# Independent

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

```python
class tensorplay.distributions.Independent(base_distribution: D, reinterpreted_batch_ndims: int, validate_args: bool | None = None)
```

Reinterprets some of the batch dims of a distribution as event dims.

This is mainly useful for changing the shape of the result of
log_prob(). For example to create a diagonal Normal distribution with
the same shape as a Multivariate Normal distribution (so they are
interchangeable), you can:

```
>>> from tensorplay.distributions.multivariate_normal import MultivariateNormal
>>> from tensorplay.distributions.normal import Normal
>>> loc = tensorplay.zeros(3)
>>> scale = tensorplay.ones(3)
>>> mvn = MultivariateNormal(loc, scale_tril=tensorplay.diag(scale))
>>> [mvn.batch_shape, mvn.event_shape]
[tensorplay.Size([]), tensorplay.Size([3])]
>>> normal = Normal(loc, scale)
>>> [normal.batch_shape, normal.event_shape]
[tensorplay.Size([3]), tensorplay.Size([])]
>>> diagn = Independent(normal, 1)
>>> [diagn.batch_shape, diagn.event_shape]
[tensorplay.Size([]), tensorplay.Size([3])]
```

Parameters:

- base_distribution ([tensorplay.distributions.distribution.Distribution](/docs/generated/tensorplay.distributions.Distribution.html#tensorplay.distributions.Distribution)) – a base distribution

- reinterpreted_batch_ndims ([int](https://docs.python.org/3/builtins/functions.html#int)) – the number of batch dims to reinterpret as event dims

```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
property event_shape: Size
```

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

```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
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```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.