# scipy.stats.kstwobign¶

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.

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() 

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