/*
 * Copyright (C) 2008-2015 by Holger Arndt
 *
 * This file is part of the Java Data Mining Package (JDMP).
 * See the NOTICE file distributed with this work for additional
 * information regarding copyright ownership and licensing.
 *
 * JDMP is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 * JDMP is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with JDMP; if not, write to the
 * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
 * Boston, MA  02110-1301  USA
 */

package org.jdmp.core.algorithm.classification.meta;

import java.util.ArrayList;
import java.util.List;

import org.jdmp.core.algorithm.classification.AbstractClassifier;
import org.jdmp.core.algorithm.classification.Classifier;
import org.jdmp.core.dataset.DataSet;
import org.jdmp.core.dataset.ListDataSet;
import org.ujmp.core.Matrix;
import org.ujmp.core.calculation.Calculation.Ret;

public class MultiClassClassifier extends AbstractClassifier {
	private static final long serialVersionUID = 466059743021340944L;

	private Classifier singleClassClassifier = null;

	private int classCount = 0;

	private boolean twoColumns = false;

	private final List singleClassClassifiers = new ArrayList();

	public MultiClassClassifier(Classifier singleClassClassifier, boolean twoColumns) {
		super();
		setLabel("MultiClassClassifier [" + singleClassClassifier.toString() + "]");
		this.singleClassClassifier = singleClassClassifier;
		this.twoColumns = twoColumns;
	}

	public Matrix predictOne(Matrix input) {
		double[] results = new double[classCount];
		for (int i = 0; i < classCount; i++) {
			Classifier c = singleClassClassifiers.get(i);
			results[i] = c.predictOne(input).getAsDouble(0, 0);
		}
		return Matrix.Factory.linkToArray(results).transpose();
	}

	public void reset() {
		singleClassClassifiers.clear();
	}

	public void trainAll(ListDataSet dataSet) {
		reset();
		classCount = getClassCount(dataSet);

		for (int i = 0; i < classCount; i++) {
			System.out.println("Training class " + i);
			Classifier c = singleClassClassifier.emptyCopy();
			singleClassClassifiers.add(c);
			Matrix input = dataSet.getInputMatrix();
			Matrix target = dataSet.getTargetMatrix().selectColumns(Ret.LINK, i);
			if (twoColumns) {
				Matrix target2 = target.minus(1).abs(Ret.NEW);
				target = Matrix.Factory.horCat(target, target2);
			}
			ListDataSet ds = DataSet.Factory.linkToInputAndTarget(input, target);
			c.trainAll(ds);
		}

	}

	public Classifier emptyCopy() {
		return new MultiClassClassifier(singleClassClassifier, twoColumns);
	}
}