/*
* The JCS Conflation Suite (JCS) is a library of Java classes that
* can be used to build automated or semi-automated conflation solutions.
*
* Copyright (C) 2003 Vivid Solutions
*
* 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.
*
* For more information, contact:
*
* Vivid Solutions
* Suite #1A
* 2328 Government Street
* Victoria BC V8T 5G5
* Canada
*
* (250)385-6040
* www.vividsolutions.com
*/
package com.vividsolutions.jcs.conflate.polygonmatch;
import com.vividsolutions.jts.util.Assert;
import com.vividsolutions.jump.feature.Feature;
import com.vividsolutions.jump.feature.FeatureCollection;
import com.vividsolutions.jump.feature.FeatureSchema;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.TreeMap;
/**
* Runs multiple FeatureMatchers, and combines their scores using a weighted
* average.
*/
public class WeightedMatcher implements FeatureMatcher {
/**
* Creates a WeightedMatcher with the given matchers and their weights.
* @param matchersAndWeights alternates between FeatureMatchers and Doubles
*/
public WeightedMatcher(Object[] matchersAndWeights) {
Assert.isTrue(matchersAndWeights.length % 2 == 0);
for (int i = 0; i < matchersAndWeights.length; i += 2) {
add((FeatureMatcher) matchersAndWeights[i+1],
((Number) matchersAndWeights[i]).doubleValue());
//Number rather than Double so parties (e.g. Jython) can pass in Integers. [Jon Aquino]
}
}
/**
* Adds a matcher to the WeightedMatcher's matchers. If weight is 0, the
* matcher will be ignored.
* @param matcher a matcher to add
* @param weight the weight given to scores returned by the matcher
*/
private void add(FeatureMatcher matcher, double weight) {
Assert.isTrue(weight >= 0);
if (weight == 0) {
return;
}
matcherToWeightMap.put(matcher, new Double(weight));
}
private Map matcherToWeightMap = new HashMap();
/**
* Searches a collection of candidate features for those that match the given
* target feature, using each FeatureMatcher.
* @param target the feature to match
* @param candidates the features to search for matches
* @return the candidates that pass at least one FeatureMatcher. Each score is
* a weighted average of the scores from the FeatureMatchers.
*/
@Override
public Matches match(Feature target, FeatureCollection candidates) {
if (weightTotal() == 0) { return new Matches(candidates.getFeatureSchema()); }
Map matcherToMatchesMap = matcherToMatchesMap(target, candidates);
Map featureToScoreMap = featureToScoreMap(matcherToMatchesMap);
return toMatches(featureToScoreMap, candidates.getFeatureSchema());
}
private Matches toMatches(Map featureToScoreMap, FeatureSchema schema) {
Matches matches = new Matches(schema);
for (Iterator i = featureToScoreMap.keySet().iterator(); i.hasNext(); ) {
Feature feature = (Feature) i.next();
double score = ((Double) featureToScoreMap.get(feature)).doubleValue();
matches.add(feature, score);
}
return matches;
}
private Map matcherToMatchesMap(Feature feature, FeatureCollection candidates) {
HashMap matcherToMatchesMap = new HashMap();
for (Iterator i = matcherToWeightMap.keySet().iterator(); i.hasNext(); ) {
FeatureMatcher matcher = (FeatureMatcher) i.next();
if (normalizedWeight(matcher) == 0) { continue; }
matcherToMatchesMap.put(matcher, matcher.match(feature, candidates));
}
return matcherToMatchesMap;
}
private Map featureToScoreMap(Map matcherToMatchesMap) {
TreeMap featureToScoreMap = new TreeMap();
for (Iterator i = matcherToMatchesMap.keySet().iterator(); i.hasNext(); ) {
FeatureMatcher matcher = (FeatureMatcher) i.next();
Matches matches = (Matches) matcherToMatchesMap.get(matcher);
addToFeatureToScoreMap(matches, matcher, featureToScoreMap);
}
return featureToScoreMap;
}
private void addToFeatureToScoreMap(Matches matches, FeatureMatcher matcher,
Map featureToScoreMap) {
for (int i = 0; i < matches.size(); i++) {
double score = matches.getScore(i) * normalizedWeight(matcher);
addToFeatureToScoreMap(matches.getFeature(i), score, featureToScoreMap);
}
}
private void addToFeatureToScoreMap(Feature feature, double score, Map featureToScoreMap) {
Double oldScore = (Double) featureToScoreMap.get(feature);
if (oldScore == null) { oldScore = new Double(0); }
featureToScoreMap.put(feature, new Double(oldScore.doubleValue() + score));
}
private double normalizedWeight(FeatureMatcher matcher) {
return ((Double)matcherToWeightMap.get(matcher)).doubleValue() / weightTotal();
}
private double weightTotal() {
double weightTotal = 0;
for (Iterator i = matcherToWeightMap.values().iterator(); i.hasNext(); ) {
Double weight = (Double) i.next();
weightTotal += weight.doubleValue();
}
return weightTotal;
}
}