Var_utils.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:
// 
//  1. Redistributions of source code must retain the above copyright
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/* *******************************************************      
   * $Id: Var_utils.cc,v 1.3 2004/02/20 21:11:54 chrish42 Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "AbsVariable.h"
#include "ColumnIndexVariable.h"
#include "ExpVariable.h"
#include "LogVariable.h"
#include "PLogPVariable.h"
#include "PowVariable.h"
#include "SumVariable.h"
#include "Var_utils.h"
//#include "Var_all.h"
#include "Var_operators.h"

namespace PLearn {
using namespace std;

Var mean(Var v)
{ return sum(v)/real(v->nelems()); }

Var neg_log_pi(Var p, Var index)
{
  if(index->isScalar())  return -log(p[index]);
  else return -log(matrixIndex(p,index));
}

Var softmax(Var input, int index)
{ 
  return 1.0/sum(exp(input-input[index])); 
}

Var pownorm(Var input, real n)
{
  if(n==2.0)
    return sum(square(input));
  else if(n==1.0)
    return sum(abs(input));
  else
    return sum(pow(abs(input),n));
}

Var norm(Var input, real n)
{
  if(n==2.0)
    return sqrt(sum(square(input)));
  else if(n==1.0)
    return sum(abs(input));
  else
    return pow(sum(pow(abs(input),n)),1.0/n);
}

Var entropy(Var v, bool normalize)
{
  if(normalize)
    {
      Var absx = abs(v);
      Var normalized = absx/sum(absx);
      return sum(plogp(normalized))*(-1.0/log(2.0));
    }
  else
    return sum(plogp(v))*(-1.0/log(2.0));
}

Var distance(Var input1, Var input2, real n)
{ return norm(input1-input2, n); }

Var powdistance(Var input1, Var input2, real n)
{ return pownorm(input1-input2, n); }



} // end of namespace PLearn

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