public void TestGaussian()
{
IRadialBasisFunction radial = new GaussianFunction(0.0, 1.0, 1.0);
var bubble = new NeighborhoodRBF1D(radial);
Assert.AreEqual(0.0, bubble.Function(5, 0), 0.1);
Assert.AreEqual(1.0, bubble.Function(5, 5), 0.1);
Assert.AreEqual(0.6, bubble.Function(5, 4), 0.1);
}
/// <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);
}
