def buildGraph(wordFile):
d = {}
g = Graph()
wfile = open(wordFile, 'r')
# 创建词桶
for line in wfile:
word = line[:-1]
for i in range(len(word)):
bucket = word[:i] + '_' + word[i+1:]
if bucket in d:
d[bucket].append(word)
else:
d[bucket] = [word]
# 为同一个桶中的单词添加顶点和边
for bucket in d.keys():
for word1 in d[bucket]:
for word2 in d[bucket]:
if word1 != word2:
g.addEdge(word1, word2)
return g
def buildGraph():
d = {}
g = Graph()
# create buckets of words that differ by one letter
with open('words4', 'r') as wfile:
for line in wfile:
word = line.strip()
for i in range(4):
bucket = word[:i] + '_' + word[i + 1:]
if bucket in d:
d[bucket].append(word)
else:
d[bucket] = [word]
# add vertices and edges for words in the same bucket
for bucket in d.keys():
for word1 in d[bucket]:
for word2 in d[bucket]:
if word1 != word2:
g.addEdge(word1, word2)
return g
def KruskalMST(g):
c = [] # empty set of clusters
for v in g.vertices:
c.append(Graph(v))
q = sorted(g.edges, reverse=True) # use the edge weights as keys
T = Graph() # empty tree
counter = 0
while len(T) < len(g) - 1:
counter += 1
e = q.pop()
origGraph = graphWithVertex(c, e.origin)
destGraph = graphWithVertex(c, e.destination)
if origGraph is not None and destGraph is not None:
if origGraph is not destGraph:
T.addEdge(e)
# remove the graphs for union
c.remove(origGraph)
c.remove(destGraph)
c.append(origGraph.getUnion(destGraph))
T.render('Step {}'.format(counter))
return T
def generateFullMaze(width, height):
g = Graph()
for i in range(0, width * height):
v = Vertex(i)
g.addVertex(v)
# Create a fully connected maze graph
for i in range(0, width * height):
# Connect up
if i > width - 1:
g.addEdge(g.getVertex(i), g.getVertex(i - width))
# Connect down
if i < (width * height) - width:
g.addEdge(g.getVertex(i), g.getVertex(i + width))
# Connect right
if i % width != (width - 1):
g.addEdge(g.getVertex(i), g.getVertex(i + 1))
return g
def buildAtomTraceGraph(ag, tracedAtoms, pairScores = None, weighting = TRACE_GRAPH_WEIGHTS_UNWEIGHTED,
sources = None, target = None, minCommonAtoms = 1, skipnodes = set(),
reactionDirConstraints = {}):
g = Graph()
#ccc = 0
for u in ag.edges:
#ccc += 1
#print ccc, len(ag.edges)
types = ag.atomTypes[u]
for v in ag.edges[u]:
pairs = ag.edges[u][v]
# Each molecule pair might have multiple rpairs
for pair in pairs:
amtypes = ag.atomMapTypes[pair]
tracedCount = 0
for type in amtypes:
if type in tracedAtoms:
tracedCount += 1
if tracedCount < minCommonAtoms: # Does this rpair carry enough traced atoms?
continue
# Find out the score of this rpair
if weighting == TRACE_GRAPH_WEIGHTS_RSCORE:
assert(pairScores)
if pair in pairScores:
#print pairScores[pair]
score = convScore(pairScores[pair])
#score = 1
else:
score = convScore(NONE_SCORE)
elif weighting == TRACE_GRAPH_WEIGHTS_NATOMS:
#score = 2**(-tracedCount) # As described in the 03/2009 manuscript
score = 1.0 / tracedCount
#score = 1.0
elif weighting == TRACE_GRAPH_WEIGHTS_UNWEIGHTED:
score = 1.0
else:
print "unknown TRACE_GRAPH_SCORE: %d" % (TRACE_GRAPH_SCORE)
assert(0)
# Generate edges for each mapped atom pair of this rpair
amap = ag.atomMap[pair]
for src in amap:
if types[src] not in tracedAtoms:
continue
tgt = amap[src]
srcnode = "%s-%d" % (u, src)
tgtnode = "%s-%d" % (v, tgt)
fwdpair = "%s_f-%d-%d" % (pair, src, tgt)
revpair = "%s_r-%d-%d" % (pair, src, tgt)
if srcnode in skipnodes or tgtnode in skipnodes:
continue
# forward direction
g.addEdge(srcnode, fwdpair, score)
g.addEdge(fwdpair, tgtnode, 0.0)
# reverse direction
g.addEdge(tgtnode, revpair, score)
g.addEdge(revpair, srcnode, 0.0)
# Add source and target root nodes, if necessary
if sources != None:
for source in sources: # source = mol
g.addEdge("root", source, 0.0)
for i in sources[source]: # i = mol-atom
g.addEdge(source, i)
if target != None:
g.addEdge(i, target, DUMMY_DIST)
if target != None:
for i in range(ag.atomCount[target]):
g.addEdge("%s-%d" % (target, i + 1), target, 0.0)
g.addEdge(target, "%s-%d" % (target, i + 1), DUMMY_DIST)
return g
