public static void PreTrainACS(BPNetwork idn)
{
Console.Write("Pre-training ACS...");
pABEBlackGun = abeBlackGun + (((r.NextDouble() * 2) - 1) * abeMaxTemp);
pABEWhiteGun = abeWhiteGun + (((r.NextDouble() * 2) - 1) * abeMaxTemp);
List <ActivationCollection> dataSets = new List <ActivationCollection>();
List <DeclarativeChunk> primes = new List <DeclarativeChunk>();
primes.AddRange(white_faces);
primes.AddRange(black_faces);
List <DeclarativeChunk> targets = new List <DeclarativeChunk>();
targets.AddRange(guns);
targets.AddRange(tools);
foreach (DeclarativeChunk p in primes)
{
foreach (DeclarativeChunk t in targets)
{
ActivationCollection ds = ImplicitComponentInitializer.NewDataSet();
ds.AddRange(p, 1);
ds.AddRange(t, 1);
dataSets.Add(ds);
}
}
ImplicitComponentInitializer.Train(idn, trainer, numIterations: numTrainingTrials, randomTraversal: true, dataSets: dataSets.ToArray());
Console.WriteLine("Finished");
}
static void SetupBPNetwork(Agent reasoner)
{
//Chunks for the whales, tuna, and bears
DeclarativeChunk TunaChunk = World.NewDeclarativeChunk("Tuna");
DeclarativeChunk WhaleChunk = World.NewDeclarativeChunk("Whale");
DeclarativeChunk BearChunk = World.NewDeclarativeChunk("Bear");
//The 2 properties (as DV pairs)
DimensionValuePair livesinwater = World.NewDimensionValuePair("lives in", "water");
DimensionValuePair eatsfish = World.NewDimensionValuePair("eats", "fish");
//The BP network to be used in the bottom level of the NACS
BPNetwork net = AgentInitializer.InitializeAssociativeMemoryNetwork(reasoner, BPNetwork.Factory);
//Adds the properties (as inputs) and chunks (as outputs) to the BP network
net.Input.Add(livesinwater);
net.Input.Add(eatsfish);
net.Output.Add(TunaChunk);
net.Output.Add(WhaleChunk);
net.Output.Add(BearChunk);
reasoner.Commit(net);
//Adds the chunks to the GKS
reasoner.AddKnowledge(TunaChunk);
reasoner.AddKnowledge(WhaleChunk);
reasoner.AddKnowledge(BearChunk);
//Initializes a trainer to use to train the BP network
GenericEquation trainer = ImplicitComponentInitializer.InitializeTrainer(GenericEquation.Factory, (Equation)trainerEQ);
//Adds the properties (as inputs) and chunks (as outputs) to the trainer
trainer.Input.Add(livesinwater);
trainer.Input.Add(eatsfish);
trainer.Output.Add(TunaChunk);
trainer.Output.Add(WhaleChunk);
trainer.Output.Add(BearChunk);
trainer.Commit();
//Sets up data sets for each of the 2 properties
List <ActivationCollection> sis = new List <ActivationCollection>();
ActivationCollection si = ImplicitComponentInitializer.NewDataSet();
si.Add(livesinwater, 1);
sis.Add(si);
si = ImplicitComponentInitializer.NewDataSet();
si.Add(eatsfish, 1);
sis.Add(si);
Console.Write("Training AMN...");
//Trains the BP network to report associative knowledge between the properties and the chunks
ImplicitComponentInitializer.Train(net, trainer, sis, ImplicitComponentInitializer.TrainingTerminationConditions.SUM_SQ_ERROR);
Console.WriteLine("Finished!");
}
static void DoTraining(BPNetwork target, FoodDrive foodDr)
{
DriveEquation trainer = ImplicitComponentInitializer.InitializeTrainer(DriveEquation.Factory, foodDr);
trainer.Commit();
List <ActivationCollection> data = new List <ActivationCollection>();
data.Add(ImplicitComponentInitializer.NewDataSet());
foreach (var i in foodDr.Input)
{
ImplicitComponentInitializer.AddRange(i.WORLD_OBJECT, 0, 1, .25);
data[0].Add(i);
}
Console.WriteLine("Performing Pre-Training (see the trace log for results)");
ImplicitComponentInitializer.Train(target, trainer, data, ImplicitComponentInitializer.TrainingTerminationConditions.BOTH, numTrials);
}
