def multi_sampling():
f = FaceData()
G = f.create_graph() # 获取原始数据
_degree = dict() # 分析重组数据
chushu = 0
for i in xrange(38):
mhrw = common.metropolis_hastings_random_walk(G, None, 10000, "unique")
# impore = improve_MH.impore_02(G, None, 10000, "unique")
for i in mhrw:
_degree[G.degree(i)] = _degree.get(G.degree(i), 0) + 1
chushu += len(mhrw)
print "step "
# 处理数据,取平均的度分布
x = sorted(_degree.iterkeys()) #生成x轴序列，从1到最大度
num = chushu
y = []
for i in x:
y.append(float(_degree[i]) / num)
# 保存数据
f = open("../compare/degree_plot/dup_Twitter_Mh.txt", "w")
try:
for i in x:
f.write(str(i) + " " + str(float(_degree[i]) / num) + "\n")
finally:
f.close()
##################
_degree = dict() # 分析重组数据
chushu = 0
for i in xrange(38):
# mhrw = common.metropolis_hastings_random_walk(G,None,10000,"unique")
impore = improve_MH.impore_02(G, None, 10000, "unique")
for i in impore:
_degree[G.degree(i)] = _degree.get(G.degree(i), 0) + 1
chushu += len(impore)
print "step "
# 处理数据,取平均的度分布
x = sorted(_degree.iterkeys()) #生成x轴序列，从1到最大度
num = chushu
y = []
for i in x:
y.append(float(_degree[i]) / num)
# 保存数据
f = open("../compare/degree_plot/dup_Twitter_Ud.txt", "w")
try:
for i in x:
f.write(str(i) + " " + str(float(_degree[i]) / num) + "\n")
finally:
f.close()
def ori_avg_degree():
f = FaceData()
G = f.create_graph() # 获取原始数据
rw = common.random_walk(G, None, 16000, "unique")
mhrw = common.metropolis_hastings_random_walk(G, None, 16000, "unique")
bfs = common.BFS(G, None, 16000, "unique")
print "原始网络的平均度为: ", degree.avg_degree(G)
print "RW抽样得到的平均度为: ", degree.avg_degree(G, rw)
print "MHRW抽样得到的平均度为: ", degree.avg_degree(G, mhrw)
print "BFS抽样得到的平均度为: ", degree.avg_degree(G, bfs)
def duplicate_epinions_Mhrw():
t = FaceData()
G = t.create_graph() # 获取原始数据
f = open("../compare/avg_degree/dup_Epinion_Mhrw.txt", "w")
try:
for i in xrange(100, 10000, 100):
mhrw = common.metropolis_hastings_random_walk(G, None, i, "unique")
f.write(str(i) + " " + str(mhrw) + "\n")
finally:
f.close()
def compare_press():
f = FaceData()
G = f.create_graph() # 获取原始数据
rw = common.random_walk(G, None, 10000, "unique")
mhrw = common.metropolis_hastings_random_walk(G, None, 10000, "unique")
# print round(1/impore - 1, 4)
# 保存数据
f = open("../compare/alpha_dup/com_dup.txt", "w")
try:
f.write(str("rw") + " " + str(rw) + "\n")
f.write(str("mhrw") + " " + str(mhrw) + "\n")
finally:
f.close()
def ego_degree():
f = FaceData()
G = f.create_graph() # 获取原始数据
# rw = common.random_walk(G,None,10000,"unique")
mhrw = common.metropolis_hastings_random_walk(G, None, 10000, "unique")
# bfs = common.BFS(G,None,8000,"unique")
impore = improve_MH.impore_03(G, None, 10000, "unique")
_plt = degree.degree(G, plt, "Original", "k-") # 绘制原始网络的度分布
# _plt = degree.ego_degree(G,bfs,_plt,"ego-bfs","c*")
# _plt = degree.ego_degree(G,rw,_plt,"ego-RW","k-")
# _plt = degree.ego_degree(G,mhrw,_plt,"MHRW","b-.")
_plt = degree.ego_degree(G, mhrw, _plt, "MHRW", "bv-")
# _plt = degree.ego_degree(G,impore,_plt,"UD","r:")
_plt = degree.ego_degree(G, impore, _plt, "UD", "rh-")
_plt.ylabel(r'$P(k_\upsilon=k) $')
_plt.xlabel(u'node degree k')
# _plt.xlim(0,100)
_plt.legend(loc="upper right") # 加入图例
_plt.show()
def record_con(self, sample="MHRW", lengend=False):
# 在G中抽取节点
if sample == "MHRW":
nodes = common.metropolis_hastings_random_walk(self.G, None, 10000, "total")
elif sample == "RW":
nodes = common.random_walk(self.G, None, 10000, "total")
elif sample == "BFS":
nodes = common.BFS(self.G, 10000)
else:
nodes = improve_MH.impore_03(self.G, None, 10000, "total")
x = range(self.x) # 横坐标
y = []
numerator = 0 # 分子
denominator = 0 # 分母
for i in nodes[0:self.x]:
denominator += 1
if self.G.degree(i) <= self.limit:
numerator += 1
y.append(float(numerator)/denominator)
if sample == "MHRW":
if lengend is True:
self.plt.plot(x, y, "b--", label="MHRW", linewidth=3.0)
else:
self.plt.plot(x, y, "b--", linewidth=3.0)
elif sample == "RW":
if lengend is True:
self.plt.plot(x, y, "g:", label="RW", linewidth=3.0)
else:
self.plt.plot(x, y, "g:", linewidth=3.0)
elif sample == "BFS":
if lengend is True:
self.plt.plot(x, y, "c-.", label="BFS", linewidth=3.0)
else:
self.plt.plot(x, y, "c-.", linewidth=3.0)
else:
if lengend is True:
self.plt.plot(x, y, "r-", label="UD", linewidth=3.0)
else:
self.plt.plot(x, y, "r-", linewidth=3.0)
def record_con(self, sample="MHRW", lengend=False):
# 在G中抽取节点
if sample == "MHRW":
nodes = common.metropolis_hastings_random_walk(self.G, None, self.x, "total")
elif sample == "RW":
nodes = common.random_walk(self.G, None, self.x, "total")
elif sample == "BFS":
nodes = common.BFS(self.G, self.x)
else:
nodes = improve_MH.impore_03(self.G, None, self.x, "total")
x = range(self.x) # 横坐标
y = []
list_x = []
for i in nodes:
list_x.append(self.G.degree(i))
x,y = self.Z_test(list_x)
if sample == "MHRW":
if lengend is True:
self.plt.plot(x, y, c='b',marker='v', label="MHRW", linewidth=1.0)
else:
self.plt.plot(x, y, c='b',marker='v', linewidth=1.0)
elif sample == "RW":
if lengend is True:
self.plt.plot(x, y, c='m',marker='*', label="RW", linewidth=1.0)
else:
self.plt.plot(x, y, c='m',marker='*', linewidth=1.0)
elif sample == "BFS":
if lengend is True:
self.plt.plot(x, y, c='g',marker='s', label="BFS", linewidth=1.0)
else:
self.plt.plot(x, y, c='g',marker='s', linewidth=1.0)
else:
if lengend is True:
self.plt.plot(x, y, c='r',marker='o', label="UD", linewidth=1.0)
else:
self.plt.plot(x, y, c='r',marker='o', linewidth=1.0)