public void Train()
{
var network = createNetwork();
IMLDataSet trainingSet = EncogUtility.LoadCSV2Memory(normFile, network.InputCount, 1, false, CSVFormat.English, false);
IMLTrain train = new Backpropagation(network, trainingSet);
int epoch = 1;
int truecases = 0;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.05);
train.FinishTraining();
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = network.Compute(pair.Input);
Console.WriteLine(@" actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
if (pair.Ideal[0] == 1 && output[0] > 0.5)
{
truecases++;
}
else if (pair.Ideal[0] == 0 && output[0] < 0.5)
{
truecases++;
}
}
Console.WriteLine(truecases);
SerializeObject.Save(networkFile, network);
}
public void geneticNeural()
{
FeedforwardNetwork network = createNetwork();
// train the neural network
Console.WriteLine("Determining initial scores");
TicTacToeGenetic train = new TicTacToeGenetic(network, true,
NeuralTicTacToe.POPULATION_SIZE,
NeuralTicTacToe.MUTATION_PERCENT, NeuralTicTacToe.MATE_PERCENT,
this.player2.GetType());
train.UseThreadPool = true;
ThreadPool.SetMaxThreads(NeuralTicTacToe.THREAD_POOL_SIZE, NeuralTicTacToe.THREAD_POOL_SIZE);
int epoch = 1;
DateTime started = DateTime.Now;
int minutes = 0;
do
{
train.Iteration();
TimeSpan span = (DateTime.Now - started);
minutes = span.Minutes;
Console.WriteLine("Epoch #" + epoch + " Error:" + train.getScore()
+ ",minutes left="
+ (NeuralTicTacToe.TRAIN_MINUTES - minutes));
epoch++;
} while (minutes < NeuralTicTacToe.TRAIN_MINUTES);
SerializeObject.Save("tictactoe.net", train.Network);
}
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()
{
SupportVectorMachine network = Create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
SupportVectorMachine network2 = (SupportVectorMachine)SerializeObject.Load(SERIAL_FILENAME.ToString());
Validate(network2);
}
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()
{
BasicPNN network = create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
BasicPNN network2 = (BasicPNN)SerializeObject.Load(SERIAL_FILENAME.ToString());
XOR.VerifyXOR(network2, 0.001);
}
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 void SavePAPContainer(string pathName, string fileName)
{
if (playerNetworkPool == null)
{
throw new Exception("Network must be set before the PAP container can be saved out.");
}
//Save out the PAPContainer (without the network)
SerializeObject.Save(pathName + fileName + ".PAPc", this);
//Save out the network
NNLoadSave.saveNetwork(playerNetworkPool.BaseNetwork, fileName + ".eNN", pathName);
}
public void Serialize()
{
using (SaveFileDialog dialog = new SaveFileDialog())
{
dialog.Filter = "ennq files (*.ennq)|*.ennq";
dialog.FilterIndex = 2;
dialog.RestoreDirectory = true;
if (dialog.ShowDialog() == DialogResult.OK)
{
SerializeObject.Save(dialog.FileName, Network);
}
}
}
public void Process()
{
this.network = NetworkUtil.CreateNetwork();
Console.WriteLine("Preparing training sets...");
this.common = new CommonWords(Config.FILENAME_COMMON_WORDS);
this.histogramGood = new WordHistogram(this.common);
this.histogramBad = new WordHistogram(this.common);
// load the good words
this.histogramGood.BuildFromFile(Config.FILENAME_GOOD_TRAINING_TEXT);
this.histogramGood.BuildComplete();
// load the bad words
this.histogramBad.BuildFromFile(Config.FILENAME_BAD_TRAINING_TEXT);
this.histogramBad.BuildComplete();
// remove low scoring words
this.histogramGood
.RemoveBelow((int)this.histogramGood.CalculateMean());
this.histogramBad.RemovePercent(0.99);
// remove common words
this.histogramGood.RemoveCommon(this.histogramBad);
this.histogramGood.Trim(Config.INPUT_SIZE);
this.goodAnalysis = new AnalyzeSentences(this.histogramGood,
Config.INPUT_SIZE);
this.badAnalysis = new AnalyzeSentences(this.histogramGood,
Config.INPUT_SIZE);
this.goodAnalysis.Process(this.trainingSet, 0.9,
Config.FILENAME_GOOD_TRAINING_TEXT);
this.badAnalysis.Process(this.trainingSet, 0.1,
Config.FILENAME_BAD_TRAINING_TEXT);
this.sampleCount = this.trainingSet.Ideal.Count;
Console.WriteLine("Processing " + this.sampleCount + " training sets.");
AllocateTrainingSets();
CopyTrainingSets();
TrainNetworkBackpropBackprop();
SerializeObject.Save(Config.FILENAME_WHENBORN_NET, this.network);
SerializeObject.Save(Config.FILENAME_HISTOGRAM, this.histogramGood);
Console.WriteLine("Training complete.");
}
public void geneticNeural()
{
Stopwatch sw = new Stopwatch();
FeedforwardNetwork network = createNetwork();
// train the neural network
Console.WriteLine("Determining initial scores");
TicTacToeGenetic train = new TicTacToeGenetic(network, true,
NeuralTicTacToe.POPULATION_SIZE,
NeuralTicTacToe.MUTATION_PERCENT, NeuralTicTacToe.MATE_PERCENT,
this.player2.GetType());
train.UseThreadPool = true;
sw.Stop();
string duration = String.Format("Training_time: {0}", sw.Elapsed.Minutes);
Console.WriteLine(duration);
ThreadPool.SetMaxThreads(NeuralTicTacToe.THREAD_POOL_SIZE, NeuralTicTacToe.THREAD_POOL_SIZE);
int epoch = 1;
DateTime started = DateTime.Now;
int minutes = 0;
double error = train.getScore();
do
{
sw.Start();
error = train.getScore();
if (error > 0)
{
train.Iteration();
}
sw.Stop();
minutes = sw.Elapsed.Minutes;
string observation = String.Format("Epoch: {0}, Error:{1}, minutes_left : {2}", epoch, error, (NeuralTicTacToe.TRAIN_MINUTES - minutes));
Console.WriteLine(observation);
epoch++;
} while (minutes < NeuralTicTacToe.TRAIN_MINUTES && error > 0.00001d);
SerializeObject.Save("tictactoe.net", train.Network);
}
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));
}
public static void Generate(ForestConfig config, bool useOneOf)
{
var generate = new GenerateData(config);
generate.Step1();
generate.Step2();
DataNormalization norm = generate.Step3(useOneOf);
// save the normalize object
SerializeObject.Save(config.NormalizeFile.ToString(), norm);
// create and save the neural network
BasicNetwork network =
EncogUtility.SimpleFeedForward(norm.GetNetworkInputLayerSize(),
config.HiddenCount, 0, norm.GetNetworkOutputLayerSize(), true);
EncogDirectoryPersistence.SaveObject(config.TrainedNetworkFile, network);
}
/// <summary>
/// Saves a normalization to the specified folder with the specified name.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <param name="normTosave">The norm tosave.</param>
public static void SaveNormalization(string directory, string file, DataNormalization normTosave)
{
SerializeObject.Save(directory + file, normTosave);
}
public void saveNeuralNetwork()
{
SerializeObject.Save("sp500.net", network);
}
