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

## Testing

C++ Standards:

Compilers:
Visual Studio: 2017, 2019, 2022
GNU: 6.5, 7.5, 8.4, 9.3, 10.1, 11.1
Clang: 6, 7, 8, 9, 10, 11, 12

Boost Versions:
1.68+

## 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

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 > 0]
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)

### 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)

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