Error in following code while converting cell to matrix

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Balaji M. Sontakke
Balaji M. Sontakke 2018-6-21
评论： Balaji M. Sontakke 2018-6-22
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);

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回答（2 个）

Majid Farzaneh
Majid Farzaneh 2018-6-21
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.
  1 个评论
Balaji M. Sontakke
Balaji M. Sontakke 2018-6-22
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

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Andrei Bobrov
Andrei Bobrov 2018-6-22
编辑：Andrei Bobrov 2018-6-22
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,[]));
  1 个评论
Balaji M. Sontakke
Balaji M. Sontakke 2018-6-22
Sir, how to provide this fusion to knn classification, because I required P_Test and P_train (Please see attached code)

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