ml::ConjugateGradientLogisticRegression Class Reference

Conjugate gradient logistic regression, as described in Sec. 4 of Thomas P. Minka, "A comparison of numerical optimizers for logistic regression". More...

#include <LogisticRegression.hpp>

Inheritance diagram for ml::ConjugateGradientLogisticRegression:

Collaboration diagram for ml::ConjugateGradientLogisticRegression:

Public Member Functions
Result	fit (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y) const override
	Fits the model and returns the result. More...
Public Member Functions inherited from ml::AbstractLogisticRegression
	AbstractLogisticRegression ()
	Constructor setting default parameter values.
double	lam () const
	Returns the regularisation parameter. More...
double	absolute_tolerance () const
	Returns absolute tolerance for fitted weights.
double	relative_tolerance () const
	Returns relative tolerance for fitted weights.
unsigned int	maximum_steps () const
	Returns maximum number of steps allowed.
void	set_lam (double lam)
	Sets the regularisation parameter. More...
void	set_absolute_tolerance (double absolute_tolerance)
	Sets absolute tolerance for weight convergence. More...
void	set_relative_tolerance (double relative_tolerance)
	Sets relative tolerance for weight convergence. More...
void	set_maximum_steps (unsigned int maximum_steps)
	Sets maximum number of steps.
Public Member Functions inherited from ml::LogisticRegression
virtual	~LogisticRegression ()
	Virtual destructor.

Additional Inherited Members
Static Public Member Functions inherited from ml::LogisticRegression
static double	probability (Eigen::Ref< const Eigen::VectorXd > x, double y, Eigen::Ref< const Eigen::VectorXd > w)
	Calculates the probability of label given model weights and feature vector. More...
static double	log_likelihood (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, Eigen::Ref< const Eigen::VectorXd > w, double lam)
	Calculates the posterior log-likelihood of data given model weights. More...
static void	grad_log_likelihood (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, Eigen::Ref< const Eigen::VectorXd > w, double lam, Eigen::Ref< Eigen::VectorXd > g)
	Calculates the gradient of the posterior log-likelihood of data given model weights, over those weights. More...
static void	hessian_log_likelihood (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, Eigen::Ref< const Eigen::VectorXd > w, double lam, Eigen::Ref< Eigen::MatrixXd > H)
	Calculates the Hessian of the posterior log-likelihood of data given model weights, over those weights. More...
Protected Member Functions inherited from ml::AbstractLogisticRegression
bool	weights_converged (Eigen::Ref< const Eigen::VectorXd > old_weights, Eigen::Ref< const Eigen::VectorXd > new_weights) const
	Check if weight fitting converged. More...

Detailed Description

Conjugate gradient logistic regression, as described in Sec. 4 of Thomas P. Minka, "A comparison of numerical optimizers for logistic regression".

Member Function Documentation

fit()

Result ml::ConjugateGradientLogisticRegression::fit ( Eigen::Ref< const Eigen::MatrixXd >  X, Eigen::Ref< const Eigen::VectorXd >  y  ) const

overridevirtual

Fits the model and returns the result.

If fitting with intercept is desired, include a row of 1's in the X values.

Parameters

X	D x N matrix of X values, with data points in columns.
y	Y vector with length N. Values should be -1 or 1.

Exceptions

std::invalid_argument

if N or D are zero, or if dimensions of X and y do not match.

Returns

Result object.

Implements ml::LogisticRegression.

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

• ML/LogisticRegression.hpp