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Reworked random.py so that it no longer depends on, and offers all the 

functionality of, whrandom.py.  Also closes all the "XXX" todos in
random.py.  New frequently-requested functions/methods getstate() and
setstate().  All exported functions are now bound methods of a hidden
instance.  Killed all unintended exports.  Updated the docs.
FRED:  The more I fiddle the docs, the less I understand the exact
intended use of the \var, \code, \method tags.  Please review critically.
GUIDO:  See email.  I updated NEWS as if whrandom were deprecated; I
think it should be.
This commit is contained in:
Tim Peters 2001-01-25 03:36:26 +00:00
parent 83125775e0
commit d7b5e88e8e
3 changed files with 501 additions and 308 deletions
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86
Doc/lib/librandom.tex
View File

@ -32,6 +32,18 @@ functions from multiple threads, you should explicitly serialize the calls.
Else, because no critical sections are implemented internally, calls
from different threads may see the same return values.
The functions supplied by this module are actually bound methods of a
hidden instance of the \var{random.Random} class. You can instantiate
your own instances of \var{Random} to get generators that don't share state.
This may be especially useful for multi-threaded programs, although there's
no simple way to seed the distinct generators to ensure that the generated
sequences won't overlap. Class \var{Random} can also be subclassed if you
want to use a different basic generator of your own devising: in that
case, override the \method{random()}, \method{seed()}, \method{getstate()}
and \method{setstate()} methods.
Bookkeeping functions:
\begin{funcdesc}{seed}{\optional{x}}
Initialize the basic random number generator.
@ -45,15 +57,19 @@ from different threads may see the same return values.
the module is first imported.
\end{funcdesc}
\begin{funcdesc}{choice}{seq}
Return a random element from the non-empty sequence \var{seq}.
\end{funcdesc}
\begin{funcdesc}{getstate}{}
Return an object capturing the current internal state of the generator.
This object can be passed to \code{setstate()} to restore the state.
\end{funcdesc}
\begin{funcdesc}{randint}{a, b}
\deprecated{2.0}{Use \function{randrange()} instead.}
Return a random integer \var{N} such that
\code{\var{a} <= \var{N} <= \var{b}}.
\end{funcdesc}
\begin{funcdesc}{setstate}{state}
\var{state} should have been obtained from a previous call to
\code{getstate()}, and \code{setstate()} restores the internal state
of the generate to what it was at the time \code{setstate()} was called.
\end{funcdesc}
Functions for integers:
\begin{funcdesc}{randrange}{\optional{start,} stop\optional{, step}}
Return a randomly selected element from \code{range(\var{start},
@ -63,6 +79,37 @@ from different threads may see the same return values.
\versionadded{1.5.2}
\end{funcdesc}
\begin{funcdesc}{randint}{a, b}
\deprecated{2.0}{Use \function{randrange()} instead.}
Return a random integer \var{N} such that
\code{\var{a} <= \var{N} <= \var{b}}.
\end{funcdesc}
Functions for sequences:
\begin{funcdesc}{choice}{seq}
Return a random element from the non-empty sequence \var{seq}.
\end{funcdesc}
\begin{funcdesc}{shuffle}{x\optional{, random}}
Shuffle the sequence \var{x} in place.
The optional argument \var{random} is a 0-argument function
returning a random float in [0.0, 1.0); by default, this is the
function \function{random()}.
Note that for even rather small \code{len(\var{x})}, the total
number of permutations of \var{x} is larger than the period of most
random number generators; this implies that most permutations of a
long sequence can never be generated.
\end{funcdesc}
The following functions generate specific real-valued distributions.
Function parameters are named after the corresponding variables in the
distribution's equation, as used in common mathematical practice; most of
these equations can be found in any statistics text.
\begin{funcdesc}{random}{}
Return the next random floating point number in the range [0.0, 1.0).
\end{funcdesc}
@ -72,14 +119,6 @@ from different threads may see the same return values.
\code{\var{a} <= \var{N} < \var{b}}.
\end{funcdesc}
The following functions are defined to support specific distributions,
and all return real values. Function parameters are named after the
corresponding variables in the distribution's equation, as used in
common mathematical practice; most of these equations can be found in
any statistics text.
\begin{funcdesc}{betavariate}{alpha, beta}
Beta distribution. Conditions on the parameters are
\code{\var{alpha} > -1} and \code{\var{beta} > -1}.
@ -143,21 +182,6 @@ any statistics text.
\end{funcdesc}
This function does not represent a specific distribution, but
implements a standard useful algorithm:
\begin{funcdesc}{shuffle}{x\optional{, random}}
Shuffle the sequence \var{x} in place.
The optional argument \var{random} is a 0-argument function
returning a random float in [0.0, 1.0); by default, this is the
function \function{random()}.
Note that for even rather small \code{len(\var{x})}, the total
number of permutations of \var{x} is larger than the period of most
random number generators; this implies that most permutations of a
long sequence can never be generated.
\end{funcdesc}
\begin{seealso}
\seetext{Wichmann, B. A. \& Hill, I. D., ``Algorithm AS 183:
An efficient and portable pseudo-random number generator'',

710
Lib/random.py
View File

@ -1,7 +1,17 @@
"""Random variable generators.
integers
--------
uniform within range
sequences
---------
pick random element
generate random permutation
distributions on the real line:
------------------------------
uniform
normal (Gaussian)
lognormal
negative exponential
@ -17,328 +27,429 @@ Translated from anonymously contributed C/C++ source.
Multi-threading note: the random number generator used here is not
thread-safe; it is possible that two calls return the same random
value. See whrandom.py for more info.
value.
"""
# XXX The docstring sucks.
import whrandom
from whrandom import random, uniform, randint, choice, randrange # For export!
from math import log, exp, pi, e, sqrt, acos, cos, sin
from math import log as _log, exp as _exp, pi as _pi, e as _e
from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
# Interfaces to replace remaining needs for importing whrandom
# XXX TO DO: make the distribution functions below into methods.
def makeseed(a=None):
"""Turn a hashable value into three seed values for whrandom.seed().
None or no argument returns (0, 0, 0), to seed from current time.
"""
if a is None:
return (0, 0, 0)
a = hash(a)
a, x = divmod(a, 256)
a, y = divmod(a, 256)
a, z = divmod(a, 256)
x = (x + a) % 256 or 1
y = (y + a) % 256 or 1
z = (z + a) % 256 or 1
return (x, y, z)
def seed(a=None):
"""Seed the default generator from any hashable value.
None or no argument seeds from current time.
"""
x, y, z = makeseed(a)
whrandom.seed(x, y, z)
class generator(whrandom.whrandom):
"""Random generator class."""
def __init__(self, a=None):
"""Constructor. Seed from current time or hashable value."""
self.seed(a)
def seed(self, a=None):
"""Seed the generator from current time or hashable value."""
x, y, z = makeseed(a)
whrandom.whrandom.seed(self, x, y, z)
def new_generator(a=None):
"""Return a new random generator instance."""
return generator(a)
# Housekeeping function to verify that magic constants have been
# computed correctly
def verify(name, expected):
def _verify(name, expected):
computed = eval(name)
if abs(computed - expected) > 1e-7:
raise ValueError, \
'computed value for %s deviates too much (computed %g, expected %g)' % \
(name, computed, expected)
raise ValueError(
"computed value for %s deviates too much "
"(computed %g, expected %g)" % (name, computed, expected))
NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
_verify('NV_MAGICCONST', 1.71552776992141)
TWOPI = 2.0*_pi
_verify('TWOPI', 6.28318530718)
LOG4 = _log(4.0)
_verify('LOG4', 1.38629436111989)
SG_MAGICCONST = 1.0 + _log(4.5)
_verify('SG_MAGICCONST', 2.50407739677627)
del _verify
# Translated by Guido van Rossum from C source provided by
# Adrian Baddeley.
class Random:
VERSION = 1 # used by getstate/setstate
def __init__(self, x=None):
"""Initialize an instance.
Optional argument x controls seeding, as for Random.seed().
"""
self.seed(x)
self.gauss_next = None
# Specific to Wichmann-Hill generator. Subclasses wishing to use a
# different core generator should override seed(), random(), getstate()
# and setstate().
def __whseed(self, x=0, y=0, z=0):
"""Set the Wichmann-Hill seed from (x, y, z).
These must be integers in the range [0, 256).
"""
if not type(x) == type(y) == type(z) == type(0):
raise TypeError('seeds must be integers')
if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256):
raise ValueError('seeds must be in range(0, 256)')
if 0 == x == y == z:
# Initialize from current time
import time
t = long(time.time()) * 256
t = int((t&0xffffff) ^ (t>>24))
t, x = divmod(t, 256)
t, y = divmod(t, 256)
t, z = divmod(t, 256)
# Zero is a poor seed, so substitute 1
self._seed = (x or 1, y or 1, z or 1)
def seed(self, a=None):
"""Seed from hashable value
None or no argument seeds from current time.
"""
if a is None:
self.__whseed()
a = hash(a)
a, x = divmod(a, 256)
a, y = divmod(a, 256)
a, z = divmod(a, 256)
x = (x + a) % 256 or 1
y = (y + a) % 256 or 1
z = (z + a) % 256 or 1
self.__whseed(x, y, z)
def getstate(self):
"""Return internal state; can be passed to setstate() later."""
return self.VERSION, self._seed, self.gauss_next
def __getstate__(self): # for pickle
self.getstate()
def setstate(self, state):
"""Restore internal state from object returned by getstate()."""
version = state[0]
if version == 1:
version, self._seed, self.gauss_next = state
else:
raise ValueError("state with version %s passed to "
"Random.setstate() of version %s" %
(version, self.VERSION))
def __setstate__(self, state): # for pickle
self.setstate(state)
def random(self):
"""Get the next random number in the range [0.0, 1.0)."""
# Wichman-Hill random number generator.
#
# Wichmann, B. A. & Hill, I. D. (1982)
# Algorithm AS 183:
# An efficient and portable pseudo-random number generator
# Applied Statistics 31 (1982) 188-190
#
# see also:
# Correction to Algorithm AS 183
# Applied Statistics 33 (1984) 123
#
# McLeod, A. I. (1985)
# A remark on Algorithm AS 183
# Applied Statistics 34 (1985),198-200
# This part is thread-unsafe:
# BEGIN CRITICAL SECTION
x, y, z = self._seed
x = (171 * x) % 30269
y = (172 * y) % 30307
z = (170 * z) % 30323
self._seed = x, y, z
# END CRITICAL SECTION
# Note: on a platform using IEEE-754 double arithmetic, this can
# never return 0.0 (asserted by Tim; proof too long for a comment).
return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0
def randrange(self, start, stop=None, step=1, int=int, default=None):
"""Choose a random item from range(start, stop[, step]).
This fixes the problem with randint() which includes the
endpoint; in Python this is usually not what you want.
Do not supply the 'int' and 'default' arguments.
"""
# This code is a bit messy to make it fast for the
# common case while still doing adequate error checking
istart = int(start)
if istart != start:
raise ValueError, "non-integer arg 1 for randrange()"
if stop is default:
if istart > 0:
return int(self.random() * istart)
raise ValueError, "empty range for randrange()"
istop = int(stop)
if istop != stop:
raise ValueError, "non-integer stop for randrange()"
if step == 1:
if istart < istop:
return istart + int(self.random() *
(istop - istart))
raise ValueError, "empty range for randrange()"
istep = int(step)
if istep != step:
raise ValueError, "non-integer step for randrange()"
if istep > 0:
n = (istop - istart + istep - 1) / istep
elif istep < 0:
n = (istop - istart + istep + 1) / istep
else:
raise ValueError, "zero step for randrange()"
if n <= 0:
raise ValueError, "empty range for randrange()"
return istart + istep*int(self.random() * n)
def randint(self, a, b):
"""Get a random integer in the range [a, b] including
both end points.
(Deprecated; use randrange below.)
"""
return self.randrange(a, b+1)
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
return seq[int(self.random() * len(seq))]
def shuffle(self, x, random=None, int=int):
"""x, random=random.random -> shuffle list x in place; return None.
Optional arg random is a 0-argument function returning a random
float in [0.0, 1.0); by default, the standard random.random.
Note that for even rather small len(x), the total number of
permutations of x is larger than the period of most random number
generators; this implies that "most" permutations of a long
sequence can never be generated.
"""
if random is None:
random = self.random
for i in xrange(len(x)-1, 0, -1):
# pick an element in x[:i+1] with which to exchange x[i]
j = int(random() * (i+1))
x[i], x[j] = x[j], x[i]
# -------------------- uniform distribution -------------------
def uniform(self, a, b):
"""Get a random number in the range [a, b)."""
return a + (b-a) * self.random()
# -------------------- normal distribution --------------------
NV_MAGICCONST = 4*exp(-0.5)/sqrt(2.0)
verify('NV_MAGICCONST', 1.71552776992141)
def normalvariate(mu, sigma):
# mu = mean, sigma = standard deviation
def normalvariate(self, mu, sigma):
# mu = mean, sigma = standard deviation
# Uses Kinderman and Monahan method. Reference: Kinderman,
# A.J. and Monahan, J.F., "Computer generation of random
# variables using the ratio of uniform deviates", ACM Trans
# Math Software, 3, (1977), pp257-260.
while 1:
u1 = random()
u2 = random()
z = NV_MAGICCONST*(u1-0.5)/u2
zz = z*z/4.0
if zz <= -log(u2):
break
return mu+z*sigma
# -------------------- lognormal distribution --------------------
def lognormvariate(mu, sigma):
return exp(normalvariate(mu, sigma))
# -------------------- circular uniform --------------------
def cunifvariate(mean, arc):
# mean: mean angle (in radians between 0 and pi)
# arc: range of distribution (in radians between 0 and pi)
return (mean + arc * (random() - 0.5)) % pi
# -------------------- exponential distribution --------------------
def expovariate(lambd):
# lambd: rate lambd = 1/mean
# ('lambda' is a Python reserved word)
u = random()
while u <= 1e-7:
u = random()
return -log(u)/lambd
# -------------------- von Mises distribution --------------------
TWOPI = 2.0*pi
verify('TWOPI', 6.28318530718)
def vonmisesvariate(mu, kappa):
# mu: mean angle (in radians between 0 and 2*pi)
# kappa: concentration parameter kappa (>= 0)
# if kappa = 0 generate uniform random angle
# Based upon an algorithm published in: Fisher, N.I.,
# "Statistical Analysis of Circular Data", Cambridge
# University Press, 1993.
# Thanks to Magnus Kessler for a correction to the
# implementation of step 4.
if kappa <= 1e-6:
return TWOPI * random()
a = 1.0 + sqrt(1.0 + 4.0 * kappa * kappa)
b = (a - sqrt(2.0 * a))/(2.0 * kappa)
r = (1.0 + b * b)/(2.0 * b)
while 1:
u1 = random()
z = cos(pi * u1)
f = (1.0 + r * z)/(r + z)
c = kappa * (r - f)
u2 = random()
if not (u2 >= c * (2.0 - c) and u2 > c * exp(1.0 - c)):
break
u3 = random()
if u3 > 0.5:
theta = (mu % TWOPI) + acos(f)
else:
theta = (mu % TWOPI) - acos(f)
return theta
# -------------------- gamma distribution --------------------
LOG4 = log(4.0)
verify('LOG4', 1.38629436111989)
def gammavariate(alpha, beta):
# beta times standard gamma
ainv = sqrt(2.0 * alpha - 1.0)
return beta * stdgamma(alpha, ainv, alpha - LOG4, alpha + ainv)
SG_MAGICCONST = 1.0 + log(4.5)
verify('SG_MAGICCONST', 2.50407739677627)
def stdgamma(alpha, ainv, bbb, ccc):
# ainv = sqrt(2 * alpha - 1)
# bbb = alpha - log(4)
# ccc = alpha + ainv
if alpha <= 0.0:
raise ValueError, 'stdgamma: alpha must be > 0.0'
if alpha > 1.0:
# Uses R.C.H. Cheng, "The generation of Gamma
# variables with non-integral shape parameters",
# Applied Statistics, (1977), 26, No. 1, p71-74
# Uses Kinderman and Monahan method. Reference: Kinderman,
# A.J. and Monahan, J.F., "Computer generation of random
# variables using the ratio of uniform deviates", ACM Trans
# Math Software, 3, (1977), pp257-260.
random = self.random
while 1:
u1 = random()
u2 = random()
v = log(u1/(1.0-u1))/ainv
x = alpha*exp(v)
z = u1*u1*u2
r = bbb+ccc*v-x
if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= log(z):
return x
z = NV_MAGICCONST*(u1-0.5)/u2
zz = z*z/4.0
if zz <= -_log(u2):
break
return mu + z*sigma
elif alpha == 1.0:
# expovariate(1)
# -------------------- lognormal distribution --------------------
def lognormvariate(self, mu, sigma):
return _exp(self.normalvariate(mu, sigma))
# -------------------- circular uniform --------------------
def cunifvariate(self, mean, arc):
# mean: mean angle (in radians between 0 and pi)
# arc: range of distribution (in radians between 0 and pi)
return (mean + arc * (self.random() - 0.5)) % _pi
# -------------------- exponential distribution --------------------
def expovariate(self, lambd):
# lambd: rate lambd = 1/mean
# ('lambda' is a Python reserved word)
random = self.random
u = random()
while u <= 1e-7:
u = random()
return -log(u)
return -_log(u)/lambd
else: # alpha is between 0 and 1 (exclusive)
# -------------------- von Mises distribution --------------------
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
def vonmisesvariate(self, mu, kappa):
# mu: mean angle (in radians between 0 and 2*pi)
# kappa: concentration parameter kappa (>= 0)
# if kappa = 0 generate uniform random angle
# Based upon an algorithm published in: Fisher, N.I.,
# "Statistical Analysis of Circular Data", Cambridge
# University Press, 1993.
# Thanks to Magnus Kessler for a correction to the
# implementation of step 4.
random = self.random
if kappa <= 1e-6:
return TWOPI * random()
a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa)
b = (a - _sqrt(2.0 * a))/(2.0 * kappa)
r = (1.0 + b * b)/(2.0 * b)
while 1:
u = random()
b = (e + alpha)/e
p = b*u
if p <= 1.0:
x = pow(p, 1.0/alpha)
else:
# p > 1
x = -log((b-p)/alpha)
u1 = random()
if not (((p <= 1.0) and (u1 > exp(-x))) or
((p > 1) and (u1 > pow(x, alpha - 1.0)))):
z = _cos(_pi * u1)
f = (1.0 + r * z)/(r + z)
c = kappa * (r - f)
u2 = random()
if not (u2 >= c * (2.0 - c) and u2 > c * _exp(1.0 - c)):
break
return x
u3 = random()
if u3 > 0.5:
theta = (mu % TWOPI) + _acos(f)
else:
theta = (mu % TWOPI) - _acos(f)
return theta
# -------------------- gamma distribution --------------------
def gammavariate(self, alpha, beta):
# beta times standard gamma
ainv = _sqrt(2.0 * alpha - 1.0)
return beta * self.stdgamma(alpha, ainv, alpha - LOG4, alpha + ainv)
def stdgamma(self, alpha, ainv, bbb, ccc):
# ainv = sqrt(2 * alpha - 1)
# bbb = alpha - log(4)
# ccc = alpha + ainv
random = self.random
if alpha <= 0.0:
raise ValueError, 'stdgamma: alpha must be > 0.0'
if alpha > 1.0:
# Uses R.C.H. Cheng, "The generation of Gamma
# variables with non-integral shape parameters",
# Applied Statistics, (1977), 26, No. 1, p71-74
while 1:
u1 = random()
u2 = random()
v = _log(u1/(1.0-u1))/ainv
x = alpha*_exp(v)
z = u1*u1*u2
r = bbb+ccc*v-x
if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):
return x
elif alpha == 1.0:
# expovariate(1)
u = random()
while u <= 1e-7:
u = random()
return -_log(u)
else: # alpha is between 0 and 1 (exclusive)
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
while 1:
u = random()
b = (_e + alpha)/_e
p = b*u
if p <= 1.0:
x = pow(p, 1.0/alpha)
else:
# p > 1
x = -_log((b-p)/alpha)
u1 = random()
if not (((p <= 1.0) and (u1 > _exp(-x))) or
((p > 1) and (u1 > pow(x, alpha - 1.0)))):
break
return x
# -------------------- Gauss (faster alternative) --------------------
gauss_next = None
def gauss(mu, sigma):
def gauss(self, mu, sigma):
# When x and y are two variables from [0, 1), uniformly
# distributed, then
#
# cos(2*pi*x)*sqrt(-2*log(1-y))
# sin(2*pi*x)*sqrt(-2*log(1-y))
#
# are two *independent* variables with normal distribution
# (mu = 0, sigma = 1).
# (Lambert Meertens)
# (corrected version; bug discovered by Mike Miller, fixed by LM)
# When x and y are two variables from [0, 1), uniformly
# distributed, then
#
# cos(2*pi*x)*sqrt(-2*log(1-y))
# sin(2*pi*x)*sqrt(-2*log(1-y))
#
# are two *independent* variables with normal distribution
# (mu = 0, sigma = 1).
# (Lambert Meertens)
# (corrected version; bug discovered by Mike Miller, fixed by LM)
# Multithreading note: When two threads call this function
# simultaneously, it is possible that they will receive the
# same return value. The window is very small though. To
# avoid this, you have to use a lock around all calls. (I
# didn't want to slow this down in the serial case by using a
# lock here.)
# Multithreading note: When two threads call this function
# simultaneously, it is possible that they will receive the
# same return value. The window is very small though. To
# avoid this, you have to use a lock around all calls. (I
# didn't want to slow this down in the serial case by using a
# lock here.)
global gauss_next
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()))
z = _cos(x2pi) * g2rad
self.gauss_next = _sin(x2pi) * g2rad
z = gauss_next
gauss_next = None
if z is None:
x2pi = random() * TWOPI
g2rad = sqrt(-2.0 * log(1.0 - random()))
z = cos(x2pi) * g2rad
gauss_next = sin(x2pi) * g2rad
return mu + z*sigma
return mu + z*sigma
# -------------------- beta --------------------
def betavariate(alpha, beta):
def betavariate(self, alpha, beta):
# Discrete Event Simulation in C, pp 87-88.
# Discrete Event Simulation in C, pp 87-88.
y = expovariate(alpha)
z = expovariate(1.0/beta)
return z/(y+z)
y = self.expovariate(alpha)
z = self.expovariate(1.0/beta)
return z/(y+z)
# -------------------- Pareto --------------------
def paretovariate(alpha):
# Jain, pg. 495
def paretovariate(self, alpha):
# Jain, pg. 495
u = random()
return 1.0 / pow(u, 1.0/alpha)
u = self.random()
return 1.0 / pow(u, 1.0/alpha)
# -------------------- Weibull --------------------
def weibullvariate(alpha, beta):
# Jain, pg. 499; bug fix courtesy Bill Arms
def weibullvariate(self, alpha, beta):
# Jain, pg. 499; bug fix courtesy Bill Arms
u = random()
return alpha * pow(-log(u), 1.0/beta)
# -------------------- shuffle --------------------
# Not quite a random distribution, but a standard algorithm.
# This implementation due to Tim Peters.
def shuffle(x, random=random, int=int):
"""x, random=random.random -> shuffle list x in place; return None.
Optional arg random is a 0-argument function returning a random
float in [0.0, 1.0); by default, the standard random.random.
Note that for even rather small len(x), the total number of
permutations of x is larger than the period of most random number
generators; this implies that "most" permutations of a long
sequence can never be generated.
"""
for i in xrange(len(x)-1, 0, -1):
# pick an element in x[:i+1] with which to exchange x[i]
j = int(random() * (i+1))
x[i], x[j] = x[j], x[i]
u = self.random()
return alpha * pow(-_log(u), 1.0/beta)
# -------------------- test program --------------------
def test(N = 200):
print 'TWOPI =', TWOPI
print 'LOG4 =', LOG4
print 'NV_MAGICCONST =', NV_MAGICCONST
print 'SG_MAGICCONST =', SG_MAGICCONST
test_generator(N, 'random()')
test_generator(N, 'normalvariate(0.0, 1.0)')
test_generator(N, 'lognormvariate(0.0, 1.0)')
test_generator(N, 'cunifvariate(0.0, 1.0)')
test_generator(N, 'expovariate(1.0)')
test_generator(N, 'vonmisesvariate(0.0, 1.0)')
test_generator(N, 'gammavariate(0.5, 1.0)')
test_generator(N, 'gammavariate(0.9, 1.0)')
test_generator(N, 'gammavariate(1.0, 1.0)')
test_generator(N, 'gammavariate(2.0, 1.0)')
test_generator(N, 'gammavariate(20.0, 1.0)')
test_generator(N, 'gammavariate(200.0, 1.0)')
test_generator(N, 'gauss(0.0, 1.0)')
test_generator(N, 'betavariate(3.0, 3.0)')
test_generator(N, 'paretovariate(1.0)')
test_generator(N, 'weibullvariate(1.0, 1.0)')
def test_generator(n, funccall):
def _test_generator(n, funccall):
import time
print n, 'times', funccall
code = compile(funccall, funccall, 'eval')
@ -356,9 +467,54 @@ def test_generator(n, funccall):
t1 = time.time()
print round(t1-t0, 3), 'sec,',
avg = sum/n
stddev = sqrt(sqsum/n - avg*avg)
stddev = _sqrt(sqsum/n - avg*avg)
print 'avg %g, stddev %g, min %g, max %g' % \
(avg, stddev, smallest, largest)
def _test(N=200):
print 'TWOPI =', TWOPI
print 'LOG4 =', LOG4
print 'NV_MAGICCONST =', NV_MAGICCONST
print 'SG_MAGICCONST =', SG_MAGICCONST
_test_generator(N, 'random()')
_test_generator(N, 'normalvariate(0.0, 1.0)')
_test_generator(N, 'lognormvariate(0.0, 1.0)')
_test_generator(N, 'cunifvariate(0.0, 1.0)')
_test_generator(N, 'expovariate(1.0)')
_test_generator(N, 'vonmisesvariate(0.0, 1.0)')
_test_generator(N, 'gammavariate(0.5, 1.0)')
_test_generator(N, 'gammavariate(0.9, 1.0)')
_test_generator(N, 'gammavariate(1.0, 1.0)')
_test_generator(N, 'gammavariate(2.0, 1.0)')
_test_generator(N, 'gammavariate(20.0, 1.0)')
_test_generator(N, 'gammavariate(200.0, 1.0)')
_test_generator(N, 'gauss(0.0, 1.0)')
_test_generator(N, 'betavariate(3.0, 3.0)')
_test_generator(N, 'paretovariate(1.0)')
_test_generator(N, 'weibullvariate(1.0, 1.0)')
# Initialize from current time.
_inst = Random()
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
shuffle = _inst.shuffle
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate
cunifvariate = _inst.cunifvariate
expovariate = _inst.expovariate
vonmisesvariate = _inst.vonmisesvariate
gammavariate = _inst.gammavariate
stdgamma = _inst.stdgamma
gauss = _inst.gauss
betavariate = _inst.betavariate
paretovariate = _inst.paretovariate
weibullvariate = _inst.weibullvariate
getstate = _inst.getstate
setstate = _inst.setstate
if __name__ == '__main__':
test()
_test()

13
Misc/NEWS
View File

@ -1,3 +1,16 @@
What's New in Python 2.1 alpha 2?
=================================
Core language, builtins, and interpreter
Standard library
- random.py is now self-contained, and offers all the functionality of
the now-deprecated whrandom.py. See the docs for details. random.py
also supports new functions getstate() and setstate(), for saving
and restoring the internal state of all the generators.
What's New in Python 2.1 alpha 1?
=================================