sample.save

c++实现的KNN库:建立高维度的K-d tree,实现K邻域搜索

Data Points:
(-0.297462, 0.176102)
(0.565538, -0.361496)
(0.909313, -0.182785)
(0.920712, 0.478408)
(0.167682, 0.0499836)
(0.305223, -0.0805835)
(0.114973, 0.882453)
(0.742916, 0.16376)
(0.0724605, -0.826775)
(0.69096, -0.559284)
(0.188485, -0.643934)
(0.749427, -0.942415)
(-0.970662, -0.223466)
(0.91611, 0.879597)
(0.927417, -0.382593)
(-0.711327, 0.278713)
(-0.519172, 0.986146)
(0.135338, 0.924588)
(-0.0837537, 0.61687)
(0.0520465, 0.896306)
Query point: (0.0902484, -0.207129)
	NN:	Index	Distance
	0	5	0.249455
Query point: (-0.419567, 0.485743)
	NN:	Index	Distance
	0	0	0.332847
Query point: (0.826225, -0.30962)
	NN:	Index	Distance
	0	14	0.124759
Query point: (0.694758, 0.987088)
	NN:	Index	Distance
	0	13	0.246071
Query point: (-0.410807, -0.465182)
	NN:	Index	Distance
	0	8	0.60357
Query point: (-0.836501, 0.490184)
	NN:	Index	Distance
	0	15	0.245741
Query point: (0.588289, 0.656408)
	NN:	Index	Distance
	0	3	0.37708
Query point: (0.325807, 0.38721)
	NN:	Index	Distance
	0	4	0.372458
Query point: (-0.532226, -0.727036)
	NN:	Index	Distance
	0	8	0.612857
Query point: (-0.52506, -0.853508)
	NN:	Index	Distance
	0	8	0.598118