# Binomial

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

```python
class tensorplay.distributions.Binomial(total_count: Tensor | int = 1, probs: Tensor | None = None, logits: Tensor | None = None, validate_args: bool | None = None)
```

Creates a Binomial distribution parameterized by total_count and
either probs or logits (but not both). total_count must be
broadcastable with probs/logits.

Example:

```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Binomial(100, tensorplay.tensor([0 , .2, .8, 1]))
>>> x = m.sample()
tensor([   0.,   22.,   71.,  100.])

>>> m = Binomial(tensorplay.tensor([[5.], [10.]]), tensorplay.tensor([0.5, 0.8]))
>>> x = m.sample()
tensor([[ 4.,  5.],
        [ 7.,  6.]])
```

Parameters:

- total_count ([int](https://docs.python.org/3/builtins/functions.html#int) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – number of Bernoulli trials

- probs ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – Event probabilities

- logits ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – Event log-odds

```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
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_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.