53 lines
1.2 KiB
Mathematica
53 lines
1.2 KiB
Mathematica
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% CSCI 5521 Introduction to Machine Learning
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% Rui Kuang
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% Demonstration of Classification by 2-D Gaussians
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mu1 = [-1 -1];
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mu2 = [1 1];
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% Equal diagnoal covariance matrix
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Sigma1 = [1 0; 0 1];
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Sigma2 = [1 0; 0 1];
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% Diagnoal covariance matrix
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% Sigma1 = [1 0; 0 0.5];
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% Sigma2 = [1 0; 0 0.5];
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% Shared covariance matrix
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% Sigma1 = [1 0.3; 0.3 0.5];
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% Sigma2 = [1 0.3; 0.3 0.5];
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x1 = -10:.1:10; x2 = -10:.1:10;
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% covariance matrix (increase the range for visualization)
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% Sigma1 = [1 0.1; 0.1 0.5];
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% Sigma2 = [0.5 0.3; 0.3 1];
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% x1 = -40:.1:40; x2 = -40:.1:40;
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[X1,X2] = meshgrid(x1,x2);
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%pdf1
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F1 = mvnpdf([X1(:) X2(:)],mu1,Sigma1);
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F1 = reshape(F1,length(x2),length(x1));
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subplot(1,2,1);
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surf(x1,x2,F1); hold on;
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%pdf2
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F2 = mvnpdf([X1(:) X2(:)],mu2,Sigma2);
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F2 = reshape(F2,length(x2),length(x1));
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surf(x1,x2,F2);
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caxis([min(F2(:))-.5*range(F2(:)),max(F2(:))]);
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axis([-4 4 -4 4 0 .4])
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xlabel('x1'); ylabel('x2'); zlabel('Probability Density');
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%decosopm boundary
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%F1 = mvnpdf([X1(:) X2(:)],mu1,Sigma1);
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%F1 = reshape(F1,length(x2),length(x1));
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%F2 = mvnpdf([X1(:) X2(:)],mu2,Sigma2);
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%F2 = reshape(F2,length(x2),length(x1));
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cmp = F1 > F2;
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subplot(1,2,2);
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imagesc(X1(:),X2(:),cmp);
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xlabel('x1'); ylabel('x2');
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