# Distribution

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

```python
class tensorplay.distributions.Distribution(batch_shape: Size = (), event_shape: Size = (), validate_args: bool | None = None)
```

Distribution is the abstract base class for probability distributions.

Parameters:

- batch_shape ([tensorplay.Size](/docs/generated/tensorplay.Size.html#tensorplay.Size)) – The shape over which parameters are batched.

- event_shape ([tensorplay.Size](/docs/generated/tensorplay.Size.html#tensorplay.Size)) – The shape of a single sample (without batching).

- validate_args ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – Whether to validate arguments. Default: None.

```python
property arg_constraints: dict[str, Constraint]
```

Returns a dictionary from argument names to
Constraint objects that
should be satisfied by each argument of this distribution. Args that
are not tensors need not appear in this dict.

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```python
cdf(value: Tensor) → Tensor
```

Returns the cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
entropy() → Tensor
```

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```python
enumerate_support(expand: bool = True) → Tensor
```

Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be (cardinality,) + batch_shape + event_shape
(where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step
[[0, 0], [1, 1], …]. With expand=False, enumeration happens
along dim 0, but with the remaining batch dimensions being
singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use
itertools.product(m.enumerate_support()).

Parameters:

expand ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – whether to expand the support over the
batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

```python
property event_shape: Size
```

Returns the shape of a single sample (without batching).

```python
expand(batch_shape: Size | Sequence[int], _instance=None)
```

Returns a new distribution instance (or populates an existing instance
provided by a derived class) with batch dimensions expanded to
batch_shape. This method calls expand on
the distribution’s parameters. As such, this does not allocate new
memory for the expanded distribution instance. Additionally,
this does not repeat any args checking or parameter broadcasting in
__init__.py, when an instance is first created.

Parameters:

- batch_shape ([tensorplay.Size](/docs/generated/tensorplay.Size.html#tensorplay.Size)) – the desired expanded size.

- _instance – new instance provided by subclasses that need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to
batch_shape.

```python
icdf(value: Tensor) → Tensor
```

Returns the inverse cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
log_prob(value: Tensor) → Tensor
```

Returns the log of the probability density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
property mean: Tensor
```

Returns the mean of the distribution.

```python
property mode: Tensor
```

Returns the mode of the distribution.

```python
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```python
rsample(sample_shape: Size | Sequence[int] = ()) → Tensor
```

Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched.

```python
sample(sample_shape: Size | Sequence[int] = ()) → Tensor
```

Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched.

```python
sample_n(n: int) → Tensor
```

Generates n samples or n batches of samples if the distribution
parameters are batched.

```python
static set_default_validate_args(value: bool) → None
```

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation
is on by default, but is disabled if Python is run in optimized mode
(via python -O). Validation may be expensive, so you may want to
disable it once a model is working.

Parameters:

value ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – Whether to enable validation.

```python
property stddev: Tensor
```

Returns the standard deviation of the distribution.

```python
property support: Constraint | None
```

Returns a Constraint object
representing this distribution’s support.

```python
property variance: Tensor
```

Returns the variance of the distribution.