numpy.random.random_sample()

numpy.random.random_sample(size=None)

Return random floats in the half-open interval [0.0, 1.0).

Results are from the ?continuous uniform? distribution over the stated interval. To sample $Unif[a, b), b > a$ multiply the output of random_sample by (b-a) and add a:

(b - a) * random_sample() + a

Parameters:

Parameters:	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.
Returns:	out : float or ndarray of floats Array of random floats of shape `size` (unless `size=None`, in which case a single float is returned).

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.

Returns:

out : float or ndarray of floats

Array of random floats of shape size (unless size=None, in which case a single float is returned).

Examples

>>> np.random.random_sample()
0.47108547995356098
>>> type(np.random.random_sample())
<type 'float'>
>>> np.random.random_sample((5,))
array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])

Three-by-two array of random numbers from [-5, 0):

>>> 5 * np.random.random_sample((3, 2)) - 5
array([[-3.99149989, -0.52338984],
       [-2.99091858, -0.79479508],
       [-1.23204345, -1.75224494]])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.random.random_sample.html

doc_NumPy

2017-01-10 18:18:13

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