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 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))