# mars.tensor.random.choice¶

mars.tensor.random.choice(a, size=None, replace=True, p=None, chunk_size=None, gpu=None)[source]

Generates a random sample from a given 1-D array

Parameters
• a (1-D array-like or int) – If a tensor, a random sample is generated from its elements. If an int, the random sample is generated as if a were mt.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

• 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.

• chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

• gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

Returns

samples – The generated random samples

Return type

single item or tensor

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

randint, shuffle, permutation

Examples

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

>>> import mars.tensor as mt

>>> mt.random.choice(5, 3).execute()
array([0, 3, 4])
>>> #This is equivalent to mt.random.randint(0,5,3)


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

>>> mt.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]).execute()
array([3, 3, 0])


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

>>> mt.random.choice(5, 3, replace=False).execute()
array([3,1,0])
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]


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

>>> mt.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]).execute()
array([2, 3, 0])


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

>>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],
dtype='|S11')