mlpack_regression_LinearRegression (3)

Public Member Functions

LinearRegression (const arma::mat &predictors, const arma::vec &responses, const double lambda=0)

Creates the model. LinearRegression (const std::string &filename)

Initialize the model from a file. LinearRegression (const LinearRegression &linearRegression)

Copy constructor. LinearRegression ()

Empty constructor. double ComputeError (const arma::mat &points, const arma::vec &responses) const

Calculate the L2 squared error on the given predictors and responses using this linear regression model. double Lambda () const

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

Modify the Tikhonov regularization parameter for ridge regression. const arma::vec & Parameters () const

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

Modify the parameters (the b vector). void Predict (const arma::mat &points, arma::vec &predictions) const

Calculate y_i for each data point in points. std::string ToString () const

Private Attributes

double lambda

The Tikhonov regularization parameter for ridge regression (0 for linear regression). arma::vec parameters

The calculated B.

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 35 of file linear_regression.hpp.

mlpack::regression::LinearRegression::LinearRegression (const arma::mat &predictors, const arma::vec &responses, const doublelambda = \fC0\fP)

Creates the model.

Parameters:

predictors X, matrix of data points to create B with.

responses y, the measured data for each point in X

mlpack::regression::LinearRegression::LinearRegression (const std::string &filename)

Initialize the model from a file.

Parameters:

filename the name of the file to load the model from.

mlpack::regression::LinearRegression::LinearRegression (const \fBLinearRegression\fP &linearRegression)

Copy constructor.

Parameters:

linearRegression the other instance to copy parameters from.

mlpack::regression::LinearRegression::LinearRegression ()\fC [inline]\fP

Empty constructor.

Definition at line 65 of file linear_regression.hpp.

double mlpack::regression::LinearRegression::ComputeError (const arma::mat &points, const arma::vec &responses) const

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

\[ (1 / n) * \| y - X B \|^2_2 \].PP where $ y $ is the responses vector, $ X $ is the matrix of predictors, and $ B $ is the parameters of the trained linear regression model.

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

Parameters:

predictors Matrix of predictors (X).

responses Vector of responses (y).

double mlpack::regression::LinearRegression::Lambda () const\fC [inline]\fP

Return the Tikhonov regularization parameter for ridge regression.

Definition at line 101 of file linear_regression.hpp.

References lambda.

double& mlpack::regression::LinearRegression::Lambda ()\fC [inline]\fP

Modify the Tikhonov regularization parameter for ridge regression.

Definition at line 103 of file linear_regression.hpp.

References lambda.

const arma::vec& mlpack::regression::LinearRegression::Parameters () const\fC [inline]\fP

Return the parameters (the b vector).

Definition at line 96 of file linear_regression.hpp.

References parameters.

arma::vec& mlpack::regression::LinearRegression::Parameters ()\fC [inline]\fP

Modify the parameters (the b vector).

Definition at line 98 of file linear_regression.hpp.

References parameters.

void mlpack::regression::LinearRegression::Predict (const arma::mat &points, arma::vec &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.

std::string mlpack::regression::LinearRegression::ToString () const

double mlpack::regression::LinearRegression::lambda\fC [private]\fP

The Tikhonov regularization parameter for ridge regression (0 for linear regression).

Definition at line 119 of file linear_regression.hpp.

Referenced by Lambda().

arma::vec mlpack::regression::LinearRegression::parameters\fC [private]\fP

The calculated B. Initialized and filled by constructor to hold the least squares solution.

Definition at line 113 of file linear_regression.hpp.

Referenced by Parameters().

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