/**
* This file is part of FNLP (formerly FudanNLP).
*
* FNLP is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* FNLP is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with FudanNLP. If not, see <http://www.gnu.org/licenses/>.
*
* Copyright 2009-2014 www.fnlp.org. All rights reserved.
*/
package org.fnlp.demo.ml;
import java.io.File;
import org.fnlp.ml.classifier.hier.Linear;
import org.fnlp.ml.classifier.hier.PATrainer;
import org.fnlp.ml.classifier.hier.inf.MultiLinearMax;
import org.fnlp.ml.classifier.linear.inf.Inferencer;
import org.fnlp.ml.feature.BaseGenerator;
import org.fnlp.ml.loss.ZeroOneLoss;
import org.fnlp.ml.types.Instance;
import org.fnlp.ml.types.InstanceSet;
import org.fnlp.ml.types.alphabet.AlphabetFactory;
import org.fnlp.ml.types.alphabet.LabelAlphabet;
import org.fnlp.ml.types.sv.HashSparseVector;
import org.fnlp.ml.types.sv.ISparseVector;
/**
* 层次分类器使用示例
*
* @author xpqiu
*
*/
public class HierClassifierUsage1 {
static InstanceSet trainset;
static InstanceSet test;
static AlphabetFactory factory = AlphabetFactory.buildFactory();
//将类别标签转换为数字
static LabelAlphabet lf = factory.DefaultLabelAlphabet();
static String path = null;
public static void main(String[] args) throws Exception {
long start = System.currentTimeMillis();
float data[][] = {{1,1,-1},{0,0,0},{1,1,1},{1,1,1},{1,-1,1}};
String target[] = {"Y","Y","N","N","N"};
//构建训练集
trainset = new InstanceSet(factory);
for(int i=0;i<data.length;i++){
ISparseVector sv = new HashSparseVector(data[i],true);
int l = lf.lookupIndex(target[i]);
Instance inst = new Instance(sv,l);
trainset.add(inst);
}
lf.setStopIncrement(true);
//构建测试集
System.out.println("Train Number: " + trainset.size());
System.out.println("Class Number: " + lf.size());
float c = 1.0f;
int round = 10;
BaseGenerator featureGen = new BaseGenerator();
ZeroOneLoss loss = new ZeroOneLoss();
Inferencer msolver = new MultiLinearMax(featureGen, lf, null,2);
PATrainer trainer = new PATrainer(msolver, featureGen, loss, round,c, null);
Linear pclassifier = trainer.train(trainset, null);
String modelFile = "./tmp/m.gz";
pclassifier.saveTo(modelFile);
pclassifier = null;
System.out.println("分类器测试");
pclassifier = Linear.loadFrom(modelFile);
float[] tdata = new float[]{1,0,1};
ISparseVector sv = new HashSparseVector(tdata,true);
Instance inst = new Instance(sv);
String lab = pclassifier.getStringLabel(inst);
System.out.println("分类结果：\t"+lab);
long end = System.currentTimeMillis();
System.out.println("Total Time: " + (end - start));
System.out.println("End!");
(new File(modelFile)).deleteOnExit();
System.exit(0);
}
}