我正在尝试使用sklearn进行凝聚聚类。在拟合步骤中,我收到此错误。错误并不是一直显示,如果我改变了数据点的数量,那么我可能不会得到错误和凝聚聚类。我不太清楚如何调试它。我已经确保我的数据阵列中没有填充nannan的NaN值。任何关于为什么会发生这种情况的想法都会有所帮助。
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ValueError Traceback (most recent call last)
<ipython-input-38-8acbe956f76e> in <module>()
13 agg = AgglomerativeClustering(n_clusters=k,affinity="euclidean",linkage="ward")
14 init = time.time()
---> 15 agg.fit(data)
16 atime = time.time()
17 labels = agg.labels_
C:\Python27\lib\site-packages\sklearn\cluster\hierarchical.pyc in fit(self, X, y)
754 n_components=self.n_components,
755 n_clusters=n_clusters,
--> 756 **kwargs)
757 # Cut the tree
758 if compute_full_tree:
C:\Python27\lib\site-packages\sklearn\externals\joblib\memory.pyc in __call__(self, *args, **kwargs)
279
280 def __call__(self, *args, **kwargs):
--> 281 return self.func(*args, **kwargs)
282
283 def call_and_shelve(self, *args, **kwargs):
C:\Python27\lib\site-packages\sklearn\cluster\hierarchical.pyc in ward_tree(X, connectivity, n_components, n_clusters, return_distance)
189 'for the specified number of clusters',
190 stacklevel=2)
--> 191 out = hierarchy.ward(X)
192 children_ = out[:, :2].astype(np.intp)
193
C:\Python27\lib\site-packages\scipy\cluster\hierarchy.pyc in ward(y)
463
464 """
--> 465 return linkage(y, method='ward', metric='euclidean')
466
467
C:\Python27\lib\site-packages\scipy\cluster\hierarchy.pyc in linkage(y, method, metric)
662 Z = np.zeros((n - 1, 4))
663 _hierarchy.linkage(dm, Z, n,
--> 664 int(_cpy_euclid_methods[method]))
665 return Z
666
scipy\cluster\_hierarchy.pyx in scipy.cluster._hierarchy.linkage (scipy\cluster\_hierarchy.c:8759)()
C:\Python27\lib\site-packages\scipy\cluster\_hierarchy.pyd in View.MemoryView.memoryview_copy_contents (scipy\cluster\_hierarchy.c:22026)()
C:\Python27\lib\site-packages\scipy\cluster\_hierarchy.pyd in View.MemoryView._err_extents (scipy\cluster\_hierarchy.c:21598)()
ValueError: got differing extents in dimension 0 (got 704882705 and 4999850001)
答案 0 :(得分:1)
这是一个溢出的问题,请注意4999850001 - 2 ** 32 = 704882705(输出的最后一行)。有些东西太大,不适合32位整数。您应该尝试使用更少的数据点。