scipy.stats.kstwobign

scipy.stats.kstwobign = <scipy.stats._continuous_distns.kstwobign_gen object at 0x4502f2ac>

Kolmogorov-Smirnov two-sided test for large N.

As an instance of the rv_continuous class, kstwobign 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.

Methods

rvs(loc=0, scale=1, size=1, random_state=None)	Random variates.
pdf(x, loc=0, scale=1)	Probability density function.
logpdf(x, loc=0, scale=1)	Log of the probability density function.
cdf(x, loc=0, scale=1)	Cumulative density function.
logcdf(x, loc=0, scale=1)	Log of the cumulative density function.
sf(x, loc=0, scale=1)	Survival function (1 - cdf — sometimes more accurate).
logsf(x, loc=0, scale=1)	Log of the survival function.
ppf(q, loc=0, scale=1)	Percent point function (inverse of cdf — percentiles).
isf(q, loc=0, scale=1)	Inverse survival function (inverse of sf).
moment(n, loc=0, scale=1)	Non-central moment of order n
stats(loc=0, scale=1, moments='mv')	Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(loc=0, scale=1)	(Differential) entropy of the RV.
fit(data, loc=0, scale=1)	Parameter estimates for generic data.
expect(func, 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(loc=0, scale=1)	Median of the distribution.
mean(loc=0, scale=1)	Mean of the distribution.
var(loc=0, scale=1)	Variance of the distribution.
std(loc=0, scale=1)	Standard deviation of the distribution.
interval(alpha, loc=0, scale=1)	Endpoints of the range that contains alpha percent of the distribution
Examples
>>> from scipy.stats import kstwobign
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> mean, var, skew, kurt = kstwobign.stats(moments='mvsk')
Display the probability density function (pdf):
>>> x = np.linspace(kstwobign.ppf(0.01),
... kstwobign.ppf(0.99), 100)
>>> ax.plot(x, kstwobign.pdf(x),
... ‘r-‘, lw=5, alpha=0.6, label=’kstwobign 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 = kstwobign()
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
Check accuracy of cdf and ppf:
>>> vals = kstwobign.ppf([0.001, 0.5, 0.999])
>>> np.allclose([0.001, 0.5, 0.999], kstwobign.cdf(vals))
True
Generate random numbers:
>>> r = kstwobign.rvs(size=1000)
And compare the histogram:
>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()