Version 4.1.5

PMarkov Class Reference

Parcimonious Markov modelling, estimation and simulation. More...

#include <seqpp/PMarkov.h>

Inheritance diagram for PMarkov:


Public Member Functions
	PMarkov (Partition &p, const SequenceSet &seqset, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., const string &xmlfile=string())
	Constructor 1 from a SequenceSet.
	PMarkov (Partition &p, const Sequence &seq, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., const Translator &trans=Translator(), const string &xmlfile=string())
	Constructor 2 from a Sequence.
	PMarkov (Partition &p, unsigned long *count, short size, short order, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., const Translator &trans=Translator(), const string &xmlfile=string())
	Constructor 3 from a coded-word count.
	PMarkov (Partition &part, const string &count_file, short size, short order, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., const Translator &trans=Translator(), const string &xmlfile=string())
	Constructor 4 from a file containing coded-word count.
	PMarkov (Partition &part, short size, short order, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., bool alloc=true)
	Basic Constructor 5. No estimation.
void	select (unsigned long *count, bool decal_required, const Translator &trans=Translator(), const string &xmlfile=string())
	performs the A Posteriori Maximisation
template<class TSeq1, class TSeq2>
double	mean_post_log_likelihood (const TSeq1 &tseq_train, const TSeq2 &tseq_eval, short initial_phase_train=0, short initial_phase_eval=0)
	return the mean post likelihood over the parameters and the trees
template<class TSeq>
double	mean_post_log_likelihood (const TSeq &tseq_eval, short initial_phase_eval=0)
	return the mean post likelihood over the parameters and the trees
double	mean_post_log_likelihood (unsigned long count_train, bool decal_required_t, unsigned long count_eval, bool decal_required_e)
	return the mean post likelihood over the parameters and the trees
double	mean_post_log_likelihood (unsigned long *count_eval, bool decal_required_e)
	return the mean post likelihood over the parameters and the trees
double	mean_post_log_likelihood ()
	return the mean post likelihood over the parameters and the trees
template<class TSeq>
double	post_log_likelihood (const TSeq &tseq_eval)
	compute the mean posterior likelihood over the parameters
double	post_log_likelihood (unsigned long *count_eval, bool decal_required_e)
	compute the mean posterior likelihood over the parameters
void	draw (unsigned long count, bool decal_required, gsl_rng r, const Translator &trans=Translator(), const string &xmlfile=string())
	draws a model
void	draw (gsl_rng *r, const Translator &trans, const string &xmlfile=string())
	draws a model
void	info_nb_leaves () const
	returns info on the number of leaves

Detailed Description

Parcimonious Markov modelling, estimation and simulation.

This is a special case of a phased Parcimonious Markov [PhasedPMarkov] model when only one phase is considered, and also a Markov object.

Constructor & Destructor Documentation

PMarkov::PMarkov ( Partition & p,

const SequenceSet & seqset,

const string & prior_alpha_file = string(),

bool motif_prior = true,

double penalty = 0.,

const string & xmlfile = string()

) [inline]

Constructor 1 from a SequenceSet.

Parameters:

p associated partition

seqset a set of sequences for estimation

prior_alpha_file file containing the alpha for the a priori law, one value per alphabet element

motif_prior activate a weight-function on the priors, weight proportionnal on each motif in the tree

penalty penalty on the leaves number, by default 0

xmlfile xmlfile for tree saving (if xml2 activated)

PMarkov::PMarkov ( Partition & p,

const Sequence & seq,

const string & prior_alpha_file = string(),

bool motif_prior = true,

double penalty = 0.,

const Translator & trans = Translator(),

const string & xmlfile = string()

) [inline]

Constructor 2 from a Sequence.

Parameters:

p associated partition

seq sequence for estimation

prior_alpha_file file containing the alpha for the a priori law, one value per alphabet element

motif_prior activate a weight-function on the priors, weight proportionnal on each motif in the tree

penalty penalty on the leaves number, by default 0

trans a Translator is required only for the xml saving

xmlfile xmlfile for tree saving (if xml2 activated)

PMarkov::PMarkov ( Partition & p,

unsigned long * count,

short size,

short order,

const string & prior_alpha_file = string(),

bool motif_prior = true,

double penalty = 0.,

const Translator & trans = Translator(),

const string & xmlfile = string()

) [inline]

Constructor 3 from a coded-word count.

Parameters:

p associated partition

count count of all the coded word(base size) of size order+1 for each phase, for estimation

size alphabet size

order markovian order associated to the word count

prior_alpha_file file containing the alpha for the a priori law, one value per alphabet element

motif_prior activate a weight-function on the priors, weight proportionnal on each motif in the tree

penalty penalty on the leaves number, by default 0

trans a Translator is required only for the xml saving

xmlfile xmlfile for tree saving (if xml2 activated)

PMarkov::PMarkov ( Partition & part,

const string & count_file,

short size,

short order,

const string & prior_alpha_file = string(),

bool motif_prior = true,

double penalty = 0.,

const Translator & trans = Translator(),

const string & xmlfile = string()

) [inline]

Constructor 4 from a file containing coded-word count.

Parameters:

part associated partition

count_file file with count

size alphabet size

order markovian order associated to the word count

prior_alpha_file file containing the alpha for the a priori law, one value per alphabet element

motif_prior activate a weight-function on the priors, weight proportionnal on each motif in the tree

penalty penalty on the leaves number, by default 0

trans a Translator is required only for the xml saving

xmlfile xmlfile for tree saving (if xml2 activated)

PMarkov::PMarkov ( Partition & part,

short size,

short order,

const string & prior_alpha_file = string(),

bool motif_prior = true,

double penalty = 0.,

bool alloc = true

) [inline]

Basic Constructor 5. No estimation.

Parameters:

part associated partition

size alphabet size

order markovian order associated to the word count

prior_alpha_file file containing the alpha for the a priori law, one value per alphabet element

motif_prior activate a weight-function on the priors, weight proportionnal on each motif in the tree

penalty penalty on the leaves number, by default 0

alloc true for matrices memory allocation

Member Function Documentation

void PMarkov::draw ( gsl_rng * r,

const Translator & trans,

const string & xmlfile = string()

) [inline]

draws a model

Parameters:

r random generator

trans a Translator is required for xml tree saving (if xml2 activated)

xmlfile xmlfile for tree saving (if xml2 activated)

void PMarkov::draw ( unsigned long * count,

bool decal_required,

gsl_rng * r,

const Translator & trans = Translator(),

const string & xmlfile = string()

) [inline]

draws a model

Parameters:

count count of all the coded word(base size) of size order+1, for estimation

decal_required necessary when using a count of word from 1-word to (_max_depth+1)-word

r random generator

trans a Translator is required for xml tree saving (if xml2 activated)

xmlfile xmlfile for tree saving (if xml2 activated)

double PMarkov::mean_post_log_likelihood ( unsigned long * count_eval,

bool decal_required_e

) [inline]

return the mean post likelihood over the parameters and the trees

Parameters:

count_eval count of all the coded word(base size) of size order+1 for evaluation step

decal_required_e necessary when using a count of word from 1-word to (_max_depth+1)-word

double PMarkov::mean_post_log_likelihood ( unsigned long * count_train,

bool decal_required_t,

unsigned long * count_eval,

bool decal_required_e

) [inline]

return the mean post likelihood over the parameters and the trees

Parameters:

count_train count of all the coded word(base size) of size order+1, for learning step

decal_required_t necessary when using a count of word from 1-word to (_max_depth+1)-word

count_eval count of all the coded word(base size) of size order+1 for each phase, for evaluation step

decal_required_e necessary when using a count of word from 1-word to (_max_depth+1)-word

template<class TSeq>

double PMarkov::mean_post_log_likelihood ( const TSeq & tseq_eval,

short initial_phase_eval = 0

) [inline]

return the mean post likelihood over the parameters and the trees

Parameters:

tseq_eval sequence(s) (set) for the evaluation step

initial_phase_eval phase of the first element of each sequence

template<class TSeq1, class TSeq2>

double PMarkov::mean_post_log_likelihood ( const TSeq1 & tseq_train,

const TSeq2 & tseq_eval,

short initial_phase_train = 0,

short initial_phase_eval = 0

) [inline]

return the mean post likelihood over the parameters and the trees

Parameters:

tseq_train sequence(s) (set) for the training step

initial_phase_train phase of the first element of each sequence

tseq_eval sequence(s) (set) for the evaluation step

initial_phase_eval phase of the first element of each sequence

double PMarkov::post_log_likelihood ( unsigned long * count_eval,

bool decal_required_e

) [inline]

compute the mean posterior likelihood over the parameters

Parameters:

count_eval count of all the coded word(base size) of size order+1 for each phase, for evaluation step

decal_required_e necessary when using a count of word from 1-word to (_max_depth+1)-word

template<class TSeq>

double PMarkov::post_log_likelihood ( const TSeq & tseq_eval ) [inline]

compute the mean posterior likelihood over the parameters

Parameters:

tseq_eval sequence(s) (set) for the evaluation step

void PMarkov::select ( unsigned long * count,

bool decal_required,

const Translator & trans = Translator(),

const string & xmlfile = string()

) [inline]

performs the A Posteriori Maximisation

Parameters:

count count of all the coded word(base size) of size order+1 for each phase, for estimation

decal_required necessary when using a count of word from 1-word to (_max_depth+1)-word

trans a Translator is required for xml tree saving (if xml2 activated)

xmlfile xmlfile for tree saving (if xml2 activated)

The documentation for this class was generated from the following file:

PMarkov.h

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Contributors : M.Baudry, P.Y.Bourguignon, M.Hoebeke, V.Miele, P.Nicolas, G.Nuel, H.Richard, D.Robelin

Generated on Thu Aug 4 20:33:13 2005 for seqpp by

1.3.9.1

PMarkov Class Reference

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation