scipy.stats.weibull_max#
- scipy.stats.weibull_max = <scipy.stats._continuous_distns.weibull_max_gen object>[source]#
Weibull maximum continuous random variable.
The Weibull Maximum Extreme Value distribution, from extreme value theory (Fisher-Gnedenko theorem), is the limiting distribution of rescaled maximum of iid random variables. This is the distribution of -X if X is from the
weibull_min
function.As an instance of the
rv_continuous
class,weibull_max
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.See also
Notes
The probability density function for
weibull_max
is:\[f(x, c) = c (-x)^{c-1} \exp(-(-x)^c)\]for \(x < 0\), \(c > 0\).
weibull_max
takesc
as a shape parameter for \(c\).The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the
loc
andscale
parameters. Specifically,weibull_max.pdf(x, c, loc, scale)
is identically equivalent toweibull_max.pdf(y, c) / scale
withy = (x - loc) / scale
. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.References
https://en.wikipedia.org/wiki/Weibull_distribution
https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem
Examples
>>> from scipy.stats import weibull_max >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1)
Calculate the first four moments:
>>> c = 2.87 >>> mean, var, skew, kurt = weibull_max.stats(c, moments='mvsk')
Display the probability density function (
pdf
):>>> x = np.linspace(weibull_max.ppf(0.01, c), ... weibull_max.ppf(0.99, c), 100) >>> ax.plot(x, weibull_max.pdf(x, c), ... 'r-', lw=5, alpha=0.6, label='weibull_max 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 = weibull_max(c) >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
Check accuracy of
cdf
andppf
:>>> vals = weibull_max.ppf([0.001, 0.5, 0.999], c) >>> np.allclose([0.001, 0.5, 0.999], weibull_max.cdf(vals, c)) True
Generate random numbers:
>>> r = weibull_max.rvs(c, size=1000)
And compare the histogram:
>>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) >>> ax.legend(loc='best', frameon=False) >>> plt.show()
Methods
rvs(c, loc=0, scale=1, size=1, random_state=None)
Random variates.
pdf(x, c, loc=0, scale=1)
Probability density function.
logpdf(x, c, loc=0, scale=1)
Log of the probability density function.
cdf(x, c, loc=0, scale=1)
Cumulative distribution function.
logcdf(x, c, loc=0, scale=1)
Log of the cumulative distribution function.
sf(x, c, loc=0, scale=1)
Survival function (also defined as
1 - cdf
, but sf is sometimes more accurate).logsf(x, c, loc=0, scale=1)
Log of the survival function.
ppf(q, c, loc=0, scale=1)
Percent point function (inverse of
cdf
— percentiles).isf(q, c, loc=0, scale=1)
Inverse survival function (inverse of
sf
).moment(order, c, loc=0, scale=1)
Non-central moment of the specified order.
stats(c, loc=0, scale=1, moments=’mv’)
Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
entropy(c, loc=0, scale=1)
(Differential) entropy of the RV.
fit(data)
Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments.
expect(func, args=(c,), 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(c, loc=0, scale=1)
Median of the distribution.
mean(c, loc=0, scale=1)
Mean of the distribution.
var(c, loc=0, scale=1)
Variance of the distribution.
std(c, loc=0, scale=1)
Standard deviation of the distribution.
interval(confidence, c, loc=0, scale=1)
Confidence interval with equal areas around the median.