package python.lib

*Available on python*

`staticbetavariate(alpha:Float, beta:Float):Float`

Beta distribution. Conditions on the parameters are `alpha > 0`

and `beta > 0`

. Returned values range between 0 and 1.

`staticexpovariate(lambd:Float):Float`

Exponential distribution. `lambd`

is 1.0 divided by the desired mean. It should be nonzero. Returned values range from 0 to positive infinity if `lambd`

is positive, and from negative infinity to 0 if `lambd`

is negative.

`staticgammavariate(alpha:Float, beta:Float):Float`

Gamma distribution. (Not the gamma function!) Conditions on the parameters are `alpha > 0`

and `beta > 0`

.

`staticgauss(mu:Float, sigma:Float):Float`

Gaussian distribution. `mu`

is the mean, and `sigma`

is the standard deviation. This is slightly faster than the `normalvariate`

function defined below.

`staticgetrandbits(k:Int):Int`

Returns a Python integer with `k`

random bits. This method is supplied with the `MersenneTwister`

generator and some other generators may also provide it as an optional part of the API. When available, `getrandbits`

() enables `randrange`

() to handle arbitrarily large ranges.

`staticgetstate():RandomState`

Return an object capturing the current internal state of the generator. This object can be passed to setstate() to restore the state.

`staticlognormvariate(mu:Float, sigma:Float):Float`

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.

`staticnormalvariate(mu:Float, sigma:Float):Float`

Normal distribution. `mu`

is the mean, and `sigma`

is the standard deviation.

`staticparetovariate(alpha:Float):Float`

Pareto distribution. alpha is the `shape`

parameter.

`staticrandint(a:Int, b:Int):Int`

Return a random integer N such that `a <= N <= b`

. Alias for `randrange(a, b+1)`

.

`staticrandom():Float`

Return the next random floating point number in the range [0.0, 1.0).

`staticrandrange(start:Int, stop:Int, ?step:Int):Int`

`staticrandrange(stop:Int):Int`

Return a randomly selected element from `range(start, stop, step)`

. This is equivalent to `choice(range(start, stop, step))`

, but doesn’t actually build a range object.

`staticseed(?a:Int, ?version:Int):Float`

Initialize the random number generator.

If `a`

is omitted or `null`

, the current system time is used. If randomness sources are provided by the operating system, they are used instead of the system time (see the os.urandom() function for details on availability).

If `a`

is an int, it is used directly.

With `version`

2 (the default), a str, bytes, or bytearray object gets converted to an int and all of its bits are used. With version 1, the hash() of a is used instead.

`staticsetstate(state:RandomState):Void`

`state`

should have been obtained from a previous call to `getstate`

(), and `setstate`

() restores the internal state of the generator to what it was at the time `getstate`

() was called.

`statictriangular(?low:Float, ?high:Float, ?mode:Float):Float`

Return a random floating point number N such that `low <= N <= high`

and with the specified `mode`

between those bounds. The `low`

and `high`

bounds default to zero and one. The `mode`

argument defaults to the midpoint between the bounds, giving a symmetric distribution.

`staticuniform(a:Float, b:Float):Float`

Return a random floating point number N such that `a <= N <= b`

for `a <= b`

and `b <= N <= a`

for `b < a`

.

`staticvonmisesvariate(mu:Float, kappa:Float):Float`

`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.

`staticweibullvariate(alpha:Float, beta:Float):Float`

Weibull distribution. `alpha`

is the scale parameter and `beta`

is the shape parameter.

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Licensed under a MIT license.

https://api.haxe.org/python/lib/Random.html