//
// File: T92.h
// Created by: Julien Dutheil
// Created on: Mon May 26 14:41:24 2003
//

/*
   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
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   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 _T92_H_
#define _T92_H_

#include "NucleotideSubstitutionModel.h"
#include "../AbstractSubstitutionModel.h"

#include <Bpp/Numeric/Constraints.h>

// From SeqLib:
#include <Bpp/Seq/Alphabet/NucleicAlphabet.h>
#include <Bpp/Seq/Container/SequenceContainer.h>

namespace bpp
{
/**
 * @brief The Tamura (1992) substitution model for nucleotides.
 *
 * This model is similar to the K80 model,
 * but allows distinct equilibrium frequencies between GC and AT.
 * This models hence includes two parameters, the transition / transversion
 * relative rate \f$\kappa\f$ and the frequency of GC, \f$\theta\f$.
 * \f[
 * S = \begin{pmatrix}
 * \cdots & r & \kappa r & r \\
 * r & \cdots & r & \kappa r \\
 * \kappa r & r & \cdots & r \\
 * r & \kappa r & r & \cdots \\
 * \end{pmatrix}
 * \f]
 * \f[
 * \pi = \left(\frac{1-\theta}{2}, \frac{\theta}{2}, \frac{\theta}{2}, \frac{1 - \theta}{2}\right)
 * \f]
 * Normalization: \f$r\f$ is set so that \f$\sum_i Q_{i,i}\pi_i = -1\f$:
 * \f[
 * S = \frac{1}{P}\begin{pmatrix}
 * \frac{2\theta\kappa + 2}{\theta - 1} & 2 & 2\kappa & 2 \\
 * 2 & \frac{2(\theta-1)\kappa - 2}{\theta} & 2 & 2\kappa \\
 * 2\kappa & 2 & \frac{2(\theta-1)\kappa - 2}{\theta} & 2 \\
 * 2 & 2\kappa & 2 & \frac{2\theta\kappa + 2}{\theta - 1} \\
 * \end{pmatrix}
 * \f]
 * with \f$P=1+2\theta\kappa-2\theta^2\kappa\f$.
 *
 * The normalized generator is obtained by taking the dot product of \f$S\f$ and \f$\pi\f$:
 * \f[
 * Q = S . \pi = \frac{1}{P}\begin{pmatrix}
 * -(\theta\kappa+1) & \theta & \theta\kappa & 1-\theta \\
 * 1-\theta & -((1-\theta)\kappa+1) & \theta & (1-\theta)\kappa \\
 * (1-\theta)\kappa & \theta & -((1-\theta)\kappa+1) & 1-\theta \\
 * 1-\theta & \theta\kappa & \theta & -(\theta\kappa+1) \\
 * \end{pmatrix}
 * \f]
 *
 * The eigen values are \f$\left(0, -\frac{\kappa+1}{P}, -\frac{\kappa+1}{P}, -\frac{2}{P}\right)\f$,
 * the left eigen vectors are, by row:
 * \f[
 * U = \begin{pmatrix}
 * \frac{1-\theta}{2} &  \frac{\theta}{2} &  \frac{\theta}{2} & \frac{1-\theta}{2} \\
 *                  0 &          1-\theta &                 0 & \theta-1 \\
 *             \theta &                   0 &         -\theta & 0 \\
 * \frac{1-\theta}{2} & -\frac{\theta}{2} &  \frac{\theta}{2} & -\frac{1-\theta}{2} \\
 * \end{pmatrix}
 * \f]
 * and the right eigen vectors are, by column:
 * \f[
 * U^-1 = \begin{pmatrix}
 * 1 &  0 &  1 &  1 \\
 * 1 &  1 &  0 & -1 \\
 * 1 &  0 & -\frac{1-\theta}{\theta} &  1 \\
 * 1 & -\frac{\theta}{1-\theta} &  0 & -1 \\
 * \end{pmatrix}
 * \f]
 *
 * In addition, a rate_ factor defines the mean rate of the model.
 *
 * The probabilities of changes are computed analytically using the formulas:
 * \f{multline*}
 * P_{i,j}(t) = \\
 * \begin{pmatrix}
 * \theta A + \frac{1-\theta}{2}B + \frac{1-\theta}{2} & \frac{\theta}{2} - \frac{\theta}{2}B & -\theta A + \frac{\theta}{2}B + \frac{\theta}{2} & \frac{1-\theta}{2} - \frac{1-\theta}{2}B \\
 * \frac{1-\theta}{2} - \frac{1-\theta}{2}B & (1-\theta)A + \frac{\theta}{2}B + \frac{\theta}{2} & \frac{\theta}{2} - \frac{\theta}{2}B & -(1-\theta)A + \frac{1-\theta}{2}B + \frac{1-\theta}{2} \\
 * -(1-\theta)A + \frac{1-\theta}{2}B + \frac{1-\theta}{2} & \frac{\theta}{2} - \frac{\theta}{2}B & (1-\theta)A + \frac{\theta}{2}B + \frac{\theta}{2} & \frac{1-\theta}{2} - \frac{1-\theta}{2}B \\
 * \frac{1-\theta}{2} - \frac{1-\theta}{2}B & -\theta A + \frac{\theta}{2}B + \frac{\theta}{2} & \frac{\theta}{2} - \frac{\theta}{2}B & \theta A + \frac{1-\theta}{2}B + \frac{1-\theta}{2} \\
 * \end{pmatrix}
 * \f}
 * with \f$A=e^{-\frac{rate\_ * (\kappa+1)t}{P}}\f$ and \f$B = e^{-\frac{rate\_ * 2t}{P}}\f$.
 *
 * First and second order derivatives are also computed analytically using the formulas:
 * \f{multline*}
 * \frac{\partial P_{i,j}(t)}{\partial t} = rate\_ * \\
 * \frac{1}{P}
 * \begin{pmatrix}
 * -\theta(\kappa+1)A - (1-\theta)B & \theta B & \theta(\kappa+1)A - \theta B & (1-\theta)B \\
 * (1-\theta)B & -(1-\theta)(\kappa+1)A - \theta B & \theta B & (1-\theta)(\kappa+1)A - (1-\theta)B \\
 * (1-\theta)(\kappa+1)A - (1-\theta)B & \theta B & -(1-\theta)(\kappa+1)A - \theta B & (1-\theta)B \\
 * (1-\theta)B & \theta(\kappa+1)A - \theta B & \theta B & -\theta(\kappa+1)A - (1-\theta)B \\
 * \end{pmatrix}
 * \f}
 * \f{multline*}
 * \frac{\partial^2 P_{i,j}(t)}{\partial t^2} = rate\_^2 * \\
 * \frac{1}{P^2}
 * \begin{pmatrix}
 * \theta{(\kappa+1)}^2A + 2(1-\theta)B & -2\theta B & -\theta{(\kappa+1)}^2A + 2\theta B & -2(1-\theta)B \\
 * -2(1-\theta)B & (1-\theta){(\kappa+1)}^2A + 2\theta B & -2\theta B & -(1-\theta){(\kappa+1)}^2A + 2(1-\theta)B \\
 * -(1-\theta){(\kappa+1)}^2A + 2(1-\theta)B & -2\theta B & (1-\theta){(\kappa+1)}^2A + 2\theta B & -2(1-\theta)B \\
 * -2(1-\theta)B & -\theta{(\kappa+1)}^2A + 2\theta B & -2\theta B & \theta{(\kappa+1)}^2A + 2(1-\theta)B \\
 * \end{pmatrix}
 * \f}
 *
 * The parameters are named \c "kappa" and \c "theta"
 * and their values may be retrieve with the commands
 * \code
 * getParameterValue("kappa")
 * getParameterValue("theta")
 * \endcode
 *
 * Reference:
 * - Tamura K (1992), Molecular_ Biology And Evolution_ 9(5) 814-25.
 */
class T92 :
  public virtual NucleotideSubstitutionModel,
  public AbstractReversibleSubstitutionModel
{
private:
  double kappa_, theta_, k_, r_, piA_, piC_, piG_, piT_;
  mutable double exp1_, exp2_, l_;
  mutable RowMatrix<double> p_;

public:
  T92(const NucleicAlphabet* alpha, double kappa = 1., double theta = 0.5);

  virtual ~T92() {}

#ifndef NOVIRTUAL_COV_
  T92*
#else
  Clonable*
#endif
  clone() const { return new T92(*this); }

public:
  double Pij_t    (size_t i, size_t j, double d) const;
  double dPij_dt  (size_t i, size_t j, double d) const;
  double d2Pij_dt2(size_t i, size_t j, double d) const;
  const Matrix<double>& getPij_t(double d) const;
  const Matrix<double>& getdPij_dt(double d) const;
  const Matrix<double>& getd2Pij_dt2(double d) const;

  std::string getName() const { return "T92"; }


  /**
   * @brief This method is over-defined to actualize the 'theta' parameter too.
   */
  void setFreq(std::map<int, double>& freqs);

protected:
  void updateMatrices();
};
} // end of namespace bpp.

#endif  // _T92_H_

