Python 从scipy矩阵中删除行

我有一个scipy稀疏矩阵

data

和一个整数

n

，它与我要删除的

data

中的一行关联。要删除此行，我尝试了以下方法：

data = sparse.csr_matrix(np.delete(np.array(data),n, axis=0))

但是，这产生了以下错误：

Traceback (most recent call last):
  File "...", line 260, in <module>
    X_labeled = sparse.csr_matrix(np.delete(np.array(X_labeled),n, axis=0))
  File "/anaconda3/lib/python3.6/site-packages/scipy/sparse/compressed.py", line 79, in __init__
    self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
  File "/anaconda3/lib/python3.6/site-packages/scipy/sparse/coo.py", line 177, in __init__
    self.row, self.col = M.nonzero()
SystemError: <built-in method nonzero of numpy.ndarray object at 0x113c883f0> returned a result with an error set

Traceback (most recent call last):
  File "...", line 261, in <module>
    X_labeled = np.delete(X_labeled.toarray(),n, axis=0)
  File "/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py", line 4839, in delete
    "size %i" % (obj, axis, N))
IndexError: index 86 is out of bounds for axis 0 with size 4

我得到这个错误：

Traceback (most recent call last):
  File "...", line 260, in <module>
    X_labeled = sparse.csr_matrix(np.delete(np.array(X_labeled),n, axis=0))
  File "/anaconda3/lib/python3.6/site-packages/scipy/sparse/compressed.py", line 79, in __init__
    self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
  File "/anaconda3/lib/python3.6/site-packages/scipy/sparse/coo.py", line 177, in __init__
    self.row, self.col = M.nonzero()
SystemError: <built-in method nonzero of numpy.ndarray object at 0x113c883f0> returned a result with an error set

Traceback (most recent call last):
  File "...", line 261, in <module>
    X_labeled = np.delete(X_labeled.toarray(),n, axis=0)
  File "/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py", line 4839, in delete
    "size %i" % (obj, axis, N))
IndexError: index 86 is out of bounds for axis 0 with size 4

我明白了：

<class 'scipy.sparse.csr.csr_matrix'>
(4, 2740)
(4, 2740)

(4, 2740)
(4, 2740)

将稀疏矩阵转换为密集矩阵的正确方法是使用

toarray

，而不是

np.array（…）

：

delete

适用于此正确数组：

In [414]: data = sparse.csr_matrix(np.delete(M.toarray(),1, axis=0))
In [415]: data
Out[415]: 
<2x3 sparse matrix of type '<class 'numpy.float64'>'
    with 2 stored elements in Compressed Sparse Row format>
In [416]: data.A
Out[416]: 
array([[ 1.,  0.,  0.],
       [ 0.,  0.,  1.]])

---

你的数据看起来像什么？用这个简单的数据运行你的行是有效的：

data=np.array（[1,2,3,4,5,6]）

。将稀疏矩阵转换为密集数组的正确方法是

data.toarray（）

为什么不将

数据

索引到除要删除的一个之外的所有行索引？我想你是指

todense（）

方法。。无论如何，这不是关于矩阵的。

todense

构成了一个

np.matrix

，

toarray

构成了一个

np.ndarray

（有一个

.a

捷径）。

In [411]: M.toarray()
Out[411]: 
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

In [414]: data = sparse.csr_matrix(np.delete(M.toarray(),1, axis=0))
In [415]: data
Out[415]: 
<2x3 sparse matrix of type '<class 'numpy.float64'>'
    with 2 stored elements in Compressed Sparse Row format>
In [416]: data.A
Out[416]: 
array([[ 1.,  0.,  0.],
       [ 0.,  0.,  1.]])

In [417]: M[[0,2],:]
Out[417]: 
<2x3 sparse matrix of type '<class 'numpy.float64'>'
    with 2 stored elements in Compressed Sparse Row format>
In [418]: _.A
Out[418]: 
array([[ 1.,  0.,  0.],
       [ 0.,  0.,  1.]])
In [420]: M[np.array([True,False,True]),:].A
Out[420]: 
array([[ 1.,  0.,  0.],
       [ 0.,  0.,  1.]])

In [421]: mask=np.ones((3,),bool)
In [422]: mask[1]=False
In [423]: M[mask,:].A