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
def grid_graph(m, corners=False):
k=8
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z, k=8, metric='euclidean')
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, k*m**2//2))
return A
def grid_graph(m, corners=False):
"""
返回 graph 的连接矩阵(symmetric, nearest k neighbors)
"""
num_edges = 8
z = graph.grid(m)
dist, idx = graph.distance_sklearn_metrics(z, k=8, metric='euclidean')
A = graph.adjacency(dist, idx)
print('A.nnz: {}'.format(A.nnz))
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, num_edges * (m**2) // 2))
return A