STK_IMultiStatModel.h

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2016  Serge Iovleff, Université Lille 1, Inria
 
    This program 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 2 of the
    License, or (at your option) any later version.
 
    This program 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
    License along with this program; if not, write to the
    Free Software Foundation, Inc.,
    59 Temple Place,
    Suite 330,
    Boston, MA 02111-1307
    USA
 
    Contact : S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
*/
 
/*
 * Project:  stkpp::Model
 * created on: 22 juil. 2011
 * Purpose: define the class IMultiStatModel.
 * Author:   iovleff, S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 *
 **/
 
#ifndef STK_IMULTISTATMODEL_H
#define STK_IMULTISTATMODEL_H
 
#include <cmath>
#include <Sdk.h>
 
#include "STK_IStatModelBase.h"
 
#include <DManager/include/STK_DataBridge.h>
#include <STatistiK/include/STK_MultiLaw_IMultiLaw.h>
 
 
namespace STK
{
 
namespace hidden
{
template<class Derived> struct StatModelTraits;
 
} // namespace hidden
 
template <int Id> struct ModelParameters;
 
template <class Derived>
class IMultiStatModel: public IStatModelBase, public IRecursiveTemplate<Derived>
{
  public:
    typedef typename hidden::StatModelTraits<Derived>::Data Data;
    typedef typename hidden::StatModelTraits<Derived>::Type Type;
    typedef typename hidden::StatModelTraits<Derived>::WColVector WColVector;
    typedef typename hidden::StatModelTraits<Derived>::Parameters Parameters;
 
  protected:
    IMultiStatModel(): IStatModelBase(), p_data_(0), param_() {}
    IMultiStatModel( Data const& data): IStatModelBase(), p_data_(&data), param_(data.dataij().cols())
    { this->initialize(data.dataij().sizeRows(), data.dataij().sizeCols());}
    IMultiStatModel( Data const* p_data): IStatModelBase(), p_data_(p_data), param_()
    {
      if (p_data)
      {
        this->initialize(p_data->sizeRows(), p_data->sizeCols());
        param_.resize(p_data_->cols());
      }
    }
    IMultiStatModel( IMultiStatModel const& model)
                   : IStatModelBase(model)
                   , p_data_(model.p_data_)
                   , param_(param_)
    {}
    ~IMultiStatModel() {}
 
  public:
    inline Data const* const p_data() const { return (p_data_);}
    inline Parameters const& param() const { return (param_);}
    inline String const& error() const { return msg_error_;}
    inline void setData( Data const* p_data)
    {
      p_data_ = p_data;
      update();
    }
    bool run()
    {
#ifdef STK_DEBUG
      if (!p_data())
      { this->msg_error_ = STKERROR_NO_ARG(IMultiStatModel::run,data have not be set);
        return false;
      }
#endif
      try
      {
        // compute parameters
        this->asDerived().computeParameters();
        // compute log-likelihood
        this->setLnLikelihood(computeLnLikelihood());
        // set the number of free parameters
        this->setNbFreeParameter(this->asDerived().computeNbFreeParameters());
      }
      catch (Exception const& e)
      { this->msg_error_ = e.error(); return false;}
      return true;
    }
    bool run(WColVector const& weights)
    {
#ifdef STK_DEBUG
      if (!p_data())
      { this->msg_error_ = STKERROR_NO_ARG(IMultiStatModel::run(weights),data have not be set);
        return false;
      }
#endif
      try
      {
        // compute weighted parameters
        this->asDerived().computeParameters(weights);
        // compute log-likelihood
        this->setLnLikelihood(computeLnLikelihood());
        // set the number of free parameters
        this->setNbFreeParameter(this->asDerived().computeNbFreeParameters());
      }
      catch (Exception const& e)
      { this->msg_error_ = e.error(); return false;}
      return true;
    }
    void writeParameters(ostream &os) { this->asDerived().writeParametersImpl(os);}
    void writeParametersImpl(ostream &os) // default implementation
    {
#ifdef STK_DEBUG
        stk_cout << _T("You should implement this method in your derived class\n");
#endif
    }
 
  protected:
    Data const* p_data_;
    Parameters param_;
    String msg_error_;
    inline Parameters& param() { return (param_);}
    Real computeLnLikelihood() const
    {
      Real sum = 0.0;
      for (int i= p_data()->dataij().beginRows(); i< p_data()->dataij().endRows(); i++)
      { sum += this->asDerived().computeLnLikelihood(p_data()->dataij().row(i));}
      return(sum);
    }
    void update()
    {
      if (p_data_)
      {
        this->initialize(p_data()->dataij().sizeRows(), p_data()->dataij().sizeCols());
        param_.resize(p_data()->dataij().cols());
      }
      else
      { this->initialize(0,0); }
    }
};
 
} // namespace STK
 
#endif /* STK_IMULTISTATMODEL_H */