/**
* {@inheritDoc}
*/
public Object Read(Stream istream)
{
var result = new SOMNetwork();
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("SOM")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<String, String> p = section.ParseParams();
EngineArray.PutAll(p, result.Properties);
}
if (section.SectionName.Equals("SOM")
&& section.SubSectionName.Equals("NETWORK"))
{
IDictionary<String, String> p = section.ParseParams();
result.Weights = EncogFileSection.ParseMatrix(p,
PersistConst.Weights)
;
}
}
return result;
}
public void TestSOM()
{
// create the training set
IMLDataSet training = new BasicMLDataSet(
SOMInput, null);
// Create the neural network.
var network = new SOMNetwork(4, 2) {Weights = new Matrix(MatrixArray)};
var train = new BasicTrainSOM(network, 0.4,
training, new NeighborhoodSingle()) {ForceWinner = true};
int iteration = 0;
for (iteration = 0; iteration <= 100; iteration++)
{
train.Iteration();
}
IMLData data1 = new BasicMLData(
SOMInput[0]);
IMLData data2 = new BasicMLData(
SOMInput[1]);
int result1 = network.Classify(data1);
int result2 = network.Classify(data2);
Console.WriteLine(result1 + " result 2 :"+result2);
Assert.IsTrue(result1 != result2);
}
/**
* {@inheritDoc}
*/
public Object Read(Stream istream)
{
var result = new SOMNetwork();
var reader = new EncogReadHelper(istream);
EncogFileSection section;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("SOM") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> p = section.ParseParams();
EngineArray.PutAll(p, result.Properties);
}
if (section.SectionName.Equals("SOM") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> p = section.ParseParams();
result.Weights = EncogFileSection.ParseMatrix(p,
PersistConst.Weights)
;
}
}
return(result);
}
/// <summary>
/// Construct the object.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
public SOMClusterCopyTraining(SOMNetwork network, IMLDataSet training)
: base(TrainingImplementationType.OnePass)
{
_network = network;
Training = training;
if (_network.OutputCount < training.Count)
{
throw new NeuralNetworkError(
"To use cluster copy training you must have at least as many output neurons as training elements.");
}
}
public SOMColors()
{
InitializeComponent();
network = CreateNetwork();
gaussian = new NeighborhoodRBF(RBFEnum.Gaussian, WIDTH, HEIGHT);
train = new BasicTrainSOM(network, 0.01, null, gaussian);
train.ForceWinner = false;
samples = new List<IMLData>();
for (int i = 0; i < 15; i++)
{
IMLData data = new BasicMLData(3);
data.Data[0] = RangeRandomizer.Randomize(-1, 1);
data.Data[1] = RangeRandomizer.Randomize(-1, 1);
data.Data[2] = RangeRandomizer.Randomize(-1, 1);
samples.Add(data);
}
train.SetAutoDecay(100, 0.8, 0.003, 30, 5);
}
public BasicTrainSOM(SOMNetwork network, double learningRate, IMLDataSet training, INeighborhoodFunction neighborhood)
: base(TrainingImplementationType.Iterative)
{
this._x3d0ce91dbd8e0b1b = neighborhood;
while (true)
{
this.Training = training;
while (0 == 0)
{
this._x9b481c22b6706459 = learningRate;
this._x87a7fc6a72741c2e = network;
this._x57202a8751db8895 = network.InputCount;
this._x0c37ff3adde9bc44 = network.OutputCount;
this._xf7c1f63ae5cd87f7 = false;
this.Error = 0.0;
this._x42cc0dadfb982948 = new Matrix(this._x57202a8751db8895, this._x0c37ff3adde9bc44);
this._x703660d5c67ed8fb = new BestMatchingUnit(network);
if (0 == 0)
{
return;
}
}
}
}
public BestMatchingUnit(SOMNetwork som)
{
this._x7af5fe5ee7d4a6c7 = som;
}
/// <summary>
/// Generate the RSOM network.
/// </summary>
public IMLMethod Generate()
{
var som = new SOMNetwork(_inputNeurons, _outputNeurons);
som.Reset();
return som;
}
public void TestSOM2()
{
// create the training set
IMLDataSet training = new BasicMLDataSet(
SOMInput2, null);
// Create the neural network.
var network = new SOMNetwork(4,4);
var train = new BasicTrainSOM(network, 0.01,
training, new NeighborhoodSingle()) { ForceWinner = true };
int iteration = 0;
for (iteration = 0; iteration <= 1000; iteration++)
{
train.Iteration();
}
IMLData data1 = new BasicMLData(
SOMInput2[2]);
IMLData data2 = new BasicMLData(
SOMInput2[0]);
IMLData data3 = new BasicMLData(
SOMInput2[1]);
IMLData data4 = new BasicMLData(
SOMInput2[3]);
int result1 = network.Classify(data1);
int result2 = network.Classify(data2);
int result3 = network.Classify(data3);
int result4 = network.Classify(data4);
Console.WriteLine("Winner in someinput 2 "+network.Winner(new BasicMLData(SOMInput2[0])));
Console.WriteLine("First :" +result1);
Console.WriteLine("Second "+result2);
Console.WriteLine("Third :" + result3);
Console.WriteLine("Fourth " + result4);
Assert.IsTrue(result1 != result2);
train.TrainPattern(new BasicMLData(SOMInput2[2]));
Console.WriteLine("After training pattern: " + network.Winner(new BasicMLData(SOMInput2[1])));
var result = new SupportVectorMachine(4, SVMType.SupportVectorClassification, KernelType.Sigmoid);
training = new BasicMLDataSet(
SOMInput2, SOMInput2);
SVMTrain trainsvm = new SVMTrain(result, training);
trainsvm.Iteration(50);
result1 = result.Classify(data1);
result2 = result.Classify(data2);
result3 = result.Classify(data3);
result4 = result.Classify(data4);
Console.WriteLine("SVM classification : EURUSD 1 :"+result1 + " GBPUSD:"+result2 + " EURCHF :"+result3+ " EURJPY:"+result4 );
}
private void Validate(SOMNetwork network)
{
Assert.AreEqual(4, network.InputCount);
Assert.AreEqual(2, network.OutputCount);
Assert.AreEqual(8, network.Weights.ToPackedArray().Length);
}
private SOMNetwork Create()
{
SOMNetwork network = new SOMNetwork(4, 2);
return network;
}
private SOMNetwork CreateNetwork()
{
var result = new SOMNetwork(3, WIDTH*HEIGHT);
result.Reset();
return result;
}
private SOMNetwork CreateNetwork()
{
SOMNetwork result = new SOMNetwork(3, SOMColors.WIDTH * SOMColors.HEIGHT);
result.Reset();
return result;
}
public IMLMethod Generate()
{
SOMNetwork network = new SOMNetwork(this._xcfe830a7176c14e5, this._x8f581d694fca0474);
network.Reset();
return network;
}
/// <summary>
/// Construct the object.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
public SOMClusterCopyTraining(SOMNetwork network, IMLDataSet training)
: base(TrainingImplementationType.OnePass)
{
_network = network;
Training = training;
}
private void btnBeginTraining_Click(object sender, EventArgs e)
{
if (!this.training)
{
int inputCount = OCRForm.DOWNSAMPLE_HEIGHT * OCRForm.DOWNSAMPLE_WIDTH;
this.trainingSet = new BasicMLDataSet();
foreach (char ch in this.letterData.Keys)
{
BasicMLData item = new BasicMLData(inputCount);
bool[] data = this.letterData[ch];
for (int i = 0; i < inputCount; i++)
{
item[i] = data[i] ? 0.5 : -0.5;
}
this.trainingSet.Add(item);
}
this.network = new SOMNetwork(this.downsampled.Length, this.letterData.Count);
this.network.Reset();
this.training = true;
Thread thread = new Thread(TrainNetwork);
thread.Start();
}
else
{
this.training = false;
}
}
public SOMClusterCopyTraining(SOMNetwork network, IMLDataSet training)
: base(TrainingImplementationType.OnePass)
{
this._x87a7fc6a72741c2e = network;
this.Training = training;
}
public object Read(Stream mask0)
{
EncogReadHelper helper;
EncogFileSection section;
SOMNetwork network = new SOMNetwork();
goto Label_00FC;
Label_000B:
if (section.SubSectionName.Equals("NETWORK"))
{
IDictionary<string, string> paras = section.ParseParams();
network.Weights = EncogFileSection.ParseMatrix(paras, "weights");
network.OutputCount = EncogFileSection.ParseInt(paras, "outputCount");
network.InputCount = EncogFileSection.ParseInt(paras, "inputCount");
if (0 != 0)
{
if (0 == 0)
{
goto Label_0055;
}
goto Label_0039;
}
}
Label_001D:
if ((section = helper.ReadNextSection()) != null)
{
if (!section.SectionName.Equals("SOM"))
{
goto Label_003E;
}
goto Label_00A0;
}
if (15 != 0)
{
return network;
}
goto Label_00FC;
Label_0039:
if (0 == 0)
{
goto Label_001D;
}
goto Label_000B;
Label_003E:
if (!section.SectionName.Equals("SOM"))
{
goto Label_0039;
}
if (1 != 0)
{
goto Label_000B;
}
return network;
Label_0055:
if (0 != 0)
{
if (0 != 0)
{
goto Label_001D;
}
}
else
{
goto Label_003E;
}
Label_00A0:
if (section.SubSectionName.Equals("PARAMS"))
{
EngineArray.PutAll<string, string>(section.ParseParams(), network.Properties);
}
goto Label_003E;
Label_00FC:
if (4 == 0)
{
goto Label_0055;
}
helper = new EncogReadHelper(mask0);
goto Label_001D;
}