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
def Renewal(*args, **kargs):
"""Renewal
Construction of a (either ordinary or equilibrium) renewal process from an inter-event distribution or from an ASCII file.
:Usage:
.. doctest::
:options: +SKIP
>>> Renewal("BINOMIAL", inf_bound, sup_bound, proba, Type="Equilibrium", ObservationTime=40)
>>> Renewal("POISSON", inf_bound, param, Type="Equilibrium", ObservationTime=40)
>>> Renewal("NEGATIVE_BINOMIAL", inf_bound, param, proba, Type="Equilibrium", ObservationTime=40)
>>> Renewal(inter_event, Type="Equilibrium", ObservationTime=40)
>>> Renewal(file_name, Type="Equilibrium", ObservationTime=40)
:Arguments:
* inf_bound (int): lower bound to the range of possible values (shift parameter),
* sup_bound (int): upper bound to the range of possible values (only relevant for binomial or uniform distributions),
* param (int, real): parameter of either the Poisson distribution or the negative binomial distribution.
* proba (int, real): probability of 'success' (only relevant for binomial or negative binomial distributions).
.. note:: the names of the parametric discrete distributions can be summarized by their first letters: "B" ("BINOMIAL"), "P" ("POISSON"), "NB" ("NEGATIVE_BINOMIAL").
* inter_event (distribution, mixture, convolution, compound): inter-event distribution,
* file_name (string).
:Optional Arguments:
* Type (string): type of renewal process: "Ordinary" or "Equilibriun" (the default).
* ObservationTime (int): length of the observation period for the computation of the intensity and counting distributions (default value: 20),
:Returned Object:
If the construction succeeds, an object of type renewal is returned, otherwise no object is returned.
:Background:
A renewal process is built from a discrete distribution termed the inter-event
distribution which represents the time interval between consecutive events. Two types
of renewal processes are available:
* ordinary renewal process where the start of the observation period coincides
with the occurrence time of an event (synchronism assumption),
* equilibrium or stationary renewal process where the start of the observation
period is independent of the process which generates the data (asynchronism
assumption).
In the case where the arguments are the name and the parameters of the inter-event \
distribution, the constraints on parameters described in the definition of the syntactic
form of the type distribution apply (cf. File Syntax).
.. seealso::
:func:`~openalea.stat_tool.output.Save`,
:func:`~openalea.sequence_analysis.simulate.Simulate` (renewal process)
.. todo :: ident should correspond to Binomail,B, NegativeBinomial and so on
"""
#todo: move this enym to enumerate.py
type_map = {
"Equilibrium":'e',
"Ordinary": 'o'
}
Type = error.ParseKargs(kargs, "Type", "Equilibrium", type_map)
ObservationTime = kargs.get("ObservationTime", DEFAULT_TIME)
Scale = kargs.get("Scale", None) #todo check default values !
a = [str]
a.extend(model_distribution_types)
error.CheckType([args[0]], [a])
# a filename constructor. check that only one argument, which is a string
# ------------------ todo ----------- not tested
if len(args)==1 and isinstance(args[0], str):
filename = args[0]
if os.path.isfile(filename):
renewal = _Renewal(filename)
else:
raise IOError("bad file name")
# otherwise, we switch to a constructor from a distribution
elif isinstance(args[0], str):
if args[0] == "BINOMIAL" or args[0] == "B":
error.CheckArgumentsLength(args, 4, 4)
error.CheckType([args[1], args[2], args[3]],
[int, int, [int, float]])
inf_bound = args[1]
sup_bound = args[2]
probability = args[3]
parameter = -1
elif args[0] == "NEGATIVE_BINOMIAL" or args[0] == "NB":
error.CheckArgumentsLength(args, 4, 4)
error.CheckType([args[1], args[2], args[3]],
[int, [int, float], [int, float]])
inf_bound = args[1]
sup_bound = -1
parameter = args[2]
probability = args[3]
elif args[0] == "POISSON" or args[0] == "P":
error.CheckArgumentsLength(args, 4, 4)
error.CheckType([args[1], args[2], args[3]],
[int, [float, int], [int, float]])
inf_bound = args[1]
sup_bound = -1
parameter = args[2]
probability = args[3]
else:
raise NotImplemented("""case not implemented. First arg must be a
valid filename or a "BINOMIAL", "NEGATIVE_BINOMIAL, or "POISSON"
""")
# if all keys in distribution_identifier_type are used, we can move this
# piece of call before the if and remove the NotImplemented above
ident = distribution_identifier_type[args[0]]
RENEWAL_THRESHOLD = 1.
inter_event = _DiscreteParametric(ident , inf_bound , sup_bound , parameter ,
probability , RENEWAL_THRESHOLD)
if Scale:
error.CheckType([Scale], [float])
scaled_inter_event = _DiscreteParametric(inter_event , Scale)
renewal = _Renewal(scaled_inter_event , Type , ObservationTime)
else:
renewal = _Renewal(inter_event , Type , ObservationTime)
# renewal = _Renewal(args[0], range(0,len(args[0])),
# index_parameter_type) or may be provided by the user.
elif type(args[0]) in model_distribution_types:
renewal = _Renewal(_DiscreteParametric(args[0]), Type, ObservationTime)
return renewal