Recursive multivariate Ordinary Least Squares. More...

Public Member Functions
	RecursiveMultivariateOLS ()
	Initialises without data.

	RecursiveMultivariateOLS (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y)
	Initialises with the first sample and calculates the first beta estimate. More...

void	update (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y)
	Updates the beta estimate with a new sample. More...

unsigned int	n () const
	Returns the number of data points seen so far.

unsigned int	d () const
	Returns the dimension of data points. If n() == 0, returs 0.

const Eigen::VectorXd &	beta () const
	Returns the current estimate of beta. If n() == 0, returns an empty vector.

Detailed Description

Recursive multivariate Ordinary Least Squares.

Given a stream of pairs \((X_i, \vec{y}_i)\), updates the least-squares estimate for \(\vec{\beta}\) using the model

\( \vec{y}_0 = X_0^T \vec{\beta} + \vec{e}_0 \)

\( \vec{y}_1 = X_1^T \vec{\beta} + \vec{e}_1 \)

...

where \(\vec{e}_i\) are i.i.d. Gaussian.

Constructor & Destructor Documentation

ml::LinearRegression::RecursiveMultivariateOLS::RecursiveMultivariateOLS	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y
	)

Initialises with the first sample and calculates the first beta estimate.

Parameters

[in]	X	D x N matrix of X values, with data points in columns.
[in]	y	Y vector with length N.

Exceptions

std::invalid_argument If y.size() != X.cols() or X.cols() < X.rows().

void ml::LinearRegression::RecursiveMultivariateOLS::update	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y
	)

Updates the beta estimate with a new sample.

Parameters

[in]	X	D x N matrix of X values, with data points in columns.
[in]	y	Y vector with length N.

Exceptions

std::invalid_argument If (X, y) is the first sample (i.e., n() == 0) and X.cols() < X.rows(), or y.size() != X.cols().

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