/snap/core18/2999/usr/lib/python3.6/__pycache__
Edit: /snap/core18/2999/usr/lib/python3.6/__pycache__/random.cpython-36.pyc (19330B)
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Z>e7j?Z?e7j@Z@e7jAZAe7jBZBe7jCZCe7jDZDe7jEZEe7jFZFe7jGZGe7jHZHe7jIZIe7jJZJe7jKZKe7jLZLe7jMZMeNd1kre6 d S )4a Random variable generators.
integers
--------
uniform within range
sequences
---------
pick random element
pick random sample
pick weighted random sample
generate random permutation
distributions on the real line:
------------------------------
uniform
triangular
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.
* The random() method is implemented in C, executes in a single Python step,
and is, therefore, threadsafe.
)warn)
MethodTypeBuiltinMethodType)logexppieceil)sqrtacoscossin)urandom)SetSequence)sha512NRandomseedrandomuniformrandintchoicesample randrangeshuffle
normalvariatelognormvariate expovariatevonmisesvariate gammavariate
triangulargauss betavariate
paretovariateweibullvariategetstatesetstate getrandbitschoices SystemRandom g ?g @g @g ?g @5 c s e Zd ZdZdZd;ddZd< fdd Z fd d
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Used to instantiate instances of Random to get generators that don't
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(), and setstate().
Optionally, implement a getrandbits() method so that randrange()
can cover arbitrarily large ranges.
Nc C s | j | d| _dS )zeInitialize an instance.
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system specific randomness source if available.
If *a* is an int, all bits are used.
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bytes, or bytearray. For version 1 (provided for reproducing random
sequences from older versions of Python), the algorithm for str and
bytes generates a narrower range of seeds.
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zChoose a random item from range(start, stop[, step]).
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z!non-integer arg 1 for randrange()Nr zempty range for randrange()z non-integer stop for randrange()r4 z'empty range for randrange() (%d,%d, %d)z non-integer step for randrange()zzero step for randrange())rO
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zRandom.randrangec C s | j ||d S )zJReturn random integer in range [a, b], including both end points.
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random float in [0.0, 1.0); if it is the default None, the
standard random.random will be used.
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a= Chooses k unique random elements from a population sequence or set.
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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 range as an argument.
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large population: sample(range(10000000), 60)
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r< |S X ||kr`d| }d| }|| }}||| || d S )zTriangular distribution.
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and having a given mode value in-between.
http://en.wikipedia.org/wiki/Triangular_distribution
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zRandom.normalvariatec C s t | j||S )zLog normal distribution.
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)_expr )r/ r r r1 r1 r2 r s zRandom.lognormvariatec C s t d| j | S )a^ Exponential distribution.
lambd is 1.0 divided by the desired mean. It should be
nonzero. (The parameter would be called "lambda", but that is
a reserved word in Python.) Returned values range from 0 to
positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
g ?)rp r )r/ Zlambdr1 r1 r2 r s zRandom.expovariatec C s | j }|dkrt| S d| }|td|| }xN| }tt| }||| }| } | d|| k s~| d| t| kr6P q6W d| }
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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
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to a uniform random angle over the range 0 to 2*pi.
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| S dS ) aZ Gamma distribution. Not the gamma function!
Conditions on the parameters are alpha > 0 and beta > 0.
The probability distribution function is:
x ** (alpha - 1) * math.exp(-x / beta)
pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
g z*gammavariate: alpha and beta must be > 0.0g ?g @gHz>gP?g @N)rO r r LOG4rp r
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zRandom.gammavariatec C s` | j }| j}d| _|dkrT| t }tdtd| }t|| }t|| | _||| S )zGaussian 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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z Random.gaussc C s0 | j |d}|dkrdS ||| j |d S dS )zBeta distribution.
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zRandom.betavariatec C s d| j }d|d| S )z3Pareto distribution. alpha is the shape parameter.g ?)r )r/ r r r1 r1 r2 r# s zRandom.paretovariatec C s" d| j }|t| d| S )zfWeibull distribution.
alpha is the scale parameter and beta is the shape parameter.
g ?)r rp )r/ r r r r1 r1 r2 r$ s zRandom.weibullvariate)N)Nr, )N)N)r~ r N)$__name__
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S ) r) zAlternate random number generator using sources provided
by the operating system (such as /dev/urandom on Unix or
CryptGenRandom on Windows).
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c C s t jtddd? t S )z3Get the next random number in the range [0.0, 1.0).r5 r6 r- )rC rD _urandom RECIP_BPF)r/ r1 r1 r2 r s zSystemRandom.randomc C sP |dkrt d|t|kr$td|d d }tjt|d}||d | ? S )z:getrandbits(k) -> x. Generates an int with k random bits.r z(number of bits must be greater than zeroz#number of bits should be an integerr5 r6 )rO rC rP rD r )r/ ra Znumbytesr0 r1 r1 r2 r' s zSystemRandom.getrandbitsc O s dS )z& sn
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