有聊的程序员看这里。评选我心目中的处女级C/C++代码

[phoneix818]

写出自己的处女级C/C++代码哈。我先上一个

#include <stdio.h>
#define sex int
void touch(sex male, sex female)
{
		printf("%d*%d=%d\t", male, female, male*female);
		if (male==female)
		{
			printf("\n");
		}
}

#define makelove touch(male,female)
void exchange(sex male, sex female)
{
	if (female==1)
	{
		makelove;
	}
	else if (male==1)
	{
		exchange(female-1, female-1);
		makelove;
	}
	else
	{
		exchange(male-1, female);
		makelove;
	}
}

sex main(void)
{
	exchange(9,9);

	return 0;
}

[A_Zhao]

printf("Hello Kitty!");

[珍惜生命远离CPP]

[muqiuyu1988]

#include<stdio.h>

int main()
{
printf("Hello World!");
return 0;
}

[phoneix818]

常回家看看---恋爱篇 (转载)
发信站: BBS 水木清华站 (Thu Apr 19 20:59:44 2001)

找点儿空闲，找点儿时间
寻个MM，很希望谈谈
问问学校，看看照片
只要PP，很希望谈谈
哥哥看上去有些腼腆
妹妹却不是十分害羞
如果想脱光不要罗嗦
假如要失明就别怕麻烦
很希望谈谈，希望谈谈
哪怕是青蛙王子长得不好看
妹妹不图哥哥长相到底有多帅呀
爱上你不容易就图个有人撒赖
很希望谈谈，希望谈谈
哪怕是恐龙妹妹侏罗纪公园
哥哥不图妹妹美貌比过饭岛爱呀
搞定你不容易总图个有人做饭

标 题: 常回家看看---灌水篇 (转载)
发信站: BBS 水木清华站 (Thu Apr 19 20:59:29 2001)

找点儿空闲，找点儿时间
选个ID，常上来灌灌
喝瓶啤酒，抽根香烟
无论多晚，常上来灌灌
华健准备了一些好坑
大家回敬了一版好水
生活的烦恼不要再多说
工作的事情也不必再谈
常上来灌灌，上来灌灌
哪怕和妹妹几几歪歪聊聊天
挖坑不图是否真有多少人来灌呀
一通宵不容易就图个好好玩玩
常上来灌灌，上来灌灌
哪怕被版主封闭权限N多天
灌水不图最终版主是不是MARK呀
一晚上总熬夜只图个平平安安

[phoneix818]

标题写错了，应该叫无聊的人看这里

[测试NULL]

随便发个代码：

/**************************************************************************/
/* This is a simple genetic algorithm implementation where the */
/* evaluation function takes positive values only and the      */
/* fitness of an individual is the same as the value of the    */
/* objective function                                          */
/**************************************************************************/
/* population[POPSIZE].fitness保存最好的适应度*/

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

/* Change any of these parameters to match your needs */

#define POPSIZE 50               /* population size染色体个数 */
#define MAXGENS 1000             /* max. number of generations迭代次数 */
#define NVARS 3                  /* no. of problem variables 变量个数*/
#define PXOVER 0.8               /* probability of crossover 交叉概率*/
#define PMUTATION 0.15           /* probability of mutation变异概率*/
#define TRUE 1
#define FALSE 0

int generation;                  /* current generation no. 当前代数*/
int cur_best;                    /* best individual 最好个体*/
FILE *galog;                     /* an output file 输出文件*/
/*染色体结构体*/
struct genotype /* genotype (GT), a member of the population 染色体成员*/
{
  double gene[NVARS];        /* a string of variables一连串变量 */
  double fitness;            /* GT's fitness 个体适应度*/
  double upper[NVARS];       /* GT's variables upper bound 个体上界*/
  double lower[NVARS];       /* GT's variables lower bound 个体下界*/
  double rfitness;  /* relative fitness相关适应度等于个体适应度比所有个体适应度之和**/
  double cfitness;           /* cumulative fitness 累积适应度是相关是适应度的累积*/
};
/*声明个体*/
struct genotype population[POPSIZE+1];    /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
                                          /* replaces the */
                                          /* old generation */

/* Declaration of procedures used by this genetic algorithm */
/*声明的程序使用的遗传算法*/
void initialize(void);/*初始化函数*/
doublerandval(double, double);
void evaluate(void);/*评估*/
void keep_the_best(void); /*保持最好的*/
void elitist(void);
void select(void);/*选择*/
void crossover(void);/*交叉*/
voidXover(int,int);
void swap(double *, double *);
void mutate(void);/*变异*/
void report(void);

/***************************************************************/
/* Initialization function: Initializes the values of genes    */
/* within the variables bounds. It also initializes (to zero)  */
/* all fitness values for each member of the population. It    */
/* reads upper and lower bounds of each variable from the      */
/* input file `gadata.txt'. It randomly generates values       */
/* between these bounds for each gene of each genotype in the  */
/* population. The format of the input file `gadata.txt' is    */
/* var1_lower_bound var1_upper bound                           */
/* var2_lower_bound var2_upper bound ...                       */
/***************************************************************/
/*初始化函数：在定义域内初始化基因并计算适应度，从文件“gadata.txt”中读定义域*/
void initialize(void) /*初始化*/
{
FILE *infile;
int i, j;
doublelbound, ubound;

if ((infile = fopen("gadata.txt","r"))==NULL)
      {
fprintf(galog,"\nCannot open input file!\n");
exit(1);
      }

/* initialize variables within the bounds */
/*NVARS变量的个数*/
for (i = 0; i < NVARS; i++)
      {
fscanf(infile, "%lf",&lbound);
fscanf(infile, "%lf",&ubound);
/*POPSIZE 种群规模*/
for (j = 0; j < POPSIZE; j++)
           {
population[j].fitness = 0;
population[j].rfitness = 0;
population[j].cfitness = 0;
population[j].lower[i] = lbound;
population[j].upper[i]= ubound;
population[j].gene[i] = randval(population[j].lower[i],
population[j].upper[i]);
           }
      }
fclose(infile);
}
/*每个变量初始popsize个个体*/
/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/
/*Randval随机在定义域内取值*/
doublerandval(double low, double high)
{
doubleval;
val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
return(val);
}

/*************************************************************/
/* Evaluation function: This takes a user defined function.  */
/* Each time this is changed, the code has to be recompiled. */
/* The current function is:  x[1]^2-x[1]*x[2]+x[3]  目标函数 */
/*************************************************************/

void evaluate(void)
{
int mem;
int i;
double x[NVARS+1];

for (mem = 0; mem < POPSIZE; mem++)
      {
for (i = 0; i < NVARS; i++)
            x[i+1] = population[mem].gene[i]; /*个体populartion变量gene*/

population[mem].fitness = (x[1]*x[1]) - (x[1]*x[2]) + x[3];
      }
}


/***************************************************************/
/* Keep_the_best function: This function keeps track of the    */
/* best member of the population. Note that the last entry in  */
/* the array Population holds a copy of the best individual    */
/***************************************************************/
/*最好个体函数保持副本*/
void keep_the_best()
{
int mem;
int i;
cur_best = 0; /* stores the index of the best individual */
for (mem = 0; mem < POPSIZE; mem++)
      {
if (population[mem].fitness > population[POPSIZE].fitness)
            {
            cur_best = mem;
population[POPSIZE].fitness = population[mem].fitness;
            }
      }
/* once the best member in the population is found, copy the genes */
for (i = 0; i < NVARS; i++)
population[POPSIZE].gene[i] = population[cur_best].gene[i];
}

/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of    */
/* the current generation is worse then the best member of the  */
/* previous generation, the latter one would replace the worst  */
/* member of the current population                             */
/****************************************************************/
/*当前代数的最好个体和目前已找到的最好个体比较，最好的保存下来淘汰最差的*/
void elitist()
{
int i;
double best, worst;             /* best and worst fitness values */
intbest_mem, worst_mem; /* indexes of the best and worst member */

best = population[0].fitness;
worst = population[0].fitness;
for (i = 0; i < POPSIZE - 1; ++i)
      {
if(population[i].fitness > population[i+1].fitness)
            {      
if (population[i].fitness >= best)
                  {
best = population[i].fitness;
best_mem = i;
                  }
if (population[i+1].fitness <= worst)
                  {
worst = population[i+1].fitness;
worst_mem = i + 1;
                  }
            }
else
            {
if (population[i].fitness <= worst)
                  {
worst = population[i].fitness;
worst_mem = i;
                  }
if (population[i+1].fitness >= best)
                  {
best = population[i+1].fitness;
best_mem = i + 1;
                  }
            }
      }
/* if best individual from the new population is better than */
/* the best individual from the previous population, then    */
/* copy the best from the new population; else replace the   */
/* worst individual from the current population with the     */
/* best one from the previous generation                     */

if (best >= population[POPSIZE].fitness)
    {
    for (i = 0; i < NVARS; i++)  /*取代当前已找到最好的个体*/
population[POPSIZE].gene[i] = population[best_mem].gene[i];
population[POPSIZE].fitness = population[best_mem].fitness;
    }
else
    {
    for (i = 0; i < NVARS; i++)/*淘汰当代中最差的个体*/
population[worst_mem].gene[i] = population[POPSIZE].gene[i];
population[worst_mem].fitness = population[POPSIZE].fitness;
    } 
}

[测试NULL]

/**************************************************************/
/* Selection function: Standard proportional selection for    */
/* maximization problems incorporating elitist model - makes  */
/* sure that the best member survives                         */
/**************************************************************/
/*比例选择*/
void select(void)
{
int mem, i, j, k;
double sum = 0;
double p;
/* find total fitness of the population */
for (mem = 0; mem < POPSIZE; mem++)
      {
sum += population[mem].fitness;
      }

/* calculate relative fitness 相关适应度等于个体适应度比所有个体适应度之和*/
for (mem = 0; mem < POPSIZE; mem++)
      {
population[mem].rfitness =  population[mem].fitness/sum;
      }
population[0].cfitness = population[0].rfitness;

/* calculate cumulative fitness */
for (mem = 1; mem < POPSIZE; mem++)
      {
population[mem].cfitness =  population[mem-1].cfitness +       
population[mem].rfitness;
      }
/* finally select survivors using cumulative fitness. */
/*选择依据累积适应度*/
for (i = 0; i < POPSIZE; i++)
      { 
      p = rand()%1000/1000.0;
if (p < population[0].cfitness)
newpopulation[i] = population[0];      
else
            {
for (j = 0; j <POPSIZE;j++)      
if (p >= population[j].cfitness&&
                              p<population[j+1].cfitness)
newpopulation[i] = population[j+1];
            }
      }
/* once a new population is created, copy it back */
for (i = 0; i < POPSIZE; i++)
population[i] = newpopulation[i];      
}

/***************************************************************/
/* Crossover selection: selects two parents that take part in  */
/* the crossover. Implements a single point crossover          */
/***************************************************************/
/*单点交叉*/
void crossover(void)
{
int i, mem, one;
int first  =  0; /* count of the number of members chosen */
double x;

for (mem = 0; mem < POPSIZE; ++mem)
      {
      x = rand()%1000/1000.0;
if (x < PXOVER)
            {
            ++first;
if (first % 2 == 0)
Xover(one, mem);
else
one = mem;
            }
      }
}
/**************************************************************/
/* Crossover: performs crossover of the two selected parents. */
/**************************************************************/

voidXover(int one, int two)
{
int i;
int point; /* crossover point */

/* select crossover point */
if(NVARS > 1)
   {
if(NVARS == 2)
point = 1;
else
point = (rand() % (NVARS - 1)) + 1;

for (i = 0; i < point; i++)
swap(&population[one].gene[i], &population[two].gene[i]);

   }
}

/*************************************************************/
/* Swap: A swap procedure that helps in swapping 2 variables */
/*************************************************************/

void swap(double *x, double *y)
{
double temp;

temp = *x;
*x = *y;
*y = temp;

}

/**************************************************************/
/* Mutation: Random uniform mutation. A variable selected for */
/* mutation is replaced by a random value between lower and   */
/* upper bounds of this variable                              */
/**************************************************************/
/*随机均匀变异*/
void mutate(void)
{
int i, j;
doublelbound, hbound;
double x;

for (i = 0; i < POPSIZE; i++)
for (j = 0; j < NVARS; j++)
            {
            x = rand()%1000/1000.0;
if (x < PMUTATION)
                  {
                  /* find the bounds on the variable to be mutated */
lbound = population[i].lower[j];
hbound = population[i].upper[j];  
population[i].gene[j] = randval(lbound, hbound);
                  }
            }
}

/***************************************************************/
/* Report function: Reports progress of the simulation. Data   */
/* dumped into the  output file are separated by commas        */
/***************************************************************/

void report(void)
{
int i;
doublebest_val;            /* best population fitness */
doubleavg;                 /* avg population fitness */
doublestddev;              /* std. deviation of population fitness 偏差*/
doublesum_square;          /* sum of square for std. calc */
doublesquare_sum;          /* square of sum for std. calc */
double sum;                 /* total population fitness */

sum = 0.0;
sum_square = 0.0;

for (i = 0; i < POPSIZE; i++)
      {
sum += population[i].fitness;
sum_square += population[i].fitness * population[i].fitness;
      }

avg = sum/(double)POPSIZE;
square_sum = avg * avg * POPSIZE;
stddev = sqrt((sum_square - square_sum)/(POPSIZE - 1));
best_val = population[POPSIZE].fitness;

fprintf(galog, "\n%5d,      %6.3f, %6.3f, %6.3f \n\n", generation, 
best_val, avg, stddev);
}
/**************************************************************/
/* Main function: Each generation involves selecting the best */
/* members, performing crossover & mutation and then          */
/* evaluating the resulting population, until the terminating */
/* condition is satisfied                                     */
/**************************************************************/
int main(void)
{
int i;

if ((galog = fopen("galog.txt","w"))==NULL)
      {
exit(1);
      }
generation = 0;

fprintf(galog, "\n generation  best  average  standard \n");
fprintf(galog, " number      value fitness  deviation \n");

initialize();/*初始化*/
evaluate();/*评估*/
keep_the_best();
while(generation<MAXGENS)
      {
generation++;
select();
crossover();
mutate();
report();
evaluate();
      elitist(); /*保存最好的淘汰最差的*/
      }
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");

for (i = 0; i < NVARS; i++)
   {
fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene[i]);
   }
fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness);
fclose(galog);
printf("Success\n");
}
/***************************************************************/

[羽飞]

无聊

[phoneix818]

《至少还有你》世贸版—转自高中同学录，不知道old否？
发信站: BBS 水木清华站 (Wed Oct 17 07:03:03 2001)

我怕来不及，我要撞掉你，
直到看到整个纽约有了地震的痕迹。
直到肯定你是倒了，
直到烟灭灰飞，
为报仇，我愿意。
人民也心痛，股市也颤动，
直到仿佛整个白宫有了痛苦的回忆。
直到总统变得愤怒，
直到球赛延期，
让我们欢庆胜利。

如果上天再给我一架飞机，
我也要用它撞向美国大地，
布什能躲过就是生命的奇迹。
也许阿富汗我也可以躲避，

只是不愿意失去父母兄弟，
为真理正义我要坚决斗到底。

我们好不容易，我们精心算计。
我怕速度太快，不够将你撞彻底；
我怕角度太刁，让你倒的太容易。
恨不得亲自驾驶飞机，直接奔向你。