├── 1 ├── bet_cen_enron_data.txt ├── twitter_nodes.py ├── graph_create.py ├── between_central.py ├── closeness_enron.py ├── degree_enron.py ├── degree_twitter.py ├── closeness_twitter.py ├── twitter_between_central.py ├── bet_cen_closeness.py ├── between_centrality.py ├── degree_closeness_enron.py ├── bet_cen_degree.py ├── graph_laplacian.py ├── ann_graph.py ├── cnn_graph.py ├── ann_twitter.py ├── ann_twitter.py~ ├── Untitled Document~ ├── ann_degree.py ├── ann_bet_cen.py ├── ann_closeness.py ├── ann_bet_cen_degree.py ├── ann_bet_cen_closeness.py └── bet_cen_enron_data.csv /bet_cen_enron_data.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /twitter_nodes.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import networkx as nx 3 | 4 | data = pd.read_csv('higgs-social_network.edgelist', header = None, sep = " ") 5 | 6 | data_a = data.iloc[:,0].values 7 | data_b = data.iloc[:,1].values 8 | 9 | nodes = [] 10 | for i, j in zip(data_a, data_b): 11 | if i not in nodes: 12 | nodes.append(i) 13 | if j not in nodes: 14 | nodes.append(j) 15 | 16 | print len(nodes) 17 | -------------------------------------------------------------------------------- /graph_create.py: -------------------------------------------------------------------------------- 1 | # 14855843 edges 2 | # anomaly detection datasets social networks 3 | 4 | import pandas as pd 5 | import networkx as nx 6 | 7 | dataset_graph = pd.read_csv('higgs-social_network.edgelist', sep= " ", header = None) 8 | 9 | node_A = dataset_graph.iloc[:,0].values 10 | node_B = dataset_graph.iloc[:,1].values 11 | 12 | # print node_A 13 | 14 | 15 | G = nx.Graph() 16 | 17 | for i, j in zip(node_A, node_B): 18 | G.add_edge(i, j) 19 | 20 | 21 | # nx.write_graphml(G, 'twitter.graphml') 22 | print len(G.edges()), len(G.nodes()) 23 | 24 | 25 | 26 | 27 | 28 | -------------------------------------------------------------------------------- /between_central.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';') 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | 22 | d_bet_cen = dict() 23 | d_bet_cen = nx.betweenness_centrality(G) 24 | 25 | output = open('bet_cen_dict.pkl', 'wb') 26 | pickle.dump(d_bet_cen, output) 27 | output.close() 28 | 29 | pkl_file = open('bet_cen_dict.pkl','rb') 30 | d_bet_cen =pickle.load(pkl_file) 31 | 32 | l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items()) 33 | print len(l) 34 | 35 | # print min(d_bet_cen.items(), key = lambda x:x[1]) 36 | 37 | # print d_bet_cen 38 | 39 | outfile = open('bet_cen_enron_data.txt', 'w') 40 | for i, j in d_bet_cen.iteritems(): 41 | if j >= 0.0000001: 42 | outfile.write(str(i) + " 1\n") 43 | else: 44 | outfile.write(str(i) + " 0\n") 45 | 46 | outfile.close() 47 | pkl_file.close() 48 | 49 | -------------------------------------------------------------------------------- /closeness_enron.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';', header = None) 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | # d = nx.closeness_centrality(G) 22 | 23 | # print max(d.items(), key = lambda x:x[1]) 24 | # print min(d.items(), key = lambda x:x[1]) 25 | 26 | ''' 27 | output = open('closeness_dict.pkl', 'wb') 28 | pickle.dump(d, output) 29 | output.close() 30 | ''' 31 | 32 | pkl_file = open('closeness_dict.pkl','rb') 33 | d = pickle.load(pkl_file) 34 | 35 | cnt = 0 36 | for a, b in d.iteritems(): 37 | if b>0.25: 38 | cnt += 1 39 | print cnt 40 | 41 | 42 | outfile = open('closeness_enron_data.txt', 'w') 43 | for i, j in d.iteritems(): 44 | if j > 0.25: 45 | outfile.write(str(i) + " 1\n") 46 | else: 47 | outfile.write(str(i) + " 0\n") 48 | 49 | 50 | outfile.close() 51 | pkl_file.close() 52 | 53 | 54 | -------------------------------------------------------------------------------- /degree_enron.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';', header = None) 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | d = G.degree(G.nodes()) 22 | 23 | # print d 24 | 25 | max_ = 0 26 | ind = 0 27 | for a, b in d: 28 | if b > max_: 29 | max_ = b 30 | ind = a 31 | 32 | print max_, ind 33 | 34 | ''' 35 | output = open('degree_list_tup.pkl', 'wb') 36 | pickle.dump(d, output) 37 | output.close() 38 | ''' 39 | 40 | pkl_file = open('degree_list_tup.pkl','rb') 41 | d = pickle.load(pkl_file) 42 | 43 | cnt = [item for item in d if item[1]>70] 44 | print len(cnt) 45 | 46 | 47 | outfile = open('degree_enron_data.txt', 'w') 48 | for i, j in d: 49 | if j > 70: 50 | outfile.write(str(i) + " 1\n") 51 | else: 52 | outfile.write(str(i) + " 0\n") 53 | 54 | 55 | outfile.close() 56 | pkl_file.close() 57 | 58 | 59 | -------------------------------------------------------------------------------- /degree_twitter.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | ''' 7 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 8 | dataset_nodes = pd.read_csv('Enron.true', sep = ';', header = None) 9 | 10 | node_from = dataset_edges.iloc[:,0].values 11 | node_to = dataset_edges.iloc[:,1].values 12 | 13 | num_nodes = len(dataset_nodes) 14 | 15 | print num_nodes 16 | 17 | # Creare graph 18 | G = nx.Graph() 19 | for i, j in zip(node_from, node_to): 20 | G.add_edge(i, j) 21 | ''' 22 | num_nodes = 76851 23 | G = nx.read_graphml('twitter.graphml') 24 | d = G.degree(G.nodes()) 25 | 26 | # print d 27 | 28 | max_ = 0 29 | ind = 0 30 | for a, b in d: 31 | if b > max_: 32 | max_ = b 33 | ind = a 34 | 35 | print max_, ind 36 | 37 | print 'Dict done' 38 | output = open('degree_list_tup.pkl', 'wb') 39 | pickle.dump(d, output) 40 | output.close() 41 | ''' 42 | 43 | pkl_file = open('degree_list_tup.pkl','rb') 44 | d = pickle.load(pkl_file) 45 | 46 | cnt = [item for item in d if item[1]>70] 47 | print len(cnt) 48 | 49 | 50 | outfile = open('degree_enron_data.txt', 'w') 51 | for i, j in d: 52 | if j > 70: 53 | outfile.write(str(i) + " 1\n") 54 | else: 55 | outfile.write(str(i) + " 0\n") 56 | 57 | 58 | outfile.close() 59 | pkl_file.close() 60 | 61 | ''' 62 | -------------------------------------------------------------------------------- /closeness_twitter.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | ''' 7 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 8 | dataset_nodes = pd.read_csv('Enron.true', sep = ';', header = None) 9 | 10 | node_from = dataset_edges.iloc[:,0].values 11 | node_to = dataset_edges.iloc[:,1].values 12 | 13 | num_nodes = len(dataset_nodes) 14 | 15 | print num_nodes 16 | 17 | # Creare graph 18 | G = nx.Graph() 19 | for i, j in zip(node_from, node_to): 20 | G.add_edge(i, j) 21 | 22 | ''' 23 | num_nodes = 76851 24 | G = nx.read_graphml('twitter.graphml') 25 | d = nx.closeness_centrality(G) 26 | 27 | print max(d.items(), key = lambda x:x[1]) 28 | print min(d.items(), key = lambda x:x[1]) 29 | 30 | print 'Dict done' 31 | output = open('closeness_dict.pkl', 'wb') 32 | pickle.dump(d, output) 33 | output.close() 34 | 35 | ''' 36 | pkl_file = open('closeness_dict.pkl','rb') 37 | d = pickle.load(pkl_file) 38 | 39 | cnt = 0 40 | for a, b in d.iteritems(): 41 | if b>0.25: 42 | cnt += 1 43 | print cnt 44 | 45 | 46 | outfile = open('closeness_enron_data.txt', 'w') 47 | for i, j in d.iteritems(): 48 | if j > 0.25: 49 | outfile.write(str(i) + " 1\n") 50 | else: 51 | outfile.write(str(i) + " 0\n") 52 | 53 | 54 | outfile.close() 55 | pkl_file.close() 56 | 57 | ''' 58 | -------------------------------------------------------------------------------- /twitter_between_central.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | ''' 7 | dataset_edges = pd.read_csv('higgs-social_network.edgelist', sep = " ", header = None) 8 | # dataset_nodes = pd.read_csv('Enron.true', sep = ';') 9 | 10 | node_from = dataset_edges.iloc[:,0].values 11 | node_to = dataset_edges.iloc[:,1].values 12 | 13 | num_nodes = 76851 14 | 15 | print num_nodes 16 | 17 | # Creare graph 18 | G = nx.Graph() 19 | for i, j in zip(node_from, node_to): 20 | G.add_edge(i, j) 21 | 22 | ''' 23 | num_nodes = 76851 24 | G = nx.read_graphml('twitter.graphml') 25 | print num_nodes 26 | d_bet_cen = dict() 27 | d_bet_cen = nx.betweenness_centrality(G) 28 | 29 | print 'Dict done' 30 | output = open('twitter_bet_cen_dict.pkl', 'wb') 31 | pickle.dump(d_bet_cen, output) 32 | output.close() 33 | 34 | ''' 35 | pkl_file = open('bet_cen_dict.pkl','rb') 36 | d_bet_cen =pickle.load(pkl_file) 37 | 38 | l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items()) 39 | print len(l) 40 | 41 | # print min(d_bet_cen.items(), key = lambda x:x[1]) 42 | 43 | # print d_bet_cen 44 | 45 | outfile = open('bet_cen_enron_data.txt', 'w') 46 | for i, j in d_bet_cen.iteritems(): 47 | if j >= 0.0000001: 48 | outfile.write(str(i) + " 1\n") 49 | else: 50 | outfile.write(str(i) + " 0\n") 51 | 52 | outfile.close() 53 | pkl_file.close() 54 | ''' 55 | -------------------------------------------------------------------------------- /bet_cen_closeness.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';') 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | ''' 22 | d_bet_cen = dict() 23 | d_bet_cen = nx.betweenness_centrality(G) 24 | 25 | output = open('bet_cen_dict.pkl', 'wb') 26 | pickle.dump(d_bet_cen, output) 27 | output.close() 28 | 29 | ''' 30 | pkl_file = open('bet_cen_dict.pkl','rb') 31 | d_bet_cen =pickle.load(pkl_file) 32 | 33 | pkl_file2 = open('closeness_dict.pkl', 'rb') 34 | d_closeness = pickle.load(pkl_file2) 35 | 36 | # l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items()) 37 | # print len(l) 38 | 39 | # print min(d_bet_cen.items(), key = lambda x:x[1]) 40 | 41 | # print d_bet_cen 42 | 43 | cnt = 0 44 | outfile = open('bet_cen_closeness_enron_data.txt', 'w') 45 | for (k1, v1), (k2, v2) in zip(d_bet_cen.iteritems(), d_closeness.iteritems()): 46 | if v1 > 0.0000001 and v2>0.25 and k1 == k2: 47 | cnt += 1 48 | outfile.write(str(i) + " 1\n") 49 | else: 50 | outfile.write(str(i) + " 0\n") 51 | 52 | print cnt 53 | 54 | outfile.close() 55 | pkl_file.close() 56 | pkl_file2.close() 57 | -------------------------------------------------------------------------------- /between_centrality.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | 11 | dataset_edges = pd.read_csv('./Enron/enron_dataset_final.csv') 12 | dataset_nodes = pd.read_csv('./Enron/Enron.true', sep = ';') 13 | 14 | node_from = dataset_edges.iloc[:,0].values 15 | node_to = dataset_edges.iloc[:,1].values 16 | 17 | num_nodes = len(dataset_nodes) 18 | print "Number of nodes: " + str(num_nodes) 19 | 20 | #Stores the node as key, it's neighbors as values 21 | d = dict() 22 | for i in range(0, (num_nodes+1)): 23 | d[i] = [] 24 | for (i,j) in zip(node_from, node_to): 25 | if j not in d[i]: 26 | d[i].append(j) 27 | if i not in d[j]: 28 | d[j].append(i) 29 | 30 | train_nodes = int(0.8*13533) #10826 31 | test_nodes = int(0.2*13533) #2706 32 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 33 | 34 | #Full dataset graph 35 | H = nx.Graph() 36 | for k, v in d.iteritems(): 37 | for j in v: 38 | H.add_edge(k, j) 39 | 40 | # Training graph 41 | G = nx.Graph() 42 | for i in range(train_nodes): 43 | for j in d[i]: 44 | G.add_edge(i, j) 45 | 46 | #Testing graph 47 | test_id = [i for i in H.nodes() if i not in G.nodes()] 48 | P = nx.Graph() 49 | for i in test_id: 50 | for j in d[i]: 51 | P.add_edge(i, j) 52 | 53 | print nx.betweenness_centrality(G) -------------------------------------------------------------------------------- /1: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';') 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | ''' 22 | d_bet_cen = dict() 23 | d_bet_cen = nx.betweenness_centrality(G) 24 | d_degree = G.degree(G.nodes()) 25 | 26 | output = open('bet_cen_dict.pkl', 'wb') 27 | pickle.dump(d_bet_cen, output) 28 | output.close() 29 | 30 | ''' 31 | #Reading between central dictionary pickle file 32 | pkl_file = open('bet_cen_dict.pkl','rb') 33 | d_bet_cen =pickle.load(pkl_file) 34 | 35 | #Reading degree dictionary pickle file 36 | pkl_file2 = open('degree_list_tup.pkl', 'rb') 37 | d_degree = pickle.load(pkl_file2) 38 | 39 | # l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items()) 40 | # print len(l) 41 | # print min(d_bet_cen.items(), key = lambda x:x[1]) 42 | 43 | cnt = 0 44 | for k1, v1, k2, v2 in zip(d_bet_cen.iteritems(),d_degree.iteritems()): 45 | if k1>0.0000001 and k2>70: 46 | cnt+= 1 47 | 48 | print cnt 49 | ''' 50 | outfile = open('bet_cen_enron_data.txt', 'w') 51 | for i, j in d_bet_cen.iteritems(): 52 | if j >= 0.0000001: 53 | outfile.write(str(i) + " 1\n") 54 | else: 55 | outfile.write(str(i) + " 0\n") 56 | 57 | outfile.close() 58 | pkl_file.close() 59 | ''' 60 | -------------------------------------------------------------------------------- /degree_closeness_enron.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';', header = None) 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | d = G.degree(G.nodes()) 22 | 23 | # print d 24 | 25 | max_ = 0 26 | ind = 0 27 | for a, b in d: 28 | if b > max_: 29 | max_ = b 30 | ind = a 31 | 32 | print max_, ind 33 | 34 | ''' 35 | output = open('degree_list_tup.pkl', 'wb') 36 | pickle.dump(d, output) 37 | output.close() 38 | ''' 39 | 40 | pkl_file = open('degree_list_tup.pkl','rb') 41 | d = pickle.load(pkl_file) 42 | 43 | pkl_file2 = open('closeness_dict.pkl', 'rb') 44 | d_closeness = pickle.load(pkl_file2) 45 | 46 | closeness_nodes = [] 47 | for k,v in d_closeness.iteritems(): 48 | if v>0.25: 49 | closeness_nodes.append(k) 50 | 51 | cnt = 0 52 | for item in d: 53 | if item[0] in closeness_nodes and item[1]>70: 54 | cnt += 1 55 | 56 | print cnt 57 | 58 | cnt = [item for item in d if item[1]>70] 59 | print len(cnt) 60 | 61 | 62 | outfile = open('degree_enron_data.txt', 'w') 63 | for i, j in d: 64 | if j > 70: 65 | outfile.write(str(i) + " 1\n") 66 | else: 67 | outfile.write(str(i) + " 0\n") 68 | 69 | 70 | outfile.close() 71 | pkl_file.close() 72 | 73 | 74 | -------------------------------------------------------------------------------- /bet_cen_degree.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import networkx as nx 4 | import pickle 5 | 6 | dataset_edges = pd.read_csv('enrondatasetfinal.csv') 7 | dataset_nodes = pd.read_csv('Enron.true', sep = ';') 8 | 9 | node_from = dataset_edges.iloc[:,0].values 10 | node_to = dataset_edges.iloc[:,1].values 11 | 12 | num_nodes = len(dataset_nodes) 13 | 14 | print num_nodes 15 | 16 | # Creare graph 17 | G = nx.Graph() 18 | for i, j in zip(node_from, node_to): 19 | G.add_edge(i, j) 20 | 21 | ''' 22 | d_bet_cen = dict() 23 | d_bet_cen = nx.betweenness_centrality(G) 24 | d_degree = G.degree(G.nodes()) 25 | 26 | output = open('bet_cen_dict.pkl', 'wb') 27 | pickle.dump(d_bet_cen, output) 28 | output.close() 29 | 30 | ''' 31 | #Reading between central dictionary pickle file 32 | pkl_file = open('bet_cen_dict.pkl','rb') 33 | d_bet_cen =pickle.load(pkl_file) 34 | 35 | #Reading degree dictionary pickle file 36 | pkl_file2 = open('degree_list_tup.pkl', 'rb') 37 | d_degree = pickle.load(pkl_file2) 38 | 39 | # l = filter(lambda x:x[1]>=0.0000001,d_bet_cen.items()) 40 | # print len(l) 41 | # print min(d_bet_cen.items(), key = lambda x:x[1]) 42 | 43 | bet_nodes = [] 44 | for k1, v1 in d_bet_cen.iteritems(): 45 | if v1>0.0000001: 46 | bet_nodes.append(k1) 47 | 48 | cnt = 0 49 | outfile = open('bet_cen_degree_enron_data.txt', 'w') 50 | for item in d_degree: 51 | if item[0] in bet_nodes and item[1]>70: 52 | outfile.write(str(item[0]) + " 1\n") 53 | cnt += 1 54 | else: 55 | outfile.write(str(item[0]) + " 0\n") 56 | 57 | print cnt 58 | outfile.close() 59 | pkl_file.close() 60 | pkl_file2.close() 61 | -------------------------------------------------------------------------------- /graph_laplacian.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | from numpy import linalg as LA 11 | 12 | #Numpy error 13 | def laplacian(G): 14 | # Normalized Laplacian Matrix 15 | # L = 1 - D^(-1/2).W.D^(-1/2) 16 | # D = degree matrix 17 | # W = adjacency matrix 18 | W = nx.adjacency_matrix(G) 19 | D = [] 20 | for i in range(0, len(G.nodes())): 21 | temp = np.zeros(len(G.nodes())) 22 | temp[i] = len(list(G.neighbors(list(G.nodes())[i]))) 23 | D.append(temp) 24 | 25 | D = np.array(D) 26 | W = np.array(W) 27 | L = np.array([]) 28 | L = np.matmul(W, np.sqrt(D)) 29 | return L 30 | 31 | def eigenvector_matrix(G): 32 | W = np.array(nx.adjacency_matrix(G).todense()) 33 | print W.shape[0], W.shape[1] 34 | return LA.eig(W) 35 | 36 | 37 | 38 | dataset_edges = pd.read_csv('./Enron/enron_dataset_final.csv') 39 | dataset_nodes = pd.read_csv('./Enron/Enron.true', sep = ';') 40 | 41 | node_from = dataset_edges.iloc[:,0].values 42 | node_to = dataset_edges.iloc[:,1].values 43 | 44 | num_nodes = len(dataset_nodes) 45 | print "Number of nodes: " + str(num_nodes) 46 | 47 | #Stores the node as key, it's neighbors as values 48 | d = dict() 49 | for i in range(0, (num_nodes+1)): 50 | d[i] = [] 51 | for (i,j) in zip(node_from, node_to): 52 | if j not in d[i]: 53 | d[i].append(j) 54 | if i not in d[j]: 55 | d[j].append(i) 56 | 57 | train_nodes = int(0.8*13533) #10826 58 | test_nodes = int(0.2*13533) #2706 59 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 60 | 61 | #Full dataset graph 62 | H = nx.Graph() 63 | for k, v in d.iteritems(): 64 | for j in v: 65 | H.add_edge(k, j) 66 | # nx.write_graphml(H, 'dataset_graph.graphml') 67 | # H = nx.read_graphml('dataset_graph.graphml') 68 | 69 | # Training graph 70 | G = nx.Graph() 71 | for i in range(train_nodes): 72 | for j in d[i]: 73 | G.add_edge(i, j) 74 | # nx.write_graphml(G, 'train_graph.graphml') 75 | # G = nx.read_graphml('train_graph.graphml') 76 | 77 | #Testing graph 78 | test_id = [i for i in H.nodes() if i not in G.nodes()] 79 | P = nx.Graph() 80 | for i in test_id: 81 | for j in d[i]: 82 | P.add_edge(i, j) 83 | # nx.write_graphml(P, 'test_graph.graphml') 84 | # P = nx.read_graphml('test_graph.graphml') 85 | 86 | print len(H.nodes()) 87 | print len(G.nodes()) 88 | print len(P.nodes()) 89 | 90 | L = nx.normalized_laplacian_matrix(G) 91 | # print L 92 | # w: eigenvector, v: eigenvalue 93 | # w,v = eigenvector_matrix(G) 94 | -------------------------------------------------------------------------------- /ann_graph.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | 11 | dataset_edges = pd.read_csv('./Enron/enron_dataset_final.csv') 12 | dataset_nodes = pd.read_csv('./Enron/Enron.true', sep = ';') 13 | 14 | node_from = dataset_edges.iloc[:,0].values 15 | node_to = dataset_edges.iloc[:,1].values 16 | 17 | num_nodes = len(dataset_nodes) 18 | print "Number of nodes: " + str(num_nodes) 19 | 20 | #Stores the node as key, it's neighbors as values 21 | d = dict() 22 | for i in range(0, (num_nodes+1)): 23 | d[i] = [] 24 | for (i,j) in zip(node_from, node_to): 25 | if j not in d[i]: 26 | d[i].append(j) 27 | if i not in d[j]: 28 | d[j].append(i) 29 | 30 | train_nodes = int(0.8*13533) #10826 31 | test_nodes = int(0.2*13533) #2706 32 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 33 | 34 | #Full dataset graph 35 | H = nx.Graph() 36 | for k, v in d.iteritems(): 37 | for j in v: 38 | H.add_edge(k, j) 39 | 40 | # Training graph 41 | G = nx.Graph() 42 | for i in range(train_nodes): 43 | for j in d[i]: 44 | G.add_edge(i, j) 45 | 46 | #Testing graph 47 | test_id = [i for i in H.nodes() if i not in G.nodes()] 48 | P = nx.Graph() 49 | for i in test_id: 50 | for j in d[i]: 51 | P.add_edge(i, j) 52 | 53 | print len(H.nodes()) 54 | # print len(H.edges()) 55 | print len(G.nodes()) 56 | print len(P.nodes()) 57 | 58 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 59 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 60 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 61 | # print B.shape[0], B.shape[1] 62 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 63 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 64 | 65 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 66 | # print test_adjacency_matrix.shape[0] 67 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 68 | # print B.shape[0], B.shape[1] 69 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 70 | # print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 71 | 72 | d_labels = dict() 73 | for i in range(num_nodes+1): 74 | d_labels[i] = [] 75 | for i,k in zip(dataset_nodes["0"], dataset_nodes["0.1"]): 76 | d_labels[i] = k 77 | 78 | y_train = [d_labels[i] for i in G.nodes()] 79 | y_test = [d_labels[i] for i in P.nodes()] 80 | 81 | y_train = np.array(y_train) 82 | y_test = np.array(y_test) 83 | classifier = Sequential() 84 | 85 | #First hidden layer 86 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 87 | 88 | #Second hidden layer 89 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 90 | 91 | #Output Layer 92 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 93 | 94 | #Compiling model 95 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 96 | 97 | #Training 98 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 99 | scores = model.evaluate(train_adjacency_matrix, y_train) 100 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 101 | #Prediction 102 | y_pred = classifier.predict(test_adjacency_matrix) 103 | y_pred = (y_pred > 0.5) 104 | 105 | scores = model.evaluate(test_adjacency_matrix, y_test) 106 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 107 | 108 | -------------------------------------------------------------------------------- /cnn_graph.py: -------------------------------------------------------------------------------- 1 | from keras.models import Sequential 2 | from keras.layers import Conv2D 3 | from keras.layers import MaxPooling2D 4 | from keras.layers import Flatten 5 | from keras.layers import Dense 6 | import numpy as np 7 | import pandas as pd 8 | import matplotlib.pyplot as plt 9 | import networkx as nx 10 | from sklearn.cross_validation import train_test_split 11 | import keras 12 | from keras.models import Sequential 13 | from keras.layers import Dense 14 | from sklearn.metrics import confusion_matrix 15 | 16 | ####### DATA PREPROCESSING ####### 17 | 18 | dataset_edges = pd.read_csv('./Enron/enron_dataset_final.csv') 19 | dataset_nodes = pd.read_csv('./Enron/Enron.true', sep = ';') 20 | 21 | node_from = dataset_edges.iloc[:,0].values 22 | node_to = dataset_edges.iloc[:,1].values 23 | 24 | num_nodes = len(dataset_nodes) 25 | print "Number of nodes: " + str(num_nodes) 26 | 27 | #Stores the node as key, it's neighbors as values 28 | d = dict() 29 | for i in range(0, (num_nodes+1)): 30 | d[i] = [] 31 | for (i,j) in zip(node_from, node_to): 32 | if j not in d[i]: 33 | d[i].append(j) 34 | if i not in d[j]: 35 | d[j].append(i) 36 | 37 | train_nodes = int(0.8*13533) #10826 38 | test_nodes = int(0.2*13533) #2706 39 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 40 | 41 | #Full dataset graph 42 | H = nx.Graph() 43 | for k, v in d.iteritems(): 44 | for j in v: 45 | H.add_edge(k, j) 46 | 47 | # Training graph 48 | G = nx.Graph() 49 | for i in range(train_nodes): 50 | for j in d[i]: 51 | G.add_edge(i, j) 52 | 53 | #Testing graph 54 | test_id = [i for i in H.nodes() if i not in G.nodes()] 55 | P = nx.Graph() 56 | for i in test_id: 57 | for j in d[i]: 58 | P.add_edge(i, j) 59 | 60 | print len(H.nodes()) 61 | # print len(H.edges()) 62 | print len(G.nodes()) 63 | print len(P.nodes()) 64 | 65 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 66 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 68 | # print B.shape[0], B.shape[1] 69 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 70 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 71 | 72 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 73 | # print test_adjacency_matrix.shape[0] 74 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 75 | # print B.shape[0], B.shape[1] 76 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 77 | # print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 78 | 79 | d_labels = dict() 80 | for i in range(num_nodes+1): 81 | d_labels[i] = [] 82 | for i,k in zip(dataset_nodes["0"], dataset_nodes["0.1"]): 83 | d_labels[i] = k 84 | 85 | y_train = [d_labels[i] for i in G.nodes()] 86 | y_test = [d_labels[i] for i in P.nodes()] 87 | 88 | y_train = np.array(y_train) 89 | y_test = np.array(y_test) 90 | 91 | ####### DATA PREPROCESSING ENDS ####### 92 | 93 | ####### CNN ####### 94 | 95 | # Initialising the CNN 96 | classifier = Sequential() 97 | 98 | # Convolution 99 | classifier.add(Conv2D(32, (3, 3), input_shape = (11422, 11703, 3), activation = 'relu')) 100 | 101 | # Pooling 102 | classifier.add(MaxPooling2D(pool_size = (2, 2))) 103 | 104 | # Adding a second convolutional layer 105 | # classifier.add(Conv2D(32, (3, 3), activation = 'relu')) 106 | # classifier.add(MaxPooling2D(pool_size = (2, 2))) 107 | 108 | # Flattening 109 | classifier.add(Flatten()) 110 | 111 | # Full connection 112 | classifier.add(Dense(units = 128, activation = 'relu')) 113 | classifier.add(Dense(units = 1, activation = 'sigmoid')) 114 | 115 | # Compiling the CNN 116 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 117 | 118 | # Fitting the CNN 119 | 120 | # classifier.fit_generator(train_adjacency_matrix, steps_per_epoch = 8000, epochs = 25, validation_data = y_train, validation_steps = 2000) -------------------------------------------------------------------------------- /ann_twitter.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(76841) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(56830) + ", Test nodes: " + str(20431) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | # print len(H.nodes()) 56 | # print len(H.edges()) 57 | # print len(G.nodes()) 58 | # print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | #print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | #print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_twitter.py~: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(76841) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(56830) + ", Test nodes: " + str(20431) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | # print len(H.nodes()) 56 | # print len(H.edges()) 57 | # print len(G.nodes()) 58 | # print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /Untitled Document~: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(76841) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(56830) + ", Test nodes: " + str(20431) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | # print len(H.nodes()) 56 | # print len(H.edges()) 57 | # print len(G.nodes()) 58 | # print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_degree.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('degree_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(num_nodes) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | print len(H.nodes()) 56 | # print len(H.edges()) 57 | print len(G.nodes()) 58 | print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_bet_cen.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(num_nodes) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | print len(H.nodes()) 56 | # print len(H.edges()) 57 | print len(G.nodes()) 58 | print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_closeness.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('closeness_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(num_nodes) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | print len(H.nodes()) 56 | # print len(H.edges()) 57 | print len(G.nodes()) 58 | print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_enron_data.csv', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_bet_cen_degree.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(num_nodes) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | print len(H.nodes()) 56 | # print len(H.edges()) 57 | print len(G.nodes()) 58 | print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_degree_enron_data.txt', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /ann_bet_cen_closeness.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import networkx as nx 5 | from sklearn.cross_validation import train_test_split 6 | import keras 7 | from keras.models import Sequential 8 | from keras.layers import Dense 9 | from sklearn.metrics import confusion_matrix 10 | import csv 11 | 12 | dataset_edges = pd.read_csv('enrondatasetfinal.csv', sep=',') 13 | # dataset_nodes = pd.read_csv('bet_cen_enron_data.csv', sep = ' ', header = None, quoting = csv.QUOTE_NONE, error_bad_lines = False) 14 | dataset_true = pd.read_csv('Enron.true', header = None, sep = ';') 15 | 16 | node_from = dataset_edges.iloc[:,0].values 17 | node_to = dataset_edges.iloc[:,1].values 18 | 19 | num_nodes = len(dataset_true) 20 | print "Number of nodes: " + str(num_nodes) 21 | 22 | #Stores the node as key, it's neighbors as values 23 | d = dict() 24 | for i in range(0, num_nodes): 25 | d[i] = [] 26 | for (i,j) in zip(node_from, node_to): 27 | if j not in d[i]: 28 | d[i].append(j) 29 | if i not in d[j]: 30 | d[j].append(i) 31 | 32 | train_nodes = int(0.8*13533) #10826 33 | test_nodes = int(0.2*13533) #2706 34 | print 'Train nodes: ' + str(train_nodes) + ", Test nodes: " + str(test_nodes) 35 | 36 | #Full dataset graph 37 | H = nx.Graph() 38 | for k, v in d.iteritems(): 39 | for j in v: 40 | H.add_edge(k, j) 41 | 42 | # Training graph 43 | G = nx.Graph() 44 | for i in range(train_nodes): 45 | for j in d[i]: 46 | G.add_edge(i, j) 47 | 48 | #Testing graph 49 | test_id = [i for i in H.nodes() if i not in G.nodes()] 50 | P = nx.Graph() 51 | for i in test_id: 52 | for j in d[i]: 53 | P.add_edge(i, j) 54 | 55 | print len(H.nodes()) 56 | # print len(H.edges()) 57 | print len(G.nodes()) 58 | print len(P.nodes()) 59 | 60 | train_adjacency_matrix = nx.adjacency_matrix(G).todense() 61 | # print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 62 | # np zero is added to show the if there is an edge between all the other nodes 63 | B = np.zeros((len(G.nodes()), (len(H.nodes()) - len(G.nodes())))) 64 | # print B.shape[0], B.shape[1] 65 | train_adjacency_matrix = np.concatenate((train_adjacency_matrix, B), axis = 1) 66 | print train_adjacency_matrix.shape[0], train_adjacency_matrix.shape[1] 67 | 68 | test_adjacency_matrix = nx.adjacency_matrix(P).todense() 69 | # print test_adjacency_matrix.shape[0] 70 | B = np.zeros((len(P.nodes()), (len(H.nodes()) - len(P.nodes())))) 71 | # print B.shape[0], B.shape[1] 72 | test_adjacency_matrix = np.concatenate((test_adjacency_matrix, B), axis = 1) 73 | print test_adjacency_matrix.shape[0], test_adjacency_matrix.shape[1] 74 | 75 | d_labels = dict() 76 | for i in range(0, num_nodes): 77 | d_labels[i] = [] 78 | with open('bet_cen_closeness_enron_data.txt', 'r') as infile: 79 | for line in infile.readlines(): 80 | token = line.strip().split(" ") 81 | d_labels[int(token[0])].append(int(token[1])) 82 | 83 | 84 | # print d_labels 85 | 86 | y_train = [v for k,v in d_labels.iteritems() if k in G.nodes()] 87 | y_test = [d_labels[i] for i in P.nodes()] 88 | # print y_train[10] 89 | 90 | y_train = np.array(y_train) 91 | y_test = np.array(y_test) 92 | classifier = Sequential() 93 | 94 | 95 | #First hidden layer 96 | classifier.add(Dense(output_dim = 5711, init = 'uniform', activation = 'relu', input_dim = 11703)) 97 | 98 | #Second hidden layer 99 | # classifier.add(Dense(units = 11703, kernel_initializer = 'uniform', activation = 'relu')) 100 | 101 | #Output Layer 102 | classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) 103 | 104 | #Compiling model 105 | classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) 106 | 107 | #Training 108 | classifier.fit(train_adjacency_matrix, y_train, batch_size = 10, epochs = 100) 109 | 110 | scores = model.evaluate(train_adjacency_matrix, y_train) 111 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 112 | #Prediction 113 | y_pred = classifier.predict(test_adjacency_matrix) 114 | y_pred = (y_pred > 0.5) 115 | 116 | scores = model.evaluate(test_adjacency_matrix, y_test) 117 | print "Train scores: " + str(classifier.metrics_names[1]) + str(scores[1]*100) 118 | 119 | -------------------------------------------------------------------------------- /bet_cen_enron_data.csv: -------------------------------------------------------------------------------- 1 | 0 1 2 | 1 0 3 | 2 0 4 | 3 1 5 | 5 1 6 | 6 0 7 | 8 0 8 | 9 0 9 | 10 0 10 | 11 1 11 | 13 0 12 | 14 0 13 | 15 1 14 | 17 1 15 | 18 1 16 | 19 1 17 | 20 0 18 | 21 0 19 | 22 0 20 | 23 0 21 | 24 1 22 | 25 1 23 | 26 1 24 | 27 0 25 | 28 1 26 | 29 0 27 | 30 0 28 | 31 1 29 | 32 0 30 | 33 1 31 | 34 0 32 | 35 1 33 | 36 0 34 | 37 0 35 | 38 0 36 | 39 1 37 | 40 0 38 | 41 0 39 | 42 0 40 | 43 0 41 | 44 0 42 | 45 1 43 | 46 0 44 | 47 0 45 | 48 0 46 | 49 0 47 | 50 0 48 | 51 0 49 | 52 1 50 | 53 0 51 | 54 0 52 | 57 1 53 | 58 0 54 | 59 1 55 | 60 1 56 | 61 0 57 | 63 1 58 | 64 1 59 | 65 0 60 | 66 0 61 | 68 0 62 | 69 0 63 | 70 0 64 | 71 0 65 | 72 1 66 | 73 1 67 | 74 0 68 | 75 1 69 | 76 0 70 | 77 1 71 | 78 0 72 | 79 0 73 | 80 0 74 | 81 1 75 | 82 1 76 | 83 0 77 | 84 1 78 | 85 0 79 | 86 1 80 | 87 1 81 | 88 0 82 | 90 0 83 | 91 1 84 | 92 1 85 | 93 1 86 | 94 1 87 | 95 1 88 | 96 0 89 | 97 1 90 | 98 1 91 | 99 0 92 | 100 0 93 | 101 1 94 | 102 1 95 | 103 1 96 | 104 0 97 | 107 0 98 | 108 0 99 | 109 0 100 | 110 0 101 | 111 1 102 | 112 1 103 | 115 1 104 | 116 0 105 | 117 1 106 | 118 0 107 | 120 0 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1512 | 1738 1 1513 | 1739 0 1514 | 1740 1 1515 | 1741 1 1516 | 1742 0 1517 | 1743 1 1518 | 1744 1 1519 | 1745 0 1520 | 1746 1 1521 | 1747 1 1522 | 1748 1 1523 | 1749 0 1524 | 1751 1 1525 | 1752 0 1526 | 1753 0 1527 | 1754 0 1528 | 1755 1 1529 | 1756 1 1530 | 1757 1 1531 | 1759 0 1532 | 1760 1 1533 | 1761 0 1534 | 1762 0 1535 | 1763 1 1536 | 1765 0 1537 | 1766 1 1538 | 1767 1 1539 | 1768 0 1540 | 1769 0 1541 | 1770 0 1542 | 1771 1 1543 | 1772 0 1544 | 1773 0 1545 | 1774 1 1546 | 1775 1 1547 | 1776 0 1548 | 1777 1 1549 | 1778 0 1550 | 1779 1 1551 | 1780 0 1552 | 1783 0 1553 | 1784 1 1554 | 1785 0 1555 | 1787 0 1556 | 1788 1 1557 | 1789 0 1558 | 1791 0 1559 | 1792 1 1560 | 1793 0 1561 | 1794 1 1562 | 1795 0 1563 | 1796 1 1564 | 1797 0 1565 | 1798 1 1566 | 1799 0 1567 | 1800 1 1568 | 1801 1 1569 | 1802 1 1570 | 1803 0 1571 | 1804 0 1572 | 1806 1 1573 | 1807 0 1574 | 1808 0 1575 | 1809 1 1576 | 1810 1 1577 | 1811 0 1578 | 1813 0 1579 | 1815 0 1580 | 1816 0 1581 | 1817 1 1582 | 1818 1 1583 | 1819 0 1584 | 1820 0 1585 | 1821 0 1586 | 1822 0 1587 | 1823 0 1588 | 1824 0 1589 | 1826 0 1590 | 1827 1 1591 | 1829 1 1592 | 1830 1 1593 | 1831 0 1594 | 1832 1 1595 | 1833 1 1596 | 1834 1 1597 | 1836 0 1598 | 1837 1 1599 | 1838 1 1600 | 1840 1 1601 | 1841 1 1602 | 1842 0 1603 | 1843 0 1604 | 1844 1 1605 | 1845 1 1606 | 1846 0 1607 | 1847 0 1608 | 1848 0 1609 | 1849 1 1610 | 1850 0 1611 | 1851 1 1612 | 1853 0 1613 | 1854 0 1614 | 1855 1 1615 | 1856 0 1616 | 1857 0 1617 | 1858 0 1618 | 1859 1 1619 | 1860 1 1620 | 1861 0 1621 | 1862 1 1622 | 1863 1 1623 | 1865 1 1624 | 1866 0 1625 | 1867 1 1626 | 1869 0 1627 | 1870 1 1628 | 1871 1 1629 | 1873 0 1630 | 1874 0 1631 | 1875 1 1632 | 1876 1 1633 | 1877 0 1634 | 1878 1 1635 | 1879 1 1636 | 1880 1 1637 | 1882 1 1638 | 1883 0 1639 | 1884 1 1640 | 1886 1 1641 | 1887 1 1642 | 1888 0 1643 | 1889 0 1644 | 1890 0 1645 | 1891 0 1646 | 1892 1 1647 | 1893 0 1648 | 1894 0 1649 | 1897 0 1650 | 1898 1 1651 | 1899 1 1652 | 1900 1 1653 | 1901 0 1654 | 1902 0 1655 | 1903 0 1656 | 1904 1 1657 | 1906 0 1658 | 1907 1 1659 | 1908 1 1660 | 1909 0 1661 | 1910 0 1662 | 1911 1 1663 | 1912 1 1664 | 1913 0 1665 | 1915 0 1666 | 1916 1 1667 | 1918 1 1668 | 1919 0 1669 | 1920 1 1670 | 1921 0 1671 | 1922 0 1672 | 1923 0 1673 | 1924 0 1674 | 1925 0 1675 | 1926 0 1676 | 1927 1 1677 | 1928 1 1678 | 1930 1 1679 | 1931 1 1680 | 1932 1 1681 | 1933 0 1682 | 1934 0 1683 | 1936 0 1684 | 1937 1 1685 | 1938 0 1686 | 1940 1 1687 | 1941 1 1688 | 1943 1 1689 | 1944 0 1690 | 1945 0 1691 | 1946 1 1692 | 1947 0 1693 | 1948 0 1694 | 1949 0 1695 | 1951 1 1696 | 1952 1 1697 | 1953 1 1698 | 1955 1 1699 | 1956 0 1700 | 1957 1 1701 | 1958 0 1702 | 1959 0 1703 | 1960 0 1704 | 1961 1 1705 | 1962 1 1706 | 1964 0 1707 | 1965 0 1708 | 1966 0 1709 | 1967 0 1710 | 1968 0 1711 | 1970 1 1712 | 1972 1 1713 | 1973 0 1714 | 1975 1 1715 | 1976 0 1716 | 1977 1 1717 | 1978 0 1718 | 1979 1 1719 | 1980 0 1720 | 1981 0 1721 | 1982 1 1722 | 1983 0 1723 | 1984 0 1724 | 1985 1 1725 | 1986 0 1726 | 1987 0 1727 | 1988 0 1728 | 1989 0 1729 | 1990 1 1730 | 1991 1 1731 | 1992 0 1732 | 1993 1 1733 | 1994 1 1734 | 1995 1 1735 | 1996 1 1736 | 1997 0 1737 | 1998 1 1738 | 1999 1 1739 | 2000 1 1740 | 2001 0 1741 | 2002 0 1742 | 2003 1 1743 | 2005 0 1744 | 2006 1 1745 | 2007 1 1746 | 2008 1 1747 | 2009 1 1748 | 2010 1 1749 | 2011 0 1750 | 2012 1 1751 | 2013 1 1752 | 2014 1 1753 | 2016 0 1754 | 2017 1 1755 | 2018 1 1756 | 2019 0 1757 | 2020 1 1758 | 2021 0 1759 | 2022 0 1760 | 2023 1 1761 | 2024 1 1762 | 2025 0 1763 | 2026 0 1764 | 2027 0 1765 | 2028 1 1766 | 2030 0 1767 | 2031 1 1768 | 2032 1 1769 | 2033 1 1770 | 2034 0 1771 | 2035 0 1772 | 2039 0 1773 | 2040 0 1774 | 2041 1 1775 | 2042 0 1776 | 2043 1 1777 | 2046 0 1778 | 2047 0 1779 | 2048 1 1780 | 2049 1 1781 | 2050 0 1782 | 2051 1 1783 | 2052 0 1784 | 2054 0 1785 | 2055 1 1786 | 2056 0 1787 | 2057 1 1788 | 2058 0 1789 | 2059 0 1790 | 2060 0 1791 | 2063 1 1792 | 2065 0 1793 | 2069 0 1794 | 2070 1 1795 | 2071 0 1796 | 2072 1 1797 | 2073 1 1798 | 2074 0 1799 | 2075 1 1800 | 2076 1 1801 | 2077 0 1802 | 2078 0 1803 | 2079 0 1804 | 2080 0 1805 | 2082 0 1806 | 2084 1 1807 | 2085 1 1808 | 2087 0 1809 | 2089 0 1810 | 2090 0 1811 | 2091 1 1812 | 2093 0 1813 | 2094 1 1814 | 2095 1 1815 | 2096 0 1816 | 2097 0 1817 | 2098 1 1818 | 2099 0 1819 | 2102 1 1820 | 2103 1 1821 | 2104 0 1822 | 2105 0 1823 | 2106 0 1824 | 2108 0 1825 | 2109 0 1826 | 2110 1 1827 | 2111 1 1828 | 2112 1 1829 | 2113 1 1830 | 2114 1 1831 | 2115 0 1832 | 2116 0 1833 | 2119 0 1834 | 2120 1 1835 | 2121 0 1836 | 2122 1 1837 | 2123 1 1838 | 2124 1 1839 | 2126 1 1840 | 2127 0 1841 | 2128 0 1842 | 2129 0 1843 | 2131 1 1844 | 2133 0 1845 | 2136 0 1846 | 2137 0 1847 | 2138 0 1848 | 2139 0 1849 | 2140 0 1850 | 2141 1 1851 | 2142 0 1852 | 2143 0 1853 | 2144 0 1854 | 2145 0 1855 | 2146 0 1856 | 2147 0 1857 | 2148 1 1858 | 2149 0 1859 | 2150 0 1860 | 2151 0 1861 | 2152 0 1862 | 2153 1 1863 | 2154 1 1864 | 2155 1 1865 | 2156 1 1866 | 2157 0 1867 | 2158 0 1868 | 2159 0 1869 | 2160 0 1870 | 2161 1 1871 | 2162 0 1872 | 2163 1 1873 | 2164 0 1874 | 2165 1 1875 | 2168 0 1876 | 2169 1 1877 | 2170 0 1878 | 2171 1 1879 | 2172 0 1880 | 2173 0 1881 | 2174 0 1882 | 2175 1 1883 | 2176 0 1884 | 2177 0 1885 | 2178 1 1886 | 2179 0 1887 | 2180 0 1888 | 2181 0 1889 | 2182 0 1890 | 2183 0 1891 | 2185 1 1892 | 2186 0 1893 | 2188 0 1894 | 2190 0 1895 | 2191 0 1896 | 2192 0 1897 | 2193 0 1898 | 2194 0 1899 | 2195 0 1900 | 2196 0 1901 | 2197 1 1902 | 2198 1 1903 | 2199 0 1904 | 2201 0 1905 | 2202 1 1906 | 2203 1 1907 | 2204 1 1908 | 2205 0 1909 | 2206 1 1910 | 2207 0 1911 | 2208 1 1912 | 2209 0 1913 | 2210 1 1914 | 2211 1 1915 | 2212 0 1916 | 2213 0 1917 | 2214 1 1918 | 2215 0 1919 | 2216 0 1920 | 2217 1 1921 | 2219 1 1922 | 2220 1 1923 | 2221 1 1924 | 2222 0 1925 | 2223 0 1926 | 2224 0 1927 | 2225 0 1928 | 2226 0 1929 | 2228 1 1930 | 2230 0 1931 | 2231 1 1932 | 2232 0 1933 | 2234 0 1934 | 2235 0 1935 | 2236 1 1936 | 2237 1 1937 | 2238 1 1938 | 2239 0 1939 | 2240 0 1940 | 2241 0 1941 | 2242 0 1942 | 2243 0 1943 | 2244 1 1944 | 2245 1 1945 | 2246 1 1946 | 2247 1 1947 | 2248 1 1948 | 2249 1 1949 | 2250 1 1950 | 2251 0 1951 | 2253 0 1952 | 2254 1 1953 | 2255 1 1954 | 2257 0 1955 | 2258 1 1956 | 2259 1 1957 | 2260 0 1958 | 2261 1 1959 | 2263 0 1960 | 2264 1 1961 | 2265 0 1962 | 2266 1 1963 | 2267 0 1964 | 2268 0 1965 | 2270 1 1966 | 2271 1 1967 | 2272 0 1968 | 2273 1 1969 | 2274 0 1970 | 2275 1 1971 | 2276 0 1972 | 2277 1 1973 | 2278 0 1974 | 2279 1 1975 | 2280 1 1976 | 2281 0 1977 | 2282 0 1978 | 2283 1 1979 | 2284 1 1980 | 2285 0 1981 | 2286 0 1982 | 2287 1 1983 | 2288 1 1984 | 2289 0 1985 | 2290 1 1986 | 2291 1 1987 | 2292 0 1988 | 2293 0 1989 | 2294 0 1990 | 2297 1 1991 | 2299 1 1992 | 2300 1 1993 | 2302 0 1994 | 2304 0 1995 | 2306 0 1996 | 2307 0 1997 | 2308 0 1998 | 2309 1 1999 | 2310 1 2000 | 2311 0 2001 | 2312 0 2002 | 2313 0 2003 | 2314 0 2004 | 2315 0 2005 | 2316 0 2006 | 2317 0 2007 | 2318 1 2008 | 2319 0 2009 | 2320 0 2010 | 2321 1 2011 | 2322 0 2012 | 2323 0 2013 | 2324 1 2014 | 2325 0 2015 | 2326 0 2016 | 2327 1 2017 | 2328 1 2018 | 2329 1 2019 | 2330 1 2020 | 2331 1 2021 | 2334 0 2022 | 2335 0 2023 | 2336 0 2024 | 2337 0 2025 | 2338 1 2026 | 2339 1 2027 | 2340 1 2028 | 2341 0 2029 | 2342 1 2030 | 2343 0 2031 | 2344 1 2032 | 2345 0 2033 | 2346 1 2034 | 2347 1 2035 | 2348 0 2036 | 2349 0 2037 | 2350 0 2038 | 2351 1 2039 | 2352 0 2040 | 2353 0 2041 | 2355 1 2042 | 2356 1 2043 | 2357 0 2044 | 2358 1 2045 | 2359 0 2046 | 2360 0 2047 | 2362 0 2048 | 2363 1 2049 | 2364 1 2050 | 2365 1 2051 | 2366 1 2052 | 2367 0 2053 | 2368 1 2054 | 2369 1 2055 | 2370 0 2056 | 2371 1 2057 | 2372 0 2058 | 2373 0 2059 | 2374 0 2060 | 2375 1 2061 | 2376 1 2062 | 2377 0 2063 | 2378 0 2064 | 2379 0 2065 | 2380 0 2066 | 2381 0 2067 | 2382 1 2068 | 2383 1 2069 | 2384 1 2070 | 2385 1 2071 | 2387 0 2072 | 2388 0 2073 | 2389 1 2074 | 2390 0 2075 | 2393 1 2076 | 2394 1 2077 | 2397 0 2078 | 2399 1 2079 | 2401 1 2080 | 2402 1 2081 | 2404 0 2082 | 2405 0 2083 | 2406 1 2084 | 2407 0 2085 | 2409 1 2086 | 2410 0 2087 | 2411 1 2088 | 2412 0 2089 | 2413 1 2090 | 2414 0 2091 | 2415 1 2092 | 2416 0 2093 | 2417 0 2094 | 2418 1 2095 | 2419 0 2096 | 2421 1 2097 | 2422 1 2098 | 2423 0 2099 | 2424 0 2100 | 2426 0 2101 | 2427 1 2102 | 2428 1 2103 | 2429 0 2104 | 2430 0 2105 | 2431 1 2106 | 2432 1 2107 | 2433 0 2108 | 2434 0 2109 | 2435 1 2110 | 2436 0 2111 | 2437 1 2112 | 2438 1 2113 | 2439 0 2114 | 2441 1 2115 | 2442 0 2116 | 2444 0 2117 | 2446 0 2118 | 2447 1 2119 | 2448 0 2120 | 2449 0 2121 | 2450 1 2122 | 2451 0 2123 | 2452 0 2124 | 2453 0 2125 | 2454 1 2126 | 2455 1 2127 | 2456 0 2128 | 2457 0 2129 | 2458 1 2130 | 2459 1 2131 | 2461 1 2132 | 2462 0 2133 | 2463 0 2134 | 2464 0 2135 | 2465 0 2136 | 2466 0 2137 | 2468 0 2138 | 2469 1 2139 | 2470 1 2140 | 2472 0 2141 | 2473 1 2142 | 2474 1 2143 | 2475 0 2144 | 2477 0 2145 | 2478 0 2146 | 2479 0 2147 | 2480 0 2148 | 2481 0 2149 | 2482 1 2150 | 2483 1 2151 | 2484 1 2152 | 2485 0 2153 | 2486 0 2154 | 2487 0 2155 | 2488 0 2156 | 2489 0 2157 | 2490 1 2158 | 2491 0 2159 | 2492 1 2160 | 2493 1 2161 | 2494 0 2162 | 2495 1 2163 | 2496 0 2164 | 2497 0 2165 | 2498 1 2166 | 2499 0 2167 | 2500 0 2168 | 2501 1 2169 | 2503 0 2170 | 2505 1 2171 | 2507 1 2172 | 2508 0 2173 | 2509 0 2174 | 2510 1 2175 | 2511 0 2176 | 2512 0 2177 | 2513 1 2178 | 2514 1 2179 | 2515 1 2180 | 2516 0 2181 | 2517 1 2182 | 2518 0 2183 | 2519 0 2184 | 2520 0 2185 | 2521 1 2186 | 2522 0 2187 | 2523 1 2188 | 2525 0 2189 | 2527 0 2190 | 2528 1 2191 | 2529 0 2192 | 2530 0 2193 | 2532 0 2194 | 2535 0 2195 | 2536 0 2196 | 2537 1 2197 | 2538 0 2198 | 2539 1 2199 | 2541 0 2200 | 2542 0 2201 | 2543 1 2202 | 2544 0 2203 | 2545 0 2204 | 2546 0 2205 | 2547 1 2206 | 2548 0 2207 | 2549 1 2208 | 2550 0 2209 | 2551 1 2210 | 2552 0 2211 | 2553 0 2212 | 2554 1 2213 | 2555 0 2214 | 2557 0 2215 | 2558 0 2216 | 2559 1 2217 | 2560 0 2218 | 2561 1 2219 | 2562 0 2220 | 2563 0 2221 | 2564 1 2222 | 2565 0 2223 | 2566 1 2224 | 2567 1 2225 | 2569 0 2226 | 2570 1 2227 | 2571 0 2228 | 2572 0 2229 | 2574 0 2230 | 2575 1 2231 | 2576 1 2232 | 2577 1 2233 | 2578 1 2234 | 2580 0 2235 | 2582 1 2236 | 2583 1 2237 | 2584 1 2238 | 2585 0 2239 | 2586 0 2240 | 2587 0 2241 | 2588 1 2242 | 2590 1 2243 | 2591 0 2244 | 2592 1 2245 | 2593 1 2246 | 2595 0 2247 | 2596 1 2248 | 2597 1 2249 | 2598 1 2250 | 2599 1 2251 | 2600 1 2252 | 2601 0 2253 | 2602 0 2254 | 2603 0 2255 | 2604 1 2256 | 2605 1 2257 | 2606 0 2258 | 2607 0 2259 | 2608 0 2260 | 2609 0 2261 | 2610 1 2262 | 2611 0 2263 | 2614 0 2264 | 2615 1 2265 | 2617 0 2266 | 2618 1 2267 | 2619 1 2268 | 2620 0 2269 | 2621 0 2270 | 2622 1 2271 | 2623 0 2272 | 2624 0 2273 | 2625 1 2274 | 2626 0 2275 | 2627 0 2276 | 2629 0 2277 | 2630 1 2278 | 2631 1 2279 | 2632 0 2280 | 2633 0 2281 | 2635 1 2282 | 2636 0 2283 | 2638 0 2284 | 2639 0 2285 | 2641 0 2286 | 2642 1 2287 | 2643 0 2288 | 2644 0 2289 | 2645 1 2290 | 2646 0 2291 | 2648 0 2292 | 2650 1 2293 | 2651 1 2294 | 2652 0 2295 | 2653 1 2296 | 2654 0 2297 | 2655 1 2298 | 2656 1 2299 | 2657 0 2300 | 2658 1 2301 | 2659 1 2302 | 2661 0 2303 | 2662 1 2304 | 2663 0 2305 | 2666 1 2306 | 2667 0 2307 | 2668 1 2308 | 2669 1 2309 | 2670 0 2310 | 2671 0 2311 | 2672 0 2312 | 2673 0 2313 | 2674 1 2314 | 2675 0 2315 | 2676 1 2316 | 2677 1 2317 | 2678 1 2318 | 2679 1 2319 | 2681 1 2320 | 2682 1 2321 | 2683 0 2322 | 2684 0 2323 | 2685 1 2324 | 2686 1 2325 | 2687 0 2326 | 2688 0 2327 | 2689 1 2328 | 2690 0 2329 | 2691 1 2330 | 2692 0 2331 | 2693 1 2332 | 2694 1 2333 | 2695 0 2334 | 2696 0 2335 | 2698 1 2336 | 2699 1 2337 | 2700 0 2338 | 2701 0 2339 | 2702 1 2340 | 2704 1 2341 | 2705 1 2342 | 2706 0 2343 | 2707 1 2344 | 2708 1 2345 | 2709 0 2346 | 2710 0 2347 | 2711 0 2348 | 2712 0 2349 | 2713 0 2350 | 2714 1 2351 | 2715 1 2352 | 2716 0 2353 | 2718 1 2354 | 2719 1 2355 | 2721 1 2356 | 2722 1 2357 | 2723 1 2358 | 2724 0 2359 | 2725 1 2360 | 2726 1 2361 | 2727 1 2362 | 2728 0 2363 | 2729 0 2364 | 2730 1 2365 | 2732 1 2366 | 2733 0 2367 | 2734 0 2368 | 2735 0 2369 | 2736 0 2370 | 2737 1 2371 | 2738 0 2372 | 2740 0 2373 | 2741 0 2374 | 2742 0 2375 | 2743 0 2376 | 2744 0 2377 | 2745 1 2378 | 2746 1 2379 | 2747 1 2380 | 2748 1 2381 | 2749 0 2382 | 2750 0 2383 | 2751 1 2384 | 2752 0 2385 | 2753 1 2386 | 2754 1 2387 | 2755 1 2388 | 2756 0 2389 | 2757 1 2390 | 2758 1 2391 | 2759 0 2392 | 2760 0 2393 | 2761 1 2394 | 2762 0 2395 | 2763 1 2396 | 2764 0 2397 | 2765 0 2398 | 2766 0 2399 | 2767 1 2400 | 2768 0 2401 | 2769 0 2402 | 2771 1 2403 | 2772 0 2404 | 2773 1 2405 | 2774 1 2406 | 2775 1 2407 | 2776 1 2408 | 2777 1 2409 | 2778 0 2410 | 2780 0 2411 | 2781 0 2412 | 2782 1 2413 | 2783 0 2414 | 2784 0 2415 | 2785 0 2416 | 2786 1 2417 | 2787 1 2418 | 2788 1 2419 | 2790 1 2420 | 2791 0 2421 | 2792 1 2422 | 2793 0 2423 | 2794 0 2424 | 2796 1 2425 | 2798 0 2426 | 2799 0 2427 | 2800 0 2428 | 2801 0 2429 | 2802 0 2430 | 2803 1 2431 | 2804 1 2432 | 2805 0 2433 | 2806 0 2434 | 2807 0 2435 | 2808 1 2436 | 2809 0 2437 | 2811 0 2438 | 2812 1 2439 | 2813 1 2440 | 2814 1 2441 | 2815 1 2442 | 2816 0 2443 | 2817 0 2444 | 2818 0 2445 | 2819 1 2446 | 2821 0 2447 | 2822 1 2448 | 2823 1 2449 | 2824 0 2450 | 2825 1 2451 | 2826 1 2452 | 2827 0 2453 | 2828 1 2454 | 2829 1 2455 | 2830 0 2456 | 2831 0 2457 | 2832 1 2458 | 2833 1 2459 | 2834 0 2460 | 2835 1 2461 | 2836 1 2462 | 2837 0 2463 | 2838 1 2464 | 2840 1 2465 | 2841 0 2466 | 2843 0 2467 | 2844 1 2468 | 2847 1 2469 | 2848 0 2470 | 2849 1 2471 | 2850 0 2472 | 2851 1 2473 | 2852 1 2474 | 2853 1 2475 | 2854 0 2476 | 2855 1 2477 | 2856 1 2478 | 2857 0 2479 | 2858 0 2480 | 2859 0 2481 | 2860 0 2482 | 2861 1 2483 | 2862 0 2484 | 2863 1 2485 | 2865 1 2486 | 2866 1 2487 | 2867 0 2488 | 2868 1 2489 | 2870 0 2490 | 2871 1 2491 | 2872 0 2492 | 2873 1 2493 | 2874 1 2494 | 2875 0 2495 | 2876 0 2496 | 2877 0 2497 | 2878 0 2498 | 2879 0 2499 | 2880 1 2500 | 2881 0 2501 | 2882 0 2502 | 2883 1 2503 | 2884 0 2504 | 2885 0 2505 | 2886 0 2506 | 2887 0 2507 | 2888 1 2508 | 2889 0 2509 | 2890 1 2510 | 2891 0 2511 | 2892 1 2512 | 2893 1 2513 | 2894 0 2514 | 2895 0 2515 | 2896 1 2516 | 2897 1 2517 | 2898 0 2518 | 2899 0 2519 | 2900 0 2520 | 2901 0 2521 | 2902 1 2522 | 2903 0 2523 | 2904 1 2524 | 2905 1 2525 | 2906 0 2526 | 2907 0 2527 | 2908 1 2528 | 2909 0 2529 | 2910 1 2530 | 2911 1 2531 | 2912 1 2532 | 2913 1 2533 | 2914 0 2534 | 2915 0 2535 | 2916 0 2536 | 2917 0 2537 | 2919 0 2538 | 2921 0 2539 | 2923 0 2540 | 2924 1 2541 | 2925 0 2542 | 2927 1 2543 | 2928 1 2544 | 2929 0 2545 | 2930 1 2546 | 2931 0 2547 | 2932 0 2548 | 2933 1 2549 | 2934 0 2550 | 2935 0 2551 | 2936 0 2552 | 2937 1 2553 | 2938 1 2554 | 2940 1 2555 | 2941 0 2556 | 2942 0 2557 | 2943 0 2558 | 2945 1 2559 | 2946 1 2560 | 2948 1 2561 | 2949 0 2562 | 2950 1 2563 | 2951 0 2564 | 2952 1 2565 | 2953 0 2566 | 2954 0 2567 | 2955 0 2568 | 2956 0 2569 | 2957 1 2570 | 2958 1 2571 | 2959 0 2572 | 2960 1 2573 | 2961 0 2574 | 2962 0 2575 | 2963 0 2576 | 2964 0 2577 | 2965 0 2578 | 2966 1 2579 | 2967 1 2580 | 2968 1 2581 | 2969 0 2582 | 2970 0 2583 | 2971 1 2584 | 2972 0 2585 | 2973 0 2586 | 2974 0 2587 | 2975 0 2588 | 2976 0 2589 | 2977 0 2590 | 2978 1 2591 | 2979 0 2592 | 2980 1 2593 | 2981 1 2594 | 2982 0 2595 | 2983 1 2596 | 2984 1 2597 | 2985 1 2598 | 2986 1 2599 | 2987 0 2600 | 2988 1 2601 | 2989 1 2602 | 2990 0 2603 | 2991 0 2604 | 2992 1 2605 | 2993 1 2606 | 2994 0 2607 | 2995 0 2608 | 2996 1 2609 | 2997 0 2610 | 2998 0 2611 | 2999 1 2612 | 3000 1 2613 | 3001 1 2614 | 3003 1 2615 | 3004 0 2616 | 3005 0 2617 | 3006 1 2618 | 3007 0 2619 | 3008 1 2620 | 3009 0 2621 | 3010 0 2622 | 3012 1 2623 | 3013 1 2624 | 3014 1 2625 | 3015 1 2626 | 3016 1 2627 | 3017 1 2628 | 3019 1 2629 | 3020 1 2630 | 3021 0 2631 | 3022 0 2632 | 3023 1 2633 | 3024 1 2634 | 3025 0 2635 | 3026 0 2636 | 3027 0 2637 | 3030 0 2638 | 3031 1 2639 | 3033 0 2640 | 3034 0 2641 | 3035 0 2642 | 3037 1 2643 | 3038 1 2644 | 3040 1 2645 | 3041 1 2646 | 3042 0 2647 | 3044 1 2648 | 3045 0 2649 | 3046 0 2650 | 3047 0 2651 | 3048 1 2652 | 3050 0 2653 | 3051 1 2654 | 3052 1 2655 | 3053 0 2656 | 3054 0 2657 | 3055 1 2658 | 3056 0 2659 | 3057 0 2660 | 3059 0 2661 | 3060 1 2662 | 3061 0 2663 | 3062 0 2664 | 3063 0 2665 | 3065 1 2666 | 3066 0 2667 | 3067 0 2668 | 3068 0 2669 | 3069 0 2670 | 3070 0 2671 | 3072 1 2672 | 3073 0 2673 | 3074 0 2674 | 3075 1 2675 | 3076 1 2676 | 3077 1 2677 | 3078 1 2678 | 3079 0 2679 | 3080 0 2680 | 3082 0 2681 | 3083 1 2682 | 3084 0 2683 | 3085 0 2684 | 3086 0 2685 | 3087 0 2686 | 3088 1 2687 | 3089 0 2688 | 3090 0 2689 | 3091 0 2690 | 3092 1 2691 | 3093 1 2692 | 3094 0 2693 | 3095 0 2694 | 3096 0 2695 | 3097 0 2696 | 3098 1 2697 | 3099 1 2698 | 3100 0 2699 | 3101 0 2700 | 3102 0 2701 | 3103 1 2702 | 3104 0 2703 | 3105 0 2704 | 3106 1 2705 | 3107 0 2706 | 3108 0 2707 | 3109 1 2708 | 3110 1 2709 | 3111 0 2710 | 3112 0 2711 | 3113 1 2712 | 3114 0 2713 | 3115 1 2714 | 3116 1 2715 | 3117 0 2716 | 3118 1 2717 | 3119 0 2718 | 3120 0 2719 | 3121 1 2720 | 3122 0 2721 | 3124 1 2722 | 3125 0 2723 | 3126 1 2724 | 3127 0 2725 | 3128 1 2726 | 3129 1 2727 | 3130 0 2728 | 3131 0 2729 | 3132 0 2730 | 3133 0 2731 | 3134 1 2732 | 3136 1 2733 | 3138 1 2734 | 3139 1 2735 | 3140 0 2736 | 3141 0 2737 | 3142 0 2738 | 3143 1 2739 | 3144 0 2740 | 3146 1 2741 | 3147 0 2742 | 3148 0 2743 | 3149 0 2744 | 3150 1 2745 | 3151 1 2746 | 3152 0 2747 | 3154 1 2748 | 3155 1 2749 | 3156 1 2750 | 3157 1 2751 | 3158 0 2752 | 3159 1 2753 | 3160 0 2754 | 3161 0 2755 | 3163 0 2756 | 3164 1 2757 | 3165 1 2758 | 3166 1 2759 | 3167 0 2760 | 3169 0 2761 | 3171 0 2762 | 3172 0 2763 | 3173 1 2764 | 3174 0 2765 | 3175 0 2766 | 3177 1 2767 | 3178 1 2768 | 3182 0 2769 | 3183 0 2770 | 3184 0 2771 | 3185 0 2772 | 3186 1 2773 | 3187 0 2774 | 3188 1 2775 | 3189 1 2776 | 3190 1 2777 | 3191 0 2778 | 3192 1 2779 | 3193 1 2780 | 3195 0 2781 | 3197 0 2782 | 3199 0 2783 | 3201 1 2784 | 3202 1 2785 | 3203 0 2786 | 3204 1 2787 | 3206 0 2788 | 3207 1 2789 | 3208 1 2790 | 3209 0 2791 | 3210 1 2792 | 3211 1 2793 | 3212 0 2794 | 3213 1 2795 | 3214 1 2796 | 3215 0 2797 | 3216 1 2798 | 3217 1 2799 | 3219 0 2800 | 3221 1 2801 | 3222 0 2802 | 3223 0 2803 | 3226 0 2804 | 3227 0 2805 | 3228 0 2806 | 3229 0 2807 | 3231 1 2808 | 3232 0 2809 | 3233 0 2810 | 3234 0 2811 | 3236 0 2812 | 3237 1 2813 | 3238 0 2814 | 3239 1 2815 | 3240 1 2816 | 3241 0 2817 | 3242 1 2818 | 3243 1 2819 | 3244 1 2820 | 3245 0 2821 | 3247 0 2822 | 3248 0 2823 | 3249 0 2824 | 3250 1 2825 | 3251 0 2826 | 3252 0 2827 | 3253 0 2828 | 3254 0 2829 | 3255 1 2830 | 3256 1 2831 | 3257 0 2832 | 3258 0 2833 | 3259 1 2834 | 3260 1 2835 | 3261 0 2836 | 3262 0 2837 | 3263 0 2838 | 3264 1 2839 | 3265 0 2840 | 3267 1 2841 | 3268 1 2842 | 3269 1 2843 | 3270 0 2844 | 3271 0 2845 | 3272 0 2846 | 3273 0 2847 | 3274 0 2848 | 3275 1 2849 | 3276 1 2850 | 3277 1 2851 | 3278 1 2852 | 3279 0 2853 | 3280 1 2854 | 3281 0 2855 | 3282 0 2856 | 3283 1 2857 | 3284 1 2858 | 3285 0 2859 | 3286 1 2860 | 3287 0 2861 | 3288 1 2862 | 3289 1 2863 | 3290 0 2864 | 3292 1 2865 | 3293 1 2866 | 3294 1 2867 | 3295 1 2868 | 3296 1 2869 | 3297 1 2870 | 3298 0 2871 | 3299 0 2872 | 3300 0 2873 | 3301 1 2874 | 3302 1 2875 | 3303 0 2876 | 3304 0 2877 | 3305 1 2878 | 3306 1 2879 | 3307 0 2880 | 3308 1 2881 | 3309 0 2882 | 3310 0 2883 | 3311 1 2884 | 3312 1 2885 | 3314 0 2886 | 3315 0 2887 | 3316 1 2888 | 3317 0 2889 | 3319 0 2890 | 3320 1 2891 | 3321 1 2892 | 3322 1 2893 | 3323 1 2894 | 3324 0 2895 | 3325 1 2896 | 3326 0 2897 | 3327 0 2898 | 3328 0 2899 | 3329 0 2900 | 3330 0 2901 | 3332 0 2902 | 3333 0 2903 | 3334 0 2904 | 3335 1 2905 | 3336 1 2906 | 3337 1 2907 | 3338 0 2908 | 3339 0 2909 | 3340 1 2910 | 3341 0 2911 | 3342 0 2912 | 3343 1 2913 | 3344 1 2914 | 3345 0 2915 | 3346 0 2916 | 3347 0 2917 | 3348 0 2918 | 3349 1 2919 | 3350 0 2920 | 3351 1 2921 | 3352 0 2922 | 3353 0 2923 | 3354 1 2924 | 3355 0 2925 | 3356 0 2926 | 3357 0 2927 | 3358 0 2928 | 3359 0 2929 | 3360 0 2930 | 3361 1 2931 | 3362 0 2932 | 3364 1 2933 | 3365 1 2934 | 3366 0 2935 | 3367 0 2936 | 3368 1 2937 | 3369 1 2938 | 3370 0 2939 | 3371 0 2940 | 3372 1 2941 | 3374 0 2942 | 3375 1 2943 | 3376 1 2944 | 3377 1 2945 | 3378 1 2946 | 3379 1 2947 | 3380 0 2948 | 3381 0 2949 | 3382 0 2950 | 3383 1 2951 | 3384 1 2952 | 3385 0 2953 | 3386 1 2954 | 3387 1 2955 | 3388 1 2956 | 3390 1 2957 | 3391 1 2958 | 3392 0 2959 | 3394 0 2960 | 3395 1 2961 | 3396 1 2962 | 3397 1 2963 | 3398 0 2964 | 3399 0 2965 | 3400 1 2966 | 3401 1 2967 | 3402 0 2968 | 3403 1 2969 | 3405 1 2970 | 3406 1 2971 | 3407 0 2972 | 3408 0 2973 | 3409 0 2974 | 3410 0 2975 | 3411 0 2976 | 3413 1 2977 | 3414 1 2978 | 3416 0 2979 | 3417 1 2980 | 3418 0 2981 | 3419 1 2982 | 3420 1 2983 | 3421 1 2984 | 3423 1 2985 | 3424 0 2986 | 3425 1 2987 | 3426 1 2988 | 3427 1 2989 | 3428 1 2990 | 3429 0 2991 | 3430 0 2992 | 3431 0 2993 | 3432 0 2994 | 3433 0 2995 | 3434 1 2996 | 3436 0 2997 | 3437 1 2998 | 3438 0 2999 | 3439 0 3000 | 3440 0 3001 | 3441 1 3002 | 3442 0 3003 | 3443 0 3004 | 3444 0 3005 | 3445 1 3006 | 3446 0 3007 | 3447 1 3008 | 3448 0 3009 | 3451 0 3010 | 3452 0 3011 | 3454 1 3012 | 3455 0 3013 | 3457 0 3014 | 3459 1 3015 | 3460 1 3016 | 3461 1 3017 | 3462 1 3018 | 3463 1 3019 | 3464 1 3020 | 3465 0 3021 | 3466 0 3022 | 3467 1 3023 | 3469 0 3024 | 3470 1 3025 | 3471 1 3026 | 3472 1 3027 | 3474 0 3028 | 3475 0 3029 | 3476 0 3030 | 3478 1 3031 | 3479 0 3032 | 3480 0 3033 | 3481 0 3034 | 3482 0 3035 | 3483 0 3036 | 3484 0 3037 | 3485 0 3038 | 3486 0 3039 | 3487 0 3040 | 3488 1 3041 | 3489 0 3042 | 3491 1 3043 | 3492 0 3044 | 3494 0 3045 | 3496 1 3046 | 3498 0 3047 | 3499 1 3048 | 3500 0 3049 | 3501 0 3050 | 3502 1 3051 | 3503 0 3052 | 3504 0 3053 | 3505 0 3054 | 3506 0 3055 | 3507 0 3056 | 3508 1 3057 | 3509 1 3058 | 3510 0 3059 | 3511 1 3060 | 3512 1 3061 | 3513 0 3062 | 3514 0 3063 | 3515 1 3064 | 3516 1 3065 | 3517 0 3066 | 3518 1 3067 | 3519 1 3068 | 3520 1 3069 | 3521 0 3070 | 3522 0 3071 | 3525 0 3072 | 3526 1 3073 | 3528 1 3074 | 3529 1 3075 | 3530 0 3076 | 3531 1 3077 | 3532 0 3078 | 3533 0 3079 | 3534 0 3080 | 3536 0 3081 | 3537 0 3082 | 3538 0 3083 | 3539 0 3084 | 3540 1 3085 | 3541 1 3086 | 3542 1 3087 | 3543 1 3088 | 3544 0 3089 | 3545 1 3090 | 3546 1 3091 | 3547 1 3092 | 3548 1 3093 | 3549 0 3094 | 3550 0 3095 | 3552 0 3096 | 3553 1 3097 | 3554 0 3098 | 3555 1 3099 | 3556 1 3100 | 3557 1 3101 | 3558 0 3102 | 3559 1 3103 | 3560 0 3104 | 3561 1 3105 | 3562 1 3106 | 3563 0 3107 | 3564 0 3108 | 3565 0 3109 | 3566 0 3110 | 3567 1 3111 | 3568 0 3112 | 3569 1 3113 | 3570 1 3114 | 3571 0 3115 | 3573 0 3116 | 3574 0 3117 | 3575 1 3118 | 3576 0 3119 | 3577 0 3120 | 3578 0 3121 | 3579 0 3122 | 3580 0 3123 | 3581 0 3124 | 3582 0 3125 | 3583 0 3126 | 3584 0 3127 | 3585 0 3128 | 3586 1 3129 | 3587 0 3130 | 3588 0 3131 | 3590 1 3132 | 3591 0 3133 | 3592 0 3134 | 3593 0 3135 | 3594 0 3136 | 3595 0 3137 | 3596 1 3138 | 3597 1 3139 | 3599 0 3140 | 3600 0 3141 | 3601 0 3142 | 3603 0 3143 | 3604 1 3144 | 3605 0 3145 | 3606 0 3146 | 3607 0 3147 | 3609 1 3148 | 3610 1 3149 | 3611 0 3150 | 3612 0 3151 | 3613 0 3152 | 3614 1 3153 | 3615 0 3154 | 3616 0 3155 | 3617 1 3156 | 3618 0 3157 | 3619 0 3158 | 3620 0 3159 | 3621 0 3160 | 3622 0 3161 | 3623 1 3162 | 3624 0 3163 | 3625 1 3164 | 3626 0 3165 | 3627 0 3166 | 3628 0 3167 | 3629 1 3168 | 3630 0 3169 | 3631 0 3170 | 3632 0 3171 | 3633 1 3172 | 3634 1 3173 | 3636 1 3174 | 3637 0 3175 | 3638 0 3176 | 3640 0 3177 | 3641 1 3178 | 3642 1 3179 | 3643 0 3180 | 3644 1 3181 | 3645 1 3182 | 3646 1 3183 | 3648 0 3184 | 3649 1 3185 | 3650 1 3186 | 3651 1 3187 | 3652 0 3188 | 3653 1 3189 | 3654 1 3190 | 3655 0 3191 | 3657 1 3192 | 3659 0 3193 | 3661 1 3194 | 3662 0 3195 | 3663 1 3196 | 3664 0 3197 | 3665 0 3198 | 3667 0 3199 | 3668 0 3200 | 3669 0 3201 | 3670 1 3202 | 3671 1 3203 | 3672 1 3204 | 3673 1 3205 | 3674 0 3206 | 3675 1 3207 | 3676 1 3208 | 3677 0 3209 | 3678 0 3210 | 3679 0 3211 | 3680 1 3212 | 3681 1 3213 | 3682 1 3214 | 3683 1 3215 | 3684 1 3216 | 3685 0 3217 | 3686 0 3218 | 3688 1 3219 | 3690 0 3220 | 3691 1 3221 | 3692 0 3222 | 3693 0 3223 | 3694 0 3224 | 3695 0 3225 | 3696 0 3226 | 3697 0 3227 | 3698 0 3228 | 3699 0 3229 | 3700 1 3230 | 3701 0 3231 | 3702 0 3232 | 3703 0 3233 | 3704 0 3234 | 3705 0 3235 | 3706 1 3236 | 3707 1 3237 | 3708 1 3238 | 3709 0 3239 | 3711 1 3240 | 3712 1 3241 | 3713 1 3242 | 3714 1 3243 | 3715 1 3244 | 3716 1 3245 | 3717 1 3246 | 3718 1 3247 | 3719 1 3248 | 3720 1 3249 | 3721 1 3250 | 3722 0 3251 | 3723 1 3252 | 3724 0 3253 | 3725 0 3254 | 3726 1 3255 | 3727 1 3256 | 3728 0 3257 | 3730 1 3258 | 3732 0 3259 | 3733 0 3260 | 3734 0 3261 | 3735 1 3262 | 3737 0 3263 | 3738 1 3264 | 3739 0 3265 | 3740 0 3266 | 3741 0 3267 | 3742 0 3268 | 3744 0 3269 | 3745 0 3270 | 3746 0 3271 | 3747 0 3272 | 3748 1 3273 | 3749 0 3274 | 3750 1 3275 | 3751 0 3276 | 3752 0 3277 | 3753 0 3278 | 3754 0 3279 | 3757 0 3280 | 3758 1 3281 | 3761 0 3282 | 3762 0 3283 | 3763 0 3284 | 3765 0 3285 | 3766 0 3286 | 3767 0 3287 | 3768 0 3288 | 3769 0 3289 | 3770 0 3290 | 3771 1 3291 | 3772 0 3292 | 3774 0 3293 | 3776 0 3294 | 3777 0 3295 | 3778 0 3296 | 3780 0 3297 | 3782 1 3298 | 3783 0 3299 | 3784 1 3300 | 3785 1 3301 | 3786 1 3302 | 3787 0 3303 | 3788 0 3304 | 3789 0 3305 | 3790 1 3306 | 3791 1 3307 | 3792 0 3308 | 3793 1 3309 | 3794 1 3310 | 3795 1 3311 | 3796 0 3312 | 3797 1 3313 | 3798 0 3314 | 3799 1 3315 | 3801 0 3316 | 3802 1 3317 | 3804 0 3318 | 3805 0 3319 | 3807 0 3320 | 3808 1 3321 | 3809 1 3322 | 3811 1 3323 | 3812 0 3324 | 3813 0 3325 | 3814 0 3326 | 3815 1 3327 | 3816 1 3328 | 3817 0 3329 | 3818 1 3330 | 3819 0 3331 | 3820 0 3332 | 3821 0 3333 | 3822 1 3334 | 3823 0 3335 | 3824 1 3336 | 3825 0 3337 | 3826 0 3338 | 3827 0 3339 | 3829 0 3340 | 3830 1 3341 | 3834 1 3342 | 3835 0 3343 | 3836 0 3344 | 3838 0 3345 | 3839 0 3346 | 3840 0 3347 | 3841 0 3348 | 3842 1 3349 | 3843 1 3350 | 3844 1 3351 | 3845 0 3352 | 3846 0 3353 | 3847 0 3354 | 3848 0 3355 | 3850 0 3356 | 3851 0 3357 | 3852 0 3358 | 3853 1 3359 | 3854 0 3360 | 3857 0 3361 | 3858 0 3362 | 3859 1 3363 | 3860 0 3364 | 3861 0 3365 | 3862 1 3366 | 3863 0 3367 | 3864 0 3368 | 3865 0 3369 | 3866 0 3370 | 3867 0 3371 | 3868 1 3372 | 3869 0 3373 | 3870 0 3374 | 3872 1 3375 | 3873 0 3376 | 3874 0 3377 | 3875 1 3378 | 3876 0 3379 | 3877 0 3380 | 3878 0 3381 | 3879 0 3382 | 3881 0 3383 | 3882 0 3384 | 3883 0 3385 | 3884 0 3386 | 3885 1 3387 | 3886 0 3388 | 3887 0 3389 | 3888 0 3390 | 3889 0 3391 | 3890 1 3392 | 3891 0 3393 | 3892 1 3394 | 3893 0 3395 | 3894 0 3396 | 3895 1 3397 | 3896 1 3398 | 3897 0 3399 | 3898 1 3400 | 3902 1 3401 | 3903 1 3402 | 3904 1 3403 | 3906 0 3404 | 3907 1 3405 | 3908 0 3406 | 3909 1 3407 | 3910 0 3408 | 3911 0 3409 | 3912 1 3410 | 3913 0 3411 | 3914 0 3412 | 3915 0 3413 | 3916 1 3414 | 3917 0 3415 | 3918 1 3416 | 3920 0 3417 | 3921 0 3418 | 3922 1 3419 | 3923 0 3420 | 3924 0 3421 | 3926 0 3422 | 3927 1 3423 | 3928 0 3424 | 3929 0 3425 | 3930 0 3426 | 3931 0 3427 | 3932 0 3428 | 3933 0 3429 | 3934 1 3430 | 3936 0 3431 | 3939 0 3432 | 3941 1 3433 | 3942 0 3434 | 3944 1 3435 | 3945 1 3436 | 3947 1 3437 | 3948 1 3438 | 3949 1 3439 | 3950 0 3440 | 3951 1 3441 | 3952 1 3442 | 3953 0 3443 | 3954 0 3444 | 3955 0 3445 | 3956 0 3446 | 3957 1 3447 | 3958 0 3448 | 3959 0 3449 | 3960 1 3450 | 3961 1 3451 | 3962 0 3452 | 3964 1 3453 | 3965 0 3454 | 3966 0 3455 | 3967 0 3456 | 3968 1 3457 | 3971 1 3458 | 3972 1 3459 | 3974 1 3460 | 3975 1 3461 | 3976 0 3462 | 3977 0 3463 | 3978 1 3464 | 3981 1 3465 | 3982 1 3466 | 3983 0 3467 | 3984 0 3468 | 3986 0 3469 | 3987 1 3470 | 3988 1 3471 | 3989 1 3472 | 3990 0 3473 | 3991 0 3474 | 3992 1 3475 | 3993 1 3476 | 3994 1 3477 | 3995 0 3478 | 3996 0 3479 | 3997 0 3480 | 3998 0 3481 | 3999 0 3482 | 4000 0 3483 | 4001 1 3484 | 4002 1 3485 | 4003 0 3486 | 4004 0 3487 | 4005 0 3488 | 4006 0 3489 | 4007 0 3490 | 4008 0 3491 | 4009 0 3492 | 4011 0 3493 | 4012 0 3494 | 4013 0 3495 | 4014 0 3496 | 4015 0 3497 | 4016 1 3498 | 4017 1 3499 | 4018 1 3500 | 4019 1 3501 | 4020 0 3502 | 4021 1 3503 | 4023 0 3504 | 4024 1 3505 | 4025 1 3506 | 4026 1 3507 | 4027 1 3508 | 4028 0 3509 | 4029 1 3510 | 4030 1 3511 | 4032 0 3512 | 4033 0 3513 | 4034 1 3514 | 4035 0 3515 | 4036 0 3516 | 4037 0 3517 | 4038 1 3518 | 4039 0 3519 | 4040 0 3520 | 4041 0 3521 | 4042 0 3522 | 4043 0 3523 | 4044 1 3524 | 4045 0 3525 | 4046 1 3526 | 4047 1 3527 | 4048 1 3528 | 4049 1 3529 | 4050 0 3530 | 4051 1 3531 | 4052 0 3532 | 4054 1 3533 | 4055 0 3534 | 4056 0 3535 | 4057 0 3536 | 4058 0 3537 | 4059 1 3538 | 4061 0 3539 | 4062 0 3540 | 4063 0 3541 | 4065 0 3542 | 4066 0 3543 | 4067 1 3544 | 4068 0 3545 | 4070 0 3546 | 4071 0 3547 | 4072 1 3548 | 4073 0 3549 | 4074 1 3550 | 4075 1 3551 | 4076 0 3552 | 4077 1 3553 | 4078 1 3554 | 4079 1 3555 | 4081 0 3556 | 4082 0 3557 | 4083 0 3558 | 4084 0 3559 | 4085 0 3560 | 4087 1 3561 | 4088 1 3562 | 4089 0 3563 | 4090 1 3564 | 4091 1 3565 | 4092 1 3566 | 4094 0 3567 | 4095 0 3568 | 4097 0 3569 | 4098 1 3570 | 4099 0 3571 | 4100 0 3572 | 4101 0 3573 | 4102 0 3574 | 4104 1 3575 | 4105 1 3576 | 4106 1 3577 | 4107 0 3578 | 4108 1 3579 | 4109 1 3580 | 4110 1 3581 | 4111 1 3582 | 4112 0 3583 | 4114 0 3584 | 4115 0 3585 | 4116 0 3586 | 4119 1 3587 | 4120 1 3588 | 4121 0 3589 | 4122 0 3590 | 4124 0 3591 | 4125 0 3592 | 4126 0 3593 | 4127 0 3594 | 4128 0 3595 | 4129 1 3596 | 4130 0 3597 | 4131 0 3598 | 4132 0 3599 | 4134 0 3600 | 4135 0 3601 | 4136 0 3602 | 4137 0 3603 | 4138 0 3604 | 4139 1 3605 | 4140 0 3606 | 4141 1 3607 | 4142 1 3608 | 4143 0 3609 | 4144 1 3610 | 4145 1 3611 | 4146 0 3612 | 4148 0 3613 | 4149 1 3614 | 4150 1 3615 | 4151 0 3616 | 4152 0 3617 | 4153 1 3618 | 4154 1 3619 | 4156 0 3620 | 4157 0 3621 | 4158 0 3622 | 4160 0 3623 | 4161 1 3624 | 4162 0 3625 | 4163 0 3626 | 4164 0 3627 | 4166 1 3628 | 4168 0 3629 | 4169 1 3630 | 4170 0 3631 | 4171 1 3632 | 4173 0 3633 | 4174 1 3634 | 4175 0 3635 | 4176 1 3636 | 4177 0 3637 | 4178 0 3638 | 4179 0 3639 | 4180 1 3640 | 4185 0 3641 | 4186 0 3642 | 4187 0 3643 | 4188 0 3644 | 4189 0 3645 | 4191 1 3646 | 4192 1 3647 | 4193 1 3648 | 4194 0 3649 | 4196 1 3650 | 4197 0 3651 | 4198 0 3652 | 4199 0 3653 | 4200 0 3654 | 4201 1 3655 | 4203 0 3656 | 4205 1 3657 | 4206 0 3658 | 4207 0 3659 | 4208 0 3660 | 4209 0 3661 | 4210 1 3662 | 4211 0 3663 | 4212 0 3664 | 4213 0 3665 | 4214 1 3666 | 4215 0 3667 | 4216 1 3668 | 4217 0 3669 | 4218 0 3670 | 4219 1 3671 | 4221 1 3672 | 4222 0 3673 | 4223 0 3674 | 4224 1 3675 | 4226 1 3676 | 4227 1 3677 | 4228 0 3678 | 4229 1 3679 | 4230 1 3680 | 4231 1 3681 | 4232 1 3682 | 4233 0 3683 | 4234 1 3684 | 4235 1 3685 | 4236 0 3686 | 4238 1 3687 | 4239 1 3688 | 4240 0 3689 | 4241 0 3690 | 4243 1 3691 | 4244 1 3692 | 4245 0 3693 | 4246 0 3694 | 4248 0 3695 | 4249 0 3696 | 4250 1 3697 | 4251 1 3698 | 4252 0 3699 | 4256 0 3700 | 4257 0 3701 | 4258 1 3702 | 4260 0 3703 | 4261 0 3704 | 4263 0 3705 | 4266 1 3706 | 4267 1 3707 | 4268 1 3708 | 4269 1 3709 | 4270 0 3710 | 4271 0 3711 | 4272 1 3712 | 4273 1 3713 | 4275 1 3714 | 4276 0 3715 | 4277 0 3716 | 4278 1 3717 | 4279 1 3718 | 4280 0 3719 | 4281 1 3720 | 4283 1 3721 | 4285 0 3722 | 4287 1 3723 | 4288 1 3724 | 4289 1 3725 | 4290 0 3726 | 4292 1 3727 | 4293 1 3728 | 4294 1 3729 | 4295 0 3730 | 4296 1 3731 | 4298 1 3732 | 4299 0 3733 | 4301 0 3734 | 4302 0 3735 | 4303 1 3736 | 4304 0 3737 | 4305 0 3738 | 4306 1 3739 | 4307 1 3740 | 4308 1 3741 | 4309 0 3742 | 4310 0 3743 | 4311 0 3744 | 4312 0 3745 | 4313 0 3746 | 4314 1 3747 | 4315 0 3748 | 4316 0 3749 | 4317 1 3750 | 4318 1 3751 | 4319 0 3752 | 4320 1 3753 | 4321 1 3754 | 4324 1 3755 | 4325 0 3756 | 4326 1 3757 | 4327 1 3758 | 4328 1 3759 | 4329 1 3760 | 4331 1 3761 | 4332 0 3762 | 4333 0 3763 | 4334 1 3764 | 4335 1 3765 | 4336 1 3766 | 4337 1 3767 | 4338 0 3768 | 4339 0 3769 | 4340 1 3770 | 4341 0 3771 | 4342 0 3772 | 4343 0 3773 | 4344 0 3774 | 4345 0 3775 | 4347 0 3776 | 4348 1 3777 | 4350 0 3778 | 4351 1 3779 | 4352 0 3780 | 4353 0 3781 | 4354 0 3782 | 4355 1 3783 | 4356 1 3784 | 4357 1 3785 | 4358 0 3786 | 4359 0 3787 | 4360 1 3788 | 4361 1 3789 | 4362 0 3790 | 4363 1 3791 | 4367 0 3792 | 4368 0 3793 | 4370 0 3794 | 4371 1 3795 | 4372 0 3796 | 4373 0 3797 | 4374 0 3798 | 4375 0 3799 | 4376 0 3800 | 4377 1 3801 | 4378 1 3802 | 4379 0 3803 | 4381 1 3804 | 4383 0 3805 | 4384 0 3806 | 4385 0 3807 | 4386 0 3808 | 4387 0 3809 | 4388 1 3810 | 4389 1 3811 | 4390 0 3812 | 4393 1 3813 | 4394 0 3814 | 4395 0 3815 | 4396 0 3816 | 4397 0 3817 | 4398 0 3818 | 4399 1 3819 | 4400 1 3820 | 4402 0 3821 | 4403 1 3822 | 4404 0 3823 | 4405 1 3824 | 4406 1 3825 | 4408 0 3826 | 4409 1 3827 | 4411 1 3828 | 4413 1 3829 | 4414 0 3830 | 4415 1 3831 | 4416 0 3832 | 4417 0 3833 | 4418 0 3834 | 4419 0 3835 | 4420 0 3836 | 4421 0 3837 | 4422 1 3838 | 4423 0 3839 | 4424 0 3840 | 4426 0 3841 | 4427 1 3842 | 4428 0 3843 | 4429 1 3844 | 4430 0 3845 | 4431 0 3846 | 4432 1 3847 | 4433 1 3848 | 4434 0 3849 | 4435 0 3850 | 4436 0 3851 | 4437 1 3852 | 4438 1 3853 | 4439 0 3854 | 4441 1 3855 | 4442 1 3856 | 4443 1 3857 | 4444 1 3858 | 4445 0 3859 | 4446 1 3860 | 4448 0 3861 | 4449 1 3862 | 4450 0 3863 | 4451 0 3864 | 4452 0 3865 | 4453 0 3866 | 4454 0 3867 | 4455 0 3868 | 4456 0 3869 | 4457 0 3870 | 4458 1 3871 | 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3943 | 4549 1 3944 | 4550 1 3945 | 4551 0 3946 | 4552 1 3947 | 4553 1 3948 | 4554 1 3949 | 4555 1 3950 | 4556 1 3951 | 4557 0 3952 | 4559 0 3953 | 4560 0 3954 | 4562 0 3955 | 4563 0 3956 | 4564 1 3957 | 4565 1 3958 | 4566 1 3959 | 4567 0 3960 | 4568 1 3961 | 4570 1 3962 | 4571 0 3963 | 4572 0 3964 | 4574 0 3965 | 4575 0 3966 | 4577 0 3967 | 4578 1 3968 | 4579 0 3969 | 4581 1 3970 | 4582 1 3971 | 4583 1 3972 | 4584 0 3973 | 4585 0 3974 | 4587 1 3975 | 4590 1 3976 | 4592 1 3977 | 4593 0 3978 | 4594 0 3979 | 4596 0 3980 | 4597 1 3981 | 4599 1 3982 | 4600 1 3983 | 4601 1 3984 | 4602 1 3985 | 4603 0 3986 | 4604 1 3987 | 4605 0 3988 | 4607 1 3989 | 4608 0 3990 | 4609 0 3991 | 4610 1 3992 | 4611 1 3993 | 4612 1 3994 | 4613 1 3995 | 4614 1 3996 | 4615 1 3997 | 4616 0 3998 | 4617 0 3999 | 4618 0 4000 | 4620 1 4001 | 4621 1 4002 | 4622 1 4003 | 4623 0 4004 | 4624 1 4005 | 4625 0 4006 | 4626 1 4007 | 4627 1 4008 | 4629 0 4009 | 4630 0 4010 | 4631 1 4011 | 4632 0 4012 | 4633 1 4013 | 4634 1 4014 | 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4515 | 5218 0 4516 | 5219 1 4517 | 5220 0 4518 | 5221 0 4519 | 5222 0 4520 | 5223 0 4521 | 5224 0 4522 | 5225 1 4523 | 5226 0 4524 | 5227 1 4525 | 5228 0 4526 | 5229 0 4527 | 5230 1 4528 | 5231 0 4529 | 5232 0 4530 | 5233 1 4531 | 5234 0 4532 | 5235 1 4533 | 5236 0 4534 | 5237 0 4535 | 5238 0 4536 | 5239 1 4537 | 5240 0 4538 | 5241 0 4539 | 5242 0 4540 | 5243 1 4541 | 5244 1 4542 | 5246 1 4543 | 5247 1 4544 | 5248 1 4545 | 5249 0 4546 | 5250 1 4547 | 5251 1 4548 | 5252 1 4549 | 5253 0 4550 | 5254 0 4551 | 5255 0 4552 | 5256 1 4553 | 5257 1 4554 | 5258 1 4555 | 5259 1 4556 | 5260 0 4557 | 5261 1 4558 | 5262 0 4559 | 5263 1 4560 | 5264 0 4561 | 5265 1 4562 | 5266 0 4563 | 5267 1 4564 | 5268 1 4565 | 5269 1 4566 | 5270 1 4567 | 5272 1 4568 | 5273 1 4569 | 5274 1 4570 | 5279 0 4571 | 5280 1 4572 | 5281 1 4573 | 5282 1 4574 | 5283 1 4575 | 5284 0 4576 | 5285 1 4577 | 5286 0 4578 | 5287 1 4579 | 5288 0 4580 | 5289 1 4581 | 5291 0 4582 | 5292 1 4583 | 5293 1 4584 | 5294 1 4585 | 5296 1 4586 | 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4658 | 5378 1 4659 | 5379 1 4660 | 5380 0 4661 | 5381 1 4662 | 5382 1 4663 | 5383 1 4664 | 5384 0 4665 | 5385 0 4666 | 5386 1 4667 | 5387 0 4668 | 5388 1 4669 | 5390 0 4670 | 5391 0 4671 | 5392 0 4672 | 5393 0 4673 | 5395 1 4674 | 5396 1 4675 | 5397 0 4676 | 5398 1 4677 | 5399 0 4678 | 5400 0 4679 | 5402 0 4680 | 5403 1 4681 | 5406 0 4682 | 5407 1 4683 | 5408 1 4684 | 5409 1 4685 | 5410 0 4686 | 5412 1 4687 | 5414 1 4688 | 5415 0 4689 | 5416 0 4690 | 5417 0 4691 | 5418 0 4692 | 5419 0 4693 | 5420 1 4694 | 5421 0 4695 | 5422 1 4696 | 5423 0 4697 | 5424 1 4698 | 5425 1 4699 | 5427 0 4700 | 5428 0 4701 | 5429 0 4702 | 5430 1 4703 | 5431 1 4704 | 5433 1 4705 | 5434 1 4706 | 5435 1 4707 | 5436 0 4708 | 5437 1 4709 | 5438 0 4710 | 5439 0 4711 | 5441 0 4712 | 5442 1 4713 | 5443 0 4714 | 5444 0 4715 | 5445 0 4716 | 5446 1 4717 | 5447 1 4718 | 5448 1 4719 | 5449 1 4720 | 5450 1 4721 | 5451 0 4722 | 5452 0 4723 | 5453 1 4724 | 5455 1 4725 | 5456 0 4726 | 5457 0 4727 | 5458 0 4728 | 5461 1 4729 | 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4801 | 5543 1 4802 | 5544 1 4803 | 5545 0 4804 | 5548 0 4805 | 5549 1 4806 | 5550 0 4807 | 5553 0 4808 | 5554 1 4809 | 5555 0 4810 | 5556 0 4811 | 5559 1 4812 | 5560 1 4813 | 5561 1 4814 | 5562 1 4815 | 5563 0 4816 | 5564 0 4817 | 5565 0 4818 | 5566 1 4819 | 5567 1 4820 | 5568 1 4821 | 5569 0 4822 | 5570 0 4823 | 5571 0 4824 | 5572 1 4825 | 5573 1 4826 | 5574 0 4827 | 5575 0 4828 | 5577 0 4829 | 5578 0 4830 | 5579 1 4831 | 5580 0 4832 | 5581 0 4833 | 5582 0 4834 | 5583 1 4835 | 5584 0 4836 | 5588 1 4837 | 5590 0 4838 | 5591 1 4839 | 5592 0 4840 | 5593 0 4841 | 5594 1 4842 | 5595 0 4843 | 5596 1 4844 | 5597 0 4845 | 5598 1 4846 | 5599 1 4847 | 5600 1 4848 | 5601 0 4849 | 5602 1 4850 | 5603 1 4851 | 5604 1 4852 | 5605 0 4853 | 5606 1 4854 | 5607 1 4855 | 5608 0 4856 | 5609 1 4857 | 5610 0 4858 | 5611 0 4859 | 5612 1 4860 | 5614 1 4861 | 5615 1 4862 | 5616 0 4863 | 5617 1 4864 | 5618 1 4865 | 5619 1 4866 | 5620 0 4867 | 5621 0 4868 | 5622 0 4869 | 5623 0 4870 | 5624 1 4871 | 5625 0 4872 | 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4944 | 5714 1 4945 | 5715 1 4946 | 5716 1 4947 | 5717 1 4948 | 5718 1 4949 | 5719 0 4950 | 5720 1 4951 | 5721 1 4952 | 5722 0 4953 | 5723 0 4954 | 5724 1 4955 | 5725 0 4956 | 5726 1 4957 | 5727 0 4958 | 5728 0 4959 | 5729 0 4960 | 5730 0 4961 | 5731 1 4962 | 5732 0 4963 | 5733 0 4964 | 5734 1 4965 | 5735 1 4966 | 5736 1 4967 | 5737 0 4968 | 5738 1 4969 | 5739 0 4970 | 5740 1 4971 | 5741 0 4972 | 5742 0 4973 | 5743 0 4974 | 5744 1 4975 | 5745 1 4976 | 5746 0 4977 | 5747 1 4978 | 5748 0 4979 | 5749 0 4980 | 5751 1 4981 | 5752 0 4982 | 5754 1 4983 | 5755 0 4984 | 5756 1 4985 | 5757 1 4986 | 5758 1 4987 | 5759 0 4988 | 5760 1 4989 | 5761 0 4990 | 5762 1 4991 | 5763 1 4992 | 5764 1 4993 | 5765 0 4994 | 5766 1 4995 | 5767 0 4996 | 5768 1 4997 | 5769 0 4998 | 5770 0 4999 | 5771 1 5000 | 5772 0 5001 | 5773 1 5002 | 5774 0 5003 | 5775 1 5004 | 5776 1 5005 | 5777 1 5006 | 5778 0 5007 | 5779 0 5008 | 5780 0 5009 | 5781 0 5010 | 5782 0 5011 | 5783 1 5012 | 5784 1 5013 | 5785 0 5014 | 5786 0 5015 | 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5087 | 5873 0 5088 | 5874 0 5089 | 5875 0 5090 | 5876 1 5091 | 5878 1 5092 | 5879 0 5093 | 5880 1 5094 | 5881 1 5095 | 5882 1 5096 | 5883 0 5097 | 5884 0 5098 | 5885 0 5099 | 5888 0 5100 | 5889 0 5101 | 5890 0 5102 | 5891 1 5103 | 5892 0 5104 | 5893 0 5105 | 5894 1 5106 | 5895 0 5107 | 5896 1 5108 | 5898 0 5109 | 5899 1 5110 | 5900 1 5111 | 5901 0 5112 | 5902 1 5113 | 5903 1 5114 | 5904 0 5115 | 5905 0 5116 | 5906 1 5117 | 5907 0 5118 | 5908 0 5119 | 5909 1 5120 | 5910 0 5121 | 5911 0 5122 | 5912 0 5123 | 5914 1 5124 | 5915 0 5125 | 5916 0 5126 | 5917 1 5127 | 5918 0 5128 | 5919 1 5129 | 5921 1 5130 | 5922 0 5131 | 5924 0 5132 | 5926 0 5133 | 5927 1 5134 | 5928 1 5135 | 5929 0 5136 | 5931 0 5137 | 5932 1 5138 | 5933 0 5139 | 5934 0 5140 | 5935 1 5141 | 5936 1 5142 | 5938 0 5143 | 5939 0 5144 | 5940 1 5145 | 5941 1 5146 | 5942 0 5147 | 5943 1 5148 | 5944 1 5149 | 5945 0 5150 | 5946 1 5151 | 5947 0 5152 | 5949 1 5153 | 5950 1 5154 | 5951 0 5155 | 5952 0 5156 | 5953 1 5157 | 5954 0 5158 | 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5230 | 6036 1 5231 | 6038 0 5232 | 6039 0 5233 | 6040 1 5234 | 6041 0 5235 | 6042 1 5236 | 6043 1 5237 | 6044 0 5238 | 6046 0 5239 | 6047 0 5240 | 6048 1 5241 | 6049 1 5242 | 6050 0 5243 | 6051 0 5244 | 6052 1 5245 | 6053 1 5246 | 6054 0 5247 | 6055 0 5248 | 6056 0 5249 | 6057 1 5250 | 6058 1 5251 | 6059 1 5252 | 6060 1 5253 | 6061 0 5254 | 6062 0 5255 | 6063 0 5256 | 6064 1 5257 | 6065 1 5258 | 6067 0 5259 | 6068 0 5260 | 6070 0 5261 | 6071 0 5262 | 6072 0 5263 | 6073 0 5264 | 6074 1 5265 | 6075 0 5266 | 6076 1 5267 | 6077 1 5268 | 6078 1 5269 | 6079 1 5270 | 6080 1 5271 | 6082 0 5272 | 6083 1 5273 | 6084 1 5274 | 6085 1 5275 | 6088 1 5276 | 6089 1 5277 | 6090 1 5278 | 6091 0 5279 | 6092 1 5280 | 6094 0 5281 | 6095 0 5282 | 6097 1 5283 | 6098 0 5284 | 6099 0 5285 | 6100 1 5286 | 6101 0 5287 | 6102 0 5288 | 6103 1 5289 | 6104 1 5290 | 6105 0 5291 | 6107 1 5292 | 6108 0 5293 | 6109 1 5294 | 6110 1 5295 | 6111 0 5296 | 6113 0 5297 | 6114 0 5298 | 6115 0 5299 | 6116 1 5300 | 6117 0 5301 | 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5516 | 6373 0 5517 | 6375 0 5518 | 6376 0 5519 | 6377 1 5520 | 6378 0 5521 | 6379 1 5522 | 6380 0 5523 | 6381 1 5524 | 6382 1 5525 | 6383 0 5526 | 6385 1 5527 | 6386 1 5528 | 6387 1 5529 | 6389 1 5530 | 6390 0 5531 | 6391 0 5532 | 6392 1 5533 | 6393 1 5534 | 6394 0 5535 | 6395 1 5536 | 6396 1 5537 | 6397 0 5538 | 6398 1 5539 | 6399 1 5540 | 6400 0 5541 | 6401 0 5542 | 6402 0 5543 | 6403 1 5544 | 6404 0 5545 | 6405 1 5546 | 6406 1 5547 | 6407 0 5548 | 6408 0 5549 | 6409 1 5550 | 6410 1 5551 | 6411 1 5552 | 6412 0 5553 | 6413 0 5554 | 6414 1 5555 | 6415 1 5556 | 6416 1 5557 | 6418 0 5558 | 6420 1 5559 | 6421 0 5560 | 6422 0 5561 | 6423 0 5562 | 6424 1 5563 | 6425 0 5564 | 6426 0 5565 | 6427 1 5566 | 6429 0 5567 | 6430 0 5568 | 6431 0 5569 | 6432 1 5570 | 6433 0 5571 | 6434 0 5572 | 6435 1 5573 | 6436 0 5574 | 6437 0 5575 | 6438 0 5576 | 6439 0 5577 | 6440 0 5578 | 6441 1 5579 | 6442 1 5580 | 6443 1 5581 | 6444 1 5582 | 6445 1 5583 | 6446 0 5584 | 6447 0 5585 | 6449 1 5586 | 6450 0 5587 | 6451 1 5588 | 6452 0 5589 | 6455 0 5590 | 6456 0 5591 | 6457 0 5592 | 6458 0 5593 | 6459 0 5594 | 6460 1 5595 | 6462 0 5596 | 6463 0 5597 | 6464 1 5598 | 6465 1 5599 | 6466 1 5600 | 6467 0 5601 | 6468 0 5602 | 6469 1 5603 | 6471 1 5604 | 6472 0 5605 | 6473 0 5606 | 6474 0 5607 | 6475 0 5608 | 6476 1 5609 | 6478 0 5610 | 6479 0 5611 | 6481 0 5612 | 6482 0 5613 | 6483 1 5614 | 6484 1 5615 | 6485 0 5616 | 6486 0 5617 | 6487 0 5618 | 6488 1 5619 | 6489 0 5620 | 6490 1 5621 | 6491 0 5622 | 6492 0 5623 | 6493 1 5624 | 6494 0 5625 | 6495 0 5626 | 6496 1 5627 | 6497 0 5628 | 6498 0 5629 | 6500 0 5630 | 6501 1 5631 | 6504 0 5632 | 6505 0 5633 | 6507 1 5634 | 6508 0 5635 | 6509 0 5636 | 6510 0 5637 | 6511 0 5638 | 6512 1 5639 | 6513 1 5640 | 6514 0 5641 | 6515 0 5642 | 6516 1 5643 | 6517 1 5644 | 6518 0 5645 | 6519 0 5646 | 6520 1 5647 | 6521 0 5648 | 6522 1 5649 | 6523 0 5650 | 6524 1 5651 | 6525 1 5652 | 6526 0 5653 | 6527 1 5654 | 6528 0 5655 | 6529 0 5656 | 6530 0 5657 | 6531 1 5658 | 6533 1 5659 | 6534 1 5660 | 6535 1 5661 | 6536 1 5662 | 6537 0 5663 | 6538 0 5664 | 6539 0 5665 | 6540 0 5666 | 6541 1 5667 | 6543 0 5668 | 6544 1 5669 | 6545 0 5670 | 6546 1 5671 | 6547 1 5672 | 6548 1 5673 | 6549 1 5674 | 6550 0 5675 | 6551 0 5676 | 6552 0 5677 | 6553 0 5678 | 6554 1 5679 | 6555 0 5680 | 6557 0 5681 | 6558 0 5682 | 6560 1 5683 | 6562 1 5684 | 6563 0 5685 | 6565 1 5686 | 6566 1 5687 | 6567 1 5688 | 6568 1 5689 | 6570 0 5690 | 6571 1 5691 | 6572 0 5692 | 6573 1 5693 | 6574 0 5694 | 6575 1 5695 | 6576 0 5696 | 6577 0 5697 | 6578 0 5698 | 6579 0 5699 | 6580 1 5700 | 6581 1 5701 | 6582 0 5702 | 6583 1 5703 | 6584 1 5704 | 6585 0 5705 | 6586 1 5706 | 6587 1 5707 | 6588 0 5708 | 6589 1 5709 | 6590 0 5710 | 6591 1 5711 | 6592 0 5712 | 6593 0 5713 | 6594 1 5714 | 6595 1 5715 | 6596 1 5716 | 6597 1 5717 | 6598 1 5718 | 6599 1 5719 | 6600 0 5720 | 6601 0 5721 | 6603 0 5722 | 6604 1 5723 | 6605 0 5724 | 6606 1 5725 | 6607 0 5726 | 6608 1 5727 | 6609 0 5728 | 6610 1 5729 | 6611 0 5730 | 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5802 | 6697 1 5803 | 6698 0 5804 | 6699 0 5805 | 6700 1 5806 | 6701 0 5807 | 6702 1 5808 | 6703 0 5809 | 6704 1 5810 | 6705 0 5811 | 6706 1 5812 | 6707 0 5813 | 6708 1 5814 | 6709 0 5815 | 6711 0 5816 | 6712 0 5817 | 6713 0 5818 | 6714 0 5819 | 6715 1 5820 | 6716 0 5821 | 6717 1 5822 | 6718 1 5823 | 6719 0 5824 | 6720 0 5825 | 6721 1 5826 | 6722 0 5827 | 6723 0 5828 | 6724 0 5829 | 6725 1 5830 | 6726 0 5831 | 6727 0 5832 | 6728 0 5833 | 6729 1 5834 | 6730 0 5835 | 6731 1 5836 | 6733 0 5837 | 6734 1 5838 | 6735 0 5839 | 6736 0 5840 | 6737 0 5841 | 6738 1 5842 | 6739 0 5843 | 6740 0 5844 | 6741 0 5845 | 6742 1 5846 | 6743 1 5847 | 6744 0 5848 | 6745 0 5849 | 6746 1 5850 | 6747 0 5851 | 6748 1 5852 | 6750 1 5853 | 6751 0 5854 | 6752 1 5855 | 6753 1 5856 | 6755 1 5857 | 6756 1 5858 | 6757 0 5859 | 6758 0 5860 | 6759 0 5861 | 6760 1 5862 | 6761 0 5863 | 6762 0 5864 | 6765 0 5865 | 6766 0 5866 | 6767 0 5867 | 6769 0 5868 | 6770 0 5869 | 6772 1 5870 | 6773 1 5871 | 6774 1 5872 | 6776 0 5873 | 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5945 | 6864 1 5946 | 6865 0 5947 | 6866 0 5948 | 6867 1 5949 | 6869 1 5950 | 6871 0 5951 | 6872 1 5952 | 6873 0 5953 | 6874 1 5954 | 6876 1 5955 | 6878 0 5956 | 6879 0 5957 | 6880 1 5958 | 6881 0 5959 | 6882 1 5960 | 6883 0 5961 | 6884 1 5962 | 6885 1 5963 | 6886 0 5964 | 6887 0 5965 | 6888 0 5966 | 6889 0 5967 | 6890 0 5968 | 6891 0 5969 | 6893 1 5970 | 6894 0 5971 | 6895 0 5972 | 6896 0 5973 | 6898 1 5974 | 6899 1 5975 | 6900 0 5976 | 6901 0 5977 | 6902 1 5978 | 6903 1 5979 | 6904 0 5980 | 6905 1 5981 | 6906 0 5982 | 6907 1 5983 | 6908 1 5984 | 6909 0 5985 | 6910 1 5986 | 6911 0 5987 | 6912 1 5988 | 6913 1 5989 | 6914 1 5990 | 6915 0 5991 | 6916 1 5992 | 6917 0 5993 | 6918 0 5994 | 6920 0 5995 | 6921 1 5996 | 6923 0 5997 | 6924 1 5998 | 6925 0 5999 | 6926 1 6000 | 6927 1 6001 | 6928 1 6002 | 6929 0 6003 | 6930 0 6004 | 6932 0 6005 | 6933 1 6006 | 6934 1 6007 | 6935 0 6008 | 6936 1 6009 | 6938 1 6010 | 6939 1 6011 | 6940 0 6012 | 6941 1 6013 | 6942 0 6014 | 6943 1 6015 | 6944 0 6016 | 6945 1 6017 | 6946 0 6018 | 6947 0 6019 | 6949 0 6020 | 6951 0 6021 | 6952 1 6022 | 6953 1 6023 | 6954 1 6024 | 6955 1 6025 | 6956 0 6026 | 6957 0 6027 | 6959 0 6028 | 6960 1 6029 | 6961 1 6030 | 6962 1 6031 | 6965 1 6032 | 6966 0 6033 | 6969 1 6034 | 6970 0 6035 | 6971 0 6036 | 6972 1 6037 | 6973 1 6038 | 6974 0 6039 | 6975 1 6040 | 6976 1 6041 | 6977 1 6042 | 6978 1 6043 | 6979 0 6044 | 6980 1 6045 | 6981 1 6046 | 6982 0 6047 | 6983 0 6048 | 6984 0 6049 | 6985 0 6050 | 6986 0 6051 | 6987 0 6052 | 6988 0 6053 | 6989 0 6054 | 6990 0 6055 | 6991 1 6056 | 6993 1 6057 | 6995 1 6058 | 6996 1 6059 | 6997 0 6060 | 6998 0 6061 | 6999 1 6062 | 7000 0 6063 | 7001 1 6064 | 7002 1 6065 | 7003 1 6066 | 7004 0 6067 | 7005 0 6068 | 7006 1 6069 | 7007 0 6070 | 7008 1 6071 | 7009 0 6072 | 7010 0 6073 | 7011 0 6074 | 7012 1 6075 | 7013 0 6076 | 7014 0 6077 | 7015 0 6078 | 7016 0 6079 | 7017 1 6080 | 7019 1 6081 | 7020 0 6082 | 7022 1 6083 | 7023 0 6084 | 7024 0 6085 | 7025 1 6086 | 7026 1 6087 | 7027 0 6088 | 7028 1 6089 | 7029 1 6090 | 7030 0 6091 | 7031 1 6092 | 7032 0 6093 | 7033 0 6094 | 7034 0 6095 | 7035 1 6096 | 7036 1 6097 | 7037 0 6098 | 7039 0 6099 | 7040 0 6100 | 7041 0 6101 | 7042 1 6102 | 7043 1 6103 | 7045 1 6104 | 7046 1 6105 | 7048 0 6106 | 7049 0 6107 | 7050 1 6108 | 7051 1 6109 | 7052 1 6110 | 7053 0 6111 | 7054 1 6112 | 7055 0 6113 | 7056 1 6114 | 7057 0 6115 | 7058 1 6116 | 7059 1 6117 | 7060 1 6118 | 7061 0 6119 | 7062 0 6120 | 7063 0 6121 | 7064 1 6122 | 7065 1 6123 | 7066 0 6124 | 7067 1 6125 | 7068 1 6126 | 7069 0 6127 | 7070 0 6128 | 7071 0 6129 | 7072 0 6130 | 7073 1 6131 | 7074 1 6132 | 7075 1 6133 | 7076 0 6134 | 7077 1 6135 | 7078 0 6136 | 7079 1 6137 | 7081 1 6138 | 7082 1 6139 | 7083 0 6140 | 7084 0 6141 | 7085 1 6142 | 7086 1 6143 | 7087 1 6144 | 7088 1 6145 | 7089 1 6146 | 7090 1 6147 | 7091 0 6148 | 7092 1 6149 | 7094 1 6150 | 7096 1 6151 | 7098 1 6152 | 7099 0 6153 | 7100 0 6154 | 7102 1 6155 | 7103 0 6156 | 7104 0 6157 | 7105 0 6158 | 7106 0 6159 | 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6231 | 7188 0 6232 | 7189 1 6233 | 7191 1 6234 | 7192 1 6235 | 7193 0 6236 | 7195 1 6237 | 7196 0 6238 | 7197 0 6239 | 7198 1 6240 | 7199 0 6241 | 7200 0 6242 | 7201 0 6243 | 7202 1 6244 | 7203 0 6245 | 7205 0 6246 | 7206 0 6247 | 7210 1 6248 | 7211 1 6249 | 7212 1 6250 | 7213 1 6251 | 7214 1 6252 | 7215 0 6253 | 7216 1 6254 | 7217 1 6255 | 7218 1 6256 | 7219 1 6257 | 7220 0 6258 | 7221 1 6259 | 7222 0 6260 | 7225 1 6261 | 7226 0 6262 | 7228 1 6263 | 7229 0 6264 | 7230 0 6265 | 7231 0 6266 | 7232 1 6267 | 7233 1 6268 | 7234 1 6269 | 7235 0 6270 | 7236 1 6271 | 7237 0 6272 | 7238 1 6273 | 7239 0 6274 | 7240 0 6275 | 7241 0 6276 | 7242 1 6277 | 7243 1 6278 | 7244 0 6279 | 7245 0 6280 | 7246 0 6281 | 7247 0 6282 | 7248 1 6283 | 7249 0 6284 | 7250 1 6285 | 7251 0 6286 | 7252 1 6287 | 7253 0 6288 | 7254 1 6289 | 7255 0 6290 | 7257 1 6291 | 7258 0 6292 | 7259 0 6293 | 7260 0 6294 | 7261 1 6295 | 7263 0 6296 | 7264 0 6297 | 7265 0 6298 | 7266 0 6299 | 7267 0 6300 | 7269 0 6301 | 7270 0 6302 | 7271 1 6303 | 7272 1 6304 | 7274 1 6305 | 7275 1 6306 | 7276 0 6307 | 7277 1 6308 | 7278 0 6309 | 7279 0 6310 | 7280 0 6311 | 7281 0 6312 | 7282 0 6313 | 7283 0 6314 | 7284 0 6315 | 7285 1 6316 | 7288 0 6317 | 7289 1 6318 | 7290 1 6319 | 7291 1 6320 | 7292 1 6321 | 7293 0 6322 | 7294 1 6323 | 7295 0 6324 | 7296 0 6325 | 7297 0 6326 | 7299 1 6327 | 7300 1 6328 | 7302 1 6329 | 7303 1 6330 | 7304 0 6331 | 7305 1 6332 | 7306 1 6333 | 7307 0 6334 | 7308 1 6335 | 7309 1 6336 | 7310 1 6337 | 7311 1 6338 | 7313 1 6339 | 7314 1 6340 | 7315 1 6341 | 7316 0 6342 | 7317 0 6343 | 7318 1 6344 | 7319 1 6345 | 7321 0 6346 | 7322 0 6347 | 7324 0 6348 | 7325 1 6349 | 7327 0 6350 | 7329 0 6351 | 7330 0 6352 | 7333 1 6353 | 7334 1 6354 | 7335 0 6355 | 7336 1 6356 | 7337 0 6357 | 7340 0 6358 | 7341 0 6359 | 7342 0 6360 | 7343 0 6361 | 7344 0 6362 | 7345 1 6363 | 7346 1 6364 | 7347 0 6365 | 7348 1 6366 | 7349 1 6367 | 7350 0 6368 | 7351 1 6369 | 7352 1 6370 | 7353 1 6371 | 7354 1 6372 | 7355 1 6373 | 7356 0 6374 | 7357 1 6375 | 7359 0 6376 | 7360 0 6377 | 7361 1 6378 | 7362 0 6379 | 7363 0 6380 | 7364 0 6381 | 7366 1 6382 | 7367 0 6383 | 7368 1 6384 | 7369 0 6385 | 7371 0 6386 | 7372 1 6387 | 7373 1 6388 | 7374 1 6389 | 7375 1 6390 | 7376 0 6391 | 7377 0 6392 | 7378 1 6393 | 7379 0 6394 | 7380 0 6395 | 7382 1 6396 | 7384 0 6397 | 7385 0 6398 | 7386 0 6399 | 7387 1 6400 | 7388 1 6401 | 7389 0 6402 | 7390 1 6403 | 7391 0 6404 | 7394 0 6405 | 7395 0 6406 | 7396 1 6407 | 7398 0 6408 | 7399 0 6409 | 7400 1 6410 | 7401 1 6411 | 7402 0 6412 | 7403 0 6413 | 7404 1 6414 | 7405 0 6415 | 7406 1 6416 | 7407 0 6417 | 7408 0 6418 | 7410 1 6419 | 7411 1 6420 | 7412 0 6421 | 7413 0 6422 | 7414 0 6423 | 7415 1 6424 | 7416 0 6425 | 7417 0 6426 | 7418 0 6427 | 7419 0 6428 | 7420 0 6429 | 7422 0 6430 | 7424 0 6431 | 7425 1 6432 | 7426 1 6433 | 7427 1 6434 | 7428 0 6435 | 7429 1 6436 | 7430 0 6437 | 7432 0 6438 | 7433 1 6439 | 7434 0 6440 | 7435 0 6441 | 7437 0 6442 | 7438 0 6443 | 7439 1 6444 | 7440 1 6445 | 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6517 | 7522 1 6518 | 7523 0 6519 | 7524 1 6520 | 7525 1 6521 | 7526 0 6522 | 7527 1 6523 | 7529 0 6524 | 7530 1 6525 | 7531 0 6526 | 7533 1 6527 | 7536 1 6528 | 7538 1 6529 | 7539 0 6530 | 7542 1 6531 | 7543 1 6532 | 7544 0 6533 | 7545 0 6534 | 7546 0 6535 | 7547 1 6536 | 7549 1 6537 | 7550 0 6538 | 7551 0 6539 | 7552 0 6540 | 7554 0 6541 | 7555 1 6542 | 7556 0 6543 | 7557 1 6544 | 7558 0 6545 | 7559 1 6546 | 7560 1 6547 | 7562 1 6548 | 7563 0 6549 | 7564 1 6550 | 7565 0 6551 | 7567 1 6552 | 7568 1 6553 | 7570 1 6554 | 7571 1 6555 | 7572 1 6556 | 7574 1 6557 | 7575 0 6558 | 7576 1 6559 | 7577 0 6560 | 7579 1 6561 | 7580 1 6562 | 7581 1 6563 | 7583 1 6564 | 7584 0 6565 | 7585 1 6566 | 7586 0 6567 | 7588 1 6568 | 7589 0 6569 | 7590 0 6570 | 7591 1 6571 | 7593 0 6572 | 7595 0 6573 | 7596 0 6574 | 7598 0 6575 | 7600 0 6576 | 7601 1 6577 | 7602 1 6578 | 7603 0 6579 | 7604 1 6580 | 7605 0 6581 | 7606 0 6582 | 7607 0 6583 | 7608 1 6584 | 7609 0 6585 | 7610 0 6586 | 7611 0 6587 | 7612 0 6588 | 7613 0 6589 | 7615 0 6590 | 7616 1 6591 | 7618 0 6592 | 7619 0 6593 | 7620 0 6594 | 7621 1 6595 | 7622 1 6596 | 7625 1 6597 | 7626 0 6598 | 7627 1 6599 | 7628 0 6600 | 7629 1 6601 | 7630 1 6602 | 7631 1 6603 | 7632 1 6604 | 7635 0 6605 | 7637 1 6606 | 7639 1 6607 | 7640 0 6608 | 7641 0 6609 | 7642 1 6610 | 7643 0 6611 | 7645 1 6612 | 7646 0 6613 | 7647 0 6614 | 7648 1 6615 | 7650 1 6616 | 7651 1 6617 | 7652 1 6618 | 7653 1 6619 | 7654 0 6620 | 7655 1 6621 | 7656 0 6622 | 7658 1 6623 | 7659 1 6624 | 7660 1 6625 | 7661 0 6626 | 7662 1 6627 | 7663 0 6628 | 7664 1 6629 | 7665 0 6630 | 7666 1 6631 | 7667 1 6632 | 7668 0 6633 | 7669 0 6634 | 7670 1 6635 | 7671 0 6636 | 7672 0 6637 | 7673 1 6638 | 7674 0 6639 | 7675 0 6640 | 7676 0 6641 | 7677 0 6642 | 7678 0 6643 | 7679 0 6644 | 7680 0 6645 | 7681 0 6646 | 7682 0 6647 | 7683 0 6648 | 7684 0 6649 | 7685 0 6650 | 7686 0 6651 | 7687 1 6652 | 7688 0 6653 | 7690 1 6654 | 7691 1 6655 | 7692 1 6656 | 7693 0 6657 | 7694 0 6658 | 7695 0 6659 | 7696 1 6660 | 7698 0 6661 | 7699 1 6662 | 7701 0 6663 | 7704 1 6664 | 7705 0 6665 | 7706 1 6666 | 7707 0 6667 | 7708 0 6668 | 7709 1 6669 | 7710 0 6670 | 7711 0 6671 | 7712 0 6672 | 7714 0 6673 | 7716 1 6674 | 7717 0 6675 | 7718 1 6676 | 7719 1 6677 | 7721 0 6678 | 7722 0 6679 | 7723 1 6680 | 7724 1 6681 | 7725 1 6682 | 7726 0 6683 | 7727 0 6684 | 7728 0 6685 | 7730 1 6686 | 7731 1 6687 | 7732 0 6688 | 7733 0 6689 | 7734 1 6690 | 7735 1 6691 | 7736 1 6692 | 7737 1 6693 | 7738 0 6694 | 7740 1 6695 | 7741 0 6696 | 7742 0 6697 | 7743 1 6698 | 7744 0 6699 | 7745 1 6700 | 7747 1 6701 | 7748 1 6702 | 7749 1 6703 | 7750 0 6704 | 7751 0 6705 | 7752 1 6706 | 7753 0 6707 | 7754 0 6708 | 7755 0 6709 | 7757 0 6710 | 7758 1 6711 | 7759 1 6712 | 7760 0 6713 | 7761 1 6714 | 7762 1 6715 | 7763 0 6716 | 7765 0 6717 | 7766 0 6718 | 7767 1 6719 | 7768 0 6720 | 7769 0 6721 | 7770 1 6722 | 7771 1 6723 | 7772 0 6724 | 7773 0 6725 | 7774 1 6726 | 7775 0 6727 | 7776 0 6728 | 7778 1 6729 | 7779 0 6730 | 7780 0 6731 | 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6803 | 7861 1 6804 | 7862 1 6805 | 7863 1 6806 | 7864 1 6807 | 7865 1 6808 | 7866 1 6809 | 7867 0 6810 | 7868 0 6811 | 7869 1 6812 | 7870 1 6813 | 7871 1 6814 | 7872 1 6815 | 7873 1 6816 | 7874 1 6817 | 7875 0 6818 | 7876 1 6819 | 7877 0 6820 | 7879 1 6821 | 7880 0 6822 | 7881 1 6823 | 7883 0 6824 | 7885 0 6825 | 7886 0 6826 | 7887 0 6827 | 7888 1 6828 | 7889 1 6829 | 7892 0 6830 | 7893 1 6831 | 7894 0 6832 | 7895 0 6833 | 7896 0 6834 | 7897 1 6835 | 7898 0 6836 | 7899 0 6837 | 7900 1 6838 | 7901 1 6839 | 7902 1 6840 | 7903 0 6841 | 7904 0 6842 | 7906 1 6843 | 7907 1 6844 | 7908 1 6845 | 7909 0 6846 | 7910 0 6847 | 7911 1 6848 | 7912 0 6849 | 7914 1 6850 | 7915 0 6851 | 7916 1 6852 | 7917 0 6853 | 7918 1 6854 | 7919 1 6855 | 7921 1 6856 | 7922 1 6857 | 7924 1 6858 | 7925 0 6859 | 7926 1 6860 | 7928 0 6861 | 7929 1 6862 | 7930 0 6863 | 7931 0 6864 | 7933 0 6865 | 7934 0 6866 | 7935 0 6867 | 7936 1 6868 | 7937 1 6869 | 7938 0 6870 | 7940 0 6871 | 7941 0 6872 | 7942 0 6873 | 7943 1 6874 | 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6946 | 8024 1 6947 | 8025 1 6948 | 8026 1 6949 | 8027 1 6950 | 8028 0 6951 | 8029 0 6952 | 8030 0 6953 | 8031 0 6954 | 8032 0 6955 | 8033 0 6956 | 8034 0 6957 | 8035 1 6958 | 8036 0 6959 | 8037 1 6960 | 8038 0 6961 | 8040 0 6962 | 8045 0 6963 | 8046 0 6964 | 8047 0 6965 | 8048 1 6966 | 8049 1 6967 | 8050 1 6968 | 8051 0 6969 | 8052 1 6970 | 8054 0 6971 | 8055 0 6972 | 8056 0 6973 | 8057 1 6974 | 8058 0 6975 | 8060 0 6976 | 8061 1 6977 | 8062 0 6978 | 8063 1 6979 | 8064 1 6980 | 8067 1 6981 | 8068 1 6982 | 8069 1 6983 | 8070 1 6984 | 8071 0 6985 | 8072 0 6986 | 8073 1 6987 | 8074 1 6988 | 8075 0 6989 | 8076 1 6990 | 8078 1 6991 | 8079 0 6992 | 8080 1 6993 | 8081 0 6994 | 8082 0 6995 | 8083 1 6996 | 8084 1 6997 | 8085 0 6998 | 8086 0 6999 | 8087 1 7000 | 8088 1 7001 | 8089 0 7002 | 8090 1 7003 | 8091 0 7004 | 8092 0 7005 | 8093 0 7006 | 8094 0 7007 | 8095 1 7008 | 8096 1 7009 | 8098 0 7010 | 8099 1 7011 | 8101 0 7012 | 8102 1 7013 | 8103 0 7014 | 8104 1 7015 | 8105 0 7016 | 8107 0 7017 | 8108 0 7018 | 8109 1 7019 | 8110 0 7020 | 8111 1 7021 | 8112 1 7022 | 8113 1 7023 | 8114 1 7024 | 8115 0 7025 | 8116 0 7026 | 8119 0 7027 | 8120 0 7028 | 8121 0 7029 | 8122 1 7030 | 8123 0 7031 | 8124 0 7032 | 8125 0 7033 | 8126 0 7034 | 8127 0 7035 | 8128 1 7036 | 8129 1 7037 | 8130 0 7038 | 8131 0 7039 | 8132 1 7040 | 8133 1 7041 | 8134 1 7042 | 8135 1 7043 | 8137 1 7044 | 8138 1 7045 | 8139 0 7046 | 8140 1 7047 | 8141 1 7048 | 8143 1 7049 | 8144 0 7050 | 8145 1 7051 | 8146 1 7052 | 8148 0 7053 | 8149 0 7054 | 8150 0 7055 | 8151 1 7056 | 8152 1 7057 | 8153 1 7058 | 8154 0 7059 | 8155 1 7060 | 8157 0 7061 | 8158 1 7062 | 8159 0 7063 | 8160 0 7064 | 8161 0 7065 | 8162 1 7066 | 8164 1 7067 | 8165 0 7068 | 8166 1 7069 | 8167 1 7070 | 8168 0 7071 | 8171 0 7072 | 8172 1 7073 | 8173 1 7074 | 8174 0 7075 | 8175 1 7076 | 8176 0 7077 | 8178 0 7078 | 8182 1 7079 | 8183 0 7080 | 8184 1 7081 | 8185 0 7082 | 8186 0 7083 | 8187 1 7084 | 8189 0 7085 | 8190 0 7086 | 8191 1 7087 | 8192 1 7088 | 8193 1 7089 | 8194 1 7090 | 8195 1 7091 | 8196 1 7092 | 8197 1 7093 | 8198 1 7094 | 8199 1 7095 | 8200 0 7096 | 8201 0 7097 | 8202 0 7098 | 8203 1 7099 | 8204 0 7100 | 8205 0 7101 | 8206 0 7102 | 8208 1 7103 | 8209 1 7104 | 8210 1 7105 | 8211 1 7106 | 8212 1 7107 | 8214 0 7108 | 8216 1 7109 | 8218 0 7110 | 8219 1 7111 | 8221 0 7112 | 8222 1 7113 | 8223 0 7114 | 8224 1 7115 | 8225 0 7116 | 8226 0 7117 | 8228 0 7118 | 8229 0 7119 | 8230 0 7120 | 8231 1 7121 | 8232 0 7122 | 8233 1 7123 | 8234 0 7124 | 8235 0 7125 | 8236 1 7126 | 8237 1 7127 | 8238 1 7128 | 8240 0 7129 | 8241 1 7130 | 8242 0 7131 | 8244 0 7132 | 8246 0 7133 | 8247 0 7134 | 8248 0 7135 | 8249 0 7136 | 8250 1 7137 | 8251 0 7138 | 8252 0 7139 | 8253 0 7140 | 8255 0 7141 | 8256 0 7142 | 8257 1 7143 | 8258 1 7144 | 8259 1 7145 | 8260 1 7146 | 8261 0 7147 | 8262 1 7148 | 8263 0 7149 | 8265 0 7150 | 8266 0 7151 | 8267 1 7152 | 8268 0 7153 | 8269 1 7154 | 8270 1 7155 | 8271 1 7156 | 8272 0 7157 | 8274 1 7158 | 8276 1 7159 | 8277 0 7160 | 8278 0 7161 | 8279 0 7162 | 8280 0 7163 | 8281 1 7164 | 8283 0 7165 | 8284 0 7166 | 8285 0 7167 | 8286 1 7168 | 8287 1 7169 | 8289 0 7170 | 8290 1 7171 | 8291 0 7172 | 8292 0 7173 | 8293 0 7174 | 8294 1 7175 | 8295 1 7176 | 8296 0 7177 | 8298 1 7178 | 8299 0 7179 | 8302 0 7180 | 8303 1 7181 | 8304 1 7182 | 8305 1 7183 | 8306 1 7184 | 8307 0 7185 | 8308 1 7186 | 8309 1 7187 | 8310 1 7188 | 8311 1 7189 | 8312 0 7190 | 8313 0 7191 | 8314 1 7192 | 8315 1 7193 | 8317 1 7194 | 8319 1 7195 | 8320 0 7196 | 8322 0 7197 | 8323 1 7198 | 8324 1 7199 | 8325 0 7200 | 8326 0 7201 | 8328 1 7202 | 8329 0 7203 | 8330 0 7204 | 8331 0 7205 | 8333 0 7206 | 8334 0 7207 | 8335 1 7208 | 8336 0 7209 | 8337 1 7210 | 8338 1 7211 | 8339 0 7212 | 8340 1 7213 | 8341 0 7214 | 8342 0 7215 | 8344 0 7216 | 8345 1 7217 | 8346 0 7218 | 8347 1 7219 | 8348 1 7220 | 8349 1 7221 | 8351 0 7222 | 8352 1 7223 | 8353 0 7224 | 8354 1 7225 | 8355 1 7226 | 8356 0 7227 | 8359 0 7228 | 8360 1 7229 | 8361 1 7230 | 8362 1 7231 | 8363 1 7232 | 8364 0 7233 | 8366 1 7234 | 8367 0 7235 | 8368 1 7236 | 8369 1 7237 | 8370 1 7238 | 8371 0 7239 | 8372 1 7240 | 8373 0 7241 | 8374 0 7242 | 8375 0 7243 | 8376 1 7244 | 8378 0 7245 | 8379 0 7246 | 8381 0 7247 | 8383 0 7248 | 8384 1 7249 | 8385 1 7250 | 8386 1 7251 | 8387 1 7252 | 8388 0 7253 | 8389 1 7254 | 8390 0 7255 | 8391 0 7256 | 8393 1 7257 | 8394 0 7258 | 8395 1 7259 | 8396 1 7260 | 8397 0 7261 | 8398 0 7262 | 8399 0 7263 | 8400 1 7264 | 8401 0 7265 | 8403 1 7266 | 8404 0 7267 | 8406 1 7268 | 8407 1 7269 | 8408 0 7270 | 8409 1 7271 | 8410 1 7272 | 8411 1 7273 | 8413 0 7274 | 8414 0 7275 | 8415 0 7276 | 8417 0 7277 | 8419 0 7278 | 8420 0 7279 | 8421 0 7280 | 8422 1 7281 | 8423 1 7282 | 8424 1 7283 | 8425 1 7284 | 8428 0 7285 | 8429 1 7286 | 8430 0 7287 | 8432 0 7288 | 8433 0 7289 | 8434 0 7290 | 8435 1 7291 | 8436 0 7292 | 8437 0 7293 | 8438 0 7294 | 8439 0 7295 | 8440 0 7296 | 8441 1 7297 | 8442 0 7298 | 8443 0 7299 | 8444 0 7300 | 8445 1 7301 | 8446 0 7302 | 8449 0 7303 | 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7375 | 8537 0 7376 | 8538 0 7377 | 8539 1 7378 | 8540 0 7379 | 8541 1 7380 | 8542 1 7381 | 8543 1 7382 | 8545 1 7383 | 8546 1 7384 | 8547 1 7385 | 8548 0 7386 | 8550 1 7387 | 8551 1 7388 | 8553 0 7389 | 8554 1 7390 | 8555 1 7391 | 8557 1 7392 | 8558 0 7393 | 8559 1 7394 | 8560 1 7395 | 8561 0 7396 | 8563 0 7397 | 8564 1 7398 | 8565 0 7399 | 8567 0 7400 | 8568 0 7401 | 8569 1 7402 | 8570 0 7403 | 8572 1 7404 | 8573 0 7405 | 8574 1 7406 | 8575 0 7407 | 8576 1 7408 | 8577 1 7409 | 8578 0 7410 | 8579 1 7411 | 8580 1 7412 | 8581 1 7413 | 8582 0 7414 | 8583 0 7415 | 8584 1 7416 | 8585 1 7417 | 8586 1 7418 | 8587 0 7419 | 8588 0 7420 | 8589 1 7421 | 8590 0 7422 | 8592 0 7423 | 8595 1 7424 | 8596 1 7425 | 8597 1 7426 | 8599 1 7427 | 8600 0 7428 | 8601 0 7429 | 8602 1 7430 | 8603 1 7431 | 8604 1 7432 | 8605 1 7433 | 8606 1 7434 | 8608 0 7435 | 8610 1 7436 | 8611 1 7437 | 8612 1 7438 | 8613 1 7439 | 8615 0 7440 | 8616 1 7441 | 8617 1 7442 | 8618 1 7443 | 8619 0 7444 | 8622 1 7445 | 8623 1 7446 | 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7518 | 8711 0 7519 | 8712 1 7520 | 8714 0 7521 | 8715 1 7522 | 8716 0 7523 | 8717 1 7524 | 8718 0 7525 | 8719 0 7526 | 8720 0 7527 | 8721 1 7528 | 8722 0 7529 | 8723 0 7530 | 8724 1 7531 | 8725 1 7532 | 8726 1 7533 | 8727 1 7534 | 8728 0 7535 | 8729 1 7536 | 8730 1 7537 | 8731 1 7538 | 8732 0 7539 | 8734 1 7540 | 8736 1 7541 | 8737 1 7542 | 8738 0 7543 | 8739 1 7544 | 8741 0 7545 | 8743 1 7546 | 8744 0 7547 | 8745 1 7548 | 8746 0 7549 | 8747 0 7550 | 8748 1 7551 | 8749 0 7552 | 8750 0 7553 | 8751 1 7554 | 8752 1 7555 | 8753 0 7556 | 8755 0 7557 | 8756 1 7558 | 8757 1 7559 | 8758 1 7560 | 8759 1 7561 | 8760 0 7562 | 8761 1 7563 | 8762 0 7564 | 8763 0 7565 | 8764 1 7566 | 8765 1 7567 | 8767 1 7568 | 8768 1 7569 | 8769 0 7570 | 8770 1 7571 | 8772 1 7572 | 8773 0 7573 | 8774 0 7574 | 8775 1 7575 | 8776 1 7576 | 8778 1 7577 | 8779 1 7578 | 8780 0 7579 | 8781 0 7580 | 8782 1 7581 | 8784 0 7582 | 8785 0 7583 | 8786 0 7584 | 8787 0 7585 | 8788 1 7586 | 8789 1 7587 | 8791 1 7588 | 8792 0 7589 | 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7661 | 8873 0 7662 | 8874 1 7663 | 8875 1 7664 | 8876 0 7665 | 8877 0 7666 | 8878 0 7667 | 8879 0 7668 | 8880 1 7669 | 8881 0 7670 | 8882 1 7671 | 8883 0 7672 | 8884 0 7673 | 8885 1 7674 | 8886 0 7675 | 8887 0 7676 | 8888 0 7677 | 8889 0 7678 | 8890 1 7679 | 8891 1 7680 | 8892 0 7681 | 8893 1 7682 | 8894 0 7683 | 8895 1 7684 | 8896 0 7685 | 8897 0 7686 | 8898 0 7687 | 8899 0 7688 | 8900 0 7689 | 8902 0 7690 | 8903 1 7691 | 8904 1 7692 | 8905 0 7693 | 8906 1 7694 | 8907 1 7695 | 8908 0 7696 | 8909 1 7697 | 8911 0 7698 | 8913 0 7699 | 8914 0 7700 | 8915 1 7701 | 8916 1 7702 | 8917 0 7703 | 8918 1 7704 | 8919 1 7705 | 8920 1 7706 | 8921 1 7707 | 8922 1 7708 | 8923 1 7709 | 8924 0 7710 | 8925 1 7711 | 8926 1 7712 | 8928 1 7713 | 8930 0 7714 | 8931 1 7715 | 8932 0 7716 | 8933 0 7717 | 8934 1 7718 | 8935 1 7719 | 8936 1 7720 | 8937 1 7721 | 8938 0 7722 | 8940 1 7723 | 8942 1 7724 | 8943 1 7725 | 8944 0 7726 | 8945 0 7727 | 8946 1 7728 | 8947 1 7729 | 8948 1 7730 | 8949 0 7731 | 8950 0 7732 | 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7804 | 9041 1 7805 | 9042 1 7806 | 9043 1 7807 | 9044 0 7808 | 9045 0 7809 | 9046 1 7810 | 9047 0 7811 | 9048 1 7812 | 9049 1 7813 | 9050 0 7814 | 9051 1 7815 | 9052 1 7816 | 9053 0 7817 | 9054 0 7818 | 9055 0 7819 | 9056 0 7820 | 9057 1 7821 | 9058 1 7822 | 9060 0 7823 | 9061 1 7824 | 9063 1 7825 | 9064 1 7826 | 9065 1 7827 | 9066 0 7828 | 9067 1 7829 | 9068 0 7830 | 9069 0 7831 | 9070 0 7832 | 9071 1 7833 | 9072 0 7834 | 9073 1 7835 | 9074 1 7836 | 9075 0 7837 | 9076 0 7838 | 9077 0 7839 | 9078 0 7840 | 9079 1 7841 | 9080 1 7842 | 9081 1 7843 | 9082 0 7844 | 9083 1 7845 | 9085 1 7846 | 9086 1 7847 | 9087 0 7848 | 9088 1 7849 | 9089 1 7850 | 9090 0 7851 | 9091 0 7852 | 9092 0 7853 | 9093 1 7854 | 9094 0 7855 | 9095 0 7856 | 9096 0 7857 | 9097 1 7858 | 9098 1 7859 | 9099 0 7860 | 9100 0 7861 | 9101 1 7862 | 9102 0 7863 | 9103 0 7864 | 9104 1 7865 | 9105 0 7866 | 9106 1 7867 | 9107 0 7868 | 9108 1 7869 | 9109 1 7870 | 9110 1 7871 | 9111 0 7872 | 9113 1 7873 | 9114 1 7874 | 9115 1 7875 | 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7947 | 9196 1 7948 | 9197 0 7949 | 9198 0 7950 | 9199 0 7951 | 9200 0 7952 | 9203 0 7953 | 9204 1 7954 | 9205 0 7955 | 9206 1 7956 | 9207 0 7957 | 9209 0 7958 | 9210 1 7959 | 9211 1 7960 | 9213 0 7961 | 9214 1 7962 | 9215 0 7963 | 9216 0 7964 | 9218 0 7965 | 9219 1 7966 | 9221 0 7967 | 9222 0 7968 | 9223 1 7969 | 9225 0 7970 | 9226 1 7971 | 9227 0 7972 | 9228 0 7973 | 9231 0 7974 | 9232 0 7975 | 9233 0 7976 | 9236 0 7977 | 9237 1 7978 | 9238 0 7979 | 9239 0 7980 | 9240 1 7981 | 9241 0 7982 | 9242 1 7983 | 9243 1 7984 | 9244 0 7985 | 9245 0 7986 | 9247 0 7987 | 9248 1 7988 | 9249 1 7989 | 9250 1 7990 | 9251 1 7991 | 9252 1 7992 | 9253 1 7993 | 9254 1 7994 | 9255 0 7995 | 9256 1 7996 | 9257 1 7997 | 9260 1 7998 | 9261 0 7999 | 9262 1 8000 | 9263 0 8001 | 9264 1 8002 | 9265 0 8003 | 9266 0 8004 | 9268 1 8005 | 9270 0 8006 | 9271 1 8007 | 9272 0 8008 | 9273 0 8009 | 9274 1 8010 | 9275 0 8011 | 9276 0 8012 | 9277 1 8013 | 9279 1 8014 | 9281 0 8015 | 9282 0 8016 | 9283 1 8017 | 9284 0 8018 | 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8090 | 9365 1 8091 | 9366 1 8092 | 9367 0 8093 | 9368 1 8094 | 9370 1 8095 | 9371 0 8096 | 9372 0 8097 | 9374 0 8098 | 9375 0 8099 | 9376 1 8100 | 9377 1 8101 | 9378 1 8102 | 9379 1 8103 | 9380 0 8104 | 9381 1 8105 | 9382 1 8106 | 9383 1 8107 | 9384 0 8108 | 9386 0 8109 | 9387 0 8110 | 9388 0 8111 | 9389 1 8112 | 9390 0 8113 | 9391 0 8114 | 9392 1 8115 | 9393 1 8116 | 9394 1 8117 | 9395 1 8118 | 9396 1 8119 | 9397 1 8120 | 9398 0 8121 | 9399 0 8122 | 9400 0 8123 | 9402 0 8124 | 9404 0 8125 | 9405 0 8126 | 9406 1 8127 | 9407 0 8128 | 9409 1 8129 | 9410 0 8130 | 9411 1 8131 | 9412 0 8132 | 9413 0 8133 | 9414 0 8134 | 9415 0 8135 | 9416 0 8136 | 9417 0 8137 | 9418 1 8138 | 9419 1 8139 | 9420 1 8140 | 9421 1 8141 | 9422 0 8142 | 9423 1 8143 | 9424 0 8144 | 9426 1 8145 | 9428 1 8146 | 9429 1 8147 | 9431 0 8148 | 9432 1 8149 | 9433 1 8150 | 9434 1 8151 | 9435 0 8152 | 9437 0 8153 | 9438 0 8154 | 9439 0 8155 | 9441 0 8156 | 9443 1 8157 | 9444 0 8158 | 9445 0 8159 | 9446 0 8160 | 9447 1 8161 | 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8233 | 9532 0 8234 | 9533 0 8235 | 9534 1 8236 | 9535 0 8237 | 9536 0 8238 | 9538 1 8239 | 9539 1 8240 | 9540 0 8241 | 9541 1 8242 | 9542 0 8243 | 9543 1 8244 | 9544 0 8245 | 9545 0 8246 | 9546 0 8247 | 9547 0 8248 | 9548 0 8249 | 9549 1 8250 | 9551 1 8251 | 9552 1 8252 | 9553 1 8253 | 9555 1 8254 | 9556 1 8255 | 9557 0 8256 | 9558 1 8257 | 9559 0 8258 | 9560 1 8259 | 9561 1 8260 | 9562 0 8261 | 9563 0 8262 | 9564 0 8263 | 9565 1 8264 | 9566 0 8265 | 9567 0 8266 | 9568 0 8267 | 9569 0 8268 | 9570 1 8269 | 9572 0 8270 | 9573 0 8271 | 9574 1 8272 | 9575 0 8273 | 9576 0 8274 | 9577 0 8275 | 9578 1 8276 | 9579 1 8277 | 9581 0 8278 | 9582 0 8279 | 9583 1 8280 | 9585 1 8281 | 9586 0 8282 | 9587 1 8283 | 9588 1 8284 | 9591 0 8285 | 9592 0 8286 | 9594 0 8287 | 9595 1 8288 | 9597 1 8289 | 9598 0 8290 | 9599 0 8291 | 9600 0 8292 | 9602 1 8293 | 9603 0 8294 | 9604 0 8295 | 9605 0 8296 | 9606 1 8297 | 9607 1 8298 | 9608 1 8299 | 9609 1 8300 | 9610 1 8301 | 9611 1 8302 | 9612 0 8303 | 9613 0 8304 | 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8376 | 9699 0 8377 | 9700 1 8378 | 9701 1 8379 | 9702 1 8380 | 9703 0 8381 | 9706 1 8382 | 9707 0 8383 | 9708 0 8384 | 9709 1 8385 | 9710 0 8386 | 9711 1 8387 | 9713 0 8388 | 9715 0 8389 | 9716 1 8390 | 9718 1 8391 | 9719 1 8392 | 9720 1 8393 | 9721 0 8394 | 9722 0 8395 | 9723 1 8396 | 9724 1 8397 | 9725 0 8398 | 9726 1 8399 | 9728 0 8400 | 9730 1 8401 | 9731 0 8402 | 9732 1 8403 | 9733 1 8404 | 9734 0 8405 | 9735 0 8406 | 9736 1 8407 | 9737 0 8408 | 9738 0 8409 | 9739 1 8410 | 9740 1 8411 | 9743 0 8412 | 9744 1 8413 | 9745 1 8414 | 9746 1 8415 | 9747 0 8416 | 9748 1 8417 | 9749 1 8418 | 9750 1 8419 | 9752 1 8420 | 9753 0 8421 | 9754 0 8422 | 9755 0 8423 | 9756 0 8424 | 9757 1 8425 | 9758 1 8426 | 9759 0 8427 | 9760 1 8428 | 9761 0 8429 | 9762 1 8430 | 9763 0 8431 | 9764 1 8432 | 9765 0 8433 | 9766 0 8434 | 9767 1 8435 | 9769 1 8436 | 9770 1 8437 | 9771 0 8438 | 9772 0 8439 | 9773 0 8440 | 9775 1 8441 | 9776 1 8442 | 9777 1 8443 | 9778 1 8444 | 9780 1 8445 | 9782 0 8446 | 9784 1 8447 | 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8519 | 9872 1 8520 | 9873 1 8521 | 9874 0 8522 | 9875 1 8523 | 9876 0 8524 | 9877 0 8525 | 9878 0 8526 | 9879 0 8527 | 9880 0 8528 | 9881 0 8529 | 9882 1 8530 | 9883 0 8531 | 9884 1 8532 | 9885 0 8533 | 9886 0 8534 | 9888 1 8535 | 9889 1 8536 | 9890 1 8537 | 9891 1 8538 | 9892 1 8539 | 9893 0 8540 | 9894 0 8541 | 9896 0 8542 | 9901 0 8543 | 9902 0 8544 | 9905 0 8545 | 9906 0 8546 | 9907 1 8547 | 9908 0 8548 | 9909 0 8549 | 9910 0 8550 | 9911 0 8551 | 9912 0 8552 | 9913 0 8553 | 9914 1 8554 | 9915 1 8555 | 9916 0 8556 | 9917 0 8557 | 9918 0 8558 | 9919 0 8559 | 9921 1 8560 | 9922 1 8561 | 9923 1 8562 | 9924 0 8563 | 9925 0 8564 | 9927 0 8565 | 9930 0 8566 | 9931 0 8567 | 9932 0 8568 | 9933 1 8569 | 9934 1 8570 | 9935 0 8571 | 9936 1 8572 | 9937 0 8573 | 9938 1 8574 | 9939 0 8575 | 9941 0 8576 | 9942 0 8577 | 9943 1 8578 | 9944 1 8579 | 9945 1 8580 | 9947 0 8581 | 9948 1 8582 | 9949 0 8583 | 9950 0 8584 | 9951 1 8585 | 9952 0 8586 | 9953 1 8587 | 9954 0 8588 | 9956 0 8589 | 9957 1 8590 | 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8726 | 10118 1 8727 | 10120 1 8728 | 10121 0 8729 | 10122 0 8730 | 10123 0 8731 | 10124 0 8732 | 10126 0 8733 | 10128 1 8734 | 10130 0 8735 | 10131 0 8736 | 10132 1 8737 | 10133 0 8738 | 10134 0 8739 | 10135 1 8740 | 10136 0 8741 | 10137 0 8742 | 10138 0 8743 | 10141 1 8744 | 10142 0 8745 | 10143 1 8746 | 10144 1 8747 | 10145 0 8748 | 10146 0 8749 | 10147 0 8750 | 10148 0 8751 | 10150 0 8752 | 10151 0 8753 | 10152 0 8754 | 10153 1 8755 | 10155 0 8756 | 10156 1 8757 | 10157 1 8758 | 10158 1 8759 | 10160 0 8760 | 10161 0 8761 | 10162 0 8762 | 10163 0 8763 | 10164 1 8764 | 10165 0 8765 | 10166 0 8766 | 10168 0 8767 | 10169 1 8768 | 10170 0 8769 | 10171 0 8770 | 10173 1 8771 | 10174 1 8772 | 10175 1 8773 | 10176 0 8774 | 10178 1 8775 | 10179 0 8776 | 10180 1 8777 | 10181 0 8778 | 10182 1 8779 | 10185 1 8780 | 10186 1 8781 | 10187 1 8782 | 10189 0 8783 | 10190 1 8784 | 10191 0 8785 | 10192 1 8786 | 10194 0 8787 | 10195 0 8788 | 10196 1 8789 | 10197 1 8790 | 10198 0 8791 | 10199 0 8792 | 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8992 | 10432 1 8993 | 10433 1 8994 | 10434 1 8995 | 10435 0 8996 | 10438 0 8997 | 10439 1 8998 | 10441 1 8999 | 10442 0 9000 | 10443 0 9001 | 10444 1 9002 | 10445 0 9003 | 10446 0 9004 | 10447 1 9005 | 10448 1 9006 | 10449 0 9007 | 10450 0 9008 | 10451 0 9009 | 10452 0 9010 | 10453 0 9011 | 10454 1 9012 | 10455 1 9013 | 10456 0 9014 | 10457 0 9015 | 10458 0 9016 | 10459 0 9017 | 10460 0 9018 | 10461 1 9019 | 10462 1 9020 | 10463 0 9021 | 10464 1 9022 | 10465 1 9023 | 10466 0 9024 | 10468 1 9025 | 10469 0 9026 | 10470 0 9027 | 10471 0 9028 | 10472 1 9029 | 10473 0 9030 | 10474 1 9031 | 10475 0 9032 | 10476 0 9033 | 10477 1 9034 | 10478 1 9035 | 10479 0 9036 | 10480 1 9037 | 10481 0 9038 | 10482 0 9039 | 10483 1 9040 | 10484 0 9041 | 10485 1 9042 | 10486 1 9043 | 10487 1 9044 | 10489 1 9045 | 10490 1 9046 | 10491 0 9047 | 10492 0 9048 | 10493 1 9049 | 10494 0 9050 | 10495 0 9051 | 10496 1 9052 | 10497 1 9053 | 10499 0 9054 | 10501 0 9055 | 10503 1 9056 | 10506 1 9057 | 10507 1 9058 | 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9125 | 10588 1 9126 | 10589 1 9127 | 10592 0 9128 | 10593 1 9129 | 10595 0 9130 | 10596 0 9131 | 10597 0 9132 | 10598 1 9133 | 10599 1 9134 | 10600 0 9135 | 10601 0 9136 | 10602 1 9137 | 10603 1 9138 | 10604 0 9139 | 10607 1 9140 | 10609 1 9141 | 10610 1 9142 | 10611 1 9143 | 10612 0 9144 | 10613 0 9145 | 10614 0 9146 | 10615 0 9147 | 10616 0 9148 | 10617 0 9149 | 10618 0 9150 | 10619 0 9151 | 10620 1 9152 | 10621 1 9153 | 10622 0 9154 | 10623 0 9155 | 10625 0 9156 | 10626 1 9157 | 10627 0 9158 | 10628 1 9159 | 10630 0 9160 | 10631 0 9161 | 10632 1 9162 | 10633 0 9163 | 10634 0 9164 | 10635 1 9165 | 10636 0 9166 | 10637 0 9167 | 10638 0 9168 | 10639 1 9169 | 10640 0 9170 | 10641 1 9171 | 10644 0 9172 | 10645 1 9173 | 10647 1 9174 | 10648 1 9175 | 10649 0 9176 | 10651 1 9177 | 10653 1 9178 | 10654 1 9179 | 10655 0 9180 | 10656 1 9181 | 10657 1 9182 | 10658 0 9183 | 10659 1 9184 | 10661 1 9185 | 10662 0 9186 | 10664 1 9187 | 10665 0 9188 | 10667 0 9189 | 10668 1 9190 | 10669 0 9191 | 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9391 | 10899 1 9392 | 10900 0 9393 | 10901 1 9394 | 10903 0 9395 | 10904 1 9396 | 10905 0 9397 | 10906 0 9398 | 10907 0 9399 | 10908 0 9400 | 10909 0 9401 | 10910 1 9402 | 10911 1 9403 | 10912 1 9404 | 10913 1 9405 | 10914 0 9406 | 10915 1 9407 | 10916 1 9408 | 10918 1 9409 | 10919 1 9410 | 10920 0 9411 | 10921 0 9412 | 10922 0 9413 | 10923 0 9414 | 10924 0 9415 | 10925 0 9416 | 10928 1 9417 | 10929 1 9418 | 10930 1 9419 | 10931 0 9420 | 10932 0 9421 | 10933 0 9422 | 10934 1 9423 | 10935 0 9424 | 10936 0 9425 | 10937 0 9426 | 10939 1 9427 | 10940 0 9428 | 10941 1 9429 | 10942 0 9430 | 10943 0 9431 | 10944 0 9432 | 10945 0 9433 | 10946 0 9434 | 10947 0 9435 | 10948 0 9436 | 10949 1 9437 | 10950 1 9438 | 10951 1 9439 | 10952 0 9440 | 10953 1 9441 | 10954 0 9442 | 10955 1 9443 | 10956 0 9444 | 10957 1 9445 | 10958 0 9446 | 10959 0 9447 | 10960 0 9448 | 10961 0 9449 | 10962 1 9450 | 10963 1 9451 | 10965 1 9452 | 10966 0 9453 | 10967 0 9454 | 10968 1 9455 | 10969 1 9456 | 10970 1 9457 | 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9524 | 11048 0 9525 | 11049 0 9526 | 11050 0 9527 | 11051 1 9528 | 11052 1 9529 | 11053 0 9530 | 11054 0 9531 | 11055 1 9532 | 11056 1 9533 | 11057 0 9534 | 11058 1 9535 | 11059 0 9536 | 11060 0 9537 | 11061 1 9538 | 11062 0 9539 | 11064 1 9540 | 11065 1 9541 | 11066 0 9542 | 11067 1 9543 | 11068 0 9544 | 11069 1 9545 | 11070 1 9546 | 11071 1 9547 | 11072 1 9548 | 11073 0 9549 | 11074 0 9550 | 11075 0 9551 | 11076 1 9552 | 11077 0 9553 | 11078 0 9554 | 11079 0 9555 | 11080 0 9556 | 11081 0 9557 | 11082 0 9558 | 11083 0 9559 | 11085 1 9560 | 11088 1 9561 | 11089 0 9562 | 11090 0 9563 | 11091 1 9564 | 11092 1 9565 | 11094 1 9566 | 11095 1 9567 | 11097 0 9568 | 11098 1 9569 | 11099 0 9570 | 11100 1 9571 | 11101 0 9572 | 11102 1 9573 | 11103 0 9574 | 11104 1 9575 | 11105 0 9576 | 11109 1 9577 | 11110 0 9578 | 11111 0 9579 | 11112 1 9580 | 11113 0 9581 | 11114 0 9582 | 11115 0 9583 | 11116 0 9584 | 11117 0 9585 | 11119 0 9586 | 11120 0 9587 | 11121 0 9588 | 11122 1 9589 | 11123 0 9590 | 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9657 | 11198 0 9658 | 11199 0 9659 | 11200 1 9660 | 11201 0 9661 | 11202 1 9662 | 11204 1 9663 | 11205 1 9664 | 11206 1 9665 | 11207 0 9666 | 11208 0 9667 | 11210 0 9668 | 11211 1 9669 | 11212 0 9670 | 11214 0 9671 | 11215 1 9672 | 11216 0 9673 | 11217 0 9674 | 11218 0 9675 | 11219 0 9676 | 11220 0 9677 | 11221 0 9678 | 11222 0 9679 | 11224 0 9680 | 11225 1 9681 | 11226 0 9682 | 11227 1 9683 | 11229 1 9684 | 11230 1 9685 | 11231 1 9686 | 11232 1 9687 | 11233 0 9688 | 11234 0 9689 | 11235 1 9690 | 11236 0 9691 | 11237 0 9692 | 11238 0 9693 | 11239 1 9694 | 11240 0 9695 | 11241 1 9696 | 11243 1 9697 | 11244 1 9698 | 11245 0 9699 | 11246 0 9700 | 11247 0 9701 | 11249 1 9702 | 11250 1 9703 | 11251 1 9704 | 11252 1 9705 | 11253 0 9706 | 11254 1 9707 | 11255 1 9708 | 11256 0 9709 | 11257 1 9710 | 11258 1 9711 | 11259 1 9712 | 11260 0 9713 | 11261 1 9714 | 11262 0 9715 | 11263 0 9716 | 11264 0 9717 | 11265 0 9718 | 11266 0 9719 | 11269 1 9720 | 11270 0 9721 | 11271 1 9722 | 11272 0 9723 | 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9790 | 11352 0 9791 | 11353 0 9792 | 11354 0 9793 | 11355 1 9794 | 11356 0 9795 | 11357 1 9796 | 11358 1 9797 | 11359 1 9798 | 11360 0 9799 | 11361 0 9800 | 11362 0 9801 | 11363 1 9802 | 11364 1 9803 | 11365 0 9804 | 11366 0 9805 | 11367 1 9806 | 11368 0 9807 | 11369 0 9808 | 11370 0 9809 | 11371 0 9810 | 11372 1 9811 | 11373 1 9812 | 11374 1 9813 | 11375 0 9814 | 11376 0 9815 | 11377 0 9816 | 11378 1 9817 | 11379 0 9818 | 11380 1 9819 | 11381 0 9820 | 11382 1 9821 | 11384 1 9822 | 11385 0 9823 | 11386 0 9824 | 11387 1 9825 | 11388 0 9826 | 11389 1 9827 | 11390 1 9828 | 11391 1 9829 | 11392 0 9830 | 11394 0 9831 | 11397 0 9832 | 11399 0 9833 | 11400 0 9834 | 11401 1 9835 | 11402 0 9836 | 11403 1 9837 | 11404 0 9838 | 11405 1 9839 | 11406 0 9840 | 11408 0 9841 | 11409 0 9842 | 11410 0 9843 | 11411 0 9844 | 11413 1 9845 | 11414 0 9846 | 11416 0 9847 | 11417 0 9848 | 11419 1 9849 | 11420 0 9850 | 11423 0 9851 | 11424 1 9852 | 11425 0 9853 | 11426 0 9854 | 11427 0 9855 | 11428 0 9856 | 11429 0 9857 | 11430 0 9858 | 11431 1 9859 | 11432 0 9860 | 11434 0 9861 | 11435 0 9862 | 11436 0 9863 | 11437 1 9864 | 11438 0 9865 | 11439 1 9866 | 11440 0 9867 | 11441 1 9868 | 11442 1 9869 | 11443 1 9870 | 11444 0 9871 | 11445 0 9872 | 11446 1 9873 | 11447 0 9874 | 11449 1 9875 | 11450 0 9876 | 11451 1 9877 | 11452 1 9878 | 11453 1 9879 | 11454 1 9880 | 11455 0 9881 | 11456 0 9882 | 11458 1 9883 | 11459 0 9884 | 11460 0 9885 | 11461 0 9886 | 11462 1 9887 | 11463 1 9888 | 11465 0 9889 | 11466 0 9890 | 11467 0 9891 | 11468 0 9892 | 11469 1 9893 | 11470 1 9894 | 11471 0 9895 | 11472 0 9896 | 11473 0 9897 | 11474 1 9898 | 11475 1 9899 | 11476 1 9900 | 11477 0 9901 | 11478 0 9902 | 11479 0 9903 | 11480 1 9904 | 11481 0 9905 | 11482 0 9906 | 11483 0 9907 | 11484 1 9908 | 11486 1 9909 | 11488 0 9910 | 11489 0 9911 | 11490 1 9912 | 11492 1 9913 | 11494 1 9914 | 11495 1 9915 | 11496 0 9916 | 11497 1 9917 | 11498 0 9918 | 11499 0 9919 | 11500 0 9920 | 11501 0 9921 | 11502 0 9922 | 11503 0 9923 | 11504 0 9924 | 11505 1 9925 | 11506 0 9926 | 11509 0 9927 | 11510 0 9928 | 11511 1 9929 | 11512 1 9930 | 11513 1 9931 | 11515 0 9932 | 11516 0 9933 | 11517 1 9934 | 11518 0 9935 | 11519 0 9936 | 11520 1 9937 | 11521 0 9938 | 11522 0 9939 | 11523 0 9940 | 11524 1 9941 | 11526 0 9942 | 11527 1 9943 | 11528 1 9944 | 11529 0 9945 | 11530 1 9946 | 11531 1 9947 | 11532 1 9948 | 11533 0 9949 | 11535 0 9950 | 11538 1 9951 | 11540 0 9952 | 11541 1 9953 | 11542 1 9954 | 11543 1 9955 | 11545 1 9956 | 11547 0 9957 | 11548 1 9958 | 11549 0 9959 | 11550 1 9960 | 11551 1 9961 | 11553 1 9962 | 11554 1 9963 | 11556 1 9964 | 11558 0 9965 | 11559 0 9966 | 11560 1 9967 | 11561 0 9968 | 11562 1 9969 | 11563 1 9970 | 11564 1 9971 | 11566 0 9972 | 11567 1 9973 | 11568 0 9974 | 11569 1 9975 | 11570 1 9976 | 11571 0 9977 | 11572 1 9978 | 11574 0 9979 | 11576 1 9980 | 11577 0 9981 | 11578 1 9982 | 11579 1 9983 | 11580 0 9984 | 11581 1 9985 | 11582 1 9986 | 11583 0 9987 | 11584 0 9988 | 11585 1 9989 | 11587 0 9990 | 11588 0 9991 | 11589 1 9992 | 11590 1 9993 | 11592 0 9994 | 11593 0 9995 | 11594 1 9996 | 11595 0 9997 | 11596 0 9998 | 11597 0 9999 | 11598 0 10000 | 11599 0 10001 | 11600 0 10002 | 11601 0 10003 | 11602 0 10004 | 11603 0 10005 | 11604 0 10006 | 11605 1 10007 | 11606 1 10008 | 11607 0 10009 | 11608 1 10010 | 11609 1 10011 | 11610 1 10012 | 11612 0 10013 | 11613 0 10014 | 11614 0 10015 | 11615 0 10016 | 11616 1 10017 | 11617 1 10018 | 11618 0 10019 | 11619 0 10020 | 11620 0 10021 | 11621 0 10022 | 11622 1 10023 | 11624 0 10024 | 11625 0 10025 | 11626 1 10026 | 11627 1 10027 | 11629 1 10028 | 11630 0 10029 | 11633 1 10030 | 11634 1 10031 | 11635 1 10032 | 11636 0 10033 | 11637 1 10034 | 11640 1 10035 | 11641 0 10036 | 11642 1 10037 | 11643 0 10038 | 11644 0 10039 | 11645 1 10040 | 11646 0 10041 | 11647 0 10042 | 11648 1 10043 | 11649 1 10044 | 11650 1 10045 | 11651 1 10046 | 11652 0 10047 | 11653 0 10048 | 11654 0 10049 | 11655 1 10050 | 11656 0 10051 | 11658 0 10052 | 11659 1 10053 | 11660 0 10054 | 11661 1 10055 | 11662 1 10056 | 11663 0 10057 | 11664 0 10058 | 11665 0 10059 | 11666 1 10060 | 11667 1 10061 | 11668 0 10062 | 11669 0 10063 | 11670 1 10064 | 11671 0 10065 | 11672 1 10066 | 11673 0 10067 | 11674 0 10068 | 11675 1 10069 | 11676 0 10070 | 11677 1 10071 | 11679 0 10072 | 11680 1 10073 | 11681 1 10074 | 11682 0 10075 | 11683 1 10076 | 11685 1 10077 | 11686 1 10078 | 11687 0 10079 | 11688 0 10080 | 11690 1 10081 | 11691 1 10082 | 11692 0 10083 | 11693 1 10084 | 11694 1 10085 | 11695 0 10086 | 11696 0 10087 | 11697 1 10088 | 11699 0 10089 | 11700 0 10090 | 11701 0 10091 | 11702 1 10092 | 11703 0 10093 | 11704 0 10094 | 11705 1 10095 | 11706 0 10096 | 11707 1 10097 | 11708 0 10098 | 11711 1 10099 | 11712 1 10100 | 11713 0 10101 | 11714 1 10102 | 11715 1 10103 | 11716 0 10104 | 11717 1 10105 | 11718 1 10106 | 11719 1 10107 | 11720 0 10108 | 11721 1 10109 | 11722 1 10110 | 11723 1 10111 | 11724 0 10112 | 11725 0 10113 | 11726 1 10114 | 11727 0 10115 | 11731 1 10116 | 11732 1 10117 | 11734 1 10118 | 11735 0 10119 | 11736 0 10120 | 11737 0 10121 | 11738 1 10122 | 11739 0 10123 | 11740 0 10124 | 11742 0 10125 | 11743 0 10126 | 11744 0 10127 | 11745 1 10128 | 11746 0 10129 | 11747 1 10130 | 11748 1 10131 | 11749 0 10132 | 11750 0 10133 | 11751 0 10134 | 11753 0 10135 | 11754 1 10136 | 11755 0 10137 | 11758 1 10138 | 11759 1 10139 | 11760 1 10140 | 11762 1 10141 | 11763 0 10142 | 11764 1 10143 | 11765 0 10144 | 11767 0 10145 | 11768 1 10146 | 11769 0 10147 | 11770 1 10148 | 11772 0 10149 | 11773 0 10150 | 11774 1 10151 | 11775 0 10152 | 11776 0 10153 | 11777 1 10154 | 11778 1 10155 | 11779 1 10156 | 11780 0 10157 | 11781 0 10158 | 11782 0 10159 | 11783 0 10160 | 11784 1 10161 | 11785 0 10162 | 11787 0 10163 | 11788 1 10164 | 11789 0 10165 | 11790 1 10166 | 11791 1 10167 | 11792 0 10168 | 11793 1 10169 | 11794 0 10170 | 11795 1 10171 | 11796 0 10172 | 11797 1 10173 | 11798 1 10174 | 11799 0 10175 | 11800 0 10176 | 11801 1 10177 | 11802 0 10178 | 11803 1 10179 | 11804 0 10180 | 11805 0 10181 | 11806 1 10182 | 11807 1 10183 | 11808 0 10184 | 11809 1 10185 | 11811 1 10186 | 11812 1 10187 | 11813 0 10188 | 11815 1 10189 | 11816 1 10190 | 11817 0 10191 | 11818 1 10192 | 11819 1 10193 | 11820 1 10194 | 11821 1 10195 | 11822 0 10196 | 11824 0 10197 | 11825 0 10198 | 11826 0 10199 | 11827 0 10200 | 11828 1 10201 | 11829 0 10202 | 11830 1 10203 | 11831 1 10204 | 11832 0 10205 | 11833 0 10206 | 11834 1 10207 | 11835 0 10208 | 11836 0 10209 | 11837 0 10210 | 11838 1 10211 | 11839 1 10212 | 11841 0 10213 | 11842 0 10214 | 11843 1 10215 | 11844 0 10216 | 11845 0 10217 | 11847 1 10218 | 11849 0 10219 | 11850 0 10220 | 11851 1 10221 | 11852 1 10222 | 11853 1 10223 | 11855 0 10224 | 11856 1 10225 | 11858 1 10226 | 11859 1 10227 | 11860 0 10228 | 11861 0 10229 | 11862 0 10230 | 11863 1 10231 | 11864 0 10232 | 11865 1 10233 | 11866 0 10234 | 11867 0 10235 | 11868 1 10236 | 11869 1 10237 | 11870 1 10238 | 11871 0 10239 | 11872 1 10240 | 11873 1 10241 | 11874 0 10242 | 11875 0 10243 | 11876 0 10244 | 11877 1 10245 | 11878 1 10246 | 11879 1 10247 | 11880 0 10248 | 11881 0 10249 | 11882 1 10250 | 11883 1 10251 | 11884 1 10252 | 11885 0 10253 | 11886 0 10254 | 11887 0 10255 | 11888 1 10256 | 11889 0 10257 | 11890 1 10258 | 11891 1 10259 | 11892 0 10260 | 11893 0 10261 | 11895 1 10262 | 11896 0 10263 | 11897 0 10264 | 11898 1 10265 | 11899 1 10266 | 11900 1 10267 | 11901 0 10268 | 11902 0 10269 | 11903 0 10270 | 11904 0 10271 | 11905 1 10272 | 11906 1 10273 | 11908 0 10274 | 11909 1 10275 | 11911 0 10276 | 11912 1 10277 | 11913 0 10278 | 11914 0 10279 | 11916 1 10280 | 11917 1 10281 | 11918 0 10282 | 11919 0 10283 | 11920 1 10284 | 11921 1 10285 | 11922 0 10286 | 11923 0 10287 | 11924 0 10288 | 11925 0 10289 | 11926 1 10290 | 11927 0 10291 | 11929 1 10292 | 11931 1 10293 | 11933 0 10294 | 11934 0 10295 | 11935 1 10296 | 11936 1 10297 | 11937 0 10298 | 11938 1 10299 | 11939 1 10300 | 11940 1 10301 | 11941 0 10302 | 11942 0 10303 | 11943 0 10304 | 11944 1 10305 | 11945 1 10306 | 11946 0 10307 | 11947 1 10308 | 11948 1 10309 | 11949 0 10310 | 11950 0 10311 | 11951 0 10312 | 11952 0 10313 | 11954 0 10314 | 11955 0 10315 | 11956 1 10316 | 11957 0 10317 | 11959 1 10318 | 11962 1 10319 | 11963 1 10320 | 11964 0 10321 | 11965 0 10322 | 11966 1 10323 | 11967 0 10324 | 11968 1 10325 | 11970 0 10326 | 11971 1 10327 | 11972 0 10328 | 11973 0 10329 | 11974 1 10330 | 11975 0 10331 | 11976 1 10332 | 11977 0 10333 | 11978 1 10334 | 11979 1 10335 | 11981 1 10336 | 11983 1 10337 | 11984 1 10338 | 11985 0 10339 | 11986 0 10340 | 11987 0 10341 | 11988 0 10342 | 11989 0 10343 | 11990 1 10344 | 11991 1 10345 | 11992 0 10346 | 11993 1 10347 | 11994 1 10348 | 11995 1 10349 | 11996 1 10350 | 11997 0 10351 | 11998 1 10352 | 11999 1 10353 | 12000 1 10354 | 12001 1 10355 | 12002 0 10356 | 12004 1 10357 | 12005 1 10358 | 12006 1 10359 | 12007 0 10360 | 12010 0 10361 | 12011 0 10362 | 12013 1 10363 | 12014 0 10364 | 12015 1 10365 | 12016 1 10366 | 12017 1 10367 | 12018 0 10368 | 12019 1 10369 | 12020 1 10370 | 12021 1 10371 | 12022 0 10372 | 12023 1 10373 | 12024 0 10374 | 12025 1 10375 | 12026 1 10376 | 12027 1 10377 | 12028 1 10378 | 12029 1 10379 | 12030 1 10380 | 12031 1 10381 | 12032 0 10382 | 12033 1 10383 | 12034 0 10384 | 12035 1 10385 | 12036 1 10386 | 12037 0 10387 | 12038 1 10388 | 12039 0 10389 | 12041 0 10390 | 12042 0 10391 | 12043 1 10392 | 12044 1 10393 | 12045 0 10394 | 12047 0 10395 | 12048 0 10396 | 12049 0 10397 | 12050 1 10398 | 12051 1 10399 | 12054 1 10400 | 12055 0 10401 | 12056 1 10402 | 12057 1 10403 | 12058 0 10404 | 12059 0 10405 | 12060 0 10406 | 12061 0 10407 | 12062 1 10408 | 12063 1 10409 | 12064 1 10410 | 12065 1 10411 | 12066 0 10412 | 12068 1 10413 | 12069 0 10414 | 12070 0 10415 | 12072 0 10416 | 12073 1 10417 | 12074 0 10418 | 12075 1 10419 | 12076 1 10420 | 12077 0 10421 | 12078 0 10422 | 12079 0 10423 | 12081 0 10424 | 12082 0 10425 | 12083 0 10426 | 12084 0 10427 | 12085 1 10428 | 12086 0 10429 | 12087 1 10430 | 12088 0 10431 | 12089 0 10432 | 12090 1 10433 | 12091 1 10434 | 12092 0 10435 | 12093 1 10436 | 12094 0 10437 | 12095 0 10438 | 12096 1 10439 | 12098 0 10440 | 12099 1 10441 | 12100 0 10442 | 12101 1 10443 | 12102 1 10444 | 12103 0 10445 | 12104 0 10446 | 12105 0 10447 | 12106 1 10448 | 12107 1 10449 | 12108 0 10450 | 12109 0 10451 | 12111 0 10452 | 12112 0 10453 | 12114 1 10454 | 12115 1 10455 | 12117 1 10456 | 12118 1 10457 | 12119 1 10458 | 12120 1 10459 | 12121 0 10460 | 12123 1 10461 | 12124 0 10462 | 12126 1 10463 | 12127 1 10464 | 12128 1 10465 | 12129 0 10466 | 12130 0 10467 | 12131 0 10468 | 12132 1 10469 | 12133 0 10470 | 12134 0 10471 | 12136 0 10472 | 12137 0 10473 | 12138 1 10474 | 12139 1 10475 | 12140 1 10476 | 12141 0 10477 | 12142 1 10478 | 12143 0 10479 | 12144 0 10480 | 12145 1 10481 | 12146 0 10482 | 12147 0 10483 | 12148 1 10484 | 12149 0 10485 | 12150 1 10486 | 12151 0 10487 | 12152 1 10488 | 12154 1 10489 | 12155 1 10490 | 12156 0 10491 | 12157 1 10492 | 12158 0 10493 | 12160 0 10494 | 12161 0 10495 | 12162 1 10496 | 12163 0 10497 | 12164 1 10498 | 12165 1 10499 | 12166 0 10500 | 12167 0 10501 | 12168 0 10502 | 12169 0 10503 | 12170 0 10504 | 12172 1 10505 | 12173 1 10506 | 12174 0 10507 | 12175 1 10508 | 12176 0 10509 | 12177 1 10510 | 12178 1 10511 | 12179 0 10512 | 12180 0 10513 | 12181 1 10514 | 12182 1 10515 | 12183 0 10516 | 12184 0 10517 | 12185 1 10518 | 12186 1 10519 | 12187 1 10520 | 12188 1 10521 | 12189 1 10522 | 12190 1 10523 | 12191 0 10524 | 12192 0 10525 | 12194 1 10526 | 12195 0 10527 | 12196 0 10528 | 12197 0 10529 | 12198 1 10530 | 12199 1 10531 | 12200 1 10532 | 12201 0 10533 | 12202 1 10534 | 12204 0 10535 | 12205 1 10536 | 12206 1 10537 | 12207 0 10538 | 12208 0 10539 | 12209 1 10540 | 12210 1 10541 | 12211 1 10542 | 12212 0 10543 | 12214 1 10544 | 12216 0 10545 | 12217 0 10546 | 12218 0 10547 | 12219 1 10548 | 12220 1 10549 | 12221 1 10550 | 12222 1 10551 | 12223 1 10552 | 12224 0 10553 | 12225 0 10554 | 12227 1 10555 | 12228 0 10556 | 12229 0 10557 | 12230 0 10558 | 12231 1 10559 | 12232 0 10560 | 12233 0 10561 | 12234 1 10562 | 12235 0 10563 | 12236 0 10564 | 12237 1 10565 | 12238 1 10566 | 12239 1 10567 | 12240 1 10568 | 12241 0 10569 | 12242 1 10570 | 12243 1 10571 | 12244 0 10572 | 12245 0 10573 | 12247 1 10574 | 12249 0 10575 | 12250 0 10576 | 12252 0 10577 | 12254 1 10578 | 12255 0 10579 | 12256 0 10580 | 12257 1 10581 | 12258 1 10582 | 12260 1 10583 | 12262 1 10584 | 12263 1 10585 | 12264 1 10586 | 12265 0 10587 | 12266 0 10588 | 12267 1 10589 | 12268 0 10590 | 12269 0 10591 | 12270 1 10592 | 12271 0 10593 | 12272 0 10594 | 12273 1 10595 | 12274 0 10596 | 12275 0 10597 | 12276 1 10598 | 12277 0 10599 | 12278 1 10600 | 12279 0 10601 | 12280 0 10602 | 12281 1 10603 | 12282 1 10604 | 12283 1 10605 | 12285 0 10606 | 12286 1 10607 | 12287 0 10608 | 12288 0 10609 | 12289 1 10610 | 12290 0 10611 | 12291 1 10612 | 12292 1 10613 | 12293 0 10614 | 12294 1 10615 | 12295 1 10616 | 12296 1 10617 | 12298 0 10618 | 12299 0 10619 | 12300 0 10620 | 12302 1 10621 | 12303 1 10622 | 12304 1 10623 | 12305 0 10624 | 12306 0 10625 | 12307 1 10626 | 12308 1 10627 | 12309 0 10628 | 12310 1 10629 | 12311 1 10630 | 12313 1 10631 | 12314 0 10632 | 12315 0 10633 | 12316 1 10634 | 12317 0 10635 | 12318 0 10636 | 12319 0 10637 | 12321 0 10638 | 12322 1 10639 | 12323 0 10640 | 12324 0 10641 | 12325 0 10642 | 12326 1 10643 | 12327 1 10644 | 12328 1 10645 | 12329 1 10646 | 12330 1 10647 | 12332 1 10648 | 12333 1 10649 | 12336 0 10650 | 12337 1 10651 | 12339 1 10652 | 12340 1 10653 | 12341 0 10654 | 12342 1 10655 | 12343 1 10656 | 12344 1 10657 | 12345 0 10658 | 12346 0 10659 | 12347 1 10660 | 12349 0 10661 | 12350 0 10662 | 12351 1 10663 | 12352 0 10664 | 12354 0 10665 | 12355 1 10666 | 12356 0 10667 | 12357 1 10668 | 12358 0 10669 | 12359 0 10670 | 12361 0 10671 | 12362 1 10672 | 12363 1 10673 | 12364 0 10674 | 12365 0 10675 | 12366 0 10676 | 12367 1 10677 | 12368 1 10678 | 12369 1 10679 | 12370 0 10680 | 12371 1 10681 | 12372 1 10682 | 12373 1 10683 | 12374 0 10684 | 12375 0 10685 | 12376 0 10686 | 12377 1 10687 | 12378 1 10688 | 12379 0 10689 | 12380 0 10690 | 12381 1 10691 | 12382 1 10692 | 12383 1 10693 | 12384 1 10694 | 12385 1 10695 | 12386 0 10696 | 12388 1 10697 | 12389 1 10698 | 12390 1 10699 | 12391 0 10700 | 12392 1 10701 | 12393 0 10702 | 12394 1 10703 | 12395 0 10704 | 12396 1 10705 | 12398 0 10706 | 12399 1 10707 | 12400 1 10708 | 12401 1 10709 | 12402 0 10710 | 12403 0 10711 | 12404 0 10712 | 12405 1 10713 | 12406 0 10714 | 12407 0 10715 | 12408 0 10716 | 12409 0 10717 | 12410 1 10718 | 12411 0 10719 | 12412 0 10720 | 12413 0 10721 | 12414 0 10722 | 12415 0 10723 | 12416 1 10724 | 12418 0 10725 | 12419 1 10726 | 12420 0 10727 | 12421 1 10728 | 12422 0 10729 | 12423 0 10730 | 12424 1 10731 | 12425 1 10732 | 12427 1 10733 | 12428 1 10734 | 12429 1 10735 | 12431 1 10736 | 12432 1 10737 | 12433 1 10738 | 12434 0 10739 | 12436 0 10740 | 12439 1 10741 | 12440 0 10742 | 12441 1 10743 | 12442 1 10744 | 12443 1 10745 | 12444 0 10746 | 12445 1 10747 | 12446 1 10748 | 12447 0 10749 | 12448 0 10750 | 12449 0 10751 | 12450 0 10752 | 12452 0 10753 | 12453 0 10754 | 12454 1 10755 | 12455 0 10756 | 12457 0 10757 | 12458 1 10758 | 12459 1 10759 | 12460 0 10760 | 12461 0 10761 | 12462 1 10762 | 12464 0 10763 | 12465 0 10764 | 12467 1 10765 | 12468 1 10766 | 12469 0 10767 | 12470 0 10768 | 12471 0 10769 | 12473 0 10770 | 12474 1 10771 | 12475 1 10772 | 12476 1 10773 | 12477 1 10774 | 12478 0 10775 | 12479 1 10776 | 12480 0 10777 | 12481 1 10778 | 12482 0 10779 | 12483 0 10780 | 12484 1 10781 | 12485 1 10782 | 12486 0 10783 | 12488 1 10784 | 12489 0 10785 | 12490 0 10786 | 12491 0 10787 | 12492 0 10788 | 12493 0 10789 | 12495 1 10790 | 12496 0 10791 | 12497 1 10792 | 12499 1 10793 | 12500 0 10794 | 12501 1 10795 | 12503 0 10796 | 12504 1 10797 | 12505 1 10798 | 12506 0 10799 | 12507 1 10800 | 12508 1 10801 | 12509 1 10802 | 12510 1 10803 | 12511 0 10804 | 12512 1 10805 | 12513 0 10806 | 12515 0 10807 | 12516 1 10808 | 12517 0 10809 | 12518 1 10810 | 12519 0 10811 | 12520 0 10812 | 12521 1 10813 | 12522 0 10814 | 12523 0 10815 | 12524 1 10816 | 12525 0 10817 | 12526 0 10818 | 12527 1 10819 | 12528 0 10820 | 12529 1 10821 | 12530 0 10822 | 12531 1 10823 | 12532 0 10824 | 12533 1 10825 | 12534 1 10826 | 12535 0 10827 | 12536 0 10828 | 12537 0 10829 | 12538 1 10830 | 12539 1 10831 | 12540 0 10832 | 12541 1 10833 | 12542 0 10834 | 12544 1 10835 | 12545 1 10836 | 12546 0 10837 | 12547 0 10838 | 12548 0 10839 | 12549 0 10840 | 12552 1 10841 | 12553 0 10842 | 12554 0 10843 | 12555 0 10844 | 12556 1 10845 | 12557 1 10846 | 12559 0 10847 | 12560 0 10848 | 12561 1 10849 | 12562 0 10850 | 12563 1 10851 | 12564 1 10852 | 12565 0 10853 | 12566 1 10854 | 12567 0 10855 | 12568 0 10856 | 12569 0 10857 | 12570 1 10858 | 12571 1 10859 | 12572 1 10860 | 12573 0 10861 | 12574 1 10862 | 12575 0 10863 | 12577 1 10864 | 12578 0 10865 | 12579 0 10866 | 12580 0 10867 | 12581 1 10868 | 12583 0 10869 | 12584 0 10870 | 12585 0 10871 | 12586 1 10872 | 12587 1 10873 | 12588 0 10874 | 12591 1 10875 | 12592 1 10876 | 12593 0 10877 | 12594 1 10878 | 12595 0 10879 | 12596 0 10880 | 12597 1 10881 | 12599 1 10882 | 12601 0 10883 | 12602 0 10884 | 12604 0 10885 | 12606 0 10886 | 12607 0 10887 | 12608 0 10888 | 12609 1 10889 | 12610 1 10890 | 12611 0 10891 | 12612 1 10892 | 12613 0 10893 | 12614 1 10894 | 12615 1 10895 | 12616 0 10896 | 12617 0 10897 | 12619 0 10898 | 12621 1 10899 | 12622 1 10900 | 12624 1 10901 | 12625 1 10902 | 12626 1 10903 | 12629 1 10904 | 12630 0 10905 | 12631 0 10906 | 12632 1 10907 | 12633 1 10908 | 12634 0 10909 | 12636 0 10910 | 12637 0 10911 | 12638 0 10912 | 12639 0 10913 | 12640 0 10914 | 12641 1 10915 | 12642 1 10916 | 12643 1 10917 | 12644 1 10918 | 12645 0 10919 | 12646 0 10920 | 12647 1 10921 | 12648 0 10922 | 12649 0 10923 | 12650 1 10924 | 12651 1 10925 | 12652 0 10926 | 12653 1 10927 | 12654 0 10928 | 12656 1 10929 | 12657 0 10930 | 12658 0 10931 | 12659 1 10932 | 12660 0 10933 | 12661 0 10934 | 12663 1 10935 | 12664 1 10936 | 12665 0 10937 | 12666 1 10938 | 12667 0 10939 | 12668 0 10940 | 12669 0 10941 | 12670 0 10942 | 12671 1 10943 | 12672 1 10944 | 12673 0 10945 | 12674 1 10946 | 12675 0 10947 | 12676 0 10948 | 12677 1 10949 | 12678 1 10950 | 12679 0 10951 | 12680 0 10952 | 12681 1 10953 | 12683 1 10954 | 12684 0 10955 | 12686 1 10956 | 12687 0 10957 | 12688 0 10958 | 12689 1 10959 | 12690 0 10960 | 12691 1 10961 | 12692 0 10962 | 12693 0 10963 | 12694 1 10964 | 12696 0 10965 | 12697 0 10966 | 12698 1 10967 | 12699 1 10968 | 12700 1 10969 | 12701 1 10970 | 12702 0 10971 | 12703 0 10972 | 12704 0 10973 | 12705 0 10974 | 12707 0 10975 | 12709 1 10976 | 12710 0 10977 | 12711 0 10978 | 12712 0 10979 | 12713 1 10980 | 12714 0 10981 | 12716 0 10982 | 12717 0 10983 | 12718 0 10984 | 12719 1 10985 | 12721 0 10986 | 12722 0 10987 | 12723 0 10988 | 12724 1 10989 | 12725 0 10990 | 12726 0 10991 | 12727 1 10992 | 12728 0 10993 | 12729 1 10994 | 12730 0 10995 | 12731 0 10996 | 12732 1 10997 | 12733 1 10998 | 12734 1 10999 | 12735 1 11000 | 12736 1 11001 | 12737 0 11002 | 12738 1 11003 | 12739 0 11004 | 12740 0 11005 | 12741 1 11006 | 12742 1 11007 | 12743 1 11008 | 12745 1 11009 | 12746 0 11010 | 12747 0 11011 | 12748 1 11012 | 12749 1 11013 | 12750 0 11014 | 12751 0 11015 | 12752 1 11016 | 12754 0 11017 | 12755 0 11018 | 12756 1 11019 | 12757 0 11020 | 12758 1 11021 | 12759 1 11022 | 12760 1 11023 | 12761 0 11024 | 12762 0 11025 | 12763 1 11026 | 12764 0 11027 | 12765 0 11028 | 12766 1 11029 | 12768 1 11030 | 12769 0 11031 | 12770 0 11032 | 12771 0 11033 | 12772 0 11034 | 12773 0 11035 | 12774 0 11036 | 12775 1 11037 | 12776 0 11038 | 12777 0 11039 | 12779 0 11040 | 12780 1 11041 | 12781 1 11042 | 12782 1 11043 | 12783 0 11044 | 12784 0 11045 | 12785 1 11046 | 12786 0 11047 | 12787 1 11048 | 12788 1 11049 | 12789 0 11050 | 12790 1 11051 | 12791 1 11052 | 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