Dummy estimator that uses a user-defined function to make predictions. More...

#include <ModelEstimator.h>

Inheritance diagram for lucid::ModelEstimator:

Public Member Functions
	ModelEstimator (std::function< Matrix(ConstMatrixRef)> model_function)
	Construct a new Model Estimator object.

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

Matrix	predict (ConstMatrixRef x) const override
	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 \( \mathcal{Y} \).

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

std::string	to_string () const override
	Obtain the string representation of 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 \( \mathcal{Y} \).

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.

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.

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.

Private Member Functions
Estimator &	consolidate_impl (ConstMatrixRef training_inputs, ConstMatrixRef training_outputs, Requests requests) override
	Concrete implementation of the consolidate method.

Private Attributes
std::function< Matrix(ConstMatrixRef)>	model_function_
	Function that defines the model.

Additional Inherited Members
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`.

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

Detailed Description

Dummy estimator that uses a user-defined function to make predictions.

Useful for testing, in a context that assumes perfect knowledge of the system dynamics.

Constructor & Destructor Documentation

◆ ModelEstimator()

lucid::ModelEstimator::ModelEstimator ( std::function< Matrix(ConstMatrixRef)> model_function )

explicit

Construct a new Model Estimator object.

Parameters

model_function Function that defines the model.

Member Function Documentation

◆ clone()

std::unique_ptr< Estimator > lucid::ModelEstimator::clone ( ) const

nodiscardoverridevirtual

Clone the estimator by creating a new instance with the same parameters.

Returns: new instance of the estimator

Implements lucid::Estimator.

◆ consolidate_impl()

Estimator & lucid::ModelEstimator::consolidate_impl	(	ConstMatrixRef	training_inputs,
		ConstMatrixRef	training_outputs,
		Requests	requests )

overrideprivatevirtual

Concrete implementation of the consolidate method.

Parameters

training_inputs	training input data
training_outputs	training output data
requests	requests for the consolidation process

Returns: reference to the estimator

Implements lucid::Estimator.

◆ predict()

Matrix lucid::ModelEstimator::predict ( ConstMatrixRef x ) const

nodiscardoverridevirtual

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 \( \mathcal{Y} \).

Precondition: The estimator should be fitted before calling this method.

Parameters

x	\( n \times d_x \) matrix of row vectors in \( \mathcal{X} \)

Returns: \( n \times d_y \) matrix of row vectors in \( \mathcal{Y} \)

Implements lucid::Estimator.

◆ score()

double lucid::ModelEstimator::score	(	ConstMatrixRef	evaluation_inputs,
		ConstMatrixRef	evaluation_outputs ) const

nodiscardoverridevirtual

Score the estimator assigning a numerical value to its accuracy in predicting the evaluation_outputs given the evaluation_inputs.

Given the evaluation inputs \( x = \{ x_1, \dots, x_n \} \), where \( x_i \in \mathcal{X} \subseteq \mathbb{R}^{d_x}, 0 \le i \le n \), we want to give a numerical score to the model's predictions \( \hat{y} = \{ \hat{y}_1, \dots, \hat{y}_n \} \) with respect to the true outputs \( y = \{ y_1, \dots, y_n \} \), where \( y_i \in \mathcal{Y} \subseteq \mathbb{R}^{d_y}, 0 \le i \le n \).

Note: By default, the score is computed using the scorer::r2_score.

Precondition: The methods fit or consolidate should have been called at least once before calling this method.

Parameters

evaluation_inputs	\( \texttip{n}{Number of samples} \times \texttip{d_x}{Dimension of the input vector space} \) evaluation input data
evaluation_outputs	\( \texttip{n}{Number of samples} \times \texttip{d_y}{Dimension of the output vector space} \) evaluation output data

Returns: score of the model

Implements lucid::Estimator.

◆ to_string()

std::string lucid::ModelEstimator::to_string ( ) const

nodiscardoverridevirtual

Obtain the string representation of this object.

Returns: string representation of this object

Reimplemented from lucid::Estimator.

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

lucid/model/ModelEstimator.h
lucid/model/ModelEstimator.cpp

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ ModelEstimator()

Member Function Documentation

◆ clone()

◆ consolidate_impl()

◆ predict()

◆ score()

◆ to_string()