armadillo

问题 Hi I just recently discovered the armadillo library for C++ and I quite like it. I am building an application after prototyping it in MATLAB so that I can execute it much faster. The problem I am facing is that when I try to load a matrix from a .mat file as follows: A.load("A.mat",raw_ascii); //raw_ascii because I can port data to MATLAB as well. it takes about 10 seconds. I was wondering if there was a faster way of doing it in armadillo or C++ in general. Any thoughts are much appreciated,

问题 Hi I installed Armadillo3.0.1 in my own working directory /home/me/package/armadillo3.0.1/ as the README.txt said. But when I try an example: g++ -I /home/me/package/armadillo3.0.1/usr/include/ example.cpp -o example -O1 It always shows the error: /tmp/ccZAE9pj.o: In function void arma::gemm<false, false, false, false>::apply_blas_type<double>(arma::Mat<double>&, arma::Mat<double> const&, arma::Mat<double> const&, double, double)': example.cpp:(.text._ZN4arma4gemmILb0ELb0ELb0ELb0EE15apply

问题 i'm converting a MATLAB program in c++ using Armadillo for matrix algebra. i'm stuck on cell type. someone has some hints? 回答1: That's because 'cell' is not really a type - it is a placeholder for anything you want to place in it. The closest thing I can think of in languages such as C# and Python is a 'tuple', which intrinsically can contain anonymous types. Since C++ does not have a built-in tuple type, I suggest you take a look at Boost, which is a very comprehensive, mature and open

问题 In armadillo, the advanced constructor provide the way to share memory like mat B(10,10); mat A(B.memptr(),2,50,false, true); but in c++ program about class, one should first declare variables in head file,like mat A,B; and realize other things in cpp file. So, anyone can tell me how to share memory between mat A and mat B in cpp file with the declaration of mat A and B in head file? 回答1: You can declare the B matrix as reference the A matrix when declaring your class. For example: class foo

问题 I have a function within which I want to convert an Eigen::MatrixXd object to arma::mat . I am aware of this question but I can't seem to be able to correct the behavior that I'm getting. Calling matrixxd_to_armamat from R would cause no issues, the problem is when I have this conversion in another function in C. This is a little confusing and I would like to understand what's going on. #include <RcppArmadillo.h> #include <RcppEigen.h> // [[Rcpp::depends(RcppEigen)]] // [[Rcpp::depends

问题 My idea is to calculate several statistics in a moving window (2 by 2). For example, the code below calculate the mean value in a moving window. It works well when the input data hasn't got NA values, however gives bad results (NAs are treated as the lowest int) when NAs are in the dataset. Can you guide me how it can be improved - for example by excluding NA in these calculations? #include <RcppArmadillo.h> using namespace Rcpp; // [[Rcpp::depends(RcppArmadillo)]] // [[Rcpp::export]] Rcpp:

问题 I'm trying to execute the expmat() function from the Armadillo c++ library but it gets stacked when i run it. First i tried in Matlab with the expm function, and with different values in the C++ program and it works in both cases. So, the problem is with something related with the values i put on the matrix, seem that some of them are too small... This is the squared [14x14] matrix i want to calculate the exponential: mat A; A << 0 << -0.0000769006 << -0.0000511322 << -0.0000915495 << 0 << 0

问题 I use boost serialization in my C++ / CLI program, and some classes have datamembers that contain some double which are initialized to arma::datum::nan (the "not-a-number" value for armadillo library). I serialize via a boost::archive::text_oarchive. When I try to deserialize via boost::archive::text_iarchive oa, a boost serialization archive exception is launched when it tries to read a arma::datum::nan value. I tried using a binary archive, but it seems other types (std::vector> for

问题 I am trying to program the naive parallel version of Conjugate gradient, so I started with the simple Wikipedia algorithm, and I want to change the dot-products and MatrixVector products by their appropriate parallel version, The Rcppparallel documentation has the code for the dot-product using parallelReduce; I think I'm gonna use that version for my code, but I'm trying to make the MatrixVector multiplication, but I haven't achieved good results compared to R base (no parallel) Some

问题 We are trying to implement a new C++ code in my research group to perform large numerical simulations (finite elements, finite difference methods, topology optimization, etc.) The software will be used by people from academia and industry alike. For the dense linear algebra piece of the software, we want to use either Eigen or Armadillo. We wish to build a wrapper around these packages for two reasons: 1. to expose our own API to the users rather than the third-party API; and 2. in case we