PLearner.h

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// -*- C++ -*-4 1999/10/29 20:41:34 dugas

// PLearner.h
//
// Copyright (C) 1998-2002 Pascal Vincent
// Copyright (C) 1999-2002 Yoshua Bengio, Nicolas Chapados, Charles Dugas, Rejean Ducharme, Universite de Montreal
// Copyright (C) 2001,2002 Francis Pieraut, Jean-Sebastien Senecal
// Copyright (C) 2002 Frederic Morin, Xavier Saint-Mleux, Julien Keable
// 
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 
//  1. Redistributions of source code must retain the above copyright
//     notice, this list of conditions and the following disclaimer.
// 
//  2. Redistributions in binary form must reproduce the above copyright
//     notice, this list of conditions and the following disclaimer in the
//     documentation and/or other materials provided with the distribution.
// 
//  3. The name of the authors may not be used to endorse or promote
//     products derived from this software without specific prior written
//     permission.
// 
// THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
// NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
// 
// This file is part of the PLearn library. For more information on the PLearn
// library, go to the PLearn Web site at www.plearn.org


 

/* *******************************************************      
   * $Id: PLearner.h,v 1.27 2004/08/16 15:45:44 dorionc Exp $
   ******************************************************* */


#ifndef PLearner_INC
#define PLearner_INC

#include <plearn/base/Object.h>
#include <plearn/vmat/VMat.h>
#include <plearn/math/VecStatsCollector.h>

namespace PLearn {
using namespace std;

class PLearner: public Object
{

private:

  typedef Object inherited;

  mutable int n_train_costs_;
  mutable int n_test_costs_;

  mutable Vec tmp_output;

public:
    
  // Build options

  string expdir; 

  long seed_; 
  int stage; 





  int nstages;






  bool report_progress; 
  int verbosity; 

protected:



  VMat train_set;  

  // learnt options all obtained from train_set when doing setTrainingSet
  int inputsize_, targetsize_, weightsize_, n_examples;

  VMat validation_set;

  PP<VecStatsCollector> train_stats;

public:

  PLearner();
  virtual ~PLearner();

  virtual void setTrainingSet(VMat training_set, bool call_forget=true);

  inline VMat getTrainingSet() const { return train_set; }

  virtual void setValidationSet(VMat validset);

  VMat getValidationSet() const { return validation_set; }

  virtual void setTrainStatsCollector(PP<VecStatsCollector> statscol);

  inline PP<VecStatsCollector> getTrainStatsCollector()
  { return train_stats; }

  virtual void setExperimentDirectory(const string& the_expdir);

  string getExperimentDirectory() const { return expdir; }

  virtual int inputsize() const;

  virtual int targetsize() const; 

  virtual int outputsize() const =0;

public:

  virtual void build();

protected:
  virtual void build_from_train_set() {}

private:
  void build_();

public:

  virtual void forget() =0;



  virtual void train() =0;


  virtual void computeOutput(const Vec& input, Vec& output) const =0;

  virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 
                                       const Vec& target, Vec& costs) const =0;
                                
  virtual void computeOutputAndCosts(const Vec& input, const Vec& target,
                                     Vec& output, Vec& costs) const;

  virtual void computeCostsOnly(const Vec& input, const Vec& target, Vec& costs) const;


  virtual void use(VMat testset, VMat outputs) const;

  virtual void useOnTrain(Mat& outputs) const;

  virtual void test(VMat testset, PP<VecStatsCollector> test_stats, 
                    VMat testoutputs=0, VMat testcosts=0) const;

  virtual TVec<string> getTestCostNames() const =0;

  virtual TVec<string> getTrainCostNames() const =0;

  virtual int nTestCosts() const;

  virtual int nTrainCosts() const;

  int getTestCostIndex(const string& costname) const;

  int getTrainCostIndex(const string& costname) const;

  virtual void resetInternalState();

  virtual bool isStatefulLearner() const;

protected:

  static void declareOptions(OptionList& ol);
  virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);

public:

  PLEARN_DECLARE_ABSTRACT_OBJECT(PLearner);

  virtual void matlabSave(const string& matlab_subdir){}
};

DECLARE_OBJECT_PTR(PLearner);

// void parse_statname(const string& statname, string& extstat, string& intstat, int& setnum, string& errorname);

// Vec trainTestLearner(PP<PLearner> learner, const VMat &dataset, PP<Splitter> splitter, TVec<string> statnames);


} // end of namespace PLearn

#endif

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