//                                               -*- C++ -*-
/**
 *  @file  KernelSmoothing.hxx
 *  @brief This class acts like a (possibly truncated) KernelMixture factory, implementing density estimation
 *         using the kernel smoothing method. It uses Silverman's rule for product kernel.
 *
 *  Copyright (C) 2005-2014 Airbus-EDF-Phimeca
 *
 *  This library is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Lesser General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This library is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public
 *  along with this library.  If not, see <http://www.gnu.org/licenses/>.
 *
 *  @author schueller
 *  @date   2012-04-18 17:56:46 +0200 (Wed, 18 Apr 2012)
 */
#ifndef OPENTURNS_KERNELSMOOTHING_HXX
#define OPENTURNS_KERNELSMOOTHING_HXX

#include "PersistentObject.hxx"
#include "Distribution.hxx"
#include "NumericalSample.hxx"

BEGIN_NAMESPACE_OPENTURNS

/**
 * @class KernelSmoothing
 *
 * The class describes the probabilistic concept of KernelSmoothing.
 */
class KernelSmoothing : public PersistentObject
{
  CLASSNAME;
public:

  /** Default constructor */
  explicit KernelSmoothing(const String & name = "KernelSmoothing");

  /** Default constructor */
  explicit KernelSmoothing(const Distribution & kernel,
                           const Bool & bined = true,
                           const UnsignedLong binNumber = ResourceMap::GetAsUnsignedLong( "KernelSmoothing-BinNumber" ),
                           const String & name = "KernelSmoothing");

  /** Virtual constructor */
  virtual KernelSmoothing * clone() const;

  /** Build a Normal kernel mixture based on the given sample. If no bandwith has already been set, Silverman's rule is used */
  virtual Distribution build(const NumericalSample & sample,
                             const Bool boundaryCorrection = false);

  /** Build a (possibly truncated) kernel mixture based on the given sample and bandwidth */
  virtual Distribution build(const NumericalSample & sample,
                             const NumericalPoint & bandwidth,
                             const Bool boundaryCorrection = false);

  /** Bandwidth accessor */
  NumericalPoint getBandwidth() const;

  /** Kernel accessor */
  Distribution getKernel() const;

  /** Compute the bandwidth according to Silverman's rule */
  NumericalPoint computeSilvermanBandwidth(const NumericalSample & sample);

  /** Compute the bandwidth according to the plugin rule. Warning!
   * it can take a lot of time for large samples, as the cost is
   * quadratic with the sample size
   */
  NumericalPoint computePluginBandwidth(const NumericalSample & sample);

  /** Compute the bandwidth according to a mixed rule:
   * simply use the plugin rule for small sample, and
   * estimate the ratio between the plugin rule and
   * the Silverman rule on a small sample, then
   * scale the Silverman bandwidth computed on the full
   * sample with this ratio
   */
  NumericalPoint computeMixedBandwidth(const NumericalSample & sample);

  /** Method save() stores the object through the StorageManager */
  virtual void save(Advocate & adv) const;

  /** Method load() reloads the object from the StorageManager */
  virtual void load(Advocate & adv);

private:

  void setBandwidth(const NumericalPoint & bandwidth);

  // Bandwith of the smoothing
  NumericalPoint bandwidth_;

  // 1D kernel for kernel product
  Distribution kernel_;

  // Flag to tell if we compute a bined version of the estimator
  Bool bined_;

  // Number of bins
  UnsignedLong binNumber_;

}; /* class KernelSmoothing */


END_NAMESPACE_OPENTURNS

#endif /* OPENTURNS_KERNELSMOOTHING_HXX */
