nlcpy.random.Generator.random

Generator.random(self, size=None, dtype='d', out=None)

Returns 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 by (b-a) and add a:

(b - a) * random() + a

Parameters

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.
dtypestr or dtype, optional: Desired dtype of the result, either 'd' (or 'float64') or 'f' (or 'float32'). All dtypes are determined by their name. The default value is 'd'.
outndarray, optional: Alternative output array in which to place the result. If size is not None, it must have the same shape as the provided size and must match the type of the output values.

Returns

outndarray of floats: Array of random floats of shape size.

Examples

>>> import nlcpy as vp
>>> rng = vp.random.default_rng()
>>> rng.random()       
array(0.11406583) # random
>>> type(rng.random())
<class 'nlcpy.core.core.ndarray'>
>>> rng.random((5,))   
array([0.68285923, 0.36783394, 0.193222  , 0.38130933, 0.67406266])

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

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