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Z5ded ed Z6edZ7ded Z8dZ9de9 Z:dZ;G dd de0j<Ze>j?Z?e>j@Z@e>jAZAe>jBZBe>jCZCe>jDZDe>jEZEe>jFZFe>jGZGe>jHZHe>jIZIe>jJZJe>jKZKe>jLZLe>jMZMe>jNZNe>jOZOe>jPZPe>jQZQe>jRZRe>jSZSe>jTZTe>jUZUdd ZVd#d d ZWeXe/d re/jYe>j?d! eZd"kreW d S 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)taufloorisfinite)urandom)SetSequence)index)
accumulaterepeat)bisectN)sha512)Random SystemRandom betavariatechoicechoices expovariate gammavariategauss getrandbitsgetstatelognormvariate
normalvariate
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triangularuniformvonmisesvariateweibullvariate g @ @ ? @5 c s. e Zd ZdZdZdBddZdC fdd Z fd d
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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.
Nc C s | | d| _dS )zeInitialize an instance.
Optional argument x controls seeding, as for Random.seed().
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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.
r7 zlatin-1r iCB l r6 bigNzSeeding based on hashing is deprecated
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s |d }|dkr|\}}| _ t | dS |dkrC|\}}| _ z tdd |D }W n ty: } zt|d}~ww t | dS td|| jf )z:Restore internal state from object returned by getstate().r r8 r6 c s s | ]}|d V qdS )l Nr< ).0r; r< r< r= s z"Random.setstate..Nz?state with version %s passed to Random.setstate() of version %s)r9 rS r* tuple
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__reduce__ s zRandom.__reduce__c K sP | j D ]"}d|jv r
dS d|jv r| j| _ dS d|jv r%| j| _ dS qdS )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.
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zRandom.__init_subclass__c C s: |sdS | j }| }||}||kr||}||ks|S )z;Return a random int in the range [0,n). Returns 0 if n==0.r )r
bit_length)r: nr krr< r< r= ri s z"Random._randbelow_with_getrandbitsr7 c C sp | j }||krtd t| | S |dkrdS || }|| | }| }||kr0| }||ks)t|| | S )zReturn a random int in the range [0,n). Returns 0 if n==0.
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|dkrtd||| | S )zChoose a random item from range(start, stop[, step]).
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z.randrange() will raise TypeError in the futurer6 z!non-integer arg 1 for randrange()zwnon-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent versionNz Missing a non-None stop argumentr zempty range for randrange()z non-integer stop for randrange()z non-integer step for randrange()r7 z(empty range for randrange() (%d, %d, %d)zzero step for randrange())_indexr` rL rQ rR r_ _ONErf ) r: startstopstepZistartZistopwidthZistepro r< r< r= r' $ st
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r7 )r' r: rT br< r< r= r% n s zRandom.randintc C s || t| S )z2Choose a random element from a non-empty sequence.)rf rI )r: seqr< r< r= r w s z
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v s|| | || < q|S )am Chooses k unique random elements from a population sequence or set.
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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)
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http://en.wikipedia.org/wiki/Triangular_distribution
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zRandom.triangularc C sR | j } | }d| }t|d | }|| d }|t| kr"nq||| S )z\Normal distribution.
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zRandom.normalvariatec C s` | j }| j}d| _|du r*| t }tdtd| }t|| }t|| | _||| S )zGaussian distribution.
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)_expr" )r: r r r< r< r= r! u 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
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positive infinity if lambd is positive, and from negative
infinity to 0 if lambd is negative.
r3 )r r& )r: Zlambdr< r< r= r s zRandom.expovariatec C s | j }|dkr t| S d| }|td|| } | }tt| }||| }| } | d|| k s?| d| t| kr@nqd| }
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r3 r )r )r: r r yr< r< r= r s zRandom.betavariatec C s d| }|d| S )z3Pareto distribution. alpha is the shape parameter.r3 g r )r: r r r< r< r= r# s zRandom.paretovariatec C s" d| }|t| d| S )zfWeibull distribution.
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r3 )r& r )r: r r r r< r< r= r/ s zRandom.weibullvariaterb )Nr6 )r r3 N)%__name__
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