def getiCDF(self, xx):
"""
A Arcisine inverse cumulative density function.
:param Arcsine self:
An instance of Arcisine class.
:param xx:
A matrix of points at which the inverse cumulative density function needs to be evaluated.
:return:
Inverse cumulative density function values of the Arcisine distribution.
"""
return arcsine.ppf(xx)
def getiCDF(self, xx):
"""
A Arcisine inverse cumulative density function.
:param Arcsine self:
An instance of Arcisine class.
:param xx:
A matrix of points at which the inverse cumulative density function needs to be evaluated.
:return:
Inverse cumulative density function values of the Arcisine distribution.
"""
return arcsine.ppf(xx)
from scipy.stats import arcsine
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
mean, var, skew, kurt = arcsine.stats(moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(arcsine.ppf(0.01),
arcsine.ppf(0.99), 100)
ax.plot(x, arcsine.pdf(x),
'r-', lw=5, alpha=0.6, label='arcsine 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 = arcsine()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = arcsine.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], arcsine.cdf(vals))
# True
# Generate random numbers:
