There is some error while calling function fitcknn. I passed parameters like fitcknn(P_train,train_label,'Distance','euclidean','NumNeighbors',5) here size of P_train is 176 X 180 and train_label is 180 1

Question

Balaji M. Sontakke 2018-3-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/391143-there-is-some-error-while-calling-function-fitcknn-i-passed-parameters-like-fitcknn-p_train-train_l

评论： Balaji M. Sontakke 2018-3-31

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 201)
X and Y do not have the same number of observations.
Error in classreg.learning.classif.FullClassificationModel.prepareData (line 487)
                classreg.learning.FullClassificationRegressionModel.prepareDataCR(...
Error in ClassificationKNN.prepareData (line 868)
                prepareData@classreg.learning.classif.FullClassificationModel(X,Y,varargin{:},'OrdinalIsCategorical',true);
Error in classreg.learning.FitTemplate/fit (line 213)
                    this.PrepareData(X,Y,this.BaseFitObjectArgs{:});
Error in ClassificationKNN.fit (line 853)
            this = fit(temp,X,Y);
Error in fitcknn (line 315)
    this = ClassificationKNN.fit(X,Y,RemainingArgs{:});
Error in main_new (line 621)
mdl = fitcknn(P_train,train_label,'Distance','euclidean','NumNeighbors',5);

-----

%%Code%%%%%%%%%%%%%%%
%%Dorsal hand vein recognition using  SVM
clc
clear all;
c=[];
addpath train;
addpath test;
mapping=getmapping(8,'u2'); %LBP
%        W=[2,1,1,1,1,1,2; ...
%           2,4,4,1,4,4,2; ...
%           1,1,1,0,1,1,1; ...
%           0,1,1,0,1,1,0; ...
%           0,1,1,1,1,1,0; ...
%           0,1,1,2,1,1,0; ...
%           0,1,1,1,1,1,0]; 
       W=[0,0,0,0,0,0,0; ...
          0,1,1,1,1,1,0; ...
          0,1,2,4,2,1,0; ...
          0,1,4,4,4,1,0; ...
          0,1,2,4,2,1,0; ...
          0,1,1,1,1,1,0; ...
          0,0,0,0,0,0,0];
for i=1:9
  B=imread(strcat('train\','1\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','2\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','3\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','4\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','5\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','6\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','7\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','8\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','9\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','10\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','11\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','12\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','13\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','14\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','15\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','16\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','17\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','18\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','19\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
for i=1:9
  B=imread(strcat('train\','20\',num2str(i),'.bmp'));  
  X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  % imshow(lpqhist);
  a=[H2,lpqhist];
  c=[c;a];
  disp(sprintf('Done',i));
end
d=[];
for i=1:3
  B=imread(strcat('test\','1\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','2\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','3\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','4\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','5\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','6\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear'); 
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','7\',num2str(i),'.bmp'));
    X = double(B);
 X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','8\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear'); 
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','9\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','10\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','11\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','12\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','13\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','14\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','15\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','16\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear'); 
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','17\',num2str(i),'.bmp'));
    X = double(B);
 X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','18\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear'); 
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','19\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
for i=1:3
  B=imread(strcat('test\','20\',num2str(i),'.bmp'));
    X = double(B);
  X = imresize(X,[60 60],'bilinear');  
  H2=DSLBP(X,mapping,W);
  Gray=X;
  Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
  lpqhist=lpq(Gray,3,1,1,'nh');       
  a=[H2,lpqhist];
  d=[d;a];
  disp(sprintf('Done',i));
end
P_train=c;
P_test=d;
% %%PCA low dimension reduction
% 
P_train = P_train';
model = perform_pca(P_train,rank(P_train)-1);                             
test_features= linear_subspace_projection(P_test, model, 1);        
P_train=model.train';
P_test=test_features';
%%Normalisation
    P_train=P_train/256;
    P_test=P_test/256;
% %%%%%%%%load label %%%%%%%%%%%%
 train_label=load('train_label.txt');  
 test_label=load('test_label.txt');    
P_train = P_train';
P_test = P_test';
%%classification K Nearest Neighour
% results = nn_classification_PhD(P_train,train_label, P_test, test_label, size(P_test,1), 'euc');
mdl = fitcknn(P_train,train_label,'Distance','euclidean','NumNeighbors',5);
predict_label = predict(mdl,P_test);

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Answer 1

Walter Roberson 2018-3-28

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/391143-there-is-some-error-while-calling-function-fitcknn-i-passed-parameters-like-fitcknn-p_train-train_l#answer_312340

https://www.mathworks.com/help/stats/fitcknn.html#bt6cr9l-X

"Predictor data, specified as numeric matrix.

Each row corresponds to one observation (also known as an instance or example), and each column corresponds to one predictor variable (also known as a feature).

The length of Y and the number of rows of X must be equal."

So, you are passing in X data that has 176 samples, each with 180 features, but you are passing in 180 feature labels.

Chances are that you want to pass in X.'

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Balaji M. Sontakke 2018-3-28

编辑：Walter Roberson 2018-3-28

在 MATLAB Online 中打开

yes sir I did it but still error is remained i.e.

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 201)
X and Y do not have the same number of observations.
%%Dorsal hand vein recognition using  SVM
clc
clear all;
c=[];
addpath train;
addpath test;
mapping=getmapping(8,'u2'); %LBP
%        W=[2,1,1,1,1,1,2; ...
%           2,4,4,1,4,4,2; ...
%           1,1,1,0,1,1,1; ...
%           0,1,1,0,1,1,0; ...
%           0,1,1,1,1,1,0; ...
%           0,1,1,2,1,1,0; ...
%           0,1,1,1,1,1,0]; 
       W=[0,0,0,0,0,0,0; ...
          0,1,1,1,1,1,0; ...
          0,1,2,4,2,1,0; ...
          0,1,4,4,4,1,0; ...
          0,1,2,4,2,1,0; ...
          0,1,1,1,1,1,0; ...
          0,0,0,0,0,0,0];
   %%Training images
   for folder_idx = 1 : 20 
   for i = 1 : 9 
   thisfile = fullfile('train', num2str(folder_idx), [num2str(i) '.bmp ']); 
   B = imread(thisfile ); 
   X = double(B);
    X = imresize(X,[60 60],'bilinear');  
    H2=DSLBP(X,mapping,W);
    Gray=X;
    Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
    lpqhist=lpq(Gray,3,1,1,'nh');       
    % imshow(lpqhist);
    a=[H2,lpqhist];
    c=[c;a];
    disp(sprintf('Done',i));
   end 
   end
%%Testing images
d=[];
for folder_idx = 1 : 20 
 for i = 1 : 3 
   thisfile = fullfile('test', num2str(folder_idx), [num2str(i) '.bmp ']); 
   B = imread(thisfile ); 
    X = double(B);
    X = imresize(X,[60 60],'bilinear');  
    H2=DSLBP(X,mapping,W);
    Gray=X;
    Gray=(Gray-mean(Gray(:)))/std(Gray(:))*20+60;
    lpqhist=lpq(Gray,3,1,1,'nh');       
    a=[H2,lpqhist];
    d=[d;a];
    disp(sprintf('Done',i));
   end 
   end
P_train=c;
P_test=d;
% %%PCA low dimension reduction
% 
P_train = P_train';
% if classes are 20 then eiganvectors not exceed then 179 
model = perform_pca(P_train,179);          %rank(P_train)-1
test_features= linear_subspace_projection(P_test, model, 1);        
P_train=model.train';
P_test=test_features';
%%Normalisation
    P_train=P_train/256;
    P_test=P_test/256;
% %%%%%%%%load label %%%%%%%%%%%%
 train_label=load('train_label.txt');  
 test_label=load('test_label.txt');    
P_train = P_train';
P_test = P_test';
%%classification K Nearest Neighour
% results = nn_classification_PhD(P_train,train_label, P_test, test_label, size(P_test,1), 'euc');
   mdl = fitcknn(P_train',train_label,'Distance','euclidean','NumNeighbors',5);
   predict_label = predict(mdl,P_test);

Balaji M. Sontakke 2018-3-29

It works sir, Thank u very much

Balaji M. Sontakke 2018-3-31

Sir, In this function [predict_label,score,cost] = predict(mdl,P_test); the score matrix contains o and 1 values but how to see a matrix contains distances.

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