Logistic Regression Matlab Code-Iterative

% this is for two class problem for more than two class code changes

% % this is for two class problem for more than two class code changes
% % ————-Parameters————-
% numIteration =1000; The Number of maximum iterations
% % errorBound = 0.0001; This is the permissible error.
% The experiments have been done keep in view both the error condition
% reached or maximum iteration reached whichever comes first.
% % eta = 0.5; This is the learning rate, experiments have been
% done on various learning rates

function logisticRegression2Class()
disp(‘..Starting logistic regression Algorithm….’);

%reading the data

A = load('BananaData.mat');
data = A.data;    
[N,col]= size(data);

vtot=[0, 0, 0, 0,0, 0,  0 , 0];

%5 folds with 70-30 ratio

for i = 1:5

    P=.3;
    groups=data(:,3);
    [train,test] = crossvalind('holdout',groups, P);
    train1= data(train, 1: 3);
    test1=data(test, 1:3);
    [trainLengthRow, trainLengthCol]=size(train1);
    [rowtest,coltest]= size(test1);

    trainingSet = train1(1:trainLengthRow, 1 : trainLengthCol -1 );
    trainingLabels = train1(1:trainLengthRow, trainLengthCol );

    testSet = test1(1:rowtest, 1 : coltest -1 );
    testLabels = test1(1:rowtest, coltest );

%initilizating weights
weights(1:trainLengthCol) = 0;
weight0=0;

[weight0, weights] = trainLogReg(weight0, weights, trainingSet,trainingLabels);

[correctlyClassified,count0,count1,unClassified,v] = testLogReg(testSet,testLabels, weight0, weights)
vtot = vtot +v ;
end

disp(‘TP, TN, FP, FN, TP/(TP+FP), TP/P, 2PR / (P+R) , correctlyClassified/trainLengthRow’);
vtot = vtot ./ 5

end

%This mathod is for tarining the logestic regression problem
% —–Parameters—-
%trainingSet: the training set
%trainingLabels: the labels corresponding to the traiining set
%weights: the initial weights obtained from traiining
%weight0: The initial bias weight
%—–Return Types——
%weights: the final weights obtained from traiining
%weight0: The bias weight
%
function [weight0, weights] = trainLogReg(weight0, weights, trainingSet,trainingLabels)

numIteration =100000;
eta = 0.5;            
errorBound = 0.0001;
error =1.0;
[trainLengthRow, trainLengthCol] = size(trainingSet); 
del_l_by_del_w_i(1:trainLengthCol) = 0;
weightsFinal(1:trainLengthCol) = 0;
k=0
while ((k < numIteration) && (error > errorBound))        
    error=0.0;
for i=1:trainLengthCol
    Y1_X = 0;
    del_l_by_del_w_i(i) = 0;                
    del_l_by_del_w_0 = 0;

    for t=1: trainLengthRow        
        sum = weight0;

        for j=1: trainLengthCol
        sum = sum +  weights(j)*trainingSet(t,j);
        end;

        Y1_X = 1/(1+ exp(-1*sum));

        del_l_by_del_w_i(i) = del_l_by_del_w_i(i) + trainingSet(t,i) *(trainingLabels(t) - Y1_X ) ;        
        del_l_by_del_w_0 = del_l_by_del_w_0 + 1 *(trainingLabels(t) - Y1_X ) ;        
    end;        

end;

for i=1:trainLengthCol
weightsFinal(i)= weights(i) + eta *  del_l_by_del_w_i(i);  
error = error + (weightsFinal(i)-weights(i))*(weightsFinal(i)-weights(i));
end;

weight0new = weight0 +    eta *  del_l_by_del_w_0;
error = error + (weight0new-weight0)*(weight0new-weight0);
error=sqrt(error);
weights=weightsFinal;
weight0 = weight0new;
k=k+1;
end

k
%Now computing the final y using the final weights

y1(1:trainLengthRow)=0;
y0(1:trainLengthRow)=0;

for i =1: trainLengthRow

    sum = weight0;

    for j=1: trainLengthCol
    sum = sum +  weightsFinal(j)*trainingSet(i,j);
    end;

    y1(i) = 1/(1+ exp(-1*sum));
    y0(i) = 1/(1+ exp(sum));

 end;

% Following is the code for plotting the data
% data(1:trainLengthRow, 1:trainLengthCol+1)=0;
% data(1:trainLengthRow, 1:trainLengthCol)= trainingSet;
% for p=1:trainLengthRow
% data(p, trainLengthCol+1)= y1(p);
% end;
%
% %figure
% % parallelcoords(data,’Labels’,labels);
%
% for p=1:trainLengthRow
% x1(p)= trainingSet(p,1);
% end;
%
% for p=1:trainLengthRow
% x2(p)= trainingSet(p,2);
% end;
%
%
% for p=1:trainLengthRow
% yOrginal(p)= trainingLabels(p);
% end;
%
%
% size(x1)
% size(x2)
% size(trainingLabels)
%
% figure
% scatter3(x1,x2,trainingLabels,10);
% axis([-10,10,-10,10,-10,10])
%
% figure
% plot3(x1,x2,y1);
% axis([-10,10,-10,10,-10,10])
%
%
% xx=[-10:1:10];
% yy=[-10:1:10];
% [xx1,yy1]=meshgrid(xx,yy);
%
% sum = -1 .* (weight0+weightsFinal(1).xx1+weightsFinal(2).yy1);
% zz= 1 ./(1 + expm(sum));
% figure
% surf(xx1,yy1,zz);
% title(‘title’);
% xlabel(‘x’);
% ylabel(‘y’)
% zlabel(‘z’);

end

%This is the method that is called to test the accuracy of the methods
%———————–Parameters————————–
%testSet: the set of samples to be considered for testing
%testLabels: the labels corresponding to testset
%weight0, weight: the weights corresponding to logistic regression
%———————–Return Values————————
%correctlyClassified: The number of correctly classified samples
%unClassified: The array containing 5 unclassified data samples from each
%classification type
%v: The vecor that returns the computed values of TP;TN; FP; FN ,P; R; F, accuracy
function [correctlyClassified,count0,count1,unClassified,v] = testLogReg(testSet,testLabels, weight0, weights)

correctlyClassified = 0;
count0 = 0; count1=0;   TP=0;    TN=0;     FP=0;     FN =0; P=0; R=0; F=0;

[testLengthRow,testLengthCol]=size(testSet);
unClassified(1:10 ,1: testLengthCol) = 0;

% checking accuracy by  number of correctly classified   

for k=(1: testLengthRow )
    x=[1, testSet(k,1:testLengthCol)];
    w =[weight0,weights];
    O1=    x' .* w' ;

    %computing the value of vector with plane
    sum =0;
    for p=1:length(O1)
        sum = sum +O1(p);
    end

     y1x = 1/(1+ exp(-1*sum));
     if(y1x>=0.5)
         %disp('class 1');
         O =1;
     else
         %disp('class 0');
         O =-1;
     end

     %    error as output approaching target
    if (O == testLabels(k))
        % correctly classified examples
        correctlyClassified=correctlyClassified+1;

        %compute  TP, TN
        if(testLabels(k)==1)
            TP = TP+1;
        else
            TN = TN +1;
        end

    else
        % wrongly classified examples
        if(testLabels(k)==1)
            FN = FN+1;
        else
            FP = FP +1;
        end
        %storing 5 misclassified  classes from each class
        if(count1<5 && testLabels(k)==1)
            count1 = count1 + 1;
            unClassified(count1,1: testLengthCol) = testSet(k,1: testLengthCol);
        end
        if(count0<5 && testLabels(k)==-1 )
            count0 = count0 + 1;
            unClassified(count0,1: testLengthCol) = testSet(k,1: testLengthCol);                
        end
    end

end

  k
P= TP/(TP+FP)
R=  TP/(TP+FN)
v=[TP,    TN,     FP,     FN,     P,     R,      2*P*R / (P+R) , correctlyClassified/testLengthRow]
disp('TP,    TN,     FP,     FN,     TP/(TP+FP),      TP/P,      2*P*R / (P+R) , correctlyClassified/trainLengthRow');


 unClassified;
 accuracy = correctlyClassified/testLengthRow  ;   
 accuracy

end

Published by Nidhika

I have an eager research-based approach to solve problems in the various areas using my expertise. I find solutions based on my experience, research skills, strong knowledge Apart from profession, I have inherent interest in writing especially poems, stories, doing painting, cooking, photography, music to mention a few! And most important on my website you can find my views, ideas, my poems, stories, novels, my comments, my proposals, my blogs, my interests, my personal experiences and short occasional glimpses of my research work as well.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: