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How to save the several results of a program in an array?

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clc;
clear;
close all;
tic
load colon.mat
data=colon;
[n,m]=size(data);
%%
%supervised
d=10;
l=1;
t=1;
for i=1:n
if data(i,m)==0
data(i,m)=2;
end
end
data1=[];
data2=[];
for i=1:n
if data(i,m)==1
data1(l,:)=data(i,1:m-1);
l=l+1;
else
data2(t,:)=data(i,1:m-1);
t=t+1;
end
end
if t>l
data1(l:t-1,1:m-1)=0;
else
data2(t:l-1,1:m-1)=0;
end
%computing Distance measures
for i=1: m-1
thisCol1=data1(:,i);
thisCol2=data2(:,i);
a6(i)=fTonimotoDist(thisCol1,thisCol2);
end
%sorting the distances
[A6,indA6]=sort(a6,'descend'); %Tonimoto
%selecting Threshold
datas6=data(:,indA6(1:d));
data6=[datas6 data(:,m)];
%%data6 classify%%tanimoto
[n,m]=size(data6);
for k=1:it
test=data6(test_rows,:);
train=data6(train_rows,:);
xtest=test(:,1:m-1);
ytest=test(:,m);
xtrain=train(:,1:m-1);
ytrain=train(:,m);
[rforest, DT , sk ] = classificationa(xtest,xtrain,ytrain);
[Arforest6(k), ADT6(k) , Ask6(k)] = allaccuracydata(rforest, DT , sk , ytest);
end
averf6=mean(Arforest6);
avedt6=mean(ADT6);
avesk6=mean(Ask6);
x6=[averf6, avedt6 , avesk6];
disp('tanimoto'); disp(x6);
In this code d is the number of selected features(columns) so we use 10 features of the data(colon attached) to classify It, my question is suppose that we want to obtain the average of Arforest (averf6) once for d=10, once for d=20,for d=30,d=40 and d=50 and save the results of averf6 for each of them in one array(forexample a) to plot the array.
how can I save the results of these different runing the program, in one array based on changing in d [10 20 30 40 50], I have problem in this part, and also how to define d that have several values, should I define d as an array forexample d=[10,20,30,40,50]; ?
Thanks

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Accepted Answer

Stephen Cobeldick
Stephen Cobeldick on 23 Dec 2019
Edited: Stephen Cobeldick on 23 Dec 2019
Assuming that those means are scalar values, then a simple loop:
dV = [10,20,30,40,50];
nV = numel(dV);
averf6 = nan(1,nV);
avedt6 = nan(1,nV);
avesk6 = nan(1,nV);
for jj = 1:nV
d = dV(jj);
... your code
averf6(jj) = mean(...)
avedt6(jj) = mean(...)
avesk6(jj) = mean(...)
end

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Stephen Cobeldick
Stephen Cobeldick on 23 Dec 2019
@phdcomputer Eng: please indent your code consistently. Inconsistently indented code, such as yours, hides the code's structure and hides bugs. You should use the MATLAB editor's default indentation rules, which are applied by default. You can also apply the editor's indentation rules to existing code: select the entire code, the press ctrl+i.
"Do you think this code is wrong?"
The basic concept seems correct, but the inconsistent indentation makes it difficult to tell if it has been implemented correctly.
It is not clear to me why you only preallocated two ouput arrays, although there are apparently six of them. It is also unclear why you hardcoded the preallocated array sizes, even though you have the required array dimensions in the variables nV and it.
You can simplify your code by calling mean after the loops, e.g.:
dV = [10,20,30,40,50];
nV = numel(dV);
it = 10; % mmoved from inside your loops.
Arf6 = nan(nV,it); % preallocate.
ADT6 = nan(nV,it); % preallocate.
Ask6 = nan(nV,it); % preallocate.
for jj = 1:nV
d = dV(jj);
...
for k = 1:it
...
[Arf6(jj,k), ADT6(jj,k), Ask6(jj,k)] = allaccuracydata(...);
end
end
averf6 = mean(Arf6,2); % mean of each row
avedt6 = mean(ADT6,2); % mean of each row
avesk6 = mean(Ask6,6); % mean of each row
This assumes that the outputs of allaccuracydata are all scalars (which, judging by your code, they appear to be).
phdcomputer Eng
phdcomputer Eng on 23 Dec 2019
sorry, I don't know how to indent the codes well I use the code part , sure I use ctrl+i to indent the codes.I hope that the structue will be better. I used these codes based on your advices but still the results are NaN
clc;
clear;
close all;
tic
load colon.mat
data=colon;
[n,m]=size(data);
%%
%supervised
it=10;
dV = [10,20,30,40,50];
nV = numel(dV);
arf66=nan(nV,it);
adt66=nan(nV,it);
ask66=nan(nV,it);
l=1;
t=1;
for i=1:n
if data(i,m)==0
data(i,m)=2;
end
end
data1=[];
data2=[];
for i=1:n
if data(i,m)==1
data1(l,:)=data(i,1:m-1);
l=l+1;
else
data2(t,:)=data(i,1:m-1);
t=t+1;
end
end
if t>l
data1(l:t-1,1:m-1)=0;
else
data2(t:l-1,1:m-1)=0;
end
%computing Distance measures
for i=1: m-1
thisCol1=data1(:,i);
thisCol2=data2(:,i);
a6(i)=fTonimotoDist(thisCol1,thisCol2);
end
% %sorting the distances
[A6,indA6]=sort(a6,'descend'); %Tonimoto
%selecting Threshold
for jj = 1:nV
d = dV(jj);
datas6=data(:,indA6(1:d)); %Tonimoto
data6=[datas6 data(:,m)];
rows=(1:n);
test_count=floor((0.2)*n);
test_rows=randsample(rows,test_count);
train_rows=setdiff(rows,test_rows);
%%data6 classify%%tanimoto
[n,m]=size(data6);
for k=1:it
test=data6(test_rows,:);
train=data6(train_rows,:);
xtest=test(:,1:m-1);
ytest=test(:,m);
xtrain=train(:,1:m-1);
ytrain=train(:,m);
[rforest, DT , sk ] = classificationa(xtest,xtrain,ytrain);
[Arforest6(jj,k), ADT6(jj,k) , Ask6(jj,k)] = allaccuracydata(rforest, DT , sk , ytest);
end
averf6=mean(Arforest6,2);
avedt6=mean(ADT6,2);
avesk6=mean(Ask6,2);
end
x6=[averf6, avedt6 , avesk6];
disp('tanimoto'); disp(x6);
plot(averf6);
plot(avedt6);
plot(avesk6);
Stephen Cobeldick
Stephen Cobeldick on 23 Dec 2019
"...sure I use ctrl+i to indent the codes.I hope that the structue will be better."
Your code has exactly the same indentation as before. I cannot follow it.
Please align your code consistently in the MATLAB editor
  1. select all of the code
  2. press ctrl + i

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More Answers (1)

phdcomputer Eng
phdcomputer Eng on 23 Dec 2019
Edited: phdcomputer Eng on 23 Dec 2019
@Stephen Cobeldick I attaced the allaccuracydata,classificationa and ftanimotodist functions. Thanks greatly

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Show 5 older comments
phdcomputer Eng
phdcomputer Eng on 25 Dec 2019
Thanks , should I use these lines in the for k=1:it loop?
disp(k) % do you get 10 iterations?
[XX,YY,ZZ] = allaccuracydata(rforest, DT , sk , ytest) % display!
Arforest6(jj,k) = XX
ADT6(jj,k) = YY
Ask6(jj,k) = ZZ
and I put the averages lines out of the two loops but still shows the same result(Nan)
Stephen Cobeldick
Stephen Cobeldick on 26 Dec 2019
"should I use these lines in the for k=1:it loop?"
Yes. That is exactly what I showed you. Then you can check what results are generated on each loop iteration, and continue to investigate the iterations that are not generating the results that you expect.
phdcomputer Eng
phdcomputer Eng on 28 Dec 2019
@Stephen Cobeldick Thanks greatly, I Edited the codes :
clc;
clear;
close all;
tic
load colon.mat
data=colon;
[n,m]=size(data);
%%
%supervised
it=10;
dV = [10,20,30,40,50];
nV = numel(dV);
Arforest6=nan(nV,it);
Adt6=nan(nV,it);
Ask6=nan(nV,it);
averf6 = nan(nV,1);
avedt6 = nan(nV,1);
avesk6 = nan(nV,1);
l=1;
t=1;
for i=1:n
if data(i,m)==0
data(i,m)=2;
end
end
data1=[];
data2=[];
for i=1:n
if data(i,m)==1
data1(l,:)=data(i,1:m-1);
l=l+1;
else
data2(t,:)=data(i,1:m-1);
t=t+1;
end
end
if t>l
data1(l:t-1,1:m-1)=0;
else
data2(t:l-1,1:m-1)=0;
end
%computing Distance measures
for i=1: m-1
thisCol1=data1(:,i);
thisCol2=data2(:,i);
a6(i)=fTonimotoDist(thisCol1,thisCol2);
end
% %sorting the distances
[A6,indA6]=sort(a6,'descend'); %Tonimoto
%selecting Threshold
for jj = 1:nV
d = dV(jj);
datas6=data(:,indA6(1:d)); %Tonimoto
data6=[datas6 data(:,m)];
rows=(1:n);
test_count=floor((0.2)*n);
test_rows=randsample(rows,test_count);
train_rows=setdiff(rows,test_rows);
%%data6 classify%%tanimoto
[n,m]=size(data6);
for k=1:it
test=data6(test_rows,:);
train=data6(train_rows,:);
xtest=test(:,1:m-1);
ytest=test(:,m);
xtrain=train(:,1:m-1);
ytrain=train(:,m);
disp(k);
[rforest, DT , sk ] = classificationa(xtest,xtrain,ytrain);
[XX, YY , ZZ] = allaccuracydata(rforest, DT , sk , ytest);
Arforest6(jj,k) = XX;
ADT6(jj,k) = YY;
Ask6(jj,k) = ZZ;
end
end
averf6=mean(Arforest6,2);
avedt6=mean(ADT6,2);
avesk6=mean(Ask6,2);
x6=[averf6, avedt6 , avesk6];
disp('tanimoto'); disp(x6);
plot(averf6);
figure
plot(avedt6);
figure
plot(avesk6);
the results of disp(k) is countiong from 1 to 10 five times, In first iteration the XX,YY,ZZ are 0.8333,0.6667, 0.7500 for all the ten outputs, but for next four iterations all the outputs are NaN.
tanimoto
0.8167 0.5833 0.8333
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
NaN NaN NaN
and the results of plots are empty. It seems the codes are right but the results are NaN.

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