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https://github.com/atsushisakai/numpycpp
A c++ header library for matrix operation inspired Numpy Scipy, MATLAB only using Eigen.
https://github.com/atsushisakai/numpycpp
cpp cpp-library eigen header-only matrix-library numpy scipy
Last synced: about 2 months ago
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A c++ header library for matrix operation inspired Numpy Scipy, MATLAB only using Eigen.
- Host: GitHub
- URL: https://github.com/atsushisakai/numpycpp
- Owner: AtsushiSakai
- License: mit
- Created: 2017-03-28T03:33:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-04-10T22:28:42.000Z (over 7 years ago)
- Last Synced: 2024-11-07T09:03:25.875Z (about 2 months ago)
- Topics: cpp, cpp-library, eigen, header-only, matrix-library, numpy, scipy
- Language: C++
- Homepage:
- Size: 14.6 KB
- Stars: 45
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# numpycpp
[](https://travis-ci.org/AtsushiSakai/numpycpp)
A c++ header library for matrix operation inspired Numpy, Scipy and MATLAB only using Eigen.
This library has some APIs which Numpy, Scipy, MATLAB has, but Eigen doesn't.
You can use it with only Eigen, and only include it.
# Requrements
- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page)
# How to use
Just add a compile option to add the Eigen path, and include numpycpp.h in your code.
# APIs
The test code: numpycppTest.cpp helps to understand APIs.
## reshape
Gives a new shape to an array without changing its data.
This function is based on numpy.reshape
see: https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html
Eigen::MatrixXf x(6,1);
x<<1.0,2.0,3.0,4.0,5.0,6.0;
PRINT(x);
Eigen::MatrixXf rx = reshape(x,2,3);
PRINT(rx);
//rx:
//1 3 5
//2 4 6
Eigen::MatrixXf rx2 = reshape(x,3,2);
PRINT(rx2);
//rx2:
//1 4
//2 5
//3 6
## isdiag
Detemine if matrix is diagonal
If matrix is not square, return false
It is inspired by MATLAB isdiag function.
see: https://www.mathworks.com/help/matlab/ref/isdiag.html
Eigen::MatrixXf x(3,1);
x<<5.0,6.0,7.0;
bool flag = isdiag(x);//return false
Eigen::MatrixXf x2(2,2);
x2<<5.0,6.0,7.0,1.0;
bool flag2 = isdiag(x2);//return false
Eigen::MatrixXf x3(3,3);
x3<<1.0,0.0,0.0,
0.0,1.0,0.0,
0.0,0.0,1.0;
bool flag3 = isdiag(x3);//return true
## vstack
Stack matrix in sequence vertically
imspired by numpy.vstack
see :https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html
Eigen::MatrixXf x(3,1);
x<<5.0,
6.0,
7.0;
Eigen::MatrixXf y(3,1);
y<<1.0,
10.0,
100.0;
Eigen::MatrixXf a = vstack(x,y);
//ans<<5,
// 6,
// 7,
// 1,
// 10,
// 100;
## hstack
Stack matrix in sequence horizontally
imspired by numpy.hstack
see: https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html
Eigen::MatrixXf x(3,1);
x<<5.0,
6.0,
7.0;
Eigen::MatrixXf y(3,1);
y<<1.0,
10.0,
100.0;
Eigen::MatrixXf a = hstack(x,y);
//a=
//5 1
//6 10
//7 100
## kron
Compute the Kronecker product
A composite array made of blocks of the second array scaled by the first.
Inspired numpy.kron.
see: https://docs.scipy.org/doc/numpy/reference/generated/numpy.kron.html
Eigen::MatrixXf x(1,3);
x<<1.0,10.0,100.0;
Eigen::MatrixXf y(1,3);
y<<5.0,6.0,7.0;
Eigen::MatrixXf a = kron(x,y);
// a = [5 50 500 6 60 600 7 70 700]
## block_diag
Create a block diagonal matrix from provided matrices
inspired scipy.linalg.block_diag
see: https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.linalg.block_diag.html
Eigen::MatrixXf x(3,1);
x<<5.0,
6.0,
7.0;
Eigen::MatrixXf y(1,3);
y<<1.0,10.0,100.0;
Eigen::MatrixXf a = block_diag(x,y);
//a= 5, 0, 0, 0,
// 6, 0, 0, 0,
// 7, 0, 0, 0,
// 0, 1, 10,100;
# License
MIT
# Author
Atsushi Sakai ([@Atsushi_twi](https://twitter.com/Atsushi_twi))