Package org.fnlp.nlp.pipe

Examples of org.fnlp.nlp.pipe.SeriesPipes

178
179
180
181
182
183
184
185
186
    // 特征集合
    IFeatureAlphabet features = factory.DefaultFeatureAlphabet();

    featurePipe = new Sequence2FeatureSequence(templets, features, labels);

    Pipe pipe = new SeriesPipes(new Pipe[] { new Target2Label(labels), featurePipe });

    return pipe;
  }
View Full Code Here
72
73
74
75
76
77
78
79
80
81
82
83
84
    Pipe indexpp = new StringArray2IndexArray(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,indexpp});
   
    SeriesPipes  pp2 = new SeriesPipes(new Pipe[]{prePipe, ngrampp,targetpp,indexpp});
   
    InstanceSet instset = new InstanceSet(pp2,af);


View Full Code Here
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
     
      Pipe lpipe = new Target2Label(al);
      Pipe fpipe = new StringArray2IndexArray(factory, true);
      //构造转换器组
      SeriesPipes pipe = new SeriesPipes(new Pipe[]{lpipe,fpipe});



      InstanceSet instset = new InstanceSet(pipe,factory);
      instset.loadThruStagePipes(new SimpleFileReader(trainFile," ",true,Type.LabelData));
      Generator gen = new SFGenerator();
      ZeroOneLoss l = new ZeroOneLoss();
      Inferencer ms = new LinearMax(gen, factory.getLabelSize());
      Update update = new LinearMaxPAUpdate(l);
      OnlineTrainer trainer = new OnlineTrainer(ms, update,l, factory.getFeatureSize(), 50,0.005f);
      Linear pclassifier = trainer.train(instset,instset);
      pipe.removeTargetPipe();
      pclassifier.setPipe(pipe);
      factory.setStopIncrement(true);
      pclassifier.saveTo(modelFile);
    }
View Full Code Here
63
64
65
66
67
68
69
70
71
72
73
    //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});

    System.out.print("\nReading data......\n");
    InstanceSet instset = new InstanceSet(pp,af); 
    Reader reader = new MyDocumentReader(trainDataPath,"gbk");
    instset.loadThruStagePipes(reader);
View Full Code Here
20
21
22
23
24
25
26
27
28
*/
public class BayesTrainer{

  public AbstractClassifier train(InstanceSet trainset) {
    AlphabetFactory af=trainset.getAlphabetFactory();
    SeriesPipes pp=(SeriesPipes) trainset.getPipes();
    pp.removeTargetPipe();
    return train(trainset,af,pp);
  }
View Full Code Here
97
98
99
100
101
102
103
104
105
106
107
   
 
      dictPipe = new DictLabel(dict, labels);

    oldfeaturePipe = featurePipe;
    featurePipe = new SeriesPipes(new Pipe[] { dictPipe, featurePipe });

    LinearViterbi dv = new ConstraintViterbi(
        (LinearViterbi) getClassifier().getInferencer());
    getClassifier().setInferencer(dv);
  }
View Full Code Here
60
61
62
63
64
65
66
67
68
69
70
    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);
    SimpleFileReader reader = new SimpleFileReader (path,true);
    train.loadThruStagePipes(reader);
View Full Code Here
131
132
133
134
135
136
137
138
139
140
    if(cws != null && isSetSegDict)
      cws.setDictionary(dict);
    dictPipe = null;
    dictPipe = new DictPOSLabel(dict, labels);
    oldfeaturePipe = featurePipe;
    featurePipe = new SeriesPipes(new Pipe[] { dictPipe, featurePipe });
    LinearViterbi dv = new ConstraintViterbi(
        (LinearViterbi) getClassifier().getInferencer(),labels.size());
    getClassifier().setInferencer(dv);
  }
View Full Code Here
78
79
80
81
82
83
84
85
86
87
88
    IFeatureAlphabet features = factory.DefaultFeatureAlphabet();
    // 将样本通过Pipe抽取特征
   
    featurePipe = new Sequence2FeatureSequence(templets, features, labels);

    Pipe pipe = new SeriesPipes(new Pipe[] { new Target2Label(labels), featurePipe });


    System.out.print("读入训练数据 ...");
    InstanceSet trainSet = new InstanceSet(pipe, factory);
View Full Code Here
62
63
64
65
66
67
68
69
70
71
72
    path = "./example-data/data-classification.txt";

    Pipe lpipe = new Target2Label(al);
    Pipe fpipe = new StringArray2IndexArray(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);
    SimpleFileReader reader = new SimpleFileReader (path,true);
    train.loadThruStagePipes(reader);
View Full Code Here
TOP

Related Classes of org.fnlp.nlp.pipe.SeriesPipes

  • org.fnlp.app.tc.TextClassifier
  • org.fnlp.demo.ml.HierClassifierUsage2
  • org.fnlp.demo.ml.SequenceLabeling
  • org.fnlp.demo.ml.SimpleClassifier2
  • org.fnlp.demo.nlp.tc.TextClassificationBasedOnBayes
  • org.fnlp.demo.nlp.tc.TextClassificationBasedOnBayes2
  • org.fnlp.demo.nlp.tc.TextClassificationBasedOnKNN
  • org.fnlp.demo.nlp.tc.TextClassificationCustom
  • org.fnlp.demo.nlp.tc.TextClassificationCustom1
  • org.fnlp.demo.nlp.tc.TextClassificationTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.