wehub-resource-sync
33 lines
1.0 KiB
Matlab
Executable File
33 lines
1.0 KiB
Matlab
Executable File
function [X, z, model] = mixGaussRnd(d, k, n)
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% Genarate samples form a Gaussian mixture model.
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% Input:
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% d: dimension of data
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% k: number of components
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% n: number of data
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% Output:
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% X: d x n data matrix
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% z: 1 x n response variable
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% model: model structure
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% Written by Mo Chen (sth4nth@gmail.com).
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alpha0 = 1; % hyperparameter of Dirichlet prior
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W0 = eye(d); % hyperparameter of inverse Wishart prior of covariances
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v0 = d+1; % hyperparameter of inverse Wishart prior of covariances
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mu0 = zeros(d,1); % hyperparameter of Guassian prior of means
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beta0 = nthroot(k,d); % hyperparameter of Guassian prior of means % in volume x^d there is k points: x^d=k
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w = dirichletRnd(alpha0,ones(1,k)/k);
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z = discreteRnd(w,n);
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mu = zeros(d,k);
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Sigma = zeros(d,d,k);
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X = zeros(d,n);
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for i = 1:k
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idx = z==i;
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Sigma(:,:,i) = iwishrnd(W0,v0); % invpd(wishrnd(W0,v0));
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mu(:,i) = gaussRnd(mu0,beta0*Sigma(:,:,i));
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X(:,idx) = gaussRnd(mu(:,i),Sigma(:,:,i),sum(idx));
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end
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model.mu = mu;
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model.Sigma = Sigma;
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model.weight = w; |