| /* |
| * 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.tools.ml.maxent.quasinewton; |
| |
| import java.util.Arrays; |
| |
| import opennlp.tools.ml.ArrayMath; |
| import opennlp.tools.ml.model.DataIndexer; |
| import opennlp.tools.ml.model.OnePassRealValueDataIndexer; |
| |
| /** |
| * Evaluate negative log-likelihood and its gradient from DataIndexer. |
| */ |
| public class NegLogLikelihood implements Function { |
| |
| protected int dimension; |
| protected int numOutcomes; |
| protected int numFeatures; |
| protected int numContexts; |
| |
| // Information from data index |
| protected final float[][] values; |
| protected final int[][] contexts; |
| protected final int[] outcomeList; |
| protected final int[] numTimesEventsSeen; |
| |
| // For calculating negLogLikelihood and gradient |
| protected double[] tempSums; |
| protected double[] expectation; |
| |
| protected double[] gradient; |
| |
| public NegLogLikelihood(DataIndexer indexer) { |
| |
| // Get data from indexer. |
| if (indexer instanceof OnePassRealValueDataIndexer) { |
| this.values = indexer.getValues(); |
| } else { |
| this.values = null; |
| } |
| |
| this.contexts = indexer.getContexts(); |
| this.outcomeList = indexer.getOutcomeList(); |
| this.numTimesEventsSeen = indexer.getNumTimesEventsSeen(); |
| |
| this.numOutcomes = indexer.getOutcomeLabels().length; |
| this.numFeatures = indexer.getPredLabels().length; |
| this.numContexts = this.contexts.length; |
| this.dimension = numOutcomes * numFeatures; |
| |
| this.expectation = new double[numOutcomes]; |
| this.tempSums = new double[numOutcomes]; |
| this.gradient = new double[dimension]; |
| } |
| |
| public int getDimension() { |
| return this.dimension; |
| } |
| |
| public double[] getInitialPoint() { |
| return new double[dimension]; |
| } |
| |
| /** |
| * Negative log-likelihood |
| */ |
| public double valueAt(double[] x) { |
| |
| if (x.length != dimension) |
| throw new IllegalArgumentException( |
| "x is invalid, its dimension is not equal to domain dimension."); |
| |
| int ci, oi, ai, vectorIndex, outcome; |
| double predValue, logSumOfExps; |
| double negLogLikelihood = 0; |
| |
| for (ci = 0; ci < numContexts; ci++) { |
| for (oi = 0; oi < numOutcomes; oi++) { |
| tempSums[oi] = 0; |
| for (ai = 0; ai < contexts[ci].length; ai++) { |
| vectorIndex = indexOf(oi, contexts[ci][ai]); |
| predValue = values != null ? values[ci][ai] : 1.0; |
| tempSums[oi] += predValue * x[vectorIndex]; |
| } |
| } |
| |
| logSumOfExps = ArrayMath.logSumOfExps(tempSums); |
| |
| outcome = outcomeList[ci]; |
| negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci]; |
| } |
| |
| return negLogLikelihood; |
| } |
| |
| /** |
| * Compute gradient |
| */ |
| public double[] gradientAt(double[] x) { |
| |
| if (x.length != dimension) |
| throw new IllegalArgumentException( |
| "x is invalid, its dimension is not equal to the function."); |
| |
| int ci, oi, ai, vectorIndex; |
| double predValue, logSumOfExps; |
| int empirical; |
| |
| // Reset gradient |
| Arrays.fill(gradient, 0); |
| |
| for (ci = 0; ci < numContexts; ci++) { |
| for (oi = 0; oi < numOutcomes; oi++) { |
| expectation[oi] = 0; |
| for (ai = 0; ai < contexts[ci].length; ai++) { |
| vectorIndex = indexOf(oi, contexts[ci][ai]); |
| predValue = values != null ? values[ci][ai] : 1.0; |
| expectation[oi] += predValue * x[vectorIndex]; |
| } |
| } |
| |
| logSumOfExps = ArrayMath.logSumOfExps(expectation); |
| |
| for (oi = 0; oi < numOutcomes; oi++) { |
| expectation[oi] = StrictMath.exp(expectation[oi] - logSumOfExps); |
| } |
| |
| for (oi = 0; oi < numOutcomes; oi++) { |
| empirical = outcomeList[ci] == oi ? 1 : 0; |
| for (ai = 0; ai < contexts[ci].length; ai++) { |
| vectorIndex = indexOf(oi, contexts[ci][ai]); |
| predValue = values != null ? values[ci][ai] : 1.0; |
| gradient[vectorIndex] += |
| predValue * (expectation[oi] - empirical) * numTimesEventsSeen[ci]; |
| } |
| } |
| } |
| |
| return gradient; |
| } |
| |
| protected int indexOf(int outcomeId, int featureId) { |
| return outcomeId * numFeatures + featureId; |
| } |
| } |