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

Author: David Pilger [email protected]

Version: 1.2

License

Copyright 2019 David Pilger

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files(the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Testing

C++ Standards: C++11, C++14, and C++17
Compilers: VS 2017/2019, GCC 7.4.0/8.3/9.1, and Clang 6.0/8.0 Boost Versions: 1.68 and 1.70

Documentation

GitHub

Installation

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 NumpCpp 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<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 NdArrays 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

NumpCpp 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)

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 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)

ITERATION

NumpCpp 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 NumpCpp 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 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)

I/O

Print and file output methods. All NumpCpp 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

NumpCpp 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)