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python - scikit学习凝聚聚类错误

转载 作者:太空宇宙 更新时间:2023-11-03 16:22:39 25 4
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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。该错误不会一直出现,如果我更改数据点的数量,那么我可能不会得到错误和凝聚聚类。我不太确定如何调试这个。我已经使用 fillnan 确保我的数据数组中没有 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。

---------------------------------------------------------------------------
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)

最佳答案

这是一个溢出问题,请注意 4999850001 - 2**32 = 704882705(输出的最后一行)。有些东西太大了,无法容纳 32 位整数。您应该尝试使用更少的数据点。

关于python - scikit学习凝聚聚类错误,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38275541/

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