;ò ðŒü?c@s+dZdklZdklZlZl Z l Z dkl ZlZlZlZdklZddddd d d d d dddddddddddddddgZded ƒedƒZde Zed ƒZd!ed"ƒZd#Zd$kZdeifd%„ƒYZdefd&„ƒYZd'„Z d(d)„Z!eƒZ"e"i#Z#e"i$Z$e"i%Z%e"i&Z&e"i'Z'e"i(Z(e"i)Z)e"i*Z*e"i+Z+e"i,Z,e"i-Z-e"i.Z.e"i/Z/e"i0Z0e"i1Z1e"i2Z2e"i3Z3e"i4Z4e"i5Z5e"i6Z6e"i7Z7e"i8Z8e9d*jo e!ƒnd$S(+s;Random variable generators. integers -------- uniform within range sequences --------- pick random element pick random sample generate random permutation distributions on the real line: ------------------------------ uniform 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 * Without a direct way to compute N steps forward, the semantics of jumpahead(n) are weakened to simply jump to another distant state and rely on the large period to avoid overlapping sequences. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe. (sBuiltinMethodType(slogsexpspise(ssqrtsacosscosssin(sfloorsRandomsseedsrandomsuniformsrandintschoicessamples randrangesshuffles normalvariateslognormvariates cunifvariates expovariatesvonmisesvariates gammavariatesstdgammasgausss betavariates paretovariatesweibullvariatesgetstatessetstates jumpaheads WichmannHillif0.5f2.0f4.0f1.0f4.5i5NcBs*tZdZdZed„Zed„Zd„Zd„Zd„Z d„Z d„Z ed e ed e >ed „Zd „Ze d e >eee d „Zd„Zee d„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Z d„Z!d„Z"RS(sbRandom number generator base class used by bound module functions. Used to instantiate instances of Random to get generators that don't share state. Especially useful for multi-threaded programs, creating a different instance of Random for each thread, and using the jumpahead() method to ensure that the generated sequences seen by each thread don't overlap. 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(), setstate() and jumpahead(). icCs|i|ƒt|_dS(seInitialize an instance. Optional argument x controls seeding, as for Random.seed(). N(sselfsseedsxsNones gauss_next(sselfsx((s#/mit/python/lib/python2.3/random.pys__init__Qs cCsS|tjo#dk}t|iƒdƒ}ntt|ƒi|ƒt|_dS(s´Initialize internal state from hashable object. None or no argument seeds from current time. If a is not None or an int or long, hash(a) is used instead. Ni( sasNonestimeslongssupersRandomsselfsseeds gauss_next(sselfsastime((s#/mit/python/lib/python2.3/random.pysseedZs   cCs&|itt|ƒiƒ|ifSdS(s9Return internal state; can be passed to setstate() later.N(sselfsVERSIONssupersRandomsgetstates gauss_next(sself((s#/mit/python/lib/python2.3/random.pysgetstatehscCs`|d}|djo,|\}}|_tt|ƒi|ƒntd||i fƒ‚dS(s:Restore internal state from object returned by getstate().iis?state with version %s passed to Random.setstate() of version %sN( sstatesversions internalstatesselfs gauss_nextssupersRandomssetstates ValueErrorsVERSION(sselfsstatesversions internalstate((s#/mit/python/lib/python2.3/random.pyssetstatels   cCs|iƒSdS(N(sselfsgetstate(sself((s#/mit/python/lib/python2.3/random.pys __getstate__|scCs|i|ƒdS(N(sselfssetstatesstate(sselfsstate((s#/mit/python/lib/python2.3/random.pys __setstate__scCs|if|iƒfSdS(N(sselfs __class__sgetstate(sself((s#/mit/python/lib/python2.3/random.pys __reduce__‚silc CsD||ƒ}||jo td‚n||jof|djoL||jot|i ƒ|jo|i |ƒSn||i ƒ|ƒSntd‚n||ƒ} | |jo td‚n| |} |djo | djo`| |jot|i ƒ|jo|||i | ƒƒSn||||i ƒ| ƒƒSn|djo td‚n||ƒ} | |jo td‚n| djo| | d| } n-| djo| | d| } n td‚| djo td‚n| |jot|i ƒ|jo||i | ƒSn|| ||i ƒ| ƒSdS( sChoose 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. Do not supply the 'int', 'default', and 'maxwidth' arguments. s!non-integer arg 1 for randrange()isempty range for randrange()s non-integer stop for randrange()is non-integer step for randrange()szero step for randrange()N(sintsstartsistarts ValueErrorsstopsdefaultsmaxwidthstypesselfsrandoms_BuiltinMethods _randbelowsistopswidthsstepsistepsn( sselfsstartsstopsstepsintsdefaultsmaxwidths_BuiltinMethodsistartswidthsnsistopsistep((s#/mit/python/lib/python2.3/random.pys randrange‡sB     #      #"          #cCs|i||dƒSdS(sJReturn random integer in range [a, b], including both end points. iN(sselfs randrangesasb(sselfsasb((s#/mit/python/lib/python2.3/random.pysrandintÆsc Csª|d||ddƒƒ} |i}|}xs||joe||ƒ|ƒ}|} x7| | jo)||>||ƒ|ƒB}| |7} qXW|| | L}q/W|SdS(s£Return a random int in the range [0,n) Handles the case where n has more bits than returned by a single call to the underlying generator. f1.0000100000000001iiN( sints_logsnsksselfsrandomsrslongsmaxwidthsbpfsbits( sselfsnsbpfsmaxwidthslongs_logsintsrandomsrsbitssk((s#/mit/python/lib/python2.3/random.pys _randbelowÌs   cCs"|t|iƒt|ƒƒSdS(s2Choose a random element from a non-empty sequence.N(sseqsintsselfsrandomslen(sselfsseq((s#/mit/python/lib/python2.3/random.pyschoiceåscCs~|tjo |i}nx]tt|ƒdddƒD]?}||ƒ|dƒ}||||f\||<|| shuffle list x in place; return None. Optional arg random is a 0-argument function returning a random float in [0.0, 1.0); by default, the standard random.random. Note that for even rather small len(x), the total number of permutations of x is larger than the period of most random number generators; this implies that "most" permutations of a long sequence can never be generated. iiiÿÿÿÿN( srandomsNonesselfsxrangeslensxsisintsj(sselfsxsrandomsintsjsi((s#/mit/python/lib/python2.3/random.pysshuffleés   c Cs—t|ƒ}d|jo |jn o td‚n|i}t}t g|}|d|jobt |ƒ} xt |ƒD]A}||ƒ||ƒ}| |||<| ||d| | 0 and beta > 0. f0.0s*gammavariate: alpha and beta must be > 0.0f1.0f2.0f9.9999999999999995e-08f0.99999990000000005f4.5iN(salphasbetas ValueErrorsselfsrandoms_sqrtsainvsLOG4sbbbscccsTruesu1su2s_logsvs_expsxszsrs SG_MAGICCONSTsus_esbspspow(sselfsalphasbetasrandomsainvsu1sbbbsu2sbspsrsusvsxszsccc((s#/mit/python/lib/python2.3/random.pys gammavariate½sL       ,       I cCs0dk}|idtdƒ|i|dƒSdS(Ns@The stdgamma function is deprecated; use gammavariate() instead.if1.0(swarningsswarnsDeprecationWarningsselfs gammavariatesalpha(sselfsalphasainvsbbbscccswarnings((s#/mit/python/lib/python2.3/random.pysstdgammaýs   cCs‰|i}|i}t|_|tjoQ|ƒt}tdtd|ƒƒƒ}t |ƒ|}t |ƒ||_n|||SdS(sØGaussian 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. f-2.0f1.0N(sselfsrandoms gauss_nextszsNonesTWOPIsx2pis_sqrts_logsg2rads_coss_sinsmussigma(sselfsmussigmasrandomsx2piszsg2rad((s#/mit/python/lib/python2.3/random.pysgausss     cCsC|i|dƒ}|djodSn|||i|dƒSdS(sBeta distribution. Conditions on the parameters are alpha > -1 and beta} > -1. Returned values range between 0 and 1. f1.0if0.0N(sselfs gammavariatesalphasysbeta(sselfsalphasbetasy((s#/mit/python/lib/python2.3/random.pys betavariateHs  cCs)d|iƒ}dt|d|ƒSdS(s3Pareto distribution. alpha is the shape parameter.f1.0N(sselfsrandomsuspowsalpha(sselfsalphasu((s#/mit/python/lib/python2.3/random.pys paretovariateZscCs0d|iƒ}|tt|ƒ d|ƒSdS(sfWeibull distribution. alpha is the scale parameter and beta is the shape parameter. f1.0N(sselfsrandomsusalphaspows_logsbeta(sselfsalphasbetasu((s#/mit/python/lib/python2.3/random.pysweibullvariatecs(#s__name__s __module__s__doc__sVERSIONsNones__init__sseedsgetstatessetstates __getstate__s __setstate__s __reduce__sintsBPFs_BuiltinMethodTypes randrangesrandintslongs_logs _randbelowschoicesshufflessamplesuniforms normalvariateslognormvariates cunifvariates expovariatesvonmisesvariates gammavariatesstdgammasgausss betavariates paretovariatesweibullvariate(((s#/mit/python/lib/python2.3/random.pysRandom@s6       ?   :     2 @  5  cBs\tZdZed„Zd„Zd„Zd„Zd„Zdddd„Z ed „Z RS( NicCsÓ|tjo#dk}t|iƒdƒ}nt|ttfƒ ot|ƒ}nt|dƒ\}}t|dƒ\}}t|dƒ\}}t|ƒdt|ƒdt|ƒdf|_ t|_ dS(s°Initialize internal state from hashable object. None or no argument seeds from current time. If a is not None or an int or long, hash(a) is used instead. If a is an int or long, a is used directly. Distinct values between 0 and 27814431486575L inclusive are guaranteed to yield distinct internal states (this guarantee is specific to the default Wichmann-Hill generator). Nii= 0i«i=vi¬icviªisvN( sns ValueErrorsselfs_seedsxsyszsintspow(sselfsnsysxsz((s#/mit/python/lib/python2.3/random.pys jumpahead½s    icCst|ƒt|ƒjot|ƒjo tjn otdƒ‚nd|jo djno9d|jo djnod|jo djn otdƒ‚nd|jo|jo |jnozdk}t|iƒdƒ}t|d@|d?Aƒ}t |dƒ\}}t |dƒ\}}t |dƒ\}}n|pd|pd|pdf|_ t |_dS( sjSet the Wichmann-Hill seed from (x, y, z). These must be integers in the range [0, 256). sseeds must be integersiisseeds must be in range(0, 256)Niÿÿÿii(stypesxsyszsints TypeErrors ValueErrorstimeslongstsdivmodsselfs_seedsNones gauss_next(sselfsxsyszststime((s#/mit/python/lib/python2.3/random.pys__whseedÓs=[* 'cCsÀ|tjo|iƒdSnt|ƒ}t|dƒ\}}t|dƒ\}}t|dƒ\}}||dpd}||dpd}||dpd}|i|||ƒdS(sbSeed from hashable object's hash code. None or no argument seeds from current time. It is not guaranteed that objects with distinct hash codes lead to distinct internal states. This is obsolete, provided for compatibility with the seed routine used prior to Python 2.1. Use the .seed() method instead. Nii( sasNonesselfs_WichmannHill__whseedshashsdivmodsxsysz(sselfsasysxsz((s#/mit/python/lib/python2.3/random.pyswhseedês    ( s__name__s __module__sVERSIONsNonesseedsrandomsgetstatessetstates jumpaheads_WichmannHill__whseedswhseed(((s#/mit/python/lib/python2.3/random.pys WichmannHillps    cCsdk} |GdG|GHt||dƒ}d} d} d}d}| iƒ}xVt |ƒD]H}t |ƒ} | | 7} | | | } t| |ƒ}t| |ƒ}qYW| iƒ} t| |dƒGdG| |}t| |||ƒ}d||||fGHdS( Nstimessevalf0.0f 10000000000.0f-10000000000.0issec,s!avg %g, stddev %g, min %g, max %g(stimesnsfunccallscompilescodestotalssqsumssmallestslargestst0srangesisevalsxsminsmaxst1sroundsavgs_sqrtsstddev(snsfunccallscodesstddevsislargestsavgst0ssmallestssqsumstimesxstotalst1((s#/mit/python/lib/python2.3/random.pys_test_generators(        iÐcCsÔt|dƒt|dƒt|dƒt|dƒt|dƒt|dƒt|dƒt|dƒt|d ƒt|d ƒt|d ƒt|d ƒt|d ƒt|dƒt|dƒt|dƒdS(Nsrandom()snormalvariate(0.0, 1.0)slognormvariate(0.0, 1.0)scunifvariate(0.0, 1.0)svonmisesvariate(0.0, 1.0)sgammavariate(0.01, 1.0)sgammavariate(0.1, 1.0)sgammavariate(0.1, 2.0)sgammavariate(0.5, 1.0)sgammavariate(0.9, 1.0)sgammavariate(1.0, 1.0)sgammavariate(2.0, 1.0)sgammavariate(20.0, 1.0)sgammavariate(200.0, 1.0)sgauss(0.0, 1.0)sbetavariate(3.0, 3.0)(s_test_generatorsN(sN((s#/mit/python/lib/python2.3/random.pys_tests                s__main__(:s__doc__stypessBuiltinMethodTypes_BuiltinMethodTypesmathslogs_logsexps_expspis_pises_essqrts_sqrtsacoss_acosscoss_cosssins_sinsfloors_floors__all__s NV_MAGICCONSTsTWOPIsLOG4s SG_MAGICCONSTsBPFs_randomsRandoms WichmannHills_test_generators_tests_instsseedsrandomsuniformsrandintschoices randrangessamplesshuffles normalvariateslognormvariates cunifvariates expovariatesvonmisesvariates gammavariatesstdgammasgausss betavariates paretovariatesweibullvariatesgetstatessetstates jumpaheads__name__(,s WichmannHills_sins_exps randrangesBPFs_acoss_randoms_testsrandoms paretovariates normalvariates gammavariatessamplesseedsweibullvariatesshuffles_floorsTWOPIs__all__srandintsvonmisesvariates_pisuniformsstdgammas expovariates betavariates_essetstateslognormvariates_cossLOG4sgaussschoices jumpaheads_logs_BuiltinMethodTypes_instsgetstates_test_generators SG_MAGICCONSTsRandoms cunifvariates_sqrts NV_MAGICCONST((s#/mit/python/lib/python2.3/random.pys?(sT  N   ÿÿ2“