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/*
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) 2012 BUI Quang Minh <email>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef MODELSET_H
#define MODELSET_H
#include "modelmarkov.h"
/**
* a set of substitution models, used eg for site-specific state frequency model or
* partition model with joint branch lengths
*/
class ModelSet : public ModelMarkov, public vector<ModelMarkov*>
{
public:
ModelSet(const char *model_name, PhyloTree *tree);
/**
* @return TRUE if this is a site-specific model, FALSE otherwise
*/
virtual bool isSiteSpecificModel() { return true; }
/**
* get the size of transition matrix, default is num_states*num_states.
* can be changed for e.g. site-specific model
*/
virtual int getTransMatrixSize() { return num_states * num_states * size(); }
/**
compute the transition probability matrix.
@param time time between two events
@param mixture (optional) class for mixture model
@param trans_matrix (OUT) the transition matrix between all pairs of states.
Assume trans_matrix has size of num_states * num_states.
*/
virtual void computeTransMatrix(double time, double *trans_matrix, int mixture = 0);
/**
compute the transition probability matrix.and the derivative 1 and 2
@param time time between two events
@param mixture (optional) class for mixture model
@param trans_matrix (OUT) the transition matrix between all pairs of states.
Assume trans_matrix has size of num_states * num_states.
@param trans_derv1 (OUT) the 1st derivative matrix between all pairs of states.
@param trans_derv2 (OUT) the 2nd derivative matrix between all pairs of states.
*/
virtual void computeTransDerv(double time, double *trans_matrix,
double *trans_derv1, double *trans_derv2, int mixture = 0);
/**
To AVOID 'hides overloaded virtual functions
compute the transition probability between two states
@param time time between two events
@param state1 first state
@param state2 second state
*/
virtual double computeTrans(double time, int state1, int state2) { return 0; }
/**
To AVOID 'hides overloaded virtual functions
compute the transition probability between two states
@param time time between two events
@param state1 first state
@param state2 second state
@param derv1 (OUT) 1st derivative
@param derv2 (OUT) 2nd derivative
*/
virtual double computeTrans(double time, int state1, int state2, double &derv1, double &derv2) { return 0; }
/**
compute the transition probability between two states at a specific site
One should override this function when defining new model.
The default is the Juke-Cantor model, valid for all kind of data (DNA, AA, Codon, etc)
@param time time between two events
@param model_id model ID
@param state1 first state
@param state2 second state
*/
virtual double computeTrans(double time, int model_id, int state1, int state2);
/**
compute the transition probability and its 1st and 2nd derivatives between two states at a specific site
One should override this function when defining new model.
The default is the Juke-Cantor model, valid for all kind of data (DNA, AA, Codon, etc)
@param time time between two events
@param model_id model ID
@param state1 first state
@param state2 second state
@param derv1 (OUT) 1st derivative
@param derv2 (OUT) 2nd derivative
*/
virtual double computeTrans(double time, int model_id, int state1, int state2, double &derv1, double &derv2);
/**
* @return pattern ID to model ID map, useful for e.g., partition model
* @param ptn pattern ID of the alignment
*/
virtual int getPtnModelID(int ptn);
/**
return the number of dimensions
*/
virtual int getNDim();
/**
write information
@param out output stream
*/
virtual void writeInfo(ostream &out);
/**
decompose the rate matrix into eigenvalues and eigenvectors
*/
virtual void decomposeRateMatrix();
virtual ~ModelSet();
/**
* compute the memory size for the model, can be large for site-specific models
* @return memory size required in bytes
*/
virtual uint64_t getMemoryRequired() {
uint64_t mem = ModelMarkov::getMemoryRequired();
for (iterator it = begin(); it != end(); it++)
mem += (*it)->getMemoryRequired();
return mem;
}
/** map from pattern ID to model ID */
IntVector pattern_model_map;
/**
join memory for eigen into one chunk
*/
void joinEigenMemory();
protected:
/**
this function is served for the multi-dimension optimization. It should pack the model parameters
into a vector that is index from 1 (NOTE: not from 0)
@param variables (OUT) vector of variables, indexed from 1
*/
virtual void setVariables(double *variables);
/**
this function is served for the multi-dimension optimization. It should assign the model parameters
from a vector of variables that is index from 1 (NOTE: not from 0)
@param variables vector of variables, indexed from 1
@return TRUE if parameters are changed, FALSE otherwise (2015-10-20)
*/
virtual bool getVariables(double *variables);
};
#endif // MODELSET_H
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