/**
* Create a LMA trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SOM)) {
throw new EncogError(
"Neighborhood training cannot be used on a method of type: "
+ method.getClass().getName());
}
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final double learningRate = holder.getDouble(MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.7);
final String neighborhoodStr =
holder.getString(MLTrainFactory.PROPERTY_NEIGHBORHOOD, false, "rbf");
final String rbfTypeStr = holder.getString(MLTrainFactory.PROPERTY_RBF_TYPE, false, "gaussian");
RBFEnum t;
if (rbfTypeStr.equalsIgnoreCase("Gaussian")) {
t = RBFEnum.Gaussian;
} else if (rbfTypeStr.equalsIgnoreCase("Multiquadric")) {
t = RBFEnum.Multiquadric;
} else if (rbfTypeStr.equalsIgnoreCase("InverseMultiquadric")) {
t = RBFEnum.InverseMultiquadric;
} else if (rbfTypeStr.equalsIgnoreCase("MexicanHat")) {
t = RBFEnum.MexicanHat;
} else {
t = RBFEnum.Gaussian;
}
NeighborhoodFunction nf = null;
if (neighborhoodStr.equalsIgnoreCase("bubble")) {
nf = new NeighborhoodBubble(1);
} else if (neighborhoodStr.equalsIgnoreCase("rbf")) {
final String str = holder.getString(MLTrainFactory.PROPERTY_DIMENSIONS, true, null);
final int[] size = NumberList.fromListInt(CSVFormat.EG_FORMAT, str);
nf = new NeighborhoodRBF(size, t);
} else if (neighborhoodStr.equalsIgnoreCase("rbf1d")) {
nf = new NeighborhoodRBF1D(t);
}
if (neighborhoodStr.equalsIgnoreCase("single")) {
nf = new NeighborhoodSingle();
}
final BasicTrainSOM result = new BasicTrainSOM((SOM) method, learningRate, training, nf);
if (args.containsKey(MLTrainFactory.PROPERTY_ITERATIONS)) {
final int plannedIterations = holder.getInt(MLTrainFactory.PROPERTY_ITERATIONS, false, 1000);
final double startRate =
holder.getDouble(MLTrainFactory.PROPERTY_START_LEARNING_RATE, false, 0.05);
final double endRate =
holder.getDouble(MLTrainFactory.PROPERTY_END_LEARNING_RATE, false, 0.05);
final double startRadius = holder.getDouble(MLTrainFactory.PROPERTY_START_RADIUS, false, 10);
final double endRadius = holder.getDouble(MLTrainFactory.PROPERTY_END_RADIUS, false, 1);
result.setAutoDecay(plannedIterations, startRate, endRate, startRadius, endRadius);
}
return result;
}
/**
* Create a Manhattan trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public final MLTrain create(
final MLMethod method, final MLDataSet training, final String argsStr) {
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final double learningRate = holder.getDouble(MLTrainFactory.PROPERTY_LEARNING_RATE, false, 0.1);
return new ManhattanPropagation((BasicNetwork) method, training, learningRate);
}
/**
* Create a SVM trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SVM)) {
throw new EncogError(
"SVM Train training cannot be used on a method of type: " + method.getClass().getName());
}
final double defaultGamma = 1.0 / ((SVM) method).getInputCount();
final double defaultC = 1.0;
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
final ParamsHolder holder = new ParamsHolder(args);
final double gamma = holder.getDouble(MLTrainFactory.PROPERTY_GAMMA, false, defaultGamma);
final double c = holder.getDouble(MLTrainFactory.PROPERTY_C, false, defaultC);
final SVMTrain result = new SVMTrain((SVM) method, training);
result.setGamma(gamma);
result.setC(c);
return result;
}
/**
* Create a SVM trainer.
*
* @param method The method to use.
* @param training The training data to use.
* @param argsStr The arguments to use.
* @return The newly created trainer.
*/
public MLTrain create(final MLMethod method, final MLDataSet training, final String argsStr) {
if (!(method instanceof SVM)) {
throw new EncogError(
"SVM Train training cannot be used on a method of type: " + method.getClass().getName());
}
final Map<String, String> args = ArchitectureParse.parseParams(argsStr);
new ParamsHolder(args);
final ParamsHolder holder = new ParamsHolder(args);
final double gammaStart =
holder.getDouble(
SVMSearchFactory.PROPERTY_GAMMA1, false, SVMSearchTrain.DEFAULT_GAMMA_BEGIN);
final double cStart =
holder.getDouble(SVMSearchFactory.PROPERTY_C1, false, SVMSearchTrain.DEFAULT_CONST_BEGIN);
final double gammaStop =
holder.getDouble(SVMSearchFactory.PROPERTY_GAMMA2, false, SVMSearchTrain.DEFAULT_GAMMA_END);
final double cStop =
holder.getDouble(SVMSearchFactory.PROPERTY_C2, false, SVMSearchTrain.DEFAULT_CONST_END);
final double gammaStep =
holder.getDouble(
SVMSearchFactory.PROPERTY_GAMMA_STEP, false, SVMSearchTrain.DEFAULT_GAMMA_STEP);
final double cStep =
holder.getDouble(
SVMSearchFactory.PROPERTY_C_STEP, false, SVMSearchTrain.DEFAULT_CONST_STEP);
final SVMSearchTrain result = new SVMSearchTrain((SVM) method, training);
result.setGammaBegin(gammaStart);
result.setGammaEnd(gammaStop);
result.setGammaStep(gammaStep);
result.setConstBegin(cStart);
result.setConstEnd(cStop);
result.setConstStep(cStep);
return result;
}