choice(self, a, size=None, replace=True, p=None, chunks='auto')

This docstring was copied from numpy.random.mtrand.RandomState.choice.

Some inconsistencies with the Dask version may exist.

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 :None:None:`Generator.choice` through its axis keyword.

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 or tuple of ints, optional

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 .

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

Returns

samples : single item or ndarray

The generated random samples

Generates a random sample from a given 1-D array

See Also

Generator.choice

which should be used in new code

permutation

randint

shuffle

Examples

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

This example is valid syntax, but we were not able to check execution

>>> np.random.choice(5, 3)  # doctest: +SKIP
array([0, 3, 4]) # random

This example is valid syntax, but we were not able to check execution

>>> #This is equivalent to np.random.randint(0,5,3)

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

This example is valid syntax, but we were not able to check execution

>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])  # doctest: +SKIP
array([3, 3, 0]) # random

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

This example is valid syntax, but we were not able to check execution

>>> np.random.choice(5, 3, replace=False)  # doctest: +SKIP
array([3,1,0]) # random

This example is valid syntax, but we were not able to check execution

>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

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

This example is valid syntax, but we were not able to check execution

>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])  # doctest: +SKIP
array([2, 3, 0]) # random

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

This example is valid syntax, but we were not able to check execution

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']  # doctest: +SKIP
... np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])  # doctest: +SKIP
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
      dtype='<U11')

See :

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File: /dask/array/random.py#206
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