public static FeedforwardNetwork loadNetwork()
{
FeedforwardNetwork result = (FeedforwardNetwork)SerializeObject
.Load("tictactoe.net");
return(result);
}
public YearBornBot()
{
this.network = (FeedforwardNetwork)SerializeObject
.Load(Config.FILENAME_WHENBORN_NET);
this.histogram = (WordHistogram)SerializeObject
.Load(Config.FILENAME_HISTOGRAM);
}
public double TestSingle(double[] input)
{
double[] outPut = new double[1];
BasicNetwork network = (BasicNetwork)SerializeObject.Load(networkFile);
network.Compute(input, outPut);
//Console.Write("The chance of winning is: " + Math.Round(output[0]*100,2) + "% | ");
return(outPut[0]);
}
public void TestPersistSerial()
{
SupportVectorMachine network = Create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
SupportVectorMachine network2 = (SupportVectorMachine)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(network2);
}
public void TestPersistSerial()
{
BasicPNN network = create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
BasicPNN network2 = (BasicPNN)SerializeObject.Load(SERIAL_FILENAME.ToString());
XOR.VerifyXOR(network2, 0.001);
}
public void TestContPersistSerial()
{
HiddenMarkovModel sourceHMM = BuildContHMM();
SerializeObject.Save(SERIAL_FILENAME.ToString(), sourceHMM);
HiddenMarkovModel resultHMM = (HiddenMarkovModel)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(resultHMM, sourceHMM);
}
public void TestPersistSerial()
{
NEATPopulation pop = Generate();
SerializeObject.Save(SERIAL_FILENAME.ToString(), pop);
NEATPopulation pop2 = (NEATPopulation)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(pop2);
}
public void TestPersistSerial()
{
BAMNetwork network = Create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
var network2 = (BAMNetwork)SerializeObject.Load(SERIAL_FILENAME.ToString());
ValidateBAM(network2);
}
public void testPersistSerial()
{
PrgPopulation pop = Create();
Validate(pop);
SerializeObject.Save(SERIAL_FILENAME.ToString(), pop);
PrgPopulation pop2 = (PrgPopulation)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(pop2);
}
public void TestPersistSerial()
{
HopfieldNetwork network = new HopfieldNetwork(4);
network.SetWeight(1, 1, 1);
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
HopfieldNetwork network2 = (HopfieldNetwork)SerializeObject.Load(SERIAL_FILENAME.ToString());
ValidateHopfield(network2);
}
public void TestPersistSerial()
{
BoltzmannMachine network = new BoltzmannMachine(4);
network.SetWeight(1, 1, 1);
network.Threshold[2] = 2;
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
BoltzmannMachine network2 = (BoltzmannMachine)SerializeObject.Load(SERIAL_FILENAME.ToString());
ValidateHopfield(network2);
}
public static PAPNetworkContainer LoadPAPContainer(string pathName, string fileName)
{
throw new NotImplementedException();
//Load the container
PAPNetworkContainer returnItem = (PAPNetworkContainer)SerializeObject.Load(pathName + fileName + ".PAPc");
//Load the network in as well
returnItem.playerNetworkPool = new NNThreadSafeNetworkPool(NNLoadSave.loadNetwork(fileName + ".eNN", pathName), returnItem.playerId.ToString(), NNThreadSafeNetworkPool.DefaultListLength);
return(returnItem);
}
public static EncogNeuralNetworkSlow DeSerialize()
{
var fileDialog = new OpenFileDialog();
fileDialog.Filter = "enns files (*.enns)|*.enns";
if (fileDialog.ShowDialog() == DialogResult.OK)
{
var ENNetwork = new EncogNeuralNetworkSlow();
ENNetwork.Network = (BasicNetwork)SerializeObject.Load(fileDialog.FileName);
return(ENNetwork);
}
return(null);
}
public static EncogNeuralNetworkQuick DeSerialize(int divisionCountX, int divisionCountY)
{
var fileDialog = new OpenFileDialog();
fileDialog.Filter = "ennq files (*.ennq)|*.ennq";
if (fileDialog.ShowDialog() == DialogResult.OK)
{
var ENNetwork = new EncogNeuralNetworkQuick();
ENNetwork.Network = (BasicNetwork)SerializeObject.Load(fileDialog.FileName);
ENNetwork._divisionCountX = divisionCountX;
ENNetwork._divisionCountY = divisionCountY;
return(ENNetwork);
}
return(null);
}
public void Execute(IExampleInterface app)
{
this.app = app;
this.app = app;
IMLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
BasicNetwork network = EncogUtility.SimpleFeedForward(2, 6, 0, 1, false);
EncogUtility.TrainToError(network, trainingSet, 0.01);
double error = network.CalculateError(trainingSet);
SerializeObject.Save("encog.ser", network);
network = (BasicNetwork)SerializeObject.Load("encog.ser");
double error2 = network.CalculateError(trainingSet);
app.WriteLine("Error before save to ser: " + Format.FormatPercent(error));
app.WriteLine("Error before after to ser: " + Format.FormatPercent(error2));
}
/// <summary>
/// Loads a normalization from the specified directory and file.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <returns>a datanormalization object</returns>
public static DataNormalization LoadNormalization(string directory, string file)
{
DataNormalization norm = null;
FileInfo networkFile = FileUtil.CombinePath(new FileInfo(@directory), @file);
if (networkFile.Exists)
{
norm = (DataNormalization)SerializeObject.Load(networkFile.FullName);
}
if (norm == null)
{
Console.WriteLine(@"Can't find normalization resource: "
+ directory + file);
return(null);
}
return(norm);
}
public DataNormalization LoadNormalization()
{
DataNormalization norm = null;
if (_config.NormalizeFile.Exists)
{
norm = (DataNormalization)SerializeObject.Load(_config.NormalizeFile.ToString());
}
if (norm == null)
{
Console.WriteLine(@"Can't find normalization resource: "
+ _config.NormalizeFile);
return(null);
}
return(norm);
}
public void loadNeuralNetwork()
{
this.network = (FeedforwardNetwork)SerializeObject.Load("sp500.net");
}
public void loadNeuralNetwork()
{
network = (BasicNetwork)SerializeObject.Load("sp500.net");
}