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#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);

// 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);

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

#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();