package org.encog.plugin.system;
import org.encog.EncogError;
import org.encog.engine.network.activation.ActivationBiPolar;
import org.encog.engine.network.activation.ActivationCompetitive;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationGaussian;
import org.encog.engine.network.activation.ActivationLOG;
import org.encog.engine.network.activation.ActivationLinear;
import org.encog.engine.network.activation.ActivationRamp;
import org.encog.engine.network.activation.ActivationSIN;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.engine.network.activation.ActivationSoftMax;
import org.encog.engine.network.activation.ActivationStep;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.factory.MLActivationFactory;
import org.encog.ml.train.MLTrain;
import org.encog.plugin.EncogPluginBase;
import org.encog.plugin.EncogPluginService1;
import org.encog.util.csv.CSVFormat;
import org.encog.util.csv.NumberList;
public class SystemActivationPlugin implements EncogPluginService1 {
/**
* {@inheritDoc}
*/
@Override
public final String getPluginDescription() {
return "This plugin provides the built in machine " +
"learning methods for Encog.";
}
/**
* {@inheritDoc}
*/
@Override
public final String getPluginName() {
return "HRI-System-Methods";
}
/**
* @return This is a type-1 plugin.
*/
@Override
public final int getPluginType() {
return 1;
}
private ActivationFunction allocateAF(String name) {
if (name.equalsIgnoreCase(MLActivationFactory.AF_BIPOLAR)) {
return new ActivationBiPolar();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_COMPETITIVE)) {
return new ActivationCompetitive();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_GAUSSIAN)) {
return new ActivationGaussian();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_LINEAR)) {
return new ActivationLinear();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_LOG)) {
return new ActivationLOG();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_RAMP)) {
return new ActivationRamp();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_SIGMOID)) {
return new ActivationSigmoid();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_SIN)) {
return new ActivationSIN();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_SOFTMAX)) {
return new ActivationSoftMax();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_STEP)) {
return new ActivationStep();
}
if (name.equalsIgnoreCase(MLActivationFactory.AF_TANH)) {
return new ActivationTANH();
}
return null;
}
/**
* {@inheritDoc}
*/
@Override
public ActivationFunction createActivationFunction(String fn) {
String name;
double[] params;
int index = fn.indexOf('[');
if (index != -1) {
name = fn.substring(0, index).toLowerCase();
int index2 = fn.indexOf(']');
if (index2 == -1) {
throw new EncogError(
"Unbounded [ while parsing activation function.");
}
String a = fn.substring(index + 1, index2);
params = NumberList.fromList(CSVFormat.EG_FORMAT, a);
} else {
name = fn.toLowerCase();
params = new double[0];
}
ActivationFunction af = allocateAF(name);
if( af==null ) {
return null;
}
if (af.getParamNames().length != params.length) {
throw new EncogError(name + " expected "
+ af.getParamNames().length + ", but " + params.length
+ " were provided.");
}
for (int i = 0; i < af.getParamNames().length; i++) {
af.setParam(i, params[i]);
}
return af;
}
/**
* {@inheritDoc}
*/
@Override
public MLMethod createMethod(String methodType, String architecture,
int input, int output) {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public MLTrain createTraining(MLMethod method, MLDataSet training,
String type, String args) {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public int getPluginServiceType() {
return EncogPluginBase.TYPE_SERVICE;
}
}