#include <Tree.h>

Inheritance diagram for Tree:

Public Types
enum	TrainingMode { BEST_ITERATIVE_SPILT =0, BEST_RANDOM_SPLIT, NUM_TRAINING_MODES }

Public Member Functions
	Tree (const UINT numSplittingSteps=100, const UINT minNumSamplesPerNode=5, const UINT maxDepth=10, const bool removeFeaturesAtEachSpilt=false, const UINT trainingMode=BEST_ITERATIVE_SPILT)

virtual	~Tree (void)

virtual bool	clear ()

virtual bool	print () const

virtual bool	getModel (std::ostream &stream) const

virtual Node *	deepCopyTree () const

const Node *	getTree () const

UINT	getTrainingMode () const

UINT	getNumSplittingSteps () const

UINT	getMinNumSamplesPerNode () const

UINT	getMaxDepth () const

UINT	getPredictedNodeID () const

bool	getRemoveFeaturesAtEachSpilt () const

bool	setTrainingMode (const UINT trainingMode)

bool	setNumSplittingSteps (const UINT numSplittingSteps)

bool	setMinNumSamplesPerNode (const UINT minNumSamplesPerNode)

bool	setMaxDepth (const UINT maxDepth)

bool	setRemoveFeaturesAtEachSpilt (const bool removeFeaturesAtEachSpilt)

Public Member Functions inherited from GRTBase
	GRTBase (void)

virtual	~GRTBase (void)

bool	copyGRTBaseVariables (const GRTBase *GRTBase)

std::string	getClassType () const

std::string	getLastWarningMessage () const

std::string	getLastErrorMessage () const

std::string	getLastInfoMessage () const

bool	setInfoLoggingEnabled (const bool loggingEnabled)

bool	setWarningLoggingEnabled (const bool loggingEnabled)

bool	setErrorLoggingEnabled (const bool loggingEnabled)

GRTBase *	getGRTBasePointer ()

const GRTBase *	getGRTBasePointer () const

Protected Attributes
UINT	trainingMode

UINT	numSplittingSteps

UINT	minNumSamplesPerNode

UINT	maxDepth

bool	removeFeaturesAtEachSpilt

Node *	tree

Protected Attributes inherited from GRTBase
std::string	classType

DebugLog	debugLog

ErrorLog	errorLog

InfoLog	infoLog

TrainingLog	trainingLog

TestingLog	testingLog

WarningLog	warningLog

Additional Inherited Members
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)

static std::string	getGRTRevison ()

Protected Member Functions inherited from GRTBase
Float	SQR (const Float &x) const

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 38 of file Tree.h.

Constructor & Destructor Documentation

GRT_BEGIN_NAMESPACE Tree::Tree	(	const UINT	numSplittingSteps = `100`,
		const UINT	minNumSamplesPerNode = `5`,
		const UINT	maxDepth = `10`,
		const bool	removeFeaturesAtEachSpilt = `false`,
		const UINT	trainingMode = `BEST_ITERATIVE_SPILT`
	)

Default Constructor

Parameters

numSplittingSteps	sets the number of steps that will be used to search for the best spliting value for each node. Default value = 100
minNumSamplesPerNode	sets the minimum number of samples that are allowed per node, if the number of samples is below that, the node will become a leafNode. Default value = 5
maxDepth	sets the maximum depth of the tree. Default value = 10
removeFeaturesAtEachSpilt	sets if a feature is removed at each spilt so it can not be used again. Default value = false
trainingMode	sets the training mode, this should be one of the TrainingMode enums. Default value = BEST_ITERATIVE_SPILT

Definition at line 25 of file Tree.cpp.

Tree::~Tree ( void )

virtual

Default Destructor

Definition at line 40 of file Tree.cpp.

Member Function Documentation

bool Tree::clear ( )

virtual

This overrides the clear function in the Regressifier base class. It will completely clear the ML module, removing any trained model and setting all the base variables to their default values.

Returns: returns true if the module was cleared succesfully, false otherwise

Reimplemented in DecisionTree, ClusterTree, and RegressionTree.

Definition at line 45 of file Tree.cpp.

Node * Tree::deepCopyTree ( ) const

virtual

Deep copies the tree, returning a pointer to the new tree. The user is in charge of cleaning up the memory so must delete the pointer when they no longer need it. NULL will be returned if the tree could not be copied.

Returns: returns a pointer to a deep copy of the tree

Reimplemented in DecisionTree, ClusterTree, and RegressionTree.

Definition at line 78 of file Tree.cpp.

UINT Tree::getMaxDepth ( ) const

Gets the maximum depth of the tree.

Returns: returns the maximum depth of the tree

Definition at line 103 of file Tree.cpp.

UINT Tree::getMinNumSamplesPerNode ( ) const

Gets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node.

Returns: returns the minimum number of samples that are allowed per node

Definition at line 99 of file Tree.cpp.

bool Tree::getModel ( std::ostream & stream ) const

virtual

This function adds the current model to the formatted stream. This function should be overwritten by the derived class.

Parameters

file	a reference to the stream the model will be added to

Returns: returns true if the model was added successfully, false otherwise

Reimplemented in DecisionTree.

Definition at line 67 of file Tree.cpp.

UINT Tree::getNumSplittingSteps ( ) const

Gets the number of steps that will be used to search for the best spliting value for each node.

If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.

Returns: returns the number of steps that will be used to search for the best spliting value for each node

Definition at line 95 of file Tree.cpp.

UINT Tree::getPredictedNodeID ( ) const

This function returns the predictedNodeID, this is ID of the leaf node that was reached during the last prediction call

Returns: returns the predictedNodeID, this will be zero if the tree does not exist or predict has not been called

Definition at line 107 of file Tree.cpp.

bool Tree::getRemoveFeaturesAtEachSpilt ( ) const

Gets if a feature is removed at each spilt so it can not be used again.

Returns: returns true if a feature is removed at each spilt so it can not be used again, false otherwise

Definition at line 116 of file Tree.cpp.

UINT Tree::getTrainingMode ( ) const

Gets the current training mode. This will be one of the TrainingModes enums.

Returns: returns the training mode

Definition at line 91 of file Tree.cpp.

const Node * Tree::getTree ( ) const

Gets a pointer to the root node of the tree. NULL will be returned if the tree model has not be trained.

Returns: returns a const pointer to the tree

Definition at line 87 of file Tree.cpp.

bool Tree::print ( ) const

virtual

Prints the tree to std::cout.

Returns: returns true if the model was printed

Reimplemented in ClusterTree, and RegressionTree.

Definition at line 56 of file Tree.cpp.

bool Tree::setMaxDepth ( const UINT maxDepth )

Sets the maximum depth of the tree, any node that reaches this depth will automatically become a leaf node. Value must be larger than zero.

Parameters

maxDepth the maximum depth of the tree

Returns: returns true if the parameter was set, false otherwise

Definition at line 147 of file Tree.cpp.

bool Tree::setMinNumSamplesPerNode ( const UINT minNumSamplesPerNode )

Sets the minimum number of samples that are allowed per node, if the number of samples at a node is below this value then the node will automatically become a leaf node. Value must be larger than zero.

Parameters

minNumSamplesPerNode the minimum number of samples that are allowed per node

Returns: returns true if the parameter was set, false otherwise

Definition at line 138 of file Tree.cpp.

bool Tree::setNumSplittingSteps ( const UINT numSplittingSteps )

Sets the number of steps that will be used to search for the best spliting value for each node.

If the trainingMode is set to BEST_ITERATIVE_SPILT, then the numSplittingSteps controls how many iterative steps there will be per feature. If the trainingMode is set to BEST_RANDOM_SPLIT, then the numSplittingSteps controls how many random searches there will be per feature.

A higher value will increase the chances of building a better model, but will take longer to train the model. Value must be larger than zero.

Parameters

numSplittingSteps sets the number of steps that will be used to search for the best spliting value for each node.

Returns: returns true if the parameter was set, false otherwise

Definition at line 129 of file Tree.cpp.

bool Tree::setRemoveFeaturesAtEachSpilt ( const bool removeFeaturesAtEachSpilt )

Sets if a feature is removed at each spilt so it can not be used again. If true then the best feature selected at each node will be removed so it can not be used in any children of that node. If false, then the feature that provides the best spilt at each node will be used, regardless of how many times it has been used again.

Parameters

removeFeaturesAtEachSpilt if true, then each feature is removed at each spilt so it can not be used again

Returns: returns true if the parameter was set, false otherwise

Definition at line 156 of file Tree.cpp.

bool Tree::setTrainingMode ( const UINT trainingMode )

Sets the training mode, this should be one of the TrainingModes enums.

Parameters

const UINT trainingMode: the new trainingMode, this should be one of the TrainingModes enums

Returns: returns true if the trainingMode was set successfully, false otherwise

Definition at line 120 of file Tree.cpp.

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

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation