我使用sklearn LabelSpreadingModel如下:
label_spreading_model = LabelSpreading()
model_s = label_spreading_model.fit(my_inputs, labels)
但是我得到了以下错误:
MemoryErrorTraceback (most recent call last)
<ipython-input-17-73adbf1fc908> in <module>()
11
12 label_spreading_model = LabelSpreading()
---> 13 model_s = label_spreading_model.fit(my_inputs, labels)
/usr/local/lib/python2.7/dist-packages/sklearn/semi_supervised/label_propagation.pyc in fit(self, X, y)
224
225 # actual graph construction (implementations should override this)
--> 226 graph_matrix = self._build_graph()
227
228 # label construction
/usr/local/lib/python2.7/dist-packages/sklearn/semi_supervised/label_propagation.pyc in _build_graph(self)
455 affinity_matrix = self._get_kernel(self.X_)
456 laplacian = graph_laplacian(affinity_matrix, normed=True)
--> 457 laplacian = -laplacian
458 if sparse.isspmatrix(laplacian):
459 diag_mask = (laplacian.row == laplacian.col)
MemoryError:
看起来输入矩阵的拉普拉斯函数有问题。是否有任何参数我可以配置或任何更改，我可以避免这个错误?谢谢!
很明显，你的电脑内存不足了。
由于您没有设置任何参数，因此默认使用rbf-kernel (proof)。
摘录自scikit-learn的文档:
The RBF kernel will produce a fully connected graph which is represented in
memory by a dense matrix. This matrix may be very large and combined with the
cost of performing a full matrix multiplication calculation for each iteration
of the algorithm can lead to prohibitively long running times
也许下面(上面文档中的下一句话)会有帮助:
On the other hand, the KNN kernel will produce a much more memory-friendly
sparse matrix which can drastically reduce running times.
但我不知道你的数据，电脑配置和其他的，只能猜测…