/* * JOrtho * * Copyright (C) 2005-2008 by i-net software * * 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 2 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 this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 * USA. * * Created on 12.12.2007 */ package com.inet.jortho; import java.util.ArrayList; import java.util.HashMap; import java.util.List; /** * A hash list of Suggestions. The list is cut with a max dissimilarity. If a suggestion already exist then the * suggestion with the lower dissimilarity will be hold. * * @author Volker Berlin */ class Suggestions { private final HashMap<Suggestion, Suggestion> map = new HashMap<Suggestion, Suggestion>(); private final int maxDiff; /** * Create a suggestions list. Suggestion with a larger dissimilarity can not be added. * @param maxDiff the max dissimilarity */ Suggestions(final int maxDiff) { this.maxDiff = maxDiff; } /** * Add a suggestion. * @param suggestion the suggestion */ void add(final Suggestion suggestion) { if (suggestion.getDissimilarity() > maxDiff) { return; } final Suggestion oldSuggestion = map.get(suggestion); if (oldSuggestion != null && oldSuggestion.getDissimilarity() <= suggestion.getDissimilarity()) { return; } map.put(suggestion, suggestion); } List<Suggestion> getlist() { final ArrayList<Suggestion> list = new ArrayList<Suggestion>(); for (final Suggestion sugg : map.values()) { list.add(sugg); } return list; } /** * Get the max dissimilarity. Suggestion with a larger value can not be added. * @return */ int getMaxDissimilarity() { return maxDiff; } }