| /* |
| * 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.ml.model; |
| |
| /** |
| * Provide a maximum entropy model with a uniform prior. |
| */ |
| public class UniformPrior implements Prior { |
| |
| private int numOutcomes; |
| private double r; |
| |
| public void logPrior(double[] dist, int[] context, float[] values) { |
| for (int oi=0;oi<numOutcomes;oi++) { |
| dist[oi] = r; |
| } |
| } |
| |
| public void logPrior(double[] dist, int[] context) { |
| logPrior(dist,context,null); |
| } |
| |
| public void setLabels(String[] outcomeLabels, String[] contextLabels) { |
| this.numOutcomes = outcomeLabels.length; |
| r = Math.log(1.0/numOutcomes); |
| } |
| } |