IGL_INLINE void igl::cat(
const int dim,
const Eigen::MatrixBase<Derived> & A,
const Eigen::MatrixBase<Derived> & B,
MatC & C)
{
assert(dim == 1 || dim == 2);
// Special case if B or A is empty
if(A.size() == 0)
{
C = B;
return;
}
if(B.size() == 0)
{
C = A;
return;
}
if(dim == 1)
{
assert(A.cols() == B.cols());
C.resize(A.rows()+B.rows(),A.cols());
C << A,B;
}else if(dim == 2)
{
assert(A.rows() == B.rows());
C.resize(A.rows(),A.cols()+B.cols());
C << A,B;
}else
{
fprintf(stderr,"cat.h: Error: Unsupported dimension %d\n",dim);
}
}
void forward_propagation(Eigen::MatrixBase<Derived1> const &input,
Eigen::MatrixBase<Derived2> const &weight,
Eigen::MatrixBase<Derived3> const &bias,
Eigen::MatrixBase<Derived4> &output,
bool no_overlap = true,
UnaryFunc func = UnaryFunc())
{
static_assert(std::is_same<Derived1::Scalar, Derived2::Scalar>::value &&
std::is_same<Derived2::Scalar, Derived3::Scalar>::value &&
std::is_same<Derived4::Scalar, Derived4::Scalar>::value,
"Data type of matrix input, weight, bias and output should be the same");
if(input.rows() != 0 && weight.rows() != 0 &&
bias.rows() != 0){
if(no_overlap){
output.noalias() = weight * input;
}else{
output = weight * input;
}
using Scalar = typename Derived3::Scalar;
using MatType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
using Mapper = Eigen::Map<const MatType, Eigen::Aligned>;
Mapper Map(&bias(0, 0), bias.size());
output.colwise() += Map;
func(output);
}
}
inline Eigen::Matrix<typename Eigen::internal::traits<Derived_T>::Scalar, 3, 3> crossMx(
Eigen::MatrixBase<Derived_T> const & v)
{
EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Eigen::MatrixBase<Derived_T>, 3);
assert((v.cols()==3 && v.rows()==1)||(v.rows()==3 && v.cols()==1));
return crossMx(v(0, 0), v(1, 0), v(2, 0));
}
IGL_INLINE void igl::vertex_triangle_adjacency(
const Eigen::MatrixBase<DerivedF> & F,
const int n,
Eigen::PlainObjectBase<DerivedVF> & VF,
Eigen::PlainObjectBase<DerivedNI> & NI)
{
typedef Eigen::Matrix<typename DerivedVF::Scalar,Eigen::Dynamic,1> VectorXI;
// vfd #V list so that vfd(i) contains the vertex-face degree (number of
// faces incident on vertex i)
VectorXI vfd = VectorXI::Zero(n);
for (int i = 0; i < F.rows(); i++)
{
for (int j = 0; j < 3; j++)
{
vfd[F(i,j)]++;
}
}
igl::cumsum(vfd,1,NI);
// Prepend a zero
NI = (DerivedNI(n+1)<<0,NI).finished();
// vfd now acts as a counter
vfd = NI;
VF.derived()= Eigen::VectorXi(3*F.rows());
for (int i = 0; i < F.rows(); i++)
{
for (int j = 0; j < 3; j++)
{
VF[vfd[F(i,j)]] = i;
vfd[F(i,j)]++;
}
}
}
IGL_INLINE void igl::per_face_normals(
const Eigen::MatrixBase<DerivedV>& V,
const Eigen::MatrixBase<DerivedF>& F,
const Eigen::MatrixBase<DerivedZ> & Z,
Eigen::PlainObjectBase<DerivedN> & N)
{
N.resize(F.rows(),3);
// loop over faces
int Frows = F.rows();
#pragma omp parallel for if (Frows>10000)
for(int i = 0; i < Frows;i++)
{
const Eigen::Matrix<typename DerivedV::Scalar, 1, 3> v1 = V.row(F(i,1)) - V.row(F(i,0));
const Eigen::Matrix<typename DerivedV::Scalar, 1, 3> v2 = V.row(F(i,2)) - V.row(F(i,0));
N.row(i) = v1.cross(v2);//.normalized();
typename DerivedV::Scalar r = N.row(i).norm();
if(r == 0)
{
N.row(i) = Z;
}else
{
N.row(i) /= r;
}
}
}
Eigen::Matrix<typename DerivedA::Scalar, matGradMultMatNumRows(DerivedA::RowsAtCompileTime, DerivedB::ColsAtCompileTime), DerivedDA::ColsAtCompileTime>
matGradMultMat(
const Eigen::MatrixBase<DerivedA>& A,
const Eigen::MatrixBase<DerivedB>& B,
const Eigen::MatrixBase<DerivedDA>& dA,
const Eigen::MatrixBase<DerivedDB>& dB) {
assert(dA.cols() == dB.cols());
const int nq = dA.cols();
const int nq_at_compile_time = DerivedDA::ColsAtCompileTime;
Eigen::Matrix<typename DerivedA::Scalar,
matGradMultMatNumRows(DerivedA::RowsAtCompileTime, DerivedB::ColsAtCompileTime),
DerivedDA::ColsAtCompileTime> ret(A.rows() * B.cols(), nq);
for (int col = 0; col < B.cols(); col++) {
auto block = ret.template block<DerivedA::RowsAtCompileTime, nq_at_compile_time>(col * A.rows(), 0, A.rows(), nq);
// A * dB part:
block.noalias() = A * dB.template block<DerivedA::ColsAtCompileTime, nq_at_compile_time>(col * A.cols(), 0, A.cols(), nq);
for (int row = 0; row < B.rows(); row++) {
// B * dA part:
block.noalias() += B(row, col) * dA.template block<DerivedA::RowsAtCompileTime, nq_at_compile_time>(row * A.rows(), 0, A.rows(), nq);
}
}
return ret;
// much slower and requires eigen/unsupported:
// return Eigen::kroneckerProduct(Eigen::MatrixXd::Identity(B.cols(), B.cols()), A) * dB + Eigen::kroneckerProduct(B.transpose(), Eigen::MatrixXd::Identity(A.rows(), A.rows())) * dA;
}
MatrixSuccessCode LUPartialPivotDecompositionSuccessful(Eigen::MatrixBase<Derived> const& LU)
{
#ifndef BERTINI_DISABLE_ASSERTS
assert(LU.rows()==LU.cols() && "non-square matrix in LUPartialPivotDecompositionSuccessful");
assert(LU.rows()>0 && "empty matrix in LUPartialPivotDecompositionSuccessful");
#endif
// this loop won't test entry (0,0). it's tested separately after.
for (unsigned int ii = LU.rows()-1; ii > 0; ii--)
{
if (IsSmallValue(LU(ii,ii)))
{
return MatrixSuccessCode::SmallValue;
}
if (IsLargeChange(LU(ii-1,ii-1),LU(ii,ii)))
{
return MatrixSuccessCode::LargeChange;
}
}
// this line is the reason for the above assert on non-empty matrix.
if (IsSmallValue(LU(0,0)))
{
return MatrixSuccessCode::SmallValue;
}
return MatrixSuccessCode::Success;
}
void normalizeVec(
const Eigen::MatrixBase<Derived>& x,
typename Derived::PlainObject& x_norm,
typename Gradient<Derived, Derived::RowsAtCompileTime, 1>::type* dx_norm,
typename Gradient<Derived, Derived::RowsAtCompileTime, 2>::type* ddx_norm) {
typename Derived::Scalar xdotx = x.squaredNorm();
typename Derived::Scalar norm_x = std::sqrt(xdotx);
x_norm = x / norm_x;
if (dx_norm) {
dx_norm->setIdentity(x.rows(), x.rows());
(*dx_norm) -= x * x.transpose() / xdotx;
(*dx_norm) /= norm_x;
if (ddx_norm) {
auto dx_norm_transpose = transposeGrad(*dx_norm, x.rows());
auto ddx_norm_times_norm = -matGradMultMat(x_norm, x_norm.transpose(), (*dx_norm), dx_norm_transpose);
auto dnorm_inv = -x.transpose() / (xdotx * norm_x);
(*ddx_norm) = ddx_norm_times_norm / norm_x;
auto temp = (*dx_norm) * norm_x;
int n = x.rows();
for (int col = 0; col < n; col++) {
auto column_as_matrix = (dnorm_inv(0, col) * temp);
for (int row_block = 0; row_block < n; row_block++) {
ddx_norm->block(row_block * n, col, n, 1) += column_as_matrix.col(row_block);
}
}
}
}
}
IGL_INLINE void igl::hessian(
const Eigen::MatrixBase<DerivedV> & V,
const Eigen::MatrixBase<DerivedF> & F,
Eigen::SparseMatrix<Scalar>& H)
{
typedef typename DerivedV::Scalar denseScalar;
typedef typename Eigen::Matrix<denseScalar, Eigen::Dynamic, 1> VecXd;
typedef typename Eigen::SparseMatrix<Scalar> SparseMat;
typedef typename Eigen::DiagonalMatrix
<Scalar, Eigen::Dynamic, Eigen::Dynamic> DiagMat;
int dim = V.cols();
assert((dim==2 || dim==3) &&
"The dimension of the vertices should be 2 or 3");
//Construct the combined gradient matric
SparseMat G;
igl::grad(Eigen::PlainObjectBase<DerivedV>(V),
Eigen::PlainObjectBase<DerivedF>(F),
G, false);
SparseMat GG(F.rows(), dim*V.rows());
GG.reserve(G.nonZeros());
for(int i=0; i<dim; ++i)
GG.middleCols(i*G.cols(),G.cols()) = G.middleRows(i*F.rows(),F.rows());
SparseMat D;
igl::repdiag(GG,dim,D);
//Compute area matrix
VecXd areas;
igl::doublearea(V, F, areas);
DiagMat A = (0.5*areas).replicate(dim,1).asDiagonal();
//Compute FEM Hessian
H = D.transpose()*A*G;
}
typename Gradient<Eigen::Matrix<typename DerivedR::Scalar, RPY_SIZE, 1>, DerivedDR::ColsAtCompileTime>::type drotmat2rpy(
const Eigen::MatrixBase<DerivedR>& R,
const Eigen::MatrixBase<DerivedDR>& dR)
{
EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Eigen::MatrixBase<DerivedR>, SPACE_DIMENSION, SPACE_DIMENSION);
EIGEN_STATIC_ASSERT(Eigen::MatrixBase<DerivedDR>::RowsAtCompileTime == RotmatSize, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);
const int nq = dR.cols();
typedef typename DerivedR::Scalar Scalar;
typedef typename Gradient<Eigen::Matrix<Scalar, RPY_SIZE, 1>, DerivedDR::ColsAtCompileTime>::type ReturnType;
ReturnType drpy(RPY_SIZE, nq);
auto dR11_dq = getSubMatrixGradient(dR, 0, 0, R.rows());
auto dR21_dq = getSubMatrixGradient(dR, 1, 0, R.rows());
auto dR31_dq = getSubMatrixGradient(dR, 2, 0, R.rows());
auto dR32_dq = getSubMatrixGradient(dR, 2, 1, R.rows());
auto dR33_dq = getSubMatrixGradient(dR, 2, 2, R.rows());
Scalar sqterm = R(2,1) * R(2,1) + R(2,2) * R(2,2);
using namespace std;
// droll_dq
drpy.row(0) = (R(2, 2) * dR32_dq - R(2, 1) * dR33_dq) / sqterm;
// dpitch_dq
Scalar sqrt_sqterm = sqrt(sqterm);
drpy.row(1) = (-sqrt_sqterm * dR31_dq + R(2, 0) / sqrt_sqterm * (R(2, 1) * dR32_dq + R(2, 2) * dR33_dq)) / (R(2, 0) * R(2, 0) + R(2, 1) * R(2, 1) + R(2, 2) * R(2, 2));
// dyaw_dq
sqterm = R(0, 0) * R(0, 0) + R(1, 0) * R(1, 0);
drpy.row(2) = (R(0, 0) * dR21_dq - R(1, 0) * dR11_dq) / sqterm;
return drpy;
}
bool allGreaterEquals(const Eigen::MatrixBase<DerivedA> & A,
const Eigen::MatrixBase<DerivedB> & B,
double tolerance = math::NUMERICAL_ZERO_DIFFERENCE)
{
assertion(A.rows() == B.rows(), "Matrices with different number of rows can't be compared.");
assertion(A.cols() == B.cols(), "Matrices with different number of cols can't be compared.");
return ((A-B).array() >= tolerance).all();
}
void assert_size(const Eigen::MatrixBase<Derived> &X, int rows_expected, int cols_expected)
{
common_assert_msg_ex(
X.rows() == rows_expected && X.cols() == cols_expected,
"matrix [row, col] mismatch" << std::endl <<
"actual: [" << X.rows() << ", " << X.cols() << "]" << std::endl <<
"expected: [" << rows_expected << ", " << cols_expected << "]",
eigen_utilities::assert_error);
}
void ba81NormalQuad::layer::detectTwoTier(Eigen::ArrayBase<T1> ¶m,
Eigen::MatrixBase<T2> &mean, Eigen::MatrixBase<T3> &cov)
{
if (mean.rows() < 3) return;
std::vector<int> orthogonal;
Eigen::Matrix<Eigen::DenseIndex, Eigen::Dynamic, 1>
numCov((cov.array() != 0.0).matrix().colwise().count());
std::vector<int> candidate;
for (int fx=0; fx < numCov.rows(); ++fx) {
if (numCov(fx) == 1) candidate.push_back(fx);
}
if (candidate.size() > 1) {
std::vector<bool> mask(numItems());
for (int cx=candidate.size() - 1; cx >= 0; --cx) {
std::vector<bool> loading(numItems());
for (int ix=0; ix < numItems(); ++ix) {
loading[ix] = param(candidate[cx], itemsMap[ix]) != 0;
}
std::vector<bool> overlap(loading.size());
std::transform(loading.begin(), loading.end(),
mask.begin(), overlap.begin(),
std::logical_and<bool>());
if (std::find(overlap.begin(), overlap.end(), true) == overlap.end()) {
std::transform(loading.begin(), loading.end(),
mask.begin(), mask.begin(),
std::logical_or<bool>());
orthogonal.push_back(candidate[cx]);
}
}
}
std::reverse(orthogonal.begin(), orthogonal.end());
if (orthogonal.size() == 1) orthogonal.clear();
if (orthogonal.size() && orthogonal[0] != mean.rows() - int(orthogonal.size())) {
mxThrow("Independent specific factors must be given after general dense factors");
}
numSpecific = orthogonal.size();
if (numSpecific) {
Sgroup.assign(numItems(), 0);
for (int ix=0; ix < numItems(); ix++) {
for (int dx=orthogonal[0]; dx < mean.rows(); ++dx) {
if (param(dx, itemsMap[ix]) != 0) {
Sgroup[ix] = dx - orthogonal[0];
continue;
}
}
}
//Eigen::Map< Eigen::ArrayXi > foo(Sgroup.data(), param.cols());
//mxPrintMat("sgroup", foo);
}
}
IGL_INLINE void igl::per_face_normals_stable(
const Eigen::MatrixBase<DerivedV>& V,
const Eigen::MatrixBase<DerivedF>& F,
Eigen::PlainObjectBase<DerivedN> & N)
{
using namespace Eigen;
typedef Matrix<typename DerivedV::Scalar,1,3> RowVectorV3;
typedef typename DerivedV::Scalar Scalar;
const size_t m = F.rows();
N.resize(F.rows(),3);
// Grad all points
for(size_t f = 0;f<m;f++)
{
const RowVectorV3 p0 = V.row(F(f,0));
const RowVectorV3 p1 = V.row(F(f,1));
const RowVectorV3 p2 = V.row(F(f,2));
const RowVectorV3 n0 = (p1 - p0).cross(p2 - p0);
const RowVectorV3 n1 = (p2 - p1).cross(p0 - p1);
const RowVectorV3 n2 = (p0 - p2).cross(p1 - p2);
// careful sum
for(int d = 0;d<3;d++)
{
// This is a little _silly_ in terms of complexity, but its recursive
// implementation is clean looking...
const std::function<Scalar(Scalar,Scalar,Scalar)> sum3 =
[&sum3](Scalar a, Scalar b, Scalar c)->Scalar
{
if(fabs(c)>fabs(a))
{
return sum3(c,b,a);
}
// c < a
if(fabs(c)>fabs(b))
{
return sum3(a,c,b);
}
// c < a, c < b
if(fabs(b)>fabs(a))
{
return sum3(b,a,c);
}
return (a+b)+c;
};
N(f,d) = sum3(n0(d),n1(d),n2(d));
}
// sum better not be sure, or else NaN
N.row(f) /= N.row(f).norm();
}
}
static void run( float a, const Eigen::MatrixBase<Derived2> & A, const Eigen::MatrixBase<Derived1> & x, float b, Eigen::MatrixBase<Derived1> &y) {
EIGEN_STATIC_ASSERT(sizeof(PREC) == sizeof(typename Derived1::Scalar), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
ASSERTMSG(A.cols() == x.rows() && A.rows() == y.rows() ,"ERROR: Vector/Matrix wrong dimension");
// b_dev = alpha * A_dev * x_old_dev + beta *b_dev
//Derived2 C = A;
#if USE_INTEL_BLAS == 1
CBLAS_ORDER order;
CBLAS_TRANSPOSE trans;
if(Derived1::Flags & Eigen::RowMajorBit) {
order = CblasRowMajor;
} else {
order = CblasColMajor;
}
trans = CblasNoTrans;
mkl_set_dynamic(false);
mkl_set_num_threads(BLAS_NUM_THREADS);
//cout << "Threads:" << mkl_get_max_threads();
cblas_sgemv(order, trans, A.rows(), A.cols(), a, const_cast<double*>(&(A.operator()(0,0))), A.outerStride(), const_cast<double*>(&(x.operator()(0,0))), 1, b, &(y.operator()(0,0)), 1);
//cblas_dgemm(order,trans,trans, A.rows(), A.cols(), A.cols(), 1.0, const_cast<double*>(&(A.operator()(0,0))), A.rows(), const_cast<double*>(&(A.operator()(0,0))), A.rows(), 1.0 , &(C.operator()(0,0)), C.rows() );
#else
#if USE_GOTO_BLAS == 1
/* static DGEMVFunc DGEMV = NULL;
if (DGEMV == NULL) {
HINSTANCE hInstLibrary = LoadLibrary("libopenblasp-r0.1alpha2.2.dll");
DGEMV = (DGEMVFunc)GetProcAddress(hInstLibrary, "DGEMV");
}*/
char trans = 'N';
BLAS_INT idx = 1;
BLAS_INT m = A.rows();
BLAS_INT n = A.cols();
sgemv(&trans, &m, &n, &a, &(A.operator()(0,0)), &m, &(x.operator()(0,0)), &idx, &b, &(y.operator()(0,0)), &idx);
// FreeLibrary(hInstLibrary);
#else
ASSERTMSG(false,"No implementation for BLAS defined!");
#endif
#endif
}
void least_squares_eqcon(Eigen::MatrixBase<data1__> &x, const Eigen::MatrixBase<data2__> &A, const Eigen::MatrixBase<data3__> &b, const Eigen::MatrixBase<data4__> &B, const Eigen::MatrixBase<data5__> &d)
{
typedef Eigen::Matrix<typename Eigen::MatrixBase<data4__>::Scalar, Eigen::Dynamic, Eigen::Dynamic> B_matrixD;
B_matrixD Q, R, temp_B, temp_R;
typedef Eigen::Matrix<typename Eigen::MatrixBase<data2__>::Scalar, Eigen::Dynamic, Eigen::Dynamic> A_matrixD;
A_matrixD A1, A2, temp_A;
typedef Eigen::Matrix<typename Eigen::MatrixBase<data5__>::Scalar, Eigen::Dynamic, Eigen::Dynamic> d_matrixD;
d_matrixD y;
typedef Eigen::Matrix<typename Eigen::MatrixBase<data3__>::Scalar, Eigen::Dynamic, Eigen::Dynamic> b_matrixD;
b_matrixD z, rhs;
typedef Eigen::Matrix<typename Eigen::MatrixBase<data1__>::Scalar, Eigen::Dynamic, Eigen::Dynamic> x_matrixD;
x_matrixD x_temp(A.cols(), b.cols());
typename A_matrixD::Index p(B.rows()), n(A.cols());
#ifdef DEBUG
typename A_matrixD::Index m(b.rows());
#endif
// build Q and R
Eigen::HouseholderQR<B_matrixD> qr(B.transpose());
Q=qr.householderQ();
temp_B=qr.matrixQR();
temp_R=Eigen::TriangularView<B_matrixD, Eigen::Upper>(temp_B);
R=temp_R.topRows(p);
assert((R.rows()==p) && (R.cols()==p));
assert((Q.rows()==n) && (Q.cols()==n));
// build A1 and A2
temp_A=A*Q;
A1=temp_A.leftCols(p);
A2=temp_A.rightCols(n-p);
#ifdef DEBUG
assert((A1.rows()==m) && (A1.cols()==p));
assert((A2.rows()==m) && (A2.cols()==n-p));
#endif
assert(A1.cols()==p);
assert(A2.cols()==n-p);
// solve for y
y=R.transpose().lu().solve(d);
// setup the unconstrained optimization
rhs=b-A1*y;
least_squares_uncon(z, A2, rhs);
// build the solution
x_temp.topRows(p)=y;
x_temp.bottomRows(n-p)=z;
x=Q*x_temp;
}
typename Derived::PlainObject transposeGrad(
const Eigen::MatrixBase<Derived>& dX, int rows_X)
{
typename Derived::PlainObject dX_transpose(dX.rows(), dX.cols());
int numel = dX.rows();
int index = 0;
for (int i = 0; i < numel; i++) {
dX_transpose.row(i) = dX.row(index);
index += rows_X;
if (index >= numel) {
index = (index % numel) + 1;
}
}
return dX_transpose;
}
Eigen::Matrix<typename DerivedDA::Scalar, matGradMultNumRows(DerivedDA::RowsAtCompileTime, Derivedb::RowsAtCompileTime), DerivedDA::ColsAtCompileTime>
matGradMult(const Eigen::MatrixBase<DerivedDA>& dA, const Eigen::MatrixBase<Derivedb>& b) {
const int nq = dA.cols();
assert(b.cols() == 1);
assert(dA.rows() % b.rows() == 0);
const int A_rows = dA.rows() / b.rows();
Eigen::Matrix<typename DerivedDA::Scalar, matGradMultNumRows(DerivedDA::RowsAtCompileTime, Derivedb::RowsAtCompileTime), DerivedDA::ColsAtCompileTime> ret(A_rows, nq);
ret.setZero();
for (int row = 0; row < b.rows(); row++) {
ret += b(row, 0) * dA.block(row * A_rows, 0, A_rows, nq);
}
return ret;
}
void printEigen(const Eigen::MatrixBase<T> &b)
{
for (int i = 0; i < b.rows(); ++i) {
printf("(");
for (int j = 0; j < b.cols(); ++j) {
if (j > 0) {
printf(",");
}
printf("%.3f", static_cast<double>(b(i, j)));
}
printf(")%s\n", i + 1 < b.rows() ? "," : "");
}
}
IGL_INLINE void igl::internal_angles_using_edge_lengths(
const Eigen::MatrixBase<DerivedL>& L,
Eigen::PlainObjectBase<DerivedK> & K)
{
// Note:
// Usage of internal_angles_using_squared_edge_lengths() is preferred to internal_angles_using_squared_edge_lengths()
// This function is deprecated and probably will be removed in future versions
typedef typename DerivedL::Index Index;
assert(L.cols() == 3 && "Edge-lengths should come from triangles");
const Index m = L.rows();
K.resize(m,3);
parallel_for(
m,
[&L,&K](const Index f)
{
for(size_t d = 0;d<3;d++)
{
const auto & s1 = L(f,d);
const auto & s2 = L(f,(d+1)%3);
const auto & s3 = L(f,(d+2)%3);
K(f,d) = acos((s3*s3 + s2*s2 - s1*s1)/(2.*s3*s2));
}
},
1000l);
}
bool checkRange(const Eigen::MatrixBase<DerivedA>& mm,
typename DerivedA::Scalar minVal, typename DerivedA::Scalar maxVal)
{
assert(minVal < maxVal);
// Go through each element in the matrix and ensure they are not
// NaN and fall within the specified min and max values.
for (int ii = 0; ii < mm.rows(); ii++)
{
for (int jj = 0; jj < mm.cols(); jj++)
{
if (std::isnan(mm(ii, jj)))
{
std::stringstream ss;
ss << "NaN detected at index (" << ii << ", " << jj << "), returning false!";
ROS_WARN_STREAM(ss.str());
return false;
}
if (mm(ii, jj) > maxVal || mm(ii, jj) < minVal)
{
std::stringstream ss;
ss << "Value of out range at index (" << ii << ", " << jj << "), returning false.\n"
" - value = " << mm(ii, jj) << "\n"
" - minVal = " << minVal << "\n"
" - maxVal = " << maxVal;
ROS_WARN_STREAM(ss.str());
return false;
}
}
}
return true;
}
IGL_INLINE void igl::face_areas(
const Eigen::MatrixBase<DerivedL>& L,
const typename DerivedL::Scalar doublearea_nan_replacement,
Eigen::PlainObjectBase<DerivedA>& A)
{
using namespace Eigen;
assert(L.cols() == 6);
const int m = L.rows();
// (unsigned) face Areas (opposite vertices: 1 2 3 4)
Matrix<typename DerivedA::Scalar,Dynamic,1>
A0(m,1), A1(m,1), A2(m,1), A3(m,1);
Matrix<typename DerivedA::Scalar,Dynamic,3>
L0(m,3), L1(m,3), L2(m,3), L3(m,3);
L0<<L.col(1),L.col(2),L.col(3);
L1<<L.col(0),L.col(2),L.col(4);
L2<<L.col(0),L.col(1),L.col(5);
L3<<L.col(3),L.col(4),L.col(5);
doublearea(L0,doublearea_nan_replacement,A0);
doublearea(L1,doublearea_nan_replacement,A1);
doublearea(L2,doublearea_nan_replacement,A2);
doublearea(L3,doublearea_nan_replacement,A3);
A.resize(m,4);
A.col(0) = 0.5*A0;
A.col(1) = 0.5*A1;
A.col(2) = 0.5*A2;
A.col(3) = 0.5*A3;
}
double SLHA_io::read_vector(const std::string& block_name, Eigen::MatrixBase<Derived>& vector) const
{
if (vector.cols() != 1) throw SetupError("Vector has more than 1 column");
auto block = data.find(data.cbegin(), data.cend(), block_name);
const int rows = vector.rows();
double scale = 0.;
while (block != data.cend()) {
for (const auto& line: *block) {
if (!line.is_data_line()) {
// read scale from block definition
if (line.size() > 3 &&
to_lower(line[0]) == "block" && line[2] == "Q=")
scale = convert_to<double>(line[3]);
continue;
}
if (line.size() >= 2) {
const int i = convert_to<int>(line[0]) - 1;
if (0 <= i && i < rows) {
const double value = convert_to<double>(line[1]);
vector(i,0) = value;
}
}
}
++block;
block = data.find(block, data.cend(), block_name);
}
return scale;
}
void SLHA_io::set_block_imag(const std::string& name,
const Eigen::MatrixBase<Derived>& matrix,
const std::string& symbol, double scale)
{
std::ostringstream ss;
ss << "Block " << name;
if (scale != 0.)
ss << " Q= " << FORMAT_SCALE(scale);
ss << '\n';
const int rows = matrix.rows();
const int cols = matrix.cols();
for (int i = 1; i <= rows; ++i) {
if (cols == 1) {
ss << boost::format(vector_formatter) % i % Im(matrix(i-1,0))
% ("Im(" + symbol + "(" + ToString(i) + "))");
} else {
for (int k = 1; k <= cols; ++k) {
ss << boost::format(mixing_matrix_formatter) % i % k % Im(matrix(i-1,k-1))
% ("Im(" + symbol + "(" + ToString(i) + "," + ToString(k) + "))");
}
}
}
set_block(ss);
}
IGL_INLINE void igl::project_to_line_segment(
const Eigen::MatrixBase<DerivedP> & P,
const Eigen::MatrixBase<DerivedS> & S,
const Eigen::MatrixBase<DerivedD> & D,
Eigen::PlainObjectBase<Derivedt> & t,
Eigen::PlainObjectBase<DerivedsqrD> & sqrD)
{
project_to_line(P,S,D,t,sqrD);
const int np = P.rows();
// loop over points and fix those that projected beyond endpoints
#pragma omp parallel for if (np>10000)
for(int p = 0;p<np;p++)
{
const DerivedP Pp = P.row(p);
if(t(p)<0)
{
sqrD(p) = (Pp-S).squaredNorm();
t(p) = 0;
}else if(t(p)>1)
{
sqrD(p) = (Pp-D).squaredNorm();
t(p) = 1;
}
}
}
IGL_INLINE bool igl::writeDMAT(
const std::string file_name,
const Eigen::MatrixBase<DerivedW> & W,
const bool ascii)
{
FILE * fp = fopen(file_name.c_str(),"wb");
if(fp == NULL)
{
fprintf(stderr,"IOError: writeDMAT() could not open %s...",file_name.c_str());
return false;
}
if(ascii)
{
// first line contains number of rows and number of columns
fprintf(fp,"%d %d\n",(int)W.cols(),(int)W.rows());
// Loop over columns slowly
for(int j = 0;j < W.cols();j++)
{
// loop over rows (down columns) quickly
for(int i = 0;i < W.rows();i++)
{
fprintf(fp,"%0.17lg\n",(double)W(i,j));
}
}
}else
{
// write header for ascii
fprintf(fp,"0 0\n");
// first line contains number of rows and number of columns
fprintf(fp,"%d %d\n",(int)W.cols(),(int)W.rows());
// reader assumes the binary part is double precision
Eigen::MatrixXd Wd = W.template cast<double>();
fwrite(Wd.data(),sizeof(double),Wd.size(),fp);
//// Loop over columns slowly
//for(int j = 0;j < W.cols();j++)
//{
// // loop over rows (down columns) quickly
// for(int i = 0;i < W.rows();i++)
// {
// double d = (double)W(i,j);
// fwrite(&d,sizeof(double),1,fp);
// }
//}
}
fclose(fp);
return true;
}
void ErrorTermFs<C>::setInvR(const Eigen::MatrixBase<DERIVED>& invR)
{
SM_ASSERT_EQ(Exception, invR.rows(), invR.cols(), "The covariance matrix must be square");
SM_ASSERT_EQ(Exception, invR.rows(), (int)dimension(), "The covariance matrix does not match the size of the error");
// http://eigen.tuxfamily.org/dox-devel/classEigen_1_1LDLT.html#details
// LDLT seems to work on positive semidefinite matrices.
sm::eigen::computeMatrixSqrt(invR, _sqrtInvR);
}
void Precision(Eigen::MatrixBase<Derived> & v, unsigned prec)
{
using bertini::Precision;
for (int ii=0; ii<v.rows(); ++ii)
for (int jj=0; jj<v.cols(); ++jj)
Precision(v(ii,jj),prec);
}
template<typename Rhs> inline const Eigen::internal::solve_retval<MyJacobiPreconditioner, Rhs>
solve(const Eigen::MatrixBase<Rhs>& b) const
{
eigen_assert(m_isInitialized && "MyJacobiPreconditioner is not initialized.");
eigen_assert(m_invdiag.size()==b.rows()
&& "MyJacobiPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
return Eigen::internal::solve_retval<MyJacobiPreconditioner, Rhs>(*this, b.derived());
}
IGL_INLINE void igl::vertex_triangle_adjacency(
const Eigen::MatrixBase<DerivedV>& V,
const Eigen::MatrixBase<DerivedF>& F,
std::vector<std::vector<IndexType> >& VF,
std::vector<std::vector<IndexType> >& VFi)
{
return vertex_triangle_adjacency(V.rows(),F,VF,VFi);
}
