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mirror of https://github.com/avitex/elixir-glicko synced 2024-12-22 08:29:57 +00:00

Initial rating implementation

This commit is contained in:
avitex 2017-11-16 12:39:07 +11:00
parent 6bafa2be0b
commit c96d357812
3 changed files with 200 additions and 4 deletions

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lib/glicko.ex Normal file
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defmodule Glicko do
alias __MODULE__.{
Player,
GameResult,
}
@epsilon 0.0000001
@default_system_constant 0.8
@doc """
Generate a new Rating from an existing rating and a series of results.
"""
@spec new_rating(player :: Player.t, results :: list(GameResult.t), sys_constant :: float) :: Player.t
def new_rating(player, results, sys_constant \\ @default_system_constant)
def new_rating(player = %Player{version: :v1}, results, sys_constant) do
player
|> Player.to_v2
|> do_new_rating(results, sys_constant)
|> Player.to_v1
end
def new_rating(player = %Player{version: :v2}, results, sys_constant) do
do_new_rating(player, results, sys_constant)
end
defp do_new_rating(player = %Player{version: :v2}, results, sys_constant) do
results = Enum.map(results, fn result ->
opponent = Player.to_v2(result.opponent)
%{
score: result.score,
opponent: opponent,
opponent_rating_deviation_g: calc_g(opponent.rating_deviation),
}
end)
ctx =
Map.new
|> Map.put(:system_constant, sys_constant)
|> Map.put(:results, results)
|> Map.put(:player, player)
|> Map.put(:player_rating_deviation_squared, :math.pow(player.rating_deviation, 2))
# Step 3
ctx = Map.put(ctx, :variance_estimate, calc_variance_estimate(ctx))
# Step 4
ctx = Map.put(ctx, :delta, calc_delta(ctx))
# Step 5.1
ctx = Map.put(ctx, :alpha, calc_alpha(ctx))
# Step 5.2
{initial_a, initial_b} = iterative_algorithm_initial(ctx)
ctx = Map.put(ctx, :initial_a, initial_a)
ctx = Map.put(ctx, :initial_b, initial_b)
# Step 5.3
ctx = Map.put(ctx, :initial_fa, calc_f(ctx, ctx.initial_a))
ctx = Map.put(ctx, :initial_fb, calc_f(ctx, ctx.initial_b))
# Step 5.4
ctx = Map.put(ctx, :a, iterative_algorithm_body(
ctx, ctx.initial_a, ctx.initial_b, ctx.initial_fa, ctx.initial_fb
))
# Step 5.5
ctx = Map.put(ctx, :new_player_volatility, calc_new_player_volatility(ctx))
# Step 6
ctx = Map.put(ctx, :prerating_period, calc_prerating_period(ctx))
# Step 7
ctx = Map.put(ctx, :new_player_rating_deviation, calc_new_player_rating_deviation(ctx))
ctx = Map.put(ctx, :new_player_rating, calc_new_player_rating(ctx))
Player.new_v2([
rating: ctx.new_player_rating,
rating_deviation: ctx.new_player_rating_deviation,
volatility: ctx.new_player_volatility,
])
end
# Calculation of the estimated variance of the player's rating based on game outcomes
defp calc_variance_estimate(%{player: player, results: results}) do
Enum.reduce(results, 0.0, fn result, acc ->
tmp_e = calc_e(player, result)
acc + :math.pow(result.opponent_rating_deviation_g, 2) * tmp_e * (1 - tmp_e)
end)
|> :math.pow(-1)
end
defp calc_delta(ctx) do
calc_results_effect(ctx) * ctx.variance_estimate
end
defp calc_f(ctx, x) do
:math.exp(x) *
(:math.pow(ctx.delta, 2) - :math.exp(x) - ctx.player_rating_deviation_squared - ctx.variance_estimate) /
(2 * :math.pow(ctx.player_rating_deviation_squared + ctx.variance_estimate + :math.exp(x), 2)) -
(x - ctx.alpha) / :math.pow(ctx.system_constant, 2)
end
defp calc_alpha(%{player: player}) do
:math.log(:math.pow(player.volatility, 2))
end
defp calc_new_player_volatility(%{a: a}) do
:math.exp(a/2)
end
defp calc_results_effect(%{player: player, results: results}) do
Enum.reduce(results, 0.0, fn result, acc ->
acc + result.opponent_rating_deviation_g * (result.score - calc_e(player, result))
end)
end
defp calc_new_player_rating(ctx) do
ctx.player.rating + :math.pow(ctx.new_player_rating_deviation, 2) * calc_results_effect(ctx)
end
defp calc_new_player_rating_deviation(ctx) do
1/:math.sqrt(1/:math.pow(ctx.prerating_period, 2) + 1/ctx.variance_estimate)
end
defp calc_prerating_period(ctx) do
:math.sqrt((:math.pow(ctx.new_player_volatility, 2) + ctx.player_rating_deviation_squared))
end
defp iterative_algorithm_initial(ctx) do
initial_a = ctx.alpha
initial_b =
if :math.pow(ctx.delta, 2) > ctx.player_rating_deviation_squared + ctx.variance_estimate do
:math.log(:math.pow(ctx.delta, 2) - ctx.player_rating_deviation_squared - ctx.variance_estimate)
else
ctx.alpha - calc_k(ctx, 1) * ctx.system_constant
end
{initial_a, initial_b}
end
defp iterative_algorithm_body(ctx, a, b, fa, fb) do
if abs(b - a) > @epsilon do
c = a + (a - b) * fa / (fb - fa)
fc = calc_f(ctx, c)
{a, fa} =
if fc * fb < 0 do
{b, fb}
else
{a, fa / 2}
end
iterative_algorithm_body(ctx, a, c, fa, fc)
else
a
end
end
defp calc_k(ctx, k) do
if calc_f(ctx, (ctx.alpha - k * ctx.system_constant)) < 0 do
calc_k(ctx, k + 1)
else
k
end
end
# g function
defp calc_g(rating_deviation) do
1 / :math.sqrt(1 + 3 * :math.pow(rating_deviation, 2) / :math.pow(:math.pi, 2))
end
# E function
defp calc_e(player, result) do
1 / (1 + :math.exp(-1 * result.opponent_rating_deviation_g * (player.rating - result.opponent.rating)))
end
end

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test/glicko_test.exs Normal file
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defmodule GlickoTest do
use ExUnit.Case
alias Glicko.{
Player,
GameResult,
}
doctest Glicko
@player Player.new_v1([rating: 1500, rating_deviation: 200])
@results [
GameResult.new(Player.new_v1([rating: 1400, rating_deviation: 30]), :win),
GameResult.new(Player.new_v1([rating: 1550, rating_deviation: 100]), :loss),
GameResult.new(Player.new_v1([rating: 1700, rating_deviation: 300]), :loss),
]
@valid_player_rating_after_results 1464.06
@valid_player_rating_deviation_after_results 151.52
test "new rating" do
%Player{rating: new_rating, rating_deviation: new_rating_deviation} =
Glicko.new_rating(@player, @results, 0.5)
assert_in_delta new_rating, @valid_player_rating_after_results, 0.1
assert_in_delta new_rating_deviation, @valid_player_rating_deviation_after_results, 0.1
end
end