def test_naive_aggregation(self):
for A in self.cases:
S = symmetric_strength_of_connection(A)
(expected, expected_Cpts) = reference_naive_aggregation(S)
(result, Cpts) = naive_aggregation(S)
assert_equal((result - expected).nnz, 0)
assert_equal(Cpts.shape[0], expected_Cpts.shape[0])
assert_equal(np.setdiff1d(Cpts, expected_Cpts).shape[0], 0)
# S is diagonal - no dofs aggregated
S = spdiags([[1, 1, 1, 1]], [0], 4, 4, format='csr')
(result, Cpts) = naive_aggregation(S)
expected = np.eye(4)
assert_equal(result.toarray(), expected)
assert_equal(Cpts.shape[0], 4)
def test_naive_aggregation(self):
for A in self.cases:
S = symmetric_strength_of_connection(A)
(expected, expected_Cpts) = reference_naive_aggregation(S)
(result, Cpts) = naive_aggregation(S)
assert_equal((result - expected).nnz, 0)
assert_equal(Cpts.shape[0], expected_Cpts.shape[0])
assert_equal(np.setdiff1d(Cpts, expected_Cpts).shape[0], 0)
# S is diagonal - no dofs aggregated
S = spdiags([[1, 1, 1, 1]], [0], 4, 4, format='csr')
(result, Cpts) = naive_aggregation(S)
expected = np.eye(4)
assert_equal(result.toarray(), expected)
assert_equal(Cpts.shape[0], 4)
def get_aggregate(A, strength, aggregate, diagonal_dominance, B, **kwargs):
def unpack_arg(v):
if isinstance(v, tuple):
return v[0], v[1]
else:
return v, {}
# Compute the strength-of-connection matrix C, where larger
# C[i,j] denote stronger couplings between i and j.
fn, kwargs = unpack_arg(strength)
if fn == 'symmetric':
C = symmetric_strength_of_connection(A, **kwargs)
elif fn == 'classical':
C = classical_strength_of_connection(A, **kwargs)
elif fn == 'distance':
C = distance_strength_of_connection(A, **kwargs)
elif (fn == 'ode') or (fn == 'evolution'):
if 'B' in kwargs:
C = evolution_strength_of_connection(A, **kwargs)
else:
C = evolution_strength_of_connection(A, B, **kwargs)
elif fn == 'energy_based':
C = energy_based_strength_of_connection(A, **kwargs)
elif fn == 'predefined':
C = kwargs['C'].tocsr()
elif fn == 'algebraic_distance':
C = algebraic_distance(A, **kwargs)
elif fn == 'affinity':
C = affinity_distance(A, **kwargs)
elif fn is None:
C = A.tocsr()
else:
raise ValueError('unrecognized strength of connection method: %s' %
str(fn))
# Avoid coarsening diagonally dominant rows
flag, kwargs = unpack_arg(diagonal_dominance)
if flag:
C = eliminate_diag_dom_nodes(A, C, **kwargs)
# Compute the aggregation matrix AggOp (i.e., the nodal coarsening of A).
# AggOp is a boolean matrix, where the sparsity pattern for the k-th column
# denotes the fine-grid nodes agglomerated into k-th coarse-grid node.
fn, kwargs = unpack_arg(aggregate)
if fn == 'standard':
AggOp = standard_aggregation(C, **kwargs)[0]
elif fn == 'naive':
AggOp = naive_aggregation(C, **kwargs)[0]
elif fn == 'lloyd':
AggOp = lloyd_aggregation(C, **kwargs)[0]
elif fn == 'pairwise':
AggOp = pairwise_aggregation(A, B, **kwargs)[0]
elif fn == 'predefined':
AggOp = kwargs['AggOp'].tocsr()
else:
raise ValueError('unrecognized aggregation method %s' % str(fn))
return AggOp
def test_naive_aggregation(self):
for A in self.cases:
S = symmetric_strength_of_connection(A)
(expected, expected_Cpts) = reference_naive_aggregation(S)
(result, Cpts) = naive_aggregation(S)
assert_equal((result - expected).nnz, 0)
assert_equal(Cpts.shape[0], expected_Cpts.shape[0])
assert_equal(np.setdiff1d(Cpts, expected_Cpts).shape[0], 0)
def test_naive_aggregation(self):
for A in self.cases:
S = symmetric_strength_of_connection(A)
(expected, expected_Cpts) = reference_naive_aggregation(S)
(result, Cpts) = naive_aggregation(S)
assert_equal((result - expected).nnz, 0)
assert_equal(Cpts.shape[0], expected_Cpts.shape[0])
assert_equal(np.setdiff1d(Cpts, expected_Cpts).shape[0], 0)