Neiro - Functional programming, software architecture
31 Jul 2016

Markov chains in Elixir

1 Markov chains

Markov chain or Markov model is a process that undergoes transitions from one state to another. The next state depends only on current state and not the sequence of previous events. This allows us to use Markov chains as statistical models for real-world processes.

Figure 1: Simple Markov chain

For the next example we will try to build simple sentence generator within Markov chain.

2 Realization

Let’s create an entry point of new application:

    # lib/elixir markov chain.ex
    defmodule ElixirMarkovChain do
      alias ElixirMarkovChain.Model
      alias ElixirMarkovChain.Generator

      def start(_type, _args) do
        case :elixir_markov_chain, :source_file) do
           {:ok, body} -> process_source body
           {:error, reason} -> IO.puts reason

        System.halt 0

      defp process_source do

At first, to process the source file for output sentences, we need to tokenize it:

    # lib/elixir_markov_chain/tokenizer.ex
    defmodule ElixirMarkovChain.Tokenizer do
      def tokenize(text) do
          |> String.downcase
          |> String.split(~r{\n}, trim: true) # split text to sentences
          |> # split sentences to words

Next we need to realize Markov model. We’ll use agents to share state in application:

    # lib/elixir_markov_chain/model.ex
    defmodule ElixirMarkovChain.Model do
      import ElixirMarkovChain.Tokenizer

      def start_link, do: Agent.start_link(fn -> %{} end) # create map for sharing through agent

      def populate(pid, text) do
        for tokens <- tokenize(text), do: modelize(pid, tokens) # populate model with tokens

      def fetch_token(state, pid) do
        tokens = fetch_tokens state, pid

        if length(tokens) > 0 do
          token = Enum.random tokens
          count = tokens |> Enum.count(&(token == &1))
          {token, count / length(tokens)} # count probability of the token
          {"", 0.0}

      def fetch_state(tokens), do: fetch_state(tokens, length(tokens))
      defp fetch_state(_tokens, id) when id == 0, do: {nil, nil}
      defp fetch_state([head | _tail], id) when id == 1, do: {nil, head}
      defp fetch_state(tokens, id) do
          |> Enum.slice(id - - 1) # fetch states by ids
          |> List.to_tuple

      # Get tokens within agent
      defp fetch_tokens(state, pid), do: Agent.get pid, &(&1[state] || [])

      # Build Markov chain model using tokens
      defp modelize(pid, tokens) do
        for {token, id} <- Enum.with_index(tokens) do
          tokens |> fetch_state(id) |> add_state(pid, token)

      # Add new state within agent
      defp add_state(state, pid, token) do
        Agent.update pid, fn(model) ->
          current_state = model[state] || []
          Map.put model, state, [token | current_state]

When our Markov model is done, we can use it in application. For this example, we’ll build a random sentence generator based on text source:

    # lib/elixir_markov_chain/generator.ex
    defmodule ElixirMarkovChain.Generator do
      alias ElixirMarkovChain.Model

      def create_sentence(pid) do
        {sentence, prob} = build_sentence pid

        # Create new sentence or convert builded based on treshold value
        if prob >= Application.get_env(:elixir_markov_chain, :treshold) do
          sentence |> Enum.join(" ") |> String.capitalize
          create_sentence pid

      # Sentence is complete when it have enough length
      # or when punctuation ends a sentence
      defp complete?(tokens) do
        length(tokens) > 15 ||
        (length(tokens) > 3 && Regex.match?(~r/[\!\?\.]\z/, List.last tokens))

      defp build_sentence(pid), do: build_sentence(pid, [], 0.0, 0.0)
      defp build_sentence(pid, tokens, prob_acc, new_tokens) do
        # Fetch Markov model state through agent
        {token, prob} = tokens |> Model.fetch_state |> Model.fetch_token(pid)

        case complete?(tokens) do
          true ->
            score = case new_tokens == 0 do
              true -> 1.0
              _ -> prob_acc / new_tokens # count new probability for this word
            {tokens, score}
          _ ->
            # Concat sentence with new token and try to continue
            build_sentence pid, tokens ++ [token], prob + prob_acc, new_tokens + 1

Now, when basic logic is implemented, we need to fill process_source function:

    # lib/elixir_markov_chain.ex
    defp process_source(text) do
      {:ok, model} = Model.start_link
      model = Model.populate model, text # populate Markov model with the source

      # Generate 10 random sentences based on text source
      Enum.each(1..10, fn(_) -> model |> Generator.create_sentence |> IO.puts end)

3 Result

Processed from Thus Spoke Zarathustra by Friedrich Nietzsche:

  • By thee pursued, my fancy!
  • Nether-world, thou exuberant star!
  • Well then! we part here!
  • Snare for me–the desire for love–that i should like to strangle me, thou fountain of delight!
  • As yet without meaning: a buffoon at heart.
  • Loved by overflowing hearts.
  • Growling bear, and sweeten thy soul!
  • Fountains shall rush down into his height!

Processed from Metamorphosis by Franz Kafka:

  • “it’s got to get up.
  • Where we have to open the door, holding himself upright as preparation for getting through the
  • Incidental damages even if he did not know that he wouldn’t have to suffer the view
  • The gentlemen bent over the dishes set in front of them were blown onto the cool,
  • Gregor then turned to look after my parents suffer!
  • “we have to overcome it because of that.
  • Does not agree to be patient.
  • “leave my home. now!”, said mr.

4 Conclusion

Elixir allows to easily build Markov chains and applicate them to real world processes. In our case we have built the random text generator, but you can find Markov models useful for another cases. To view entire application please visit this repository.

Tags: elixir functional