�`^c@ s�dZddlmZddlmZddlmZm Z
ddl m Z
mZmZmZmZddl mZmZmZm Z ddl m Z ddl!m"Z#dd l$Z%d
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e-j.fd+��YZ.d e.fd,��YZ/d"e.fd-��YZ0d.�Z1d/d0�Z2e.�Z3e3j4Z4e3j5Z5e3j6Z6e3j7Z7e3j8Z8e3j9Z9e3j:Z:e3j;Z;e3j<Z<e3j=Z=e3j>Z>e3j?Z?e3j@Z@e3jAZAe3jBZBe3jCZCe3jDZDe3jEZEe3jFZFe3jGZGe3jHZHe3jIZIeJd1kr�e2�nd S(2sPRandom 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.
* 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.
i�(tdivision(twarn(t
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triangulartgausst betavariatet
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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().
Optionally, implement a getrandbits() method so that randrange() can cover
arbitrarily large ranges.
icC s|j|�d|_dS(seInitialize an instance.
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r�}t|�nXtt|�j|�ntd||jf��dS(s:Restore internal state from object returned by getstate().iiics s |]}t|�dVqdS(ii Nl(R-(t.0R+((s /sys/lib/python2.7/random.pys <genexpr>�ss?state with version %s passed to Random.setstate() of version %sN(R)R2RR#ttuplet
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cC sWt|�t|j��}ttjd|�j�d�}tt|�j|�dS(s�Change the internal state to one that is likely far away
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tsha512iN( treprR"tintt_hashlibtnewt hexdigestR2RR$(R*tnts((s /sys/lib/python2.7/random.pyR$�s !cC s
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n*| dkr�|| d| }
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s�Choose a random item from range(start, stop[, step]).
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r ntXt|j�|ksHt|�|kr�|d||dd��}||�} x | |kr�||�} qtW| S||kr�td�n||j�|�S(s�Return a random int in the range [0,n)
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s8Chooses k unique random elements from a population sequence.
Returns a new list containing elements from the population while
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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 xrange as an argument.
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large population: sample(xrange(10000000), 60)
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Continuous distribution bounded by given lower and upper limits,
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cC sh|j}xP|�}d|�}t|d|}||d}|t|� kr Pq q |||S(s\Normal distribution.
mu is the mean, and sigma is the standard deviation.
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If you take the natural logarithm of this distribution, you'll get a
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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�RVR(R*tlambd((s /sys/lib/python2.7/random.pyR�scC s|j}|dkr t|�Sd|}|td||�}xe|�}tt|�}|||}|�} | d||ks�| d|t|�krEPqEqEd|}
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S(sFCircular 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
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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.
The probability distribution function is:
x ** (alpha - 1) * math.exp(-x / beta)
pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
gs*gammavariate: alpha and beta must be > 0.0g�@gH���>g�P��@N(R7RRtLOG4RVR|t
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mu is the mean, and sigma is the standard deviation. This is
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icC s|dkrdy tttd��d�}Wqdtk
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d|_ dS( s�Initialize internal state from hashable object.
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0 and 27814431486575L inclusive are guaranteed to yield distinct
internal states (this guarantee is specific to the default
Wichmann-Hill generator).
ii�Nii<vibvirvi( R(R-R.R/R0R1RhR>thashtdivmodt_seedR)(R*R3R1R+R�Rz((s /sys/lib/python2.7/random.pyR�s
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S( s3Get the next random number in the range [0.0, 1.0).i�i=vi�icvi�isvg@�@g��g��g�R�(R*R+R�Rz((s /sys/lib/python2.7/random.pyR�s cC s|j|j|jfS(s9Return internal state; can be passed to setstate() later.(R4R�R)(R*((s /sys/lib/python2.7/random.pyR"�scC sK|d}|dkr.|\}|_|_ntd||jf��dS(s:Restore internal state from object returned by getstate().iis?state with version %s passed to Random.setstate() of version %sN(R�R)R7R4(R*R9R:((s /sys/lib/python2.7/random.pyR#�s
cC s�|dkstd��n|j\}}}t|td|d��d}t|td|d��d}t|td|d��d}|||f|_d S(
s�Act as if n calls to random() were made, but quickly.
n is an int, greater than or equal to 0.
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consume no more than a million random numbers, create two Random
objects r1 and r2, then do
r2.setstate(r1.getstate())
r2.jumpahead(1000000)
Then r1 and r2 will use guaranteed-disjoint segments of the full
period.
isn must be >= 0i�i=vi�icvi�isvN(R7R�R>R�(R*RBR+R�Rz((s /sys/lib/python2.7/random.pyR$�s icC st|�t|�ko4t|�ko4tknsHtd��nd|ko_dkno�d|ko{dkno�d|ko�dkns�td��nd|ko�|ko�|knrNddl}t|j�d�}t|d@|d?A�}t|d�\}}t|d�\}}t|d�\}}n|pWd |p`d |pid f|_d|_ dS(
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None or no argument seeds from current time. It is not guaranteed
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states.
This is obsolete, provided for compatibility with the seed routine
used prior to Python 2.1. Use the .seed() method instead.
Nii(R(t_WichmannHill__whseedR�R�(R*R3R+R�Rz((s /sys/lib/python2.7/random.pytwhseed s
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cC s!tttd��d�d?tS(s3Get the next random number in the range [0.0, 1.0).iii(R-R.R/t RECIP_BPF(R*((s /sys/lib/python2.7/random.pyR-scC su|dkrtd��n|t|�kr<td��n|dd}ttt|��d�}||d|?S(s>getrandbits(k) -> x. Generates a long int with k random bits.is(number of bits must be greater than zeros#number of bits should be an integeriii(R7R>R8R-R.R/(R*RYtbytesR+((s /sys/lib/python2.7/random.pyR&1s cO sdS(s<Stub method. Not used for a system random number generator.N(R((R*targstkwds((s /sys/lib/python2.7/random.pyt_stub;scO std��dS(sAMethod should not be called for a system random number generator.s*System entropy source does not have state.N(R0(R*R�R�((s /sys/lib/python2.7/random.pyt_notimplemented@s( R�R�R�RR&R�RR$R�R"R#(((s /sys/lib/python2.7/random.pyR'%s
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