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mohamad-amin / madelon_pca_classification.ipynb
Created May 2, 2019
Classification on Madelon dataset, using PCA feature selection
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# Compute the probability of each component in the mixture
def _computeProbabilities(self, limits):
probabilities = np.zeros(numComponents) # numComponents refers to the number of components in this mixture
for component in range(numComponents):
# gmm is a GaussianMixture from sklearn.mixture
mean = gmm.means_[component]
cov = gmm.covariances_[component]
weight = gmm.weights_[component]
probabilities[component] = constrainedGaussianDensity(mean, cov, limits) * weight
return probabilities
mohamad-amin /
Last active Mar 15, 2017
A java sample about this issue in EasyMVP repository:
public class Main {
public static void main(String[] args) {
for (int i=0; i<10; i++) {
SimplePresenter presenter = new SimplePresenter();
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