General Kolmogorov-Smirnov one-sided test.
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
n : 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 = ksone(n, loc=0, scale=1)
Examples ——– >>> from scipy.stats import ksone >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1) Calculate a few first moments: >>> n = 1000 >>> mean, var, skew, kurt = ksone.stats(n, moments=’mvsk’) Display the probability density function (``pdf``): >>> x = np.linspace(ksone.ppf(0.01, n), ... ksone.ppf(0.99, n), 100) >>> ax.plot(x, ksone.pdf(x, n), ... ‘r-‘, lw=5, alpha=0.6, label=’ksone pdf’) Alternatively, freeze the distribution and display the frozen pdf: >>> rv = ksone(n) >>> ax.plot(x, rv.pdf(x), ‘k-‘, lw=2, label=’frozen pdf’) Check accuracy of ``cdf`` and ``ppf``: >>> vals = ksone.ppf([0.001, 0.5, 0.999], n) >>> np.allclose([0.001, 0.5, 0.999], ksone.cdf(vals, n)) True Generate random numbers: >>> r = ksone.rvs(n, 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(n, loc=0, scale=1, size=1) | Random variates. |
pdf(x, n, loc=0, scale=1) | Probability density function. |
logpdf(x, n, loc=0, scale=1) | Log of the probability density function. |
cdf(x, n, loc=0, scale=1) | Cumulative density function. |
logcdf(x, n, loc=0, scale=1) | Log of the cumulative density function. |
sf(x, n, loc=0, scale=1) | Survival function (1-cdf — sometimes more accurate). |
logsf(x, n, loc=0, scale=1) | Log of the survival function. |
ppf(q, n, loc=0, scale=1) | Percent point function (inverse of cdf — percentiles). |
isf(q, n, loc=0, scale=1) | Inverse survival function (inverse of sf). |
moment(n, n, loc=0, scale=1) | Non-central moment of order n |
stats(n, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). |
entropy(n, loc=0, scale=1) | (Differential) entropy of the RV. |
fit(data, n, loc=0, scale=1) | Parameter estimates for generic data. |
expect(func, n, 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(n, loc=0, scale=1) | Median of the distribution. |
mean(n, loc=0, scale=1) | Mean of the distribution. |
var(n, loc=0, scale=1) | Variance of the distribution. |
std(n, loc=0, scale=1) | Standard deviation of the distribution. |
interval(alpha, n, loc=0, scale=1) | Endpoints of the range that contains alpha percent of the distribution |