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Mar 20, 2025
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gh-131269: Avoid binding functions in random.py #131270

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c92ca1f
gh-131269: Avoid binding functions in random.py
colesbury Mar 15, 2025
a0a9026
Limit change to _randbelow_with_getrandbits
colesbury Mar 19, 2025
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65 changes: 27 additions & 38 deletions Lib/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -245,11 +245,10 @@ def __init_subclass__(cls, /, **kwargs):
def _randbelow_with_getrandbits(self, n):
"Return a random int in the range [0,n). Defined for n > 0."

getrandbits = self.getrandbits
k = n.bit_length()
r = getrandbits(k) # 0 <= r < 2**k
r = self.getrandbits(k) # 0 <= r < 2**k
while r >= n:
r = getrandbits(k)
r = self.getrandbits(k)
return r

def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF):
Expand All @@ -258,7 +257,6 @@ def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF):
The implementation does not use getrandbits, but only random.
"""

random = self.random
if n >= maxsize:
from warnings import warn
warn("Underlying random() generator does not supply \n"
Expand All @@ -267,9 +265,9 @@ def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF):
return _floor(random() * n)
rem = maxsize % n
limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0
r = random()
r = self.random()
while r >= limit:
r = random()
r = self.random()
return _floor(r * maxsize) % n

_randbelow = _randbelow_with_getrandbits
Expand Down Expand Up @@ -354,10 +352,9 @@ def choice(self, seq):
def shuffle(self, x):
"""Shuffle list x in place, and return None."""

randbelow = self._randbelow
for i in reversed(range(1, len(x))):
# pick an element in x[:i+1] with which to exchange x[i]
j = randbelow(i + 1)
j = self._randbelow(i + 1)
x[i], x[j] = x[j], x[i]

def sample(self, population, k, *, counts=None):
Expand Down Expand Up @@ -429,7 +426,6 @@ def sample(self, population, k, *, counts=None):
selections = self.sample(range(total), k=k)
bisect = _bisect
return [population[bisect(cum_counts, s)] for s in selections]
randbelow = self._randbelow
if not 0 <= k <= n:
raise ValueError("Sample larger than population or is negative")
result = [None] * k
Expand All @@ -441,16 +437,16 @@ def sample(self, population, k, *, counts=None):
# Invariant: non-selected at pool[0 : n-i]
pool = list(population)
for i in range(k):
j = randbelow(n - i)
j = self._randbelow(n - i)
result[i] = pool[j]
pool[j] = pool[n - i - 1] # move non-selected item into vacancy
else:
selected = set()
selected_add = selected.add
for i in range(k):
j = randbelow(n)
j = self._randbelow(n)
while j in selected:
j = randbelow(n)
j = self._randbelow(n)
selected_add(j)
result[i] = population[j]
return result
Expand All @@ -462,13 +458,12 @@ def choices(self, population, weights=None, *, cum_weights=None, k=1):
the selections are made with equal probability.

"""
random = self.random
n = len(population)
if cum_weights is None:
if weights is None:
floor = _floor
n += 0.0 # convert to float for a small speed improvement
return [population[floor(random() * n)] for i in _repeat(None, k)]
return [population[floor(self.random() * n)] for i in _repeat(None, k)]
try:
cum_weights = list(_accumulate(weights))
except TypeError:
Expand All @@ -489,7 +484,7 @@ def choices(self, population, weights=None, *, cum_weights=None, k=1):
raise ValueError('Total of weights must be finite')
bisect = _bisect
hi = n - 1
return [population[bisect(cum_weights, random() * total, 0, hi)]
return [population[bisect(cum_weights, self.random() * total, 0, hi)]
for i in _repeat(None, k)]


Expand Down Expand Up @@ -542,10 +537,9 @@ def normalvariate(self, mu=0.0, sigma=1.0):
# variables using the ratio of uniform deviates", ACM Trans
# Math Software, 3, (1977), pp257-260.

random = self.random
while True:
u1 = random()
u2 = 1.0 - random()
u1 = self.random()
u2 = 1.0 - self.random()
z = NV_MAGICCONST * (u1 - 0.5) / u2
zz = z * z / 4.0
if zz <= -_log(u2):
Expand Down Expand Up @@ -579,12 +573,11 @@ def gauss(self, mu=0.0, sigma=1.0):
# didn't want to slow this down in the serial case by using a
# lock here.)

random = self.random
z = self.gauss_next
self.gauss_next = None
if z is None:
x2pi = random() * TWOPI
g2rad = _sqrt(-2.0 * _log(1.0 - random()))
x2pi = self.random() * TWOPI
g2rad = _sqrt(-2.0 * _log(1.0 - self.random()))
z = _cos(x2pi) * g2rad
self.gauss_next = _sin(x2pi) * g2rad

Expand Down Expand Up @@ -636,25 +629,24 @@ def vonmisesvariate(self, mu, kappa):
# Thanks to Magnus Kessler for a correction to the
# implementation of step 4.

random = self.random
if kappa <= 1e-6:
return TWOPI * random()
return TWOPI * self.random()

s = 0.5 / kappa
r = s + _sqrt(1.0 + s * s)

while True:
u1 = random()
u1 = self.random()
z = _cos(_pi * u1)

d = z / (r + z)
u2 = random()
u2 = self.random()
if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):
break

q = 1.0 / r
f = (q + z) / (1.0 + q * z)
u3 = random()
u3 = self.random()
if u3 > 0.5:
theta = (mu + _acos(f)) % TWOPI
else:
Expand Down Expand Up @@ -685,7 +677,6 @@ def gammavariate(self, alpha, beta):
if alpha <= 0.0 or beta <= 0.0:
raise ValueError('gammavariate: alpha and beta must be > 0.0')

random = self.random
if alpha > 1.0:

# Uses R.C.H. Cheng, "The generation of Gamma
Expand All @@ -697,10 +688,10 @@ def gammavariate(self, alpha, beta):
ccc = alpha + ainv

while True:
u1 = random()
u1 = self.random()
if not 1e-7 < u1 < 0.9999999:
continue
u2 = 1.0 - random()
u2 = 1.0 - self.random()
v = _log(u1 / (1.0 - u1)) / ainv
x = alpha * _exp(v)
z = u1 * u1 * u2
Expand All @@ -710,20 +701,20 @@ def gammavariate(self, alpha, beta):

elif alpha == 1.0:
# expovariate(1/beta)
return -_log(1.0 - random()) * beta
return -_log(1.0 - self.random()) * beta

else:
# alpha is between 0 and 1 (exclusive)
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
while True:
u = random()
u = self.random()
b = (_e + alpha) / _e
p = b * u
if p <= 1.0:
x = p ** (1.0 / alpha)
else:
x = -_log((b - p) / alpha)
u1 = random()
u1 = self.random()
if p > 1.0:
if u1 <= x ** (alpha - 1.0):
break
Expand Down Expand Up @@ -816,11 +807,9 @@ def binomialvariate(self, n=1, p=0.5):
return n
raise ValueError("p must be in the range 0.0 <= p <= 1.0")

random = self.random

# Fast path for a common case
if n == 1:
return _index(random() < p)
return _index(self.random() < p)

# Exploit symmetry to establish: p <= 0.5
if p > 0.5:
Expand All @@ -834,7 +823,7 @@ def binomialvariate(self, n=1, p=0.5):
if not c:
return x
while True:
y += _floor(_log2(random()) / c) + 1
y += _floor(_log2(self.random()) / c) + 1
if y > n:
return x
x += 1
Expand All @@ -852,7 +841,7 @@ def binomialvariate(self, n=1, p=0.5):

while True:

u = random()
u = self.random()
u -= 0.5
us = 0.5 - _fabs(u)
k = _floor((2.0 * a / us + b) * u + c)
Expand All @@ -861,7 +850,7 @@ def binomialvariate(self, n=1, p=0.5):

# The early-out "squeeze" test substantially reduces
# the number of acceptance condition evaluations.
v = random()
v = self.random()
if us >= 0.07 and v <= vr:
return k

Expand Down
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