def __init__(self, *args, **kwds):
(dtype, name, parent_names, parents_default, docstr, logp, random,
mv, logp_partial_gradients) = new_class_args
parents = parents_default
# Figure out what argument names are needed.
arg_keys = [
'name', 'parents', 'value', 'observed', 'size', 'trace',
'rseed', 'doc', 'debug', 'plot', 'verbose'
]
arg_vals = [
None, parents, None, False, None, True, True, None, False,
None, -1
]
if 'isdata' in kwds:
warnings.warn(
'"isdata" is deprecated, please use "observed" instead.')
kwds['observed'] = kwds['isdata']
pass
# No size argument allowed for multivariate distributions.
if mv:
arg_keys.pop(4)
arg_vals.pop(4)
arg_dict_out = dict(zip(arg_keys, arg_vals))
args_needed = ['name'] + parent_names + arg_keys[2:]
# Sort positional arguments
for i in xrange(len(args)):
try:
k = args_needed.pop(0)
if k in parent_names:
parents[k] = args[i]
else:
arg_dict_out[k] = args[i]
except:
raise ValueError(
'Too many positional arguments provided. Arguments for class '
+ self.__class__.__name__ + ' are: ' +
str(args_needed))
# Sort keyword arguments
for k in args_needed:
if k in parent_names:
try:
parents[k] = kwds.pop(k)
except:
if k in parents_default:
parents[k] = parents_default[k]
else:
raise ValueError('No value given for parent ' + k)
elif k in arg_dict_out.keys():
try:
arg_dict_out[k] = kwds.pop(k)
except:
pass
# Remaining unrecognized arguments raise an error.
if len(kwds) > 0:
raise TypeError('Keywords ' + str(kwds.keys()) +
' not recognized. Arguments recognized are ' +
str(args_needed))
# Determine size desired for scalar variables.
# Notes
# -----
# Case | init_val | parents | size | value.shape | bind size
# ------------------------------------------------------------------
# 1.1 | None | scalars | None | 1 | 1
# 1.2 | None | scalars | n | n | n
# 1.3 | None | n | None | n | 1
# 1.4 | None | n | n(m) | n (Error) | 1 (-)
# 2.1 | scalar | scalars | None | 1 | 1
# 2.2 | scalar | scalars | n | n | n
# 2.3 | scalar | n | None | n | 1
# 2.4 | scalar | n | n(m) | n (Error) | 1 (-)
# 3.1 | n | scalars | None | n | n
# 3.2 | n | scalars | n(m) | n (Error) | n (-)
# 3.3 | n | n | None | n | 1
# 3.4 | n | n | n(m) | n (Error) | 1 (-)
if not mv:
shape = arg_dict_out.pop('size')
shape = None if shape is None else tuple(np.atleast_1d(shape))
init_val = arg_dict_out['value']
init_val_shape = None if init_val is None else np.shape(
init_val)
if len(parents) > 0:
pv = [np.shape(utils.value(v)) for v in parents.values()]
biggest_parent = np.argmax([(np.prod(v) if v else 0)
for v in pv])
parents_shape = pv[biggest_parent]
# Scalar parents can support any shape.
if np.prod(parents_shape) < 1:
parents_shape = None
else:
parents_shape = None
def shape_error():
raise ValueError(
'Shapes are incompatible: value %s, largest parent %s, shape argument %s'
% (shape, init_val_shape, parents_shape))
if init_val_shape is not None and shape is not None and init_val_shape != shape:
shape_error()
given_shape = init_val_shape or shape
bindshape = given_shape or parents_shape
# Check consistency of bindshape and parents_shape
if parents_shape is not None:
# Uncomment to leave broadcasting completely up to NumPy's random functions
# if bindshape[-np.alen(parents_shape):]!=parents_shape:
# Uncomment to limit broadcasting flexibility to what the Fortran likelihoods can handle.
if bindshape < parents_shape:
shape_error()
if random is not None:
random = bind_size(random, bindshape)
elif 'size' in kwds.keys():
raise ValueError(
'No size argument allowed for multivariate stochastic variables.'
)
# Call base class initialization method
if arg_dict_out.pop('debug'):
logp = debug_wrapper(logp)
random = debug_wrapper(random)
else:
Stochastic.__init__(self,
logp=logp,
random=random,
dtype=dtype,
**arg_dict_out)
def __init__(self, *args, **kwds):
new_class.__init__(self, *args, **kwds)
self.args, self.kwds = separate_shape_args(self.parents, shape_args)
self.frozen_rv = self.rv(self.args, self.kwds)
self._random = bind_size(self._random, self.shape)
def __init__(self, *args, **kwds):
new_class.__init__(self, *args, **kwds)
self.args, self.kwds = separate_shape_args(self.parents,
shape_args)
self.frozen_rv = self.rv(self.args, self.kwds)
self._random = bind_size(self._random, self.shape)
def __init__(self, *args, **kwds):
(dtype, name, parent_names, parents_default, docstr, logp, random, mv, logp_partial_gradients) = new_class_args
parents=parents_default
# Figure out what argument names are needed.
arg_keys = ['name', 'parents', 'value', 'observed', 'size', 'trace', 'rseed', 'doc', 'debug', 'plot', 'verbose']
arg_vals = [None, parents, None, False, None, True, True, None, False, None, -1]
if 'isdata' in kwds:
warnings.warn('"isdata" is deprecated, please use "observed" instead.')
kwds['observed'] = kwds['isdata']
pass
# No size argument allowed for multivariate distributions.
if mv:
arg_keys.pop(4)
arg_vals.pop(4)
arg_dict_out = dict(list(zip(arg_keys, arg_vals)))
args_needed = ['name'] + parent_names + arg_keys[2:]
# Sort positional arguments
for i in range(len(args)):
try:
k = args_needed.pop(0)
if k in parent_names:
parents[k] = args[i]
else:
arg_dict_out[k] = args[i]
except:
raise ValueError('Too many positional arguments provided. Arguments for class ' + self.__class__.__name__ + ' are: ' + str(args_needed))
# Sort keyword arguments
for k in args_needed:
if k in parent_names:
try:
parents[k] = kwds.pop(k)
except:
if k in parents_default:
parents[k] = parents_default[k]
else:
raise ValueError('No value given for parent ' + k)
elif k in list(arg_dict_out.keys()):
try:
arg_dict_out[k] = kwds.pop(k)
except:
pass
# Remaining unrecognized arguments raise an error.
if len(kwds) > 0:
raise TypeError('Keywords '+ str(list(kwds.keys())) + ' not recognized. Arguments recognized are ' + str(args_needed))
# Determine size desired for scalar variables.
# Notes
# -----
# Case | init_val | parents | size | value.shape | bind size
# ------------------------------------------------------------------
# 1.1 | None | scalars | None | 1 | 1
# 1.2 | None | scalars | n | n | n
# 1.3 | None | n | None | n | 1
# 1.4 | None | n | n(m) | n (Error) | 1 (-)
# 2.1 | scalar | scalars | None | 1 | 1
# 2.2 | scalar | scalars | n | n | n
# 2.3 | scalar | n | None | n | 1
# 2.4 | scalar | n | n(m) | n (Error) | 1 (-)
# 3.1 | n | scalars | None | n | n
# 3.2 | n | scalars | n(m) | n (Error) | n (-)
# 3.3 | n | n | None | n | 1
# 3.4 | n | n | n(m) | n (Error) | 1 (-)
if not mv:
shape = arg_dict_out.pop('size')
shape = None if shape is None else tuple(np.atleast_1d(shape))
init_val = arg_dict_out['value']
init_val_shape = None if init_val is None else np.shape(init_val)
if len(parents) > 0:
pv = [np.shape(utils.value(v)) for v in list(parents.values())]
biggest_parent = np.argmax([(np.prod(v) if v else 0) for v in pv])
parents_shape = pv[biggest_parent]
# Scalar parents can support any shape.
if np.prod(parents_shape) < 1:
parents_shape = None
else:
parents_shape = None
def shape_error():
raise ValueError('Shapes are incompatible: value %s, largest parent %s, shape argument %s'%(shape, init_val_shape, parents_shape))
if init_val_shape is not None and shape is not None and init_val_shape != shape:
shape_error()
given_shape = init_val_shape or shape
bindshape = given_shape or parents_shape
# Check consistency of bindshape and parents_shape
if parents_shape is not None:
# Uncomment to leave broadcasting completely up to NumPy's random functions
# if bindshape[-np.alen(parents_shape):]!=parents_shape:
# Uncomment to limit broadcasting flexibility to what the Fortran likelihoods can handle.
if bindshape<parents_shape:
shape_error()
if random is not None:
random = bind_size(random, bindshape)
elif 'size' in list(kwds.keys()):
raise ValueError('No size argument allowed for multivariate stochastic variables.')
# Call base class initialization method
if arg_dict_out.pop('debug'):
logp = debug_wrapper(logp)
random = debug_wrapper(random)
else:
Stochastic.__init__(self, logp=logp, random=random, dtype=dtype, **arg_dict_out)