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Recommending movies: retrieval
It can also be seen as expressesing how much the users liked the movies they did watch. ("movielens/100k-ratings", split="train") # Features of all the available movies. movies = tfds.load( The model is re-recommending some of users' already watched movies. These known-positive watches can crowd out test movies out of top K recommendations. out of the entire movies dataset. index.index(movies.batch(100).map(model.movie_model), movies) # Get
63710发布于 2021-07-28 - 来自专栏Reck Zhang
LeetCode 0620 - Not Boring Movies
Not Boring Movies Desicription X city opened a new cinema, many people would like to go to this cinema The cinema also gives out a poster indicating the movies’ ratings and descriptions. Please write a SQL query to output movies with an odd numbered ID and a description that is not ‘boring
30630发布于 2021-08-11 - 来自专栏owent
POJ PKU Lets Go to the Movies 解题报告
如果用G++要把cin和cout改成scanf和printf，然后少用string，否则会TLE)： /** * Author: OWenT * POJ PKU 3513 Let's Go to the Movies ns, long &nf, long &t, long &s, long &f); void initNode(long &pos); int main() { //freopen("d:\\movies.in.txt ","r",stdin); //freopen("d:\\movies.out.txt","w",stdout); long s,f, k = 0; long ns,nf,t;
46320发布于 2018-08-01 - 来自专栏Michael阿明学习之路
Friendly Movies Streamed Last Month
content for kids otherwise 'N' is not content for kids. content_type is the category of the content as movies Write an SQL query to report the distinct titles of the kid-friendly movies streamed in June 2020. -+----------------+---------------+---------------+ | 1 | Leetcode Movie | N | Movies | | 5 | Cinderella | Y | Movies | +------------+------------- 来源：力扣（LeetCode） 链接：https://leetcode-cn.com/problems/friendly-movies-streamed-last-month 著作权归领扣网络所有。
65020发布于 2021-02-19 - 来自专栏站长的编程笔记
【说站】Movies 1.1.1 自适应 手机平板电视均可使用
一款新出的比较新颖的盒子，支持安卓平板手机和TV盒子智能电视上安装，这款壳应该是新创的，可以自动适应横竖屏，这点和小書一样不过发现新版小書新版在手机不能自动横屏了，在电视安装就是横屏。
90020编辑于 2022-11-25 - 来自专栏深度学习之tensorflow实战篇
从零到一spark进阶之路（三） pyspark 处理movies数据集(整理ING6-20)
处理movies数据集 下面我通过PySpark对真实的数据集进行处理，并作图形来分析。首先我需要介绍下数据集以及数据处理的环境。
1.2K30发布于 2019-01-25 - 来自专栏MeteoAI
Keras系列（五） ConvLSTM 空间特征深度学习
产生样本数据 # Artificial data generation: # Generate movies with 3 to 7 moving squares inside. # The squares def generate_movies(n_samples=, n_frames=): row = col = noisy_movies = np.zeros((n_samples = noisy_movies[::, ::, :, :, ::] shifted_movies = shifted_movies[::, ::, :, :, ::] noisy_movies [noisy_movies >= ] = shifted_movies[shifted_movies >= ] = return noisy_movies, shifted_movies 开始训练 、预测 # Train the network noisy_movies, shifted_movies = generate_movies(n_samples=) seq.fit(noisy_movies
11.7K20发布于 2019-07-24 - 来自专栏一Li小麦
hand first python选读(一)
列表(list) 基本操作 比如说我要整理一个近期热映的电影列表： movies = ["venom", "My Neighbor Totor", "Aquaman"] print(movies) # ['venom', 'My Neighbor Totor', 'Aquaman'] print(len(movies)) # 3 print(movies[1]) # My Neighbor Totor movies.append('A Cool Fish') print(movies) # ['venom', 'My Neighbor Totor', 'Aquaman','A Cool Fish'] movies.pop() print(movies) # ['venom', 'My Neighbor Totor'] 两个列表相衔接，用的是 extend方法。 movies.remove('venom') print(movies) # ['My Neighbor Totor', 'Aquaman'] movies.insert(len(movies),'venom
79220发布于 2019-07-18 - 来自专栏全栈程序员必看
mongodb服务启动失败_mongodb启动不了
(new Document()) SELECT * FROM movies 条件查询 movies.Find(new Document { { “title”, “Hello Esr ” } }); SELECT * FROM movies WHERE title= ‘foobar’ 查询数量 movies.Find(new Document { { “title “gte : >= ; lte : <= ; select * from movies where num > 50 分页查询 movies.Find(new Document()) .Skip(10).Limit(20); SELECT * FROM movies limit 10,20 查询排序语句 movies.Find(new Document()). } }); INSERT INOT movies (`title`, `reauleDate`) values (‘foobar’,25) 删除语句 movies.Remove(
8.8K20编辑于 2022-08-03 - 来自专栏毛利学Python
【小白学习PyTorch教程】十五、通过PyTorch来创建一个文本分类的Bert模型
= movies_df[(movies_df["Origin/Ethnicity"]=="American") | (movies_df["Origin/Ethnicity"]=="British") ] movies_df = movies_df[["Plot", "Genre"]] drop_indices = movies_df[movies_df["Genre"] == "unknown" ] ().reset_index(name="count").query("count > 200")["index"].tolist() movies_df = movies_df[movies_df[" =1).reset_index(drop=True) #从不同类型中抽取大致相同数量的电影情节样本（以减少阶级不平衡问题） movies_df = movies_df.groupby("Genre") (movies_df["Genre"].tolist()) movies_df = movies_df[["Plot", "Genre", "genre_encoded"]] movies_df
1.2K30编辑于 2022-08-18 - 来自专栏福大大架构师每日一题
2021-11-20：一场电影开始和结束时间可以用一个小数组来表
, 19}, {20, 20}} ret := maxEnjoy2(movies) fmt.Println(ret) } func maxEnjoy2(movies [][]int) int { sort.Slice(movies, func(i, j int) bool { a := movies[i] b := movies[j] movies) { return twoSelectOne(rest == 0, 0, -1) } p1 := process2(movies, index+1, time , rest) next := twoSelectOne(movies[index][0] >= time && rest > 0, process2(movies, index+1, movies = -1, (movies[index][1] - movies[index][0] + next), -1) return getMax(p1, p2) } func twoSelectOne
57710发布于 2021-11-20 - 来自专栏SeanCheney的专栏
《Pandas 1.x Cookbook · 第二版》第02章 DataFrame基础运算
>>> type(movies[["director_name"]]) <class 'pandas.core.frame.DataFrame'> # DataFrame类型 >>> type(movies >>> type(movies.loc[:, ["director_name"]]) <class 'pandas.core.frame.DataFrame'> >>> type(movies.loc[ = movies.rename(columns=shorten) >>> movies.dtypes.value_counts() float64 13 int64 3 object "_for_reviews", "" ... ) >>> movies = movies.rename(columns=shorten) 对下面的列名进行 >>> movies.columns >>> movies = pd.read_csv("data/movie.csv") >>> movies.shape (4916, 28) >>> movies.size 137648 >>> movies.ndim
90010发布于 2021-02-05 - 来自专栏有趣的django
微信小程序实战–集阅读与电影于一体的小程序项目（六）
pages/posts/post", "pages/welcome/welcome", "pages/posts/post-detail/post-detail", "pages/movies /movies", "pages/movies/more-movie/more-movie" ], more-list-template.wxml <view class="more" catchtap category=' + category }) }, more-movie.js // pages/<em>movies</em>/more-movie/more-movie.js Page({ onLoad 25.动态设置导航栏标题 more-movie.js // pages/<em>movies</em>/more-movie/more-movie.js Page({ data:{ categoryTitle (temp) } this.setData({ <em>movies</em>: <em>movies</em> }); }, onReady: function () { wx.setNavigationBarTitle
82060发布于 2018-08-30 - 来自专栏Java开发者杂谈
Python(1):入门
大致看下来python的代码如下： def print_list(movies): if isinstance(movies, list): for movie in movies 列表的常见操作如下： 1 movies = ["movie1", "movie2"] 2 # 列表中可以嵌套列表，这里执行完后，列表中的第一个元素就是一个列表 3 movies.insert(0 = [] 2 movies.append("movie1") 3 print(movies) # ['movie1'] 4 movies.extend('movie2') 5 print( movies) # ['movie1', 'm', 'o', 'v', 'i', 'e','2'] 6 movies.append(['movie3']) 7 print(movies) print(movies) 7 定义完函数之后，我们可以直接通过 print_list(movies)来输出列表。
94580发布于 2018-03-14 - 来自专栏粽子的深度学习笔记
python学习（一）：Python入门
10. while循环 >>> count=0 >>> while count < len(movies): print(movies[count]) count = count+1 =["红海行动","盗梦空间","前目的地"] >>> print(movies) ['红海行动', '盗梦空间', '前目的地'] >>> print(movies[1]) 盗梦空间 >>> print (len(movies)) 3 >>> movies.append("云图") >>> print(movies) ['红海行动', '盗梦空间', '前目的地', '云图'] >>> movies.pop () '云图' >>> print(movies) ['红海行动', '盗梦空间', '前目的地'] >>> movies.extend(["云图","百万英镑","罗马假日"]) >>> print( movies) ['红海行动', '盗梦空间', '前目的地', '云图', '百万英镑', '罗马假日'] >>> movies.remove("红海行动") >>> print(movies) ['
64120发布于 2021-07-07 - 来自专栏猿说编程
16.python set集合
= set() movies.add("天龙八部") movies.add("射雕英雄传") print("movies集合的元素：" , movies) # issubset()方法判断是否为子集合 print("movies集合是否为c的子集合？" , c.issuperset(movies)) # 输出False # 用c集合减去books集合里的元素，不改变c集合本身 result1 = c - movies print(result1) ("天龙八部") movies.add("射雕英雄传") print("movies集合的元素：" , movies) # issubset()方法与<=运算符效果相同 print("movies集合是否为 print(inter1) 输出结果： movies集合的元素： {'天龙八部', '射雕英雄传'} movies集合是否为c的子集合？
88010发布于 2020-03-12 - 来自专栏飞鸟的专栏
Elasticsearch 高级操作-别名示例
假设我们有两个索引，一个是存储电影信息的movies索引，另一个是存储演员信息的actors索引。现在我们希望在这两个索引上执行相同的查询，以找到所有电影和演员的信息。 首先，我们需要创建别名，将其指向这两个索引：PUT /_alias/movies_actors{ "indices": ["movies", "actors"]}接下来，我们可以使用以下查询来在别名上执行搜索 : ["title", "cast.name"] } }}在上面的查询中，我们使用movies_actors别名代替了实际的索引名称。 , "alias": "movies_actors" } }, { "add": { "index": "movies_v2", "alias": "movies_actors" } } ]}在上面的命令中，我们首先使用remove操作将别名从旧索引movies中删除，然后使用add操作将别名指向新索引movies_v2
58140编辑于 2023-05-09 - 来自专栏文大师的新世界
5. ListView应用
= 'request_movies'; export const RECEIVE_MOVIES = 'receive_movies'; 新建app/home/action.js，action一般在这里请求 /common/util' import { REQUEST_MOVIES, RECEIVE_MOVIES } from '. 新建app/home/reducer.js，分发action 'use strict' import { REQUEST_MOVIES, RECEIVE_MOVIES } from '. /constant' export function moviesReducer ( state={ isFetching: true, movies: {}, : return Object.assign({}, state, { movies: action.movies,
79850发布于 2018-08-30 - 来自专栏PostgreSQL研究与原理解析
[译]理解PG如何执行一个查询-2
movies-# SELECT COUNT(*), EXTRACT( DECADE FROM birth_date ) movies-# FROM customers movies-# 下面是创建tapes和dvds表的命令： movies=# CREATE TABLE tapes ( ) INHERITS( video ); movies=# CREATE TABLE dvds movies -# ( movies(# region_id INTEGER, movies(# audio_tracks VARCHAR[] movies(# ) INHERITS ( video ) 这是一个使用hash join算子的查询计划： movies=# EXPLAIN movies-# SELECT * FROM customers, rentals movies-# WHERE =# EXPLAIN movies-# SELECT * FROM customers movies-# WHERE customer_id IN movies-# ( movies
2.3K20编辑于 2022-04-27 - 来自专栏作图丫
高级的交集可视化工具--ComplexUpset！
包安装 BiocManager::install("ComplexUpset") library(ggplot2) library(ComplexUpset) #数据展示 library(ggplot2movies ) movies = as.data.frame(ggplot2movies::movies) head(movies, 3) genres = colnames(movies)[18:24] genres movies[genres] = movies[genres] == 1 t(head(movies[genres], 3)) movies[movies$mpaa == '', 'mpaa'] = NA movies = na.omit(movies) #设置绘图区域函数 set_size = function(w, h, factor=1.5) { s = 1 * factor ('Without empty groups (Short dropped)') + #通过patchwork实现拼图 upset(movies, genres, name='genre',
2.1K20编辑于 2022-03-29
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