# randomgen.generator.ExtendedGenerator.random¶

ExtendedGenerator.random(size=None, dtype='d', out=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 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.

dtype{str, dtype}, optional

Desired dtype of the result. One of ‘d’ (‘float64’ or np.float64), ‘f’ (‘float32’ of np.float32), or ‘longdouble’ (np.longdouble). 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:
out{float, longdouble or ndarray}

Array of random floats of shape size (unless size=None, in which case a single float is returned). If dtype is np.longdouble, then the returned type is a scalar np.longdouble. Otherwise it is a float.

Examples

>>> randomgen.generator.random()
0.47108547995356098 # random
>>> type(randomgen.generator.random())
<class 'float'>
>>> randomgen.generator.random((5,))
array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428]) # random


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

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