Adjust the probabilities of a batch of connections

Adjusts the probability of one connection

Specs

cond_shift_probs(%{required([any()]) => any()}, %Markov{
  links: term(),
  sanitize_tokens: term(),
  shift: term()
}) :: %{required([any()]) => any()}

Shifts probabilities if the model has a corresponding flag

Specs

enable_token_sanitization(%Markov{
  links: term(),
  sanitize_tokens: term(),
  shift: term()
}) ::
  %Markov{links: term(), sanitize_tokens: term(), shift: term()}

Enables token sanitization on a chain. When this mode is enabled, the chain doesn't understand the difference similar textual tokens. This mode can't be disabled once it has been enabled.

Returns the modified chain.

Specs

forget_token(
  %Markov{links: term(), sanitize_tokens: term(), shift: term()},
  any()
) :: %Markov{
  links: term(),
  sanitize_tokens: term(),
  shift: term()
}

Removes a token from all generation paths chain could produce.

Returns the modifier chain

Example

iex> %Markov{} |>
...> Markov.train("a b c") |>
...> Markov.forget_token("b") |>
...> Markov.generate_text()
"a"

Specs

generate_text(%Markov{links: term(), sanitize_tokens: term(), shift: term()}, [
  any()
]) ::
  String.t()

Generates a string of text using the chain

Optionally assumes the previous two states were [state1, state2]=state.

Returns the generated text.

Example

iex> %Markov{} |> Markov.train("hello, world!") |> Markov.generate_text()
"hello, world!"

iex> %Markov{} |> Markov.train("hello, world!") |>
...> Markov.generate_text([:start, "hello,"])
"world!"

Specs

generate_tokens(
  %Markov{links: term(), sanitize_tokens: term(), shift: term()},
  [any()],
  [any()],
  integer()
) :: [any()]

Generates a list of tokens using the chain

Optionally prepends acc to it and assumes the previous two states were [state1, state2]=state. The amount of the resulting token list is limited by limit.

Returns the generated list.

Example

iex> %Markov{} |> Markov.train([:a, :b, :c]) |> Markov.generate_tokens()
[:a, :b, :c]

iex> %Markov{} |> Markov.train([:a, :b, :c]) |>
...> Markov.generate_tokens([], [:a, :b])
[:c]

Specs

next_state(
  %Markov{links: term(), sanitize_tokens: term(), shift: term()},
  any()
) :: any()

Predicts the next state of a chain assuming current state.

Note: current state conists of two tokens.

Returns the next predicted state.

Example

iex> %Markov{} |> Markov.train("1 2 3 4 5") |> Markov.next_state(["2", "3"])
"4"

iex> %Markov{} |> Markov.train("1 2") |> Markov.next_state([:start, :start])
"1"

iex> %Markov{} |> Markov.train([:a, :b, :c]) |> Markov.next_state([:a, :b])
:c

Specs

train(
  %Markov{links: term(), sanitize_tokens: term(), shift: term()},
  String.t() | [any()]
) ::
  %Markov{links: term(), sanitize_tokens: term(), shift: term()}

Trains chain using text or a list of tokens.

Returns the modified chain.

Example

chain = %Markov{}
    |> Markov.train("hello, world!")
    |> Markov.train("example string number two")
    |> Markov.train("hello, Elixir!")
    |> Markov.train("fourth string")

chain = %Markov{}
    |> Markov.train(["individual tokens", :can_be, 'arbitrary terms'])