/// <summary>
/// This constructor creates a default network to work with.
/// </summary>
/// <param name="aoe2Directory">Directory of your age of empires game.</param>
/// <param name="aiScript">Name of your ai script that you want to generate.</param>
public AITrainingModule(string aoe2Directory, string aiScript)
{
_logger = Program.Logger;
_logger.Info("Initializing Training module.");
_aoe2Directory = aoe2Directory;
_aiScript = aiScript;
_numberOfInitialCycles = 100000;
_numberOfContinuousCycles = 10000;
_numberOfNeuronRefreshes = 0;
// Keep track of random number of neurons here.
int numberOfInputNeurons = 10;
int numberOfHiddenNeurons = 10;
int numberOfOutputNeurons = 8;
double learningRate = 0.25;
_errorList = new LinkedList <double>();
LinearLayer inputLayer = new LinearLayer(numberOfInputNeurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(numberOfHiddenNeurons);
SigmoidLayer outputLayer = new SigmoidLayer(numberOfOutputNeurons);
// Wow, how hidden is really hidden. So that I think these connectors do is
// insert themselves as part of the various layers. This really hides the hidden
// layer from the network, as only the connectors then modify the hidden layer.
// In other words "trained".
var conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
var conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
_nueralNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
_nueralNetwork.SetLearningRate(learningRate);
_nueralNetwork.EndEpochEvent += BackgroundTasks; // hehe call back methods.
// Needs to make initial configuration of AI.
_logger.Warn("Begining initial training cycle...");
// If this module is being instantiated for the first time, create a comprehensive
// knowledge base/ network so it can continue where it last left off. Tweak the
// query to filter outliers.
_rawMgxStats = StreamUtilities.GetAiDataSet();
_nueralNetwork.EndEpochEvent +=
(object networkInput, TrainingEpochEventArgs args) =>
{
if (_percent % (_numberOfInitialCycles / 100) == 0 && _percent > 0)
{
_logger.Info(string.Format("Precent completed {0}%", _percent / (_numberOfInitialCycles / 100)));
}
_percent++;
};
_nueralNetwork.Learn(CompileTrainingSet(_rawMgxStats), _numberOfInitialCycles);
_logger.Warn("Finished initial training cycle.");
// Get the latest dataset so we can generate some kind of graph and push the data set to database.
var knowledgeBase = StreamUtilities.GetLatestAiEntry().ToList();
var aiTrainingSet = CompileTrainingSet(knowledgeBase);
_currentStats = knowledgeBase[knowledgeBase.Count - 1];
// push data, hacked to show simple output
//double[] veryFirstInput
// =
//{
// 0.2,0.2,0.2,0.2,0.2,
// 0.2,0.2,0.2,0.2,0.2
//};
_climber = new HillClimbing(aiTrainingSet[0].InputVector, _nueralNetwork);
// _climber = new HillClimbing(veryFirstInput, _nueralNetwork);
// Hardcoding these dimensions
int ordinalTracker = 1;
for (int i = 0; i < 4; i++)
{
for (int j = i + 1; j < 5; j++)
{
//write normalized data
StreamUtilities.SubmitPlotableData(_climber.GenerateTopologyData(i, j), ordinalTracker);
//write unnormalized data.
StreamUtilities.SubmitPlotableUnnormailizedData(_climber.GenerateUnormalizedTopologyData(i, j), ordinalTracker);
_logger.Debug(string.Format("Writing Axis{0} and Axis{1} with ordinal {2}.", i, j, ordinalTracker));
ordinalTracker++;
}
}
// If input table == output, then a new game is needed
if (StreamUtilities.CheckIfNewGameIsNeeded())
{
TriggerNewGame();
}
}