def main():
g = Digraph(13)
with open("../resources/tinyDAG.txt", ) as f:
for line in f.readlines():
vertices = " ".join(line.splitlines()).split(' ')
if len(vertices) < 2:
continue
else:
v1, v2 = int(vertices[0]), int(vertices[1])
g.add_edge(v1, v2)
print(g)
dfs = DepthFirstOrder(g)
print(dfs)
print("v pre post")
print("--------------------")
for v in range(g.get_V()):
print(f'{v} {dfs.pre(v)} {dfs.post(v)}\n')
print('Pre-order')
for v in dfs.preorder_vertices():
print(f'{v} ', end="")
print()
print('Post-order')
for v in dfs.postorder_vertices():
print(f'{v} ', end="")
print()
print(dfs.postorder_vertices())
print('Reverse Post-order')
q1 = dfs.reverse_post()
for v in q1:
print(f'{v} ', end="")
def main():
file_name = '../resources/tinyDG.txt'
with open(file_name) as f:
ints = list()
for line in f.read().split('\n'):
ints.append(line)
vertices, edges = int(ints[0]), int(ints[1])
graph = Digraph(vertices)
print(graph)
inp = ints[
2:] # skip first 2 lines in tiny.DG.txt i.e. # of vertices and edges
for i in range(edges):
v, w = inp[i].split(' ')
graph.add_edge(int(v), int(w))
print(graph)
s = 2
dfs = NonrecursiveDirectedDFS(graph, s)
print(dfs)
for v in range(graph.get_V()):
if dfs.marked(v):
print(f'{s} is connected to {v} ')
else:
print(f'{s} not connected to {v}')
def main():
file_name = '../resources/tinyDG.txt'
with open(file_name) as f:
ints = list()
for line in f.read().split('\n'):
ints.append(line)
vertices, edges = int(ints[0]), int(ints[1])
graph = Digraph(vertices)
print(graph)
inp = ints[
2:] # skip first 2 lines in tiny.DG.txt i.e. # of vertices and edges
for i in range(edges):
v, w = inp[i].split(' ')
graph.add_edge(int(v), int(w))
print(graph)
scc = KosarajuSharirSCC(graph)
m = scc.count()
print(f'{m} strong components')
components = [Queue() for _ in range(m)]
for v in range(graph.get_V()):
components[scc.id(v)].put(v)
for i in range(m):
for v in components[i].queue:
print(f'{v} ', end="")
print()
def main():
file_name = '../resources/digraph1.txt'
with open(file_name) as f:
ints = list()
for line in f.read().split('\n'):
ints.append(line)
vertices, edges = int(ints[0]), int(ints[1])
graph = Digraph(vertices)
inp = ints[
2:] # skip first 2 lines in tiny.DG.txt i.e. # of vertices and edges
for i in range(edges):
v, w = inp[i].split(' ')
graph.add_edge(int(v), int(w))
s = 3
sources = [3, 4, 5]
# bfs = BreadthFirstDirectedPaths(graph, s)
bfs = BreadthFirstDirectedPaths(graph, sources=sources)
print(bfs)
for v in range(graph.get_V()):
if bfs.has_path_to(v):
print(f'{sources} to {v} ({bfs.dist_to(v)})')
path = bfs.path_to(v)
while not path.empty():
x = path.get()
if x == s or x in sources:
print(f'{x}', end="")
else:
print(f'->{x}', end="")
print()
else:
print(f'{s} to {v} (-): not connected.\n')
class SymbolDigraph:
def __init__(self, file_name, delimiter=" "):
self._st = defaultdict(int)
# First pass builds the index by reading strings to associate
# distinct strings with an index
with open(f"../resources/{file_name}.txt", ) as f:
for line in f.readlines():
a = " ".join(line.splitlines()).split(delimiter)
for i in range(len(a)):
if a[i] not in self._st:
self._st[a[i]] = len(self._st)
# inverted index to get string keys in an array
self._keys = ['' for _ in range(len(self._st))]
for name in self._st.keys():
self._keys[self._st.get(name)] = name
# second pass builds the digraph by connecting first vertex on each
# line to all others
self._graph = Digraph(len(self._st))
with open(f"../resources/{file_name}.txt", ) as f:
for line in f.readlines():
a = " ".join(line.splitlines()).split(delimiter)
v = self._st.get(a[0])
for i in range(1, len(a)):
w = self._st.get(a[i])
self._graph.add_edge(v, w)
def contains(self, s):
return s in self._st
def index_of(self, s):
return self._st.get(s)
def name_of(self, v):
self.__validate_vertex(v)
if isinstance(v, int):
return self._keys[v]
else:
return self._keys[v.item]
def digraph(self):
return self._graph
def __validate_vertex(self, v):
n = self._graph.get_V()
if isinstance(v, int):
if v < 0 or v >= n:
raise ValueError(f'vertex {v} is not between 0 and {n - 1}')
else:
if v.item < 0 or v.item >= n:
raise ValueError(f'vertex {v} is not between 0 and {n - 1}')
def __repr__(self):
return f'<{self.__class__.__name__}(_st={self._st}, _graph={self._graph}, _keys={self._keys})>'
def main():
with open("../resources/digraph1.txt") as f:
values = "".join(f.readlines()).split('\n')
V, E = int(values[0]), int(values[1])
g = Digraph(V)
sap = SAP(g)
for line in values:
vertices = "".join(line).split(' ')
if len(vertices) < 2:
continue
else:
v, w = int(vertices[0]), int(vertices[1])
g.add_edge(v, w)
length = sap.length(v, w)
ancestor = sap.ancestor(v, w)
print(f'length = {length}, ancestor={ancestor}')
# sap = SAP(g)
print(g)
print(sap)
def main():
g = Digraph(13)
with open("../resources/tinyDG.txt", ) as f:
for line in f.readlines():
vertices = " ".join(line.splitlines()).split(' ')
if len(vertices) < 2:
continue
else:
v1, v2 = int(vertices[0]), int(vertices[1])
g.add_edge(v1, v2)
finder = DirectedCycleX(g)
print(finder)
if finder.has_cycle():
print('Directed cycle')
for v in finder.get_cycle():
print(f'{v} ', end="")
print()
else:
print('No directed cycle')
def main():
g = Digraph(13)
with open("../resources/tinyDG.txt", ) as f:
for line in f.readlines():
vertices = " ".join(line.splitlines()).split(' ')
if len(vertices) < 2:
continue
else:
v1, v2 = int(vertices[0]), int(vertices[1])
g.add_edge(v1, v2)
print(g)
euler = DirectedEulerianCycle(g)
print(euler)
print("Eulerian cycle: ")
if euler.has_eulerian_cycle():
for v in euler.cycle():
print(v + " ")
print()
else:
print("none")
print()
def main():
file_name = '../resources/tinyDG.txt'
with open(file_name) as f:
ints = list()
for line in f.read().split('\n'):
ints.append(line)
vertices, edges = int(ints[0]), int(ints[1])
graph = Digraph(vertices)
# print(graph)
inp = ints[
2:] # skip first 2 lines in tiny.DG.txt i.e. # of vertices and edges
sources = Bag()
sources.add(1)
sources.add(5)
sources.add(10)
for i in range(edges):
v, w = inp[i].split(' ')
graph.add_edge(int(v), int(w))
# print(graph)
# s = 6
# reachable from single source vertex, s
# dfs = DirectedDFS(graph, int(s))
# print(dfs)
# reachable from many source vertices, sources
dfs = DirectedDFS(graph, sources)
# print(dfs)
for v in range(graph.get_V()):
if dfs.marked(v):
print(f'{v} ')
def __init__(self, regex):
m = len(regex)
graph = Digraph(m + 1)
ops = LifoQueue() # stack of states
for i in range(m):
lp = i
# for left parentheses add Epsilon transition to next state, and push state index to stack
if regex[i] == '(' or regex[i] == '|': # '|' is operator for or
# if the regex has left parentheses '(' or the '|' meta-characters,
# push the *state index [i]* of the meta-character on the stack
ops.put(i)
# if we encounter a right parentheses, pop the corresponding left parentheses and/or '|'
elif regex[i] == ')':
_or = ops.get()
# 2-way or operator
if regex[_or] == '|':
lp = ops.get()
graph.add_edge(lp, _or + 1)
graph.add_edge(_or, i)
else:
lp = _or
# else:
# assert False
# closure operator * (uses 1-character lookahead)
if i < m - 1 and regex[i + 1] == '*':
graph.add_edge(lp, i + 1)
graph.add_edge(i + 1, lp)
if regex[i] in ("(", "*", ")"):
graph.add_edge(i, i + 1)
if ops.qsize() != 0:
raise ValueError('Invalid regular expression')
self._m = m
self._graph = graph
self._regex = regex
class WordNet:
def __init__(self, synsets=None, hypernyms=None):
self._nouns_dict = defaultdict(Bag)
self._reverse_nouns_dict = defaultdict(str)
max_id = 0
with open(synsets) as f:
lines = f.readlines()
for line in lines:
items = "".join(line.splitlines()).split(',')
nouns = items[1].split(' ')
for i in range(len(nouns)):
if nouns[i] not in self._nouns_dict:
nouns_list = Bag()
else:
nouns_list = self._nouns_dict.get(nouns[i])
nouns_list.add(int(items[0]))
self._nouns_dict[nouns[i]] = nouns_list
self._reverse_nouns_dict[int(items[0])] = items[1]
max_id = max(max_id, int(items[0]))
self._dg = Digraph(max_id + 1)
with open(hypernyms) as f1:
lines = f1.readlines()
for line in lines:
items = "".join(line.splitlines()).split(',')
v = int(items[0])
for i in range(1, len(items)):
w = int(items[i])
self._dg.add_edge(v, w)
# if not self._is_dag(self._dg):
# raise AttributeError('digraph is not acyclic')
self._sap = SAP(self._dg)
def nouns_dict(self):
return self._nouns_dict
def rev_nouns_dict(self):
return self._reverse_nouns_dict
def _is_dag(self, dg):
dc = DirectedCycle(dg)
return dc.has_cycle()
def nouns(self):
# returns all WordNet nouns
return list(self._nouns_dict.keys())
def is_noun(self, word):
# is the word a WordNet noun?
return True if word in self._nouns_dict else False
def distance(self, noun_a, noun_b):
# distance between noun_a and noun_b
if not self.is_noun(noun_a) or not self.is_noun(noun_b):
raise AttributeError(f'noun arguments to distance() not found')
return self._sap.length(
self._nouns_dict.get(noun_a).first.item,
self._nouns_dict.get(noun_b).first.item)
def sap(self, noun_a, noun_b):
# a synset (second field of synsets.txt) that is the common ancestor of noun_a and noun_b
# in a shortest ancestral path (defined below)
if not self.is_noun(noun_a) or not self.is_noun(noun_b):
raise AttributeError(f'noun arguments to distance() not found')
_id = self._sap.ancestor(
self._nouns_dict.get(noun_a).first.item,
self._nouns_dict.get(noun_b).first.item)
return self._reverse_nouns_dict.get(_id)
def __repr__(self):
return f'<{self.__class__.__name__}(' \
f'_dg={self._dg})>'