Muhammad Nadzeri Munawar
67 linhas
1.5 KiB
Matlab
67 linhas
1.5 KiB
Matlab
%
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% Name: demo_PCA2.m
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%
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% Description: Generates multivariate Gaussian samples and applies PCA to
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% extract the dimensions of maximum variance and projects the
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% samples onto them
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%
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% Author: Brian Moore
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% brimoor@umich.edu
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%
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% Date: April 26, 2015
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%
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rng(1);
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% Knobs
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n = 1000; % # samples
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d = 3; % Sample dimension
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r = 2; % # principal components
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% Generate Gaussian data
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MU = 10 * rand(d,1);
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sigma = (2 * randi([0 1],d) - 1) .* rand(d);
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SIGMA = 3 * (sigma * sigma');
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Z = myMultiGaussian(MU,SIGMA,n);
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% Perform PCA
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[Zpca,~,~,~,eigVecs] = myPCA(Z,r);
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% Plot samples + scaled eigenvectors
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figure;
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x = @(i,j) MU(i) + [0 eigVecs(i,j)];
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if (d == 2)
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% Plot 2D data
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plot(Z(1,:),Z(2,:),'g+');
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hold on;
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for j = 1:r
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plot(x(1,j),x(2,j),'b','Linewidth',3);
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end
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else
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% Plot first 3 dimensions
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plot3(Z(1,:),Z(2,:),Z(3,:),'g+');
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hold on;
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for j = 1:r
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plot3(x(1,j),x(2,j),x(3,j),'b','Linewidth',3);
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end
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end
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title(sprintf('First %i principal directions of %iD Gaussian samples',min(3,r),d));
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grid on;
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set(gca,'DataAspectRatio',[1 1 1]);
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% Plot principal componenets
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figure;
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if (r == 2)
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% Plot 2D data
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plot(Zpca(1,:),Zpca(2,:),'r+');
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else
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% Plot first 3 dimensions
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plot3(Zpca(1,:),Zpca(2,:),Zpca(3,:),'r+');
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zlabel('Principal direction #3');
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end
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title(sprintf('Zpca(1:%i,:)',min(3,r)));
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xlabel('Principal direction #1');
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ylabel('Principal direction #2');
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grid on;
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set(gca,'DataAspectRatio',[1 1 1]);
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