def load_hcp_tcgn():
time_series, labels, As = load_hcp_example()
normalized_laplacian = True
coarsening_levels = 4
graphs, perm = coarsening.coarsen(As[0],
levels=coarsening_levels,
self_connections=False)
L = [graph.laplacian(A, normalized=normalized_laplacian) for A in graphs]
# idx_train = range(40*512)
# idx_test = range(len(idx_train), time_series.shape[0])
idx_train = range(5 * 512)
idx_test = range(len(idx_train), 10 * 512)
train_data = time_series[idx_train]
train_labels = labels[idx_train]
test_data = time_series[idx_test]
test_labels = labels[idx_test]
train_data = perm_data_time(train_data, perm)
test_data = perm_data_time(test_data, perm)
return L, train_data, test_data, train_labels, test_labels
def load_hcp_tcgn(device):
time_series, labels, As = load_hcp_example()
normalized_laplacian = True
coarsening_levels = 4
graphs, perm = coarsening.coarsen(As[0],
levels=coarsening_levels,
self_connections=False)
coos = [
torch.tensor([graph.tocoo().row, graph.tocoo().col],
dtype=torch.long).to(device) for graph in graphs
]
idx_train = range(int(0.8 * time_series.shape[0]))
print('Size of train set: {}'.format(len(idx_train)))
idx_test = range(len(idx_train), time_series.shape[0])
print('Size of test set: {}'.format(len(idx_test)))
train_data = time_series[idx_train]
train_labels = labels[idx_train]
test_data = time_series[idx_test]
test_labels = labels[idx_test]
train_data = perm_data_time(train_data, perm)
test_data = perm_data_time(test_data, perm)
return graphs, coos, train_data, test_data, train_labels, test_labels
def load_hcp_tcgn(device):
time_series, labels, As = load_hcp_example()
normalized_laplacian = True
coarsening_levels = 4
graphs, perm = coarsening.coarsen(As[0],
levels=coarsening_levels,
self_connections=False)
L = [
torch.tensor(graph.rescale_L(graph.laplacian(
A, normalized=normalized_laplacian).todense(),
lmax=2),
dtype=torch.float).to(device) for A in graphs
]
L_sparse = list()
for A in graphs:
g = graph.rescale_L(graph.laplacian(A,
normalized=normalized_laplacian),
lmax=2)
coo = coo_matrix(g)
values = coo.data
indices = np.vstack((coo.row, coo.col))
i = torch.LongTensor(indices)
v = torch.FloatTensor(values)
shape = coo.shape
a = torch.sparse.FloatTensor(i, v, torch.Size(shape))
a = a.to(device)
L_sparse.append(a)
# idx_train = range(17*512)
idx_train = range(int(0.8 * time_series.shape[0]))
print('Size of train set: {}'.format(len(idx_train)))
idx_test = range(len(idx_train), time_series.shape[0])
print('Size of test set: {}'.format(len(idx_test)))
# idx_train = range(5*512)
# idx_test = range(len(idx_train), 10*512)
train_data = time_series[idx_train]
train_labels = labels[idx_train]
test_data = time_series[idx_test]
test_labels = labels[idx_test]
train_data = perm_data_time(train_data, perm)
test_data = perm_data_time(test_data, perm)
sparse = False
if sparse:
laplacian = L_sparse
else:
laplacian = L
return laplacian, train_data, test_data, train_labels, test_labels
def load_hcp_tcgn(device):
time_series, labels, As = load_hcp_example(full=True)
normalized_laplacian = True
coarsening_levels = 4
shuffled = False
A = As[0]
#A = arr.todense()
if shuffled:
B = A.toarray()
B = list(B[np.triu_indices(A.shape[0])])
random.shuffle(B)
A = np.zeros((A.shape[0], A.shape[0]))
indices = np.triu_indices(A.shape[0])
A[indices] = B
A = A + A.T - np.diag(A.diagonal())
A = sp.csr_matrix(A)
graphs, perm = coarsening.coarsen(A,
levels=coarsening_levels,
self_connections=False)
#else:
W = sp.random(As[0].shape[0],
As[0].shape[0],
density=0,
format='csr',
data_rvs=lambda s: np.random.uniform(0, 1, size=s))
#graphs, perm = coarsening.coarsen(W, levels=coarsening_levels, self_connections=False)
#graphs = [As[0]]
graphs = [W]
coos = [
torch.tensor([graph.tocoo().row, graph.tocoo().col],
dtype=torch.long).to(device) for graph in graphs
]
idx_train = range(int(0.8 * time_series.shape[0]))
print('Size of train set: {}'.format(len(idx_train)))
idx_test = range(len(idx_train), time_series.shape[0])
print('Size of test set: {}'.format(len(idx_test)))
train_data = time_series[idx_train]
train_labels = labels[idx_train]
test_data = time_series[idx_test]
test_labels = labels[idx_test]
#train_data = perm_data_time(train_data, perm)
#test_data = perm_data_time(test_data, perm)
return graphs, coos, train_data, test_data, train_labels, test_labels
