Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseMatrix.transpose()

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    GivensThinSolver qrSolver = new GivensThinSolver(m.rowSize(), m.columnSize());
    qrSolver.solve(m);

    Matrix qtm = new DenseMatrix(qrSolver.getThinQtTilde());

    assertOrthonormality(qtm.transpose(), false, SVD_EPSILON);

    Matrix aClone = new DenseMatrix(qrSolver.getThinQtTilde()).transpose()
        .times(qrSolver.getRTilde());

    System.out.println("aclone : " + aClone);
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      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if(symmetric) {
      return matrix.times(matrix.transpose());
    }
    return matrix;
  }

  public static Matrix randomHierarchicalSymmetricMatrix(int size) {
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   * @param recomputeUserFeatures
   */
  public void reCalculateTrans(boolean recomputeUserFeatures) {
    if (recomputeUserFeatures) {
      Matrix iMatrix = new DenseMatrix(itemMatrix);
      itemTransItem = iMatrix.transpose().times(iMatrix);
    } else {
      Matrix uMatrix = new DenseMatrix(userMatrix);
      userTransUser = uMatrix.transpose().times(uMatrix);
    }
  }
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    if (recomputeUserFeatures) {
      Matrix iMatrix = new DenseMatrix(itemMatrix);
      itemTransItem = iMatrix.transpose().times(iMatrix);
    } else {
      Matrix uMatrix = new DenseMatrix(userMatrix);
      userTransUser = uMatrix.transpose().times(uMatrix);
    }
  }

  private synchronized void updateMatrix(int id, Matrix m) {
    double normA = 0;
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      if (recomputeUserFeatures) {
        Matrix I = identityV(dataModel.getNumItems());
        Matrix I2 = identityV(numFeatures);
        Matrix iTi = itemTransItem.clone();
        Matrix itemM = new DenseMatrix(itemMatrix);
        XTCX = iTi.plus(itemM.transpose().times(C.minus(I)).times(itemM));

        Matrix diag = solve(XTCX.plus(I2.times(preventOverfitting)), I2);
        Matrix results = diag.times(itemM.transpose().times(C)).times(prefVector.transpose());
        updateMatrix(id, results);
      } else {
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        Matrix iTi = itemTransItem.clone();
        Matrix itemM = new DenseMatrix(itemMatrix);
        XTCX = iTi.plus(itemM.transpose().times(C.minus(I)).times(itemM));

        Matrix diag = solve(XTCX.plus(I2.times(preventOverfitting)), I2);
        Matrix results = diag.times(itemM.transpose().times(C)).times(prefVector.transpose());
        updateMatrix(id, results);
      } else {
        Matrix I = identityV(dataModel.getNumUsers());
        Matrix I2 = identityV(numFeatures);
        Matrix uTu = userTransUser.clone();
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      } else {
        Matrix I = identityV(dataModel.getNumUsers());
        Matrix I2 = identityV(numFeatures);
        Matrix uTu = userTransUser.clone();
        Matrix userM = new DenseMatrix(userMatrix);
        XTCX = uTu.plus(userM.transpose().times(C.minus(I)).times(userM));

        Matrix diag = solve(XTCX.plus(I2.times(preventOverfitting)), I2);
        Matrix results = diag.times(userM.transpose().times(C)).times(prefVector.transpose());
        updateMatrix(id, results);
      }
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        Matrix uTu = userTransUser.clone();
        Matrix userM = new DenseMatrix(userMatrix);
        XTCX = uTu.plus(userM.transpose().times(C.minus(I)).times(userM));

        Matrix diag = solve(XTCX.plus(I2.times(preventOverfitting)), I2);
        Matrix results = diag.times(userM.transpose().times(C)).times(prefVector.transpose());
        updateMatrix(id, results);
      }
      return null;
    }
  }
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      new GivensThinSolver(m.rowSize(), m.columnSize());
    qrSolver.solve(m);

    Matrix qtm = new DenseMatrix(qrSolver.getThinQtTilde());

    assertOrthonormality(qtm.transpose(), false, SVD_EPSILON);

    Matrix aClone =
      new DenseMatrix(qrSolver.getThinQtTilde()).transpose()
                                                .times(qrSolver.getRTilde());
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    EigenDecomposition Eig = new EigenDecomposition(A);
    Matrix D = Eig.getD();
    Matrix V = Eig.getV();
    check("EigenvalueDecomposition (nonsymmetric)...", A.times(V), V.times(D));

    A = A.transpose().times(A);
    Eig = new EigenDecomposition(A);
    D = Eig.getD();
    V = Eig.getV();
    check("EigenvalueDecomposition (symmetric)...", A.times(V), V.times(D));
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