Package org.apache.mahout.math.function

Examples of org.apache.mahout.math.function.DoubleDoubleFunction

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    return phi;
  }
 
  private static Vector digamma(Vector v) {
    Vector digammaGamma = new DenseVector(v.size());
    digammaGamma.assign(v, new DoubleDoubleFunction() {
      @Override
      public double apply(double unused, double g) {
        return digamma(g);
      }
    });
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  // -------- classification methods

  @Override
  public Vector classify(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    DoubleDoubleFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classify(instance), scale);
    }
    return r;
  }
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  }

  @Override
  public Vector classifyNoLink(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    DoubleDoubleFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classifyNoLink(instance), scale);
    }
    return r;
  }
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    x.viewRow(1).assign(new double[]{2, 4, 6});
    x.viewRow(2).assign(new double[]{3, 6, 9});

    OldQRDecomposition qr = new OldQRDecomposition(x);
    assertFalse(qr.hasFullRank());
    assertEquals(0, new DenseVector(new double[]{3.741657, 7.483315, 11.22497}).aggregate(qr.getR().viewRow(0), Functions.PLUS, new DoubleDoubleFunction() {
      @Override
      public double apply(double arg1, double arg2) {
        return Math.abs(arg1) - Math.abs(arg2);
      }
    }), 1.0e-5);
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    x.viewRow(0).assign(new double[]{1, 2, 3});
    x.viewRow(1).assign(new double[]{2, 4, 6});
    x.viewRow(2).assign(new double[]{3, 6, 9});

    CholeskyDecomposition rr = new CholeskyDecomposition(x.transpose().times(x), false);
    assertEquals(0, new DenseVector(new double[]{3.741657, 7.483315, 11.22497}).aggregate(rr.getL().transpose().viewRow(0), Functions.PLUS, new DoubleDoubleFunction() {
      @Override
      public double apply(double arg1, double arg2) {
        return Math.abs(arg1) - Math.abs(arg2);
      }
    }), 1.0e-5);
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    x.viewRow(1).assign(new double[]{2, 4, 6});
    x.viewRow(2).assign(new double[]{3, 6, 9});

    QRDecomposition qr = new QRDecomposition(x);
    assertFalse(qr.hasFullRank());
    assertEquals(0, new DenseVector(new double[]{3.741657, 7.483315, 11.22497}).aggregate(qr.getR().viewRow(0), Functions.PLUS, new DoubleDoubleFunction() {
      @Override
      public double apply(double arg1, double arg2) {
        return Math.abs(arg1) - Math.abs(arg2);
      }
    }), 1.0e-5);
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  // -------- classification methods

  @Override
  public Vector classify(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    DoubleDoubleFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classify(instance), scale);
    }
    return r;
  }
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  }

  @Override
  public Vector classifyNoLink(Vector instance) {
    Vector r = new DenseVector(numCategories() - 1);
    DoubleDoubleFunction scale = Functions.plusMult(1.0 / models.size());
    for (OnlineLogisticRegression model : models) {
      r.assign(model.classifyNoLink(instance), scale);
    }
    return r;
  }
View Full Code Here
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    return phi;
  }
 
  private static Vector digamma(Vector v) {
    Vector digammaGamma = new DenseVector(v.size());
    digammaGamma.assign(v, new DoubleDoubleFunction() {
      @Override
      public double apply(double unused, double g) {
        return digamma(g);
      }
    });
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    Vector output = internalResults.get(internalResults.size() - 1);

    final DoubleFunction derivativeSquashingFunction =
        NeuralNetworkFunctions.getDerivativeDoubleFunction(this.squashingFunctionList.get(this.squashingFunctionList.size() - 1));

    final DoubleDoubleFunction costFunction = NeuralNetworkFunctions.getDerivativeDoubleDoubleFunction(this.costFunctionName);

    Matrix lastWeightMatrix = this.weightMatrixList.get(this.weightMatrixList.size() - 1);

    for (int i = 0; i < deltaVec.size(); ++i) {
      double costFuncDerivative = costFunction.apply(labels.get(i), output.get(i + 1));
      // add regularization
      costFuncDerivative += this.regularizationWeight * lastWeightMatrix.viewRow(i).zSum();
      deltaVec.set(i, costFuncDerivative);
      deltaVec.set(i, deltaVec.get(i) * derivativeSquashingFunction.apply(output.get(i + 1)));
    }
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Related Classes of org.apache.mahout.math.function.DoubleDoubleFunction

  • org.apache.mahout.classifier.mlp.NeuralNetwork
  • org.apache.mahout.classifier.sgd.CrossFoldLearner
  • org.apache.mahout.clustering.lda.LDAInference
  • org.apache.mahout.math.CholeskyDecompositionTest
  • org.apache.mahout.math.OldQRDecompositionTest
  • org.apache.mahout.math.QRDecompositionTest
  • org.carrot2.matrix.factorization.LocalNonnegativeMatrixFactorization
  • org.carrot2.matrix.factorization.NonnegativeMatrixFactorizationKL
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