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/*
 * SimMetrics - SimMetrics is a java library of Similarity or Distance Metrics,
 * e.g. Levenshtein Distance, that provide float based similarity measures
 * between String Data. All metrics return consistent measures rather than
 * unbounded similarity scores.
 * 
 * Copyright (C) 2014 SimMetrics authors
 * 
 * This file is part of SimMetrics. This program is free software: you can
 * redistribute it and/or modify it under the terms of the GNU 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 General Public License for more
 * details.
 * 
 * You should have received a copy of the GNU General Public License along with
 * SimMetrics. If not, see .
 */
package org.simmetrics.metrics;

import static java.util.Collections.frequency;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.simmetrics.ListMetric;

import static java.lang.Math.sqrt;

/**
 * Euclidean Distance algorithm providing a similarity measure between two lists
 * using the vector space of combined terms as the dimensions.
 *
 * 
 * This class is immutable and thread-safe.
 * @param 
 *            type of the token
 * 
 */
public class EuclideanDistance implements ListMetric {

	@Override
	public float compare(List a, List b) {

		if (a.isEmpty() && b.isEmpty()) {
			return 1.0f;
		}

		if (a.isEmpty() || b.isEmpty()) {
			return 0.0f;
		}

		float totalPossible = (float) sqrt((a.size() * a.size()) + (b.size() * b.size()));
		return (totalPossible - distance(a, b)) / totalPossible;
	}

	private static  float distance(final List a, final List b) {
		final Set all = new HashSet<>();
		all.addAll(a);
		all.addAll(b);

		float totalDistance = 0.0f;
		for (final T token : all) {
			int frequencyInA = frequency(a, token);
			int frequencyInB = frequency(b, token);

			totalDistance += ((frequencyInA - frequencyInB) * (frequencyInA - frequencyInB));
		}

		return (float) sqrt(totalDistance);
	}

	@Override
	public String toString() {
		return "EuclideanDistance";
	}

}