Delete AdditionalPDFs.py

AdditionalPDFs.py

-import numpy as np
-from scipy.stats import rv_discrete, rv_continuous, uniform
-import scipy.special as sc
-import matplotlib.pyplot as plt
-
-
-from scipy.stats._distn_infrastructure import (
-        rv_discrete, _ncx2_pdf, _ncx2_cdf, get_distribution_names)
-
-
-class gpd_gen(rv_discrete):
-    def _argcheck(self, mu, lbda):
-        return mu >= 0.0 and lbda >= 0.0 and lbda <= 1.0
-
-    def _rvs(self, mu, lbda):
-        population = np.asarray(
-            self._random_state.poisson(mu, self._size)
-        )
-        if population.shape == ():
-            population = population.reshape(-1)
-        offspring = population.copy()
-        while np.any(offspring > 0):
-            # probability dists are NOT ufuncs
-            # print("offspring", offspring)
-            offspring[:] = [
-                self._random_state.poisson(m)
-                for m in lbda*offspring
-            ]
-            population += offspring
-        return population
-
-    def _pmf(self, k, mu, lbda):
-        return np.exp(self._logpmf(k, mu, lbda))
-
-    def _logpmf(self, k, mu, lbda):
-        mu_pls_klmb = mu + lbda*k
-        return np.log(mu) + sc.xlogy(k-1, mu_pls_klmb) - mu_pls_klmb - sc.gammaln(k+1)
-
-    def _munp(self, n, mu, lbda):
-        if n == 1:
-            return mu/(1-lbda)
-        elif n == 2:
-            return (mu/(1-lbda))**2+mu/(1-lbda)**3
-
-
-gpoisson = gpd_gen(name='gpoisson')
-
-    
-class borel_gen(rv_discrete):
-    def _argcheck(self, mu):
-        return ((mu > 0) & (mu<1))
-
-    def _logpmf(self, k, mu):
-        n = k+1
-        Pk = sc.xlogy(n-1, mu*n) - sc.gammaln(n + 1) - mu*n
-        return Pk
-
-    def _pmf(self, k, mu):
-        return np.exp(self._logpmf(k, mu))
-
-#     def _rvs(self, mu, size=None, random_state=None):
-#         u = uniform.rvs(loc=0, scale = 1, size=size)
-#         cum = np.cumsum([self._pmf(_k, mu) for _k in range(0, 100)])
-#         print(cum)
-#         rnd = [ np.argmax( cum>=_u ) for _u in u ]
-#         return rnd
-    
-    def _rvs(self, mu, size=None, random_state=None, epsilon=1e-4):
-        _u = uniform.rvs(loc=0, scale = 1-epsilon, size=size)
-        _sum = 0
-        _k=0
-        _elem = []
-        _max_u = np.max(_u)
-        
-        while(_sum<_max_u):
-            _pmf = self._pmf(_k, mu)
-            _elem.append(_pmf)
-            _sum+=_pmf
-            _k+=1
-            
-        _cum = np.cumsum(_elem)
-        _rnd = [ np.argmax( _cum>=__u ) for __u in _u ]
-        
-        return _rnd
-
-
-    def _stats(self, mu):
-        _mu = 1/(1-mu)
-        _var = mu/(1-mu)**3
-        tmp = np.asarray(mu)
-        mu_nonzero = ((tmp > 0) & (tmp<1))
-        #g1 and g2: Lagrangian Probability Distributions, 978-0-8176-4365-2, page 159
-        g1 = scipy._lib._util._lazywhere(mu_nonzero, (tmp,), lambda x: (1+2*x)/scipy.sqrt(x*(1-x)), np.inf)
-        g2 = scipy._lib._util._lazywhere(mu_nonzero, (tmp,), lambda x: 3 + (1 + 8*x+6*x**2)/(x*(1-x)), np.inf)
-        return _mu, _var, g1, g2
-
-
-borel= borel_gen(name='borel')
-    
-  
-    
-class erlang_gen(rv_discrete):
-    
-    
-    
-    def _pdf(self, x, a):
-        # gamma.pdf(x, a) = x**(a-1) * exp(-x) / gamma(a)
-        return np.exp(self._logpdf(x, a))
-
-    def _logpdf(self, k, mu, nu):
-        return sc.xlogy(a-1.0, x) - x - sc.gammaln(a)
-
-   
-    
-    
-  
-
-#     def _rvs(self, mu, nu, size=None, random_state=None):
-#         u = scipy.stats.uniform.rvs(loc=0, scale = 1, size=size)
-#         cum = np.cumsum([self._pmf(_k, mu, nu) for _k in range(0, 100)])
-#         rnd = [ np.argmax( cum>=_u ) for _u in u ]
-#         return rnd
-
-pairs = list(globals().items())
-_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_discrete)
-
-__all__ = _distn_names + _distn_gen_names