mirror of
https://github.com/furyfire/trueskill.git
synced 2025-04-19 12:24:28 +00:00
Deleted C# code (it was just there to note the fork)
This commit is contained in:
Jeff Moser
@ -1,192 +0,0 @@
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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// The whole purpose of this is to do a loop on the bottom
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internal class IteratedTeamDifferencesInnerLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, Variable<GaussianDistribution>, GaussianWeightedSumFactor, Variable<GaussianDistribution>>
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{
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private readonly TeamDifferencesComparisonLayer<TPlayer> _TeamDifferencesComparisonLayer;
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private readonly TeamPerformancesToTeamPerformanceDifferencesLayer<TPlayer>
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_TeamPerformancesToTeamPerformanceDifferencesLayer;
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public IteratedTeamDifferencesInnerLayer(TrueSkillFactorGraph<TPlayer> parentGraph,
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TeamPerformancesToTeamPerformanceDifferencesLayer<TPlayer>
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teamPerformancesToPerformanceDifferences,
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TeamDifferencesComparisonLayer<TPlayer> teamDifferencesComparisonLayer)
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: base(parentGraph)
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{
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_TeamPerformancesToTeamPerformanceDifferencesLayer = teamPerformancesToPerformanceDifferences;
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_TeamDifferencesComparisonLayer = teamDifferencesComparisonLayer;
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}
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public override IEnumerable<Factor<GaussianDistribution>> UntypedFactors
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{
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get
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{
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return
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_TeamPerformancesToTeamPerformanceDifferencesLayer.UntypedFactors.Concat(
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_TeamDifferencesComparisonLayer.UntypedFactors);
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}
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}
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public override void BuildLayer()
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{
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_TeamPerformancesToTeamPerformanceDifferencesLayer.SetRawInputVariablesGroups(InputVariablesGroups);
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_TeamPerformancesToTeamPerformanceDifferencesLayer.BuildLayer();
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_TeamDifferencesComparisonLayer.SetRawInputVariablesGroups(
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_TeamPerformancesToTeamPerformanceDifferencesLayer.GetRawOutputVariablesGroups());
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_TeamDifferencesComparisonLayer.BuildLayer();
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}
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public override Schedule<GaussianDistribution> CreatePriorSchedule()
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{
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Schedule<GaussianDistribution> loop = null;
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switch (InputVariablesGroups.Count)
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{
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case 0:
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case 1:
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throw new InvalidOperationException();
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case 2:
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loop = CreateTwoTeamInnerPriorLoopSchedule();
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break;
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default:
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loop = CreateMultipleTeamInnerPriorLoopSchedule();
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break;
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}
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// When dealing with differences, there are always (n-1) differences, so add in the 1
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int totalTeamDifferences = _TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors.Count;
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int totalTeams = totalTeamDifferences + 1;
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var innerSchedule = new ScheduleSequence<GaussianDistribution>(
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"inner schedule",
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new[]
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{
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loop,
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new ScheduleStep<GaussianDistribution>(
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"teamPerformanceToPerformanceDifferenceFactors[0] @ 1",
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[0], 1),
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new ScheduleStep<GaussianDistribution>(
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String.Format("teamPerformanceToPerformanceDifferenceFactors[teamTeamDifferences = {0} - 1] @ 2",
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totalTeamDifferences),
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[totalTeamDifferences - 1], 2)
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}
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);
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return innerSchedule;
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}
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private Schedule<GaussianDistribution> CreateTwoTeamInnerPriorLoopSchedule()
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{
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return ScheduleSequence(
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new[]
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{
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new ScheduleStep<GaussianDistribution>(
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"send team perf to perf differences",
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[0],
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0),
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new ScheduleStep<GaussianDistribution>(
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"send to greater than or within factor",
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_TeamDifferencesComparisonLayer.LocalFactors[0],
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0)
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},
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"loop of just two teams inner sequence");
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}
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private Schedule<GaussianDistribution> CreateMultipleTeamInnerPriorLoopSchedule()
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{
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int totalTeamDifferences = _TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors.Count;
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var forwardScheduleList = new List<Schedule<GaussianDistribution>>();
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for (int i = 0; i < totalTeamDifferences - 1; i++)
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{
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Schedule<GaussianDistribution> currentForwardSchedulePiece =
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ScheduleSequence(
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new Schedule<GaussianDistribution>[]
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{
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new ScheduleStep<GaussianDistribution>(
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String.Format("team perf to perf diff {0}",
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i),
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[i], 0),
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new ScheduleStep<GaussianDistribution>(
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String.Format("greater than or within result factor {0}",
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i),
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_TeamDifferencesComparisonLayer.LocalFactors[i],
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0),
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new ScheduleStep<GaussianDistribution>(
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String.Format("team perf to perf diff factors [{0}], 2",
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i),
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[i], 2)
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}, "current forward schedule piece {0}", i);
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forwardScheduleList.Add(currentForwardSchedulePiece);
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}
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var forwardSchedule =
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new ScheduleSequence<GaussianDistribution>(
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"forward schedule",
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forwardScheduleList);
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var backwardScheduleList = new List<Schedule<GaussianDistribution>>();
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for (int i = 0; i < totalTeamDifferences - 1; i++)
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{
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var currentBackwardSchedulePiece = new ScheduleSequence<GaussianDistribution>(
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"current backward schedule piece",
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new Schedule<GaussianDistribution>[]
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{
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new ScheduleStep<GaussianDistribution>(
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String.Format("teamPerformanceToPerformanceDifferenceFactors[totalTeamDifferences - 1 - {0}] @ 0",
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i),
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[
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totalTeamDifferences - 1 - i], 0),
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new ScheduleStep<GaussianDistribution>(
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String.Format("greaterThanOrWithinResultFactors[totalTeamDifferences - 1 - {0}] @ 0",
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i),
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_TeamDifferencesComparisonLayer.LocalFactors[totalTeamDifferences - 1 - i], 0),
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new ScheduleStep<GaussianDistribution>(
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String.Format("teamPerformanceToPerformanceDifferenceFactors[totalTeamDifferences - 1 - {0}] @ 1",
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i),
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_TeamPerformancesToTeamPerformanceDifferencesLayer.LocalFactors[
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totalTeamDifferences - 1 - i], 1)
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}
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);
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backwardScheduleList.Add(currentBackwardSchedulePiece);
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}
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var backwardSchedule =
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new ScheduleSequence<GaussianDistribution>(
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"backward schedule",
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backwardScheduleList);
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var forwardBackwardScheduleToLoop =
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new ScheduleSequence<GaussianDistribution>(
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"forward Backward Schedule To Loop",
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new Schedule<GaussianDistribution>[]
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{
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forwardSchedule, backwardSchedule
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});
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const double initialMaxDelta = 0.0001;
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var loop = new ScheduleLoop<GaussianDistribution>(
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String.Format("loop with max delta of {0}",
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initialMaxDelta),
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forwardBackwardScheduleToLoop,
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initialMaxDelta);
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return loop;
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}
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}
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}
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@ -1,68 +0,0 @@
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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internal class PlayerPerformancesToTeamPerformancesLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, KeyedVariable<TPlayer, GaussianDistribution>, GaussianWeightedSumFactor,
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Variable<GaussianDistribution>>
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{
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public PlayerPerformancesToTeamPerformancesLayer(TrueSkillFactorGraph<TPlayer> parentGraph)
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: base(parentGraph)
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{
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}
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public override void BuildLayer()
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{
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foreach (var currentTeam in InputVariablesGroups)
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{
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Variable<GaussianDistribution> teamPerformance = CreateOutputVariable(currentTeam);
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AddLayerFactor(CreatePlayerToTeamSumFactor(currentTeam, teamPerformance));
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// REVIEW: Does it make sense to have groups of one?
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OutputVariablesGroups.Add(new[] {teamPerformance});
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}
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}
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public override Schedule<GaussianDistribution> CreatePriorSchedule()
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{
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return ScheduleSequence(
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from weightedSumFactor in LocalFactors
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select new ScheduleStep<GaussianDistribution>("Perf to Team Perf Step", weightedSumFactor, 0),
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"all player perf to team perf schedule");
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}
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protected GaussianWeightedSumFactor CreatePlayerToTeamSumFactor(
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IList<KeyedVariable<TPlayer, GaussianDistribution>> teamMembers, Variable<GaussianDistribution> sumVariable)
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{
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return new GaussianWeightedSumFactor(sumVariable, teamMembers.ToArray(),
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teamMembers.Select(v => PartialPlay.GetPartialPlayPercentage(v.Key)).
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ToArray());
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}
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public override Schedule<GaussianDistribution> CreatePosteriorSchedule()
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{
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return ScheduleSequence(from currentFactor in LocalFactors
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from currentIteration in
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Enumerable.Range(1, currentFactor.NumberOfMessages - 1)
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select new ScheduleStep<GaussianDistribution>(
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"team sum perf @" + currentIteration,
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currentFactor,
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currentIteration),
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"all of the team's sum iterations");
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}
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private Variable<GaussianDistribution> CreateOutputVariable(
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IList<KeyedVariable<TPlayer, GaussianDistribution>> team)
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{
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string teamMemberNames = String.Join(", ", team.Select(teamMember => teamMember.Key.ToString()).ToArray());
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return ParentFactorGraph.VariableFactory.CreateBasicVariable("Team[{0}]'s performance", teamMemberNames);
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}
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}
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}
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@ -1,63 +0,0 @@
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using System.Collections.Generic;
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using System.Linq;
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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// We intentionally have no Posterior schedule since the only purpose here is to
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internal class PlayerPriorValuesToSkillsLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, DefaultVariable<GaussianDistribution>, GaussianPriorFactor,
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KeyedVariable<TPlayer, GaussianDistribution>>
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{
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private readonly IEnumerable<IDictionary<TPlayer, Rating>> _Teams;
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public PlayerPriorValuesToSkillsLayer(TrueSkillFactorGraph<TPlayer> parentGraph,
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IEnumerable<IDictionary<TPlayer, Rating>> teams)
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: base(parentGraph)
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{
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_Teams = teams;
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}
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public override void BuildLayer()
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{
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foreach (var currentTeam in _Teams)
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{
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var currentTeamSkills = new List<KeyedVariable<TPlayer, GaussianDistribution>>();
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foreach (var currentTeamPlayer in currentTeam)
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{
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KeyedVariable<TPlayer, GaussianDistribution> playerSkill =
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CreateSkillOutputVariable(currentTeamPlayer.Key);
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AddLayerFactor(CreatePriorFactor(currentTeamPlayer.Key, currentTeamPlayer.Value, playerSkill));
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currentTeamSkills.Add(playerSkill);
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}
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OutputVariablesGroups.Add(currentTeamSkills);
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}
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}
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public override Schedule<GaussianDistribution> CreatePriorSchedule()
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{
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return ScheduleSequence(
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from prior in LocalFactors
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select new ScheduleStep<GaussianDistribution>("Prior to Skill Step", prior, 0),
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"All priors");
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}
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private GaussianPriorFactor CreatePriorFactor(TPlayer player, Rating priorRating,
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Variable<GaussianDistribution> skillsVariable)
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{
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return new GaussianPriorFactor(priorRating.Mean,
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Square(priorRating.StandardDeviation) +
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Square(ParentFactorGraph.GameInfo.DynamicsFactor), skillsVariable);
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}
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private KeyedVariable<TPlayer, GaussianDistribution> CreateSkillOutputVariable(TPlayer key)
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{
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return ParentFactorGraph.VariableFactory.CreateKeyedVariable(key, "{0}'s skill", key);
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}
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}
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}
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@ -1,64 +0,0 @@
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using System.Collections.Generic;
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using System.Linq;
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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internal class PlayerSkillsToPerformancesLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, KeyedVariable<TPlayer, GaussianDistribution>, GaussianLikelihoodFactor,
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KeyedVariable<TPlayer, GaussianDistribution>>
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{
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public PlayerSkillsToPerformancesLayer(TrueSkillFactorGraph<TPlayer> parentGraph)
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: base(parentGraph)
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{
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}
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public override void BuildLayer()
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{
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foreach (var currentTeam in InputVariablesGroups)
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{
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var currentTeamPlayerPerformances = new List<KeyedVariable<TPlayer, GaussianDistribution>>();
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foreach (var playerSkillVariable in currentTeam)
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{
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KeyedVariable<TPlayer, GaussianDistribution> playerPerformance =
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CreateOutputVariable(playerSkillVariable.Key);
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AddLayerFactor(CreateLikelihood(playerSkillVariable, playerPerformance));
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currentTeamPlayerPerformances.Add(playerPerformance);
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}
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OutputVariablesGroups.Add(currentTeamPlayerPerformances);
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}
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}
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private GaussianLikelihoodFactor CreateLikelihood(KeyedVariable<TPlayer, GaussianDistribution> playerSkill,
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KeyedVariable<TPlayer, GaussianDistribution> playerPerformance)
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{
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return new GaussianLikelihoodFactor(Square(ParentFactorGraph.GameInfo.Beta), playerPerformance, playerSkill);
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}
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private KeyedVariable<TPlayer, GaussianDistribution> CreateOutputVariable(TPlayer key)
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{
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return ParentFactorGraph.VariableFactory.CreateKeyedVariable(key, "{0}'s performance", key);
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}
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public override Schedule<GaussianDistribution> CreatePriorSchedule()
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{
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return ScheduleSequence(
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from likelihood in LocalFactors
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select new ScheduleStep<GaussianDistribution>("Skill to Perf step", likelihood, 0),
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"All skill to performance sending");
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}
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public override Schedule<GaussianDistribution> CreatePosteriorSchedule()
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{
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return ScheduleSequence(
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from likelihood in LocalFactors
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select new ScheduleStep<GaussianDistribution>("name", likelihood, 1),
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"All skill to performance sending");
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}
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}
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}
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@ -1,38 +0,0 @@
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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internal class TeamDifferencesComparisonLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, Variable<GaussianDistribution>, GaussianFactor, DefaultVariable<GaussianDistribution>>
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{
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private readonly double _Epsilon;
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private readonly int[] _TeamRanks;
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public TeamDifferencesComparisonLayer(TrueSkillFactorGraph<TPlayer> parentGraph, int[] teamRanks)
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: base(parentGraph)
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{
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_TeamRanks = teamRanks;
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GameInfo gameInfo = ParentFactorGraph.GameInfo;
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_Epsilon = DrawMargin.GetDrawMarginFromDrawProbability(gameInfo.DrawProbability, gameInfo.Beta);
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}
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public override void BuildLayer()
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{
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for (int i = 0; i < InputVariablesGroups.Count; i++)
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{
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bool isDraw = (_TeamRanks[i] == _TeamRanks[i + 1]);
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Variable<GaussianDistribution> teamDifference = InputVariablesGroups[i][0];
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GaussianFactor factor =
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isDraw
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? (GaussianFactor) new GaussianWithinFactor(_Epsilon, teamDifference)
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: new GaussianGreaterThanFactor(_Epsilon, teamDifference);
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AddLayerFactor(factor);
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}
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}
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}
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}
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@ -1,43 +0,0 @@
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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using Moserware.Skills.TrueSkill.Factors;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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internal class TeamPerformancesToTeamPerformanceDifferencesLayer<TPlayer> :
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TrueSkillFactorGraphLayer
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<TPlayer, Variable<GaussianDistribution>, GaussianWeightedSumFactor, Variable<GaussianDistribution>>
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{
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public TeamPerformancesToTeamPerformanceDifferencesLayer(TrueSkillFactorGraph<TPlayer> parentGraph)
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: base(parentGraph)
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{
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}
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public override void BuildLayer()
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{
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for (int i = 0; i < InputVariablesGroups.Count - 1; i++)
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{
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Variable<GaussianDistribution> strongerTeam = InputVariablesGroups[i][0];
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Variable<GaussianDistribution> weakerTeam = InputVariablesGroups[i + 1][0];
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Variable<GaussianDistribution> currentDifference = CreateOutputVariable();
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AddLayerFactor(CreateTeamPerformanceToDifferenceFactor(strongerTeam, weakerTeam, currentDifference));
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// REVIEW: Does it make sense to have groups of one?
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OutputVariablesGroups.Add(new[] {currentDifference});
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}
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}
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private GaussianWeightedSumFactor CreateTeamPerformanceToDifferenceFactor(
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Variable<GaussianDistribution> strongerTeam, Variable<GaussianDistribution> weakerTeam,
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Variable<GaussianDistribution> output)
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{
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return new GaussianWeightedSumFactor(output, new[] {strongerTeam, weakerTeam}, new[] {1.0, -1.0});
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}
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private Variable<GaussianDistribution> CreateOutputVariable()
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{
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return ParentFactorGraph.VariableFactory.CreateBasicVariable("Team performance difference");
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}
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}
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}
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@ -1,20 +0,0 @@
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using Moserware.Numerics;
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using Moserware.Skills.FactorGraphs;
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namespace Moserware.Skills.TrueSkill.Layers
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{
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internal abstract class TrueSkillFactorGraphLayer<TPlayer, TInputVariable, TFactor, TOutputVariable>
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:
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FactorGraphLayer
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<TrueSkillFactorGraph<TPlayer>, GaussianDistribution, Variable<GaussianDistribution>, TInputVariable,
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TFactor, TOutputVariable>
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where TInputVariable : Variable<GaussianDistribution>
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where TFactor : Factor<GaussianDistribution>
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where TOutputVariable : Variable<GaussianDistribution>
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{
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public TrueSkillFactorGraphLayer(TrueSkillFactorGraph<TPlayer> parentGraph)
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: base(parentGraph)
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{
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||||
}
|
||||
}
|
||||
}
|
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Reference in New Issue
Block a user