The Bad Beginning: A Terrible Tale Do you enjoy happy endings? If so, The Bad Beginning by Lemony Snicket is a book you’ll want to avoid. This is a story with a bad beginning, a bad middle and a bad ending. Why would anyone want to read such a thoroughly bad book? It’s all in good fun!
标签： Beginning The Bad Terrible
上传时间： 2013-12-28
上传用户：笨小孩
The Linux Enterprise Cluster explains how to take a number of inexpensive computers with limited resources, place them on a normal computer network, and install free software so that the computers act together like one powerful server. This makes it possible to build a very inexpensive and reliable business system for a small business or a large corporation. The book includes information on how to build a high-availability server pair using the Heartbeat package, how to use the Linux Virtual Server load balancing software, how to configure a reliable printing system in a Linux cluster environment, and how to build a job scheduling system in Linux with no single point of failure. The book also includes information on high availability techniques that can be used with or without a cluster, making it helpful for System Administrators even if they are not building a cluster. Anyone interested in deploying Linux in an environment where low cost computer reliability is important will find this book useful.
标签： inexpensive Enterprise computers explains
上传时间： 2014-11-29
上传用户：zhangliming420
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
标签： meta-learning classifiers combining Boosting
上传时间： 2016-01-30
上传用户：songnanhua
古典密码中，主要的思想为移位算法及置换算法。 1.移位密码 密钥K为整数，且取值空间为0到25；加密函数：x = x + k (mod 26)；解密函数：x = x - k (mod 26)。当K=3时，为凯撒密码。 2.仿射密码 密钥对由a、b组成，整数a满足 gcd(a, 26) = 1，整数b的取值空间为0到25；加密函数：x = ax + b(mod 26)；解密函数：x = a*y - a*b (mod 26)。当a=1,b=3时，为凯撒密码。 3.维吉尼亚密码 首先确定密钥长度（本例中密钥只采取个位数字，所以取决于输入密钥的长度），然后输入满足这个长度的向量；加密：取明文第一个字母并将之移k1位，这里k1=1,第二个字母移k2位，k2=2,一旦到了密钥末尾，又从头开始。 4.换位密码 首先确定密钥长度，输入长度为5的0到4的整数序列，将明文分成每5个字母一组，每组字母按照密钥进行换位。
标签： 密码
上传时间： 2016-02-09
上传用户：jqy_china
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa. For example, >> project.name = MyProject >> project.id = 1234 >> project.param.a = 3.1415 >> project.param.b = 42 becomes with str=xml_format(project, off ) "<project> <name>MyProject</name> <id>1234</id> <param> <a>3.1415</a> <b>42</b> </param> </project>" On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
标签： converts Toolbox complex logical
上传时间： 2016-02-12
上传用户：a673761058
扫雷程序还会自动展开已确定没有雷的雷区。如果a[3][4]周围雷数为1，a[2][3]已被标示为地雷，那么a[2][4]，a[2][5],a[3][3]，a[3][5],a[4][3]，a[4][4]，a[4][5]将被展开，一直波及到不可确定的雷区。这也是实现的关键。我们可以把数组的元素设定为一个类对象，它们所属的类设定这样的一个事件：在被展开时，检查周围的雷数是否与周围标示出来的雷数相等，如果相等则展开周围未标示的雷区。这样新的雷区展开又触发这个事件，就这样递归下去，一直蔓延到不可展开的雷区。相信在了解以上两个要点后，把雷区这个类编写完全（如添加是否有雷标记，是否展开标记，周围雷数等，双击，左右单击的鼠标事件等），实现扫雷程序应是十分简单的一件事。
上传时间： 2016-02-13
上传用户：GHF
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
标签： performance equalizers Adaptive several
上传时间： 2016-02-16
上传用户：yan2267246
分数是两个整数的比，通常表示为 (或b/a)的形式，其中b称为分子，a称为分母，分母不能为0。分数在计算机中以整数或浮点数（有限小数）的形式表示，大多数情况下都是近似表示，具有较大的误差，例如 ，在计算机中用整数表示为0，用浮点数表示为0.333333。本实例就是要设计一个Fraction (分数) 类类型，该类型的对象可以像基本类型数据一样进行运算，结果仍为分数，运算包括四则运算，关系运算，及求一元一次分式方程的解，输入输出要求按分数方式进行。
上传时间： 2016-02-18
上传用户：zhoujunzhen
Mir2 Actor.pas if (IsFace) and (FaceIndex > -1) then begin d := aFrmMain.WFaceimg.Images[FaceIndex * 10 + (FaceFram) mod 8] /// if HorseSurface<>nil then // dSurface.Draw (dx+shiftx, dy + hpy + ShiftY-60, d.ClientRect, d, TRUE) // else if d <> nil then begin if HorseSurface <> nil then dsurface.Draw(SayX - d.Width div 2, dy + hpy + ShiftY - 60, d.ClientRect, d, True) else dsurface.Draw(SayX - d.Width div 2, dy + hpy + ShiftY - 50, d.ClientRect, d, True) end end end
标签： FaceIndex aFrmMain WFaceimg IsFace
上传时间： 2016-02-21
上传用户：ruixue198909
The problem of ¯ nding a linear discriminant function will be formulated as a problem of minimizing a criterion function
标签： problem discriminant formulated function
上传时间： 2014-11-30
上传用户：15071087253
