Interface for objects that can be optimised using gradient-based methods (e.g., LbfgsTuner). More...

#include <GradientOptimizable.h>

Inheritance diagram for lucid::GradientOptimizable:

Public Member Functions
double	objective_value () const
	Get read-only access to the objective value of the this object.

const Vector &	gradient () const
	Get read-only access to the gradient of the this object.

Public Member Functions inherited from lucid::Estimator
	Estimator (Parameters parameters=NoParameters, const std::shared_ptr< const Tuner > &tuner=nullptr)
	Construct a new Estimator object.

Matrix	operator() (ConstMatrixRef x) const
	A model is a function that takes a \( n \times d_x \) matrix of row vectors in the input space \( \mathcal{X} \) and returns a \( n \times d_y \) matrix of row vectors in the output space \( \mathcal{Y} \).

virtual Matrix	predict (ConstMatrixRef x) const =0
	A model is a function that takes a \( n \times d_x \) matrix of row vectors in the input space \( \mathcal{X} \) and returns a \( n \times d_y \) matrix of row vectors in the output space \( \mathcal{Y} \).

Estimator &	fit (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, const Tuner &tuner)
	Fit the model to the given data.

Estimator &	fit (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs)
	Fit the model to the given data.

Estimator &	fit_online (ConstMatrixRef training_inputs, const OutputComputer &training_outputs, const Tuner &tuner)
	Fit the model to the data produced by the `training_outputs` OutputComputer.

Estimator &	fit_online (ConstMatrixRef training_inputs, const OutputComputer &training_outputs)
	Fit the model to the data produced by the `training_outputs` OutputComputer.

Estimator &	consolidate (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, const Request request=Request::_)
	Consolidate the model, making sure it is ready for use.

Estimator &	consolidate (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, Requests requests)
	Consolidate the model, making sure it is ready for use.

virtual double	score (ConstMatrixRef evaluation_inputs, ConstMatrixRef evaluation_outputs) const =0
	Score the estimator assigning a numerical value to its accuracy in predicting the `evaluation_outputs` given the `evaluation_inputs`.

const std::shared_ptr< const Tuner > &	tuner () const
	Get read-only access to the tuner of the estimator.

std::shared_ptr< const Tuner > &	m_tuner ()
	Get read-write access to the tuner of the estimator.

virtual std::unique_ptr< Estimator >	clone () const =0
	Clone the estimator by creating a new instance with the same parameters.

virtual std::string	to_string () const
	Obtain the string representation of this object.

Public Member Functions inherited from lucid::Parametrizable
template<IsAnyOf< int, double, const Vector & > T>
T	get (Parameter parameter) const
	Get the value of the specified `parameter`.

template<Parameter P>
internal::ParameterType< P >::ref_type	get () const
	Get the value of the specified `parameter`.

std::variant< int, double, Vector >	get (Parameter parameter) const
	Get the value of the specified `parameter`.

void	set (Parameter parameter, const std::variant< int, double, Vector > &value)
	Set the `parameter` to the indicated `value`.

template<Parameter P>
void	set (const std::variant< int, double, Vector > &value)
	Set the `parameter` to the indicated `value`.

void	set (Parameter parameter, std::size_t idx, const std::variant< std::vector< int >, std::vector< double >, std::vector< Vector > > &values)
	Set the `parameter` to the `index`-th value among the indicated `values`.

template<Parameter P>
void	set (std::size_t idx, const std::variant< std::vector< int >, std::vector< double >, std::vector< Vector > > &values)
	Set the `parameter` to the `index`-th value among the indicated `values`.

virtual void	set (Parameter parameter, int value)
	Set the `parameter` to the indicated `value`.

virtual void	set (Parameter parameter, double value)
	Set the `parameter` to the indicated `value`.

virtual void	set (Parameter parameter, const Vector &value)
	Set the `parameter` to the indicated `value`.

template<Parameter P>
void	set (typename internal::ParameterType< P >::ref_type value)
	Set the `parameter` to the indicated `value`.

bool	has (const Parameter parameter) const
	Check whether the `parameter` is present in this object.

Parameters	parameters () const
	Get read-only access to the parameters of the parametrizable object.

std::vector< Parameter >	parameters_list () const
	Get read-only access to the list of parameters of the parametrizable object.

Parametrizable &	load (const Parametrizable &o)
	Load parameters from another `Parametrizable` object.

Protected Attributes
double	objective_value_
	Objective value of the optimisation problem.

Vector	gradient_
	Gradient of the objective function.

Protected Attributes inherited from lucid::Parametrizable
Parameters	parameters_
	Parameters supported by this object.

Additional Inherited Members
Protected Member Functions inherited from lucid::Estimator
virtual Estimator &	consolidate_impl (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, Requests requests)=0
	Concrete implementation of the consolidate method.

Protected Member Functions inherited from lucid::Parametrizable
virtual int	get_i (Parameter parameter) const
	Get the value of the specified `parameter`.

virtual double	get_d (Parameter parameter) const
	Get the value of the specified `parameter`.

virtual const Vector &	get_v (Parameter parameter) const
	Get the value of the specified `parameter`.

Detailed Description

Interface for objects that can be optimised using gradient-based methods (e.g., LbfgsTuner).

It provides access to the objective value and the gradient of the objective function needed for optimisation.

Todo: Instead of being a subclass of Estimator, we should introduce a mixin subclass that supports oll kinds of mixins

Member Function Documentation

◆ gradient()

const Vector & lucid::GradientOptimizable::gradient ( ) const

inlinenodiscard

Get read-only access to the gradient of the this object.

If it has not been computed, it will be a vector of size 0.

Returns: gradient of the this object

◆ objective_value()

double lucid::GradientOptimizable::objective_value ( ) const

inlinenodiscard

Get read-only access to the objective value of the this object.

If it has not been computed, it will be \( -\infty \).

Returns: objective value of the this object

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

lucid/model/GradientOptimizable.h

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ gradient()

◆ objective_value()