core generator for random.py.
This module implements pseudo-random number generators for various
distributions.
+
For integers, uniform selection from a range.
-For sequences, uniform selection of a random element, and a function to
-generate a random permutation of a list in-place.
+For sequences, uniform selection of a random element, a function to
+generate a random permutation of a list in-place, and a function for
+random sampling without replacement.
+
On the real line, there are functions to compute uniform, normal (Gaussian),
lognormal, negative exponential, gamma, and beta distributions.
-For generating distribution of angles, the circular uniform and
-von Mises distributions are available.
+For generating distributions of angles, the von Mises distribution
+is available.
Almost all module functions depend on the basic function
\function{random()}, which generates a random float uniformly in
-the semi-open range [0.0, 1.0). Python uses the standard Wichmann-Hill
-generator, combining three pure multiplicative congruential
-generators of modulus 30269, 30307 and 30323. Its period (how many
-numbers it generates before repeating the sequence exactly) is
-6,953,607,871,644. While of much higher quality than the \function{rand()}
-function supplied by most C libraries, the theoretical properties
-are much the same as for a single linear congruential generator of
-large modulus. It is not suitable for all purposes, and is completely
-unsuitable for cryptographic purposes.
-
-The functions in this module are not threadsafe: if you want to call these
-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 semi-open range [0.0, 1.0). Python uses the Mersenne Twister as
+the core generator. It produces 53-bit precision floats and has a
+period of 2**19937-1. The underlying implementation in C
+is both fast and threadsafe. The Mersenne Twister is one of the most
+extensively tested random number generators in existence. However, being
+completely deterministic, it is not suitable for all purposes, and is
+completely unsuitable for cryptographic purposes.
The functions supplied by this module are actually bound methods of a
hidden instance of the \class{random.Random} class. You can
that don't share state. This is especially useful for multi-threaded
programs, creating a different instance of \class{Random} for each
thread, and using the \method{jumpahead()} method to ensure that the
-generated sequences seen by each thread don't overlap (see example
-below).
+generated sequences seen by each thread don't overlap.
Class \class{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()},
\method{setstate()} and \method{jumpahead()} methods.
-Here's one way to create threadsafe distinct and non-overlapping generators:
-
-\begin{verbatim}
-def create_generators(num, delta, firstseed=None):
- """Return list of num distinct generators.
- Each generator has its own unique segment of delta elements
- from Random.random()'s full period.
- Seed the first generator with optional arg firstseed (default
- is None, to seed from current time).
- """
-
- from random import Random
- g = Random(firstseed)
- result = [g]
- for i in range(num - 1):
- laststate = g.getstate()
- g = Random()
- g.setstate(laststate)
- g.jumpahead(delta)
- result.append(g)
- return result
-
-gens = create_generators(10, 1000000)
-\end{verbatim}
-
-That creates 10 distinct generators, which can be passed out to 10
-distinct threads. The generators don't share state so can be called
-safely in parallel. So long as no thread calls its \code{g.random()}
-more than a million times (the second argument to
-\function{create_generators()}, the sequences seen by each thread will
-not overlap. The period of the underlying Wichmann-Hill generator
-limits how far this technique can be pushed.
-
-Just for fun, note that since we know the period, \method{jumpahead()}
-can also be used to ``move backward in time:''
-
-\begin{verbatim}
->>> g = Random(42) # arbitrary
->>> g.random()
-0.25420336316883324
->>> g.jumpahead(6953607871644L - 1) # move *back* one
->>> g.random()
-0.25420336316883324
-\end{verbatim}
+As an example of subclassing, the \module{random} module provides
+the \class{WichmannHill} class which implements an alternative generator
+in pure Python. The class provides a backward compatible way to
+reproduce results from earlier versions of Python which used the
+Wichmann-Hill algorithm as the core generator.
+\versionchanged[Substituted MersenneTwister for Wichmann-Hill]{2.3}
Bookkeeping functions:
If \var{x} is not \code{None} or an int or long,
\code{hash(\var{x})} is used instead.
If \var{x} is an int or long, \var{x} is used directly.
- Distinct values between 0 and 27814431486575L inclusive are guaranteed
- to yield distinct internal states (this guarantee is specific to the
- default Wichmann-Hill generator, and may not apply to subclasses
- supplying their own basic generator).
-\end{funcdesc}
-
-\begin{funcdesc}{whseed}{\optional{x}}
- This is obsolete, supplied for bit-level compatibility with versions
- of Python prior to 2.1.
- See \function{seed} for details. \function{whseed} does not guarantee
- that distinct integer arguments yield distinct internal states, and can
- yield no more than about 2**24 distinct internal states in all.
\end{funcdesc}
\begin{funcdesc}{getstate}{}
\end{funcdesc}
\begin{funcdesc}{jumpahead}{n}
- Change the internal state to what it would be if \function{random()}
- were called \var{n} times, but do so quickly. \var{n} is a
- non-negative integer. This is most useful in multi-threaded
+ Change the internal state to one different from and likely far away from
+ the current state. \var{n} is a non-negative integer which is used to
+ scramble the current state vector. This is most useful in multi-threaded
programs, in conjuction with multiple instances of the \class{Random}
- class: \method{setstate()} or \method{seed()} can be used to force
- all instances into the same internal state, and then
- \method{jumpahead()} can be used to force the instances' states as
- far apart as you like (up to the period of the generator).
+ class: \method{setstate()} or \method{seed()} can be used to force all
+ instances into the same internal state, and then \method{jumpahead()}
+ can be used to force the instances' states far apart.
\versionadded{2.1}
+ \versionchanged[Instead of jumping to a specific state, \var{n} steps
+ ahead, \method{jumpahead(\var{n})} jumps to another state likely to be
+ separated by many steps.]{2.3}
\end{funcdesc}
+
Functions for integers:
\begin{funcdesc}{randrange}{\optional{start,} stop\optional{, step}}
\var{beta} is the shape parameter.
\end{funcdesc}
+Alternative Generator
+
+\begin{classdesc}{WichmannHill}{\optional{seed}}
+Class that implements the Wichmann-Hill algorithm as the core generator.
+Has all of the same methods as \class{Random} plus the \method{whseed}
+method described below. Because this class is implemented in pure
+Python, it is not threadsafe and may require locks between calls. The
+period of the generator is 6,953,607,871,644 which is small enough to
+require care that two independent random sequences do not overlap.
+\end{classdesc}
+
+\begin{funcdesc}{whseed}{\optional{x}}
+ This is obsolete, supplied for bit-level compatibility with versions
+ of Python prior to 2.1.
+ See \function{seed} for details. \function{whseed} does not guarantee
+ that distinct integer arguments yield distinct internal states, and can
+ yield no more than about 2**24 distinct internal states in all.
+\end{funcdesc}
\begin{seealso}
+ \seetext{M. Matsumoto and T. Nishimura, ``Mersenne Twister: A
+ 623-dimensionally equidistributed uniform pseudorandom
+ number generator'',
+ \citetitle{ACM Transactions on Modeling and Computer Simulation}
+ Vol. 8, No. 1, January pp.3-30 1998.}
+
\seetext{Wichmann, B. A. \& Hill, I. D., ``Algorithm AS 183:
An efficient and portable pseudo-random number generator'',
\citetitle{Applied Statistics} 31 (1982) 188-190.}
negative exponential
gamma
beta
+ pareto
+ Weibull
distributions on the circle (angles 0 to 2pi)
---------------------------------------------
circular uniform
von Mises
-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. However,
-you can instantiate a different instance of Random() in each thread to get
-generators that don't share state, then use .setstate() and .jumpahead() to
-move the generators to disjoint segments of the full period. For example,
-
-def create_generators(num, delta, firstseed=None):
- ""\"Return list of num distinct generators.
- Each generator has its own unique segment of delta elements from
- Random.random()'s full period.
- Seed the first generator with optional arg firstseed (default is
- None, to seed from current time).
- ""\"
-
- from random import Random
- g = Random(firstseed)
- result = [g]
- for i in range(num - 1):
- laststate = g.getstate()
- g = Random()
- g.setstate(laststate)
- g.jumpahead(delta)
- result.append(g)
- return result
-
-gens = create_generators(10, 1000000)
-
-That creates 10 distinct generators, which can be passed out to 10 distinct
-threads. The generators don't share state so can be called safely in
-parallel. So long as no thread calls its g.random() more than a million
-times (the second argument to create_generators), the sequences seen by
-each thread will not overlap.
-
-The period of the underlying Wichmann-Hill generator is 6,953,607,871,644,
-and that limits how far this technique can be pushed.
-
-Just for fun, note that since we know the period, .jumpahead() can also be
-used to "move backward in time":
-
->>> g = Random(42) # arbitrary
->>> g.random()
-0.25420336316883324
->>> g.jumpahead(6953607871644L - 1) # move *back* one
->>> g.random()
-0.25420336316883324
+General notes on the underlying Mersenne Twister core generator:
+
+* The period is 2**19937-1.
+* It is one of the most extensively tested generators in existence
+* Without a direct way to compute N steps forward, the
+ semantics of jumpahead(n) are weakened to simply jump
+ to another distant state and rely on the large period
+ to avoid overlapping sequences.
+* The random() method is implemented in C, executes in
+ a single Python step, and is, therefore, threadsafe.
+
"""
-# XXX The docstring sucks.
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
"randrange","shuffle","normalvariate","lognormvariate",
"cunifvariate","expovariate","vonmisesvariate","gammavariate",
"stdgamma","gauss","betavariate","paretovariate","weibullvariate",
- "getstate","setstate","jumpahead","whseed"]
-
-def _verify(name, computed, expected):
- if abs(computed - expected) > 1e-7:
- raise ValueError(
- "computed value for %s deviates too much "
- "(computed %g, expected %g)" % (name, computed, expected))
+ "getstate","setstate","jumpahead"]
NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
-_verify('NV_MAGICCONST', NV_MAGICCONST, 1.71552776992141)
-
TWOPI = 2.0*_pi
-_verify('TWOPI', TWOPI, 6.28318530718)
-
LOG4 = _log(4.0)
-_verify('LOG4', LOG4, 1.38629436111989)
-
SG_MAGICCONST = 1.0 + _log(4.5)
-_verify('SG_MAGICCONST', SG_MAGICCONST, 2.50407739677627)
-
-del _verify
# Translated by Guido van Rossum from C source provided by
-# Adrian Baddeley.
+# Adrian Baddeley. Adapted by Raymond Hettinger for use with
+# the Mersenne Twister core generator.
-class Random:
+from _random import Random as CoreGenerator
+
+class Random(CoreGenerator):
"""Random number generator base class used by bound module functions.
Used to instantiate instances of Random to get generators that don't
"""
- VERSION = 1 # used by getstate/setstate
+ VERSION = 2 # used by getstate/setstate
def __init__(self, x=None):
"""Initialize an instance.
"""
self.seed(x)
-
-## -------------------- core generator -------------------
-
- # Specific to Wichmann-Hill generator. Subclasses wishing to use a
- # different core generator should override the seed(), random(),
- # getstate(), setstate() and jumpahead() methods.
+ self.gauss_next = None
def seed(self, a=None):
"""Initialize internal state from hashable object.
None or no argument seeds from current time.
If a is not None or an int or long, hash(a) is used instead.
-
- If a is an int or long, a is used directly. Distinct values between
- 0 and 27814431486575L inclusive are guaranteed to yield distinct
- internal states (this guarantee is specific to the default
- Wichmann-Hill generator).
"""
- if a is None:
- # Initialize from current time
- import time
- a = long(time.time() * 256)
-
- if type(a) not in (type(3), type(3L)):
- a = hash(a)
-
- a, x = divmod(a, 30268)
- a, y = divmod(a, 30306)
- a, z = divmod(a, 30322)
- self._seed = int(x)+1, int(y)+1, int(z)+1
-
+ CoreGenerator.seed(self, a)
self.gauss_next = None
- 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 getstate(self):
"""Return internal state; can be passed to setstate() later."""
- return self.VERSION, self._seed, self.gauss_next
+ return self.VERSION, CoreGenerator.getstate(self), self.gauss_next
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
+ if version == 2:
+ version, internalstate, self.gauss_next = state
+ CoreGenerator.setstate(self, internalstate)
else:
raise ValueError("state with version %s passed to "
"Random.setstate() of version %s" %
(version, self.VERSION))
- def jumpahead(self, n):
- """Act as if n calls to random() were made, but quickly.
-
- n is an int, greater than or equal to 0.
-
- Example use: If you have 2 threads and know that each will
- consume no more than a million random numbers, create two Random
- objects r1 and r2, then do
- r2.setstate(r1.getstate())
- r2.jumpahead(1000000)
- Then r1 and r2 will use guaranteed-disjoint segments of the full
- period.
- """
-
- if not n >= 0:
- raise ValueError("n must be >= 0")
- x, y, z = self._seed
- x = int(x * pow(171, n, 30269)) % 30269
- y = int(y * pow(172, n, 30307)) % 30307
- z = int(z * pow(170, n, 30323)) % 30323
- self._seed = x, y, z
-
- 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) == int:
- 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)
-
- self.gauss_next = None
-
- def whseed(self, a=None):
- """Seed from hashable object's hash code.
-
- None or no argument seeds from current time. It is not guaranteed
- that objects with distinct hash codes lead to distinct internal
- states.
-
- This is obsolete, provided for compatibility with the seed routine
- used prior to Python 2.1. Use the .seed() method instead.
- """
-
- if a is None:
- self.__whseed()
- return
- 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)
-
## ---- Methods below this point do not need to be overridden when
## ---- subclassing for the purpose of using a different core generator.
u = self.random()
return alpha * pow(-_log(u), 1.0/beta)
+## -------------------- Wichmann-Hill -------------------
+
+class WichmannHill(Random):
+
+ VERSION = 1 # used by getstate/setstate
+
+ def seed(self, a=None):
+ """Initialize internal state from hashable object.
+
+ None or no argument seeds from current time.
+
+ If a is not None or an int or long, hash(a) is used instead.
+
+ If a is an int or long, a is used directly. Distinct values between
+ 0 and 27814431486575L inclusive are guaranteed to yield distinct
+ internal states (this guarantee is specific to the default
+ Wichmann-Hill generator).
+ """
+
+ if a is None:
+ # Initialize from current time
+ import time
+ a = long(time.time() * 256)
+
+ if not isinstance(a, (int, long)):
+ a = hash(a)
+
+ a, x = divmod(a, 30268)
+ a, y = divmod(a, 30306)
+ a, z = divmod(a, 30322)
+ self._seed = int(x)+1, int(y)+1, int(z)+1
+
+ self.gauss_next = None
+
+ 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 getstate(self):
+ """Return internal state; can be passed to setstate() later."""
+ return self.VERSION, self._seed, self.gauss_next
+
+ 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 jumpahead(self, n):
+ """Act as if n calls to random() were made, but quickly.
+
+ n is an int, greater than or equal to 0.
+
+ Example use: If you have 2 threads and know that each will
+ consume no more than a million random numbers, create two Random
+ objects r1 and r2, then do
+ r2.setstate(r1.getstate())
+ r2.jumpahead(1000000)
+ Then r1 and r2 will use guaranteed-disjoint segments of the full
+ period.
+ """
+
+ if not n >= 0:
+ raise ValueError("n must be >= 0")
+ x, y, z = self._seed
+ x = int(x * pow(171, n, 30269)) % 30269
+ y = int(y * pow(172, n, 30307)) % 30307
+ z = int(z * pow(170, n, 30323)) % 30323
+ self._seed = x, y, z
+
+ 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) == int:
+ 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)
+
+ self.gauss_next = None
+
+ def whseed(self, a=None):
+ """Seed from hashable object's hash code.
+
+ None or no argument seeds from current time. It is not guaranteed
+ that objects with distinct hash codes lead to distinct internal
+ states.
+
+ This is obsolete, provided for compatibility with the seed routine
+ used prior to Python 2.1. Use the .seed() method instead.
+ """
+
+ if a is None:
+ self.__whseed()
+ return
+ 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)
+
## -------------------- test program --------------------
def _test_generator(n, funccall):
print 'avg %g, stddev %g, min %g, max %g' % \
(avg, stddev, smallest, largest)
-def _test_sample(n):
- # For the entire allowable range of 0 <= k <= n, validate that
- # the sample is of the correct length and contains only unique items
- population = xrange(n)
- for k in xrange(n+1):
- s = sample(population, k)
- uniq = dict.fromkeys(s)
- assert len(uniq) == len(s) == k
- assert None not in uniq
-
def _sample_generator(n, k):
# Return a fixed element from the sample. Validates random ordering.
return sample(xrange(n), k)[k//2]
def _test(N=2000):
- 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, 'weibullvariate(1.0, 1.0)')
_test_generator(N, '_sample_generator(50, 5)') # expected s.d.: 14.4
_test_generator(N, '_sample_generator(50, 45)') # expected s.d.: 14.4
- _test_sample(500)
-
- # Test jumpahead.
- s = getstate()
- jumpahead(N)
- r1 = random()
- # now do it the slow way
- setstate(s)
- for i in range(N):
- random()
- r2 = random()
- if r1 != r2:
- raise ValueError("jumpahead test failed " + `(N, r1, r2)`)
# Create one instance, seeded from current time, and export its methods
-# as module-level functions. The functions are not threadsafe, and state
-# is shared across all uses (both in the user's code and in the Python
-# libraries), but that's fine for most programs and is easier for the
-# casual user than making them instantiate their own Random() instance.
+# as module-level functions. The functions share state across all uses
+#(both in the user's code and in the Python libraries), but that's fine
+# for most programs and is easier for the casual user than making them
+# instantiate their own Random() instance.
+
_inst = Random()
seed = _inst.seed
random = _inst.random
getstate = _inst.getstate
setstate = _inst.setstate
jumpahead = _inst.jumpahead
-whseed = _inst.whseed
if __name__ == '__main__':
_test()
-from test import test_support
+#!/usr/bin/env python
+
+import unittest
import random
+import time
+from test import test_support
+
+class TestBasicOps(unittest.TestCase):
+ # Superclass with tests common to all generators.
+ # Subclasses must arrange for self.gen to retrieve the Random instance
+ # to be tested.
+
+ def randomlist(self, n):
+ """Helper function to make a list of random numbers"""
+ return [self.gen.random() for i in xrange(n)]
+
+ def test_autoseed(self):
+ self.gen.seed()
+ state1 = self.gen.getstate()
+ time.sleep(1)
+ self.gen.seed() # diffent seeds at different times
+ state2 = self.gen.getstate()
+ self.assertNotEqual(state1, state2)
+
+ def test_saverestore(self):
+ N = 1000
+ self.gen.seed()
+ state = self.gen.getstate()
+ randseq = self.randomlist(N)
+ self.gen.setstate(state) # should regenerate the same sequence
+ self.assertEqual(randseq, self.randomlist(N))
+
+ def test_seedargs(self):
+ for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20),
+ 3.14, 1+2j, 'a', tuple('abc')]:
+ self.gen.seed(arg)
+ for arg in [range(3), dict(one=1)]:
+ self.assertRaises(TypeError, self.gen.seed, arg)
+
+ def test_jumpahead(self):
+ self.gen.seed()
+ state1 = self.gen.getstate()
+ self.gen.jumpahead(100)
+ state2 = self.gen.getstate() # s/b distinct from state1
+ self.assertNotEqual(state1, state2)
+ self.gen.jumpahead(100)
+ state3 = self.gen.getstate() # s/b distinct from state2
+ self.assertNotEqual(state2, state3)
+
+ self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg
+ self.assertRaises(TypeError, self.gen.jumpahead, "ick") # wrong type
+ self.assertRaises(TypeError, self.gen.jumpahead, 2.3) # wrong type
+ self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many
+
+ def test_sample(self):
+ # For the entire allowable range of 0 <= k <= N, validate that
+ # the sample is of the correct length and contains only unique items
+ N = 100
+ population = xrange(N)
+ for k in xrange(N+1):
+ s = self.gen.sample(population, k)
+ self.assertEqual(len(s), k)
+ uniq = dict.fromkeys(s)
+ self.assertEqual(len(uniq), k)
+ self.failIf(None in uniq)
+
+ def test_gauss(self):
+ # Ensure that the seed() method initializes all the hidden state. In
+ # particular, through 2.2.1 it failed to reset a piece of state used
+ # by (and only by) the .gauss() method.
+
+ for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
+ self.gen.seed(seed)
+ x1 = self.gen.random()
+ y1 = self.gen.gauss(0, 1)
+
+ self.gen.seed(seed)
+ x2 = self.gen.random()
+ y2 = self.gen.gauss(0, 1)
+
+ self.assertEqual(x1, x2)
+ self.assertEqual(y1, y2)
+
+
+class WichmannHill_TestBasicOps(TestBasicOps):
+ gen = random.WichmannHill()
+
+ def test_strong_jumpahead(self):
+ # tests that jumpahead(n) semantics correspond to n calls to random()
+ N = 1000
+ s = self.gen.getstate()
+ self.gen.jumpahead(N)
+ r1 = self.gen.random()
+ # now do it the slow way
+ self.gen.setstate(s)
+ for i in xrange(N):
+ self.gen.random()
+ r2 = self.gen.random()
+ self.assertEqual(r1, r2)
+
+ def test_gauss_with_whseed(self):
+ # Ensure that the seed() method initializes all the hidden state. In
+ # particular, through 2.2.1 it failed to reset a piece of state used
+ # by (and only by) the .gauss() method.
+
+ for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
+ self.gen.whseed(seed)
+ x1 = self.gen.random()
+ y1 = self.gen.gauss(0, 1)
+
+ self.gen.whseed(seed)
+ x2 = self.gen.random()
+ y2 = self.gen.gauss(0, 1)
+
+ self.assertEqual(x1, x2)
+ self.assertEqual(y1, y2)
+
+class MersenneTwister_TestBasicOps(TestBasicOps):
+ gen = random.Random()
+
+ def test_referenceImplementation(self):
+ # Compare the python implementation with results from the original
+ # code. Create 2000 53-bit precision random floats. Compare only
+ # the last ten entries to show that the independent implementations
+ # are tracking. Here is the main() function needed to create the
+ # list of expected random numbers:
+ # void main(void){
+ # int i;
+ # unsigned long init[4]={61731, 24903, 614, 42143}, length=4;
+ # init_by_array(init, length);
+ # for (i=0; i<2000; i++) {
+ # printf("%.15f ", genrand_res53());
+ # if (i%5==4) printf("\n");
+ # }
+ # }
+ expected = [0.45839803073713259,
+ 0.86057815201978782,
+ 0.92848331726782152,
+ 0.35932681119782461,
+ 0.081823493762449573,
+ 0.14332226470169329,
+ 0.084297823823520024,
+ 0.53814864671831453,
+ 0.089215024911993401,
+ 0.78486196105372907]
+
+ self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
+ actual = self.randomlist(2000)[-10:]
+ for a, e in zip(actual, expected):
+ self.assertAlmostEqual(a,e,places=14)
+
+ def test_strong_reference_implementation(self):
+ # Like test_referenceImplementation, but checks for exact bit-level
+ # equality. This should pass on any box where C double contains
+ # at least 53 bits of precision (the underlying algorithm suffers
+ # no rounding errors -- all results are exact).
+ from math import ldexp
+
+ expected = [0x0eab3258d2231fL,
+ 0x1b89db315277a5L,
+ 0x1db622a5518016L,
+ 0x0b7f9af0d575bfL,
+ 0x029e4c4db82240L,
+ 0x04961892f5d673L,
+ 0x02b291598e4589L,
+ 0x11388382c15694L,
+ 0x02dad977c9e1feL,
+ 0x191d96d4d334c6L]
+
+ self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
+ actual = self.randomlist(2000)[-10:]
+ for a, e in zip(actual, expected):
+ self.assertEqual(long(ldexp(a, 53)), e)
+
+ def test_long_seed(self):
+ # This is most interesting to run in debug mode, just to make sure
+ # nothing blows up. Under the covers, a dynamically resized array
+ # is allocated, consuming space proportional to the number of bits
+ # in the seed. Unfortunately, that's a quadratic-time algorithm,
+ # so don't make this horribly big.
+ seed = (1L << (10000 * 8)) - 1 # about 10K bytes
+ self.gen.seed(seed)
-# Ensure that the seed() method initializes all the hidden state. In
-# particular, through 2.2.1 it failed to reset a piece of state used by
-# (and only by) the .gauss() method.
+class TestModule(unittest.TestCase):
+ def testMagicConstants(self):
+ self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
+ self.assertAlmostEqual(random.TWOPI, 6.28318530718)
+ self.assertAlmostEqual(random.LOG4, 1.38629436111989)
+ self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
-for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
- for seeder in random.seed, random.whseed:
- seeder(seed)
- x1 = random.random()
- y1 = random.gauss(0, 1)
+ def test__all__(self):
+ # tests validity but not completeness of the __all__ list
+ defined = dict.fromkeys(dir(random))
+ for entry in random.__all__:
+ self.failUnless(entry in defined)
- seeder(seed)
- x2 = random.random()
- y2 = random.gauss(0, 1)
+def test_main():
+ suite = unittest.TestSuite()
+ for testclass in (WichmannHill_TestBasicOps,
+ MersenneTwister_TestBasicOps,
+ TestModule):
+ suite.addTest(unittest.makeSuite(testclass))
+ test_support.run_suite(suite)
- test_support.vereq(x1, x2)
- test_support.vereq(y1, y2)
+if __name__ == "__main__":
+ test_main()
and 'stop' arguments so long as each is in the range of Python's
bounded integers.
+- Thanks to Raymond Hettinger, random.random() now uses a new core
+ generator. The Mersenne Twister algorithm is implemented in C,
+ threadsafe, faster than the previous generator, has an astronomically
+ large period (2**19937-1), creates random floats to full 53-bit
+ precision, and may be the most widely tested random number generator
+ in existence.
+
+ The random.jumpahead(n) method has different semantics for the new
+ generator. Instead of jumping n steps ahead, it uses n and the
+ existing state to create a new state. This means that jumpahead()
+ continues to support multi-threaded code needing generators of
+ non-overlapping sequences. However, it will break code which relies
+ on jumpahead moving a specific number of steps forward.
+
+ The attributes random.whseed and random.__whseed have no meaning for
+ the new generator. Code using these attributes should switch to a
+ new class, random.WichmannHill which is provided for backward
+ compatibility and to make an alternate generator available.
+
- New "algorithms" module: heapq, implements a heap queue. Thanks to
Kevin O'Connor for the code and François Pinard for an entertaining
write-up explaining the theory and practical uses of heaps.
--- /dev/null
+/* Random objects */
+
+/* ------------------------------------------------------------------
+ The code in this module was based on a download from:
+ http://www.math.keio.ac.jp/~matumoto/MT2002/emt19937ar.html
+
+ It was modified in 2002 by Raymond Hettinger as follows:
+
+ * the principal computational lines untouched except for tabbing.
+
+ * renamed genrand_res53() to random_random() and wrapped
+ in python calling/return code.
+
+ * genrand_int32() and the helper functions, init_genrand()
+ and init_by_array(), were declared static, wrapped in
+ Python calling/return code. also, their global data
+ references were replaced with structure references.
+
+ * unused functions from the original were deleted.
+ new, original C python code was added to implement the
+ Random() interface.
+
+ The following are the verbatim comments from the original code:
+
+ A C-program for MT19937, with initialization improved 2002/1/26.
+ Coded by Takuji Nishimura and Makoto Matsumoto.
+
+ Before using, initialize the state by using init_genrand(seed)
+ or init_by_array(init_key, key_length).
+
+ Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+ All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions
+ are met:
+
+ 1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+
+ 2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in the
+ documentation and/or other materials provided with the distribution.
+
+ 3. The names of its contributors may not be used to endorse or promote
+ products derived from this software without specific prior written
+ permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+ CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+ Any feedback is very welcome.
+ http://www.math.keio.ac.jp/matumoto/emt.html
+ email: matumoto@math.keio.ac.jp
+*/
+
+/* ---------------------------------------------------------------*/
+
+#include "Python.h"
+#include <time.h> // for seeding to current time
+
+/* Period parameters -- These are all magic. Don't change. */
+#define N 624
+#define M 397
+#define MATRIX_A 0x9908b0dfUL /* constant vector a */
+#define UPPER_MASK 0x80000000UL /* most significant w-r bits */
+#define LOWER_MASK 0x7fffffffUL /* least significant r bits */
+
+typedef struct {
+ PyObject_HEAD
+ unsigned long state[N];
+ int index;
+} RandomObject;
+
+static PyTypeObject Random_Type;
+
+#define RandomObject_Check(v) ((v)->ob_type == &Random_Type)
+
+
+/* Random methods */
+
+
+/* generates a random number on [0,0xffffffff]-interval */
+static unsigned long
+genrand_int32(RandomObject *self)
+{
+ unsigned long y;
+ static unsigned long mag01[2]={0x0UL, MATRIX_A};
+ /* mag01[x] = x * MATRIX_A for x=0,1 */
+ unsigned long *mt;
+
+ mt = self->state;
+ if (self->index >= N) { /* generate N words at one time */
+ int kk;
+
+ for (kk=0;kk<N-M;kk++) {
+ y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
+ mt[kk] = mt[kk+M] ^ (y >> 1) ^ mag01[y & 0x1UL];
+ }
+ for (;kk<N-1;kk++) {
+ y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
+ mt[kk] = mt[kk+(M-N)] ^ (y >> 1) ^ mag01[y & 0x1UL];
+ }
+ y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK);
+ mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL];
+
+ self->index = 0;
+ }
+
+ y = mt[self->index++];
+ y ^= (y >> 11);
+ y ^= (y << 7) & 0x9d2c5680UL;
+ y ^= (y << 15) & 0xefc60000UL;
+ y ^= (y >> 18);
+ return y;
+}
+
+/* random_random is the function named genrand_res53 in the original code;
+ * generates a random number on [0,1) with 53-bit resolution; note that
+ * 9007199254740992 == 2**53; I assume they're spelling "/2**53" as
+ * multiply-by-reciprocal in the (likely vain) hope that the compiler will
+ * optimize the division away at compile-time. 67108864 is 2**26. In
+ * effect, a contains 27 random bits shifted left 26, and b fills in the
+ * lower 26 bits of the 53-bit numerator.
+ * The orginal code credited Isaku Wada for this algorithm, 2002/01/09.
+ */
+static PyObject *
+random_random(RandomObject *self)
+{
+ unsigned long a=genrand_int32(self)>>5, b=genrand_int32(self)>>6;
+ return PyFloat_FromDouble((a*67108864.0+b)*(1.0/9007199254740992.0));
+}
+
+/* initializes mt[N] with a seed */
+static void
+init_genrand(RandomObject *self, unsigned long s)
+{
+ int mti;
+ unsigned long *mt;
+
+ mt = self->state;
+ mt[0]= s & 0xffffffffUL;
+ for (mti=1; mti<N; mti++) {
+ mt[mti] =
+ (1812433253UL * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti);
+ /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
+ /* In the previous versions, MSBs of the seed affect */
+ /* only MSBs of the array mt[]. */
+ /* 2002/01/09 modified by Makoto Matsumoto */
+ mt[mti] &= 0xffffffffUL;
+ /* for >32 bit machines */
+ }
+ self->index = mti;
+ return;
+}
+
+/* initialize by an array with array-length */
+/* init_key is the array for initializing keys */
+/* key_length is its length */
+static PyObject *
+init_by_array(RandomObject *self, unsigned long init_key[], unsigned long key_length)
+{
+ unsigned int i, j, k; /* was signed in the original code. RDH 12/16/2002 */
+ unsigned long *mt;
+
+ mt = self->state;
+ init_genrand(self, 19650218UL);
+ i=1; j=0;
+ k = (N>key_length ? N : key_length);
+ for (; k; k--) {
+ mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL))
+ + init_key[j] + j; /* non linear */
+ mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
+ i++; j++;
+ if (i>=N) { mt[0] = mt[N-1]; i=1; }
+ if (j>=key_length) j=0;
+ }
+ for (k=N-1; k; k--) {
+ mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL))
+ - i; /* non linear */
+ mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
+ i++;
+ if (i>=N) { mt[0] = mt[N-1]; i=1; }
+ }
+
+ mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */
+ Py_INCREF(Py_None);
+ return Py_None;
+}
+
+/*
+ * The rest is Python-specific code, neither part of, nor derived from, the
+ * Twister download.
+ */
+
+static PyObject *
+random_seed(RandomObject *self, PyObject *args)
+{
+ PyObject *result = NULL; /* guilty until proved innocent */
+ PyObject *masklower = NULL;
+ PyObject *thirtytwo = NULL;
+ PyObject *n = NULL;
+ unsigned long *key = NULL;
+ unsigned long keymax; /* # of allocated slots in key */
+ unsigned long keyused; /* # of used slots in key */
+
+ PyObject *arg = NULL;
+
+ if (!PyArg_UnpackTuple(args, "seed", 0, 1, &arg))
+ return NULL;
+
+ if (arg == NULL || arg == Py_None) {
+ time_t now;
+
+ time(&now);
+ init_genrand(self, (unsigned long)now);
+ Py_INCREF(Py_None);
+ return Py_None;
+ }
+ /* If the arg is an int or long, use its absolute value; else use
+ * the absolute value of its hash code.
+ */
+ if (PyInt_Check(arg) || PyLong_Check(arg))
+ n = PyNumber_Absolute(arg);
+ else {
+ long hash = PyObject_Hash(arg);
+ if (hash == -1)
+ goto Done;
+ n = PyLong_FromUnsignedLong((unsigned long)hash);
+ }
+ if (n == NULL)
+ goto Done;
+
+ /* Now split n into 32-bit chunks, from the right. Each piece is
+ * stored into key, which has a capacity of keymax chunks, of which
+ * keyused are filled. Alas, the repeated shifting makes this a
+ * quadratic-time algorithm; we'd really like to use
+ * _PyLong_AsByteArray here, but then we'd have to break into the
+ * long representation to figure out how big an array was needed
+ * in advance.
+ */
+ keymax = 8; /* arbitrary; grows later if needed */
+ keyused = 0;
+ key = (unsigned long *)PyMem_Malloc(keymax * sizeof(*key));
+ if (key == NULL)
+ goto Done;
+
+ masklower = PyLong_FromUnsignedLong(0xffffffffU);
+ if (masklower == NULL)
+ goto Done;
+ thirtytwo = PyInt_FromLong(32L);
+ if (thirtytwo == NULL)
+ goto Done;
+ while (PyObject_IsTrue(n)) {
+ PyObject *newn;
+ PyObject *pychunk;
+ unsigned long chunk;
+
+ pychunk = PyNumber_And(n, masklower);
+ if (pychunk == NULL)
+ goto Done;
+ chunk = PyLong_AsUnsignedLong(pychunk);
+ Py_DECREF(pychunk);
+ if (chunk == (unsigned long)-1 && PyErr_Occurred())
+ goto Done;
+ newn = PyNumber_Rshift(n, thirtytwo);
+ if (newn == NULL)
+ goto Done;
+ Py_DECREF(n);
+ n = newn;
+ if (keyused >= keymax) {
+ unsigned long bigger = keymax << 1;
+ if ((bigger >> 1) != keymax) {
+ PyErr_NoMemory();
+ goto Done;
+ }
+ key = (unsigned long *)PyMem_Realloc(key,
+ bigger * sizeof(*key));
+ if (key == NULL)
+ goto Done;
+ keymax = bigger;
+ }
+ assert(keyused < keymax);
+ key[keyused++] = chunk;
+ }
+
+ if (keyused == 0)
+ key[keyused++] = 0UL;
+ result = init_by_array(self, key, keyused);
+Done:
+ Py_XDECREF(masklower);
+ Py_XDECREF(thirtytwo);
+ Py_XDECREF(n);
+ PyMem_Free(key);
+ return result;
+}
+
+static PyObject *
+random_getstate(RandomObject *self)
+{
+ PyObject *state;
+ PyObject *element;
+ int i;
+
+ state = PyTuple_New(N+1);
+ if (state == NULL)
+ return NULL;
+ for (i=0; i<N ; i++) {
+ element = PyInt_FromLong((long)(self->state[i]));
+ if (element == NULL)
+ goto Fail;
+ PyTuple_SET_ITEM(state, i, element);
+ }
+ element = PyInt_FromLong((long)(self->index));
+ if (element == NULL)
+ goto Fail;
+ PyTuple_SET_ITEM(state, i, element);
+ return state;
+
+Fail:
+ Py_DECREF(state);
+ return NULL;
+}
+
+static PyObject *
+random_setstate(RandomObject *self, PyObject *state)
+{
+ int i;
+ long element;
+
+ if (!PyTuple_Check(state)) {
+ PyErr_SetString(PyExc_TypeError,
+ "state vector must be a tuple");
+ return NULL;
+ }
+ if (PyTuple_Size(state) != N+1) {
+ PyErr_SetString(PyExc_ValueError,
+ "state vector is the wrong size");
+ return NULL;
+ }
+
+ for (i=0; i<N ; i++) {
+ element = PyInt_AsLong(PyTuple_GET_ITEM(state, i));
+ if (element == -1 && PyErr_Occurred())
+ return NULL;
+ self->state[i] = (unsigned long)element;
+ }
+
+ element = PyInt_AsLong(PyTuple_GET_ITEM(state, i));
+ if (element == -1 && PyErr_Occurred())
+ return NULL;
+ self->index = (int)element;
+
+ Py_INCREF(Py_None);
+ return Py_None;
+}
+
+/*
+Jumpahead should be a fast way advance the generator n-steps ahead, but
+lacking a formula for that, the next best is to use n and the existing
+state to create a new state far away from the original.
+
+The generator uses constant spaced additive feedback, so shuffling the
+state elements ought to produce a state which would not be encountered
+(in the near term) by calls to random(). Shuffling is normally
+implemented by swapping the ith element with another element ranging
+from 0 to i inclusive. That allows the element to have the possibility
+of not being moved. Since the goal is to produce a new, different
+state, the swap element is ranged from 0 to i-1 inclusive. This assures
+that each element gets moved at least once.
+
+To make sure that consecutive calls to jumpahead(n) produce different
+states (even in the rare case of involutory shuffles), i+1 is added to
+each element at position i. Successive calls are then guaranteed to
+have changing (growing) values as well as shuffled positions.
+
+Finally, the self->index value is set to N so that the generator itself
+kicks in on the next call to random(). This assures that all results
+have been through the generator and do not just reflect alterations to
+the underlying state.
+*/
+
+static PyObject *
+random_jumpahead(RandomObject *self, PyObject *n)
+{
+ long i, j;
+ PyObject *iobj;
+ PyObject *remobj;
+ unsigned long *mt, tmp;
+
+ if (!PyInt_Check(n) && !PyLong_Check(n)) {
+ PyErr_Format(PyExc_TypeError, "jumpahead requires an "
+ "integer, not '%s'",
+ n->ob_type->tp_name);
+ return NULL;
+ }
+
+ mt = self->state;
+ for (i = N-1; i > 1; i--) {
+ iobj = PyInt_FromLong(i);
+ if (iobj == NULL)
+ return NULL;
+ remobj = PyNumber_Remainder(n, iobj);
+ Py_DECREF(iobj);
+ if (remobj == NULL)
+ return NULL;
+ j = PyInt_AsLong(remobj);
+ Py_DECREF(remobj);
+ if (j == -1L && PyErr_Occurred())
+ return NULL;
+ tmp = mt[i];
+ mt[i] = mt[j];
+ mt[j] = tmp;
+ }
+
+ for (i = 0; i < N; i++)
+ mt[i] += i+1;
+
+ self->index = N;
+ Py_INCREF(Py_None);
+ return Py_None;
+}
+
+static PyObject *
+random_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+ RandomObject *self;
+ PyObject *tmp;
+
+ self = (RandomObject *)type->tp_alloc(type, 0);
+ if (self == NULL)
+ return NULL;
+ tmp = random_seed(self, args);
+ if (tmp == NULL) {
+ Py_DECREF(self);
+ return NULL;
+ }
+ Py_DECREF(tmp);
+ return (PyObject *)self;
+}
+
+static PyMethodDef random_methods[] = {
+ {"random", (PyCFunction)random_random, METH_NOARGS,
+ PyDoc_STR("random() -> x in the interval [0, 1).")},
+ {"seed", (PyCFunction)random_seed, METH_VARARGS,
+ PyDoc_STR("seed([n]) -> None. Defaults to current time.")},
+ {"getstate", (PyCFunction)random_getstate, METH_NOARGS,
+ PyDoc_STR("getstate() -> tuple containing the current state.")},
+ {"setstate", (PyCFunction)random_setstate, METH_O,
+ PyDoc_STR("setstate(state) -> None. Restores generator state.")},
+ {"jumpahead", (PyCFunction)random_jumpahead, METH_O,
+ PyDoc_STR("jumpahead(int) -> None. Create new state from "
+ "existing state and integer.")},
+ {NULL, NULL} /* sentinel */
+};
+
+PyDoc_STRVAR(random_doc,
+"Random() -> create a random number generator with its own internal state.");
+
+static PyTypeObject Random_Type = {
+ PyObject_HEAD_INIT(NULL)
+ 0, /*ob_size*/
+ "_random.Random", /*tp_name*/
+ sizeof(RandomObject), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ /* methods */
+ 0, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ 0, /*tp_compare*/
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ PyObject_GenericGetAttr, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+ random_doc, /*tp_doc*/
+ 0, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ random_methods, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ PyType_GenericAlloc, /*tp_alloc*/
+ random_new, /*tp_new*/
+ _PyObject_Del, /*tp_free*/
+ 0, /*tp_is_gc*/
+};
+
+PyDoc_STRVAR(module_doc,
+"Module implements the Mersenne Twister random number generator.");
+
+PyMODINIT_FUNC
+init_random(void)
+{
+ PyObject *m;
+
+ if (PyType_Ready(&Random_Type) < 0)
+ return;
+ m = Py_InitModule3("_random", NULL, module_doc);
+ Py_INCREF(&Random_Type);
+ PyModule_AddObject(m, "Random", (PyObject *)&Random_Type);
+}
libraries=math_libs) )
exts.append( Extension('datetime', ['datetimemodule.c'],
libraries=math_libs) )
+ # random number generator implemented in C
+ exts.append( Extension("_random", ["_randommodule.c"]) )
# operator.add() and similar goodies
exts.append( Extension('operator', ['operator.c']) )
# Python C API test module
projects / python / commitdiff