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业务刚需 | meteva复现多模式降水站点值mesh图
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
40310编辑于 2024-08-21 - 来自专栏火星娃统计
mlr3_Benchmarking
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
1.1K31发布于 2021-03-09 - 来自专栏小徐学爬虫
Pymysql cur.fetchall() 返回 None
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
53910编辑于 2024-10-09 - 来自专栏DeepHub IMBA
在PyTorch中使用Seq2Seq构建的神经机器翻译模型
") 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.
2.1K10发布于 2020-09-23 - 来自专栏明明如月的技术专栏
mysql 字符集（CHARACTER SET）和…
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 ·
70340发布于 2021-08-27 - 来自专栏测试技术圈
Facebook开源的数据Mock：Memisis详解
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
85431发布于 2020-01-17 - 来自专栏python小白到大牛
利用世界杯，读懂 Python 装饰器
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！
46230发布于 2018-07-20 - 来自专栏ATYUN订阅号
神经机器翻译与代码（下）
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
1K20发布于 2020-01-02 - 来自专栏陈树义
6.Redis常用命令：Set
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
72270发布于 2018-04-13 - 来自专栏python前行者
python语言转换库snowballstemmer
'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
1.9K40发布于 2019-03-25 - 来自专栏面朝大海春暖花开
mysql自定义排序
`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
1.5K30发布于 2020-01-19 - 来自专栏InvQ的专栏
ElasticSearch Snowball token filter
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
84910发布于 2020-09-27 - 来自专栏磐创AI技术团队的专栏
TensorFlow2.0 代码实战专栏（四）：Word2Vec (Word Embedding)
, 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
3.9K20发布于 2019-12-05 - 来自专栏Java学习网
MySQL中的字符集和校对学习--MySql语法
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
1.1K30发布于 2021-07-30 - 来自专栏优雅R
mlr3基础（二）
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
3.3K10发布于 2021-09-24 - 来自专栏API技术
数据工程实践：从网络抓取到API调用，解析共享单车所需要的数据
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
99110编辑于 2024-01-05 - 来自专栏自动化办公
利用世界杯，读懂 Python 装饰器
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！
39640编辑于 2023-03-02 - 来自专栏HelloWorld杰少
实现 iOS 内购商品批量操作
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:"
1.3K20编辑于 2022-08-04 - 来自专栏学习笔记
【数学建模】——【A题 信用风险识别问题】全面解析
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。
1.1K20编辑于 2024-08-05 - 来自专栏陈树义
7.Redis常用命令：ZSet
127.0.0.1:6379> zrange CountryPower 0 -1 withscores 1) "France" 2) "85" 3) "German" 4) "88" 5) " WITHSCORES] 截取范围内的成员（自选带分数） 127.0.0.1:6379> zrange CountryPower 0 -1 withscores 1) "France" 2) "85" 3) "German withscores 1) "America" 2) "99" 3) "Russia" 4) "97" 5) "China" 6) "95" 7) "Japan" 8) "89" 9) "German withscores 1) "America" 2) "99" 3) "Russia" 4) "97" 5) "China" 6) "95" 7) "Japan" 8) "89" 9) "German 127.0.0.1:6379> zrange CountryPower 0 -1 withscores 1) "France" 2) "85" 3) "German" 4) "88" 5) "
1.1K50发布于 2018-04-13
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