/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.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.tools.math.distribution.kernel;

import java.util.HashMap;
import java.util.Map;

import com.rapidminer.tools.math.distribution.NormalDistribution;


/**
 * An updatable estimated kernel density distribution. Uses one kernel
 * for each value.
 * 
 * @author Tobias Malbrecht
 */
public class FullKernelDistribution extends KernelDistribution {

    public static final long serialVersionUID = -3205432422815818L;

    private boolean heuristicBandwidthSelection;

    private double bandwidth = DEFAULT_BANDWIDTH;

    private boolean recentlyUpdated;

    private HashMap<Double, Double> valueWeightMap;

    private double weightSum;

    private double minValue;

    private double maxValue;

    private static final double STANDARD_NORMAL_LOWER_BOUND = NormalDistribution.getLowerBound(0, 1);

    private static final double STANDARD_NORMAL_UPPER_BOUND = NormalDistribution.getUpperBound(0, 1);

    public FullKernelDistribution() {
        super();
        valueWeightMap = new HashMap<Double, Double>();
        weightSum = 0;
        minValue = Double.POSITIVE_INFINITY;
        maxValue = Double.NEGATIVE_INFINITY;
        heuristicBandwidthSelection = true;
        recentlyUpdated = false;
    }

    public FullKernelDistribution(double bandwidth) {
        super();
        this.bandwidth = bandwidth;
        valueWeightMap = new HashMap<Double, Double>();
        weightSum = 0;
        minValue = Double.POSITIVE_INFINITY;
        maxValue = Double.NEGATIVE_INFINITY;
        heuristicBandwidthSelection = false;
        recentlyUpdated = false;
    }

    @Override
    public void update(double value, double weight) {
        if (!Double.isNaN(value) && !Double.isNaN(weight)) {
            Double totalValueWeight = valueWeightMap.get(value);
            if (totalValueWeight != null) {
                totalValueWeight += weight;
            } else {
                totalValueWeight = new Double(weight);
            }
            valueWeightMap.put(value, totalValueWeight);
            weightSum += weight;
            if (value < minValue) {
                minValue = value;
            }
            if (value > maxValue) {
                maxValue = value;
            }
            recentlyUpdated = true;
        }
    }

    @Override
    public void update(double value) {
        update(value, 1.0d);
    }

    @Override
    public String getAttributeName() {
        return null;
    }

    @Override
    public int getNumberOfParameters() {
        return 0;
    }

    @Override
    public String getParameterName(int index) {
        return null;
    }

    @Override
    public double getParameterValue(int index) {
        return Double.NaN;
    }

    private void updateBandwidth() {
        if (heuristicBandwidthSelection && recentlyUpdated) {
            bandwidth = (maxValue - minValue) / Math.sqrt(weightSum);
            recentlyUpdated = false;
        }
    }

    @Override
    public double getUpperBound() {
        updateBandwidth();
        return NormalDistribution.getUpperBound(maxValue, bandwidth);
    }

    @Override
    public double getLowerBound() {
        updateBandwidth();
        return NormalDistribution.getLowerBound(minValue, bandwidth);
    }

    @Override
    public double getTotalWeight() {
        return weightSum;
    }

    @Override
    public double getProbability(double value) {
        updateBandwidth();
        double probability = 0;
        for (Map.Entry<Double, Double> entry : valueWeightMap.entrySet()) {
            double scaledValue = (value - entry.getKey().doubleValue()) / bandwidth;
            if (scaledValue < STANDARD_NORMAL_LOWER_BOUND || scaledValue > STANDARD_NORMAL_UPPER_BOUND) {
                continue;
            }
            probability += NormalDistribution.getProbability(0, 1, scaledValue) * entry.getValue().doubleValue();
        }
        probability /= bandwidth;
        if (probability == 0) {
            return Double.MIN_VALUE;
        }
        return probability / weightSum;
    }
}