def random(shape=None, **kwds):
args, kwds = separate_shape_args(kwds, shape_args)
if shape is None:
return scipy_dist.rvs(*args, **kwds)
else:
return np.reshape(scipy_dist.rvs(*args, **kwds), shape)
def random(shape=None, **kwds):
args, kwds = separate_shape_args(kwds, shape_args)
if shape is None:
return scipy_dist.rvs(*args, **kwds)
else:
return np.reshape(scipy_dist.rvs(*args, **kwds), 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 logp(value, **kwds):
args, kwds = separate_shape_args(kwds, shape_args)
if hasattr(scipy_dist, '_logp'):
return scipy_dist._logp(value, *args)
else:
return np.sum(scipy_dist.logpmf(value, *args, **kwds))
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 logp(value, **kwds):
args, kwds = separate_shape_args(kwds, shape_args)
if hasattr(scipy_dist, '_logp'):
return scipy_dist._logp(value, *args)
else:
return np.sum(scipy_dist.logpmf(value,*args,**kwds))
