Expectation Maximization of Gaussian Mixture Models via CUDA: errorellipse(mu,sigma,stdev,n) - File Exchange

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Expectation Maximization of Gaussian Mixture Models via CUDA

errorellipse(mu,sigma,stdev,n...
gmm_test(num_clusts, num_dims... GMM_TEST - runs the EM algorithm on random data on the GPU
nvmex(varargin) MEX Compile MEX-function
nvmex_helper(varargin) MEX_HELPER is a helper function that contains the code that MEX.M (an
compile.m
experiment1.m
experiment2.m
experiment3.m
gmm_example.m
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from Expectation Maximization of Gaussian Mixture Models via CUDA by Andrew Harp
CUDA enabled parallel EM for Gaussian Mixture Models, providing over 100x performance increases.

errorellipse(mu,sigma,stdev,n)

function [x y] = errorellipse(mu,sigma,stdev,n)
L = chol(sigma,'lower');
circle = [cos(2*pi*(0:n)/n); sin(2*pi*(0:n)/n)].*stdev;
ellipse = L*circle + repmat(mu,[1,n+1]);
x = ellipse(1,:);
y = ellipse(2,:);

Highlights from Expectation Maximization of Gaussian Mixture Models via CUDA

Highlights from
Expectation Maximization of Gaussian Mixture Models via CUDA