#include <DecisionStump.h>

Inheritance diagram for DecisionStump:

Public Member Functions
	DecisionStump (const UINT numRandomSplits=100)

virtual	~DecisionStump ()

	DecisionStump (const DecisionStump &rhs)

DecisionStump &	operator= (const DecisionStump &rhs)

virtual bool	deepCopyFrom (const WeakClassifier *weakClassifer)

virtual bool	train (ClassificationData &trainingData, VectorFloat &weights)

virtual Float	predict (const VectorFloat &x)

virtual bool	saveModelToFile (std::fstream &file) const

virtual bool	loadModelFromFile (std::fstream &file)

virtual void	print () const

UINT	getDecisionFeatureIndex () const

UINT	getDirection () const

UINT	getNumRandomSplits () const

Float	getDecisionValue () const

Public Member Functions inherited from WeakClassifier
	WeakClassifier ()

virtual	~WeakClassifier ()

	WeakClassifier (const WeakClassifier &rhs)

WeakClassifier &	operator= (const WeakClassifier &rhs)

bool	copyBaseVariables (const WeakClassifier *weakClassifer)

virtual Float	getPositiveClassLabel () const

virtual Float	getNegativeClassLabel () const

std::string	getWeakClassifierType () const

bool	getTrained () const

UINT	getNumInputDimensions () const

WeakClassifier *	createNewInstance () const

Protected Attributes
UINT	decisionFeatureIndex
	The dimension that the data will be spilt on.

UINT	direction
	Indicates if the decision spilt threshold is greater than (1), or less than (0)

UINT	numRandomSplits
	The number of random splits used to search for the best decision spilt.

Float	decisionValue
	The decision spilt threshold.

Protected Attributes inherited from WeakClassifier
std::string	weakClassifierType
	A string that represents the weak classifier type, e.g. DecisionStump.

bool	trained
	A flag to show if the weak classifier model has been trained.

UINT	numInputDimensions
	The number of input dimensions to the weak classifier.

TrainingLog	trainingLog

ErrorLog	errorLog

WarningLog	warningLog

Static Protected Attributes
static RegisterWeakClassifierModule< DecisionStump >	registerModule
	This is used to register the DecisionStump with the WeakClassifier base class.

Additional Inherited Members
Public Types inherited from WeakClassifier
typedef std::map< std::string, WeakClassifier ()() >	StringWeakClassifierMap

Static Public Member Functions inherited from WeakClassifier
static WeakClassifier *	createInstanceFromString (std::string const &weakClassifierType)

Static Protected Member Functions inherited from WeakClassifier
static StringWeakClassifierMap *	getMap ()

Detailed Description

GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Definition at line 36 of file DecisionStump.h.

Constructor & Destructor Documentation

DecisionStump::DecisionStump ( const UINT numRandomSplits = 100 )

Default Constructor.

Sets the number of random splits that will be used to search for the best split value.

Parameters

numRandomSplits sets the number of random splits that will be used to search for the best split value. Default value = 100

Definition at line 35 of file DecisionStump.cpp.

DecisionStump::~DecisionStump ( )

virtual

Default Destructor.

Definition at line 48 of file DecisionStump.cpp.

DecisionStump::DecisionStump ( const DecisionStump & rhs )

Default Copy Constructor. Defines how the data from the rhs GRT::DecisionStump instance is copied to this GRT::DecisionStump instance.

Definition at line 52 of file DecisionStump.cpp.

Member Function Documentation

bool DecisionStump::deepCopyFrom ( const WeakClassifier * weakClassifer )

virtual

This function enables the data from one GRT::DecisionStump instance to be copied into this GRT::DecisionStump instance.

Parameters

weakClassifer a pointer to the Classifier Base Class, this should be pointing to another GRT::DecisionStump instance

Returns: returns true if the clone was successfull, false otherwise

Reimplemented from WeakClassifier.

Definition at line 67 of file DecisionStump.cpp.

UINT DecisionStump::getDecisionFeatureIndex ( ) const

Returns: returns the index of the feature that is used to split the data into a positive or negative class

Definition at line 255 of file DecisionStump.cpp.

Float DecisionStump::getDecisionValue ( ) const

Returns: returns the decision spilt threshold

Definition at line 267 of file DecisionStump.cpp.

UINT DecisionStump::getDirection ( ) const

Returns: returns if the decision spilt threshold is greater than (1), or less than (0)

Definition at line 259 of file DecisionStump.cpp.

UINT DecisionStump::getNumRandomSplits ( ) const

Returns: returns the number of random splits that will be used to search for the best decision spilt

Definition at line 263 of file DecisionStump.cpp.

bool DecisionStump::loadModelFromFile ( std::fstream & file )

virtual

This function loads an model model from a file.

fstream file: a reference to the file you want to load the RBF model from

Returns: returns true if the model was loaded successfull, false otherwise

Reimplemented from WeakClassifier.

Definition at line 180 of file DecisionStump.cpp.

DecisionStump & DecisionStump::operator= ( const DecisionStump & rhs )

Defines how the data from the rhs GRT::DecisionStump instance is copied to this GRT::DecisionStump instance.

Definition at line 56 of file DecisionStump.cpp.

Float DecisionStump::predict ( const VectorFloat & x )

virtual

This function predicts the class label of the input vector, given the current model. The class label returned will either be positive (WEAK_CLASSIFIER_POSITIVE_CLASS_LABEL) or negative (WEAK_CLASSIFIER_NEGATIVE_CLASS_LABEL).

Parameters

weights a reference to the vector used for prediction

Returns: returns the estimated class label, which will be positive or negative

Reimplemented from WeakClassifier.

Definition at line 150 of file DecisionStump.cpp.

void DecisionStump::print ( ) const

virtual

This function prints out some basic info about the model to std::cout.

Reimplemented from WeakClassifier.

Definition at line 248 of file DecisionStump.cpp.

bool DecisionStump::saveModelToFile ( std::fstream & file ) const

virtual

This function saves the current model to a file.

fstream file: a reference to the file you want to save the RBF model to

Returns: returns true if the model was saved successfull, false otherwise

Reimplemented from WeakClassifier.

Definition at line 157 of file DecisionStump.cpp.

bool DecisionStump::train	(	ClassificationData &	trainingData,
		VectorFloat &	weights
	)

virtual

This function trains the DecisionStump model, using the weighted labelled training data.

Parameters

trainingData	the labelled training data
weights	the corresponding weights for each sample in the labelled training data

Returns: returns true if the model was trained successfull, false otherwise

Reimplemented from WeakClassifier.

Definition at line 77 of file DecisionStump.cpp.

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

Public Member Functions

Protected Attributes

Static Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation