本文整理汇总了C++中eigen::MatrixXd::format方法的典型用法代码示例。如果您正苦于以下问题：C++ MatrixXd::format方法的具体用法？C++ MatrixXd::format怎么用？C++ MatrixXd::format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eigen::MatrixXd的用法示例。
在下文中一共展示了MatrixXd::format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: fmt
bool NuTo::StructureBase::CheckHessian0_Submatrix(const BlockSparseMatrix& rHessian0, BlockSparseMatrix& rHessian0_CDF,
double rRelativeTolerance, bool rPrintWrongMatrices)
{
int row, col;
assert(rHessian0.GetNumRows() == rHessian0_CDF.GetNumRows());
assert(rHessian0.GetNumColumns() == rHessian0_CDF.GetNumColumns());
if (rHessian0.GetNumRows() == 0 or rHessian0.GetNumColumns() == 0)
return true;
bool isSubmatrixCorrect = true;
Eigen::IOFormat fmt(Eigen::StreamPrecision, 0, " ", "\n", "|", " |");
for (auto dofRow : GetDofStatus().GetActiveDofTypes())
{
for (auto dofCol : GetDofStatus().GetActiveDofTypes())
{
Eigen::MatrixXd hessian0_CDF_Full = rHessian0_CDF(dofRow, dofCol).ConvertToFullMatrix();
double scaling = 1. / rHessian0_CDF(dofRow, dofCol).AbsMax();
auto& diff = rHessian0_CDF(dofRow, dofCol);
diff.AddScal(rHessian0(dofRow, dofCol), -1.);
diff *= scaling;
double error = diff.AbsMax(row, col);
if (error > rRelativeTolerance)
{
GetLogger() << "[" << __FUNCTION__ << "] max error in (" << Node::DofToString(dofRow) << ","
<< Node::DofToString(dofCol) << ") " << error << " at entry (" << row << "," << col
<< ")\n";
GetLogger() << "hessian0(" << row << "," << col
<< ") = " << rHessian0(dofRow, dofCol).ConvertToFullMatrix()(row, col) << "\n";
GetLogger() << "hessian0_CDF(" << row << "," << col << ") = " << hessian0_CDF_Full(row, col) << "\n";
isSubmatrixCorrect = false;
if (rPrintWrongMatrices)
{
Eigen::MatrixXd diffPrint = diff.ConvertToFullMatrix();
GetLogger() << "####### relative difference\n" << diffPrint.format(fmt) << "\n";
GetLogger() << "####### hessian0\n"
<< rHessian0(dofRow, dofCol).ConvertToFullMatrix().format(fmt) << "\n";
GetLogger() << "####### hessian0_CDF\n" << hessian0_CDF_Full.format(fmt) << "\n";
}
}
}
}
return isSubmatrixCorrect;
}示例2: CleanFmt
MaxEntropy::MaxEntropy( const double txTime,
const double bandwidthKhz,
Map const * const ptrMap,
CORRE_MA_OPE* ptrMatComputer,
ClusterStructure const * const ptrCS):
m_txTimePerSlot(txTime), m_bandwidthKhz(bandwidthKhz),
m_ptrMap(ptrMap),
m_ptrCS(ptrCS), m_ptrMatComputer(ptrMatComputer),
m_type("BranchBound")
{
m_numNodes = m_ptrMap->GetNumNodes();
m_numMaxHeads = m_ptrMap->GetNumInitHeads();
m_maxPower = m_ptrMap->GetMaxPower();
Eigen::IOFormat CleanFmt(2, 0, " ", "\n", "", "");
m_A = Eigen::MatrixXd::Zero(m_numMaxHeads, m_numNodes);
m_B = Eigen::MatrixXd::Zero(m_numMaxHeads, m_numNodes);
m_C = Eigen::MatrixXd::Zero(m_numMaxHeads, m_numNodes);
m_X = Eigen::MatrixXd::Zero(m_numMaxHeads, m_numNodes);
for (int i = 0; i < m_numMaxHeads; ++i) {
for (int j = 0; j < m_numNodes; ++j) {
if (i == m_ptrCS->GetChIdxByName(j)) {
m_A(i,j) = OmegaValue(j);
}
}
}
double exponent = pow(2.0,m_ptrMap->GetIdtEntropy()/m_bandwidthKhz/m_txTimePerSlot);
for (int i = 0; i < m_numMaxHeads; ++i) {
for (int j = 0; j < m_numNodes; ++j) {
if (i != m_ptrCS->GetChIdxByName(j)) {
m_B(i,j) = (exponent-1) * m_maxPower *
m_ptrMap->GetGijByPair(m_ptrCS->GetVecHeadName()[i],j);
}
}
}
for (int i = 0; i < m_numMaxHeads; ++i) {
for (int j = 0; j < m_numNodes; ++j) {
if (i == m_ptrCS->GetChIdxByName(j)) {
int headName = m_ptrCS->GetVecHeadName()[i];
m_C(i,j) = m_maxPower * m_ptrMap->GetGijByPair(headName,j) -
(exponent -1)*m_bandwidthKhz * m_ptrMap->GetNoise() - OmegaValue(j) ;
}
}
}
Eigen::MatrixXd tmp = m_A+m_B-m_C;
#ifdef DEBUG
cout << "================ m_A ================" << endl;
cout << m_A.format(CleanFmt) << endl;
cout << "================ m_B ================" << endl;
cout << m_B.format(CleanFmt) << endl;
cout << "================ m_C ================" << endl;
cout << m_C.format(CleanFmt) << endl;
cout << "================ m_A + m_B-m_C ================" << endl;
cout << tmp.format(CleanFmt) << endl;
cout << "Varince: " << m_ptrMatComputer->GetCorrationFactor() << endl;
#endif
double corrFactor = m_ptrMatComputer->GetCorrationFactor();
double variance = 1.0;
m_Signma = Eigen::MatrixXd::Zero(m_numNodes, m_numNodes);
for (int i = 0; i < m_numNodes; ++i) {
for (int j = 0; j < m_numNodes; ++j) {
m_Signma(i,j) = variance * exp(-1*(m_ptrMatComputer->GetDijSQByPair(i,j))/corrFactor) ;
}
}
m_vecSched.resize(m_numNodes);
fill(m_vecSched.begin(), m_vecSched.end(), 0);
}