def set_dist(self, dist='uniform', kwds=None, dim=None):
r"""
TODO: Add this.
"""
distribution = dist
if (kwds is not None) and (dim is not None):
self.dist.set_dist(distribution, kwds, dim)
elif (kwds is None) and (dim is not None):
self.dist.set_dist(dist=distribution, dim=dim)
# Here we override the default errors printed by scipy.stats with our own.
elif (kwds is None) and (
distributions.supported_distributions(distribution) is 'chi2'):
raise (AttributeError(
"If you are using a chi2 distribution, please pass `df` as a key-value pair in a dictionary. Ex: {'df': 20}."
))
elif (kwds is None) and (
distributions.supported_distributions(distribution) is 'beta'):
raise (AttributeError(
"If you are using a Beta dist, please pass `a` and `b` as key-value pairs in a dictionary. Ex: {'a': 1, 'b': 1}"
))
# the following allows for manual overrides not using the parametric object.
# e.g. kwds = {'loc': [1,2,3]}
elif dim is None:
# print("INPUT: No dimension specified. You will be using `scipy.stats` for your distributions instead of the parametric object. Be warned that functions like `.pdf` may not work as expected.")
if kwds is not None:
self.dist = distributions.assign_dist(distribution, **kwds)
else:
self.dist = distributions.assign_dist(distribution)
pass
def set_observed_dist(self, distribution=None, kwds=None, dim=None):
r"""
TODO: Add this.
"""
# If `distribution = None`, we query the pushforward density for the top 5% to get a MAP estimate
# TODO print warning about the aforementioned.
# TODO check sizes, ensure dimension agreement
if distribution is not None:
if (kwds is not None) and (dim is not None):
self.observed_dist.set_dist(dim, distribution, kwds)
elif (kwds is None) and (dim is not None):
self.observed_dist.set_dist(dim, distribution)
# Here we override the default errors printed by scipy.stats with our own.
elif (kwds is None) and (
distributions.supported_distributions(distribution) is
'chi2'):
raise (AttributeError(
"If you are using a chi2 distribution, please pass `df` as a key-value pair in a dictionary. Ex: {'df': 20}."
))
elif (kwds is None) and (
distributions.supported_distributions(distribution) is
'beta'):
raise (AttributeError(
"If you are using a Beta dist, please pass `a` and `b` as key-value pairs in a dictionary. Ex: {'a': 1, 'b': 1}"
))
# the following allows for manual overrides not using the parametric object.
# e.g. kwds = {'loc': [1,2,3]}
elif dim is None:
logging.warn(
"OBS: No dimension specified. You will be using `scipy.stats` for your distributions instead of the parametric object. Be warned that functions like `.pdf` may not work as expected."
)
if kwds is not None:
self.observed_dist = distributions.assign_dist(
distribution, **kwds)
else:
self.observed_dist = distributions.assign_dist(
distribution)
else:
logging.warn("""No distribution specified.
Defaulting to normal around data_means with 0.5*data_standard_deviation"""
)
loc = np.mean(self.output.samples, axis=0)
scale = 0.5 * np.std(self.output.samples, axis=0)
self.observed_dist = distributions.assign_dist(
'normal', **{
'loc': loc,
'scale': scale
})
pass