# scipy.stats.kstwobign¶

scipy.stats.kstwobign = <scipy.stats._continuous_distns.kstwobign_gen object at 0x2b45d2d77bd0>[source]

Kolmogorov-Smirnov two-sided test for large N.

Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:

Parameters: x : array_like quantiles q : array_like lower or upper tail probability loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) size : int or tuple of ints, optional shape of random variates (default computed from input arguments ) moments : str, optional composed of letters [‘mvsk’] specifying which moments to compute where ‘m’ = mean, ‘v’ = variance, ‘s’ = (Fisher’s) skew and ‘k’ = (Fisher’s) kurtosis. (default=’mv’) Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a “frozen” continuous RV object: rv = kstwobign(loc=0, scale=1) Frozen RV object with the same methods but holding the given shape, location, and scale fixed. 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, 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()

Methods

 rvs(loc=0, scale=1, size=1) 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

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