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Depth-First Search
#!/usr/bin/env python
An iterative implementation of depth-first search from:
"Python Algorithms: Mastering Basic Algorithms in the Python Language"
by Magnus Lie Hetland
ISBN: 9781484200551
Input consists of a simple graph of { node: [list of neighbors] } plus a source and target node.
def dfs (graph, src, tgt):
"""Return a path from the source (src) to the target (tgt) in the graph using depth-first search"""
if not graph.has_key(src):
raise AttributeError("The source '%s' is not in the graph" % src)
if not graph.has_key(tgt):
raise AttributeError("The target '%s' is not in the graph" % tgt)
path = []
queue = []
while queue:
node = queue.pop()
if node not in path:
if node == tgt:
return path
Test using the graph from:
test = {
"a": ["b", "f"],
"b": ["a", "c", "g"],
"c": ["b", "d", "g", "l"],
"d": ["c", "e", "k"],
"e": ["d", "f"],
"f": ["a", "e"],
"g": ["b", "c", "h", "l"],
"h": ["g", "i"],
"i": ["h", "j", "k"],
"j": ["i", "k"],
"k": ["d", "i", "j", "l"],
"l": ["c", "g", "k"],
# unlike BFS, this will *not* always find the shortest paths
assert dfs(test, "a", "e") == ['a', 'f', 'e']
assert dfs(test, "a", "i") != ['a', 'b', 'g', 'h', 'i'] # finds: ['a', 'f', 'e', 'd', 'k', 'l', 'g', 'h', 'i']
assert dfs(test, "a", "l") != ['a', 'b', 'c', 'l'] # finds: ['a', 'f', 'e', 'd', 'k', 'l']
assert len(dfs(test, "b", "k")) == len(['b', 'c', 'd', 'k']) # finds: ['b', 'g', 'l', 'k'] which is ok b/c of length
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