/usr/include/Bpp/Phyl/Model/MixtureOfASubstitutionModel.h is in libbpp-phyl-dev 2.0.3-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | //
// File: MixtureOfASubstitutionModel.h
// Created by: David Fournier, Laurent Gueguen
//
/*
Copyright or © or Copr. CNRS, (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 _MIXTUREOFASUBSTITUTIONMODEL_H_
#define _MIXTUREOFASUBSTITUTIONMODEL_H_
#include "AbstractMixedSubstitutionModel.h"
#include <Bpp/Numeric/Prob.all>
#include <Bpp/Numeric/VectorTools.h>
#include <vector>
#include <string>
#include <map>
#include <cstring> // C lib for string copy
namespace bpp
{
/**
* @brief Substitution models defined as a mixture of nested
* substitution models.
* @author Laurent Guéguen
*
* All the nested models are of the same type (for example T92 or
* GY94), and their parameter values can follow discrete
* distributions.
*
* In this kind of model, there is no generator.
*
* There is a map with connection from parameter names to discrete
* distributions, and then a related vector of "simple" substitution
* models for all the combinations of parameter values.
*
* For example:
* HKY85(kappa=Gamma(n=3,alpha=2,beta=5),
* theta=TruncExponential(n=4,lambda=0.2,tp=1),
* theta1=0.4,
* theta2=TruncExponential(n=5,lambda=0.6,tp=1))
*
* defines 3*4*5=60 different HKY85 nested models with rate one.
*
* Optionnal arguments are used to homogeneize the rates of the nested
* models. Default values sets all the rates to 1, and given values
* are the two letters (from_ & to_) between which the substitution
* rates are the same in all nested models.
*
* For example:
* HKY85(kappa=Gamma(n=3,alpha=2,beta=5),
* theta=TruncExponential(n=4,lambda=0.2,tp=1),
* theta1=0.4,
* theta2=TruncExponential(n=5,lambda=0.6,tp=1),
* from=A, to=C)
*
* defines 3*4*5=60 different HKY85 nested models with the same A->C
* substitution rate.
*
* If a distribution parameter does not respect the constraints of
* this parameter, there is an Exception at the creation of the
* wrong model, if any.
*
* When used through a MixedTreeLikelihood objets, all the models have
* a specific probability, defined through the probabilities of the
* several parameter distributions. The computing of the likelihoods
* and probabilities are the expectation of the "simple" models
* values.
*
*/
class MixtureOfASubstitutionModel :
public AbstractMixedSubstitutionModel
{
private:
std::map<std::string, DiscreteDistribution*> distributionMap_;
int from_, to_;
public:
MixtureOfASubstitutionModel(const Alphabet* alpha,
SubstitutionModel* model,
std::map<std::string, DiscreteDistribution*> parametersDistributionsList,
int ffrom=-1, int tto=-1) throw(Exception);
MixtureOfASubstitutionModel(const MixtureOfASubstitutionModel&);
MixtureOfASubstitutionModel& operator=(const MixtureOfASubstitutionModel&);
~MixtureOfASubstitutionModel();
MixtureOfASubstitutionModel* clone() const { return new MixtureOfASubstitutionModel(*this); }
public:
std::string getName() const { return "MixtureOfASubstitutionModel"; }
void updateMatrices();
/*
*@brief Returns the vector of numbers of the submodels in the
*mixture that match a description of the parameters numbers.
*
*@param desc is the description of the class indexes of the mixed
*parameters. Syntax is like: kappa_1,gamma_3,delta_2
*
*/
Vint getSubmodelNumbers(std::string& desc) const;
/**
* @brief sets the eq frequencies of the first nested model, and
* adapts the parameters at best to it (surely there is a better way
* to manage this).
*
*/
void setFreq(std::map<int,double>&);
};
} // end of namespace bpp.
#endif // _MIXTUREOFASUBSTITUTIONMODEL_H_
|