defmodule Glicko do
  @moduledoc """
  Provides the implementation of the Glicko rating system.

  See the [specification](http://www.glicko.net/glicko/glicko2.pdf) for implementation details.

  ## Usage

  Get a player's new rating after a series of matches in a rating period.

      iex> results = [Result.new(Player.new_v1([rating: 1400, rating_deviation: 30]), :win),
      ...> Result.new(Player.new_v1([rating: 1550, rating_deviation: 100]), :loss),
      ...> Result.new(Player.new_v1([rating: 1700, rating_deviation: 300]), :loss)]
      iex> player = Player.new_v1([rating: 1500, rating_deviation: 200])
      iex> Glicko.new_rating(player, results, [system_constant: 0.5])
      {1464.0506705393013, 151.51652412385727}

  Get a player's new rating when they haven't played within a rating period.

      iex> player = Player.new_v1([rating: 1500, rating_deviation: 200])
      iex> Glicko.new_rating(player, [], [system_constant: 0.5])
      {1.5e3, 200.27141669877065}

  Calculate the probability of a player winning against an opponent.

      iex> player = Player.new_v1
      iex> opponent = Player.new_v1
      iex> Glicko.win_probability(player, opponent)
      0.5

  Calculate the probability of a player drawing against an opponent.

      iex> player = Player.new_v1
      iex> opponent = Player.new_v1
      iex> Glicko.draw_probability(player, opponent)
      1.0

  """

  alias __MODULE__.{
    Player,
    Result
  }

  @default_system_constant 0.8
  @default_convergence_tolerance 1.0e-7

  @type new_rating_opts :: [system_constant: float, convergence_tolerance: float]

  @doc """
  Calculates the probability of a player winning against an opponent.

  Returns a value between `0.0` and `1.0`.
  """
  @spec win_probability(player :: Player.t(), opponent :: Player.t()) :: float
  def win_probability(player, opponent) do
    win_probability(
      player |> Player.rating(:v2),
      opponent |> Player.rating(:v2),
      opponent |> Player.rating_deviation(:v2)
    )
  end

  @doc """
  Calculates the probability of a player winning against an opponent from a player rating, opponent rating and opponent rating deviation.

  Values provided for the player rating, opponent rating and opponent rating deviation must be *v2* based.

  Returns a value between `0.0` and `1.0`.
  """
  @spec win_probability(
          player_rating :: Player.rating(),
          opponent_rating :: Player.rating(),
          opponent_rating_deviation :: Player.rating_deviation()
        ) :: float
  def win_probability(player_rating, opponent_rating, opponent_rating_deviation) do
    calc_e(player_rating, opponent_rating, calc_g(opponent_rating_deviation))
  end

  @doc """
  Calculates the probability of a player drawing against an opponent.

  Returns a value between `0.0` and `1.0`.
  """
  @spec draw_probability(player :: Player.t(), opponent :: Player.t()) :: float
  def draw_probability(player, opponent) do
    draw_probability(
      player |> Player.rating(:v2),
      opponent |> Player.rating(:v2),
      opponent |> Player.rating_deviation(:v2)
    )
  end

  @doc """
  Calculates the probability of a player drawing against an opponent from a player rating, opponent rating and opponent rating deviation.

  Values provided for the player rating, opponent rating and opponent rating deviation must be *v2* based.

  Returns a value between `0.0` and `1.0`.
  """
  @spec draw_probability(
          player_rating :: Player.rating(),
          opponent_rating :: Player.rating(),
          opponent_rating_deviation :: Player.rating_deviation()
        ) :: float
  def draw_probability(player_rating, opponent_rating, opponent_rating_deviation) do
    1 -
      abs(win_probability(player_rating, opponent_rating, opponent_rating_deviation) - 0.5) / 0.5
  end

  @doc """
  Generate a new rating from an existing rating and a series (or lack) of results.

  Returns the updated player with the same version given to the function.
  """
  @spec new_rating(player :: Player.t(), results :: list(Result.t()), opts :: new_rating_opts) ::
          Player.t()
  def new_rating(player, results, opts \\ [])

  def new_rating(player, results, opts) when tuple_size(player) == 3 do
    do_new_rating(player, results, opts)
  end

  def new_rating(player, results, opts) when tuple_size(player) == 2 do
    player
    |> Player.to_v2()
    |> do_new_rating(results, opts)
    |> Player.to_v1()
  end

  defp do_new_rating({player_r, player_pre_rd, player_v}, [], _) do
    player_post_rd = calc_player_post_base_rd(:math.pow(player_pre_rd, 2), player_v)

    {player_r, player_post_rd, player_v}
  end

  defp do_new_rating({player_pre_r, player_pre_rd, player_pre_v}, results, opts) do
    sys_const = Keyword.get(opts, :system_constant, @default_system_constant)
    conv_tol = Keyword.get(opts, :convergence_tolerance, @default_convergence_tolerance)

    # Initialization (skips steps 1, 2 and 3)
    player_pre_rd_sq = :math.pow(player_pre_rd, 2)
    {variance_est, results_effect} = result_calculations(results, player_pre_r)
    # Step 4
    delta = calc_delta(results_effect, variance_est)
    # Step 5.1
    alpha = calc_alpha(player_pre_v)
    # Step 5.2
    k = calc_k(alpha, delta, player_pre_rd_sq, variance_est, sys_const, 1)

    {initial_a, initial_b} =
      iterative_algorithm_initial(
        alpha,
        delta,
        player_pre_rd_sq,
        variance_est,
        sys_const,
        k
      )

    # Step 5.3
    initial_fa = calc_f(alpha, delta, player_pre_rd_sq, variance_est, sys_const, initial_a)
    initial_fb = calc_f(alpha, delta, player_pre_rd_sq, variance_est, sys_const, initial_b)
    # Step 5.4
    a =
      iterative_algorithm_body(
        alpha,
        delta,
        player_pre_rd_sq,
        variance_est,
        sys_const,
        conv_tol,
        initial_a,
        initial_b,
        initial_fa,
        initial_fb
      )

    # Step 5.5
    player_post_v = calc_new_player_volatility(a)
    # Step 6
    player_post_base_rd = calc_player_post_base_rd(player_pre_rd_sq, player_post_v)
    # Step 7
    player_post_rd = calc_new_player_rating_deviation(player_post_base_rd, variance_est)
    player_post_r = calc_new_player_rating(results_effect, player_pre_r, player_post_rd)

    {player_post_r, player_post_rd, player_post_v}
  end

  defp result_calculations(results, player_pre_r) do
    {variance_estimate_acc, result_effect_acc} =
      Enum.reduce(results, {0.0, 0.0}, fn result, {variance_estimate_acc, result_effect_acc} ->
        opponent_rd_g =
          result
          |> Result.opponent_rating_deviation()
          |> calc_g

        win_probability = calc_e(player_pre_r, Result.opponent_rating(result), opponent_rd_g)

        {
          variance_estimate_acc +
            :math.pow(opponent_rd_g, 2) * win_probability * (1 - win_probability),
          result_effect_acc + opponent_rd_g * (Result.score(result) - win_probability)
        }
      end)

    {:math.pow(variance_estimate_acc, -1), result_effect_acc}
  end

  defp calc_delta(results_effect, variance_est) do
    results_effect * variance_est
  end

  defp calc_f(alpha, delta, player_pre_rd_sq, variance_est, sys_const, x) do
    :math.exp(x) *
      (:math.pow(delta, 2) - :math.exp(x) - player_pre_rd_sq - variance_est) /
      (2 * :math.pow(player_pre_rd_sq + variance_est + :math.exp(x), 2)) -
      (x - alpha) / :math.pow(sys_const, 2)
  end

  defp calc_alpha(player_pre_v) do
    :math.log(:math.pow(player_pre_v, 2))
  end

  defp calc_new_player_volatility(a) do
    :math.exp(a / 2)
  end

  defp calc_new_player_rating(results_effect, player_pre_r, player_post_rd) do
    player_pre_r + :math.pow(player_post_rd, 2) * results_effect
  end

  defp calc_new_player_rating_deviation(player_post_base_rd, variance_est) do
    1 / :math.sqrt(1 / :math.pow(player_post_base_rd, 2) + 1 / variance_est)
  end

  defp calc_player_post_base_rd(player_pre_rd_sq, player_pre_v) do
    :math.sqrt(:math.pow(player_pre_v, 2) + player_pre_rd_sq)
  end

  defp iterative_algorithm_initial(alpha, delta, player_pre_rd_sq, variance_est, sys_const, k) do
    initial_a = alpha

    initial_b =
      if :math.pow(delta, 2) > player_pre_rd_sq + variance_est do
        :math.log(:math.pow(delta, 2) - player_pre_rd_sq - variance_est)
      else
        alpha - k * sys_const
      end

    {initial_a, initial_b}
  end

  defp iterative_algorithm_body(
         alpha,
         delta,
         player_pre_rd_sq,
         variance_est,
         sys_const,
         conv_tol,
         a,
         b,
         fa,
         fb
       ) do
    if abs(b - a) > conv_tol do
      c = a + (a - b) * fa / (fb - fa)
      fc = calc_f(alpha, delta, player_pre_rd_sq, variance_est, sys_const, c)

      {a, fa} =
        if fc * fb < 0 do
          {b, fb}
        else
          {a, fa / 2}
        end

      iterative_algorithm_body(
        alpha,
        delta,
        player_pre_rd_sq,
        variance_est,
        sys_const,
        conv_tol,
        a,
        c,
        fa,
        fc
      )
    else
      a
    end
  end

  defp calc_k(alpha, delta, player_pre_rd_sq, variance_est, sys_const, k) do
    if calc_f(alpha, delta, player_pre_rd_sq, variance_est, sys_const, alpha - k * sys_const) < 0 do
      calc_k(alpha, delta, player_pre_rd_sq, variance_est, sys_const, k + 1)
    else
      k
    end
  end

  # g function
  defp calc_g(rd) do
    1 / :math.sqrt(1 + 3 * :math.pow(rd, 2) / :math.pow(:math.pi(), 2))
  end

  # E function
  defp calc_e(player_pre_r, opponent_r, opponent_rd_g) do
    1 / (1 + :math.exp(-1 * opponent_rd_g * (player_pre_r - opponent_r)))
  end
end