Copy disabled (too large)
Download .txt
Showing preview only (31,832K chars total). Download the full file to get everything.
Repository: daiquocnguyen/ConvKB
Branch: master
Commit: 0befa12fad55
Files: 107
Total size: 151.3 MB
Directory structure:
gitextract_2ysrl9k6/
├── ConvKB_pytorch/
│ ├── Config.py
│ ├── ConvKB.py
│ ├── ConvKB_1D.py
│ ├── Model.py
│ ├── base/
│ │ ├── Base.cpp
│ │ ├── Corrupt.h
│ │ ├── Random.h
│ │ ├── Reader.h
│ │ ├── Setting.h
│ │ ├── Test.h
│ │ ├── Triple.h
│ │ └── Valid.h
│ ├── benchmarks/
│ │ ├── FB15K/
│ │ │ ├── 1-1.txt
│ │ │ ├── 1-n.txt
│ │ │ ├── entity2id.txt
│ │ │ ├── n-1.txt
│ │ │ ├── n-n.py
│ │ │ ├── n-n.txt
│ │ │ ├── relation2id.txt
│ │ │ ├── test2id.txt
│ │ │ ├── test2id_all.txt
│ │ │ ├── train2id.txt
│ │ │ ├── type_constrain.txt
│ │ │ └── valid2id.txt
│ │ ├── FB15K237/
│ │ │ ├── 1-1.txt
│ │ │ ├── 1-n.txt
│ │ │ ├── entity2id.txt
│ │ │ ├── entity2id_100init.txt
│ │ │ ├── entity2vec100.init
│ │ │ ├── n-1.txt
│ │ │ ├── n-n.py
│ │ │ ├── n-n.txt
│ │ │ ├── relation2id.txt
│ │ │ ├── relation2id_100init.txt
│ │ │ ├── relation2vec100.init
│ │ │ ├── test2id.txt
│ │ │ ├── test2id_all.txt
│ │ │ ├── train2id.txt
│ │ │ ├── type_constrain.txt
│ │ │ └── valid2id.txt
│ │ ├── WN18/
│ │ │ ├── 1-1.txt
│ │ │ ├── 1-n.txt
│ │ │ ├── entity2id.txt
│ │ │ ├── n-1.txt
│ │ │ ├── n-n.py
│ │ │ ├── n-n.txt
│ │ │ ├── relation2id.txt
│ │ │ ├── test2id.txt
│ │ │ ├── test2id_all.txt
│ │ │ ├── train2id.txt
│ │ │ ├── type_constrain.txt
│ │ │ └── valid2id.txt
│ │ └── WN18RR/
│ │ ├── 1-1.txt
│ │ ├── 1-n.txt
│ │ ├── _also_see.txt
│ │ ├── _derivationally_related_form.txt
│ │ ├── _has_part.txt
│ │ ├── _hypernym.txt
│ │ ├── _instance_hypernym.txt
│ │ ├── _member_meronym.txt
│ │ ├── _member_of_domain_region.txt
│ │ ├── _member_of_domain_usage.txt
│ │ ├── _similar_to.txt
│ │ ├── _synset_domain_topic_of.txt
│ │ ├── _verb_group.txt
│ │ ├── entity2id.txt
│ │ ├── entity2id_50init.txt
│ │ ├── entity2vec50.init
│ │ ├── n-1.txt
│ │ ├── n-n.py
│ │ ├── n-n.txt
│ │ ├── relation2id.txt
│ │ ├── relation2id_50init.txt
│ │ ├── relation2vec50.init
│ │ ├── test2id.txt
│ │ ├── test2id_all.txt
│ │ ├── train2id.txt
│ │ ├── type_constrain.txt
│ │ └── valid2id.txt
│ ├── make.sh
│ └── train_ConvKB.py
├── ConvKB_tf/
│ ├── batching.py
│ ├── builddata.py
│ ├── data/
│ │ ├── FB15k-237/
│ │ │ ├── entity2id.txt
│ │ │ ├── entity2vec100.init
│ │ │ ├── relation2id.txt
│ │ │ ├── relation2vec100.init
│ │ │ ├── test.txt
│ │ │ ├── train.txt
│ │ │ └── valid.txt
│ │ └── WN18RR/
│ │ ├── entity2id.txt
│ │ ├── entity2vec100.init
│ │ ├── entity2vec50.init
│ │ ├── relation2id.txt
│ │ ├── relation2vec100.init
│ │ ├── relation2vec50.init
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── eval.py
│ ├── evalFB15k-237.sh
│ ├── evalWN18RR.sh
│ ├── model.py
│ ├── train.py
│ └── training_commands.txt
├── LICENSE
└── README.md
================================================
FILE CONTENTS
================================================
================================================
FILE: ConvKB_pytorch/Config.py
================================================
# coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import os
import time
import sys
import datetime
import ctypes
import json
import numpy as np
from tqdm import tqdm
use_gpu=False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
use_gpu=True
class MyDataParallel(nn.DataParallel):
def _getattr__(self, name):
return getattr(self.module, name)
def to_var(x):
return Variable(torch.from_numpy(x).to(device))
class Config(object):
def __init__(self):
base_file = os.path.abspath(
os.path.join(os.path.dirname(__file__), "release/Base.so")
)
self.lib = ctypes.cdll.LoadLibrary(base_file)
"""argtypes"""
"""'sample"""
self.lib.sampling.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_int64,
ctypes.c_int64,
ctypes.c_int64,
]
"""'valid"""
self.lib.getValidHeadBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.getValidTailBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.validHead.argtypes = [ctypes.c_void_p]
self.lib.validTail.argtypes = [ctypes.c_void_p]
"""test link prediction"""
self.lib.getHeadBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.getTailBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.testHead.argtypes = [ctypes.c_void_p]
self.lib.testTail.argtypes = [ctypes.c_void_p]
"""test triple classification"""
self.lib.getValidBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.getTestBatch.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.getBestThreshold.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
self.lib.test_triple_classification.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
]
"""restype"""
self.lib.getValidHit10.restype = ctypes.c_float
# for triple classification
self.lib.test_triple_classification.restype = ctypes.c_float
"""set essential parameters"""
self.in_path = "./"
self.batch_size = 100
self.bern = 0
self.work_threads = 8
self.hidden_size = 100
self.negative_ent = 1
self.negative_rel = 0
self.ent_size = self.hidden_size
self.rel_size = self.hidden_size
self.margin = 1.0
self.valid_steps = 5
self.save_steps = 5
self.opt_method = "SGD"
self.optimizer = None
self.lr_decay = 0
self.weight_decay = 0
self.lmbda = 0.0
self.lmbda_two = 0.0
self.alpah = 0.001
self.early_stopping_patience = 10
self.nbatches = 100
self.p_norm = 1
self.test_link = True
self.test_triple = False
self.model = None
self.trainModel = None
self.testModel = None
self.pretrain_model = None
self.ent_dropout = 0
self.rel_dropout = 0
self.use_init_embeddings = False
self.test_file_path = None
def init(self):
self.lib.setInPath(
ctypes.create_string_buffer(self.in_path.encode(), len(self.in_path) * 2)
)
self.lib.setTestFilePath(
ctypes.create_string_buffer(self.test_file_path.encode(), len(self.test_file_path) * 2)
)
self.lib.setBern(self.bern)
self.lib.setWorkThreads(self.work_threads)
self.lib.randReset()
self.lib.importTrainFiles()
self.lib.importTestFiles()
self.lib.importTypeFiles()
self.relTotal = self.lib.getRelationTotal()
self.entTotal = self.lib.getEntityTotal()
self.trainTotal = self.lib.getTrainTotal()
self.testTotal = self.lib.getTestTotal()
self.validTotal = self.lib.getValidTotal()
self.batch_size = int(self.trainTotal / self.nbatches)
self.batch_seq_size = self.batch_size * (
1 + self.negative_ent + self.negative_rel
)
self.batch_h = np.zeros(self.batch_seq_size, dtype=np.int64)
self.batch_t = np.zeros(self.batch_seq_size, dtype=np.int64)
self.batch_r = np.zeros(self.batch_seq_size, dtype=np.int64)
self.batch_y = np.zeros(self.batch_seq_size, dtype=np.float32)
self.batch_h_addr = self.batch_h.__array_interface__["data"][0]
self.batch_t_addr = self.batch_t.__array_interface__["data"][0]
self.batch_r_addr = self.batch_r.__array_interface__["data"][0]
self.batch_y_addr = self.batch_y.__array_interface__["data"][0]
self.valid_h = np.zeros(self.entTotal, dtype=np.int64)
self.valid_t = np.zeros(self.entTotal, dtype=np.int64)
self.valid_r = np.zeros(self.entTotal, dtype=np.int64)
self.valid_h_addr = self.valid_h.__array_interface__["data"][0]
self.valid_t_addr = self.valid_t.__array_interface__["data"][0]
self.valid_r_addr = self.valid_r.__array_interface__["data"][0]
self.test_h = np.zeros(self.entTotal, dtype=np.int64)
self.test_t = np.zeros(self.entTotal, dtype=np.int64)
self.test_r = np.zeros(self.entTotal, dtype=np.int64)
self.test_h_addr = self.test_h.__array_interface__["data"][0]
self.test_t_addr = self.test_t.__array_interface__["data"][0]
self.test_r_addr = self.test_r.__array_interface__["data"][0]
self.valid_pos_h = np.zeros(self.validTotal, dtype=np.int64)
self.valid_pos_t = np.zeros(self.validTotal, dtype=np.int64)
self.valid_pos_r = np.zeros(self.validTotal, dtype=np.int64)
self.valid_pos_h_addr = self.valid_pos_h.__array_interface__["data"][0]
self.valid_pos_t_addr = self.valid_pos_t.__array_interface__["data"][0]
self.valid_pos_r_addr = self.valid_pos_r.__array_interface__["data"][0]
self.valid_neg_h = np.zeros(self.validTotal, dtype=np.int64)
self.valid_neg_t = np.zeros(self.validTotal, dtype=np.int64)
self.valid_neg_r = np.zeros(self.validTotal, dtype=np.int64)
self.valid_neg_h_addr = self.valid_neg_h.__array_interface__["data"][0]
self.valid_neg_t_addr = self.valid_neg_t.__array_interface__["data"][0]
self.valid_neg_r_addr = self.valid_neg_r.__array_interface__["data"][0]
self.test_pos_h = np.zeros(self.testTotal, dtype=np.int64)
self.test_pos_t = np.zeros(self.testTotal, dtype=np.int64)
self.test_pos_r = np.zeros(self.testTotal, dtype=np.int64)
self.test_pos_h_addr = self.test_pos_h.__array_interface__["data"][0]
self.test_pos_t_addr = self.test_pos_t.__array_interface__["data"][0]
self.test_pos_r_addr = self.test_pos_r.__array_interface__["data"][0]
self.test_neg_h = np.zeros(self.testTotal, dtype=np.int64)
self.test_neg_t = np.zeros(self.testTotal, dtype=np.int64)
self.test_neg_r = np.zeros(self.testTotal, dtype=np.int64)
self.test_neg_h_addr = self.test_neg_h.__array_interface__["data"][0]
self.test_neg_t_addr = self.test_neg_t.__array_interface__["data"][0]
self.test_neg_r_addr = self.test_neg_r.__array_interface__["data"][0]
self.relThresh = np.zeros(self.relTotal, dtype=np.float32)
self.relThresh_addr = self.relThresh.__array_interface__["data"][0]
def set_test_link(self, test_link):
self.test_link = test_link
def set_test_triple(self, test_triple):
self.test_triple = test_triple
def set_margin(self, margin):
self.margin = margin
def set_in_path(self, in_path):
self.in_path = in_path
def set_test_file_path(self, test_file_path):
self.test_file_path = test_file_path
def set_nbatches(self, nbatches):
self.nbatches = nbatches
def set_p_norm(self, p_norm):
self.p_norm = p_norm
def set_valid_steps(self, valid_steps):
self.valid_steps = valid_steps
def set_save_steps(self, save_steps):
self.save_steps = save_steps
def set_checkpoint_dir(self, checkpoint_dir):
self.checkpoint_dir = checkpoint_dir
def set_result_dir(self, result_dir):
self.result_dir = result_dir
def set_alpha(self, alpha):
self.alpha = alpha
def set_lmbda(self, lmbda):
self.lmbda = lmbda
def set_lmbda_two(self, lmbda_two):
self.lmbda_two = lmbda_two
def set_lr_decay(self, lr_decay):
self.lr_decay = lr_decay
def set_weight_decay(self, weight_decay):
self.weight_decay = weight_decay
def set_opt_method(self, opt_method):
self.opt_method = opt_method
def set_bern(self, bern):
self.bern = bern
def set_init_embeddings(self, entity_embs, rel_embs):
self.use_init_embeddings = True
self.init_ent_embs = torch.from_numpy(entity_embs).to(device)
self.init_rel_embs = torch.from_numpy(rel_embs).to(device)
def set_config_CNN(self, num_of_filters, drop_prob, kernel_size=1):
self.out_channels = num_of_filters
self.convkb_drop_prob = drop_prob
self.kernel_size = kernel_size
def set_dimension(self, dim):
self.hidden_size = dim
self.ent_size = dim
self.rel_size = dim
def set_ent_dimension(self, dim):
self.ent_size = dim
def set_rel_dimension(self, dim):
self.rel_size = dim
def set_train_times(self, train_times):
self.train_times = train_times
def set_work_threads(self, work_threads):
self.work_threads = work_threads
def set_ent_neg_rate(self, rate):
self.negative_ent = rate
def set_rel_neg_rate(self, rate):
self.negative_rel = rate
def set_ent_dropout(self, ent_dropout):
self.ent_dropout = ent_dropout
def set_rel_dropout(self, rel_dropout):
self.rel_dropout = rel_dropout
def set_early_stopping_patience(self, early_stopping_patience):
self.early_stopping_patience = early_stopping_patience
def set_pretrain_model(self, pretrain_model):
self.pretrain_model = pretrain_model
def get_parameters(self, param_dict, mode="numpy"):
for param in param_dict:
param_dict[param] = param_dict[param].cpu()
res = {}
for param in param_dict:
if mode == "numpy":
res[param] = param_dict[param].numpy()
elif mode == "list":
res[param] = param_dict[param].numpy().tolist()
else:
res[param] = param_dict[param]
return res
def save_embedding_matrix(self, best_model):
path = os.path.join(self.result_dir, self.model.__name__ + ".json")
f = open(path, "w")
f.write(json.dumps(self.get_parameters(best_model, "list")))
f.close()
def set_train_model(self, model):
print("Initializing training model...")
self.model = model
self.trainModel = self.model(config=self)
#self.trainModel = nn.DataParallel(self.trainModel, device_ids=[2,3,4])
self.trainModel.to(device)
if self.optimizer != None:
pass
elif self.opt_method == "Adagrad" or self.opt_method == "adagrad":
self.optimizer = optim.Adagrad(
self.trainModel.parameters(),
lr=self.alpha,
lr_decay=self.lr_decay,
weight_decay=self.weight_decay,
)
elif self.opt_method == "Adadelta" or self.opt_method == "adadelta":
self.optimizer = optim.Adadelta(
self.trainModel.parameters(),
lr=self.alpha,
weight_decay=self.weight_decay,
)
elif self.opt_method == "Adam" or self.opt_method == "adam":
self.optimizer = optim.Adam(
self.trainModel.parameters(),
lr=self.alpha,
weight_decay=self.weight_decay,
)
else:
self.optimizer = optim.SGD(
self.trainModel.parameters(),
lr=self.alpha,
weight_decay=self.weight_decay,
)
print("Finish initializing")
def set_test_model(self, model, path=None):
print("Initializing test model...")
self.model = model
self.testModel = self.model(config=self)
if path == None:
path = os.path.join(self.result_dir, self.model.__name__ + ".ckpt")
self.testModel.load_state_dict(torch.load(path))
self.testModel.to(device)
self.testModel.eval()
print("Finish initializing")
def sampling(self):
self.lib.sampling(
self.batch_h_addr,
self.batch_t_addr,
self.batch_r_addr,
self.batch_y_addr,
self.batch_size,
self.negative_ent,
self.negative_rel,
)
def save_checkpoint(self, model, epoch):
path = os.path.join(
self.checkpoint_dir, self.model.__name__ + "-" + str(epoch) + ".ckpt"
)
torch.save(model, path)
def save_best_checkpoint(self, best_model):
path = os.path.join(self.result_dir, self.model.__name__ + ".ckpt")
torch.save(best_model, path)
def train_one_step(self):
self.trainModel.train()
self.trainModel.batch_h = to_var(self.batch_h)
self.trainModel.batch_t = to_var(self.batch_t)
self.trainModel.batch_r = to_var(self.batch_r)
self.trainModel.batch_y = to_var(self.batch_y)
self.optimizer.zero_grad()
loss = self.trainModel()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.trainModel.parameters(), 0.5)
self.optimizer.step()
return loss.item()
def test_one_step(self, model, test_h, test_t, test_r):
model.eval()
with torch.no_grad():
model.batch_h = to_var(test_h)
model.batch_t = to_var(test_t)
model.batch_r = to_var(test_r)
return model.predict()
def valid(self, model):
self.lib.validInit()
for i in range(self.validTotal):
sys.stdout.write("%d\r" % (i))
sys.stdout.flush()
self.lib.getValidHeadBatch(
self.valid_h_addr, self.valid_t_addr, self.valid_r_addr
)
res = self.test_one_step(model, self.valid_h, self.valid_t, self.valid_r)
self.lib.validHead(res.__array_interface__["data"][0])
self.lib.getValidTailBatch(
self.valid_h_addr, self.valid_t_addr, self.valid_r_addr
)
res = self.test_one_step(model, self.valid_h, self.valid_t, self.valid_r)
self.lib.validTail(res.__array_interface__["data"][0])
return self.lib.getValidHit10()
def training_model(self):
if not os.path.exists(self.checkpoint_dir):
os.mkdir(self.checkpoint_dir)
best_epoch = 0
best_hit10 = 0.0
best_model = None
bad_counts = 0
training_range = tqdm(range(self.train_times))
for epoch in training_range:
res = 0.0
for batch in range(self.nbatches):
self.sampling()
loss = self.train_one_step()
res += loss
training_range.set_description("Epoch %d | loss: %f" % (epoch, res))
# print("Epoch %d | loss: %f" % (epoch, res))
if (epoch + 1) % self.save_steps == 0:
training_range.set_description("Epoch %d has finished, saving..." % (epoch))
self.save_checkpoint(self.trainModel.state_dict(), epoch)
if (epoch + 1) % self.valid_steps == 0:
training_range.set_description("Epoch %d has finished | loss: %f, validating..." % (epoch, res))
hit10 = self.valid(self.trainModel)
if hit10 > best_hit10:
best_hit10 = hit10
best_epoch = epoch
best_model = self.trainModel.state_dict()
print("Best model | hit@10 of valid set is %f" % (best_hit10))
bad_counts = 0
else:
print("Hit@10 of valid set is %f | bad count is %d" % (hit10, bad_counts))
bad_counts += 1
if bad_counts == self.early_stopping_patience:
print("Early stopping at epoch %d" % (epoch))
break
if best_model == None:
best_model = self.trainModel.state_dict()
best_epoch = self.train_times - 1
best_hit10 = self.valid(self.trainModel)
print("Best epoch is %d | hit@10 of valid set is %f" % (best_epoch, best_hit10))
print("Store checkpoint of best result at epoch %d..." % (best_epoch))
if not os.path.isdir(self.result_dir):
os.mkdir(self.result_dir)
self.save_best_checkpoint(best_model)
self.save_embedding_matrix(best_model)
print("Finish storing")
print("Testing...")
self.set_test_model(self.model)
self.test()
print("Finish test")
return best_model
def valid_triple_classification(self, model):
self.lib.getValidBatch(
self.valid_pos_h_addr,
self.valid_pos_t_addr,
self.valid_pos_r_addr,
self.valid_neg_h_addr,
self.valid_neg_t_addr,
self.valid_neg_r_addr,
)
res_pos = self.test_one_step(
model, self.valid_pos_h, self.valid_pos_t, self.valid_pos_r
)
res_neg = self.test_one_step(
model, self.valid_neg_h, self.valid_neg_t, self.valid_neg_r
)
self.lib.getBestThreshold(
self.relThresh_addr,
res_pos.__array_interface__["data"][0],
res_neg.__array_interface__["data"][0],
)
return self.lib.test_triple_classification(
self.relThresh_addr,
res_pos.__array_interface__["data"][0],
res_neg.__array_interface__["data"][0],
)
def training_triple_classification(self):
if not os.path.exists(self.checkpoint_dir):
os.mkdir(self.checkpoint_dir)
best_epoch = 0
best_acc = 0.0
best_model = None
bad_counts = 0
training_range = tqdm(range(self.train_times))
for epoch in training_range:
res = 0.0
for batch in range(self.nbatches):
self.sampling()
loss = self.train_one_step()
res += loss
training_range.set_description("Epoch %d | loss: %f" % (epoch, res))
if (epoch + 1) % self.save_steps == 0:
training_range.set_description("Epoch %d has finished, saving..." % (epoch))
self.save_checkpoint(self.trainModel.state_dict(), epoch)
if (epoch + 1) % self.valid_steps == 0:
training_range.set_description("Epoch %d has finished | loss: %f, validating..." % (epoch, res))
acc = self.valid_triple_classification(self.trainModel)
if acc > best_acc:
best_acc = acc
best_epoch = epoch
best_model = self.trainModel.state_dict()
print("Best model | Acc of valid set is %f" % (best_acc))
bad_counts = 0
else:
print("Acc of valid set is %f | bad count is %d" % (acc, bad_counts))
bad_counts += 1
if bad_counts == self.early_stopping_patience:
print("Early stopping at epoch %d" % (epoch))
break
if best_model == None:
best_model = self.trainModel.state_dict()
best_epoch = self.train_times - 1
best_acc = self.valid_triple_classification(self.trainModel)
print("Best epoch is %d | Acc of valid set is %f" % (best_epoch, best_acc))
print("Store checkpoint of best result at epoch %d..." % (best_epoch))
if not os.path.isdir(self.result_dir):
os.mkdir(self.result_dir)
self.save_best_checkpoint(best_model)
self.save_embedding_matrix(best_model)
print("Finish storing")
print("Testing...")
self.set_test_model(self.model)
self.test()
print("Finish test")
return best_model
def link_prediction(self):
print("The total of test triple is %d" % (self.testTotal))
for i in range(self.testTotal):
sys.stdout.write("%d\r" % (i))
sys.stdout.flush()
self.lib.getHeadBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
res = self.test_one_step(
self.testModel, self.test_h, self.test_t, self.test_r
)
self.lib.testHead(res.__array_interface__["data"][0])
self.lib.getTailBatch(self.test_h_addr, self.test_t_addr, self.test_r_addr)
res = self.test_one_step(
self.testModel, self.test_h, self.test_t, self.test_r
)
self.lib.testTail(res.__array_interface__["data"][0])
self.lib.test_link_prediction()
def triple_classification(self):
self.lib.getValidBatch(
self.valid_pos_h_addr,
self.valid_pos_t_addr,
self.valid_pos_r_addr,
self.valid_neg_h_addr,
self.valid_neg_t_addr,
self.valid_neg_r_addr,
)
res_pos = self.test_one_step(
self.testModel, self.valid_pos_h, self.valid_pos_t, self.valid_pos_r
)
res_neg = self.test_one_step(
self.testModel, self.valid_neg_h, self.valid_neg_t, self.valid_neg_r
)
self.lib.getBestThreshold(
self.relThresh_addr,
res_pos.__array_interface__["data"][0],
res_neg.__array_interface__["data"][0],
)
self.lib.getTestBatch(
self.test_pos_h_addr,
self.test_pos_t_addr,
self.test_pos_r_addr,
self.test_neg_h_addr,
self.test_neg_t_addr,
self.test_neg_r_addr,
)
res_pos = self.test_one_step(
self.testModel, self.test_pos_h, self.test_pos_t, self.test_pos_r
)
res_neg = self.test_one_step(
self.testModel, self.test_neg_h, self.test_neg_t, self.test_neg_r
)
self.lib.test_triple_classification(
self.relThresh_addr,
res_pos.__array_interface__["data"][0],
res_neg.__array_interface__["data"][0],
)
def test(self):
if self.test_link:
self.link_prediction()
if self.test_triple:
self.triple_classification()
================================================
FILE: ConvKB_pytorch/ConvKB.py
================================================
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
from Model import Model
from numpy.random import RandomState
torch.manual_seed(123)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(123)
class ConvKB(Model):
def __init__(self, config):
super(ConvKB, self).__init__(config)
self.ent_embeddings = nn.Embedding(self.config.entTotal, self.config.hidden_size)
self.rel_embeddings = nn.Embedding(self.config.relTotal, self.config.hidden_size)
self.conv1_bn = nn.BatchNorm2d(1)
self.conv_layer = nn.Conv2d(1, self.config.out_channels, (self.config.kernel_size, 3)) # kernel size x 3
self.conv2_bn = nn.BatchNorm2d(self.config.out_channels)
self.dropout = nn.Dropout(self.config.convkb_drop_prob)
self.non_linearity = nn.ReLU() # you should also tune with torch.tanh() or torch.nn.Tanh()
self.fc_layer = nn.Linear((self.config.hidden_size - self.config.kernel_size + 1) * self.config.out_channels, 1, bias=False)
self.criterion = nn.Softplus()
self.init_parameters()
def init_parameters(self):
if self.config.use_init_embeddings == False:
nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
nn.init.xavier_uniform_(self.rel_embeddings.weight.data)
else:
self.ent_embeddings.weight.data = self.config.init_ent_embs
self.rel_embeddings.weight.data = self.config.init_rel_embs
nn.init.xavier_uniform_(self.fc_layer.weight.data)
nn.init.xavier_uniform_(self.conv_layer.weight.data)
def _calc(self, h, r, t):
h = h.unsqueeze(1) # bs x 1 x dim
r = r.unsqueeze(1)
t = t.unsqueeze(1)
conv_input = torch.cat([h, r, t], 1) # bs x 3 x dim
conv_input = conv_input.transpose(1, 2)
# To make tensor of size 4, where second dim is for input channels
conv_input = conv_input.unsqueeze(1)
conv_input = self.conv1_bn(conv_input)
out_conv = self.conv_layer(conv_input)
out_conv = self.conv2_bn(out_conv)
out_conv = self.non_linearity(out_conv)
out_conv = out_conv.view(-1, (self.config.hidden_size - self.config.kernel_size + 1) * self.config.out_channels)
input_fc = self.dropout(out_conv)
score = self.fc_layer(input_fc).view(-1)
return -score
def loss(self, score, regul):
return torch.mean(self.criterion(score * self.batch_y)) + self.config.lmbda * regul
def forward(self):
h = self.ent_embeddings(self.batch_h)
r = self.rel_embeddings(self.batch_r)
t = self.ent_embeddings(self.batch_t)
score = self._calc(h, r, t)
# regularization
l2_reg = torch.mean(h ** 2) + torch.mean(t ** 2) + torch.mean(r ** 2)
for W in self.conv_layer.parameters():
l2_reg = l2_reg + W.norm(2)
for W in self.fc_layer.parameters():
l2_reg = l2_reg + W.norm(2)
return self.loss(score, l2_reg)
def predict(self):
h = self.ent_embeddings(self.batch_h)
r = self.rel_embeddings(self.batch_r)
t = self.ent_embeddings(self.batch_t)
score = self._calc(h, r, t)
return score.cpu().data.numpy()
================================================
FILE: ConvKB_pytorch/ConvKB_1D.py
================================================
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
from Model import Model
from numpy.random import RandomState
torch.manual_seed(123)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(123)
class ConvKB(Model):
def __init__(self, config):
super(ConvKB, self).__init__(config)
self.ent_embeddings = nn.Embedding(self.config.entTotal, self.config.hidden_size)
self.rel_embeddings = nn.Embedding(self.config.relTotal, self.config.hidden_size)
self.conv1_bn = nn.BatchNorm1d(3)
self.conv_layer = nn.Conv1d(3, self.config.out_channels, self.config.kernel_size) # kernel size x 3
self.conv2_bn = nn.BatchNorm1d(self.config.out_channels)
self.dropout = nn.Dropout(self.config.convkb_drop_prob)
self.non_linearity = nn.ReLU() # you should also tune with torch.tanh() or torch.nn.Tanh()
self.fc_layer = nn.Linear((self.config.hidden_size - self.config.kernel_size + 1) * self.config.out_channels, 1, bias=False)
self.criterion = nn.Softplus()
self.init_parameters()
def init_parameters(self):
if self.config.use_init_embeddings == False:
nn.init.xavier_uniform_(self.ent_embeddings.weight.data)
nn.init.xavier_uniform_(self.rel_embeddings.weight.data)
else:
self.ent_embeddings.weight.data = self.config.init_ent_embs
self.rel_embeddings.weight.data = self.config.init_rel_embs
nn.init.xavier_uniform_(self.fc_layer.weight.data)
nn.init.xavier_uniform_(self.conv_layer.weight.data)
def _calc(self, h, r, t):
h = h.unsqueeze(1) # bs x 1 x dim
r = r.unsqueeze(1)
t = t.unsqueeze(1)
conv_input = torch.cat([h, r, t], 1) # bs x 3 x dim
conv_input = self.conv1_bn(conv_input)
out_conv = self.conv_layer(conv_input)
out_conv = self.conv2_bn(out_conv)
out_conv = self.non_linearity(out_conv)
out_conv = out_conv.view(-1, (self.config.hidden_size - self.config.kernel_size + 1) * self.config.out_channels)
input_fc = self.dropout(out_conv)
score = self.fc_layer(input_fc).view(-1)
return -score
def loss(self, score, regul):
return torch.mean(self.criterion(score * self.batch_y)) + self.config.lmbda * regul
def forward(self):
h = self.ent_embeddings(self.batch_h)
r = self.rel_embeddings(self.batch_r)
t = self.ent_embeddings(self.batch_t)
score = self._calc(h, r, t)
# regularization
l2_reg = torch.mean(h ** 2) + torch.mean(t ** 2) + torch.mean(r ** 2)
for W in self.conv_layer.parameters():
l2_reg = l2_reg + W.norm(2)
for W in self.fc_layer.parameters():
l2_reg = l2_reg + W.norm(2)
return self.loss(score, l2_reg)
def predict(self):
h = self.ent_embeddings(self.batch_h)
r = self.rel_embeddings(self.batch_r)
t = self.ent_embeddings(self.batch_t)
score = self._calc(h, r, t)
return score.cpu().data.numpy()
================================================
FILE: ConvKB_pytorch/Model.py
================================================
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
self.batch_h = None
self.batch_t = None
self.batch_r = None
self.batch_y = None
def get_positive_score(self, score):
return score[0:self.config.batch_size]
def get_negative_score(self, score):
negative_score = score[self.config.batch_size:self.config.batch_seq_size]
negative_score = negative_score.view(-1, self.config.batch_size)
negative_score = torch.mean(negative_score, 0)
return negative_score
def forward(self):
raise NotImplementedError
def predict(self):
raise NotImplementedError
================================================
FILE: ConvKB_pytorch/base/Base.cpp
================================================
#include "Setting.h"
#include "Random.h"
#include "Reader.h"
#include "Corrupt.h"
#include "Test.h"
#include "Valid.h"
#include <cstdlib>
#include <pthread.h>
extern "C"
void setInPath(char *path);
extern "C"
void setTestFilePath(char *path);
extern "C"
void setOutPath(char *path);
extern "C"
void setWorkThreads(INT threads);
extern "C"
void setBern(INT con);
extern "C"
INT getWorkThreads();
extern "C"
INT getEntityTotal();
extern "C"
INT getRelationTotal();
extern "C"
INT getTripleTotal();
extern "C"
INT getTrainTotal();
extern "C"
INT getTestTotal();
extern "C"
INT getValidTotal();
extern "C"
void randReset();
extern "C"
void importTrainFiles();
struct Parameter {
INT id;
INT *batch_h;
INT *batch_t;
INT *batch_r;
REAL *batch_y;
INT batchSize;
INT negRate;
INT negRelRate;
};
void* getBatch(void* con) {
Parameter *para = (Parameter *)(con);
INT id = para -> id;
INT *batch_h = para -> batch_h;
INT *batch_t = para -> batch_t;
INT *batch_r = para -> batch_r;
REAL *batch_y = para -> batch_y;
INT batchSize = para -> batchSize;
INT negRate = para -> negRate;
INT negRelRate = para -> negRelRate;
INT lef, rig;
if (batchSize % workThreads == 0) {
lef = id * (batchSize / workThreads);
rig = (id + 1) * (batchSize / workThreads);
} else {
lef = id * (batchSize / workThreads + 1);
rig = (id + 1) * (batchSize / workThreads + 1);
if (rig > batchSize) rig = batchSize;
}
REAL prob = 500;
for (INT batch = lef; batch < rig; batch++) {
INT i = rand_max(id, trainTotal);
batch_h[batch] = trainList[i].h;
batch_t[batch] = trainList[i].t;
batch_r[batch] = trainList[i].r;
batch_y[batch] = 1;
INT last = batchSize;
for (INT times = 0; times < negRate; times ++) {
if (bernFlag)
prob = 1000 * right_mean[trainList[i].r] / (right_mean[trainList[i].r] + left_mean[trainList[i].r]);
if (randd(id) % 1000 < prob) {
batch_h[batch + last] = trainList[i].h;
batch_t[batch + last] = corrupt_head(id, trainList[i].h, trainList[i].r);
batch_r[batch + last] = trainList[i].r;
} else {
batch_h[batch + last] = corrupt_tail(id, trainList[i].t, trainList[i].r);;
batch_t[batch + last] = trainList[i].t;
batch_r[batch + last] = trainList[i].r;
}
batch_y[batch + last] = -1;
last += batchSize;
}
for (INT times = 0; times < negRelRate; times++) {
batch_h[batch + last] = trainList[i].h;
batch_t[batch + last] = trainList[i].t;
batch_r[batch + last] = corrupt_rel(id, trainList[i].h, trainList[i].t);
batch_y[batch + last] = -1;
last += batchSize;
}
}
pthread_exit(NULL);
}
extern "C"
void sampling(INT *batch_h, INT *batch_t, INT *batch_r, REAL *batch_y, INT batchSize, INT negRate = 1, INT negRelRate = 0) {
pthread_t *pt = (pthread_t *)malloc(workThreads * sizeof(pthread_t));
Parameter *para = (Parameter *)malloc(workThreads * sizeof(Parameter));
for (INT threads = 0; threads < workThreads; threads++) {
para[threads].id = threads;
para[threads].batch_h = batch_h;
para[threads].batch_t = batch_t;
para[threads].batch_r = batch_r;
para[threads].batch_y = batch_y;
para[threads].batchSize = batchSize;
para[threads].negRate = negRate;
para[threads].negRelRate = negRelRate;
pthread_create(&pt[threads], NULL, getBatch, (void*)(para+threads));
}
for (INT threads = 0; threads < workThreads; threads++)
pthread_join(pt[threads], NULL);
free(pt);
free(para);
}
int main() {
importTrainFiles();
return 0;
}
================================================
FILE: ConvKB_pytorch/base/Corrupt.h
================================================
#ifndef CORRUPT_H
#define CORRUPT_H
#include "Random.h"
#include "Triple.h"
#include "Reader.h"
INT corrupt_head(INT id, INT h, INT r) {
INT lef, rig, mid, ll, rr;
lef = lefHead[h] - 1;
rig = rigHead[h];
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainHead[mid].r >= r) rig = mid; else
lef = mid;
}
ll = rig;
lef = lefHead[h];
rig = rigHead[h] + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainHead[mid].r <= r) lef = mid; else
rig = mid;
}
rr = lef;
INT tmp = rand_max(id, entityTotal - (rr - ll + 1));
if (tmp < trainHead[ll].t) return tmp;
if (tmp > trainHead[rr].t - rr + ll - 1) return tmp + rr - ll + 1;
lef = ll, rig = rr + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainHead[mid].t - mid + ll - 1 < tmp)
lef = mid;
else
rig = mid;
}
return tmp + lef - ll + 1;
}
INT corrupt_tail(INT id, INT t, INT r) {
INT lef, rig, mid, ll, rr;
lef = lefTail[t] - 1;
rig = rigTail[t];
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainTail[mid].r >= r) rig = mid; else
lef = mid;
}
ll = rig;
lef = lefTail[t];
rig = rigTail[t] + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainTail[mid].r <= r) lef = mid; else
rig = mid;
}
rr = lef;
INT tmp = rand_max(id, entityTotal - (rr - ll + 1));
if (tmp < trainTail[ll].h) return tmp;
if (tmp > trainTail[rr].h - rr + ll - 1) return tmp + rr - ll + 1;
lef = ll, rig = rr + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainTail[mid].h - mid + ll - 1 < tmp)
lef = mid;
else
rig = mid;
}
return tmp + lef - ll + 1;
}
INT corrupt_rel(INT id, INT h, INT t) {
INT lef, rig, mid, ll, rr;
lef = lefRel[h] - 1;
rig = rigRel[h];
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainRel[mid].t >= t) rig = mid; else
lef = mid;
}
ll = rig;
lef = lefRel[h];
rig = rigRel[h] + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainRel[mid].t <= t) lef = mid; else
rig = mid;
}
rr = lef;
INT tmp = rand_max(id, relationTotal - (rr - ll + 1));
if (tmp < trainRel[ll].r) return tmp;
if (tmp > trainRel[rr].r - rr + ll - 1) return tmp + rr - ll + 1;
lef = ll, rig = rr + 1;
while (lef + 1 < rig) {
mid = (lef + rig) >> 1;
if (trainRel[mid].r - mid + ll - 1 < tmp)
lef = mid;
else
rig = mid;
}
return tmp + lef - ll + 1;
}
bool _find(INT h, INT t, INT r) {
INT lef = 0;
INT rig = tripleTotal - 1;
INT mid;
while (lef + 1 < rig) {
INT mid = (lef + rig) >> 1;
if ((tripleList[mid]. h < h) || (tripleList[mid]. h == h && tripleList[mid]. r < r) || (tripleList[mid]. h == h && tripleList[mid]. r == r && tripleList[mid]. t < t)) lef = mid; else rig = mid;
}
if (tripleList[lef].h == h && tripleList[lef].r == r && tripleList[lef].t == t) return true;
if (tripleList[rig].h == h && tripleList[rig].r == r && tripleList[rig].t == t) return true;
return false;
}
INT corrupt(INT h, INT r){
INT ll = tail_lef[r];
INT rr = tail_rig[r];
INT loop = 0;
INT t;
while(1) {
t = tail_type[rand(ll, rr)];
if (not _find(h, t, r)) {
// printf("r:%ld\tt:%ld\n", r, t);
return t;
} else {
loop ++;
if (loop >= 1000){
// printf("drop\n");
return corrupt_head(0, h, r);
}
}
}
}
#endif
================================================
FILE: ConvKB_pytorch/base/Random.h
================================================
#ifndef RANDOM_H
#define RANDOM_H
#include "Setting.h"
#include <cstdlib>
unsigned long long *next_random;
extern "C"
void randReset() {
next_random = (unsigned long long *)calloc(workThreads, sizeof(unsigned long long));
for (INT i = 0; i < workThreads; i++)
next_random[i] = rand();
}
unsigned long long randd(INT id) {
next_random[id] = next_random[id] * (unsigned long long)25214903917 + 11;
return next_random[id];
}
INT rand_max(INT id, INT x) {
INT res = randd(id) % x;
while (res < 0)
res += x;
return res;
}
//[a,b)
INT rand(INT a, INT b){
return (rand() % (b-a))+ a;
}
#endif
================================================
FILE: ConvKB_pytorch/base/Reader.h
================================================
#ifndef READER_H
#define READER_H
#include "Setting.h"
#include "Triple.h"
#include <cstdlib>
#include <algorithm>
INT *freqRel, *freqEnt;
INT *lefHead, *rigHead;
INT *lefTail, *rigTail;
INT *lefRel, *rigRel;
REAL *left_mean, *right_mean;
Triple *trainList;
Triple *trainHead;
Triple *trainTail;
Triple *trainRel;
INT *testLef, *testRig;
INT *validLef, *validRig;
extern "C"
void importTrainFiles() {
printf("The toolkit is importing datasets.\n");
FILE *fin;
int tmp;
fin = fopen((inPath + "relation2id.txt").c_str(), "r");
tmp = fscanf(fin, "%ld", &relationTotal);
printf("The total of relations is %ld.\n", relationTotal);
fclose(fin);
fin = fopen((inPath + "entity2id.txt").c_str(), "r");
tmp = fscanf(fin, "%ld", &entityTotal);
printf("The total of entities is %ld.\n", entityTotal);
fclose(fin);
fin = fopen((inPath + "train2id.txt").c_str(), "r");
tmp = fscanf(fin, "%ld", &trainTotal);
trainList = (Triple *)calloc(trainTotal, sizeof(Triple));
trainHead = (Triple *)calloc(trainTotal, sizeof(Triple));
trainTail = (Triple *)calloc(trainTotal, sizeof(Triple));
trainRel = (Triple *)calloc(trainTotal, sizeof(Triple));
freqRel = (INT *)calloc(relationTotal, sizeof(INT));
freqEnt = (INT *)calloc(entityTotal, sizeof(INT));
for (INT i = 0; i < trainTotal; i++) {
tmp = fscanf(fin, "%ld", &trainList[i].h);
tmp = fscanf(fin, "%ld", &trainList[i].t);
tmp = fscanf(fin, "%ld", &trainList[i].r);
}
fclose(fin);
std::sort(trainList, trainList + trainTotal, Triple::cmp_head);
tmp = trainTotal; trainTotal = 1;
trainHead[0] = trainTail[0] = trainRel[0] = trainList[0];
freqEnt[trainList[0].t] += 1;
freqEnt[trainList[0].h] += 1;
freqRel[trainList[0].r] += 1;
for (INT i = 1; i < tmp; i++)
if (trainList[i].h != trainList[i - 1].h || trainList[i].r != trainList[i - 1].r || trainList[i].t != trainList[i - 1].t) {
trainHead[trainTotal] = trainTail[trainTotal] = trainRel[trainTotal] = trainList[trainTotal] = trainList[i];
trainTotal++;
freqEnt[trainList[i].t]++;
freqEnt[trainList[i].h]++;
freqRel[trainList[i].r]++;
}
std::sort(trainHead, trainHead + trainTotal, Triple::cmp_head);
std::sort(trainTail, trainTail + trainTotal, Triple::cmp_tail);
std::sort(trainRel, trainRel + trainTotal, Triple::cmp_rel);
printf("The total of train triples is %ld.\n", trainTotal);
lefHead = (INT *)calloc(entityTotal, sizeof(INT));
rigHead = (INT *)calloc(entityTotal, sizeof(INT));
lefTail = (INT *)calloc(entityTotal, sizeof(INT));
rigTail = (INT *)calloc(entityTotal, sizeof(INT));
lefRel = (INT *)calloc(entityTotal, sizeof(INT));
rigRel = (INT *)calloc(entityTotal, sizeof(INT));
memset(rigHead, -1, sizeof(INT)*entityTotal);
memset(rigTail, -1, sizeof(INT)*entityTotal);
memset(rigRel, -1, sizeof(INT)*entityTotal);
for (INT i = 1; i < trainTotal; i++) {
if (trainTail[i].t != trainTail[i - 1].t) {
rigTail[trainTail[i - 1].t] = i - 1;
lefTail[trainTail[i].t] = i;
}
if (trainHead[i].h != trainHead[i - 1].h) {
rigHead[trainHead[i - 1].h] = i - 1;
lefHead[trainHead[i].h] = i;
}
if (trainRel[i].h != trainRel[i - 1].h) {
rigRel[trainRel[i - 1].h] = i - 1;
lefRel[trainRel[i].h] = i;
}
}
lefHead[trainHead[0].h] = 0;
rigHead[trainHead[trainTotal - 1].h] = trainTotal - 1;
lefTail[trainTail[0].t] = 0;
rigTail[trainTail[trainTotal - 1].t] = trainTotal - 1;
lefRel[trainRel[0].h] = 0;
rigRel[trainRel[trainTotal - 1].h] = trainTotal - 1;
left_mean = (REAL *)calloc(relationTotal,sizeof(REAL));
right_mean = (REAL *)calloc(relationTotal,sizeof(REAL));
for (INT i = 0; i < entityTotal; i++) {
for (INT j = lefHead[i] + 1; j <= rigHead[i]; j++)
if (trainHead[j].r != trainHead[j - 1].r)
left_mean[trainHead[j].r] += 1.0;
if (lefHead[i] <= rigHead[i])
left_mean[trainHead[lefHead[i]].r] += 1.0;
for (INT j = lefTail[i] + 1; j <= rigTail[i]; j++)
if (trainTail[j].r != trainTail[j - 1].r)
right_mean[trainTail[j].r] += 1.0;
if (lefTail[i] <= rigTail[i])
right_mean[trainTail[lefTail[i]].r] += 1.0;
}
for (INT i = 0; i < relationTotal; i++) {
left_mean[i] = freqRel[i] / left_mean[i];
right_mean[i] = freqRel[i] / right_mean[i];
}
}
Triple *testList;
Triple *validList;
Triple *tripleList;
extern "C"
void importTestFiles() {
FILE *fin;
INT tmp;
fin = fopen((inPath + "relation2id.txt").c_str(), "r");
tmp = fscanf(fin, "%ld", &relationTotal);
fclose(fin);
fin = fopen((inPath + "entity2id.txt").c_str(), "r");
tmp = fscanf(fin, "%ld", &entityTotal);
fclose(fin);
if (testFilePath == "")
testFilePath = inPath + "test2id.txt";
FILE* f_kb1 = fopen(testFilePath.c_str(), "r");
FILE* f_kb2 = fopen((inPath + "train2id.txt").c_str(), "r");
FILE* f_kb3 = fopen((inPath + "valid2id.txt").c_str(), "r");
tmp = fscanf(f_kb1, "%ld", &testTotal);
tmp = fscanf(f_kb2, "%ld", &trainTotal);
tmp = fscanf(f_kb3, "%ld", &validTotal);
tripleTotal = testTotal + trainTotal + validTotal;
testList = (Triple *)calloc(testTotal, sizeof(Triple));
validList = (Triple *)calloc(validTotal, sizeof(Triple));
tripleList = (Triple *)calloc(tripleTotal, sizeof(Triple));
for (INT i = 0; i < testTotal; i++) {
tmp = fscanf(f_kb1, "%ld", &testList[i].h);
tmp = fscanf(f_kb1, "%ld", &testList[i].t);
tmp = fscanf(f_kb1, "%ld", &testList[i].r);
tripleList[i] = testList[i];
}
for (INT i = 0; i < trainTotal; i++) {
tmp = fscanf(f_kb2, "%ld", &tripleList[i + testTotal].h);
tmp = fscanf(f_kb2, "%ld", &tripleList[i + testTotal].t);
tmp = fscanf(f_kb2, "%ld", &tripleList[i + testTotal].r);
}
for (INT i = 0; i < validTotal; i++) {
tmp = fscanf(f_kb3, "%ld", &tripleList[i + testTotal + trainTotal].h);
tmp = fscanf(f_kb3, "%ld", &tripleList[i + testTotal + trainTotal].t);
tmp = fscanf(f_kb3, "%ld", &tripleList[i + testTotal + trainTotal].r);
validList[i] = tripleList[i + testTotal + trainTotal];
}
fclose(f_kb1);
fclose(f_kb2);
fclose(f_kb3);
std::sort(tripleList, tripleList + tripleTotal, Triple::cmp_head);
std::sort(testList, testList + testTotal, Triple::cmp_rel2);
std::sort(validList, validList + validTotal, Triple::cmp_rel2);
printf("The total of test triples is %ld.\n", testTotal);
printf("The total of valid triples is %ld.\n", validTotal);
testLef = (INT *)calloc(relationTotal, sizeof(INT));
testRig = (INT *)calloc(relationTotal, sizeof(INT));
memset(testLef, -1, sizeof(INT) * relationTotal);
memset(testRig, -1, sizeof(INT) * relationTotal);
for (INT i = 1; i < testTotal; i++) {
if (testList[i].r != testList[i-1].r) {
testRig[testList[i-1].r] = i - 1;
testLef[testList[i].r] = i;
}
}
testLef[testList[0].r] = 0;
testRig[testList[testTotal - 1].r] = testTotal - 1;
validLef = (INT *)calloc(relationTotal, sizeof(INT));
validRig = (INT *)calloc(relationTotal, sizeof(INT));
memset(validLef, -1, sizeof(INT)*relationTotal);
memset(validRig, -1, sizeof(INT)*relationTotal);
for (INT i = 1; i < validTotal; i++) {
if (validList[i].r != validList[i-1].r) {
validRig[validList[i-1].r] = i - 1;
validLef[validList[i].r] = i;
}
}
validLef[validList[0].r] = 0;
validRig[validList[validTotal - 1].r] = validTotal - 1;
}
INT* head_lef;
INT* head_rig;
INT* tail_lef;
INT* tail_rig;
INT* head_type;
INT* tail_type;
extern "C"
void importTypeFiles() {
head_lef = (INT *)calloc(relationTotal, sizeof(INT));
head_rig = (INT *)calloc(relationTotal, sizeof(INT));
tail_lef = (INT *)calloc(relationTotal, sizeof(INT));
tail_rig = (INT *)calloc(relationTotal, sizeof(INT));
INT total_lef = 0;
INT total_rig = 0;
FILE* f_type = fopen((inPath + "type_constrain.txt").c_str(),"r");
INT tmp;
tmp = fscanf(f_type, "%ld", &tmp);
for (INT i = 0; i < relationTotal; i++) {
INT rel, tot;
tmp = fscanf(f_type, "%ld %ld", &rel, &tot);
for (INT j = 0; j < tot; j++) {
tmp = fscanf(f_type, "%ld", &tmp);
total_lef++;
}
tmp = fscanf(f_type, "%ld%ld", &rel, &tot);
for (INT j = 0; j < tot; j++) {
tmp = fscanf(f_type, "%ld", &tmp);
total_rig++;
}
}
fclose(f_type);
head_type = (INT *)calloc(total_lef, sizeof(INT));
tail_type = (INT *)calloc(total_rig, sizeof(INT));
total_lef = 0;
total_rig = 0;
f_type = fopen((inPath + "type_constrain.txt").c_str(),"r");
tmp = fscanf(f_type, "%ld", &tmp);
for (INT i = 0; i < relationTotal; i++) {
INT rel, tot;
tmp = fscanf(f_type, "%ld%ld", &rel, &tot);
head_lef[rel] = total_lef;
for (INT j = 0; j < tot; j++) {
tmp = fscanf(f_type, "%ld", &head_type[total_lef]);
total_lef++;
}
head_rig[rel] = total_lef;
std::sort(head_type + head_lef[rel], head_type + head_rig[rel]);
tmp = fscanf(f_type, "%ld%ld", &rel, &tot);
tail_lef[rel] = total_rig;
for (INT j = 0; j < tot; j++) {
tmp = fscanf(f_type, "%ld", &tail_type[total_rig]);
total_rig++;
}
tail_rig[rel] = total_rig;
std::sort(tail_type + tail_lef[rel], tail_type + tail_rig[rel]);
}
fclose(f_type);
}
#endif
================================================
FILE: ConvKB_pytorch/base/Setting.h
================================================
#ifndef SETTING_H
#define SETTING_H
#define INT long
#define REAL float
#include <cstring>
#include <cstdio>
#include <string>
std::string inPath = "../data/FB15K/";
std::string outPath = "../data/FB15K/";
std::string testFilePath = "";
extern "C"
void setInPath(char *path) {
INT len = strlen(path);
inPath = "";
for (INT i = 0; i < len; i++)
inPath = inPath + path[i];
printf("Input Files Path : %s\n", inPath.c_str());
}
extern "C"
void setTestFilePath(char *path) {
INT len = strlen(path);
testFilePath = "";
for (INT i = 0; i < len; i++)
testFilePath = testFilePath + path[i];
printf("Test File Path : %s\n", testFilePath.c_str());
}
extern "C"
void setOutPath(char *path) {
INT len = strlen(path);
outPath = "";
for (INT i = 0; i < len; i++)
outPath = outPath + path[i];
printf("Output Files Path : %s\n", outPath.c_str());
}
/*
============================================================
*/
INT workThreads = 1;
extern "C"
void setWorkThreads(INT threads) {
workThreads = threads;
}
extern "C"
INT getWorkThreads() {
return workThreads;
}
/*
============================================================
*/
INT relationTotal = 0;
INT entityTotal = 0;
INT tripleTotal = 0;
INT testTotal = 0;
INT trainTotal = 0;
INT validTotal = 0;
extern "C"
INT getEntityTotal() {
return entityTotal;
}
extern "C"
INT getRelationTotal() {
return relationTotal;
}
extern "C"
INT getTripleTotal() {
return tripleTotal;
}
extern "C"
INT getTrainTotal() {
return trainTotal;
}
extern "C"
INT getTestTotal() {
return testTotal;
}
extern "C"
INT getValidTotal() {
return validTotal;
}
/*
============================================================
*/
INT bernFlag = 0;
extern "C"
void setBern(INT con) {
bernFlag = con;
}
#endif
================================================
FILE: ConvKB_pytorch/base/Test.h
================================================
#ifndef TEST_H
#define TEST_H
#include "Setting.h"
#include "Reader.h"
#include "Corrupt.h"
/*=====================================================================================
link prediction
======================================================================================*/
INT lastHead = 0;
INT lastTail = 0;
REAL l1_filter_tot = 0, l1_tot = 0, r1_tot = 0, r1_filter_tot = 0, l_tot = 0, r_tot = 0, l_filter_rank = 0, l_rank = 0, l_filter_reci_rank = 0, l_reci_rank = 0;
REAL l3_filter_tot = 0, l3_tot = 0, r3_tot = 0, r3_filter_tot = 0, l_filter_tot = 0, r_filter_tot = 0, r_filter_rank = 0, r_rank = 0, r_filter_reci_rank = 0, r_reci_rank = 0;
REAL l1_filter_tot_constrain = 0, l1_tot_constrain = 0, r1_tot_constrain = 0, r1_filter_tot_constrain = 0, l_tot_constrain = 0, r_tot_constrain = 0, l_filter_rank_constrain = 0, l_rank_constrain = 0, l_filter_reci_rank_constrain = 0, l_reci_rank_constrain = 0;
REAL l3_filter_tot_constrain = 0, l3_tot_constrain = 0, r3_tot_constrain = 0, r3_filter_tot_constrain = 0, l_filter_tot_constrain = 0, r_filter_tot_constrain = 0, r_filter_rank_constrain = 0, r_rank_constrain = 0, r_filter_reci_rank_constrain = 0, r_reci_rank_constrain = 0;
extern "C"
void initTest() {
lastHead = 0;
lastTail = 0;
l1_filter_tot = 0, l1_tot = 0, r1_tot = 0, r1_filter_tot = 0, l_tot = 0, r_tot = 0, l_filter_rank = 0, l_rank = 0, l_filter_reci_rank = 0, l_reci_rank = 0;
l3_filter_tot = 0, l3_tot = 0, r3_tot = 0, r3_filter_tot = 0, l_filter_tot = 0, r_filter_tot = 0, r_filter_rank = 0, r_rank = 0, r_filter_reci_rank = 0, r_reci_rank = 0;
l1_filter_tot_constrain = 0, l1_tot_constrain = 0, r1_tot_constrain = 0, r1_filter_tot_constrain = 0, l_tot_constrain = 0, r_tot_constrain = 0, l_filter_rank_constrain = 0, l_rank_constrain = 0, l_filter_reci_rank_constrain = 0, l_reci_rank_constrain = 0;
l3_filter_tot_constrain = 0, l3_tot_constrain = 0, r3_tot_constrain = 0, r3_filter_tot_constrain = 0, l_filter_tot_constrain = 0, r_filter_tot_constrain = 0, r_filter_rank_constrain = 0, r_rank_constrain = 0, r_filter_reci_rank_constrain = 0, r_reci_rank_constrain = 0;
}
extern "C"
void getHeadBatch(INT *ph, INT *pt, INT *pr) {
for (INT i = 0; i < entityTotal; i++) {
ph[i] = i;
pt[i] = testList[lastHead].t;
pr[i] = testList[lastHead].r;
}
}
extern "C"
void getTailBatch(INT *ph, INT *pt, INT *pr) {
for (INT i = 0; i < entityTotal; i++) {
ph[i] = testList[lastTail].h;
pt[i] = i;
pr[i] = testList[lastTail].r;
}
}
extern "C"
void testHead(REAL *con) {
INT h = testList[lastHead].h;
INT t = testList[lastHead].t;
INT r = testList[lastHead].r;
INT lef = head_lef[r], rig = head_rig[r];
REAL minimal = con[h];
INT l_s = 0;
INT l_filter_s = 0;
INT l_s_constrain = 0;
INT l_filter_s_constrain = 0;
for (INT j = 0; j < entityTotal; j++) {
if (j != h) {
REAL value = con[j];
if (value < minimal) {
l_s += 1;
if (not _find(j, t, r))
l_filter_s += 1;
}
while (lef < rig && head_type[lef] < j) lef ++;
if (lef < rig && j == head_type[lef]) {
if (value < minimal) {
l_s_constrain += 1;
if (not _find(j, t, r)) {
l_filter_s_constrain += 1;
}
}
}
}
}
if (l_filter_s < 10) l_filter_tot += 1;
if (l_s < 10) l_tot += 1;
if (l_filter_s < 3) l3_filter_tot += 1;
if (l_s < 3) l3_tot += 1;
if (l_filter_s < 1) l1_filter_tot += 1;
if (l_s < 1) l1_tot += 1;
if (l_filter_s_constrain < 10) l_filter_tot_constrain += 1;
if (l_s_constrain < 10) l_tot_constrain += 1;
if (l_filter_s_constrain < 3) l3_filter_tot_constrain += 1;
if (l_s_constrain < 3) l3_tot_constrain += 1;
if (l_filter_s_constrain < 1) l1_filter_tot_constrain += 1;
if (l_s_constrain < 1) l1_tot_constrain += 1;
l_filter_rank += (l_filter_s+1);
l_rank += (1+l_s);
l_filter_reci_rank += 1.0/(l_filter_s+1);
l_reci_rank += 1.0/(l_s+1);
l_filter_rank_constrain += (l_filter_s_constrain+1);
l_rank_constrain += (1+l_s_constrain);
l_filter_reci_rank_constrain += 1.0/(l_filter_s_constrain+1);
l_reci_rank_constrain += 1.0/(l_s_constrain+1);
lastHead++;
//printf("l_filter_s: %ld\n", l_filter_s);
//printf("%f %f %f %f \n", l_tot / lastHead, l_filter_tot / lastHead, l_rank / lastHead, l_filter_rank / lastHead);
}
extern "C"
void testTail(REAL *con) {
INT h = testList[lastTail].h;
INT t = testList[lastTail].t;
INT r = testList[lastTail].r;
INT lef = tail_lef[r], rig = tail_rig[r];
REAL minimal = con[t];
INT r_s = 0;
INT r_filter_s = 0;
INT r_s_constrain = 0;
INT r_filter_s_constrain = 0;
for (INT j = 0; j < entityTotal; j++) {
if (j != t) {
REAL value = con[j];
if (value < minimal) {
r_s += 1;
if (not _find(h, j, r))
r_filter_s += 1;
}
while (lef < rig && tail_type[lef] < j) lef ++;
if (lef < rig && j == tail_type[lef]) {
if (value < minimal) {
r_s_constrain += 1;
if (not _find(h, j ,r)) {
r_filter_s_constrain += 1;
}
}
}
}
}
if (r_filter_s < 10) r_filter_tot += 1;
if (r_s < 10) r_tot += 1;
if (r_filter_s < 3) r3_filter_tot += 1;
if (r_s < 3) r3_tot += 1;
if (r_filter_s < 1) r1_filter_tot += 1;
if (r_s < 1) r1_tot += 1;
if (r_filter_s_constrain < 10) r_filter_tot_constrain += 1;
if (r_s_constrain < 10) r_tot_constrain += 1;
if (r_filter_s_constrain < 3) r3_filter_tot_constrain += 1;
if (r_s_constrain < 3) r3_tot_constrain += 1;
if (r_filter_s_constrain < 1) r1_filter_tot_constrain += 1;
if (r_s_constrain < 1) r1_tot_constrain += 1;
r_filter_rank += (1+r_filter_s);
r_rank += (1+r_s);
r_filter_reci_rank += 1.0/(1+r_filter_s);
r_reci_rank += 1.0/(1+r_s);
r_filter_rank_constrain += (1+r_filter_s_constrain);
r_rank_constrain += (1+r_s_constrain);
r_filter_reci_rank_constrain += 1.0/(1+r_filter_s_constrain);
r_reci_rank_constrain += 1.0/(1+r_s_constrain);
lastTail++;
//printf("r_filter_s: %ld\n", r_filter_s);
//printf("%f %f %f %f\n", r_tot /lastTail, r_filter_tot /lastTail, r_rank /lastTail, r_filter_rank /lastTail);
}
extern "C"
void test_link_prediction() {
l_rank /= testTotal;
r_rank /= testTotal;
l_reci_rank /= testTotal;
r_reci_rank /= testTotal;
l_tot /= testTotal;
l3_tot /= testTotal;
l1_tot /= testTotal;
r_tot /= testTotal;
r3_tot /= testTotal;
r1_tot /= testTotal;
// with filter
l_filter_rank /= testTotal;
r_filter_rank /= testTotal;
l_filter_reci_rank /= testTotal;
r_filter_reci_rank /= testTotal;
l_filter_tot /= testTotal;
l3_filter_tot /= testTotal;
l1_filter_tot /= testTotal;
r_filter_tot /= testTotal;
r3_filter_tot /= testTotal;
r1_filter_tot /= testTotal;
//printf("metric:\t\t\t MRR \t\t MR \t\t hit@10 \t hit@3 \t hit@1 \n");
//printf("l(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", l_reci_rank, l_rank, l_tot, l3_tot, l1_tot);
//printf("r(raw):\t\t\t %f \t %f \t %f \t %f \t %f \n", r_reci_rank, r_rank, r_tot, r3_tot, r1_tot);
//printf("averaged(raw):\t\t %f \t %f \t %f \t %f \t %f \n",
// (l_reci_rank+r_reci_rank)/2, (l_rank+r_rank)/2, (l_tot+r_tot)/2, (l3_tot+r3_tot)/2, (l1_tot+r1_tot)/2);
printf("Final (filter) results:\n");
printf("\n");
printf("l(filter):\t\t %f \t %f \t %f \t %f \t %f \n", l_filter_reci_rank, l_filter_rank, l_filter_tot, l3_filter_tot, l1_filter_tot);
printf("r(filter):\t\t %f \t %f \t %f \t %f \t %f \n", r_filter_reci_rank, r_filter_rank, r_filter_tot, r3_filter_tot, r1_filter_tot);
printf("averaged(filter):\t %f \t %f \t %f \t %f \t %f \n",
(l_filter_reci_rank+r_filter_reci_rank)/2, (l_filter_rank+r_filter_rank)/2, (l_filter_tot+r_filter_tot)/2, (l3_filter_tot+r3_filter_tot)/2, (l1_filter_tot+r1_filter_tot)/2);
}
/*=====================================================================================
triple classification
======================================================================================*/
Triple *negTestList;
extern "C"
void getNegTest() {
negTestList = (Triple *)calloc(testTotal, sizeof(Triple));
for (INT i = 0; i < testTotal; i++) {
negTestList[i] = testList[i];
negTestList[i].t = corrupt(testList[i].h, testList[i].r);
}
/*
FILE* fout = fopen((inPath + "test_neg.txt").c_str(), "w");
for (INT i = 0; i < testTotal; i++) {
fprintf(fout, "%ld\t%ld\t%ld\t%ld\n", testList[i].h, testList[i].t, testList[i].r, INT(1));
fprintf(fout, "%ld\t%ld\t%ld\t%ld\n", negTestList[i].h, negTestList[i].t, negTestList[i].r, INT(-1));
}
fclose(fout);
*/
}
Triple *negValidList;
extern "C"
void getNegValid() {
negValidList = (Triple *)calloc(validTotal, sizeof(Triple));
for (INT i = 0; i < validTotal; i++) {
negValidList[i] = validList[i];
negValidList[i].t = corrupt(validList[i].h, validList[i].r);
}
/*
FILE* fout = fopen((inPath + "valid_neg.txt").c_str(), "w");
for (INT i = 0; i < validTotal; i++) {
fprintf(fout, "%ld\t%ld\t%ld\t%ld\n", validList[i].h, validList[i].t, validList[i].r, INT(1));
fprintf(fout, "%ld\t%ld\t%ld\t%ld\n", negValidList[i].h, negValidList[i].t, negValidList[i].r, INT(-1));
}
fclose(fout);
*/
}
extern "C"
void getTestBatch(INT *ph, INT *pt, INT *pr, INT *nh, INT *nt, INT *nr) {
getNegTest();
for (INT i = 0; i < testTotal; i++) {
ph[i] = testList[i].h;
pt[i] = testList[i].t;
pr[i] = testList[i].r;
nh[i] = negTestList[i].h;
nt[i] = negTestList[i].t;
nr[i] = negTestList[i].r;
}
}
extern "C"
void getValidBatch(INT *ph, INT *pt, INT *pr, INT *nh, INT *nt, INT *nr) {
getNegValid();
for (INT i = 0; i < validTotal; i++) {
ph[i] = validList[i].h;
pt[i] = validList[i].t;
pr[i] = validList[i].r;
nh[i] = negValidList[i].h;
nt[i] = negValidList[i].t;
nr[i] = negValidList[i].r;
}
}
REAL threshEntire;
extern "C"
void getBestThreshold(REAL *relThresh, REAL *score_pos, REAL *score_neg) {
REAL interval = 0.01;
REAL min_score, max_score, bestThresh, tmpThresh, bestAcc, tmpAcc;
INT n_interval, correct, total;
for (INT r = 0; r < relationTotal; r++) {
if (validLef[r] == -1) continue;
total = (validRig[r] - validLef[r] + 1) * 2;
min_score = score_pos[validLef[r]];
if (score_neg[validLef[r]] < min_score) min_score = score_neg[validLef[r]];
max_score = score_pos[validLef[r]];
if (score_neg[validLef[r]] > max_score) max_score = score_neg[validLef[r]];
for (INT i = validLef[r]+1; i <= validRig[r]; i++) {
if(score_pos[i] < min_score) min_score = score_pos[i];
if(score_pos[i] > max_score) max_score = score_pos[i];
if(score_neg[i] < min_score) min_score = score_neg[i];
if(score_neg[i] > max_score) max_score = score_neg[i];
}
n_interval = INT((max_score - min_score)/interval);
for (INT i = 0; i <= n_interval; i++) {
tmpThresh = min_score + i * interval;
correct = 0;
for (INT j = validLef[r]; j <= validRig[r]; j++) {
if (score_pos[j] <= tmpThresh) correct ++;
if (score_neg[j] > tmpThresh) correct ++;
}
tmpAcc = 1.0 * correct / total;
if (i == 0) {
bestThresh = tmpThresh;
bestAcc = tmpAcc;
} else if (tmpAcc > bestAcc) {
bestAcc = tmpAcc;
bestThresh = tmpThresh;
}
}
relThresh[r] = bestThresh;
}
}
REAL *testAcc;
REAL aveAcc;
extern "C"
REAL test_triple_classification(REAL *relThresh, REAL *score_pos, REAL *score_neg) {
testAcc = (REAL *)calloc(relationTotal, sizeof(REAL));
INT aveCorrect = 0, aveTotal = 0;
REAL aveAcc;
for (INT r = 0; r < relationTotal; r++) {
if (validLef[r] == -1 || testLef[r] ==-1) continue;
INT correct = 0, total = 0;
for (INT i = testLef[r]; i <= testRig[r]; i++) {
if (score_pos[i] <= relThresh[r]) correct++;
if (score_neg[i] > relThresh[r]) correct++;
total += 2;
}
testAcc[r] = 1.0 * correct / total;
aveCorrect += correct;
aveTotal += total;
}
aveAcc = 1.0 * aveCorrect / aveTotal;
printf("triple classification accuracy is %lf\n", aveAcc);
return aveAcc;
}
#endif
================================================
FILE: ConvKB_pytorch/base/Triple.h
================================================
#ifndef TRIPLE_H
#define TRIPLE_H
#include "Setting.h"
struct Triple {
INT h, r, t;
static INT minimal(INT a,INT b) {
if (a > b) return b;
return a;
}
static bool cmp_list(const Triple &a, const Triple &b) {
return (minimal(a.h, a.t) > minimal(b.h, b.t));
}
static bool cmp_head(const Triple &a, const Triple &b) {
return (a.h < b.h)||(a.h == b.h && a.r < b.r)||(a.h == b.h && a.r == b.r && a.t < b.t);
}
static bool cmp_tail(const Triple &a, const Triple &b) {
return (a.t < b.t)||(a.t == b.t && a.r < b.r)||(a.t == b.t && a.r == b.r && a.h < b.h);
}
static bool cmp_rel(const Triple &a, const Triple &b) {
return (a.h < b.h)||(a.h == b.h && a.t < b.t)||(a.h == b.h && a.t == b.t && a.r < b.r);
}
static bool cmp_rel2(const Triple &a, const Triple &b) {
return (a.r < b.r)||(a.r == b.r && a.h < b.h)||(a.r == b.r && a.h == b.h && a.t < b.t);
}
};
#endif
================================================
FILE: ConvKB_pytorch/base/Valid.h
================================================
#ifndef VALID_H
#define VALID_H
#include "Setting.h"
#include "Reader.h"
#include "Corrupt.h"
INT lastValidHead = 0;
INT lastValidTail = 0;
REAL l_valid_filter_tot = 0;
REAL r_valid_filter_tot = 0;
extern "C"
void validInit() {
lastValidHead = 0;
lastValidTail = 0;
l_valid_filter_tot = 0;
r_valid_filter_tot = 0;
}
extern "C"
void getValidHeadBatch(INT *ph, INT *pt, INT *pr) {
for (INT i = 0; i < entityTotal; i++) {
ph[i] = i;
pt[i] = validList[lastValidHead].t;
pr[i] = validList[lastValidHead].r;
}
}
extern "C"
void getValidTailBatch(INT *ph, INT *pt, INT *pr) {
for (INT i = 0; i < entityTotal; i++) {
ph[i] = validList[lastValidTail].h;
pt[i] = i;
pr[i] = validList[lastValidTail].r;
}
}
extern "C"
void validHead(REAL *con) {
INT h = validList[lastValidHead].h;
INT t = validList[lastValidHead].t;
INT r = validList[lastValidHead].r;
REAL minimal = con[h];
INT l_filter_s = 0;
for (INT j = 0; j < entityTotal; j++) {
if (j != h) {
REAL value = con[j];
if (value < minimal && ! _find(j, t, r)) {
l_filter_s += 1;
}
}
}
if (l_filter_s < 10) l_valid_filter_tot += 1;
lastValidHead ++;
// printf("head: l_valid_filter_tot = %f | l_filter_hit10 = %f\n", l_valid_filter_tot, l_valid_filter_tot / lastValidHead);
}
extern "C"
void validTail(REAL *con) {
INT h = validList[lastValidTail].h;
INT t = validList[lastValidTail].t;
INT r = validList[lastValidTail].r;
REAL minimal = con[t];
INT r_filter_s = 0;
for (INT j = 0; j < entityTotal; j++) {
if (j != t) {
REAL value = con[j];
if (value < minimal && ! _find(h, j, r)) {
r_filter_s += 1;
}
}
}
if (r_filter_s < 10) r_valid_filter_tot += 1;
lastValidTail ++;
// printf("tail: r_valid_filter_tot = %f | r_filter_hit10 = %f\n", r_valid_filter_tot, r_valid_filter_tot / lastValidTail);
}
REAL validHit10 = 0;
extern "C"
REAL getValidHit10() {
l_valid_filter_tot /= validTotal;
r_valid_filter_tot /= validTotal;
validHit10 = (l_valid_filter_tot + r_valid_filter_tot) / 2;
// printf("result: %f\n", validHit10);
return validHit10;
}
#endif
================================================
FILE: ConvKB_pytorch/benchmarks/FB15K/1-1.txt
================================================
832
2326 6886 637
4843 4843 149
9671 7174 190
8055 8055 305
9549 11909 855
13652 13585 446
1100 1100 305
1321 14529 176
11074 1002 444
4668 9007 1172
7266 11750 444
12271 12271 101
4766 6205 470
2129 13806 176
1624 10851 190
3106 3107 275
11815 8842 226
376 376 305
4142 4142 149
7929 7929 305
3939 4387 190
4753 10689 423
1785 4433 190
197 5800 423
1675 8660 190
11216 11216 149
487 6941 698
14413 3636 95
3296 8269 190
2188 5216 358
4881 13935 446
2999 4598 966
7333 6560 226
6349 1227 444
13468 11918 446
13763 13763 305
2187 2187 101
13177 3513 470
3017 270 637
9844 9844 149
13518 13518 101
11751 14295 242
4544 4544 149
9703 9703 101
0 10598 95
11863 14219 446
14396 14396 101
5438 5255 444
11017 5390 242
8703 8703 305
7023 10286 396
714 4627 190
11670 11670 101
7363 2764 964
5476 10060 667
12102 14703 1213
5333 2095 667
1391 5311 966
5288 5288 149
1432 7674 190
1016 3095 470
8103 6135 439
13611 5360 159
7951 9751 444
3117 3117 149
8053 6619 446
7655 2280 712
6023 9808 470
8020 1068 1260
162 2042 292
3245 1710 358
13397 11620 856
10834 9891 190
5216 1568 358
608 11005 444
13242 5068 1241
167 424 112
183 11914 176
8381 8381 149
10771 7953 242
2818 8616 242
3242 14324 95
9517 247 176
7624 7624 305
12311 12311 305
4729 4729 101
14211 13538 176
7098 9383 444
5891 2995 242
5788 5788 149
7071 4197 1251
8928 2226 1154
3112 397 691
410 410 101
3792 314 176
12158 2953 439
8948 8948 305
11670 9300 446
10964 10964 305
12881 546 176
12666 11941 292
10770 9227 190
1275 12025 190
11923 1966 95
1886 1885 446
2514 8204 93
8684 8684 149
2671 2671 149
12903 6011 928
13736 10192 1002
11964 11964 101
4699 5258 93
865 13006 1238
13952 13952 101
2511 1565 783
10713 5332 292
1441 2711 456
3930 12369 1222
3655 13003 295
4356 7330 292
4944 4943 93
10528 2876 112
454 4645 444
11330 11330 305
13805 13805 149
5118 3629 358
8920 1513 637
13815 10036 446
11400 7886 446
1402 712 444
8332 6242 358
6193 8728 292
5375 7952 1102
657 8481 303
9745 11402 242
2514 2439 93
12559 3772 292
4036 4036 305
9883 11004 190
524 524 149
6551 8482 396
9946 12404 242
7957 2720 673
11145 10975 176
5483 1114 190
2674 2674 305
5425 7989 873
9510 11800 396
12565 5454 242
9947 11125 242
12379 12379 101
1330 632 444
1505 4371 423
2744 3116 175
2358 4026 820
9973 7255 176
1927 1927 609
12471 12471 101
751 751 305
13291 13291 101
13195 13195 149
2407 3724 190
12693 12693 305
6338 2616 190
839 321 226
4089 5380 444
4463 12665 242
5260 1994 275
8524 1983 456
3243 6903 93
703 1123 637
9043 6506 176
9695 9695 101
7477 10458 456
13786 1700 190
11417 14174 95
6485 11218 275
3480 8180 1166
6445 8360 456
3636 7829 446
8939 8939 305
6799 12231 446
1704 3245 444
2348 9132 95
7546 756 360
12932 1711 439
8831 1360 444
6581 9609 190
2692 5922 359
1633 2504 637
7363 2765 964
9794 9794 149
14849 9912 609
2818 8616 439
5817 2353 176
1259 3074 226
5834 2280 66
3242 14629 190
7219 7218 446
13808 13808 1177
13272 13272 149
8037 8037 149
4014 10989 242
10286 11313 396
14137 14137 305
7135 7135 149
9572 4410 93
4772 5823 358
3384 3836 470
5054 5054 149
1725 2028 439
7954 7954 149
1225 2754 358
5105 2286 456
11969 880 637
3140 1527 66
1798 2063 190
2394 12623 446
2239 2469 176
4612 4388 358
7341 7341 149
1513 6527 226
13235 13235 691
12559 12907 444
4036 4036 149
2272 9838 456
8481 657 1036
8858 8858 149
9960 5553 516
6883 6883 439
758 7550 190
3674 956 93
6168 8475 295
10903 10903 101
424 12094 1211
8813 2585 444
6728 6728 966
13069 13714 176
11296 10528 516
4786 1838 444
6065 1401 358
8226 1329 698
8676 8709 360
7534 7459 190
1983 8524 456
3790 573 609
5248 5248 149
8104 11310 226
4637 10534 423
2328 10498 190
8488 8488 305
10107 5208 358
2547 8002 295
11463 11463 149
8555 8555 101
6339 5180 226
1059 576 1002
14898 8354 781
11988 11988 149
7052 2589 446
2769 11161 423
9717 2445 470
1358 10383 95
14340 14340 101
2754 1744 93
9310 677 358
5515 3292 396
5483 14035 190
2421 880 637
6013 6013 305
8028 2442 873
4456 2728 423
8478 11991 190
2042 162 423
3439 4141 444
1925 12757 95
1269 652 93
726 6761 226
3939 11300 446
11619 2928 667
5986 892 1052
2701 2701 305
4545 4545 439
6205 4766 275
3456 3456 305
3476 14455 190
13554 5025 470
11421 9357 275
2107 8695 95
6758 6758 149
9713 8883 446
4954 4954 609
10638 10638 149
13293 7530 446
5728 7606 226
12501 920 803
4349 5065 226
1279 10890 793
9102 12438 439
13791 13791 149
12005 1674 981
4917 7626 423
900 4429 190
11727 11727 305
13209 4680 1216
10861 10860 446
11800 9510 396
7078 2268 444
3951 3951 305
14452 12088 543
10805 13286 295
10172 10172 305
13427 13427 101
13430 2767 446
13793 2916 176
3203 4150 456
430 9843 396
13736 10192 659
7722 10043 275
8696 8696 101
1389 2632 697
9051 9051 305
4642 6498 93
12251 12251 305
10568 2430 396
605 8745 470
5378 5378 149
14662 14662 1081
2003 10909 470
8466 8465 242
4297 7130 176
12972 12972 101
7444 7956 637
5672 462 358
2506 2506 101
7801 1134 95
12576 12576 305
487 2842 698
677 7072 358
3756 3899 444
11854 13707 446
9817 5480 659
734 8180 618
1203 5562 456
3674 6509 358
13147 6806 295
1307 11484 446
7433 7433 305
11410 13510 295
11410 13510 242
4207 4207 149
5540 6610 791
13805 13805 305
14568 13946 444
12659 10703 439
11853 11510 176
5778 2034 190
11076 11076 101
2633 2194 536
3920 706 806
7664 4707 470
6841 6841 101
13004 8360 456
2234 10530 292
12596 163 609
1264 9197 190
13071 6821 360
1711 12932 446
5158 6934 93
3663 8957 609
4764 10425 456
12814 8563 242
5800 197 292
2608 4642 358
4587 4587 149
9173 9173 149
2665 7550 931
7283 7283 305
11618 11618 101
2765 7481 470
11892 7482 470
10816 10816 305
6512 466 242
6662 12922 470
8295 6288 190
3054 3054 305
7074 13787 446
2892 3226 444
10335 10335 101
9577 9577 305
4621 2793 444
7145 5079 444
3560 4197 1108
2018 8615 446
11384 4944 93
10910 10910 101
1624 6703 190
11110 11111 242
712 9222 444
4366 4707 470
10269 10269 609
7818 361 226
3195 5438 358
13067 1034 176
9740 11192 423
9187 8207 470
12240 12240 101
11631 11631 305
8480 9310 360
10687 3415 226
1509 1509 305
9042 11831 439
5763 7005 226
9063 13539 95
8051 8051 101
6650 6650 149
1836 53 609
2774 5333 618
9058 9058 305
5637 5637 101
7962 7961 446
2699 2407 176
11421 4706 275
890 4868 360
7736 7736 305
3341 4666 190
2718 2717 516
5341 2117 840
4397 637 444
1169 3813 190
4584 4584 1112
4680 10457 456
12045 12045 305
3612 3612 305
5900 5900 305
534 7638 93
13455 13455 305
6242 3287 93
4513 10797 470
2442 2442 305
278 13654 190
9540 7538 444
5552 1416 95
10081 11661 446
430 3915 444
908 3865 396
495 13915 909
10238 3270 836
12814 8563 295
12495 12495 149
5564 12343 456
10929 10929 305
14415 7682 961
5106 5106 149
3657 280 456
8318 8318 305
5586 1247 456
12318 12318 101
7023 11067 396
7272 14881 815
6338 4186 446
5860 10024 444
2906 13983 95
44 7966 93
1744 6903 358
2353 12296 190
3099 3099 149
1021 7723 423
14344 12792 869
11250 7952 909
2234 1023 516
12112 12112 305
12400 7511 781
11257 11257 305
1775 6388 358
8280 8280 101
7666 12035 637
10095 1774 176
10123 1463 516
4743 3381 175
6903 801 931
8540 8540 305
6431 6431 149
5337 7820 358
5438 3195 93
8476 2506 242
2764 9717 359
3702 10317 470
11062 66 637
6242 3287 444
12474 1203 667
7893 7893 101
12807 2010 446
14482 14140 446
4422 4422 149
2736 2883 470
10580 1864 869
8345 9695 242
9891 10834 176
11983 1064 396
430 3520 396
1746 10104 190
585 4748 176
1544 6402 609
8028 984 931
8002 8002 101
7984 3667 516
397 3224 303
397 3900 303
10140 10140 149
3837 3485 784
3520 430 396
13072 265 444
2368 6840 444
245 244 446
13314 4634 242
2752 2639 93
1416 5444 190
5421 7997 423
7926 7926 305
12805 13801 292
1155 1155 149
1389 1389 149
4094 4094 149
13535 12781 1282
8498 8497 242
8262 8262 305
5823 3542 358
5467 1507 637
3871 900 176
8693 6508 637
12872 1729 396
692 1704 444
4017 4017 610
2614 8107 992
11337 12340 446
2445 5691 444
3409 1310 360
13581 13581 149
5143 5143 149
5257 5257 149
1744 8956 358
5159 8479 292
2439 3372 444
9944 9571 190
4441 2630 226
5421 6096 396
10286 7023 396
10286 11067 396
842 13790 176
12160 80 275
8438 8438 149
11000 11000 149
10087 9276 175
13008 2146 176
9831 11050 712
8486 11649 697
13078 8340 439
1983 1707 456
7414 9548 966
2692 5796 964
3355 3355 149
7418 9494 446
1874 8043 226
2752 5790 358
7079 7080 295
566 9584 637
2750 3463 358
58 11911 190
7292 7292 305
12292 12292 439
12179 14513 242
7915 12949 275
3415 10687 637
9775 9775 101
7074 7074 101
9853 10449 637
2194 2633 923
1728 334 609
4197 4476 1238
4135 1481 470
11669 12645 242
2754 2755 360
4078 11706 444
2018 2018 101
8126 9737 456
9305 10441 242
5464 8857 226
10541 10541 305
4544 4544 305
9728 7493 226
5733 9647 95
397 2285 303
5118 6838 423
10883 10882 242
13281 9352 446
7931 4707 444
8811 11239 292
7543 9581 295
1284 3657 456
11491 10264 444
2720 2721 1195
75 10359 190
12989 13230 1184
6637 9842 190
10753 3106 423
3796 3797 295
8772 2107 176
2187 9084 190
7847 7847 149
12975 12975 671
12945 2764 275
11479 6022 1003
1051 4603 964
3155 9043 697
12622 13206 1140
13968 13968 101
4247 7501 637
6057 9454 444
4564 2389 95
6117 10555 456
5913 5682 423
176 4219 190
11313 11067 396
7969 7273 93
6574 4667 423
9335 6879 444
9205 3585 444
6513 8107 190
1410 11632 190
7670 7670 149
10464 10464 671
5276 8745 470
2769 1338 360
13917 13772 691
11966 11966 101
14196 14193 839
5430 627 444
2743 3516 66
1906 7953 176
12948 12948 101
3937 14432 176
12585 4459 242
2098 5619 423
2098 9400 444
5499 9395 712
8476 2506 439
10146 8656 439
8223 4527 637
6540 6540 101
942 4756 637
6193 11828 292
11351 11351 305
3293 2598 396
3450 7284 923
10481 13858 242
1481 11639 292
3746 11570 637
13409 3839 176
9629 9629 149
2543 2543 305
2188 2730 444
13231 13055 446
1674 12312 806
12372 2499 446
10276 4608 659
10548 10907 190
10731 10731 101
13722 11635 439
13829 2523 190
9740 9450 423
12207 12207 671
11830 11111 295
8223 553 637
5498 10451 712
10444 12967 470
5257 5257 305
3700 887 190
328 4280 112
6656 6656 149
489 489 149
9360 10444 966
14035 5483 176
7074 254 190
12660 12660 149
25 12536 95
239 239 439
9886 111 176
4881 4881 101
6443 11570 698
4598 2999 698
195 195 149
6237 11448 691
2114 10613 190
5065 4349 637
3390 9003 966
11889 8216 439
4854 4854 305
3482 3296 176
12622 13013 1288
11160 1160 95
2580 14608 981
5143 5143 305
5770 5770 609
6970 6970 101
9604 9604 305
11947 4483 864
12903 12903 609
8055 8055 149
3476 13647 95
7957 3296 176
1674 10757 806
12946 12978 176
10790 10790 101
11602 1325 423
8326 3262 176
3674 3294 93
1565 6981 856
5068 13242 66
175 1502 536
5877 10012 226
2300 7207 242
1650 12715 242
1624 10385 190
3117 3117 305
14089 11288 95
4207 9576 697
12434 12434 149
9476 3021 275
11570 6443 966
7818 1009 637
5531 2370 275
681 2089 1239
7916 8332 93
8122 8122 305
8318 8318 149
13374 13374 101
49 5674 295
4925 4926 242
13198 2353 242
657 10242 1291
657 11299 864
5530 5529 93
8009 6785 292
4437 1779 226
12275 2603 175
13664 3241 176
2254 2085 95
10594 4346 603
12127 12127 609
4707 2287 275
6186 6186 149
8152 521 637
6135 6135 439
11929 11928 446
10327 3414 637
9877 9878 446
6121 6121 149
8458 9326 444
1484 12355 782
7899 8515 275
9126 4165 176
3420 11849 456
3869 6418 93
8504 1416 95
11336 11336 149
8676 6192 444
11086 13108 618
9611 9611 149
4827 709 667
5280 5280 149
13532 12967 470
13588 3116 175
4967 3878 1281
4687 4687 149
10805 13286 242
2469 914 190
14645 6885 95
12665 4463 446
883 12739 439
9879 1732 175
1930 12949 964
7722 11636 275
10002 5251 744
9653 9653 149
2570 4817 855
10192 9896 275
3772 3356 444
3675 7100 618
3105 3105 700
1300 7246 242
8868 6709 470
6000 11167 190
10065 5835 444
12369 534 274
11914 1372 190
11088 1034 926
9762 5558 446
8998 7447 444
2630 3459 637
13309 13309 101
9019 1818 952
3826 5906 444
5231 12768 444
8656 144 446
678 4965 1192
11842 13302 439
983 8334 242
4468 6647 360
7728 5574 275
5090 3216 360
7997 6096 292
5674 49 446
8953 10709 609
7354 1237 456
2848 2848 149
9000 12520 439
10395 8661 226
4868 969 444
111 4226 295
10906 5645 242
2082 11406 95
12300 8101 444
11510 11853 190
13592 13592 101
8664 8664 149
3920 635 906
7094 800 175
6193 11208 444
9540 971 444
8670 13057 295
4923 4923 101
================================================
FILE: ConvKB_pytorch/benchmarks/FB15K/1-n.txt
================================================
5259
147 11738 592
3572 8632 143
12761 4179 88
3244 201 129
3244 11230 129
9085 11970 1
1963 6035 203
833 10624 263
4654 6310 40
4154 1917 114
586 7752 266
736 8733 155
920 5647 114
1180 6107 438
811 12322 850
5997 3181 114
1477 4206 203
1621 12084 129
1290 6739 231
36 7006 18
36 4767 18
36 4204 18
36 11194 18
36 8551 18
36 6579 18
36 3213 18
12381 7633 114
695 2098 1
6524 7766 203
5572 1813 40
7471 12559 129
6213 2125 391
2156 4281 314
798 9960 663
10076 2020 148
4537 6159 98
7082 943 660
403 3121 91
403 6206 91
403 10168 91
403 8289 91
403 6946 91
403 518 91
403 11831 91
403 8038 91
403 2839 91
403 7409 91
403 1115 91
403 12509 91
403 8995 91
403 2282 91
938 5114 203
13075 2784 129
2187 11377 114
214 10033 756
859 9699 113
859 11995 113
859 10540 113
715 4844 297
12886 4956 155
101 9105 91
101 10167 91
101 14270 91
101 13470 91
101 3470 91
101 2773 91
101 2570 91
101 14529 91
101 9077 91
101 14457 91
101 7362 91
101 8486 91
101 10738 91
101 5900 91
101 7719 91
101 4978 91
101 12778 91
101 6459 91
2706 10419 88
7335 4254 106
12717 4723 934
103 6741 106
1054 4098 208
1054 3726 208
6084 4945 203
1521 7228 98
3504 3528 148
2209 1337 324
375 6220 113
375 8512 113
375 9498 113
1278 14905 342
6279 3456 137
6344 9317 98
12397 8256 1155
3381 12075 102
5506 9286 253
8067 9999 268
13998 12610 1074
8704 1242 1
7161 3008 560
11653 9265 114
4086 2127 203
3839 10322 114
2241 4882 199
2241 7176 199
2241 12176 199
825 9955 1
1444 13059 821
7533 9788 340
2849 12767 137
635 11448 102
344 20 106
766 767 167
1104 7137 494
1818 3012 301
2068 8741 40
1905 6214 167
5900 5081 40
8341 1652 329
9280 187 252
9280 9804 252
9280 13173 252
11206 7343 518
11150 4154 786
6102 9437 1
8264 12929 633
2293 3963 560
7806 3247 129
7806 9499 129
2076 11401 129
3939 2210 114
412 7015 155
3905 168 179
1230 5464 208
1230 8542 208
1230 6339 208
7791 4205 203
4513 11750 1
10277 6931 1
5693 5573 114
10969 13628 256
10969 166 256
10969 8061 256
13700 315 114
3672 6109 221
2379 11539 199
657 6332 114
58 10653 170
3890 13543 199
3890 10320 199
2591 5790 1
10778 7093 129
5331 6681 40
552 378 277
2074 4388 578
6696 8836 98
10770 12982 102
3116 6619 297
5442 1135 1
4512 843 143
2280 7655 705
706 13520 102
706 6444 170
3487 7688 114
7108 1670 559
147 147 771
1008 8272 417
8560 6574 541
1794 7809 315
11250 5976 329
3191 9724 252
2879 3538 253
5064 9496 505
297 11950 1
3400 7486 137
833 11277 263
8414 10194 541
13012 13202 559
1953 1094 559
3623 9791 114
1016 286 1
12743 2829 129
4467 2762 155
4467 6109 155
8911 1782 167
888 4223 253
1846 2217 288
1621 1963 129
1621 5847 129
3108 1534 996
7436 2174 148
36 4003 350
36 1169 350
36 12248 350
36 13700 350
36 677 18
36 7239 18
36 6983 18
36 1075 18
36 9954 18
36 6262 18
36 6057 18
36 85 18
36 2430 18
36 519 18
5305 6826 129
1500 7055 167
3288 4238 96
2845 12624 199
7471 5343 129
1577 5981 167
913 619 764
8246 14726 527
874 6530 114
1474 456 129
1037 5825 203
9551 10602 114
374 4812 155
10076 10077 167
7676 1954 481
3133 6085 88
8556 9363 692
7197 15 40
403 8456 91
403 7026 91
403 7236 91
403 9852 91
403 8237 91
403 3745 91
403 35 91
403 9759 91
403 1356 91
1300 7246 297
8874 3437 155
7661 7745 203
4954 11421 40
4645 659 268
859 9613 113
8582 10226 129
8884 6683 98
7673 5369 155
101 3975 91
101 9945 91
101 11605 91
101 7463 91
101 3926 91
101 2222 91
101 9198 91
101 11488 91
101 12531 91
101 12448 91
8435 10060 40
2706 1880 88
4547 1594 735
11173 8308 106
4652 3115 129
10528 1693 266
9559 1606 314
3141 348 441
13268 11332 377
3539 8753 148
4166 3647 199
4166 13887 199
375 1755 113
1278 3234 342
1278 1294 342
1278 8217 342
1278 654 342
1278 13959 342
1278 2876 342
1278 5103 342
569 9886 185
7822 459 612
1915 6542 40
2210 649 221
10828 5449 129
13933 6363 179
654 9657 158
6993 10431 102
5954 14118 114
3839 308 114
3021 5609 1
3948 8863 98
4346 11408 114
2559 1402 1
731 13580 155
1464 5900 137
342 623 268
7747 8607 520
1277 12231 297
635 12625 102
635 5748 102
344 12432 106
6026 9553 114
753 2467 40
1104 411 248
4854 11799 374
1818 2850 162
1818 9852 407
2731 7394 18
2731 7306 18
1747 7438 902
3388 6201 1
7892 7760 114
1534 2537 256
1534 4088 256
7065 4262 277
1080 5679 1
14313 318 114
8749 6447 179
922 2529 315
5105 3659 221
10773 9022 167
4017 7502 660
9900 2332 40
10131 4981 106
12154 3828 388
2407 9648 114
7806 4967 129
8957 4179 129
1574 6724 114
89 4267 40
5285 7144 155
13567 8674 441
11926 4073 129
657 9856 114
58 75 102
58 13038 170
58 12091 170
58 1129 850
58 2972 894
1659 1658 481
3890 5516 199
1732 9874 297
1903 3585 88
6022 5824 289
4946 9645 40
4946 10015 374
3194 3805 297
4503 7355 137
75 8618 114
9308 7616 98
2044 3962 297
6931 754 203
4280 6489 106
3191 351 252
12754 5549 114
1836 8126 438
1836 10679 438
833 1211 263
954 592 268
385 7049 129
11106 11990 114
504 8833 297
10935 9275 277
920 8452 114
13408 4624 40
3004 3283 626
4852 1065 106
2987 1260 40
1290 12827 231
36 11772 350
36 2094 350
36 9008 350
36 2547 350
36 1665 18
36 9437 18
36 6661 18
36 5366 18
36 3870 18
36 9220 18
36 13665 18
36 589 18
36 418 18
36 884 18
36 4948 18
36 3691 18
36 5585 18
36 5860 18
6530 9435 167
7471 10994 129
2156 2781 314
4585 3504 129
10076 3627 167
4537 3158 98
5418 3345 155
4218 4217 466
403 6389 91
403 177 91
403 9657 91
403 10782 91
403 14415 91
403 8358 91
403 4083 91
403 1083 91
403 11694 91
10371 6392 33
12134 8402 155
229 1218 114
4961 10621 489
4645 6098 203
13495 1707 462
10032 3988 401
5278 1122 221
11663 9431 221
301 14159 162
301 12686 286
301 125 825
10491 286 462
101 7858 91
101 6481 91
101 224 91
101 1511 91
101 13407 91
101 14237 91
101 6981 91
101 4129 91
101 1303 91
101 11428 91
101 10270 91
101 7799 91
101 4860 91
101 12240 91
101 13928 91
101 12373 91
10276 8650 40
2706 6248 88
4824 3115 286
7022 11348 167
10069 5070 129
7027 4486 106
103 3051 106
1054 11264 208
11370 4642 129
3845 7504 434
3845 1473 434
9921 9921 82
1282 6484 155
2882 1559 234
4166 11394 199
375 6548 113
1278 4965 342
1278 4668 342
1278 5951 342
1278 472 342
569 12908 185
5841 8387 155
443 12340 894
10837 4581 98
1359 10667 98
7161 3551 560
9008 6840 114
2241 2953 199
2241 10653 199
2241 4168 199
2241 13347 199
2241 12932 199
3021 8846 1
3449 8715 155
3948 8448 203
5441 6855 40
4155 11982 716
9198 876 114
1524 5880 185
1524 12102 254
13790 5914 114
14119 10444 340
397 728 114
7747 226 520
5600 1897 518
344 3306 106
12944 3813 1001
8567 6151 1
12017 3876 266
1104 11161 941
1104 2583 248
1818 6816 286
1818 7185 949
2731 5757 18
1534 4139 256
2631 6434 40
4929 8158 155
4860 9984 114
11108 847 954
2427 5963 40
9647 9511 114
12661 4561 82
7490 6677 40
14107 2678 106
7142 6494 900
11197 571 114
4778 13186 297
2076 1326 129
1230 113 208
7664 11602 1
8776 7287 167
13181 3474 40
10969 3511 256
1509 4067 40
1498 9155 252
1498 1576 252
514 928 155
514 2917 155
17 11226 665
278 14072 170
13534 14640 185
2359 2528 253
10890 6934 40
2379 12871 199
2379 193 199
2379 13255 199
58 2672 792
58 4414 297
58 14580 790
58 1587 894
3890 2819 199
11880 2532 373
1702 5907 253
8926 9384 129
3242 4651 114
5447 2545 148
3116 12747 102
3557 4044 288
9533 4967 324
4964 1998 40
1221 11940 971
4503 4658 137
4503 9413 137
11281 11577 98
6182 7666 518
3066 7567 1
14768 11722 114
13230 6396 1063
13053 1455 656
11988 2475 40
3216 7155 167
6697 5867 203
1836 6285 438
1792 11611 541
833 1117 263
833 6617 263
5270 10732 88
4154 6621 114
4154 1841 114
385 6664 129
385 4500 129
7320 7967 1
3895 9805 88
3701 3107 340
2998 3327 340
5597 13122 374
888 1272 253
11784 9175 155
36 11578 350
36 4398 350
36 8180 18
36 9593 18
36 1111 18
36 10739 18
36 6503 18
36 5307 18
36 2894 18
36 3163 18
36 5486 18
36 5494 18
36 12417 18
36 6306 18
36 5438 18
9601 12535 82
2028 4141 114
2956 12726 581
2773 633 361
3288 10048 96
12502 5869 520
7592 805 167
1474 7686 129
4585 12817 717
2670 2027 289
1339 8661 148
3262 3592 114
2018 156 114
9018 1361 40
1914 1959 137
403 3099 91
403 13664 91
403 3630 91
403 5720 91
403 9379 91
403 12455 91
403 2335 91
403 14166 91
403 7598 91
10488 7056 114
229 5978 114
4039 6528 234
12512 6111 113
4645 2208 268
2144 9950 560
991 1558 40
859 3668 113
859 6690 113
859 436 113
6911 11517 520
101 10389 91
101 4882 91
101 6703 91
101 3117 91
101 10750 91
101 11077 91
101 8753 91
101 6114 91
101 14844 91
101 9296 91
101 14445 91
101 13482 91
51 10123 129
4841 2165 148
3423 3772 129
4652 6551 129
7419 10923 266
12148 7055 98
5135 8597 289
4136 11161 40
377 9903 114
9 8938 665
185 1791 732
6280 81 40
5032 1503 289
1278 6857 342
569 1421 185
569 8933 185
569 3877 254
569 1207 842
569 6746 578
569 8423 578
6344 6343 98
3381 13115 102
6789 12563 203
544 13726 842
9545 11025 129
9545 3298 129
1952 10141 1
8045 7444 106
8045 7088 106
4875 8969 98
3396 6276 155
1524 2592 254
1128 9919 98
11917 2143 88
635 9735 102
1820 5332 941
12944 3813 496
5847 6281 520
1104 2642 494
12423 10832 391
1818 13774 162
7186 7777 155
10738 10024 1
10738 7309 1
2731 2166 18
540 8681 114
8839 782 221
5706 344 921
334 815 732
13586 12449 114
8076 3351 167
4980 13309 82
7971 9968 114
1236 3217 221
10406 2136 114
6571 6647 1
1381 1966 102
4227 14678 199
1611 8232 424
4312 11359 329
4026 4622 465
11407 11374 315
10969 8034 256
11381 8908 129
819 3866 179
4656 2808 315
13639 3113 297
1956 4582 520
1956 11785 520
2196 11995 252
657 10310 114
657 7719 114
657 3317 114
58 5701 114
3890 8133 199
8926 5588 129
6696 4283 167
1732 11918 299
8398 4712 167
6433 9012 40
10642 6501 106
4946 1184 40
4946 9898 374
4473 930 1
13987 2117 463
4503 11244 137
4503 12490 137
4503 3155 137
4503 6770 137
75 8908 114
3164 3575 98
706 12980 170
7250 13781 629
3572 11363 143
3066 6350 1
4280 1705 106
3244 3230 129
4360 4355 315
5064 4300 505
13033 8547 1
1836 6347 438
3400 12741 137
833 97 263
833 3327 318
8951 9533 167
5360 5371 114
11871 11619 114
1113 3007 888
9629 11070 40
3550 10077 167
1111 1110 98
239 5670 114
1858 904 148
7436 2174 98
36 1587 350
36 9041 350
36 5503 350
36 11881 18
36 4350 18
36 14052 18
36 10519 18
36 7692 18
36 5800 18
36 5418 18
36 5686 18
36 10732 18
36 5596 18
36 8490 18
36 3208 18
36 11082 18
36 668 18
36 315 18
2940 4556 167
2028 9669 114
10280 5361 315
3288 7276 96
9055 9054 40
2845 7561 199
1474 9668 129
1474 6292 129
1474 1905 129
10709 12184 129
10865 10123 40
2211 845 98
7082 9772 660
8556 8458 692
6261 9869 518
403 3956 91
403 2013 91
403 7355 91
403 5515 91
403 11686 91
403 6343 91
403 4300 91
403 6244 91
403 9010 91
403 5311 91
403 416 91
4587 10644 40
4961 7055 106
1239 7804 329
991 4419 40
5281 5603 143
3841 2663 148
859 1316 113
859 2041 113
859 2117 113
859 3601 402
715 2254 297
1076 4358 324
2030 130 203
2820 563 155
6810 1584 373
301 10562 407
101 8029 91
101 1432 91
101 1440 91
101 12965 91
101 9129 91
101 2405 91
101 9265 91
101 9030 91
101 1225 91
101 7809 91
101 8786 91
101 8473 91
101 13690 91
101 540 91
101 11100 91
101 3702 91
101 13710 91
101 8263 91
101 5091 91
101 9423 91
6190 647 253
2706 3703 88
2706 11931 88
4824 9690 96
4824 462 286
6365 2611 692
1585 5388 391
3423 4361 129
5071 11042 512
7419 6487 376
9559 6617 88
1054 2386 208
3141 4020 441
9622 9623 82
9622 583 82
7617 5222 155
2637 7438 487
11370 11005 129
8465 2369 114
5982 12614 82
9069 6306 277
377 9774 114
9 9445 500
9 2996 500
11623 8277 98
4166 4634 199
375 13159 113
14075 3618 315
13846 9025 340
569 12961 185
3828 7302 814
1915 8812 40
11888 6082 732
6032 648 1
1098 1097 98
4574 6053 622
264 14113 88
12126 12343 40
7151 2754 252
2241 10775 199
7577 5824 1
7533 8024 340
732 5489 315
1820 6032 944
344 6218 106
3513 3039 167
3513 7388 167
753 1318 40
1818 5403 494
2731 11879 18
2731 6586 18
12303 4751 40
10213 4292 179
3485 1097 203
9402 2854 155
3255 3058 221
8514 11182 775
12124 10436 221
10748 7221 40
4879 7340 155
8264 14203 633
2689 9374 810
9184 1384 106
5498 2613 773
1230 11792 208
4528 4527 155
4422 6530 40
5786 2577 113
1498 12183 252
5375 5207 944
17 8356 665
17 6270 500
7952 1507 377
2196 2092 252
2379 13475 199
657 8193 114
6567 5297 203
3890 10798 199
3890 12140 199
500 499 253
2074 9510 578
4990 10362 465
4990 1890 465
6027 339 106
12238 2455 148
3488 291 413
3546 12797 1
6490 10458 114
4067 2893 167
12152 9171 1
6535 6281 98
135 1992 277
3572 1231 143
4280 9754 106
10003 2626 203
7800 13569 40
6881 4085 329
4417 8372 155
983 12381 894
5571 6301 40
3216 3165 148
3400 10943 137
833 11724 318
9783 1998 129
5246 2969 203
586 11559 266
5220 9492 203
5007 5827 623
5007 10649 623
247 11060 297
13035 1732 199
504 2308 297
504 13723 102
3701 268 340
7160 2867 33
7805 2886 208
9135 2595 155
1846 3358 288
6278 2015 1
13089 10023 488
917 12491 1
36 7452 350
36 3342 350
36 213 18
36 7198 18
36 6677 18
36 9272 18
36 5487 18
36 910 18
36 7665 18
36 11268 18
36 8406 18
36 5192 18
36 11361 18
36 5210 18
36 4070 18
36 9130 18
36 3732 18
2370 910 1
673 13303 1
1474 10068 129
9938 1147 1
11597 11087 129
4585 13483 129
3094 847 329
5409 7271 88
2018 8796 114
7532 13942 1
403 2571 91
403 7016 91
403 9914 91
403 5144 91
403 12408 91
403 243 91
403 12312 91
403 4603 91
403 11031 91
403 10406 91
403 4723 91
403 9835 91
403 4406 91
403 10194 91
403 8295 91
403 13232 91
399 1007 167
1847 2758 40
4954 6561 40
2187 8840 114
5472 200 33
4986 6112 934
7071 2703 421
2300 7207 82
7603 11022 148
859 10192 693
859 5606 113
715 2216 297
7159 10644 40
10445 7838 40
101 7624 91
101 12267 91
101 7613 91
101 10431 91
101 2357 91
101 5568 91
101 4544 91
101 14002 91
101 10494 91
101 8104 91
101 13366 91
101 12824 91
101 2877 91
101 13810 91
101 4935 91
5295 9316 40
5295 456 40
4407 1203 129
12856 12503 155
4824 8376 286
131 1884 148
131 6575 167
4652 3172 129
1054 7649 208
2637 4665 487
258 257 664
53 4443 438
9 9445 665
9 8423 500
12209 14809 851
4166 12485 199
2416 8524 40
3256 1549 33
375 10530 693
375 10865 113
375 11844 113
375 3117 113
375 6760 113
375 13913 113
1278 2812 342
6279 11726 137
13960 2711 846
569 12029 185
569 8292 185
569 13912 185
6502 649 98
3381 14291 102
7546 3937 466
2241 58 199
5113 7597 518
3021 3888 1
931 7983 505
1043 1061 297
11939 2833 324
5951 13014 299
6313 6775 819
3329 4582 155
8489 7832 98
3513 6538 167
9895 3666 253
1274 8028 402
1818 1594 494
10027 12595 475
2731 4944 18
2731 4460 18
18 2382 167
11128 6077 289
3485 5974 203
10139 1734 750
1508 3996 549
11694 6733 1
9258 2854 155
11762 945 106
1994 2883 1
7442 4439 114
2425 4672 98
11423 9219 1
1236 12927 221
1236 8224 221
7583 6026 199
2689 6931 649
4485 10395 518
11586 3039 106
89 374 40
2066 5482 304
9156 4938 161
9447 9208 155
11053 6104 985
10969 3513 256
1509 7627 40
11729 10202 161
12679 6071 413
7227 3293 462
2146 12711 102
10006 8625 520
13534 13712 185
2379 13231 199
2379 12271 199
6951 9450 277
7602 12418 129
657 13175 114
657 6095 114
657 7515 114
58 2588 277
58 1300 299
58 7308 541
5852 791 629
4532 4981 155
4990 2650 562
4463 11670 82
3880 1387 98
5163 14030 297
8995 2758 277
1059 8828 481
8317 7124 148
4503 12051 137
4503 6907 137
1767 5068 238
7250 9479 277
4419 452 155
3244 4049 129
11396 3144 797
3191 4964 252
3698 6753 114
833 7236 318
4154 12557 114
2372 8040 40
7856 4244 314
7318 736 315
3895 11881 88
11177 6854 88
12290 9875 340
2095 4162 309
5637 10393 114
1477 3127 148
1858 1152 167
6774 6677 40
769 2862 167
36 2085 350
36 13224 350
36 2815 18
36 7661 18
36 11383 18
36 9478 18
36 11002 18
36 6432 18
36 5773 18
36 8249 18
36 9016 18
36 439 18
36 2062 18
36 10317 18
36 6884 18
36 1996 18
638 4242 756
3224 12832 374
2185 11673 40
9414 4895 129
2845 11883 199
6443 4101 1
292 5184 148
1474 8890 129
1474 6847 129
1474 12829 129
2156 1396 314
11185 4262 1
4265 10527 221
3262 10170 114
5292 1870 155
2018 8659 114
2018 8480 114
2018 2981 114
7594 2754 1
403 5998 91
403 3926 91
403 1551 91
403 5082 91
9231 7345 297
13953 14534 266
223 931 289
3416 8344 203
5128 5279 40
9733 6445 40
11469 6931 315
2810 4206 253
859 721 113
715 2586 297
5779 6757 268
101 12130 91
101 3662 91
101 12910 91
101 8037 91
101 12912 91
101 6452 91
101 5527 91
101 3537 91
101 55 91
101 4611 91
101 6430 91
101 3185 91
101 2386 91
101 7486 91
101 13441 91
101 7557 91
6681 3236 148
2706 5853 88
4824 4300 286
4824 13301 286
4547 456 735
8049 1103 114
7027 5941 106
1054 6337 208
7357 10832 391
6084 93 203
11314 5040 402
5361 5694 155
3845 9879 434
12094 2544 1124
3232 9208 391
1130 5913 443
3539 8693 98
3539 3538 98
4166 1044 199
375 9051 113
375 4303 113
375 13455 113
375 12545 113
375 9148 113
6130 537 1
1278 12363 342
13143 4150 88
569 11 185
569 7827 254
6502 6501 203
1033 3735 129
4574 12634 622
26 4138 167
13043 13016 870
8588 5910 518
7151 7166 252
2241 13932 199
11358 14436 248
2358 621 629
8764 2243 518
10425 5259 221
5439 6428 155
344 6212 625
13387 12037 1134
1818 8452 286
1818 7053 301
1818 4619 301
3208 10643 155
2731 12466 18
2731 2608 18
2731 8501 18
14080 5278 40
7065 6045 696
1508 13743 549
2805 3878 361
12279 1958 148
9647 12123 114
4178 8761 40
1994 10797 1
5778 2745 114
11839 2588 1075
10131 2109 106
10561 6207 277
6882 1323 277
4066 5771 203
5098 10282 129
6371 4963 33
998 12958 40
9542 12329 927
7113 7112 762
2076 1822 129
412 7501 155
3319 13182 114
2384 231 612
10615 1507 98
9156 9059 40
6876 6946 251
11572 9089 114
8903 7343 148
7873 1572 1
5375 2496 494
3231 7524 40
2196 3994 252
10685 12916 40
11926 3434 129
657 6692 114
58 143 792
58 10047 170
58 12773 170
58 10906 170
58 6512 170
58 9013 170
58 13742 170
58 7466 894
3890 11889 199
3890 14096 199
6395 7872 148
5892 4181 40
5840 5839 1
191 5603 635
4946 3380 40
11281 10572 203
706 4971 581
7566 810 489
13328 862 167
3244 11173 129
3244 1310 129
3244 10927 129
10481 12985 297
3191 11844 252
7074 2774 114
5571 7033 40
1288 7921 106
3400 9577 137
833 7265 263
4154 2268 114
4154 3226 114
7856 9081 314
854 274 221
8037 846 40
11177 12666 88
381 3546 340
7472 13594 581
239 9120 114
4126 9286 253
769 5774 203
36 13628 18
36 12611 18
36 6165 18
36 6808 18
36 10121 18
36 5499 18
36 8190 18
36 1851 18
10021 3585 129
2773 5517 361
3224 12161 374
6530 1195 167
6187 6012 106
2503 945 148
1474 5412 129
1474 10574 129
1474 9283 129
9337 698 167
9316 10546 167
1598 2392 732
8756 1829 253
7594 5963 1
403 13520 91
403 511 91
403 7956 91
403 680 91
403 6112 91
403 10954 91
403 2745 91
9807 14194 88
6107 7457 221
991 7947 40
859 12573 113
715 8639 297
6957 7375 88
301 12808 162
101 4729 91
101 13958 91
101 9910 91
101 4551 91
101 4010 91
101 1310 91
101 8266 91
101 8021 91
101 3048 91
101 4713 91
101 2037 91
101 10080 91
101 10432 91
101 8748 91
101 7426 91
101 4064 91
101 13255 91
4407 4415 129
7839 7567 114
2706 12967 88
2706 4891 88
51 1613 129
2212 884 1
4652 14297 129
103 793 106
5341 5178 148
4771 2638 155
3441 10386 203
2882 890 234
1612 7026 88
8838 2042 663
4166 4159 199
375 10045 113
1278 4093 342
8443 11785 98
5772 4415 1
6279 1209 137
7576 3833 40
545 504 143
6032 11590 1
2766 12638 527
11948 3377 155
26 6309 167
9893 2716 179
9545 8905 129
8395 4739 203
7934 6214 167
332 6059 113
2849 2848 137
2559 8876 1
9198 5480 114
11786 1913 114
6313 6608 819
1524 3059 254
1464 12136 137
13863 9784 1032
7747 7597 520
635 14366 527
635 14732 102
8917 9175 148
1820 9374 494
1820 12301 944
8719 5658 518
7210 11319 40
1104 2484 248
1818 5980 649
1818 8434 301
2731 1246 18
2731 712 18
2731 8317 18
10139 6552 750
7300 3856 520
2434 2630 167
9280 8601 252
2328 2246 102
7299 3333 88
6296 10048 203
486 14635 248
486 5411 248
3702 10317 329
2108 6552 311
8264 10725 633
7901 8155 40
8692 3217 148
4873 2879 40
1574 6763 114
1230 2023 208
1230 3459 208
4528 4250 155
1509 10886 40
2442 10150 40
39 10787 155
17 4298 238
2379 1412 199
9357 4769 1
657 10994 114
6851 2218 167
58 2927 102
58 9910 170
58 6360 170
58 4827 850
5892 10857 40
6779 468 106
14460 6396 256
4946 4655 40
4503 13845 137
3143 5517 40
75 4825 114
2280 6748 743
706 3241 170
6202 9800 374
1337 11058 203
2399 973 167
8364 6509 40
833 4763 263
10807 7243 1
7643 2851 106
7728 9166 148
5007 12476 623
13814 8860 98
1207 11946 266
3550 1472 167
1111 6098 268
10342 11322 114
3503 9340 465
6044 6530 40
1336 11442 98
4899 12213 40
1621 5686 129
36 9601 350
36 14324 350
36 12729 350
36 6739 350
36 1533 18
36 13284 18
36 6077 18
36 3153 18
36 9903 18
36 3052 18
36 3673 18
36 6174 18
36 3789 18
36 6994 18
5305 2901 129
12911 12419 1
6875 2699 185
5572 2769 40
8830 90 40
325 324 377
12502 2435 520
5685 5590 155
132 6893 221
6332 4981 167
10709 4485 129
13167 14325 627
11356 9378 347
2018 6429 114
5483 4171 114
403 4197 91
403 4645 91
403 6118 91
403 1127 91
403 9735 91
403 8164 91
4677 10980 82
229 3434 933
4986 2321 277
991 9633 277
4783 1357 203
301 10142 825
101 3532 1013
101 12051 91
101 14209 91
101 6320 91
101 10955 91
101 11391 91
101 5454 91
2706 5804 88
13701 11021 1
8510 6389 252
2010 12807 297
4224 1486 40
4224 6863 40
265 4349 203
1130 9252 443
14520 14924 851
3107 10062 1
8673 3144 98
807 8127 203
375 12349 113
375 7308 402
6279 8435 137
569 12681 185
569 4382 578
1683 2881 438
5616 6748 526
3424 11387 289
613 5392 560
13995 13356 361
4086 6815 148
2241 3883 199
2241 8902 199
2241 13305 199
1260 7343 520
332 5950 113
332 4740 113
332 9076 113
3883 3883 82
11743 5076 114
6603 12942 88
4295 8162 401
340 12972 82
635 4114 458
635 11323 102
635 13890 102
1820 11438 494
1820 9060 494
344 6045 625
766 8469 167
1415 11457 243
8226 6593 722
1818 4122 286
2731 6084 18
1534 11840 256
1905 8940 167
3009 6784 33
6965 2761 106
4874 908 40
9280 8211 252
4659 10720 329
6432 11924 155
4017 1775 660
4093 6326 289
5977 9685 40
4227 1303 199
576 3202 167
4161 7622 167
2076 7663 129
4873 1664 40
412 6225 155
1230 1229 208
1230 3228 208
8445 8416 253
3672 3468 98
3672 6109 98
17 3196 238
17 2743 238
1698 6554 1
8799 11803 199
10132 4411 148
6951 9450 361
559 9412 113
657 3093 114
657 8443 114
58 12932 170
58 3891 170
58 8615 170
58 6306 541
3890 1465 199
13566 3678 106
4990 1395 465
328 7905 161
1410 10034 102
3546 298 1
8024 9493 1
1622 4757 268
4512 278 143
4503 13800 137
4503 7044 137
3356 2806 98
3572 9277 143
9352 13950 297
3244 5639 129
2201 3371 248
3191 5571 252
1836 12967 438
3400 7847 137
3400 13032 137
833 5740 263
12064 13318 82
3182 7291 161
385 3000 129
5276 8745 329
1111 659 203
12523 4240 1317
2998 1643 481
5637 2769 114
1846 5381 288
1477 10374 203
6103 3867 203
36 14633 350
36 14184 350
36 5984 18
36 4985 18
36 11322 18
36 6093 18
36 11070 18
36 7896 18
36 6153 18
36 3844 18
36 5531 18
36 3687 18
36 9612 18
36 6561 18
36 5876 18
36 1380 18
36 7314 18
36 3330 18
36 1841 18
36 2340 18
36 2938 18
5305 3402 129
2773 7383 361
2960 6520 315
5904 6856 33
2185 2184 40
6818 1331 179
2148 9561 340
8886 10544 324
1474 894 129
1474 13462 129
1474 9478 129
1474 6296 129
1037 5077 203
7128 10255 581
4585 4133 129
2211 6362 167
8556 3245 692
6261 3679 518
403 4158 91
403 5865 91
403 4817 91
403 8457 91
403 12003 91
403 1771 91
403 7076 91
403 1043 91
403 2631 91
403 2103 91
5373 8945 167
991 1458 40
991 9103 40
12623 4983 297
10032 4403 401
2820 1350 155
301 12156 286
8895 782 148
1067 11492 167
101 12459 91
101 2538 91
101 7646 91
101 426 91
101 14859 91
101 5371 91
101 5587 91
101 3178 91
101 10140 91
101 4176 91
101 310 91
101 5528 91
101 8880 91
101 4156 91
101 4281 91
101 610 91
14101 9849 1127
4824 8406 286
9265 8980 155
10050 853 1
10069 8482 129
4652 13266 129
14248 4057 129
1054 9901 208
2637 5148 487
12084 7353 33
9 1830 665
1612 10107 88
48 12967 277
4768 6266 167
375 12921 113
375 9203 113
1278 1300 342
2797 7284 148
6279 2787 137
569 3985 185
569 12370 185
569 7707 254
4584 9166 377
7939 1238 1
7822 4177 612
5615 1844 221
8588 7656 518
7151 4281 252
4357 4479 462
10082 3734 221
1043 7389 299
12476 3513 161
11296 254 900
397 2267 114
7077 1402 417
10906 5645 82
4237 354 88
2479 10479 155
6618 5280 137
1415 1966 243
5424 7192 155
8260 7987 1
14438 14929 559
6830 4731 40
1818 6554 162
1818 2683 649
2731 4244 18
2731 3333 18
14080 9863 40
8189 973 148
5207 14738 619
12609 10869 40
1534 433 256
11072 12559 88
803 8554 102
10811 9658 167
9280 10823 252
7078 4208 203
4178 2377 40
5812 13482 297
7442 6934 114
4017 4543 660
5376 6006 277
1236 4250 221
8264 3846 633
11773 7996 40
9747 6033 82
7806 1351 129
6043 10637 434
1574 2752 114
1230 9803 208
89 6334 40
7286 3585 114
12656 9407 179
2640 4820 129
2640 11102 129
2640 774 129
1509 11808 40
1514 6986 520
819 421 179
657 7075 114
657 9258 114
657 5368 114
657 8389 114
58 13249 170
58 2453 170
58 9532 170
58 10789 170
11272 4158 315
2074 10450 578
5852 11913 161
5852 369 629
1732 12323 170
3116 11056 297
5840 1673 1
5404 3136 462
6433 7555 40
6433 1253 40
6769 6489 167
6769 7759 167
3557 12349 288
3557 7817 288
5729 8365 98
4503 13791 137
9983 2218 520
2412 1783 114
789 5834 487
6389 11289 277
7586 8101 40
5571 6696 40
3400 376 137
4154 13188 114
5360 11021 114
62 6084 40
9855 7437 40
920 9852 114
10168 1567 179
2095 1017 309
303 8879 607
1477 10318 98
6103 3366 167
13917 9007 622
13917 6574 622
6774 5168 40
769 773 167
12965 3248 40
36 13810 350
36 3999 18
36 10783 18
36 12592 18
36 399 18
36 4772 18
36 4353 18
36 5300 18
36 3329 18
36 3317 18
36 2606 18
10021 8956 129
8019 210 1
650 11498 268
2661 3666 167
2845 4291 199
1474 10677 129
1037 5077 167
14749 2695 351
3094 5210 329
9338 1633 167
3262 12658 114
3262 594 114
2018 7306 114
7594 11706 1
403 3277 91
403 10219 91
403 6997 91
403 2787 91
403 11886 91
403 11788 91
859 3769 693
859 4240 113
859 2014 113
715 2107 297
715 4919 297
9548 13828 1
9910 9910 82
101 2009 91
101 7207 91
101 2883 91
101 5413 91
101 8563 91
101 2574 91
101 7492 91
101 11909 91
101 1467 91
101 9342 91
101 13066 91
101 14037 91
101 6438 91
101 3319 91
101 6173 91
101 13353 91
4407 4695 129
3072 6792 203
6889 7653 854
7478 7874 601
9559 6380 314
1054 2154 208
3610 5721 40
2882 8758 796
8838 4070 735
4166 4748 199
375 6045 402
11479 90 289
260 6006 40
569 10740 185
569 7752 185
569 4132 185
4584 1127 401
11948 2677 155
7139 13933 582
3332 3628 324
2241 1848 199
2241 4424 199
2241 6793 199
1043 12718 346
3948 8493 98
7629 1323 424
3474 5700 221
11743 10533 114
6603 8284 88
1524 4409 106
1464 2372 137
10424 8631 167
635 1641 102
344 12530 625
5424 4963 155
1818 6386 96
1818 11821 286
1818 8180 301
1818 5438 301
1818 2730 301
1818 13354 301
2731 5573 18
5207 7508 381
8959 10899 129
11072 14280 88
7065 354 696
7049 7143 203
1266 3071 253
8749 295 179
14146 13601 114
7442 6180 114
4017 5757 660
4017 4195 660
176 5583 297
1140 119 167
3190 8831 40
14107 6336 462
7816 1184 40
2553 3714 106
8306 3480 253
2285 11619 40
3974 7234 40
13423 8876 129
2076 337 129
89 358 40
10312 3498 114
8046 7371 417
7422 10759 98
5786 9794 113
9732 5706 203
7227 9504 462
4656 1319 315
5820 5475 520
5820 3462 520
13534 14665 185
7952 5647 401
2196 13173 252
414 11817 559
58 1586 792
58 7246 170
58 3655 894
3890 4270 199
3890 1270 199
3890 2818 199
5725 2189 221
10778 3172 129
5852 273 629
6940 7115 520
9214 6755 155
7219 1204 114
8024 10039 1
6433 5341 40
4964 6344 40
1221 11525 971
7670 6069 40
4503 8889 137
2352 3913 98
14687 6723 405
12252 13415 481
3572 295 143
5448 4099 148
789 11087 487
12538 8914 114
3244 3229 129
5064 9984 505
3400 9094 137
8927 7774 1164
3784 7573 243
1207 4974 376
3895 12406 88
1111 3598 203
13974 2269 203
8071 2680 40
14342 5308 114
3503 10349 465
4126 5665 253
3004 8933 266
5399 13059 957
6774 4321 40
2987 2375 40
36 8002 350
36 12549 350
36 2971 18
36 7472 18
36 8482 18
36 9554 18
36 774 18
36 12439 18
36 7677 18
36 4481 18
36 12907 18
7392 10527 520
7238 3008 40
11641 6193 129
1474 8687 129
1060 3924 340
4585 3498 129
10869 1120 98
2670 7833 525
5266 2606 381
3560 12613 340
10407 10544 40
798 2833 663
10865 8885 40
10017 10228 221
396 6144 98
1800 3601 40
403 2569 91
403 10156 91
403 7684 91
403 11044 91
1250 213 1
11718 6247 106
12487 7137 314
859 9010 113
715 314 297
1542 5951 441
301 11970 286
8895 5474 98
101 9301 91
101 10481 91
101 12284 91
101 12660 91
101 8152 91
101 13717 91
101 13291 91
101 4217 91
101 8644 91
101 9322 91
51 517 129
4547 6850 735
4547 9398 735
4547 4070 735
4652 11604 129
7419 10939 266
7027 11982 106
1054 942 208
3477 13326 102
6534 5917 484
3845 6912 434
4224 9012 40
53 8291 438
11752 6306 462
8838 9638 663
4166 5546 199
2797 2798 167
569 585 185
14357 10282 114
264 8592 106
2136 2135 155
11215 2443 347
4741 8846 114
10551 3734 268
2241 14026 199
2241 13414 199
2241 12414 199
3872 5834 1116
5606 11317 481
5614 9252 592
4295 6931 649
7437 9885 167
11202 4374 148
635 14748 527
344 8187 106
344 5526 625
1209 7576 374
1209 5497 374
5641 14515 297
9895 3537 253
2082 12938 297
2191 2027 438
8596 2407 1037
7895 10779 203
10263 10559 622
8936 9072 167
10015 3603 40
4929 1192 155
7049 1216 167
154 2514 764
1266 2116 253
1813 11696 167
7299 8254 88
11839 7629 1075
11186 13774 1
176 9947 297
176 13118 299
1140 3575 148
2172 4099 98
2407 9633 114
4227 12711 199
12525 12239 520
1230 7570 208
9726 9321 40
608 5771 148
13152 11779 340
646 647 203
14478 8231 179
559 13805 113
657 5017 114
657 4248 114
657 12159 114
1702 4534 98
6111 6808 40
4990 5957 562
5085 8842 106
8656 7009 114
3116 1537 102
8716 11099 297
12788 1913 256
11130 9164 167
4503 6002 137
706 11781 170
7589 6794 114
6202 10960 40
6202 217 40
60 1679 148
4280 3982 289
8766 9692 148
8806 11630 40
14428 1228 234
833 10996 263
833 2029 263
149 3584 520
2035 1337 114
3063 9701 381
385 3201 129
385 1236 129
1432 7789 114
2432 12635 297
11177 8001 88
381 11764 277
2998 11238 340
347 9252 462
2216 6885 297
7436 4800 203
36 12514 350
36 3299 18
36 4229 18
36 10282 18
36 727 18
36 3365 18
36 4010 18
36 7158 18
36 6015 18
36 5540 18
36 6526 18
36 3971 18
13554 3293 1
2028 10164 114
650 8253 268
3224 9553 40
1500 1943 167
7471 712 129
8830 123 40
12115 11704 405
673 9526 1
8438 7408 40
2357 4053 324
2503 2504 98
1474 9278 129
3560 6287 277
3262 2541 114
11356 10701 347
4537 6797 167
403 1741 91
403 2166 91
403 10853 91
403 8458 91
403 1989 91
403 10620 91
403 7138 91
403 4893 91
403 7875 91
403 9799 91
403 4089 91
223 12363 525
4645 1110 98
12982 7100 114
6237 9479 622
779 9870 560
3930 539 388
7381 7381 465
101 6780 91
101 2147 91
101 8123 91
101 11701 91
101 7493 91
101 9562 91
101 9696 91
101 5687 91
10276 9680 40
2706 7709 88
2706 10421 88
4824 3834 96
4824 5724 96
4824 4895 286
51 100 129
9265 3366 155
5341 3407 148
5135 5961 289
11078 8175 40
6593 787 559
3107 8118 1
8838 11367 663
4166 14483 199
1278 6187 342
4204 3321 347
11011 3056 253
5912 8728 129
3424 8272 289
5992 2342 391
1033 10609 129
1033 7849 129
443 2234 541
10269 10781 114
7506 8894 40
9545 11183 129
1725 6702 82
4086 8287 203
4086 5369 203
2667 3172 289
2241 11291 199
2241 12936 199
2241 9544 199
332 8449 113
2860 1441 560
13310 6064 137
2358 9101 629
941 4757 167
7678 5712 129
397 7681 114
9550 928 520
8719 7579 518
1104 10100 941
1818 5149 401
2731 2639 18
2731 1718 18
6747 1921 185
7065 10831 696
380 3087 88
5900 5443 40
9280 5399 252
2805 868 361
7889 10817 114
11762 343 106
12240 1128 114
8010 11061 40
7299 6107 88
6102 11810 1
176 12977 299
12101 7437 40
10543 756 314
4730 5296 98
5107 9457 329
4814 4096 329
7418 7122 114
5453 8878 179
3916 5804 462
2076 722 129
412 9360 155
4528 553 155
6867 10764 82
89 10220 40
2384 194 612
5344 8771 1
3945 2558 1
2506 6107 622
2379 9399 199
8202 4945 221
58 3829 792
58 7655 458
58 657 102
58 14003 170
58 4310 170
58 1355 170
58 11731 170
3890 6068 199
10 8938 238
2052 1256 203
5133 2571 167
5133 10048 167
5133 4351 167
1221 3900 830
4512 5312 143
3773 3772 40
1001 9188 732
4503 5811 137
4503 13594 137
8077 3040 1315
12187 14444 875
4280 9164 106
584 5449 40
687 2921 155
3100 11800 902
1836 12058 438
833 12911 318
4154 12329 114
3358 6900 40
385 3865 129
11106 7053 114
11294 5044 696
5276 606 329
1846 2674 288
7209 3522 155
6222 6221 155
6222 6501 155
36 4226 350
36 14300 350
36 699 18
36 2892 18
36 5430 18
36 6713 18
36 5602 18
36 3349 18
36 11215 18
36 7934 18
36 11838 18
36 2757 18
12841 987 40
6858 5932 40
2845 9590 199
1577 1576 167
12177 11279 129
9247 12455 88
7601 4619 40
6332 6258 167
5326 3737 161
1775 9463 251
5180 2346 619
403 2960 91
403 7620 91
403 2411 91
403 3619 91
403 6238 91
403 2193 91
403 1169 91
403 6104 91
403 7559 91
403 10554 91
403 6491 91
403 6764 91
403 5040 91
2674 7128 161
1250 8274 1
6144 8551 324
4645 6098 98
991 11011 40
7278 12827 714
859 6112 113
13148 8561 277
301 8914 162
101 12233 91
101 2272 91
101 4921 91
101 11000 91
101 6185 91
101 7673 91
101 12808 91
101 390 91
101 10777 91
101 457 91
101 12862 91
101 4977 91
101 4025 91
101 4483 91
101 14661 91
101 10547 91
2706 2198 88
4547 10310 735
8570 607 1
4771 8850 155
6366 4754 40
2637 9052 631
12084 297 33
7409 12175 340
2882 4772 234
1057 11398 391
4166 12523 199
375 9336 113
569 4221 185
569 3177 254
5616 2665 1116
545 3123 143
613 10358 560
264 12146 88
264 9059 88
9987 8004 98
11467 7465 82
676 6901 407
2358 2963 629
1524 1693 185
2505 4159 875
1464 13698 137
1277 9880 297
635 12202 170
344 6845 625
1818 5742 407
1818 5486 301
7186 5532 155
2731 2743 18
979 8777 199
11540 2237 179
6943 1185 520
11108 14888 1196
1618 13358 560
3521 3923 289
10737 7568 114
4017 8579 660
8076 6098 148
176 12713 102
14932 8112 1071
9948 13380 137
4026 6628 465
11176 8189 40
5786 389 113
2442 1330 40
1073 5506 560
11731 12615 82
278 2916 102
1785 12764 527
2379 9494 199
559 6044 113
657 3356 114
657 6166 114
657 7537 114
58 6950 792
58 6631 792
58 10186 170
58 11827 170
3890 6081 199
3890 14006 199
3890 278 199
12092 8334 82
4990 968 465
4990 134 465
3242 13765 297
5447 8484 148
13907 13907 465
9214 11115 155
3116 2628 114
3557 753 288
1903 1031 88
4702 5979 692
3567 7979 155
5993 9792 373
4503 4549 137
4503 6121 137
4503 2229 137
6518 1110 155
6182 12035 518
7858 1654 203
2297 3500 253
1008 7309 933
7566 3144 106
76 11445 866
9192 6537 148
3191 5875 252
7744 1461 1
3100 6270 902
3400 4335 137
833 4082 263
833 2831 318
4950 1359 301
4950 8726 301
5246 3272 98
1411 3712 1
7728 7727 148
385 8812 129
11558 13019 40
3503 3731 465
5997 9854 114
6044 5913 40
6472 6768 203
1621 3727 129
2987 2405 40
2055 8884 329
36 4933 350
36 10891 350
36 10663 18
36 6289 18
36 5316 18
36 9637 18
36 319 18
36 921 18
36 2329 18
36 3486 18
36 4086 18
36 7201 18
36 2853 18
36 633 18
2956 4614 989
4957 1329 98
6875 13142 185
7392 10074 520
6443 4868 1
913 1032 764
7592 6136 167
1474 9894 129
1394 5582 167
4585 6591 129
5266 208 381
798 1319 663
5409 5691 88
5166 1400 98
8556 6924 692
403 6212 91
403 1125 91
403 1593 91
403 14517 91
403 3347 91
403 5804 91
403 12724 91
403 2805 91
403 1946 91
403 1236 91
403 9118 91
10628 10355 401
5373 601 167
10525 6448 114
10929 9637 40
715 8161 297
7580 11706 114
6116 14147 1
1959 4067 40
2684 417 277
301 10833 96
301 13243 754
101 9097 91
101 4654 91
101 2456 91
101 11180 91
101 1775 91
101 11786 91
101 10578 91
101 5280 91
101 1574 91
101 8331 91
101 10134 91
10155 3357 203
2706 10526 88
4824 2596 96
6365 11941 692
4547 5631 735
1585 8348 391
6165 4208 98
4652 10099 129
4652 6720 129
6624 6623 40
103 11387 289
1054 6461 208
1054 1837 208
3281 9899 373
185 10381 732
8838 8811 663
3539 1743 98
4166 8041 199
3688 839 203
375 1245 113
375 8486 113
375 7954 113
375 4373 113
375 8703 113
1278 10595 342
2797 5941 167
6279 2427 137
569 12467 185
569 9758 185
5912 6021 129
11948 3029 155
7260 4053 324
26 6864 167
4357 3001 462
9545 467 129
12695 11102 114
11852 9657 525
2241 13379 199
8053 2497 114
4338 1374 129
4932 9692 155
931 8470 505
2904 7379 1101
12781 2966 924
2849 11558 137
4263 4177 129
774 8308 167
5723 2259 88
5199 6527 155
397 5988 114
342 1752 268
13863 2566 1032
8719 5805 518
9895 3257 253
1104 4941 941
3050 2621 505
1818 4624 162
1818 457 286
1818 7057 286
1818 9032 286
3208 11882 155
2731 2396 18
13717 6219 40
6747 6424 185
9059 7192 98
118 11015 102
1140 6375 167
7490 4642 40
3452 7248 33
6371 7192 33
7806 1838 129
11744 9725 98
2076 3210 129
3939 7215 114
3905 9901 179
10339 2715 520
4903 3459 106
1509 8812 40
6145 13964 114
7952 5467 377
7952 1981 377
3231 884 40
10132 3659 167
657 6900 114
58 13088 170
58 11451 170
58 8219 170
3890 14292 199
12334 638 546
6940 1340 520
10635 678 758
9042 11831 82
13521 3332 106
1794 2646 315
10003 2626 167
3244 8036 129
2879 5296 253
1801 7230 289
11883 1461 114
1836 8907 438
3400 48 137
3400 13478 137
833 12160 263
833 4917 263
11010 4268 518
4154 7145 114
13272 4359 40
10935 3969 277
4044 10945 40
3569 2952 167
3303 11984 106
3303 3856 106
546 12709 170
36 13821 350
36 4342 18
36 2244 18
36 10609 18
36 2288 18
36 5223 18
36 1002 18
36 8206 18
36 9502 18
36 8597 18
2028 11507 114
14604 9488 622
9230 6934 40
1458 9415 167
1389 13199 40
8438 8610 40
1474 519 129
4585 1247 129
3422 3622 221
2670 7833 607
4218 2239 466
403 748 91
403 2432 91
403 10993 91
403 9677 91
403 6469 91
403 7330 91
403 1682 91
403 5497 91
403 8169 91
403 7078 91
403 1198 91
403 13322 91
403 6775 91
403 1718 91
403 4791 91
3058 92 1
7661 7068 98
229 228 114
1802 3778 438
1601 3349 315
2590 7408 40
715 6208 297
715 8629 297
8582 13512 129
2574 3001 114
2574 11466 114
13806 3421 114
9628 5913 694
5278 150 221
9416 3562 148
2684 4818 277
1817 2969 518
3494 9380 114
101 12758 91
101 4482 91
101 9074 91
101 6220 91
101 389 91
101 13330 91
101 1501 91
101 269 91
101 11495 91
101 14432 91
101 11862 91
101 1400 91
101 12299 91
101 9132 91
101 5093 91
10276 1218 40
1967 3294 114
12561 10524 88
51 6231 129
6748 2624 347
5073 3777 155
377 7110 114
9 318 665
3504 6157 148
1278 1592 342
1278 14459 342
1278 5682 342
569 3427 185
569 11559 185
569 9907 254
5912 9975 129
7151 1000 252
9545 2521 129
5954 13119 114
2241 6471 199
2241 1631 199
2241 8656 199
8998 4268 167
409 758 82
332 9372 113
4346 5127 114
13021 4150 40
2849 13264 137
4263 417 129
1524 6521 254
1464 4620 137
3983 10029 114
635 14357 102
9550 4344 520
7387 7659 98
1209 11328 374
2509 10918 289
189 5161 155
1818 11326 494
1818 4637 286
1818 3691 286
1818 9491 301
2731 7617 18
2731 10504 18
10554 6117 752
1813 9434 167
4202 5 253
7140 6495 179
5964 6152 827
714 7383 850
4017 3245 660
2863 9698 484
1925 12849 297
5977 12089 40
3355 4415 40
11044 6320 315
3568 6165 324
1230 2165 208
1230 8631 208
89 3767 40
4026 3959 465
13070 10339 129
10969 2040 256
1509 5308 40
1509 2457 40
2876 14460 699
278 13292 102
13534 6017 254
646 10687 221
2379 7052 199
9303 3914 40
58 6459 297
58 14282 170
58 1413 299
3890 13780 199
211 6477 732
8200 9457 329
10778 10777 129
6696 5947 203
7796 4308 148
10358 11939 253
13247 7463 114
6433 11384 40
6433 3382 40
6022 6640 664
4264 8810 88
10642 8378 106
4503 9795 137
6518 2825 155
2297 2737 253
789 4388 487
5571 6789 40
3400 14160 137
3383 3382 40
3063 3736 381
504 7889 297
10043 3635 381
9945 2429 40
8179 1128 324
13089 10058 488
12763 5555 155
12763 7810 155
36 607 18
36 5352 18
36 9337 18
36 9932 18
36 4139 18
36 4680 18
36 2948 18
36 1974 18
36 11616 18
36 1011 18
36 5649 18
2028 9396 114
14604 5220 622
1992 3311 148
3571 5052 520
1458 8416 167
8270 10181 155
1474 8204 129
1474 12383 129
11142 10723 562
3560 12724 340
3560 4473 340
2211 3394 167
3133 9266 88
3133 5284 88
8556 3409 692
403 5501 91
403 4810 91
403 12486 91
403 11952 91
403 8341 91
403 1573 91
403 2407 91
403 4434 91
6409 5495 167
11469 6930 315
13006 5667 857
715 247 297
715 580 297
715 6653 297
9628 5682 694
301 11902 162
101 3392 91
101 3449 91
101 2367 91
101 4907 91
101 9417 91
4547 2465 735
4547 4420 735
103 2798 106
10528 2721 266
12949 9515 1
2637 8212 487
1612 7209 88
1612 8673 88
3504 8158 148
8838 5975 663
8838 4033 663
10949 9138 1167
375 6830 113
6279 5787 137
4584 7727 868
3810 6673 98
544 6216 185
544 9249 185
8045 6258 106
2241 10838 199
2241 13168 199
2241 10771 199
2241 2036 199
5192 1289 155
332 14082 113
676 12830 248
774 773 203
1524 3085 185
1524 914 185
1524 8416 106
7437 2821 203
397 8735 581
7048 10731 82
1818 1758 301
2731 3330 18
10729 4405 696
14593 12383 114
6953 1080 340
6747 6170 254
9275 8971 167
13692 4770 114
2434 1340 167
12240 3922 114
11017 5390 82
3521 7629 289
12726 7131 137
172 5753 167
2026 3620 114
11839 4827 1075
2507 1561 289
2507 4262 289
8514 4133 775
7401 6046 203
5098 10776 129
4934 7377 309
12768 9661 167
2689 6521 649
576 840 148
8086 14139 297
7775 11650 1
1291 13306 114
6338 12142 114
1574 12065 114
1230 6792 208
6867 6000 82
13757 9889 114
4552 8205 82
4026 2620 465
2640 2232 129
2442 7098 40
14711 9907 114
17 6039 238
278 14859 935
10132 5640 167
9540 343 148
1955 9869 148
58 5254 792
58 606 114
58 3788 102
58 8103 170
14745 10364 1055
11880 11115 373
2489 154 331
2074 9122 254
3116 11928 297
10223 4830 329
5133 6315 148
3804 13636 297
5442 1297 1
4702 10026 692
11143 3296 82
4946 8579 40
4503 10890 137
1675 4075 114
706 11448 170
3572 5215 143
3066 6349 1
4419 106 155
3244 1113 129
1638 8581 129
3100 11296 902
9961 11885 297
833 11645 263
4950 3761 301
4154 3218 114
9635 3666 221
2017 7078 40
4444 4097 329
4664 7623 289
10414 6367 179
13362 2651 391
2998 80 340
8576 5161 155
36 5662 18
36 6161 18
36 5816 18
36 4218 18
36 3965 18
36 1081 18
36 7987 18
36 1128 18
36 2802 18
36 7596 18
36 9278 18
1582 6585 315
12078 6465 114
1474 368 129
1474 2797 129
1474 5731 129
11589 629 203
4243 11128 607
8208 9643 114
14753 1613 443
2785 9744 33
403 5789 91
403 2405 91
403 2416 91
403 7984 91
403 5118 91
403 7780 91
403 1480 91
403 6724 91
403 13370 91
403 6739 91
403 10504 91
403 7219 91
1802 8032 438
1399 12061 559
8867 8539 314
11469 10520 304
493 5279 1
6756 5111 315
859 4865 113
859 9672 113
6116 5353 1
2574 3132 114
3384 6545 329
5020 4577 847
101 12212 91
101 10380 91
101 476 91
101 2034 91
101 3275 91
101 4531 91
101 10372 91
101 13155 91
101 7633 91
101 1316 91
101 906 91
101 13763 91
101 7041 91
101 3202 91
101 10964 91
101 14764 91
101 6805 91
101 8024 91
12561 13101 88
4824 8564 286
4824 9464 286
5075 1617 98
3423 9402 129
4652 13904 129
7419 7498 376
11918 13629 297
1054 2150 208
2882 11619 234
53 2554 438
2416 6077 277
13996 9650 861
5106 2973 40
1278 5197 342
2797 3849 203
11460 9284 167
569 6947 185
5172 458 98
5506 2993 253
613 4126 560
613 6190 560
8704 5686 1
7161 4428 560
12065 7644 167
2241 5358 199
2241 7961 199
8411 10409 1
332 14182 113
2849 4327 137
5614 7905 592
774 11982 203
2358 1575 629
2927 6285 814
2053 2054 732
7678 4660 129
1464 13018 137
3612 4875 40
6170 875 773
342 2081 253
8489 10515 148
5381 11094 40
344 5980 625
1415 7080 243
5499 875 776
1818 1980 162
1818 541 301
5023 7884 40
10213 6537 179
11862 11861 481
1966 12603 114
2162 561 137
9275 3993 167
7049 2905 167
13653 4070 114
9280 3668 252
9697 11122 297
1670 9222 289
4535 9206 664
7299 2928 88
5778 12422 114
14902 9230 137
1140 5728 148
4879 2113 155
8775 4088 114
2689 541 649
988 10927 886
12966 13182 1
412 6522 155
1230 3102 208
13542 4599 114
4026 6179 465
1605 9978 40
2196 3519 252
2379 3898 199
58 8128 792
58 2985 792
58 2774 814
58 10197 170
58 13389 170
58 10857 850
3890 4968 199
7796 9465 148
3116 4359 458
3116 11056 102
3557 2791 288
3557 2573 288
11023 3878 1075
4946 3247 40
13259 5100 114
706 8716 170
4555 7007 340
8552 4399 114
497 5498 902
3244 1566 129
7020 11102 129
2284 8586 248
2688 9863 114
833 5075 263
833 947 263
8927 4140 1164
2017 1996 40
149 1705 520
6021 2400 148
3503 5894 465
1336 1335 98
4126 5173 253
888 7979 253
3004 3005 266
1621 5447 129
11784 7668 155
36 13500 350
36 12276 18
36 637 18
36 10993 18
36 3416 18
36 4439 18
36 10781 18
36 2338 18
36 8736 18
36 8310 18
36 10533 18
36 7051 18
36 6887 18
36 9417 18
4960 7473 114
3570 2890 167
12911 3498 1
1992 5180 148
1992 3454 148
2845 11947 199
10020 10887 88
1474 10769 129
1474 781 129
1474 6326 129
1474 7765 129
6923 6101 40
3560 13033 340
11048 13132 1167
798 1926 663
798 5515 663
3014 4928 315
6434 8415 167
7082 3078 629
403 2354 91
403 13683 91
403 1594 91
403 4467 91
403 2769 91
403 874 91
403 8333 91
403 2755 91
403 5025 91
403 12666 91
403 4923 91
1847 10017 277
1847 12268 40
5472 2236 33
7071 4197 481
2665 4037 776
5281 25 143
5281 617 143
715 2906 297
5779 7872 268
1466 90 438
101 5448 91
101 14580 91
101 9972 91
101 1364 91
101 13648 91
101 9180 91
101 8702 91
101 5466 91
101 11282 91
101 4182 91
101 11859 91
101 10834 91
101 6721 91
101 7199 91
101 14610 91
4824 3720 286
4824 10560 286
4547 4816 735
4652 9753 129
7027 4901 106
3141 1929 441
11370 3530 129
9518 2774 438
12409 14269 40
8035 5834 626
9 318 238
1612 11974 88
8838 1496 663
375 4544 113
375 4964 113
2797 3071 98
2797 2424 148
2241 11964 199
2241 12091 199
13719 14422 581
13836 6992 185
5199 3500 155
397 13828 114
4303 5967 40
3934 6523 203
3934 6523 98
2031 3202 167
344 11427 106
2082 8212 595
1104 10614 248
359 9024 1
1818 6980 301
2731 9668 18
2731 9103 18
9254 6005 315
13717 3248 277
5023 8155 40
1852 3205 148
7065 10060 696
6943 8837 520
1905 1106 148
12855 2692 340
8003 4484 114
4445 5857 40
13935 4881 82
922 1826 315
7078 4208 167
5964 10796 827
2915 6128 114
11843 5969 1
2507 4468 289
10131 121 106
6694 5495 520
3702 813 329
11423 12812 1
1236 8862 221
8812 3366 203
13322 10807 340
4455 8279 40
9029 14129 297
89 6711 40
2442 10763 40
1880 7228 203
9743 13660 82
8821 10168 562
4656 8001 315
646 6817 98
2196 11686 252
2196 12088 252
2379 14254 199
7279 7278 1127
11926 2244 129
657 10521 114
58 5558 297
58 6660 297
58 12241 170
58 13164 170
58 6675 299
6517 4150 324
4990 7405 562
10635 14899 758
5898 8376 114
10333 7562 98
1410 8460 102
6769 4674 167
4964 5023 374
11362 4176 40
9996 5733 917
6022 6015 289
11107 12293 481
4503 3580 137
4503 5870 137
4503 11726 137
14221 1953 1069
2280 7655 743
13834 12979 340
3244 6669 129
833 9777 263
833 3702 318
5220 946 203
7502 7240 106
6589 1993 463
842 1845 764
5637 9306 114
13089 4631 488
2055 7053 1
4184 3735 114
36 11751 350
36 3891 350
36 14812 350
36 1257 18
36 8993 18
36 7394 18
36 11906 18
36 6200 18
36 9233 18
36 3287 18
36 6692 18
36 4667 18
36 6643 18
36 13774 18
36 5549 18
2773 10831 361
11521 2680 40
4569 10080 40
6875 11296 254
6524 611 203
14928 617 405
12583 7454 40
4764 7431 221
3560 5319 340
10383 8547 114
8756 11956 253
2018 1285 114
12951 1858 277
403 2092 91
403 12508 91
403 14079 91
403 10231 91
403 11845 91
403 3225 91
403 13950 91
403 14048 91
403 7363 91
403 6122 91
403 2340 91
9439 6699 114
6409 1573 148
7007 1147 1
7325 11306 462
4645 1898 268
2810 10318 253
859 2092 113
859 10206 402
14230 4560 114
3740 1432 656
4082 4083 98
1817 227 518
101 14128 91
101 8579 91
101 6955 91
101 687 91
101 11494 91
101 2343 91
101 5459 91
101 14858 91
101 12526 91
101 2858 91
101 9611 91
101 8400 91
101 14295 91
101 5231 91
101 9411 91
4547 10979 735
7478 280 601
3423 90 129
4652 13119 129
5073 5388 155
2296 3345 148
5341 6439 167
5135 10159 289
6084 93 98
2045 12439 114
53 8524 438
3504 1306 148
4166 8612 199
375 10772 113
6379 637 1
1278 1649 342
1278 4038 342
5520 11715 481
569 13391 185
569 5316 254
9054 928 221
4584 4765 377
5616 4940 725
6156 8592 167
545 176 143
4366 4446 1
13878 7300 114
7976 5383 518
1703 10932 277
4357 3136 462
648 6483 167
2241 1363 199
2241 12954 199
332 4249 113
4263 1242 129
12135 9504 40
6235 2852 1
10173 10527 155
5286 7948 622
344 5901 106
1209 5100 40
1818 11821 301
1818 6244 301
1818 4648 301
1818 484 301
3208 3734 155
3208 10795 155
979 13803 199
1631 1632 82
1534 5998 256
1545 10159 40
12582 8918 462
3282 3419 373
12192 6315 155
2787 5959 374
13543 6980 114
11839 10857 1075
13532 7422 1
8076 2215 148
7490 12365 40
11753 8448 167
14040 14170 971
4879 1201 155
8264 6410 633
7876 10663 88
561 3435 40
2553 2313 106
8306 1454 253
2076 9832 129
2076 6498 129
2076 5923 129
1736 6941 221
1230 7690 208
1230 13580 208
89 11040 40
12050 5477 106
4026 916 465
5476 6177 167
11566 5679 286
10075 1258 1129
1498 10074 252
17 5316 665
2196 7077 252
2379 11454 199
2379 9287 199
2379 8275 199
11926 6681 129
1955 953 98
657 2519 114
58 11134 102
3890 13174 199
7407 9331 98
6075 7597 167
1702 7777 98
8926 7137 129
8932 8932 1124
9259 3012 40
6844 11453 1073
9755 12482 129
3880 1387 203
7379 1805 88
4964 13775 40
1833 14484 297
4473 930 381
4503 10445 137
8359 5780 167
9555 5985 268
8817 8831 1
12761 10584 88
13465 2253 167
2879 3079 253
3100 10450 902
833 10419 263
833 194 263
833 8062 318
4154 837 114
5246 8223 98
854 5803 98
62 8874 40
9246 13321 88
3358 6919 40
385 6942 129
5007 14660 623
3569 3568 203
3303 1685 106
348 12121 102
6278 921 1
6407 538 40
769 768 203
36 6975 350
36 12857 350
36 8595 18
36 2793 18
36 2332 18
36 1109 18
36 2454 18
36 11835 18
36 1442 18
36 2686 18
36 5245 18
36 5139 18
36 11230 18
36 4651 18
36 8533 18
8019 9731 1
650 1097 167
1992 4314 148
4957 4063 167
2148 5689 340
7135 1481 40
7135 5329 40
1474 7271 129
10102 10036 484
781 3053 167
7008 3941 129
403 5794 91
403 3481 91
403 473 91
403 3865 91
403 7672 91
403 1493 91
403 12755 91
403 4612 91
13278 6370 155
13308 9408 622
1087 10546 155
4645 7749 268
9237 7308 689
991 6731 277
991 12886 40
859 5260 693
859 11471 113
12647 1225 234
10998 3211 1
471 13068 494
2684 1905 277
101 12426 91
101 11106 91
101 6461 91
101 4597 91
101 2424 91
101 4746 91
101 5930 91
101 1319 91
101 14181 91
101 13777 91
101 11022 91
101 8501 91
101 4590 91
101 1066 91
101 2376 91
10276 10281 40
5160 7578 98
4824 10576 286
4824 6930 286
51 8232 129
4547 12166 735
10363 10655 40
5887 2755 129
5071 1299 512
11328 5371 40
1054 499 208
9852 8988 203
12094 3415 1124
7324 7323 88
3610 8384 40
3256 5883 33
375 4389 113
1278 11509 342
1278 1508 342
1278 2547 342
2797 5941 203
569 6387 254
4584 7307 401
6032 8482 1
10204 2790 607
264 9214 88
1725 11680 82
11852 2189 106
2241 10565 199
2958 11473 40
1464 4573 137
3983 1988 114
1063 1133 297
397 11028 114
635 1896 850
344 9059 625
753 6871 40
1818 8118 286
1818 2500 377
2731 4456 18
6747 4940 578
1534 9322 256
4871 12304 623
14325 9861 88
14325 5980 88
11108 5672 954
6965 4914 106
11762 3394 106
4659 2481 329
8188 2771 1
12200 10560 254
4814 6504 329
2689 646 810
2076 921 129
12354 4921 234
6574 635 1097
1574 14134 114
1230 7260 208
8903 6864 167
12656 1126 179
2701 12265 40
17 2743 500
13137 2305 129
6805 6006 381
58 5834 595
58 6748 458
58 9199 170
58 6649 170
58 13727 170
3890 12521 199
3890 12695 199
8995 2758 40
3517 8282 88
12532 1827 114
4503 4327 137
4503 8858 137
8340 3686 114
2478 11969 167
3244 2252 129
1129 6849 581
9779 2865 114
1288 4210 106
1836 2519 438
3400 13380 137
833 5804 263
833 2436 263
8769 726 179
4154 11435 114
677 6803 148
586 10498 266
3296 3532 114
653 1171 732
5997 894 114
11801 8940 253
2998 3173 340
2375 5382 148
3004 1421 376
3004 4400 376
5330 10527 98
2216 6885 102
761 4665 458
769 1881 167
36 3307 350
36 12736 18
36 12634 18
36 6404 18
36 7705 18
36 9488 18
36 5308 18
36 12256 18
36 2011 18
36 7119 18
650 11498 518
2185 3474 40
325 12035 377
10020 12408 88
1474 8742 129
10353 1481 846
6135 1613 114
13611 5963 256
798 8332 663
7482 7959 98
13329 3863 114
8556 4342 692
403 4441 91
403 2883 91
403 3570 91
403 2357 91
403 6686 91
403 2459 91
403 2586 91
403 966 91
403 6848 91
8874 3202 155
13494 6906 114
399 8852 221
1294 5575 289
3152 6857 525
991 2810 40
8698 8698 82
5776 3717 1010
14484 3087 114
2684 7934 277
13637 12662 525
101 719 91
101 13340 91
101 8026 91
101 10002 91
101 4078 91
101 13659 91
101 3656 91
101 12137 91
101 12215 91
101 1774 91
101 3323 91
101 11600 91
101 14081 91
101 499 91
101 7440 91
101 998 91
101 6529 91
10276 1127 40
2706 12217 88
4547 3975 735
1704 2724 167
103 4800 106
9559 13641 88
3141 1492 441
9305 10441 82
2637 4388 487
1521 9582 203
6534 2767 484
3845 8265 434
9185 6852 40
375 1847 113
375 12341 113
1278 4243 342
1278 2107 342
2797 5780 203
2797 2449 167
6279 8748 137
102 1081 1
7822 10898 612
2459 7703 1
13874 11016 82
14405 9902 129
7161 2354 560
7611 10557 234
2241 10240 199
2241 1724 199
2241 13874 199
2241 540 199
2241 10743 199
8540 6085 40
2025 13077 161
4263 1627 129
774 5877 167
12879 12879 465
4295 861 377
12389 5333 361
10565 2518 114
10173 4726 155
635 1406 170
10518 3409 40
1415 11802 243
1415 11037 243
9006 3628 256
1818 7330 401
1818 9217 162
1818 4575 286
1818 293 286
1818 2844 301
8640 4125 1
979 10312 199
11540 4842 179
8959 10917 129
3510 1561 277
2162 8858 137
3282 3964 373
9258 9582 155
10497 171 619
49 10275 297
3605 11776 40
14675 5804 462
9563 8048 623
13926 10932 311
3104 5 106
8142 8717 40
9412 3878 40
11839 472 1075
13928 3997 340
10850 6490 971
9922 5196 268
12386 6509 1
5098 2837 129
7876 9191 88
3148 1623 315
4485 5589 518
2076 6939 129
971 744 203
1574 10615 114
2931 5352 88
10555 10541 581
11053 2186 985
10969 6006 256
1498 5196 252
1498 3964 252
12414 11146 82
3924 1240 1
12963 9638 40
17 3196 665
1698 5217 1
13193 7127 927
7602 1707 129
657 13019 114
58 11772 170
58 2857 170
58 11228 170
58 10948 170
58 354 346
3890 9305 199
549 7445 167
14650 3163 161
6940 4486 520
3557 5193 288
3557 3337 288
6518 3757 155
14128 5419 329
11396 12503 797
3191 3337 252
4417 11310 155
5571 3181 40
1836 2608 438
3400 8111 137
833 1336 263
833 5531 263
11967 5013 518
12631 4624 40
954 119 268
385 5824 129
3701 1930 340
3701 4513 340
7805 437 208
5637 1140 114
7436 2174 203
1290 1416 231
36 11106 350
36 6338 350
36 8382 350
36 3372 18
36 2680 18
36 14145 18
36 2188 18
36 3976 18
36 4615 18
36 4816 18
36 8547 18
36 12958 18
36 12182 18
36 12142 18
36 11059 18
6875 2476 185
7432 4812 520
7471 2340 129
325 1122 377
673 11033 1
1474 12566 129
2018 8399 114
7594 8576 1
3133 5220 88
7082 5515 660
403 9623 91
403 12526 91
403 5841 91
403 11870 91
403 3755 91
403 5489 91
403 10666 91
403 13242 91
403 5543 91
1300 2724 106
4 5775 559
11718 2872 106
3340 12320 40
991 6539 40
715 5546 297
715 9002 297
9548 9241 1
6840 1066 167
301 13925 286
101 6067 1013
101 8806 91
101 14082 91
101 2169 91
101 9785 91
101 11322 91
101 5807 91
101 11587 91
101 14216 91
101 5821 91
101 13518 91
101 5863 91
101 554 91
101 711 91
101 5883 91
101 943 91
101 13893 91
101 2486 91
101 552 91
2706 2175 88
3049 5758 481
4824 8489 286
4824 12083 286
4824 10511 286
2152 9329 33
7419 3534 266
5310 2339 40
2637 9510 631
10300 9454 129
4224 7667 40
2882 3463 234
9 10233 500
1612 5559 88
8838 4613 663
2416 5945 40
375 991 113
375 8048 113
1278 14912 342
1278 13623 342
1278 7833 342
1278 14695 342
2797 5877 167
569 12913 185
569 9571 185
569 9925 185
11011 3415 253
5912 1613 129
5912 4938 129
5536 2262 520
1493 2695 161
5574 12628 329
264 11436 88
554 119 155
12065 3304 167
10551 2737 268
7611 4868 234
8045 121 106
2241 13849 199
2241 6705 199
2241 6922 199
3449 945 155
1260 8088 520
14927 8226 252
774 7125 203
5951 14133 299
9405 11205 1
6313 1693 819
6094 3304 98
635 3921 541
67 3035 167
1209 13377 40
1818 11764 649
2731 10662 18
10015 7905 40
5924 1884 155
12192 1229 155
9280 11318 252
6398 1705 167
6552 4123 155
13979 2234 311
6458 272 40
7299 863 88
11839 8232 1075
11670 12952 114
10543 2042 314
2393 9938 340
2076 3749 129
2076 9463 129
9440 2727 716
13056 3844 1
89 6871 40
5100 5271 98
13413 14652 248
2640 11910 129
10075 12798 1129
10686 10683 167
2379 11546 199
2379 14177 199
58 14254 170
4545 7495 114
10 1352 665
9214 5862 155
10358 10652 253
5682 1323 424
4964 6591 40
9621 4938 88
8441 3630 167
10642 23 106
4946 3645 40
3164 2933 167
1675 2935 114
706 9147 170
3878 11456 251
3066 5397 1
13465 3666 167
3244 2683 129
3244 2769 129
7006 3568 520
13053 9839 1
2399 130 167
3400 11878 137
833 2147 318
833 6987 318
7010 1076 253
586 10180 266
1217 4634 714
2998 3058 340
1156 5575 438
6062 10123 88
1477 8418 221
36 2552 18
36 849 18
36 5332 18
36 7729 18
36 8412 18
36 8620 18
36 1041 18
36 8714 18
36 8370 18
36 1057 18
36 3825 18
36 11763 18
36 1691 18
36 4390 18
36 12488 18
36 1228 18
36 7485 18
2773 5333 161
1992 3913 148
4957 7832 167
11158 11158 465
6875 3154 185
1458 2737 167
1474 396 129
7269 10497 98
6149 4875 114
4537 7386 148
5418 764 155
403 10376 91
403 2085 91
403 6093 91
403 2699 91
403 6412 91
403 7577 91
403 13547 91
403 2540 91
403 9896 91
403 240 91
403 14291 91
7663 8990 148
3859 3858 98
14560 11923 405
13293 12196 114
1677 6134 98
859 2566 402
101 5997 91
101 7585 91
101 11633 91
101 8278 91
101 10088 91
101 8827 91
101 14561 91
101 8122 91
101 5443 91
101 1748 91
101 10185 91
101 11407 91
101 12227 91
101 8789 91
389 7049 40
10156 3296 714
4824 4304 286
131 1617 148
5887 430 129
5071 5254 512
7419 10923 376
7254 13847 82
1054 11504 208
4771 1604 155
4224 4277 40
9607 4967 402
4300 4299 98
10361 7940 438
375 11521 113
8112 2269 377
4584 521 377
5912 1323 129
545 635 143
5506 2346 253
1033 3542 129
443 11760 894
4574 2908 622
1359 5652 98
264 9559 627
9669 5056 391
5688 5687 137
9893 2978 179
2241 13329 199
2241 1742 199
9987 767 98
7229 9648 40
2790 4664 525
11939 4921 324
676 13392 162
4155 1832 716
4263 6429 129
13836 9141 185
7678 10781 129
1464 11329 137
5497 6574 40
7360 6082 732
6260 5056 268
635 5865 102
635 13806 102
3934 6575 203
1820 9460 944
7771 8432 155
1818 2853 494
1818 1165 286
1818 2520 286
1818 5670 301
1818 8812 301
2731 100 18
2731 9553 18
8936 5643 167
4871 3290 463
1038 2283 631
13690 6445 40
8341 5878 1
11368 7887 289
2805 9184 251
13571 12040 114
472 9762 581
486 1078 248
6345 5353 286
176 12977 297
8812 4947 203
7251 2761 155
9889 10187 581
7806 9984 129
3905 3866 179
11566 5984 407
1498 6049 252
1498 3070 252
5273 4095 155
5285 1259 155
2379 12461 199
2379 14465 199
2379 11432 199
58 1277 102
58 1848 170
58 12158 170
58 3796 894
8155 367 221
6765 840 155
174 7632 500
7292 291 40
1221 11940 771
4512 9929 143
2469 10110 114
4503 10148 137
999 9252 289
706 8076 114
9745 11402 82
6202 2098 40
9085 9171 1
3191 442 252
11704 2234 541
1836 535 438
3400 8703 137
4154 8578 114
7010 8768 253
7318 7869 315
5720 2527 155
7530 11516 82
2998 5507 340
2375 1076 203
2095 8170 309
36 5529 18
36 4560 18
36 4957 18
36 368 18
36 12249 18
36 3396 18
36 4786 18
36 10848 18
36 2869 18
36 6067 18
5305 11390 129
10899 3414 373
2028 13862 114
3571 2969 520
6818 6086 179
11641 9975 129
8246 2234 541
1474 10645 129
14448 9146 315
4218 4217 900
403 1258 91
403 5631 91
403 906 91
403 7896 91
403 930 91
403 11496 91
403 4088 91
403 12850 91
13944 5955 148
1847 1803 40
11718 7348 106
223 14756 607
5128 4575 40
1677 1126 203
4645 2215 98
5472 3364 33
1929 11751 297
991 4879 40
10929 10055 40
715 2695 297
12926 14571 808
301 7088 96
101 12689 91
101 3797 91
101 6009 91
101 8647 91
101 6032 91
101 4346 91
101 9394 91
101 1018 91
101 7765 91
101 12509 91
101 7070 91
2706 1947 88
4506 1093 268
131 485 167
6273 2921 98
3423 11306 129
4652 1334 129
10528 6608 266
3123 11087 458
6877 10780 88
7324 8527 88
13939 9311 114
375 4036 113
375 1155 113
375 7229 113
375 11677 113
800 14872 527
569 10484 185
569 9891 185
569 8938 578
7822 10520 612
3381 12836 102
6997 3018 114
5536 6601 520
5615 1844 203
9545 7809 129
8045 9360 106
2241 8465 199
2241 2547 199
3021 6638 1
4263 5810 129
2358 696 629
9951 4271 489
6863 526 155
7387 6557 203
2082 9052 595
6743 3782 518
951 2027 238
1818 2678 96
1818 1980 286
1818 8480 286
1818 2723 301
1818 4916 301
2731 7804 18
380 12146 88
380 4998 88
11108 5156 954
11931 4208 155
49 353 297
9563 1316 623
11125 4528 114
2425 34 203
8076 10589 167
13055 13231 82
7251 2256 155
13768 13795 88
8545 11711 114
4312 8929 1
2076 8613 129
971 2058 167
1574 2940 114
4481 3568 221
1230 8223 208
9029 13821 297
642 7501 167
1509 8884 40
2876 9006 699
514 7222 155
9222 6316 203
8558 6001 167
278 11742 527
278 2766 299
1053 5068 487
646 5774 221
58 1150 170
3890 6972 199
3890 13401 199
9020 6898 40
4545 4158 114
489 4480 40
7379 11648 88
6639 11005 40
4946 8578 40
4946 11440 40
4946 5248 374
355 3878 592
75 1147 114
2478 481 167
3244 9736 129
8766 2189 148
3191 1209 252
5571 4468 40
1288 4721 106
3400 2169 137
3400 13383 137
833 8884 263
833 3635 263
833 476 318
2493 4940 277
2017 5300 40
6915 11181 660
586 4908 266
12816 8224 106
385 8384 129
385 4481 129
5007 3182 623
1432 4514 114
2432 2433 297
2555 6175 340
504 14807 170
1111 450 203
4467 10254 155
11801 8308 253
239 1971 114
1708 3217 155
5399 931 933
5399 13058 957
5592 9960 161
36 5527 350
36 1774 350
36 2927 350
36 2881 18
36 2478 18
36 9495 18
36 4575 18
36 11384 18
36 5526 18
36 12896 18
36 8034 18
36 6873 18
36 6248 18
36 13818 18
36 6320 18
36 4281 18
2028 7660 114
2028 631 114
6975 3111 954
12122 11086 311
9230 10983 40
6530 4807 167
7471 1707 129
1389 6903 251
1474 457 129
1474 9012 129
2934 5413 315
7594 2446 1
6261 641 518
403 6202 91
403 7245 91
403 8120 91
403 6645 91
403 10065 91
403 7529 91
403 3145 91
403 11548 91
403 9037 91
4954 9417 40
13308 3755 622
12524 4255 315
4645 450 268
6594 8051 82
991 6693 40
715 11803 297
715 190 297
715 7505 297
3581 2890 98
6957 11273 88
301 8914 407
10479 6903 324
8895 5593 148
101 1676 91
101 8055 91
101 7131 91
101 4350 91
101 6607 91
101 4599 91
101 2848 91
101 14310 91
101 1465 91
101 6953 91
101 9900 91
101 11197 91
101 3044 91
101 14727 91
101 6539 91
101 4859 91
4824 2089 286
4824 3542 286
4547 366 735
2889 6941 203
5341 6899 148
5341 7815 148
5135 12038 289
6534 658 484
3120 9804 623
1612 11473 88
375 2877 113
1278 1129 342
1278 9827 342
2797 6394 203
163 7358 289
569 6525 185
225 6576 560
264 6107 88
1112 91 155
8704 8705 1
8704 2288 1
3789 873 221
1725 342 114
7611 2757 234
2667 11500 664
2241 7225 199
2241 8103 199
2241 14532 199
5113 4606 518
8053 11136 114
4875 4199 98
1260 9943 520
332 6186 113
4263 1535 129
1464 3337 137
5199 1513 155
9967 13303 40
5286 4998 622
635 11269 102
2082 10821 595
1818 1433 286
10109 11115 391
2731 2517 18
8778 8862 268
7065 7308 696
14792 11070 556
3510 10107 277
13374 1437 114
10206 1276 148
1034 13147 102
5924 7034 155
2805 2802 361
6458 12146 40
6102 6045 1
11839 1611 1075
8076 6315 148
8594 5783 40
6694 5191 148
4879 5728 155
7700 10026 324
2448 10189 129
13768 11136 88
2553 5682 277
5977 9120 40
4227 14037 199
2689 538 649
1875 3505 203
3939 6895 114
4873 11144 40
89 5155 40
608 5706 203
1498 5782 252
5585 40 203
2482 6287 886
646 3056 98
2196 1571 252
657 1664 114
657 2496 114
657 6592 106
58 2926 792
58 13995 527
58 981 297
58 10733 170
4545 7881 114
4990 3097 465
4690 9960 277
1732 14068 170
7289 3086 106
10333 4302 203
10715 10821 357
8089 8090 365
1902 6255 373
5442 3019 1
6769 4196 148
4964 8032 40
4702 2641 692
6022 5721 289
4946 8539 40
4503 7046 137
5702 945 167
6204 10560 114
3356 10397 155
2044 6705 297
11298 12344 794
13340 5410 114
5571 1692 40
5064 8810 505
1836 10519 438
1836 2426 438
1836 931 438
4950 7268 301
149 904 520
1113 4387 888
1207 7487 376
13974 10600 167
10588 4579 559
8911 3305 203
4197 269 421
4197 13458 421
13089 2018 488
36 14073 350
36 13260 350
36 9950 18
36 9081 18
36 2520 18
36 12491 18
36 10544 18
36 8397 18
36 10644 18
36 5956 18
36 8890 18
36 12515 18
36 1690 18
36 13526 18
36 4546 18
36 12854 18
36 27 18
36 14595 18
36 2981 18
36 6696 18
5305 10814 129
2956 76 989
2773 4082 361
1992 6159 148
6818 664 179
8830 639 40
8830 95 40
6187 2662 106
1389 288 40
6548 13775 40
2503 3538 167
1474 3299 129
1474 8034 129
1474 8876 129
1474 1351 129
1474 3226 129
1037 5077 98
6412 2797 161
4585 3255 129
11142 4577 562
13990 9454 40
9093 9092 790
403 2597 91
403 6021 91
403 9074 91
403 7333 91
403 11587 91
403 8499 91
403 260 91
403 3778 91
403 8188 91
403 12373 91
403 943 91
403 14454 91
403 4885 91
403 436 91
403 13517 91
6037 4575 1
7650 8360 114
8874 3433 155
9483 211 732
2249 6839 167
12512 489 113
1677 6134 203
1919 2854 98
8867 10837 314
10227 58 917
8262 1647 40
3384 1285 1
301 449 286
101 1153 91
101 9352 91
101 12616 91
101 2207 91
101 8468 91
101 9804 91
101 9647 91
101 11744 91
101 3033 91
101 1506 91
101 8656 91
10276 7396 40
4407 8701 129
12067 12439 40
4547 10594 735
7478 2519 601
4652 14280 129
5071 9935 512
11914 7581 114
7254 6164 82
6749 1992 114
15 1832 203
3120 263 623
12209 797 851
4166 12446 199
7529 7660 315
11706 14571 808
375 2464 113
375 8648 113
375 404 113
375 561 113
1278 3270 342
1278 5603 342
1278 6878 342
1278 1275 342
6279 4597 137
569 11333 185
6502 6370 98
2766 13 792
443 5864 894
6847 1844 221
1359 966 203
2241 10617 199
11667 3948 556
12476 3513 277
6603 3282 88
6356 12239 203
73 5087 33
731 935 155
5723 1611 88
397 12909 114
732 7323 315
6618 12212 137
1818 3923 494
1818 1296 286
1818 6931 649
2731 10888 18
2731 9411 18
80 12160 329
1534 7502 256
1534 1582 256
4929 7115 155
49 5540 114
5105 2218 221
4659 3543 329
5416 9783 717
10543 6871 314
2393 1721 340
4873 2222 40
1230 6667 208
89 11784 40
10379 269 481
514 3071 155
11300 8133 82
1228 5123 98
1955 3347 203
7602 6212 129
657 10915 114
657 704 114
58 13576 170
58 137 170
58 14338 170
58 1798 299
3890 14255 199
3890 5461 199
3890 7968 199
3890 10463 199
11551 2715 203
6633 3267 114
10358 973 253
3531 8030 1
4964 2928 40
5649 3783 716
2450 6395 161
3572 10742 143
3572 7512 143
76 2907 796
11673 7786 221
3244 7524 129
3191 5892 252
1930 9493 1
5571 418 40
11883 8887 114
833 11218 318
4154 2621 114
6915 4942 660
1411 13270 1
5787 11481 277
13814 5035 98
2705 8928 88
5749 13824 299
7320 11443 1
1111 3598 167
7805 94 208
8911 1428 203
4197 2022 481
36 5863 350
36 3380 18
36 13358 18
36 12398 18
36 10446 18
36 6084 18
36 5403 18
36 6734 18
36 4929 18
36 2434 18
36 4570 18
36 6131 18
36 10202 18
36 5148 18
36 4147 18
4135 5939 329
2028 2503 114
1992 1306 148
12078 9822 114
6186 3833 277
1474 3902 129
1474 7471 717
10405 1050 203
6559 14751 808
5418 7565 155
403 6879 91
403 11409 91
403 9479 91
403 5061 91
403 3775 91
403 7999 91
403 2302 91
403 9699 91
403 4636 91
403 9320 91
403 3364 91
403 14263 91
403 9123 91
403 13255 91
8626 313 221
2840 10379 481
715 1746 297
3656 235 315
9767 1884 167
101 2822 91
101 4240 91
101 6609 91
101 12703 91
101 3728 91
101 13769 91
101 2802 91
101 12855 91
101 7816 91
101 7338 91
101 11008 91
101 2022 91
101 1811 91
101 4437 91
4407 6063 129
10237 1247 40
8510 2139 252
3072 1408 98
9157 3761 324
3120 4476 623
9 8423 665
3256 6370 33
4931 9839 329
375 3612 113
1278 6208 342
11460 9937 167
4701 1298 520
7769 7770 82
4366 3181 1
7161 13433 560
4086 8739 167
1043 7301 170
4777 5657 114
332 3401 113
4155 2001 716
4263 3449 129
1531 3562 155
635 14593 102
8354 6585 315
344 8628 489
344 5451 106
12944 14154 496
9895 11492 253
3405 7576 113
2731 4311 18
2731 7347 18
1966 1965 114
13692 8117 114
8341 1437 1
9280 5144 252
11762 10583 106
4659 10955 329
11839 4358 1075
6571 8820 1
4047 4048 373
2076 4816 129
10615 4527 221
4723 7083 481
608 2468 98
1498 5941 252
8153 10419 324
2379 13481 199
12256 11446 98
14478 2716 179
657 7776 114
657 4636 114
58 1562 170
58 2561 299
58 472 541
9507 3758 82
5892 1111 40
4335 2244 40
12646 1950 167
3727 641 268
9621 550 88
12788 1136 256
6639 8282 40
7070 3550 114
355 1896 592
4503 9967 137
3244 5679 129
3244 6565 129
5571 10655 40
4036 5426 40
833 4814 318
833 3531 318
5246 8945 98
7087 517 1
385 275 129
13814 2365 221
11294 6442 277
8037 5062 40
5006 12285 1
4044 3033 40
12290 3945 340
2219 2218 203
1477 4437 98
36 10835 350
36 12985 350
36 13232 350
36 3619 18
36 12591 18
36 4082 18
36 8322 18
36 881 18
36 6246 18
36 2723 18
36 12435 18
2028 4265 114
1992 2500 148
2370 271 1
7432 316 520
913 12464 114
2148 1643 481
1474 2956 129
798 6085 663
3094 4708 1
4537 1097 98
7532 3760 1
4218 3482 888
403 9323 91
403 10600 91
403 2681 91
403 11108 91
403 14406 91
403 5953 91
403 1581 91
403 5327 91
403 11829 91
1300 1000 541
6409 1897 167
11718 6709 88
5472 6308 33
5281 3381 143
859 1534 113
1542 3476 441
11620 13825 481
6957 7110 88
8975 601 155
6115 6661 40
101 5879 91
101 8946 91
101 9761 91
101 11430 91
101 1979 91
101 15 91
101 5783 91
101 8007 91
101 10981 91
101 5100 91
101 9859 91
10276 8595 40
8512 7302 40
4824 3911 286
4547 5217 735
4547 2157 735
4547 7527 735
7478 5669 601
4652 11057 129
7419 2679 376
1054 8098 208
5504 7713 248
6534 2561 484
6826 3228 155
9 3177 500
3504 793 148
4166 11647 199
375 11362 113
1278 4632 342
2797 3071 167
545 5546 143
4574 10333 622
12739 4160 114
648 8843 167
2387 2798 253
2241 13978 199
1043 12263 102
332 331 113
4263 4300 129
1464 8122 137
1063 8358 106
342 2571 268
1277 10906 297
1181 7010 560
635 11068 102
1209 2874 374
753 8122 374
3050 10479 167
1818 67 494
1818 455 286
1818 1606 301
1818 8395 301
1818 2126 301
10109 2890 391
2731 4753 18
9280 9782 252
9280 6690 252
49 8913 297
12093 5040 40
3521 9346 289
7299 5063 88
5964 10723 827
11125 14034 114
8076 8004 167
5698 12317 114
10748 5711 40
7142 11526 900
2689 9591 248
5453 6657 179
10891 10162 82
2076 2855 129
2076 5185 129
2076 8190 129
971 8631 221
1230 6124 208
2931 5826 314
10969 10908 256
1509 11207 40
11381 10357 129
1498 7571 252
1498 8229 252
1514 3209 520
9523 8332 796
17 9445 238
278 1649 102
3231 4738 40
646 5774 148
1785 2432 297
58 1137 792
58 4496 102
5817 5818 847
5892 7150 277
5447 10290 148
2720 12208 1306
8656 11971 114
3558 2310 1
382 2831 340
3557 11216 288
3880 10862 203
7379 2678 106
4024 13066 289
5687 8322 40
4946 8250 481
8057 5438 417
2352 2737 148
2092 7941 481
2450 6395 277
789 10450 487
6202 6119 40
60 6397 148
3244 5515 129
3244 3267 129
4036 8392 40
5064 3628 505
1836 6326 438
3400 11755 137
833 4105 263
833 2475 263
833 8200 318
1411 12731 1
504 11692 170
5749 13824 297
14342 7725 114
1621 2894 129
3472 6748 40
10262 2237 98
12763 9057 155
36 6780 350
36 10975 350
36 9983 18
36 2826 18
36 4078 18
36 8613 18
36 7215 18
36 11109 18
36 13085 18
36 7375 18
36 11276 18
36 7373 18
36 11306 18
36 8102 18
36 10524 18
2940 4867 167
10899 10687 373
2028 9337 114
650 6797 203
2773 3463 361
3288 5636 96
11129 675 114
6187 7691 625
12115 7863 405
1474 6113 129
10709 1956 129
2107 13316 102
3960 7440 1
2018 1318 114
2775 6600 481
6778 472 314
10772 6847 40
403 7225 91
403 1504 91
403 3675 91
403 3384 91
403 4125 91
403 3972 91
403 6969 91
403 9751 91
13944 7475 167
13308 13345 622
2684 1905 161
101 7052 91
101 856 91
101 13514 91
101 6758 91
101 479 91
101 6404 91
101 2791 91
101 1691 91
101 11344 91
101 11475 91
101 13726 91
101 997 91
101 14549 91
101 4028 91
101 11755 91
101 12077 91
101 1862 91
2706 10689 88
4824 6375 96
8723 3825 114
5887 11401 129
10153 4328 401
103 7437 289
1054 1731 208
15 7340 167
12409 13904 40
2882 3565 234
8035 11215 626
8838 1496 735
1278 251 342
8256 2449 98
569 8006 185
4701 6070 520
14346 13715 114
5574 10563 329
4357 3921 462
8053 8479 114
332 7601 113
2003 2143 1
5606 1993 481
10118 11675 441
10173 5694 155
9550 38 520
1820 9363 494
6863 6803 155
1415 14214 243
1818 8783 286
1818 6542 286
1818 3550 301
2731 10633 18
2731 1147 18
8596 75 1037
7895 7590 203
979 5454 199
8918 3653 98
1534 6093 256
1534 3346 256
1534 6259 256
333 1485 525
1508 8016 106
6057 4782 203
8749 4757 179
9280 10156 252
2805 7128 161
583 5134 114
8893 6314 976
14456 2328 405
4017 7985 660
4017 4019 660
176 11647 299
8881 2881 443
8264 14198 633
7883 8036 1
11365 11359 114
13331 8459 114
2812 13362 114
11942 9317 167
882 6442 88
4483 4484 329
2640 8061 129
690 4018 167
10969 12408 256
5820 4453 520
3280 7374 289
17 9458 500
2379 658 199
8709 9348 203
657 5530 764
58 7643 277
58 6932 170
58 11023 790
3890 6204 199
2489 11960 331
500 499 148
2074 9510 254
6633 8036 114
3700 5953 114
8 8938 357
5892 10208 277
12374 2929 167
3531 2517 1
416 3788 484
191 4003 635
10642 5170 106
4946 1875 40
4503 13805 137
================================================
FILE: ConvKB_pytorch/benchmarks/FB15K/entity2id.txt
================================================
14951
/m/027rn 0
/m/06cx9 1
/m/017dcd 2
/m/06v8s0 3
/m/07s9rl0 4
/m/0170z3 5
/m/01sl1q 6
/m/044mz_ 7
/m/0cnk2q 8
/m/02nzb8 9
/m/02_j1w 10
/m/01cwm1 11
/m/059ts 12
/m/03h_f4 13
/m/011yn5 14
/m/01pjr7 15
/m/04nrcg 16
/m/02sdk9v 17
/m/07nznf 18
/m/014lc_ 19
/m/05cvgl 20
/m/04kxsb 21
/m/02qyp19 22
/m/02d413 23
/m/02vk52z 24
/m/01crd5 25
/m/0q9kd 26
/m/0184jc 27
/m/09w1n 28
/m/0sx8l 29
/m/03q5t 30
/m/07y_7 31
/m/0gqng 32
/m/073hkh 33
/m/0b76d_m 34
/m/029j_ 35
/m/04ztj 36
/m/012v9y 37
/m/014_x2 38
/m/012ljv 39
/m/0ds35l9 40
/m/05r4w 41
/m/015qsq 42
/m/02bjrlw 43
/m/04bdxl 44
/m/02s2ft 45
/m/0677ng 46
/m/03ckfl9 47
/m/03zj9 48
/m/059rby 49
/m/0157m 50
/m/063k3h 51
/m/01ky7c 52
/m/019v9k 53
/m/02681vq 54
/m/01w61th 55
/m/0gx_st 56
/m/09qvf4 57
/m/09c7w0 58
/m/0rs6x 59
/m/079vf 60
/m/0d90m 61
/m/015zyd 62
/m/05vsxz 63
/m/06qgvf 64
/m/04ljl_l 65
/m/03qcfvw 66
/m/0grwj 67
/m/05sxg2 68
/m/02ynfr 69
/m/0bmch_x 70
/m/018gz8 71
/m/071wvh 72
/m/02cqbx 73
/m/01jr4j 74
/m/0rh6k 75
/m/01lp8 76
/m/03mqtr 77
/m/04j4tx 78
/m/01z4y 79
/m/07zhjj 80
/m/05d7rk 81
/m/027dtxw 82
/m/05hs4r 83
/m/01pbxb 84
/m/03qcq 85
/m/084w8 86
/m/01lxd4 87
/m/0f0y8 88
/m/08815 89
/m/05bnp0 90
/m/08nhfc1 91
/m/015c4g 92
/m/0g56t9t 93
/m/062zm5h 94
/m/0c9xjl 95
/m/0160w 96
/m/0dbpyd 97
/m/053y0s 98
/m/0dz3r 99
/m/016qtt 100
/m/08mbj5d 101
/m/0b005 102
/m/07c52 103
/m/023wyl 104
/m/02wvfxl 105
/m/09sh8k 106
/m/09zzb8 107
/m/05pd94v 108
/m/01vvydl 109
/m/0gtsx8c 110
/m/0jgd 111
/m/02r0csl 112
/m/0m313 113
/m/02y_lrp 114
/m/012d40 115
/m/034qmv 116
/m/07rlg 117
/m/0b90_r 118
/m/0g22z 119
/m/05b4l5x 120
/m/018js4 121
/m/07fq1y 122
/m/02qgqt 123
/m/081jbk 124
/m/02v5xg 125
/m/028q6 126
/m/02581q 127
/m/0sxg4 128
/m/04xvlr 129
/m/02gpkt 130
/m/0fvf9q 131
/m/0l6qt 132
/m/07jnt 133
/m/0gq9h 134
/m/0jz9f 135
/m/083shs 136
/m/0njvn 137
/m/09nqf 138
/m/0d1tm 139
/m/0154j 140
/m/01br2w 141
/m/0kq0q 142
/m/03rl1g 143
/m/05kkh 144
/m/0fbq2n 145
/m/01r3hr 146
/m/060bp 147
/m/027nb 148
/m/04qvl7 149
/m/02vxq9m 150
/m/04yywz 151
/m/0140g4 152
/m/01k7d9 153
/m/0k049 154
/m/02p65p 155
/m/0337vz 156
/m/06688p 157
/m/0b2v79 158
/m/01c0cc 159
/m/01jc6q 160
/m/040njc 161
/m/01wmjkb 162
/m/0l14j_ 163
/m/0520r2x 164
/m/0cb77r 165
/m/06j0md 166
/m/05mgj0 167
/m/0ds6bmk 168
/m/05b3ts 169
/m/0g2hw4 170
/m/0ktpx 171
/m/0hqcy 172
/m/01453 173
/m/0dgrmp 174
/m/028_yv 175
/m/03rjj 176
/m/01914 177
/m/0bpbhm 178
/m/0219x_ 179
/m/05_6_y 180
/m/02b15h 181
/m/0487c3 182
/m/01j_jh 183
/m/03c7ln 184
/m/026t6 185
/m/01qvcr 186
/m/04sv4 187
/m/02pprs 188
/m/07s3vqk 189
/m/0d05q4 190
/m/0784z 191
/m/0kwgs 192
/m/0gyh 193
/m/01gbbz 194
/m/035ktt 195
/m/05zjtn4 196
/m/0197tq 197
/m/02_fm2 198
/m/02x73k6 199
/m/02lxrv 200
/m/0411q 201
/m/016z4k 202
/m/06151l 203
/m/06gp3f 204
/m/01fq7 205
/m/06w99h3 206
/m/02kdv5l 207
/m/05cljf 208
/m/08r4x3 209
/m/03_6y 210
/m/0342h 211
/m/0l14qv 212
/m/02bfmn 213
/m/05zppz 214
/m/01rk91 215
/m/0kc6x 216
/m/01xdf5 217
/m/04t2l2 218
/m/02rc4d 219
/m/0g0vx 220
/m/027qgy 221
/m/06cvj 222
/m/01mkq 223
/m/011xy1 224
/m/0dxtw 225
/m/0cq86w 226
/m/02v8kmz 227
/m/01j5ts 228
/m/06_kh 229
/m/02hrh1q 230
/m/01pcz9 231
/m/0dwl2 232
/m/01mw1 233
/m/02wh75 234
/m/0lbj1 235
/m/0c3ybss 236
/m/03cfjg 237
/m/0jzphpx 238
/m/06wxw 239
/m/0jbk9 240
/m/0np9r 241
/m/05bp8g 242
/m/01l1b90 243
/m/0p07l 244
/m/01vsl 245
/m/02vp1f_ 246
/m/03_3d 247
/m/060__y 248
/m/03rtz1 249
/m/09m6kg 250
/m/02h40lc 251
/m/047q2k1 252
/m/05p553 253
/m/01ypc 254
/m/02dwn9 255
/m/01k_0fp 256
/m/0m77m 257
/m/02_h0 258
/m/06dv3 259
/m/01rtm4 260
/m/02rchht 261
/m/0dq3c 262
/m/02zs4 263
/m/0qcr0 264
/m/0byfz 265
/m/06wzvr 266
/m/01q03 267
/m/01qn7n 268
/m/016tw3 269
/m/03tps5 270
/m/0cnl80 271
/m/083chw 272
/m/0gq_v 273
/m/0k2cb 274
/m/0qf43 275
/m/0f_nbyh 276
/m/0c0yh4 277
/m/0d060g 278
/m/05qt0 279
/m/0nk72 280
/m/01s3kv 281
/m/03g90h 282
/m/02_n3z 283
/m/06cqb 284
/m/0827d 285
/m/02tkzn 286
/m/014zcr 287
/m/05m63c 288
/m/0yyg4 289
/m/027c924 290
/m/03f0324 291
/m/040rjq 292
/m/01tv3x2 293
/m/06rqw 294
/m/0h3y 295
/m/0rtv 296
/m/090s_0 297
/m/0h0jz 298
/m/05znxx 299
/m/02hsq3m 300
/m/01kyvx 301
/m/0ckr7s 302
/m/02ky346 303
/m/0p5wz 304
/m/05ztjjw 305
/m/01gc7 306
/m/04j53 307
/m/04h68j 308
/m/01g3gq 309
/m/01vw87c 310
/m/01gbcf 311
/m/016clz 312
/m/011yrp 313
/m/0d0vqn 314
/m/049tjg 315
/m/05jf85 316
/m/046f25 317
/m/0d1swh 318
/m/0b_fw 319
/m/04t36 320
/m/011yxg 321
/m/05v38p 322
/m/03hkv_r 323
/m/025twgf 324
/m/0clpml 325
/m/0t015 326
/m/0j1z8 327
/m/07t65 328
/m/0162b 329
/m/0fnb4 330
/m/0hd7j 331
/m/07xl34 332
/m/02sgy 333
/m/0l14md 334
/m/05r5c 335
/m/0c9d9 336
/m/02g8h 337
/m/01d_h8 338
/m/0c_j9x 339
/m/0n5j_ 340
/m/0fm9_ 341
/m/04cy8rb 342
/m/07gp9 343
/m/081pw 344
/m/0gzy02 345
/m/05g8ky 346
/m/026wp 347
/m/049nq 348
/m/03t5kl 349
/m/01f2q5 350
/m/01jssp 351
/m/01l849 352
/m/0f4y_ 353
/m/08959 354
/m/0789n 355
/m/01fkr_ 356
/m/0mdxd 357
/m/03x3qv 358
/m/0g60z 359
/m/0d_84 360
/m/01hr1 361
/m/042l3v 362
/m/026mg3 363
/m/01s695 364
/m/0gtt5fb 365
/m/0tj9 366
/m/0hx4y 367
/m/01y_px 368
/m/0k611 369
/m/0hsmh 370
/m/0gx1bnj 371
/m/04gzd 372
/m/04njml 373
/m/07hgkd 374
/m/060c4 375
/m/04wlz2 376
/m/01531 377
/m/0b1f49 378
/m/01gxqf 379
/m/01_qc_ 380
/m/0g5lhl7 381
/m/0c55fj 382
/m/09xbpt 383
/m/0yxl 384
/m/02w7gg 385
/m/05f4m9q 386
/m/08lr6s 387
/m/09y20 388
/m/01nrnm 389
/m/0hl3d 390
/m/0cbd2 391
/m/0495ys 392
/m/05l2z4 393
/m/018wrk 394
/m/02bkg 395
/m/0h5f5n 396
/m/04jpl 397
/m/047gn4y 398
/m/05ty4m 399
/m/0bvn25 400
/m/026p4q7 401
/m/044lyq 402
/m/08mbj32 403
/m/01vs5c 404
/m/09jvl 405
/m/01g888 406
/m/01vrx3g 407
/m/01lmj3q 408
/m/0m27n 409
/m/0qpjt 410
/m/01c8w0 411
/m/023361 412
/m/01qbg5 413
/m/017fp 414
/m/01gsvb 415
/m/0b3wk 416
/m/01wbg84 417
/m/01r42_g 418
/m/0gr51 419
/m/04gcd1 420
/m/0ddfwj1 421
/m/0sv6n 422
/m/02wkmx 423
/m/02vxn 424
/m/0f5hyg 425
/m/0126y2 426
/m/01flzq 427
/m/05cj4r 428
/m/0bfvw2 429
/m/023tp8 430
/m/0dq626 431
/m/07sbbz2 432
/m/089tm 433
/m/0m2wm 434
/m/02zq43 435
/m/086k8 436
/m/0czyxs 437
/m/014635 438
/m/02kz_ 439
/m/0ds3t5x 440
/m/02896 441
/m/05krk 442
/m/0chghy 443
/m/0l6vl 444
/m/02tcgh 445
/m/0kvgnq 446
/m/02x4wr9 447
/m/017z49 448
/m/08p1gp 449
/m/0dnvn3 450
/m/07zrf 451
/m/016fyc 452
/m/01qscs 453
/m/01p7yb 454
/m/0p_pd 455
/m/0bl2g 456
/m/0prfz 457
/m/02pw_n 458
/m/09l3p 459
/m/0k9ctht 460
/m/0fr63l 461
/m/01q_ph 462
/m/05zr6wv 463
/m/0cjk9 464
/m/047lj 465
/m/0s3y5 466
/m/09fb5 467
/m/07xtqq 468
/m/05hcy 469
/m/0jg1q 470
/m/0c5lg 471
/m/0rlz 472
/m/02ktt7 473
/m/03w5xm 474
/m/026v437 475
/m/080dwhx 476
/m/0150jk 477
/m/05bt6j 478
/m/01k1k4 479
/m/09gq0x5 480
/m/0gtv7pk 481
/m/01ls2 482
/m/02rbdlq 483
/m/0l8v5 484
/m/095zlp 485
/m/06q2q 486
/m/0hnws 487
/m/09wj5 488
/m/0m4yg 489
/m/09c7b 490
/m/0hhbr 491
/m/03ckxdg 492
/m/02_1q9 493
/m/09n48 494
/m/020qr4 495
/m/03_r3 496
/m/02lq67 497
/m/0fvwg 498
/m/0dtw1x 499
/m/01yznp 500
/m/01tvz5j 501
/m/03rs8y 502
/m/0h1cdwq 503
/m/03rt9 504
/m/0m2kd 505
/m/0jdk_ 506
/m/064vjs 507
/m/042v_gx 508
/m/025cbm 509
/m/03qbh5 510
/m/09hnb 511
/m/052q4j 512
/m/01fxg8 513
/m/0146pg 514
/m/0bzjvm 515
/m/0cj_v7 516
/m/0jt90f5 517
/m/067xw 518
/m/025h4z 519
/m/0bp_b2 520
/m/02fqrf 521
/m/057xs89 522
/m/05y7hc 523
/m/01g0p5 524
/m/032xhg 525
/m/034qrh 526
/m/050023 527
/m/026dcvf 528
/m/0195fx 529
/m/0m_sb 530
/m/023rwm 531
/m/01t_xp_ 532
/m/021gt5 533
/m/01dw4q 534
/m/02qjj7 535
/m/0436f4 536
/m/02lfcm 537
/m/024jwt 538
/m/03v1s 539
/m/0plyy 540
/m/0z4s 541
/m/099jhq 542
/m/0sx7r 543
/m/03tmr 544
/m/02j9z 545
/m/05qhw 546
/m/0h1_w 547
/m/0789_m 548
/m/054_mz 549
/m/039n1 550
/m/099bk 551
/m/05qd_ 552
/m/0jsf6 553
/m/0fp_v1x 554
/m/09bjv 555
/m/05zkcn5 556
/m/03mh94 557
/m/022r38 558
/m/05k17c 559
/m/05vsb7 560
/m/01pl14 561
/m/01vx2h 562
/m/06x43v 563
/m/01wv9xn 564
/m/02snj9 565
/m/0dckvs 566
/m/02r96rf 567
/m/0223bl 568
/m/02vx4 569
/m/0clfdj 570
/m/09fqtq 571
/m/0m0jc 572
/m/01pfr3 573
/m/07w21 574
/m/041h0 575
/m/027z0pl 576
/m/0drnwh 577
/m/058kh7 578
/m/0mfj2 579
/m/06nnj 580
/m/0bynt 581
/m/01rrwf6 582
/m/02dtg 583
/m/052nd 584
/m/03ys48 585
/m/07y9k 586
/m/03gqb0k 587
/m/015pdg 588
/m/0m2l9 589
/m/04gxf 590
/m/0b_6q5 591
/m/0ds11z 592
/m/02rjjll 593
/m/032nwy 594
/m/0159h6 595
/m/0h5g_ 596
/m/04rs03 597
/m/0flw86 598
/m/04f2zj 599
/m/0285c 600
/m/04tz52 601
/m/03w1v2 602
/m/025_64l 603
/m/01_9c1 604
/m/05f4vxd 605
/m/09v6gc9 606
/m/0c4f4 607
/m/0bxtg 608
/m/0fd3y 609
/m/0m19t 610
/m/04v8x9 611
/m/0p9sw 612
/m/02vs3x5 613
/m/05t54s 614
/m/087c7 615
/m/01tlmw 616
/m/04w8f 617
/m/06z6r 618
/m/02wb6yq 619
/m/0ggx5q 620
/m/0bdwqv 621
/m/02ldv0 622
/m/0g5qs2k 623
/m/03ds3 624
/m/0j_t1 625
/m/03_d0 626
/m/026ps1 627
/m/02g3v6 628
/m/0ds33 629
/m/02x4w6g 630
/m/04zn7g 631
/m/04wqr 632
/m/0chsq 633
/m/0dscrwf 634
/m/07ssc 635
/m/01vrkdt 636
/m/01rr9f 637
/m/02zsn 638
/m/058ncz 639
/m/03zqc1 640
/m/016z5x 641
/m/07f8wg 642
/m/018d6l 643
/m/0xhtw 644
/m/01dys 645
/m/0343h 646
/m/0dtfn 647
/m/0147dk 648
/m/01h7bb 649
/m/06cv1 650
/m/02jknp 651
/m/01lz4tf 652
/m/05148p4 653
/m/0wzm 654
/m/07gvx 655
/m/019tzd 656
/m/02_286 657
/m/07b_l 658
/m/0b6tzs 659
/m/017149 660
/m/05hj0n 661
/m/04lgymt 662
/m/02g3gj 663
/m/0gydcp7 664
/m/02_j7t 665
/m/0lhp1 666
/m/0dxtg 667
/m/04j_gs 668
/m/06dr9 669
/m/05wh0sh 670
/m/09v82c0 671
/m/03cv_gy 672
/m/06cs95 673
/m/02nb2s 674
/m/02lhm2 675
/m/014ktf 676
/m/0mdqp 677
/m/09bcm 678
/m/05qc_ 679
/m/09h4b5 680
/m/09l65 681
/m/09sb52 682
/m/0gq6s3 683
/m/04nl83 684
/m/0309lm 685
/m/0wq3z 686
/m/02rgz4 687
/m/018jk2 688
/m/0gkvb7 689
/m/03f2_rc 690
/m/07lmxq 691
/m/06jzh 692
/m/01k_r5b 693
/m/0131kb 694
/m/02q5bx2 695
/m/07z2lx 696
/m/017s11 697
/m/0djb3vw 698
/m/01ty7ll 699
/m/033hqf 700
/m/0gqy2 701
/m/02vm9nd 702
/m/02qrv7 703
/m/03kpvp 704
/m/094vy 705
/m/02jx1 706
/m/04bs3j 707
/m/032l1 708
/m/019z7q 709
/m/01j_9c 710
/m/02mslq 711
/m/014x77 712
/m/01ln5z 713
/m/05v8c 714
/m/02j71 715
/m/0dm5l 716
/m/011k1h 717
/m/0n4m5 718
/m/0ydpd 719
/m/02211by 720
/m/01c6k4 721
/m/0c1pj 722
/m/03h_yy 723
/m/0151ns 724
/m/02gdjb 725
/m/05p1tzf 726
/m/0f830f 727
/m/027dtv3 728
/m/02mhfy 729
/m/0dcqh 730
/m/01nqfh_ 731
/m/0bk1p 732
/m/01cf93 733
/m/03_2y 734
/m/0mb8c 735
/m/01wl38s 736
/m/01d38g 737
/m/06cc_1 738
/m/02cg41 739
/m/03t5n3 740
/m/07c6l 741
/m/060v34 742
/m/03npn 743
/m/0bth54 744
/m/016tt2 745
/m/02bxjp 746
/m/05dxl_ 747
/m/040vk98 748
/m/01zkxv 749
/m/0cb4j 750
/m/01fpvz 751
/m/05m_8 752
/m/06pwq 753
/m/01sxly 754
/m/05zvj3m 755
/m/0bksh 756
/m/08vq2y 757
/m/0d35y 758
/m/0fz20l 759
/m/0gqwc 760
/m/015fr 761
/m/0l98s 762
/m/018dnt 763
/m/050r1z 764
/m/07g2b 765
/m/0184jw 766
/m/011yqc 767
/m/0f4_2k 768
/m/0g2lq 769
/m/02p_7cr 770
/m/02_1rq 771
/m/02_1sj 772
/m/0g54xkt 773
/m/0bwh6 774
/m/0f4x7 775
/m/0n0bp 776
/m/0lzb8 777
/m/02grdc 778
/m/0ch6mp2 779
/m/01tl50z 780
/m/02lf0c 781
/m/0b60sq 782
/m/071h5c 783
/m/0ggq0m 784
/m/01vvy 785
/m/04ddm4 786
/m/02n4kr 787
/m/02825kb 788
/m/0kbws 789
/m/03hr1p 790
/m/0gr4k 791
/m/0bzk8w 792
/m/03s6l2 793
/m/0lsxr 794
/m/077g7n 795
/m/03ww_x 796
/m/07j87 797
/m/07_lq 798
/m/02002f 799
/m/06m_5 800
/m/0cqt41 801
/m/02x3lt7 802
/m/02k54 803
/m/03xq0f 804
/m/08nvyr 805
/m/019f4v 806
/m/02r6c_ 807
/m/0njlp 808
/m/0p4wb 809
/m/01fc7p 810
/m/0bq0p9 811
/m/0xkq4 812
/m/03fykz 813
/m/0mkg 814
/m/0dwsp 815
/m/011yph 816
/m/025jfl 817
/m/09qwmm 818
/m/0fpkxfd 819
/m/09v42sf 820
/m/0br1x_ 821
/m/07s6fsf 822
/m/02w2bc 823
/m/0fq27fp 824
/m/03kq98 825
/m/01gvr1 826
/m/0cpllql 827
/m/02nxhr 828
/m/01kwld 829
/m/02pt27 830
/m/0dl5d 831
/m/072kp 832
/m/0ckd1 833
/m/0c40vxk 834
/m/0gx9rvq 835
/m/02pp_q_ 836
/m/0d4fqn 837
/m/02r22gf 838
/m/01cssf 839
/m/0298n7 840
/m/054g1r 841
/m/023fb 842
/m/06ryl 843
/m/014b4h 844
/m/08720 845
/m/032t2z 846
/m/0bqs56 847
/m/02q690_ 848
/m/044rvb 849
/m/028tv0 850
/m/04rcr 851
/m/0127ps 852
/m/0jbp0 853
/m/04r7jc 854
/m/080nwsb 855
/m/013x0b 856
/m/0c7ct 857
/m/04htfd 858
/m/0dq_5 859
/m/020xn5 860
/m/04n52p6 861
/m/026p_bs 862
/m/06dl_ 863
/m/02ndf1 864
/m/02p10m 865
/m/02qvgy 866
/m/0j5m6 867
/m/021sv1 868
/m/070m6c 869
/m/0kzy0 870
/m/0gbbt 871
/m/025p38 872
/m/04jwjq 873
/m/02cl1 874
/m/0jmdb 875
/m/0p3sf 876
/m/013y1f 877
/m/03lpp_ 878
/m/083jv 879
/m/0401sg 880
/m/012gbb 881
/m/01psyx 882
/m/0n22z 883
/m/02r_d4 884
/m/0f4vx 885
/m/01pv51 886
/m/0jmj7 887
/m/0284n42 888
/m/0pc62 889
/m/01pgzn_ 890
/m/0r0m6 891
/m/07gyv 892
/m/03lygq 893
/m/013tjc 894
/m/02p21g 895
/m/0dqytn 896
/m/064t9 897
/m/0168cl 898
/m/0jcgs 899
/m/0f2r6 900
/m/02jqjm 901
/m/018vs 902
/m/01pcdn 903
/m/0170_p 904
/m/033nzk 905
/m/06y9c2 906
/m/02pjc1h 907
/m/0170qf 908
/m/0cqhk0 909
/m/08wq0g 910
/m/01cv3n 911
/m/025vry 912
/m/0f2wj 913
/m/01ct6 914
/m/0g3zpp 915
/m/0bdw1g 916
/m/039fgy 917
/m/05b1610 918
/m/0dj0m5 919
/m/01cx_ 920
/m/030xr_ 921
/m/07mvp 922
/m/08c6k9 923
/m/0_b3d 924
/m/026g4l_ 925
/m/01l29r 926
/m/0n5j7 927
/m/03176f 928
/m/06n7h7 929
/m/03ldxq 930
/m/01n5309 931
/m/09qvc0 932
/m/0fgpvf 933
/m/0209xj 934
/m/0gffmn8 935
/m/015lhm 936
/m/0209hj 937
/m/0kr5_ 938
/m/06x68 939
/m/05kcgsf 940
/m/01f7v_ 941
/m/01f8gz 942
/m/01vvycq 943
/m/024n3z 944
/m/08phg9 945
/m/03rg2b 946
/m/0187y5 947
/m/01z9_x 948
/m/0248jb 949
/m/07k53y 950
/m/02vkdwz 951
/m/09q23x 952
/m/020bv3 953
/m/02qggqc 954
/m/0fg04 955
/m/05gml8 956
/m/03m8lq 957
/m/05tg3 958
/m/02qpbqj 959
/m/07kb5 960
/m/02jcc 961
/m/01b30l 962
/m/0b_j2 963
/m/0hn10 964
/m/02py4c8 965
/m/0hmr4 966
/m/01csvq 967
/m/018wng 968
/m/01vlj1g 969
/m/04fzfj 970
/m/03_gd 971
/m/0gs9p 972
/m/03cp4cn 973
/m/01bgqh 974
/m/01bx35 975
/m/07s846j 976
/m/0d2b38 977
/m/03h4fq7 978
/m/02hczc 979
/m/0p07_ 980
/m/05kj_ 981
/m/0mx4_ 982
/m/059f4 983
/m/05g3b 984
/m/09byk 985
/m/04306rv 986
/m/063g7l 987
/m/02g_6j 988
/m/05jx2d 989
/m/04zx3q1 990
/m/065y4w7 991
/m/03m73lj 992
/m/061681 993
/m/06w87 994
/m/041ly3 995
/m/0c_j5d 996
/m/02jyhv 997
/m/01w3v 998
/m/05qjt 999
/m/08f3b1 1000
/m/01vj9c 1001
/m/05q9g1 1002
/m/04dn09n 1003
/m/09q_6t 1004
/m/07lt7b 1005
/m/094qd5 1006
/m/026mfbr 1007
/m/07_dn 1008
/m/01hp5 1009
/m/0338lq 1010
/m/042rnl 1011
/m/02z3r8t 1012
/m/05gg4 1013
/m/06npd 1014
/m/09qj50 1015
/m/0kfpm 1016
/m/043djx 1017
/m/05fkf 1018
/m/03gwpw2 1019
/m/017gm7 1020
/m/0svqs 1021
/m/0274ck 1022
/m/0j7v_ 1023
/m/06s6l 1024
/m/01hp22 1025
/m/0jf1b 1026
/m/0dzfdw 1027
/m/01r97z 1028
/m/02w0dc0 1029
/m/012cj0 1030
/m/012c6x 1031
/m/04fzk 1032
/m/01qhm_ 1033
/m/06mzp 1034
/m/0l998 1035
/m/0p_sc 1036
/m/05kfs 1037
/m/0kbvb 1038
/m/096f8 1039
/m/02f6ym 1040
/m/01t110 1041
/m/037njl 1042
/m/0f8l9c 1043
/m/0glb5 1044
/m/01k2yr 1045
/m/099t8j 1046
/m/0lpjn 1047
/m/0bzm__ 1048
/m/01qzyz 1049
/m/0gkz15s 1050
/m/02k_4g 1051
/m/0fbvqf 1052
/m/018ctl 1053
/m/0cj16 1054
/m/02ryz24 1055
/m/03hjv97 1056
/m/076lxv 1057
/m/08gsvw 1058
/m/0g1rw 1059
/m/01f2w0 1060
/m/0lpp8 1061
/m/087wc7n 1062
/m/0hzlz 1063
/m/018db8 1064
/m/035xwd 1065
/m/09p35z 1066
/m/0415svh 1067
/m/01n7q 1068
/m/024tcq 1069
/m/0jm3v 1070
/m/04s0m 1071
/m/03j43 1072
/m/02vxfw_ 1073
/m/09zf_q 1074
/m/082_p 1075
/m/03hkch7 1076
/m/03hl6lc 1077
/m/07s467s 1078
/m/09jp3 1079
/m/0cwrr 1080
/m/01mvth 1081
/m/04yj5z 1082
/m/03ckwzc 1083
/m/03qd_ 1084
/m/07z31v 1085
/m/0bdw6t 1086
/m/02fgpf 1087
/m/07hpv3 1088
/m/0vgkd 1089
/m/0vrmb 1090
/m/0cbv4g 1091
/m/03hj5vf 1092
/m/0jzw 1093
/m/03k9fj 1094
/m/01jfsb 1095
/m/0dsvzh 1096
/m/084qpk 1097
/m/058kqy 1098
/m/01mvjl0 1099
/m/0288zy 1100
/m/0785v8 1101
/m/05ml_s 1102
/m/04bd8y 1103
/m/09jwl 1104
/m/01qkqwg 1105
/m/02hxhz 1106
/m/05b__vr 1107
/m/064nh4k 1108
/m/07rhpg 1109
/m/0b73_1d 1110
/m/02kxbwx 1111
/m/0m_v0 1112
/m/0hcs3 1113
/m/06wpc 1114
/m/02l840 1115
/m/01wmxfs 1116
/m/02773nt 1117
/m/02773m2 1118
/m/02778pf 1119
/m/0pv2t 1120
/m/027986c 1121
/m/0164qt 1122
/m/02sg5v 1123
/m/016jhr 1124
/m/025xt8y 1125
/m/0bs5k8r 1126
/m/01yfm8 1127
/m/0p_47 1128
/m/042d1 1129
/m/060d2 1130
/m/02lfl4 1131
/m/0521rl1 1132
/m/01bh3l 1133
/m/01_vrh 1134
/m/01pcq3 1135
/m/03h_9lg 1136
/m/07p__7 1137
/m/01km6_ 1138
/m/013n2h 1139
/m/02q_cc 1140
/m/0kx4m 1141
/m/034x61 1142
/m/066m4g 1143
/m/071t0 1144
/m/09x3r 1145
/m/02z13jg 1146
/m/016khd 1147
/m/05kjc6 1148
/m/0_3cs 1149
/m/0mwl2 1150
/m/02ndbd 1151
/m/06_wqk4 1152
/m/013kcv 1153
/m/019lwb 1154
/m/01r3y2 1155
/m/02h4rq6 1156
/m/0dhrqx 1157
/m/05jzt3 1158
/m/0hnlx 1159
/m/0fhp9 1160
/m/0ccd3x 1161
/m/03r8tl 1162
/m/05wdgq 1163
/m/088tb 1164
/m/01l7qw 1165
/m/018j2 1166
/m/01x15dc 1167
/m/01_gx_ 1168
/m/0f04v 1169
/m/0l6ny 1170
/m/0dwvl 1171
/m/05x_5 1172
/m/02z9hqn 1173
/m/06mfvc 1174
/m/0ck27z 1175
/m/09qv3c 1176
/m/02nf2c 1177
/m/0152cw 1178
/m/06fq2 1179
/m/03bwzr4 1180
/m/09vw2b7 1181
/m/0hr3c8y 1182
/m/08w7vj 1183
/m/032_jg 1184
/m/06z8s_ 1185
/m/0dky9n 1186
/m/0147sh 1187
/m/01yk13 1188
/m/0cqh46 1189
/m/02lk1s 1190
/m/047byns 1191
/m/0287477 1192
/m/0f4vbz 1193
/m/0dwtp 1194
/m/0kv2hv 1195
/m/04tc1g 1196
/m/0b13yt 1197
/m/01c_d 1198
/m/0bg539 1199
/m/0488g9 1200
/m/043h78 1201
/m/05lls 1202
/m/0pcc0 1203
/m/069ld1 1204
/m/01j5x6 1205
/m/04b4yg 1206
/m/0356lc 1207
/m/03tc8d 1208
/m/07tgn 1209
/m/0ymbl 1210
/m/048wrb 1211
/m/03m5k 1212
/m/01jfr3y 1213
/m/02vjzr 1214
/m/016vh2 1215
/m/03wbqc4 1216
/m/018mxj 1217
/m/0gs1_ 1218
/m/0344gc 1219
/m/02x1dht 1220
/m/0fkvn 1221
/m/07cfx 1222
/m/04969y 1223
/m/02f72_ 1224
/m/015f7 1225
/m/02h659 1226
/m/01v3s2_ 1227
/m/0pz7h 1228
/m/01vksx 1229
/m/07fb8_ 1230
/m/019rg5 1231
/m/0265v21 1232
/m/026dg51 1233
/m/02qrbbx 1234
/m/01srq2 1235
/m/081k8 1236
/m/073v6 1237
/m/031296 1238
/m/05zr0xl 1239
/m/03ym1 1240
/m/065b6q 1241
/m/0htlr 1242
/m/03cvwkr 1243
/m/01hxs4 1244
/m/02cttt 1245
/m/04sx9_ 1246
/m/04411 1247
/m/0ddd9 1248
/m/01yb09 1249
/m/039c26 1250
/m/050rj 1251
/m/05p1dby 1252
/m/03fg0r 1253
/m/03t97y 1254
/m/02rcdc2 1255
/m/04gknr 1256
/m/07q1v4 1257
/m/018fq 1258
/m/0sxmx 1259
/m/06r_by 1260
/m/01sp81 1261
/m/0bn9sc 1262
/m/02279c 1263
/m/0f2rq 1264
/m/0b_6rk 1265
/m/027rwmr 1266
/m/03h26tm 1267
/m/02qflgv 1268
/m/01lbp 1269
/m/0r5y9 1270
/m/01hmnh 1271
/m/05fm6m 1272
/m/01jv_6 1273
/m/06b1q 1274
/m/0jgx 1275
/m/01qb5d 1276
/m/0vmt 1277
/m/04n6k 1278
/m/026ldz7 1279
/m/05zm34 1280
/m/01vdm0 1281
/m/01ycfv 1282
/m/02hv44_ 1283
/m/017r2 1284
/m/03d_w3h 1285
/m/0fq9zdn 1286
/m/092vkg 1287
/m/04jjy 1288
/m/0963mq 1289
/m/03jn4 1290
/m/0k3p 1291
/m/02wzl1d 1292
/m/01hhvg 1293
/m/036hv 1294
/m/01hrqc 1295
/m/05zbm4 1296
/m/03_vx9 1297
/m/0cwy47 1298
/m/06f0dc 1299
/m/0hjy 1300
/m/01kwlwp 1301
/m/0n1h 1302
/m/0c75w 1303
/m/02vzc 1304
/m/02z1nbg 1305
/m/09z2b7 1306
/m/0r62v 1307
/m/0kcd5 1308
/m/05ztrmj 1309
/m/03f5spx 1310
/m/01tnxc 1311
/m/04q7r 1312
/m/03s0w 1313
/m/05sfs 1314
/m/0gg5qcw 1315
/m/04f525m 1316
/m/0fh694 1317
/m/03gm48 1318
/m/01vrnsk 1319
/m/01w40h 1320
/m/03j755 1321
/m/01s0ps 1322
/m/0d0vj4 1323
/m/012t_z 1324
/m/015grj 1325
/m/03pmty 1326
/m/01q7cb_ 1327
/m/03knl 1328
/m/017gl1 1329
/m/0yfp 1330
/m/0gh65c5 1331
/m/02qzmz6 1332
/m/0dgd_ 1333
/m/0738y5 1334
/m/04kkz8 1335
/m/04l3_z 1336
/m/06pk8 1337
/m/02r3cn 1338
/m/0glyyw 1339
/m/0dlngsd 1340
/m/03y_f8 1341
/m/075q_ 1342
/m/0134w7 1343
/m/01bdxz 1344
/m/0537y_ 1345
/m/03lfd_ 1346
/m/02x8n1n 1347
/m/01d3n8 1348
/m/0hgxh 1349
/m/02_sr1 1350
/m/01vsn38 1351
/m/08jbxf 1352
/m/0d1pc 1353
/m/044mjy 1354
/m/0l2lk 1355
/m/05w3y 1356
/m/0m_mm 1357
/m/0k6nt 1358
/m/081lh 1359
/m/01tspc6 1360
/m/0151w_ 1361
/m/0mw89 1362
/m/0mw93 1363
/m/01cr28 1364
/m/03mqj_ 1365
/m/0p9lw 1366
/m/099tbz 1367
/m/0dn16 1368
/m/07qnf 1369
/m/067jsf 1370
/m/01jrz5j 1371
/m/01dtl 1372
/m/023vrq 1373
/m/01wyz92 1374
/m/015ynm 1375
/m/01795t 1376
/m/01m1dzc 1377
/m/013b2h 1378
/m/0yfvf 1379
/m/01gf5h 1380
/m/03gj2 1381
/m/01vv7sc 1382
/m/0f94t 1383
/m/06krf3 1384
/m/03c7tr1 1385
/m/05zvq6g 1386
/m/01m13b 1387
/m/01d5z 1388
/m/07w0v 1389
/m/01vn35l 1390
/m/0gvvf4j 1391
/m/03qbnj 1392
/m/0133x7 1393
/m/06mn7 1394
/m/0gr42 1395
/m/01bpnd 1396
/m/0bwfwpj 1397
/m/06n90 1398
/m/0jtdp 1399
/m/04dsnp 1400
/m/0456xp 1401
/m/04shbh 1402
/m/01fkv0 1403
/m/019_1h 1404
/m/0f0p0 1405
/m/0124jj 1406
/m/0g133 1407
/m/0dgq80b 1408
/m/0crfwmx 1409
/m/0ctw_b 1410
/m/0cskb 1411
/m/05fhy 1412
/m/04ych 1413
/m/012x4t 1414
/m/0c7g7 1415
/m/06c62 1416
/m/02__x 1417
/m/040j2_ 1418
/m/0dsfnd 1419
/m/0czmk1 1420
/m/01x4wq 1421
/m/056xx8 1422
/m/02bn_p 1423
/m/032ft5 1424
/m/071pf2 1425
/m/0k_l4 1426
/m/02l7c8 1427
/m/011ykb 1428
/m/05z96 1429
/m/058w5 1430
/m/01wxyx1 1431
/m/0f2v0 1432
/m/08433 1433
/m/012cph 1434
/m/0jkvj 1435
/m/03vfr_ 1436
/m/01x0sy 1437
/m/01p5_g 1438
/m/07_fj54 1439
/m/010v8k 1440
/m/0250f 1441
/m/04hpck 1442
/m/04pm6 1443
/m/05khh 1444
/m/0sqc8 1445
/m/027b9j5 1446
/m/011yl_ 1447
/m/0466k4 1448
/m/01zq91 1449
/m/02t__l 1450
/m/08cn_n 1451
/m/05pcn59 1452
/m/04w391 1453
/m/08hmch 1454
/m/080h2 1455
/m/09_bl 1456
/m/05q96q6 1457
/m/04wvhz 1458
/m/06196 1459
/m/03hpr 1460
/m/0bz5v2 1461
/m/03gjzk 1462
/m/0kz2w 1463
/m/05pcjw 1464
/m/0f102 1465
/m/02_xgp2 1466
/m/0517bc 1467
/m/0bx_q 1468
/m/07bzz7 1469
/m/02qm_f 1470
/m/012t1 1471
/m/048scx 1472
/m/02hxc3j 1473
/m/041rx 1474
/m/0jdhp 1475
/m/0pv3x 1476
/m/05_k56 1477
/m/0gqzz 1478
/m/049yf 1479
/m/05gqf 1480
/m/057d89 1481
/m/02q1tc5 1482
/m/05qsxy 1483
/m/01vswwx 1484
/m/06by7 1485
/m/01sxq9 1486
/m/086vfb 1487
/m/021_rm 1488
/m/02r3zy 1489
/m/0264v8r 1490
/m/02clgg 1491
/m/04v3q 1492
/m/01rz1 1493
/m/0mq17 1494
/m/02rdxsh 1495
/m/049dyj 1496
/m/0bshwmp 1497
/m/05glt 1498
/m/018f8 1499
/m/0272kv 1500
/m/02vjp3 1501
/m/01wtlq 1502
/m/01x66d 1503
/m/0jqp3 1504
/m/0170vn 1505
/m/02d44q 1506
/m/01_mdl 1507
/m/04rjg 1508
/m/07tg4 1509
/m/01pr_j6 1510
/m/01j_06 1511
/m/03_hd 1512
/m/0dr3sl 1513
/m/0f3zf_ 1514
/m/0k2sk 1515
/m/09q5w2 1516
/m/089fss 1517
/m/0l8z1 1518
/m/01c22t 1519
/m/0l6m5 1520
/m/0171lb 1521
/m/0ktx_ 1522
/m/03td5v 1523
/m/0jm_ 1524
/m/02cllz 1525
/m/02yw5r 1526
/m/02gys2 1527
/m/0dy68h 1528
/m/01b1mj 1529
/m/03x3wf 1530
/m/01vrncs 1531
/m/01fjz9 1532
/m/06sy4c 1533
/m/0g2c8 1534
/m/02whj 1535
/m/0lk90 1536
/m/086g2 1537
/m/035gjq 1538
/m/07_53 1539
/m/01mc11 1540
/m/0gkkf 1541
/m/01nv4h 1542
/m/03qx63 1543
/m/06fvc 1544
/m/01k2wn 1545
/m/03lsq 1546
/m/07bdd_ 1547
/m/0wh3 1548
/m/0jjy0 1549
/m/02f716 1550
/m/0dw4g 1551
/m/01g4zr 1552
/m/0yc84 1553
/m/0cqh6z 1554
/m/09qvms 1555
/m/047g8h 1556
/m/01y3c 1557
/m/06b0d2 1558
/m/03lt8g 1559
/m/0_92w 1560
/m/039bp 1561
/m/0mwx6 1562
/m/09pmkv 1563
/m/05nmg_ 1564
/m/06wcbk7 1565
/m/016kjs 1566
/m/02q8ms8 1567
/m/0h1mt 1568
/m/02x8m 1569
/m/01wbgdv 1570
/m/01yx7f 1571
/m/01nvmd_ 1572
/m/0jyx6 1573
/m/0cc56 1574
/m/0bdwft 1575
/m/0c5dd 1576
/m/030pr 1577
/m/0t_07 1578
/m/0jnwx 1579
/m/018wdw 1580
/m/04vr_f 1581
/m/07c0j 1582
/m/016r9z 1583
/m/0bl06 1584
/m/057bc6m 1585
/m/02bqn1 1586
/m/059_c 1587
/m/01gjd0 1588
/m/0js9s 1589
/m/02w_6xj 1590
/m/01yfj 1591
/m/0j3b 1592
/m/012x1l 1593
/m/014zfs 1594
/m/03bxz7 1595
/m/02r8hh_ 1596
/m/07kc_ 1597
/m/02qjv 1598
/m/0h1m9 1599
/m/02qyntr 1600
/m/0249kn 1601
/m/04rzd 1602
/m/0p_th 1603
/m/0h3xztt 1604
/m/02g839 1605
/m/0lgsq 1606
/m/01vc3y 1607
/m/0ym8f 1608
/m/02lp1 1609
/m/0dhdp 1610
/m/083p7 1611
/m/02y0js 1612
/m/083q7 1613
/m/049n7 1614
/m/02x2khw 1615
/m/099c8n 1616
/m/0c0nhgv 1617
/m/01pvkk 1618
/m/05pxnmb 1619
/m/02w9895 1620
/m/0d7wh 1621
/m/03cp7b3 1622
/m/018y2s 1623
/m/06gmr 1624
/m/0473m9 1625
/m/0swff 1626
/m/02lfns 1627
/m/01qdmh 1628
/m/09gdm7q 1629
/m/0207wx 1630
/m/0k3gw 1631
/m/0tyww 1632
/m/0872p_c 1633
/m/047yc 1634
/m/02_5h 1635
/m/0jnnx 1636
/m/02qvzf 1637
/m/0g48m4 1638
/m/019y64 1639
/m/0k_6t 1640
/m/02ly_ 1641
/m/053rxgm 1642
/m/030_1m 1643
/m/02scbv 1644
/m/0cp0t91 1645
/m/01ps2h8 1646
/m/05m883 1647
/m/02n9nmz 1648
/m/059t8 1649
/m/036k0s 1650
/m/0b68vs 1651
/m/04n7njg 1652
/m/06rv5t 1653
/m/02v63m 1654
/m/0jt3tjf 1655
/m/02dwj 1656
/m/043tz8m 1657
/m/06jntd 1658
/m/03rwz3 1659
/m/04nfpk 1660
/m/033g4d 1661
/m/05h4t7 1662
/m/0y54 1663
/m/02r34n 1664
/m/06gb2q 1665
/m/028fjr 1666
/m/02pkpfs 1667
/m/02f9wb 1668
/m/07sc6nw 1669
/m/0hcr 1670
/m/0283ph 1671
/m/092c5f 1672
/m/02gvwz 1673
/m/0pqc5 1674
/m/05ksh 1675
/m/0dzf_ 1676
/m/02r5w9 1677
/m/02pqp12 1678
/m/048yqf 1679
/m/03n93 1680
/m/03pmfw 1681
/m/0478__m 1682
/m/016t_3 1683
/m/01wdl3 1684
/m/0n6ds 1685
/m/01mqh5 1686
/m/0gj8t_b 1687
/m/07ylj 1688
/m/02sjf5 1689
/m/04cf09 1690
/m/01vrt_c 1691
/m/02lnhv 1692
/m/0ws7 1693
/m/0glqh5_ 1694
/m/03fbb6 1695
/m/03fnyk 1696
/m/0988cp 1697
/m/01s81 1698
/m/0d3f83 1699
/m/0lmm3 1700
/m/07gbf 1701
/m/026dx 1702
/m/018sg9 1703
/m/0sz28 1704
/m/0gjk1d 1705
/m/01dyk8 1706
/m/0jcx 1707
/m/0k4gf 1708
/m/07xzm 1709
/m/01ztgm 1710
/m/0fvxz 1711
/m/025m98 1712
/m/0fm2_ 1713
/m/02prw4h 1714
/m/03bx2lk 1715
/m/0277jc 1716
/m/04jzj 1717
/m/03f1zdw 1718
/m/016gr2 1719
/m/07gkgp 1720
/m/02rhwjr 1721
/m/020fcn 1722
/m/01qbl 1723
/m/0t_gg 1724
/m/0kpys 1725
/m/0r111 1726
/m/011yg9 1727
/m/026g73 1728
/m/030dx5 1729
/m/0r1yc 1730
/m/05z_kps 1731
/m/059j2 1732
/m/026390q 1733
/m/01vrz41 1734
/m/0c4z8 1735
/m/04zd4m 1736
/m/01sn04 1737
/m/045bg 1738
/m/0lbbj 1739
/m/02g8mp 1740
/m/05pdbs 1741
/m/0n5kc 1742
/m/0435vm 1743
/m/02114t 1744
/m/01xvb 1745
/m/05cgv 1746
/m/02lq5w 1747
/m/01gfq4 1748
/m/0gr0m 1749
/m/027gs1_ 1750
/m/0h1nt 1751
/m/02pxmgz 1752
/m/01k5t_3 1753
/m/0fvvz 1754
/m/02sjgpq 1755
/m/0tgcy 1756
/m/07_kq 1757
/m/01tcf7 1758
/m/02c6d 1759
/m/0bs5f0b 1760
/m/0372j5 1761
/m/0gjvqm 1762
/m/02z0dfh 1763
/m/0hv1t 1764
/m/037jz 1765
/m/01wd02c 1766
/m/02qvl7 1767
/m/0j6tr 1768
/m/09728 1769
/m/0h1wg 1770
/m/01vvlyt 1771
/m/01f1jf 1772
/m/07b2lv 1773
/m/05jbn 1774
/m/0bdxs5 1775
/m/03b12 1776
/m/020mfr 1777
/m/0xwj 1778
/m/04hwbq 1779
/m/07_3qd 1780
/m/0g5pv3 1781
/m/027r9t 1782
/m/034np8 1783
/m/0dgst_d 1784
/m/0345h 1785
/m/05c1t6z 1786
/m/0277470 1787
/m/016fly 1788
/m/01tdnyh 1789
/m/07brj 1790
/m/0bm02 1791
/m/0d04z6 1792
/m/0137n0 1793
/m/01v0sx2 1794
/m/04wgh 1795
/m/01bb9r 1796
/m/06m6z6 1797
/m/04ykg 1798
/m/0pmq2 1799
/m/01bzw5 1800
/m/06ms6 1801
/m/0bkj86 1802
/m/030znt 1803
/m/0swbd 1804
/m/016hvl 1805
/m/028p0 1806
/m/0cj2nl 1807
/m/0cp0ph6 1808
/m/0358x_ 1809
/m/04wtx1 1810
/m/01wdqrx 1811
/m/0cnztc4 1812
/m/01t6b4 1813
/m/09sdmz 1814
/m/026rm_y 1815
/m/07vc_9 1816
/m/0cdf37 1817
/m/0147fv 1818
/m/06mmb 1819
/m/043q4d 1820
/m/09lxv9 1821
/m/09dt7 1822
/m/02662b 1823
/m/01v42g 1824
/m/0blbxk 1825
/m/01kx_81 1826
/m/03kwtb 1827
/m/01718w 1828
/m/07g_0c 1829
/m/02vl_pz 1830
/m/04991x 1831
/m/03s5lz 1832
/m/022_6 1833
/m/03x746 1834
/m/01f2xy 1835
/m/014mlp 1836
/m/032_wv 1837
/m/0n6f8 1838
/m/0g5879y 1839
/m/09td7p 1840
/m/0d1mp3 1841
/m/01j67j 1842
/m/01b9ck 1843
/m/011ywj 1844
/m/03v3xp 1845
/m/0325pb 1846
/m/0gl5_ 1847
/m/0mnrb 1848
/m/0b7xl8 1849
/m/07t_l23 1850
/m/015nhn 1851
/m/05k2s_ 1852
/m/017vb_ 1853
/m/07ddz9 1854
/m/0m7fm 1855
/m/0fj52s 1856
/m/08lb68 1857
/m/059x0w 1858
/m/02mjs7 1859
/m/02583l 1860
/m/054ks3 1861
/m/01s1zk 1862
/m/04mzf8 1863
/m/05fg2 1864
/m/0_2v 1865
/m/03hbzj 1866
/m/035qlx 1867
/m/02qdzd 1868
/m/05m0h 1869
/m/069q4f 1870
/m/07cbcy 1871
/m/0bkbm 1872
/m/0d1qmz 1873
/m/033srr 1874
/m/05drq5 1875
/m/04pbhw 1876
/m/07b1gq 1877
/m/05z01 1878
/m/03bdkd 1879
/m/0flddp 1880
/m/053tj7 1881
/m/07ww5 1882
/m/01l8t8 1883
/m/0416y94 1884
/m/0drsm 1885
/m/0xy28 1886
/m/0hvvf 1887
/m/01vs14j 1888
/m/01qvgl 1889
/m/0gqz2 1890
/m/04t7ts 1891
/m/05q7cj 1892
/m/0n5fl 1893
/m/02qsjt 1894
/m/01lhy 1895
/m/0kn4c 1896
/m/03m4mj 1897
/m/01vfqh 1898
/m/03qlv7 1899
/m/01l4zqz 1900
/m/07cyl 1901
/m/098sv2 1902
/m/02knxx 1903
/m/026q3s3 1904
/m/0pz91 1905
/m/0ftf0f 1906
/m/09qr6 1907
/m/017cjb 1908
/m/03cs_z7 1909
/m/07s6tbm 1910
/m/05lb87 1911
/m/02lyr4 1912
/m/0h0wc 1913
/m/02p0qmm 1914
/m/0c_zj 1915
/m/049bmk 1916
/m/01jz6x 1917
/m/025syph 1918
/m/06n9lt 1919
/m/0pgjm 1920
/m/0jmfv 1921
/m/01ngz1 1922
/m/06mz5 1923
/m/0w0d 1924
/m/035qy 1925
/m/086qd 1926
/m/0kyk 1927
/m/01fkxr 1928
/m/0h7x 1929
/m/0vhm 1930
/m/0215n 1931
/m/01lfvj 1932
/m/02knnd 1933
/m/0284jb 1934
/m/09lbv 1935
/m/089pg7 1936
/m/0g768 1937
/m/01xqqp 1938
/m/026mml 1939
/m/08pgl8 1940
/m/01c9f2 1941
/m/019bk0 1942
/m/01f69m 1943
/m/0gqyl 1944
/m/058x5 1945
/m/047msdk 1946
/m/01pny5 1947
/m/04qftx 1948
/m/012vd6 1949
/m/06cm5 1950
/m/01swck 1951
/m/07ng9k 1952
/m/02jjt 1953
/m/073tm9 1954
/m/0136g9 1955
/m/03_fk9 1956
/m/02b5_l 1957
/m/0bwhdbl 1958
/m/07tl0 1959
/m/0sjqm 1960
/m/01fmz6 1961
/m/0gmcwlb 1962
/m/0prjs 1963
/m/01kff7 1964
/m/015rmq 1965
/m/095w_ 1966
/m/02hrh0_ 1967
/m/07wj1 1968
/m/01cpjx 1969
/m/0262zm 1970
/m/01963w 1971
/m/0gpjbt 1972
/m/0249fn 1973
/m/05cv94 1974
/m/01wcp_g 1975
/m/07_f2 1976
/m/0gcdzz 1977
/m/04dqdk 1978
/m/01yh3y 1979
/m/048lv 1980
/m/044g_k 1981
/m/017ztv 1982
/m/059y0 1983
/m/05mt_q 1984
/m/01c427 1985
/m/0bjrnt 1986
/m/05f7s1 1987
/m/02mxw0 1988
/m/04hhv 1989
/m/073q1 1990
/m/01lj_c 1991
/m/06q8hf 1992
/m/0f721s 1993
/m/019nnl 1994
/m/0fr59 1995
/m/01k8rb 1996
/m/01j4ls 1997
/m/011zf2 1998
/m/03xgm3 1999
/m/013cr 2000
/m/0sxfd 2001
/m/0c4xc 2002
/m/099pks 2003
/m/01by1l 2004
/m/04mz10g 2005
/m/04y79_n 2006
/m/0r7fy 2007
/m/05fly 2008
/m/0ftps 2009
/m/04q42 2010
/m/031zkw 2011
/m/0zc6f 2012
/m/01y0y6 2013
/m/061v5m 2014
/m/01yhvv 2015
/m/01r32 2016
/m/0bjqh 2017
/m/01_d4 2018
/m/067mj 2019
/m/02b6n9 2020
/m/01p9hgt 2021
/m/024rbz 2022
/m/09p0ct 2023
/m/01797x 2024
/m/0n85g 2025
/m/0_24q 2026
/m/012gx2 2027
/m/030qb3t 2028
/m/015pxr 2029
/m/0162c8 2030
/m/043q6n_ 2031
/m/03fghg 2032
/m/02vw1w2 2033
/m/01yhm 2034
/m/0bxbb 2035
/m/0bx9y 2036
/m/02rqwhl 2037
/m/07nqn 2038
/m/07qg8v 2039
/m/0p_2r 2040
/m/084l5 2041
/m/01rh0w 2042
/m/04m1bm 2043
/m/07z1m 2044
/m/0mnzd 2045
/m/01t_vv 2046
/m/0j90s 2047
/m/02nhxf 2048
/m/03bxwtd 2049
/m/02g3ft 2050
/m/01f7gh 2051
/m/022_lg 2052
/m/0395lw 2053
/m/0239kh 2054
/m/0ddd0gc 2055
/m/0clvcx 2056
/m/0gs96 2057
/m/0dr_4 2058
/m/0xzly 2059
/m/02ht1k 2060
/m/0l4h_ 2061
/m/037w7r 2062
/m/0512p 2063
/m/019n8z 2064
/m/014y6 2065
/m/02rlzj 2066
/m/0284gcb 2067
/m/017zq0 2068
/m/099cng 2069
/m/0hr6lkl 2070
/m/054krc 2071
/m/0bscw 2072
/m/018ljb 2073
/m/07bs0 2074
/m/0292l3 2075
/m/033tf_ 2076
/m/01wjrn 2077
/m/059x66 2078
/m/0pb33 2079
/m/04zyhx 2080
/m/0jqn5 2081
/m/06qd3 2082
/m/0qm98 2083
/m/0cz8mkh 2084
/m/05ywg 2085
/m/04w_7 2086
/m/02g87m 2087
/m/03n08b 2088
/m/06w2sn5 2089
/m/0xmlp 2090
/m/01vt9p3 2091
/m/0l8sx 2092
/m/0gtvrv3 2093
/m/0fq8f 2094
/m/07t31 2095
/m/01grpq 2096
/m/03mg5f 2097
/m/03rl84 2098
/m/098s2w 2099
/m/0cymp 2100
/m/02vklm3 2101
/m/03bx0bm 2102
/m/01vsxdm 2103
/m/0cd25 2104
/m/05sq84 2105
/m/03twd6 2106
/m/07t21 2107
/m/01f2y9 2108
/m/0286hyp 2109
/m/09sr0 2110
/m/0319l 2111
/m/0g2dz 2112
/m/03fts 2113
/m/05qx1 2114
/m/06jk5_ 2115
/m/07y9w5 2116
/m/054lpb6 2117
/m/01ckbq 2118
/m/01c6qp 2119
/m/02r79_h 2120
/m/02g9p4 2121
/m/05strv 2122
/m/06f5j 2123
/m/020d5 2124
/m/03mr85 2125
/m/014kg4 2126
/m/02q0k7v 2127
/m/0d06vc 2128
/m/02qhlm 2129
/m/0d_q40 2130
/m/02lgj6 2131
/m/0bwgc_ 2132
/m/045c66 2133
/m/05sxzwc 2134
/m/01719t 2135
/m/0244r8 2136
/m/0fpkhkz 2137
/m/0krdk 2138
/m/0cv9b 2139
/m/0gwf191 2140
/m/05j82v 2141
/m/0_7z2 2142
/m/0168dy 2143
/m/0263ycg 2144
/m/028kj0 2145
/m/015qh 2146
/m/0124k9 2147
/m/03mdt 2148
/m/01xn6mc 2149
/m/04jkpgv 2150
/m/01znc_ 2151
/m/03y1mlp 2152
/m/0f6_4 2153
/m/0cvkv5 2154
/m/01njxvw 2155
/m/029sk 2156
/m/01pw2f1 2157
/m/0byq0v 2158
/m/02r4qs 2159
/m/0wp9b 2160
/m/01t8sr 2161
/m/01_9fk 2162
/m/01vsksr 2163
/m/03rzvv 2164
/m/032zq6 2165
/m/018z_c 2166
/m/0340hj 2167
/m/044ntk 2168
/m/017d77 2169
/m/033smt 2170
/m/07f_t4 2171
/m/02s5v5 2172
/m/027cyf7 2173
/m/0ckt6 2174
/m/01h4rj 2175
/m/0g9wdmc 2176
/m/044mm6 2177
/m/07f3xb 2178
/m/0ft5vs 2179
/m/01_1pv 2180
/m/0161h5 2181
/m/0jg77 2182
/m/01271h 2183
/m/059j1m 2184
/m/0187nd 2185
/m/0km5c 2186
/m/04f_d 2187
/m/01tj34 2188
/m/04w7rn 2189
/m/0qmhk 2190
/m/013zdg 2191
/m/07lx1s 2192
/m/016pns 2193
/m/025sc50 2194
/m/01wp8w7 2195
/m/04k4rt 2196
/m/0py9b 2197
/m/02lkcc 2198
/m/07z542 2199
/m/03qnvdl 2200
/m/04_tv 2201
/m/0fdjb 2202
/m/07s3m4g 2203
/m/01_4z 2204
/m/01c6nk 2205
/m/049g_xj 2206
/m/02rv_dz 2207
/m/011yhm 2208
/m/09rx7tx 2209
/m/05183k 2210
/m/05prs8 2211
/m/01cvtf 2212
/m/07qcbw 2213
/m/01p45_v 2214
/m/02r1c18 2215
/m/06c1y 2216
/m/0bx8pn 2217
/m/0dp7wt 2218
/m/045cq 2219
/m/05sw5b 2220
/m/02sb1w 2221
/m/03jldb 2222
/m/02wcx8c 2223
/m/07y2s 2224
/m/0blg2 2225
/m/01c72t 2226
/m/0crx5w 2227
/m/01tbp 2228
/m/04hgpt 2229
/m/01jv1z 2230
/m/01r9fv 2231
/m/04nw9 2232
/m/04syw 2233
/m/0d1_f 2234
/m/0ggbhy7 2235
/m/0pvms 2236
/m/0d6b7 2237
/m/028c_8 2238
/m/01d6g 2239
/m/0mbwf 2240
/m/02hcv8 2241
/m/0n1v8 2242
/m/0168ls 2243
/m/0j582 2244
/m/05tk7y 2245
/m/020lpx 2246
/m/0gj9qxr 2247
/m/0g5b0q5 2248
/m/06cgy 2249
/m/061fhg 2250
/m/03qmj9 2251
/m/02xwq9 2252
/m/05sy_5 2253
/m/01mjq 2254
/m/027f7dj 2255
/m/0mcl0 2256
/m/05hqv 2257
/m/026n4h6 2258
/m/07_m2 2259
/m/0lbd9 2260
/m/0fpjd_g 2261
/m/01hqhm 2262
/m/024rgt 2263
/m/01g257 2264
/m/01c4_6 2265
/m/0ggl02 2266
/m/0f2df 2267
/m/01pl9g 2268
/m/02wgk1 2269
/m/0_ytw 2270
/m/071x0k 2271
/m/03g5jw 2272
/m/02r1tx7 2273
/m/01sb5r 2274
/m/0l15bq 2275
/m/01d650 2276
/m/03ttfc 2277
/m/05tfm 2278
/m/0169t 2279
/m/0jm2v 2280
/m/0ctt4z 2281
/m/01vb403 2282
/m/03cvfg 2283
/m/01445t 2284
/m/0hsb3 2285
/m/03cdg 2286
/m/030p35 2287
/m/0f6_x 2288
/m/01713c 2289
/m/02x4wb 2290
/m/03h502k 2291
/m/01kvqc 2292
/m/089g0h 2293
/m/047vp1n 2294
/m/02gx2k 2295
/m/02633g 2296
/m/021yc7p 2297
/m/0qm8b 2298
/m/02rdyk7 2299
/m/0mx7f 2300
/m/0dw6b 2301
/m/0tyql 2302
/m/05p09zm 2303
/m/013knm 2304
/m/016k6x 2305
/m/0160nk 2306
/m/02c4s 2307
/m/01xd9 2308
/m/05v10 2309
/m/01nczg 2310
/m/0m4mb 2311
/m/0d5fb 2312
/m/05pbl56 2313
/m/0lk8j 2314
/m/0c34mt 2315
/m/0dlglj 2316
/m/02_n7 2317
/m/0ntpv 2318
/m/03yl2t 2319
/m/025txtg 2320
/m/0203v 2321
/m/01nqj 2322
/m/073hmq 2323
/m/0fb7c 2324
/m/05zh9c 2325
/m/01l_pn 2326
/m/0gxtknx 2327
/m/06bnz 2328
/m/0154d7 2329
/m/0199wf 2330
/m/021r7r 2331
/m/01ypsj 2332
/m/05z_p6 2333
/m/038bht 2334
/m/0gztl 2335
/m/0vg8 2336
/m/06rzwx 2337
/m/019l68 2338
/m/01xcqc 2339
/m/01mqz0 2340
/m/02m501 2341
/m/04vq33 2342
/m/01304j 2343
/m/05w3f 2344
/m/02kth6 2345
/m/02tktw 2346
/m/09blyk 2347
/m/025ndl 2348
/m/0g78xc 2349
/m/01sbf2 2350
/m/033q4k 2351
/m/07s93v 2352
/m/0fvyg 2353
/m/0fx0j2 2354
/m/05hmp6 2355
/m/01f1jy 2356
/m/02847m9 2357
/m/04l8xw 2358
/m/09rp4r_ 2359
/m/02lxj_ 2360
/m/051kv 2361
/m/03q4nz 2362
/m/01fwj8 2363
/m/03fn8k 2364
/m/0436yk 2365
/m/05z7c 2366
/m/013jz2 2367
/m/0dvld 2368
/m/027cxsm 2369
/m/08jgk1 2370
/m/02rl201 2371
/m/02607j 2372
/m/01l2fn 2373
/m/03xb2w 2374
/m/01g1lp 2375
/m/024y8p 2376
/m/0j5q3 2377
/m/0pmpl 2378
/m/02fqwt 2379
/m/02rlj20 2380
/m/07l24 2381
/m/0cd2vh9 2382
/m/07s8r0 2383
/m/0d63kt 2384
/m/04h41v 2385
/m/0bq8tmw 2386
/m/09pjnd 2387
/m/09txzv 2388
/m/0156q 2389
/m/02b1gz 2390
/m/073h1t 2391
/m/05ljv7 2392
/m/01nzs7 2393
/m/01f62 2394
/m/06vkl 2395
/m/01fh9 2396
/m/02lg9w 2397
/m/01l1sq 2398
/m/03h304l 2399
/m/03wjm2 2400
/m/0464pz 2401
/m/01bpc9 2402
/m/04kngf 2403
/m/01hww_ 2404
/m/0l12d 2405
/m/07csf4 2406
/m/0h7h6 2407
/m/09ykwk 2408
/m/015zxh 2409
/m/034h1h 2410
/m/0dzst 2411
/m/0r2l7 2412
/m/02bxd 2413
/m/01w4dy 2414
/m/02jgm0 2415
/m/04rwx 2416
/m/028lc8 2417
/m/06s_2 2418
/m/03v6t 2419
/m/02tr7d 2420
/m/06_sc3 2421
/m/0gp9mp 2422
/m/02_hj4 2423
/m/031t2d 2424
/m/032v0v 2425
/m/01v_pj6 2426
/m/01j_cy 2427
/m/05fnl9 2428
/m/021vwt 2429
/m/02zyy4 2430
/m/05q54f5 2431
/m/017v_ 2432
/m/05x30m 2433
/m/03ktjq 2434
/m/0946bb 2435
/m/026_dcw 2436
/m/0hn821n 2437
/m/06_x996 2438
/m/019pm_ 2439
/m/06lpmt 2440
/m/02ck7w 2441
/m/07szy 2442
/m/0bwjj 2443
/m/02sf_r 2444
/m/03ft8 2445
/m/03mp9s 2446
/m/0djtky 2447
/m/07mqps 2448
/m/072x7s 2449
/m/030_1_ 2450
/m/024l2y 2451
/m/035s95 2452
/m/0nm9y 2453
/m/0gcs9 2454
/m/03lrht 2455
/m/0gh4g0 2456
/m/01w923 2457
/m/02ny8t 2458
/m/01ft14 2459
/m/0gls4q_ 2460
/m/080dyk 2461
/m/0182r9 2462
/m/010dft 2463
/m/01nkcn 2464
/m/02jt1k 2465
/m/0bh8yn3 2466
/m/04x4s2 2467
/m/0c00zd0 2468
/m/094jv 2469
/m/024d8w 2470
/m/05cj_j 2471
/m/06hhrs 2472
/m/05p3738 2473
/m/06v_gh 2474
/m/09gffmz 2475
/m/01v3x8 2476
/m/06h4y9 2477
/m/0ksf29 2478
/m/04bpm6 2479
/m/02y_rq5 2480
/m/0q9zc 2481
/m/02g_6x 2482
/m/0fgg8c 2483
/m/0nbcg 2484
/m/03q2t9 2485
/m/0f2w0 2486
/m/075wx7_ 2487
/m/0fsm8c 2488
/m/0kpw3 2489
/m/013gwb 2490
/m/09tqxt 2491
/m/02_ssl 2492
/m/0jmk7 2493
/m/01n4f8 2494
/m/02pb53 2495
/m/05sj55 2496
/m/06kb_ 2497
/m/0c3kw 2498
/m/0p2rj 2499
/m/07h9gp 2500
/m/01hwc6 2501
/m/02p68d 2502
/m/09pl3f 2503
/m/047csmy 2504
/m/04swx 2505
/m/03v_5 2506
/m/041y2 2507
/m/01n4w_ 2508
/m/0w7c 2509
/m/01mxqyk 2510
/m/016ckq 2511
/m/0fxgg9 2512
/m/03t9sp 2513
/m/015pkc 2514
/m/029zqn 2515
/m/01r2lw 2516
/m/01l9p 2517
/m/032w8h 2518
/m/01d494 2519
/m/0170pk 2520
/m/015rkw 2521
/m/0bjy7 2522
/m/03y9p40 2523
/m/0b_6zk 2524
/m/0b_c7 2525
/m/0c8tkt 2526
/m/035yn8 2527
/m/0sxkh 2528
/m/01p95y0 2529
/m/01w56k 2530
/m/0bcndz 2531
/m/0k4kk 2532
/m/023l9y 2533
/m/0735l 2534
/m/0bzm81 2535
/m/01756d 2536
/m/01fl3 2537
/m/0b44shh 2538
/m/02hnl 2539
/m/05crg7 2540
/m/01dzz7 2541
/m/07s5fz 2542
/m/041pnt 2543
/m/0fdv3 2544
/m/02q5g1z 2545
/m/01grnp 2546
/m/01x73 2547
/m/02khs 2548
/m/0crjn65 2549
/m/0dbdy 2550
/m/01kkg5 2551
/m/06w33f8 2552
/m/0d6qjf 2553
/m/03gkn5 2554
/m/0gsg7 2555
/m/02bg8v 2556
/m/0lp_cd3 2557
/m/02wrhj 2558
/m/02zv4b 2559
/m/0m2cb 2560
/m/04rrd 2561
/m/09kvv 2562
/m/04rtpt 2563
/m/015pkt 2564
/m/06rmdr 2565
/m/02vptk_ 2566
/m/09fqdt 2567
/m/0fhpv4 2568
/m/03hmt9b 2569
/m/03sb38 2570
/m/0b7l4x 2571
/m/09cm54 2572
/m/0bthb 2573
/m/06pr6 2574
/m/049k07 2575
/m/01gzm2 2576
/m/02_jjm 2577
/m/01c59k 2578
/m/0845v 2579
/m/03gk2 2580
/m/013t9y 2581
/m/011ydl 2582
/m/039v1 2583
/m/0137g1 2584
/m/0ft7sr 2585
/m/01z88t 2586
/m/01z215 2587
/m/034rd 2588
/m/0488g 2589
/m/049dk 2590
/m/0bbm7r 2591
/m/039g82 2592
/m/0fpzzp 2593
/m/07scx 2594
/m/0gj9tn5 2595
/m/013q07 2596
/m/091z_p 2597
/m/0gbwp 2598
/m/02jm0n 2599
/m/0gs5q 2600
/m/0hkq4 2601
/m/0m_xy 2602
/m/01pj7 2603
/m/0fhzy 2604
/m/0dth6b 2605
/m/058s57 2606
/m/03gr7w 2607
/m/070yzk 2608
/m/07sgfvl 2609
/m/050xxm 2610
/m/01pq5j7 2611
/m/0f1vrl 2612
/m/02ptzz0 2613
/m/05g_nr 2614
/m/08hp53 2615
/m/02pyyld 2616
/m/0f9rw9 2617
/m/076xkps 2618
/m/03_2td 2619
/m/0cjyzs 2620
/m/015_30 2621
/m/01c92g 2622
/m/030h95 2623
/m/0jmbv 2624
/m/049sb 2625
/m/02q52q 2626
/m/01g6gs 2627
/m/064p92m 2628
/m/018nnz 2629
/m/01dyvs 2630
/m/0lfgr 2631
/m/027yf83 2632
/m/02f6g5 2633
/m/01pgp6 2634
/m/0n5hh 2635
/m/0k5p1 2636
/m/06sks6 2637
/m/04k9y6 2638
/m/030hcs 2639
/m/09vc4s 2640
/m/015882 2641
/m/0770cd 2642
/m/016s_5 2643
/m/016zgj 2644
/m/02dgq2 2645
/m/01jgkj2 2646
/m/02b71x 2647
/m/02yv_b 2648
/m/09cr8 2649
/m/0ftlkg 2650
/m/0283_zv 2651
/m/06mnps 2652
/m/01kc4s 2653
/m/04vn5 2654
/m/03h42s4 2655
/m/0835q 2656
/m/05ztm4r 2657
/m/0806vbn 2658
/m/09mq4m 2659
/m/090q32 2660
/m/01515w 2661
/m/0333t 2662
/m/0bs8s1p 2663
/m/02qgyv 2664
/m/04g9sq 2665
/m/0jm9w 2666
/m/03g3w 2667
/m/02wbm 2668
/m/01vc5m 2669
/m/062z7 2670
/m/01y888 2671
/m/02bp37 2672
/m/03z5xd 2673
/m/0g8rj 2674
/m/03t22m 2675
/m/01ky2h 2676
/m/0h1x5f 2677
/m/02yvct 2678
/m/07r78j 2679
/m/0b7t3p 2680
/m/049dzz 2681
/m/0bhtzw 2682
/m/016_mj 2683
/m/06rq1k 2684
/m/0kq9l 2685
/m/012zng 2686
/m/01c40n 2687
/m/05l5n 2688
/m/015cjr 2689
/m/047jhq 2690
/m/015x74 2691
/m/09kn9 2692
/m/01htzx 2693
/m/02jyr8 2694
/m/04g61 2695
/m/0ynfz 2696
/m/0241jw 2697
/m/02rb84n 2698
/m/07l4z 2699
/m/03wnh 2700
/m/01k8q5 2701
/m/0kfv9 2702
/m/017jv5 2703
/m/01kf3_9 2704
/m/01l2m3 2705
/m/0gk4g 2706
/m/02lz1s 2707
/m/052gzr 2708
/m/025m8y 2709
/m/016dj8 2710
/m/01c58j 2711
/m/0177s6 2712
/m/026lj 2713
/m/05r79 2714
/m/0m491 2715
/m/0b76t12 2716
/m/05rrtf 2717
/m/03h40_7 2718
/m/04qw17 2719
/m/0cv72h 2720
/m/051q5 2721
/m/01t2h2 2722
/m/03k7bd 2723
/m/02rx2m5 2724
/m/025s7j4 2725
/m/016ywr 2726
/m/0cc7hmk 2727
/m/0hvb2 2728
/m/02bkdn 2729
/m/01t07j 2730
/m/01g63y 2731
/m/01vh3r 2732
/m/02qsqmq 2733
/m/044crp 2734
/m/03mz9r 2735
/m/0584r4 2736
/m/0cz_ym 2737
/m/05rgl 2738
/m/06x58 2739
/m/0261g5l 2740
/m/0217m9 2741
/m/02bh_v 2742
/m/06qjgc 2743
/m/03k50 2744
/m/016s0m 2745
/m/02qw1zx 2746
/m/016v46 2747
/m/02k84w 2748
/m/0p5mw 2749
/m/03xmy1 2750
/m/028cg00 2751
/m/01gq0b 2752
/m/0d2psv 2753
/m/0j1yf 2754
/m/0320jz 2755
/m/01y9xg 2756
/m/02d9k 2757
/m/073bb 2758
/m/0c_n9 2759
/m/07s6prs 2760
/m/026gyn_ 2761
/m/09146g 2762
/m/02q3n9c 2763
/m/06r4f 2764
/m/06qw_ 2765
/m/05kr_ 2766
/m/04rrx 2767
/m/02lmk 2768
/m/022q32 2769
/m/01yc02 2770
/m/0mj1l 2771
/m/011yth 2772
/m/07wbk 2773
/m/083pr 2774
/m/03xsby 2775
/m/0fq7dv_ 2776
/m/0ch26b_ 2777
/m/01p1v 2778
/m/08m4c8 2779
/m/0_7w6 2780
/m/0gd_s 2781
/m/02664f 2782
/m/021p26 2783
/m/0479b 2784
/m/0c6g29 2785
/m/0cq7tx 2786
/m/02zd460 2787
/m/03mkk4 2788
/m/04n2r9h 2789
/m/0g26h 2790
/m/01rgdw 2791
/m/0bs0bh 2792
/m/03mg35 2793
/m/02x258x 2794
/m/018wl5 2795
/m/04mn81 2796
/m/06pj8 2797
/m/0h21v2 2798
/m/0bs5vty 2799
/m/0by1wkq 2800
/m/060ny2 2801
/m/06bss 2802
/m/0mhfr 2803
/m/04r1t 2804
/m/0d075m 2805
/m/02stbw 2806
/m/02qx69 2807
/m/0gt_k 2808
/m/0btyf5z 2809
/m/0c94fn 2810
/m/0tn9j 2811
/m/04ly1 2812
/m/0b82vw 2813
/m/0784v1 2814
/m/040wdl 2815
/m/01s5q 2816
/m/0lz8d 2817
/m/0mxcf 2818
/m/0mx6c 2819
/m/04zwjd 2820
/m/03sxd2 2821
/m/07ymr5 2822
/m/02krf9 2823
/m/03hj3b3 2824
/m/03cffvv 2825
/m/033071 2826
/m/02bqm0 2827
/m/07ym0 2828
/m/05d1y 2829
/m/02_5x9 2830
/m/02906 2831
/m/014dq7 2832
/m/03bnv 2833
/m/0fy34l 2834
/m/04myfb7 2835
/m/06t61y 2836
/m/065jlv 2837
/m/021_z5 2838
/m/019g40 2839
/m/0fb0v 2840
/m/03n0cd 2841
/m/05qbckf 2842
/m/0162v 2843
/m/02wgln 2844
/m/03bdv 2845
/m/0f25w9 2846
/m/07hnp 2847
/m/07w5rq 2848
/m/01_srz 2849
/m/01lly5 2850
/m/016z9n 2851
/m/042z_g 2852
/m/0jvtp 2853
/m/09d38d 2854
/m/06s6hs 2855
/m/07kh6f3 2856
/m/0nvrd 2857
/m/0s69k 2858
/m/050f0s 2859
/m/015h31 2860
/m/01cgz 2861
/m/050gkf 2862
/m/0l_j_ 2863
/m/01gvxh 2864
/m/0zjpz 2865
/m/01nd2c 2866
/m/070fnm 2867
/m/0136p1 2868
/m/04y9dk 2869
/m/0jcg8 2870
/m/02qfv5d 2871
/m/01jmyj 2872
/m/02hft3 2873
/m/0yjf0 2874
/m/01b3l 2875
/m/06ntj 2876
/m/037s9x 2877
/m/03z19 2878
/m/04ktcgn 2879
/m/034b6k 2880
/m/063vn 2881
/m/0c8wxp 2882
/m/0gz5hs 2883
/m/01c57n 2884
/m/09k56b7 2885
/m/0gjc4d3 2886
/m/0cjsxp 2887
/m/0fvxg 2888
/m/01twdk 2889
/m/0k0rf 2890
/m/0r540 2891
/m/02lf70 2892
/m/0dt8xq 2893
/m/0140t7 2894
/m/07yk1xz 2895
/m/01wz3cx 2896
/m/0fxwx 2897
/m/0b76kw1 2898
/m/0509bl 2899
/m/0g51l1 2900
/m/01wsl7c 2901
/m/0l_tn 2902
/m/016pjk 2903
/m/051_y 2904
/m/0gd0c7x 2905
/m/0d0kn 2906
/m/0w6w 2907
/m/03fvqg 2908
/m/0sg6b 2909
/m/04kj2v 2910
/m/0mtdx 2911
/m/0pzpz 2912
/m/01z0lb 2913
/m/05k79 2914
/m/052p7 2915
/m/0694j 2916
/m/031778 2917
/m/0dq6p 2918
/m/03d34x8 2919
/m/04913k 2920
/m/0j6b5 2921
/m/0jmfb 2922
/m/07vk2 2923
/m/06brp0 2924
/m/01sh2 2925
/m/01gtbb 2926
/m/05k7sb 2927
/m/0bvzp 2928
/m/03f7xg 2929
/m/0hpt3 2930
/m/0g02vk 2931
/m/04t53l 2932
/m/02vqhv0 2933
/m/047cx 2934
/m/045bs6 2935
/m/04kzqz 2936
/m/0279c15 2937
/m/01vyp_ 2938
/m/0278rq7 2939
/m/01n9d9 2940
/m/028knk 2941
/m/02hzz 2942
/m/026mmy 2943
/m/02c8d7 2944
/m/01wbl_r 2945
/m/0h_cssd 2946
/m/01ycbq 2947
/m/0f7h2g 2948
/m/0cq8qq 2949
/m/022xml 2950
/m/0bj9k 2951
/m/047qxs 2952
/m/0tzt_ 2953
/m/0_9wr 2954
/m/0gvrws1 2955
/m/045m1_ 2956
/m/0mrs1 2957
/m/031n8c 2958
/m/02zn1b 2959
/m/0dtd6 2960
/m/066l3y 2961
/m/02z5x7l 2962
/m/0bdx29 2963
/m/01xr2s 2964
/m/0lv1x 2965
/m/06f41 2966
/m/02k6rq 2967
/m/0cwt70 2968
/m/0hmm7 2969
/m/03t5b6 2970
/m/01vvyd8 2971
/m/050l8 2972
/m/02y7sr 2973
/m/045c7b 2974
/m/040p3y 2975
/m/01fmys 2976
/m/035dk 2977
/m/09tqkv2 2978
/m/0g9zjp 2979
/m/02b2np 2980
/m/012s5j 2981
/m/0l2q3 2982
/m/0r3tq 2983
/m/05fjf 2984
/m/070mff 2985
/m/05d8vw 2986
/m/02s62q 2987
/m/0jym0 2988
/m/0clz1b 2989
/m/0dwr4 2990
/m/054ky1 2991
/m/0b1zz 2992
/m/0292qb 2993
/m/09f0bj 2994
/m/0r80l 2995
/m/0f1pyf 2996
/m/02ryyk 2997
/m/09d5h 2998
/m/02xhpl 2999
/m/043s3 3000
/m/0h25 3001
/m/01cyd5 3002
/m/015npr 3003
/m/0355pl 3004
/m/01fwqn 3005
/m/02s7tr 3006
/m/05g76 3007
/m/0gl88b 3008
/m/05x2t7 3009
/m/02fb1n 3010
/m/0x44q 3011
/m/02l4rh 3012
/m/018lg0 3013
/m/01czx 3014
/m/0j2pg 3015
/m/085q5 3016
/m/09g8vhw 3017
/m/06lgq8 3018
/m/01541z 3019
/m/05cwl_ 3020
/m/03ln8b 3021
/m/01bvw5 3022
/m/0dnkmq 3023
/m/0jlv5 3024
/m/01j8wk 3025
/m/0gmdkyy 3026
/m/02zmh5 3027
/m/048qrd 3028
/m/0kvgxk 3029
/m/0h1p 3030
/m/099ty 3031
/m/025hwq 3032
/m/06lj1m 3033
/m/02rh1dz 3034
/m/08l0x2 3035
/m/01z77k 3036
/m/0yvjx 3037
/m/0371rb 3038
/m/023p33 3039
/m/0653m 3040
/m/05b7q 3041
/m/02hwhyv 3042
/m/02mp0g 3043
/m/01m15br 3044
/m/01lyv 3045
/m/064n1pz 3046
/m/022769 3047
/m/016fmf 3048
/m/01dtcb 3049
/m/018grr 3050
/m/034qzw 3051
/m/036c_0 3052
/m/0j_tw 3053
/m/02dq8f 3054
/m/0pf2 3055
/m/0ddt_ 3056
/m/02jxk 3057
/m/06zsk51 3058
/m/01kwsg 3059
/m/02w9sd7 3060
/m/016z68 3061
/m/0260bz 3062
/m/06hwzy 3063
/m/0164nb 3064
/m/0fbtbt 3065
/m/02rzdcp 3066
/m/01yzl2 3067
/m/0dck27 3068
/m/016z7s 3069
/m/0bm2g 3070
/m/011xg5 3071
/m/04hw4b 3072
/m/05dbyt 3073
/m/02p86pb 3074
/m/073hgx 3075
/m/01y49 3076
/m/02psgvg 3077
/m/05zrvfd 3078
/m/02c638 3079
/m/02661h 3080
/m/09p30_ 3081
/m/09ntbc 3082
/m/011k11 3083
/m/05qw5 3084
/m/05g49 3085
/m/047n8xt 3086
/m/018swb 3087
/m/0f6_dy 3088
/m/01xcfy 3089
/m/027b9k6 3090
/m/01kv4mb 3091
/m/0126rp 3092
/m/01j7rd 3093
/m/07c72 3094
/m/03xp8d5 3095
/m/02pgky2 3096
/m/0gr07 3097
/m/0443y3 3098
/m/03qdm 3099
/m/02lpp7 3100
/m/02__34 3101
/m/04y5j64 3102
/m/04gtdnh 3103
/m/0d1w9 3104
/m/02c_4 3105
/m/01pcmd 3106
/m/0n2bh 3107
/m/04rlf 3108
/m/042y1c 3109
/m/040fb 3110
/m/03_wj_ 3111
/m/0dplh 3112
/m/0l9rg 3113
/m/0mwht 3114
/m/02vmzp 3115
/m/03rk0 3116
/m/03x23q 3117
/m/0fht9f 3118
/m/027lf1 3119
/m/023k2 3120
/m/07w3r 3121
/m/0yzvw 3122
/m/06mkj 3123
/m/02qhqz4 3124
/m/04b7xr 3125
/m/0196bp 3126
/m/0407yfx 3127
/m/011_6p 3128
/m/01dnws 3129
/m/01njml 3130
/m/07024 3131
/m/02ck1 3132
/m/04p3w 3133
/m/01y67v 3134
/m/014kq6 3135
/m/040db 3136
/m/04xjp 3137
/m/03_9r 3138
/m/06ybb1 3139
/m/09r1j5 3140
/m/02l6h 3141
/m/03jqw5 3142
/m/01jtp7 3143
/m/0bx0l 3144
/m/0k4bc 3145
/m/04zpv 3146
/m/02f77l 3147
/m/013w8y 3148
/m/01w60_p 3149
/m/0144l1 3150
/m/01jq34 3151
/m/01jzxy 3152
/m/015gw6 3153
/m/051vz 3154
/m/01ptt7 3155
/m/02_2v2 3156
/m/02_06s 3157
/m/0f4_l 3158
/m/0325dj 3159
/m/0c3ns 3160
/m/071ywj 3161
/m/02v1m7 3162
/m/0l56b 3163
/m/01f7j9 3164
/m/0fvr1 3165
/m/04jlgp 3166
/m/0cpz4k 3167
/m/056878 3168
/m/01vsnff 3169
/m/0163v 3170
/m/0c8tk 3171
/m/046rfv 3172
/m/03czz87 3173
/m/02pbp9 3174
/m/0fphgb 3175
/m/05f5sr9 3176
/m/02y9ln 3177
/m/01vwbts 3178
/m/02tj96 3179
/m/012x03 3180
/m/011zd3 3181
/m/01cszh 3182
/m/01yndb 3183
/m/02pl5bx 3184
/m/0gfzgl 3185
/m/0bfvd4 3186
/m/02jjdr 3187
/m/0pyg6 3188
/m/0c3z0 3189
/m/07w4j 3190
/m/09g7thr 3191
/m/0mwh1 3192
/m/05g3v 3193
/m/0121c1 3194
/m/01tfck 3195
/m/0c11mj 3196
/m/0266sb_ 3197
/m/0g284 3198
/m/0dq630k 3199
/m/07gql 3200
/m/015wnl 3201
/m/07p62k 3202
/m/041mt 3203
/m/0bvfqq 3204
/m/07nt8p 3205
/m/02nbqh 3206
/m/0gvstc3 3207
/m/02cyfz 3208
/m/011yd2 3209
/m/08664q 3210
/m/02lq10 3211
/m/0161c 3212
/m/01364q 3213
/m/01vvyfh 3214
/m/05zksls 3215
/m/026c1 3216
/m/0h1v19 3217
/m/0m0nq 3218
/m/0h98b3k 3219
/m/0274v0r 3220
/m/0457w0 3221
/m/03zbg0 3222
/m/05cc1 3223
/m/0gjv_ 3224
/m/0345gh 3225
/m/0klh7 3226
/m/02t_zq 3227
/m/05c9zr 3228
/m/09k2t1 3229
/m/07ss8_ 3230
/m/01s0_f 3231
/m/05b4rcb 3232
/m/03kxj2 3233
/m/09bg4l 3234
/m/016jll 3235
/m/02_fz3 3236
/m/02f5qb 3237
/m/0d193h 3238
/m/0l6mp 3239
/m/0ymc8 3240
/m/0jt5zcn 3241
/m/05sb1 3242
/m/04xrx 3243
/m/0x67 3244
/m/01vs_v8 3245
/m/025m8l 3246
/m/0453t 3247
/m/0j3v 3248
/m/02p11jq 3249
/m/09prnq 3250
/m/01y9pk 3251
/m/0ptk_ 3252
/m/0fqpc7d 3253
/m/03jvmp 3254
/m/01q415 3255
/m/0bytfv 3256
/m/0gvs1kt 3257
/m/03lty 3258
/m/0167_s 3259
/m/01qncf 3260
/m/01679d 3261
/m/0dclg 3262
/m/040fv 3263
/m/03y_46 3264
/m/0568qz 3265
/m/07srw 3266
/m/01w724 3267
/m/06j6l 3268
/m/01wyzyl 3269
/m/02rxj 3270
/m/0z1cr 3271
/m/01b195 3272
/m/07f1x 3273
/m/06wrt 3274
/m/04jbyg 3275
/m/0lgxj 3276
/m/025tdwc 3277
/m/01lb14 3278
/m/03rj0 3279
/m/01lj9 3280
/m/071jv5 3281
/m/0dh73w 3282
/m/085v7 3283
/m/054kmq 3284
/m/047g6m 3285
/m/04pbsq 3286
/m/02jg92 3287
/m/09_gdc 3288
/m/01xllf 3289
/m/0300cp 3290
/m/03qh03g 3291
/m/01zmpg 3292
/m/013v5j 3293
/m/05dbf 3294
/m/04qhdf 3295
/m/013yq 3296
/m/06xj93 3297
/m/04smkr 3298
/m/02vy5j 3299
/m/01wj9y9 3300
/m/07hbxm 3301
/m/0721cy 3302
/m/018h2 3303
/m/02s4l6 3304
/m/0f40w 3305
/m/02725hs 3306
/m/02wt0 3307
/m/07z5n 3308
/m/0cg39k 3309
/m/01q_y0 3310
/m/0gyv0b4 3311
/m/050yyb 3312
/m/0311wg 3313
/m/051hrr 3314
/m/02x7vq 3315
/m/0mp3l 3316
/m/0161sp 3317
/m/01gjw 3318
/m/01qh7 3319
/m/025tn92 3320
/m/0jm3b 3321
/m/033hn8 3322
/m/01ww2fs 3323
/m/0b_77q 3324
/m/02plv57 3325
/m/05218gr 3326
/m/01bv8b 3327
/m/026zlh9 3328
/m/06k02 3329
/m/01vhb0 3330
/m/01fdc0 3331
/m/064q5v 3332
/m/0gthm 3333
/m/018qb4 3334
/m/06ncr 3335
/m/0gghm 3336
/m/07wrz 3337
/m/0h5k 3338
/m/02_7t 3339
/m/01jzyx 3340
/m/03spz 3341
/m/06t2t 3342
/m/0537b 3343
/m/04fhxp 3344
/m/06v9_x 3345
/m/0lccn 3346
/m/03ydlnj 3347
/m/01r216 3348
/m/0136pk 3349
/m/01ck6h 3350
/m/05k2xy 3351
/m/02581c 3352
/m/01nm8w 3353
/m/020y73 3354
/m/07wjk 3355
/m/021bk 3356
/m/0pdp8 3357
/m/0lyjf 3358
/m/024qqx 3359
/m/024tsn 3360
/m/0gxkm 3361
/m/07nvmx 3362
/m/0dc3_ 3363
/m/0g3zrd 3364
/m/04y8r 3365
/m/08sfxj 3366
/m/01kckd 3367
/m/047sxrj 3368
/m/0288fyj 3369
/m/03dn9v 3370
/m/05smlt 3371
/m/01p4vl 3372
/m/02g1px 3373
/m/0j06n 3374
/m/01dcqj 3375
/m/0d_2fb 3376
/m/0fpv_3_ 3377
/m/02x17s4 3378
/m/0ct5zc 3379
/m/01_vfy 3380
/m/0d05w3 3381
/m/01w9ph_ 3382
/m/01jq0j 3383
/m/07g9f 3384
/m/09qftb 3385
/m/0cg9y 3386
/m/025vl4m 3387
/m/02_1ky 3388
/m/01nhkxp 3389
/m/01hvjx 3390
/m/029bkp 3391
/m/01wx756 3392
/m/02sp_v 3393
/m/0661m4p 3394
/m/0q19t 3395
/m/0kvrb 3396
/m/02b1mc 3397
/m/0bymv 3398
/m/01gkgk 3399
/m/01rs41 3400
/m/01hnb 3401
/m/0783m_ 3402
/m/080knyg 3403
/m/03ln9 3404
/m/05c0jwl 3405
/m/0nzw2 3406
/m/05p1qyh 3407
/m/03yvf2 3408
/m/0kszw 3409
/m/014v6f 3410
/m/01hw6wq 3411
/m/065zr 3412
/m/075mb 3413
/m/0f4yh 3414
/m/0ddjy 3415
/m/02fcs2 3416
/m/03g52k 3417
/m/02n9bh 3418
/m/075cph 3419
/m/0hky 3420
/m/01hb6v 3421
/m/0427y 3422
/m/048z7l 3423
/m/0fdys 3424
/m/07tlg 3425
/m/0dqcs3 3426
/m/03z2rz 3427
/m/0yyts 3428
/m/02xb2bt 3429
/m/0l6px 3430
/m/0cc97st 3431
/m/02mx98 3432
/m/01jft4 3433
/m/016z2j 3434
/m/02d4ct 3435
/m/06z68 3436
/m/026f__m 3437
/m/06y0xx 3438
/m/0
gitextract_2ysrl9k6/ ├── ConvKB_pytorch/ │ ├── Config.py │ ├── ConvKB.py │ ├── ConvKB_1D.py │ ├── Model.py │ ├── base/ │ │ ├── Base.cpp │ │ ├── Corrupt.h │ │ ├── Random.h │ │ ├── Reader.h │ │ ├── Setting.h │ │ ├── Test.h │ │ ├── Triple.h │ │ └── Valid.h │ ├── benchmarks/ │ │ ├── FB15K/ │ │ │ ├── 1-1.txt │ │ │ ├── 1-n.txt │ │ │ ├── entity2id.txt │ │ │ ├── n-1.txt │ │ │ ├── n-n.py │ │ │ ├── n-n.txt │ │ │ ├── relation2id.txt │ │ │ ├── test2id.txt │ │ │ ├── test2id_all.txt │ │ │ ├── train2id.txt │ │ │ ├── type_constrain.txt │ │ │ └── valid2id.txt │ │ ├── FB15K237/ │ │ │ ├── 1-1.txt │ │ │ ├── 1-n.txt │ │ │ ├── entity2id.txt │ │ │ ├── entity2id_100init.txt │ │ │ ├── entity2vec100.init │ │ │ ├── n-1.txt │ │ │ ├── n-n.py │ │ │ ├── n-n.txt │ │ │ ├── relation2id.txt │ │ │ ├── relation2id_100init.txt │ │ │ ├── relation2vec100.init │ │ │ ├── test2id.txt │ │ │ ├── test2id_all.txt │ │ │ ├── train2id.txt │ │ │ ├── type_constrain.txt │ │ │ └── valid2id.txt │ │ ├── WN18/ │ │ │ ├── 1-1.txt │ │ │ ├── 1-n.txt │ │ │ ├── entity2id.txt │ │ │ ├── n-1.txt │ │ │ ├── n-n.py │ │ │ ├── n-n.txt │ │ │ ├── relation2id.txt │ │ │ ├── test2id.txt │ │ │ ├── test2id_all.txt │ │ │ ├── train2id.txt │ │ │ ├── type_constrain.txt │ │ │ └── valid2id.txt │ │ └── WN18RR/ │ │ ├── 1-1.txt │ │ ├── 1-n.txt │ │ ├── _also_see.txt │ │ ├── _derivationally_related_form.txt │ │ ├── _has_part.txt │ │ ├── _hypernym.txt │ │ ├── _instance_hypernym.txt │ │ ├── _member_meronym.txt │ │ ├── _member_of_domain_region.txt │ │ ├── _member_of_domain_usage.txt │ │ ├── _similar_to.txt │ │ ├── _synset_domain_topic_of.txt │ │ ├── _verb_group.txt │ │ ├── entity2id.txt │ │ ├── entity2id_50init.txt │ │ ├── entity2vec50.init │ │ ├── n-1.txt │ │ ├── n-n.py │ │ ├── n-n.txt │ │ ├── relation2id.txt │ │ ├── relation2id_50init.txt │ │ ├── relation2vec50.init │ │ ├── test2id.txt │ │ ├── test2id_all.txt │ │ ├── train2id.txt │ │ ├── type_constrain.txt │ │ └── valid2id.txt │ ├── make.sh │ └── train_ConvKB.py ├── ConvKB_tf/ │ ├── batching.py │ ├── builddata.py │ ├── data/ │ │ ├── FB15k-237/ │ │ │ ├── entity2id.txt │ │ │ ├── entity2vec100.init │ │ │ ├── relation2id.txt │ │ │ ├── relation2vec100.init │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ └── WN18RR/ │ │ ├── entity2id.txt │ │ ├── entity2vec100.init │ │ ├── entity2vec50.init │ │ ├── relation2id.txt │ │ ├── relation2vec100.init │ │ ├── relation2vec50.init │ │ ├── test.txt │ │ ├── train.txt │ │ └── valid.txt │ ├── eval.py │ ├── evalFB15k-237.sh │ ├── evalWN18RR.sh │ ├── model.py │ ├── train.py │ └── training_commands.txt ├── LICENSE └── README.md
SYMBOL INDEX (144 symbols across 18 files)
FILE: ConvKB_pytorch/Config.py
class MyDataParallel (line 20) | class MyDataParallel(nn.DataParallel):
method _getattr__ (line 21) | def _getattr__(self, name):
function to_var (line 25) | def to_var(x):
class Config (line 29) | class Config(object):
method __init__ (line 30) | def __init__(self):
method init (line 139) | def init(self):
method set_test_link (line 215) | def set_test_link(self, test_link):
method set_test_triple (line 218) | def set_test_triple(self, test_triple):
method set_margin (line 221) | def set_margin(self, margin):
method set_in_path (line 224) | def set_in_path(self, in_path):
method set_test_file_path (line 227) | def set_test_file_path(self, test_file_path):
method set_nbatches (line 230) | def set_nbatches(self, nbatches):
method set_p_norm (line 233) | def set_p_norm(self, p_norm):
method set_valid_steps (line 236) | def set_valid_steps(self, valid_steps):
method set_save_steps (line 239) | def set_save_steps(self, save_steps):
method set_checkpoint_dir (line 242) | def set_checkpoint_dir(self, checkpoint_dir):
method set_result_dir (line 245) | def set_result_dir(self, result_dir):
method set_alpha (line 248) | def set_alpha(self, alpha):
method set_lmbda (line 251) | def set_lmbda(self, lmbda):
method set_lmbda_two (line 254) | def set_lmbda_two(self, lmbda_two):
method set_lr_decay (line 257) | def set_lr_decay(self, lr_decay):
method set_weight_decay (line 260) | def set_weight_decay(self, weight_decay):
method set_opt_method (line 263) | def set_opt_method(self, opt_method):
method set_bern (line 266) | def set_bern(self, bern):
method set_init_embeddings (line 269) | def set_init_embeddings(self, entity_embs, rel_embs):
method set_config_CNN (line 274) | def set_config_CNN(self, num_of_filters, drop_prob, kernel_size=1):
method set_dimension (line 279) | def set_dimension(self, dim):
method set_ent_dimension (line 284) | def set_ent_dimension(self, dim):
method set_rel_dimension (line 287) | def set_rel_dimension(self, dim):
method set_train_times (line 290) | def set_train_times(self, train_times):
method set_work_threads (line 293) | def set_work_threads(self, work_threads):
method set_ent_neg_rate (line 296) | def set_ent_neg_rate(self, rate):
method set_rel_neg_rate (line 299) | def set_rel_neg_rate(self, rate):
method set_ent_dropout (line 302) | def set_ent_dropout(self, ent_dropout):
method set_rel_dropout (line 305) | def set_rel_dropout(self, rel_dropout):
method set_early_stopping_patience (line 308) | def set_early_stopping_patience(self, early_stopping_patience):
method set_pretrain_model (line 311) | def set_pretrain_model(self, pretrain_model):
method get_parameters (line 314) | def get_parameters(self, param_dict, mode="numpy"):
method save_embedding_matrix (line 327) | def save_embedding_matrix(self, best_model):
method set_train_model (line 333) | def set_train_model(self, model):
method set_test_model (line 369) | def set_test_model(self, model, path=None):
method sampling (line 380) | def sampling(self):
method save_checkpoint (line 391) | def save_checkpoint(self, model, epoch):
method save_best_checkpoint (line 397) | def save_best_checkpoint(self, best_model):
method train_one_step (line 401) | def train_one_step(self):
method test_one_step (line 416) | def test_one_step(self, model, test_h, test_t, test_r):
method valid (line 424) | def valid(self, model):
method training_model (line 444) | def training_model(self):
method valid_triple_classification (line 495) | def valid_triple_classification(self, model):
method training_triple_classification (line 522) | def training_triple_classification(self):
method link_prediction (line 572) | def link_prediction(self):
method triple_classification (line 590) | def triple_classification(self):
method test (line 631) | def test(self):
FILE: ConvKB_pytorch/ConvKB.py
class ConvKB (line 15) | class ConvKB(Model):
method __init__ (line 17) | def __init__(self, config):
method init_parameters (line 33) | def init_parameters(self):
method _calc (line 45) | def _calc(self, h, r, t):
method loss (line 64) | def loss(self, score, regul):
method forward (line 67) | def forward(self):
method predict (line 82) | def predict(self):
FILE: ConvKB_pytorch/ConvKB_1D.py
class ConvKB (line 15) | class ConvKB(Model):
method __init__ (line 17) | def __init__(self, config):
method init_parameters (line 33) | def init_parameters(self):
method _calc (line 45) | def _calc(self, h, r, t):
method loss (line 61) | def loss(self, score, regul):
method forward (line 64) | def forward(self):
method predict (line 79) | def predict(self):
FILE: ConvKB_pytorch/Model.py
class Model (line 8) | class Model(nn.Module):
method __init__ (line 9) | def __init__(self, config):
method get_positive_score (line 17) | def get_positive_score(self, score):
method get_negative_score (line 20) | def get_negative_score(self, score):
method forward (line 26) | def forward(self):
method predict (line 29) | def predict(self):
FILE: ConvKB_pytorch/base/Base.cpp
type Parameter (line 52) | struct Parameter {
function sampling (line 117) | void sampling(INT *batch_h, INT *batch_t, INT *batch_r, REAL *batch_y, I...
function main (line 137) | int main() {
FILE: ConvKB_pytorch/base/Corrupt.h
function INT (line 7) | INT corrupt_head(INT id, INT h, INT r) {
function INT (line 39) | INT corrupt_tail(INT id, INT t, INT r) {
function INT (line 72) | INT corrupt_rel(INT id, INT h, INT t) {
function _find (line 105) | bool _find(INT h, INT t, INT r) {
function INT (line 118) | INT corrupt(INT h, INT r){
FILE: ConvKB_pytorch/base/Random.h
function randReset (line 9) | void randReset() {
function randd (line 15) | unsigned long long randd(INT id) {
function INT (line 20) | INT rand_max(INT id, INT x) {
function INT (line 28) | INT rand(INT a, INT b){
FILE: ConvKB_pytorch/base/Reader.h
function importTrainFiles (line 23) | void importTrainFiles() {
function importTestFiles (line 128) | void importTestFiles() {
function importTypeFiles (line 213) | void importTypeFiles() {
FILE: ConvKB_pytorch/base/Setting.h
function setInPath (line 14) | void setInPath(char *path) {
function setTestFilePath (line 23) | void setTestFilePath(char *path) {
function setOutPath (line 32) | void setOutPath(char *path) {
function setWorkThreads (line 47) | void setWorkThreads(INT threads) {
function INT (line 52) | INT getWorkThreads() {
function INT (line 68) | INT getEntityTotal() {
function INT (line 73) | INT getRelationTotal() {
function INT (line 78) | INT getTripleTotal() {
function INT (line 83) | INT getTrainTotal() {
function INT (line 88) | INT getTestTotal() {
function INT (line 93) | INT getValidTotal() {
function setBern (line 103) | void setBern(INT con) {
FILE: ConvKB_pytorch/base/Test.h
function initTest (line 18) | void initTest() {
function getHeadBatch (line 28) | void getHeadBatch(INT *ph, INT *pt, INT *pr) {
function getTailBatch (line 37) | void getTailBatch(INT *ph, INT *pt, INT *pr) {
function testHead (line 46) | void testHead(REAL *con) {
function testTail (line 109) | void testTail(REAL *con) {
function test_link_prediction (line 170) | void test_link_prediction() {
function getNegTest (line 219) | void getNegTest() {
function getNegValid (line 237) | void getNegValid() {
function getTestBatch (line 254) | void getTestBatch(INT *ph, INT *pt, INT *pr, INT *nh, INT *nt, INT *nr) {
function getValidBatch (line 267) | void getValidBatch(INT *ph, INT *pt, INT *pr, INT *nh, INT *nt, INT *nr) {
function getBestThreshold (line 280) | void getBestThreshold(REAL *relThresh, REAL *score_pos, REAL *score_neg) {
function REAL (line 321) | REAL test_triple_classification(REAL *relThresh, REAL *score_pos, REAL *...
FILE: ConvKB_pytorch/base/Triple.h
function cmp_list (line 5) | struct Triple {
function cmp_head (line 18) | static bool cmp_head(const Triple &a, const Triple &b) {
function cmp_tail (line 22) | static bool cmp_tail(const Triple &a, const Triple &b) {
function cmp_rel (line 26) | static bool cmp_rel(const Triple &a, const Triple &b) {
function cmp_rel2 (line 30) | static bool cmp_rel2(const Triple &a, const Triple &b) {
FILE: ConvKB_pytorch/base/Valid.h
function validInit (line 14) | void validInit() {
function getValidHeadBatch (line 22) | void getValidHeadBatch(INT *ph, INT *pt, INT *pr) {
function getValidTailBatch (line 31) | void getValidTailBatch(INT *ph, INT *pt, INT *pr) {
function validHead (line 40) | void validHead(REAL *con) {
function validTail (line 60) | void validTail(REAL *con) {
function REAL (line 81) | REAL getValidHit10() {
FILE: ConvKB_pytorch/train_ConvKB.py
function get_term_id (line 69) | def get_term_id(filename):
function get_init_embeddings (line 80) | def get_init_embeddings(relinit, entinit):
FILE: ConvKB_tf/batching.py
function randn (line 26) | def randn(*args): return np.random.randn(*args).astype('f')
class Batch_Loader (line 28) | class Batch_Loader(object):
method __init__ (line 29) | def __init__(self, train_triples, words_indexes, indexes_words, headTa...
method __call__ (line 57) | def __call__(self):
FILE: ConvKB_tf/builddata.py
function read_from_id (line 8) | def read_from_id(filename='../data/WN18RR/entity2id.txt'):
function init_norm_Vector (line 20) | def init_norm_Vector(relinit, entinit, embedding_size):
function getID (line 39) | def getID(folder='data/WN18RR/'):
function parse_line (line 85) | def parse_line(line):
function load_triples_from_txt (line 97) | def load_triples_from_txt(filename, words_indexes=None, parse_line=parse...
function build_data (line 150) | def build_data(name='WN18', path='../data'):
function dic_of_chars (line 198) | def dic_of_chars(words_indexes):
function convert_to_seq_chars (line 208) | def convert_to_seq_chars(x_batch, lstChars, indexes_words):
function _pad_sequences (line 217) | def _pad_sequences(sequences, pad_tok, max_length):
function pad_sequences (line 228) | def pad_sequences(sequences, pad_tok):
FILE: ConvKB_tf/eval.py
function predict (line 171) | def predict(x_batch, y_batch, writer=None):
function test_prediction (line 181) | def test_prediction(x_batch, y_batch, head_or_tail='head'):
FILE: ConvKB_tf/model.py
class ConvKB (line 4) | class ConvKB(object):
method __init__ (line 6) | def __init__(self, sequence_length, num_classes, embedding_size, filte...
FILE: ConvKB_tf/train.py
function train_step (line 128) | def train_step(x_batch, y_batch):
Copy disabled (too large)
Download .json
Condensed preview — 107 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (35,101K chars).
[
{
"path": "ConvKB_pytorch/Config.py",
"chars": 23656,
"preview": "# coding:utf-8\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.optim as optim\nimport"
},
{
"path": "ConvKB_pytorch/ConvKB.py",
"chars": 3368,
"preview": "import torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim "
},
{
"path": "ConvKB_pytorch/ConvKB_1D.py",
"chars": 3195,
"preview": "import torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim "
},
{
"path": "ConvKB_pytorch/Model.py",
"chars": 798,
"preview": "import torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim "
},
{
"path": "ConvKB_pytorch/base/Base.cpp",
"chars": 3453,
"preview": "#include \"Setting.h\"\n#include \"Random.h\"\n#include \"Reader.h\"\n#include \"Corrupt.h\"\n#include \"Test.h\"\n#include \"Valid.h\"\n#"
},
{
"path": "ConvKB_pytorch/base/Corrupt.h",
"chars": 3271,
"preview": "#ifndef CORRUPT_H\n#define CORRUPT_H\n#include \"Random.h\"\n#include \"Triple.h\"\n#include \"Reader.h\"\n\nINT corrupt_head(INT id"
},
{
"path": "ConvKB_pytorch/base/Random.h",
"chars": 603,
"preview": "#ifndef RANDOM_H\n#define RANDOM_H\n#include \"Setting.h\"\n#include <cstdlib>\n\nunsigned long long *next_random;\n\nextern \"C\"\n"
},
{
"path": "ConvKB_pytorch/base/Reader.h",
"chars": 9364,
"preview": "#ifndef READER_H\n#define READER_H\n#include \"Setting.h\"\n#include \"Triple.h\"\n#include <cstdlib>\n#include <algorithm>\n\nINT "
},
{
"path": "ConvKB_pytorch/base/Setting.h",
"chars": 1761,
"preview": "#ifndef SETTING_H\n#define SETTING_H\n#define INT long\n#define REAL float\n#include <cstring>\n#include <cstdio>\n#include <s"
},
{
"path": "ConvKB_pytorch/base/Test.h",
"chars": 13035,
"preview": "#ifndef TEST_H\n#define TEST_H\n#include \"Setting.h\"\n#include \"Reader.h\"\n#include \"Corrupt.h\"\n\n/*========================="
},
{
"path": "ConvKB_pytorch/base/Triple.h",
"chars": 893,
"preview": "#ifndef TRIPLE_H\n#define TRIPLE_H\n#include \"Setting.h\"\n\nstruct Triple {\n\n\tINT h, r, t;\n\n\tstatic INT minimal(INT a,INT b)"
},
{
"path": "ConvKB_pytorch/base/Valid.h",
"chars": 2182,
"preview": "#ifndef VALID_H\n#define VALID_H\n#include \"Setting.h\"\n#include \"Reader.h\"\n#include \"Corrupt.h\"\n\nINT lastValidHead = 0;\nIN"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/1-1.txt",
"chars": 11973,
"preview": "832\n2326 6886 637\n4843 4843 149\n9671 7174 190\n8055 8055 305\n9549 11909 855\n13652 13585 446\n1100 1100 305\n1321 14529 176\n"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/1-n.txt",
"chars": 70522,
"preview": "5259\n147 11738 592\n3572 8632 143\n12761 4179 88\n3244 201 129\n3244 11230 129\n9085 11970 1\n1963 6035 203\n833 10624 263\n4654"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/entity2id.txt",
"chars": 225015,
"preview": "14951\n/m/027rn\t0\n/m/06cx9\t1\n/m/017dcd\t2\n/m/06v8s0\t3\n/m/07s9rl0\t4\n/m/0170z3\t5\n/m/01sl1q\t6\n/m/044mz_\t7\n/m/0cnk2q\t8\n/m/02nz"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/n-1.txt",
"chars": 114333,
"preview": "8637\n3136 4357 588\n6675 58 267\n719 2241 326\n5864 443 110\n13576 138 55\n76 1278 230\n9368 2018 42\n3956 6313 1056\n7654 12726"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/n-n.py",
"chars": 3125,
"preview": "lef = {}\nrig = {}\nrellef = {}\nrelrig = {}\n\ntriple = open(\"train2id.txt\", \"r\")\nvalid = open(\"valid2id.txt\", \"r\")\ntest = o"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/n-n.txt",
"chars": 567778,
"preview": "44343\n453 1347 37\n8663 4522 307\n2404 8386 186\n722 806 37\n1248 10937 26\n9182 1043 20\n706 14564 28\n706 14004 28\n213 9353 9"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/relation2id.txt",
"chars": 89799,
"preview": "1345\n/location/country/form_of_government\t0\n/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor\t1\n/media_common/"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/test2id.txt",
"chars": 764592,
"preview": "59071\n453 1347 37\n3136 4357 588\n8663 4522 307\n2404 8386 186\n722 806 37\n1248 10937 26\n9182 1043 20\n706 14564 28\n706 14004"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/test2id_all.txt",
"chars": 882734,
"preview": "59071\n3\t453 1347 37\n2\t3136 4357 588\n3\t8663 4522 307\n3\t2404 8386 186\n3\t722 806 37\n3\t1248 10937 26\n3\t9182 1043 20\n3\t706 14"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/train2id.txt",
"chars": 6251267,
"preview": "483142\n0 1 0\n2 3 1\n4 5 2\n6 7 3\n8 9 4\n10 11 5\n12 13 6\n14 15 7\n16 17 8\n18 19 9\n20 21 10\n22 23 11\n24 25 12\n26 27 13\n28 29 1"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/type_constrain.txt",
"chars": 1597866,
"preview": "1345\n1343\t1\t11904\n1343\t1\t11904\n1200\t4\t3123\t1034\t58\t5733\n1200\t4\t12123\t4388\t11087\t11088\n1175\t2\t13219\t14400\n1175\t5\t487\t4584"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K/valid2id.txt",
"chars": 647201,
"preview": "50000\n5167 1427 52\n239 2379 326\n837 9339 3\n7674 4431 272\n4528 8708 155\n71 5412 32\n5041 5979 26\n11273 3632 390\n10994 36 1"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/1-1.txt",
"chars": 2657,
"preview": "192\n5335 6299 230\n5299 10806 226\n14151 12928 94\n5692 5692 146\n11618 11618 56\n986 13237 94\n6680 7830 94\n1080 10505 94\n101"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/1-n.txt",
"chars": 17040,
"preview": "1293\n13757 3763 50\n2279 130 71\n2279 9410 71\n6559 12201 1\n1180 4006 102\n2955 4214 22\n8313 11808 132\n4709 8227 189\n4770 12"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/entity2id.txt",
"chars": 233099,
"preview": "14541\r\n/m/027rn\t0\r\n/m/06cx9\t1\r\n/m/017dcd\t2\r\n/m/06v8s0\t3\r\n/m/07s9rl0\t4\r\n/m/0170z3\t5\r\n/m/01sl1q\t6\r\n/m/044mz_\t7\r\n/m/0cnk2q\t"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/entity2id_100init.txt",
"chars": 233092,
"preview": "/m/027rn\t0\r\n/m/06cx9\t1\r\n/m/017dcd\t2\r\n/m/06v8s0\t3\r\n/m/07s9rl0\t4\r\n/m/0170z3\t5\r\n/m/01sl1q\t6\r\n/m/044mz_\t7\r\n/m/0cnk2q\t8\r\n/m/0"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/n-1.txt",
"chars": 52880,
"preview": "4185\n422 2438 154\n12013 90 33\n44 774 118\n6850 1652 24\n5308 10758 124\n8254 494 110\n10469 718 186\n10778 911 80\n5900 90 34\n"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/n-n.py",
"chars": 3125,
"preview": "lef = {}\nrig = {}\nrellef = {}\nrelrig = {}\n\ntriple = open(\"train2id.txt\", \"r\")\nvalid = open(\"valid2id.txt\", \"r\")\ntest = o"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/n-n.txt",
"chars": 186511,
"preview": "14796\n6180 2861 148\n1454 5951 92\n6651 637 13\n1124 14037 15\n1124 13245 15\n140 6827 6\n1899 6401 7\n98 2794 37\n9317 3257 31\n"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/relation2id.txt",
"chars": 15764,
"preview": "237\r\n/location/country/form_of_government\t0\r\n/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor\t1\r\n/media_commo"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/relation2id_100init.txt",
"chars": 15759,
"preview": "/location/country/form_of_government\t0\r\n/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor\t1\r\n/media_common/net"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/relation2vec100.init",
"chars": 225364,
"preview": "-0.100144\t-0.109701\t-0.064256\t0.022187\t0.047555\t0.064904\t0.028120\t-0.049784\t0.100976\t-0.005863\t0.133728\t0.004563\t0.10196"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/test2id.txt",
"chars": 259074,
"preview": "20466\n6180 2861 148\n1454 5951 92\n6651 637 13\n1124 14037 15\n1124 13245 15\n140 6827 6\n1899 6401 7\n98 2794 37\n9317 3257 31\n"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/test2id_all.txt",
"chars": 300006,
"preview": "20466\n3\t6180 2861 148\n3\t1454 5951 92\n3\t6651 637 13\n3\t1124 14037 15\n3\t1124 13245 15\n3\t140 6827 6\n3\t1899 6401 7\n3\t98 2794 "
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/train2id.txt",
"chars": 3416118,
"preview": "272115\n0 1 0\n2 3 1\n4 5 2\n6 7 3\n8 9 4\n10 11 5\n12 13 6\n14 15 7\n16 17 8\n18 19 9\n20 21 10\n22 23 11\n24 25 12\n26 27 13\n28 29 1"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/type_constrain.txt",
"chars": 828462,
"preview": "237\n216\t23\t9229\t4633\t12816\t6727\t5526\t9228\t10434\t13204\t5983\t978\t13322\t3416\t9787\t9157\t13430\t11024\t10843\t5579\t6500\t13090\t10"
},
{
"path": "ConvKB_pytorch/benchmarks/FB15K237/valid2id.txt",
"chars": 222093,
"preview": "17535\n6404 1211 31\n4089 1437 154\n8549 434 85\n8379 3498 47\n2364 235 13\n5042 23 11\n8252 160 51\n14424 1694 127\n3929 12645 7"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/1-1.txt",
"chars": 605,
"preview": "42\n20391 18987 14\n39837 12647 14\n37344 24682 14\n961 30170 14\n14581 24254 14\n14264 11347 14\n23007 4630 14\n14674 7581 14\n4"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/1-n.txt",
"chars": 26270,
"preview": "1847\n7951 38768 8\n13820 25808 10\n16395 29310 3\n11866 20457 1\n12799 21410 13\n19036 2722 10\n566 37370 10\n12652 34179 13\n61"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/entity2id.txt",
"chars": 603041,
"preview": "40943\n05451384\t0\n04958634\t1\n00620424\t2\n09887034\t3\n12149144\t4\n03257343\t5\n02757462\t6\n03757925\t7\n02453611\t8\n14108324\t9\n0445"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/n-1.txt",
"chars": 26818,
"preview": "1981\n18323 197 15\n39889 11075 5\n24460 2195 15\n6067 31615 5\n27845 12500 5\n12730 6128 5\n11108 18068 5\n15590 19118 5\n32278 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/n-n.py",
"chars": 3125,
"preview": "lef = {}\nrig = {}\nrellef = {}\nrelrig = {}\n\ntriple = open(\"train2id.txt\", \"r\")\nvalid = open(\"valid2id.txt\", \"r\")\ntest = o"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/n-n.txt",
"chars": 15230,
"preview": "1130\n24257 24051 2\n24484 3510 2\n38166 8808 2\n14244 17173 2\n23148 1594 2\n1550 4544 2\n1091 39703 17\n22912 4228 2\n12735 356"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/relation2id.txt",
"chars": 363,
"preview": "18\n_member_of_domain_topic\t0\n_member_meronym\t1\n_derivationally_related_form\t2\n_member_of_domain_region\t3\n_similar_to\t4\n_"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/test2id.txt",
"chars": 68910,
"preview": "5000\n7951 38768 8\n20391 18987 14\n13820 25808 10\n16395 29310 3\n11866 20457 1\n18323 197 15\n39889 11075 5\n24460 2195 15\n606"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/test2id_all.txt",
"chars": 78910,
"preview": "5000\n1\t7951 38768 8\n0\t20391 18987 14\n1\t13820 25808 10\n1\t16395 29310 3\n1\t11866 20457 1\n2\t18323 197 15\n2\t39889 11075 5\n2\t2"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/train2id.txt",
"chars": 1955191,
"preview": "141442\n27536 33729 10\n25546 10838 5\n1213 38780 6\n4207 6437 2\n27541 34437 2\n8217 20390 7\n23993 10605 10\n40739 23109 2\n283"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/type_constrain.txt",
"chars": 1035718,
"preview": "18\n11\t419\t23997\t18885\t34975\t14916\t14179\t37693\t27281\t11545\t21764\t23690\t19010\t36665\t21645\t11265\t24713\t9939\t33034\t2914\t1792"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18/valid2id.txt",
"chars": 69123,
"preview": "5000\n18394 30466 5\n4484 32979 2\n9388 13671 9\n26213 3112 1\n21878 36397 5\n7802 35451 7\n19886 33640 5\n1247 16285 15\n18521 3"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/1-1.txt",
"chars": 554,
"preview": "42\n17935 35175 9\n65 439 9\n1122 23212 9\n25017 18392 9\n6882 10563 9\n34810 22728 9\n11632 24469 9\n18458 31641 9\n15601 15600 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/1-n.txt",
"chars": 6306,
"preview": "475\n1678 9202 7\n785 23537 8\n16176 33688 4\n16748 29001 6\n5945 10270 6\n1255 24915 6\n30663 40225 4\n31355 35983 4\n34114 1598"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_also_see.txt",
"chars": 732,
"preview": "56\n38200 8055 3\n25183 11235 3\n2507 4435 3\n18377 4166 3\n11585 21329 3\n12153 19756 3\n17011 17169 3\n11643 3908 3\n24578 2311"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_derivationally_related_form.txt",
"chars": 14130,
"preview": "1074\n26184 32091 1\n4194 6975 1\n24512 24859 1\n12274 9268 1\n12842 15886 1\n13394 3789 1\n18270 18269 1\n29179 10971 1\n5734 37"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_has_part.txt",
"chars": 2286,
"preview": "172\n16748 29001 6\n5945 10270 6\n1255 24915 6\n40287 21134 6\n271 40026 6\n258 35861 6\n31306 34770 6\n22455 27199 6\n637 40404 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_hypernym.txt",
"chars": 16266,
"preview": "1251\n18671 11241 0\n15582 4030 0\n19165 2969 0\n31740 1183 0\n15602 4367 0\n33182 5053 0\n5109 22368 0\n20774 8060 0\n36698 586 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_instance_hypernym.txt",
"chars": 1544,
"preview": "122\n38212 121 2\n8415 7033 2\n11449 6220 2\n8306 25327 2\n13604 251 2\n21703 1136 2\n27870 2023 2\n35882 2524 2\n11490 2023 2\n23"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_member_meronym.txt",
"chars": 3396,
"preview": "253\n16176 33688 4\n30663 40225 4\n31355 35983 4\n34114 1598 4\n15430 617 4\n10581 27666 4\n20809 32206 4\n35258 25396 4\n1038 40"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_member_of_domain_region.txt",
"chars": 321,
"preview": "26\n785 23537 8\n1559 14508 8\n840 19493 8\n785 6770 8\n3408 38801 8\n785 36761 8\n3408 28506 8\n840 36647 8\n785 1493 8\n785 3874"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_member_of_domain_usage.txt",
"chars": 313,
"preview": "24\n1678 9202 7\n1678 30089 7\n785 11761 7\n1678 18000 7\n1678 35669 7\n6633 12050 7\n1823 36030 7\n1046 18788 7\n1678 36781 7\n66"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_similar_to.txt",
"chars": 44,
"preview": "3\n15601 15600 10\n6093 24981 10\n849 13402 10\n"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_synset_domain_topic_of.txt",
"chars": 1441,
"preview": "114\n40728 914 5\n14576 2971 5\n25546 914 5\n25625 2176 5\n36561 129 5\n5335 9988 5\n15250 2644 5\n31958 914 5\n33711 19257 5\n118"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/_verb_group.txt",
"chars": 512,
"preview": "39\n17935 35175 9\n65 439 9\n1122 23212 9\n25017 18392 9\n6882 10563 9\n34810 22728 9\n11632 24469 9\n18458 31641 9\n17534 12584 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/entity2id.txt",
"chars": 643985,
"preview": "40943\r\n00260881\t0\r\n00260622\t1\r\n01332730\t2\r\n03122748\t3\r\n06066555\t4\r\n00645415\t5\r\n09322930\t6\r\n09360122\t7\r\n07193596\t8\r\n00784"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/entity2id_50init.txt",
"chars": 643978,
"preview": "00260881\t0\r\n00260622\t1\r\n01332730\t2\r\n03122748\t3\r\n06066555\t4\r\n00645415\t5\r\n09322930\t6\r\n09360122\t7\r\n07193596\t8\r\n00784342\t9\r\n"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/n-1.txt",
"chars": 19243,
"preview": "1487\n18671 11241 0\n15582 4030 0\n19165 2969 0\n31740 1183 0\n15602 4367 0\n33182 5053 0\n5109 22368 0\n20774 8060 0\n36698 586 "
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/n-n.py",
"chars": 3125,
"preview": "lef = {}\nrig = {}\nrellef = {}\nrelrig = {}\n\ntriple = open(\"train2id.txt\", \"r\")\nvalid = open(\"valid2id.txt\", \"r\")\ntest = o"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/n-n.txt",
"chars": 14859,
"preview": "1130\n26184 32091 1\n4194 6975 1\n24512 24859 1\n12274 9268 1\n12842 15886 1\n13394 3789 1\n38200 8055 3\n18270 18269 1\n29179 10"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/relation2id.txt",
"chars": 229,
"preview": "11\r\n_hypernym\t0\r\n_derivationally_related_form\t1\r\n_instance_hypernym\t2\r\n_also_see\t3\r\n_member_meronym\t4\r\n_synset_domain_to"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/relation2id_50init.txt",
"chars": 225,
"preview": "_hypernym\t0\r\n_derivationally_related_form\t1\r\n_instance_hypernym\t2\r\n_also_see\t3\r\n_member_meronym\t4\r\n_synset_domain_topic_"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/relation2vec50.init",
"chars": 5232,
"preview": "0.074439\t-0.078413\t-0.060850\t-0.074264\t0.093673\t-0.073760\t-0.028944\t0.062929\t0.045091\t-0.057606\t-0.101286\t-0.073069\t-0.0"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/test2id.txt",
"chars": 40950,
"preview": "3134\n1678 9202 7\n17935 35175 9\n785 23537 8\n16176 33688 4\n18671 11241 0\n15582 4030 0\n19165 2969 0\n16748 29001 6\n31740 118"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/test2id_all.txt",
"chars": 47218,
"preview": "3134\n1\t1678 9202 7\n0\t17935 35175 9\n1\t785 23537 8\n1\t16176 33688 4\n2\t18671 11241 0\n2\t15582 4030 0\n2\t19165 2969 0\n1\t16748 2"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/train2id.txt",
"chars": 1124910,
"preview": "86835\n0 1 0\n2 3 1\n4 5 1\n6 7 2\n8 9 1\n10 11 1\n12 13 0\n14 15 0\n16 17 0\n18 19 1\n20 21 0\n22 23 3\n24 25 0\n26 27 0\n28 29 0\n30 3"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/type_constrain.txt",
"chars": 618450,
"preview": "11\n10\t82\t15601\t15600\t4898\t16473\t16472\t961\t8156\t3441\t19682\t2437\t455\t7032\t13402\t9779\t3515\t12045\t24127\t25225\t26292\t4553\t662"
},
{
"path": "ConvKB_pytorch/benchmarks/WN18RR/valid2id.txt",
"chars": 39634,
"preview": "3034\n33512 4410 0\n16031 32320 1\n40543 172 5\n15278 39692 4\n22936 14838 0\n31887 743 2\n29405 27360 0\n35494 15192 1\n18925 32"
},
{
"path": "ConvKB_pytorch/make.sh",
"chars": 81,
"preview": "g++ ./base/Base.cpp -fPIC -shared -o ./release/Base.so -pthread -O3 -march=native"
},
{
"path": "ConvKB_pytorch/train_ConvKB.py",
"chars": 5554,
"preview": "from Config import Config\nfrom ConvKB import ConvKB\nimport json\nimport os\nimport numpy as np\n\nfrom argparse import Argum"
},
{
"path": "ConvKB_tf/batching.py",
"chars": 4476,
"preview": "import sys, os\nimport logging\nimport numpy as np\nimport colorsys\n\n\n# Current path\ncur_path = os.path.dirname(os.path.rea"
},
{
"path": "ConvKB_tf/builddata.py",
"chars": 8238,
"preview": "import scipy\nimport scipy.io\nimport random\n\nfrom batching import *\n\n\ndef read_from_id(filename='../data/WN18RR/entity2id"
},
{
"path": "ConvKB_tf/data/FB15k-237/entity2id.txt",
"chars": 233092,
"preview": "/m/027rn\t0\r\n/m/06cx9\t1\r\n/m/017dcd\t2\r\n/m/06v8s0\t3\r\n/m/07s9rl0\t4\r\n/m/0170z3\t5\r\n/m/01sl1q\t6\r\n/m/044mz_\t7\r\n/m/0cnk2q\t8\r\n/m/0"
},
{
"path": "ConvKB_tf/data/FB15k-237/relation2id.txt",
"chars": 15759,
"preview": "/location/country/form_of_government\t0\r\n/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor\t1\r\n/media_common/net"
},
{
"path": "ConvKB_tf/data/FB15k-237/relation2vec100.init",
"chars": 225364,
"preview": "-0.100144\t-0.109701\t-0.064256\t0.022187\t0.047555\t0.064904\t0.028120\t-0.049784\t0.100976\t-0.005863\t0.133728\t0.004563\t0.10196"
},
{
"path": "ConvKB_tf/data/FB15k-237/test.txt",
"chars": 1519499,
"preview": "/m/08966\t/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month\t/m/05lf_\r\n/m/01hww_\t/music/"
},
{
"path": "ConvKB_tf/data/FB15k-237/valid.txt",
"chars": 1303101,
"preview": "/m/07pd_j\t/film/film/genre\t/m/02l7c8\r\n/m/06wxw\t/location/location/time_zones\t/m/02fqwt\r\n/m/01t94_1\t/people/person/spouse"
},
{
"path": "ConvKB_tf/data/WN18RR/entity2id.txt",
"chars": 643978,
"preview": "00260881\t0\r\n00260622\t1\r\n01332730\t2\r\n03122748\t3\r\n06066555\t4\r\n00645415\t5\r\n09322930\t6\r\n09360122\t7\r\n07193596\t8\r\n00784342\t9\r\n"
},
{
"path": "ConvKB_tf/data/WN18RR/relation2id.txt",
"chars": 225,
"preview": "_hypernym\t0\r\n_derivationally_related_form\t1\r\n_instance_hypernym\t2\r\n_also_see\t3\r\n_member_meronym\t4\r\n_synset_domain_topic_"
},
{
"path": "ConvKB_tf/data/WN18RR/relation2vec100.init",
"chars": 10463,
"preview": "0.006882\t0.075964\t0.036331\t0.034198\t0.016211\t-0.008821\t-0.020629\t-0.014837\t0.052503\t0.037201\t-0.029670\t-0.056403\t0.04463"
},
{
"path": "ConvKB_tf/data/WN18RR/relation2vec50.init",
"chars": 5232,
"preview": "0.074439\t-0.078413\t-0.060850\t-0.074264\t0.093673\t-0.073760\t-0.028944\t0.062929\t0.045091\t-0.057606\t-0.101286\t-0.073069\t-0.0"
},
{
"path": "ConvKB_tf/data/WN18RR/test.txt",
"chars": 113180,
"preview": "06845599\t_member_of_domain_usage\t03754979\n00789448\t_verb_group\t01062739\n08860123\t_member_of_domain_region\t05688486\n02233"
},
{
"path": "ConvKB_tf/data/WN18RR/train.txt",
"chars": 3135377,
"preview": "00260881\t_hypernym\t00260622\n01332730\t_derivationally_related_form\t03122748\n06066555\t_derivationally_related_form\t0064541"
},
{
"path": "ConvKB_tf/data/WN18RR/valid.txt",
"chars": 110415,
"preview": "02174461\t_hypernym\t02176268\n05074057\t_derivationally_related_form\t02310895\n08390511\t_synset_domain_topic_of\t08199025\n020"
},
{
"path": "ConvKB_tf/eval.py",
"chars": 12747,
"preview": "import tensorflow as tf\nimport numpy as np\nfrom scipy.stats import rankdata\n\nnp.random.seed(1234)\nimport os\nimport time\n"
},
{
"path": "ConvKB_tf/evalFB15k-237.sh",
"chars": 1164,
"preview": "nohup python eval.py --embedding_dim 100 --num_filters 50 --name FB15k-237 --useConstantInit --model_name fb15k237 --num"
},
{
"path": "ConvKB_tf/evalWN18RR.sh",
"chars": 976,
"preview": "nohup python eval.py --embedding_dim 50 --num_filters 500 --name WN18RR --model_name wn18rr --num_splits 8 --testIdx 0 &"
},
{
"path": "ConvKB_tf/model.py",
"chars": 3673,
"preview": "import tensorflow as tf\nimport numpy as np\nimport math\nclass ConvKB(object):\n\n def __init__(self, sequence_length, nu"
},
{
"path": "ConvKB_tf/train.py",
"chars": 7006,
"preview": "import tensorflow as tf\nimport numpy as np\n\nnp.random.seed(1234)\nimport os\nimport time\nimport datetime\nfrom argparse imp"
},
{
"path": "ConvKB_tf/training_commands.txt",
"chars": 260,
"preview": "python train.py --embedding_dim 50 --num_filters 500 --learning_rate 0.0001 --name WN18RR --model_name wn18rr --saveStep"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 4196,
"preview": "<p align=\"center\">\n\t<img src=\"https://github.com/daiquocnguyen/ConvKB/blob/master/convkb_logo.png\">\n</p>\n\n# A Novel Embe"
}
]
// ... and 6 more files (download for full content)
About this extraction
This page contains the full source code of the daiquocnguyen/ConvKB GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 107 files (151.3 MB), approximately 8.0M tokens, and a symbol index with 144 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.