# Uniform

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

```python
class tensorplay.distributions.Uniform(low: Tensor | float, high: Tensor | float, validate_args: bool | None = None)
```

Generates uniformly distributed random samples from the half-open interval
[low, high).

Example:

```
>>> m = Uniform(tensorplay.tensor([0.0]), tensorplay.tensor([5.0]))
>>> m.sample()  # uniformly distributed in the range [0.0, 5.0)
>>> # xdoctest: +SKIP
tensor([ 2.3418])
```

Parameters:

- low ([float](https://docs.python.org/3/builtins/functions.html#float) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – lower range (inclusive).

- high ([float](https://docs.python.org/3/builtins/functions.html#float) or [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – upper range (exclusive).

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```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
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```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.