openModeller  Version 1.4.0
Public Member Functions | Protected Member Functions | Private Member Functions | Private Attributes
MinimumDistance Class Reference

#include <minimum_distance.hh>

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List of all members.

Public Member Functions

 MinimumDistance ()
virtual ~MinimumDistance ()
int initialize ()
int iterate ()
int done () const
Scalar getValue (const Sample &x) const
int getConvergence (Scalar *val)

Protected Member Functions

virtual void _getConfiguration (ConfigurationPtr &) const
virtual void _setConfiguration (const ConstConfigurationPtr &)

Private Member Functions

Scalar findDist (const Sample &x, const Sample &pnt) const

Private Attributes

bool _done
Scalar _dist
 Parameter for MaxDistance.
bool _hasCategorical
int _numLayers
Sample _isCategorical
std::vector< Sample > _envPoints

Detailed Description

Declaration of Minimum Distance algorithm.

Author:
Mauro E S Muņoz (mauro@cria.org.br)
Date:
2003-09-20
Id:
minimum_distance.hh 3382 2007-07-19 19:09:18Z rdg

LICENSE INFORMATION

Copyright(c) 2003 by CRIA - Centro de Referencia em Informacao Ambiental

http://www.cria.org.br

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details:

http://www.gnu.org/copyleft/gpl.html Algorithm to generate models based on the cartesian distance from the closest occurrence point.

Definition at line 40 of file minimum_distance.hh.

Constructor & Destructor Documentation

MinimumDistance::MinimumDistance ( )

Occurrences within this distance will be considered the same one.

Definition at line 131 of file minimum_distance.cpp.

References AlgorithmImpl::_normalizerPtr.

MinimumDistance::~MinimumDistance ( ) [virtual]

Definition at line 145 of file minimum_distance.cpp.

Member Function Documentation

void MinimumDistance::_getConfiguration ( ConfigurationPtr &  config) const [protected, virtual]

Reimplemented from AlgorithmImpl.

Definition at line 288 of file minimum_distance.cpp.

References _dist, _done, _envPoints, and _isCategorical.

void MinimumDistance::_setConfiguration ( const ConstConfigurationPtr &  config) [protected, virtual]

Reimplemented from AlgorithmImpl.

Definition at line 312 of file minimum_distance.cpp.

References _dist, _done, _envPoints, _hasCategorical, _isCategorical, _numLayers, and Sample::size().

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int MinimumDistance::done ( ) const [virtual]

Return != 0 if algorithm finished.

Reimplemented from AlgorithmImpl.

Definition at line 219 of file minimum_distance.cpp.

References _done.

Scalar MinimumDistance::findDist ( const Sample &  x,
const Sample &  pnt 
) const [private]

Calculate cartesian distance between 'x' and 'pnt', with dimensions equal to dim.

Definition at line 264 of file minimum_distance.cpp.

References _hasCategorical, _isCategorical, _numLayers, and Sample::norm().

Referenced by getValue().

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int MinimumDistance::getConvergence ( Scalar *  val)

Definition at line 254 of file minimum_distance.cpp.

Scalar MinimumDistance::getValue ( const Sample &  x) const [virtual]

The algorithm must return the occurrence probability at the given environment conditions.

Parameters:
xEnvironmental conditions.
Returns:
The occurrence probability in the range [0,1].

Implements AlgorithmImpl.

Definition at line 228 of file minimum_distance.cpp.

References _dist, _envPoints, findDist(), and min().

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int MinimumDistance::initialize ( ) [virtual]

Initiate a new training.

Implements AlgorithmImpl.

Definition at line 153 of file minimum_distance.cpp.

References _dist, _done, _envPoints, _hasCategorical, _isCategorical, _numLayers, AlgorithmImpl::_samp, Log::error(), AlgorithmImpl::getParameter(), Log::instance(), MAXDIST_ID, Sample::resize(), and Log::warn().

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int MinimumDistance::iterate ( ) [virtual]

One step further on the training. Return 0 if something wrong happened.

Reimplemented from AlgorithmImpl.

Definition at line 210 of file minimum_distance.cpp.

Member Data Documentation

Scalar MinimumDistance::_dist [private]

Parameter for MaxDistance.

Definition at line 68 of file minimum_distance.hh.

Referenced by _getConfiguration(), _setConfiguration(), getValue(), and initialize().

bool MinimumDistance::_done [private]

Definition at line 62 of file minimum_distance.hh.

Referenced by _getConfiguration(), _setConfiguration(), done(), and initialize().

std::vector<Sample> MinimumDistance::_envPoints [private]

Definition at line 74 of file minimum_distance.hh.

Referenced by _getConfiguration(), _setConfiguration(), getValue(), and initialize().

bool MinimumDistance::_hasCategorical [private]

Definition at line 70 of file minimum_distance.hh.

Referenced by _setConfiguration(), findDist(), and initialize().

Sample MinimumDistance::_isCategorical [private]

Definition at line 72 of file minimum_distance.hh.

Referenced by _getConfiguration(), _setConfiguration(), findDist(), and initialize().

int MinimumDistance::_numLayers [private]

Definition at line 71 of file minimum_distance.hh.

Referenced by _setConfiguration(), findDist(), and initialize().

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