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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.
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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 |
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_test(N=2000) | |||
_test_generator(n, func, args) | |||
betavariate(alpha,
beta)
Beta distribution. |
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choice(seq)
Choose a random element from a non-empty sequence. |
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expovariate(lambd)
Exponential distribution. |
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gammavariate(alpha,
beta)
Gamma distribution. |
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gauss(mu,
sigma)
Gaussian distribution. |
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getrandbits(k)
Generates a long int with k random bits. |
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getstate()
Return internal state; can be passed to setstate() later. |
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jumpahead(int)
Create new state from existing state and integer. |
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lognormvariate(mu,
sigma)
Log normal distribution. |
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normalvariate(mu,
sigma)
Normal distribution. |
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paretovariate(alpha)
Pareto distribution. |
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randint(a,
b)
Return random integer in range [a, b], including both end points. |
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random() | |||
randrange(start,
stop={},
step=1,
int=<type 'int'>,
default={},
maxwidth=9007199254740992L)
Choose a random item from range(start, stop[, step]). |
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sample(population,
k)
Chooses k unique random elements from a population sequence. |
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seed(a={})
Initialize internal state from hashable object. |
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setstate(state)
Restore internal state from object returned by getstate(). |
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shuffle(x,
random={},
int=<type 'int'>)
x, random=random.random -> shuffle list x in place; return None. |
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uniform(a,
b)
Get a random number in the range [a, b). |
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vonmisesvariate(mu,
kappa)
Circular data distribution. |
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weibullvariate(alpha,
beta)
Weibull distribution. |
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BPF | |||
LOG4 | |||
NV_MAGICCONST | |||
RECIP_BPF | |||
SG_MAGICCONST | |||
TWOPI | |||
_e | |||
_inst | |||
_pi |
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Beta distribution. Conditions on the parameters are alpha > -1 and beta} > -1. Returned values range between 0 and 1. |
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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 distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. |
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. |
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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. |
Normal distribution. mu is the mean, and sigma is the standard deviation. |
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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. |
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) |
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. |
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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. |
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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. |
Weibull distribution. alpha is the scale parameter and beta is the shape parameter. |
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BPF
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LOG4
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NV_MAGICCONST
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RECIP_BPF
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SG_MAGICCONST
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TWOPI
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_e
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_inst
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_pi
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