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Commit 6d7b11db authored by Raymond Hettinger's avatar Raymond Hettinger
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Remove defunct parts of the random module

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Doc/library/random.rst
View file @ 6d7b11db
......@@ -28,25 +28,14 @@ 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 instantiate your own
instances of :class:`Random` to get generators 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 :meth:`jumpahead` method to make
it likely that the generated sequences seen by each thread don't overlap.
instances of :class:`Random` to get generators that don't share state.
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 :meth:`random`,
:meth:`seed`, :meth:`getstate`, :meth:`setstate` and :meth:`jumpahead` methods.
:meth:`seed`, :meth:`getstate`, and :meth:`setstate`.
Optionally, a new generator can supply a :meth:`getrandombits` method --- this
allows :meth:`randrange` to produce selections over an arbitrarily large range.
As an example of subclassing, the :mod:`random` module provides the
:class:`WichmannHill` class that 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. Note that this Wichmann-Hill generator can no longer be recommended:
its period is too short by contemporary standards, and the sequence generated is
known to fail some stringent randomness tests. See the references below for a
recent variant that repairs these flaws.
Bookkeeping functions:
......@@ -79,17 +68,6 @@ Bookkeeping functions:
the time :func:`setstate` was called.
.. function:: jumpahead(n)
Change the internal state to one different from and likely far away from the
current state. *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:
:meth:`setstate` or :meth:`seed` can be used to force all instances into the
same internal state, and then :meth:`jumpahead` can be used to force the
instances' states far apart.
.. function:: getrandbits(k)
Returns a python integer with *k* random bits. This method is supplied with
......@@ -224,24 +202,6 @@ be found in any statistics text.
Alternative Generators:
.. class:: WichmannHill([seed])
Class that implements the Wichmann-Hill algorithm as the core generator. Has all
of the same methods as :class:`Random` plus the :meth:`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.
.. function:: whseed([x])
This is obsolete, supplied for bit-level compatibility with versions of Python
prior to 2.1. See :func:`seed` for details. :func:`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.
.. class:: SystemRandom([seed])
Class that uses the :func:`os.urandom` function for generating random numbers
......@@ -281,6 +241,4 @@ Examples of basic usage::
equidistributed uniform pseudorandom number generator", ACM Transactions on
Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998.
Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and portable
pseudo-random number generator", Applied Statistics 31 (1982) 188-190.
Lib/random.py
View file @ 6d7b11db
......@@ -30,9 +30,6 @@ 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.
......@@ -49,7 +46,7 @@ __all__ = ["Random","seed","random","uniform","randint","choice","sample",
"randrange","shuffle","normalvariate","lognormvariate",
"expovariate","vonmisesvariate","gammavariate",
"gauss","betavariate","paretovariate","weibullvariate",
"getstate","setstate","jumpahead", "WichmannHill", "getrandbits",
"getstate","setstate", "getrandbits",
"SystemRandom"]
NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
......@@ -70,14 +67,11 @@ class Random(_random.Random):
"""Random number generator base class used by bound module functions.
Used to instantiate instances of Random to get generators that don't
share state. Especially useful for multi-threaded programs, creating
a different instance of Random for each thread, and using the jumpahead()
method to ensure that the generated sequences seen by each thread don't
overlap.
share state.
Class Random can also be subclassed if you want to use a different basic
generator of your own devising: in that case, override the following
methods: random(), seed(), getstate(), setstate() and jumpahead().
methods: random(), seed(), getstate(), and setstate().
Optionally, implement a getrandombits() method so that randrange()
can cover arbitrarily large ranges.
......@@ -615,156 +609,6 @@ class Random(_random.Random):
u = 1.0 - 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 or from an operating
system specific randomness source if available.
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:
try:
a = int(_hexlify(_urandom(16)), 16)
except NotImplementedError:
import time
a = int(time.time() * 256) # use fractional seconds
if not isinstance(a, int):
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 = int(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)
## --------------- Operating System Random Source ------------------
class SystemRandom(Random):
......@@ -789,10 +633,9 @@ class SystemRandom(Random):
x = int(_hexlify(_urandom(bytes)), 16)
return x >> (bytes * 8 - k) # trim excess bits
def _stub(self, *args, **kwds):
def seed(self, *args, **kwds):
"Stub method. Not used for a system random number generator."
return None
seed = jumpahead = _stub
def _notimplemented(self, *args, **kwds):
"Method should not be called for a system random number generator."
......@@ -866,7 +709,6 @@ paretovariate = _inst.paretovariate
weibullvariate = _inst.weibullvariate
getstate = _inst.getstate
setstate = _inst.setstate
jumpahead = _inst.jumpahead
getrandbits = _inst.getrandbits
if __name__ == '__main__':
......
Lib/test/test_generators.py
View file @ 6d7b11db
......@@ -444,7 +444,7 @@ Subject: Re: PEP 255: Simple Generators
>>> roots = sets[:]
>>> import random
>>> gen = random.WichmannHill(42)
>>> gen = random.Random(42)
>>> while 1:
... for s in sets:
... print(" %s->%s" % (s, s.find()), end='')
......@@ -458,29 +458,29 @@ Subject: Re: PEP 255: Simple Generators
... else:
... break
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
merged D into G
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
merged C into F
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
merged I into A
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
merged D into C
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
merged K into H
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
merged L into A
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
merged H into E
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
merged B into E
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
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
merged E into A
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
merged B into G
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
merged A into F
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
merged H into G
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
merged F into J
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
merged M into C
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
merged J into G
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
merged E into G
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
merged M into G
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
merged I into K
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
merged K into A
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
merged F into A
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
merged A into G
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
merged C into G
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
"""
......
Lib/test/test_random.py
View file @ 6d7b11db
......@@ -42,21 +42,6 @@ class TestBasicOps(unittest.TestCase):
self.assertRaises(TypeError, self.gen.seed, 1, 2)
self.assertRaises(TypeError, type(self.gen), [])
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
......@@ -157,48 +142,6 @@ class TestBasicOps(unittest.TestCase):
f.close()
self.assertEqual(r.randrange(1000), value)
class WichmannHill_TestBasicOps(TestBasicOps):
gen = random.WichmannHill()
def test_setstate_first_arg(self):
self.assertRaises(ValueError, self.gen.setstate, (2, None, None))
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 range(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)
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]
......
Misc/NEWS
View file @ 6d7b11db
......@@ -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.
......
Modules/_randommodule.c
View file @ 6d7b11db
......@@ -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.")},
......
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