dask.array.random.power

dask.array.random.power¶

dask.array.random.power(*args, **kwargs)¶

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

This docstring was copied from numpy.random.mtrand.RandomState.power.

Some inconsistencies with the Dask version may exist.

Also known as the power function distribution.

Note

New code should use the ~numpy.random.Generator.power method of a ~numpy.random.Generator instance instead; please see the Quick Start.

Parameters

afloat or array_like of floats: Parameter of the distribution. Must be non-negative.
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. If size is None (default), a single value is returned if a is a scalar. Otherwise, np.array(a).size samples are drawn.

Returns

outndarray or scalar: Drawn samples from the parameterized power distribution.

Raises

ValueError: If a <= 0.

See also

random.Generator.power: which should be used for new code.

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

1: Christian Kleiber, Samuel Kotz, “Statistical size distributions in economics and actuarial sciences”, Wiley, 2003.
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 = np.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 = np.linspace(0, 1, 100)  
>>> y = a*x**(a-1.)  
>>> normed_y = samples*np.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 
>>> rvs = np.random.power(5, 1000000)  
>>> rvsp = np.random.pareto(5, 1000000)  
>>> xx = np.linspace(0,1,100)  
>>> powpdf = stats.powerlaw.pdf(xx,5)  

>>> plt.figure()  
>>> plt.hist(rvs, bins=50, density=True)  
>>> plt.plot(xx,powpdf,'r-')  
>>> plt.title('np.random.power(5)')  

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

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

dask.array.random.poisson

dask.array.random.randint