mygsl::linear_regression Class Reference

The linear regression algorithm. More...

#include <bayeux/mygsl/linear_regression.h>

Classes
class	fit_data
	The serializable result data of the linear regression algorithm. More...
class	function
	A linear regression fit function. More...

Public Member Functions
bool	is_initialized () const
	Check initialization flag. More...
bool	can_weighted () const
	Check if a weighted fit can pe performed. More...
const fit_data &	get_fit_data () const
	Return the result of the fit. More...
	linear_regression ()
	Default constructor. More...
	linear_regression (const std::vector< datapoint > &p_)
	Construct from an array of datapoints (x,y,w) triplets. More...
	linear_regression (size_t npoints_, const double *x_, const double *y_, const double *w_=0)
	Construct from plain arrays of x, y and w values. More...
	linear_regression (const std::vector< double > &x_, const std::vector< double > &y_, const std::vector< double > &w_)
	Construct from vectors of x, y and w values. More...
	linear_regression (const std::vector< double > &x_, const std::vector< double > &y_)
	Construct from vectors of x and y values (no weights) More...
virtual	~linear_regression ()
	Destructor. More...
void	init (const std::vector< datapoint > &p_)
	Initialize from an array of datapoints (x,y,w) triplets. More...
void	init (size_t npoints_, const double *x_, const double *y_, const double *w_=0)
	Initialize from plain arrays of x, y and optional w values. More...
void	init (const std::vector< double > &x_, const std::vector< double > &y_, const std::vector< double > &w_)
	Initialize from vectors of x, y and w values. More...
void	init (const std::vector< double > &x_, const std::vector< double > &y_)
	Construct from vectors of x and y values (no weights) More...
void	reset ()
	Reset. More...
bool	fit_linear ()
	Perform the linear unweighted fit. More...
bool	fit_weighted_linear ()
	Perform the linear weighted fit. More...
bool	fit_linear_no_constant ()
	Perform the linear unweighted fit without constant parameter. More...
bool	fit_weighted_linear_no_constant ()
	Perform the linear weighted fit without constant parameter. More...

Static Public Attributes
static const unsigned int	MINIMUM_NUMBER_OF_DATA_POINTS = 2
	Minimum number of data points to perform the linear regression fit. More...

Detailed Description

The linear regression algorithm.

Constructor & Destructor Documentation

linear_regression() [1/5]

mygsl::linear_regression::linear_regression

(

)

Default constructor.

linear_regression() [2/5]

mygsl::linear_regression::linear_regression

(

const std::vector< datapoint > &

p_

)

Construct from an array of datapoints (x,y,w) triplets.

linear_regression() [3/5]

mygsl::linear_regression::linear_regression	(	size_t	npoints_,
		const double *	x_,
		const double *	y_,
		const double *	w_ = 0
	)

Construct from plain arrays of x, y and w values.

linear_regression() [4/5]

mygsl::linear_regression::linear_regression	(	const std::vector< double > &	x_,
		const std::vector< double > &	y_,
		const std::vector< double > &	w_
	)

Construct from vectors of x, y and w values.

linear_regression() [5/5]

mygsl::linear_regression::linear_regression	(	const std::vector< double > &	x_,
		const std::vector< double > &	y_
	)

Construct from vectors of x and y values (no weights)

~linear_regression()

virtual mygsl::linear_regression::~linear_regression ( )

virtual

Destructor.

Member Function Documentation

can_weighted()

bool mygsl::linear_regression::can_weighted

(

)

const

Check if a weighted fit can pe performed.

fit_linear()

bool mygsl::linear_regression::fit_linear

(

)

Perform the linear unweighted fit.

fit_linear_no_constant()

bool mygsl::linear_regression::fit_linear_no_constant

(

)

Perform the linear unweighted fit without constant parameter.

fit_weighted_linear()

bool mygsl::linear_regression::fit_weighted_linear

(

)

Perform the linear weighted fit.

fit_weighted_linear_no_constant()

bool mygsl::linear_regression::fit_weighted_linear_no_constant

(

)

Perform the linear weighted fit without constant parameter.

get_fit_data()

const fit_data& mygsl::linear_regression::get_fit_data

(

)

const

Return the result of the fit.

init() [1/4]

void mygsl::linear_regression::init

(

const std::vector< datapoint > &

p_

)

Initialize from an array of datapoints (x,y,w) triplets.

init() [2/4]

void mygsl::linear_regression::init	(	size_t	npoints_,
		const double *	x_,
		const double *	y_,
		const double *	w_ = 0
	)

Initialize from plain arrays of x, y and optional w values.

init() [3/4]

void mygsl::linear_regression::init	(	const std::vector< double > &	x_,
		const std::vector< double > &	y_,
		const std::vector< double > &	w_
	)

Initialize from vectors of x, y and w values.

init() [4/4]

void mygsl::linear_regression::init	(	const std::vector< double > &	x_,
		const std::vector< double > &	y_
	)

Construct from vectors of x and y values (no weights)

is_initialized()

bool mygsl::linear_regression::is_initialized

(

)

const

Check initialization flag.

reset()

void mygsl::linear_regression::reset

(

)

Reset.

Member Data Documentation

MINIMUM_NUMBER_OF_DATA_POINTS

const unsigned int mygsl::linear_regression::MINIMUM_NUMBER_OF_DATA_POINTS = 2

static

Minimum number of data points to perform the linear regression fit.

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

• bayeux/mygsl/linear_regression.h