def grid_graph(m, corners=False):
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z,
k=number_edges,
metric=metric)
A = graph.adjacency(dist, idx)
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)
# Connections are only vertical or horizontal on the grid.
# Corner vertices are connected to 2 neightbors only.
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, number_edges * m**2 // 2))
return A
def grid_graph(m, corners=False):
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z,
k=number_edges,
metric=metric)
A = graph.adjacency(dist, idx)
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, number_edges * m**2 // 2))
return A
def grid_graph(m, corners=False):
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z, k=number_edges, metric=metric)
A = graph.adjacency(dist, idx)
# Connections are only vertical or horizontal on the grid.
# Corner vertices are connected to 2 neightbors only.
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, number_edges * m ** 2 // 2))
return A
def grid_graph(m, corners=False):
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z,
k=number_edges,
metric=metric)
A = graph.adjacency(dist, idx)
#A = sp.random(A.shape[0], A.shape[0], density=0.01, format="csr", data_rvs=lambda s: np.random.uniform(0, 0.5, size=s))
# Connections are only vertical or horizontal on the grid.
# Corner vertices are connected to 2 neightbors only.
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, number_edges * m**2 // 2))
return A