Package org.fnlp.nlp.pipe

Examples of org.fnlp.nlp.pipe.StringArray2SV


    //建立字典管理器
    AlphabetFactory af = AlphabetFactory.buildFactory();
    //使用n元特征
    Pipe ngrampp = new NGram(new int[] {2,3});
    //将字符特征转换成字典索引; 
    Pipe sparsepp=new StringArray2SV(af);
    //将目标值对应的索引号作为类别
    Pipe targetpp = new Target2Label(af.DefaultLabelAlphabet())
    //建立pipe组合
    SeriesPipes pp = new SeriesPipes(new Pipe[]{removepp,segpp,s2spp,targetpp,sparsepp});
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    long start = System.currentTimeMillis();

    path = "./example-data/data-classification.txt";

    Pipe lpipe = new Target2Label(al);
    Pipe fpipe = new StringArray2SV(factory, true);
    //构造转换器组
    Pipe pipe = new SeriesPipes(new Pipe[]{lpipe,fpipe});
   
    //构建训练集
    train = new InstanceSet(pipe, factory);
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    //建立字典管理器
    AlphabetFactory af = AlphabetFactory.buildFactory();
    //使用n元特征
    Pipe ngrampp = new NGram(new int[] {2,3});
    //将字符特征转换成字典索引; 
    Pipe sparsepp=new StringArray2SV(af);
    //将目标值对应的索引号作为类别
    Pipe targetpp = new Target2Label(af.DefaultLabelAlphabet())
    //建立pipe组合
    SeriesPipes pp = new SeriesPipes(new Pipe[]{removepp,segpp,s2spp,targetpp,sparsepp});
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    //建立字典管理器
    AlphabetFactory af = AlphabetFactory.buildFactory();
    //使用n元特征
    Pipe ngrampp = new NGram(new int[] {2,3});
    //将字符特征转换成字典索引; 
    Pipe sparsepp=new StringArray2SV(af);
    //将目标值对应的索引号作为类别
    Pipe targetpp = new Target2Label(af.DefaultLabelAlphabet())
    //建立pipe组合
    SeriesPipes pp = new SeriesPipes(new Pipe[]{ngrampp,targetpp,sparsepp});

    System.out.print("\nReading data......\n");
    InstanceSet instset = new InstanceSet(pp,af)
    Reader reader = new MyDocumentReader(trainDataPath,"gbk");
    instset.loadThruStagePipes(reader);
    System.out.print("..Reading data complete\n");
   
    //将数据集分为训练是和测试集
    System.out.print("Sspliting....");
    float percent = 0.9f;
    InstanceSet[] splitsets = instset.split(percent);
   
    InstanceSet trainset = splitsets[0];
    InstanceSet testset = splitsets[1]
    System.out.print("..Spliting complete!\n");

    System.out.print("Training...\n");
    BayesTrainer trainer=new BayesTrainer();
    BayesClassifier classifier= (BayesClassifier) trainer.train(trainset);
    pp.removeTargetPipe();
    classifier.setPipe(pp);
    af.setStopIncrement(true);
    System.out.print("..Training complete!\n");
    System.out.print("Saving model...\n");
    classifier.saveTo(bayesModelFile)
    classifier = null;
    System.out.print("..Saving model complete!\n");
    /**
     * 测试
     */
    System.out.print("Loading model...\n");
    BayesClassifier bayes;
    bayes =BayesClassifier.loadFrom(bayesModelFile);
//    bayes =classifier;
    System.out.print("..Loading model complete!\n");
   
    System.out.println("Testing Bayes...");
    int count=0;
    for(int i=0;i<testset.size();i++){
      Instance data = testset.getInstance(i);
      Integer gold = (Integer) data.getTarget();
      Predict<String> pres=bayes.classify(data, Type.STRING, 3);
      String pred_label=pres.getLabel();
//      String pred_label = bayes.getStringLabel(data);
      String gold_label = bayes.getLabel(gold);
     
      if(pred_label.equals(gold_label)){
        //System.out.println(pred_label+" : "+testsetbayes.getInstance(i).getTempData());
        count++;
      }
      else{
//        System.err.println(gold_label+"->"+pred_label+" : "+testset.getInstance(i).getTempData());
//        for(int j=0;j<3;j++)
//          System.out.println(pres.getLabel(j)+":"+pres.getScore(j));
      }
    }
    int bayesCount=count;
    System.out.println("..Testing Bayes complete!");
    System.out.println("Bayes Precision:"+((float)bayesCount/testset.size())+"("+bayesCount+"/"+testset.size()+")");


    /**
     * Knn
     */
    System.out.print("\nKnn\n");
    //建立字典管理器
    AlphabetFactory af2 = AlphabetFactory.buildFactory();
    //使用n元特征
    ngrampp = new NGram(new int[] {2,3});
    //将字符特征转换成字典索引; 
    sparsepp=new StringArray2SV(af2);
    //将目标值对应的索引号作为类别
    targetpp = new Target2Label(af2.DefaultLabelAlphabet())
    //建立pipe组合
    pp = new SeriesPipes(new Pipe[]{ngrampp,targetpp,sparsepp});

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    //分词
//    CWSTagger tag = new CWSTagger("../models/seg.m");
//    Pipe segpp=new CNPipe(tag);
    //将字符特征转换成字典索引
    Pipe indexpp = new StringArray2IndexArray(af)
    Pipe sparsepp=new StringArray2SV(af);
    //将目标值对应的索引号作为类别
    Pipe targetpp = new Target2Label(af.DefaultLabelAlphabet())
    //建立pipe组合
    SeriesPipes pp = new SeriesPipes(new Pipe[]{ngrampp,targetpp,sparsepp});
   
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