# OneHotCategorical

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

```python
class tensorplay.distributions.OneHotCategorical(probs: Tensor | None = None, logits: Tensor | None = None, validate_args: bool | None = None)
```

Creates a one-hot categorical distribution parameterized by probs or
logits.

Samples are one-hot coded vectors of size probs.size(-1).

> **Note**
>
> The probs argument must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1 along the last dimension. probs will return this normalized value. The logits argument will be interpreted as unnormalized log probabilities and can therefore be any real number. It will likewise be normalized so that the resulting probabilities sum to 1 along the last dimension. logits will return this normalized value.

See also: [tensorplay.distributions.Categorical()](/docs/generated/tensorplay.distributions.Categorical.html#tensorplay.distributions.Categorical) for specifications of
probs and logits.

Example:

```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = OneHotCategorical(tensorplay.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
>>> m.sample()  # equal probability of 0, 1, 2, 3
tensor([ 0.,  0.,  0.,  1.])
```

Parameters:

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

- logits ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – event log probabilities (unnormalized)

```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.