mlpack::cf::ZScoreNormalization Class Reference

This normalization class performs z-score normalization on raw ratings. More...

#include <z_score_normalization.hpp>

Public Member Functions
void	Normalize (arma::mat &data)
	Normalize the data to zero mean and one standard deviation. More...
void	Normalize (arma::sp_mat &cleanedData)
	Normalize the data to zero mean and one standard deviation. More...
double	Denormalize (const size_t, const size_t, const double rating) const
	Denormalize computed rating by adding mean and multiplying stddev. More...
void	Denormalize (const arma::Mat< size_t > &, arma::vec &predictions) const
	Denormalize computed rating by adding mean and multiplying stddev. More...
double	Mean () const
	Return mean.
double	Stddev () const
	Return stddev.
template<typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialization.

Detailed Description

This normalization class performs z-score normalization on raw ratings.

An example of how to use ZScoreNormalization in CF is shown below:

extern arma::mat data; // data is a (user, item, rating) table.
// Users for whom recommendations are generated.
extern arma::Col<size_t> users;
arma::Mat<size_t> recommendations; // Resulting recommendations.

// Use ZScoreNormalization as normalization method.
CFType<NMFPolicy, ZScoreNormalization> cf(data);

// Generate 10 recommendations for all users.
cf.GetRecommendations(10, recommendations);

Member Function Documentation

Denormalize() [1/2]

double mlpack::cf::ZScoreNormalization::Denormalize ( const size_t  , const size_t  , const double  rating  ) const

inline

Denormalize computed rating by adding mean and multiplying stddev.

Parameters

*	(user) User ID.
*	(item) Item ID.
rating	Computed rating before denormalization.

Denormalize() [2/2]

void mlpack::cf::ZScoreNormalization::Denormalize ( const arma::Mat< size_t > &  , arma::vec &  predictions  ) const

inline

Denormalize computed rating by adding mean and multiplying stddev.

Parameters

*	(combinations) User/Item combinations.
predictions	Predicted ratings for each user/item combination.

Normalize() [1/2]

void mlpack::cf::ZScoreNormalization::Normalize ( arma::mat &  data )

inline

Normalize the data to zero mean and one standard deviation.

Parameters

data	Input dataset in the form of coordinate list.

Normalize() [2/2]

void mlpack::cf::ZScoreNormalization::Normalize ( arma::sp_mat &  cleanedData )

inline

Normalize the data to zero mean and one standard deviation.

Parameters

cleanedData

Input data as a sparse matrix.

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

• src/mlpack/methods/cf/normalization/z_score_normalization.hpp