Hướng dẫn python fit gumbel distribution
A right-skewed Gumbel continuous random variable. As an instance of the
Notes The probability density function for \[f(x) = \exp(-(x + e^{-x}))\] The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett distribution. It is also related to the extreme value distribution, log-Weibull and Gompertz distributions. The
probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the Examples >>> from scipy.stats import gumbel_r >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(1, 1) Calculate the first four moments: >>> mean, var, skew, kurt = gumbel_r.stats(moments='mvsk') Display
the probability density function ( >>> x = np.linspace(gumbel_r.ppf(0.01), ... gumbel_r.ppf(0.99), 100) >>> ax.plot(x, gumbel_r.pdf(x), ... 'r-', lw=5, alpha=0.6, label='gumbel_r 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 >>> rv = gumbel_r() >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') Check accuracy of >>> vals = gumbel_r.ppf([0.001, 0.5, 0.999]) >>> np.allclose([0.001, 0.5, 0.999], gumbel_r.cdf(vals)) True Generate random numbers: >>> r = gumbel_r.rvs(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
|