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Version 4.1.5 |
#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 |
This is a special case of a phased Parcimonious Markov [PhasedPMarkov] model when only one phase is considered, and also a Markov object.
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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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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 |