/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.ml.factory;
import org.encog.Encog;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.plugin.EncogPluginBase;
import org.encog.plugin.EncogPluginService1;
/**
* This factory is used to create trainers for machine learning methods.
*
*/
public class MLTrainFactory {
/**
* K2 training for Bayesian.
*/
public static final String TYPE_NELDER_MEAD = "nm";
/**
* K2 training for Bayesian.
*/
public static final String TYPE_BAYESIAN = "bayesian";
/**
* String constant for RPROP training.
*/
public static final String TYPE_RPROP = "rprop";
/**
* String constant for backprop training.
*/
public static final String TYPE_BACKPROP = "backprop";
/**
* String constant for SCG training.
*/
public static final String TYPE_SCG = "scg";
/**
* String constant for LMA training.
*/
public static final String TYPE_LMA = "lma";
/**
* String constant for LMA training.
*/
public static final String TYPE_NEAT_GA = "neat-ga";
/**
* String constant for LMA training.
*/
public static final String TYPE_EPL_GA = "epl-ga";
/**
* String constant for SVM training.
*/
public static final String TYPE_SVM = "svm-train";
/**
* String constant for SVM-Search training.
*/
public static final String TYPE_SVM_SEARCH = "svm-search";
/**
* String constant for SOM-Neighborhood training.
*/
public static final String TYPE_SOM_NEIGHBORHOOD = "som-neighborhood";
/**
* String constant for SOM-Cluster training.
*/
public static final String TYPE_SOM_CLUSTER = "som-cluster";
/**
* Property for learning rate.
*/
public static final String PROPERTY_LEARNING_RATE = "LR";
/**
* Property for momentum.
*/
public static final String PROPERTY_LEARNING_MOMENTUM = "MOM";
/**
* Property for init update.
*/
public static final String PROPERTY_INITIAL_UPDATE = "INIT_UPDATE";
/**
* Property for max step.
*/
public static final String PROPERTY_MAX_STEP = "MAX_STEP";
/**
* Property for bayes reg.
*/
public static final String PROPERTY_BAYESIAN_REGULARIZATION = "BAYES_REG";
/**
* Property for gamma.
*/
public static final String PROPERTY_GAMMA = "GAMMA";
/**
* Property for constant.
*/
public static final String PROPERTY_C = "C";
/**
* Property for neighborhood.
*/
public static final String PROPERTY_PROPERTY_NEIGHBORHOOD = "NEIGHBORHOOD";
/**
* Property for iterations.
*/
public static final String PROPERTY_ITERATIONS = "ITERATIONS";
/**
* Property for starting learning rate.
*/
public static final String PROPERTY_START_LEARNING_RATE = "START_LR";
/**
* Property for ending learning rate.
*/
public static final String PROPERTY_END_LEARNING_RATE = "END_LR";
/**
* Property for starting radius.
*/
public static final String PROPERTY_START_RADIUS = "START_RADIUS";
/**
* Property for ending radius.
*/
public static final String PROPERTY_END_RADIUS = "END_RADIUS";
/**
* Property for neighborhood.
*/
public static final String PROPERTY_NEIGHBORHOOD = "NEIGHBORHOOD";
/**
* Property for rbf type.
*/
public static final String PROPERTY_RBF_TYPE = "RBF_TYPE";
/**
* Property for dimensions.
*/
public static final String PROPERTY_DIMENSIONS = "DIM";
/**
* The number of cycles.
*/
public static final String CYCLES = "cycles";
/**
* The starting temperature.
*/
public static final String PROPERTY_TEMPERATURE_START = "startTemp";
/**
* The ending temperature.
*/
public static final String PROPERTY_TEMPERATURE_STOP = "stopTemp";
/**
* Use simulated annealing.
*/
public static final String TYPE_ANNEAL = "anneal";
/**
* Population size.
*/
public static final String PROPERTY_POPULATION_SIZE = "population";
/**
* Genetic training.
*/
public static final String TYPE_GENETIC = "genetic";
/**
* Manhattan training.
*/
public static final String TYPE_MANHATTAN = "manhattan";
/**
* RBF-SVD training.
*/
public static final String TYPE_SVD = "rbf-svd";
/**
* PNN training.
*/
public static final String TYPE_PNN = "pnn";
/**
* QPROP training.
*/
public static final String TYPE_QPROP = "qprop";
public static final String PROPERTY_MAX_PARENTS = "MAXPARENTS";
public static final String PROPERTY_PARTICLES = "PARTICLES";
public static final String TYPE_PSO = "pso";
/**
* Create a trainer.
* @param method The method to train.
* @param training The training data.
* @param type Type type of trainer.
* @param args The training args.
* @return The new training method.
*/
public MLTrain create(final MLMethod method,
final MLDataSet training,
final String type, final String args) {
for (EncogPluginBase plugin : Encog.getInstance().getPlugins()) {
if (plugin instanceof EncogPluginService1) {
MLTrain result = ((EncogPluginService1) plugin).createTraining(
method, training, type, args);
if (result != null) {
return result;
}
}
}
throw new EncogError("Unknown training type: " + type);
}
}