svmtrain function is not working in my matlab 2016a.

Question

Sajja Tulasi Krishna 2018-4-25

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https://ww2.mathworks.cn/matlabcentral/answers/397252-svmtrain-function-is-not-working-in-my-matlab-2016a

回答： farid jafary 2020-4-23

采纳的回答： Steven Lord

I want to run svmtrain function on 2016a version of matlab. what will do

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Answer 1

Steven Lord 2018-4-25

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https://ww2.mathworks.cn/matlabcentral/answers/397252-svmtrain-function-is-not-working-in-my-matlab-2016a#answer_317133

Do you have Statistics and Machine Learning Toolbox installed? You can check this using the ver function. Since svmtrain is part of this toolbox, you must have that toolbox installed in order to use this function.

But if you have Statistics and Machine Learning Toolbox included in your installation, you might want to use the newer functions / classes introduced in release R2014a like fitcsvm, ClassificationSVM, and CompactClassificationSVM. This will be especially important if you want your code to work in release R2018a and later, as the Release Notes to which I linked indicate that svmtrain will error starting in release R2018a.

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Sajja Tulasi Krishna 2018-4-25

编辑：Walter Roberson 2018-4-25

在 MATLAB Online 中打开

%

AUTHOR: Jaime Ivan Cervantes
%LAST UPDATED: December 13, 2011
%DESCRIPTION: This code implements PCA, Fisher LDA, and SVM to find matching faces.
% Clear memory and console
close all
clear
clc
% Choose simple, pose variation, or illumination variation dataset 
dataset = input('Please select your dataset: \n(1) simple.m \n(2) pose.m\n');
if dataset == 1
     load 'E:/Ph.D Work/Softwares/PhD_tool/demos/svm/simple.mat'
     %Define variables
     k = 200;% Number of classes
     n = 3;% Number of images per class 
elseif dataset == 2
     load 'E:/Ph.D Work/Softwares/PhD_tool/demos/svm/pose.mat'
     face = pose;
    %Define variables
     k = 68;% Number of classes
     n = 13;% Number of images per class
else
    display('Input not valid');
end
testNum = input('Enter the number of the face for which you want to find the match: ');
classType = input('Choose (1) for SVM or (2) for Euclidean Distance: ');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Principal Component Analysis (PCA)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
display('Loading please wait...')
% Read images in T matrix
[nRow nCol M] = size(face);
% T is a matrix containing the reshaped vectors for each image
T = reshape(face,[nRow*nCol M]);
% mTot is the mean of the entire set of training images
mTot = mean(T,2);
% substract mean
A = T-repmat(mTot,1,M);
% Obtaining eigenvalues and eigenvectors of A'A
[V,D] = eig(A'*A);
% Obtaning more relevant eigenvalues and eigenvectors
eval = diag(D);
peval = [];
pevec = [];
for i = M:-1:k+1
    peval = [peval eval(i)];
    pevec = [pevec V(:,i)];
end
% Obtaining the eigenvectors
U = A * pevec; 
% Obtaining PCA weights
 Wpca = U'*A;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Fisher's Linear Discriminant Analysis
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Obtaining Sb and Sw
cMean = zeros(M-k,M-k);
Sb = zeros(M-k,M-k);
Sw = zeros(M-k,M-k);
pcaMean = mean(Wpca,2);
for i = 1:k
    cMean = mean(Wpca(:,n*i-(n-1):n*i),2);
    Sb = Sb + (cMean-pcaMean)*(cMean-pcaMean)';
end
Sb = n*Sb;
for i = 1:k
    cMean = mean(Wpca(:,n*i-(n-1):n*i),2);
    for j = n*i-(n-1):n*i
         Sw = Sw + (Wpca(:,j)-cMean)*(Wpca(:,j)-cMean)';
    end
end
% Obtaining Fisher eigenvectors and eigenvalues
[Vf, Df] = eig(Sb,Sw);
% Calculating weights
 Df = fliplr(diag(Df));
 Vf = fliplr(Vf);
% Calculating fisher weights
Wf = Vf'*Wpca;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Support Vector Machine (LIBSCM Dependency)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if classType == 1
      % testNum = 13*20;
      % Reshape the selected face
      Tr = reshape(face(:,:,testNum),[nRow*nCol 1]);
      Ar = Tr-mTot;
      % Obtain the weights of the normalized selected face
      Wrec = Vf'*U'*Ar;
      % SVM parameters. The kernel is a polynomial
      c=1e9;
      params=[' -t ' int2str(1) ' -c ' int2str(c)];
      % prevArray starts with an array containing each class. Winner array is the
      %classes that are selected in the binary tree
      prevArr = [1:k]; 
      winnerArr = [];
      % This section of the code computes a binary SVM tree, by solving a 2 class
      % problem with SVM for every 2 classes (1 and 2, 3 and 4, 5 and 6 etc). The
      % that you selected is classified according to each 2-class problem and the
      % class selected goes on to compete with the other classes selected.
      for max = 1:1000
          winnerArr = []; 
          for winRep = 1:2:length(prevArr) 
              % Selects the two classes to train the SVM
              if winRep >= length(prevArr)
                  i = prevArr(winRep) ;       
                  j = prevArr(winRep-1) ;   
              else
                  i = prevArr(winRep)  ;      
                  j = prevArr(winRep+1)  ; 
              end
              % Selects the features of the 2 classes
              feature = [Wf(1:2,n*i-(n-1):n*i),Wf(1:2,n*j-(n-1):n*j)]';
              % Assigns the labels for each class
              for m1 = 1:n
                 label(m1) = 1; 
              end
              for n1 = n+1:2*n
                 label(n1) = -1; 
              end
              % The SVM is trained
              model=svmtrain(label', feature, params);
              % model = svmtrain(data(tr,:),groups(tr),'showplot',false,'kernel_function','linear');
              % The face that the user selected is classified to any of the two
              % classes
              guessLab(1) = 1;
              predLabel=svmpredict(guessLab',[Wrec(1) Wrec(2)],model);
              predLabel;
              % A winner class is selected
              if predLabel == 1 
                  winnerArr = [winnerArr i];
              elseif predLabel == -1 
                  winnerArr = [winnerArr j];
              end
              if winnerArr > 1
                  for c1 = 2:length(winnerArr)
                      if winnerArr(c1) == winnerArr(c1-1)
                         winnerArr(c1) = []; 
                      end
                  end
              end
          end
          prevArr = winnerArr;
         if length(winnerArr) < 2
             winnerArr;
              break
          end
      end
      % This is the class that was selected
      CLASS = winnerArr;
      display('The class that matches your face is:')
      display(CLASS)
      % Plot the selected face
      figure(1)
      imagesc(reshape(Tr,nRow,nCol));
      colormap gray;
      title('Face selected')
      % Plot the faces of the matching class
      for i = 1:n
          figure(i+1)
          imagesc(reshape(T(:,winnerArr*n-i+1),nRow,nCol));
          colormap gray;
          title(strcat('Matching face number: ',num2str(winnerArr*n-i+1)));
      end
  end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SELECT FACE BASED ON THE SHORTEST EUCLIDEAN DISTANCE
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if classType == 2
      % Calculate euclidean distance
      %testNum = 134;
      % Normalize selected image
      Tr = reshape(face(:,:,testNum),[nRow*nCol 1]);
      Ar = Tr-mTot;
      % Obtain weights of the selected face
      Wrec = Vf'*U'*Ar;
      temp = 0;
      % Obtaining an array of euclidean distances to each face
      eDist = [];
      for i = 1:M
          eDist = [eDist sqrt(( norm( Wrec - Wf(:,i)) )^2)]; 
      end
      % Find minimum distance and the corresponding index
      minDis = 999999;
      minIndex = 0;
      for i = 1:length(eDist)
         if minDis > eDist(i) && i ~= testNum
             minDis = eDist(i);
             minIndex = i;
         end
      end
      Matching_index = minIndex;
      % Matching index
      display(Matching_index);
      % Plot selected face
      figure(1)
      imagesc(reshape(Tr,nRow,nCol));
      colormap gray;
      title('Face selected')
      % Plot best match
      figure(2)
      imagesc(reshape(T(:,minIndex),nRow,nCol));
      colormap gray;
      title('Best match')
  end

This is my code, when i ran this code while seclecting svm option

it gives this error

Please select your dataset: 
(1) simple.m 
(2) pose.m
1
Enter the number of the face for which you want to find the match: 5
Choose (1) for SVM or (2) for Euclidean Distance: 1
Loading please wait...
Error using svmtrain (line 234)
Y must be a vector or a character array.
Error in svmface (line 158)
            model=svmtrain(label', feature, params);