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

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.
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.
double phi (const arma::vec &x, const arma::vec &mean, const arma::mat &cov)
 Calculates the multivariate Gaussian probability density function.
double phi (const double x, const double mean, const double var)
 Calculates the univariate Gaussian probability density function.

Detailed Description

Gaussian Mixture Models.


Function Documentation

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.

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.

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 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().


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