# StickBreakingTransform

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributions.StickBreakingTransform.html

```python
class tensorplay.distributions.StickBreakingTransform(cache_size: int = 0)
```

Transform from unconstrained space to the simplex of one additional
dimension via a stick-breaking process.

This transform arises as an iterated sigmoid transform in a stick-breaking
construction of the Dirichlet distribution: the first logit is
transformed via sigmoid to the first probability and the probability of
everything else, and then the process recurses.

This is bijective and appropriate for use in HMC; however it mixes
coordinates together and is less appropriate for optimization.

```python
property inv: Transform
```

Returns the inverse [Transform](/docs/generated/tensorplay.distributions.Transform.html#tensorplay.distributions.Transform) of this transform.
This should satisfy t.inv.inv is t.

```python
property sign: int
```

Returns the sign of the determinant of the Jacobian, if applicable.
In general this only makes sense for bijective transforms.