SpiralDistribution.cc

Go to the documentation of this file.

// -*- C++ -*-

// SpiralDistribution.cc
//
// 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

/* *******************************************************      
   * $Id: SpiralDistribution.cc,v 1.10 2004/07/21 16:30:56 chrish42 Exp $ 
   ******************************************************* */

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

namespace PLearn {
using namespace std;

SpiralDistribution::SpiralDistribution() 
  : lambda(0.04),
    alpha(1),
    tmin(3),
    tmax(15),
    sigma(0.01),
    uniformity(1),
    include_t(false)
  {
    // build_();
  }

  PLEARN_IMPLEMENT_OBJECT(SpiralDistribution, "Generates samples drawn from a 2D spiral", 
      "SpiralDistribution is a generative model that generates 2D (x,y) samples in the following manner:\n"
      " t ~ uniform([tmin, tmax])^uniformity \n"
      " x = lambda*t*sin(alpha*t) + N(0,sigma) \n"
      " y = lambda*t*cos(alpha*t) + N(0,sigma) \n");

  void SpiralDistribution::declareOptions(OptionList& ol)
  {
  declareOption(ol, "lambda", &SpiralDistribution::lambda, OptionBase::buildoption,"");
  declareOption(ol, "alpha", &SpiralDistribution::alpha, OptionBase::buildoption,"");
  declareOption(ol, "tmin", &SpiralDistribution::tmin, OptionBase::buildoption,"");
  declareOption(ol, "tmax", &SpiralDistribution::tmax, OptionBase::buildoption,"");
  declareOption(ol, "sigma", &SpiralDistribution::sigma, OptionBase::buildoption,"");
  declareOption(ol, "uniformity", &SpiralDistribution::uniformity, OptionBase::buildoption,"");
  declareOption(ol, "include_t", &SpiralDistribution::include_t, OptionBase::buildoption,
                "If true, then t will be appended to the generated sample, along with x and y.");
  
  inherited::declareOptions(ol);
  }

  void SpiralDistribution::build_()
  {
    // ### This method should do the real building of the object,
    // ### according to set 'options', in *any* situation. 
    // ### Typical situations include:
    // ###  - Initial building of an object from a few user-specified options
    // ###  - Building of a "reloaded" object: i.e. from the complete set of all serialised options.
    // ###  - Updating or "re-building" of an object after a few "tuning" options have been modified.
    // ### You should assume that the parent class' build_() has already been called.
  }

  // ### Nothing to add here, simply calls build_
  void SpiralDistribution::build()
  {
    inherited::build();
    build_();
  }

  void SpiralDistribution::makeDeepCopyFromShallowCopy(map<const void*, void*>& copies)
  {
    inherited::makeDeepCopyFromShallowCopy(copies);

    // ### Call deepCopyField on all "pointer-like" fields 
    // ### that you wish to be deepCopied rather than 
    // ### shallow-copied.
    // ### ex:
    // deepCopyField(trainvec, copies);

    // ### Remove this line when you have fully implemented this method.
    PLERROR("SpiralDistribution::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!");
  }

real SpiralDistribution::log_density(const Vec& x) const
{ PLERROR("density not implemented for SpiralDistribution"); return 0; }

real SpiralDistribution::survival_fn(const Vec& x) const
{ PLERROR("survival_fn not implemented for SpiralDistribution"); return 0; }

real SpiralDistribution::cdf(const Vec& x) const
{ PLERROR("cdf not implemented for SpiralDistribution"); return 0; }

void SpiralDistribution::expectation(Vec& mu) const
{ PLERROR("expectation not implemented for SpiralDistribution"); }

void SpiralDistribution::variance(Mat& covar) const
{ PLERROR("variance not implemented for SpiralDistribution"); }

void SpiralDistribution::curve(real t, real& x, real& y) const
{
  x = lambda*t*sin(alpha*t);
  y = lambda*t*cos(alpha*t);
}

void SpiralDistribution::generate(Vec& v) const
{ 
  v.resize(inputsize());
  
  real x, y;
  real u =  bounded_uniform(0,1);
  real t = (uniformity==1)?u:pow(u,uniformity);
  t = tmin+(tmax-tmin)*t;
  curve(t,x,y);
  x += gaussian_mu_sigma(0, sigma);
  y += gaussian_mu_sigma(0, sigma);

  v[0] = x;
  v[1] = y;
  if(inputsize()==3)
    v[2] = t;
}


// Default version of inputsize returns learner->inputsize()
// If this is not appropriate, you should uncomment this and define
// it properly in the .cc
int SpiralDistribution::inputsize() const 
{ return include_t ?3 :2; }

void SpiralDistribution::resetGenerator(long g_seed) const
{
  manual_seed(g_seed);  
}


} // end of namespace PLearn

---