Abstract class for all classes making supervised learning. More...

#include <STK_IRunner.h>

Inheritance diagram for STK::IRunnerSupervised< YArray_, XArray_, Weights_ >:

Public Member Functions
virtual void	setX (XArray_ const &x)
	set the x data set (predictors).

virtual void	setY (YArray_ const &y)
	set the data set.

virtual void	setData (YArray_ const &y, XArray_ const &x)
	set the data set.

virtual bool	run ()=0
	run the computations.

virtual bool	run (Weights_ const &weights)=0
	run the weighted computations.

Public Member Functions inherited from STK::IRunnerBase
String const &	error () const
	get the last error message.

Protected Member Functions
	IRunnerSupervised ()
	default constructor

	IRunnerSupervised (YArray_ const const &p_y, XArray_ const const &p_x)
	constructor

	IRunnerSupervised (YArray_ const &y, XArray_ const &x)
	default constructor

	IRunnerSupervised (IRunnerSupervised const &runner)
	copy constructor

	~IRunnerSupervised ()
	destructor

virtual void	updateY ()
	update the runner when y data set is set.

virtual void	updateX ()
	update the runner when x data set is set.

virtual void	update ()
	update the runner.

Protected Member Functions inherited from STK::IRunnerBase
	IRunnerBase ()
	default constructor

	IRunnerBase (IRunnerBase const &runner)
	copy constructor

virtual	~IRunnerBase ()
	destructor

Protected Attributes
YArray_ const *	p_y_
	A pointer on the y data set.

XArray_ const *	p_x_
	A pointer on the x data set.

Protected Attributes inherited from STK::IRunnerBase
String	msg_error_
	String with the last error message.

bool	hasRun_
	`true` if run has been used, `false` otherwise

Detailed Description

template<typename YArray_, typename XArray_, class Weights_>
class STK::IRunnerSupervised< YArray_, XArray_, Weights_ >

Abstract class for all classes making supervised learning.

This Interface is designed for supervised learning purpose. In an unsupervised learning setting, use IRunnerUnsupervised. The data sets x and y are not copied. There is two pointers on the data sets stored internally.

The pure virtual method to implement are

bool run();

bool run(weights);

STK::IRunnerSupervised::run

virtual bool run()=0

run the computations.

STK::MultidimRegression

The MultidimRegression class allows to regress a multidimensional output variable among a multivariat...

Definition STK_MultidimRegression.h:52

Definition at line 269 of file STK_IRunner.h.

Constructor & Destructor Documentation

◆ IRunnerSupervised() [1/4]

template<typename YArray_ , typename XArray_ , class Weights_ >

STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::IRunnerSupervised ( )

inlineprotected

default constructor

Definition at line 273 of file STK_IRunner.h.

273: p_y_(0), p_x_(0) {}

STK::IRunnerSupervised::p_y_

YArray_ const * p_y_

A pointer on the y data set.

Definition STK_IRunner.h:345

STK::IRunnerSupervised::p_x_

XArray_ const * p_x_

A pointer on the x data set.

Definition STK_IRunner.h:347

◆ IRunnerSupervised() [2/4]

template<typename YArray_ , typename XArray_ , class Weights_ >

STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::IRunnerSupervised	(	YArray_ const *const &	p_y,
		XArray_ const *const &	p_x
	)

inlineprotected

constructor

Parameters

p_y,p_x pointers on the y and x data sets

Definition at line 277 of file STK_IRunner.h.

278 : p_y_(p_y), p_x_(p_x)

279 {}

◆ IRunnerSupervised() [3/4]

template<typename YArray_ , typename XArray_ , class Weights_ >

STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::IRunnerSupervised	(	YArray_ const &	y,
		XArray_ const &	x
	)

inlineprotected

default constructor

Parameters

y,x	y and x data sets

Definition at line 283 of file STK_IRunner.h.

284 : p_y_(&y), p_x_(&x)

285 {}

◆ IRunnerSupervised() [4/4]

template<typename YArray_ , typename XArray_ , class Weights_ >

STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::IRunnerSupervised ( IRunnerSupervised< YArray_, XArray_, Weights_ > const & runner )

inlineprotected

copy constructor

Parameters

runner the runner to copy

Definition at line 289 of file STK_IRunner.h.

                     : IRunnerBase(runner)
                     , p_y_(runner.p_y_), p_x_(runner.p_x_)
    {}

◆ ~IRunnerSupervised()

template<typename YArray_ , typename XArray_ , class Weights_ >

STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::~IRunnerSupervised ( )

inlineprotected

destructor

Definition at line 294 of file STK_IRunner.h.

294{}

Member Function Documentation

◆ run() [1/2]

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual bool STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::run ( )

pure virtual

run the computations.

Returns: true if no error occur during the running process, false otherwise

Implements STK::IRunnerBase.

Implemented in STK::IClassification< YArray_, XArray_, Weights_ >, STK::IRegression< YArray, XArray, Weights >, STK::IRegression< Array, Array, Weight >, STK::IRegression< YArray, XArray, VectorX >, and STK::IRegression< YArray, XVector, VectorX >.

◆ run() [2/2]

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual bool STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::run ( Weights_ const & weights )

pure virtual

run the weighted computations.

Parameters

weights the weights of the samples

Returns: true if no error occur during the running process, false otherwise

Implemented in STK::IRegression< YArray, XArray, Weights >, and STK::IClassification< YArray_, XArray_, Weights_ >.

◆ setData()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setData	(	YArray_ const &	y,
		XArray_ const &	x
	)

inlinevirtual

set the data set.

If the state of the runner change when a new data set is set, the user of this class have to overload the udpate() method.

Parameters

y	The y data set to run
x	The x data set to run

Definition at line 324 of file STK_IRunner.h.

    {
      p_y_ = &y;
      p_x_ = &x;
      update();
      this->hasRun_ = false;
    }

References STK::IRunnerBase::hasRun_, STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_x_, STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_y_, and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::update().

Referenced by STK::LocalVariance< Array >::computeAxis(), STK::LocalVariance< Array >::computeCovarianceMatrices(), STK::LocalVariance< Array >::computeCovarianceMatrices(), STK::CategoricalBridge< Id, Data >::create(), STK::DiagGaussianBridge< Id, Data >::create(), STK::GammaBridge< Id, Data >::create(), STK::HDMatrixGaussianBridge< IdRow, IdCol, Data >::create(), STK::HDGaussianBridge< Id, Data >::create(), STK::PoissonBridge< Id, Data >::create(), STK::List1D< Type_ >::operator=(), STK::Stat::Bivariate< XTYPE, YTYPE, TContainer1D >::setData(), and STK::MultiLaw::Normal< RowVector >::setParameters().

◆ setX()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setX ( XArray_ const & x )

inlinevirtual

set the x data set (predictors).

If the state of the runner change when a new x data set is set, the user of this class have to overload the udpate() method.

Parameters

x	The x data set to run

Definition at line 302 of file STK_IRunner.h.

    {
      p_x_ = &x;
      updateX();
      this->hasRun_ = false;
    }

References STK::IRunnerBase::hasRun_, STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_x_, and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateX().

◆ setY()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setY ( YArray_ const & y )

inlinevirtual

set the data set.

If the state of the runner change when a new data set is set, the user of this class have to overload the udpate() method.

Parameters

y	The y data set to run

Definition at line 313 of file STK_IRunner.h.

    {
      p_y_ = &y;
      updateY();
      this->hasRun_ = false;
    }

References STK::IRunnerBase::hasRun_, STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_y_, and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateY().

◆ update()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::update ( )

inlineprotectedvirtual

update the runner.

This virtual method will be called when the state of the runner will change, i.e. when new x and y data sets are set. By default do nothing.

Reimplemented from STK::IRunnerBase.

Definition at line 362 of file STK_IRunner.h.

362{ updateX(); updateY();}

References STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateX(), and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateY().

Referenced by STK::IQr< Derived >::insertCol(), STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setData(), and STK::HDMatrixModelParameters< Array_ >::updateStatistics().

◆ updateX()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateX ( )

inlineprotectedvirtual

update the runner when x data set is set.

This virtual method will be called when the state of the runner will change, i.e. when a new x data set is set. By default do nothing.

Definition at line 357 of file STK_IRunner.h.

357{}

Referenced by STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setX(), and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::update().

◆ updateY()

template<typename YArray_ , typename XArray_ , class Weights_ >

virtual void STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::updateY ( )

inlineprotectedvirtual

update the runner when y data set is set.

This virtual method will be called when the state of the runner will change, i.e. when a new y data is set is set. By default do nothing.

Definition at line 352 of file STK_IRunner.h.

352{}

Referenced by STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setY(), and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::update().

Member Data Documentation

◆ p_x_

template<typename YArray_ , typename XArray_ , class Weights_ >

XArray_ const* STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_x_

protected

A pointer on the x data set.

Definition at line 347 of file STK_IRunner.h.

Referenced by STK::AdditiveBSplineRegression< YArray, XArray, Weights >::initializeStep(), STK::MultidimRegression< Array, Weight >::predictionStep(), STK::MultidimRegression< Array, Weight >::regressionStep(), STK::MultidimRegression< Array, Weight >::regressionStep(), STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setData(), and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setX().

◆ p_y_

template<typename YArray_ , typename XArray_ , class Weights_ >

YArray_ const* STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::p_y_

protected

A pointer on the y data set.

Definition at line 345 of file STK_IRunner.h.

Referenced by STK::AdditiveBSplineRegression< YArray, XArray, Weights >::regressionStep(), STK::BSplineRegression< YArray, XVector, Weights >::regressionStep(), STK::MultidimRegression< Array, Weight >::regressionStep(), STK::MultidimRegression< Array, Weight >::regressionStep(), STK::AdditiveBSplineRegression< YArray, XArray, Weights >::regressionStep(), STK::BSplineRegression< YArray, XVector, Weights >::regressionStep(), STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setData(), and STK::IRunnerSupervised< YArray_, XArray_, Weights_ >::setY().

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

STK_IRunner.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ IRunnerSupervised() [1/4]

◆ IRunnerSupervised() [2/4]

◆ IRunnerSupervised() [3/4]

◆ IRunnerSupervised() [4/4]

◆ ~IRunnerSupervised()

Member Function Documentation

◆ run() [1/2]

◆ run() [2/2]

◆ setData()

◆ setX()

◆ setY()

◆ update()

◆ updateX()

◆ updateY()

Member Data Documentation

◆ p_x_

◆ p_y_