Interface base class for Regression methods. More...

#include <STK_IClassification.h>

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

Public Member Functions
virtual	~IClassification ()
	virtual destructor.

int	nbClass () const

int	nbFreeParameter () const

virtual bool	run ()
	run the computations.

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

Public Member Functions inherited from STK::IRunnerSupervised< YArray_, XArray_, Weights_ >
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.

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

Protected Types
typedef IRunnerSupervised< YArray_, XArray_, Weights_ >	Base

Protected Member Functions
	IClassification ()
	Default constructor.

	IClassification (YArray_ const p_y, XArray_ const p_x)
	Constructor.

	IClassification (YArray_ const &y, XArray_ const &x)
	Constructor.

virtual bool	initializeStep ()
	perform any computation needed before the call of the classification method.

virtual bool	predictionStep ()
	Compute the predicted outputs by the classification function and store the result in the p_predicted_ array.

virtual bool	finalizeStep ()
	perform any computation needed after the call of the classification method.

Protected Member Functions inherited from STK::IRunnerSupervised< YArray_, XArray_, Weights_ >
	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
int	nbClass_
	number of class

int	nbFreeParameter_
	number of parameter of the classification method.

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

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

Protected Attributes inherited from STK::IRunnerSupervised< YArray_, XArray_, Weights_ >
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

Private Member Functions
virtual bool	estimationStep ()=0
	compute the classification function.

virtual bool	estimationStep (Weights_ const &weights)=0
	compute the weighted classification function.

virtual int	computeNbFreeParameter () const =0
	Compute the number of parameter of the classification function.

Detailed Description

template<class YArray_, class XArray_, class Weights_>
class STK::IClassification< YArray_, XArray_, Weights_ >

Interface base class for Regression methods.

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.

In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance.

Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function.

An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.

In this interface, the pure virtual function to implement are

virtual bool estimationStep() =0;
virtual bool estimationStep( Weights_ const& weights) =0;
virtual bool predictionStep() =0;
virtual int computeNbFreeParameter() const =0;
virtual YArray_ extrapolate(XArray_ const& x) const =0;

The virtual function

virtual void initializeStep();

STK::IClassification::initializeStep

virtual bool initializeStep()

perform any computation needed before the call of the classification method.

Definition STK_IClassification.h:189

can be overloaded.

The default behavior of the run methods is

if (!initializeStep()) { flag = false;}
if (!estimationStep()) { flag = false;}
if (!finalizeStep()) { flag = false;}
nbFreeParameter_ = computeNbFreeParameter();
if (!predictionStep()) { flag = false;}

and it can be overloaded in derived class.

Definition at line 103 of file STK_IClassification.h.

Member Typedef Documentation

◆ Base

template<class YArray_ , class XArray_ , class Weights_ >

typedef IRunnerSupervised<YArray_, XArray_, Weights_> STK::IClassification< YArray_, XArray_, Weights_ >::Base

protected

Definition at line 106 of file STK_IClassification.h.

Constructor & Destructor Documentation

◆ IClassification() [1/3]

template<class YArray_ , class XArray_ , class Weights_ >

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

protected

Default constructor.

Initialize the data members.

Definition at line 214 of file STK_IClassification.h.

214: Base(), nbClass_(0), nbFreeParameter_(0) {}

STK::IClassification::Base

IRunnerSupervised< YArray_, XArray_, Weights_ > Base

Definition STK_IClassification.h:106

STK::IClassification::nbClass_

int nbClass_

number of class

Definition STK_IClassification.h:182

◆ IClassification() [2/3]

template<class YArray_ , class XArray_ , class Weights_ >

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

protected

Constructor.

Initialize the data members.

Parameters

p_y,p_x pointer array with the observed output and output

Definition at line 220 of file STK_IClassification.h.

                                                          : Base(p_y, p_x)
                                                          , nbClass_(0)
                                                          , nbFreeParameter_(0)
{}

◆ IClassification() [3/3]

template<class YArray_ , class XArray_ , class Weights_ >

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

protected

Constructor.

Initialize the data members.

Parameters

y,x	arrays with the observed output and input

Definition at line 230 of file STK_IClassification.h.

                                                          : Base(y, x)
                                                          , nbClass_(0)
                                                          , nbFreeParameter_(0)
{}

◆ ~IClassification()

template<class YArray_ , class XArray_ , class Weights_ >

virtual STK::IClassification< YArray_, XArray_, Weights_ >::~IClassification ( )

inlinevirtual

virtual destructor.

Definition at line 122 of file STK_IClassification.h.

122{}

Member Function Documentation

◆ computeNbFreeParameter()

template<class YArray_ , class XArray_ , class Weights_ >

virtual int STK::IClassification< YArray_, XArray_, Weights_ >::computeNbFreeParameter ( ) const

privatepure virtual

Compute the number of parameter of the classification function.

Returns: the number of parameter of the classification function

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ estimationStep() [1/2]

template<class YArray_ , class XArray_ , class Weights_ >

virtual bool STK::IClassification< YArray_, XArray_, Weights_ >::estimationStep ( )

privatepure virtual

compute the classification function.

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ estimationStep() [2/2]

template<class YArray_ , class XArray_ , class Weights_ >

virtual bool STK::IClassification< YArray_, XArray_, Weights_ >::estimationStep ( Weights_ const & weights )

privatepure virtual

compute the weighted classification function.

Parameters

weights the weights of the samples

◆ finalizeStep()

template<class YArray_ , class XArray_ , class Weights_ >

virtual bool STK::IClassification< YArray_, XArray_, Weights_ >::finalizeStep ( )

inlineprotectedvirtual

perform any computation needed after the call of the classification method.

Default implementation is do nothing.

Definition at line 197 of file STK_IClassification.h.

197{return true;}

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ initializeStep()

template<class YArray_ , class XArray_ , class Weights_ >

virtual bool STK::IClassification< YArray_, XArray_, Weights_ >::initializeStep ( )

inlineprotectedvirtual

perform any computation needed before the call of the classification method.

Default implementation is do nothing.

Definition at line 189 of file STK_IClassification.h.

189{return true;}

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ nbClass()

template<class YArray_ , class XArray_ , class Weights_ >

int STK::IClassification< YArray_, XArray_, Weights_ >::nbClass ( ) const

inline

Returns: number of class

Definition at line 125 of file STK_IClassification.h.

125{ return nbClass_;}

References STK::IClassification< YArray_, XArray_, Weights_ >::nbClass_.

◆ nbFreeParameter()

template<class YArray_ , class XArray_ , class Weights_ >

int STK::IClassification< YArray_, XArray_, Weights_ >::nbFreeParameter ( ) const

inline

Returns: number of parameters of the classification function

Definition at line 127 of file STK_IClassification.h.

127{ return nbFreeParameter_;}

References STK::IClassification< YArray_, XArray_, Weights_ >::nbFreeParameter_.

◆ predictionStep()

template<class YArray_ , class XArray_ , class Weights_ >

virtual bool STK::IClassification< YArray_, XArray_, Weights_ >::predictionStep ( )

inlineprotectedvirtual

Compute the predicted outputs by the classification function and store the result in the p_predicted_ array.

Default implementation is do nothing.

Definition at line 193 of file STK_IClassification.h.

193{return true;};

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ run() [1/2]

template<class YArray_ , class XArray_ , class Weights_ >

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

inlinevirtual

run the computations.

Default Implementation.

Implements STK::IRunnerSupervised< YArray_, XArray_, Weights_ >.

Definition at line 130 of file STK_IClassification.h.

    {
      if (!p_x_ || !p_y_)
      { this->msg_error_ = STKERROR_NO_ARG(IClassification::run,missing data sets);
        return false;
      }
      bool flag=true;
      // perform any initialization step needed before the classification step
      if (!initializeStep()) { flag = false;}
      // compute the classification
      if (!estimationStep()) { flag = false;}
 
      // perform any post-operation needed after the classification step
      if (!finalizeStep()) { flag = false;}
      // Compute the number of parameter of the classification function.
      nbFreeParameter_ = computeNbFreeParameter();
      // create array of the predicted value and compute prediction
      if (!predictionStep()) { flag = false;}
      // return the result of the computations
      this->hasRun_ = true;
      return flag;
    }

Referenced by STK::IClassification< YArray_, XArray_, Weights_ >::run(), and STK::IClassification< YArray_, XArray_, Weights_ >::run().

◆ run() [2/2]

template<class YArray_ , class XArray_ , class Weights_ >

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

inlinevirtual

run the weighted computations.

Parameters

weights weights of the samples

Implements STK::IRunnerSupervised< YArray_, XArray_, Weights_ >.

Definition at line 155 of file STK_IClassification.h.

    {
 
      if (!p_x_ || !p_y_)
      { this->msg_error_ = STKERROR_NO_ARG(IClassification::run,missing data sets);
        return false;
      }
      bool flag=true;
      // perform any pre-operation needed before the classification step
      if (!initializeStep()) { flag = false;}
      // compute weighted classification
      if (!estimationStep(weights)) { flag = false;}
 
      // perform any post-operation needed after the classification step
      if (!finalizeStep()) { flag = false;}
      // Compute the number of parameter of the classification function.
      nbFreeParameter_ = computeNbFreeParameter();
      // create array of the predicted value and compute prediction
      if (!predictionStep()) { flag = false;}
 
      // return the result of the computations
      this->hasRun_ = true;
      return flag;
    }