flopscope.numpy.random.power

fnp.random.power(a, size=None)[flopscope source]

Draws samples in [0, 1] from a power distribution with positive exponent a - 1.

Adapted from NumPy docs np.random.power

Arearandom

Typecustom

NumPy Refnp.random.power

Cost

16×per-operation

Flopscope Context

Sampling; cost = numel(output).

Also known as the power function distribution.

Note.

New code should use the power method of a Generator instance instead; please see the random-quick-start.

Parameters

a:float or array_like of floats: Parameter of the distribution. Must be non-negative.
size:int or tuple of ints, optional: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if a is a scalar. Otherwise, flops.array(a).size samples are drawn.

Returns

out:ndarray or scalar: Drawn samples from the parameterized power distribution.

Raises

:ValueError: If a <= 0.

Notes

The probability density function is

P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.

The power function distribution is just the inverse of the Pareto distribution. It may also be seen as a special case of the Beta distribution.

It is used, for example, in modeling the over-reporting of insurance claims.

References

footnote

1

Christian Kleiber, Samuel Kotz, "Statistical size distributions
in economics and actuarial sciences", Wiley, 2003.

footnote

2

Heckert, N. A. and Filliben, James J. "NIST Handbook 148:
Dataplot Reference Manual, Volume 2: Let Subcommands and Library
Functions", National Institute of Standards and Technology
Handbook Series, June 2003.
https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf

Examples

Draw samples from the distribution:

>>> a = 5. # shape
>>> samples = 1000
>>> s = flops.random.power(a, samples)

Display the histogram of the samples, along with the probability density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, bins=30)
>>> x = flops.linspace(0, 1, 100)
>>> y = a*x**(a-1.)
>>> normed_y = samples*flops.diff(bins)[0]*y
>>> plt.plot(x, normed_y)
>>> plt.show()

Compare the power function distribution to the inverse of the Pareto.

>>> from scipy import stats # doctest: +SKIP
>>> rvs = flops.random.power(5, 1000000)
>>> rvsp = flops.random.pareto(5, 1000000)
>>> xx = flops.linspace(0,1,100)
>>> powpdf = stats.powerlaw.pdf(xx,5)  # doctest: +SKIP

>>> plt.figure()
>>> plt.hist(rvs, bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP
>>> plt.title('flops.random.power(5)')

>>> plt.figure()
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP
>>> plt.title('inverse of 1 + flops.random.pareto(5)')

>>> plt.figure()
>>> plt.hist(1./(1.+rvsp), bins=50, density=True)
>>> plt.plot(xx,powpdf,'r-')  # doctest: +SKIP
>>> plt.title('inverse of stats.pareto(5)')