*Last updated: 2023-03-16.*
# tf_quant_finance.math.random.uniform
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Generates draws from a uniform distribution on [0, 1).
```python
tf_quant_finance.math.random.uniform(
dim, sample_shape, random_type=None, dtype=None, seed=None, name=None, **kwargs
)
```
Allows generating either (pseudo) random or quasi-random draws based on the
`random_type` parameter. Dimension parameter `dim` is required since for
quasi-random draws one needs to know the dimensionality of the space as
opposed to just sample shape.
#### Example:
```python
sample_shape = [10] # Generates 10 draws.
# `Tensor` of shape [10, 1]
uniform_samples = uniform(1, sample_shape)
# `Tensor` of shape [10, 5]
sobol_samples = uniform(5, sample_shape, RandomType.SOBOL)
```
#### Args:
* `dim`: A positive Python `int` representing each sample's `event_size.`
* `sample_shape`: Rank 1 `Tensor` of positive `int32`s. Should specify a valid
shape for a `Tensor`. The shape of the samples to be drawn.
* `random_type`: Enum value of `RandomType`. The type of draw to generate.
Default value: None which is mapped to RandomType.PSEUDO.
* `dtype`: Optional `dtype` (eithier `tf.float32` or `tf.float64`). The dtype of
the output `Tensor`.
Default value: `None` which maps to `tf.float32`.
* `seed`: Seed for the random number generator. The seed is
only relevant if `random_type` is one of
`[STATELESS, PSEUDO, HALTON_RANDOMIZED]`. For `PSEUDO`, and
`HALTON_RANDOMIZED` the seed should be a Python integer. For
`STATELESS` must be supplied as an integer `Tensor` of shape `[2]`.
Default value: `None` which means no seed is set.
* `name`: Python `str` name prefixed to ops created by this class.
Default value: `None` which is mapped to the default name
`uniform_distribution`.
* `**kwargs`: parameters, specific to a random type:
(1) `skip` is an `int` 0-d `Tensor`. The number of initial points of the
Sobol or Halton sequence to skip. Used only when `random_type` is 'SOBOL',
'HALTON', or 'HALTON_RANDOMIZED', otherwise ignored.
(2) `randomization_params` is an instance of
`tff.math.random.HaltonParams` that fully describes the randomization
behavior. Used only when `random_type` is 'HALTON_RANDOMIZED', otherwise
ignored (see halton.sample args for more details). If this parameter is
provided when random_type is `HALTON_RANDOMIZED`, the `seed` parameter is
ignored.
Default value: `None`. In this case with randomized = True, the necessary
randomization parameters will be computed from scratch.
#### Returns:
* `samples`: A `Tensor` of shape `sample_shape + [dim]`. The draws
from the uniform distribution of the requested random type.
#### Raises:
* `ValueError`: if `random_type` is `STATELESS` and the `seed` is `None`.