/// <summary>
/// Parse an int array from a name-value collection of params.
/// </summary>
///
/// <param name="paras">The name-value pairs.</param>
/// <param name="name">The name to parse.</param>
/// <returns>The parsed int array value.</returns>
public static int[] ParseIntArray(IDictionary <String, String> paras,
String name)
{
String v = null;
try
{
v = paras[name];
if (v == null)
{
throw new PersistError("Missing property: " + name);
}
return(NumberList.FromListInt(CSVFormat.EgFormat, v));
}
catch (FormatException)
{
throw new PersistError("Field: " + name + ", "
+ "invalid integer: " + v);
}
}
/// <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);
}