UniformVMatrix.cc

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// -*- C++ -*-

// PLearn (A C++ Machine Learning Library)
// Copyright (C) 1998 Pascal Vincent
// Copyright (C) 1999-2001 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
// Copyright (C) 2002 Pascal Vincent, Julien Keable, Xavier Saint-Mleux
//
// 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.
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/* *******************************************************      
   * $Id: UniformVMatrix.cc,v 1.5 2004/07/21 16:30:55 chrish42 Exp $
   ******************************************************* */

#include "UniformVMatrix.h"
#include <plearn/math/random.h>

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(UniformVMatrix, "ONE LINE DESC", "NO HELP");

UniformVMatrix::UniformVMatrix()
  : minval(0), maxval(1)
{
}

UniformVMatrix::UniformVMatrix(real the_minval, real the_maxval, int the_width)
  : inherited(-1,the_width),minval(the_minval), maxval(the_maxval)
{}

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

void
UniformVMatrix::build_()
{
}

void
UniformVMatrix::declareOptions(OptionList &ol)
{
    declareOption(ol, "minval", &UniformVMatrix::minval, OptionBase::buildoption, "");
    declareOption(ol, "maxval", &UniformVMatrix::maxval, OptionBase::buildoption, "");
    inherited::declareOptions(ol);
}

real UniformVMatrix::get(int i, int j) const
{
  double scale = maxval-minval;
  return uniform_sample()*scale+minval;
}

void UniformVMatrix::getSubRow(int i, int j, Vec v) const
{
  double scale = maxval-minval;
  for(int k=0; k<v.length(); k++)
    v[k] = uniform_sample()*scale+minval;
}


} // end of namespcae PLearn

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