/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 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;

import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.NominalMapping;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.operator.learner.functions.kernel.functions.DotKernel;
import com.rapidminer.operator.learner.functions.kernel.functions.Kernel;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;

/**
 * The perceptron is a type of artificial neural network invented in 1957 by Frank Rosenblatt. 
 * It can be seen as the simplest kind of feedforward neural network: a linear classifier.
 * Beside all biological analogies, the single layer perceptron is simply a linear classifier
 * which is efficiently trained by a simple update rule: for all wrongly classified data points,
 * the weight vector is either increased or decreased by the corresponding example values.
 * 
 * @author Sebastian Land
 * @version $Id: Perceptron.java,v 1.9 2008/05/09 19:22:56 ingomierswa Exp $
 */
public class Perceptron extends AbstractLearner {

	public static final String PARAMETER_ROUNDS = "rounds";
	
	public static final String PARAMETER_LEARNING_RATE = "learning_rate";

	public Perceptron(OperatorDescription description) {
		super(description);
	}
	
	public Model learn(ExampleSet exampleSet) throws OperatorException {
        Kernel kernel = getKernel();
		kernel.init(exampleSet);
		
		double initLearnRate = getParameterAsDouble(PARAMETER_LEARNING_RATE);
		NominalMapping labelMapping = exampleSet.getAttributes().getLabel().getMapping();
		String classNeg = labelMapping.getNegativeString();
		String classPos = labelMapping.getPositiveString();
		double classValueNeg = labelMapping.getNegativeIndex();
		int numberOfAttributes = exampleSet.getAttributes().size();
		HyperplaneModel model = new HyperplaneModel(exampleSet, classNeg, classPos, kernel);
		model.init(new double[numberOfAttributes], 0);
		for (int round = 0; round <= getParameterAsInt(PARAMETER_ROUNDS); round++) {
			double learnRate = getLearnRate(round, getParameterAsInt(PARAMETER_ROUNDS), initLearnRate);
			Attributes attributes = exampleSet.getAttributes();
			for (Example example: exampleSet) {
				double prediction = model.predict(example);
				if (prediction != example.getLabel()) {
					double direction = (example.getLabel() == classValueNeg)? -1 : 1;
					// adapting intercept
					model.setIntercept(model.getIntercept() + learnRate * direction);
					// adapting coefficients
					double coefficients[] = model.getCoefficients();
					int i = 0;
					for (Attribute attribute: attributes) {
						coefficients[i] += learnRate * direction * example.getValue(attribute);
						i++;
					}
				}
			}
		}
		return model;
	}

	protected Kernel getKernel() throws UndefinedParameterError {
		return new DotKernel();
	}
	
	public double getLearnRate(int time, int maxtime, double initLearnRate) {
		return initLearnRate * Math.pow(((initLearnRate * 0.1d) / initLearnRate), (((double) time) / ((double) maxtime)));
	}
	
	public boolean supportsCapability(LearnerCapability lc) {
        if (lc == LearnerCapability.NUMERICAL_ATTRIBUTES)
            return true;
        if (lc == LearnerCapability.BINOMINAL_CLASS)
            return true;
        if (lc == LearnerCapability.WEIGHTED_EXAMPLES)
            return true;
        return false;	
    }
	
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeInt(PARAMETER_ROUNDS, "The number of datascans used to adapt the hyperplane.", 0, Integer.MAX_VALUE, 3));
		types.add(new ParameterTypeDouble(PARAMETER_LEARNING_RATE, "The hyperplane will adapt with this rate to each example.", 0.0d, 1.0d, 0.05d));
		return types;
	}
}