I use C++ 14 with Eigen and CodeBlocsks IDE.
How should I integrate/install Eigen correct?
First of all, please see this imagine
Is this the right way to append Eigen to my project? In other topics I observed #include<Eigen/Dense> but this won't work for me.
The main problem is that when I run my project (Build and run) it load too slow, too much time to open the console, this problem come from Eigen? From Eigen I use only linear algebra facilities (Matrix, Vector, methods for determinants, QR decomposition) Can I make something to load only this file instead of all Eigen components? or how should I solve the problem about slow loading?
Do not put Eigen code directly in your project. Instead, install it to another location on your system and connect your project to it - for example, if you're using CMake, use find_package(Eigen).
The easiest way to install libraries is to use a library package manager such as vcpkg.
Related
I know there are ways of using Tensorflow in C++ they even have a documentation for it but I can seem to be able to get the library for it. I've checked the build from source instructions but it seems to builds a pip package rather than a library I can link to my project. I also found a tutorial but when I tried it out I ran out of memory and my computer crashed. My question is, how can I actually get the C++ library to work on my project? I do have these requirements, I have to work on windows with Visual Studio in C++. What I would love to is if I could get a pre-compiled DLL that I could just link but I haven't found such a thing and I'm open to other alternatives.
I can't comment so I am writing this as an answer.
If you don't mind using Keras, you could use the package frugally deep. I haven't seen a library myself either, but I came across frugally deep and it seemed easy to implement. I am currently trying to use it, so I cannot guarantee it will work.
You could check out neural2D from here:
https://github.com/davidrmiller/neural2d
It is a neural network implementation without any dependent libraries (all written from scratch).
I would say that the best option is to use cppflow, an easy wrapper that I created to use Tensorflow from C++ easily.
You won't need to install anything, just download the TF C API, and place it somewhere in your computer. You can take a look to the docs to see how to do it, and how to use the library.
The answer seems to be that it is hard :-(
Try this to start. You can follow the latest instructions for building from source on Windows up to the point of building the pip package. But don't do that - do this/these instead:
bazel -config=opt //tensorflow:tensorflow.dll
bazel -config=opt //tensorflow:tensorflow.lib
bazel -config=opt tensorflow:install_headers
That much seems to work fine. The problems really start when you try to use Any of the header files - you will probably get compilation errors, at least with TF version >= 2.0. I have tried:
Build the label_image example (instructions in the readme.md file)
It builds and runs fine on Windows, meaning all the headers and source are there somewhere
Try incorporating that source into Windows console executable: runs into compiler errors due to conflicts with std::min & std::max, probably due to Windows SDK.
Include c_api.h in a Windows console application: won't compile.
Include TF-Lite header files: won't compile.
There is little point investing the lengthy compile time in the first two bazel commands if you can't get the headers to compile :-(
You may have time to invest in resolving these errors; I don't. At this stage Tensorflow lacks sufficient support for Windows C++ to rely on it, particularly in a commercial setting. I suggest exploring these options instead:
If TF-Lite is an option, watch this
Windows ML/Direct ML (requires conversion of TF models to ONNX format)
CPPFlow
Frugally Deep
Keras2CPP
UPDATE: having explored the list above, I eventually found the following worked best in my context (real-time continuous item recognition):
convert models to ONNX format (use tf2onnx or keras2onnx
use Microsoft's ONNX runtime
Even though Microsoft recommends using DirectML where milliseconds matter, the performance of ONNX runtime using DirectML as an execution provider means we can run a 224x224 RGB image through our Intel GPU in around 20ms, which is quick enough for us. But it was still hard finding our way to this answer
I am developing part of my R package in C++ using Rcpp and I need to use a Linear Programming Solver.
After comparing some benchmarks using the solvers implementation into R (lpSolveAPI, Rglpk, Rsymphony and so on) I have decided to use GLPK. However, I have found no good way to use it in my C++ code developing under Windows.
Simply put, there is no simple way to just install GLPK and call it using something like
#include <glpk.h>
and I have found no implementations in R packages so that I can use a shortcut using Rcpp attributes like
// [[Rcpp::depends(package)]]
Any ideas?
I'm sure you are aware of the Rglpk package and its predecessor glpk. Often in these cases, it's helpful to stand on the shoulders of those that came before us. Having said that, we gleam from the package sources the following:
The source of Rglpk requires a pre-existing system install, does not enable linking, and is specific to Linux.
The source of glpk installs the library headers directly in R and it seems to also provide direct wrappers into the library.
Given the current implementations and your requirements, you would basically have to create an RcppGLPK package. This is primarily because no one really has a solution for what you need. I would highly suggest that you look at how RcppGSL is structured.
I'm currently working on a project for my university, where I have to develop an iOS app. For this I would like to work with octave. My question is, if it's possible to use my octave code as a c++ library without having octave installed, which I just can include to my Objective-C project and give the input to the library, which computes the result and return the output to my project.
I already searched the web, but I couldn't find anything :/
Best regards,
Carsten
What I have done is following the guidline in this
website
Now, I want to use some functions like rgb2gray() and imresize()... but I dont know how to use them, or I dont know which header file should I include in my project?
I have tried other way by creating a C++ shared-library in Matlab, then used it in VS 2012, but I could not add the DLL file to my project when I added new references. it is like that:
Please help me!
thanks in advance.
If you really want to call Matlab inside visual-studio, there are two ways:
Distribute MATLAB into independent shared library: check out my blog-post on how to do this (with detail steps and example).
Call MATLAB Engine directly: Refer to another blog of mine for more info.
On the other hand, it seems that you don't need to call Matlab to achieve your goal. OpenCV library will offer functions similar to rgb2gray() and imresize().
I would like to use the libLAS C/C++ library functions within R to import, analyse, export terrestrial lidar data. libLAS is a C/C++ library for reading and writing the very common LAS LiDAR format ( http://liblas.org/index.html ).
Would it be possible to use the Rcpp package to run this library (or other packages)? http://dirk.eddelbuettel.com/code/rcpp.html
Or should I compile and install it in order to use it following the compilation instructions http://liblas.org/compilation.html ? I am working on a MacOSx 10.6.5. As such I could also use it within Open Source GIS GRASS as described in the following wiki http://grass.osgeo.org/wiki/LIDAR#Micro-tutorial_for_LAS_data_import .
All advice is welcome related to reading and processing LIDAR data with R/GRASS.
Thanks,
Jan
For the question
Would it be possible to use the Rcpp
package to run this library (or other
packages)?
the answer is whopping Yup! as using it for glueing R to a given C/C++ library was pretty much the reason Rcpp was written for. Come and see the documentation and/or the rcpp-devel list for examples. There is some exciting new stuff happening with Rcpp modules but you can also get going the old-fashioned way of writing your wrapper. Rcpp makes mapping and R and C++ types (in both directions) a lot easier.