mirror of https://github.com/python/cpython
Remove defunct parts of the random module
This commit is contained in:
Raymond Hettinger
parent
f7ec7a81a5
commit
28de64fd0f
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@ -28,25 +28,14 @@ for cryptographic purposes.
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The functions supplied by this module are actually bound methods of a hidden
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instance of the :class:`random.Random` class. You can instantiate your own
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instances of :class:`Random` to get generators that don't share state. This is
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especially useful for multi-threaded programs, creating a different instance of
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:class:`Random` for each thread, and using the :meth:`jumpahead` method to make
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it likely that the generated sequences seen by each thread don't overlap.
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instances of :class:`Random` to get generators that don't share state.
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Class :class:`Random` can also be subclassed if you want to use a different
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basic generator of your own devising: in that case, override the :meth:`random`,
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:meth:`seed`, :meth:`getstate`, :meth:`setstate` and :meth:`jumpahead` methods.
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:meth:`seed`, :meth:`getstate`, and :meth:`setstate`.
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Optionally, a new generator can supply a :meth:`getrandombits` method --- this
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allows :meth:`randrange` to produce selections over an arbitrarily large range.
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As an example of subclassing, the :mod:`random` module provides the
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:class:`WichmannHill` class that implements an alternative generator in pure
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Python. The class provides a backward compatible way to reproduce results from
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earlier versions of Python, which used the Wichmann-Hill algorithm as the core
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generator. Note that this Wichmann-Hill generator can no longer be recommended:
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its period is too short by contemporary standards, and the sequence generated is
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known to fail some stringent randomness tests. See the references below for a
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recent variant that repairs these flaws.
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Bookkeeping functions:
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@ -79,17 +68,6 @@ Bookkeeping functions:
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the time :func:`setstate` was called.
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.. function:: jumpahead(n)
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Change the internal state to one different from and likely far away from the
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current state. *n* is a non-negative integer which is used to scramble the
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current state vector. This is most useful in multi-threaded programs, in
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conjuction with multiple instances of the :class:`Random` class:
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:meth:`setstate` or :meth:`seed` can be used to force all instances into the
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same internal state, and then :meth:`jumpahead` can be used to force the
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instances' states far apart.
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.. function:: getrandbits(k)
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Returns a python integer with *k* random bits. This method is supplied with
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@ -224,24 +202,6 @@ be found in any statistics text.
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Alternative Generators:
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.. class:: WichmannHill([seed])
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Class that implements the Wichmann-Hill algorithm as the core generator. Has all
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of the same methods as :class:`Random` plus the :meth:`whseed` method described
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below. Because this class is implemented in pure Python, it is not threadsafe
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and may require locks between calls. The period of the generator is
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6,953,607,871,644 which is small enough to require care that two independent
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random sequences do not overlap.
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.. function:: whseed([x])
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This is obsolete, supplied for bit-level compatibility with versions of Python
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prior to 2.1. See :func:`seed` for details. :func:`whseed` does not guarantee
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that distinct integer arguments yield distinct internal states, and can yield no
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more than about 2\*\*24 distinct internal states in all.
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.. class:: SystemRandom([seed])
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Class that uses the :func:`os.urandom` function for generating random numbers
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@ -281,6 +241,4 @@ Examples of basic usage::
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equidistributed uniform pseudorandom number generator", ACM Transactions on
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Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998.
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Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and portable
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pseudo-random number generator", Applied Statistics 31 (1982) 188-190.
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166
Lib/random.py
166
Lib/random.py
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@ -30,9 +30,6 @@ General notes on the underlying Mersenne Twister core generator:
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* The period is 2**19937-1.
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* It is one of the most extensively tested generators in existence.
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* Without a direct way to compute N steps forward, the semantics of
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jumpahead(n) are weakened to simply jump to another distant state and rely
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on the large period to avoid overlapping sequences.
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* The random() method is implemented in C, executes in a single Python step,
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and is, therefore, threadsafe.
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@ -49,7 +46,7 @@ __all__ = ["Random","seed","random","uniform","randint","choice","sample",
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"randrange","shuffle","normalvariate","lognormvariate",
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"expovariate","vonmisesvariate","gammavariate",
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"gauss","betavariate","paretovariate","weibullvariate",
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"getstate","setstate","jumpahead", "WichmannHill", "getrandbits",
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"getstate","setstate", "getrandbits",
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"SystemRandom"]
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NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
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@ -70,14 +67,11 @@ class Random(_random.Random):
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"""Random number generator base class used by bound module functions.
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Used to instantiate instances of Random to get generators that don't
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share state. Especially useful for multi-threaded programs, creating
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a different instance of Random for each thread, and using the jumpahead()
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method to ensure that the generated sequences seen by each thread don't
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overlap.
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share state.
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Class Random can also be subclassed if you want to use a different basic
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generator of your own devising: in that case, override the following
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methods: random(), seed(), getstate(), setstate() and jumpahead().
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methods: random(), seed(), getstate(), and setstate().
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Optionally, implement a getrandombits() method so that randrange()
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can cover arbitrarily large ranges.
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@ -615,156 +609,6 @@ class Random(_random.Random):
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u = 1.0 - self.random()
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return alpha * pow(-_log(u), 1.0/beta)
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## -------------------- Wichmann-Hill -------------------
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class WichmannHill(Random):
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VERSION = 1 # used by getstate/setstate
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def seed(self, a=None):
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"""Initialize internal state from hashable object.
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None or no argument seeds from current time or from an operating
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system specific randomness source if available.
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If a is not None or an int or long, hash(a) is used instead.
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If a is an int or long, a is used directly. Distinct values between
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0 and 27814431486575L inclusive are guaranteed to yield distinct
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internal states (this guarantee is specific to the default
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Wichmann-Hill generator).
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"""
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if a is None:
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try:
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a = int(_hexlify(_urandom(16)), 16)
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except NotImplementedError:
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import time
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a = int(time.time() * 256) # use fractional seconds
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if not isinstance(a, int):
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a = hash(a)
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a, x = divmod(a, 30268)
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a, y = divmod(a, 30306)
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a, z = divmod(a, 30322)
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self._seed = int(x)+1, int(y)+1, int(z)+1
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self.gauss_next = None
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def random(self):
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"""Get the next random number in the range [0.0, 1.0)."""
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# Wichman-Hill random number generator.
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#
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# Wichmann, B. A. & Hill, I. D. (1982)
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# Algorithm AS 183:
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# An efficient and portable pseudo-random number generator
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# Applied Statistics 31 (1982) 188-190
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#
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# see also:
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# Correction to Algorithm AS 183
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# Applied Statistics 33 (1984) 123
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#
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# McLeod, A. I. (1985)
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# A remark on Algorithm AS 183
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# Applied Statistics 34 (1985),198-200
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# This part is thread-unsafe:
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# BEGIN CRITICAL SECTION
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x, y, z = self._seed
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x = (171 * x) % 30269
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y = (172 * y) % 30307
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z = (170 * z) % 30323
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self._seed = x, y, z
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# END CRITICAL SECTION
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# Note: on a platform using IEEE-754 double arithmetic, this can
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# never return 0.0 (asserted by Tim; proof too long for a comment).
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return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0
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def getstate(self):
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"""Return internal state; can be passed to setstate() later."""
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return self.VERSION, self._seed, self.gauss_next
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def setstate(self, state):
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"""Restore internal state from object returned by getstate()."""
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version = state[0]
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if version == 1:
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version, self._seed, self.gauss_next = state
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else:
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raise ValueError("state with version %s passed to "
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"Random.setstate() of version %s" %
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(version, self.VERSION))
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def jumpahead(self, n):
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"""Act as if n calls to random() were made, but quickly.
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n is an int, greater than or equal to 0.
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Example use: If you have 2 threads and know that each will
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consume no more than a million random numbers, create two Random
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objects r1 and r2, then do
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r2.setstate(r1.getstate())
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r2.jumpahead(1000000)
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Then r1 and r2 will use guaranteed-disjoint segments of the full
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period.
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"""
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if not n >= 0:
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raise ValueError("n must be >= 0")
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x, y, z = self._seed
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x = int(x * pow(171, n, 30269)) % 30269
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y = int(y * pow(172, n, 30307)) % 30307
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z = int(z * pow(170, n, 30323)) % 30323
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self._seed = x, y, z
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def __whseed(self, x=0, y=0, z=0):
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"""Set the Wichmann-Hill seed from (x, y, z).
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These must be integers in the range [0, 256).
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"""
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if not type(x) == type(y) == type(z) == int:
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raise TypeError('seeds must be integers')
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if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256):
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raise ValueError('seeds must be in range(0, 256)')
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if 0 == x == y == z:
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# Initialize from current time
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import time
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t = int(time.time() * 256)
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t = int((t&0xffffff) ^ (t>>24))
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t, x = divmod(t, 256)
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t, y = divmod(t, 256)
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t, z = divmod(t, 256)
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# Zero is a poor seed, so substitute 1
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self._seed = (x or 1, y or 1, z or 1)
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self.gauss_next = None
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def whseed(self, a=None):
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"""Seed from hashable object's hash code.
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None or no argument seeds from current time. It is not guaranteed
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that objects with distinct hash codes lead to distinct internal
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states.
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This is obsolete, provided for compatibility with the seed routine
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used prior to Python 2.1. Use the .seed() method instead.
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"""
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if a is None:
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self.__whseed()
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return
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a = hash(a)
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a, x = divmod(a, 256)
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a, y = divmod(a, 256)
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a, z = divmod(a, 256)
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x = (x + a) % 256 or 1
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y = (y + a) % 256 or 1
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z = (z + a) % 256 or 1
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self.__whseed(x, y, z)
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## --------------- Operating System Random Source ------------------
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class SystemRandom(Random):
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@ -789,10 +633,9 @@ class SystemRandom(Random):
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x = int(_hexlify(_urandom(bytes)), 16)
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return x >> (bytes * 8 - k) # trim excess bits
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def _stub(self, *args, **kwds):
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def seed(self, *args, **kwds):
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"Stub method. Not used for a system random number generator."
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return None
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seed = jumpahead = _stub
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def _notimplemented(self, *args, **kwds):
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"Method should not be called for a system random number generator."
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@ -866,7 +709,6 @@ paretovariate = _inst.paretovariate
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weibullvariate = _inst.weibullvariate
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getstate = _inst.getstate
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setstate = _inst.setstate
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jumpahead = _inst.jumpahead
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getrandbits = _inst.getrandbits
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if __name__ == '__main__':
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@ -444,7 +444,7 @@ Subject: Re: PEP 255: Simple Generators
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>>> roots = sets[:]
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>>> import random
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>>> gen = random.WichmannHill(42)
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>>> gen = random.Random(42)
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>>> while 1:
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... for s in sets:
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... print(" %s->%s" % (s, s.find()), end='')
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@ -458,29 +458,29 @@ Subject: Re: PEP 255: Simple Generators
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... else:
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... break
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A->A B->B C->C D->D E->E F->F G->G H->H I->I J->J K->K L->L M->M
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merged D into G
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A->A B->B C->C D->G E->E F->F G->G H->H I->I J->J K->K L->L M->M
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merged C into F
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A->A B->B C->F D->G E->E F->F G->G H->H I->I J->J K->K L->L M->M
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merged I into A
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A->A B->B C->C D->D E->E F->F G->G H->H I->A J->J K->K L->L M->M
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merged D into C
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A->A B->B C->C D->C E->E F->F G->G H->H I->A J->J K->K L->L M->M
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merged K into H
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A->A B->B C->C D->C E->E F->F G->G H->H I->A J->J K->H L->L M->M
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merged L into A
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A->A B->B C->F D->G E->E F->F G->G H->H I->I J->J K->K L->A M->M
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merged H into E
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A->A B->B C->F D->G E->E F->F G->G H->E I->I J->J K->K L->A M->M
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merged B into E
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A->A B->E C->F D->G E->E F->F G->G H->E I->I J->J K->K L->A M->M
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A->A B->B C->C D->C E->E F->F G->G H->H I->A J->J K->H L->A M->M
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merged E into A
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A->A B->B C->C D->C E->A F->F G->G H->H I->A J->J K->H L->A M->M
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merged B into G
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A->A B->G C->C D->C E->A F->F G->G H->H I->A J->J K->H L->A M->M
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merged A into F
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A->F B->G C->C D->C E->F F->F G->G H->H I->F J->J K->H L->F M->M
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merged H into G
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A->F B->G C->C D->C E->F F->F G->G H->G I->F J->J K->G L->F M->M
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merged F into J
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A->J B->G C->C D->C E->J F->J G->G H->G I->J J->J K->G L->J M->M
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merged M into C
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A->J B->G C->C D->C E->J F->J G->G H->G I->J J->J K->G L->J M->C
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merged J into G
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A->A B->E C->F D->G E->E F->F G->G H->E I->I J->G K->K L->A M->M
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merged E into G
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A->A B->G C->F D->G E->G F->F G->G H->G I->I J->G K->K L->A M->M
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merged M into G
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A->A B->G C->F D->G E->G F->F G->G H->G I->I J->G K->K L->A M->G
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merged I into K
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A->A B->G C->F D->G E->G F->F G->G H->G I->K J->G K->K L->A M->G
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merged K into A
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A->A B->G C->F D->G E->G F->F G->G H->G I->A J->G K->A L->A M->G
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merged F into A
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A->A B->G C->A D->G E->G F->A G->G H->G I->A J->G K->A L->A M->G
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merged A into G
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A->G B->G C->C D->C E->G F->G G->G H->G I->G J->G K->G L->G M->C
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merged C into G
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A->G B->G C->G D->G E->G F->G G->G H->G I->G J->G K->G L->G M->G
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"""
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@ -42,21 +42,6 @@ class TestBasicOps(unittest.TestCase):
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self.assertRaises(TypeError, self.gen.seed, 1, 2)
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self.assertRaises(TypeError, type(self.gen), [])
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def test_jumpahead(self):
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self.gen.seed()
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state1 = self.gen.getstate()
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self.gen.jumpahead(100)
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state2 = self.gen.getstate() # s/b distinct from state1
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self.assertNotEqual(state1, state2)
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self.gen.jumpahead(100)
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state3 = self.gen.getstate() # s/b distinct from state2
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self.assertNotEqual(state2, state3)
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self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg
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self.assertRaises(TypeError, self.gen.jumpahead, "ick") # wrong type
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self.assertRaises(TypeError, self.gen.jumpahead, 2.3) # wrong type
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self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many
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def test_sample(self):
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# For the entire allowable range of 0 <= k <= N, validate that
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# the sample is of the correct length and contains only unique items
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@ -157,48 +142,6 @@ class TestBasicOps(unittest.TestCase):
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f.close()
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self.assertEqual(r.randrange(1000), value)
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class WichmannHill_TestBasicOps(TestBasicOps):
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gen = random.WichmannHill()
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def test_setstate_first_arg(self):
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self.assertRaises(ValueError, self.gen.setstate, (2, None, None))
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def test_strong_jumpahead(self):
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# tests that jumpahead(n) semantics correspond to n calls to random()
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N = 1000
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s = self.gen.getstate()
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self.gen.jumpahead(N)
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r1 = self.gen.random()
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# now do it the slow way
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self.gen.setstate(s)
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for i in range(N):
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self.gen.random()
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r2 = self.gen.random()
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self.assertEqual(r1, r2)
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def test_gauss_with_whseed(self):
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# Ensure that the seed() method initializes all the hidden state. In
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# particular, through 2.2.1 it failed to reset a piece of state used
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# by (and only by) the .gauss() method.
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for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
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self.gen.whseed(seed)
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x1 = self.gen.random()
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y1 = self.gen.gauss(0, 1)
|
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|
||||
self.gen.whseed(seed)
|
||||
x2 = self.gen.random()
|
||||
y2 = self.gen.gauss(0, 1)
|
||||
|
||||
self.assertEqual(x1, x2)
|
||||
self.assertEqual(y1, y2)
|
||||
|
||||
def test_bigrand(self):
|
||||
# Verify warnings are raised when randrange is too large for random()
|
||||
with test_support.catch_warning():
|
||||
warnings.filterwarnings("error", "Underlying random")
|
||||
self.assertRaises(UserWarning, self.gen.randrange, 2**60)
|
||||
|
||||
class SystemRandom_TestBasicOps(TestBasicOps):
|
||||
gen = random.SystemRandom()
|
||||
|
||||
|
|
@ -214,10 +157,6 @@ class SystemRandom_TestBasicOps(TestBasicOps):
|
|||
# Doesn't need to do anything except not fail
|
||||
self.gen.seed(100)
|
||||
|
||||
def test_jumpahead(self):
|
||||
# Doesn't need to do anything except not fail
|
||||
self.gen.jumpahead(100)
|
||||
|
||||
def test_gauss(self):
|
||||
self.gen.gauss_next = None
|
||||
self.gen.seed(100)
|
||||
|
|
@ -541,8 +480,7 @@ class TestModule(unittest.TestCase):
|
|||
|
||||
|
||||
def test_main(verbose=None):
|
||||
testclasses = [WichmannHill_TestBasicOps,
|
||||
MersenneTwister_TestBasicOps,
|
||||
testclasses = [MersenneTwister_TestBasicOps,
|
||||
TestDistributions,
|
||||
TestModule]
|
||||
|
||||
|
|
|
|||
|
|
@ -352,6 +352,9 @@ Core and Builtins
|
|||
Library
|
||||
-------
|
||||
|
||||
- Removed defunct parts of the random module (the Wichmann-Hill generator
|
||||
and the jumpahead() method).
|
||||
|
||||
- Patch #467924: add ZipFile.extract() and ZipFile.extractall() in the
|
||||
zipfile module.
|
||||
|
||||
|
|
|
|||
|
|
@ -369,72 +369,6 @@ random_setstate(RandomObject *self, PyObject *state)
|
|||
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 (!PyLong_Check(n)) {
|
||||
PyErr_Format(PyExc_TypeError, "jumpahead requires an "
|
||||
"integer, not '%s'",
|
||||
Py_TYPE(n)->tp_name);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
mt = self->state;
|
||||
for (i = N-1; i > 1; i--) {
|
||||
iobj = PyLong_FromLong(i);
|
||||
if (iobj == NULL)
|
||||
return NULL;
|
||||
remobj = PyNumber_Remainder(n, iobj);
|
||||
Py_DECREF(iobj);
|
||||
if (remobj == NULL)
|
||||
return NULL;
|
||||
j = PyLong_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_getrandbits(RandomObject *self, PyObject *args)
|
||||
{
|
||||
|
|
@ -506,9 +440,6 @@ static PyMethodDef random_methods[] = {
|
|||
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.")},
|
||||
{"getrandbits", (PyCFunction)random_getrandbits, METH_VARARGS,
|
||||
PyDoc_STR("getrandbits(k) -> x. Generates a long int with "
|
||||
"k random bits.")},
|
||||
|
|
|
|||
Loading…
Reference in New Issue