def estimate_parametric(histo,
ident,
MinInfBound=0,
InfBoundStatus="Free"):
""" Estimate a parametric discrete distribution (binomial,
Poisson or negative binomial distribution with an additional shift
parameter)
:Parameters:
* histo (histogram, mixture_data, convolution_data, compound_data),
* ident ("BINOMIAL", "POISSON", "NEGATIVE_BINOMIAL", "UNIFORM")
* MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
* InfBoundStatus (string): shifting or not of the distribution:
"Free" (default value) or "Fixed". T
:Usage:
.. doctest::
:options: +SKIP
>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")
"""
error.CheckType([ident, MinInfBound, InfBoundStatus], [str, int, str])
flag = bool(InfBoundStatus == "Free")
try:
ident_id = dist_type[ident]
except KeyError:
raise KeyError("Valid type are %s" % (str(dist_type.keys())))
return histo.parametric_estimation(ident_id, MinInfBound, flag)
def estimate_parametric(histo, ident,
MinInfBound=0,
InfBoundStatus="Free"):
""" Estimate a parametric discrete distribution (binomial,
Poisson or negative binomial distribution with an additional shift
parameter)
:Parameters:
* histo (histogram, mixture_data, convolution_data, compound_data),
* ident ("BINOMIAL", "POISSON", "NEGATIVE_BINOMIAL", "UNIFORM")
* MinInfBound (int): lower bound to the range of possible values (0 - default value - or 1).
* InfBoundStatus (string): shifting or not of the distribution:
"Free" (default value) or "Fixed". T
:Usage:
.. doctest::
:options: +SKIP
>>> estimate_parametric(histo, ident, MinInfBound=0, InfBoundStatus="Free")
>>> Estimate(histo, "NB", MinInfBound=1, InfBoundStatus="Fixed")
"""
error.CheckType([ident, MinInfBound, InfBoundStatus],
[str, int, str])
flag = bool(InfBoundStatus == "Free")
try:
ident_id = dist_type[ident]
except KeyError:
raise KeyError("Valid type are %s" % (str(dist_type.keys())))
return histo.parametric_estimation(ident_id, MinInfBound, flag)
def Distribution(utype, *args):
"""
Construction of a parametric discrete distribution (either binomial,
Poisson, negative binomial or uniform) from the name and the parameters
of the distribution or from an ASCII file.
A supplementary shift parameter (argument inf_bound) is required with
respect to the usual definitions of these discrete distributions.
Constraints over parameters are given in the file syntax corresponding
to the type distribution(cf. File Syntax).
:Parameters:
* `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, float) : probability of success \
(only relevant for binomial or negative binomial distributions),
* `file_name` (string).
.. note:: the names of the parametric discrete distributions can be
summarized by their first letters:
* "B" ("BINOMIAL"),
* "P" ("POISSON"),
* "NB" ("NEGATIVE_BINOMIAL"),
* "U" ("UNIFORM"),
* "M" ("MULTINOMIAL"),
:Returns:
If the construction succeeds, an object of type distribution is
returned, otherwise no object is returned.
:Examples:
.. doctest::
:options: +SKIP
>>> Distribution("BINOMIAL", inf_bound, sup_bound, proba)
>>> Distribution("POISSON", inf_bound, param)
>>> Distribution("NEGATIVE_BINOMIAL", inf_bound, param, proba)
>>> Distribution("UNIFORM", inf_bound, sup_bound)
>>> Distribution(file_name)
.. seealso::
:func:`~openalea.stat_tool.output.Save`,
:func:`~openalea.stat_tool.estimate.Estimate`
:func:`~openalea.stat_tool.simulate.Simulate`.
"""
# Constructor from Filename or Histogram or parametricmodel
if(len(args) == 0):
error.CheckType([utype],
[[str, _DiscreteDistributionData, _DiscreteParametricModel]],
arg_id=[1])
result = _DiscreteParametricModel(utype)
# from parameters
if len(args)>0:
error.CheckArgumentsLength(args, 1)
if utype in ["B", "BINOMIAL"]:
result = Binomial(*args)
elif utype in ["P", "POISSON"]:
result = Poisson(*args)
elif utype in ["M", "MULTINOMIAL"]:
raise NotImplementedError("Multinomial not yet implemented")
elif utype in ["NB", "NEGATIVE_BINOMIAL"]:
result = NegativeBinomial(*args)
elif utype in ["U", "UNIFORM"]:
result = Uniform(*args)
else:
raise KeyError(" %s not found. Allowed keys are %s"
% (utype, distribution_identifier_type.keys()))
return result