for j in range(b.shape[1]):
col = b[:, j].toarray()
xj = Afactsolve(ravel(col))
w = nonzero(xj)[0]
tempj.fill(j)
x = x + A.__class__((xj[w], (w, tempj[:len(w)])),
shape=b.shape, dtype=A.dtype)
After Change
// Create a sparse output matrix by repeatedly applying
// the sparse factorization to solve columns of b.
data_segs = []
row_segs = []
col_segs = []
for j in range(b.shape[1]):
bj = b[:, j].A.ravel()
xj = Afactsolve(bj)
w = np.flatnonzero(xj)
segment_length = w.shape[0]
row_segs.append(w)
col_segs.append(np.ones(segment_length, dtype=int)*j)
data_segs.append(np.asarray(xj[w], dtype=A.dtype))
sparse_data = np.concatenate(data_segs)
sparse_row = np.concatenate(row_segs)
sparse_col = np.concatenate(col_segs)
x = A.__class__((sparse_data, (sparse_row, sparse_col)),
shape=b.shape, dtype=A.dtype)
