Module random
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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.
WichmannHill
SystemRandom
Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows).
Functions [hide private]
 
_test_generator(n, func, args)
 
_test(N=2000)
 
seed(a=None)
Initialize internal state from hashable object.
x in the interval [0, 1).
random()
 
uniform(a, b)
Get a random number in the range [a, b).
 
randint(a, b)
Return random integer in range [a, b], including both end points.
 
choice(seq)
Choose a random element from a non-empty sequence.
 
randrange(start, stop=None, step=1, int=<type 'int'>, default=None, maxwidth=9007199254740992)
Choose a random item from range(start, stop[, step]).
 
sample(population, k)
Chooses k unique random elements from a population sequence.
 
shuffle(x, random=None, int=<type 'int'>)
x, random=random.random -> shuffle list x in place; return None.
 
normalvariate(mu, sigma)
Normal distribution.
 
lognormvariate(mu, sigma)
Log normal distribution.
 
expovariate(lambd)
Exponential distribution.
 
vonmisesvariate(mu, kappa)
Circular data distribution.
 
gammavariate(alpha, beta)
Gamma distribution.
 
gauss(mu, sigma)
Gaussian distribution.
 
betavariate(alpha, beta)
Beta distribution.
 
paretovariate(alpha)
Pareto distribution.
 
weibullvariate(alpha, beta)
Weibull distribution.
 
getstate()
Return internal state; can be passed to setstate() later.
 
setstate(state)
Restore internal state from object returned by getstate().
None
jumpahead(int)
Create new state from existing state and integer.
x
getrandbits(k)
Generates a long int with k random bits.
Variables [hide private]
  NV_MAGICCONST = 1.71552776992
  TWOPI = 6.28318530718
  LOG4 = 1.38629436112
  SG_MAGICCONST = 2.50407739678
  BPF = 53
  RECIP_BPF = 1.11022302463e-16
  _inst = Random()

Imports: _warn, _MethodType, _BuiltinMethodType, _log, _exp, _pi, _e, _ceil, _sqrt, _acos, _cos, _sin, _urandom, _hexlify, _random


Function Details [hide private]

seed(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.

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

 

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)

 

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)

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

 

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.

normalvariate(mu, sigma)

 

Normal distribution.

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

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.

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.

vonmisesvariate(mu, kappa)

 

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.

gammavariate(alpha, beta)

 

Gamma distribution. Not the gamma function!

Conditions on the parameters are alpha > 0 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.

Not thread-safe without a lock around calls.

betavariate(alpha, beta)

 
Beta distribution.

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

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.