# NumCpp **Repository Path**: eric__tech/NumCpp ## Basic Information - **Project Name**: NumCpp - **Description**: C++开发者的福音，Numpy的C++版本，搬运自https://github.com/dpilger26/NumCpp.git - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 1 - **Created**: 2020-09-03 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ![NumCpp logo](https://github.com/dpilger26/NumCpp/raw/master/docs/logo/NumCppLogo.png) [![GitHub watchers](https://img.shields.io/github/watchers/dpilger26/NumCpp.svg?style=social&label=Watch&maxAge=2592000)](https://GitHub.com/dpilger26/NumCpp/watchers/) [![GitHub stars](https://img.shields.io/github/stars/dpilger26/NumCpp.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/dpilger26/NumCpp/stargazers/) [![GitHub forks](https://img.shields.io/github/forks/dpilger26/NumCpp.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/dpilger26/NumCpp/network/) [![C++14](https://img.shields.io/badge/C%2B%2B-14-blue.svg)](https://isocpp.org/std/the-standard) ![GitHub tag (latest by date)](https://img.shields.io/github/tag-date/dpilger26/NumCpp.svg?color=blue&label=Official%20Release&style=popout) [![Build status](https://ci.appveyor.com/api/projects/status/slxre2j64qc8ubvl/branch/master?svg=true)](https://ci.appveyor.com/project/dpilger26/numcpp/branch/master) [![Build Status](https://travis-ci.org/dpilger26/NumCpp.svg?branch=master)](https://travis-ci.org/dpilger26/NumCpp) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/ea24c08d892e4beebd46cfca260823c1)](https://app.codacy.com/manual/dpilger26/NumCpp?utm_source=github.com&utm_medium=referral&utm_content=dpilger26/NumCpp&utm_campaign=Badge_Grade_Dashboard) # NumCpp: A Templatized Header Only C++ Implementation of the [Python NumPy Library](http://www.numpy.org) ## Author: David Pilger dpilger26@gmail.com ## Version: 2.1.0 ## License [![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/) Copyright 2020 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++14 C++17 C++2a **Compilers:** Visual Studio: 2017, 2019 GNU: 6.5, 7.5, 8.4, 9.3, 10.1 Clang: 6, 7, 8, 9, 10 **Boost Versions:** 1.68, 1.70, 1.72, and 1.73 ## [Documentation](https://dpilger26.github.io/NumCpp) ## [GitHub](https://github.com/dpilger26/NumCpp) ## [Installation](https://dpilger26.github.io/NumCpp/doxygen/html/md__c_1__github__num_cpp_docs_markdown__installation.html) ## [Building](https://dpilger26.github.io/NumCpp/doxygen/html/md__c_1__github__num_cpp_docs_markdown__building.html) ## Release Notes ### Version 2.1.0 * Improved installation and usage with CMake find_package support * Various minor improvements ### Version 2.0.0 * Dropped support of C++11, now requires a C++14 or higher compiler * Added support for `std::complex`, closing __Issue #58__ * Added more `NdArray` constructors for STL containers including `std::vector>`, closing __Issue #59__ * Added `polyfit` routine inline with Numpy `polyfit`, closing __Issue #61__ * Added ability to use `NdArray` as container for generic structs * Non-linear least squares fitting using Gauss-Newton * Root finding routines * Numerical integration routines * `lu_decomposition` and `pivotLU_decomposition` added to `Linalg` namespace * New STL iterators added to `NdArray` * `iterator` * `const_iterator` * `reverse_iterator` * `const_reverse_iterator` * `column_iterator` * `const_column_iterator` * `reverse_column_iterator` * `const_reverse_column_iterator` * Added `rodriguesRotation` and `wahbasProblem` to `Rotations` namespace * Various efficiency and/or bug fixes ## 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](https://dpilger26.github.io/NumCpp). ### 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 a = { {1, 2}, {3, 4}, {5, 6} }``` | | ```a.reshape([2, 3])``` | ```a.reshape(2, 3)``` | | ```a.astype(np.double)``` | ```a.astype()``` | ### INITIALIZERS Many initializer functions are provided that return `NdArray`s for common needs. | **NumPy** | **NumCpp** | |:--------------------------------------------------------:|:--------------------------------------------------------:| | ```np.linspace(1, 10, 5)``` | ```nc::linspace(1, 10, 5)``` | | ```np.arange(3, 7)``` | ```nc::arange(3, 7)``` | | ```np.eye(4)``` | ```nc::eye(4)``` | | ```np.zeros([3, 4])``` | ```nc::zeros(3, 4)``` | | | ```nc::NdArray(3, 4) a = 0``` | | ```np.ones([3, 4])``` | ```nc::ones(3, 4)``` | | | ```nc::NdArray(3, 4) a = 1``` | | ```np.nans([3, 4])``` | ```nc::nans(3, 4)``` | | | ```nc::NdArray(3, 4) a = nc::constants::nan``` | | ```np.empty([3, 4])``` | ```nc::empty(3, 4)``` | | | ```nc::NdArray(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 > 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(nc::Shape(3,4))``` | | | ```nc::random::randn({3, 4})``` | | ```np.random.randint(0, 10, [3, 4])``` | ```nc::random::randInt(nc::Shape(3,4),0,10)``` | | | ```nc::random::randInt({3, 4},0,10)``` | | ```np.random.rand(3, 4)``` | ```nc::random::rand(nc::Shape(3,4))``` | | | ```nc::random::rand({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(filename, "\n")``` | | ```np.dump(a, filename)``` | ```nc::dump(a, filename)``` | | ```np.load(filename)``` | ```nc::load(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)``` |