Question

我有coo_matrix a形状(40106, 2048)和列numpy数组b形状(40106,)。

我想要做的是简单地连接矩阵和数组（即结果数据结构将具有形状(40106, 2049)）。我尝试使用hstack，如下所示

concat = hstack([a, b])

但是我收到以下错误：

File "/Users/usr/anaconda/lib/python3.5/site-packages/scipy/sparse/construct.py", line 464, in hstack
    return bmat([blocks], format=format, dtype=dtype)
File "/Users/usr/anaconda/lib/python3.5/site-packages/scipy/sparse/construct.py", line 581, in bmat
    'row dimensions' % i)
ValueError: blocks[0,:] has incompatible row dimensions

由于a和b具有相同的行数，因此我无法理解尺寸不匹配的原因。

Answer 1

将第二个数组1D转换为2D，然后使用hstack -

hstack([A,B[:,None]])

示例运行 -

In [86]: from scipy.sparse import coo_matrix, hstack

# Sample inputs as a coo_matrix and an array
In [87]: A = coo_matrix([[1, 2, 0], [3, 0, 4]])
    ...: B = np.array([5, 6])
    ...: 

# Use proposed solution
In [88]: out = hstack([A,B[:,None]])

# Print the dense version to visually verify
In [89]: out.toarray()
Out[89]: 
array([[1, 2, 0, 5],
       [3, 0, 4, 6]])

Answer 2

我认为是sparse.hstack。转换为矩阵时，b为(1,40106)。尝试将其转换为正确的稀疏矩阵，然后再将其传递给hstack。 hstack将作业传递给bmat，最终加入所有输入矩阵的coo属性，从而制作新矩阵

In [66]: from scipy import sparse
In [67]: A = sparse.coo_matrix(np.eye(3))
In [68]: b = np.ones(3)
In [69]: sparse.hstack((A,b))
....
ValueError: blocks[0,:] has incompatible row dimensions
In [70]: B=sparse.coo_matrix(b)
In [71]: B
Out[71]: 
<1x3 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in COOrdinate format>
In [72]: sparse.hstack((A,B.T))
Out[72]: 
<3x4 sparse matrix of type '<class 'numpy.float64'>'
    with 6 stored elements in COOrdinate format>
In [73]: _.A
Out[73]: 
array([[ 1.,  0.,  0.,  1.],
       [ 0.,  1.,  0.,  1.],
       [ 0.,  0.,  1.,  1.]])

这也有效（如在Divakar的回答中）：

In [74]: sparse.hstack((A,b[:,None]))
Out[74]: 
<3x4 sparse matrix of type '<class 'numpy.float64'>'
    with 6 stored elements in COOrdinate format>

我的hastack确实：

return bmat([blocks], format=format, dtype=dtype)

所以直接调用bmat也可以使用

In [93]: sparse.bmat([[A, B.T]])
Out[93]: 
<3x4 sparse matrix of type '<class 'numpy.float64'>'
    with 6 stored elements in COOrdinate format>

sparse.bmat([A, B.T])会产生blocks must be 2d错误。

如何将coo_matrix与列numpy数组连接起来

2 个答案: