C++ implementation of the Python Numpy library


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NumCpp: A Templatized Header Only C++ Implementation of the Python NumPy Library

Author: David Pilger [email protected]

Version: GitHub tag (latest by date)

License MIT license


C++ Standards:
C++14 C++17 C++20

Visual Studio: 2017, 2019
GNU: 6.5, 7.5, 8.4, 9.3, 10.1
Clang: 6, 7, 8, 9, 10

Boost Versions:





Release Notes

From NumPy To NumCpp – A Quick Start Guide

This quick start guide is meant as a very brief overview of some of the things that can be done with NumCpp. For a full breakdown of everything available in the NumCpp library please visit the Full Documentation.


The main data structure in NumCpp is the NdArray. It is inherently a 2D array class, with 1D arrays being implemented as 1xN arrays. There is also a DataCube class that is provided as a convenience container for storing an array of 2D NdArrays, but it has limited usefulness past a simple container.

NumPy NumCpp
a = np.array([[1, 2], [3, 4], [5, 6]]) nc::NdArray a = { {1, 2}, {3, 4}, {5, 6} }
a.reshape([2, 3]) a.reshape(2, 3)
a.astype(np.double) a.astype()


Many initializer functions are provided that return NdArrays for common needs.

NumPy NumCpp
np.linspace(1, 10, 5) nc::linspace(1, 10, 5)
np.arange(3, 7) nc::arange(3, 7)
np.eye(4) nc::eye(4)
np.zeros([3, 4]) nc::zeros(3, 4)
nc::NdArray(3, 4) a = 0
np.ones([3, 4]) nc::ones(3, 4)
nc::NdArray(3, 4) a = 1
np.nans([3, 4]) nc::nans(3, 4)
nc::NdArray(3, 4) a = nc::constants::nan
np.empty([3, 4]) nc::empty(3, 4)
nc::NdArray(3, 4) a


NumCpp offers NumPy style slicing and broadcasting.

NumPy NumCpp
a[2, 3] a(2, 3)
a[2:5, 5:8] a(nc::Slice(2, 5), nc::Slice(5, 8))
a({2, 5}, {5, 8})
a[:, 7] a(a.rSlice(), 7)
a[a > 5] a[a > 50]
a[a > 5] = 0 a.putMask(a > 50, 666)


The random module provides simple ways to create random arrays.

NumPy NumCpp
np.random.seed(666) nc::random::seed(666)
np.random.randn(3, 4) nc::random::randN(nc::Shape(3,4))
nc::random::randN({3, 4})
np.random.randint(0, 10, [3, 4]) nc::random::randInt(nc::Shape(3,4),0,10)
nc::random::randInt({3, 4},0,10)
np.random.rand(3, 4) nc::random::rand(nc::Shape(3,4))
nc::random::rand({3, 4})
np.random.choice(a, 3) nc::random::choice(a, 3)


Many ways to concatenate NdArray are available.

NumPy NumCpp
np.stack([a, b, c], axis=0) nc::stack({a, b, c}, nc::Axis::ROW)
np.vstack([a, b, c]) nc::vstack({a, b, c})
np.hstack([a, b, c]) nc::hstack({a, b, c})
np.append(a, b, axis=1) nc::append(a, b, nc::Axis::COL)


The following return new NdArrays.

NumPy NumCpp
np.diagonal(a) nc::diagonal(a)
np.triu(a) nc::triu(a)
np.tril(a) nc::tril(a)
np.flip(a, axis=0) nc::flip(a, nc::Axis::ROW)
np.flipud(a) nc::flipud(a)
np.fliplr(a) nc::fliplr(a)


NumCpp follows the idioms of the C++ STL providing iterator pairs to iterate on arrays in different fashions.

NumPy NumCpp
for value in a for(auto it = a.begin(); it < a.end(); ++it)
for(auto& value : a)


Logical FUNCTIONS in NumCpp behave the same as NumPy.

NumPy NumCpp
np.where(a > 5, a, b) nc::where(a > 5, a, b)
np.any(a) nc::any(a)
np.all(a) nc::all(a)
np.logical_and(a, b) nc::logical_and(a, b)
np.logical_or(a, b) nc::logical_or(a, b)
np.isclose(a, b) nc::isclose(a, b)
np.allclose(a, b) nc::allclose(a, b)


NumPy NumCpp
np.equal(a, b) nc::equal(a, b)
a == b
np.not_equal(a, b) nc::not_equal(a, b)
a != b
rows, cols = np.nonzero(a) auto [rows, cols] = nc::nonzero(a)


NumPy NumCpp
np.min(a) nc::min(a)
np.max(a) nc::max(a)
np.argmin(a) nc::argmin(a)
np.argmax(a) nc::argmax(a)
np.sort(a, axis=0) nc::sort(a, nc::Axis::ROW)
np.argsort(a, axis=1) nc::argsort(a, nc::Axis::COL)
np.unique(a) nc::unique(a)
np.setdiff1d(a, b) nc::setdiff1d(a, b)
np.diff(a) nc::diff(a)


Reducers accumulate values of NdArrays along specified axes. When no axis is specified, values are accumulated along all axes.

NumPy NumCpp
np.sum(a) nc::sum(a)
np.sum(a, axis=0) nc::sum(a, nc::Axis::ROW)
np.prod(a) nc::prod(a)
np.prod(a, axis=0) nc::prod(a, nc::Axis::ROW)
np.mean(a) nc::mean(a)
np.mean(a, axis=0) nc::mean(a, nc::Axis::ROW)
np.count_nonzero(a) nc::count_nonzero(a)
np.count_nonzero(a, axis=0) nc::count_nonzero(a, nc::Axis::ROW)


Print and file output methods. All NumCpp classes support a print() method and << stream operators.

NumPy NumCpp
print(a) a.print()
std::cout << a
a.tofile(filename, sep=’\n’) a.tofile(filename, "\n")
np.fromfile(filename, sep=’\n’) nc::fromfile(filename, "\n")
np.dump(a, filename) nc::dump(a, filename)
np.load(filename) nc::load(filename)


NumCpp universal functions are provided for a large set number of mathematical functions.


NumPy NumCpp
np.abs(a) nc::abs(a)
np.sign(a) nc::sign(a)
np.remainder(a, b) nc::remainder(a, b)
np.clip(a, 3, 8) nc::clip(a, 3, 8)
np.interp(x, xp, fp) nc::interp(x, xp, fp)


NumPy NumCpp
np.exp(a) nc::exp(a)
np.expm1(a) nc::expm1(a)
np.log(a) nc::log(a)
np.log1p(a) nc::log1p(a)


NumPy NumCpp
np.power(a, 4) nc::power(a, 4)
np.sqrt(a) nc::sqrt(a)
np.square(a) nc::square(a)
np.cbrt(a) nc::cbrt(a)


NumPy NumCpp
np.sin(a) nc::sin(a)
np.cos(a) nc::cos(a)
np.tan(a) nc::tan(a)


NumPy NumCpp
np.sinh(a) nc::sinh(a)
np.cosh(a) nc::cosh(a)
np.tanh(a) nc::tanh(a)


NumPy NumCpp
np.isnan(a) nc::isnan(a)
np.isinf(a) nc::isinf(a)


NumPy NumCpp
np.linalg.norm(a) nc::norm(a)
np.dot(a, b) nc::dot(a, b)
np.linalg.det(a) nc::linalg::det(a)
np.linalg.inv(a) nc::linalg::inv(a)
np.linalg.lstsq(a, b) nc::linalg::lstsq(a, b)
np.linalg.matrix_power(a, 3) nc::linalg::matrix_power(a, 3)
Np.linalg.multi_dot(a, b, c) nc::linalg::multi_dot({a, b, c})
np.linalg.svd(a) nc::linalg::svd(a)
  • NdArrayCore.hpp Visual Studio error

    NdArrayCore.hpp Visual Studio error

    In https://github.com/dpilger26/NumCpp/blob/master/include/NumCpp/NdArray/NdArrayCore.hpp Line 4690, You've commented: // NOTE: this needs to be defined outside of the class to get rid of a compiler // error in Visual Studio

    But I see it's already outside of the class, yet I get a Visual Studio compilation error:

    Severity Code Description Project File Line Suppression State Error(active) E0135 class template "nc::NdArray<dtype, Allocator>" has no member "nonzero" my_lib C : \NumCpp - master\include\NumCpp\NdArray\NdArrayCore.hpp 4693

    What is needed to be done in order to fix this error ?

    opened by TomAvrech 8
  • Problem for random seed

    Problem for random seed

    nc::random::seed(unsigned int(time(NULL)));
    cout << nc::random::randFloat<float>(0, 1) << endl;

    Here is my code, but the final executable program products the same number every time it starts. However, the following code can generate different numbers each time.

    mt19937_64 generator;
    generator.seed(unsigned int(time(NULL)));
    boost::random::uniform_real_distribution<float> rand(0, 1);
    cout << rand(generator) << endl;
    opened by linlll 8
  • Numpy diag in Numcpp

    Numpy diag in Numcpp

    Hey, I was wondering if you could let me know how I can get numpy.diag in Numcpp. I could not find anything regarding that method in the documentation. There is nc:diagonal, which is different with numpy.diag. Wondering if you have already implemented numpy.diag .

    Thanks in advance.

    opened by Houman-HM 7
  • Compilation error: ambiguous overload for operator

    Compilation error: ambiguous overload for operator

    I just installed Numcpp on a new machine and tried to compile a piece of code which was working on another machine. However, on the new machine, I get compilation error: ambiguous overload for ‘operator+’ (operand types are ‘nc::NdArray<double>’ and ‘int’). There are errors related to the ambiguity for nearly all the operators. I was wondering if you could help me fix this issue. I am using Boost 1.7, Clang 6.0, and tried different versions of gcc, including 7 , 8.3.0, 9.2. Thanks in advance.

    opened by Houman-HM 7
  • there are some bugs when i try to build Readme

    there are some bugs when i try to build Readme


    i try to build Numcpp on ubuntu1604. there are some bugs . cake is 3.15.2 and boost is 1.68. ***there is the log Scanning dependencies of target Example [ 50%] Building CXX object CMakeFiles/Example.dir/ReadMe.cpp.o In file included from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions.hpp:57:0, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp.hpp:37, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/ReadMe.cpp:1: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(const std::array<_Tp, _Nm>&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:55:23: error: missing template arguments before '(' token return NdArray(inArray); ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(const std::vector<_RealType>&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:72:23: error: missing template arguments before '(' token return NdArray(inVector); ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(const std::deque<_Tp>&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:89:23: error: missing template arguments before '(' token return NdArray(inDeque); ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(const std::set&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:106:23: error: missing template arguments before '(' token return NdArray(inSet); ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(std::initializer_list<Tp>&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:124:23: error: missing template arguments before '(' token return NdArray(inList); ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp: In function 'nc::NdArray nc::asarray(std::initializer_list<std::initializer_list<Tp> >&)': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Functions/asarray.hpp:142:23: error: missing template arguments before '(' token return NdArray(inList); ^ In file included from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/DCM.hpp:35:0, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations.hpp:31, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp.hpp:44, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/ReadMe.cpp:1: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp: At global scope: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp:61:70: error: array must be initialized with a brace-enclosed initializer std::array<double, 4> components = { 0.0, 0.0, 0.0, 1.0 }; ^ /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp:61:70: error: too many initializers for 'std::array<double, 4ul>' In file included from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/DCM.hpp:35:0, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations.hpp:31, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp.hpp:44, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/ReadMe.cpp:1: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp: In static member function 'static nc::rotations::Quaternion nc::rotations::Quaternion::identity()': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp:357:35: error: use of deleted function 'constexpr nc::rotations::Quaternion::Quaternion()' return Quaternion(); ^ In file included from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/DCM.hpp:35:0, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations.hpp:31, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp.hpp:44, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/ReadMe.cpp:1: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp:179:13: note: 'constexpr nc::rotations::Quaternion::Quaternion() noexcept' is implicitly deleted because its exception-specification does not match the implicit exception-specification 'noexcept (false)' Quaternion() noexcept = default; ^ In file included from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/DCM.hpp:35:0, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations.hpp:31, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp.hpp:44, from /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/ReadMe.cpp:1: /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp: In member function 'nc::NdArray nc::rotations::Quaternion::toNdArray() const': /home/lisas/numcpp_new/numcpp_new/NumCpp/examples/ReadMe/../../include/NumCpp/Rotations/Quaternion.hpp:655:31: error: missing template arguments before '(' token return NdArray(components); ^ CMakeFiles/Example.dir/build.make:62: recipe for target 'CMakeFiles/Example.dir/ReadMe.cpp.o' failed make[2]: *** [CMakeFiles/Example.dir/ReadMe.cpp.o] Error 1 CMakeFiles/Makefile2:75: recipe for target 'CMakeFiles/Example.dir/all' failed make[1]: *** [CMakeFiles/Example.dir/all] Error 2 Makefile:83: recipe for target 'all' failed make: *** [all] Error 2

    best lisa shi

    opened by shixinlishixinli 6
  • can stack function change the input parameter type to list or vector?

    can stack function change the input parameter type to list or vector?

    i'm try to use vstack to stack a series ndarray,but the size of series is uncertain and the initializer_list can't convert from vector or list. so,can i change the implement of the stack function?

    opened by novioleo 6
  • where is install and build step in ubuntu and windows

    where is install and build step in ubuntu and windows

    where is install and build step in ubuntu and windows

    opened by fatalfeel 5
  • Creating NdArray from cv::Mat having double values

    Creating NdArray from cv::Mat having double values

    Hi I am trying to use NumCpp in my iOS project. I have a cv::Mat object with double values in it. But I am unable to create NdArray from this source. It is only allowing me to use nc::uint8 as type.

    auto ncArray = nc::NdArray(textSource.data, textSource.rows, textSource.cols);

    What can I do to have NdArray with double type from cv::Mat object ? Kindly provide some guidance on it.

    opened by nasircsms 5
  • How to init NdArray with 2d array?

    How to init NdArray with 2d array?

    Describe the solution you'd like When I init a NdArray with a 2d array, what should I do with code? I tried code as follows:

    float a[4][3] = {{0.0, 0.0, 0.0},
                         {0.0, 0.0, 1.0},
                         {5.0, 5.0, 5.0},
                         {1.0, 1.0, 1.0}};
    b = a;
    nc::NdArray<float> x = nc::NdArray<float>(a, 4, 3); // not worked
    nc::NdArray<float> x = nc::NdArray<float>(b, 4, 3); // not worked

    My purpose is write a Python extension with C++. I chose numcpp for finishing some tasks which are done by numpy in Python, so the input of functions are lists from Python in form of 2d array such as const int (*a)[3], I need to initial nc::NdArray<dtype> via a, how can I do that? Or is there some better ways to make C++ extension for Python? What I know is using ctypes

    opened by AlbertoWang 4
  • Unable to change the value of an array slice.

    Unable to change the value of an array slice.

    Describe the bug Unable to change the value of an array slice.

    To Reproduce nc::NdArray *mels = new nc::NdArray{1,2,3,4,5}; std::cout << mels << std::endl; float aaa = new float[2]{1000, 1000}; (*mels)(0, {0, 2}) = *aaa; std::cout << (*mels)(0, {0, 2}) << std::endl; std::cout << *mels << std::endl;

    Expected behavior The expected output should be [[1, 2, 3, 4, 5, ]] [[1000, 1000, ]] [[1000, 1000, 3, 4, 5, ]]

    But the actual output is [[1, 2, 3, 4, 5, ]] [[1, 2, ]] [[1, 2, 3, 4, 5, ]]

    I'm not sure if this is a bug or I'm using NumCpp the wrong way. Thanks for your help!

    opened by MuyangDu 4
  • Fatal Error with Boost

    Fatal Error with Boost

    In file included from /usr/local/include/NumCpp/Polynomial.hpp:31:0,
                     from /usr/local/include/NumCpp.hpp:52,
                     from /mnt/d/Deepedge/CPPinference/foxeye-pair-match/inference.cpp:14:
    /usr/local/include/NumCpp/Polynomial/chebyshev_t.hpp:36:10: fatal error: boost/math/special_functions/chebyshev.hpp: No such file or directory
     #include "boost/math/special_functions/chebyshev.hpp"
    compilation terminated.

    When try to build the file where included "NumCpp.hpp", I get the fatal error as above. Have tried reinstall boost as well, can't figure out a solution.

    opened by nefario7 1
  • nc::linalg::lstsq very slow

    nc::linalg::lstsq very slow

    Describe the bug It takes forever to do SVD::decompose()

    To Reproduce Run linalg.lstsq with big NdArray, my was 8905 rows and 316 cols

    Expected behavior It must be much more faster

    enhancement help wanted 
    opened by 66Ton99 10
  • multidimensional arrays support

    multidimensional arrays support

    Hi folks!

    As I can see, currently NumCpp lacks support for multidimensional arrays (for example, 3D or 4D datacubes). We oftenly use such arrays in our numpy code, and it really stops us from starting using NumCpp in our C++ part.

    Can you add multidimensonal arays some day?

    opened by Felix-neko 1
  • maximum can't deal with different array shape  like in python

    maximum can't deal with different array shape like in python

    ` np.maximum(np.eye(2), [0.5, 2]) # broadcasting

    array([[ 1. , 2. ], [ 0.5, 2. ]])`

    opened by bing1zhi2 0
  • Benchmark against eigen, xtensor, blaze, ...

    Benchmark against eigen, xtensor, blaze, ...

    Would be nice to include some benchmarks against another matrix/linalg libraries, such as Eigen, xtensor, numpy (py version), blaze, ...

    opened by breznak 0
  • histogramdd function

    histogramdd function

    I want histogramdd function just like it in numPy

    It is often used together with opencv to treat multi channel component historgram.

    I hope NumCpp+opencv in C++ can easily finish most of those in numpy +opencv-python.

    opened by yinjilong 0
  • Version_2.4.1(Feb 5, 2021)

  • Version_2.4.0(Jan 31, 2021)

    • Compile with NO_USE_BOOST definition to remove the Boost libraries as a dependency, with reduced functionality:
      • gcd with a pair of values (still available using a C++17 compliant compiler)
      • gcd array
      • lcm with a pair of values (still available using a C++17 compliant compiler)
      • lcm array
      • polynomial::chebyshev_t
      • polynomial::chebyshev_u
      • polynomial::hermite (still available using a C++17 compliant compiler)
      • polynomial::laguerre (still available using a C++17 compliant compiler)
      • polynomial::legendre_p (still available using a C++17 compliant compiler)
      • polynomial::legendre_q
      • polynomial::spherical_harmonic
      • random::beta
      • random::laplace
      • random::nonCentralChiSquared
      • random::triangle
      • random::uniformOnSphere
      • special::airy_ai
      • special::airy_ai_prime
      • special::airy_bi
      • special::airy_bi_prime
      • special::bernoulli
      • special::bessel_in (still available using a C++17 compliant compiler)
      • special::bessel_in_prime
      • special::bessel_jn (still available using a C++17 compliant compiler)
      • special::bessel_jn_prime
      • special::bessel_kn (still available using a C++17 compliant compiler)
      • special::bessel_kn_prime
      • special::bessel_yn (still available using a C++17 compliant compiler)
      • special::bessel_yn_prime
      • special::beta (still available using a C++17 compliant compiler)
      • special::cyclic_hankel_1
      • special::cyclic_hankel_2
      • special::digamma
      • special::erf
      • special::erf_inv
      • special::erfc
      • special::erfc_inv
      • special::gamma
      • special::gamma1pm1
      • special::log_gamma
      • special::polygamma
      • special::prime
      • special::riemann_zeta (still available using a C++17 compliant compiler)
      • special::spherical_bessel_jn (still available using a C++17 compliant compiler)
      • special::spherical_bessel_yn (still available using a C++17 compliant compiler)
      • special::spherical_hankel_1
      • special::spherical_hankel_2
      • special::trigamma
    • Added replace option into random::choice
    • Added nan_to_num function
    • Added complete and incomplete elliptical integrals of the first, second, and third kind to special namespace (requires either Boost or C++17 compliant compiler)
    • Added exponential integral to special namespace (requires either Boost or C++17 compliant compiler)
    • Added NO_MULTITHREAD compile definition to turn off algorithm multithreading from compliant compilers
    Source code(tar.gz)
    Source code(zip)
  • Version_1.3.0(Nov 20, 2020)

  • Version_2.3.1(Sep 9, 2020)

  • Version_2.3.0(Aug 31, 2020)

  • Version_2.2.0(Aug 20, 2020)

  • Version_2.1.0(Jul 22, 2020)

  • Version_2.0.0(Jun 20, 2020)

    • Dropped support of C++11, now requires a C++14 or higher compiler
    • Added support for std::complex<T>, closing Issue #58
    • Added more NdArray constructors for STL containers including std::vector<std::vector<T>>, closing Issue #59
    • Added polyfit routine inline with Numpy polyfit, closing Issue #61
    • Added ability to use NdArray as container for generic structs
    • Non-linear least squares fitting using Gauss-Newton
    • Root finding routines
    • Numerical integration routines
    • lu_decomposition and pivotLU_decomposition added to Linalg namespace
    • New STL iterators added to NdArray
      • iterator
      • const_iterator
      • reverse_iterator
      • const_reverse_iterator
      • column_iterator
      • const_column_iterator
      • reverse_column_iterator
      • const_reverse_column_iterator
    • Added rodriguesRotation and wahbasProblem to Rotations namespace
    • Various efficiency and/or bug fixes
    Source code(tar.gz)
    Source code(zip)

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