问题
Are there any C++ (or C) libs that have NumPy-like arrays with support for slicing, vectorized operations, adding and subtracting contents element-by-element, etc.?
回答1:
Here are several free software that may suit your needs.
The GNU Scientific Library is a GPL software written in C. Thus, it has a C-like allocation and way of programming (pointers, etc.). With the GSLwrap, you can have a C++ way of programming, while still using the GSL. GSL has a BLAS implementation, but you can use ATLAS instead of the default CBLAS, if you want even more performances.
The boost/uBLAS library is a BSL library, written in C++ and distributed as a boost package. It is a C++-way of implementing the BLAS standard. uBLAS comes with a few linear algebra functions, and there is an experimental binding to ATLAS.
eigen is a linear algebra library written in C++, distributed under the LGPL3 (or GPL2). It's a C++ way of programming, but more integrated than the two others (more algorithms and data structures are available). Eigen claim to be faster than the BLAS implementations above, while not following the de-facto standard BLAS API. Eigen does not seem to put a lot of effort on parallel implementation.
Armadillo is LGPL3 library for C++. It has binding for LAPACK (the library used by numpy). It uses recursive templates and template meta-programming, which is a good point (I don't know if other libraries are doing it also?).
xtensor is a C++ library that is BSD licensed. It offers A C++ API very similar to that of NumPy. See https://xtensor.readthedocs.io/en/latest/numpy.html for a cheat sheet.
These alternatives are really good if you just want to get data structures and basic linear algebra. Depending on your taste about style, license or sysadmin challenges (installing big libraries like LAPACK may be difficult), you may choose the one that best suits your needs.
回答2:
Try out xtensor. (See the NumPy to Xtensor Cheat Sheet).
xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.
xtensor provides
- an extensible expression system enabling numpy-style broadcasting.
- an API following the idioms of the C++ standard library.
- tools to manipulate array expressions and build upon xtensor.
Example
Initialize a 2-D array and compute the sum of one of its rows and a 1-D array.
#include <iostream>
#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"
xt::xarray<double> arr1
{{1.0, 2.0, 3.0},
{2.0, 5.0, 7.0},
{2.0, 5.0, 7.0}};
xt::xarray<double> arr2
{5.0, 6.0, 7.0};
xt::xarray<double> res = xt::view(arr1, 1) + arr2;
std::cout << res;
Outputs
{7, 11, 14}
Initialize a 1-D array and reshape it inplace.
#include <iostream>
#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"
xt::xarray<int> arr
{1, 2, 3, 4, 5, 6, 7, 8, 9};
arr.reshape({3, 3});
std::cout << arr;
Outputs
{{1, 2, 3},
{4, 5, 6},
{7, 8, 9}}
回答3:
DyND is designed to be, among other things, a NumPy-like library for C++. Things like broadcasting, arithmetic operators, and slicing all work fine. On the other hand, it is still very experimental and many features haven't been implemented yet.
Here's a simple implementation of the de Casteljau algorithm in C++ using DyND arrays:
#include <iostream>
#include <dynd/array.hpp>
using namespace dynd;
nd::array decasteljau(nd::array a, double t){
size_t e = a.get_dim_size();
for(size_t i=0; i < e-1; i++){
a = (1.-t) * a(irange()<(e-i-1)) + t * a(0<irange());
}
return a;
}
int main(){
nd::array a = {1., 2., 2., -1.};
std::cout << decasteljau(a, .25) << std::endl;
}
I wrote a blog post a little while back with more examples and side-by-side comparisons of the syntax for Fortran 90, DyND in C++, and NumPy in Python.
Disclaimer: I'm one of the current DyND developers.
回答4:
Eigen is a good linear algebra library.
http://eigen.tuxfamily.org/index.php?title=Main_Page
It is quite easy to install since it's a header-only library. It relies on template in order to to generate well optimized code. It vectorizes automatically the matrix operations.
It also fully support coefficient wise operations, such as the "per element multiplication" between two matrices for instance. It is what you need?
回答5:
Blitz++ supports arrays with an arbitrary number of axes, whereas Armadillo only supports up to three (vectors, matrices, and cubes). Eigen only supports vectors and matrices (not cubes). The downside is that Blitz++ doesn't have linear algebra functions beyond the basic entrywise operations and tensor contractions. Development seems to have slowed down quite some time ago, but perhaps that's just because the library does what it does and not many changes need to be made.
回答6:
VIGRA contains a good N-dimensional array implementation:
http://ukoethe.github.io/vigra/doc/vigra/Tutorial.html
I use it extensively, and find it very simple and effective. It's also header only, so very easy to integrate into your development environment. It's the closest thing I've come across to using NumPy in terms of it's API.
The main downside is that it isn't so widely used as the others, so you won't find much help online. That, and it's awkwardly named (try searching for it!)
回答7:
Eigen is a template library for linear algebra (matrices, vectors…). It is header only and free to use (LGPL).
回答8:
The GSL is great, it does all of what you're asking and much more. It is licensed under the GPL though.
回答9:
While GLM is designed to mesh easily with OpenGL and GLSL, it is a fully functional header only math library for C++ with a very intuitive set of interfaces.
It declares vector & matrix types as well as various operations on them.
Multiplying two matrices is a simple as (M1 * M2). Subtracting two vectors (V1- V2).
Accessing values contained in vectors or matrices is equally simple. After declaring a vec3 vector for example, one can access its first element with vector.x. Check it out.
回答10:
Use LibTorch (PyTorch frontend for C++) and be happy.
来源：https://stackoverflow.com/questions/11169418/numpy-style-arrays-for-c