Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from datetime import datetime
# SMAWK utilities
def reduce(r,c,lookup):
n = len(r)
m = len(c)
if n == m :
return c
t = np.zeros((n) , dtype=int)
a = 0
b = 1
t[0] = c[0]
while True :
if ( a < (n-1)) & (lookup(r[a], t[a]) >= lookup(r[a], c[b])) :
a +=1
t[a] = c[b]
b +=1
elif (a == (n-1)) & (lookup(r[a], t[a]) >= lookup(r[a], c[b])) :
b +=1
elif lookup(r[a], t[a]) < lookup(r[a], c[b]):
if a>0:
a = a - 1
else:
t[0] = c[b]
b +=1
if ((a+1+(m-b+1))<= n) or (b == m) :
break
while a < (n-1):
a += 1
print('%d %d %d %d'%(a,b,m,n))
t[a] = c[b]
b += 1
if (b == m):
break
return t
def Maxcompute(rows,cols,lookup):
if not rows: return {}
cols = reduce(rows,cols,lookup)
result = Maxcompute([rows[i] for i in range(1,len(rows),2)],cols,lookup)
# go back and fill in the even rows
c = 0
for r in range(0,len(rows),2):
row = rows[r]
if r == len(rows) - 1:
cc = len(cols)-1 # if r is last row, search through last col
else:
cc = c # otherwise only until pos of max in row r+1
target = result[rows[r+1]]
while cols[cc] != target:
cc += 1
result[row] = max([ (lookup(row,cols[x]),-x,cols[x]) \
for x in range(c,cc+1) ]) [2]
c = cc
return result
# Ref: David Eppstein's SMAWK Python code
def smawk(rows,cols,lookup):
# base case of recursion
if not rows: return {}
# reduce phase: make number of columns at most equal to number of rows
stack = []
for c in cols:
while len(stack) >= 1 and \
lookup(rows[len(stack)-1],stack[-1]) < lookup(rows[len(stack)-1],c):
stack.pop()
if len(stack) != len(rows):
stack.append(c)
cols = stack
# recursive call to search for every odd row
result = smawk([rows[i] for i in range(1,len(rows),2)],cols,lookup)
# go back and fill in the even rows
c = 0
for r in range(0,len(rows),2):
row = rows[r]
if r == len(rows) - 1:
cc = len(cols)-1 # if r is last row, search through last col
else:
cc = c # otherwise only until pos of max in row r+1
target = result[rows[r+1]]
while cols[cc] != target:
cc += 1
result[row] = max([ (lookup(row,cols[x]),-x,cols[x]) \
for x in range(c,cc+1) ]) [2]
c = cc
return result
# Graph processing
def getGraph(nodes_arr, temporal_graph):
G=nx.Graph()
for _vertex in nodes_arr:
G.add_node(_vertex)
for _edge in temporal_graph:
G.add_edge(_edge[0], _edge[2])
return G
# Generate Plots
def generate_plots(G,group_assignment,lambda_estimates,num_roles,num_segments,dest_folder,nodes,change_points_arr,t_df,refValue):
color_map = []
print('Number of nodes: {}'.format( len(nodes)))
# supports for 6 clusters
for node in G:
if group_assignment[node] == 0:
color_map.append('blue')
elif group_assignment[node] == 1:
color_map.append('red')
elif group_assignment[node] == 2:
color_map.append('green')
elif group_assignment[node] == 3:
color_map.append('yellow')
elif group_assignment[node] == 4:
color_map.append('black')
else:
color_map.append('white')
plt.figure(figsize=(10,10))
nx.draw_spring(G, node_color=color_map, with_labels=True)
_file_name = dest_folder+'spring.png'
plt.savefig(_file_name)
plt.figure(figsize=(10,10))
nx.draw_random(G, node_color=color_map, with_labels=True)
_file_name = dest_folder+'random.png'
plt.savefig(_file_name)
plt.figure(figsize=(10,10))
nx.draw_circular(G, node_color=color_map, with_labels=True)
_file_name = dest_folder+'circular.png'
plt.savefig(_file_name)
list_of_groups= [[] for _ in range(num_roles)]
for idx, val in group_assignment.items():
list_of_groups[val].append(idx)
print('group assignments: {}'.format(list_of_groups))
print('lambdas....')
for k in range(0, num_roles):
for g in range(k,num_roles):
print(lambda_estimates[k,g,:])
_y_limit = np.max(lambda_estimates[k,g,:])
fig, ax = plt.subplots()
for d in range(0, num_segments):
p = change_points_arr[k,g,d]
q = change_points_arr[k,g,d+1]
plt.hlines(y=lambda_estimates[k,g,d], xmin=datetime.utcfromtimestamp(p+refValue), xmax=datetime.utcfromtimestamp(q+refValue))
plt.xlabel('t')
plt.title(r'$\lambda_{ %d%d}(t)$'%(k+1,g+1))
plt.ylim(0,_y_limit)
plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
plt.autoscale()
_file_name = dest_folder+'/lamda'+str(k+1)+str(g+1)+'.pdf'
plt.savefig(_file_name)