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);
Assert.IsTrue(result1 != result2);
}
public void Init(IMLTrain train)
{
basicTrainSOM = train as BasicTrainSOM;
if (basicTrainSOM == null)
{
throw new ArgumentException(
String.Format("Argument shoud be of {0} type.", typeof(BasicTrainSOM)), "train");
}
}
/// <summary>
/// Uczy siec Kohonena z podanymi parametrami.
/// </summary>
/// <param name="learningRate">Początkowy współczynnik nauki.</param>
/// <param name="learningChangeRate">Współczynnik zmiany współczynnika nauki</param>
/// <param name="neighbourhoodRate">Początkowy współczynnik sąsiedztwa.</param>
/// <param name="neighbourhoodChangeRate">Współczynnik zmiany współczynnika sąsiedztwa.</param>
/// <param name="trainIterations">Na ilu przykladach przebiega nauka.</param>
/// <param name="learningSet">Zbiór danych uczących.</param>
public void Train(double learningRate, double learningChangeRate,
double neighbourhoodRate, double neighbourhoodChangeRate,
int trainIterations, InputDataSet learningSet)
{
var basicMlDataSet = new BasicMLDataSet(learningSet.InputSet, null);
INeighborhoodFunction neighborhoodFunc =
new KohonenNeighbourhoodFunction(neighbourhoodRate, rows, columns);
var train = new BasicTrainSOM(network, learningRate, basicMlDataSet, neighborhoodFunc);
IStrategy strategy = new KohonenTrainStrategy(learningChangeRate, neighbourhoodChangeRate);
strategy.Init(train);
train.Strategies.Add(strategy);
train.Iteration(trainIterations);
train.FinishTraining();
}
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);
}
/// <summary>
/// Create a LMA trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is SupportVectorMachine))
{
throw new EncogError(
"Neighborhood training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
double learningRate = holder.GetDouble(
MLTrainFactory.PropertyLearningRate, false, 0.7d);
String neighborhoodStr = holder.GetString(
MLTrainFactory.PropertyNeighborhood, false, "rbf");
String rbfTypeStr = holder.GetString(
MLTrainFactory.PropertyRBFType, false, "gaussian");
RBFEnum t;
if (rbfTypeStr.Equals("Gaussian", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Gaussian;
}
else if (rbfTypeStr.Equals("Multiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.Multiquadric;
}
else if (rbfTypeStr.Equals("InverseMultiquadric", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.InverseMultiquadric;
}
else if (rbfTypeStr.Equals("MexicanHat", StringComparison.InvariantCultureIgnoreCase))
{
t = RBFEnum.MexicanHat;
}
else
{
t = RBFEnum.Gaussian;
}
INeighborhoodFunction nf = null;
if (neighborhoodStr.Equals("bubble", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodBubble(1);
}
else if (neighborhoodStr.Equals("rbf", StringComparison.InvariantCultureIgnoreCase))
{
String str = holder.GetString(
MLTrainFactory.PropertyDimensions, true, null);
int[] size = NumberList.FromListInt(CSVFormat.EgFormat, str);
nf = new NeighborhoodRBF(size, t);
}
else if (neighborhoodStr.Equals("rbf1d", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodRBF1D(t);
}
if (neighborhoodStr.Equals("single", StringComparison.InvariantCultureIgnoreCase))
{
nf = new NeighborhoodSingle();
}
var result = new BasicTrainSOM((SOMNetwork) method,
learningRate, training, nf);
if (args.ContainsKey(MLTrainFactory.PropertyIterations))
{
int plannedIterations = holder.GetInt(
MLTrainFactory.PropertyIterations, false, 1000);
double startRate = holder.GetDouble(
MLTrainFactory.PropertyStartLearningRate, false, 0.05d);
double endRate = holder.GetDouble(
MLTrainFactory.PropertyEndLearningRate, false, 0.05d);
double startRadius = holder.GetDouble(
MLTrainFactory.PropertyStartRadius, false, 10);
double endRadius = holder.GetDouble(
MLTrainFactory.PropertyEndRadius, false, 1);
result.SetAutoDecay(plannedIterations, startRate, endRate,
startRadius, endRadius);
}
return result;
}
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 );
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
IDictionary<string, string> dictionary;
ParamsHolder holder;
double num;
string str;
string str2;
RBFEnum mexicanHat;
INeighborhoodFunction function;
string str3;
int[] numArray;
BasicTrainSOM nsom;
int num2;
double num3;
double num4;
double num6;
if (method is SupportVectorMachine)
{
dictionary = ArchitectureParse.ParseParams(argsStr);
holder = new ParamsHolder(dictionary);
num = holder.GetDouble("LR", false, 0.7);
str = holder.GetString("NEIGHBORHOOD", false, "rbf");
if (2 != 0)
{
goto Label_03DF;
}
goto Label_039F;
}
goto Label_03F4;
Label_0083:
num4 = holder.GetDouble("END_LR", false, 0.05);
double startRadius = holder.GetDouble("START_RADIUS", false, 10.0);
if ((((uint) num4) + ((uint) num4)) > uint.MaxValue)
{
return nsom;
}
if ((((uint) num3) + ((uint) num2)) <= uint.MaxValue)
{
num6 = holder.GetDouble("END_RADIUS", false, 1.0);
nsom.SetAutoDecay(num2, num3, num4, startRadius, num6);
return nsom;
}
Label_00E4:
if (4 == 0)
{
if ((((uint) num3) + ((uint) num2)) > uint.MaxValue)
{
goto Label_0292;
}
goto Label_02F8;
}
Label_00EE:
nsom = new BasicTrainSOM((SOMNetwork) method, num, training, function);
do
{
if (dictionary.ContainsKey("ITERATIONS"))
{
do
{
num2 = holder.GetInt("ITERATIONS", false, 0x3e8);
num3 = holder.GetDouble("START_LR", false, 0.05);
}
while ((((uint) num3) | 15) == 0);
goto Label_0083;
}
}
while ((((uint) num6) | 0xff) == 0);
if (0 == 0)
{
if ((((uint) num3) + ((uint) num3)) >= 0)
{
return nsom;
}
goto Label_03F4;
}
if ((((uint) num2) - ((uint) startRadius)) <= uint.MaxValue)
{
goto Label_00E4;
}
goto Label_0083;
Label_0184:
if (!str.Equals("single", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_00EE;
}
function = new NeighborhoodSingle();
if ((((uint) num6) - ((uint) num3)) >= 0)
{
if ((((uint) num) - ((uint) startRadius)) >= 0)
{
goto Label_00E4;
}
goto Label_0324;
}
if ((((uint) num2) & 0) == 0)
{
goto Label_0233;
}
Label_01E2:
while (!str.Equals("rbf1d", StringComparison.InvariantCultureIgnoreCase))
{
if (0 == 0)
{
if ((((uint) num3) - ((uint) num6)) >= 0)
{
goto Label_0184;
}
goto Label_0233;
}
}
function = new NeighborhoodRBF1D(mexicanHat);
if ((((uint) num2) + ((uint) num)) >= 0)
{
if (((uint) num6) < 0)
{
goto Label_01E2;
}
goto Label_0184;
}
if (((uint) num2) < 0)
{
goto Label_03DF;
}
goto Label_01E2;
Label_0233:
function = new NeighborhoodRBF(numArray, mexicanHat);
goto Label_0184;
Label_0243:
if (!str.Equals("rbf", StringComparison.InvariantCultureIgnoreCase))
{
if ((((uint) num2) & 0) != 0)
{
goto Label_03DF;
}
goto Label_01E2;
}
Label_0292:
str3 = holder.GetString("DIM", true, null);
if ((((uint) num3) + ((uint) num)) > uint.MaxValue)
{
goto Label_0292;
}
numArray = NumberList.FromListInt(CSVFormat.EgFormat, str3);
if ((((uint) num6) & 0) == 0)
{
goto Label_0233;
}
goto Label_0243;
Label_02F8:
if (str.Equals("bubble", StringComparison.InvariantCultureIgnoreCase))
{
function = new NeighborhoodBubble(1);
goto Label_0184;
}
if ((((uint) num3) & 0) == 0)
{
goto Label_0243;
}
goto Label_0292;
Label_0324:
function = null;
goto Label_02F8;
Label_0362:
mexicanHat = RBFEnum.Multiquadric;
goto Label_0324;
Label_039F:
mexicanHat = RBFEnum.Gaussian;
goto Label_0324;
Label_03DF:
str2 = holder.GetString("RBF_TYPE", false, "gaussian");
if (str2.Equals("Gaussian", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_039F;
}
if (((uint) startRadius) <= uint.MaxValue)
{
if (str2.Equals("Multiquadric", StringComparison.InvariantCultureIgnoreCase))
{
goto Label_0362;
}
if (!str2.Equals("InverseMultiquadric", StringComparison.InvariantCultureIgnoreCase) || ((((uint) num2) + ((uint) num2)) < 0))
{
if (str2.Equals("MexicanHat", StringComparison.InvariantCultureIgnoreCase))
{
mexicanHat = RBFEnum.MexicanHat;
}
else
{
mexicanHat = RBFEnum.Gaussian;
}
goto Label_0324;
}
}
else if (((uint) num3) <= uint.MaxValue)
{
goto Label_0362;
}
if ((((uint) num3) - ((uint) num3)) <= uint.MaxValue)
{
mexicanHat = RBFEnum.InverseMultiquadric;
goto Label_0324;
}
goto Label_00E4;
Label_03F4:
throw new EncogError("Neighborhood training cannot be used on a method of type: " + method.GetType().FullName);
}