Package org.apache.mahout.math.function

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

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      nextLayerDelta = nextLayerDelta.viewPart(1, nextLayerDelta.size() - 1);
    }

    Vector delta = weightMatrix.transpose().times(nextLayerDelta);

    delta = delta.assign(curLayerOutput, new DoubleDoubleFunction() {
      @Override
      public double apply(double deltaElem, double curLayerOutputElem) {
        return deltaElem * derivativeSquashingFunction.apply(curLayerOutputElem);
      }
    });
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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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    Vector output = internalResults.get(internalResults.size() - 1);

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

    final DoubleDoubleFunction costFunction =
        NeuralNetworkFunctions.getDerivativeDoubleDoubleFunction(costFunctionName);

    Matrix lastWeightMatrix = weightMatrixList.get(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 += 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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      nextLayerDelta = nextLayerDelta.viewPart(1, nextLayerDelta.size() - 1);
    }

    Vector delta = weightMatrix.transpose().times(nextLayerDelta);

    delta = delta.assign(curLayerOutput, new DoubleDoubleFunction() {
      @Override
      public double apply(double deltaElem, double curLayerOutputElem) {
        return deltaElem * derivativeSquashingFunction.apply(curLayerOutputElem);
      }
    });
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        DoubleMatrix2D VT = new DenseDoubleMatrix2D(n, k);
        DoubleMatrix2D UT = new DenseDoubleMatrix2D(m, k);
        double [] work = new double [U.columns()];

        // Colt functions
        DoubleDoubleFunction invDiv = Functions.swapArgs(Functions.DIV);
        DoubleFunction plusEps = Functions.plus(eps);

        if (stopThreshold >= 0)
        {
            updateApproximationError();
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        DoubleMatrix2D VT = new DenseDoubleMatrix2D(A.columns(), k);
        DoubleMatrix2D UT = new DenseDoubleMatrix2D(A.rows(), k);
        double [] work = new double [U.columns()];

        // Colt functions
        DoubleDoubleFunction invDiv = Functions.swapArgs(Functions.DIV);
        DoubleDoubleFunction sqrtMult = Functions.chain(Functions.SQRT, Functions.MULT);
        DoubleFunction plusEps = Functions.plus(eps);

        if (stopThreshold >= 0)
        {
            updateApproximationError();
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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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