There is some error while calling function fitcknn. I passed parameters like fitcknn(P_​train,trai​n_label,'D​istance','​euclidean'​,'NumNeigh​bors',5) here size of P_train is 176 X 180 and train_label is 180 1

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Balaji M. Sontakke
Balaji M. Sontakke 2018-3-28
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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采纳的回答

Walter Roberson
Walter Roberson 2018-3-28
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.'
  7 个评论
显示 5更早的评论
Balaji M. Sontakke
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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