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# 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()