def __init__(self, task_parameters):
# parameters
vocab_size = task_parameters['Voc']
nb_of_clust = task_parameters['nb_clusters_target']
clust_size_min = task_parameters['size_min']
clust_size_max = task_parameters['size_max']
p = task_parameters['p']
q = task_parameters['q']
self_loop = True
# block model
W, c = block.unbalanced_block_model(nb_of_clust, clust_size_min,
clust_size_max, p, q)
# add self loop
if self_loop:
for i in range(W.shape[0]):
W[i, i] = 1
# shuffle
W, c, idx = block.schuffle(W, c)
# signal on block model
u = np.zeros(c.shape[0])
for r in range(nb_of_clust):
cluster = np.where(c == r)[0]
s = cluster[np.random.randint(cluster.shape[0])]
u[s] = r + 1
# target
target = c
# convert to pytorch
u = torch.from_numpy(u)
u = u.long()
target = torch.from_numpy(target)
target = target.long()
# mapping matrices
W_coo = sp.coo_matrix(W)
nb_edges = W_coo.nnz
nb_vertices = W.shape[0]
edge_to_starting_vertex = sp.coo_matrix(
(np.ones(nb_edges), (np.arange(nb_edges), W_coo.row)),
shape=(nb_edges, nb_vertices))
edge_to_ending_vertex = sp.coo_matrix(
(np.ones(nb_edges), (np.arange(nb_edges), W_coo.col)),
shape=(nb_edges, nb_vertices))
# attribute
#self.adj_matrix=torch.from_numpy(W).type(default_type)
#self.edge_to_starting_vertex=torch.from_numpy(edge_to_starting_vertex.toarray()).type(default_type)
#self.edge_to_ending_vertex=torch.from_numpy(edge_to_ending_vertex.toarray()).type(default_type)
self.adj_matrix = W
self.edge_to_starting_vertex = edge_to_starting_vertex
self.edge_to_ending_vertex = edge_to_ending_vertex
self.signal = u
self.target = target
def __init__(self, task_parameters):
# parameters
vocab_size = task_parameters['Voc']
nb_of_clust = task_parameters['nb_clusters_target']
clust_size_min = task_parameters['size_min']
clust_size_max = task_parameters['size_max']
p = task_parameters['p']
q = task_parameters['q']
self_loop = True
W0 = task_parameters['W0']
u0 = task_parameters['u0']
# create block model graph and put random signal on it
W, c = block.unbalanced_block_model(nb_of_clust, clust_size_min,
clust_size_max, p, q)
u = np.random.randint(vocab_size, size=W.shape[0]) # len(c)=n (1-dim)
# add the subgraph to be detected
W, c = block.add_a_block(W0, W, c, nb_of_clust, q)
u = np.concatenate((u, u0), axis=0)
# shuffle
W, c, idx = block.schuffle(W, c)
u = u[idx]
u = torch.from_numpy(u)
u = u.long()
# add self loop
if self_loop:
for i in range(W.shape[0]):
W[i, i] = 1
# create the target
target = (c == nb_of_clust).astype(float)
target = torch.from_numpy(target)
target = target.long()
# mapping matrices
W_coo = sp.coo_matrix(W)
nb_edges = W_coo.nnz
nb_vertices = W.shape[0]
edge_to_starting_vertex = sp.coo_matrix(
(np.ones(nb_edges), (np.arange(nb_edges), W_coo.row)),
shape=(nb_edges, nb_vertices))
edge_to_ending_vertex = sp.coo_matrix(
(np.ones(nb_edges), (np.arange(nb_edges), W_coo.col)),
shape=(nb_edges, nb_vertices))
# attribute
#self.adj_matrix=torch.from_numpy(W).type(default_type)
#self.edge_to_starting_vertex=torch.from_numpy(edge_to_starting_vertex.toarray()).type(default_type)
#self.edge_to_ending_vertex=torch.from_numpy(edge_to_ending_vertex.toarray()).type(default_type)
self.adj_matrix = W
self.edge_to_starting_vertex = edge_to_starting_vertex
self.edge_to_ending_vertex = edge_to_ending_vertex
self.signal = u
self.target = target