choice

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choice#

class brainstate.random.choice(a, size=None, replace=True, p=None, key=None)#

Generates a random sample from a given 1-D array

Parameters:

  • a (1-D array-like or int) – If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if it were np.arange(a)

  • size (int | Sequence[int] | integer | Sequence[integer] | None) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

  • replace (boolean, optional) – Whether the sample is with or without replacement. Default is True, meaning that a value of a can be selected multiple times.

  • p (1-D array-like, optional) – The probabilities associated with each entry in a. If not given, the sample assumes a uniform distribution over all entries in a.

  • key (int | Array | ndarray | None) – The key for the random number generator. If not given, the default random number generator is used.

Returns:

samples – The generated random samples

Return type:

single item or ndarray

Raises:

ValueError – If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size

See also

randint, shuffle, permutation

Generator.choice

which should be used in new code

Notes

Setting user-specified probabilities through p uses a more general but less efficient sampler than the default. The general sampler produces a different sample than the optimized sampler even if each element of p is 1 / len(a).

Sampling random rows from a 2-D array is not possible with this function, but is possible with Generator.choice through its axis keyword.

Examples

Generate a uniform random sample from np.arange(5) of size 3:

>>> import brainstate
>>> result = brainstate.random.choice(5, 3)
>>> print(result.shape)  # (3,)
>>> print((result >= 0).all() and (result < 5).all())  # True

Generate a non-uniform random sample from np.arange(5) of size 3:

>>> result = brainstate.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
>>> print(result.shape)  # (3,)
>>> print(set(result).issubset({0, 2, 3}))  # True (only non-zero prob elements)

Generate a uniform random sample from np.arange(5) of size 3 without replacement:

>>> result = brainstate.random.choice(5, 3, replace=False)
>>> print(result.shape)  # (3,)
>>> print(len(set(result)) == 3)  # True (all unique)

Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:

>>> result = brainstate.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
>>> print(result.shape)  # (3,)
>>> print(len(set(result)) == 3)  # True (all unique)

Any of the above can be repeated with an arbitrary array-like instead of just integers:

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> result = brainstate.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
>>> print(result.shape)  # (5,)
>>> print(result.dtype.kind)  # 'U' (unicode string)
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