List of norm2() Examples

Examples of norm2()

Jama.Matrix.norm2()
Two norm @return maximum singular value.
de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.norm2()
Two norm @return maximum singular value.
org.jblas.DoubleMatrix.norm2()
The Euclidean norm of the matrix as vector, also the Frobenius norm of the matrix.

Examples of Jama.Matrix.norm2()

            final double dev = yMatrix.get(i, 0) - mean;
            sst += dev * dev;
        }


        final Matrix residuals = xMatrix.times(beta).minus(yMatrix);
        sse = residuals.norm2() * residuals.norm2();


        for (int i = 0; i < this.algorithm.getLongTermMemory().length; i++) {
            this.algorithm.getLongTermMemory()[i] = beta.get(i, 0);
        }

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Examples of Jama.Matrix.norm2()

            final double dev = yMatrix.get(i, 0) - mean;
            sst += dev * dev;
        }


        final Matrix residuals = xMatrix.times(beta).minus(yMatrix);
        sse = residuals.norm2() * residuals.norm2();


        for (int i = 0; i < this.algorithm.getLongTermMemory().length; i++) {
            this.algorithm.getLongTermMemory()[i] = beta.get(i, 0);
        }

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.norm2()

      }


      Matrix strong_ev1 = pca1.getStrongEigenvectors();
      Matrix weak_ev2 = pca2.getWeakEigenvectors();
      Matrix m1 = weak_ev2.getColumnDimensionality() == 0 ? strong_ev1.transpose() : strong_ev1.transposeTimes(weak_ev2);
      double d1 = m1.norm2();


      WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
      WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());


      double affineDistance = Math.max(df1.distance(o1, o2).doubleValue(), df2.distance(o1, o2).doubleValue());

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Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix.norm2()

      }


      Matrix strong_ev1 = pca1.getStrongEigenvectors();
      Matrix weak_ev2 = pca2.getWeakEigenvectors();
      Matrix m1 = weak_ev2.getColumnDimensionality() == 0 ? strong_ev1.transpose() : strong_ev1.transposeTimes(weak_ev2);
      double d1 = m1.norm2();


      WeightedDistanceFunction df1 = new WeightedDistanceFunction(pca1.similarityMatrix());
      WeightedDistanceFunction df2 = new WeightedDistanceFunction(pca2.similarityMatrix());


      double affineDistance = Math.max(df1.distance(o1, o2).doubleValue(), df2.distance(o1, o2).doubleValue());

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Examples of org.jblas.DoubleMatrix.norm2()

            double ReturnedCost = Func.valueAt(x);
            double[] ReturnedGradient = Func.derivativeAt(x);
            double[] NumericalGradient = new double[size];
            double PartCosts;


            double Mean = 2e-6 * ((1 + xMat.norm2()) / p);
            for (int i = 0; i < size; i++) {
                double[] e = DoubleMatrix.zeros(size).data;
                e[i] = 1;
                DoubleArrays.scale(e, Mean);
                double[] y = DoubleArrays.add(x, e);

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