dfs python
###############
#The Algorithm (In English):
# 1) Pick any node.
# 2) If it is unvisited, mark it as visited and recur on all its
# adjacent nodes.
# 3) Repeat until all the nodes are visited, or the node to be
# searched is found.
# The graph below (declared as a Python dictionary)
# is from the linked website and is used for the sake of
# testing the algorithm. Obviously, you will have your own
# graph to iterate through.
graph = {
'A' : ['B','C'],
'B' : ['D', 'E'],
'C' : ['F'],
'D' : [],
'E' : ['F'],
'F' : []
}
visited = set() # Set to keep track of visited nodes.
##################
# The Algorithm (In Code)
def dfs(visited, graph, node):
if node not in visited:
print (node)
visited.add(node)
for neighbour in graph[node]:
dfs(visited, graph, neighbour)
# Driver Code to test in python yourself.
# Note that when calling this, you need to
# call the starting node. In this case it is 'A'.
dfs(visited, graph, 'A')
# NOTE: There are a few ways to do DFS, depending on what your
# variables are and/or what you want returned. This specific
# example is the most fleshed-out, yet still understandable,
# explanation I could find.