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
q : array_like
loc : array_like, optional
scale : array_like, optional
size : int or tuple of ints, optional
moments : str, optional
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)
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() |
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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 |