The Error
When you get an error that you don't understand, take a bit of time to look at the traceback. Or at least show it to us!
In [288]: M = sparse.random(5,5,.2, 'csr')
In [289]: M
Out[289]:
<5x5 sparse matrix of type '<class 'numpy.float64'>'
with 5 stored elements in Compressed Sparse Row format>
In [290]: print(M)
(1, 1) 0.17737340878962138
(2, 2) 0.12362174819457106
(2, 3) 0.24324155883057885
(3, 0) 0.7666429046432961
(3, 4) 0.21848551209470246
In [291]: SparseMatrix(M)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-291-cca56ea35868> in <module>
----> 1 SparseMatrix(M)
/usr/local/lib/python3.6/dist-packages/sympy/matrices/sparse.py in __new__(cls, *args, **kwargs)
206 else:
207 # handle full matrix forms with _handle_creation_inputs
--> 208 r, c, _list = Matrix._handle_creation_inputs(*args)
209 self.rows = r
210 self.cols = c
/usr/local/lib/python3.6/dist-packages/sympy/matrices/matrices.py in _handle_creation_inputs(cls, *args, **kwargs)
1070 if 0 in row.shape:
1071 continue
-> 1072 elif not row:
1073 continue
1074
/usr/local/lib/python3.6/dist-packages/scipy/sparse/base.py in __bool__(self)
281 return self.nnz != 0
282 else:
--> 283 raise ValueError("The truth value of an array with more than one "
284 "element is ambiguous. Use a.any() or a.all().")
285 __nonzero__ = __bool__
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().
A full understanding requires reading the sympy code, but a cursory look indicates that it's trying to handle your input as "full matrix", and looks at rows. The error isn't the result of you doing logical operations on the entries, but that sympy is doing a logical test on your sparse matrix. It's trying to check if the row is empty (so it can skip it).
SparseMatrix docs may not be the clearest, but most examples either show a dict of points, or a flat array of ALL values plus shape, or a ragged list of lists. I suspect it's trying to treat your matrix that way, looking at it row by row.
But the row of M is itself a sparse matrix:
In [295]: [row for row in M]
Out[295]:
[<1x5 sparse matrix of type '<class 'numpy.float64'>'
with 0 stored elements in Compressed Sparse Row format>,
<1x5 sparse matrix of type '<class 'numpy.float64'>'
with 1 stored elements in Compressed Sparse Row format>,
...]
And trying to check if that row is empty not row produces this error:
In [296]: not [row for row in M][0]
...
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().
So clearly SparseMatrix cannot handle a scipy.sparse matrix as is (at least not in the csr or csc format, and probably not the others. Plus scipy.sparse is not mentioned anywhere in the SparseMatrix docs!
from dense array
Converting the sparse matrix to its dense equivalent does work:
In [297]: M.A
Out[297]:
array([[0. , 0. , 0. , 0. , 0. ],
[0. , 0.17737341, 0. , 0. , 0. ],
[0. , 0. , 0.12362175, 0.24324156, 0. ],
[0.7666429 , 0. , 0. , 0. , 0.21848551],
[0. , 0. , 0. , 0. , 0. ]])
In [298]: SparseMatrix(M.A)
Out[298]:
⎡ 0 0 0 0 0 ⎤
...⎦
Or a list of lists:
SparseMatrix(M.A.tolist())
from dict
The dok format stores a sparse matrix as a dict, which then can be
In [305]: dict(M.todok())
Out[305]:
{(3, 0): 0.7666429046432961,
(1, 1): 0.17737340878962138,
(2, 2): 0.12362174819457106,
(2, 3): 0.24324155883057885,
(3, 4): 0.21848551209470246}
Which works fine as an input:
SparseMatrix(5,5,dict(M.todok()))
I don't know what's most efficient. Generally when working with sympy we (or at least I) don't worry about efficiency. Just get it to work is enough. Efficiency is more relevant in numpy/scipy where arrays can be large, and using the fast compiled numpy methods makes a big difference in speed.
Finally - numpy and sympy are not integrated. That applies also to the sparse versions. sympy is built on Python, not numpy. So inputs in the form of lists and dicts makes most sense.
