该文件是一个简短的语法分析文法，是用来实现LL1文法的，适用于初级学者！...

Standar Snake Using Pascal

一个用flex、bison和vc开发的堆栈机，堆栈机为一解释器，支持简单的表达式和赋值、条件和循环语句。-A used flex, bison and vc development stack machine, stack machine for one interpreter to support the simple expressions and assignment, conditions and loop.

编译原理中的语法分析，是对以ＰＡＳＣＡＬ的一个子集编写的编译程序，功能还行，自己看着用吧-Compiler Construction Principles of grammar analysis, is to a subset of PASCAL compiler preparation procedures, functions also visit with your own watch

词法分析器一个很不错的程序，源代码。 看看吧- A morphology analyzer very good procedure, source code. Has a look

本代码是语音识别的应用程序，语音识别的产品是新加坡的INFO，需要的请下载-This code is speech recognition applications, speech recognition products in Singapore INFO, need to download

一个由LEX编写的C-词法分析程序。为锻炼使用者能力，未加载LEX已编译的代码。建立时最好建立控制台程序。-LEX written by a C-lexical analysis program. The user the ability to exercise without load LEX compiled code. Is best to establish procedures for the establishment of the console.

解法:对原文法消除左递归,根据消除左递归后的等价文法建立语法树,而后对此语法树 进行后根遍历,即可得到后缀式.- Solution: Eliminates the left recursion to the original grammar, after equal in value grammar establishment grammar tree which eliminates the left recursion, after but the grammar tree carries on the root to spread the calendar regarding this, after then obtains decorates the type

很初级的，我们刚刚开编译原理- Very primary, we just opened the translation principle

(1)构造文法G的LR(0)项目 (2)构造文法G的LR(0)项目集规范族及识别活前缀的DFA (3)证明文法G不是LR(0)文法而是SLR(1)文法，并构造SLR(1)分析表 (4)设计LR语法分析程序，且能输出分析过程 (5)列举两个例子测试语法分析程序（识别失败一例，识别成功一例，后者推导步骤不得少于10步） -(1) construct grammar G of the LR (0) item (2) construct grammar G of the LR (0) item sets standards and identification of family living prefix DFA (3) prove that the grammar G is not LR (0) grammar, but the SLR (1 ) grammar, and constructs SLR (1) analysis table (4) design LR parser, and can output analysis process (5) cited two examples of parsing test procedures (identification fails an example, identify the success of a case, the latter shall not be derived step Less than 10 steps)

K-nearest neighbor classifier (k-NN) is a nonparametric approach for classification. It does not require the priori knowledge such as priori probabilities and the conditional probabilities. It operates directly towards the samples and is categorized as an instance-based classification method.