先看一下语言模型的输出格式 \data\ ngram = ngram = ngram = \-grams: -5.24036 'cause -0.2084827 -4.675221 'em -0.221857 -4.989297 'n -0.05809768 -5.365303 'til -0.1855581 -2.111539 </s> 0.0 - <s> -0.7736475 -1.128404 <unk> -0.8049794 -2.271447 a -0.616

SRILM是一个建立和使用统计语言模型的开源工具包,从1995年开始由SRI 口语技术与研究实验室(SRI Speech Technology and Research Laboratory)开发,现在仍然不断推出新版本,被广泛应用于语音识别.机器翻译等领域.这个工具包包含一组C++类库.一组进行语言模型训练和应用的可执行程序等.利用它可以非常方便地训练和应用语言模型.给定一组连续的词,调用SRILM提供的接口,可以得到这组词出现的概率. http://www.jianshu.com/p/5b1

目录: 一基本训练 二语言模型打分 三语言模型剪枝 四语言模型合并 五语言模型使用词典限制 一.基本训练 #功能 读取分词后的text文件或者count文件,然后用来输出最后汇总的count文件或者语言模型 #参数 输入文本: -read 读取count文件 -text 读取分词后的文本文件 词典文件: -vocab 限制text和count文件的单词,没有出现在词典的单词替换为<unk>:如果没有,所有的单词将会被自动加入词典 -limit-vocab 只限制count文件的单词(对text

[本文链接] http://www.cnblogs.com/hellogiser/p/random-generator-with-equal-or-unequal-probability.html 1. 等概率生成 (1) rand5生成rand3 现在有一个Rand5函数,可以生成等概率的[0, 5)范围内的随机整数,要求利用此函数写一个Rand3函数(除此之外,不能再使用任何能产生随机数的函数或数据源),生成等概率的[0, 3)范围内的随机整数.  C++ Code  12345678910

作者:韩信子@ShowMeAI 教程地址:http://www.showmeai.tech/tutorials/36 本文地址:http://www.showmeai.tech/article-detail/239 声明:版权所有,转载请联系平台与作者并注明出处 收藏ShowMeAI查看更多精彩内容 本系列为斯坦福CS224n<自然语言处理与深度学习(Natural Language Processing with Deep Learning)>的全套学习笔记,对应的课程视频可以在 这里 查看

作者:韩信子@ShowMeAI,路遥@ShowMeAI,奇异果@ShowMeAI 教程地址:http://www.showmeai.tech/tutorials/36 本文地址:http://www.showmeai.tech/article-detail/252 声明:版权所有,转载请联系平台与作者并注明出处 收藏ShowMeAI查看更多精彩内容 ShowMeAI为斯坦福CS224n<自然语言处理与深度学习(Natural Language Processing with Deep Learn

这个源之于一个朋友问我的一个问题,他说他们的需求是在一天之内随机抽取数据生成订单,还不能让客户看出来. 随机生成的订单还分概率抽取不一定的状态值,那么根据我之前写的定时器线程执行器,我们设计需要一个定时器去执行. 那么我们的定时器坑定需要一直运行,包括每天的情况. 创建 SecondsTimerTask 由于是测试条件下,我们声明5秒运行一次 /// <summary> /// 每秒执行的任务 /// </summary> public class SecondsTimerTask

本篇主要是参考 lua连续随机数 这篇文章完成.大家可以去原贴查看学习. 生成随机数组,暂时发现两种方法 1.把生成的数放到一个表里面,每次随机时判断这个表里是否有,若有再随机一次(问了朋友,很多人都想到这个方法) 2.先生成一个连续的数字表t,每次随机一个数n,把t[n]保存,并移除t[n] 首先,说一下我的真实需求是给定一定范围,然后生成指定个数的不重复随机数组.这个给定范围,可能很大,可能很小,指定个数可能比数组个数大,也可能只比数组个数小一点,也可能比数组个数小很多. 例如,1.从1~1

我们都知道使用Random可以生成随机数,默认的无参的构造函数New Random().使用与时间相关的默认种子值,初始化 System.Random 类的新实例. 这种方式生成随机数时重复的概率很大.可以传入一个种子,用来计算伪随机数序列起始值的数字. /// <summary> /// 创建加密随机数生成器 生成强随机种子 /// </summary> /// <returns></returns> private static int GetRando

1. 遗传编程简介 0x1:什么是遗传编程算法,和传统机器学习算法有什么区别 传统上,我们接触的机器学习算法,都是被设计为解决某一个某一类问题的确定性算法.对于这些机器学习算法来说,唯一的灵活性体现在参数搜索空间上,向算法输入样本,算法借助不同的优化手段,对参数进行调整,以此来得到一个对训练样本和测试样本的最佳适配参数组. 遗传编程算法完全走了另一外一条路,遗传编程算法的目标是编写一个程度,这个程序会尝试自动构造出解决某一问题的最佳程度.从本质上看,遗传编程算法构造的是一个能够构造算法的算法.

1. 从纳什均衡(Nash equilibrium)说起 我们先来看看纳什均衡的经济学定义: 所谓纳什均衡,指的是参与人的这样一种策略组合,在该策略组合上,任何参与人单独改变策略都不会得到好处.换句话说,如果在一个策略组合上,当所有其他人都不改变策略时,没有人会改变自己的策略,则该策略组合就是一个纳什均衡. B站上有一个关于”海滩2个兄弟卖雪糕“形成纳什均衡的视频,讲的很生动. 不管系统中的双方一开始处于什么样的状态,只要系统中参与竞争的个体都是”理性经济人“,即每个人在考虑其他人的可能动作的基

在分布式系统中,有一些场景需要使用全局唯一 ID ,可以和业务场景有关,比如支付流水号,也可以和业务场景无关,比如分库分表后需要有一个全局唯一 ID,或者用作事务版本号.分布式链路追踪等等,好的全局唯一 ID 需要具备这些特点: 全局唯一:这是最基本的要求,不能重复: 递增:有些特殊场景是必须递增的,比如事务版本号,后面生成的 ID 一定要大于前面的 ID :有些场景递增比不递增要好,因为递增有利于数据库索引的性能: 高可用:如果是生成唯一 ID 的系统或服务,那么一定会有大量的调用,那么保证其

本文系原创,转载请说明出处:信安科研人 关注微信公众号 信安科研人 获取更多网络安全学术技术资讯 今日介绍一篇发表在2021 NDSS会议上的一项有关协议逆向的工作: @ 目录 1 网络协议逆向工程简介 1.1 协议逆向工程的主流技术 案例 1.2 基于对齐的聚类 1.3 基于标识的聚类 1.4 NETPLIER的构思 2 NETPLIER--系统设计 2.1 预处理 2.2 关键字段候选列表生成 2.2.1 序列对齐 2.2.2 生成字段 2.3 基于概率的关键字段识别 2.4 迭代对齐和聚类

摘要:面对庞大服务接口群,完备的接口测试覆盖和业务上下文场景测试看护才有可能保障产品服务的质量和可信.如果你想低成本实现产品和服务的测试高覆盖和高质量看护,这篇文章将为你提供你想要的. 本文分享自华为云社区<基于语义感知SBST的API场景测试智能生成[智能云测]>,作者:DevAI. 1 背景 据2022年统计数据,华为云已发布商用的云服务达到220+.以单个服务500+接口数量计算,已商用的云服务的接口总数高达10W+.每接口参数数量更是平均超过20个,全组合测试会带来组合爆炸,费时费资源

第四章 N元语法 本章开篇的两句话很有意思,代表了当时两个学派的思想和矛盾. 一句是"有史以来最伟大的语言学家"乔姆斯基说的:"句子的概率,在任何已知的对于这个术语的解释中,都是一个完全无用的概念". 他提出了形式语言,坚信可以通过语法规则来处理文字,对概率不屑一顾. 而另一句是语言处理大师贾里尼克说的:"每当我开除一个语言学家,语言识别率就能提高一个百分比". 此前他曾有和语言学家不愉快的合作经历,加之当时概率学习方法取得了显著成果,因此也是心

从决策树学习谈到贝叶斯分类算法.EM.HMM     引言 最近在面试中,除了基础 &  算法 & 项目之外,经常被问到或被要求介绍和描述下自己所知道的几种分类或聚类算法(当然,这完全不代表你将来的面试中会遇到此类问题,只是因为我的简历上写了句:熟悉常见的聚类 & 分类算法而已),而我向来恨对一个东西只知其皮毛而不得深入,故写一个有关数据挖掘十大算法的系列文章以作为自己备试之用,甚至以备将来常常回顾思考.行文杂乱,但侥幸若能对读者起到一点帮助,则幸甚至哉. 本文借鉴和参考了两本书,

从决策树学习谈到贝叶斯分类算法.EM.HMM                (Machine Learning & Recommend Search交流新群:172114338) 引言 log0为0). 如果写代码实现熵的计算,则例如以下所看到的: //依据详细属性和值来计算熵 double ComputeEntropy(vector <vector <string> > remain_state, string attribute, string value,bool i

两周以前读了些文档自动摘要的论文,并针对其中两篇( [2] 和 [3] )做了presentation.下面把相关内容简单整理一下. 文本自动摘要(Automatic Text Summarization)就是说在不改变文档原意的情况下,利用计算机程序自动地总结出文档的主要内容.自动摘要的应用场景非常多,例如新闻标题生成.科技文献摘要生成.搜索结果片段(snippets)生成.商品评论摘要等.在信息爆炸的互联网大数据时代,如果能用简短的文本来表达信息的主要内涵,无疑将有利于缓解信息过载问题. 一

本文来自李纪为博士的论文 A Diversity-Promoting Objective Function for Neural Conversation Models 1,概述 对于seq2seq模型类的对话系统,无论输入是什么,都倾向于生成安全,通用的回复(例如 i don't know 这一类的回复),因为这种响应更符合语法规则,在训练集中出现频率也较高,最终生成的概率也最大,而有意义的响应生成概率往往比他们小.如下表所示: 上面的表中是seq2seq对话系统产生的结果,分数最高的回复通常

Lattice是一个无环WFSA,结点可以是hmm状态.hmm(音素).词,每个结点是一段音频在某个时间的对齐 用训练好的声学模型.现成的语言模型和发音字典构建解码网络(wfst),最后将提取的测试集的语音特征输入以上解码网络,得到网格结构(lattice)的语音识别结果. Lattice可用于语言模型的得分重估计 一个Lattice是为一段音频产生的,Lattice是一个带权无向图,其中每个点代表一个声学单元,每条弧包含两个权重(声学权重和语言权重).解码时,会使用Viterbi算法?结合两种