# random_uniform¶

Graph.random_uniform(shape, lower_bound, upper_bound, seed=None, *, name=None)

Create a sample of uniformly distributed random numbers.

Parameters
• shape (tuple or list) – The shape of the sampled random numbers.

• lower_bound (int or float) – The inclusive lower bound of the interval of the uniform distribution.

• upper_bound (int or float) – The exclusive upper bound of the interval of the uniform distribution.

• seed (int, optional) – A seed for the random number generator. Defaults to None, in which case a random value for the seed is used.

• name (str, optional) – The name of the node.

Returns

A tensor containing a sample of uniformly distributed random numbers with shape shape.

Return type

Tensor

calculate_stochastic_optimization()

Function to find the minimum of generic stochastic functions.

random_choices()

Create random samples from the data that you provide.

random_normal()

Create a sample of uniformly distributed random numbers.

Examples

Create a random tensor by sampling uniformly from $$[0,\, 1)$$.

>>> samples = graph.random_uniform(
...     shape=(3, 1), lower_bound=0, upper_bound=1, seed=0, name="samples"
... )
>>> result = qctrl.functions.calculate_graph(graph=graph, output_node_names=["samples"])
>>> result.output["samples"]["value"]
array([[0.8069013], [0.79011373], [0.38818516]])


See more examples in the How to optimize controls robust to strong noise sources user guide.