-
Notifications
You must be signed in to change notification settings - Fork 0
/
generate_plots.py
132 lines (111 loc) · 4.36 KB
/
generate_plots.py
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
import input as i
import alg as a
import alg_google as a_g
import numpy as np
from importlib import reload
reload(i); reload(a);
import matplotlib.pyplot as plt
B = 20
w_list = np.linspace(0, 0.9, 10)
# print(w_list)
repeat = 5000
std = 90
# competitive ratio against omega
# alg_ratio = np.zeros((len(w_list), repeat))
# rand_ratio = np.zeros((len(w_list), repeat))
# google_ratio = np.zeros((len(w_list), repeat))
# google_r_ratio = np.zeros((len(w_list), repeat))
#
# for idx, w in enumerate(w_list):
# for j in np.arange(repeat):
# ins = i.Instance(B=B, time_dependant=True, predictor_std=std)
# alg = a.DPOA(ins, w=w)
# rand = a.RPOA(ins, w=w)
# google = a_g.DPOA_google(ins, w)
# g_rand = a_g.RPOA_google(ins, w)
# opt = a.OPT(ins)
#
# alg_ratio[idx, j] = alg / opt
# rand_ratio[idx, j] = rand / opt
# google_ratio[idx, j] = google / opt
# google_r_ratio[idx, j] = g_rand / opt
# avg_ratio = np.mean(alg_ratio, axis=1)
# ci = 1.96 * np.std(alg_ratio,axis=1)/np.sqrt(repeat)
# avg_r_ratio = np.mean(rand_ratio, axis=1)
# ci_r = 1.96 * np.std(rand_ratio,axis=1)/np.sqrt(repeat)
# avg_ratio_g = np.mean(google_ratio, axis=1)
# ci_g = 1.96 * np.std(google_ratio,axis=1)/np.sqrt(repeat)
# avg_ratio_gr = np.mean(google_r_ratio, axis=1)
# ci_gr = 1.96 * np.std(google_r_ratio,axis=1)/np.sqrt(repeat)
#
# plt.clf()
#
# positions = np.arange(1, len(w_list)+1)
#
# plt.plot(positions, avg_ratio, label="dSPW", color='b')
# plt.fill_between(positions, (avg_ratio-ci), (avg_ratio+ci), color='b', alpha=.1)
#
# plt.plot(positions, avg_r_ratio, label="rSPW", color='g')
# plt.fill_between(positions, (avg_r_ratio-ci_r), (avg_r_ratio+ci_r), color='g', alpha=.1)
#
#
# plt.plot(positions, avg_ratio_g, label="d-KPS18", color='r')
# plt.fill_between(positions, (avg_ratio_g-ci_g), (avg_ratio_g+ci_g), color='r', alpha=.1)
#
# plt.plot(positions, avg_ratio_gr, label="r-KPS18", color='m')
# plt.fill_between(positions, (avg_ratio_gr-ci_gr), (avg_ratio_gr+ci_gr), color='m', alpha=.1)
#
#
# formated_list = ['%.2f' % elem for elem in w_list ]
# plt.xticks(positions, formated_list);
#
# plt.legend(loc='upper right')
# plt.xlabel("$\omega$", fontsize=16)
# plt.ylabel("Competitive Ratio", fontsize=16)
#
# plt.savefig("competitive ratio against omega")
# competitive ratio against sigma
std_list = np.arange(0,100,10)
w = 0.3
alg_ratio = np.zeros((len(w_list), repeat))
rand_ratio = np.zeros((len(w_list), repeat))
google_ratio = np.zeros((len(w_list), repeat))
google_r_ratio = np.zeros((len(w_list), repeat))
for idx, std in enumerate(std_list):
for j in np.arange(repeat):
ins = i.Instance(B=B, time_dependant=True, predictor_std=std)
alg = a.DPOA(ins, w=w)
rand = a.RPOA(ins, w=w)
google = a_g.DPOA_google(ins, w)
g_rand = a_g.RPOA_google(ins, w)
opt = a.OPT(ins)
alg_ratio[idx, j] = alg / opt
rand_ratio[idx, j] = rand / opt
google_ratio[idx, j] = google / opt
google_r_ratio[idx, j] = g_rand / opt
avg_ratio = np.mean(alg_ratio, axis=1)
ci = 1.96 * np.std(alg_ratio,axis=1)/np.sqrt(repeat)
avg_r_ratio = np.mean(rand_ratio, axis=1)
ci_r = 1.96 * np.std(rand_ratio,axis=1)/np.sqrt(repeat)
avg_ratio_g = np.mean(google_ratio, axis=1)
ci_g = 1.96 * np.std(google_ratio,axis=1)/np.sqrt(repeat)
avg_ratio_gr = np.mean(google_r_ratio, axis=1)
ci_gr = 1.96 * np.std(google_r_ratio,axis=1)/np.sqrt(repeat)
plt.clf()
# plt.boxplot(alg_ratio.transpose(), "DPOA")
# plt.boxplot(google_ratio.transpose(), "Google");
positions = np.arange(1, len(std_list)+1)
plt.plot(positions, avg_ratio, label="dSPW", color='b')
plt.fill_between(positions, (avg_ratio-ci), (avg_ratio+ci), color='b', alpha=.1)
plt.plot(positions, avg_r_ratio, label="rSPW", color='g')
plt.fill_between(positions, (avg_r_ratio-ci_r), (avg_r_ratio+ci_r), color='g', alpha=.1)
plt.plot(positions, avg_ratio_g, label="d-KPS18", color='r')
plt.fill_between(positions, (avg_ratio_g-ci_g), (avg_ratio_g+ci_g), color='r', alpha=.1)
plt.plot(positions, avg_ratio_gr, label="r-KPS18", color='m')
plt.fill_between(positions, (avg_ratio_gr-ci_gr), (avg_ratio_gr+ci_gr), color='m', alpha=.1)
formated_list = ['%d' % elem for elem in std_list ]
plt.xticks(positions, formated_list);
plt.legend(loc='upper right')
plt.xlabel("$\sigma$", fontsize=16)
plt.ylabel("Competitive Ratio", fontsize=16)
plt.savefig("competitive ratio against sigma")