def transform(x, *args, **kwargs):
"""Transform a continuous random variable to the unconstrained space.
Transform selects among a number of defaults transformations which depend
on the support of the provided random variable.
Args:
x : RandomVariable.
Continuous random variable to transform.
*args, **kwargs : optional.
Arguments to overwrite when forming the ``TransformedDistribution``.
For example, one can manually specify the transformation by
passing in the ``bijector`` argument.
Returns:
RandomVariable.
A ``TransformedDistribution`` random variable, or the provided random
variable if no transformation was applied.
#### Examples
```python
x = Gamma(1.0, 1.0)
y = ed.transform(x)
sess = tf.Session()
sess.run(y)
-2.2279539
```
"""
if len(args) != 0 or kwargs.get('bijector', None) is not None:
return TransformedDistribution(x, *args, **kwargs)
try:
support = x.support
except AttributeError as e:
msg = """'{}' object has no 'support'
so cannot be transformed.""".format(type(x).__name__)
raise ValueError(msg)
if support == '01':
bij = bijectors.Invert(bijectors.Sigmoid())
elif support == 'nonnegative':
bij = bijectors.Invert(bijectors.Softplus())
elif support == 'simplex':
bij = bijectors.Invert(bijectors.SoftmaxCentered(event_ndims=1))
elif support == 'real' or support == 'multivariate_real':
return x
else:
msg = "'transform' does not handle supports of type '{}'".format(
support)
raise NotImplementedError(msg)
return TransformedDistribution(x, bij, *args, **kwargs)
def test_kwargs(self):
with self.test_session():
x = Normal(-100.0, 1.0)
y = ed.transform(x, bijector=bijectors.Softplus())
sample = y.sample(10).eval()
self.assertTrue((sample >= 0.0).all())