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 opennlp.model; import java.io.IOException; import java.util.ArrayList; import java.util.Arrays; import java.util.HashMap; import java.util.HashSet; import java.util.Iterator; import java.util.LinkedList; import java.util.List; import java.util.Map; import java.util.Set; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; /** * An indexer for maxent model data which handles cutoffs for uncommon * contextual predicates and provides a unique integer index for each of the * predicates. */ public class OnePassDataIndexer extends AbstractDataIndexer { private static final Log LOG = LogFactory.getLog(OnePassDataIndexer.class); /** * One argument constructor for DataIndexer which calls the two argument * constructor assuming no cutoff. * * @param eventStream * An Event[] which contains the a list of all the Events seen in the * training data. */ public OnePassDataIndexer(EventStream eventStream) throws IOException { this(eventStream, 0); } public OnePassDataIndexer(EventStream eventStream, int cutoff) throws IOException { this(eventStream, cutoff, true); } /** * Two argument constructor for DataIndexer. * * @param eventStream * An Event[] which contains the a list of all the Events seen in the * training data. * @param cutoff * The minimum number of times a predicate must have been observed in * order to be included in the model. */ public OnePassDataIndexer(EventStream eventStream, int cutoff, boolean sort) throws IOException { Map<String, Integer> predicateIndex = new HashMap<String, Integer>(); LinkedList<Event> events; List eventsToCompare; LOG.info("Indexing events using cutoff of " + cutoff); LOG.info("Computing event counts... "); events = computeEventCounts(eventStream, predicateIndex, cutoff); LOG.info("done. " + events.size() + " events"); LOG.info("Indexing... "); eventsToCompare = index(events, predicateIndex); // done with event list events = null; // done with predicates predicateIndex = null; LOG.info("done."); LOG.info("Sorting and merging events... "); sortAndMerge(eventsToCompare, sort); LOG.info("Done indexing."); } /** * Reads events from <tt>eventStream</tt> into a linked list. The predicates * associated with each event are counted and any which occur at least * <tt>cutoff</tt> times are added to the <tt>predicatesInOut</tt> map along * with a unique integer index. * * @param eventStream * an <code>EventStream</code> value * @param predicatesInOut * a <code>TObjectIntHashMap</code> value * @param cutoff * an <code>int</code> value * @return a <code>TLinkedList</code> value */ private LinkedList<Event> computeEventCounts(EventStream eventStream, Map<String, Integer> predicatesInOut, int cutoff) throws IOException { Set predicateSet = new HashSet(); Map<String, Integer> counter = new HashMap<String, Integer>(); LinkedList<Event> events = new LinkedList<Event>(); while (eventStream.hasNext()) { Event ev = eventStream.next(); events.addLast(ev); update(ev.getContext(), predicateSet, counter, cutoff); } predCounts = new int[predicateSet.size()]; int index = 0; for (Iterator pi = predicateSet.iterator(); pi.hasNext(); index++) { String predicate = (String) pi.next(); predCounts[index] = counter.get(predicate); predicatesInOut.put(predicate, index); } return events; } protected List index(LinkedList<Event> events, Map<String, Integer> predicateIndex) { Map<String, Integer> omap = new HashMap<String, Integer>(); int numEvents = events.size(); int outcomeCount = 0; List eventsToCompare = new ArrayList(numEvents); List<Integer> indexedContext = new ArrayList<Integer>(); for (int eventIndex = 0; eventIndex < numEvents; eventIndex++) { Event ev = (Event) events.removeFirst(); String[] econtext = ev.getContext(); ComparableEvent ce; int ocID; String oc = ev.getOutcome(); if (omap.containsKey(oc)) { ocID = omap.get(oc); } else { ocID = outcomeCount++; omap.put(oc, ocID); } for (int i = 0; i < econtext.length; i++) { String pred = econtext[i]; if (predicateIndex.containsKey(pred)) { indexedContext.add(predicateIndex.get(pred)); } } // drop events with no active features if (indexedContext.size() > 0) { int[] cons = new int[indexedContext.size()]; for (int ci = 0; ci < cons.length; ci++) { cons[ci] = indexedContext.get(ci); } ce = new ComparableEvent(ocID, cons); eventsToCompare.add(ce); } else { LOG.debug("Dropped event " + ev.getOutcome() + ":" + Arrays.asList(ev.getContext())); } // recycle the TIntArrayList indexedContext.clear(); } outcomeLabels = toIndexedStringArray(omap); predLabels = toIndexedStringArray(predicateIndex); return eventsToCompare; } }