def test_with_data(self):
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([45., 55., 75., 95.])
m = CSCMatrix((data, (row, col)), shape=(4, 4))
data = m.data
m2 = self.basic_m._with_data(data)
self.assertTrue(np.allclose(m.todense(), m2.todense()))
m2 = self.basic_m._with_data(data, copy=False)
self.assertTrue(np.allclose(m.todense(), m2.todense()))
def test_mul_sparse_matrix(self):
#from pyomo.contrib.pynumero.sparse.block_matrix import BlockMatrix
# test unsymmetric times unsymmetric
m = self.basic_m
dense_m = m.toarray()
res = m * m
dense_res = np.matmul(dense_m, dense_m)
self.assertFalse(res.is_symmetric)
self.assertTrue(np.allclose(res.toarray(), dense_res))
# test symmetric result
m = self.basic_m
dense_m = m.toarray()
res = m.transpose() * m
dense_res = np.matmul(dense_m.transpose(), dense_m)
self.assertTrue(res.is_symmetric)
self.assertTrue(np.allclose(res.toarray(), dense_res))
# test unsymmetric with rectangular
m = self.basic_m
dense_m2 = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]])
m2 = CSCMatrix(dense_m2)
res = m * m2
dense_res = np.matmul(m.toarray(), dense_m2)
self.assertFalse(res.is_symmetric)
self.assertTrue(np.allclose(res.toarray(), dense_res))
# test unsymmetric with rectangular scipycsr
m = self.basic_m
dense_m2 = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]])
m2 = csc_matrix(dense_m2)
with self.assertRaises(Exception) as context:
res = m * m2
# test product with symmetric matrix
m = self.basic_m
dense_m = m.todense()
m2 = self.basic_sym_m
dense_m2 = m2.todense()
res = m * m2
res_dense = np.matmul(dense_m, dense_m2)
self.assertTrue(np.allclose(res.todense(), res_dense))
"""