/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.linear.learner.perceptron;

import java.util.Arrays;
import java.util.List;

import org.openimaj.math.model.EstimatableModel;
import org.openimaj.ml.linear.learner.OnlineLearner;
import org.openimaj.util.pair.IndependentPair;

/**
 * 
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 */
public class SimplePerceptron implements OnlineLearner<double[], Integer>, EstimatableModel<double[], Integer> {
        private static final double DEFAULT_LEARNING_RATE = 0.01;
        private static final int DEFAULT_ITERATIONS = 1000;
        double alpha = DEFAULT_LEARNING_RATE;
        private double[] w;
        private int iterations = DEFAULT_ITERATIONS;

        private SimplePerceptron(double[] w) {
                this.w = w;
        }

        /**
         * 
         */
        public SimplePerceptron() {
        }

        @Override
        public void process(double[] pt, Integer clazz) {
                // System.out.println("Testing: " + Arrays.toString(pt) + " = " +
                // clazz);
                if (w == null) {
                        initW(pt.length);
                }
                final int y = predict(pt);
                System.out.println("w: " + Arrays.toString(w));
                w[0] = w[0] + alpha * (clazz - y);
                for (int i = 0; i < pt.length; i++) {
                        w[i + 1] = w[i + 1] + alpha * (clazz - y) * pt[i];
                }
                // System.out.println("neww: " + Arrays.toString(w));
        }

        private void initW(int length) {
                w = new double[length + 1];
                w[0] = 1;
        }

        @Override
        public Integer predict(double[] x) {
                if (w == null)
                        return 0;
                return (w[0] + project(x)) > 0 ? 1 : 0;
        }

        private double project(double[] x) {
                double sum = 0;
                for (int i = 0; i < x.length; i++) {
                        sum += x[i] * w[i + 1];
                }
                return sum;
        }

        @Override
        public boolean estimate(List<? extends IndependentPair<double[], Integer>> data) {
                this.w = new double[] { 1, 0, 0 };

                for (int i = 0; i < iterations; i++) {
                        iteration(data);

                        final double error = calculateError(data);
                        if (error < 0.01)
                                break;
                }
                return true;
        }

        private void iteration(List<? extends IndependentPair<double[], Integer>> pts) {
                for (int i = 0; i < pts.size(); i++) {
                        final IndependentPair<double[], Integer> pair = pts.get(i);
                        process(pair.firstObject(), pair.secondObject());
                }
        }

        @Override
        public int numItemsToEstimate() {
                return 1;
        }

        protected double calculateError(List<? extends IndependentPair<double[], Integer>> pts) {
                double error = 0;

                for (int i = 0; i < pts.size(); i++) {
                        final IndependentPair<double[], Integer> pair = pts.get(i);
                        error += Math.abs(predict(pts.get(i).firstObject()) - pair.secondObject());
                }

                return error / pts.size();
        }

        /**
         * Compute NaN-coordinate of a point on the hyperplane given
         * non-NaN-coordinates. Only one x coordinate may be nan. If more NaN are
         * seen after the first they are assumed to be 0
         * 
         * @param x
         *            the coordinates, only one may be NaN, all others must be
         *            provided
         * @return the y-ordinate
         */
        public double[] computeHyperplanePoint(double[] x) {
                double total = w[0];
                int nanindex = -1;
                final double[] ret = new double[x.length];
                for (int i = 0; i < x.length; i++) {
                        double value = x[i];
                        if (nanindex != -1 && Double.isNaN(value)) {
                                value = 0;
                        }
                        else if (Double.isNaN(value)) {
                                nanindex = i;
                                continue;
                        }
                        ret[i] = value;
                        total += w[i + 1] * value;
                }
                if (nanindex != -1)
                        ret[nanindex] = total / -w[nanindex + 1];
                return ret;
        }

        @Override
        public SimplePerceptron clone() {
                return new SimplePerceptron(w);
        }

        public double[] getWeights() {
                return this.w;
        }
}