mlpack::svd::BiasSVDFunction< MatType > Class Template Reference

This class contains methods which are used to calculate the cost of BiasSVD's objective function, to calculate gradient of parameters with respect to the objective function, etc. More...

#include <bias_svd_function.hpp>

Public Member Functions
	BiasSVDFunction (const MatType &data, const size_t rank, const double lambda)
	Constructor for BiasSVDFunction class. More...
void	Shuffle ()
	Shuffle the points in the dataset. More...
double	Evaluate (const arma::mat &parameters) const
	Evaluates the cost function over all examples in the data. More...
double	Evaluate (const arma::mat &parameters, const size_t start, const size_t batchSize=1) const
	Evaluates the cost function for one training example. More...
void	Gradient (const arma::mat &parameters, arma::mat &gradient) const
	Evaluates the full gradient of the cost function over all the training examples. More...
template<typename GradType >
void	Gradient (const arma::mat &parameters, const size_t start, GradType &gradient, const size_t batchSize=1) const
	Evaluates the gradient of the cost function over one training example. More...
const arma::mat &	GetInitialPoint () const
	Return the initial point for the optimization.
const arma::mat &	Dataset () const
	Return the dataset passed into the constructor.
size_t	NumFunctions () const
	Return the number of training examples. Useful for SGD optimizer.
size_t	NumUsers () const
	Return the number of users in the data.
size_t	NumItems () const
	Return the number of items in the data.
double	Lambda () const
	Return the regularization parameters.
size_t	Rank () const
	Return the rank used for the factorization.

Detailed Description

template<typename MatType = arma::mat>
class mlpack::svd::BiasSVDFunction< MatType >

This class contains methods which are used to calculate the cost of BiasSVD's objective function, to calculate gradient of parameters with respect to the objective function, etc.

Template Parameters

MatType

The matrix type of the dataset.

Constructor & Destructor Documentation

BiasSVDFunction()

template<typename MatType >

mlpack::svd::BiasSVDFunction< MatType >::BiasSVDFunction	(	const MatType &	data,
		const size_t	rank,
		const double	lambda
	)

Constructor for BiasSVDFunction class.

The constructor calculates the number of users and items in the passed data. It also randomly initializes the parameter values.

Parameters

data	Dataset for which SVD is calculated.
rank	Rank used for matrix factorization.
lambda	Regularization parameter used for optimization.

Member Function Documentation

Evaluate() [1/2]

template<typename MatType >

double mlpack::svd::BiasSVDFunction< MatType >::Evaluate

(

const arma::mat &

parameters

)

const

Evaluates the cost function over all examples in the data.

Parameters

parameters

Parameters(user/item matrices/bias) of the decomposition.

Evaluate() [2/2]

template<typename MatType >

double mlpack::svd::BiasSVDFunction< MatType >::Evaluate	(	const arma::mat &	parameters,
		const size_t	start,
		const size_t	batchSize = 1
	)		const

Evaluates the cost function for one training example.

Useful for the SGD optimizer abstraction which uses one training example at a time.

Parameters

parameters	Parameters(user/item matrices/bias) of the decomposition.
start	First index of the training examples to be used.
batchSize	Size of batch to evaluate.

Gradient() [1/2]

template<typename MatType >

void mlpack::svd::BiasSVDFunction< MatType >::Gradient	(	const arma::mat &	parameters,
		arma::mat &	gradient
	)		const

Evaluates the full gradient of the cost function over all the training examples.

Parameters

parameters	Parameters(user/item matrices/bias) of the decomposition.
gradient	Calculated gradient for the parameters.

Gradient() [2/2]

template<typename MatType >

template<typename GradType >

void mlpack::svd::BiasSVDFunction< MatType >::Gradient	(	const arma::mat &	parameters,
		const size_t	start,
		GradType &	gradient,
		const size_t	batchSize = 1
	)		const

Evaluates the gradient of the cost function over one training example.

This function is useful for optimizers like SGD. The type of the gradient parameter is a template argument to allow the computation of a sparse gradient.

Template Parameters

GradType

The type of the gradient out-param.

Parameters

parameters	Parameters(user/item matrices/bias) of the decomposition.
start	The first index of the training examples to use.
gradient	Calculated gradient for the parameters.
batchSize	Size of batch to calculate gradient for.

Shuffle()

template<typename MatType >

void mlpack::svd::BiasSVDFunction< MatType >::Shuffle

(

)

Shuffle the points in the dataset.

This may be used by optimizers.

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

• src/mlpack/methods/bias_svd/bias_svd_function.hpp
• src/mlpack/methods/bias_svd/bias_svd_function_impl.hpp