public void Test_Interbreed_2()
{
var initSet = new InitNeuroSet(_inputSize, _outputSize).BuildNetworks().Take(2).ToArray();
NeuralNetwork res = null;
for (int i = 0; i < 100; i++)
{
res = _interbreeder.Interbreed(initSet[0], initSet[1]);
}
Console.Write(res.ToString());
res.Serialize(@"C:\Temp\graph.graphml");
}
static void Main(string[] args)
{
var reqSerializer = (ISerializer <Request>) new BinarySerializer();
var respSerializer = (ISerializer <Response>) new BinarySerializer();
var log = LogManager.GetLogger("Main");
var testingTime = TimeSpan.FromSeconds(5);
var inputSize = 9;
var outputSize = 2;
var initSetCount = 5;
var initSet = new InitNeuroSet(inputSize, outputSize);
using (var remoteTestingController = new RemoteTestingController(testingTime, new ImageManager(), log))
using (var server = new TcpServer(52200))
{
var trainingSupervisor = new TrainingSupervisor(new Interbreeder(2, outputSize), null, remoteTestingController);
server.OnProcess += bytes =>
{
var request = reqSerializer.Deserialize(bytes);
var response = remoteTestingController.Compute(request);
return(respSerializer.Serialize(response));
};
server.Start();
Task.Factory.StartNew(() =>
{
try
{
var networks = initSet.BuildNetworks().Take(initSetCount).ToList().AsReadOnly();
trainingSupervisor.Train(networks);
remoteTestingController.Dispose();
}
catch (Exception e)
{
log.Error(e);
throw;
}
});
Console.Read();
}
}
