mlpack: mlpack Documentation

A class that represents a univariate conditionally Gaussian distribution. More...

Public Member Functions
	RegressionDistribution ()
	Default constructor, which creates a Gaussian with zero dimension. More...

mlpack_deprecated	RegressionDistribution (const arma::mat &predictors, const arma::vec &responses)
	Create a Conditional Gaussian distribution with conditional mean function obtained by running RegressionFunction on predictors, responses. More...

	RegressionDistribution (const arma::mat &predictors, const arma::rowvec &responses)
	Create a Conditional Gaussian distribution with conditional mean function obtained by running RegressionFunction on predictors, responses. More...

size_t	Dimensionality () const
	Return the dimensionality. More...

const GaussianDistribution &	Err () const
	Return error distribution. More...

GaussianDistribution &	Err ()
	Modify error distribution. More...

double	LogProbability (const arma::vec &observation) const
	Evaluate log probability density function of given observation. More...

const arma::vec &	Parameters () const
	Return the parameters (the b vector). More...

mlpack_deprecated void	Predict (const arma::mat &points, arma::vec &predictions) const
	Calculate y_i for each data point in points. More...

void	Predict (const arma::mat &points, arma::rowvec &predictions) const
	Calculate y_i for each data point in points. More...

double	Probability (const arma::vec &observation) const
	Evaluate probability density function of given observation. More...

const regression::LinearRegression &	Rf () const
	Return regression function. More...

regression::LinearRegression &	Rf ()
	Modify regression function. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the distribution. More...

void	Train (const arma::mat &observations)
	Estimate the Gaussian distribution directly from the given observations. More...

mlpack_deprecated void	Train (const arma::mat &observations, const arma::vec &weights)
	Estimate parameters using provided observation weights. More...

void	Train (const arma::mat &observations, const arma::rowvec &weights)
	Estimate parameters using provided observation weights. More...

Detailed Description

A class that represents a univariate conditionally Gaussian distribution.

Can be used as an emission distribution with the hmm class to implement HMM regression (HMMR) as described in https://www.ima.umn.edu/preprints/January1994/1195.pdf The hmm observations should have the dependent variable in the first row, with the independent variables in the other rows.

Definition at line 31 of file regression_distribution.hpp.

Constructor & Destructor Documentation

◆ RegressionDistribution() [1/3]

RegressionDistribution ( )

inline

Default constructor, which creates a Gaussian with zero dimension.

Definition at line 43 of file regression_distribution.hpp.

◆ RegressionDistribution() [2/3]

mlpack_deprecated RegressionDistribution	(	const arma::mat &	predictors,
		const arma::vec &	responses
	)

inline

Create a Conditional Gaussian distribution with conditional mean function obtained by running RegressionFunction on predictors, responses.

Parameters

predictors	Matrix of predictors (X).
responses	Vector of responses (y).

Definition at line 52 of file regression_distribution.hpp.

◆ RegressionDistribution() [3/3]

RegressionDistribution	(	const arma::mat &	predictors,
		const arma::rowvec &	responses
	)

inline

Create a Conditional Gaussian distribution with conditional mean function obtained by running RegressionFunction on predictors, responses.

Parameters

predictors	Matrix of predictors (X).
responses	Vector of responses (y).

Definition at line 64 of file regression_distribution.hpp.

References LinearRegression::ComputeError(), GaussianDistribution::Covariance(), and LinearRegression::Train().

Member Function Documentation

◆ Dimensionality()

size_t Dimensionality ( ) const

inline

Return the dimensionality.

Definition at line 156 of file regression_distribution.hpp.

References LinearRegression::Parameters().

◆ Err() [1/2]

const GaussianDistribution& Err ( ) const

inline

Return error distribution.

Definition at line 90 of file regression_distribution.hpp.

◆ Err() [2/2]

GaussianDistribution& Err ( )

inline

Modify error distribution.

Definition at line 92 of file regression_distribution.hpp.

References mlpack_deprecated, RegressionDistribution::Probability(), and RegressionDistribution::Train().

◆ LogProbability()

double LogProbability ( const arma::vec & observation ) const

inline

Evaluate log probability density function of given observation.

Parameters

observation Point to evaluate log probability at.

Definition at line 130 of file regression_distribution.hpp.

References mlpack_deprecated, RegressionDistribution::Predict(), and RegressionDistribution::Probability().

◆ Parameters()

const arma::vec& Parameters ( ) const

inline

Return the parameters (the b vector).

Definition at line 153 of file regression_distribution.hpp.

References LinearRegression::Parameters().

◆ Predict() [1/2]

mlpack_deprecated void Predict	(	const arma::mat &	points,
		arma::vec &	predictions
	)		const

Calculate y_i for each data point in points.

Parameters

points	The data points to calculate with.
predictions	Y, will contain calculated values on completion.

Referenced by RegressionDistribution::LogProbability().

◆ Predict() [2/2]

void Predict	(	const arma::mat &	points,
		arma::rowvec &	predictions
	)		const

Calculate y_i for each data point in points.

Parameters

points	The data points to calculate with.
predictions	Y, will contain calculated values on completion.

◆ Probability()

double Probability ( const arma::vec & observation ) const

Evaluate probability density function of given observation.

Parameters

observation Point to evaluate probability at.

Referenced by RegressionDistribution::Err(), and RegressionDistribution::LogProbability().

◆ Rf() [1/2]

const regression::LinearRegression& Rf ( ) const

inline

Return regression function.

Definition at line 85 of file regression_distribution.hpp.

◆ Rf() [2/2]

regression::LinearRegression& Rf ( )

inline

Modify regression function.

Definition at line 87 of file regression_distribution.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

inline

Serialize the distribution.

Definition at line 78 of file regression_distribution.hpp.

◆ Train() [1/3]

void Train ( const arma::mat & observations )

Estimate the Gaussian distribution directly from the given observations.

Parameters

observations List of observations.

Referenced by RegressionDistribution::Err().

◆ Train() [2/3]

mlpack_deprecated void Train	(	const arma::mat &	observations,
		const arma::vec &	weights
	)

Estimate parameters using provided observation weights.

Parameters

observations	List of observations.
weights	Probability that given observation is from distribution.

◆ Train() [3/3]

void Train	(	const arma::mat &	observations,
		const arma::rowvec &	weights
	)

Estimate parameters using provided observation weights.

Parameters

observations	List of observations.
weights	Probability that given observation is from distribution.

The documentation for this class was generated from the following file:

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/core/dists/regression_distribution.hpp