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这一个好像就够用了\n\n","description":"","id":"https://blog.littleji.com/blog/20160510somethingaboutsitemanagement/","path":"/blog/20160510somethingaboutsitemanagement/","title":"建站中的一些问题"},"https://blog.littleji.com/blog/20160512adjacencylistandadjacencymatrix/":{"body":"\n邻接矩阵\n\n概述\n邻接矩阵,其只要是用来存储一个图结构.\n首先一个无向图如下所示\n\n\n\n\n\n\n\n性质\n\n无向图的邻接矩阵对称矩阵\n由上图发现该图其实是个对称矩阵,这是由无向图的性质决定的,无向图的各个顶点之间的连线是具有相互性的,于是每有一个顶点之间的连线就要延伸出一组关系,表现在邻接矩阵上就是对称点的值相同(a12 = a21 依次类推)\n\n邻接矩阵的大小=顶点数*顶点数\n很容易了解,一个图的顶点数决定了其对应邻接矩阵的大小,例如上图是个6个顶点的图,则对应的矩阵需要6*6的大小方可表达其结构\n\n数据压缩的属性\n\n由于事实上每一组顶点关系只需要一位来来表达,处理一个有4个顶点的图仅仅需要 44/8个=2个字节,这是很节省空间的,如果是一个无向图的话,所需要的空间则可以再减少一半的空间 44/16个=1个字节,通过这个方法已经接近信息论中表达一个n个顶点所需要字节数的下界.\n但是也存在一些问题,例如其可能需要存储那些本来不存在的边\n\n\n邻接链表\n\n概述\n邻接链表也是一种表达图的方式\n假设有无向图如下所示\n \n\n \n则相对应的表示为三个list{b,c},{a,c},{a,b}\n但是换出去空间的节省后,查询时间也会相应的边长\n例如:查询a与b是否有关系需要首先定位到a的链表{b,c},再对这个链表进行遍历,如果没有b,也就说明对其进行了完整的遍历,浪费了较多的时间,当然可以采用快速搜索的方式进行优化\n\n事实上邻接列表还有一点是其应用在大型稀疏矩阵中,因为邻接矩阵不需要浪费空间来表达那些不存在的边缘\n\n\n参考:\n\nAdjacency_matrix\nAdjacency_list\n\n","description":"","id":"https://blog.littleji.com/blog/20160512adjacencylistandadjacencymatrix/","path":"/blog/20160512adjacencylistandadjacencymatrix/","title":"邻接矩阵,邻接列表一些基本概念和应用(Adjacency list and Adjacency matrix)"},"https://blog.littleji.com/blog/20161115almostperfectgitconfiguration/":{"body":"\n概述(abstract)\nGithub最近又开始不稳定,而在bower等安装的过程中会利用到其上面的源, 在包的下载总会因为网络或者的问题,导致中断安装,而每次重新安装的时间较长.\n为了让大家不再因为此浪费时间,给大家一些可能有用的配置建议.\n\n具体配置(configuration)\n\n卸载和安装最新(remove and update git)\n\n\n使用较快的hosts(use faster host for github.com)\n\n","description":"","id":"https://blog.littleji.com/blog/20161115almostperfectgitconfiguration/","path":"/blog/20161115almostperfectgitconfiguration/","title":"Centos7下git较为完整安装与配置,解决git clone速度慢等问题(Almost perfect git configuration)"},"https://blog.littleji.com/blog/20161128don-tforgettheresultmapwhenyouusemybatis/":{"body":"\n问题\n最近需要使用Mybatis来操作MySQL,由于入门就随意的拿来一篇入门介绍和官方的教程,照着走一遍,奈何写好了Mapper,Bean,以及配置好数据库,以及对应的xml后会发现无论如何也无法得到实例化后的Bean.事实上,并没有一些bug信息,也没有抛异常.\n\n解决1\n判断是否是数据库本身的连接的问题.\n尝试更改mybatis的数据库mysql配置 => 数据库连接没问题\n\n解决2\n判断是否是数据库得命令没有正常执行\n查找mysql的general日志,发现没有,于是查看general的设置使用下面的命令\n\n返回下面的信息\n\n打开general_log\n\n如果需要自定义日志的路径,同理设置不再赘述.\n使用tail -F /tmp/mysql/general.log 监控mysql的查询信息\n运行自己得mybatis 测试用例,可以看到上面的命令打印到了控制台,结果如下:\n\n说明数据库的命令执行也没问题\n\n解决3\n回想起,自身项目与参考的项目唯一的不同就是Bean不同,MySQL表不同\n将Bean中的属性名称完全与MySQL一一对应,运行后解决\n\n总结\n查看mybatis的Result_Maps文档,发现定义了一个Java Bean后,select语句会精确的匹配JavaBean中的属性,然后映射到结果集.这个就是关键所在,当然可以不完全的对应起来,不过需要 ResultMap来,使用select语句的别名,最终匹配到对应的属性上,如下所示\n\n\n参考\nhttp://www.mybatis.org/mybatis-3/zh/sqlmap-xml.html#Result_Maps\n","description":"","id":"https://blog.littleji.com/blog/20161128don-tforgettheresultmapwhenyouusemybatis/","path":"/blog/20161128don-tforgettheresultmapwhenyouusemybatis/","title":"别忘了使用Mybatis ResultMap(Don't forget the ResultMap when you use Mybatis)"},"https://blog.littleji.com/blog/20161128usemultilineplugintodecodeawholejsonfile/":{"body":"\n概述(abstract)\n如果希望得到配置文件请直接查看\"配置(configuration)\" if you just want to get the configure file of logstash,so just look at \"Configuration\".\n最近需要通过logstah处理json格式的日志,最好的结果是配置配置Logstash中的config,用用其他轮子插件,自动化的解析.例如这样一段待分析的信息:\n\n最终解析得结果如下:\n\n\n问题1(trouble No.1)\n在处理日志的时候会遇到这样的问题:logstash 默认会将所监视文件的每一行进行拆分,并单独的作为一个文事件于是就有一个完整的json file 被拆成了数个事件,这违背初衷,所以首先解决其自动拆分的问题.\nmultiline的插件就是为此而生,下面贴出multiline的简单的配置:\n\n\n解释1\npattern:表示组成多行的json中, 每一行都具有什么样的特征,这个特征通过正则表达式进行匹配,如果匹配成功则认定此为多行块中的一行\nwhat:有两个值 \"previous\"和\"next\", 如果是previous,则将该行归到上面一个多行块中,如果是next,则归到下面一个代码块中\nmax_age:如果没有再有新行添加到多行中,那么在max_age后,这个多行块将被推送,默认是5,单位是秒\n\n问题2(trouble No.2)\n现在我们通过multiline插件获得了一个多行json块,剩下的就是将这个json串解析.\n这个比较简单,logstash 中的filter有 json 这个插件,配置上就可以搞定,前提是之前的json必须是一个合规的json串\n下面贴出其配置项\n\n\n解释2\nsource 就是存储json串的字段,默认是message字段\n最好是将该字段分析后删除,可以使用mutate这个插件,并开启remove_field这个选项,否则不仅分析后的数据进入到了es,原json串也进入,导致存储的数据,占用双倍的空间.\n并且该选项也是支持正则表达式的.\n\n\n完整的配置\n\n\n参考\nMultiline Plugin\n","description":"","id":"https://blog.littleji.com/blog/20161128usemultilineplugintodecodeawholejsonfile/","path":"/blog/20161128usemultilineplugintodecodeawholejsonfile/","title":"使用LogStash中的multiline插件来进行json文件解析(use the multiline plugin to decode a whole json file)"},"https://blog.littleji.com/blog/20161130usejavaapiforelasticsearch1/":{"body":"\n配置(configuration)\n\nMaven\n要使用相应的API,必须引入所需要的jar包,这里使用的ElasticSearch5.0版本相应的Maven配置如下所示:\n\n\n日志(Logger)\n之后配置日志,这里使用的是log4j2,在src/main/resources下添加名为log4f2.properties的文件,并在其中添加如下的内容:\n\n\n整体打包(Embedding jar with dependencies)\n将所使用的jar依赖包和你的应用同时打包为一个jar文件,此时不应该使用maven-assembly-plugin, 因为没有Lucene的jar包,导致该插件无法解析META-INF/service的结构,相应的你可以使用maven-shade-plugin插件,配置如下所示\n\n如果有main.class 可加入下面的配置:\n\n\n使用客户端(Client)\n客户端可以做以下几件事情:\n\n在已有的集群之上进行,索引,插入,删除,搜索等操作\n在已有的集群上运行管理员的任务\n\n\n注意: 客户端的主版本号必须同集群节点的版本号相一致\n一般通过TransportClient 来连接ES集群\n\n\nTransportClient\n当你使用TansportClient来连接一个ES集群的时候,你并不参与到这个集群中,而是获得这个集群的一个或多个地址,并在实际执行操作的时候,依次的操作它们,尽管大多数的操作都可能是两段式的(It does not join the cluster, but simply gets one or more initial transport addresses and communicates with them in round robin fashion on each action(though most actions will probably be \"two hop\" operations).)\n下面是如何开始和结束一个TransportClient\n\n其中得Setting,按照下面所示进行设置\n\nTransportClient 自身有一个集群发现的功能,其能够动态的添加host和移除之前已有的.\n一旦发现功能启用,transport client就将根据其配置的节点列表进行连接,而节点列表的配置则是通过addTransportAddress来配置的.之后,客户端将调用集群内部的状态API,来发现可用的数据节点.内部客户端的节点列表将替换为只有数据节点,并且这份列表默认每过5秒,刷新一次\n如果需要开启发现功能,设置client.trasport.sniff为true\n\n参考\nElasticSearch Java API\n","description":"","id":"https://blog.littleji.com/blog/20161130usejavaapiforelasticsearch1/","path":"/blog/20161130usejavaapiforelasticsearch1/","title":"使用ElasticSearch的JavaAPI 1(UseJavaApiForElasticSearch 1)"},"https://blog.littleji.com/blog/20161201usejavaapiforelasticsearch2/":{"body":"\n单独文档的APIs\n\nIndex API\n\n生成Json\n\n手动\n使用Map\n使用第三方库如Jackson\n使用內建的jsonBuilder()\n\n\n建立索引文档\n下面的例子是建立一个索引为twitter,类型为tweet,id为1的文档,\n\n或者通过另一种建立json的方式\n\n之后你可以通过调用上面的response来查看建立的结果\n\n\nGetAPI\n下面使用client调用 index是 twitter type是tweet id为1的文档\n\n更多的getapi则需要参考get docs\n其它的delete update multiget bulkapi都需要查看es的相应rest调用文档\n\nBulkAPI\nbulk API 允许你通过一个请求来检索或者删除多个文档,增加吞吐量,下面是一个应用:\n\n\n使用Using Bulk Processor\nBulkProcessor类提供了一个简单的接口可以通过请求的数量或者给定的时间来自动的进行flush操作.\n使用这个类,首先要创建一个BulkProcessor实例:\n\n其中的setConcurrentRequests(1)指同时允许多少个并发,0的话意味着不允许,1则表示允许一个并发请求.\nsetBackoffPolicy则可以允许用户自定义当一个或者多个bulk请求失败后,该执行如何操作,而这个失败是要求基于EsRejectedExecutionException,也就是说集群内的计算资源不够导致的请求失败,如果不设定的话使用BackoffPolicy.noBackoff()\n当所有的需要执行的操作加载到bulk中之后,可使用两种方式来关闭这个bulk\n\n这两个操作都会flush所有的剩余bulk操作,前者会等待一段时间,如果在这段时间 flush成功,则返回true 否则返回false,\n后者则会直接退出,不在等待所有的bulk执行操作完成\n\n参考\nElasticSearch Java API\n","description":"","id":"https://blog.littleji.com/blog/20161201usejavaapiforelasticsearch2/","path":"/blog/20161201usejavaapiforelasticsearch2/","title":"使用ElasticSearch的JavaAPI2(UseJavaApiForElasticSearch2)"},"https://blog.littleji.com/blog/20161206quickstartforjsonrpc4j/":{"body":"\n概述\n目前有个项目需要向外提供服务:\n\n不打算用spring相对来说比较重的框架\n后来考虑过jersey+jetty的restful框架,但是restful本身的应用场景,或者说是抽象较为局限,主要是编者水平有限\n再之后又看了thrift的使用,稍微上手,认为操作还是较为繁杂.\njson-rpc以前并没有接触过,json相对来说又有传输占用带宽较小等广为人知的优点,便以此篇为个契机学习一下.\njsonrpc4j是briandilley开发,项目的地址是github\njsonrpc4j 服从json-rpc的规范,规范链接在此\n其主要支持下面这4类客户端:\nStreaming server (InputStream \\ OutputStream)\nHTTP Server (HttpServletRequest \\ HttpServletResponse)\nPortlet Server (ResourceRequest \\ ResourceResponse)\nSocket Server (StreamServer)\n这次主要使用的是HTTP Server\n\n\nMaven\njsonrpc项目通过Maven构建,首先需要在pom.xm中加入对jsonrpc4j的依赖,如下面所示:\n\n但是仅仅加入上面的依赖还不够,完整的pom.xml如下所示:\n\n\n配置测试用的model,service\n\nmodel\n\n\nService和其Implement\n\n\n\n配置server端\n\n\nServlet的web.xml\n\n\n测试类\n\n\nStreaming server and client\njsonrpc4j 主要是通过流服务器和客户端来处理应用(不仅仅是HTTP)的请求,而JsonRpcClient和JsonRpcServer有一些简单的方法来获得输入和输出流,且在库中附带着一个JsonRpcHttpClient,用来扩展JsonRpcClient对于HTTP的支持\n\nWithout the Spring Framework\njsonrpc4j可以不使用spring框架,事实上二者均可运行于Android环境中\n\n对测试的解释\n其实测试类就是一个客户端的简单使用\n下面是一个客户端使用JSON-RPC服务的例子,具体如下:\n\n或者使用ProxyUtil类结合接口,来创建一个动态的代理,如下所示:\n\n\n参考\njsonrpc4j\nJSON-RPC（jsonrpc4j）使用demo\n","description":"","id":"https://blog.littleji.com/blog/20161206quickstartforjsonrpc4j/","path":"/blog/20161206quickstartforjsonrpc4j/","title":"jsonrpc4j初步使用(Quick Start For Jsonrpc4j)"},"https://blog.littleji.com/blog/20161212usejsonrpcformattogetwhatyouwant/":{"body":"\n概述\n本文承接上文的jsonrpc4j初步使用,在知道了如何部署Server端以及通过Java的接口调用部署的服务后,下面就是通过json-rpc本身的标准来实际的调用rpc借口和处理返回的相关信息.\n\njson-rpc的标准详细说明\n这方面具体请参考:\n\n中文\nEnglish\n这里拿出一些必要且简单的说明.\n\n\nrpc请求所必要的参数\n\n请求的对象需要通过json的格式发往server端\n请求对象需要包括下面四个对象\njsonrpc:来说明所使用的JSON-RPC的版本\nmethode:来说明说调用的方法名称\nparams:调用方法的结构化参数值\nid:来唯一的表明发起的请求,客户端同样通过这个id来唯一的表示响应的信息,这个id可以是字符串,数字和NULL.如果没有这个值则认为这个请求是一个通知\n\n\nrpc响应所必要的参数\n\njsonrpc:同请求\nresult: 如果一个请求成功的调用了方法,则必须有这一项,否则不必有这一项\nerror:错误的对象.如果一个方法失败了,必须有该项,否则不必有\nid:该成员必须有,同请求.\n\n\n错误对象所必要的参数\n\ncode:使用整数来表明的异常错误类型,-32768到-32000为保留的错误代码,详细的错误代码所对应的解释请参考 中文\nmessage:描述错误信息\ndata:包含错误复杂信息的成员,可忽略.\n\n\n使用curl来测试已经建立的json-rpc服务\n\n需要按照前文,建立好对应的服务\n\n\n测试单个请求\n通过使用curl来发送http的请求,当然也可以通过java的HttpClient来发送,假设RpcServer部署相应的服务,且url为/rpc,我们使用server端的\"getString\"方法,server端部署在192.168.1.10的8080端口上,那么代码如下\n\n可以看到服务端返回这样的信息\n\n说明发送的信息不是一个合法的JSON串,检查后发现因为curl发送数据的问题,需要对引号进行转义,更改后的请求命令如下:\n\n服务端返回信息:\n\n信息正常的返回了,说明调用成功\n\n测试批的方式发送请求\n原理非常简单,只需要将发送的一组请求使用数组的形式括起来即可,代码如下:\n\n服务端返回:\n\n","description":"","id":"https://blog.littleji.com/blog/20161212usejsonrpcformattogetwhatyouwant/","path":"/blog/20161212usejsonrpcformattogetwhatyouwant/","title":"json-rpc的标准调用(UseJsonRPCFormatToGetWhatYouWant)"},"https://blog.littleji.com/blog/20161219mysql-1146-1017/":{"body":"\n概述(abstract)\n问题的产生原因是由于在Windows下使用Navicat客户端,操作位于CentOS上MySQL 5.6时,具体的执行一个重命名字段的操作时,Navicat停止了运行,然后将该Navicat强制的关闭后,再运行Navicat打开刚才重命名字段所在的表,再次卡死,反复操作几次,结果并没有改变.\n\n解决1(figure it 1)\n首先,去MySQL数据库所在的服务器,对MySQL服务进行重启\n\n再去用Navicat操作MySQL ,报 ERROR 1146 table '' doesn't exist 的错误\n\n解决2(figure it 2)\n之后,通过查阅stackoverflow,可能是数据库文件权限的问题,然后照葫芦画瓢,使用下面的命令对其进行修改:\n\n直接报,没有该文件\n\n解决3(figure it 3)\n需要通过下面的命令查询mysql data文件具体在什么位置\n\n找到下面这行\n\n再通过语句修改对应数据表所在数据库的权限,比如出现错误的表所在数据库的名字是'tmp_db'\n\n重新启动数据库\n\n\n参考\nstackoverflow#1\n","description":"","id":"https://blog.littleji.com/blog/20161219mysql-1146-1017/","path":"/blog/20161219mysql-1146-1017/","title":"使用navicat 修改MySQL数据库的某个表后,无法再读取该表(MySQL-#1146-#1017)"},"https://blog.littleji.com/blog/20170510mybooklist/":{"body":"\n算法\n程序员实用算法 原书\n算法导论->大餐 9.4\nAlgorithms Unlocked->开胃\n算法引论----一种创造性方法（建议先看第5章，真是爽爆了）\nThe Science of Programming\n求解数学题《怎样解题》\n计算机程序设计艺术 9.3\n基础数学 9.5\n怎样解题 9.1\nAn Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition 9.6\n如何求解问题 8.5\n数理逻辑 8.9\n集合论基础 9.1\n概率论及其应用 9.1\n图论导引 8.8\n来自圣经的证明 9.4\n\n计算机原理\n计算机程序的构造与解释 9.5\n领域特定语言 9.1\n编译原理 9.1\n\nJava\nEffective Akka\nJava 8 in action\njava puzz\njava 并发编程实战 9.0\neffective java 9.1\n\nPython\nLearning Python\nPython Programming\n\nC/C++\nEffective C++/STL\n\n数据库\n高性能MySQL 第三版\n\n安全\n白帽子讲web安全 8.2\n黑客攻防技术宝典：web实战篇(第2版) 8.6\n加密与解密(第3版) 8.4\nWeb之困 - 现代Web应用安全指南\nWeb前端黑客技术揭秘\n码的奥秘 9.2\nWeb安全深度剖析 7.9\nSQL注入攻击与防御（第2版） 7.8\n精通脚本黑客 8.0\n0day安全 （第2版） 8.4\n社会工程 8.0\n\n软件工程\n教科书:构建之法构建之法中提到的书单\n参考书:代码大全 Rapid Development 快速软件开发 项目管理艺术 人件\n敏捷软件开发 原则，模式与实践\n硝烟中的Scrum和XP : 我们如何实施Scrum 8.6\n认知与设计 8.6\n门后的秘密 8.2\n点石成金 8.5\n启示录 8.5\n项目百态 8.3\n精益开发实战 8.3\n敏捷武士 7.7\n敏捷软件开发 9.1\n持续交付 8.6\n精益企业 8.8\n精益思想 8.6\n看板方法 7.8\n系统的思考 8.0\n管理3.0 8.0\n第五项修炼\n\nUI设计\n简约至上 8.4\n\n营销\n增长黑客 7.9\n\n标准\nHTTP 权威指南 8.7\nTCP/IP详解 9.2\n\n软件设计\n设计模式 : 可复用面向对象软件的基础 9.4\n企业应用架构模式 9.0\n软件框架设计的艺术\n发布!软件的设计与部署 7.7\n实现领域驱动设计 8.8\n\n运维\n网站运维 8.3\n\n测试\nGoogle软件测试之道 8.1\n#程序员实践\n卓有成效的程序员\n高效程序员的修炼\n程序员的思维训练 8.3\n程序员的职业素养 8.6\n高效程序员的45个习惯——敏捷开发修炼之道\n程序员修炼之道——从小工到专家\n重构 9.0(《重构与模式》一样，充其量只是一本案例集，无法达到《重构》的高度)\n编写可读代码的艺术 8.6\n编程珠玑 9.2\n编码 9.2\n\n程序员泛读\n哥德尔、艾舍尔、巴赫 9.4\n暗时间 8.4\n把时间当朋友\n影响力 8.6\n黑客 8.3\n编程人生 8.4\n","description":"","id":"https://blog.littleji.com/blog/20170510mybooklist/","path":"/blog/20170510mybooklist/","title":"我的程序员书单(My Programmer Book List)"},"https://blog.littleji.com/blog/20180117bitcoin/":{"body":"\n比特币白皮书\n\n0x00摘要\n一个真正意义上的点对点的在线电子交易系统，应该满足交易的双方在不需第三方金融机构介入的情况下进行。虽说数字签名技术，部分解决了这个问题，但是又需要面临，在离线交易过程中可能出现的“重复支付”（double-spending ）问题。我们在这里给出了一种解决的办法，这种方法将网络时间戳通过hash转换，加入到一种基于hash的、持续伸展的“工作证明链”（以下简称工作链）中。攻击者除非能够将之前所做的工作再做一遍，否则无法更改这个工作证明链中已有的工作证明记录。也就是说，最长的工作链一方面证明了其见证了最多的交易记录，另一方面也说明了其所掌握的CPU计算能力（译注：HASH的计算能力）。只要大多数的CPU计算能力掌握在各个交易的个体手中，那么攻击者无法对该交易网络进行攻击。当然，这个交易系统也需要最基本的结构，个个节点也会最大范围的通知自己所记录的交易行为。由于最长的工作链存有所有的交易记录，所以每个节点可以在任意的时间脱离或者回到这个交易系统，而不会丢失其未曾见证的交易记录。\n\n0x01说明\n目前，互联网上的电子商务几乎完全依赖作为可信第三方的金融机构来处理电子支付。在大多数情况下，该交易模式可以应对，但它仍然受到基于信任模型的固有弱点的影响。比如，不可避免的调解纠纷，而调解成本又增加了交易成本，限制了最小的实际交易规模，并且切断了小型临时交易的可能性。尤其是，当需要运作而又无法正常的运作一笔“不可逆的交易”时，交易成本会大大的增加。由于该系统的提供信息有可能存在掺了假的或者过时的信息，所以交易的双方都需要花更多的成本在获取双方的信任上，进一步增加了交易的成本。这种交易机制在没有任何可信任的第三方见证的情况下，交易无法进行下去。\n事实上，目前所需要的是一种基于密码证明而不是信任的电子支付系统，这个系统允许任何两个愿意相互交易的双方，在不需要可信任的第三方的情况下进行交易。一方面，由于在实施欺骗需要付出巨大的计算代价，从而保护了卖家免受欺诈；另一方面，使容易被采用的常规托管机制，来保护买家。在本文中，我们提出了一个解决重复支付问题的解决方案，这个方案使用分布式时间戳服务器来顺序生成基于交易时间的工作证明。并且只要该系统中的所有坏人节点的计算能力不超过系统内的其他节点，那么就能保证这个系统是安全的。（译注：51%攻击）\n\n0x02交易\n这里定义一种电子货币保存一系列的数字签名。在交易的过程中，交易中的付款方首先将之前的交易与付款方的公钥一同进行hash，最终添加到付款方所持有的电子货币中，并传给收款方，收款方使用付款方的公钥对该电子货币进行验证是否为收款方的身份。\n问题在于，收款方无法确认付款方是否多花了钱。一般的解决方法是引入一个可信的第三方，或者说造币厂，对每一笔交易进行审查。具体过程是，每一笔交易发生时，付款方将所持有的货币传回给造币厂，造币厂则将这个货币摧毁，产生一个新的货币给收款方。这样以来，整个交易的过程安全性完全依赖于造币厂。\n而我们需要一种让收款者知道付款方没有进行二次交易。为了这个目的，我们可以在交易的过程中认为，只有最早的那笔交易是算数的，这样避免了二次交易情况的发生。收款方通过遍历所有的交易来验证付款方发出交易请求而收款方没有收到这一情况。要想脱离造币厂的模型，交易必须被广而告之，并且需要一个系统来帮助所有的参与者决策交易的到达顺序，收款方在判断一个交易的到达时间时需要这个系统内的其他节点进行投票决定，少数服从多数。\n\n0x03时间戳服务器\n时间戳服务器是一切的开始，时间戳服务器工作原理是对一个block进行hash，并对其结果进行广播。建立时间戳服务器的意义主要是为了证明某一个数据在一个时间点一定存在。在组成一个链的时候，每一个时间戳的生成都会考虑到之前时间戳的信息。\n\n参考\n比特币白皮书\n","description":"","id":"https://blog.littleji.com/blog/20180117bitcoin/","path":"/blog/20180117bitcoin/","title":"比特币白皮书"},"https://blog.littleji.com/blog/20180907gettingstartedwithi3windowmanager/":{"body":"注：本文首发于linux中国\n本篇文章会介绍如何在Fedora系统中，安装、配置、使用i3桌面管理器。\n\n在我的文章i3 桌面让Linux更好的5个理由，我分享了选择 i3 桌面管理器 作为一种Linux桌面替代方案的最主要五个理由。\n在本篇文章中，我将向大家展示，如何在 Fedora 28 上安装与配置 i3。\n\n1. 安装\n首先进入 Fedora 系统中，打开一个终端。使用 dnf 来安装需要的软件包，如下：\n\n注意：在这个命令中，我排除了 rxvt-unicode 这个包，因为我更喜欢 terminator 作为我的终端模拟器。\n据用户目前的系统状态，在命令执行过程中可能会安装很多依赖。等待所有的依赖安装完成，之后重启你的电脑。\n\n2. 登录与初始化\n在你的机器重启之后，你便可以第一次体验 i3 了。在 GNOME Display Manager （GDM），选择你的用户名，之后先别着急输密码，点击下方的密码输入框下方的小齿轮，之后选择 i3 ，像下方这样：\n\n输入你的密码，并点击 Sign In。在你第一次登入之后，会先看到 i3 的配置界面：\n\n点击 ENTER 就会在 $HOME/.config/i3 生成一个配置文件，之后你可以通过这个配置文件来定制化 i3's 的一些行为。\n在下一屏，你需要选择你的 Mod 键。这一步很关键，因为 Mod 键通常都会作为 i3's 命令快捷键的发起键。按 ENTER 会选择 Win 键作为默认的 Mod 键。如果你的键盘没有 Win 键，用 Alt 键做替代，用方向键键选择后按 ENTER 确认。\n\n现在你就登录到了 i3 的系统中。由于 i3 是一个最小化的窗口管理器，你会看到一个黑屏窗口，以及屏幕底端显式的状态栏：\n\n接下来，让我们看看 i3 的如何实际使用。\n\n3. 基本的快捷键\n现在你已经登录到了 i3 的会话中，你需要几个基本的快捷键来应对基本的操作。\n大多数的 i3 快捷键都会用到之前配置的 Mod 键。在下面的例子中，当我提到 Mod 键，请根据情况使用你定义的做替换。通常使用 Win 键或者 Alt 键。\n首先，打开一个终端，使用 Mod+ENTER。重复打开几个终端，观察 i3 是如何自动将它们在桌面中排列。默认情况下， i3 会在水平的方向分割屏幕；使用 Mod + v 来垂直分割，再按 Mod + h 会恢复水平分割模式。\n\n当需要启动其他的应用，按 Mod + d 来打开 dmenu，一个简单的文字应用菜单。默认情况下，dmenu 会呈现出所有在你 $PATH 中设置的应用。使用方向键来选择你想启动的应用，同时你可以键入应用的名称，来缩小选择的范围，之后按 ENTER 来启动选择的应用。\n\n如果你的应用没有提供退出的方法，你可以使用 i3 来关闭对应的窗口，通过按 Mod + Shift +q。注意，你可能会丢失未保存的工作内容。\n最后，当你想关闭会话并退出 i3，按 Mod + Shift +e。之后会在窗口的上方提示你是否退出。点击 Yes, exit i3 退出，或选择 X 来取消。\n\n这些就是 i3 中最基本的快捷键，如果想了解更多，请查阅官方文档 documentation。\n\n4. 替换GDM\n使用 i3 window manager 会降低你操作系统的内存占用；然而，Fedora 依然会使用 GDM 作为登录的窗口。GDM 会载入几个与 GNOME 相关的库从而占用内存。\n如果你想进一步的降低你的内存占用，你可以使用一些更轻量级的窗口管理器来替换 GDM，比如 lightdm：\n\n之后重启你的机器来使用 Lightdm 的登录界面。\n现在你可以继续登录并使用 i3了。\n\n\nvia: https://opensource.com/article/18/8/getting-started-i3-window-manager\n作者：Ricardo Gerardi\n选题：lujun9972\n译者：littleji\n校对：校对者ID\n本文由 LCTT 原创编译，Linux中国 荣誉推出\n","description":"","id":"https://blog.littleji.com/blog/20180907gettingstartedwithi3windowmanager/","path":"/blog/20180907gettingstartedwithi3windowmanager/","title":"在Linux上使用 i3 作为窗口管理器的入门教程(i3-tiling-window-manager)"},"https://blog.littleji.com/blog/20180925installdvwaonalpinefromscratch/":{"body":"\n背景\n\nalpine系统\n目前 alpine 系统已经作为 docker 容器的默认安装系统，其最大的特点是安装的体积非常小，镜像的大小最少只有 5M ，这对于在 docker 集群中大规模使用容器的情况下，极大的降低了磁盘占用，并且其还有：\n\n因体积较小，从而提升了启动速度\n2.因体积小，组件少，从而暴露面降低，最终提升系统的安全\n\n当我们能够熟练的配置该系统之后，可以大大加快 devops 的整体流程，迅速搭建原型系统，提高资源的利用率。\n\nDVWA\nDVWA:Damn Vulnerable Web Application\n老牌的安全测试靶场，后端使用的是 PHP + MySQL 的组合，可以方便快捷的让安全从业者，对常见的安全漏洞的原理进行学习，并在实践中巩固自己的学习成果。\nDVWA的官网在这里。\n\n安装alpine系统\n首先从 alpine 的官网，根据需要下载指定的alpine安装包，这里选择了 3.8.1 的Extend版本。\n之后，可以将下载的iso文件制作成U盘启动器进行安装，这里由于演示使用 VMware Fusion 进行安装演示。\n虚拟机的配置为默认配置如下：\n\n\n启动后直接使用 root 账号登录，并输入 setup-alpine ，进入配置界面，这里的操作的目的是将目前运行在内存中的系统，配置完整后安装至实际的硬盘上，如下图所示：\n\n图中可根据自己的需要配置，如键盘的布局、主机名、网卡信息、时间服务器组件、root密码、时区等，本文选择时区为Asia/Shanghai，重点是选择安装的实际物理磁盘（ sda ）以及安装的用途（ sys ）\n\n安装完成后 reboot\n\n安装系统必备组件\n\n安装Bash、Vim\n\n\n开启远程root登录的权限以及扩展的包镜像源\n方便直接通过远程终端进行操作，打开vim /etc/ssh/sshd_config，在配置文件的末尾添加：\n\n重启 ssh 服务，service sshd restart\n\n开启community testing edge包仓库\n\n将其中所有对应的库链接注释打开\n\n安装配置 MySQL/MariaDB\n\n安装MariaDB\n\n\n配置MariaDB\nMariaDB安装后还不能直接使用，需要创建用户等配置工作\n\n设定环境变量，稍后使用\n\n\n启动配置MariaDB\n\n\n启动 MariaDB 并添加默认启动\n\n\n改 root 密码\n\n\n创建一个dvwa用户，以及dvwa数据库\n建议添加该用户，不建议使用 MariaDB 的 root 账户登录MariaDB\n\n\n重启主机，验证是否安装完成\n\n\n安装PHP 以及所需要的库组件\n\n\n安装配置 lighttp\n\n取消掉注释include \"mod_fastcgi.conf\"\n\n替换/usr/bin/php-cgi 为 /usr/bin/php-cgi5\n打开对应的选项，便于漏洞学习\n\n找到allow_url_open 和 allow_url_include 均设置为 On\n启动Lighttpd\n\n\n安装配置 DVWA\n\n创建项目文件夹\n\n\n下载DVWA\n\n\n将DVWA放到指定的web项目目录中\n\n\n配置DVWA\n\n设置用户名 密码 以及端口号\n\n\n验证安装情况\n打开http://IP/dvwa/setup.php\n查看 Setup Check 各项应均为绿色\n\n点击Create/Reset Database，创建对应的数据库，安装完成\n\n参考\nhttps://wiki.alpinelinux.org/wiki/Damn_Vulnerable_Web_Application_(DVWA)\nhttps://wiki.alpinelinux.org/wiki/MariaDB\n","description":"","id":"https://blog.littleji.com/blog/20180925installdvwaonalpinefromscratch/","path":"/blog/20180925installdvwaonalpinefromscratch/","title":"在alpine上，从零开始安装DVWA(Alpine for DWVA from Scratch)"},"https://blog.littleji.com/blog/20181016formallanguagegenerality/":{"body":"\n背景\n形式语言作为精确描述语言的工具，广泛的应用在机器翻译等自然语言处理的领域\n描述语言的常用三个方法：\n\n穷举法，对于句子数目有限的语言可以使用这种方法将所有的句子进行枚举，从而定义。\n文法，可以理解为我们在英文、中文中学习的语法，通过严格定义规则，来生成合法的句子，重点在于生成句子。\n自动机，不同于文法，自动机更偏向与对于输入句子的合法性检测，从而区分哪些是语言中的句子，哪些不是。\n\n文法与自动机二者皆有所长，一定条件下可以相互转换。\n\n符号与符号串\n\n字母表，符号集\n字母或者符号的又穷非空集合。\n例：汉语字母表为汉字、数字、标点符号。\n\n符号串\n字母表中的符号组成的任何有穷序列。\n例：有符号集\n\n符号串为：0，1，01，10，11，00\n\n空字符串\n表示，长度为0\n\n符号串的头、尾、固有头、固有尾\n例：如果z=abc，则\nz的头为：，a，ab，abc\nz的尾为：，c，bc，cba\nz的固有头：，a，ab\nz的固有尾：，c，bc\n\n符号串连接\n有符号串x，y，连接xy指y符号串写在x符号串之后\n例：x=ab，y=cd，xy=abcd\n\n符号串的方幂\n有符号串x，z=xxxx....，称z为x的方幂，记为\n且\n\n\n符号串的相乘\n有符号串\n例：有A={a,b} B={c,d},则AB={ac,ad,bc,bd}\n\n闭包\n字母表上所有又穷长的字符串的集合用\n来表示，其中正闭包不包括空集\n\n文法和语言的形式定义\n\n规则\n也称为产生式：\n\n其中，\n\n文法\n定义：G定义为四元组\n\n\n句型\n\n句子一定是句型，句型不一定是句子\n\n语言\n记为L(G)\n\n文法类型\n\n\n上下文无关文法\n\n重点是在规则的左部只有一个非终结字符\n\n正规文法\n\n\n语法树\n语法树表明了推倒过程中使用了什么样的产生式和用到了哪些非终结字符，并不表明顺序。\n例：有下图\n\n构造aabbaa的语法树，步骤如下\n\n\n文法的二义性\n\n\n句型分析\n识别一个符号串是否为某文法的句型是整个推导的构建过程\n\n自上而下分析法\n由非终结字符串推导至终结字符串，并查看该终结字符串是否匹配\n\n自下而上分析法\n有终结字符串进行规约，最终生成的非终结字符串，是否符合规则\n\n回溯法\n\n这时候使用回溯法进行计算\n\n简化文法\n主要是去除规则中的两种类型的规则，有害规则和多与规则\n\n有害规则\n例如：\nU->U这种产生式，会引起文法的二义性。\n多余规则：指文法中任何句子的推倒都不会用到的规则，主要有两种：\n\n非终结字符不在任何规则的右部出现。\n非终结字符无法推出终结符号串\n\n\n参考\n宗成庆. 统计自然语言处理[M]. 清华大学出版社, 2008.\n北京大学编译原理课程\n","description":"","id":"https://blog.littleji.com/blog/20181016formallanguagegenerality/","path":"/blog/20181016formallanguagegenerality/","title":"形式语言概述(OverviewOfFromalLanguage)"},"https://blog.littleji.com/blog/20181022probabilisticgraphicalmodels1/":{"body":"\n概率图概述\n概率图模型使用图的方法来表示概率分布，在该模型中，结点表示变量，节点之间的边表示变量之间的概略关系。\n根据图模型中的边是否有向，将概率图的代表模型如下：\n\n\n\n无向图模型和有向图模型的区别\n这里要说明的是,采用有向图的贝叶斯网络的\"有向\"表示的是依存关系,具有因果推断,即A->B->C,反之则不可.\n而无向图的代表马尔科夫网络,采用的无向图\n各个图模型的演变关系如下：\n\n其中横向，由点到线（序列结构），最终到面。\n纵向则是在一定的条件下生成式模型转换为判别式模型。\n下面假设有观测序列x，状态序列y，并依次来说明生成式与判别式模型的区别。\n\n生成式模型\n生成式模型的定义是：“状态（输出）序列y按照一定的规律生成观测（输入）序列x”。生成式模型的本质是对于联合概率分布p(x,y)进行建模，并根据生成概率最大的生成序列来获取y。\n这类模型中，一般有严格的独立性假设，模型变量之间的关系清楚，处理单类问题时较为灵活。\n弱点是模型的推导与学习较为复杂。\n主要的模型有：n-gram，HMM、朴素贝叶斯、概率上下文无关文法。\n\n判别式模型\n判别式模型的定义是：“状态（输出）序列y是由观测序列（输入）所决定的。”判别式模型的本质是对后验概率p（y|x）进行建模，优点是处理多累问题或分辨某一类与其他类差异时更为灵活，模型构造简单。\n缺点是模型的描述能力有限，变量之间的关系不清楚。大多数模型都是有监督学习，不能很好地扩展为无监督学习。\n主要模型有：最大熵模型、最大熵马尔科夫模型、支持向量机、条件随机场、感知机。\n\n贝叶斯网络\n贝叶斯网络也称为belief networks，是一种基于概率推理的数学模型，理论基础为贝叶斯公式。\n贝叶斯网络形式上是一个有向无环图（DAG directed acyclic graph），结点表示随机变量，结点之间的边表示条件依存关系，箭头出发的节点为父节点，箭头到达的节点为子节点，子节点依存于父节点。\n如果两个节点没有连接关系，表示两个随机变量能够在某些特定情况下条件独立。\n\n构造贝叶斯网络\n构造贝叶斯网络是一个复杂的任务，其主要有三个方面的问题：表示、推断、学习\n\n表示\n在简单某一随机变量的组合上\n\n即便是随机变量只有两个取值，那么联合概率分布P需要对 2^n 种不同取值下的概率情况进行说明，然而这件事的计算代价非常高。\n\n推断\n由于贝叶斯网络是变量以及关系的完整模型，那么在观测到某些变量变化的时候就需要使用一些推断方法，来得知另一些变量子集的变化。\n概率推理：在已知某些证据的情况下，计算变量的后验分布的过程。\n常用的精确推理方法有两种\n\n变量消除法 variable elimination\n团树法 clique tree\n\n常用的近似推理\n\n重要性抽样 importance sampling\n随机马尔科夫链蒙特卡洛模拟法 Markov chain Monte Carlo，MCMC\n循环信念传播法 loopy belief propagation\n泛化信念传播法 generalized belief propagation\n\n\n学习\n参数学习的目的就是得知各个变量结点之间相互依存的强度。\n比如给定某个节点X，需要计算 P(X|父节点1，父节点2...)，这些概率分布可以是任意形式，通常是离散分布与高斯分布。\n常用的参数学习方法包括：\n\n最大似然估计 MLE\n最大后验概率 MAP\n期望最大 EM\n贝叶斯估计方法，贝叶斯图模型中，使用较多的是该种方法\n\n除了参数学习，还需要结构学习来学习各个变量之间的图关系，简单的贝叶斯可以由有经验的专家来构造，但一般情况下人工构造一个贝叶斯网络的结构几乎不可能，所以自动结构学习是一项颇具挑战的任务。\n\nMLE MAP 贝叶斯估计之间的关系\n其中，最大似然估计（MLE）是频率派的代表，贝叶斯估计（Bayes）是贝叶斯派的代表，最大后验估计是频率派和贝叶斯派的合成，是一种规则化后的最大似然估计。\n一般来说，在我们对于先验概率一无所知时，只能假设每种猜测的先验概率是均等的（其实这也是人类经验的结果），这个时候就只有用最大似然了，但不能忘记一个重要的前提是所有的采样都是独立同分布。\n如果我们有足够的自信，训练集中的样本分布的确很接近真实的情况，这时就应该用贝叶斯方法。贝叶斯学派强调的是“靠谱的先验概率”。所以说贝叶斯学派的适用范围更广，关键要先验概率靠谱，而频率学派有效的前提也是他们的先验概率同样是经验统计的结果。但也说明了贝叶斯计算要更为复杂，因为需要选择一个常用分布，并确定一个初始参数集作为先验分布。\n再者，MAP与MLE最大区别是MAP中加入了模型参数本身的概率分布，即是否考虑了先验知识。或者说。MLE中认为模型参数本身的概率的是均匀的，即该概率为一个固定值。\n\n参考\n统计自然语言处理（第2版）\n1\n2\n3\n4\n5\n","description":"","id":"https://blog.littleji.com/blog/20181022probabilisticgraphicalmodels1/","path":"/blog/20181022probabilisticgraphicalmodels1/","title":"概率图模型之一(ProbabilisticGraphicalModel-PGM)"},"https://blog.littleji.com/blog/20181025logstashgrokoverview/":{"body":"\n背景\nGrok 插件让 Logstash 在处理一定格式的日志文件的时候有了一定程度“正则表达”的功能。\n特别适用于解析一些非结构化的数据，并从中提取真正相关的信息至一些结构化 field 中。\n主要的使用场景在 syslog、apache 与其他的 webserver 具有这样特点的日志：这些日志都具有易人读但不易机读的特点。\nLogstash 默认有很多 patterns，你可以在 https://github.com/logstash-plugins/logstash-patterns-core/tree/master/patterns 找到。当然你也可以添加你自己的 pattern，这点我们稍后就会讲到。\n\n测试你的 Grok pattern\nDebug 是人类进步的阶梯，想起来当时被正则表达式支配的恐惧了么？那时我们有一些工具来测试我们的正则表达式是否足够“强壮”，grok 的作者也提供了两个网站，允许你通过这个来测试你的 grok 表达式，就跟你在测试正表达式的时候一样。分别是 http://grokdebug.herokuapp.com 和 http://grokconstructor.appspot.com/\n\nGrok 的一些基本设定\n\n语法\nGrok 使用如下的语法进行匹配操作。\n\n其中 SYNTAX是你想匹配的表达式的名称，SEMANTIC 则是存储匹配后的文本的变量名，比如：\n\n那么 IPV4 和 TIMESTAMP_ISO8601就是 SYNTAX，ip 和 time 就是 SEMANTIC。\n\n一个实际的例子\n一个 logstash 的配置文件，内容如下：\n\n运行后，这条 message 将会在 grok 过滤之后上报一条 event，内容如下：\n\n\n正则表达式库\nGrok 使用了 oniguruma 的正则表达式，所以所有在该文档中出现的匹配语法都可以使用。\n\n自定义 pattern\nGrok 自带了一些常用的匹配规则，比如：IPV4、IPV6、YEAR等，但是当我们想要写一些自己的规则的时候，grok 也提供了支持，这里主要有两种方式：\n\n第一种方式添加自定义 pattern\n直接使用下面的 pattern 结构：\n\n其中 field_name 可以理解为上文的 SEMANTIC，后面则跟的是实际的匹配表达式，比如这样：\n\n\n第二种方式添加自定义 pattern\n第二种方式就如同我们一开始使用的一样，在指定的 pattern 文件夹，添加即可比如：\n\n在实际匹配的时候写入\n\n使用的效果与第一种方式完全一致。\nLogstash 的配置文件中可以指定 自定义 pattern 的位置，比如：\n\n就是读取当前目录下的 patterns 文件。\n\nGrok 插件的一些配置项\n如果需要定制化 Grok 插件的一些匹配配置\n配置项名称匹配项类型是否必须\nbreak_on_matchbooleanno\nkeep_empty_capturebooleanno\nmatchhashno\nnamed_captures_onlybooleanno\noverwritearrayno\npattern_definitionshashno\npatterns_dirarrayno\npatterns_files_globstringno\ntag_on_failurearrayno\ntag_on_timeoutstringno\ntimeout_millisnumberno\n\n\nnamed_captures_only\n默认值： true\n意义：当该值为 true 时，只会从数据中捕获有 SEMANTIC 的信息，其他的匹配项不存储。\n\noverwrite\n默认值：[]\n意义：这个属性允许你在输出匹配结果之前，重写该匹配结果中的一项或多项，比如：\n\n可以看到，原来的 message 属性存储的是原始的输入信息，增加了 overwrite 选项后，则由匹配到的 message 信息将其替换。\n\npattern_definitions\n默认值：空\n意义：可以再该项中，以键值对的方式输入 grok 表达式，也就是把patterns_dir 文件内的 pattern 写在这个属性里，比如：\n\n\ntimeout_millis\n默认值：30000\n意义：在执行了所定义的时间后，终止正则匹配，设置为0则没有超时时间。\n\nadd_tag\n默认值：[]\n意义：在匹配成功后，增加一个或多个 tag 字段\n\nadd_field\n默认值：{}\n意义：在匹配成功后，增加一个或多个 field、value对，例如：\n\n\n参考\ngrok manual\n","description":"","id":"https://blog.littleji.com/blog/20181025logstashgrokoverview/","path":"/blog/20181025logstashgrokoverview/","title":"Logstash Grok 概览(OverviewOfLogstashGrok)"},"https://blog.littleji.com/blog/20181127generativevsdiscriminative/":{"body":"\n\"one should solve\nthe [classification] problem directly and never solve a more general problem as an\nintermediate step [such as modeling p(xly)].\" ---Vapnik\n\n\n背景\n笔者在 NLP 概率图学习的过程中,发现解决同一种问题可以使用若干种模型,而大多数的 NLP 模型的种类主要集中在有监督学习.\n通过概率图,我们自然会联想到对应的概率问题,而每当概率问题出现,无可避免的会有频率学派与贝叶斯派的竞争,两派为了解决同一个问题开发出了不同的概率图模型,自然在这些概率图的模型分类中也出现了对应分支,分别为判别式与生成式.\n两个模型本身是解释同一种问题的不同角度,笔者最近对于该两个模型之间的对比与解释进行了学习,整理如下:\n\n二个模型在分类问题上的处理方式\n判别模型不关心数据是如何生成的，它只是对给定数据进行分类。\n因此，判别算法试图直接从数据中学习P（y | x），然后尝试对数据进行分类。\n另一方面，生成模型试图学习p（x，y），后来根据条件概率公式，可以将其转换为p（y | x）来对数据进行分类。\n\n为了便于理解这里举两个例子\n\n第一个例子\n当我们需要判断两种不同的语言(比如中文和英文)的时候:\n\n生成式模型:先去学习这两种不同的语言,再根据语言的输入去判断语言的种类\n判断式模型:直接根据输入判断到底属于哪种语言\n\n\n第二个例子\n假设有\n\n以及\n\n以及对应的几个数据:\n\n那么根据定义就可以求出联合概率分布为\np(x,y)y=0y=1\nx=11/20\nx=21/41/4\n\n对应的条件分布概率则为\np(y|x)y=0y=1\nx=110\nx=21/21/2\n\n\n一张图理解生成模型与判别模型的关系\n\n学习的目标是正确的将未知的数据进行分类,从图中我们可以很容易的看出:\n\n判别模型学习得到的是那条分类的曲线,其关注点在于分类的边界学习\n生成模型则学习得到的则是两类数据的具体分布情况\n\n\n生成模型与判别模型的优缺点\n\n生成模型的优点\n\n在不平衡的数据样本上,表现依然优异\n可输出所有类别下的估算概率\n更好的模型解释性\n更像是通用型 AI,可以产生有语法错误答案 有口音的语音等,可以使用 p（x，y）生成类似于现有数据的新数据\n当样本数量较多时，生成模型能更快地收敛于真实模型\n生成模型能够应付存在隐变量的情况，比如混合高斯模型就是含有隐变量的生成方法\n只有生成模型能检测异常值。由于生成模型完全学习了所有的分布，所以它可以用来检测某个值是否异常：P(X)是否太小\n\n\n生成模型的缺点\n\n联合分布是能提供更多的信息，但也需要更多的样本和更多计算，尤其是为了更准确估计类别条件分布，需要增加样本的数目，而且类别条件概率的许多信息是我们做分类用不到，因而如果我们只需要做分类任务，就浪费了计算资源\n\n\n判别模型的优点\n\n在拥有大量的数据集的时候,相对于生成式模型,其准确度更高\n由于直接学习P(\\tilde{c}|\\tilde{x} )，而不需要求解类别条件概率，所以允许我们对输入进行抽象（比如降维、构造等），从而能够简化学习问题\n相对于生成模型来说,其计算资源大大地节省了,性能较好\n所需要的样本数量少于生成模型\n\n\n判别模型的缺点\n\n不适合应用在不平衡的数据集中\n只能应用在监督学习的任务中\n模型的解释性差\n尽管判别模型不需要对观察到的变量的分布进行建模，但它们通常不能表达观察变量和目标变量之间的复杂关系。在分类和回归任务中，它们不一定比生成模型表现更好。\n\n\n主要的生成模型\n\nLDA\nHMM\n朴素贝叶斯\n混合高斯模型\n概率上下无关文法\n变分自动编码器\nGAN\n\n\n主要的判别模型\n\nLR\nSVM\nCRF\nBoosting\nDecision tree\nK-neighbor\n最大熵模型\n感知机\n神经网络\n\n\n参考\n知乎:机器学习“判定模型”和“生成模型”有什么区别？\nGenerative model\n","description":"","id":"https://blog.littleji.com/blog/20181127generativevsdiscriminative/","path":"/blog/20181127generativevsdiscriminative/","title":"生成模型VS判别模型(Gnerative VS Discriminative)"},"https://blog.littleji.com/blog/20181218mlreview1/":{"body":"\n1 机器学习的一些概念\n\n1.1 什么是机器学习?\n计算机系统能够通过现有的数据，不断地改进其解决某一问题的性能，称为机器学习。\n\n1.2 什么是有监督与无监督学习？\n对于机器学习的过程中，对于所要学习的数据，如果其为标注的数据则称为有监督学习，否则称为无监督学习。\n\n1.3 什么是泛化能力？\n泛化能力指一个经过学习后的模型，能够适应与处理新样本的能力，这也是机器学习的主要目标。\n\n1.4 什么是过拟合与欠拟合？\n\n过拟合：过拟合指在学习的过程中，模型把样本本身的特点而非目标模型的特点进行了学习，导致整体性能下降的情况。\n欠拟合：欠拟合则是对目标模型的一般性质还没有习得，主要是由于模型本身的学习与表达能力不够。\n\n\n1.5 过拟合与欠拟合如何解决？\n\n欠拟合的解决：通过增加模型的学习能力，如：增加学习轮数、增加神经网络的复杂度以提升其表现能力等。\n过拟合的解决：过拟合一般来说是只可缓解无法彻底解决，通过对学习使用的模型进行改进，如 ：减少相应的模型参数、降低神经网络复杂度等。\n\n\n1.6 什么是方差Variance和偏差Bias？\n方差与偏差是解释学习算法泛化性能的一种重要的工具，下面我们来说明二者的侧重点。\n首先,我们将所得到的训练数据等分成A,B,C3份,再借由这3份训练数据分别得出3个训练模型,那么\"方差\"指的是向这3个模型输入同样的测试样本,得出的输出结果的方差,借此来判断我们选择的模型对于不同的学习样本展现出的\"模型本身训练的稳定性\".\n其次,如果我们将上面的所有训练数据进行训练,并输入同样的测试样本,得到对应的输出结果X,将输出结果与真实的结果T进行比较,得到的差值就为偏差, 偏差则侧重于\"模型本身预测的精准度\"的衡量.\n方差,偏差,噪声三者共同主导了学习模型的误差,在学习初期由于模型的拟合能力不强,这个时候主要是由偏差主导了误差,当学习后期模型的拟合能力增强后,微小的数据扰动都会被模型所捕捉到,此时由方差来主导模型的误差.\n\n1.7 交叉验证\n为了能够具体的衡量模型的性能,交叉验证提供了这样一种性能评测的手段.\n交叉验证的思想是重复的利用数据,把数据进行切分为训练数据集与测试数据集,并在这个基础上进行反复的训练与测试,选取具有较好性能指标的模型.\n具体的思想是,在数据集上划分k个大小相同且互斥的子集,使用k-1个子集进行训练,最后一个进行测试,得出结果后,再选择不同的k-1个自己训练,最后一个进行测试,容易看出上面可最终得到k个模型,通过求取其平均误差得到该模型的测试误差.\n其中典型的k值为10.\n\n2 线性模型\n\n2.1 线性回归的原理\n\n线性模型:即通过将给定的一些特征进行线性组合所得到的模型.\n线性回归:通过学习得到一个线性的模型能够尽可能准确的预测输入数据的真实标记.\n线性回归的原理:使用了均方误差最小化的方法也就是常说的最小二乘法,即试图找到这么一条直线,使样本到直线上的欧氏距离之和最小.\n\n\n2.2 线性回归损失函数、代价函数、目标函数\n\n\n损失函数指的是单一训练集上产生的误差.\n\n\n代价函数则值得是模型在整个训练集上产生的平均误差.\n\n\n设学习后的模型f,面对测试样本X,模型对应的模拟输出f(X)以及X实际的输出Y,拟合的程度.有如下的表示方式\n$$\nL(Y, f(X))=(Y-f(X))^2\n$$\n\n\n但是仅仅通过损失函数来就纠正拟合误差并非我们的目标,我们的是目标是是让模型精确地同时,又尽量的减少模型的复杂度,于是就引入了正则化函数来衡量模型的复杂度,最终我们的目标函数是最小化误差与最小化模型复杂度之和也就是\n$$\n\\frac{1}{N} \\sum_{i=1}^{N}L(y_i,f(x_i))+\\lambda J(f)\n$$\n前者是最小化经验风险,后者则是最小化结构风险\n\n\n\n3 线性模型的优化方法\n当我们得到了对应目标函数后,那么就需要具体的对该模型各个参数求最优解,也就是常见的最优化问题,这里有以下这么几个主要的算法.\n\n3.1 梯度下降法\n梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向.\n\n3.2 随机梯度下降法\n由于梯度下降法使用了固定步长的,这样带来了后期收敛慢,其进入极小点的情况,这里通过选用随机梯度的下降,更容易突破局部极小点,从而收敛至全局极值点,不过也有迭代次数增加等缺点.\n\n3.3 牛顿法\n牛顿法不同于梯度向量,而是使用了二阶海森矩阵的逆矩阵来求解,相对来说比普通的梯度下降算法收敛速度更快,但是其要求必须具有二阶海森矩阵的逆矩阵条件,其计算非常复杂,该条件在大规模数据下往往无法保证.\n\n3.4 拟牛顿法\n拟牛顿法则是通过找到一个与海森矩阵类似性质的矩阵来替代,从而让计算更为容易.\n其中DFP BFGS L-BFGS都是比较重要的拟牛顿方法.\n\n3.5 其他自适应学习方法\nadagrad,adadelta,rmsprop,adam等一系列adaptive learning rate方法\n\n4 线性回归的评估指标\n\n4.1查准率 全查率与F1\n二分类问题有四种预测的情况:\n真正例:true positive\n假正例:false positive\n真反例:true negative\n假反例:false negative\n查准率P与全查率R为\n$$\nP=\\frac{TP}{TP+FP}\n$$\n$$\nR=\\frac{TP}{TP+FN}\n$$\n二者一般矛盾,因为查全率高意味着查的个数多,这样又会导致查准率下降\n由查全率作为横轴,查准率作为纵轴形成P-R曲线,比较学习期的的性能时,比较该曲线的面积是一个方法\n平衡点(Break-Event Point)是查准率等于查全率的取值,这样可以权衡查全率和查准率\nBEP还是有些简化,更常用的是F1度量,目的是为了找出更好的学习器\nF1:基于查准率和查全率的调和平均值(harmonic mean):\n$$\nF1=\\frac{2PR}{P+R}=\\frac{2TP}{样例总数+TP-TN}\n$$\n$$\nF_\\beta=\\frac{(1+\\beta^{2})PR}{\\beta^2P +R}\n$$\n其中β>0, β=1时就退化为标准的F1,β<1时则查准更重要,β>1则查全更重要\n很多时候我们有多个二分类混淆矩阵,例如进行多次训练和测试,每次都有一个,或者执行多分类任务,每每两个组合都对应一个混淆矩阵\n一种直接的做法是在所有的混淆矩阵都计算,然后计算所有的平均值也就是宏查重率macro-P和macro-R以及对应的macro F1\n\n4.2 ROC与AUC\n学习预测就是将样本进行排序,最可能是正例的排在前面\n神经网络一般情形下对每个测试样本预测出一个0-1的实值,然后将这个值作为截断点,大于这个截断点的样本为正例,其他的为反例,如果更重视查准率则可选择排序中靠前的位置,重视查全率则选择较后的截断点\n排序本身的质量好环则是体现了学习器在不同任务下的期望泛化性能,ROC曲线就是从这个角度来研究学习器泛化性能\nROC(Receiver Operating Characteristic)的操作方式与之前的P-R图类似,并提出了两个概念,真正利率TPR,假正利率FPR\n$$\nTPR=TP/(TP+FN)\n$$\n$$\nFPR=FP/(FP+TN)\n$$\n\n4.3 ROC绘制过程\n设正例数目为m+ 反例数目为m-\n\n均是先对所有的样例根据学习器的结果进行排序,然后将分类的阀值设置为最大,这时候均是反例,TPR=FPR=0\n2.调整阀值为依次每个样例的值,然后观察次样本是否为真正例,如果是坐标为(x,y+1/m+),如果不是坐标为(x+1/m-,y)\n比较ROC曲线的面积也就是AUC\n\nAUC更考虑的是样本预测的排序质量\n\n5 sklearn参数详解\n下面对于一个最普通的sklearn 线性模型的使用方式进行说明\n\n除了上面的模型还有包括 lasso ridge等回归模型在linear_model包内\n\n讨论\n\n如何提升泛化能力?\n\n参考\nscikit-learn 0.20 document\nazure machine-learning-reference\ndeeplearning\n机器学习-周志华\n统计学习方法-李航\n[机器学习中的Bias(偏差,Error(误差)和Variance(方差))有什么区别与联系]((https://www.zhihu.com/question/27068705)\n理解机器学习中常用优化方法\n机器学习中的目标函数\\损失函数\\代价函数\n梯度下降or拟牛顿法?\n梯度下降法\\牛顿法和拟牛顿法\n","description":"","id":"https://blog.littleji.com/blog/20181218mlreview1/","path":"/blog/20181218mlreview1/","title":"ML梳理之线性回归(linearRegression)"},"https://blog.littleji.com/blog/20181222mlreview2/":{"body":"\n逻辑回归与线性回归的联系与区别\n逻辑回归事实上是将线性回归的输出进行了非线性函数的映射,而这个映射即是:\n$$\ny=\\frac{1}{1+e^{-(w^{T}x+b)}}\n$$\n\n逻辑回归的原理\n逻辑回归的主要原理是将之前的线性空间通过非线性函数进行再输出,让对应的输出范围集中在要么靠近0,要么靠近1的区域内,从而完成将对应的数据分类的目的\n3、逻辑回归损失函数推导及优化\n假设\nP(y=1|x,θ)=hθ(x)\nP(y=0|x,θ)=1−hθ(x)\n则有\nP(y|x,θ)=hθ(x)y(1−hθ(x))1−y\n很容易得到似然函数表达式:\nL(θ)=∏i=1m(hθ(x(i)))y(i)(1−hθ(x(i)))1−y(i)\n取对数得:\nJ(θ)=−lnL(θ)=−∑i=1m(y(i)log(hθ(x(i)))+(1−y(i))log(1−hθ(x(i))))\n4、 正则化与模型评估指标\n逻辑回归也需要处理过拟合的问题那么正则化的方法提供了一个很好地思路\n逻辑回归的L1正则化的损失函数表达式如下，相比普通的逻辑回归损失函数，增加了L1的范数做作为惩罚，超参数α作为惩罚系数，调节惩罚项的大小。\n二元逻辑回归的L1正则化损失函数表达式如下：\nJ(θ)=−Y⊙loghθ(X)−(E−Y)⊙log(E−hθ(X))+||θ||1\n其中||θ||1为θ的L1范数。\n二元逻辑回归的L2正则化损失函数表达式如下：\nJ(θ)=−Y⊙loghθ(X)−(E−Y)⊙log(E−hθ(X))+12α||θ||22\n其中||θ||2为θ的L2范数。\n5、逻辑回归的优缺点\n优点:可以给出概率,解释性较好\n缺点:容易欠拟合,对于非线性的特征还需要进一步的转化,\n6、样本不均衡问题解决办法\n类别不平衡问题指的是当正反例的数目偏差过大的时候，所造成的困扰\n类别不平衡的一个基本策略是-再缩放(rescaling)\n主要有三个途径:\n\n\n对训练集中的反类样例进行欠采样,去除一些反例\n\n\n对训练集里的正例进行过采样\n\n\n直接学习但在预测的时候进行阀值的改变\n类别不平衡学习通常是较小类的代价更高,\n\n\nsklearn参数\n逻辑回归具体的位置在:\nfrom sklearn.linear_model import LogisticRegression\n主要有C penalty tol solver 等几个参数\nC:正则化系数的倒数,默认为1\npenalty:用来指定正则化的参数\ntol:迭代终止的误差范围\nsolver:决定使用什么样的优化方法\n\n\n","description":"","id":"https://blog.littleji.com/blog/20181222mlreview2/","path":"/blog/20181222mlreview2/","title":"ML梳理之线性回归(LogisticRegression)"},"https://blog.littleji.com/blog/20181225mlreview3/":{"body":"\n信息论基础\n信息论的基础由香农博士于1948年奠定.下面说明关于信息论的一些基本概念.\n\n熵\n上表示一个随机变量不确定的数量.如果一个随机变量的熵越大,那么其不确定也就越大.\n如果$X$为离散型变量,取值为$\\mathbb R$,其概率分布为$p(x)=P(X=x),x\\in \\mathbb R$,那么X的熵$H(X)$定义为:\n$$\nH(X)=-\\sum_{x \\in R}p(x)log_2p(x)\n$$\n\n联合熵\n联合熵其实就是描述一对随机变量平均所需要的信息量.\n如果$X,Y$是一对离散型随机变量 $X,Y ~ p(x,y),X,Y$的联合熵为$H(X,Y)$为:\n$$\nH(X,Y)=-\\sum_{x \\in X}\\sum_{y \\in Y}p(x,y)logp(x,y)\n$$\n\n条件熵\n条件熵$H(Y|X)$的意思是,在X发生的条件下,Y的不确定性有\n$$\nH(Y|X)=\\sum_{x \\in X}\\sum_{y \\in Y}p(x, y)logp(y | x)\n$$\n将联合概率进行展开后发现:\n$$\nH(X, Y)=-\\sum_{x \\in X}p(x)logp(x)-\\sum_{x \\in X}\\sum_{y \\in Y}p(x, y)logp(y | x) = H(X)+H(Y|X)\n$$\n\n信息增益\n现在有属性a, 其可能有v个可能的取值,如果使用属性a来对样本D进行划分的话,易知会产生v个节点,那么所有属性为$a_v$的样本可记为$D^v$.,这时候再根据各个节点对应所占的比例$|D^v|/|D|$分配权重,就可以知道使用属性a对D进行划分的时候所获得的信息增益,也就是说使用整个样本的信息熵,减去通过属性a划分的信息熵之和就是信息增益.\n现在假设样本D的信息熵为\n$$\nEnt(D)=-\\sum_{k=1}^{|v|}p_klog_2p_k\n$$\n那么信息增益为:\n$$\nGain(D,a)=Ent(D)-\\sum_{v=1}^{V}\\frac{|D^v|}{|D|}Ent(D^v)\n$$\n\n基尼不纯度\n基尼不纯度是CART算法划分属性所使用的度量方法,其直观上的理解是从一个数据集D中任意抽取两个样本,其类别不一致的概率.其具体的公式如下:\n$$\nGini(D)=\\sum_{k=1}{|y|}\\sum_{k^{'}\\neq k}(p_kp_k')\n$$\n\n决策树的不同分类算法\n\nID3算法\n流程具体如下:\n\n首先考虑样本中只有一个类或者没有属性的情况\n计算各个属性的信息增益后\n选择信息增益最多的属性进行节点分类,建立各个节点分支\n再依次的再各个节点中进行选择计算信息增益,返回步骤2重复迭代\n到达指定的退出条件,没有特征或者信息增益较小\n由于ID3 算法只有生成树的过程,没有剪枝等过程,所以可能过拟合.\n\n\nC4.5\n首先,信息增益比的定义是信息增益G(D,a)与训练数据集熵H(D)的比\n$$\ng_R(D,a)=\\frac{g(D,a)}{H(D)}\n$$\n该C4.5算法则是针对于ID3算法的改进,在生成树的过程中使用了信息增益比来选择,而不是单纯的使用信息增益\n算法过程如下:\n假设 数据集D 特征集A 阀值ε\n\n如果数据中均为同一个类,则返回,算法结束\n如果 $A=\\varnothing$, 则返回一个单节点的树,并选择实例数最多的类,为该节点的类别,算法结束\n选择其中信息增益比最大的节点\n再依次选择各个节点,计算当前节点的内的信息增益比,进行迭代\n最终达到指定的退出条件,即信息增益比过低,或者没有更多的特征时退出算法\n\n上面的构建的节点树都是分类树,只不过节点划分的方式不同.那么什么是回归树呢?\n\n回归树原理\n回归树对于样本的划分,通过遍历所有输入变量，找到最优的切分变量j和最优的切分点s，即选择第j个特征$x^j$和它的取值s将输入空间划分为两部分，然后重复这个操作,对于连续性的样本值非常有效.\n具体算法如下\n\n选择最优的切分变量j和最优的切分点s，求解\n$$\nmin_{j,s}[min_{c_{1}}\\sum_{x_{i}\\in R_{1}(j,s)}(y_{i}-c_{1})^2+min_{c_{2}}\\sum_{x_{i}\\in R_{2}(j,s)}(y_{i}-c_{2})^2]\n$$\n遍历所有特征，对固定的特征扫描所有取值，找到使上式达到最小值的对(j,s).\n用选定的对 (j,s)划分区域，并确定该区域的预测值；\n继续对两个字区域调用上述步骤，直至满足停止条件；\n\n\nCART分类树\nCART分类树的全称是分类与回归树,主要的原理思想是将内部的节点特征取值为\"是\"或\"否\"两个值,左分支为是,右分支为否,这样整个决策树就可以在整个样本空间中求取对应的条件概率分布.\n算法由特征选择和生成树以及前面两种算法所没有的剪枝构成,算法主要包括两个部分:树的生成与剪枝\n\nCART的生成\n从根节点开始，对节点计算现有特征的基尼指数，对每一个特征，例如AA，再对其每个可能的取值如aa,根据样本点对A=aA=a的结果的”是“与”否“划分为两个部分，利用\n$$\nGini(D,A=a)=\\frac{|D_{1}|}{|D|}Gini(D_{1})+\\frac{|D_{2}|}{|D|}Gini(D_{2})\n$$\n进行计算；在所有可能的特征AA以及该特征所有的可能取值a中，选择基尼指数最小的特征及其对应的取值作为最优特征和最优切分点。然后根据最优特征和最优切分点，将本节点的数据集二分，生成两个子节点\n对两个字节点递归地调用上述步骤，直至节点中的样本个数小于阈值，或者样本集的基尼指数小于阈值，或者没有更多特征后停止；\n\nCART的剪枝\n剪枝就是对生成的树进行裁剪简化的过程,其一般是通过极小化决策树整体的损失函数或代价函数来实现.\nCART的剪枝是通过两个步骤:\n\n从树的底部不断地剪枝直到根节点,形成对应的子树序列\n通过交叉验证法,对子树的序列进行测试,并从中选取最优的子树\n\n\n决策树防止过拟合手段\n决策树过拟合主要有两个手段,分别为early stopping与剪枝.\n\nearlystopping:限制选取的分类节点的总数,树的深度,节点中的实例数,阈值等\n剪枝,即当前节点的划分无法带来决策树泛化性能的提升,增删除对应的节点\n\n\n模型评估\n可以使用之前梳理的AUC ROC 交叉验证 随机抽样等方法,这里就不再赘述了.\n\npython可视化决策树与对应的函数实现\n\n主要的函数为\n\n参考\n统计自然语言处理-宗成庆\n机器学习-周志华\n统计学习方法-李航\n决策树(分类树、回归树\nDecision tree visual example\n","description":"","id":"https://blog.littleji.com/blog/20181225mlreview3/","path":"/blog/20181225mlreview3/","title":"ML梳理之线性回归(DecisionTree)"},"https://blog.littleji.com/blog/20190219handovermemolist/":{"body":"\n1 项目交接的场景\n\n由于同事离职，将工作交接给自己。\n由于自己离职，将工作交接给同事。\n由于项目变动，将工作交给其他项目组。\n\n\n2 文档相关注意点\n\n确定每一期的产品需求文档，PRD文档，概要设计文档，详细设计文档（流程图、设计框架图、上下游组件交互图），接口设计文档。\n确定开发过程中可能使用到的PSD等视觉稿或切图。\n确定每一期的测试覆盖文档，单元测试文档，遗留Bug。\n确定目前这一期的开发进度文档，包括尚未开发，开发过程中，开发完成。\n确定每一期的使用说明文档。\n确定FAQ文档。\n确定每一期的产品人员，开发人员，测试人员。\n确定每一期的开始开发时间，提测时间，上线时间。\n确定之前所编写的专利、软件著作权等文档。\n了解相关程序风险，遗留问题等。\n其他的一些约定如Scrum、XP、瀑布等软件开发方法。\n\n\n3 项目源码相关注意点\n\n将开发人员最后修改的代码提交。\n确定项目源码且需要有规范的详细注释。\n确定源码目录结构说明，明确结构中各个部分的意义。\n确定项目对外API。\n确定项目定时脚本。\n确定项目日志查看平台。\n开通SVN的权限。\n开通GIT的权限。\n开通涉及交接的任何系统权限。\n了解上线部署脚本、流程。\n了解代码规范。\n\n\n4 数据库相关注意点\n\n相关数据库与数据表结构\n查看是否有未注释的库名、表名、字段名，将其确定。\n最好了解每一个数据库、表、字段的意义，更新到文档。\n最好将每一个表涉及到哪一个模块进行确认，更新到文档。\n\n\n5 环境相关注意点\n\n5.1 本地开发环境配置\n\n在自己本地电脑上配置环境，将项目在自己机器上运行成功。\n确认是否有其他的扩展，如需账号、端口，记得做记录。\n确认是否有配置文件，如果有则对于所有的配置项进行说明。\n\n\n5.2 测试环境\n开通测试账号。\n\n5.3 预上线环境\n\n开通预上线环境账号\n确定预上线环境的域名地址，是否需要指定的Host等等。\n\n\n5.4 正式环境\n开通正式环境账号\n\n6 对接人相关注意点\n\n确定测试对接人。\n确定产品对接人。\n确定项目跨部门对接人。\n确定运维和DBA对接人。\n\n\n7 交接的形式\n\n具体的形式最好为举行一个交接会议，叫上相关产品、开发、测试，对于交接人员提出的问题，仔细逐一讲解、解答。\n使用交接双方共同参与,代码讲解的方式\n\n\n备注\n\n最好能在员工提离职的时候就开始项目交接，而不是员工走的时候再做交接。\n离职要提前与上级领导沟通，给领导留出找对接人的时间。\n交接过程中遇到有疑问的地方，一定要确认清楚，做好记录。\n\n\n参考\n程序员如何做好交接\n项目交接小总结\n文档交接说明书\n前端项目交接文档\n","description":"","id":"https://blog.littleji.com/blog/20190219handovermemolist/","path":"/blog/20190219handovermemolist/","title":"软件项目交接清单(Software project handover checklist)"},"https://blog.littleji.com/blog/20190318headfirstforgitlab/":{"body":"\nGitLab 入门\n\n我不想再尴尬下去了\n当你在照镜子时\n\n当你觉得在正常地开发用户的需求时\n\n当你作为一个萌新准备接手别人的代码时\n\n当你在准备跟用户进行展示你开发的产品时\n\n当你在咆哮这段恶心的代码是谁写的时候\n\n当你准备跟甲方好好唠嗑的时候\n\n\n上面到底是什么问题？\n\n软件开发进度难以预测\n用户对产品功能难以满足\n软件产品质量无法保证\n软件产品难以维护\n软件缺少适当的文档资料\n\n归根结底是因为我们使用了一种小作坊式/游击队式地开发方式\n\n从游击队转向正规军\n以前在游击队里面人们在无数次的管理混乱导致项目搁浅（钱没了，什么都没做出来）的教训下，逐渐摸索出了一些“办事流程”，这些办事流程在实践中被证明效果还不错，被广泛采用，借以提高项目的成功率，以及降低掉头发的速率。\n技术思维得有，工程思维也少不了\n工程思维的起点是流程。流程的背后是科学，以既定的步骤、阶段性的输入/输出去完成价值创造，通过过程控制确保最终结果让人满意。\n\n为什么使用GitLab搞这一套东西\nGitLab 本身是一个工具,是一个让大家达成共识的工具，帮助各位进行工程化地软件开发与管理，最终目的是拯救程序员们日益稀少的头发。\n\n\nGitLab中的组织结构\n主要使用group subgroup project的方式进行组织,一个典型的人员组织结构如下所示:\n\nOrganization Group - GitLab\n\nCategory Subgroup - Marketing\n\n(project) Design\n(project) General\n\n\nCategory Subgroup - Software\n\n(project) GitLab CE\n(project) GitLab EE\n(project) Omnibus GitLab\n(project) GitLab Runner\n(project) GitLab Pages daemon\n\n\n\n\n\ngroup的作用是让团队人员可以一次性授权并访问多个项目\n再比如我现在建立的一个示范性的\n\n\nteam\n\n\n产品/用研user_research/UR\n用户体验user_experience/UE\n用户界面user_interface/UI\n开发develop/DEV\n质量quality_assurance/QA\n运维operation_maintenance/OP\n文档documentation/DOC\n开发经理pproject_manager/PM\n\n\n迭代时间的长度\n\nsprint:译作短跑,指一个迭代,一般包括几个功能和bug修复,开发周期为2-4周\nmilestone:译作里程碑,为了便于大家进行回顾是否偏离的航道或者进度,一般为3个sprint,也就是之后的一个季度\nrelease:一般是一个产品的deadline\n\n从点子到产品\n\n\nIDEA： 每一个从点子开始的项目，通常来源于一次闲聊。在这个阶段，项目组内的所有人对需求提出自己的想法.\nISSUE： 最有效的讨论一个点子的方法，就是为这个点子建立一个工单讨论。你的团队和你的合作伙伴可以在 工单追踪器issue tracker 中帮助你去提升这个点子\nPLAN： 一旦讨论得到一致的同意，就是开始编码的时候了。首先，我们需要优先考虑组织我们的工作流。对于此，我们可以使用 工单看板Issue Board。\nCODE： 现在，当一切准备就绪，我们可以开始写代码了。\nCOMMIT： 当我们为我们的初步成果欢呼的时候，我们就可以在版本控制下，提交代码到功能分支了。\nTEST： 通过 GitLab CI，我们可以运行脚本来构建和测试我们的应用。\nREVIEW： 一旦脚本成功运行，我们测试和构建成功，我们就可以进行 代码复审code review 以及批准。\nSTAGING：： 现在是时候将我们的代码部署到演示环境来检查一下，看看是否一切就像我们预估的那样顺畅——或者我们可能仍然需要修改。\nPRODUCTION： 当一切都如预期，就是部署到生产环境的时候了！\nFEEDBACK： 现在是时候返回去看我们项目中需要提升的部分了。我们使用周期分析 Cycle Analytics来对当前项目中关键的部分进行的反馈。\n\n\n为什么说要使用一个issue开始一切\n\n在issue中,通过规范化的模板,项目中的任何人可以知道这个需求或者bug的任何详细信息和讨论以及历史\n通过issue中的文档信息,开发者与项目经理可以进行实时交流某一个feature的最新设计思路,这个可以作为一份实时的共享编辑文档\n使用issue的label功能可以实现工作流\\时间估计等较复杂的功能\n\n\nissue本身需要包含哪些东西\n一个 feature类型 issue基本上长成这个样子\n\n\nissue的type\n\nfeature:开发功能点\nbug: 软件缺陷\ntech debt:不改变功能的情况下增强产品性能\nux debt:用户体验需要提升\n\n\n对issues进行plan\nplan说是计划，实则是沟通\n\n\n\nprocess-needs confirmed\nprocess-reject(拒绝)\nprocess-doing\n\n\n优先级\n优先级的意义是便于在同时处理大量需求的时候有一个计划。\n\n\nP1 特高级 目前的sprint搞定\nP2 高级 下个sprint搞定\nP3 中级 目前的里程碑再搞定\nP4 低级 下个里程碑再搞定\n\n\n严重性\n\nS1 完蛋 系统崩溃/数据丢失/数据泄漏/coredump 没有数据可用性\nS2 严重 无法在前台查询数据,但是数据库完整 部分数据可用性丢失\nS3 中级 前台显示错位 数据无丢失可用性受损\nS4 初级 前台颜色显示错误 数据无丢失 可用性也无受损\n\n\nissue board，工作看板\n我们做了这么多准备，都是为了这个，让整个项目组成员清晰的了解到，目前的计划与进度，加快信息的传递\n\n正儿八经的issue应该有三个标签、责任人、截止时间：\n\nprocess-*\n优先级\n团队，以团队为基础建立起来的看板可以通过拖拽自动加上团队标签\n\n\nFQA\n\n\n问：能不能想svn那样单独的文件夹设置权限？\n\n答：不可以,同一个repo的每个文件夹的权限是一致,如果想根据人来设置请使用两个repo\n\n\n\n问：分支权限可以做什么？\n\n答：只允许部分人进行merge、只允许部分人进行push。\n\n\n\n\n参考\n程序员的那些事儿\n奶头乐维基百科\nGitLab工作流概览\nGitLab Workflow: An Overview\nAlways start with an issue \nscrum-sprint-vs-milestone-vs-release\n我们是怎么scrum\n別再傻傻分不清，究竟PM, UX, UI, Web designer, Front-End Developer的專業差在哪？\n","description":"","id":"https://blog.littleji.com/blog/20190318headfirstforgitlab/","path":"/blog/20190318headfirstforgitlab/","title":"Gitlab 入门与工作流(HeadfirstForGitlab)"},"https://blog.littleji.com/blog/nlp/":{"body":"\nNLP流水线总览\nNLP处理套路无非以下该图中描述\n\n该文的重点则对所有该流水线之前的任务进行补充\n\n数据本身处理\n\n简繁体转换或其他同义转换(中文)\n全角半角转换\n不在dictionary(正则与替换对照词典)以内的单词就用UNK取代\n可以在句首加上 <BOS>，在句末加上 <EOS>\nurl，at，表情符号等统一替换\n稀有词替换为 <UNK>(词频小于某一个阈值)Latest posts\n编码转换\n小写转换\n去除标点符号(根据具体的任务也可替换)\n去除停用词\n去除频现词\n去除稀疏词\n略缩词替换\n错词纠正(将词替换为词典中最近的词或者 <UNK>)\n单位替换(将文本中的单位替换为统一格式如：将4kgs、4kg统一替换为4 kg，将4k替换为4000，将100或100 100或100100或100替换为100 dollar)\n词形还原(lemmatization)\n其他语言进行翻译(比如对于中文中的英文单词归一化为<E>)\n数字归一化(比如将小于10的为如果在之后的实体识别中需要对应的原始数字则跳过该步骤.1:NUM 大于10<2:NUM>)\n\n\n数据增强\n\n长句截断\ndropout\nshuffle\n\n文档裁减(这样我将获得更多的数据。开始的时候我尝试从文档中抽取几个句子并创建10个新文档。这些新创建的文档句子间没有逻辑关系，所以用它们训练得到的分类器性能很差。第二次，我尝试将每篇文章分成若干段，每段由文章中五个连续的句子组成。这个方法就运行得非常好，让分类器的性能提升很大)\n文本对齐\n同义词替换\n回译\n迁移学习\nGAN\nBERT\n\n\n实体识别的分类\n\nPERSON People, including fictional.\nNORP Nationalities or religious or political groups.\nFAC Buildings, airports, highways, bridges, etc.\nORG Companies, agencies, institutions, etc.\nGPE Countries, cities, states.\nLOC Non-GPE locations, mountain ranges, bodies of water.\nPRODUCT Objects, vehicles, foods, etc. (Not services.)\nEVENT Named hurricanes, battles, wars, sports events, etc.\nWORK_OF_ART Titles of books, songs, etc.\nLAW Named documents made into laws.\nLANGUAGE Any named language.\nDATE Absolute or relative dates or periods.\nTIME Times smaller than a day.\nPERCENT Percentage, including ”%“.\nMONEY Monetary values, including unit.\nQUANTITY Measurements, as of weight or distance.\nORDINAL “first”, “second”, etc.\nCARDINAL Numerals that do not fall under another type.\n\n\n参考\n\nIntrodecton to pipelines\n文本数据处理的终极指南-NLP入门\n使用re正则化进行文本清理\n自然语言处理时，通常的文本清理流程是什么？\n中文自然语言处理时，英文单词和数字怎么处理？\n几千条文本库也能做机器学习！NLP小数据集训练指南\n知乎“看山杯” 夺冠记\n\n","description":"","id":"https://blog.littleji.com/blog/nlp/","path":"/blog/nlp/","title":"实施NLP流水线之前干点什么(BeforANLPWork)"},"https://blog.littleji.com/blog/return/":{"body":"\nI guess it comes down to a simple choice:get busy living or get busy dying.\nThe Shawshank Redemption\n\n\n概述\n此文简单的概述重新建立博客的理由,以及过程\n\n原因\n\n重新建立博客 原因有这么几条:\n\n\n希望跟更多的人交流和分享一些心得\n实习有了一些收入\n科学上网的需求\n\n\n过程\n\n首先,上一次写博客大概是去年8月份的时候,由于用的是Amazon ec2,国内的浏览效果不好,SSH速度也很慢丢包严重,每次远程连接调一些配置都是挺煎熬一件事,于是就不再想去管它 ,并且,自己将平常的所看所想的一些东西写在为知笔记里面,也基本可以满足知识积累要求.\n其次,赶上写论文,实习等等事情,实在是没有精力再去维护博客服务器.\n最后,如同肖申克救赎里面的台词, I guess it comes down to a simple choice:get busy living or get busy dying,不再给自身找借口,拿出一天来重新建立博客.\n\n\nupdate\n\n好吧 最终还是用了Hugo来完成博客的编写,源于最近的香港服务器线路改道,也就不再更换服务器了.\n好吧 再次换到了hexo, 只是因为我的新机子不想装go了,而且正好机子里面有node,并且hugo国内貌似不太行,换着来呗\n好吧 换到了zola and tabi, 继续折腾。\n\n","description":"kill time","id":"https://blog.littleji.com/blog/return/","path":"/blog/return/","title":"重来"},"https://blog.littleji.com/pages/":{"body":"","description":"","id":"https://blog.littleji.com/pages/","path":"/pages/","title":""},"https://blog.littleji.com/pages/about/":{"body":"\n联系\n\n如果有问题 请留下评论，或提issue\n\n","description":"","id":"https://blog.littleji.com/pages/about/","path":"/pages/about/","title":"关于(About)"}},"docInfo":{"https://blog.littleji.com/":{"body":5,"description":0,"path":0,"title":2},"https://blog.littleji.com/archive/":{"body":0,"description":0,"path":1,"title":1},"https://blog.littleji.com/blog/":{"body":0,"description":0,"path":1,"title":2},"https://blog.littleji.com/blog/20160510somethingaboutsitemanagement/":{"body":164,"description":0,"path":2,"title":5},"https://blog.littleji.com/blog/20160512adjacencylistandadjacencymatrix/":{"body":334,"description":0,"path":2,"title":16},"https://blog.littleji.com/blog/20161115almostperfectgitconfiguration/":{"body":80,"description":0,"path":2,"title":19},"https://blog.littleji.com/blog/20161128don-tforgettheresultmapwhenyouusemybatis/":{"body":260,"description":0,"path":3,"title":13},"https://blog.littleji.com/blog/20161128usemultilineplugintodecodeawholejsonfile/":{"body":338,"description":0,"path":2,"title":20},"https://blog.littleji.com/blog/20161130usejavaapiforelasticsearch1/":{"body":347,"description":0,"path":2,"title":4},"https://blog.littleji.com/blog/20161201usejavaapiforelasticsearch2/":{"body":273,"description":0,"path":2,"title":4},"https://blog.littleji.com/blog/20161206quickstartforjsonrpc4j/":{"body":298,"description":0,"path":2,"title":7},"https://blog.littleji.com/blog/20161212usejsonrpcformattogetwhatyouwant/":{"body":358,"description":0,"path":2,"title":5},"https://blog.littleji.com/blog/20161219mysql-1146-1017/":{"body":171,"description":0,"path":2,"title":16},"https://blog.littleji.com/blog/20170510mybooklist/":{"body":406,"description":0,"path":2,"title":8},"https://blog.littleji.com/blog/20180117bitcoin/":{"body":828,"description":0,"path":2,"title":5},"https://blog.littleji.com/blog/20180907gettingstartedwithi3windowmanager/":{"body":677,"description":0,"path":2,"title":17},"https://blog.littleji.com/blog/20180925installdvwaonalpinefromscratch/":{"body":512,"description":0,"path":2,"title":12},"https://blog.littleji.com/blog/20181016formallanguagegenerality/":{"body":528,"description":0,"path":2,"title":5},"https://blog.littleji.com/blog/20181022probabilisticgraphicalmodels1/":{"body":951,"description":0,"path":2,"title":6},"https://blog.littleji.com/blog/20181025logstashgrokoverview/":{"body":612,"description":0,"path":2,"title":4},"https://blog.littleji.com/blog/20181127generativevsdiscriminative/":{"body":730,"description":0,"path":2,"title":8},"https://blog.littleji.com/blog/20181218mlreview1/":{"body":1528,"description":0,"path":2,"title":6},"https://blog.littleji.com/blog/20181222mlreview2/":{"body":403,"description":0,"path":2,"title":6},"https://blog.littleji.com/blog/20181225mlreview3/":{"body":1167,"description":0,"path":2,"title":6},"https://blog.littleji.com/blog/20190219handovermemolist/":{"body":494,"description":0,"path":2,"title":8},"https://blog.littleji.com/blog/20190318headfirstforgitlab/":{"body":1068,"description":0,"path":2,"title":6},"https://blog.littleji.com/blog/nlp/":{"body":480,"description":0,"path":2,"title":10},"https://blog.littleji.com/blog/return/":{"body":242,"description":2,"path":2,"title":2},"https://blog.littleji.com/pages/":{"body":0,"description":0,"path":1,"title":0},"https://blog.littleji.com/pages/about/":{"body":11,"description":0,"path":2,"title":2}},"length":30},"lang":"Chinese"}