def cdf(self, x: float) -> float:
"""
Returns CDF(x)
Parameters
----------
x : float
x in [0, +inf]
Returns
-------
"""
return betaprime.cdf(x, a=self.a, b=self.b)
def beta_prime_cdf_wrapper(cls, x: float, a: float, b: float, loc: float):
"""
A wrapper for the beta prime cdf. In order to be usable by curve fit
Parameters
----------
x : float
a : float
b : float
loc: float
scale: float
Returns
-------
evaluated beta prime function cdf
"""
print("DEBUG####################")
print(x, a, b, loc)
return betaprime.cdf(x, a, b, loc=loc)
x = np.linspace(betaprime.ppf(0.01, a, b),
betaprime.ppf(0.99, a, b), 100)
ax.plot(x, betaprime.pdf(x, a, b),
'r-', lw=5, alpha=0.6, label='betaprime 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 = betaprime(a, b)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = betaprime.ppf([0.001, 0.5, 0.999], a, b)
np.allclose([0.001, 0.5, 0.999], betaprime.cdf(vals, a, b))
# True
# Generate random numbers:
r = betaprime.rvs(a, b, size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()