mlpack
1.0.12
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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... | |
Gaussian Mixture Models.
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inline |
Calculates the univariate Gaussian probability density function.
Example use:
x | Observation. |
mean | Mean of univariate Gaussian. |
var | Variance of univariate Gaussian. |
Definition at line 38 of file phi.hpp.
References M_PI.
Referenced by mlpack::distribution::GaussianDistribution::Probability().
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inline |
Calculates the multivariate Gaussian probability density function.
Example use:
x | Observation. |
mean | Mean of multivariate Gaussian. |
cov | Covariance of multivariate Gaussian. |
Definition at line 60 of file phi.hpp.
References M_PI.
|
inline |
Calculates the multivariate Gaussian probability density function and also the gradients with respect to the mean and the variance.
Example use:
Definition at line 86 of file phi.hpp.
References M_PI.
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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.
x | List of observations. |
mean | Mean of multivariate Gaussian. |
cov | Covariance of multivariate Gaussian. |
probabilities | Output probabilities for each input observation. |
Definition at line 130 of file phi.hpp.
References M_PI.