DistanceEstimation.h

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//
// File: DistanceEstimation.h
// Created by: Julien Dutheil
//             Vincent Ranwez
// Created on: Wed jun 08 10:39 2005
//

/*
Copyright or © or Copr. Bio++ Development Team, (November 16, 2004)

This software is a computer program whose purpose is to provide classes
for phylogenetic data analysis.

This software is governed by the CeCILL  license under French law and
abiding by the rules of distribution of free software.  You can  use, 
modify and/ or redistribute the software under the terms of the CeCILL
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info". 

As a counterpart to the access to the source code and  rights to copy,
modify and redistribute granted by the license, users are provided only
with a limited warranty  and the software's author,  the holder of the
economic rights,  and the successive licensors  have only  limited
liability. 

In this respect, the user's attention is drawn to the risks associated
with loading,  using,  modifying and/or developing or reproducing the
software by the user in light of its specific status of free software,
that may mean  that it is complicated to manipulate,  and  that  also
therefore means  that it is reserved for developers  and  experienced
professionals having in-depth computer knowledge. Users are therefore
encouraged to load and test the software's suitability as regards their
requirements in conditions enabling the security of their systems and/or 
data to be ensured and,  more generally, to use and operate it in the 
same conditions as regards security. 

The fact that you are presently reading this means that you have had
knowledge of the CeCILL license and that you accept its terms.
*/

#ifndef _DISTANCEESTIMATION_H_
#define _DISTANCEESTIMATION_H_

#include "../Model/SubstitutionModel.h"
#include "../Likelihood/AbstractTreeLikelihood.h"
#include "../Likelihood/DRHomogeneousTreeLikelihood.h"
#include "../Likelihood/PseudoNewtonOptimizer.h"

#include <Bpp/Clonable.h>
#include <Bpp/Numeric/ParameterList.h>
#include <Bpp/Numeric/Prob/DiscreteDistribution.h>
#include <Bpp/Numeric/Function/Optimizer.h>
#include <Bpp/Numeric/Function/SimpleMultiDimensions.h>
#include <Bpp/Numeric/Function/MetaOptimizer.h>

// From SeqLib:
#include <Bpp/Seq/Container/SiteContainer.h>

namespace bpp
{

class TwoTreeLikelihood:
  public AbstractDiscreteRatesAcrossSitesTreeLikelihood  
{
  private:
    SiteContainer* shrunkData_;
    std::vector<std::string> seqnames_;
    SubstitutionModel* model_;
    ParameterList brLenParameters_;
    
    mutable VVVdouble pxy_;

    mutable VVVdouble dpxy_;

    mutable VVVdouble d2pxy_;

    std::vector<size_t> rootPatternLinks_;

    std::vector<unsigned int> rootWeights_;

    //some values we'll need:
    size_t nbSites_,         //the number of sites in the container
           nbClasses_,       //the number of rate classes
           nbStates_,        //the number of states in the alphabet
           nbDistinctSites_; //the number of distinct sites in the container

    mutable VVVdouble rootLikelihoods_;
    mutable VVdouble rootLikelihoodsS_;
    mutable Vdouble rootLikelihoodsSR_;
    mutable Vdouble dLikelihoods_;
    mutable Vdouble d2Likelihoods_;
    mutable VVdouble leafLikelihoods1_, leafLikelihoods2_;
  
    double minimumBrLen_;
    Constraint* brLenConstraint_;
    double brLen_;

  public:
    TwoTreeLikelihood(
      const std::string& seq1, const std::string& seq2,  
      const SiteContainer& data,
      SubstitutionModel* model,
      DiscreteDistribution* rDist,
      bool verbose)  throw (Exception);

    TwoTreeLikelihood(const TwoTreeLikelihood& lik);
    
    TwoTreeLikelihood& operator=(const TwoTreeLikelihood& lik);

    TwoTreeLikelihood* clone() const { return new TwoTreeLikelihood(*this); } 

    virtual ~TwoTreeLikelihood();

  public:

    size_t getNumberOfStates() const { return model_->getNumberOfStates(); } 
    
    const std::vector<int>& getAlphabetStates() const { return model_->getAlphabetStates(); } 
    
    int getAlphabetStateAsInt(size_t i) const { return model_->getAlphabetStateAsInt(i); }
  
    std::string getAlphabetStateAsChar(size_t i) const { return model_->getAlphabetStateAsChar(i); }
    
    TreeLikelihoodData* getLikelihoodData() throw (NotImplementedException)
    {
      throw NotImplementedException("TwoTreeLikelihood::getLikelihoodData.");
    }
    const TreeLikelihoodData* getLikelihoodData() const throw (NotImplementedException)
    {
      throw NotImplementedException("TwoTreeLikelihood::getLikelihoodData.");
    }  
    double getLikelihood() const;
    double getLogLikelihood() const;
    double getLikelihoodForASite (size_t site) const;
    double getLogLikelihoodForASite(size_t site) const;
    ParameterList getBranchLengthsParameters() const;
    ParameterList getSubstitutionModelParameters() const;
    SubstitutionModel* getSubstitutionModel(int nodeId, size_t siteIndex) throw (NodeNotFoundException) { return model_; }
    const SubstitutionModel* getSubstitutionModel(int nodeId, size_t siteIndex) const throw (NodeNotFoundException) { return model_; }
    const std::vector<double>& getRootFrequencies(size_t siteIndex) const { return model_->getFrequencies(); }
    size_t getSiteIndex(size_t site) const throw (IndexOutOfBoundsException) { return rootPatternLinks_[site]; }
    VVVdouble getTransitionProbabilitiesPerRateClass(int nodeId, size_t siteIndex) const { return pxy_; }
    void setData(const SiteContainer& sites) throw (Exception) {}
    void initialize() throw(Exception);
    double getLikelihoodForASiteForARateClass(size_t site, size_t rateClass) const;
    double getLogLikelihoodForASiteForARateClass(size_t site, size_t rateClass) const;
    double getLikelihoodForASiteForARateClassForAState(size_t site, size_t rateClass, int state) const;
    double getLogLikelihoodForASiteForARateClassForAState(size_t site, size_t rateClass, int state) const;
    const SubstitutionModel* getSubstitutionModel() const { return model_; }
    
    SubstitutionModel* getSubstitutionModel() { return model_; }

    ConstBranchModelIterator* getNewBranchModelIterator(int nodeId) const throw (NotImplementedException)
    {
      throw NotImplementedException("TwoTreeLikelihood::getNewBranchSiteModelIterator. This class does not (yet) provide support for partition models.");
    }

    ConstSiteModelIterator* getNewSiteModelIterator(size_t siteIndex) const throw (NotImplementedException)
    {
      throw NotImplementedException("TwoTreeLikelihood::getNewSiteModelIterator. This class is for inner use only and does not provide site model iterators.");
    }

    

    void setParameters(const ParameterList& parameters) throw (ParameterNotFoundException, ConstraintException);
    double getValue() const throw(Exception);
    
    double getFirstOrderDerivative(const std::string& variable) const throw (Exception);
    double getSecondOrderDerivative(const std::string& variable) const throw (Exception);
    double getSecondOrderDerivative(const std::string& variable1, const std::string& variable2) const throw (Exception) { return 0; } // Not implemented for now.
    virtual void initBranchLengthsParameters();

    virtual void setMinimumBranchLength(double minimum)
    {
      minimumBrLen_ = minimum;
      if (brLenConstraint_) delete brLenConstraint_;
      brLenConstraint_ = new IntervalConstraint(1, minimumBrLen_, true);
      initBranchLengthsParameters();
    }

    virtual double getMinimumBranchLength() const { return minimumBrLen_; }

  protected:
    
    virtual void initTreeLikelihoods(const SequenceContainer & sequences) throw (Exception);

    void fireParameterChanged(const ParameterList & params);
    virtual void computeTreeLikelihood();
    virtual void computeTreeDLikelihood();
    virtual void computeTreeD2Likelihood();
    virtual void initParameters();

    virtual void applyParameters() throw (Exception);  

};

class DistanceEstimation:
  public virtual Clonable
{
  private:
    auto_ptr<SubstitutionModel> model_;
    auto_ptr<DiscreteDistribution> rateDist_;
    const SiteContainer* sites_;
    DistanceMatrix* dist_;
    Optimizer* optimizer_;
    MetaOptimizer* defaultOptimizer_;
    size_t verbose_;
    ParameterList parameters_;

  public:
  
    DistanceEstimation(
        SubstitutionModel* model,
        DiscreteDistribution* rateDist,
        size_t verbose = 1) :
      model_(model),
      rateDist_(rateDist),
      sites_(0),
      dist_(0),
      optimizer_(0),
      defaultOptimizer_(0),
      verbose_(verbose),
      parameters_()
    {
      init_();
    }
  
    DistanceEstimation(
        SubstitutionModel* model,
        DiscreteDistribution* rateDist,
        const SiteContainer* sites,
        size_t verbose = 1,
        bool computeMat = true) :
      model_(model),
      rateDist_(rateDist),
      sites_(sites),
      dist_(0),
      optimizer_(0),
      defaultOptimizer_(0),
      verbose_(verbose),
      parameters_()
    {
      init_();
      if(computeMat) computeMatrix();
    }
    
    DistanceEstimation(const DistanceEstimation& distanceEstimation):
      model_(distanceEstimation.model_->clone()),
      rateDist_(distanceEstimation.rateDist_->clone()),
      sites_(distanceEstimation.sites_),
      dist_(0),
      optimizer_(dynamic_cast<Optimizer *>(distanceEstimation.optimizer_->clone())),
      defaultOptimizer_(dynamic_cast<MetaOptimizer *>(distanceEstimation.defaultOptimizer_->clone())),
      verbose_(distanceEstimation.verbose_),
      parameters_(distanceEstimation.parameters_)
    {
      if(distanceEstimation.dist_ != 0)
        dist_ = new DistanceMatrix(*distanceEstimation.dist_);
      else
        dist_ = 0;
    }

    DistanceEstimation& operator=(const DistanceEstimation& distanceEstimation)
    {
      model_.reset(distanceEstimation.model_->clone());
      rateDist_.reset(distanceEstimation.rateDist_->clone());
      sites_      = distanceEstimation.sites_;
      if (distanceEstimation.dist_ != 0)
        dist_     = new DistanceMatrix(*distanceEstimation.dist_);
      else
        dist_     = 0;
      optimizer_  = dynamic_cast<Optimizer *>(distanceEstimation.optimizer_->clone());
      // _defaultOptimizer has already been initialized since the default constructor has been called.
      verbose_    = distanceEstimation.verbose_;
      parameters_ = distanceEstimation.parameters_;
      return *this;
    }

    virtual ~DistanceEstimation()
    {
      if (dist_) delete dist_;
      delete defaultOptimizer_;
      delete optimizer_;
    }

#ifndef NO_VIRTUAL_COV
    DistanceEstimation*
#else
    Clonable*
#endif
    clone() const { return new DistanceEstimation(*this); }
    
  private:
    void init_()
    {
      MetaOptimizerInfos* desc = new MetaOptimizerInfos();
      std::vector<std::string> name;
      name.push_back("BrLen");
      desc->addOptimizer("Branch length", new PseudoNewtonOptimizer(0), name, 2, MetaOptimizerInfos::IT_TYPE_FULL);
      ParameterList tmp = model_->getParameters();
      tmp.addParameters(rateDist_->getParameters());
      desc->addOptimizer("substitution model and rate distribution", new SimpleMultiDimensions(0), tmp.getParameterNames(), 0, MetaOptimizerInfos::IT_TYPE_STEP);
      defaultOptimizer_ = new MetaOptimizer(0, desc);
      defaultOptimizer_->setMessageHandler(0);
      defaultOptimizer_->setProfiler(0);
      defaultOptimizer_->getStopCondition()->setTolerance(0.0001);
      optimizer_ = dynamic_cast<Optimizer*>(defaultOptimizer_->clone());
    }

  public:

    void computeMatrix() throw (NullPointerException);
    
    DistanceMatrix* getMatrix() const { return dist_ == 0 ? 0 : new DistanceMatrix(*dist_); }

    bool hasSubstitutionModel() const { return model_.get(); }

    const SubstitutionModel& getSubstitutionModel() const throw (Exception) {
      if (hasSubstitutionModel())
        return *model_;
      else
        throw Exception("DistanceEstimation::getSubstitutionModel(). No model assciated to this instance.");
    }

    void resetSubstitutionModel(SubstitutionModel* model = 0) { model_.reset(model); }

    bool hasRateDistribution() const { return rateDist_.get(); }

    const DiscreteDistribution& getRateDistribution() const throw (Exception) {
      if (hasRateDistribution())
        return *rateDist_;
      else
        throw Exception("DistanceEstimation::getRateDistribution(). No rate distribution assciated to this instance.");
    }

    void resetRateDistribution(DiscreteDistribution* rateDist = 0) { rateDist_.reset(rateDist); }

    void setData(const SiteContainer* sites) { sites_ = sites; }
    const SiteContainer* getData() const { return sites_; }
    void resetData() { sites_ = 0; }
    
    void setOptimizer(const Optimizer * optimizer)
    { 
      if (optimizer_) delete optimizer_;
      optimizer_ = dynamic_cast<Optimizer *>(optimizer->clone());
    }
    const Optimizer* getOptimizer() const { return optimizer_; }
    Optimizer* getOptimizer() { return optimizer_; }
    void resetOptimizer() { optimizer_ = dynamic_cast<Optimizer*>(defaultOptimizer_->clone()); }

    void setAdditionalParameters(const ParameterList& parameters)
    {
      parameters_ = parameters;
    }

    void resetAdditionalParameters()
    {
      parameters_.reset();
    }

    void setVerbose(size_t verbose) { verbose_ = verbose; }
    size_t getVerbose() const { return verbose_; }
};

} //end of namespace bpp.

#endif //_DISTANCEESTIMATION_H_