mlpack::gmm Namespace Reference

Gaussian Mixture Models. More...

Classes
class	DiagonalConstraint
	Force a covariance matrix to be diagonal. More...
class	EigenvalueRatioConstraint
	Given a vector of eigenvalue ratios, ensure that the covariance matrix always has those eigenvalue ratios. More...
class	EMFit
	This class contains methods which can fit a GMM to observations using the EM algorithm. More...
class	GMM
	A Gaussian Mixture Model (GMM). More...
class	NoConstraint
	This class enforces no constraint on the covariance matrix. More...
class	PositiveDefiniteConstraint
	Given a covariance matrix, force the matrix to be positive definite. More...

Functions
double	phi (const double x, const double mean, const double var)
	Calculates the univariate Gaussian probability density function. More...
double	phi (const arma::vec &x, const arma::vec &mean, const arma::mat &cov)
	Calculates the multivariate Gaussian probability density function. More...
double	phi (const arma::vec &x, const arma::vec &mean, const arma::mat &cov, const std::vector< arma::mat > &d_cov, arma::vec &g_mean, arma::vec &g_cov)
	Calculates the multivariate Gaussian probability density function and also the gradients with respect to the mean and the variance. More...
void	phi (const arma::mat &x, const arma::vec &mean, const arma::mat &cov, arma::vec &probabilities)
	Calculates the multivariate Gaussian probability density function for each data point (column) in the given matrix, with respect to the given mean and variance. More...

Detailed Description

Gaussian Mixture Models.

Function Documentation

double mlpack::gmm::phi ( const double  x, const double  mean, const double  var  )

inline

Calculates the univariate Gaussian probability density function.

Example use:

double x, mean, var;
....
double f = phi(x, mean, var);

Parameters

x	Observation.
mean	Mean of univariate Gaussian.
var	Variance of univariate Gaussian.

Returns

Probability of x being observed from the given univariate Gaussian.

Definition at line 46 of file phi.hpp.

References M_PI.

Referenced by mlpack::distribution::GaussianDistribution::Probability().

double mlpack::gmm::phi ( const arma::vec &  x, const arma::vec &  mean, const arma::mat &  cov  )

inline

Calculates the multivariate Gaussian probability density function.

Example use:

extern arma::vec x, mean;
extern arma::mat cov;
....
double f = phi(x, mean, cov);

Parameters

x	Observation.
mean	Mean of multivariate Gaussian.
cov	Covariance of multivariate Gaussian.

Returns

Probability of x being observed from the given multivariate Gaussian.

Definition at line 68 of file phi.hpp.

References M_PI.

double mlpack::gmm::phi ( const arma::vec &  x, const arma::vec &  mean, const arma::mat &  cov, const std::vector< arma::mat > &  d_cov, arma::vec &  g_mean, arma::vec &  g_cov  )

inline

Calculates the multivariate Gaussian probability density function and also the gradients with respect to the mean and the variance.

Example use:

extern arma::vec x, mean, g_mean, g_cov;
std::vector<arma::mat> d_cov; // the dSigma
....
double f = phi(x, mean, cov, d_cov, &g_mean, &g_cov);

Definition at line 94 of file phi.hpp.

References M_PI.

void mlpack::gmm::phi ( const arma::mat &  x, const arma::vec &  mean, const arma::mat &  cov, arma::vec &  probabilities  )

inline

Calculates the multivariate Gaussian probability density function for each data point (column) in the given matrix, with respect to the given mean and variance.

Parameters

x	List of observations.
mean	Mean of multivariate Gaussian.
cov	Covariance of multivariate Gaussian.
probabilities	Output probabilities for each input observation.

Definition at line 138 of file phi.hpp.

References M_PI.