基于文本来推荐相似酒店

基于文本来推荐相似酒店

查看数据集基本信息

import pandas as pd
import numpy as np
from nltk.corpus import stopwords
from sklearn.metrics.pairwise import linear_kernel
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.feature_extraction.text import TfidfVectorizer
import re
import random
import cufflinks
import cufflinks
from plotly.offline import iplot

df=pd.read_csv("Seattle_Hotels.csv",encoding="latin-1")
df.head()
nameaddressdesc
0Hilton Garden Seattle Downtown1821 Boren Avenue, Seattle Washington 98101 USALocated on the southern tip of Lake Union, the...
1Sheraton Grand Seattle1400 6th Avenue, Seattle, Washington 98101 USALocated in the city's vibrant core, the Sherat...
2Crowne Plaza Seattle Downtown1113 6th Ave, Seattle, WA 98101Located in the heart of downtown Seattle, the ...
3Kimpton Hotel Monaco Seattle1101 4th Ave, Seattle, WA98101What?s near our hotel downtown Seattle locatio...
4The Westin Seattle1900 5th Avenue, Seattle, Washington 98101 USASituated amid incredible shopping and iconic a...
df.shape

(152, 3)

df['desc'][0]

"Located on the southern tip of Lake Union, the Hilton Garden Inn Seattle Downtown hotel is perfectly located for business and leisure. \nThe neighborhood is home to numerous major international companies including Amazon, Google and the Bill & Melinda Gates Foundation. A wealth of eclectic restaurants and bars make this area of Seattle one of the most sought out by locals and visitors. Our proximity to Lake Union allows visitors to take in some of the Pacific Northwest's majestic scenery and enjoy outdoor activities like kayaking and sailing. over 2,000 sq. ft. of versatile space and a complimentary business center. State-of-the-art A/V technology and our helpful staff will guarantee your conference, cocktail reception or wedding is a success. Refresh in the sparkling saltwater pool, or energize with the latest equipment in the 24-hour fitness center. Tastefully decorated and flooded with natural light, our guest rooms and suites offer everything you need to relax and stay productive. Unwind in the bar, and enjoy American cuisine for breakfast, lunch and dinner in our restaurant. The 24-hour Pavilion Pantry? stocks a variety of snacks, drinks and sundries."

查看酒店描述中主要介绍信息

vec=CountVectorizer().fit(df['desc'])

vec=CountVectorizer().fit(df['desc'])
bag_of_words=vec.transform(df['desc'])
sum_words=bag_of_words.sum(axis=0)
words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]
sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)
sum_words[1:10]

[('and', 1062),('of', 536),('seattle', 533),('to', 471),('in', 449),('our', 359),('you', 304),('hotel', 295),('with', 280)]

bag_of_words=vec.transform(df['desc'])
bag_of_words.shape

(152, 3200)

bag_of_words.toarray()

array([[0, 1, 0, ..., 0, 0, 0],[0, 0, 0, ..., 0, 0, 0],[0, 0, 0, ..., 0, 0, 0],...,[0, 0, 0, ..., 0, 0, 0],[0, 0, 0, ..., 0, 0, 0],[0, 0, 0, ..., 1, 0, 0]], dtype=int64)

sum_words=bag_of_words.sum(axis=0)
sum_words

matrix([[ 1, 11, 11, ...,  2,  6,  2]], dtype=int64)

words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]

sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)

sum_words[1:10]

[('and', 1062),('of', 536),('seattle', 533),('to', 471),('in', 449),('our', 359),('you', 304),('hotel', 295),('with', 280)]

将以上信息整合成函数

def get_top_n_words(corpus,n=None):vec=CountVectorizer().fit(df['desc'])bag_of_words=vec.transform(df['desc'])sum_words=bag_of_words.sum(axis=0)words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)return sum_words[:n]

common_words=get_top_n_words(df['desc'],20)
common_words

[('the', 1258),('and', 1062),('of', 536),('seattle', 533),('to', 471),('in', 449),('our', 359),('you', 304),('hotel', 295),('with', 280),('is', 271),('at', 231),('from', 224),('for', 216),('your', 186),('or', 161),('center', 151),('are', 136),('downtown', 133),('on', 129)]

df1=pd.DataFrame(common_words,columns=['desc','count'])

common_words=get_top_n_words(df['desc'],20)
df2=pd.DataFrame(common_words,columns=['desc','count'])
chart_info2=df2.groupby(['desc']).sum().sort_values('count',ascending=False)
chart_info2.plot(kind='barh',figsize=(14,10),title='top 20 before remove stopwords')

<AxesSubplot:title={'center':'top 20 before remove stopwords'}, ylabel='desc'>

在这里插入图片描述

chart_info1=df1.groupby(['desc']).sum().sort_values('count',ascending=False)

chart_info1.plot(kind='barh',figsize=(14,10),title='top 20 before remove stopwords')

<AxesSubplot:title={'center':'top 20 before remove stopwords'}, ylabel='desc'>

在这里插入图片描述

def get_any1_top_n_words_after_stopwords(corpus,n=None):vec=CountVectorizer(stop_words='english',ngram_range=(1,1)).fit(df['desc'])bag_of_words=vec.transform(df['desc'])sum_words=bag_of_words.sum(axis=0)words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)return sum_words[:n]

common_words=get_any1_top_n_words_after_stopwords(df['desc'],20)
df2=pd.DataFrame(common_words,columns=['desc','count'])
chart_info2=df2.groupby(['desc']).sum().sort_values('count',ascending=False)
chart_info2.plot(kind='barh',figsize=(14,10),title='top 20 before after stopwords')

<AxesSubplot:title={'center':'top 20 before after stopwords'}, ylabel='desc'>

在这里插入图片描述

def get_any2_top_n_words_after_stopwords(corpus,n=None):vec=CountVectorizer(stop_words='english',ngram_range=(2,2)).fit(df['desc'])bag_of_words=vec.transform(df['desc'])sum_words=bag_of_words.sum(axis=0)words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)return sum_words[:n]common_words=get_any2_top_n_words_after_stopwords(df['desc'],20)
df2=pd.DataFrame(common_words,columns=['desc','count'])
chart_info2=df2.groupby(['desc']).sum().sort_values('count',ascending=False)
chart_info2.plot(kind='barh',figsize=(14,10),title='top 20 before after stopwords')

<AxesSubplot:title={'center':'top 20 before after stopwords'}, ylabel='desc'>

在这里插入图片描述

def get_any3_top_n_words_after_stopwords(corpus,n=None):vec=CountVectorizer(stop_words='english',ngram_range=(3,3)).fit(df['desc'])bag_of_words=vec.transform(df['desc'])sum_words=bag_of_words.sum(axis=0)words_freq=[(word,sum_words[0,idx]) for word,idx in vec.vocabulary_.items()]sum_words=sorted(words_freq,key= lambda x: x[1],reverse=True)return sum_words[:n]common_words=get_any3_top_n_words_after_stopwords(df['desc'],20)
df2=pd.DataFrame(common_words,columns=['desc','count'])
chart_info2=df2.groupby(['desc']).sum().sort_values('count',ascending=False)
chart_info2.plot(kind='barh',figsize=(14,10),title='top 20 before after stopwords')

<AxesSubplot:title={'center':'top 20 before after stopwords'}, ylabel='desc'>

在这里插入图片描述

描述的一些统计信息

df=pd.read_csv("Seattle_Hotels.csv",encoding="latin-1")
df['desc'][0]

"Located on the southern tip of Lake Union, the Hilton Garden Inn Seattle Downtown hotel is perfectly located for business and leisure. \nThe neighborhood is home to numerous major international companies including Amazon, Google and the Bill & Melinda Gates Foundation. A wealth of eclectic restaurants and bars make this area of Seattle one of the most sought out by locals and visitors. Our proximity to Lake Union allows visitors to take in some of the Pacific Northwest's majestic scenery and enjoy outdoor activities like kayaking and sailing. over 2,000 sq. ft. of versatile space and a complimentary business center. State-of-the-art A/V technology and our helpful staff will guarantee your conference, cocktail reception or wedding is a success. Refresh in the sparkling saltwater pool, or energize with the latest equipment in the 24-hour fitness center. Tastefully decorated and flooded with natural light, our guest rooms and suites offer everything you need to relax and stay productive. Unwind in the bar, and enjoy American cuisine for breakfast, lunch and dinner in our restaurant. The 24-hour Pavilion Pantry? stocks a variety of snacks, drinks and sundries."

df['word_count']=df['desc'].apply(   lambda x:len(str(x).split(' '))    )
df.head()
nameaddressdescword_count
0Hilton Garden Seattle Downtown1821 Boren Avenue, Seattle Washington 98101 USALocated on the southern tip of Lake Union, the...184
1Sheraton Grand Seattle1400 6th Avenue, Seattle, Washington 98101 USALocated in the city's vibrant core, the Sherat...152
2Crowne Plaza Seattle Downtown1113 6th Ave, Seattle, WA 98101Located in the heart of downtown Seattle, the ...147
3Kimpton Hotel Monaco Seattle1101 4th Ave, Seattle, WA98101What?s near our hotel downtown Seattle locatio...151
4The Westin Seattle1900 5th Avenue, Seattle, Washington 98101 USASituated amid incredible shopping and iconic a...151
df['word_count'].plot(kind='hist',bins=50)

<AxesSubplot:ylabel='Frequency'>

在这里插入图片描述

文本处理

sub_replace=re.compile('[^0-9a-z#-]')

from nltk.corpus import stopwords

stopwords=set(stopwords.words('english'))

def clean_txt(text):text.lower()text=sub_replace.sub(' ',text)''.join(    word   for word in text.split(' ')  if word not in stopwords               )return  text

df['desc_clean']=df['desc'].apply(clean_txt)

df['desc_clean'][0]

' ocated on the southern tip of  ake  nion  the  ilton  arden  nn  eattle  owntown hotel is perfectly located for business and leisure    he neighborhood is home to numerous major international companies including  mazon   oogle and the  ill    elinda  ates  oundation    wealth of eclectic restaurants and bars make this area of  eattle one of the most sought out by locals and visitors   ur proximity to  ake  nion allows visitors to take in some of the  acific  orthwest s majestic scenery and enjoy outdoor activities like kayaking and sailing  over 2 000 sq  ft  of versatile space and a complimentary business center   tate-of-the-art     technology and our helpful staff will guarantee your conference  cocktail reception or wedding is a success   efresh in the sparkling saltwater pool  or energize with the latest equipment in the 24-hour fitness center   astefully decorated and flooded with natural light  our guest rooms and suites offer everything you need to relax and stay productive   nwind in the bar  and enjoy  merican cuisine for breakfast  lunch and dinner in our restaurant   he 24-hour  avilion  antry  stocks a variety of snacks  drinks and sundries '

相似度计算

df.index

RangeIndex(start=0, stop=152, step=1)

df.head()
nameaddressdescword_countdesc_clean
0Hilton Garden Seattle Downtown1821 Boren Avenue, Seattle Washington 98101 USALocated on the southern tip of Lake Union, the...184ocated on the southern tip of ake nion the...
1Sheraton Grand Seattle1400 6th Avenue, Seattle, Washington 98101 USALocated in the city's vibrant core, the Sherat...152ocated in the city s vibrant core the herat...
2Crowne Plaza Seattle Downtown1113 6th Ave, Seattle, WA 98101Located in the heart of downtown Seattle, the ...147ocated in the heart of downtown eattle the ...
3Kimpton Hotel Monaco Seattle1101 4th Ave, Seattle, WA98101What?s near our hotel downtown Seattle locatio...151hat s near our hotel downtown eattle locatio...
4The Westin Seattle1900 5th Avenue, Seattle, Washington 98101 USASituated amid incredible shopping and iconic a...151ituated amid incredible shopping and iconic a...
df.set_index('name' ,inplace=True)

df.index[:5]

Index(['Hilton Garden Seattle Downtown', 'Sheraton Grand Seattle','Crowne Plaza Seattle Downtown', 'Kimpton Hotel Monaco Seattle ','The Westin Seattle'],dtype='object', name='name')

tf=TfidfVectorizer(analyzer='word',ngram_range=(1,3),stop_words='english')#将原始文档集合转换为TF-IDF特性的矩阵。
tf

TfidfVectorizer(ngram_range=(1, 3), stop_words='english')

tfidf_martix=tf.fit_transform(df['desc_clean'])
tfidf_martix.shape

(152, 27694)

cosine_similarity=linear_kernel(tfidf_martix,tfidf_martix)
cosine_similarity.shape

(152, 152)

cosine_similarity[0]

array([1.        , 0.01354605, 0.02855898, 0.00666729, 0.02915865,0.01258837, 0.0190937 , 0.0152567 , 0.00689703, 0.01852763,0.01241924, 0.00919602, 0.01189826, 0.01234794, 0.01200711,0.01596218, 0.00979221, 0.04374643, 0.01138524, 0.02334485,0.02358692, 0.00829121, 0.00620275, 0.01700472, 0.0191396 ,0.02340334, 0.03193292, 0.00678849, 0.02272962, 0.0176494 ,0.0125159 , 0.03702338, 0.01569165, 0.02001584, 0.03656467,0.03189017, 0.00644231, 0.01008181, 0.02428547, 0.03327365,0.01367507, 0.00827835, 0.01722986, 0.04135263, 0.03315194,0.01529834, 0.03568623, 0.01294482, 0.03480617, 0.01447235,0.02563783, 0.01650068, 0.03328324, 0.01562323, 0.02703264,0.01315504, 0.02248426, 0.02690816, 0.00565479, 0.02899467,0.02900863, 0.00971019, 0.0439659 , 0.03020971, 0.02166199,0.01487286, 0.03182626, 0.00729518, 0.01764764, 0.01193849,0.02405471, 0.01408249, 0.02632335, 0.02027866, 0.01978292,0.04879328, 0.00244737, 0.01937539, 0.01388813, 0.02996677,0.00756079, 0.01429659, 0.0050572 , 0.00630326, 0.01496956,0.04104425, 0.00911942, 0.00259554, 0.00645944, 0.01460694,0.00794788, 0.00592598, 0.0090397 , 0.00532289, 0.01445326,0.01156657, 0.0098189 , 0.02077998, 0.0116756 , 0.02593775,0.01000463, 0.00533785, 0.0026153 , 0.02261775, 0.00680343,0.01859473, 0.03802118, 0.02078981, 0.01196228, 0.03744293,0.05164375, 0.00760035, 0.02627101, 0.01579335, 0.01852171,0.06768183, 0.01619049, 0.03544484, 0.0126264 , 0.01613638,0.00662941, 0.01184946, 0.01843151, 0.0012407 , 0.00687414,0.00873796, 0.04397665, 0.06798914, 0.00794379, 0.01098165,0.01520306, 0.01257289, 0.02087956, 0.01718063, 0.0292332 ,0.00489742, 0.03096065, 0.01163736, 0.01382631, 0.01386944,0.01888652, 0.02391748, 0.02814364, 0.01467017, 0.00332169,0.0023627 , 0.02348599, 0.00762246, 0.00390889, 0.01277579,0.00247891, 0.00854051])

求酒店的推荐

indices=pd.Series(df.index)
indices[:5]

0    Hilton Garden Seattle Downtown
1            Sheraton Grand Seattle
2     Crowne Plaza Seattle Downtown
3     Kimpton Hotel Monaco Seattle 
4                The Westin Seattle
Name: name, dtype: object

def recommendation(name,cosine_similarity):recommend_hotels=[]idx=indices[indices==name].index[0]score_series=pd.Series(cosine_similarity[idx]).sort_values(ascending=False)top_10_indexes=list(score_series[1:11].index)for i in top_10_indexes:recommend_hotels.append(list(df.index)[i])return recommend_hotels                                

recommendation('Hilton Garden Seattle Downtown',cosine_similarity)

['Staybridge Suites Seattle Downtown - Lake Union','Silver Cloud Inn - Seattle Lake Union','Residence Inn by Marriott Seattle Downtown/Lake Union','MarQueen Hotel','The Charter Hotel Seattle, Curio Collection by Hilton','Embassy Suites by Hilton Seattle Tacoma International Airport','SpringHill Suites Seattle\xa0Downtown','Courtyard by Marriott Seattle Downtown/Pioneer Square','The Loyal Inn','EVEN Hotel Seattle - South Lake Union']

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/pingmian/19065.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

C++：类和对象

C++：类和对象

一、前言 C是面向对象的语言&#xff0c;本文将通过上、中、下三大部分&#xff0c;带你深入了解类与对象。 目录 一、前言 二、部分&#xff1a;上 1.面向过程和面向对象初步认识 2.类的引入 3.类的定义 4.类的访问限定符及封装 5.类的作用域 6.类的实例化 7.类的…
阅读更多...
【busybox记录】【shell指令】readlink

【busybox记录】【shell指令】readlink

目录 内容来源&#xff1a; 【GUN】【readlink】指令介绍 【busybox】【readlink】指令介绍 【linux】【readlink】指令介绍 使用示例&#xff1a; 打印符号链接或规范文件名的值 - 默认输出 打印符号链接或规范文件名的值 - 打印规范文件的全路径 打印符号链接或规范文…
阅读更多...
（函数）颠倒字符串顺序（C语言）

（函数）颠倒字符串顺序（C语言）

一、运行结果&#xff1b; 二、源代码&#xff1b; # define _CRT_SECURE_NO_WARNINGS # include <stdio.h> # include <string.h>//声明颠倒函数; void reverse(char a[]) {//初始化变量值&#xff1b;int i, j;char t;//循环颠倒&#xff1b;for (i 0, j strl…
阅读更多...
【使用ChatGPT构建应用程序】应用程序开发概述：1. 管理秘钥、2. 数据安全、3. 与应用程序解耦、4. 注意提示语的注入攻击

【使用ChatGPT构建应用程序】应用程序开发概述：1. 管理秘钥、2. 数据安全、3. 与应用程序解耦、4. 注意提示语的注入攻击

文章目录 一. 首先注意的两个方面1. 管理API密钥1.1. 用户提供API密钥1.2. 你自己提供API密钥 2. 数据安全和数据隐私 二. 软件架构设计原则&#xff1a;与应用程序解耦三. 注意LLM提示语的注入攻击1. 分析输入和输出2. 监控和审计3. 其他要注意的注入情况 在了解了ChatGPT的文…
阅读更多...
一篇文章讲透排序算法之快速排序

一篇文章讲透排序算法之快速排序

前言 本篇博客难度较高&#xff0c;建议在学习过程中先阅读一遍思路、浏览一遍动图&#xff0c;之后研究代码&#xff0c;之后仔细体会思路、体会动图。之后再自己进行实现。 一.快排介绍与思想 快速排序相当于一个对冒泡排序的优化&#xff0c;其大体思路是先在文中选取一个…
阅读更多...
基于Python实现 HR 分析（逻辑回归和基于树的机器学习）【500010104】

基于Python实现 HR 分析（逻辑回归和基于树的机器学习）【500010104】

介绍 数据集说明 此数据集包含与员工有关的综合属性集合&#xff0c;从人口统计细节到与工作相关的因素。该分析的主要目的是预测员工流动率并辨别导致员工流失的潜在因素。 在这个数据集中&#xff0c;有14,999行&#xff0c;10列&#xff0c;以及这些变量&#xff1a;满意度…
阅读更多...
ClickHouse 与其他数仓架构的对比——Clickhouse 架构篇（四）

ClickHouse 与其他数仓架构的对比——Clickhouse 架构篇（四）

文章目录 前言ClickHouse与Hive的对比计算引擎的差异ClickHouse比Hive查询速度快的原因 ClickHouse与HBase的对比HBase的存储系统与ClickHouse的异同HBase的适用场景及ClickHouse不适合的原因 ClickHouse与Kylin的对比Kylin的架构Kylin解决性能问题的思路Kylin方案的缺陷ClickH…
阅读更多...
我觉得 “砍需求” 是程序员最牛逼的本领

我觉得 “砍需求” 是程序员最牛逼的本领

在下认为&#xff0c;不会 “砍需求” 的程序员不是好程序员&#xff0c;工作经验越丰富的程序员&#xff0c;砍需求的本领一般就越高。即使现在我多了一个身份 —— 管理团队&#xff0c;我也会帮开发同学去跟产品砍需求。 没错&#xff0c;从管理者的角度&#xff0c;我希望…
阅读更多...
web练习

web练习

[CISCN 2022 初赛]ezpop ThinkPHP V6.0.12LTS 反序列化漏洞 漏洞分析 ThinkPHP6.0.12LTS反序列漏洞分析 - FreeBuf网络安全行业门户 解题过程 ThinkPHP V6.0.12LTS反序列化的链子可以找到&#xff0c;找到反序列化的入口就行 反序列化的入口在index.php/index/test 链子 …
阅读更多...
latex中伪代码后面多出=0

latex中伪代码后面多出=0

这latex简直就是憨猪&#xff01;&#xff01;&#xff01; \usepackage{algpseudocode} 注释掉&#xff0c;或者删除就可以了 还有&#xff0c;引用包的时候一般begin{}中括号里是什么就引入什么包。 这下面这几行&#xff0c;开始全爆红说没定义&#xff0c;我就去一行一行问…
阅读更多...
【SPSS】基于因子分析法对水果茶调查问卷进行分析

【SPSS】基于因子分析法对水果茶调查问卷进行分析

&#x1f935;‍♂️ 个人主页&#xff1a;艾派森的个人主页 ✍&#x1f3fb;作者简介&#xff1a;Python学习者 &#x1f40b; 希望大家多多支持&#xff0c;我们一起进步&#xff01;&#x1f604; 如果文章对你有帮助的话&#xff0c; 欢迎评论 &#x1f4ac;点赞&#x1f4…
阅读更多...
P2341 受欢迎的牛

P2341 受欢迎的牛

题目描述 每一头牛的愿望就是变成一头最受欢迎的牛。现在有 N 头牛&#xff0c;给你 M 对整数&#xff0c;表示牛 A 认为牛 B 受欢迎。这种关系是具有传递性的&#xff0c;如果 A 认为 B 受欢迎&#xff0c;B 认为 C 受欢迎&#xff0c;那么牛 A 也认为牛 C 受欢迎。你的任务是…
阅读更多...
MATLAB学习：频谱图的绘制

MATLAB学习：频谱图的绘制

1.概述 时域信号经FFT变换后得到了频谱,在作图时还必须设置正确的频率刻度,这样才能从图中得到正确的结果。 2.案例分析 下面透过一个简单的例子来分析频谱图中频率刻度(横坐标)的设置的重要性。一余弦信号,信号频率为30Hz,采样频率100Hz,信号长128,在FFT后做谱图&#xff0…
阅读更多...
Iphone自动化指令每隔固定天数打开闹钟关闭闹钟(二)

Iphone自动化指令每隔固定天数打开闹钟关闭闹钟(二)

1.首先在搜索和操作里搜索“查找日期日程" 1.1.然后过滤条件开始日期选择”是今天“ 1.2.增加过滤条件&#xff0c;日历是这里选择”工作“ 1.3.增加过滤条件&#xff0c;选择标题&#xff0c;是这里选择”workDay“ 1.4选中限制&#xff0c;日历日程只要一个&#xff0c;…
阅读更多...
k8s部署calico遇到的问题

k8s部署calico遇到的问题

kubernetes安装calico calico官网 环境&#xff1a;centos7.9&#xff0c;calico 3.23&#xff0c;kuberadm 1.26 问题1&#xff1a;执行kubectl create -f calico.yml后报错如下 error: resource mapping not found for name: “tigera-operator” namespace: “” from “…
阅读更多...
echarts-dataset,graphic,dataZoom, toolbox

echarts-dataset,graphic,dataZoom, toolbox

dataset数据集配置数据 dataset数据集&#xff0c;也可以完成数据的映射&#xff0c;一般用于一段数据画多个图表 例子&#xff1a; options {tooltip: {},dataset: {source: [["product", "2015", "2016", "2017"],["test&q…
阅读更多...
HTTP Basic Access Authentication Schema

HTTP Basic Access Authentication Schema

HTTP Basic Access Authentication Schema 背景介绍流程安全缺陷参考 背景 本文内容大多基于网上其他参考文章及资料整理后所得&#xff0c;并非原创&#xff0c;目的是为了需要时方便查看。 介绍 HTTP Basic Access Authentication Schema&#xff0c;HTTP 基本访问认证模式…
阅读更多...
ThreadLocal：熟悉的陌生词，你应该要知道的。

ThreadLocal：熟悉的陌生词，你应该要知道的。

Hi,大家好&#xff0c;我是抢老婆酸奶的小肥仔。 在很多的地方&#xff0c;我们都能看到ThreadLocal的身影&#xff0c;也会用到它&#xff0c;但是我们真的就了解它吗&#xff1f; 今天我们来叨叨这个我们既熟悉又陌生的小伙伴&#xff0c;废话不多说开整。 1、啥是ThreadL…
阅读更多...
云原生架构内涵_3.主要架构模式

云原生架构内涵_3.主要架构模式

云原生架构有非常多的架构模式&#xff0c;这里列举一些对应用收益更大的主要架构模式&#xff0c;如服务化架构模式、Mesh化架构模式、Serverless模式、存储计算分离模式、分布式事务模式、可观测架构、事件驱动架构等。 1.服务化架构模式 服务化架构是云时代构建云原生应用的…
阅读更多...
[ C++ ] 深入理解模板( 初 阶 )

[ C++ ] 深入理解模板( 初 阶 )

函数模板 函数模板格式 template <typename T1, typename T2,......,typename Tn> 返回值类型 函数名(参数列表){} 注意&#xff1a; typename是用来定义模板参数关键字&#xff0c;也可以使用class(切记&#xff1a;不能使用struct代替class) 函数模板的实例化 模板参数…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023