这是一个最小的工作示例,演示了OpenNLP Maxent API的用法.

它包括以下内容：

>从文件中存储的数据训练maxent模型.

>将经过训练的模型存储到文件中.

>从文件中加载经过训练的模型.

>使用模型进行分类.

>注意：结果是每个训练样本中的第一要素

>注意：这些值可以是任意字符串,例如xyz = s0methIng

import java.io.File;

import java.io.FileInputStream;

import java.io.IOException;

import java.io.InputStream;

import java.util.zip.GZIPInputStream;

import opennlp.maxent.GIS;

import opennlp.maxent.io.GISModelReader;

import opennlp.maxent.io.SuffixSensitiveGISModelWriter;

import opennlp.model.AbstractModel;

import opennlp.model.AbstractModelWriter;

import opennlp.model.DataIndexer;

import opennlp.model.DataReader;

import opennlp.model.FileEventStream;

import opennlp.model.MaxentModel;

import opennlp.model.OnePassDataIndexer;

import opennlp.model.PlainTextFileDataReader;

...

String trainingFileName = "training-file.txt";

String modelFileName = "trained-model.maxent.gz";

// Training a model from data stored in a file.

// The training file contains one training sample per line.

// Outcome (result) is the first element on each line.

// Example:

// result=1 a=1 b=1

// result=0 a=0 b=1

// ...

DataIndexer indexer = new OnePassDataIndexer( new FileEventStream(trainingFileName));

MaxentModel trainedMaxentModel = GIS.trainModel(100, indexer); // 100 iterations

// Storing the trained model into a file for later use (gzipped)

File outFile = new File(modelFileName);

AbstractModelWriter writer = new SuffixSensitiveGISModelWriter((AbstractModel) trainedMaxentModel, outFile);

writer.persist();

// Loading the gzipped model from a file

FileInputStream inputStream = new FileInputStream(modelFileName);

InputStream decodedInputStream = new GZIPInputStream(inputStream);

DataReader modelReader = new PlainTextFileDataReader(decodedInputStream);

MaxentModel loadedMaxentModel = new GISModelReader(modelReader).getModel();

// Now predicting the outcome using the loaded model

String[] context = {"a=1", "b=0"};

double[] outcomeProbs = loadedMaxentModel.eval(context);

String outcome = loadedMaxentModel.getBestOutcome(outcomeProbs);