def estimate_convolution(histo, *args, **kargs):
""" Estimate a convolution
:Usage:
.. doctest::
:options: +SKIP
>>> Estimate(histo, "CONVOLUTION", dist,
MinInfBound=1, Parametric=False)
Estimate(histo, "CONVOLUTION", dist,
InitialDistribution=initial_dist, Parametric=False)
"""
if len(args) == 0:
raise ValueError("expect at least two argument")
known_distribution = args[0]
Weight = kargs.get("Weight", -1)
NbIteration = kargs.get("NbIteration", -1)
InitialDistribution = kargs.get("InitialDistribution", None)
MinInfBound = kargs.get("MinInfBound", 0)
Estimator = error.ParseKargs(kargs, "Estimator", "Likelihood",
estimator_type)
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
if isinstance(known_distribution, _DiscreteParametricModel):
known_distribution = _DiscreteParametric(known_distribution)
elif type(known_distribution) in [
_DiscreteMixture, _Convolution, _Compound
]:
known_distribution = _Distribution(known_distribution)
else:
raise TypeError("""
argument "known_distribution" must be of type _DiscreteMixture, _COnvolution,
_Compound or _DiscreteParametricModel""")
if InitialDistribution:
return histo.convolution_estimation1(known_distribution,
InitialDistribution,
Estimator, NbIteration,
Weight, Penalty, Outside)
else:
return histo.convolution_estimation2(known_distribution,
MinInfBound, Estimator,
NbIteration, Weight, Penalty,
Outside)
def estimate_convolution(histo, *args, **kargs):
""" Estimate a convolution
:Usage:
.. doctest::
:options: +SKIP
>>> Estimate(histo, "CONVOLUTION", dist,
MinInfBound=1, Parametric=False)
Estimate(histo, "CONVOLUTION", dist,
InitialDistribution=initial_dist, Parametric=False)
"""
if len(args)==0:
raise ValueError("expect at least two argument")
known_distribution = args[0]
Weight = kargs.get("Weight", -1)
NbIteration = kargs.get("NbIteration", -1)
InitialDistribution = kargs.get("InitialDistribution", None)
MinInfBound = kargs.get("MinInfBound", 0)
Estimator = error.ParseKargs(kargs, "Estimator", "Likelihood",
estimator_type)
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
if isinstance(known_distribution, _DiscreteParametricModel):
known_distribution = _DiscreteParametric(known_distribution)
elif type(known_distribution) in [_DiscreteMixture, _Convolution, _Compound]:
known_distribution = _Distribution(known_distribution)
else:
raise TypeError("""
argument "known_distribution" must be of type _DiscreteMixture, _COnvolution,
_Compound or _DiscreteParametricModel""")
if InitialDistribution:
return histo.convolution_estimation1(known_distribution,
InitialDistribution, Estimator,
NbIteration, Weight, Penalty,
Outside)
else :
return histo.convolution_estimation2(known_distribution,
MinInfBound,
Estimator, NbIteration, Weight,
Penalty, Outside)
def estimate_compound(histo, *args, **kargs):
"""estimate a compound
:Usage:
.. doctest::
:options: +SKIP
>>> Estimate(histo, "COMPOUND", dist, unknown,
Parametric=False, MinInfBound=0)
Estimate(histo, "COMPOUND", dist, unknown,
InitialDistribution=initial_dist, Parametric=False)
"""
if len(args)<2:
raise ValueError("expect at least three arguments")
known_distribution = args[0]
##if isinstance(known_distribution, _DiscreteParametricModel):
# known_distribution = _DiscreteParametric(known_distribution)
#elif type(known_distribution) in [_DiscreteMixture, _Convolution, _Compound]:
# known_distribution = _Distribution(known_distribution)
#else:
# raise TypeError("""
# argument "known_distribution" must be of type _DiscreteMixture,
# _COnvolution, _Compound or _DiscreteParametricModel""")
Type = args[1]
error.CheckType([Type], [str])
Weight = kargs.get("Weight", -1)
NbIteration = kargs.get("NbIteration", -1)
InitialDistribution = kargs.get("InitialDistribution", None)
MinInfBound = kargs.get("MinInfBound", 0)
Estimator = error.ParseKargs(kargs, "Estimator", "Likelihood",
estimator_type)
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
if MinInfBound and InitialDistribution:
raise ValueError("""MinInfBound and InitialDistribution cannot be
used together.""")
#if Estimator != _stat_tool.PENALIZED_LIKELIHOOD:
# if Penalty or Weight or Outside:
# raise ValueError("""Estimator cannot be used with O
# utside or Weight or Penalty option""")
#The second argument is either a string (e.g.,"Sum") or an unknown
#distribution.
try:
if Type:
Type = compound_type[Type]
except KeyError:
raise AttributeError("Bad type. Possible types are %s"
% (str(compound_type.keys())))
#The second argument is either a string (e.g.,"Sum") or an unknown
#distribution.
unknown_distribution = None
if InitialDistribution:
unknown_distribution = InitialDistribution
if isinstance(unknown_distribution, _Distribution):
unknown_distribution = _DiscreteParametric(unknown_distribution)
elif type(unknown_distribution) in \
[_DiscreteMixture, _Convolution, _Compound]:
unknown_distribution = _Distribution(unknown_distribution)
else:
raise TypeError("""
argument "known_distribution" must be of type
_DiscreteMixture, _COnvolution, _Compound or _DiscreteParametricModel""")
if Type == 's':
return histo.compound_estimation1(
unknown_distribution, known_distribution, Type,
Estimator, NbIteration, Weight, Penalty, Outside)
elif Type == 'e':
return histo.compound_estimation1(
known_distribution, unknown_distribution, Type,
Estimator, NbIteration, Weight, Penalty, Outside)
else:
raise KeyError("should not enter here.")
else:
return histo.compound_estimation2(
known_distribution, Type, MinInfBound, Estimator,
NbIteration, Weight, Penalty, Outside)
def estimate_DiscreteMixture(histo, *args, **kargs):
""" Estimate a finite mixture of discrete distributions
:Parameters:
* histo (histogram, mixture_data, convolution_data, compound_data),
* distributions (list) : a list of distribution object
or distribution label(string) : 'B', 'NB', 'U', 'P', ...
* unknown (string): type of unknown distribution: "Sum" or "Elementary".
:Keywords:
* MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). \
This optional argument cannot be used in conjunction \
with the optional argument InitialDistribution.
* InfBoundStatus (string): shifting or not of the distribution: "Free" (default value) or "Fixed".
* DistInfBoundStatus (string): shifting or not of the subsequent components of \
the mixture: "Free" (default value) or "Fixed".
* NbComponent (string): estimation of the number of components of the mixture: \
"Fixed" (default value) or "Estimated". Le number of estimated \
components is comprised between\
1 and a maximum number which is given by the number of specified \
parametric distributions in the mandatory arguments \
(all of these distributions are assumed to be unknown).
* Penalty (string): type of Penalty function for model selection: \
"AIC" (Akaike Information Criterion), \
"AICc" (corrected Akaike Information Criterion) \
"BIC" (Bayesian Information Criterion - default value). \
"BICc" (corrected Bayesian Information Criterion). \
This optional argument can only be used if the optional argument
NbComponent is set at "Estimated".
:Examples:
.. doctest::
:options: +SKIP
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed",
NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed",
NbComponent="Estimated", Penalty="AIC")
"""
#alias
#error.CheckArgumentsLength(args, 1, 1)
# get user arguments
# list of distributions can be either a list or several arguments
# e.g.: estimate_DiscreteMixture(["B","B"]) or estimate_DiscreteMixture("B", "B")
if len(args)==1 and type(args[0])==list:
distributions = args[0]
else:
distributions = list(args)
InfBoundStatus = kargs.get("InfBoundStatus","Free")
DistInfBoundStatus = kargs.get("DistInfBoundStatus", "Free")
NbComponent = kargs.get("NbComponent", "Fixed")
MinInfBound = kargs.get("MinInfBound", 0)
Penalty = error.ParseKargs(kargs, "Penalty", "AIC",
likelihood_penalty_type)
#should be before the conversion to booleans
error.CheckType([MinInfBound, InfBoundStatus, DistInfBoundStatus,
NbComponent, Penalty],
[int, str, str, str, LikelihoodPenaltyType])
# transform into boolean when needed
InfBoundStatus = bool(InfBoundStatus == "Free")
DistInfBoundStatus = bool(DistInfBoundStatus == "Free")
NbComponent = bool(NbComponent == "Estimated")
estimate = [] # list of bool
pcomponent = [] # list of distribution
ident = [] # list of distribution identifier
# Parse list of distribution that could be defined by a distribution,
# compound, mixture, convolution or simplya string such as "B",
# "Mixture", ...
for dist in distributions:
if isinstance(dist, str):
dist_authorised = ["BINOMIAL", "B", "POISSON",
"P", "NB", "NEGATIVE_BINOMIAL"]
if dist not in dist_authorised:
raise ValueError("""If distribution is a string, then it
must be in %s. You provided %s"""
% (dist_authorised, dist))
#todo: check that poisson is allowed
pcomponent.append(_DiscreteParametric(0, dist_type[dist]))
ident.append(dist_type[dist])
estimate.append(True)
elif isinstance(dist, _DiscreteParametricModel):
pcomponent.append(_DiscreteParametric(dist))
ident.append(None)
estimate.append(False)
elif type(dist) in [_DiscreteMixture, _Convolution, _Compound]:
pcomponent.append(_Distribution(dist))
ident.append(None)
estimate.append(False)
else:
raise ValueError("""In the case of a MIXTURE estimation,
argument related to distributions must be either string, or
Distribution, Mixture, Convolution, Compound. %s provided"""
% dist)
# check parameters
if not NbComponent and Penalty:
raise TypeError("""
Penalty can only be used with NbComponent set to 'Estimated'""")
if not NbComponent: # "FIXED"
imixt = _DiscreteMixture(pcomponent)
ret = histo.discrete_mixture_estimation1(imixt, estimate, MinInfBound,
InfBoundStatus, DistInfBoundStatus)
return ret
else: # "ESTIMATED"
ret = histo.discrete_mixture_estimation2(ident, MinInfBound, InfBoundStatus,
DistInfBoundStatus, Penalty)
return ret
def _estimate_renewal_interval_data(obj, **kargs):
"""
Estimate switch renewal_count_data
.. todo:: to be completed and validated with tests
see stat_func4 in aml
"""
#only LIKELIHOOD and PENALIZED_LIKELIHOOD
Estimator = error.ParseKargs(kargs, "Estimator",
'Likelihood', estimator_type)
NbIteration = kargs.get("NbIteration", I_DEFAULT)
error.CheckType([NbIteration], [int])
# distribution
InitialInterEvent = kargs.get("InitialInterEvent", None)
error.CheckType([InitialInterEvent], [[type(None), _DiscreteParametricModel,
_DiscreteMixture, _Convolution, _Compound]])
if isinstance(InitialInterEvent, _DiscreteParametricModel):
InitialInterEvent = _DiscreteParametric(InitialInterEvent)
else:
InitialInterEvent = _Distribution(InitialInterEvent)
#cast initialInterEvent to parametric ?
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Weight = kargs.get("Weight", D_DEFAULT)
error.CheckType([Weight], [[int, float]])
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
error.CheckType([Weight], [[int, float]])
InterEventMean = error.ParseKargs(kargs, "InterEventMean",
'Computed', mean_computation_map)
if Estimator == estimator_type['PenalizedLikelihood']:
if kargs.get("InterEventMean") is None:
InterEventMean = ONE_STEP_LATE
elif InterEventMean == COMPUTED:
raise ValueError("""
Incompatible options Estimator and InterEventMean""")
else:
if kargs.get("Penalty"):
raise ValueError("""Incompatible options Penalty with type o""")
if kargs.get("Weight"):
raise ValueError("""Incompatible options Weight with type o""")
if kargs.get("Outside"):
raise ValueError("""Incompatible options Outside with type o""")
if isinstance(obj, _FrequencyDistribution):
if InitialInterEvent:
renew = obj.estimation_inter_event(InitialInterEvent,
Estimator, NbIteration,
InterEventMean, Weight,
Penalty, Outside)
else:
renew = obj.estimation(Estimator, NbIteration,
InterEventMean ,
Weight, Penalty, Outside)
else:
if InitialInterEvent:
renew = obj.estimation_inter_event(InitialInterEvent,
Estimator, NbIteration,
InterEventMean, Weight,
Penalty, Outside)
else:
renew = obj.estimation(Estimator, NbIteration,
InterEventMean ,
Weight, Penalty, Outside)
return renew
def _estimate_renewal_count_data(obj, itype, **kargs):
"""
Estimate switch renewal_count_data
"""
Type = 'v'
error.CheckType([obj, itype], [[_TimeEvents, _RenewalData], str])
if isinstance(itype, str):
if itype == "Ordinary":
Type = 'o'
elif itype == "Equilibrium":
Type = 'e'
else:
raise AttributeError("type must be Ordinary or Equilibrium")
else:
raise AttributeError("type must be Ordinary or Equilibrium")
Estimator = error.ParseKargs(kargs, "Estimator",
'Likelihood', estimator_type)
NbIteration = kargs.get("NbIteration", I_DEFAULT)
error.CheckType([NbIteration], [int])
InitialInterEvent = kargs.get("InitialInterEvent", None)
error.CheckType([InitialInterEvent], [[type(None), _DiscreteParametricModel,
_DiscreteMixture, _Convolution, _Compound]])
EquilibriumEstimator = error.ParseKargs(kargs, "EquilibriumEstimator",
'CompleteLikelihood', estimator_semi_markov_type)
InterEventMean = error.ParseKargs(kargs, "InterEventMean",
'Computed', mean_computation_map)
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
Weight = kargs.get("Weight", -1.)
error.CheckType([Weight], [[int, float]])
if Type != 'e':
if kargs.get("EquilibriumEstimator"):
raise Exception("EquilibriumEstimator cannot be used with type='e'")
if kargs.get("InterEventMean"):
raise Exception("InterEventMean be used with type='e'")
if Estimator == estimator_type['PenalizedLikelihood']:
if kargs.get("InterEventMean") is None:
InterEventMean = ONE_STEP_LATE
elif InterEventMean == COMPUTED:
raise ValueError("""
Incompatible options Estimator and InterEventMean""")
else:
if kargs.get("Penalty"):
raise ValueError("""Incompatible options Penalty with type o""")
if kargs.get("Weight"):
raise ValueError("""Incompatible options Weight with type o""")
if kargs.get("Outside"):
raise ValueError("""Incompatible options Outside with type o""")
if InitialInterEvent:
#cast from InitialInterEvent to Mixture, Compound should be done
if isinstance(InitialInterEvent, _DiscreteParametricModel):
InitialInterEvent = _DiscreteParametric(InitialInterEvent)
else:
InitialInterEvent = _Distribution(InitialInterEvent)
renew = obj.estimation_inter_event_type(Type, InitialInterEvent,
Estimator, NbIteration,
EquilibriumEstimator,
InterEventMean, Weight,
Penalty, Outside)
else:
renew = obj.estimation_type(Type, Estimator, NbIteration,
EquilibriumEstimator, InterEventMean ,
Weight, Penalty, Outside)
return renew
def estimate_compound(histo, *args, **kargs):
"""estimate a compound
:Usage:
.. doctest::
:options: +SKIP
>>> Estimate(histo, "COMPOUND", dist, unknown,
Parametric=False, MinInfBound=0)
Estimate(histo, "COMPOUND", dist, unknown,
InitialDistribution=initial_dist, Parametric=False)
"""
if len(args) < 2:
raise ValueError("expect at least three arguments")
known_distribution = args[0]
##if isinstance(known_distribution, _DiscreteParametricModel):
# known_distribution = _DiscreteParametric(known_distribution)
#elif type(known_distribution) in [_DiscreteMixture, _Convolution, _Compound]:
# known_distribution = _Distribution(known_distribution)
#else:
# raise TypeError("""
# argument "known_distribution" must be of type _DiscreteMixture,
# _COnvolution, _Compound or _DiscreteParametricModel""")
Type = args[1]
error.CheckType([Type], [str])
Weight = kargs.get("Weight", -1)
NbIteration = kargs.get("NbIteration", -1)
InitialDistribution = kargs.get("InitialDistribution", None)
MinInfBound = kargs.get("MinInfBound", 0)
Estimator = error.ParseKargs(kargs, "Estimator", "Likelihood",
estimator_type)
Penalty = error.ParseKargs(kargs, "Penalty", "SecondDifference",
smoothing_penalty_type)
Outside = error.ParseKargs(kargs, "Outside", "Zero", outside_type)
if MinInfBound and InitialDistribution:
raise ValueError("""MinInfBound and InitialDistribution cannot be
used together.""")
#if Estimator != _stat_tool.PENALIZED_LIKELIHOOD:
# if Penalty or Weight or Outside:
# raise ValueError("""Estimator cannot be used with O
# utside or Weight or Penalty option""")
#The second argument is either a string (e.g.,"Sum") or an unknown
#distribution.
try:
if Type:
Type = compound_type[Type]
except KeyError:
raise AttributeError("Bad type. Possible types are %s" %
(str(compound_type.keys())))
#The second argument is either a string (e.g.,"Sum") or an unknown
#distribution.
unknown_distribution = None
if InitialDistribution:
unknown_distribution = InitialDistribution
if isinstance(unknown_distribution, _Distribution):
unknown_distribution = _DiscreteParametric(
unknown_distribution)
elif type(unknown_distribution) in \
[_DiscreteMixture, _Convolution, _Compound]:
unknown_distribution = _Distribution(unknown_distribution)
else:
raise TypeError("""
argument "known_distribution" must be of type
_DiscreteMixture, _COnvolution, _Compound or _DiscreteParametricModel"""
)
if Type == 's':
return histo.compound_estimation1(unknown_distribution,
known_distribution, Type,
Estimator, NbIteration,
Weight, Penalty, Outside)
elif Type == 'e':
return histo.compound_estimation1(known_distribution,
unknown_distribution, Type,
Estimator, NbIteration,
Weight, Penalty, Outside)
else:
raise KeyError("should not enter here.")
else:
return histo.compound_estimation2(known_distribution, Type,
MinInfBound, Estimator,
NbIteration, Weight, Penalty,
Outside)
def estimate_DiscreteMixture(histo, *args, **kargs):
""" Estimate a finite mixture of discrete distributions
:Parameters:
* histo (histogram, mixture_data, convolution_data, compound_data),
* distributions (list) : a list of distribution object
or distribution label(string) : 'B', 'NB', 'U', 'P', ...
* unknown (string): type of unknown distribution: "Sum" or "Elementary".
:Keywords:
* MinInfBound (int): lower bound to the range of possible values (0 -default- or 1). \
This optional argument cannot be used in conjunction \
with the optional argument InitialDistribution.
* InfBoundStatus (string): shifting or not of the distribution: "Free" (default value) or "Fixed".
* DistInfBoundStatus (string): shifting or not of the subsequent components of \
the mixture: "Free" (default value) or "Fixed".
* NbComponent (string): estimation of the number of components of the mixture: \
"Fixed" (default value) or "Estimated". Le number of estimated \
components is comprised between\
1 and a maximum number which is given by the number of specified \
parametric distributions in the mandatory arguments \
(all of these distributions are assumed to be unknown).
* Penalty (string): type of Penalty function for model selection: \
"AIC" (Akaike Information Criterion), \
"AICc" (corrected Akaike Information Criterion) \
"BIC" (Bayesian Information Criterion - default value). \
"BICc" (corrected Bayesian Information Criterion). \
This optional argument can only be used if the optional argument
NbComponent is set at "Estimated".
:Examples:
.. doctest::
:options: +SKIP
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", dist,...,,
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed")
>>> estimate_DiscreteMixture(histo, "MIXTURE", "B", "NB",...,,
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed",
NbComponent="Estimated", Penalty="AIC")
>>> Estimate(histo, "MIXTURE", "B", dist, MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed")
>>> Estimate(histo, "MIXTURE", "B", "NB",
MinInfBound=1, InfBoundStatus="Fixed",
DistInfBoundStatus="Fixed",
NbComponent="Estimated", Penalty="AIC")
"""
#alias
#error.CheckArgumentsLength(args, 1, 1)
# get user arguments
# list of distributions can be either a list or several arguments
# e.g.: estimate_DiscreteMixture(["B","B"]) or estimate_DiscreteMixture("B", "B")
if len(args) == 1 and type(args[0]) == list:
distributions = args[0]
else:
distributions = list(args)
InfBoundStatus = kargs.get("InfBoundStatus", "Free")
DistInfBoundStatus = kargs.get("DistInfBoundStatus", "Free")
NbComponent = kargs.get("NbComponent", "Fixed")
MinInfBound = kargs.get("MinInfBound", 0)
Penalty = error.ParseKargs(kargs, "Penalty", "AIC",
likelihood_penalty_type)
#should be before the conversion to booleans
error.CheckType([
MinInfBound, InfBoundStatus, DistInfBoundStatus, NbComponent,
Penalty
], [int, str, str, str, LikelihoodPenaltyType])
# transform into boolean when needed
InfBoundStatus = bool(InfBoundStatus == "Free")
DistInfBoundStatus = bool(DistInfBoundStatus == "Free")
NbComponent = bool(NbComponent == "Estimated")
estimate = [] # list of bool
pcomponent = [] # list of distribution
ident = [] # list of distribution identifier
# Parse list of distribution that could be defined by a distribution,
# compound, mixture, convolution or simplya string such as "B",
# "Mixture", ...
for dist in distributions:
if isinstance(dist, str):
dist_authorised = [
"BINOMIAL", "B", "POISSON", "P", "NB", "NEGATIVE_BINOMIAL"
]
if dist not in dist_authorised:
raise ValueError("""If distribution is a string, then it
must be in %s. You provided %s""" %
(dist_authorised, dist))
#todo: check that poisson is allowed
pcomponent.append(_DiscreteParametric(0, dist_type[dist]))
ident.append(dist_type[dist])
estimate.append(True)
elif isinstance(dist, _DiscreteParametricModel):
pcomponent.append(_DiscreteParametric(dist))
ident.append(None)
estimate.append(False)
elif type(dist) in [_DiscreteMixture, _Convolution, _Compound]:
pcomponent.append(_Distribution(dist))
ident.append(None)
estimate.append(False)
else:
raise ValueError("""In the case of a MIXTURE estimation,
argument related to distributions must be either string, or
Distribution, Mixture, Convolution, Compound. %s provided""" %
dist)
# check parameters
if not NbComponent and Penalty:
raise TypeError("""
Penalty can only be used with NbComponent set to 'Estimated'""")
if not NbComponent: # "FIXED"
imixt = _DiscreteMixture(pcomponent)
ret = histo.discrete_mixture_estimation1(imixt, estimate,
MinInfBound,
InfBoundStatus,
DistInfBoundStatus)
return ret
else: # "ESTIMATED"
ret = histo.discrete_mixture_estimation2(ident, MinInfBound,
InfBoundStatus,
DistInfBoundStatus,
Penalty)
return ret
