private void RemoveExistingExperiment()
{
if (_ea != null)
{
//_ea.Stop();
_ea.Dispose();
}
if (_experiment != null)
{
//_experiment.
}
_experiment = null;
_ea = null;
_experimentArgs = null;
_hyperneatArgs = null;
_harness = null;
_harnessArgs = null;
_evalArgs = null;
_winningBrain = null;
}
public TrainingSession(ShipDNA dna, ShipExtraArgs shipExtraArgs, int inputCount, int outputCount, Func <WorldAccessor, TrainingRoom, IPhenomeTickEvaluator <IBlackBox, NeatGenome> > getNewEvaluator, int roomCount = 25, double roomSize = ROOMSIZE)
{
DNA = dna;
ShipExtraArgs = shipExtraArgs;
double roomMargin = roomSize / 10;
Arena = new ArenaAccessor(roomCount, roomSize, roomMargin, false, false, new Type[] { typeof(Bot) }, new Type[] { typeof(Bot) }, shipExtraArgs.NeuralPoolManual, (.1, .25));
Arena.WorldCreated += Arena_WorldCreated;
foreach (var(room, _) in Arena.AllRooms)
{
room.Evaluator = getNewEvaluator(Arena.WorldAccessor, room);
}
#region experiment args
ExperimentInitArgs experimentArgs = new ExperimentInitArgs()
{
Description = "Trains an individual BrainNEAT part",
InputCount = inputCount,
OutputCount = outputCount,
IsHyperNEAT = false,
PopulationSize = roomCount, // may want to do a few more than rooms in case extra are made
SpeciesCount = Math.Max(2, (roomCount / 4d).ToInt_Ceiling()),
Activation = GetActivationFunctionArgs(),
Complexity_RegulationStrategy = ComplexityCeilingType.Absolute,
Complexity_Threshold = 200,
};
#endregion
Experiment = new ExperimentNEATBase();
// Use the overload that takes the tick phenome
Experiment.Initialize("BrainNEAT Trainer", experimentArgs, Arena.AllRooms.Select(o => o.room.Evaluator).ToArray(), Arena.WorldAccessor.RoundRobinManager, Arena.WorldAccessor.UpdateWorld);
EA = Experiment.CreateEvolutionAlgorithm();
// look in AnticipatePositionWindow for an example. The update event just displays stats
//ea.UpdateEvent += EA_UpdateEvent;
//ea.PausedEvent += EA_PausedEvent;
}
private void Reset_Click(object sender, RoutedEventArgs e)
{
try
{
#region ANNOYING XML
// string xml = "<?xml version=\"1.0\" encoding=\"utf - 8\" ?>" + @"
// <Experiments>
// " + "<Experiment name=\"Pick a Number\"" + @">
// <AssemblyPath>SharpNeatDomains.dll</AssemblyPath >
// <ClassName>SharpNeat.Domains.FunctionRegression.FunctionRegressionExperiment</ClassName>
// <Config>
// <PopulationSize>150</PopulationSize>
// <SpecieCount>10</SpecieCount>
// <Activation>
// <Scheme>Acyclic</ Scheme >
// </Activation>
// <ComplexityRegulationStrategy>Absolute</ ComplexityRegulationStrategy >
// <ComplexityThreshold>20</ComplexityThreshold>
// <Description>Pick a Number</Description>
// </Config>
// </Experiment>
//</Experiments>
//";
// //< Function > Sine </ Function >
// //< SampleResolution > 21 </ SampleResolution >
// //< SampleMin > -3.14159 </ SampleMin >
// //< SampleMax > 3.14159 </ SampleMax >
// XmlDocument doc = new XmlDocument();
// doc.LoadXml(xml);
// XmlElement element = doc.SelectSingleNode("Config") as XmlElement;
// _experiment.Initialize("Pick a Number", element);
#endregion
int[] trueValues = UtilityCore.RandomRange(0, 8, StaticRandom.Next(1, 8)).ToArray();
ExperimentInitArgs config = new ExperimentInitArgs()
{
Description = "Some numbers are chosen, the NN needs to train itself to return true when one of those numbers are presented",
InputCount = 3,
OutputCount = 1,
PopulationSize = 150,
SpeciesCount = 10,
Activation = new ExperimentInitArgs_Activation_Acyclic(),
Complexity_RegulationStrategy = ComplexityCeilingType.Absolute,
Complexity_Threshold = 20,
};
PAN_Evaluator evaluator = new PAN_Evaluator(config.InputCount, PAN_Experiment.GetTrueVectors(config.InputCount, trueValues));
_experiment = new PAN_Experiment(config.InputCount, trueValues);
_experiment.Initialize("pick a number", config, evaluator);
//_experiment.NeatGenomeParameters.InitialInterconnectionsProportion = .1;
_ea = _experiment.CreateEvolutionAlgorithm();
// Attach update event listener.
_ea.UpdateEvent += EA_UpdateEvent;
_ea.PausedEvent += EA_PausedEvent;
ShowBestGenome();
panPlot.ResetLabels(trueValues);
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString(), this.Title, MessageBoxButton.OK, MessageBoxImage.Error);
}
}
private void Reset2_Click(object sender, RoutedEventArgs e)
{
try
{
// If not hyperneat, just replace with a new net
// If hyperneat, compare other args and retain genomes if the only change is input/output resolution
string prevGenomeXML = null;
if (chkHyperNEAT.IsChecked.Value && _experiment != null && _ea != null)
{
//_ea.Stop(); // currently, Stop just calls RequestPause, so don't use it. There needs to be a dispose that removes the underlying thread
_ea.RequestPauseAndWait();
prevGenomeXML = ExperimentNEATBase.SavePopulation(_ea.GenomeList);
}
RemoveExistingExperiment();
// My stuff
#region harness args
_harnessArgs = new HarnessArgs(
trkMapSize.Value,
trkVisionSize.Value,
trkOutputSize.Value,
trkInputPixels.Value.ToInt_Round(),
trkOutputPixels.Value.ToInt_Round(),
trkDelayBetweenInstances.Value);
#endregion
#region eval args
if (chkRandomStartingConditions.IsChecked.Value)
{
_evalArgs = new EvaluatorArgs(
trkEvalIterations.Value.ToInt_Round(),
trkDelay.Value,
trkEvalElapsedTime.Value,
trkMaxSpeed.Value,
chkBounceOffWalls.IsChecked.Value,
new[] { (TrackedItemType)cboTrackedItemType.SelectedValue },
trkNewItemDuration.Value,
trkNewItemErrorMultiplier.Value,
(ScoreLeftRightBias)cboErrorBias.SelectedValue);
}
else
{
Point position = Math3D.GetRandomVector(_harnessArgs.MapSize / 2).ToPoint2D();
Vector velocity = Math3D.GetRandomVector_Circular(trkMaxSpeed.Value).ToVector2D();
// Don't let the velocity be in the same quadrant as the position (otherwise, you could have something spawn next to a wall, heading
// toward the wall). These if statements force it to cross the x,y axiis
if (Math.Sign(position.X) == Math.Sign(velocity.X))
{
velocity = new Vector(-velocity.X, velocity.Y);
}
if (Math.Sign(position.Y) == Math.Sign(velocity.Y))
{
velocity = new Vector(velocity.X, -velocity.Y);
}
_evalArgs = new EvaluatorArgs(
trkEvalIterations.Value.ToInt_Round(),
trkDelay.Value,
trkEvalElapsedTime.Value,
new[] { Tuple.Create((TrackedItemType)cboTrackedItemType.SelectedValue, position, velocity, chkBounceOffWalls.IsChecked.Value) },
trkNewItemDuration.Value,
trkNewItemErrorMultiplier.Value,
(ScoreLeftRightBias)cboErrorBias.SelectedValue);
}
#endregion
// SharpNEAT
#region experiment args
_experimentArgs = new ExperimentInitArgs()
{
Description = "Input is a pixel array. Output is a pixel array. The NN needs to watch the object and anticipate where it will be at some fixed time in the future",
InputCount = _harnessArgs.InputSizeXY * _harnessArgs.InputSizeXY,
OutputCount = _harnessArgs.OutputSizeXY * _harnessArgs.OutputSizeXY,
IsHyperNEAT = chkHyperNEAT.IsChecked.Value,
PopulationSize = trkPopulationSize.Value.ToInt_Round(),
SpeciesCount = trkSpeciesCount.Value.ToInt_Round(),
Activation = new ExperimentInitArgs_Activation_CyclicFixedTimesteps()
{
TimestepsPerActivation = trkTimestepsPerActivation.Value.ToInt_Round(),
FastFlag = true
},
Complexity_RegulationStrategy = ComplexityCeilingType.Absolute,
Complexity_Threshold = trkComplexityThreshold.Value.ToInt_Round(),
};
#endregion
#region hyperneat args
_hyperneatArgs = null;
if (chkHyperNEAT.IsChecked.Value)
{
// Use two square sheets
var hyperPoints = HyperNEAT_Args.GetSquareSheets(trkVisionSize.Value, trkOutputSize.Value, _harnessArgs.InputSizeXY, _harnessArgs.OutputSizeXY);
_hyperneatArgs = new HyperNEAT_Args()
{
InputPositions = hyperPoints.inputs,
OutputPositions = hyperPoints.outputs,
};
}
#endregion
#region create harness
_harness = new TrackedItemHarness(_harnessArgs);
_harness.ItemRemoved += (s1, e1) =>
{
_harness.SetItem(AntPos_Evaluator.GetNewItem(_harness, _evalArgs));
};
_harness.SetItem(AntPos_Evaluator.GetNewItem(_harness, _evalArgs));
#endregion
#region create evaluator
AntPos_Evaluator evaluator = new AntPos_Evaluator(_harnessArgs, _evalArgs);
//FitnessInfo score = evaluator.Evaluate(new RandomBlackBoxNetwork(_harness.InputSizeXY * _harness.InputSizeXY, _harness.OutputSizeXY * _harness.OutputSizeXY, true)); // this is a good place to unit test the evaluator
#endregion
#region create experiment
_experiment = new ExperimentNEATBase();
_experiment.Initialize("anticipate position", _experimentArgs, evaluator);
#endregion
#region create evolution algorithm
if (prevGenomeXML == null)
{
if (chkHyperNEAT.IsChecked.Value)
{
_ea = _experiment.CreateEvolutionAlgorithm(_hyperneatArgs);
}
else
{
_ea = _experiment.CreateEvolutionAlgorithm();
}
}
else
{
List <NeatGenome> genomeList;
if (_hyperneatArgs == null)
{
genomeList = ExperimentNEATBase.LoadPopulation(prevGenomeXML, _experimentArgs.Activation, _experimentArgs.InputCount, _experimentArgs.OutputCount);
}
else
{
genomeList = ExperimentNEATBase.LoadPopulation(prevGenomeXML, _experimentArgs.Activation, _hyperneatArgs);
}
// The factory is the same for all items, so just grab the first one
NeatGenomeFactory genomeFactory = genomeList[0].GenomeFactory;
_ea = _experiment.CreateEvolutionAlgorithm(genomeFactory, genomeList, _hyperneatArgs);
}
_ea.UpdateEvent += EA_UpdateEvent;
_ea.PausedEvent += EA_PausedEvent;
#endregion
ShowBestGenome(); // this ensures the neural viewer is created
_winningBrainTime = DateTime.UtcNow - TimeSpan.FromDays(1); // put it way in the past so the first tick will request a new winner
_winningBrain = null;
//_winningBrain = new RandomBlackBoxNetwork(_harness.InputSizeXY * _harness.InputSizeXY, _harness.OutputSizeXY * _harness.OutputSizeXY, true);
_tickCounter = _evalArgs.TotalNumberEvaluations * 2; // force the timer to get the winning NN right away (otherwise it will do a round before refreshing)
_ea.StartContinue(); // this needs to be done last
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString(), this.Title, MessageBoxButton.OK, MessageBoxImage.Error);
}
}
