dask.array.random.random_integers

dask.array.random.random_integers¶

dask.array.random.random_integers(*args, **kwargs)¶

Random integers of type numpy.int_ between low and high, inclusive.

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

Some inconsistencies with the Dask version may exist.

Return random integers of type numpy.int_ from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the default), then results are from [1, low]. The numpy.int_ type translates to the C long integer type and its precision is platform dependent.

This function has been deprecated. Use randint instead.

Deprecated since version 1.11.0.

Parameters

lowint: Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer).
highint, optional: If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None).
sizeint 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.

Returns

outint or ndarray of ints: size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.

See also

randint: Similar to random_integers, only for the half-open interval [low, high), and 0 is the lowest value if high is omitted.

Notes

To sample from N evenly spaced floating-point numbers between a and b, use:

a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)

Examples

>>> np.random.random_integers(5)  
4 # random
>>> type(np.random.random_integers(5))  
<class 'numpy.int64'>
>>> np.random.random_integers(5, size=(3,2))  
array([[5, 4], # random
       [3, 3],
       [4, 5]])

Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (i.e., from the set \({0, 5/8, 10/8, 15/8, 20/8}\)):

>>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.  
array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ]) # random

Roll two six sided dice 1000 times and sum the results:

>>> d1 = np.random.random_integers(1, 6, 1000)  
>>> d2 = np.random.random_integers(1, 6, 1000)  
>>> dsums = d1 + d2  

Display results as a histogram:

>>> import matplotlib.pyplot as plt  
>>> count, bins, ignored = plt.hist(dsums, 11, density=True)  
>>> plt.show()  

dask.array.random.random

dask.array.random.random_sample