Accuracy of ANN confusion matrix is not correct

Hi, i been working on ANN to design a prediction model.This is my coding.But after i run the coding the accuracy in confusion matrix is not same as the final accuracy using formula

clear, close all

clc

% help nndatasets

load ('ann.mat');

RandStream.setGlobalStream(RandStream ('mt19937ar','seed',1));

x = [aiparameter.mc1,aiparameter.torque1];

y = aiparameter.output;

a = isnan(y);

[u,~] = find(a==0);

x = x(u,:); y = y(u);

x = x'; y = y';

% x=rows2vars(m);

% y=rows2vars(n);

%[y,n]=vec2ind(y1);%

%y(y == 2) = 0;

len_ = length(y);

temp = rand(1,length(y));

[~, ind] = sort(temp);

x = x(:, ind);

y = y(:, ind);

y_store = categorical(y);

b = 1:4;

for ii = 1:len_

yy(:,ii) = (b == y(ii));

end

y = single(yy);

net = patternnet;

% Divide data into k-folds

kfold = 5; %10

fold = cvpartition(len_, 'kfold', kfold);

% NN train for every fold

for k = 1 : kfold

% Call index of training & testing sets

trainIdx = fold.training(k); testIdx = fold.test(k);

% Call training & testing features and labels

xtrain = x(:,trainIdx);

ytrain = y(:,trainIdx);

xtest = x(:,testIdx);

ytest = y(:,testIdx);

% configure network

net = configure(net,x,y);

trInd = find(trainIdx); tstInd = find(testIdx);

net.divideFcn = 'divideind';

net.divideParam.trainInd = trInd;

net.divideParam.testInd = tstInd;

% Initialize network

net.layers{1}.name='Hidden Layer 1';

net.layers{2}.name='Output Layer';

net.layers{1}.size = 12; % number of hidden neuron (1-10)

net.layers{1}.transferFcn = 'satlin';

net.trainFcn = 'trainlm'; % training algorithm (trainlm, trainscg, etc)

net.performFcn = 'mse';

net.trainParam.epochs = 10000;

% Train the Network

[net, tr] = train(net, x, y);

% Test the Network

yhat = net(x);

% adjust threshold

thr = 0.5;

% for ii = 1:1:len_

% if yhat(:,ii) >= thr

% yhat(:,ii) = 1;

% else

% yhat(:,ii) = 0;

% end

% end

clear Ypred

for ii = 1:len_

[~,Ypred(ii)] = max(yhat(:,ii));

end

Ypred = categorical(Ypred);

% validate trained network

e = gsubtract(y, yhat);

performance = perform(net, y, yhat);

perf_fold(k)=performance;

% Calculate Training and Testing Performance (Confusion Matrix)

x_trn = x(:,tr.trainInd);

x_tst = x(:,tr.testInd);

y_trn = y_store(:,tr.trainInd);

y_tst = y_store(:,tr.testInd);

yhat_trn = Ypred(:,tr.trainInd);

yhat_tst = Ypred(:,tr.testInd);

figure, plotconfusion(y_trn, yhat_trn, 'Training');

figure, plotconfusion(y_tst, yhat_tst, 'Testing');

% for ii = 1:10%length(y_tst)

% [y_class(ii),~] = find(y_tst(:,ii) == 1);

% [yhat_class(ii),~] = find(yhat_tst(:,ii) == 1);

% end

C=confusionmat(y_tst,yhat_tst)

tp(k)=C(1,1);

fn(k)=C(1,2);

tn(k)=C(2,2);

fp(k)=C(2,1);

end

TP=round(mean(tp));

FN=round(mean(fn));

TN=round(mean(tn));

FP=round(mean(fp));

accuracy=(TP+TN)/(TP+FN+TN+FP)*100

sensitivity=TP/(TP+FN)*100

specificity=TN/(TN+FP)*100

[Poorna]

Can you provide the "ann.mat" file?