gplib::gp_reg Class Reference

#include <gp.hpp>

Collaboration diagram for gplib::gp_reg:

Classes
struct	implementation

Public Member Functions
	gp_reg ()
	Constructor. More...
	~gp_reg ()
	Destructor. More...
void	set_kernel (const std::shared_ptr< kernel_class > &k)
	Sets the kernel to be used during the regression process. More...
std::shared_ptr< kernel_class >	get_kernel () const
	Returns the current kernel set to be used during the regression process. More...
void	set_training_set (const arma::mat &X, const arma::vec &y)
	Sets the training set to be used during the training process. More...
double	train (int max_iter, double tol)
	Trains the model using the provided training set. More...
mv_gauss	full_predict (const arma::mat &new_data) const
	Uses the already trained model to predict output values for new inputs provided in the parameter, this method returns the complete multivariate gaussian distribution resulting from the regression process. More...
arma::vec	predict (const arma::mat &new_data) const
	Uses the already trained model to predict output values for new inputs provided in the parameter, this method returns only the mean of the multivariate gaussian distribution resulting from the regression process, which is the "best guess" for each new input. More...

Private Attributes
implementation *	pimpl

Constructor & Destructor Documentation

gplib::gp_reg::gp_reg

(

)

Constructor.

gplib::gp_reg::~gp_reg

(

)

Destructor.

Member Function Documentation

mv_gauss gplib::gp_reg::full_predict

(

const arma::mat &

new_data

)

const

Uses the already trained model to predict output values for new inputs provided in the parameter, this method returns the complete multivariate gaussian distribution resulting from the regression process.

Parameters

new_data

: A matrix containing points for which output data is unknown.

shared_ptr< kernel_class > gplib::gp_reg::get_kernel

(

)

const

Returns the current kernel set to be used during the regression process.

arma::vec gplib::gp_reg::predict

(

const arma::mat &

new_data

)

const

Uses the already trained model to predict output values for new inputs provided in the parameter, this method returns only the mean of the multivariate gaussian distribution resulting from the regression process, which is the "best guess" for each new input.

Parameters

new_data

: A matrix containing points for which output data is unknown.

void gplib::gp_reg::set_kernel

(

const std::shared_ptr< kernel_class > &

k

)

Sets the kernel to be used during the regression process.

Parameters

k	: A kernel_class kernel.

void gplib::gp_reg::set_training_set	(	const arma::mat &	X,
		const arma::vec &	y
	)

Sets the training set to be used during the training process.

Parameters

X	: Matrix of known inputs, each row represents one input.
y	: Vector of known outputs corresponding to the known inputs.

double gplib::gp_reg::train	(	int	max_iter,
		double	tol
	)

Trains the model using the provided training set.

Parameters

max_iter	: Maximum number of iterations.
tol	: Relative tolerance on the optimization parameters.

Member Data Documentation

implementation* gplib::gp_reg::pimpl

private

---

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

• src/gplib/gp.hpp
• src/gplib/gpreg.cc