brainpy.math.random.random_integers#
- brainpy.math.random.random_integers(low, high=None, size=None, key=None)[source]#
Random integers of type np.int_ between low and high, inclusive.
Return random integers of type np.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 np.int_ type translates to the C long integer type and its precision is platform dependent.
- Parameters:
low (int) – Lowest (signed) integer to be drawn from the distribution (unless
high=None
, in which case this parameter is the highest such integer).high (int, optional) – If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if
high=None
).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.
- Returns:
out – size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.
- Return type:
int or ndarray of ints
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) * (bm.random.random_integers(N) - 1) / (N - 1.)
Examples
>>> import brainpy.math as bm >>> bm.random.random_integers(5) 4 # random >>> type(bm.random.random_integers(5)) <class 'numpy.int64'> >>> bm.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 * (bm.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 = bm.random.random_integers(1, 6, 1000) >>> d2 = bm.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()