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

Author: David Pilger dpilger26@gmail.com

Version:

License

Testing

C++ Standards:

Compilers:
Visual Studio: 2022
GNU: 11.3 Clang: 14

Boost Versions:
1.73+

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 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

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 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

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 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 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)`