from createGraph import createGraph,createUser, createFriends
from queryGraph import checkConnectivity, getDegree
a = createGraph()
g = a.createGraph()
m = a.createMapping()
count = 0
while 1:
if count==0 or count==1:
choice = raw_input("\n\nChoose:\n0. Exit\n1. Add a user\n")
elif count>=2:
choice = raw_input("\n\nChoose:\n0. Exit\n1. Add a user\n2. Add a friend link\n3. Check connectivity\n4. Check degree of connection\n")
count = count+1
if choice=='0':
break
elif choice=='1':
username=raw_input("Enter a username:\t")
b = createUser(username,g,m)
if b.makeNode():
print "User created"
else:
print "User already exists"
elif choice=='2':
user1,user2 = raw_input("Enter the usernames of each user seperated by a tab:\t").split('\t')
c = createFriends(user1,user2,g,m)
if c.makeLinks():
print "Friendship link established"
else:
edge_list.append((node, nbhr, w))
# Sort list using edge weight as key and return in reverse of asc. order
return sorted(edge_list, key = lambda edge : edge[2], reverse=True)
"""
Get random node
"""
def getRandomNode(self):
index = randint(0, len(self.node_list) - 1)
return self.node_list[index]
if __name__ == '__main__':
# Create biparitite graph from existing Advertisement and Keywords
(g, list_of_test_sessions) = createGraph()
# Convert bipartite graph to directed graph using similarity function
# of ads
g_new = Graph()
g_new.bipartiteConversion(g)
# Get list of sorted edges by weight
sorted_edge_list = g_new.sortedListOfEdges()
# create list of singleton clusters
cluster_list = []
node_cluster_dict = {}
for node in g_new.node_list:
cluster = Cluster(node)
node_cluster_dict[node] = cluster
for cluster in list_1:
l.append(cluster)
else:
l.append(list_1)
if isinstance(list_2, list):
for cluster in list_2:
l.append(cluster)
else:
l.append(list_2)
return l
if __name__ == '__main__':
# Create SPECTRAL biparitite graph from existing Advertisement and Keywords
(spectralG, list_of_test_sessions) = createGraph(True)
spectralG.setWeightMatrix()
#cluster1, cluster2 = spectralG.partition()
list_of_clusters = produceClusters(spectralG)
# print number of clusters
print "Number of clusters:", len(list_of_clusters)
# map each test session to its keyword
list_test_keywords = map(lambda x : Keyword(x.keyword_id), list_of_test_sessions)
# compute predictions for CTR
list_CTR_predictions = []
count = 0
for kw in list_test_keywords:
#createGraph(xaxis,yaxis,start,end,size) (length, xaxis, yaxis, cdf, cdfW) = StandardizeDistributionW(xaxis, yaxis) ylimStart = 0 ylimEnd = 1 plt.figure(1) multiplier = -10 plt.subplot(4, 2, 1) fromN = 10**(0 + multiplier) fromN = 0 toN = 10**(2 + multiplier) plt.title(str(fromN) + ' - ' + str(toN)) (x, y) = createGraph(xaxis, yaxis, fromN, toN, 1000) plt.ylim(ylimStart, ylimEnd) plt.plot(x, y) plt.subplot(4, 2, 2) fromN = 10**(2 + multiplier) fromN = 0 toN = 10**(4 + multiplier) plt.title(str(fromN) + ' - ' + str(toN)) (x, y) = createGraph(xaxis, yaxis, fromN, toN, 1000) plt.ylim(ylimStart, ylimEnd) plt.plot(x, y) plt.subplot(4, 2, 3) fromN = 10**(4 + multiplier) fromN = 0
