def example_instance():
nP = 2
jobs = [1, 2, 3, 4]
diff = []
embedding = {
0: [1760, 1632, 1764],
1: [1628, 1636, 1634],
2: [1638, 1630, 1627],
3: [1752, 1624, 1759, 1767],
4: [1763, 1635, 1765],
5: [1761, 1633],
6: [1754, 1626, 1758, 1766],
7: [1631, 1639, 1625],
8: [1637, 1629]
}
# base_sampler = neal.SimulatedAnnealingSampler()
base_sampler = DWaveSampler()
#base_sampler.properties['extended_j_range'] = [-10.0, 10.0]
G = dnx.chimera_graph(16, 16, 4)
nodelist = G.nodes()
edgelist = G.edges()
sampler = dimod.StructureComposite(base_sampler, nodelist, edgelist)
new_sampler = FixedEmbeddingComposite(sampler, embedding)
# print new_sampler.properties
# print new_sampler.parameters
#new_sampler = VirtualGraphComposite(sampler, embedding, chain_strength=-6.0)
Q = create.createQUBO(jobs, nP, diff)
solutions = {
'energy': [],
"valid": [],
'max_time': [],
'chain_break_fraction': []
}
for k in range(10):
s = solve.solveQUBO(Q, jobs, nP, new_sampler, 0)
solutions['energy'].append(s['energy'])
solutions['valid'].append(s['valid'])
solutions['max_time'].append(s['max_time'])
solutions['chain_break_fraction'].append(s['chain_break_fraction'])
solve.evaluate(solutions)
print(solutions)
print("Durchschnittliche Anteil gebrochender Ketten: ",
sum(solutions['chain_break_fraction']) / 100)
def compare_coefficients():
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
rc('text', usetex=True)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['text.latex.unicode'] = True
jobs = [1, 2, 3, 4]
nP = 2
diff = [1]
for c2 in range(4, 5):
ran = []
gap = []
rel = []
for c1 in range(1, 6):
Q = create.createQUBO(jobs, nP, diff, c1, c2)
best, second, worst = solve.solution_distribution(
Q, len(jobs), nP, diff,
max(jobs) * c1 + 1, 1)
ran.append(worst - best)
gap.append(second - best)
rel.append(float(second - best) / float(worst - best))
x_ran = list(np.arange(0.8, 5.8, 1))
x_gap = list(np.arange(1.2, 6.2, 1))
x_rel = list(range(1, 6, 1))
f, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.bar(x_ran, ran, width=0.4)
ax1.bar(x_gap, gap, color='g', width=0.4)
ax1.set_yscale('log')
ax2.plot(x_rel, rel, 'r')
ax2.tick_params(axis='y', colors='r')
plt.title(str(c1) + ", " + str(c2))
ax1.legend(['Spannweite', u'Lucke'])
ax1.set_ylabel("Energiespektrum")
ax1.set_xlabel("Koeffizent A")
ax2.set_ylabel(u"Verhaltnis")
tikz_save(str(c1) + ", " + str(c2),
figureheight='\\figureheight',
figurewidth='\\figurewidth')
def example_instance_delete_given(percentage):
nP = 3
jobs = [5, 7, 3, 5, 9, 6, 3, 1, 3, 1]
diff = [1, 2, 2, 5]
base_sampler = DWaveSampler()
nodelist = base_sampler.nodelist
edgelist = base_sampler.edgelist
# print new_sampler.properties
# print new_sampler.parameters
# new_sampler = VirtualGraphComposite(sampler, embedding, chain_strength=-6.0)
Q, num_entries, max_entry = create.createQUBO(jobs, nP, diff)
# nur noch nach absolutwerten sortiert, enthält nur die keys nicht die values
sorted_q = sorted(Q, key=lambda dict_key: abs(Q[dict_key]))
embedding = {
0: [
622, 614, 606, 598, 726, 594, 722, 978, 630, 638, 850, 593, 465,
337, 602, 1106, 590
],
1: [
1090, 834, 1110, 578, 1102, 1094, 962, 1118, 706, 1134, 1126, 1142,
1150, 710, 836, 718, 1112, 702, 583
],
2: [894, 830, 838, 846, 854, 886, 878, 870, 862, 851, 723, 595, 979],
3: [362, 106, 490, 234, 618, 749, 1002, 746, 874, 110, 118, 126, 1130],
4: [
342, 334, 326, 318, 382, 374, 569, 953, 366, 358, 350, 313, 441,
697, 825, 1081
],
5: [
315, 325, 341, 333, 317, 365, 357, 349, 443, 571, 699, 381, 827,
955, 373
],
6: [123, 635, 379, 1019, 763, 251, 891, 1147, 507, 1275, 1151],
7: [764, 756, 716, 748, 740, 732, 724, 708, 700],
8: [
591, 599, 607, 345, 985, 601, 729, 857, 615, 473, 639, 1113, 623,
631
],
9: [
511, 455, 704, 448, 471, 463, 960, 576, 479, 487, 496, 495, 503,
320, 624, 832, 1088
],
10: [
709, 1141, 971, 1099, 1109, 843, 717, 844, 715, 701, 1101, 1117,
1125, 1133, 725, 733, 1149, 741
],
11: [761, 1017, 633, 889, 505, 377, 249, 1145, 121, 1273, 127],
12: [893, 829, 885, 877, 837, 845, 853, 861, 869, 880, 752, 1008],
13: [1007, 999, 991, 959, 967, 975, 983, 1015, 1023, 977, 849, 721],
14: [
1127, 961, 1119, 833, 1095, 1103, 1111, 1089, 705, 839, 1135, 831,
847, 1120, 1248, 1254, 1262, 1270, 1278, 855, 863
],
15: [
608, 589, 605, 1098, 597, 613, 480, 352, 586, 842, 714, 970, 224,
96, 101, 109, 117, 125
],
16: [611, 355, 483, 227, 253, 245, 237, 229, 739, 867, 871, 995, 1123],
17: [491, 1003, 107, 363, 619, 747, 246, 238, 235, 875, 254, 1131],
18: [482, 758, 766, 750, 742, 994, 738, 610, 866, 1122, 354],
19: [
988, 835, 1004, 996, 980, 707, 964, 972, 992, 864, 868, 876, 884,
892, 956, 963
],
20: [632, 1144, 888, 504, 1016, 248, 376, 760, 1272, 120],
21: [
982, 958, 966, 974, 950, 946, 818, 690, 990, 998, 1006, 1014, 1022,
692
],
22: [
442, 452, 468, 460, 1082, 444, 570, 698, 826, 954, 476, 484, 458,
492, 500, 508, 314
],
23: [728, 216, 344, 856, 472, 236, 228, 220, 600, 244, 984, 734, 252],
24: [
510, 502, 494, 865, 486, 481, 478, 353, 609, 993, 737, 1121, 470,
462
],
25: [
84, 72, 456, 200, 328, 92, 76, 100, 98, 226, 968, 108, 116, 584,
461, 469, 712, 124, 840, 1096
],
26: [703, 751, 719, 727, 767, 759, 743, 735, 711, 695, 736],
27: [475, 493, 485, 477, 987, 603, 731, 859, 501, 509, 347, 1115],
28:
[1011, 629, 371, 621, 627, 637, 755, 243, 499, 883, 1139, 115, 1267],
29: [
1005, 997, 989, 1013, 965, 976, 973, 981, 957, 1021, 848, 720, 592,
596, 588
],
30:
[329, 343, 335, 585, 383, 359, 351, 457, 367, 375, 713, 841, 969, 338],
31: [
340, 332, 324, 364, 348, 356, 316, 312, 440, 568, 696, 824, 952,
372, 380, 1080
],
32: [
1116, 1114, 1124, 1084, 1092, 1100, 1108, 986, 730, 1132, 858,
1140, 1148, 1097, 1083
],
33: [1018, 762, 250, 634, 122, 506, 378, 890, 1020, 1146, 1274],
34: [757, 626, 1010, 754, 882, 498, 1138, 370, 242, 765, 114, 1266],
35:
[1001, 617, 636, 628, 620, 873, 489, 745, 361, 233, 105, 1129, 612],
36: [1009, 625, 881, 497, 369, 241, 895, 753, 887, 1137, 113, 1265],
37: [
255, 247, 872, 239, 488, 616, 232, 360, 744, 1000, 1128, 240, 112,
231
]
}
sampler = dimod.StructureComposite(base_sampler, nodelist, edgelist)
new_sampler = FixedEmbeddingComposite(sampler, embedding)
alldata = []
n_deleted = []
v_deleted = []
for p in percentage:
print("Percentage: ", p)
Q_deleted, max, deleted = get_given_percentage_qubo(
p, copy.deepcopy(Q), copy.deepcopy(sorted_q), num_entries)
n_deleted.append(deleted)
print("Größter gelöschter Wert: ", max)
v_deleted.append(max)
solution = []
kaputt = 0
for i in range(100):
result = solve.solveQUBO(Q_deleted, jobs, nP, new_sampler)
#print(result)
solution.append(result)
if result['valid'] == False:
kaputt += 1
print(kaputt, " kaputte Lösungen für Threshold und ", p)
alldata.append(solution)
dist = []
for i in range(len(alldata)):
dist.append(
[alldata[i][j]['max_time'] for j in range(len(alldata[i]))])
print(dist)
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
## for Palatino and other serif fonts use:
# rc('font',**{'family':'serif','serif':['Palatino']})
rc('text', usetex=True)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.boxplot(dist)
plt.xticks([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21
], [
0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6,
0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0
])
plt.xlabel("Prozentanzahl entfernter Elemente")
plt.ylabel("Umsteigedistanzen")
plt.title("Jeweils 200 Aufrufe, beste Ergebnisse, Datensatz tai12, " +
u'12 Flüge, 12 Gates, keine Kosten')
tikz_save('mms_given.tikz',
figureheight='\\figureheight',
figurewidth='\\figurewidth')
#plt.show()
return alldata, n_deleted, v_deleted
def example_instance_big():
nP = 3
jobs = [5, 7, 3, 5, 9, 6, 3, 1, 3, 1]
diff = [1, 2, 2, 5]
base_sampler = DWaveSampler()
nodelist = base_sampler.nodelist
edgelist = base_sampler.edgelist
Q, num_entries, max_value = create.createQUBO(jobs, nP, diff)
embedding = {
0: [
622, 614, 606, 598, 726, 594, 722, 978, 630, 638, 850, 593, 465,
337, 602, 1106, 590
],
1: [
1090, 834, 1110, 578, 1102, 1094, 962, 1118, 706, 1134, 1126, 1142,
1150, 710, 836, 718, 1112, 702, 583
],
2: [894, 830, 838, 846, 854, 886, 878, 870, 862, 851, 723, 595, 979],
3: [362, 106, 490, 234, 618, 749, 1002, 746, 874, 110, 118, 126, 1130],
4: [
342, 334, 326, 318, 382, 374, 569, 953, 366, 358, 350, 313, 441,
697, 825, 1081
],
5: [
315, 325, 341, 333, 317, 365, 357, 349, 443, 571, 699, 381, 827,
955, 373
],
6: [123, 635, 379, 1019, 763, 251, 891, 1147, 507, 1275, 1151],
7: [764, 756, 716, 748, 740, 732, 724, 708, 700],
8: [
591, 599, 607, 345, 985, 601, 729, 857, 615, 473, 639, 1113, 623,
631
],
9: [
511, 455, 704, 448, 471, 463, 960, 576, 479, 487, 496, 495, 503,
320, 624, 832, 1088
],
10: [
709, 1141, 971, 1099, 1109, 843, 717, 844, 715, 701, 1101, 1117,
1125, 1133, 725, 733, 1149, 741
],
11: [761, 1017, 633, 889, 505, 377, 249, 1145, 121, 1273, 127],
12: [893, 829, 885, 877, 837, 845, 853, 861, 869, 880, 752, 1008],
13: [1007, 999, 991, 959, 967, 975, 983, 1015, 1023, 977, 849, 721],
14: [
1127, 961, 1119, 833, 1095, 1103, 1111, 1089, 705, 839, 1135, 831,
847, 1120, 1248, 1254, 1262, 1270, 1278, 855, 863
],
15: [
608, 589, 605, 1098, 597, 613, 480, 352, 586, 842, 714, 970, 224,
96, 101, 109, 117, 125
],
16: [611, 355, 483, 227, 253, 245, 237, 229, 739, 867, 871, 995, 1123],
17: [491, 1003, 107, 363, 619, 747, 246, 238, 235, 875, 254, 1131],
18: [482, 758, 766, 750, 742, 994, 738, 610, 866, 1122, 354],
19: [
988, 835, 1004, 996, 980, 707, 964, 972, 992, 864, 868, 876, 884,
892, 956, 963
],
20: [632, 1144, 888, 504, 1016, 248, 376, 760, 1272, 120],
21: [
982, 958, 966, 974, 950, 946, 818, 690, 990, 998, 1006, 1014, 1022,
692
],
22: [
442, 452, 468, 460, 1082, 444, 570, 698, 826, 954, 476, 484, 458,
492, 500, 508, 314
],
23: [728, 216, 344, 856, 472, 236, 228, 220, 600, 244, 984, 734, 252],
24: [
510, 502, 494, 865, 486, 481, 478, 353, 609, 993, 737, 1121, 470,
462
],
25: [
84, 72, 456, 200, 328, 92, 76, 100, 98, 226, 968, 108, 116, 584,
461, 469, 712, 124, 840, 1096
],
26: [703, 751, 719, 727, 767, 759, 743, 735, 711, 695, 736],
27: [475, 493, 485, 477, 987, 603, 731, 859, 501, 509, 347, 1115],
28:
[1011, 629, 371, 621, 627, 637, 755, 243, 499, 883, 1139, 115, 1267],
29: [
1005, 997, 989, 1013, 965, 976, 973, 981, 957, 1021, 848, 720, 592,
596, 588
],
30:
[329, 343, 335, 585, 383, 359, 351, 457, 367, 375, 713, 841, 969, 338],
31: [
340, 332, 324, 364, 348, 356, 316, 312, 440, 568, 696, 824, 952,
372, 380, 1080
],
32: [
1116, 1114, 1124, 1084, 1092, 1100, 1108, 986, 730, 1132, 858,
1140, 1148, 1097, 1083
],
33: [1018, 762, 250, 634, 122, 506, 378, 890, 1020, 1146, 1274],
34: [757, 626, 1010, 754, 882, 498, 1138, 370, 242, 765, 114, 1266],
35:
[1001, 617, 636, 628, 620, 873, 489, 745, 361, 233, 105, 1129, 612],
36: [1009, 625, 881, 497, 369, 241, 895, 753, 887, 1137, 113, 1265],
37: [
255, 247, 872, 239, 488, 616, 232, 360, 744, 1000, 1128, 240, 112,
231
]
}
sampler = dimod.StructureComposite(base_sampler, nodelist, edgelist)
new_sampler = FixedEmbeddingComposite(sampler, embedding)
solutions = {
'energy': [],
"valid": [],
'max_time': [],
'chain_break_fraction': []
}
for k in range(10):
s = solve.solveQUBO(Q, jobs, nP, new_sampler)
solutions['energy'].append(s['energy'])
solutions['valid'].append(s['valid'])
solutions['max_time'].append(s['max_time'])
solutions['chain_break_fraction'].append(s['chain_break_fraction'])
solve.evaluate(solutions)
print(solutions)
print("Durchschnittliche Anteil gebrochender Ketten: ",
sum(solutions['chain_break_fraction']) / 100)
def check_if_proper():
nP = 2
instances = [[[1, 2, 3, 4], [0]], [[2, 3, 4, 5], [0]], [[2, 4, 6, 8], [0]],
[[1, 2, 3, 6], [0]], [[2, 3, 4, 7], [2]],
[[10, 11, 12, 15], [2]], [[10, 20, 30, 40], [0]]]
for i in instances:
jobs = i[0]
diff = i[1]
table = []
print
print("#########################")
print("Instance ", i)
print(
"c1 | c2 | Optimum | Range | Gap |Relation"
)
for c1_mult in range(1, 11):
c1 = max(jobs) * c1_mult + 1
Q = create.createQUBO(jobs, nP, diff, c1_mult, 1)
best, second, worst = solve.solution_distribution(
Q, len(jobs), nP, diff, c1, 1)
print(c1, " | 1 | ", best, " | ", worst - best,
" | ", second - best, " | ",
float(second - best) / float(worst - best))
embedding = {
0: [1760, 1632, 1764],
1: [1628, 1636, 1634],
2: [1638, 1630, 1627],
3: [1752, 1624, 1759, 1767],
4: [1763, 1635, 1765],
5: [1761, 1633],
6: [1754, 1626, 1758, 1766],
7: [1631, 1639, 1625],
8: [1637, 1629]
}
base_sampler = neal.SimulatedAnnealingSampler()
G = dnx.chimera_graph(16, 16, 4)
nodelist = G.nodes()
edgelist = G.edges()
source_edgelist = list(Q)
sampler = dimod.StructureComposite(base_sampler, nodelist,
edgelist)
new_sampler = FixedEmbeddingComposite(sampler, embedding)
solution = {'energy': [], "valid": [], 'max_time': []}
for k in range(100):
s = solve.solveQUBO(Q, jobs, nP, new_sampler)
solution['energy'].append(s['energy'])
solution['valid'].append(s['valid'])
solution['max_time'].append(s['max_time'])
table.append(solution)
print("Solutions ")
print(
"c1 | c2 | Energies | Valid | Max_Time "
)
for c1_mult in range(1, 11):
print(
max(jobs) * c1_mult + 1, " | 1 | ",
table[c1_mult - 1]['energy'], " | ",
table[c1_mult - 1]['valid'], " | ",
table[c1_mult - 1]['max_time'])