lti::crispDecisionTree::univariateCrispDecisionFunction Class Reference

This class implements the most common decision function: Each node has a real-valued threshold in one dimension only. More...

#include <ltiCrispDecisionTree.h>

Inheritance diagram for lti::crispDecisionTree::univariateCrispDecisionFunction:

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Collaboration diagram for lti::crispDecisionTree::univariateCrispDecisionFunction:

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List of all members.

Public Member Functions
	univariateCrispDecisionFunction ()
	univariateCrispDecisionFunction (const int &dim, const double &thresh)
	univariateCrispDecisionFunction (const univariateCrispDecisionFunction &other)
	~univariateCrispDecisionFunction ()
const char *	getTypeName () const
univariateCrispDecisionFunction &	copy (const univariateCrispDecisionFunction &other)
univariateCrispDecisionFunction &	operator= (const univariateCrispDecisionFunction &other)
virtual crispDecisionFunction *	clone () const
virtual bool	apply (const dvector &data) const
void	setCondition (const int &dim, const double &thresh)
virtual bool	write (ioHandler &handler, const bool complete=true) const
virtual bool	read (ioHandler &handler, const bool complete=true)
Protected Attributes
int	dimension
double	threshold

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Detailed Description

This class implements the most common decision function: Each node has a real-valued threshold in one dimension only.

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Constructor & Destructor Documentation

lti::crispDecisionTree::univariateCrispDecisionFunction::univariateCrispDecisionFunction

(

)

default constructor

lti::crispDecisionTree::univariateCrispDecisionFunction::univariateCrispDecisionFunction	(	const int &	dim,
		const double &	thresh
	)

Sets the condition of the decision function.

Parameters:

	dim	dimension for the decision
	thresh	the threshold

lti::crispDecisionTree::univariateCrispDecisionFunction::univariateCrispDecisionFunction

(

const univariateCrispDecisionFunction &

other

)

copy constructor

Parameters:

other

the univariateCrispDecisionFunction object to be copied

lti::crispDecisionTree::univariateCrispDecisionFunction::~univariateCrispDecisionFunction

(

)

destructor

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Member Function Documentation

virtual bool lti::crispDecisionTree::univariateCrispDecisionFunction::apply

(

const dvector &

data

)

const [virtual]

Evaluate the condition implemented in the cdf.

Parameters:

data

value to be evaluated

Returns:

true if it meets the condition (left), false if not

Implements lti::crispDecisionTree::crispDecisionFunction.

virtual crispDecisionFunction* lti::crispDecisionTree::univariateCrispDecisionFunction::clone

(

)

const [virtual]

returns a pointer to a clone of the univariateCrispDecisionFunction

Implements lti::crispDecisionTree::crispDecisionFunction.

univariateCrispDecisionFunction& lti::crispDecisionTree::univariateCrispDecisionFunction::copy

(

const univariateCrispDecisionFunction &

other

)

copy the contents of a univariateCrispDecisionFunction object

Parameters:

other

the univariateCrispDecisionFunction object to be copied

Returns:

a reference to this univariateCrispDecisionFunction object

Reimplemented from lti::crispDecisionTree::crispDecisionFunction.

const char* lti::crispDecisionTree::univariateCrispDecisionFunction::getTypeName

(

)

const [virtual]

returns name of this type

Reimplemented from lti::crispDecisionTree::crispDecisionFunction.

univariateCrispDecisionFunction& lti::crispDecisionTree::univariateCrispDecisionFunction::operator=

(

const univariateCrispDecisionFunction &

other

)

copy the contents of a univariateCrispDecisionFunction object

Parameters:

other

the univariateCrispDecisionFunction object to be copied

Returns:

a reference to this univariateCrispDecisionFunction object

Reimplemented from lti::crispDecisionTree::crispDecisionFunction.

virtual bool lti::crispDecisionTree::univariateCrispDecisionFunction::read	(	ioHandler &	handler,
		const bool	complete = true
	)			[virtual]

read the univariateCrispDecisionFunction from the given ioHandler

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:

true if write was successful

Reimplemented from lti::crispDecisionTree::crispDecisionFunction.

void lti::crispDecisionTree::univariateCrispDecisionFunction::setCondition	(	const int &	dim,
		const double &	thresh
	)

Sets the condition to be tested.

Parameters:

	dim	the dimension to be tested.
	thresh	the threshold in that dimension

virtual bool lti::crispDecisionTree::univariateCrispDecisionFunction::write	(	ioHandler &	handler,
		const bool	complete = true
	)			const [virtual]

write the univariateCrispDecisionFunction in the given ioHandler

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:

true if write was successful

Reimplemented from lti::crispDecisionTree::crispDecisionFunction.

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Member Data Documentation

int lti::crispDecisionTree::univariateCrispDecisionFunction::dimension [protected]

The dimension to be tested.

double lti::crispDecisionTree::univariateCrispDecisionFunction::threshold [protected]

Threshold against which the data is tested in the given dimension.

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

• ltiCrispDecisionTree.h