def breadth_first_search(graph, root=None, filter=filters.null()):
"""
Breadth-first search.
@type graph: graph
@param graph: Graph.
@type root: node
@param root: Optional root node (will explore only root's connected component)
@rtype: tuple
@return: A tuple containing a dictionary and a list.
1. Generated spanning tree
2. Graph's level-based ordering
"""
def bfs():
"""
Breadth-first search subfunction.
"""
while (queue != []):
node = queue.pop(0)
for other in graph[node]:
if (other not in spanning_tree and filter(other, node)):
queue.append(other)
ordering.append(other)
spanning_tree[other] = node
queue = [] # Visiting queue
spanning_tree = {} # Spanning tree
ordering = []
filter.configure(graph, spanning_tree)
# BFS from one node only
if (root is not None):
if (filter(root, None)):
queue.append(root)
ordering.append(root)
spanning_tree[root] = None
bfs()
return spanning_tree, ordering
# Algorithm
for each in graph:
if (each not in spanning_tree):
if (filter(each, None)):
queue.append(each)
ordering.append(each)
spanning_tree[each] = None
bfs()
return spanning_tree, ordering
def breadth_first_search(graph, root=None, filter=filters.null()):
"""
Breadth-first search.
@type graph: graph
@param graph: Graph.
@type root: node
@param root: Optional root node (will explore only root's connected component)
@rtype: tuple
@return: A tuple containing a dictionary and a list.
1. Generated spanning tree
2. Graph's level-based ordering
"""
def bfs():
"""
Breadth-first search subfunction.
"""
while (queue != []):
node = queue.pop(0)
for other in graph[node]:
if (other not in spanning_tree and filter(other, node)):
queue.append(other)
ordering.append(other)
spanning_tree[other] = node
queue = [] # Visiting queue
spanning_tree = {} # Spanning tree
ordering = []
filter.configure(graph, spanning_tree)
# BFS from one node only
if (root is not None):
if (filter(root, None)):
queue.append(root)
ordering.append(root)
spanning_tree[root] = None
bfs()
return spanning_tree, ordering
# Algorithm
for each in graph:
if (each not in spanning_tree):
if (filter(each, None)):
queue.append(each)
ordering.append(each)
spanning_tree[each] = None
bfs()
return spanning_tree, ordering
def depth_first_search(graph, root=None, filter=filters.null()):
"""
Depth-first search.
@type graph: graph
@param graph: Graph.
@type root: node
@param root: Optional root node (will explore only root's connected component)
@rtype: tuple
@return: A tupple containing a dictionary and two lists:
1. Generated spanning tree
2. Graph's preordering
3. Graph's postordering
"""
def dfs(node):
"""
Depht-first search subfunction.
"""
visited[node] = 1
pre.append(node)
# Explore recursively the connected component
for each in graph[node]:
if (each not in visited and filter(each, node)):
spanning_tree[each] = node
dfs(each)
post.append(node)
visited = {} # List for marking visited and non-visited nodes
spanning_tree = {} # Spanning tree
pre = [] # Graph's preordering
post = [] # Graph's postordering
filter.configure(graph, spanning_tree)
# DFS from one node only
if (root is not None):
if (filter(root, None)):
spanning_tree[root] = None
dfs(root)
return spanning_tree, pre, post
# Algorithm loop
for each in graph:
# Select a non-visited node
if (each not in visited and filter(each, None)):
spanning_tree[each] = None
# Explore node's connected component
dfs(each)
return spanning_tree, pre, post
def depth_first_search(graph, root=None, filter=filters.null()):
"""
Depth-first search.
@type graph: graph
@param graph: Graph.
@type root: node
@param root: Optional root node (will explore only root's connected component)
@rtype: tuple
@return: A tupple containing a dictionary and two lists:
1. Generated spanning tree
2. Graph's preordering
3. Graph's postordering
"""
def dfs(node):
"""
Depht-first search subfunction.
"""
visited[node] = 1
pre.append(node)
# Explore recursively the connected component
for each in graph[node]:
if (each not in visited and filter(each, node)):
spanning_tree[each] = node
dfs(each)
post.append(node)
visited = {} # List for marking visited and non-visited nodes
spanning_tree = {} # Spanning tree
pre = [] # Graph's preordering
post = [] # Graph's postordering
filter.configure(graph, spanning_tree)
# DFS from one node only
if (root is not None):
if (filter(root, None)):
spanning_tree[root] = None
dfs(root)
return spanning_tree, pre, post
# Algorithm loop
for each in graph:
# Select a non-visited node
if (each not in visited and filter(each, None)):
spanning_tree[each] = None
# Explore node's connected component
dfs(each)
return spanning_tree, pre, post