refactor interface to use a hash instead of paralle arrays

lib/saulabs/trueskill/factor_graph.rb

@@ -1,38 +1,38 @@
 # -*- encoding : utf-8 -*-
 module Saulabs
   module TrueSkill
-    
+
     class FactorGraph
-      
+
       # @return [Array<Array<TrueSkill::Rating>>]
       attr_reader :teams
-      
+
       # @return [Float]
       attr_reader :beta
-      
+
       # @return [Float]
       attr_reader :beta_squared
-      
+
       # @return [Float]
       attr_reader :draw_probability
-      
+
       # @return [Float]
       attr_reader :epsilon
-      
+
       # @private
       attr_reader :layers
 
       # @return [Boolean]
       attr_reader :skills_additive
-      
-      
+
+
       # Creates a new trueskill factor graph for calculating the new skills based on the given game parameters
       #
-      # @param [Array<Array<TrueSkill::Rating>>] teams 
+      # @param [Array<Array<TrueSkill::Rating>>] teams
       #   player-ratings grouped in Arrays by teams
-      # @param [Array<Integer>] ranks 
+      # @param [Array<Integer>] ranks
       #   team rankings, example: [2,1,3] first team  in teams finished 2nd, second team 1st and third team 3rd
-      # @param [Hash] options 
+      # @param [Hash] options
       #   the options hash to configure the factor graph constants beta, draw_probability and skills_additive
       #
       # @option options [Float] :beta (4.166667)
@@ -44,30 +44,31 @@ class FactorGraph
       #   true is valid for games like Halo etc, where skill is additive (2 players are better than 1),
       #   false for card games like Skat, Doppelkopf, Bridge where skills are not additive,
       #         two players dont make the team stronger, skills averaged)
-      # 
+      #
       # @example Calculating new skills of a two team game, where one team has one player and the other two
       #
       #  require 'rubygems'
       #  require 'saulabs/trueskill'
-      # 
+      #
       #  include Saulabs::TrueSkill
-      # 
+      #
       #  # team 1 has just one player with a mean skill of 27.1, a skill-deviation of 2.13
       #  # and a play activity of 100 %
       #  team1 = [Rating.new(27.1, 2.13, 1.0)]
-      # 
+      #
       #  # team 2 has two players
       #  team2 = [Rating.new(22.0, 0.98, 0.8), Rating.new(31.1, 5.33, 0.9)]
-      # 
+      #
       #  # team 1 finished first and team 2 second
       #  graph = FactorGraph.new([team1, team2], [1,2])
-      # 
+      #
       #  # update the Ratings
       #  graph.update_skills
       #
-      def initialize(teams, ranks, options = {})
-        @teams = teams
-        @ranks = ranks
+      def initialize(ranks_teams_hash, options = {})
+        @teams = ranks_teams_hash.keys
+        @ranks = ranks_teams_hash.values
+
         opts = {
             :beta => 25/6.0,
             :draw_probability => 0.1,
@@ -79,25 +80,25 @@ def initialize(teams, ranks, options = {})
         @beta_squared = @beta**2
         @epsilon = -Math.sqrt(2.0 * @beta_squared) * Gauss::Distribution.inv_cdf((1.0 - @draw_probability) / 2.0)
         @skills_additive = opts[:skills_additive]
-        
+
         @prior_layer = Layers::PriorToSkills.new(self, @teams)
         @layers = [
           @prior_layer,
           Layers::SkillsToPerformances.new(self),
           Layers::PerformancesToTeamPerformances.new(self, @skills_additive),
           Layers::IteratedTeamPerformances.new(self,
             Layers::TeamPerformanceDifferences.new(self),
-            Layers::TeamDifferenceComparision.new(self, ranks)
+            Layers::TeamDifferenceComparision.new(self, @ranks)
           )
         ]
       end
-      
+
       def draw_margin
         Gauss::Distribution.inv_cdf(0.5*(@draw_probability + 1)) * Math.sqrt(1 + 1) * @beta
       end
-      
+
       # Updates the skills of the players inplace
-      # 
+      #
       # @return [Float] the probability of the games outcome
       def update_skills
         build_layers
@@ -109,9 +110,9 @@ def update_skills
         end
         ranking_probability
       end
-      
+
     private
-      
+
       def ranking_probability
         # factor_list = []
         # sum_log_z, sum_log_s = 0.0
@@ -125,11 +126,11 @@ def ranking_probability
         # Math.exp(sum_log_z + sum_log_s)
         0.0
       end
-      
+
       def updated_skills
         @prior_layer.output
       end
-      
+
       def build_layers
         output = nil
         @layers.each do |layer|
@@ -138,13 +139,13 @@ def build_layers
           output = layer.output
         end
       end
-      
+
       def run_schedule
         schedules = @layers.map(&:prior_schedule) + @layers.reverse.map(&:posterior_schedule)
         Schedules::Sequence.new(schedules.compact).visit
       end
-      
+
     end
-    
+
   end
 end

lib/saulabs/trueskill/layers/prior_to_skills.rb

@@ -3,15 +3,15 @@ module Saulabs
   module TrueSkill
     # @private
     module Layers
-      
+
       # @private
       class PriorToSkills < Base
-      
+
         def initialize(graph, teams)
           super(graph)
           @teams = teams
         end
-        
+
         def build
           @teams.each do |team|
             team_skills = []
@@ -23,13 +23,13 @@ def build
             @output << team_skills
           end
         end
-        
+
         def prior_schedule
           Schedules::Sequence.new(@factors.map { |f| Schedules::Step.new(f, 0) })
         end
-        
+
       end
-    
+
     end
   end
 end