def tocsc(self, copy=False):
"""
Convert this matrix to Compressed Sparse Column format
Returns
-------
CSCMatrix
"""
from pyomo.contrib.pynumero.sparse.csc import CSCMatrix
if self.nnz == 0:
return CSCMatrix(self.shape, dtype=self.dtype)
else:
M, N = self.shape
idx_dtype = get_index_dtype((self.col, self.row),
maxval=max(self.nnz, M))
row = self.row.astype(idx_dtype, copy=False)
col = self.col.astype(idx_dtype, copy=False)
indptr = np.empty(N + 1, dtype=idx_dtype)
indices = np.empty_like(row, dtype=idx_dtype)
data = np.empty_like(self.data, dtype=upcast(self.dtype))
# TODO: check why scipy does this and not coo_tocsc
coo_tocsr(N, M, self.nnz, col, row, self.data, indptr, indices,
data)
x = CSCMatrix((data, indices, indptr), shape=self.shape)
if not self.has_canonical_format:
x.sum_duplicates()
return x
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 setUp(self):
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4., 5., 7., 9.])
m = CSCMatrix((data, (row, col)), shape=(4, 4))
m.name = 'basic_matrix'
self.basic_m = m
row = np.array([0, 3, 1, 2, 3])
col = np.array([0, 0, 1, 2, 3])
data = np.array([2., 1., 3., 4., 5.])
m = CSCSymMatrix((data, (row, col)), shape=(4, 4))
m.name = 'basic_sym_matrix'
self.basic_sym_m = m
def tocsc(self, copy=False):
idx_dtype = get_index_dtype((self.indptr, self.indices),
maxval=max(self.nnz, self.shape[0]))
indptr = np.empty(self.shape[1] + 1, dtype=idx_dtype)
indices = np.empty(self.nnz, dtype=idx_dtype)
data = np.empty(self.nnz, dtype=upcast(self.dtype))
csr_tocsc(self.shape[0], self.shape[1], self.indptr.astype(idx_dtype),
self.indices.astype(idx_dtype), self.data, indptr, indices,
data)
from pyomo.contrib.pynumero.sparse.csc import CSCMatrix
A = CSCMatrix((data, indices, indptr), shape=self.shape)
A.has_sorted_indices = True
return A
def test_is_symmetric_numerically(self):
test_m = np.array([[2., 0., 0., 1.], [0., 3., 0., 0.],
[0., 0., 4., 0.], [1., 0., 0., 5.]])
m = CSCMatrix(test_m)
self.assertTrue(_is_symmetric_numerically(m))
self.assertFalse(_is_symmetric_numerically(self.basic_m))
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))
"""
def test_convert_matrix_to_symmetric(self):
test_m = np.array([[2., 0., 0., 1.], [0., 3., 0., 0.],
[0., 0., 4., 0.], [1., 0., 0., 5.]])
m = CSCMatrix(test_m)
sm = _convert_matrix_to_symmetric(m)
self.assertTrue(sm.is_symmetric)
mm = sm.toarray()
self.assertTrue(np.allclose(mm, test_m, atol=1e-6))
def transpose(self, axes=None, copy=False):
if axes is not None:
raise ValueError(("Sparse matrices do not support "
"an 'axes' parameter because swapping "
"dimensions is the only logical permutation."))
M, N = self.shape
from pyomo.contrib.pynumero.sparse.csc import CSCMatrix
return CSCMatrix((self.data, self.indices, self.indptr),
shape=(N, M),
copy=copy)
def getcol(self, j):
from pyomo.contrib.pynumero.sparse.csc import CSCMatrix
return CSCMatrix(self.toscipy().getcol(j))