SciPy

scipy.stats.genpareto

scipy.stats.genpareto = <scipy.stats._continuous_distns.genpareto_gen object at 0x7fa40e987590>[source]

A generalized Pareto continuous random variable.

As an instance of the rv_continuous class, genpareto object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

Notes

The probability density function for genpareto is:

genpareto.pdf(x, c) = (1 + c * x)**(-1 - 1/c)

defined for x >= 0 if c >=0, and for 0 <= x <= -1/c if c < 0.

genpareto takes c as a shape parameter.

For c == 0, genpareto reduces to the exponential distribution, expon:

genpareto.pdf(x, c=0) = exp(-x)

For c == -1, genpareto is uniform on [0, 1]:

genpareto.cdf(x, c=-1) = x

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, genpareto.pdf(x, c, loc, scale) is identically equivalent to genpareto.pdf(y, c) / scale with y = (x - loc) / scale.

Examples

>>> from scipy.stats import genpareto
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)

Calculate a few first moments:

>>> c = 0.1
>>> mean, var, skew, kurt = genpareto.stats(c, moments='mvsk')

Display the probability density function (pdf):

>>> x = np.linspace(genpareto.ppf(0.01, c),
...                 genpareto.ppf(0.99, c), 100)
>>> ax.plot(x, genpareto.pdf(x, c),
...        'r-', lw=5, alpha=0.6, label='genpareto pdf')

Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = genpareto(c)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

Check accuracy of cdf and ppf:

>>> vals = genpareto.ppf([0.001, 0.5, 0.999], c)
>>> np.allclose([0.001, 0.5, 0.999], genpareto.cdf(vals, c))
True

Generate random numbers:

>>> r = genpareto.rvs(c, size=1000)

And compare the histogram:

>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()

(Source code)

../_images/scipy-stats-genpareto-1.png

Methods

rvs(c, loc=0, scale=1, size=1, random_state=None) Random variates.
pdf(x, c, loc=0, scale=1) Probability density function.
logpdf(x, c, loc=0, scale=1) Log of the probability density function.
cdf(x, c, loc=0, scale=1) Cumulative density function.
logcdf(x, c, loc=0, scale=1) Log of the cumulative density function.
sf(x, c, loc=0, scale=1) Survival function (1 - cdf — sometimes more accurate).
logsf(x, c, loc=0, scale=1) Log of the survival function.
ppf(q, c, loc=0, scale=1) Percent point function (inverse of cdf — percentiles).
isf(q, c, loc=0, scale=1) Inverse survival function (inverse of sf).
moment(n, c, loc=0, scale=1) Non-central moment of order n
stats(c, loc=0, scale=1, moments='mv') Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(c, loc=0, scale=1) (Differential) entropy of the RV.
fit(data, c, loc=0, scale=1) Parameter estimates for generic data.
expect(func, c, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Expected value of a function (of one argument) with respect to the distribution.
median(c, loc=0, scale=1) Median of the distribution.
mean(c, loc=0, scale=1) Mean of the distribution.
var(c, loc=0, scale=1) Variance of the distribution.
std(c, loc=0, scale=1) Standard deviation of the distribution.
interval(alpha, c, loc=0, scale=1) Endpoints of the range that contains alpha percent of the distribution

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