[Python-checkins] python/dist/src/Lib random.py,1.26.6.6,1.26.6.7
rhettinger@users.sourceforge.net
rhettinger@users.sourceforge.net
Sat, 05 Oct 2002 07:42:54 -0700
Update of /cvsroot/python/python/dist/src/Lib
In directory usw-pr-cvs1:/tmp/cvs-serv17518
Modified Files:
Tag: release22-maint
random.py
Log Message:
Backport 1.33 (omitting the deprecation warning but keeping the deprecation
comment):
Deprecated Random.cunifvariate clearing bug 506647.
Also, added docstrings.
Index: random.py
===================================================================
RCS file: /cvsroot/python/python/dist/src/Lib/random.py,v
retrieving revision 1.26.6.6
retrieving revision 1.26.6.7
diff -C2 -d -r1.26.6.6 -r1.26.6.7
*** random.py 23 Sep 2002 14:52:40 -0000 1.26.6.6
--- random.py 5 Oct 2002 14:42:52 -0000 1.26.6.7
***************
*** 108,111 ****
--- 108,124 ----
class Random:
+ """Random number generator base class used by bound module functions.
+
+ Used to instantiate instances of Random to get generators that don't
+ share state. Especially useful for multi-threaded programs, creating
+ a different instance of Random for each thread, and using the jumpahead()
+ method to ensure that the generated sequences seen by each thread don't
+ overlap.
+
+ Class Random can also be subclassed if you want to use a different basic
+ generator of your own devising: in that case, override the following
+ methods: random(), seed(), getstate(), setstate() and jumpahead().
+
+ """
VERSION = 1 # used by getstate/setstate
***************
*** 372,375 ****
--- 385,393 ----
def normalvariate(self, mu, sigma):
+ """Normal distribution.
+
+ mu is the mean, and sigma is the standard deviation.
+
+ """
# mu = mean, sigma = standard deviation
***************
*** 392,395 ****
--- 410,420 ----
def lognormvariate(self, mu, sigma):
+ """Log normal distribution.
+
+ If you take the natural logarithm of this distribution, you'll get a
+ normal distribution with mean mu and standard deviation sigma.
+ mu can have any value, and sigma must be greater than zero.
+
+ """
return _exp(self.normalvariate(mu, sigma))
***************
*** 397,400 ****
--- 422,436 ----
def cunifvariate(self, mean, arc):
+ """Circular uniform distribution.
+
+ mean is the mean angle, and arc is the range of the distribution,
+ centered around the mean angle. Both values must be expressed in
+ radians. Returned values range between mean - arc/2 and
+ mean + arc/2 and are normalized to between 0 and pi.
+
+ Deprecated in version 2.3. Use:
+ (mean + arc * (Random.random() - 0.5)) % Math.pi
+
+ """
# mean: mean angle (in radians between 0 and pi)
# arc: range of distribution (in radians between 0 and pi)
***************
*** 405,408 ****
--- 441,451 ----
def expovariate(self, lambd):
+ """Exponential distribution.
+
+ lambd is 1.0 divided by the desired mean. (The parameter would be
+ called "lambda", but that is a reserved word in Python.) Returned
+ values range from 0 to positive infinity.
+
+ """
# lambd: rate lambd = 1/mean
# ('lambda' is a Python reserved word)
***************
*** 417,420 ****
--- 460,471 ----
def vonmisesvariate(self, mu, kappa):
+ """Circular data distribution.
+
+ mu is the mean angle, expressed in radians between 0 and 2*pi, and
+ kappa is the concentration parameter, which must be greater than or
+ equal to zero. If kappa is equal to zero, this distribution reduces
+ to a uniform random angle over the range 0 to 2*pi.
+
+ """
# mu: mean angle (in radians between 0 and 2*pi)
# kappa: concentration parameter kappa (>= 0)
***************
*** 459,462 ****
--- 510,518 ----
def gammavariate(self, alpha, beta):
+ """Gamma distribution. Not the gamma function!
+
+ Conditions on the parameters are alpha > 0 and beta > 0.
+
+ """
# alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
***************
*** 538,541 ****
--- 594,605 ----
def gauss(self, mu, sigma):
+ """Gaussian distribution.
+
+ mu is the mean, and sigma is the standard deviation. This is
+ slightly faster than the normalvariate() function.
+
+ Not thread-safe without a lock around calls.
+
+ """
# When x and y are two variables from [0, 1), uniformly
***************
*** 583,586 ****
--- 647,657 ----
def betavariate(self, alpha, beta):
+ """Beta distribution.
+
+ Conditions on the parameters are alpha > -1 and beta} > -1.
+ Returned values range between 0 and 1.
+
+ """
+
# This version due to Janne Sinkkonen, and matches all the std
# texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
***************
*** 594,597 ****
--- 665,669 ----
def paretovariate(self, alpha):
+ """Pareto distribution. alpha is the shape parameter."""
# Jain, pg. 495
***************
*** 602,605 ****
--- 674,682 ----
def weibullvariate(self, alpha, beta):
+ """Weibull distribution.
+
+ alpha is the scale parameter and beta is the shape parameter.
+
+ """
# Jain, pg. 499; bug fix courtesy Bill Arms