Examples of Jama.LUDecomposition

Jama.LUDecomposition
LU Decomposition.
For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U. If m < n, then L is m-by-m and U is m-by-n.
The LU decompostion with pivoting always exists, even if the matrix is singular, so the constructor will never fail. The primary use of the LU decomposition is in the solution of square systems of simultaneous linear equations. This will fail if isNonsingular() returns false.

        }
        int n = A.getColumnDimension();


        A = A.getMatrix(0, n - 1, 0, n - 1);
        A.set(0, 0, 0.);
        LUDecomposition LU = A.lu();


        try {
            check(A.getMatrix(LU.getPivot(), 0, n - 1),
                    LU.getL().times(LU.getU()));
            try_success("LUDecomposition...", "");
        } catch (java.lang.RuntimeException e) {
            errorCount = try_failure(errorCount, "LUDecomposition...",
                    "incorrect LU decomposition calculation");
        }

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            print(
                    c < 1 / eps
                            ? fixedWidthDoubletoString(c, 12, 3)
                            : "         Inf");


            LUDecomposition LU = new LUDecomposition(M);
            Matrix L = LU.getL();
            Matrix U = LU.getU();
            int[] p = LU.getPivot();
            Matrix R = L.times(U).minus(M.getMatrix(p, 0, n - 1));
            double res = R.norm1() / (n * eps);


            print(fixedWidthDoubletoString(res, 12, 3));

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                    hessian[j][i] += r[i] * r[j] * weights[k];
                }
            }
        }


        final LUDecomposition lu = new LUDecomposition(new Matrix(hessian));


        if (lu.isNonsingular()) {
            deltas = lu.solve(gradient);
        } else {
            throw new AIFHError("Matrix Non singular");
        }


        final double[] prev = this.algorithm.getLongTermMemory().clone();

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   * for each <em>n</em>.
   */
  private Matrix actuallySolveMatrix() {
    MatrixPrimitive prim = this.toMatrixPrimitive();
    Matrix m = new Matrix(prim.body);
    LUDecomposition decomp = new LUDecomposition(m);
    Matrix solution = new Matrix(prim.solution);


    try {
      return decomp.solve(solution);
    } catch (RuntimeException e) {
      if (e.getMessage().equals("Matrix is singular.")) {
        throw new AmbiguousLayoutException();
      } else {
        throw e;

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com.heatonresearch.aifh.regression.TrainReweightLeastSquares

edu.cmu.relativelayout.matrix.RelativeMatrix

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