List of svd() Examples

Examples of svd()

Jama.Matrix.svd()
Singular Value Decomposition @return SingularValueDecomposition @see SingularValueDecomposition
org.ejml.simple.SimpleMatrix.svd()
uk.ac.cam.ch.wwmm.ptc.experimental.indexersearcher.SVDHarness.svd()
weka.core.matrix.Matrix.svd()
Singular Value Decomposition @return SingularValueDecomposition @see SingularValueDecomposition

Examples of Jama.Matrix.svd()

        yVal += xVal;
        A.set(i, j, xVal / sd[i]);
      }
      b[i] = yVal / sd[i];
    }
    SVD = A.svd();
    U = (SVD.getU()).getArray();
    V = (SVD.getV()).getArray();
    w = SVD.getSingularValues();


    for (int j = 0; j <= order; j++) {

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Examples of Jama.Matrix.svd()

            try_success("QRDecomposition...", "");
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount, "QRDecomposition...",
                    "incorrect QR decomposition calculation");
        }
        SingularValueDecomposition SVD = A.svd();


        try {
            check(A, SVD.getU().times(SVD.getS().times(SVD.getV().transpose())));
            try_success("SingularValueDecomposition...", "");
        } catch (java.lang.RuntimeException e) {

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Examples of Jama.Matrix.svd()

        }


    if (result==null) {


      Matrix m = new Matrix(matrix);
      SingularValueDecomposition svd = m.svd();
      Matrix sInv = svd.getS().inverse();


      int i = 0;
      while (i < svd.getS().getRowDimension() && svd.getS().get(i, i) > minSV && (nSV <= 0 || i < nSV)) {
                i++;

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Examples of org.ejml.simple.SimpleMatrix.svd()

        checkUncountable(NormOps.inducedP2(zeroMatrix));


        // make sure the largest singular value is being returned not just the first
        for( int i = 0; i < 20; i++ ) {
            SimpleMatrix A = SimpleMatrix.random(5,5,-10,10,rand);
            double largest = A.svd().getW().get(0);


            assertEquals(largest,NormOps.inducedP2(A.getMatrix()),1e-8);
        }
    }

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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.indexersearcher.SVDHarness.svd()

      }
      wordNo++;
    }
    System.out.println("Harness ready...");
    int values = 50;
    svdh.svd(values);
    Matrix ut = svdh.getUt(); // Terms
    //Matrix vt = svdh.getVt(); // Features
    
    //System.out.println(ut.getColumnDimension() + "\t" + ut.getRowDimension());
    //System.out.println(vt.getColumnDimension() + "\t" + vt.getRowDimension());

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Examples of uk.ac.cam.ch.wwmm.ptc.experimental.indexersearcher.SVDHarness.svd()

        int fno = featureIndex.get(feature);
        svdh.set(fno, i, wv.get(feature));
      }
    }
    System.out.println("Harness ready...");
    svdh.svd(10);
    Matrix lm = svdh.getUt().transpose();
    System.out.println(lm.getRowDimension());
    System.out.println(lm.getColumnDimension());
    System.out.println(termNo);
    System.out.println(featureNo);

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Examples of weka.core.matrix.Matrix.svd()

    // svd requires rows >= columns, so transpose data if necessary
    if (m_numAttributes < m_numInstances) {
      m_transpose = true;
      trainMatrix = trainMatrix.transpose();
    }
    SingularValueDecomposition trainSVD = trainMatrix.svd();
    m_u = trainSVD.getU(); // left singular vectors
    m_s = trainSVD.getS(); // singular values
    m_v = trainSVD.getV(); // right singular vectors
    
    // find actual rank to use

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Examples of weka.core.matrix.Matrix.svd()

    // svd requires rows >= columns, so transpose data if necessary
    if (m_numAttributes < m_numInstances) {
      m_transpose = true;
      trainMatrix = trainMatrix.transpose();
    }
    SingularValueDecomposition trainSVD = trainMatrix.svd();
    m_u = trainSVD.getU(); // left singular vectors
    m_s = trainSVD.getS(); // singular values
    m_v = trainSVD.getV(); // right singular vectors
    
    // find actual rank to use

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