42 lines
1.1 KiB
Matlab
42 lines
1.1 KiB
Matlab
% CSCI 5521 Introduction to Machine Learning
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% Rui Kuang
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% Perceptron function
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function [w] = perceptron(X,Y,w_init,rate)
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w = w_init;
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N = size(X,2);
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pos_idx = (Y==1);
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neg_idx = (Y==-1);
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mxx = max(X(1,:));
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mnx=-0.1*mxx;%for visualization %mnx = min(X(1,:));
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mxy = max(X(2,:));
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mny=-0.1*mxy; % for visualization %mny = min(X(2,:));
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figure;
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ginput(1);
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err = 1;
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round = 0;
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while err > 0
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for ii = 1 : N %cycle through training set
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if sign(w'*X(:,ii)) ~= Y(ii) %wrong decision?
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w = w + rate*X(:,ii) * Y(ii); %then add (or subtract) this point to w
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x1=mnx:0.01:mxx;
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x2=-(w(1)*x1+w(3))/w(2);
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%figure;
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clf;
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hold on
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plot(X(1,pos_idx),X(2,pos_idx),'b*','MarkerSize',10);
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plot(X(1,neg_idx),X(2,neg_idx),'r+','MarkerSize',10);
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plot(X(1,ii),X(2,ii),'ko','MarkerSize',15);
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plot(x1,x2);
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xlim([mnx mxx]);
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ylim([mny mxy]);
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%ginput(1);
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pause(0.5); %change the delay
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end
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end
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round = round + 1
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err = sum(sign(w'*X)~=Y')/N %show misclassification rate
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end |