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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)
MethodTypeBuiltinMethodType)logexppieceil)sqrtacoscossin)urandom)SetSequence)sha512NRandomseedrandomuniformrandintchoicesample randrangeshuffle
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triangulargauss betavariate
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Class Random can also be subclassed if you want to use a different basic
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methods: random(), seed(), getstate(), and setstate().
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can cover arbitrarily large ranges.
Nc C s | j | d| _dS )zeInitialize an instance.
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a= Chooses k unique random elements from a population sequence or set.
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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.
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large population: sample(range(10000000), 60)
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r< |S X ||kr`d| }d| }|| }}||| || d S )zTriangular distribution.
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http://en.wikipedia.org/wiki/Triangular_distribution
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)_expr )r/ r r r1 r1 r2 r s zRandom.lognormvariatec C s t d| j | 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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infinity to 0 if lambd is negative.
g ?)rp r )r/ Zlambdr1 r1 r2 r s zRandom.expovariatec C s | j }|dkrt| S d| }|td|| }xN| }tt| }||| }| } | d|| k s~| d| t| kr6P q6W d| }
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| S dS ) aZ Gamma distribution. Not the gamma function!
Conditions on the parameters are alpha > 0 and beta > 0.
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pdf(x) = --------------------------------------
math.gamma(alpha) * beta ** alpha
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zRandom.gammavariatec C s` | j }| j}d| _|dkrT| t }tdtd| }t|| }t|| | _||| S )zGaussian distribution.
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z Random.gaussc C s0 | j |d}|dkrdS ||| j |d S dS )zBeta distribution.
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zRandom.betavariatec C s d| j }d|d| S )z3Pareto distribution. alpha is the shape parameter.g ?)r )r/ r r r1 r1 r2 r# s zRandom.paretovariatec C s" d| j }|t| d| S )zfWeibull distribution.
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