| /** |
| * 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.nutch.parsefilter.naivebayes; |
| |
| import java.io.BufferedReader; |
| import java.io.IOException; |
| import java.util.HashMap; |
| import java.io.InputStreamReader; |
| import org.apache.hadoop.conf.Configuration; |
| import org.apache.hadoop.fs.FileSystem; |
| import org.apache.hadoop.fs.Path; |
| |
| public class Classify { |
| |
| private static int uniquewords_size = 0; |
| |
| private static int numof_ir = 0; |
| private static int numwords_ir = 0; |
| private static HashMap<String, Integer> wordfreq_ir = null; |
| |
| private static int numof_r = 0; |
| private static int numwords_r = 0; |
| private static HashMap<String, Integer> wordfreq_r = null; |
| private static boolean ismodel = false; |
| |
| public static HashMap<String, Integer> unflattenToHashmap(String line) { |
| HashMap<String, Integer> dict = new HashMap<String, Integer>(); |
| |
| String dictarray[] = line.split(","); |
| |
| for (String field : dictarray) { |
| |
| dict.put(field.split(":")[0], Integer.valueOf(field.split(":")[1])); |
| } |
| |
| return dict; |
| |
| } |
| |
| public static String classify(String line) throws IOException { |
| |
| double prob_ir = 0; |
| double prob_r = 0; |
| |
| String result = "1"; |
| |
| String[] linearray = line.replaceAll("[^a-zA-Z ]", "").toLowerCase() |
| .split(" "); |
| |
| // read the training file |
| // read the line |
| if (!ismodel) { |
| Configuration configuration = new Configuration(); |
| FileSystem fs = FileSystem.get(configuration); |
| |
| BufferedReader bufferedReader = new BufferedReader(new InputStreamReader( |
| fs.open(new Path("naivebayes-model")))); |
| |
| uniquewords_size = Integer.valueOf(bufferedReader.readLine()); |
| bufferedReader.readLine(); |
| |
| numof_ir = Integer.valueOf(bufferedReader.readLine()); |
| numwords_ir = Integer.valueOf(bufferedReader.readLine()); |
| wordfreq_ir = unflattenToHashmap(bufferedReader.readLine()); |
| bufferedReader.readLine(); |
| numof_r = Integer.valueOf(bufferedReader.readLine()); |
| numwords_r = Integer.valueOf(bufferedReader.readLine()); |
| wordfreq_r = unflattenToHashmap(bufferedReader.readLine()); |
| |
| ismodel = true; |
| |
| bufferedReader.close(); |
| |
| } |
| |
| // update probabilities |
| |
| for (String word : linearray) { |
| if (wordfreq_ir.containsKey(word)) |
| prob_ir += Math.log(wordfreq_ir.get(word)) + 1 |
| - Math.log(numwords_ir + uniquewords_size); |
| else |
| prob_ir += 1 - Math.log(numwords_ir + uniquewords_size); |
| |
| if (wordfreq_r.containsKey(word)) |
| prob_r += Math.log(wordfreq_r.get(word)) + 1 |
| - Math.log(numwords_r + uniquewords_size); |
| else |
| prob_r += 1 - Math.log(numwords_r + uniquewords_size); |
| |
| } |
| |
| prob_ir += Math.log(numof_ir) - Math.log(numof_ir + numof_r); |
| prob_r += Math.log(numof_r) - Math.log(numof_ir + numof_r); |
| |
| if (prob_ir > prob_r) |
| result = "0"; |
| else |
| result = "1"; |
| |
| return result; |
| } |
| |
| } |