# LowerCholeskyTransform

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

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

Transform from unconstrained matrices to lower-triangular matrices with
nonnegative diagonal entries.

This is useful for parameterizing positive definite matrices in terms of
their Cholesky factorization.

```python
forward_shape(shape)
```

Infers the shape of the forward computation, given the input shape.
Defaults to preserving shape.

```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
inverse_shape(shape)
```

Infers the shapes of the inverse computation, given the output shape.
Defaults to preserving shape.

```python
log_abs_det_jacobian(x, y)
```

Computes the log det jacobian log |dy/dx| given input and output.

```python
property sign: int
```

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