lr

以下脚本，定义两个一样的字符数组，对比后，打印出result的值： Action() { int result; char string1[] = "We can see the string:nancy"; char string2[] = "We can see the string:nancy"; lr_output_message("the string1 is %s.",string1); lr_output_message("the string2 is %s.",string2); result = strcmp(string1,string2); if ( result == 0 ) { lr_output_message("the result is 0."); } else { lr_output_message("the result is not 0."); } return 0; } 　　 　　 运行结果： Ending action vuser_init. Running Vuser... Starting iteration 1. Starting action Action. Action.c(7): the string1 is We can see the string:nancy. Action.c(8): the string2 is We

一、首先要理解 lr_convert_string_encoding 函数对中文进行UTF-8转码 的使用 int lr_convert_string_encoding ( const char *sourceString, const char *fromEncoding, const char *toEncoding, const char *paramName); 四个参数代表的含义： 　　sourceString：被转换的源字符串。 　　fromEncoding：转换前的字符编码。 　　toEncoding：要转换成为的字符编码。 　　paramName：转换后的目标字符串。 二、解决乱码问题 　　1.request乱码：将中文消息使用lr_convert_string_encoding先转码，再传入请求参数 　　注意：请求转码函数必须写在请求函数之前，如： //中文请求参数转换//char tmp[50]; lr_convert_string_encoding("中欧国际班列", LR_ENC_SYSTEM_LOCALE, LR_ENC_UTF8, "str"); //strcpy(tmp,lr_eval_string("{str}")); lr_save_string(lr_eval_string("{str}"),"payerName"); 　　 　　2

I used to think C++ was the "weird" one with all the ambiguities with < and > , but after trying to implement a parser I think I found an example which breaks just about every language that uses < and > for generic types: f(g<h, i>(j)); This could be syntactically either interpreted as a generic method call ( g ), or it could be interpreted as giving f the results of two comparisons. How do such languages (especially Java, which I thought was supposed to be LALR(1)-parsable? ) get around this syntactic ambiguity? I just can't imagine any non-hacky/context-free way of dealing with this, and I'm

为高效找到使损失函数的值最小的参数，关于最优化（optimization）提了很多方法。 其中包括： SGD（stochastic gradient descent，随机梯度下降） Momentum（冲量算法） Adagrad Adam 各优化算法比较实验（python） # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from collections import OrderedDict class SGD: """随机梯度下降法（Stochastic Gradient Descent）""" def __init__(self, lr=0.01): self.lr = lr def update(self, params, grads): for key in params.keys(): params[key] -= self.lr * grads[key] class Momentum: """Momentum SGD""" def __init__(self, lr=0.01, momentum=0.9): self.lr = lr self.momentum = momentum self.v = None def update(self, params, grads)

What advantages do LL parsers have over LR parsers to warrant their relative popularity in today's parser generator tools ? According to Wikipedia , LR parsing appears to have advantages over LL: LR parsing can handle a larger range of languages than LL parsing, and is also better at error reporting, i.e. it detects syntactic errors when the input does not conform to the grammar as soon as possible. This is in contrast to an LL(k) (or even worse, an LL(*) parser) which may defer error detection to a different branch of the grammar due to backtracking, often making errors harder to localize

I am working on my compilers concepts however I am a little confused... Googling got me nowhere to a definite answer. Is SLR and LR(0) parsers one and same? If not, whats the difference? Both LR(0) and SLR(1) parsers are bottom-up, directional, predictive parsers . This means that The parsers attempt to apply productions in reverse to reduce the input sentence back to the start symbol ( bottom-up ) The parsers scan the input from left-to-right ( directional ) The parsers attempt to predict what reductions to apply without necessarily seeing all of the input ( predictive ) Both LR(0) and SLR(1)

关联函数： 关联： web_reg_save_param("uid",//参数 "LB=value=",//左边界 "RB=>",//右边界 LAST);//关闭 检查点函数： 检查点： web_reg_find("Search=Body", "SaveCount=login1", "Text=Welcome, <b>jojo", LAST); if(atoi(lr_eval_string("{login1}"))>0){ lr_end_transaction("a1",LR_PASS); }else{ lr_end_transaction("a1",LR_FAIL); } 思考时间函数： 思考时间：lr_think_time(13); 集合点函数： 集合点： lr_rendezvous("11"); 参数：点击选中右键倒数第二个. 删除票业务： char a[1024]="BODY=";//定义a的数组 char b[100]; 定义b的数组 char c[100]; 定义c的数组 int len,ran,i; 定义：len，ran，i，整数类型变量 关联票的序号1：128001862-790-JM web_reg_save_param("uid1", "LB=name=\"flightID\" value=\"", "RB=\"", "ORD=All", LAST);

在Markdown中，一段流程图语法以 “ 开头，以 “ 结尾。 在 “` 后另起一行，书写graph XX，用以确定将要绘制的流程图及其类型（XX表示流程图类型）。 功能整理 流程图分为竖向和横向两大类，竖向包括自上而下和自下而上两种顺序，横向包括从右到左和从左到右两种顺序。 其对应语法分别为：graph TB/graph BT/graph RL/graph LR。 TB - top bottom（自上而下） graph TB A --> B graph TB A --> B BT - bottom top（自下而上） graph BT A --> B graph BT A --> B RL - right left（从右到左） graph RL A --> B graph RL A --> B LR - left right（从左到右） graph LR A --> B graph LR A --> B 对框线形状调整 圆角四边形 () graph LR A（这是圆角四边形） graph LR A(这是圆角四边形) 直角四边形 [ ] graph LR A[这是直角四边形] graph LR A[这是直角四边形] 圆形 (( )) graph LR A((这是圆形)) graph LR A((这是圆形)) 菱形 { } graph LR A{这是菱形} graph LR A

　　在录制，增强，整合 LoadRunner 脚本时，遇到了中文乱码问题。在此记录一下中文乱码问题的解决办法。 　 　一、录制回放中文乱码 　　我录制登陆的脚本，用户名中出现中文，回放的时候总是提示登陆失败。如下图： 　　 图1 LR回放中文乱码 　　解决中文乱码可以在录制的时候在Virtual User Gen的 Tools->Recoding Options -> Advanced -> Support charset -> UTF-8。重新录制后中文乱码问题得到解决。 　　 二、整合脚本中文乱码 　　录制增强（参数化，关联，检查点，事务）脚本后决定将几个脚本整合在一起。于是新建了一个空的脚本，将登陆退出公用操作分别放在vuser_init和vuser_end中，其他操作放在各自的Action中。整理完成回放后又出现中文乱码。为解决这个问题，最关键的是要把本地GBK编码的汉字转换成UTF-8编码格式的信息，为此我们引进loadrunner自带的编码函数lr_convert_string_encoding。 　　int lr_convert_string_encoding ( const char *sourceString, const char *fromEncoding, const char *toEncoding, const char *paramName); 　　

Is there a good resource online with a collection of grammars for some of the major parsing algorithms (LL(1), LR(1), LR(0), LALR(1))? I've found many individual grammars that fall into these families, but I know of no good resource where someone has written up a large set of example grammars. Does anyone know of such a resource? Parsing Techniques - A Practical Guide has several examples (i.e. probably half a dozen or so per type) of almost every type of grammar. You can purchase the 2nd edition book, although the 1st edition is available for free on the author's website in PDF form (near