Package org.apache.mahout.math

Examples of org.apache.mahout.math.SingularValueDecomposition

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      throw new CardinalityException(m.numRows(), m.numCols());
    }
    // See http://www.mlahanas.de/Math/svd.htm for details,
    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
    Matrix sInv = svd.getS();
    // Inverse Diagonal Elems
    for (int i = 0; i < sInv.numRows(); i++) {
      double diagElem = sInv.get(i,i);
      if (diagElem > 0.0) {
        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
  }
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      throw new CardinalityException(m.numRows(), m.numCols());
    }
    // See http://www.mlahanas.de/Math/svd.htm for details,
    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
    Matrix sInv = svd.getS();
    // Inverse Diagonal Elems
    for (int i = 0; i < sInv.numRows(); i++) {
      double diagElem = sInv.get(i, i);
      if (diagElem > 0.0) {
        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
    Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
  }
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    mx.setQuick(1, 2, 6);
    mx.setQuick(2, 0, 7);
    mx.setQuick(2, 1, 8);
    mx.setQuick(2, 2, 9);

    SingularValueDecomposition svd2 = new SingularValueDecomposition(mx);
    double[] svaluesControl = svd2.getSingularValues();

    for (int i = 0; i < kp; i++) {
      System.out.printf("%.3e ", svaluesControl[i]);
    }
    System.out.println();
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    mx.setQuick(1, 2, 6);
    mx.setQuick(2, 0, 7);
    mx.setQuick(2, 1, 8);
    mx.setQuick(2, 2, 9);

    SingularValueDecomposition svd2 = new SingularValueDecomposition(mx);
    double[] svaluesControl = svd2.getSingularValues();

    for (int i = 0; i < kp; i++) {
      System.out.printf("%e ", svaluesControl[i]);
    }
    System.out.println();
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    // try to run the same thing without stochastic algo
    double[][] a = SSVDSolver.loadDistributedRowMatrix(fs, aPath, conf);

    // SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
    // Array2DRowRealMatrix(a));
    SingularValueDecomposition svd2 =
      new SingularValueDecomposition(new DenseMatrix(a));

    double[] svalues2 = svd2.getSingularValues();
    dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      assertTrue(Math.abs(svalues2[i] - stochasticSValues[i]) <= s_epsilon);
    }
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    LSMR r = new LSMR();
    Vector x1 = r.solve(m, b);

//    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    double norm = new SingularValueDecomposition(m).getS().viewDiagonal().norm(2);
    double actual = m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2);
    System.out.printf("%.4f\n", actual / norm * 1.0e6);
    assertEquals(0, actual, norm * 1.0e-5);

    // and we need to check that the error estimates are pretty good.
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    // L L' = B B'
    cd2 = new CholeskyDecomposition(b.times(b.transpose()));

    // U_0 D V_0' = L
    svd = new SingularValueDecomposition(cd2.getL());
  }
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      throw new CardinalityException(m.numRows(), m.numCols());
    }
    // See http://www.mlahanas.de/Math/svd.htm for details,
    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
    Matrix sInv = svd.getS();
    // Inverse Diagonal Elems
    for (int i = 0; i < sInv.numRows(); i++) {
      double diagElem = sInv.get(i, i);
      if (diagElem > 0.0) {
        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
    Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
  }
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      for (int j = 0; j < n; j++) {
        a[i][j] -= xi.getQuick(j);
      }
    }

    SingularValueDecomposition svd2 =
      new SingularValueDecomposition(new DenseMatrix(a));

    Vector svalues2 = new DenseVector(svd2.getSingularValues());
    LocalSSVDSolverSparseSequentialTest.dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      assertTrue(Math.abs(svalues2.getQuick(i) - stochasticSValues.getQuick(i)) <= s_epsilon);
    }
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    // try to run the same thing without stochastic algo
    double[][] a = SSVDHelper.loadDistributedRowMatrix(fs, aPath, conf);

    // SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
    // Array2DRowRealMatrix(a));
    SingularValueDecomposition svd2 =
      new SingularValueDecomposition(new DenseMatrix(a));

    Vector svalues2 = new DenseVector(svd2.getSingularValues());
    dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      assertTrue(Math.abs(svalues2.getQuick(i) - stochasticSValues.getQuick(i)) <= s_epsilon);
    }
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Related Classes of org.apache.mahout.math.SingularValueDecomposition

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  • org.apache.mahout.common.distance.MahalanobisDistanceMeasure
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDPCADenseTest
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDSolverDenseTest
  • org.apache.mahout.math.hadoop.stochasticsvd.LocalSSVDSolverSparseSequentialTest
  • org.apache.mahout.math.hadoop.stochasticsvd.SSVDPrototype
  • org.apache.mahout.math.solver.LSMRTest
  • org.apache.mahout.math.ssvd.SequentialBigSvd
  • org.apache.mahout.math.ssvd.SequentialBigSvdTest
  • org.apache.mahout.math.ssvd.SequentialOutOfCoreSvd
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