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 1D array
 Parameters
a (1D arraylike 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)
, thenm * 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 (1D arraylike, 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 1dimensional, if a is an arraylike 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
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 nonuniform 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 nonuniform 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 arraylike 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')