The skeleton selMapSY is a stochastic variant of mapSY. It has an internal stochastic process and selects one out of two combinatorial functions depending on the output of the stochastic process.

The skeleton selScanlSY is a stochastic variant of scanlSY.

selMealySY is the stochastic variant of mealySY. Both the next-state and the output function is randomly selected based on a uniform distribution.

selMooreSY is the stochastic variant of mooreSY. Both the next-state and the output function is randomly selected based on a uniform distribution.

sigmaUn generates a signal list of uniformly distributed Int within the given range and with a given seed.

sigmaGe is a more general stochastic process. The first argument is a function f which describes the distribution. For each value v in the given range (r1,r2), f(v) is the probability that v is generated.

Note, that the user has to make sure that sum(f(v))=1 for v in (r1,r2).

For illustration consider the following example.

pdist :: Float -> Float
pdist d = 1\/\(2**d\)
pdistsum 1 = pdist 1
pdistsum d = \(pdist d\) + \(pdistsum \(d-1\)\)

pdistnorm :: Float -> Float -> Float
pdistnorm dmax d = 1\/((pdistsum dmax) * (2**d))

pdistnorm dmax d gives the probability of a value <= d;

pdistnorm dmax dmax is always 1.0

Hence, using pdistnorm as a function in sigmaGe gives an exponantial distribution for values in the range \[0, dmax\].