{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":59551,"title":"Range of Values in a Matrix","description":"Create a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 63px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 31.5px; transform-origin: 407px 31.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCreate a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function valueRange = find_range(matrixInput)\r\n % Your code goes here\r\nend\r\n","test_suite":"%% Test case 1\r\nx = [1, 2, 3; 4, 5, 6; 7, 8, 9];\r\ny_correct = 8;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 2\r\nx = [0.5, 0.75; 0.55, 0.7];\r\ny_correct = 0.25;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 3\r\nx = [100];\r\ny_correct = 0;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 4\r\nx = [2, 2, 2; 2, 2, 2];\r\ny_correct = 0;\r\nassert(isequal(find_range(x), y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3551517,"edited_by":3551517,"edited_at":"2024-01-09T19:35:19.000Z","deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2024-01-09T19:35:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-09T19:27:40.000Z","updated_at":"2026-03-23T06:24:32.000Z","published_at":"2024-01-09T19:28:19.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47430,"title":"Find the standard deviation of an array","description":"given an array, find it's standard deviation without using the std() function. Use sample formula (n-1) for standard deviation and round it to the nearest integer.\r\nIf array is empty, return -1.\r\nnote: std() function also uses the sample formula (n-1) for standard deviation. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 102px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 51px; transform-origin: 407px 51px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133.5px 8px; transform-origin: 133.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003egiven an array, find it's standard deviation \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 98.5px 8px; transform-origin: 98.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003ewithout using the std() function.\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 124px 8px; transform-origin: 124px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Use sample formula (n-1) for standard deviation and round it to the nearest integer.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83px 8px; transform-origin: 83px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf array is empty, return -1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 246px 8px; transform-origin: 246px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003enote: std() function also uses the sample formula (n-1) for standard deviation. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = my_sd_deviation(x)\r\n y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('my_sd_deviation.m');\r\nassert(isempty(strfind(filetext, 'std')))\r\n\r\n%%\r\nx = [3 5 10 8 6 12 9 7 5 2 4 11 16];\r\ny_correct = 4;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [];\r\ny_correct = -1;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [1 1];\r\ny_correct = 0;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [1 2 3 4 5 6 7 8 9 10];\r\ny_correct = 3;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":600646,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":30,"test_suite_updated_at":"2021-11-02T05:43:42.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-11-08T21:24:50.000Z","updated_at":"2026-02-17T08:37:54.000Z","published_at":"2020-11-08T21:24:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003egiven an array, find it's standard deviation \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithout using the std() function.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Use sample formula (n-1) for standard deviation and round it to the nearest integer.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf array is empty, return -1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003enote: std() function also uses the sample formula (n-1) for standard deviation. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44278,"title":"Find the outlier in a set of samples","description":"Given an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.","description_html":"\u003cp\u003eGiven an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.\u003c/p\u003e","function_template":"function [p v] = outx(x)\r\n p=18;\r\n v=36;\r\nend","test_suite":"%%\r\nx = [ 3 4 71 8 10];\r\n[p v]=outx(x);\r\nassert(p==3)\r\nassert(v==71)\r\n\r\n%%\r\nx = [ 0 3 -7 -714 8 -10];\r\n[p v]=outx(x);\r\nassert(p==4)\r\nassert(v== -714)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":50,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-08-08T15:20:18.000Z","updated_at":"2025-11-27T00:30:22.000Z","published_at":"2017-08-08T15:20:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44281,"title":"Check if any duplicates in your data","description":"Given an array x, return a number n equal to the largest number of repetitions in your data.\r\nFor example:\r\nIf x=[1 2 3 6 8 4 9] then n=1.\r\nIf x=[2 3 6 2 6 2 2] then n=4.\r\n ","description_html":"\u003cp\u003eGiven an array x, return a number n equal to the largest number of repetitions in your data.\r\nFor example:\r\nIf x=[1 2 3 6 8 4 9] then n=1.\r\nIf x=[2 3 6 2 6 2 2] then n=4.\u003c/p\u003e","function_template":"function n = rept(x)\r\n n = mean(x);\r\nend","test_suite":"%%\r\nx=[1 2 3 6 8 4 9];\r\nn=1;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[2 3 6 2 6 2 2 -2 -7]; \r\nn=4;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[2 3 6 2 6 2 2 -2 -7 -7 6 6 6]; \r\nn=5;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[8 8 8 6 8 8 8]; \r\nn=6;\r\nassert(isequal(rept(x),n))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-08-10T11:08:42.000Z","updated_at":"2026-03-05T16:43:10.000Z","published_at":"2017-08-10T11:08:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array x, return a number n equal to the largest number of repetitions in your data. For example: If x=[1 2 3 6 8 4 9] then n=1. If x=[2 3 6 2 6 2 2] then n=4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47673,"title":"Area under standard normal curve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 25.5px; transform-origin: 407px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCalculate the area under standard normal curve between two points. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10px; transform-origin: 404px 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ediff(normcdf([-3 0])) = 0.4987\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function area = areaUnderStandardNormalCurve(a,b)\r\n\r\nend","test_suite":"%%\r\na = -3;\r\nb = 0;\r\ny_correct = 0.4987;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -9;\r\nb = 0;\r\ny_correct = 0.5;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -1;\r\nb = 1;\r\ny_correct = 0.6827;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -2;\r\nb = 2;\r\ny_correct = 0.9545;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -3;\r\nb = 3;\r\ny_correct = 0.9973;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -1;\r\nb = 2.789;\r\ny_correct = 0.8387;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n\r\n%%\r\na = 1;\r\nb = 2.4;\r\ny_correct = 0.1505;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n\r\n%%\r\na = -2.7;\r\nb = -0.89;\r\ny_correct = 0.1833;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-03T11:38:39.000Z","updated_at":"2026-03-28T21:01:32.000Z","published_at":"2020-12-03T11:38:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the area under standard normal curve between two points. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[diff(normcdf([-3 0])) = 0.4987]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60831,"title":"\"Identify and Replace Outliers in a Numeric Array\"","description":"Given a numeric array, identify outliers that are more than two standard deviations away from the mean and replace them with the mean of the non-outlier values.\r\nExample: \r\nInput:\r\nx = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\r\nProcessing:\r\nThe mean and standard deviation of the array are calculated.\r\nValues that are more than 2 standard deviations from the mean are considered outliers.\r\nOutliers are replaced by the mean of the remaining non-outlier elements.\r\nOutput:\r\ny = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 334.312px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 167.156px; transform-origin: 408px 167.156px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a numeric array, identify outliers that are more than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etwo standard deviations\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e away from the mean and replace them with the mean of the non-outlier values.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ex = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProcessing:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 392px 30.6562px; transform-origin: 392px 30.6562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe mean and standard deviation of the array are calculated.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValues that are more than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e2 standard deviations\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e from the mean are considered outliers.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutliers are replaced by the mean of the remaining non-outlier elements.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n y = x; % Replace with your solution\r\nend\r\n","test_suite":"% Test 1: Basic case with outliers\r\nx = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 2: No outliers in the dataset\r\nx = [5, 6, 5, 6, 5, 6, 5, 6, 5, 6];\r\ny_correct = x; % No change since no outliers\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 3: Identical elements in the array\r\nx = [10, 10, 10, 10, 10, 10, 10, 10, 10, 10];\r\ny_correct = x; % No outliers possible\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 4: Handling negative values with outliers\r\nx = [-100, -10, -10, -10, -5, -10, -10, -5, -1000];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 5: Single value input\r\nx = [42];\r\ny_correct = [42]; % No outliers in a single-value array\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 6: Mixed dataset with multiple outliers\r\nx = [1, 2, 3, 4, 5, 100, 6, 7, 8, 9, 10, -200];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 7: Multiple identical outliers\r\nx = [1, 1, 1, 1, 1, 50, 50, 50, 1, 1, 1, 1];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 8: All elements are outliers\r\nx = [1000, 2000, 3000, 4000, 5000];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 9: Edge case with only two elements\r\nx = [1, 100];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 10: Large dataset stress test\r\nx = randi([-500, 500], 1, 1000); % Generate random dataset\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\ndisp('All test cases passed successfully!');\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":4814940,"edited_by":4814940,"edited_at":"2025-03-24T01:12:34.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2025-03-24T01:12:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-03-24T01:04:46.000Z","updated_at":"2025-06-08T10:19:26.000Z","published_at":"2025-03-24T01:04:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a numeric array, identify outliers that are more than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etwo standard deviations\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e away from the mean and replace them with the mean of the non-outlier values.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProcessing:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe mean and standard deviation of the array are calculated.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValues that are more than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2 standard deviations\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e from the mean are considered outliers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutliers are replaced by the mean of the remaining non-outlier elements.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47613,"title":"Calculate F statistics ( One-way Analysis of Variance)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 232px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 116px; transform-origin: 407px 116px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCalculate F statistics by following the steps \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWikipedia\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAssume balanced design.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 140px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 70px; transform-origin: 404px 70px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex = [6\t8\t13\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e8\t12\t9\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e4\t9\t11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e5\t11\t8\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e3\t6\t7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e4\t8\t12];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eF = 9.2647\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function F = oneWayAnovaF(x)\r\n\r\nend","test_suite":"%%\r\nx = [6\t8\t13\r\n8\t12\t9\r\n4\t9\t11\r\n5\t11\t8\r\n3\t6\t7\r\n4\t8\t12];\r\ny_correct = 9.2647;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n%%\r\nx =[ 25 6 21 22 14\r\n 28 30 3 2 13\r\n 5 29 26 10 24\r\n 28 16 29 3 25\r\n 20 25 21 4 7\r\n 4 6 23 25 16\r\n 10 14 23 22 14\r\n 17 28 13 11 20\r\n 29 24 21 29 22\r\n 29 29 6 2 23];\r\ny_correct = 1.0699;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%%\r\nx =[ 5.8526 0.7585 0.1190 2.6297\r\n 5.4972 0.5395 3.3712 6.5408\r\n 9.1719 5.3080 1.6218 6.8921\r\n 2.8584 7.7917 7.9428 7.4815\r\n 7.5720 9.3401 3.1122 4.5054\r\n 7.5373 1.2991 5.2853 0.8382\r\n 3.8045 5.6882 1.6565 2.2898\r\n 5.6782 4.6939 6.0198 9.1334];\r\ny_correct = 1.0378;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%% F is (almost) an integer\r\nx =[ -0.5536 -3.9960 1.0098\r\n -2.6001 -0.2397 8.1808\r\n -6.8236 0.9040 2.9747\r\n -9.8205 0.7594 1.1360\r\n -9.4491 -6.8518 4.7317]\r\ny_correct = 9;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%% F is (almost) an integer\r\nx =[ -45.4808 91.7694 96.2151 -57.5649\r\n -20.5585 -31.8710 12.1575 -18.6353\r\n -1.0294 4.8946 28.6612 -3.6424\r\n -77.9321 14.2147 32.9444 -31.2570\r\n 18.1578 49.8679 0.1815 -21.2512\r\n 1.0996 68.3178 32.0347 -2.8978\r\n -73.2486 9.3672 51.8217 -17.7058]\r\ny_correct = 7;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-25T08:04:51.000Z","updated_at":"2026-03-16T10:26:38.000Z","published_at":"2020-11-25T08:04:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate F statistics by following the steps \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWikipedia\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume balanced design.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [6\\t8\\t13\\n8\\t12\\t9\\n4\\t9\\t11\\n5\\t11\\t8\\n3\\t6\\t7\\n4\\t8\\t12];\\n]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eF = 9.2647\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44933,"title":"Vogel-Dobbener entropy","description":"*Vogel-Dobbener entropy* is a measure of dispersion for ordinal variables. \r\n\r\nGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\r\n\r\n VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))\r\n\r\nwhere F_1, ... F_k are the cumulative relative frequences, i.e.\r\n\r\n F_i = sum_{j=1}^i f_i\r\n\r\nThe Vogel-Dobbener entropy of a sample satisfies 0 \u003c= VD \u003c= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\r\n\r\n VD* = VD / (k - 1)\r\n\r\n*For example*, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\r\n\r\n* k = 5 (there are five distinct observations);\r\n* u_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\r\n* f_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\r\n* F_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\r\n* VD = 3.2183; (approx.)\r\n* VD* = 0.8046. (approx.)\r\n\r\n*Your task* is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample. *Round to four decimal digits.*\r\n\r\n*Hint*: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.","description_html":"\u003cp\u003e\u003cb\u003eVogel-Dobbener entropy\u003c/b\u003e is a measure of dispersion for ordinal variables.\u003c/p\u003e\u003cp\u003eGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\u003c/p\u003e\u003cpre\u003e VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))\u003c/pre\u003e\u003cp\u003ewhere F_1, ... F_k are the cumulative relative frequences, i.e.\u003c/p\u003e\u003cpre\u003e F_i = sum_{j=1}^i f_i\u003c/pre\u003e\u003cp\u003eThe Vogel-Dobbener entropy of a sample satisfies 0 \u0026lt;= VD \u0026lt;= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\u003c/p\u003e\u003cpre\u003e VD* = VD / (k - 1)\u003c/pre\u003e\u003cp\u003e\u003cb\u003eFor example\u003c/b\u003e, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\u003c/p\u003e\u003cul\u003e\u003cli\u003ek = 5 (there are five distinct observations);\u003c/li\u003e\u003cli\u003eu_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\u003c/li\u003e\u003cli\u003ef_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\u003c/li\u003e\u003cli\u003eF_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\u003c/li\u003e\u003cli\u003eVD = 3.2183; (approx.)\u003c/li\u003e\u003cli\u003eVD* = 0.8046. (approx.)\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eYour task\u003c/b\u003e is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample. \u003cb\u003eRound to four decimal digits.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHint\u003c/b\u003e: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.\u003c/p\u003e","function_template":"function VDstar = vogel_dobbener_normalized(x)\r\n VDstar = 0;\r\nend","test_suite":"%%\r\nx = [2.7 3.3 2.0 3.3 1.7 3.7];\r\nvds_correct = 0.8046;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [linspace(0, 0, 5) linspace(1, 1, 20) linspace(2, 2, 43) linspace(3, 3, 13)];\r\nvds_correct = 0.6205;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [linspace(10, 10, 4) linspace(11, 11, 17) linspace(99, 99, 38) linspace(7777, 7777, 22)];\r\nvds_correct = 0.6511;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [-pi -pi -pi i i i];\r\nvds_correct = 1;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [7777 7777 7777 7777 7777 7777];\r\nvds_correct = 0;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":332395,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-07-08T09:39:50.000Z","updated_at":"2025-11-06T15:58:01.000Z","published_at":"2019-07-16T15:59:52.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eVogel-Dobbener entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a measure of dispersion for ordinal variables.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere F_1, ... F_k are the cumulative relative frequences, i.e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ F_i = sum_{j=1}^i f_i]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Vogel-Dobbener entropy of a sample satisfies 0 \u0026lt;= VD \u0026lt;= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ VD* = VD / (k - 1)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFor example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ek = 5 (there are five distinct observations);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eu_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eF_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVD = 3.2183; (approx.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVD* = 0.8046. (approx.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eYour task\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRound to four decimal digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55250,"title":"Find the minimum of the column-maximums of a matrix","description":"Given a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\r\n\r\nA = [5 3 4 3;1 2 6 3;1 1 4 4];\r\nm = minimax(A)\r\nm =\r\n 3\r\nNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 751.733px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 375.867px; transform-origin: 407px 375.867px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 284.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 142.25px; 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qsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+FJqYY+dI/7YBu5xCv/6eHurnvE5u7uQTVigTy1Y8qn1A75NBrRdinQUHhSqKFP3cNW2dCnvu4jquUvztNszDvbn0l6JE/9DHQZpaHwpFBDT8urvoae2j5z92S3ZimYp56kosIjuRxR+t2IhsKJMg21i6jqGrqSpp6iT8KGzi+VTxSplw25MaLdmoeGwpMiDQ1Xe7U1dCuhksWva+jmmPkRLX4kFZf0NBSelGhofMJcWUO3E6qIqKyhO2NmF6r8kVRdLNNQOFGgocNrjnU1dLTwH576pf4wekaaHVFVQ4cx72zK0Z7+Hhk0Q45eVX6Qj0hD4Yu+oeMFZrtySVb+0MvxpdKwN3fxixoaBxq9/Dm8f5N7RCVDDn/CoyMZRxRd0dNQOCFv6CihVTU0zjVf43Hx23YqTUM3xqxqyDDMdMQ4+fhbVBIaCk/UDZ28X1tTQ8Ngy8uk9SS8mKahYZj5odva/0KKIWMsbTvYPsIvREPhibahsx8arKmh/UAt2xzZasILSRoaZll2aPOGF1EMGf6MF9eb4QbbTEZD4Ym0obFGpqKGhouktSea4ba8cSUN7edYLaUmUIIhwx/y8mht3/IyNBSeCBu6/JsrFTV0N0F2W941nqKhoUJrpbebbh76ne9GdpBzn8zTUHiia2hYW60QrIoa2s+zNZHkGk/RUDuGq1NKAqU7kmsHi4bigFQNHRW0XUP2P/U0dO8CbxjeNtMoGtpPsTFlO2T3LzitfwWX0jV07c9WchxpKHwRNXSc0HaV2//V09D91b1f2AsJ8mRz5F7H7RAMaReba8eKhuKA5A3tfzTc/p+GvoxNmTXGPsGQ7eF6eLhbPZI0FAekbugpm5ONfIo8tev+bn3l19PQfoj6G7qJ10NxQNqGhr+feNqqqaG7JNdP+UOGlNtmCWWPZD99/h87DYUnyoYOf8O722p5aWgl78tbQ1VHbU3RIxm+FeVeR9NQeKJr6PifCO4etOWkoeH679Y/NmQN8trQcBizr6NpKDxRNXT6Mzfdg7acNDRcP+WNK2toPJL2G9+Uv/Ct5JEMV/PZ09JQeCJq6Ez3oC0nDe1nbeU9B80fcvJTQ9NfSlfnL84bi1eh+T+ZRUPhCQ2Nl6GZz0Hzh+yHOI2x/Huz45dK0pU6kqN58+ekofCEhsYLqMzFr2xonGlM8aP3JY7k0+SSWZB6GgpPaGi4gpL8VGPOkCGcw0gzgojKj2Q/2IjiapmGwpPDNzT2Knf16xq6kVDZS43lGqr5A6eh8OToDY29yp5V1tD4RN5+39vomXJ2RNVHcvqig+LVhhYNhScHb2hsQAWXeNMeja+Lh4rmHtGyDW0PI8/lcTTHbuiQgPy1r23otOnDTbYjVemGSq5FaSg8OXRDlQnVNnReonhbZqLUR3LyQ6wn+ZeiNBSeHLmh0oRKG7osZbzRthPJG/rw1B+709+oMrlHk4bCkwM3dFj2ioRKG2p7xsIVX94xLXMke8Mlae7TeRoKT47bUHFClQ1dPW52W16gCjZ09HeVFC840FA4cdiGqhOqbKjtmAoD22aakg0dHdO8Q0pD4clRGypPqLCh64dN8mS+bEPDn7viYpmGwoljNnT00qMqocUbKjmohRsaXxO17TQ0FJ4csqElEips6MZMdumcdY1XuKHx8j4/9DQUThyxoaMfatQlVDBkP1HLNuc8NFT3ggMNhRMHbOjwUmjm+8dTNLTTj6gYkobCieM1tFBCBUOGyWxzzq7xaGg6Ggq9wzW0VEJpaE92sUxD4cTBGlrk3aST/CHDi4m2OSfL0xUauvU1XISGwpNjNbRgQoUN3X9fPuugCo7k09PDw93mFP2IXIfiQA7V0JIJVQzZT7Z52PZvvUz+kP0M25E8c/NFaCg8OVJDh4RqfknmlGDI3WfrYfqs0fOH3H9FIQyZH3oaCieO1NDwWl3eVdIWXUPXX0ysq6EbL3jSUBzOgRpaNqGKIXd/Qn0/XhfKH3J3xjhkfuhpKJw4TkPD4i+UUMmQ/Xwt2xyTXOEphuynaNnmRBhSEHoaCicO09C4vAslVDJk6PzKjJIrPMWQYZC1P9y92y5HQ+HJYRraD9QqlVDJkHHKRSk1l6GKIeP1/LLm8SbbTkRD4clRGrqz8kUkDd0aM15FZ46vGDJcbC6+G8UhFaGnoXDiIA1VNWiHpKExUNNBVXWSDBmHmUU0jp57qU9D4clBGhqv73ZlBVbT0BiocYmG6W1HMsmQMZbjP+Dhtyllf5+iofDkIA3txzmrgoaOItrc9cfvadT/7KtozZBDLu8e+pHGM4qGpKFw4hgNHYVpTw0N3Zs1u06qIYeILomGpKFw4hgNveypfB0N3Y5ofp1kQ25HVDUkDYUTx2hoP815dTR0/NLimKBOuiE3Iir5yTEaCk9o6EglDV0tlOZYFg69IvM0FL7Q0JFqGrp49eH01k0+5ZAPs4qqZqShcKVMQ8W0eSpEPeTTw90pUvbet4R4yIcSM9JQuEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeNI39Ef/bd3+KUOKuBjyvd2QNBROfLw7X4HavGYnqBQNhR4NRZVoKJygoagSDYUTv92dr9/4TXX7xm7It9pGrRhSpR/yQ3aCStFQ6PG+vApDqvC+PDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4Umphj50j/tgG7lKrXwXQ77xxl33yM2dbWXxMyQNhROFGvrUPWzteXIxZBiz8obKh6ShcKJQQ09XJZXnycWQoU51N1Q/JA2FE2Ua2j9Jrj1PLoYMpa+8ofohaSicKNLQcFVSdZ5cDDmMWXVDCwxJQ+FEiYbGFVVznlwMORqz5oaWGJKGwokCDR1WVMV5cjFkKzxJrrqhJYakoXBC39ChThXnycWQLXvNtlNvQ4sMSUPhhLyhozrVmycXQ7bGc1bb0DJD0lA4oW7o6Jqk3jy5GLLTD2iqbWg/naGhOBxtQ5+GV8Y6debJxZC9yaC1NrTQkDQUTkgbOn5W16kyTy6G7E0nrbShpYakoXBC2NDZ9V2rwjy5GNL047Vdsv/Y3ix+hqShcELX0PG7s/bf+vLkYkhjA4bq19nQYkPSUDihauj4bZq7cHFSW55cDBnYk+SHkKkqG1puSBoKJ0QNHdfpKT7BqyxPLoYMrE5tlCpuaMEhaSickDf0rq1T9Q2teMjAotQOWnFDCw5JQ+GEuqGnIk028qkbWvOQxmbthqu3oSWHpKFwQtvQ/vquddqqtKF1D3kyPEmuuKFFh6ShcELZ0BCnqhta+5An/WT9k+SKG9qPVWpIGgondA0d4lRxQ+sfsmdXzKdha21o2SFpKJxQNfRhFKdqG+phyM74SXK1DS08JA2FE6KGznQP2qryaXLUj1jlkP1cTWNblTa0H6rckDQUTtDQ+oa0HoWL5jobWnpIGgonaGh1Q06fJFfa0OJD0lA4QUNrG9LqFJ4k19nQ8kPSUDhBQ2sb0mo0vP1VY0PLD0lD4QQNrWxIu8IbTVVhQ68wJA2FEzS0riEXT5JrbOg1hqShcIKG1jWktWj8g6z1NfQaQ9JQOEFDqxrS/u7PZKbqGnqVIWkonKChNQ1pT5KnJaqtodcZkobCCRpa05D9QNMnyfU1tJ+m+JA0FE7Q0IqGtBDNJqqsoVcakobCCRpaz5CrT5Jra+i1hqShcIKG1jNkP878SXJtDe1nucKQNBRO0NBqhrQMzetUV0OvNiQNhRM0tJYhN54k19XQ6w1JQ+EEDa1kSKvT8gqvpoZecUgaCidoaCVD2g+un5eVKT9D0lA4QUNp6OVoKDBHQ2no5WgoMEdDaejlaCgwR0Np6OVoKDBHQ2no5WgoMEdDa2no3ZZ+ypZtLn+u6AX8DElD4QQNrXvIVkU/H7qNnw/FUdFQGqpAQ3FUNJSGKtBQHBUNpaEKNBRHRUNpqAINxVHRUBqqQENxVDSUhirQUBwVDaWhCjQUR0VDaagCDcVR0VAaqkBDcVQ0lIYq0FAcFQ2loQo0FEdFQ2moAg3FUdFQGqpAQ3FUZRoqVmzlKzGkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8KRv6K/9Zd1+nyFF/AxJQ+HEx7vzFajNa3aCStFQ6NFQVImGwgkaiirRUDhBQ1ElGgov3tv6tdoxpIqbIe301KKhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaigLe3nrv+yvHkCpuhvw7Oz+laCj0Pt4AFXrNTlApGgo9Gooq0VA4QUNRJRoKJ/5bd75+53fX7R8ypIifIX/FTlApGgq9P+nO1997o25/zJAifob8QztBpWgo9GioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKTKQRv62H3ZLduEFzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeHLAhj49Pdx1D9jc3T3YLgV5nkZjyuYs11Cb1bay0FB4Imto/8e/JbMBypX/dFrsgS6j4tA/9ONFkjgVbOhTPyUNzUBDnVI11NbQhmoaOitoR1XRgqHv6fJUoKHhj5+GpqOhTqkaOrtsmqmloetTaq7xhHnaGPPJbs5QqqGh+Qdt6PPjff/l398/Ptquhcd7u8/j47PtmtI19Ll/qHYY2zaP67uX7Mu54J5bMiZ4Pt2pO062p3aHaujK1d2JoE7CPBUcs1BD47OQIzb01IvBWiGssdFaHsLD2GbLPujeNgfPpxuaGOPxxz4Pn+t+qPVkyvkjTh5v8uWMHmFf5gTB6ENbdpjGj90Kd1l5jPjRl46toGpoP/imOhq62SZJRGV5KjlmmYYOL+Qcr6EhP2PzxT3pgllWdFaKln3cyxo6LtXwsdO988aMHm/x9WzVbiZzgpNpQTv9vUaP3YkjLkbbvqWkAzV09JLtQx8je9+7I1j7qjyNhnqSj1mmodLj6Kqhy0Xfm/RhUSUzj8isFK2Uhs4HOn3wcszVEZ/Xhr2sR5kT9OaZ7XQfPT8yccjZQ8T9l42sImro/ltKVTR0GHF0NRdXf/7iF+UpvigyevlzeIsp80CWaejodZyDNXSlOL3x2l7GI5itdElDl5+t++i1GcYzxsdb/YIuKlLmBJ21+/QfvTgy8Z62fTIMbzuuRNRQW0aSJbQkWfmhQ9MZY1kreZoc55mOGSNq26lKNHT8/fNYDV1f9K1RHTbv05rGSdHQtQY+rs8wftT4ePbfmXns1mRO0No6UvfbR2b6EPEBLplXSNvQ3MukDYqVH1e6bQfhIip79WvyFGI5P5Jb+1+oREP7ucyhGrq16MeLePs+nUkCFA019/YzACfxxnb3aP/oFdl5+KZ3XBlgKXOC+QOM72hv1E8uL23P+CHi/a+cUFVD+9klb82sUaz8EKHFiOEG20wmyVMo/bJFmze8SIGGxkvkzpEaOl709uNK4e33uIrHITm9X/+8+fb0shT20S9saJjFNgPbPbyCO3rYSUNjlobhJ7FblznBeIT4Xn44nOEDbHcn3j3cd9hzSfGljtLQEKflddz2LS8jyVM/yWqKNKnXN3T8TP5QDR0t+riOW304YgT6m3vjDI12jz407LXNVkiIbQ7C544fvfqIQwNbK7ttuzUOmO3qvCRLmRPEC8vx/VqTR7B9vfj5bPsFs04ec9dlOdY0NCwk21QTrPzwlH2l8lan3OWvyFM4jmvfi+ymvNTrG9oP1R49+4/tzeKjof3X25kvtHaJhgzYPVbvY4ZiaBo6KtCojJMwhU8+ZH245/Rzxf22vSNzgvjh20dqNkW44fQBw2fqN3eNH3Lf8sivkTY091Jui2Dl9/O1bHOsoobuvawsGVPeUJsq/OTAcRq6uei79Wy9iIt1eZ+VmyQNndx3iMVkd7jzsHMo0Lh0rfAItrkjb4I4wORuveEhZlOEG/oQx3vNvoA1o0c8YznNGk1Dy76lJGzo2oThEtU2Uyny1M+x8ZJIO2b3LzjlvVyibmj85nm0hu4s+vbG00Levc9y0UsaOklI3Dsri+0cHjdOOn/hMzzCfP9S3gTxfrY9NiTPdpg4dPvQL0lopQ0NVyO2qSZY+TsT1tNQK5IkROvEDR0GPlpDw6LfW2Vhra7fJ9wa8xQe0jZbdpflx2831LaN7ZzvDp/aNqc5mrL9L2iobRvbeW4C21xPoN02f4w49f0w/0XRC5/8vGs2tP+ExV4O1az8p4eHu9UJ62moTVLqW1FL3NDhO9PRGtp/ta296x67y8Z94rq3bUlDZ60LtZjtXnymxSyR7b+8oUkTDDVcE5tn21G4Id7hsubFu591xYaGt0JsU0688mdEy18wZD+Ho4Za87sXSA7W0LDo99oSOrG1EMPtIYSLsoXFfr2GLr+czRHmsiYImxvfkezWRUMXNbTdZ8w/atv1G1rq5dDCDe1nz58+f8jS34pa0iM5funhYA09s+h7Yana5sI8XIuybQdss6GzecIIs92LDxc2NGmCM4dq++Zwy8nsk1yLpKGj65EiijY0PJXPvfqTNbTYYWxJj2Q/rR24gzW0/2JbtrnqbH5Ot8cHKdDQjd1XbOhlE9jW1mdZHpkgPE5v9jmuRtJQW0D9Yirxy4pKNjRc/GVf/eUPWfpbUUt5JCev3h6zocvmjJy9zywNy1K8vKG2GWzs3mzoskIvbqhtBpdNYFtbh2rjQTqjiJ6fsRBJQ/svoavQ7LcA1fhrNuZs8eeXS9bQeEFs344q/W40fiZ/tIZuN2fE7nO+ofYoy1IcpaHnDme43TYnbMAbJlTS0Pg63rSgHcXvryjZ0HgVmr/484e0Dp0O2fTbUYXfjfq54uU7DZ07f5/ZPWjo8tMbu922JmJDV2+9CmVDVylWVamGjn71W37r84fsBzlVaflL6Wr7bjQJPg1dOn+f2T1o6PLTG7vdtsaGhN7uQlTR0OXl55hgWZVo6NPkQk8QKGVDV78rVfXdaPpMnoYunb/P7B40dPnpjd1uWyPh4Xu3imj5hgoCJW9oP9aI4hove8j4kkgI0lxF342GWc0xG7r3ntL5+8z6sszNURp67qXjcG/bHIQbzGaDp0bXrmdcVmVFQ/tPZ+5Of6E7vDvfy15YhRuqea1R19CNhCoKpTqSNuLwvedYDT3/flHr7H1mfVnmhoaehHvbZhT2R5dFtO6GTl60G1KQu7LUDZ0+VZase2FD43T2m/NGl/m1fDeyEUfffA7W0EvScrrLcuVHswc5cEPPfJaNB4lHeGip3bDPPtsFzn/ZHUFDhyLNnhIPN2Re6ZVt6Cz9qWQNNaOZhormXjJrjmSY1DY7x2zo7poNK3/z4shuP9vQ5WdZPPRGZTZ219rQ5Rd6YrfObw4f9Qr8myNxhS9KNFTBdiQq3VDJ2tc2dDqQ6kCKjuTimfzhGhrSsPdkPizsrfuEpRxuX+ZmI0ArN2zcc2N3dQ0N91o/VLGQtm2GhI7uckn1Km7oysVcXPt510/qhq68CZZ/KSpt6LxF8bbMSEmOpB2+yZ/pwRp6/ol6y+6yUqbOogzL3GymxfYPd80q2MqO6DoNnV+ST4XHmD5IPH79Y8Qurh/siQob+kb3r8rdrSU0Lq3MtSVv6MNTP+34ja/1+V9A2dDl4Yo32nYixZG0UaYzHq2h44ugLWHtr99n8QjL3IRMbDV0eOC8gt2+oeFgrBUwxnLyIHGvfUgM49pDTMW7nnX+y+4oGrqnH6WVdSGqbujIcEmau/yVDbU9Y2HQ21/R92PMv+ccraHzJTyh+nfsty7PwgMPbfXe0HC3+f06Q/JsRyce23gMbPvC7imVbqhk6Rds6OhvBGWuf2FDVw+W3ZY3peBI2vGazXi0hu5d98h+n1K81+xC1PaOPnVewW7f0J0CxiO1emSG+8eqnp9VrHRDJUu/ZEOH1xsyn80LG2o7psKYtpkm/0iuPpM/YEPjql+s2TYbloa4rBf3GcJgO1oruQm7pnGLH2zbrcyC3b6h8SvdPlLjB1k5BMPRXrz0UVjxhiqWftmGxtcbbnyJFxu6fs1eyRV9P8Py+83xGhpXfXM/XrT9Sg5piMt6urCHDx3vXsuN7ZrULfbjzMe2NnYvCrbYEV2rocNXdT8e4nmc0OFB4t7JwOt7y6Oho9dEbTtN6YaGet22ofbHubhkP15DhyXbVfTUs2dLRlzE4wQ8nl4mfRzvG7dpLTdhXwx1+AydfsfJRqo2dtfY0LCnFb8nhS82HDHbPdx3Nm88ssuvo6TiDQ2BynmiXLihsfO3zVNs6MahUmQqd8iNZ/KHbOgkkFPDGt6+T2eSptXc2L4141BkFqyChk6P1H3L/rdrqv2P3TMmdDFV+Jjz4yrR0FZdT5O3LocraGjI/PIP84gN3Q7kVhpmpkt9NTexFwvn++uroZtH6n72INsJ3bupIBra6Se88SWeh4bGVz3OyR/SQ0M3l/0laVgs9PXcxCzMTD84s2BrSTu5YkM3jlT7qacPEu+2nHY4WqPXioujoZ1+wps31CK51VA7kjQ0nbqhs/c8osnq3qrgPAFncjN1UX8vLthq0nrXbGi881j3mScPspfQ0UOs3loGDe1UcInXoqEnjhq6vu7nC3irDVOTUoysdXp+mbXxsRu7a23oypfaf6HjBwn/v3WlGR/Btq+AhnbO1Osi+UOGQ2WbczyXz1agoe2qni38x5VLoLjyTyY/DRVs5KZ1/jNsfOzG7mobOv9S7QsdPUj4wO2JwgOcH1lF0NCnp6eHlm3NnQnDRQQN7Wa823zTqJ/w5tehZ77dWENv+cYXDV33GN5Fvl8LaO/50fJwvxrQcy74DK+K8KW6+ULzG9p/vdurxm6+cUP7Ec4OeeOGhjE2Krl/62VoKCCW39D958Hhp2FuufJb+8+DKxlyf8rtnyt6gdyG3m3pR2vZZv6QNBRO6Bq6HqAzz08vI2to3aHfn7KGhm7bzf8L0VB4kt/QUMn15WM3Zj2VF6z8MOR6JUO7bp2n3Sn3vw1ciIYCYoL3lLpH6NjmhOQKT7Hy+ylatjkRhrx9nvopWrY5Vs2RXEdDcVSChoYLpLX1YzflXeEpVn4Yci1Be7ddTpGnnWv6MOTNj+Q6GoqjEjQ0LPyV5b2X1xcQrPydIeNNtp1Ikqd+jtZiSs1lKA0F1AQNjQt/EaGQ0MyLJ8nK38x5fCZfQ562Uh+HrOBIrqKhOCpFQ+PCn/1IS0xoZp0kKz9WaLbM45C5y1+Tp/VvO6rO01BATdHQYeGP13hc9/kLS7Ly14cc9mZe4YnytHrU4jepzJcbaKj9F9CRNHTUp+ahb9HTsOzzF75o5Q9D3q0MmZtQVZ6GiLZj9jtGU9Yy5BINxVFpGjpa+EvZC1+18kelX6hmyL1jWc+QCzQUR6Vp6Pg58UzeX/s7Ua387YhWNOR2RGsaco6G4qhEDd3sk25RHWbIrW9IgoTSUEBN1tD1la9Y98XzVNuQq63PfUf+hIYCYrqGvvHGw2zpK57G95Qrfz7k6e0lAW2exm93tVRT0lBATNnQ1sOD/TNo9ta3hnjluxjyjacSUxZrqBINhSfihpbhZ+UzpAANhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe0FAVhlShofCEhqowpAoNhSc0VIUhVWgoPKGhKgypQkPhCQ1VYUgVGgpPaKgKQ6rQUHhCQ1UYUoWGwhMaqsKQKjQUntBQFYZUoaHwhIaqMKQKDYUnNFSFIVVoKDyhoSoMqUJD4QkNVWFIFRoKT2ioCkOq0FB4QkNVGFKFhsITGqrCkCo0FJ7QUBWGVKGh8ISGqjCkCg2FJzRUhSFVaCg8oaEqDKlCQ+EJDVVhSBUaCk9oqApDqtBQeEJDVRhShYbCExqqwpAqNBSe9A39D5+u268zpIifIWkonPh4d74CtXnNTlApGgo9Gooq0VA4QUNRJRoKJ2goqkRD4cXPtD5RO4ZUcTOknZ5aNBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUABIR0MBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUABIR0MBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUABIR0MBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUACvul+JbIcQDYVcE3zddgA39XU7IZvmrbZHiIZC7W12ujbN12wPcFNfsxOyad5me4RoKNS+1U7Xpvmq7QFu6qt2QjbNt9oeIRoKtW+307VpXrc9wE29bidk03y77RGioVD7Ljtdm+Yrtge4qa/YCdk032V7hGgo1N5up2vTfMn2ADf1JTshm+bttkeIhkLtu+10bZov2h7gpr5oJ2TTfLftEaKhUPseO12b5gu2B7ipL9gJ2TTfY3uEaCjU3mmna9N8zvYAN/U5OyGb5p22R4iGQu377HRtms/aHuCmPmsnZNN8n+0RoqFQe5edrk3zGdsD3NRn7IRsmnfZHiEaCrV32+naNM+2B7ipZzshm+bdtkeIhkLtPXa6Ns2nbA9wU5+yE7Jp3mN7hGgo1P6lna5N80nbA9zUJ+2EbJp/YXuEaCjU/o2drk3zn2wPcFO/aydk0/xr2yNEQ6H2c3a6Ns2v2x7gpj5mJ2TT/JztEaKhUPslO12b5iO2B7ipj9gJ2TS/ZHuEaCjUftVO16b5sO0BburDdkI2za/aHiEaCrXftNO1aT5ge4Cb+oCdkE3zm7ZHiIZC7RN2ujbNT9ke4KZ+2k7IpvmE7RGioVD7Iztdi/wwHvBy93ZCNs0f2R4hGgq1P7PTtWl+0PYAN/VDdkI2zZ/ZHiEaCrXP2+la5B+8BV7uHXZCNs3nbY8QDYXa39vp2jTfYHuAm3qrnZBN8/e2R4iGQu7b7Hxtmi/bHuCGvmynY9N8m+1RoqGQ+147YZvmL2wPcEN/Yadj03yv7VGioZD7YTthm+bTtge4oU/b6dg0P2x7lGgo5H7CTtim+QPbA9zQH9jp2DQ/YXuUaCjkfsZO2KZ5v+0Bbuj9djo2zc/YHiUaCrlfthO2aT5oe4Ab+qCdjk3zy7ZHiYZC7vfthG2ae9sD3NDw15T+i+1RoqGQG357TYHfRAu81PDbukv8hi8aCrm/tRO2ad5qe4AbGn7E/m9tjxINhd7wQ/YF/mod8DLDXz4u8SP2NBQF/KCdsk3zZHuAm3myk7HQP4JDQ6H3z+2UbZrftj3AzfyWnYxlfjyUhqKAX7RTtsivAANeZvgliUV+OQ0Nhd5v2ynbND9ie4Cb+VE7GQs9LaKh0PtTO2Wb5jtsD3Az32knY9P8qe2RoqHQe8NO2dbnbBdwI5+zU7H1hu2SoqEoYPih5gK/Awx4ieF3JJb5Kx80FAX8pJ20TfOztge4kZ+1U7FpftL2aNFQFPAxO2mb5l22B7iRd9mp2DQfsz1aNBQF/LmdtK2/tl3ATfy1nYitP7ddWjQUJXyLnbVN8zu2B7iJ37ETsWm+xfaI0VCU8D47bZvmx20PcBM/bidi07zP9ojRUJTwG3batv7SdgE38Jd2GrZ+w3aJ0VCU8Fk7bZHqbT/w839jB7P1Nz//A2+zG5Dqs3YwxWgoini3nbdI900PdjDffPgm24V077aDqUZDUcRH7cRFjg+dDua/s03k+OjpYMrRUBTx+jfbmYsc/7k7lv/TNpDjm1/vT0w9GooyfsFOXeT4ri+9+ebf/WPbQI5fsBNTjoaijL/7R3buIke78vlupNB9NyqDhqKQ/2EnL3J88+u8KiLRvypSBA1FKVw/KXyUd+cU7N25EmgoiuEncgTezU+J5Rt+SqwAGopy/ubnv5+fDMeNve37x39bQY+GAvUZ/Q3FAX9rtko0FKjQj1k3R37MbkJdaChQoeFfbIv4VwTrREOBCo3+5eCAf826TjQUqNHwGywMv1WlUjQUqNHwm9QMv92vUjQUqNHwG33Nf7UbUBkaCtToryyd0V/ZDagMDQWq9B3WTvMdthu1oaFAlX7E4ml+1HajNjQUqNJrFk/zc7YbtaGhQJV+y+Jpfst2ozY0FKjSk8XTPNlu1IaGAlX6vMXTfN52ozY0FKjTW62evbfaTlSHhgJ1eqfls/dO24nq0FCgTveWz9697UR1aChQpw9aPnsftJ2oDg0F6vR+y2fv/bYT1aGhQJ3+wPLZ+4TtRHVoKFCnT1s+e//HdqI6NBSo019YPnv/13aiOjQUqNOXLZ+9L9tOVIeGApUa/ZD9N9gu1IeGApV6hwW09XbbhfrQUKBSP2QBbf2g7UJ9aChQqdFfVHqP7UJ9aChQqZ+2gLZ+ynahPjQUqNQHLKCtD9gu1IeGApX6sAW09WHbhfrQUKBSH7GAtj5iu1AfGgpU6mMW0NbHbBfqQ0OBSv2uBbT1u7YL9aGhQKU+aQFtfdJ2oT40FKjUpyygrU/ZLtSHhgKVeraAtp5tF+pDQ4FKfcYC2vqM7UJ9aChQqc9aQFuftV2oDw0FKvU5C2jrc7YL9aGhQKW+YAFtfcF2oT40FKjUFy2grS/aLtSHhgKV+pIFtPUl24X60FCgUl+xgLa+YrtQHxoKVOp1C2jrdduF+tBQoFJftYC2vmq7UB8aClTqaxbQ1tdsF+pDQ4FKfd0C2vq67UJ9aChQq/jLkd9hO1AhGgrU6n2W0OZ9tgMVoqFAreI/fve/bQcqREOBan3olNAP2SZqREOBen3qfe94x3v5B5irRkMBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUABIR0MBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUANLRUABIR0MBIB0NBYB0NBQA0tFQAEhHQwEgHQ0FgHQ0FADS0VAASEdDASAdDQWAdDQUQOf5+fG+6dzfP9ouXICGAnjzzcc+nwMyeikaChTVXdw9PttGrZ6tnGP3dhv20VCgpNPT46buiNqQc7WXvw40FCgo1sm2a7R2EXrCpegFaChQkMWo5tcXtxNKRC9BQ4Fyhndq6m3o5In8/f30eT0RPYuGAuUMDa02RqM35MNbX/YzTj1eEz2HhgLlDM+Ta70OHSYcV359L9bQUKAgK1G97ynFy9DZBWe8FOUHRc+goUBB4YKu1qfE8YJzMWCIKBeiZ9BQoKTnrkX31b6qGC5Dl6WMdbVtbKChQFnPNb8tE642V2YMN/Fkfh8NBQ7MOrl6sWk38WR+Hw0Fjis8YV+91rQLURq6j4YCx/V4f7L6ckN4Mm+bWEdDAawK7zfZJtbRUACraOhFaCiAVTT0IjQUwCprKO8p7aOhAFadEsrPh55BQwGsqf2vqdaChgJYw8uhl6GhAFaEy1BeDj2DhgIXO/3jxPezX9Npv5d9vvtSj6d/Oj71wwvZ/gedMEVDgR0Wkv59lfDktjV6n2X8j74vg7P2oqJ9QH+B1/+zTlE9b9/EhHIZeg4NBXZYSdq4TWsXojjbu0jOmYbOPrqSq77n0XeLKgaqGg0FdlhJHocLs6CPy2LvPKL7DV0k9ObNen6cXFeT0PNoKLDDUvK4jGWXl5W9s4juNnQlobeu1mwkEnoeDQV2WEumF2dmNaGz7uw1dPSMeWT+asB1Tb9OEnoBGgrssJiY+8lvX48b3b8eZ//bsQ/t7TQ0mH30bcM1+fpsH3bRUGCH5aRzf3rXfPEukr2ZPlxVjiN4rqHxX+4cXdPanptYnQ17aCiww3LSGq7KJk/hV3aPf0LpTENXd59L1+Tz73txBaffILgSvQANBXZYTKY5GYVmvDu2zbY7+w2dJi7sP/dTotdrKBG9AA0FdlhLpjUaRWwSKds33rnb0Fmhwn1v2dCO/b2rHhE9i4YCOywls5bYzvnu8JLopQ217ch2n3tBtHRDW8OLvkT0HBoK7LCSzK4N4/tH090rF5J7DV3kKTysbW65QkNHoT93VXx4NBTYYSGZZS1WzLYD23thQxd1qqmh21fLmKKhwA7rSJGG2uZg7c4rrtPQ7dRjgoYCO6wjs6fdoWLzZ+OWndHuEg29FpuGV0T30VBgx0ZHNna/Wg3dnBNjNBTYYRmZP5/d2P2ihi6v7ypr6NZXiQkaCuzYyMjG7lezoctBMUJDgR2WkWM2dHNQjNBQYMepIgdt6IU/a3VwNBTYYRWhodhCQ4EdVpGqGhrudoG8GtPQS9BQYIdVhIZiCw0FdlhFXtGGdv+Efmv+ZQQ2KA3dRUOBHVaRV7Shdq/lJCdnbkaPhgI7LCOv6nWo3c02Z8Ln2bpMRY+GAjs2MrKx22tD1ysZXg498yBHR0OBHZaRV7ShoZLLUVrx09g21tFQYIdlpKqGCtknXL0Q3b9IRUBDgR0bHdnY7a6hoZMrn3LnJozRUGCHdeRVbWi8EN36UtbGxAQNBXZYSF7ZhsZUNvfjzxomaV1xGJ9oKLDDQvLKNnQU0Sb8rP3zqKCLLxFzNBTYsVGSjd0OGzruZev+fhTV1nJIzNBQYIel5NVt6DyiUyT0PBoK7LCWvMINffN5euU5Nv/6sIKGAjs2YrKx22VD40AL157DJxoK7LCavNoNXb8U5SL0MjQU2LHRk43dXhu6UlGuQS9FQwF0nh9P78nf3z8S0BegoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACko6EAkI6GAkA6GgoA6WgoAKSjoQCQjoYCQDoaCgDpaCgApKOhAJCOhgJAOhoKAOloKACkewsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAu8Ja3/H+npqQ98UMv3AAAAABJRU5ErkJggg==\" alt=\"A 3-by-4 matrix. Beneath it is a 1-by-4 row vector with the maximum value of each column of the matrix independently. Beneath that is the single minimum value of the row vector.\" data-image-state=\"image-loaded\" width=\"337\" height=\"279\"\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [5 3 4 3;1 2 6 3;1 1 4 4];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em = minimax(A)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371px 8px; transform-origin: 371px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 284.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 142.25px; text-align: left; transform-origin: 384px 142.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg 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alt=\"On the left, a 3-by-1 column vector; underneath is the scalar maximum value; under that is the minimum, which is the same value. On the right, a 1-by-4 row vector; underneath that is the same vector (representing the column maximums); under that is the minimum value.\" data-image-state=\"image-loaded\" width=\"583\" height=\"279\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function m = minimax(A)\r\nm = A;\r\nend","test_suite":"%% Check various size matrices\r\n A = [0.57 -0.74 -0.46 -0.7 2.06;-3.44 3.36 -1.14 1.94 -3.91;0.62 2.31 2.76 4.45 -1.1;1.95 -1.4 2.34 2.84 0.91];\r\n assert( isequal(minimax(A),1.95) )\r\n %%\r\n A = [-2.71 1.69;3.34 0;-4.84 -2.82;3.64 0.72;-4.22 -3.78];\r\n assert( isequal(minimax(A),1.69) )\r\n %%\r\n A = [-4.44 1.17 0.17 4.17 0.86 4.39;-4.44 0.2 -3.57 4.87 2.63 0.9;-3.47 3.64 0.59 0.05 -4.17 -0.59;-4.8 -4.02 -4.95 -2.29 1.62 4.42;-0.65 4.08 2.67 -3.99 0.17 1.56;3.32 -3.92 3.49 0.08 -3.29 -0.48];\r\n assert( isequal(minimax(A),2.63) )\r\n %%\r\n A = [0.54 -3.87 -1.4 -0.75 -1.06;1.8 -0.56 -3.6 -3.81 -4.97;-1.33 -2 -2.4 -0.05 -2.79;-2.61 -0.99 -4.13 2.06 -4.99;0.79 3.33 -0.71 -2.56 -3.11;3.67 -0.96 -2.43 2.85 -3.58;-0.93 -1.1 -2.02 -4.26 -2.32];\r\n assert( isequal(minimax(A),-1.06) )\r\n %%\r\n A = [0.99 -2.79;4.01 -0.17;4.39 -1.24];\r\n assert( isequal(minimax(A),-0.17) )\r\n %%\r\n A = [-4.32 -0.69 2.06;-0.64 4.62 1.45;-3.26 2.62 0.52;-4.74 -4.93 -2.82;4.55 1.8 2.72];\r\n assert( isequal(minimax(A),2.72) )\r\n %%\r\n A = [3.91 -1.82 4.09 3.53;3.56 1.09 0.92 -0.58;-0.98 4.1 -1.67 4.04];\r\n assert( isequal(minimax(A),3.91) )\r\n %%\r\n A = [2.16 -1.63 -1.78 0.49 0.53;-3.21 -3.12 -0.96 -4.51 -2.25];\r\n assert( isequal(minimax(A),-1.63) )\r\n\r\n %% Check row vector\r\n A = [3.19 2.28 -3.24 -1.4 -3.11 -4.99 -1.84 2];\r\n assert( isequal(minimax(A),-4.99) )\r\n for k = 1:5\r\n n = randi(7);\r\n A = rand(1,n);\r\n assert( isequal(minimax(A),min(A)) )\r\n end\r\n\r\n %% Check column vector\r\n A = [0.43;-0.61;-2.13;0.02;2.62;2.62];\r\n assert( isequal(minimax(A),2.62) )\r\n for k = 1:5\r\n n = randi(7);\r\n A = rand(n,1);\r\n assert( isequal(minimax(A),max(A)) )\r\n end\r\n\r\n %% Check scalar\r\n for k = 1:5\r\n A = randn;\r\n assert( isequal(minimax(A),A) )\r\n end","published":true,"deleted":false,"likes_count":8,"comments_count":3,"created_by":287,"edited_by":287,"edited_at":"2022-10-03T14:28:07.000Z","deleted_by":null,"deleted_at":null,"solvers_count":632,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T15:15:22.000Z","updated_at":"2026-04-05T16:28:34.000Z","published_at":"2022-10-03T14:28:07.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"279\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"337\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A 3-by-4 matrix. Beneath it is a 1-by-4 row vector with the maximum value of each column of the matrix independently. Beneath that is the single minimum value of the row vector.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [5 3 4 3;1 2 6 3;1 1 4 4];\\nm = minimax(A)\\nm =\\n 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"279\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"583\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"On the left, a 3-by-1 column vector; underneath is the scalar maximum value; under that is the minimum, which is the same value. On the right, a 1-by-4 row vector; underneath that is the same vector (representing the column maximums); under that is the minimum value.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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- Average Partnership Contribution","description":"The (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-n matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for n innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be NaNs for the partnerships that did not occur. (This means the total team score is the last non-NaN value in each column.) The average across the n sample innings should ignore NaNs.\r\nIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\r\n\r\nx = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\r\nx =\r\n 0 50\r\n 20 80\r\n 30 120\r\n 70 130\r\n 70 140\r\n 80 180\r\n 100 180\r\n 110 200\r\n 140 NaN\r\n 150 NaN\r\n\r\navg = partnershipcontrib(x)\r\navg =\r\n 0.1250\r\n 0.1417\r\n 0.1333\r\n 0.1583\r\n 0.0250\r\n 0.1333\r\n 0.0667\r\n 0.0833\r\n 0.2000\r\n 0.0667","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1289.83px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 644.917px; transform-origin: 407px 644.917px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 127.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63.75px; text-align: left; transform-origin: 384px 63.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365px 8px; transform-origin: 365px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.5px 8px; transform-origin: 256.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003es for the partnerships that did not occur. (This means the total team score is the last non-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117.5px 8px; transform-origin: 117.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e value in each column.) The average across the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 96px 8px; transform-origin: 96px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sample innings should ignore \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.5px 8px; transform-origin: 5.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003es.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 369.5px 8px; transform-origin: 369.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 581.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 290.75px; text-align: left; transform-origin: 384px 290.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 969px;height: 576px\" 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alt=\"A two-column matrix of nondecreasing values, with the last non-NaN value highlighted in each column. This is converted to another 2-column matrix of the differences from row to row. The total score is shown below, being the highlighted values from before. The matrix is again converted to a new 2-column matrix where each value is the proportion of the total (given by the total score values from before). Finally, a column vector is calculated from the matrix by averaging across the columns.\" data-image-state=\"image-loaded\" width=\"969\" height=\"576\"\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 510.833px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 255.417px; transform-origin: 404px 255.417px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 304px 8.5px; tab-size: 4; transform-origin: 304px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 50\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 20 80\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 30 120\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 70 130\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 70 140\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 80 180\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 100 180\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 110 200\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 140 NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 150 NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eavg = partnershipcontrib(x)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eavg =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1250\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1417\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1333\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1583\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0250\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1333\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0667\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0833\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.2000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0667\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function avg = partnershipcontrib(s)\r\n\r\navg = mean(s);\r\n\r\nend","test_suite":"%% 12 innings\r\nx = [93 10 9 14 5 46 165 7 42 14 8 88;164 44 10 31 58 55 169 24 72 18 9 98;227 53 10 34 100 144 173 31 145 63 10 228;NaN 58 25 63 NaN 178 241 35 158 69 32 356;NaN 67 143 178 NaN 195 497 58 180 84 45 372;NaN 67 151 342 NaN 277 592 69 188 88 60 379;NaN 88 224 370 NaN 311 592 75 206 151 102 476;NaN 90 NaN 376 NaN 319 643 131 226 185 102 502;NaN 93 NaN 396 NaN 325 668 131 NaN 219 105 522;NaN 101 NaN 431 NaN 326 707 136 NaN 253 109 554];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.127561166808451;0.12977999987294;0.155703562754948;0.0927761241656379;0.177085480116408;0.108446996646594;0.163626020105591;0.0902197552243652;0.0409854550106841;0.0605319499146062])) \u003c 1e-5 )\r\n%% 15 innings\r\nx = [0 36 0 16 40 2 107 49 0 58 15 0 31 35 15;47 149 13 35 43 14 115 92 0 136 106 6 62 51 35;68 204 255 96 102 23 197 183 49 136 133 45 141 72 50;71 237 382 300 114 29 211 199 55 173 137 71 142 78 102;118 308 NaN 507 132 85 217 304 62 173 204 79 162 78 133;132 319 NaN 542 251 96 238 321 67 173 248 87 181 157 150;172 NaN NaN 566 251 143 276 350 67 208 248 99 182 225 183;NaN NaN NaN 572 266 166 NaN 414 71 237 264 117 194 252 197;NaN NaN NaN 572 275 171 NaN 433 75 239 277 119 195 282 197;NaN NaN NaN 606 282 213 NaN 436 90 248 278 129 221 282 250];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.0935178799346019;0.123522199506716;0.214786109140686;0.107581946541405;0.144429580185992;0.105164272050944;0.100678940349023;0.0802158893817775;0.0295136677949011;0.0817011534864528])) \u003c 1e-5 )\r\n%% 10 innings\r\nx = [2 61 9 66 70 24 30 34 2 11;144 176 174 66 126 83 46 82 27 16;NaN 225 259 66 149 159 90 122 74 42;NaN 228 277 153 184 193 100 156 80 43;NaN 229 279 NaN 189 198 133 282 81 51;NaN 261 480 NaN 194 284 163 336 81 82;NaN 263 508 NaN 219 286 197 359 93 124;NaN 263 NaN NaN 220 299 214 466 98 159;NaN 269 NaN NaN 236 300 238 468 105 165;NaN 276 NaN NaN 239 313 239 526 112 193];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.131857056901008;0.25577103343828;0.166489867398518;0.115025818107455;0.0590566909925436;0.149512322121404;0.0855127326608772;0.0780370592130176;0.0413840570886391;0.0573537352490243])) \u003c 1e-5 )\r\n%% 17 innings\r\nx = [47 132 4 29 4 33 14 1 185 16 80 10 39 96 22 49 44;48 175 8 30 11 86 14 20 189 28 139 44 53 NaN 33 113 101;49 260 15 31 13 139 44 131 289 244 142 51 124 NaN NaN 179 103;60 277 70 77 13 161 187 143 321 331 146 55 124 NaN NaN 201 134;90 355 85 88 15 209 200 171 322 339 149 85 124 NaN NaN NaN 142;179 444 114 180 18 294 204 210 347 345 180 106 126 NaN NaN NaN 142;237 510 119 199 21 311 236 255 354 346 181 119 130 NaN NaN NaN 158;248 520 124 205 21 410 320 293 361 350 182 155 156 NaN NaN NaN 159;252 NaN 124 225 21 442 337 301 381 363 227 159 165 NaN NaN NaN 185;257 NaN 124 246 47 NaN 337 312 382 365 233 167 168 NaN NaN NaN 190];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.240994776523359;0.124242738355593;0.166998542784811;0.123630511079241;0.0693601120017177;0.133635916354558;0.0730614634765755;0.0807820974677455;0.0569861900798804;0.0682662046838182])) \u003c 1e-5 )\r\n%% 16 innings\r\nx = [38 32 56 7 87 67 6 0 42 53 8 78 28 0 19 6;140 NaN 75 51 87 100 320 21 55 119 196 100 28 63 47 48;168 NaN 92 148 87 110 329 130 55 122 218 101 32 89 101 48;213 NaN 136 283 219 115 337 176 65 167 318 117 43 196 121 85;266 NaN 150 306 252 169 339 182 86 226 357 373 50 215 121 89;290 NaN 160 347 331 NaN 379 230 106 254 415 439 61 246 130 189;NaN NaN 206 375 333 NaN 418 261 202 301 424 452 64 345 165 197;NaN NaN 206 384 333 NaN 418 273 210 314 455 494 69 353 172 239;NaN NaN 240 394 362 NaN 446 305 237 388 466 NaN 85 353 173 252;NaN NaN 248 403 NaN NaN 453 305 286 419 466 NaN 89 365 181 254];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.182240310458828;0.175892478952746;0.0875416554422805;0.152836201456442;0.112910555705271;0.12396440347931;0.113191687593507;0.0426419111443832;0.0783210269004195;0.0404773549272449])) \u003c 1e-5 )\r\n%% 6 innings\r\nx = [132 23 8 4 1 5;175 52 8 4 10 18;260 68 63 4 33 18;277 80 74 36 36 64;355 80 177 125 43 71;444 112 208 147 43 108;510 113 216 208 43 109;520 148 231 240 52 119;NaN 149 251 253 82 166;NaN 155 265 253 82 167];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.0817279080428618;0.0762315818040129;0.125787053267087;0.0983562962636804;0.169623320455451;0.13384999984987;0.0684430187318784;0.0996265908248745;0.156119498902202;0.0195055780101392])) \u003c 1e-5 )\r\n%% 2 innings\r\nx = [0 79;23 144;40 167;47 177;53 215;68 249;77 249;108 261;110 284;132 320];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.1234375;0.188683712121212;0.100331439393939;0.0421401515151515;0.0821022727272727;0.109943181818182;0.0340909090909091;0.136174242424242;0.0435132575757576;0.139583333333333])) \u003c 1e-5 )\r\n%% 7 innings\r\nx = [82 77 39 17 25 16 9;100 77 146 28 88 57 35;146 91 262 102 119 242 35;163 98 279 114 286 242 39;247 103 305 168 362 277 47;503 173 307 172 368 299 50;557 206 364 194 489 304 59;559 220 605 258 493 309 73;570 267 615 277 554 314 83;NaN NaN 631 293 556 315 86];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.107497876929982;0.11206962590314;0.173226871930385;0.0672585891886349;0.104632115131127;0.120518985511512;0.103128650710132;0.120309237160835;0.0739838287054595;0.0243239063603082])) \u003c 1e-5 )\r\n%% Only one partnership (non-NaN)\r\nx = [51 135 80 56 0 73 12 95 25 158 19 13 10 164 3 1 49 5;99 162 NaN 75 35 146 12 117 115 249 53 63 20 208 34 34 90 39;100 165 NaN 92 73 161 49 117 163 267 80 142 21 218 54 34 99 90;NaN 225 NaN 136 73 351 76 311 NaN 310 92 170 51 330 115 59 128 113;NaN 241 NaN 150 74 389 205 313 NaN 321 143 188 94 364 115 90 152 119;NaN 281 NaN 160 84 394 224 315 NaN 326 170 196 100 388 137 127 208 170;NaN 288 NaN 206 123 422 233 376 NaN 370 178 196 100 396 185 149 241 203;NaN 294 NaN 206 181 449 245 434 NaN 447 178 205 100 408 210 173 267 239;NaN 312 NaN 240 193 475 258 451 NaN 460 226 205 104 424 210 200 283 270;NaN 343 NaN 248 202 481 270 460 NaN NaN 226 231 104 424 210 200 426 271];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.211390656192859;0.168194620890675;0.0927330577536002;0.177194557307618;0.114920288442186;0.0786823138140911;0.0873894127938098;0.0799241281835184;0.0661291359874339;0.0496857216889986])) \u003c 1e-5 )\r\n%% No NaNs at all\r\nx = [2 114;9 116;18 195;20 266;50 294;206 316;215 326;229 340;242 390;247 422];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.139119673043345;0.0165397087322755;0.111820519216379;0.0881718057447666;0.0939041003895082;0.341855824395111;0.0300669647140089;0.0449277586967784;0.0855574956348217;0.0480361494330065])) \u003c 1e-5 )\r\n%% Only one innings - no NaNs\r\nx = [20;35;39;111;123;123;128;133;149;153];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.130718954248366;0.0980392156862745;0.0261437908496732;0.470588235294118;0.0784313725490196;0;0.0326797385620915;0.0326797385620915;0.104575163398693;0.0261437908496732])) \u003c 1e-5 )\r\n%% Only one innings - with NaNs\r\nx = [107;152;204;250;NaN;NaN;NaN;NaN;NaN;NaN];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.428;0.18;0.208;0.184;NaN;NaN;NaN;NaN;NaN;NaN])) \u003c 1e-5 )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T20:18:04.000Z","deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:20:52.000Z","updated_at":"2025-12-06T12:44:27.000Z","published_at":"2022-11-08T15:10:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003es for the partnerships that did not occur. (This means the total team score is the last non-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e value in each column.) The average across the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sample innings should ignore \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003es.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"576\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"969\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A two-column matrix of nondecreasing values, with the last non-NaN value highlighted in each column. This is converted to another 2-column matrix of the differences from row to row. The total score is shown below, being the highlighted values from before. The matrix is again converted to a new 2-column matrix where each value is the proportion of the total (given by the total score values from before). Finally, a column vector is calculated from the matrix by averaging across the columns.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\\nx =\\n 0 50\\n 20 80\\n 30 120\\n 70 130\\n 70 140\\n 80 180\\n 100 180\\n 110 200\\n 140 NaN\\n 150 NaN\\n\\navg = partnershipcontrib(x)\\navg =\\n 0.1250\\n 0.1417\\n 0.1333\\n 0.1583\\n 0.0250\\n 0.1333\\n 0.0667\\n 0.0833\\n 0.2000\\n 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- Career Bowling Statistics","description":"Given a vector s of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\r\nNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\r\nCareer statistics are defined as:\r\nAverage = (total runs conceded) / (total wickets taken)\r\nSR = (total balls delivered) / (total wickets taken)\r\nEcon = (total runs conceded) / (total overs delivered)\r\n(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\r\nFor example:\r\ns = [\"23.1-8-55-5\";\"24-5-84-0\"];\r\nstats = bowlingstats(s)\r\nstats =\r\n 27.8000 56.6000 2.9470\r\nHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 472.533px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 236.267px; transform-origin: 407px 236.267px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 330px 8px; transform-origin: 330px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100.5px 8px; transform-origin: 100.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCareer statistics are defined as:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 170.5px 8px; transform-origin: 170.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSR = (total balls delivered) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 166.5px 8px; transform-origin: 166.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355.5px 8px; transform-origin: 355.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; tab-size: 4; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e\"23.1-8-55-5\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003e\"24-5-84-0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = bowlingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 27.8000 56.6000 2.9470\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = bowlingstats(bf)\r\n\r\ns = mean(bf);\r\n\r\nend","test_suite":"%% Random figures (of various levels of realism) part I\r\ns = [\"47-5-6-9\";\"4.4-43-167-1\";\"35-3-73-1\";\"37-8-158-1\";\"43-11-176-4\";\"28.3-39-136-2\";\"43-18-110-10\"];\r\nassert( max(abs(bowlingstats(s) - [29.5 51.0357142857143 3.46815955213436])) \u003c 0.001 )\r\ns = [\"38-8-148-4\";\"35-42-147-6\";\"39-45-164-1\";\"46.3-4-107-8\";\"16.4-24-113-9\"];\r\nassert( max(abs(bowlingstats(s) - [24.25 37.5357142857143 3.87630827783064])) \u003c 0.001 )\r\ns = [\"2-41-199-5\";\"46.1-48-41-4\";\"5-50-76-1\";\"43-22-142-6\";\"27-22-33-3\";\"1.3-17-65-6\";\"7.5-23-162-1\";\"30-39-44-7\";\"37-38-66-5\"];\r\nassert( max(abs(bowlingstats(s) - [21.7894736842105 31.5 4.15037593984962])) \u003c 0.001 )\r\ns = [\"16-4-85-5\";\"15.1-46-74-4\";\"22-33-171-2\";\"28-48-197-6\";\"7.3-28-11-2\";\"7.3-16-72-4\";\"12.5-5-200-10\"];\r\nassert( max(abs(bowlingstats(s) - [24.5454545454545 19.8181818181818 7.43119266055046])) \u003c 0.001 )\r\ns = [\"13-9-186-6\";\"38-1-177-3\";\"17.5-44-124-8\";\"19.5-43-179-7\";\"25-17-42-2\";\"11.5-5-52-3\";\"15-32-1-2\";\"15.5-21-19-6\"];\r\nassert( max(abs(bowlingstats(s) - [21.0810810810811 25.3513513513514 4.98933901918977])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part II\r\ns = [\"43.5-36-199-6\";\"2-10-104-3\"];\r\nassert( max(abs(bowlingstats(s) - [33.6666666666667 30.5555555555556 6.61090909090909])) \u003c 0.001 )\r\ns = [\"26.2-48-119-8\";\"26-31-180-7\";\"24.4-39-102-5\";\"8-4-107-6\"];\r\nassert( max(abs(bowlingstats(s) - [19.5384615384615 19.6153846153846 5.97647058823529])) \u003c 0.001 )\r\ns = [\"26-32-62-4\";\"7.3-49-89-2\";\"14-37-134-9\";\"47-49-165-7\";\"30-39-64-9\";\"27-27-132-10\";\"47.3-23-102-7\";\"13-32-32-6\"];\r\nassert( max(abs(bowlingstats(s) - [14.4444444444444 23.5555555555556 3.67924528301887])) \u003c 0.001 )\r\ns = [\"50-45-186-2\";\"32.1-27-22-8\";\"43.3-14-167-8\";\"25.1-9-31-1\";\"1.2-3-172-2\";\"13-9-120-7\"];\r\nassert( max(abs(bowlingstats(s) - [24.9285714285714 35.3928571428571 4.22603430877901])) \u003c 0.001 )\r\ns = [\"14-13-8-7\";\"40-10-88-7\";\"31-44-14-4\";\"35.4-2-196-5\"];\r\nassert( max(abs(bowlingstats(s) - [13.304347826087 31.4782608695652 2.53591160220994])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part III\r\ns = \"11-16-166-7\";\r\nassert( max(abs(bowlingstats(s) - [23.7142857142857 9.42857142857143 15.0909090909091])) \u003c 0.001 )\r\ns = [\"45-43-122-9\";\"13.3-6-153-3\";\"48.1-36-33-1\";\"23-28-99-7\";\"45-27-153-6\";\"24.2-16-61-3\";\"30-9-168-10\";\"1.4-16-136-10\";\"37.2-34-66-5\";\"21.3-27-108-7\"];\r\nassert( max(abs(bowlingstats(s) - [18.016393442623 28.4754098360656 3.79620034542314])) \u003c 0.001 )\r\ns = [\"15-37-129-8\";\"1-20-101-10\";\"15-35-107-2\";\"34-4-148-4\";\"8.2-38-55-1\";\"12-3-48-10\"];\r\nassert( max(abs(bowlingstats(s) - [16.8 14.6285714285714 6.890625])) \u003c 0.001 )\r\ns = [\"37-7-85-7\";\"40-33-108-10\";\"29.2-2-93-10\";\"19.1-12-126-8\";\"9-49-101-2\";\"45-48-159-9\";\"46.1-50-143-2\";\"38-4-114-8\";\"19-10-32-1\"];\r\nassert( max(abs(bowlingstats(s) - [16.859649122807 29.7543859649123 3.3997641509434])) \u003c 0.001 )\r\ns = [\"3-22-47-3\";\"46-33-148-4\";\"9-4-176-9\";\"5.4-20-155-4\";\"16.3-22-8-5\";\"34.1-21-111-8\";\"11.4-14-118-5\"];\r\nassert( max(abs(bowlingstats(s) - [20.0789473684211 19.8947368421053 6.05555555555556])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part IV\r\ns = [\"34-25-192-3\";\"5-32-164-3\";\"50.4-38-85-1\";\"29-35-164-6\";\"11-7-89-10\";\"5-46-77-8\";\"4-19-142-3\";\"2.2-27-48-7\";\"17-50-36-1\"];\r\nassert( max(abs(bowlingstats(s) - [23.7380952380952 22.5714285714286 6.31012658227848])) \u003c 0.001 )\r\ns = [\"17-27-112-5\";\"17-1-158-3\";\"39-14-83-4\";\"20-50-17-7\";\"11.1-36-118-6\";\"29-11-128-5\";\"12.2-33-26-4\";\"9.4-7-174-6\"];\r\nassert( max(abs(bowlingstats(s) - [20.4 23.275 5.25886143931257])) \u003c 0.001 )\r\ns = \"28-17-161-2\";\r\nassert( max(abs(bowlingstats(s) - [80.5 84 5.75])) \u003c 0.001 )\r\ns = \"23.3-35-107-3\";\r\nassert( max(abs(bowlingstats(s) - [35.6666666666667 47 4.5531914893617])) \u003c 0.001 )\r\ns = [\"21-29-169-6\";\"22.5-28-158-1\"];\r\nassert( max(abs(bowlingstats(s) - [46.7142857142857 37.5714285714286 7.46007604562738])) \u003c 0.001 )\r\n%% Tim Southee's stats\r\nts = [\"23.1-8-55-5\";\"24-5-84-0\";\"16-2-59-0\";\"18-3-63-4\";\"16.2-5-62-1\";\"27-1-100-0\";\"18-1-94-2\";\"12-2-58-0\";\"17-4-62-1\";\"31-8-64-2\";\"16-2-62-1\";\"11-4-41-3\";\"19-4-68-0\";\"19-3-61-4\";\"23-4-89-2\";\"33-6-119-3\";\"4-0-11-0\";\"29-5-94-1\";\"32-10-82-2\";\"1.4-0-10-0\";\"28-7-102-2\";\"15-2-49-2\";\"28.2-5-103-2\";\"1-0-11-0\";\"12-2-32-1\";\"19-3-77-2\";\"3.5-0-8-2\";\"8-2-20-0\";\"10-1-40-0\";\"26-4-100-0\";\"19-5-70-2\";\"14-4-30-1\";\"24-6-64-7\";\"18-3-68-1\";\"18-4-46-4\";\"22-4-62-5\";\"20-5-58-3\";\"15-3-45-1\";\"36-8-94-0\";\"32-9-77-0\";\"23.2-9-44-3\";\"30-6-77-2\";\"28.2-8-58-4\";\"19-4-50-6\";\"26-6-76-2\";\"15-4-51-0\";\"16-1-52-4\";\"29.1-4-101-2\";\"15.5-2-58-2\";\"11-2-24-3\";\"28-3-79-4\";\"8.5-5-12-3\";\"19-6-38-3\";\"23-4-81-3\";\"20-0-93-3\";\"16-3-50-2\";\"16.2-9-19-4\";\"9-2-32-2\";\"30-9-69-1\";\"4-1-21-0\";\"21-8-63-1\";\"16-4-28-3\";\"23-5-62-0\";\"9-0-33-0\";\"30-5-67-3\";\"11-3-21-1\";\"24-4-54-1\";\"11-3-20-0\";\"12-4-17-2\";\"37-8-91-4\";\"26-3-87-1\";\"17.4-6-41-1\";\"24-1-104-1\";\"34-4-162-2\";\"30-5-83-4\";\"18-7-43-1\";\"24-8-70-1\";\"29-6-88-0\";\"25-4-97-4\";\"17-1-50-1\";\"16-1-58-0\";\"27-4-71-3\";\"21-6-52-3\";\"21-5-63-3\";\"12.3-2-26-4\";\"31-5-87-2\";\"25-4-85-0\";\"7-2-30-1\";\"17-8-28-2\";\"15-3-68-2\";\"28-14-73-1\";\"14-7-35-1\";\"23-3-80-1\";\"35-5-114-1\";\"16-6-46-3\";\"19-11-20-2\";\"23.4-10-53-3\";\"21-4-80-6\";\"24-6-60-2\";\"34-5-158-2\";\"13-5-34-1\";\"28.3-7-94-5\";\"12.5-2-48-3\";\"27-7-98-2\";\"6-2-17-1\";\"19-9-34-2\";\"19-3-71-2\";\"10-3-25-4\";\"26-4-86-1\";\"26-7-62-6\";\"19-4-65-1\";\"25-5-56-1\";\"12-3-42-3\";\"27-7-68-6\";\"25-8-52-2\";\"15-5-35-3\";\"27-13-61-2\";\"14-2-76-3\";\"24-4-98-3\";\"15-2-52-1\";\"12-1-57-0\";\"7-3-17-0\";\"12-2-33-1\";\"29-7-63-4\";\"12-6-15-2\";\"32-7-88-4\";\"20-4-60-1\";\"37-4-90-2\";\"30.2-7-93-4\";\"21.1-6-69-5\";\"33.1-6-103-3\";\"15-3-44-0\";\"20.1-5-49-4\";\"21-6-61-5\";\"13-5-38-2\";\"11-2-36-3\";\"19-7-35-4\";\"15-2-62-1\";\"17.4-6-32-5\";\"22-4-96-2\";\"26-7-69-2\";\"23-8-33-2\";\"23-7-61-2\";\"20-8-45-0\";\"25.1-8-43-6\";\"17-1-37-1\";\"22-6-64-1\";\"19-4-48-4\";\"27.4-6-69-5\";\"22-2-75-3\";\"22-6-43-0\";\"13-2-31-0\";\"38-4-114-2\";\"5-2-21-1\";\"12-4-28-3\";\"17-6-54-1\";\"12-2-33-1\";\"17.4-6-35-5\";\"32-11-75-1\";\"26-5-90-2\";\"14-3-55-4\";\"23.5-5-87-0\";\"32-1-154-0\";\"11-0-67-1\";\"23-2-100-3\";\"19-5-68-1\"];\r\nassert( max(abs(bowlingstats(ts) - [28.9942363112392 57.8933717579251 3.00492807008811])) \u003c 0.001 )\r\n%% No wickets\r\nfor k = 1:4\r\n n = randi(10);\r\n o = randi([1 50],n,1);\r\n m = randi(50,n,1);\r\n r = randi([1 200],n,1);\r\n w = zeros(n,1);\r\n bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n s = bowlingstats(bf);\r\n assert( isinf(s(1)) )\r\n assert( isinf(s(2)) )\r\nend\r\n%% No runs or wickets\r\nfor k = 1:4\r\n n = randi(10);\r\n o = randi([1 50],n,1);\r\n m = randi(50,n,1);\r\n r = zeros(n,1);\r\n w = zeros(n,1);\r\n bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n s = bowlingstats(bf);\r\n assert( isnan(s(1)) )\r\n assert( isinf(s(2)) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":28,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:38:20.000Z","updated_at":"2025-12-06T12:54:56.000Z","published_at":"2022-11-08T15:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \\\"O-M-R-W\\\", such as \\\"31.4-8-123-4\\\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that overs can be given as integers (\\\"31\\\" meaning 31 complete overs) or as pseudo decimals (\\\"31.4\\\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCareer statistics are defined as:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSR = (total balls delivered) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"23.1-8-55-5\\\";\\\"24-5-84-0\\\"];\\nstats = bowlingstats(s)\\nstats =\\n 27.8000 56.6000 2.9470]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44449,"title":"Moving Median Absolute Deviation","description":"The median absolute deviation (MAD) is defined as\r\nMAD = median(abs(A − median(A)))\r\nfor a rolling window of length n. For example:\r\nmove_mad(1:10, 5) \r\nreturns [1 1 1 1 1 1];\r\n\r\nmove_mad(logspace(0, 1, 10), 3) \r\nreturns [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]\r\nRound the result to 3 digits.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 225.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 112.8px; transform-origin: 407px 112.8px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 161px 8px; transform-origin: 161px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe median absolute deviation (MAD) is defined as\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 76px 8.5px; transform-origin: 76px 8.5px; \"\u003eMAD = median(abs(A \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 8px 8.5px; text-decoration: none; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 8px 8.5px; \"\u003e− \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003emedian(A)))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 140.5px 8px; transform-origin: 140.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003efor a rolling window of length n. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emove_mad(1:10, 5) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 88px 8.5px; transform-origin: 88px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003ereturns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e[1 1 1 1 1 1]\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emove_mad(logspace(0, 1, 10), 3) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 228px 8.5px; transform-origin: 228px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003ereturns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 196px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 196px 8.5px; \"\u003e[0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87px 8px; transform-origin: 87px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRound the result to 3 digits.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function stats = move_mad(v, n)\r\n \r\nend","test_suite":"%%\r\nv = 1:10;\r\nn = 5;\r\nstats_correct = [1 1 1 1 1 1];\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);\r\n\r\n%%\r\nv = logspace(0, 1, 10);\r\nn = 3;\r\nstats_correct = [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748];\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);\r\n\r\n%%\r\nrng('default');\r\nv = randn(1000, 1);\r\nn = 990;\r\nstats_correct = 0.660 * ones(1, 11);\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);","published":true,"deleted":false,"likes_count":0,"comments_count":6,"created_by":8269,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2021-07-03T05:49:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-12T08:08:02.000Z","updated_at":"2025-11-18T17:43:42.000Z","published_at":"2017-12-12T08:08:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe median absolute deviation (MAD) is defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[MAD = median(abs(A − median(A)))]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor a rolling window of length n. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[move_mad(1:10, 5) \\nreturns [1 1 1 1 1 1];\\n\\nmove_mad(logspace(0, 1, 10), 3) \\nreturns [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRound the result to 3 digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44940,"title":"Get Next Combination","description":"Return next combination\r\n\r\nFor example three-element combinations of 1:5\r\n \r\n 1 2 3\r\n\r\n 1 2 4\r\n\r\n 1 2 5\r\n\r\n 1 3 4\r\n\r\n 1 3 5\r\n\r\n 1 4 5\r\n\r\n 2 3 4\r\n\r\n 2 3 5\r\n\r\n 2 4 5\r\n\r\n 3 4 5\r\n\r\nfunction should return\r\n\r\n nextComb([1 3 5],5)\r\n ans = [1 4 5] \r\n\r\nFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\r\n","description_html":"\u003cp\u003eReturn next combination\u003c/p\u003e\u003cp\u003eFor example three-element combinations of 1:5\u003c/p\u003e\u003cpre\u003e 1 2 3\u003c/pre\u003e\u003cpre\u003e 1 2 4\u003c/pre\u003e\u003cpre\u003e 1 2 5\u003c/pre\u003e\u003cpre\u003e 1 3 4\u003c/pre\u003e\u003cpre\u003e 1 3 5\u003c/pre\u003e\u003cpre\u003e 1 4 5\u003c/pre\u003e\u003cpre\u003e 2 3 4\u003c/pre\u003e\u003cpre\u003e 2 3 5\u003c/pre\u003e\u003cpre\u003e 2 4 5\u003c/pre\u003e\u003cpre\u003e 3 4 5\u003c/pre\u003e\u003cp\u003efunction should return\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003enextComb([1 3 5],5)\r\nans = [1 4 5] \r\n\u003c/pre\u003e\u003cp\u003eFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\u003c/p\u003e","function_template":"function y = nextComb(vec,n)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(nextComb([1 2 3],5),[1 2 4]))\r\n%%\r\nassert(isequal(nextComb([2 4 5],5),[3 4 5]))\r\n%%\r\nassert(isequal(nextComb([3 4 5],5),[3 4 5])) % if it is the last combination in the list; return itself\r\n%%\r\nassert(isequal(nextComb([1 3 4 5],5),[2 3 4 5]))\r\n%%\r\nassert(isequal(nextComb([5 6 7 10],10),[5 6 8 9]))\r\n%%\r\nassert(isequal(nextComb([2 3 4 5 10],10),[2 3 4 6 7]))\r\n%%\r\nassert(isequal(nextComb([5 7 8 9 10],10),[6 7 8 9 10]))\r\n%%\r\nassert(isequal(nextComb([8 10 17 19 20],20),[8 10 18 19 20]))\r\n%%\r\nassert(isequal(nextComb([11 13 17 18 20],20),[11 13 17 19 20]))\r\n%%\r\nassert(isequal(nextComb([7 8 13 17 19 20],20),[7 8 13 18 19 20]))\r\n%%\r\nassert(isequal(nextComb([10 11 17 18 19 20],20),[10 12 13 14 15 16]))\r\n%%\r\nassert(isequal(nextComb([12 16 17 18 19 20],20),[13 14 15 16 17 18]))\r\n%%\r\nassert(isequal(nextComb([1:6 9 14 19 20 21 23 26 29 30],30),[1:6 9 14 19 20 21 23 27 28 29]))\r\n%%\r\nassert(isequal(nextComb([1:5 7 11 13 15 18 19 21 22 27 29],30),[1:5 7 11 13 15 18 19 21 22 27 30]))\r\n%%\r\nassert(isequal(nextComb([1:4 6 10 13 14 15 18 19 20 23 24 30],30),[1:4 6 10 13 14 15 18 19 20 23 25 26]))\r\n%%\r\nassert(isequal(nextComb([1:17 19 23 27 33 40 42 48 50],50),[1:17 19 23 27 33 40 42 49 50]))\r\n%%\r\nassert(isequal(nextComb([1:17 22 24 27 30 35 36 38 44],50),[1:17 22 24 27 30 35 36 38 45]))\r\n%%\r\nassert(isequal(nextComb([1:16 18 19 22 23 27 44 45 46 48],50),[1:16 18 19 22 23 27 44 45 46 49]))\r\n%%\r\nassert(isequal(nextComb([1:13 15 23 25 26 28 30 32 38 39 42 46 50],50),[1:13 15 23 25 26 28 30 32 38 39 42 47 48]))\r\n\r\n\r\n\r\n%%\r\nassert(isequal(nextComb(1,9),2))\r\n%%\r\nassert(isequal(nextComb(2,9),3))\r\n%%\r\nassert(isequal(nextComb(3,9),4))\r\n%%\r\nassert(isequal(nextComb(4,9),5))\r\n%%\r\nassert(isequal(nextComb(5,9),6))\r\n%%\r\nassert(isequal(nextComb(6,9),7))\r\n%%\r\nassert(isequal(nextComb(7,9),8))\r\n%%\r\nassert(isequal(nextComb(8,9),9))\r\n%%\r\nassert(isequal(nextComb(9,9),9))\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":1,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2019-08-08T07:06:35.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-08T05:11:06.000Z","updated_at":"2025-12-04T21:02:10.000Z","published_at":"2019-08-08T05:11:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn next combination\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example three-element combinations of 1:5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1 2 3\\n\\n 1 2 4\\n\\n 1 2 5\\n\\n 1 3 4\\n\\n 1 3 5\\n\\n 1 4 5\\n\\n 2 3 4\\n\\n 2 3 5\\n\\n 2 4 5\\n\\n 3 4 5]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efunction should return\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[nextComb([1 3 5],5)\\nans = [1 4 5]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56498,"title":"Cricket - Career Batting Statistics","description":"Given a vector s of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\"42\") or a number with an asterisk (\"42*\") to represent \"not-out\".\r\nBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\r\nHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\r\nCenturies is the number of scores greater than 99 (whether out or not-out)\r\nDucks is the number of scores of 0 out (0* should not be counted)\r\nFor example:\r\ns = [\"53\";\"163*\";\"6\";\"186\";\"147*\";\"0\";\"89*\";\"0*\"];\r\nstats = battingstats(s)\r\nstats =\r\n 161 2 3 1\r\nIn this case, the batter has a total of sum([53 163 6 186 147 0 89 0]) = 644 runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 381.9px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 190.95px; transform-origin: 407px 190.95px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 325px 8px; transform-origin: 325px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\"42\") or a number with an asterisk (\"42*\") to represent \"not-out\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347px 8px; transform-origin: 347px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 240.5px 8px; transform-origin: 240.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 234.5px 8px; transform-origin: 234.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCenturies is the number of scores greater than 99 (whether out or not-out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDucks is the number of scores of 0 out (0* should \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 11.5px 8px; transform-origin: 11.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enot\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 40px 8px; transform-origin: 40px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e be counted)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 200px 8.5px; tab-size: 4; transform-origin: 200px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e\"53\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003e\"163*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e\"6\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e\"186\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003e\"147*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e\"0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e\"89*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e\"0*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = battingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 161 2 3 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 115px 8px; transform-origin: 115px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn this case, the batter has a total of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144px 8px; transform-origin: 144px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003esum([53 163 6 186 147 0 89 0]) = 644\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 107.5px 8px; transform-origin: 107.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function stats = battingstats(s)\r\nstats = mean(s);\r\nend","test_suite":"%% 1 score\r\ns = \"0\";\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 0) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 0 1]) )\r\n%% 3 scores\r\ns = [\"0\",\"196*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 98) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 1 2]) )\r\n%% 4 scores\r\ns = [\"27\",\"0\",\"34\",\"9*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 23.3333) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 0 1]) )\r\n%% 7 scores\r\ns = [\"13\",\"31*\",\"55\",\"39*\",\"16*\",\"0\",\"0*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 51.3333) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[1 0 1]) )\r\n%% 11 scores\r\ns = [\"46\",\"16\",\"0\",\"79\",\"84\",\"23*\",\"21*\",\"55\",\"86\",\"85*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 61.875) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 0 2]) )\r\n%% 16 scores\r\ns = [\"14\",\"72*\",\"64\",\"2\",\"47\",\"87\",\"3\",\"14\",\"3\",\"82*\",\"41\",\"27*\",\"56\",\"22\",\"159*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 57.75) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 1 1]) )\r\n%% 22 scores\r\ns = [\"104\",\"0\",\"59\",\"0*\",\"82*\",\"97*\",\"12*\",\"108*\",\"112\",\"11\",\"0*\",\"69*\",\"0\",\"186\",\"71*\",\"43*\",\"14*\",\"2*\",\"0\",\"0\",\"154\",\"34*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 115.8) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 5 4]) )\r\n%% 26 scores\r\ns = [\"62*\",\"106*\",\"12*\",\"50\",\"5\",\"39\",\"8*\",\"48*\",\"60*\",\"127*\",\"0\",\"58\",\"28*\",\"0\",\"0\",\"0*\",\"6*\",\"128*\",\"67*\",\"6\",\"26\",\"77\",\"5\",\"0\",\"69*\",\"44\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 79.3077) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[7 3 4]) )\r\n%% 30 scores\r\ns = [\"6*\",\"0\",\"0\",\"9\",\"0\",\"17\",\"0*\",\"0\",\"39*\",\"0\",\"26\",\"68*\",\"49\",\"50\",\"17*\",\"4*\",\"162*\",\"78*\",\"39\",\"17\",\"0*\",\"52\",\"104*\",\"81\",\"0\",\"87\",\"95\",\"0*\",\"160*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 64.4444) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[7 3 7]) )\r\n%% 32 scores\r\ns = [\"0\",\"55*\",\"50\",\"125\",\"46\",\"76\",\"29*\",\"0\",\"21\",\"74\",\"0*\",\"62\",\"64\",\"0\",\"0\",\"90\",\"73*\",\"33*\",\"0*\",\"14*\",\"33*\",\"29*\",\"0\",\"53*\",\"7\",\"78*\",\"152\",\"20\",\"79*\",\"87\",\"92*\",\"45\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 78.2632) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[13 2 5]) )\r\n%% 34 scores\r\ns = [\"25\",\"62*\",\"108\",\"26\",\"124*\",\"50\",\"82\",\"16\",\"69*\",\"0*\",\"10\",\"65*\",\"23*\",\"91*\",\"0\",\"76\",\"50*\",\"0*\",\"76\",\"21\",\"96*\",\"89*\",\"129*\",\"29*\",\"1*\",\"0\",\"17\",\"0\",\"42\",\"19*\",\"55\",\"55\",\"0\",\"97\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 84.3684) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[14 3 4]) )\r\n%% 35 scores\r\ns = [\"15*\",\"0*\",\"65*\",\"26\",\"11\",\"36*\",\"12\",\"70*\",\"0*\",\"16*\",\"128\",\"0\",\"34*\",\"0*\",\"12\",\"1*\",\"0\",\"164\",\"0\",\"49*\",\"34*\",\"0\",\"55\",\"134\",\"0\",\"95*\",\"125*\",\"140\",\"2*\",\"108\",\"23*\",\"60\",\"35\",\"34*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 82.4444) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 6 6]) )\r\n%% 36 scores\r\ns = [\"0\",\"29\",\"34\",\"31*\",\"72*\",\"57*\",\"139*\",\"81*\",\"14*\",\"162*\",\"33*\",\"59*\",\"7*\",\"135*\",\"165*\",\"146\",\"100*\",\"75*\",\"0*\",\"0\",\"49\",\"46*\",\"13*\",\"85\",\"0*\",\"1*\",\"11*\",\"36*\",\"0*\",\"201\",\"0\",\"9*\",\"38*\",\"0\",\"43\",\"30\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 158.4167) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[6 7 4]) )\r\n%% 40 scores\r\ns = [\"81*\",\"51\",\"46\",\"0*\",\"8\",\"14*\",\"8*\",\"133\",\"9\",\"128*\",\"0*\",\"65*\",\"112*\",\"64\",\"0\",\"0\",\"0*\",\"0\",\"0\",\"0\",\"94*\",\"0\",\"0*\",\"1\",\"79*\",\"48*\",\"84*\",\"97\",\"71\",\"11*\",\"25\",\"27*\",\"10*\",\"25*\",\"0\",\"71\",\"28*\",\"0\",\"0*\",\"98*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 78.3158) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[11 3 8]) )\r\n\r\n%% No runs, no outs (average = NaN, ducks = 0)\r\nfor k = 1:4\r\n s = repmat(\"0*\",randi(8),1);\r\n stats = battingstats(s);\r\n assert( isnan(stats(1)) \u0026 isequal(stats(2:4),[0 0 0]) )\r\nend\r\n\r\n%% Runs, no outs (average = Inf, ducks = 0)\r\nfor k = 1:4\r\n s = string(randi([1 99],randi(8),1)) + \"*\";\r\n stats = battingstats(s);\r\n assert( isinf(stats(1)) \u0026 isequal(stats(3:4),[0 0]) )\r\nend\r\n\r\n%% Runs, all half-centuries, no not-outs\r\nfor k = 1:10\r\n n = randi(50);\r\n x = randi([51 99],n,1);\r\n s = string(x);\r\n stats = battingstats(s);\r\n % batting average = mean\r\n assert( abs(mean(x) - stats(1)) \u003c 1e-8 )\r\n % No ducks, no centuries, all 50s\r\n assert( isequal(stats(2:4),[n 0 0]) )\r\nend\r\n\r\n%% Ducks, 50s, 100s, no not-outs\r\nfor k = 1:10\r\n n1 = randi(10);\r\n n2 = randi(10);\r\n n3 = randi(10);\r\n x = [zeros(n1,1);50;99;randi([50 99],n2,1);100;randi([100 400],n3,1)];\r\n s = string(x);\r\n stats = battingstats(s);\r\n % batting average = mean\r\n assert( abs(mean(x) - stats(1)) \u003c 1e-8 )\r\n % No ducks, no centuries, all 50s\r\n assert( isequal(stats(2:4),[(n2+2) (n3+1) n1]) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:11:27.000Z","deleted_by":null,"deleted_at":null,"solvers_count":35,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:31:11.000Z","updated_at":"2025-12-06T12:50:07.000Z","published_at":"2022-11-08T15:11:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\\\"42\\\") or a number with an asterisk (\\\"42*\\\") to represent \\\"not-out\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCenturies is the number of scores greater than 99 (whether out or not-out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDucks is the number of scores of 0 out (0* should \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be counted)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"53\\\";\\\"163*\\\";\\\"6\\\";\\\"186\\\";\\\"147*\\\";\\\"0\\\";\\\"89*\\\";\\\"0*\\\"];\\nstats = battingstats(s)\\nstats =\\n 161 2 3 1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this case, the batter has a total of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esum([53 163 6 186 147 0 89 0]) = 644\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":54485,"title":"Dog Statistics","description":"The vectors ht and wt contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by NaN (Not a Number). \r\nWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 115px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 57.5px; transform-origin: 407px 57.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 43px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.5px; text-align: left; transform-origin: 384px 21.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39.5px 8px; transform-origin: 39.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe vectors \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003eht\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003ewt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 299px 8px; transform-origin: 299px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54px 8px; transform-origin: 54px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (Not a Number). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 370px 8px; transform-origin: 370px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,p] = retrieveWeight(x1,x2)\r\n\r\nht = [24.8, 23.8, NaN, 20.5, 19.7, ...\r\n 25.2, 15.0, 23.4, 18.7, NaN,...\r\n NaN, 25.8, 19.8, 18.3, 18.2,...\r\n 24.6, 17.4, 18.7, 24.0, 22.6];\r\n\r\nwt = [62.6, 69.2, 64.7, 61.2, 60.4,...\r\n 68.0, 71.6, NaN, 62.0, 73.0,...\r\n 62.4, 71.8, 74.6, 59.7, 73.2,...\r\n 67.0, 70.8, 59.2, 70.4, 63.8];\r\n\r\ny = x1;\r\np = x2;\r\n\r\nend","test_suite":"%%\r\n[y,n] = retrieveWeight(20,23);\r\nassert(isequal(y,62.5) \u0026 isequal(n,10))\r\n%%\r\n[y,n] = retrieveWeight(23,26)\r\nassert(abs(y-68.1667)\u003c1e-4 \u0026 isequal(n,35))\r\n%%\r\n[y,n] = retrieveWeight(19,26);\r\nassert(abs(y-66.9)\u003c1e-4 \u0026 abs(n-55)\u003c1e-4)\r\n%%\r\n[y,n] = retrieveWeight(17,23);\r\nassert(abs(y-64.9889)\u003c1e-4 \u0026 abs(n-45)\u003c1e-4)","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":571375,"edited_by":223089,"edited_at":"2022-10-04T18:03:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":405,"test_suite_updated_at":"2022-10-04T18:03:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-05-03T20:06:40.000Z","updated_at":"2026-03-31T14:48:30.000Z","published_at":"2022-10-03T14:27:27.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe vectors \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eht\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (Not a Number). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61059,"title":"Leaderboard for the Nedball World Cup","description":"At the upcoming inaugural Nedball World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most hilbs. After each match, they need the current leader and the total number of hilbs they have scored.\r\nGiven an m-by-n matrix, S, representing the individual scores of n participants in the m matches in the Nedball World Cup, return two m-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the m events.\r\n\r\n\r\nThat is, S(j,k) represents the number of hilbs scored by player k in match j. The jth element of the first output vector will be the total number of hilbs scored by the player currently in the lead for the Golden Toeplitz. The jth element of the second output will be the index of that current leading player (from 1 to n).\r\nNote that the index of the current leader should only change if the leading score exceeds that of the previous leader. That is, the leader does not change if points are tied.\r\nHowever, the players are already ordered by their International Nedball Association condition number (a very important statistic for Nedballers), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\r\nExample\r\n\r\nAssumptions\r\nTo the annoyance of Nedball purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of S) will be finite and real-valued. Even though Nedball is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that S will not be empty (that is, m,n ≥ 1). Other than that, do not make any assumptions on the values of the scores, or the dimensions m and n.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1310.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 655.3px; transform-origin: 408px 655.3px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAt the upcoming inaugural \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. After each match, they need the current leader and the total number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e they have scored.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-by-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, representing the individual scores of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e participants in the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matches in the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, return two \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 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\" alt=\"Example showing input and output for this problem. The input is a matrix, shown as a table with column headers of the player number and row headers of the match number. The output is two vectors, total score and player. The third element of both output vectors is highlighted. The value of the score is 14 and the value of the player is 6. An annotation explains the meaning: after the third match, player 6 is in the lead with a total of 14 hilbs.\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThat is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) represents the number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e scored by player \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in match \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e element of the first output vector will be the total number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e scored by the player currently in the lead for the Golden Toeplitz. The \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e element of the second output will be the index of that current leading player (from 1 to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that the index of the current leader should only change if the leading score \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eexceeds\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that of the previous leader. That is, the leader does not change if points are tied.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHowever, the players are already ordered by their International \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Association condition number (a very important statistic for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedballers\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 478.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 239.4px; text-align: left; transform-origin: 385px 239.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"697\" height=\"473\" style=\"vertical-align: baseline;width: 697px;height: 473px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAssumptions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 42px; text-align: left; transform-origin: 385px 42px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo the annoyance of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) will be finite and real-valued. Even though \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will not be empty (that is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ≥ 1). Other than that, do not make any assumptions on the values of the scores, or the dimensions \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [mxval,idx] = leaderboardmax(v)\r\n[mxval,idx] = max(v);\r\nend","test_suite":"%% Test case #1: v = 6-by-5\r\nv = [-5 -2 -1 3 5;-5 -2 0 -4 -5;-1 4 -1 0 -3;5 3 0 -3 1;0 -1 2 1 -2;5 4 -4 -3 -4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;0;0;3;2;6]))\r\nassert(isequal(k,[5;5;2;2;2;2]))\r\n\r\n%% Test case #2: v = 4-by-10\r\nv = [0 2 1 1 0 1 2 1 2 1;0 2 2 0 1 0 2 2 0 0;0 1 1 0 2 1 0 2 0 2;2 1 1 1 1 0 0 2 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4;5;7]))\r\nassert(isequal(k,[2;2;2;8]))\r\n\r\n%% Test case #3: v = 16-by-9\r\nv = [1 7 7 3 7 5 4 5 7;5 5 2 4 6 7 7 7 3;6 3 5 3 5 2 7 6 0;3 5 0 0 7 4 3 0 2;3 4 6 2 4 7 2 7 2;7 5 6 4 3 3 7 7 3;5 6 4 1 7 2 3 5 3;6 4 1 0 1 4 7 0 5;3 4 7 2 1 4 1 7 2;2 3 1 5 5 1 0 3 7;3 0 0 3 2 4 0 3 2;5 7 4 3 1 1 4 4 5;1 3 4 0 2 2 3 4 7;3 3 3 0 2 4 2 7 4;0 0 4 2 0 6 6 6 7;1 4 5 2 1 5 3 3 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[7;13;18;25;29;32;39;40;44;47;50;54;58;65;71;74]))\r\nassert(isequal(k,[2;5;5;5;5;5;5;5;8;8;8;8;8;8;8;8]))\r\n\r\n%% Test case #4: v = 12-by-1\r\nv = [-2;2;1;-3;-5;-5;-3;0;1;-4;-4;-2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[-2;0;1;-2;-7;-12;-15;-15;-14;-18;-22;-24]))\r\nassert(isequal(k,[1;1;1;1;1;1;1;1;1;1;1;1]))\r\n\r\n%% Test case #5: v = 2-by-5\r\nv = [2 6 3 4 2;1 4 1 1 1];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[6;10]))\r\nassert(isequal(k,[2;2]))\r\n\r\n%% Test case #6: v = 7-by-4\r\nv = [1 1 2 1;1 2 1 1;1 2 1 1;2 2 1 1;1 2 1 2;1 2 2 1;2 2 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;3;5;7;9;11;13]))\r\nassert(isequal(k,[3;3;2;2;2;2;2]))\r\n\r\n%% Test case #7: v = 9-by-9\r\nv = [0 -5 -1 4 1 4 1 -2 3;-5 -5 -5 0 2 -4 3 1 -1;-3 2 -5 2 -1 -5 1 0 2;0 -2 -3 4 1 1 2 2 4;0 -5 -4 0 -4 -5 0 -4 -2;4 4 1 0 3 -4 -5 0 -4;1 -5 -1 0 -3 1 -4 -2 0;-1 -5 -4 1 -2 2 -1 4 -2;1 0 -1 2 -2 3 0 4 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[4;4;6;10;10;10;10;11;13]))\r\nassert(isequal(k,[4;4;4;4;4;4;4;4;4]))\r\n\r\n%% Test case #8: v = 4-by-3\r\nv = [0 2 3;4 5 3;6 6 1;3 4 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;7;13;17]))\r\nassert(isequal(k,[3;2;2;2]))\r\n\r\n%% Test case #9: v = 10-by-9\r\nv = [2 1 2 1 1 1 1 1 2;1 2 1 1 1 2 2 1 2;2 1 2 2 2 2 2 1 2;1 1 2 2 1 1 1 1 1;2 2 1 2 2 2 2 2 2;2 1 1 1 2 2 2 2 2;1 1 2 2 1 2 1 2 2;1 2 1 2 2 2 1 1 1;2 2 1 2 1 1 1 2 2;2 2 1 1 2 1 1 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4;6;7;9;11;13;14;16;18]))\r\nassert(isequal(k,[1;9;9;9;9;9;9;9;9;9]))\r\n\r\n%% Test case #10: v = 3-by-10\r\nv = [3 3 3 0 3 1 3 0 2 3;4 4 4 1 1 2 0 3 3 2;2 2 4 3 1 0 4 4 1 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;7;11]))\r\nassert(isequal(k,[1;1;3]))\r\n\r\n%% Test case #11: v = 17-by-5\r\nv = [4 5 1 1 2;3 1 5 5 5;4 3 3 3 3;2 2 1 3 1;5 1 4 5 3;0 4 3 1 5;5 4 1 5 1;4 2 3 1 4;3 1 0 5 1;0 2 0 5 0;5 1 4 1 0;4 0 0 0 0;2 2 4 1 5;1 1 4 3 5;1 1 4 4 0;3 3 4 3 4;4 5 5 1 5];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;7;11;13;18;19;23;27;30;34;35;39;41;42;43;46;50]))\r\nassert(isequal(k,[2;1;1;1;1;5;1;1;1;4;4;1;1;1;1;1;1]))\r\n\r\n%% Test case #12: v = 5-by-1\r\nv = [5;-5;0;-4;0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;0;0;-4;-4]))\r\nassert(isequal(k,[1;1;1;1;1]))\r\n\r\n%% Test case #13: v = 12-by-7\r\nv = [2 -5 1 4 5 -4 1;3 -2 0 4 5 2 -5;-4 -2 0 4 0 2 1;2 6 2 2 2 -2 0;4 -3 1 -2 3 3 2;-1 0 3 3 -4 -1 4;-1 0 6 0 1 -3 -3;3 4 4 1 -3 -3 6;-3 -5 0 0 -5 -1 1;6 4 -4 0 -5 -5 5;-4 1 6 -3 6 -1 2;1 -5 -1 -2 1 1 -4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;10;12;14;15;15;15;17;17;16;19;18]))\r\nassert(isequal(k,[5;5;4;4;5;4;4;3;3;4;3;3]))\r\n\r\n%% Test case #14: v = 2-by-1\r\nv = [2;2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4]))\r\nassert(isequal(k,[1;1]))\r\n\r\n%% Test case #15: v = 4-by-2\r\nv = [3 1;3 0;5 0;0 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;6;11;11]))\r\nassert(isequal(k,[1;1;1;1]))\r\n\r\n%% Test case #16: v = 4-by-8\r\nv = [1 0 3 2 1 2 3 3;1 3 0 3 2 3 0 2;2 2 3 0 3 3 2 1;3 3 0 1 1 3 2 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;5;8;11]))\r\nassert(isequal(k,[3;4;6;6]))\r\n\r\n%% Test case #17: v = 3-by-3\r\nv = [1 8 3;7 8 1;5 2 4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[8;16;18]))\r\nassert(isequal(k,[2;2;2]))\r\n\r\n%% Test case #18: v = 9-by-4\r\nv = [5 5 4 6;1 0 4 4;5 0 2 6;2 3 0 0;0 0 3 5;6 6 3 6;6 6 3 1;0 6 5 6;3 6 4 4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[6;10;16;16;21;27;28;34;38]))\r\nassert(isequal(k,[4;4;4;4;4;4;4;4;4]))\r\n\r\n%% Test case #19: v = 15-by-7\r\nv = [5 4 2 4 4 0 0;0 5 2 2 3 4 2;5 1 0 3 5 1 3;1 0 1 5 2 5 0;4 4 5 5 5 4 2;2 2 5 0 5 4 1;3 4 4 2 2 2 3;2 5 0 3 4 1 2;2 1 3 4 5 4 2;4 1 0 5 5 5 0;5 3 4 4 0 5 1;5 3 4 1 0 5 1;3 3 5 5 3 5 4;5 3 0 5 0 2 3;3 0 1 2 1 2 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;9;12;14;19;24;26;30;35;40;40;40;45;48;50]))\r\nassert(isequal(k,[1;2;5;5;5;5;5;5;5;5;5;5;6;4;4]))\r\n\r\n%% Test case #20: v = 3-by-1\r\nv = [0;0;1];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[0;0;1]))\r\nassert(isequal(k,[1;1;1]))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2025-11-10T02:17:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":116,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-04T20:25:18.000Z","updated_at":"2026-03-25T10:57:18.000Z","published_at":"2025-11-10T02:17:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAt the upcoming inaugural \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. After each match, they need the current leader and the total number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e they have scored.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-by-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, representing the individual scores of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e participants in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matches in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, return two \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e events.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"295\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"690\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Example showing input and output for this problem. The input is a matrix, shown as a table with column headers of the player number and row headers of the match number. The output is two vectors, total score and player. The third element of both output vectors is highlighted. The value of the score is 14 and the value of the player is 6. An annotation explains the meaning: after the third match, player 6 is in the lead with a total of 14 hilbs.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThat is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) represents the number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e scored by player \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in match \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e element of the first output vector will be the total number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e scored by the player currently in the lead for the Golden Toeplitz. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e element of the second output will be the index of that current leading player (from 1 to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that the index of the current leader should only change if the leading score \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexceeds\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that of the previous leader. That is, the leader does not change if points are tied.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, the players are already ordered by their International \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Association condition number (a very important statistic for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedballers\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"473\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"697\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAssumptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo the annoyance of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) will be finite and real-valued. Even though \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will not be empty (that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ≥ 1). 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Kifpb+G9M0yeVyVJaXsvMO2xU6HCGEENsILWdC0lmHdDqLqeFeDyedxVl3RKzneXot5183gHe9hviVUriebhv/DQxTvxyy2SzZbLbhNCyj4a18bl2fjfU5eMWdwy95nvrP+1DkOeRyOWpraggGgwRDIeKJeMN9KItq8yz9kqJh1tDztHsfJYQQQmNaFiF77tQapRThgK/QoWy2eCpDy4oSbJ+PkmhYm67dGwoG/Phsm4DfT2k0gqVjDn4fPp+NaRh65hBoWFrj9/kA1ZCDRrNqjVzPI+D3YdkWpdEwlmazCNCYQ8NnQstnCfACfkJB/WaWhRBC6EvL5VizFi+nJpHS8svecT12a9ucFqURspp1jG6kFPhsC08pXFez2Zx1lKJh6Y8Cx3X1zgFwHI1zsCww9M7BskxMwyCnaQ4ApmEW9XI4IYQQTYuWMyH/7x+TeHvm95RquCckVp/iqdHDOWH/rvxcE9Nu6QY0LKOpKIuScxwSybS2OZSXRnA9j3gipeXSPs9TlJVEUChi8aS2OZREw1imQW2sXtscopEQPtuipi6hZw5KEQr4qSjTpweQEEIIvWlZhPgsi4Bt4dfwrZ1lW9K3RAghhBBCbNO0LEIM49+nHOlGx5iFEEIIIYTIJ/02VQghhBBCCCG0puVMyK/JOS6O52GbJj4Nl2wJIYQQQgjRlDW5mZBYKkNFNETnti0oj4aIJdOFDkkIIYQQQgixgSY1ExJPZxhx2D7cNPxwysJBfo4nuXr8W/zj/S+Jyhn4QgghhBBCFIUmMxOSyubYtU0z7vnT0aysjXPWff8kk8tx38iB7NSiinROz54cxaDxEABdDwPYkO456B7/hppCHoah7z+hTeUzLYQQQk9NZiZEKSgNBXlzxjzumfQxkyZNo8tOrTnviP1pXhZl4cq1oF+D9YJSSmHbNpFIhA17WiYS9QWMasuEQiFc1yWV0muZnmEYlJWVkclk8DyPUChEbW1docP6TZRShMNhlFIkk6lCh/Ob6J5DNBrdqPhIZTIo5RUwIiGEENsafV/j/UIk6OObJSsYeOsTTPpyDu26tufY/bqy5OdavlmygqC/ydRbW00wFGLJ4sUMGng0Rx3Zl/79jqB/vyOYP38eAb9eFZ1SiqqqSu6/7x5OPnE4pmVimno8/oZhEAwG+cuf76Tv4YfR9/BDufWWsfh8Pu0a4ymlqKqu4plnnmbQgP7kcrmGjukaaXiWqnjppRc4+qgjSKVT2LYe/74YZsNzf/HoC9d/pvsd8UcmPDOe0mik0OEJIYTYhujxzbkJlALbsvBUhvYtK3np8lOoiIbpe+PfiKcylGjYXb3Q/D4/c+bM4Ycf5nPT2FtJp1N4nkdlZRWO6xY6vE2mlKKsrIx//etfXHHZpeyw444Nb4E1WYZSVlbGQw8+wDVXjeHpZ54lGAxy6sknUVlZxQXnnUNCozfx0WiUOd/NYfSoCwgEAnjKwzAMNphoK3rhcJgFCxYw6rxzyWSzuK7b8DxpkIRlWsTjMd6e/BbnX3AhzZs3J5vN0n7nDqSzWaJ2qNAhCiGE2EY0mSLEMAxqEkl2374V/7zkRFyl2OPiu5g9bwll1eXrBjrFP0goJqZpsGrVSg44oCfHHTuIrAt+C36uieM4+hQhPp+PdDrNZZdczIiRZzJr5kw8V5+lJ46TA+De+x9g6JBBABwzeAhvT57MBeedgx6lFJjr3sJfNOp8hp9wItOnf4mnUTELDf/O+P0BLrzgPI4ZPISvZkzHdfTZb2aaBjU1NbRs2ZIzzzobn8/GZ0Ii45BJ1hMNSxEihBBi69BjPcomSKQzHLDbDrx3w0h2aF7Bh98uoE+3Dtxwen+679CKVCZb6BC1Y9sWdXV1fDxtGgMGHsOBPffnkssux3EdLI2W0FRWlHLl5ZfSuUtXTjrlVGLxeKFD2izxeILTTj+D4SecxM81MZJZl2lTP6LHfvsBoENprZSiuqqcsTfeQFl5OeePuohYLFbosDaLUorq6gruvOM2TNPk0ssuJ1anVw6WZZFIxFm0cBGnnHQCvXsewJBjhzJ/3jzCISlAhBBCbD1NZiYkk3PYZ+ftyDouy9bGGNSjCyf03gOlFKvqEkybs4igZvsYCi2dStOpUyeOOLIfxwweDApOPmk4ruvx5ztuJ5Mt/sKupKSE116bxBeff86/Pv+c997/ANM0CUf0Wf9uGAb19fX4/X5KS0s59eQTqays4OxzziWVLv57AA3LsKZN+5TXX3+VDz+axqJFi8GASCSizelMkUiEL7+YwYRnn+XtKe9Rt64AacxBh4lW13UpKyun/9EDOPSww+jYsQM33nADp550Ah9/+mmhwxNCCLENaTJFSEU0zBPvz2D8RzP/488c16U8Gi5AVHqLJxIcfMihDBkyiHgiRVk0xIUXXcxjf3sU7ri96AePtm1TV1fH2WeOZMixQ/ngvSl8+OFUYrEYU955mz/+sU/R5wANb+CDwRA+v4+hQwaxYsUKXnrlVcDAdV2KfT2WaZpks1lGnHEa+/bYj+lffsbnX8wgmajnnbffZsDR/fH5ivufItM08ZRixJ9OY7dOnZj9zSxmzvyaZDLJO++8zaBjjiGkQS+ibDZLs2bNuO+B+8lmHUzL4qabb2X/HnuzeMmP7NZx50KHKIQQYhvRZJZjGUDOdalPZ//jfznXK/ZxWtExDIOSkhJOOP44brttHGXRhqUa07/8gpYtWwLFvw/XMAxidXXss+++LFy4gHF3/pk3Jr3O2rVreH7Cs3iuq8UJWbZtk06nGDr4GALBIB9/8jHNm1VhGKYWRZRpmsTjcbrtsQe1tTXcfvsdvPTiC9TW1THhuWdIp9NFfx8MwyARj9Oly+6k02luu+12Jv7zeerqapnw7LMkk/VaLFEsLy/nsb89Qr8jjiAQsAnaBl/PmomBQUV5eaHDE0IIsQ0xVJ52a8+cOZOlS5dy5JFHsmzZMsaNG0c2m+Xyyy+nbdu2+bjEekfd/Djvzpqv5YlXNfUpnhx1HMN6dGHZmlrMIh5ERqNRXnxhIldefik9e/UmnU4za9ZMHnv8SXruvy/1yRSJZLqoc2gsppRSRII+Xn9jMqMuOI/pX32D32+Sy2aJJ1JFfdRteUU5J58wnOcnPEffI44EIJVK0aFDR+69915cz6W2LlHUORiGQTQaBQzCQZt/ffYlQwYP5IvpM6koK8V1HWpj9UWdAzQs7wODYNDm61mz6XfE4Xzy2Re0aN4Mz3WoKfL7EAgE+PHHHzl+6BBatW5Nu3ZtmTx5MqMuHM1FF12IXcSxCyGEaFryUoS89dZb9O3bl2HDhjF+/Hg6d+7M8uXLadmyJel0mhkzZlCex7dsUoRsHUopSktL+eGHH3jn7bcwDIMj+/WnZavW+C3IOU7RFyEbsiyLmpoaFi5YwO7dulNeGsFx3aIvQmzbZsb0L4nH48RicTyv4UjY0tIyjjyiL57yqNNgAN/Isizi8Thz586ha9dulJeVYBpoUYQ0Mk2TZDLJd7Nn06VrV8rLSrEts+iLEKUUoVCIVCrFG5NeZ9WqVfTosR977bMPuUyairKSQocohBBiG5GXImSvvfbikEMO4eabb+b1119n4MCBLF26lNatW9OlSxcuueQSTjnllHzEC0C/sX/Xugh56sJhWhQh0DBoCQSChNYd3ZmsT5JOp6ksL9GuCGnsAB8MhojH45SXRnA9r+iLEKUU4UgE3y8a4uVyDrYJCkUsnizqHDaklMKyLMLhMLFYgpJoCMs0tCpCNswhHk8QCQfx2VbRFyHw789BOBLBMk0ymQz1ySShgF+KECGEEFvNFu8G9TyPWCzGiBEjsG2biRMnsvfee9O6dWsA9tlnHxYtWrSll9lIzvXIOC5+jXpVNHJzLq7X0KOiyLdUAA3LaLLZDNlsZsMfrv9PHXJoZBgNG7nr6xMbxV3sORiGQSqZ5JctCV1PUV7ScMpXseewIcMw8DyPRCKB2iByXXPwlF45NH4O4hsckaxD3EIIIZqWLS5CDMPAtm0++ugj2rRpwwsvvMANN9wANLxxmzp1Kn369NniQDc07uQjWZtIYhf5Ztb/Jud6dG7bAkyT6vJSXZp2b0Qp8NkWPp9NwO/XNgfbtkAp/D6fvjms2wzts229czCgusLSNgfLMjENg+oKfT/Tlob/ngohhNBXXpZjPfzww5x55plEo1Gi0Shz585l9uzZDB/Qr2LfAAAgAElEQVQ+HM/zmD17NuFw/o7IvfTJSXw5fynhgH59P+LpDNcMPYw9t6tm6aq1RX8q0H/jei5tWzUnlc7yc00tlln8pwL9kuu6tG7ZDMdxWLWmRtscWjWvBqVYvnqNFqcz/ZLruTSvqsC2bZatWK1tDtUV5YQCfn5csUrLZ8nzPEqjEdq2bl7oUIQQQmwj8nI4/8iRI2nTpg1z5sxhyJAhlJaWks1mOeywwxgzZkxeCxCA6T8sY8rM7wmH9dsTkkykWN5nX5wWZcQTSSxLvyIk57rkcg6ZbJZYIolPw4FjznFplsuRyzla51BVkQOlGnKwNczBdSkviaI0z6EkEsa2TG2fJdd1i75XixBCiKYlb986/fr1o1+/fuv/f+/evendu3e+fv1Ggn6bYNBPJOD/XX7/7ynruNhWQ38H0zS1nAmxlMIwDAzDwNI1B7Pp5NDw35rm0BSepSaQg1JKmwMmhBBCNA15+7b87rvvOPfccznkkEOYMGECs2bN4rjjjsv7pnQhhBBCCCGE3vJShHzyySd069aNzz//nKVLlzJt2jTKysr47rvvOOigg4jH4/m4zGbJUw9GIYQQQgghRJ7lpQi5+OKLGTlyJJ999hkjR46ktraW7bffnlmzZmEYBq+99lo+LvOrDMMglc1Rk0hRn8lRU5+iPpPV8rQaIYQQQgghmqq89AlZuXIl55xzDgCJRGKjE27+8Ic/MH/+/C29zK8ygGQmy86tqjnviP3ovkNr5i5bzYNv/YvpC5cS9ut3kpYQQgghhBBN0RbPhJimSSAQYNq0aQCEQiF8voYBv+u6fPzxx+y6665beplflXFcmpdFmTr2LM44dG/qEkkG9+jCa2NOZfvqcjI553ePQQghhBBCCPHr8nI61kUXXcSZZ57J8uXLmT9/PmvXrmX8+PHceuutVFRUcNRRR+XjMv+T63m0LC/hX/N+5MkPpvP4PyZxwVmDuOuM/uzUoooFK9cSkCMoN5tSimAwSDAYAiCVSpJLJgsc1eZRShEIBAiFGo6KTqVS5HL1BY5q82yUgwFpDXNo1JhLMBRibc3aQofzmyil8PsDhMMh1tbUFDqczRaJRLEsEwUYGCST9bKPTgghxFaVl1H5iBEjsG2b66+/nqVLlwLwwQcf0LdvX+677z5CoVA+LvM/hf0+5ixdTZ/rH6VVRQl3XH06A/fpxLuz5vPZ90u0bGxYaEopIpEoP8yfx3vvvo1pWvzxyH40b9FSmwFLYw4//riYt9+chAIO6/NH2rTdXqscwpEIPy1Zwjtvv0kul6PP4X1p224HlFLotOVJKUUoFObHJYv5/F+fcOgfj9DySNtgMMSKFcuY9tGHHHZ4X21yaDzS+aMP32P50qUoFJ6n6LjLrvTq1bPA0QkhhNiW5OWbc/Xq1Zx22mksWrSIRYsWMXfuXJYvX87zzz9P8+ZbqQOvYeB5Hp5SVETDnNC7O+1bVvHTmjpyroeh1VCtOEQiET7/7FNOGjaYOd/NZuZX0zl52BAWL1q4fmak2IXCYWbNnMEJxx7D3Dnf8f3cuYw47URmzfyKcCi/TTR/L6FQiDmzv2X40IHM+PILvp87h1NOGMo333xNKM+NQH9vtm1jWiajLzib6666As91tRnANzJNE7/fx2UXj+K6qy4nm81q0+ndNE0cx+HGa6/ijUmv8dEH7/PR+1NYvHgRfp+8qBFCCLH15OXbv0ePHjzxxBMAtGnThp122oloNJqPX73Jco5L2+py+nbvyIqaGK1OvYHRj7/GKQf/geG9ulObTG/VeJoCvz/AixMn8McjjuLvTz3HP8Y/T6vWbfjnc89q063e5/Px6SfTOLzvkTz82FM89dw/2WW3zkyc8AyhcLDQ4W2SYDDEa6+8xF779OCJZ/7J3596jm7d92T8k48T0qQYhIZZkPKKSu79651kMhl22W03PM8rdFibRSlFRWUlD913DzVr19Jxl121ysEwDOrq6igvL+fxp57jny9P4ukJL3JU/wHEYrFChyeEEGIbkpciZMiQIYwfPz4fv+o3i6UyHNh5J964+nTO/mMPwpaFb93byXDAp83Sm2JSV1fLhRdfysWXXcnSn34km82gAL/fjy5/m/FYjJNPPYMrr7meRYsW8Mm0aaxd8zP77rc/2Wyu0OFtkrq6Wk49YwQ3jL2NFcuXkUmnQal190GPO6GUorS0jI/en8KUdyZz3U23kMvltPpcKqWIlpTw2aef8OorL3LDzbfjup5WOViWRX0ijlKKiROe5bKLR/HiC8+DYWDZsmdOCCHE1pOXb51OnTrxl7/8hf79+9OpUydc193oz8866yx23nnnfFzq/1QSDPDqF7MZ0Wcfbjj+cMYMOYSAz2busp95+bPZlIT0eHNfTJRSlJVXoDyPqupqnn7ycX76cTF3/PVe6utThQ5vkyml8Pn9THj2KZ78+2N06LgL/foPIJXUI4fGAXzjW/jXXnmJL774jCeffYFUvR6b030+H6lUkrE3Xssll19F8xYtyWYyGIY+S7Fs28bJOdx43VWMGn0JbdttTyaT1ioHwzDxPI+6ujqmT/+cqupqxl5/DYf2+SO333FnocMTQgixDclLEfLRRx+x++67M2/ePGbPnr1+8yM0DKAGDRr0uxchfp/J2niKfmP/Tt89OtKhVTULVq7lra/m8XOsnnDAr9Uby2LhuS5V1dU88fe/8edxt3DfQ4/RslVrsplMoUPbJA0boUPkslkGH3s8Rw8czNjrr+H6q6/kiSeeIJ3JFjrETeK6LtXNmvHKixO55srLuO3Ou9i5Q0fSmUzR73YyDINgMMi1V11B9z32pP+AY/jgvXfBMLRqJBoKhbn5xmtp374Dg4cez2effoyhWQ6pVJKdO3TkxdfeIhKNUl5ezoEHHsKI009m9OiLaVlVVugQhRBCbCPyUoQ8+uij+fg1W0QpCAVsEukM/3jvS1xPYZkGkYBflmNtgYqKCh598H6eHf8kz7/4Ol27dWfFiuWFDmuTNAx+Q9x0/TUceNDBHDVgEOFQmMMO78tf/zwO13U2KpiLWWVlFc+Nf4oH7r2Lx59+jgN6HcjyZcswjFDDw1/ELMtizZo1vPfu27Rq1ZoBRx5GLBZj5coVXDp6FE89/RQVFZWFDvN/siybWF0t7779FpWVlRxz1B9JxOOsWrWSyy4exRNPPkXr1q0KHeavCofDvD35TZxcjmOHnUA6laayuhrTMMjl9FieKIQQomnI6yLg+vp6vv32WxKJBNtttx0dO3bM56//VUqBz7Ioj4T+4+di80UiEW67+UYeuv8ezr/wYmbN/IoPP5hCdbPmnHzC8ZAu7tmQxn4UkUiEq664BAwDn8/PQ/ffw+F/PBLb9qFU8W8qjkajPHT/Pdx28w2c9qczWbRwIdO//IKysnJOPOH4ot8Y7boufr+fvz/5LPXJemzLZu6c2dx0/dWcec55hEJhXLe4m4m6rothmjzy9yepr6/HNE0WLVrINVdcwsizzyNaUoLrFHcOAJZtozzFJRedTzKZpP3OHbjrztvpvucfaLNd20KHJ4QQYhuStyLk9ttv58YbbySRSKz/2V577cX48ePp0KFDvi4jthLDMMjmcqRSKY446mjmzvmOWTNnAAYddtkV65STtJhFiMXqGDX6Upo1b84/HnsEFAwcfCwnnHwaiUS80OFtEtf1iMViHNGvP8uW/sSCH+YDsF3bdpx04vCiX46llMIwDNptvz3GukKwpLSEIccOo3OXrpimqcFMZUMO27XbHtMwsG0fVdXVDB56PF1274ZlWRrkAIl4nL5H9iMQeIznnxvP66++zC677sbZ519ILqfH0kQhhBBNg6Hy8M35wAMPcM455zBu3DgOPfRQwuEwS5YsYdy4cXz77bfMnj2bsrL8rTU+6ubHeXfWfC03m9fUp3jqwmEc1rENP/y0Cssq3k2tjacB2Za90SlM6XSGVtVlJNMZlq9ag13kPRIMw6CktBQn56BQ+H1+ampqaNemOY7j8tOK1UWdQ2PDRZ/Pt9F9yKSzVFdEQSkWL12JbRdvDhtSSmHbNuFwmNq6Otq0qMbns1m4ZLlWOViWRSQSpbaulpbNKomEgsxfvLSon6VG0WgUDAMn5xAI+InFYpREQnTYUWZDhBBCbB15mQm5++67ue+++zjnnHPW/2yXXXahT58+7LDDDrz88sucfPLJ+bgUAPFUhnQihVPky1D+GyeRJOu4KKVwXLfoj1itq6v7j585rkur6jI8z8P5xUloxWrt2rUNDSuNhgGkk3NRoE0Osfh/9nBwHJfKsggoheO4FP2UyAbcbJZMNovjuuubjGqXg+eRra1Zn0PjZ1oHtXV16z8PifoEruMS0aRvjhBCiKZhi4sQz/NIpVLsu+++//XPd911V5YsWbKll9nIib33YJ8ObQn49DvXPpXJ0aVtC4LBIK2aV2GaGo261vE8RSgYWLeMBj1zcBWRUBDX56N182p9cwgHMYDWLaoxLQ1z8BQlkTCWbWmdQ2k0QsCv8bPkKSlChBBCbFVbPIo3TZOqqiqeeeYZ/vCHP2z0Z3V1dXz22WecffbZW3qZjbRrVo5tmfg0WbqxoWzOoTQcJFoSxfb7Cx3Ob+bz2QT8fsIhfQcuPrthmVkwqN+yvkY+21rXQNJX6FB+M9u2MDBo1UK/lwqNbMvCMA1aNa8qdCi/mWUW79JQIYQQTU9evvXHjh3LEUccwXfffcfhhx9OaWkpc+fO5W9/+xu77bYb/fr1y8dl/n29f07hoxnzsDTcE+LWJ3n8spNpX13GzzUxTA02d/+S5ykqy6PkHId4fVrbHCrKIrieRyye0vbtdXlpBIWiLpbUNofSkjCWaVBTV69tDiXRED7bYm1tQs8clCIc9FNZXlroUIQQQmwj8lKE9O3bl7fffpurrrqKMWPGkEqlqK6u5thjj2XcuHHYdn7fcJaGgwRLwnpuTDeN9cvITNPQcgAPrGvSZjSdHDQcOALrTyjTPYemcB+0zkGhxWl3Qgghmo68VQeHHXYYhx12GKlUimQySVWVvssShBBCCCGEEL+fvC0CXrNmDVOnTiUUClFVVcXXX3/N1KlT8/XrhRBCCCGEEE1EXoqQ+fPn07FjR84999z1P3v99dfp1asXl1xyST4usVkMw8BxPWrrUyQzOVlmIIQQQgghRBHJSxFy4YUX0qlTJyZNmrT+Z6NHj+all17ijjvu4Msvv8zHZTZZJudQEgpw6sF7sf+u25PJOVv1+kIIIYQQQoj/2xYXIUopvv76a26++WbatGmz/ud+v58BAwbQq1cv3nnnnS29zGbFE09lGHfKkTx89iBOPnBP4qn0Vru+EEIIIYQQ4n/Ly0yIYRisXr36v/5ZTU0N4XA4H5fZhDhgTTzJaYfsxdD9dwcgnZPlWFvKMAx8Ph8+n0/7v0vL0q+3zC9ZltXwsGuqKT1LkoMQQgjx22xxEWIYBv3792fkyJFMnTqVVCqF4zisWLGCK6+8km+//ZajjjoqH7H+7ziARDrLrts158Ezj+H5T74m57jSgGsLKKXw+XxEIhGWL1/OypUriUaj2LaNUoWObvMYpkkkEiHn5FC6Bb+OYRhEIhEcx0F5XqHD2SxKKSzLorS0lNWrVrFs2TLC4TB+v1/L+9F4H1zXLXQov1k4EsHzPBxHlqsKIYTY+vIyQr/tttvYd9996dWrF1VVVVRXV9OqVSvuvPNOnn32WXbcccd8XOZ/cpXC8xSPnjOYGQuXcvHjr+OzLRzXw805Or84Lhjb9pHJZDjtlJM4YdhQhh07mLPPHInj5LBtPYq7xkKqoryce+++i4N79aS2tlarDuNKKWyfj8rKCh55+CEO7Lkfq1evIhDwFzq0TWZZFpZlcdGo8xky+BhOGj6ME44/jtraGvw+Pe5FYyFVXV3JhOeepXfP/Vi0cCGhUKjQoW0WwzCorq7kow8/4MBeB/DJx9MoKSkpdFhCCCG2MXnpExIOh3n99deZOnUqn376Kclkkvbt29OnTx+aN2+ej0v8qlgyzSUDD2T/XbbnzAdfoGu7FgC0riilfetmrK1PYcusyGYpKyvl8ssuYebMr3hnyge4rkufQw/ib488zNVXXUk6ky10iL8qGAzy45IlnH3mCFasWM6aNWtwHEerJSiBQIAVK1YwaMBRLP3pJ1atWkUuq9cyw9LSUh5+8AFemDiR9z+aRlVVJYMGDuDWW8by6MMPkUwV/8yOz+cjHo9zwvHHseCHH1i2bCnpdBrDNAA9ZnPMdf8GnnLSSfzr009Ytnw5iUQ9lmUh8yFCCCG2pry2Mu/Zsyc9e/bM56/cZK4H+3ZoC8BDZw1a//N+e+1KIpNl+F+foSq6dfamNBWxWIzBg4/l1NNOp22blhhAy5atyGQyhQ5tk3meh+u5nP6nEXTq1Jnjjh2Mp+FSJtdxOOnkU+m+xx4MHng0rqfXMqBEIsF+BxzApDcns+uuHbCAdu221+pZUkqRy2YZeMxgevXuzcD+R+I4uUKHtVmUgmw2wwE9e3H1tddz/NAhZLMZLZfECSGE0FvepgbGjRvHmDFjgIYeIR06dGCHHXbghRdeyNcl/qfySIBrn3uHQ659hF5jHuTUe54H4O2Z33Pl029SFg5ulTiaklwuR6cuXdhll1257/4H6NX7QCKRMBde/P/IanLscTabpW3bdpx+2in4fD6y2eKfvfmlbDZLy1atOOOM0wgGglrmkMvlaN9+Z/bYszvPjH+OvkccyQ8/fM+NN91CJptDh5kEx3Eor6hgxMgRlJaWkclktJqNAlDKw+/3c/oZI9iubduGmRzNchBCCNE05G1PyKWXXko0GqW2tpaBAwfSpUsXhg4dyvDhw5k3b14+LvM/2ZbJghVr+HTeEj6Zt5gPZy9kytfzeXPGPBasXIvPkqVYv4Xf58NxHFq1ak3PXr2IxxN8+P77WJr8fRqGgeM41MWTuK6r5YDLMAxc16UuVq9tDgA+n59czqW6WTU99tuPQCDI5LfewNbkxDLDMPA8j7q6OI6j13K4DSmliMXqyGpYRAkhhGg6tng5llKKxx57jKeffprhw4dz33334ff7efHFFwH46quveOONN+jYseMWB/u/44CAzybgA/ATS6XpN/ZxfLZF87KoLDf4DSzL4vXXXmX3bt046ugBDD5mADfcdDPXXD2GQQP703AmmRC/zu/38847k9m+3fYccsih9O1zKB06dOSSi0dzysknrd+rIIQQQohtQ16KEKUUe++9NwCvvPIKBx988Po/r66uJh6Pb+llNptpGERDAYx1MYrNYxgGpaWl/OXOO2iz3XY8/ewEsi589dUMWrdu8+u/oAgppcjl9D2iF/TNobS0hGfHP83ixYt4Z8oHWCE/X3z+GdXNqrFtm2xWr70VgJb34Zccx9Fuj5QQQoimYYtfP5qmSbNmzbjjjjuYMmUKkydP5owzzsDzPGbNmsVrr71WsM3q8p7+t1NKkU6nufeBh1iyZDEH9z6Ag3ofwPJlS7nltttxXBcd1vFvyLIsysvLtX7rbpom5eXl2jVdjMfjjL3lNiKRCIcc2ItDDjqI996bwp1/uQvP81BKr4GwYRiUrbsPOhcipaWl2vZqEUIIoTdD5eHb57PPPuOII45g7dq1HH300bz88su88sorDBgwgEGDBjFx4sR8xLreUTc/zruz5lMSCuT1924NNfUpnhx1HMN6dGHZmlrMIl6TrZQiHA6Ty+X46qsZAHTvvgeWZRPwWTiuQyKZLuocGq3fG1JXR3l5OYZhUlYSxvU84okUpqlHDq7rUltbS1lZGaZpURoNo1DE4smizkEpRTAYxDAMZn71FelMmt1370Y4HMHAw7ZMamP1RZ3DhpRS1NbUUFJaimXZRMJBfLZFTV1CmxwMw6CmZi2hUBh/IEDQ76OiTPqFCCGE2DryckTvPvvsw/fff8+KFSvo1KkTAJ07d+a1116jX79++bjERhzXI+e45By9jikFcBwXb13dpyjyuQTDIJlMYlkWe+29DwDZTIZMJkmwvGGwUvQ5rKM8D8uyadmyJdlsFtf995t3pfTJwTTNdTnkNurWXfQ5GAbpdBrTNOnWvTuGYZDJZEkkEpSWRAANcmikFIZh0LxlS5xcDsfR6D40Ug0nZTVr1hzXdclJ13QhhBBbWd76hFRWVlJZWbn+/7dv35727dvn69dvpKokTKvKUqJBfTpGNwoGfIT8PjAMbMvUYhYBIJdt6OdgGGDbFoZhYGqWA4DrOliWiWEY/87BNrV5ew2NORgYhrX+dCOdcsjlGo4YbnyWzHX3QqccADzXwTQNvXPw3H/fB42XKQohhNBPXpZjbW1zl66mLpnG0vBL0/U82resoiISJKNZ1+tGSin8Ph+e8nAcPY+MVUrh89mgIKdZB/VGSil8dsN7BO1zMCCX0zeHhkLEJJvT9zNtWRZ+X1771wohhBD/Jy2/ca597m0++HahljMhtck0D589iAPbt2LB0tXa9NvYkOu67NSuNfWpNCtXr9VukzSA67hs37YluZzDspU/a5tD2zYtQCl+XLYKy9YwB9eldYtqfD6bxT+u0DaHFs0qiYSCLFiyTM9nyXWpLC9hp3Z6nnwnhBBCP797EfLEE0/QrVs3unXrlrffWZdMszpWT0aTrt0bqqlPks45KKXWrenXbiIKx3VRSuEpte6ULP00pRxozEG/F/A4bsMeKdWUctCQ67ob7ZMSQgghfm+/uQhJpVLEYrH/c+mBaZosW7aMU045hWeeeSavRYhlmvgsE1vDWQTbstbvoWjcl6CbxpgNJIdC2jBmnXMwNvxvyaEgdI1bCCGEvn5zEfLkk09y1lln/Z9fXI3HiXbt2pW+ffv+5gCFEEIIIYQQTctvLkL69OnDhAkT/uuJKo1d1KuqqujRowehUGiLgtwc3i/PyDTQ6vQmIYQQQgghmrrfXITsuOOO7LjjjvmMJS88TzWssVGAAYai4SxQIYQQQgghRFHI28b0BQsW8OGHH65voOY4DsFgkEwmw5FHHsl2222Xr0v9V0opHM/jobMGsedObQj7fQDM/mkVQ+94CtPUq5+FEEIIIYQQTVVeipA33niD/v374/f7SaVSAESjURKJBOXl5XTt2vV3L0JcBQHbpk+3DtTWp3jli++IBHysrKvfaOOoEEIIIYQQorDyUoSMGTOGM888k1tvvZXrrruOZs2aMWrUKP785z8zd+5c9ttvv3xc5n/yPI/SkhAVkRB/e+dz7nj5Q8IBHwtXrKGiJIJpGejXlrE4RKJRbNvGALK5HLFYrNAhbSZFJBLF9jXMjjk5h7q6ugLHtPkikSg+nw+FwnUcamv1ywEaZi1DoTD+gJ+1a9cWOpzN1tDo0kc0WkI8HiPn6HVUeMPff4hgMLR+/15tXS3yD6QQQoitaYuLENd1qa+vZ/To0ZSUlFBZWUkqlSIUCjFmzBj2339/PvzwQ3r37p2PeP9PnucRWrcEa/TRvfh/Axqu9+zUmZz/6Ms4rqdlh/VCi0ajvPv2ZF5/7WUs0+ToYwaz7349Cx3WplMQKSnho/ff45WXJqKAfv0H0LP3wYWObLOEIxE+fH8Kr778Ao7j0K//QA48+NBCh7XZlFKUlpbyybSpTH5zEueMGq1dc7+qqmqW/vQjjz50P/0HDqK6WfNCh7TJlFKEwxG+nzeHp594nLq6Ovbp0YNBQ4ZhanYfhBBC6G2LR+WmaaKUYubMmQC0bt2ab775Zv2ft2zZkg8++GBLL/OrLMsinXV44K1PufH5d+l15QM8/8nXDOvZjQH7dCKWyvzuMTQ10ZISXpz4PKNHnUP3PfZk1906c9nFFzL1w/cIRyKFDm+TREtKePP1VznvrDPo3KUru+/ejWuuuJR3Jr9JNBItdHibJBKN8t47kzn3rDPYsf3OdN29O5dfciHvTXmHiCY5NAoEAtTW1nLxqHN5/rnxeK6nTX+Kxl4aE555mtNPHsa9d/2ZtWvW4PP5Cx3aJgsEgvz442JOHDYYpRS9DjyIRx96gAfvu5toSWmhwxNCCLEN2eKZEMMwGDp0KMcddxw33XQTxx9/PKNGjWLixIlUVVUxadIkjjvuuHzE+r/jAAI+myc/mM6MBctIr1pLKODj2P260qayTLoB/wae55FM1nP1dTcx8uzzKCkJ8NmnH/PRB+9z5p9OLXR4m8RTHvF4nCuuuo5zLriIsrIgX82Yzofvvcv5555Z6PA2iWmazPnuO0acdS5XXn09fr+f+fPn8epLL3DmyD9Rnyh0hJsuGi3h6isuoVOXLqxdsxbP06fDuG3brFq1ildefoFhw0/mH39/BLexa70mfD6b7+fNpfdBh3D7X+6mtLQM0zR44N67ueba6wodnhBCiG1IXvaE3HDDDaTTaSZPnsxll13G5ZdfzpAhQwAYOHAgxxxzTD4u8z/FUxl67rYD948cyFMfzODul97n1EP2AmD+8jX4bFlqsLnqEwmGnXAyPtvHx1M/5P+zd9/RUVXbA8e/t01PJpPQLICV9uxiw4c+0Wd/VhRBbCgKqE8UG3Z/FhTBLhYUxa48ey9YsGDHriCgoIiAhCTTZ+695/dHCMqzUF7MzAn7s1bWwhgye3NvMmffs885M6Z/TTKZZP8D+5LRZGYpnUpx8CH9CASCvDP1LWZ9M4MFC35i+IizyWRypQ5vpTTU1zNo8AnYts2P836guqYNc779li226onv61FcK6Worq7hgfvuZv6P8zj3wksYftKQUoe1SorFIlVVVdx170Ok0ynG3zqu1CGtsnQ6Ta8de7NLn92WzuI4zJj+NR3WWhvbabbNEoUQQogVapZ3HdM0GTNmzLL/Puecc+jXrx/ZbJZNNtmkOV5ihSojQf4z9TN22WRDBu68JQN33hKA219+n6c//IqKcLBF4mhNDMOgkM9jmRavvzqZR//zMOt27Mh6G2yAp8liXMMwKIEoMr0AACAASURBVBQK2LbDW2+8zqSHHqBtm7ZsuNFGuBrl4Ps+xWKRmjZtuW7saBYtWsigwSeQTukxDRIKhZg75zsmjL+FW+6YiFt08TwPZ+lmAbowDINsNkMqmSx1KKtNKUUul6O6poYXn3uWx/4zidvuvJdiUY+fByGEEK3DX7ZSe8MNN2yxAgTANk2Kns8xN06i18hxHDrmPrY/+yb+fccT2JaJpUnfebnJ5XJkMmmOGTyEl15/m5qaNow841Qi0Qi6bHycy+VIp1MMPHIQL776JuttsAGnn3oy4XBYm/UIpmmSqK7miksv4qEH7uW2O+8hHq+iWCyWOrQVMgwD0zQ558zT2G6HXlRX1zBr5gxc12XevB/wfX3WhQBatV/9kTZt2vLkY49w2inDuHLsdWyx1dZkM+lShyWEEGIN0iwzIaeeeiovv/zycp8zDINoNIpSilGjRrHLLn/tbkQKcCwT2zT4dM5PfDR7Ho5tEQ0Flx2gLlaeYRiEw2GOH3QEe+2zH8edMJRAIMjGXbvx+KOPaLGbZ9M9OHTwMezYe2dOHj4Cx3Ho2q07U996a+l6hPIf/DZdi0suPI/PP/2E5ye/QfsOHVi0cGHj4L3ML4ZpmixZsoRMNsM306dz/DFHkM6kWfzzz4y54jK2euABEonqUoe5xqisjPPAfXdz5+23ct/Dj9Frx94sXLAAU3YPFEII0YKapQjp2rUr6XR62dPMpvaRBx98kO7du9OxY8fmeJmVYhgGkaAD6NXmUW6UUpiWxS677s6YKy5jwU/zAXjg3rs5+dQzUMpHlXlpp5QCw2C33ffkqlGXsKR2MYFAgPvvmcixQ07ENC2UKv81FRUVlVx0/tncf89EDjioL1dfNYpUMkmHtdbmggvOx/fKe3G353mEQiEm3P0ArusSDIb45OOP+Pew47nsyrHEYhW4bvnP6PyaUoqG+no8z9NqFqeispJHJj3IyDNOZYdef+eJR//DxDvGYzsOZ509stThCSGEWIM0SxEyZMjvLzA98cQTOfbYY6mulqecOkolkwwYeBSd11uPl198HoArxl7Lttv3IpXUYy1CKpnkoEP6sc66HXnhuafxfZ//GzWaHXbciWRSj0MXc7ksm22xFWuvvS7pdIpisUiiuppYRUWpQ1slhmEQCAQwTZNOnToz4syRxGIV+L6PZenzFN73fQLBIGeMPI+111mHQiEPhEod1kopFgq0bdeeEWeeQz6fI5fLUZVIYFmWzIQIIYRoUX/pdiibb745nucxceJETj311L/ypcRfJJ1O0evvO/GPXXYDoFAsNJ6Y3qaqxJGtvHQqxbbbbc/fe+8MQLFYoL6+AWr0OBchn8+z/4EH/2YRdz5XwPO8sm/HatK0k5frulQlEhw24AiW1C3RsghxHIeBRx5NKpUil9djpziAbDbL9jv04h99lj/o0nVdbEOP+0gIIUTr8JfvyVhfX099fX2zfs9kNk8ulcHV8OwPN5Wh4HooX+G6Xtm3NAGNRcevuG7j2Qi+7+O65d0K1KQhmQR+2dHIdT18jXL4vZ8ht+hRVREG1XgvabC8ZRmvUCBfqG28Dr6Pr2EOi2trAfA8H9/3UU05lLlUOk0qvfwidM/1qKmOlygiIYQQayJDNcNWLzfffDMffPDBctP5vu8zbdo0pk2bxgcffMDWW2/9v77MMpM/m8n82iSOrc/T0yaFokfvHuvToSJMQyarVT95E+UrKiuiuK5HJpvDMPXMoSIWwfcV6UxW2xxisQgoRSqtbw7RSAjTNEmmMtrmEAmHsG2LhmRazxyUIhgIEK+IljoUIYQQa4hmmQn58ssveeONN5YbUNu2TceOHXnmmWeatQABmD7vZ6bPW0QooN/hWpl8ge4d27FeuyqKmm1N2kQpsC0Lw4AwITRMoTEH28JXinBY3xwcy0KB1jnYjoNp6J2D49hYpql1DrYc6CqEEKIFNcso/oYbbmiOb7PSHnrrE6ZMm4Ed0e8AQjeVYZuNO9JzvbWoT2YwNRyx+L7CqjIoui7JdE7bHMx4FM/3aUhmMTV8eu37CqMyikJR35DRNofKigiWaVDfkNY2h4pYGMe29M1BKSKhAOFgoNShCCGEWEPoN5UAVISDhGJhLU9BXwIElj5xNE1DywE8NO501HgIXSvJQcOBI7BsJk33HFrDddA6B4WWs7JCCCH0tdpFyMMPP8zFF18MNB5GFo/HSaVSy53gHIvFMAyjRQ4rFEIIIYQQQuhhtYuQtdZaix133HHZ/vLjxo1jm222YYcddgAaF6bfc889bLjhhnTq1KnZAhZCCCGEEELobbWLkN69e9O7d28AbrvtNvr27cukSZOW+5pTTjmFww8/nFCoZQ7yMgBPKZLZPJ7ng2FQEQ4QsC1djlIQQgghhBCi1WuWPW7Hjh3L4MGDf/P5Hj160KZNG+66667meJkVcn2ffNGlf+8tGHfCgZx14M5UhIPkCq5Oxw8IIYQQQgjRqjXLwnSlFO+//z677777b/7fvHnzlp2U/FdSCrIFlxuO249jd92G2lSG6liEI3beij4X3kau4GJrdCqzEEIIIYQQrVWzFCFDhgxhxIgRZLNZdtttN2KxGHPmzGHChAlMnz6dfv36NcfL/Kl0vkCvrp04dtdtuG/KNAZeMoEDdtuWY/r0pHPbBF/MXSBFyP9AKUUwGMRxHJLJVKnDWSVKKWzbJhwOA5DNZvG84gr+VnkyDINQKEQulwP07DFUSuE4DqFQiIaG5Ir/Qhlqug75fB7fL/9T0v+bYRiEw2FM06JQyJPL50sdkhBCiDVMsxQhp512GqlUijFjxnDZZZct+3zXrl154YUX6NKlS3O8zJ8qFF223GAdAL6Zv5grT+yL7ytG3vscsxfUUhkJ0QyHw6+RlFKEQiEWLlzI3Llz2GSTzbTZzlMpRTgcJpPJ8MaU1/F9nx3/3ptwOKzd/RAIBDAti+lff81aa6+NbTulDmm1BAIBUqkkH7z/Pptsqs+91MRxHAKBAF9/9RXt2rcnGGyZNW/NoakAdByHqVPfpnbxYrbYcivWWXdd3IIUIkIIIVpOs50TcsEFF3DmmWcye/ZsMpkM7dq1a9FdsRQQdBrP3zj7oH+wsC5Jp7YJhuyxHbtceBuz5i8mFNBz0FZqtm3jOA6HHXowDfUNvPfhNG0GjqFQiB9++J7DDu1LKBTGMMBzPe576GE2Wn89bZ4AO45DNpvhvHNG8sD99zH1vQ/o3Hm9Uoe1ygzDoLKygqOOPJzJL73EVzNmY1uWNrMJtm3jFl0uufgixo27kZcnv8ZmW2yBLrNSlmVhYHD0kQP57LNP6dSxI7NmzeKGm25mzz1/204rhBBC/FWatT8pFArRo0cPevbsWZJteT2vcSBw6p1P03ngRZx42+NURcPsvnkX0nk9229KTSlFdXWcK6+4nO/nfk/7Dh20mkGIVUS55uqxVFbGmfrue7z9znso4Labb8ZZemhkuQuFQsydO4fddvkH3347m6qqBJ6nx6D915RS1LRJMP6223h36lTWW399fPXXrxdrLo7jUFtby+677cK0aR9SVVWF6+r1eyUai/HCC8/x2quv8vIrr/HKK6+w/wEHcfFFF2KZevw8CCGEaB1Wuwg599xz6dmzJwBDhw6lW7du9OjR4zcf3bp1Y+rUqc0W8B8J2BZf/rAQgC5rtwHfp2lLrKLrye5Yq0EpRTxexcsvvcLTTz3FlVeNoVDIa7XdcX1dA8ceN5g7JtxFMpnCMaFTp07U1taWOrSVViwWiUVj3HXPvYy//U4sy2qRzR6aWzQa5YvPvuTG66/jmutuwLZslEZ5eJ6H49jcdMttPPDQf4hGIriuW+qwVkk2k2HTzTbjhZcmE49X4QMbd+lCKpXE0+haCCGE0N9qt2NtsMEG9OrVC4BtttkGx3GWOy3916qrq1f3ZVZaNBTgtc9n8eyHX3Pqvn9nry270m2dtsz9uY6nP/yaWCj4l8fQ2jiOQyaT5vQRwxl15VWYpklek/alJsVikR49/obnecRiMd54+x2mvP4ajz35tDaDLtd1iVdV0bFTJ6Z//bU2rUu/ZpomjuNw8olDGXrSyWyxxZakM+lSh7VKfN8nGo2x5ZZbsWDBT7iuq01bYpNischaa62DbVsYhsGSuiTXjL2K44cMk407hBBCtKjVLkKOPfbYZX/+17/+xaBBg5oloNVlGQauYXD0jZMY1KcnO2+yAS9+PINbX3qXH2sbiIYcrZ7gl4NwOMLpp53CLn12Zd+99uDxJ5/Gtm0qKivB16MNxTAMstksbdvWMHXqOxx8wH5cfMllbL99L20KKsMw8H2fbDarVSvcr8ViMcaMvpIOa63FycOG8OHHn2JZFpWVlVoN5HW/DoZhUCwWCIUqSCaT/Gufvdh+h178+5ThNCQzVFVGSx2iEEKINUSzPPo65phjqK6u5vTTT+fDDz9sjm+5ypoWpru+z9gn32D/URM57a6n+f7neqKhgBQgqygYDPLNNzO49eZxfDNjOof178/oK0bx7ezZDD/5RJRSmGb5PzlVStG2bQ0vvfQyhx/Wj6uvvZ5/nzSMdFqvp/A6s22bxYsXM3bMaObPn8+AAQM4Y8Rp/DR/PicOHUJDsgHbbrY9MsSfUEpRGY+zcOFC9t17D3r12pH777t36ayg/JIUQgjRcprlnf+6667jwQcf5P777+e6666jR48eDBgwgP79+7fsDlkKbNMkEQv/1+flzXVVFYtF4vE4D056hIaGBhzH4Z2pb/PNNzPYaed/YBiGFv+u0WiU++69j6OPHMgFF13MppttzkuTXyUSibL1Vlto05LVRClFOp3Wak2I7/sYhsGdE+9hyZIlAMyaOZP333+Xf+yyC8FAUKt8YPnrYBgGujzlCIfDfPH5Z+y/795ss+12DDpuMK++/iau77H5ppuWOjwhhBBrkGYpQjbccEPOPfdczj33XD777DMmTpzIvffey80338yWW27JqFGj6NatW3O8lGghnucRjUbZ91/7NR5sFrBYd92OLK6t5cCDDga8si9CDMPAMAyee/YZNtl0U1579VWefuopUIpNNt2MCRNux/P0GfwqpQgEAmyzzbaEQiF8v7z//Zv4vo/jOOy+x56YpkkwYPHFl9P59NNPOLTfYQQCAa3WuSilsCyLnttsQzQaw/M8bFOPlrJgMMiU11+nsjJOQ0MDg489Gt/3CUci3HPPfdDmr1+/J4QQQgAY6i8aSb7//vsMHjyYTz75hEmTJtG3b99m+977Xn4Xkz+dSUVYv8XmS9JZ7jmlH4dtvwk/Lq7D1Kgf3jRNLMsiny+QiMcoui6pTK7sc7BtG9M0l9vJyPcVlbEwnu+TTGUxNRlEGobReFaF6+J5PvGKKApFQzKjTQ7wSx75fIGKWATLNKhrSGuVg+M4y65DLBrGsS2W1KfKPgfTNLFtG8/75UGCrxRBxyYRryhxdEIIIdYUzdqI/emnn/Lwww8zadIk5s+fz7bbbsuDDz7Innvu2ZwvQ7ZQJJfLa7WgtYmby+MuffqulNKqC9vzfHxfLRu4KKW0yMF1PQxj+SftjXn8KodyT2IppRRF10X5/rJ/ewVa5QC/yuNXceuWw3LXQaMcfN//zU6GvgZxCyGEaF2apQi5/fbbGTNmDNOnT2eDDTZgwIABHHnkkWy88cbN8e1/Y/sunQhYFpGgfiegJ3MF1qmuBNMkEgpqWUgpZWNZJgEcIiGlaQ4K27YwlUEkrOt1UDiWhULpm4Nj4zgWpqH3dQg4FpZlap6DbA4ghBCi5TTLu86UKVPYZpttmDhxItttt11zfMs/1XeHTemzyYZYGu5r73o+PdZtT7HoYpqGpgMWMGiMXescDDCU3jlgNF4LnXMwaLwWWudgGPpfBw3jFkIIoa+/bE3IX2m3i29n8scziIZDpQ5llaXTGe49YyB7dF2Xb75foOUBYUXPo8t665LO5pi34Gccyyp1SKus6Hps2HltikWXufMXapvD+h3XAqX49oefcGwNc/A8Oq3VDsexmTXnR21zWKd9G6LhEDO++0HLe8n1PGqq4nTZoGOpQxFCCLGGWO2ZkJEjR/L8888zbdo0hg0bxiuvvPKHX3vXXXex/fbbr+5L/UbQsQkGAoQD+rVj5YoBbNPEMAws09TirI3/Zim1bOcpbXMwW08OjX/WNIfWcC+1khzKfUG9EEKI1mW1i5CuXbuSzWYB2HTTTTGXDqz/m1KKRCKx+hH+jqb2DR27B3SMWQghhBBCiOa02kXI0UcfvezPQ4cObY5YhBBCCCGEEGuA1S5CXNcln1/xNrlKKUKhENZf3CdtAK7vU/R8fh2RAhzLwjaNst9KVgghhBBCiDXBahchd999NyeffPKyg69isRh1dXXL/r9SimAwSCgUYvz48ey9997NEvAf8ZQiFgoSj4Twf7XW3jQMlqSzZAvFsj9UTwghhBBCiDXBahchO+ywA1dffTWGYVAoFBgxYgQDBw6kV69e+L6P53lcf/31JBIJevbs2Zwx/666dI6BO23FtYP2XfY531fkXJeR9zzHTc9PpToW+cvjEEIIIYQQQvy51S5CunfvTvfu3QE4//zzOfbYYxk3btxyX3PooYey0047sWjRItq1a/e/RboCkYDDtG/nceVjr+ErSGbz9Nl0Q3bbbCMWNaSxNNyxply0pvMDDMNAw12pl2m6Fjrn0MQ0TfC8FX9hGWstOXia5yCEEEI/zXJY4f3338/48eN/8/nq6mo6d+7MAw88wKWXXtocL/WHwkGHj7/9kTe/+g7f98Hz6bvDpnw9bxFPf/g1FeHgX/r6rZFSCsuyCIfDyw16vXR66Ul5+lBKEYlEsCyLdDpd6nBWmWEYxCoqyGWzS3OJLm1/1Os6NAmFQo0D32Kx1KGsHqUIhsP4GufQ2DIbQqEoum6pwxFCCLGGaZbpgWg0yqOPPvqbzxeLRT7++GOqq6ub42X+lFKKoGPTpiKCaZoM3nN7ttpgba587DUy+YKsB1kNoVCIeT98z7FHHc7Rh/fj6IGNH999O5tgOFzq8FZKUyHVvsNaTHntVYYOPoZkQwO23Sz1d4sJhcPcOf7WZdfg2qtHY1ompqnXwXhKKaoSCR6d9BDHHjUAt1j8yzetaG5KKaqqq3n26Sc5+vB+5LJZ7e4npRTxeJxpH73P4YccwIKf5hMIyIMaIYQQLadZ3jnPO+88+vXrx3fffceee+5JOBzm22+/5f7778d1XQYMGNAcL7NSir5PVTTMeYfsyncLl/D4u18Qj+gxYC43juMwa9ZMFsz/kZHnX0wul8XzfapranA1efrrOAHq6mq5+IJzeOP1V6mvq6NQLGh1oFxlZSUP3ncPV4+5glvvuJtIJMIZw08iEAhy2aWX0lBft+JvUhYU0WiMGV9/zWX/dwHhcATfV9q1+0UiEb6bPYtLLjgHz/fxfV+7HAKBAPX19Vx8/jnMnjWTXC6n1c+EEEII/TVLEXLooYdimiaXX345p512Gp7nEY/H2WWXXRg9ejQdOnRojpdZIQOoS2U5eZ8d6dSmihNve5yGbJ42FRFNm1ZKyzBNFv/8M1v23Ib9DjyY2trFVFZWMn/+fFxN2jdMy6RuyRKqq2u4Ysy1XHTe2fieX+qwVkk+n2edjp0Yd+sEdt9jL6qqK7jv7ruYNfMbrWYRTLPx183/XXAOffv15+OPPsL39VqLYCzdDfCi80fyrwMO4pNp07RcT1FRWclpJw9ly616EgqFtHmoIIQQovVoth6Cvn370rdvX4rFIoVCgWg02lzfeqUVPI91auJc3G83flhczwNvfkJVJCQFyGqyLZtkQwPvvTOV/gfvx88/L2LLrbdh6EnDl7af5Esd4grlczk6r7c+F196BZ999gn5fPnH/N9yuRzbbd+LSCTC888+zZtTXuOHH+Zy6RVjSaVSpQ5vpSilSFQnGHXJxdS0acORRx/Lm1NeL3VYq0QpRU2imuuuHk0oGGLwCSdy9MB+pQ5rlTReh2oenfQws2fN5PaJ93Pogfuu+C8KIYQQzaxZG5mTySTQuIi2oaEB13WX9R63RM90Nl+k7w6bkikUuWTSZBqyeWpiYSlCVlMul6Nr9+7sufe+7LHXPmAYnDLseJSC22+/lYZU+S/wNgwDz/Ooq1tCoVDQrm2mia98im6RJUtqmTfvB3zfp76+HtPSo4UmGo3y7tS3ePON13jkief47rvZAEQi0aXXpPx/SiORCNM++oAXnnuGBx95kiW1tUs/H9XmvgoEAvz4ww/cfutNXHPDzVQlEvi+R3jppg1CCCFES2mWEcyMGTPo3bs3NTU1VFdXU11dTZs2bWjfvj0dOnTgzTffbI6XWaGKSIgXps1g69Nv4OG3PqVaCpD/STqdYpttt2fk+RfRpVt3tt9hRwYddwJvvvE6nqdfH7yuLMvi29mzWLRwIfvufyD3PPAfdt9zb84581RMw4Qyvw6maVIoFDhrxHC6devO+++9wysvvUgmk+bVyS9RKBQa8yhjpmni+z5njRjOBhtsxCcfT+OF558hk8nw6isvabGmwjAMwuEw5408g1isgoULF/LopIfIZjK8+fpry4oqIYQQoiU0y/TEoEGDmDdvHjfeeCPxeLxxi9xf6dGjR3O8zAoZQKZQxFcKW57q/U8MwyAWq2DwMQPZbodenH72uRgKpn30IR06dMCyTN126QX0PF+jorKSq0ePom27dlw/bnzj7M7SWcYyrz+AxnspnU6zdc9tyWYzTJwwnmSygVQyycsvPU/fgw8gXPa7rRlk0mm22GorUskUEyeMJ51OkUolmfzi8xx84AEk4vFSB7lC2WyWDTfamAU/zefuO8fjui7JVIopr73CAQccAGxQ6hCFEEKsIf7nIkQpxddff83999/P7rvv3hwx/U9Mw5DteJuBUgoM6H/4EVx+yYV8+flnFAoFZs+ayVXX3kg+l0eHFppfU0pRKBS0K0QK+TzDTh7O6cNPYmC/gwiHw3w87SPOPOf8xtmoMs/H8zxCoRCjrroaaHwaP+2jDxl63FFcfuXVxGIVeF55b3Tg+x6WbXPJqKswDINgMMj0r77kqMP7cdmVV5OorsZ1y3txd9P9f+bI8zGtxgX2yYYG9ty1N+dc8H9suNHGpQ5RCCHEGuR/LkIMw2CjjTZi9uzZzRGPKCPpVIpdd9+Tbj3+xltvTMEwDC66dBRViWqy2Wypw1slxWKR9u3bc+Mtd1CVSFDUaDegTCbDppttzn0PPcqU11/FLRY5afgI1u3YmVQqhQ4lt1Jq2ZqxdDpFTZsaRl9zA0W3iOu62LYeM5epphxMk4qKSsZceyOKxsG9HSr/czYaZ6VSy/7seR5jrr2J6poastkMUFXaAIUQQqwxmqUd66qrruLkk09m7733pmPHjr9pxzJNs1nXDyil8Jd+6MZXSpv5A8MwSCWTtG3bjsMGHAFANpshnU5jtE2UOLpV43kewWCInttsRzaboVDQpwhpameqjMfp268/Bgb5fI5UMkmbqljZz4T8N8/ziESibLl1T5LJpHYzU7D0fgqF2GrrnjQkk3r+LvJ9TNNkq617kk6ntdxqWAghhL6apQi55557+OSTT+jcuTPt2rVbrghRSvHYY4/Ru3fv5ngpADxf4fk+nq/XeQ8AvuejlFpWSJkaDF4KhQKFQmHZf6umj6U56DKI9H1/2VPgxvj1yqFYLC43g9N0DdAohyae55FJp5fFrRTa5dB4PzXmwNKHC7rloJQinU7jSgEihBCihRmqGd4xH374YRYsWIDrur85xE4pRb9+/ejcufP/+jLLfD73J5aks1hlvhvN73E9n+7rtqMqHCCXL2q5w5RSilAogO/7FAquvjkEA/hKUSjoex2CwQCgyGt8LwUCDqZhkMvruYVyYw42pmmSy+mbg21bhDVoKRNCCNE6NNuJ6S1pwisf8P43PxAJOS36us0hmc1zUb9/svtmG9GQzmq5iN73FcGAg+f5FFxX2xwCARvfVxSKLqapZw6OY6NA6+tg2xaYhtY5WJaFgcb3klJaFk9CCCH0tdpFSDqdpq6uboVvXEopampqCIVCq/tSv/HpnJ9488vZRMPN9z1bSjqdYWF9CnyfXL6g7aArEg5SdD2tcwiHArhN10HHgaO/dDYHRS6nbw5OwMFWhtY52LaNYaDvvaTU0g0Oyn2rZCGEEK3Fahchd999N8OGDVupr33ssceW7kHfPEKOTSgYIBLUbyYk7wawl55ybRiGlk8fm0I2jFaQA5JDKRkGy3b30jqHZT8TmuYAWsYthBBCX6tdhPTp04fx48djreBQQN/32WqrrVb3ZYQQQgghhBCtzGoXIV27dqVr167NGUuzUtLjLIQQQgghRFlqloXp5cIwDDL5ArmiS8C2KLoeIccmHAxotW2mEEIIIYQQrVmrKkKy+QIbdqjhjP13YrP11uaLuT9x1eOvM2P+z4QD+q0fEUIIIYQQojXS76CNP+D5PkHH5uERh9Nvx82ZNusH9tu2B5POGEgkGMD19DvYUAghhBBCiNao1RQhrufTvipGl7XbcMsL73DUv8dy07NT2aB9NW0ro7ganq5eLpRSOI5DPB7Xcp1N4+GKIRLV1VRXVxMKhbRtz2vKpaoqUepQVlnj4YpBEonG6xAOh7W7DoZhEI/HqapKUFVVRVVVAsfRq92z8WBCm6qqBNXV1cRisVKHJIQQYg3UatqxArbFnEV1PPLOZxzSazO44kQO2mFTHn3nc+YsWkLIaTWptrhEdTXzf/yRZ55+il367IrjBEod0kpTShGLxfjqq6946onH8X2fAw8+mI03Lt9NFf5IUy5ffPEFU99+i0P79ccwDC0GwEopotEo3377LY8/+gjZbJZ/7bc/m2y2GVD+8QOYpkmhUOChBx9g0cKFYBj4vs9OO+3MlltsjlLl/6CjqYhNp9NMuGM8tbW19Nrx7/xjlz54+Zp0VwAAIABJREFUxUKpwxNCCLEGaTUzIU0W1qdpXxXjwB02Ze3qShY1pBsHaqUOTENNZx68Mvll9tt3L4456khyuRyW/efbMpeTWCzGO1PfZo/ddmH69OnMnDmTffbcg88++4RgUJ9iCiAQCFB0iww47FDOO+dsoHFgrINIJMJXX33JP/vszEcffci8H+ex37578+YbU4iEgqUOb6U4jsPin39mxKmnMGXK67z15hu89eYbLFy4EMvS4zoEAgHq6+vZc/ddef65Z8mmU5w0dAi3jLuJyopoqcMTQgixBmk10wMNmTyH9NqUoXtsz3n3v8BlE57itIF7MvbofXn76zk88OYnVEX1O2G9lMLhCB+8/x5nnTGCHf/em0w6g69ZW1swGGTSpIfZ/4ADuevOCQD8c489mHD77fx9h+1KHN2qSVRVcMopw4lGI3Tv3kOraxEKh3ji8cfYodeOPPH4YwD0HzCQW2++mX323J1fjiwsX6ZpsmDBT2yy6WY89eQTyz7/85IUmWyOgAazrU4gwJdffM6663TkP489TiwcoGu3HowdcxWnnXZqqcMTQgixBin/d82V5CtFRbixyIiFgsSqYsSjYQAqIkF8DVolyk02m2HDjTZiyptT+W7Odzz1xBOgQevPry1ZsoSzR55LOBxm4eI62tVUUSwUiUT1eeqrlCJRXc2jjz7OO2+/zVVjrmHEqado0YbVpG5JHYOPP4FgMLTsOhQKeSKRSKlDW2mWZVNfX0+hUOCGm25m9qxZ9Nl1V3b6xy6Ymsy1ZtJptu65DZMefYz6+jp++inHG29MYa+999GgDBRCCNGatJoipCIc5Mn3v2BA7y04+6B/MGL/3jiWxTsz5vLke19SEdaj5aOcKKUIh8M4jkNDfX2pw1ktSikqKipQStG2poo77pzI1199ybhbbqPoeqUOb6UEg0F+XrSI884dya233U4wGKLoFrVpxYKm9SyN16GmporHnniKVyZP5vmXXl56Hcp/EG8YjS2KtYsX882MGQRDQY4bdAz/Hj6cc0aeTS6XL3WIK82ybN577z3OG3k2i2sX8/Irr+O6Hlag1bwlCCGEKHOt5h3HsU1S+QIHjb6HPbbcmG7rtGP6vEU8P206BdcjFLB1e4hfFnzfx/P0GKz/Ed/3adcmwa233c65I8/mgYcepvN661EoFEsd2goZhkEgEGDoCcfzz3/uzs69d+TZ518C0O66eJ5Hu7bVTJr0CMOGHM+tt9/BZptvQTaXw9ZgTUVDQwN9dv0nb73Th1isgpBj0K1bdy4471yGnzIc2y7/X6dNO915nsu2227H8y9N5q4Jd3DE4f155dVXCQYqSx2iEEKINUT5v2uuJKUgZNu4vs+ktz/D83wsyyQWChBypABZk9XUJLjm2uu5Y/xtTHlrKn/rtjH1qRy2Wf43hW3bzJ8/nylTXqNz58703mkn6urq+OH77zn+uGN48MGHCEfCpQ5zxZSiTdsa7rxzIleOupynn3uBHbbtSUO6oM22z9FolDtuH0/Hjp3oe9D+AFRXV+P7Hr4mv2Di8TjjbrqRhQsXMvqKywEYMPAIrhh1GT8tWEgiLkWIEEKIltFqihBobOiwTJN4JPSbz4vVZxgGnudRV1en1ToEgIqKCs4/7zyuuPwyRpxxJu9MfZtnnn6KDh060O/QQygW3VKH+Kdc1yUajfLCi5NJZzIEg0Hefecdzhl5JuedfxHBYFCLGZHKeJxrr7mWM0acyvFDhvL1V1/x5pQpVFVX0/+ww7QoRAKBALlclqOPPJxk8mYS1QnOPusMDjjoYGLRsBbtWL7v06VLF84ZeSZt27Zlk03+xvXXXccWW25F506dSh2eEEKINUirKkLEX6NYLFJdXcMxg44lGAxqsyuTYRgUi0U8z2fA4QOZO2cOM6ZPB6BLl64M6N+PMq9BUEphmiYdO3VqbM0KBjFNk2OPG0zXbt0wlp5VUe48zyObydB/wOHULanjsUcfAWCddTsyoP9hjQsuylxdXT0n/fsUOnRYi0kPP0ShUGDgwCMZdtLJZDI5TLP8c0gmk/TZ7Z888ugT3HHHeF584Xm6dOnKaaefocU5J0IIIVoPQ+n2aBvY9/K7mPzpTC0Xmy9JZ7nnlH4ctv0m/Li4DlODwVfTCcsVFRXU19fjeT6JeIyi65LK5Mo6B6UUlZWVOI6zXEteoVDApLGNJpnKajGAhF96+qPRKEuW1BGviKJQNCQzZZ2DUopYRQXBQGC561AsFlG+i2Ua1DWkyzqHJpWVcUDh+z62bVNXV084FCTgWCypT5V9Dk2bNViWjesWCQSCNCQbsAyorpJ2LCGEEC1Dy5mQZDZPLpnB9fR7cuemMuTdxsfvvq90OB4BgGLRpXbJElCNcSvV+KFDDvX1Db+J0fcUiXj0lxw0UigUKRTqfrkO6JFDsiFJ8neuQ2VFBLX0vtJBXV3dsombX+JWWt1LDQ3JX3Ighe8rIiG9Du8UQgihNy2LkGP69KR39/UIanA42H/LFops0XltsCyqKmM6dKH8hlIQcBxs28KybG1zCAZsfAWmaWmcgwMojLipbQ4Bx8Y0oCqu88+DjWkYWufgaLBDmRBCiNZDv1E80LYySr7o4thWqUNZZfmiSzQUIJvNkkyVdwvNH/F9hVUZo+h6pDP6tDL9mu8rzIoovq9IpvW9DkYsCiit76WKaBjTNEmm9GjH+m++r4hGQji2rXUO4VCAYFBmQ4QQQrQMLYuQKx97jTemTccMh1b8xWXGT2WYOPIo9urekW/nLsDS8Omj63l02aAj6UyWeT/9jG3pVwy6rseG661DsVhk7o8Ltc1h/U5rg/L59vufsDUsyl3Po9Pa7XAch9lzftQ2h3U6tCEaCTN77o9a3kue51GTiFNVWVHqUIQQQqwhtCxCKiMhQhVRPRemmwZBx8Y0zaXtTPoVIRhgGsayHHQcdAGYZuvIAbU0Bw0H8Bhgmiamaeifg2Foey8ZBnr+LhJCCKEtedcRQgghhBBCtCgpQoQQQgghhBAtSst2rD9iAK6vSOcLeL5PwLKIyraTQgghhBBClJVWU4QYQN71sEyDvbbsytrVFXz9wyKmzphDNBjA0HHfTCGEEEIIIVqhVlOEuL6PY1vc8+9+7Llll2Wf/79Jk7n8P69SGdFvEbsQQgghhBCtUatZE5LM5tmvZ3f23LIL1z79FusPupQXPp7BBYfsytYbrkMmXyx1iFozDANLw11/oDF227aX+9B5Zsw0TRxH7zZD0zRxAnrnYJgmAd1zMAwCAZkpFkII0fJaTRHi+j4d21QB8NoXs/nu89nc+coHAGzXpSO5ghQhq8s0TULhMPl8HqVUqcNZZUop0uk09fV11NfXU19fh+d5Wg68TLPxR3bRogUo9LsW0JiDYRgsWrhAy/sJlhblpsnChQtQvq/lvQRg2w4LFy7AdV1tcxBCCKGnVlOEhByHqTPmADDu+P25d/RJjD1qHwDaVEQ1Ha6VllIKxwkQiUa56bprGHDIAWSzWWxbny6+UCjED9/PZUDf/Tlh0FGcMOhIThh0JDO/mU44HC51eKssXlXFlZdfwqCBhwFoOTtVlUhw3dWjOfzQg3CLrlb3EzT+XCSqq7l13A0ceuC/yOVymuZQwzNPPs7+e/+Tn36aTzAoLatCCCFajl7vnH+iIhxk8qcz2f+Kuzlj/53o2rE9z06bzuDdtiWVK5Q6PC2FwmG+mz2LkWecRiqVJJlswPc8DPR5YmpZNt9/P5eqRIJx4+8in89RKBSIRmNks9lSh7fSlFIkEtU8/+zTPDrpIdbt2FG7WQSlFFVVCV59+SUeuPce2rRti1J+qcNaJUopKuNx3n5jChMnjCdWUYHv65UDQCQS5ZsZX3P9tVehfIVbdEGjn2shhBD6azUzIdl8kd491mejDjUcef1DbHP0/zF3UR0AX89biCOnAa8y3/MwDJPjh57EmGtvJBgM4ms28DUtk7oltXTqvD4BJ0Aum2W99TegoiKu1eAxGAzx0/wfufHasZx+9jlEIhGt4gcIBILULv6ZMVdexmlnnE1lpV7XAMBxAqSSSUZdehH/PvUMamraaJeDYZpYtsVF551N/4FHsf4GG+K60q4qhBCiZbWakbnn+6Bg7NH7MOXSITx0xTAu6b87733zPa98NotYSFoNVlWhUGDdjh3Z78CDAfA8t8QRrTrLskilUrz95hRGnDKMs0acwpGH9eXHH38gGAyVOryVYgDRaJQLzzuLPfbalx3/vhPpdLrUYa2yisoKLr7gHHb6Rx/6/HN30qlkqUNaZfF4nMv+7wK27rkte++7H6lUqtQhrRKlFNXV1Yy7/loikQjHDh5CMllf6rCEEEKsgVpNERINBXj3m+85ZMx9zFm0hG037sSEyR8w4NoH8H2FZUqrwaoyDAPXdUklk0uf9ur3b5hOpei1406cd9EljL7mBu57+DGcQIDRl1+ydE1I+ecUq6jkgfvuxvM8Lrp0FMViEcu2qayMa7OYOBar4NFJD1FfX8+lV4zBcz1My9Iqh2gsxtNPPc687+dy5dXX4fs+pmlSWRlfumFA+c8ShsNhPnjvXV55+QWuvek2AsEghmFQUVGp3boWIYQQerMuuuiii0odxKq6/42P+XZBLUFn+TfNoG3x6dyfuO/1aYx/+T0effdziq5HOOiUzfAgV3Q5aPtN2KCmkiUNaUwNiiPbdli0aCHPPfMkfQ/tTygUoqoyStF1SaWzy3ZsKke+75NJp9i657Y4ToB4vArXLfL8s88wZOhQCoUiDal02eZg2zbJZAODjxnIhhttzFdffs6rk1/iqy8/J5fL06vXDtiWRV1DqmxzsCyLXC7LcUcPpGOnTsya+Q2vvPwiX37+GZlshl47bE8oGKK2rqFsczBNC891Oe7ow2nffi3mzPmOyS+9wOeffUI6lWK77bYjFo2yeEl92eZgGAbhcJhhJwzCV4pMJs2Lzz/DtA8/oK6+js232IK1O7QrdZhCCCHWEK3q0ZcCKsNBlGrcvDQRbdz9SLcFvOVIKUWxqF/feGVlnOEnDSESiXDtTbeSyaR48rFH6Nq9O4FAoOzvDaUUvudx8vARNNTXk0wl8TwPt1j8ZS1Cmc8kKKVwXZdhJ59CfV0dyfoGPM+juFwOpY1xxRSFQpEThp5MbW0tDfX1+J6//HUo/yTIZjIcNuAIfpo/n7oltfi+/8t1KPOfBSGEEK1LqypCmhiGTvs3lT+lFJZlkUhUa9M60ySfz3HO+Rdz4bln0f/g/VHKJxKNctY5F5DNZij3FhrP8wiFwxx3wjBMwyQcifD+O1OZO2cOJ51yGsFgELfMi0Pf9wkEAgw6bkjjmTOhMJ99+jEzZ8zg5OEjCIcjeJ5X6jD/lO/7WJbFkYOOwzItgsEg33wzna++/IKTho8gVlmJ55X3dVBKUXRdDunXH9t2GmfZUg188N67nDD0ZNZZt2OpQxRCCLEGMVS5Pwr+HftefheTP51JRVi/xeZL0lnuHX4Yu3VZh1k/LMTSYNcuwzDwPJd0OkMsFsPzfTbqvA7pbI75Cxdjl/FZFUopwuEwvlJM/+pLlFJ0694Dz/NZq10C1/X44adFZZ3DrzWt08lmM4RCETqv2wGUYs68Bdi2ZjlkMgTDYTqu1Q7Hsfl27nxtcoCmVr80wXCYtdu3IRoOMXPOPG3uJVh6kGcqRTAYpG2bajbsvE6pQxJCCLGG0HImpOj5FFyPglveT09/j+t6jdvcqsYBgA41oK8UpmlRlUg0tp8sjbkp/nLPIZPNYhomXbt1BwwKhQKFQhEMQ5scmvhLZ6XiVQly2Vxj3BrdS/CrHBIJcrncsvZJnXKAxq1uf50D6JcDQDyRoJDPa7fVsBBCCL1pWYR0qIqxXtsE0VCg1KGssvpIiGgwgGEaOI6txUzILxSOY2OaJqZhYFkWAcfRJgffbyxabdvEwMYyDXzNcviFItB0/ygac7B1zMHBsiws02jMR6OZkEaNOdiWhWkY2t5Ltm1rNQslhBBCf1q2Y02ft4j6TE7LbXddX7FRhxoS0RCFoqvl2hUFOI7deNKy5+mbg22jULiuzjlYKNA6B9u2MDAouvr+PNiWhWEaFDX+mbZME8fR8rmUEEIIDWn5jnPu/S/w2ueziYX1mwmpS+e4/cSD6bvt36hLpjA1W+gN4PuKqsoYRdclnc1pm0O8Iorn+0u3GdYzh8pYFIUimcpom0MsGsEyDeqTemxZ/d98XxGNhHHspq2SNcxBKUKBAFVOrNShCCGEWENoWYSk8wXqMzn8Mt/Z6PfUZ3IUXQ+UwveVDrt6/obv/7KOotXkoKFlOdAKclC0ghw0vg4armURQgihNy2LEMs0sS0Tu0wPBfsztmVqt82tEEIIIYQQzUm/UbwQQgghhBBCa622CPGVwvud1gjP9yl6OjZyCSGEEEII0Tq0uiLEALKFIp7n/2b3rIZsHseyqAgFSGbzFF1PWqOEEEIIIYRoYVquCfkjvlIsbkiz9UbrMm7wAXhKceiY+0jnC2TyRQb8fXMuPmx3aioiPPn+l5w+8RnSuQKO7I8vhBBCCCFEi2k1RYjnKxzLZNwJB3LC7tthGga1qSyGYZDO5um5UUcmnHQIM378mSff/5LD/r45yVyBYbc+RiIWLnX4QgghhBBCrDFaTTuW5/tURcMM3GlL/vP2Z3y7sHbplpOKQtHlgO16ADDyvufpP2ois36qpf+Om9G+Kobr+aUNvsxVVsZJJBIkEgkqKipKHc5qUUoRDIaorq7GNE103I20MYfgr3LQKwmlFLFYjEQiQVUiQWVlHC33d6Yxl3A4TFVVlXYtnUopIpEIiepqqqqqqKpKaHk/CSGE0FurKUIcy6Ihm2OLEddzxPUPUXS9X7bwNQzWa5sA4IfF9WBbzPhxEbFwkDaVUSlC/kQ0GuXBB+7jkIMPpN8hB/Pcs88QjUZLHdZKU0phmiZrtavhm2+mc/ZZZ5BMJnEcfVrwlFIYhkGHdjXMmfMdZ591BnVLluA4TqlDW2lKKSorK3n1lVc4vH8/Dj34QO69924i0bCWg/h4PM7UqW8z+NhjyOWyWJYe91NTIfjpJx9zzJEDOfjAA7hm7FVYpqVNDkIIIVqHVlOEGAYUXY+F9SliwSCWaS63A1bTqd6Nh+sZy3bOciwLJXtl/a54vIqJd05g2JDj2Wnnndm6Z0+OPnIgb745hVBQj9Pqbdsmn89zxegxHLjfv7hm7BiymQy2RuuAbNvGdV3GXn0t+++7D9eMHUMylcRx9OmmrKioYPLLL3HwgfuxxZZbsdfee3PxBedx14Q7iIZDpQ5vlQSDQeobGjjumKO49567cV0XU5Mzi0KhMLNmzmSP3XcjFotx4IH7c/PNN3HF5ZdSWaHPwwUhhBD602cUsxIMwyBgW79t8FCKn5MZACrDQcjmWaemkqLn83MyreWhhy1BKZ9Zs2Zx2agrGXHqKQC8/dZbPPrII+yz5+4ljm7lBAIBpk//mimvv8ZZI8/hmrFj8Hxfq3Ysx3H4dvZsJr/8IqefeRZXjxmN53latc8opUg2JLls1JWcMeJUAD795FNefPF5hv/7JHRqy6qsjHH0UUexyaab0aZtW1zXLXVIK81xbKbPmM4BBx7EzbfeggV4Plx5xeVcPmoUQY1mCIUQQuit1Y6+LdPEMk3AwLIsnvtoOgAn7rUDgw/YmS3XX5vXPp/F/NoktrQh/K76+nrOv/Aijj1uMItq68kWPGbPnkWXLl1KHdpKy2azbLTRxjz9zNP02XU3stlsqUNaZblcjk6dO/P0s8+y1157a5lDKpVi3/32Y/ipp/L+Rx9z973388knH3PCCcNwPQ80mI1USlFdU83EuyYya+ZMRl0xmnQqXeqwVkkymWSXXfpw8y23sWhRLQAffzyNTp06yy6BQgghWlSrmgn5tYLrkl/6hDIWDvLypzO5+YV3GLrH9hy0/SZMn7eIkfc+j22ZaNaS3mIMw6BQKGDbNjXVcYYNPZFoNMagYwdTKOrz9BcgmcqSTqW0W3/wa8lkhpSmOTTdS4FAgA/ef5/bbrkZDIONu3TB88q/AAEIhULM/W4OY68azQMPTyIUCuN5HoFAEMMwtJhdMwwD3/fJZrK0bVvNI489wYP338czz71IoVgkFNBnnZEQQgi9tboipOmAwkPH3I9pGuSLLgHbwjJNTr/rGe5/42Pi4RDTvv2R+kyOaCigVVtLS1JKEQgEqKioYNAxg3j//fd4+tkXsB0b1/VKHd4qaQ3XWPcc0uk0nufRf8DhHH/CYM4793yOOLw/7777Lr4q70lZwzCwbZuhQ45n6549ScQreOfdDygWi8ya9Q3t29VoM6NqGAbt2iS48667OWPEqdx1971stXVPMumkFCFCCCFaTKsrQprMXlgLCkKBxhQt0yASDDBt9jw8XxEOOESDjvYDu7+SbdvYts2go4+koaGBTz77HNuAhnQeHdpnRPmoSlRx9BED6dS5M6OvGAXA3/72NybeOQHP8yn3NSGmaVJfX0+hUOC7777jsMP6k8lk+PnnRVx84QU89PDDdGjfvtRhrpR4PM5N427h1lvG8dobb7Npj65kir7sjiWEEKJFtdoiJPQ7OwcZBkR+tauTDKP/mGEYRCIRBhx2KE8+8ThHHTOIYUOGkE6n6da9B+ecfRZeLlfqMFdaUxtKXV0dvu9r2YL32xz0ScItuhxyaD9OGHws2WyWRCLBxLvuZMjQYQQCNtlsed9Lnudh2zb/efRxPM8jGAzy+Wefsf+/9uHW8XdQU9OGYrFY6jBXKB6Pc+eEOzj5xKHs/I9dGH/bLdQuXkwwFOLCCy8iLjtkCSGEaCGttggR/xulFJlMhr33+Rd/770TDQ31eK5Hm7Ztadu2Hb7ytZpFKhQKtG3blrHXXEtVVRXFoqtd60mxWCSRSDD2muto06YthUKRgCaLiZPJJHvutTePP/UMj0x6mEULFzH26mv55x57ks7kMDWpp5rOnfGVYu111uGK0VdRWRnH87xlraDlLJ/P07nzeoy+aizZbJZsLkssFsNxAjITIoQQokUZSqeR5FL7Xn4Xkz+dSUU4WOpQVtmSdJZ7TunHYdtvwo+L65adX1KOmg5ls+3la9V8voBl+LieRyqTK+scmiilsCyLyso4DQ31FIseiXgUz/dJprKYGgwgf5uDS1VlDIWiIZkp+xyaTuoOh8MAFIpFGuobqIhFsEyDuoZ02efQRCmFbdtUVFSwZEkd0UgIx7ZYUp8q6xz++xo08XyfQi4rMyFCCCFajJYzIclsnlwqo+VJ5246Q2Hpou7GgxNLHNAK1NXV/+Zzvq+oroqhlNIihyau61Fb27gtqVJq2Yfv61OHL5/D0jzQJ4d0OkM6nVn2377fdB3QJocmxaJLbe2SX+Wgx3X472sA4CtFJKTHAaRCCCFaBy1nQp79aDrzFtdrua99oejSZ7ON2Kh9NelcXqu+/iZKKYIBB99XFF1X3xwcB4WiUNQ3h4DjgPY52BgY5ItFbXNwbLtxN76CvjnYlkVQsxZFIYQQ+tKyCBFCCCGEEELoq7w35xdCCCGEEEK0OlKECCGEEEIIIVqUFCFCCCGEEEKIFiVFiBBCCCGEEKJFSREihBBCCCGEaFFShAghhBBCCCFalBQhQgghhBBCiBYlRYgQQgghhBCiRUkRIoQQQgghhGhRdqkDEEJ3CqhPZ/F8tXJfrxSVkRAB2/prAxNCCCGEKFNShAjxPyoUXfa74m5mzf+ZUMD5069VQDKT486TD2Hfrbu3TIBCCCGEEGVGihAhmkFdOsvPyQzhlShCGjI5Cq7XMoEJIYQQQpQhKUKEaAaOZRGwrRW2WCkgYFuYhtEygQkhhBBClCFZmC6EEEIIIYRoUVKECCGEEEIIIVqUFCFCCCGEEEKIFiVFiBBCCCGEEKJFSREihBBCCCGEaFFShAghhBBCCCFalBQhQgghhBBCiBYlRYgQQgghhBCiRUkRIoQQQgghhGhRUoQIIYQQQgghWpQUIUIIIYQQQogWJUWIEEIIIYQQokVJESLE/7N33+FRVHsDx7/TdrYngSQEQu+IdBAVlaIoCnrBhg1FREWlCiiKBSsgimLvDfXKtXvtirz2goCo9N5LerbvTnn/2CSCohdQk4yez/P4+OyS3Tln5szs+Z0qCIIgCIIgVCsRhAiCIAiCIAiCUK1EECIIgiAIgiAIQrUSQYggCIIgCIIgCNVKBCGCIAiCIAiCIFQrEYQIwh+kayqyJGHv59/bNnhc2l+aJkEQBEEQhNpMrekECIJTWLbN16s2kzAMdE2lMupImiaxZApFkvbrezRFZunGHQTc+l7vl8fi9GjZiLoB75+ddEEQBEEQhFpFBCGCsJ8kYN2uIi649z/IsoRb00hHIhIel5oOTPbjO/xuF7e9/DGmZVW8YxMpi3Bq7y4c3rrxX5oHQRAEQRCE2kCybXt/R5EIggC8+vVPDLt3Hook4XZp2LaNtJ+9IJXSd1361issj3Bu7648PfoMVEWMkBQEQRAE4e9PBCGCcBBe+2YZw+a8iCzLeFwqB3sXFZSFOa93V54ZcyayfGCBjCAIgiAIglOJZldBOAhDerZn7rizsG2beNLgADtCACgoDzOsT1eeFgGIIAiCIAj/MCIIEYSDNKRne54dOxTTsg44ECkoC3PeMekARBEBiCAIgiAI/zAiCBGEP6CyR8S0bGJJg/0JJwrKKntAzkA+mC4UQRAEQRAEhxNBiCD8QelAZCiWZRNL/X6PSOUckKdHn4kii9tPEARBEIR/JlELEoQ/QWUgUjVHZB9/83MPiJgDIgiCIAjCP5sIQgThTzKkZ3vmjh2KZdvEf9EjUlAeSQcgo8UcEEEQBEEQBBGECMKfaPAegUjlHJGCsjDDenfhqdGiB0QQBEEQBAHEPiGC8Jd4/dtlDJszj3AoyrDjDhOT0AVBEARBEPZFZCItAAAgAElEQVQgghBB+Is8/+kS3v9+jQhABEEQBEEQfkEEIYIgCIIgCIIgVCsxJ0QQBEEQBEEQhGolghBBEARBEARBEKqVCEIEQRAEQRAEQahWIggRBEEQBEEQBKFaiSBEEARBEARBEIRqJYIQQRAEQRAEQRCqlQhCBEEQBEEQBEGoViIIEQRBEARBEAShWokgRBAEQRAEQRCEaiWCEEEQBEEQBEEQqpVa0wk4GE8vWMTaHYXomvOSH0sanNe7C6u3FbBw7VbcLuflIZpIcdZRndheXM5nKzbgcWk1naQDFk2kGHL4oYRjCT5cugav7sw8DOreFtuGtxetdGwe+ndqRcCj8+rXPzkyD7FkimMOaUZeVpB5ny91ZB7iSYNDG9fj7KM7V9sxP1i6hgU/rnPk+YomUvRq14RB3dpV2zFf+fonFq3b6tjn7YAurendvnm1HfOZBYtY49B6giA4hQ0kUgbDenelbX7OAX/ekXfnE/MX8vniVched00n5YBZ4Rhdm+fz5sLlzH33S2Sfp6aTdMCsUIR2DXP4ds1WHnjlY2S/t6aTdMCs8gj5dTLYXlLOjLnvIgecmYcMnxvbtpkx9x3kgK+mk3TArFAEWRpIg7pBZjz7DnLQgXkIRwmfcSxdm+c79zpEYgw6unO1BiHvf7+a2c+/78xrHopwyZA+1RqEvPTVD8x7/xvHPm89LrVag5CnPv6OTxatdGQ9QRCcwgbsaJzuLRr+c4KQDK8bd9BHwKPXdFIOWKks41IV/G4XrqCPDAc+IIsl0DUVn9uFFvSR6cBAqghwu1S8uoYS9FHH78w8eFwatm1X5MF5lZNiCby6hltTkYM+6jowGCyRJXy6C11THXsdyhS52p+nPt3l2GteLIHP7arWYwbcOmrQR5ZDn7fV3YMTdHA9QRCcwgZCqoJLVQ7q82JOiCAIgiAIgiAI1UoEIYIgCIIgCIIgVCtHDsf6pwjHk1iWRcCjI0lSTSfngNg2RBJJUqaJKsv43C5kh+UBIJpIkjBMZEnCq2u4FAW7phN1gCTAtG3Ko3FkWSbDozsmD5IkEU+miKeMvd73u12oioztkIxIEiRSJtFEEkiXJV1THJN+ofaTJEgZFuF4Ak1V8Lt17P0sYJIkEYrFSaRMsvweFElyzDNCqDm2bVMSie31HJNliYBbR1Od83wWao4IQmqhpGESSSTp2745Qa+bL1dtIllREXYCy7aJJw16tW1C6wbZbCks45Nl61FkGVVxRudbZRDVrUU+XZo1oDgU5dMVGygJx/Dqrv3+ca8NTNvGsmwu7NedaCLFO4tXIssyTihNScOgVf1sWuTVBdIBFRIsXLuV0kgMRa795amygpdfJ4N+HTpimBYLflpPQXnYcWVJqL0SKZNG2RnMHHYi7yxaxZMff4d/P+etxJIpTj+iA23zc3nsw28pj8UdcW8JNce0bPxujcsHHIFH13CpCpZls35XMa9/u4xoIoWuOq/RTqheIgipZUzLxqu7GH3iEdx6zgkkUgaHTribaFnkoCf+VLdIPMlNZ/XnqsG9q96b98VSLn34NSzbdkQwFY4nuGpIb24a2r/qvTU7Chkycy5bCksds+yjBBSFokw85WjuvGAgq7cX8vrC5eiS5IjetfJogssGHM5Fx/bY6/3+Nz3BFys24vdU7+TggxGOJejUtAEvTz6P/DpBAHaVhTnxlidZs6PQkUuu1oR0MJfAtCx0TSWaSCIhEfS6SRoGsWQKWUq/VmQJ207fx0nDBMClqvg9LkzTojyaQNcUfB6dWDxJLGng9+i4VMWxQaFp2XhdGk1z65Ad9GHZNuWxBACaIu/z/EQSSRIpAzMa5+yjO3Nilza8+PlSisNRLBsi8QSmZaMqMsGKHvnSSCzdo1px3sOxJB5dq1okQ/hnMC0Lr+7iprPSv5Ep00RT0nWUM4/syOl3Podp2ZiWRTiRxLJstIoFMCRJQpIglkgRTRrYdrqMBdw6iiJj2zahWIKUmb53PS4Nb8Vz0qz4N8O0UGQJn1vHpcpgQ1k0jiRJqL8s76Jnr9ZyRk3qH8IwTTwuF9/eMZr8OkESKYNIIumoLs140qB1gxyuGtybr1dv5uJ7XmTmyFMY2qsTT87/js9WbNzv1rmaYpgWdQNeJgw6isXrtzPwlifo36UNz449kzN7dWTavA8dEYRIQFksQbcW+dxx/kkAhGJxR/SAVJIkiZygn91lYfrf9Dg24NZUdpWG8bprf+Xdtm0kSeLei06hQVaQHlfdj64pPHrZaQzs1paZr30igpD9lEwZ9O/UEp+us2p7AYMPO4SiUJRnFiyic7MGDOjSmi2FZbz05Q8YJqQMk2MOaU6/Di2QZYmPlq7l02XraVqvLuce04KlG3fwzfKNdGjRgMNbN+aLlZtYv6vYMY09v6SrCtuKy5n95mes3l6Arsic2K0tScNkZ0mIgd3asqUofX5MC6LxJD1bN+b4zq1Ysn47oYqAxcYmnjLICfo4v3cXWjXI5uvVm3n9m2UEPW4uOb4nO4pDvL1oBfl1MzjrqE6s2V7EwrVbHPFcFP48lUHn5ys3cvLtz+B3u3j16mH0ObQ5HRrn8dmy9eRnZ3Lhsd1plluHL1Zu5K3vVuJ2qSSSBi3r12Vg13bUzwrw7dotvL1oJYZpkjIs+h7agn4dWpA0TF77Zhkrt+3GpSoYpsWpPQ/lsFaNWLeziNe/XUZZNI6uqQzpeSixVIrdpWEGdm/LloIyXvrqB0xwROPnP5F4YtQmkoSmKrz//Wr+u3AF153Rj/aN6tV0qg6IIkukTJNp8z7iox/W8tPyjSzduJOTurZFkWVsB7RHKLKMadn0ueFRSiMxdJdG45xMUobJqm0FuBRnVFJSpoWmyMwdO5QvV27Co2v4dOcsV2nb6Zaz7KCXWDLFGUd2JOjReXfJKn7ctNMRS28mDJNmuVn0aNmQdxevwqtrtG6QzcgHX+GnTTvI8rkdcEfUDuF4kvGDjqLvoS3YXlxO3YAXXVM5++jONKybQb0MP6oikxP0cetz7zLm1L7ce9Ep/LhpJ0gw6ZRjOOfuf/PBwhVcNbg3AbdOh7F3MXfcWTTKzqDThHtqOot/SNwwaFYvi8cvP425nyzm/Lte4Kah/WmTn8P24nJyK85P/awA0559hzP6dOPFieeQMkwKQ1GyA16iyRRJwyTT6+btqRfSvF4dlm/ZxRUnHsmT8xcy6oGXOalrW07q2oZOV97DlScfzQV9u3HctMcwLIvaf0cKfwXTtCmNxLAsC0VK92RsLSwlLyvAe9ePoH5WgNXbChlz0pE88O5XjH70dY5o15TPbxtFcSjKiq27ueLEI3j0w28Zde88xp3al7svHMRnyzfQMDuTa0/rS+/rH+GL5Rt4auyZDOvdlYLyCDlBH6NPOpKTb3+aTQUl3HR2f1rXz06X90w/qixTL9PP7a8sIMuBy/D/E4hBn7WIKstEE0kueehV3li43JEtci5NYVdpiFtfns8XP66lS492jBvYi2VbdvH1ms14XbW7FwTSEzwN02Tl1gIiiRRfzriCW88+ng+WruGNb5fhd0DlF6A0EuOWs4+nSU4mQ2Y+SzxpgJQeLucENlTsqaPTJCeLwT3bM+LYHrx73QjO79OVsmi8ppP4P1mWXbWPzlHtmvLBDSN57LLT+Pimi+nfqXXVcBnhf5MkiWgiBcDlj75O01Ez2V5cTrfm+Qye8Swdr7yHRMpgULd2KC4NXVX5z5c/MODWJ7n4wVcAGNyzPUVlYc6a/QK6S+X9my6mTYNszrn7RTYVlqJrznvmVpJIz8cDKu719PmKJVOcedcLtB17J6FYgiE926O4NMaffBS2DT2vfoCOE+5h3a5idFUhFEtwXu8udGpanxte/JChM+fy78++56Jje9CjTROG3fNvNu4u4bWrz+eCvt248cUPmf/jOvx67X+2C3++UCxBr7ZN2PnEdex44jra5GczbM481m7ayWUnHkG7hrlMee49ht4xlze+Xc4VJx7BoU3z8Okar3+zjMEzn2XwzLlsKihhYNe22IrMkW2aADDvix+44N553Pryx8QSSY5q35xhvbsy95PF5J1/E2fc+Txt83O4bMDhpBIpohX/nXHn87QbexfheJLBPduja6oYKlhLiSCkFsr0uQk6pKL7S7ad7kmwbDimUys+nnYxxeEoZ81+gUTSQJWd0SUqSRIeXcMwTM6Y9Rx3vP4JJ3RpzbhBR1Eerf0Vx3A8yQmdWzN+0FHM++IHGtcJUj8rgE930attU2RJqvXD/OSKsb3D5rxI98n30XXivfS8+n5My2LEsT0q8lDLMwEYlgXA0o07aDN6Fn2ufwRdVZj4r6MBRE/IAdAUmZRp8c2azewsDVFQHmFnaYjlW3aytaiMsmgcl6Ygqyrfrt2C3+1i/rSLeXL0GUC6kq76PXzx4zpe+/on2jXMZf6P63jz65/I8nlq/T1xoDRNpigcZfH6bewsCVMSiaFrKgG/lyY5mWzYXcyanYUUl4VYtG5rejK6Dc3q1QFg/KCj+HzWGLq3bMiaHYXUywxQvKOY+979kub16rCzJMQ9b31Oplf06P1TqYpMUSjKe0tW8u3aLWiKwqGN80CCZrnpcnTNqX347I7RtGuYy5odhWQHfKzYsovicJTHLjuNBTdfQm6Gn3A8gcfj5raXP+arVZu5/+J/8emto2iRV4cNu4o5pGEuAO8tWY1lWSxevw2AQxvVA0nCpcoUhiIV5T1EaTSGV9fQFFmUz1pKBCHCn0oiPTnslB7teGfqhfy0eSedrryHn9ZuRVOVdDdDLRdNpGibn8PCO0Zz4bHd+fKz77nnrS9QZZljO7R0xJCyRCpF1xb5AFzQtxuLZo+neb06NMrOYN7Ec9AUGbOiclxbJVImDetmcu1p/ejWIp9UKEJxKIpp2SgOmVjvUhW2FJYSiSdxayqbtu7m+407SKTM9I7hssTfrub7F6o8Ux5XejUeRZaQJQl3xWtZkkimDDy6xmtXn0/3Fg05/qbHOffufwPpwNaIxunWpgmDurdjd3mEo9s1pXfHlhWTWmsub38F207n2bPH+bFtSKQMQrEEdf3edOCRMsnLDKQ/A1XzQ8Y8/gZNL7qNUQ+/yktf/sj6XUVk5tVh5LE9KAnHyMsKMKyiV9IJ96Pw5/O4NH7aspPhdzxH3+sfYfH6bUw5tQ95eXUpLI8AMPLBV2g68jbGPP4GL335Ixt2FzN7xMmMPO4wxj35X3pd+yA7S0LoLhWXquBzu7jhxQ/oPHEOs9/8jLOP6sxVp/ZlS2EZAI2zM6E8iq+i9y0US4Bt77O8m5YTfrH/ucSckFos4NFxa6qjHu7lsQQndG7N61efD4CuqcwdN5SAW+fZT5Yw9/8WE/TW7l4eTZXZsLuELJ+H2cMH0aNFw3TLDvDO4pU1nLr9k+nz8J8vf+CTZRuwbRtZlnjv+hFEEykG3vYUSdOs9UtwKrJEQVmYPoc2Z2ivjhzXsSWtG+TgUhWe+WQRhmnV+ntDVWR2lYW5563PmXp6P/7vjtG4VBW3S2XuJ4tJGSaSGMayX2zbxud2oSkykpR+HfDoBL3uqr/J8LoJeH7eH8OlqfyrxyEMPrw9kO6l9bp1XrzyHEzLpt/Uh/nw5kuYN/Ecuk++L73HhkPmfP2Szc+Tbz0uDfZxfipXxoolkrxZMTdm7rihROMp+ndqBYBX13j5qx+5anBvZg47kda5dbh8UC+a16vDs58s5v5LBtOuYS5HXvMg157Wl/tH/otF67bx0+adYpGFf5jK52+Wz4Oe4SeRTPHO4lUc3rox/+pxCE/P/46xA3sx6/yTeH7BIsYP7k1+nSCPvPd11Xf0bNWIM4/sQLN6ddhWXE55NM7ok47k7KM6cfmjr1MUigJQFonx2bJ1bNxdwi1nH08dr5vju7YB4MmPFyFXrLz1y/JuWiIEqc1EEFILSaR/TN5bsor6WUFShuGolR10TeXDpWswTAu/20VdvxcbUCQJJww+cSkKJZEYJ9z8BFcP6cNR7ZpRFIpyzXPv8fhHC8nY4yFXW6mKTFF5lO3F5VhWugPq5a9+JJ40WLZlFwGPTm0fGacpMqF4gn43PMrVQ/rQu30zSiNxxj35Jk/NX+SI6wAQ9LiZ9canhBNJLujTDcu2uWruOzzywbeOyUNt4FJVPl22gZJwnJRh4VJV3luyCr+7YslP4NVvfqKgLEIyZTD8vv9w7Wl9GTvoKOZ9sZRQLMHnKzbS45CmbCkq5YYXP2TZ+m2MeuQ1xg/qRa+2TXhz4QrHBiGqLBNJJHl38SoWrd+Grmu8t2RVxZKoABJvfruceCpF0Otm1huf4NNdnNi1DQvXbuWWl+bTtXk+iizz48ad/GvGs4wb2IsrT+3Dmu0FXP7o6xiGRb0MPze++CFfLd/AdYkULk2l76Et+GHTjpo+BUI1kiWJRMrg3cWrWLZlF5oio7p15v+wliNaNybT52Hp+m2cesdcxpx0JBOG9GbF1t2MfPBldpaUc/srC3BrGhf26867S1bxwLtf4Xe7CHh0psx9l6JQhImnHE08ZXD7Kwt49MNvSZkWp0x/himn9uGcPl3ZUljK0LteYP4Pawj4PLy7eBU+3VW1BPAb3y4jEk8v1VvLf+7+sSTbCYOqf2HQ7U8z/4e1jlgd55dKI3Fennwe7y1ZxRPzf79CW1t3TC8OR3l+/FksXr+du//7WdXEW0i3jCRTBtFk6lefc2sq7lqylnxRKMojo05lZ2mIafM+os4vVs5IT4JNr6Hvc+sYpkk8ZZBRi9YcLwpFmX3hIGzbZtIzb1PH7/3dvw/FEkiSRMDtqhXph3RZumlof/KyAlzy0KvUDeydB0mSiCXTk2v9ugvDskhUXIfack+URGJMOuUYOjatz7A5L/7qOlTuWF8WiePR0y3F8WSqVuWhLBrntMMP5YUJZ1fbMW948UNue/njX13z3yJJEpF4EsM0CXjcyHJ635DKHhFIl3FZkvB7dEIVG+7JkkQ8ZSABmqrgcWmUR+OoSnrYRzSRJGmYFXsU7N9cqeJwlLEDezF7+KA/cAYOzMUPvcLTCxaR5dv3Kj+V5SwUS6CrKh5d2+f5kSQIeNykTJNwLIlP14iljPSXVPytLMuUR+PYgE/XiCZSVZ8rr1gQIuh1k0il92fx6hou9bcn/xaFosw4bwCT99g76q92yvRn+HDpGkfWE5zCrtiLRlXkiqX3JZKGQTSRwu1ScWtaeqge6R62dDmSCHp1ookUhmmha0rV+3JFGavcf8bvdmHaNrFEOnDWFLlqbxu/WydhGJiWnd7DBgjFk/so71Kt3xbAySqHb74y+TwGdmt7wJ8XPSG1mBNvHNu20VSFjN9Y2as2BCD7w7ZtPK70BlyWbaPKatVQA2fk4NcqH8xOSr9t2+ngVVOxbBtFkR035KNymEyW31O1epFb7KdwwGzbxqtrgFb1+pfPyKoybtv43XrV+f7l/hWVQzb2vM/Tr//KHPy1KstZxh55+73zo8oymT43lm0TUPW9WoorK3I2YFt2RW8Te1XwbNvGpSpVqzg65dku/HmkvcobgI2mKGR4fy4TQY+Obf98T0pSetVAt6Ziq+n5Gns2xqaXZleqnpcKPz8vrT1/lys256xsyLH5dZ1JBKC1n/glFIT/wUlD4f7O/g7X4e+QBycR5/t/+71zJAFSbR+3KdR6ksQ+e32l/zFM6vfKpizK5d+CI4MQ07JImRaGWbtX99mXlGlh23ZFHkxH5sE0TSzbxrRsx+bBqsiDZdmYDs+DbTs3D6ZpYll2+lo4NA+GaWJW5MGp1yFlmtW+WpplOfeamxXXvFqPaVkYDj1fVsV9Xp2cXE8QBKew+bleezAcGYR4XBoBtwufA4crGaaFosi4NZWAW3dkHpKGgaYo6Jri2DwkUgaaquDSFHweh+bBMHCpSsWqQc7MQ9IwcGkKmqLgdeh1SJkmuqagKbJjr4NpWbireZibS3PuNU8aBu5q3tjQrWn4HVq+EhX3eXVycj1BEJzErhgqfTAcOTF9d1mYeMpZK0ZVsmybnKCPWDJFOJ50Zh4sm+ygl6RhUl4xEdRpLNsmOzuAaVuUReKO7Nq1LJssvwfbtCkujjg2Dxk+N4osUxyKOjYPQa+OpioUlTs0D3Z6fHV20FdtxyyNxCmPOvfeC3h0svz7niT+VygKRYnEk449X5k+917Lp/7VnFxPEAQnqazXHsx8TUcGIYLwh6Vg13MbsBKgyaojNiD8JQmJZCqJv12QYL+cmk6OIAiCIAjCfnPkcKylG3dQHI7W+s3W9sUwLdo3rkcdr04snnTEDuK/Ytt43DqmZZFMppyXBxnMgiRFXxXirmeS8hvYlsPyAEgyRNdDPJFCOtyH7cShz7aNS3fhJoUVDTmvLAHYNrLuIan5iMfijs2Dpql43NW3mkw8mSSZcODzA9Ll1qXhrsaNJmPxBKmU4djzpesu9Goc8heNxTFME8QOEYLw17JtvB436m+sivp7HBmETHzmbeZ/vxqfx3kbfUUiUZ6bfB4ntGnI2i27DnocXU0yTJNWTRsSicXZvqsQ1Wmbe6kgldj4dI3so8J48guxkg7LAyDrFrvfyqUonqRw82akhPN6cwzTpEGDBjS2dhNftRDJ5bx72k4lcDdqQ0mdtqzfsMl59wPpidZ1sjJo3axRtR2zoLCErTsKDuqHq6YZpkn93Lo0bVi/2o65fVchu4tKHFm+DMOkSX4eDfKyq+2Ym7bvoqws7MjfWEFwEtO0aNOiEVkZwQP+rCODEF1V0F0aHpfzkh9PaaiynN6YR5aRHdibI9t2xY6kDs2DDJKc7jawkmDFwfr13oq1nw22ScUmTzKS7LwgpLIsIcmgKCA7r4KFooDk7Hvatu1qHzvv5PNVVW6rkaPPl1z950t28PkSBCex7X0vwbw/nFeLh6oKcG3ZbfhAODHNglA9nHpvODXdgiAIglBz/rZNBJX7WAiCIAiCIAiCULv87YIQCYglU5imheLApQwFQRAEQRAE4e/ubxWEWLbN7rIwbfNzef+Gi3jjmgvQNVXsmCoIgiAIgiAItYgj54Tsi2nZaIrMg5eeyqgTeiIBxeEYkiQ5cAcIQRAEQRCcTpIk9rUd22+9/1cc6498H/CHv/O35sKmd9pWsCzrTz8XB+KPnLe/4jr+GQ72vFZ3fv5GQYhFTtDHuUd3Zt4XS+nZqjFBT/Wtef93Z9s2Pp8fWZYpKS2p6eQclHQefMi6QqSkrKaTs98qH9R6wLfXHgFWMpx+WDho1KFt27hcLnw+P+XlZdiGUdNJ2m826RV33AE/7DHU0ywrSy8P4jDpa6Hj8XooKy2tlT+kNa3y3gsGg3vde2VlZeJ8/Q6/P1CxNK6EYRiUlTnneftnc7l0ZEUmFo0C6TKlqiqqqpJIJP6040iShMvl+tO+07ZtVE1DApLJ5B9aVEeWZZSKpaVlWcay0qNTDMMgGo2gaS4UWf5TG4wrV0Yz9uM3RnO5MA0D0zjwfXhcLhepVKoqT9VJkiQURcUwUr96v/K8HujqcNWdn7/NcCxNVSiPxek8aQ4XzPkPKcNAFUvz/Sls2yYzM4v/vvEq02+9EVVVHbfKl23bZGRm8v5773DLjdell7SVnFE+XJpKaXmYidMf4sKrZzBiykwuvHoGP67diNdBgbYkSeTk5LJ79y5m3HYTxUVFaFr1bV72R6mKTDyRZOrsx7lwykxGTJnJ8Ktm8OUPK5A8nppO3gFKB+RbNm9i0rgrKCgowOVyTlmqLpqmEQ6FuPXmG5g07gomjR/NxLFXsHL5cjyOu+bVw+f38+7bb3L5JSMYc9lIPp7/AV6fr6aTVe3kiqX4r7h0BE88+hCZWVlVDWELv/mKKZPGo6pqVSXxj6z46XK52LJlM5MnjCGZTFZV+Cu/d19+73iSJBEMBnlh7tM8+vADBALB/f7cLwUCQf77xmucN/Q0hp19evr/Z53OsLNOY+2aVVx/zWTe/u8b+H9xjP9FlmWCwQwURflVg4AkSSQSCcrLyn71XXumX5IkNE3juimT+G7hN3h+o5zuKz2yLGOaBlddOZb169ai6+7f/fsDsT/nV5IqA/y9G4Ury92okcP54vNP8fn9+/XdlUHb1RPHseynH3G7/7z8/B5n1ML2gwSkDJOCsgh+t/6nR9X/ZD6fj3VrV3P1xHEs+OgjFEVxXBDi9XrZsmkTV00Ywwfvv4skO2eJZ1XTWL9tJ8++NZ9WjfNpnp9Hs/w83C4XlkNWgKts/XrisYe56PxzePyRBykvL3NUEKIpCruKSnn89Q9oWj+X5g3zaN4wD6/bjdO2q5cVFSSJG6ZexZuvv0o4HEJV/zYd438aVdPYvn0bb7z6Co2bNKVhw8Y0bNQI3a075t6rToFgkJfn/ZspkyZw1NG96dq9B9dPmcwnC+bj9f/TApH078uKZT8x+47pLFr4bVWlubCwkMWLvqv6DfL7/bg9HnRdJxgMVvVS+vz+qgp2IBjE6/NVvfYHAlUVcFmWiUYiLPru26oWbJfLRTAYRNM0gsEMXC7Xz98VCODxeNJ/k5GBLMtV3xPMyEDX3WTVqcPWLZtYvWoFqqpVBCbp7/F4vfgq0iJJEj6/H7/fj9fr/dVZiMWidO9xGJOmTOXkU4bww9IljLj4UiZMmkK9vAZcde0N9Du2f9UzKBjMQNM0AoEAbrd7nz2OiqJQVlbGU48/QiKRQNd1qKjxVTaaLvzmK6ZMnkBObj0URUGWZTIyMtPp93jw+/1AOs9LFi+iqLAQRVHwen14PF4sy8LtdhMIBNLnMCMDVVWr0lM5bGnxdwsJh0NVlfjK8+f3+/H5/ViWhcfrxePxYFkWkiSRkZGJ7nZjWRayLBMIBADQdTeBQHCv41mWhaIoZGRkomku/IEAbrcHr9fLpo0bGD96FP5AAJdLx7btqiFY0++YTfcePYlGIvh8Pvx+f8Vn3fiACX0AACAASURBVFXHs227qsy5PR78gQBLFn1HWWkpiqLgcrkI7FGGNC1dhvz+AJrmwrIsvD7fb16n/fG3+tWRJAmXqjhpdEqtV9lKc8u06znu+AGUlpZgO+zHV5IkVFXjlmnX0btPP4rKimqk6/SgyRI7Covp0+1Qrr31SghHAQlKdrF5aRIcsL+fqqrs2rmTTxfM5+JLL+fRhx/ANE1HDWmRZJldxSV0bdeCG2+eAEkjXc/YvgMrFgeH1LFs2yYrK4uZt92Mx+OhTdt2mIbhqGtRXWRZpqBgN926d2f6rFmEw3EkSWLrtq3E4zEgq6aTWKuYhoFpmtwy/Q6GX3QxgYCbLz79hK+//JyRI4bXdPKqnWVZ5NWvT7v27ZkyaTyvv/0Bfn8AVVXTFXbbxuf3897b/+W1V14C4MheR3PJ5WN4Ye7TFBcXcdHFl2EYBtNvmUZ+w0YMu2AE8XicWdNv5dj+J9ChY2cgXVa93vRDSHe72bVjB3PunkWorIxgRiZjJ0wiOycHSZJ4/dWXee+dt1AUhTZt2zHy0itQVRlFkZlz1yy+X7KItu0OIRyOEAgEkWRw6ToP3X8P3y9ZjCRJnHXuMPr0O47du3bxxKMPk5ObyycL5nP9tFvJzskhlUoPEUqlUjRo2Ih27Tvg9XrJzMrisMOPIDsnl2QiwWMPP0inzl044qijKS8r466Zt7Nt21Y0zcVlo8fSqk1bopFIVcCm6zqyLNOgQT4lJcXccO1V3PvgY1iWjWGk8Hq9LP1+MY88eD8b1q/j0hHDuHzMeJo0bcadM25j9eqVyLLMoFMGM+CkQRiGgdfrRVEUsurUYd7zc/nu22+Yct001q5ZxQP33UMqmSQ/vyFjJkzC5XLtNcTL6/Uiy+kgR1VVHpgzmyWLv8Pl0jl96NmceNLJ3Dnzdpo2a8agU4ZQVlbCpPFXMOCkQfTrfwK7d+3irjumc/mY8ezevYv77r6TRCJBXv36jJ0wGZ/fTzgUYs7sWWzbugWXy8XISy+nTt1s7ph+C8t/+pExo0Zy3vCL6N79MOLxGLIs8967b3P0MX3p3KUrD9x7N/Xy8vh+8Xds3rSJ/gNO5KxzzwfbZtvWrcyZfQeRcJheR/cmEAyiqiq67mb79m3cO3sW4VCIzKwsxk+agq7rXDdlEheOvJQOnTrxwtxn2Lp1C7NmTj+oe+Rv0xPyS4oso4jhWH+Ibdtk1anDnNmzyM7OZtjwi4iEwzWdrAOSzkNdHrp/DrquM3LUFYQjzsoDikJpOMraLTu5fNR1nHLueB64/xks20ZVnVHGk8kkObm5PDn3RfqfeBKJRLymk3TgFIWycJTNOwoYO/5mTjlnLLNmPUo8mUJySI+ObdsEgkG++OxTPvrgPW6+7Q4MEYD8JlVRCIdCbN60mYuHD+fMIYO4e9YMbMsSPUf7EIlEOPOscxh82hl8+n8LuHPGLMLhMINPO5NIxZyIf5pwOMwVY68kOzuHm66/hozMzKp/83h9fP3lF8y+YzqXXD6aKVNv4JWXXuSlfz9P3exs5j71BJZlUlJSxDNPPs68F55DkiW2b9/KW2++Rk5ODpZl7nU8WZKwLIurJ40jL68+t868i+ycHK6eOA63x8OSxd8x9epJnHPeBVxz/TTee+ct5r0wlwb5DXjo/jm89sp/uGLsBNq0bcdnn/wfmuYiEAjyxCMP8eknC7h+2q2cfe753Hz9tSz/6Sd0l4unn3yUD959m9POGIo/EMA0f06TJEkYqRTRSIR4PI5t28RiMWLRKIqi8PZ/32D16pVkZGRyy43XUR4q55bps+h5xBFMnjCG0pISNC3dE2NZFiuWL+OHpd/z/ZJF/GvI6YTDISaNvwLDMJAVmVQqRf36DejW4zCys7P515DTadK0GXNmz+L9995m6g03MfSc87hl2nWsW7sWl57uQahTN5v333mbG6ZeTf8BJxGLRZk8YQxHHNmLW6bfQUlJCTffOLVimNKvm7r9wQB33zWTD957hzHjJ3HCiQOZMnEcPyxdAtg898xTBIJB1qxezRuvvcJ/33iNjIxMvl/8HQu/+QpddzNp/Gi69TiMW6fPIhqJMu26KWTn5PDIA/fyw/eLuf2O2RxyaAcmjLkMj8dD7z79yMyqw8BTBtOgQT6pVBIkCVmWeeOVl9i6eRM+n4/3332LRx68j979juPMs8/lzpm3s3rlChRFZcxlI3HpOuMnT2HXzh1s3LgBt8eDaZpMmTiORo2bcOvMuwhmZDJ5whhycnPxeD3cdcft7Nyxg7tnzaBjpy5IB9n874wazEFIGgYJB016rY38fj/ffPUlH3/0AdNn3U0qmQTS3bxO4fP5WLpkEf9941VmzLqn6uHopDxYpkl2ZpBGedkc1bEt5w46lulPvsQtj71IhsOGOEQiYSLhsGOGwu3JsiyCPi/N8+vRrU0Lhp96Ao+99j5X3vUYktcZ8wNUVSUeizPjtpu46trradi4MclkAk1zIYlGm18xTZOMzEwa5OfTsXMXhpx2Bk89/gj33XMX/oohDcLPKsfiW6bFV198xtNPPobu1mmQn49lmv/7C/6GLMvEMk1m3/cQb77xKu++/SYZmZnYto3b7ebdt96kXl59ouEIWzZvpmnT5rz4wlz6HXc8Pp+fNatXsfCbrxlw0kBycnJYu3o1ixZ+S/tDO9KsRcuqHodKLl1n9coVrF65kuYtWvLdwm9o1rwFSxZ/x6qVK8jPb8SDjz5JoyZNKCosJC+vPps3bSSZTPHu2/9l7PhJHNOnL8MuvIj+xw8gFo2STCZ57523aH9oB1avWpGeVyHLfPT+O3i8XrIys7jq2us5ZchpVUOI9kd6aI8fr8fLzh3b+eqLz2jfvgNLFn1HTm49tm7ZzOJFC3F7PCiKQiIe5/45s7lzxq3MvO1mbr/lRmLRKG+9+Trr1q1Bd7lJpVLUy8ujQ8dOZNWpy4CTTkZRFHr3O5bps+4mEokiSzKaqrFj+zY0TcPj8fB/H3/EnTNu5d4HHqX/8QP49P8+pqy0lOycXL5fvJi27Q5hwUcfUFxcjKruPfxAkWXKSkp45803mHjVtRx2+JGcN3wEffodxxOPPczQs8+jsLCAgt27+OyTBZx6+pkUFhRQWLCbr778nBMHnsLqVSso3L2bevXqs3jxd7Rp145PFsxn965d1G/QgILdu/lkwXz6Hz+A2fc+iNfno1uPngSDQY4/4URy6+XtVRb8gQCqpmFZFqqmcdElozj9zLP516mn06xZc3bs2M6K5T9RWFjAdTfcQoeOnZkw+Rpyc+sBsGL5T6xZs5qmzZrz3cJv0mXp269Zvuwnbrx5OrIsc8bggQy/6GL6HtufaCx2UPfH364pp3KDwjPvfAFZlkikDFRF/LgeKEVRCIfDTL1qIqcMPpXy8nI2blxPIpFg08YN5NerU9NJ/J9kWSGRSHDN5Cs5fsBJJJIJ1q9bS7IiD83tnANeCaMmxMNRTundk1NO7APJJAQDWKkUNz/1HKObjUeWnRNQObnFPR6NcUzX9vQ+5jBIpsDnJeh2c/41M7gvFEbOrv3PGa/Px/VTJhEIBGjXrj1LFn+HYRhs2byRLp07IYYX7S0cDnNM776ceNLJJJIJgsEMTNNk1ozbuP3228HvjOCzOkWjESzL4sKRl3L52AlMHHcFU6+6krfefremk1ZDJCLRCK1at+GaqdOYMmkCo64Yi0vXsWybcDiEZVl8v2QxkUiYtoccQpt2h+DW3RzSoQOff/oJO3du54STBrJ0yRK++uIzVq5czmGHH/Gb8yVC5eVIksT6dWtJpZIoispV115Pdt1sysrL+M+Lz+N2u2nVpi1l5WV4vF7i8RipVIo62XUpLw+h6y78AT+JZIJkIk4iEaesrIzvlywmmUwy9JzzOLLX0YTDYbSKym5ZWVl6zqgsk0ok9vv3VVYUIpEItm2xfdtWdu/eBbbNmAmTaNW6DYl4HMMw8Hi93HP/w0B6/kQoVM6YUSO56baZdOjYqWrYlmEYxONxTNMkEkk3epUWF/P83Gdo3qIldevWxTRNNE1Lz4tw6Xz+6f/h8Xo54qhjiCfilJWVIisyK5cvIxqNoOtuplx3Y9XSt3s2pCmKQiiUvo51s3MoLyvD6/NSv0E+SxZ/R/OWrcjOyeHT/1vAmlWruGLcBO675y4++2QB69etY8hpZ7J165b08VYsIxaLorvcTLluGrFolFFXjCO3Xh6vvjyPe+++kwEnDaJjp87EYlEsyyISiaAov71gUOXqZKHycmKxKFJFEFZaWoLP50fVVEJlpSiqisfjQZIkysvTk/rXrllNKplEUVWmXHcjAX8Ar89Hy1Zt+OiD9+h5RK+K4Me9z2P/z2t/UJ9ygPW7i1m7oyg9caqmE+NAiqJSULAbl8vFl19+xhWXjODfzz3Lju3bufeeO7EqJrHVZooiU1RYiKqqLP5uIVdcMoJnn3qcXdt3Mmf2LEzTqPV5AHD7vIye8RBPPP86ZARAVdhRVILH7UJVZEdX7J3EHfAz9YG53PHQXAj6waWys6gEl6YiqyrU8qUwJEkimUhQWlpKMpFk/JhRTL/5RoqLinjkwfvYsnlTTSex1vEHAky/dRpPPv5wxcRQjcLCAnTdjaL87drw/hBJkvB6vUwaP5q3//s6Obn1yKpTl2bNmrN9+/Z/9HNKkiRKiosYftEltDukPXfOuK2qspeTWw9/IMCse+7k/ocfptfRvdm2ZQuWZdG7T790K//atfQ47HC6H9aT1199iU0bNtCn73FEIz8PcbMsCyOVwuPxUK9+fZBgxMhR3PvgQ1xz/TRC5eW4dJ17Z88CSeL5/7zGrTOm06hRY2LRKH5/gGBGBqtXrSQnJweP18vOHTvAtvF6fQQCQTp17sq9989hzgMPUbduNgUFu6smtVcuZ11aUsKunTuQlf2frGgYBnXq1kVRNQaeMpj7Hn6A6Xfeg23bv+o5t0lXqhOJOFdPHMdRx/Rh+IiLiUYie39pxSTtrDpZmKbJjddN4axzh/HEs08z9srJuD3uqrkdoVCISVOm0qJFS84/+3QUWaFxk6bYNkyaMpV7H3yYSy8fy66dO/cqx7YNiUQCl66TWy8PzaWxbu1qcuvl4vf7WbVyBU2aNEPXdQ4/ohePPXw/SNClW3c6d+nKww/ch8vlSvfaZKUbdq+cfA33Pvgwl48dT8HuXbhcLu6fM5tWrdvw3vwFPPXcizz71BOsXLECXXdjpFIEgsG9Fh7Yl717p2wM0yC/YSPKykopKSkmr0GD9Ipi5WXYtkW9vPpISFw86grufeghrp56A6UlpbjdHj7/9P/49JOPuXrqjUy77hqSiQSqenBDkv+2T1G39rfNWrVIJhPk5tbjhZdfxzAMfH4/b7/5OvfcNZPbZ86uGndam6VSKTKzspj74svpyWc+Hws++oBbbr2e6bPuRp1nOGKIgCRL9GjfmivvepzVW3eiqQpPvPo+9143smKFjppO4YGxbZvy8jJM03TUsCzJsji8U1uGX383OwpLyQj6eezld5g6/HQkB6yWZNs2iUSCGXfOwbJMZFkmHA5x8oB+TL3xFlq2al3TSax1JKBjpy7cdvMNbN+2FZdL56UXX2D85Gv2WgZVSJcvWVE4ceAp3H3nDLZv24Zt27z68jyunHwttsNWkPuzhMrLq+ZdRaNRbrptJscdczg7tm0jGo1wwYiRDD93KBcPH0HbdofwwJy7OWfYBRimyTF9+nH1xHF06tKVvPoN6KG5uOLHH+h5xJG0bNWa0oo9uwzTJL9hQwzD4IapVzPtlhkMOmUIFw4byvkXjuTjjz5g65bNjBk/kQ6dOvPC3Gd4+IF7Wb92Dd989SUnDjwZVVU474ILmX7LNGRJoqCggE//72OO7T8AJIkrxk3g+msmE4/HSSYSPPX4Izz0+NNVebRMk2AgyOhLRrB2zWre+eiTvfabkCQJ0zQp32OPHUmSCIdDxGMx6taty/CLLubKsZezauVoVixbxsfzP+TEk07eu+JvWVikeykvHzOe9od2oLCwYK9znkgmad+hIzu2b2XKxPFcdMlldOnanbfeeI1kIsHXX37O7l27qpbrLSsrJRaN8sCjTzLw+L5cOOws7nvoMQ7t0JFzzxzC4FPP4D//fh6f308wGCQWi6Xn1wUCNMjP5647bmf2vQ8ydsJkZs+aTmlpKZs3bWDN6lVMvuY6iouKGDDwZB66fw7nXXAhuu6m77H9ufvOmVx08WXobg/dD+tJp85dOXfoEE474yxe+c+LqJpGTm4uZaUlXHHpRVw2Zhwb1q2jabNm1M3OJiMzg0QywdSrruTcC0bQqlUbkqn0sPlQKEQqlUo/50OhvfZ6CYVCRMJhDjnkULp268GYURdz6hlD+earL9m1cyfJRJK27dpz/ICTGH7uUIYNH8EH771DYUEB5194EVdfOY4Rl4zi8jHj+frLz7n5xqnM+/fzB3V/KNOmTZt2UJ+sQS989j0bdhWjOzDQiKcMTj38UJrXDVJSHkGWa3clTKqY5IQk4dbdtGzVmqbNWpAZ9JNMGYQjsVrfm1CZB0mScOk6zVu1pGXDlqg/pvC3iKEFothW7c2DkUzRvWMbju3ekWVrNxJPJLjx0nM4sd9hFCyViUsezHYqUu2PpwCQJJlGjZrQrn17FFXF5/ORYUcxincg1eLWZSOZ4tA2zRnUqzsrNmyhPBThquGnc9aAY7BkN1FPNqWlZY64HyrvCVmWaZDfiHbt2pORESAz6P/fX/AnKQ9HCIWjtfp8JRIJOnTszOFH9GLN6lXEYjFGjR5Hv+OOB9uo1vNVUhYiGovX6vOVTCbp3LUbHTp2ZvWqFRhGistGj6d3336oEgT8v17C9a9SVFJGIpGq8d/Y+g3yaXdIezweD8lkgoyMDLofdjhtD2lPkyZNyczMpO9x/VmzZhW7du7gnPMuYMgZQwmVl+PxeGjVug19+/UnJzcXWZZp0bIV/Y47geycnPTmeqRbuQOBAIcdfiRlZaW0bNWa/icMQFEUli/7kebNW3L11BuRZZkOHTvj8/tYtWIFnbt2Y8jpZ9KseQsCwQzatz+UZs1bsmLFctod0p6zzz2flq1ak5mVRcuWrejQsRM/LP0ew0gxacpUOnbuQiwapWHDRrRudwiqqlI3O4eeRxxJ4yZN95qgXrlJY4uWrWjTtl3FPhsmOTm5HNK+A7qu071HT/Ly6vPD0u/JyMzk2htuol5eHolEYu/ekIqlZevXT7fe/7IHwDQMcuvVo1OXbhQWFNC6TRtOOvlfbN2ymV07dzL41DM4+pi+NG3WDLfbQ05OLm3atiMYzOS44wdQXFRIfqNGDD71DMrKSlmzehWH9TyCcROvwjTNvQKrXkf1JpGIk5ObR59+x9GwcWN+XPo9Xq+PKVNvpHHjJoTDITIzM8lv2Ihj+5+Arut4vT4a5Dfk2P7H43a7sUyT/iecSCQSYfWqlXTt3oMJk6YQjyc4vNdR1K2bzU8/LMXtdjNu4lVkZ2fjdns4rOcRFBUX0aRJU7KzszFME2ybenl5VeWuTnY27dt3IBAIYBgG9eql/83r9dGn37HEYlG2bdvCkNPOoHe/Y2nWogW6y8UxffshyxLLl/1Ey9ZtmHrDTRQXFZJTrx6DTz2dcChEt+6HEY1G6dq1Mx73ge81JdkO7CMddPvTzP9hLQEHbdRWqSQS47nxZ3Fc63zWbd1dsats7Ve507Xb7aGktISWTfKJxOLs2F2E6pAWQdu205PQAl7Cm0vx/DtB/eNL8NQvxErV/jx4PDroFfM/EikSZpSC93MpkbJInKojJZ1xK0uSRCAYJBIOE08kaNCgAY2sAmJrFiFptfyetu30g7byYZsyiJaX4c5vRWGdNmzYuNkx9wNUXItAkPLyMoJ+L62aNaq2Y2/dsZttOwt/NcmzNnJ7PLgrNiNLJhOUh0Lk5dShSX5etaVh/ebtFBSXOqJ8eb3eqs0vE8kEofIQjRvUo369utWWhlXrN1NWHqnx39hgMEg0GiWVSlXtLeH1+dBUtWoneZeuV+2xYaQMIhUrONq2TTAYxDBMotH0cKNgRkZ6talo9FcVc4/Xi67rlFd8r98fQFZksG0ikUhVy7g/EECWZVLJVEUFPv3vsizj9/tRVY2UkcK2LGyoGurk9frQdRc2kIjHicViVftchCMRjFQKn89f1cv6y/Spqorf76e8vLyqIh8IBEkmE1WBhs/vT688Z0M0FiX5iwDkQFSWw1CoPH0+KhaTiMdiaJpGPB4nlUoRDGYQj8fSQ6tcLnx+P6Hynz8jSRK2ZREOh/fqwa+sF3l9PiLhMKlUCr/fn95PA5tYNLpXAJWRkUksFiUej6MoCsGMjKq/gfTcEr8/gCRL2JZdcbz0sHGfz49S8azc8zMejxe3200kEt6rtyMQCFblKZiRQTKRqPpMMBgkHo+TSCTQNC19TEkiHo+jairxWOxXZaVyaBykF/ypnAPjcrnQNJ36OUEygwe+WEftbXb8HZF4kngkjuW8+AkjFsd0geSRsXUJW6ndPSE/k0hikEyGsHUJ2atASsIwTUfNuUkkEiTMBJKZLjuyaiO7LEdMUE+YMYj+vAKF7LKQZBvbsjEtC8l0znCHkuJiAEzLSg9jsixsIwkO2MU+GjFgj/HHtpEEK73niWFaB71UYU0pKSnBqIE9WyzLTj8/HHC6IuHwXsuTG3u0hlYXy7Ic87wNhUJAqOp1+nxVb/kyK85XTc/VKikp+dV74VBor9fxWIz4b6wuVFpauvfrfXxfpV+W08rhWnuyLOs3v8M0zarAaF/C4RC/XKXfsqy98hgOh/gtyWSS4opnf6Wysr3zV/47xz9QvyyHxUVF+/y7Pc9TYo/KOvz8W/Vb4vE48fjPS86XVwQv+1Jc/PPxDcP4VXrS5/LXx0tP+i/91ftQseLkPrYd2DNPZb8sQ3u8Tl+TfZ+X3yoryYqVUiGd/3AkSl72wa0W6MiekK9WbaagPOyIFqE9SUCkPE5bK0j9gI9QNO6oMfGVbNvG73Ej11Gx66o13t19wGQwC5MUTN+AXjeB6k1h2w7LAyDJNtFNKlrbutQZ1QgMx93KWLaNx+3GSxIzXAq1eKjJb7ItFLefuCtAxMH3tEtTCHgPboWTgxGLx4nGks57fpAut27dhc9TfecrHI2RSKaQHVi+LMvG59FxH8RwjYMVikRJpQxH3o+C4CS2bRPwedEOYoqEI3tCPl2+nh837cSjO2ODsEoJLJolvHRfVZ8iqRBZcd6kYkh3GuwuTeHvnU2dCxphJZzTAg8Vje1uDf/hmZhxm0TKdkRr7C/ZNrg7wf+zd97hURRtAP/t3l5vSQgdKQooXUWUJlLEQhEEVIqAYANExQaCKFVBRYqoIEU6WFAEUUFAulQFAZEiSBUSSL++tzvfH5ecoPipEElO7/c8eZ5c7rLzztzO7LzzNmslGwZJjonK6b9FFgJZUdCzzxI+cxTJEFtzGkBoYUSR0pjDIYwn90Ehjmv5QzQNyZ0MtiqXrUlZklEUQ0xuEmUhkC+z1c4gyyiGGB0vWVz2OjQGWUYYDDFh5Y4TJ6YR4qLnWQw+LWHZjgOs2XkAc4zFhARFmFrGZLoVKUOxZhkYE7IR4dg7+ZUUndTlyaiawBPwI/yxpYQIQMiCoj3Loek62Tm+2DyN1QUJbgd6WCfjTA6GmHHt+xVNF2A0Y/RmET51CMkUW3MaQKghZJMFXSQSSDmKZIydui15CFXFiMBU+vIpIcGQSo7XH63tFEtousBhE1gtl++7DgRDeANBDDG4qdZ0gSxLmE2X75DBHwgSiFHLUZw4sYQuBEaTEeNFxPfFpBJit5iw2CwxF5juE2EchshDS7FpKM5wzCohkiKASJadWDtpkgBdgO6P+HWLgI6IwY2Q0AW6UUMQecDH5AmpFPk+kGUkxRiTlhCEANkAUiz3AZAv7+NAkiRkKYbv28ssd3y8/h6xPF5x4sQSMlx0rFpMKiH/BoQuIbTIT8whSQUd6xcnTpw4ceLEiRMnhom9Y/g4ceLEiRMnTpw4ceLENP8qS4gkQSisk+MPAgIJCafVjFGRYzIAPE6cOHHixIkTJ06cfyP/KiUkqGo4LCZ63XYT15Quyg/HU5i7dge+kIopBgpixYkTJ06cOHHixInzX+Bfo4ToeiTN6oKnO3NzlfJ4gyG6m2tz53VX0/71uWi6iMksLHHixIkTJ06cOHHi/Nv418SE+IIhbqx4BTdXKc/bX26iyH2DWbRlD01qXEXNciUIhNSCFjFmMSoGrHYbVpsVOQaLyQkhMBgMOBwOHA4HBoPhsleHvhSEEJjNZux2OzZb5Mdut2OxXL5CafmBJEnYbDZs9l/7YDAYYi7HgUGWsdqsWO3WmCuYmocsSVitFqx2G0ZFiSea+D+cv344Y279KBCEQJZlrDbbfz47VUJCIlarNXrPCCEwmUy43e58ayPvHnW7E/JtvIUQ2Gw27Hb7Jd3vQggsVitJSUm/+1EUBZfLjcViKbA5JYTA5XZjMpkuSga3242iKIVuTUhITMRsNv9tudxuN0aj8bL1519jCbGYjBxJzaDN6NnsOnqKYEiNxoFk+4NxK8hFEFmErGRkZrN67VZsVjPNb7oWCuGE+yOEEFgsFoLBEJ8v/QwBNG7cpEAXvb+Loijs3fsD6enpyJKMyM3J7XK5qVWjekz0Q5IkwprGjh3fkZOTg0Tku7mqYiXKupwFLd5fInIvmVFDKl+s20JIDdP8pmuxms1oMfAdQCSbsNGooMgyqzbvIMvjpdH11Ul22wnFSB8uJ3kbMa/Xy6qVK0CSaNKkKVZr7KwfBYHVZkNVVfbs3kX58hWQ5dhU1i8FSZIQQjDjvWnUrFWLmjVr4fF4MJnMHDp0iD27d9OyVSuEEJd8LymKQlpaGp8tWcztd9yJ0WhE1y+tfpfFYuGbjRsJhoI0anQLwWDwoq5jpEX/SgAAIABJREFUs1nZuGEDmzZtRFGMuJxOMrMy0XVBh3vu5cP3F3B97do0v+12fD7fX76uJEkYjUZCodDv3osc3FmwWMxkZWX9X8VMURQ++uB9qlWvToUKV6Kqf+3AWpIkNE1jwfx51K1Xj2LFihMOh/+y/JeKEAKj0YjdbicrK+s8uSRJ4tmn+tG6TRvq1q2H3+//0+vljdH7C+Zzfe3alClzxV8ei0sh35SQ9evXc/LkSTp27MihQ4cYNmwYwWCQ4cOHc/XVV+dXM3+IUZE5m+Nl2Y79hPxBHmnTiHZ1qzNz9bf8cDwFty22To0LA2arhR8OHaXj869SrmQx0jKzeXPBEj6eMAhFMfD7qV/4MJlMnD17lq5dOuKwO5ANBt4cP5aZs+dRvmwZghdYwAobVquVDxYsYNOmjdGFNzU1FavVxnfffUfoMiwUl4rZbOaXw4e5p11bypevgCRJ6EIw6IUXqVzxyoIW7y9hNBk5m5lNh2dexmhUMMgyI6d+wJKxAylR+uqYMCYoBhlV17m3/yhOp2WQnJTA8xNnsXD0c9SoV6agxSt0WCwWfvnlJPd37kRCQgKhUJDxb4xhwYcLKV6saEGLVygxGo2kpKTwdL8nWLd2Dd/u3E1CYmJBi3XZkWUZXdd57pmncDic7PrhR8xmM1arje3btjJy2FDuatOGcDiMoijY7XYkScLn8xMMBrBarbmvIxtzh8OBpun4/T4kScJutxMMBlFVFbPZzIkTxxnQ/1kaN2mK2WxG13UcDgeKYkQNq/i83qjCYzKZsOVaqYLBID6fL6o0Wa1WLBYLFqPMggXzSE9Pp0WLFgQCASwWC1arFV0IvB4P4XAYWZax5SqdFosVjycHTdOim1qDwUBGRgZHfj5CSsppVny1nHbtO2Cz2fD7fThdLpRzTt5tNhsmkwlN0/B6vRdUpmRZxuv1kpWVxVVXXYXX642+J4TA7XazZcsWVq9ayZBhQ8jIyEZVVWw2W9Q64PP5UFUVk8nEkBcHM/CFwVStVh18PsxmCx5PDpIkRb0nQqHQeUqSLMuEQiEGDujP9JmzuOKKspExsFqxWizouo7X6yUcDmO329E0jUAggCRJOJ1OgsEgwWAQWZax2+34fD6EEH/Y3rnj4vF4sFqtHD16lHlzZvPSsOGEgsHo9fPuF0VR0IXAbrcTCqmYTEaMRiN+vz/6WUmSsDscmHL/PnDAc4ybMJGKFStFZVcUI6oawuv1RsfE6/WiaRpmswXDJcRc54sSsmjRItq1a8cDDzxAhw4daNasGZqmUbp0aRo3bsyuXbsoWvSfXbDzDhJUTWfAPc0Y3fVOFm7azePTFmO9jFVa/y1IEhiMCk+++i43Vb+aaeNfRM/MoWb7XkyYs4g+pZ/mTEEL+RdwOp28NXECXq+XzZs2AXDjjTcxZ/YsRgwbEhNKiMfj4aWhw5DliBWkSIKT9h3uocwVZVEMMoFg4a9YL8syp079QuWrr2H9+nXogN8fIifHgzekYS9oAf8CRpuNEWOmogvBqrnjQJZp89DzPDd2OvM+aIx2+Q7BLhqTw8bkmR+zY/9hDi19D0OSm6cHj6H3y2+x8cvGBS1eocNutzP0pRcpUqQIXy1fRlhAk1tuYeTwYUyfNqWgxSt02Gw2du7YwYM9u1OhQgWSkpIQl3giH8sIIahc+Wp+/vkwzz3zNDNmvkcwpGMymXC53dFNf0ZGBvPnzSUYDHLHnS246qor2bZtG4qiUKVKVYQQbNm8GYfTQeXKldF12PTNRoqXKEnp0qXPccdyRzeWDoeDr75azr59P1KlSlVuvfU2vD4vFrOF9PQ05s+bSyDgp0HDRtSoUQOPx4vT5WLvnj2sWbOaG264gcTERILBIEKAy+Vi348/snLlClwuF23a3o3NZsPjyWHb1t2UKFmStWvW0Py223G73WiaBkBOjodWrVvTpdO9bN2+gx07vuOdyVMonpxIRnZE+UlMTCQUCuFyudi4cQPbt22jTJkytGp9F2FNQ/uNhUGWZYyKkUHP9+e222/niSceJ/VMOhA5eExJSWHG9Kl88cXnVKxUiRtvvImy5cqxY8d3bFi/HrvdTouWrUhKSkLXdVwuF0ajEYvJQMrpbDZ9s4wmTZtitVr55OOFnDhxghvq1KF+g4bkZGefJ0ueO5ae69b1494f+HrVKhITE2nV+i7sNhsbN26gVKnSlClTBk3TWLVyJVdddRUlSpZEVVXWrP6aatVrkJyczKeLPuHYsWNcX7s2DRs2IicnG5vNxtatW/h2+3ZKlSpF67va4PV6mTnzPaZOnsRVFStyU916lClTBlVVkSSJu9u3p2hyUfSwypbt2yhbthw7vvuWY8eOcfvtd1C2XDlCwRBmq4Uvli7l6NEjNLu1OcWLl0BRIqqBzW5n+bIv2b9/P9WqV6d58+akpp5h3bq13HxzI6wWMydOnuTQTwe5847bL2qO5IuD/7Bhwxg2bBgzZszgk08+4ejRo+zatYvNmzdTsmRJlixZkh/N/F+EEKiazqv338HorncyZcUW7h//AQZZxmz875mCLx0JQiqHfznNnfVrgxpGTnTT975WLFm3FU3TufgamZcPTdMoUaIkIJGW5SEzJ3LaULpMmZg4uc5D07Toide6jZvZsnkTfR9/kpAaAztffj0NczgcrFj5Ne9Nn8nxY8dwOByx8z1Igj2HjtGkTk0wm0HA8w90YOP3ewll5cRGvJTBwJ5DR7mp+tUYkhPB66d/9/b8fDKFX06cKmjpCh2apnPgwH6aNm0GgCLBE0/2Y+2a1fE4wwsQDAYpXrw4ixYvZfSrYwiHtf+025oQAlVVGTdhIp8vXcLceQuwmuTomJhMJtLT07in/d1s27aVkyeO077tXRw+9BPbt22j96MPY7PZ0HWdNq1b8ky/J3G5nHi9Hh5+sAdpaWcxGn9/yOp0Ohg5YjgvDx9GyO9j+JCXeGXkcJKTEjl16iTNGjdi44b1pKWl0bnjPWxYv46kRBerv15F65Z3cODAPqZPn8ann3yM0+nEaIAN69fT8b4OpKedZdXKFXS6rwOhUJBwWKNH9250u78LK1d8hQTnrYWSJOH3+8n2BcnKykIIQUZGBulZHkwmE48+/BBLFi+maJKbmTPe44nH+hDweZg6ZTJP9O2D2WSKnu7nKVcWi5WSpYrx3szZLPzoQ97/4EOcLhcQca9KOX2agwcOoIU1li/7EiRY/Oki7r6rFelpZ9m4YT333dMer8cTjX+w2Wx4cjzc3aYVO777lgS3k8f79mHa1Cn4fR769unFzPem48pt57c4rGaWL/uSe9rfzdEjh/lsyWLuatUCXdeYO3sWI4YNpUiCk9OnTtGmdQveeectiiQ42b9vH3379MbhcPDMU08yedLbBHwe+j3+GNOmTKZokUQWfvQhT/TtQyjoZ/q0qfTo1hW/38/u73ciSRIrV3xFamoKRqMRWZZRFIWuXTqxYcN6iiS6eWHgAO5u04qln33GurVraHtXS9LS0khIdDNowHMMer4/x478zJAXX+CXX05isViwW4yMGDqE0a+8TCjg46XBgxg96hVKFEtmzGuvMvmdt0lwOej5QDe2bduGyXhxNo1LtoTouo7P56Nz585AxCpSv359EnPNr7Vq1eLEiROX2syfku7x81TrhjzX9hYAWt9QlXY3VUcxGBg0bxlTV24l0W79x+X4tyCEALOJutWv5tVZC6lZrRJn0zKZ98Ua1HCYYDgcEwGH2dnZ9Oj5IB++v4C6dWojARWuvJIePR8kECz8VpA88h5aFouJ118dRes2bSlfoSxBf6CAJftrGAwGgsEgO3d8x9SpUwgFgwwb8iJjx79J+3Z3F7R4fw0BzerUZNLCL2h/WyNkWWL8/MX4AkECwSAmd+GfD6hhmtSpyUMj3mTdyg0UL1aESfOXkOENkOOLjXvpciLLEg0b3szUKZNpfvvtBAMBpk+bSjAUJBAIYolb2c8jHA6TVKQITqeTLZs3/6cVkDx8fh+Vr76a8W++Re9HH+HOO+7Aao3sRZx2C6NenoTdbmfGzJkoEmRkZjL4hRd4Z/IUJowfy/Hjxzl86BAVK1UipIb45ZfTbN+2lVKly1Cnzo34fL7zkpSYTCYOHvyJWTPeY8nSL7ih9rU0v6MFre68nUd69eLo0aPccWcLJk96G4D9+/axcOFH3Hl7c4YPHUKX+7sx9o3X0YBmTZri9/mRgFdGDqdjp86MGDYUXzBM7WtrMHf2bHo8+BD+gJ8+j/Wl5wPdSE3LJBQK/aX9QV7SFbPZjDcQYsxrr/Lq62O4p/3d9Dj+C7WvrcnGjRto2PBmgsEg2dnZ9On1CB5PDkJEPB10XdCl431s2LSFGjVq4vV6ueHG2vTq8xhvv/Um8+bOIaQL5s6ZzcujXuXhB3uQmeOjepXKbP92Oy1atkBRFILBIF27duX662/g9ddGs3zF16z5+ms2bt7CFaVKcPU1Ven/7DN0uOdeDL9JSCJLMpquM3jQ8/Tq1YcB/Z8F4Mab6jJ+/Hge7f0YD/XsjqoJ1qz5mho1a3Jg/3504OtVK2narBknThzniy8+Z/3GzVQoW5rqNa/lib59eLRXb9atXUNSUhFeHDyYPo8/ySsjhpOQmMiLLw2jT69HmDVnPqFQEK/XG5XNYrFEf1dVlcaNm/D2WxMJhgXXVLqSnTu+IxxWmTVrJl+vWceNta9j9979NGpYD6PRyMHDR5gzexafL/uK62pVp0mz22hzV0t6PvgQ09+bQfdu95ORmUFSUhLPD3qBkBq+KEXkkpUQSZIwm80sW7aMTp068cknnzB27FgAAoEAq1evZsyYMZfazJ9iNRnZezyVUZ+sAQQWoxFZkhAIDp46i+UitbT/MiFfgKlDnuTRERPp9MzLVCxXmqpXXsGeI0cwxIACAuBwOpj45nj8gQDvTH4XWZIYMXwYE9+cwNP9nrisgWSXSsQku42tW7bw+hvjyM7KwWIyxoQlITs7m9tuu4Prrq9NpUqVcFhNDBr8IkNfGszd7dqixEDiiGC2h8GPdCLb66Pn4DcoXTyZKhXKkOB0IEtSTGSYCni8dGp9KydT03jy1ckUTXRzU42rKZHkivQhznlkZ2czfOQoAoEAPbt344qyZbnyqqs4cuQIBkMMWL4uM3nBuhcbxPxvRJIk0tPSuL9zR+bNncOjjzxM1+4PIOWueceOHSXl9Gk63Xcfqqri9Xq55pprKFU8mauuqsimb77h4MH9dOzUmT27d7Hpm41s3bqFOnVuxGo1nxcPAREl5MSJ43i9XkaPfgU1FEKWZapVr0F2djY3N7yZw4cO0anL/dhtNnbv2sVdbdui6nAmNYWbG92CN6BiNhu59rrrSE1JRQdSU1NYt3Ytd7frgCRBctFiuFxu1HAYt9tN9Ro1yPT40XX9bx1QSpKEoiicPZtOdnYWs2fNZO6c2RgMBipXrhy1quh6xI2t8/1do31KTEzklZEjaNrsVsqWLRtVflRVj8YsZHsDhMNhevR8kHcnTaJTl/tRFAVPrhVEApwuFy8Mep7y5cqz+NNFhAUcOvQTfr+PZ57qRyAQOaCpXqMGgUAAm812Xh8URSH1TBrpaWk0bNSILE8Au91Co1tuYcO6tQwa9AJGxcjOnd/zzcaNPNf/eaZPm8qOnbvZvn0bXbt356eDP+H3+xnw3DPReI1qNWqSlp7OsBEjeax3L66pUpVKlSox8IXBuF0OMrMy0TSNzMwMFEU5b9zP/V2WDVx73fWENPD5fCQmJSHJMj/99BPFihbjqqsqcjYjmyuuuIKSJUsigOPHjuPzeXll5HBCueNdo0ZNMjOzuLZGVVq0bM3I4UP57vs9QMRbg4JSQgYOHMj9999Pv379KF++PD169GDdunV06NCB5ORkWrdufanN/CkOi4kNPx5h+c4DF3zPZr649Gv/ZXQtzOHjp5g9/Gk0BMbSJXju+VcpmZyIxWwiu5CPpyRJyJLMzBkz6PngQzRv2hiAvXvv4c0J43n2qSeRYsClLA+b3cKro0fRslUrKlW6kpSUszFzEmswKHzzzddcfc01Ef9Z4IY6NzJn1ixUHZQY+B6ELLH3p6O89EgnRvfriZzkZt7cxXy1YQt2t+uSs9FcFgwGDv50hAdaN+PZBzqA1cK2rTuZ+ekySpVILmjpCh1CCPbt+5GRr4zGbDaT4LTx2pixuFwu7L/ZiMSJ80cIIQiGdaZMe4+G9W/C4/HgdDqjWQJvqFOHKdPeIz09nYyMdFJTUgnrcEvjxiz6eCECwQsvvoTNZmPRJx+TmZnJE/2ewuf7VdnL2/ybzGZMJhNOp4Mxb4yLZoL8Yc9uypYtx6hRr7Dk00VMmfYeta6tyaMPP4LX48UgRZIKeDw5WCxGDJDrTqeDiMTddn+gBx3uuRefz8eRn3/GZDbj9/nQdT0aO2K325FlAzk52X84HhcaH5PJhKIoDHzhRSpVqoSmafy4dy/FS5QgEAig6zpGo5Hbbrs9kv7ZYmTEiJex2KzMX/AhwHmB2ZqmYTAYsNst+HxB7u/UkSpVqzJ02HASEhLZvnUroVAIAfj9Ph588GEWLvyQtye9y2O9H0VRjBQrVpx3p07H5/PlyvMDBoMBXdejMZpCCExmEzarDZAIBAKYzWYUCXxeLxarFZMiUevaa5k06W2ysrJo2aoVG9avY8q7k/B6PTRq1JjVX6+iaHJRJr07NdrfvT/8AMDZM2eZOXsup0+d4oMPFtCmVUu2bP8WxRBRPKxWK9oFYmfOJfL9ROTVdR2EwOGIBMhruobNakOSZTRNy83AacLpdPLGuAmYTCZ0XeeHPXtITk4mNS2TbzZuoG69+sx4bzpvjB2LHr4499R8Ocrp3Lkza9euZcqUKWzatCkaxd+zZ0/WrFmD2WzOj2b+L7oQWEwKRZy23/2YjbGTUrYwYXE5GTXjI5r3Gcxpj5cvPl7GpIVf8Hin1gRVlcJ+9Ctyc9XXq1efGdOnsWPXHnb/sI/p06bQoOHNkc8U8j7kYbPZWLN6LWu+XsXTzzxHTo6PWIjJycPpdLBq5Qpat7yT3bt38eO+A7w66mUa3HwzNkWKiQ28xWHnk9XfcH3nJ/n59Bm2ffMdT4+dRu97WiLFyBpjsVrY/uNPXN2uF98fOMy+A4d5YMg4Ot3eCHti/tUt+LfgTkhg/tw53Nr0FlJTT7Ni1WpeGTmCR3v1obCvfwWJJEXmtMfjiYl58U+SdyIfCqkUK16cCW++xYqvlpOZkYmq6dxzX0c+X7qUbdu2YTQaeOapfkx5dzKKDHe1acvSpUs4duwoVatWp0nTZnz04QecPXuG+vUbEPBHsicJIXA6IvEG69au4dprryWpSBHeeWsiZqPCyhVfcX+XTmhamOPHjlKqdGlq1arBpk2bWbliOZIMsgQNG93CG6+/xtGjx1mxajUfvr8g9zAPWrZuzZsTxpOenk5mZib3tL+bb7dvw5qbwlrXdZx2C6NfeZlHH+6J6ZxYDvjVSnbuPZGX/cvn81GyWBK1rr2OsWNeQ0Jn794f6NCuDWdSUzGZTNF+er1e/H4/Px06gt/nY+Jbk5Bl+TwFRNc0ipcowaFDh9i0aQsej4eTv5yk9g11KFGsGO8vmM+BgwdyN/GQdjaNGjVrMnfefJ59+inmzJ1P69atyMrKYt7c2VjNJmbPmsHjj/XBYrHkZhKLuDz5A37Wr1uH3WamUePGjHp5JGfOpLBh0xY+XvgR93XqjC6gXYd7mDF9Gm63myS3kztbtGTK5EmUKXMFJYsn06BBQ3w+H7NnzcRqNjF/3lz69HqYIokJDB82hMf69KJ8+bK0bdsOIXRycjwUK16Mk7+cZMP6dfh8PuRz3MR8uZm5gGgmsLzx8Xm9eDwerrvuWgSCiRPGk552hrfenMCB/fvRwhq1r7sWl8vN5EnvYDYqLF/2Jd26dsZsNvNUvycoVaoUX61YwdIli5k/fz7mizwQNQwdOnToRf3nbyhXrhzXXXcddnskz02ZMmW49dZbo6/zk/nrd/JzSjrmGHOxUtEpIVu521Qee2UvBmsQ9EK8kdR1bqldg027fmTqh1+wcecPDOvVmda31if1OwnhcmKqaYdw4X3IqKpK4yZNOHz4MO9OeofFixdRu/YNDBvxMhazCU3XCYUKf3yL1WplzqxZVKtenfs6dspdyMFijizOwZBaqPsQCoVo2uxW0tPSmPzO23y66BOqVq3OqFdfw2ixoXjT0TJOIRkK75wWapgba1bhl9SzTJj7Kcs3bufBNs15vGMrNMWBZLSgpZ1AKsQ1EXQ1TI3KFUDAmFkfs/jrb2he9zpG9u6CMFiRi1y+NL2hkEpILdxzL6yq1KtfnwMHDjD5nbdZs/prevV5jO4PPEAoFLroB+/FEAiGUMNaoR6vPGRZJicnh8OHD9Gy9V2YzWaMigHTBYKo/yn8gSCa9vdcg/KTvJS33+/cyS23NKF4iRJ4cnKoUbMmXp8Po1Hh9jtbUqVqVZxOFxPGjWXJ4k9JSkpi5CujQDaQnFyMfT/upV69+jRu0hi73cEPe/bQuEkzGjdugj/XTUjTNIoVK4YaVln40Ye0adOWO1u2ZM7sWXy88CO2bd3CS0OHU6N6dapWq8HyZV/y0UcfkpKSQrXq1SlfvgI33FCHG268iS2bNzF3zmyOHTtGrVq1KF/hSurWq0+9+g04eOAAU6dMZulnS7jzzpb06tOHnOwc9uzZza3NmlO8WDKff/456enptGl7dzRDFkSKvGZlZ3P48CHatGmbG8ci8f33O6hZsxaVr76Gxk2aseKr5cybN5dVK1fyWN/HubNFy9/VD9F1HYvFwu133IGiKOcpIBCJTSpXrjxnUs+wYP5cGjVqTMObb+bNCeNZvvxLEhMTKV26NDfWrUupUqX5dtt2atSsSZNbGlGlWnXefvst2t59N01vbc7bb77J4sWf8vPhnxn9+pio21deMHuxYsX46MP3qV6jFg907866deuZNXMGy778kocefpSuXbvh8fooUaIEW7ds4d77IhYZp8vNls2buL9bd66sWBmbzcZNdevy7qR3+HTxIg4ePMio116nTJkyXHt9bVZ+tZz58+dFrvvIozS79VYSEhNR1RBzZs+mUqXKVKpcmXCusrHz+x3cVLceFcqXZ9v27Vx3XcQVOhwO8/33O7mhzo1UveZqqlSrwbQpU/j886W4XG5Kly5NvXr1qFChAg1ubsTs2TP5ZOFCvv12O6NHv4piNPHpok94bcxYiiUnU7xEKT5bspg2d7X5XazMX5onIp+OKbZt28a4ceM4cOAATz75JNWrV2fAgAGMHz+eqlWr5kcTUVq9MpNVu37Caf3nLSz5iU+EudaQxCx7Y4q1TMWYlIUIF16/YiEEZpMRg9lEVkY2dqsFxWom4M/h9JJiaGVK4uhSFOEvvKfYeQV9HA4naWlnEUKQnJxMdnYOdqsJXQhyPH7kQh6TkGfVsVis+HNPv3Rd4HbaEQiyc3yFug9CiNzquC7S09MJh8MkJyfj8XgxWW040g8RPPQdkrHwzulIHwyYbFZyMrIxGhUsTjv+jAyU0tcg2xMI7t+EpJgKWtT/iyRJWBx2vNk5kbz0CS6CmVlISaUwVWlw2eTI8fjI8RbuuXduQbCzZ89itlhwOZ1kZGRis5pxOy9fcunMbA++QDBmYnfy4kWDwYgy4HLacNguX3KY9MzsQnE4YzabUVU1au2VJAmbzU44rEZjZ1wuN1lZmQQCAYoXL04gEIjWkLBYrOj6rzUmrFYruq7/buOdV3/C7/cTCoWw2mzIksSZM2dISEjAYrGQlZWN1WpBlmXS09MoUiQ5WiskUjPDjMVi5uyZM7gTEqI1JVRVRVEUHA4HqampGAyG3OdoNrquYzabCYVC6LqOzWbPrePh+d1YyLKMyWSKugYBmMxmtHCYcDgcDVJPTU3BZrPjcrnOK8T3V8lLWexwOvF5vaiqitPpxOv1EggEKFq0KCFVJRQMomkaFouFcK4MkZoaIQKBAG53JOYlIz2d5ORkJFnG6/FEx10IgdPpRFVVAoEARqMRm81GamoqFosFtzuB7OwsdF3HYDBE2wkGgxgMBswWC2FVjRZctDscaOEw6enpFClSBIPBQE6OB6vNilFRSE1NxWaz4XK7yc4twpj3nf+2sGDkvgujaeHz+pc3L/Ne59Us8Xg8FC1aNJJ4KBBA07RI7IskcfbMGRJzK7DnZbmMWPdCWK1WQqEQTrsVq+XvP7/z5dhx5cqV3HbbbTRv3hxd19m2bRtNmzYlEAjQpEkT9u3bF82WlR94gyECvkDMmXmDIozXGAI7yEaBbNIRhVcHASBMCC0Uwu5UEEIlGAwhm3QkOeIjmh/VXv9pQqpKRkY6imIECTIyMtA0gd1qRggdXQikwt0FBEQLLAkEEhEXRCEiLmWx0Idw7uIqyzIms5nMzEw0TWCy2UDXEWEVCnma23AwjBYKYjLIoIXxpWcgtBCyGWSrFJkbSuFVyvMIBrMxW2VAIhjIRihhDJbLu1kTIjbu25CqomZmYjKbEbpOekYGun75173oeF3WVi8OAUhCEAgEonIX1HgV9IoS9cWH6Lrt8XiQpF+zHmZlZUaL1mVlZUU/q+mR4oSRdT7yeb/fH33NOWOqC3FedXCfz4ecu0lVVZVgKIQkgT8QQJYk7HZHblC7BLl5NYKhICE1osD4A4FIpW1JQgBqOExmZiYWqxVy0+zmyXluH32+yDXznlN5CEDkFuzL67cAQuf8byAYJBgMYbc7Iql8MzMv+n4PaxpZmZnR8cjOycGQq9TljVNeu+fKn1eQz2AwkOPxRMfQ7/dH59+593JOTk60jZCqomZlYbPZEEKQmZkR/ZymadGChCL3tf+c1xKRmmB57QUCgUh7EpHigrnpiYUQZGVm/u47P/c6/KZP5/6OEOe9zs6JpJe3WH8/Lt5z7qFQKEQgV3ltYkwkAAAgAElEQVTK+w4j8TSRauwXq+znixIycOBA+vfvz+jRo3njjTfYtWsXpUuXZt26dVSuXJnPPvuMbt265UdTANxeqzKlEl1YTIXXdeNChNApG7AiDuv4jplQMh0ILRYeKecjGQSqR8KoyNjNVoRU+DddvyVywmnAIGSc9oufQAWJEAKT0YDAgNNujdk+mBUDBpsTS4lyYIiNQPs8FECoKt6DgBxEPemCQuxS9odoYeR0GVMgG1slJ5Lyz99LJpMRJ7E89y7vvWoxm5BlOT5efxGLxfy7jEFx4sTJf/IsTxdDvtQJSU9Pp3v37kBEkztXmBo1anD06NFLbeY8bqlWgSplimGMsRSJmgyWsxrhowHSv3WjC3csxRb/igBUDZNZEA6FQSvkR5kXQNdzT+d0QVjTYsbF4Vx0XaCbIkdi4bBWqN1a/ghdCEzhEAFncbxKQuxtGGQJEdA4O/MQenYGkrlUbMYrSyBCOrLlIBVH1sLg/OcVKV3XY/q+VZTLG/ujxfJ46QJhvLyHVbqmx+zaHidOLKELcZ5V7u9wyU8aWZax2Wx89dVXVKlSJZoZCyLmwHXr1tG1a9dLbeY8Bs1bzpodBzDZCq//+IXQEYQDYT55/H4aXlmCo6fOIseYIgURM2L50iXwhAP8ePAIihR7fVDDGhXLlyYUCnPsVArGi9TiCxI1rFGhbCkQgp+Pn8J4mTdF+YGqaZQtWRyjSeHQkV9irw8ySEGBXYOiN+vYyh9DhGJvPkgmHe9BGxm7S1y2g5Gz6VkcP5Uae985kfu2VLEiVLii1GVr83RqGilpGTG7VpUvXZLSJS/fM/v46VQyszwoMfiMjRMnlghrOlUqliXRfeFq8v+PfHPH6tKlC4cOHeLkyZNkZ2czceJE3njjDSpUqECLFi3yo5kodosJi90Sc4HpAGm6j4ATDEkKmldCMsTeKY2mSRiSjRi8GgaPfNFmuIJEiNw6IrKEYjDEbB/yTvlitg9EqlLLkoRikGOvAJwccU8ECYNVYHSp6MEY6wMgm3UMVu3PP5ifbcby3INoEbXLhSzLsTtegstuwTFExyv25mOcOLGFVLAxIZ07d8ZisTBkyBCOHj2Krut8//33tG3bljFjxkQtI3Eim0ZZAxEWSJf3mZ9vSBoIVcSkG1acOP8UQgehSTEZ5yU0CVGY04XHiRMnTpx/HfmihBw9epR27drRrl07srKyCIVCJCUlxeSJTZw4ceLEiRMnTpw4cf5Z8sVO2bBhQ6ZOnQqA2+2maNGiBaqABNUwnkCIgBqOyRjROHHixIkTJ06cOHH+zeSLEvLII4+wYMGC/LjUJSGEINsXoHSSiwbXlKNCsUQ8/iC6HldF4sSJEydOnDhx4sQpLOSLO1ZycjJr1qyhYcOGXHPNNWjar8EOQggGDBhAlSpV8qOpP0QA/lCYwfc047k2jTAbFYQQTFq+hRfmLwMhx1P1xYkTJ06cOHHixIlTCMgXJWTv3r00btwYj8fDt99+e16UvK7r5OTk5Ecz/xd/UKVW+RIM7tCUb/Yf5fnpn9G3bSP63FGXRVv2sHHfERwXUVL+v0Zetcy/+15h4t/Qhz8iLyOPrsdegcg8YqkPeWvZb+8ZSZYj1Y+JhT4A/PF9b5BlRAz043LxR9953nuSJIEWo1lFLiN5YxUL8zzOP48syxhzC1aqqoqu67lFg40YFIWA339RGZbyXP+1QjAnJUnCYrGcVxU+VlAUBV3XL/t8zRclZOLEiflxmUvCpBg4k+2j3Wtz+OFYCgeOp9A6NVLa3hdU41aQP0EIgdliwagY8ft9aJp23oJgs9mASO2XwooQArPZjNFoumAfrFYbkiTh8xfePvwRsizjcDjxeCIKvdPpIjMjs4Cl+ntIkoTT6cTr9SKEwOlykZlZOPsghEBRjFitFoLBIKFQKHovOZ2u6P3lcrvwpXsRIlDAEl8Yg0HGZLGghUKEQmqeRhJBCMxmE+FC8PAuDORtiPI2Eaqq/m5TZDKbUUOhApIwdnA6nZF5o6q4XG4yM7NiblMWJ38QQmC12QirKvv3/YgQgoqVKmOz2QkE/KSmpJCVlUnFSpX/tiIhSRI+rxcAi9VaoPeYJEkEg0H2/biXipUqoyhKTN3z2dlZmExmjEbjZZU7XxNoh0Ihdu3axTfffMORI0fy89J/ilGRScvxsmjzHoyKAf/CUTzXthGvfbqWzQeOYTfH0wT/MRJFixbj+NEjPPpgN3Z9vwO73R7diBUtVpwvli6h10MPEPD7UQz/fDXliyG5aFFOnz5F74e6s33bFux2R24fFIoVK86qFct4uMf95GRnoSiFsw8XQpIkTCYTE8ePoWfXTjxw/33MnD4Fu8Ne0KL9ZSRJwmK1MvntifTo2pEHutzH1ElvYbPZCl2VdCEEbncCAP2ffoL3583B6XQCYLfbmTt7Bt0730OPrh0Z9uIgwmG10N1PQgisTjuywcCDL43ljTmLMDts57/vdrL2293Uuf8pTqScxWw2FqDEBYsQgoTEJEKhEE893pvPFi+Kfud57ycmJTF/zkweeqALmqYhy/HsjxfC4XCyeNHHPHD/ffTs2pFRI4cAAkMMFqWMc+lYLBZ+PvQTD3S5l6GDn2fksBfp2rE9O77dRnLRYmxYv4bxY15FMRqRZRkp11KuKArybxIcRWp7yciyjMvtpnjxkrzz1ngmvfUmLrc7+jmDwYCiKH/4bMmzxhsMBgzn7GcURblgUiWDopx3vTw5zpXLbLaQmnKaoYOfJzMzI9qfX9v59bq/bedC18t7LUnS+WNyzt/PfX2h/p17vXNlP7f9vP3FU0/0Ycumjdjs9vM+/9v/v9A1LoV8e3JOmDCBoUOHnneyWb9+febOnUuFChXyq5n/g4SmCyRJIsPrZ/D85dzXoCYdG9Ri8da9fH/kFLb/8EP2j8hzK3l5+BCWffEZx48dpV2He1EUBZPZTGpKCgOeeYLtW7cQCPgJh8OFbtMoSRKywcCY0S+zdMkijh45wh0tW2M0KphMJtLT0hjU/xm2bvmGrKwsVFW97IXGLgWn08Xkt9/k4w/f5613p+P1enm810M43Qk80bcPOdnZBS3in+JwOpn93jTmzn6Ptya/h6aF6fvog9jsDp595hn8fm9BixjFZrfzycIPmDHtXQ4e2I/D6cJoMuFwOFn25VJeGTGEd6fPokTJUvR/5nHeGj+WkRUHA/+82+lfxWyz8v4XqxkzYyE7D/5Mt5ZNkY1GwA+AyWwiMyOLXq+8w4GjJ/EHQ/9pa7HVZmPurPeYN3sGh346SNlyFTDm1rcSQuBwOtm1cwevjxpJYlISQohCtw4WBmx2Oxs3rGXYiwMZ9fo4rqpYib69H0Yxmhg3blxBixenALBYLIwc9hIlS5Vm3FuT0XWd0SOH8dzTT7Bs1TruatueFq3uIhQMRqyRJhMmo5HTp0/hdLqwWKz4fF7MZjMmk5mMjHTsDgfvvj2RqypWQgiB15t9nqX67NkzBPx+SpUuTSgUOs+SDZE9g8Pp5OyZVCRJpkhyMkLXSUk5jdVqw+Fw4vV6UIxG7DY7p0/9gqZrlChZilAwhM/nxWq1Rl2YFEUhJzuLMleUZfaCjzAaTaihEAaDAYcj0o4syyQlJ6PrOqmnT2O12XA4HPh8vv+7lgghMBmNKIpCakoKTpcbl8uN3+8jPS2NpORkDEJE+5jnTmU0GqN/UxSFQCCA2WzBbDFz6uRJDAYDxUuUwJ/rBpeRnk4wGIwqHOdaQyKvQQgdk8mEyWTml5MnsFgtJCcXuySPhnxRQqZNm0a/fv0YMmQIt912G3a7nePHj/P666/TuHFjfvjhBxwOR3409Yfk+AO0vqEqnW6uxZD3V/DGWx+x70QqS1/owa21KrJp/9G4EnIBJCAYCCBJ8OobExj+0guoqooQAlmSyMrMoGKlq+lwXyeGvTgIvRCaFyVJIhQMomkar7w6ltEvD0MNhaIbheysLMqVL889HTsx+Pln0bVY81EWLP/yc7o+8CA31WuABPR5oh8L5szk8b59znexKaTIsswXn39Gx85dubnRLei6Tr9nBzB31gye6tevUG3ohK6TmZnBE089x4rlXxAKRR6OoVCQ4sVLMGnKDG67oyVFiripc1M9ju4/jFRJikymwoIQZGRkM+DhjqzetIMsry9StpqImIrdxuMvjaVZnVo4bVZUNZzPdvHYQZIkdE0jJzubAYNe4sP350a/c4icPoZVlZFDX6RLtx58u20rIh7ncEFMJhPzZs2gY5duPPjow6SnZfPx4i84fOgQnmwP9qIJBS1inMuOhBCRWANJknA4nAwY9BLNmt+GLMusW/0169atZtzEyXz15ed8/NH7lChZit27duL3+RgyYhTX176Bs2fO8NroAYSCQVJOn8bhdNKi9V2sX7cmas1wOBzMfG8Kyz7/DLvdgdPl4sVhL2Oz2QiHwxgUheysTEYMGUy58hXYu2c3qakp9Hy4F7+cPMnmbzaQlZXJ84OH0LBRYzw5Obz0Qn8O/XQQi8VKYmISI0e/zqlfTvLqy8OZ8Pa7JCUn0+fhHrRp14Frr7uelwb2Z8To1wGJlwY+x1WVKrNn9/ecSU3l4V6PcfTIz2zdvImsrExeGDKcZs1vZ+a0Keze9T3DR72GhMSniz5iy8aNjH97Ml8s/YyPP3qfUqXLsGfX92i6Ru++T/LZ4kWcOHYUi9XK62Mn4kpIQNc0hBA81bcXDz3ahxtuqotRMTJq5FASExPp3vNhnnq8N1mZmei6TuWrr+G5519AkiSMuZYbp9PJa6+MIDEpiUd69wVg3GujMZpM9Hu2P2dSUxnY/2myMjPx+bw0v/1OuvV4iIt9AObLY2fcuHFRS0j9+vWpVasWrVq1Yu3atei6zqeffpofzfxfdB1UTaPtjdWY268j/R5uy0v33QrAvhNnMMVNwRdE13WMJhP9B71I7RtuJBj81bc9EAhQsVJlBg4eSnJyUUKhYAFK+sfouo7BYOCZ/oOoW7/BeXIGg0HKli/PoBeHUaxESYLBwtmH/4cky7hcLvbt2xtRuNQQOTk5pJ5Jxef1xoRVR5IkEhIS2L/vR3RdJ6yFyc7O4uzZM3g8OYXKtcXv99O9x8N0uLcT4XCYvGJDwWCQ6jVq0vz2O1m98isee/QRvvt2G88MGITf56MwFSUK+gP07tiae+5tiZYbAAqRUzVLgot5Hy/j55MpvPH0g+R4/QUsbcEihCAYDPJI7760uutuVFU9900SEhN5ffTLVLjyKu7r1AWPp/BbHgsCSZIIBAKkpZ3FaDTS5+FHuPfulsyeMY3K11SJJz/4jxIMBuk/8EV+OniApg1v4vFeD7J+7dc0uqUpNruDM6kpHDywD5MpEsu5cvmXVKlajZnzPqTy1VV4a/wbJCYlMXbMaDRNY+zESbRq05ZwOEyNGrVQc5/3NpuNjRvWsWDuLN56dzoLFi7GYDAwZvRI7PbIIbgkSSBgw/q1hIJB3p7yHj0eeoQXBz5HYmIiM+Z9QL36DXnnzXG4XW5WrljOhnVrmTpjLrPmf8T+fXtZMG829Rs2oljx4kx+ZyILP1hAamoKt93egqzMLPbu2R2NbVm3bg1aOMykqTPp+kBPBvV/mmLFijNz3gfUubEukyZOwGy2cvbsGX7++XDUjSw9LY3Dh3/CaDTh9XpZvWoFtW+ow4y575OcXJSBzz1Fpy7dmDprHqmpqSz+9GPsdju6rpOUVAR3YiIL5s3GYXeQnp7Gl0uX0PCWJsyZ+R6nTp5gzoKFTJ42k2VffMaGdWux2X917VYUhePHjnH61CmUXDe0EyeOc+qXE7jdCYx9fRQG2cCChYt5a/J0Fsydzfq1a6Jxw3+XS7aE6LqO1+ulQYMGF3y/WrVqlyU+xGkz89XOgwyY8yWPt6jPuEfaciojh2EfruTL7/bjtFr+cRlimeysrIg/5m9MluFwmMzMjAsGaRY2IoFVJs7VyCVJQtO0SB9+Y5KNFULBEE88/Rz9+j5Kx/Z3YbVaSU1JwWq1out6TCghAX+Avk8+zeO9H+K+9nfhcDg4k5qK1WordNlzJEnC6/VEM7n8FlVV8ft90cQAKadOUVUuczlF/FMkScLv82M1nR9kaDKbOHn8FGPmfMKn417E6nKgCx2HzYJBlWMqkDI/kSQJjyfndz7OdoeDtatXsfO77Xz6xQp+2L0LWZKx5/pNxzkfoeuoqsq6tavp2Lkrdes3YNTwIaSeOcO7775b0OLFKQD8fh833lSPhYu/ZPWqFaxds4qB/Z9m0ccfMWnqTIxmM2azBSEEqqpStXoN7u3YBdlgoM6NN/HRB/MJq2FO/XKSprfeRoUKpaleoxZTJ71NVlYWssGAFtYwmy1s3bwJg0Hhk48+RFVDqKrKwYMHznPH0nWdokWL0aV7D5KKFKFqtRqUKFmKNu064HK6qNfgZrZu2czZtLPc3KgxJUuWZNWK5Xg9HoQQeHI8BAMBRox6nS733s3SxYt4/5MlGBRD5JDHas11i9IoXrw4nbs9QGJS0nntOJ0u6jVoyI7vthMM+DEajZjNZoQQCCJxrL+OSYhrqlSjdZu7MShGrqlSlXBYpXGTZmi6znXX1ebkiePIUmQfEAj46Xx/d/o//QQebw5bN39DkeRkqlarTmJiItVq1GDhhwsi651sICcnG9nw6x5CCIHJZDovQN1kMmE2W/B5vez87lsqX30Nr48aiclkQgjBrp07MD3Y7aLuj0tWQmRZJjExkY8//pjatWuf957X62X79u088sgjl9rMnyIBVpOR8Us3MHP1dhLtNjJ9fjI8fpxWcyx4rMSJc0G8Xg/X31CHT5YsY93a1VSqdDWHD//EtHcnYbVa8fsL/0m2z+elWo2afLxkGWtXr6J8hStJTTnNuDdew2qzxYR7i6IoHPrpIC53Ak2aNadt+3uZM2cGLzz/LKu7f1X4lUFJQjGbeWjoeJw2K7uPnGDV5p14fH4Wfb2FjtVKY1SKFrSUhQaDbCDL52Pgc09Rv2EjVq1Yzq6dO8jJyWH5l1/QseO9BS1iocNoMmEymWjb7h76PN4PVVXJyszizXFjCAVVsMRdov9L5B1kfrpoITfeWJc2d7en/b33cWDfj7S8rQnf79yBxWI973+EEPh8XixWK6HcuApN1+nSrQevDHsJT04OG9ev4/7uPXG5XITD4agl3e/zkZRUhPIVKpCZmUmFK6+ieImSBIK/T5nr9/lQVZVQKJh7mO4hMTEJVY3EvVrMZtZ+u41Jb02gUeOmVK1eA4fDiSxLUUVBkiTUsIpR+eOMUr+2E4q04/Hgcrmjbu/wqzdHXkC62fxrOYm8+Ayfz4fVZkNVVVRVxefzRcZGCyPEr/MqEAhQ67rrSUxKYtuWLaxd8zXN72iB253A3Jnv8emihTS/7U4qXHklVps1ah2C89Pn57m4KUoktlYgCGthVFWlzBVlKVe+AsGAn4EvDqVsuQp4fD4cF2ENyZen5vDhwxk1ahTt27dn2rRpzJs3jxEjRlC1alWuvPJKWrVqlR/N/CmSBG6bhbCmcyozGzWs4bZZ/tMBl38XIcQfTiYhCv9GEf6kDzGw2f0tLreb114ZwScLP+SxJ/pRr0FDZs+YTr2GN0cWqxg4vXa7E5jwxmvMmz2D3n2fpHHTZsx6bxp1bqyLzWZHL6T31rn3ksvlZsb0KQx5YQDWvJTVXk80W0th5dw+aIEA11epSLmSxZj58ZcsWbuZbK+fFVu+Jz0zByXuthodr4g1yUeDm28hEAjw0fvz2br5GzyeHDasW0NYDRe0qIUKIQQGg4G69Ruy8MMFpKenYZBldu/aSXLRohhNcQXkv0akboaVIYMGMHvm9IingoBAMIgky5jNJoQ431303Gd33mtZkti2eRP1GtyM3e6ge8+H6d33ScLhMGFVJRjwoygK11StSmrqadq068CzA57F7nDyycIPztvU5133/NfnPn8ECIHJZGbG9CncVK8+b08cT+u77iaYG3vqcrkZMXQwNa+9jr5PPkP/Z57AqBhzFQb9D67Lb97LlUGKBJGfSU3BbLaABN9u3xZ5P1cBudCY/HFfBBazmRYt7+KdN8ex/8e93NmiFV6Ph+lTJtGpSzfeeH0UtzRpRjAQjK51gUCAcDgckQE4eyYVtzuBrMxM9uzeFQ2yL1W6DKqq0ufxPvR54ik2f7OBXTt3YDZdXB2+fAlMb926NUuXLuWFF16gd+/ehMNh3G437dq1Y/z48bkuMpcPgyxjKMSbgsJMRKv+/aZWCEE4HBv1BLTc4KzfIoQg/AfvFWZ0TaN+w0a8MOAZ9uz+nrNnzmC1Wnmkd188nv+xd57RUVVdA35umd5SgNBCSShKR4oKWGhKUQQUwQKiIlYUEVFBlI4o2BABFVGKigooqC9iBxRpCgoigvQaCCnT79zy/ZhkSET9EGMy0XnWYi0mydyz97mn7XP22dtX1uKdEaqq0vaiSxgxbAg7ft5Obm4Ouq5xz33D8PviJ6rUbymavMkf8HPn3fdy39230693DzzJyfzww/cMe/ARLPkWdD0+7wpouo6m62AYhJUIE++7GSQRJAnN56fOVbcx85HbSY+ks2d/+bszVdJomha7t2Sz25k0ZRoIAnabnfXfrmXoPbczYcrUmCGa4BQ+r49Bt9/Fd5s20Kdnd9LSKvPTtq1MevIZNDUCJAyR/xK6rmOz2Zg4ZSrjxzzK5u834XZ72LL5e27oP5DGTZvz9ZrVsZxehmEUyxVS+FkQBVIrVmTlio/w+bzk5+fxxoLXmfDEU3S/8ioeeXAYz06dwo0Db2H1V1/S64ouNG7SlM8/Xcktt91xWloBTVWLGT5F1zaGrqNqGqqq0uvqa5n94nRuu/0ucnNz8Pt9uD1JrFzxEV+vXsW7739ExUppfLjsPV6Z/SKdLru8mPyaqv1xOQW6hYJBLu3YmddefZnr+1yF2Wzh2NEjpKRWKPZ3Rev0t59/69Ls8/npfuVVTJkUvfhep259ImqEXtdcy6svz2LP7l85mZ0dCxZgMpno2OkyXpz+DPXPaUC/G/oz5I5BXHfNVei6htebjyTJKIrCQyNHM/SeO7ihb18ikQg//7SNftcPIKKqmM5iE0swSnhFlpeXRygUIi0trSQfW4wrJr3GZz/swmUrfxnQc/xBFgztR6d61fj1YBaSFD/GkiAI/LprJxUrViIpOTnW0EVRJBgMsn/fXjIy64AgkFmjKv5giCNZ2cglECu6pBAEgd2/7iIlNZWUlNSYDoWJhPbt3UPtjEwEQaRWemUiEZWDR4/HlQ6/xTAMHE4nRw4f5ttv1pCUnEzbdhej6waVKyYjAPsOHYvrXWzDMHA4HGRlHeObNatxudy0u/hiDEQqJrswm03s2X8krnSQJIkD+/chiiLVqqejqio2mw2/38/Xa1YR8Ps5r3UrMqplor54nMptgjjrHEIPx0+fFiWJfYeOohkGGdWroBeZvARBQNN1tu85QN2MSmh7Uzm+vgp1xjdAcv7zeU8OHsni0NETcffO9+3dg9lioUqVqsUme1EUCQT8HDp4kBq1alM1rQI1qv5z89xv2b3/MMdP5sb9WGW2WNA1jVVffoHf7+X8C9pSKa0yKW4HVStXKDVZduzeT16+P67m2P8ihSGujx09wqYN6wkGg9Q/51waN2lGKBziRFZWLFnhyexsTpw4TkZmHURRJPvECbw+L9WqVad/v2sYO/EJLmzbDl3X6dv7SqpVT2fG7Dl8s2Y1qqrSqHETDMNg1ZdfkH3iOE2aNaf+OQ3w+byx0LOqqrJ3z27Sa9TEZouG/z2wfz8ZmZmYTGby8nI5npVFRmYdbDYbGzesZ9/ePbRs1RpZNoFAQUAYiVoZGeiazonjWRw/Hv3Ont2/UjsjE4Ddv+6iZq1aWK3ReePggf1kZNbBZDKRm5vDieNRXS0WC4cOHmDTxg1UT08ns049so4dJSOzDtnZJ8g+cYLMOnURRJGjRw4TDAapXTsDEDh4IDpHVa2WjqadOp2VZZkdP28nKTmZtLTKaJqG2Wzh6zVfkX3iBG3aXYTf78dmtZFaIdovt3z/HZXSKlM7M5Mft2xmx8/badykKS63m4A/QNVq1bBabQXz+FfIsomLLr4Um8NJjSqpJHvcf7l9lJgRkp+fz86dO2P3Qnbs2EF+fj6tWrUqiccXo9vEuXy6ZWe5NULeuP86Otevzq4D8WSERJuBzW4noijRqECxC94GoihitVoJBoNEVJW6tarjD4Y4fCyejJACHWxRv0lVjXC6DjaCoSARJUJGzapEIioHjsS3ERIlGj/darGi6zrBUBAlrFArvQoYBnsPxrcREqUwI7UN3dAJBYOEFYX0KpUwmWR274svIwSMgqNpoyCqmgAFvsBWa9SXNqyGieSHsM1XqHJJAFf9/WiheOnTgAEWixkECIdPz/QtCAJmqwVdCJGzxcGJb6uXqhFy8Eh8GSFgYLVG+5iiKBQPO2kgShIWiwWfz0daxRRqVatcapLt3n+YrOz4NkKiRN2ybDYbgiASDocIBILUrFaZKmmppSbFjt37yc1LGCFlT3ReNplMWKxWBAQikQjhcAhdNzCZozkwgoEAJpMJk8kUu+com0yYTWYUJcy9dw3G7Y5eHM/NzWHZ0iU8MOIR2l1yKUbBaUAoFCpoe3ZESURRFMKhwoif0fG7MHFuOBQqCOwiYbVaCAaDUZdCWcZsikbqAgG73YYkyYRCQQRRjLp0F9zdKHy22WxGEEWUcBirzUaoQH6bzUaosBxJwmo5VY4sy5hi5RDLg6KqESKqiixJBIPB0+rEbDYjihKhUDD2PQNQYnPUqTovDE0cHcui473d7kAUBYLBqAubqkbveUiShNVqQ1HCRFQVu82GbDJF574C1zClILpo4TxuYETn8bBC/cx0ktynEryeKSUy0+zZs4c2bdpQsWJFfvjhBwAWL17MqFGjGJRVrZEAACAASURBVDVqFBMmTCiJYmKYJAmLScZiiq8sxWeCxVQ042U8pXiIClLYeYpHfokekwZ/53dxqUPoz3QInPa7+NLhjxBQIxF8RcKHlksdVBVfEfer+NZBiIV7PiVnNNqa31/gBicWZJOVDXy/Sih5qRhqXClxRgiyjfAxM4JUuq6K8fjOC8N4nx79SsDQ9VNjZClLBvFYX7+HEIuaGftJGQldPurr3070Baiqivob92FRFNBUFU1VEUURTdNirlkQdZsKqiqyLPP0czNY8b8POXzoIDabjSlPP8c555yLz+eL/b0oirGL7bHSf7MOgOLrHMPQi61tdE0jpAVj3/uzwC+Ff1M0rHeoyN8Xfa6hFy8nquupcgqTKhbyR3USLStS7Ht/pOfvrdmK1k1Rb5ei6yOx4I4IoVMpG4o+58/m8b9KiZyEXHXVVRw9epRFixZRq1YtIKr8smXL6NevH99//z3NmjX7u8XE+PnQcfICISSx/I0uqqZTt0oFkmxmQhE1rvKbnSmGARaLCV3Xiaha+dWhIHypElHL5URlGGA2Rw1xRSnHOpiiUUbCSqT86SAI6CGNo1N/IXxIw9BN8ZW08EwxQJA1zBUFMh5tVConIZGIWq77nskkYf6DMM7/BGElgqqV3/HWbJIxleLGYSisoOl6uayvBMUxAFmSsBQJbqAZ0UhQifdb9hiA1Ww6LcT5mfC3RwTDMNi8eTPz5s2LGSAQPYbq27cv06dPZ+XKlSVqhDy9fDVrf96Hw1q6F95LgrxAiKk3dad783oEguFyGblL1w1kWURVNQIhpdzqIEkiuq5H30M5NGh1PepiZmCUax2iYQmF8qmDJKCHVTRFwN1IxVLpZDk9CTEIHzUTPFZ6Ga0jEbV8vnOi7daGuVSNECUSIRSOlNvxVhCFUjVCFCVSYOSWv/pK8Pv4/uOJVeMVwzAwSVLZGCGF5Obm/u7PfT7faeHR/i57s3LYeuBYubwT4vUHyQ0EwTBQIuV3QtF1K5qul3MdLKd0KKcLIV23YGCUax2sejTBZLnUQRcwIhqGKuDI0HDXPxlfd0LOEMmqk7/VReBwcqmVWd77XuEpZGmhauV7vLXppbtpGFE1lHKQZDdBgvKOYRjoZ+lU9bdHUUEQ6NatG7fffjtVq1alWbNmmEwmsrOzmT59Olu2bOHKK6/8u8UUwyxLWE0y1nJ4JyRokmPhgwujNZQ3ioqc0KHsSOgQBxQ6nQugR0ALSXEVHevMEdAjpS93uXznlM09A4FEff3VMstrfSVI8F+hRFbxTz31FDt27KB169YkJSVhtVrJysrCMAzmzZtHRkZGSRSTIEGCBAkSJEiQIEGCfwElYoQ4nU4+//xzPv/8c9atW0deXh7169enS5cuVKlSpSSKOCt0w4jtHiVIkCBBggQJEiRIkCA+KFF/pg4dOtChQ4eSfORZIQgQKLjAJ0siUsIISZAgQYIECRIkSJAgbigRJ2DDMHjkkUe47777AHjrrbdIS0sjNTWVuXPnlkQRZ4wgCJz0BuncpC6fPD6Ie7q2IT8Y+v+/mCBBggQJEiRIkCBBglKhRIyQMWPGMG3aNOrWrcvx48e57rrr6NatGw8++CC3334727ZtK4lizohgWKFykovZd/Sidd10MiunElG1Uis/QYIECRIkSJAgQXH+KFDAv8ll/t+kS2lQInlC3nzzTRYtWkSvXr145plncLlcsROQNWvW8PHHH9OwYcO/LeyZyOJXIiwYeh1OWzT8aqRIpskEf44gCLg9HgJ+P4qiFKs3wzBwOByYzGZOZueUoZR/jiAIuN1ugsEg4XC4ICOqgd1ux2azAdFEmj6f//95Utni8XgIhULRZEwFOlgsFhwOJ4IAfr+fQCC+Y6a7PR7Cv9HBZrNht9uBaKIprzd+34NhGLjd7oIM6f7T+oPVasXudpBzOLsMpfx/MAxsTjvoBoHAqey8NqcdJCmaZUoAjCD5fz9v7b8Ct9tNJBIhEIhmD5YkCbfbUyzCU36+lxLI8/uvxDAMzGYzTqcToSDzstfr+/+/mOBfiWEY0T7k8UQzdBsgyxL5+fmoqorZbEaW5T/NTP5H6LqBy+UEwOf1IZRhuO9CPa1WK6FQqFiW83jHMAySkpKKrZtKixIxQgAaNGgAwLJly+jUqVPs9w6H46wa119FEASO5/u5u+uF9Gh1LrfMeJeX7uiNJJTHcJmlj8PhwAAWvfkGjZs0pVat2kQiCnBqMfb112vYtnUr191wY9x1rkIjSZQk3l70Fuc2aECdOnUJh8M4XU5+2LKF95cuRdd1ru7ThwYNGpW1yKdRaCyZzWbefedtMuvU5ZxzziEcDmO328k6doznnnmaUDjE9Tf0p1at2tH+F0evImpo2LFarSxZ/A7pNWrQsGFjwuEQTqeTX3bsYPG77xAMBunRsyfntWhJdCUcPxQafG63k2XLlmO32Wh9wQUo4VP9weFw8OuuXXz08Yfc2Kc/ovjXkzT9kxiGgcVkQnI7+OyLbxFFkXbnNUJTVQRg6crV7D54FAPQdZ22rerRzHo+J8ta8DKi0Kh0Oh0sXbKE1AoVaN78PDRNIxDw887bizh5MhsQwDC4vEtXmjT+5zfWyhuFY1h2djYzX5zByezsIv1cL2vxEpQBkiQhSTJPT32KL7/4HEEQaNOmLXfcdTfJSR5ee+01Pln5MXPmvk44HD4j476wvzocdl55+RUEAW7sPwC/v+w2taxWK7/u2sX99w1h1stzqFy5SmwNFc8IgoDJZGL8uDF07NiZFi1bEgqV3hWGv71CF0WRatWqMXbsWJYsWcKXX37JbbfdRiQS4euvv2b58uW0b9++JGT9U4Jhhea1qzJ1QHdGLlzBoq+3IEsi4YiKFlLibtEcT8iyzLZt2xjY/wZuvqk/23/aFtutBrBabeTknKTP1b14ZtpTmE1y3NWnyWRix46fue2WgQwccCNbNm/G7nDgdDpZvWoVXS/rzJEjh8nKOkaP7l3ZvPk7LJbSTZ71/2Eymfh11y7uGDyIATdez8YN63E4nZjNZk5mZ9Plsk788MMWDh86TLfLO7N375641GHf3j3cc9cd3HBdX75eswan04HdbufHH7bQueOl7Nz5C16fl949e/DVF59jj7OkoyaTiePHs3h05Ciu7tmDJYsXx3bbINpfRFHkloEDePShh4koyllliv0nkWSZY7n5TJw+j053Psrryz/D5LAhiSKKqvLA03P46rutbP5lN9/v2M2REznIcaZDaWIymTh8+DDDhw2jb5+r+fh//8Pliva9AwcOMPLhEfyw5Qd+2LKZLVs2k5efF8v3lOAUFouFEydO0PXyTqxbtxbd0OnZ4wqWLn4Xu81a1uIlKAPcbjcTxo3hpdkz6T/gJm4ZdBtvvfUGN17fD8MwOK9FC264cQCKEsFsNuN2u3E4HKSkpJCcnIIkSTHDxOVykZKSQsWKqez+9Vf27t3L6lVf8tmnn2C1Wwo2kKwkp6SQnJyMy+U+TR5BEPB4PNjsdpJTUkhJScFsNmOz2Ugp+GwymTAMI7rh5HQWe55hGMgmE0lJybETfrfHgyzLJCUlccddd+N0ODAMHU9SEja7vchzzdhspz7LshxN2Guz4Xa7Y2VarVbcbndBUlQzbrenSJ0kF5zOuklJScGTlHTaeiwpKbmYDk6nM7rRbBi43G6Sk5NJTkmJ/UyWZRbOn8+uXTsxWyw4nU7sdjuGbmDo0TpwOBzo+qn3UPgMq9X6t06FSyQ61vPPP0+3bt148803ufHGG+natStLly6ld+/eDBw4kDZt2pREMX+KLxxh1DXtsZpl2p1Ti0saRnOTdG95DkN7t2f2ym9xWuNrsRMPSJJEKBRixPBhnHvuudStVw9N02KNShAEnE47d985mDqZdbA77Gh6fO1ci6KIGonw8IgHqVW7Nuee2yCmg9liZtn779G333W8/NIsALp07cZrr77KJe3++XZ5poiiiGHAqJEPk5aWRuMmTWI6uFwunp72FE6Xk6VLFgPQvkNHnn1mGnPnvEI4TjZbRFFEFEUef+xRnE4Xzc9rga5pGEZ0l+jDDz+kY6fOvPP2IgBCwRAvvzSbHld0I56Oc+x2O6MeeYojh4/QvkNHwKBwjDUMg5QUD6NGjkLTNRo3a4Km6SUbZrAEMNusPDNjHjv2HqJ7u5ZRNwXDQBQFTuZ7qZSSxLKZE6BiCoTC4DtJ1sYAUHoZ0+MFQRCwWKxMmjiecDjEhW3aous6hgGiKHH0yBEubNOWd95+K/ad4ye9+IMhkkzOP3nyfw+ny8noUSOpUrkKy99/H4DWrc/n2NFjaJqGKMdbT0lQGnz88f+4/oYb6X/DdQDUqVOX2TNn4PMH0HWdYDCI1SKzdetP7NjxM0lJSSxZspj06uncPWQIFosVq9XK8mXvs2nTJhx2O19++QWDBg0itUIFvPn5GHo0XcSuXbt45aVZBINBOnTsRM9evfH5ou6AoigSCARYsvhdMuvUYemSxWiaxvAHH2Lf3r0sWDCPlJQUhtw7FI/HjclkZvVXX8bm3Q4dO9Oz51V8v3kza1av4rbBd2C2WFg4fx5paZVp2aoVwVAISZbx+/0seutN6tWrz+J338HA4MERD/Prrp28sXAhqRVSGXLvUFJTklm7di0HDx6g99XXIAgCmzZt4tddO7nuun5s+eFHdvz8M263m+XLl5GZWYe77r6bOS+/xLp139KqVWsGDLw55kIfDod59plp9OzZiwoVK2E2m1j58cf4/T769OnD0qVL+fSTlciyiZ69enPRxRcTUVU8Hg9msxmTKPD24qU4HE4u7RA9QPjog+XIskynzp2RZRPvvxd9hs1m45ZBg6lbt+5Zt40S2cpp3LgxO3fuZP/+/cyfPx+Ali1bsmbNmlKLjmWRZX7cd5RPf9hFqstOeqon+nOTTJLDetYp5f/taJqGKIq89fY7zJg5E1mSUVUVKFhwpSYze9YssrKyGP34GHxeX7x5z6DrOrph8Pr8N3jl5ZcwW8wxHXJycnlk5KNMmDSZrOxcAAKBAG736TskZYmu66hqhJdeeZXX5r6KzWYjEolE3fUNg/Xr1tGhQydUAxQdOne+jO82bQJAjJNTKV3XCYfDTJ8xkwXzX8flchGJRADIzc1l8O13MH3GTI6diN4p8vm8uOLsPQDk5+cz8tHRLFv2HtWqVSMcDgPR9+BJSuLTTz7jw+XLef6FF1GUSFzeDQj5/Iy8pQ/L5k2jdrXKhAosVVEQyfcFiKgqM+Yv4f77xvHWwvdB05Dl/+ZJiGEY+P0+Jk2ewrtvv02FChVQlGh9ybJEXl4ePp+Pp6Y9zd1D7mXZh//DbDEhJk5CiiEIAhElwsaNG7jm2r6s/uZbnps+g+bnteC+oUNibTDBfwvDMBh4861Mf+5Zht7/ACs//ZzMOnV4afYs3G43n3/2GZMnjMPtdLBr105uGTiAxe++w/mtWvLO24uYNGE8qcluXps7h0kTJ9CkcUPWrfuWqlWrcuMN1+PN9wIgi3D48CFuvL4fFSul0aVrV8aPG8Prr82NzfeSJKEoCsOHDWXi+HE0OPccjh45QtfLOvH663Np3aoln336CY89OpLUJDdr1qym51VX0KBhQy6+5FKG3X8vn3zyCZm1azHrxRl89OGH7N61i0ceGkGNGulkZWUxYvj9BAIBNFVl6L33MHnSBBo1PJeDBw7Q5bKOLJg/j9atW/Lxiv8xbszjuJ12vvzyc2bPmonNZsdut/PN12t4ccYLuBx2ftmxg0G3DGTlyo85v1VLXn9tLpd37sjOXTtp2ao1o0Y+zEcffYDL5SrYJEvhrTcX8uqrr5CU5EIQBEY+MoJgMMDSpUsZdOvNtLvoYho1asQtA/uzbdtWbNZTp5QmWWThgvl8sHwZDqcdh9PO22+9yZLF7+Jx2lmwYD7jxj5Ol65dqZSWxo3X9+XAgQOYznKDocS2JaxWK+np6bHP6enpxT7/07jtFp5etoYpS78iFFZoUKMK2567n482/cyYBSuoVCEpLhcL8YAoiphMJnJz89F1PXa0Z7fb+WXHTp57ZhoffLSCHb/8gqZrcTn5iqKI2WwizxsofiGswEfZMAwqpSYxc/bL7Nq5k5fnzI27qGmCIGCz2fAGwsV00DSdvLxcUlNT0bTo8WpySjLBYBBV0yFOjBA4pUNA0VBVNaZD4aV0gAqpSSx6ZzGrVn3Fys++QIloxJtla7c7iOigKEpMbpPJRMDvZ8SDDzDxiSlUrVoNRQkX3AeJlK3Av4PDaoVgiLASOdUfRAEDUCIqO/cfpmJaKo+9uIA1W7fw9DUjOV6mEpctdrsdRSNq/MfabdT9Ljc3hxMnTuByubnlpv48Mmo09w+9r4wljj8ikQiqGmHOKy9Rs2YtJEli8qQJPPvcdPpc07usxUtQBuTn53PPkHupWasWL8+exbvvLMJmtzN69BgG9L8+6nrkiW4a64ZBZp06vDjrJdwOK6oO815/DQFYuGA+d999D/1vvJGmzVtyRbfLCUV0ZFlCVVVE4O1Fb2GxmOnb7zokSeSKK6/ihenPcfMttxZxWTLweJIYN34Cl1zUlvMvaEuXyzsxdux4MmvXICk5lScmTSQYUhAFkRdnvkS/667H6/VSt25dVq9eRY8ruvHynNd4aMRwZFlm4uQnaNakEes3fh9z09INg+SUFMZPmETbC1vTvGVrruzWhfETJ1MrvSoOp5tnnp4KgM1mL+KORcwdC6JzZ926dXnu+RewW2QOHj7CpytX8vyzzwDwzddr+Hr1aq69tm/B5XiRu++5l+efe4ZJkyfz3XebCIfDXNOnH59+8jHz5i/ksi5dOH4si+nTn+P7777j/JbnFXtnp9yxop8dTifWgnnw5dkz6d37Gpo2a06Tps14Y8EC3nxjARPGjT2r9vGvORs1DAOrWcaCjN1iwhsM0XfaGxw6mUeSx5kwQP4fCk9EChFFAV3Xufeeu7ih/wDq181k3bp1GIaBElGA+HNt0/5gN1fXdSpVSOa56TMYP24M7yxeSvXq1VGU+Fs4atrphpEoClitNrw+b8F7iUb4MptNSJIISny17aLufEXRdZ20iinMX/AG9983hFfnzqNhg4aEwmFkKb4MW63AjawQgehEMeTuO2ncpCk9undl9TfrAFCUMLayEfNP0XQdfvMeQsEQjTJr8uPbL4DJBC4HXVs1pfO9j3Bv21sxmyqUkbRlz++127y8XK7q2YurevbCZrNhkQUqV67M9OefZcg9d2M2/Wum0BJBkiQ0TeOyy7owZcpkBODhkaOYMH4sfa+9pqzFS1AGaJpGTk4OPXv2omfPXmRlZTH31TkMumUgbdq1xVpkF15XVTyeqEtoWC2MtGbCALpfcSVvLFxA06aNeeWVOZxzzrmYZQFNOxXw4HhWFn6fnyefmEQgEMBstnDDDf2LBUcq3BCz2eyENfD7AzgcDgAUDURJip7qqRr169fnvaWLWb5sGdWqVeHA/gN06nwZER06tr8YURTZsvl7Btw0EG+geFSpU+XYCGsQ9PtxOqNrUUWL9hVdj8ouCMVD+xZdi+m6jssVjdIYMWQ0VSMpKYmwGnWvNZvNsdN6AK/PT5eu3Xhi8kR27NjJyhUraNfuIpLcDurVr89TU57gzTcWUqFCBXJzcrDZbL+7BVgojyAUuFoLIpoRjUL23Xeb2Ld/H5qqcmGbNpx3Xks0TTuru5H/uhFUAGRRJKBEeG/9NsyyhN1iThghfxGTSeSnn/awZfP3hEIhPl25khPZJziwfz/33Hk7CxcujMsTkd8jJSWZKVOe4s03FrJ23UbqZtQkzxdGFuO/TRiAJImce+65fLt2LSYREAW+XfstGRmZMXet8kBqhRRmzX6Z5559mk8+/5LzmjbGGygfQSMkEXJzc1izehVuj4d2F12Ez+fjyKHD3HH7IJ5uPJkKpvgzzH+LyWFn0Udf4AsEuW3gNRBRcdptCKIQu3SYIIogRE9Hnnv2GZo0aUrPHt2B6C6hrunE0z2meKAwqlxmZh3y8vNitVOtWnUURcEw4urQNkEpIEkSuqbRvevljJ84iSu7dSG9ahoPPTySZ6Y9xcGDh4q78QgCuq7FFudF5zaXy43NbmfJkveonp7OlKemIRbcgXA6o0aE0+miWvXqzH11DgB79h9i9apV6LpR7FmGYRTZ8Iv+rrDMQm8Ql9PGTQPuQ9cNFr37LlZZpPsVV+Lz+TGJMH/hm1gsFtp36MiE8eOYOH7saXNx0XKMgs+/LQdAVVV0Xcdmjq6pQqFQ7O8K/7ZonUTvrEXjq+u6XmwtpqkqVaqkcckll/LqKy+xccMGHnzoYQzD4Oab+nPJJe2Z9sw0JGDdt9/GImFFIpFYQCJFUaIGTsFj/X4/FosVUYh6BPTtdx0339QfgBUrP0MQhd/ud50x/4gRUvRSc1lgEPWT99itxeRJ8P+Tm5uLoihEIjppaWl8teYbgsEgDruDD5Yv4+mnp/L42PHR40Y9/kIuCoJAXl4u4XAYURBxuVw88tBDTJv6ZNQf9eMVvJWdTZWq1RjQ/3oURS1rkU8jqkNeLL9GIBDk1kGD6dThEsZPnIzH42H5svdY8t5yNN2IM0emKIIgkJ+fH9VBjEZImfrkk4x8+CEGDb6ddd9+y4cffEBKaio3DRgQl4aIIIDP54tOAER3g97/4COCgQCyLLNjx8/ceutNjB07gaRPk1HV/LIW+XQEAV8whDcQBEFAEsDlsDNg9DR8wTB1MtKZNGshnVs3o3bVNPb9Gl8uiqWNIAh4vV4CgQCCEI32FIko3HzTjWQ//SxWq5WxYx7jpoG3YDL9N+/Q/BmhUIj77h9Gzyu7U716OvXq1eXJJyZz6223UYYpHBKUEZqmkZScQtNmzbhz8CAOjn6c5ORk5s97narVqtG8WVM2rF9Hfn507FQUhby8vIKkhhAOh8nPy0cA9u3dS/aJEwQCfvx+H28veotBgwaRWSeT+fNeZ/2GDdw0cCDz5r3GuAmTaH/pJYwYMYKqVatybd++5OZG74QahkFubm6By3NUxtycHAzDQBCii/G8vDwgmq9r7969bN60gdVrvuGLzz+jVevzOXDwMA8+cD/z33iLc889lxbNm3Jp+/ZUTqtMbm5ubM2Zm5tTYGxEDY2i5RTqqhnQpElTnnxiMrNfnoPf72fmjOmk16hxWp2IAgSDAbz5+dHPIvhjl+5P1XswGOamgbdw6cVtaX5eCy65tD0RLeqGdvx4FuvWruV/Kz5my5bNiKKIAFSoWJEF8+dx8UXtaNmyFTNemM7557fmp+0/88HyZdw08GYEYNDg23ns0VGkplYgFAoy6JaBzHrplbO+UyiNGTNmzFl98zesWLGCwYMHM27cuJgPfteuXWnSpAk1CiqzpHhj9Wb2HDuJpRwehYciKr0vaESj6pXwBkNxt/gKh8O0at2aipXSoiHmPMmkpKSQWrEiZrOZJE8Sl3fpiiyJaLqOElHjRodCMUKhEC1atqRKlaqoqsqG9euoU6cu+fl57Nq1k2PHjgLQsWMHVE0r7jNfxhTKEQoFad78PKpVTycQCJCeXoPzz7+ApYvf4ddfdzF23AQ6dOyMVnABPx51CAYDNGnSjJo1a6IbBhvWrye9Rg3C4TC//LKDY0ePoigKnTp1RBBFQnEWSlsQBILBAHXr1af+OeeiRqIRRJJTUkhNrYDT6UQyy3Tr0h1tYxBrlRCWFC+GFl86hEMh6taoSpO6tVD8QRrWrc2lLRrxwar1fPPdVto1b8i0EbcSzJLxHXSQ0qEiovmfP+VUlEhcjR9AgdHvp0GDhmTWqYvP5+PS9h2onp7Oe0uXsHnz9wy46WbuunsIqqpiMZtKTbZQWCGixncCNEVRqF07g1atW7P8/ffYtHEjN/QfwJ13DUHTtVJ1XwuGwmiaHtf19V9AVVW6duuO3eFg5ccr+P67TWRk1OHZ56eTnJyM1+ujQoUKschSnqQkWrVujWEYhEIhKlWqRLNmzXj33Xdp1KgR7S66mLS0NJ58YjJ5eXkMf3AEx44ew+sPcPllnWnX7hI+WP4+a9asoWXLVowdPxFNU9F1I7Z5qusaF1xwIQ6Hg7CiYDKbaNu2HbLJRDgcxuVy0bxFK9q0bcf2n7bx2WefUbt2Bpd36UqDBg05fPgItTMyuLZvPxwOJ5UqVeLw4UM0bdYcXdO4sE0bZFlG0zQubNMWu82OoiiYLZZoObJMOBzG7XbT7LyWZGbWweVy89GHyxEQuPmWW6lZsybNmjUjGAyTVFAnum4QDAapll6dZs3PQ1U1/IEAdevVo179c2KBYFRVpXKVKgQCAXr26kXDxo0IhUJcdNHFrF71Fd98s5aWLVtxYZu2nNugAZUrV6FFq9b89NM2KleuylU9e5Kdnc3KlZ9Qs1ZNrunTl1q1MsisW48WrVrjdrt49523+emnbQx/8CGu7HEVBsZZhXkXjBI4JigMx9u/f38OHjxIgwYNePzxx7njjjv45JNP2LVrF5UqVfq7xcS4YtJrfPbDLlxxll/gTMjxB5l/X1/6XdCIw9m5cRPZqBBPUhLBQOC0rJlR30wLdruNnJxckj1OIqqKLxCKKx0KM38WZhuH6G6GySQXOy4MKxFEQ0U3DLy+IGIcbdMVRmEKh8OEgsFYLHKn04ksm4DocWxubh5JbicGBvneQPzp4PGgKErMH9flcmGxmIu9h0hERdciSKJAbr4/7nQojDjy22y8hbHVXUluco+eJHfqISq28eGqdwg9FD9uioZhYHPYAIGgP1DQpw2sdjvIEug6SCJ6xMfJH+1kr69GnfENkJz//GLR6wvg9cdf33O7o/7Xfr+/YEdWwOV2x1wfBUEkNzcXu82Cx+UoNdly830EQuG4Gm9/j8KxymQyRXeBRZHcnFxcThtOe+ndnjqZmx9XmzP/VWIZ0wsuWuu6jizL+Hw+QqEwKKMC0QAAIABJREFUNpsVi8VCbm4eVqsFm80WO7WwWq3Y7Q6OH8+idYvmTH5iClf06EEwGKR3zx506nQZ4yaOR9eim48+nx+Hw47FYkFVVUwmUywz+6k7DtE8IV6vF1VVo+O4201ebi66rmM2m7Hb7eTm5mIymXA6XahqBEEUwYga2rquYbfbycvLi80TmqYTCgXxeDxFTlKSyM/PQ9O06LNcrlg5FsspXUVRxO3xYBS4VhXKm5eXX6xODAMcDjuyyUR+Xh6GAS63CwwDr9dXbCwVBIHk5GTCYQWfzxurT5vNhqpqBfdQREKhYMF7sOFw2PF6vWiahqcgw32h4aZpGj5fdI4ulLWwTvPy8kj2uLCeRd6yEjFCmjZtSt++fRk5ciRTp07lxx9/5PXXXwegUaNGDB06lEGDBv3dYmJc/OgsVn//C2I5NEJ0X4DXHh7ATRc14+DxnLifUH4PXTdISYoaIV5/ELEcZqXXdYNkjwNN1wsW8OVEB4GCxVB0MC80QvLy/eVHhyLouoHbZUcSBXLyfOVPB1nACGh4nztCShM/jlrH0JXyo0PUJAHRrOPb5SB3axp1xpWOEZLv9ZPvK799z+mwkeQuvTwhOXnR3CTlZrz9TX153A5cDvv/+7WSIjsnj1BYQSgv9fVfoEib+CvYbDZWrfqSl2fPQhRFNE2jabPm3Hf/MAzdON01/CzLKUmZz6oooRTK+Qv6/Jk8glAQ09IAw9BJSfJgs/51I+RvzzS6ruPz+ejVqxcQvcBS9IZ8vXr1OHr06N8tphj3dm/LVa0bYC6HiY9CEZXWddJBkvAkJ5fLq42GEb24bpJlTCZTudXBbJajO9qSXC4vTBoGmE1y9PJ6klRudTCZovWfkuQufzqIArqsEbAYnFxr4uT6knU9LU0M3UB2ld49L5vVgiyX375X2nlVHHYrFou53I63plJ2n3Y67Iks7f8SDMPgqiu6cdUV3fD6/FisVsyyRFhR0XStXPaJfxtn62r5t0cFURTxeDy8++67jB49GofDEQu5dvz4cT777DMGDx78d4sphqrpKKqOQPm7RKmoGhoC5B7ClnMcQSx/lxsNXUOuWIOThhX/b44Aywu6bpCc5ELXdPK88eUGdKboukGSxwlG1FWjvOrgcTkQJZGcXG/500EEI6RjaAay00C0KfGW8uTMEEALCmCU3ngUCIXizo3wTNF1A6fThtnkKrUyff4g/kCo3NZXktuJyVl6JyH5Pj+hsFIuvQ0SnE5OnjfmHun1hzCM+AuM819FNwwqJnuQpL/unVQiWxNjx46lR48ebNu2jRMnTuD3+xk1ahQzZ86kdevWXHbZZSVRTIwXV6wtv+5Y/gDVKqTQqJZCeM92BLn0LjaWFIaqINmcnNQ8HDty7KwuI5U1qqphMslEIhH2H84qtzpIkgSGzv5Dx8plxmtV06hRtRImk6l86iCBEDZwhgXS2qg46xxCD5c/9w/RouPd7iR7Q9VSKzMv38eBw8fL3zsn2m6rpqWS7C49IyQ7N5+sEznldqwSBRFXKRohx7Nzyc3zRXMpJUiQ4B9D03ScditWaxkZIVdeeSXffPMNI0eOZNeuXYTDYU6cOME999zD6NGjS9zn1223YnXZy+fFdFHAYpJAlBFMlnJphESz10hIhogsS+VyUoRoEkBRLP86UPgeyuFijsJESKJQPnUQQZAMIP5CPcc7sb5X3t45xNptaSL9G8aqUkSSovWVMEISJPhnKTyhOhtKxAhZu3YtDRs25IsvvgCicZZNpnK4uE6QIEGCs8Uo8q+8UV7lTpAgQYIE5ZYS2SKYMmUKSUlJ9O7dm+XLlycMkAQJEiRIkCBBggQJEvwhJWKEzJ07lzfeeINQKMR1111H5cqVue2221i7dm1JPP4v4QuFyfWHyAuEyPWH8IXCpS5DggQJEiRIkCBBggQJ/pgSccdKTk6mX79+9OvXj+PHj7N06VLee+89+vTpQ+XKlZk9ezYtWrQoiaL+EINoGLf2jTJJKbj8JggCeYEg3/y8DwQhEcYtQYIECRIkSJAgQYI4oMQDd1esWJHBgwdTrVo1HnjgATZt2sShQ4f+eSNEN9ANnemDriIjLSX2811Hs2n14HQkSUxkTk2QIEGCBAkSJEiQIA4oMSMkEAjwxRdfsGDBAlauXIkgCHTv3p05c+bQunXrkirmD9EMA7fNStUUN699sZGRCz+OnYiYymP0lTJCliR0XUcvSJMpS9JpxptuxHd+FkmWMXQ9lkVVEAQkST6VHlqIhpSLZyRJxjD0YplgBUEouG8lEIkoZSfcGSJJEoZxejZbWZYRRQlVjYAW522pIBKRVkROWZZj6WIFCTQlviNjFUYHKmzzoigii2Kxe+iirCdOigv4bbuVJAlRFE9lDhbASGxo/b+YTCYEQSwXY9V/BUEQsNvt8JverqoRNE3DZrfjzc//y881DAOb3Y6uaYTD4RLd8DUMA1mWMZvNBIPBEnmmIAhYbTZCwSBGaaRCLwVsNjuKEkZV1XK14V4iRsj48eN56qmn8Pl8XHzxxTz99NP06tULt9tdEo8/I3Rdx2Y2YZIlqiS7Gd2nI/6QwptrNuMPKzit5jNKU/9fRQCsdht5eV4EAcwFwQVO5nuJqKcykoqSiEUwSC4zSf8cu91Bfn4ehmFgNptjiwm/L5dIJBL7O6vNHrcd1V4wEeiGjtlsQdd1TGYzZpOJPbt3o6oqmXXqAAKGYcTl4tFut+P1etE0DYvFEhvoXW43Rw8f5uTJbGpnZGK1WuN2EiicpMLhcDE5vd58lHB0YWWIBk7ZgTVO25LNaiEUVgiGwtgKYrgHQiH8gVCs/QuCgNUpounlL+R5SWOz2QkE/EQiESyWaH0Uvm9BjBqeoigiShIkl978Vp4QRRGH08mBffvw+X3UqVMXQRDjtp//VxBFkVAoxLdrv0bTNBwOB6qqEg6HqVKlKg6Hg/XfruWKq3ohCMJfel+yLLN921bsDgdVq1VHU0tuY0aWZXJzczh29Bh169X72+1IEAQikQg7ft5O7doZmC3WuEt86Ha7CQQCRCKRM16n/PjDZqpUqYrL7T5t469EKLjSUNL9uESMkLy8PIYPH86gQYOoWrX0kl0VxTAMHFYzhm5wYf2aVHI7aJ5RjdsvO58Oj7/Mz4eysJkTUbt+D0EQsLqdLFu5inuffIlZI+/m8kvOx1AiPPT8a2zesTsaB1oUCYfCnJNeicUfdkbwlbXkpxAEAbfHw+efrmTcYyN58JHRdO12BZFIhJ2/7OCeOwbhcp1KLDbysXE0aVgPRYn8yVNLF0EQcLs9rFn9JaMfGcG9Q4dz1dXXEPD7CYdCDL/vbvbs2Y0kilSpWpXxk6diMqWhRuJHBwC3J4n1337DyBHDGHT7XfS7vj/5+Xm4XC7mzJ7J4nfewu3xoOs6o8dOpNpFbdG1+DlNMDBwuzzs2vULw4bcSfuOnRk24hGCgQAns7MZfEt/RFGMTdRD7h7GFY72gL+sRT+FYWBzOdm9/xB9HpxM+xaNeeqhOwCDeR9+zoxFH0R1ILo4yVfyeX/o81hNtcpY8LLD7faw9cctDB96D7379GXwnfegqSrPPf0k6775BqEgr1BEVXE6XSz/8KOyFjnuEAQBq9XKkxPHseqrL7DZbDhdLh4f/wTpVSuVtXj/aSRJIhAIsHDeXDRV4/vvNpGSmkrNmrW4oE07LmjTljWrv6Rbj6uQxejS0GKxIAgiihIudhr8W+wOB+PHPMp5LVrx6NgJ5J48WeS3BrJswmQ2o2s6ihIutpC1WCyIkoSqqkSUU6dmoihisVhwOBx8+fmnTH92GkuWf4yuaxiGgcVqRQDC4TC6rsc2HnVdR5IkdN0gElFOLeIFAYvZgsViYf/+vdw/5E5enruAWrUzUJQwZrMZSZJR1UixDUtBEDBbLIiiiBJW0IrMVSazGVmSUbXishf9vaqqmE0mRFGMyVpIYZkRNYJaYHDIssy7b7/FhW3aUTEtjWAgUMwQKawXwzCKnTo9/MB9PDgyuu4JBkOEw6FiZRXWs6aqKIoSO2ESRbFgs9CKpmso4VPBnCRJQpJlBEA2mQj4/UVOpiwYhk44/PeCP521ERIOhwmFQng8HqZOnRr7uaqqf+B+8c8mDLKYTBw6mUfrh14g1x9iz8Fj3H5FO2bd3otOTeqwafehhBHyO0iiiKrrXH3vGLbvPkCu10cgFI527kiEJ4bchKpGBx9nkpu+D0ygVtU0MJni5mRJFEUQBO67+3a2b9tKfl4+oWAw5oZ18MB+KlVK46W58wmHQoQVBYvFWmJHuyVB4eAzYtgQvv9uE/n5eQSCAcQCw+TJyePZ+uMW3vvwE0wmE317X8nrc1/hhenTyc3JKWvxgYLB2mxm9CPD+fabr8nPyyMQ8CNIAna7g2/WrGbG9GeYO/8tzmvRitGPPMirL82ic4f2hOPILcvldPHCc9N4b/E7BENBvF5v1CVHkjh+PAtJlpk953Vk2UQ4EsImWAm87cdZ1oIXwepy8OSrb/Pyux8RCCvk+vwIokAo3881HdrQrW0LDAOcHhevvvMRCz75lOppFTi6J36MwdJCEARsNjtPTBzLJx//D7/Ph6/gnXvz87l18J0MuHkQhmGQkpLKmEcfJqwo2O2OshY97nC6XCx++y2WLn6HRUuXU716De6541beWjifthdML2vx/tNEIhGSk5N55bWF2J1OelzegY6XdWH4Q6M4mX2CQCDAmAlTkMSoC7bFamXzd5sI+P00bX4edofjtAVxUWLrvCILA8MwcDic5OScZOvab0hOSaFxk6YEC9yg7HY7P2//iaNHj5CRUYf0mjXx+3yYzWYEQWDTxvUkJ6dgs9kxmcwFC2ATgijw3Yb1KJEIzc9rgcViQVEUTpw4jtvt4cD+fciyTNVq1VFVFVEUsdls/LRtK36/j2rV07FarUDUC8TlcrFr504OHTxAZt26VKuejjc/H0mSsNls/LBlM3m5OTRq0gyPJwm/34fT5WL/3r3s3bubmjVrU6t2Bn6/r5jux48do1JaGtt/2kYoGKRJs+YIgoCmaQVl/sKhgwfJrFuX6tVrEAj42f3rLqZMGsewBx/hkvYdcLuTYoaX1WpFVVXWr1uLyWSmabPmACiKgslsxuFwsOPn7Rw7dowmTZsVGGN6sXqunZFJjZq1CAYC+LxeACxWK99tXE2VqtWoVbs2fr8fh9OJNz+ffTu243Yn8dLMF2javDkDbx1M1rFjbFj/LU6ni6bNzvtba6mzNkKGDRvG+++/z8GDB+nXrx8rVqz43cZpGAbLli3j4osvPmshzwR/OEyPVg3pfUFDXvhoLXt+2Yc3GLXQCq4BJPgdDMMgElG57vJL6NCqCR3vGEW4YBfAMAxSPe7oAtlmYdvPv7Jj7yFef+weCAQBe9kKX4BhGKiRCJ06X86ox8Zyx603xax5URLJzcmhenp6bJCqV/8clMjpxnJZoygKF1/ageEPP8p9dw0mHA5hAH6/j/MvbEvHzpfj8XjweJJo3qIle/fsRijlrM1/hmFEdbiw7UUMGTqch4YNIRwKgRHdhVn23hKu7NGL9h07s2vnL4yZ+ATZ2dnk5+dhjqMNgnA4zDkNGvLa1X2YOf3ZmN+wJIrk5eaSllYZSZQ4dvQImfXrYdFNqNrxsha7GJGwQpPMGnw0exIzFrzHsZO5QLSvOO023E4HkiggmE3M/+gLHr3jWmwWM7oeJzsLpUh0RzFEs+YtuOmW25g0/jFCoRCGYWAYBm63B0EUsZjNHDt2lI0b1jFj9lwURQFsZS1+XCEKIsveW8Kg2++kUeOm7N+7h5kvzyUr6zj5eXk4rKllLeJ/GsMwCIVCiAX3nlRVJRQMIssy32/ayPRnp7Fg0WJMJjOPPjycwwcPklqhIs8/O5Unpz1PtfT0YjvlRfm9rNl2u52ftv7IY6NGULdeffbt20vjps14ZNQYDAwmjR/DhnVrady0GdM2TeKuIfdzRY+e5OfnMWzIneTl5VGpUiXyvV5sNhuyLKOqKg8NvY9AwIfd5mDmC88y7dkZ1MrIZNL4x9m/dw/BYIjb7riLjMw6+Hw+bDYbTz0xkS8//5TMOnUxdB1VVZFlCYfLyawXnmPZe0vJyMxk5y87uHXwnfS65lrUSISJYx/jp21bqZ6ezjPTnmTiE1Npfl5L3ln0Bq+/+gqNmjRh83eb6N2nLwNuHoTf50OSJBRFYdi9d1KxUhqGbnDgwD7OObcBk596BrvdwZyXXmTZ+0vJyIiWedsdd9Pr6mt5c8HrhEMh3nnrDQJ+HwNvHYzf78disXD8eBYjht2LzWqLumqJIlOfnY7b7cFqtTLnpZmYzRYOHTxA9fQaPP38i1itNiZPeJz1366lSdNmTN24gbuGDKXfDf2ZN3cOc16exTnnnIvX62X7T1sZM34yXbv3YOOGdTwxYQyZdeqxccN6mjRtSo+eV7Nj+3aG338PNWrWIuvYUdJr1GT02IkIwtmtRc7aCOnevTv16tUD4Prrr+fCCy/8XSNEEAQyMjLOtpi/gECOL8C1bZpwRYtzWbFxO5e3OAd/SOGTzb9gt8TPIiee0A0Di9nENV0vhUiEULi4D6JasEMtyw4en7WQnpdeQOX0qhhx5AJkGAaSJNHtyqvgN0eUkiTh8/v4es0qHnnwfnJzcpAkiXGTn6JW9db4fPHhQmMYBoIg0LV7DyRJIhQ65bMfDoc5/4ILY4uiPbt/5bOVHzN63KQ/nBDKBgPDgC5du2M2W4rpoOkaObknkSWZm2/sy769e3C53IwaM566GTXjyh1LURTad+iE2+0hFAphtUYXmpIk4w/42fzdRkY+9AA+r5dAKMDY0ZNo5TgHCJWt4EVQlQhd2rUCjyt6slmkT2u6jqbryB4X8xevQNN1rulyCdnfBwBP2QldhqiqSqfLu+B0OAmHgsXrS9NA0/C4Pcx+8QWaNGlO46bNCIWC/Ffr6/cQBIFQOETA7+eXHT/Tv9/VHD58iGrVqjNqzIREEuM4oXAeKfoZou08GAzgcDpZ8s4iftr6I//7dBXJKSkMufM2npw8npfmzj/jOUcQBERR4olJY7myZ29GPjqWffv3cnn7tlzaviMNGzXh+00bmPnKa7Rr04qxYybwyuwZXHdDf2bNeI49e3az4rNV2Gx2Hh4+lI0b1uFyu5g5/TmyT2Sx7H+fYbXZGHBdH56d9iSvvL4Qb34+kUiEufPfxGK1FhggdjZuWM9bC+exeNkKGjdtxry5r/D1muizt2/byswXnmfBosW0Ov8CPnh/KQ89cB8dOl3Gdxs3sGb1V/zv06+oWq06jz48nMkTxrB8xefMmzuHFq1aMefVV1i8ZDkLXn8VpcjaQxAEjhw+zPkXtmXU4+P5+aet9Ol5BYcPH0IJh5n5wnMsWLSUVudfwLL3FvPwA0M5/8K2PDpmIhs3rGfEyEdpfUGb2GmFzW7nqckTcLs9zF2wCL/Py4Dr+/D16lX0uuZavF4vzc5rwdgJU9i/by89unbi1107SatShe83bWTmy6/Rrm0rxo2dwMuzZnDdjTeh6xrHjh5h6jMvcGHbdjxw710snP8a11x7HVOnTOLiSzvy2LiJLJg3lzkvzaRSpTQevH8I51/QhqeemU72iRN0vrQNHy5/n4eG339W7fGsjZBu3brF/t+jR4+zfUyJ4bSaWb19L1dOfp07L7+A5nXT+Xzr/7F33tFRVG8Dfqbs7sy2NBJ6771JU0CliQICigVBsaGgqNiRriJVsaPYOxZAEcXeABEFaUqRIkWK1CTby5Tvj0lWAljAmGy+3z7n5Jyc7Gbufe/MnXvf+7ZtPLJwKRv3HMSj2PnfO+P7Z5imSTQYQhQFTmRpdSgO1v/8C8vWbmTFaw+j+4OI2clnWzrajHv03zp1Pptq1WrQtn0HXC4Xw4dexfTJ99P9888IBEKl2OPjCQatU5ujZRAEgVAohMfj5ciRwwwZfAm9zu9Hz/N6EwwmhxJ1NFafjj8VMw2Tffv3cPeY8dSsVYfpk+/nzltvYsnS7+AUT1H+CwrH224vGqgdDodo0rQZk6Y+SOs2bckql82EcaO4d/xoPhjwblIlOhAEgXA4guqwn/BzURQwYnEefHU+tw/uDzb5fzpwWBAEQsEgNtnKPncsNrudnTu28/mnH/P0cy8RCgZJ86SsIMciAPF4jIMHD3LL7XeRk5PDXbfezL3j7uH9hR+UdvdS/AWW+7KEzWZj08YNRCIRRt0xklg8xqGDBzFNg1gs9o+D1kVRJC8vl3179/Djih8YMugiZNmGw+HgwP79dO1WkfH3T2b+O2/x2ssvsmnjBlRVxTBN1qxeRZduPXC53ESjEbp078HatasBgV82bcTv9zNyxDB0XefQwQOkZ2Sg6zq6rtOx81nklK/AwYMHALA77Kxbs5p69RtSt159jhw+RKczzyI7pwKSLLHi++XUqFmLRo2bsP/33+lwRifcHi+bNqxnx45ficViTBg7ilg0Sn5eHoZh4MvP56aRtzNt8n107tiZMzp2ZuzESUUUPMMw8Hg8dO/Rk1gsituThjctDdM0WfnD90e1uY/Tz+iE2+Php3Vr6NqtR+L//8jwKRIMBNiw/iduu/MeopEIkUiEl19/h3g8hj8/H5vNRtfu56AbOi63i4zMTHw+H02aNmfC/ZOZP/ctXnvlRTZtXI+iKOi6Tiwao279BjRt3hy/z0eVqtXYtm0rmqbhdDoTrmPBQIBwKITP52PXrp3EolGuvvxSZNmGJEnW4e4pLn/FEpg+YsQIOnbsyKWXXnrcZ5dddhmXXnppiSgqHsXO52u38Omazah2G+FYHEkU8aipzFj/BtGpcO8zc+jVqS2Va1QltP9AmXFC0OIa8XiMjmeeZWVtcXno1acvjz8yk1jsn2eeKE1M0yQtLZ3dv+3k2isH061HT0aPv5fDh4+Q7lZKu3v/CEmU8Hq9VK9Rg97n9ycvL5fBQ65m6FWDE0HryY6VZS1AhzM64XS6EEWRPv0u4IuFHxHw+8lIIte4v8PhdjFnwWeEozEG9e6C5g8CqRiHE2Fi4vF4mDFlEo0aNaZFq9YcPHgwpYQcg2ma2B0OPB4vZ3fpxllduhKNRLnkssE8NH2qlVFOSVlDkh4T4rEYtWrXZujwG8k9cgSbzYZhGETCYTweL7quEQgEjls/RdFSYmTZhixLRKNWEHrPXn2oV68B8XiMiwcOonLlKqxetZJbRwznkssG0/ec88jOWczHHy4suI6IoesF1hQRMWFdgFgsSsNGjbnuhhHkHjmCLMsIokgwGEQQBKLRaJHAckwSsRHWNYQCN2bTSrMuWIevha7NgiAgFnwnHo9TuUoVhg67saAtG6ZpkJeXS5t27XllzlyWLV3Me/Pe4ZuvvuDZl14rEv9smmYi/qUwoD3htiYIVsY9hESiE2vTbySCvwuxvi4U/IiJdP2/7doBCFQsSAgVDocxDQNd1xMxN1s2b2LkiGFcdOkg+va/kJzy5flgwXuJvugFGdLsNntB/IyAiUnf/gN45KFpHDx4gO2/buOecfciyxJaPE6X7j1o2+50wpEwFw8cjMvlIhAK43GdvIv+v1o14/E4gUCA5cuXs3HjRoLBYJGf3bt38/777+MvMCf915iAR3XgdSrIkojXqaRS854kx8ZKOBQHq1et57Plq7nn6oswgqGkD7CJx+OWDIKAx+tl5oypjLzxegB8vjzmz32LRk2aJlL4JiOWDDoCVraZtWtWccmF59O1Ww9uuPlWtm/bRu6Rw6Xdzb9Ei8cTGVVisRi9+vTjvflzWfzNVxiGwYJ351KxUmXS0tKTLj7HQkDTNDRNg4Lsa2+89jJXDBxAOBxC0zTemfM61avXwJv2H6VFLAY0XSeu/RH4L4oCsUiUic/M4dZBfbE5VfQknQelgaZp1nNbsPFx2B1s27qFhe/N4/obbyYaSSYXyOTCNE3O7d2HZ2fPYsPPPxEKBvjg/feoW69+IuVxiuQgnng/W8+5YRjE43FisSgdO5/JT2vX4lSdnHl2F7764nNmPf4I6enpPPv0k7w37x1UtagSrmsaPl8+hw4e5PDhg+zf/zvetDTq1K3Pd0sX07pNG+rVb8C94+9h69YtbNn8C4ZhcNud99CgYUN+XreWaDSGaRi073AGn36yiAMH9hOJRFi44F0i4TC6btCx81msXPEDWVnl6HTm2SyYP5eXn38Gj8fzx9w9apMSjUZp3aYtmzdvYvWqlaiqk0ULF7Bv7x40TaNdh9PZvv1Xvv9uGZmZWXzx2ScEQ0EaNGxE69Zt2PzLJkwTzjq7K6tXrWDmjClkZGZyzZDL+O7bJQwfcRN33jOWTRvX4/f7iigh8Xi8yB4jHo8RCUdo1+F0dvz6Kz98t4zMzEw+++RjQqEgzZu3BKysXr/t2oVZsKYYhoHL5aZFy9a89carVharWIxrhwzi++Xf4nS5/9j3HNW2zWbn55/WoeuF49zYGueY5TZWeM8tLKtHYZ2Rb776nNPatqNKlapcdMlAmjZrjqo6adqsOUu++ZomzZrTvHlLpj1wL98vX4ZTPbVDmX9lCXnmmWe4/fbbicfjrF27lhkzZiQG3HIHCFOrVi169Ojxb5o5aYSC9lOcPOWz0lGPqusg2mSenf8Jl/ToTK061Qnnnnwho5LENE2yssqhKCqmYRCNxhg38X4mjr2Hywb0wzQNMjIzuXv0OEKhECShk54lQxaq6ixIO+jghedmEwmH+fHHFVx6QR8Mw6Bm7Tq89trrf5k6sbQwTZOMzCxcLhemaRIMBjirazeuvHooI2+8nqysLCKRCPdPmY5hGkmXpx3ANA3S0tJxKAqmYRAMBLn51juYOPYerhg4wCrGJok8MGUG2mI9KRVa0zRJ97ixHeVuZVcUFn65DLeqcFXfHsR8AUgdUAMF45WegdvtSWwAFEXl3blvc1rb9rRp2568KaAAAAAgAElEQVT8/LxS7mXyEvD7ufjSQWzZvJlrrrgMj8eDzWbn/mkPEomEcSnJb/H8X6BwjXG53JimdfLucDjIKleOYDBIl+7n8PNP67ju6supVLkKv+/by+jx96EbBh8ufI96DRpy4SUDiUQihRekarUaLPnmS9atWZVImTtp6oNMe+hRbh4+lMsG9CMYClK3bgNatGxFMBjk3blvc+mF55OWloYkSVSrXh2fz8cFF13K8u++ZchlF5FTvgI2u43qNWri9/nof+FF/LJxA1cOupjs7ByOHDnM+PumEA6HSE9Px+12F1lPIpEwTZo156prruOOW26kes1apKWl0aBhI6LRKPUbNOSue8Zx7/jRVKlShd/37WPCfZPxeLy0Oq0tQ4eN4Obh11Kteg327P6NkXeMwibbGHzFVTz/zFN89cVn7Nmzm+EjRpKZVY5wKJQocluuXDY2my0R81kuOwdd16jfsDl3jbbarFylKr/v28vESVMpl52NCVw+5Bqef+YpDh7Yz9BhIwgGAwSDAe64ewyj7hzJoIv7E4tGadOuA337DyAY8JNVrlyRmlzlymUTi0Xp2v0c3pv/DgMvPJ/0jAwEQaRq1WrouoaiqmRmZRW4kRm4XC4yMjLRdZ3yFSryzVdfYpomPyxfxhOPPsT9Ux5k4gPTuHn4UAYO6Iuh62RmZdGjZy9C4Qge18krIoL5L1bO9evXs3z5csaPH0/r1q2Pc7lKS0ujW7duZGQUb2m73pNf4ot1W/GoZe9kJTcY5pWRlzGwapzQjs0IcnKt/qFIFLtNtkyBBY9Gri9AZpqbwuJ4phZDrd+OHbqXA/v3I0vJVZE+Eg4j22wJE7KiKAiiyNbNv2CaJnXr1ScWi1OpfCaaprP794NJJYOlwIes3OoFwZyhUAjDsHKsJ9K9mQJNGtUD02Tnnv3IcrLJEEaSpITFSRAE3G43u3buJD8/j5q1aiPb7GR6ndjtNrbv2pd0MhQG1zscVtFIu92O3e5g29bNRKJR6tSvh02XMGfnUuH0MO46ezCiyeOWZQULxzAxE4cLgijgC4Sw22RcqoKm6YgOA/9GN4dXVqLO/Y2Q3MXiqfuX7N53gD2/H0rCex5GFETsheMlCPjy83F7PAm3Dk3XqZCdSfXKFUqsb7/u2svBI3lJ9a46EaIo4nS6+HXbVsLhEHXq1sMwISfDS6UK5UqsH7/8uot8XxBJSp75mCwUrjGSZFUiB8sCGIvFEjGJLpeb7b9uIz8/j9p16uJ0ugiHrXXo6DpJhdeLRiKWK5QWRyiwRLjdbjzeNDQtzuZfNqGoKnXr1kusDYZhsGXzL2SVy6Z8+fLk5eWiKFYWLFmW2fzLJlwuN5WrVEl8JooiqtPJti2bCQaD1K1XvyARilVYVhSFghoWZhF5nS4X27dtJRqNUrd+A3z5+Ym0wm6Ph727d3PgwO9UrVaDrKxy+P0+BFEsWLN2cPjQIWrWqo03LY2A34/X6+XQoYPs2rmTctnZVK1mpRc+mnA4jMPhSMgaiYRxOJS/blMQUJ1ODh44gGHopKVlFFEUBUFg8y+bsNnt1Klbj2gkiq5rhMNh7HY7siwn3MDsdjuqqqLretFxzs1FdTqJx+NompawasVjVlHWcDjMxf168cbcBbRp2x5d1+nauT1NmjRjyoyHOXLkCJt/2YgkydSr34BIJELNquXJSDv5Aq7/aqVp3LgxjRs3Zv/+/bRv354uXbr8m8v9Y2KaTiQWx5bkL+MTocXjGDJgA0EyEeTkOj1NT1PRDQPDKHBHMKF8thfDtPwMBUEA00CwCxCzzHlGEgUVg4nT5cYw9IRZNhwOI4giNWrVRgDCkQjx2B9piA3DxBCS6T4YOJ0uDMNMWDmcLhfiMeOcMPUWypBU6VWt3OSGaR5lHjfJ9/kol51DTvkKaFqcYDBAptf5x31IMhkKM2NZ/ryWv3EsFqNK1eqJyrvxcAQnAoIMot2AJDPquB1WsLWu6wknhcxMa6OhaXFEm9VvQSrZsU/ae646ochza+BNT4eClKYgYBhFMwyVBMn5rjoew9Dw+31UqlylYI7ErKKwJVxhvnC8hCQfr9KhYI0x/1hjREnC5XIXPOMGPr+PCpUqUalKFbR4PFEDQ5IkTJNj3J4MHKqKekztHE3XCIWCiKJE/QaNME2TQCCQSA8sStbfdV0jHIngdLoSylBc06hdpx6GoR/1mY5haAT8/iLv4FDIStCiqOoxc/cPeQN+P5UqVwUBwqEQiqKi61pBoLmPjMxMsrKz0QrcykDA1K3PsrPLU75CRbS4VpCtSsDn8+F0umjctBmGblhhB8c8ai6XOxEwjyDgcnvQtb9p0zQIBYNWELthJtYewEqvLIrUqVvf8jAoGEuwlEb9qH1PYduFKZmLjLPLVZCi2FJCtYIK97LNjiSJ2B0Knc/uxthRd1K/YaNE7M2lg67g0KGDaJpG3XoNMDEJhUKJ9+KpUCzHXaNHjwYgNzc3IQyQ8DlLT09PaNvFQZ0KWRyqVRnXn2R+SVoEyM0PoeaaxBSI59khySwhVs1P6a//pgmIezUEh45TVZCSMiD3z/uk2G3YJMk6gTXBqTqSVAb4qykqIBeVIVlP/GwnksHEbpORJBGbbMmR3DKc6MDDxGGXEUwJ9BC5ywT863NAL4OuoJJJPE/CjMVLzEPRJstJfs9P8NwW/E03DGwnfK7/O+w2W5K/q06ElQJelqQigbYlgd1uQ1UdSGIZnI8lyTHPsU0+dl9lvauPnw9/fz9l6ej9jQkCSMft20xEWcJWsMewFbGMmoiSiFzwjrAdZzW13sHH9eVv5mZhH04oqyzBCa2zJnabdIK1wESUBGTpxPtRm1x0vtqO+95ftCnxp2vPicbSdsy+p2jbfz7Ox46rKIpMnf4Qa9esYu/ePZZrWuvTUFXVsu7YbRQuFJLDhl6wlp8K/8odq5Dff/+dwYMHs3Tp0oQvXCwWK8hG5OLdd9/lzDPP/LfNJNi4+wD5oUgZexmDIZjIBzTcbxzBNMr2i9GIaqSfnU25gVVBK3snTSYUuDWYaLqR7LH2J+QPGazg45QMpYAARszg8II96PkGehme16Jo4vDK5FxYFVH5763Muq6X6bknSWKJukZpmo5ulN3xkiUxsT8oCeKaZllCSqzFFCn+f2BiGRHsRylzmm4UsaYf+32bLBUJyv+nFFuK3l27dvHiiy8yZ84c0tPT6d+/PzNmzKBatWq0adOmOJpJcNcri/jip7IXExIyNVpIWbysdsbbIQ/ZG8Qsg6emgmRyeEkakYBGXjiIGU6+wOi/wzAgzeNENwwCwQhlTJ8FLBm8bicmJv5AuMzK4HapSKJAvj9U9mQwwTAh7ZLK2CWJI74AYhk8eTVME1Wxk2Y7+RSLp0ooHCUQKrtzz6k6SPOUXFrjQChMOBqjDD5eGAZ43CpuZ8mlNfYHQsTi2glrX6VIkaL4ME1I97pRTsE76V8rIYZhsHbtWt544w3atGnDtm3bcDqd9O/fn7PPPpvevXtz5MgRnM7iW9x000TXrZ+yhG6a6IKBKQgolWLYs4KY8bK3Ags2g/yVHjQKq6+Wdo9OnmMrxppm2VupUjIkB6ZpYoR1dBmMsE5Z3CUapmnFgZVwAfCyfM9Lq92yeLZfmuOV9DnlU6Qo4/yb+V1sTpqFMR8ej4eNGzcCkJ6eTk5ODnPnzmXkyJHF1RSiICCKQpk7cRRNIVF0x4wLGDERUyuDSohJmVQ8UqRIkSJFihQpUiQH/3oHLIoi9evXZ8SIEfzyyy+0a9eOjz76CJ/Ph2EYbNiwISnz56dIkSJFihQpUqRIkaJ0KBZLyGOPPUaXLl3o2bMn27dvp1atWlSpUgWn00ksFuOiiy4qjmb+MYZpBRtLolDmgtdTpEiRIkWKFClSpPj/TrEoIbVq1WLz5s1s3boVgAULFvDoo4/i9/u56667KFeuZAoUCYJAIBJFEkVyvC78kRj5oQjeMhbAniJFihQpUqRIkSLF/2eKNSakUaNGAHi9XsaNGwfA3LlzqVu3Ls2bNy+upk6IAPjDUZrXqMjDV/WmXb1q7Dmcz/g3P2PO0jV4lJQikiJFihQpUqRIkSJFMnDKSkg4HMbns0rMnwhBENi7dy8XXXQRc+bM+c+VkJiuk+VRmXfnYDLdKne9+AGDzmrFCzcOYM32vWzZdwjVnlyFAVOkSJEiRYoUKf4XKKwTU1ilHay4YlEUixS6PhkK96DJEHssCAKSLKPF46XdlZOmtMbxlJWQV199lWHDhv2lEqLrOk2bNqVnz56n3MF/SjASo1/bxlTK9HLLCwt5bP5XfLJuKzlpbnyhiFWRMsUJMU0Tp1MFmw1MAwSBWCBkFRITCj5XHKA4iPgCpd3dP8U0TWRZJi0tjUAgQDQaBSzLnM1WVAGNxuKY5qm99P5LCmXwpqURDAaJRiKJOebxeJAkCbPge3m5eaXb2RNgPUtOFFVF13UkUcTv9xMveCm7XG7sdjsmJoauk5fnK+Ue/zmCIJCWlkYsFiMYDCIIAqZpoqgqTtWJYegIokhebl5SZ4tLT08nHo8TCAQSMrhcLuwOB6ZhAJCXn1/KvSxdTNNEURRcLhdawXMbCASIxWJ/3PeCz/Py8tD15Ht3JBNHvwfycnMprK6c4n8P0zRxOByoqsrBgwcxTZOcnByi0SiRSJRoNEI8ruFyuU56A1y4XgLE4/E/3Y+WFIZh4M/Nxe12l2o/TgWbzYamaWVHCenevTvvvPPOn9500zTJzMykffv2qOp/X6BIN0wqZXoBuKxTC6Zefi7RWJwnPvqOqfN3oTpSVpATYoLTqfL92o08/MYCAsEwnVs34ZZB/ZBEwaqamZXOyu/X8OYnixlzzcW4PSdfkKYkyMrKIjc3l1F33cm55/Wibbv2mKbB1CmT2bhhPaIoFiyOLmrXrs0994wiGouVdrcTFM4Zvz/AmHvupkuXrpzRqTPhUAiPx8PC9xfw+muvous6ffv155KBlyU2SMlA4eZ206ZNPDzzQQ4fOkTTZs247fY7sDsUVEVh5YoVPPH4o4RDIa648irO690HjOQqdmmaJh6PB5vNxrQpk6lStSoDLrqYYDCIy+Vix44dPDLzQfbt20eHDqcz/MabkKXkSoBhmiZutxtFcTBj2jQyMjMZNPhygsEgXq+XH77/nllPPk4wGKTnuedy5dXXYmhl7/SuuFBVld927eKhB6ezd+9e6tarxx133o3X60XTNMpnZ7J67c88/9wzjLjpFsqVyy7tLiclhZvC7Mw0vl78LfPmvcPtd9yFqpZcEcwUyYXdbicvL48bhl3P5l82IUoi1avX4L5JD1C/Xl0ef+xx3n9/Ae+9/wGRSCSxnv3dybzH48Gl2BgzdgKCCKPHjsOX7/tH/1vIsd/7s//7J9dTFIVNmzZy5RWDeWfeu1SuXIVYwf7in7bzd5zoOqey/h99HUEQsNlsDLvuWgZeNohu3XsQDAZPui+nyimvnDVr1uTCCy/kggsuOOHPhRdeyNlnn10iCkghhTU4DMPk9mcXsHbHPsYO6EKf0xrgC0VKrB9lCYdiZ8O2XZw59B6qVchmYL8ePDV3EffNfgNHhhdfOMJ9Dz9Pr5vv5dE57xOLa0mXcUwQBGRZZs4br9P73HN4eOaDbN++HUVR0HWdGjVq0KxZcxo3acIZHTuxceN6Fi36EJssJc8BnSBgt9uZN/cdep93Dg/NmM6WLVtQFAWv18t7787n8kED6da9O/0vuJCJE8bx+muvoirJoxDa7XYOHjzIeed0QxJFhgy5gs8/+5TbRt5CVoaXHTt20L9vb2rXqUPP83pxzVVD+PKLL3CqSml3vQiKqvLtt0vp37cPE8aP5bvvlqE6Fex2O7m5ufTr0wvDMLj6qit57bVXuP22W3A5k0wGRWXFD98z4IL+jBk9iiWLv0FVFZxOJ+vWrWXAhf1o3LgJgwYPZsrkB3ji0UdI85a907viQJZlQqEQvc7twZHcI1w55ApWr1rF9UOvwel0ous6U6dNp9/5vZj1xOP4/X5kW7GFU/6/wmazEQwEGDt+AhcP6M/Ts54kFoshJpmSnqLkcLvd3HXHbezcsZ1XXn+DV19/k1AoxID+/QiFwvTtfwGPPfEkkUgEWZbxeL1409Kw2x243W4cDgeGaSKKIunp6bjdbjIzM3l/wXt8veRb9uzZzdbNm7HZ5ILDFw+qqiLLMunp6YnDx0IEQcDj8eD1elFVFVV14nK58Hg8OBwOXC4XiqJgFGzQ09PTsTsc2Gw20tPTrc23IOD1ehEL9kKFhxWVK1fhzbfmkpVVDk3TirbjdOJ0Ht2OO9FOYbtW8VYTu92O2+3GME1sNps1Jl4vDocDt8eSz+v1YrfbSUtLszwkCmQsHAO73Z64nstljaMgCGRkZGCz2XA4HKSlWdVpRVFk1Y8/cujQISRJsqyYipL4f1V1oqpqkfsgyzKqquLxeEq/WOHixYvZtWvXcVYRu91OPB6nU6dOVK1atTia+lNEQSAvaCkaD72/mHlzPmVXrp8zG9eiTsVyxLXkOm1NGiSRbb/t5ep+3Zk+5kZwOYkHQ0x98R0euPM6duz+nQ1bdjL1piFMfuFtjAL3jWRCEARisRgfLfqQIVddjaZraFocwzSIxWJcOvAyZJsNWbKKLL780guMGTsa0wQzSbQQQRDQNJ1FH37AwMsGYZoG8Xgc0zTRdA1d13n08Se5Ydh1ACxZvJivv/qCG4ddlzT1gCVZZueOHXTrcQ6zn30el2Ijs1wOQwZfRlwzeO2Vl2nUqDHTpkwGYNWPPzLricfo2/tckqmqsU2W+fSTjznttDaoqoKh65gmuD1unnj8UTKzsnjpxRcA6NDxTL784gsCoUhSWUPsdhufffYpjRo3xuVyYRgGZoHV89VXXqZ58+ZMnGAlD1EUlZG33MTwG4aTkeYt5Z6XPJIksfu33zitTVueff5FstI91K7XkJ7du3Lw4EHy8vL4btkyxo2bwJTJk9D1ApeF5Hlkkwabzc727dvZsH49E+69n5kPzki4/KX438Q0TX7dto12HTrQpGF9AJ597kXmz5+Lphv8/NNP/PD9csaMGc03S5by+Wefkl0um0WLPsTr8TB52nSqVq2Kphk8MOk+tm3bRiwa5dChQ4waNQrVqWKa1vvN6/Xy0UeLeHb20wiCQPMWLbh71GjAcpWSJAmfz8fkSfdTp25dPlr0IYGAn3HjJ7J161befutNZFlm0gNTaNCwAYZh8vhjj/DZp59gs9lo2ao1d989itVr1vLcM0/z0MxHSU9PZ9Rdd9K+w+l0OP10nn/+WW6/4y6i0Sj3TZxA/QYNWPThBwSDQSbcex+bNmxg7tx3sNlsPDBlKk0aN2LuvPls2riBu0eNQRDgo0WL+PHHFdw7cSJffvUNX3z+KVnlyvHpJ5+QnZ3N6DFjeeGF51m7ejV169Vj0uSpyLKMaUIsFuWmEcO57vrhNGjYEIfdzqwnn0AQBIZdfz33ThjPj6tWIgoiPc45h2uHXo+uGzidTmRZxmGTePSRmaSnZ3D5kCEAPD1rFnabjWuGDiUWi3PvxAmsXvUjJibXXHMd5/XqdcrPR7Gsmk8++SRXX311kZ+rrrqKiy++mOuvv54NGzYURzN/ieqw8c36X4nrBg8MOocevTtyTbc2AGzafSAVE/InRINhep7emlljbiRyOA+iMZb/tIm61SqBptO0TnXefOp+Tm/WgHAkeVyXjsYwDARB4PkXX+HGG4YV8WsUBAG/30/ukSMYBsyYPh27zc55vXoTiSaPPKZhYBg6Tz/zHLfcdGPBptGSIegP0q//BQy97jqWLV/BU7OfZeuWzQwbdiOarieJGgWRcIgmTZvy3PMv4vf7Afjh++VUrFQJmySydu0aWp12GnETInGD09q0Yft2a84KYvLs6Px+a1G6794JOBxKImBSQOCndeto1bo1r7/5FldfM5QPFi7gkoEDATNp3OIAfD4fd919D1MemISqqn8EfQrWqVckEk18t2KlSvh9fvbs2VtKvS1dotEo1WvU4JXX3rDeHcCKFT+QmZmJqqpUrFiRdxe8R/ce5xCOpCzqf0UoFKRR48bMnzeXtm3bEQ6HS7tLKUoZTdMYNXoMb7/1Jp3PPIup0x8kN/cIN4+4AY/Hzfr1P7PgvXdRFQcHDxzgoRnTycvLY8qUyfj8fiaOH0e6x8X0aZNZ8s033HXnHVSsVIn09HR6ntODUIHrkE2ENWtWc/cdtzHy1tt4avZsln27lKlTJuP1WocroihiGDqzn57FF59/xujR99CuXQf69+3Dls2bmTx5MoqiMG7saNLcThZ9+AETxo1l3IR7eXDmo7z6ysvMefNN2rRuyZYtW3juuWf48osveHf+PDp37sihQ4eY88ZrxGIxTNNk1pOP883XXzFmzBhan3Yaffv0Yvv27UyePBlJkpg4fhyqw87GDRv44vPPsdvtOBwONm/+hc8+/QTFYWf//t+Z+dCDGIbBlCmT2bVrF73PO5cGDRoyacpUPli4kHfefhOPx4Nh6GRnZ7Nrxw6enf0UaR4noVCIxx99hLp16/Lsc8/x4ovP89DMRxg3YSKTJ93Pt0uXFrHkS6LA0iVLWPXjjzgcdhwOO8u/W8aKH77Hpdh5cMY0vl26mKdmz2bkyNu5687b+PHHldhPMfFTsSghL7zwAvv37z/uZ+LEiZx11ll07969OJr5SxSbzOa9Bxk6ay7ZXhef3Hcd/do24uEPlvLxms14UrVCTowgoOk6kVAEJSudefM/Zt4Xy5hx27XosRiGaYIvQDASLfWgr79CEASCwSD5/tAJP5ckifx8H889M5tbbru9wMyYXCd0giAQCoXwh6JFzceiZenRNJ0ff1zJc8/ORtN1KlWugmEkz8YXBAzDIBAIkFUuk8+/+oaZD85g6vQHEQQIBAK43R5MA/SC+BEtHkeLa0n1bAmCQDQaJaZbWVyO7pthGHy8aBHffP01derU4sEZ0xl23VCcikIyHY0XWgdjRlEZgsEwV155NVu3bOGiSy7lrrtHMXrUXZbF7X84JsQ0TQKBAOkZGaxYuZrxY+7hgSlTE+5Y+fmBRGB/ij+n0Ec9PxAmFAqlxisFfn+ACwdcwFffLKV9+w7Mm/s2Z3Y6nbvuvgdJsGIpPB4PAHFNo0nTZtw3aRKnd+jARZdcyo4dOwBYumQJAwcNokXz5lxzzVBWrPiBSFxPBKYLwIcfLMTlcuP3+/hu2XfUqVuP9+bPQ9P+WGMMw6B8+QqMn3AvHc84gwEXXUxaWhp33HU3Hdq3Y/AVQ9i3by/+YJhGjRvzzrx3sdvt7Ny5g5zsHH7dthVVcfDSy6/y1pw3uOnG4Tz/4suUL18eXdPwetMQBAFD16lYqVJBO6czYMBFeL1e7rx7lNXO5Vfw22+/AaCqCm63O+H+5HA4cLutMdE0jaZNmzHqnnvo0L493bp3p379+tw4/Ho6n9GB7j16sP7nn5EKLPGapnHjTTezZMlionGd75Ytw+lUObtLV9q2bcerr72BLz+fQwcP4vZ42LVzBzap6Dx1uVyoqmp5i5jgdDpxuix33YXvL6BR4yYs+3YZfr+VIffDDz5IhEOcLMXijuVyuXC5XMf9fcKECdSuXZtPPvmEc889tzia+ku8ToU3v13H4g07qFspiwN5ATbuOYjbYU+9DP8CQRBQMtN57vX3GP3Yy8yfOZYGdWsQzvdDGRq3PzuJNk2TtLQ0Hnv0EdweD3379sPn85HmSb5gyT+TIS8vj3g8zpArr2LEjcO58867ufKKQSz/7rtTTm34X5Gdncl77y5g6DVX88Ssp+narRtxHdLS0jh8+BCiBLIpkp+fj0NRsdttxGLJtQE+0X0QBEuZPevsLjzz9FMAtGnbngEX9mfatGlkpKeXdDf/khPJEAoGada8Ocu+/4FnZj+N2+3h7lFjGHbdtTidyTcfSgrTNMnOzuKrL79m4CUXce/9D3DhgAEcOnwkiVTLFCnKFlaWVI0fvl9JvfoNmDJtCpoGn3y8iP59+zDo8suLxg0XxECEQmEURSUei1mxHsDV11zLg9OnIYkCc+fOo1ev3ig2CU3745DF5/MhyRIbNmzA5/NRrVp1etx/DuFwuMj7UJZlYrEYUR1LWRZFgoEAsXLl0HUdEQFRkggFQzzy8EwqVKhA/fr1CYfDKIpC3IDadWoh22zs3/87LVq2JBItmp3LPEE7hVn3MjIy0HU9cRBqFsRaFP7/sdk8rbi1CKpTIRa1MvZF4gaSZMW7HJ3yOBgM0vnMs7Db7axetYpPPv6Ic3qei8fpYP+B/Twy8yHq1q1H9erV0TTNypJ41P06uj8AoljQHwF0E6KRKKFgkPXrfyYajTJs+A2cfnpH4pqGTT55leI/d2LOzMxk8eLF/3UzCbyqg9xAiCUbdrBt/xG8qgMxiVw9kg0BUNxOHn/+LZ56+0PWvPMEnc7pDCYIopg0rj7/BlmW8fv9PPn4Y4y89fYiQVzJjoCVYvW2kTfz+GOP4nUpCECt2rU5sH9/gRzJ83x7vWm8/tobjLlnFB989DGXDxqIaVovsrZt2/Hl558jAA5Z4OOPF9GocWMkMXkyfP0VumHQvkMHVq78gVjBOz8/Px9ZlrHZbGVCBo/Hw9tvv8U7b7/F9KlTGD92NKtX/4jb46ZG9Rql3b1SofCQYuH7HzDihut5bc6b3HTjcEzTig8qC/c1RYpkRBRFZFnmgn59ePutOUiAQ4a27drjdDoJBY+3lhVaAwp/L2T79u20an0a+w8c4uJLLuXp2U8Dljtl4TWqVKmKKFpuTjMfnEHffv3ZtHEjkiT/aRvHYsV7CTgVO2NGj6JmzVq8+srLjB0zmnLZ2UQiEWwiPHD/JKpUqcKQq65hxA3DcSnHuyP9XTtHb/qDwQCKTcAhC+zcsaNILNU/vQ5Ylp70dC+9evXh0YdnsiAYQVMAACAASURBVGLFD1x08aXEdIPbR95Mn/P78tyzsxk3djQulzuRPj8SieBwWB5D8XicSCSCXbQsFbt27kQUBCQB0tLTada8BZMn3c9DM6bjcrrYvfu3hNJyshSLJWT37t3HFS40TZN169axcuVKbrnlluJo5h9jkyUr81GKv0XxunlmzkJunj6bbu1a8MBzb5HvC+BSFe6/YTCZHjcIArphkOcPJv2CLAgCPp8vkd8frI3X5Afux263MeCii/H5krc2BRTGsfisOieCgBaPM+jyKxgx/Hry83Jxud3Mef01brvjLgQBjCRxK/N6vSz6cCFDLh9Ey5ateO2VV3j04ZkgCIwfP5Hrrr+OOXNe54J+F1C+fHlW/PADCz/4qCBpRPI9V4JgnSpZL3nw+/xcMeQq5s+bR49uXejU8QxefvllbrjxJtLTPEXiLJIFQbBO4Kw6J9Z7uVrVatw84gZ+/fVXFEVh7ttvMWv2s/+zhzUej4elS5dwQb8+1K1Xj4ULFvDi888BMPK222nYsJEVo2UY5OfnJ2LQkvGZTRYK64Tl5+cn/ZqR4r9D13W8Xi8jb7udcWNHs3rVj1Zmq/cXcObZXWjdqiVLlnyTiCGMx2P4C/aSgmApGH6/HwEQRYHvl3+HaRp8v/w7vl++nEkPPECnzp2YMG4sb79zLtdecw3z5r7NJZcOpFPHjjz22GP06tMHVVWIxaz3s2ma+BLzmCLPqSBYG3Cfz4dhQseOnZg/fy5PPfUUGzdtZt3aNXTr3oNVa9bxxBOP8eXXi6lRsybNmzTixZdfpW279viOeubz/0E7cd3kzDPPZsa0qQwbfgO6rvPpp59QtWo1AGKxomMSCYcTrqGCYFm3C38vJBAIMejyK2jRtBEdO3XmtDZt0HWD0zt24t15c3EqdtasXceunTsA6xizRcuWTJ82lWbNmtK3X39uuelG3G4n+38/wMqVK6hTrx4AY8aO54ZhQwmFgkQjUZ6Z/TRz3vrzch1/hzRx4sSJp/SfRzF48GBuueUWnnzyycTPrFmzmDdvHv369WPcuHHHmZf+DW8sWcP2/UdwlLE0iXEMKogq/e01cNULIqlRMEp/4Y9FY5zerCE1K5fHpTiokJVBxXKZtKpfC5ssoxsGdlmiad0aNK5VHUkG/yYXpteDvZkLtORZZEzToHr1GrRs2Qq3x4Oh6xiGQW5uLpcOHETFihULsk6B4rBS2MViyRWTYBgGVatVo2WrVqR50wiFQjRt2oyzu3Rl27atxONxRt52O/36X4Be4IoVjZV+oSawNrzNW7SkYaNG2B0OyueUJycnhyZNm5GTnUPv8/uyZ/duJFli6rQHadCoEZqmI4rCcebs0sYwDHJyytOqdWvK51QgFotht9vpf8GFBAJ+Dh46wjXXDuXKq68hHosXyBBLLhl0g3LZ2bRqfRoVK1YiHA5TrVo1+vbtz9YtW6zgyPsm0f700wmHwqhKycXOxWJxYkkQD2TFkwVo3KQpLVu1wmazk1Pw3DZt1iyRflOSJOo3aEDjJk2RJRlZllAcJZciOxKNEdf0Uh+vf4JhGNhtNho1bkzDhg0RBBG7XcZejPuAvyMciaLrRpkYr//PRKNROnXqTPsOHdi7dy/hcJj+F1zI6DFj0Q0Dp+qiefMWVK1WHUV10rRpc2rUrImm6Xg8blq2bEVO+fI8PHMmt9x6G1dddQ1dunXjiccfZfPmLUycOJ7yFSohyTKNmzahz/n92Ld3L3v27mXQ4Mu57vrhRepeiKJI3Xr1LCu8LCPLMo0bN6F+g4aAgKoqNGnSjMpVq3HGGR1RFJVff91Ot+7duXTgZdRv0BCf30ePc3rS+rQ2mKZJ27btiMVi1K1Xjzp161Kvfn1sNpvVTqPGSJKMTbbRuElT6jdoAFi1iZo2bUaVqtWoWrUqbdq2Y+eunTRq3ISRt95G4yZNqFy5CqrTSdNmzalRoyZaXCc9I4NWrVtTpUpVYrE42dk5tGzZiuyc8gm3LE3TyMjIoF79+vS74AKys3OIRCJ069adUDjEvn2/c+FFF9G7z/nUqVMXl9tL57POxuFwUK5cNl27dqVa9ers2b2Hbt27c/W1Q6lbtx5Z2TnUq9+A9u3bs27tOhBg0gNTaN6iBZgm8ikc/gtmMRxTrFu3jv379xeZ7LIsU6FCBRoUDHhx0nvyS3y+biveMhZsHjQ1WkqZvOw6iwr99mPLysPUSjetp1UJ2KqGfswHxAosH2bBwyU7VWKBEEga++ZXQKtUCfegbMxwcpzEwx+uFZFIJGGmLSw+ZxhGovK1YZike13ohoE/EE6qU+BCGayKspGEDE6nM+E/a50QBUjzuDAx8flDpS5DYUCd6wTVYn35+cQ1rSDgzo1A4Ql9CK/HhSQK5PmCpS7D0RQW/DNNCAb/qDZus9lwuz2IokA8Hic/34fbpWKTJXLzA0kng8vlTiQGOLb6N0A4HCYQDOJUHGSkeUqsb/5ACH+w9OdeIi+/53jZ/T6f5a4gCIiiSJrXi8/nJx6P43appHmOj4X8r8jzBQhFoqccAFqSFBYtdHs8+PLz0TUDr9eJ21lydcOO5PmS5nAmBTidLmuvAWiaTiDgR9N0VNWqwZSf70NRHCiKQn5+PmAFrauqSl5eHj26ns255/Xi3PPOI9+Xz9jR9zBs+I1cfe21YFobb78/gN1uw+PxIAjW+zkQCBxXJ8Tr9eIPBNA1rchzahgGdrsdVVXJz89HFEW83jRESSRasBYXxmDabHb8fsurwuVyJRLLeL3ehLeF15uG3+9D13Vk2Ybb4z5hO2BZZO12O5qmEYvFkGUbPt8xY2KC0+VEkmX8BW0UBrQX7m2OJj09nVgsllDCCuuwCIJAOBRCttmIFVSut9utdS0YChKPxfB605BliWj0j0Q5wUAQBHC6XKiKggmEQyFCoRCZ6d5TOpQpFiWkpOky4Vm+Wv0LSpIVCPs7IqZGC1sWc7N64G2Vi+QKYSaBJeRkEUST3O+9UDcH18DkUkL+KYZhkp7mRtcN/IHS38CfCoZhkuZ1W0pIkm3g/ymGYeJxu5AlIek28P8UwzBxu5zYbRJH8vxlUwbTRHXYyUwvuTohPn8If7Dszj2XUyG9BAs85ub7y4wSciyGYeL1uvCUoBJyODc/pYT8P8HucLD7t9944flnOXL4MDabna7du9O7z/mJFL0pSg/TNEtfCVmxYgVffvkleXl5iQCVwktfd9111KhRoziaAeCHLb9xyBdMquJg/wRdAPWQQcX3giDarGjdsqcDWjEigTDpZ2aRfVl1TL3sKSGmiWU6NE003ShLScASmCbIkmX+1HS9bMsgUJDlpLR7dPKYppU1SxQocJcp7R6dPKYJkiiekjn9VNF1vUzPPUkSE/OvJNA0Hd0ou+MlyxLSKQavngpxTcMwzDI5XimKYhomjhNscGMFrtWpe1y6FCbxOJUDpWIJqnjssce45ZZbcLvdeAtMPUfTq1evYlVCPlmzhXU79qE6ylZMSBSD6hGVoVQn5xwftqwwplb2Zo8gmxz4wI0vP0Lg930I0bKnSOm6QYWcLDRN51BuXokujsWFrhvkZGcimCb7D+Um8oSXJXTDoFxGOrIs8fuBw2VWhsJToL37D5XNZ8kw8LqdVCqfXWJtHsn3c/hIfpm95xlpHsqXyyyxNg8eySPfHyibz5dukJOVQWZGyVnafj94hGAoglQGLW0p/orC+1n29h3/X9ENkyoVs0/J3bJYdvFTp07l9ttvZ9KkSSjKf+8i9dXPW/lq9eYy6451fWZNJFsQSc7D/O+zJBc7gmwgigqxmEbYH0AIl72XgabpZKR7iMU1juT5S/REs7jQNJ00rxtM05KhDGaE03Qdl1PFbshlWgZFcSAKQpl9lnRdL3E3n3AkWqbvucNeckHpAMFQpMw+X5qm43WXXPwMQCAYIi8/WCaV3BQpyhK6blC+XDpQSkqIoij07t27RBQQAKfDjuJUylwVdMmUcEnWwmUaIqYhYRpl8AVpCGAKCIKAJIoIUtlTQgpza4uCgCSJZXKhOjo/eJmVARNRKHiWUjKUIuYp53k/Vcr03MMs8VgWUSzD43VMLYOSQBTFMjteKVKUNU51fhfL7Lzkkkt46aWXiuNSKVKkSJEiRYoUKVKk+H9OsVhCpkyZQrNmzbj22mupWLHicZ9fddVV1KpVqziaSpEiRYoUKVKkSJEiRRmnWJSQmTNnsmHDBn777TdcLleRnMymadKjR4//XAkRBIFILE4gEjvuM7diR7HbUpVbU6RIkSJFihQpUqRIAorNEnLrrbcyefLkYq2MfjKEojHa1a3G+W0aohsmCFbVVsOERT9uYuW23aj20ulbihQpUqRIkSJFihQp/qBYlBBVVenZs2epKSAAkZhGwyo53NzrjOM+O+IP8c2GX1NKSIoUKVKkSJEiRYoUSUCxKCE33HADr776Kl27di2Oy50S6S6Fd7//mc/XbcE04ZA/yGNXn8/ATi34bO0WXKdQyfF/CVEUcSgOEAWMWJxoLP7HZ4KAQ1VAFNAiMTTzeJe3ZEJRVbR4nHjcqpZrmiayLKOoVvq4SDhMPK6Vci//GkVR0bQ/ZADL5VBRVSRJIhqJoCWpDIIgoKoqoiQRj8eJRiJFPjdNE5vdjuJwkJefX0q9/HtUVUWSZXRNIxwOJ/4uCCKqU0UURCKRMHEtOe8DWM+RbJPRNZ1IJJxwSRVFEUVVEUWRWDRGOBwq5Z6WPoIgoCgqkiyhxTUikXDRzxJzL4qmh//iSimgYE1xKEXGMcX/Li6XO3FQHdfihIJBdF1HVZ3YHXb8Pt9JX9M0TVwuK/VzMBgs8Qxsx/ZFlmXcHg9+nw9d10u1PyeDaZp4vWlEImFisViJ9rtYlJDatWszduxY4vE4DRo0QDtmUR4yZEgJxYRo+CNRwlGNGjnpDDi9Ka8tXsWPv+4h21uyOcrLCqYJNpuMAHy8ZAWH83x0aN6AWtUqEw6GkG02JFHgk6UrOJzvp3v7lmTneJMyvkaWZVTVyc8/rSU9PYPMrCw0TUNRFCLhMB998TkAHTufiaIoSSmDJEm4XC7W//wTbo+HcuWy0XUdSZKx2+0sX/YtBw/sp03b9mSXr2ClviztTh+FJEnYbDaWL/uW33/fR8NGjWnQsDHBYCDxHbvDQX5uLss3/EyzFq2S7kUtCAKq08na1avY/us2atSsRctWrQkGg0iyjCxJLP76S3w+H506n4XqdCbdsyQIAk6nk5/WrWHrli1UrVad1qe1IRwOI0kSkiSx+KsvycvLpVnzFlSrUTPpZChJrEMYhVUrV/Dbrp3UqVuPps1aEAwGkCQJu93BD98tY//+3zmtTTvKV6z4Pz1ef4eqqkSjMX7+aS01atZKujmeomRxuVws/uZLvvnyC0ygY6czOatrN0RB5PNPP2b1qhWMvP1u4vH4P55XoiihKA6WLvkGgA6ndyQajf6HUvw1drudfXv3ct+EMdx86x1kZGQetxdOVmRZ5t3579CoUWOq16hJPB7/+38qJqSJEydO/LcXmTZtGvF4nC1btrBy5UpWr16d+Fm1ahU9e/akWrVqxdBdizeWrGH7/iM4bEV1KEEQkEURfyTKzKt607pWZa6dNZe8UAR7EhTEimNQQVTpb6+Bq14QSY1aNTdKEVkWies6/W69n/lfLuO3w7lMff5tGtWsSv36tYnHYvS/bRKLlq5k54FDTH7uTdo2rUuFUB0CNhm9noyQBPNMFCU0TeOF52Zz+8030LhZc5o1a45pmhw+dIhhQ69k147tbNn8C2+/+TodO59F1coVicc1fIFQiddIOLEMIoZh8OrLLzDyxuupW7ceLU9rQzwex2aTufPWm/hw4Xvk5h7hqScepUbNWpzWuhXxeJx8f7DUZRAEAZvdzug7b+PNN14h4Pfz3OxZeLxeWrY+LXHC4nZ7GDHsGp6bPYsrrryWclkZCALk5QdKXQYAl9vNwzOm8ujM6UTCYV556XkCfj9nntUV0zC47eYb+OzjRezauYPXX3mRM8/uRqWKFZFEgSP5/qSQwe328ORjM5kx5QEikQhzXn2ZQ4cO0qVrDyLRKLfeNJwvPvuYvNw8nn/2abKzy9O6dSs8rpMvNnWq+AJB/Ekw9yzLnZNJE8fx7NNPEA6FePG52ZimSYczOgEmo+64hfffnUd+fh6znniEqlWr06pVK1wlWKsqN99PKBwp9fH6O2TZxv79+xh956089cQjXDrochRFxeNS8bidJdaPw7n5RKPxEq/nkqIoHo+H115+kemT76dl69PweL3MevwR9u7ezTnn9WLP7t2EgkGaNGuOJEk4FAUBAbfHjc1uP2pDbOJwKDidThwOhXAoiCBKzHr8ETauX0/fCwYQDoUS1girbp2ApscRjjqqEwQBp8uFifWuVxwK8XgcSZJwezzYHQ60eBzDMABwOBy43G4cigKCgBaPY7PZUFUn8XgM0zRxOl3Isozf72P3zl00adYCRVFRVfX4dgr6Z7c70LQ4hqHjcDiwOxzEY5aXid3uQFEcRKNRbHY7DkVBFARcbjeyzUY8FkNRVVwuF5IkF1EahILvGbqekEFRVWyyTDweQ1FUXG4XDod1EKvrVvsjhl9LzVq1adKsOWLBQVWhElXoFaAVtKMoCi63G7vDga5paJpOucw0FMfJvw+LxRLyyiuvFMdlioVgJEbr2pUZclZr5n//M6t+3UuWp+RefGUNu6qw4KOv+XnbTrYueBalSkXuGT+TCU+9Ts8+3Xj0mTfYsXc/6xc+DxlpTJr0OF9+v4aGVbpCkpzBi6KIaRrcfMN1GIZOpcqV0Qsmj8vt5oVnn0aLx3lj7gIkSaJn1068O/dtOp/RnlAoOVwFBEFAlERuvWk4oWCQKlWqoumWDG63mzmvv8rqH1fy6dffUrVadR6c9gDffbuUywcNhCQ5ZVQUhTWrV7H4my95d+EnNG3eglmPP8ysxx+hT9/+CIJAZmYWzz/zFFs3b6ZmrdoYhl7a3S6CzWZjz2+/8dYbr/HUcy/Ro+d5LFzwLv/H3pkH2FT2D/xzlrvfO/uMsWYna0ihDdGuECKtZN+XRJKo7CqKCmXJkjYpZYkQpUUktKnsY2bMevd77ll+f9yZQXp/lbfX3Kn7+WvuMvdZznPO83z3YQP70qvPAHbu2MbuL7/go+2fUb5CRTre2p75zz/LstdWoASjw6VJlk1kZ2WyfOliZs+dz+0dO7N50wYeur8nDz7Uj19/PsTxY0dZ9dZaatSsxfSnJ/P8nNncfffdpd31UsFsNvPzzz+xbu07LF/9Dq2uvpaVy5cy6bFxPNC7D+vee5cvv/icTVt3cknVajw7azq7dn1Kjx53lXbXow6bzcaB/d8yZuRQqteoSVJyMroesxj9mxEEgddXvkaPnvcx+7lZFHpCNGvWnAUvvkBuTg7Vq9cgMTERUZQ4lXGSgoI8nM44Pt68kUqVq9C23Q2Ew2EcDhc//fgD+7/9hoSERN5cvZJbbrud1NRUPG4Puq5jsVgiSpflS/F6PFx1zXXUrVcfr8eDUFRQNhxWOPjlfipWqsS2LZsRRJHOXbqRc/o0G5YtJiUllRtuuqXIndDCqYwMPt68CV3XaHX1tVxarz4//fgDGRknadnqakRRZN83e5AkiZq1atO2/Q04HA6CQT/7vtlD5cpV+HjzJiRZpnOXbmRlZbJp/YekpqVxw023YLFYOXrkMIWFhTRo2AiAEyeOkZuTQ5OmzTh58gQF+fnY7HY+2fYx1arX4Kabb2PHJ9vY/+03NGp8GZc3b0EwGEAQRZRQiC8/38VlTZthsViQZZlffj6Ex+3myhat+P67g+z8ZBsms4k2bdtHzkuahssVh8lkQpZlvtnzNWazmeo1awHw3cEDiKJIzVq1sVit/PjD9+zcvhWH08XNt3bAYrVdsGU4ulUqF0AwrDKm43UAzFq74zxrSYxzCfqDtGx0KTtemY4sSxAIUCElCSWsgqqy/tOvGdbjdvIKPeza+AmP9urG+IH3UuD2AtGxuUSkeZWx4yeybOWbJCQmlWgUVFWlQsVK6LpB5qkMTmWcRFM1KlWpElWmUsMwCCthRo4Zx8o315CSmhaxHCAgihLbP95M1+53gwHr1q7hzq7deWzik3g8nohPXRQQCoWoVKkyK994l+SUFAoLCkhNK4eua2iahsPp5Nt9e1m96jWeeGoaQNS5tKiqhs1u57XX36Z+g4bknM4mJSW1SJtq8NmO7bS86mpSUlIJ+P10uL0Te3Z/RcDvjxqXE01TMZlMLF35Bk2bXU52dhZJyclIkoTbXUj9ho1Y+ea7mM1msjJPcfLEcepeWg/hX6oxDofDJCYmseLNd7mkWnXycnNJTU3DwMAwYNvHm+nUpSuSJPH+2jXc0elOJj45JXLvxTiHUChEuXLpLFm+mofHjkdV1ai7x2NcXARB4NrWbVi1Yhlz58zjuwMHuLZNW5aufBOn08XG9R8wfuwoklNSOHhwP73vu5u5z84kNzeXKZMnsmzxK6SXL88n27YwZuRQcnNyeHn+8yQkJHDfg73xeiOuvrIsEwoGGTLgIb7e/RWKojB8cD92bN+Kw+kEQJJlgoEgIwb359Exozhx/DjvvLWa/g89wJxnZpCXm8ucZ2by0gtzSE1L49Chn+je5XaOHjmM2+1mYN9eHDywn/iERCaMe5j9+77B6/UybGBfdF0nNyeHgQ89gM/nRVHCDOnfm8fGjubkyRO8tXolA/s8yNxnZpGXl8uzM6ex8KV5pJevwKYNH/LsrOnYHQ6cThfbt25m1rSnSU5OYf++ffS+724WvTyf3JzTPPn4eAb3683bb75O1qlTDO7Xm693f4ndbgfDwOVyMXvGFN5b8zZxcfFYLFaeGD+Wgwe+Ze+e3dx3dxfc7kKOHz1Kv973kZl5CvNZFgyXy8Wil1/krTdex+Vy4XK5WPrqIlYtX0q59PLs2rmD4YP6oSgKe7/+isH9e+P3+5DlC0v89Lec0EeMGMHmzZvPeU8QhJKaIVOnTqVNmzZ/R1P/L96gwo2X1aZbq0a8v/t7vvjpWMwK8gcYmk65pAQkUUQ0SRRm5zJ96Vs8Peg+UDWCisKbmz9lzdbP8fgDFHi8rJg2kor2VkTLFmwYBqIoUb1GTVRVRT0rmNvn9XJn1+689+47dLr1BhAEqlWvzp1du58TpxANCIJA9eo10TTtnIB0TVMJhoJ89ukO9u3dg9/vJyfnNBMmT+HOjncQ8PtKuecRdF3HFRdHYlIyggCKEuKZGdO4s2t34uMTCIcVJo4fS5/+g6lZq/Y5wd7RgmHoWK1WqteogabpWG02Zkx9kuvaXE/FSpXJyMiIrDNNRVXDJCWnEAwGCQQDxLucpd19IHI/mC0WqteohaapOB1OZk59mhatrqJater4/X4SEhLYvvVjZs+YQm5uDq+//f5F9QOOJnRdx+FwkJCQgG4YSJLEzKlPcfOtt5OUnITP5+XLz3cx6GBksz2dnc34J56i4+0dSrvrUYeu68TFJ+B0Ovlm7+6YABIDj8fD0BEPExcXz/PPzSbg91O3Xj2GjRzDFVe2jLhgFbkGCYDd4eDxyU/ToGFjypVL58N1a3l47HheWfASXe7qwYQnHqN12+sZNqgfXq8XURDQMHA4nLzy7ot4PR4WL1+N3WZHUzXmPjuL69qcSZokCAIGBg/06kOPe+5jy0cb6XlXZ95cs45r27Sldp26LF70MuM0neNHj9Kte0+eeGoaqqqy5+vdrP/gfZ6ePpsx4ybw3DMzSExMomv3u7m2dRt2f/klVputyOoSaevBh/rRtfvdbFz/Affd3ZU16zZy9TXXUr16DVYuX1rkbh1J1GIYBgYGsmzCUvQawOWK4/FJT1G77qXI8iA+3ryRjVs/JSkpmVOnMti25SOuvuY6/H4/FquVO7v14MN179Fv4GB++P4gWVmnuLNrdz5ct5ahIx5m+Kgx5Ofl0r51K77Y9Rl333t/yfwYhhFxDzObS9ovdhcTRYEXX5jDHZ3uZPjoRwgGAtx2Y1vWvP0GLac8eUHr42+xhNSpU4eWLVvSqlUrWrVqxVVXXUWLFi04ePAghmFQuXLlv6OZP0QSBWRJZNIbm5mwalMsI9afQYCwqiJazGTmFHDVfaPo0u4q7u96C/j9hMMql5RPZfXsR9m5cg4tGtZlyLSXMMnSOX6W0YCihEp8IItxOJwsfHkeoWCQWXPmMXvOPBQlzMKX5uNwRMeh8Wx+bwwIApqmk5iYxMxnX+CNNetof+PNTH78UQxDR4giH3FN0yLZhVSVe3t0oXqNGgwZPhpd13n+udlUrVaNvgMGEwxGAqTtdkfUWBCK0XUdwzCwWq0M7PMgXo+bSU9PJxgIIMvyWdlDBDRNRRTFIpfA6Dlw6bqOrmvY7Q6GDurLyZPHeHr6bILBIGZz5LlYu25dpsx4hptvvZ1JE8aV+Pv+G9E0DQQBSZToff/dWG02xk14Ar/Pj6bpxMcnMOOZuax+Zx233HY7kyc8GvmfGOeh6xqKEt0ZFGNcPDRNw2q1MnzUI7z7wSbmLXiV+PgEet3bnaysTMwWS8keEA6HSU8vT1JSMllZmZFsdZKEpmvUa9CAb7/Zy08/HmH71o9xOp1YrbYSRwBJksg4eQK3282YkUPp8+A9fP/9QerXb0gwGCxpQ9d1nM44KlepQnZ2VkSZlJRMYlIS2ZlZOJ0uQMTjcXPNda2Ji4un1709GDV0IMeOHsHpdFJQkE+3HvcQCobY9vEWho18mIKCghKXr+J2XHFxVKpcuagdleTkFBITEsnKzMLpcqFpGpqqRjyqhXPjVop/R1XDpKal4XS6yDl9GkmSqFS5CgIiBfn5xMcn4D8ru6Hf5+PmW24jJ+c0J0+eZMcn26jfoBGpqWm0v/Fm8nJzuL9nN8aNGUlBfn4k1uUPEAQBURTx+Xzk5eXyxee7GPDQeRko8AAAIABJREFUA4wYMoC0cukkJSajX+Dz8G+xhPTv3/933x80aBC9e/cmOTn572jmD7FbzGw78CsffP0DTqsZmyVWJf2PMAwDe5yTw8cy6DR8Mj1vbcO4UX2g0AMWC+VTk7gkPY24yhVACdPuisbs2PdtxF3rAoKQLipF2og1b79J9x730u6GmzCMiH/jogUvMvmJx6PuAHw+BrIkk56ejt3hpEbNmvj8PlpedTVvv7GaYDB6AlUNw8DhdOIuKGBgv17UrVuP6c/MJawoZGdnsWLZEmrWrs1dnTuQm5tLVlYm4x4ewYKFC0iITyjt7gOUCB8AQwY8hK7rvLn2w0jSAMOgatVq7P92X0RzZbVx6NBPJCQm4nQ40fXocO8r1mRJsszIoQMoLCxgzfubMFssKEqId99+k1AoxIO9+1K9ek3SyqVz/TUtOJWRQdVK6aXd/YuOYRjYbHYUJcTQgX1JTExi7osL0FQNQRQol56OLEnUqFULn9dHy1ZXs2r5awQDASA61m2MGNGIKEkooch91X/QUJo2a07zK1vQtFlzmjSoxaGffihRikDksKtpGqqqIptM6LpW9J5O48uasXjRS8yYOplgMMjsOfMjKcbDCjarLZLlUxCoXqMmc+cvoCA/H6/Xw7GjR1F/k3XLMPRzUvjruo6qqiXtCwLEueJ4dMxIfvzxe6bMeIaaNWvTt9e9+P3+IpepLei6Rs1atVnz9pv0vPeB886buv5H7RQLGhFlls1mQ5ZMSJJU8lvF3y1WehiGgaqq6LqGJEklc1RMOBymcpVLaNCwMevWrmHXpzu4s2t3TGYTj4wajsPpZOz4x0kvX4HvDx4oSaFfHCAvFaXWN5lMWG02ZCmS1MnQjRKXqy7dutPhjs4EgwF+/fUXDN0gEFJw2v96YpP/6emlcePGaJrG0qVL/5fNlGAYBlazTLLLjsUkxwSQP4HFZuXLfd/TuOtAaletxA1XXc7mD7ey5bOvUQJBhna/ndnL32X92o/4/uAhpi95ixtbNcVqMUft/Pr9/ohLFpGg9StbtGLl8qXs2L6VXZ9+wopliyMBZVJ0aa+LEQQBv98fcY8RBILBAHf1uIf33n2bZUte4eeffuLF5+fQrPkVuFxx51tOSgmLxcqxI4fpdNuNYEDHO7uyaf0HbN60gVAwyIuLltC7zwA6du5K2+vbIwpiJKjNYkGLkjGYTGYKCwvpfucdfP/dAXr16c/OT7bz0Yb1nMo4Sacu3dj/7Te8sWo5X37+GUtfWUinLnchSlLUxOaYTCZ8Ph/3dOvMl1/sok//wXy+61M2rV8X0XxZLEyaMI71H77PoZ9+ZN6cZ6hQsSLp5cuXdtdLBbPFwunsbLrccQtZmafoed8DbN3yEZs/2sDp7Gx63vsAGz5cx5JFC/jl55+Y//xzXNasGXHx8aXd9ShGQNd1fD5fVD5jY1wcDD1iRSzIz+exsaP56svPOXhgP1OenIjJZKJ23Xr4fF4CAT+CIKKqKn5/RKsfCSIPE/AHMJlkvt79JYmJSSQmJlGjRk0KCvKLfj+eb/ft5acffqTDHZ3Zt3cPG9d/iGEYzJj6FC/Nm4vLFXemT4aB3+dD13UE4dx1KggCqqoW9UcgKyuT8uUrUKlSZT7atJ4vPv8Mi9WKx+Nm3MMj6NWnPzOfe57ZM6bwww/fYTab8Rf9lmEY+M5pRzuvHb/fj27oVKxUmW/2fs0Xuz5j04YPWLb4FTRdQxTPnxNFUQgEAiXWklAwSCgUOk8Q6XJXd2bPmMrp7Gyubd0Wr8fL6ewsqlWrTnJyCmvffosfvv8OWZIjAemSzBef78Lj8VCpUiW2fbyFA9/uY+VrS9mwfh2iJGK322l/480sfGkep06dJPPUKfr1upfvDh7A9icsKr/H/zxqu7CwkIKCgv91MzEuEMEks/vAT9StVhmPz8+wqfMxAJfdRoMal3Bj25Y8M7I3k15cjigIXHVZPaYNf5CCDwIQH33uTLqu0+zyK0hLT0cNhyPpSEeNQZYlJowbAwJc2aIVg4aNxOuNjliK36JpGk2bXU75ChVRw2ECAT/Nml/Bk1NmsOTVhQgIVK1endFjH4sUmYuSTd5sNvPD99+RmJSM3WFn2lOT0A0dAYEnp87gyhYtUZQwZouFX37+iYMH9tO6bTtk2YRhRIkQYjZx9PCvaJpGlUuq8czMqWhqRAM1fPQY2ra7kWmznuOVBS8CcM/9D3JXj3twuwuxW6PDMmgymfjpxx8IBPzUrFWbuc/MLEnCMGjYCDp3uQu3283L857HMCJufs8+/xIm07/TfdVsMvPzoR+xWCwkJacwc+pTRe5ZMHb8E7S86iqenDaLxQtfYvmyxVxySVVGj30sUoQvLhZz+Hvouobd4aBFy6uQ5ZhC8N+KYRgoSojn5r3M3GdnMnnieERRxOV0MX/BYpKTU0hOTqHxZc0IhYKkpKbStFnzEgtC+fIVuKxZM3w+P1arFVmWkWUTeXl5jBo2kCHDRzNwyHDGjh7BqhXLGPHwI0yeMoNXFsxn5bLFOJxOJj45paRgZnFBwStatCxKsRvG4XDSomUrZDmS6jY5OZmmzZrj83kZ9cijPD1pAoP69aZ2nbp07daDatVqsGXTRppdfgU33doBs9lM97vvZeOHH9Dxzi40b9ESWY4cra9s2QqrzUY4HMbpdNGiZasSS0NKSirNLm+Ox+Oh3Q038dWXnzNx/CNUrFSZ2zt2xufzEQgGSU1No+nlZ+bkkqrViiwqkTTCteteisVqPSemz+/3cXnzK7n8iito2epqHE4nSijEhElP88yMqfz4w3c0atyEO7veRXxCAkpIYfTY8Sx8cR47t2+j/+Ch/PLLz4weNohL6zegx933US49nbzcXAYOHUEoFOSRUcMAgbt63MOtt3fE6/MT7/rr9fgE4294Orz44ovs3r37HLcQXddLaoXs3r2bZs2a/bfNlHDblCVs+fZnXBcxR/vfgd9QuUxKYqmjNekdszAlF2CopexKYxiYTDKiyQS6Hkl4JUTeL66abnE60AJBlLCKLd6JHvZz4o00ChMTCN1qQQhG1wZjsVpRw+GSg5csyxE3ocJCDMMgPiGBwoJCKldIRVU1TmSeLjE5RgsWi+W8AHWn00UoFNF6xMcn4PV6KZ+WhAAcPZkVyW5WihQ/4M1mS5F15sy6UBTlnIOIKIqYzWa8Ph+V0lMxmWQOHztV6mMo7lskUFI/p8/hcBhN03C6XAQDAVRVjWj5Cgool5qIw2bl56Mno2ItFaeXLNbIFRMuyn8fFxdHIBAgGAgQn5BIKBTEZjFRq9rFid8DOHEqm5OZOaV+zY2iYHSLxYpu6OcI9Uo4jK5puFwuQqEQoWCQ+IREvD4vSfFOqla6eNajX49lcDqvICrW158hco9bCIWChMMqVSqUo3y5i+OaDfDjr8codPuQpOhwV/23YhgGJpMZh9OBx+1GVcPExcWjaTo+nxeT2YwsSQSDwRIhIxQKRvYTkwmL2UJBQT63tL+OFxcs5vobbkINh+l0243UqFWLabPm4Pd58fsDkUQcLhehYBC/309SUhKKopxjKRAEAYslUoND1/WiYqTmktfFfQgGA1itVgRBwONxk5CQiGEYhEIK4bCC0+kkEAgUuXPaCAaDGIDVYiEYjBRNLK71cXY7wWCwZK8sbqe40LLHU4jNZi+JPQyFQufNidlsRhDFEjeq4uf8byudR+of2dA0vUQIs9vtJZaehIRENC0SvxURxhyAQCDgx2QyYTKZ8Xo9OF0uAJRQqKSeisPppCA/H1EUS/bAWtUqkhDn+svr42+xhBw8eJBPPvnknAmQZZlKlSrxwQcf/K0CCESyYAV9AfQypl1RDBWvKQQOCJy0oLidGFrpxyQICL9b8sMwIppRHyAKdgQBAsd0BNGGHpQxiBS6EbToug4Bv7/oHBHpl6IoKHn5RUKyQH5ePuEiAUXXDVRNi7IQe/AHAkXdPzO3hYUFCKKIIIgUFBQQVlUMinxEi/xYSxtFCRMOnx8bETkInxmLpkV8YzVNQ9eNqBpDpG/nW8mKx+AuLIxcBwTy8/MJa+q5Y7j4XT6PyBjODxQsHkN+fj6CKCLKMoXuQlRVxWq+uO5FJfdeFEyYrhv/7zUvLL7mohS59zQVI+6va/3+uz7qUbO+/gzF97hhgKppF32/1nS9yI8+uvanfyPFyjNBFBAEiUK3u0TYV0IKSpHiU1HCKOHwOZ+FlTAOp4shw0Yzc/oUli15Fb/fR0pqGn0HDiU7O+tMkgjjzPPZZDaf084ZBDTtzBkhcu+rJa8je1gkhsTvj7iJybIZt9tzzjnJ4/WW/LbXV/TsMMBf8tsR1/A/046iKCjhMKIgnvmfs+bk7EryweKq8EWvA0XCyO95RHi93qJjhFHyWhBFJMlEYaG7qI1If7xeH4IQ+aqmFbt4ieemIi+KaynIL0AUJYr3ElXTLtja+bdYQi42s9/bwcHjWVjLWA0QBZ1KQQs9fy6PFhbQjWjLL/XnMABRVRGvtCN1SIBQmVtC6LpOWkoimqaTm18YNcHdfwVd10lNTgTD4HReQZkdQ3JiPJIkkp2TX2bHkBjvwmIxk5mdW2bH4HLYSU+7eJrq3PxC8grcZXa+EuJdpCZdvMD0rJw83B5fmZ2vlMQEEhP+uqb0QjmZlYPfH4xVTP+H4IqLIy83l+ysTOx2B5WqVEEJhVCK4j9jlB66blAhPRmH7a8Hpv8tQsjx48epVKlSiSUkNzcXj8dD1apV/9uf/l12fn+E7EJvmTFLF6MJBo5cnarrQggmA6MM3zmaWyPuulTS7q6CrpY9IQRAlqQia050xCNcCMX3gFqG04VKkoRAWR+DWBRsWHbHIIoCJvniKXZUTSvT954kihfVlUxVtahJ4HAhyJKIdBH37LCqxqq1/8OQJAlJEtH1iBdGGdSh/2MxydIFKUj+qx3n+PHjdO3alZ9//plTp05hMkXSd61du5bevXvTs2dPXnnlFSx/cyrXx1ZtZPuenzDZy1ZMSNhQaWRK4Z2UG0lpnYOc6C79mJALQJB1sjekEPRp5Po86IGytzHqukFinANN13F7A2VSW6brBgkuJwY6hR5/mR1DnNOOJArku31ldgwuhw2TLJNX6CmbYzAM7BYzSQlxf/zlvwm/P4jbV3bvPafdSkLcxUvO4fH58QVDiNHgv/YX0XWDeJcdl+PiBfK7PT6CSpjoT8MeI0bZxjAMkhLisF1Abb4LFkIURaF9+/b4/X7mz59fIoAAdOjQgTlz5jBy5EgUReGNN9640GZ+F6fVgtVpK5OB6S4p0mfJpiE71DIrhAjSmRzWZXFTRDhTEEgs02OI/FG2x/BPuA6RmjRldgxw0Q9r/4RrflGbjM3XX2uyaK5iQkiMGP9bikNYLoQLFkLWrl3LoUOHOHXqFGlpaed8lpqaytChQ2nUqBFt2rRh//79NGzY8EKb+kdi6AKGJkRFYPpfRhBisX4xYsSIESNGjBgxLpgLVsOvW7eOm2666TwB5Gxat25NlSpV+PTTTy+0mRgxYsSIESNGjBgxYvzDuGBLSGFhISkpKX/4vfj4eNxu94U2c0EEwyqqpiGLElZz2cqgFSNGjBgxYsSIESPGP50LtoTUrl2bffv2/b/fCQQC/Pjjj9SuXftCm/lLGIAnEKJaWiJtGtSgRnoS3qASy6AQI0aMGDFixIgRI0YUccFCSI8ePdi3bx+LFi36j98ZOXIkkiTRpk2bC23mL+ELKoy6/Vp2zxzCB+Mf5KsZQxjT8Vr8SjgWwhAjRowYMWLEiBEjRpRwwb5KTZo0YcyYMfTp04dNmzbRpUsXqlWrhqqqfPfddyxevJhPP/2UVatWER//v6/Eq6gaFZJcPHX3DXy8/xf6zV3NnAGdeeKu9qzcsY/sAg/mMlbc8GIjigICArqhlxTfFITfKagYxdlGRDFSr0HX9fMsYMU5rPUoz7X/Tx+DIAiIonimym2UUtzP346hOJuXYURuBV2P3nH8pzH89rPfq7b7b+T35uvs6x15DYYR3fdftBCZt9jaihGp8WE2R1K4KopSUufDbDYjy3JRdfK/drYwDKMkM2s4XPrpmEVRxGqzEfD7y9y6N5lMaJp20c8W/9WpfPr06VSuXJlJkybx5ptvnvNZrVq1eO+99+jQocN/1cG/gq4bKKpGrsfPsaw88r0BFFVD1bRSX5zRjs3pIBwIElQUXPEutJCCooSRZalo7gTAiPwtaVFpWYqLiyMQCBAIBEhMTERRFILBIIIgEB+fgNtdiGEYxMfHk59fWNrdPY/ivgWDQfx+PwmJiYSVMMFgoGgM8Xg8HjRNIyEhgYKC6BxDXFwciqLg8/lISEhAVVUCgQDAOZ8lJyfj9fqKHtbRc38ahoHT6cQwDAoLC4mLi0MURbxeL6IoYrZYMM56UEdjQTTDMHA4HAiCQGFBAU6XC0mS8Xo9AGc+KywkITERRQlj6Gop97r0MAwDl8uFpmm43e4SxZnP50OWZQRRLFmhgiAgSdF3zaMNh8OBrusl936MfyfFz6JgMMjXu3djGAYNGjbA6XQSCAQ4ceIEuTk5NGzU6C8rpiRJorAwsg86XU6MUnwWi6KI3+/n691f0bBRY0wmU5kRRARBICcnB5vNhsViuaj9/q9NA4MHD6Zv374cPHiQzMxMJEmiSpUq1K1b9+/o35/GYpI4kp3PpNWbebrnjXReOwtJgAmrNnEkO5/UeEdM2fcfsNltvPrWh8x74wMEQaBWlQrMGdOPlJREBkyay75Dh0ty05tkmUZ1K/PYpRPxRdHB0el0suK1Zcyf/wIAVapcwoxZs0lNTcNisfDC83N4feUKdMOgx913M2DgEAQhuhaEy+XizTdWM+fZZzAwKF++AjNmzaZixUqYzWYWvvwSS5cuQdc1Ot/ZhWHDRxVpZaNnHC6Xi/feW8us6dPQDZ20tHJMnzGLS6pWxWKxsO7993j2mdmoapgaNWry9NTpVCxfLqosO06nky8+/5yJE8bjD/hxOBxMfmoKLVu0IOPUKfrf3Z2CggJEIWI1HD1mLF06d0RRwqXd9RIcDgd79+5hwqPj8Hg9WK02npg0mZatrkIQBPbt+4YJ4x/F5/WSmJTEk09PpWGDeqXd7VLD5XKx9eMtPDV5EkpYIT4+ganTZtCsaRMeeeQRdu74BFEUMQCz2UT5ChVZvHhJaXc76ijWbCcnuHh54Su8vmolryxeitPlKu2uxSglbHY7Bw8eZFD/voiShMkk4/P5mDZjFjfd2J6VK5bzwbr3eeOtd0r2Ml3XMZlM6Lp+jmBSbKk0DIiLj8MqC4wfNxZRFJg7bz75efkAEcWBIKCq6u/uj8XWTlmWQRBQw5Fnd7HgoKrqOW3KcuSoXPx7xdbR4n1LFEUsFiuHf/2VRx4ezYrXV1OxYiVUVUPXtaL/F1DV32/nt7939uszllgDWZZL5kQURSRJ+l3rRfH7Z4+3eF6Lx2MAWlH7FouF++/tyaDBQ+h8Zxd8Pl/J93/7/2fPyW/n6kL4Wyrlmc1mmjRpws0338wNN9xw0QUQgICi0qBKOg+2vZw9v55k+MvvsOvHozzQ9nIaVS1PIPTv1fL9f9jsVnbs/pbek+cy7sGuvDbrUY5lnmb07EWIJhMPdbqBKYPu48kB9/DcuIGIokCh14/NYo4aa4jdbuerr76kb5/eDBw0mOXLV1BYWMiYUaNIineyaeMGHn/sUSY/PYXpM2Yy+YmJrP/wA6yW6Cl2abPZOHBgPw/cdw8P9n6I5StWoOs6I4YNJTHOwSefbGfMw6N4bMJEnnluLjOmTWPNmrexWaNnDBaLlUOHDnH3Xd3o0bMnK1aswGazMXhgf+LiHOzfv58+vR7kvvsfYPnyFWRmZjJ+3FisFjPRYgmRZZnCwkI6d7qdy6+4ghUrVnJZk6Y81OsBwqrK6dOnOfzrLzw+cRJPPPkUj0+cRL169QmGlNLuegmSJOEP+Lmz4x1cWq8eK1as5JprruGhXg8QCoXwuD306NaVyy+/nOUrVlCpUmUG9OtT2t0uNcxmM5mnTtGlc0fa33gjK1as5JKqVenbpxeBUIgu3boxYeIkHp3wOLOffY6UlFSyMjOx26yl3fWow2Kxciojgw6338HECePZ8/VuVFWNeSL8i7FarYwZPZJatWuz64sv+Xj7Tq6/vj0D+vYhv8DNXd178OqSZYRCoZLDtcPhIDPzFEoohN1uRy8Sbu12Ox6PB0GAqU8/xeq33kGSRHw+X8lhPT4+noKCfE6eOIHdbsdsNp8niEQsn3Hk5eVRkJ9PXFw8DoeTrMxMfD4fdrsDwzCQJAmXy0VWVhYZGRnY7HZMJhOBQCAixJhMiKKIKIrk5+dStVo1PtiwibS0ciiKgmHoZ9opKG7HQWZJO/aS/pwtSJz9Wtd1RFHEZrORmZmJqqokJCQgCAKnTmVErPNnjVHTNBRFKRF0igW3YDCIyWTC4XCQkXGS01lZOJxOJEkCwF1YgKIoJcKPfo61/8zr4utw8sSJyNzFx/9X9/c/JkjCH1Ro17gWNcsn0/O511n56vtkuH28PeZe2jeuxTNHd8TS9f4OqhaJ/3jx0UF06XA9WC0M7HoLkxesgmCI5vVrR5ygzSZys3M5lnmaBY8PwP9NCOKcpd19gBLNwMzZz3Df/b2wyNCv/wDGPTIGgOWvvUbXbndx6003ANDpzi4sW7qEbl06ES2HX103CIVCTJs+k4f69sNmEhkwcDADB/RFA15fuYLbOtxOpztuA+Cee+9j2ZIl3H/P3fxO1E4pYRAIBHh6ylQGDBqC3SIzaMhQenbvhqrqLF3yKu1uuIEB/fqgAx+s38iB/Qfw+oOI0TIEwO12M2z4CIYOG0liYhxDh41gxWvLyMzKprCgkFq163DLrTeRn+8mJTGOQm+QsKphiZKYM0EQcBe66T9wEIOHDCU5OYnBQ4axaMHL5ObmsnPHJyQkJDBzxnQM4OVFC9jz9Td4i5QL/zYMw8Dj9TL20ccYOWo0TqeNocNG0L7tdWRlZdO48WVFWkgTwWCA7747yHNzXogcDqS/RY/3j0EQBNxuN82vuJJ+/QcyZNCAc1wXY/z7EABZkvH5fQSDIVx2C09OmUqH22/HZDKz9t21bNzwIStWLGfte+t4ZdFCKlepwr5v9uIudDPn+Rdo1aoVmVnZjB0zGl3XOX78GCnJqXTp3JEtH32EJEkIRKzYs2fO4P331xIXF4/VamXuC/OJi4sjHA4jyzL5+fkMHTyQ6jVq8N2BA5w8eZLBQ4dy4sQJdu74hJzTOUydPpObb76JQreHfn16c/jwr9jtdpxOFy++9DIZGScZNWIYq1a/Rbly5eh0Rwfuf6AXza+4gn59e7Ng4asIgkDfPr24tO6l7P/2WzIyMhg6YgSHf/2VXZ99Ss7pHGbOfoZbb7mJefNfYvdXX/HSgkUIAry2bClbP97C8uWv8c6a93j1lYVcUrUq3+77hlAoxOiHx/D2W29x/PgxZElmyWsrSElJQVUj1o+7u3dl5KiHua51G0wmEyNHDKNcWjn6DxjAA/fdg8/nQ9c0atWuzfSZsxFFEVmOCFRWs8SYJyeRmpLKmHHjAHhs3DgsFjNPTJrEqcxshg4eiN/vx+12c8uttzJy1MMXLIj8Y56gZpPMwWOZAEzoej2jh3fnie7tATh4PAuzSSrN7kUtSjDE1U3q0b9HB0KFHtA0Vq7fznXNGoDZTCAYIhAIgiQxYd5rXFG/NrXq1SIQRZrfQCBA06bNGDR4GAUFEXPs6tdXcWWLFgAcPXKYupfWI6SBohnUrVOX48ePAdEigkAwGKBBg4YMGzESd5GP6+urVtC0aTMk4NdffqHupZeiaBBUderUrUtGxkl0w4iaPAHBYJA6deowcvTDeD0eBCLCU8NGjbHIIkePHCEuLo6HxzzC9W3a8vCokVxS9RKkKDrIhcNhUlJSeGzCxIgZXYC33nyD9PLlqVKpAnl5uezZ8zUdb7+Dm29sR8dOnTlx4niRNSc6UNUwCQkJTHh8IqIoYhJhzZp3SExO5pIqlfn+++9JK1eOp6dO5/rr23FPj7uJi4/H5XSUdtdLBUVRqFSpEo+Of4xgMIgErH59JdWr1yA9PR2fz4fX68VkEpk9aybp6em0bdcOfyBY2l2POoLBALXr1OHxxx4lKSmJUChU2l2KUcoEg0GmTJvO0SNHqF+3Nl273cV7a9/l6muuxW63cvp0Nj//9BOyJBEIBNi0cQMtWrRk/fqNNLu8OVOefgqHzcITjz+G2WzmtWXLuP/+XgSCfho2bIiiRNaYSYSPNm1i6dLFvPPu+3y0aSOuuDgee3QsTmdEYVp8UP5k+zYkUeKtt9/m4UfGMnzoECpWrMSHH67npptvYeb0qditZta9/x6f7/qMD9ZvYtPGjRw6dIhXX32VVlc2p3r1GkyfOoVFCxdQkJ9Px463U5Cfz/cHD6JpGgLwybZtmMxm3l7zDsNHjWLooIFUq1adDz9cz/Xt2zNzxnRkSSInJ4cjRw4jSRKyLJOXl8fhw4eRJQm/38fHWzbT9vp2rN+wgSpVLmHo4EEMGTqcDzZsoqCggNdXrcDhdKLrGsnJSZQvX4FFCxfgtFs4ffo0a9es4eZbbmXhwoXk5eWxZctm1q77kA/Wvc9HmzbhsJ+x6oqCwInjx8nMzESWJWRZ4uTJE2RkZGCWJR6fMB6Xy8VHmzbyzrvvsWzJYjZuWI/FbLqg9REdqru/AafVzM7vj9DvpXcYfce1zOzVgZ9P5TJo4bt8cvAwrihyW4kmBEEgpISRNA1LYhwTpr3ELydOsXzaGML+SECh2Wwi4+hJ3t32OZvmT8YIBBGE6Dl0CYKAoiioqkpKSiKPT5zE3j172L7zU4JhDSWsnGOuNJnNJf6SUXJ+RxAEwuEwbreb1JREpkybwfbt29i2fSdh3UBRzhqDEbkmuq5eYw3bAAAgAElEQVQRTTHdxWMoLCwkNSWR5+a+wLr332PLtk/QjIjFaueOHQwZOpzr27Vj8hNP0OuB+1j3/vuElOgQaotN0fn5+aSmJvHWmrXMmDaFt9e8BwZUrVqVvn37c/Mtt5KalsagAf0YPLA/W7ZsjqKYkDNjSElJYt2HG3n8sfGsXP0GNosJw9A5sP9brmvdhjFjxrBk6VI63nYLu774AqvlwjaSsowgCGiaRkFBAakpiSxZtpxFC17mww0fAWcy8OSczuW1pUuYO28+4XA4qmKxooViP/xCbyAqshXFKH38fj8tr7ycT3buYtPGjWzcsJ7hQwax4rVlvP3OO1gsFizWyCE4FArR6LIm3HPvvYgiXNmiBT8s+Q6AzFOnuOGmm7DbbdSrX5+fp/1MSNWRJKkkLuHTT3cgCiIvzX8BRQnj9Xg4dvQIoVCoZC3quka5cuk81KcviUmJ1KvfgMpVqtCtW3fi4uO4+tpr2bb1YwrcPm66+WYqV67MqpUr8Hq9qOEwPp8XzYDn573I9a2v5a03V7Nz1xcIooRhGFhttsh9oGmkly/PQw/1JTExkfr1G1C5cmW63RVp55prruXTnTuAyH5eHBBuGBG3YEuRu7iiKDRs2Ig77+yMJEC9evUJBYO0vvYqDODy5s05fuxYiTdBIBDkob79ePD+e3H7gmzftpX08uk0aNiQpORkLrusCfPmv4TH4wFBwOv1IP3mNjWbzUXuXGdeW6xWDGD3V19St+6ljBk7DovZgq7r7N2zhy6dO17Q+vjHCCEADquZJVu/5q1dB0hy2sj3BXD7gyQ4bKXdtejFAJNJRnbYGTzxObZ99S1bX5lOYpyTgNcPAkhOB9NmLeSK+rVo0LAuIU8+EFfaPS+h+JAQHx/HqBEjWbfufTZ8tIW0tHKAQGJiEidPnsAkR+60rKxMEhISIv9biv3+LZIkkZgUz6PjxvP6qpVs2LSZylWqoOsCySkpnDxxEpMsoCOQlZWFyxWHJJ5JHRoNSJJEUlI8kyc9yaKFL/PB+k3UrFkLzYj4Bt9y220MHTIQAJPJQrcunSn0eEp8Y6MBQRBITUnk1VeX8siYUSxfuZprr7uW3Nx80stX4LHHJxIKhbDbrYx5ZCw9e3TH7fFht0XPc0ZAICU1kddXrWbIoIG8sngpN954A4oeCRSt36Ahjz/2KGED6jdsTP1La/Pzz7/QvFmT0u56qSCIIqnJCcyd+zxTn36Kt999j6bNmpGbmwtAfEIcU558inLp6dx4403k5+fHYkJixPgDRFFEURSWLFtBm7Zt6XxnZ+7q2pkfDo3jsob12PvNvnOem4IgYOg6Pp8fm82OoihIkoRhQN/+Axg9cgT5ubns3LmToUOHY5FFwmG1xJru9/spV64cjZs0IS8vn8uaNCE9vTzBYPA8pYHP5yOsgVIUi+L1ekhOSSZcFMNks1l5f937PDNzBrd1uJ3GjRuTmJRU4v5sFOVo13UDXdP/o1v02e3ouo7H4yEhIeEcRYauR4SpSHwJWK3nzomu6/h8AewOG+FwOJL5M6wjSSKqqpakPoaIZ8jllzcnLS2NHdu3s2njBu7o2Am7ReatN9/grTdW06lLF2rVqo2rKGFE8cycHYAeCdoHkxAJXMcA3QA1HKZ2nTo0aNCQQCDArGfnUL1adZSwekFlMKLHD+JvIr7IrHTa7cMwiAkgf4AoiSiGTq9x0/nh8HG+Xv0C5SuUQwuFQRAwm8ycOHKclRu28dhD3TFCoag6uEPk4CsIAgP69ePLL77gs8+/olatGgQCAURRpE3b63n7rTdRFI2warDm7bdo3aYtAEaUjCbikykzfMhQNm/exKeff0n9+pfi9/sBgzZt27L23TV4fUEE4I3XX+fa61oD0ZMdSxAETCYTjzz8CGveeZudu76kadPGJf6nN910C++89SaZ2XkAfP75LuLj47HbbFGVHctms/Hcc3OZ8tQkNn60hdtuuRG320NyciJPTnqC7t264nRYkQX4eMsWkpKTcNijbAx2Gy+/tIBHxz3C+x+up0vnOyh0ewkFFdq1a8+er3fz1Z5vMAmwZ8/X6LpOampqaXe7VBAEAavVylNPTeHFeS+wdftO2lx7NR6PJ+LOZjJxOjuHl196kVGjHy7RVsb4Y6LpnohROthsNkaPHMbzc5/DJEYOvAX5+QiCiN1mPye70+8HaEdcjrdt3Urb69uRklaOwUOHMXzkSCDiQluczalJk6YcP3GcTp060rf3g0iyzCuLFmK1nqsw+O26PKdNXUc3dCwmiRfnvcB1rdvw5OQnuKNTJ7xeD7qhIwkwYtgQWrZsyeNPTKJ3rwciddZE4byA7v/YjmGU7N1ms5mMjJOY5Uh86I5PtpXUIvr/gtYjr89twzAMLFYLXbrexZSnJnNg/34639mFkKrzwvNz6NOvP+PHPsJ117XG543Mm0BEeFGKPBIEQYj0R4DTeQV89eWXSLKEJMAlVavhcXvo2eMuHur1AOs/WMcXX3yOSb6wkId/lCWkGEkUkMRYDMifweJyMGvh6yx+bzOtGl/KrYMfJxAI4nLYWTJpBOlVKjBt5stc2aAOlzdrQLDAHXWrJj4+nnkvPM8rixZwefPmdO96Jz6fD5vNxsuLXmHo0KFs/XgLra+9ClmWKV++Av0HDCIUjp6MaXFxcSx+9RXmvfA8TZo25b6ePfD6vJhMJl58eRF9+/bjo00baXvdNdjsdhxOJ0OHDSekRNcYVr++itmzZtCoUWN6P3g/Xq8HQRB5Yf5LPPDgA2zb9jHXt7mW2rVrs2fvHmbMfAZZlqPGd9zpdPL5rl2MGjGMatWq8+jYRygsLAADps6YyajRo+jR/S7atb0eh9PJdwcP8tzcF6KqgJ3d7mDfN3sZNKAflStXZvITEykoKMAwDMY/NoE7OtzKoCFD6XpnJ5o1bcqevXt5bMJEKlWqWNpdLxVcLhcbN25g4oTx1K5Th2FDB+N2FwIC02fO4tqrWzHxiUmUL1+e2zrcgdvtLu0ulw3OOmTF+Hei6zp2u52Fryxm5PCh7P7qK+Lj49i7dy+Dhw6jYYN6bNmy+Q9+JbKG0tPTWbRoAR6Pm4KCAua/8DwvzJtP9x496NenN3Oef4F+/fqxZfNHXNPqKho3bsTmzVsYOfrhkgxQZ37SOOvP31mnBoQ1nQd79WbqlKdQwyE8Pj+KopCYkMjmj7fz6ac7+WTnZ6SVS2P166t49tlnuaNjp3PcK4w/aodIZtdbb+vAi/Pm0bZtW2w2O6ezs0lKTv5Tc2wY5ysivR4vXbp2Y+yY0dx+R0fq1KlDOKzSq/dDPPvMLPbv24vH60MqqQMHHTt1Zsa0qdSrV4++/frTvVtXbrnlVkRJIhQKYjaZ0IGp02dy3z096NHzHsKKwqFDhxg6fCS6biD91q/rTyAYZfApcduUJWz59mdctrIV5+E3VC6TkljqaE3ardmYkgox1NI1RomSxMms0+QWevAFQiWxErIs0bRuDcxmMwd/PkKF1GQSE1zoqoYg62S+l4ZWqTzOnqkYgdI9fMmyTEZGBrk5OfgDftQiX2RRkmjUqDFxcS4UJcxHmzYiiiLXt2uPKErYLDK6YeDxBhBLOT2TLMtkZp7idHY2gcAZf2pBEGnYqBEJ8fGomsZHmzah6xrXt2uPyWTGYpJAALfHHxVjyM7KIjMrk2AwSLgo3R8INGzUiPi4OGSTie3bt5GdlUXzK66kWvUaGJqKLAkUuH2lPgZJkigoKODYsaOEw2FCRcUuAWrXqUOlSpXwen1s37YVn8/HVVdfQ4UKFdG1MGaTTH6hNyrG4C4s5MjRI6hhtaTYJUCt2rVJTU3DZrfz1ZdfcOinn6hXrz6NmzQhHAqSGH/x6jl4vH48vtK/9yRJIjc3l4yTJwkpIZSz/Mfr1W9AYkICP/z4AwkJCZQvXx5FCaPrBg67lXjXxQvmL3B78QdDJTWbohlRFAkEAhw/doxq1asjihIupw2n/eJ5JuQVuAkpsbiU0qa4gG1WVhZffL4Lv99P/QYNaNSoMX6/n5yc0xQWFlKjRg3y8wvIzckpWjMiubm5eD0eypdPp327try8YBHXXNUSgNZt2lCpUmWWv7aML3bvQVHCNGhQH1GU2Prxx2SfzqZZs2bUqXspHre7JIWvqqocOXyYylWqYLPZ8Pv9HD9+jOrVa2AymSgsLOT06WyqV6+B3e7gq6++4NdffqVFyxbIciTtrc/rRTaZqFq1KpqukZuTS1ZWJjVq1OTw4V+pVq06AL/++guXXFIVq9WKz+fjxInjJe0UFBSQk3Oa6tVrYLVaOXnyJF98sYtLLqlKrVq1ycw8RbVq1cjLyycvN5fqNWogiAJZpzIJBAJUrVYNgBMnjiMKIhUqVjynNogsy/z0448kJCaSlpaGpmlYrVZ2fLKd7OzTXNe6NV6vF6vFQlJyMpIksXfPHtLKlaN6tWrsP7CfgwcP0uSyJsTFx+P1eqlYsSJWq5WcnNNs27oVk8lEm7bXY7fbsVpMF5RdsUwKITc/tZgt3x4qo0JIMstdbUjvlIk5pQA9XPoecWaTjGD6TUCqYaAEQxHTnsWCoWsoRZYD0aST8VY6avkKOO8pfSHEMAxMZjMWs/m894vzeUuyHIk7MAz8fj/hsEpivBNN16NCCCmOa7H8pnaJYUAg4C/xGbXbI4eeQMCPEg6TGO9Cx4gKIeQ/jQEi/dW0SL5zu92OJImEQpHMay6nA0mAgsLSF0KKc8Pbfie+IxgMoqoqsixjKwo+DAaDkbSTTjsmWYoKIeTPjCHi82xDlk2Ewwr+QACbxfyvFEKKi4D91mUDIi4KxZu3YRglAa4xIeT/p3gNWqxWAv7IvR8f54gJIf9SivcGq9WKIIiEw0pJnIbJZEKWZQKBALIsYzKZCQT8QKSon9lsRgmHeeDenphMJq5r3Zq8vDzeXfMOT02ZRus2bdE1Dd0wCAWDJXuMKEooSojgWYqkojAObDYbwWCwZF+1WKwEAv6SZ4HJbCbg9xc9J+3IslTi3l1c8K+49gZE3KlEMbKn2Ww2AoFIUh+bzU4wGPjDdiASd2GxWAiHw6iqiiTLBH9nTsxmM6IkESxqw2K1wlnPppI5B+w2O6qmohR5GgiCiMNhL6nuLssymqYRDocj6XltNpRQCFVVsdlsmExmQqFgSb2RYo8Fk8mEzWYrOZ+Ew2GSEuIuKEtkmRRCOs94ja37f8FZFoUQMYkFYiusNb1IthBR4sHxlxBEcB+wIjdKw3VfOXR/2RuEoRvEx9nRdAOvN4AQTYUq/gQCoGkGLpsNQzDwRIEQciHouoHTYUc2ibi9/jJ3HSCylhwOG7IskR8F1pwLQTcMbGYziXEX71Dt9QXw+srevQeRa263W4lzXryECoUeH4FgqEweqg3dwOWy47iIwfz5hZ5IxroyOF8xzqX4sPz+e2s5dvQIDoeTdu1voE7dukXFC2PXuFQxDOLjnP8eIeSHk6dx+4NIYulbEf4KumAg5ISJ2+hF0mQ0vcxNfQmioeK6IpGEm9KjK8XUn8QwDEyyjIGBqmpl7yEmCgR/9ZL96hEMQ0Q3RMrkhQAETSWxXRrx7ctF0m+UMSJaLQkJUMNK2VtLRBI0iKKEZLp4qbdVVUPVyuC9R9E1lyI59C8WYVVF0/QyO18mWTrfN/9/SDisohs6UZPDPMYFU/yMlX+zfoKhsvm8/ecROU+JF3Amj7IQ4z/HjHe38+n3R3BYo6dWxZ9BAPJ8AZ7t3YHraqRzJCOnTFbc1TSNapUr4w8EOfjj4fMeDGUBVdOoUrEcalgl43QuchlLZGCYBaQjCqYCnfgrFETyMPQytpaEiJLSvcdF9tF8Th0PIQTLnhCi6hopKalUltxov+xFkMperQ1DC0NyRaSazS9am6dz8zl1Oq9sPj90jbTkBCqlp120NjOycsgtcJe5ZxVEnrcVy6VSLjXxorV5LCMLt9df5pSVMWKUNTRdp0aV8sS5nH/5f8ukEHIyr5BDp3Jw2cuWOxaA2xcgoITRdR0lHEYqawdHIhuKruuomk4oHC6TaRjDqoam6aiaRkgJo0tlawyGIPB/7J13eBTV2sB/07aXVJLQpUgHQVAUVFQs14JSRBRRUFERUUBEEBS86kVQeke6HUSKFEF6lyYiVTqEIiTZZLN9Z2e+PzaJoPjdK9dLNrg/nzxPZDcz7zlzzpnznrfJqorBaMB5fQRZd6FpJWw6CyAKOv7DNrwahMJhhHDJU0LCkeiJPqhoAR+CXLIORwB0NYwYvrIZyormnlyy5h4UPHM18u+/+BeiqiVzrYLC9fbK9ldYVQmFwkU1JOLEifO/IRLR0C7TqaqE7VqiKJKEUZExyiVPfFmWERFAjxYU+6MCN7GMUCQ/iAUZJ0oaUbmj+bBLZBuKZNbQghDRJXSthJ2QCqALOrpG0bMoaY8BLpgDggCiGP0paYhiNNjrClJi5x7Fs+7F++vPUZgNqST2V5w4JYn/Zo6VwLdlnDhx4sSJEydOnDhxSjJXpRKiw2WbhuLEiRMnTpw4ceLEifO/peT5M/0/CALk+0Nouo5RlgiEVUyKjMmgxCu3xokTJ06cOHHixIkTI1w1SohAVAG5o05luj/QlEppSWw5eJKBs5aTmZ2H1aCU0ASmceLEiRMnTpw4ceJcXVw17li+UJha5dL4uncHGlQqw/ZDJ2nXtB6zX22P2aCglsAMTnHixIkTJ06cOFcr8cQB/xtKSr9eNZYQXzDMXfWqYJAlXpg4l9lfraDnC60Y+tT93FW3CnM27cZpvXLVWksSuq5jMBqxWCwIgkA4HMbr8RR9ZjabMZnMAPj9PsJeb3GKe0l0XcdgMGCxWhEEATWs4vV60DQNSZKwOxwXfT8vNy/mXPR0XUcxGLBarAiigKqqeD2ei+QsfB4Guxkv54tR2ktTWARSsZqjGZfUCEGvD03X0XUdo0FBspijvpOBAIFgoLhF/h26riPJMjarDVES0SIaHk9+UYpRk8mE2WwBAQJ+P/kFcyXWEAUBo80CkgSaRsjrI6JG0OHiZxQM4csPF7e4xYqu60iSjM1uQxRFIpEIXo+n6JkbDEYs1uj66PfF5hoYa+i6jsVqRZFlcnJcxS1OseFwOIlEVDweD4IgFL2rTCYz+fnuv+Qe0WJ+MhaLFY8n/y95t+m6jtliQRQEvF7vZW9qdV3HZDIV7SEQKKqr6/X+2ifhcPgv3TgXZkb7dyUEdF3H7nAQDAQIhf588UO73YHP50VV1WLZ+IuieMk2KgYDkUgE9U/2q93uiO7z/uLn8UdcNUqIIEAgpAJQr2I666qUIyPBDkDVjJS4JeQPKNzUnjv3C5O/mkWuy0XjJk1pdntz/H4fVquVA/v3sXDBPGRZ5qFWbShTtnxMbeALF7mcnGymTp5IdlYWjW5szJ133YMaDuN25/H17C9xu/OiA0XXueue+6hUoQyq6itu8YFoG4wmE3m5ucyc+hHnzv1C/esbcfe99xEKBtELNvE2m42fdv3I9zs20b5xO0Qxdk47dMBgMOAPBhk95RtOn8uiQc2qtLv3NkKhMEaTgfNZuXw0dTb5Pj8dW9xJjaoVYmosASiKgqZpfPrxNA4fPEilKlV5uFUbJEnGbDZx+PAhFsydQzgcpsXDraharXrMtUGURGRJYtpXS9h96BiVymbQ6aG7kBUZAYGQqjJu+lecPHueh5o15pYGNQkVt9DFSOEznzHlI06cOM611arT4uFWQHRMnz93jknjPycQ8NOydVvKX1Mp5p55LKHrOna7g00b1/PzgX20bPPo365wuSCIgM6Afq9zfaMbaNGyNXm5uZjNZn7YsZ3VK5fz0is90TTtvx5LBoOBU5knmf3l5zzX5SWMRuN/Xb/LarMx/+vZ+Hx+Hmv/JD7f5SneNrudhfPnsnjhAiRJwmQ24/f50HWdl17pydTJE7m5yS20avMoHk/+f3xdURQxmy34fN4/7L9/t4kWBAFZlhk2ZBC3NruDevUbEAz8ZwdjoiiiqmEGv/c2D7duS8VrriEUurKr6KVSUBdWLX/hmSdp36EjdzS/G+9/cGhSeJ0hg/7Jvfc9SPUaNQkG//e1o64adyy72cSczbvZeiiTfq3v4PTMgXS843oADHIJq59wBTEYDGSdP88TbVux68edWG023uz7Gl98OpNSaWns+nEnzz/zFIIgEAgE6NzxCY4cPoTJbC5u0YtQFIW8vFw6tGvNti2bcTgcvDOgHzOmfERKSipHjxxmxNDBZGae5MSxoxw/doxAIFA0WWMBWVHweb10bN+W9evW4HQmMPi9t5k0bjR2e9SKYzAY8Pv9dOvSmXGjRxRUHI+hNkgi/nCIO59/g0Xrt2JLSeLdyV/Sa9gUTA4buflebn22Dz8dOkYgGKJZ577sPngUqzl2LJSCIKAoCj26deGTmdNJSk7ms4+n07/PayQmJrJv7x6efepxfF4vuq7xbMf27NyxHavVWtyiFyEIArLBwBP9PmTwjDk4UpP5ctla2vUZjCCKSIpM61ffY/byDRgsFh7u+S4LVm7EYLUUt+jFgiiKCILAi507sWDe16SlpfHpzOkM7N8Hm81OXm4eTz7+CEePHMbn89Ox/aMcOXgQcwytgbGGyWQiOzuLrs91YsbUySiSXGLcQ/4qCtu76Jv59Or+EseOHMFqtWIwKJw4foxv5n9d9A4ymc04nQk4nE6sVhuapmEym7HZbEUHUNHPrOh6VLlwOJzIilJkBXG5XMz/+itUVUUURSRJwul04nA4cToTkCSp6FoWi6XofoXvl8KNvN1ux+lMILVUKXZs38bG9WsxGqOFoW02Gw6nE2dCAkaTCV3XEQQBu91RdM3fPudwOEy5cuW54867qVDxGhbOn0v96xty2+13YLPbaduuPXXr1ccf8CMIAg6HIypzQgKKcumkQpIk4Xa7Wb5sCbIsI0m/7vF0XScxMZEfdmzjrTdeJyOjDHJBTTm7w1Ekv9lsLug7iYXfzOfkieMoioLJZMLucEQ/UxScCQk4HNF+LFQsC5+vpmksmDuH7OwsJCl6D1tB/zkTEjCZTGiahs1mw2w2o2kagiCQkJiIseAzURRxOJ1FCtGF9xNFsaCPo9+5sF+sViunMjPp3fNlEhISMRpNRc9X13U6PvMcVa6tRiAQwGa3Y7ZYiv7eav11XBXeMyExEZvNxvy5X3P2zJmifnU6EwrGkBNRjPaz0+lElmU0TcNssRRd73K4aiwhiiSS7w/S5oNPuKFqOfJ9AcqmJDD5xdaczc0vkUXQrgRGo5Ht27ZQtnx5psz8jKSkZBITk/j8s4/p+kpPlixaQNVrqzF89Fg0DW5vcgMrvlvKA/c1hxixsBuNRjasW0tKaimmzvyC1FKlKF2mDB9NGEvXl3tw7pdzNLnlNj6f9QUer0ooFOJUZiaBgL+4RS/CaDCwY+sWbDY70z7+gvSM0lSqXIWhQ/7Fs8+/GF2oHE769u5BWloapQzpaBGNWDpHkCWJQyfOUC4thc8H9cZQuQLN61an1avvMaz/S8yYvxyDQebLMf8Eq5nMJ7MYPHU245veWtyiFyHLCpknT3LmzGkmTZlJvfoNuPve+2jX+iGys88zbfIkrqt/PcNGjwd0BFFk+Ifvc+/9D0AwNhYZURRx57kJh1UWjXiLyjc3oEfLeyh3XydyXXnsO5bJtr0HOblkOubKFbBIIm+N/4QWj7QtbtGLBUmS+OWXs1htNoaOGkuduvW47fY7ad+2JaFwiDmzP8dqszFhygyMRhNtWz7A5InjaNbs8+IWPSYRBAGz2ULf13pwbbXqKIqByN/UaqRpGhUqVkSNqPR9rQefzZ5XdNBhtzuKrNs/7vyBr778HDWi0vyue7nvgRYsXDCXQDDIP+57gEhE4+PpU0hNLcWdd92Lqob5eMZU6l3XgGurVStwJ4y6HRduZgOBAKOGf0jmyZOUK1+eJzs9i6IoyIrCjm1b+Wb+XCKRCNc3uoEHH2pJMBjEbLYwb+4cNq5bS5161yFLctGG3Gq1sXjhAtavXYPRZKRd+yepVq0G2dlZLFm4gFLp6az8binPdelGUnIyqhr1TAkGAtSsU5emtzZj65bvmTtnFo8+9gSpqal4PB527fwBq8WCIsvoksT0qZPZt2c3iUlJdHy6M4lJSVHrRMEmTpJkJEkkKSmJFcuXsXHDet4b/CG5LldUeTOZOLB/H5Mnjmf3rh/p37cXjzz6OJUqVWb2F5+xY9tWjEYjLVq2pt51DYioKna7HVmWcTidrF+zmo3r1/J05xdw5+UxatgHuHJyqFGrFo+1f5JwmIusTHaHI6oECWC12lg4fx7r163GZrXxSLvHqV23Hp/MmEaFChVp1Pgm/D4fo4cP5cabbqZe/Qbku/OYPPFjWjzcGkmSGDNiKNnZ2VSrXoPHOzxVpIjMnDaF/Xt3k5CYxJMdn0GSJEaP+JC1a1Yx8M2+PPhwK2rVqk0gGEQUBFw5OZQuXRaj0cicLz+nTLlybNvyPcePHeO+B1pwa7PbUVUVv8/H+DEjOffLWe5ofjcpKanIsowsK6iqhxFDB3P61CnKV6xIp6efIxgOM3rEUFo90o6y5cqxbvUqDh8+RN/Xe13WHImdHcx/idsf5O7rqrLxX11IsJrY8vMJOt7eAID1+45hMSjFLGFs4vV6aXLLbYybNI283Fw8nnxcrhzsdjvhcIibm9yCKyeH75YtY/HCBQRDQW5sfBPBQOw4b/h8Pho2uoFJUz/G48knPz8fV04ONpsdg8FAfn4eubku+vZ+g24vPMvib+ZjMBhj6mTO7/dTu25dpsz4lEAwSL7bTU5ODlarreg0Yu6c2fy8fx99+g3A5/PFXLa3YChM5bLpzBkxgGAwzMFNP/DpktW0uaspmIys+2EPtzesC5oGefncc3MDfvz5KL5gCCFG3MrC4RCJSUl88vlXJCYlk5OdhSsnB5qrF8UAACAASURBVMWgYDSZyc11kVoqDQGBcFjltmZ3cPToUVwuF4IUG8tpJBLBZDDw1fA3KZuewpEtPzL+qyXcdn1tkstmsGrbT9SpWhGz0w6nf+GOG+px5nwOOVk5xS16sRAOh0lOTmHsxKkYDEY2bdzA3Dmzue325jgdTrZv28oNN96Eqqrku900aXore/fuIXQFXBVKGtFT6CRmTP0It9tN15d74vXGZszUlcLn89HnjQHk5roYOWwIiUnJRafGJpOJ/fv28lqPbtSpdx13Nr+H9997m1UrvyMQCDBsyCAkScLn8/LOgP6MHTUco9FIVtZ5xo8ZicGg8NsT1kIlp3+fXhw5fJhH2j3Ozz8foH+fXjgcTvbv3cPTTz5G5SpVufe+BxgzcijfzJ9LRunSfPHZTN5/ZyCNb26CJz+fb5cswmQyY3c4mDP7C8aPGcl9D7SgQsVr6N71eU6dOokkSQz+1zuMHjGUsuXKF1kYLpQnFAySl5eH1xuNcczPd+N2u5FlmQljR7Ft6/ekpJZi2Afvs3L5Ulq3bUc4HKZ7txcIhkJIBZYMXddxu3PJyc7GnZfHG/3f5tiRwwz51zsoilIUX2I0mUhJScFqs5GRUZqU1FQ+mjSO4R9ED4yuqVSZni934fTpUyhGA7qu40xIYPeuH+nSuSMZpaPWk+4vvUAkEqF120dZvvRbhg4ZhNVqu+RzdjgcTJ86iVHDh9D01mYkJiXRudMTnMrM5PDBn5k4bjSJiUkcP3aUkcOGMOvzT0lOTmb3T7v4/JOZWKxWer7chUAgQJu27Vi9agVDBr1DWnoG06dM4uvZX9D2sSfwejw893QHREkiLT0dq9VKWno6ZtOvlhZJkhg1/EP279uLw+nk4xlTeaP3q9hsdipVrsxrPV7i+LFjmC0WerzchR9/2EGzO+9i5fJlHD9+FJPZjKwo9Hv9VU6eOM4j7R5n7+6f6NenFxmlS7N/315GD/8QXdfp3/c1zAVK5OUQG2/NvwCLQWHdvmOcyc3noy6tyfnkbZrWuIa+n3zL3sxzmOJKyCXRdT06eUURpzOBbVu28OnH03m5ey+8Xi9Nb22G2WymZ7cu9HqlKxkZpWl4Q2OCgdh5AReaTQutBbt37eSjieN4qfuraLqOohjw5HuIRFQSk5Lo8XIX5n49q8gMHQtEzaIKoiRhs9k5+PMBxo4cxosv98BisXLmzGlGj/iQge++j9liIRKJxJQ7WSE6gKZx4PgpHus9iM+/XUunh+4CIMftIcVpjyohEY1Eu41AKEwwFEaMIYVQlCRkRcFoNODz+3mr/+s83qEjyckp3P/gQ8yZ/QWjRw5l+uSJjBr2IbqmEQ6HfrcZKG70sEpWbj4d3xrOgImf8ti9tyE6bJzLySPRXvAi1TTsFhOaruPxx86cLg4iEZUzp0/Rq/tLfP7JDB5q1QZZlsnLyyUhIZFIJEJEi+BwOggGg4RCf+9g/kthtlg4+PN+Zk6fwnuDh4IQPTUWpb+vS3Q4HMZssTBs1DgmjB3F1i2bsdntBW5RVr6a9QVlypSl8U1NqFa9BvUbNGTCmFHc90ALZFnm6JEjbN+2hVtubYbD4eDYsSPs2L6VatVrUL1mrd8pwwaDgQMH9rNl8yYeafc4CQkJtHmkHatWfMehgwdQFAMD332fdo93oHbdelSvXpOdP2wH4LOPZ9D1lZ483fkF+vR/iyZNb8Hv8xKJRPji049pfve9lCtfgdvvvAtd11kwdw42mw2r1UqffgPo/lofDEZjUUKHf0dh7JDZYuX8uV9Y9M08Wj/SjqTkZB5u1YYD+/exdfOm6KZYlgn4/fR9rScvd32OF5/rxMsvdsYf8DNh7Cj27P4Jk9lMMBikUqXK3N78btLTS/Piyz1wOBxklC7N8DETaHRDY5re1gxdhyOHDkYPmAxGDuzbR59e3enRqw+dnnmOVSuWc+pUJg+3akNycioPtWrDnFmf43LlXOT+BVHrs9fj4ePpU3m19xu079CRfgPfoW69+kwcN4oOHZ/h1KlMcnNdrF+3htvvvIuzZ07jzstj4/q13Hvfgxw+dJAjhw/TsnVbkpKTadn6EeZ+NYucnGwCgUDROOrZuy8vdX8Vq9VGi4dakZKSyvNdXuKaSpUviuFwOBzRWLeIhiTLPPFUJ17t/QavvNqbjNJlOHr0MD/v38+e3T8xdNQ4WrV5lDfffq/A7Uxg/97dbNv6PW0efZyEhERat23Hd0sX8/OBA3wwYgynTmXyRNtW3HXPvbRt1x6v7/Lia68qdyxvIMR9707jzjpVSHVa2X74FDuOnMIRQz7nsYimaSSnpLBx3Vq6vdiZ1994k1ub3YGqhnnrjd6kpKYyeeZnaJEIPbq9wKB3BvLR5Il4LnPQ/S/QNI2k5GR2bN3CC8925OXuvbjrnn9w5vQpWrZuS6tHHsWgGEgo8NP8eNoUevXoHlPWEE3TSExKYvdPu+jc8Qk6v/AiLR5ujd/vZ0C/12l2R3PqXdeAZd8uRte1ggUntvzSBYBIhFqVy7Nu2gcsWvM97foM5oevxpHosOH2+qIZmTQdXyCIoshRM3wMuWvomobZbic3J4en2relwfWNeLlHL06fyqTd4x2wWm3MnTOLMmXK0qJlayZPGo8iK6DHVqYvQYCUBDtLx/6TvYeP0/LV97iuRhXSUxI4ePJ09EuiQCAUjgZ5/s0PajRdp0zZcsz4dBbbtn5P/z69qFatOgkJCXi9+VGlXwe/PxB1a7nMk7+rFVEUiagqb73xOm3atqNCxYps3rgOTdMI+P1AQnGLWCwIgkC+O49mD7Ximc5d6NmtC891eQlZUdB0nVyXi7y8PCZPGk/A78dkMnHfgw/hcDq5tlp1Nm9aT+aJE/zjwRbs/vFHtmzaxJ7du7i+0Q2XPIiSZBlXTjaqqrJowTzC4RCiJNGufQeMJjPOhERmf/kpK5cvpWzZ8hw/foxrKlfB6/Xi9/upUKEC2dlZGI1GSqWlkZ2dTcDvw+fzsn/fXiZNGENEjXBTk6bUv74h/gKZLRYLOdnZCIJQlGHuP0WSJNx5eajhMOvWrmbj+nUAPNSyNRllyhAOh1FVFZPZzLuDPkDTNZSCQ7te3bvS+YWu1Kxdh4DfX5Th0+/zEYmo5OXmous65ctX5KMJYzGaTDgcDgJ+PwajEfSo8vzpzGmkly5Np2efw+P1kJV1HlVV+fKzj6Nz3qDQ/slOAEWxML/KL+JyuQiHgpSvUJGs8+exWC1UqXotO3/YQfWaNXE4HGzZvIk9P+3i6c7PM2ncGL7fvJED+/fx/IvdOHMqk4iqMuvLTwlccL98t5ter/fDbLbQu+fLhEMhOj3zHDa7jfz8aNbGvLw8pP8n7koURZxOJ7m5LgJ+HwaDAUVWOHfuLAkJiVitVrLO/YIkyzgKXPpysrOJqCrfzPuacDiEJEk83qEjsiSSnp7BjY1vYuSwDxjw7iBCoSBwefvsq2YV1QGjLKFqGnO/301E1zHKMnazsbhFi2l0XSchMZEVy5Yy6N2BDBs1nodatSE76zw6sGP7Vp59vivly1dAEAVuuPFm5s+dg1DwXyxQaEpdv3Y1b7/5Bv8aMoy2j7UnK+s8ZrOFMaOG0bjxzdx59z1Ry4jBgB5jzky6ruN0JrD1+0307/Ma/Qe+w5MdnyEvL5fs7Cz279uLKyeHVi3uJd/t5uyJ04wZMZRBjd+OLjwx0ByT1cKXi1axdPMOpg3tBzq0vrMJnd8dTdbZLBrVqMKCtd+DQQGblXU/7KFSmTSsZiPntBhoABT5Pp85dYpXXnqe+x98mH4D38Gdl4vVamXB/K8pXboMcxYswWIW6dXjNZKTk0lISkL3Zha3+AAYTUZ27PmZgRM/Z+HIt8Bm4fqMRpiNRn7af5ib6lRnzJeLIBCE9FS27lmEw2ImOTmxuEUvFixWK2tWrmDu17MYP2k6kixx51330LdXd06cOE6t2nXZuGEdJpMJRVHYsW0L5ctXwGSKH25diKJEA66PHD5IRIuwetUKXDk5nDl9ioH9+zBjxoziFrH4EARcrhx6vt6XbVs3M3LoEBKTkxEFAYvVSsVKlZgyYyY+X5Afdmxj357dqGGVprc245v5c7FaLbR/qhMGxcCcWV+go9Pm0cfwX3AQqGkauqZhMZtJSEjEYDDQb8A7lC5TBq/Hw+wvP8PpdPLe229y8uRJPvliDmnppXjysbZ4vR7MZgtms5nTp0+TmJiEKArku93omo7RZMZgMPCP+x/kha7dCAaDLJw/D0NBGlhd14us85qmFaXG/08PlyKRCDaHA0mSeb7LSzS+uSnBYJDZX3yKLElFMRiCIJCUnIwoioiiRJ9er1C5SlX6vjmQXJfrovtFIpFoEHhCAi6Xi+4vvUCnZ5+j68s9CQQCbFy/jnBBNiuvJ5+evfvy9ewv6f7SC4yZMIWkpGSsVgsfjBiLQVHIyjrPgnlzLpJb1/WocmQyk5iUhCBKnDt3joY3NMZgUDhz+hSpqaWwWm3Uv74RUyaNQ5YVbrypCevXrmHKpAmAQP0GDdm4fh0Wi4UhQ0cVZfuc9/VXSJLEN/Pn8ki7x+n1+husXL6M5595ioY3No7uZQqsSaFQmGBQ/cNdWaFSqBfIHdEiZKSWxpOfTyAQID2jNJ58N36/H13XcCakYDAYeeuf75GWnk5+fj5fffE5VpudH3fuYOWK73i8w1MMfu+fTJg8A0G4PGtn7Plz/BfogCSKOCwmEq1mLMa/98nef4LdbmfRN/Pp3OkJKlS8huPHjjKw3+tMHDeaYDBIqzaPMnr4h3w0YSzjR49k6kcTaPPoY+i6FjMbeZvNzsrly3jq8bZkZJTm7NkzDOjXh/FjRqKqYdB1er78Ip/MmMaUSeOZPGEcjz7eAaEg4CsWsNpsbFi/hsfaPExSUjK5LhcD+vVh5NAhqGqYL79ewKAPhvHhyLE83+UlTDYznTo/j6qqMdMGTVW5rnol5q3axGv/HMXcpWto2+d9qpTLoFy5DDo8eCeHMs/y1qBxjB//CZ99u4aeT7QkFFaLW/QiTCYTJ44f5b67mpF9/jwJCQm8O7A/wz94n7Nnz5DvdvPU422ZNGEMA/oPZMbUj3ixW4+CJAGx8RwiYZUqFcvy84lTtH19MPOWraNX/6F4AwFurl+L2264jlKJTtr3+5BPPp1Pv7Ef0739Q4hGQ3GLXiyEQ2GurV6D7Vu20Oe1HixZ9A29XumKw5lA9Rq1eLjVIxw5dIj33h7AqGEfsmzpEjo+0/mKp+OMdUKhEKml0pg1bxHvDvqQ4aPH8/gTT2F3OOjVp19xi1dsuPPyUFW1aLP+3pBhHDt2lLNnzuAP+Gn/ZEe+37iBf73zT5YuWcQLzzzFDzui7lF3Nr+b7Vu/Jycnm/LlK9L45qZs2rieQCBAzdp1CAajY1DTNFJSU8nNzWXi+DFUrVad+tc3pNsLz7By+TLefusNhn84GKPJRHJKCnm5LnZs38rgf73Liu+WIggCJpOJFg+3YuSwIcz9ahYjh37AwgXzgKjL9hNPPc3QIYP4ZOZ0Ppkxna7PP4M7LxrX4c7LIxKJ4LA7GNi/Lx3bP4piMFxkqREEIXpqX2CZKPy3/Px8/D4v6ekZ3PdgC3p1f4nFCxcwctgH9O/zWjTG4YLrFPZlVtZ57rrnH/Tu+yZ5ubkXBYuHwyqVq1Tl6NHDjB87iry8XFJLleL40aOsXb2SAf1e59ixI4hSNDNeTk4OisHAlI8/Z93a1fTu+TJ3NL8bpzOR7l2fZ83qFfTq3o1PP56BrSAmpPDAymQ2M3PaZEKhII+178D77w5k6ZKFjBkxjA3r1vLEUx3Jy83l/gcfYt2a1aSkpGC3O7ij+T2sXrmcChWvwe5wcONNN5OcmsrLXZ9jzaqV9O75MjOnTSYtLZ2N69fw3NMd2LB+LZmZJ0lKTsZkMlOmTFlysrMYP3Ykp09nYjD8uobnu92Ew1Erd36++6IaKPluNx6Ph9p161G6TFne6N2TlcuX8c+3+pF58gThUJg69epRu05dunXpzKoVyxnQrw9jRg1DlmV6dX+JO+68i+GjJ+By5TBi6GCsl5ldURo4cODAy/rLYuSzdTs5+ksORqXkGXICYZVWjWtTKdmBy+0t9joPsqJw8MB+EpOSsVgsnDh+jNzcXHxeL9fVv55bmt2O3eFg5fJlHDt6hKee7szDbdpiVCTCYRWP11/ssQmKInPo0EGcDicOp5Pjx46Sm+vCk59Pvevqc9c995GSmsqK75Zy5MghOj7dmZatHkGRoin23B5fsbdBVhSOHD6E1WYjKSmZ48eOkJsbNdM3uqExpdLSsFisOJxOjEYTVqeNprWaIP8cwV5HRSQX9OJtQ0RVyUhLpsUtN7Bmx2427dhDpTLpjO7TBYvJSKLTTvNGdZm/ZguHTp5mULcnufuWRpzbIRF0GolUkhCKWR+RZYUzp08TDoeoXKUqx44dxZWdQ25uLpWrVOWef9xPekYGy5YsJjsnm56v9eWmJrcAGgliEDX7FIJYvP7vEV3HYjbR9q5b2HP4OKu/34kiy4zr8yKVyqYD8PBtN7Ju5162/7Sflx59gBda30NYMCCllLticro9XvJjYO5FIiqJiYk0v/sf7Nq5g63fbyYpKZk3336vKHVloxtuZMWKZWSePEHP1/rSuMktiGgkOC4dpPq/wJWXj88fW6nFf4soilittoI0r04URSExMYmbbm6KzWLGZr1y7qPZrjyCwXAxvmOFojSo9eo3wOFw4Pf7KV26NBWvqURG6dLUrlOXjIzSNGjYiOXLvuXHnTu48657eK5LV3w+LzabHbPZzC233k6lKlVRDAYUg4Hb72hOpUpVorFoRE+5ExOTqFS5Crt37aJBw0b84/4HOXYsuukWRZGB7/wLpzOR2nXrcf78OdavXUN6RgZ333sf5SpUJKN0aerWq48oSaxa8R3OhEQeaPEwFa+pRJmyZaldtx5JSYksW7KYo0eO0KXbK9zZ/B48Hg+iIFKvQYOiYonlK1akTr3r0C5yyYoqIWaLhesb3Yhc4Iarqio1a9UhMTGRm5rcgs/nY+V3S8nLddH3zbepVqMm/gI3q0IKa4NVr1GLcDj0u5ooqhqmdJkypKVn8MP2rdS9rj73/OMB1q1Zze6ffqTprbdRv0FDqlarjsPpJBQKUb1GTSpUvIZbb23G1u83U6duPVq0bM2O7VvYtGE9aekZ9B/4Lgbjr9YfWZapV78BB/bvo2zZcrR4uDWRSITvli7h3Plz9OrzBvUbNMLtziM5OYVIJMK99z9AYlISVqsVVQ1z3wMtSExMBEHgzuZ389POnWzcsI6UlFTefPs9JEmmcZOmeNz5rFy+jF/OnqVbj15UrlIFq81GpcpV+GH7Vipecw2lS5clUpCVLBKJUKduPRISkwgEAtSqXYfU1LQiJa5W7bqklirFzU1vYfdPu/hh+1aa3HIr9RtcT7UaNXE6E7jtjuYcPXKYdWtWYVAMvPv+B+S783C73Tz7/IuoYZXadepy8OefadbsVkyXcZAl6LFyjPoneOBf01mx61CJdLVyef180r0dza8tw+HMc0jFnFEnWqwwaoa90LKh6zruvLyiaqKFrle6ruHKy6VK+TJ4/QHOnMtGLuagw+iCZMZisVxsndEhLy+3yFxZuIhpukauK5drymcQDqtknj0fI20wYbFYf2dhKjxlKsz8oSgK5gQbvp1ZWL7VKPtUAEk/gq7FgFKu65hMRjAZIaKBJKL7/AQLgnhNZlPUHUsH9AhBr5czX5fBXc5BqLkBIVC8y1Fh/xblzr/gWXjyPYTDIewOB6IgIggQVlXy8vJIS0+nopSH/8AWBLn4LQoXVacveA4EQvgKCnGZjAZEsymaJADw5+YhpZTFUP3mKyZj5plznDqbhRwDdZyKqjqbzWgRDVES8fv8BIPR/rJYLCgGA+jRl3ueO5f01GQqlEm/YjIeOXGa8zm5xb5W/acUVQY3m3HluChfOo2MtOQrdv8DR06Q5/YW+zvW4XTi83qLKlDruo7Nbo/GQeTmovPr+NIi0boRHk9+kfuM05mAGlHxFlRcdzidqOHw76qYF15XkRVyc11IBUlOIhEVqSCo2+/3oygKNpsten1BQNd0NC2C1+uNpvm1O6K1SC74zOPxIIoiNrs9uhMQBHRNIz8/GitldzjwejyEw2HsdgeiJOLOy/udfLIsY7Pbi/YWuq7jcDgIhkIE/H4kScZutxckMxAJh0L4fL7Ljt202x1IcjTeRJbl6PwudBcTpaLK4E6nE78/QDAYwGQyYbZYyMvNRVYUrBYrakRFlmV8Xu9FFoXCYs8ms5l8t5tIJFKU0lgURcLhML6C5yQIAk5nAn6/D7/fH816mZCAz+crKpBoMBiw/OZ+wWAQWVGw22yokQiSJBVdF6J1SRRFwevxXCSbvSDuJRgM4nQmEAwGCAaD0bouF3xmMpkwmy0FRTM1RDGajS0UChWMFXuRPH6fD7UgpXF+fj6qqmI2m5EkhfRUO4mOP5/sJwZ2LX8ety9AIN9HSP3PA59iBc3jIxhW0TQNVY3EhCuNx+PB4/njNIquHNev1W51UCMRNF0vakMseKF4vd7/tyqoy/WbNqgRNC3W2uDD6/33wf6RSBC/K4SiaaDrhHMgohvQ9diYziFAIJqlI9qtIhA9MAjl6ggFtbl1QBCM6BEBTddQIxEEtfgfRCQSJBA4/4ef57pyLxpLkYhGRNNBiKCHgxADcxogqIbAF30B6nCRXIHCz4i+SHU1jBS5stmeiuZejFA0/wR+tx7k53t+swZq/3VF6j9LJIbWqv8Uvz+A3x8oWG+vcH9FYuMdm5P9+9TXebl5F/3/b8fXRX+fc/HfX+p6l7quqkYIBrN/d91gMEQwlHPJe2maGr3fJeaApkV+t/ZBtJ8vlCkv7+K2XUgkEorKdAEuV+4F9wj/ev9LyPdncbl+LWYWCoXx+f2XvG5Ozq/f8/n8+HzR76lqhIA/cMn+KMTj8eLx/Lr3+O1+6UKysrKKftc0lazzWRd9Xjhffnu/UDBEdvDS/fLbsXSRHIW/u1x/+JnP5//DfgkGQ5ccQ9kXPG+v10ckopGeYr+kHP+O2Ni1/ElebXErbW+uWyIroQfCKo2vLY/VZqFiufRid8e6HDRNx2YxYzQoGBSlxLbBbrMUBdCVuDZIAqrXjzd8mrMLRBDKF7dEl43ui5BQ34qhTCkIlaAdVgGarmO1mBF0CVOV66GY3bEuC01DsF7ZlNWJzmiBsBI394iuH1bLlc1Ml5rkxGo2ldj+ctisV/SeaalJJDhsJbK/4sQpSWiajvkyE3WUSCXE7Q+Qle/DqJS8l70/pBIIqVHXIUGMkfxSfw5dj8ZhSJKIJEkltg3GghSJol2MtRIP/xZBEfGbIV8HIQy6WELHUsGPJeymlMeDHi55rdAjEUQhmVBiefyp9hI3liD6DBRR4PJCCy8Pk9GIKEols790UK7wIZi5IDtXCewudB0Mhiu73bCaTZiMhhLZX3HilCR0Ln89LJFKyEfLt7JuxwEkS8mLCYl4/FROT6Ju2VRceZ6YKtL2n6JpOkkJNsKqSr43UGLbkOi0EtE03Pn+EndaJphFVI8PUVEodW8YUT+FrpcwpVwAAZ3za9MI5+QROHkaLVjyliQ9HMRYphphexnyc3NL3FiCqDXHYjJgMVw5a4g/EMTtKXlzDwqswVZTtGL1FcLnD+D1B0vseut0XH5V5cvB6/MTCIZjqhZUnDhXI7quk5TgwHwZ8Wol740POMxGTHZLyQxMF4SirF6iKJTIFwpQFGh11bShhG2ECmVHEDGVBkn3xUZg+p9BAEHQERQNBAlBNiJESpgiVYgkI0CJHEsA6FzxzVpJnXuFFFt/leD19krfTxSFuBISJ87/GF3nsi3aJWzXEidOnN+ihUHQRXQtdlN3XpICJaQkBdrGiRMnTpw4cf4aStiuJU6cOHHixIkTJ06cOCWdq9YS4vYF0AGHxYRANMOYqmnk+4NoOpgNMuYr6M8bJ06cOHHixIkTJ06cKFedEuIPhQmpER5oWAOzQWbFrsOoEY1gRMUgy3S8vSEOi5F1e4/x04mz2E2GuDdInDhx4sSJEydOnDhXkKtKCYloOmkJdrr+4yZ6PNAUTyBErVeG4Q2GUCSRT7u34+56VQEIhlXafPgJy38smZXX48SJEydOnDhx4sQpqVw1MSEhNYLTYmTX8O70eKAp/lAYTyCIDnj8QVreWJu761VlxML1NHljHLIk8U67u5FFsdgrqsYKBqMRk8mE9Js0a4IgYDQaMRqNMZ9pxGAwXLINF34mirE97KNympEvSGcpCAIGg+Gin1h+FgZFxmw0IF9iLJkMCiajIeazIimyhNlo+F1aUUEQMBa1IbbHkizLmArqS/yW/2+u/F1RFAWTyXTR3IPCNdBUItaPWEHXdWRZxmi8vCJmcf56BEHAbrfjcDgu+ilcIxISEi7rurquY7FYMBqNf/l+qnAcWa1/XbFLQRCw2WxX1Vy2Wq3Islzi9rNXjSVEFKKpC6cs38qCrXsZ+UwLSic6AB1d02lSvQIAc7/fw8YfDvL9wRPcXK0CGUkOzuV5SmT19b8KURSx2mz8fGA/+e58ql57LTabjXyPB0PBS3nP7t0IokDNmrXwen3FLfLvEEURm83GoUMHcblcVKlSFYfDQb7HgyxJ2O12Dh48SG6ui1q1aiPLSsxN1sIXxOHDh8nJzqZylco4nQl4vV40TeP8uXMEg8FogJNRxB6KTUXEbLNw4uQZslxuKpVLJyHBgd/jQ1ZkFEVh7+HjhMIqdapWQJRicNHUdcw2K+fPZ3Pi7HnKpqWQlpaCP9+LJEsYjEb2Hz5OIBiiVuUKKEYDWoy1Qdd1HA4Hv/zyCydOHCcjozTlypXDnedGlESsVhuHDv5Mbl4u115bDYvFgqaGi1vsYsXhcHDy+QXaJQAAIABJREFU5EnOnDlN+fIVSEtLJ8+dh0ExYDAa2LtnN6qqUrNmLaQYXD9iDYPBgN/v59y5X0jPKF3c4vztEUURv9/PsqXfEolEsNvthMNh/IEAFcpXwJngZNWKFTzx5FMIgvCnxreiKGzbthW73c41lSqhhtW/TG5ZlsnKOs/JEyepW6/efz3vBEEgFArxw44d1KhZE7PZjKZpf5G0fw0JCQl4PB7C4f+8zs2mTRupUKEiSUlJRCKRv1ymQjn+6nXvqlFCZEkkzxfklSnfYJAlTBcqFQI4CgobeoMhMCrk5PsBcFpMnMlxw99UCSm0crzxem++XbKY5OQk/P4AI0ePpX796wipKs8+3ZE9u3cT0SI0adKUD4YOj6kTBEEQMJlM9H29NwvmzyU5JQW/38/Y8RNp0OB6RFGkX98+LFm8CGeCExAYO34idWvXJBAMFrf4QLQNFouFgQPeZNYXX5CamkK+x8PI0WO5/bZb2bV7D60ealEgPyDAey+8SwNrbSBQrLJfiMliZui0r5g4Zwlpqcm4XHkMffVZmt/SiIDXx5NvDmP/0ZMYFAWb1cS0d3pgNJQBYuM5AJisFj5fvIq3xn1MeloK58/n0PfptjzZ8m4C/gCd3hzKjv2HsZiMKLLM1De7Ujm9cnGLfRF2u515c7/mrTf7kZGewblzv9D1pZd55rnniagqvXv1ZMnihSQlJREMhRg7fhI3NLq+uMUuNmw2G1Mmf8SIYUPJyEjn/Pnz9HtzAI880obcvHw6P9ORw4cPocgKDqeTcRMmUbF8ueIWO2YRBAGr1Uq7tm04c/o0a9ZvRBRi553xd0SWZXw+H598PBNVVVm/bi1paWlUvbYat93WjFtvu41NmzfR/okOiAWWQLPZjCCIBIOB/3dDbLPZ6d2rJ41vupnhI0eSnZVT9Jmu6xgMBoxGI5FIhEAgULSRLXx3S5JEOByOHrIVIIoiZrMZm8XIt0sW887bA9m0ZVvRBttkNiMAgUAAVY0qPUajEU3TkGWZSCRCKBQqkrnwXiaTmcOHD9LpqQ4sWLSYKlWqEgwGMRqNyIpCOBQiFAoVySEIAiazGVEUCQYu7ofCv1F/I3shRqORcDiM0WgsUgIjkcgFf29CVuSiewqCgKIofDRpInfdfQ9lypTB4/Fc1O+SJGEymdB1nUDg13d/l+c7M2jwB7Ru3RKfL0Qg4EdV1aKaYkaTCfmCfi60MEmSRCQSwWQyE4moF11TlmVkWUEQovf1eDzouo6iKBhNJnRNw+/3/1eKyVW1KggCJNrM2C4RbK5GopquLImgaUWWj2BYRYhxt5D/JVarlbVr1jBj+lS+mPUVa9Zv4Kabm9C7V0/sVjPTp05hzZrVfPvdchZ8s5ivZs9i9uxZGGMos5jVamXVqpWMGT2Sz778ig0bN3HjjTfR9/XeOKwmvpo1i+nTpzJrzlzWrd9I5cqV+WjSRGRZImpWKH4sFgubN23iww+GMP3jT1i/cRPNm99Fn96vIcsymSczSU9PZ/nKNSz+9jvmLVlM3br18Hm9xS16EUaDwoEjJ3j3oy+YNrA76z4dQedW99J10Dgko5FPFq5ky+6fWTl5MBs/H4VBlnln4mc4rJbiFr0IWZZw5bp5dehHDHj+cdZ9OoIh3Z+m1/AphAJB5i3fwPItP7J80iA2fD6KlEQHb46diWCJHZcTWZZxu9306d2Ll1/pwao1axkydDgDB7xFnsvF6lUrmThhPF9+NZcNmzZTt+51vNG3N4ZLuGz9HTAYDGRmZjJwQH/ee38wq9as49XXXuf13r0AgU8+nslPu3bx7bIVrN24CV3TGDJ4EGaTobhFj0l0XSclJZGJ48ezbt1arDYrcaNR8RMKhUhKSuLreQtYsfw76ta7js7PvcCK5d/xSo+eVKl6LUOGfIgoSYiiiMlsZv36dSxZvBCfz4fVav3DzWahohF1B7r43+12Oy6Xi4XffMPWrVuwWCyIolh08LZz5w8sWDCfI0eOYLXZiq4lShKrVq5k46YtmM3moo23oigoisLqlStZ+u0SVFXFYrEgSRKnT58iEAiwZ89ujh49gsEQnaOiKGKxWNmxfTtLly5B13VsdhsQVTIcDgc/HzjA4oULOZWZicPhQNd1JEnCYrHw/eZNLFr4DW63G5vNhq6D1Wrj8OHDLPpmAYcOHcRms/+uT06ePIEoimzZ8j2rVq5AILo+F97zwIH90XueysTpdCKKIj/9tIu3+vdjyaJFZJ48WeQaqut6kdVm+XfLWLd2DbIsYzAY0HUdk8mE1Wpl+7YfWLHiOyKRCIqiFPSzlV07d0b7+fBhbDYbkiSRm5tLVtZ5AgE/3367mAMH9mOxWAraZ8Xj8bB1y/f8/PMBnnv2aWbN+pL0Usn4fD6WLFrIhg3rL+m++me4aiwhf4wAOuw5eQ6AGmVKse1gJnUrpnM218NZVz6KdFXpYn8Kv99PterVWLV2PdWq1UARBZxOJ5YC/8sF8+fzRIcnKZNeCoCHHm7FvK+/pvPTHYmVDXwwGKR06TLMX7iY2rVroygyzW6/ndWrVwLw5Zef8+KLL1GnZjWOnjjF5GkzcOW48PkCxEqlvFAoREpKCvPmL6RBg4ZIikyz2+9k/rx5hCMaLlcOZcqVw+12c/r0KerdUB89J8gvmqu4RS8iHFZJTXCweeZQqlUqD2YjSQ4bVrMJzefnviYNub9pIxKTE8BqwWmzYDH99T7E/w2RiIYsCqyY8B7VK5YFg4HUBAc2i4lgIESzhnXZOHUIqalJYLeSYLMiRGLLjSkSiaBpGnPmLqBK1aookkBKSgo2m42wGqFcufIsWLiI6tWrY1BkbmvWjMHvDyKsRjAqfz+LsKqqmM1mvl26gmurVUORBJKSkrFabXi8fu67/wEeeOBBklNSMEpgdzgwm2NHcY4ldF3HZrOzfdsPTJnyEf8aNJgvPvs0pub43xld1/F6vQiCDU3TCIVChDWQJJnNmzbyztsDWbF6LYoi89wzT3Pu3FnS0tL513vvMmXaDCpXrnzRSfmFFJ64X4jVZmPb1q1069qF6+pfx8GDh6hTty7Dho9EB3p0f5ktmzfTqFFD3h7wJq/3eYPHHn8MlyuP9o89is/nIyMjnaysbMwWM7KiEAoG6fRUB1RVxWq1MWTw+8z4+FOurVqZV3t05+DPBwiHw7z6Wm9q1KiJqoaxWCz0fu1VViz/jpo1ahAMhQmFQsiyjN1m4d133mXOV7OpXqM6A3f9RLdXXqHTM88SCobo/ko39u3dS+VK1/DeP99m3IRJ3HhDQ6ZNn8H4cWNp1PB63vnn2zz+eHu6v9qLfLcbSZIIBoM81aE96enpSJLEsaPHqFa9OlOmz8BoMDLk/UHMmTOb6tWrM/Ctn3ilR086Pf00M6dPIxgM8MknMwkGA7z08it4PB5MJhOnMjPp1LEDiYmJBIMhwqEQMz/9jMSkZEwmM0M/GILVauXEieOULl2GT7/4EpPJRI9XurF582ZuaNSQt9/qz2uv9+WZTh2Z+/Uchn4wmLr1rsPr9bBj+w5GjBpNq1Yt2bhxE31ef41atWqzYf06bmh8E+0efYQfdv7E052epGbNmmRmnqJMmTKMGTfhsr1jrlolxGkxkWCN1ggxGxVmbdjFqy1uYfxzLenT6nbSE+wM+PI7XF4/iTZzcYtbbKiqSmJiEhWvuYZlS5cyYtiHHD58mMVLlhEMRzh//hwVKlQkENYQRJEKFSqwe/dPxS32RYRCISpVqkSNGjUIBoMYjEZGjRhOy5atAXC789i3by8PPdySzMyTJCclM3zUGFKTnAQuMLsWJ6FQiPIVKlD12msJBAJYLEaGD/uA+x98AKMsku92s27Nal7p1pXcXBd+NcCoHiOpYEkDcotbfAA0XcdmMZFcKoVduw/Qf/wnbNvzMwtGvIUoiqQnJ6LYrXw+dyljZy1C1SJMfacHWQt9SDEyBXVdx6Ao1Li2EpmnztK9zxDWbN/FR292w5GUgNnrRbHbmLNwJaO/WIDb62fVhHfQvX5wFLf0UQpPC2vVrsPpU5k89+zTrF69iiEfDCWjdAZ2u42q11YjHA4RCCuMHTOK1m0ewfA3VEAANE3DZDJRu05d9u/fy4A3+7N500amTJuBzWZFENJwOh1MnTKFKZMnIYoiU6bNwBcIYjHFMyteiCzLIEC3rl14tVdvSqWVwh/wF7dYcS5A1/WLlMLCXzVNIxgMYrfbmDZ1Kvv37WXHjzuRgJe6vUK/vq8z/5tFf6iE/BahIE73jb696fBUR3r36kluvo86tapz/wMP0rDhDez+aRdzFyykcsVyDB85mlEjhvN0xyd5f8J4MjNPsnPXHiwmheeff4G1a9bgtJn51+hR5ObmsmH9OgDaPfY4/xz4Fp9/9ime/HxESWLJoiUoilJkuVi/bh0fz5jOpu+3UbtmNaZMm8HaNauj1pEfdjJ82IesWL2O66+rwzeLv6Vjhydo8VBLNm3cwLq1a/jhx91YTQpvDXybPr17sWHDej6aOIGbmzRh3JjRrN/4PRPGjyXg9xcpYqIocPbMWe655x+8P+g9du87QNObbiTzxEn8fj/Dhn3AqtXraXBdHRYsXEzHJ5/gjjubM2zEKDasX8+g94dw62234XLlIghRb4l+b/QhI6M08+Z+jTcQ5sH77mX1qlW0f+IJ3O48mjZtyvARwzl67AQNG1zH/r37KFuuHLt3/8S8C/p55IjhPPt0RzRNIysrm779+nP9dXXp2u0VJn80kccefYS33uxPixYP0++NPsz+eh4fvD+IlJQU/q+98w6PqkwX+O+UaZmZTEIKJCQKSgIJxYKFIja6SNdNCFWvFVSwoaKu3VVRRAWxLGIXvbssAooNELGsiquiSKRLE0jPTKaddv84M0MC6Cr33mRYz+95eHhmJjNfOV973+8tF026iP79BzLzoQdQdOhaXMQbry/kyssvPaLx+B93BSAI5r+Fn3zL86u+QtV1Upx2dlTWMuKBF1n9w1ZC0SgzXnmH2cs+wedOHjOKlsLhcGDoBrk5uZSOKaNr12488/S8A39gQONbDyFJbkDixB3NFEXB4XRScsEoXCkp/PnOOwlGNRRFpaqqkuk3z+Afby7FZrcz+crLsdnkpGlLvA3RaBS32824sjKUqMK99z9ATV2Ac/r249n5z7PghZdYuXoNHTt14pabboxFLGvp2psYhoFdtoEAGT4vJQPPpN/pJzL3jbcIhkLYUpygaXRsl8ekkQNJTUnhucXv4Uwi0z7DMMyIXrHIWBf0682YgWey4M0PqKioxOZOAU2nQ34OE4YPILtVGs/8fTmCLXn0OXEzAptNxu5wMHTYcEpLy1j42qvs2rkDwzDQNBWbzcYFI4eTkZHJjFtvpz6QfAEnmoO40CZJZnCLESNHcd6QoSx4bj7V1VWxGySVjp06MXHSxTgdTl568YU/rPnar+HxeHjwL/dTVFzMxPFlRCPRWNSlJJHQLX4RQRCQJAlZFlm//nsCDQEumjiJ0jFjKS/fgGEYTXws/h2SJFFdXc3uXbtZ/eEqSkrHcNXkK0hxpVBZUUFOmywenDmLZ56ax9XXTGPpksUJC4y1X37O4POG4HLaCEVUBg0+L2Fa9cP676mpqWH8hImMKRvHnj17UFUVA1A1lXPOPZesjDQikYgZVdLhYO3aL+ncpSuFHTtSXd/A2eeeS5s2OciSxCcff0xBYSFdunShorqOM87ogy/Nx3ffrWPr1q2EQ2EmX3EZJaVlfPH550iyTDiqcsddd7Nq5Qr69DmT9957hwcemgkccNzWdZ3U1FQGnzeEsKLj9nhJT0/HAD755GMKCgopjpXZ58yz8PnS+NdXaxPRClVVjfl0mOZkfr+fdeu+5U8lpYQVnYaGBpYuf5cRI0dRW1uH3W5nwODziKrgcqWQkZFBfX09rVtn8+DMR3jm6XlcPXUaS95cTEqKqfWLRCIUFRXRrVs3ojrk5x+TEErT0tISvjGVFRWEI2GCoSjbt2/jX//6itKysVw0aSKyLFNXV3fEt53Js3P+HyHGJsj1z7+FYRj43C5EQcDrcvDFpp2MeOBFJEkkoqj4UpxIgpAkBjktg8vlYuWKD5AkmUHnDeLkE7vSo2dvep7enRum30Lbtm3ZvHkTTpvZr1u3baV1TpsWrnVTzPCAbjRN5YIRw5BkmeXvvp9YVNPS0ujZsxe9e5wKwKSLLmbqNVejaFrS+APF7T0FQaDkwtEEg0HeX/khss1GQ6CB2poaevXujcvlwi6LjBw9mltenWZqrpJEkLI5HHy+/kcqqusYOqAPY487htJ+Z5DZt4xrxwxjV0UVedmZnHxyF04+83TaZvi45N4nGHbZpdiTJNCBzW5j0097+ObHrYw5/1xKh/endPRgjulzIWvWfofX66GVO4Xup3TlhN7d6ZDbmlHT7mTa1GuSJtSt3e7gp+3b+OqrtYwpG0vZ2LFMHD+W448/nmVLlzD16quorKln9MjheDxelr39D1RNQ1OS41awuXE6nXz//fds3bKZktIxTJw0gbHjJpCXk8XatWtRogpt27al9xm9ObtPb9LT07n+2mlMmTLZ9DG0AExl1pYtW7jvnru48E8lXHfddWwo/5EdO3Zw1513MuOWW1q6iha/EUVR6NSpiNv+fCdVVZVIokQkGiUYCuHz+VBVDb+//hCBRJIk7KLpZyVJEpIkYRiGqdzsdgLRaIRwOEx+/jF8+ulnjB9byuQpV1NaWsKKlav47zcWAiDLNhRFQYz9piCYZvUCEI0qnHTSSdx+x51UVVUjCgKarqHppiAVDodRGgeHiikZtEYHeimWmsGARFhbQRAQRTHhr2I6cUc5tn07br39DqoqK00zq2iUmto6up96Ku++v5KPPlrNyy8+z/vvvcuSZe80MUsyDINgMGgqflQ1UY4c6xdRFBLlCQJIMYf6uGKkcf+KoogoiLHviTgcDjb88AOiKNCuXXsAQsEghqEnfsPtdvPdunWMKytl8pSrKCkpYeXKVbz26iuA2V+qqhKJRLHb7aiqgiiaZ+I/lZRy7z13sX//PrZv386sRx9DkmRURWXEyJGcfU5fQsEg0Wg0YeprPwJl3H/sCmqaY7kSxzPDMHA77XicDlw2mfSYcPJHFkDAFELKN2xgbFkJmzdtwR8Ms2TJYnypPtLTfQwdOpyXX3qBbTt2s37DRhb97b8ZOWp07NvJ0Xt2u539+/cxsH9fNF1n3lPPsHv3brZu2UI0GqV0TBlz5zzB1+vWEwiFeeXllygqKsYmSRh68rShpqaGIYMHUltTw7PzF7B/3z62bN6Mx+Ph0VkPc8Go4YTDYWrrAjz312cpLu6Mw+EgWZ6DzS6zr6qWEdffx7r1G9FCYRZ/+E8A2rfLY9lHXzL+tkeoqa4lUlHFktVf0D43G6c9eULcypJERFEou3Um7675Ei0S5fNPv6K6rp5uhcex8vNvKJ3xEJUV1USraln84T/Jb5OJ7HImjd27JInous5ll1zMkjcXEw6F+Pizz9m3bx9FRcX8tHMn/fueg8PhYM6T89i5cwfbtm1NujCVzYUsywQCfiZNGMdHq1cTDJpOmqFQiM7FnVm29E2uuPwSqqqqqWsIsfztt2jXvr11E3IQmqbhcDiYNfsxOnfpSnpGFl6vF0PXycjIbOnqWRxENBpNaNqBmI9IhEhEZcCAQXz55RcA9DztFBYt+hsP3H8v6T4vD/zlPl54/jlSGgUUiR9mq6qq2LV3H7t372LHjp9ITU2la7duLH/7LU7sWkz79scx5YrLKS/fwKZNmzF0g5un30CHwk589umnhMNmhKlzzjmXxYsWsXP3Xvz+el55+SWCoQY0AwYOGsSaj1bjcXvoeVp35v/1WeY8/hiyaApPjdsEZvSsM87ow/r137PmozXYJJHXXn2VnTt3oCgKZ511Fls2b2bFB+/j9bj5x6K/E2wI0rXbCfTq3Yf1339HQyBAz9NPYdWqFfz5thmk+byMGHo+y99+i3FjSrj9z3exYcMG/P76JkJIJBJB1/WEMBGJRAgGGzjr7LNjZX5AqtfNor//jWAoxKmnnmY+GyXKpk0bE5G4dF3H7fHQs1cvnn36KcLhMIFAgJHDzufTTz7B43ETjR5als1uY9267zB0g5um30hBYSf++dmnRCKmSV08gpj5DM3XSlRBAN59Zzk9evai/XHHUzZuPEXFnXHZRU47/XTeWraMoqKOFHfuzHXXTmXNmo+OSACB/8CbkH+H+XySQ3OcDNTV1XHZFVeyY+cOSi4chcftIRQK8eTTz2LoOhMuuohvv/2GIYMHIAoiky7+Ly644EIi0eRxxvV4PDy/4Dl+LC+nqLiYEcPOR1EUXCkuFrzwMuPGj6d8wwbKSi4kNTUVh9PJ3HlPo6gqRpIc4N1uNwtfe411335Dp6JiRo8cjqKYpgwvvbKQRx97nClXXsGgAf0QBMjMzWbmI7MIvhPEkxxNINwQZNg5PbnnynGMv/VhUr0e/IEGnpwxBa/Xzf3XTOLK++bQ/5KbsdlkHDaZZ26/GnWdmjQH+HA4wonFHZh/x1RunD0f34I38PsbuHfyBDocl8+tl5SwZ38Vgy67BbvDjigIPHfnNDBImkN8OBymqLiYZ+cv4P5772HunMepr/dz4/Sb6df3XB586GG2btmMw2Fn2PmDTcfsFDcLF75ORrqvpavf7DQ0NNCnz5k8NPMRrp12NWlpadTX1XP/Aw+Rl5/H3ffcx9RrrmLwgH7YbXYcTiePPTHXNP9MIlPClkZRFHw+H5OvugYBsEvwznsfUFNbw5SrrkKNJk8Y7j86hmGQk5OLLy0NTdPN6EouF7m5eQQCAQafdx6Tp1xF6YWjycnNoaqqmpkPP4Kmw7KlS+nSpQsXX3IpoaDp76PpGgUFhXy4aiVDBg3C0HUMDObMfYqnn3mWCePHMWDgQBoagpx62mn06NGTQCDA6wtfpW+//mRkZOD2uCksLMQfjDBuwkQ+/ngN5w8ZTG5uLh6vl05FRdTVBxhTNpYffljP8KFDyMrKwh/wM2v2E6iaTlZ2Nmnp6WjagbU4FApx4kkncf2NN3HZpRfTvl172uTk0L37KUQiEbp0LmbmI49y0/QbyWnThqqqKp54ch5er5cePXpwy4zbmTRxHPn5+ezbt4+77r4PURCYdu31zHr4IZa/tZSf9+7jttvvICsri4aGhtituEBeXl4iNLEgCOTl56NpOsXFRTw8azY333gDbdq0pqqqmrnzniYrKwsDuGbqtTw55wkq9u/nhuk34ff7Cfj93HPfX7jy8ksZ1L8vkWiEAQMHUTZ2HPX1fnJz83A6nQfKyssjGlUYOvR83nj9Vfr160dmZhYpKW4KCgrRdB2Px0NOTk7MRNc0H2vdpg2appGTm8uqFSuQZZnPPv2E++6+iyfmzmXWo7OZNHEC/fv2Rdd12rdvz8hRo1FU9ZDEvr8FwUiW3f93cP79z7Ni3Wa8rqPPKbCmIcRLU0so7dGFPVW1CfOxliIeK9rj8bJ9+zbq6mpp3/443G4P9f56bLKM0+Vi08aNyLJMhw4dqK/34/OmoKgqgWC4xdsQ96cwpfgoumGYYmYsK6osy3i8XrZv20ogEKBDQSEY4LCJ6IaBPxBq8ezdTdqgKIkDbTzefkpKCjabjU2bNqKpGoUnFBH5rp6q+bvJHx9FMrZh6C2vUxAEAafHzf59FeyvruWYNtmkpqUS8gew2WzIDhtbf9pNVFEpbNcWURLYvDATV06EzDP2o0eSw6TJ5XVTW13Lrr2VtMlsRWZ2K0L+BmRZwuZwsH3HHkKRCIXHtkVCR/W1JXrsydTW1Lb4WALzbsyX6qO6qpKdu3aSlZVN27w8/PV1hMOmxqzxXNGB7MwMWjWj7b4/EMTf0PJzL47Pl8bevT+z9+efyc3NJbt1a2pra2M5DpxsjmkmOxQWxkxDdNJSPc1Wv9r6AMFwpMXX29+Druuoqoos20j1puBJab4IFNW19USivz3Z2x+NaDSKKIrYbGbizfizstvticS527Zto7a2loKCApxOJ8FgMBEFq4nSRRASuS5UVSHuOepKScHrNUPalpeX43K5KCgoaHRQh40bN5KVmRmbbzXY7Q5kWcbhcPBjeTluj5u8vHxqamrM0L2x5MpbNm+iIdBAYceOyLJMKBRGUQ7k2mh8tBUEAY/Xy7YtW4hEIhR27EhdXR2SKCKIIqmpqezZs4d9+/Zy7LHtSE9vRX19XSyUro8dO36iqrKS444/PpHI2ZeaSnV1Fdu3/0R2dhZ5ecfg99c37eNIBNlmS9yORCKRRP/+Wplut5vKyko0TcXrTU0EE3A4zL75sbwcm91OQUEBwWAQTVWJKgqyLCNKEhiGeRNis8WsJWDTxo1kxvq5pqYGp9OJqqpompaok6qqCf+TM3v3YOXqjynu2AGAnj17cVL37jw55wnqAiHKyzcgyzIdO3YiFArhcbtwOX5/2PKjUgjpf9df+eCbTXhSjj4hJBAI8fINZYzt1Y3dlTVJtaHYYpMlPjDjCIKAzW4HwyCqKOiaTqs0L1FVJdAQSoo2iKJoTr7GGKajmulpJcTaJ8SubDXSfR5UXcfvDybFQeiwbYAmtqRxO1FF0oh+7yf4ajX5kyJIxhYMPXm0sjZZQhJFVE1D1TTM20ezDXZZNjctTcUwNHYtOgZndojM3nuTRggB06xJliQ0TY+1wTzcC4DdZsZ6VxQVNRrG0baQ6LHdqamuSYqxFEeS5ETiLjWWEf1w40zXDeyySLrPe7if+X+hPhDEH0iOuRfHTN4lo2lqIgEa0HTuxdYPd4qLtFR3s9Wtpi5AMNzySp/fgyCISJJINKqQmurG24xCSFVNnSWE/AqyLKPrehOFlyRJTca9zWaP7Zkqum6ugXE/j4NvfiVJQjjIty++dzUWdhRFSQgI8fdVTUPXNERJQouVLwgCdruyVUpqAAACwklEQVQdTdfRVLVp3QQBe6P5GK+LWTcSdW2KgD12c6koSiJJX7wusmyLlaEcknHcPDsc+pkky8iSjK6bysPD9fGBMgRk+beWaf6teUPRtC6CIGK3H9qXTctq+vqX+lmMCWHxPo/vDaIoMuPm6ZRv2EBxcTHV1dVUVFQw+/E5tG3bFkVVsTd6nrqu0yotFecRCCEtrzo9Atplp1N8TGvcR2GyqPpgGF+KExCwxZLWJAvxAS8IwiHJZ/TYRJAlCUMwHakkUUyuNhxGnpYbHbZMh63Ywieb2hxJiD2HZDkIHaYNjR2eE4utLJoLrm4QrQARJ+jJc4A/4OIsc/AykzDKEGwgyBhREGx2hBQfQhI5+urE2iEfMOCM/5/YbmQQbU4EuwtBILnGUgxNMzeYJnP6oHEmYBxxnPcjRRKTbO7FOGx/cWDuxdcPqZnHqiQl2Xr7O7DJMlIz19uM9GQclf3VXMQdsRvTeNwf2DMFRPHA+3En7kM4aF1pvHfFD9MHB/DQNA2h0fuNyz/4O40/azwfG9fFdD4//NG2sbByaF3MqIGHO//EIwoe7rNfWi/iHNze31qm2ZbDv/9Lfflrr3+tn5vuDToYMHv243z99dfs3PETvrQ0Tj3tNOx2B6FQEEkUm9Qh7lx/JByVNyGarh+1GVgNzM33aNJmWSQnDT/Ws/3BDYg20TwdH21DKjaHtQaV7OFtyb4gP1l87H8nsfsRa05bWFhYWBzlxC0vfuv7/xuOSiHEwsIC9LBGeJdpo3tUT2LdwJbhwNbq6LvZtLCwsLCwsDgyLCHEwsLCwsLCwsLCwqJZSR4DbAsLCwsLCwsLCwuLPwSWEGJhYWFhYWFhYWFh0axYQoiFhYWFhYWFhYWFRbNiCSEWFhYWFhYWFhYWFs2KJYRYWFhYWFhYWFhYWDQrlhBiYWFhYWFhYWFhYdGs/A+pZvbe6DWceAAAAABJRU5ErkJggg==\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42875,"title":"Assignment Problem","description":"Given a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\r\n\r\nFor example, if presented with the following matrix:\r\n\r\n x = [1,3,4,7; \r\n 2,3,1,3;\r\n 4,2,3,7;\r\n 6,4,2,2;]\r\n\r\nYour output array would be:\r\n\r\n a = [1,3,2,4].\r\n\r\nWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\r\n\r\nThis gives us a total time of 1+1+2+2=6 which is minimal.","description_html":"\u003cp\u003eGiven a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\u003c/p\u003e\u003cp\u003eFor example, if presented with the following matrix:\u003c/p\u003e\u003cpre\u003e x = [1,3,4,7; \r\n 2,3,1,3;\r\n 4,2,3,7;\r\n 6,4,2,2;]\u003c/pre\u003e\u003cp\u003eYour output array would be:\u003c/p\u003e\u003cpre\u003e a = [1,3,2,4].\u003c/pre\u003e\u003cp\u003eWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\u003c/p\u003e\u003cp\u003eThis gives us a total time of 1+1+2+2=6 which is minimal.\u003c/p\u003e","function_template":"function y = Assignment(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [2,12,8,5; 4,4,5,12; 3,10,8,3; 12,0,1,2];\r\ny_correct1 = [1,2,4,3];\r\ny_correct2 = [1,4,2,3];\r\ntest1 = isequal(Assignment(x),y_correct1);\r\ntest2 = isequal(Assignment(x),y_correct2);\r\nassert(test1||test2)\r\n\r\n%%\r\nx = [7,5,3,2,6; 6,3,6,6,5; 3,3,9,4,7; 4,5,3,3,4; 2,3,4,3,5];\r\ny_correct = [5,3,4,1,2];\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = eye(4);\r\nx(x==0) = 3;\r\ny_correct = 1:4;\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = 5 * ones(5);\r\nx(1,4) = 1;\r\nx(2,2) = 1;\r\nx(3,5) = 1;\r\nx(4,1) = 1;\r\nx(5,3) = 1;\r\ny_correct = [4,2,5,1,3];\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = 1:4;\r\nx = [x; circshift(x,1); circshift(x,2); circshift(x,3)];\r\ny_correct = 1:4;\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = [7,5,4; 14,16,2; 7,1,9];\r\ny_correct = [1,3,2];\r\nassert(isequal(Assignment(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":6,"created_by":64486,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":"2018-07-30T13:18:15.000Z","rescore_all_solutions":false,"group_id":43,"created_at":"2016-06-02T23:08:15.000Z","updated_at":"2026-03-11T23:46:24.000Z","published_at":"2016-06-02T23:18:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if presented with the following matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [1,3,4,7; \\n 2,3,1,3;\\n 4,2,3,7;\\n 6,4,2,2;]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour output array would be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ a = [1,3,2,4].]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis gives us a total time of 1+1+2+2=6 which is minimal.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44451,"title":"Rate of event occurence: find percentiles of the distribution (for smallish rates)","description":"*In this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter _μ_ (the mean rate parameter).*\r\n\r\n_What follows is just a longer introduction to the concepts, including two examples._ \r\n\r\nSay that you manage the inventory of a business for which orders *on average* come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, .... \r\n\r\nThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average. \r\n\r\nIn this problem, N follows a *\u003chttps://www.britannica.com/topic/Poisson-distribution Poisson distribution\u003e* :\r\nN ~ Poisson(μ). \r\nSo there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ. \r\n\r\nThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: \r\nPr(N=k) = μ^k × exp(−μ) / k!\r\n\r\nThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation. \r\n\r\nIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula. \r\n\r\nHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine _two_ values of k:\r\n\r\n* A _lower_ limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\r\n* An _upper_ limit on the number of customers, k, that will not be exceeded more than 5% of the time\r\n\r\nHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits. \r\n\r\nEXAMPLE 1:\r\n\r\n % Input\r\n mu = 10\r\n % Output\r\n lowerLimit = 4\r\n upperLimit = 15\r\n safeLimits = [lowerLimit upperLimit]\r\n\r\nNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'. \r\n\r\nEXAMPLE 2:\r\n\r\n % Input\r\n mu = 100\r\n % Output\r\n lowerLimit = 83\r\n upperLimit = 117\r\n safeLimits = [lowerLimit upperLimit]\r\n\r\nNOTE: the two limits are returned as the vector safeLimits, as shown.\r\n\r\n|NOTE: Toolbox functions are generally not available from within Cody.|","description_html":"\u003cp\u003e\u003cb\u003eIn this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter \u003ci\u003eμ\u003c/i\u003e (the mean rate parameter).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003ci\u003eWhat follows is just a longer introduction to the concepts, including two examples.\u003c/i\u003e\u003c/p\u003e\u003cp\u003eSay that you manage the inventory of a business for which orders \u003cb\u003eon average\u003c/b\u003e come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, ....\u003c/p\u003e\u003cp\u003eThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average.\u003c/p\u003e\u003cp\u003eIn this problem, N follows a \u003cb\u003e\u003ca href = \"https://www.britannica.com/topic/Poisson-distribution\"\u003ePoisson distribution\u003c/a\u003e\u003c/b\u003e :\r\nN ~ Poisson(μ). \r\nSo there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ.\u003c/p\u003e\u003cp\u003eThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: \r\nPr(N=k) = μ^k × exp(−μ) / k!\u003c/p\u003e\u003cp\u003eThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation.\u003c/p\u003e\u003cp\u003eIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula.\u003c/p\u003e\u003cp\u003eHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine \u003ci\u003etwo\u003c/i\u003e values of k:\u003c/p\u003e\u003cul\u003e\u003cli\u003eA \u003ci\u003elower\u003c/i\u003e limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\u003c/li\u003e\u003cli\u003eAn \u003ci\u003eupper\u003c/i\u003e limit on the number of customers, k, that will not be exceeded more than 5% of the time\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits.\u003c/p\u003e\u003cp\u003eEXAMPLE 1:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Input\r\nmu = 10\r\n% Output\r\nlowerLimit = 4\r\nupperLimit = 15\r\nsafeLimits = [lowerLimit upperLimit]\r\n\u003c/pre\u003e\u003cp\u003eNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'.\u003c/p\u003e\u003cp\u003eEXAMPLE 2:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Input\r\nmu = 100\r\n% Output\r\nlowerLimit = 83\r\nupperLimit = 117\r\nsafeLimits = [lowerLimit upperLimit]\r\n\u003c/pre\u003e\u003cp\u003eNOTE: the two limits are returned as the vector safeLimits, as shown.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eNOTE: Toolbox functions are generally not available from within Cody.\u003c/tt\u003e\u003c/p\u003e","function_template":"% Here you can describe your strategy.\r\nfunction safeLimits = getLimits(mu)\r\n % Here you can write your code.\r\nend","test_suite":"%% From EXAMPLE 1\r\nmu = 10;\r\nsafeLimits = [4 15];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%% From EXAMPLE 2:\r\nmu = 100;\r\nsafeLimits = [83 117];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%% Handle lower limit of zero:\r\nmu = 3;\r\nsafeLimits = [0 6];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 30;\r\nsafeLimits = [20 39];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 4;\r\nsafeLimits = [0 8];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 40;\r\nsafeLimits = [29 51];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 11;\r\nsafeLimits = [5 17];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 12;\r\nsafeLimits = [6 18];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 13;\r\nsafeLimits = [6 19];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 14;\r\nsafeLimits = [7 20];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 15;\r\nsafeLimits = [8 22];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 20;\r\nsafeLimits = [12 28];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 110;\r\nsafeLimits = [92 128];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 120;\r\nsafeLimits = [101 138];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n\r\n%% High rates\r\n% Not asserted in this problem\r\n\r\nmu = 150;\r\nsafeLimits = [129 170];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 200;\r\nsafeLimits = [176 224];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 300;\r\nsafeLimits = [271 329];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 1000;\r\nsafeLimits = [947 1052];\r\n%assert(isequal(getLimits(mu), safeLimits))","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-13T04:58:52.000Z","updated_at":"2025-11-21T18:54:27.000Z","published_at":"2017-12-13T07:41:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eIn this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eμ\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e (the mean rate parameter).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWhat follows is just a longer introduction to the concepts, including two examples.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSay that you manage the inventory of a business for which orders\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eon average\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, ....\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, N follows a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.britannica.com/topic/Poisson-distribution\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePoisson distribution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e : N ~ Poisson(μ). So there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: Pr(N=k) = μ^k × exp(−μ) / k!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etwo\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values of k:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elower\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eupper\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e limit on the number of customers, k, that will not be exceeded more than 5% of the time\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Input\\nmu = 10\\n% Output\\nlowerLimit = 4\\nupperLimit = 15\\nsafeLimits = [lowerLimit upperLimit]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Input\\nmu = 100\\n% Output\\nlowerLimit = 83\\nupperLimit = 117\\nsafeLimits = [lowerLimit upperLimit]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: the two limits are returned as the vector safeLimits, as shown.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE: Toolbox functions are generally not available from within Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1959,"title":"German tank problem","description":"The German tank problem is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its far reaching implications...)\r\nYou do not know how many tanks are in circulation (let's call this number N), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of k tanks, and find that, among the observed tanks, the maximum serial number is n. Your job is to predict N, the total number of tanks in circulation, from this observation alone (from the values of k and n).\r\nExample:\r\nYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be at least 77 tanks in circulation (although it seems unlikely that there were only 77). What is your best estimate of the total number of tanks in circulation?\r\nDetails:\r\nYour function takes the values n and k and must return your best estimate of the value of N.\r\nTo pass this problem you must be exactly correct in at least 10% of the testsuite cases.\r\nTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\r\nHints: (why my solution is not working? this formula is on wikipedia!!!)\r\nIn a typical scenario you may not really care about being \"exactly correct\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 459px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 229.5px; transform-origin: 407px 229.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGerman tank problem\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 273px 8px; transform-origin: 273px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003efar reaching implications\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.5px 8px; transform-origin: 8.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e...)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 227px 8px; transform-origin: 227px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou do not know how many tanks are in circulation (let's call this number\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 145.5px 8px; transform-origin: 145.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 37px 8px; transform-origin: 37px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e tanks, and find that, among the observed tanks, the maximum serial number is\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 68.5px 8px; transform-origin: 68.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Your job is to predict\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91.5px 8px; transform-origin: 91.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the total number of tanks in circulation, from this observation alone (from the values of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5px 8px; transform-origin: 31.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 305px 8px; transform-origin: 305px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23px 8px; transform-origin: 23px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eat least\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 77 tanks in circulation (although it seems unlikely that there were\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13px 8px; transform-origin: 13px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eonly\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 198px 8px; transform-origin: 198px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 77). What is your best estimate of the total number of tanks in circulation?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eDetails:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 95px 8px; transform-origin: 95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function takes the values\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 159px 8px; transform-origin: 159px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and must return your best estimate of the value of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 105.5px 8px; transform-origin: 105.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo pass this problem you must be\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52px 8px; transform-origin: 52px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eexactly correct\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 119.5px 8px; transform-origin: 119.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in at least 10% of the testsuite cases.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 329.5px 8px; transform-origin: 329.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20px 8px; transform-origin: 20px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHints:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 199px 8px; transform-origin: 199px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (why my solution is not working? this formula is on wikipedia!!!)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a typical scenario you may not really care about being \"exactly correct\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function N = tank_problem(k,m)\r\n% k: number of observations\r\n% m: maximum observed serial number\r\n% N: estimated number of tanks\r\n\r\nN=1;\r\nend","test_suite":"%%\r\n Ntest=1e4;\r\n N=randi([10,100],Ntest,1);\r\n K=randi([1 10],Ntest,1);\r\n M=arrayfun(@(N,K)max(randi(N,1,K)),N,K);\r\n N_hat=arrayfun(@tank_problem,K,M);\r\n rate=mean(N==N_hat);\r\n assert(rate\u003e=.1,sprintf('hit rate too low (%0.2f)',rate*100));\r\n fprintf('hit rate = %0.2f\\n',rate*100);\r\n","published":true,"deleted":false,"likes_count":7,"comments_count":1,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":33,"test_suite_updated_at":"2022-01-15T07:59:10.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-25T21:34:05.000Z","updated_at":"2026-03-02T17:47:43.000Z","published_at":"2013-10-25T22:07:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGerman tank problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003efar reaching implications\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e...)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou do not know how many tanks are in circulation (let's call this number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e tanks, and find that, among the observed tanks, the maximum serial number is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Your job is to predict\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the total number of tanks in circulation, from this observation alone (from the values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eat least\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 77 tanks in circulation (although it seems unlikely that there were\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eonly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 77). What is your best estimate of the total number of tanks in circulation?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDetails:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function takes the values\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and must return your best estimate of the value of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo pass this problem you must be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexactly correct\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in at least 10% of the testsuite cases.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHints:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (why my solution is not working? this formula is on wikipedia!!!)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a typical scenario you may not really care about being \\\"exactly correct\\\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44676,"title":"Mean = Standard Deviation","description":"Create a series with following properties;\r\n\r\n# All of the members should be positive integer\r\n# Mean of the series should be integer\r\n# Standard deviation of the series should be integer (use a normalization factor of N instead of N-1)\r\n# Mean should be equal to standard deviation\r\n\r\nFor example if input is 6, you can return the following series;\r\n\r\n out = [12 44 2 24 2 6]\r\n\r\n mean(out) = 15;\r\n std(out,1) = 15;\r\n \r\nAnother example; if input is 4, a possible solution;\r\n\r\n out = [24 2 2 48];\r\n mean(out) = 19;\r\n std(out,1) = 19;\r\n\r\n ","description_html":"\u003cp\u003eCreate a series with following properties;\u003c/p\u003e\u003col\u003e\u003cli\u003eAll of the members should be positive integer\u003c/li\u003e\u003cli\u003eMean of the series should be integer\u003c/li\u003e\u003cli\u003eStandard deviation of the series should be integer (use a normalization factor of N instead of N-1)\u003c/li\u003e\u003cli\u003eMean should be equal to standard deviation\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eFor example if input is 6, you can return the following series;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout = [12 44 2 24 2 6]\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emean(out) = 15;\r\nstd(out,1) = 15;\r\n\u003c/pre\u003e\u003cp\u003eAnother example; if input is 4, a possible solution;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout = [24 2 2 48];\r\nmean(out) = 19;\r\nstd(out,1) = 19;\r\n\u003c/pre\u003e","function_template":"function y = meanEqualsStd(x)\r\n y = x;\r\nend","test_suite":"%%\r\nfor idx = 5:50\r\n x = idx;\r\n Series = meanEqualsStd(x);\r\n mSeries = mean(Series);\r\n sSeries = std(Series,1);\r\n assert(length(Series)==x);\r\n assert(round(mSeries)==mSeries);\r\n assert(round(sSeries)==sSeries);\r\n assert(all(round(Series) == Series));\r\n assert(all(round(Series) \u003e= 1));\r\n assert(mSeries == sSeries);\r\nend\r\n\r\n%%\r\nfiletext = fileread('meanEqualsStd.m');\r\nassert(isempty(strfind(filetext, 'echo')))\r\nassert(isempty(strfind(filetext, 'assert')))","published":true,"deleted":false,"likes_count":5,"comments_count":11,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2018-06-14T07:03:27.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2018-06-07T01:30:24.000Z","updated_at":"2025-11-14T13:50:04.000Z","published_at":"2018-06-07T01:30:24.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate a series with following properties;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll of the members should be positive integer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMean of the series should be integer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStandard deviation of the series should be integer (use a normalization factor of N instead of N-1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMean should be equal to standard deviation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if input is 6, you can return the following series;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out = [12 44 2 24 2 6]\\n\\nmean(out) = 15;\\nstd(out,1) = 15;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnother example; if input is 4, a possible solution;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out = [24 2 2 48];\\nmean(out) = 19;\\nstd(out,1) = 19;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":3074,"title":"Compute the cokurtosis of a given portfolio.","description":"As input data, you are given a nObs-by-nAssets matrix _portRet_ of return series for assets in a portfolio along with an nAssets-by-1 vector _portWeights_ of portfolio weights. Example: \r\n\r\n \u003e\u003e nObs = 504; % Number of observations\r\n\r\n \u003e\u003e nAssets = 5; % Number of assets in the portfolio\r\n\r\n \u003e\u003e portRet = randn(nObs, nAssets); % Sample portfolio return series\r\n\r\n \u003e\u003e portWeights = rand(nAssets, 1); \r\n\r\n \u003e\u003e portWeights = portWeights/sum(portWeights); % Portfolio weights are \u003e=0 and sum to 1.\r\n\r\nThe task is to compute the *portfolio cokurtosis* , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\r\n\r\nhttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\r\n\r\nWrite a function that accepts _portRet_ and _portWeights_ as input arguments and returns the scalar statistic _portCokurt_ as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eAs input data, you are given a nObs-by-nAssets matrix \u003ci\u003eportRet\u003c/i\u003e of return series for assets in a portfolio along with an nAssets-by-1 vector \u003ci\u003eportWeights\u003c/i\u003e of portfolio weights. Example:\u003c/p\u003e\u003cpre\u003e \u0026gt;\u0026gt; nObs = 504; % Number of observations\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; nAssets = 5; % Number of assets in the portfolio\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portRet = randn(nObs, nAssets); % Sample portfolio return series\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portWeights = rand(nAssets, 1); \u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portWeights = portWeights/sum(portWeights); % Portfolio weights are \u0026gt;=0 and sum to 1.\u003c/pre\u003e\u003cp\u003eThe task is to compute the \u003cb\u003eportfolio cokurtosis\u003c/b\u003e , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\"\u003ehttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\u003c/a\u003e\u003c/p\u003e\u003cp\u003eWrite a function that accepts \u003ci\u003eportRet\u003c/i\u003e and \u003ci\u003eportWeights\u003c/i\u003e as input arguments and returns the scalar statistic \u003ci\u003eportCokurt\u003c/i\u003e as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\u003c/p\u003e","function_template":"function portCokurt = computePortCokurt(portRet, portWeights)\r\n\r\n\r\nend","test_suite":"%%\r\nrng('default')\r\nR = randn(504, 5);\r\nw = ones(5, 1)/5;\r\nassert(abs(computePortCokurt(R, w)-0.119749008958925)\u003c1e-3)\r\n\r\n%%\r\nrng('default')\r\nR = randn(252, 15);\r\nw = ones(15, 1)/15;\r\nassert(abs(computePortCokurt(R, w)-0.013012357540290)\u003c1e-3)\r\n\r\n%% \r\nrng('default')\r\nR = randn(100, 1);\r\nw = 1;\r\nassert(abs(computePortCokurt(R, w)-6.280759230562035)\u003c1e-3)\r\n\r\n%%\r\nrng('default')\r\nR = randn(5, 10);\r\nw = [0.1*ones(5, 1); 0.5; zeros(4, 1)];\r\nassert(abs(computePortCokurt(R, w)-0.169198885214440)\u003c1e-3)","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":2328,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-03-10T11:04:22.000Z","updated_at":"2015-03-11T18:00:35.000Z","published_at":"2015-03-10T11:25:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs input data, you are given a nObs-by-nAssets matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportRet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of return series for assets in a portfolio along with an nAssets-by-1 vector\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportWeights\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of portfolio weights. Example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ \u003e\u003e nObs = 504; % Number of observations\\n\\n \u003e\u003e nAssets = 5; % Number of assets in the portfolio\\n\\n \u003e\u003e portRet = randn(nObs, nAssets); % Sample portfolio return series\\n\\n \u003e\u003e portWeights = rand(nAssets, 1); \\n\\n \u003e\u003e portWeights = portWeights/sum(portWeights); % Portfolio weights are \u003e=0 and sum to 1.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe task is to compute the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportfolio cokurtosis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that accepts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportRet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportWeights\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as input arguments and returns the scalar statistic\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportCokurt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":59551,"title":"Range of Values in a Matrix","description":"Create a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 63px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 31.5px; transform-origin: 407px 31.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCreate a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function valueRange = find_range(matrixInput)\r\n % Your code goes here\r\nend\r\n","test_suite":"%% Test case 1\r\nx = [1, 2, 3; 4, 5, 6; 7, 8, 9];\r\ny_correct = 8;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 2\r\nx = [0.5, 0.75; 0.55, 0.7];\r\ny_correct = 0.25;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 3\r\nx = [100];\r\ny_correct = 0;\r\nassert(isequal(find_range(x), y_correct))\r\n\r\n%% Test case 4\r\nx = [2, 2, 2; 2, 2, 2];\r\ny_correct = 0;\r\nassert(isequal(find_range(x), y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3551517,"edited_by":3551517,"edited_at":"2024-01-09T19:35:19.000Z","deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2024-01-09T19:35:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-09T19:27:40.000Z","updated_at":"2026-03-23T06:24:32.000Z","published_at":"2024-01-09T19:28:19.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate a function that accepts a matrix of real numbers as input and returns the range of the values contained in the matrix. The range is defined as the difference between the maximum and minimum element. For a single-element matrix or a matrix with identical elements, the function should return 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47430,"title":"Find the standard deviation of an array","description":"given an array, find it's standard deviation without using the std() function. Use sample formula (n-1) for standard deviation and round it to the nearest integer.\r\nIf array is empty, return -1.\r\nnote: std() function also uses the sample formula (n-1) for standard deviation. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 102px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 51px; transform-origin: 407px 51px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133.5px 8px; transform-origin: 133.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003egiven an array, find it's standard deviation \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 98.5px 8px; transform-origin: 98.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003ewithout using the std() function.\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 124px 8px; transform-origin: 124px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Use sample formula (n-1) for standard deviation and round it to the nearest integer.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83px 8px; transform-origin: 83px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf array is empty, return -1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 246px 8px; transform-origin: 246px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003enote: std() function also uses the sample formula (n-1) for standard deviation. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = my_sd_deviation(x)\r\n y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('my_sd_deviation.m');\r\nassert(isempty(strfind(filetext, 'std')))\r\n\r\n%%\r\nx = [3 5 10 8 6 12 9 7 5 2 4 11 16];\r\ny_correct = 4;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [];\r\ny_correct = -1;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [1 1];\r\ny_correct = 0;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n%%\r\nx = [1 2 3 4 5 6 7 8 9 10];\r\ny_correct = 3;\r\nassert(isequal(my_sd_deviation(x),y_correct));\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":600646,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":30,"test_suite_updated_at":"2021-11-02T05:43:42.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-11-08T21:24:50.000Z","updated_at":"2026-02-17T08:37:54.000Z","published_at":"2020-11-08T21:24:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003egiven an array, find it's standard deviation \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewithout using the std() function.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Use sample formula (n-1) for standard deviation and round it to the nearest integer.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf array is empty, return -1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003enote: std() function also uses the sample formula (n-1) for standard deviation. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44278,"title":"Find the outlier in a set of samples","description":"Given an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.","description_html":"\u003cp\u003eGiven an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.\u003c/p\u003e","function_template":"function [p v] = outx(x)\r\n p=18;\r\n v=36;\r\nend","test_suite":"%%\r\nx = [ 3 4 71 8 10];\r\n[p v]=outx(x);\r\nassert(p==3)\r\nassert(v==71)\r\n\r\n%%\r\nx = [ 0 3 -7 -714 8 -10];\r\n[p v]=outx(x);\r\nassert(p==4)\r\nassert(v== -714)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":50,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-08-08T15:20:18.000Z","updated_at":"2025-11-27T00:30:22.000Z","published_at":"2017-08-08T15:20:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array x of numbers. Assumed that this array has one outlier. Find the position p and the value v of the outlier in this array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44281,"title":"Check if any duplicates in your data","description":"Given an array x, return a number n equal to the largest number of repetitions in your data.\r\nFor example:\r\nIf x=[1 2 3 6 8 4 9] then n=1.\r\nIf x=[2 3 6 2 6 2 2] then n=4.\r\n ","description_html":"\u003cp\u003eGiven an array x, return a number n equal to the largest number of repetitions in your data.\r\nFor example:\r\nIf x=[1 2 3 6 8 4 9] then n=1.\r\nIf x=[2 3 6 2 6 2 2] then n=4.\u003c/p\u003e","function_template":"function n = rept(x)\r\n n = mean(x);\r\nend","test_suite":"%%\r\nx=[1 2 3 6 8 4 9];\r\nn=1;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[2 3 6 2 6 2 2 -2 -7]; \r\nn=4;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[2 3 6 2 6 2 2 -2 -7 -7 6 6 6]; \r\nn=5;\r\nassert(isequal(rept(x),n))\r\n\r\n%%\r\nx=[8 8 8 6 8 8 8]; \r\nn=6;\r\nassert(isequal(rept(x),n))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":166,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-08-10T11:08:42.000Z","updated_at":"2026-03-05T16:43:10.000Z","published_at":"2017-08-10T11:08:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array x, return a number n equal to the largest number of repetitions in your data. For example: If x=[1 2 3 6 8 4 9] then n=1. If x=[2 3 6 2 6 2 2] then n=4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47673,"title":"Area under standard normal curve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 25.5px; transform-origin: 407px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCalculate the area under standard normal curve between two points. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10px; transform-origin: 404px 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ediff(normcdf([-3 0])) = 0.4987\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function area = areaUnderStandardNormalCurve(a,b)\r\n\r\nend","test_suite":"%%\r\na = -3;\r\nb = 0;\r\ny_correct = 0.4987;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -9;\r\nb = 0;\r\ny_correct = 0.5;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -1;\r\nb = 1;\r\ny_correct = 0.6827;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -2;\r\nb = 2;\r\ny_correct = 0.9545;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -3;\r\nb = 3;\r\ny_correct = 0.9973;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n%%\r\na = -1;\r\nb = 2.789;\r\ny_correct = 0.8387;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n\r\n%%\r\na = 1;\r\nb = 2.4;\r\ny_correct = 0.1505;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n\r\n%%\r\na = -2.7;\r\nb = -0.89;\r\ny_correct = 0.1833;\r\nassert(abs(areaUnderStandardNormalCurve(a,b)-y_correct)\u003c0.0001)\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-03T11:38:39.000Z","updated_at":"2026-03-28T21:01:32.000Z","published_at":"2020-12-03T11:38:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the area under standard normal curve between two points. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[diff(normcdf([-3 0])) = 0.4987]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60831,"title":"\"Identify and Replace Outliers in a Numeric Array\"","description":"Given a numeric array, identify outliers that are more than two standard deviations away from the mean and replace them with the mean of the non-outlier values.\r\nExample: \r\nInput:\r\nx = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\r\nProcessing:\r\nThe mean and standard deviation of the array are calculated.\r\nValues that are more than 2 standard deviations from the mean are considered outliers.\r\nOutliers are replaced by the mean of the remaining non-outlier elements.\r\nOutput:\r\ny = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 334.312px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 167.156px; transform-origin: 408px 167.156px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a numeric array, identify outliers that are more than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etwo standard deviations\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e away from the mean and replace them with the mean of the non-outlier values.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ex = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProcessing:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 392px 30.6562px; transform-origin: 392px 30.6562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe mean and standard deviation of the array are calculated.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValues that are more than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e2 standard deviations\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e from the mean are considered outliers.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 364px 10.2188px; text-align: left; transform-origin: 364px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutliers are replaced by the mean of the remaining non-outlier elements.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = your_fcn_name(x)\r\n y = x; % Replace with your solution\r\nend\r\n","test_suite":"% Test 1: Basic case with outliers\r\nx = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 2: No outliers in the dataset\r\nx = [5, 6, 5, 6, 5, 6, 5, 6, 5, 6];\r\ny_correct = x; % No change since no outliers\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 3: Identical elements in the array\r\nx = [10, 10, 10, 10, 10, 10, 10, 10, 10, 10];\r\ny_correct = x; % No outliers possible\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 4: Handling negative values with outliers\r\nx = [-100, -10, -10, -10, -5, -10, -10, -5, -1000];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 5: Single value input\r\nx = [42];\r\ny_correct = [42]; % No outliers in a single-value array\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 6: Mixed dataset with multiple outliers\r\nx = [1, 2, 3, 4, 5, 100, 6, 7, 8, 9, 10, -200];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 7: Multiple identical outliers\r\nx = [1, 1, 1, 1, 1, 50, 50, 50, 1, 1, 1, 1];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 8: All elements are outliers\r\nx = [1000, 2000, 3000, 4000, 5000];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 9: Edge case with only two elements\r\nx = [1, 100];\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\n% Test 10: Large dataset stress test\r\nx = randi([-500, 500], 1, 1000); % Generate random dataset\r\ny_correct = your_fcn_name(x); % Compute expected output dynamically\r\nassert(isequal(your_fcn_name(x), y_correct));\r\n\r\ndisp('All test cases passed successfully!');\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":4814940,"edited_by":4814940,"edited_at":"2025-03-24T01:12:34.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2025-03-24T01:12:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-03-24T01:04:46.000Z","updated_at":"2025-06-08T10:19:26.000Z","published_at":"2025-03-24T01:04:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a numeric array, identify outliers that are more than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etwo standard deviations\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e away from the mean and replace them with the mean of the non-outlier values.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex = [10, 12, 11, 50, 13, 9, 12, 8, 11, 200];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProcessing:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe mean and standard deviation of the array are calculated.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValues that are more than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2 standard deviations\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e from the mean are considered outliers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutliers are replaced by the mean of the remaining non-outlier elements.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey = [10, 12, 11, 13, 13, 9, 12, 8, 11, 13];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47613,"title":"Calculate F statistics ( One-way Analysis of Variance)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 232px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 116px; transform-origin: 407px 116px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCalculate F statistics by following the steps \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWikipedia\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eAssume balanced design.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 140px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 70px; transform-origin: 404px 70px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex = [6\t8\t13\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e8\t12\t9\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e4\t9\t11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e5\t11\t8\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e3\t6\t7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e4\t8\t12];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eF = 9.2647\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function F = oneWayAnovaF(x)\r\n\r\nend","test_suite":"%%\r\nx = [6\t8\t13\r\n8\t12\t9\r\n4\t9\t11\r\n5\t11\t8\r\n3\t6\t7\r\n4\t8\t12];\r\ny_correct = 9.2647;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n%%\r\nx =[ 25 6 21 22 14\r\n 28 30 3 2 13\r\n 5 29 26 10 24\r\n 28 16 29 3 25\r\n 20 25 21 4 7\r\n 4 6 23 25 16\r\n 10 14 23 22 14\r\n 17 28 13 11 20\r\n 29 24 21 29 22\r\n 29 29 6 2 23];\r\ny_correct = 1.0699;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%%\r\nx =[ 5.8526 0.7585 0.1190 2.6297\r\n 5.4972 0.5395 3.3712 6.5408\r\n 9.1719 5.3080 1.6218 6.8921\r\n 2.8584 7.7917 7.9428 7.4815\r\n 7.5720 9.3401 3.1122 4.5054\r\n 7.5373 1.2991 5.2853 0.8382\r\n 3.8045 5.6882 1.6565 2.2898\r\n 5.6782 4.6939 6.0198 9.1334];\r\ny_correct = 1.0378;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%% F is (almost) an integer\r\nx =[ -0.5536 -3.9960 1.0098\r\n -2.6001 -0.2397 8.1808\r\n -6.8236 0.9040 2.9747\r\n -9.8205 0.7594 1.1360\r\n -9.4491 -6.8518 4.7317]\r\ny_correct = 9;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n\r\n%% F is (almost) an integer\r\nx =[ -45.4808 91.7694 96.2151 -57.5649\r\n -20.5585 -31.8710 12.1575 -18.6353\r\n -1.0294 4.8946 28.6612 -3.6424\r\n -77.9321 14.2147 32.9444 -31.2570\r\n 18.1578 49.8679 0.1815 -21.2512\r\n 1.0996 68.3178 32.0347 -2.8978\r\n -73.2486 9.3672 51.8217 -17.7058]\r\ny_correct = 7;\r\nassert(abs(oneWayAnovaF(x)-y_correct)\u003c0.01)\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-25T08:04:51.000Z","updated_at":"2026-03-16T10:26:38.000Z","published_at":"2020-11-25T08:04:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate F statistics by following the steps \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/One-way_analysis_of_variance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWikipedia\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume balanced design.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [6\\t8\\t13\\n8\\t12\\t9\\n4\\t9\\t11\\n5\\t11\\t8\\n3\\t6\\t7\\n4\\t8\\t12];\\n]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eF = 9.2647\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44933,"title":"Vogel-Dobbener entropy","description":"*Vogel-Dobbener entropy* is a measure of dispersion for ordinal variables. \r\n\r\nGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\r\n\r\n VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))\r\n\r\nwhere F_1, ... F_k are the cumulative relative frequences, i.e.\r\n\r\n F_i = sum_{j=1}^i f_i\r\n\r\nThe Vogel-Dobbener entropy of a sample satisfies 0 \u003c= VD \u003c= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\r\n\r\n VD* = VD / (k - 1)\r\n\r\n*For example*, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\r\n\r\n* k = 5 (there are five distinct observations);\r\n* u_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\r\n* f_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\r\n* F_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\r\n* VD = 3.2183; (approx.)\r\n* VD* = 0.8046. (approx.)\r\n\r\n*Your task* is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample. *Round to four decimal digits.*\r\n\r\n*Hint*: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.","description_html":"\u003cp\u003e\u003cb\u003eVogel-Dobbener entropy\u003c/b\u003e is a measure of dispersion for ordinal variables.\u003c/p\u003e\u003cp\u003eGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\u003c/p\u003e\u003cpre\u003e VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))\u003c/pre\u003e\u003cp\u003ewhere F_1, ... F_k are the cumulative relative frequences, i.e.\u003c/p\u003e\u003cpre\u003e F_i = sum_{j=1}^i f_i\u003c/pre\u003e\u003cp\u003eThe Vogel-Dobbener entropy of a sample satisfies 0 \u0026lt;= VD \u0026lt;= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\u003c/p\u003e\u003cpre\u003e VD* = VD / (k - 1)\u003c/pre\u003e\u003cp\u003e\u003cb\u003eFor example\u003c/b\u003e, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\u003c/p\u003e\u003cul\u003e\u003cli\u003ek = 5 (there are five distinct observations);\u003c/li\u003e\u003cli\u003eu_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\u003c/li\u003e\u003cli\u003ef_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\u003c/li\u003e\u003cli\u003eF_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\u003c/li\u003e\u003cli\u003eVD = 3.2183; (approx.)\u003c/li\u003e\u003cli\u003eVD* = 0.8046. (approx.)\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cb\u003eYour task\u003c/b\u003e is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample. \u003cb\u003eRound to four decimal digits.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHint\u003c/b\u003e: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.\u003c/p\u003e","function_template":"function VDstar = vogel_dobbener_normalized(x)\r\n VDstar = 0;\r\nend","test_suite":"%%\r\nx = [2.7 3.3 2.0 3.3 1.7 3.7];\r\nvds_correct = 0.8046;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [linspace(0, 0, 5) linspace(1, 1, 20) linspace(2, 2, 43) linspace(3, 3, 13)];\r\nvds_correct = 0.6205;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [linspace(10, 10, 4) linspace(11, 11, 17) linspace(99, 99, 38) linspace(7777, 7777, 22)];\r\nvds_correct = 0.6511;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [-pi -pi -pi i i i];\r\nvds_correct = 1;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n%%\r\nx = [7777 7777 7777 7777 7777 7777];\r\nvds_correct = 0;\r\nassert(isequal(vogel_dobbener_normalized(x), vds_correct))\r\n\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":332395,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-07-08T09:39:50.000Z","updated_at":"2025-11-06T15:58:01.000Z","published_at":"2019-07-16T15:59:52.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eVogel-Dobbener entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a measure of dispersion for ordinal variables.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an ordered list of distinct observations u_(1), ..., u_(k) with observed relative frequencies f_1, ..., f_k, the Vogel-Dobbener entropy is defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ VD = -(sum_{i=1}^{k-1} (F_i log2(F_i) + (1 - F_i) log2(1 - F_i))]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere F_1, ... F_k are the cumulative relative frequences, i.e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ F_i = sum_{j=1}^i f_i]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Vogel-Dobbener entropy of a sample satisfies 0 \u0026lt;= VD \u0026lt;= (k - 1); the normalized Vogel-Dobbener entropy is thus defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ VD* = VD / (k - 1)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFor example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, suppose that your sample is [2.7 3.3 2.0 3.3 1.7 3.7]. Then:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ek = 5 (there are five distinct observations);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eu_(1), ..., u_(k) = [1.7 2.0 2.7 3.3 3.7]; (note that 3.3 only appears once);\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef_1, ..., f_k = [1/6 1/6 1/6 2/6 1/6];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eF_1, ..., F_k = [1/6 2/6 3/6 5/6 6/6];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVD = 3.2183; (approx.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVD* = 0.8046. (approx.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eYour task\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is to write a function that, given a list of observations (unordered and possibly containing duplicates) computes the normalized Vogel-Dobbener entropy VD* of the sample.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRound to four decimal digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHint\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: if all observations in the sample are the same, then k = 1, the sum in the definition of VD is empty, and VD equals zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55250,"title":"Find the minimum of the column-maximums of a matrix","description":"Given a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\r\n\r\nA = [5 3 4 3;1 2 6 3;1 1 4 4];\r\nm = minimax(A)\r\nm =\r\n 3\r\nNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 751.733px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 375.867px; transform-origin: 407px 375.867px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 284.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 142.25px; 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\" alt=\"A 3-by-4 matrix. Beneath it is a 1-by-4 row vector with the maximum value of each column of the matrix independently. Beneath that is the single minimum value of the row vector.\" data-image-state=\"image-loaded\" width=\"337\" height=\"279\"\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA = [5 3 4 3;1 2 6 3;1 1 4 4];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em = minimax(A)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003em =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371px 8px; transform-origin: 371px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 284.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 142.25px; text-align: left; transform-origin: 384px 142.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg 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alt=\"On the left, a 3-by-1 column vector; underneath is the scalar maximum value; under that is the minimum, which is the same value. On the right, a 1-by-4 row vector; underneath that is the same vector (representing the column maximums); under that is the minimum value.\" data-image-state=\"image-loaded\" width=\"583\" height=\"279\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function m = minimax(A)\r\nm = A;\r\nend","test_suite":"%% Check various size matrices\r\n A = [0.57 -0.74 -0.46 -0.7 2.06;-3.44 3.36 -1.14 1.94 -3.91;0.62 2.31 2.76 4.45 -1.1;1.95 -1.4 2.34 2.84 0.91];\r\n assert( isequal(minimax(A),1.95) )\r\n %%\r\n A = [-2.71 1.69;3.34 0;-4.84 -2.82;3.64 0.72;-4.22 -3.78];\r\n assert( isequal(minimax(A),1.69) )\r\n %%\r\n A = [-4.44 1.17 0.17 4.17 0.86 4.39;-4.44 0.2 -3.57 4.87 2.63 0.9;-3.47 3.64 0.59 0.05 -4.17 -0.59;-4.8 -4.02 -4.95 -2.29 1.62 4.42;-0.65 4.08 2.67 -3.99 0.17 1.56;3.32 -3.92 3.49 0.08 -3.29 -0.48];\r\n assert( isequal(minimax(A),2.63) )\r\n %%\r\n A = [0.54 -3.87 -1.4 -0.75 -1.06;1.8 -0.56 -3.6 -3.81 -4.97;-1.33 -2 -2.4 -0.05 -2.79;-2.61 -0.99 -4.13 2.06 -4.99;0.79 3.33 -0.71 -2.56 -3.11;3.67 -0.96 -2.43 2.85 -3.58;-0.93 -1.1 -2.02 -4.26 -2.32];\r\n assert( isequal(minimax(A),-1.06) )\r\n %%\r\n A = [0.99 -2.79;4.01 -0.17;4.39 -1.24];\r\n assert( isequal(minimax(A),-0.17) )\r\n %%\r\n A = [-4.32 -0.69 2.06;-0.64 4.62 1.45;-3.26 2.62 0.52;-4.74 -4.93 -2.82;4.55 1.8 2.72];\r\n assert( isequal(minimax(A),2.72) )\r\n %%\r\n A = [3.91 -1.82 4.09 3.53;3.56 1.09 0.92 -0.58;-0.98 4.1 -1.67 4.04];\r\n assert( isequal(minimax(A),3.91) )\r\n %%\r\n A = [2.16 -1.63 -1.78 0.49 0.53;-3.21 -3.12 -0.96 -4.51 -2.25];\r\n assert( isequal(minimax(A),-1.63) )\r\n\r\n %% Check row vector\r\n A = [3.19 2.28 -3.24 -1.4 -3.11 -4.99 -1.84 2];\r\n assert( isequal(minimax(A),-4.99) )\r\n for k = 1:5\r\n n = randi(7);\r\n A = rand(1,n);\r\n assert( isequal(minimax(A),min(A)) )\r\n end\r\n\r\n %% Check column vector\r\n A = [0.43;-0.61;-2.13;0.02;2.62;2.62];\r\n assert( isequal(minimax(A),2.62) )\r\n for k = 1:5\r\n n = randi(7);\r\n A = rand(n,1);\r\n assert( isequal(minimax(A),max(A)) )\r\n end\r\n\r\n %% Check scalar\r\n for k = 1:5\r\n A = randn;\r\n assert( isequal(minimax(A),A) )\r\n end","published":true,"deleted":false,"likes_count":8,"comments_count":3,"created_by":287,"edited_by":287,"edited_at":"2022-10-03T14:28:07.000Z","deleted_by":null,"deleted_at":null,"solvers_count":632,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-07-28T15:15:22.000Z","updated_at":"2026-04-05T16:28:34.000Z","published_at":"2022-10-03T14:28:07.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix A, find the maximum value of each column, then return the smallest of those maximum values (ie return the minimum of the column maximums).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"279\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"337\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A 3-by-4 matrix. Beneath it is a 1-by-4 row vector with the maximum value of each column of the matrix independently. Beneath that is the single minimum value of the row vector.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A = [5 3 4 3;1 2 6 3;1 1 4 4];\\nm = minimax(A)\\nm =\\n 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that your function should work for matrices with one row or column (ie vectors) and scalars (in which case m = A).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"279\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"583\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"On the left, a 3-by-1 column vector; underneath is the scalar maximum value; under that is the minimum, which is the same value. On the right, a 1-by-4 row vector; underneath that is the same vector (representing the column maximums); under that is the minimum value.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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- Average Partnership Contribution","description":"The (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-n matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for n innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be NaNs for the partnerships that did not occur. (This means the total team score is the last non-NaN value in each column.) The average across the n sample innings should ignore NaNs.\r\nIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\r\n\r\nx = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\r\nx =\r\n 0 50\r\n 20 80\r\n 30 120\r\n 70 130\r\n 70 140\r\n 80 180\r\n 100 180\r\n 110 200\r\n 140 NaN\r\n 150 NaN\r\n\r\navg = partnershipcontrib(x)\r\navg =\r\n 0.1250\r\n 0.1417\r\n 0.1333\r\n 0.1583\r\n 0.0250\r\n 0.1333\r\n 0.0667\r\n 0.0833\r\n 0.2000\r\n 0.0667","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1289.83px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 644.917px; transform-origin: 407px 644.917px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 127.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63.75px; text-align: left; transform-origin: 384px 63.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365px 8px; transform-origin: 365px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.5px 8px; transform-origin: 256.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003es for the partnerships that did not occur. (This means the total team score is the last non-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117.5px 8px; transform-origin: 117.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e value in each column.) The average across the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 96px 8px; transform-origin: 96px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sample innings should ignore \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.5px 8px; transform-origin: 5.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003es.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 369.5px 8px; transform-origin: 369.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 581.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 290.75px; text-align: left; transform-origin: 384px 290.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 969px;height: 576px\" 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alt=\"A two-column matrix of nondecreasing values, with the last non-NaN value highlighted in each column. This is converted to another 2-column matrix of the differences from row to row. The total score is shown below, being the highlighted values from before. The matrix is again converted to a new 2-column matrix where each value is the proportion of the total (given by the total score values from before). Finally, a column vector is calculated from the matrix by averaging across the columns.\" data-image-state=\"image-loaded\" width=\"969\" height=\"576\"\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 510.833px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 255.417px; transform-origin: 404px 255.417px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 304px 8.5px; tab-size: 4; transform-origin: 304px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 50\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 20 80\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 30 120\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 70 130\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 70 140\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 80 180\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 100 180\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 110 200\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 140 NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 150 NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eavg = partnershipcontrib(x)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eavg =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1250\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1417\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1333\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1583\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0250\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.1333\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0667\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0833\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.2000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0.0667\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function avg = partnershipcontrib(s)\r\n\r\navg = mean(s);\r\n\r\nend","test_suite":"%% 12 innings\r\nx = [93 10 9 14 5 46 165 7 42 14 8 88;164 44 10 31 58 55 169 24 72 18 9 98;227 53 10 34 100 144 173 31 145 63 10 228;NaN 58 25 63 NaN 178 241 35 158 69 32 356;NaN 67 143 178 NaN 195 497 58 180 84 45 372;NaN 67 151 342 NaN 277 592 69 188 88 60 379;NaN 88 224 370 NaN 311 592 75 206 151 102 476;NaN 90 NaN 376 NaN 319 643 131 226 185 102 502;NaN 93 NaN 396 NaN 325 668 131 NaN 219 105 522;NaN 101 NaN 431 NaN 326 707 136 NaN 253 109 554];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.127561166808451;0.12977999987294;0.155703562754948;0.0927761241656379;0.177085480116408;0.108446996646594;0.163626020105591;0.0902197552243652;0.0409854550106841;0.0605319499146062])) \u003c 1e-5 )\r\n%% 15 innings\r\nx = [0 36 0 16 40 2 107 49 0 58 15 0 31 35 15;47 149 13 35 43 14 115 92 0 136 106 6 62 51 35;68 204 255 96 102 23 197 183 49 136 133 45 141 72 50;71 237 382 300 114 29 211 199 55 173 137 71 142 78 102;118 308 NaN 507 132 85 217 304 62 173 204 79 162 78 133;132 319 NaN 542 251 96 238 321 67 173 248 87 181 157 150;172 NaN NaN 566 251 143 276 350 67 208 248 99 182 225 183;NaN NaN NaN 572 266 166 NaN 414 71 237 264 117 194 252 197;NaN NaN NaN 572 275 171 NaN 433 75 239 277 119 195 282 197;NaN NaN NaN 606 282 213 NaN 436 90 248 278 129 221 282 250];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.0935178799346019;0.123522199506716;0.214786109140686;0.107581946541405;0.144429580185992;0.105164272050944;0.100678940349023;0.0802158893817775;0.0295136677949011;0.0817011534864528])) \u003c 1e-5 )\r\n%% 10 innings\r\nx = [2 61 9 66 70 24 30 34 2 11;144 176 174 66 126 83 46 82 27 16;NaN 225 259 66 149 159 90 122 74 42;NaN 228 277 153 184 193 100 156 80 43;NaN 229 279 NaN 189 198 133 282 81 51;NaN 261 480 NaN 194 284 163 336 81 82;NaN 263 508 NaN 219 286 197 359 93 124;NaN 263 NaN NaN 220 299 214 466 98 159;NaN 269 NaN NaN 236 300 238 468 105 165;NaN 276 NaN NaN 239 313 239 526 112 193];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.131857056901008;0.25577103343828;0.166489867398518;0.115025818107455;0.0590566909925436;0.149512322121404;0.0855127326608772;0.0780370592130176;0.0413840570886391;0.0573537352490243])) \u003c 1e-5 )\r\n%% 17 innings\r\nx = [47 132 4 29 4 33 14 1 185 16 80 10 39 96 22 49 44;48 175 8 30 11 86 14 20 189 28 139 44 53 NaN 33 113 101;49 260 15 31 13 139 44 131 289 244 142 51 124 NaN NaN 179 103;60 277 70 77 13 161 187 143 321 331 146 55 124 NaN NaN 201 134;90 355 85 88 15 209 200 171 322 339 149 85 124 NaN NaN NaN 142;179 444 114 180 18 294 204 210 347 345 180 106 126 NaN NaN NaN 142;237 510 119 199 21 311 236 255 354 346 181 119 130 NaN NaN NaN 158;248 520 124 205 21 410 320 293 361 350 182 155 156 NaN NaN NaN 159;252 NaN 124 225 21 442 337 301 381 363 227 159 165 NaN NaN NaN 185;257 NaN 124 246 47 NaN 337 312 382 365 233 167 168 NaN NaN NaN 190];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.240994776523359;0.124242738355593;0.166998542784811;0.123630511079241;0.0693601120017177;0.133635916354558;0.0730614634765755;0.0807820974677455;0.0569861900798804;0.0682662046838182])) \u003c 1e-5 )\r\n%% 16 innings\r\nx = [38 32 56 7 87 67 6 0 42 53 8 78 28 0 19 6;140 NaN 75 51 87 100 320 21 55 119 196 100 28 63 47 48;168 NaN 92 148 87 110 329 130 55 122 218 101 32 89 101 48;213 NaN 136 283 219 115 337 176 65 167 318 117 43 196 121 85;266 NaN 150 306 252 169 339 182 86 226 357 373 50 215 121 89;290 NaN 160 347 331 NaN 379 230 106 254 415 439 61 246 130 189;NaN NaN 206 375 333 NaN 418 261 202 301 424 452 64 345 165 197;NaN NaN 206 384 333 NaN 418 273 210 314 455 494 69 353 172 239;NaN NaN 240 394 362 NaN 446 305 237 388 466 NaN 85 353 173 252;NaN NaN 248 403 NaN NaN 453 305 286 419 466 NaN 89 365 181 254];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.182240310458828;0.175892478952746;0.0875416554422805;0.152836201456442;0.112910555705271;0.12396440347931;0.113191687593507;0.0426419111443832;0.0783210269004195;0.0404773549272449])) \u003c 1e-5 )\r\n%% 6 innings\r\nx = [132 23 8 4 1 5;175 52 8 4 10 18;260 68 63 4 33 18;277 80 74 36 36 64;355 80 177 125 43 71;444 112 208 147 43 108;510 113 216 208 43 109;520 148 231 240 52 119;NaN 149 251 253 82 166;NaN 155 265 253 82 167];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.0817279080428618;0.0762315818040129;0.125787053267087;0.0983562962636804;0.169623320455451;0.13384999984987;0.0684430187318784;0.0996265908248745;0.156119498902202;0.0195055780101392])) \u003c 1e-5 )\r\n%% 2 innings\r\nx = [0 79;23 144;40 167;47 177;53 215;68 249;77 249;108 261;110 284;132 320];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.1234375;0.188683712121212;0.100331439393939;0.0421401515151515;0.0821022727272727;0.109943181818182;0.0340909090909091;0.136174242424242;0.0435132575757576;0.139583333333333])) \u003c 1e-5 )\r\n%% 7 innings\r\nx = [82 77 39 17 25 16 9;100 77 146 28 88 57 35;146 91 262 102 119 242 35;163 98 279 114 286 242 39;247 103 305 168 362 277 47;503 173 307 172 368 299 50;557 206 364 194 489 304 59;559 220 605 258 493 309 73;570 267 615 277 554 314 83;NaN NaN 631 293 556 315 86];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.107497876929982;0.11206962590314;0.173226871930385;0.0672585891886349;0.104632115131127;0.120518985511512;0.103128650710132;0.120309237160835;0.0739838287054595;0.0243239063603082])) \u003c 1e-5 )\r\n%% Only one partnership (non-NaN)\r\nx = [51 135 80 56 0 73 12 95 25 158 19 13 10 164 3 1 49 5;99 162 NaN 75 35 146 12 117 115 249 53 63 20 208 34 34 90 39;100 165 NaN 92 73 161 49 117 163 267 80 142 21 218 54 34 99 90;NaN 225 NaN 136 73 351 76 311 NaN 310 92 170 51 330 115 59 128 113;NaN 241 NaN 150 74 389 205 313 NaN 321 143 188 94 364 115 90 152 119;NaN 281 NaN 160 84 394 224 315 NaN 326 170 196 100 388 137 127 208 170;NaN 288 NaN 206 123 422 233 376 NaN 370 178 196 100 396 185 149 241 203;NaN 294 NaN 206 181 449 245 434 NaN 447 178 205 100 408 210 173 267 239;NaN 312 NaN 240 193 475 258 451 NaN 460 226 205 104 424 210 200 283 270;NaN 343 NaN 248 202 481 270 460 NaN NaN 226 231 104 424 210 200 426 271];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.211390656192859;0.168194620890675;0.0927330577536002;0.177194557307618;0.114920288442186;0.0786823138140911;0.0873894127938098;0.0799241281835184;0.0661291359874339;0.0496857216889986])) \u003c 1e-5 )\r\n%% No NaNs at all\r\nx = [2 114;9 116;18 195;20 266;50 294;206 316;215 326;229 340;242 390;247 422];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.139119673043345;0.0165397087322755;0.111820519216379;0.0881718057447666;0.0939041003895082;0.341855824395111;0.0300669647140089;0.0449277586967784;0.0855574956348217;0.0480361494330065])) \u003c 1e-5 )\r\n%% Only one innings - no NaNs\r\nx = [20;35;39;111;123;123;128;133;149;153];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.130718954248366;0.0980392156862745;0.0261437908496732;0.470588235294118;0.0784313725490196;0;0.0326797385620915;0.0326797385620915;0.104575163398693;0.0261437908496732])) \u003c 1e-5 )\r\n%% Only one innings - with NaNs\r\nx = [107;152;204;250;NaN;NaN;NaN;NaN;NaN;NaN];\r\navg = partnershipcontrib(x);\r\nassert( max(abs(avg - [0.428;0.18;0.208;0.184;NaN;NaN;NaN;NaN;NaN;NaN])) \u003c 1e-5 )","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T20:18:04.000Z","deleted_by":null,"deleted_at":null,"solvers_count":37,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:20:52.000Z","updated_at":"2025-12-06T12:44:27.000Z","published_at":"2022-11-08T15:10:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe (infamous) Duckworth-Lewis method uses statistical models of how much each wicket partnership is worth to an innings (on average). Given an 10-by-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix representing the score at the end of each partnership (either by the fall of a wicket or the innings ending) for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e innings, return a 10-element vector that gives the average proportion of the total score each wicket contributes. For innings that are completed without all 10 wickets falling, there will be \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003es for the partnerships that did not occur. (This means the total team score is the last non-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e value in each column.) The average across the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sample innings should ignore \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003es.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the total innings score is 200 and the first wicket fell at a score of 40, the first wicket was worth 0.2 of the total. If the second wicket fell at 60, it was worth 20 runs = 0.1 of the total. And so on.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"576\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"969\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"A two-column matrix of nondecreasing values, with the last non-NaN value highlighted in each column. This is converted to another 2-column matrix of the differences from row to row. The total score is shown below, being the highlighted values from before. The matrix is again converted to a new 2-column matrix where each value is the proportion of the total (given by the total score values from before). Finally, a column vector is calculated from the matrix by averaging across the columns.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [0 50;20 80;30 120;70 130;70 140;80 180;100 180;110 200;140 NaN;150 NaN]\\nx =\\n 0 50\\n 20 80\\n 30 120\\n 70 130\\n 70 140\\n 80 180\\n 100 180\\n 110 200\\n 140 NaN\\n 150 NaN\\n\\navg = partnershipcontrib(x)\\navg =\\n 0.1250\\n 0.1417\\n 0.1333\\n 0.1583\\n 0.0250\\n 0.1333\\n 0.0667\\n 0.0833\\n 0.2000\\n 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- Career Bowling Statistics","description":"Given a vector s of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\r\nNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\r\nCareer statistics are defined as:\r\nAverage = (total runs conceded) / (total wickets taken)\r\nSR = (total balls delivered) / (total wickets taken)\r\nEcon = (total runs conceded) / (total overs delivered)\r\n(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\r\nFor example:\r\ns = [\"23.1-8-55-5\";\"24-5-84-0\"];\r\nstats = bowlingstats(s)\r\nstats =\r\n 27.8000 56.6000 2.9470\r\nHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 472.533px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 236.267px; transform-origin: 407px 236.267px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 330px 8px; transform-origin: 330px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \"O-M-R-W\", such as \"31.4-8-123-4\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that overs can be given as integers (\"31\" meaning 31 complete overs) or as pseudo decimals (\"31.4\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100.5px 8px; transform-origin: 100.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCareer statistics are defined as:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 170.5px 8px; transform-origin: 170.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSR = (total balls delivered) / (total wickets taken)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 166.5px 8px; transform-origin: 166.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355.5px 8px; transform-origin: 355.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; tab-size: 4; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e\"23.1-8-55-5\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 44px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 44px 8.5px; \"\u003e\"24-5-84-0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = bowlingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 120px 8.5px; tab-size: 4; transform-origin: 120px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 27.8000 56.6000 2.9470\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = bowlingstats(bf)\r\n\r\ns = mean(bf);\r\n\r\nend","test_suite":"%% Random figures (of various levels of realism) part I\r\ns = [\"47-5-6-9\";\"4.4-43-167-1\";\"35-3-73-1\";\"37-8-158-1\";\"43-11-176-4\";\"28.3-39-136-2\";\"43-18-110-10\"];\r\nassert( max(abs(bowlingstats(s) - [29.5 51.0357142857143 3.46815955213436])) \u003c 0.001 )\r\ns = [\"38-8-148-4\";\"35-42-147-6\";\"39-45-164-1\";\"46.3-4-107-8\";\"16.4-24-113-9\"];\r\nassert( max(abs(bowlingstats(s) - [24.25 37.5357142857143 3.87630827783064])) \u003c 0.001 )\r\ns = [\"2-41-199-5\";\"46.1-48-41-4\";\"5-50-76-1\";\"43-22-142-6\";\"27-22-33-3\";\"1.3-17-65-6\";\"7.5-23-162-1\";\"30-39-44-7\";\"37-38-66-5\"];\r\nassert( max(abs(bowlingstats(s) - [21.7894736842105 31.5 4.15037593984962])) \u003c 0.001 )\r\ns = [\"16-4-85-5\";\"15.1-46-74-4\";\"22-33-171-2\";\"28-48-197-6\";\"7.3-28-11-2\";\"7.3-16-72-4\";\"12.5-5-200-10\"];\r\nassert( max(abs(bowlingstats(s) - [24.5454545454545 19.8181818181818 7.43119266055046])) \u003c 0.001 )\r\ns = [\"13-9-186-6\";\"38-1-177-3\";\"17.5-44-124-8\";\"19.5-43-179-7\";\"25-17-42-2\";\"11.5-5-52-3\";\"15-32-1-2\";\"15.5-21-19-6\"];\r\nassert( max(abs(bowlingstats(s) - [21.0810810810811 25.3513513513514 4.98933901918977])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part II\r\ns = [\"43.5-36-199-6\";\"2-10-104-3\"];\r\nassert( max(abs(bowlingstats(s) - [33.6666666666667 30.5555555555556 6.61090909090909])) \u003c 0.001 )\r\ns = [\"26.2-48-119-8\";\"26-31-180-7\";\"24.4-39-102-5\";\"8-4-107-6\"];\r\nassert( max(abs(bowlingstats(s) - [19.5384615384615 19.6153846153846 5.97647058823529])) \u003c 0.001 )\r\ns = [\"26-32-62-4\";\"7.3-49-89-2\";\"14-37-134-9\";\"47-49-165-7\";\"30-39-64-9\";\"27-27-132-10\";\"47.3-23-102-7\";\"13-32-32-6\"];\r\nassert( max(abs(bowlingstats(s) - [14.4444444444444 23.5555555555556 3.67924528301887])) \u003c 0.001 )\r\ns = [\"50-45-186-2\";\"32.1-27-22-8\";\"43.3-14-167-8\";\"25.1-9-31-1\";\"1.2-3-172-2\";\"13-9-120-7\"];\r\nassert( max(abs(bowlingstats(s) - [24.9285714285714 35.3928571428571 4.22603430877901])) \u003c 0.001 )\r\ns = [\"14-13-8-7\";\"40-10-88-7\";\"31-44-14-4\";\"35.4-2-196-5\"];\r\nassert( max(abs(bowlingstats(s) - [13.304347826087 31.4782608695652 2.53591160220994])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part III\r\ns = \"11-16-166-7\";\r\nassert( max(abs(bowlingstats(s) - [23.7142857142857 9.42857142857143 15.0909090909091])) \u003c 0.001 )\r\ns = [\"45-43-122-9\";\"13.3-6-153-3\";\"48.1-36-33-1\";\"23-28-99-7\";\"45-27-153-6\";\"24.2-16-61-3\";\"30-9-168-10\";\"1.4-16-136-10\";\"37.2-34-66-5\";\"21.3-27-108-7\"];\r\nassert( max(abs(bowlingstats(s) - [18.016393442623 28.4754098360656 3.79620034542314])) \u003c 0.001 )\r\ns = [\"15-37-129-8\";\"1-20-101-10\";\"15-35-107-2\";\"34-4-148-4\";\"8.2-38-55-1\";\"12-3-48-10\"];\r\nassert( max(abs(bowlingstats(s) - [16.8 14.6285714285714 6.890625])) \u003c 0.001 )\r\ns = [\"37-7-85-7\";\"40-33-108-10\";\"29.2-2-93-10\";\"19.1-12-126-8\";\"9-49-101-2\";\"45-48-159-9\";\"46.1-50-143-2\";\"38-4-114-8\";\"19-10-32-1\"];\r\nassert( max(abs(bowlingstats(s) - [16.859649122807 29.7543859649123 3.3997641509434])) \u003c 0.001 )\r\ns = [\"3-22-47-3\";\"46-33-148-4\";\"9-4-176-9\";\"5.4-20-155-4\";\"16.3-22-8-5\";\"34.1-21-111-8\";\"11.4-14-118-5\"];\r\nassert( max(abs(bowlingstats(s) - [20.0789473684211 19.8947368421053 6.05555555555556])) \u003c 0.001 )\r\n%% Random figures (of various levels of realism) part IV\r\ns = [\"34-25-192-3\";\"5-32-164-3\";\"50.4-38-85-1\";\"29-35-164-6\";\"11-7-89-10\";\"5-46-77-8\";\"4-19-142-3\";\"2.2-27-48-7\";\"17-50-36-1\"];\r\nassert( max(abs(bowlingstats(s) - [23.7380952380952 22.5714285714286 6.31012658227848])) \u003c 0.001 )\r\ns = [\"17-27-112-5\";\"17-1-158-3\";\"39-14-83-4\";\"20-50-17-7\";\"11.1-36-118-6\";\"29-11-128-5\";\"12.2-33-26-4\";\"9.4-7-174-6\"];\r\nassert( max(abs(bowlingstats(s) - [20.4 23.275 5.25886143931257])) \u003c 0.001 )\r\ns = \"28-17-161-2\";\r\nassert( max(abs(bowlingstats(s) - [80.5 84 5.75])) \u003c 0.001 )\r\ns = \"23.3-35-107-3\";\r\nassert( max(abs(bowlingstats(s) - [35.6666666666667 47 4.5531914893617])) \u003c 0.001 )\r\ns = [\"21-29-169-6\";\"22.5-28-158-1\"];\r\nassert( max(abs(bowlingstats(s) - [46.7142857142857 37.5714285714286 7.46007604562738])) \u003c 0.001 )\r\n%% Tim Southee's stats\r\nts = [\"23.1-8-55-5\";\"24-5-84-0\";\"16-2-59-0\";\"18-3-63-4\";\"16.2-5-62-1\";\"27-1-100-0\";\"18-1-94-2\";\"12-2-58-0\";\"17-4-62-1\";\"31-8-64-2\";\"16-2-62-1\";\"11-4-41-3\";\"19-4-68-0\";\"19-3-61-4\";\"23-4-89-2\";\"33-6-119-3\";\"4-0-11-0\";\"29-5-94-1\";\"32-10-82-2\";\"1.4-0-10-0\";\"28-7-102-2\";\"15-2-49-2\";\"28.2-5-103-2\";\"1-0-11-0\";\"12-2-32-1\";\"19-3-77-2\";\"3.5-0-8-2\";\"8-2-20-0\";\"10-1-40-0\";\"26-4-100-0\";\"19-5-70-2\";\"14-4-30-1\";\"24-6-64-7\";\"18-3-68-1\";\"18-4-46-4\";\"22-4-62-5\";\"20-5-58-3\";\"15-3-45-1\";\"36-8-94-0\";\"32-9-77-0\";\"23.2-9-44-3\";\"30-6-77-2\";\"28.2-8-58-4\";\"19-4-50-6\";\"26-6-76-2\";\"15-4-51-0\";\"16-1-52-4\";\"29.1-4-101-2\";\"15.5-2-58-2\";\"11-2-24-3\";\"28-3-79-4\";\"8.5-5-12-3\";\"19-6-38-3\";\"23-4-81-3\";\"20-0-93-3\";\"16-3-50-2\";\"16.2-9-19-4\";\"9-2-32-2\";\"30-9-69-1\";\"4-1-21-0\";\"21-8-63-1\";\"16-4-28-3\";\"23-5-62-0\";\"9-0-33-0\";\"30-5-67-3\";\"11-3-21-1\";\"24-4-54-1\";\"11-3-20-0\";\"12-4-17-2\";\"37-8-91-4\";\"26-3-87-1\";\"17.4-6-41-1\";\"24-1-104-1\";\"34-4-162-2\";\"30-5-83-4\";\"18-7-43-1\";\"24-8-70-1\";\"29-6-88-0\";\"25-4-97-4\";\"17-1-50-1\";\"16-1-58-0\";\"27-4-71-3\";\"21-6-52-3\";\"21-5-63-3\";\"12.3-2-26-4\";\"31-5-87-2\";\"25-4-85-0\";\"7-2-30-1\";\"17-8-28-2\";\"15-3-68-2\";\"28-14-73-1\";\"14-7-35-1\";\"23-3-80-1\";\"35-5-114-1\";\"16-6-46-3\";\"19-11-20-2\";\"23.4-10-53-3\";\"21-4-80-6\";\"24-6-60-2\";\"34-5-158-2\";\"13-5-34-1\";\"28.3-7-94-5\";\"12.5-2-48-3\";\"27-7-98-2\";\"6-2-17-1\";\"19-9-34-2\";\"19-3-71-2\";\"10-3-25-4\";\"26-4-86-1\";\"26-7-62-6\";\"19-4-65-1\";\"25-5-56-1\";\"12-3-42-3\";\"27-7-68-6\";\"25-8-52-2\";\"15-5-35-3\";\"27-13-61-2\";\"14-2-76-3\";\"24-4-98-3\";\"15-2-52-1\";\"12-1-57-0\";\"7-3-17-0\";\"12-2-33-1\";\"29-7-63-4\";\"12-6-15-2\";\"32-7-88-4\";\"20-4-60-1\";\"37-4-90-2\";\"30.2-7-93-4\";\"21.1-6-69-5\";\"33.1-6-103-3\";\"15-3-44-0\";\"20.1-5-49-4\";\"21-6-61-5\";\"13-5-38-2\";\"11-2-36-3\";\"19-7-35-4\";\"15-2-62-1\";\"17.4-6-32-5\";\"22-4-96-2\";\"26-7-69-2\";\"23-8-33-2\";\"23-7-61-2\";\"20-8-45-0\";\"25.1-8-43-6\";\"17-1-37-1\";\"22-6-64-1\";\"19-4-48-4\";\"27.4-6-69-5\";\"22-2-75-3\";\"22-6-43-0\";\"13-2-31-0\";\"38-4-114-2\";\"5-2-21-1\";\"12-4-28-3\";\"17-6-54-1\";\"12-2-33-1\";\"17.4-6-35-5\";\"32-11-75-1\";\"26-5-90-2\";\"14-3-55-4\";\"23.5-5-87-0\";\"32-1-154-0\";\"11-0-67-1\";\"23-2-100-3\";\"19-5-68-1\"];\r\nassert( max(abs(bowlingstats(ts) - [28.9942363112392 57.8933717579251 3.00492807008811])) \u003c 0.001 )\r\n%% No wickets\r\nfor k = 1:4\r\n n = randi(10);\r\n o = randi([1 50],n,1);\r\n m = randi(50,n,1);\r\n r = randi([1 200],n,1);\r\n w = zeros(n,1);\r\n bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n s = bowlingstats(bf);\r\n assert( isinf(s(1)) )\r\n assert( isinf(s(2)) )\r\nend\r\n%% No runs or wickets\r\nfor k = 1:4\r\n n = randi(10);\r\n o = randi([1 50],n,1);\r\n m = randi(50,n,1);\r\n r = zeros(n,1);\r\n w = zeros(n,1);\r\n bf = o + \"-\" + m + \"-\" + r + \"-\" + w;\r\n s = bowlingstats(bf);\r\n assert( isnan(s(1)) )\r\n assert( isinf(s(2)) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":28,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:38:20.000Z","updated_at":"2025-12-06T12:54:56.000Z","published_at":"2022-11-08T15:13:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a bowler's individual innings records, return their career statistics as a 3-element (numeric) row vector representing: average, strike rate, and economy rate. The strings will be in the standard form: \\\"O-M-R-W\\\", such as \\\"31.4-8-123-4\\\" to represent 31 overs and 4 balls, 8 maidens, 123 runs conceded, 4 wickets taken.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that overs can be given as integers (\\\"31\\\" meaning 31 complete overs) or as pseudo decimals (\\\"31.4\\\" meaning 31 overs and 4 balls). All overs can be assumed to be 6-balls. All other statistics will be integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCareer statistics are defined as:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAverage = (total runs conceded) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSR = (total balls delivered) / (total wickets taken)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEcon = (total runs conceded) / (total overs delivered)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Note that economy is given as runs/over. However, it can be calculated as runs/ball then converted to runs/over, assuming 6 balls/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"23.1-8-55-5\\\";\\\"24-5-84-0\\\"];\\nstats = bowlingstats(s)\\nstats =\\n 27.8000 56.6000 2.9470]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the bowler has delivered 47.1 overs (= 47*6 + 1 = 282 + 1 = 283 balls), conceded 139 runs, and taken 5 wickets. Hence their average is 139/5 = 27.8, their strike rate is 283/5 = 56.6, and their economy rate is 139/47.1667 = 2.947. (Or equivalently, economy = 139/283 = 0.4912 runs/ball, which converts to 0.4912*6 = 2.947 runs/over.)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44449,"title":"Moving Median Absolute Deviation","description":"The median absolute deviation (MAD) is defined as\r\nMAD = median(abs(A − median(A)))\r\nfor a rolling window of length n. For example:\r\nmove_mad(1:10, 5) \r\nreturns [1 1 1 1 1 1];\r\n\r\nmove_mad(logspace(0, 1, 10), 3) \r\nreturns [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]\r\nRound the result to 3 digits.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 225.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 112.8px; transform-origin: 407px 112.8px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 161px 8px; transform-origin: 161px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe median absolute deviation (MAD) is defined as\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 76px 8.5px; transform-origin: 76px 8.5px; \"\u003eMAD = median(abs(A \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(255, 0, 0); border-block-start-color: rgb(255, 0, 0); border-bottom-color: rgb(255, 0, 0); border-inline-end-color: rgb(255, 0, 0); border-inline-start-color: rgb(255, 0, 0); border-left-color: rgb(255, 0, 0); border-right-color: rgb(255, 0, 0); border-top-color: rgb(255, 0, 0); caret-color: rgb(255, 0, 0); color: rgb(255, 0, 0); column-rule-color: rgb(255, 0, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(255, 0, 0); perspective-origin: 8px 8.5px; text-decoration: none; text-decoration-color: rgb(255, 0, 0); text-emphasis-color: rgb(255, 0, 0); transform-origin: 8px 8.5px; \"\u003e− \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003emedian(A)))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 140.5px 8px; transform-origin: 140.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003efor a rolling window of length n. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.167px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 51.0833px; transform-origin: 404px 51.0833px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 72px 8.5px; transform-origin: 72px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emove_mad(1:10, 5) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 88px 8.5px; transform-origin: 88px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003ereturns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003e[1 1 1 1 1 1]\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 128px 8.5px; transform-origin: 128px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emove_mad(logspace(0, 1, 10), 3) \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 228px 8.5px; transform-origin: 228px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 32px 8.5px; transform-origin: 32px 8.5px; \"\u003ereturns \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 196px 8.5px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 196px 8.5px; \"\u003e[0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87px 8px; transform-origin: 87px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRound the result to 3 digits.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function stats = move_mad(v, n)\r\n \r\nend","test_suite":"%%\r\nv = 1:10;\r\nn = 5;\r\nstats_correct = [1 1 1 1 1 1];\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);\r\n\r\n%%\r\nv = logspace(0, 1, 10);\r\nn = 3;\r\nstats_correct = [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748];\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);\r\n\r\n%%\r\nrng('default');\r\nv = randn(1000, 1);\r\nn = 990;\r\nstats_correct = 0.660 * ones(1, 11);\r\nstats = move_mad(v, n);\r\nassert(sum(abs(stats - stats_correct)) \u003c 1e-3);","published":true,"deleted":false,"likes_count":0,"comments_count":6,"created_by":8269,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2021-07-03T05:49:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-12T08:08:02.000Z","updated_at":"2025-11-18T17:43:42.000Z","published_at":"2017-12-12T08:08:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe median absolute deviation (MAD) is defined as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[MAD = median(abs(A − median(A)))]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor a rolling window of length n. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[move_mad(1:10, 5) \\nreturns [1 1 1 1 1 1];\\n\\nmove_mad(logspace(0, 1, 10), 3) \\nreturns [0.292 0.377 0.486 0.628 0.811 1.048 1.353 1.748]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRound the result to 3 digits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44940,"title":"Get Next Combination","description":"Return next combination\r\n\r\nFor example three-element combinations of 1:5\r\n \r\n 1 2 3\r\n\r\n 1 2 4\r\n\r\n 1 2 5\r\n\r\n 1 3 4\r\n\r\n 1 3 5\r\n\r\n 1 4 5\r\n\r\n 2 3 4\r\n\r\n 2 3 5\r\n\r\n 2 4 5\r\n\r\n 3 4 5\r\n\r\nfunction should return\r\n\r\n nextComb([1 3 5],5)\r\n ans = [1 4 5] \r\n\r\nFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\r\n","description_html":"\u003cp\u003eReturn next combination\u003c/p\u003e\u003cp\u003eFor example three-element combinations of 1:5\u003c/p\u003e\u003cpre\u003e 1 2 3\u003c/pre\u003e\u003cpre\u003e 1 2 4\u003c/pre\u003e\u003cpre\u003e 1 2 5\u003c/pre\u003e\u003cpre\u003e 1 3 4\u003c/pre\u003e\u003cpre\u003e 1 3 5\u003c/pre\u003e\u003cpre\u003e 1 4 5\u003c/pre\u003e\u003cpre\u003e 2 3 4\u003c/pre\u003e\u003cpre\u003e 2 3 5\u003c/pre\u003e\u003cpre\u003e 2 4 5\u003c/pre\u003e\u003cpre\u003e 3 4 5\u003c/pre\u003e\u003cp\u003efunction should return\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003enextComb([1 3 5],5)\r\nans = [1 4 5] \r\n\u003c/pre\u003e\u003cp\u003eFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\u003c/p\u003e","function_template":"function y = nextComb(vec,n)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(nextComb([1 2 3],5),[1 2 4]))\r\n%%\r\nassert(isequal(nextComb([2 4 5],5),[3 4 5]))\r\n%%\r\nassert(isequal(nextComb([3 4 5],5),[3 4 5])) % if it is the last combination in the list; return itself\r\n%%\r\nassert(isequal(nextComb([1 3 4 5],5),[2 3 4 5]))\r\n%%\r\nassert(isequal(nextComb([5 6 7 10],10),[5 6 8 9]))\r\n%%\r\nassert(isequal(nextComb([2 3 4 5 10],10),[2 3 4 6 7]))\r\n%%\r\nassert(isequal(nextComb([5 7 8 9 10],10),[6 7 8 9 10]))\r\n%%\r\nassert(isequal(nextComb([8 10 17 19 20],20),[8 10 18 19 20]))\r\n%%\r\nassert(isequal(nextComb([11 13 17 18 20],20),[11 13 17 19 20]))\r\n%%\r\nassert(isequal(nextComb([7 8 13 17 19 20],20),[7 8 13 18 19 20]))\r\n%%\r\nassert(isequal(nextComb([10 11 17 18 19 20],20),[10 12 13 14 15 16]))\r\n%%\r\nassert(isequal(nextComb([12 16 17 18 19 20],20),[13 14 15 16 17 18]))\r\n%%\r\nassert(isequal(nextComb([1:6 9 14 19 20 21 23 26 29 30],30),[1:6 9 14 19 20 21 23 27 28 29]))\r\n%%\r\nassert(isequal(nextComb([1:5 7 11 13 15 18 19 21 22 27 29],30),[1:5 7 11 13 15 18 19 21 22 27 30]))\r\n%%\r\nassert(isequal(nextComb([1:4 6 10 13 14 15 18 19 20 23 24 30],30),[1:4 6 10 13 14 15 18 19 20 23 25 26]))\r\n%%\r\nassert(isequal(nextComb([1:17 19 23 27 33 40 42 48 50],50),[1:17 19 23 27 33 40 42 49 50]))\r\n%%\r\nassert(isequal(nextComb([1:17 22 24 27 30 35 36 38 44],50),[1:17 22 24 27 30 35 36 38 45]))\r\n%%\r\nassert(isequal(nextComb([1:16 18 19 22 23 27 44 45 46 48],50),[1:16 18 19 22 23 27 44 45 46 49]))\r\n%%\r\nassert(isequal(nextComb([1:13 15 23 25 26 28 30 32 38 39 42 46 50],50),[1:13 15 23 25 26 28 30 32 38 39 42 47 48]))\r\n\r\n\r\n\r\n%%\r\nassert(isequal(nextComb(1,9),2))\r\n%%\r\nassert(isequal(nextComb(2,9),3))\r\n%%\r\nassert(isequal(nextComb(3,9),4))\r\n%%\r\nassert(isequal(nextComb(4,9),5))\r\n%%\r\nassert(isequal(nextComb(5,9),6))\r\n%%\r\nassert(isequal(nextComb(6,9),7))\r\n%%\r\nassert(isequal(nextComb(7,9),8))\r\n%%\r\nassert(isequal(nextComb(8,9),9))\r\n%%\r\nassert(isequal(nextComb(9,9),9))\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":1,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2019-08-08T07:06:35.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-08T05:11:06.000Z","updated_at":"2025-12-04T21:02:10.000Z","published_at":"2019-08-08T05:11:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn next combination\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example three-element combinations of 1:5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1 2 3\\n\\n 1 2 4\\n\\n 1 2 5\\n\\n 1 3 4\\n\\n 1 3 5\\n\\n 1 4 5\\n\\n 2 3 4\\n\\n 2 3 5\\n\\n 2 4 5\\n\\n 3 4 5]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efunction should return\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[nextComb([1 3 5],5)\\nans = [1 4 5]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFirst input is the vector of which the next combination is searched. Second input is the scalar which indicates the length of the vector (1:n).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56498,"title":"Cricket - Career Batting Statistics","description":"Given a vector s of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\"42\") or a number with an asterisk (\"42*\") to represent \"not-out\".\r\nBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\r\nHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\r\nCenturies is the number of scores greater than 99 (whether out or not-out)\r\nDucks is the number of scores of 0 out (0* should not be counted)\r\nFor example:\r\ns = [\"53\";\"163*\";\"6\";\"186\";\"147*\";\"0\";\"89*\";\"0*\"];\r\nstats = battingstats(s)\r\nstats =\r\n 161 2 3 1\r\nIn this case, the batter has a total of sum([53 163 6 186 147 0 89 0]) = 644 runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 381.9px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 190.95px; transform-origin: 407px 190.95px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a vector \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 325px 8px; transform-origin: 325px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\"42\") or a number with an asterisk (\"42*\") to represent \"not-out\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347px 8px; transform-origin: 347px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 240.5px 8px; transform-origin: 240.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 234.5px 8px; transform-origin: 234.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCenturies is the number of scores greater than 99 (whether out or not-out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDucks is the number of scores of 0 out (0* should \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 11.5px 8px; transform-origin: 11.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enot\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 40px 8px; transform-origin: 40px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e be counted)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 200px 8.5px; tab-size: 4; transform-origin: 200px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es = [\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e\"53\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003e\"163*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e\"6\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e\"186\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 24px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 24px 8.5px; \"\u003e\"147*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e\"0\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e\"89*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e\"0*\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats = battingstats(s)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003estats =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 161 2 3 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 115px 8px; transform-origin: 115px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn this case, the batter has a total of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144px 8px; transform-origin: 144px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003esum([53 163 6 186 147 0 89 0]) = 644\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 107.5px 8px; transform-origin: 107.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function stats = battingstats(s)\r\nstats = mean(s);\r\nend","test_suite":"%% 1 score\r\ns = \"0\";\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 0) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 0 1]) )\r\n%% 3 scores\r\ns = [\"0\",\"196*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 98) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 1 2]) )\r\n%% 4 scores\r\ns = [\"27\",\"0\",\"34\",\"9*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 23.3333) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[0 0 1]) )\r\n%% 7 scores\r\ns = [\"13\",\"31*\",\"55\",\"39*\",\"16*\",\"0\",\"0*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 51.3333) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[1 0 1]) )\r\n%% 11 scores\r\ns = [\"46\",\"16\",\"0\",\"79\",\"84\",\"23*\",\"21*\",\"55\",\"86\",\"85*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 61.875) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 0 2]) )\r\n%% 16 scores\r\ns = [\"14\",\"72*\",\"64\",\"2\",\"47\",\"87\",\"3\",\"14\",\"3\",\"82*\",\"41\",\"27*\",\"56\",\"22\",\"159*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 57.75) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 1 1]) )\r\n%% 22 scores\r\ns = [\"104\",\"0\",\"59\",\"0*\",\"82*\",\"97*\",\"12*\",\"108*\",\"112\",\"11\",\"0*\",\"69*\",\"0\",\"186\",\"71*\",\"43*\",\"14*\",\"2*\",\"0\",\"0\",\"154\",\"34*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 115.8) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 5 4]) )\r\n%% 26 scores\r\ns = [\"62*\",\"106*\",\"12*\",\"50\",\"5\",\"39\",\"8*\",\"48*\",\"60*\",\"127*\",\"0\",\"58\",\"28*\",\"0\",\"0\",\"0*\",\"6*\",\"128*\",\"67*\",\"6\",\"26\",\"77\",\"5\",\"0\",\"69*\",\"44\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 79.3077) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[7 3 4]) )\r\n%% 30 scores\r\ns = [\"6*\",\"0\",\"0\",\"9\",\"0\",\"17\",\"0*\",\"0\",\"39*\",\"0\",\"26\",\"68*\",\"49\",\"50\",\"17*\",\"4*\",\"162*\",\"78*\",\"39\",\"17\",\"0*\",\"52\",\"104*\",\"81\",\"0\",\"87\",\"95\",\"0*\",\"160*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 64.4444) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[7 3 7]) )\r\n%% 32 scores\r\ns = [\"0\",\"55*\",\"50\",\"125\",\"46\",\"76\",\"29*\",\"0\",\"21\",\"74\",\"0*\",\"62\",\"64\",\"0\",\"0\",\"90\",\"73*\",\"33*\",\"0*\",\"14*\",\"33*\",\"29*\",\"0\",\"53*\",\"7\",\"78*\",\"152\",\"20\",\"79*\",\"87\",\"92*\",\"45\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 78.2632) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[13 2 5]) )\r\n%% 34 scores\r\ns = [\"25\",\"62*\",\"108\",\"26\",\"124*\",\"50\",\"82\",\"16\",\"69*\",\"0*\",\"10\",\"65*\",\"23*\",\"91*\",\"0\",\"76\",\"50*\",\"0*\",\"76\",\"21\",\"96*\",\"89*\",\"129*\",\"29*\",\"1*\",\"0\",\"17\",\"0\",\"42\",\"19*\",\"55\",\"55\",\"0\",\"97\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 84.3684) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[14 3 4]) )\r\n%% 35 scores\r\ns = [\"15*\",\"0*\",\"65*\",\"26\",\"11\",\"36*\",\"12\",\"70*\",\"0*\",\"16*\",\"128\",\"0\",\"34*\",\"0*\",\"12\",\"1*\",\"0\",\"164\",\"0\",\"49*\",\"34*\",\"0\",\"55\",\"134\",\"0\",\"95*\",\"125*\",\"140\",\"2*\",\"108\",\"23*\",\"60\",\"35\",\"34*\",\"0\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 82.4444) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[5 6 6]) )\r\n%% 36 scores\r\ns = [\"0\",\"29\",\"34\",\"31*\",\"72*\",\"57*\",\"139*\",\"81*\",\"14*\",\"162*\",\"33*\",\"59*\",\"7*\",\"135*\",\"165*\",\"146\",\"100*\",\"75*\",\"0*\",\"0\",\"49\",\"46*\",\"13*\",\"85\",\"0*\",\"1*\",\"11*\",\"36*\",\"0*\",\"201\",\"0\",\"9*\",\"38*\",\"0\",\"43\",\"30\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 158.4167) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[6 7 4]) )\r\n%% 40 scores\r\ns = [\"81*\",\"51\",\"46\",\"0*\",\"8\",\"14*\",\"8*\",\"133\",\"9\",\"128*\",\"0*\",\"65*\",\"112*\",\"64\",\"0\",\"0\",\"0*\",\"0\",\"0\",\"0\",\"94*\",\"0\",\"0*\",\"1\",\"79*\",\"48*\",\"84*\",\"97\",\"71\",\"11*\",\"25\",\"27*\",\"10*\",\"25*\",\"0\",\"71\",\"28*\",\"0\",\"0*\",\"98*\"];\r\nstats = battingstats(s);\r\nassert( abs(stats(1) - 78.3158) \u003c 0.001 )\r\nassert( isequal(stats(2:4),[11 3 8]) )\r\n\r\n%% No runs, no outs (average = NaN, ducks = 0)\r\nfor k = 1:4\r\n s = repmat(\"0*\",randi(8),1);\r\n stats = battingstats(s);\r\n assert( isnan(stats(1)) \u0026 isequal(stats(2:4),[0 0 0]) )\r\nend\r\n\r\n%% Runs, no outs (average = Inf, ducks = 0)\r\nfor k = 1:4\r\n s = string(randi([1 99],randi(8),1)) + \"*\";\r\n stats = battingstats(s);\r\n assert( isinf(stats(1)) \u0026 isequal(stats(3:4),[0 0]) )\r\nend\r\n\r\n%% Runs, all half-centuries, no not-outs\r\nfor k = 1:10\r\n n = randi(50);\r\n x = randi([51 99],n,1);\r\n s = string(x);\r\n stats = battingstats(s);\r\n % batting average = mean\r\n assert( abs(mean(x) - stats(1)) \u003c 1e-8 )\r\n % No ducks, no centuries, all 50s\r\n assert( isequal(stats(2:4),[n 0 0]) )\r\nend\r\n\r\n%% Ducks, 50s, 100s, no not-outs\r\nfor k = 1:10\r\n n1 = randi(10);\r\n n2 = randi(10);\r\n n3 = randi(10);\r\n x = [zeros(n1,1);50;99;randi([50 99],n2,1);100;randi([100 400],n3,1)];\r\n s = string(x);\r\n stats = battingstats(s);\r\n % batting average = mean\r\n assert( abs(mean(x) - stats(1)) \u003c 1e-8 )\r\n % No ducks, no centuries, all 50s\r\n assert( isequal(stats(2:4),[(n2+2) (n3+1) n1]) )\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2022-11-08T15:11:27.000Z","deleted_by":null,"deleted_at":null,"solvers_count":35,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T18:31:11.000Z","updated_at":"2025-12-06T12:50:07.000Z","published_at":"2022-11-08T15:11:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of strings representing a batter's individual innings scores, return their career statistics as a 4-element (numeric) row vector representing: average, half-centuries, centuries, and ducks. The strings will be in the form of a number (\\\"42\\\") or a number with an asterisk (\\\"42*\\\") to represent \\\"not-out\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBatting average is defined as total runs divided by number of dismissals (ie number of innings - number of not-outs).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHalf-centuries is the number of scores from 50 to 99 (whether out or not-out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCenturies is the number of scores greater than 99 (whether out or not-out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDucks is the number of scores of 0 out (0* should \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enot\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e be counted)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s = [\\\"53\\\";\\\"163*\\\";\\\"6\\\";\\\"186\\\";\\\"147*\\\";\\\"0\\\";\\\"89*\\\";\\\"0*\\\"];\\nstats = battingstats(s)\\nstats =\\n 161 2 3 1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this case, the batter has a total of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esum([53 163 6 186 147 0 89 0]) = 644\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs from 4 completed innings (8 innings - 4 not-outs). Hence their average is 644/4 = 161. They have two half-centuries (53 and 89*), three centuries (163*, 186, 147*), and one duck (0 - the 0* does not count as a duck).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":54485,"title":"Dog Statistics","description":"The vectors ht and wt contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by NaN (Not a Number). \r\nWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 115px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 57.5px; transform-origin: 407px 57.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 43px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.5px; text-align: left; transform-origin: 384px 21.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39.5px 8px; transform-origin: 39.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe vectors \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003eht\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003ewt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 299px 8px; transform-origin: 299px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 12px 8.5px; transform-origin: 12px 8.5px; \"\u003eNaN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54px 8px; transform-origin: 54px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (Not a Number). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 370px 8px; transform-origin: 370px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,p] = retrieveWeight(x1,x2)\r\n\r\nht = [24.8, 23.8, NaN, 20.5, 19.7, ...\r\n 25.2, 15.0, 23.4, 18.7, NaN,...\r\n NaN, 25.8, 19.8, 18.3, 18.2,...\r\n 24.6, 17.4, 18.7, 24.0, 22.6];\r\n\r\nwt = [62.6, 69.2, 64.7, 61.2, 60.4,...\r\n 68.0, 71.6, NaN, 62.0, 73.0,...\r\n 62.4, 71.8, 74.6, 59.7, 73.2,...\r\n 67.0, 70.8, 59.2, 70.4, 63.8];\r\n\r\ny = x1;\r\np = x2;\r\n\r\nend","test_suite":"%%\r\n[y,n] = retrieveWeight(20,23);\r\nassert(isequal(y,62.5) \u0026 isequal(n,10))\r\n%%\r\n[y,n] = retrieveWeight(23,26)\r\nassert(abs(y-68.1667)\u003c1e-4 \u0026 isequal(n,35))\r\n%%\r\n[y,n] = retrieveWeight(19,26);\r\nassert(abs(y-66.9)\u003c1e-4 \u0026 abs(n-55)\u003c1e-4)\r\n%%\r\n[y,n] = retrieveWeight(17,23);\r\nassert(abs(y-64.9889)\u003c1e-4 \u0026 abs(n-45)\u003c1e-4)","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":571375,"edited_by":223089,"edited_at":"2022-10-04T18:03:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":405,"test_suite_updated_at":"2022-10-04T18:03:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-05-03T20:06:40.000Z","updated_at":"2026-03-31T14:48:30.000Z","published_at":"2022-10-03T14:27:27.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe vectors \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eht\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e contains the heights and weights of 20 golden retrievers. In some cases, it was not possible to make both measurements, so any empty cells are represented by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNaN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (Not a Number). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that calculates the average weight of all golden retrievers within a certain height range. Your function should take the lower and upper bound for the height range as inputs. It should output the average weight of the dogs in that height range, as well as the percentage of dogs in that range. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61059,"title":"Leaderboard for the Nedball World Cup","description":"At the upcoming inaugural Nedball World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most hilbs. After each match, they need the current leader and the total number of hilbs they have scored.\r\nGiven an m-by-n matrix, S, representing the individual scores of n participants in the m matches in the Nedball World Cup, return two m-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the m events.\r\n\r\n\r\nThat is, S(j,k) represents the number of hilbs scored by player k in match j. The jth element of the first output vector will be the total number of hilbs scored by the player currently in the lead for the Golden Toeplitz. The jth element of the second output will be the index of that current leading player (from 1 to n).\r\nNote that the index of the current leader should only change if the leading score exceeds that of the previous leader. That is, the leader does not change if points are tied.\r\nHowever, the players are already ordered by their International Nedball Association condition number (a very important statistic for Nedballers), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\r\nExample\r\n\r\nAssumptions\r\nTo the annoyance of Nedball purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of S) will be finite and real-valued. Even though Nedball is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that S will not be empty (that is, m,n ≥ 1). Other than that, do not make any assumptions on the values of the scores, or the dimensions m and n.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1310.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 655.3px; transform-origin: 408px 655.3px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAt the upcoming inaugural \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. After each match, they need the current leader and the total number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e they have scored.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-by-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, representing the individual scores of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e participants in the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matches in the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e World Cup, return two \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 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\" alt=\"Example showing input and output for this problem. The input is a matrix, shown as a table with column headers of the player number and row headers of the match number. The output is two vectors, total score and player. The third element of both output vectors is highlighted. The value of the score is 14 and the value of the player is 6. An annotation explains the meaning: after the third match, player 6 is in the lead with a total of 14 hilbs.\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThat is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e(\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) represents the number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e scored by player \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in match \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e element of the first output vector will be the total number of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ehilbs\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e scored by the player currently in the lead for the Golden Toeplitz. The \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ej\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e element of the second output will be the index of that current leading player (from 1 to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNote that the index of the current leader should only change if the leading score \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eexceeds\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that of the previous leader. That is, the leader does not change if points are tied.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 31.5px; text-align: left; transform-origin: 385px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHowever, the players are already ordered by their International \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Association condition number (a very important statistic for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedballers\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 478.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 239.4px; text-align: left; transform-origin: 385px 239.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"697\" height=\"473\" style=\"vertical-align: baseline;width: 697px;height: 473px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAssumptions\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 42px; text-align: left; transform-origin: 385px 42px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo the annoyance of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) will be finite and real-valued. Even though \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNedball\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eS\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e will not be empty (that is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ≥ 1). Other than that, do not make any assumptions on the values of the scores, or the dimensions \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003em\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [mxval,idx] = leaderboardmax(v)\r\n[mxval,idx] = max(v);\r\nend","test_suite":"%% Test case #1: v = 6-by-5\r\nv = [-5 -2 -1 3 5;-5 -2 0 -4 -5;-1 4 -1 0 -3;5 3 0 -3 1;0 -1 2 1 -2;5 4 -4 -3 -4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;0;0;3;2;6]))\r\nassert(isequal(k,[5;5;2;2;2;2]))\r\n\r\n%% Test case #2: v = 4-by-10\r\nv = [0 2 1 1 0 1 2 1 2 1;0 2 2 0 1 0 2 2 0 0;0 1 1 0 2 1 0 2 0 2;2 1 1 1 1 0 0 2 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4;5;7]))\r\nassert(isequal(k,[2;2;2;8]))\r\n\r\n%% Test case #3: v = 16-by-9\r\nv = [1 7 7 3 7 5 4 5 7;5 5 2 4 6 7 7 7 3;6 3 5 3 5 2 7 6 0;3 5 0 0 7 4 3 0 2;3 4 6 2 4 7 2 7 2;7 5 6 4 3 3 7 7 3;5 6 4 1 7 2 3 5 3;6 4 1 0 1 4 7 0 5;3 4 7 2 1 4 1 7 2;2 3 1 5 5 1 0 3 7;3 0 0 3 2 4 0 3 2;5 7 4 3 1 1 4 4 5;1 3 4 0 2 2 3 4 7;3 3 3 0 2 4 2 7 4;0 0 4 2 0 6 6 6 7;1 4 5 2 1 5 3 3 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[7;13;18;25;29;32;39;40;44;47;50;54;58;65;71;74]))\r\nassert(isequal(k,[2;5;5;5;5;5;5;5;8;8;8;8;8;8;8;8]))\r\n\r\n%% Test case #4: v = 12-by-1\r\nv = [-2;2;1;-3;-5;-5;-3;0;1;-4;-4;-2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[-2;0;1;-2;-7;-12;-15;-15;-14;-18;-22;-24]))\r\nassert(isequal(k,[1;1;1;1;1;1;1;1;1;1;1;1]))\r\n\r\n%% Test case #5: v = 2-by-5\r\nv = [2 6 3 4 2;1 4 1 1 1];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[6;10]))\r\nassert(isequal(k,[2;2]))\r\n\r\n%% Test case #6: v = 7-by-4\r\nv = [1 1 2 1;1 2 1 1;1 2 1 1;2 2 1 1;1 2 1 2;1 2 2 1;2 2 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;3;5;7;9;11;13]))\r\nassert(isequal(k,[3;3;2;2;2;2;2]))\r\n\r\n%% Test case #7: v = 9-by-9\r\nv = [0 -5 -1 4 1 4 1 -2 3;-5 -5 -5 0 2 -4 3 1 -1;-3 2 -5 2 -1 -5 1 0 2;0 -2 -3 4 1 1 2 2 4;0 -5 -4 0 -4 -5 0 -4 -2;4 4 1 0 3 -4 -5 0 -4;1 -5 -1 0 -3 1 -4 -2 0;-1 -5 -4 1 -2 2 -1 4 -2;1 0 -1 2 -2 3 0 4 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[4;4;6;10;10;10;10;11;13]))\r\nassert(isequal(k,[4;4;4;4;4;4;4;4;4]))\r\n\r\n%% Test case #8: v = 4-by-3\r\nv = [0 2 3;4 5 3;6 6 1;3 4 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;7;13;17]))\r\nassert(isequal(k,[3;2;2;2]))\r\n\r\n%% Test case #9: v = 10-by-9\r\nv = [2 1 2 1 1 1 1 1 2;1 2 1 1 1 2 2 1 2;2 1 2 2 2 2 2 1 2;1 1 2 2 1 1 1 1 1;2 2 1 2 2 2 2 2 2;2 1 1 1 2 2 2 2 2;1 1 2 2 1 2 1 2 2;1 2 1 2 2 2 1 1 1;2 2 1 2 1 1 1 2 2;2 2 1 1 2 1 1 2 2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4;6;7;9;11;13;14;16;18]))\r\nassert(isequal(k,[1;9;9;9;9;9;9;9;9;9]))\r\n\r\n%% Test case #10: v = 3-by-10\r\nv = [3 3 3 0 3 1 3 0 2 3;4 4 4 1 1 2 0 3 3 2;2 2 4 3 1 0 4 4 1 3];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;7;11]))\r\nassert(isequal(k,[1;1;3]))\r\n\r\n%% Test case #11: v = 17-by-5\r\nv = [4 5 1 1 2;3 1 5 5 5;4 3 3 3 3;2 2 1 3 1;5 1 4 5 3;0 4 3 1 5;5 4 1 5 1;4 2 3 1 4;3 1 0 5 1;0 2 0 5 0;5 1 4 1 0;4 0 0 0 0;2 2 4 1 5;1 1 4 3 5;1 1 4 4 0;3 3 4 3 4;4 5 5 1 5];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;7;11;13;18;19;23;27;30;34;35;39;41;42;43;46;50]))\r\nassert(isequal(k,[2;1;1;1;1;5;1;1;1;4;4;1;1;1;1;1;1]))\r\n\r\n%% Test case #12: v = 5-by-1\r\nv = [5;-5;0;-4;0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;0;0;-4;-4]))\r\nassert(isequal(k,[1;1;1;1;1]))\r\n\r\n%% Test case #13: v = 12-by-7\r\nv = [2 -5 1 4 5 -4 1;3 -2 0 4 5 2 -5;-4 -2 0 4 0 2 1;2 6 2 2 2 -2 0;4 -3 1 -2 3 3 2;-1 0 3 3 -4 -1 4;-1 0 6 0 1 -3 -3;3 4 4 1 -3 -3 6;-3 -5 0 0 -5 -1 1;6 4 -4 0 -5 -5 5;-4 1 6 -3 6 -1 2;1 -5 -1 -2 1 1 -4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;10;12;14;15;15;15;17;17;16;19;18]))\r\nassert(isequal(k,[5;5;4;4;5;4;4;3;3;4;3;3]))\r\n\r\n%% Test case #14: v = 2-by-1\r\nv = [2;2];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[2;4]))\r\nassert(isequal(k,[1;1]))\r\n\r\n%% Test case #15: v = 4-by-2\r\nv = [3 1;3 0;5 0;0 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;6;11;11]))\r\nassert(isequal(k,[1;1;1;1]))\r\n\r\n%% Test case #16: v = 4-by-8\r\nv = [1 0 3 2 1 2 3 3;1 3 0 3 2 3 0 2;2 2 3 0 3 3 2 1;3 3 0 1 1 3 2 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[3;5;8;11]))\r\nassert(isequal(k,[3;4;6;6]))\r\n\r\n%% Test case #17: v = 3-by-3\r\nv = [1 8 3;7 8 1;5 2 4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[8;16;18]))\r\nassert(isequal(k,[2;2;2]))\r\n\r\n%% Test case #18: v = 9-by-4\r\nv = [5 5 4 6;1 0 4 4;5 0 2 6;2 3 0 0;0 0 3 5;6 6 3 6;6 6 3 1;0 6 5 6;3 6 4 4];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[6;10;16;16;21;27;28;34;38]))\r\nassert(isequal(k,[4;4;4;4;4;4;4;4;4]))\r\n\r\n%% Test case #19: v = 15-by-7\r\nv = [5 4 2 4 4 0 0;0 5 2 2 3 4 2;5 1 0 3 5 1 3;1 0 1 5 2 5 0;4 4 5 5 5 4 2;2 2 5 0 5 4 1;3 4 4 2 2 2 3;2 5 0 3 4 1 2;2 1 3 4 5 4 2;4 1 0 5 5 5 0;5 3 4 4 0 5 1;5 3 4 1 0 5 1;3 3 5 5 3 5 4;5 3 0 5 0 2 3;3 0 1 2 1 2 0];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[5;9;12;14;19;24;26;30;35;40;40;40;45;48;50]))\r\nassert(isequal(k,[1;2;5;5;5;5;5;5;5;5;5;5;6;4;4]))\r\n\r\n%% Test case #20: v = 3-by-1\r\nv = [0;0;1];\r\n[mx,k] = leaderboardmax(v);\r\nassert(isequal(mx,[0;0;1]))\r\nassert(isequal(k,[1;1;1]))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":287,"edited_by":287,"edited_at":"2025-11-10T02:17:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":116,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-04T20:25:18.000Z","updated_at":"2026-03-25T10:57:18.000Z","published_at":"2025-11-10T02:17:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAt the upcoming inaugural \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, organizers need to track who is in the lead for the coveted Golden Toeplitz trophy, awarded to the player who scores the most \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. After each match, they need the current leader and the total number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e they have scored.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-by-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matrix, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, representing the individual scores of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e participants in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e matches in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e World Cup, return two \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-element column vectors representing the leading (cumulative) score and the index of the corresponding participant, respectively, after each of the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e events.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"295\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"690\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Example showing input and output for this problem. The input is a matrix, shown as a table with column headers of the player number and row headers of the match number. The output is two vectors, total score and player. The third element of both output vectors is highlighted. The value of the score is 14 and the value of the player is 6. An annotation explains the meaning: after the third match, player 6 is in the lead with a total of 14 hilbs.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThat is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e(\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) represents the number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e scored by player \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in match \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e element of the first output vector will be the total number of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ehilbs\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e scored by the player currently in the lead for the Golden Toeplitz. The \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ej\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e element of the second output will be the index of that current leading player (from 1 to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that the index of the current leader should only change if the leading score \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexceeds\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that of the previous leader. That is, the leader does not change if points are tied.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, the players are already ordered by their International \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Association condition number (a very important statistic for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedballers\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), so if there are multiple tied candidates for a new leader (including for the first event), return the lowest index.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"473\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"697\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAssumptions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo the annoyance of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e purists, the organizers have decided to simplify the rules so that the individual hilb scores (that is, the elements of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e) will be finite and real-valued. Even though \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eNedball\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is famous for its incomprehensibility, a tournament with no matches or no players isn't very interesting, so you can also assume that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e will not be empty (that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ≥ 1). 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42875,"title":"Assignment Problem","description":"Given a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\r\n\r\nFor example, if presented with the following matrix:\r\n\r\n x = [1,3,4,7; \r\n 2,3,1,3;\r\n 4,2,3,7;\r\n 6,4,2,2;]\r\n\r\nYour output array would be:\r\n\r\n a = [1,3,2,4].\r\n\r\nWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\r\n\r\nThis gives us a total time of 1+1+2+2=6 which is minimal.","description_html":"\u003cp\u003eGiven a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\u003c/p\u003e\u003cp\u003eFor example, if presented with the following matrix:\u003c/p\u003e\u003cpre\u003e x = [1,3,4,7; \r\n 2,3,1,3;\r\n 4,2,3,7;\r\n 6,4,2,2;]\u003c/pre\u003e\u003cp\u003eYour output array would be:\u003c/p\u003e\u003cpre\u003e a = [1,3,2,4].\u003c/pre\u003e\u003cp\u003eWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\u003c/p\u003e\u003cp\u003eThis gives us a total time of 1+1+2+2=6 which is minimal.\u003c/p\u003e","function_template":"function y = Assignment(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [2,12,8,5; 4,4,5,12; 3,10,8,3; 12,0,1,2];\r\ny_correct1 = [1,2,4,3];\r\ny_correct2 = [1,4,2,3];\r\ntest1 = isequal(Assignment(x),y_correct1);\r\ntest2 = isequal(Assignment(x),y_correct2);\r\nassert(test1||test2)\r\n\r\n%%\r\nx = [7,5,3,2,6; 6,3,6,6,5; 3,3,9,4,7; 4,5,3,3,4; 2,3,4,3,5];\r\ny_correct = [5,3,4,1,2];\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = eye(4);\r\nx(x==0) = 3;\r\ny_correct = 1:4;\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = 5 * ones(5);\r\nx(1,4) = 1;\r\nx(2,2) = 1;\r\nx(3,5) = 1;\r\nx(4,1) = 1;\r\nx(5,3) = 1;\r\ny_correct = [4,2,5,1,3];\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = 1:4;\r\nx = [x; circshift(x,1); circshift(x,2); circshift(x,3)];\r\ny_correct = 1:4;\r\nassert(isequal(Assignment(x),y_correct))\r\n\r\n%%\r\nx = [7,5,4; 14,16,2; 7,1,9];\r\ny_correct = [1,3,2];\r\nassert(isequal(Assignment(x),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":6,"created_by":64486,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":"2018-07-30T13:18:15.000Z","rescore_all_solutions":false,"group_id":43,"created_at":"2016-06-02T23:08:15.000Z","updated_at":"2026-03-11T23:46:24.000Z","published_at":"2016-06-02T23:18:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix where row i corresponds to person i, and column j corresponds to task j and cell (i,j) corresponds to the time taken for person i to complete task j. Output an assignment array for the minimal time taken for all tasks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if presented with the following matrix:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = [1,3,4,7; \\n 2,3,1,3;\\n 4,2,3,7;\\n 6,4,2,2;]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour output array would be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ a = [1,3,2,4].]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhere person one is assigned to task one, person three is assigned to task two , person two is assigned to task three and person four is assigned to task four.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis gives us a total time of 1+1+2+2=6 which is minimal.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44451,"title":"Rate of event occurence: find percentiles of the distribution (for smallish rates)","description":"*In this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter _μ_ (the mean rate parameter).*\r\n\r\n_What follows is just a longer introduction to the concepts, including two examples._ \r\n\r\nSay that you manage the inventory of a business for which orders *on average* come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, .... \r\n\r\nThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average. \r\n\r\nIn this problem, N follows a *\u003chttps://www.britannica.com/topic/Poisson-distribution Poisson distribution\u003e* :\r\nN ~ Poisson(μ). \r\nSo there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ. \r\n\r\nThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: \r\nPr(N=k) = μ^k × exp(−μ) / k!\r\n\r\nThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation. \r\n\r\nIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula. \r\n\r\nHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine _two_ values of k:\r\n\r\n* A _lower_ limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\r\n* An _upper_ limit on the number of customers, k, that will not be exceeded more than 5% of the time\r\n\r\nHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits. \r\n\r\nEXAMPLE 1:\r\n\r\n % Input\r\n mu = 10\r\n % Output\r\n lowerLimit = 4\r\n upperLimit = 15\r\n safeLimits = [lowerLimit upperLimit]\r\n\r\nNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'. \r\n\r\nEXAMPLE 2:\r\n\r\n % Input\r\n mu = 100\r\n % Output\r\n lowerLimit = 83\r\n upperLimit = 117\r\n safeLimits = [lowerLimit upperLimit]\r\n\r\nNOTE: the two limits are returned as the vector safeLimits, as shown.\r\n\r\n|NOTE: Toolbox functions are generally not available from within Cody.|","description_html":"\u003cp\u003e\u003cb\u003eIn this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter \u003ci\u003eμ\u003c/i\u003e (the mean rate parameter).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003ci\u003eWhat follows is just a longer introduction to the concepts, including two examples.\u003c/i\u003e\u003c/p\u003e\u003cp\u003eSay that you manage the inventory of a business for which orders \u003cb\u003eon average\u003c/b\u003e come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, ....\u003c/p\u003e\u003cp\u003eThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average.\u003c/p\u003e\u003cp\u003eIn this problem, N follows a \u003cb\u003e\u003ca href = \"https://www.britannica.com/topic/Poisson-distribution\"\u003ePoisson distribution\u003c/a\u003e\u003c/b\u003e :\r\nN ~ Poisson(μ). \r\nSo there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ.\u003c/p\u003e\u003cp\u003eThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: \r\nPr(N=k) = μ^k × exp(−μ) / k!\u003c/p\u003e\u003cp\u003eThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation.\u003c/p\u003e\u003cp\u003eIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula.\u003c/p\u003e\u003cp\u003eHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine \u003ci\u003etwo\u003c/i\u003e values of k:\u003c/p\u003e\u003cul\u003e\u003cli\u003eA \u003ci\u003elower\u003c/i\u003e limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\u003c/li\u003e\u003cli\u003eAn \u003ci\u003eupper\u003c/i\u003e limit on the number of customers, k, that will not be exceeded more than 5% of the time\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits.\u003c/p\u003e\u003cp\u003eEXAMPLE 1:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Input\r\nmu = 10\r\n% Output\r\nlowerLimit = 4\r\nupperLimit = 15\r\nsafeLimits = [lowerLimit upperLimit]\r\n\u003c/pre\u003e\u003cp\u003eNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'.\u003c/p\u003e\u003cp\u003eEXAMPLE 2:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Input\r\nmu = 100\r\n% Output\r\nlowerLimit = 83\r\nupperLimit = 117\r\nsafeLimits = [lowerLimit upperLimit]\r\n\u003c/pre\u003e\u003cp\u003eNOTE: the two limits are returned as the vector safeLimits, as shown.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eNOTE: Toolbox functions are generally not available from within Cody.\u003c/tt\u003e\u003c/p\u003e","function_template":"% Here you can describe your strategy.\r\nfunction safeLimits = getLimits(mu)\r\n % Here you can write your code.\r\nend","test_suite":"%% From EXAMPLE 1\r\nmu = 10;\r\nsafeLimits = [4 15];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%% From EXAMPLE 2:\r\nmu = 100;\r\nsafeLimits = [83 117];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%% Handle lower limit of zero:\r\nmu = 3;\r\nsafeLimits = [0 6];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 30;\r\nsafeLimits = [20 39];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 4;\r\nsafeLimits = [0 8];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 40;\r\nsafeLimits = [29 51];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 11;\r\nsafeLimits = [5 17];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 12;\r\nsafeLimits = [6 18];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 13;\r\nsafeLimits = [6 19];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 14;\r\nsafeLimits = [7 20];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 15;\r\nsafeLimits = [8 22];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 20;\r\nsafeLimits = [12 28];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 110;\r\nsafeLimits = [92 128];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n%%\r\nmu = 120;\r\nsafeLimits = [101 138];\r\nassert(isequal(getLimits(mu), safeLimits))\r\n\r\n\r\n%% High rates\r\n% Not asserted in this problem\r\n\r\nmu = 150;\r\nsafeLimits = [129 170];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 200;\r\nsafeLimits = [176 224];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 300;\r\nsafeLimits = [271 329];\r\n%assert(isequal(getLimits(mu), safeLimits))\r\n\r\nmu = 1000;\r\nsafeLimits = [947 1052];\r\n%assert(isequal(getLimits(mu), safeLimits))","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-12-13T04:58:52.000Z","updated_at":"2025-11-21T18:54:27.000Z","published_at":"2017-12-13T07:41:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eIn this problem you need to find the 5th and 95th percentiles of a Poisson distribution defined by parameter\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eμ\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e (the mean rate parameter).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eWhat follows is just a longer introduction to the concepts, including two examples.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSay that you manage the inventory of a business for which orders\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eon average\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e come in at a known rate (daily, say), but for any given period the number of orders can be somewhat above or somewhat below the expected value. If it helps your motivation, you can imagine you're running a stall selling vegan sausages, or in booth handing out free beer, or at a barbecue serving pancakes, ....\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe variable of interest, N, is the number of customers/orders within a given period — one day, let's say. The average rate multiplied by the period of interest yields the mean of the distribution (in this problem), which is the 'expected value', or average.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, N follows a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.britannica.com/topic/Poisson-distribution\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePoisson distribution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e : N ~ Poisson(μ). So there is no influence of any external factors (weather, trends, etc.). The only parameter having a deterministic effect is μ.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen the probability of having k customers in a period, given the expected number of customers in that period being equal to μ, is given by: Pr(N=k) = μ^k × exp(−μ) / k!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe caret (^) represents the power operator, and the exclamation mark (!) represents the factorial operation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf μ and k are given, it is therefore straightforward to compute the probability, Pr(N=k), using the above formula.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, in this problem you will be given μ, representing the mean ('expected') daily rate, and you thereby need to determine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etwo\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e values of k:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elower\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e limit on the number of customers, k, for which a lower demand will not be faced more than 5% of the time\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eupper\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e limit on the number of customers, k, that will not be exceeded more than 5% of the time\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHence, you will be sure that, in the long run, the number of customers you see each day will not often (no more than 10% of the time) be outside the range specified by the above two limits.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Input\\nmu = 10\\n% Output\\nlowerLimit = 4\\nupperLimit = 15\\nsafeLimits = [lowerLimit upperLimit]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that the outputs shall be integers, because there is no physical meaning to 'half a customer'.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Input\\nmu = 100\\n% Output\\nlowerLimit = 83\\nupperLimit = 117\\nsafeLimits = [lowerLimit upperLimit]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: the two limits are returned as the vector safeLimits, as shown.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE: Toolbox functions are generally not available from within Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1959,"title":"German tank problem","description":"The German tank problem is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its far reaching implications...)\r\nYou do not know how many tanks are in circulation (let's call this number N), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of k tanks, and find that, among the observed tanks, the maximum serial number is n. Your job is to predict N, the total number of tanks in circulation, from this observation alone (from the values of k and n).\r\nExample:\r\nYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be at least 77 tanks in circulation (although it seems unlikely that there were only 77). What is your best estimate of the total number of tanks in circulation?\r\nDetails:\r\nYour function takes the values n and k and must return your best estimate of the value of N.\r\nTo pass this problem you must be exactly correct in at least 10% of the testsuite cases.\r\nTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\r\nHints: (why my solution is not working? this formula is on wikipedia!!!)\r\nIn a typical scenario you may not really care about being \"exactly correct\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 459px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 229.5px; transform-origin: 407px 229.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGerman tank problem\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 273px 8px; transform-origin: 273px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003efar reaching implications\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.5px 8px; transform-origin: 8.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e...)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 227px 8px; transform-origin: 227px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou do not know how many tanks are in circulation (let's call this number\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 145.5px 8px; transform-origin: 145.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 37px 8px; transform-origin: 37px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e tanks, and find that, among the observed tanks, the maximum serial number is\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 68.5px 8px; transform-origin: 68.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Your job is to predict\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91.5px 8px; transform-origin: 91.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the total number of tanks in circulation, from this observation alone (from the values of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5px 8px; transform-origin: 31.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 305px 8px; transform-origin: 305px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23px 8px; transform-origin: 23px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eat least\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 77 tanks in circulation (although it seems unlikely that there were\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13px 8px; transform-origin: 13px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eonly\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 198px 8px; transform-origin: 198px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 77). What is your best estimate of the total number of tanks in circulation?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eDetails:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 95px 8px; transform-origin: 95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function takes the values\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ek\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 159px 8px; transform-origin: 159px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and must return your best estimate of the value of\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5px 8px; transform-origin: 5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 105.5px 8px; transform-origin: 105.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo pass this problem you must be\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52px 8px; transform-origin: 52px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eexactly correct\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 119.5px 8px; transform-origin: 119.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in at least 10% of the testsuite cases.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 329.5px 8px; transform-origin: 329.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20px 8px; transform-origin: 20px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHints:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 199px 8px; transform-origin: 199px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (why my solution is not working? this formula is on wikipedia!!!)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374px 8px; transform-origin: 374px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a typical scenario you may not really care about being \"exactly correct\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function N = tank_problem(k,m)\r\n% k: number of observations\r\n% m: maximum observed serial number\r\n% N: estimated number of tanks\r\n\r\nN=1;\r\nend","test_suite":"%%\r\n Ntest=1e4;\r\n N=randi([10,100],Ntest,1);\r\n K=randi([1 10],Ntest,1);\r\n M=arrayfun(@(N,K)max(randi(N,1,K)),N,K);\r\n N_hat=arrayfun(@tank_problem,K,M);\r\n rate=mean(N==N_hat);\r\n assert(rate\u003e=.1,sprintf('hit rate too low (%0.2f)',rate*100));\r\n fprintf('hit rate = %0.2f\\n',rate*100);\r\n","published":true,"deleted":false,"likes_count":7,"comments_count":1,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":33,"test_suite_updated_at":"2022-01-15T07:59:10.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-25T21:34:05.000Z","updated_at":"2026-03-02T17:47:43.000Z","published_at":"2013-10-25T22:07:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGerman tank problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a well-known statistical problem that attempts to estimate the maximum of a discrete distribution from a limited number of samples (you may know it from some of its\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003efar reaching implications\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e...)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou do not know how many tanks are in circulation (let's call this number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), but you know that they have been assigned serial numbers sequentially starting at 1 (ranging from 1 to N). You manage to observe a random subset of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e tanks, and find that, among the observed tanks, the maximum serial number is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Your job is to predict\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the total number of tanks in circulation, from this observation alone (from the values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou observe the serial number of 3 tanks, which are 36, 77, and 28. You know that there must be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eat least\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 77 tanks in circulation (although it seems unlikely that there were\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eonly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 77). What is your best estimate of the total number of tanks in circulation?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eDetails:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function takes the values\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and must return your best estimate of the value of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo pass this problem you must be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexactly correct\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in at least 10% of the testsuite cases.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest suite checks 10,000 cases, with N values ranging from 10 to 100, and k values ranging from 1 to 10.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHints:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (why my solution is not working? this formula is on wikipedia!!!)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a typical scenario you may not really care about being \\\"exactly correct\\\" in at least 10% of all possible cases (i.e. with relatively high likelihood). This problem is intended to have you thinking about frequentist approaches, Bayesian, and maximum likelihood estimators\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44676,"title":"Mean = Standard Deviation","description":"Create a series with following properties;\r\n\r\n# All of the members should be positive integer\r\n# Mean of the series should be integer\r\n# Standard deviation of the series should be integer (use a normalization factor of N instead of N-1)\r\n# Mean should be equal to standard deviation\r\n\r\nFor example if input is 6, you can return the following series;\r\n\r\n out = [12 44 2 24 2 6]\r\n\r\n mean(out) = 15;\r\n std(out,1) = 15;\r\n \r\nAnother example; if input is 4, a possible solution;\r\n\r\n out = [24 2 2 48];\r\n mean(out) = 19;\r\n std(out,1) = 19;\r\n\r\n ","description_html":"\u003cp\u003eCreate a series with following properties;\u003c/p\u003e\u003col\u003e\u003cli\u003eAll of the members should be positive integer\u003c/li\u003e\u003cli\u003eMean of the series should be integer\u003c/li\u003e\u003cli\u003eStandard deviation of the series should be integer (use a normalization factor of N instead of N-1)\u003c/li\u003e\u003cli\u003eMean should be equal to standard deviation\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eFor example if input is 6, you can return the following series;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout = [12 44 2 24 2 6]\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emean(out) = 15;\r\nstd(out,1) = 15;\r\n\u003c/pre\u003e\u003cp\u003eAnother example; if input is 4, a possible solution;\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout = [24 2 2 48];\r\nmean(out) = 19;\r\nstd(out,1) = 19;\r\n\u003c/pre\u003e","function_template":"function y = meanEqualsStd(x)\r\n y = x;\r\nend","test_suite":"%%\r\nfor idx = 5:50\r\n x = idx;\r\n Series = meanEqualsStd(x);\r\n mSeries = mean(Series);\r\n sSeries = std(Series,1);\r\n assert(length(Series)==x);\r\n assert(round(mSeries)==mSeries);\r\n assert(round(sSeries)==sSeries);\r\n assert(all(round(Series) == Series));\r\n assert(all(round(Series) \u003e= 1));\r\n assert(mSeries == sSeries);\r\nend\r\n\r\n%%\r\nfiletext = fileread('meanEqualsStd.m');\r\nassert(isempty(strfind(filetext, 'echo')))\r\nassert(isempty(strfind(filetext, 'assert')))","published":true,"deleted":false,"likes_count":5,"comments_count":11,"created_by":8703,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2018-06-14T07:03:27.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2018-06-07T01:30:24.000Z","updated_at":"2025-11-14T13:50:04.000Z","published_at":"2018-06-07T01:30:24.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate a series with following properties;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll of the members should be positive integer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMean of the series should be integer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStandard deviation of the series should be integer (use a normalization factor of N instead of N-1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMean should be equal to standard deviation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if input is 6, you can return the following series;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out = [12 44 2 24 2 6]\\n\\nmean(out) = 15;\\nstd(out,1) = 15;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnother example; if input is 4, a possible solution;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out = [24 2 2 48];\\nmean(out) = 19;\\nstd(out,1) = 19;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":3074,"title":"Compute the cokurtosis of a given portfolio.","description":"As input data, you are given a nObs-by-nAssets matrix _portRet_ of return series for assets in a portfolio along with an nAssets-by-1 vector _portWeights_ of portfolio weights. Example: \r\n\r\n \u003e\u003e nObs = 504; % Number of observations\r\n\r\n \u003e\u003e nAssets = 5; % Number of assets in the portfolio\r\n\r\n \u003e\u003e portRet = randn(nObs, nAssets); % Sample portfolio return series\r\n\r\n \u003e\u003e portWeights = rand(nAssets, 1); \r\n\r\n \u003e\u003e portWeights = portWeights/sum(portWeights); % Portfolio weights are \u003e=0 and sum to 1.\r\n\r\nThe task is to compute the *portfolio cokurtosis* , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\r\n\r\nhttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\r\n\r\nWrite a function that accepts _portRet_ and _portWeights_ as input arguments and returns the scalar statistic _portCokurt_ as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eAs input data, you are given a nObs-by-nAssets matrix \u003ci\u003eportRet\u003c/i\u003e of return series for assets in a portfolio along with an nAssets-by-1 vector \u003ci\u003eportWeights\u003c/i\u003e of portfolio weights. Example:\u003c/p\u003e\u003cpre\u003e \u0026gt;\u0026gt; nObs = 504; % Number of observations\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; nAssets = 5; % Number of assets in the portfolio\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portRet = randn(nObs, nAssets); % Sample portfolio return series\u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portWeights = rand(nAssets, 1); \u003c/pre\u003e\u003cpre\u003e \u0026gt;\u0026gt; portWeights = portWeights/sum(portWeights); % Portfolio weights are \u0026gt;=0 and sum to 1.\u003c/pre\u003e\u003cp\u003eThe task is to compute the \u003cb\u003eportfolio cokurtosis\u003c/b\u003e , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\"\u003ehttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\u003c/a\u003e\u003c/p\u003e\u003cp\u003eWrite a function that accepts \u003ci\u003eportRet\u003c/i\u003e and \u003ci\u003eportWeights\u003c/i\u003e as input arguments and returns the scalar statistic \u003ci\u003eportCokurt\u003c/i\u003e as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\u003c/p\u003e","function_template":"function portCokurt = computePortCokurt(portRet, portWeights)\r\n\r\n\r\nend","test_suite":"%%\r\nrng('default')\r\nR = randn(504, 5);\r\nw = ones(5, 1)/5;\r\nassert(abs(computePortCokurt(R, w)-0.119749008958925)\u003c1e-3)\r\n\r\n%%\r\nrng('default')\r\nR = randn(252, 15);\r\nw = ones(15, 1)/15;\r\nassert(abs(computePortCokurt(R, w)-0.013012357540290)\u003c1e-3)\r\n\r\n%% \r\nrng('default')\r\nR = randn(100, 1);\r\nw = 1;\r\nassert(abs(computePortCokurt(R, w)-6.280759230562035)\u003c1e-3)\r\n\r\n%%\r\nrng('default')\r\nR = randn(5, 10);\r\nw = [0.1*ones(5, 1); 0.5; zeros(4, 1)];\r\nassert(abs(computePortCokurt(R, w)-0.169198885214440)\u003c1e-3)","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":2328,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-03-10T11:04:22.000Z","updated_at":"2015-03-11T18:00:35.000Z","published_at":"2015-03-10T11:25:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs input data, you are given a nObs-by-nAssets matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportRet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of return series for assets in a portfolio along with an nAssets-by-1 vector\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportWeights\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of portfolio weights. Example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ \u003e\u003e nObs = 504; % Number of observations\\n\\n \u003e\u003e nAssets = 5; % Number of assets in the portfolio\\n\\n \u003e\u003e portRet = randn(nObs, nAssets); % Sample portfolio return series\\n\\n \u003e\u003e portWeights = rand(nAssets, 1); \\n\\n \u003e\u003e portWeights = portWeights/sum(portWeights); % Portfolio weights are \u003e=0 and sum to 1.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe task is to compute the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportfolio cokurtosis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e , which is a scalar statistic associated with the portfolio. A full description of this statistic, along with sample MATLAB code for computing it, can be found here:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.quantatrisk.com/2013/01/20/coskewness-and-cokurtosis/\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that accepts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportRet\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportWeights\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as input arguments and returns the scalar statistic\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eportCokurt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as its output. You can use the code at the website above as a starting point, but try to simplify and shorten it in the spirit of Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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