Current File : //snap/core/current/usr/lib/python3.5/__pycache__/random.cpython-35.pyc
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d! � e d" � Z( d" e Z) e d# � Z* d$ e d% � Z+ d& Z, d' e, Z- d d( l. Z. Gd) d � d e. j/ � Z/ Gd* d � d e/ � Z0 d+ d, � Z1 d- d. d/ � Z2 e/ � Z3 e3 j4 Z4 e3 j5 Z5 e3 j6 Z6 e3 j7 Z7 e3 j8 Z8 e3 j9 Z9 e3 j: Z: e3 j; Z; e3 j< Z< e3 j= Z= e3 j> Z> e3 j? Z? e3 j@ Z@ e3 jA ZA e3 jB ZB e3 jC ZC e3 jD ZD e3 jE ZE e3 jF ZF e3 jG ZG e3 jH ZH eI d0 k rxe2 � d( S)1a� Random variable generators.
integers
--------
uniform within range
sequences
---------
pick random element
pick 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.
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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 | _ d S)zeInitialize an instance.
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z�Choose a random item from range(start, stop[, step]).
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rO )r )r. rA �br0 r0 r1 r � s zRandom.randintc
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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.
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large population: sample(range(10000000), 60)
z>Population must be a sequence or set. For dicts, use list(d).r z Sample larger than populationN� � r) r, rO )r: �_SetrH � _SequencerJ rP r_ rI �_ceil�_log�listrd �set�add) r. Z
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rL | SYn X| | k rz d | } d | } | | } } | | | | | d S)z�Triangular distribution.
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zRandom.triangularc C sf | j } xN | � } d | � } t | d | } | | d } | t | � k r Pq W| | | S)z\Normal distribution.
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)�_expr )r. rv rw r0 r0 r1 r � s zRandom.lognormvariatec C s t d | j � � | S)a^ Exponential distribution.
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nonzero. (The parameter would be called "lambda", but that is
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positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
g �?)rl r )r. Zlambdr0 r0 r1 r � s zRandom.expovariatec C s | j } | d k r t | � Sd | } | t d | | � } xc | � } t t | � } | | | } | � } | d | | k s� | d | t | � k rE PqE Wd | }
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to a uniform random angle over the range 0 to 2*pi.
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Conditions on the parameters are alpha > 0 and beta > 0.
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mu is the mean, and sigma is the standard deviation. This is
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z Random.gaussc C s> | j | d � } | d k r" d S| | | j | d � Sd S)z�Beta 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� rs r0 r0 r1 r# i s zRandom.paretovariatec C s' d | j � } | t | � d | S)zfWeibull distribution.
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