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More informative docstrings in the random module (gh-109745)

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Raymond Hettinger 2023-09-26 08:20:17 -05:00 • committed by GitHub
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1 changed files with 34 additions and 5 deletions
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39
Lib/random.py
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@ -492,7 +492,14 @@ class Random(_random.Random):
## -------------------- real-valued distributions -------------------
def uniform(self, a, b):
"Get a random number in the range [a, b) or [a, b] depending on rounding."
"""Get a random number in the range [a, b) or [a, b] depending on rounding.
The mean (expected value) and variance of the random variable are:
E[X] = (a + b) / 2
Var[X] = (b - a) ** 2 / 12
"""
return a + (b - a) * self.random()
def triangular(self, low=0.0, high=1.0, mode=None):
@ -503,6 +510,11 @@ class Random(_random.Random):
http://en.wikipedia.org/wiki/Triangular_distribution
The mean (expected value) and variance of the random variable are:
E[X] = (low + high + mode) / 3
Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18
"""
u = self.random()
try:
@ -593,12 +605,15 @@ class Random(_random.Random):
positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
"""
# lambd: rate lambd = 1/mean
# ('lambda' is a Python reserved word)
The mean (expected value) and variance of the random variable are:
E[X] = 1 / lambd
Var[X] = 1 / lambd ** 2
"""
# we use 1-random() instead of random() to preclude the
# possibility of taking the log of zero.
return -_log(1.0 - self.random()) / lambd
def vonmisesvariate(self, mu, kappa):
@ -654,8 +669,12 @@ class Random(_random.Random):
pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
The mean (expected value) and variance of the random variable are:
E[X] = alpha * beta
Var[X] = alpha * beta ** 2
"""
# alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
# Warning: a few older sources define the gamma distribution in terms
# of alpha > -1.0
@ -714,6 +733,11 @@ class Random(_random.Random):
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
The mean (expected value) and variance of the random variable are:
E[X] = alpha / (alpha + beta)
Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1))
"""
## See
## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html
@ -766,6 +790,11 @@ class Random(_random.Random):
Returns an integer in the range: 0 <= X <= n
The mean (expected value) and variance of the random variable are:
E[X] = n * p
Var[x] = n * p * (1 - p)
"""
# Error check inputs and handle edge cases
if n < 0: