38 lines
876 B
Matlab
38 lines
876 B
Matlab
% CSCI 5521 Introduction to Machine Learning
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% Rui Kuang
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% Run perceptron on random data points in two classes
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% n = 200; %set the number of data points
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% mydata = rand(n,2);
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%
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% shiftidx = abs(mydata(:,1)-mydata(:,2))>0.00005;
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% mydata = mydata(shiftidx,:);
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% myclasses = mydata(:,1)>mydata(:,2); % labels
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% n = size(mydata,1);
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% X = [mydata ones(1,n)']'; Y=myclasses;
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% Y = Y * 2 -1;
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%
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% % init weigth vector
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% %%% w = [mean(mydata) 0]';
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% w = [1 0 0];
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for i = 1:1
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%%% w=rand(1,3)'
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w = [0.6842
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0.5148
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0]
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w(3,1)=0%go through the origin for visualization
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% call perceptron
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wtag=perceptron(X,Y,w,10);
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end
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% call perceptron
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% wtag=perceptron(X,Y,w);
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% predict
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ytag=wtag'*X;
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% plot prediction over origianl data
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%plot(X(1,ytag<0),X(2,ytag<0),'bo')
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%plot(X(1,ytag>0),X(2,ytag>0),'ro')
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%legend('class -1','class +1','pred -1','pred +1') |