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

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Balaji M. Sontakke 2020-2-22

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https://ww2.mathworks.cn/matlabcentral/answers/506930-undefined-function-min-for-input-arguments-of-type-struct

评论： dpb 2020-2-24

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
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