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Version 4.1.5 |
#include <seqpp/PhasedPMarkov.h>
Inheritance diagram for PhasedPMarkov:
Public Member Functions | |
PhasedPMarkov (Partition &part, const SequenceSet &seqset, short phase, short initial_phase=0, const string &prior_alpha_file=string(), bool motif_prior=true, double penalty=0., const string &xmlfile=string()) | |
Constructor 1 from a SequenceSet. | |
PhasedPMarkov (Partition &part, const Sequence &seq, short phase, short initial_phase=0, 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. | |
PhasedPMarkov (Partition &part, unsigned long **count, short size, short order, short phase, 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. | |
PhasedPMarkov (Partition &part, const string &count_file, short size, short order, short phase, 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. | |
PhasedPMarkov (Partition &part, short size, short order, short phase, 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) |
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 | |
template<class TSeq> | |
double | mean_post_log_likelihood (const TSeq &tseq_eval, short initial_phase_eval=0) |
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, short initial_phase_eval=0) |
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=Translator(), const string &xmlfile=string()) |
draws a model | |
void | info_nb_leaves () const |
returns info on the number of leaves for each phase | |
~PhasedPMarkov () | |
Destructor. | |
Protected Attributes | |
vector< pmm_forest * > | _p_f |
Parcimonious Context Trees. |
PhasedPMarkov is a PhasedMarkov object with a different estimation step. This object performs the estimation with the Parcimonious Markov algorithm and then transfoms, once per phase, the parcimonious context tree in a markovian matrix. xml outputs can be activated to save the associated trees.
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Constructor 1 from a SequenceSet.
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Constructor 2 from a Sequence.
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Constructor 3 from a coded-word count.
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Constructor 4 from a file containing coded-word count.
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Basic Constructor 5. No estimation.
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draws a model
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draws a model
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return the mean post likelihood over the parameters and the trees
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return the mean post likelihood over the parameters and the trees
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compute the mean posterior likelihood over the parameters
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compute the mean posterior likelihood over the parameters
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performs the A Posteriori Maximisation
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Contributors : M.Baudry, P.Y.Bourguignon, M.Hoebeke, V.Miele, P.Nicolas, G.Nuel, H.Richard, D.Robelin |