Error in following code while converting cell to matrix

 Error in following code
 Error using cell2mat (line 52)
 CELL2MAT does not support cell arrays containing cell arrays or objects.

 Error in Fusion_Minutie_InvMoment (line 18)
 fusion = cell2mat(out);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 V = {load('db1.mat'),load('db2.mat'),load('db3.mat'),load('db4.mat')};
 n = cellfun(@fieldnames,V,'un',0);
 V1 = cellfun(@(x,y)[x.(y)]',V,[n{:}],'un',0);
 V2 = [V1{:}];
 V3 = cell2mat(reshape(V2,1200,[]));
 V4 = mat2cell(V3,4*ones(size(V3,1)/4,1),7);
 out = mat2cell(V4,[400,800],1);

 fusion = cell2mat(out);

Answer 1

[Majid Farzaneh]

Hi, you can do it like this:

V = {load('db1.mat'),load('db2.mat'),load('db3.mat'),load('db4.mat')};
 n = cellfun(@fieldnames,V,'un',0);
 V1 = cellfun(@(x,y)[x.(y)]',V,[n{:}],'un',0);
 V2 = [V1{:}];
 V3 = cell2mat(reshape(V2,1200,[]));
 V4 = mat2cell(V3,4*ones(size(V3,1)/4,1),7);
 out = mat2cell(V4,[400,800],1)
 fusion1 = cell2mat(out{1});
 fusion2= cell2mat(out{2});
 fusion=[fusion1;fusion2];

In your cell matrix (out) there are 2 other cell matrix. For using cell2mat you should have normal matrix in 'out' cell matrix.

[Balaji M. Sontakke]

Sir, am having the four mat files (attached herewith), which of two contains features from minutiae and other two contains feature from invariant moment. I did classification by loading first two mat files (see following code), In your code (fusion) contains fusion of all mat files, then how i provide the value of reduced_testdata and reduced_traindata for further processing.

 clear all; 
 clc; 

 tic;                  %%calculating elapsed time for execution

 %load mat files

 load('db3.mat');
 load('db4.mat');

 %%reshape into row vector

 reduced_testdata = reshape(reduced_testdata,1,4,100);        
 reduced_traindata = reshape(reduced_traindata,1,4,200);       

 %%adjust dimension
 % Adjust matrix dimension

 P_test = cell2mat(reduced_testdata);                                          % Convert cell array to matrix

 P_train = cell2mat(reduced_traindata); 

 %%rearranges the dimensions of P_test and P_train 

 C = permute(P_test,[1 3 2]);
 P_test = reshape(C,[],size(P_test,2),1);

 C = permute(P_train,[1 3 2]);
 P_train = reshape(C,[],size(P_train,2),1);

 %%labeling class

train_label=load('train_label.txt'); test_label=load('test_label.txt');

 %%Normalisation by Z - Scores 

 P_train = zscore(P_train,0,2);
 P_test  =zscore(P_test,0,2); 

 %%classfication

 predictlabel = knnclassify(P_test, P_train,   train_label,3,'euclidean','nearest');
 cp = classperf(test_label,predictlabel);

 Conf_Mat = confusionmat(test_label,predictlabel);
 disp(Conf_Mat);

 %%Evaluate Performance

 [FPR, TPR,Thr, AUC, OPTROCPT]  = perfcurve(predictlabel, test_label,1);

 figure,

 plot(TPR,FPR,'r-','LineWidth',4);
 xlabel('False positive rate')
 ylabel('True positive rate') 
 title('ROC Curve for Classification ')
 Tbl = table(FPR, TPR,Thr)

 [FPR, FNR, Thr]  = perfcurve(predictlabel, test_label,1,'xCrit','fall','xCrit','miss');
Thr = Thr/10;

 figure,

 plot(Thr,FPR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
 xlabel('Threshold')
 ylabel('False positive rate / FAR') 
 title('False Acceptance Rate / FAR ')

 figure,

 plot(Thr,FNR,'r--','LineWidth',2,'MarkerEdgeColor','k','MarkerFaceColor','g','MarkerSize',10);
 xlabel('Threshold')
 ylabel('False Negative rate / FRR') 
 title('False Rejection Rate / FRR ')
 Tbl = table(FPR, FNR,Thr) 

 fprintf('\n\n Overall accuracy:%f%%\n',cp.CorrectRate*100);

 %%calculating elapsed time for execution
 toc

Answer 2

[Andrei Bobrov]

fusion = cell2mat(cat(1,out{:}));

or just

V = {load('db1.mat'),load('db2.mat'),load('db3.mat'),load('db4.mat')};

n = cellfun(@fieldnames,V,'un',0);
V1 = cellfun(@(x,y)[x.(y)],V,[n{:}],'un',0);
fusion = cell2mat(reshape(cat(1,V1{:}),1200,[]));

[Balaji M. Sontakke]

Sir, how to provide this fusion to knn classification, because I required P_Test and P_train (Please see attached code)