Examples of train()

Examples of opennlp.tools.ml.EventModelSequenceTrainer.train()

341
342
343
344
345
346
347
348
349
350
351
    else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator());

      EventModelSequenceTrainer trainer = TrainerFactory.getEventModelSequenceTrainer(
              trainParams.getSettings(), manifestInfoEntries);
      nameFinderModel = trainer.train(ss);
    } else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
              trainParams.getSettings(), manifestInfoEntries);

      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator(), false);
View Full Code Here

Examples of opennlp.tools.ml.EventTrainer.train()

333
334
335
336
337
338
339
340
341
342
343
    if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
      ObjectStream<Event> eventStream = new NameFinderEventStream(samples, type,
              factory.createContextGenerator(), factory.createSequenceCodec());

      EventTrainer trainer = TrainerFactory.getEventTrainer(trainParams.getSettings(), manifestInfoEntries);
      nameFinderModel = trainer.train(eventStream);
    } // TODO: Maybe it is not a good idea, that these two don't use the context generator ?!
    // These also don't use the sequence codec ?!
    else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator());
View Full Code Here

Examples of opennlp.tools.ml.SequenceTrainer.train()

347
348
349
350
351
352
353
354
355
356
357
    } else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer trainer = TrainerFactory.getSequenceModelTrainer(
              trainParams.getSettings(), manifestInfoEntries);

      NameSampleSequenceStream ss = new NameSampleSequenceStream(samples, factory.createContextGenerator(), false);
      seqModel = trainer.train(ss);
    } else {
      throw new IllegalStateException("Unexpected trainer type!");
    }

    if (seqModel != null) {
View Full Code Here

Examples of org.apache.ctakes.relationextractor.eval.ModifierExtractorEvaluation.train()

79
80
81
82
83
84
85
86
87
88
89
        modelsDir,
        "-t", "0",   // svm kernel index
        "-c", "1000" // svm cost
        );
    CollectionReader collectionReader = evaluation.getCollectionReader(trainFiles);
    evaluation.train(collectionReader, modelsDir);
   
    // create the description
    AnalysisEngineDescription modifierExtractorDescription = AnalysisEngineFactory.createPrimitiveDescription(
        ModifierExtractorAnnotator.class,
        GenericJarClassifierFactory.PARAM_CLASSIFIER_JAR_PATH,
View Full Code Here

Examples of org.apache.ctakes.relationextractor.eval.RelationExtractorEvaluation.train()

123
124
125
126
127
128
129
130
131
132
133
          dataWriterClass,
          additionalParameters,
          trainingArguments);
     
      CollectionReader collectionReader = evaluation.getCollectionReader(trainFiles);
      evaluation.train(collectionReader, modelsDir);
     
      // create the description
      AnalysisEngineDescription relationExtractorDescription = AnalysisEngineFactory.createPrimitiveDescription(
          annotatorClass,
          RelationExtractorAnnotator.PARAM_PROBABILITY_OF_KEEPING_A_NEGATIVE_EXAMPLE,
View Full Code Here

Examples of org.apache.hama.ml.ann.SmallLayeredNeuralNetwork.train()

175
176
177
178
179
180
181
182
183
184
185
      Map<String, String> trainingParameters = new HashMap<String, String>();
      trainingParameters.put("tasks", "5");
      trainingParameters.put("training.max.iterations", "" + iteration);
      trainingParameters.put("training.batch.size", "300");
      trainingParameters.put("convergence.check.interval", "1000");
      ann.train(new Path(trainingDataPath), trainingParameters);
    }

  }

  private static void printUsage() {
View Full Code Here

Examples of org.apache.hama.ml.recommendation.cf.OnlineCF.train()

93
94
95
96
97
98
99
100
101
102
103
      recommender.setMatrixRank(3);
      recommender.setSkipCount(1);
      recommender.setTaskCount(2);
      recommender.setUpdateFunction(MeanAbsError.class);
      recommender.setOutputPath(outputFileName);
      assertEquals(true, recommender.train());

      recommender.load(outputFileName, false);
      int correct = 0;
      for (Preference<Integer, Integer> test : test_prefs) {
        double actual = test.getValue().get();
View Full Code Here

Examples of org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.train()

161
162
163
164
165
166
167
168
169
170
171
    for (File file : files) {
      String ng = file.getParentFile().getName();
      int actual = newsGroups.intern(ng);

      Vector v = encodeFeatureVector(file);
      learningAlgorithm.train(actual, v);

      k++;

      int bump = bumps[(int) Math.floor(step) % bumps.length];
      int scale = (int) Math.pow(10, Math.floor(step / bumps.length));
View Full Code Here

Examples of org.apache.mahout.classifier.sgd.OnlineLogisticRegression.train()

53
54
55
56
57
58
59
60
61
62
63
        .lambda(0.000001)
        .stepOffset(10000)
        .decayExponent(0.2);
      for (int pass = 0; pass < 20; pass++) {
        for (TelephoneCall observation : train) {
          lr.train(observation.getTarget(), observation.asVector());
        }
        if (pass % 5 == 0) {
          Auc eval = new Auc(0.5);
          for (TelephoneCall testCall : test) {
            eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));
View Full Code Here

Examples of org.apache.mahout.clustering.classify.ClusterClassifier.train()

116
117
118
119
120
121
122
123
124
125
126
        // policy selects weights for models given those probabilities
        Vector weights = classifier.getPolicy().select(probabilities);
        // training causes all models to observe data
        for (Vector.Element e : weights.nonZeroes()) {
          int index = e.index();
          classifier.train(index, vector, weights.get(index));
        }
      }
      // compute the posterior models
      classifier.close();
      // update the policy
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. 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.