/*
* 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;
}
}