本文整理汇总了C#中System.Matrix.CopyTo方法的典型用法代码示例。如果您正苦于以下问题：C# Matrix.CopyTo方法的具体用法？C# Matrix.CopyTo怎么用？C# Matrix.CopyTo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System.Matrix的用法示例。
在下文中一共展示了Matrix.CopyTo方法的10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: Create
/// <summary>
/// Initializes a new instance of the <see cref="UserEvd"/> class. This object will compute the
/// the eigenvalue decomposition when the constructor is called and cache it's decomposition.
/// </summary>
/// <param name="matrix">The matrix to factor.</param>
/// <exception cref="ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
/// <exception cref="ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
public static UserEvd Create(Matrix<float> matrix)
{
if (matrix.RowCount != matrix.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSquare);
}
var order = matrix.RowCount;
// Initialize matricies for eigenvalues and eigenvectors
var eigenVectors = Matrix<float>.Build.SameAs(matrix, order, order);
var blockDiagonal = Matrix<float>.Build.SameAs(matrix, order, order);
var eigenValues = new LinearAlgebra.Complex.DenseVector(order);
var isSymmetric = true;
for (var i = 0; isSymmetric && i < order; i++)
{
for (var j = 0; isSymmetric && j < order; j++)
{
isSymmetric &= matrix.At(i, j) == matrix.At(j, i);
}
}
var d = new float[order];
var e = new float[order];
if (isSymmetric)
{
matrix.CopyTo(eigenVectors);
d = eigenVectors.Row(order - 1).ToArray();
SymmetricTridiagonalize(eigenVectors, d, e, order);
SymmetricDiagonalize(eigenVectors, d, e, order);
}
else
{
var matrixH = matrix.ToArray();
NonsymmetricReduceToHessenberg(eigenVectors, matrixH, order);
NonsymmetricReduceHessenberToRealSchur(eigenVectors, matrixH, d, e, order);
}
for (var i = 0; i < order; i++)
{
blockDiagonal.At(i, i, d[i]);
if (e[i] > 0)
{
blockDiagonal.At(i, i + 1, e[i]);
}
else if (e[i] < 0)
{
blockDiagonal.At(i, i - 1, e[i]);
}
}
for (var i = 0; i < order; i++)
{
eigenValues[i] = new Complex(d[i], e[i]);
}
return new UserEvd(eigenVectors, eigenValues, blockDiagonal, isSymmetric);
}示例2: DoAdd
/// <summary>
/// Adds another matrix to this matrix.
/// </summary>
/// <param name="other">The matrix to add to this matrix.</param>
/// <param name="result">The matrix to store the result of the addition.</param>
/// <exception cref="ArgumentOutOfRangeException">If the two matrices don't have the same dimensions.</exception>
protected override void DoAdd(Matrix<Complex> other, Matrix<Complex> result)
{
// diagonal + diagonal = diagonal
var diagOther = other as DiagonalMatrix;
var diagResult = result as DiagonalMatrix;
if (diagOther != null && diagResult != null)
{
Control.LinearAlgebraProvider.AddArrays(_data, diagOther._data, diagResult._data);
return;
}
other.CopyTo(result);
for (int i = 0; i < _data.Length; i++)
{
result.At(i, i, result.At(i, i) + _data[i]);
}
}示例3: Solve
/// <summary>
/// Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
/// </summary>
/// <param name="input">The right hand side <see cref="Matrix{T}"/>, <b>B</b>.</param>
/// <param name="result">The left hand side <see cref="Matrix{T}"/>, <b>X</b>.</param>
public override void Solve(Matrix<Complex32> input, Matrix<Complex32> result)
{
if (input == null)
{
throw new ArgumentNullException("input");
}
if (result == null)
{
throw new ArgumentNullException("result");
}
// Check for proper dimensions.
if (result.RowCount != input.RowCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameRowDimension);
}
if (result.ColumnCount != input.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameColumnDimension);
}
if (input.RowCount != CholeskyFactor.RowCount)
{
throw new ArgumentException(Resources.ArgumentMatrixDimensions);
}
input.CopyTo(result);
var order = CholeskyFactor.RowCount;
for (var c = 0; c < result.ColumnCount; c++)
{
// Solve L*Y = B;
Complex32 sum;
for (var i = 0; i < order; i++)
{
sum = result.At(i, c);
for (var k = i - 1; k >= 0; k--)
{
sum -= CholeskyFactor.At(i, k) * result.At(k, c);
}
result.At(i, c, sum / CholeskyFactor.At(i, i));
}
// Solve L'*X = Y;
for (var i = order - 1; i >= 0; i--)
{
sum = result.At(i, c);
for (var k = i + 1; k < order; k++)
{
sum -= CholeskyFactor.At(k, i).Conjugate() * result.At(k, c);
}
result.At(i, c, sum / CholeskyFactor.At(i, i));
}
}
}示例4: Solve
/// <summary>
/// Solves a system of linear equations, <c>AX = B</c>, with A LU factorized.
/// </summary>
/// <param name="input">The right hand side <see cref="Matrix{T}"/>, <c>B</c>.</param>
/// <param name="result">The left hand side <see cref="Matrix{T}"/>, <c>X</c>.</param>
public override void Solve(Matrix<Complex> input, Matrix<Complex> result)
{
// Check for proper arguments.
if (input == null)
{
throw new ArgumentNullException("input");
}
if (result == null)
{
throw new ArgumentNullException("result");
}
// Check for proper dimensions.
if (result.RowCount != input.RowCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameRowDimension);
}
if (result.ColumnCount != input.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameColumnDimension);
}
if (input.RowCount != Factors.RowCount)
{
throw Matrix.DimensionsDontMatch<ArgumentException>(input, Factors);
}
// Copy the contents of input to result.
input.CopyTo(result);
for (var i = 0; i < Pivots.Length; i++)
{
if (Pivots[i] == i)
{
continue;
}
var p = Pivots[i];
for (var j = 0; j < result.ColumnCount; j++)
{
var temp = result.At(p, j);
result.At(p, j, result.At(i, j));
result.At(i, j, temp);
}
}
var order = Factors.RowCount;
// Solve L*Y = P*B
for (var k = 0; k < order; k++)
{
for (var i = k + 1; i < order; i++)
{
for (var j = 0; j < result.ColumnCount; j++)
{
var temp = result.At(k, j)*Factors.At(i, k);
result.At(i, j, result.At(i, j) - temp);
}
}
}
// Solve U*X = Y;
for (var k = order - 1; k >= 0; k--)
{
for (var j = 0; j < result.ColumnCount; j++)
{
result.At(k, j, (result.At(k, j)/Factors.At(k, k)));
}
for (var i = 0; i < k; i++)
{
for (var j = 0; j < result.ColumnCount; j++)
{
var temp = result.At(k, j)*Factors.At(i, k);
result.At(i, j, result.At(i, j) - temp);
}
}
}
}示例5: Solve
/// <summary>
/// Solves a system of linear equations, <b>AX = B</b>, with A Cholesky factorized.
/// </summary>
/// <param name="input">The right hand side <see cref="Matrix{T}"/>, <b>B</b>.</param>
/// <param name="result">The left hand side <see cref="Matrix{T}"/>, <b>X</b>.</param>
public override void Solve(Matrix<float> input, Matrix<float> result)
{
if (result.RowCount != input.RowCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameRowDimension);
}
if (result.ColumnCount != input.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameColumnDimension);
}
if (input.RowCount != Factor.RowCount)
{
throw Matrix.DimensionsDontMatch<ArgumentException>(input, Factor);
}
input.CopyTo(result);
var order = Factor.RowCount;
for (var c = 0; c < result.ColumnCount; c++)
{
// Solve L*Y = B;
float sum;
for (var i = 0; i < order; i++)
{
sum = result.At(i, c);
for (var k = i - 1; k >= 0; k--)
{
sum -= Factor.At(i, k)*result.At(k, c);
}
result.At(i, c, sum/Factor.At(i, i));
}
// Solve L'*X = Y;
for (var i = order - 1; i >= 0; i--)
{
sum = result.At(i, c);
for (var k = i + 1; k < order; k++)
{
sum -= Factor.At(k, i)*result.At(k, c);
}
result.At(i, c, sum/Factor.At(i, i));
}
}
}示例6: UserEvd
/// <summary>
/// Initializes a new instance of the <see cref="UserEvd"/> class. This object will compute the
/// the eigenvalue decomposition when the constructor is called and cache it's decomposition.
/// </summary>
/// <param name="matrix">The matrix to factor.</param>
/// <exception cref="ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
/// <exception cref="ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
public UserEvd(Matrix<float> matrix)
{
if (matrix == null)
{
throw new ArgumentNullException("matrix");
}
if (matrix.RowCount != matrix.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSquare);
}
var order = matrix.RowCount;
// Initialize matricies for eigenvalues and eigenvectors
MatrixEv = matrix.CreateMatrix(order, order);
MatrixD = matrix.CreateMatrix(order, order);
VectorEv = new LinearAlgebra.Complex.DenseVector(order);
IsSymmetric = true;
for (var i = 0; IsSymmetric && i < order; i++)
{
for (var j = 0; IsSymmetric && j < order; j++)
{
IsSymmetric &= matrix.At(i, j) == matrix.At(j, i);
}
}
var d = new float[order];
var e = new float[order];
if (IsSymmetric)
{
matrix.CopyTo(MatrixEv);
d = MatrixEv.Row(order - 1).ToArray();
SymmetricTridiagonalize(d, e, order);
SymmetricDiagonalize(d, e, order);
}
else
{
var matrixH = matrix.ToArray();
NonsymmetricReduceToHessenberg(matrixH, order);
NonsymmetricReduceHessenberToRealSchur(matrixH, d, e, order);
}
for (var i = 0; i < order; i++)
{
MatrixD.At(i, i, d[i]);
if (e[i] > 0)
{
MatrixD.At(i, i + 1, e[i]);
}
else if (e[i] < 0)
{
MatrixD.At(i, i - 1, e[i]);
}
}
for (var i = 0; i < order; i++)
{
VectorEv[i] = new Complex(d[i], e[i]);
}
}示例7: Create
/// <summary>
/// Initializes a new instance of the <see cref="UserEvd"/> class. This object will compute the
/// the eigenvalue decomposition when the constructor is called and cache it's decomposition.
/// </summary>
/// <param name="matrix">The matrix to factor.</param>
/// <param name="symmetricity">If it is known whether the matrix is symmetric or not the routine can skip checking it itself.</param>
/// <exception cref="ArgumentNullException">If <paramref name="matrix"/> is <c>null</c>.</exception>
/// <exception cref="ArgumentException">If EVD algorithm failed to converge with matrix <paramref name="matrix"/>.</exception>
public static UserEvd Create(Matrix<double> matrix, Symmetricity symmetricity)
{
if (matrix.RowCount != matrix.ColumnCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSquare);
}
var order = matrix.RowCount;
// Initialize matricies for eigenvalues and eigenvectors
var eigenVectors = Matrix<double>.Build.SameAs(matrix, order, order);
var blockDiagonal = Matrix<double>.Build.SameAs(matrix, order, order);
var eigenValues = new LinearAlgebra.Complex.DenseVector(order);
bool isSymmetric;
switch (symmetricity)
{
case Symmetricity.Symmetric:
case Symmetricity.Hermitian:
isSymmetric = true;
break;
case Symmetricity.Asymmetric:
isSymmetric = false;
break;
default:
isSymmetric = matrix.IsSymmetric();
break;
}
var d = new double[order];
var e = new double[order];
if (isSymmetric)
{
matrix.CopyTo(eigenVectors);
d = eigenVectors.Row(order - 1).ToArray();
SymmetricTridiagonalize(eigenVectors, d, e, order);
SymmetricDiagonalize(eigenVectors, d, e, order);
}
else
{
var matrixH = matrix.ToArray();
NonsymmetricReduceToHessenberg(eigenVectors, matrixH, order);
NonsymmetricReduceHessenberToRealSchur(eigenVectors, matrixH, d, e, order);
}
for (var i = 0; i < order; i++)
{
blockDiagonal.At(i, i, d[i]);
if (e[i] > 0)
{
blockDiagonal.At(i, i + 1, e[i]);
}
else if (e[i] < 0)
{
blockDiagonal.At(i, i - 1, e[i]);
}
}
for (var i = 0; i < order; i++)
{
eigenValues[i] = new Complex(d[i], e[i]);
}
return new UserEvd(eigenVectors, eigenValues, blockDiagonal, isSymmetric);
}示例8: TestImageDFT
public void TestImageDFT()
{
Image<Gray, float> matA = EmguAssert.LoadImage<Gray, float>("stuff.jpg");
//The matrix to be convolved with matA, a bluring filter
Matrix<float> matB = new Matrix<float>(
new float[,] {
{1.0f / 16.0f, 1.0f / 16.0f, 1.0f / 16.0f},
{1.0f / 16.0f, 8.0f / 16.0f, 1.0f / 16.0f},
{1.0f / 16.0f, 1.0f / 16.0f, 1.0f / 16.0f}});
Image<Gray, float> convolvedImage = new Image<Gray, float>(new Size(matA.Width + matB.Width -1, matA.Height + matB.Height -1));
Matrix<float> dftA = new Matrix<float>(
CvInvoke.GetOptimalDFTSize(convolvedImage.Rows),
CvInvoke.GetOptimalDFTSize(convolvedImage.Cols));
matA.CopyTo(dftA.GetSubRect(matA.ROI));
CvInvoke.Dft(dftA, dftA, Emgu.CV.CvEnum.DxtType.Forward, matA.Rows);
Matrix<float> dftB = new Matrix<float>(dftA.Size);
matB.CopyTo(dftB.GetSubRect(new Rectangle(Point.Empty, matB.Size)));
CvInvoke.Dft(dftB, dftB, Emgu.CV.CvEnum.DxtType.Forward, matB.Rows);
CvInvoke.MulSpectrums(dftA, dftB, dftA, Emgu.CV.CvEnum.MulSpectrumsType.Default, false);
CvInvoke.Dft(dftA, dftA, Emgu.CV.CvEnum.DxtType.Inverse, convolvedImage.Rows);
dftA.GetSubRect(new Rectangle(Point.Empty, convolvedImage.Size)).CopyTo(convolvedImage);
}示例9: ForwardAlgorithm
private static void ForwardAlgorithm(ref List<Particle> particleListNew, Matrix<float> matRnormalized, Matrix<float> stateMat, Matrix<float> condMat, int tempModel = 5)
{
//Matrix<float> condMat = new Matrix<float>(3, 2);
//condMat[0, 0] = 0.7f;
//condMat[0, 1] = 0.3f;
//condMat[1, 0] = 0.7f;
//condMat[1, 1] = 0.3f;
//condMat[2, 0] = 0.3f;
//condMat[2, 1] = 0.7f;
//stateMat = new Matrix<float>(3, 3);
//stateMat[0, 0] = 0.8f;
//stateMat[0, 1] = 0.15f;
//stateMat[0, 2] = 0.05f;
//stateMat[1, 0] = 0.2f;
//stateMat[1, 1] = 0.5f;
//stateMat[1, 2] = 0.3f;
//stateMat[2, 0] = 0.05f;
//stateMat[2, 1] = 0.25f;
//stateMat[2, 2] = 0.7f;
int Dcount = 0;
int Acount = 0;
for (int i = 0; i < particleListNew.Count; i++)
{
if (particleListNew[i].isReady() == false)
{
particleListNew[i].pAlone.Add(0.5f);
continue;
}
particleListNew[i].pAlone.Add(matRnormalized[i, i]);
particleListNew[i].pAlone.RemoveAt(0);
//float obserPA = particleListNew[i].pAlone[particleListNew[i].pAlone.Count - tempModel - 1];
Matrix<float> obserMat0 = new Matrix<float>(3, 3);
Matrix<float> obserMat1 = new Matrix<float>(3, 3);
for (int j = 0; j < 3; j++)
{
obserMat0[j, j] = condMat[j, 0];
obserMat1[j, j] = condMat[j, 1];
}
Matrix<float> S = new Matrix<float>(3, tempModel + 1);
S[0, 0] = 0.33f;
S[1, 0] = 0.33f;
S[2, 0] = 0.33f;
for (int j = 0; j < tempModel; j++)
{
Matrix<float> inMat = new Matrix<float>(3, 1);
inMat[0, 0] = S[0, j];
inMat[1, 0] = S[1, j];
inMat[2, 0] = S[2, j];
Matrix<float> outMat = new Matrix<float>(3, 1);
float obserPA = particleListNew[i].pAlone[particleListNew[i].pAlone.Count - tempModel + j];
Matrix<float> obserMat = new Matrix<float>(3, 3);
if (obserPA > 0.5)
obserMat0.CopyTo(obserMat);
else
obserMat1.CopyTo(obserMat);
outMat = obserMat * stateMat.Transpose() * inMat;
outMat = outMat / outMat.Sum;
S[0, j + 1] = outMat[0, 0];
S[1, j + 1] = outMat[1, 0];
S[2, j + 1] = outMat[2, 0];
}
double pDEAD = S[0, tempModel];
double pDYING = S[1, tempModel];
double pALIVE = S[2, tempModel];
//for (int j = 0; j < particleListNew.Count; j++)
//{
// if (i == j)
// continue;
// pG += matRnormalized[i, j] * stateMat[1, particleListNew[i].lastState];
//}
//double pA = 1 - pG;
//swHere.WriteLine("P(A) = {0}\nP(G) = {1}\nsum = {2}\n", pA, pG, pG+pA);
Matrix<float> pOut = new Matrix<float>(3, 1);
pOut[0, 0] = (float)pDEAD;
pOut[1, 0] = (float)pDYING;
pOut[2, 0] = (float)pALIVE;
Matrix<float> pResult = new Matrix<float>(4, 1);
Point maxValP = new Point();
Point minValP = new Point();
double minVal, maxVal;
pOut.MinMax(out minVal, out maxVal, out minValP, out maxValP);
switch (maxValP.Y)
{
case 0:
particleListNew[i].lastState = particleListNew[i].state;
particleListNew[i].state = 0;
break;
//.........这里部分代码省略.........
示例10: TestImageDFT
public void TestImageDFT()
{
Image<Gray, float> matA = new Image<Gray, float>("stuff.jpg");
//The matrix to be convoled with matA, a bluring filter
Matrix<float> matB = new Matrix<float>(
new float[,] {
{1.0f/16.0f, 1.0f/16.0f, 1.0f/16.0f},
{1.0f/16.0f, 8.0f/16.0f, 1.0f/16.0f},
{1.0f/16.0f, 1.0f/16.0f, 1.0f/16.0f}});
Image<Gray, float> convolvedImage = new Image<Gray, float>(matA.Size + matB.Size - new Size(1, 1));
Matrix<float> dftA = new Matrix<float>(
CvInvoke.cvGetOptimalDFTSize(convolvedImage.Rows),
CvInvoke.cvGetOptimalDFTSize(convolvedImage.Cols));
matA.CopyTo(dftA.GetSubRect(matA.ROI));
CvInvoke.cvDFT(dftA, dftA, Emgu.CV.CvEnum.CV_DXT.CV_DXT_FORWARD, matA.Rows);
Matrix<float> dftB = new Matrix<float>(dftA.Size);
matB.CopyTo(dftB.GetSubRect(new Rectangle(Point.Empty, matB.Size)));
CvInvoke.cvDFT(dftB, dftB, Emgu.CV.CvEnum.CV_DXT.CV_DXT_FORWARD, matB.Rows);
CvInvoke.cvMulSpectrums(dftA, dftB, dftA, Emgu.CV.CvEnum.MUL_SPECTRUMS_TYPE.DEFAULT);
CvInvoke.cvDFT(dftA, dftA, Emgu.CV.CvEnum.CV_DXT.CV_DXT_INVERSE, convolvedImage.Rows);
dftA.GetSubRect(new Rectangle(Point.Empty, convolvedImage.Size)).CopyTo(convolvedImage);
}