Nonsubstantive code edits

binomial.py

@@ -13,7 +13,6 @@
     realIsLess,
 )
 
-
 class BinomialSampler:
     def __init__(self, rg=None):
         self.rg = randomgen.RandomGen() if rg == None else rg

fixed.py

@@ -1,7 +1,6 @@
 import decimal as dec
 import math
 
-
 class Fixed:
     """
     Fixed-point numbers, represented using integers that store multiples
@@ -758,7 +757,6 @@ def __str__(self):
     def __repr__(self):
         return str(dec.Decimal(self.value) / dec.Decimal((1 << Fixed.BITS)))
 
-
 if __name__ == "__main__":
 
     def asserteq(a, b):

interval.py

@@ -18,7 +18,6 @@
 #  precision in bits.
 #
 
-
 class FInterval:
     """An interval of two Fractions.  x.sup holds the upper bound, and x.inf holds
     the lower bound."""
@@ -344,7 +343,6 @@ def cos(self, precision):
     def __repr__(self):
         return "[%s, %s]" % (float(self.inf), float(self.sup))
 
-
 def _polynomialProduct(a, b):
     # Finds the product of two polynomials.  Each polynomial
     # is a list of the following form:
@@ -356,15 +354,13 @@ def _polynomialProduct(a, b):
             ret[i + j] += a[i] * b[j]
     return ret
 
-
 def _polynomialProductA(a, b0, b1):
     ret = [0 for i in range(len(a) + 1)]
     for i in range(len(a)):
         ret[i] += a[i] * b0  # b0 is 0th-order coefficient
         ret[i + 1] += a[i] * b1  # b1 is 1st-order coefficient
     return ret
 
-
 def _polynomialIntegral(p, x=1):
     # Finds the integral of a polynomial at the point x.
     if x == 1:

keaneobrien.py

@@ -1,7 +1,6 @@
 from sympy import *
 import random
 
-
 def roughmaxiopen(func, x):
     # Rough version of maxiopen that samples points
     # in the open interval (0, 1) to find an approximate
@@ -10,7 +9,6 @@ def roughmaxiopen(func, x):
         *[ceiling(func.subs(x, S(i) / 100) * 100000) / 100000 for i in range(1, 100)]
     )
 
-
 def maxi(func, x):
     # Finds the maximum of a function whose domain is [0,1].
     # Unfortunately, SymPy's 'maximum' does not always work
@@ -22,7 +20,6 @@ def maxi(func, x):
     # func=target function; x=variable used in 'func'
     return maximum(func, x, Interval(0, 1))
 
-
 def maxiopen(func, x):
     # Finds the maximum of a function whose domain is (0,1).
     # Unfortunately, SymPy's 'maximum' does not always work
@@ -34,7 +31,6 @@ def maxiopen(func, x):
     # func=target function; x=variable used in 'func'
     return maximum(func, x, Interval.open(0, 1))
 
-
 def bernpoly(a, x):
     # Create a polynomial in Bernstein form using the given
     # array of coefficients and the symbol x.  The polynomial's
@@ -49,7 +45,6 @@ def bernpoly(a, x):
         ret += a[i] * bino * pt**i * (1 - pt) ** (n - i)
     return ret
 
-
 def kobevents(func, p, numpolys=12):
     # Generates 'func', a Bernoulli factory function,
     # as a convex combination of polynomials
@@ -150,7 +145,6 @@ def kobevents(func, p, numpolys=12):
     # plot(*[r[2] for r in results], (p, 0, 1))
     return fevents
 
-
 def kob(fevents, p):
     # Simulates a convex combination of polynomials
     # returned by kobevents, which will generally be an
@@ -166,7 +160,6 @@ def kob(fevents, p):
     s = sum(1 if random.random() < p else 0 for i in range(len(fevents[geo]) - 1))
     return fevents[geo][s]
 
-
 p = symbols("p")  # Variable used in the example function
 # Example function
 ofunc = sin(2 * pi * p) / 4 + S.Half + S(1) / 100

logconcave.py

@@ -7,7 +7,6 @@
 import math
 import random
 
-
 class TConcaveDiscreteSampler:
     """
     Generates a random variate that follows a
@@ -100,7 +99,6 @@ def codegen(self, name, pdfcall=None):
         ret += "        if u*u <= %s(ret): return ret\n\n" % (pdfcall)
         return ret
 
-
 class TConcaveSampler:
     """
     Generates a random variate that follows a
@@ -232,7 +230,6 @@ def _simplesampleOne(self):
             if u * u <= self.f(ret):
                 return ret
 
-
 class LogConcaveSampler:
     """
     Generates a random variate that follows a distribution
@@ -357,7 +354,6 @@ def sampleOne(self):
             elif logchi <= pr:
                 return ret
 
-
 class UnimodalSampler:
     """
     Generates a random variate that follows a unimodal distribution
@@ -433,7 +429,6 @@ def sampleOne(self):
                     self.mode, self.mode + 1, self.modecdf, self.modecdfright
                 )
 
-
 class LogConcaveSampler2:
     """
     Generates a random variate that follows a distribution
@@ -490,7 +485,6 @@ def sampleOne(self):
                 if ret != None:
                     return ret
 
-
 class LogConcaveSamplerMonotone:
     """
     Generates a random variate that follows a distribution
@@ -668,7 +662,6 @@ def sampleOne(self):
             if ret != None:
                 return ret
 
-
 class NormalDist:
     def __init__(self, mu=0, sigma=1):
         psi = lambda x: -(x * x) / (2 * sigma * sigma)
@@ -678,7 +671,6 @@ def __init__(self, mu=0, sigma=1):
     def sample(self, n):
         return [x + self.mu for x in self.sampler.sample(n)]
 
-
 class GenInvGaussianAlphaBeta:
     def __init__(self, lamda, alpha, beta):
         # Three-parameter version (Hörmann, W., Leydold, J.,
@@ -697,7 +689,6 @@ def fromLambdaPsiChi(lamda, psi, chi):
             lamda, math.sqrt(psi / chi), math.sqrt(psi * chi)
         )
 
-
 class GammaDist:
     # Devroye 2014
     def __init__(self, a):
@@ -722,7 +713,6 @@ def sample(self, n):
             for x in self.sampler.sample(n)
         ]
 
-
 class GenInvGaussian:
     def __init__(self, lamda, omega):
         # omega>0, lamda>=0. Two-parameter version described in Devroye 2014
@@ -741,7 +731,6 @@ def _trans(self, v):
     def sample(self, n):
         return [self._trans(x) for x in self.sampler.sample(n)]
 
-
 if __name__ == "__main__":
 
     def bucket(v, ls, buckets):