Xtensor & Xtensor-blas Library - Numpy for C++

Intro - What & Why?

I am currently working on my own deep learning & optimization library in C++, for my research in Data Science and Analytics Course at Maynooth University, Ireland. While searching for an existing tensor library (eigen/armadillo/trilinos - do not support tensors). I discovered Xtensor and Xtensor-blas, which has syntax like numpy and is avaliable for for C++ and Python.

Capabilities/Advantages (Xtensor to Numpy cheatsheet)

• Numpy Like Syntax

  typedef xt::xarray<double> dtensor;
  
  dtensor arr1 {{1.0, 2.0, 3.0},   {2.0, 5.0, 7.0},   {2.0, 5.0, 7.0}}; // 2d array of double
  
  dtensor arr2 {5.0, 6.0, 7.0}; // 1d array of doubles
  
  cout << arr2 << "\n"; // outputs : {5.0, 6.0, 7.0}

• Intuitive Syntax For Operation

  typedef xt::xarray<double> dtensor;
  
  dtensor arr1 {{1.0, 2.0, 3.0},   {2.0, 5.0, 7.0},   {2.0, 5.0, 7.0}}; // 2d array of double
  
  dtensor arr2 {5.0, 6.0, 7.0}; // 1d array of doubles
  
  cout << arr2 << "\n"; // outputs : {5.0, 6.0, 7.0}
  
  // Reshape
  arr1.reshape({1, 9});
  arr2.reshape({1,9});
  cout << arr1 << "\n"; // outputs : {1.0, 2.0, 3.0, 2.0, 5.0, 7.0, 2.0, 3.0, 7.0}
  
  // Addition, Subtraction, Multiplication, Division
  dtensor arr3 = arr1 + arr2;
  dtensor arr3 = arr1 - arr2;
  dtensor arr3 = arr1 * arr2;
  dtensor arr3 = arr1 / arr2;
  
  // Logical Operations
  dtensor filtered_out = xt::where(a > 5, a, b);
  dtensor var = xt::where(a > 5);
  dtensor logical_and = a && b;
  dtensor var = xt::equal(a, b);
  
  // Random numbers
  dtensor random_seed = xt::random::seed(0);
  dtensor random_ints = xt::random::randint<int>({10, 10});
  
  // Basic operations
  dtensor summation_of_a = xt::sum(a);
  dtensor mean = xt::mean(a);
  dtensor abs_vals = xt::abs(a);
  dtensor clipped_vals = xt::clip(a, min, max);
  
  // Exponential & Power Functions
  dtensor exp_of_a = xt::exp(a);
  dtensor log_of_a = xt::log(a);
  dtensor a_raise_to_b = xt::pow(a, b);

• Easy Linear Algebra

  // Vector product
  dtensor dot_product = xt::linalg::dot(a, b)
  dtensor outer_product = xt::linalg::outer(a, b)
  
  // Inverse & solving system of equation
  xt::linalg::inv(a)
  xt::linalg::pinv(a)
  xt::linalg::solve(A, b)
  xt::linalg::lstsq(A, b)
  
  // Decomposition
  dtensor SVD_of_a = xt::linalg::svd(a)
  
  // Norms & determinants
  dtensor matrix_norm = xt::linalg::norm(a, 2)
  dtensor matrix_determinant = xt::linalg::det(a)

  
  

Installation

• Install Xtensor
  

  cd ~ ; git clone https://github.com/QuantStack/xtensor
  cd xtensor; mkdir build && cd build;
  cmake -DBUILD_TESTS=ON -DDOWNLOAD_GTEST=ON ..
  make
  sudo make install

• Install xtensor-blas
  

  cd ~ ; git clone https://github.com/QuantStack/xtensor-blas
  cd xtensor-blas; mkdir build && cd build;
  cmake ..
  make
  sudo make install

Use In Your Code

• It is a header only library
  

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

• Linking & Compilation flags
  

  g++ -std=c++14 ./myprog.cpp -lblas

Where have I used it?

As mentioned in the intro, Xtensor and Xtensor-blas are the core component on which I have built my own deep learning & optimization library. This library is a monumental shift in C++ and ease of computation. In upcoming series of posts I will show you how to create your own library using xtensor.

Next Post

In the next post, I will give an overview of the architecture of the project for your own library. And alongside I will introduce blas routines.