5 가까운 이웃 KD 트리를 사용

Question

빨간 점 (T-SNE2)에서 청색 점 (T-SNE1)의 각 점에 대해 5 개의 가장 가까운 이웃을 찾고 싶습니다. 그래서 나는이 코드를 작성하여 올바른 방법을 찾았지만 그렇게 할 수있는 것이 옳지 않은지 확실하지 않습니다.

X = np.random.random((10, 2)) # 10 points in 3 dimensions 
Y = np.random.random((10, 2)) # 10 points in 3 dimensions 
NNlist=[] 
treex = KDTree(X, leaf_size=2) 
for i in range(len(Y)): 
    dist, ind = treex.query([Y[i]], k=5) 
    NNlist.append(ind[0][0]) 
    print(ind) # indices of 5 closest neighbors 
    print(dist) 
    print("the nearest index is:" ,ind[0][0],"with distance:",dist[0][0], "for Y",i) 
print(NNlist) 

출력

[[9 5 4 6 0]] 
[[ 0.21261486 0.32859024 0.41598597 0.42960146 0.43793039]] 
the nearest index is: 9 with distance: 0.212614862956 for Y 0 
[[0 3 2 6 1]] 
[[ 0.10907128 0.11378059 0.13984741 0.18000197 0.27475481]] 
the nearest index is: 0 with distance: 0.109071275144 for Y 1 
[[8 2 3 0 1]] 
[[ 0.21621245 0.30543878 0.40668179 0.4370689 0.49372232]] 
the nearest index is: 8 with distance: 0.216212445449 for Y 2 
[[8 3 2 6 0]] 
[[ 0.16648482 0.2989508 0.40967709 0.42511931 0.46589575]] 
the nearest index is: 8 with distance: 0.166484820786 for Y 3 
[[1 2 5 0 4]] 
[[ 0.15331281 0.25121761 0.29305736 0.30173474 0.44291615]] 
the nearest index is: 1 with distance: 0.153312811422 for Y 4 
[[2 3 8 0 6]] 
[[ 0.20441037 0.20917797 0.25121628 0.2903253 0.33914051]] 
the nearest index is: 2 with distance: 0.204410367254 for Y 5 
[[2 1 0 3 5]] 
[[ 0.08400022 0.1484925 0.17356156 0.32387147 0.33789602]] 
the nearest index is: 2 with distance: 0.0840002184199 for Y 6 
[[8 2 3 7 0]] 
[[ 0.2149891 0.40584999 0.50054235 0.53307269 0.5389266 ]] 
the nearest index is: 8 with distance: 0.21498909502 for Y 7 
[[1 0 2 5 9]] 
[[ 0.07265268 0.11687068 0.19065327 0.20004392 0.30269591]] 
the nearest index is: 1 with distance: 0.0726526838766 for Y 8 
[[5 9 4 1 0]] 
[[ 0.21563204 0.25067242 0.29904262 0.36745386 0.39634179]] 
the nearest index is: 5 with distance: 0.21563203953 for Y 9 
[9, 0, 8, 8, 1, 2, 2, 8, 1, 5] 

Answer 1

import numpy as np 
from scipy.spatial import KDTree 

X = np.random.random((10, 2)) # 10 points in 3 dimensions 
Y = np.random.random((10, 2)) # 10 points in 3 dimensions 
NNlist=[] 

for i in range(len(X)): 
    treey = KDTree(np.concatenate([Y.tolist(), np.expand_dims(X[i], axis=0)], axis=0)) 
    dist, ind = treey.query([X[i]], k=6) 
    print('index', ind) # indices of 5 closest neighbors 
    print('distance', dist) 
    print('5 nearest neighbors') 
    for j in ind[0][1:]: 
     print(Y[j]) 
    print() 

당신이 얻을 수있는 ...

index [[10 5 8 9 1 2]] 
distance [[ 0.   0.3393312 0.38565112 0.40120109 0.44200758 0.47675255]] 
5 nearest neighbors 
[ 0.6298789 0.18283264] 
[ 0.42952574 0.83918788] 
[ 0.26258905 0.4115705 ] 
[ 0.61789523 0.96261285] 
[ 0.92417172 0.13276541] 

index [[10 1 3 8 4 9]] 
distance [[ 0.   0.09176157 0.18219064 0.21845335 0.28876942 0.60082231]] 
5 nearest neighbors 
[ 0.61789523 0.96261285] 
[ 0.51031835 0.99761715] 
[ 0.42952574 0.83918788] 
[ 0.3744326 0.97577322] 
[ 0.26258905 0.4115705 ] 

index [[10 7 0 9 5 6]] 
distance [[ 0.   0.15771386 0.2751765 0.3457175 0.49918935 0.70597498]] 
5 nearest neighbors 
[ 0.19803817 0.23495888] 
[ 0.41293849 0.05585981] 
[ 0.26258905 0.4115705 ] 
[ 0.6298789 0.18283264] 
[ 0.04527532 0.78806495] 

index [[10 0 5 7 9 2]] 
distance [[ 0.   0.09269963 0.20597988 0.24505542 0.31104979 0.49743673]] 
5 nearest neighbors 
[ 0.41293849 0.05585981] 
[ 0.6298789 0.18283264] 
[ 0.19803817 0.23495888] 
[ 0.26258905 0.4115705 ] 
[ 0.92417172 0.13276541] 

index [[10 9 5 7 0 8]] 
distance [[ 0.   0.20406876 0.26125464 0.30645317 0.33369641 0.45509834]] 
5 nearest neighbors 
[ 0.26258905 0.4115705 ] 
[ 0.6298789 0.18283264] 
[ 0.19803817 0.23495888] 
[ 0.41293849 0.05585981] 
[ 0.42952574 0.83918788] 

index [[10 5 2 0 7 9]] 
distance [[ 0.   0.13641503 0.17524716 0.34224271 0.56393988 0.56893897]] 
5 nearest neighbors 
[ 0.6298789 0.18283264] 
[ 0.92417172 0.13276541] 
[ 0.41293849 0.05585981] 
[ 0.19803817 0.23495888] 
[ 0.26258905 0.4115705 ] 

index [[10 7 9 0 5 6]] 
distance [[ 0.   0.04152391 0.22807566 0.25709252 0.43421854 0.61332497]] 
5 nearest neighbors 
[ 0.19803817 0.23495888] 
[ 0.26258905 0.4115705 ] 
[ 0.41293849 0.05585981] 
[ 0.6298789 0.18283264] 
[ 0.04527532 0.78806495] 

index [[10 5 1 2 8 3]] 
distance [[ 0.   0.40641681 0.43652515 0.44861766 0.45186271 0.51705369]] 
5 nearest neighbors 
[ 0.6298789 0.18283264] 
[ 0.61789523 0.96261285] 
[ 0.92417172 0.13276541] 
[ 0.42952574 0.83918788] 
[ 0.51031835 0.99761715] 

index [[10 6 9 7 8 4]] 
distance [[ 0.   0.17568369 0.2841519 0.40184611 0.43110847 0.47835169]] 
5 nearest neighbors 
[ 0.04527532 0.78806495] 
[ 0.26258905 0.4115705 ] 
[ 0.19803817 0.23495888] 
[ 0.42952574 0.83918788] 
[ 0.3744326 0.97577322] 

index [[10 9 7 5 0 8]] 
distance [[ 0.   0.11723769 0.2275565 0.32111803 0.32446146 0.4643181 ]] 
5 nearest neighbors 
[ 0.26258905 0.4115705 ] 
[ 0.19803817 0.23495888] 
[ 0.6298789 0.18283264] 
[ 0.41293849 0.05585981] 
[ 0.42952574 0.83918788]