Examples of UniformPrior

Examples of opennlp.tools.ml.model.UniformPrior

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   *          The parameter associated with the correction feature.
   */
  public GISModel(Context[] params, String[] predLabels, String[] outcomeNames,
      int correctionConstant, double correctionParam) {
    this(params, predLabels, outcomeNames, correctionConstant, correctionParam,
        new UniformPrior());
  }
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Examples of opennlp.tools.ml.model.UniformPrior

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  @Test
  public void testMaxentOnPrepAttachData2Threads() throws IOException {
    AbstractModel model =
        new GISTrainer(true).trainModel(100,
            new TwoPassDataIndexer(createTrainingStream(), 1),
            new UniformPrior(), 1, 2);

    testModel(model, 0.7997028967566229);
  }
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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//  QuickTestConfig qtf = new QuickTestConfig("src/test/resources/data/synth/R_synth_multi_4coef_test.csv", 5, 10, 5 );
//  @Test
  public void test() throws Exception {
   
    ParallelOnlineLinearRegression polr = new ParallelOnlineLinearRegression(
        qtf.feature_size, new UniformPrior()).alpha(1)
        .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5)
        .learningRate(qtf.learningRate);
   
    RCV1RecordFactory factory = new RCV1RecordFactory();
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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  @Test
  public void TestBaseRSquaredCalc( ) throws Exception {
   

    ParallelOnlineLinearRegression polr = new ParallelOnlineLinearRegression(
        2, new UniformPrior()).alpha(1)
        .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5)
        .learningRate(17);
   
    RCV1RecordFactory factory = new RCV1RecordFactory();
   
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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      // ----- this normally is generated from the POLRModelParams ------
     
     
      this.polr = new ParallelOnlineLinearRegression(
          this.FeatureVectorSize, new UniformPrior()).alpha(1).stepOffset(1000)
          .decayExponent(0.9).lambda(this.Lambda).learningRate(this.LearningRate);
     
      polr_modelparams.setPOLR(polr);
     
    }
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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        .getTargetCategories());

    // ----- this normally is generated from the POLRModelParams ------

    this.polr = new ParallelOnlineLinearRegression(
        this.FeatureVectorSize, new UniformPrior()).alpha(1)
        .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5)
        .learningRate(this.LearningRate);

    polr_modelparams.setPOLR(polr);
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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        .getTargetCategories());

    // ----- this normally is generated from the POLRModelParams ------

    this.polr = new ParallelOnlineLinearRegression(
        this.FeatureVectorSize, new UniformPrior()).alpha(1)
        .stepOffset(1000).decayExponent(0.9).lambda(3.0e-5)
        .learningRate(this.LearningRate);

    polr_modelparams.setPOLR(polr);
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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        .getTargetCategories());
   
    // ----- this normally is generated from the POLRModelParams ------
   
    this.polr = new ParallelOnlineLogisticRegression(this.num_categories,
        this.FeatureVectorSize, new UniformPrior()).alpha(1).stepOffset(1000)
        .decayExponent(0.9).lambda(this.Lambda).learningRate(this.LearningRate);
   
    polr_modelparams.setPOLR(polr);
    // this.bSetup = true;
   
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Examples of org.apache.mahout.classifier.sgd.UniformPrior

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        .getTargetCategories());
   
    // ----- this normally is generated from the POLRModelParams ------
   
    this.polr = new ParallelOnlineLogisticRegression(this.num_categories,
        this.FeatureVectorSize, new UniformPrior()).alpha(1).stepOffset(1000)
        .decayExponent(0.9).lambda(this.Lambda).learningRate(this.LearningRate);
   
    polr_modelparams.setPOLR(polr);
   
    // this.bSetup = true;
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