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.example.kddcup.track1; import java.io.File; import java.io.IOException; import org.apache.commons.cli2.OptionException; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.example.TasteOptionParser; import org.apache.mahout.cf.taste.example.kddcup.KDDCupDataModel; import org.apache.mahout.cf.taste.model.DataModel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; public final class Track1RecommenderEvaluatorRunner { private static final Logger log = LoggerFactory.getLogger(Track1RecommenderEvaluatorRunner.class); private Track1RecommenderEvaluatorRunner() { } public static void main(String... args) throws IOException, TasteException, OptionException { File dataFileDirectory = TasteOptionParser.getRatings(args); if (dataFileDirectory == null) { throw new IllegalArgumentException("No data directory"); } if (!dataFileDirectory.exists() || !dataFileDirectory.isDirectory()) { throw new IllegalArgumentException("Bad data file directory: " + dataFileDirectory); } Track1RecommenderEvaluator evaluator = new Track1RecommenderEvaluator(dataFileDirectory); DataModel model = new KDDCupDataModel(KDDCupDataModel.getTrainingFile(dataFileDirectory)); double evaluation = evaluator.evaluate(new Track1RecommenderBuilder(), null, model, Float.NaN, Float.NaN); log.info(String.valueOf(evaluation)); } }