def main():
print '[Initialization]: Building Graphs and Links...'
#read file and build graph and link object by the data in it
G1 = Graph_builder()
G1.build_graph_from('G1.txt')
G2 = Graph_builder()
G2.build_graph_from('G2.txt')
L = Link_builder()
L.build_link_from('L.txt')
SortedByDegG1 = max(nx.degree(G1.Graph).values()) #max degree for node in G1
SortedByDegG2 = max(nx.degree(G2.Graph).values()) #max degree for node in G2
MaxMinDeg = min(SortedByDegG1, SortedByDegG2) #Minimum of the maximum of degree for G1 and G2
log_D = math.floor(math.log(MaxMinDeg, 2)) #parameter in Korula Algorithm
max_node_num = G1.max_node + 1 #to avoid 0-based, remains 0 when index = 0
#N x N matrix (N is the # of nodes in G1 or G2, assume # of nodes in G1 = # of nodes in G2)
print 'Allocate Matrix for Score of Similarity...'
matrix = Matrix = [[0 for x in xrange(max_node_num)] for x in xrange(max_node_num)] #initialize all value as 0
#parameter for matching algorithm
it = 100 #iteration
threshold = 1 #
weight_unit = 1 #similarity unit
#write file
f = open('output.txt','w')
for element in L.content:
f.write(str(element[0]) + ' ' + str(element[1]) + '\n')
#count similarity for all pairs at beginning by scaning existing links stored in link object
print 'Initializing the Scores of Similarity for each pair...'
for pair in L.content: #links currently exist
for nb1 in G1.Graph.neighbors(pair[0]): # for each neighbor of node with id = pair[0]
for nb2 in G2.Graph.neighbors(pair[1]): # for each neighbor of node with id = pair[1]
matrix[nb1][nb2] += weight_unit #add score of similarity
print 'Processing Korula Algorithm...'
#Korula Algorithm
for i in range(it):
print '-------------------------'
print 'Iteraion:' + str(1+i)
for j in range(int(log_D)): #iterate from logD to 1
degree = 2**(log_D - j)
#map reduce
G1_nodes, G2_nodes = G1.Graph.nodes(), G2.Graph.nodes()
G1_pool, G2_pool = [], []#nodes that have degree > degree threshold
size = len(G1_nodes)#G1 and G2 should have the same size in # of nodes
for idx in range(size):
#filter the nodes that could satistify the condition we set
if not L.outlink_fromG1_hash.has_key(G1_nodes[idx]) and len(G1.Graph.neighbors(G1_nodes[idx])) > degree:
G1_pool.append(G1_nodes[idx])
if not L.outlink_fromG2_hash.has_key(G2_nodes[idx]) and len(G2.Graph.neighbors(G2_nodes[idx])) > degree:
G2_pool.append(G2_nodes[idx])
#select a best pair and insert to L
candidate_pairs_from_G1 = []
for u1 in G1_pool: #for each candidate in G1_pool
max_value = threshold #set threshold
candidate_u2 = []
for u2 in G2_pool: #for each candidate in G2_pool
if matrix[u1][u2] >= threshold: #if node's weight >= threshold we set
if matrix[u1][u2] == max_value:
if len(candidate_u2) == 0:
candidate_u2.append(u2)
else: #!= 0
if (len(G1.Graph.neighbors(u1)) + len(G2.Graph.neighbors(u2))) > (len(G1.Graph.neighbors(u1)) + len(G2.Graph.neighbors(candidate_u2[0]))):
candidate_u2= [] #reset
candidate_u2.append(u2)
else:
pass
elif matrix[u1][u2] > max_value:
max_value = matrix[u1][u2]#update
candidate_u2 = []#reset
candidate_u2.append(u2)
else:
pass
if len(candidate_u2) > 0:
for candidate in candidate_u2:
candidate_pairs_from_G1.append((u1,candidate))
else:#all scores of similarity are lower than threshold
#no update for candidate_pairs
continue
#Vice versa
candidate_pairs_from_G2 = []
for u2 in G2_pool:
max_value = threshold
candidate_u1 = []
for u1 in G1_pool:
if matrix[u1][u2] >= threshold:
if matrix[u1][u2] == max_value:
if len(candidate_u1) == 0:
candidate_u1.append(u1)
else:
if (len(G1.Graph.neighbors(u1)) + len(G2.Graph.neighbors(u2))) > (len(G1.Graph.neighbors(candidate_u1[0])) + len(G2.Graph.neighbors(u2))):
candidate_u1= []
candidate_u1.append(u1)
else:
pass
elif matrix[u1][u2] > max_value:
max_value = matrix[u1][u2]#update
candidate_u1 = []#clear
candidate_u1.append(u1)
else:
pass
if len(candidate_u1) > 0:
for candidate in candidate_u1:
candidate_pairs_from_G2.append((candidate,u2))
else:#all scores of similarity are lower than threshold
#no update for candidate_pairs
continue
candidate_pairs = []
for candidate_pair in candidate_pairs_from_G1:
#node in candidate_pairs_from G1 is the node in G1 with maximum similarity score to u2
#and u2 is node in G2 with maximum similarity cscore to u1
if candidate_pair in candidate_pairs_from_G2:
candidate_pairs.append(candidate_pair)
if len(candidate_pairs) == 0:
candidate_pairs_from_G1.extend(candidate_pairs_from_G2)
candidate_pairs = candidate_pairs_from_G1
#Choose best candidate
best_candidate = []
if len(candidate_pairs) == 0: #happen in last iteration
print 'No Candidate Pairs'
continue
elif len(candidate_pairs) == 1:
# if candidate_pairs[0] in L.content:
# print 'duplicate'
#else:
best_candidate.append(candidate_pairs[0])
else:#len(candidate_pairs) > 1:
max_value = threshold #minimum value
for candidate in candidate_pairs:
if matrix[candidate[0]][candidate[1]] > max_value:
best_candidate = [] #clear
best_candidate.append(candidate)
max_value = matrix[candidate[0]][candidate[1]]#update
#best_candidate.append(candidate)
elif matrix[candidate[0]][candidate[1]] == max_value:
best_candidate.append(candidate)
#best_candidate.append(candidate)
else: # <
pass
for candidate in best_candidate:
print '(' + str(candidate[0]) + ',' + str(candidate[1]) + ')'
# if not candidate in Ans.content:
# print 'incorrect output'
#print 'Updating Scores of similarity'
#update similarity score and mew links
for candidate in best_candidate:
L.add_link_from(candidate)
f.write(str(candidate[0]) + ' ' + str(candidate[1]) + '\n')
for u1 in G1.Graph.neighbors(candidate[0]):
for u2 in G2.Graph.neighbors(candidate[1]):
matrix[u1][u2] += weight_unit #add score of similarity
print 'Total # of new links found:'
print len(L.content) - L.ori_size
print 'Done'
f.close()
def main():
try:
response = requests.get(start_q) #sending start-query to the server
except requests.exceptions.RequestException as e:
print e
time.sleep(5)
exit(1)
dic = response.json() #catch the contetnt returned in json format
task, start_node, dest_node = dic['task'], str(dic['problem'][0]), str(dic['problem'][1])
#task: 1-3 responding to different requirement to reach the destination given(in README file)
path_str = '' + start_node
current_node = '' + start_node
visited_nodes = [start_node]
dijkstra_path = []
lock_nodes = {} #positions that can't move anymore (circle, one neighbor only...etc)
visieted_nodes = {}
path_list = [] #list for current path from start_node to end_node
#showing infomation
print 'Token number: ' + uni
print 'Find the way from node ' + start_node + ' to ' + dest_node + ' in task ' + str(task)
#load model packed by pickle
fn = 'model.pkl'
with open(fn, 'r') as f:
train_model = pickle.load(f)
userID_table = train_model.ID2Coordinate #key:userID; value: coordinate
#get neighbors of start node
response = requests.get(friend_list_q + start_node)
print 'Processing...'
friend_list = response.json()['neighbors'] #list of neighbor
#building graph
gb = Graph_builder(start_node, dest_node, friend_list, userID_table)
gb.build_graph_from(current_node, friend_list)
visited_nodes[current_node] = True
#exploring the social network
while True:
print 'current_position = ' + current_node
#previous_node = '' + current_node
#if succeed thne commit the path, break loop and report.
if current_node == dest_node:
print path_str
payload = {'path': path_str }
response = requests.post(commit_path_q ,data=payload)
result = response.text
print result
print 'Succeed :) !!'
print 'Quesry: ' + str(result['queries'])
print 'Cost: ' + str(result['cost'])
print 'Target_distance: ' + str(result['target_distance'])
break
#graph update
if current_node not in visieted_node and current_node not in lock_nodes:
try:
response = requests.get(friend_list_q + current_node)
except requests.exceptions.RequestException as e:
print e
time.sleep(10)
continue
result = response.json()
#if error (reach the query limit), breal loop and report.
if 'error' in result: #reach query limit
print path_str
payload = {'path': path_str }
response = requests.post(commit_path_q ,data=payload)
result = response.text
print result
print 'fail :( !!'
print 'Step exceeds limit'
print 'Quesry: ' + str(result['queries'])
print 'Cost: ' + str(result['cost'])
print 'Target_distance: ' + str(result['target_distance'])
break
else: #exploring
friend_list = response.json()['neighbors']
gb.build_graph_from(current_node, friend_list)
visited_nodes[current_node] = True
#stop_list: can't extend anymore, roll back
if (current_node != start_node and len(gb.Graph.neighbors(current_node)) <= 1) or DetectBlockOrCircle(current_node, gb.Graph, visited_nodes, lock_nodes):
print 'block point!!'
lock_nodes[path_list.pop()] = True #add new lock to the dictionary
path_str = path_str.replace(',' + current_node, '') #remove the last node in current path
current_node = '' + path_list[-1] #roll back to previous node
print "lock_nodes: "
print lock_nodes.keys()
time.sleep( 3 )
continue
#path exploration
if task == 1: #task 1: minimum number of query.
deg = -999999 #set a small weight for default
next_node = ''
for neighbor in gb.Graph.neighbors(current_node): #scan the neighbors of current node
#print neighbor
if neighbor not in path_list and neighbor not in lock_nodes: #prevent from formimg a circle
if neighbor == dest_node:
current_node = '' + dest_node #update current node
next_node = '' + dest_node
break
else:
if neighbor in train_model.Graph.nodes() and dest_node in train_model.G.nodes():
if nx.has_path(train_model.G, neighbor, dest_node):
next_node = '' + neighbor
deg = 999999 #set a large weight for priority
#upadate
else:
degree = gb.Graph.node[neighbor]['deg']
if degree >= deg:
deg = degree
next_node = '' + neighbor
if next_node != '':
current_node = '' + next_node
path_list.append(current_node)
path_str += ',' + current_node
print path_str
else: #minimum geography distance of path.
#greedy rounting. With query limit, the recursion method seems to be relatively unstable
distance_t = 999999
next_node = ''
for neighbor in gb.Graph.neighbors(current_node): #scan the neighbors of current node
#print neighbor
if neighbor not in path_list and neighbor not in block_nodes: #prevent from formimg a circle
if neighbor == dest_node:
print 'Dijkstra Algorithm processing...'
current_node = '' + dest_node #update current node
next_node = '' + dest_node
#don't need to updat
dijkstra_path = nx.dijkstra_path(gb.Graph, start_node, dest_node)
path_str = dijkstra_path[0]
for i in range(len(dijkstra_path)-2):
path_str += ',' + dijkstra_path[i+1]
break
else:
if neighbor in train_model.Graph.nodes() and dest_node in train_model.Graph.nodes():
if nx.has_path(train_model.Graph, neighbor, dest_node):
next_node = '' + neighbor
distance_t = -999999
else:
dis = distance(neighbor, current_node, userID_table)
if dis <= distance_t:
distance_t = dis
next_node = '' + neighbor
if next_node != '':
current_node = '' + next_node
path_list.append(current_node)
path_str += ',' + current_node
print path_str
