def __init__(self, base_dist: Distribution, skewness, validate_args=None):
assert (
base_dist.event_shape == skewness.shape[-1:]
), "Sine Skewing is only valid with a skewness parameter for each dimension of `base_dist.event_shape`."
batch_shape = jnp.broadcast_shapes(base_dist.batch_shape, skewness.shape[:-1])
event_shape = skewness.shape[-1:]
self.skewness = jnp.broadcast_to(skewness, batch_shape + event_shape)
self.base_dist = base_dist.expand(batch_shape)
super().__init__(batch_shape, event_shape, validate_args=validate_args)
def __init__(self, base_dist: Distribution, skewness, validate_args=None):
batch_shape = jnp.broadcast_shapes(base_dist.batch_shape,
skewness.shape[:-1])
event_shape = skewness.shape[-1:]
self.skewness = jnp.broadcast_to(skewness, batch_shape + event_shape)
self.base_dist = base_dist.expand(batch_shape)
super().__init__(batch_shape, event_shape, validate_args=validate_args)
if self._validate_args and base_dist.mean.device != skewness.device:
raise ValueError(
f"base_density: {base_dist.__class__.__name__} and SineSkewed "
f"must be on same device.")
def __init__(self):
self._kernel = lambda x, y: jnp.broadcast_to(0., jnp.broadcast_shapes(x.shape, y.shape))
self._minderivable = (sys.maxsize, sys.maxsize)
self._maxderivable = (sys.maxsize, sys.maxsize)
self.initargs = None