markov
markov v4.1.3

Markov

Text generation library based on nth-order Markov chains

Hex.pm Hex.pm

features

 Features

  • Token sanitation (optional): ignores letter case and punctuation when switching states, but still keeps the output as-is
  • Operation history (optional): recalls the operations it was instructed to perform, incl. past training data
  • Probability shifting (optional): gives less frequent generation paths more chance to get used, which makes the output more original but may produce nonsense
  • Tagging (optional): you can tag your source data and alter the probabilities of tagged generation paths according to your rules
  • Prompted generation (optional) grants your model the ability to answer questions given to it provided that the training data consists mostly of Q&A pairs
  • Managed disk storage so you don't have to worry about storing and loading the models
  • Transparent fragmentation reduces RAM usage and loading times with huge models

usage

 Usage

In mix.exs:

defp deps do
  [{:markov, "~> 4.0"}]
end

Unlike Markov 1.x, this version has very strong opinions on how you should create and persist your models (that also differs from 2.x and 3.x).

Example workflow (click here for full docs):

# The model will be stored under this path
{:ok, model} = Markov.load("./model_path", sanitize_tokens: true, store_log: [:train])

# train using four strings
:ok = Markov.train(model, "hello, world!")
:ok = Markov.train(model, "example string number two")
:ok = Markov.train(model, "hello, Elixir!")
:ok = Markov.train(model, "fourth string")

# generate text
{:ok, text} = Markov.generate_text(model)
IO.puts(text)

# commit all changes and unload
Markov.unload(model)

# these will return errors because the model is unloaded
# Markov.generate_text(model)
# Markov.train(model, "hello, world!")

# load the model again
{:ok, model} = Markov.load("./model_path")

# enable probability shifting and generate text
:ok = Markov.configure(model, shift_probabilities: true)
{:ok, text} = Markov.generate_text(model)
IO.puts(text)

# print uninteresting stats
model |> Markov.dump_partition(0) |> IO.inspect
model |> Markov.read_log |> IO.inspect

# this will also write our new just-set option
Markov.unload(model)

credits

 Credits