def __init__(self,
logits: Optional[Numeric] = None,
probs: Optional[Numeric] = None,
dtype: jnp.dtype = jnp.int_):
"""Initializes a Bernoulli distribution.
Args:
logits: Logit transform of the probability of a `1` event (`0` otherwise),
i.e. `probs = sigmoid(logits)`. Only one of `logits` or `probs` can be
specified.
probs: Probability of a `1` event (`0` otherwise). Only one of `logits` or
`probs` can be specified.
dtype: The type of event samples.
"""
super().__init__()
# Validate arguments.
chex.assert_exactly_one_is_none(probs, logits)
if not (jnp.issubdtype(dtype, bool) or jnp.issubdtype(
dtype, jnp.integer) or jnp.issubdtype(dtype, jnp.floating)):
raise ValueError(
f'The dtype of `{self.name}` must be boolean, integer or '
f'floating-point, instead got `{dtype}`.')
# Parameters of the distribution.
self._probs = None if probs is None else conversion.as_float_array(
probs)
self._logits = None if logits is None else conversion.as_float_array(
logits)
self._dtype = dtype
def __init__(self, low: Numeric = 0., high: Numeric = 1.):
"""Initializes a Uniform distribution.
Args:
low: Lower bound.
high: Upper bound.
"""
super().__init__()
self._low = conversion.as_float_array(low)
self._high = conversion.as_float_array(high)
self._batch_shape = jax.lax.broadcast_shapes(self._low.shape,
self._high.shape)
def __init__(self, loc: Numeric, scale: Numeric):
"""Initializes a Normal distribution.
Args:
loc: Mean of the distribution.
scale: Standard deviation of the distribution.
"""
super().__init__()
self._loc = conversion.as_float_array(loc)
self._scale = conversion.as_float_array(scale)
self._batch_shape = jax.lax.broadcast_shapes(
self._loc.shape, self._scale.shape)
def __init__(self, loc: Numeric, scale: Numeric) -> None:
"""Initializes a Logistic distribution.
Args:
loc: Mean of the distribution.
scale: Spread of the distribution.
"""
super().__init__()
self._loc = conversion.as_float_array(loc)
self._scale = conversion.as_float_array(scale)
self._batch_shape = jax.lax.broadcast_shapes(self._loc.shape,
self._scale.shape)
def __init__(self,
loc: Optional[Array] = None,
scale_diag: Optional[Array] = None):
"""Initializes a MultivariateNormalDiag distribution.
Args:
loc: Mean vector of the distribution. Can also be a batch of vectors. If
not specified, it defaults to zeros. At least one of `loc` and
`scale_diag` must be specified.
scale_diag: Vector of standard deviations. Can also be a batch of vectors.
If not specified, it defaults to ones. At least one of `loc` and
`scale_diag` must be specified.
"""
super().__init__()
chex.assert_not_both_none(loc, scale_diag)
if scale_diag is not None and not scale_diag.shape:
raise ValueError(
'If provided, argument `scale_diag` must have at least '
'1 dimension.')
if loc is not None and not loc.shape:
raise ValueError('If provided, argument `loc` must have at least '
'1 dimension.')
if loc is not None and scale_diag is not None and (
loc.shape[-1] != scale_diag.shape[-1]):
raise ValueError(
f'The last dimension of arguments `loc` and '
f'`scale_diag` must coincide, but {loc.shape[-1]} != '
f'{scale_diag.shape[-1]}.')
if scale_diag is None:
self._loc = conversion.as_float_array(loc)
self._scale_diag = jnp.ones(self._loc.shape[-1], self._loc.dtype)
elif loc is None:
self._scale_diag = conversion.as_float_array(scale_diag)
self._loc = jnp.zeros(self._scale_diag.shape[-1],
self._scale_diag.dtype)
else:
self._loc = conversion.as_float_array(loc)
self._scale_diag = conversion.as_float_array(scale_diag)
self._batch_shape = jax.lax.broadcast_shapes(
self._loc.shape[:-1], self._scale_diag.shape[:-1])
def __init__(self,
loc: Numeric,
log_scale: Numeric,
max_scale: Optional[float] = None):
"""Initializes a LogStddevNormal distribution.
Args:
loc: Mean of the distribution.
log_scale: Log of the distribution's scale. This is often the
pre-activated output of a neural network.
max_scale: Maximum value of the scale that this distribution will saturate
at. This parameter can be useful for numerical stability. It is not a
hard maximum; rather, we compute scale as per the following formula:
log(max_scale) - softplus(log(max_scale) - log_scale).
"""
if max_scale is not None:
max_log_scale = math.log(max_scale)
self._log_scale = max_log_scale - jax.nn.softplus(
max_log_scale - conversion.as_float_array(log_scale))
else:
self._log_scale = conversion.as_float_array(log_scale)
scale = jnp.exp(self._log_scale)
super().__init__(loc, scale)
def test_on_invalid_array(self, dtype):
x = jnp.zeros([], dtype)
with self.assertRaises(ValueError):
conversion.as_float_array(x)
def test_on_int_array(self, dtype):
x = jnp.zeros([], dtype)
y = conversion.as_float_array(x)
self.assertIsInstance(y, jnp.ndarray)
self.assertEqual(
y.dtype, jnp.float64 if jax.config.x64_enabled else jnp.float32)
def test_on_float_array(self, dtype):
x = jnp.zeros([], dtype)
y = conversion.as_float_array(x)
self.assertIs(y, x)
def test_on_invalid_scalar(self, x):
with self.assertRaises(ValueError):
conversion.as_float_array(x)
def test_on_valid_scalar(self, x):
y = conversion.as_float_array(x)
self.assertIsInstance(y, jnp.ndarray)
self.assertEqual(
y.dtype, jnp.float64 if jax.config.x64_enabled else jnp.float32)
