C# System.Numerics.Complex.Copy方法代码示例

        /// <summary>
        /// Computes the inverse of a previously factored matrix.
        /// </summary>
        /// <param name="a">The LU factored N by N matrix.  Contains the inverse On exit.</param>
        /// <param name="order">The order of the square matrix <paramref name="a"/>.</param>
        /// <param name="ipiv">The pivot indices of <paramref name="a"/>.</param>
        /// <remarks>This is equivalent to the GETRI LAPACK routine.</remarks>
        public virtual void LUInverseFactored(Complex[] a, int order, int[] ipiv)
        {
            if (a == null)
            {
                throw new ArgumentNullException("a");
            }

            if (ipiv == null)
            {
                throw new ArgumentNullException("ipiv");
            }

            if (a.Length != order*order)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "a");
            }

            if (ipiv.Length != order)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "ipiv");
            }

            var inverse = new Complex[a.Length];
            for (var i = 0; i < order; i++)
            {
                inverse[i + (order*i)] = Complex.One;
            }

            LUSolveFactored(order, a, order, ipiv, inverse);
            inverse.Copy(a);
        }

        /// <summary>
        /// Solves A*X=B for X using LU factorization.
        /// </summary>
        /// <param name="columnsOfB">The number of columns of B.</param>
        /// <param name="a">The square matrix A.</param>
        /// <param name="order">The order of the square matrix <paramref name="a"/>.</param>
        /// <param name="b">On entry the B matrix; on exit the X matrix.</param>
        /// <remarks>This is equivalent to the GETRF and GETRS LAPACK routines.</remarks>
        public virtual void LUSolve(int columnsOfB, Complex[] a, int order, Complex[] b)
        {
            if (a == null)
            {
                throw new ArgumentNullException("a");
            }

            if (b == null)
            {
                throw new ArgumentNullException("b");
            }

            if (a.Length != order*order)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "a");
            }

            if (b.Length != order*columnsOfB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "b");
            }

            if (ReferenceEquals(a, b))
            {
                throw new ArgumentException(Resources.ArgumentReferenceDifferent);
            }

            var ipiv = new int[order];
            var clone = new Complex[a.Length];
            a.Copy(clone);
            LUFactor(clone, order, ipiv);
            LUSolveFactored(columnsOfB, clone, order, ipiv, b);
        }

        /// <summary>
        /// Solves A*X=B for X using Cholesky factorization.
        /// </summary>
        /// <param name="a">The square, positive definite matrix A.</param>
        /// <param name="orderA">The number of rows and columns in A.</param>
        /// <param name="b">On entry the B matrix; on exit the X matrix.</param>
        /// <param name="columnsB">The number of columns in the B matrix.</param>
        /// <remarks>This is equivalent to the POTRF add POTRS LAPACK routines.</remarks>
        public virtual void CholeskySolve(Complex[] a, int orderA, Complex[] b, int columnsB)
        {
            if (a == null)
            {
                throw new ArgumentNullException("a");
            }

            if (b == null)
            {
                throw new ArgumentNullException("b");
            }

            if (b.Length != orderA*columnsB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "b");
            }

            if (ReferenceEquals(a, b))
            {
                throw new ArgumentException(Resources.ArgumentReferenceDifferent);
            }

            var clone = new Complex[a.Length];
            a.Copy(clone);
            CholeskyFactor(clone, orderA);
            CholeskySolveFactored(clone, orderA, b, columnsB);
        }

        /// <summary>
        /// Solves A*X=B for X using the singular value decomposition of A.
        /// </summary>
        /// <param name="a">On entry, the M by N matrix to decompose.</param>
        /// <param name="rowsA">The number of rows in the A matrix.</param>
        /// <param name="columnsA">The number of columns in the A matrix.</param>
        /// <param name="b">The B matrix.</param>
        /// <param name="columnsB">The number of columns of B.</param>
        /// <param name="x">On exit, the solution matrix.</param>
        public virtual void SvdSolve(Complex[] a, int rowsA, int columnsA, Complex[] b, int columnsB, Complex[] x)
        {
            if (a == null)
            {
                throw new ArgumentNullException("a");
            }

            if (b == null)
            {
                throw new ArgumentNullException("b");
            }

            if (x == null)
            {
                throw new ArgumentNullException("x");
            }

            if (b.Length != rowsA*columnsB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "b");
            }

            if (x.Length != columnsA*columnsB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "b");
            }

            var work = new Complex[rowsA];
            var s = new Complex[Math.Min(rowsA, columnsA)];
            var u = new Complex[rowsA*rowsA];
            var vt = new Complex[columnsA*columnsA];

            var clone = new Complex[a.Length];
            a.Copy(clone);
            SingularValueDecomposition(true, clone, rowsA, columnsA, s, u, vt, work);
            SvdSolveFactored(rowsA, columnsA, s, u, vt, b, columnsB, x);
        }

        /// <summary>
        /// Scales an array. Can be used to scale a vector and a matrix.
        /// </summary>
        /// <param name="alpha">The scalar.</param>
        /// <param name="x">The values to scale.</param>
        /// <param name="result">This result of the scaling.</param>
        /// <remarks>This is similar to the SCAL BLAS routine.</remarks>
        public virtual void ScaleArray(Complex alpha, Complex[] x, Complex[] result)
        {
            if (x == null)
            {
                throw new ArgumentNullException("x");
            }

            if (alpha.IsZero())
            {
                Array.Clear(result, 0, result.Length);
            }
            else if (alpha.IsOne())
            {
                x.Copy(result);
            }
            else
            {
                if (Control.ParallelizeOperation(x.Length))
                {
                    CommonParallel.For(0, x.Length, 4096, (a, b) =>
                        {
                            for (int i = a; i < b; i++)
                            {
                                result[i] = alpha*x[i];
                            }
                        });
                }
                else
                {
                    for (var index = 0; index < x.Length; index++)
                    {
                        result[index] = alpha*x[index];
                    }
                }
            }
        }

        /// <summary>
        /// Solves A*X=B for X using QR factorization of A.
        /// </summary>
        /// <param name="a">The A matrix.</param>
        /// <param name="rows">The number of rows in the A matrix.</param>
        /// <param name="columns">The number of columns in the A matrix.</param>
        /// <param name="b">The B matrix.</param>
        /// <param name="columnsB">The number of columns of B.</param>
        /// <param name="x">On exit, the solution matrix.</param>
        /// <param name="work">The work array. The array must have a length of at least N,
        /// but should be N*blocksize. The blocksize is machine dependent. On exit, work[0] contains the optimal
        /// work size value.</param>
        /// <param name="method">The type of QR factorization to perform. <seealso cref="QRMethod"/></param>
        /// <remarks>Rows must be greater or equal to columns.</remarks>
        public virtual void QRSolve(Complex[] a, int rows, int columns, Complex[] b, int columnsB, Complex[] x, Complex[] work, QRMethod method = QRMethod.Full)
        {
            if (a == null)
            {
                throw new ArgumentNullException("a");
            }

            if (b == null)
            {
                throw new ArgumentNullException("b");
            }

            if (x == null)
            {
                throw new ArgumentNullException("x");
            }

            if (work == null)
            {
                throw new ArgumentNullException("work");
            }

            if (a.Length != rows*columns)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "a");
            }

            if (b.Length != rows*columnsB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "b");
            }

            if (rows < columns)
            {
                throw new ArgumentException(Resources.RowsLessThanColumns);
            }

            if (x.Length != columns*columnsB)
            {
                throw new ArgumentException(Resources.ArgumentArraysSameLength, "x");
            }

            if (work.Length < rows*columns)
            {
                work[0] = rows*columns;
                throw new ArgumentException(Resources.WorkArrayTooSmall, "work");
            }

            var clone = new Complex[a.Length];
            a.Copy(clone);

            if (method == QRMethod.Full)
            {
                var q = new Complex[rows*rows];
                QRFactor(clone, rows, columns, q, work);
                QRSolveFactored(q, clone, rows, columns, null, b, columnsB, x, method);
            }
            else
            {
                var r = new Complex[columns*columns];
                ThinQRFactor(clone, rows, columns, r, work);
                QRSolveFactored(clone, r, rows, columns, null, b, columnsB, x, method);
            }

            work[0] = rows*columns;
        }

        /// <summary>
        /// Adds a scaled vector to another: <c>result = y + alpha*x</c>.
        /// </summary>
        /// <param name="y">The vector to update.</param>
        /// <param name="alpha">The value to scale <paramref name="x"/> by.</param>
        /// <param name="x">The vector to add to <paramref name="y"/>.</param>
        /// <param name="result">The result of the addition.</param>
        /// <remarks>This is similar to the AXPY BLAS routine.</remarks>
        public virtual void AddVectorToScaledVector(Complex[] y, Complex alpha, Complex[] x, Complex[] result)
        {
            if (y == null)
            {
                throw new ArgumentNullException("y");
            }

            if (x == null)
            {
                throw new ArgumentNullException("x");
            }

            if (y.Length != x.Length)
            {
                throw new ArgumentException(Resources.ArgumentVectorsSameLength);
            }

            if (y.Length != x.Length)
            {
                throw new ArgumentException(Resources.ArgumentVectorsSameLength);
            }

            if (alpha.IsZero())
            {
                y.Copy(result);
            }
            else if (alpha.IsOne())
            {
                if (Control.ParallelizeOperation(x.Length))
                {
                    CommonParallel.For(0, y.Length, 4096, (a, b) =>
                        {
                            for (int i = a; i < b; i++)
                            {
                                result[i] = y[i] + x[i];
                            }
                        });
                }
                else
                {
                    for (var index = 0; index < x.Length; index++)
                    {
                        result[index] = y[index] + x[index];
                    }
                }
            }
            else
            {
                if (Control.ParallelizeOperation(x.Length))
                {
                    CommonParallel.For(0, y.Length, 4096, (a, b) =>
                        {
                            for (int i = a; i < b; i++)
                            {
                                result[i] = y[i] + (alpha*x[i]);
                            }
                        });
                }
                else
                {
                    for (var index = 0; index < x.Length; index++)
                    {
                        result[index] = y[index] + (alpha*x[index]);
                    }
                }
            }
        }