本文整理汇总了C#中System.Matrix.getColumnDimension方法的典型用法代码示例。如果您正苦于以下问题：C# Matrix.getColumnDimension方法的具体用法？C# Matrix.getColumnDimension怎么用？C# Matrix.getColumnDimension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.getColumnDimension方法的6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: CholeskyDecomposition
/* ------------------------
Constructor
* ------------------------ */
/** Cholesky algorithm for symmetric and positive definite matrix.
@param A Square, symmetric matrix.
@return Structure to access L and isspd flag.
*/
public CholeskyDecomposition(Matrix Arg)
{
// Initialize.
double[][] A = Arg.getArray();
n = Arg.getRowDimension();
L = new double[n][];
for(int i = 0; i < n; i++)
{
L[n] = new double[n];
}
isspd = (Arg.getColumnDimension() == n);
// Main loop.
for (int j = 0; j < n; j++) {
double[] Lrowj = L[j];
double d = 0.0;
for (int k = 0; k < j; k++) {
double[] Lrowk = L[k];
double s = 0.0;
for (int i = 0; i < k; i++) {
s += Lrowk[i]*Lrowj[i];
}
Lrowj[k] = s = (A[j][k] - s)/L[k][k];
d = d + s*s;
isspd = isspd & (A[k][j] == A[j][k]);
}
d = A[j][j] - d;
isspd = isspd & (d > 0.0);
L[j][j] = Math.Sqrt(Math.Max(d,0.0));
for (int k = j+1; k < n; k++) {
L[j][k] = 0.0;
}
}
}示例2: EigenvalueDecomposition
/* ------------------------
Constructor
* ------------------------ */
/** Check for symmetry, then construct the eigenvalue decomposition
@param A Square matrix
@return Structure to access D and V.
*/
public EigenvalueDecomposition(Matrix Arg)
{
double[][] A = Arg.getArray();
n = Arg.getColumnDimension();
V = new double[n][];
for(int i = 0; i < n ; i++)
{
V[i]= new double [n];
}
d = new double[n];
e = new double[n];
issymmetric = true;
for (int j = 0; (j < n) & issymmetric; j++) {
for (int i = 0; (i < n) & issymmetric; i++) {
issymmetric = (A[i][j] == A[j][i]);
}
}
if (issymmetric) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
V[i][j] = A[i][j];
}
}
// Tridiagonalize.
tred2();
// Diagonalize.
tql2();
} else {
H = new double[n][];
for(int i = 0; i < n ; i++)
{
H[i]= new double [n];
}
ort = new double[n];
for (int j = 0; j < n; j++) {
for (int i = 0; i < n; i++) {
H[i][j] = A[i][j];
}
}
// Reduce to Hessenberg form.
orthes();
// Reduce Hessenberg to real Schur form.
hqr2();
}
}示例3: SingularValueDecomposition
/* ------------------------
Constructor
* ------------------------ */
/** Construct the singular value decomposition
@param A Rectangular matrix
@return Structure to access U, S and V.
*/
public SingularValueDecomposition(Matrix Arg)
{
// Derived from LINPACK code.
// Initialize.
double[][] A = Arg.getArrayCopy();
m = Arg.getRowDimension();
n = Arg.getColumnDimension();
/* Apparently the failing cases are only a proper subset of (m<n),
so let's not throw error. Correct fix to come later?
if (m<n) {
throw new IllegalArgumentException("Jama SVD only works for m >= n"); }
*/
int nu = Math.Min(m,n);
s = new double [Math.Min(m+1,n)];
U = new double [m][];
for (int i = 0; i < m; i++)
{
U[i] = new double[nu];
}
V = new double [n][];
for(int i = 0; i < n; i++)
{
V[i] = new double[n];
}
double[] e = new double [n];
double[] work = new double [m];
bool wantu = true;
bool wantv = true;
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
int nct = Math.Min(m-1,n);
int nrt = Math.Max(0,Math.Min(n-2,m));
for (int k = 0; k < Math.Max(nct,nrt); k++) {
if (k < nct) {
// Compute the transformation for the k-th column and
// place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
s[k] = 0;
for (int i = k; i < m; i++) {
s[k] = Maths.hypot(s[k],A[i][k]);
}
if (s[k] != 0.0) {
if (A[k][k] < 0.0) {
s[k] = -s[k];
}
for (int i = k; i < m; i++) {
A[i][k] /= s[k];
}
A[k][k] += 1.0;
}
s[k] = -s[k];
}
for (int j = k+1; j < n; j++) {
if ((k < nct) & (s[k] != 0.0)) {
// Apply the transformation.
double t = 0;
for (int i = k; i < m; i++) {
t += A[i][k]*A[i][j];
}
t = -t/A[k][k];
for (int i = k; i < m; i++) {
A[i][j] += t*A[i][k];
}
}
// Place the k-th row of A into e for the
// subsequent calculation of the row transformation.
e[j] = A[k][j];
}
if (wantu & (k < nct)) {
// Place the transformation in U for subsequent back
// multiplication.
for (int i = k; i < m; i++) {
U[i][k] = A[i][k];
}
}
if (k < nrt) {
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
//.........这里部分代码省略.........
示例4: solve
/** Solve A*X = B
@param B A Matrix with as many rows as A and any number of columns.
@return X so that L*L'*X = B
@exception IllegalArgumentException Matrix row dimensions must agree.
@exception RuntimeException Matrix is not symmetric positive definite.
*/
public Matrix solve(Matrix B)
{
if (B.getRowDimension() != n)
{
throw new IllegalArgumentException("Matrix row dimensions must agree.");
}
if (!isspd)
{
throw new RuntimeException("Matrix is not symmetric positive definite.");
}
// Copy right hand side.
double[][] X = B.getArrayCopy();
int nx = B.getColumnDimension();
// Solve L*Y = B;
for (int k = 0; k < n; k++)
{
for (int j = 0; j < nx; j++)
{
for (int i = 0; i < k; i++)
{
X[k][j] -= X[i][j] * L[k][i];
}
X[k][j] /= L[k][k];
}
}
// Solve L'*X = Y;
for (int k = n - 1; k >= 0; k--)
{
for (int j = 0; j < nx; j++)
{
for (int i = k + 1; i < n; i++)
{
X[k][j] -= X[i][j] * L[i][k];
}
X[k][j] /= L[k][k];
}
}
return new Matrix(X, n, nx);
}示例5: LUDecomposition
/* ------------------------
Constructor
* ------------------------ */
/** LU Decomposition
@param A Rectangular matrix
@return Structure to access L, U and piv.
*/
public LUDecomposition(Matrix A)
{
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
LU = A.getArrayCopy();
m = A.getRowDimension();
n = A.getColumnDimension();
piv = new int[m];
for (int i = 0; i < m; i++)
{
piv[i] = i;
}
pivsign = 1;
double[] LUrowi;
double[] LUcolj = new double[m];
// Outer loop.
for (int j = 0; j < n; j++)
{
// Make a copy of the j-th column to localize references.
for (int i = 0; i < m; i++)
{
LUcolj[i] = LU[i][j];
}
// Apply previous transformations.
for (int i = 0; i < m; i++)
{
LUrowi = LU[i];
// Most of the time is spent in the following dot product.
int kmax = Math.Min(i, j);
double s = 0.0;
for (int k = 0; k < kmax; k++)
{
s += LUrowi[k] * LUcolj[k];
}
LUrowi[j] = LUcolj[i] -= s;
}
// Find pivot and exchange if necessary.
int p = j;
for (int i = j + 1; i < m; i++)
{
if (Math.Abs(LUcolj[i]) > Math.Abs(LUcolj[p]))
{
p = i;
}
}
if (p != j)
{
for (int k = 0; k < n; k++)
{
double t = LU[p][k]; LU[p][k] = LU[j][k]; LU[j][k] = t;
}
int z = piv[p];
piv[p] = piv[j];
piv[j] = z;
pivsign = -pivsign;
}
// Compute multipliers.
if (j < m & LU[j][j] != 0.0)
{
for (int i = j + 1; i < m; i++)
{
LU[i][j] /= LU[j][j];
}
}
}
}示例6: B
/** Solve A*X = B
@param B A Matrix with as many rows as A and any number of columns.
@return X so that L*U*X = B(piv,:)
@exception IllegalArgumentException Matrix row dimensions must agree.
@exception RuntimeException Matrix is singular.
*/
public Matrix solve(Matrix B)
{
if (B.getRowDimension() != m)
{
throw new IllegalArgumentException("Matrix row dimensions must agree.");
}
//EDGAR ver pra q serve
//if (!this.isNonsingular())
//{
// throw new RuntimeException("Matrix is singular.");
//}
// Copy right hand side with pivoting
int nx = B.getColumnDimension();
Matrix Xmat = B.getMatrix(piv, 0, nx - 1);
double[][] X = Xmat.getArray();
// Solve L*Y = B(piv,:)
for (int k = 0; k < n; k++)
{
for (int i = k + 1; i < n; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
// Solve U*X = Y;
for (int k = n - 1; k >= 0; k--)
{
for (int j = 0; j < nx; j++)
{
X[k][j] /= LU[k][k];
}
for (int i = 0; i < k; i++)
{
for (int j = 0; j < nx; j++)
{
X[i][j] -= X[k][j] * LU[i][k];
}
}
}
return Xmat;
}