numpy.random.random_sample#
- 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
Note
New code should use the
random
method of aGenerator
instance instead; please see the Quick start.- Parameters:
- 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.
- Returns:
- outfloat or ndarray of floats
Array of random floats of shape
size
(unlesssize=None
, in which case a single float is returned).
See also
random.Generator.random
which should be used for new code.
Examples
>>> np.random.random_sample() 0.47108547995356098 # random >>> type(np.random.random_sample()) <class 'float'> >>> np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428]) # random
Three-by-two array of random numbers from [-5, 0):
>>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], # random [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]])