本文整理汇总了C++中MatrixD::transpose方法的典型用法代码示例。如果您正苦于以下问题：C++ MatrixD::transpose方法的具体用法？C++ MatrixD::transpose怎么用？C++ MatrixD::transpose使用的例子？那么, 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MatrixD的用法示例。
在下文中一共展示了MatrixD::transpose方法的5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: pinv_damped
bool pinv_damped(const MatrixD &A, MatrixD *invA, Scalar lambda_max, Scalar eps) {
//A (m x n) usually comes from a redundant task jacobian, therfore we consider m<n
int m = A.rows() - 1;
VectorD sigma; //vector of singular values
Scalar lambda2;
int r = 0;
JacobiSVD<MatrixD> svd_A(A.transpose(), ComputeThinU | ComputeThinV);
sigma = svd_A.singularValues();
if (((m > 0) && (sigma(m) > eps)) || ((m == 0) && (A.array().abs() > eps).any())) {
for (int i = 0; i <= m; i++) {
sigma(i) = 1.0 / sigma(i);
}
(*invA) = svd_A.matrixU() * sigma.asDiagonal() * svd_A.matrixV().transpose();
return true;
} else {
lambda2 = (1 - (sigma(m) / eps) * (sigma(m) / eps)) * lambda_max * lambda_max;
for (int i = 0; i <= m; i++) {
if (sigma(i) > EPSQ)
r++;
sigma(i) = (sigma(i) / (sigma(i) * sigma(i) + lambda2));
}
//only U till the rank
MatrixD subU = svd_A.matrixU().block(0, 0, A.cols(), r);
MatrixD subV = svd_A.matrixV().block(0, 0, A.rows(), r);
(*invA) = subU * sigma.asDiagonal() * subV.transpose();
return false;
}
}示例2: pinv_forBarP
bool pinv_forBarP(const MatrixD &W, const MatrixD &P, MatrixD *inv) {
MatrixD barW;
int rowsBarW = 0;
MatrixD tmp;
bool invertible;
for (int i = 0; i < W.rows(); i++) {
if (W(i, i) > 0.99) { //equal to 1 (safer)
rowsBarW++;
barW = (MatrixD(rowsBarW, W.cols()) << barW, W.row(i)).finished();
}
}
tmp = barW * P * barW.transpose();
FullPivLU < MatrixD > inversePbar(tmp);
invertible = inversePbar.isInvertible();
if (invertible) {
(*inv) = P * barW.transpose() * inversePbar.inverse() * barW;
return true;
} else {
(*inv) = MatrixD::Zero(W.rows(), W.rows());
return false;
}
}示例3: pinv_QR
bool pinv_QR(const MatrixD &A, MatrixD *invA, Scalar eps) {
MatrixD At = A.transpose();
HouseholderQR < MatrixD > qr = At.householderQr();
int m = A.rows();
//int n = A.cols();
MatrixD Rt = MatrixD::Zero(m, m);
bool invertible;
MatrixD hR = (MatrixD) qr.matrixQR();
MatrixD Y = ((MatrixD) qr.householderQ()).leftCols(m);
//take the useful part of R
for (int i = 0; i < m; i++) {
for (int j = 0; j <= i; j++)
Rt(i, j) = hR(j, i);
}
FullPivLU < MatrixD > invRt(Rt);
invertible = fabs(invRt.determinant()) > eps;
if (invertible) {
*invA = Y * invRt.inverse();
return true;
} else {
return false;
}
}示例4: isOptimal
bool OSNSVelocityIK::isOptimal(int priority, const VectorD& dotQ,
const MatrixD& tildeP, MatrixD* W,
VectorD* dotQn, double eps) {
VectorD barMu;
bool isOptimal = true;
barMu = tildeP.transpose() * dotQ;
for (int i = 0; i < n_dof; i++) {
if ((*W)(i, i) < 0.1) { //equal to 0.0 (safer)
if (abs(dotQ(i) - dotQmax(i)) < eps)
barMu(i) = -barMu(i);
if (barMu(i) < 0.0) {
(*W)(i, i) = 1.0;
(*dotQn)(i) = 0.0;
isOptimal = false;
}
}
}
return isOptimal;
}示例5: pinv
bool pinv(const MatrixD &A, MatrixD *invA, Scalar eps) {
//A (m x n) usually comes from a redundant task jacobian, therfore we consider m<n
int m = A.rows() - 1;
VectorD sigma; //vector of singular values
JacobiSVD<MatrixD> svd_A(A.transpose(), ComputeThinU | ComputeThinV);
sigma = svd_A.singularValues();
if (((m > 0) && (sigma(m) > eps)) || ((m == 0) && (A.array().abs() > eps).any())) {
for (int i = 0; i <= m; i++) {
sigma(i) = 1.0 / sigma(i);
}
(*invA) = svd_A.matrixU() * sigma.asDiagonal() * svd_A.matrixV().transpose();
return true;
} else {
return false;
}
}