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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. i���c�������������C���s���|��j��|���d�|��_�d�S(���ue���Initialize an instance. Optional argument x controls seeding, as for Random.seed(). N(���u���seedu���Noneu ���gauss_next(���u���selfu���x(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���__init__T���s���� u���Random.__init__i���c���������� ������s����|�d�k�rb�y�t�j�t�d���d���}�Wqb�t�k �r^�d�d�l�}�t�|�j����d���}�Yqb�Xn��|�d�k�r��t�|�t�t�t �f���r��t�|�t���r��|�j ����}�n��|�t�|���j����7}�t�j�|�d���}�q��n��t ����j�|���d�|��_�d�S(���u���Initialize internal state from hashable object. None or no argument seeds from current time or from an operating system specific randomness source if available. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1, the hash() of *a* is used instead. If *a* is an int, all bits are used. i ���u���bigi����Ni���i���(���u���Noneu���intu ���from_bytesu���_urandomu���NotImplementedErroru���timeu ���isinstanceu���stru���bytesu ���bytearrayu���encodeu���_sha512u���digestu���superu���seedu ���gauss_next(���u���selfu���au���versionu���time(���u ���__class__(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���seed]���s���� u���Random.seedc����������������s���|��j��t����j����|��j�f�S(���u9���Return internal state; can be passed to setstate() later.(���u���VERSIONu���superu���getstateu ���gauss_next(���u���self(���u ���__class__(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���getstate{���s����u���Random.getstatec����������������s����|�d�}�|�d�k�r;�|�\�}�}�|��_��t����j�|���n��|�d�k�r��|�\�}�}�|��_��y�t�d�d����|�D����}�Wn.�t�k �r��}�z�t�|���WYd�d�}�~�Xn�Xt����j�|���n�t�d�|�|��j�f�����d�S(���u:���Restore internal state from object returned by getstate().i����i���i���c�������������s���s���|��]�}�|�d�Vq�d�S(���i���i ���Nl��������(����(���u���.0u���x(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���<genexpr>����s����u"���Random.setstate.<locals>.<genexpr>Nu?���state with version %s passed to Random.setstate() of version %s(���u ���gauss_nextu���superu���setstateu���tupleu ���ValueErroru ���TypeErroru���VERSION(���u���selfu���stateu���versionu ���internalstateu���e(���u ���__class__(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���setstate���s���� u���Random.setstatec�������������C���s ���|��j�����S(���N(���u���getstate(���u���self(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���__getstate__����s����u���Random.__getstate__c�������������C���s���|��j��|���d��S(���N(���u���setstate(���u���selfu���state(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���__setstate__����s����u���Random.__setstate__c�������������C���s���|��j��f��|��j����f�S(���N(���u ���__class__u���getstate(���u���self(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���__reduce__����s����u���Random.__reduce__i���c������� ������C���s���|�|���}�|�|�k�r'�t��d�����n��|�d �k�r[�|�d�k�rL�|��j�|���St��d�����n��|�|���}�|�|�k�r��t��d�����n��|�|�}�|�d�k�r��|�d�k�r��|�|��j�|���S|�d�k�r��t��d�|�|�|�f�����n��|�|���}�|�|�k�rt��d�����n��|�d�k�r%|�|�d�|�} �n-�|�d�k��rF|�|�d�|�} �n�t��d�����| �d�k�rmt��d�����n��|�|�|��j�| ���S( ���u����Choose 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. u!���non-integer arg 1 for randrange()i����u���empty range for randrange()u ���non-integer stop for randrange()i���u'���empty range for randrange() (%d,%d, %d)u ���non-integer step for randrange()u���zero step for randrange()N(���u ���ValueErroru���Noneu ���_randbelow( ���u���selfu���startu���stopu���stepu���_intu���istartu���istopu���widthu���istepu���n(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���randrange����s4���� u���Random.randrangec�������������C���s���|��j��|�|�d���S(���uJ���Return random integer in range [a, b], including both end points. i���(���u ���randrange(���u���selfu���au���b(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���randint����s����u���Random.randintc������� ������C���s����|��j��}�|�|��j���|�k�s0�|�|���|�k�rk�|�j����}�|�|���} �x�| �|�k�rf�|�|���} �qK�W| �S|��j�} �|�|�k�r��t�d���|�| ����|���S|�|�}�|�|�|�}�| ����} �x�| �|�k�r��| ����} �q��W|�| �|���|�S(���uC���Return a random int in the range [0,n). Raises ValueError if n==0.u����Underlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.(���u���getrandbitsu���randomu ���bit_lengthu���_warn( ���u���selfu���nu���intu���maxsizeu���typeu���Methodu ���BuiltinMethodu���getrandbitsu���ku���ru���randomu���remu���limit(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���_randbelow����s"���� ' u���Random._randbelowc�������������C���sB���y�|��j��t�|�����}�Wn�t�k �r9�t�d�����Yn�X|�|�S(���u2���Choose a random element from a non-empty sequence.u$���Cannot choose from an empty sequence(���u ���_randbelowu���lenu ���ValueErroru ���IndexError(���u���selfu���sequ���i(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���choice����s ���� u ���Random.choicec�������������C���s����|�d�k�rk�|��j�}�x��t�t�d�t�|�������D]3�}�|�|�d���}�|�|�|�|�|�|�<|�|�<q1�Wn`�t�}�xW�t�t�d�t�|�������D]:�}�|�|����|�d���}�|�|�|�|�|�|�<|�|�<q��Wd�S(���u����x, random=random.random -> shuffle list x in place; return None. Optional arg random is a 0-argument function returning a random float in [0.0, 1.0); by default, the standard random.random. i���N(���u���Noneu ���_randbelowu���reversedu���rangeu���lenu���int(���u���selfu���xu���randomu ���randbelowu���iu���ju���_int(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���shuffle����s���� "$"u���Random.shufflec�������������C���s���t��|�t���r�t�|���}�n��t��|�t���s<�t�d�����n��|��j�}�t�|���}�d�|�k�oh�|�k�n�s|�t�d�����n��d �g�|�}�d�}�|�d�k�r��|�d�t �t �|�d�d�����7}�n��|�|�k�r%t�|���}�x��t�|���D]:�}�|�|�|���} �|�| �|�|�<|�|�|�d�|�| �<q��Wnl�t ����} �| �j�}�xW�t�|���D]I�}�|�|���} �x�| �| �k�rt|�|���} �qYW|�| ���|�| �|�|�<qDW|�S( ���u=��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) u>���Population must be a sequence or set. For dicts, use list(d).i����u���Sample larger than populationi���i���i���i���i���N(���u ���isinstanceu���_Setu���tupleu ���_Sequenceu ���TypeErroru ���_randbelowu���lenu ���ValueErroru���Noneu���_ceilu���_logu���listu���rangeu���setu���add(���u���selfu ���populationu���ku ���randbelowu���nu���resultu���setsizeu���poolu���iu���ju���selectedu���selected_add(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���sample��s6���� $ u ���Random.samplec�������������C���s���|�|�|�|��j�����S(���uH���Get a random number in the range [a, b) or [a, b] depending on rounding.(���u���random(���u���selfu���au���b(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���uniformP��s����u���Random.uniformg��������g�������?c�������������C���sx���|��j�����}�|�d�k�r�d�n�|�|�|�|�}�|�|�k�r`�d�|�}�d�|�}�|�|�}�}�n��|�|�|�|�|�d�S(���u����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 g�������?g�������?N(���u���randomu���None(���u���selfu���lowu���highu���modeu���uu���c(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���triangularV��s���� $ u���Random.triangularc�������������C���sh���|��j��}�xP�|����}�d�|����}�t�|�d�|�}�|�|�d�}�|�t�|���k�r�Pq�q�|�|�|�S(���u\���Normal distribution. mu is the mean, and sigma is the standard deviation. g�������?g�������?g������@(���u���randomu ���NV_MAGICCONSTu���_log(���u���selfu���muu���sigmau���randomu���u1u���u2u���zu���zz(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���normalvariatei��s���� u���Random.normalvariatec�������������C���s���t��|��j�|�|�����S(���u����Log 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. (���u���_expu ���normalvariate(���u���selfu���muu���sigma(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���lognormvariate���s����u���Random.lognormvariatec�������������C���s���t��d�|��j������|�S(���u^��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�������?(���u���_logu���random(���u���selfu���lambd(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���expovariate���s����u���Random.expovariatec�������������C���s��|��j��}�|�d�k�r �t�|����Sd�|�}�|�t�d�|�|���}�xe�|����}�t�t�|���}�|�|�|�}�|����} �| �d�|�|�k��s��| �d�|�t�|���k�rE�PqE�qE�d�|�} �| �|�d�| �|�}�|����}�|�d�k�r��|�t�|���t�} �n�|�t�|���t�} �| �S(���uF��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. g���ư>g�������?g�������?(���u���randomu���TWOPIu���_sqrtu���_cosu���_piu���_expu���_acos(���u���selfu���muu���kappau���randomu���su���ru���u1u���zu���du���u2u���qu���fu���u3u���theta(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���vonmisesvariate���s&���� . u���Random.vonmisesvariatec�������������C���s��|�d�k�s�|�d�k�r'�t��d�����n��|��j�}�|�d�k�rt�d�|�d���}�|�t�}�|�|�}�x�|����}�d�|�k��o��d�k��n�s��qg�n��d�|����}�t�|�d�|���|�} �|�t�| ���} �|�|�|�}�|�|�| �| �}�|�t�d�|�d�k�s|�t�|���k�rg�| �|�Sqg�n��|�d�k�r_|����} �x�| �d�k�rO|����} �q7Wt�| ���|�Sx��|����} �t�|�t�}�|�| �}�|�d�k�r�|�d�|�} �n�t�|�|�|���} �|����}�|�d�k�r�|�| �|�d�k�r�Pq�qb|�t�| ���k�rbPqbqb| �|�Sd�S( ���uZ��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 g��������u*���gammavariate: alpha and beta must be > 0.0g�������?g�������@gH�����z>g�P���?g������@N(���u ���ValueErroru���randomu���_sqrtu���LOG4u���_logu���_expu ���SG_MAGICCONSTu���_e(���u���selfu���alphau���betau���randomu���ainvu���bbbu���cccu���u1u���u2u���vu���xu���zu���ru���uu���bu���p(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���gammavariate���sJ���� * u���Random.gammavariatec�������������C���s����|��j��}�|��j�}�d�|��_�|�d�k�rw�|����t�}�t�d�t�d�|��������}�t�|���|�}�t�|���|�|��_�n��|�|�|�S(���u����Gaussian 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. g�������@g�������?Ng��������(���u���randomu ���gauss_nextu���Noneu���TWOPIu���_sqrtu���_logu���_cosu���_sin(���u���selfu���muu���sigmau���randomu���zu���x2piu���g2rad(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���gauss��s���� u���Random.gaussc�������������C���s>���|��j��|�d���}�|�d�k�r"�d�S|�|�|��j��|�d���Sd�S(���u����Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. g�������?i����g��������N(���u���gammavariate(���u���selfu���alphau���betau���y(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���betavariateN��s���� u���Random.betavariatec�������������C���s ���d�|��j�����}�d�|�d�|�S(���u3���Pareto distribution. alpha is the shape parameter.g�������?(���u���random(���u���selfu���alphau���u(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu ���paretovariate`��s����u���Random.paretovariatec�������������C���s'���d�|��j�����}�|�t�|���d�|�S(���uf���Weibull distribution. alpha is the scale parameter and beta is the shape parameter. g�������?(���u���randomu���_log(���u���selfu���alphau���betau���u(����(����u+���/opt/alt/python33/lib64/python3.3/random.pyu���weibullvariatei��s����u���Random.weibullvariateN(#���u���__name__u ���__module__u���__qualname__u���__doc__u���VERSIONu���Noneu���__init__u���seedu���getstateu���setstateu���__getstate__u���__setstate__u ���__reduce__u���intu ���randrangeu���randintu���BPFu���typeu���_MethodTypeu���_BuiltinMethodTypeu ���_randbelowu���choiceu���shuffleu���sampleu���uniformu ���triangularu ���normalvariateu���lognormvariateu���expovariateu���vonmisesvariateu���gammavariateu���gaussu���betavariateu ���paretovariateu���weibullvariate(���u ���__locals__(����(���u ���__class__u+���/opt/alt/python33/lib64/python3.3/random.pyu���RandomD���s6��� , >0H5 c�������������B���sT���|��Ee��Z�d��Z�d�Z�d�d����Z�d�d����Z�d�d����Z�d�d ����Z�e�Z�Z �d �S(���u���SystemRandomu����Alternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). 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