Multi-dimensional arrays with broadcasting and lazy computing.

Initialize a 2-D array and compute the sum of one of its rows and a 1-D array



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#include <iostream>

#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"
#include "xtensor/xview.hpp"



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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::view(arr1, 1) + arr2



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arr1

Initialize a 1-D array and reshape it inplace



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#include <iostream>
#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"



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xt::xarray<int> arr
  {1, 2, 3, 4, 5, 6, 7, 8, 9};

arr.reshape({3, 3});

arr

Broadcasting the `xt::pow` universal functions



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#include <iostream>
#include "xtensor/xarray.hpp"
#include "xtensor/xmath.hpp"
#include "xtensor/xio.hpp"



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xt::xarray<double> arr3
  {1.0, 2.0, 3.0};

xt::xarray<unsigned int> arr4
  {4, 5, 6, 7};

arr4.reshape({4, 1});

xt::pow(arr3, arr4)

Random arrays with the random module



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#include <iostream>
#include "xtensor/xrandom.hpp"



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xt::xarray<double> arr5 = xt::random::randn<double>({4, 3});
arr5



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xt::random::randn<double>({5, 3, 8, 10}) > 0.2

Using `linspace`, `arange`, `ones`, `zeros`



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#include "xtensor/xbuilder.hpp"



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xt::xarray<double> ar = xt::linspace<double>(0.0, 10.0, 12);
ar.reshape({4, 3});
ar



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xt::xarray<double> fones = xt::ones<float>({2, 2});
fones



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xt::arange<int>(1569325055)

Using `xt::broadcast`



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#include <vector>
#include "xtensor/xbroadcast.hpp"



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xt::broadcast(xt::linspace<double>(0.0, 10.0, 4),
              std::vector<std::size_t>({3, 4}))

Using standard algorithms with xexpressions



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#include <algorithm>



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xt::xarray<double> frand = xt::random::randn<double>({2, 2});

// begin() and end() provide and iterator pair iterating over the xexpression in a row-major fashion
std::cout << std::accumulate(frand.begin(), frand.end(), 0.0);

frand

Iterating over a prescribed broadcasted shape



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// begin(shape) and end(shape) provide and iterator pair iterating
// over the xexpression broadcasted to the prescrived shape in a row-major fashion
std::vector<std::size_t> shape = {3, 2, 2};
std::cout << std::accumulate(frand.begin(shape), frand.end(shape), 0.0);

Blas bindings for xtensor.

xtensor-blas is an extension to the xtensor library, offering bindings to BLAS and LAPACK libraries through cxxblas and cxxlapack from the FLENS project.

xtensor-blas currently provides non-broadcasting dot, norm (1- and 2-norm for vectors), inverse, solve, eig, cross, det, slogdet, matrix_rank, inv, cholesky, qr, svd in the xt::linalg namespace (check the corresponding xlinalg.hpp header for the function signatures). The functions, and signatures, are trying to be 1-to-1 equivalent to NumPy. Low-level functions to interface with BLAS or LAPACK with xtensor containers are also offered in the blas and lapack namespace.

xtensor and xtensor-blas require a modern C++ compiler supporting C++14. The following C++ compilers are supported:

On Windows platforms, Visual C++ 2015 Update 2, or more recent
On Unix platforms, gcc 4.9 or a recent version of Clang



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#include "xtensor-blas/xlinalg.hpp"



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xt::xtensor<double, 2> m = {{1.5, 0.5}, {0.7, 1.0}};



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std::cout << "Matrix rank: " << std::endl << xt::linalg::matrix_rank(m);



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std::cout << "Matrix inverse: " << std::endl << xt::linalg::inv(m);



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std::cout << "Eigen values: " << std::endl << xt::linalg::eigvals(m);



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xt::xarray<double> arg1 = xt::arange<double>(9);
xt::xarray<double> arg2 = xt::arange<double>(18);
arg1.reshape({3, 3});
arg2.reshape({2, 3, 3});
std::cout << xt::linalg::dot(arg1, arg2) << std::endl;



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