本文整理汇总了C#中Matrix.Initialize方法的典型用法代码示例。如果您正苦于以下问题：C# Matrix.Initialize方法的具体用法？C# Matrix.Initialize怎么用？C# Matrix.Initialize使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Matrix的用法示例。
在下文中一共展示了Matrix.Initialize方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: Main
static void Main(string[] args)
{
Matrix matrix = new Matrix();
matrix.Initialize();
bool[,] passedCells = new bool[Matrix.SIZE, Matrix.SIZE];
List<Matrix> result = new List<Matrix>();
for (int row = 0; row < Matrix.SIZE; row++)
{
for (int col = 0; col < Matrix.SIZE; col++)
{
if (!passedCells[row, col] && matrix[row, col] != Matrix.OBSTACLE_SYMBOL)
{
Console.WriteLine(1);
Program.currCellCount = 0;
Program.FindAllAreaOfEmptyCells(matrix, row, col, passedCells);
Matrix currMatrix = new Matrix();
Matrix.Copy(matrix,currMatrix);
result.Add(currMatrix);
matrix.Reset();
}
}
}
Console.WriteLine();
Console.WriteLine("Possible areas:");
foreach (Matrix currMatrix in result)
{
Console.WriteLine();
currMatrix.PrintOnConsole();
}
}示例2: MultiplyByMatrix_MATLAB
/*
需要添加matlab的运行库作为引用
/// <summary>
/// 使用matlab库运算矩阵乘法
/// </summary>
/// <param name="m"></param>
/// <returns></returns>
public Matrix MultiplyByMatrix_MATLAB(Matrix m)
{
MLAppClass matlab = new MLAppClass();
double[,] pia = new double[m.nRows, m.nColumns];
double[,] pib = new double[this.nRows, this.nColumns];
matlab.PutFullMatrix("A", "base", m.Cell, pia);
matlab.PutFullMatrix("B", "base", this.Cell, pib);
matlab.Execute("C = A*B;");
Matrix result =new Matrix(m.nRows, this.nColumns);
System.Array pir = new double[m.nRows,this.nColumns];
System.Array prr = new double[m.nRows, this.nColumns];
matlab.GetFullMatrix("C", "base",ref prr,ref pir);
for(int i =0;i<prr.GetLength(0);i++)
{
for (int j = 0; j < prr.GetLength(1); j++) result.Cell[i, j] = (double)(prr.GetValue(i, j));
}
return result;
}
*/
/// <summary>
/// 求实对称矩阵特征值与特征向量的雅可比法(来自http://download.csdn.net/detail/jian_5030624/1082723)
/// </summary>
/// <param name="dblEigenValue">一维数组，长度为矩阵的阶数，返回时存放特征值</param>
/// <param name="mtxEigenVector">返回时存放特征向量矩阵，其中第i列为与数组dblEigenValue中第i个特征值对应的特征向量</param>
/// <param name="nMaxIt">迭代次数</param>
/// <param name="eps">计算精度</param>
/// <returns>bool型，求解是否成功</returns>
public bool ComputeEvJacobi(double[] dblEigenValue, Matrix mtxEigenVector, int nMaxIt, double eps)
{
int i, j, p = 0, q = 0, l;
//int u, w, t, s;
double fm, cn, sn, omega, x, y, d;
//SyncElements();
mtxEigenVector.Initialize(this.numColumns, this.numColumns);
l = 1;
for (i = 0; i <= numColumns - 1; i++)
{
mtxEigenVector.Cell[i, i] = 1.0;
for (j = 0; j <= numColumns - 1; j++)
if (i != j)
mtxEigenVector.Cell[i, j] = 0.0;
}
while (true)
{
fm = 0.0;
for (i = 1; i <= numColumns - 1; i++)
{
for (j = 0; j <= i - 1; j++)
{
d = Math.Abs(cell[i, j]);
if ((i != j) && (d > fm))
{
fm = d;
p = i;
q = j;
}
}
}
if (fm < eps || l>nMaxIt)
{
//this.SyncCell();
for (i = 0; i < numColumns; ++i)
dblEigenValue[i] = cell[i, i];
//mtxEigenVector.SyncCell();
return true;
}
/*if (l > nMaxIt)
return false;*/
l = l + 1;
//u = p * numColumns + q;
// w = p * numColumns + p;
//t = q * numColumns + p;
//s = q * numColumns + q;
x = -cell[p,q];
y = (cell[q,q] - cell[p,p]) / 2.0;
omega = x / Math.Sqrt(x * x + y * y);
if (y < 0.0)
omega = -omega;
sn = 1.0 + Math.Sqrt(1.0 - omega * omega);
sn = omega / Math.Sqrt(2.0 * sn);
cn = Math.Sqrt(1.0 - sn * sn);
fm = cell[p, p];
//.........这里部分代码省略.........
示例3: Matrix
public static Matrix operator *(Matrix lMat,Matrix rMat)
{
if(lMat.Columns == rMat.Rows)
{
Matrix temp = new Matrix(lMat.Rows,rMat.Columns);
temp.Initialize();
for(int rw=0;rw<temp.Rows;rw++)
for(int cl=0;cl<temp.Columns;cl++)
for(int cl2=0;cl2<lMat.Columns;cl2++)
temp.m_MatArr[rw,cl] =temp.m_MatArr[rw,cl] + lMat.m_MatArr[rw,cl2] * rMat.m_MatArr[cl2,cl];
return temp;
}
else
{
throw new Exception("Number of Columns of Left Matrix must be equal to Rows of Right Matrix.");
}
}