| /* |
| * 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.opennlp.utils.ngram; |
| |
| import java.util.Arrays; |
| import java.util.Collection; |
| import java.util.HashSet; |
| |
| /** |
| * utility class for calculating probabilities of tri/bi/uni-grams |
| */ |
| public class NGramUtils { |
| |
| private static <T> Double count(T x0, T x1, T x2, Collection<T[]> sentences) { |
| Double count = 0d; |
| for (T[] sentence : sentences) { |
| int idx0 = contains(sentence, x0); |
| if (idx0 >= 0) { |
| if (idx0 + 2 < sentence.length && x1.equals(sentence[idx0+1]) && x2.equals(sentence[idx0+2])) { |
| count++; |
| } |
| } |
| } |
| return count; |
| } |
| |
| private static <T> int contains(T[] sentence, T word) { |
| for (int i = 0; i < sentence.length; i++) { |
| if (word.equals(sentence[i])){ |
| return i; |
| } |
| } |
| return -1; |
| } |
| |
| private static <T> Double count(T sequentWord, T precedingWord, Collection<T[]> set) { |
| Double result = 0d; |
| boolean foundPreceding = false; |
| for (T[] sentence : set) { |
| for (T w : sentence) { |
| if (precedingWord.equals(w)) { |
| foundPreceding = true; |
| continue; |
| } |
| if (foundPreceding && sequentWord.equals(w)) { |
| foundPreceding = false; |
| result++; |
| } |
| else |
| foundPreceding = false; |
| } |
| } |
| return result; |
| } |
| |
| private static <T> Double count(T word, Collection<T[]> set) { |
| Double result = 0d; |
| for (T[] sentence : set) { |
| for (T w : sentence) { |
| if (word.equals(w)) |
| result++; |
| } |
| } |
| return result; |
| } |
| |
| public static <T> Double calculateLaplaceSmoothingProbability(T sequentWord, T precedingWord, Collection<T[]> set, Double k) { |
| return (count(sequentWord, precedingWord, set) + k) / (count(precedingWord, set) + k * set.size()); |
| } |
| |
| public static <T> Double calculateBigramMLProbability(T sequentWord, T precedingWord, Collection<T[]> set) { |
| return count(sequentWord, precedingWord, set)/ count(precedingWord, set); |
| } |
| |
| public static <T> Double calculateTrigramMLProbability(T x0, T x1, T x2, Collection<T[]> sentences) { |
| return count(x0, x1, x2, sentences)/ count(x1, x0, sentences); |
| } |
| |
| public static Double calculateBigramPriorSmoothingProbability(String sequentWord, String precedingWord, Collection<String[]> set, Double k) { |
| return (count(sequentWord, precedingWord, set) + k * calculateUnigramMLProbability(sequentWord, set)) / (count(precedingWord, set) + k * set.size()); |
| } |
| |
| public static <T> Double calculateUnigramMLProbability(T word, Collection<T[]> set) { |
| double vocSize = 0d; |
| for (T[] s : set) { |
| vocSize+= s.length; |
| } |
| return count(word, set) / vocSize; |
| } |
| |
| public static <T> Double calculateLinearInterpolationProbability(T x0, T x1, T x2, Collection<T[]> sentences, |
| Double lambda1, Double lambda2, Double lambda3) { |
| assert lambda1 + lambda2 + lambda3 == 1 : "lambdas sum should be equals to 1"; |
| assert lambda1 > 0 && lambda2 > 0 && lambda3 > 0 : "lambdas should all be greater than 0"; |
| |
| return lambda1 * calculateTrigramMLProbability(x0, x1, x2, sentences) + |
| lambda2 * calculateBigramMLProbability(x2, x1, sentences) + |
| lambda3 * calculateUnigramMLProbability(x2, sentences); |
| |
| } |
| |
| private static <T> Collection<T> flatSet(Collection<T[]> set) { |
| Collection<T> flatSet = new HashSet<T>(); |
| for (T[] sentence : set){ |
| flatSet.addAll(Arrays.asList(sentence)); |
| } |
| return flatSet; |
| } |
| |
| public static <T> Double calculateMissingBigramProbabilityMass(T x1, Double discount, Collection<T[]> set) { |
| Double missingMass = 0d; |
| Double countWord = count(x1, set); |
| for (T word : flatSet(set)) { |
| missingMass += (count(word, x1, set) - discount)/ countWord; |
| } |
| return 1 - missingMass; |
| } |
| |
| } |