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Edit File: random.cpython-38.pyc
U >hp @ s d Z ddlmZ ddlmZmZm Z m Z m Z ddlmZmZmZmZ ddlmZ ddlmZ m Z ddl m Z!m"Z# ddl$m$Z% dd lZ&zdd l'm(Z' W n e)k r dd l*m(Z' Y nX d d d ddddddddddddddd d d d d d!d"gZ+d#ed$ ed% Z,d%e Z-ed&Z.d'ed( Z/d)Z0d*e0 Z1dd l2Z2G d+d d e2j3Z3G d,d" d"e3Z4d-d. Z5d5d0d1Z6e3 Z7e7j8Z8e7j9Z9e7j:Z:e7j;Z;e7j<Z<e7j=Z=e7j>Z>e7j?Z?e7j@Z@e7jAZAe7jBZBe7jCZCe7jDZDe7jEZEe7jFZFe7jGZGe7jHZHe7jIZIe7jJZJe7jKZKe7jLZLe7jMZMeNe&d2re&jOe7j8d3 ePd4kre6 d S )6a 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)logexppieceil)sqrtacoscossin)urandom)SetSequence) accumulaterepeat)bisectN)sha512Randomseedrandomuniformrandintchoicesample randrangeshuffle normalvariatelognormvariate expovariatevonmisesvariate gammavariate triangulargauss betavariate paretovariateweibullvariategetstatesetstate getrandbitschoices SystemRandom g @ @ ? @5 c s$ e Zd ZdZdZd?ddZdd Zd@ fd d Z fd d Z fd dZ dd Z dd Z dd Z dde fddZdd Zdd Ze de> fd d ZeZd d ZdAd d!Zd"d# ZdBddd$d%d&Zd'd( ZdCd+d,Zd-d. Zd/d0 Zd1d2 Z d3d4 Z d5d6 Z d7d8 Z d9d: Z d;d< Z!d=d> Z" Z#S )Dr a Random number generator base class used by bound module functions. 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 | | d| _dS )zeInitialize an instance. Optional argument x controls seeding, as for Random.seed(). N)r gauss_next)selfx r6 /usr/lib/python3.8/random.py__init__^ s zRandom.__init__c K sJ | j D ]>}d|jkr qFd|jkr.| j| _ qFd|jkr| j| _ qFqdS )a Control how subclasses generate random integers. The algorithm a subclass can use depends on the random() and/or getrandbits() implementation available to it and determines whether it can generate random integers from arbitrarily large ranges. _randbelowr( r N)__mro____dict___randbelow_with_getrandbitsr9 _randbelow_without_getrandbits)clskwargscr6 r6 r7 __init_subclass__g s zRandom.__init_subclass__r1 c s |dkrt |ttfrt |tr*|dn|}|rBt|d d> nd}tt|D ]}d| |A d@ }qP|t|N }|dkr~dn|}|d krt |tttfrt |tr| }|t | 7 }t |d }t | d | _d S ) 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. zlatin-1r iCB l r1 bigN) isinstancestrbytesdecodeordmaplen bytearrayencode_sha512Zdigestint from_bytessuperr r3 )r4 aversionr5 r@ __class__r6 r7 r { s z Random.seedc s | j t | jfS )z9Return internal state; can be passed to setstate() later.)VERSIONrS r& r3 r4 rV r6 r7 r& s zRandom.getstatec s |d }|dkr*|\}}| _ t | nt|dkr|\}}| _ ztdd |D }W n( tk r| } z t|W 5 d}~X Y nX t | ntd|| jf dS )z:Restore internal state from object returned by getstate().r r2 r1 c s s | ]}|d V qdS )l Nr6 ).0r5 r6 r6 r7 <genexpr> s z"Random.setstate.<locals>.<genexpr>Nz?state with version %s passed to Random.setstate() of version %s)r3 rS r' tuple ValueError TypeErrorrX )r4 staterU Z internalstater rV r6 r7 r' s zRandom.setstatec C s | S N)r& rY r6 r6 r7 __getstate__ s zRandom.__getstate__c C s | | d S r` )r' )r4 r_ r6 r6 r7 __setstate__ s zRandom.__setstate__c C s | j d| fS )Nr6 )rW r& rY r6 r6 r7 __reduce__ s zRandom.__reduce__rB c C s ||}||krt d|dkr:|dkr2| |S t d||}||krRt d|| }|dkrx|dkrx|| | S |dkrt d|||f ||}||krt d|dkr|| d | } n"|dk r|| d | } nt d | dkrt d||| | S ) zChoose 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. z!non-integer arg 1 for randrange()Nr zempty range for randrange()z non-integer stop for randrange()rB z(empty range for randrange() (%d, %d, %d)z non-integer step for randrange()zzero step for randrange())r] r9 ) r4 startstopstep_intZistartZistopwidthZistepnr6 r6 r7 r s4 zRandom.randrangec C s | ||d S )zJReturn random integer in range [a, b], including both end points. rB )r r4 rT br6 r6 r7 r s zRandom.randintc C s, | j }| }||}||kr(||}q|S )zCReturn a random int in the range [0,n). Raises ValueError if n==0.)r( bit_length)r4 ri r( krr6 r6 r7 r< s z"Random._randbelow_with_getrandbitsc C sn | j }||kr$td || | S |dkr4td|| }|| | }| }||kr^| }qN||| | S )zReturn a random int in the range [0,n). Raises ValueError if n==0. The implementation does not use getrandbits, but only random. zUnderlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.r zBoundary cannot be zero)r _warnr] )r4 ri rQ maxsizer Zremlimitrn r6 r6 r7 r= s z%Random._randbelow_without_getrandbitsc C s: z| t|}W n tk r0 tddY nX || S )z2Choose a random element from a non-empty sequence.z$Cannot choose from an empty sequenceN)r9 rM r] IndexError)r4 seqir6 r6 r7 r s z Random.choicec C s |dkrN| j }ttdt|D ]*}||d }|| || ||< ||< q nHt}ttdt|D ]0}|| |d }|| || ||< ||< qddS )zShuffle list x in place, and return None. Optional argument random is a 0-argument function returning a random float in [0.0, 1.0); if it is the default None, the standard random.random will be used. NrB )r9 reversedrangerM rQ )r4 r5 r randbelowrt jrg r6 r6 r7 r % s zRandom.shufflec C s t |trt|}t |ts$td| j}t|}d| krF|ksPn tddg| }d}|dkr|dtt |d d 7 }||krt |}t |D ]0}||| } || ||< ||| d || < qnHt } | j } t |D ]2}||} | | kr||} q| | || ||< q|S ) a= Chooses k unique random elements from a population sequence or set. 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 range as an argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) z>Population must be a sequence or set. For dicts, use list(d).r z,Sample larger than population or is negativeN r+ r2 rB )rG _Setr\ _Sequencer^ r9 rM r] _ceil_loglistrv setadd) r4 populationrm rw ri resultZsetsizeZpoolrt rx ZselectedZ selected_addr6 r6 r7 r ; s6 ) z Random.sample) cum_weightsrm c s | j tdkrV|dkrHt d7 fddtd|D S tt|n|dk rftdtkrztdtd d d fd dtd|D S ) zReturn a k sized list of population elements chosen with replacement. If the relative weights or cumulative weights are not specified, the selections are made with equal probability. N c s g | ]} qS r6 r6 rZ rt )rg ri r r r6 r7 <listcomp> s z"Random.choices.<locals>.<listcomp>z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationrD rB c s$ g | ] } d qS )r r6 r )r r hir r totalr6 r7 r s ) r rM rQ _repeatr _accumulater^ r] _bisect)r4 r Zweightsr rm r6 )rg r r r ri r r r r7 r) s$ zRandom.choicesc C s ||| | S )zHGet a random number in the range [a, b) or [a, b] depending on rounding.r rj r6 r6 r7 r s zRandom.uniformr r. c C s| | }z |dkrdn|| || }W n tk r@ | Y S X ||krdd| }d| }|| }}||| t|| S )zTriangular 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 N ?r. )r ZeroDivisionError_sqrt)r4 ZlowZhighmodeur@ r6 r6 r7 r! s zRandom.triangularc C sP | j }| }d| }t|d | }|| d }|t| krqDq||| S )z\Normal distribution. mu is the mean, and sigma is the standard deviation. r. r r- )r NV_MAGICCONSTr~ )r4 musigmar u1u2zZzzr6 r6 r7 r s zRandom.normalvariatec C s t | ||S )zLog 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. )_expr )r4 r r r6 r6 r7 r s zRandom.lognormvariatec C s t d| | 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. r. )r~ r )r4 Zlambdr6 r6 r7 r s zRandom.expovariatec C s | j }|dkrt| S d| }|td|| }| }tt| }||| }| } | d|| k s| d| t| kr4qq4d| } | | d| | } | } | dkr|t| t } n|t| t } | S )aF 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. gư>r r. )r TWOPIr _cos_pir _acos)r4 r Zkappar srn r r dr qfZu3Zthetar6 r6 r7 r s$ $zRandom.vonmisesvariatec C s~ |dks|dkrt d| j}|dkrtd| d }|t }|| }| }d| k r`dk sdqF qFd| }t|d| | } |t| } || | } ||| | } | t d| dks| t| krF| | S qFn|dkrtd| | S | } t| t }|| }|dkr$|d| } nt|| | } | }|dkr^|| |d krpqrq|t| krqrq| | 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 r z*gammavariate: alpha and beta must be > 0.0r. r, gHz>gP?r/ N)r] r r LOG4r~ r SG_MAGICCONST_e)r4 alphabetar ZainvZbbbZcccr r vr5 r rn r rk pr6 r6 r7 r # s@ 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. Ng r. )r r3 r r r~ r _sin)r4 r r r r Zx2piZg2radr6 r6 r7 r" h s z Random.gaussc C s0 | |d}|dkrdS ||| |d S dS )zBeta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. r. r r N)r )r4 r r yr6 r6 r7 r# s zRandom.betavariatec C s d| }d|d| S )z3Pareto distribution. alpha is the shape parameter.r. r )r4 r r r6 r6 r7 r$ s zRandom.paretovariatec C s" d| }|t| d| S )zfWeibull distribution. alpha is the scale parameter and beta is the shape parameter. r. )r r~ )r4 r r r r6 r6 r7 r% s zRandom.weibullvariate)N)Nr1 )N)N)r r. N)$__name__ __module__ __qualname____doc__rX r8 rA r r& r' ra rb rc rQ r r r< BPFr= r9 r r r r) r r! r r r r r r" r# r$ r% __classcell__r6 r6 rV r7 r N s< , G 0E5 c @ s8 e Zd ZdZdd Zdd Zdd Zdd Ze ZZ d S ) r* zAlternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). Not available on all systems (see os.urandom() for details). c C s t tddd? t S )z3Get the next random number in the range [0.0, 1.0).rC rF r2 )rQ rR _urandom RECIP_BPFrY r6 r6 r7 r s zSystemRandom.randomc C s< |dkrt d|d d }tt|d}||d | ? S )z:getrandbits(k) -> x. Generates an int with k random bits.r z(number of bits must be greater than zerorC rF )r] rQ rR r )r4 rm Znumbytesr5 r6 r6 r7 r( s zSystemRandom.getrandbitsc O s dS )z<Stub method. Not used for a system random number generator.Nr6 r4 argskwdsr6 r6 r7 r s zSystemRandom.seedc O s t ddS )zAMethod should not be called for a system random number generator.z*System entropy source does not have state.N)NotImplementedErrorr r6 r6 r7 _notimplemented s z SystemRandom._notimplementedN) r r r r r r( r r r& r' r6 r6 r6 r7 r* s c C s dd l }t| d|j d}d}d}d}| }t| D ]4} || } || 7 }|| | }t| |}t| |}q6| } tt| | dddd || } t|| | | } td | | ||f d S ) Nr timesr g _Bg _r2 zsec, )endz"avg %g, stddev %g, min %g, max %g ) timeprintr perf_counterrv minmaxroundr )ri funcr r r ZsqsumZsmallestZlargestZt0rt r5 t1ZavgZstddevr6 r6 r7 _test_generator s( r c C s t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td t | td d S )Nr6 )r r. )g{Gz?r. )皙?r. )r r, )r r. )g?r. )r. r. )r, r. )g 4@r. )g i@r. ) @r )r r. gUUUUUU?) r r r r r r r" r# r! )Nr6 r6 r7 _test s r fork)Zafter_in_child__main__)r )Qr warningsr ro Zmathr r~ r r r r r r r r} r r r r r r r r osr r _collections_abcr r{ r r| itertoolsr r r r r r _osrP r ImportErrorZhashlib__all__r r r r r r Z_randomr r* r r Z_instr r r r! r r r r r r) r r r r r r" r# r$ r% r& r' r( hasattrregister_at_forkr r6 r6 r6 r7 <module> s ' {
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