def test_docformat():
udd = doccer.unindent_dict(doc_dict)
formatted = doccer.docformat(docstring, udd)
yield assert_equal, formatted, filled_docstring
single_doc = 'Single line doc %(strtest1)s'
formatted = doccer.docformat(single_doc, doc_dict)
# Note - initial indent of format string does not
# affect subsequent indent of inserted parameter
yield assert_equal, formatted, """Single line doc Another test
def test_docformat():
with suppress_warnings() as sup:
sup.filter(category=DeprecationWarning)
udd = doccer.unindent_dict(doc_dict)
formatted = doccer.docformat(docstring, udd)
assert_equal(formatted, filled_docstring)
single_doc = 'Single line doc %(strtest1)s'
formatted = doccer.docformat(single_doc, doc_dict)
# Note - initial indent of format string does not
# affect subsequent indent of inserted parameter
assert_equal(formatted, """Single line doc Another test
with some indent""")
x = _process_quantiles(x, self.dim)
out = self._mnorm._logpdf(x, self.mean, self.prec_U, self._log_det_cov)
return _squeeze_output(out)
def pdf(self, x):
return np.exp(self.logpdf(x))
def rvs(self, size=1):
return self._mnorm.rvs(self.mean, self.cov, size)
def entropy(self):
"""
Computes the differential entropy of the multivariate normal.
Returns
-------
h : scalar
Entropy of the multivariate normal distribution
"""
return 1 / 2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)
# Set frozen generator docstrings from corresponding docstrings in
# multivariate_normal_gen and fill in default strings in class docstrings
for name in ['logpdf', 'pdf', 'rvs']:
method = multivariate_normal_gen.__dict__[name]
method_frozen = multivariate_normal_frozen.__dict__[name]
method_frozen.__doc__ = doccer.docformat(method.__doc__, docdict_noparams)
method.__doc__ = doccer.docformat(method.__doc__, docdict_params)
def __init__(self):
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
x = _process_quantiles(x, self.dim)
out = self._mnorm._logpdf(x, self.mean, self.prec_U, self._log_det_cov)
return _squeeze_output(out)
def pdf(self, x):
return np.exp(self.logpdf(x))
def rvs(self, size=1):
return self._mnorm.rvs(self.mean, self.cov, size)
def entropy(self):
"""
Computes the differential entropy of the multivariate normal.
Returns
-------
h : scalar
Entropy of the multivariate normal distribution
"""
return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)
# Set frozen generator docstrings from corresponding docstrings in
# multivariate_normal_gen and fill in default strings in class docstrings
for name in ['logpdf', 'pdf', 'rvs']:
method = multivariate_normal_gen.__dict__[name]
method_frozen = multivariate_normal_frozen.__dict__[name]
method_frozen.__doc__ = doccer.docformat(method.__doc__, docdict_noparams)
method.__doc__ = doccer.docformat(method.__doc__, docdict_params)
def __init__(self):
super(conditional_mix_normal_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__,
mix_normal_docdict_params)
def __init__(self):
super(conditional_student_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, student_docdict_params)
def __init__(self):
super(invwishart_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, wishart_docdict_params)
def __init__(self):
super(conditional_student_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, student_docdict_params)
def __init__(self):
super(invwishart_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, wishart_docdict_params)
def __init__(self):
super(multivariate_student_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
def __init__(self, seed=None):
super(multivariate_t_gen, self).__init__(seed)
self.__doc__ = doccer.docformat(self.__doc__, mvt_docdict_params)
def __init__(self):
super(markov_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
def __init__(self):
super(markov_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
def __init__(self):
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
def __init__(self):
super(conditional_mix_normal_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, mix_normal_docdict_params)
def __init__(self):
super(multivariate_student_gen, self).__init__()
self.__doc__ = doccer.docformat(self.__doc__, docdict_params)
