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This libary contains the functionalities required to train and handle Gaussian Mixture Models, aka GMM. If you believe to be not really familiar with this object, have a look at ./doc/Gaussian_Mixture_Model.pdf.

The construction of a GMM can be done by explicitly define the clusters:

#include <GaussianMixtureModel/GaussianMixtureModel.h>

// A gaussian mixture model (gmm) is made of clusters,
// which are basically gaussian distribution with an associated weight.
//
// You can create a gmm by firstly defining the clusters.
std::vector<gauss::gmm::Cluster> clusters;
// add the first cluster
Eigen::VectorXd cluster_mean = ; // fill the mean values
Eigen::MatrixXd cluster_covariance = ; // fill the covariance values
double cluster_weight = 0.1;
std::unique_ptr<gauss::GaussianDistribution> cluster_distributon = std::make_unique<gauss::GaussianDistribution>(cluster_mean, cluster_covariance);
clusters.push_back(gauss::gmm::Cluster{ cluster_weight, std::move(cluster_distributon) });
// similarly, add the second and all the others cluster
clusters.push_back(...);
// now that the clusters are defined, build the gmm
gauss::gmm::GaussianMixtureModel gmm_model(clusters);

Or by traininig using the Expectation Maximization algorithm:

// the samples from which the gmm should be deduced
std::vector<Eigen::VectorXd> samples;
// apply expectation maximization (EM) to compute the set of clusters that
// best fit the given samples.
// The number of expected clusters should be specified
const std::size_t clusters_size = 4;
std::vector<gauss::gmm::Cluster> clusters = gauss::gmm::ExpectationMaximization(samples, clusters_size);
// use the computed clusters to build a gmm
gauss::gmm::GaussianMixtureModel gmm_model(clusters);

It is also possible to specify the initial clusters from which the iterations of the Expectation Maximization start. Otherwise, when non specifying anything, the k-means is internally called to create the starting clusters. k-means is also exposed as a callable stand-alone algorithm.

You can also draw samples from an already built GMM:

std::vector<Eigen::VectorXd> samples = gmm_model.drawSamples(5000)

And generate a completely random GMM:

#include <GaussianMixtureModel/GaussianMixtureModelFactory.h>

const std::size_t space_size = 4;
const std::size_t clusters_size = 3;
gauss::gmm::GaussianMixtureModelFactory model_factory(space_size, clusters_size); // this factory will generate model living in R^4, adopting 3 random clusters
std::unique_ptr<gauss::gmm::GaussianMixtureModel> random_gmm_model = model_factory.makeRandomModel();

This package is completely cross-platform: use CMake to configure the project containig the libary and some samples.

This library uses Eigen as internal linear algebra engine. Eigen is by default fetched from the official gitlab repo by CMake and made available. However, if you already have installed Eigen on your machine you can also decide to use that local version, by setting the CMake option EIGEN_INSTALL_FOLDER equal to the root folder storing the local Eigen you want to use.

If you have found this library useful, take the time to leave a star ;)