/**
 * 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;

import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.nnet.comp.layer.InputLayer;
import org.neuroph.nnet.comp.neuron.BiasNeuron;
import org.neuroph.nnet.learning.LMS;
import org.neuroph.util.ConnectionFactory;
import org.neuroph.util.LayerFactory;
import org.neuroph.util.NeuralNetworkFactory;
import org.neuroph.util.NeuralNetworkType;
import org.neuroph.util.NeuronProperties;
import org.neuroph.util.TransferFunctionType;

/**
 * Adaline neural network architecture with LMS learning rule.
 * Uses bias input, bipolar inputs [-1, 1] and ramp transfer function
 * It can be also created using binary inputs and linear transfer function,
 * but that dont works for some problems.
 * @author Zoran Sevarac 
 */
public class Adaline extends NeuralNetwork {
	
	/**
	 * The class fingerprint that is set to indicate serialization
	 * compatibility with a previous version of the class.
	 */	
	private static final long serialVersionUID = 1L;

	/**
	 * Creates new Adaline network with specified number of neurons in input
	 * layer
	 * 
	 * @param inputNeuronsCount
	 *            number of neurons in input layer
	 */
	public Adaline(int inputNeuronsCount) {
		this.createNetwork(inputNeuronsCount);
	}

	/**
	 * Creates adaline network architecture with specified number of input neurons
	 * 
	 * @param inputNeuronsCount
         *              number of neurons in input layer
	 */
	private void createNetwork(int inputNeuronsCount) {
		// set network type code
		this.setNetworkType(NeuralNetworkType.ADALINE);
                
                // create input layer neuron settings for this network
		NeuronProperties inNeuronProperties = new NeuronProperties();
		inNeuronProperties.setProperty("transferFunction", TransferFunctionType.LINEAR);

		// createLayer input layer with specified number of neurons
		//Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, inNeuronProperties);
                Layer inputLayer = new InputLayer(inputNeuronsCount);
                inputLayer.addNeuron(new BiasNeuron()); // add bias neuron (always 1, and it will act as bias input for output neuron)
		this.addLayer(inputLayer);
                
               // create output layer neuron settings for this network
		NeuronProperties outNeuronProperties = new NeuronProperties();
		outNeuronProperties.setProperty("transferFunction", TransferFunctionType.LINEAR); // was RAMP
//		outNeuronProperties.setProperty("transferFunction.slope", new Double(1));
//		outNeuronProperties.setProperty("transferFunction.yHigh", new Double(1));
//		outNeuronProperties.setProperty("transferFunction.xHigh", new Double(1));
//		outNeuronProperties.setProperty("transferFunction.yLow", new Double(-1));
//		outNeuronProperties.setProperty("transferFunction.xLow", new Double(-1));

		// createLayer output layer (only one neuron)
		Layer outputLayer = LayerFactory.createLayer(1, outNeuronProperties);
		this.addLayer(outputLayer);

		// createLayer full conectivity between input and output layer
		ConnectionFactory.fullConnect(inputLayer, outputLayer);

		// set input and output cells for network
		NeuralNetworkFactory.setDefaultIO(this);

		// set LMS learning rule for this network
		this.setLearningRule(new LMS());
	}

}