int myGRBaddconstrs(GRBmodel* model, MatrixBase<DerivedA> const& A,
MatrixBase<DerivedB> const& b, char sense,
double sparseness_threshold = 1e-14) {
int i, j, nnz, error = 0;
/*
// todo: it seems like I should just be able to do something like this:
SparseMatrix<double, RowMajor> sparseAeq(Aeq.sparseView());
sparseAeq.makeCompressed();
error =
GRBaddconstrs(model, nq_con, sparseAeq.nonZeros(), sparseAeq.InnerIndices(), sparseAeq.OuterStarts(), sparseAeq.Values(), beq.data(), NULL);
*/
int* cind = new int[A.cols()];
double* cval = new double[A.cols()];
for (i = 0; i < A.rows(); i++) {
nnz = 0;
for (j = 0; j < A.cols(); j++) {
if (abs(A(i, j)) > sparseness_threshold) {
cval[nnz] = A(i, j);
cind[nnz++] = j;
}
}
error = GRBaddconstr(model, nnz, cind, cval, sense, b(i), NULL);
if (error) break;
}
delete[] cind;
delete[] cval;
return error;
}
bool CSRMatrix::eq(const MatrixBase &other) const
{
unsigned row = this->nrows();
if (row != other.nrows() or this->ncols() != other.ncols())
return false;
if (is_a<CSRMatrix>(other)) {
const CSRMatrix &o = down_cast<const CSRMatrix &>(other);
if (this->p_[row] != o.p_[row])
return false;
for (unsigned i = 0; i <= row; i++)
if (this->p_[i] != o.p_[i])
return false;
for (unsigned i = 0; i < this->p_[row]; i++)
if ((this->j_[i] != o.j_[i]) or neq(*this->x_[i], *(o.x_[i])))
return false;
return true;
} else {
return this->MatrixBase::eq(other);
}
}
Derived lidarBoostEngine::apply_optical_flow(const MatrixBase<Derived>& I, std::vector< Derived > uv)
{
Derived moved_I(beta*n, beta*m);
// W = SparseMatrix<double>( beta*n, beta*m );
// W.reserve(VectorXi::Constant(n, m));
// T = SparseMatrix<double>( beta*n, beta*m );
// MatrixXi W( beta*n, beta*m ), T( beta*n, beta*m );
W = MatrixXd( beta*n, beta*m );
int new_i, new_j;
for( int i = 0; i < I.rows(); i++ )
{
for( int j = 0; j < I.cols(); j++ )
{
new_i = round( beta * (i + uv[0](i, j)) );
new_j = round( beta * (j + uv[1](i, j)) );
if(new_i > 0 && new_i < beta*n && new_j > 0 && new_j < beta*m)
{
moved_I(new_i, new_j) = I(i ,j);
W(new_i, new_j) = 1;
}
}
}
return moved_I;
}
inline auto convolution(
MatrixBase< DerivedM > const& m,
MatrixBase< DerivedK > const& k
){
using ScalarK = typename DerivedK::Scalar;
using ResultType = Matrix< ScalarK, Eigen::Dynamic, Eigen::Dynamic >;
int kr = k.rows();
int kc = k.cols();
int res_r = m.rows() - kr + 1;
int res_c = m.cols() - kc + 1;
ResultType res(res_r, res_c);
for(int my = 0; my < res_r; ++my){
for(int mx = 0; mx < res_c; ++mx){
ScalarK b = 0;
for(int ky = 0; ky < kr; ++ky){
for(int kx = 0; kx < kc; ++kx){
b += m(my + ky, mx + kx) * k(ky, kx);
}
}
res.coeffRef(my, mx) = b;
}
}
return res;
}
Vector<T> GaussianElimination<T>::operator()(MatrixBase<T>& A, const Vector<T>& b)
{
Vector<T> x( b.getSize() );
Matrix<T> newA(A.getNumRows(),A.getNumCols());
newA = A;
newA.addColumn(b);
// Forward Elimination
for(int i=1; i < newA.getNumRows(); i++)
{
reduceDown(newA, i);
}
// Backward Elimination
for(int i=newA.getNumRows()-2; i >= 0; i--)
{
reduceUp(newA, i);
}
// Solve for x
for(int i=0; i < x.getSize(); i++)
{
x[i] = newA(i,newA.getNumCols()-1) / newA(i,i);
}
return x;
}
void setLinearIndices(MatrixBase<Derived>& A)
{
for (int col = 0; col < A.cols(); col++) {
for (int row = 0; row < A.rows(); row++) {
A(row, col) = row + col * A.rows() + 1;
}
}
}
std::vector < Derived > lidarBoostEngine::lk_optical_flow( const MatrixBase<Derived>& I1, const MatrixBase<Derived> &I2, int win_sz)
{
//Instantiate optical flow matrices
std::vector < Derived > uv(2);
//Create masks
Matrix2d robX, robY, robT;
robX << -1, 1,
-1, 1;
robY << -1, -1,
1, 1;
robT << -1, -1,
-1, -1;
Derived Ix, Iy, It, A, solutions, x_block, y_block, t_block;
//Apply masks to images and average the result
Ix = 0.5 * ( conv2d( I1, robX ) + conv2d( I2, robX ) );
Iy = 0.5 * ( conv2d( I1, robY ) + conv2d( I2, robY ) );
It = 0.5 * ( conv2d( I1, robT ) + conv2d( I2, robT ) );
uv[0] = Derived::Zero( I1.rows(), I1.cols() );
uv[1] = Derived::Zero( I1.rows(), I1.cols() );
int hw = win_sz/2;
for( int i = hw+1; i < I1.rows()-hw; i++ )
{
for ( int j = hw+1; j < I1.cols()-hw; j++ )
{
//Take a small block of window size in the filtered images
x_block = Ix.block( i-hw, j-hw, win_sz, win_sz);
y_block = Iy.block( i-hw, j-hw, win_sz, win_sz);
t_block = It.block( i-hw, j-hw, win_sz, win_sz);
//Convert these blocks in vectors
Map<Derived> A1( x_block.data(), win_sz*win_sz, 1);
Map<Derived> A2( y_block.data(), win_sz*win_sz, 1);
Map<Derived> B( t_block.data(), win_sz*win_sz, 1);
//Organize the vectors in a matrix
A = Derived( win_sz*win_sz, 2 );
A.block(0, 0, win_sz*win_sz, 1) = A1;
A.block(0, 1, win_sz*win_sz, 1) = A2;
//Solve the linear least square system
solutions = (A.transpose() * A).ldlt().solve(A.transpose() * B);
//Insert the solutions in the optical flow matrices
uv[0](i, j) = solutions(0);
uv[1](i, j) = solutions(1);
}
}
return uv;
}
void save(const char *filename, const MatrixBase<Derived>& m)
{
unsigned int rows = m.rows(), cols = m.cols();
std::ofstream f(filename, std::ios::out | std::ios::binary);
f.write((char *)&rows, sizeof(rows));
f.write((char *)&cols, sizeof(cols));
f.write((char *)m.derived().data(), sizeof(typename Derived::Scalar) * rows * cols);
f.close();
}
void vFSMNLayer::Compute( const MatrixBase& feature, MatrixBase* output ) {
m_linear.Compute( feature, output );
m_memory.Reshape( feature.Rows(), feature.Columns() );
m_memory.VfsmnMemory( feature, m_filter, m_position );
output->Sgemm( 1.0f, 1.0f, m_memory, CUBLAS_OP_N, m_weight, CUBLAS_OP_N );
output->ReLU( *output );
}
void load(const char *filename, MatrixBase<Derived>& m)
{
int nrows, ncols;
std::ifstream f(filename, std::ios::binary);
f.read(reinterpret_cast<char*>(&nrows), sizeof(int));
f.read(reinterpret_cast<char*>(&ncols), sizeof(int));
m.derived().resize(nrows, ncols);
f.read((char*)m.derived().data(), sizeof(typename Derived::Scalar) * nrows * ncols);
}
bool hasInf(const MatrixBase& expr)
{
for (int i = 0; i != expr.cols(); ++i) {
for (int j = 0; j != expr.rows(); ++j) {
if (std::isinf(expr.coeff(j, i)))
return true;
}
}
return false;
}
bool hasNaN(const MatrixBase& expr)
{
for (int j = 0; j != expr.cols(); ++j) {
for (int i = 0; i != expr.rows(); ++i) {
if (std::isnan(expr.coeff(i, j)))
return true;
}
}
return false;
}
void getCols(std::set<int> &cols, MatrixBase<DerivedA> const &M,
MatrixBase<DerivedB> &Msub) {
if (static_cast<int>(cols.size()) == M.cols()) {
Msub = M;
return;
}
int i = 0;
for (std::set<int>::iterator iter = cols.begin(); iter != cols.end(); iter++)
Msub.col(i++) = M.col(*iter);
}
void DenseMatrix::add_matrix(const MatrixBase &other, MatrixBase &result) const
{
SYMENGINE_ASSERT(row_ == result.nrows() and col_ == result.ncols());
if (is_a<DenseMatrix>(other) and is_a<DenseMatrix>(result)) {
const DenseMatrix &o = static_cast<const DenseMatrix &>(other);
DenseMatrix &r = static_cast<DenseMatrix &>(result);
add_dense_dense(*this, o, r);
}
}
void Matrix<T>::copy(const MatrixBase<T>& a)
{
setSize(a.getNumRows(), a.getNumCols());
for(int i=0; i < a.getNumRows(); i++)
{
for(int j=0; j < a.getNumCols(); j++)
{
head[i][j] = a(i,j);
}
}
}
void getRows(std::set<int> &rows, MatrixBase<DerivedA> const &M,
MatrixBase<DerivedB> &Msub) {
if (static_cast<int>(rows.size()) == M.rows()) {
Msub = M;
return;
}
int i = 0;
for (std::set<int>::iterator iter = rows.begin(); iter != rows.end(); iter++)
Msub.row(i++) = M.row(*iter);
}
void fillSubMatrix ( MatrixBase<T> & fillMatrix, MatrixBase<T> & subMatrix, unsigned int startRow, unsigned int startCol )
{
for ( unsigned int i = 0; i < subMatrix.size(); i++ )
{
for ( unsigned int j = 0; j < subMatrix.size(); j++ )
{
fillMatrix( startRow + i , startCol + j ) = subMatrix ( i , j );
}
}
return;
}
// TODO(#2274) Fix NOLINTNEXTLINE(runtime/references).
void getCols(std::set<int>& cols, MatrixBase<DerivedA> const& M,
// TODO(#2274) Fix NOLINTNEXTLINE(runtime/references).
MatrixBase<DerivedB>& Msub) {
if (static_cast<int>(cols.size()) == M.cols()) {
Msub = M;
return;
}
int i = 0;
for (std::set<int>::iterator iter = cols.begin(); iter != cols.end(); iter++)
Msub.col(i++) = M.col(*iter);
}
MatrixXd stack( const MatrixBase<D1>& m1, const MatrixBase<D2>& m2 )
{
assert( m1.cols() == m2.cols() );
const int m1r=m1.rows(), m2r=m2.rows(), mr=m1r+m2r, mc=m1.cols();
MatrixXd res( mr,mc );
for( int i=0;i<m1r;++i )
for( int j=0;j<mc;++j ) res(i,j) = m1(i,j);
for( int i=0;i<m2r;++i )
for( int j=0;j<mc;++j ) res(m1r+i,j) = m2(i,j);
return res;
}
bool MatrixBase::eq(const MatrixBase &other) const
{
if (this->nrows() != other.nrows() || this->ncols() != other.ncols())
return false;
for (unsigned i = 0; i < this->nrows(); i++)
for (unsigned j = 0; j < this->ncols(); j++)
if(neq(this->get(i, j), other.get(i, j)))
return false;
return true;
}
void sFSMNLayer::Compute( const MatrixBase& feature, MatrixBase* output ) {
m_linear.Compute( feature, output );
m_memory.Reshape( feature.Rows(), feature.Columns() );
m_memory.Strmm( 1.0f,
m_block_diagonal, CUBLAS_SIDE_LEFT, CUBLAS_FILL_MODE_LOWER,
feature, CUBLAS_OP_N ); // @xmb20160226
// m_memory.Sgemm( 0.0f, 1.0f, m_block_diagonal, CUBLAS_OP_N, feature, CUBLAS_OP_N );
output->Sgemm( 1.0f, 1.0f, m_memory, CUBLAS_OP_N, m_weight, CUBLAS_OP_N );
output->ReLU( *output );
}
void angleDiff(MatrixBase<DerivedPhi1> const &phi1, MatrixBase<DerivedPhi2> const &phi2, MatrixBase<DerivedD> &d) {
d = phi2 - phi1;
for (int i = 0; i < phi1.rows(); i++) {
for (int j = 0; j < phi1.cols(); j++) {
if (d(i,j) < -M_PI) {
d(i,j) = fmod(d(i,j) + M_PI, 2*M_PI) + M_PI;
} else {
d(i,j) = fmod(d(i,j) + M_PI, 2*M_PI) - M_PI;
}
}
}
}
void dumpPointsMatrix(std::string filePath, const MatrixBase<Derived>& v)
{
std::fstream l(filePath.c_str());
if (!l.good())
{
GRSF_ERRORMSG("Could not open file: " << filePath << std::endl)
}
for (unsigned int i = 0; i < v.cols(); i++)
{
l << v.col(i).transpose().format(MyMatrixIOFormat::SpaceSep) << std::endl;
}
}
Matrix<Scalar, Dynamic, 1> dynamicsBiasTermTemp(const RigidBodyTree &model, KinematicsCache<Scalar> &cache, const MatrixBase<DerivedF> &f_ext_value) {
// temporary solution.
eigen_aligned_unordered_map<const RigidBody *, Matrix<Scalar, 6, 1> > f_ext;
if (f_ext_value.size() > 0) {
assert(f_ext_value.cols() == model.bodies.size());
for (DenseIndex i = 0; i < f_ext_value.cols(); i++) {
f_ext.insert({model.bodies[i].get(), f_ext_value.col(i)});
}
}
return model.dynamicsBiasTerm(cache, f_ext);
};
Derived lidarBoostEngine::conv2d(const MatrixBase<Derived>& I, const MatrixBase<Derived2> &kernel )
{
Derived O = Derived::Zero(I.rows(),I.cols());
typedef typename Derived::Scalar Scalar;
typedef typename Derived2::Scalar Scalar2;
int col=0,row=0;
int KSizeX = kernel.rows();
int KSizeY = kernel.cols();
int limitRow = I.rows()-KSizeX;
int limitCol = I.cols()-KSizeY;
Derived2 block ;
Scalar normalization = kernel.sum();
if ( normalization < 1E-6 )
{
normalization=1;
}
for ( row = KSizeX; row < limitRow; row++ )
{
for ( col = KSizeY; col < limitCol; col++ )
{
Scalar b=(static_cast<Derived2>( I.block(row,col,KSizeX,KSizeY ) ).cwiseProduct(kernel)).sum();
O.coeffRef(row,col) = b;
}
}
return O/normalization;
}
Matrix<Scalar, Dynamic, 1> dynamicsBiasTermTemp(
const RigidBodyTreed& model, KinematicsCache<Scalar>& cache,
const MatrixBase<DerivedF>& f_ext_value) {
// temporary solution.
typename RigidBodyTree<Scalar>::BodyToWrenchMap external_wrenches;
if (f_ext_value.size() > 0) {
DRAKE_ASSERT(f_ext_value.cols() == model.bodies.size());
for (Eigen::Index i = 0; i < f_ext_value.cols(); i++) {
external_wrenches.insert({model.bodies[i].get(), f_ext_value.col(i)});
}
}
return model.dynamicsBiasTerm(cache, external_wrenches);
}
MatrixBase<T>& Matrix<T>::operator*=(const MatrixBase<T>& rhs)
{
if( getNumCols() != rhs.getNumRows() ) throw SizeError(getNumCols(), "operator*= matrix");
MatrixBase<T>* retVal = new Matrix<T>(getNumRows(), rhs.getNumCols());
for(int i=0; i < getNumRows(); i++)
{
for(int j=0; j < rhs.getNumCols(); j++)
{
retVal->operator()(i,j) = getRow(i) * rhs.getColumn(j);
}
}
*this = *retVal;
delete retVal;
return *this;
}
// print the features (debugging)
void FeatureFile::print(MatrixBase<float> &mFeatures, unsigned int iDelta) {
assert((mFeatures.getCols()%iDelta) == 0);
cout << endl;
for(unsigned int i=0 ; i < mFeatures.getRows() ; ++i) {
for(unsigned int j=0 ; j < iDelta ; ++j) {
for(unsigned int h=0 ; h < mFeatures.getCols()/iDelta ; ++h) {
cout << " " << FLT(9,6) << mFeatures(i,h+j*(mFeatures.getCols()/iDelta));
}
cout << endl;
}
cout << endl;
}
}
void vFSMNLayer::BackPropagate( MatrixBase& outDiff,
const MatrixBase& in,
const MatrixBase& out,
float learningRate,
MatrixBase* inDiff ) {
outDiff.DiffReLU( out, outDiff );
m_linear.BackPropagate( outDiff, in, Matrix(), learningRate, inDiff );
if ( m_w_momentum.Rows() != m_weight.Rows() ||
m_w_momentum.Columns() != m_weight.Columns() ) {
m_w_momentum.Reshape( m_weight.Rows(), m_weight.Columns(), kSetZero );
}
float avg_lr = -learningRate / (float)outDiff.Rows();
m_w_momentum.Sgemm( m_momentum,
avg_lr,
m_memory,
CUBLAS_OP_T,
outDiff,
CUBLAS_OP_N );
if ( m_weight_decay != 0.0f ) {
m_w_momentum.Add( 1.0f, -learningRate * m_weight_decay, m_weight );
}
m_memory_diff.Reshape( m_memory.Rows(), m_memory.Columns() );
m_memory_diff.Sgemm( 0.0f, 1.0f, outDiff, CUBLAS_OP_N, m_weight, CUBLAS_OP_T );
if ( NULL != inDiff ) {
inDiff->ComputeVfsmnHiddenDiff( m_memory_diff, m_filter, m_position );
}
if ( m_norm_type == "Clip" ) {
float range = m_norm_param * (-avg_lr);
m_w_momentum.Clip( -range, range );
}
if ( m_norm_type == "L2Norm" ) {
float w_norm = m_w_momentum.L2Norm() * (-avg_lr);
if ( w_norm > m_norm_param ) {
m_w_momentum.Scale( 1.0f / w_norm );
}
}
m_filter.UpdateVfsmnFilter( m_memory_diff, in, m_position, avg_lr );
m_weight.Add( 1.0f, 1.0f, m_w_momentum );
}
Matrix<Scalar, Dynamic, DerivedPoints::ColsAtCompileTime> forwardJacDotTimesVTemp(
const RigidBodyTree &tree, const KinematicsCache<Scalar> &cache, const MatrixBase<DerivedPoints> &points, int current_body_or_frame_ind, int new_body_or_frame_ind, int rotation_type) {
auto Jtransdot_times_v = tree.transformPointsJacobianDotTimesV(cache, points, current_body_or_frame_ind, new_body_or_frame_ind);
if (rotation_type == 0) {
return Jtransdot_times_v;
}
else {
Matrix<Scalar, Dynamic, 1> Jrotdot_times_v(rotationRepresentationSize(rotation_type));
if (rotation_type == 1) {
Jrotdot_times_v = tree.relativeRollPitchYawJacobianDotTimesV(cache, current_body_or_frame_ind, new_body_or_frame_ind);
}
else if (rotation_type == 2) {
Jrotdot_times_v = tree.relativeQuaternionJacobianDotTimesV(cache, current_body_or_frame_ind, new_body_or_frame_ind);
}
else {
throw runtime_error("rotation type not recognized");
}
Matrix<Scalar, Dynamic, 1> Jdot_times_v((3 + rotationRepresentationSize(rotation_type)) * points.cols(), 1);
int row_start = 0;
for (int i = 0; i < points.cols(); i++) {
Jdot_times_v.template middleRows<3>(row_start) = Jtransdot_times_v.template middleRows<3>(3 * i);
row_start += 3;
Jdot_times_v.middleRows(row_start, Jrotdot_times_v.rows()) = Jrotdot_times_v;
row_start += Jrotdot_times_v.rows();
}
return Jdot_times_v;
}
};
