/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.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.learner.functions.neuralnet;

import com.rapidminer.example.Example;

/**
 * This function represents a linear activation function by calculating the
 * identity function on the weighted sum. The linear activation function
 * is usually used for the output layer of regression problems.
 *
 * @author Ingo Mierswa
 */
public class LinearFunction extends ActivationFunction {

    private static final long serialVersionUID = 1L;

    @Override
    public String getTypeName() {
        return "Linear";
    }

    @Override
    public double calculateValue(InnerNode node, Example example) {
        Node[] inputs = node.getInputNodes();
        double[] weights = node.getWeights();
        double weightedSum = weights[0]; // bias
        for (int i = 0; i < inputs.length; i++) {
            weightedSum += inputs[i].calculateValue(true, example) * weights[i + 1];
        }
        return weightedSum;
    }

    @Override
    public double calculateError(InnerNode node, Example example) {
        Node[] outputs = node.getOutputNodes();
        int[] numberOfOutputs = node.getOutputNodeInputIndices();
        double errorSum = 0;
        for (int i = 0; i < outputs.length; i++) {
            errorSum += outputs[i].calculateError(true, example) * outputs[i].getWeight(numberOfOutputs[i]);
        }
        return errorSum;
    }
}