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Undefined function 'min' for input arguments of type 'struct'.

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Balaji M. Sontakke
Balaji M. Sontakke on 22 Feb 2020
Commented: dpb on 24 Feb 2020
Undefined function 'min' for input arguments of type 'struct'.
Error in mapminmax.create (line 12)
xmin = nnet.array.safeGather(min(x,[],2));
Error in mapminmax (line 51)
[y,settings] = mapminmax.create(x,param);
Error in GLCM_MainTotalDemo (line 46)
P_train=mapminmax(P_train,0,1);
%********* program **************************
clear all;
clc;
%% BUILD DORSAL HAND VEIN TEMPLATE DATABASE
tic; %% calculating elapsed time for execution
%% load mat files
load('db5.mat');
load('db6.mat');
%% reshape into row vector
reduced_testdata = reshape(reduced_testdata,1,4,10); % one row,four column and 15(60/4) group for 20 classes
reduced_traindata = reshape(reduced_traindata,1,4,20); % one row,four column and 45(180/4) group for 20 classes
%% 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_2.txt');
test_label=load('test_label_2.txt');
% %%% Normalisation
%
% P_train=P_train/256;
% P_test=P_test/256;
%% Normalisation by min max
P_train=mapminmax(P_train,0,1);
P_test=mapminmax(P_test,0,1);
%% %%PCA low dimension reduction
P_train = P_train';
%%% if classes are 20 then eiganvectors not exceed then 179
model = perform_pca(P_train,rank(P_train)-1); %rank(P_train)-1
test_features= linear_subspace_projection(P_test, model, 1);
P_train=model.train';
P_test=test_features';
%% 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);
[c_matrix,Result,RefereceResult]= confusion.getMatrix(test_label,predictlabel);
%% % Evaluate Performance
[FPR, TPR,Thr, AUC, OPTROCPT] = perfcurve(predictlabel, test_label,1);
figure,
plot(TPR,FPR,'r-','LineWidth',1);
xlabel('False positive rate')
ylabel('True positive rate')
title('ROC Curve for Classification ')
Tbl = table(FPR, TPR, Thr)
%% FAR = FPR = FP/(FP + TN) and FRR = FNR = FN/(FN + TP)
fprintf('\n\n Overall accuracy:%f%%\n',cp.CorrectRate*100);
%% calculating elapsed time for execution
toc

  10 Comments

Show 7 older comments
dpb
dpb on 23 Feb 2020
Huh. I'd forgotten cell2mat will return an array of an underlying type including a struct array...was thinking that would have had to have erred if result wasn't a "real" matrix.
Walter Roberson
Walter Roberson on 24 Feb 2020
It depends on the release which datatypes are supported by cell2mat()
dpb
dpb on 24 Feb 2020
Well, mayhaps hadn't forgotten but had never realized... :)

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