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

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.


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
>>> 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))
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)

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