/// <inheritdoc />
public override void Init(BasicNetwork theNetwork, IMLDataSet theTraining)
{
base.Init(theNetwork, theTraining);
int weightCount = theNetwork.Structure.Flat.Weights.Length;
_training = theTraining;
_network = theNetwork;
_hessianMatrix = new Matrix(weightCount, weightCount);
_hessian = _hessianMatrix.Data;
// create worker(s)
var determine = new DetermineWorkload(
ThreadCount, _training.Count);
_workers = new ChainRuleWorker[determine.ThreadCount];
int index = 0;
// handle CPU
foreach (IntRange r in determine.CalculateWorkers())
{
_workers[index++] = new ChainRuleWorker((FlatNetwork) _flat.Clone(),
_training.OpenAdditional(), r.Low,
r.High);
}
}
public static double EvaluateNetworks(BasicNetwork network, BasicMLDataSet set)
{
int count = 0;
int correct = 0;
foreach (IMLDataPair pair in set)
{
IMLData input = pair.Input;
IMLData actualData = pair.Ideal;
IMLData predictData = network.Compute(input);
double actual = actualData[0];
double predict = predictData[0];
double diff = Math.Abs(predict - actual);
Direction actualDirection = DetermineDirection(actual);
Direction predictDirection = DetermineDirection(predict);
if (actualDirection == predictDirection)
correct++;
count++;
Console.WriteLine(@"Number" + @"count" + @": actual=" + Format.FormatDouble(actual, 4) + @"(" + actualDirection + @")"
+ @",predict=" + Format.FormatDouble(predict, 4) + @"(" + predictDirection + @")" + @",diff=" + diff);
}
double percent = correct / (double)count;
Console.WriteLine(@"Direction correct:" + correct + @"/" + count);
Console.WriteLine(@"Directional Accuracy:"
+ Format.FormatPercent(percent));
return percent;
}
/// <summary>
/// Measure the performance of the network
/// </summary>
/// <param name = "network">Network to analyze</param>
/// <param name = "dataset">Dataset with input and ideal data</param>
/// <returns>Error % of correct bits, returned by the network.</returns>
public static double MeasurePerformance(BasicNetwork network, BasicNeuralDataSet dataset)
{
int correctBits = 0;
float threshold = 0.0f;
IActivationFunction activationFunction = network.GetActivation(network.LayerCount - 1); //get the activation function of the output layer
if (activationFunction is ActivationSigmoid)
{
threshold = 0.5f; /* > 0.5, range of sigmoid [0..1]*/
}
else if (activationFunction is ActivationTANH)
{
threshold = 0.0f; /*> 0, range of bipolar sigmoid is [-1..1]*/
}
else
throw new ArgumentException("Bad activation function");
int n = (int) dataset.Count;
Parallel.For(0, n, (i) =>
{
IMLData actualOutputs = network.Compute(dataset.Data[i].Input);
lock (LockObject)
{
for (int j = 0, k = actualOutputs.Count; j < k; j++)
if ((actualOutputs[j] > threshold && dataset.Data[i].Ideal[j] > threshold)
|| (actualOutputs[j] < threshold && dataset.Data[i].Ideal[j] < threshold))
correctBits++;
}
});
long totalOutputBitsCount = dataset.Count*dataset.Data[0].Ideal.Count;
return (double) correctBits/totalOutputBitsCount;
}
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]);
}
}
public TrainAdaline(BasicNetwork network, IMLDataSet training, double learningRate)
: base(TrainingImplementationType.Iterative)
{
if (((uint) learningRate) > uint.MaxValue)
{
goto Label_003B;
}
Label_0009:
if (network.LayerCount > 2)
{
goto Label_003B;
}
Label_0012:
this._x87a7fc6a72741c2e = network;
this._x823a2b9c8bf459c5 = training;
this._x9b481c22b6706459 = learningRate;
return;
Label_003B:
throw new NeuralNetworkError("An ADALINE network only has two layers.");
if (0x7fffffff == 0)
{
goto Label_0009;
}
goto Label_0012;
}
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 void evaluateNetwork(BasicNetwork network, IMLDataSet training)
{
double total = 0;
int seed = 0;
int completed = 0;
Stopwatch sw = new Stopwatch();
sw.Start();
while (completed < SAMPLE_SIZE)
{
new ConsistentRandomizer(-1, 1, seed).Randomize(network);
int iter = Evaluate(network, training);
if (iter == -1)
{
seed++;
}
else
{
total += iter;
seed++;
completed++;
}
}
sw.Stop();
Console.WriteLine(network.GetActivation(1).GetType().Name + ": time="
+ Format.FormatInteger((int)sw.ElapsedMilliseconds)
+ "ms, Avg Iterations: "
+ Format.FormatInteger((int)(total / SAMPLE_SIZE)));
}
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();
}
public void TestSingleOutput()
{
BasicNetwork 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();
(new ConsistentRandomizer(-1, 1)).Randomize(network);
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
HessianFD testFD = new HessianFD();
testFD.Init(network, trainingData);
testFD.Compute();
HessianCR testCR = new HessianCR();
testCR.Init(network, trainingData);
testCR.Compute();
//dump(testFD, "FD");
//dump(testCR, "CR");
Assert.IsTrue(testCR.HessianMatrix.equals(testFD.HessianMatrix, 4));
}
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;
}
public static long BenchmarkEncog(double[][] input, double[][] output)
{
var 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(23); // constant seed for repeatable testing
IMLDataSet trainingSet = new BasicMLDataSet(input, output);
// train the neural network
IMLTrain train = new Backpropagation(network, trainingSet, 0.7, 0.7);
var 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]);
}
}
/// <summary>
/// Saves the network to the specified directory with the specified parameter name.
/// </summary>
/// <param name="directory">The directory.</param>
/// <param name="file">The file.</param>
/// <param name="anetwork">The network to save..</param>
public static void SaveNetwork(string directory, string file, BasicNetwork anetwork)
{
FileInfo networkFile = FileUtil.CombinePath(new FileInfo(directory), file);
EncogDirectoryPersistence.SaveObject(networkFile, anetwork);
return;
}
public JacobianChainRule(BasicNetwork network, IMLDataSet indexableTraining)
{
BasicMLData data;
BasicMLData data2;
if (0 == 0)
{
goto Label_0055;
}
Label_0009:
this._x61830ac74d65acc3 = new BasicMLDataPair(data, data2);
return;
Label_0055:
this._xb12276308f0fa6d9 = indexableTraining;
if (0 == 0)
{
}
this._x87a7fc6a72741c2e = network;
this._xabb126b401219ba2 = network.Structure.CalculateSize();
this._x530ae94d583e0ea1 = (int) this._xb12276308f0fa6d9.Count;
this._xbdeab667c25bbc32 = EngineArray.AllocateDouble2D(this._x530ae94d583e0ea1, this._xabb126b401219ba2);
this._xc8a462f994253347 = new double[this._x530ae94d583e0ea1];
data = new BasicMLData(this._xb12276308f0fa6d9.InputSize);
data2 = new BasicMLData(this._xb12276308f0fa6d9.IdealSize);
if (-2147483648 != 0)
{
goto Label_0009;
}
goto Label_0055;
}
/// <summary>
/// Randomize the connections between two layers.
/// </summary>
/// <param name="network">The network to randomize.</param>
/// <param name="fromLayer">The starting layer.</param>
private void RandomizeSynapse(BasicNetwork network, int fromLayer)
{
int toLayer = fromLayer + 1;
int toCount = network.GetLayerNeuronCount(toLayer);
int fromCount = network.GetLayerNeuronCount(fromLayer);
int fromCountTotalCount = network.GetLayerTotalNeuronCount(fromLayer);
IActivationFunction af = network.GetActivation(toLayer);
double low = CalculateRange(af, Double.NegativeInfinity);
double high = CalculateRange(af, Double.PositiveInfinity);
double b = 0.7d * Math.Pow(toCount, (1d / fromCount)) / (high - low);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
if (fromCount != fromCountTotalCount)
{
double w = RangeRandomizer.Randomize(-b, b);
network.SetWeight(fromLayer, fromCount, toNeuron, w);
}
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = RangeRandomizer.Randomize(0, b);
network.SetWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
public NeuralRobot(BasicNetwork network, bool track, Position source, Position destination)
{
_hStats = new NormalizedField(NormalizationAction.Normalize, "Heading", 359, 0, .9, -.9);
_CanGoStats = new NormalizedField(NormalizationAction.Normalize, "CanGo", 1, 0, 0.9, -0.9);
_track = track;
_network = network;
sim = new RobotSimulator(source, destination);
}
public void Execute()
{
_normalizedTrainingData = NormalizeData(trainingData);
_normalizedPredictionData = NormalizeData(predictionData);
network = CreateNetwork();
IMLDataSet training = GenerateTraining();
Train(training);
Predict();
}
public MainWindow()
{
neuralNetIter = null;
neuralNetPair = null;
rep = new TrainLogRepository(AppConfigHelper.GetCollectionName, AppConfigHelper.GetDBName, AppConfigHelper.GetMongoDBConnectionString);
InitializeComponent();
}
public void EvaluateNetwork(BasicNetwork trainedNetwork, BasicMLDataSet trainingData)
{
foreach (var trainingItem in trainingData)
{
var output = trainedNetwork.Compute(trainingItem.Input);
Console.WriteLine("Input:{0}, {1} Ideal: {2} Actual : {3}", trainingItem.Input[0], trainingItem.Input[1], trainingItem.Ideal, output[0]);
}
Console.ReadKey();
}
/// <summary>
/// Create a new tester form
/// </summary>
/// <param name="network">Trained neural network to test</param>
/// <param name="inputFields">List of input fields from Encog Analyst</param>
/// <param name="outputFields">List of output fields from Encog Analyst</param>
public frmTest(BasicNetwork network, List<AnalystField> inputFields,
List<AnalystField> outputFields)
{
InitializeComponent();
m_network = network;
m_inputFields = inputFields;
m_outputFields = outputFields;
foreach(AnalystField field in inputFields)
{
switch(field.Name)
{
case "vCoverageType":
foreach (ClassItem item in field.Classes)
cmbCoverageType.Items.Add(item.Name);
cmbCoverageType.SelectedIndex = 0;
break;
case "vTransaction":
foreach (ClassItem item in field.Classes)
cmbTransactionType.Items.Add(item.Name);
cmbTransactionType.SelectedIndex = 0;
break;
case "nLoanAmount":
trkLoanAmount.Minimum = (int)field.ActualLow;
trkLoanAmount.Maximum = (int)field.ActualHigh;
trkLoanAmount.TickFrequency = (int)(field.ActualHigh / 10.0);
txtLoanAmount.Text = String.Format("{0:C2}", trkLoanAmount.Value);
break;
case "nLiens":
trkLiens.Minimum = (int)field.ActualLow;
trkLiens.Maximum = (int)field.ActualHigh;
trkLiens.TickFrequency = (int)(field.ActualHigh / 10.0);
txtLiens.Text = trkLiens.Value.ToString();
break;
case "nActions":
trkActions.Minimum = (int)field.ActualLow;
trkActions.Maximum = (int)field.ActualHigh;
trkActions.TickFrequency = (int)(field.ActualHigh / 10.0);
txtActions.Text = trkActions.Value.ToString();
break;
case "nAuditEntriesPerDay":
trkAuditEntries.Minimum = (int)field.ActualLow;
trkAuditEntries.Maximum = (int)field.ActualHigh;
trkAuditEntries.TickFrequency = (int)(field.ActualHigh / 10.0);
txtAuditEntries.Text = trkAuditEntries.Value.ToString();
break;
case "nTotalNotesPerDay":
trkNotesLogged.Minimum = (int)field.ActualLow;
trkNotesLogged.Maximum = (int)field.ActualHigh;
trkNotesLogged.TickFrequency = (int)(field.ActualHigh / 10.0);
txtNotesLogged.Text = trkNotesLogged.Value.ToString();
break;
}
}
}
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;
}
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 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 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;
}
/// <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);
}
private BasicNetwork ConstructNetwork()
{
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationTANH(), true, VanDerWaerdenGameRules.VanDerWaerdenNumber(this.NColors, this.ProgressionLength) - 1));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, VanDerWaerdenGameRules.VanDerWaerdenNumber(this.NColors, this.ProgressionLength)));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 1));
network.Structure.FinalizeStructure();
return network;
Debug.Print("Created new Network with parameters nColors = {0} and progression length = {1}.", NColors, ProgressionLength);
}
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);
}
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 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;
}
public NeuralPilot(BasicNetwork network, bool track)
{
_fuelStats = new NormalizedField(NormalizationAction.Normalize, "fuel", 200, 0, -0.9, 0.9);
_altitudeStats = new NormalizedField(NormalizationAction.Normalize, "altitude", 10000, 0, -0.9, 0.9);
_velocityStats = new NormalizedField(NormalizationAction.Normalize, "velocity",
LanderSimulator.TerminalVelocity, -LanderSimulator.TerminalVelocity,
-0.9, 0.9);
_track = track;
_network = network;
}
/// <summary>
/// Compare the two neural networks. For them to be equal they must be of the
/// same structure, and have the same matrix values.
/// </summary>
/// <param name="other">The other neural network.</param>
/// <returns>True if the two networks are equal.</returns>
public bool Equals(BasicNetwork other)
{
return(Equals(other,
EncogFramework.DEFAULT_PRECISION));
}
/// <summary>
/// Compare the two neural networks. For them to be equal they must be of the
/// same structure, and have the same matrix values.
/// </summary>
///
/// <param name="other">The other neural network.</param>
/// <returns>True if the two networks are equal.</returns>
public bool Equals(BasicNetwork other)
{
return(Equals(other, EncogFramework.DefaultPrecision));
}
/// <summary>
/// Determine if this neural network is equal to another. Equal neural
/// networks have the same weight matrix and bias values, within a specified
/// precision.
/// </summary>
///
/// <param name="other">The other neural network.</param>
/// <param name="precision">The number of decimal places to compare to.</param>
/// <returns>True if the two neural networks are equal.</returns>
public bool Equals(BasicNetwork other, int precision)
{
return(NetworkCODEC.Equals(this, other, precision));
}
/// <summary>
/// Read an object.
/// </summary>
///
public Object Read(Stream mask0)
{
var result = new BasicNetwork();
var flat = new FlatNetwork();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
flat.BeginTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagBeginTraining);
flat.ConnectionLimit = EncogFileSection.ParseDouble(p,
BasicNetwork.TagConnectionLimit);
flat.ContextTargetOffset = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetOffset);
flat.ContextTargetSize = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagContextTargetSize);
flat.EndTraining = EncogFileSection.ParseInt(p,
BasicNetwork.TagEndTraining);
flat.HasContext = EncogFileSection.ParseBoolean(p,
BasicNetwork.TagHasContext);
flat.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
flat.LayerCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerCounts);
flat.LayerFeedCounts = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerFeedCounts);
flat.LayerContextCount = EncogFileSection.ParseIntArray(
p, BasicNetwork.TagLayerContextCount);
flat.LayerIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagLayerIndex);
flat.LayerOutput = section.ParseDoubleArray(p, PersistConst.Output);
flat.LayerSums = new double[flat.LayerOutput.Length];
flat.OutputCount = EncogFileSection.ParseInt(p,
PersistConst.OutputCount);
flat.WeightIndex = EncogFileSection.ParseIntArray(p,
BasicNetwork.TagWeightIndex);
flat.Weights = section.ParseDoubleArray(p, PersistConst.Weights);
flat.BiasActivation = section.ParseDoubleArray(p, BasicNetwork.TagBiasActivation);
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("ACTIVATION"))
{
int index = 0;
flat.ActivationFunctions = new IActivationFunction[flat.LayerCounts.Length];
foreach (String line in section.Lines)
{
IActivationFunction af;
IList <String> cols = EncogFileSection
.SplitColumns(line);
String name = ReflectionUtil.AfPath
+ cols[0];
try
{
af = (IActivationFunction)ReflectionUtil.LoadObject(name);
}
catch (TypeLoadException e)
{
throw new PersistError(e);
}
catch (TargetException e)
{
throw new PersistError(e);
}
catch (MemberAccessException e)
{
throw new PersistError(e);
}
for (int i = 0; i < af.ParamNames.Length; i++)
{
af.Params[i] =
CSVFormat.EgFormat.Parse(cols[i + 1]);
}
flat.ActivationFunctions[index++] = af;
}
}
}
result.Structure.Flat = flat;
return(result);
}
/// <summary>
/// Mencari solusi model neural network
/// </summary>
private void searchSolution()
{
// Normalize Data
switch (this.selectedActivationFunction)
{
case ActivationFunctionEnumeration.SemiLinearFunction:
this.activationFunction = new SemiLinearFunction();
this.normalizeData(0.1, 0.9);
break;
case ActivationFunctionEnumeration.SigmoidFunction:
this.activationFunction = new SigmoidFunction();
this.normalizeData(0.1, 0.9);
break;
case ActivationFunctionEnumeration.BipolarSigmoidFunction:
this.activationFunction = new BipolarSigmoidFunction();
this.normalizeData(-0.9, 0.9);
break;
case ActivationFunctionEnumeration.HyperbolicTangentFunction:
this.activationFunction = new HyperbolicTangentFunction();
this.normalizeData(-0.9, 0.9);
break;
default:
this.activationFunction = new BipolarSigmoidFunction();
this.normalizeData(-0.9, 0.9);
break;
}
//create network
this.network = new BasicNetwork();
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.inputLayerNeurons));
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.hiddenLayerNeurons));
this.network.AddLayer(new FeedforwardLayer(this.activationFunction, this.outputLayerNeurons));
this.network.Reset();
//variable for looping
//needToStop = false;
double mse = 0.0, error = 0.0, mae = 0.0;
int iteration = 1;
// parameters
double msle = 0.0, mspe = 0.0, generalizationLoss = 0.0, pq = 0.0;
double[] trainingErrors = new double[this.strip];
for (int i = 0; i < this.strip; i++)
{
trainingErrors[i] = double.MaxValue / strip;
}
double lastMSE = double.MaxValue;
// advanced early stopping
int n = this.data.Length - this.network.InputLayer.NeuronCount;
int validationSet = (int)Math.Round(this.validationSetRatio * n);
int trainingSet = n - validationSet;
double[][] networkTrainingInput = new double[trainingSet][];
double[][] networkTrainingOutput = new double[trainingSet][];
for (int i = 0; i < trainingSet; i++)
{
networkTrainingInput[i] = new double[this.network.InputLayer.NeuronCount];
networkTrainingOutput[i] = new double[1];
}
for (int i = 0; i < trainingSet; i++)
{
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
networkTrainingInput[i][j] = this.networkInput[i][j];
}
networkTrainingOutput[i][0] = this.networkOutput[i][0];
}
// validation set
double[] solutionValidation = new double[validationSet];
double[] inputForValidation = new double[this.network.InputLayer.NeuronCount];
double[] inputForValidationNetwork = new double[this.network.InputLayer.NeuronCount];
// array for saving neural weights and parameters
this.bestValidationError = double.MaxValue;
this.bestWeightMatrix = new double[this.network.Layers.Count - 1][, ];
this.bestSolution = new double[n];
for (int i = 0; i < this.network.Layers.Count - 1; i++)
{
this.bestWeightMatrix[i] = new double[this.network.Layers[i].WeightMatrix.Rows, this.network.Layers[i].WeightMatrix.Cols];
}
//best network criterion
double bestNetworkError = double.MaxValue, bestNetworkMSE = double.MaxValue, bestNetworkMAE = double.MaxValue;
// build array for graph
this.solutionData = new double[n];
this.predictedPoint = new cPoint[n];
this.validationPoint = new cPoint[validationSet];
//initialize point for graph
predictedDS.Samples = predictedPoint;
validationDS.Samples = validationPoint;
this.predictedDS.Active = true;
// prepare training data
INeuralDataSet dataset;
if (this.useAdvanceEarlyStopping)
{
dataset = new BasicNeuralDataSet(networkTrainingInput, networkTrainingOutput);
}
else
{
dataset = new BasicNeuralDataSet(this.networkInput, this.networkOutput);
}
// initialize trainer
this.learning = new Backpropagation(this.network, dataset, this.learningRate, this.momentum);
//training
while (!needToStop)
{
double sse = 0.0;
double sae = 0.0;
double ssle = 0.0;
double sspe = 0.0;
this.learning.Iteration();
error = learning.Error;
if (this.useAdvanceEarlyStopping)
{
this.validationDS.Active = true;
}
else
{
this.validationDS.Active = false;
}
for (int i = 0; i < n; i++)
{
INeuralData neuraldata = new BasicNeuralData(this.networkInput[i]);
this.solutionData[i] = (this.network.Compute(neuraldata)[0]
- this.minNormalizedData) / this.factor + this.minData;
this.predictedPoint[i].x = i + this.network.InputLayer.NeuronCount;
this.predictedPoint[i].y = (float)this.solutionData[i];
sse += Math.Pow(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount], 2);
sae += Math.Abs(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount]);
//calculate advance early stopping
if (this.useAdvanceEarlyStopping)
{
if (i < n - validationSet)
{
ssle += Math.Pow(this.solutionData[i] - this.data[i + this.network.InputLayer.NeuronCount], 2);
}
else
{
// initialize the first validation set input
if (i == n - validationSet)
{
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
inputForValidation[this.network.InputLayer.NeuronCount - 1 - j] = this.data[this.data.Length - (n - i) - 1 - j];
}
}
for (int j = 0; j < this.network.InputLayer.NeuronCount; j++)
{
inputForValidationNetwork[j] = (inputForValidation[j] - this.minData) * this.factor + this.minNormalizedData;
}
INeuralData neuraldataval = new BasicNeuralData(inputForValidationNetwork);
solutionValidation[i - n + validationSet] = (this.network.Compute(neuraldataval)[0] - this.minNormalizedData) / this.factor + this.minData;
this.validationPoint[i - n + validationSet].x = i + this.network.InputLayer.NeuronCount;
this.validationPoint[i - n + validationSet].y = (float)solutionValidation[i - n + validationSet];
sspe += Math.Pow(this.data[i + this.network.InputLayer.NeuronCount] - solutionValidation[i - n + validationSet], 2);
// initialize the next validation set input from the current validation set input
for (int j = 0; j < this.network.InputLayer.NeuronCount - 1; j++)
{
inputForValidation[j] = inputForValidation[j + 1];
}
inputForValidation[this.network.InputLayer.NeuronCount - 1] = solutionValidation[i - n + validationSet];
}
}
}
mse = sse / this.solutionData.Length;
mae = sae / this.solutionData.Length;
//Console.WriteLine(error.ToString());
//Display it
this.iterationBox.Text = iteration.ToString();
this.maeBox.Text = mae.ToString("F5");
this.mseBox.Text = mse.ToString("F5");
this.errorBox.Text = error.ToString("F5");
seriesGraph.Refresh();
if (this.useAdvanceEarlyStopping)
{
//calculate advance early stopping 2
mspe = sspe / validationSet;
msle = ssle / (this.solutionData.Length - validationSet);
//save best weight
if (this.bestValidationError > mspe)
{
this.bestValidationError = mspe;
this.bestSolution = this.solutionData;
// weight matrix
for (int i = 0; i < this.network.Layers.Count - 1; i++)
{
for (int j = 0; j < this.network.Layers[i].WeightMatrix.Rows; j++)
{
for (int k = 0; k < this.network.Layers[i].WeightMatrix.Cols; k++)
{
this.bestWeightMatrix[i][j, k] = this.network.Layers[i].WeightMatrix[j, k];
}
}
}
bestNetworkError = error;
bestNetworkMAE = mae;
bestNetworkMSE = mse;
}
//calculate generalization loss &pq
generalizationLoss = 100 * (mspe / this.bestValidationError - 1);
trainingErrors[(iteration - 1) % this.strip] = msle;
double minStripTrainingError = double.MaxValue, sumStripTrainingError = 0.0;
for (int i = 0; i < this.strip; i++)
{
sumStripTrainingError += trainingErrors[i];
if (trainingErrors[i] < minStripTrainingError)
{
minStripTrainingError = trainingErrors[i];
}
}
double trainingProgress = 1000 * ((sumStripTrainingError / (this.strip * minStripTrainingError)) - 1);
pq = generalizationLoss / trainingProgress;
//display advance early stopping
this.learningErrorBox.Text = msle.ToString("F5");
this.validationErrorBox.Text = mspe.ToString("F5");
this.generalizationLossBox.Text = generalizationLoss.ToString("F5");
this.pqBox.Text = pq.ToString("F5");
this.seriesGraph.Refresh();
//stopping
switch (this.advanceStoppingMethod)
{
case AdvanceStoppingMethodEnumeration.GeneralizationLoss:
if (generalizationLoss > this.generalizationLossTreshold)
{
needToStop = true;
}
break;
case AdvanceStoppingMethodEnumeration.ProgressQuotient:
if (pq > this.pqTreshold)
{
needToStop = true;
}
break;
}
}
if (this.withCheckingCycle && iteration % this.checkingCycle == 0)
{
switch (this.checkingMethod)
{
case CheckingMethodEnumeration.byMSEValue:
if (mse <= this.byMSEValueStopping)
{
needToStop = true;
}
break;
case CheckingMethodEnumeration.byMSEChange:
if (lastMSE - mse <= this.byMSEChangeStopping)
{
needToStop = true;
}
break;
}
lastMSE = mse;
}
if (iteration >= this.maxIteration)
{
needToStop = true;
}
iteration++;
}
//restore weight
if (this.useAdvanceEarlyStopping)
{
this.solutionData = this.bestSolution;
// weight matrix
for (int i = 0; i < this.network.Layers.Count - 1; i++)
{
for (int j = 0; j < this.network.Layers[i].WeightMatrix.Rows; j++)
{
for (int k = 0; k < this.network.Layers[i].WeightMatrix.Cols; k++)
{
this.network.Layers[i].WeightMatrix[j, k] = this.bestWeightMatrix[i][j, k];
}
}
}
//best network criterion
this.error = bestNetworkError;
this.mse = bestNetworkMSE;
this.mae = bestNetworkMAE;
}
else
{
this.error = error;
this.mse = mse;
this.mae = mae;
}
this.enableControls(true);
}