scipy.stats.kstwobign¶
-
scipy.stats.
kstwobign
= <scipy.stats._continuous_distns.kstwobign_gen object>[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
andppf
:>>> 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()