# Index in position 2 exceeds array bounds (must not exceed 1).

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sun rise on 29 May 2022
Answered: sun rise on 31 May 2022
%WEIGHTEDVOTING is a proposed ensemble method
k = max(actual_label); % determining number of classes [1-K]
heus = [];
%HOG_SVM = multisvm(Feat1,Group_Train1,Feat2,Group_Test1);
rec = recall(predict_label, actual_label); prec = precision(predict_label, actual_label);
heus = [heus; 2*f1(prec,rec)-FPR(predict_label, actual_label)];
%LBP_SVM = multisvm(Feat1,Group_Train1,Feat2,Group_Test1);
rec = recall(predict_label2, actual_label); prec = precision(predict_label2, actual_label);
heus = [heus; 2*f1(prec,rec)-FPR(predict_label2, actual_label)];
res = zeros(length(actual_label),length(k));
for i=1:length(actual_label)
res(i,predict_label(i)) = res(i,predict_label(i)) + heus(1);
res(i,predict_label2(i)) = res(i,predict_label2(i)) + heus(2);
end
[~, preds] = max(res,[],2);
Index in position 2 exceeds array bounds (must not exceed 1).
Error in weightedVoting (line 27)
res(i,predict_label(i)) = res(i,predict_label(i)) + heus(1);

KSSV on 29 May 2022
This is a simple error. You are trying to extract more number of elements then present in the array.
% Example
A = rand(1,5) ; % size of A is 1x5
A(1) % no error
ans = 0.2750
A(4) % no error
ans = 0.3824
A(end) % no error, this will give you 5th element
ans = 0.8093
A(6) % error, there is no 6th element
Index exceeds the number of array elements. Index must not exceed 5.
Like wise check the dimensions of each array and use looping.

Walter Roberson on 29 May 2022
A predicted label might exceed the number of actual labels.
Or heus might be empty, if some of the other variables are empty.
sun rise on 31 May 2022
The error is due to the difference in the type of the array. How can I change the type of the array so that the code works.Look at the workspace
Error using categorical/max (line 63)
Relational comparisons are not allowed for categorical arrays that are not ordinal.
Error in recall (line 4)
K = max(actual_label); %number of classes
Error in weightedVoting (line 14)
rec = recall(predict_label, Group_Test1); prec = precision(predict_label, Group_Test1); sun rise on 31 May 2022
%label.m
setup_folders;
% sad = dir(pwd); % Returns both folders and files
% cell_array_of_train_folder_names( strncmp( cell_array_of_train_folder_names, ".", 1 ) ) = []; % Remove '.' and '..'
%sorted_cell_array_of_train_folder_names = sort_nat( cell_array_of_train_folder_names );
% sorted_cell_array_of_train_folder_names = cell_array_of_train_folder_names; % if you don't have sort_nat
whos sorted_cell_array_of_train_folder_names
%----------------
np = numel(sorted_cell_array_of_train_folder_names); % number of subfolders
train_cats = categorical(sorted_cell_array_of_train_folder_names);
Group_Train1 = cell(np,1);
for i = 1 : np
SFN = sorted_cell_array_of_train_folder_names{i};% extract his name
tifListdinfo = dir(traintifpattern); % list all jpg files
tifList = fullfile({tifListdinfo.folder}, {tifListdinfo.name});
ms1 = numel(tifList); % ms = number of image files found
Group_Train1{i} = repmat(train_cats(i), ms1, 1);
end
Group_Train1 = vertcat(Group_Train1{:});
save('Group_Train','Group_Train1');
%---------------------
whos sorted_cell_array_of_test_folder_names
%----------------
np = numel(sorted_cell_array_of_test_folder_names); %number of subfolders
test_cats = categorical(sorted_cell_array_of_test_folder_names);
Group_Test1 = cell(np, 1);
for i = 1 : np
SFN = sorted_cell_array_of_test_folder_names{i};% extract his name
tifListdinfo = dir(testtifpattern); % list all jpg files
tifList = fullfile({tifListdinfo.folder}, {tifListdinfo.name});
ms1 = numel(tifList); % ms = number of image files found
Group_Test1{i} = repmat(test_cats(i), ms1, 1);
end
Group_Test1 = vertcat(Group_Test1{:});
save('Group_Test','Group_Test1');
%HOG.M
setup_folders;
FileList = dir(fullfile(Folder, '**', tiffwild));
Feature = cell(1, numel(FileList)); % Pre-allocation
for iFile = 1:numel(FileList)
File = fullfile(FileList(iFile).folder, FileList(iFile).name);
Img = imresize(Img, [128, 128]);
Feature{iFile} = extractHOGFeatures(Img,'CellSize',[2 2],'BlockSize',[2 2]);
end
% Maybe:
Feat1 = cat(1, Feature{:}); % Or cat(2, ...) ?!
clear Feature
save('featurs_T.mat', '-v7.3', 'Feat1');
%clear Feat1
FileList2 = dir(fullfile(Folder2, '**', tiffwild));
Feature2 = cell(1, numel(FileList2)); % Pre-allocation
for iFile2 = 1:numel(FileList2)
File2 = fullfile(FileList2(iFile2).folder, FileList2(iFile2).name);
%Img2 = imresize(Img2, [128, 128]);
Img2 = imresize(Img2, [128, 128]);
%Feature2{iFile2} = hog_feature_vector(Img2);
Feature2{iFile2} = extractHOGFeatures(Img2,'CellSize',[2 2],'BlockSize',[2 2]);
end
% Maybe:
Feat2 = cat(1, Feature2{:}); % Or cat(2, ...) ?!
clear Feature2
save('featurs_S.mat', '-v7.3', 'Feat2');
%clear Feat2
%maltisvm
function [preds] = multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1)
%Models a given training set with a corresponding group vector and
%classifies a given test set using an SVM classifier according to a
%one vs. all relation.
%
%This code was written by Cody Neuburger cneuburg@fau.edu
%Florida Atlantic University, Florida USA...
%This code was adapted and cleaned from Anand Mishra's multisvm function
%found at http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine/
u=unique(Group_Train1);
numClasses=length(u);
%preds = zeros(length(TestSet(:,1)),1);
%build models
preds = categorical.empty();
for k=1:numClasses
%Vectorized statement that binarizes Group
%where 1 is the current class and 0 is all other classes
G1vAll=(Group_Train1==u(k));
models{k} = fitcsvm(TrainingSet,G1vAll,'KernelFunction','polynomial','polynomialorder',3,'Solver','ISDA','Verbose',0,'Standardize',true);
if ~models{k}.ConvergenceInfo.Converged
fprintf('Training did not converge for class "%s"\n', string(u(k)));
end
end
%classify test cases
for t=1:size(TestSet,1)
matched = false;
for k = numClasses:-1:1
% for k =1: numClasses
if(predict(models{k},TestSet(t,: )))
matched = true;
break;
end
end
if matched
preds(t,1) = u(k);
%result(t) = u(k);
else
preds(t,1) = 'No Match';
%--------------------------------
end
end
Accuracy = mean(Group_Test1==preds) * 100;
fprintf('Accuracy = %.2f\n', Accuracy);
fprintf('error rate = %.2f\n ', mean(preds ~= Group_Test1 ) * 100);
%confusionchart(result,Group_Test1);
end
%maltisvmcall
fprintf("HOG_SVM:\n")
HOG_SVM= multisvm(Feat1,Group_Train1,Feat2,Group_Test1);
predict_label= HOG_SVM;
%predict_label= double( predict_label);
save('predict_label.mat', 'predict_label');

R2019a

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