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:¶

size=None¶

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='d'¶

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’.

out=None¶

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 – 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.

Return type:¶

{float, longdouble or ndarray}

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]])