/**
 * Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
 *
 * 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.
 */

package org.neuroph.nnet.learning;

import org.neuroph.core.learning.TrainingData;
import org.neuroph.core.Connection;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.Neuron;
import org.neuroph.core.Weight;


/**
 * Backpropagation learning rule with momentum.
 * @author Zoran Sevarac <sevarac@gmail.com>
 */
public class MomentumBackpropagation extends BackPropagation {
	
	/**
	 * The class fingerprint that is set to indicate serialization
	 * compatibility with a previous version of the class.
	 */	
	private static final long serialVersionUID = 1L;
	
	/**
	 * Momentum factor
	 */
	protected double momentum = 0.25d;

     
	/**
	 * Creates new instance of MomentumBackpropagation learning
         */
	public MomentumBackpropagation() {
		super();
                this.setTrainingDataBufferSize(2); // batch weights sum and previous weight value
	}


	/**
	 * This method implements weights update procedure for the single neuron
	 * for the back propagation with momentum factor
	 * @param neuron
	 *            neuron to update weights
	 */
	@Override
	protected void updateNeuronWeights(Neuron neuron) {
		for(Connection connection : neuron.getInputConnections() ) {
			double input = connection.getInput();
			if (input == 0) {
				continue;
			}

                        // get the error for specified neuron,
                        double neuronError = neuron.getError();

                        // tanh can be used to minimise the impact of big error values, which can cause network instability
                        // suggested at https://sourceforge.net/tracker/?func=detail&atid=1107579&aid=3130561&group_id=238532
                        // double neuronError = Math.tanh(neuron.getError());
			
			Weight weight = connection.getWeight();
			
			double currentWeighValue = weight.getValue();
			double previousWeightValue = weight.getTrainingData().get(TrainingData.PREVIOUS_WEIGHT);
			double deltaWeight = this.learningRate * neuronError * input +
				momentum * (currentWeighValue - previousWeightValue);
                        // save previous weight value
                        weight.getTrainingData().set(TrainingData.PREVIOUS_WEIGHT, currentWeighValue);
                        this.applyWeightChange(weight, deltaWeight);
		}
	}

	/**
	 * Returns the momentum factor 
	 * 
	 * @return momentum factor 
	 */
	public double getMomentum() {
		return momentum;
	}

	/**
	 * Sets the momentum factor 
	 * 
	 * @param momentum
	 *            momentum factor
	 */	
	public void setMomentum(double momentum) {
		this.momentum = momentum;
	}

}