├── EUTB ├── EUTB.py ├── README.md └── data │ └── sample.txt ├── Naive_Bayesian_EM ├── 1.gif ├── 2.gif ├── README.md └── k_7_com_features.py ├── README.md ├── gSpan ├── README.md ├── data_readme.txt ├── graph.data ├── intro │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ └── 5.png └── main.cpp └── ullmann ├── Q4.my ├── README.md ├── Ullmann介绍.pdf ├── main.cpp └── mygraphdb.data /EUTB/EUTB.py: -------------------------------------------------------------------------------- 1 | # !/usr/bin/python 2 | # -*- coding:utf-8 -*- 3 | 4 | import numpy as np 5 | from scipy.stats import multivariate_normal 6 | from sklearn.mixture import GMM 7 | #from mpl_toolkits.mplot3d import Axes3D 8 | #import matplotlib as mpl 9 | #import matplotlib.pyplot as plt 10 | from sklearn.cross_validation import train_test_split 11 | from sklearn.naive_bayes import MultinomialNB 12 | from sklearn.naive_bayes import GaussianNB 13 | from sklearn.linear_model import LogisticRegression 14 | import random 15 | import math 16 | import sys 17 | 18 | # reload(sys) 19 | # sys.setdefaultencoding('utf8') 20 | 21 | # mpl.rcParams['font.sans-serif'] = [u'SimHei'] 22 | # mpl.rcParams['axes.unicode_minus'] = False 23 | 24 | 25 | class EUTB(): 26 | 27 | iter_tot = 100 28 | k1 = 10 29 | k2 = 10 30 | n = 10 # 调整权重时，滑动窗口大小 31 | lamda = 0.5 # 空间正则的占比 32 | yipu = 20 # 时间正则的占比 33 | gamma = 0.5 # 牛顿法迭代步长 34 | alpha = 1.2 # 阈值 35 | 36 | user = set() # user: name -> id 对应 37 | time = set() # time: name -> id 对应 38 | word = set() # words: name -> id 对应 39 | user_dict = dict() 40 | time_dict = dict() 41 | word_dict = dict() 42 | # M 43 | 44 | 45 | def __init__(self): 46 | with open('./data/checkins.txt') as f: 47 | self.data = f.readlines() 48 | self.data = self.data[0:50] 49 | 50 | def pre_deal_w(self): 51 | """ 52 | 预处理统计所有词汇，得到词表； 53 | 同时得到W[u,t,w]词频数据，t按照同一天的计算 54 | """ 55 | # user & time & word 56 | for line in self.data: 57 | line = line.split('\t') 58 | self.user.add(line[0]) # user 59 | self.time.add(line[4][:10]) # time 60 | line = line[6].split('|') # words 61 | for w in line: 62 | self.word.add(w) 63 | self.user_size = len(self.user) 64 | self.time_size = len(self.time) 65 | self.word_size = len(self.word) 66 | print("user_size = " + str(len(self.user))) 67 | print("time size = " + str(len(self.time))) 68 | print("word_size = " + str(len(self.word))) 69 | 70 | # 构造user/time/word下标映射 71 | i = 0 72 | for line in self.user: 73 | self.user_dict[line] = i 74 | i+=1 75 | i = 0 76 | for line in self.time: 77 | self.time_dict[line] = i 78 | i+=1 79 | i = 0 80 | for line in self.word: 81 | self.word_dict[line] = i 82 | i+=1 83 | 84 | # 构造M[u,t,w]词频数组 85 | self.M = np.array([[[0] * len(self.word)] * len(self.time)] * len(self.user)) 86 | print("M.shape = " + str(self.M.shape)) 87 | for line in self.data: 88 | line = line.split('\t') 89 | u = self.user_dict[line[0]] # user 90 | t = self.time_dict[line[4][:10]] # time 91 | line = line[6].split('|') # words 92 | for w in line: 93 | w = self.word_dict[w] 94 | self.M[u][t][w] += 1 95 | 96 | 97 | 98 | def pre_deal_data(self): 99 | """ 100 | 数据预处理： 101 | （1）分词，统计所有词汇；同时得到W[u,t,w]词频数据，t按照一天算 102 | （2）获取π(u,v) 103 | （3）get_burst_degree()，计算改进3需要的预处理内容 104 | """ 105 | print("pre_deal data...") 106 | self.pre_deal_w() 107 | self.get_pai() 108 | self.get_burst_degree() # 第三项：这个只需要计算一次，放在初始化中 109 | print("pre_deal data end!\n") 110 | 111 | def get_pai(self): 112 | # 目前没有找到社交网络上u/v之间好友信息，暂时保留不处理 113 | # deal with π(u,v) 114 | 115 | return 0 116 | 117 | def get_burst_degree(self): 118 | """ 119 | 处理第三个提升，获得burst_degree数组 120 | """ 121 | # 统计每个词在各个时刻出现的次数，把矩阵M的User积掉即可 122 | freq = np.array([[0.0]* self.time_size] * self.word_size) 123 | for i in range(self.word_size): 124 | # cnt = 0 125 | for j in range(self.time_size): 126 | tot = 0 127 | for k in range(self.user_size): 128 | tot += self.M[k][j][i] 129 | freq[i][j] = tot 130 | # cnt += tot 131 | # print(i,cnt) 132 | """ 133 | for i in range(self.time_size): 134 | cnt = 0 135 | for j in range(self.word_size): 136 | cnt += freq[j][i] 137 | print(i, cnt) 138 | print([x[10] for x in freq]) 139 | """ 140 | # 计算burst_degree 141 | self.burst_degree = np.array([[0.0] * self.time_size] * self.word_size) 142 | for i in range(self.word_size): 143 | self.burst_degree[i][0] = self.alpha # 第一个不知道应该赋值什么，先赋值为alpha吧 144 | for j in range(1, self.time_size): 145 | # 对窗口n中的数据计算 [i-n, i-1] 146 | u = 0.0 147 | for k in range(1, self.n): 148 | if j-k<0: 149 | break 150 | u += freq[i][j-k] # 这个里面大多数都是0，很有可能前面计算的均值和方差都是0，这样就没办法用 151 | u /= (float(min(self.n, j+1))) 152 | 153 | thita = 0.0 154 | for k in range(1, self.n): 155 | if j-k<0: 156 | break 157 | thita += (freq[i][j-k]-u)*(freq[i][j-k]-u) 158 | thita /= (float(min(self.n, j+1))) 159 | thita = math.sqrt(thita) 160 | 161 | # 取一个max，有超参数alpha的影响 162 | if thita==0: 163 | self.burst_degree[i][j] = self.alpha 164 | else: 165 | self.burst_degree[i][j] = max(abs(freq[i][j] - u)/thita, self.alpha) 166 | # if freq[i][j] > 0: 167 | # print(u, thita, self.burst_degree[i][j]) 168 | 169 | # 打印出burst_degree看一下 170 | # print(self.burst_degree[1]) 171 | 172 | return 1 173 | 174 | def iterator_theta(self): 175 | 176 | return 0 177 | 178 | 179 | def spatial_regularization(self): 180 | """ 181 | 第一个正则 182 | 待补充 183 | """ 184 | # theta_u_new = [[0.0]*self.user_size] * self.k1 185 | 186 | return 0 187 | 188 | def temporal_regularization(self): 189 | """ 190 | 第二个正则 191 | 处理theta_t二维数组，迭代 192 | """ 193 | gamma = self.gamma 194 | theta_t_new = np.array([[0.0]*self.k2] * self.time_size) 195 | theta_t_new2 = np.array([[0.0]*self.k2] * self.time_size) 196 | 197 | for i in range(len(self.theta_t)): 198 | for j in range(len(self.theta_t[0])): 199 | theta_t_new2[i][j] = self.theta_t[i][j] 200 | theta_t_new[i][j] = self.theta_t[i][j] 201 | expect = self.com_expect(theta_t_new2) 202 | expect2 = expect + 0.01 203 | 204 | while(expect2 > expect and (expect2-expect)>=0.001): #期望需要增加，且需要增加足够多 205 | for i in range(len(theta_t_new2)): 206 | for j in range(len(theta_t_new2[0])): 207 | theta_t_new[i][j] = theta_t_new2[i][j] 208 | expect = expect2 209 | for i in range(self.k2): 210 | theta_t_new2[0][i] = theta_t_new[0][i] 211 | theta_t_new2[self.time_size-1][i] = theta_t_new[self.time_size-1][i] 212 | for t in range(1, self.time_size-1): 213 | for i in range(self.k2): 214 | # theta_t_new2[t][i] = (1.0-gamma)*theta_t_new[t][i] + gamma*(theta_t_new[t-1][i] + theta_t_new[t+1][i])/2.0 215 | theta_t_new2[t][i] = (1.0-gamma)*theta_t_new[t][i] + gamma*(theta_t_new[t-1][i] + theta_t_new[t+1][i])/2.0 216 | 217 | # 归一化 218 | for i in range(len(theta_t_new2)): 219 | tot = 0.0 220 | for j in range(len(theta_t_new2[0])): 221 | tot += (theta_t_new2[i][j]) 222 | for j in range(len(theta_t_new2[0])): 223 | theta_t_new2[i][j] /= tot 224 | expect2 = self.com_expect(theta_t_new2) 225 | # print("E------:" + str(expect2) + "," + str(expect)) 226 | 227 | if expect > self.com_expect(self.theta_t): 228 | for i in range(len(self.theta_t)): 229 | for j in range(len(self.theta_t[0])): 230 | self.theta_t[i][j] = theta_t_new[i][j] 231 | """ 232 | # 在上面归一化过了 233 | for i in range(len(self.theta_t)): 234 | tot = 0.0 235 | for j in range(len(self.theta_t[0])): 236 | tot += (self.theta_t[i][j]) 237 | for j in range(len(self.theta_t[0])): 238 | self.theta_t[i][j] /= tot 239 | """ 240 | 241 | return 1 242 | 243 | def burst_weighted(self): 244 | """ 245 | 第三个提升 246 | 对phi_t进行权重调整，需要重新归一化 247 | """ 248 | for w in range(len(self.phi_t[0])): 249 | t1 = set() 250 | for t in range(self.time_size): 251 | if self.burst_degree[w][t]>self.alpha: 252 | t1.add(t) 253 | for k in range(len(self.phi_t)): 254 | t2 = set() 255 | maxc = -1.0 256 | for t in range(self.time_size): 257 | if self.theta_t[t][k] > maxc: 258 | maxc = self.theta_t[t][k] 259 | maxc *= 0.9 260 | for t in range(self.time_size): 261 | if self.theta_t[t][k] > maxc: 262 | t2.add(t) 263 | t = t1 & t2 # 取两个set的交集，在这些时间下的主题k，下的词w， 264 | # print(t) 265 | maxc = -1.0 266 | for j in t: 267 | if self.burst_degree[w][j] > maxc: 268 | maxc = self.burst_degree[w][j] 269 | # if math.sqrt(max(maxc, self.alpha)) > 1.0: 270 | # print(math.sqrt(max(maxc, self.alpha)), self.phi_t[k][w], self.phi_t[k][w]*math.sqrt(max(maxc, self.alpha))) 271 | self.phi_t[k][w] *= math.sqrt(max(maxc, self.alpha)) 272 | # print(math.sqrt(max(maxc, self.alpha))) 273 | # 归一化 274 | for k in range(self.k2): 275 | tot = 0.0 276 | for w in range(self.word_size): 277 | tot += self.phi_t[k][w] 278 | for w in range(self.word_size): 279 | self.phi_t[k][w] /= tot 280 | 281 | return 1 282 | 283 | def com_expect(self, theta_t_new): 284 | """ 285 | 按照输入的theta_t计算E 286 | """ 287 | E = 0.0 288 | for u in range(self.user_size): 289 | for t in range(self.time_size): 290 | for w in range(self.word_size): 291 | cnt1 = 0.0 292 | for k in range(self.k1): 293 | cnt1 += self.theta_u[u][k]*self.phi_u[k][w] 294 | cnt2 = 0.0 295 | for k in range(self.k2): 296 | cnt2 += self.theta_t[t][k]*self.phi_t[k][w] 297 | E += ((math.log(self.lamda_u * cnt1 + (1-self.lamda_u) * cnt2))*self.M[u][t][w]) 298 | 299 | # print("E:" + str(E)) 300 | 301 | # 下面计算正则 302 | # 第一项：缺少数据，暂时不加 303 | tot = 0.0 304 | 305 | # 第二项 306 | tot = 0.0 307 | for t in range(len(theta_t_new)-1): 308 | for k in range(self.k2): 309 | tot += (theta_t_new[t][k] - theta_t_new[t+1][k]) * (theta_t_new[t][k] - theta_t_new[t+1][k]) 310 | # print("err:" + str(tot)) 311 | E -= self.yipu*tot 312 | 313 | return E 314 | 315 | def EM(self): 316 | """ 317 | 开始迭代 318 | （1） 319 | （2） 320 | （3） 321 | """ 322 | print("lamda = " + str(self.lamda)) 323 | # 随机初始化theta_u/theta_t/phi_u/phi_t 324 | self.theta_u = np.random.random((self.user_size,self.k1)) 325 | self.theta_t = np.random.random((self.time_size,self.k2)) 326 | self.phi_u = np.random.random((self.k1,self.word_size)) 327 | self.phi_t = np.random.random((self.k2,self.word_size)) 328 | self.lamda_u = np.random.random(1) # 第一轮随机，之后在M步迭代计算 329 | print("lamda_u = " + str(self.lamda_u)) 330 | 331 | iter_num = 0 332 | while(iter_num < self.iter_tot): 333 | 334 | # E 335 | # print("E...") 336 | # 对每一个单词来说，计算其属于stable和temporal主题的概率分布；这里数据结构不行，最好是每个u/t下单词的数量，为第三维度的大小 337 | p_u = np.array([[[[0.0] * self.k1] * self.word_size] * self.time_size] * self.user_size) 338 | p_t = np.array([[[[0.0] * self.k2] * self.word_size] * self.time_size] * self.user_size) 339 | """ 340 | B_u = 0.0 341 | B_t = 0.0 342 | for line in self.data: 343 | line = line.split('\t') 344 | u = self.user_dict[line[0]] # user 345 | t = self.time_dict[line[4][:10]] # time 346 | line = line[6].split('|') # words 347 | for w in line: 348 | w = self.word_dict[w] 349 | for k in range(self.k1): 350 | B_u += self.theta_u[u][k]*self.phi_u[k][w] 351 | for k in range(self.k2): 352 | B_t += self.theta_t[u][k]*self.phi_t[k][w] 353 | 354 | B = self.lamda_u*B_u + (1-self.lamda_u)*B_t 355 | """ 356 | for line in self.data: 357 | line = line.split('\t') 358 | u = self.user_dict[line[0]] # user 359 | t = self.time_dict[line[4][:10]] # time 360 | line = line[6].split('|') # words 361 | words = set() 362 | for w in line: 363 | words.add(self.word_dict[w]) 364 | for w in words: 365 | # 已经是换好的序号下标了 366 | B_u = 0.0 367 | B_t = 0.0 368 | for k in range(self.k1): 369 | B_u += self.theta_u[u][k]*self.phi_u[k][w] 370 | for k in range(self.k2): 371 | B_t += self.theta_t[t][k]*self.phi_t[k][w] 372 | B = self.lamda_u*B_u + (1-self.lamda_u)*B_t 373 | 374 | #注意这里对每个单词只计算一次，没用到词频（原数据集中没进行处理，所以这里需要单独处理一下） 375 | for k in range(self.k1): 376 | p_u[u][t][w][k] += self.lamda_u * self.theta_u[u][k] * self.phi_u[k][w] / B 377 | for k in range(self.k2): 378 | p_t[u][t][w][k] += (1-self.lamda_u)*self.theta_t[t][k] * self.phi_t[k][w] / B 379 | 380 | # M 381 | # print("M...theta_u/t") 382 | # (1)正常计算 383 | self.theta_u = np.array([[0.0] *self.k1] * self.user_size) 384 | self.theta_t = np.array([[0.0] *self.k2] * self.time_size) 385 | tot_u = [0.0] * self.user_size 386 | tot_t = [0.0] * self.time_size 387 | for line in self.data: 388 | line = line.split('\t') 389 | u = self.user_dict[line[0]] # user 390 | # print(u, line[0]) 391 | t = self.time_dict[line[4][:10]] # time 392 | line = line[6].split('|') # words 393 | for w in line: 394 | w = self.word_dict[w] 395 | for k in range(self.k1): 396 | tot_u[u] += p_u[u][t][w][k] 397 | for k in range(self.k2): 398 | tot_t[t] += p_t[u][t][w][k] 399 | 400 | for line in self.data: 401 | line = line.split('\t') 402 | u = self.user_dict[line[0]] # user 403 | t = self.time_dict[line[4][:10]] # time 404 | line = line[6].split('|') # words 405 | for w in line: 406 | w = self.word_dict[w] 407 | for k in range(self.k1): 408 | self.theta_u[u][k] += p_u[u][t][w][k] 409 | for k in range(self.k2): 410 | self.theta_t[t][k] += p_t[u][t][w][k] 411 | # 归一化 412 | for i in range(len(self.theta_u)): 413 | for k in range(self.k1): 414 | self.theta_u[i][k] /= tot_u[i] 415 | for i in range(len(self.theta_t)): 416 | for k in range(self.k2): 417 | self.theta_t[i][k] /= tot_t[i] 418 | 419 | # phi_u & phi_t 420 | # print("M...phi_u/t") 421 | # self.phi_u = 0.0 422 | # self.phi_t = 0.0 423 | self.phi_u = np.array([[0.0] * self.word_size] * self.k1) 424 | self.phi_t = np.array([[0.0] * self.word_size] * self.k2) 425 | tot_u = [0.0] * self.k1 426 | tot_t = [0.0] * self.k2 427 | for line in self.data: 428 | line = line.split('\t') 429 | u = self.user_dict[line[0]] # user 430 | t = self.time_dict[line[4][:10]] # time 431 | line = line[6].split('|') # words 432 | for w in line: 433 | w = self.word_dict[w] 434 | for k in range(self.k1): 435 | tot_u[k] += p_u[u][t][w][k] 436 | for k in range(self.k2): 437 | tot_t[k] += p_t[u][t][w][k] 438 | 439 | for line in self.data: 440 | line = line.split('\t') 441 | u = self.user_dict[line[0]] # user 442 | t = self.time_dict[line[4][:10]] # time 443 | line = line[6].split('|') # words 444 | for w in line: 445 | w = self.word_dict[w] 446 | for k in range(self.k1): 447 | self.phi_u[k][w] += p_u[u][t][w][k] 448 | for k in range(self.k2): 449 | self.phi_t[k][w] += p_t[u][t][w][k] 450 | 451 | # 归一化 452 | for k in range(len(self.phi_u)): 453 | for w in range(len(self.phi_u[0])): 454 | self.phi_u[k][w] /= tot_u[k] 455 | for k in range(len(self.phi_t)): 456 | for w in range(len(self.phi_t[0])): 457 | self.phi_t[k][w] /= tot_t[k] 458 | 459 | # lamda_u 460 | tot = 0.0 461 | self.lamda_u = 0.0 462 | for line in self.data: 463 | line = line.split('\t') 464 | u = self.user_dict[line[0]] # user 465 | t = self.time_dict[line[4][:10]] # time 466 | line = line[6].split('|') # words 467 | for w in line: 468 | w = self.word_dict[w] 469 | for k in range(self.k1): 470 | self.lamda_u += p_u[u][t][w][k] 471 | tot += p_u[u][t][w][k] 472 | for k in range(self.k2): 473 | tot += p_t[u][t][w][k] 474 | self.lamda_u /= tot 475 | 476 | # (2)两个正则 477 | #self.spatial_regularization() 478 | # print("temporal_regularization...") 479 | self.temporal_regularization() 480 | 481 | # (3)词权重调整 482 | # print("burst_weighted...") 483 | # self.burst_weighted() 484 | 485 | if iter_num%1==0: 486 | # 输出生成概率，保证递增（正确性） 487 | print("iter_num = " + str(iter_num) + ", E: " + str(self.com_expect(self.theta_t))) 488 | 489 | iter_num += 1 490 | return 0 491 | 492 | def print_ans(self): 493 | """ 494 | 对temporal topics输出，输出所有topic的Top K的词来表征这个主题 495 | """ 496 | ans = self.word_dict.keys() 497 | top_num = 6 498 | 499 | for k in range(self.k2): 500 | """ 501 | maxc = -1.0 502 | flag = 0 503 | for k in range(self.k2): 504 | if self.theta_t[t][k] > maxc: 505 | maxc = self.theta_t[t][k] 506 | flag = k 507 | """ 508 | flag = k 509 | value = np.array([0.0]*top_num) #输出前top K的词 510 | words = np.array([0] * top_num) 511 | for w in range(self.word_size): 512 | # 对每一个词，查看其在当前flag主题下，是否在top 20中 513 | minc = 1000000.0 514 | wei = 0 515 | for v in range(len(value)): 516 | if(value[v] < minc): 517 | minc = value[v] 518 | wei = v 519 | if(self.phi_t[flag][w] > minc): 520 | value[wei] = self.phi_t[flag][w] 521 | words[wei] = w 522 | # 输出当前主题下top 20的单词，用来看当前主题如何（默认的时间区间为1天之内，这里查看某天主题分布情况） 523 | i = 0 524 | for w in words: 525 | print (i,ans[w],value[i]), 526 | i += 1 527 | print("\n") 528 | return 1 529 | 530 | def run(self): 531 | self.pre_deal_data() 532 | self.EM() 533 | return 0 534 | 535 | 536 | if __name__ == '__main__': 537 | eutb = EUTB() 538 | eutb.run() 539 | eutb.print_ans() 540 | 541 | 542 | 543 | 544 | 545 | 546 | 547 | -------------------------------------------------------------------------------- /EUTB/README.md: -------------------------------------------------------------------------------- 1 | # 论文算法实现 2 | A Unified Model for Stable and Temporal Topic Detection from Social Media Data 3 | 4 | # 输入数据参见data/sample.txt文件 5 | 可以在任意网站下载具有相关user-time-document的数据集（主要指twitter以及微博等） 6 | 7 | # 运行环境： 8 | python 2.7 9 | 10 | # 运行： 11 | python ./EUTB.py（注意修改input文件及其对应格式） 12 | -------------------------------------------------------------------------------- /EUTB/data/sample.txt: -------------------------------------------------------------------------------- 1 | 0 0 39.497277 -76.232232 2010-08-10 14:35:18 0 coffee|espresso|rest|stop|shop|shops| 2 | 0 1 39.856301 -75.076342 2010-08-10 16:00:28 1 atm|convenience|store|gas|deli|bodega|delis|bodegas| 3 | 0 2 39.376797 -74.435366 2010-08-10 18:45:42 2 casino|concert|hall|entertainment|gambling|gym|hotel|karaoke|music|venue|nightclub|nightlife|pizza|poker|pool|shopping|slots|spa|vh1|casinos|resort|resorts|hotels| 4 | 0 3 39.376797 -74.435366 2010-08-11 00:49:08 3 borgata|noodles|pho|ramen|noodle|house|chinese|restaurant|restaurants|asian| 5 | 0 4 39.942663 -75.158286 2010-08-11 19:02:10 4 fine|employees|food|grocery|organic|shopping|total|babes|whole|foods|yummy|store|stores|deli|bodega|delis|bodegas| 6 | 0 5 39.678294 -75.654413 2010-08-11 20:42:22 5 boutiques|fashionista|photobooth|shop|mall|malls| 7 | 0 6 39.005349 -78.338016 2010-08-25 05:46:10 6 american|restaurant|restaurants| 8 | 0 7 36.632045 -82.123238 2010-08-25 13:11:39 7 coffee|shop|shops|caf|cafs| 9 | 0 8 35.900615 -84.147460 2010-08-26 01:35:03 8 neighborhood|market|supercenter|walmart|wally|world|department|store|stores|miscellaneous|shop|shops| 10 | 0 9 35.899523 -84.158567 2010-08-26 17:10:08 9 barbeque|american|restaurant|restaurants| 11 | 0 10 35.918306 -84.084710 2010-08-26 19:04:22 10 apple|cameras|electronics|geek|squad|hardware|music|phones|retail|shopping|software|stereo|tv|video|games|wifi|xbox|store|stores| 12 | 0 11 35.941971 -83.978500 2010-08-26 20:11:22 11 grocery|store|stores| 13 | 0 12 35.900662 -84.157763 2010-08-27 00:16:39 12 mexican|restaurant|restaurants| 14 | 0 13 36.042741 -83.929064 2010-08-27 10:28:20 13 coffee|doughnut|donut|shop|shops|donuts| 15 | 0 14 40.183340 -80.220848 2010-10-16 00:21:45 14 neighborhood|market|supercenter|walmart|wally|world|food|shopping|department|store|stores|miscellaneous|shop|shops|parking| 16 | 0 15 39.180610 -86.525016 2010-10-16 17:25:52 15 college|football|indiana|university|iu|sports|stadium|stadiums|field|fields| 17 | 0 16 39.166387 -86.528084 2010-10-17 03:09:07 16 bar|beer|pong|college|kok|patios|tvs|bars|sports|cocktail| 18 | 0 17 40.418201 -86.816889 2010-10-18 16:07:10 6 american|restaurant|restaurants| 19 | 0 18 41.485499 -81.799178 2010-10-21 00:56:50 17 beer|grilled|cheese|trendy|vegan|friendly|vegetarian|sandwich|place|places|sandwiches|bar|bars| 20 | 0 19 41.499593 -81.690641 2010-10-21 17:31:08 18 bodies|cleveland|exhibit|exhibition|east|4th|education|gallery|general|entertainment|college|university|colleges|universities| 21 | 0 20 41.508513 -81.695430 2010-10-21 18:27:26 19 gallery|hall|of|fame|historychannel|museum|music|venue|rock|and|roll|the|examiner|museums|venues| 22 | 0 21 40.450709 -79.985588 2010-10-21 22:45:12 20 atm|beer|bravo|cash|only|food|fries|landmark|sandwich|place|places|sandwiches|bar|bars|american|restaurant|restaurants| 23 | 0 22 39.377983 -74.433918 2010-11-02 19:27:17 2 casino|concert|hall|entertainment|gambling|gym|hotel|karaoke|music|venue|nightclub|nightlife|pizza|poker|pool|shopping|slots|spa|vh1|casinos|resort|resorts|hotels| 24 | 0 23 39.376797 -74.435366 2010-11-02 23:37:33 3 borgata|noodles|pho|ramen|noodle|house|chinese|restaurant|restaurants|asian| 25 | 0 24 38.907729 -76.864433 2010-11-16 00:51:06 21 autocross|cheerleaders|concert|cones|fedexfield|football|hot|dogs|ichsan|rachman|live|men|nfl|quad|redskins|scca|soccer|solo|stadium|stadiums| 26 | 1 25 40.555760 -105.098112 2010-08-12 02:19:06 22 billiards|classroom|dance|floor|gymnasium|hot|tub|pool|poolbilliardsclassrooms|poolpoolclassrooms|stage|swimming|gym|fitness|center|gyms|or|centers| 27 | 1 26 40.553028 -105.077815 2010-08-14 14:48:53 23 gym|yoga|college|gyms|studio|studios| 28 | 1 27 40.521793 -105.055993 2010-08-14 17:48:12 24 healthy|juice|smoothies|wheatgrass|bar|bars| 29 | 1 28 40.525619 -105.022683 2010-08-15 21:37:13 25 department|store|stores|grocery|drugstore|pharmacy|drugstores|pharmacies| 30 | 1 29 40.555760 -105.098112 2010-08-17 00:53:42 22 billiards|classroom|dance|floor|gymnasium|hot|tub|pool|poolbilliardsclassrooms|poolpoolclassrooms|stage|swimming|gym|fitness|center|gyms|or|centers| 31 | 1 30 40.555760 -105.098112 2010-08-17 00:53:43 22 billiards|classroom|dance|floor|gymnasium|hot|tub|pool|poolbilliardsclassrooms|poolpoolclassrooms|stage|swimming|gym|fitness|center|gyms|or|centers| 32 | 1 31 40.523873 -105.033801 2010-08-21 00:37:48 6 american|restaurant|restaurants| 33 | 1 32 40.553028 -105.077815 2010-08-21 16:09:55 23 gym|yoga|college|gyms|studio|studios| 34 | 1 33 40.553028 -105.077815 2010-08-21 16:09:55 23 gym|yoga|college|gyms|studio|studios| 35 | 1 34 40.585847 -105.075028 2010-08-21 17:32:55 26 theme|park|parks| 36 | 1 35 40.585048 -105.076529 2010-08-21 18:05:43 27 burgers|french|fries|burger|joint|joints|american|restaurant|restaurants| 37 | 1 36 40.585847 -105.075028 2010-08-22 16:05:38 26 theme|park|parks| 38 | 1 37 40.585847 -105.075028 2010-08-22 16:05:39 26 theme|park|parks| 39 | 1 38 39.849930 -104.673786 2010-08-24 18:17:54 28 airlines|airplane|airport|atm|concourse|den|denver|transportation|international|dia|free|wifi|gate|mile|high|planes|public|art|southwest|terminal|tourist|travel|uso|airports| 40 | 1 39 29.986869 -95.341673 2010-08-24 20:48:05 29 airplanes|airport|iah|jetsetter|people|watching|restaurants|terminal|airports| 41 | 1 40 29.726994 -95.418332 2010-08-24 22:34:16 30 barbeque|bbq|beef|brisket|catering|jalapeo|cheese|bread|mesquite|grilled|online|store|order|pecan|pie|sausage|smoked|turkey|spicy|pork|and|packages|joint|joints| 42 | 1 41 29.740865 -95.423786 2010-08-25 18:04:15 31 french|restaurant|restaurants|jazz|club|clubs|cajun|creole| 43 | 1 42 29.942585 -95.332500 2010-08-26 03:33:37 32 bar|bars| 44 | 1 43 29.986869 -95.341673 2010-08-26 15:59:23 29 airplanes|airport|iah|jetsetter|people|watching|restaurants|terminal|airports| 45 | 1 44 40.587359 -105.075352 2010-08-28 01:12:59 33 beer|billiards|brewpub|karaoke|pub|fare|pubs|pizza|place|places|brewery|breweries| 46 | 1 45 39.933364 -105.132648 2010-08-28 19:45:13 34 apple|store|cupertino|imac|ipad|iphone|ipod|macbook|pro|macintosh|steve|jobs|wifi|electronics|stores| 47 | 1 46 40.533915 -105.083181 2010-08-29 15:29:22 35 basketball|court|bbq|pits|picnic|tables|pond|public|bathroom|tennis|park|parks|playground|playgrounds|courts| 48 | 1 47 40.533915 -105.083181 2010-08-29 15:29:22 35 basketball|court|bbq|pits|picnic|tables|pond|public|bathroom|tennis|park|parks|playground|playgrounds|courts| 49 | 1 48 40.523711 -105.074283 2010-08-31 14:39:32 36 coffee|shop|shops| 50 | 1 49 40.345244 -105.530322 2010-09-05 17:46:39 37 lake|lakes|other|great|outdoors| 51 | 1 50 40.377392 -105.521514 2010-09-06 15:46:13 38 american|restaurant|restaurants|breakfast|spot|spots| 52 | 1 51 40.553028 -105.077815 2010-09-11 16:09:52 23 gym|yoga|college|gyms|studio|studios| 53 | 1 52 40.555760 -105.098112 2010-09-16 00:51:18 22 billiards|classroom|dance|floor|gymnasium|hot|tub|pool|poolbilliardsclassrooms|poolpoolclassrooms|stage|swimming|gym|fitness|center|gyms|or|centers| 54 | 1 53 40.584516 -105.077426 2010-09-26 01:02:17 39 cocktails|entertainment|jazz|lounge|lounges|hotel|bar|bars| 55 | 1 54 40.529531 -105.075331 2010-10-02 01:26:19 40 arts|crafts|store|stores| 56 | 1 55 40.528995 -105.075115 2010-10-02 01:57:19 41 thai|restaurant|restaurants| 57 | 1 56 40.549212 -105.075806 2010-10-08 00:15:27 42 indian|restaurant|restaurants| 58 | 1 57 40.565698 -105.053224 2010-10-09 22:16:49 43 basketball|disc|golf|nature|park|tennis|parks| 59 | 1 58 40.542556 -105.073462 2010-10-10 21:04:15 44 kids|store|stores| 60 | 1 59 40.524724 -105.076320 2010-10-11 23:49:00 45 drugstore|pharmacy|drugstores|pharmacies|doctors|office|offices| 61 | 1 60 40.525619 -105.022683 2010-10-12 01:09:35 7 coffee|shop|shops|caf|cafs| 62 | 1 61 40.522803 -105.064744 2010-10-21 23:45:35 46 arcade|bar|american|restaurant|restaurants|burger|joint|joints|burgers| 63 | 1 62 40.565801 -105.057262 2010-10-22 23:29:56 47 medical|center|centers| 64 | 1 63 40.479905 -104.903652 2010-10-23 17:00:46 48 bridal|shop|shops| 65 | 1 64 40.528145 -105.076396 2010-10-23 18:39:59 48 bridal|shop|shops| 66 | 1 65 40.536575 -105.076533 2010-10-23 20:01:56 49 bagels|coffee|free|wifi|bakery|deli|bodega|delis|bodegas|sandwich|place|places|sandwiches|shop|shops| 67 | 1 66 40.584007 -105.077682 2010-10-24 01:43:34 50 caf|cafs|dessert|shop|shops|desserts| 68 | 1 67 40.584516 -105.077426 2010-10-28 02:44:45 39 cocktails|entertainment|jazz|lounge|lounges|hotel|bar|bars| 69 | 1 68 40.584007 -105.077682 2010-10-28 02:45:38 50 caf|cafs|dessert|shop|shops|desserts| 70 | 1 69 40.584007 -105.077682 2010-10-28 02:45:38 50 caf|cafs|dessert|shop|shops|desserts| 71 | 1 70 40.407394 -105.010368 2010-11-06 23:39:50 51 beer|american|restaurant|restaurants|pizza|place|places|bar|bars| 72 | 1 71 40.548627 -105.077158 2010-11-09 15:37:48 52 breakfast|spot|spots| 73 | 1 72 40.523372 -105.031509 2010-11-13 01:38:43 53 food|sweet|tea|teddy|grahams|deli|bodega|delis|bodegas| 74 | 1 73 40.500744 -105.069096 2010-11-14 17:52:33 54 pet|store|stores| 75 | 1 74 40.525450 -105.038686 2010-11-19 16:13:01 55 bakery|coffee|bagel|shop|shops|bagels| 76 | 2 75 37.803963 -122.448531 2010-04-14 16:06:20 56 chirp|developers|twitter|general|entertainment| 77 | 2 76 37.805021 -122.433450 2010-04-15 17:09:51 57 general|entertainment|event|space|spaces| 78 | 2 77 37.778048 -122.405681 2010-04-16 05:18:01 58 club|dj|live|music|red|bull|redbull|ticketweb|nightclub|nightclubs| 79 | 2 78 37.805800 -122.267000 2010-04-17 02:59:26 59 vietnamese|restaurant|restaurants| 80 | 2 79 37.807718 -122.270060 2010-04-17 03:25:41 60 bar|blak|concert|entertainment|good|times|live|music|venue|pool|table|trivia|night|venues|rock|club|clubs|movie|theater|theaters| 81 | 2 80 37.764922 -122.396415 2010-04-17 04:31:50 61 hipsters|hot|rod|live|music|patio|rock|club|clubs|venue|venues|concert|hall|halls| 82 | 2 81 36.311241 -119.348405 2010-04-18 00:19:25 11 grocery|store|stores| 83 | 2 82 36.240038 -119.766649 2010-04-18 06:39:22 62 casino|dining|hotel|hotels|casinos| 84 | 2 83 36.327917 -119.341476 2010-04-18 09:48:57 63 breakfast|spot|spots|american|restaurant|restaurants| 85 | 2 84 36.325743 -119.106264 2010-04-18 20:58:18 64 american|restaurant|restaurants|steakhouse|steakhouses| 86 | 2 85 37.755083 -122.423036 2010-04-19 03:41:19 65 japanese|mission|sashimi|sushi|restaurant|restaurants| 87 | 2 86 37.784509 -122.404274 2010-04-20 04:27:05 66 authentic|japanese|food|fresh|fish|cuisine|real|sushi|traditional|white|restaurant|restaurants| 88 | 2 87 37.785114 -122.404979 2010-04-20 04:54:51 67 bar|cocktail|lounge|cocktails|hotel|restaurant|skyline|view|bars|lounges| 89 | 2 88 37.807718 -122.270060 2010-04-21 03:58:31 60 bar|blak|concert|entertainment|good|times|live|music|venue|pool|table|trivia|night|venues|rock|club|clubs|movie|theater|theaters| 90 | 2 89 37.778481 -122.412238 2010-04-23 02:27:23 68 absinthe|agricole|rum|asian|fusion|bar|seating|charles|phan|chinese|cocktail|heaven|cocktails|happy|hour|lounge|restaurant|share|plates|slanted|door|team|small|restaurants|vietnamese|lounges| 91 | 2 90 37.776915 -122.424351 2010-04-24 01:52:49 69 gourmet|hamburgers|burger|joint|joints|burgers|food| 92 | 2 91 37.703900 -122.475000 2010-04-24 20:36:19 70 brunch|cocktails|daly|city|happy|hour|restaurant|diner|diners|caf|cafs|american|restaurants| 93 | 2 92 37.786743 -122.404531 2010-04-25 17:36:54 71 pool|swimming|bar|boxing|cafe|fitness|gym|health|club|jungshin|martial|arts|pilates|sauna|spa|spinning|steam|room|vinyasa|yoga|class|gyms|center|or|centers|pools| 94 | 2 93 37.776800 -122.424000 2010-04-25 23:47:43 72 macarons|paulette|tea|dessert|shop|shops|desserts|bakery|bakeries| 95 | 2 94 37.755083 -122.423036 2010-04-26 04:21:57 65 japanese|mission|sashimi|sushi|restaurant|restaurants| 96 | 2 95 37.786743 -122.404531 2010-04-26 14:54:14 71 pool|swimming|bar|boxing|cafe|fitness|gym|health|club|jungshin|martial|arts|pilates|sauna|spa|spinning|steam|room|vinyasa|yoga|class|gyms|center|or|centers|pools| 97 | 2 96 37.786743 -122.404531 2010-04-27 02:37:58 71 pool|swimming|bar|boxing|cafe|fitness|gym|health|club|jungshin|martial|arts|pilates|sauna|spa|spinning|steam|room|vinyasa|yoga|class|gyms|center|or|centers|pools| 98 | 2 97 37.786743 -122.404531 2010-04-28 15:13:19 71 pool|swimming|bar|boxing|cafe|fitness|gym|health|club|jungshin|martial|arts|pilates|sauna|spa|spinning|steam|room|vinyasa|yoga|class|gyms|center|or|centers|pools| 99 | 2 98 37.776611 -122.395765 2010-04-28 22:00:04 73 dentists|office|offices| 100 | 2 99 36.325743 -119.106264 2010-05-02 18:13:54 64 american|restaurant|restaurants|steakhouse|steakhouses| 101 | -------------------------------------------------------------------------------- /Naive_Bayesian_EM/1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/Naive_Bayesian_EM/1.gif -------------------------------------------------------------------------------- /Naive_Bayesian_EM/2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/Naive_Bayesian_EM/2.gif -------------------------------------------------------------------------------- /Naive_Bayesian_EM/README.md: -------------------------------------------------------------------------------- 1 | # 介绍 2 | 样例文件是聚类数目为7的朴素贝叶斯非监督实现，聚类主题数改变直接改变code中K即可。 3 | 4 | # 运行环境 5 | * Core: 4 * Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz 6 | * OS: Linux 4.8.4-1-ARCH #1 SMP PREEMPT x86_64 GNU/Linux 7 | * Language: Python 2.7 8 | * Data Set: Activity Recognition from a Single Chest-Mounted Accelerometer 9 | 10 | # 问题描述 11 | * 对输入数据 [X1, X2...Xn ] 进行聚类。 12 | * 每个 Xi 表示一个 L 维度的样本,样本个数为 N。使用 聚类结果与原有数据类别做比较,查看聚类效果。 13 | 14 | # 输入数据 15 | * https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer 16 | * 注:我们将数据集切分,0.8 进行训练,0.2 进行测试 17 | 18 | # 第一部分:朴素贝叶斯非监督EM算法推导 19 | 20 | ![github](https://github.com/songjs1993/model/raw/master/Naive_Bayesian_EM/1.gif)   21 | 22 | ![github](https://github.com/songjs1993/model/raw/master/Naive_Bayesian_EM/2.gif) 23 | 24 | # 第二部分：测试 25 | 26 | 1. 数据预处理 27 | • 数据集： 28 | • 取数据集中第一个文件 1.csv 作为处理数据; 29 | • 类别 0 只有一个样本,将其作为异常点删除; 30 | • 离散化处理各属性; 31 | • 划分数据为 80% 训练集,20% 验证集。 32 | 33 | 2. 实验过程 34 | 我们目标是利用 EM 算法进行样本聚类。 35 | 注:因为聚类结果没有顺序,所以对于每一个聚类类别,我们将在此聚类中出现实际类别最多的 类作为其分类;如聚类 1 中出现的实际类的个数为:1 类别 10 个,2 类别 20 个,3 类别 0 个, 4 类别 50 个,那么我们认为此聚类类别 1 代表实际类别 4。 36 | 37 | 3. 实验结果 38 | 我们采用 300 轮迭代充分收敛的聚类精度作为分析。 39 | * EM 训练集，聚类7时：73.78% 40 | * EM 测试集，聚类7时：73.75% 41 | -------------------------------------------------------------------------------- /Naive_Bayesian_EM/k_7_com_features.py: -------------------------------------------------------------------------------- 1 | # !/usr/bin/python 2 | # -*- coding:utf-8 -*- 3 | 4 | import numpy as np 5 | from scipy.stats import multivariate_normal 6 | from sklearn.mixture import GMM 7 | #from mpl_toolkits.mplot3d import Axes3D 8 | #import matplotlib as mpl 9 | #import matplotlib.pyplot as plt 10 | from sklearn.cross_validation import train_test_split 11 | from sklearn.naive_bayes import MultinomialNB 12 | from sklearn.naive_bayes import GaussianNB 13 | from sklearn.linear_model import LogisticRegression 14 | import random 15 | import math 16 | import sys 17 | 18 | # reload(sys) 19 | # sys.setdefaultencoding('utf8') 20 | 21 | # mpl.rcParams['font.sans-serif'] = [u'SimHei'] 22 | # mpl.rcParams['axes.unicode_minus'] = False 23 | 24 | K = 7 25 | 26 | def pre_deal_data(data1,data2): 27 | # 数据离散化处理 28 | for i in range(len(data1[0])): 29 | a = np.array([x[i] for x in data1]) # 取某一列 30 | sp = np.percentile(a,[x*5 for x in range(21)]) 31 | for j in range(len(data1)): 32 | # t = data1[j][i] 33 | flag = 0 34 | for k in range(1,len(sp)): 35 | if(data1[j][i] <= sp[k]): 36 | flag = k 37 | break 38 | # data1[j][i] = k + i*100 39 | data1[j][i] = flag + i*100 40 | for j in range(len(data2)): 41 | # t = data2[j][i] 42 | flag = 0 43 | for k in range(1,len(sp)): 44 | if(data2[j][i] <= sp[k]): 45 | flag = k 46 | break 47 | # data2[j][i] = k + i*100 48 | data2[j][i] = flag + i*100 49 | 50 | 51 | def change(y_hat, y): 52 | # 聚类结果与真实值结果找对应：例如聚类类别1，可能对应真实类别3 53 | lable = np.array([[0]*(K+1)]*(K+1)) 54 | # print(lable.shape) 55 | for i in range(len(y)): 56 | lable[int(y_hat[i])][int(y[i])] += 1 57 | 58 | z = [0]*(K+1) 59 | # print(lable) 60 | # tot = 0 61 | for i in range(1,(K+1)): 62 | y_max = 0 63 | num_max = -1 64 | for j in range(1,(K+1)): 65 | if lable[i][j] > num_max: 66 | num_max = lable[i][j] 67 | y_max = j 68 | z[i] = y_max 69 | for i in range(len(y_hat)): 70 | y_hat[i] = z[y_hat[i]] 71 | 72 | 73 | # 注：数据预处理，将最后一列删掉了 74 | 75 | 76 | def pre(x_test, phi, pi): 77 | # 使用训练好的模型对测试集进行预测 78 | gamma = np.array([[0.0]*K]*len(x_test)) 79 | data = x_test 80 | for i in range(len(data)): 81 | tot = 0.0 82 | for k in range(K): 83 | # gamma[i][k] = pi[k] * (phi[0][k][data[i][0]]+lamda) * (phi[1][k][data[i][1]]+lamda) * (phi[2][k][data[i][2]]+lamda) 84 | gamma[i][k] = pi[k] * (phi[0][k][data[i][0]]) * (phi[1][k][data[i][1]]) * (phi[2][k][data[i][2]]) 85 | tot += gamma[i][k] 86 | for k in range(K): 87 | gamma[i][k] /= tot 88 | 89 | y_test_hat = [0.0]*len(data) 90 | for i in range(len(data)): 91 | maxc = gamma[i][0] 92 | lable = 0 93 | for k in range(1,K): 94 | if maxc < gamma[i][k]: 95 | maxc = gamma[i][k] 96 | lable = k 97 | y_test_hat[i] = lable + 1 98 | return np.array(y_test_hat) 99 | 100 | def print_ans(gamma, phi, pi, data, y, x_test, y_test, n_iter): 101 | # 计算预测结果并打印 102 | y_hat = [0.0]*len(data) 103 | for i in range(len(data)): 104 | maxc = gamma[i][0] 105 | lable = 0 106 | for k in range(1,K): 107 | if maxc < gamma[i][k]: 108 | maxc = gamma[i][k] 109 | lable = k 110 | y_hat[i] = lable + 1 111 | 112 | y_hat = np.array(y_hat) 113 | y_test_hat = pre(x_test, phi, pi) 114 | 115 | if n_iter % 1 ==0: 116 | x_ = np.c_[data, y_hat.reshape(len(y_hat), 1)] 117 | x_test_ = np.c_[x_test, y_test_hat.reshape(len(y_test_hat), 1)] 118 | # 朴素贝叶斯分类：多项式 119 | multinomialNB(x_,y,x_test_,y_test) 120 | # 朴素贝叶斯分类：高斯分布 121 | gaussianNB(x_,y,x_test_,y_test) 122 | logistic(x_,y,x_test_,y_test) 123 | gmm_deal(x_,y,x_test_,y_test) 124 | 125 | change(y_hat, y) 126 | change(y_test_hat, y_test) 127 | acc = np.mean(y_hat.ravel() == y.ravel()) 128 | acc_test = np.mean(y_test_hat.ravel() == y_test.ravel()) 129 | acc_str = u'EM训练集准确率：%.2f%%' % (acc * 100) 130 | acc_test_str = u'EM测试集准确率：%.2f%%' % (acc_test * 100) 131 | print(acc_str) 132 | print(acc_test_str) 133 | 134 | 135 | def gmm_deal(x,y,x_test,y_test): 136 | gmm = GMM(n_components=7, covariance_type='full', tol=0.0001, n_iter=100, random_state=0) 137 | gmm.fit(x) 138 | 139 | y_hat = gmm.predict(x) 140 | y_test_hat = gmm.predict(x_test) 141 | print(np.min(y_test_hat, axis=0)) 142 | print(np.max(y_test_hat, axis=0)) 143 | 144 | change(y_hat, y) 145 | change(y_test_hat, y_test) 146 | 147 | # 准确率计算需要改动 148 | # 对聚类结果，每一类别中实际类别占比最多的，就是当前类别取值 149 | acc = np.mean(y_hat.ravel() == y.ravel()) 150 | acc_test = np.mean(y_test_hat.ravel() == y_test.ravel()) 151 | print "GMM：" 152 | acc_str = u'训练集准确率：%.2f%%' % (acc * 100) 153 | acc_test_str = u'测试集准确率：%.2f%%' % (acc_test * 100) 154 | print acc_str 155 | print acc_test_str 156 | 157 | 158 | def multinomialNB(x,y,x_test,y_test): 159 | # 多项式分布：朴素贝叶斯 160 | #create the Multinomial Naive Bayesian Classifier 161 | clf = MultinomialNB(alpha = 0.01) 162 | clf.fit(x,y); 163 | y_hat = clf.predict(x) 164 | y_test_hat = clf.predict(x_test) 165 | 166 | acc = np.mean(y_hat.ravel() == y.ravel()) 167 | acc_test = np.mean(y_test_hat.ravel() == y_test.ravel()) 168 | 169 | print("朴素贝叶斯：多项式分布") 170 | acc_str = u'训练集准确率：%.2f%%' % (acc * 100) 171 | acc_test_str = u'测试集准确率：%.2f%%' % (acc_test * 100) 172 | print(acc_str) 173 | print(acc_test_str) 174 | 175 | def logistic(x,y,x_test,y_test): 176 | classifier = LogisticRegression() # 使用类，参数全是默认的 177 | classifier.fit(x, y) # 训练数据来学习，不需要返回值 178 | y_hat = classifier.predict(x) 179 | y_test_hat = classifier.predict(x_test) 180 | acc = np.mean(y_hat.ravel() == y.ravel()) 181 | acc_test = np.mean(y_test_hat.ravel() == y_test.ravel()) 182 | print("logistic回归:") 183 | acc_str = u'训练集准确率：%.2f%%' % (acc * 100) 184 | acc_test_str = u'测试集准确率：%.2f%%' % (acc_test * 100) 185 | print(acc_str) 186 | print(acc_test_str) 187 | 188 | def gaussianNB(x,y,x_test,y_test): 189 | # 高斯分布朴素贝叶斯 190 | #create the Multinomial Naive Bayesian Classifier 191 | clf = GaussianNB() 192 | clf.fit(x,y); 193 | y_hat = clf.predict(x) 194 | y_test_hat = clf.predict(x_test) 195 | 196 | acc = np.mean(y_hat.ravel() == y.ravel()) 197 | acc_test = np.mean(y_test_hat.ravel() == y_test.ravel()) 198 | 199 | print("朴素贝叶斯：高斯分布") 200 | acc_str = u'训练集准确率：%.2f%%' % (acc * 100) 201 | acc_test_str = u'测试集准确率：%.2f%%' % (acc_test * 100) 202 | print(acc_str) 203 | print(acc_test_str) 204 | 205 | def init_with_bayes(pi, phi, data, y): 206 | # 使用贝叶斯统计信息来初始化EM算法参数: pi / phi 207 | pi = np.array([0.0] * K) 208 | for i in range(len(data)): 209 | pi[int(y[i])-1] += 1 210 | pi = pi/sum(pi) 211 | 212 | phi = np.array([[[0.0]*21] *K] *3) 213 | print(phi.shape) 214 | for i in range(3): 215 | for j in range(len(data)): 216 | k = int(data[j][i]) 217 | lable = int(y[j])-1 218 | phi[i][lable][k] += 1 219 | for j in range(K): 220 | phi[i][j] = phi[i][j]/sum(phi[i][j]) 221 | 222 | def EM(): 223 | data = np.loadtxt('1.csv', dtype=np.float, delimiter=',', skiprows=1) 224 | # data = data[50000:70000] 225 | print(data.shape) 226 | xx, x, y = np.split(data, [1,4, ], axis=1) 227 | x, x_test, y, y_test = train_test_split(x, y, train_size=0.8, random_state=0) 228 | 229 | # gmm_deal(x,y,x_test,y_test) 230 | # 朴素贝叶斯分类：多项式 231 | # multinomialNB(x,y,x_test,y_test) 232 | # 朴素贝叶斯分类：高斯分布 233 | # gaussianNB(x,y,x_test,y_test) 234 | # logistic(x,y,x_test,y_test) 235 | 236 | 237 | # 预处理，连续数据离散化 238 | pre_deal_data(x,x_test) 239 | # 朴素贝叶斯分类：多项式 240 | # multinomialNB(x,y,x_test,y_test) 241 | # 朴素贝叶斯分类：高斯分布 242 | # gaussianNB(x,y,x_test,y_test) 243 | # logistic(x,y,x_test,y_test) 244 | # gmm_deal(x,y,x_test,y_test) 245 | 246 | # 朴素贝叶斯非监督分类 247 | x = x%100 248 | x_test = x_test%100 #对于前面的k+i*100特征进行处理 249 | data = x 250 | n, d = data.shape 251 | print(data.shape) 252 | 253 | # 初始化参数 254 | # 选择每个类别概率pi 255 | pi = abs(np.random.standard_normal(K)) # 7个类别[0-6] 256 | pi = pi/sum(pi) 257 | # 每个维度属性下：各个类别选择具体取值的多项式分布，随机初始化 258 | phi = np.array([[[0.0]*21] *K] *3) 259 | print(phi.shape) 260 | for i in range(3): 261 | for j in range(K): 262 | phi[i][j] = np.array(abs(np.random.standard_normal(21))) 263 | # print(phi[i][j]) 264 | phi[i][j] = phi[i][j]/sum(phi[i][j]) 265 | # 每个样本所属各个类别概率 266 | gamma = np.array([[0.0]*K]*len(data)) 267 | 268 | # 使用先验统计参数初始化 pi 和 phi 269 | # init_with_bayes(pi, phi, data, y) 270 | 271 | print(pi) # 查看每次是否是初始化 272 | num_iter = 300 273 | # EM 274 | for n_iter in range(num_iter): 275 | # E 276 | expect = 0.0 277 | for i in range(len(data)): 278 | tot = 0.0 279 | for k in range(K): 280 | gamma[i][k] = pi[k] * (phi[0][k][data[i][0]]) * (phi[1][k][data[i][1]]) * (phi[2][k][data[i][2]]) 281 | tot += gamma[i][k] 282 | for k in range(K): 283 | gamma[i][k] /= tot 284 | expect += tot 285 | 286 | # M 287 | # pi 288 | for k in range(K): 289 | tot = 0.0 290 | for i in range(len(data)): 291 | tot += gamma[i][k] 292 | pi[k] = tot/len(data) 293 | 294 | # phi 295 | for i in range(3): # 对于第i维数据 296 | for j in range(len(data)): 297 | lable = data[j][i] # 数据j的第i维 298 | for k in range(K): 299 | phi[i][k][lable] += gamma[j][k] 300 | for k in range(K): # 按行归一化 301 | tot = 0.0 302 | for j in range(20): 303 | tot += phi[i][k][j] 304 | for j in range(20): 305 | phi[i][k][j] /= tot 306 | 307 | if n_iter % 1 == 0: 308 | print(n_iter, ":\t", math.log(expect)) 309 | # print(pi,gamma[0]) 310 | print_ans(gamma, phi, pi, data, y, x_test, y_test, n_iter) 311 | 312 | """ 313 | # 朴素贝叶斯分类：多项式 314 | multinomialNB(x,y,x_test,y_test) 315 | # 朴素贝叶斯分类：高斯分布 316 | gaussianNB(x,y,x_test,y_test) 317 | """ 318 | 319 | 320 | if __name__ == '__main__': 321 | EM() 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 介绍 2 | 对机器学习、概率图模型、主题模型领域一些模型进行实现，主要涉及一些近年高水平会议论文中提到的算法。 3 | 4 | # Model 5 | ## 1.EUTB 6 | 挖掘stable以及temporal主题：《A Unified Model for Stable and Temporal Topic Detection from Social Media Data》 7 | 8 | 注：EM-style算法求解 9 | 10 | ## 2.Naive_Bayesian_EM 11 | 朴素贝叶斯的非监督形式实现 12 | 13 | ## 3.gSpan 14 | 频繁子图挖掘算法：《gSpan: Graph-Based Substructure Pattern Mining》 15 | 16 | ## 4.ullmann 17 | 子图同构检测算法：《An Algorithm for Subgraph Isomorphism》 18 | 19 | ## 5.机器学习_概率解释.pdf 20 | 参见：https://github.com/songjs1993/ML/ 21 | 22 | 主要介绍一些常见机器学习算法，同时从损失函数与概率的角度解释了一些机器学习领域内的基本概念。 23 | 24 | ## 6.UniWalk 25 | #### 6.1 Online SimRank：UniWalk 26 | #### 6.2《On Top-k Structural Similarity Search》- 2012 IEEE 27 | 参见：https://github.com/songjs1993/DeepSimRank 28 | 29 | ## 7. DeepLearning 30 | #### 7.1 Ordinary neural network 31 | 参见：https://github.com/songjs1993/DeepLearning 32 | #### 7.2 AutoEncoder 33 | 参见：https://github.com/songjs1993/DeepLearning 34 | 35 | ## 8. Graph Embedding 36 | #### 8.1 《node2vec: Scalable Feature Learning for Networks - 2016/08 KDD》 37 | 参见：https://github.com/songjs1993/Graph-Embedding/tree/master/node2vec 38 | #### 8.2 IsoMap：《A Global Geometric Framework for Nonlinear Dimensionality Reduction》 39 | 参见：https://github.com/songjs1993/Graph-Embedding/tree/master/isomap 40 | -------------------------------------------------------------------------------- /gSpan/README.md: -------------------------------------------------------------------------------- 1 | # 介绍 2 | 《gSpan: Graph-Based Substructure Pattern Mining》算法实现 3 | 4 | 5 | 6 | ![github](https://github.com/songjs1993/model/raw/master/gSpan/intro/1.png)   7 | 8 | ![github](https://github.com/songjs1993/model/raw/master/gSpan/intro/2.png) 9 | 10 | ![github](https://github.com/songjs1993/model/raw/master/gSpan/intro/3.png) 11 | 12 | ![github](https://github.com/songjs1993/model/raw/master/gSpan/intro/4.png) 13 | 14 | ![github](https://github.com/songjs1993/model/raw/master/gSpan/intro/5.png) 15 | 16 | -------------------------------------------------------------------------------- /gSpan/data_readme.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/data_readme.txt -------------------------------------------------------------------------------- /gSpan/intro/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/intro/1.png -------------------------------------------------------------------------------- /gSpan/intro/2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/intro/2.png -------------------------------------------------------------------------------- /gSpan/intro/3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/intro/3.png -------------------------------------------------------------------------------- /gSpan/intro/4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/intro/4.png -------------------------------------------------------------------------------- /gSpan/intro/5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/gSpan/intro/5.png -------------------------------------------------------------------------------- /gSpan/main.cpp: -------------------------------------------------------------------------------- 1 | // 2 | // main.cpp 3 | // gSpan 4 | // 5 | // Created by songjs on 16/10/17. 6 | // Copyright © 2016年 songjs. All rights reserved. 7 | // 8 | 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | #include 17 | #include 18 | #include 19 | #include 20 | #include 21 | 22 | using namespace std; 23 | 24 | template inline void _swap(T &x, T &y){ 25 | T z = x; 26 | x = y; 27 | y = z; 28 | } 29 | struct Edge{ 30 | int from,to,elabel; // [from, to , elabel] 31 | unsigned int id; // edge id 32 | Edge(): from(0), to(0), elabel(0), id(0){}; 33 | }; 34 | 35 | class Vertex{ 36 | public: 37 | typedef vector::iterator edge_iterater; 38 | int label; 39 | vector edge; // ? 40 | void push(int from, int to, int elabel){ 41 | edge.resize(edge.size() + 1); 42 | edge[edge.size()-1].from = from; 43 | edge[edge.size()-1].to = to; 44 | edge[edge.size()-1].elabel = elabel; 45 | return ; 46 | } 47 | 48 | }; 49 | 50 | 51 | 52 | class Graph: public vector{ 53 | private: 54 | unsigned int edge_size_; 55 | public: 56 | typedef vector::iterator vertex_iterater; 57 | Graph(){} 58 | unsigned int edge_size(){ return edge_size_; } 59 | unsigned int vertex_size(){ return (unsigned int)size();} 60 | void buildEdge(){ 61 | char buf[512]; 62 | map tmp; // tmp只是临时用一下 63 | unsigned int id = 0; 64 | for(int from=0; from<(int)size(); ++from){ 65 | for(Vertex::edge_iterater it = (*this)[from].edge.begin(); it!=(*this)[from].edge.end(); it++){ 66 | if(from <= it->to) sprintf(buf, "%d %d %d", from, it->to, it->elabel); 67 | else sprintf(buf, "%d %d %d", it->to, from, it->elabel); 68 | 69 | if(tmp.find(buf) == tmp.end()){ 70 | it->id = id; 71 | tmp[buf] = id++; 72 | }else{ 73 | it->id = tmp[buf]; 74 | } 75 | } 76 | } 77 | edge_size_ = id; 78 | } 79 | 80 | }; 81 | 82 | struct PDFS{ 83 | unsigned int id; //原始输入图id 84 | Edge *edge; 85 | PDFS *prev; 86 | PDFS(): id(0), edge(0), prev(0){}; 87 | }; 88 | 89 | class Projected: public vector{ 90 | public: 91 | void push(int id, Edge *edge, PDFS *prev){ 92 | resize(size() + 1); 93 | PDFS &d = (*this)[size()-1]; 94 | d.id = id; d.edge = edge; d.prev = prev; 95 | } 96 | }; 97 | 98 | 99 | 100 | typedef vector EdgeList; 101 | 102 | bool get_forward_root(Graph &g, Vertex &v, EdgeList &result){ 103 | result.clear(); 104 | for(Vertex::edge_iterater it = v.edge.begin(); it!=v.edge.end(); it++){ 105 | assert(it->to >=0 && it->to to].label) 107 | result.push_back(&(*it)); 108 | } 109 | return (!result.empty()); 110 | } 111 | 112 | 113 | class DFS{ 114 | public: 115 | int from,to,fromlabel,elabel,tolabel; 116 | friend bool operator == (const DFS &a, const DFS &b){ 117 | return (a.from==b.from && a.to==b.to && a.fromlabel==b.fromlabel && a.tolabel==b.tolabel && a.elabel==b.elabel); 118 | } 119 | friend bool operator != (const DFS &a, const DFS &b){ return (!(a==b));} 120 | DFS(): from(0), to(0), fromlabel(0), elabel(0), tolabel(0){}; 121 | }; 122 | 123 | typedef vector RMPath; //最右路径 124 | 125 | struct DFSCode: public vector{ 126 | private: 127 | RMPath rmpath; 128 | public: 129 | const RMPath& buildRMPath(){ 130 | rmpath.clear(); 131 | int old_from = -1; 132 | 133 | for(int i=(int)size()-1 ;i>=0; i--){ 134 | // 前向边 135 | if((*this)[i].from < (*this)[i].to && (rmpath.empty() || old_from == (*this)[i].to)){ 136 | rmpath.push_back(i); 137 | old_from = (*this)[i].from; 138 | } 139 | } 140 | return rmpath; 141 | } 142 | 143 | // 将当前DFS编码转为图 144 | bool toGraph(Graph &g){ 145 | g.clear(); 146 | for(DFSCode::iterator it = begin(); it!=end(); it++){ 147 | g.resize(max(max(it->from, it->to), (int)g.size()) + 1); // a bug before. 148 | 149 | if(it->fromlabel != -1) 150 | g[it->from].label = it->fromlabel; 151 | if(it->tolabel != -1) 152 | g[it->to].label = it->tolabel; 153 | g[it->from].push(it->from, it->to, it->elabel); 154 | g[it->to].push(it->to, it->from, it->elabel); 155 | } 156 | g.buildEdge(); 157 | return true; 158 | } 159 | 160 | // 从当前图重建DFS编码 161 | bool fromGraph(Graph &g); 162 | 163 | // 返回图中节点数目 164 | unsigned int nodeCound(void){ 165 | unsigned int nodecount = 0; 166 | for(DFSCode::iterator it = begin(); it!=end(); it++) 167 | nodecount = max(nodecount, (unsigned int)(max(it->from, it->to)+1)); 168 | return nodecount; 169 | } 170 | 171 | void push(int from, int to, int fromlabel, int elabel, int tolabel){ 172 | resize(size() + 1); 173 | DFS &d = (*this)[size() - 1]; 174 | d.from = from; d.to = to; d.fromlabel=fromlabel; d.elabel = elabel; d.tolabel = tolabel; 175 | } 176 | void pop(){ 177 | resize(size() - 1); 178 | } 179 | 180 | 181 | }; 182 | 183 | class History: public vector{ 184 | private: 185 | vector edge; 186 | vector vertex; 187 | 188 | public: 189 | bool hasEdge(unsigned int id){ return (bool)edge[id];} 190 | bool hasVertex(unsigned int id){ return (bool)vertex[id];} 191 | void build(Graph &graph, PDFS *e){ 192 | clear();edge.clear(); edge.resize(graph.edge_size()); 193 | vertex.clear(); vertex.resize(graph.size()); 194 | if(e){ 195 | push_back(e->edge); 196 | edge[e->edge->id] = vertex[e->edge->from] = vertex[e->edge->to] = 1; //1 表示当前顶点或者边访问过！那么下次再继续 197 | for(PDFS *p = e->prev; p ;p=p->prev){ 198 | push_back(p->edge); 199 | edge[p->edge->id] = vertex[p->edge->from] = vertex[p->edge->to] = 1; 200 | } 201 | reverse(begin(), end()); //vector 翻转 202 | } 203 | 204 | } 205 | History(){} 206 | History(Graph &g, PDFS *p){ build(g,p);} 207 | }; 208 | 209 | Edge *get_backward(Graph &graph, Edge* e1, Edge* e2, History &history){ 210 | if(e1 == e2) return 0; 211 | 212 | for(Vertex::edge_iterater it = graph[e2->to].edge.begin(); it!=graph[e2->to].edge.end(); it++){ 213 | if(history.hasEdge(it->id)) continue; 214 | if((it->to == e1->from) && ((e1->elabel < it->elabel) || ((e1->elabel == it->elabel) && (graph[e1->to].label <= graph[e2->to].label)))){ 215 | return &(*it); 216 | } 217 | } 218 | return 0; 219 | } 220 | 221 | bool get_forward_pure(Graph &graph, Edge *e, int minlabel, History &history, EdgeList &result){ 222 | result.clear(); 223 | 224 | for(Vertex::edge_iterater it=graph[e->to].edge.begin(); it!=graph[e->to].edge.end(); it++){ 225 | assert(it->to >=0 && it->to < graph.size()); 226 | if(minlabel > graph[it->to].label || history.hasVertex(it->to)) 227 | continue; 228 | result.push_back(&(*it)); 229 | } 230 | return (!(result.empty())); 231 | } 232 | 233 | bool get_forward_rmpath(Graph &graph, Edge *e, int minlabel, History &history, EdgeList &result){ 234 | result.clear(); 235 | int tolabel = graph[e->to].label; 236 | 237 | for(Vertex::edge_iterater it=graph[e->from].edge.begin(); it!=graph[e->from].edge.end(); it++){ 238 | int tolabel2 = graph[it->to].label; 239 | if(e->to == it->to || minlabel>tolabel2 || history.hasVertex(it->to)) 240 | continue; 241 | if(e->elabel < it->elabel || (e->elabel == it->elabel && tolabel <= tolabel2)) 242 | result.push_back(&(*it)); 243 | } 244 | return (!result.empty()); 245 | } 246 | 247 | class gSpan{ 248 | private: 249 | typedef map > > Projected_map3; 250 | typedef map > Projected_map2; 251 | typedef map Projected_map1; 252 | typedef map > >::iterator Projected_iterator3; 253 | typedef map >::iterator Projected_iterator2; 254 | typedef map::iterator Projected_iterator1; 255 | typedef map > >::reverse_iterator Projected_riterator3; 256 | 257 | vector TRANS; 258 | DFSCode DFS_CODE; 259 | DFSCode DFS_CODE_IS_MIN; 260 | Graph GRAPH_IS_MIN; 261 | 262 | public: 263 | char *filename; // filename 264 | int minsup; 265 | int maxpat_max,maxpat_min; // the number of nodes in the fre.. graph. 266 | 267 | 268 | /* 269 | singular vertex. 270 | */ 271 | map > singleVertex; 272 | map singleVertexLabel; 273 | 274 | int answer_number = 0; 275 | 276 | clock_t start,end; 277 | public: 278 | 279 | gSpan(){ 280 | filename = new char[150]; 281 | 282 | 283 | } 284 | gSpan(int argc, const char * argv[]){ 285 | filename = new char[150]; 286 | cout<<"请输入图文件路径:(150字符以内)"<>filename; 288 | // cout<<"请输入最小支持度、挖掘频繁子图中最少与最多节点个数(无限制请输入-1)："<>minsup; //>>maxpat_min>>maxpat_max; 291 | if(maxpat_min == -1) maxpat_min = 0; 292 | if(maxpat_max == -1) maxpat_max = 0xffffffff; 293 | 294 | // strcpy(filename, "/Users/songjs/Desktop/workspace/gSpan/gSpan/graph.data"); 295 | start = clock(); 296 | read(); 297 | } 298 | ~gSpan(){ 299 | delete[] filename; 300 | } 301 | void read(){ 302 | ifstream in; in.open(filename); 303 | 304 | vector result; 305 | string line; Graph g; g.clear(); 306 | while(in>>line){ 307 | if(line[0]=='t'){ 308 | getline(in, line); 309 | if(!g.empty()){ 310 | // 新的一个图，加进来 311 | g.buildEdge(); // 给edge编号，赋值id 312 | TRANS.push_back(g); 313 | g.clear(); 314 | } 315 | if(line[4]=='-') break; // end of the file 316 | } 317 | else if(line[0]=='v'){ 318 | int id,label; 319 | in>>id>>label; 320 | getline(in,line); 321 | g.resize(max((int)g.size(), id+1)); 322 | g[id].label = label; 323 | }else if(line[0]=='e'){ 324 | int from,to,elabel; 325 | in>>from>>to>>elabel; 326 | g.resize(max((int)g.size(), from+1)); 327 | g.resize(max((int)g.size(), to+1)); 328 | g[from].push(from, to, elabel); 329 | g[to].push(to, from, elabel); 330 | getline(in, line); 331 | } 332 | 333 | } 334 | 335 | } 336 | unsigned int support(Projected &projected){ 337 | // 计算当前子图的频繁数 338 | unsigned int oid = 0xffffffff; 339 | unsigned int size = 0; 340 | for(Projected::iterator cur = projected.begin(); cur!=projected.end(); cur++){ 341 | if(oid != cur->id){ 342 | size++; 343 | } 344 | oid = cur->id; 345 | } 346 | return size; 347 | 348 | } 349 | bool project_is_min(Projected &projected){ 350 | // projected中有当前子图的多条边，从每一条开始尝试进行判断 351 | const RMPath & rmpath = DFS_CODE_IS_MIN.buildRMPath(); // rmpath是倒序，0的话就是min Code的搜索起始点 352 | int minlabel = DFS_CODE_IS_MIN[0].fromlabel; // 这里DFS_CODE_IS_MIN是有序的，在map中会自动按照label排序 353 | int maxtoc = DFS_CODE_IS_MIN[rmpath[0]].to; 354 | { 355 | Projected_map1 root; 356 | bool flg = false; 357 | int newto = 0; 358 | 359 | for(int i=(int)rmpath.size()-1; !flg && i>=1; i--){ // [5,4,3,0] 360 | for(unsigned int n=0;nelabel].push(0, e, cur); 366 | newto = DFS_CODE_IS_MIN[rmpath[i]].from; 367 | flg = true; 368 | } 369 | } 370 | } 371 | if(flg){ 372 | Projected_iterator1 elabel = root.begin(); 373 | DFS_CODE_IS_MIN.push(maxtoc, newto, -1, elabel->first, -1); 374 | 375 | // 剪枝，新的DFS_CODE不断增多，每次都要和MIN_CODE相同才继续向下找。 376 | if(DFS_CODE[DFS_CODE_IS_MIN.size()-1] != DFS_CODE_IS_MIN[DFS_CODE_IS_MIN.size() - 1]) return false; 377 | return project_is_min(elabel->second); 378 | } 379 | 380 | } 381 | { 382 | // 找forward 383 | bool flg = false; 384 | int newfrom = 0; 385 | Projected_map2 root; 386 | EdgeList edges; 387 | for(unsigned int n=0; nelabel][GRAPH_IS_MIN[(*it)->to].label].push(0, *it, cur); 395 | } 396 | } 397 | } 398 | for(int i=0; !flg && i<(int)rmpath.size(); i++){ 399 | for(unsigned int n=0;nelabel][GRAPH_IS_MIN[(*it)->to].label].push(0, *it, cur); 407 | } 408 | } 409 | } 410 | } 411 | if(flg){ 412 | Projected_iterator2 elabel = root.begin(); 413 | Projected_iterator1 tolabel = elabel->second.begin(); 414 | DFS_CODE_IS_MIN.push(newfrom, maxtoc+1, -1, elabel->first, tolabel->first); 415 | if(DFS_CODE[DFS_CODE_IS_MIN.size()-1] != DFS_CODE_IS_MIN[DFS_CODE_IS_MIN.size()-1]) return false; 416 | return project_is_min(tolabel->second); 417 | } 418 | } 419 | return true; 420 | } 421 | bool is_min(){ 422 | // 此函数所有操作都基于新构建的子图上，与原数据集没有联系 423 | if(DFS_CODE.size()==1) return true; 424 | DFS_CODE.toGraph(GRAPH_IS_MIN); //这里重建的图重新给边编了id，且边没有用指针 425 | DFS_CODE_IS_MIN.clear(); 426 | 427 | Projected_map3 root; 428 | EdgeList edges; 429 | for(unsigned int from = 0;fromelabel][GRAPH_IS_MIN[(*it)->to].label].push(0, *it, 0); //原图id都设为了0，这里没有现在图与原图的映射 433 | } 434 | } 435 | } 436 | 437 | Projected_iterator3 fromlabel = root.begin(); 438 | Projected_iterator2 elabel = fromlabel->second.begin(); 439 | Projected_iterator1 tolabel = elabel->second.begin(); 440 | DFS_CODE_IS_MIN.push(0, 1, fromlabel->first, elabel->first, tolabel->first); // 从每一条边开始查找？ 441 | return (project_is_min(tolabel->second)); 442 | } 443 | void print(Graph &g){ 444 | // 输出当前图 445 | cout<<"t # "< maxpat_min && DFS_CODE.nodeCound()>maxpat_max) return ; 465 | 466 | // 满足两个检查，输出当前频繁集，DFS_CODE 467 | // if(DFS_CODE.nodeCound()==3) { 468 | Graph g; 469 | DFS_CODE.toGraph(g); 470 | print(g); 471 | // } 472 | 473 | // 扩展图 n -> (n+1) 474 | const RMPath &rmpath = DFS_CODE.buildRMPath(); 475 | int minlabel = DFS_CODE[0].fromlabel; 476 | int maxtoc = DFS_CODE[rmpath[0]].to; 477 | 478 | Projected_map3 new_fwd_root; //前向边 479 | Projected_map2 new_bck_root; //后向边 480 | EdgeList edges; 481 | 482 | // 枚举所有扩展边的可能 483 | for(unsigned int n=0;n=1;i--){ 490 | Edge *e = get_backward(TRANS[id], history[rmpath[i]], history[rmpath[0]], history); 491 | if(e){ 492 | new_bck_root[DFS_CODE[rmpath[i]].from][e->elabel].push(id, e, cur); 493 | } 494 | } 495 | // 最右节点扩展边 496 | if(get_forward_pure(TRANS[id], history[rmpath[0]], minlabel, history, edges)) 497 | for(EdgeList::iterator it = edges.begin(); it!=edges.end(); it++) 498 | new_fwd_root[maxtoc][(*it)->elabel][TRANS[id][(*it)->to].label].push(id, *it, cur); 499 | // 最右路径（除最右节点）扩展边 500 | for(int i=0;i<(int)rmpath.size();i++){ 501 | if(get_forward_rmpath(TRANS[id], history[rmpath[i]], minlabel, history, edges)){ 502 | for(EdgeList::iterator it = edges.begin(); it!=edges.end(); it++){ 503 | new_fwd_root[DFS_CODE[rmpath[i]].from][(*it)->elabel][TRANS[id][(*it)->to].label].push(id, *it, cur); 504 | } 505 | } 506 | } 507 | } 508 | 509 | // 测试所有扩展的子图： 510 | // backward 511 | for(Projected_iterator2 to = new_bck_root.begin(); to!=new_bck_root.end(); to++){ 512 | for(Projected_iterator1 elabel = to->second.begin(); elabel!=to->second.end(); elabel++){ 513 | DFS_CODE.push(maxtoc, to->first, -1, elabel->first, -1); 514 | project(elabel->second); 515 | DFS_CODE.pop(); 516 | } 517 | } 518 | // forward 519 | for(Projected_riterator3 from = new_fwd_root.rbegin(); from!=new_fwd_root.rend(); from++){ 520 | for(Projected_iterator2 elabel = from->second.begin(); elabel!=from->second.end(); elabel++){ 521 | for(Projected_iterator1 tolabel = elabel->second.begin(); tolabel!=elabel->second.end(); tolabel++){ 522 | DFS_CODE.push(from->first, maxtoc+1, -1, elabel->first, tolabel->first); 523 | project(tolabel->second); 524 | DFS_CODE.pop(); 525 | } 526 | } 527 | } 528 | 529 | return ; 530 | 531 | } 532 | void run(){ 533 | if(maxpat_min <= 1){ // one node subgraphs 534 | for(unsigned int id=0; id 536 | if(singleVertex[id][TRANS[id][nid].label] == 0) // the idth Graph 537 | singleVertexLabel[TRANS[id][nid].label]+=1; // all Graph vertexLabel. 538 | singleVertex[id][TRANS[id][nid].label] += 1; 539 | } 540 | } 541 | 542 | for(map::iterator it = singleVertexLabel.begin(); it!=singleVertexLabel.end(); it++){ 543 | if((*it).second < minsup) continue; 544 | unsigned int frequent_label = (*it).first; 545 | 546 | // the answer is stored in the format of graph. 547 | Graph g; 548 | g.resize(1); 549 | g[0].label = frequent_label; 550 | 551 | vector counts(TRANS.size()); 552 | for(map>::iterator it2 = singleVertex.begin(); it2!=singleVertex.end(); it2++){ 553 | counts[(*it2).first] = (*it2).second[frequent_label]; // label在图(*it2).first中出现多少次 554 | } 555 | 556 | } 557 | } 558 | EdgeList edges; 559 | Projected_map3 root; 560 | for(unsigned int id=0; idelabel][g[(*it)->to].label].push(id, *it, 0); // 将每一条边，对应图id，PDFS编码为0，放入root中 566 | } 567 | } 568 | } 569 | } 570 | for(Projected_iterator3 fromlabel=root.begin(); fromlabel!=root.end(); fromlabel++){ 571 | for(Projected_iterator2 elabel = fromlabel->second.begin(); elabel!=fromlabel->second.end(); elabel++){ 572 | for(Projected_iterator1 tolabel = elabel->second.begin(); tolabel!=elabel->second.end(); tolabel++){ 573 | // 建立初始的两节点一条边的子图 574 | DFS_CODE.push(0, 1, fromlabel->first, elabel->first, tolabel->first); 575 | 576 | // 注意一个projected中保存的是一种类型边[from_label, elabel, to_label]在各个图中出现的情况：[图1 边1][图1 边3]...[图8 边5]... 577 | // 扩展子图（增加边）的话，使用PDFS *prev指针来指向下一条边 578 | 579 | // 从每一个两节点一条边的子图进行搜索，递归树 580 | project(tolabel->second); 581 | DFS_CODE.pop(); 582 | } 583 | } 584 | } 585 | 586 | } 587 | void run_time(){ 588 | end = clock(); 589 | cout<<"run time : "<<(double)(end-start)/CLOCKS_PER_SEC<<"s."<run(); 597 | g->run_time(); 598 | 599 | return 0; 600 | } 601 | 602 | 603 | 604 | 605 | 606 | -------------------------------------------------------------------------------- /ullmann/Q4.my: -------------------------------------------------------------------------------- 1 | t # 0 2 | v 0 2 3 | v 1 2 4 | v 2 2 5 | v 3 2 6 | v 4 2 7 | e 0 1 2 8 | e 1 2 2 9 | e 2 3 2 10 | e 3 4 2 11 | t # 1 12 | v 0 5 13 | v 1 2 14 | v 2 2 15 | v 3 2 16 | v 4 5 17 | e 0 1 2 18 | e 1 2 2 19 | e 1 3 2 20 | e 3 4 2 21 | t # 2 22 | v 0 2 23 | v 1 5 24 | v 2 2 25 | v 3 2 26 | v 4 2 27 | e 0 1 2 28 | e 0 2 2 29 | e 1 4 2 30 | e 1 3 2 31 | t # 3 32 | v 0 2 33 | v 1 3 34 | v 2 2 35 | v 3 2 36 | v 4 2 37 | e 0 1 2 38 | e 1 2 2 39 | e 2 3 2 40 | e 2 4 2 41 | t # 4 42 | v 0 2 43 | v 1 6 44 | v 2 3 45 | v 3 3 46 | v 4 3 47 | e 0 1 2 48 | e 1 2 3 49 | e 1 3 3 50 | e 1 4 2 51 | t # 5 52 | v 0 2 53 | v 1 2 54 | v 2 2 55 | v 3 2 56 | v 4 3 57 | e 0 1 2 58 | e 1 3 2 59 | e 2 3 2 60 | e 3 4 2 61 | t # 6 62 | v 0 2 63 | v 1 6 64 | v 2 2 65 | v 3 3 66 | v 4 3 67 | e 0 1 2 68 | e 0 2 5 69 | e 1 3 3 70 | e 1 4 3 71 | t # 7 72 | v 0 2 73 | v 1 2 74 | v 2 2 75 | v 3 2 76 | v 4 2 77 | e 0 1 2 78 | e 1 2 2 79 | e 1 3 2 80 | e 1 4 2 81 | t # 8 82 | v 0 2 83 | v 1 3 84 | v 2 2 85 | v 3 2 86 | v 4 3 87 | e 0 2 2 88 | e 0 3 2 89 | e 1 2 2 90 | e 2 4 2 91 | t # 9 92 | v 0 2 93 | v 1 2 94 | v 2 2 95 | v 3 2 96 | v 4 2 97 | e 0 1 5 98 | e 1 2 5 99 | e 2 4 5 100 | e 3 4 5 101 | t # 10 102 | v 0 2 103 | v 1 6 104 | v 2 3 105 | v 3 3 106 | v 4 3 107 | e 0 1 2 108 | e 1 2 3 109 | e 1 3 3 110 | e 1 4 2 111 | t # 11 112 | v 0 2 113 | v 1 2 114 | v 2 2 115 | v 3 2 116 | v 4 2 117 | e 0 1 2 118 | e 1 2 5 119 | e 1 3 5 120 | e 2 4 2 121 | t # 12 122 | v 0 2 123 | v 1 2 124 | v 2 2 125 | v 3 2 126 | v 4 2 127 | e 0 1 2 128 | e 1 2 2 129 | e 2 3 2 130 | e 3 4 2 131 | t # 13 132 | v 0 2 133 | v 1 2 134 | v 2 2 135 | v 3 2 136 | v 4 2 137 | e 0 1 5 138 | e 0 2 5 139 | e 1 3 5 140 | e 1 4 5 141 | t # 14 142 | v 0 2 143 | v 1 2 144 | v 2 2 145 | v 3 2 146 | v 4 2 147 | e 0 2 2 148 | e 0 1 5 149 | e 1 3 5 150 | e 3 4 5 151 | t # 15 152 | v 0 2 153 | v 1 2 154 | v 2 2 155 | v 3 2 156 | v 4 3 157 | e 0 2 2 158 | e 0 3 5 159 | e 1 4 2 160 | e 1 3 5 161 | t # 16 162 | v 0 2 163 | v 1 2 164 | v 2 2 165 | v 3 2 166 | v 4 2 167 | e 0 1 2 168 | e 1 2 2 169 | e 2 3 3 170 | e 3 4 2 171 | t # 17 172 | v 0 2 173 | v 1 2 174 | v 2 2 175 | v 3 2 176 | v 4 2 177 | e 0 1 2 178 | e 1 2 2 179 | e 2 3 2 180 | e 3 4 2 181 | t # 18 182 | v 0 2 183 | v 1 2 184 | v 2 5 185 | v 3 2 186 | v 4 2 187 | e 0 1 5 188 | e 0 2 5 189 | e 1 3 5 190 | e 3 4 5 191 | t # 19 192 | v 0 2 193 | v 1 2 194 | v 2 2 195 | v 3 2 196 | v 4 5 197 | e 0 2 5 198 | e 1 3 5 199 | e 2 3 5 200 | e 3 4 2 201 | t # 20 202 | v 0 2 203 | v 1 2 204 | v 2 2 205 | v 3 2 206 | v 4 2 207 | e 0 2 5 208 | e 1 3 2 209 | e 1 2 5 210 | e 3 4 3 211 | t # 21 212 | v 0 2 213 | v 1 2 214 | v 2 2 215 | v 3 2 216 | v 4 3 217 | e 0 1 2 218 | e 0 2 2 219 | e 2 3 2 220 | e 3 4 2 221 | t # 22 222 | v 0 2 223 | v 1 2 224 | v 2 2 225 | v 3 2 226 | v 4 2 227 | e 0 2 5 228 | e 0 1 5 229 | e 2 4 2 230 | e 2 3 5 231 | t # 23 232 | v 0 2 233 | v 1 3 234 | v 2 3 235 | v 3 2 236 | v 4 2 237 | e 0 1 2 238 | e 0 2 3 239 | e 0 3 2 240 | e 3 4 2 241 | t # 24 242 | v 0 2 243 | v 1 2 244 | v 2 3 245 | v 3 2 246 | v 4 2 247 | e 0 2 3 248 | e 0 3 2 249 | e 1 4 5 250 | e 1 3 5 251 | t # 25 252 | v 0 2 253 | v 1 5 254 | v 2 2 255 | v 3 5 256 | v 4 5 257 | e 0 1 2 258 | e 1 2 2 259 | e 2 3 3 260 | e 2 4 2 261 | t # 26 262 | v 0 2 263 | v 1 2 264 | v 2 2 265 | v 3 2 266 | v 4 2 267 | e 0 1 2 268 | e 1 2 3 269 | e 2 3 2 270 | e 3 4 3 271 | t # 27 272 | v 0 2 273 | v 1 2 274 | v 2 2 275 | v 3 2 276 | v 4 2 277 | e 0 1 2 278 | e 1 3 2 279 | e 1 2 3 280 | e 2 4 2 281 | t # 28 282 | v 0 2 283 | v 1 2 284 | v 2 2 285 | v 3 5 286 | v 4 2 287 | e 0 1 5 288 | e 1 2 2 289 | e 2 4 2 290 | e 3 4 2 291 | t # 29 292 | v 0 3 293 | v 1 2 294 | v 2 2 295 | v 3 2 296 | v 4 3 297 | e 0 1 2 298 | e 0 3 2 299 | e 2 3 2 300 | e 3 4 2 301 | t # 30 302 | v 0 2 303 | v 1 2 304 | v 2 3 305 | v 3 3 306 | v 4 2 307 | e 0 1 2 308 | e 1 2 2 309 | e 1 3 3 310 | e 2 4 2 311 | t # 31 312 | v 0 9 313 | v 1 5 314 | v 2 2 315 | v 3 2 316 | v 4 2 317 | e 0 1 2 318 | e 1 2 5 319 | e 2 4 5 320 | e 2 3 5 321 | t # 32 322 | v 0 2 323 | v 1 2 324 | v 2 6 325 | v 3 3 326 | v 4 3 327 | e 0 1 5 328 | e 1 2 2 329 | e 2 3 3 330 | e 2 4 2 331 | t # 33 332 | v 0 2 333 | v 1 2 334 | v 2 5 335 | v 3 2 336 | v 4 3 337 | e 0 1 5 338 | e 1 2 2 339 | e 2 3 2 340 | e 3 4 3 341 | t # 34 342 | v 0 2 343 | v 1 2 344 | v 2 2 345 | v 3 2 346 | v 4 2 347 | e 0 2 2 348 | e 1 3 5 349 | e 1 4 2 350 | e 2 4 3 351 | t # 35 352 | v 0 2 353 | v 1 2 354 | v 2 2 355 | v 3 2 356 | v 4 3 357 | e 0 2 5 358 | e 1 3 5 359 | e 2 3 5 360 | e 3 4 2 361 | t # 36 362 | v 0 2 363 | v 1 2 364 | v 2 2 365 | v 3 2 366 | v 4 2 367 | e 0 1 5 368 | e 1 2 5 369 | e 1 3 2 370 | e 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| e 1 2 2 449 | e 2 3 5 450 | e 3 4 5 451 | t # 45 452 | v 0 2 453 | v 1 3 454 | v 2 2 455 | v 3 5 456 | v 4 2 457 | e 0 1 2 458 | e 0 2 2 459 | e 2 3 2 460 | e 3 4 2 461 | t # 46 462 | v 0 2 463 | v 1 2 464 | v 2 2 465 | v 3 2 466 | v 4 2 467 | e 0 1 2 468 | e 0 2 2 469 | e 2 3 5 470 | e 3 4 5 471 | t # 47 472 | v 0 2 473 | v 1 2 474 | v 2 2 475 | v 3 5 476 | v 4 3 477 | e 0 1 2 478 | e 0 2 2 479 | e 2 4 3 480 | e 2 3 2 481 | t # 48 482 | v 0 2 483 | v 1 2 484 | v 2 2 485 | v 3 2 486 | v 4 5 487 | e 0 1 2 488 | e 0 2 2 489 | e 1 3 2 490 | e 3 4 2 491 | t # 49 492 | v 0 5 493 | v 1 2 494 | v 2 2 495 | v 3 2 496 | v 4 2 497 | e 0 1 2 498 | e 1 2 5 499 | e 2 3 5 500 | e 3 4 5 501 | t # 50 502 | v 0 3 503 | v 1 2 504 | v 2 2 505 | v 3 2 506 | v 4 2 507 | e 0 2 2 508 | e 1 3 2 509 | e 2 4 2 510 | e 3 4 2 511 | t # 51 512 | v 0 5 513 | v 1 2 514 | v 2 2 515 | v 3 2 516 | v 4 2 517 | e 0 1 2 518 | e 1 2 5 519 | e 2 3 5 520 | e 3 4 5 521 | t # 52 522 | v 0 2 523 | v 1 2 524 | v 2 2 525 | v 3 2 526 | v 4 2 527 | e 0 1 5 528 | e 0 2 5 529 | e 1 3 5 530 | e 2 4 5 531 | t # 53 532 | v 0 2 533 | v 1 2 534 | v 2 2 535 | v 3 3 536 | v 4 2 537 | e 0 1 2 538 | e 1 3 3 539 | e 1 2 2 540 | e 2 4 2 541 | t # 54 542 | v 0 2 543 | v 1 2 544 | v 2 2 545 | v 3 5 546 | v 4 2 547 | e 0 1 5 548 | e 1 2 5 549 | e 2 4 5 550 | e 3 4 5 551 | t # 55 552 | v 0 2 553 | v 1 2 554 | v 2 2 555 | v 3 2 556 | v 4 2 557 | e 0 1 2 558 | e 1 2 2 559 | e 2 3 5 560 | e 2 4 5 561 | t # 56 562 | v 0 2 563 | v 1 5 564 | v 2 2 565 | v 3 2 566 | v 4 2 567 | e 0 1 2 568 | e 0 2 2 569 | e 1 3 2 570 | e 1 4 2 571 | t # 57 572 | v 0 2 573 | v 1 2 574 | v 2 6 575 | v 3 2 576 | v 4 3 577 | e 0 2 2 578 | e 0 1 5 579 | e 1 3 5 580 | e 2 4 3 581 | t # 58 582 | v 0 2 583 | v 1 2 584 | v 2 2 585 | v 3 2 586 | v 4 2 587 | e 0 1 5 588 | e 0 2 5 589 | e 1 3 5 590 | e 2 4 5 591 | t # 59 592 | v 0 2 593 | v 1 2 594 | v 2 5 595 | v 3 2 596 | v 4 2 597 | e 0 1 2 598 | e 1 2 2 599 | e 2 3 2 600 | e 3 4 5 601 | t # 60 602 | v 0 2 603 | v 1 2 604 | v 2 2 605 | v 3 2 606 | v 4 3 607 | e 0 1 2 608 | e 0 2 5 609 | e 2 4 2 610 | e 2 3 5 611 | t # 61 612 | v 0 6 613 | v 1 2 614 | v 2 2 615 | v 3 5 616 | v 4 2 617 | e 0 1 2 618 | e 1 2 2 619 | e 2 3 2 620 | e 3 4 2 621 | t # 62 622 | v 0 2 623 | v 1 2 624 | v 2 2 625 | v 3 2 626 | v 4 2 627 | e 0 1 2 628 | e 0 2 2 629 | e 2 4 2 630 | e 3 4 2 631 | t # 63 632 | v 0 5 633 | v 1 2 634 | v 2 2 635 | v 3 2 636 | v 4 3 637 | e 0 1 2 638 | e 1 3 2 639 | e 2 4 2 640 | e 3 4 2 641 | t # 64 642 | v 0 2 643 | v 1 2 644 | v 2 5 645 | v 3 2 646 | v 4 5 647 | e 0 1 5 648 | e 1 2 2 649 | e 1 3 5 650 | e 2 4 3 651 | t # 65 652 | v 0 2 653 | v 1 2 654 | v 2 2 655 | v 3 3 656 | v 4 2 657 | e 0 2 2 658 | e 0 3 2 659 | e 0 4 2 660 | e 1 4 2 661 | t # 66 662 | v 0 2 663 | v 1 2 664 | v 2 3 665 | v 3 2 666 | v 4 6 667 | e 0 2 2 668 | e 0 3 5 669 | e 1 4 2 670 | e 1 3 5 671 | t # 67 672 | v 0 2 673 | v 1 2 674 | v 2 2 675 | v 3 2 676 | v 4 2 677 | e 0 1 2 678 | e 1 2 2 679 | e 2 3 2 680 | e 3 4 2 681 | t # 68 682 | v 0 2 683 | v 1 2 684 | v 2 2 685 | v 3 2 686 | v 4 2 687 | e 0 2 5 688 | e 1 3 5 689 | e 2 4 5 690 | e 3 4 5 691 | t # 69 692 | v 0 2 693 | v 1 2 694 | v 2 2 695 | v 3 3 696 | v 4 2 697 | e 0 2 2 698 | e 1 3 3 699 | e 1 4 2 700 | e 2 4 5 701 | t # 70 702 | v 0 3 703 | v 1 2 704 | v 2 3 705 | v 3 2 706 | v 4 2 707 | e 0 1 2 708 | e 1 2 3 709 | e 1 3 2 710 | e 3 4 5 711 | t # 71 712 | v 0 2 713 | v 1 2 714 | v 2 3 715 | v 3 2 716 | v 4 2 717 | e 0 1 2 718 | e 1 2 3 719 | e 1 3 2 720 | e 3 4 2 721 | t # 72 722 | v 0 2 723 | v 1 2 724 | v 2 3 725 | v 3 2 726 | v 4 2 727 | e 0 1 2 728 | e 1 2 2 729 | e 1 3 2 730 | e 3 4 2 731 | t # 73 732 | v 0 2 733 | v 1 2 734 | v 2 2 735 | v 3 2 736 | v 4 2 737 | e 0 1 5 738 | e 1 2 5 739 | e 2 4 5 740 | e 3 4 5 741 | t # 74 742 | v 0 2 743 | v 1 2 744 | v 2 5 745 | v 3 2 746 | v 4 5 747 | e 0 1 5 748 | e 1 2 2 749 | e 1 3 5 750 | e 3 4 2 751 | t # 75 752 | v 0 2 753 | v 1 2 754 | v 2 2 755 | v 3 2 756 | v 4 2 757 | e 0 1 5 758 | e 1 2 5 759 | e 2 4 5 760 | e 3 4 5 761 | t # 76 762 | v 0 2 763 | v 1 2 764 | v 2 2 765 | v 3 2 766 | v 4 2 767 | e 0 1 5 768 | e 1 3 5 769 | e 1 4 5 770 | e 2 3 5 771 | t # 77 772 | v 0 2 773 | v 1 2 774 | v 2 2 775 | v 3 5 776 | v 4 2 777 | e 0 1 5 778 | e 1 2 2 779 | e 2 3 3 780 | e 3 4 2 781 | t # 78 782 | v 0 2 783 | v 1 2 784 | v 2 5 785 | v 3 2 786 | v 4 2 787 | e 0 1 2 788 | e 1 2 2 789 | e 2 4 2 790 | e 3 4 2 791 | t # 79 792 | v 0 3 793 | v 1 2 794 | v 2 2 795 | v 3 2 796 | v 4 2 797 | e 0 1 2 798 | e 0 2 2 799 | e 2 3 5 800 | e 3 4 5 801 | t # 80 802 | v 0 3 803 | v 1 2 804 | v 2 2 805 | v 3 2 806 | v 4 2 807 | e 0 1 2 808 | e 1 2 5 809 | e 1 3 5 810 | e 3 4 5 811 | t # 81 812 | v 0 2 813 | v 1 2 814 | v 2 2 815 | v 3 2 816 | v 4 2 817 | e 0 1 2 818 | e 1 2 3 819 | e 2 4 2 820 | e 2 3 2 821 | t # 82 822 | v 0 2 823 | v 1 2 824 | v 2 2 825 | v 3 5 826 | v 4 3 827 | e 0 1 5 828 | e 1 2 2 829 | e 2 3 2 830 | e 2 4 3 831 | t # 83 832 | v 0 2 833 | v 1 2 834 | v 2 2 835 | v 3 2 836 | v 4 3 837 | e 0 1 2 838 | e 0 2 5 839 | e 1 3 2 840 | e 1 4 2 841 | t # 84 842 | v 0 2 843 | v 1 2 844 | v 2 2 845 | v 3 2 846 | v 4 3 847 | e 0 2 2 848 | e 1 3 5 849 | e 1 2 2 850 | e 2 4 3 851 | t # 85 852 | v 0 2 853 | v 1 2 854 | v 2 2 855 | v 3 6 856 | v 4 2 857 | e 0 1 2 858 | e 1 2 5 859 | e 2 4 5 860 | e 2 3 2 861 | t # 86 862 | v 0 2 863 | v 1 2 864 | v 2 2 865 | v 3 2 866 | v 4 2 867 | e 0 1 5 868 | e 0 2 5 869 | e 2 3 5 870 | e 3 4 5 871 | t # 87 872 | v 0 2 873 | v 1 2 874 | v 2 2 875 | v 3 2 876 | v 4 2 877 | e 0 1 5 878 | e 1 2 5 879 | e 2 3 5 880 | e 3 4 5 881 | t # 88 882 | v 0 2 883 | v 1 2 884 | v 2 2 885 | v 3 2 886 | v 4 2 887 | e 0 1 5 888 | e 1 2 2 889 | e 2 3 2 890 | e 2 4 2 891 | t # 89 892 | v 0 2 893 | v 1 2 894 | v 2 2 895 | v 3 2 896 | v 4 2 897 | e 0 1 5 898 | e 1 2 5 899 | e 2 3 5 900 | e 3 4 5 901 | t # 90 902 | v 0 3 903 | v 1 2 904 | v 2 2 905 | v 3 2 906 | v 4 2 907 | e 0 1 2 908 | e 1 2 5 909 | e 1 3 5 910 | e 3 4 5 911 | t # 91 912 | v 0 2 913 | v 1 5 914 | v 2 2 915 | v 3 2 916 | v 4 2 917 | e 0 1 2 918 | e 1 2 2 919 | e 2 3 5 920 | e 3 4 5 921 | t # 92 922 | v 0 2 923 | v 1 2 924 | v 2 2 925 | v 3 2 926 | v 4 3 927 | e 0 1 2 928 | e 1 2 5 929 | e 1 3 5 930 | e 3 4 2 931 | t # 93 932 | v 0 2 933 | v 1 2 934 | v 2 2 935 | v 3 2 936 | v 4 2 937 | e 0 1 5 938 | e 0 2 5 939 | e 2 3 5 940 | e 3 4 5 941 | t # 94 942 | v 0 2 943 | v 1 2 944 | v 2 2 945 | v 3 3 946 | v 4 2 947 | e 0 1 5 948 | e 0 2 5 949 | e 2 3 2 950 | e 3 4 2 951 | t # 95 952 | v 0 2 953 | v 1 2 954 | v 2 2 955 | v 3 2 956 | v 4 2 957 | e 0 1 2 958 | e 0 2 5 959 | e 2 3 2 960 | e 3 4 2 961 | t # 96 962 | v 0 2 963 | v 1 2 964 | v 2 2 965 | v 3 2 966 | v 4 2 967 | e 0 1 5 968 | e 0 2 5 969 | e 1 3 5 970 | e 2 4 5 971 | t # 97 972 | v 0 2 973 | v 1 2 974 | v 2 2 975 | v 3 2 976 | v 4 6 977 | e 0 1 5 978 | e 1 3 5 979 | e 2 4 2 980 | e 2 3 5 981 | t # 98 982 | v 0 2 983 | v 1 2 984 | v 2 5 985 | v 3 3 986 | v 4 2 987 | e 0 1 2 988 | e 1 2 2 989 | e 1 3 3 990 | e 2 4 2 991 | t # 99 992 | v 0 2 993 | v 1 2 994 | v 2 2 995 | v 3 2 996 | v 4 2 997 | e 0 1 5 998 | e 0 2 5 999 | e 1 3 5 1000 | e 2 4 5 1001 | t # 100 1002 | v 0 2 1003 | v 1 2 1004 | v 2 5 1005 | v 3 2 1006 | v 4 3 1007 | e 0 1 2 1008 | e 1 2 2 1009 | e 2 3 2 1010 | e 3 4 3 1011 | t # 101 1012 | v 0 2 1013 | v 1 2 1014 | v 2 6 1015 | v 3 3 1016 | v 4 3 1017 | e 0 1 5 1018 | e 1 2 2 1019 | e 2 3 3 1020 | e 2 4 3 1021 | t # 102 1022 | v 0 5 1023 | v 1 2 1024 | v 2 2 1025 | v 3 2 1026 | v 4 2 1027 | e 0 1 2 1028 | e 1 2 5 1029 | e 1 3 5 1030 | e 3 4 5 1031 | t # 103 1032 | v 0 2 1033 | v 1 2 1034 | v 2 2 1035 | v 3 2 1036 | v 4 2 1037 | e 0 1 5 1038 | e 0 2 5 1039 | e 1 3 5 1040 | e 2 4 5 1041 | t # 104 1042 | v 0 2 1043 | v 1 2 1044 | v 2 2 1045 | v 3 2 1046 | v 4 2 1047 | e 0 2 2 1048 | e 1 3 2 1049 | e 1 4 2 1050 | e 2 4 3 1051 | t # 105 1052 | v 0 2 1053 | v 1 2 1054 | v 2 2 1055 | v 3 5 1056 | v 4 2 1057 | e 0 1 5 1058 | e 1 2 5 1059 | e 2 3 2 1060 | e 3 4 2 1061 | t # 106 1062 | v 0 2 1063 | v 1 2 1064 | v 2 2 1065 | v 3 2 1066 | v 4 2 1067 | e 0 1 2 1068 | e 1 2 2 1069 | e 2 3 2 1070 | e 3 4 2 1071 | t # 107 1072 | v 0 2 1073 | v 1 2 1074 | v 2 2 1075 | v 3 2 1076 | v 4 2 1077 | e 0 2 2 1078 | e 0 3 5 1079 | e 1 4 2 1080 | e 1 3 5 1081 | t # 108 1082 | v 0 2 1083 | v 1 2 1084 | v 2 23 1085 | v 3 23 1086 | v 4 23 1087 | e 0 1 2 1088 | e 0 2 2 1089 | e 1 3 2 1090 | e 1 4 2 1091 | t # 109 1092 | v 0 2 1093 | v 1 2 1094 | v 2 2 1095 | v 3 2 1096 | v 4 3 1097 | e 0 1 5 1098 | e 0 2 5 1099 | e 1 3 5 1100 | e 3 4 2 1101 | t # 110 1102 | v 0 2 1103 | v 1 2 1104 | v 2 2 1105 | v 3 2 1106 | v 4 3 1107 | e 0 1 2 1108 | e 1 2 5 1109 | e 2 3 5 1110 | e 3 4 2 1111 | t # 111 1112 | v 0 2 1113 | v 1 5 1114 | v 2 2 1115 | v 3 2 1116 | v 4 2 1117 | e 0 2 5 1118 | e 1 3 5 1119 | e 2 4 5 1120 | e 3 4 5 1121 | t # 112 1122 | v 0 2 1123 | v 1 2 1124 | v 2 3 1125 | v 3 3 1126 | v 4 2 1127 | e 0 2 2 1128 | e 0 1 5 1129 | e 1 3 2 1130 | e 2 4 2 1131 | t # 113 1132 | v 0 2 1133 | v 1 2 1134 | v 2 2 1135 | v 3 2 1136 | v 4 2 1137 | e 0 1 5 1138 | e 1 2 5 1139 | e 2 3 2 1140 | e 3 4 2 1141 | t # 114 1142 | v 0 2 1143 | v 1 2 1144 | v 2 2 1145 | v 3 2 1146 | v 4 2 1147 | e 0 1 2 1148 | e 0 2 2 1149 | e 0 3 2 1150 | e 2 4 2 1151 | t # 115 1152 | v 0 2 1153 | v 1 2 1154 | v 2 2 1155 | v 3 2 1156 | v 4 2 1157 | e 0 1 2 1158 | e 1 2 2 1159 | e 2 3 2 1160 | e 3 4 2 1161 | t # 116 1162 | v 0 2 1163 | v 1 2 1164 | v 2 2 1165 | v 3 2 1166 | v 4 2 1167 | e 0 1 2 1168 | e 1 2 2 1169 | e 2 3 2 1170 | e 3 4 2 1171 | t # 117 1172 | v 0 5 1173 | v 1 5 1174 | v 2 2 1175 | v 3 6 1176 | v 4 5 1177 | e 0 1 2 1178 | e 1 2 2 1179 | e 2 3 3 1180 | e 2 4 2 1181 | t # 118 1182 | v 0 2 1183 | v 1 2 1184 | v 2 2 1185 | v 3 5 1186 | v 4 2 1187 | e 0 1 2 1188 | e 0 2 5 1189 | e 1 3 2 1190 | e 2 4 2 1191 | t # 119 1192 | v 0 2 1193 | v 1 2 1194 | v 2 3 1195 | v 3 2 1196 | v 4 3 1197 | e 0 1 5 1198 | e 1 2 2 1199 | e 1 3 5 1200 | e 3 4 2 1201 | t # 120 1202 | v 0 2 1203 | v 1 2 1204 | v 2 3 1205 | v 3 2 1206 | v 4 3 1207 | e 0 1 2 1208 | e 1 2 2 1209 | e 1 3 2 1210 | e 3 4 2 1211 | t # 121 1212 | v 0 3 1213 | v 1 2 1214 | v 2 2 1215 | v 3 3 1216 | v 4 2 1217 | e 0 1 2 1218 | e 1 2 5 1219 | e 2 3 2 1220 | e 2 4 5 1221 | t # 122 1222 | v 0 2 1223 | v 1 5 1224 | v 2 3 1225 | v 3 2 1226 | v 4 2 1227 | e 0 1 2 1228 | e 0 2 3 1229 | e 1 3 2 1230 | e 3 4 2 1231 | t # 123 1232 | v 0 2 1233 | v 1 5 1234 | v 2 2 1235 | v 3 5 1236 | v 4 3 1237 | e 0 1 2 1238 | e 1 2 2 1239 | e 2 3 2 1240 | e 2 4 3 1241 | t # 124 1242 | v 0 2 1243 | v 1 5 1244 | v 2 2 1245 | v 3 2 1246 | v 4 2 1247 | e 0 1 2 1248 | e 1 3 2 1249 | e 2 4 2 1250 | e 3 4 2 1251 | t # 125 1252 | v 0 2 1253 | v 1 3 1254 | v 2 2 1255 | v 3 2 1256 | v 4 2 1257 | e 0 1 3 1258 | e 0 2 2 1259 | e 2 3 2 1260 | e 2 4 2 1261 | t # 126 1262 | v 0 2 1263 | v 1 2 1264 | v 2 2 1265 | v 3 2 1266 | v 4 5 1267 | e 0 3 5 1268 | e 1 2 5 1269 | e 1 4 2 1270 | e 2 3 5 1271 | t # 127 1272 | v 0 2 1273 | v 1 5 1274 | v 2 2 1275 | v 3 2 1276 | v 4 2 1277 | e 0 1 2 1278 | e 1 2 2 1279 | e 2 3 2 1280 | e 3 4 2 1281 | t # 128 1282 | v 0 2 1283 | v 1 2 1284 | v 2 2 1285 | v 3 2 1286 | v 4 3 1287 | e 0 1 5 1288 | e 1 2 5 1289 | e 1 4 2 1290 | e 2 3 5 1291 | t # 129 1292 | v 0 2 1293 | v 1 3 1294 | v 2 3 1295 | v 3 2 1296 | v 4 2 1297 | e 0 1 2 1298 | e 0 2 3 1299 | e 0 3 2 1300 | e 1 4 2 1301 | t # 130 1302 | v 0 2 1303 | v 1 2 1304 | v 2 2 1305 | v 3 2 1306 | v 4 2 1307 | e 0 1 2 1308 | e 0 2 2 1309 | e 1 3 5 1310 | e 1 4 5 1311 | t # 131 1312 | v 0 2 1313 | v 1 2 1314 | v 2 2 1315 | v 3 2 1316 | v 4 2 1317 | e 0 1 5 1318 | e 1 3 5 1319 | e 2 4 5 1320 | e 3 4 5 1321 | t # 132 1322 | v 0 2 1323 | v 1 2 1324 | v 2 3 1325 | v 3 2 1326 | v 4 2 1327 | e 0 1 5 1328 | e 1 2 2 1329 | e 2 3 2 1330 | e 3 4 5 1331 | t # 133 1332 | v 0 2 1333 | v 1 2 1334 | v 2 2 1335 | v 3 2 1336 | v 4 2 1337 | e 0 1 5 1338 | e 0 2 2 1339 | e 2 3 2 1340 | e 3 4 2 1341 | t # 134 1342 | v 0 2 1343 | v 1 2 1344 | v 2 5 1345 | v 3 2 1346 | v 4 2 1347 | e 0 1 2 1348 | e 1 2 2 1349 | e 1 3 2 1350 | e 3 4 2 1351 | t # 135 1352 | v 0 2 1353 | v 1 2 1354 | v 2 3 1355 | v 3 5 1356 | v 4 2 1357 | e 0 1 2 1358 | e 0 2 3 1359 | e 1 3 2 1360 | e 3 4 2 1361 | t # 136 1362 | v 0 2 1363 | v 1 2 1364 | v 2 5 1365 | v 3 2 1366 | v 4 3 1367 | e 0 1 5 1368 | e 1 2 2 1369 | e 2 3 2 1370 | e 3 4 3 1371 | t # 137 1372 | v 0 2 1373 | v 1 2 1374 | v 2 5 1375 | v 3 2 1376 | v 4 3 1377 | e 0 1 2 1378 | e 1 2 2 1379 | e 2 3 2 1380 | e 3 4 3 1381 | t # 138 1382 | v 0 2 1383 | v 1 5 1384 | v 2 3 1385 | v 3 2 1386 | v 4 2 1387 | e 0 1 2 1388 | e 0 2 3 1389 | e 1 3 2 1390 | e 3 4 2 1391 | t # 139 1392 | v 0 2 1393 | v 1 2 1394 | v 2 2 1395 | v 3 2 1396 | v 4 5 1397 | e 0 1 2 1398 | e 1 2 2 1399 | e 2 3 2 1400 | e 3 4 2 1401 | t # 140 1402 | v 0 2 1403 | v 1 2 1404 | v 2 2 1405 | v 3 3 1406 | v 4 2 1407 | e 0 1 5 1408 | e 1 2 5 1409 | e 2 3 2 1410 | e 3 4 2 1411 | t # 141 1412 | v 0 5 1413 | v 1 2 1414 | v 2 2 1415 | v 3 2 1416 | v 4 3 1417 | e 0 1 2 1418 | e 0 2 2 1419 | e 1 3 2 1420 | e 1 4 3 1421 | t # 142 1422 | v 0 2 1423 | v 1 2 1424 | v 2 2 1425 | v 3 2 1426 | v 4 3 1427 | e 0 1 5 1428 | e 1 2 5 1429 | e 1 3 5 1430 | e 3 4 2 1431 | t # 143 1432 | v 0 2 1433 | v 1 2 1434 | v 2 2 1435 | v 3 3 1436 | v 4 2 1437 | e 0 2 5 1438 | e 1 2 5 1439 | e 2 3 2 1440 | e 3 4 2 1441 | t # 144 1442 | v 0 2 1443 | v 1 2 1444 | v 2 2 1445 | v 3 2 1446 | v 4 3 1447 | e 0 1 5 1448 | e 0 2 5 1449 | e 1 3 5 1450 | e 3 4 2 1451 | t # 145 1452 | v 0 2 1453 | v 1 2 1454 | v 2 2 1455 | v 3 2 1456 | v 4 2 1457 | e 0 1 5 1458 | e 1 2 2 1459 | e 1 3 5 1460 | e 3 4 5 1461 | t # 146 1462 | v 0 2 1463 | v 1 2 1464 | v 2 2 1465 | v 3 2 1466 | v 4 3 1467 | e 0 1 2 1468 | e 0 2 2 1469 | e 2 3 3 1470 | e 3 4 2 1471 | t # 147 1472 | v 0 2 1473 | v 1 2 1474 | v 2 2 1475 | v 3 2 1476 | v 4 2 1477 | e 0 1 5 1478 | e 1 2 5 1479 | e 2 4 5 1480 | e 3 4 5 1481 | t # 148 1482 | v 0 2 1483 | v 1 2 1484 | v 2 2 1485 | v 3 2 1486 | v 4 2 1487 | e 0 1 2 1488 | e 0 2 2 1489 | e 1 3 2 1490 | e 3 4 2 1491 | t # 149 1492 | v 0 2 1493 | v 1 2 1494 | v 2 5 1495 | v 3 2 1496 | v 4 3 1497 | e 0 1 2 1498 | e 0 2 2 1499 | e 2 3 2 1500 | e 3 4 3 1501 | t # 150 1502 | v 0 27 1503 | v 1 5 1504 | v 2 5 1505 | v 3 5 1506 | v 4 2 1507 | e 0 1 2 1508 | e 0 2 2 1509 | e 0 3 2 1510 | e 3 4 5 1511 | t # 151 1512 | v 0 2 1513 | v 1 3 1514 | v 2 2 1515 | v 3 2 1516 | v 4 6 1517 | e 0 1 2 1518 | e 1 2 2 1519 | e 2 3 2 1520 | e 3 4 5 1521 | t # 152 1522 | v 0 2 1523 | v 1 2 1524 | v 2 2 1525 | v 3 2 1526 | v 4 2 1527 | e 0 2 5 1528 | e 1 3 5 1529 | e 2 4 5 1530 | e 3 4 5 1531 | t # 153 1532 | v 0 2 1533 | v 1 2 1534 | v 2 2 1535 | v 3 2 1536 | v 4 2 1537 | e 0 1 2 1538 | e 1 2 2 1539 | e 1 3 2 1540 | e 3 4 2 1541 | t # 154 1542 | v 0 2 1543 | v 1 3 1544 | v 2 2 1545 | v 3 2 1546 | v 4 3 1547 | e 0 1 2 1548 | e 1 2 2 1549 | e 2 3 2 1550 | e 2 4 3 1551 | t # 155 1552 | v 0 7 1553 | v 1 2 1554 | v 2 2 1555 | v 3 2 1556 | v 4 2 1557 | e 0 1 2 1558 | e 1 2 5 1559 | e 2 3 5 1560 | e 3 4 5 1561 | t # 156 1562 | v 0 2 1563 | v 1 5 1564 | v 2 5 1565 | v 3 2 1566 | v 4 2 1567 | e 0 1 2 1568 | e 0 2 2 1569 | e 2 3 2 1570 | e 3 4 2 1571 | t # 157 1572 | v 0 5 1573 | v 1 2 1574 | v 2 2 1575 | v 3 2 1576 | v 4 3 1577 | e 0 1 2 1578 | e 1 2 2 1579 | e 1 3 2 1580 | e 2 4 3 1581 | t # 158 1582 | v 0 2 1583 | v 1 2 1584 | v 2 2 1585 | v 3 2 1586 | v 4 2 1587 | e 0 2 5 1588 | e 1 3 5 1589 | e 2 4 5 1590 | e 3 4 5 1591 | t # 159 1592 | v 0 2 1593 | v 1 2 1594 | v 2 2 1595 | v 3 2 1596 | v 4 2 1597 | e 0 1 2 1598 | e 1 2 5 1599 | e 1 3 5 1600 | e 2 4 5 1601 | t # 160 1602 | v 0 2 1603 | v 1 2 1604 | v 2 5 1605 | v 3 2 1606 | v 4 2 1607 | e 0 1 2 1608 | e 1 2 2 1609 | e 2 3 2 1610 | e 3 4 2 1611 | t # 161 1612 | v 0 2 1613 | v 1 3 1614 | v 2 2 1615 | v 3 2 1616 | v 4 3 1617 | e 0 1 2 1618 | e 1 2 2 1619 | e 2 3 2 1620 | e 2 4 3 1621 | t # 162 1622 | v 0 2 1623 | v 1 2 1624 | v 2 2 1625 | v 3 2 1626 | v 4 3 1627 | e 0 1 2 1628 | e 1 2 2 1629 | e 2 3 2 1630 | e 3 4 2 1631 | t # 163 1632 | v 0 2 1633 | v 1 2 1634 | v 2 2 1635 | v 3 2 1636 | v 4 2 1637 | e 0 2 2 1638 | e 0 3 5 1639 | e 1 2 2 1640 | e 3 4 5 1641 | t # 164 1642 | v 0 2 1643 | v 1 2 1644 | v 2 5 1645 | v 3 5 1646 | v 4 2 1647 | e 0 1 2 1648 | e 1 2 3 1649 | e 2 3 2 1650 | e 3 4 2 1651 | t # 165 1652 | v 0 2 1653 | v 1 2 1654 | v 2 2 1655 | v 3 2 1656 | v 4 2 1657 | e 0 1 2 1658 | e 1 2 2 1659 | e 2 3 2 1660 | e 3 4 2 1661 | t # 166 1662 | v 0 2 1663 | v 1 2 1664 | v 2 2 1665 | v 3 2 1666 | v 4 2 1667 | e 0 1 5 1668 | e 1 3 5 1669 | e 2 4 5 1670 | e 3 4 5 1671 | t # 167 1672 | v 0 2 1673 | v 1 3 1674 | v 2 3 1675 | v 3 2 1676 | v 4 3 1677 | e 0 2 3 1678 | e 0 3 2 1679 | e 1 3 2 1680 | e 3 4 2 1681 | t # 168 1682 | v 0 2 1683 | v 1 2 1684 | v 2 3 1685 | v 3 2 1686 | v 4 2 1687 | e 0 1 2 1688 | e 1 2 2 1689 | e 1 3 2 1690 | e 2 4 2 1691 | t # 169 1692 | v 0 2 1693 | v 1 2 1694 | v 2 2 1695 | v 3 3 1696 | v 4 2 1697 | e 0 1 2 1698 | e 1 2 2 1699 | e 2 3 2 1700 | e 2 4 2 1701 | t # 170 1702 | v 0 2 1703 | v 1 2 1704 | v 2 2 1705 | v 3 3 1706 | v 4 2 1707 | e 0 1 2 1708 | e 1 2 2 1709 | e 2 3 2 1710 | e 2 4 2 1711 | t # 171 1712 | v 0 2 1713 | v 1 3 1714 | v 2 2 1715 | v 3 2 1716 | v 4 3 1717 | e 0 1 2 1718 | e 1 2 2 1719 | e 2 3 2 1720 | e 2 4 3 1721 | t # 172 1722 | v 0 2 1723 | v 1 3 1724 | v 2 2 1725 | v 3 2 1726 | v 4 2 1727 | e 0 1 2 1728 | e 0 2 2 1729 | e 2 3 2 1730 | e 2 4 2 1731 | t # 173 1732 | v 0 2 1733 | v 1 2 1734 | v 2 2 1735 | v 3 2 1736 | v 4 2 1737 | e 0 1 5 1738 | e 1 3 5 1739 | e 2 3 5 1740 | e 3 4 2 1741 | t # 174 1742 | v 0 2 1743 | v 1 5 1744 | v 2 2 1745 | v 3 2 1746 | v 4 2 1747 | e 0 1 2 1748 | e 1 2 2 1749 | e 2 3 2 1750 | e 3 4 2 1751 | t # 175 1752 | v 0 2 1753 | v 1 2 1754 | v 2 2 1755 | v 3 2 1756 | v 4 3 1757 | e 0 2 2 1758 | e 1 3 2 1759 | e 1 2 2 1760 | e 2 4 2 1761 | t # 176 1762 | v 0 2 1763 | v 1 2 1764 | v 2 2 1765 | v 3 5 1766 | v 4 3 1767 | e 0 2 5 1768 | e 1 3 2 1769 | e 1 2 5 1770 | e 2 4 2 1771 | t # 177 1772 | v 0 2 1773 | v 1 2 1774 | v 2 2 1775 | v 3 2 1776 | v 4 3 1777 | e 0 1 2 1778 | e 1 2 2 1779 | e 1 3 2 1780 | e 3 4 2 1781 | t # 178 1782 | v 0 2 1783 | v 1 2 1784 | v 2 3 1785 | v 3 2 1786 | v 4 2 1787 | e 0 2 2 1788 | e 1 3 2 1789 | e 2 3 2 1790 | e 3 4 2 1791 | t # 179 1792 | v 0 2 1793 | v 1 2 1794 | v 2 2 1795 | v 3 3 1796 | v 4 2 1797 | e 0 2 5 1798 | e 1 3 2 1799 | e 1 2 5 1800 | e 2 4 2 1801 | t # 180 1802 | v 0 2 1803 | v 1 2 1804 | v 2 5 1805 | v 3 3 1806 | v 4 2 1807 | e 0 1 2 1808 | e 1 2 2 1809 | e 1 3 3 1810 | e 2 4 2 1811 | t # 181 1812 | v 0 3 1813 | v 1 2 1814 | v 2 2 1815 | v 3 2 1816 | v 4 2 1817 | e 0 1 2 1818 | e 0 2 2 1819 | e 2 3 5 1820 | e 3 4 5 1821 | t # 182 1822 | v 0 2 1823 | v 1 2 1824 | v 2 2 1825 | v 3 2 1826 | v 4 2 1827 | e 0 1 2 1828 | e 1 2 2 1829 | e 2 3 2 1830 | e 3 4 2 1831 | t # 183 1832 | v 0 2 1833 | v 1 2 1834 | v 2 2 1835 | v 3 2 1836 | v 4 3 1837 | e 0 1 5 1838 | e 1 3 5 1839 | e 2 3 5 1840 | e 3 4 2 1841 | t # 184 1842 | v 0 2 1843 | v 1 2 1844 | v 2 2 1845 | v 3 2 1846 | v 4 3 1847 | e 0 2 2 1848 | e 1 3 2 1849 | e 1 2 2 1850 | e 2 4 2 1851 | t # 185 1852 | v 0 2 1853 | v 1 2 1854 | v 2 3 1855 | v 3 2 1856 | v 4 2 1857 | e 0 1 2 1858 | e 0 2 2 1859 | e 2 3 2 1860 | e 3 4 3 1861 | t # 186 1862 | v 0 2 1863 | v 1 2 1864 | v 2 2 1865 | v 3 2 1866 | v 4 3 1867 | e 0 2 5 1868 | e 1 3 5 1869 | e 2 3 5 1870 | e 3 4 2 1871 | t # 187 1872 | v 0 2 1873 | v 1 2 1874 | v 2 2 1875 | v 3 2 1876 | v 4 3 1877 | e 0 2 5 1878 | e 1 3 5 1879 | e 2 4 2 1880 | e 2 3 5 1881 | t # 188 1882 | v 0 2 1883 | v 1 3 1884 | v 2 2 1885 | v 3 2 1886 | v 4 3 1887 | e 0 1 2 1888 | e 1 2 2 1889 | e 2 3 2 1890 | e 2 4 3 1891 | t # 189 1892 | v 0 2 1893 | v 1 2 1894 | v 2 2 1895 | v 3 5 1896 | v 4 2 1897 | e 0 1 5 1898 | e 1 2 5 1899 | e 2 3 2 1900 | e 3 4 2 1901 | t # 190 1902 | v 0 2 1903 | v 1 2 1904 | v 2 3 1905 | v 3 2 1906 | v 4 2 1907 | e 0 1 2 1908 | e 1 2 2 1909 | e 1 3 2 1910 | e 3 4 2 1911 | t # 191 1912 | v 0 2 1913 | v 1 2 1914 | v 2 2 1915 | v 3 2 1916 | v 4 3 1917 | e 0 1 2 1918 | e 0 2 2 1919 | e 2 4 2 1920 | e 3 4 2 1921 | t # 192 1922 | v 0 2 1923 | v 1 2 1924 | v 2 2 1925 | v 3 2 1926 | v 4 3 1927 | e 0 2 5 1928 | e 1 3 5 1929 | e 2 3 5 1930 | e 3 4 2 1931 | t # 193 1932 | v 0 2 1933 | v 1 2 1934 | v 2 2 1935 | v 3 2 1936 | v 4 2 1937 | e 0 1 2 1938 | e 0 2 3 1939 | e 2 3 2 1940 | e 3 4 3 1941 | t # 194 1942 | v 0 2 1943 | v 1 2 1944 | v 2 3 1945 | v 3 2 1946 | v 4 2 1947 | e 0 1 2 1948 | e 1 2 2 1949 | e 1 3 2 1950 | e 3 4 2 1951 | t # 195 1952 | v 0 3 1953 | v 1 2 1954 | v 2 2 1955 | v 3 2 1956 | v 4 5 1957 | e 0 1 2 1958 | e 1 2 5 1959 | e 2 3 2 1960 | e 3 4 3 1961 | t # 196 1962 | v 0 2 1963 | v 1 2 1964 | v 2 2 1965 | v 3 2 1966 | v 4 3 1967 | e 0 1 2 1968 | e 1 2 2 1969 | e 1 3 2 1970 | e 3 4 2 1971 | t # 197 1972 | v 0 2 1973 | v 1 2 1974 | v 2 3 1975 | v 3 2 1976 | v 4 3 1977 | e 0 1 2 1978 | e 1 2 3 1979 | e 1 3 2 1980 | e 3 4 2 1981 | t # 198 1982 | v 0 2 1983 | v 1 2 1984 | v 2 2 1985 | v 3 2 1986 | v 4 2 1987 | e 0 1 3 1988 | e 1 2 2 1989 | e 2 3 2 1990 | e 2 4 2 1991 | t # 199 1992 | v 0 2 1993 | v 1 2 1994 | v 2 2 1995 | v 3 3 1996 | v 4 2 1997 | e 0 1 5 1998 | e 1 2 2 1999 | e 2 3 3 2000 | e 2 4 2 2001 | t # 200 2002 | v 0 2 2003 | v 1 3 2004 | v 2 2 2005 | v 3 2 2006 | v 4 2 2007 | e 0 1 2 2008 | e 1 2 2 2009 | e 2 3 3 2010 | e 3 4 2 2011 | t # 201 2012 | v 0 2 2013 | v 1 2 2014 | v 2 2 2015 | v 3 2 2016 | v 4 2 2017 | e 0 1 5 2018 | e 0 2 5 2019 | e 2 3 5 2020 | e 3 4 5 2021 | t # 202 2022 | v 0 2 2023 | v 1 2 2024 | v 2 2 2025 | v 3 3 2026 | v 4 2 2027 | e 0 1 5 2028 | e 1 3 2 2029 | e 2 4 2 2030 | e 3 4 2 2031 | t # 203 2032 | v 0 2 2033 | v 1 2 2034 | v 2 2 2035 | v 3 2 2036 | v 4 2 2037 | e 0 1 5 2038 | e 1 2 5 2039 | e 2 3 5 2040 | e 2 4 2 2041 | t # 204 2042 | v 0 2 2043 | v 1 2 2044 | v 2 2 2045 | v 3 2 2046 | v 4 5 2047 | e 0 2 5 2048 | e 1 3 5 2049 | e 2 4 2 2050 | e 2 3 5 2051 | t # 205 2052 | v 0 2 2053 | v 1 2 2054 | v 2 2 2055 | v 3 2 2056 | v 4 2 2057 | e 0 1 2 2058 | e 1 2 3 2059 | e 2 3 2 2060 | e 3 4 2 2061 | t # 206 2062 | v 0 2 2063 | v 1 2 2064 | v 2 2 2065 | v 3 5 2066 | v 4 3 2067 | e 0 1 5 2068 | e 1 2 2 2069 | e 2 3 3 2070 | e 3 4 2 2071 | t # 207 2072 | v 0 2 2073 | v 1 2 2074 | v 2 3 2075 | v 3 2 2076 | v 4 2 2077 | e 0 1 5 2078 | e 1 2 2 2079 | e 1 3 5 2080 | e 3 4 2 2081 | t # 208 2082 | v 0 2 2083 | v 1 2 2084 | v 2 2 2085 | v 3 3 2086 | v 4 2 2087 | e 0 1 2 2088 | e 0 2 2 2089 | e 2 3 2 2090 | e 2 4 2 2091 | t # 209 2092 | v 0 2 2093 | v 1 2 2094 | v 2 2 2095 | v 3 3 2096 | v 4 2 2097 | e 0 1 5 2098 | e 1 3 2 2099 | e 1 2 5 2100 | e 2 4 2 2101 | t # 210 2102 | v 0 2 2103 | v 1 3 2104 | v 2 2 2105 | v 3 2 2106 | v 4 2 2107 | e 0 1 2 2108 | e 0 2 2 2109 | e 1 3 2 2110 | e 3 4 2 2111 | t # 211 2112 | v 0 2 2113 | v 1 3 2114 | v 2 2 2115 | v 3 2 2116 | v 4 3 2117 | e 0 1 2 2118 | e 1 2 2 2119 | e 2 3 2 2120 | e 2 4 2 2121 | t # 212 2122 | v 0 2 2123 | v 1 2 2124 | v 2 2 2125 | v 3 2 2126 | v 4 3 2127 | e 0 1 5 2128 | e 0 2 5 2129 | e 2 4 2 2130 | e 2 3 5 2131 | t # 213 2132 | v 0 3 2133 | v 1 2 2134 | v 2 2 2135 | v 3 2 2136 | v 4 2 2137 | e 0 1 2 2138 | e 1 2 5 2139 | e 2 3 5 2140 | e 3 4 5 2141 | t # 214 2142 | v 0 2 2143 | v 1 2 2144 | v 2 5 2145 | v 3 2 2146 | v 4 3 2147 | e 0 1 5 2148 | e 1 2 2 2149 | e 1 3 5 2150 | e 2 4 3 2151 | t # 215 2152 | v 0 2 2153 | v 1 2 2154 | v 2 3 2155 | v 3 2 2156 | v 4 2 2157 | e 0 1 2 2158 | e 0 2 2 2159 | e 2 3 2 2160 | e 3 4 3 2161 | t # 216 2162 | v 0 2 2163 | v 1 2 2164 | v 2 3 2165 | v 3 2 2166 | v 4 2 2167 | e 0 1 2 2168 | e 1 2 3 2169 | e 1 3 2 2170 | e 3 4 3 2171 | t # 217 2172 | v 0 2 2173 | v 1 2 2174 | v 2 2 2175 | v 3 2 2176 | v 4 2 2177 | e 0 1 2 2178 | e 0 2 2 2179 | e 0 3 2 2180 | e 1 4 2 2181 | t # 218 2182 | v 0 2 2183 | v 1 2 2184 | v 2 2 2185 | v 3 2 2186 | v 4 2 2187 | e 0 1 2 2188 | e 0 2 2 2189 | e 1 3 5 2190 | e 2 4 2 2191 | t # 219 2192 | v 0 2 2193 | v 1 2 2194 | v 2 2 2195 | v 3 2 2196 | v 4 2 2197 | e 0 1 5 2198 | e 1 2 5 2199 | e 2 4 5 2200 | e 3 4 5 2201 | t # 220 2202 | v 0 2 2203 | v 1 2 2204 | v 2 3 2205 | v 3 2 2206 | v 4 2 2207 | e 0 1 2 2208 | e 1 2 2 2209 | e 1 3 2 2210 | e 3 4 2 2211 | t # 221 2212 | v 0 2 2213 | v 1 2 2214 | v 2 2 2215 | v 3 2 2216 | v 4 3 2217 | e 0 1 5 2218 | e 1 2 5 2219 | e 1 3 5 2220 | e 2 4 2 2221 | t # 222 2222 | v 0 2 2223 | v 1 2 2224 | v 2 2 2225 | v 3 2 2226 | v 4 2 2227 | e 0 1 5 2228 | e 0 2 5 2229 | e 1 3 2 2230 | e 3 4 2 2231 | t # 223 2232 | v 0 2 2233 | v 1 2 2234 | v 2 2 2235 | v 3 2 2236 | v 4 3 2237 | e 0 1 2 2238 | e 1 3 2 2239 | e 2 3 2 2240 | e 3 4 2 2241 | t # 224 2242 | v 0 2 2243 | v 1 2 2244 | v 2 2 2245 | v 3 3 2246 | v 4 3 2247 | e 0 2 5 2248 | e 1 3 2 2249 | e 1 2 5 2250 | e 2 4 2 2251 | t # 225 2252 | v 0 2 2253 | v 1 3 2254 | v 2 2 2255 | v 3 5 2256 | v 4 3 2257 | e 0 1 2 2258 | e 1 2 2 2259 | e 2 3 2 2260 | e 2 4 3 2261 | t # 226 2262 | v 0 2 2263 | v 1 2 2264 | v 2 2 2265 | v 3 3 2266 | v 4 2 2267 | e 0 1 2 2268 | e 1 2 2 2269 | e 2 3 2 2270 | e 2 4 2 2271 | t # 227 2272 | v 0 2 2273 | v 1 2 2274 | v 2 3 2275 | v 3 2 2276 | v 4 3 2277 | e 0 1 2 2278 | e 0 2 2 2279 | e 1 3 2 2280 | e 3 4 2 2281 | t # 228 2282 | v 0 2 2283 | v 1 5 2284 | v 2 2 2285 | v 3 2 2286 | v 4 2 2287 | e 0 1 2 2288 | e 1 2 2 2289 | e 1 3 2 2290 | e 2 4 2 2291 | t # 229 2292 | v 0 2 2293 | v 1 2 2294 | v 2 2 2295 | v 3 3 2296 | v 4 2 2297 | e 0 1 2 2298 | e 1 3 2 2299 | e 1 2 2 2300 | e 2 4 2 2301 | t # 230 2302 | v 0 2 2303 | v 1 2 2304 | v 2 2 2305 | v 3 2 2306 | v 4 2 2307 | e 0 1 2 2308 | e 1 2 5 2309 | e 1 3 5 2310 | e 2 4 5 2311 | t # 231 2312 | v 0 2 2313 | v 1 2 2314 | v 2 2 2315 | v 3 2 2316 | v 4 3 2317 | e 0 1 5 2318 | e 0 2 2 2319 | e 1 3 5 2320 | e 3 4 2 2321 | t # 232 2322 | v 0 2 2323 | v 1 2 2324 | v 2 2 2325 | v 3 2 2326 | v 4 3 2327 | e 0 1 5 2328 | e 1 3 5 2329 | e 2 4 2 2330 | e 2 3 5 2331 | t # 233 2332 | v 0 2 2333 | v 1 2 2334 | v 2 2 2335 | v 3 2 2336 | v 4 2 2337 | e 0 1 2 2338 | e 1 2 2 2339 | e 2 3 2 2340 | e 3 4 2 2341 | t # 234 2342 | v 0 2 2343 | v 1 2 2344 | v 2 2 2345 | v 3 2 2346 | v 4 3 2347 | e 0 1 5 2348 | e 0 2 5 2349 | e 2 3 5 2350 | e 3 4 2 2351 | t # 235 2352 | v 0 2 2353 | v 1 2 2354 | v 2 2 2355 | v 3 3 2356 | v 4 2 2357 | e 0 1 3 2358 | e 1 2 2 2359 | e 2 3 2 2360 | e 3 4 2 2361 | t # 236 2362 | v 0 2 2363 | v 1 5 2364 | v 2 3 2365 | v 3 2 2366 | v 4 2 2367 | e 0 1 3 2368 | e 1 2 2 2369 | e 2 3 2 2370 | e 3 4 2 2371 | t # 237 2372 | v 0 2 2373 | v 1 2 2374 | v 2 2 2375 | v 3 2 2376 | v 4 2 2377 | e 0 1 2 2378 | e 1 2 2 2379 | e 2 3 2 2380 | e 3 4 2 2381 | t # 238 2382 | v 0 2 2383 | v 1 2 2384 | v 2 3 2385 | v 3 2 2386 | v 4 2 2387 | e 0 1 2 2388 | e 1 2 2 2389 | e 2 3 2 2390 | e 3 4 2 2391 | t # 239 2392 | v 0 2 2393 | v 1 2 2394 | v 2 2 2395 | v 3 5 2396 | v 4 5 2397 | e 0 1 2 2398 | e 0 2 5 2399 | e 1 3 3 2400 | e 2 4 2 2401 | t # 240 2402 | v 0 2 2403 | v 1 2 2404 | v 2 2 2405 | v 3 2 2406 | v 4 2 2407 | e 0 1 2 2408 | e 1 2 5 2409 | e 1 3 5 2410 | e 3 4 5 2411 | t # 241 2412 | v 0 2 2413 | v 1 3 2414 | v 2 2 2415 | v 3 2 2416 | v 4 3 2417 | e 0 1 2 2418 | e 1 2 2 2419 | e 2 3 2 2420 | e 2 4 3 2421 | t # 242 2422 | v 0 2 2423 | v 1 2 2424 | v 2 2 2425 | v 3 3 2426 | v 4 2 2427 | e 0 2 2 2428 | e 1 3 2 2429 | e 2 4 2 2430 | e 3 4 2 2431 | t # 243 2432 | v 0 2 2433 | v 1 2 2434 | v 2 2 2435 | v 3 2 2436 | v 4 2 2437 | e 0 1 5 2438 | e 1 3 5 2439 | e 2 3 5 2440 | e 3 4 2 2441 | t # 244 2442 | v 0 2 2443 | v 1 2 2444 | v 2 3 2445 | v 3 2 2446 | v 4 3 2447 | e 0 1 2 2448 | e 1 2 3 2449 | e 1 3 2 2450 | e 3 4 2 2451 | t # 245 2452 | v 0 2 2453 | v 1 2 2454 | v 2 2 2455 | v 3 3 2456 | v 4 2 2457 | e 0 1 5 2458 | e 0 2 2 2459 | e 1 3 2 2460 | e 3 4 2 2461 | t # 246 2462 | v 0 2 2463 | v 1 3 2464 | v 2 2 2465 | v 3 2 2466 | v 4 3 2467 | e 0 1 2 2468 | e 1 2 2 2469 | e 2 3 2 2470 | e 3 4 2 2471 | t # 247 2472 | v 0 2 2473 | v 1 2 2474 | v 2 3 2475 | v 3 2 2476 | v 4 3 2477 | e 0 2 2 2478 | e 1 3 2 2479 | e 2 3 2 2480 | e 3 4 2 2481 | t # 248 2482 | v 0 3 2483 | v 1 2 2484 | v 2 2 2485 | v 3 2 2486 | v 4 5 2487 | e 0 1 2 2488 | e 1 2 5 2489 | e 2 3 2 2490 | e 3 4 2 2491 | t # 249 2492 | v 0 2 2493 | v 1 2 2494 | v 2 2 2495 | v 3 2 2496 | v 4 3 2497 | e 0 1 2 2498 | e 1 2 2 2499 | e 1 3 2 2500 | e 3 4 2 2501 | t # 250 2502 | v 0 2 2503 | v 1 2 2504 | v 2 2 2505 | v 3 2 2506 | v 4 3 2507 | e 0 2 2 2508 | e 0 3 2 2509 | e 1 3 2 2510 | e 3 4 2 2511 | t # 251 2512 | v 0 2 2513 | v 1 3 2514 | v 2 2 2515 | v 3 3 2516 | v 4 2 2517 | e 0 1 3 2518 | e 0 2 2 2519 | e 2 3 2 2520 | e 3 4 2 2521 | t # 252 2522 | v 0 2 2523 | v 1 2 2524 | v 2 2 2525 | v 3 2 2526 | v 4 3 2527 | e 0 1 3 2528 | e 1 2 2 2529 | e 2 3 2 2530 | e 3 4 2 2531 | t # 253 2532 | v 0 2 2533 | v 1 2 2534 | v 2 2 2535 | v 3 2 2536 | v 4 3 2537 | e 0 1 2 2538 | e 1 3 2 2539 | e 1 2 2 2540 | e 2 4 2 2541 | t # 254 2542 | v 0 2 2543 | v 1 2 2544 | v 2 2 2545 | v 3 2 2546 | v 4 2 2547 | e 0 2 2 2548 | e 0 1 5 2549 | e 1 3 2 2550 | e 2 4 3 2551 | t # 255 2552 | v 0 2 2553 | v 1 2 2554 | v 2 3 2555 | v 3 3 2556 | v 4 3 2557 | e 0 1 2 2558 | e 0 2 2 2559 | e 1 3 2 2560 | e 1 4 3 2561 | t # 256 2562 | v 0 3 2563 | v 1 2 2564 | v 2 2 2565 | v 3 2 2566 | v 4 2 2567 | e 0 1 2 2568 | e 1 2 3 2569 | e 1 3 2 2570 | e 2 4 2 2571 | t # 257 2572 | v 0 5 2573 | v 1 2 2574 | v 2 2 2575 | v 3 2 2576 | v 4 2 2577 | e 0 1 2 2578 | e 1 2 2 2579 | e 2 3 2 2580 | e 2 4 3 2581 | t # 258 2582 | v 0 3 2583 | v 1 2 2584 | v 2 2 2585 | v 3 2 2586 | v 4 2 2587 | e 0 1 2 2588 | e 1 2 5 2589 | e 2 3 5 2590 | e 3 4 5 2591 | t # 259 2592 | v 0 2 2593 | v 1 2 2594 | v 2 3 2595 | v 3 2 2596 | v 4 2 2597 | e 0 1 2 2598 | e 1 2 2 2599 | e 1 3 2 2600 | e 3 4 2 2601 | t # 260 2602 | v 0 2 2603 | v 1 5 2604 | v 2 2 2605 | v 3 3 2606 | v 4 2 2607 | e 0 1 2 2608 | e 1 2 2 2609 | e 2 3 3 2610 | e 2 4 2 2611 | t # 261 2612 | v 0 2 2613 | v 1 2 2614 | v 2 2 2615 | v 3 2 2616 | v 4 2 2617 | e 0 1 2 2618 | e 1 2 2 2619 | e 1 3 3 2620 | e 3 4 2 2621 | t # 262 2622 | v 0 2 2623 | v 1 2 2624 | v 2 2 2625 | v 3 2 2626 | v 4 3 2627 | e 0 2 2 2628 | e 0 3 2 2629 | e 1 3 2 2630 | e 3 4 2 2631 | t # 263 2632 | v 0 2 2633 | v 1 2 2634 | v 2 2 2635 | v 3 2 2636 | v 4 3 2637 | e 0 2 2 2638 | e 1 3 2 2639 | e 2 3 2 2640 | e 3 4 2 2641 | t # 264 2642 | v 0 2 2643 | v 1 2 2644 | v 2 2 2645 | v 3 2 2646 | v 4 2 2647 | e 0 1 2 2648 | e 0 2 2 2649 | e 2 3 2 2650 | e 3 4 2 2651 | t # 265 2652 | v 0 2 2653 | v 1 2 2654 | v 2 2 2655 | v 3 5 2656 | v 4 2 2657 | e 0 2 2 2658 | e 1 3 2 2659 | e 2 3 2 2660 | e 3 4 2 2661 | t # 266 2662 | v 0 3 2663 | v 1 2 2664 | v 2 3 2665 | v 3 2 2666 | v 4 2 2667 | e 0 1 2 2668 | e 1 2 2 2669 | e 2 3 2 2670 | e 3 4 3 2671 | t # 267 2672 | v 0 2 2673 | v 1 2 2674 | v 2 3 2675 | v 3 2 2676 | v 4 2 2677 | e 0 2 2 2678 | e 0 3 2 2679 | e 1 4 2 2680 | e 3 4 2 2681 | t # 268 2682 | v 0 2 2683 | v 1 3 2684 | v 2 2 2685 | v 3 2 2686 | v 4 2 2687 | e 0 1 2 2688 | e 0 2 2 2689 | e 1 3 2 2690 | e 2 4 2 2691 | t # 269 2692 | v 0 2 2693 | v 1 2 2694 | v 2 2 2695 | v 3 2 2696 | v 4 5 2697 | e 0 2 5 2698 | e 1 3 5 2699 | e 2 4 2 2700 | e 2 3 5 2701 | t # 270 2702 | v 0 2 2703 | v 1 2 2704 | v 2 2 2705 | v 3 5 2706 | v 4 2 2707 | e 0 1 2 2708 | e 0 2 5 2709 | e 2 3 2 2710 | e 3 4 2 2711 | t # 271 2712 | v 0 6 2713 | v 1 2 2714 | v 2 2 2715 | v 3 2 2716 | v 4 2 2717 | e 0 1 2 2718 | e 1 2 2 2719 | e 1 3 2 2720 | e 3 4 2 2721 | t # 272 2722 | v 0 5 2723 | v 1 2 2724 | v 2 2 2725 | v 3 5 2726 | v 4 6 2727 | e 0 1 2 2728 | e 1 2 2 2729 | e 1 3 3 2730 | e 2 4 2 2731 | t # 273 2732 | v 0 2 2733 | v 1 2 2734 | v 2 2 2735 | v 3 2 2736 | v 4 2 2737 | e 0 2 2 2738 | e 1 2 2 2739 | e 2 4 2 2740 | e 3 4 2 2741 | t # 274 2742 | v 0 2 2743 | v 1 2 2744 | v 2 2 2745 | v 3 2 2746 | v 4 2 2747 | e 0 1 2 2748 | e 1 2 2 2749 | e 1 3 2 2750 | e 3 4 2 2751 | t # 275 2752 | v 0 2 2753 | v 1 2 2754 | v 2 2 2755 | v 3 3 2756 | v 4 2 2757 | e 0 1 3 2758 | e 1 2 2 2759 | e 2 3 2 2760 | e 2 4 2 2761 | t # 276 2762 | v 0 2 2763 | v 1 2 2764 | v 2 2 2765 | v 3 3 2766 | v 4 3 2767 | e 0 1 2 2768 | e 1 2 2 2769 | e 2 3 2 2770 | e 2 4 3 2771 | t # 277 2772 | v 0 5 2773 | v 1 2 2774 | v 2 2 2775 | v 3 2 2776 | v 4 2 2777 | e 0 1 2 2778 | e 0 2 2 2779 | e 2 3 2 2780 | e 3 4 2 2781 | t # 278 2782 | v 0 3 2783 | v 1 2 2784 | v 2 2 2785 | v 3 2 2786 | v 4 2 2787 | e 0 1 2 2788 | e 1 2 5 2789 | e 1 3 5 2790 | e 2 4 5 2791 | t # 279 2792 | v 0 3 2793 | v 1 2 2794 | v 2 2 2795 | v 3 2 2796 | v 4 5 2797 | e 0 1 2 2798 | e 1 2 5 2799 | e 2 3 2 2800 | e 3 4 3 2801 | t # 280 2802 | v 0 5 2803 | v 1 5 2804 | v 2 2 2805 | v 3 2 2806 | v 4 2 2807 | e 0 1 2 2808 | e 1 2 2 2809 | e 1 3 2 2810 | e 3 4 2 2811 | t # 281 2812 | v 0 5 2813 | v 1 2 2814 | v 2 2 2815 | v 3 2 2816 | v 4 2 2817 | e 0 2 2 2818 | e 1 3 5 2819 | e 1 4 2 2820 | e 2 4 2 2821 | t # 282 2822 | v 0 5 2823 | v 1 2 2824 | v 2 2 2825 | v 3 2 2826 | v 4 2 2827 | e 0 1 2 2828 | e 1 2 2 2829 | e 2 3 2 2830 | e 2 4 3 2831 | t # 283 2832 | v 0 5 2833 | v 1 5 2834 | v 2 2 2835 | v 3 2 2836 | v 4 2 2837 | e 0 1 2 2838 | e 1 2 2 2839 | e 2 3 2 2840 | e 3 4 2 2841 | t # 284 2842 | v 0 2 2843 | v 1 2 2844 | v 2 2 2845 | v 3 2 2846 | v 4 3 2847 | e 0 1 5 2848 | e 1 2 5 2849 | e 2 3 5 2850 | e 3 4 2 2851 | t # 285 2852 | v 0 2 2853 | v 1 2 2854 | v 2 2 2855 | v 3 5 2856 | v 4 2 2857 | e 0 2 2 2858 | e 0 3 2 2859 | e 1 3 2 2860 | e 3 4 2 2861 | t # 286 2862 | v 0 5 2863 | v 1 2 2864 | v 2 3 2865 | v 3 2 2866 | v 4 2 2867 | e 0 1 2 2868 | e 1 2 3 2869 | e 1 3 2 2870 | e 3 4 3 2871 | t # 287 2872 | v 0 2 2873 | v 1 2 2874 | v 2 3 2875 | v 3 2 2876 | v 4 3 2877 | e 0 1 2 2878 | e 1 3 2 2879 | e 2 3 2 2880 | e 3 4 2 2881 | t # 288 2882 | v 0 2 2883 | v 1 3 2884 | v 2 2 2885 | v 3 2 2886 | v 4 3 2887 | e 0 1 2 2888 | e 1 2 2 2889 | e 2 3 2 2890 | e 2 4 3 2891 | t # 289 2892 | v 0 2 2893 | v 1 3 2894 | v 2 2 2895 | v 3 2 2896 | v 4 2 2897 | e 0 1 3 2898 | e 0 2 2 2899 | e 2 3 3 2900 | e 3 4 2 2901 | t # 290 2902 | v 0 5 2903 | v 1 2 2904 | v 2 5 2905 | v 3 2 2906 | v 4 2 2907 | e 0 1 2 2908 | e 0 2 2 2909 | e 1 3 2 2910 | e 3 4 5 2911 | t # 291 2912 | v 0 2 2913 | v 1 2 2914 | v 2 2 2915 | v 3 2 2916 | v 4 3 2917 | e 0 1 2 2918 | e 1 2 2 2919 | e 2 3 2 2920 | e 2 4 2 2921 | t # 292 2922 | v 0 2 2923 | v 1 3 2924 | v 2 2 2925 | v 3 2 2926 | v 4 2 2927 | e 0 1 2 2928 | e 0 2 2 2929 | e 2 3 3 2930 | e 3 4 2 2931 | t # 293 2932 | v 0 5 2933 | v 1 2 2934 | v 2 2 2935 | v 3 2 2936 | v 4 2 2937 | e 0 1 2 2938 | e 0 2 2 2939 | e 1 3 2 2940 | e 3 4 2 2941 | t # 294 2942 | v 0 2 2943 | v 1 2 2944 | v 2 2 2945 | v 3 2 2946 | v 4 2 2947 | e 0 1 5 2948 | e 1 3 5 2949 | e 1 4 5 2950 | e 2 3 5 2951 | t # 295 2952 | v 0 2 2953 | v 1 3 2954 | v 2 2 2955 | v 3 2 2956 | v 4 2 2957 | e 0 1 3 2958 | e 0 2 5 2959 | e 2 3 5 2960 | e 2 4 5 2961 | t # 296 2962 | v 0 3 2963 | v 1 2 2964 | v 2 2 2965 | v 3 5 2966 | v 4 2 2967 | e 0 1 2 2968 | e 1 2 5 2969 | e 2 3 2 2970 | e 3 4 2 2971 | t # 297 2972 | v 0 2 2973 | v 1 2 2974 | v 2 2 2975 | v 3 3 2976 | v 4 3 2977 | e 0 1 2 2978 | e 1 2 2 2979 | e 2 3 3 2980 | e 2 4 2 2981 | t # 298 2982 | v 0 5 2983 | v 1 2 2984 | v 2 2 2985 | v 3 2 2986 | v 4 2 2987 | e 0 1 2 2988 | e 1 2 2 2989 | e 2 3 5 2990 | e 3 4 5 2991 | t # 299 2992 | v 0 2 2993 | v 1 3 2994 | v 2 2 2995 | v 3 2 2996 | v 4 2 2997 | e 0 1 2 2998 | e 0 2 2 2999 | e 1 3 2 3000 | e 3 4 2 3001 | t # 300 3002 | v 0 2 3003 | v 1 2 3004 | v 2 2 3005 | v 3 2 3006 | v 4 3 3007 | e 0 1 2 3008 | e 1 2 2 3009 | e 1 3 2 3010 | e 3 4 2 3011 | t # 301 3012 | v 0 2 3013 | v 1 2 3014 | v 2 2 3015 | v 3 2 3016 | v 4 3 3017 | e 0 2 5 3018 | e 1 3 2 3019 | e 1 2 5 3020 | e 2 4 2 3021 | t # 302 3022 | v 0 2 3023 | v 1 2 3024 | v 2 2 3025 | v 3 2 3026 | v 4 2 3027 | e 0 1 3 3028 | e 1 3 2 3029 | e 2 3 2 3030 | e 3 4 2 3031 | t # 303 3032 | v 0 2 3033 | v 1 3 3034 | v 2 2 3035 | v 3 2 3036 | v 4 3 3037 | e 0 1 2 3038 | e 1 2 2 3039 | e 2 3 2 3040 | e 2 4 3 3041 | t # 304 3042 | v 0 2 3043 | v 1 3 3044 | v 2 2 3045 | v 3 2 3046 | v 4 3 3047 | e 0 1 2 3048 | e 1 2 2 3049 | e 2 3 2 3050 | e 2 4 3 3051 | t # 305 3052 | v 0 5 3053 | v 1 2 3054 | v 2 2 3055 | v 3 5 3056 | v 4 2 3057 | e 0 1 2 3058 | e 1 2 2 3059 | e 2 3 2 3060 | e 3 4 2 3061 | t # 306 3062 | v 0 2 3063 | v 1 2 3064 | v 2 5 3065 | v 3 2 3066 | v 4 2 3067 | e 0 1 2 3068 | e 1 2 2 3069 | e 2 3 2 3070 | e 3 4 2 3071 | t # 307 3072 | v 0 2 3073 | v 1 2 3074 | v 2 3 3075 | v 3 2 3076 | v 4 2 3077 | e 0 2 2 3078 | e 0 3 5 3079 | e 1 3 5 3080 | e 3 4 2 3081 | t # 308 3082 | v 0 2 3083 | v 1 3 3084 | v 2 2 3085 | v 3 3 3086 | v 4 2 3087 | e 0 1 2 3088 | e 1 2 2 3089 | e 2 3 2 3090 | e 2 4 2 3091 | t # 309 3092 | v 0 2 3093 | v 1 2 3094 | v 2 2 3095 | v 3 2 3096 | v 4 3 3097 | e 0 2 2 3098 | e 1 2 5 3099 | e 2 3 5 3100 | e 3 4 2 3101 | t # 310 3102 | v 0 2 3103 | v 1 2 3104 | v 2 2 3105 | v 3 2 3106 | v 4 2 3107 | e 0 1 3 3108 | e 1 2 2 3109 | e 2 3 2 3110 | e 3 4 2 3111 | t # 311 3112 | v 0 5 3113 | v 1 2 3114 | v 2 3 3115 | v 3 2 3116 | v 4 2 3117 | e 0 1 2 3118 | e 1 2 3 3119 | e 1 3 2 3120 | e 3 4 2 3121 | t # 312 3122 | v 0 2 3123 | v 1 2 3124 | v 2 2 3125 | v 3 2 3126 | v 4 2 3127 | e 0 1 3 3128 | e 1 2 2 3129 | e 2 3 3 3130 | e 3 4 2 3131 | t # 313 3132 | v 0 2 3133 | v 1 2 3134 | v 2 2 3135 | v 3 3 3136 | v 4 2 3137 | e 0 1 5 3138 | e 0 2 2 3139 | e 1 3 2 3140 | e 3 4 2 3141 | t # 314 3142 | v 0 3 3143 | v 1 2 3144 | v 2 2 3145 | v 3 2 3146 | v 4 2 3147 | e 0 1 2 3148 | e 1 2 5 3149 | e 2 3 5 3150 | e 3 4 5 3151 | t # 315 3152 | v 0 2 3153 | v 1 2 3154 | v 2 2 3155 | v 3 2 3156 | v 4 5 3157 | e 0 1 5 3158 | e 1 2 2 3159 | e 1 3 5 3160 | e 2 4 3 3161 | t # 316 3162 | v 0 2 3163 | v 1 5 3164 | v 2 2 3165 | v 3 2 3166 | v 4 2 3167 | e 0 1 2 3168 | e 1 2 2 3169 | e 1 3 2 3170 | e 1 4 2 3171 | t # 317 3172 | v 0 2 3173 | v 1 2 3174 | v 2 2 3175 | v 3 2 3176 | v 4 2 3177 | e 0 1 2 3178 | e 1 2 5 3179 | e 1 3 5 3180 | e 2 4 5 3181 | t # 318 3182 | v 0 2 3183 | v 1 2 3184 | v 2 2 3185 | v 3 2 3186 | v 4 2 3187 | e 0 1 2 3188 | e 1 2 5 3189 | e 2 3 5 3190 | e 3 4 5 3191 | t # 319 3192 | v 0 3 3193 | v 1 2 3194 | v 2 2 3195 | v 3 2 3196 | v 4 2 3197 | e 0 1 2 3198 | e 1 2 3 3199 | e 2 3 2 3200 | e 3 4 2 3201 | t # 320 3202 | v 0 2 3203 | v 1 2 3204 | v 2 3 3205 | v 3 2 3206 | v 4 2 3207 | e 0 1 5 3208 | e 0 2 2 3209 | e 2 3 2 3210 | e 3 4 5 3211 | t # 321 3212 | v 0 2 3213 | v 1 5 3214 | v 2 2 3215 | v 3 3 3216 | v 4 2 3217 | e 0 1 2 3218 | e 1 2 3 3219 | e 2 3 2 3220 | e 2 4 2 3221 | t # 322 3222 | v 0 2 3223 | v 1 2 3224 | v 2 2 3225 | v 3 3 3226 | v 4 2 3227 | e 0 1 3 3228 | e 1 2 2 3229 | e 2 3 2 3230 | e 2 4 2 3231 | t # 323 3232 | v 0 2 3233 | v 1 2 3234 | v 2 2 3235 | v 3 2 3236 | v 4 2 3237 | e 0 1 2 3238 | e 1 2 5 3239 | e 2 3 5 3240 | e 3 4 5 3241 | t # 324 3242 | v 0 2 3243 | v 1 5 3244 | v 2 2 3245 | v 3 3 3246 | v 4 2 3247 | e 0 1 2 3248 | e 1 2 2 3249 | e 2 3 3 3250 | e 2 4 2 3251 | t # 325 3252 | v 0 2 3253 | v 1 2 3254 | v 2 2 3255 | v 3 2 3256 | v 4 3 3257 | e 0 1 5 3258 | e 0 2 5 3259 | e 2 3 5 3260 | e 3 4 2 3261 | t # 326 3262 | v 0 2 3263 | v 1 3 3264 | v 2 2 3265 | v 3 2 3266 | v 4 2 3267 | e 0 1 2 3268 | e 0 2 2 3269 | e 2 3 2 3270 | e 2 4 2 3271 | t # 327 3272 | v 0 2 3273 | v 1 2 3274 | v 2 3 3275 | v 3 2 3276 | v 4 2 3277 | e 0 1 2 3278 | e 1 2 2 3279 | e 1 3 2 3280 | e 3 4 2 3281 | t # 328 3282 | v 0 5 3283 | v 1 2 3284 | v 2 5 3285 | v 3 5 3286 | v 4 2 3287 | e 0 1 2 3288 | e 1 2 2 3289 | e 2 3 2 3290 | e 2 4 2 3291 | t # 329 3292 | v 0 2 3293 | v 1 3 3294 | v 2 2 3295 | v 3 2 3296 | v 4 3 3297 | e 0 2 2 3298 | e 0 1 2 3299 | e 2 4 2 3300 | e 2 3 2 3301 | t # 330 3302 | v 0 2 3303 | v 1 2 3304 | v 2 2 3305 | v 3 2 3306 | v 4 2 3307 | e 0 1 2 3308 | e 0 2 2 3309 | e 1 3 5 3310 | e 3 4 2 3311 | t # 331 3312 | v 0 2 3313 | v 1 2 3314 | v 2 2 3315 | v 3 2 3316 | v 4 2 3317 | e 0 1 5 3318 | e 1 2 2 3319 | e 2 3 2 3320 | e 3 4 2 3321 | t # 332 3322 | v 0 2 3323 | v 1 2 3324 | v 2 3 3325 | v 3 2 3326 | v 4 5 3327 | e 0 2 3 3328 | e 0 3 2 3329 | e 1 3 3 3330 | e 3 4 2 3331 | t # 333 3332 | v 0 2 3333 | v 1 2 3334 | v 2 2 3335 | v 3 3 3336 | v 4 2 3337 | e 0 1 5 3338 | e 1 3 2 3339 | e 1 4 5 3340 | e 2 4 5 3341 | t # 334 3342 | v 0 2 3343 | v 1 2 3344 | v 2 2 3345 | v 3 2 3346 | v 4 2 3347 | e 0 2 5 3348 | e 1 3 5 3349 | e 2 4 2 3350 | e 2 3 5 3351 | t # 335 3352 | v 0 2 3353 | v 1 2 3354 | v 2 3 3355 | v 3 2 3356 | v 4 2 3357 | e 0 1 2 3358 | e 1 2 2 3359 | e 2 3 2 3360 | e 3 4 2 3361 | t # 336 3362 | v 0 2 3363 | v 1 2 3364 | v 2 2 3365 | v 3 2 3366 | v 4 3 3367 | e 0 1 2 3368 | e 1 2 2 3369 | e 2 3 2 3370 | e 3 4 2 3371 | t # 337 3372 | v 0 2 3373 | v 1 2 3374 | v 2 2 3375 | v 3 8 3376 | v 4 2 3377 | e 0 2 5 3378 | e 1 3 2 3379 | e 1 4 5 3380 | e 2 4 5 3381 | t # 338 3382 | v 0 2 3383 | v 1 2 3384 | v 2 3 3385 | v 3 3 3386 | v 4 2 3387 | e 0 2 2 3388 | e 0 1 5 3389 | e 1 3 2 3390 | e 3 4 2 3391 | t # 339 3392 | v 0 2 3393 | v 1 2 3394 | v 2 2 3395 | v 3 2 3396 | v 4 3 3397 | e 0 1 2 3398 | e 1 2 2 3399 | e 1 3 2 3400 | e 3 4 2 3401 | t # 340 3402 | v 0 2 3403 | v 1 2 3404 | v 2 2 3405 | v 3 3 3406 | v 4 2 3407 | e 0 1 5 3408 | e 1 3 2 3409 | e 1 2 5 3410 | e 3 4 2 3411 | t # 341 3412 | v 0 2 3413 | v 1 2 3414 | v 2 2 3415 | v 3 5 3416 | v 4 5 3417 | e 0 2 5 3418 | e 1 3 5 3419 | e 2 4 2 3420 | e 2 3 5 3421 | t # 342 3422 | v 0 2 3423 | v 1 3 3424 | v 2 2 3425 | v 3 3 3426 | v 4 2 3427 | e 0 1 2 3428 | e 1 2 2 3429 | e 2 3 2 3430 | e 3 4 2 3431 | t # 343 3432 | v 0 2 3433 | v 1 2 3434 | v 2 2 3435 | v 3 2 3436 | v 4 2 3437 | e 0 1 5 3438 | e 1 3 5 3439 | e 2 4 5 3440 | e 3 4 5 3441 | t # 344 3442 | v 0 2 3443 | v 1 2 3444 | v 2 3 3445 | v 3 3 3446 | v 4 2 3447 | e 0 1 2 3448 | e 1 2 2 3449 | e 1 3 3 3450 | e 2 4 2 3451 | t # 345 3452 | v 0 2 3453 | v 1 2 3454 | v 2 2 3455 | v 3 2 3456 | v 4 2 3457 | e 0 1 2 3458 | e 0 2 2 3459 | e 2 3 5 3460 | e 3 4 5 3461 | t # 346 3462 | v 0 3 3463 | v 1 2 3464 | v 2 5 3465 | v 3 2 3466 | v 4 2 3467 | e 0 1 3 3468 | e 1 2 2 3469 | e 1 3 2 3470 | e 2 4 2 3471 | t # 347 3472 | v 0 5 3473 | v 1 2 3474 | v 2 2 3475 | v 3 2 3476 | v 4 2 3477 | e 0 2 5 3478 | e 1 2 5 3479 | e 2 3 5 3480 | e 3 4 5 3481 | t # 348 3482 | v 0 2 3483 | v 1 2 3484 | v 2 2 3485 | v 3 2 3486 | v 4 2 3487 | e 0 1 2 3488 | e 1 2 5 3489 | e 2 3 5 3490 | e 3 4 5 3491 | t # 349 3492 | v 0 2 3493 | v 1 2 3494 | v 2 2 3495 | v 3 2 3496 | v 4 2 3497 | e 0 1 2 3498 | e 1 2 2 3499 | e 2 3 2 3500 | e 3 4 2 3501 | t # 350 3502 | v 0 2 3503 | v 1 2 3504 | v 2 2 3505 | v 3 2 3506 | v 4 2 3507 | e 0 1 2 3508 | e 0 2 5 3509 | e 1 3 2 3510 | e 3 4 2 3511 | t # 351 3512 | v 0 2 3513 | v 1 3 3514 | v 2 2 3515 | v 3 2 3516 | v 4 2 3517 | e 0 2 2 3518 | e 0 1 2 3519 | e 2 3 5 3520 | e 3 4 5 3521 | t # 352 3522 | v 0 2 3523 | v 1 2 3524 | v 2 2 3525 | v 3 2 3526 | v 4 2 3527 | e 0 2 2 3528 | e 0 3 2 3529 | e 0 4 2 3530 | e 1 3 2 3531 | t # 353 3532 | v 0 3 3533 | v 1 2 3534 | v 2 2 3535 | v 3 2 3536 | v 4 3 3537 | e 0 1 2 3538 | e 1 3 2 3539 | e 1 2 2 3540 | e 2 4 2 3541 | t # 354 3542 | v 0 2 3543 | v 1 3 3544 | v 2 2 3545 | v 3 2 3546 | v 4 2 3547 | e 0 1 2 3548 | e 1 2 2 3549 | e 2 3 2 3550 | e 3 4 2 3551 | t # 355 3552 | v 0 2 3553 | v 1 2 3554 | v 2 3 3555 | v 3 2 3556 | v 4 2 3557 | e 0 1 5 3558 | e 1 2 2 3559 | e 1 3 5 3560 | e 2 4 2 3561 | t # 356 3562 | v 0 3 3563 | v 1 2 3564 | v 2 2 3565 | v 3 2 3566 | v 4 2 3567 | e 0 1 2 3568 | e 1 2 5 3569 | e 1 3 5 3570 | e 2 4 2 3571 | t # 357 3572 | v 0 2 3573 | v 1 2 3574 | v 2 2 3575 | v 3 2 3576 | v 4 2 3577 | e 0 1 5 3578 | e 1 2 5 3579 | e 2 3 2 3580 | e 2 4 5 3581 | t # 358 3582 | v 0 2 3583 | v 1 3 3584 | v 2 2 3585 | v 3 2 3586 | v 4 3 3587 | e 0 1 2 3588 | e 0 2 2 3589 | e 2 4 2 3590 | e 2 3 2 3591 | t # 359 3592 | v 0 2 3593 | v 1 2 3594 | v 2 2 3595 | v 3 2 3596 | v 4 2 3597 | e 0 1 5 3598 | e 1 3 5 3599 | e 2 4 5 3600 | e 3 4 5 3601 | t # 360 3602 | v 0 2 3603 | v 1 2 3604 | v 2 2 3605 | v 3 2 3606 | v 4 2 3607 | e 0 1 3 3608 | e 1 2 3 3609 | e 2 3 3 3610 | e 3 4 2 3611 | t # 361 3612 | v 0 2 3613 | v 1 5 3614 | v 2 2 3615 | v 3 2 3616 | v 4 2 3617 | e 0 1 5 3618 | e 1 2 5 3619 | e 2 4 5 3620 | e 2 3 5 3621 | t # 362 3622 | v 0 2 3623 | v 1 2 3624 | v 2 2 3625 | v 3 3 3626 | v 4 2 3627 | e 0 2 2 3628 | e 0 1 5 3629 | e 1 3 2 3630 | e 2 4 2 3631 | t # 363 3632 | v 0 2 3633 | v 1 2 3634 | v 2 3 3635 | v 3 2 3636 | v 4 2 3637 | e 0 1 2 3638 | e 0 2 2 3639 | e 1 3 2 3640 | e 2 4 2 3641 | t # 364 3642 | v 0 2 3643 | v 1 2 3644 | v 2 2 3645 | v 3 5 3646 | v 4 6 3647 | e 0 2 2 3648 | e 1 3 2 3649 | e 2 3 2 3650 | e 3 4 2 3651 | t # 365 3652 | v 0 2 3653 | v 1 2 3654 | v 2 2 3655 | v 3 6 3656 | v 4 2 3657 | e 0 2 5 3658 | e 1 3 5 3659 | e 2 4 5 3660 | e 2 3 5 3661 | t # 366 3662 | v 0 2 3663 | v 1 2 3664 | v 2 2 3665 | v 3 2 3666 | v 4 2 3667 | e 0 1 5 3668 | e 0 2 5 3669 | e 2 3 5 3670 | e 3 4 5 3671 | t # 367 3672 | v 0 2 3673 | v 1 2 3674 | v 2 2 3675 | v 3 2 3676 | v 4 2 3677 | e 0 1 2 3678 | e 1 2 5 3679 | e 2 3 5 3680 | e 3 4 5 3681 | t # 368 3682 | v 0 2 3683 | v 1 2 3684 | v 2 3 3685 | v 3 2 3686 | v 4 2 3687 | e 0 1 2 3688 | e 0 2 2 3689 | e 1 3 2 3690 | e 3 4 2 3691 | t # 369 3692 | v 0 2 3693 | v 1 2 3694 | v 2 2 3695 | v 3 2 3696 | v 4 3 3697 | e 0 1 2 3698 | e 1 2 3 3699 | e 2 3 2 3700 | e 3 4 3 3701 | t # 370 3702 | v 0 2 3703 | v 1 2 3704 | v 2 2 3705 | v 3 2 3706 | v 4 3 3707 | e 0 2 5 3708 | e 1 3 5 3709 | e 2 3 5 3710 | e 3 4 2 3711 | t # 371 3712 | v 0 5 3713 | v 1 6 3714 | v 2 5 3715 | v 3 2 3716 | v 4 2 3717 | e 0 2 5 3718 | e 1 3 5 3719 | e 2 3 5 3720 | e 3 4 2 3721 | t # 372 3722 | v 0 2 3723 | v 1 5 3724 | v 2 2 3725 | v 3 2 3726 | v 4 2 3727 | e 0 1 5 3728 | e 1 3 5 3729 | e 2 4 5 3730 | e 3 4 5 3731 | t # 373 3732 | v 0 2 3733 | v 1 2 3734 | v 2 2 3735 | v 3 2 3736 | v 4 3 3737 | e 0 1 5 3738 | e 0 2 5 3739 | e 1 3 5 3740 | e 3 4 5 3741 | t # 374 3742 | v 0 2 3743 | v 1 2 3744 | v 2 2 3745 | v 3 2 3746 | v 4 2 3747 | e 0 1 5 3748 | e 1 2 5 3749 | e 2 3 2 3750 | e 2 4 5 3751 | t # 375 3752 | v 0 2 3753 | v 1 2 3754 | v 2 3 3755 | v 3 2 3756 | v 4 2 3757 | e 0 1 2 3758 | e 0 2 3 3759 | e 1 3 2 3760 | e 1 4 2 3761 | t # 376 3762 | v 0 2 3763 | v 1 2 3764 | v 2 2 3765 | v 3 2 3766 | v 4 5 3767 | e 0 1 2 3768 | e 0 2 2 3769 | e 1 3 2 3770 | e 1 4 2 3771 | t # 377 3772 | v 0 2 3773 | v 1 2 3774 | v 2 2 3775 | v 3 2 3776 | v 4 2 3777 | e 0 1 2 3778 | e 1 2 5 3779 | e 2 3 5 3780 | e 3 4 5 3781 | t # 378 3782 | v 0 2 3783 | v 1 2 3784 | v 2 2 3785 | v 3 2 3786 | v 4 2 3787 | e 0 2 5 3788 | e 1 3 5 3789 | e 2 4 5 3790 | e 3 4 5 3791 | t # 379 3792 | v 0 2 3793 | v 1 7 3794 | v 2 2 3795 | v 3 2 3796 | v 4 2 3797 | e 0 1 2 3798 | e 1 2 2 3799 | e 2 3 5 3800 | e 2 4 5 3801 | t # 380 3802 | v 0 7 3803 | v 1 3 3804 | v 2 2 3805 | v 3 2 3806 | v 4 2 3807 | e 0 1 2 3808 | e 0 2 2 3809 | e 1 3 2 3810 | e 3 4 2 3811 | t # 381 3812 | v 0 2 3813 | v 1 2 3814 | v 2 2 3815 | v 3 2 3816 | v 4 2 3817 | e 0 1 2 3818 | e 0 2 2 3819 | e 1 3 2 3820 | e 2 4 2 3821 | t # 382 3822 | v 0 2 3823 | v 1 2 3824 | v 2 2 3825 | v 3 5 3826 | v 4 5 3827 | e 0 2 5 3828 | e 0 4 2 3829 | e 1 2 5 3830 | e 2 3 5 3831 | t # 383 3832 | v 0 2 3833 | v 1 3 3834 | v 2 2 3835 | v 3 2 3836 | v 4 2 3837 | e 0 1 2 3838 | e 1 2 2 3839 | e 2 3 2 3840 | e 3 4 5 3841 | t # 384 3842 | v 0 2 3843 | v 1 2 3844 | v 2 2 3845 | v 3 2 3846 | v 4 3 3847 | e 0 1 5 3848 | e 1 2 5 3849 | e 2 4 5 3850 | e 2 3 5 3851 | t # 385 3852 | v 0 10 3853 | v 1 3 3854 | v 2 3 3855 | v 3 2 3856 | v 4 2 3857 | e 0 1 2 3858 | e 0 2 2 3859 | e 2 3 2 3860 | e 3 4 2 3861 | t # 386 3862 | v 0 2 3863 | v 1 2 3864 | v 2 2 3865 | v 3 2 3866 | v 4 8 3867 | e 0 1 5 3868 | e 1 3 5 3869 | e 2 3 5 3870 | e 3 4 2 3871 | t # 387 3872 | v 0 5 3873 | v 1 10 3874 | v 2 3 3875 | v 3 2 3876 | v 4 2 3877 | e 0 1 2 3878 | e 1 2 2 3879 | e 1 3 2 3880 | e 3 4 5 3881 | t # 388 3882 | v 0 2 3883 | v 1 5 3884 | v 2 2 3885 | v 3 2 3886 | v 4 2 3887 | e 0 1 2 3888 | e 1 2 2 3889 | e 1 3 2 3890 | e 2 4 2 3891 | t # 389 3892 | v 0 2 3893 | v 1 3 3894 | v 2 2 3895 | v 3 2 3896 | v 4 2 3897 | e 0 1 3 3898 | e 0 2 2 3899 | e 2 4 2 3900 | e 3 4 2 3901 | t # 390 3902 | v 0 2 3903 | v 1 2 3904 | v 2 2 3905 | v 3 2 3906 | v 4 2 3907 | e 0 1 2 3908 | e 1 3 5 3909 | e 1 2 5 3910 | e 3 4 5 3911 | t # 391 3912 | v 0 2 3913 | v 1 5 3914 | v 2 2 3915 | v 3 2 3916 | v 4 2 3917 | e 0 1 2 3918 | e 1 2 2 3919 | e 2 3 2 3920 | e 3 4 2 3921 | t # 392 3922 | v 0 5 3923 | v 1 10 3924 | v 2 2 3925 | v 3 2 3926 | v 4 2 3927 | e 0 1 2 3928 | e 0 2 2 3929 | e 1 3 2 3930 | e 3 4 5 3931 | t # 393 3932 | v 0 2 3933 | v 1 2 3934 | v 2 2 3935 | v 3 2 3936 | v 4 2 3937 | e 0 1 2 3938 | e 1 2 5 3939 | e 2 4 5 3940 | e 3 4 5 3941 | t # 394 3942 | v 0 2 3943 | v 1 2 3944 | v 2 2 3945 | v 3 5 3946 | v 4 2 3947 | e 0 1 5 3948 | e 0 2 5 3949 | e 2 3 5 3950 | e 3 4 5 3951 | t # 395 3952 | v 0 2 3953 | v 1 2 3954 | v 2 2 3955 | v 3 2 3956 | v 4 2 3957 | e 0 1 2 3958 | e 0 2 2 3959 | e 1 4 2 3960 | e 2 3 3 3961 | t # 396 3962 | v 0 2 3963 | v 1 3 3964 | v 2 2 3965 | v 3 2 3966 | v 4 3 3967 | e 0 1 2 3968 | e 0 3 2 3969 | e 1 2 2 3970 | e 2 4 2 3971 | t # 397 3972 | v 0 2 3973 | v 1 2 3974 | v 2 2 3975 | v 3 2 3976 | v 4 3 3977 | e 0 1 2 3978 | e 1 2 2 3979 | e 2 4 2 3980 | e 2 3 2 3981 | t # 398 3982 | v 0 7 3983 | v 1 5 3984 | v 2 5 3985 | v 3 7 3986 | v 4 5 3987 | e 0 1 3 3988 | e 0 2 2 3989 | e 1 3 2 3990 | e 2 4 5 3991 | t # 399 3992 | v 0 2 3993 | v 1 2 3994 | v 2 2 3995 | v 3 2 3996 | v 4 2 3997 | e 0 2 2 3998 | e 1 3 2 3999 | e 2 3 2 4000 | e 3 4 2 4001 | t # 400 4002 | v 0 2 4003 | v 1 2 4004 | v 2 5 4005 | v 3 2 4006 | v 4 5 4007 | e 0 1 5 4008 | e 0 2 2 4009 | e 2 3 2 4010 | e 3 4 5 4011 | t # 401 4012 | v 0 2 4013 | v 1 5 4014 | v 2 2 4015 | v 3 3 4016 | v 4 2 4017 | e 0 1 2 4018 | e 1 2 2 4019 | e 2 3 3 4020 | e 2 4 2 4021 | t # 402 4022 | v 0 2 4023 | v 1 2 4024 | v 2 2 4025 | v 3 3 4026 | v 4 2 4027 | e 0 1 2 4028 | e 1 2 2 4029 | e 2 3 2 4030 | e 3 4 2 4031 | t # 403 4032 | v 0 2 4033 | v 1 2 4034 | v 2 2 4035 | v 3 2 4036 | v 4 2 4037 | e 0 1 5 4038 | e 1 3 5 4039 | e 2 4 5 4040 | e 3 4 5 4041 | t # 404 4042 | v 0 2 4043 | v 1 2 4044 | v 2 2 4045 | v 3 2 4046 | v 4 3 4047 | e 0 2 2 4048 | e 1 3 2 4049 | e 1 2 2 4050 | e 2 4 2 4051 | t # 405 4052 | v 0 5 4053 | v 1 2 4054 | v 2 2 4055 | v 3 2 4056 | v 4 2 4057 | e 0 1 5 4058 | e 1 2 5 4059 | e 2 3 5 4060 | e 3 4 5 4061 | t # 406 4062 | v 0 2 4063 | v 1 2 4064 | v 2 2 4065 | v 3 2 4066 | v 4 3 4067 | e 0 2 2 4068 | e 1 2 2 4069 | e 2 3 2 4070 | e 2 4 2 4071 | t # 407 4072 | v 0 2 4073 | v 1 2 4074 | v 2 2 4075 | v 3 2 4076 | v 4 2 4077 | e 0 1 5 4078 | e 1 3 5 4079 | e 2 3 5 4080 | e 3 4 2 4081 | t # 408 4082 | v 0 6 4083 | v 1 2 4084 | v 2 2 4085 | v 3 2 4086 | v 4 2 4087 | e 0 1 2 4088 | e 1 2 5 4089 | e 1 3 5 4090 | e 2 4 5 4091 | t # 409 4092 | v 0 2 4093 | v 1 2 4094 | v 2 2 4095 | v 3 5 4096 | v 4 2 4097 | e 0 1 2 4098 | e 1 2 5 4099 | e 1 3 5 4100 | e 3 4 5 4101 | t # 410 4102 | v 0 2 4103 | v 1 2 4104 | v 2 2 4105 | v 3 2 4106 | v 4 2 4107 | e 0 1 2 4108 | e 1 2 5 4109 | e 1 3 5 4110 | e 2 4 5 4111 | t # 411 4112 | v 0 2 4113 | v 1 2 4114 | v 2 2 4115 | v 3 2 4116 | v 4 2 4117 | e 0 2 2 4118 | e 0 3 2 4119 | e 1 4 2 4120 | e 3 4 2 4121 | t # 412 4122 | v 0 2 4123 | v 1 2 4124 | v 2 3 4125 | v 3 5 4126 | v 4 2 4127 | e 0 1 2 4128 | e 0 2 3 4129 | e 0 3 2 4130 | e 1 4 2 4131 | t # 413 4132 | v 0 6 4133 | v 1 3 4134 | v 2 2 4135 | v 3 2 4136 | v 4 2 4137 | e 0 1 3 4138 | e 0 2 2 4139 | e 2 3 5 4140 | e 3 4 5 4141 | t # 414 4142 | v 0 2 4143 | v 1 2 4144 | v 2 2 4145 | v 3 2 4146 | v 4 5 4147 | e 0 1 5 4148 | e 1 2 5 4149 | e 2 3 2 4150 | e 3 4 3 4151 | t # 415 4152 | v 0 2 4153 | v 1 2 4154 | v 2 2 4155 | v 3 2 4156 | v 4 2 4157 | e 0 1 2 4158 | e 1 2 2 4159 | e 1 3 2 4160 | e 3 4 2 4161 | t # 416 4162 | v 0 2 4163 | v 1 2 4164 | v 2 2 4165 | v 3 3 4166 | v 4 3 4167 | e 0 1 2 4168 | e 1 3 2 4169 | e 1 2 2 4170 | e 2 4 2 4171 | t # 417 4172 | v 0 2 4173 | v 1 2 4174 | v 2 2 4175 | v 3 2 4176 | v 4 2 4177 | e 0 1 2 4178 | e 1 2 2 4179 | e 2 3 5 4180 | e 3 4 5 4181 | t # 418 4182 | v 0 2 4183 | v 1 2 4184 | v 2 2 4185 | v 3 2 4186 | v 4 2 4187 | e 0 1 5 4188 | e 1 3 5 4189 | e 2 3 5 4190 | e 3 4 2 4191 | t # 419 4192 | v 0 2 4193 | v 1 3 4194 | v 2 2 4195 | v 3 2 4196 | v 4 2 4197 | e 0 1 2 4198 | e 1 2 2 4199 | e 2 3 5 4200 | e 2 4 5 4201 | t # 420 4202 | v 0 2 4203 | v 1 2 4204 | v 2 5 4205 | v 3 5 4206 | v 4 2 4207 | e 0 1 5 4208 | e 0 2 5 4209 | e 1 3 5 4210 | e 3 4 5 4211 | t # 421 4212 | v 0 2 4213 | v 1 5 4214 | v 2 2 4215 | v 3 2 4216 | v 4 2 4217 | e 0 1 5 4218 | e 0 2 5 4219 | e 1 3 5 4220 | e 2 4 5 4221 | t # 422 4222 | v 0 2 4223 | v 1 2 4224 | v 2 2 4225 | v 3 3 4226 | v 4 3 4227 | e 0 2 2 4228 | e 1 3 2 4229 | e 1 2 2 4230 | e 2 4 2 4231 | t # 423 4232 | v 0 2 4233 | v 1 2 4234 | v 2 2 4235 | v 3 2 4236 | v 4 2 4237 | e 0 1 2 4238 | e 0 2 2 4239 | e 2 3 5 4240 | e 3 4 5 4241 | t # 424 4242 | v 0 2 4243 | v 1 2 4244 | v 2 2 4245 | v 3 2 4246 | v 4 2 4247 | e 0 1 5 4248 | e 1 2 5 4249 | e 2 3 5 4250 | e 3 4 5 4251 | t # 425 4252 | v 0 2 4253 | v 1 2 4254 | v 2 2 4255 | v 3 2 4256 | v 4 2 4257 | e 0 2 5 4258 | e 0 3 5 4259 | e 1 2 5 4260 | e 2 4 5 4261 | t # 426 4262 | v 0 2 4263 | v 1 2 4264 | v 2 2 4265 | v 3 3 4266 | v 4 2 4267 | e 0 1 5 4268 | e 1 2 5 4269 | e 2 3 2 4270 | e 2 4 5 4271 | t # 427 4272 | v 0 2 4273 | v 1 3 4274 | v 2 2 4275 | v 3 2 4276 | v 4 5 4277 | e 0 1 3 4278 | e 0 2 2 4279 | e 2 3 2 4280 | e 2 4 2 4281 | t # 428 4282 | v 0 2 4283 | v 1 2 4284 | v 2 2 4285 | v 3 2 4286 | v 4 2 4287 | e 0 1 2 4288 | e 1 2 2 4289 | e 2 3 3 4290 | e 3 4 2 4291 | t # 429 4292 | v 0 2 4293 | v 1 2 4294 | v 2 5 4295 | v 3 3 4296 | v 4 3 4297 | e 0 1 5 4298 | e 1 2 2 4299 | e 2 3 3 4300 | e 2 4 2 4301 | t # 430 4302 | v 0 2 4303 | v 1 2 4304 | v 2 2 4305 | v 3 3 4306 | v 4 3 4307 | e 0 2 2 4308 | e 0 4 2 4309 | e 1 2 2 4310 | e 2 3 2 4311 | t # 431 4312 | v 0 2 4313 | v 1 2 4314 | v 2 2 4315 | v 3 2 4316 | v 4 3 4317 | e 0 3 5 4318 | e 1 2 5 4319 | e 1 4 2 4320 | e 2 3 5 4321 | t # 432 4322 | v 0 2 4323 | v 1 2 4324 | v 2 5 4325 | v 3 2 4326 | v 4 2 4327 | e 0 1 2 4328 | e 1 2 2 4329 | e 2 4 2 4330 | e 2 3 2 4331 | t # 433 4332 | v 0 2 4333 | v 1 2 4334 | v 2 2 4335 | v 3 2 4336 | v 4 2 4337 | e 0 1 5 4338 | e 1 2 5 4339 | e 1 4 5 4340 | e 2 3 5 4341 | t # 434 4342 | v 0 6 4343 | v 1 2 4344 | v 2 2 4345 | v 3 2 4346 | v 4 2 4347 | e 0 1 2 4348 | e 1 4 2 4349 | e 2 3 2 4350 | e 2 4 2 4351 | t # 435 4352 | v 0 2 4353 | v 1 2 4354 | v 2 2 4355 | v 3 2 4356 | v 4 5 4357 | e 0 1 2 4358 | e 1 2 2 4359 | e 2 3 2 4360 | e 3 4 2 4361 | t # 436 4362 | v 0 2 4363 | v 1 2 4364 | v 2 2 4365 | v 3 2 4366 | v 4 2 4367 | e 0 3 2 4368 | e 1 2 2 4369 | e 2 3 2 4370 | e 3 4 2 4371 | t # 437 4372 | v 0 2 4373 | v 1 2 4374 | v 2 2 4375 | v 3 3 4376 | v 4 3 4377 | e 0 1 2 4378 | e 0 3 2 4379 | e 1 4 2 4380 | e 2 3 2 4381 | t # 438 4382 | v 0 2 4383 | v 1 6 4384 | v 2 2 4385 | v 3 2 4386 | v 4 2 4387 | e 0 1 5 4388 | e 1 2 5 4389 | e 2 3 5 4390 | e 2 4 5 4391 | t # 439 4392 | v 0 2 4393 | v 1 2 4394 | v 2 2 4395 | v 3 6 4396 | v 4 2 4397 | e 0 1 5 4398 | e 0 4 2 4399 | e 1 3 5 4400 | e 2 3 5 4401 | t # 440 4402 | v 0 2 4403 | v 1 2 4404 | v 2 2 4405 | v 3 3 4406 | v 4 2 4407 | e 0 1 2 4408 | e 1 2 2 4409 | e 1 3 3 4410 | e 2 4 2 4411 | t # 441 4412 | v 0 2 4413 | v 1 2 4414 | v 2 2 4415 | v 3 2 4416 | v 4 2 4417 | e 0 1 2 4418 | e 1 2 2 4419 | e 2 3 2 4420 | e 3 4 2 4421 | t # 442 4422 | v 0 2 4423 | v 1 2 4424 | v 2 2 4425 | v 3 2 4426 | v 4 2 4427 | e 0 1 2 4428 | e 0 4 2 4429 | e 1 2 2 4430 | e 2 3 2 4431 | t # 443 4432 | v 0 2 4433 | v 1 3 4434 | v 2 2 4435 | v 3 3 4436 | v 4 2 4437 | e 0 1 2 4438 | e 0 2 2 4439 | e 2 3 2 4440 | e 2 4 2 4441 | t # 444 4442 | v 0 2 4443 | v 1 2 4444 | v 2 2 4445 | v 3 5 4446 | v 4 5 4447 | e 0 3 2 4448 | e 1 2 5 4449 | e 2 4 2 4450 | e 3 4 3 4451 | t # 445 4452 | v 0 2 4453 | v 1 2 4454 | v 2 2 4455 | v 3 3 4456 | v 4 5 4457 | e 0 1 5 4458 | e 0 2 2 4459 | e 2 3 3 4460 | e 2 4 2 4461 | t # 446 4462 | v 0 2 4463 | v 1 2 4464 | v 2 2 4465 | v 3 2 4466 | v 4 2 4467 | e 0 1 2 4468 | e 0 3 2 4469 | e 1 2 5 4470 | e 3 4 5 4471 | t # 447 4472 | v 0 2 4473 | v 1 2 4474 | v 2 5 4475 | v 3 2 4476 | v 4 3 4477 | e 0 1 2 4478 | e 0 4 3 4479 | e 1 2 2 4480 | e 2 3 2 4481 | t # 448 4482 | v 0 2 4483 | v 1 2 4484 | v 2 2 4485 | v 3 2 4486 | v 4 2 4487 | e 0 1 5 4488 | e 0 4 5 4489 | e 1 2 5 4490 | e 3 4 5 4491 | t # 449 4492 | v 0 2 4493 | v 1 2 4494 | v 2 2 4495 | v 3 6 4496 | v 4 3 4497 | e 0 2 5 4498 | e 0 3 2 4499 | e 1 2 5 4500 | e 3 4 3 4501 | t # 450 4502 | v 0 5 4503 | v 1 2 4504 | v 2 3 4505 | v 3 2 4506 | v 4 2 4507 | e 0 1 2 4508 | e 1 2 3 4509 | e 1 3 2 4510 | e 3 4 5 4511 | t # 451 4512 | v 0 2 4513 | v 1 2 4514 | v 2 2 4515 | v 3 2 4516 | v 4 3 4517 | e 0 1 2 4518 | e 0 3 2 4519 | e 1 2 2 4520 | e 1 4 2 4521 | t # 452 4522 | v 0 2 4523 | v 1 2 4524 | v 2 2 4525 | v 3 5 4526 | v 4 2 4527 | e 0 1 5 4528 | e 1 2 5 4529 | e 1 4 2 4530 | e 2 3 5 4531 | t # 453 4532 | v 0 2 4533 | v 1 2 4534 | v 2 2 4535 | v 3 2 4536 | v 4 3 4537 | e 0 1 2 4538 | e 1 2 2 4539 | e 2 4 2 4540 | e 2 3 2 4541 | t # 454 4542 | v 0 2 4543 | v 1 2 4544 | v 2 2 4545 | v 3 2 4546 | v 4 2 4547 | e 0 1 5 4548 | e 1 2 5 4549 | e 1 3 2 4550 | e 2 4 2 4551 | t # 455 4552 | v 0 2 4553 | v 1 2 4554 | v 2 2 4555 | v 3 5 4556 | v 4 3 4557 | e 0 2 2 4558 | e 0 4 2 4559 | e 1 2 2 4560 | e 2 3 2 4561 | t # 456 4562 | v 0 2 4563 | v 1 2 4564 | v 2 2 4565 | v 3 2 4566 | v 4 2 4567 | e 0 1 5 4568 | e 0 2 5 4569 | e 1 4 5 4570 | e 3 4 5 4571 | t # 457 4572 | v 0 2 4573 | v 1 2 4574 | v 2 2 4575 | v 3 2 4576 | v 4 2 4577 | e 0 1 5 4578 | e 1 4 5 4579 | e 2 3 5 4580 | e 3 4 5 4581 | t # 458 4582 | v 0 2 4583 | v 1 2 4584 | v 2 3 4585 | v 3 2 4586 | v 4 5 4587 | e 0 1 2 4588 | e 0 3 2 4589 | e 1 2 2 4590 | e 3 4 2 4591 | t # 459 4592 | v 0 2 4593 | v 1 2 4594 | v 2 2 4595 | v 3 2 4596 | v 4 2 4597 | e 0 1 2 4598 | e 1 2 2 4599 | e 2 3 2 4600 | e 3 4 2 4601 | t # 460 4602 | v 0 5 4603 | v 1 2 4604 | v 2 2 4605 | v 3 3 4606 | v 4 5 4607 | e 0 1 2 4608 | e 1 2 2 4609 | e 2 3 3 4610 | e 2 4 2 4611 | t # 461 4612 | v 0 2 4613 | v 1 2 4614 | v 2 2 4615 | v 3 3 4616 | v 4 3 4617 | e 0 1 2 4618 | e 1 2 2 4619 | e 2 3 3 4620 | e 2 4 2 4621 | t # 462 4622 | v 0 2 4623 | v 1 2 4624 | v 2 2 4625 | v 3 2 4626 | v 4 2 4627 | e 0 2 5 4628 | e 1 2 5 4629 | e 2 3 5 4630 | e 3 4 5 4631 | t # 463 4632 | v 0 5 4633 | v 1 2 4634 | v 2 2 4635 | v 3 2 4636 | v 4 3 4637 | e 0 1 2 4638 | e 1 2 2 4639 | e 1 3 2 4640 | e 3 4 3 4641 | t # 464 4642 | v 0 2 4643 | v 1 2 4644 | v 2 2 4645 | v 3 2 4646 | v 4 2 4647 | e 0 1 2 4648 | e 1 2 2 4649 | e 2 3 2 4650 | e 2 4 2 4651 | t # 465 4652 | v 0 2 4653 | v 1 3 4654 | v 2 2 4655 | v 3 2 4656 | v 4 5 4657 | e 0 1 2 4658 | e 0 3 2 4659 | e 1 2 2 4660 | e 2 4 2 4661 | t # 466 4662 | v 0 2 4663 | v 1 2 4664 | v 2 5 4665 | v 3 3 4666 | v 4 2 4667 | e 0 1 2 4668 | e 1 2 2 4669 | e 1 3 3 4670 | e 2 4 2 4671 | t # 467 4672 | v 0 2 4673 | v 1 2 4674 | v 2 2 4675 | v 3 2 4676 | v 4 2 4677 | e 0 1 2 4678 | e 1 2 2 4679 | e 2 3 5 4680 | e 2 4 5 4681 | t # 468 4682 | v 0 2 4683 | v 1 2 4684 | v 2 2 4685 | v 3 2 4686 | v 4 2 4687 | e 0 1 2 4688 | e 1 2 2 4689 | e 2 3 2 4690 | e 3 4 2 4691 | t # 469 4692 | v 0 3 4693 | v 1 2 4694 | v 2 2 4695 | v 3 5 4696 | v 4 2 4697 | e 0 2 2 4698 | e 1 2 2 4699 | e 2 3 2 4700 | e 3 4 5 4701 | t # 470 4702 | v 0 2 4703 | v 1 2 4704 | v 2 2 4705 | v 3 5 4706 | v 4 2 4707 | e 0 1 2 4708 | e 1 2 2 4709 | e 2 3 2 4710 | e 3 4 2 4711 | t # 471 4712 | v 0 2 4713 | v 1 3 4714 | v 2 7 4715 | v 3 3 4716 | v 4 3 4717 | e 0 1 2 4718 | e 1 2 2 4719 | e 2 3 3 4720 | e 2 4 2 4721 | t # 472 4722 | v 0 2 4723 | v 1 2 4724 | v 2 5 4725 | v 3 2 4726 | v 4 5 4727 | e 0 1 2 4728 | e 1 2 2 4729 | e 1 3 2 4730 | e 3 4 2 4731 | t # 473 4732 | v 0 5 4733 | v 1 2 4734 | v 2 2 4735 | v 3 3 4736 | v 4 2 4737 | e 0 1 2 4738 | e 1 2 2 4739 | e 1 4 2 4740 | e 2 3 3 4741 | t # 474 4742 | v 0 2 4743 | v 1 2 4744 | v 2 2 4745 | v 3 2 4746 | v 4 2 4747 | e 0 1 5 4748 | e 0 4 5 4749 | e 1 2 5 4750 | e 2 3 5 4751 | t # 475 4752 | v 0 2 4753 | v 1 2 4754 | v 2 3 4755 | v 3 3 4756 | v 4 2 4757 | e 0 4 2 4758 | e 1 2 2 4759 | e 2 4 2 4760 | e 3 4 3 4761 | t # 476 4762 | v 0 2 4763 | v 1 2 4764 | v 2 2 4765 | v 3 2 4766 | v 4 3 4767 | e 0 1 5 4768 | e 1 2 5 4769 | e 1 4 2 4770 | e 2 3 5 4771 | t # 477 4772 | v 0 2 4773 | v 1 2 4774 | v 2 3 4775 | v 3 3 4776 | v 4 2 4777 | e 0 4 2 4778 | e 1 2 2 4779 | e 2 4 2 4780 | e 3 4 3 4781 | t # 478 4782 | v 0 5 4783 | v 1 2 4784 | v 2 5 4785 | v 3 2 4786 | v 4 6 4787 | e 0 1 2 4788 | e 1 2 5 4789 | e 1 4 5 4790 | e 2 3 5 4791 | t # 479 4792 | v 0 2 4793 | v 1 2 4794 | v 2 2 4795 | v 3 2 4796 | v 4 2 4797 | e 0 1 2 4798 | e 0 2 2 4799 | e 1 3 2 4800 | e 1 4 2 4801 | t # 480 4802 | v 0 2 4803 | v 1 2 4804 | v 2 2 4805 | v 3 3 4806 | v 4 5 4807 | e 0 1 2 4808 | e 0 2 2 4809 | e 2 3 3 4810 | e 2 4 2 4811 | t # 481 4812 | v 0 2 4813 | v 1 2 4814 | v 2 2 4815 | v 3 2 4816 | v 4 2 4817 | e 0 1 5 4818 | e 0 4 5 4819 | e 2 3 5 4820 | e 3 4 5 4821 | t # 482 4822 | v 0 2 4823 | v 1 2 4824 | v 2 6 4825 | v 3 3 4826 | v 4 3 4827 | e 0 1 5 4828 | e 1 2 2 4829 | e 2 3 3 4830 | e 2 4 3 4831 | t # 483 4832 | v 0 5 4833 | v 1 2 4834 | v 2 2 4835 | v 3 2 4836 | v 4 2 4837 | e 0 2 2 4838 | e 1 4 2 4839 | e 1 2 5 4840 | e 2 3 5 4841 | t # 484 4842 | v 0 2 4843 | v 1 2 4844 | v 2 2 4845 | v 3 2 4846 | v 4 2 4847 | e 0 1 2 4848 | e 1 2 5 4849 | e 1 4 5 4850 | e 3 4 5 4851 | t # 485 4852 | v 0 2 4853 | v 1 2 4854 | v 2 2 4855 | v 3 2 4856 | v 4 2 4857 | e 0 1 5 4858 | e 0 2 2 4859 | e 1 3 5 4860 | e 2 4 2 4861 | t # 486 4862 | v 0 2 4863 | v 1 2 4864 | v 2 2 4865 | v 3 2 4866 | v 4 2 4867 | e 0 1 2 4868 | e 0 3 2 4869 | e 1 4 2 4870 | e 2 3 2 4871 | t # 487 4872 | v 0 3 4873 | v 1 7 4874 | v 2 3 4875 | v 3 3 4876 | v 4 2 4877 | e 0 1 3 4878 | e 1 2 2 4879 | e 1 3 2 4880 | e 1 4 2 4881 | t # 488 4882 | v 0 2 4883 | v 1 5 4884 | v 2 2 4885 | v 3 2 4886 | v 4 2 4887 | e 0 1 2 4888 | e 0 2 3 4889 | e 2 3 2 4890 | e 3 4 5 4891 | t # 489 4892 | v 0 2 4893 | v 1 2 4894 | v 2 2 4895 | v 3 2 4896 | v 4 2 4897 | e 0 3 5 4898 | e 1 2 5 4899 | e 1 4 2 4900 | e 2 3 5 4901 | t # 490 4902 | v 0 2 4903 | v 1 2 4904 | v 2 2 4905 | v 3 2 4906 | v 4 2 4907 | e 0 1 5 4908 | e 1 2 5 4909 | e 1 4 5 4910 | e 2 3 5 4911 | t # 491 4912 | v 0 2 4913 | v 1 2 4914 | v 2 2 4915 | v 3 2 4916 | v 4 2 4917 | e 0 4 5 4918 | e 0 1 5 4919 | e 1 2 5 4920 | e 2 3 5 4921 | t # 492 4922 | v 0 2 4923 | v 1 2 4924 | v 2 2 4925 | v 3 2 4926 | v 4 5 4927 | e 0 1 3 4928 | e 0 4 2 4929 | e 1 2 2 4930 | e 2 3 5 4931 | t # 493 4932 | v 0 2 4933 | v 1 2 4934 | v 2 5 4935 | v 3 3 4936 | v 4 2 4937 | e 0 4 5 4938 | e 1 2 2 4939 | e 1 4 5 4940 | e 2 3 3 4941 | t # 494 4942 | v 0 5 4943 | v 1 2 4944 | v 2 3 4945 | v 3 2 4946 | v 4 2 4947 | e 0 1 2 4948 | e 1 2 3 4949 | e 1 3 2 4950 | e 3 4 5 4951 | t # 495 4952 | v 0 2 4953 | v 1 2 4954 | v 2 2 4955 | v 3 2 4956 | v 4 2 4957 | e 0 1 2 4958 | e 1 2 2 4959 | e 2 3 5 4960 | e 2 4 5 4961 | t # 496 4962 | v 0 2 4963 | v 1 3 4964 | v 2 2 4965 | v 3 2 4966 | v 4 2 4967 | e 0 1 2 4968 | e 1 3 2 4969 | e 2 3 2 4970 | e 3 4 2 4971 | t # 497 4972 | v 0 2 4973 | v 1 5 4974 | v 2 2 4975 | v 3 3 4976 | v 4 2 4977 | e 0 1 2 4978 | e 1 2 2 4979 | e 2 3 3 4980 | e 2 4 2 4981 | t # 498 4982 | v 0 2 4983 | v 1 2 4984 | v 2 3 4985 | v 3 2 4986 | v 4 2 4987 | e 0 1 2 4988 | e 1 2 2 4989 | e 1 4 2 4990 | e 2 3 2 4991 | t # 499 4992 | v 0 5 4993 | v 1 5 4994 | v 2 2 4995 | v 3 3 4996 | v 4 4 4997 | e 0 4 2 4998 | e 1 2 5 4999 | e 1 4 2 5000 | e 3 4 2 5001 | t # 500 5002 | v 0 2 5003 | v 1 2 5004 | v 2 2 5005 | v 3 2 5006 | v 4 2 5007 | e 0 1 2 5008 | e 1 2 2 5009 | e 1 4 2 5010 | e 2 3 2 5011 | t # 501 5012 | v 0 2 5013 | v 1 2 5014 | v 2 2 5015 | v 3 2 5016 | v 4 2 5017 | e 0 1 2 5018 | e 0 4 2 5019 | e 1 2 2 5020 | e 3 4 2 5021 | t # 502 5022 | v 0 2 5023 | v 1 2 5024 | v 2 2 5025 | v 3 2 5026 | v 4 5 5027 | e 0 1 2 5028 | e 1 2 2 5029 | e 1 3 2 5030 | e 3 4 2 5031 | t # 503 5032 | v 0 2 5033 | v 1 2 5034 | v 2 2 5035 | v 3 2 5036 | v 4 3 5037 | e 0 1 2 5038 | e 1 2 2 5039 | e 1 4 2 5040 | e 2 3 2 5041 | t # 504 5042 | v 0 2 5043 | v 1 2 5044 | v 2 2 5045 | v 3 2 5046 | v 4 2 5047 | e 0 1 5 5048 | e 0 4 5 5049 | e 2 3 5 5050 | e 3 4 5 5051 | t # 505 5052 | v 0 2 5053 | v 1 2 5054 | v 2 5 5055 | v 3 2 5056 | v 4 3 5057 | e 0 1 2 5058 | e 1 2 2 5059 | e 2 3 2 5060 | e 3 4 2 5061 | t # 506 5062 | v 0 2 5063 | v 1 2 5064 | v 2 6 5065 | v 3 3 5066 | v 4 3 5067 | e 0 1 5 5068 | e 1 2 2 5069 | e 2 3 3 5070 | e 2 4 3 5071 | t # 507 5072 | v 0 2 5073 | v 1 2 5074 | v 2 2 5075 | v 3 3 5076 | v 4 2 5077 | e 0 2 3 5078 | e 0 1 2 5079 | e 1 4 2 5080 | e 2 3 2 5081 | t # 508 5082 | v 0 2 5083 | v 1 2 5084 | v 2 2 5085 | v 3 2 5086 | v 4 2 5087 | e 0 1 2 5088 | e 1 2 5 5089 | e 1 4 5 5090 | e 2 3 5 5091 | t # 509 5092 | v 0 2 5093 | v 1 7 5094 | v 2 2 5095 | v 3 2 5096 | v 4 2 5097 | e 0 1 3 5098 | e 1 2 2 5099 | e 2 4 5 5100 | e 3 4 5 5101 | t # 510 5102 | v 0 2 5103 | v 1 2 5104 | v 2 2 5105 | v 3 2 5106 | v 4 2 5107 | e 0 1 5 5108 | e 1 2 5 5109 | e 2 3 5 5110 | e 3 4 5 5111 | t # 511 5112 | v 0 5 5113 | v 1 2 5114 | v 2 2 5115 | v 3 5 5116 | v 4 2 5117 | e 0 1 2 5118 | e 1 2 2 5119 | e 2 3 2 5120 | e 3 4 5 5121 | t # 512 5122 | v 0 2 5123 | v 1 2 5124 | v 2 3 5125 | v 3 2 5126 | v 4 5 5127 | e 0 1 5 5128 | e 0 3 2 5129 | e 1 2 3 5130 | e 3 4 2 5131 | t # 513 5132 | v 0 5 5133 | 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5205 | v 3 2 5206 | v 4 2 5207 | e 0 1 2 5208 | e 1 2 3 5209 | e 1 3 2 5210 | e 3 4 2 5211 | t # 521 5212 | v 0 2 5213 | v 1 2 5214 | v 2 2 5215 | v 3 2 5216 | v 4 2 5217 | e 0 1 2 5218 | e 1 2 2 5219 | e 2 3 2 5220 | e 3 4 2 5221 | t # 522 5222 | v 0 2 5223 | v 1 2 5224 | v 2 2 5225 | v 3 5 5226 | v 4 2 5227 | e 0 1 2 5228 | e 1 2 2 5229 | e 2 3 2 5230 | e 3 4 2 5231 | t # 523 5232 | v 0 3 5233 | v 1 2 5234 | v 2 2 5235 | v 3 2 5236 | v 4 2 5237 | e 0 1 2 5238 | e 1 2 5 5239 | e 2 3 5 5240 | e 3 4 5 5241 | t # 524 5242 | v 0 5 5243 | v 1 2 5244 | v 2 3 5245 | v 3 3 5246 | v 4 2 5247 | e 0 1 2 5248 | e 1 2 3 5249 | e 1 3 2 5250 | e 3 4 2 5251 | t # 525 5252 | v 0 2 5253 | v 1 2 5254 | v 2 2 5255 | v 3 2 5256 | v 4 3 5257 | e 0 1 2 5258 | e 1 2 2 5259 | e 2 3 2 5260 | e 3 4 3 5261 | t # 526 5262 | v 0 5 5263 | v 1 2 5264 | v 2 2 5265 | v 3 2 5266 | v 4 2 5267 | e 0 1 2 5268 | e 0 3 2 5269 | e 1 2 2 5270 | e 3 4 2 5271 | t # 527 5272 | v 0 2 5273 | v 1 5 5274 | v 2 2 5275 | v 3 5 5276 | 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5348 | e 0 4 5 5349 | e 2 3 3 5350 | e 3 4 2 5351 | t # 535 5352 | v 0 2 5353 | v 1 2 5354 | v 2 2 5355 | v 3 3 5356 | v 4 2 5357 | e 0 1 5 5358 | e 0 3 2 5359 | e 1 2 5 5360 | e 3 4 2 5361 | t # 536 5362 | v 0 2 5363 | v 1 2 5364 | v 2 2 5365 | v 3 2 5366 | v 4 3 5367 | e 0 1 2 5368 | e 0 3 2 5369 | e 1 2 2 5370 | e 3 4 2 5371 | t # 537 5372 | v 0 2 5373 | v 1 2 5374 | v 2 5 5375 | v 3 2 5376 | v 4 3 5377 | e 0 1 2 5378 | e 1 3 2 5379 | e 2 3 2 5380 | e 3 4 3 5381 | t # 538 5382 | v 0 2 5383 | v 1 2 5384 | v 2 2 5385 | v 3 2 5386 | v 4 3 5387 | e 0 2 2 5388 | e 1 2 2 5389 | e 2 3 2 5390 | e 3 4 3 5391 | t # 539 5392 | v 0 2 5393 | v 1 3 5394 | v 2 2 5395 | v 3 2 5396 | v 4 3 5397 | e 0 1 2 5398 | e 0 3 2 5399 | e 2 3 2 5400 | e 3 4 2 5401 | t # 540 5402 | v 0 2 5403 | v 1 3 5404 | v 2 2 5405 | v 3 5 5406 | v 4 2 5407 | e 0 1 3 5408 | e 0 2 2 5409 | e 2 3 2 5410 | e 3 4 5 5411 | t # 541 5412 | v 0 5 5413 | v 1 2 5414 | v 2 2 5415 | v 3 2 5416 | v 4 3 5417 | e 0 2 2 5418 | e 0 1 5 5419 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5562 | v 0 2 5563 | v 1 2 5564 | v 2 2 5565 | v 3 3 5566 | v 4 2 5567 | e 0 1 5 5568 | e 0 4 2 5569 | e 1 2 5 5570 | e 1 3 2 5571 | t # 557 5572 | v 0 2 5573 | v 1 2 5574 | v 2 2 5575 | v 3 3 5576 | v 4 3 5577 | e 0 3 2 5578 | e 1 2 5 5579 | e 1 4 2 5580 | e 2 3 2 5581 | t # 558 5582 | v 0 2 5583 | v 1 3 5584 | v 2 3 5585 | v 3 3 5586 | v 4 15 5587 | e 0 1 3 5588 | e 1 4 2 5589 | e 2 4 2 5590 | e 3 4 2 5591 | t # 559 5592 | v 0 2 5593 | v 1 2 5594 | v 2 2 5595 | v 3 2 5596 | v 4 2 5597 | e 0 1 2 5598 | e 0 4 2 5599 | e 2 3 2 5600 | e 3 4 2 5601 | t # 560 5602 | v 0 3 5603 | v 1 2 5604 | v 2 2 5605 | v 3 2 5606 | v 4 2 5607 | e 0 1 2 5608 | e 1 2 2 5609 | e 2 4 5 5610 | e 3 4 5 5611 | t # 561 5612 | v 0 2 5613 | v 1 5 5614 | v 2 5 5615 | v 3 2 5616 | v 4 2 5617 | e 0 3 5 5618 | e 1 2 5 5619 | e 2 4 5 5620 | e 2 3 5 5621 | t # 562 5622 | v 0 2 5623 | v 1 3 5624 | v 2 2 5625 | v 3 2 5626 | v 4 3 5627 | e 0 1 2 5628 | e 1 2 2 5629 | e 2 3 2 5630 | e 2 4 3 5631 | t # 563 5632 | v 0 2 5633 | 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v 4 2 5777 | e 0 1 2 5778 | e 1 2 2 5779 | e 2 3 3 5780 | e 2 4 2 5781 | t # 578 5782 | v 0 3 5783 | v 1 2 5784 | v 2 2 5785 | v 3 3 5786 | v 4 2 5787 | e 0 1 2 5788 | e 1 2 2 5789 | e 2 3 2 5790 | e 3 4 2 5791 | t # 579 5792 | v 0 2 5793 | v 1 2 5794 | v 2 3 5795 | v 3 2 5796 | v 4 3 5797 | e 0 1 2 5798 | e 0 4 2 5799 | e 1 2 2 5800 | e 1 3 2 5801 | t # 580 5802 | v 0 2 5803 | v 1 2 5804 | v 2 2 5805 | v 3 2 5806 | v 4 2 5807 | e 0 2 2 5808 | e 0 4 2 5809 | e 1 4 2 5810 | e 2 3 3 5811 | t # 581 5812 | v 0 2 5813 | v 1 5 5814 | v 2 2 5815 | v 3 2 5816 | v 4 5 5817 | e 0 1 2 5818 | e 1 2 2 5819 | e 2 3 2 5820 | e 3 4 2 5821 | t # 582 5822 | v 0 2 5823 | v 1 2 5824 | v 2 2 5825 | v 3 2 5826 | v 4 2 5827 | e 0 2 2 5828 | e 0 3 2 5829 | e 1 3 2 5830 | e 3 4 2 5831 | t # 583 5832 | v 0 2 5833 | v 1 2 5834 | v 2 2 5835 | v 3 5 5836 | v 4 2 5837 | e 0 1 2 5838 | e 1 2 2 5839 | e 2 3 2 5840 | e 3 4 2 5841 | t # 584 5842 | v 0 2 5843 | v 1 3 5844 | v 2 2 5845 | v 3 2 5846 | v 4 3 5847 | e 0 1 2 5848 | e 1 2 2 5849 | e 2 3 2 5850 | e 2 4 3 5851 | t # 585 5852 | v 0 2 5853 | v 1 2 5854 | v 2 2 5855 | v 3 2 5856 | v 4 3 5857 | e 0 2 3 5858 | e 1 4 2 5859 | e 1 2 2 5860 | e 2 3 2 5861 | t # 586 5862 | v 0 2 5863 | v 1 2 5864 | v 2 3 5865 | v 3 2 5866 | v 4 3 5867 | e 0 1 2 5868 | e 1 2 2 5869 | e 2 3 2 5870 | e 3 4 3 5871 | t # 587 5872 | v 0 2 5873 | v 1 2 5874 | v 2 2 5875 | v 3 3 5876 | v 4 3 5877 | e 0 2 2 5878 | e 0 3 2 5879 | e 1 2 2 5880 | e 2 4 2 5881 | t # 588 5882 | v 0 2 5883 | v 1 2 5884 | v 2 3 5885 | v 3 2 5886 | v 4 3 5887 | e 0 1 2 5888 | e 1 2 2 5889 | e 2 3 2 5890 | e 3 4 3 5891 | t # 589 5892 | v 0 5 5893 | v 1 2 5894 | v 2 2 5895 | v 3 2 5896 | v 4 3 5897 | e 0 1 2 5898 | e 1 2 2 5899 | e 1 3 2 5900 | e 3 4 2 5901 | t # 590 5902 | v 0 5 5903 | v 1 2 5904 | v 2 2 5905 | v 3 3 5906 | v 4 5 5907 | e 0 1 2 5908 | e 1 2 2 5909 | e 2 3 3 5910 | e 2 4 2 5911 | t # 591 5912 | v 0 2 5913 | v 1 2 5914 | v 2 2 5915 | v 3 2 5916 | v 4 5 5917 | e 0 1 2 5918 | e 1 2 2 5919 | e 2 3 5 5920 | e 2 4 5 5921 | t # 592 5922 | v 0 2 5923 | v 1 2 5924 | v 2 5 5925 | v 3 2 5926 | v 4 5 5927 | e 0 1 2 5928 | e 1 2 5 5929 | e 2 3 5 5930 | e 3 4 5 5931 | t # 593 5932 | v 0 5 5933 | v 1 2 5934 | v 2 3 5935 | v 3 2 5936 | v 4 2 5937 | e 0 1 2 5938 | e 1 2 3 5939 | e 1 3 2 5940 | e 3 4 5 5941 | t # 594 5942 | v 0 5 5943 | v 1 2 5944 | v 2 2 5945 | v 3 3 5946 | v 4 5 5947 | e 0 1 5 5948 | e 1 2 5 5949 | e 1 3 2 5950 | e 2 4 2 5951 | t # 595 5952 | v 0 2 5953 | v 1 2 5954 | v 2 5 5955 | v 3 2 5956 | v 4 2 5957 | e 0 1 5 5958 | e 1 2 2 5959 | e 2 3 3 5960 | e 3 4 2 5961 | t # 596 5962 | v 0 2 5963 | v 1 3 5964 | v 2 2 5965 | v 3 3 5966 | v 4 2 5967 | e 0 1 2 5968 | e 1 2 2 5969 | e 2 3 3 5970 | e 2 4 2 5971 | t # 597 5972 | v 0 2 5973 | v 1 2 5974 | v 2 5 5975 | v 3 5 5976 | v 4 2 5977 | e 0 3 2 5978 | e 0 1 5 5979 | e 1 2 2 5980 | e 3 4 3 5981 | t # 598 5982 | v 0 5 5983 | v 1 2 5984 | v 2 3 5985 | v 3 2 5986 | v 4 2 5987 | e 0 1 2 5988 | e 1 3 2 5989 | e 2 3 2 5990 | e 3 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7987 | v 0 2 7988 | v 1 2 7989 | v 2 2 7990 | v 3 2 7991 | v 4 2 7992 | e 0 1 2 7993 | e 1 2 2 7994 | e 2 3 2 7995 | e 3 4 2 7996 | t # 800 7997 | v 0 12 7998 | v 1 2 7999 | v 2 2 8000 | v 3 5 8001 | v 4 2 8002 | e 0 1 2 8003 | e 1 3 4 8004 | e 0 4 2 8005 | e 2 3 2 8006 | t # 801 8007 | v 0 4 8008 | v 1 7 8009 | v 2 7 8010 | v 3 2 8011 | v 4 2 8012 | e 0 1 2 8013 | e 0 2 2 8014 | e 2 3 2 8015 | e 2 4 2 8016 | t # 802 8017 | v 0 2 8018 | v 1 2 8019 | v 2 2 8020 | v 3 2 8021 | v 4 2 8022 | e 0 4 5 8023 | e 1 2 5 8024 | e 2 3 5 8025 | e 3 4 5 8026 | t # 803 8027 | v 0 2 8028 | v 1 2 8029 | v 2 2 8030 | v 3 2 8031 | v 4 2 8032 | e 0 1 5 8033 | e 2 3 5 8034 | e 1 4 5 8035 | e 0 2 5 8036 | t # 804 8037 | v 0 2 8038 | v 1 2 8039 | v 2 2 8040 | v 3 2 8041 | v 4 2 8042 | e 0 1 2 8043 | e 1 2 2 8044 | e 2 3 2 8045 | e 3 4 2 8046 | t # 805 8047 | v 0 7 8048 | v 1 2 8049 | v 2 2 8050 | v 3 12 8051 | v 4 2 8052 | e 0 1 2 8053 | e 0 2 2 8054 | e 0 3 2 8055 | e 3 4 2 8056 | t # 806 8057 | v 0 2 8058 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2 8130 | v 3 2 8131 | v 4 2 8132 | e 1 2 5 8133 | e 2 3 5 8134 | e 0 4 5 8135 | e 3 4 5 8136 | t # 814 8137 | v 0 2 8138 | v 1 2 8139 | v 2 7 8140 | v 3 2 8141 | v 4 7 8142 | e 0 1 2 8143 | e 0 4 2 8144 | e 1 2 2 8145 | e 2 3 2 8146 | t # 815 8147 | v 0 2 8148 | v 1 2 8149 | v 2 2 8150 | v 3 2 8151 | v 4 2 8152 | e 0 1 5 8153 | e 0 4 5 8154 | e 1 2 5 8155 | e 2 3 5 8156 | t # 816 8157 | v 0 7 8158 | v 1 35 8159 | v 2 2 8160 | v 3 2 8161 | v 4 7 8162 | e 0 1 2 8163 | e 0 2 2 8164 | e 1 4 2 8165 | e 3 4 2 8166 | t # 817 8167 | v 0 2 8168 | v 1 2 8169 | v 2 2 8170 | v 3 2 8171 | v 4 2 8172 | e 0 1 5 8173 | e 1 2 5 8174 | e 2 3 5 8175 | e 3 4 5 8176 | t # 818 8177 | v 0 7 8178 | v 1 2 8179 | v 2 2 8180 | v 3 2 8181 | v 4 2 8182 | e 0 1 2 8183 | e 0 2 2 8184 | e 0 4 2 8185 | e 2 3 5 8186 | t # 819 8187 | v 0 2 8188 | v 1 2 8189 | v 2 2 8190 | v 3 2 8191 | v 4 2 8192 | e 0 4 5 8193 | e 1 2 5 8194 | e 2 3 5 8195 | e 3 4 5 8196 | t # 820 8197 | v 0 2 8198 | v 1 2 8199 | v 2 2 8200 | v 3 2 8201 | v 4 2 8202 | e 0 1 5 8203 | e 1 2 5 8204 | e 2 3 5 8205 | e 3 4 5 8206 | t # 821 8207 | v 0 2 8208 | v 1 2 8209 | v 2 2 8210 | v 3 2 8211 | v 4 2 8212 | e 0 1 5 8213 | e 2 3 5 8214 | e 0 4 5 8215 | e 3 4 5 8216 | t # 822 8217 | v 0 50 8218 | v 1 2 8219 | v 2 2 8220 | v 3 2 8221 | v 4 2 8222 | e 0 1 2 8223 | e 0 2 2 8224 | e 0 3 2 8225 | e 0 4 2 8226 | t # 823 8227 | v 0 2 8228 | v 1 2 8229 | v 2 2 8230 | v 3 50 8231 | e 0 1 3 8232 | e 1 2 2 8233 | e 2 3 2 8234 | e 0 3 2 8235 | t # 824 8236 | v 0 2 8237 | v 1 2 8238 | v 2 2 8239 | v 3 5 8240 | v 4 2 8241 | e 0 2 2 8242 | e 0 1 5 8243 | e 2 3 2 8244 | e 3 4 2 8245 | t # 825 8246 | v 0 2 8247 | v 1 2 8248 | v 2 2 8249 | v 3 2 8250 | v 4 2 8251 | e 0 1 2 8252 | e 1 2 2 8253 | e 1 3 2 8254 | e 1 4 2 8255 | t # 826 8256 | v 0 7 8257 | v 1 23 8258 | v 2 23 8259 | v 3 23 8260 | v 4 23 8261 | e 0 1 2 8262 | e 0 2 2 8263 | e 0 3 2 8264 | e 0 4 2 8265 | t # 827 8266 | v 0 16 8267 | v 1 2 8268 | v 2 16 8269 | v 3 2 8270 | v 4 2 8271 | e 0 1 2 8272 | e 0 2 2 8273 | e 2 3 2 8274 | e 3 4 2 8275 | t # 828 8276 | v 0 35 8277 | v 1 7 8278 | v 2 2 8279 | v 3 2 8280 | v 4 2 8281 | e 0 1 2 8282 | e 1 2 2 8283 | e 1 3 2 8284 | e 3 4 2 8285 | t # 829 8286 | v 0 2 8287 | v 1 2 8288 | v 2 2 8289 | v 3 2 8290 | v 4 2 8291 | e 0 1 3 8292 | e 1 2 2 8293 | e 2 4 2 8294 | e 3 4 2 8295 | t # 830 8296 | v 0 2 8297 | v 1 2 8298 | v 2 2 8299 | v 3 5 8300 | v 4 2 8301 | e 0 1 5 8302 | e 1 3 2 8303 | e 2 4 2 8304 | e 2 3 2 8305 | t # 831 8306 | v 0 2 8307 | v 1 2 8308 | v 2 2 8309 | v 3 2 8310 | v 4 2 8311 | e 0 1 5 8312 | e 0 2 5 8313 | e 2 3 5 8314 | e 1 4 5 8315 | t # 832 8316 | v 0 2 8317 | v 1 3 8318 | v 2 2 8319 | v 3 2 8320 | v 4 2 8321 | e 0 3 5 8322 | e 1 2 5 8323 | e 1 3 5 8324 | e 3 4 5 8325 | t # 833 8326 | v 0 2 8327 | v 1 5 8328 | v 2 2 8329 | v 3 2 8330 | v 4 5 8331 | e 0 2 5 8332 | e 1 2 5 8333 | e 2 3 5 8334 | e 3 4 5 8335 | t # 834 8336 | v 0 2 8337 | v 1 2 8338 | v 2 2 8339 | v 3 2 8340 | v 4 2 8341 | e 0 1 5 8342 | e 0 4 5 8343 | e 1 2 5 8344 | e 3 4 5 8345 | t # 835 8346 | v 0 2 8347 | v 1 7 8348 | v 2 2 8349 | v 3 2 8350 | v 4 2 8351 | e 0 1 2 8352 | e 1 2 2 8353 | e 2 3 5 8354 | e 3 4 5 8355 | t # 836 8356 | v 0 2 8357 | v 1 2 8358 | v 2 2 8359 | v 3 2 8360 | v 4 3 8361 | e 0 1 2 8362 | e 0 2 2 8363 | e 2 3 2 8364 | e 3 4 2 8365 | t # 837 8366 | v 0 5 8367 | v 1 2 8368 | v 2 2 8369 | v 3 2 8370 | v 4 2 8371 | e 0 1 5 8372 | e 0 3 2 8373 | e 1 2 5 8374 | e 3 4 2 8375 | t # 838 8376 | v 0 5 8377 | v 1 2 8378 | v 2 2 8379 | v 3 5 8380 | v 4 3 8381 | e 0 1 2 8382 | e 1 2 5 8383 | e 2 3 2 8384 | e 3 4 2 8385 | t # 839 8386 | v 0 2 8387 | v 1 2 8388 | v 2 2 8389 | v 3 3 8390 | v 4 2 8391 | e 0 1 5 8392 | e 1 2 5 8393 | e 2 3 2 8394 | e 3 4 2 8395 | t # 840 8396 | v 0 2 8397 | v 1 2 8398 | v 2 2 8399 | v 3 5 8400 | v 4 2 8401 | e 0 1 2 8402 | e 1 4 2 8403 | e 2 3 3 8404 | e 2 4 2 8405 | t # 841 8406 | v 0 5 8407 | v 1 2 8408 | v 2 2 8409 | v 3 2 8410 | v 4 2 8411 | e 0 1 2 8412 | e 1 2 2 8413 | e 2 3 5 8414 | e 3 4 5 8415 | t # 842 8416 | v 0 2 8417 | v 1 2 8418 | v 2 2 8419 | v 3 2 8420 | v 4 5 8421 | e 0 1 5 8422 | e 0 2 2 8423 | e 0 3 5 8424 | e 3 4 5 8425 | t # 843 8426 | v 0 2 8427 | v 1 2 8428 | v 2 2 8429 | v 3 2 8430 | v 4 2 8431 | e 0 1 2 8432 | e 1 4 5 8433 | e 2 3 5 8434 | e 3 4 5 8435 | t # 844 8436 | v 0 2 8437 | v 1 5 8438 | v 2 3 8439 | v 3 60 8440 | v 4 2 8441 | e 1 3 2 8442 | e 0 4 2 8443 | e 2 3 2 8444 | e 1 4 3 8445 | t # 845 8446 | v 0 2 8447 | v 1 2 8448 | v 2 2 8449 | v 3 2 8450 | v 4 6 8451 | e 0 2 5 8452 | e 1 2 5 8453 | e 2 3 5 8454 | e 3 4 2 8455 | t # 846 8456 | v 0 3 8457 | v 1 2 8458 | v 2 2 8459 | v 3 2 8460 | v 4 5 8461 | e 0 2 2 8462 | e 1 2 5 8463 | e 2 3 5 8464 | e 3 4 5 8465 | t # 847 8466 | v 0 2 8467 | v 1 2 8468 | v 2 5 8469 | v 3 5 8470 | v 4 5 8471 | e 0 1 5 8472 | e 0 3 2 8473 | e 1 4 2 8474 | e 2 3 3 8475 | t # 848 8476 | v 0 2 8477 | v 1 2 8478 | v 2 2 8479 | v 3 2 8480 | v 4 2 8481 | e 0 1 5 8482 | e 0 3 5 8483 | e 1 2 5 8484 | e 3 4 5 8485 | t # 849 8486 | v 0 2 8487 | v 1 2 8488 | v 2 2 8489 | v 3 2 8490 | v 4 3 8491 | e 0 2 5 8492 | e 1 3 2 8493 | e 1 2 5 8494 | e 3 4 2 8495 | t # 850 8496 | v 0 2 8497 | v 1 2 8498 | v 2 2 8499 | v 3 2 8500 | v 4 3 8501 | e 0 1 2 8502 | e 1 2 2 8503 | e 2 4 2 8504 | e 2 3 2 8505 | t # 851 8506 | v 0 2 8507 | v 1 3 8508 | v 2 2 8509 | v 3 3 8510 | v 4 2 8511 | e 0 1 2 8512 | e 1 2 2 8513 | e 2 3 3 8514 | e 2 4 2 8515 | t # 852 8516 | v 0 2 8517 | v 1 3 8518 | v 2 2 8519 | v 3 2 8520 | v 4 2 8521 | e 0 1 2 8522 | e 0 3 2 8523 | e 1 2 2 8524 | e 3 4 2 8525 | t # 853 8526 | v 0 2 8527 | v 1 2 8528 | v 2 2 8529 | v 3 3 8530 | v 4 3 8531 | e 0 1 5 8532 | e 0 2 2 8533 | e 2 3 3 8534 | e 2 4 2 8535 | t # 854 8536 | v 0 2 8537 | v 1 2 8538 | v 2 3 8539 | v 3 2 8540 | v 4 3 8541 | e 0 1 2 8542 | e 1 2 2 8543 | e 1 3 2 8544 | e 3 4 2 8545 | t # 855 8546 | v 0 2 8547 | v 1 2 8548 | v 2 2 8549 | v 3 3 8550 | v 4 2 8551 | e 0 1 2 8552 | e 0 2 2 8553 | e 0 3 2 8554 | e 3 4 2 8555 | t # 856 8556 | v 0 2 8557 | v 1 2 8558 | v 2 2 8559 | v 3 2 8560 | v 4 2 8561 | e 0 1 2 8562 | e 1 2 2 8563 | e 2 3 2 8564 | e 3 4 2 8565 | t # 857 8566 | v 0 2 8567 | v 1 2 8568 | v 2 3 8569 | v 3 2 8570 | v 4 2 8571 | e 0 2 2 8572 | e 0 1 2 8573 | e 2 3 2 8574 | e 3 4 2 8575 | t # 858 8576 | v 0 2 8577 | v 1 2 8578 | v 2 2 8579 | v 3 2 8580 | v 4 2 8581 | e 0 1 2 8582 | e 1 2 2 8583 | e 2 3 2 8584 | e 3 4 2 8585 | t # 859 8586 | v 0 2 8587 | v 1 2 8588 | v 2 2 8589 | v 3 2 8590 | v 4 2 8591 | e 0 1 2 8592 | e 1 2 2 8593 | e 2 3 2 8594 | e 3 4 2 8595 | t # 860 8596 | v 0 2 8597 | v 1 3 8598 | v 2 2 8599 | v 3 3 8600 | v 4 2 8601 | e 0 1 2 8602 | e 0 2 2 8603 | e 2 3 2 8604 | e 3 4 2 8605 | t # 861 8606 | v 0 2 8607 | v 1 2 8608 | v 2 2 8609 | v 3 2 8610 | v 4 2 8611 | e 0 1 2 8612 | e 1 2 2 8613 | e 2 3 2 8614 | e 3 4 2 8615 | t # 862 8616 | v 0 2 8617 | v 1 2 8618 | v 2 5 8619 | v 3 2 8620 | v 4 3 8621 | e 0 1 5 8622 | e 1 2 5 8623 | e 2 3 5 8624 | e 3 4 3 8625 | t # 863 8626 | v 0 2 8627 | v 1 3 8628 | v 2 2 8629 | v 3 2 8630 | v 4 3 8631 | e 0 1 2 8632 | e 1 2 2 8633 | e 2 3 2 8634 | e 3 4 2 8635 | t # 864 8636 | v 0 2 8637 | v 1 5 8638 | v 2 2 8639 | v 3 2 8640 | v 4 2 8641 | e 0 1 2 8642 | e 1 2 5 8643 | e 2 4 5 8644 | e 3 4 5 8645 | t # 865 8646 | v 0 2 8647 | v 1 2 8648 | v 2 3 8649 | v 3 2 8650 | v 4 2 8651 | e 0 1 2 8652 | e 1 2 2 8653 | e 2 3 2 8654 | e 3 4 2 8655 | t # 866 8656 | v 0 5 8657 | v 1 2 8658 | v 2 2 8659 | v 3 5 8660 | v 4 2 8661 | e 0 1 5 8662 | e 1 4 5 8663 | e 2 3 5 8664 | e 3 4 5 8665 | t # 867 8666 | v 0 2 8667 | v 1 2 8668 | v 2 2 8669 | v 3 2 8670 | v 4 2 8671 | e 0 4 2 8672 | e 1 4 5 8673 | e 2 3 5 8674 | e 3 4 5 8675 | t # 868 8676 | v 0 6 8677 | v 1 2 8678 | v 2 6 8679 | v 3 2 8680 | v 4 2 8681 | e 0 1 2 8682 | e 1 3 3 8683 | e 1 2 2 8684 | e 3 4 2 8685 | t # 869 8686 | v 0 2 8687 | v 1 2 8688 | v 2 2 8689 | v 3 2 8690 | v 4 2 8691 | e 0 1 5 8692 | e 1 3 5 8693 | e 2 3 5 8694 | e 3 4 2 8695 | t # 870 8696 | v 0 2 8697 | v 1 2 8698 | v 2 3 8699 | v 3 2 8700 | v 4 3 8701 | e 0 1 5 8702 | e 0 2 5 8703 | e 2 3 5 8704 | e 3 4 3 8705 | t # 871 8706 | v 0 2 8707 | v 1 2 8708 | v 2 2 8709 | v 3 3 8710 | v 4 5 8711 | e 0 1 2 8712 | e 1 2 2 8713 | e 2 3 3 8714 | e 2 4 2 8715 | t # 872 8716 | v 0 2 8717 | v 1 2 8718 | v 2 2 8719 | v 3 5 8720 | v 4 2 8721 | e 0 1 2 8722 | e 0 2 2 8723 | e 2 3 2 8724 | e 3 4 2 8725 | t # 873 8726 | v 0 2 8727 | v 1 2 8728 | v 2 2 8729 | v 3 2 8730 | v 4 2 8731 | e 0 1 2 8732 | e 1 2 2 8733 | e 2 3 2 8734 | e 2 4 2 8735 | t # 874 8736 | v 0 5 8737 | v 1 2 8738 | v 2 2 8739 | v 3 2 8740 | v 4 2 8741 | e 0 1 2 8742 | e 1 2 2 8743 | e 1 4 2 8744 | e 2 3 2 8745 | t # 875 8746 | v 0 2 8747 | v 1 2 8748 | v 2 2 8749 | v 3 2 8750 | v 4 2 8751 | e 0 1 5 8752 | e 1 2 5 8753 | e 1 3 5 8754 | e 3 4 5 8755 | t # 876 8756 | v 0 2 8757 | v 1 2 8758 | v 2 2 8759 | v 3 2 8760 | v 4 2 8761 | e 0 1 5 8762 | e 0 2 5 8763 | e 2 3 5 8764 | e 3 4 5 8765 | t # 877 8766 | v 0 2 8767 | v 1 2 8768 | v 2 2 8769 | v 3 5 8770 | v 4 2 8771 | e 0 1 5 8772 | e 0 2 5 8773 | e 1 3 5 8774 | e 3 4 5 8775 | t # 878 8776 | v 0 5 8777 | v 1 2 8778 | v 2 2 8779 | v 3 2 8780 | v 4 2 8781 | e 0 1 5 8782 | e 1 2 5 8783 | e 2 3 5 8784 | e 2 4 5 8785 | t # 879 8786 | v 0 2 8787 | v 1 2 8788 | v 2 2 8789 | v 3 2 8790 | v 4 2 8791 | e 0 4 5 8792 | e 1 2 5 8793 | e 2 3 5 8794 | e 3 4 5 8795 | t # 880 8796 | v 0 2 8797 | v 1 2 8798 | v 2 2 8799 | v 3 2 8800 | v 4 2 8801 | e 0 1 5 8802 | e 0 4 5 8803 | e 1 2 5 8804 | e 3 4 5 8805 | t # 881 8806 | v 0 2 8807 | v 1 2 8808 | v 2 2 8809 | v 3 2 8810 | v 4 2 8811 | e 0 1 5 8812 | e 1 2 5 8813 | e 2 3 5 8814 | e 3 4 5 8815 | t # 882 8816 | v 0 2 8817 | v 1 2 8818 | v 2 2 8819 | v 3 2 8820 | v 4 3 8821 | e 0 1 2 8822 | e 0 3 2 8823 | e 1 2 2 8824 | e 2 4 3 8825 | t # 883 8826 | v 0 2 8827 | v 1 2 8828 | v 2 2 8829 | v 3 2 8830 | v 4 2 8831 | e 0 1 5 8832 | e 1 2 5 8833 | e 2 3 5 8834 | e 3 4 5 8835 | t # 884 8836 | v 0 5 8837 | v 1 2 8838 | v 2 2 8839 | v 3 2 8840 | v 4 2 8841 | e 0 1 2 8842 | e 1 2 5 8843 | e 2 3 5 8844 | e 3 4 5 8845 | t # 885 8846 | v 0 2 8847 | v 1 2 8848 | v 2 2 8849 | v 3 2 8850 | v 4 2 8851 | e 0 1 5 8852 | e 1 2 5 8853 | e 2 3 5 8854 | e 2 4 2 8855 | t # 886 8856 | v 0 2 8857 | v 1 5 8858 | v 2 2 8859 | v 3 3 8860 | v 4 2 8861 | e 0 1 2 8862 | e 0 3 3 8863 | e 1 4 2 8864 | e 1 2 2 8865 | t # 887 8866 | v 0 2 8867 | v 1 2 8868 | v 2 2 8869 | v 3 2 8870 | v 4 2 8871 | e 0 1 2 8872 | e 1 2 5 8873 | e 2 3 5 8874 | e 2 4 2 8875 | t # 888 8876 | v 0 2 8877 | v 1 2 8878 | v 2 2 8879 | v 3 2 8880 | v 4 2 8881 | e 0 1 5 8882 | e 1 2 5 8883 | e 2 3 5 8884 | e 3 4 5 8885 | t # 889 8886 | v 0 2 8887 | v 1 2 8888 | v 2 2 8889 | v 3 3 8890 | v 4 2 8891 | e 0 1 2 8892 | e 0 3 2 8893 | e 1 2 2 8894 | e 1 4 2 8895 | t # 890 8896 | v 0 2 8897 | v 1 2 8898 | v 2 2 8899 | v 3 2 8900 | v 4 2 8901 | e 0 1 2 8902 | e 1 2 5 8903 | e 2 3 5 8904 | e 3 4 5 8905 | t # 891 8906 | v 0 2 8907 | v 1 5 8908 | v 2 2 8909 | v 3 2 8910 | v 4 2 8911 | e 0 1 2 8912 | e 1 2 2 8913 | e 2 4 5 8914 | e 3 4 5 8915 | t # 892 8916 | v 0 2 8917 | v 1 5 8918 | v 2 3 8919 | v 3 2 8920 | v 4 2 8921 | e 0 1 2 8922 | e 0 2 3 8923 | e 1 3 2 8924 | e 3 4 5 8925 | t # 893 8926 | v 0 2 8927 | v 1 2 8928 | v 2 3 8929 | v 3 2 8930 | v 4 2 8931 | e 0 1 2 8932 | e 0 2 2 8933 | e 1 3 2 8934 | e 3 4 2 8935 | t # 894 8936 | v 0 5 8937 | v 1 2 8938 | v 2 2 8939 | v 3 2 8940 | v 4 2 8941 | e 0 2 2 8942 | e 0 1 2 8943 | e 2 3 5 8944 | e 3 4 5 8945 | t # 895 8946 | v 0 2 8947 | v 1 2 8948 | v 2 2 8949 | v 3 2 8950 | v 4 2 8951 | e 0 1 5 8952 | e 1 2 5 8953 | e 2 3 5 8954 | e 3 4 5 8955 | t # 896 8956 | v 0 5 8957 | v 1 2 8958 | v 2 2 8959 | v 3 2 8960 | v 4 2 8961 | e 0 2 2 8962 | e 1 4 5 8963 | e 1 2 5 8964 | e 3 4 5 8965 | t # 897 8966 | v 0 2 8967 | v 1 2 8968 | v 2 2 8969 | v 3 5 8970 | v 4 2 8971 | e 0 1 2 8972 | e 1 2 2 8973 | e 1 4 2 8974 | e 3 4 2 8975 | t # 898 8976 | v 0 2 8977 | v 1 2 8978 | v 2 2 8979 | v 3 2 8980 | v 4 2 8981 | e 0 1 5 8982 | e 0 4 5 8983 | e 1 2 5 8984 | e 2 3 5 8985 | t # 899 8986 | v 0 2 8987 | v 1 2 8988 | v 2 2 8989 | v 3 2 8990 | v 4 2 8991 | e 0 1 5 8992 | e 1 2 5 8993 | e 2 3 5 8994 | e 2 4 2 8995 | t # 900 8996 | v 0 2 8997 | v 1 2 8998 | v 2 5 8999 | v 3 3 9000 | v 4 3 9001 | e 0 1 5 9002 | e 1 2 2 9003 | e 2 3 3 9004 | e 2 4 2 9005 | t # 901 9006 | v 0 2 9007 | v 1 2 9008 | v 2 2 9009 | v 3 2 9010 | v 4 2 9011 | e 0 1 5 9012 | e 0 4 5 9013 | e 1 2 5 9014 | e 2 3 5 9015 | t # 902 9016 | v 0 2 9017 | v 1 5 9018 | v 2 2 9019 | v 3 2 9020 | v 4 2 9021 | e 0 2 2 9022 | e 1 2 2 9023 | e 2 3 2 9024 | e 3 4 5 9025 | t # 903 9026 | v 0 2 9027 | v 1 2 9028 | v 2 2 9029 | v 3 2 9030 | v 4 8 9031 | e 0 1 5 9032 | e 1 2 5 9033 | e 2 3 5 9034 | e 3 4 2 9035 | t # 904 9036 | v 0 2 9037 | v 1 5 9038 | v 2 2 9039 | v 3 3 9040 | v 4 5 9041 | e 0 2 2 9042 | e 0 3 2 9043 | e 1 2 2 9044 | e 3 4 2 9045 | t # 905 9046 | v 0 3 9047 | v 1 5 9048 | v 2 2 9049 | v 3 2 9050 | v 4 2 9051 | e 0 1 2 9052 | e 1 2 2 9053 | e 2 3 5 9054 | e 2 4 5 9055 | t # 906 9056 | v 0 2 9057 | v 1 2 9058 | v 2 2 9059 | v 3 2 9060 | v 4 2 9061 | e 0 1 2 9062 | e 0 3 2 9063 | e 1 2 2 9064 | e 1 4 2 9065 | t # 907 9066 | v 0 2 9067 | v 1 5 9068 | v 2 2 9069 | v 3 3 9070 | v 4 3 9071 | e 0 2 2 9072 | e 0 4 2 9073 | e 1 2 2 9074 | e 2 3 3 9075 | t # 908 9076 | v 0 5 9077 | v 1 2 9078 | v 2 2 9079 | v 3 2 9080 | v 4 2 9081 | e 0 1 2 9082 | e 1 2 2 9083 | e 2 4 5 9084 | e 3 4 5 9085 | t # 909 9086 | v 0 6 9087 | v 1 2 9088 | v 2 5 9089 | v 3 5 9090 | v 4 2 9091 | e 0 1 5 9092 | e 1 2 5 9093 | e 1 3 2 9094 | e 3 4 2 9095 | t # 910 9096 | v 0 3 9097 | v 1 2 9098 | v 2 2 9099 | v 3 2 9100 | v 4 2 9101 | e 0 1 2 9102 | e 1 2 2 9103 | e 2 3 2 9104 | e 3 4 2 9105 | t # 911 9106 | v 0 2 9107 | v 1 2 9108 | v 2 2 9109 | v 3 2 9110 | v 4 3 9111 | e 0 2 2 9112 | e 1 4 2 9113 | e 1 2 2 9114 | e 2 3 2 9115 | t # 912 9116 | v 0 2 9117 | v 1 2 9118 | v 2 3 9119 | v 3 2 9120 | v 4 3 9121 | e 0 1 2 9122 | e 1 2 2 9123 | e 2 3 2 9124 | e 3 4 3 9125 | t # 913 9126 | v 0 2 9127 | v 1 2 9128 | v 2 2 9129 | v 3 2 9130 | v 4 2 9131 | e 0 1 5 9132 | e 1 2 5 9133 | e 2 3 5 9134 | e 3 4 5 9135 | t # 914 9136 | v 0 5 9137 | v 1 2 9138 | v 2 5 9139 | v 3 2 9140 | v 4 2 9141 | e 0 1 5 9142 | e 1 2 5 9143 | e 2 3 5 9144 | e 3 4 5 9145 | t # 915 9146 | v 0 2 9147 | v 1 6 9148 | v 2 5 9149 | v 3 2 9150 | v 4 2 9151 | e 0 1 3 9152 | e 0 2 2 9153 | e 2 3 2 9154 | e 3 4 5 9155 | t # 916 9156 | v 0 2 9157 | v 1 2 9158 | v 2 2 9159 | v 3 2 9160 | v 4 8 9161 | e 0 1 5 9162 | e 1 2 5 9163 | e 1 4 2 9164 | e 2 3 5 9165 | t # 917 9166 | v 0 2 9167 | v 1 2 9168 | v 2 2 9169 | v 3 2 9170 | v 4 3 9171 | e 0 1 5 9172 | e 1 2 5 9173 | e 2 3 2 9174 | e 3 4 2 9175 | t # 918 9176 | v 0 2 9177 | v 1 2 9178 | v 2 3 9179 | v 3 2 9180 | v 4 2 9181 | e 0 1 2 9182 | e 1 3 2 9183 | e 1 4 2 9184 | e 2 3 2 9185 | t # 919 9186 | v 0 2 9187 | v 1 2 9188 | v 2 2 9189 | v 3 2 9190 | v 4 2 9191 | e 0 1 2 9192 | e 1 2 2 9193 | e 2 3 2 9194 | e 3 4 2 9195 | t # 920 9196 | v 0 2 9197 | v 1 2 9198 | v 2 2 9199 | v 3 2 9200 | v 4 2 9201 | e 0 1 5 9202 | e 0 4 5 9203 | e 2 3 5 9204 | e 3 4 5 9205 | t # 921 9206 | v 0 2 9207 | v 1 2 9208 | v 2 2 9209 | v 3 3 9210 | v 4 5 9211 | e 0 1 5 9212 | e 1 2 2 9213 | e 2 3 3 9214 | e 2 4 2 9215 | t # 922 9216 | v 0 2 9217 | v 1 2 9218 | v 2 2 9219 | v 3 2 9220 | v 4 5 9221 | e 0 1 5 9222 | e 1 4 5 9223 | e 2 3 5 9224 | e 2 4 5 9225 | t # 923 9226 | v 0 2 9227 | v 1 5 9228 | v 2 5 9229 | v 3 2 9230 | v 4 5 9231 | e 0 1 2 9232 | e 0 3 2 9233 | e 2 4 2 9234 | e 2 3 2 9235 | t # 924 9236 | v 0 7 9237 | v 1 2 9238 | v 2 2 9239 | v 3 2 9240 | v 4 2 9241 | e 0 1 2 9242 | e 1 2 5 9243 | e 1 4 5 9244 | e 3 4 5 9245 | t # 925 9246 | v 0 2 9247 | v 1 2 9248 | v 2 2 9249 | v 3 2 9250 | v 4 2 9251 | e 0 1 2 9252 | e 1 2 2 9253 | e 2 3 2 9254 | e 3 4 2 9255 | t # 926 9256 | v 0 2 9257 | v 1 2 9258 | v 2 2 9259 | v 3 2 9260 | v 4 2 9261 | e 0 1 5 9262 | e 0 2 2 9263 | e 2 3 2 9264 | e 2 4 2 9265 | t # 927 9266 | v 0 5 9267 | v 1 5 9268 | v 2 2 9269 | v 3 3 9270 | v 4 2 9271 | e 0 1 2 9272 | e 1 2 2 9273 | e 2 3 3 9274 | e 2 4 2 9275 | t # 928 9276 | v 0 2 9277 | v 1 2 9278 | v 2 2 9279 | v 3 3 9280 | v 4 3 9281 | e 0 1 2 9282 | e 1 2 2 9283 | e 1 3 2 9284 | e 2 4 2 9285 | t # 929 9286 | v 0 3 9287 | v 1 2 9288 | v 2 2 9289 | v 3 2 9290 | v 4 2 9291 | e 0 1 2 9292 | e 1 2 2 9293 | e 1 4 2 9294 | e 2 3 2 9295 | t # 930 9296 | v 0 2 9297 | v 1 2 9298 | v 2 5 9299 | v 3 2 9300 | v 4 2 9301 | e 0 1 2 9302 | e 1 2 2 9303 | e 2 3 2 9304 | e 3 4 2 9305 | t # 931 9306 | v 0 2 9307 | v 1 2 9308 | v 2 2 9309 | v 3 2 9310 | v 4 8 9311 | e 0 3 5 9312 | e 1 2 5 9313 | e 2 3 5 9314 | e 3 4 2 9315 | t # 932 9316 | v 0 2 9317 | v 1 6 9318 | v 2 2 9319 | v 3 2 9320 | v 4 2 9321 | e 0 2 2 9322 | e 1 2 2 9323 | e 2 3 3 9324 | e 3 4 2 9325 | t # 933 9326 | v 0 2 9327 | v 1 2 9328 | v 2 8 9329 | v 3 3 9330 | v 4 2 9331 | e 0 3 2 9332 | e 0 1 5 9333 | e 1 2 2 9334 | e 3 4 2 9335 | t # 934 9336 | v 0 2 9337 | v 1 2 9338 | v 2 3 9339 | v 3 2 9340 | v 4 2 9341 | e 0 1 2 9342 | e 1 2 3 9343 | e 1 3 2 9344 | e 3 4 5 9345 | t # 935 9346 | v 0 2 9347 | v 1 2 9348 | v 2 2 9349 | v 3 2 9350 | v 4 8 9351 | e 0 1 5 9352 | e 0 4 2 9353 | e 1 2 5 9354 | e 2 3 5 9355 | t # 936 9356 | v 0 2 9357 | v 1 2 9358 | v 2 2 9359 | v 3 8 9360 | v 4 2 9361 | e 0 1 5 9362 | e 1 4 5 9363 | e 2 3 2 9364 | e 2 4 5 9365 | t # 937 9366 | v 0 2 9367 | v 1 2 9368 | v 2 2 9369 | v 3 5 9370 | v 4 3 9371 | e 0 2 5 9372 | e 1 3 2 9373 | e 3 4 3 9374 | e 1 2 5 9375 | t # 938 9376 | v 0 2 9377 | v 1 2 9378 | v 2 2 9379 | v 3 2 9380 | v 4 2 9381 | e 0 1 2 9382 | e 0 4 2 9383 | e 1 2 2 9384 | e 2 3 2 9385 | t # 939 9386 | v 0 5 9387 | v 1 2 9388 | v 2 2 9389 | v 3 2 9390 | v 4 2 9391 | e 0 1 2 9392 | e 0 2 2 9393 | e 2 3 2 9394 | e 2 4 2 9395 | t # 940 9396 | v 0 5 9397 | v 1 2 9398 | v 2 2 9399 | v 3 2 9400 | v 4 2 9401 | e 0 1 2 9402 | e 0 2 2 9403 | e 2 3 2 9404 | e 2 4 2 9405 | t # 941 9406 | v 0 2 9407 | v 1 2 9408 | v 2 2 9409 | v 3 2 9410 | v 4 2 9411 | e 0 1 2 9412 | e 1 2 2 9413 | e 2 3 2 9414 | e 3 4 2 9415 | t # 942 9416 | v 0 2 9417 | v 1 2 9418 | v 2 2 9419 | v 3 2 9420 | v 4 2 9421 | e 0 1 5 9422 | e 1 2 5 9423 | e 2 3 5 9424 | e 3 4 5 9425 | t # 943 9426 | v 0 2 9427 | v 1 2 9428 | v 2 2 9429 | v 3 2 9430 | v 4 5 9431 | e 0 3 5 9432 | e 1 2 5 9433 | e 2 4 5 9434 | e 2 3 5 9435 | t # 944 9436 | v 0 2 9437 | v 1 3 9438 | v 2 2 9439 | v 3 3 9440 | v 4 2 9441 | e 0 4 2 9442 | e 1 2 2 9443 | e 1 4 2 9444 | e 2 3 3 9445 | t # 945 9446 | v 0 2 9447 | v 1 2 9448 | v 2 5 9449 | v 3 2 9450 | v 4 3 9451 | e 0 1 5 9452 | e 1 2 5 9453 | e 2 3 5 9454 | e 3 4 3 9455 | t # 946 9456 | v 0 2 9457 | v 1 2 9458 | v 2 2 9459 | v 3 2 9460 | v 4 2 9461 | e 0 1 2 9462 | e 1 2 2 9463 | e 2 3 2 9464 | e 3 4 2 9465 | t # 947 9466 | v 0 5 9467 | v 1 2 9468 | v 2 3 9469 | v 3 5 9470 | v 4 2 9471 | e 0 1 2 9472 | e 1 2 3 9473 | e 1 3 2 9474 | e 3 4 2 9475 | t # 948 9476 | v 0 2 9477 | v 1 2 9478 | v 2 5 9479 | v 3 2 9480 | v 4 3 9481 | e 0 1 2 9482 | e 1 3 5 9483 | e 2 3 5 9484 | e 3 4 3 9485 | t # 949 9486 | v 0 2 9487 | v 1 2 9488 | v 2 2 9489 | v 3 2 9490 | v 4 2 9491 | e 0 1 5 9492 | e 0 2 5 9493 | e 1 4 5 9494 | e 3 4 5 9495 | t # 950 9496 | v 0 2 9497 | v 1 2 9498 | v 2 2 9499 | v 3 2 9500 | v 4 3 9501 | e 0 1 5 9502 | e 1 2 5 9503 | e 1 4 2 9504 | e 3 4 2 9505 | t # 951 9506 | v 0 2 9507 | v 1 2 9508 | v 2 2 9509 | v 3 2 9510 | v 4 5 9511 | e 0 1 5 9512 | e 1 2 5 9513 | e 1 4 2 9514 | e 2 3 5 9515 | t # 952 9516 | v 0 2 9517 | v 1 2 9518 | v 2 8 9519 | v 3 2 9520 | v 4 2 9521 | e 0 1 5 9522 | e 0 4 5 9523 | e 1 2 2 9524 | e 3 4 5 9525 | t # 953 9526 | v 0 2 9527 | v 1 2 9528 | v 2 2 9529 | v 3 2 9530 | v 4 2 9531 | e 0 1 5 9532 | e 1 2 5 9533 | e 2 3 5 9534 | e 2 4 5 9535 | t # 954 9536 | v 0 2 9537 | v 1 2 9538 | v 2 3 9539 | v 3 2 9540 | v 4 2 9541 | e 0 1 2 9542 | e 1 2 2 9543 | e 2 3 2 9544 | e 3 4 2 9545 | t # 955 9546 | v 0 2 9547 | v 1 2 9548 | v 2 2 9549 | v 3 2 9550 | v 4 2 9551 | e 0 4 5 9552 | e 1 2 5 9553 | e 2 3 5 9554 | e 3 4 5 9555 | t # 956 9556 | v 0 2 9557 | v 1 2 9558 | v 2 2 9559 | v 3 2 9560 | v 4 2 9561 | e 0 4 5 9562 | e 1 4 5 9563 | e 2 3 5 9564 | e 3 4 5 9565 | t # 957 9566 | v 0 2 9567 | v 1 5 9568 | v 2 5 9569 | v 3 2 9570 | v 4 2 9571 | e 0 1 3 9572 | e 1 2 2 9573 | e 2 3 2 9574 | e 3 4 2 9575 | t # 958 9576 | v 0 2 9577 | v 1 2 9578 | v 2 2 9579 | v 3 2 9580 | v 4 2 9581 | e 0 3 5 9582 | e 1 2 5 9583 | e 1 4 2 9584 | e 2 3 5 9585 | t # 959 9586 | v 0 2 9587 | v 1 2 9588 | v 2 2 9589 | v 3 2 9590 | v 4 3 9591 | e 0 1 5 9592 | e 0 2 5 9593 | e 0 3 2 9594 | e 2 4 2 9595 | t # 960 9596 | v 0 2 9597 | v 1 2 9598 | v 2 2 9599 | v 3 5 9600 | v 4 5 9601 | e 0 1 5 9602 | e 0 3 2 9603 | e 1 2 5 9604 | e 3 4 3 9605 | t # 961 9606 | v 0 2 9607 | v 1 2 9608 | v 2 2 9609 | v 3 2 9610 | v 4 2 9611 | e 0 1 5 9612 | e 0 4 5 9613 | e 2 3 5 9614 | e 3 4 5 9615 | t # 962 9616 | v 0 2 9617 | v 1 2 9618 | v 2 6 9619 | v 3 3 9620 | v 4 2 9621 | e 0 2 2 9622 | e 0 1 5 9623 | e 2 3 3 9624 | e 2 4 2 9625 | t # 963 9626 | v 0 2 9627 | v 1 2 9628 | v 2 2 9629 | v 3 2 9630 | v 4 2 9631 | e 0 1 5 9632 | e 1 2 5 9633 | e 2 3 5 9634 | e 3 4 5 9635 | t # 964 9636 | v 0 2 9637 | v 1 3 9638 | v 2 2 9639 | v 3 2 9640 | v 4 5 9641 | e 0 4 2 9642 | e 1 2 2 9643 | e 2 3 2 9644 | e 3 4 2 9645 | t # 965 9646 | v 0 2 9647 | v 1 3 9648 | v 2 3 9649 | v 3 2 9650 | v 4 2 9651 | e 0 1 2 9652 | e 1 4 2 9653 | e 2 3 2 9654 | e 3 4 2 9655 | t # 966 9656 | v 0 2 9657 | v 1 3 9658 | v 2 2 9659 | v 3 2 9660 | v 4 3 9661 | e 0 1 2 9662 | e 1 3 2 9663 | e 2 3 2 9664 | e 3 4 3 9665 | t # 967 9666 | v 0 2 9667 | v 1 2 9668 | v 2 2 9669 | v 3 2 9670 | v 4 3 9671 | e 0 1 5 9672 | e 1 4 2 9673 | e 1 2 5 9674 | e 2 3 2 9675 | t # 968 9676 | v 0 2 9677 | v 1 2 9678 | v 2 5 9679 | v 3 2 9680 | v 4 2 9681 | e 0 1 2 9682 | e 0 2 2 9683 | e 2 3 2 9684 | e 3 4 2 9685 | t # 969 9686 | v 0 5 9687 | v 1 2 9688 | v 2 2 9689 | v 3 2 9690 | v 4 2 9691 | e 0 1 2 9692 | e 0 3 2 9693 | e 1 2 2 9694 | e 3 4 2 9695 | t # 970 9696 | v 0 2 9697 | v 1 2 9698 | v 2 2 9699 | v 3 2 9700 | v 4 2 9701 | e 0 1 5 9702 | e 0 4 5 9703 | e 2 3 5 9704 | e 3 4 5 9705 | t # 971 9706 | v 0 21 9707 | v 1 2 9708 | v 2 2 9709 | v 3 2 9710 | v 4 2 9711 | e 0 1 2 9712 | e 1 2 2 9713 | e 2 3 2 9714 | e 3 4 2 9715 | t # 972 9716 | v 0 2 9717 | v 1 2 9718 | v 2 2 9719 | v 3 2 9720 | v 4 2 9721 | e 0 1 5 9722 | e 0 4 5 9723 | e 2 3 5 9724 | e 3 4 5 9725 | t # 973 9726 | v 0 2 9727 | v 1 2 9728 | v 2 2 9729 | v 3 3 9730 | v 4 2 9731 | e 0 1 2 9732 | e 0 2 2 9733 | e 2 3 2 9734 | e 3 4 2 9735 | t # 974 9736 | v 0 2 9737 | v 1 3 9738 | v 2 6 9739 | v 3 3 9740 | v 4 2 9741 | e 0 1 2 9742 | e 1 2 2 9743 | e 2 3 3 9744 | e 2 4 2 9745 | t # 975 9746 | v 0 2 9747 | v 1 2 9748 | v 2 3 9749 | v 3 5 9750 | v 4 2 9751 | e 0 1 2 9752 | e 1 2 3 9753 | e 1 3 2 9754 | e 3 4 2 9755 | t # 976 9756 | v 0 2 9757 | v 1 2 9758 | v 2 2 9759 | v 3 2 9760 | v 4 5 9761 | e 0 1 5 9762 | e 0 3 5 9763 | e 2 4 5 9764 | e 2 3 5 9765 | t # 977 9766 | v 0 2 9767 | v 1 5 9768 | v 2 2 9769 | v 3 2 9770 | v 4 2 9771 | e 0 1 2 9772 | e 1 2 2 9773 | e 1 4 2 9774 | e 3 4 2 9775 | t # 978 9776 | v 0 2 9777 | v 1 2 9778 | v 2 2 9779 | v 3 2 9780 | v 4 2 9781 | e 0 1 5 9782 | e 0 4 5 9783 | e 2 3 5 9784 | e 2 4 5 9785 | t # 979 9786 | v 0 2 9787 | v 1 2 9788 | v 2 2 9789 | v 3 2 9790 | v 4 2 9791 | e 0 1 5 9792 | e 1 2 5 9793 | e 2 3 5 9794 | e 3 4 5 9795 | t # 980 9796 | v 0 2 9797 | v 1 2 9798 | v 2 5 9799 | v 3 3 9800 | v 4 3 9801 | e 0 1 5 9802 | e 1 2 2 9803 | e 2 3 3 9804 | e 2 4 2 9805 | t # 981 9806 | v 0 2 9807 | v 1 2 9808 | v 2 2 9809 | v 3 2 9810 | v 4 2 9811 | e 0 1 5 9812 | e 0 4 5 9813 | e 1 2 5 9814 | e 2 3 5 9815 | t # 982 9816 | v 0 2 9817 | v 1 2 9818 | v 2 2 9819 | v 3 2 9820 | v 4 2 9821 | e 0 1 5 9822 | e 1 2 5 9823 | e 2 3 5 9824 | e 3 4 5 9825 | t # 983 9826 | v 0 2 9827 | v 1 2 9828 | v 2 5 9829 | v 3 3 9830 | v 4 2 9831 | e 0 1 5 9832 | e 1 2 2 9833 | e 2 4 2 9834 | e 3 4 3 9835 | t # 984 9836 | v 0 2 9837 | v 1 5 9838 | v 2 2 9839 | v 3 2 9840 | v 4 6 9841 | e 0 3 5 9842 | e 0 4 3 9843 | e 1 2 5 9844 | e 2 3 5 9845 | t # 985 9846 | v 0 2 9847 | v 1 2 9848 | v 2 5 9849 | v 3 2 9850 | v 4 2 9851 | e 0 1 5 9852 | e 0 3 5 9853 | e 1 2 5 9854 | e 1 4 5 9855 | t # 986 9856 | v 0 2 9857 | v 1 2 9858 | v 2 5 9859 | v 3 2 9860 | v 4 2 9861 | e 0 1 2 9862 | e 1 2 2 9863 | e 2 3 2 9864 | e 3 4 2 9865 | t # 987 9866 | v 0 2 9867 | v 1 2 9868 | v 2 2 9869 | v 3 2 9870 | v 4 6 9871 | e 0 1 5 9872 | e 0 4 2 9873 | e 1 2 5 9874 | e 2 3 5 9875 | t # 988 9876 | v 0 2 9877 | v 1 2 9878 | v 2 2 9879 | v 3 2 9880 | v 4 2 9881 | e 0 1 5 9882 | e 1 2 5 9883 | e 2 3 5 9884 | e 3 4 5 9885 | t # 989 9886 | v 0 2 9887 | v 1 2 9888 | v 2 5 9889 | v 3 2 9890 | v 4 2 9891 | e 0 1 5 9892 | e 0 2 2 9893 | e 2 3 2 9894 | e 3 4 5 9895 | t # 990 9896 | v 0 2 9897 | v 1 2 9898 | v 2 2 9899 | v 3 2 9900 | v 4 8 9901 | e 0 3 5 9902 | e 1 2 5 9903 | e 2 4 2 9904 | e 2 3 5 9905 | t # 991 9906 | v 0 6 9907 | v 1 2 9908 | v 2 2 9909 | v 3 2 9910 | v 4 2 9911 | e 0 1 2 9912 | e 1 2 5 9913 | e 1 4 5 9914 | e 3 4 5 9915 | t # 992 9916 | v 0 2 9917 | v 1 2 9918 | v 2 2 9919 | v 3 2 9920 | v 4 2 9921 | e 0 1 2 9922 | e 1 2 2 9923 | e 2 3 2 9924 | e 3 4 2 9925 | t # 993 9926 | v 0 2 9927 | v 1 2 9928 | v 2 2 9929 | v 3 2 9930 | v 4 2 9931 | e 0 1 5 9932 | e 1 2 5 9933 | e 2 3 5 9934 | e 3 4 5 9935 | t # 994 9936 | v 0 2 9937 | v 1 2 9938 | v 2 2 9939 | v 3 2 9940 | v 4 2 9941 | e 0 1 2 9942 | e 1 2 5 9943 | e 1 4 5 9944 | e 3 4 5 9945 | t # 995 9946 | v 0 2 9947 | v 1 5 9948 | v 2 2 9949 | v 3 2 9950 | v 4 2 9951 | e 0 1 5 9952 | e 1 2 5 9953 | e 2 3 5 9954 | e 3 4 5 9955 | t # 996 9956 | v 0 2 9957 | v 1 3 9958 | v 2 2 9959 | v 3 2 9960 | v 4 2 9961 | e 0 1 2 9962 | e 1 2 2 9963 | e 2 3 5 9964 | e 2 4 5 9965 | t # 997 9966 | v 0 2 9967 | v 1 2 9968 | v 2 2 9969 | v 3 2 9970 | v 4 2 9971 | e 0 1 5 9972 | e 0 4 5 9973 | e 1 2 5 9974 | e 2 3 5 9975 | t # 998 9976 | v 0 2 9977 | v 1 2 9978 | v 2 2 9979 | v 3 3 9980 | v 4 3 9981 | e 0 1 2 9982 | e 1 3 2 9983 | e 1 2 2 9984 | e 2 4 2 9985 | t # 999 9986 | v 0 2 9987 | v 1 2 9988 | v 2 2 9989 | v 3 2 9990 | v 4 2 9991 | e 0 1 2 9992 | e 1 2 2 9993 | e 2 3 2 9994 | e 2 4 2 9995 | t # -1 -------------------------------------------------------------------------------- /ullmann/README.md: -------------------------------------------------------------------------------- 1 | # Ullmann 子图同构算法原理与实现 2 | 具体参考 Ullmann介绍.pdf 3 | -------------------------------------------------------------------------------- /ullmann/Ullmann介绍.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Junshuai-Song/Model-Alan/bdb32d0837cddfcd8766525d95c53193a27dc61c/ullmann/Ullmann介绍.pdf -------------------------------------------------------------------------------- /ullmann/main.cpp: -------------------------------------------------------------------------------- 1 | // 2 | // main.cpp 3 | // ullmann 4 | // 5 | // Created by songjs on 16/10/29. 6 | // Copyright © 2016年 songjs. All rights reserved. 7 | // 8 | 9 | #include 10 | #include 11 | #include 12 | #include 13 | #include 14 | #include 15 | #include 16 | #include 17 | #include 18 | #include 19 | #include 20 | 21 | using namespace std; 22 | 23 | typedef struct Edge{ 24 | int from,to,elabel; 25 | }Edge; 26 | 27 | typedef struct Vertex{ 28 | int id,label; 29 | vector edge; 30 | void push(int from,int to,int elabel){ 31 | edge.resize(edge.size() + 1); 32 | edge[edge.size() - 1].from = from; 33 | edge[edge.size() - 1].to = to; 34 | edge[edge.size() - 1].elabel = elabel; 35 | return ; 36 | } 37 | 38 | }Vector; 39 | 40 | class Graph: public vector{ 41 | private: 42 | unsigned int edge_size_; 43 | 44 | public: 45 | int id; 46 | typedef vector::iterator vertex_iterater; 47 | unsigned int edge_size(){ return edge_size_; } 48 | unsigned int vertex_size(){ return (unsigned int)size();} 49 | int **M; 50 | 51 | void build(){ 52 | // 邻接矩阵 53 | // cout<<"vertex_size="<::iterator iter2 = (*iter).edge.begin(); iter2!=(*iter).edge.end(); iter2++){ 60 | M[(*iter2).from][(*iter2).to] = 1; //无向图 61 | M[(*iter2).to][(*iter2).from] = 1; 62 | } 63 | } 64 | } 65 | int find_label(int n){ 66 | // 找到顶点n的label 67 | return (*this)[n].label; 68 | } 69 | int count(int n){ 70 | return (int)(*this)[n].edge.size(); 71 | } 72 | void print(){ 73 | for(vertex_iterater iter = (*this).begin(); iter!=(*this).end(); iter++){ 74 | for(vector::iterator iter2 = (*iter).edge.begin(); iter2!=(*iter).edge.end(); iter2++){ 75 | cout<<(*iter2).from<<" "<<(*iter2).to< graph; 84 | vector query_graph; 85 | 86 | public: 87 | char *filename1,*filename2; 88 | int answer_number; 89 | clock_t start,end; 90 | int **MA,**MB,**M,**M_,**MC; 91 | int *vis; 92 | 93 | void read(char filename[], vector &graph_){ 94 | ifstream in; in.open(filename); 95 | string line; Graph g; g.clear(); int id=0; 96 | while(in>>line){ 97 | if(line[0]=='t'){ 98 | getline(in, line); 99 | if(!g.empty()){ 100 | // 新的一个图，加进来 101 | g.build(); //构建邻接矩阵 102 | g.id = id++; 103 | // cout<>id_>>label; 112 | getline(in,line); 113 | g.resize(max((int)g.size(), id_+1)); 114 | g[id_].label = label; 115 | }else if(line[0]=='e'){ 116 | int from,to,elabel; 117 | in>>from>>to>>elabel; 118 | g.resize(max((int)g.size(), from+1)); 119 | g.resize(max((int)g.size(), to+1)); 120 | g[from].push(from, to, elabel); 121 | g[to].push(to, from, elabel); 122 | getline(in, line); 123 | } 124 | } 125 | in.close(); 126 | } 127 | 128 | void pre_check(Graph &Q, Graph &G){ 129 | MA = Q.M; MB = G.M; 130 | // 构建M和M_ 131 | M = new int*[Q.vertex_size()]; 132 | M_ = new int*[Q.vertex_size()]; 133 | MC = new int*[Q.vertex_size()]; 134 | for(int i=0;i G.count(j)){ M[i][j] = 0;continue; } 146 | } 147 | } 148 | int num = 1; 149 | while(num>0){ //这个过程迭代进行 150 | num = 0; 151 | for(int i=0;i0) break; 196 | } 197 | M__[i][j] = tot; 198 | } 199 | } 200 | for(int i=0;i0) break; 206 | } 207 | MC[i][j] = tot; 208 | } 209 | } 210 | for(int i=0;i::iterator iter = Q[i].edge.begin(); iter!=Q[i].edge.end(); iter++){ 249 | if((*iter).to==j){ 250 | lable = (*iter).elabel; 251 | break; 252 | } 253 | } 254 | int lable_ = -1; 255 | for(vector::iterator iter = G[v[i]].edge.begin(); iter!=G[v[i]].edge.end(); iter++){ 256 | if((*iter).to==v[j]){ 257 | lable_ = (*iter).elabel; 258 | break; 259 | } 260 | } 261 | if(lable == lable_) continue; 262 | else{ 263 | flag=0; 264 | i = Q.vertex_size(); 265 | break; 266 | } 267 | } 268 | } 269 | } 270 | if(flag==0) return false; //当前映射，边lable不对应，不输出 271 | 272 | cout<<"查询图id #"< "< G.edge_size()) return ; 299 | pre_check(Q,G); //使用顶点 & refinement规则尽可能减少M矩阵中的 300 | 301 | int flag=1; 302 | for(int i=0;i::iterator iter = query_graph.begin(); iter!=query_graph.end(); iter++){ 326 | Graph Q = (*iter); 327 | int number = 0; 328 | for(vector::iterator iter2 = graph.begin(); iter2!=graph.end(); iter2++){ 329 | Graph G = (*iter2); 330 | // 保证Q是小图，进行同构匹配 331 | if(Q.vertex_size() > G.vertex_size()) continue; 332 | check(Q,G); 333 | number++; 334 | if(number%1==0) 335 | cout<<"G_number="<>filename2; 349 | strcpy(filename1, "/Users/songjs/Desktop/workspace/ullmann/ullmann/mygraphdb.data"); 350 | strcpy(filename2, "/Users/songjs/Desktop/workspace/ullmann/ullmann/Q4.my"); 351 | // strcpy(filename1, "/home/songjunshuai/workspace/ullmann/mygraphdb.data"); 352 | // strcpy(filename2, "/home/songjunshuai/workspace/ullmann/Q8.my"); 353 | read(filename1, graph); 354 | read(filename2, query_graph); 355 | cout<