libkdtree

A KD-Tree model written in C++(with C and Python interface).

Usage

Python

importkdtreeasktimportnumpyasnpX=np.random.rand(60000, 250)
y=np.random.rand(60000)
clf=kt.KNeighborsRegressor(k=5)
clf.fit(X, y)
ypred=clf.predict(X[:10]) print(ypred) 

C

#include"kdtree.h"#include<stdio.h>intmain()
{
doubledatas[100] = {1.3, 1.3, 1.3,
8.3, 8.3, 8.3,
2.3, 2.3, 2.3,
1.2, 1.2, 1.2,
7.3, 7.3, 7.3,
9.3, 9.3, 9.3,
15, 15, 15,
3, 3, 3,
1.1, 1.1, 1.1,
12, 12, 12,
4, 4, 4,
5, 5, 5};
doublelabels[100];
for(size_ti=0; i<12; ++i)
labels[i] = (double)i;
tree_model*model=build_kdtree(datas, labels, 12, 3, 2);
double*ans=k_nearests_neighbor(model, test, 2, 5, false);
printf("k Nearest Neighbors Regressor: \n%.2f %.2f\n", ans[0], ans[1]);
free(ans);
free_tree_memory(model->root);
}

Build

Linux and Mac OS

• mkdir build
• cd build
• cmake ..
• make
• cd ..
• cp build/libkdtree* python/kdtree (if you would like to build python package)

Windows

Use cmake-gui to build(If your operating system is Windows 10 X64 and you have Visual Studio 2017 installed, you can also run build.bat).

Build Python Package

• cd python
• python setup.py install
• python ./demo.py(run a demo)

Build MKL Support

Intel® Math Kernel Library is a very popular library product from Intel that accelerates math processing routines to increase application performance. If you want to build with MKL support, please add #define USE_INTEL_MKL to kdtree.cpp.

......
#include<queue>
#include<cstring>
#defineMalloc(type, n) (type *)malloc((n)*sizeof(type))
// If you need to use Intel MKL to accelerate, // you can cancel the next line comment.// #define USE_INTEL_MKL
#ifdef USE_INTEL_MKL
#include<mkl.h>
#endif
......