def test_coord_cost(self):
d_mat = np.array([[1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
c_mat = np.diag(np.ones([8]))
c = ClusterMatrix.from_mat(c_mat)
pow_cc = 1
cg = ClusterGenerator(dsm_mat=d)
initial_cost = ClusterGenerator._coord_cost(d, c, cg.params)
assert (initial_cost == 64)
d_mat = np.array([[1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
c_mat = np.diag(np.ones([8]))
c = ClusterMatrix.from_mat(c_mat)
pow_cc = 1
# cg = ClusterGenerator(dsm_mat = d)
initial_cost = ClusterGenerator._coord_cost(d, c, cg.params)
assert (initial_cost == 80)
def test_bid(self):
d_mat = np.array([[1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 1, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
c_mat = np.diag(np.ones([8]))
c = ClusterMatrix.from_mat(c_mat)
cg = ClusterGenerator(dsm_mat=d)
cg.params = ClusterParameters(pow_cc=1,
pow_bid=1,
pow_dep=4,
max_cluster_size=16,
rand_accept=32,
rand_bid=32,
times=2,
stable_limit=2,
max_repeat=10)
elmt = 7
cluster_bid = cg._make_bid(elmt, d, c)
# print(cluster_bid)
assert np.equal(cluster_bid, np.zeros([8, 1])).all()
d_mat2 = np.array([[1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
d2 = DSMMatrix(d_mat2, activity_labels=d_list)
cg2 = ClusterGenerator(dsm_mat=d2)
cg2.params = ClusterParameters(pow_cc=1,
pow_bid=1,
pow_dep=4,
max_cluster_size=16,
rand_accept=32,
rand_bid=32,
times=2,
stable_limit=2,
max_repeat=10)
elmt = 0
cluster_bid_2 = cg2._make_bid(elmt, d, c)
# print("CB2: ", cluster_bid_2)
assert np.equal(cluster_bid_2,
np.array([[0], [0], [16], [1], [0], [0], [0],
[0]])).all()
def test_coord_cost_2(self):
d_mat = np.array([[1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
c_mat = np.array([[1., 0., 1., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 1.],
[0., 0., 0., 0., 0., 0., 0., 0.]])
c = ClusterMatrix.from_mat(c_mat)
pow_cc = 1
cg = ClusterGenerator(dsm_mat=d)
# import pdb; pdb.set_trace()
initial_cost = ClusterGenerator._coord_cost(d, c, cg.params)
print(initial_cost)
assert (initial_cost == 20)
def test_cluster(self):
d_mat = np.array([[1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 1, 0, 0], [0, 1, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
# print(d.mat.shape)
cg = ClusterGenerator(dsm_mat=d)
def test_place_diag(self):
d_mat = np.array([[0, 0, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
d_diag = DSMMatrix.place_diag(d)
d_mat_result = np.array([[1, 0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0],
[1, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 1]])
d_result = DSMMatrix(d_mat_result, activity_labels=d_list)
assert np.equal(d_diag.mat, d_result.mat).all()
async def new_dsm(dsm_item: DSMItem):
d_mat = np.array(dsm_item.mat)
labels = dsm_item.labels
system_elements = dsm_item.system_elements
try:
d = DSMMatrix(d_mat, activity_labels=labels)
d.clear_elements(system_elements)
app_state.dsm = d
except AssertionError as err:
return {"result": "AssertionError: " + str(err)}
except:
return {"result": "Unexpected error: " + str(sys.exc_info()[0])}
return dsm_item
def test_clear_systemelems(self):
d_mat = np.array([[1, 0, 1, 0], [1, 1, 1, 0], [1, 1, 1, 0],
[0, 0, 1, 1]])
d = DSMMatrix(d_mat)
d.clear_elements([0])
assert (d.mat == np.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0],
[0, 0, 1, 1]])).all()
d.clear_elements([1, 3])
# print(d.mat)
assert (d.mat == np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 0, 0, 0]])).all()
d.clear_elements([])
assert (d.mat == np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 0, 0, 0]])).all()
self.assertRaises(AssertionError, d.clear_elements, ["e"])
self.assertRaises(AssertionError, d.clear_elements, ["2", 1])
self.assertRaises(AssertionError, d.clear_elements, [6])
def test_setunset(self):
d_mat = np.array([[1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])
d_list = ["a", "b", "c", "d"]
d = DSMMatrix(d_mat, activity_labels=d_list)
d_mat[1, 0] = 1
d.set(1, 0)
assert (d.mat == np.array([[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])).all()
d_mat[2, 1] = 1
d.set(2, 1)
assert (d.mat == np.array([[1, 0, 0, 0], [1, 0, 0, 0], [1, 1, 1, 0],
[0, 0, 1, 1]])).all()
d_mat[3, 2] = 0
d.unset(3, 2)
assert (d.mat == np.array([[1, 0, 0, 0], [1, 0, 0, 0], [1, 1, 1, 0],
[0, 0, 0, 1]])).all()
# d_mat[3,2] = 0
self.assertRaises(ArithmeticError, d.unset, 4, 6)
self.assertRaises(ArithmeticError, d.unset, -1, 0)
def test_cluster(self):
d_mat = np.array([[1, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]])
d_list = ["a", "b", "c", "d", "e", "f", "g", "h"]
d = DSMMatrix(d_mat, activity_labels=d_list)
cg = ClusterGenerator(default_size=8)
# try:
cluster_matrix = cg.cluster(d)
total_coord_cost = cg.total_coord_cost
cost_history = cg.cost_history
# except:
# extype, value, tb = sys.exc_info()
# traceback.print_exc()
# pdb.post_mortem(tb)
# pdb.runcall(DSMMatrix.reorder_by_cluster, DSM_matrix, cluster_matrix)
c_mat_result = np.array([[1, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])
c_result = ClusterMatrix.from_mat(c_mat_result)
c_size_result = np.array([[2], [1], [2], [1], [1], [1], [0], [0]])
print(cluster_matrix.mat)
# import pdb; pdb.set_trace()
# assert np.equal(cluster_matrix.mat, c_result.mat).all()
initial_cost = ClusterGenerator._coord_cost(d, cluster_matrix,
cg.params)
print("Cost: " + str(initial_cost))
assert (initial_cost == 14 or initial_cost == 16)
def test_reorder2(self):
d_mat_2 = np.array([[1, 0, 0, 1], [0, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])
d_list_2 = ["1", "2", "3", "4"]
d_2 = DSMMatrix(d_mat_2, d_list_2)
# print("Original DSM: ")
# print(d_2.mat)
c_mat_2 = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0],
[1, 0, 0, 0]])
c_2 = ClusterMatrix.from_mat(c_mat_2)
# print("Original Cluster: ")
# print(c_2.mat)
new_d_mat_2 = DSMMatrix.reorder_by_cluster(d_2, c_2)
# print("Reordered DSM: ")
# print(new_d_mat_2.mat)
# print(new_d_mat_2.labels)
val_comp_2 = new_d_mat_2.mat == np.array([[0, 1, 0, 0], [0, 0, 0, 1],
[0, 0, 0, 0], [1, 0, 0, 0]])
lab_comp_2 = new_d_mat_2.labels == ['4', '3', '2', '1']
assert val_comp_2.all()
assert lab_comp_2
def test_reorder1(self):
# d_mat = np.identity(3)
d_mat = np.array([[1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])
d_list = ["1", "2", "3", "4"]
d = DSMMatrix(d_mat, d_list)
# print("Original DSM: ")
# print(d.mat)
c_mat = np.array([[1, 0, 1, 1], [0, 1, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0]])
c = ClusterMatrix.from_mat(c_mat)
# print("Original Cluster: ")
# print(c.mat)
new_d_mat = DSMMatrix.reorder_by_cluster(d, c)
# print("Reordered DSM: ")
# print(new_d_mat.mat)
# print(new_d_mat.labels)
val_comp_1 = new_d_mat.mat == np.array([[0, 0, 0, 0], [1, 0, 0, 0],
[0, 1, 0, 0], [0, 0, 0, 0]])
lab_comp_1 = new_d_mat.labels == ['1', '3', '4', '2']
assert val_comp_1.all()
assert lab_comp_1
def block_exit():
plt.show()
if __name__ == "__main__":
d_mat = np.array([
[1, 0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 0, 1, 1, 0],
[1, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 1]
])
d = DSMMatrix(d_mat)
c_mat = np.array([
[0, 0, 0, 1, 0, 1, 0, 0],
[1, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]
])
c = ClusterMatrix.from_mat(c_mat)
graph_matrix(d, cluster_matrix=c)
graph_matrix(c)
def test_auto_labels(self):
d2_mat = np.array([[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])
d2 = DSMMatrix(d2_mat)
# print(d2.labels)
assert d2.labels == ['1', '2', '3', '4']
def test_manual_labels(self):
d_mat = np.array([[1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 1, 0],
[0, 0, 1, 1]])
d_list = ["a", "b", "c", "d"]
d = DSMMatrix(d_mat, activity_labels=d_list)