mlpack_svd_RegularizedSVDFunction (3)

Public Member Functions

RegularizedSVDFunction (const arma::mat &data, const size_t rank, const double lambda)

Constructor for RegularizedSVDFunction class. const arma::mat & Dataset () const

Return the dataset passed into the constructor. double Evaluate (const arma::mat ¶meters) const

Evaluates the cost function over all examples in the data. double Evaluate (const arma::mat ¶meters, const size_t i) const

Evaluates the cost function for one training example. const arma::mat & GetInitialPoint () const

Return the initial point for the optimization. void Gradient (const arma::mat ¶meters, arma::mat &gradient) const

Evaluates the full gradient of the cost function over all the training examples. double Lambda () const

Return the regularization parameters. size_t NumFunctions () const

Return the number of training examples. Useful for SGD optimizer. size_t NumItems () const

Return the number of items in the data. size_t NumUsers () const

Return the number of users in the data. size_t Rank () const

Return the rank used for the factorization.

Private Attributes

const arma::mat & data

Rating data. arma::mat initialPoint

Initial parameter point. double lambda

Regularization parameter for the optimization. size_t numItems

Number of items in the given dataset. size_t numUsers

Number of users in the given dataset. size_t rank

Rank used for matrix factorization.

Definition at line 32 of file regularized_svd_function.hpp.

mlpack::svd::RegularizedSVDFunction::RegularizedSVDFunction (const arma::mat &data, const size_trank, const doublelambda)

Constructor for RegularizedSVDFunction 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.

const arma::mat& mlpack::svd::RegularizedSVDFunction::Dataset () const\fC [inline]\fP

Return the dataset passed into the constructor.

Definition at line 80 of file regularized_svd_function.hpp.

References data.

double mlpack::svd::RegularizedSVDFunction::Evaluate (const arma::mat ¶meters) const

Evaluates the cost function over all examples in the data.

Parameters:

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

double mlpack::svd::RegularizedSVDFunction::Evaluate (const arma::mat ¶meters, const size_ti) 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) of the decomposition.

i Index of the training example to be used.

const arma::mat& mlpack::svd::RegularizedSVDFunction::GetInitialPoint () const\fC [inline]\fP

Return the initial point for the optimization.

Definition at line 77 of file regularized_svd_function.hpp.

References initialPoint.

void mlpack::svd::RegularizedSVDFunction::Gradient (const arma::mat ¶meters, arma::mat &gradient) const

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

Parameters:

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

gradient Calculated gradient for the parameters.

double mlpack::svd::RegularizedSVDFunction::Lambda () const\fC [inline]\fP

Return the regularization parameters.

Definition at line 92 of file regularized_svd_function.hpp.

References lambda.

size_t mlpack::svd::RegularizedSVDFunction::NumFunctions () const\fC [inline]\fP

Return the number of training examples. Useful for SGD optimizer.

Definition at line 83 of file regularized_svd_function.hpp.

size_t mlpack::svd::RegularizedSVDFunction::NumItems () const\fC [inline]\fP

Return the number of items in the data.

Definition at line 89 of file regularized_svd_function.hpp.

References numItems.

size_t mlpack::svd::RegularizedSVDFunction::NumUsers () const\fC [inline]\fP

Return the number of users in the data.

Definition at line 86 of file regularized_svd_function.hpp.

References numUsers.

size_t mlpack::svd::RegularizedSVDFunction::Rank () const\fC [inline]\fP

Return the rank used for the factorization.

Definition at line 95 of file regularized_svd_function.hpp.

References rank.

const arma::mat& mlpack::svd::RegularizedSVDFunction::data\fC [private]\fP

Rating data.

Definition at line 99 of file regularized_svd_function.hpp.

Referenced by Dataset().

arma::mat mlpack::svd::RegularizedSVDFunction::initialPoint\fC [private]\fP

Initial parameter point.

Definition at line 101 of file regularized_svd_function.hpp.

Referenced by GetInitialPoint().

double mlpack::svd::RegularizedSVDFunction::lambda\fC [private]\fP

Regularization parameter for the optimization.

Definition at line 105 of file regularized_svd_function.hpp.

Referenced by Lambda().

size_t mlpack::svd::RegularizedSVDFunction::numItems\fC [private]\fP

Number of items in the given dataset.

Definition at line 109 of file regularized_svd_function.hpp.

Referenced by NumItems().

size_t mlpack::svd::RegularizedSVDFunction::numUsers\fC [private]\fP

Number of users in the given dataset.

Definition at line 107 of file regularized_svd_function.hpp.

Referenced by NumUsers().

size_t mlpack::svd::RegularizedSVDFunction::rank\fC [private]\fP

Rank used for matrix factorization.

Definition at line 103 of file regularized_svd_function.hpp.

Referenced by Rank().

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