本文整理汇总了Python中igraph.Graph.es['weight']方法的典型用法代码示例。如果您正苦于以下问题：Python Graph.es['weight']方法的具体用法？Python Graph.es['weight']怎么用？Python Graph.es['weight']使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类igraph.Graph的用法示例。
在下文中一共展示了Graph.es['weight']方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _gen_graph
# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import es['weight'] [as 别名]
def _gen_graph(self):
# make it lazy to support optional addslaves command
from igraph import Graph
g = Graph().as_directed()
g.add_vertices(self.vertex_count)
g.es['weight'] = 1 # enable weight
return g示例2: gen_graph
# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import es['weight'] [as 别名]
def gen_graph():
masters = sum(ms, [])
slaves = sum(ss, [])
frees = sum(fs, [])
ct = machine_count
g = Graph().as_directed()
g.add_vertices(2 * ct + 2)
g.es['weight'] = 1
assert g.is_weighted()
lin = map(len, fs)
# lin = map(operator.add, map(len, ss), map(len, fs))
rout = map(len, ms)
s = 2 * ct
t = s + 1
for i, m in enumerate(lin):
g[s, i] = m
for i, m in enumerate(rout):
g[i + ct, t] = m
cap = defaultdict(dict)
for i, m in enumerate(lin):
for j, n in enumerate(rout):
if i == j:
continue
cap[i][j] = m
assert len(ss) == ct
# for i in range(ct):
# for slave in ss[i]:
# cap[i][slave.master.machine_tag] -= 1
# assert cap[i][slave.master.machine_tag] >= 0
print cap
for i in range(ct):
for j in range(ct):
if i == j:
continue
masters_in_j = set(slave.master.tag for slave in ss[i] if slave.master.machine_tag == j) # to fight existing distribution error
limit = len(ms[j]) - len(masters_in_j)
cap[i][j] = min(limit, map(len, fs)[i])
for i, m in enumerate(lin):
if m == 0:
continue
for j, n in enumerate(rout):
if i == j:
continue
g[i, ct + j] = cap[i][j]
print g
start = datetime.utcnow()
mf = g.maxflow(s, t, g.es['weight'])
end = datetime.utcnow()
print 'result:', end - start
print mf.value, sum(rout)
print mf.flow
print map(float, g.es['weight']), len(g.es['weight'])示例3: _gen_graph
# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import es['weight'] [as 别名]
def _gen_graph(self):
g = Graph().as_directed()
g.add_vertices(self.vertex_count)
g.es['weight'] = 1 # enable weight
assert g.is_weighted()
return g示例4: contributor_network
# 需要导入模块: from igraph import Graph [as 别名]
# 或者: from igraph.Graph import es['weight'] [as 别名]
#.........这里部分代码省略.........
# journal editors are at the issue level
for issue in j.issue_set.all():
editors = issue.editors.all()
for i, editor in enumerate(editors):
# only add if not already present
if editor.network_id not in graph.vs['name']:
graph.add_vertex(editor.network_id,
type=editor.network_type,
label=editor.firstname_lastname)
add_edge(((editor.network_id, j.network_id), 'editor'))
# add a co-editor rel to any other editors on this issue
for co_editor in editors[i+1:]:
add_edge(((editor.network_id, co_editor.network_id),
'co-editor'))
# authors and translators are at the item level
for item in issue.item_set.all():
authors = item.creators.all()
for i, author in enumerate(authors):
# only add person if not already present in the graph
if author.network_id not in graph.vs['name']:
graph.add_vertex(author.network_id,
label=author.firstname_lastname,
type=author.network_type)
# author is a journal contributor
add_edge(((author.network_id, j.network_id),
'contributor'))
# each author is connected to the issue editors who
# edited their work
for editor in editors:
add_edge(((editor.network_id, author.network_id),
'edited'))
# add a co-author to any other authors on this item
for co_author in authors[i+1:]:
add_edge(((author.network_id, co_author.network_id),
'co-author'))
for translator in item.translators.all():
# only add person if not already present in the graph
if translator.network_id not in graph.vs['name']:
graph.add_vertex(translator.network_id,
label=translator.firstname_lastname,
type=translator.network_type)
# translators are connected to the journal they contributed to
add_edge(((translator.network_id, j.network_id),
'translator'))
# and to the author whose work they translated
for author in authors:
add_edge(((translator.network_id, author.network_id),
'translated'))
logger.debug('Added %d nodes and %d edges for %s in %.2f sec',
len(graph.vs()) - vtx_count, len(edges) - edge_count,
j, time.time() - start)
# add person-school associations
# - only a fairly small number of people are associated with
# schools, so it should be most efficient to handle separately
start = time.time()
schooled_people = Person.objects.filter(schools__isnull=False) \
.prefetch_related('schools')
for person in schooled_people:
try:
graph.vs.find(name=person.network_id)['schools'] = \
[s.name for s in person.schools.all()]
except ValueError:
# it's possible we have people associated with schools
# who are not contributors to our journals, so this is
# not an error, but providea warning.
logger.warn('School-associated person %s not found in contributor network graph',
person)
logger.debug('Added school associations for %d people in %.2f sec',
schooled_people.count(), time.time() - start)
start = time.time()
# split edge information into source/target tuple and edge label
edge_src_target, edge_labels = zip(*edges.keys())
# add the edges to the graph
graph.add_edges(edge_src_target)
# set the edge labels
graph.es['label'] = edge_labels
# set edge weight based on number of occurrences
graph.es['weight'] = edges.values()
logger.debug('Added edges and edge sizes in %.2f sec',
time.time() - start)
logger.debug('Complete journal contributor graph (%d nodes, %d edges) generated in %.2f sec',
len(graph.vs()), len(graph.es()), time.time() - full_start)
# store the generated graph in the cache for the next time
# for now, set cached graph to never time out
cache.set(cls.contributor_network_cache_key, graph, None)
return graph