dask.array.random.choice
dask.array.random.choice¶
- dask.array.random.choice(*args, **kwargs)¶
Generates a random sample from a given 1-D array
This docstring was copied from numpy.random.mtrand.RandomState.choice.
Some inconsistencies with the Dask version may exist.
New in version 1.7.0.
Note
New code should use the ~numpy.random.Generator.choice method of a ~numpy.random.Generator instance instead; please see the Quick start.
Warning
This function uses the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy’s default integer is 32bit on 32bit platforms and 64bit on 64bit platforms.
- Parameters
- a1-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)
- sizeint 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.- replaceboolean, optional
Whether the sample is with or without replacement. Default is True, meaning that a value of
a
can be selected multiple times.- p1-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
.
- Returns
- samplessingle item or ndarray
The generated random samples
- 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
random.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 ofp
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:
>>> np.random.choice(5, 3) array([0, 3, 4]) # random >>> #This is equivalent to np.random.randint(0,5,3)
Generate a non-uniform random sample from np.arange(5) of size 3:
>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]) array([3, 3, 0]) # random
Generate a uniform random sample from np.arange(5) of size 3 without replacement:
>>> np.random.choice(5, 3, replace=False) array([3,1,0]) # random >>> #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:
>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]) array([2, 3, 0]) # random
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'], # random dtype='<U11')