adtreelearner.h

来自「MultiBoost 是c++实现的多类adaboost酸法。与传统的adabo」· C头文件 代码 · 共 161 行

/*
* 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 WeakLearnerTemplate.h A brief description of the file
*/

#ifndef __ADTREE_LEARNER_H
#define __ADTREE_LEARNER_H

#include "WeakLearners/SingleStumpLearner.h"
#include "Utils/Args.h"
#include "IO/InputData.h"

using namespace std;

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

namespace MultiBoost {

/**
* Template weak learner class. Use it to write your own weak learner!
*/
class ADTreeLearner : public SingleStumpLearner
{

public:

   ADTreeLearner() : _parent(NULL) {}

   /**
   * 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 01/2/2006
   */
   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 01/2/2006
   */
   virtual void initOptions(nor_utils::Args& args);

   /**
   * 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 01/2/2006
   */
   virtual BaseLearner* create() { return new ADTreeLearner(); }

   /**
   * Creates an InputData object that it is good for the
   * weak learner. Overridden to return SortedData.
   * @see InputData
   * @see BaseLearner::createInputData()
   * @see SortedData
   * @warning The object \b must be destroyed by the caller.
   * @date 01/2/2006
   */
   //virtual InputData* createInputData() { return new YourInputData(); }

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

   /**
   * 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
   * @return +1 if the classifier thinks that \a val belongs to class 
   * \a classIdx, -1 if it does not and 0 if it abstain. If ABST_REAL is selected
   * the value returned is a range between -1 and +1 which holds the confidence
   * in the classification.
   * @date 11/11/2005
   */
   virtual double classify(InputData* pData, int idx, int classIdx);

   /**
   * Save the current object information needed for classification,
   * that is the single threshold.
   * @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 11/11/2005
   */
   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 11/11/2005
   */
   virtual void load(nor_utils::StreamTokenizer& st);

protected:

   //void  partitionData(); //!< Partition the data 

   ADTreeLearner*         _parent;
   vector<ADTreeLearner*> _children;

   // The base rule is composed by the decision over
   // _threshold
   // and the real values are stored in the vector
   // _v

   /**
   * A direct access array with the size of the whole dataset, and 
   * with 1 if it belongs to this partition, and 0 otherwise.
   */ 
   vector<char>    _partitionedData;

};

// ------------------------------------------------------------------------------

} // end of namespace MultiBoost


#endif // __ADTREE_LEARNER_H