/**
 * Copyright (C) 2001-2017 by RapidMiner and the contributors
 * 
 * Complete list of developers available at our web site:
 * 
 * http://rapidminer.com
 * 
 * This program is free software: you can redistribute it and/or modify it under the terms of the
 * GNU Affero General Public License as published by the Free Software Foundation, either version 3
 * of the License, or (at your option) any later version.
 * 
 * This program 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
 * Affero General Public License for more details.
 * 
 * You should have received a copy of the GNU Affero General Public License along with this program.
 * If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.performance;

import com.rapidminer.operator.Model;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.learner.functions.kernel.KernelModel;
import com.rapidminer.operator.learner.functions.kernel.SupportVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;

import java.util.List;


/**
 * Returns a performance vector just counting the number of support vectors of a given support
 * vector based model (kernel model). Please note that this operator will try to derive the number
 * of support vectors of the first delivered model and might fail on this task if no appropriate
 * kernel based model is delivered. Currently, at least the models delivered by the operator JMySVM,
 * MyKLR, LibSVM, GPLearner, KernelLogisticRegression, RVM, and the EvoSVM should be supported.
 * 
 * @author Ingo Mierswa
 */
public class SupportVectorCounter extends Operator {

	/**
	 * The parameter name for "Indicates if the fitness should for maximal or minimal number of
	 * features."
	 */
	public static final String PARAMETER_OPTIMIZATION_DIRECTION = "optimization_direction";

	private double lastCount = Double.NaN;

	private InputPort modelInput = getInputPorts().createPort("model", KernelModel.class);
	private InputPort performanceInput = getInputPorts().createPort("performance vector");

	private OutputPort modelOutput = getOutputPorts().createPort("model");
	private OutputPort performanceOutput = getOutputPorts().createPort("performance vector");

	public SupportVectorCounter(OperatorDescription description) {
		super(description);

		getTransformer().addGenerationRule(performanceOutput, PerformanceVector.class);
		getTransformer().addPassThroughRule(modelInput, modelOutput);

		addValue(new ValueDouble("support_vectors", "The number of the currently used support vectors.") {

			@Override
			public double getDoubleValue() {
				return lastCount;
			}
		});
	}

	@Override
	public void doWork() throws OperatorException {
		Model model = modelInput.getData(Model.class);
		if (!(model instanceof KernelModel)) {
			throw new UserError(this, 122, "'support vector based model (kernel model)'");
		}

		PerformanceVector inputPerformance = performanceInput.getDataOrNull(PerformanceVector.class);

		PerformanceVector performance = count((KernelModel) model, inputPerformance);

		modelOutput.deliver(model);
		performanceOutput.deliver(performance);
	}

	/**
	 * Creates a new performance vector if the given one is null. Adds a new estimated criterion. If
	 * the criterion was already part of the performance vector before it will be overwritten.
	 */
	private PerformanceVector count(KernelModel model, PerformanceVector performanceCriteria) throws OperatorException {
		if (performanceCriteria == null) {
			performanceCriteria = new PerformanceVector();
		}

		this.lastCount = 0;
		int svNumber = model.getNumberOfSupportVectors();
		for (int i = 0; i < svNumber; i++) {
			SupportVector sv = model.getSupportVector(i);
			if (Math.abs(sv.getAlpha()) > 0.0d) {
				this.lastCount++;
			}
		}
		EstimatedPerformance svCriterion = new EstimatedPerformance("number_of_support_vectors", lastCount, 1,
				getParameterAsInt(PARAMETER_OPTIMIZATION_DIRECTION) == MDLCriterion.MINIMIZATION);
		performanceCriteria.addCriterion(svCriterion);
		return performanceCriteria;
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeCategory(PARAMETER_OPTIMIZATION_DIRECTION,
				"Indicates if the fitness should be maximal for the maximal or the minimal number of support vectors.",
				MDLCriterion.DIRECTIONS, MDLCriterion.MINIMIZATION));
		return types;
	}
}