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Stochastic externals

These are externals to be compiled and used with the Pure Data visual programming language.

You will need to download Pure Data http://msp.ucsd.edu/software.html to use these externals.

Below is a list of these objects that I created with a little explanation about them. These use different ideas from probability theory and some of these were inspired from the Python language’s “random” library, however, these are all written in C.

annealing – Uses the simulated annealing algorithm by using an array table lookup. States are simplified by comparing floats from the array and giving their output as you step through the algorithm.

gauss – Outputs a Gaussian distribution. Might be cool to try some type of granular synthesis using this object (hint hint)…

randomsample – Takes a list of numbers and outputs a portion of them randomly. Could be useful for generating chords or sequences of notes from a larger population of notes.

reallyrandom – This just uses a different random number generated (different from Pd’s built-in object) that is a little better. It should be seeded differently each time you open Pd.

weightedlist – This takes two lists: one that contains the elements and the other that contains the probabilities corresponding to each element. This object is extremely useful for quickly and easily implementing Markov chains.

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These are externals to be compiled and used with the Pure Data visual programming language.

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