#include <polynomialRegression.hpp>

Inheritance diagram for julian::PolynomialRegression:

Public Member Functions
	PolynomialRegression ()
	constructor More...

	PolynomialRegression (int order)
	constructor More...

void	estimate (const std::vector< double > &x, const std::vector< double > &y)
	estimates the parameters basing on provided data More...

void	estimate (const std::vector< double > &x, const std::vector< double > &y, const std::vector< double > &w)
	weighted estimates the parameters basing on provided data More...

std::vector< double >	getCoefficient () const
	return coefficients of the regression More...

double	operator() (double) const
	evaluates the linear regression model for x More...

Public Member Functions inherited from julian::DeeplyCopyableRegression< PolynomialRegression >
virtual Regression *	clone () const
	virtual copy constructor More...

Public Member Functions inherited from julian::Regression
	Regression ()
	Constructor. More...

virtual	~Regression ()
	Destructor. More...

Private Attributes
int	order_
	Order of polynomial. More...

std::vector< double >	coefs_
	Vector of coefficients. More...

Detailed Description

Polynomial regression is regression model with a single explanatory variable. The parameters of the regression are calculated by minimizing the OLS. In other words class computes the best-fit linear regression coefficients $(c_0, c_1, ..., c_n)$ of the model $Y = c_0 + \Sigma^{i=n}_{i=1}c_i x^i$ for the dataset (x, y) by minimizing the sum: $\sum_i w_i (y_i - \sum_j X_{ij} c_j)^2 = || y - Xc ||_W^2$ .

For more information see Wikipedia

Remarks: Class uses algorithm implemented in GSL

Constructor & Destructor Documentation

julian::PolynomialRegression::PolynomialRegression ( )

inline

constructor

Note: If order of polynomial is not given, order of polynomial is set to 2.

julian::PolynomialRegression::PolynomialRegression ( int order )

inline

constructor

Parameters

order Order of polynomial fitted

Member Function Documentation

void julian::PolynomialRegression::estimate	(	const std::vector< double > &	x,
		const std::vector< double > &	y
	)

virtual

estimates the parameters basing on provided data

Implements julian::Regression.

void julian::PolynomialRegression::estimate	(	const std::vector< double > &	x,
		const std::vector< double > &	y,
		const std::vector< double > &	w
	)

weighted estimates the parameters basing on provided data

std::vector< double > julian::PolynomialRegression::getCoefficient ( ) const

virtual

return coefficients of the regression

The i-th term of vector represents coefficient of i-th power of x

Implements julian::Regression.

double julian::PolynomialRegression::operator() ( double x ) const

virtual

evaluates the linear regression model for x

Implements julian::Regression.

Member Data Documentation

std::vector<double> julian::PolynomialRegression::coefs_

private

Vector of coefficients.

int julian::PolynomialRegression::order_

private

Order of polynomial.

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

C:/Unix/home/OEM/jULIAN/src/mathematics/regressions/polynomialRegression.hpp
C:/Unix/home/OEM/jULIAN/src/mathematics/regressions/polynomialRegression.cpp

Public Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation