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(python2.1-lib.info)random
Generate pseudo-random numbers
==============================
Generate pseudo-random numbers with various common distributions.
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. 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.
Almost all module functions depend on the basic 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 `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 functions supplied by this module are actually bound methods of a
hidden instance of the `random.Random' class. You can instantiate your
own instances of `Random' to get generators that don't share state.
This is 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 (see example below).
Class `Random' can also be subclassed if you want to use a different
basic generator of your own devising: in that case, override the
`random()', `seed()', `getstate()', `setstate()' and `jumpahead()'
methods.
Here's one way to create threadsafe distinct and non-overlapping
generators:
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 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
Bookkeeping functions:
`seed([x])'
Initialize the basic random number generator. Optional argument X
can be any hashable object. If X is omitted or `None', current
system time is used; current system time is also used to
initialize the generator when the module is first imported. If X
is not `None' or an int or long, `hash(X)' is used instead. If X
is an int or long, 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).
`whseed([x])'
This is obsolete, supplied for bit-level compatibility with
versions of Python prior to 2.1. See `seed' for details.
`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.
`getstate()'
Return an object capturing the current internal state of the
generator. This object can be passed to `setstate()' to restore
the state. _Added in Python version 2.1_
`setstate(state)'
STATE should have been obtained from a previous call to
`getstate()', and `setstate()' restores the internal state of the
generator to what it was at the time `setstate()' was called.
_Added in Python version 2.1_
`jumpahead(n)'
Change the internal state to what it would be if `random()' were
called N times, but do so quickly. N is a non-negative integer.
This is most useful in multi-threaded programs, in conjuction with
multiple instances of the `Random' class: `setstate()' or `seed()'
can be used to force all instances into the same internal state,
and then `jumpahead()' can be used to force the instances' states
as far apart as you like (up to the period of the generator).
_Added in Python version 2.1_
Functions for integers:
`randrange([start,] stop[, step])'
Return a randomly selected element from `range(START, STOP,
STEP)'. This is equivalent to `choice(range(START, STOP, STEP))',
but doesn't actually build a range object. _Added in Python
version 1.5.2_
`randint(a, b)'
_This is deprecated in Python 2.0. Use `randrange()' instead._
Return a random integer N such that `A <= N <= B'.
Functions for sequences:
`choice(seq)'
Return a random element from the non-empty sequence SEQ.
`shuffle(x[, random])'
Shuffle the sequence X in place. The optional argument RANDOM is
a 0-argument function returning a random float in [0.0, 1.0); by
default, this is the function `random()'.
Note that for even rather small `len(X)', the total number of
permutations of X is larger than the period of most random number
generators; this implies that most permutations of a long sequence
can never be generated.
The following functions generate specific real-valued distributions.
Function parameters are named after the corresponding variables in the
distribution's equation, as used in common mathematical practice; most
of these equations can be found in any statistics text.
`random()'
Return the next random floating point number in the range [0.0,
1.0).
`uniform(a, b)'
Return a random real number N such that `A <= N < B'.
`betavariate(alpha, beta)'
Beta distribution. Conditions on the parameters are `ALPHA > -1'
and `BETA > -1'. Returned values range between 0 and 1.
`cunifvariate(mean, arc)'
Circular uniform distribution. MEAN is the mean angle, and ARC is
the range of the distribution, centered around the mean angle.
Both values must be expressed in radians, and can range between 0
and _pi_. Returned values range between `MEAN - ARC/2' and `MEAN
+ ARC/2'.
`expovariate(lambd)'
Exponential distribution. LAMBD is 1.0 divided by the desired
mean. (The parameter would be called "lambda", but that is a
reserved word in Python.) Returned values range from 0 to
positive infinity.
`gamma(alpha, beta)'
Gamma distribution. (_Not_ the gamma function!) Conditions on
the parameters are `ALPHA > -1' and `BETA > 0'.
`gauss(mu, sigma)'
Gaussian distribution. MU is the mean, and SIGMA is the standard
deviation. This is slightly faster than the `normalvariate()'
function defined below.
`lognormvariate(mu, sigma)'
Log normal distribution. If you take the natural logarithm of this
distribution, you'll get a normal distribution with mean MU and
standard deviation SIGMA. MU can have any value, and SIGMA must
be greater than zero.
`normalvariate(mu, sigma)'
Normal distribution. MU is the mean, and SIGMA is the standard
deviation.
`vonmisesvariate(mu, kappa)'
MU is the mean angle, expressed in radians between 0 and 2*_pi_,
and KAPPA is the concentration parameter, which must be greater
than or equal to zero. If KAPPA is equal to zero, this
distribution reduces to a uniform random angle over the range 0 to
2*_pi_.
`paretovariate(alpha)'
Pareto distribution. ALPHA is the shape parameter.
`weibullvariate(alpha, beta)'
Weibull distribution. ALPHA is the scale parameter and BETA is
the shape parameter.
See also:
Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and
portable pseudo-random number generator", 31 (1982) 188-190.
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