def pseudo_peripheral_node(A):
"""
Algorithm in Saad
"""
from pyamg.graph import breadth_first_search
n = A.shape[0]
valence = np.diff(A.indptr)
# select an initial node x, set delta = 0
x = int(np.random.rand() * n)
delta = 0
while 1:
# do a level-set traversal from x
order, level = breadth_first_search(A, x)
# select a node y in the last level with min degree
maxlevel = level.max()
lastnodes = np.where(level == maxlevel)[0]
lastnodesvalence = valence[lastnodes]
minlastnodesvalence = lastnodesvalence.min()
y = np.where(lastnodesvalence == minlastnodesvalence)[0][0]
y = lastnodes[y]
# if d(x,y)>delta, set, and go to bfs above
if level[y] > delta:
x = y
delta = level[y]
else:
return x, order, level
def pseudo_peripheral_node(A):
"""
Algorithm in Saad
"""
from pyamg.graph import breadth_first_search
n = A.shape[0]
valence = np.diff(A.indptr)
# select an initial node x, set delta = 0
x = int(np.random.rand() * n)
delta = 0
while 1:
# do a level-set traversal from x
order, level = breadth_first_search(A, x)
# select a node y in the last level with min degree
maxlevel = level.max()
lastnodes = np.where(level == maxlevel)[0]
lastnodesvalence = valence[lastnodes]
minlastnodesvalence = lastnodesvalence.min()
y = np.where(lastnodesvalence == minlastnodesvalence)[0][0]
y = lastnodes[y]
# if d(x,y)>delta, set, and go to bfs above
if level[y] > delta:
x = y
delta = level[y]
else:
return x, order, level
def pseudo_peripheral_node(A):
"""Find a pseudo peripheral node.
Parameters
----------
A : sparse matrix
Sparse matrix
Returns
-------
x : int
Locaiton of the node
order : array
BFS ordering
level : array
BFS levels
Notes
-----
Algorithm in Saad
"""
from pyamg.graph import breadth_first_search
n = A.shape[0]
valence = np.diff(A.indptr)
# select an initial node x, set delta = 0
x = int(np.random.rand() * n)
delta = 0
while True:
# do a level-set traversal from x
order, level = breadth_first_search(A, x)
# select a node y in the last level with min degree
maxlevel = level.max()
lastnodes = np.where(level == maxlevel)[0]
lastnodesvalence = valence[lastnodes]
minlastnodesvalence = lastnodesvalence.min()
y = np.where(lastnodesvalence == minlastnodesvalence)[0][0]
y = lastnodes[y]
# if d(x,y)>delta, set, and go to bfs above
if level[y] > delta:
x = y
delta = level[y]
else:
return x, order, level
def pseudo_peripheral_node(A):
"""Find a pseudo peripheral node.
Parameters
----------
A : sparse matrix
Sparse matrix
Returns
-------
x : int
Locaiton of the node
order : array
BFS ordering
level : array
BFS levels
Notes
-----
Algorithm in Saad
"""
from pyamg.graph import breadth_first_search
n = A.shape[0]
valence = np.diff(A.indptr)
# select an initial node x, set delta = 0
x = int(np.random.rand() * n)
delta = 0
while True:
# do a level-set traversal from x
order, level = breadth_first_search(A, x)
# select a node y in the last level with min degree
maxlevel = level.max()
lastnodes = np.where(level == maxlevel)[0]
lastnodesvalence = valence[lastnodes]
minlastnodesvalence = lastnodesvalence.min()
y = np.where(lastnodesvalence == minlastnodesvalence)[0][0]
y = lastnodes[y]
# if d(x,y)>delta, set, and go to bfs above
if level[y] > delta:
x = y
delta = level[y]
else:
return x, order, level