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NumCpp: A Templatized Header Only C++ Implementation of the Python NumPy Library
Author: David Pilger dpilger26@gmail.com
Version: ![GitHub tag (latest by date)](https://img.shields.io/github/tag-date/dpilger26/NumCpp.svg?color=blue&label=Official%20Release&style=popout)
License ![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)
Testing
C++ Standards:
![C++20](https://img.shields.io/badge/C%2B%2B-20-blue.svg)
Compilers:
Visual Studio: 2022
GNU: 11.3
Clang: 14
Boost Versions:
1.73+
![Star History Chart](https://api.star-history.com/svg?repos=dpilger26/NumCpp&type=Date)
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.
CONTAINERS
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 NdArray
s, but it has limited usefulness past a simple container.
NumPy |
NumCpp |
a = np.array([[1, 2], [3, 4], [5, 6]]) | nc::NdArray<int> a = { {1, 2}, {3, 4}, {5, 6} } |
a.reshape([2, 3]) | a.reshape(2, 3) |
a.astype(np.double) | a.astype<double>() |
INITIALIZERS
Many initializer functions are provided that return NdArray
s for common needs.
NumPy |
NumCpp |
np.linspace(1, 10, 5) | nc::linspace<dtype>(1, 10, 5) |
np.arange(3, 7) | nc::arange<dtype>(3, 7) |
np.eye(4) | nc::eye<dtype>(4) |
np.zeros([3, 4]) | nc::zeros<dtype>(3, 4) |
|
nc::NdArray<dtype>(3, 4) a = 0 |
np.ones([3, 4]) | nc::ones<dtype>(3, 4) |
|
nc::NdArray<dtype>(3, 4) a = 1 |
np.nans([3, 4]) | nc::nans(3, 4) |
|
nc::NdArray<double>(3, 4) a = nc::constants::nan |
np.empty([3, 4]) | nc::empty<dtype>(3, 4) |
|
nc::NdArray<dtype>(3, 4) a |
SLICING/BROADCASTING
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 > 5] |
a[a > 5] = 0 | a.putMask(a > 5, 0) |
RANDOM
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<double>(nc::Shape(3, 4)) |
|
nc::random::randN<double>({3, 4}) |
np.random.randint(0, 10, [3, 4]) | nc::random::randInt<int>(nc::Shape(3, 4), 0, 10) |
|
nc::random::randInt<int>({3, 4}, 0, 10) |
np.random.rand(3, 4) | nc::random::rand<double>(nc::Shape(3,4)) |
|
nc::random::rand<double>({3, 4}) |
np.random.choice(a, 3) | nc::random::choice(a, 3) |
CONCATENATION
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) |
DIAGONAL, TRIANGULAR, AND FLIP
The following return new NdArray
s.
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) |
ITERATION
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
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) |
COMPARISONS
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) |
MINIMUM, MAXIMUM, SORTING
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
Reducers accumulate values of NdArray
s 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) |
I/O
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<dtype>(filename, '\n') |
np.dump(a, filename) | nc::dump(a, filename) |
np.load(filename) | nc::load<dtype>(filename) |
MATHEMATICAL FUNCTIONS
NumCpp universal functions are provided for a large set number of mathematical functions.
BASIC 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) |
EXPONENTIAL FUNCTIONS
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) |
POWER FUNCTIONS
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) |
TRIGONOMETRIC FUNCTIONS
NumPy |
NumCpp |
np.sin(a) | nc::sin(a) |
np.cos(a) | nc::cos(a) |
np.tan(a) | nc::tan(a) |
HYPERBOLIC FUNCTIONS
NumPy |
NumCpp |
np.sinh(a) | nc::sinh(a) |
np.cosh(a) | nc::cosh(a) |
np.tanh(a) | nc::tanh(a) |
CLASSIFICATION FUNCTIONS
NumPy |
NumCpp |
np.isnan(a) | nc::isnan(a) |
np.isinf(a) | nc::isinf(a) |
LINEAR ALGEBRA
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) |