Package org.apache.mahout.math.matrix

Examples of org.apache.mahout.math.matrix.DoubleMatrix1D

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    Mult div = Mult.div(0);
    PlusMult minusMult = PlusMult.minusMult(0);

    IntArrayList nonZeroIndexes =
        new IntArrayList(); // sparsity
    DoubleMatrix1D bRowk = org.apache.mahout.math.matrix.DoubleFactory1D.dense.make(nx); // blocked row k

    // Solve L*Y = B(piv,:)
    int cutOff = 10;
    for (int k = 0; k < n; k++) {
      // blocking (make copy of k-th row to localize references)
      bRowk.assign(brows[k]);

      // sparsity detection
      int maxCardinality = nx / cutOff; // == heuristic depending on speedup
      bRowk.getNonZeros(nonZeroIndexes, null, maxCardinality);
      int cardinality = nonZeroIndexes.size();
      boolean sparse = cardinality < maxCardinality;

      for (int i = k + 1; i < n; i++) {
        //for (int j = 0; j < nx; j++) B[i][j] -= B[k][j]*LU[i][k];
        //for (int j = 0; j < nx; j++) B.set(i,j, B.get(i,j) - B.get(k,j)*LU.get(i,k));

        minusMult.setMultiplicator(-lu.getQuick(i, k));
        if (minusMult.getMultiplicator() != 0) {
          if (sparse) {
            brows[i].assign(bRowk, minusMult, nonZeroIndexes);
          } else {
            brows[i].assign(bRowk, minusMult);
          }
        }
      }
    }

    // Solve U*B = Y;
    for (int k = n - 1; k >= 0; k--) {
      // for (int j = 0; j < nx; j++) B[k][j] /= LU[k][k];
      // for (int j = 0; j < nx; j++) B.set(k,j, B.get(k,j) / LU.get(k,k));
      div.setMultiplicator(1 / lu.getQuick(k, k));
      brows[k].assign(div);

      // blocking
      //if (bRowk == null) {
      //  bRowk = org.apache.mahout.math.matrix.DoubleFactory1D.dense.make(B.columns());
      //}
      bRowk.assign(brows[k]);

      // sparsity detection
      int maxCardinality = nx / cutOff; // == heuristic depending on speedup
      bRowk.getNonZeros(nonZeroIndexes, null, maxCardinality);
      int cardinality = nonZeroIndexes.size();
      boolean sparse = cardinality < maxCardinality;

      //Browk.getNonZeros(nonZeroIndexes,null);
      //boolean sparse = nonZeroIndexes.size() < nx/10;
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        QR.setQuick(k, k, QR.getQuick(k, k) + 1);

        // Apply transformation to remaining columns.
        for (int j = k + 1; j < n; j++) {
          DoubleMatrix1D QRcolj = QR.viewColumn(j).viewPart(k, m - k);
          double s = QRcolumnsPart[k].zDotProduct(QRcolj);
          /*
          // fixes bug reported by John Chambers
          DoubleMatrix1D QRcolj = QR.viewColumn(j).viewPart(k,m-k);
          double s = QRcolumnsPart[k].zDotProduct(QRcolumns[j]);
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   */
  public DoubleMatrix2D getQ() {
    DoubleMatrix2D Q = QR.like();
    //double[][] Q = X.getArray();
    for (int k = n - 1; k >= 0; k--) {
      DoubleMatrix1D QRcolk = QR.viewColumn(k).viewPart(k, m - k);
      Q.setQuick(k, k, 1);
      for (int j = k; j < n; j++) {
        if (QR.getQuick(k, k) != 0) {
          DoubleMatrix1D Qcolj = Q.viewColumn(j).viewPart(k, m - k);
          double s = QRcolk.zDotProduct(Qcolj);
          s = -s / QR.getQuick(k, k);
          Qcolj.assign(QRcolk, Functions.plusMult(s));
        }
      }
    }
    return Q;
  }
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   * @throws IndexOutOfBoundsException if <tt>index<0 || width<0 || index+width>size()</tt>.
   */
  @Override
  public DoubleMatrix1D viewPart(final int index, int width) {
    checkRange(index, width);
    DoubleMatrix1D view = new WrapperDoubleMatrix1D(this) {
      @Override
      public double getQuick(int i) {
        return content.get(index + i);
      }

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    }

    checkIndexes(indexes);
    final int[] idx = indexes;

    DoubleMatrix1D view = new WrapperDoubleMatrix1D(this) {
      @Override
      public double getQuick(int i) {
        return content.get(idx[i]);
      }
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  @Override
  public DoubleMatrix1D viewStrides(final int theStride) {
    if (stride <= 0) {
      throw new IndexOutOfBoundsException("illegal stride: " + stride);
    }
    DoubleMatrix1D view = new WrapperDoubleMatrix1D(this) {
      @Override
      public double getQuick(int index) {
        return content.get(index * theStride);
      }

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    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());
      // the eigenvectors live as columns of V, in reverse order.  Weird but true.
      DoubleMatrix1D ejCol = eigenVects.viewColumn(basis.numRows() - i - 1);
      for (int j = 0; j < ejCol.size(); j++) {
        double d = ejCol.getQuick(j);
        realEigen.assign(basis.getRow(j), new PlusMult(d));
      }
      realEigen = realEigen.normalize();
      eigenVectors.assignRow(i, realEigen);
      log.info("Eigenvector {} found with eigenvalue {}", i, eigenVals.get(i));
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    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.
      DoubleMatrix1D ejCol = eigenVects.viewColumn(i - row - 1);
      int size = ejCol.size();
      for (int j = 0; j < size; j++) {
        double d = ejCol.get(j);
        Vector rowJ = state.getBasisVector(j);
        if(realEigen == null) {
          realEigen = rowJ.like();
        }
        realEigen.assign(rowJ, new PlusMult(d));
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   * @throws IndexOutOfBoundsException if <tt>index<0 || width<0 || index+width>size()</tt>.
   */
  @Override
  public DoubleMatrix1D viewPart(final int index, int width) {
    checkRange(index, width);
    DoubleMatrix1D view = new WrapperDoubleMatrix1D(this) {
      @Override
      public double getQuick(int i) {
        return content.get(index + i);
      }

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    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.DoubleMatrix1D

  • org.apache.mahout.math.decomposer.lanczos.LanczosSolver
  • org.apache.mahout.math.decomposer.lanczos.TestLanczosSolver
  • org.apache.mahout.math.DenseVector
  • org.apache.mahout.math.list.IntArrayList
  • org.apache.mahout.math.matrix.doublealgo.Formatter
  • org.apache.mahout.math.matrix.doublealgo.Partitioning
  • org.apache.mahout.math.matrix.doublealgo.Sorting
  • org.apache.mahout.math.matrix.impl.DenseDoubleMatrix1D
  • org.apache.mahout.math.matrix.impl.WrapperDoubleMatrix1D
  • org.apache.mahout.math.matrix.linalg.LUDecompositionQuick
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