eigen

What i am trying to do is to compile project which was built by CMake. In my code i have next method: /** "in-place" version of TriangularView::solve() where the result is written in \a other * * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here. * This function will const_cast it, so constness isn't honored here. * * See TriangularView:solve() for the details. */ template<typename MatrixType, unsigned int Mode> template<int Side, typename OtherDerived> void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived>&

I would like to use eigens Tensor class from the unsupported module . This site suggests to include something like #include <Eigen/CXX11/Tensor> to be able to use it. I installed eigen via Homebrew (Version 3.2.4) on my Mac on OS X Yosemite. Although I get eigen to work properly, I cannot find the necessary module in the unsupported folder: #include <eigen3/unsupported/Eigen/???> What might I have forgotten or done wrong? Or do I have an outdated version which does not have yet the Tensor class? The unsupported Tensor module is only found in the unstable version for now (Current stable: 3.2.5)

Let's say I have two eigen matrices A and B, and I want to create a third matrix defined by C(i,j) = 5.0 if A(i,j) > B(i,j), 0 otherwise I guess it is possible to do it without an explicit for loop. But I am not very proficient with Eigen yet. What whould be the best approach? Assuming A , B and C are MatrixXd you can do: C = (A.array()>B.Array()).cast<double>() * 5.0; 来源： https://stackoverflow.com/questions/22228332/comparing-two-matrices-with-eigen

I am trying to write a little unwrapper for meshes. This uses a finite-element-method to solve for minimal linear stress between flattened and the raw surface. At the moment there are some vertices pinned to get a result. Without this the triangles are rotated and translated randomly... But as this pinning isn't necessary for the problem, the better solution would be to directly solve the singular matrix. Petsc does provide some methodes to solve a singular system by providing some information on the nullspace. http://www.mcs.anl.gov/petsc/petsc-current/docs/manual.pdf#section.4.6 I wonder if

I'm trying to implement a lightweight (minimal library dependencies) version of a TensorFlow graph in c++ and I'm trying to use Eigen Tensor objects to perform the graphs operations. Right now I'm stuck trying to use the Eigen Tensor.convolve() method to try and replicate the behaviour of TensorFlow's Conv2D operation. To keep things simple my initial Conv2D operation has no padding and strides of one. The input to convolutional layer is a 51x51x1 tensor which is being convolved with a filter bank of size 3x3x1x16. In tensorflow this generates an output tensor of size 49x49x16. Setting up this

I have an affine transform in 3D and I wish to discard any z-axis information from. Is there a convenient way to convert from an Affine3d to and Affine2d ? Try following: Affine2d S2d = Translation2d(S3d.translation().topRows<2>()) * S3d.linear().topLeftCorner<2,2>(); Demo: #include <Eigen/Dense> #include <iostream> #include <string> int main() { using namespace Eigen; using namespace std; Vector3d p3d(1.,2.,3.); cout << p3d << endl << endl; Affine3d S3d = Translation3d(2.,2.,2.)*Scaling(3.,2.,5.); Vector3d scalled = S3d*p3d; cout << S3d.matrix() << endl << endl; cout << scalled << endl <<