SVD Class Reference

Implements a Singular Value Decomposition (SVD) function for machine learning. More...

#include <SVD.h>

Inheritance diagram for SVD:

Public Member Functions
	SVD (float *bu, float *bi, float *pu, float *qi, int numUser, int numItem, int latentDims)
	Constructor for SVD.
void	apply (const SelectivityVector &rows, std::vector< VectorPtr > &args, const TypePtr &outputType, exec::EvalCtx &context, VectorPtr &output) const override
	Applies the SVD function to the input data.
float *	getTensor () const override
	Returns the tensor associated with the function.
std::string	getFuncName ()
	Returns the name of the function.
std::string	getWeightsFile ()
	Returns the path to the weights file.
void	setWeights (float *weights)
	Sets the weights for the function.
CostEstimate	getCost (std::vector< int > inputDims)
	Estimates the cost of the function.
Public Member Functions inherited from MLFunction
virtual	~MLFunction ()=default
	Virtual destructor.
virtual std::vector< int >	getDims ()
	Returns the dimensions of the function.
virtual int	getNumDims ()
	Returns the number of dimensions of the function.

Static Public Member Functions
static std::vector< std::shared_ptr< exec::FunctionSignature > >	signatures ()
	Returns the function signatures.
static std::string	getName ()
	Returns the name of the function.

Additional Inherited Members
Protected Member Functions inherited from MLFunction
double	getWeightedCost (std::string name, float cost)
	Calculates the weighted cost of the function.
std::vector< double >	getCoefficientVector (std::string name)
	Retrieves the cost coefficients for the function.
Protected Attributes inherited from MLFunction
std::vector< int >	dims
	Dimensions of the function.

Detailed Description

Implements a Singular Value Decomposition (SVD) function for machine learning.

Constructor & Destructor Documentation

SVD()

SVD::SVD ( float * bu, float * bi, float * pu, float * qi, int numUser, int numItem, int latentDims )

inline

Constructor for SVD.

Parameters

bu	Array of user biases.
bi	Array of item biases.
pu	Array of user latent factors.
qi	Array of item latent factors.
numUser	Number of users.
numItem	Number of items.
latentDims	Number of latent dimensions.

Member Function Documentation

apply()

void SVD::apply ( const SelectivityVector & rows, std::vector< VectorPtr > & args, const TypePtr & outputType, exec::EvalCtx & context, VectorPtr & output ) const

inlineoverride

Applies the SVD function to the input data.

Parameters

rows	Selectivity vector indicating which rows to process.
args	Vector of input arguments.
outputType	Type of the output vector.
context	Evaluation context.
output	Output vector to store the results.

getCost()

CostEstimate SVD::getCost ( std::vector< int > inputDims )

inlinevirtual

Estimates the cost of the function.

Parameters

inputDims

Dimensions of the input.

Returns

Cost estimate.

Reimplemented from MLFunction.

getFuncName()

std::string SVD::getFuncName ( )

inlinevirtual

Returns the name of the function.

Returns

Function name.

Reimplemented from MLFunction.

getName()

static std::string SVD::getName ( )

inlinestatic

Returns the name of the function.

Returns

Function name.

getTensor()

float * SVD::getTensor ( ) const

inlineoverridevirtual

Returns the tensor associated with the function.

Returns

Pointer to the tensor.

Implements MLFunction.

getWeightsFile()

std::string SVD::getWeightsFile ( )

inline

Returns the path to the weights file.

Returns

Path to the weights file.

setWeights()

void SVD::setWeights ( float * weights )

inline

Sets the weights for the function.

Parameters

weights

Pointer to the weights array.

signatures()

static std::vector< std::shared_ptr< exec::FunctionSignature > > SVD::signatures ( )

inlinestatic

Returns the function signatures.

Returns

Vector of function signatures.

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

• SVD.h