eigen

Converting some Matlab code to C++. Questions (how to in C++): Concatenate two vectors in a matrix. (already found the solution) Normalize each array ("pts" col) dividing it by its 3rd value Matlab code for 1 and 2: % 1. A 3x1 vector. d0, d1 double. B = [d0*A (d0+d1)*A]; % B is 3x2 % 2. Normalize a set of 3D points % Divide each col by its 3rd value % pts 3xN. C 3xN. % If N = 1 you can do: C = pts./pts(3); if not: C = bsxfun(@rdivide, pts, pts(3,:)); C++ code for 1 and 2: // 1. Found the solution for that one! B << d0*A, (d0 + d1)*A; // 2. for (int i=0, i<N; i++) { // Something like this, but

I'm getting puzzling results while doing fairly simple tasks to compare the performance of: Eigen::Matrix boost::multi_array boost::multi_array mapped to Eigen::Matrix using Eigen::Map This is an abridged version of my test code; a fuller version can be found at: http://pastebin.com/faZ7TvJG . boost::multi_array<double, 2, Eigen::aligned_allocator<double> > boost_multi_array; Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> eigen_matrix; Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> > boost_multi_array_mapped(boost_multi_array.data(),

Everything seems to work in my package, but I wanted to check if the steps to make it were correct and about memory use using "Map". (It's a simple example, somewhere in-between the inline examples and the fastLm() example.) Here is an inline function that takes the maximum over each column of a matrix: library(Rcpp); library(inline); library(RcppEigen); maxOverColCpp <- ' using Eigen::Map; using Eigen::MatrixXd; // Map the double matrix AA from R const Map<MatrixXd> A(as<Map<MatrixXd> >(AA)); // evaluate and columnwise maximum entry of A const MatrixXd Amax(A.colwise().maxCoeff()); return

We are facing errors compiling against Eigen 3.3.7 (and probably older versions) against the latest versions of GCC 8.2.1 supplied by MSYS2. Strangely, this only happens with the latest builds of the same package ( mingw-w64-x86_64-gcc 8.2.1): 8.2.1+20181123-1 : fine 8.2.1+20181130-1 : error 8.2.1+20181207-1 : error The error is: In file included from C:/Users/donald/msys64/mingw64/include/eigen3/Eigen/SparseCore:50, from C:/Users/donald/msys64/mingw64/include/eigen3/Eigen/Sparse:26, from C:/Users/donald/msys64/mingw64/include/eigen3/Eigen/Eigen:2, from src/registration/transform/search.h:21,

NOTE: I allready asked this question, but it was closed because of "too broad" without much explanation. I can't see how this question could be more specific (it deals with a specific class of a specific library for a specific usage...), so I assume that it was something like a "moderator's mistake" and ask it again... I would like to perfom sparse matrix/matrix multiplication using Eigen on sparse matrices. These matrices are already defined in the code I am working on in standard 3-arrays compressed row/column strorage. Then I would like to use the Eigen::SparseMatrix class as a wrapper on

In JavaScript I have a list of "lines", each of which consists of indefinite number of "points", each of which has a form of [x, y] . So it is a 3D ragged array. Now I need to pass it to my C++ code with the help from emscripten ( embind ). Here's declaration of the C++ function: Eigen::MatrixXd f(const std::vector<std::vector<std::vector<double>>>& lines); And I would like to get a list of lists ( [[m11, m12],[m22, m22],...] ) in JavaScript after calling f . How to write the binding code in this case (the stuff inside EMSCRIPTEN_BINDINGS , for example)? UPDATE : I can now pass the JavaScript

通过Eigen的矩阵运算，将点云进行Z轴旋转45°，再沿X轴平移2.5. pcl::PointCloud<pcl::PointXYZ>::Ptr source_cloud(new pcl::PointCloud<pcl::PointXYZ>()); pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud(new pcl::PointCloud<pcl::PointXYZ>()); float theta = M_PI / 4; // The angle of rotation in radians Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity(); // Define a translation of 2.5 meters on the x axis. transform_2.translation() << 2.5, 0.0, 0.0; // The same rotation matrix as before; theta radians around Z axis transform_2.rotate(Eigen::AngleAxisf(theta, Eigen::Vector3f::UnitZ())); // Print the transformation

The C++ interface to TensorFlow doesn't seem to have a reshape method. Does anyone have an idea how to convert e.g. [A,B,C,D] into [A*B,C,D] ? It looks like the only way to do this is to use Eigen? However, the documentation there is very slim and the code is template hell and not easy to parse. Solution with checking whether reshaped tensor has the same number of elements of the source tensor: // Extracted image features from MobileNet_224 tensorflow::Tensor image_features(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 14, 14, 512})); tensorflow::Tensor image_features_reshaped(tensorflow: