Commit 46fbaed4 by Gaspard Ducamp

[pyAgrum/doc] BNCLlearner doc partially added

parent 4d0695ed
Pipeline #13668682 passed with stage
in 4 minutes 13 seconds
%feature("docstring") gum::learning::BNLearner
"
"
%feature("docstring") gum::learning::BNLearner::asIApproximationSchemeConfiguration
"
Warnings
--------
A Faire
"
%feature("docstring") gum::learning::BNLearner::learnBN
"
learn a BayesNet from a file (must have read the db before)
Returns
-------
pyAgrum.BayesNet
the learned BayesNet
"
%feature("docstring") gum::learning::BNLearner::learnParameters
"
learns a BN (its parameters) when its structure is known.
Parameters
----------
dag : pyAgrum.DAG
bn : pyAgrum.BayesNet
take_into_account_score : bool
The dag passed in argument may have been learnt from a structure learning. In this case, if the score used to learn the structure has an implicit apriori (like K2 which has a 1-smoothing apriori), it is important to also take into account this implicit apriori for parameter learning. By default, if a score exists, we will learn parameters by taking into account the apriori specified by methods useAprioriXXX () + the implicit apriori of the score, else we just take into account the apriori specified by useAprioriXXX ()
Returns
-------
pyAgrum.BayesNet
the learned BayesNet
Warnings
--------
MissingVariableInDatabase if a variable of the BN is not found in the database
Warnings
--------
UnknownLabelInDatabase raise if a label is found in the database that do not correspond to the variable
"
%feature("docstring") gum::learning::BNLearner::setInitialDAG
"
Parameters
----------
dag : pyAgrum.DAG
an initial DAG structure
"
\ No newline at end of file
%feature("docstring") gum::IApproximationSchemeConfiguration::addForbiddenArc
"
Parameters
----------
arc : pyAgrum
an arc
head :
a variable's id (int)
tail :
a variable's id (int)
head :
a variable's name (str)
tail :
a variable's name (str)
Warnings
--------
A continuer ?
"
%feature("docstring") gum::IApproximationSchemeConfiguration::addMandatoryArc
"
Allow to add prior structural knowledge
Parameters
----------
arc : pyAgrum
an arc
head :
a variable's id (int)
tail :
a variable's id (int)
head :
a variable's name (str)
tail :
a variable's name (str)
Warnings
--------
A continuer ?
"
%feature("docstring") gum::IApproximationSchemeConfiguration::burnIn
"
Returns
-------
int
size of burn in on number of iteration
"
%feature("docstring") gum::IApproximationSchemeConfiguration::currentTime
"
Returns
-------
double
get the current running time in second (double)
"
%feature("docstring") gum::IApproximationSchemeConfiguration::epsilon
"
Returns
-------
double
the value of epsilon
"
%feature("docstring") gum::IApproximationSchemeConfiguration::eraseForbiddenArc
"
Parameters
----------
arc : pyAgrum
an arc
head :
a variable's id (int)
tail :
a variable's id (int)
head :
a variable's name (str)
tail :
a variable's name (str)
Warnings
--------
A continuer ?
"
%feature("docstring") gum::IApproximationSchemeConfiguration::eraseMandatoryArc
"
Parameters
----------
arc : pyAgrum
an arc
head :
a variable's id (int)
tail :
a variable's id (int)
head :
a variable's name (str)
tail :
a variable's name (str)
Warnings
--------
A continuer ?
"
%feature("docstring") gum::IApproximationSchemeConfiguration::history
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::idFromName
"
Parameters
----------
var_names : str
a variable's name
Returns
-------
int
the node id corresponding to a variable name
Warnings
--------
MissingVariableInDatabase raised if a variable of the BN is not found in the database.
"
%feature("docstring") gum::IApproximationSchemeConfiguration::learnDAG
"
learn a structure from a file (must have read the db before)
Returns
-------
pyAgrum.DAG
the learned DAG
"
%feature("docstring") gum::IApproximationSchemeConfiguration::maxIter
"
Returns
-------
int
the criterion on number of iterations
"
%feature("docstring") gum::IApproximationSchemeConfiguration::maxTime
"
Returns
-------
double
the timeout(in seconds)
"
%feature("docstring") gum::IApproximationSchemeConfiguration::messageApproximationScheme
"
Returns
-------
str
the approximation scheme message
"
%feature("docstring") gum::IApproximationSchemeConfiguration::minEpsilonRate
"
Returns
-------
double
the value of the minimal epsilon rate
"
%feature("docstring") gum::IApproximationSchemeConfiguration::modalities
"
Returns
-------
vector<pos,size>
the number of modalities of the database's variables.
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::nameFromId
"
Parameters
----------
id
a node id
Returns
-------
str
the variable's name
"
%feature("docstring") gum::IApproximationSchemeConfiguration::names
"
Returns
-------
str
the names of the variables in the database
"
%feature("docstring") gum::IApproximationSchemeConfiguration::nbrIterations
"
Returns
-------
int
the number of iterations
Warnings
--------
a completer
"
%feature("docstring") gum::IApproximationSchemeConfiguration::periodSize
"
Returns
-------
int
the number of samples between 2 stopping
Warnings
--------
OutOfLowerBound raised if p<1
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setAprioriWeight
"
Parameters
----------
weight : double
the apriori weight
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setBurnIn
"
Parameters
----------
b : int
size of burn in on number of iteration
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setEpsilon
"
Parameters
----------
eps : double
the epsilon we want to use
Warnings
--------
OutOfLowerBound if eps<0
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setMaxIndegree
"
Parameters
----------
max_indegree : int
the limit number of parents
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setMaxIter
"
Parameters
----------
max : int
the maximum number of iteration
Warnings
--------
OutOfLowerBound raised if max <= 1
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setMaxTime
"
Parameters
----------
tiemout : double
stopping criterion on timeout (in seconds)
Warnings
--------
OutOfLowerBound raised if timeout<=0.0
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setMinEpsilonRate
"
Parameters
----------
rate : double
the minimal epsilon rate
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setPeriodSize
"
Parameters
----------
p : int
number of samples between 2 stopping
Warnings
--------
OutOfLowerBound raised if p<1
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setSliceOrder
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::setVerbosity
"
Parameters
----------
v : bool
verbosity
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useAprioriDirichlet
"
Use the Dirichlet apriori.
Parameters
----------
filename : str
the Dirichlet related database
Warnings
--------
à compléter
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useAprioriSmoothing
"
Use the apriori smoothing.
Parameters
----------
weight : double
pass in argument a weight if you wish to assign a weight to the smoothing, else the current weight of the learner will be used.
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useGreedyHillClimbing
"
Indicate that we wish to use a greedy hill climbing algorithm.
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useK2
"
Indicate that we wish to use K2.
Parameters
----------
order : list
a list of ids
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useLocalSearchWithTabuList
"
indicate that we wish to use a local search with tabu list
Parameters
----------
tabu_size : int
The size of the tabu list
nb_decrease : int
The max number of changes decreasing the score consecutively that we allow to apply
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useNoApriori
"
Use no apriori.
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreAIC
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreBD
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreBDeu
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreBIC
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreK2
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::useScoreLog2Likelihood
"
Warnings
--------
A Faire
"
%feature("docstring") gum::IApproximationSchemeConfiguration::verbosity
"
Warnings
--------
A Faire
"
\ No newline at end of file
......@@ -7,11 +7,8 @@ Model
-----
.. autoclass:: pyAgrum.BayesNet
:members:
.. autoclass:: pyAgrum.pyAgrum.BayesNet_double
:members:
:inherited-members:
Inference
---------
......@@ -19,18 +16,14 @@ Inference
.. autoclass:: pyAgrum.LazyPropagation
.. autoclass:: pyAgrum.pyAgrum.LazyPropagation_double
:members:
:inherited-members:
.. autoclass:: pyAgrum.GibbsInference
.. autoclass:: pyAgrum.pyAgrum.GibbsInference_double
:members:
:herited-members:
Learning
--------
.. autoclass:: pyAgrum.BNLearner
.. autoclass:: pyAgrum.pyAgrum.BNLearner_double
:members:
.. autoclass:: pyAgrum.pyAgrum.BNLearner_double
\ No newline at end of file
......@@ -18,4 +18,7 @@
%include "doc_BayesNet.i"
%include "doc_DAGmodel.i"
%include "doc_IBayesNet.i"
%include "doc_LazyPropagation.i"
\ No newline at end of file
%include "doc_LazyPropagation.i"
%include "doc_BNLearner.i"
%include "doc_IApproximationSchemeConfiguration.i"
\ No newline at end of file
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