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110 lines
3.0 KiB
C++
110 lines
3.0 KiB
C++
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/**
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* Declaration of Algorithm class.
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*
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* @file
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* @author Mauro E S Mu<EFBFBD>oz (mauro@cria.org.br)
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* @date 2003-05-26
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* $Id$
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*
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* LICENSE INFORMATION
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*
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* Copyright(c) 2003 by CRIA -
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* Centro de Referencia em Informacao Ambiental
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*
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* http://www.cria.org.br
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*
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* This program is free software; you can redistribute it and/or
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* modify it under the terms of the GNU General Public License
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* as published by the Free Software Foundation; either version 2
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* of the License, or (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details:
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*
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* http://www.gnu.org/copyleft/gpl.html
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*/
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#ifndef _ALGORITHMHH_
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#define _ALGORITHMHH_
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#include <sampler.hh>
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/****************************************************************/
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/*************************** Algorithm **************************/
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/**
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* Base class for all distribution modeling algorithms. Provide
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* methods to all derived classes so that they can do data sampling
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* and access the environmental layers to build the model.
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*/
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class Algorithm
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{
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public:
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Algorithm( Sampler *samp );
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virtual ~Algorithm();
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virtual char *name() { return "Undefined"; }
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/** The algorithm should return != 0 if it needs normalization
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* of environmental variables (non categorical ones). */
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virtual int needNormalization( Scalar *min, Scalar *max )
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{ return 0; }
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/** Initiate a new training. If 'ncicle' != 0, then the
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* new training will have 'ncicle' cicles. */
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virtual int initialize( int ncicle ) { return 1; }
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/** One step further on the training. Return 0 if something
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* wrong happened. */
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virtual int iterate() = 0;
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/** Return != 0 if algorithm finished. */
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virtual int done() = 0;
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/** Read algorithm state. */
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virtual int load( int fd ) { return 0; }
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/** Store algorithm state. */
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virtual int save( int fd ) { return 0; }
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/** Return value of model on coordinates (x1,...,xN).
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* Note that N is known as 'Sampler'.
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* The value will be better visualized if within the range [-1,+1]. */
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virtual Scalar getValue( Scalar *x ) = 0;
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/** Return value of the convergence function. */
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virtual int getConvergence( Scalar *val ) { return 0; }
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protected:
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Sampler *getSampler() { return f_samp; }
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int *getCategories() { return f_categ; }
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/** Return != 0 if variable "i" in the domain is categorical.
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* Obs: i = 0 is the probability of occurrrence, therefore it is
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* never categorical. */
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int isCategorical( int i ) { return f_categ[i]; }
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/** Dimension of problem domain: number of independent
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* variables (climatic + soil) added to the number of
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* dependent variables (occurrence prediction). */
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int dimDomain() { return f_samp->dim(); }
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private:
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Sampler *f_samp;
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int *f_categ; ///< f_categ[i] != 0 if map "i" is categorical.
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};
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#endif
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