/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 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.lazy;

import java.util.ArrayList;
import java.util.Collection;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.UpdateablePredictionModel;
import com.rapidminer.tools.Tools;
import com.rapidminer.tools.container.Tupel;
import com.rapidminer.tools.math.container.GeometricDataCollection;

/**
 * An implementation of a knn model.
 * 
 * @author Sebastian Land
 * 
 */
public class KNNClassificationModel extends UpdateablePredictionModel {

	private static final long serialVersionUID = -6292869962412072573L;

	private int k;

	private int size;

	private GeometricDataCollection<Integer> samples;

	private ArrayList<String> sampleAttributeNames;

	private boolean weightByDistance;

	public KNNClassificationModel(ExampleSet trainingSet, GeometricDataCollection<Integer> samples, int k, boolean weightByDistance) {
		super(trainingSet);
		this.k = k;
		this.size = trainingSet.size();
		this.samples = samples;
		this.weightByDistance = weightByDistance;

		// finding training attributes
		Attributes attributes = trainingSet.getAttributes();
		sampleAttributeNames = new ArrayList<String>(attributes.size());
		for (Attribute attribute : attributes) {
			sampleAttributeNames.add(attribute.getName());
		}
	}

	@Override
	public ExampleSet performPrediction(ExampleSet exampleSet, Attribute predictedLabel) throws OperatorException {
		// building attribute order from trainingset
		ArrayList<Attribute> sampleAttributes = new ArrayList<Attribute>(sampleAttributeNames.size());
		Attributes attributes = exampleSet.getAttributes();
		for (String attributeName : sampleAttributeNames) {
			sampleAttributes.add(attributes.get(attributeName));
		}

		double[] values = new double[sampleAttributes.size()];
		for (Example example : exampleSet) {
			// reading values
			int i = 0;
			for (Attribute attribute : sampleAttributes) {
				values[i] = example.getValue(attribute);
				i++;
			}

			// counting frequency of labels
			double[] counter = new double[predictedLabel.getMapping().size()];
			double totalDistance = 0;
			if (!weightByDistance || k == 1) {
				// finding next k neighbours
				Collection<Integer> neighbourLabels = samples.getNearestValues(k, values);
				// distance is 1 for complete neighbourhood
				totalDistance = k;

				// counting frequency of labels
				for (int index : neighbourLabels) {
					counter[index] += 1 / totalDistance;
				}
			} else {
				// finding next k neighbours and their distances
				Collection<Tupel<Double, Integer>> neighbours = samples.getNearestValueDistances(k, values);
				for (Tupel<Double, Integer> tupel : neighbours) {
					totalDistance += tupel.getFirst();
				}

				double totalSimilarity = 0.0d;
				if (totalDistance == 0) {
					totalDistance = 1;
					totalSimilarity = k;
				} else {
					totalSimilarity = Math.max(k - 1, 1);
				}

				// counting frequency of labels
				for (Tupel<Double, Integer> tupel : neighbours) {
					counter[tupel.getSecond()] += (1d - tupel.getFirst() / totalDistance) / totalSimilarity;
				}
			}
			// finding most frequent class
			int mostFrequentIndex = Integer.MIN_VALUE;
			double mostFrequentFrequency = Double.NEGATIVE_INFINITY;
			for (int index = 0; index < counter.length; index++) {
				if (mostFrequentFrequency < counter[index]) {
					mostFrequentFrequency = counter[index];
					mostFrequentIndex = index;
				}
			}
			// setting prediction
			if (mostFrequentIndex == Integer.MIN_VALUE)
				example.setValue(predictedLabel, Double.NaN);
			else
				example.setValue(predictedLabel, mostFrequentIndex);

			// setting confidence
			for (int index = 0; index < counter.length; index++) {
				example.setConfidence(predictedLabel.getMapping().mapIndex(index), counter[index]);
			}
		}
		return exampleSet;
	}

	@Override
	public void update(ExampleSet updateSet) throws OperatorException {
		Attribute label = updateSet.getAttributes().getLabel();
		// check if exampleset header is correct
		if (label.isNominal()) {
			Attributes attributes = updateSet.getAttributes();

			int valuesSize = attributes.size();
			for (Example example : updateSet) {
				double[] values = new double[valuesSize];
				int i = 0;
				for (Attribute attribute : attributes) {
					values[i] = example.getValue(attribute);
					i++;
				}
				int labelValue = (int) example.getValue(label);
				samples.add(values, labelValue);
			}
		}
	}

	@Override
	public String toString() {
		StringBuffer buffer = new StringBuffer();
		if (weightByDistance)
			buffer.append("Weighted ");
		buffer.append(k + "-Nearest Neighbour model for classification." + Tools.getLineSeparator());
		buffer.append("The model contains " + size + " examples with " + sampleAttributeNames.size() + " dimensions of the following classes:");
		buffer.append(Tools.getLineSeparator());
		for (String value: getTrainingHeader().getAttributes().getLabel().getMapping().getValues()) {
			buffer.append("  " + value + Tools.getLineSeparator());
		}
		return buffer.toString();
	}
}