TXT text segmentation tool, does not produce garbled, CStudioFile category by li...

一个结构化存储的文件通常也称之为OLE复合文档。 复合文档具有很多特性，比如： （1）可以加载和保存局部文档，这可以避免为了读取一小段数据而载入整个文件。 （2）Windows本身在内部就提供了对复合文档的支持。举个例子，即便系统中没有安装Word，我们在资源管理器中察看Word文档的属性时，也可以显示它的大量信息，比如摘要、统计、作者、字数等。因为这些信息就是从Word的复合文档中读取的。 微软公司声称结构化存储将是未来Windows的缺省文件格式。MS-Office就已经大量的使用了结构化存储。-a structured storage files are also known as OLE compound documents. Compound document has many features, such as : (a) can be loaded and preserving local documents, it can be avoided in order to read a small portion of the entire data contained in documents. (2) within Windows itself on the right compound document support. For example, even if the system had not been installed Word, in which resources management for Word documents look at the properties, it can also show that the vast amount of information, such as summary statistics, the author and not so. Because this information is from the compound Word document read. Microsoft claims that the structure of storage will be a future Windows is the default file format. MS-Office on the

重载过的cfile 读取大于2G的文件-Heavy-duty off CFile read a document larger than 2G

TinyXML是一个开源的解析XML的解析库。这个解析库的模型通过解析XML文件，然后在内存中生成DOM模型，从而让我们很方便的遍历这课XML树。给出了tinyxml应用示例，vc实现。-TinyXML is an open-source parsing XML parsing library. The analytical model of the library by parsing XML file, and then generate the DOM model in memory, so that we are very convenient to traverse the XML tree that lesson. Tinyxml application examples are given, vc achieved.

超级记事本.zip-super notepad.zip

如何将多个文件捆绑到一个与源代码匹配的可执行文件中。拉链

netty-springmvc整合netty和springmvc 框架，提高开发效率

手写体字符识别在多媒体设备人机交互、邮政信件分拣、银行票据自动处理等领域有着重要的应用。由于手写体字符的随意性，使其识别难度大于印刷体字符。通常，手写体字符识别包括联机手写体字符识别和脱机手写字符识别。联机手写体字符识别是指经过各种输入设备，将字符的轮廓及其笔划顺序按顺序送入计算机中；而脱机手写体字符没有笔划顺序这一重要特征，输入到计算机中的只是二维图像，而且相对于印刷体字符，手写体字符由于不同的人而不同，造成即使是同一字符，不同的人书写也会因字体、习惯不同而大不一样，因此，从识别技术的难度来说，脱机手写体字符识别的难度高于联机手写体识别。

将SQL脚本里的日本语文字转换成UTF-8格式.-Lane SQL script Japanese language text into UTF-8 format.

将RGB565格式的位图转换为C语言的代码-to RGB565 format bitmap conversion to the C language code

应用背景开始是做movielense的数据，可以把原始数据任意划分为train.dat和test.dat，主要是为了做验证实验。非常简单明了，适合初学者看看，如果不喜欢，请轻喷。关键技术# -*- coding: cp936 -*- from sklearn import cross_validation c = [] filename = r"Raw.data" #原始数据 out_train = open(r"train.txt","w") #训练集 out_test = open(r"test.txt","w") #测试集 for line in open(filename):     items = line.strip().split(",")      c.append(items)   c_train,c_test = cross_validation.train_test_split(c,test_size=0.1)#size =你需要的比例 for i in c_train:     out_train.write(",".join(i)+" ") for i in c_test:     out_test.write(",".join(i)+" ")