fhours对应预报时效列表,point对应需要查询站点的经纬度,point_name就是站点名 def draw(members=["ECMWF_HR","GERMAN_HR","GRAPES_GFS ECMWF_HR 120 done ECMWF_HR 132 done ECMWF_HR 144 done ECMWF_HR 156 done ECMWF_HR 168 done GERMAN_HR 12 done GERMAN_HR 24 done GERMAN_HR 36 done GERMAN_HR 48 done GERMAN_HR 60 done GERMAN_HR 72 done GERMAN_HR 84 done GERMAN_HR 96 done GERMAN_HR 108 done GERMAN_HR 120 done GERMAN_HR 132 done GERMAN_HR 144 done GERMAN_HR 156 done GERMAN_HR 168 done GRAPES_GFS 12 done
cv ## 3: 3 <ResampleResult[21]> german_credit classif.ranger cv ## 4: 4 <ResampleResult classif.featureless 4 4 ## 2: german_credit classif.rpart 3 3 ## 3: german_credit classif.ranger 1 1 ## 4: german_credit classif.kknn 对单个任务进行绘制roc曲线 autoplot(bmr$clone()$filter(task_id = "german_credit"), type = "roc") ? 提取重抽样结果 本质上和之前的代码没什么区别 不过,需要学习data.table的语法 tab = bmr$aggregate(measures) rr = tab[task_id == "german_credit
当地时间2025年7月4日,荷兰法院正式判处German Aksenov三年有期徒刑。在此之前,荷兰移民局还对其实施了20 年的入境禁令。 German Aksenov 是一名 43 岁的工程师,其曾在俄罗斯大学接受教育,并在俄罗斯科技公司工作。 2015年,German Aksenov开始为总部位于荷兰代尔夫特的Mapper Lithography公司工作,这是一家开发电子束光刻工具的公司。 此后,German Aksenov开始为ASML的“应用”部门工作。ASML则将 Mapper 的电子束技术用于计量和芯片检查。 起诉书还显示,German Aksenov自2024年8月以来就因涉嫌挪用公款和违反制裁法而被审前拘留。
sql += " PRIMARY KEY (`ID`)\n" sql += ") ENGINE=MyISAM DEFAULT CHARSET=latin1 COLLATE=latin1_german1 _ci NOT NULL,\n" sql += " `Unit` varchar(10) COLLATE latin1_german1_ci NOT NULL,\n" sql += " PRIMARY KEY (`ID`)\n" sql += ") ENGINE=MyISAM DEFAULT CHARSET=latin1 COLLATE=latin1_german1 _ci NOT NULL,\n" sql += " `Value` varchar(100) COLLATE latin1_german1_ci NOT NULL,\n" PRIMARY KEY (`Parameter`)\n" sql += ") ENGINE=MyISAM DEFAULT CHARSET=latin1 COLLATE=latin1_german1
") def tokenize_german(text): return [token.text for token in spacy_german.tokenizer(text)] Length - 15 German - ein mann lächelt einen ausgestopften löwen an . Length - 12 German - jungen tanzen mitten in der nacht auf pfosten . <eos>" German : "Kinder spielen im Park." <eos>" German : "Diese Stadt verdient eine bessere Klasse von Verbrechern.
示例1:表和列定义 CREATE TABLE t1 ( c1 CHAR(10) CHARACTER SET latin1 COLLATE latin1_german1_ci ) DEFAULT CHARACTER SET latin2 COLLATE latin2_bin; 在这里我们有一个列使用latin1字符集和latin1_german1_ci校对规则。 _ci; · 使用AS: · SELECT k COLLATE latin1_german2_ci AS k1 · FROM _ci; · 使用聚合函数: · SELECT MAX(k COLLATE latin1_german2_ci) · FROM t1; · 使用DISTINCT: · SELECT DISTINCT k COLLATE latin1_german2_ci ·
git@github.com:lk-geimfari/mimesis.git 支持多语言 Code Name Native Name cs Czech Česky da Danish Dansk de German Deutsch de-at Austrian german Deutsch de-ch Swiss german Deutsch el Greek Ελληνικά en English English
return result return rooftop_status@guess_windef german_team(): print('德国必胜!') 复制代码 输出结果: 德国必胜! 比如在上面的例子中我们在压德国队赢的时候,原本的 german_team() 函数只是输出德国必胜,但在使用装饰器(guess_win)后,它的功能多了一项:输出「天台已满,请排队!」。 x = german_team() print(x) 复制代码 输出结果: 德国必胜! 天台已满,请排队! 赢了会所嫩模!输了下海干活! return result return rooftop_status@guess_windef german_team(arg): print('{}必胜!'. x = german_team('德国') y = german_team('西班牙') print(x) 复制代码 输出结果: 德国必胜! 天台已满,请排队! 西班牙必胜! 天台已满,请排队!
tar_vocab, activation='softmax'))) return model # load datasets dataset = load_clean_sentences('english-german-both.pkl ') train = load_clean_sentences('english-german-train.pkl') test = load_clean_sentences('english-german-test.pkl English Vocabulary Size: %d' % eng_vocab_size) print('English Max Length: %d' % (eng_length)) # prepare german ]) ger_vocab_size = len(ger_tokenizer.word_index) + 1 ger_length = max_length(dataset[:, 1]) print('German Vocabulary Size: %d' % ger_vocab_size) print('German Max Length: %d' % (ger_length)) # prepare training
Singapore Vietnam (integer) 6 127.0.0.1:6379> sadd DevelopedCty America Japan Korea Singapore France German Vietnam" 3) "Thailand" 127.0.0.1:6379> sdiff DevelopedCty AsiaCountry //找到发达国家中国的非亚洲国家 1) "America" 2) "German Japan" 3) "China" 4) "Korea" 5) "Thailand" 6) "Singapore" 127.0.0.1:6379> smembers DevelopedCty 1) "German AsiaCountry 1) "Japan" 2) "China" 3) "Korea" 4) "Singapore" 127.0.0.1:6379> smembers DevelopedCty 1) "German 6379> sunionstore totalCty AsiaCountry DevelopedCty (integer) 7 127.0.0.1:6379> smembers totalCty 1) "German
'danish': 丹麦语, 'dutch': 荷兰语, 'english': 英语, 'finnish': 芬兰语, 'french': 法语, 'german snowballstemmer >>> snowballstemmer.algorithms() ['danish', 'dutch', 'english', 'finnish', 'french', 'german
`id` int(20) NOT NULL AUTO_INCREMENT, `name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_german2 (`type`) USING BTREE ) ENGINE = InnoDB AUTO_INCREMENT = 8 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_german2
语言参数可以控制除梗器,有如下的语言可供选择: Armenian, Basque, Catalan, Danish, Dutch, English, Finnish, French, German, German2, Hungarian, Italian, Kp, Lithuanian, Lovins, Norwegian, Porter, Portuguese, Romanian, Russian
, from, in, film, see, "britain" nearest neighbors: several, first, modern, part, government, german, include, may, or, which, other, there, "american" nearest neighbors: born, french, british, english, german computer, control, systems, either, these, large, small, other, "american" nearest neighbors: born, german large, control, research, using, information, either, "american" nearest neighbors: english, french, german , research, some, information, large, "american" nearest neighbors: born, english, french, british, german
Compiled | Sortlen | +---------------------+---------+----+---------+----------+---------+ | latin1_german1 Yes | 1 | | latin1_danish_ci | latin1 | 15 | | | 0 | | latin1_german2 -------------------+---------+----+---------+----------+---------+ latin1校对规则有下面的含义: 校对规则 含义 latin1_german1 _ci 德国DIN-1 latin1_swedish_ci 瑞典/芬兰 latin1_danish_ci 丹麦/挪威 latin1_german2_ci 德国 DIN-2 latin1_bin 符合latin1
library("mlr3verse") design = benchmark_grid( tasks = tsks(c("spam", "german_credit", "sonar")), ' (iter 3/3) out INFO [21:44:40.423] [mlr3] Applying learner 'classif.ranger' on task 'german_credit ' (iter 1/3) out INFO [21:44:47.537] [mlr3] Applying learner 'classif.rpart' on task 'german_credit classif.ranger 1 1 out 5: german_credit classif.rpart 2 2 out 6: german_credit classif.featureless 3 3 out 7: sonar classif.ranger
return city_df else: print("Error:", response.status_code) return None# List of German cities ( herre you can add more cities)german_cities = ['Berlin', 'Frankfurt']# Create an empty DataFrame pd.DataFrame(columns=['City', 'Country', 'Latitude', 'Longitude', 'Population'])# Iterate and scrape data for German citiesfor city_name in german_cities: wiki_link = f"https://en.wikipedia.org/wiki/{city_name}" = pd.concat([german_cities_df, city_data], ignore_index=True)# Display the DataFrameprint(german_cities_df
return result return rooftop_status @guess_win def german_team(): print('德国必胜!') 比如在上面的例子中我们在压德国队赢的时候,原本的 german_team() 函数只是输出德国必胜,但在使用装饰器(guess_win)后,它的功能多了一项:输出「天台已满,请排队!」。 x = german_team() print(x) 输出结果: 德国必胜! 天台已满,请排队! 赢了会所嫩模!输了下海干活! return result return rooftop_status @guess_win def german_team(arg): print('{}必胜!'. x = german_team('德国') y = german_team('西班牙') print(x) 输出结果: 德国必胜! 天台已满,请排队! 西班牙必胜! 天台已满,请排队! 赢了会所嫩模!
Description has at least 10 characters" }, 'es-ES': { name:"1test name es-ES", description:"German Spaceship::Tunes::IAPType::CONSUMABLE, versions: { 'es-ES': { name:"test name german1 ", description:"German has at least 10 characters" } }, reference_name:"
import pandas as pd from sklearn.preprocessing import StandardScaler # 读取数据 german_credit_data = pd.read_csv ('附件1.csv') australian_credit_data = pd.read_csv('附件2.csv') # 处理缺失值 german_credit_data.fillna(german_credit_data.mean , german_credit_data['target']) 3.3 嵌入法 通过LASSO回归进行特征选择,通过L1正则化压缩不重要的特征系数。 , german_credit_data['target']) selected_features = german_credit_data.columns[lasso.coef_ ! , german_credit_data['target'], test_size=0.3, random_state=42) 4.2 处理不平衡数据 使用SMOTE和欠采样技术处理数据不平衡问题。