Hi, Ok, I got it to work now but - damn, it's ugly. I thought I'd have to watch the differences between ndarray and matrix type but it turns out sparseMatrix is yet again different from matrix in several respects when it comes to certain operations. Is this intended or something that will be mended as sparse stuff becomes more mature?
Concrete examples for matrix/vector multiplication: testMat = asmatrix(around(10 * random.randn(3,2))) testSp = sparse.lil_matrix(testMat) testMat * array([1, 2]) # matrix([[-24., -26., -26.]]) testMat * array([[1, 2]]) # ValueError: matrices are not aligned testMat * array([[1, 2]]).transpose() # matrix([[-24.], # [-26.], # [-26.]]) ... and "dot" has the same effect as "*". Basically mat * rank1 array = row matrix and mat * rank2 array = column matrix For sparse matrices on the other hand the same operations: testSp * array([1, 2]) # array([-24., -26., -26.]) testSp * array([[1, 2]]) # array([-24., -26., -26.]) testSp * array([[1, 2]]).transpose() # array([-24., -26., -26.]) ... and "dot" != "*" (don't know what the former does exactly). So unlike with regular matrices, "*" operations return a (dense) rank1 array regardless of the shape/rank of the vector. I find this confusing and it makes for very ugly code, since a lot of ndim, shape checking is involved if a routine is supposed to accept array AND matrix as input. Any comments or hints? /David
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