SequentialLearner.h

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// -*- C++ -*-

// SequentialLearner.h
//
// Copyright (C) 2003 Rejean Ducharme, Yoshua Bengio
// Copyright (C) 2003 Pascal Vincent
//
// 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



#ifndef SEQUENTIAL_LEARNER
#define SEQUENTIAL_LEARNER

#include <plearn_learners/generic/PLearner.h>

namespace PLearn {
using namespace std;

class SequentialLearner: public PLearner
{
public:
  typedef PLearner inherited;

protected:  
  int last_train_t; 
  int last_call_train_t; 
  mutable int last_test_t; 

public:
  int get_last_train_t(){ return last_train_t; }
  int get_last_call_train_t(){ return last_call_train_t; }
  int get_last_test_t(){ return last_test_t; }
  
public:
  
  int init_train_size;
  int max_seq_len;    
  int max_train_len;  
  int train_step;     
  int horizon;        
  int outputsize_;

  Mat predictions; 

  Mat errors; 

private:
  void build_();
  
protected:
  static void declareOptions(OptionList& ol);
  
  public:
  
  SequentialLearner();
  
  virtual void build();
  
  virtual int outputsize() const;

  virtual int nTestCosts() const;

  virtual void setTrainingSet(VMat training_set, bool call_forget=true);
  
    virtual void train() =0;
 
    virtual void test(VMat testset, PP<VecStatsCollector> test_stats,
        VMat testoutputs=0, VMat testcosts=0) 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 computeOutput(const Vec& input, Vec& output) const;

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

  int sequenceStart() const { return init_train_size; }
  int sequenceLength() const { return last_test_t - init_train_size + 1; }
  Vec getCostSequence(int cost_index) const
    { return errors.subMat(sequenceStart(), cost_index, sequenceLength(), 1).toVecCopy(); }
  Vec getCostSequence(string cname) const 
    { return getCostSequence(getTestCostIndex(cname)); }

  virtual void matlabSave(const string& matlab_subdir);
  
  virtual void forget();

  PLEARN_DECLARE_ABSTRACT_OBJECT(SequentialLearner);
  virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
};

DECLARE_OBJECT_PTR(SequentialLearner);

} // end of namespace PLearn

#endif

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