Module random

Random variable generators.

    integers
    --------
           uniform within range

    sequences
    ---------
           pick random element
           pick random sample
           generate random permutation

    distributions on the real line:
    ------------------------------
           uniform
           normal (Gaussian)
           lognormal
           negative exponential
           gamma
           beta
           pareto
           Weibull

    distributions on the circle (angles 0 to 2pi)
    ---------------------------------------------
           circular uniform
           von Mises

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.

Classes

[hide private]

Random
Random number generator base class used by bound module functions.

SystemRandom
Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows).

WichmannHill

Functions

[hide private]

_test(N=2000)

source code

_test_generator(n, func, args)

source code

betavariate(alpha, beta)
Beta distribution.

source code

choice(seq)
Choose a random element from a non-empty sequence.

source code

expovariate(lambd)
Exponential distribution.

source code

gammavariate(alpha, beta)
Gamma distribution.

source code

gauss(mu, sigma)
Gaussian distribution.

source code

getrandbits(k)
Generates a long int with k random bits.

source code

getstate()
Return internal state; can be passed to setstate() later.

source code

jumpahead(int)
Create new state from existing state and integer.

source code

lognormvariate(mu, sigma)
Log normal distribution.

source code

normalvariate(mu, sigma)
Normal distribution.

source code

paretovariate(alpha)
Pareto distribution.

source code

randint(a, b)
Return random integer in range [a, b], including both end points.

source code

random()
Returns: x in the interval [0, 1).

source code

randrange(start, stop=None, step=1, int=<type 'int'>, default=None, maxwidth=9007199254740992)
Choose a random item from range(start, stop[, step]).

source code

sample(population, k)
Chooses k unique random elements from a population sequence.

source code

seed(a=None)
Initialize internal state from hashable object.

source code

setstate(state)
Restore internal state from object returned by getstate().

source code

shuffle(x, random=None, int=<type 'int'>)
x, random=random.random -> shuffle list x in place; return None.

source code

uniform(a, b)
Get a random number in the range [a, b).

source code

vonmisesvariate(mu, kappa)
Circular data distribution.

source code

weibullvariate(alpha, beta)
Weibull distribution.

source code

Variables

[hide private]

BPF = 53

LOG4 = 1.38629436112

NV_MAGICCONST = 1.71552776992

RECIP_BPF = 1.11022302463e-16

SG_MAGICCONST = 2.50407739678

TWOPI = 6.28318530718

__allow_access_to_unprotected_subobjects__ = 1

_e = 2.71828182846

_inst = <random.Random object at 0x1868610>

_pi = 3.14159265359

Function Details

[hide private]

betavariate(alpha, beta)

source code

Beta distribution.

Conditions on the parameters are alpha > -1 and beta} > -1.
Returned values range between 0 and 1.

expovariate(lambd)

source code

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.

gammavariate(alpha, beta)

source code

Gamma distribution. Not the gamma function!

Conditions on the parameters are alpha > 0 and beta > 0.

gauss(mu, sigma)

source code

Gaussian distribution.

mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function.

Not thread-safe without a lock around calls.

getrandbits(k)

source code

Generates a long int with k random bits.

Returns:

jumpahead(int)

source code

Create new state from existing state and integer.

Returns:

None

lognormvariate(mu, sigma)

source code

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)

source code

Normal distribution.

mu is the mean, and sigma is the standard deviation.

paretovariate(alpha)

source code

Pareto distribution. alpha is the shape parameter.

random()

source code

Returns:

x in the interval [0, 1).

randrange(start, stop=None, step=1, int=<type 'int'>, default=None, maxwidth=9007199254740992)

source code

Choose a random item from range(start, stop[, step]).

This fixes the problem with randint() which includes the endpoint; in Python this is usually not what you want. Do not supply the 'int', 'default', and 'maxwidth' arguments.

sample(population, k)

source code

Chooses k unique random elements from a population sequence.

Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).

Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.

To choose a sample in a range of integers, use xrange as an argument. This is especially fast and space efficient for sampling from a large population: sample(xrange(10000000), 60)

seed(a=None)

source code

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.

shuffle(x, random=None, int=<type 'int'>)

source code

x, random=random.random -> shuffle list x in place; return None.

Optional arg random is a 0-argument function returning a random float in [0.0, 1.0); by default, the standard random.random.

vonmisesvariate(mu, kappa)

source code

Circular data distribution.

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.

weibullvariate(alpha, beta)

source code

Weibull distribution.

alpha is the scale parameter and beta is the shape parameter.