static void Main(string[] args)
{
//create a neural network withtout using a factory
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
IMLTrain train = new ResilientPropagation(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine($"Epoch #{epoch} Error: {train.Error}");
epoch++;
} while (train.Error > 0.01);
train.FinishTraining();
Console.WriteLine("Neural Network Results:");
foreach (IMLDataPair iPair in trainingSet)
{
IMLData output = network.Compute(iPair.Input);
Console.WriteLine($"{iPair.Input[0]}, {iPair.Input[0]}, actual={output[0]}, ideal={iPair.Ideal[0]}");
}
EncogFramework.Instance.Shutdown();
Console.ReadKey();
}
static void Main(string[] args)
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
var trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var train = new ResilientPropagation(network, trainingSet);
var epoch = 1;
do
{
train.Iteration();
} while (train.Error > 0.01);
train.FinishTraining();
foreach (var pair in trainingSet)
{
var output = network.Compute(pair.Input);
Console.WriteLine(pair.Input[0] + @", " + pair.Input[1] + @" , actual=" + output[0] + @", ideal=" + pair.Ideal[0]);
}
EncogFramework.Instance.Shutdown();
Console.ReadLine();
}
public static long BenchmarkEncog(double[][] input, double[][] output)
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true,
input[0].Length));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true,
HIDDEN_COUNT));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false,
output[0].Length));
network.Structure.FinalizeStructure();
network.Reset();
IMLDataSet trainingSet = new BasicMLDataSet(input, output);
// train the neural network
IMLTrain train = new Backpropagation(network, trainingSet, 0.7, 0.7);
Stopwatch sw = new Stopwatch();
sw.Start();
// run epoch of learning procedure
for (int i = 0; i < ITERATIONS; i++)
{
train.Iteration();
}
sw.Stop();
return sw.ElapsedMilliseconds;
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
// create a neural network, without using a factory
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
// create training data
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
// train the neural network
IMLTrain train = new ResilientPropagation(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
// test the neural network
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = network.Compute(pair.Input);
Console.WriteLine(pair.Input[0] + @"," + pair.Input[1]
+ @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
}
}
private void Preprocessing_Completed(object sender, RunWorkerCompletedEventArgs e)
{
worker.ReportProgress(0, "Creating Network...");
BasicNetwork Network = new BasicNetwork();
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, DataContainer.NeuralNetwork.Data.InputSize));
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 50));
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, DataContainer.NeuralNetwork.Data.IdealSize));
Network.Structure.FinalizeStructure();
Network.Reset();
DataContainer.NeuralNetwork.Network = Network;
ResilientPropagation training = new ResilientPropagation(DataContainer.NeuralNetwork.Network, DataContainer.NeuralNetwork.Data);
worker.ReportProgress(0, "Running Training: Epoch 0");
for(int i = 0; i < 200; i++)
{
training.Iteration();
worker.ReportProgress(0, "Running Training: Epoch " + (i+1).ToString() + " Current Training Error : " + training.Error.ToString());
if(worker.CancellationPending == true)
{
completed = true;
return;
}
}
completed = true;
}
/// <summary>
/// Metodo responsavel por criar a rede neural
/// </summary>
/// <param name="source">FileInfo com o path do network</param>
private static void CreateNetwork(FileInfo source)
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationLinear(), true, 4));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 6));
network.AddLayer(new BasicLayer(new ActivationTANH(), false, 2));
network.Structure.FinalizeStructure();
network.Reset();
EncogDirectoryPersistence.SaveObject(source, (BasicNetwork)network);
}
public BasicNetwork generateNetwork()
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(MultiThreadBenchmark.INPUT_COUNT));
network.AddLayer(new BasicLayer(MultiThreadBenchmark.HIDDEN_COUNT));
network.AddLayer(new BasicLayer(MultiThreadBenchmark.OUTPUT_COUNT));
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public void Create(int inputnodes,int hiddennodes)
{
network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, inputnodes));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, hiddennodes));
network.AddLayer(new BasicLayer(new ActivationLinear(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
this.hiddennodes = hiddennodes;
}
public BasicNetwork generateNetwork()
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(INPUT_COUNT));
network.AddLayer(new BasicLayer(HIDDEN_COUNT));
network.AddLayer(new BasicLayer(OUTPUT_COUNT));
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public static void CreateNetwork(FileOps fileOps)
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationLinear(),true,4));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 6));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 2));
network.Structure.FinalizeStructure();
network.Reset();
EncogDirectoryPersistence.SaveObject(fileOps.TrainedNeuralNetworkFile, network);
}
private static BasicNetwork CreateNetwork()
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public static BasicNetwork createElliott()
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, INPUT_OUTPUT));
network.AddLayer(new BasicLayer(new ActivationElliottSymmetric(), true, HIDDEN));
network.AddLayer(new BasicLayer(new ActivationElliottSymmetric(), false, INPUT_OUTPUT));
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public PSO()
{
network = new BasicNetwork();
network.AddLayer(new BasicLayer(5));
network.AddLayer(new BasicLayer(1));
network.AddLayer(new BasicLayer(1));
network.Structure.FinalizeStructure();
network.Reset();
IMLDataSet dataSet = new BasicMLDataSet();
dataSet.Add(new BasicMLData(new double[] { 1.0, 4.0, 3.0, 4.0, 5.0}) , new BasicMLData(new double[] { 2.0, 4.0, 6.0 , 8.0, 10} ));
train = new NeuralPSO(network, new RangeRandomizer(0, 10), new TrainingSetScore(dataSet),5);
}
public IMLMethod Generate()
{
BasicLayer layer;
BasicLayer layer2;
BasicNetwork network = new BasicNetwork();
if ((0 != 0) || (0 == 0))
{
network.AddLayer(layer2 = new BasicLayer(this._x2a5a4034520336f3, true, this._xcfe830a7176c14e5));
}
network.AddLayer(layer = new BasicLayer(this._x2a5a4034520336f3, true, this._xdf89f9cf9fc3d06f));
network.AddLayer(new BasicLayer(null, false, this._x8f581d694fca0474));
layer2.ContextFedBy = layer;
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public static void Experiment()
{
BasicNetwork net = new BasicNetwork();
net.AddLayer(
new BasicLayer(new ActivationLinear(), false, 3));
net.AddLayer(
new BasicLayer(new ActivationTANH(), true, 3));
net.AddLayer(
new BasicLayer(new ActivationLinear(), false, 2));
net.Structure.FinalizeStructure();
//Задание случайных весов?
net.Reset();
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="args">Not used.</param>
public static void Main(String[] args)
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
network.Structure.FinalizeStructure();
network.Reset();
IMLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
EncogUtility.TrainToError(network,trainingSet,0.01);
// test the neural network
Console.WriteLine("Neural Network Results:");
EncogUtility.Evaluate(network,trainingSet);
}
public void BuildModel()
{
Model = new BasicNetwork();
Model.AddLayer(new BasicLayer(null, UseBias, FirstLayerSize));
foreach (int layer in Layers)
{
if (Activation == ActivationType.Bipolar)
Model.AddLayer(new BasicLayer(new ActivationTANH(), UseBias, layer));
else
Model.AddLayer(new BasicLayer(new ActivationSigmoid(), UseBias, layer));
}
Model.AddLayer(new BasicLayer(Activation == ActivationType.Bipolar ? new ActivationTANH() as IActivationFunction : new ActivationSigmoid() as IActivationFunction, false, LastLayerSize));
Model.Structure.FinalizeStructure();
Model.Reset();
}
public int Train(DataSet dataSet)
{
Network = new BasicNetwork();
Network.AddLayer(new BasicLayer(null, true, 8 * 21));
var first = ((8 * 21 + 4) * FirstLayerParameter);
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, (int)first));
var second = ((8 * 21 + 4) * SecondLayerParameter);
Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, (int)second));
Network.AddLayer(new BasicLayer(null, false, 1));
// Network.AddLayer(new );
Network.Structure.FinalizeStructure();
Network.Reset();
//IMLData x = new BasicNeuralData();
var set = new double[dataSet.Signatures.Count + dataSet.Forgeries.Count][];
var ideal = new double[dataSet.Signatures.Count + dataSet.Forgeries.Count][];
for (int i = 0; i < dataSet.Signatures.Count; i++)
{
set[i] = dataSet.Signatures[i].Data.Cast<double>().ToArray();
ideal[i] = new double[] {1};
}
for (int i = dataSet.Signatures.Count; i < dataSet.Signatures.Count + dataSet.Forgeries.Count; i++)
{
set[i] = dataSet.Forgeries[i- dataSet.Signatures.Count].Data.Cast<double>().ToArray();
ideal[i] = new double[] { 0 };
}
IMLDataSet trainingSet = new BasicMLDataSet(set, ideal);
IMLTrain train = new ResilientPropagation(Network, trainingSet);
int epoch = 1;
var errors = new List<double>();
do
{
train.Iteration();
// Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
errors.Add(train.Error);
} while ( epoch < 10000);
train.FinishTraining();
return 1;
}
public Predictor(TextBox txtOutput, CSVData data, int hiddenNodes, double percentValidation)
{
m_txtOutputWindow = txtOutput;
m_data = data;
// Populate the input and output arrays
LoadData(percentValidation);
// Create Neural Network
m_network = new BasicNetwork();
m_network.AddLayer(new BasicLayer(null, true, m_data.InputNodes));
m_network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, hiddenNodes));
m_network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, m_data.OutputNodes));
m_network.Structure.FinalizeStructure();
m_network.Reset();
m_train = new Backpropagation(m_network, new BasicMLDataSet(m_inputTraining, m_outputTraining));
}
public MLPNetwork(int layersCount, int neuronsCount, bool bias, ActivationFunctionType aft, ProblemType problemType, string inputFileName)
{
this.layersCount = layersCount;
this.neuronsCount = neuronsCount;
this.bias = bias;
this.activationFunType = aft;
this.problemType = problemType;
LoadTrainingData(inputFileName);
network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, bias, trainingData.InputSize));
for (int i = 0; i < layersCount; i++)
network.AddLayer(new BasicLayer(CreateActivationFunction(), bias, neuronsCount));
network.AddLayer(new BasicLayer(CreateActivationFunction(), false, outputSize));
network.Structure.FinalizeStructure();
network.Reset();
}
public void testPersistLargeEG()
{
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 200));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 200));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 200));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 200));
network.Structure.FinalizeStructure();
network.Reset();
EncogDirectoryPersistence.SaveObject(EG_FILENAME, network);
BasicNetwork network2 = (BasicNetwork)EncogDirectoryPersistence.LoadObject(EG_FILENAME);
double d = EngineArray.EuclideanDistance(network.Structure.Flat.Weights,
network2.Structure.Flat.Weights);
Assert.IsTrue(d < 0.01);
}
public void Execute(IExampleInterface app)
{
// create a neural network, without using a factory
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, 2));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
network.Structure.FinalizeStructure();
network.Reset();
// create training data
var trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
EncogUtility.TrainToError(network, trainingSet, 0.01);
EncogUtility.Evaluate(network, trainingSet);
var ff = new FreeformNetwork(network);
EncogUtility.Evaluate(ff, trainingSet);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Trains a random trainer.
/// </summary>
/// <param name="inputs">The inputs.</param>
/// <param name="predictWindow">The predict window.</param>
public static double RandomTrainerMethod(int inputs, int predictWindow)
{
double[] firstinput = MakeInputs(inputs);
double[] SecondInput = MakeInputs(inputs);
double[] ThirdInputs = MakeInputs(inputs);
double[] FourthInputs = MakeInputs(inputs);
double[] inp5 = MakeInputs(inputs);
double[] inp6 = MakeInputs(inputs);
var pair = TrainerHelper.ProcessPairs(firstinput, firstinput, inputs, predictWindow);
var pair2 = TrainerHelper.ProcessPairs(SecondInput, firstinput, inputs, predictWindow);
var pair3 = TrainerHelper.ProcessPairs(ThirdInputs, firstinput, inputs, predictWindow);
var pair4 = TrainerHelper.ProcessPairs(FourthInputs, firstinput, inputs, predictWindow);
var pair5 = TrainerHelper.ProcessPairs(inp5, firstinput, inputs, predictWindow);
var pair6 = TrainerHelper.ProcessPairs(inp6, firstinput, inputs, predictWindow);
BasicMLDataSet SuperSet = new BasicMLDataSet();
SuperSet.Add(pair);
SuperSet.Add(pair2);
SuperSet.Add(pair3);
SuperSet.Add(pair4);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationTANH(), true, SuperSet.InputSize));
network.AddLayer(new BasicLayer(new ActivationTANH(), false, 20));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 0));
network.AddLayer(new BasicLayer(new ActivationLinear(), true, predictWindow));
//var layer = new BasicLayer(new ActivationTANH(), true, SuperSet.InputSize);
//layer.Network = network;
network.Structure.FinalizeStructure();
network.Reset();
// var network = (BasicNetwork)CreateEval.CreateElmanNetwork(SuperSet.InputSize, SuperSet.IdealSize);
return CreateEval.TrainNetworks(network, SuperSet);
//Lets create an evaluation.
//Console.WriteLine(@"Last error rate on random trainer:" + error);
}
public double Run(List<int> topoplogy, int iterations)
{
_Network = new BasicNetwork();
_Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, _Features));
foreach (int layer in topoplogy)
{
_Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, layer));
}
_Network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
_Network.Structure.FinalizeStructure();
_Network.Reset();
//Encog.Neural.Networks.Training.Propagation.Gradient.
ITrain train = new ResilientPropagation(_Network, _TrainingSet);
for (int i = 0; i < iterations; i++)
{
train.Iteration();
}
return train.Error;
}
public void BasicSlidingSineSignal()
{
var listSize = 30 * 200;
var inputList = new List<double>(listSize);
var idealList = new List<double>(listSize);
var rand = new Random(23);
for(int i = 0; i < listSize; i++)
{
idealList.Add(Math.Sin(Math.PI * 2.0 * i / 30));
inputList.Add(idealList[idealList.Count - 1] + (rand.NextDouble() - 0.5) * 0.1);
}
var input = new SlidingWindowMLDataProvider(inputList, 10, 0, 1);
var ideal = new SlidingWindowMLDataProvider(idealList, 2, 11, 1); // predecit the eleventh, twelth item from the ten previous to it
var ds = new DynamicMLDataSet(input, ideal);
Assert.AreEqual(10, input.WindowSize);
Assert.AreEqual(10, ds.InputSize);
Assert.AreEqual(2, ds.IdealSize);
Assert.AreEqual(listSize, ds.Count);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(ds.InputSize));
network.AddLayer(new BasicLayer(ds.InputSize + 3));
network.AddLayer(new BasicLayer(ds.IdealSize));
network.Structure.FinalizeStructure();
network.Reset(42);
var trainer = new Encog.Neural.Networks.Training.Propagation.Resilient.ResilientPropagation(network, ds);
int maxIteration = 300;
int iteration = 0;
do
{
trainer.Iteration();
Debug.WriteLine(++iteration + ": Error = " + trainer.Error);
} while(trainer.Error > 0.001 && maxIteration > iteration);
Assert.IsTrue(iteration < maxIteration);
}
public void Perform(int thread)
{
var stopwatch = new Stopwatch();
stopwatch.Start();
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(INPUT_COUNT));
network.AddLayer(new BasicLayer(HIDDEN_COUNT));
network.AddLayer(new BasicLayer(OUTPUT_COUNT));
network.Structure.FinalizeStructure();
network.Reset();
IMLDataSet training = RandomTrainingFactory.Generate(1000, 50000,
INPUT_COUNT, OUTPUT_COUNT, -1, 1);
var rprop = new ResilientPropagation(network, training);
rprop.ThreadCount = thread;
for (int i = 0; i < 5; i++)
{
rprop.Iteration();
}
stopwatch.Stop();
Console.WriteLine("Result with " + thread + " was " + stopwatch.ElapsedMilliseconds + "ms");
}
public override void Run()
{
testNetwork = new BasicNetwork();
testNetwork.AddLayer(new BasicLayer(null, true, 2));
testNetwork.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 4));
testNetwork.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
testNetwork.Structure.FinalizeStructure();
testNetwork.Reset();
// create training data
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
// train the neural network
IMLTrain train = new Backpropagation(testNetwork, trainingSet);
//IMLTrain train = new ResilientPropagation(testNetwork, trainingSet); //Encog manual says it is the best general one
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.0001);
// test the neural network
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = testNetwork.Compute(pair.Input);
Console.WriteLine(pair.Input[0] + @"," + pair.Input[1]
+ @", actual=" + output[0] + @",ideal=" + pair.Ideal[0]);
}
}
/// <summary>
/// Generate the Elman neural network.
/// </summary>
///
/// <returns>The Elman neural network.</returns>
public IMLMethod Generate()
{
BasicLayer hidden, input;
var network = new BasicNetwork();
network.AddLayer(input = new BasicLayer(_activation, true,
_inputNeurons));
network.AddLayer(hidden = new BasicLayer(_activation, true,
_hiddenNeurons));
network.AddLayer(new BasicLayer(null, false, _outputNeurons));
input.ContextFedBy = hidden;
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
public void TestDynamicXOR()
{
Func<int, int, double> inputFunc = OnInputFunc;
Func<int, int, double> idealFunc = delegate(int chunk, int index) { return XOR.XORIdeal[chunk][index]; };
var input = new FuncMLDataProvider(inputFunc, XOR.XORInput.Length, XOR.XORInput[0].Length);
var ideal = new FuncMLDataProvider(idealFunc, XOR.XORIdeal.Length, XOR.XORIdeal[0].Length);
var ds = new DynamicMLDataSet(input, ideal);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, ds.InputSize));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, ds.InputSize + 5));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, ds.IdealSize));
network.Structure.FinalizeStructure();
network.Reset(42);
var trainer = new Encog.Neural.Networks.Training.Propagation.Resilient.ResilientPropagation(network, ds);
int maxIteration = 300;
int iteration = 0;
do
{
trainer.Iteration();
Debug.WriteLine(++iteration + ": Error = " + trainer.Error);
} while(trainer.Error > 0.0001 && maxIteration > iteration);
Assert.IsTrue(iteration < maxIteration);
}
public static BasicNetwork CreateThreeLayerNet()
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(2));
network.AddLayer(new BasicLayer(3));
network.AddLayer(new BasicLayer(1));
network.Structure.FinalizeStructure();
network.Reset();
return network;
}
