haarmultistumplearner.h

MultiBoost 是c++实现的多类adaboost酸法。与传统的adaboost算法主要解决二类分类问题不同

/*
* This file is part of MultiBoost, a multi-class 
* AdaBoost learner/classifier
*
* Copyright (C) 2005-2006 Norman Casagrande
* For informations write to [e-mail]
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library 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
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
*
*/

/**
* \file HaarMultiStumpLearner.h A learner that uses the Haar-like features for
* input and the MultiStumpLearner for training.
* @date 17/12/2005
*/

#ifndef __HAAR_MULTI_STUMP_LEARNER_H
#define __HAAR_MULTI_STUMP_LEARNER_H

#include "WeakLearners/HaarLearner.h"
#include "WeakLearners/MultiStumpLearner.h"

#include "Utils/Utils.h"

using namespace std;

//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////

namespace MultiBoost {

class HaarData;

/**
* A learner that uses the Haar-like features for input and the MultiStumpLearner 
* for training.
* @see HaarLearner
* @see MultiStumpLearner
* @date 17/12/2005
*/
class HaarMultiStumpLearner : public HaarLearner, public MultiStumpLearner
{
public:

   /**
   * The destructor. Must be declared (virtual) for the proper destruction of 
   * the object.
   */
   virtual ~HaarMultiStumpLearner() {}

   /**
   * Declare weak-learner-specific arguments.
   * These arguments will be added to the list of arguments under 
   * the group specific of the weak learner. It is called
   * automatically in main, when the list of arguments is built up.
   * Use this method to declare the arguments that belongs to
   * the weak learner only.
   * @param args The Args class reference which can be used to declare
   * additional arguments.
   * @date 28/11/2005
   */
   virtual void declareArguments(nor_utils::Args& args);

   /**
   * Set the arguments of the algorithm using the standard interface
   * of the arguments. Call this to set the arguments asked by the user.
   * @param args The arguments defined by the user in the command line.
   * @date 14/11/2005
   */
   virtual void initOptions(nor_utils::Args& args);

   /**
   * Return {+1, -1} for the given class and value using the learned classifier.
   * @param pData The pointer to the data
   * @param idx The index of the example to classify
   * @param classIdx The index of the class
   * @remark This override classify of StumpLearner, as the data must be
   * transformed to haar-like feature space.
   * @remark Passing the data and the index to the example is not nice at all.
   * This will soon be replace with the passing of the example itself in some
   * form (probably a structure to the example).
   * @return +1 if the classifier thinks that \a val belongs to class 
   * \a classIdx, -1 if it does not and 0 if it abstain
   * @date 13/11/2005
   */
   virtual double classify(InputData* pData, int idx, int classIdx);

   /**
   * Returns itself as object.
   * @remark It uses the trick described in 
   * http://www.parashift.com/c++-faq-lite/serialization.html#faq-36.8
   * for the auto-registering classes.
   * @date 14/11/2005
   */
   virtual BaseLearner* create() { return new HaarMultiStumpLearner(); }

   /**
   * Creates an InputData object that it is good for the
   * weak learner. Overridden to return HaarData.
   * @see InputData
   * @see BaseLearner::createInputData()
   * @see SortedData
   * @warning The object \b must be destroyed by the caller.
   * @date 21/11/2005
   */
   virtual InputData* createInputData();

   /**
   * Run the learner to build the classifier on the given data.
   * @param pData The pointer to the data
   * @see SingleStumpLearner::run
   * @date 11/11/2005
   */
   virtual void run(InputData* pData);

   /**
   * Save the current object information needed for classification.
   * @param outputStream The stream where the data will be saved
   * @param numTabs The number of tabs before the tag. Useful for indentation
   * @remark To fully save the object it is \b very \b important to call
   * also the super-class method.
   * @see StumpLearner::save()
   * @date 13/11/2005
   */
   virtual void save(ofstream& outputStream, int numTabs = 0);

   /**
   * Load the xml file that contains the serialized information
   * needed for the classification and that belongs to this class.
   * @param st The stream tokenizer that returns tags and values as tokens
   * @see save()
   * @date 13/11/2005
   */
   virtual void load(nor_utils::StreamTokenizer& st);

};

} // end of namespace MultiBoost

#endif // __HAAR_MULTI_STUMP_LEARNER_H