# runarorama/Malakov

Markov Chains for Scala

Language: Scala

# Malakov 3.0

## A Markov Chain library for Scala

Markov chains represent stochastic processes where the probability distribution of the next step depends nontrivially on the current step, but does not depend on previous steps. Give this library some training data and it will generate new random data that statistically resembles it. Give it your business plan and it will generate an even more bullshit business plan. Give it a sequence of notes and it will generate a new melody. Give it some stock ticker data and it will predict the future price of that stock (disclaimer: will not actually predict the future).

For example:

``````import scalaz.stream.Process
Markov.run(2, Process.emitAll("Wayne went to Wales to watch walruses. ".toStream), 0)
``````

This runs a Markov chain beginning with `Wa`. The next step is either `y` or `l` with equal probability. If `y` is picked, then `ay` is followed by `n` with 100% probability. Otherwise, `al` is followed by either `e` or `r`.

A `run` of the process is parameterized by a window size `n`, a sequence `d` called the "dictionary", a start index `i` into the dictionary, and optionally a random number generator `g`. From the dictionary, we construct a weighted list of all allowable transitions. The run starts at `d(i)` and the next transition is randomly selected from the weighted distribution of states that are allowed to follow it, based on the dictionary. The window size `n` determines how many elements of the sequence constitute a discrete step.

For another example, here is the output of a `runMulti` using the "Markov Chain" Wikipedia entry as a dictionary:

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This library is based on the Data.MarkovChain Haskell library by Henning Thielemann.

It is released under a permissive BSD license.

#### Project Statistics

 Sourcerank 5 Repository Size 59.6 KB Stars 82 Forks 12 Watchers 7 Open issues 0 Dependencies 0 Contributors 2 Tags 0 Created Apr 3, 2012 Last updated Apr 7, 2018 Last pushed Jul 24, 2016

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