package org.apache.lucene.index; /* * 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. */ import java.util.ArrayList; import java.util.Arrays; import java.util.Collections; import java.util.List; import org.apache.lucene.analysis.MockAnalyzer; import org.apache.lucene.analysis.MockTokenizer; import org.apache.lucene.document.Document; import org.apache.lucene.document.Field; import org.apache.lucene.search.similarities.TFIDFSimilarity; import org.apache.lucene.store.Directory; import org.apache.lucene.util.BytesRef; import org.apache.lucene.util.LuceneTestCase; import org.apache.lucene.util.TestUtil; /** * Tests the maxTermFrequency statistic in FieldInvertState */ public class TestMaxTermFrequency extends LuceneTestCase { Directory dir; IndexReader reader; /* expected maxTermFrequency values for our documents */ ArrayList<Integer> expected = new ArrayList<>(); @Override public void setUp() throws Exception { super.setUp(); dir = newDirectory(); IndexWriterConfig config = newIndexWriterConfig(TEST_VERSION_CURRENT, new MockAnalyzer(random(), MockTokenizer.SIMPLE, true)).setMergePolicy(newLogMergePolicy()); config.setSimilarity(new TestSimilarity()); RandomIndexWriter writer = new RandomIndexWriter(random(), dir, config); Document doc = new Document(); Field foo = newTextField("foo", "", Field.Store.NO); doc.add(foo); for (int i = 0; i < 100; i++) { foo.setStringValue(addValue()); writer.addDocument(doc); } reader = writer.getReader(); writer.close(); } @Override public void tearDown() throws Exception { reader.close(); dir.close(); super.tearDown(); } public void test() throws Exception { NumericDocValues fooNorms = MultiDocValues.getNormValues(reader, "foo"); for (int i = 0; i < reader.maxDoc(); i++) { assertEquals(expected.get(i).intValue(), fooNorms.get(i) & 0xff); } } /** * Makes a bunch of single-char tokens (the max freq will at most be 255). * shuffles them around, and returns the whole list with Arrays.toString(). * This works fine because we use lettertokenizer. * puts the max-frequency term into expected, to be checked against the norm. */ private String addValue() { List<String> terms = new ArrayList<>(); int maxCeiling = TestUtil.nextInt(random(), 0, 255); int max = 0; for (char ch = 'a'; ch <= 'z'; ch++) { int num = TestUtil.nextInt(random(), 0, maxCeiling); for (int i = 0; i < num; i++) terms.add(Character.toString(ch)); max = Math.max(max, num); } expected.add(max); Collections.shuffle(terms, random()); return Arrays.toString(terms.toArray(new String[terms.size()])); } /** * Simple similarity that encodes maxTermFrequency directly as a byte */ class TestSimilarity extends TFIDFSimilarity { @Override public float lengthNorm(FieldInvertState state) { return state.getMaxTermFrequency(); } @Override public long encodeNormValue(float f) { return (byte) f; } @Override public float decodeNormValue(long norm) { return norm; } @Override public float coord(int overlap, int maxOverlap) { return 0; } @Override public float queryNorm(float sumOfSquaredWeights) { return 0; } @Override public float tf(float freq) { return 0; } @Override public float idf(long docFreq, long numDocs) { return 0; } @Override public float sloppyFreq(int distance) { return 0; } @Override public float scorePayload(int doc, int start, int end, BytesRef payload) { return 0; } } }