def build_graph(cls, args):
number_edges = args.number_edges
metric = args.metric
normalized_laplacian = args.normalized_laplacian
coarsening_levels = args.coarsening_levels
def grid_graph(m, corners=False):
z = graph.grid(m)
# compute pairwise distance
dist, idx = graph.distance_sklearn_metrics(z, k=number_edges, metric=metric)
A = graph.adjacency(dist, idx) # build adjacent matrix
# Connections are only vertical or horizontal on the grid.
# Corner vertices are connected to 2 neightbors only.
if corners:
A = A.toarray()
A[A < A.max()/1.5] = 0
A = scipy.sparse.csr_matrix(A)
print('{} edges'.format(A.nnz))
print("{} > {} edges".format(A.nnz//2, number_edges*m**2//2))
return A
g = grid_graph(28, corners=False)
g = graph.replace_random_edges(g, 0)
graphs, perm = coarsening.coarsen(g, levels=coarsening_levels, self_connections=False)
laplacians = [graph.laplacian(g, normalized=True) for g in graphs]
cls.perm = perm
cls.graphs = graphs
cls.laplacians = laplacians
def build_graph(cls, args):
number_edges = args.number_edges
metric = args.metric
normalized_laplacian = args.normalized_laplacian
coarsening_levels = args.coarsening_levels
data_dir = 'data/20news'
embed_path = os.path.join(data_dir, 'embeddings.npy')
graph_data = np.load(embed_path).astype(np.float32)
dist, idx = graph.distance_sklearn_metrics(graph_data,
k=number_edges,
metric=metric)
adj_matrix = graph.adjacency(dist, idx)
print("{} > {} edges".format(adj_matrix.nnz // 2,
number_edges * graph_data.shape[0] // 2))
adj_matrix = graph.replace_random_edges(adj_matrix, 0)
graphs, perm = coarsening.coarsen(adj_matrix,
levels=coarsening_levels,
self_connections=False)
laplacians = [
graph.laplacian(g, normalized=normalized_laplacian) for g in graphs
]
cls.perm = perm
cls.graphs = graphs
cls.laplacians = laplacians
if corners:
import scipy.sparse
A = A.toarray()
A[A < A.max()/1.5] = 0
A = scipy.sparse.csr_matrix(A)
print('{} edges'.format(A.nnz))
print("{} > {} edges".format(A.nnz//2, FLAGS.number_edges*m**2//2))
return A
t_start = time.process_time()
A = grid_graph(28, corners=False)
graph.replace_random_edges(A, 0)
graphs, perm = coarsening.coarsen(A, levels=FLAGS.coarsening_levels, self_connections=False)
L = [graph.laplacian(A, normalized=True,renormalized=True) for A in graphs]
#print(L.dtype)
#print(L)
print('Execution time: {:.2f}s'.format(time.process_time() - t_start))
graph.plot_spectrum(L)
print("DONE")
del A
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(FLAGS.dir_data, one_hot=False)
train_data = mnist.train.images.astype(np.float32)