plLearnUnKHistogram Class Reference

Class for learning histograms parametrical form for variables wich definition set is unknown. More...

#include <plLearnUnKHistogram.h>

Inheritance diagram for plLearnUnKHistogram:

[legend]Collaboration diagram for plLearnUnKHistogram:

[legend]List of all members.

Public Member Functions
	plLearnUnKHistogram (const plLearnUnKHistogram &plLUKH)
	The copy-constructor.
	plLearnUnKHistogram (const plVariablesConjunction &vars, map< plDataValues, plLearnFrequence > *const initial, bool copie=false)
	init is a pointer on an map of initial values of frequencies.
	plLearnUnKHistogram (const plVariablesConjunction &vars, const map< plDataValues, plLearnFrequence > &initial)
	An other way to specifie that the map must be copied.
void	reset (void)
	Resets learning.
void	internal_addPoint (const plDataValues &point, double weight=1.0)
	Adds a point {point} with a given weight {weight} and updates statistics.
virtual void	get_boundaries (plValues &min_ref, plValues &max_ref) const
	Get the boundaries of each unknown definition set.
virtual void	get_frequencies (map< plDataValues, plLearnFrequence > &map_ref) const
	Add the learn frequencies to the given map.
virtual void	select_frequencies (vector< plLearnFrequence > &frequencies_ref, const vector< plValues > &selected_values) const
	The selected plValues are given in a vector, an empty vector is filled with the correspondant actual frequencies.
virtual const map< plDataValues, plLearnFrequence > &	get_map_histo (void) const
	Return a const reference on the map of learn frequencies.
virtual void	get_plProbValue (map< plDataValues, plProbValue > &prob_map_ref) const
	If the given map is empty : return a map of plProbValues with the actual learn probabilities.
virtual void	select_plProbValue (vector< plProbValue > &prob_ref, const vector< plValues > &selected_values) const
	The selected values are given in a vector of plVlaues and a vector of plProbValues of this range id filled with the actual learn probabilities.
virtual plProbValue *	select_plProbValue (const vector< plValues > &selected_values) const
	The selected values are given in a vector of plVlaues and an array of plProbValues of this range with the actual learn probabilities is returned.
void	get_frequencies (plLearnFrequence *frequencies) const
	It is supposed you know the length of the linear array of frequencies.
void	get_frequencies (vector< plLearnFrequence > &frequencies_ref) const
	It is not i.e.
void	get_frequencies (plLearnFrequence *frequencies, const plValues &min, const plValues &max) const
	The boundaries are given in min and max and a table for this range is filled with the actual frequencies.
virtual void	get_plProbValue (vector< plProbValue > &prob_ref) const
	Fill a vector of plProbValues with the actual learn probabilities.
virtual void	get_plProbValue (vector< plProbValue > &prob_ref, const plValues &min, const plValues &max) const
	The boundaries are given in min and max and a table for this range is filled with the actual probabilites.
virtual plProbValue *	get_plProbValue (const plValues &min, const plValues &max) const
	The boundaries are given in min and max and an array of plProbValues of this range with the actual learn probabilities is returned.
void	get_params (plValues &params) const
	Returns all learnt parameters of the object.
plKernel	get_distribution (const void *parameters=NULL) const
	Returns the probability table corresponding to the learnt distribution.
Protected Member Functions
void	get_boundaries (plDataValues &min_ref, plDataValues &max_ref) const
	Get the boundaries of each unknown definition set.

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Detailed Description

Class for learning histograms parametrical form for variables wich definition set is unknown.

Definition at line 122 of file plLearnUnKHistogram.h.

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Constructor & Destructor Documentation

plLearnUnKHistogram::plLearnUnKHistogram (  const plVariablesConjunction &  vars, map< plDataValues, plLearnFrequence > *const   initial, bool  copie = false )

init is a pointer on an map of initial values of frequencies. The map is copied when the flag is on true. If the flag copied is positionned on false the object access the map passed when the initialization. By default the map is not copied.

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Member Function Documentation

virtual void plLearnUnKHistogram::get_boundaries (  plValues &  min_ref, plValues &  max_ref )  const [virtual]

Get the boundaries of each unknown definition set. Useful for construct a C array of plLearnFrequence or plProbValue.

void plLearnUnKHistogram::get_boundaries (  plDataValues &  min_ref, plDataValues &  max_ref )  const [protected]

Get the boundaries of each unknown definition set. Useful for construct a C array of plLearnFrequence or plProbValue.

void plLearnUnKHistogram::get_frequencies (  vector< plLearnFrequence > &  frequencies_ref  )  const

It is not i.e. your vector of frequencies is always resized

void plLearnUnKHistogram::get_frequencies (  plLearnFrequence *  frequencies  )  const

It is supposed you know the length of the linear array of frequencies. You can get it with the get_boundaries function.

void plLearnUnKHistogram::get_params (  plValues &  params  )  const [virtual]

Returns all learnt parameters of the object. This virtual method has to be implemented in all derived classes. Implements plNonCndLearnObject.

virtual plProbValue* plLearnUnKHistogram::get_plProbValue (  const plValues &  min, const plValues &  max )  const [virtual]

The boundaries are given in min and max and an array of plProbValues of this range with the actual learn probabilities is returned. The result is normalized. Reimplemented in plLearnUnKLaplace.

virtual void plLearnUnKHistogram::get_plProbValue (  vector< plProbValue > &  prob_ref  )  const [virtual]

Fill a vector of plProbValues with the actual learn probabilities. The result is resized and normalized according to the known boundaries.

virtual void plLearnUnKHistogram::get_plProbValue (  map< plDataValues, plProbValue > &  prob_map_ref  )  const [virtual]

If the given map is empty : return a map of plProbValues with the actual learn probabilities. The sum of the inserted plProbValues is normalized. Reimplemented in plLearnUnKLaplace. Referenced by get_distribution().

void plLearnUnKHistogram::internal_addPoint (  const plDataValues &  point, double  weight = 1.0 )  [virtual]

Adds a point {point} with a given weight {weight} and updates statistics. This virtual method has to be implemented by all derived classes. This is an internal method, do not use it directly. Use addNewPoint instead. Implements plLearnObject.

void plLearnUnKHistogram::reset (  void   )  [virtual]

Resets learning. This virtual method has to be implemented by all derived classes. Implements plLearnObject.

virtual plProbValue* plLearnUnKHistogram::select_plProbValue (  const vector< plValues > &  selected_values  )  const [virtual]

The selected values are given in a vector of plVlaues and an array of plProbValues of this range with the actual learn probabilities is returned. The result is normalized. Reimplemented in plLearnUnKLaplace.

virtual void plLearnUnKHistogram::select_plProbValue (  vector< plProbValue > &  prob_ref, const vector< plValues > &  selected_values )  const [virtual]

The selected values are given in a vector of plVlaues and a vector of plProbValues of this range id filled with the actual learn probabilities. The result is normalized. Reimplemented in plLearnUnKLaplace. Referenced by get_distribution().

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

• plLearnUnKHistogram.h

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