aivika-transformers-4.5.1: Transformers for the Aivika simulation library

CopyrightCopyright (c) 2009-2016 David Sorokin <david.sorokin@gmail.com>
LicenseBSD3
MaintainerDavid Sorokin <david.sorokin@gmail.com>
Stabilityexperimental
Safe HaskellNone
LanguageHaskell2010

Simulation.Aivika.Trans.Net.Random

Description

Tested with: GHC 8.0.1

This module defines some useful random network computations that hold the current process for the corresponding time interval, when processing every input element.

Synopsis

Documentation

randomUniformNet Source #

Arguments

:: MonadDES m 
=> Double

the minimum time interval

-> Double

the maximum time interval

-> Net m a a 

When processing every input element, hold the process for a random time interval distributed uniformly.

randomUniformIntNet Source #

Arguments

:: MonadDES m 
=> Int

the minimum time interval

-> Int

the maximum time interval

-> Net m a a 

When processing every input element, hold the process for a random time interval distributed uniformly.

randomTriangularNet Source #

Arguments

:: MonadDES m 
=> Double

the minimum time interval

-> Double

the median of the time interval

-> Double

the maximum time interval

-> Net m a a 

When processing every input element, hold the process for a random time interval having the triangular distribution.

randomNormalNet Source #

Arguments

:: MonadDES m 
=> Double

the mean time interval

-> Double

the time interval deviation

-> Net m a a 

When processing every input element, hold the process for a random time interval distributed normally.

randomLogNormalNet Source #

Arguments

:: MonadDES m 
=> Double

the mean of a normal distribution which this distribution is derived from

-> Double

the deviation of a normal distribution which this distribution is derived from

-> Net m a a 

When processing every input element, hold the process for a random time interval having the lognormal distribution.

randomExponentialNet Source #

Arguments

:: MonadDES m 
=> Double

the mean time interval (the reciprocal of the rate)

-> Net m a a 

When processing every input element, hold the process for a random time interval distributed exponentially with the specified mean (the reciprocal of the rate).

randomErlangNet Source #

Arguments

:: MonadDES m 
=> Double

the scale (the reciprocal of the rate)

-> Int

the shape

-> Net m a a 

When processing every input element, hold the process for a random time interval having the Erlang distribution with the specified scale (the reciprocal of the rate) and shape parameters.

randomPoissonNet Source #

Arguments

:: MonadDES m 
=> Double

the mean time interval

-> Net m a a 

When processing every input element, hold the process for a random time interval having the Poisson distribution with the specified mean.

randomBinomialNet Source #

Arguments

:: MonadDES m 
=> Double

the probability

-> Int

the number of trials

-> Net m a a 

When processing every input element, hold the process for a random time interval having the binomial distribution with the specified probability and trials.

randomGammaNet Source #

Arguments

:: MonadDES m 
=> Double

the shape

-> Double

the scale (a reciprocal of the rate)

-> Net m a a 

When processing every input element, hold the process for a random time interval having the Gamma distribution with the specified shape and scale.

randomBetaNet Source #

Arguments

:: MonadDES m 
=> Double

shape (alpha)

-> Double

shape (beta)

-> Net m a a 

When processing every input element, hold the process for a random time interval having the Beta distribution with the specified shape parameters (alpha and beta).

randomWeibullNet Source #

Arguments

:: MonadDES m 
=> Double

shape

-> Double

scale

-> Net m a a 

When processing every input element, hold the process for a random time interval having the Weibull distribution with the specified shape and scale.

randomDiscreteNet Source #

Arguments

:: MonadDES m 
=> DiscretePDF Double

the discrete probability density function

-> Net m a a 

When processing every input element, hold the process for a random time interval having the specified discrete distribution.