file loaded

Perceptron.m

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+
+clear all;
+close all;
+clc;
+
+load("bim488_hw2.mat")
+c1x = classes(1,1:100);
+c2x = classes(1,101:200);
+c1y = classes(2,1:100);
+c2y = classes(2,101:200);
+
+plot(c1x, c1y, 'o');
+hold
+plot(c2x, c2y, '.');
+
+rho=0.01;
+w_init1 = [1, 1, -0.5]';
+w_init2 = [1, 1, 0.5]';
+
+% input for bias is 1, so I added them to 3rd row
+A= ones(1,200);
+classes= [classes;A];
+
+[weight_1]=perceptron(classes,class_labels,w_init1,rho);
+disp('Final weight vector for w_init1 is:')
+disp(weight_1');
+x1(1,1)=0;
+x1(1,2)=-weight_1(3,1)/weight_1(2,1);
+x2(1,1)=-weight_1(3,1)/weight_1(1,1);
+x2(1,2)=0;
+line(x1,x2,Color="red") % point1 = (0, -b/w2) and point2 = (-b/w1 ,0)
+
+[weight_2]=perceptron(classes,class_labels,w_init2,rho);
+disp('Final weight vector for w_init2 is:')
+disp(weight_2');
+x12(1,1)=0;
+x12(1,2)=-weight_2(3,1)/weight_2(2,1); 
+x22(1,1)=-weight_2(3,1)/weight_2(1,1); 
+x22(1,2)=0; 
+line(x12,x22); % point1 = (0, -b/w2) and point2 = (-b/w1 ,0)
+
+function [w]=perceptron(X,y,w_init,rho)
+
+[row,col]=size(X);
+max_iter=50000; % for control
+w=w_init;        
+iter_num=0;         
+missclasified=col;     
+
+while(missclasified>0)&&(iter_num<max_iter)
+    iter_num=iter_num+1;
+    missclasified=0;
+
+    sum_symbolpart=zeros(row,1);
+    for i=1:col
+        if((X(:,i)'*w)<=0 && (y(i)==1) ) % missclasified case-1
+            % e(i) = d(i) - y(i)
+            % I asssumed that transfer function is +1 for c1 and 0 for c2
+            % So, e(i) = 1 - 0 = 1
+            % (But if it is +1 for c1 and -1 for c2, e(i) would be 2 for c1
+            % and -2 for c2.)
+            e(i)=1;
+            missclasified=missclasified+1;
+            sum_symbolpart=sum_symbolpart+(e(i)*X(:,i));
+
+        elseif ((X(:,i)'*w)>0 && (y(i)==2) ) %missclasified case-2
+            % e(i) = -1 - 0 = -1
+            e(i)=-1;
+            missclasified=missclasified+1;
+            sum_symbolpart=sum_symbolpart+(e(i)*X(:,i));
+        end        
+    end
+    w=w+rho*sum_symbolpart;
+end
+end
+
+