我正在尝试完成一个尝试使用稀疏矩阵的分类任务 . 当我将训练集传递给逻辑回归函数时，在scikit中，它会返回以下错误：
ValueError: Array contains NaN or infinity.
我作为逻辑回归的参数传递的矩阵的类型是 scipy.sparse.csr.csr_matrix .
这是我用NaNs的numpy数组创建稀疏矩阵的方法：
X=sparse.csr_matrix(X_hat)
我究竟做错了什么？我得到了与 train_test_split 相同的错误，根据文档，这些函数应该接受稀疏矩阵作为参数 .
我正在使用0.14版本的scikit-learn .
更新：这是完整的TraceBack .
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-65-acdaac419166> in <module>()
1
----> 2 model2.fit(X_train, y_train)
C:\Anaconda\lib\site-packages\sklearn\svm\base.pyc in fit(self, X, y)
666 " one.")
667
--> 668 X = atleast2d_or_csr(X, dtype=np.float64, order="C")
669
670 self.class_weight_ = compute_class_weight(self.class_weight,
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in atleast2d_or_csr(X, dtype, order, copy, force_all_finite)
132 """
133 return _atleast2d_or_sparse(X, dtype, order, copy, sparse.csr_matrix,
--> 134 "tocsr", force_all_finite)
135
136
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in _atleast2d_or_sparse(X, dtype, order, copy, sparse_class, convmethod, force_all_finite)
105 X = sparse_class(X, dtype=dtype)
106 if force_all_finite:
--> 107 _assert_all_finite(X.data)
108 X.data = np.array(X.data, copy=False, order=order)
109 else:
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in _assert_all_finite(X)
25 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
26 and not np.isfinite(X).all()):
---> 27 raise ValueError("Array contains NaN or infinity.")
28
29
ValueError: Array contains NaN or infinity.