Package org.apache.mahout.math.matrix.linalg

Examples of org.apache.mahout.math.matrix.linalg.EigenvalueDecomposition

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    }
    startTime(TimingSection.TRIDIAG_DECOMP);

    log.info("Lanczos iteration complete - now to diagonalize the tri-diagonal auxiliary matrix.");
    // at this point, have tridiag all filled out, and basis is all filled out, and orthonormalized
    EigenvalueDecomposition decomp = new EigenvalueDecomposition(triDiag);

    DoubleMatrix2D eigenVects = decomp.getV();
    DoubleMatrix1D eigenVals = decomp.getRealEigenvalues();
    endTime(TimingSection.TRIDIAG_DECOMP);
    startTime(TimingSection.FINAL_EIGEN_CREATE);

    for (int i = 0; i < basis.numRows() - 1; i++) {
      Vector realEigen = new DenseVector(corpus.numCols());
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    }
    startTime(TimingSection.TRIDIAG_DECOMP);

    log.info("Lanczos iteration complete - now to diagonalize the tri-diagonal auxiliary matrix.");
    // at this point, have tridiag all filled out, and basis is all filled out, and orthonormalized
    EigenvalueDecomposition decomp = new EigenvalueDecomposition(triDiag);

    DoubleMatrix2D eigenVects = decomp.getV();
    DoubleMatrix1D eigenVals = decomp.getRealEigenvalues();
    endTime(TimingSection.TRIDIAG_DECOMP);
    startTime(TimingSection.FINAL_EIGEN_CREATE);
    for (int row = 0; row < i; row++) {
      Vector realEigen = null;
      // the eigenvectors live as columns of V, in reverse order.  Weird but true.
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    int desiredRank = 80;
    LanczosState state = new LanczosState(m, desiredRank, initialVector);
    // set initial vector?
    solver.solve(state, desiredRank, true);

    EigenvalueDecomposition decomposition = new EigenvalueDecomposition(m);
    DoubleMatrix1D eigenvalues = decomposition.getRealEigenvalues();

    float fractionOfEigensExpectedGood = 0.6f;
    for(int i = 0; i < fractionOfEigensExpectedGood * desiredRank; i++) {
      double s = state.getSingularValue(desiredRank - i - 1);
      double e = eigenvalues.get(eigenvalues.size() - i - 1);
      log.info("{} : L = {}, E = {}", new Object[] {i, s, e});
      assertTrue("Singular value differs from eigenvalue", Math.abs((s-e)/e) < ERROR_TOLERANCE);
      Vector v = state.getRightSingularVector(i);
      Vector v2 = decomposition.getV().viewColumn(eigenvalues.size() - i - 1).toVector();
      double error = 1 - Math.abs(v.dot(v2)/(v.norm(2) * v2.norm(2)));
      log.info("error: {}", error);
      assertTrue(i + ": 1 - cosAngle = " + error, error < ERROR_TOLERANCE);
    }
  }
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    }
    startTime(TimingSection.TRIDIAG_DECOMP);

    log.info("Lanczos iteration complete - now to diagonalize the tri-diagonal auxiliary matrix.");
    // at this point, have tridiag all filled out, and basis is all filled out, and orthonormalized
    EigenvalueDecomposition decomp = new EigenvalueDecomposition(triDiag);

    DoubleMatrix2D eigenVects = decomp.getV();
    DoubleMatrix1D eigenVals = decomp.getRealEigenvalues();
    endTime(TimingSection.TRIDIAG_DECOMP);
    startTime(TimingSection.FINAL_EIGEN_CREATE);

    for (int i = 0; i < basis.numRows() - 1; i++) {
      Vector realEigen = new DenseVector(corpus.numCols());
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    }
    startTime(TimingSection.TRIDIAG_DECOMP);

    log.info("Lanczos iteration complete - now to diagonalize the tri-diagonal auxiliary matrix.");
    // at this point, have tridiag all filled out, and basis is all filled out, and orthonormalized
    EigenvalueDecomposition decomp = new EigenvalueDecomposition(triDiag);

    DoubleMatrix2D eigenVects = decomp.getV();
    DoubleMatrix1D eigenVals = decomp.getRealEigenvalues();
    endTime(TimingSection.TRIDIAG_DECOMP);
    startTime(TimingSection.FINAL_EIGEN_CREATE);
    for (int row = 0; row < i; row++) {
      Vector realEigen = null;
      // the eigenvectors live as columns of V, in reverse order.  Weird but true.
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    LanczosSolver solver = new LanczosSolver();
    LanczosState state = new LanczosState(m, size, desiredRank, initialVector);
    // set initial vector?
    solver.solve(state, desiredRank, true);

    EigenvalueDecomposition decomposition = new EigenvalueDecomposition(m);
    DoubleMatrix1D eigenvalues = decomposition.getRealEigenvalues();

    float fractionOfEigensExpectedGood = 0.6f;
    for(int i = 0; i < fractionOfEigensExpectedGood * desiredRank; i++) {
      double s = state.getSingularValue(desiredRank - i - 1);
      double e = eigenvalues.get(eigenvalues.size() - i - 1);
      log.info(i + " : L = {}, E = {}", s, e);
      assertTrue("Singular value differs from eigenvalue", Math.abs((s-e)/e) < ERROR_TOLERANCE);
      Vector v = state.getRightSingularVector(i);
      Vector v2 = decomposition.getV().viewColumn(eigenvalues.size() - i - 1).toVector();
      double error = 1 - Math.abs(v.dot(v2)/(v.norm(2) * v2.norm(2)));
      log.info("error: {}", error);
      assertTrue(i + ": 1 - cosAngle = " + error, error < ERROR_TOLERANCE);
    }
  }
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Related Classes of org.apache.mahout.math.matrix.linalg.EigenvalueDecomposition

  • org.apache.mahout.math.decomposer.lanczos.LanczosSolver
  • org.apache.mahout.math.decomposer.lanczos.TestLanczosSolver
  • org.apache.mahout.math.matrix.impl.DenseDoubleMatrix1D
  • org.apache.mahout.math.matrix.impl.DenseDoubleMatrix2D
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