IrisHoldOut.m

clc;
clear all;
close all;
load iris_dataset.mat;


X=irisInputs';
Y=irisTargets';
for i=1:size(Y,1)
    [~,l(:,i)]=max(Y(i,:));%index at which the maximum value of Y(i) occurs
end

c=cvpartition(l,'HoldOut',0.3);

trIdx=c.training;
teidx=c.test;
xtr=X(trIdx,:);
ytr=Y(trIdx,:);
xte=X(teidx,:);
yte=Y(teidx,:);
xtrain=xtr;
ytrain=ytr;
xtest=xte;
ytest=yte;

tic
NumberofHiddenNeurons=30;
NumberofTrainingData=size(xtrain,1);
NumberofTestingData=size(xtest,1);
NumberofInputNeurons=size(xtrain,2);
randommat=randn(NumberofInputNeurons+1,NumberofHiddenNeurons);

tempH=[ones(size(xtrain,1),1) xtrain]*randommat;

H=tanh(tempH);
[m,n]=size(H);

OutputWeight=pinv(H)*ytrain;

testH=[ones(size(xtest,1),1) xtest]*randommat;

Ht=tanh(testH);
yn=Ht*OutputWeight;

for i=1:size(xtest,1)
    [~, lp(:,i)]=max(yn(i,:));
    [~, lt(:,i)]=max(ytest(i,:));
end

[cmt,order]=confusionmat(lp,lt);
display(cmt);
display(order);
%summ=sum(cmt(:));
%test_acc=(cmt(1,1)+cmt(2,2)/summ)*100;
%test_sen=(cmt(1,1)/(cmt(1,1)+cmt(1,2)))*100;
%test_spe=(cmt(2,2)/(cmt(2,2)+cmt(2,1)))*100;
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