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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.
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>()
Many initializer functions are provided that return NdArrays for common needs.
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 NumCppa[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)
The random module provides simple ways to create random arrays.
NumPy NumCppnp.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)
Many ways to concatenate NdArray are available.
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.
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 NumCppfor 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 NumCppnp.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) 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 accumulate values of NdArrays along specified axes. When no axis is specified, values are accumulated along all axes.
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.
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)
NumCpp universal functions are provided for a large set number of mathematical functions.
NumPy NumCppnp.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)
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