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

load featurs_T
load featurs_S
load Group_Train
load Group_Test
load actual_label
load predict_label
load predict_label

%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);

Answer 1

[KSSV]

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.

Answer 2

[Walter Roberson]

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]

The sizes of the matrices are shown in the workspace

[Walter Roberson]

k is the maximum label, which is a scalar. length(k) is 1. You use length(k) as the upper bound for the size of res so res is 120 by 1 instead of 120 by the number of labels

[sun rise]

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);

Answer 3

[sun rise]

%label.m


setup_folders;

sadtrdinfo = dir(sad1train);  % Returns both folders and files
% sad = dir(pwd);  % Returns both folders and files
sorted_cell_array_of_train_folder_names = setdiff({sadtrdinfo([sadtrdinfo.isdir]).name},{'..','.'});  % Select folder names
% 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
    traintifpattern = fullfile(sad1train, SFN, tiffwild);
	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');

%---------------------
sadtedinfo = dir(sad1test);  % Returns both folders and files
sorted_cell_array_of_test_folder_names = setdiff({sadtedinfo([sadtedinfo.isdir]).name},{'..','.'});  % Select folder names
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
    testtifpattern = fullfile(sad1test, SFN, tiffwild);
	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;

Folder   = sad1train;
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  = imread(File);
   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


Folder2   = sad1test;
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  = imread(File2);
   %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 [e-mail]
%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


load featurs_T
load featurs_S
load Group_Train
load Group_Test

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');