61 lines
2.1 KiB
Mathematica
61 lines
2.1 KiB
Mathematica
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%generate two-class data
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rng(1); % For reproducibility
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N = 20;
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r = sqrt(1*rand(N,1)); % Radius
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t = 2*pi*rand(N,1); % Angle
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data1 = [r.*cos(t), r.*sin(t)]; % Points
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r2 = sqrt(8*rand(N,1)+1); % Radius
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t2 = 2*pi*rand(N,1); % Angle
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data2 = [r2.*cos(t2), r2.*sin(t2)]; % points
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%combine the two class data
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data3 = [data1;data2];
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theclass = ones(2*N,1);
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theclass(1:N) = -1;
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figure;
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for degree = [1 2 4]
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%TO DO: replace fitcsvm with kernel_perceptron
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%SVM classification demo;
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% alphas = fitcsvm(data3,theclass,'KernelFunction','polynomial','PolynomialOrder',degree);%tune the sigma parameter here, 1, 3, 0.1
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[alphas, b] = kernel_perceptron(data3, theclass, degree);
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%[alphas,b] = kernel_perceptron(data3,theclass,degree);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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d = 0.02;
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[x1Grid,x2Grid] = meshgrid(min(data3(:,1)):d:max(data3(:,1)),...
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min(data3(:,2)):d:max(data3(:,2)));
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xGrid = [x1Grid(:),x2Grid(:)];
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%TO DO: replace predict with kp_predict
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%SVM prediction demo; replace with kernel perceptron
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% [~,scores1] = predict(alphas,data3); %replace it with mypredict(...)
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% [~,scores2] = predict(alphas,xGrid); %replace it with mypredict(...)
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% scores1 = kp_predict(alphas, b, degree, data3, theclass, data3)
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% scores2 = scores2(:,2);
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% training_error = CalculateErrorRate(theclass,sign(scores1(:,2)));
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scores1 = kp_predict(alphas,b,degree,data3,theclass,data3); %return the prediction scores
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scores2 = kp_predict(alphas,b,degree,data3,theclass,xGrid); %return the prediction scores
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training_error = CalculateErrorRate(theclass,sign(scores1));
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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subplot(5,1,degree);
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h(1:2) = gscatter(data3(:,1),data3(:,2),theclass,'rb','.');
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hold on
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contour(x1Grid,x2Grid,reshape(scores2,size(x1Grid)),[0 0],'k');
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plottitle = sprintf("Degree = %d; Training Error = %f",degree,training_error);
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title(plottitle);
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end
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function error_rate = CalculateErrorRate(y_pred,y_label)
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N = length(y_label);
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error_rate = sum(y_pred ~= y_label)/N;
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end
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