Assignment_2_1_2.m

clc;
clear all;
close all;

Q1 = 7;
Q2 = 3;

m = 3;
mu = 0.75;
a = 0.0001;
T = 1000;
MSETarget = 1e-20;
average_accuracy=0;
data = xlsread('dataset.xlsx');
data(:,1:end-1) = (data(:,1:end-1)-mean(data(:,1:end-1)))./std(data(:,1:end-1));
k_fold=5;
X = data(:,1:end-1); %Inputs
Y = data(:,end); %Target outputs

for i = 1:length(Y)
    if(Y(i)==1)
        Zy(i,:) = [0 1];   
    else  
        Zy(i,:) = [1 0];
    end
end

C = cvpartition(Y,'KFold',k_fold);
for j=1:C.NumTestSets
    tr = C.training(j);
    te = C.test(j);
    Xtr = X(tr,:);
    Xte = X(te,:);
    Ztr = Zy(tr,:);
    Zte = Zy(te,:);
    
    [p,N] = size(Xtr);
    [p2,N2] = size(Xte);
    bias = -1;
    Xtr = [bias*ones(p,1) Xtr];
    Xte = [bias*ones(p2,1) Xte];
    
    W1 = rand(Q1,N+1);
    W2 = rand(Q2,Q1+1);
    W3 = rand(m-1,Q2+1);
    
    MSETemp = zeros(1,T);
    %yaah tak error nahi hai
    for i = 1:T
        V1 = W1*Xtr';
        Z1 = 1./(1+exp(-V1));
        S1 = [bias*ones(1,p);Z1];
        
        V2 = W2*S1;
        Z2 = 1./(1+exp(-V2));
        S2 = [bias*ones(1,p);Z2];
        
        G = W3*S2;
        Y = 1./(1+exp(-G));
        E = Ztr - Y';
        mse = (mean(mean(E.^2)));
        MSETemp(i) = mse;
        if(mse<MSETarget)
            MSE = MSETemp(1:i);
            return
        end
        
        df = Y.*(1-Y);
        dG3 = df.*E';
        DW3 = mu/N * dG3*S2';
        W3 = (1+a)*W3 + DW3;
        
        df = S2.*(1-S2);
        dG2 = df.*(W3' * dG3);
        dG2 = dG2(2:end,:);
        DW2 = mu/N * dG2*S1';
        W2 = (1+a)*W2 + DW2;
        
        df = S1.*(1-S1);
        dG1 = df.*(W2' * dG2);
        dG1 = dG1(2:end,:);
        DW1 = mu/N * dG1*Xtr;
        W1 = (1+a)*W1 + DW1;
        
    end
    
    V1 = W1*Xte';
    Z1 = 1./(1+exp(-V1));
    S1 = [bias*ones(1,p2);Z1];
    
    V2 = W2*S1;
    Z2 = 1./(1+exp(-V2));
    S2 = [bias*ones(1,p2);Z2];
    
    G = W3*S2;
    Yp = 1./(1+exp(-G));
    Ypp = Yp';
    for k = 1:size(Ypp,1)
        [~, pl(k)] = max(Ypp(k,:));
        [~, pa(k)] = max(Zte(k,:));
    end
    [cm,order] = confusionmat(pa,pl);
    Accuracy = ((trace(cm))/sum(cm(:)))*100;
    average_accuracy=average_accuracy+Accuracy;
    %display(cm);
    %display(Accuracy);
end
average_accuracy=average_accuracy/k_fold;
display(average_accuracy);