使用 flann & kdtree 簡易範例－！壞人必需做好事！

#include <cmath>
#include <cstdio>
#include <flann/flann.hpp>

int main(int argc, char** argv)
{
   // 你要找幾個最近點用nn
   int nn = 3;
   // 資料總數
   const int rows = 9000;
   // 資料維度
   const int cols = 3;
   // 查找的位置數
   const int qsize = 2;
   float* rdata = new float[cols * rows];
   float* qdata = new float[cols * qsize];
   // 產生亂數資料
   for (int i = 0; i < rows; ++i)
   {
       for (int j = 0; j < cols; ++j)
       {
           rdata[i * cols + j] = rand() * 0.01f;
       }
   }
   for (int i = 0; i < qsize; ++i)
   {
       for (int j = 0; j < cols; ++j)
       {
           qdata[i * cols + j] = rand() * 0.01f;
           printf("%d %f\n", i * cols + j, qdata[i * cols + j]);
       }
   }
   // 初始化
   flann::Matrix<float> dataset(rdata, rows, cols);
   flann::Matrix<float> query(qdata, qsize, cols);
   flann::Matrix<int> indices(new int[query.rows * nn], query.rows, nn);
   flann::Matrix<float> dists(new float[query.rows * nn], query.rows, nn);
   // construct an randomized kd-tree index using 4 kd-trees
   flann::Index<flann::L2<float> > index(dataset, flann::KDTreeIndexParams(4));
   index.buildIndex();
   printf("index.knnSearch\n");
   // do a knn search, using 128 checks
   index.knnSearch(query, indices, dists, nn, flann::SearchParams(128));
   for (int i = 0; i < query.rows; ++i)
   {
       for (int j = 0; j < nn; ++j)
       {
           int idx = indices[i][j];
           printf("index:%d distance:%f position:(%f, %f, %f)\n",
                   idx, dists[i][j], rdata[idx * cols], rdata[idx * cols + 1],
                   rdata[idx * cols + 2]);
       }
   }
   delete[] dataset.ptr();
   delete[] query.ptr();
   delete[] indices.ptr();
   delete[] dists.ptr();
   return 0;
}