Java tutorial
/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.mahout.cf.taste.impl.similarity; import java.util.Collection; import org.apache.mahout.cf.taste.common.Refreshable; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.impl.common.FastIDSet; import org.apache.mahout.cf.taste.impl.common.RefreshHelper; import org.apache.mahout.cf.taste.model.DataModel; import org.apache.mahout.cf.taste.similarity.PreferenceInferrer; import org.apache.mahout.cf.taste.similarity.UserSimilarity; /** * Implementation of City Block distance (also known as Manhattan distance) - the absolute value of the difference of * each direction is summed. The resulting unbounded distance is then mapped between 0 and 1. */ public final class CityBlockSimilarity extends AbstractItemSimilarity implements UserSimilarity { public CityBlockSimilarity(DataModel dataModel) { super(dataModel); } /** * @throws UnsupportedOperationException */ @Override public void setPreferenceInferrer(PreferenceInferrer inferrer) { throw new UnsupportedOperationException(); } @Override public void refresh(Collection<Refreshable> alreadyRefreshed) { Collection<Refreshable> refreshed = RefreshHelper.buildRefreshed(alreadyRefreshed); RefreshHelper.maybeRefresh(refreshed, getDataModel()); } @Override public double itemSimilarity(long itemID1, long itemID2) throws TasteException { DataModel dataModel = getDataModel(); int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1); int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2); int intersection = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2); return doSimilarity(preferring1, preferring2, intersection); } @Override public double[] itemSimilarities(long itemID1, long[] itemID2s) throws TasteException { DataModel dataModel = getDataModel(); int preferring1 = dataModel.getNumUsersWithPreferenceFor(itemID1); double[] distance = new double[itemID2s.length]; for (int i = 0; i < itemID2s.length; ++i) { int preferring2 = dataModel.getNumUsersWithPreferenceFor(itemID2s[i]); int intersection = dataModel.getNumUsersWithPreferenceFor(itemID1, itemID2s[i]); distance[i] = doSimilarity(preferring1, preferring2, intersection); } return distance; } @Override public double userSimilarity(long userID1, long userID2) throws TasteException { DataModel dataModel = getDataModel(); FastIDSet prefs1 = dataModel.getItemIDsFromUser(userID1); FastIDSet prefs2 = dataModel.getItemIDsFromUser(userID2); int prefs1Size = prefs1.size(); int prefs2Size = prefs2.size(); int intersectionSize = prefs1Size < prefs2Size ? prefs2.intersectionSize(prefs1) : prefs1.intersectionSize(prefs2); return doSimilarity(prefs1Size, prefs2Size, intersectionSize); } /** * Calculate City Block Distance from total non-zero values and intersections and map to a similarity value. * * @param pref1 number of non-zero values in left vector * @param pref2 number of non-zero values in right vector * @param intersection number of overlapping non-zero values */ private static double doSimilarity(int pref1, int pref2, int intersection) { int distance = pref1 + pref2 - 2 * intersection; return 1.0 / (1.0 + distance); } }