theplu::yat::regression::MultiDimensionalWeighted Class Reference

MultiDimesional fitting. More...

#include <yat/regression/MultiDimensionalWeighted.h>

List of all members.

Public Member Functions
	MultiDimensionalWeighted (void)
	Default Constructor.
	~MultiDimensionalWeighted (void)
	Destructor.
double	chisq (void) const
void	fit (const utility::Matrix &X, const utility::VectorBase &y, const utility::VectorBase &w)
double	predict (const utility::VectorBase &x) const
double	prediction_error2 (const utility::VectorBase &x, const double w=1) const
double	standard_error2 (const utility::VectorBase &x) const
const utility::Vector &	fit_parameters (void) const
double	s2 (const double w=1.0) const

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Detailed Description

MultiDimesional fitting.

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Member Function Documentation

double theplu::yat::regression::MultiDimensionalWeighted::chisq

(

void

)

const

Returns:

sum of squared residuals

void theplu::yat::regression::MultiDimensionalWeighted::fit	(	const utility::Matrix &	X,
		const utility::VectorBase &	y,
		const utility::VectorBase &	w
	)

See also:

gsl_multifit_wlinear

Exceptions:

A	GSL_error exception is thrown if memory allocation fails or the underlying GSL calls fails (usually matrix dimension errors).

const utility::Vector& theplu::yat::regression::MultiDimensionalWeighted::fit_parameters

(

void

)

const

Returns:

parameters of fitted model

double theplu::yat::regression::MultiDimensionalWeighted::predict

(

const utility::VectorBase &

x

)

const

Returns:

value in x according to fitted model

double theplu::yat::regression::MultiDimensionalWeighted::prediction_error2	(	const utility::VectorBase &	x,
		const double	w = 1
	)		const

Returns:

expected squared prediction error for a new data point in x

double theplu::yat::regression::MultiDimensionalWeighted::s2

(

const double

w = 1.0

)

const

Returns:

variance of residuals

double theplu::yat::regression::MultiDimensionalWeighted::standard_error2

(

const utility::VectorBase &

x

)

const

Returns:

error of model value in x

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

• yat/regression/MultiDimensionalWeighted.h