void setUpForModuleRunning(ModulePacket oldModule, ModulePacket newModule)
{
if (oldModule.mainScript != null)
{
foreach (Rule rule in oldModule.rules)
{
Destroy(rule.detector);
}
Destroy(oldModule.mainScript);
}
moduleRunning = newModule;
for (int i = 0; i < moduleRunning.rules.Length; i++)
{
moduleRunning.rules[i].detector = gameObject.AddComponent(Type.GetType(moduleRunning.rules[i].detectModuleType)) as AIDetectModule;
foreach (InputVar inputVar in moduleRunning.rules[i].vars)
{
moduleRunning.rules[i].detector.vars.Add(inputVar.name, Convert.ChangeType(inputVar.var, Type.GetType(inputVar.varType)));
}
}
moduleRunning.mainScript = gameObject.AddComponent(Type.GetType(moduleRunning.moduleType)) as AIModule;
foreach (InputVar inputVar in moduleRunning.vars)
{
moduleRunning.mainScript.vars.Add(inputVar.name, Convert.ChangeType(inputVar.var, Type.GetType(inputVar.varType)));
}
}
public GLWindowProvider(ModulePacket packet) :
base(GameWindowSettings.Default, new NativeWindowSettings()
{
Size = new OpenTK.Mathematics.Vector2i(1600, 900), APIVersion = new Version(4, 6)
})
{
_packet = packet;
_taskManager = packet.CreateScoped <ITaskManager>();
FrameQueue = new Queue <Action>();
}
public BindingsManager(ModulePacket packet)
{
_configurationManager = packet.GetSingleton <IConfigurationManager>();
if (_configurationManager.Contains <InputBindingsConfiguration>())
{
_bindingsDictionary = _configurationManager.Get <InputBindingsConfiguration>().Bindings;
}
else
{
_bindingsDictionary = new Dictionary <string, List <Binding> >
{
{ "default", Default.ToList() }
};
}
}
public TaskManager(ModulePacket packet)
{
_packet = packet;
_tasks = _packet.GetInstances().OfType <ITask>();
// excuse this shit
_startupTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.Startup).OrderBy(task => task.Priority).ToArray();
_backgroundTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.Background).OrderBy(task => task.Priority).ToArray();
_windowRenderTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowRender).OrderBy(task => task.Priority).ToArray();
_windowUpdateTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowUpdate).OrderBy(task => task.Priority).ToArray();
_windowResizeTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowResize).OrderBy(task => task.Priority).ToArray();
_windowEnterTextTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowEnterText).OrderBy(task => task.Priority).ToArray();
_windowScrollTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowMouseScroll).OrderBy(task => task.Priority).ToArray();
_windowDisposingTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowDisposing).OrderBy(task => task.Priority).ToArray();
_windowReadyTasks = _tasks.Where((x) => x.Schedule == TaskSchedule.WindowReady).OrderBy(task => task.Priority).ToArray();
}
public Parser(ModulePacket packet)
{
_commands = packet.GetInstances().OfType <ICommand>().ToList();
}
public SDKTask(ModulePacket packet)
{
_packet = packet;
}
static public ModularNetwork DecodeToModularNetwork(NeatGenome.NeatGenome g)
{
int inputCount = g.InputNeuronCount;
int outputCount = g.OutputNeuronCount;
int neuronCount = g.NeuronGeneList.Count;
IActivationFunction[] activationFunctions = new IActivationFunction[neuronCount];
float[] biasList = new float[neuronCount];
Dictionary <long, int> neuronLookup = new Dictionary <long, int>(neuronCount);
// Create an array of the activation functions for each non-module node node in the genome.
// Start with a bias node if there is one in the genome.
// The genome's neuron list is assumed to be ordered by type, with the bias node appearing first.
int neuronGeneIndex = 0;
for (; neuronGeneIndex < neuronCount; neuronGeneIndex++)
{
if (g.NeuronGeneList[neuronGeneIndex].NeuronType != NeuronType.Bias)
{
break;
}
activationFunctions[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].ActivationFunction;
neuronLookup.Add(g.NeuronGeneList[neuronGeneIndex].InnovationId, neuronGeneIndex);
}
int biasCount = neuronGeneIndex;
for (; neuronGeneIndex < neuronCount; neuronGeneIndex++)
{
activationFunctions[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].ActivationFunction;
neuronLookup.Add(g.NeuronGeneList[neuronGeneIndex].InnovationId, neuronGeneIndex);
biasList[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].Bias;
}
// Create an array of the activation functions, inputs, and outputs for each module in the genome.
ModulePacket[] modules = new ModulePacket[g.ModuleGeneList.Count];
for (int i = g.ModuleGeneList.Count - 1; i >= 0; i--)
{
modules[i].function = g.ModuleGeneList[i].Function;
// Must translate input and output IDs to array locations.
modules[i].inputLocations = new int[g.ModuleGeneList[i].InputIds.Count];
for (int j = g.ModuleGeneList[i].InputIds.Count - 1; j >= 0; j--)
{
modules[i].inputLocations[j] = neuronLookup[g.ModuleGeneList[i].InputIds[j]];
}
modules[i].outputLocations = new int[g.ModuleGeneList[i].OutputIds.Count];
for (int j = g.ModuleGeneList[i].OutputIds.Count - 1; j >= 0; j--)
{
modules[i].outputLocations[j] = neuronLookup[g.ModuleGeneList[i].OutputIds[j]];
}
}
// ConnectionGenes point to a neuron's innovation ID. Translate this ID to the neuron's index in the neuron array.
FloatFastConnection[] connections = new FloatFastConnection[g.ConnectionGeneList.Count];
for (int connectionGeneIndex = g.ConnectionGeneList.Count - 1; connectionGeneIndex >= 0; connectionGeneIndex--)
{
ConnectionGene connectionGene = g.ConnectionGeneList[connectionGeneIndex];
connections[connectionGeneIndex].sourceNeuronIdx = neuronLookup[connectionGene.SourceNeuronId];
connections[connectionGeneIndex].targetNeuronIdx = neuronLookup[connectionGene.TargetNeuronId];
connections[connectionGeneIndex].weight = (float)connectionGene.Weight;
connections[connectionGeneIndex].learningRate = connectionGene.learningRate;
connections[connectionGeneIndex].A = connectionGene.A;
connections[connectionGeneIndex].B = connectionGene.B;
connections[connectionGeneIndex].C = connectionGene.C;
connections[connectionGeneIndex].D = connectionGene.D;
connections[connectionGeneIndex].modConnection = connectionGene.modConnection;
}
ModularNetwork mn = new ModularNetwork(biasCount, inputCount, outputCount, neuronCount, connections, biasList, activationFunctions, modules);
if (g.networkAdaptable)
{
mn.adaptable = true;
}
if (g.networkModulatory)
{
mn.modulatory = true;
}
mn.genome = g;
return(mn);
}
public AssimpImporter(ModulePacket packet)
{
_packet = packet;
}
public MainWindowTask(ModulePacket packet)
{
_packet = packet;
}
public SDKBase(ModulePacket packet)
{
_parser = packet.CreateScoped <IParser>();
}
static public ModularNetwork DecodeToModularNetwork(NeatGenome.NeatGenome g)
{
int inputCount = g.InputNeuronCount;
int outputCount = g.OutputNeuronCount;
int neuronCount = g.NeuronGeneList.Count;
IActivationFunction[] activationFunctions = new IActivationFunction[neuronCount];
float[] biasList = new float[neuronCount];
Dictionary <uint, int> neuronLookup = new Dictionary <uint, int>(neuronCount);
// Schrum: In case there are output neurons out of order
g.NeuronGeneList.NeuronSortCheck();
// Create an array of the activation functions for each non-module node node in the genome.
// Start with a bias node if there is one in the genome.
// The genome's neuron list is assumed to be ordered by type, with the bias node appearing first.
int neuronGeneIndex = 0;
for (; neuronGeneIndex < neuronCount; neuronGeneIndex++)
{
if (g.NeuronGeneList[neuronGeneIndex].NeuronType != NeuronType.Bias)
{
break;
}
activationFunctions[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].ActivationFunction;
neuronLookup.Add(g.NeuronGeneList[neuronGeneIndex].InnovationId, neuronGeneIndex);
}
int biasCount = neuronGeneIndex;
// Schrum: debug
//Console.WriteLine("start (after bias): " + g.GenomeId);
// Schrum: Debugging
//NeuronType expectedType = NeuronType.Input;
for (; neuronGeneIndex < neuronCount; neuronGeneIndex++)
{
activationFunctions[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].ActivationFunction;
// Schrum: Debug
/*
* if (expectedType != g.NeuronGeneList[neuronGeneIndex].NeuronType)
* {
* if (expectedType == NeuronType.Input && g.NeuronGeneList[neuronGeneIndex].NeuronType == NeuronType.Output)
* {
* expectedType = NeuronType.Output;
* }
* else if (expectedType == NeuronType.Output && g.NeuronGeneList[neuronGeneIndex].NeuronType == NeuronType.Hidden)
* {
* expectedType = NeuronType.Hidden;
* }
* else
* {
* // Error condition:
* Console.WriteLine("Error with genome: " + g.GenomeId);
*
* XmlDocument doc = new XmlDocument();
* XmlGenomeWriterStatic.Write(doc, (SharpNeatLib.NeatGenome.NeatGenome)g);
* FileInfo oFileInfo = new FileInfo("ProblemGenome.xml");
* doc.Save(oFileInfo.FullName);
*
* Environment.Exit(1);
* }
* }
*/
neuronLookup.Add(g.NeuronGeneList[neuronGeneIndex].InnovationId, neuronGeneIndex);
biasList[neuronGeneIndex] = g.NeuronGeneList[neuronGeneIndex].Bias;
}
// Create an array of the activation functions, inputs, and outputs for each module in the genome.
ModulePacket[] modules = new ModulePacket[g.ModuleGeneList.Count];
for (int i = g.ModuleGeneList.Count - 1; i >= 0; i--)
{
modules[i].function = g.ModuleGeneList[i].Function;
// Must translate input and output IDs to array locations.
modules[i].inputLocations = new int[g.ModuleGeneList[i].InputIds.Count];
for (int j = g.ModuleGeneList[i].InputIds.Count - 1; j >= 0; j--)
{
modules[i].inputLocations[j] = neuronLookup[g.ModuleGeneList[i].InputIds[j]];
}
modules[i].outputLocations = new int[g.ModuleGeneList[i].OutputIds.Count];
for (int j = g.ModuleGeneList[i].OutputIds.Count - 1; j >= 0; j--)
{
modules[i].outputLocations[j] = neuronLookup[g.ModuleGeneList[i].OutputIds[j]];
}
}
// ConnectionGenes point to a neuron's innovation ID. Translate this ID to the neuron's index in the neuron array.
FloatFastConnection[] connections = new FloatFastConnection[g.ConnectionGeneList.Count];
for (int connectionGeneIndex = g.ConnectionGeneList.Count - 1; connectionGeneIndex >= 0; connectionGeneIndex--)
{
ConnectionGene connectionGene = g.ConnectionGeneList[connectionGeneIndex];
connections[connectionGeneIndex].sourceNeuronIdx = neuronLookup[connectionGene.SourceNeuronId];
connections[connectionGeneIndex].targetNeuronIdx = neuronLookup[connectionGene.TargetNeuronId];
connections[connectionGeneIndex].weight = (float)connectionGene.Weight;
connections[connectionGeneIndex].learningRate = connectionGene.learningRate;
connections[connectionGeneIndex].A = connectionGene.A;
connections[connectionGeneIndex].B = connectionGene.B;
connections[connectionGeneIndex].C = connectionGene.C;
connections[connectionGeneIndex].D = connectionGene.D;
connections[connectionGeneIndex].modConnection = connectionGene.modConnection;
}
ModularNetwork mn = new ModularNetwork(biasCount, inputCount, outputCount, g.OutputsPerPolicy, neuronCount, connections, biasList, activationFunctions, modules);
if (g.networkAdaptable)
{
mn.adaptable = true;
}
if (g.networkModulatory)
{
mn.modulatory = true;
}
mn.genome = g;
return(mn);
}
public IOManager(ModulePacket packet)
{
_packet = packet;
_exporters = (List <IExporter>)_packet.GetInstances().OfType <IExporter>();
_importers = (List <IImporter>)_packet.GetInstances().OfType <IImporter>();
}
public FluincyTask(ModulePacket packet)
{
_configurationManager = packet.GetSingleton <IConfigurationManager>();
}
public MainWindow(ModulePacket packet)
{
_packet = packet;
Controls = new List <IGuiComponent>();
_ghosts = new List <IGhost>();
}
public VSync(ModulePacket packet)
{
_packet = packet;
}
