aivika-transformers-5.7: Transformers for the Aivika simulation library

Simulation.Aivika.Trans.Dynamics.Random

Description

Tested with: GHC 8.0.1

This module defines the random functions that always return the same values in the integration time points within a single simulation run. The values for another simulation run will be regenerated anew.

For example, the computations returned by these functions can be used in the equations of System Dynamics.

Also it is worth noting that the values are generated in a strong order starting from starttime with step dt. This is how the memo0Dynamics function actually works.

Synopsis

# Documentation

Arguments

 :: MonadSD m => Dynamics m Double minimum -> Dynamics m Double maximum -> Simulation m (Dynamics m Double)

Computation that generates random numbers distributed uniformly and memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Int minimum -> Dynamics m Int maximum -> Simulation m (Dynamics m Int)

Computation that generates random integer numbers distributed uniformly and memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double minimum -> Dynamics m Double median -> Dynamics m Double maximum -> Simulation m (Dynamics m Double)

Computation that generates random numbers from the triangular distribution and memoizes the numbers in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double mean -> Dynamics m Double deviation -> Simulation m (Dynamics m Double)

Computation that generates random numbers distributed normally and memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double the mean of a normal distribution which this distribution is derived from -> Dynamics m Double the deviation of a normal distribution which this distribution is derived from -> Simulation m (Dynamics m Double)

Computation that generates random numbers from the lognormal distribution and memoizes the numbers in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double the mean (the reciprocal of the rate) -> Simulation m (Dynamics m Double)

Computation that generates exponential random numbers with the specified mean (the reciprocal of the rate) and memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double the scale (the reciprocal of the rate) -> Dynamics m Int the shape -> Simulation m (Dynamics m Double)

Computation that generates the Erlang random numbers with the specified scale (the reciprocal of the rate) and integer shape but memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double the mean -> Simulation m (Dynamics m Int)

Computation that generats the Poisson random numbers with the specified mean and memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double the probability -> Dynamics m Int the number of trials -> Simulation m (Dynamics m Int)

Computation that generates binomial random numbers with the specified probability and trials but memoizes them in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double shape -> Dynamics m Double scale (a reciprocal of the rate) -> Simulation m (Dynamics m Double)

Computation that generates random numbers from the Gamma distribution with the specified shape and scale but memoizes the numbers in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double shape (alpha) -> Dynamics m Double shape (beta) -> Simulation m (Dynamics m Double)

Computation that generates random numbers from the Beta distribution by the specified shape parameters and memoizes the numbers in the integration time points.

Arguments

 :: MonadSD m => Dynamics m Double shape -> Dynamics m Double scale -> Simulation m (Dynamics m Double)

Computation that generates random numbers from the Weibull distribution with the specified shape and scale but memoizes the numbers in the integration time points.

memoRandomDiscreteDynamics :: (MonadSD m, MonadMemo m a) => Dynamics m (DiscretePDF a) -> Simulation m (Dynamics m a) Source #

Computation that generates random values from the specified discrete distribution and memoizes the values in the integration time points.