mlpack  3.0.4
LinearRegression Class Reference

A simple linear regression algorithm using ordinary least squares. More...

Public Member Functions

LinearRegression (const arma::mat &predictors, const arma::rowvec &responses, const double lambda=0, const bool intercept=true)
Creates the model. More...

LinearRegression (const arma::mat &predictors, const arma::rowvec &responses, const arma::rowvec &weights, const double lambda=0, const bool intercept=true)
Creates the model with weighted learning. More...

LinearRegression ()
Empty constructor. More...

double ComputeError (const arma::mat &points, const arma::rowvec &responses) const
Calculate the L2 squared error on the given predictors and responses using this linear regression model. More...

bool Intercept () const
Return whether or not an intercept term is used in the model. More...

double Lambda () const
Return the Tikhonov regularization parameter for ridge regression. More...

double & Lambda ()
Modify the Tikhonov regularization parameter for ridge regression. More...

const arma::vec & Parameters () const
Return the parameters (the b vector). More...

arma::vec & Parameters ()
Modify the parameters (the b vector). More...

void Predict (const arma::mat &points, arma::rowvec &predictions) const
Calculate y_i for each data point in points. More...

template
<
typename
Archive
>
void serialize (Archive &ar, const unsigned int)
Serialize the model. More...

void Train (const arma::mat &predictors, const arma::rowvec &responses, const bool intercept=true)
Train the LinearRegression model on the given data. More...

void Train (const arma::mat &predictors, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept=true)
Train the LinearRegression model on the given data and weights. More...

Detailed Description

A simple linear regression algorithm using ordinary least squares.

Optionally, this class can perform ridge regression, if the lambda parameter is set to a number greater than zero.

Definition at line 26 of file linear_regression.hpp.

◆ LinearRegression() [1/3]

 LinearRegression ( const arma::mat & predictors, const arma::rowvec & responses, const double lambda = `0`, const bool intercept = `true` )

Creates the model.

Parameters
 predictors X, matrix of data points. responses y, the measured data for each point in X. lambda Regularization constant for ridge regression. intercept Whether or not to include an intercept term.

◆ LinearRegression() [2/3]

 LinearRegression ( const arma::mat & predictors, const arma::rowvec & responses, const arma::rowvec & weights, const double lambda = `0`, const bool intercept = `true` )

Creates the model with weighted learning.

Parameters
 predictors X, matrix of data points. responses y, the measured data for each point in X. weights Observation weights (for boosting). lambda Regularization constant for ridge regression. intercept Whether or not to include an intercept term.

◆ LinearRegression() [3/3]

 LinearRegression ( )
inline

Empty constructor.

This gives a non-working model, so make sure Train() is called (or make sure the model parameters are set) before calling Predict()!

Definition at line 62 of file linear_regression.hpp.

◆ ComputeError()

 double ComputeError ( const arma::mat & points, const arma::rowvec & responses ) const

Calculate the L2 squared error on the given predictors and responses using this linear regression model.

This calculation returns

where is the responses vector, is the matrix of predictors, and is the parameters of the trained linear regression model.

As this number decreases to 0, the linear regression fit is better.

Parameters
 points Matrix of predictors (X). responses Transposed vector of responses (y^T).

◆ Intercept()

 bool Intercept ( ) const
inline

Return whether or not an intercept term is used in the model.

Definition at line 135 of file linear_regression.hpp.

◆ Lambda() [1/2]

 double Lambda ( ) const
inline

Return the Tikhonov regularization parameter for ridge regression.

Definition at line 130 of file linear_regression.hpp.

◆ Lambda() [2/2]

 double& Lambda ( )
inline

Modify the Tikhonov regularization parameter for ridge regression.

Definition at line 132 of file linear_regression.hpp.

◆ Parameters() [1/2]

 const arma::vec& Parameters ( ) const
inline

Return the parameters (the b vector).

Definition at line 125 of file linear_regression.hpp.

Referenced by RegressionDistribution::Dimensionality(), and RegressionDistribution::Parameters().

◆ Parameters() [2/2]

 arma::vec& Parameters ( )
inline

Modify the parameters (the b vector).

Definition at line 127 of file linear_regression.hpp.

◆ Predict()

 void Predict ( const arma::mat & points, arma::rowvec & predictions ) const

Calculate y_i for each data point in points.

Parameters
 points the data points to calculate with. predictions y, will contain calculated values on completion.

Referenced by LinearRegression::LinearRegression().

◆ serialize()

 void serialize ( Archive & ar, const unsigned int )
inline

Serialize the model.

Definition at line 141 of file linear_regression.hpp.

◆ Train() [1/2]

 void Train ( const arma::mat & predictors, const arma::rowvec & responses, const bool intercept = `true` )

Train the LinearRegression model on the given data.

Careful! This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm. To set the regularization parameter lambda, call Lambda() or set a different value in the constructor.

Parameters
 predictors X, the matrix of data points to train the model on. responses y, the responses to the data points. intercept Whether or not to fit an intercept term.

◆ Train() [2/2]

 void Train ( const arma::mat & predictors, const arma::rowvec & responses, const arma::rowvec & weights, const bool intercept = `true` )

Train the LinearRegression model on the given data and weights.

Careful! This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm. To set the regularization parameter lambda, call Lambda() or set a different value in the constructor.

Parameters
 predictors X, the matrix of data points to train the model on. responses y, the responses to the data points. intercept Whether or not to fit an intercept term. weights Observation weights (for boosting).

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