我想为蓝色点(T-SNE1)中的每个点从红色点(T-SNE2)中找到5个最近邻。我编写了这段代码只是为了找出正确的做法，但我并不确定这样做是对还是错？
X = np.random.random((10, 2)) # 10 points in 3 dimensionsY = np.random.random((10, 2)) # 10 points in 3 dimensionsNNlist=[]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)
输出
[[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]回答：
import numpy as npfrom scipy.spatial import KDTreeX = np.random.random((10, 2)) # 10 points in 3 dimensionsY = np.random.random((10, 2)) # 10 points in 3 dimensionsNNlist=[]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()你可以得到…
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]