#include <yat/regression/OneDimensional.h>
Public Member Functions | |
OneDimensional (void) | |
The default constructor. | |
virtual | ~OneDimensional (void) |
The destructor. | |
double | chisq (void) const |
Chi-squared. | |
virtual void | fit (const utility::VectorBase &x, const utility::VectorBase &y)=0 |
virtual double | predict (const double x) const =0 |
double | prediction_error2 (const double x) const |
std::ostream & | print (std::ostream &os, const double min, double max, const unsigned int n) const |
print output to ostream os | |
double | r2 (void) const |
virtual double | s2 (void) const =0 |
virtual double | standard_error2 (const double x) const =0 |
Protected Member Functions | |
double | variance (void) const |
Protected Attributes | |
statistics::AveragerPair | ap_ |
double | chisq_ |
double theplu::yat::regression::OneDimensional::chisq | ( | void | ) | const |
Chi-squared.
Chi-squared is defined as the
virtual void theplu::yat::regression::OneDimensional::fit | ( | const utility::VectorBase & | x, | |
const utility::VectorBase & | y | |||
) | [pure virtual] |
This function computes the best-fit given a model (see specific class for details) by minimizing , where is the fitted value.
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Naive, and theplu::yat::regression::Polynomial.
virtual double theplu::yat::regression::OneDimensional::predict | ( | const double | x | ) | const [pure virtual] |
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Naive, and theplu::yat::regression::Polynomial.
double theplu::yat::regression::OneDimensional::prediction_error2 | ( | const double | x | ) | const |
The prediction error is defined as the expected squared deviation a new data point will have from value the model provides: and is typically divided into the conditional variance ( see s2() ) given and the squared standard error ( see standard_error2() ) of the model estimation in .
std::ostream& theplu::yat::regression::OneDimensional::print | ( | std::ostream & | os, | |
const double | min, | |||
double | max, | |||
const unsigned int | n | |||
) | const |
print output to ostream os
Printing estimated model to os in the points defined by min, max, and n. The values printed for each point is the x-value, the estimated y-value, and the estimated standard deviation of a new data poiunt will have from the y-value given the x-value (see prediction_error()).
os | Ostream printout is sent to | |
n | number of points printed | |
min | smallest x-value for which the model is printed | |
max | largest x-value for which the model is printed |
double theplu::yat::regression::OneDimensional::r2 | ( | void | ) | const |
virtual double theplu::yat::regression::OneDimensional::s2 | ( | void | ) | const [pure virtual] |
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Naive, and theplu::yat::regression::Polynomial.
virtual double theplu::yat::regression::OneDimensional::standard_error2 | ( | const double | x | ) | const [pure virtual] |
The standard error is defined as
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Naive, and theplu::yat::regression::Polynomial.
double theplu::yat::regression::OneDimensional::variance | ( | void | ) | const [protected] |
Variance of y
Averager for pair of x and y
double theplu::yat::regression::OneDimensional::chisq_ [protected] |