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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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paretovariate�weibullvariate�getstate�setstate� getrandbits� SystemRandom� g �?g @g @g �?g @�5 � Nc s e Z d Z d Z d Z d d d � Z d d � f d d � Z � f d 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 | _ d S)zeInitialize an instance.
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a 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 an int, all bits are used.
For version 2 (the default), all of the bits are used if *a* is a str,
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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z!non-integer arg 1 for randrange()Nr zempty range for randrange()z non-integer stop for randrange()r5 z'empty range for randrange() (%d,%d, %d)z non-integer step for randrange()zzero step for randrange())rP �
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zRandom.randrangec C s | j | | d � S)zJReturn random integer in range [a, b], including both end points.
r5 )r )r. rG �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).
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population contains repeats, then each occurrence is a possible
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large population: sample(range(10000000), 60)
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rL | SYn X| | k rz d | } d | } | | } } | | | | | d S)z�Triangular distribution.
Continuous distribution bounded by given lower and upper limits,
and having a given mode value in-between.
http://en.wikipedia.org/wiki/Triangular_distribution
Ng �?g �?)r �ZeroDivisionError)r. ZlowZhigh�mode�urI r0 r0 r1 r e s (
zRandom.triangularc C sf | j } xN | � } d | � } t | d | } | | d } | t | � k r Pq W| | | S)z\Normal distribution.
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zRandom.normalvariatec C s t | j | | � � S)z�Log normal distribution.
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)�_expr )r. rz r{ r0 r0 r1 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 �?)rq 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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S)aF Circular data distribution.
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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.
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Conditions on the parameters are alpha > 0 and beta > 0.
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pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
g z*gammavariate: alpha and beta must be > 0.0g �?g @gH�����z>g�P���?g @N)rP r r� �LOG4rq r �
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zRandom.gammavariatec C s� | j } | j } d | _ | d k rt | � t } t d t d | � � � } t | � | } t | � | | _ | | | S)z�Gaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
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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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Returned values range between 0 and 1.
g �?r g N)r )r. r� r� �yr0 r0 r1 r" ` s
zRandom.betavariatec C s d | j � } d | d | S)z3Pareto distribution. alpha is the shape parameter.g �?)r )r. r� rx r0 r0 r1 r# r s zRandom.paretovariatec C s' d | j � } | t | � d | S)zfWeibull distribution.
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S) r( z�Alternate random number generator using sources provided
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