/*
 * Encog(tm) Core v3.0 - Java Version
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 
 * Copyright 2008-2011 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 final 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;
	}
}
