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"); }