SemiSupervisedProbClassCostVariable.cc

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// -*- C++ -*-

// PLearn (A C++ Machine Learning Library)
// Copyright (C) 1998 Pascal Vincent
// Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
// Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
// Copyright (C) 2002 Xiangdong Wang, Christian Dorion

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// modification, are permitted provided that the following conditions are met:
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//  1. Redistributions of source code must retain the above copyright
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/* *******************************************************      
   * $Id: SemiSupervisedProbClassCostVariable.cc,v 1.4 2004/04/27 16:03:35 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "SemiSupervisedProbClassCostVariable.h"
#include "Var_utils.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(SemiSupervisedProbClassCostVariable,
                        "ONE LINE DESCR",
                        "NO HELP");

SemiSupervisedProbClassCostVariable::SemiSupervisedProbClassCostVariable(Var prob_, Var target_, Var prior_, real ff)
  : inherited(prob_ & target_ & (VarArray)prior_,1,1), flatten_factor(ff)
{
    build_();
}

void
SemiSupervisedProbClassCostVariable::build()
{
    inherited::build();
    build_();
}

void
SemiSupervisedProbClassCostVariable::build_()
{
    if (varray.size() >= 3 && varray[0] && varray[1] && varray[2]) {
        // varray[0], varray[1] and varray[2] are (respectively) prob_, target_ and prior_ from constructor
        if (varray[2]->length()>0 && varray[0]->length() != varray[2]->length())
            PLERROR("In SemiSupervisedProbClassCostVariable: If prior.length()>0 then prior and prob must have the same size");
        if (!varray[1]->isScalar())
            PLERROR("In SemiSupervisedProbClassCostVariable: target must be a scalar");
        raised_prob.resize(varray[0]->length());
    }
    if (flatten_factor <= 0)
        PLERROR("In SemiSupervisedProbClassCostVariable: flatten_factor must be positive, and even > 1 for normal use.");
}

void
SemiSupervisedProbClassCostVariable::declareOptions(OptionList &ol)
{
    declareOption(ol, "flatten_factor", &SemiSupervisedProbClassCostVariable::flatten_factor, OptionBase::buildoption, "");
    inherited::declareOptions(ol);
}

void SemiSupervisedProbClassCostVariable::recomputeSize(int& l, int& w) const
{ l=1; w=1; }


void SemiSupervisedProbClassCostVariable::fprop()
{
  real target_value = target()->valuedata[0];
  int n=prob()->size();
  real* p=prob()->valuedata;
  if (finite(target_value)) // supervised case
    {
      int t = int(target_value);
      if (t<0 || t>=n)
        PLERROR("In SemiSupervisedProbClassCostVariable: target must be either missing or between 0 and %d incl.\n",prob()->size()-1);
      valuedata[0] = -safeflog(p[t]);
    }
  else // unsupervised case
    {
      sum_raised_prob=0;
      real* priorv = prior()->valuedata;
      for (int i=0;i<n;i++)
      {
        raised_prob[i] = pow(priorv[i]*p[i],flatten_factor);
        sum_raised_prob += raised_prob[i];
      }
      valuedata[0] = - safeflog(sum_raised_prob)/flatten_factor;
    }
}

void SemiSupervisedProbClassCostVariable::bprop()
{
  real target_value = target()->valuedata[0];
  int n=prob()->size();
  real* dprob=prob()->gradientdata;
  real* p=prob()->valuedata;
  if (finite(target_value)) // supervised case
    {
      int t = int(target_value);
      for (int i=0;i<n;i++)
        if (i==t && p[t]>0)
          dprob[i] += -gradientdata[0]/p[t];
    }
  else // unsupervised case
    {
      for (int i=0;i<n;i++)
        if (p[i]>0)
          {
            real grad = - gradientdata[0]*raised_prob[i]/(p[i]*sum_raised_prob);
            if (finite(grad))
              dprob[i] += grad;
          }
    }
}


void SemiSupervisedProbClassCostVariable::symbolicBprop()
{
  PLERROR("SemiSupervisedProbClassCostVariable::symbolicBprop() not implemented");
}


void SemiSupervisedProbClassCostVariable::rfprop()
{
  PLERROR("SemiSupervisedProbClassCostVariable::rfprop() not implemented");
}



} // end of namespace PLearn

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