Linear MultiDimesional regression.
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#include <yat/regression/MultiDimensional.h>
Linear MultiDimesional regression.
const utility::Matrix& theplu::yat::regression::MultiDimensional::covariance |
( |
void |
| ) |
const |
covariance of parameters
The covariance of fit parameters is calculated as where is the variance of error residuals.
Function fitting parameters of the linear model by miminizing the quadratic deviation between model and data.
Number of rows in X must match size of y.
- Exceptions
-
A | GSL_error exception is thrown if memory allocation fails or the underlying GSL calls fails (usually matrix dimension errors). |
Implements theplu::yat::regression::Multivariate.
const utility::Vector& theplu::yat::regression::MultiDimensional::fit_parameters |
( |
void |
| ) |
const |
|
virtual |
double theplu::yat::regression::MultiDimensional::predict |
( |
const utility::VectorBase & |
x | ) |
const |
|
virtual |
double theplu::yat::regression::MultiDimensional::prediction_error2 |
( |
const utility::VectorBase & |
x | ) |
const |
- Returns
- expected squared prediction error for a new data point in x
double theplu::yat::regression::MultiDimensional::standard_error2 |
( |
const utility::VectorBase & |
x | ) |
const |
- Returns
- squared error of model value in x
The documentation for this class was generated from the following file: