flopscope.numpy.random.Generator.standard_exponential
fnp.random.Generator.standard_exponential(self, size=None, dtype=<class 'numpy.float64'>, method='zig', out=None)
Draw samples from the standard exponential distribution.
Adapted from NumPy docs np.random.Generator.standard_exponential
Standard exponential; cost = numel(output).
standard_exponential is identical to the exponential distribution
with a scale parameter of 1.
Parameters
- size:int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), thenm * n * ksamples are drawn. Default is None, in which case a single value is returned.- dtype:dtype, optional
Desired dtype of the result, only
float64andfloat32are supported. Byteorder must be native. The default value is flops.float64.- method:str, optional
Either 'inv' or 'zig'. 'inv' uses the default inverse CDF method. 'zig' uses the much faster Ziggurat method of Marsaglia and Tsang.
- out:ndarray, 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 or ndarray
Drawn samples.
Examples
Output a 3x8000 array:
>>> rng = flops.random.default_rng()
>>> n = rng.standard_exponential((3, 8000))