UltimateFishing/Assets/Scripts/Assembly-CSharp/CSML/Matrix.cs

using System;
using System.Collections;

namespace CSML
{
	public class Matrix
	{
		public enum DefinitenessType
		{
			PositiveDefinite = 0,
			PositiveSemidefinite = 1,
			NegativeDefinite = 2,
			NegativeSemidefinite = 3,
			Indefinite = 4
		}

		private ArrayList Values;

		private int rowCount;

		private int columnCount;

		public int RowCount
		{
			get
			{
				return rowCount;
			}
		}

		public int ColumnCount
		{
			get
			{
				return columnCount;
			}
		}

		public virtual Complex this[int i, int j]
		{
			get
			{
				if (i > 0 && i <= rowCount && j > 0 && j <= columnCount)
				{
					return (Complex)((ArrayList)Values[i - 1])[j - 1];
				}
				throw new ArgumentOutOfRangeException("Indices must not exceed size of matrix.");
			}
			set
			{
				if (i <= 0 || j <= 0)
				{
					throw new ArgumentOutOfRangeException("Indices must be real positive.");
				}
				if (i > rowCount)
				{
					for (int k = 0; k < i - rowCount; k++)
					{
						Values.Add(new ArrayList(columnCount));
						for (int l = 0; l < columnCount; l++)
						{
							((ArrayList)Values[rowCount + k]).Add(Complex.Zero);
						}
					}
					rowCount = i;
				}
				if (j > columnCount)
				{
					for (int m = 0; m < rowCount; m++)
					{
						for (int n = 0; n < j - columnCount; n++)
						{
							((ArrayList)Values[m]).Add(Complex.Zero);
						}
					}
					columnCount = j;
				}
				((ArrayList)Values[i - 1])[j - 1] = value;
			}
		}

		public virtual Complex this[int i]
		{
			get
			{
				if (RowCount == 1)
				{
					return (Complex)((ArrayList)Values[0])[i - 1];
				}
				if (ColumnCount == 1)
				{
					return (Complex)((ArrayList)Values[i - 1])[0];
				}
				throw new InvalidOperationException("General matrix acces requires double indexing.");
			}
			set
			{
				if (rowCount == 1)
				{
					if (i > columnCount)
					{
						for (int j = 0; j < i - columnCount; j++)
						{
							((ArrayList)Values[0]).Add(Complex.Zero);
						}
						columnCount = i;
					}
					((ArrayList)Values[0])[i - 1] = value;
					return;
				}
				if (columnCount == 1)
				{
					if (i > rowCount)
					{
						for (int k = 0; k < i - rowCount; k++)
						{
							Values.Add(new ArrayList(columnCount));
							((ArrayList)Values[rowCount + k]).Add(Complex.Zero);
						}
						rowCount = i;
					}
					((ArrayList)Values[i - 1])[0] = value;
					return;
				}
				throw new InvalidOperationException("Cannot access multidimensional matrix via single index.");
			}
		}

		public Matrix()
		{
			Values = new ArrayList();
			rowCount = 0;
			columnCount = 0;
		}

		public Matrix(int m, int n)
		{
			rowCount = m;
			columnCount = n;
			Values = new ArrayList(m);
			for (int i = 0; i < m; i++)
			{
				Values.Add(new ArrayList(n));
				for (int j = 0; j < n; j++)
				{
					((ArrayList)Values[i]).Add(Complex.Zero);
				}
			}
		}

		public Matrix(int n)
		{
			rowCount = n;
			columnCount = n;
			Values = new ArrayList(n);
			for (int i = 0; i < n; i++)
			{
				Values.Add(new ArrayList(n));
				for (int j = 0; j < n; j++)
				{
					((ArrayList)Values[i]).Add(Complex.Zero);
				}
			}
		}

		public Matrix(Complex x)
		{
			rowCount = 1;
			columnCount = 1;
			Values = new ArrayList(1);
			Values.Add(new ArrayList(1));
			((ArrayList)Values[0]).Add(x);
		}

		public Matrix(Complex[,] values)
		{
			if (values == null)
			{
				Values = new ArrayList();
				columnCount = 0;
				rowCount = 0;
			}
			rowCount = (int)values.GetLongLength(0);
			columnCount = (int)values.GetLongLength(1);
			Values = new ArrayList(rowCount);
			for (int i = 0; i < rowCount; i++)
			{
				Values.Add(new ArrayList(columnCount));
				for (int j = 0; j < columnCount; j++)
				{
					((ArrayList)Values[i]).Add(values[i, j]);
				}
			}
		}

		public Matrix(Complex[] values)
		{
			if (values == null)
			{
				Values = new ArrayList();
				columnCount = 0;
				rowCount = 0;
			}
			rowCount = values.Length;
			columnCount = 1;
			Values = new ArrayList(rowCount);
			for (int i = 0; i < rowCount; i++)
			{
				Values.Add(new ArrayList(1));
				((ArrayList)Values[i]).Add(values[i]);
			}
		}

		public Matrix(double x)
		{
			rowCount = 1;
			columnCount = 1;
			Values = new ArrayList(1);
			Values.Add(new ArrayList(1));
			((ArrayList)Values[0]).Add(new Complex(x));
		}

		public Matrix(double[,] values)
		{
			if (values == null)
			{
				Values = new ArrayList();
				columnCount = 0;
				rowCount = 0;
			}
			rowCount = (int)values.GetLongLength(0);
			columnCount = (int)values.GetLongLength(1);
			Values = new ArrayList(rowCount);
			for (int i = 0; i < rowCount; i++)
			{
				Values.Add(new ArrayList(columnCount));
				for (int j = 0; j < columnCount; j++)
				{
					((ArrayList)Values[i]).Add(new Complex(values[i, j]));
				}
			}
		}

		public Matrix(double[] values)
		{
			if (values == null)
			{
				Values = new ArrayList();
				columnCount = 0;
				rowCount = 0;
			}
			rowCount = values.Length;
			columnCount = 1;
			Values = new ArrayList(rowCount);
			for (int i = 0; i < rowCount; i++)
			{
				Values.Add(new ArrayList(1));
				((ArrayList)Values[i]).Add(new Complex(values[i]));
			}
		}

		public Matrix(string matrix_string)
		{
			matrix_string = matrix_string.Replace(" ", string.Empty);
			string[] array = matrix_string.Split(';');
			rowCount = array.Length;
			Values = new ArrayList(rowCount);
			columnCount = 0;
			for (int i = 0; i < rowCount; i++)
			{
				Values.Add(new ArrayList());
			}
			for (int j = 1; j <= rowCount; j++)
			{
				string[] array2 = array[j - 1].Split(',');
				for (int k = 1; k <= array2.Length; k++)
				{
					this[j, k] = new Complex(Convert.ToDouble(array2[k - 1]));
				}
			}
		}

		public static Matrix E(int n, int j)
		{
			Matrix matrix = Zeros(n, 1);
			matrix[j] = Complex.One;
			return matrix;
		}

		public static Complex KroneckerDelta(int i, int j)
		{
			return new Complex(Math.Min(Math.Abs(i - j), 1));
		}

		public static Matrix ChessboardMatrix(int m, int n, bool even)
		{
			Matrix matrix = new Matrix(m, n);
			if (even)
			{
				for (int i = 1; i <= m; i++)
				{
					for (int j = 1; j <= n; j++)
					{
						matrix[i, j] = KroneckerDelta((i + j) % 2, 0);
					}
				}
			}
			else
			{
				for (int k = 1; k <= m; k++)
				{
					for (int l = 1; l <= n; l++)
					{
						matrix[k, l] = KroneckerDelta((k + l) % 2, 1);
					}
				}
			}
			return matrix;
		}

		public static Matrix ChessboardMatrix(int n, bool even)
		{
			Matrix matrix = new Matrix(n);
			if (even)
			{
				for (int i = 1; i <= n; i++)
				{
					for (int j = 1; j <= n; j++)
					{
						matrix[i, j] = KroneckerDelta((i + j) % 2, 0);
					}
				}
			}
			else
			{
				for (int k = 1; k <= n; k++)
				{
					for (int l = 1; l <= n; l++)
					{
						matrix[k, l] = KroneckerDelta((k + l) % 2, 1);
					}
				}
			}
			return matrix;
		}

		public static Matrix Zeros(int m, int n)
		{
			return new Matrix(m, n);
		}

		public static Matrix Zeros(int n)
		{
			return new Matrix(n);
		}

		public static Matrix Ones(int m, int n)
		{
			Matrix matrix = new Matrix(m, n);
			for (int i = 0; i < m; i++)
			{
				for (int j = 0; j < n; j++)
				{
					((ArrayList)matrix.Values[i])[j] = Complex.One;
				}
			}
			return matrix;
		}

		public static Matrix Ones(int n)
		{
			Matrix matrix = new Matrix(n);
			for (int i = 0; i < n; i++)
			{
				for (int j = 0; j < n; j++)
				{
					((ArrayList)matrix.Values[i])[j] = Complex.One;
				}
			}
			return matrix;
		}

		public static Matrix Identity(int n)
		{
			return Diag(Ones(n, 1));
		}

		public static Matrix Eye(int n)
		{
			return Identity(n);
		}

		public static Matrix VerticalConcat(Matrix A, Matrix B)
		{
			Matrix matrix = A.Column(1);
			for (int i = 2; i <= A.ColumnCount; i++)
			{
				matrix.InsertColumn(A.Column(i), i);
			}
			for (int j = 1; j <= B.ColumnCount; j++)
			{
				matrix.InsertColumn(B.Column(j), matrix.ColumnCount + 1);
			}
			return matrix;
		}

		public static Matrix VerticalConcat(Matrix[] A)
		{
			if (A == null)
			{
				throw new ArgumentNullException();
			}
			if (A.Length == 1)
			{
				return A[0];
			}
			Matrix matrix = VerticalConcat(A[0], A[1]);
			for (int i = 2; i < A.Length; i++)
			{
				matrix = VerticalConcat(matrix, A[i]);
			}
			return matrix;
		}

		public static Matrix HorizontalConcat(Matrix A, Matrix B)
		{
			Matrix matrix = A.Row(1);
			for (int i = 2; i <= A.RowCount; i++)
			{
				matrix.InsertRow(A.Row(i), i);
			}
			for (int j = 1; j <= B.RowCount; j++)
			{
				matrix.InsertRow(B.Row(j), matrix.RowCount + 1);
			}
			return matrix;
		}

		public static Matrix HorizontalConcat(Matrix[] A)
		{
			if (A == null)
			{
				throw new ArgumentNullException();
			}
			if (A.Length == 1)
			{
				return A[0];
			}
			Matrix matrix = HorizontalConcat(A[0], A[1]);
			for (int i = 2; i < A.Length; i++)
			{
				matrix = HorizontalConcat(matrix, A[i]);
			}
			return matrix;
		}

		public static Matrix Diag(Matrix diag_vector)
		{
			int num = diag_vector.VectorLength();
			if (num == 0)
			{
				throw new ArgumentException("diag_vector must be 1xN or Nx1");
			}
			Matrix matrix = new Matrix(num, num);
			for (int i = 1; i <= num; i++)
			{
				matrix[i, i] = diag_vector[i];
			}
			return matrix;
		}

		public static Matrix Diag(Matrix diag_vector, int offset)
		{
			int num = diag_vector.VectorLength();
			if (num == 0)
			{
				throw new ArgumentException("diag_vector must be 1xN or Nx1.");
			}
			Matrix matrix = new Matrix(num + Math.Abs(offset), num + Math.Abs(offset));
			num = matrix.RowCount;
			if (offset >= 0)
			{
				for (int i = 1; i <= num - offset; i++)
				{
					matrix[i, i + offset] = diag_vector[i];
				}
			}
			else
			{
				for (int j = 1; j <= num + offset; j++)
				{
					matrix[j - offset, j] = diag_vector[j];
				}
			}
			return matrix;
		}

		public static Matrix TriDiag(Complex l, Complex d, Complex u, int n)
		{
			if (n <= 1)
			{
				throw new ArgumentException("Matrix dimension must greater than one.");
			}
			return Diag(l * Ones(n - 1, 1), -1) + Diag(d * Ones(n, 1)) + Diag(u * Ones(n - 1, 1), 1);
		}

		public static Matrix TriDiag(Matrix l, Matrix d, Matrix u)
		{
			int num = l.VectorLength();
			int num2 = d.VectorLength();
			int num3 = u.VectorLength();
			if (num * num2 * num3 == 0)
			{
				throw new ArgumentException("At least one of the paramter matrices is not a vector.");
			}
			if (num != num3)
			{
				throw new ArgumentException("Lower and upper secondary diagonal must have the same length.");
			}
			if (num + 1 != num2)
			{
				throw new ArgumentException("Main diagonal must have exactly one element more than the secondary diagonals.");
			}
			return Diag(l, -1) + Diag(d) + Diag(u, 1);
		}

		public static Complex Dot(Matrix v, Matrix w)
		{
			int num = v.VectorLength();
			int num2 = w.VectorLength();
			if (num == 0 || num2 == 0)
			{
				throw new ArgumentException("Arguments need to be vectors.");
			}
			if (num != num2)
			{
				throw new ArgumentException("Vectors must be of the same length.");
			}
			Complex zero = Complex.Zero;
			for (int i = 1; i <= num; i++)
			{
				zero += v[i] * w[i];
			}
			return zero;
		}

		public static Complex Fib(int n)
		{
			Matrix matrix = Ones(2, 2);
			matrix[2, 2] = Complex.Zero;
			return (matrix ^ (n - 1))[1, 1];
		}

		public static Matrix RandomGraph(int n)
		{
			Matrix matrix = Random(n, n);
			return matrix - Diag(matrix.DiagVector());
		}

		public static Matrix RandomGraph(int n, double p)
		{
			Matrix matrix = new Matrix(n);
			Random random = new Random();
			for (int i = 1; i <= n; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					if (i == j)
					{
						matrix[i, j] = Complex.Zero;
					}
					else if (random.NextDouble() < p)
					{
						matrix[i, j] = new Complex(random.NextDouble());
					}
					else
					{
						matrix[i, j] = new Complex(double.PositiveInfinity);
					}
				}
			}
			return matrix;
		}

		public static Matrix Random(int m, int n)
		{
			Matrix matrix = new Matrix(m, n);
			Random random = new Random();
			for (int i = 1; i <= m; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					matrix[i, j] = new Complex(random.NextDouble());
				}
			}
			return matrix;
		}

		public static Matrix Random(int n)
		{
			Matrix matrix = new Matrix(n);
			Random random = new Random();
			for (int i = 1; i <= n; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					matrix[i, j] = new Complex(random.NextDouble());
				}
			}
			return matrix;
		}

		public static Matrix Random(int n, int lo, int hi)
		{
			Matrix matrix = new Matrix(n);
			Random random = new Random();
			for (int i = 1; i <= n; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					matrix[i, j] = new Complex(random.Next(lo, hi));
				}
			}
			return matrix;
		}

		public static Matrix RandomZeroOne(int m, int n, double p)
		{
			Matrix matrix = new Matrix(m, n);
			Random random = new Random();
			for (int i = 1; i <= m; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					if (random.NextDouble() <= p)
					{
						matrix[i, j] = Complex.One;
					}
				}
			}
			return matrix;
		}

		public static Matrix RandomZeroOne(int n, double p)
		{
			Matrix matrix = new Matrix(n, n);
			Random random = new Random();
			for (int i = 1; i <= n; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					if (random.NextDouble() <= p)
					{
						matrix[i, j] = Complex.One;
					}
				}
			}
			return matrix;
		}

		public static Matrix Random(int m, int n, int lo, int hi)
		{
			Matrix matrix = new Matrix(m, n);
			Random random = new Random();
			for (int i = 1; i <= m; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					matrix[i, j] = new Complex(random.Next(lo, hi));
				}
			}
			return matrix;
		}

		public static Matrix Vandermonde(Complex[] x)
		{
			if (x == null || x.Length < 1)
			{
				throw new ArgumentNullException();
			}
			int num = x.Length - 1;
			Matrix matrix = new Matrix(num + 1);
			for (int i = 0; i <= num; i++)
			{
				for (int j = 0; j <= num; j++)
				{
					matrix[i + 1, j + 1] = Complex.Pow(x[i], j);
				}
			}
			return matrix;
		}

		public static Matrix[] Floyd(Matrix adjacence_matrix)
		{
			if (!adjacence_matrix.IsSquare())
			{
				throw new ArgumentException("Expected square matrix.");
			}
			if (!adjacence_matrix.IsReal())
			{
				throw new ArgumentException("Adjacence matrices are expected to be real.");
			}
			int num = adjacence_matrix.RowCount;
			Matrix matrix = adjacence_matrix.Clone();
			Matrix matrix2 = new Matrix(num);
			for (int i = 1; i <= num; i++)
			{
				for (int j = 1; j <= num; j++)
				{
					for (int k = 1; k <= num; k++)
					{
						double num2 = matrix[j, i].Re + matrix[i, k].Re;
						if (num2 < matrix[j, k].Re)
						{
							matrix[j, k].Re = num2;
							matrix2[j, k].Re = i;
						}
					}
				}
			}
			return new Matrix[2] { matrix, matrix2 };
		}

		public static ArrayList FloydPath(Matrix P, int i, int j)
		{
			if (!P.IsSquare())
			{
				throw new ArgumentException("Path matrix must be square.");
			}
			if (!P.IsReal())
			{
				throw new ArgumentException("Adjacence matrices are expected to be real.");
			}
			ArrayList arrayList = new ArrayList();
			arrayList.Add(i);
			while (P[i, j] != 0.0)
			{
				i = Convert.ToInt32(P[i, j]);
				arrayList.Add(i);
			}
			arrayList.Add(j);
			return arrayList;
		}

		public static Matrix DFS(Matrix adjacence_matrix, int root)
		{
			if (!adjacence_matrix.IsSquare())
			{
				throw new ArgumentException("Adjacence matrices are expected to be square.");
			}
			if (!adjacence_matrix.IsReal())
			{
				throw new ArgumentException("Adjacence matrices are expected to be real.");
			}
			int num = adjacence_matrix.RowCount;
			if (root < 1 || root > num)
			{
				throw new ArgumentException("Root must be a vertex of the graph, e.i. in {1, ..., n}.");
			}
			Matrix matrix = new Matrix(num);
			bool[] array = new bool[num + 1];
			Stack stack = new Stack();
			stack.Push(root);
			array[root] = true;
			ArrayList[] array2 = new ArrayList[num + 1];
			for (int i = 1; i <= num; i++)
			{
				array2[i] = new ArrayList();
				for (int j = 1; j <= num; j++)
				{
					if (adjacence_matrix[i, j].Re != 0.0 && adjacence_matrix[i, j].Im != double.PositiveInfinity)
					{
						array2[i].Add(j);
					}
				}
			}
			while (stack.Count > 0)
			{
				int num2 = (int)stack.Peek();
				if (array2[num2].Count > 0)
				{
					int num3 = (int)array2[num2][0];
					if (!array[num3])
					{
						array[num3] = true;
						matrix[num2, num3].Re = 1.0;
						stack.Push(num3);
					}
					array2[num2].RemoveAt(0);
				}
				else
				{
					stack.Pop();
				}
			}
			return matrix;
		}

		public static Matrix BFS(Matrix adjacence_matrix, int root)
		{
			if (!adjacence_matrix.IsSquare())
			{
				throw new ArgumentException("Adjacence matrices are expected to be square.");
			}
			if (!adjacence_matrix.IsReal())
			{
				throw new ArgumentException("Adjacence matrices are expected to be real.");
			}
			int num = adjacence_matrix.RowCount;
			if (root < 1 || root > num)
			{
				throw new ArgumentException("Root must be a vertex of the graph, e.i. in {1, ..., n}.");
			}
			Matrix matrix = new Matrix(num);
			bool[] array = new bool[num + 1];
			Queue queue = new Queue();
			queue.Enqueue(root);
			array[root] = true;
			ArrayList[] array2 = new ArrayList[num + 1];
			for (int i = 1; i <= num; i++)
			{
				array2[i] = new ArrayList();
				for (int j = 1; j <= num; j++)
				{
					if (adjacence_matrix[i, j].Re != 0.0 && adjacence_matrix[i, j].Re != double.PositiveInfinity)
					{
						array2[i].Add(j);
					}
				}
			}
			while (queue.Count > 0)
			{
				int num2 = (int)queue.Peek();
				if (array2[num2].Count > 0)
				{
					int num3 = (int)array2[num2][0];
					if (!array[num3])
					{
						array[num3] = true;
						matrix[num2, num3].Re = 1.0;
						queue.Enqueue(num3);
					}
					array2[num2].RemoveAt(0);
				}
				else
				{
					queue.Dequeue();
				}
			}
			return matrix;
		}

		public static Matrix ZeroOneRandom(int m, int n, double p)
		{
			Random random = new Random();
			Matrix matrix = Zeros(m, n);
			for (int i = 1; i <= m; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					if (random.NextDouble() <= p)
					{
						matrix[i, j] = Complex.One;
					}
				}
			}
			return matrix;
		}

		public static Matrix ZeroOneRandom(int n, double p)
		{
			Random random = new Random();
			Matrix matrix = Zeros(n);
			for (int i = 1; i <= n; i++)
			{
				for (int j = 1; j <= n; j++)
				{
					if (random.NextDouble() <= p)
					{
						matrix[i, j] = Complex.One;
					}
				}
			}
			return matrix;
		}

		private static Matrix[] HouseholderVector(Matrix x)
		{
			int num = x.VectorLength();
			if (num == 0)
			{
				throw new InvalidOperationException("Expected vector as argument.");
			}
			Matrix matrix = x / x.Norm();
			Matrix matrix2 = matrix.Extract(2, num, 1, 1);
			Complex complex = Dot(matrix2, matrix2);
			Matrix matrix3 = Zeros(num, 1);
			matrix3[1] = Complex.One;
			matrix3.Insert(2, 1, matrix2);
			double x2 = 0.0;
			if (complex != 0.0)
			{
				Complex complex2 = Complex.Sqrt(matrix[1] * matrix[1] + complex);
				if (matrix[1].Re <= 0.0)
				{
					matrix3[1] = matrix[1] - complex2;
				}
				else
				{
					matrix3[1] = -complex / (matrix[1] + complex2);
				}
				x2 = 2.0 * matrix3[1].Re * matrix3[1].Re / (complex.Re + matrix3[1].Re * matrix3[1].Re);
				matrix3 /= matrix3[1];
			}
			return new Matrix[2]
			{
				matrix3,
				new Matrix(x2)
			};
		}

		public static Matrix BlockMatrix(Matrix A, Matrix B, Matrix C, Matrix D)
		{
			if (A.RowCount != B.RowCount || C.RowCount != D.RowCount || A.ColumnCount != C.ColumnCount || B.ColumnCount != D.ColumnCount)
			{
				throw new ArgumentException("Matrix dimensions must agree.");
			}
			Matrix matrix = new Matrix(A.RowCount + C.RowCount, A.ColumnCount + B.ColumnCount);
			for (int i = 1; i <= matrix.rowCount; i++)
			{
				for (int j = 1; j <= matrix.columnCount; j++)
				{
					if (i <= A.RowCount)
					{
						if (j <= A.ColumnCount)
						{
							matrix[i, j] = A[i, j];
						}
						else
						{
							matrix[i, j] = B[i, j - A.ColumnCount];
						}
					}
					else if (j <= C.ColumnCount)
					{
						matrix[i, j] = C[i - A.RowCount, j];
					}
					else
					{
						matrix[i, j] = D[i - A.RowCount, j - C.ColumnCount];
					}
				}
			}
			return matrix;
		}

		public static Matrix Solve(Matrix A, Matrix b)
		{
			Matrix matrix = A.Clone();
			Matrix matrix2 = b.Clone();
			if (!matrix.IsSquare())
			{
				throw new InvalidOperationException("Cannot uniquely solve non-square equation system.");
			}
			int n = matrix.RowCount;
			Matrix matrix3 = matrix.LUSafe();
			matrix2 = matrix3 * matrix2;
			(matrix.ExtractLowerTrapeze() - Diag(matrix.DiagVector()) + Identity(n)).ForwardInsertion(matrix2);
			matrix.ExtractUpperTrapeze().BackwardInsertion(matrix2);
			return matrix2;
		}

		public Matrix Re()
		{
			Matrix matrix = new Matrix(rowCount, columnCount);
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					matrix[i, j] = new Complex(this[i, j].Re);
				}
			}
			return matrix;
		}

		public Matrix Im()
		{
			Matrix matrix = new Matrix(rowCount, columnCount);
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					matrix[i, j] = new Complex(this[i, j].Im);
				}
			}
			return matrix;
		}

		public Matrix[] HessenbergHouseholder()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot perform Hessenberg Householder decomposition of non-square matrix.");
			}
			int num = rowCount;
			Matrix matrix = Identity(num);
			Matrix matrix2 = Clone();
			Matrix[] array = new Matrix[2];
			for (int i = 1; i <= num - 2; i++)
			{
				array = HouseholderVector(matrix2.Extract(i + 1, num, i, i));
				Matrix a = Identity(i);
				Matrix matrix3 = Zeros(i, num - i);
				int n = array[0].VectorLength();
				Matrix matrix4 = Identity(n) - array[1][1, 1] * array[0] * array[0].Transpose();
				matrix2.Insert(i + 1, i, matrix4 * matrix2.Extract(i + 1, num, i, num));
				matrix2.Insert(1, i + 1, matrix2.Extract(1, num, i + 1, num) * matrix4);
				Matrix matrix5 = BlockMatrix(a, matrix3, matrix3.Transpose(), matrix4);
				matrix *= matrix5;
			}
			return new Matrix[2] { matrix2, matrix };
		}

		public Matrix Extract(int i1, int i2, int j1, int j2)
		{
			if (i2 < i1 || j2 < j1 || i1 <= 0 || j2 <= 0 || i2 > rowCount || j2 > columnCount)
			{
				throw new ArgumentException("Index exceeds matrix dimension.");
			}
			Matrix matrix = new Matrix(i2 - i1 + 1, j2 - j1 + 1);
			for (int k = i1; k <= i2; k++)
			{
				for (int l = j1; l <= j2; l++)
				{
					matrix[k - i1 + 1, l - j1 + 1] = this[k, l];
				}
			}
			return matrix;
		}

		public Matrix ExtractLowerTrapeze()
		{
			Matrix matrix = new Matrix(rowCount, columnCount);
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= i; j++)
				{
					matrix[i, j] = this[i, j];
				}
			}
			return matrix;
		}

		public Matrix ExtractUpperTrapeze()
		{
			Matrix matrix = new Matrix(rowCount, columnCount);
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = i; j <= columnCount; j++)
				{
					matrix[i, j] = this[i, j];
				}
			}
			return matrix;
		}

		public Matrix[] ColumnVectorize()
		{
			Matrix[] array = new Matrix[columnCount];
			for (int i = 1; i <= array.Length; i++)
			{
				array[i] = Column(i);
			}
			return array;
		}

		public Matrix[] RowVectorize()
		{
			Matrix[] array = new Matrix[rowCount];
			for (int i = 1; i <= array.Length; i++)
			{
				array[i] = Row(i);
			}
			return array;
		}

		public void VerticalFlip()
		{
			Values.Reverse();
		}

		public void HorizontalFlip()
		{
			for (int i = 0; i < rowCount; i++)
			{
				((ArrayList)Values[i]).Reverse();
			}
		}

		public void SwapColumns(int j1, int j2)
		{
			if (j1 <= 0 || j1 > columnCount || j2 <= 0 || j2 > columnCount)
			{
				throw new ArgumentException("Indices must be positive and <= number of cols.");
			}
			if (j1 != j2)
			{
				j1--;
				j2--;
				for (int i = 0; i < rowCount; i++)
				{
					object value = ((ArrayList)Values[i])[j1];
					((ArrayList)Values[i])[j1] = ((ArrayList)Values[i])[j2];
					((ArrayList)Values[i])[j2] = value;
				}
			}
		}

		public void SwapRows(int i1, int i2)
		{
			if (i1 <= 0 || i1 > rowCount || i2 <= 0 || i2 > rowCount)
			{
				throw new ArgumentException("Indices must be positive and <= number of rows.");
			}
			if (i1 != i2)
			{
				ArrayList value = (ArrayList)Values[--i1];
				Values[i1] = Values[--i2];
				Values[i2] = value;
			}
		}

		public void DeleteRow(int i)
		{
			if (i <= 0 || i > rowCount)
			{
				throw new ArgumentException("Index must be positive and <= number of rows.");
			}
			Values.RemoveAt(i - 1);
			rowCount--;
		}

		public void DeleteColumn(int j)
		{
			if (j <= 0 || j > columnCount)
			{
				throw new ArgumentException("Index must be positive and <= number of cols.");
			}
			for (int i = 0; i < rowCount; i++)
			{
				((ArrayList)Values[i]).RemoveAt(j - 1);
			}
			columnCount--;
		}

		public Matrix ExtractRow(int i)
		{
			Matrix result = Row(i);
			DeleteRow(i);
			return result;
		}

		public Matrix ExtractColumn(int j)
		{
			if (j <= 0 || j > columnCount)
			{
				throw new ArgumentException("Index must be positive and <= number of cols.");
			}
			Matrix result = Column(j);
			DeleteColumn(j);
			return result;
		}

		public void InsertRow(Matrix row, int i)
		{
			int num = row.VectorLength();
			if (num == 0)
			{
				throw new InvalidOperationException("Row must be a vector of length > 0.");
			}
			if (i <= 0)
			{
				throw new ArgumentException("Row index must be positive.");
			}
			if (i > rowCount)
			{
				this[i, num] = Complex.Zero;
			}
			else if (num > columnCount)
			{
				this[i, num] = Complex.Zero;
				rowCount++;
			}
			else
			{
				rowCount++;
			}
			Values.Insert(--i, new ArrayList(num));
			for (int j = 1; j <= num; j++)
			{
				((ArrayList)Values[i]).Add(row[j]);
			}
			for (int k = num; k < columnCount; k++)
			{
				((ArrayList)Values[i]).Add(Complex.Zero);
			}
		}

		public void Insert(int i, int j, Matrix M)
		{
			for (int k = 1; k <= M.rowCount; k++)
			{
				for (int l = 1; l <= M.columnCount; l++)
				{
					this[i + k - 1, j + l - 1] = M[k, l];
				}
			}
		}

		public void InsertColumn(Matrix col, int j)
		{
			int num = col.VectorLength();
			if (num == 0)
			{
				throw new InvalidOperationException("Row must be a vector of length > 0.");
			}
			if (j <= 0)
			{
				throw new ArgumentException("Row index must be positive.");
			}
			if (j > columnCount)
			{
				this[num, j] = Complex.Zero;
			}
			else
			{
				columnCount++;
			}
			if (num > rowCount)
			{
				this[num, j] = Complex.Zero;
			}
			j--;
			for (int i = 0; i < num; i++)
			{
				((ArrayList)Values[i]).Insert(j, col[i + 1]);
			}
			for (int k = num; k < rowCount; k++)
			{
				((ArrayList)Values[k]).Insert(j, 0);
			}
		}

		public Matrix Inverse()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot invert non-square matrix.");
			}
			Complex complex = Determinant();
			if (complex == Complex.Zero)
			{
				throw new InvalidOperationException("Cannot invert (nearly) singular matrix.");
			}
			int num = columnCount;
			if (num == 1)
			{
				return new Matrix(1.0 / complex);
			}
			if (IsReal() && IsOrthogonal())
			{
				return Transpose();
			}
			if (IsUnitary())
			{
				return ConjTranspose();
			}
			if (IsDiagonal())
			{
				Matrix matrix = DiagVector();
				for (int i = 1; i <= num; i++)
				{
					matrix[i] = 1.0 / matrix[i];
				}
				return Diag(matrix);
			}
			Complex[,] array = new Complex[num, num];
			for (int j = 0; j < num; j++)
			{
				for (int k = 0; k < num; k++)
				{
					array[j, k] = Math.Pow(-1.0, j + k) * Minor(k + 1, j + 1).Determinant();
				}
			}
			return new Matrix(array) / complex;
		}

		public Matrix InverseLeverrier()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot invert non-square matrix.");
			}
			int num = rowCount;
			Matrix matrix = Identity(num);
			Matrix matrix2 = matrix;
			Complex complex;
			for (int i = 1; i < num; i++)
			{
				Matrix matrix3 = this * matrix2;
				complex = 1.0 / (double)i * matrix3.Trace();
				matrix2 = complex * matrix - matrix3;
			}
			complex = (this * matrix2).Trace() / num;
			if (complex != Complex.Zero)
			{
				return matrix2 / complex;
			}
			throw new InvalidOperationException("WARNING: Matrix nearly singular or badly scaled.");
		}

		public Matrix Minor(int i, int j)
		{
			Matrix matrix = Clone();
			matrix.DeleteRow(i);
			matrix.DeleteColumn(j);
			return matrix;
		}

		public Matrix Clone()
		{
			Matrix matrix = new Matrix();
			matrix.rowCount = rowCount;
			matrix.columnCount = columnCount;
			for (int i = 0; i < rowCount; i++)
			{
				matrix.Values.Add(((ArrayList)Values[i]).Clone());
			}
			return matrix;
		}

		public Matrix DiagVector()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot get diagonal of non-square matrix.");
			}
			Matrix matrix = new Matrix(columnCount, 1);
			for (int i = 1; i <= columnCount; i++)
			{
				matrix[i] = this[i, i];
			}
			return matrix;
		}

		public Matrix Column(int j)
		{
			Matrix matrix = new Matrix(rowCount, 1);
			for (int i = 1; i <= rowCount; i++)
			{
				matrix[i] = this[i, j];
			}
			return matrix;
		}

		public Matrix Row(int i)
		{
			if (i <= 0 || i > rowCount)
			{
				throw new ArgumentException("Index exceed matrix dimension.");
			}
			Matrix matrix = new Matrix(columnCount, 1);
			for (int j = 1; j <= columnCount; j++)
			{
				matrix[j] = this[i, j];
			}
			return matrix;
		}

		public Matrix Transpose()
		{
			Matrix matrix = new Matrix(columnCount, rowCount);
			for (int i = 1; i <= columnCount; i++)
			{
				for (int j = 1; j <= rowCount; j++)
				{
					matrix[i, j] = this[j, i];
				}
			}
			return matrix;
		}

		public Matrix Conjugate()
		{
			Matrix matrix = new Matrix(rowCount, columnCount);
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					matrix[i, j] = Complex.Conj(this[i, j]);
				}
			}
			return matrix;
		}

		public Matrix ConjTranspose()
		{
			return Transpose().Conjugate();
		}

		public void LU()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot perform LU-decomposition of non-square matrix.");
			}
			int num = columnCount;
			for (int i = 1; i <= num; i++)
			{
				if (this[i, i] == 0.0)
				{
					throw new DivideByZeroException("Warning: Matrix badly scaled or close to singular. Try LUSafe() instead. Check if det != 0.");
				}
				for (int j = 1; j < i; j++)
				{
					for (int k = j + 1; k <= num; k++)
					{
						this[k, i] -= this[k, j] * this[j, i];
					}
				}
				for (int l = i + 1; l <= num; l++)
				{
					this[l, i] /= this[i, i];
				}
			}
		}

		public Matrix LUSafe()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot perform LU-decomposition of non-square matrix.");
			}
			int num = columnCount;
			Matrix matrix = Identity(num);
			for (int i = 1; i <= num; i++)
			{
				if (i < num)
				{
					int num2 = i;
					for (int j = i + 1; j <= num; j++)
					{
						if (Complex.Abs(this[j, i]) > Complex.Abs(this[num2, i]))
						{
							num2 = j;
						}
					}
					if (num2 > i)
					{
						matrix.SwapRows(i, num2);
						SwapRows(i, num2);
					}
					if (this[i, i] == 0.0)
					{
						throw new DivideByZeroException("Warning: Matrix close to singular.");
					}
				}
				for (int k = 1; k < i; k++)
				{
					for (int l = k + 1; l <= num; l++)
					{
						this[l, i] -= this[l, k] * this[k, i];
					}
				}
				for (int m = i + 1; m <= num; m++)
				{
					this[m, i] /= this[i, i];
				}
			}
			return matrix;
		}

		public void Cholesky()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot perform Cholesky decomposition of non-square matrix.");
			}
			if (!IsSPD())
			{
				throw new InvalidOperationException("Cannot perform Cholesky decomposition of matrix not being symmetric positive definite.");
			}
			int num = rowCount;
			for (int i = 1; i < num; i++)
			{
				this[i, i] = Complex.Sqrt(this[i, i]);
				for (int j = 1; j <= num - i; j++)
				{
					this[i + j, i] /= this[i, i];
				}
				for (int k = i + 1; k <= num; k++)
				{
					for (int l = 0; l <= num - k; l++)
					{
						this[k + l, k] -= this[k + l, i] * this[k, i];
					}
				}
			}
			this[num, num] = Complex.Sqrt(this[num, num]);
		}

		public void CholeskyUndo()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot undo cholesky decomposition on non-square matrix.");
			}
			this[1, 1] = Sqr(this[1, 1]);
			for (int i = 2; i <= rowCount; i++)
			{
				Complex zero = Complex.Zero;
				for (int j = 1; j <= i - 1; j++)
				{
					zero += Sqr(this[i, j]);
				}
				this[i, i] = Sqr(this[i, i]) + zero;
			}
			SymmetrizeDown();
		}

		public void ForwardInsertion(Matrix b)
		{
			if (!IsLowerTriangular())
			{
				throw new InvalidOperationException("Cannot perform forward insertion for matrix not being lower triangular.");
			}
			if (DiagProd() == 0.0)
			{
				throw new InvalidOperationException("Warning: Matrix is nearly singular.");
			}
			int num = rowCount;
			if (b.VectorLength() != num)
			{
				throw new ArgumentException("Parameter must vector of the same height as matrix.");
			}
			for (int i = 1; i <= num - 1; i++)
			{
				b[i] /= this[i, i];
				for (int j = 1; j <= num - i; j++)
				{
					b[i + j] -= b[i] * this[i + j, i];
				}
			}
			b[num] /= this[num, num];
		}

		public void BackwardInsertion(Matrix b)
		{
			if (!IsUpperTriangular())
			{
				throw new InvalidOperationException("Cannot perform backward insertion for matrix not being upper triangular.");
			}
			if (DiagProd() == 0.0)
			{
				throw new InvalidOperationException("Warning: Matrix is nearly singular.");
			}
			int num = rowCount;
			if (b.VectorLength() != num)
			{
				throw new ArgumentException("Parameter must vector of the same height as matrix.");
			}
			for (int num2 = num; num2 >= 2; num2--)
			{
				b[num2] /= this[num2, num2];
				for (int i = 1; i <= num2 - 1; i++)
				{
					b[i] -= b[num2] * this[i, num2];
				}
			}
			b[1] /= this[1, 1];
		}

		public void SymmetrizeDown()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot symmetrize non-square matrix.");
			}
			for (int i = 1; i <= columnCount; i++)
			{
				for (int j = i + 1; j <= columnCount; j++)
				{
					this[j, i] = this[i, j];
				}
			}
		}

		public void SymmetrizeUp()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot symmetrize non-square matrix.");
			}
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = i + 1; j <= columnCount; j++)
				{
					this[i, j] = this[j, i];
				}
			}
		}

		public Matrix[] QRGramSchmidt()
		{
			int num = rowCount;
			int num2 = columnCount;
			Matrix matrix = Clone();
			Matrix matrix2 = new Matrix(num, num2);
			Matrix matrix3 = new Matrix(num2, num2);
			for (int i = 1; i <= num; i++)
			{
				matrix2[i, 1] = matrix[i, 1];
			}
			matrix3[1, 1] = Complex.One;
			for (int j = 1; j <= num2; j++)
			{
				matrix3[j, j] = new Complex(matrix.Column(j).Norm());
				for (int k = 1; k <= num; k++)
				{
					matrix2[k, j] = matrix[k, j] / matrix3[j, j];
				}
				for (int l = j + 1; l <= num2; l++)
				{
					matrix3[j, l] = Dot(matrix2.Column(j), matrix.Column(l));
					for (int m = 1; m <= num; m++)
					{
						matrix[m, l] -= matrix2[m, j] * matrix3[j, l];
					}
				}
			}
			return new Matrix[2] { matrix2, matrix3 };
		}

		public Matrix Eigenvalues()
		{
			return QRIterationBasic(40).DiagVector();
		}

		public Matrix Eigenvector(Complex eigenvalue)
		{
			throw new NotImplementedException();
		}

		public Matrix SolveCG(Matrix b)
		{
			if (!IsSPD())
			{
				throw new InvalidOperationException("CG method only works for spd matrices.");
			}
			if (!IsReal())
			{
				throw new InvalidOperationException("CG method only works for real matrices.");
			}
			int m = rowCount;
			int num = 150;
			double num2 = 1E-06;
			Matrix matrix = Ones(m, 1);
			Matrix matrix2 = b - this * matrix;
			Matrix matrix3 = matrix2;
			double num3 = matrix2.Norm();
			num3 *= num3;
			num2 *= num2;
			Matrix matrix4 = Zeros(m, 1);
			if (num3 <= num2)
			{
				return matrix;
			}
			for (int i = 0; i < num; i++)
			{
				matrix4 = this * matrix3;
				double re = Dot(matrix4, matrix3).Re;
				if (Math.Abs(re) <= num2)
				{
					return matrix;
				}
				double num4 = num3 / re;
				matrix += num4 * matrix3;
				matrix2 -= num4 * matrix4;
				double num5 = num3;
				num3 = matrix2.Norm();
				num3 *= num3;
				if (num3 <= num2)
				{
					return matrix;
				}
				matrix3 = matrix2 + num3 / num5 * matrix3;
			}
			return matrix;
		}

		public Matrix QRIterationBasic(int max_iterations)
		{
			if (!IsReal())
			{
				throw new InvalidOperationException("Basic QR iteration is possible only for real matrices.");
			}
			Matrix matrix = Clone();
			Matrix[] array = new Matrix[2];
			for (int i = 0; i < max_iterations; i++)
			{
				array = matrix.QRGramSchmidt();
				matrix = array[1] * array[0];
			}
			return matrix;
		}

		public Matrix QRIterationHessenberg(int max_iterations)
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot perform QR iteration of non-square matrix.");
			}
			int num = RowCount;
			Matrix[] array = HessenbergHouseholder();
			Matrix matrix = array[0];
			for (int i = 1; i <= max_iterations; i++)
			{
				Matrix[] array2 = matrix.QRGivens();
				matrix = array2[1];
				for (int j = 1; j <= num - 1; j++)
				{
					matrix.Gacol(array2[2][j], array2[3][j], 1, j + 1, j, j + 1);
				}
			}
			return matrix;
		}

		public Matrix[] QRGivens()
		{
			Matrix matrix = Clone();
			int num = matrix.ColumnCount;
			Matrix matrix2 = Zeros(num - 1, 1);
			Matrix matrix3 = Zeros(num - 1, 1);
			for (int i = 1; i <= num - 1; i++)
			{
				Complex[] array = GivensCS(matrix[i, i], matrix[i + 1, i]);
				matrix2[i] = array[0];
				matrix3[i] = array[1];
				Garow(matrix2[i], matrix3[i], 1, i + 1, i, i + 1);
			}
			return new Matrix[4]
			{
				GivProd(matrix2, matrix3, num),
				matrix,
				matrix2,
				matrix3
			};
		}

		private Matrix GivProd(Matrix c, Matrix s, int n)
		{
			int num = n - 1;
			int num2 = n - 2;
			Matrix matrix = Eye(n);
			matrix[num, num] = c[num];
			matrix[n, n] = c[num];
			matrix[num, n] = s[num];
			matrix[n, num] = -s[num];
			for (int num3 = num2; num3 >= 1; num3--)
			{
				int num4 = num3 + 1;
				matrix[num3, num3] = c[num3];
				matrix[num4, num3] = -s[num3];
				Matrix matrix2 = matrix.Extract(num4, num4, num4, n);
				matrix.Insert(num3, num4, s[num3] * matrix2);
				matrix.Insert(num4, num4, c[num3] * matrix2);
			}
			return matrix;
		}

		private void Garow(Complex c, Complex s, int i, int k, int j1, int j2)
		{
			for (int l = j1; l <= j2; l++)
			{
				Complex complex = this[i, l];
				Complex complex2 = this[k, l];
				this[i, l] = c * complex - s * complex2;
				this[k, l] = s * complex + c * complex2;
			}
		}

		public void Gacol(Complex c, Complex s, int j1, int j2, int i, int k)
		{
			for (int l = j1; l <= j2; l++)
			{
				Complex complex = this[l, i];
				Complex complex2 = this[l, k];
				this[l, i] = c * complex - s * complex2;
				this[l, k] = s * complex + c * complex2;
			}
		}

		private Complex[] GivensCS(Complex xi, Complex xk)
		{
			Complex zero = Complex.Zero;
			Complex complex = Complex.Zero;
			if (xk == 0.0)
			{
				zero = Complex.One;
			}
			else if (Complex.Abs(xk) > Complex.Abs(xi))
			{
				Complex complex2 = -xi / xk;
				complex = 1.0 / Complex.Sqrt(1.0 + complex2 * complex2);
				zero = complex * complex2;
			}
			else
			{
				Complex complex3 = -xk / xi;
				zero = 1.0 / Complex.Sqrt(1.0 + complex3 * complex3);
				complex = zero * complex3;
			}
			return new Complex[2] { zero, complex };
		}

		public Complex Determinant()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot calc determinant of non-square matrix.");
			}
			if (columnCount == 1)
			{
				return this[1, 1];
			}
			if (IsTrapeze())
			{
				return DiagProd();
			}
			Matrix matrix = Clone();
			Matrix matrix2 = matrix.LUSafe();
			return matrix2.Signum() * matrix.DiagProd();
		}

		public double ColumnSumNorm()
		{
			return TaxiNorm();
		}

		public double RowSumNorm()
		{
			return MaxNorm();
		}

		public Complex Permanent()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot compute permanent of non-square matrix.");
			}
			if (HasZeroRowOrColumn())
			{
				return Complex.Zero;
			}
			if (this == Ones(rowCount))
			{
				return new Complex(Factorial(rowCount));
			}
			if (IsPermutation())
			{
				return Complex.One;
			}
			Complex zero = Complex.Zero;
			int minRow = GetMinRow();
			int minColumn = GetMinColumn();
			if (AbsRowSum(minRow) < AbsColumnSum(minColumn))
			{
				for (int i = 1; i <= columnCount; i++)
				{
					if (this[minRow, i] != 0.0)
					{
						zero += this[minRow, i] * Minor(minRow, i).Permanent();
					}
				}
			}
			else
			{
				for (int j = 1; j <= rowCount; j++)
				{
					if (this[j, minColumn] != 0.0)
					{
						zero += this[j, minColumn] * Minor(j, minColumn).Permanent();
					}
				}
			}
			return zero;
		}

		public int GetMinRow()
		{
			double num = AbsRowSum(1);
			int result = 1;
			for (int i = 2; i <= rowCount; i++)
			{
				double num2 = AbsRowSum(i);
				if (num2 < num)
				{
					num = num2;
					result = i;
				}
			}
			return result;
		}

		public int GetMinColumn()
		{
			double num = AbsColumnSum(1);
			int result = 1;
			for (int i = 2; i <= columnCount; i++)
			{
				double num2 = AbsColumnSum(i);
				if (num2 < num)
				{
					num = num2;
					result = i;
				}
			}
			return result;
		}

		private double Factorial(int x)
		{
			double num = 1.0;
			for (int i = 2; i <= x; i++)
			{
				num *= (double)i;
			}
			return num;
		}

		public double Signum()
		{
			double num = 1.0;
			int num2 = rowCount;
			int num3 = 0;
			for (int i = 1; i < num2; i++)
			{
				double num4;
				for (num4 = 1.0; num4 < (double)num2 && this[i, (int)num4] != Complex.One; num4 += 1.0)
				{
					num3 = num3++;
				}
				for (int j = i + 1; j <= num2; j++)
				{
					double num5;
					for (num5 = 1.0; num5 <= (double)num2 && this[j, (int)num5] != Complex.One; num5 += 1.0)
					{
					}
					num *= (num4 - num5) / (double)(i - j);
				}
			}
			return num;
		}

		public double Condition()
		{
			return TaxiNorm() * Inverse().TaxiNorm();
		}

		public double Condition(int p)
		{
			return PNorm(p) * Inverse().PNorm(p);
		}

		public double ConditionFro()
		{
			return FrobeniusNorm() * Inverse().FrobeniusNorm();
		}

		public double PNorm(double p)
		{
			if (p <= 0.0)
			{
				throw new ArgumentException("Argument must be greater than zero.");
			}
			if (p == 1.0)
			{
				return TaxiNorm();
			}
			if (p == double.PositiveInfinity)
			{
				return MaxNorm();
			}
			int num = VectorLength();
			if (num == 0)
			{
				throw new InvalidOperationException("Cannot calc p-norm of matrix.");
			}
			double num2 = 0.0;
			for (int i = 1; i <= num; i++)
			{
				num2 += Math.Pow(Complex.Abs(this[i]), p);
			}
			return Math.Pow(num2, 1.0 / p);
		}

		public double Norm()
		{
			return PNorm(2.0);
		}

		public double FrobeniusNorm()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot compute frobenius norm of non-square matrix.");
			}
			int num = columnCount;
			double num2 = 0.0;
			for (int i = 1; i <= num; i++)
			{
				for (int j = 1; j <= num; j++)
				{
					num2 += (this[i, j] * Complex.Conj(this[i, j])).Re;
				}
			}
			return Math.Sqrt(num2);
		}

		public double TaxiNorm()
		{
			double num = 0.0;
			int num2 = VectorLength();
			if (num2 != 0)
			{
				for (int i = 1; i <= num2; i++)
				{
					num += Complex.Abs(this[i]);
				}
			}
			else
			{
				double num3 = 0.0;
				for (int j = 1; j <= columnCount; j++)
				{
					num3 = AbsColumnSum(j);
					if (num3 > num)
					{
						num = num3;
					}
				}
			}
			return num;
		}

		public double MaxNorm()
		{
			double num = 0.0;
			double num2 = 0.0;
			int num3 = VectorLength();
			if (num3 != 0)
			{
				for (int i = 1; i <= num3; i++)
				{
					num2 = Complex.Abs(this[i]);
					if (num2 > num)
					{
						num = num2;
					}
				}
			}
			else
			{
				for (int j = 1; j <= rowCount; j++)
				{
					num2 = AbsRowSum(j);
					if (num2 > num)
					{
						num = num2;
					}
				}
			}
			return num;
		}

		public Complex ColumnSum(int j)
		{
			if (j <= 0 || j > columnCount)
			{
				throw new ArgumentException("Index out of range.");
			}
			Complex zero = Complex.Zero;
			j--;
			for (int i = 0; i < rowCount; i++)
			{
				zero += (Complex)((ArrayList)Values[i])[j];
			}
			return zero;
		}

		public double AbsColumnSum(int j)
		{
			if (j <= 0 || j > columnCount)
			{
				throw new ArgumentException("Index out of range.");
			}
			double num = 0.0;
			for (int i = 1; i <= rowCount; i++)
			{
				num += Complex.Abs(this[i, j]);
			}
			return num;
		}

		public Complex RowSum(int i)
		{
			if (i <= 0 || i > rowCount)
			{
				throw new ArgumentException("Index out of range.");
			}
			Complex zero = Complex.Zero;
			ArrayList arrayList = (ArrayList)Values[i - 1];
			for (int j = 0; j < columnCount; j++)
			{
				zero += (Complex)arrayList[j];
			}
			return zero;
		}

		public double AbsRowSum(int i)
		{
			if (i <= 0 || i > rowCount)
			{
				throw new ArgumentException("Index out of range.");
			}
			double num = 0.0;
			for (int j = 1; j <= columnCount; j++)
			{
				num += Complex.Abs(this[i, j]);
			}
			return num;
		}

		public Complex DiagProd()
		{
			Complex one = Complex.One;
			int num = Math.Min(rowCount, columnCount);
			for (int i = 1; i <= num; i++)
			{
				one *= this[i, i];
			}
			return one;
		}

		public Complex Trace()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Cannot calc trace of non-square matrix.");
			}
			Complex zero = Complex.Zero;
			for (int i = 1; i <= rowCount; i++)
			{
				zero += this[i, i];
			}
			return zero;
		}

		private Complex Sqr(Complex x)
		{
			return x * x;
		}

		public bool IsNormal()
		{
			return this * ConjTranspose() == ConjTranspose() * this;
		}

		public bool IsUnitary()
		{
			if (!IsSquare())
			{
				return false;
			}
			return ConjTranspose() * this == Identity(rowCount);
		}

		public bool IsHermitian()
		{
			if (!IsSquare())
			{
				return false;
			}
			return ConjTranspose() == this;
		}

		public bool IsReal()
		{
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					if (!this[i, j].IsReal())
					{
						return false;
					}
				}
			}
			return true;
		}

		public bool IsSymmetricPositiveDefinite()
		{
			return IsSymmetric() && Definiteness() == DefinitenessType.PositiveDefinite;
		}

		public bool IsSPD()
		{
			return IsSymmetricPositiveDefinite();
		}

		public DefinitenessType Definiteness()
		{
			if (!IsSquare())
			{
				throw new InvalidOperationException("Definiteness undefined for non-square matrices.");
			}
			if (this == Zeros(rowCount, columnCount))
			{
				return DefinitenessType.Indefinite;
			}
			if (!IsSymmetric())
			{
				throw new InvalidOperationException("This test works only for symmetric matrices.");
			}
			if (!IsReal())
			{
				throw new InvalidOperationException("This test only works for real matrices.");
			}
			int num = rowCount;
			Matrix[] array = new Matrix[num + 1];
			for (int i = 0; i <= num; i++)
			{
				array[i] = Zeros(num, 1);
			}
			array[1] = Column(1);
			for (int j = 2; j <= num; j++)
			{
				Matrix matrix = Column(j);
				Matrix matrix2 = Zeros(num, 1);
				for (int k = 1; k < j; k++)
				{
					matrix2 += array[k] * Dot(matrix, this * array[k]) / Dot(array[k], this * array[k]);
				}
				array[j] = matrix - matrix2;
			}
			bool flag = true;
			for (int l = 1; l < num; l++)
			{
				Complex complex = Dot(array[l], this * array[l]) * Dot(array[l + 1], this * array[l + 1]);
				if (complex == 0.0)
				{
					flag = false;
				}
				else if (complex.Re < 0.0)
				{
					return DefinitenessType.Indefinite;
				}
			}
			if (Dot(array[1], this * array[1]).Re >= 0.0)
			{
				if (flag)
				{
					return DefinitenessType.PositiveDefinite;
				}
				return DefinitenessType.PositiveSemidefinite;
			}
			if (flag)
			{
				return DefinitenessType.NegativeDefinite;
			}
			return DefinitenessType.NegativeSemidefinite;
		}

		public bool HasZeroRowOrColumn()
		{
			for (int i = 1; i <= rowCount; i++)
			{
				if (AbsRowSum(i) == 0.0)
				{
					return true;
				}
			}
			for (int j = 1; j <= columnCount; j++)
			{
				if (AbsColumnSum(j) == 0.0)
				{
					return true;
				}
			}
			return false;
		}

		public bool IsZeroOneMatrix()
		{
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					if (this[i, j] != Complex.Zero && this[i, j] != Complex.One)
					{
						return false;
					}
				}
			}
			return true;
		}

		public bool IsPermutation()
		{
			return !IsSquare() && IsZeroOneMatrix() && IsInvolutary();
		}

		public bool IsDiagonal()
		{
			return Clone() - Diag(DiagVector()) == Zeros(rowCount, columnCount);
		}

		public int VectorLength()
		{
			if (columnCount > 1 && rowCount > 1)
			{
				return 0;
			}
			return Math.Max(columnCount, rowCount);
		}

		public bool IsSquare()
		{
			return columnCount == rowCount;
		}

		public bool IsInvolutary()
		{
			return this * this == Identity(rowCount);
		}

		public bool IsSymmetric()
		{
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					if (this[i, j] != this[j, i])
					{
						return false;
					}
				}
			}
			return true;
		}

		public bool IsOrthogonal()
		{
			return IsSquare() && this * Transpose() == Identity(rowCount);
		}

		public bool IsTrapeze()
		{
			return IsUpperTrapeze() || IsLowerTrapeze();
		}

		public bool IsTriangular()
		{
			return IsLowerTriangular() || IsUpperTriangular();
		}

		public bool IsUpperTriangular()
		{
			return IsSquare() && IsUpperTrapeze();
		}

		public bool IsLowerTriangular()
		{
			return IsSquare() && IsLowerTrapeze();
		}

		public bool IsUpperTrapeze()
		{
			for (int i = 1; i <= columnCount; i++)
			{
				for (int j = i + 1; j <= rowCount; j++)
				{
					if (this[j, i] != 0.0)
					{
						return false;
					}
				}
			}
			return true;
		}

		public bool IsLowerTrapeze()
		{
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = i + 1; j <= columnCount; j++)
				{
					if (this[i, j] != 0.0)
					{
						return false;
					}
				}
			}
			return true;
		}

		public override string ToString()
		{
			string text = string.Empty;
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					Complex complex = this[i, j];
					text = ((complex.Re != double.PositiveInfinity && complex.Re != double.NegativeInfinity && complex.Im != double.PositiveInfinity && complex.Im != double.NegativeInfinity) ? ((complex.Re != double.NaN && complex.Im != double.NaN) ? (text + complex.ToString()) : (text + "?")) : (text + "oo"));
					text += ";\t";
				}
				text = text + "\\" + Environment.NewLine;
			}
			return text;
		}

		public string ToString(string format)
		{
			string text = string.Empty;
			for (int i = 1; i <= rowCount; i++)
			{
				for (int j = 1; j <= columnCount; j++)
				{
					Complex complex = this[i, j];
					text = ((complex.Re != double.PositiveInfinity && complex.Re != double.NegativeInfinity && complex.Im != double.PositiveInfinity && complex.Im != double.NegativeInfinity) ? ((complex.Re != double.NaN && complex.Im != double.NaN) ? (text + complex.ToString(format)) : (text + "?")) : (text + "oo"));
					text += ";\t";
				}
				text = text + "\\" + Environment.NewLine;
			}
			return text;
		}

		public override bool Equals(object obj)
		{
			return obj.ToString() == ToString();
		}

		public override int GetHashCode()
		{
			return -1;
		}

		public static bool operator ==(Matrix A, Matrix B)
		{
			if (A.RowCount != B.RowCount || A.ColumnCount != B.ColumnCount)
			{
				return false;
			}
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					if (A[i, j] != B[i, j])
					{
						return false;
					}
				}
			}
			return true;
		}

		public static bool operator !=(Matrix A, Matrix B)
		{
			return !(A == B);
		}

		public static Matrix operator +(Matrix A, Matrix B)
		{
			if (A.RowCount != B.RowCount || A.ColumnCount != B.ColumnCount)
			{
				throw new ArgumentException("Matrices must be of the same dimension.");
			}
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					A[i, j] += B[i, j];
				}
			}
			return A;
		}

		public static Matrix operator -(Matrix A, Matrix B)
		{
			if (A.RowCount != B.RowCount || A.ColumnCount != B.ColumnCount)
			{
				throw new ArgumentException("Matrices must be of the same dimension.");
			}
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					A[i, j] -= B[i, j];
				}
			}
			return A;
		}

		public static Matrix operator -(Matrix A)
		{
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					A[i, j] = -A[i, j];
				}
			}
			return A;
		}

		public static Matrix operator *(Matrix A, Matrix B)
		{
			if (A.ColumnCount != B.RowCount)
			{
				throw new ArgumentException("Inner matrix dimensions must agree.");
			}
			Matrix matrix = new Matrix(A.RowCount, B.ColumnCount);
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= B.ColumnCount; j++)
				{
					matrix[i, j] = Dot(A.Row(i), B.Column(j));
				}
			}
			return matrix;
		}

		public static Matrix operator *(Matrix A, Complex x)
		{
			Matrix matrix = new Matrix(A.rowCount, A.columnCount);
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					matrix[i, j] = A[i, j] * x;
				}
			}
			return matrix;
		}

		public static Matrix operator *(Complex x, Matrix A)
		{
			Matrix matrix = new Matrix(A.RowCount, A.ColumnCount);
			for (int i = 1; i <= A.RowCount; i++)
			{
				for (int j = 1; j <= A.ColumnCount; j++)
				{
					matrix[i, j] = A[i, j] * x;
				}
			}
			return matrix;
		}

		public static Matrix operator *(Matrix A, double x)
		{
			return new Complex(x) * A;
		}

		public static Matrix operator *(double x, Matrix A)
		{
			return new Complex(x) * A;
		}

		public static Matrix operator /(Matrix A, Complex x)
		{
			return 1.0 / x * A;
		}

		public static Matrix operator /(Matrix A, double x)
		{
			return new Complex(1.0 / x) * A;
		}

		public static Matrix operator ^(Matrix A, int k)
		{
			if (k < 0)
			{
				if (A.IsSquare())
				{
					return A.InverseLeverrier() ^ -k;
				}
				throw new InvalidOperationException("Cannot take non-square matrix to the power of zero.");
			}
			switch (k)
			{
			case 0:
				if (A.IsSquare())
				{
					return Identity(A.RowCount);
				}
				throw new InvalidOperationException("Cannot take non-square matrix to the power of zero.");
			case 1:
				if (A.IsSquare())
				{
					return A;
				}
				throw new InvalidOperationException("Cannot take non-square matrix to the power of one.");
			default:
			{
				Matrix result = A;
				for (int i = 1; i < k; i++)
				{
					result *= A;
				}
				return result;
			}
			}
		}
	}
}