/*
* 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.method;
import java.util.List;
import org.encog.EncogError;
import org.encog.ml.MLMethod;
import org.encog.ml.factory.parse.ArchitectureLayer;
import org.encog.ml.factory.parse.ArchitectureParse;
import org.encog.ml.svm.KernelType;
import org.encog.ml.svm.SVM;
import org.encog.ml.svm.SVMType;
/**
* A factory that is used to create support vector machines (SVM).
*
*/
public class SVMFactory {
/**
* The max layer count.
*/
public static final int MAX_LAYERS = 3;
/**
* Create the SVM.
* @param architecture The architecture string.
* @param input The input count.
* @param output The output count.
* @return The newly created SVM.
*/
public MLMethod create(final String architecture, final int input,
final int output) {
final List<String> layers = ArchitectureParse.parseLayers(architecture);
if (layers.size() != MAX_LAYERS) {
throw new EncogError(
"SVM's must have exactly three elements, separated by ->.");
}
final ArchitectureLayer inputLayer = ArchitectureParse.parseLayer(
layers.get(0), input);
final ArchitectureLayer paramsLayer = ArchitectureParse.parseLayer(
layers.get(1), input);
final ArchitectureLayer outputLayer = ArchitectureParse.parseLayer(
layers.get(2), output);
final String name = paramsLayer.getName();
final String kernelStr = paramsLayer.getParams().get("KERNEL");
final String svmTypeStr = paramsLayer.getParams().get("TYPE");
SVMType svmType = SVMType.NewSupportVectorClassification;
KernelType kernelType = KernelType.RadialBasisFunction;
boolean useNew = true;
if (svmTypeStr == null) {
useNew = true;
} else if (svmTypeStr.equalsIgnoreCase("NEW")) {
useNew = true;
} else if (svmTypeStr.equalsIgnoreCase("OLD")) {
useNew = false;
} else {
throw new EncogError("Unsupported type: " + svmTypeStr
+ ", must be NEW or OLD.");
}
if (name.equalsIgnoreCase("C")) {
if (useNew) {
svmType = SVMType.NewSupportVectorClassification;
} else {
svmType = SVMType.SupportVectorClassification;
}
} else if (name.equalsIgnoreCase("R")) {
if (useNew) {
svmType = SVMType.NewSupportVectorRegression;
} else {
svmType = SVMType.EpsilonSupportVectorRegression;
}
} else {
throw new EncogError("Unsupported mode: " + name
+ ", must be C for classify or R for regression.");
}
if (kernelStr == null) {
kernelType = KernelType.RadialBasisFunction;
} else if ("linear".equalsIgnoreCase(kernelStr)) {
kernelType = KernelType.Linear;
} else if ("poly".equalsIgnoreCase(kernelStr)) {
kernelType = KernelType.Poly;
} else if ("precomputed".equalsIgnoreCase(kernelStr)) {
kernelType = KernelType.Precomputed;
} else if ("rbf".equalsIgnoreCase(kernelStr)) {
kernelType = KernelType.RadialBasisFunction;
} else if ("sigmoid".equalsIgnoreCase(kernelStr)) {
kernelType = KernelType.Sigmoid;
} else {
throw new EncogError("Unsupported kernel: " + kernelStr
+ ", must be linear,poly,precomputed,rbf or sigmoid.");
}
final int inputCount = inputLayer.getCount();
final int outputCount = outputLayer.getCount();
if (outputCount != 1) {
throw new EncogError("SVM can only have an output size of 1.");
}
final SVM result = new SVM(inputCount, svmType, kernelType);
return result;
}
}