I have the matrices Gx and Gy both sparse of type coo.
I perform the following operations with them:
A = np.hstack((Gx.transpose(),Gy.transpose()))
B = np.vstack((Gx,Gy))
L = np.dot(A,B)
I want to visualize the solution, C so I have used C.toarray() and C.todense(), but the answer is the following:
In [391]: C
Out[391]:
array([ <16x16 sparse matrix of type '<type 'numpy.float64'>'
with 64 stored elements in Compressed Sparse Row format>], dtype=object)
In [392]: C.toarray()
Traceback (most recent call last):
File "<ipython-input-390-86c90f8dce51>", line 1, in <module>
C.toarray()
AttributeError: 'numpy.ndarray' object has no attribute 'toarray'
How could I do to see the matrix C in a dense form?
From:
array([ <16x16 sparse matrix of type '<type 'numpy.float64'>'
with 64 stored elements in Compressed Sparse Row format>], dtype=object)
I deduce that C is a 1 element dense array with dtype=object. That one element is a sparse matrix.
So I expect that
C[0].toarray()
will work. As the error says, a numpy array does not have a toarray method. But in this case its element does.
Since Gx and Gy are sparse, then you need to use the sparse versions of hstack and vstack, not the numpy versions. Check the type of A and B. I be those are numpy arrays, not sparse matrices.
Look what happens when I use np.hstack with a couple of sparse matrices:
In [70]: M=sparse.csr_matrix([[0,1,2],[2,3,4]])
In [71]: np.hstack([M,M])
/usr/lib/python3/dist-packages/scipy/sparse/compressed.py:298: SparseEfficiencyWarning: Comparing sparse matrices using >= and <= is inefficient, using <, >, or !=, instead.
"using <, >, or !=, instead.", SparseEfficiencyWarning)
Out[71]:
array([ <2x3 sparse matrix of type '<class 'numpy.int32'>'
with 5 stored elements in Compressed Sparse Row format>,
<2x3 sparse matrix of type '<class 'numpy.int32'>'
with 5 stored elements in Compressed Sparse Row format>], dtype=object)
The result is not sparse, rather it dense with 2 sparse elements.