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random.cpython-39.opt-1.pyc
a DOgz @ s d Z ddlmZ ddlmZmZm Z m Z m Z ddlmZmZmZmZ ddlmZmZ ddlm Z ddl m Z m!Z" ddl#m$Z%m&Z' dd l(m(Z) dd lZ*dd l+Z+zdd l,m-Z, W n e.y dd l/m-Z, Y n0 g d Z0d ed ed Z1edZ2ded Z3dZ4de4 Z5G dd de+j6Z6G dd de6Z7e6 Z8e8j9Z9e8j:Z:e8j;Z;e8j<Z<e8j=Z=e8j>Z>e8j?Z?e8j@Z@e8jAZAe8jBZBe8jCZCe8jDZDe8jEZEe8jFZFe8jGZGe8jHZHe8jIZIe8jJZJe8jKZKe8jLZLe8jMZMe8jNZNe8jOZOdd ZPd!d d ZQeRe*d re*jSe8j9d eTd kreQ d S )"a Random variable generators. bytes ----- uniform bytes (values between 0 and 255) integers -------- uniform within range sequences --------- pick random element pick random sample pick weighted random sample generate random permutation distributions on the real line: ------------------------------ uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi) --------------------------------------------- circular uniform von Mises General notes on the underlying Mersenne Twister core generator: * The period is 2**19937-1. * It is one of the most extensively tested generators in existence. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe. )warn)logexppieceil)sqrtacoscossin)taufloor)urandom)SetSequence) accumulaterepeat)bisectN)sha512)Random SystemRandom betavariatechoicechoices expovariate gammavariategauss getrandbitsgetstatelognormvariate normalvariate paretovariate randbytesrandintrandom randrangesampleseedsetstateshuffle triangularuniformvonmisesvariateweibullvariate g @ @ ? @5 c s* e Zd ZdZdZdBddZdC fdd Z fd d Z fd d Zd d Z dd Z dd Z dd Z dd Z de> fddZe Zdd ZdDd d Zd d Zd d! ZdEd"d#Zdd$d%d&ZdFddd'd(d)Zd*d+ ZdGd.d/Zd0d1 Zd2d3 Zd4d5 Z d6d7 Z d8d9 Z d:d; Z d<d= Z d>d? Z!d@dA Z" Z#S )Hr 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 r9 /usr/lib64/python3.9/random.py__init__u s zRandom.__init__r4 c s |dkrt |ttfrt |tr*|dn|}|rBt|d d> nd}tt|D ]}d| |A d@ }qP|t|N }|dkr~dn|}nj|d krt |tttfrt |tr| }t |t | d }n&t |t d t ttttfstd td t | d | _d S ) a\ Initialize internal state from a seed. The only supported seed types are None, int, float, str, bytes, and bytearray. 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 r4 bigNzSeeding based on hashing is deprecated since Python 3.9 and will be removed in a subsequent version. The only supported seed types are: None, int, float, str, bytes, and bytearray.) isinstancestrbytesdecodeordmaplen bytearrayencodeint from_bytes_sha512digesttypefloat_warnDeprecationWarningsuperr' r6 )r7 aversionr8 c __class__r9 r: r' ~ s" z Random.seedc s | j t | jfS )z9Return internal state; can be passed to setstate() later.)VERSIONrR r r6 r7 rV r9 r: r s zRandom.getstatec s |d }|dkr*|\}}| _ t | nv|dkr|\}}| _ ztdd |D }W n* ty~ } zt|W Y d}~n d}~0 0 t | ntd|| jf dS )z:Restore internal state from object returned by getstate().r r5 r4 c s s | ]}|d V qdS )l Nr9 ).0r8 r9 r9 r: <genexpr> z"Random.setstate.<locals>.<genexpr>Nz?state with version %s passed to Random.setstate() of version %s)r6 rR r( tuple ValueError TypeErrorrX )r7 staterT internalstater rV r9 r: r( s zRandom.setstatec C s | S N)r rY r9 r9 r: __getstate__ s zRandom.__getstate__c C s | | d S rb )r( )r7 r` r9 r9 r: __setstate__ s zRandom.__setstate__c C s | j d| fS )Nr9 )rW r rY r9 r9 r: __reduce__ s zRandom.__reduce__c K sJ | j D ]>}d|jv r qFd|jv r.| j| _ qFd|jv r| 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_getrandbitsrf _randbelow_without_getrandbits)clskwargsrU r9 r9 r: __init_subclass__ s zRandom.__init_subclass__c C s4 |sdS | j }| }||}||kr0||}q |S )z;Return a random int in the range [0,n). Returns 0 if n==0.r )r bit_length)r7 nr krr9 r9 r: ri s z"Random._randbelow_with_getrandbitsr< c C sj | j }||kr$td t| | S |dkr0dS || }|| | }| }||krZ| }qJt|| | S )zReturn a random int in the range [0,n). Returns 0 if n==0. The implementation does not use getrandbits, but only random. zUnderlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.r )r$ rP _floor)r7 ro maxsizer$ remlimitrq r9 r9 r: rj s z%Random._randbelow_without_getrandbitsc C s | |d |dS )Generate n random bytes. little)r to_bytesr7 ro r9 r9 r: r" s zRandom.randbytesc C s t |}||krtd|du r:|dkr2| |S tdt |}||krRtd|| }|dkrx|dkrx|| | S |dkrtd|||f t |}||krtd|dkr|| d | }n"|dk r|| d | }ntd |dkrtd||| | 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()r< z(empty range for randrange() (%d, %d, %d)z non-integer step for randrange()zzero step for randrange())rJ r^ rf ) r7 startstopstepistartistopwidthistepro r9 r9 r: r% " s4 zRandom.randrangec C s | ||d S )zJReturn random integer in range [a, b], including both end points. r< )r% r7 rS br9 r9 r: r# N s zRandom.randintc C s || t| S )z2Choose a random element from a non-empty sequence.)rf rG )r7 seqr9 r9 r: r W s z Random.choicec C s |du rN| j }ttdt|D ]*}||d }|| || ||< ||< q nTtdtd t}ttdt|D ]0}|| |d }|| || ||< ||< qpdS )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. Nr< zuThe *random* parameter to shuffle() has been deprecated since Python 3.9 and will be removed in a subsequent version.r4 )rf reversedrangerG rP rQ rr )r7 r8 r$ randbelowijr r9 r9 r: r) \ s zRandom.shuffle)countsc s t tr tdtd tt ts0tdt}|durtt |t|kr`t d }t |t sztd|dkrt d| j t||d }t fd d |D S | j}d| kr|ksn t d dg| }d } |dkr | dtt|d d 7 } || kr\t} t|D ]2} ||| } | | || < | || d | | < q&nNt } | j}t|D ]8} ||} | | v r||} q||| | || < qp|S )am 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. Repeated elements can be specified one at a time or with the optional counts parameter. For example: sample(['red', 'blue'], counts=[4, 2], k=5) is equivalent to: sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) To choose a sample from a range of integers, use range() for the population argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) z\Sampling from a set deprecated since Python 3.9 and will be removed in a subsequent version.r4 zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationzCounts must be integersr z)Total of counts must be greater than zero)rp c s g | ]} | qS r9 r9 )rZ sr cum_counts populationr9 r: <listcomp> r\ z!Random.sample.<locals>.<listcomp>z,Sample larger than population or is negative r. r5 r< )rA _SetrP rQ r] _Sequencer_ rG list _accumulater^ poprJ r& r _bisectrf _ceil_logsetadd)r7 r rp r ro total selectionsr resultsetsizepoolr r selected selected_addr9 r r: r& v sT 5 z Random.sample) cum_weightsrp c s | j tdu r|du rHtd7 fddtd|D S ztt|W q ty t|tsr |}td|dY q0 n|durtdtkrt dd d dkrt d t 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 r9 r9 rZ r )r ro r r$ r9 r: r r\ z"Random.choices.<locals>.<listcomp>z4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationr> z*Total of weights must be greater than zeror< c s$ g | ] } d qS )r r9 r )r r hir r$ r r9 r: r s ) r$ rG rr _repeatr r r_ rA rJ r^ r )r7 r weightsr rp r9 )r r r r ro r r$ r r: r s< zRandom.choicesc C s ||| | S )zHGet a random number in the range [a, b) or [a, b] depending on rounding.r$ r r9 r9 r: r+ s zRandom.uniformr r1 c C sz | }z |du rdn|| || }W n ty> | Y S 0 ||krbd| }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 ?r1 )r$ ZeroDivisionError_sqrt)r7 lowhighmodeurU r9 r9 r: 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. r1 r r0 )r$ NV_MAGICCONSTr )r7 musigmar$ u1u2zzzr9 r9 r: r s zRandom.normalvariatec C s` | j }| j}d| _|du rT| 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 r1 )r$ r6 TWOPIr r _cos_sin)r7 r r r$ r x2pig2radr9 r9 r: r , s z Random.gaussc 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 )r7 r r r9 r9 r: r 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. r1 )r r$ )r7 lambdr9 r9 r: 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 r1 )r$ r r r _pir _acos)r7 r kappar$ r rq r r dr qfu3thetar9 r9 r: r, m 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 > 0.0r1 r/ gHz>gP?r2 N)r^ r$ r LOG4r r SG_MAGICCONST_e)r7 alphabetar$ ainvbbbcccr r vr8 r rq r r pr9 r9 r: r s@ zRandom.gammavariatec C s( | |d}|r$||| |d S dS )zBeta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. r1 r )r )r7 r r yr9 r9 r: r s zRandom.betavariatec C s d| }d|d| S )z3Pareto distribution. alpha is the shape parameter.r1 r )r7 r r r9 r9 r: r! s zRandom.paretovariatec C s" d| }|t| d| S )zfWeibull distribution. alpha is the scale parameter and beta is the shape parameter. r1 )r$ r )r7 r r r r9 r9 r: r- s zRandom.weibullvariate)N)Nr4 )Nr< )N)N)r r1 N)$__name__ __module__ __qualname____doc__rX r; r' r r( rc rd re rm ri BPFrj rf r" r% r# r r) r& r r+ r* r r r r r, r r r! r- __classcell__r9 r9 rV r: r e s> * ! , c& & *Ar c @ s@ e Zd ZdZdd Zdd Zdd Zdd Zd d Ze Z Z d S ) r zAlternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). Not available on all systems (see os.urandom() for details). c C s t tddd? t S )z3Get the next random number in the range [0.0, 1.0).r= r@ r5 )rJ rK _urandom RECIP_BPFrY r9 r9 r: r$ s zSystemRandom.randomc C s< |dk rt 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 non-negativer= rw r@ )r^ rJ rK r )r7 rp numbytesr8 r9 r9 r: r s zSystemRandom.getrandbitsc C s t |S )rv )r rz r9 r9 r: r" s zSystemRandom.randbytesc O s dS )z<Stub method. Not used for a system random number generator.Nr9 r7 argskwdsr9 r9 r: 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 r9 r9 r: _notimplemented) s z SystemRandom._notimplementedN) r r r r r$ r r" r' r r r( r9 r9 r9 r: r s r c s ddl m}m} ddlm} | } fddt| D }| }||} ||| } t|} t|} t|| dd| dj td | | | | f d S ) Nr )stdevfmean) perf_counterc s g | ] } qS r9 r9 r r funcr9 r: r X r\ z#_test_generator.<locals>.<listcomp>z.3fz sec, z times z"avg %g, stddev %g, min %g, max %g ) statisticsr r timer r minmaxprintr ) ro r r r meanr t0datat1xbarr r r r9 r r: _test_generatorS 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 )Nr9 )r r1 )g{Gz?r1 )皙?r1 )r r/ )r r1 )g?r1 )r1 r1 )r/ r1 )g 4@r1 )g i@r1 ) @r )r r1 gUUUUUU?) r r$ r r r, r r r r* )Nr9 r9 r: _testd s r fork)after_in_child__main__)r )Ur warningsr rP mathr r r r r r r r r r r r r r r r r r r r r rr osr r _collections_abcr r r r itertoolsr r r r r r _os_randomrL r ImportErrorhashlib__all__r r r r r r r _instr' r$ r+ r* r# r r% r& r) r r r r r, r r r r! r- r r( r r" r r hasattrregister_at_forkr r9 r9 r9 r: <module> sr / *,