eigen-git-mirror | THIS MIRROR IS DEPRECATED -- New url https
kandi X-RAY | eigen-git-mirror Summary
kandi X-RAY | eigen-git-mirror Summary
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QUESTION
I'm trying to add tests for our iOS app on the newest Apple Sillicon M1 chip. One of the dependency that our application has on 3rd party libraries is on the Ceres-Solver. I've been trying for a couple of days now to compile ceres for the newest platform but all my attempts have failed.
So we're generating the build files using CMake and then I tried compiling both with Xcode and with xcodebuild. The build is successful but whenever I tried to link the libceres.a library to our application, I get a:
Building for Mac Catalyst, but the linked library 'libceresMacOS.a' was built for macOS. You may need to restrict the platforms for which this library should be linked in the target editor, or replace it with an XCFramework that supports both platforms.
I find this quite strange because I do build in Xcode and I am targeting the same platform ("My Mac") in compiling ceres and our application. One of my suspicions is that I'm setting some wrong flags in the CMake command, this is what I'm running it with
...ANSWER
Answered 2021-Mar-01 at 16:33After some more researching I figured out how to make this work, in case anyone stumbles upon the same problem.
I ended up reading this issue description (link) and made me think I should try and use a different code generator.
My cmake configuration was correct however apparently there is a bug with using XCode as the code generator (in here -G"$GENERATOR_NAME" ). After I set GENERATOR_NAME=Ninja I managed to compile a version of the library that is for Mac Catalyst.
QUESTION
I'm writing a library that has a few dependencies that are pulled in via CMake's FetchContent. In my toplevel CMakeLists.txt, close to the top I have:
include(external/dependencies.cmake) which contains
ANSWER
Answered 2021-Jan-27 at 08:44Because in fmt, install rules are disabled if it's a subproject...
QUESTION
The following minimal benchmark rebuilds single-threaded code with -O3 -march=native on each machine, multiplying matrices that are either square or highly non-square (one dimension = 2).
ANSWER
Answered 2020-May-19 at 09:13As suggested by Peter Cordes in his comment, it seems to boil down to memory throughput.
Results of mbw 1000 show it:
i5-6600:
QUESTION
When multiplying two data-mapped matrices (Eigen::Map) I notice a significant performance difference depending on how the memory was allocated. When using memory coming from a custom allocation, it's almost twice as fast compared to using (also aligned) memory coming from an std::vector with data allocated also by Eigen::aligned_allocator.
Minimal benchmark:
...ANSWER
Answered 2020-May-10 at 19:17As pointed out in the comments by PeterT and chtz, the manually allocated version does not initialize the memory (in contrast to std::vector), accessing it is undefined behavior, and thus the MMU likely does something smart, i.e., not actually accessing the memory.
When also initializing the memory in the second part, both versions show similar performance:
QUESTION
I am working on improving my C++ skills - specifically the use of templates. I am creating a filtering mathematical library that through the use of templates is compatible for different vector/matrix classes including Boost and Eigen. So far I am very happy with the library, but am encountering issues as I am expanding functionality.
Eigen and Boost are different in that for example the latter does not have multiply operators (see 1) so this presented a choice in my implementation. I decided to make sure that the matrix/vector methods and operators used within the template library use those that are defined for Eigen so that at least one library works really well with the mathematical library. In my library I would use * to multiply matrices which is unavailable for boost. For boost I created a wrapper class for both matrix and vector to allow the use of * through custom operators. Example of wrapper for matrix is below:
...ANSWER
Answered 2019-Nov-17 at 00:41So I found a solution for the problem of wrapping the boost class, but the solution is a little messy. The block function as deduced in the question above must return a reference. If it is on the right hand side of the equal sign then we need to return the submatrix wrapped within matrixBoost for the other */+ multiplications that might be in the expression. However, what if this is called on the left hand side of the equal sign?
My solution was to use a boolean flag (subMatrixCopy) where I would set it to true as well as backup the full matrix value in valBack through the use of std::swap and return with the submatrix. This modification would allow for proper expression evaluation on the right hand side. For the left hand side, once the = operator is called then the boolean flag makes sure that the right hand side is properly assigned to the specified block of the backed up matrix value in valBack.
QUESTION
Can I use pybind1 to pass a three-dimensional numpy array to a c++ function accepting an Eigen::Tensor as argument. For example, consider the following c++ function:
ANSWER
Answered 2019-Oct-16 at 12:17It is not directly supported, here's some discussion (including some code to do the mapping if you want to add that to your project): https://github.com/pybind/pybind11/issues/1377
QUESTION
I've been successfully using cppyy for automatic python bindings for a C++ project I'm working on. I recently included the Eigen library, but I'm having trouble using this together with cppyy. Does anyone have any experience doing this, or know how I should do this?
I have the following structure for the repo (only relevant parts shown):
...ANSWER
Answered 2018-Dec-30 at 17:42When calling help(), there is:
QUESTION
I write a function that takes two DenseBase as arguments.
The function uses .derived().array() to convert both Array and Matrix to Array.
I got tired of writing derived for many times and use auto.
But auto leads to strange error. Eigen complains that x2 and y2 don't have same shape.
If I don't want to write .derived().array() for many times, what can I use?
Eigen is from https://github.com/eigenteam/eigen-git-mirror.git
...ANSWER
Answered 2018-Nov-15 at 22:14You can fix the runtime issue with auto x2 = x.array().derived();, that is: reverse array and derived. But auto is not desirable here. Here is why. Say you have:
QUESTION
I want CMake to git clone a header only library without building it. I tried:
...ANSWER
Answered 2018-Sep-03 at 17:19According to the documentation, you need to:
- Set the
CONFIGURE_COMMANDparameter as an empty string (i.e."") in order to stop the CMake configuration (the project is assumed to be CMake-base by default). - Set the
BUILD_COMMANDparameter as an empty string to disable the build step. - Set the
INSTALL_COMMANDparameter as an empty string to force the install step to do nothing.
However, if there are no complicated conditions for the inclusion of Eigen, i.e. it is always a required component for your project, you might need to consider the use of git submodules (this might be a better approach even if Eigen is not a mandatory requirement).
QUESTION
I search through the entire source code of Eigen(https://github.com/eigenteam/eigen-git-mirror), but can not fine the definition of some types such as MatrixXd and VectorXd.
The document of Eigen(https://eigen.tuxfamily.org/dox/group__matrixtypedefs.html#ga99b41a69f0bf64eadb63a97f357ab412) says that MatrixXd is defined as
...ANSWER
Answered 2018-May-11 at 03:23MatrixXd is defined by the expansion of the macro EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d) on line 451 of Matrix.h.
In fact, that macro ends up expanding to the typedefs for Matrix2d, Vector2d, RowVector2d, Matrix3d, Vector3d, RowVector3d, Matrix4d, Vector4d, RowVector4d, MatrixXd, VectorXd, RowVectorXd, Matrix2Xd, MatrixX2d, Matrix3Xd, MatrixX3d, Matrix4Xd, and MatrixX4d.
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