为什么所有标签都为-1？由DBSCAN在Python中生成]] <<

Question

！[在此处输入图片描述] [1]

from sklearn.cluster import DBSCAN
dbscan = DBSCAN(eps=0.001, min_samples=10) 
clustering = dbscan.fit(X)

示例向量：

array([[ 0.05811029, -1.089355  , -1.9143777 , ...,  1.235167  ,
    -0.6473859 ,  1.5684978 ],
   [-0.7117326 , -0.31876346, -0.45949244, ...,  0.17786546,
     1.9377285 ,  2.190525  ],
   [ 1.1685177 , -0.18201494,  0.19475089, ...,  0.7026453 ,
     0.3937522 , -0.78675956],
   ...,
   [ 1.4172379 ,  0.01070347, -1.3984257 , ..., -0.70529956,
     0.19471683, -0.6201791 ],
   [ 0.6171041 , -0.8058429 ,  0.44837445, ...,  1.216958  ,
    -0.10003573, -0.19012968],
   [ 0.6433722 ,  1.1571665 , -1.2123466 , ...,  0.592805  ,
     0.23889546,  1.6207514 ]], dtype=float32)

X是从model = word2vec.Word2Vec(sent, min_count=1,size= 50,workers=3, window =3, sg = 1)生成的model.wv.vectors

结果如下：

array（[-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1， -1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1，-1 ，-1，-1，-1，-1，-1，-1]]] >>

！[在此处输入图像描述] [1] from sklearn.cluster import DBSCAN dbscan = DBSCAN（eps = 0.001，min_samples = 10）聚类= dbscan.fit（X）示例向量：array（[[[0.05811029，-1.089355， -1 ....

Answer 1

基于docs：

labels_array，形状= [n_samples]