11、平击球：flat 12、头号种子：top seed 13、外卡：wild card 14、种子选手：seeded player
t.set_httpseeds(["http://httpseed.com"]) self.assertEqual(t.get_httpseeds(), ["http://httpseed.com" 开发者ID:synctext, ▲点赞 1▼ # 需要导入模块: from Tribler.Core.TorrentDef import TorrentDef [as 别名]# 或者: from Tribler.Core.TorrentDef.TorrentDef importset_httpseeds[as 别名]...
set 3 see 3 eft 6 efs 6 eds 4 tet 3 def 7 dee 4 4 letter words with DEFTEST Points fees 7 teds 5 teed 5 tees 4 test 4 tets 4 stet 4 sett 4 seed 5 fets 7 fete 7 fest 7 feet 7 feed 8 feds 8 efts 7 dees 5
self.config.set_overlay(False) self.config.set_megacache(False)defsetUpPostSession(self):""" override TestAsServer """TestAsServer.setUpPostSession(self)deftearDown(self):print>> sys.stderr, time.asctime(),"-","Test: Sleep before tear down"time.sleep(10) TestAsServer.tearDown(self)defcrea...
"seed": 1, "sel": [46, 92], "set_davg_zero": False, "trainable": True, "type": "se_e2_a", "type_one_side": False, }, "fitting_net": { "activation_function": "tanh", "atom_ener": [], "neuron": [6, 6, 6], "numb_aparam": 0, "numb_fparam": 0, "precision":...
Here's the main comp with the problem essential properties ("dimensions"and "random seed") set to default, but other essential properties adjusted ("paper scale" and "main paper z pos"). Here the shadow has correctly adjusted along with the parameters: Here's the comp with the problem ...
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# 导入必要的库 library(randomForest) library(caret) # 读取数据 data <- read.csv('data.csv') # 划分训练集和测试集 set.seed(42) trainIndex <- createDataPartition(data$target, p=0.8, list=FALSE, times=1) train <- data[trainIndex, ] test <- data[-trainIndex, ] # 构建随机森林回归模...
self.seed_config.set_megacache_enabled(True) self.seed_config.set_tunnel_community_socks5_listen_ports(self.get_socks5_ports())ifself.session2isNone: self.session2 = Session(self.seed_config) self.session2.start() tdef =TorrentDef() ...