本文整理汇总了C++中LLT::info方法的典型用法代码示例。如果您正苦于以下问题：C++ LLT::info方法的具体用法？C++ LLT::info怎么用？C++ LLT::info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LLT的用法示例。
在下文中一共展示了LLT::info方法的2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: train_machine
bool CLinearRidgeRegression::train_machine(CFeatures* data)
{
REQUIRE(m_labels,"No labels set\n");
if (!data)
data=features;
REQUIRE(data,"No features provided and no featured previously set\n");
REQUIRE(m_labels->get_num_labels() == data->get_num_vectors(),
"Number of training vectors (%d) does not match number of labels (%d)\n",
m_labels->get_num_labels(), data->get_num_vectors());
REQUIRE(data->get_feature_class() == C_DENSE,
"Expected Dense Features (%d) but got (%d)\n",
C_DENSE, data->get_feature_class());
REQUIRE(data->get_feature_type() == F_DREAL,
"Expected Real Features (%d) but got (%d)\n",
F_DREAL, data->get_feature_type());
CDenseFeatures<float64_t>* feats=(CDenseFeatures<float64_t>*) data;
int32_t num_feat=feats->get_num_features();
int32_t num_vec=feats->get_num_vectors();
SGMatrix<float64_t> kernel_matrix(num_feat,num_feat);
SGMatrix<float64_t> feats_matrix(feats->get_feature_matrix());
SGVector<float64_t> y(num_feat);
SGVector<float64_t> tau_vector(num_feat);
tau_vector.zero();
tau_vector.add(m_tau);
Map<MatrixXd> eigen_kernel_matrix(kernel_matrix.matrix, num_feat,num_feat);
Map<MatrixXd> eigen_feats_matrix(feats_matrix.matrix, num_feat,num_vec);
Map<VectorXd> eigen_y(y.vector, num_feat);
Map<VectorXd> eigen_labels(((CRegressionLabels*)m_labels)->get_labels(),num_vec);
Map<VectorXd> eigen_tau(tau_vector.vector, num_feat);
eigen_kernel_matrix = eigen_feats_matrix*eigen_feats_matrix.transpose();
eigen_kernel_matrix.diagonal() += eigen_tau;
eigen_y = eigen_feats_matrix*eigen_labels ;
LLT<MatrixXd> llt;
llt.compute(eigen_kernel_matrix);
if(llt.info() != Eigen::Success)
{
SG_WARNING("Features covariance matrix was not positive definite\n");
return false;
}
eigen_y = llt.solve(eigen_y);
set_w(y);
return true;
}示例2: BA
//.........这里部分代码省略.........
tmpP_inv *= huber_r_w/r_w ;
}
H_i_2_j_T = H_i_2_j.transpose();
H_i_2_j_T *= tmpP_inv;
tmpHTH = H_i_2_j_T * H_i_2_j;
tmpHTb = H_i_2_j_T * residualCamera;
int currentStateIDIndex = currentStateID - head;
if ( currentStateIDIndex < 0){
currentStateIDIndex += slidingWindowSize;
}
int linkIDIndex = linkID - head ;
if (linkIDIndex < 0){
linkIDIndex += slidingWindowSize;
}
HTH.block(currentStateIDIndex * 9, currentStateIDIndex * 9, 9, 9) += tmpHTH.block(0, 0, 9, 9);
HTH.block(currentStateIDIndex * 9, linkIDIndex * 9, 9, 9) += tmpHTH.block(0, 9, 9, 9);
HTH.block(linkIDIndex * 9, currentStateIDIndex * 9, 9, 9) += tmpHTH.block(9, 0, 9, 9);
HTH.block(linkIDIndex * 9, linkIDIndex * 9, 9, 9) += tmpHTH.block(9, 9, 9, 9);
HTb.segment(currentStateIDIndex * 9, 9) -= tmpHTb.segment(0, 9);
HTb.segment(linkIDIndex * 9, 9) -= tmpHTb.segment(9, 9);
}
}
// printf("[numList in BA]=%d\n", numList ) ;
//solve the BA
//cout << "HTH\n" << HTH << endl;
LLT<MatrixXd> lltOfHTH = HTH.llt();
ComputationInfo info = lltOfHTH.info();
if (info == Success)
{
VectorXd dx = lltOfHTH.solve(HTb);
// printf("[BA] %d %f\n",iterNum, dx.norm() ) ;
// cout << iterNum << endl ;
// cout << dx.transpose() << endl ;
//cout << "iteration " << iterNum << "\n" << dx << endl;
#ifdef DEBUG_INFO
geometry_msgs::Vector3 to_pub ;
to_pub.x = dx.norm() ;
printf("%d %f\n",iterNum, to_pub.x ) ;
pub_BA.publish( to_pub ) ;
#endif
VectorXd errorUpdate(9);
for (int i = 0; i < numOfState; i++)
{
int k = head + i;
if (k >= slidingWindowSize){
k -= slidingWindowSize;
}
errorUpdate = dx.segment(i * 9, 9);
T[k] += errorUpdate.segment(0, 3);
vel[k] += errorUpdate.segment(3, 3);
Quaterniond q(R[k]);
Quaterniond dq;
dq.x() = errorUpdate(6) * 0.5;
dq.y() = errorUpdate(7) * 0.5;