文件名：Classifier.h
#ifndef CLASSIFIER_H
#define CLASSIFIER_H
#include <iostream>
#include <stdio.h>
#define SELF0
#define NONSELF 1
#define MASKVALUE 2
// detector class
class Detector
{
public:
Detector(const unsigned int length)
Detector::~Detector(void)
unsigned int length
unsigned int *value
double threshold
unsigned int type
void save(FILE *outputStream)
void show(void) { save(stdout)}
}
#endif
/**********************************************/
//文件名：Classifier.cpp
#include "Classifier.h"
// detector class public methods
Detector::Detector(const unsigned int length)
{
this->length = length
threshold = 0.0
value = new unsigned int [length]
type = 0
}
Detector::~Detector(void)
{
delete [] value
}
void Detector::save(FILE *outputStream)
{
register unsigned int i
fprintf(outputStream, \
"%-3d %-.10f %-1d\n", \
length, \
threshold, \
type \
)
for(i = 0i <lengthi++)
fprintf(outputStream, "%-1d ", value[i])
fprintf(outputStream, "\n")
fflush(outputStream)
}
/**********************************************/
//文件名：EvolutionaryAlgorithm.h
#ifndef EVOLUTIONARYALGORITHM_H
#define EVOLUTIONARYALGORITHM_H
#include "Classifier.h"
#include <stdio.h>
// genome class
class Genome
{
public:
Genome(const unsigned int length)
~Genome(void)
unsigned int size
unsigned int *locus
unsigned int type
double mutationProbability
double crossoverProbability
double fitness, scaledFitness
unsigned int thresholdLength, patternLength
double generalityBias
double typeBias
void copyGenome(Genome *genome)
void uniformCrossover(Genome *genome1, Genome *genome2)
void mutateBinary(void)
void randomiseBinary(void)
void setDetector(Detector *detector)
void save(FILE *outputStream)
void show(void) { save(stdout)}
}
// species class
class Species
{
public:
Species(const unsigned int speciesSize, const unsigned int genomeLength)
~Species(void)
unsigned int speciesSize
Genome **genome
unsigned int fittestIndividual
double speciesScaledFitnessSum
double meanSpeciesFitness
Genome *FPSelection(void)
void randomise(void)
void copySpecies(Species *species)
void save(FILE *outputStream)
void show(void) { save(stdout)}
}
#endif
/**********************************************************/
//文件名：EvolutionaryAlgorithm.cpp
#include "EvolutionaryAlgorithm.h"
#include <stdlib.h>
// genome class public methods
Genome::Genome(const unsigned int length)
{
thresholdLength = 8
patternLength = length
size = thresholdLength + 2 * patternLength
locus = new unsigned int [size]
mutationProbability = 2.0 / double(size)
crossoverProbability = 0.6
generalityBias = typeBias = 0.5
fitness = 0.0
type = 0
}
Genome::~Genome(void)
{
delete [] locus
}
void Genome::copyGenome(Genome *genome)
{
register unsigned int i = size
register unsigned int *from = genome->locus
register unsigned int *to = locus
while(i--)
to[i] = from[i]
mutationProbability = genome->mutationProbability
crossoverProbability = genome->crossoverProbability
fitness = genome->fitness
scaledFitness = genome->scaledFitness
generalityBias = genome->generalityBias
size = genome->size
patternLength = genome->patternLength
thresholdLength = genome->thresholdLength
type = genome->type
}
void Genome::uniformCrossover(Genome *genome1, Genome *genome2)
{
register unsigned int i = size
register unsigned int *from1 = genome1->locus
register unsigned int *from2 = genome2->locus
register unsigned int *to = locus
register double cp = crossoverProbability
while(i--)
{
if(drand48() <cp)
to[i] = from1[i]
else
to[i] = from2[i]
}
if(drand48() <cp)
type = genome1->type
else
type = genome2->type
}
void Genome::mutateBinary(void)
{
register unsigned int i = size
register unsigned int *loci = locus
register double mp = mutationProbability
while(i--)
if(drand48() <mp)
loci[i] = 1 - loci[i]
if(drand48() <mp)
type = 1 - type
}
void Genome::randomiseBinary(void)
{
register unsigned int index, i
index = 0
i = thresholdLength
while(i--)
locus[index++] = int((double(rand()) * 2.0) / double(RAND_MAX + 1.0))
i = patternLength
while(i--)
locus[index++] = int((double(rand()) * 2.0) / double(RAND_MAX + 1.0))
i = patternLength
while(i--)
if(drand48() <generalityBias)
locus[index++] = 0
else
locus[index++] = 1
if(drand48() <typeBias)
type = SELF
else
type = NONSELF
}
void Genome::save(FILE *outputStream)
{
register unsigned int i
Detector *detector = new Detector(patternLength)
fprintf(outputStream, \
"%-3d %-3d %-3d %-1d %-10f %-10f %-10f %-10f %-10f %-10f\n", \
size, \
thresholdLength, \
patternLength, \
type, \
fitness, \
scaledFitness, \
mutationProbability, \
crossoverProbability, \
generalityBias, \
typeBias \
)
for(i = 0i <sizei++)
fprintf(outputStream, "%-2d ", locus[i])
fprintf(outputStream, "\n")
setDetector(detector)
detector->save(outputStream)
delete detector
fflush(outputStream)
}
void Genome::setDetector(Detector *detector)
{
register unsigned int i, loci = 0, sum, lastLoci
// set activation threshold
// gray coding for threshold gene
sum = lastLoci = locus[loci++]
while(loci <thresholdLength)
{
sum = (sum <<1) | (lastLoci ^ locus[loci])
lastLoci = locus[loci++]
}
detector->threshold = double(sum) / 255.0// !!!!!!!!!!!!!!!!!!!!
for(i = 0i <patternLengthi++)
detector->value[i] = locus[loci++]
for(i = 0i <patternLengthi++)
if(!locus[loci++])
detector->value[i] = MASKVALUE
detector->type = type
detector->length = patternLength
}
// species class public methods
Species::Species(const unsigned int speciesSize, \
const unsigned int genomeLength)
{
register unsigned int i = speciesSize
this->speciesSize = speciesSize
fittestIndividual = 0
speciesScaledFitnessSum = meanSpeciesFitness = 0.0
genome = new Genome * [speciesSize]
while(i--)
genome[i] = new Genome(genomeLength)
}
Species::~Species(void)
{
register unsigned int i = speciesSize
while(i--)
delete genome[i]
delete genome
}
Genome *Species::FPSelection(void)
{
register unsigned int i = 0
register double dtmp1, dtmp2
dtmp1 = drand48() * speciesScaledFitnessSum
dtmp2 = 0.0
while((i <speciesSize) &&((dtmp2 = dtmp2 + genome[i]->scaledFitness) \
<dtmp1))
i++
return((i <speciesSize) ? genome[i] : genome[i - 1])
}
void Species::randomise(void)
{
register unsigned int i = speciesSize
while(i--)
genome[i]->randomiseBinary()
}
void Species::save(FILE *outputStream)
{
fprintf(outputStream, \
"%-4d %-4d %-5.10f %-.10f\n", \
speciesSize, \
fittestIndividual, \
speciesScaledFitnessSum, \
meanSpeciesFitness \
)
genome[fittestIndividual]->save(outputStream)
fflush(outputStream)
}
void Species::copySpecies(Species *species)
{
register unsigned int i = species->speciesSize
speciesSize = i
while(i--)
genome[i]->copyGenome(species->genome[i])
fittestIndividual = species->fittestIndividual
speciesScaledFitnessSum = species->speciesScaledFitnessSum
meanSpeciesFitness = species->meanSpeciesFitness
}
//补充了下，刚才少了个Classifier.cpp。
分类: 电脑/网络 >>程序设计 >>其他编程语言问题描述:
我想学下编程，但是想这些东西经常听老师们提起，他们有关联吗？如果有的话，请告诉我学习的顺序，先学哪个，在学哪个，谢谢各位大侠拉~~！！小弟在这谢谢拉~~！
解析:
basic就不要提了，几十年前就被淘汰了
先说说VB吧：
建议不学VB，因为VB垃圾、简单至极、功能不全、开发出来的软件对系统资源依赖性强（俗话-运行的这么卡呢？）、以上原因导致VB已经被社会淘汰了，已经没有客户要买用VB开发出来的软件了。
VB的开发实际上就是在一个窗口里“画”按钮之类的东西，然后再在按钮里加代码，这个所谓“按钮”实际上是VB众多被称为“控件”的东西的其中之一，是别人为你编写好的，但这些“控件”是用什么编写的呢？，是用C++编写的（VB想要实现什么功能，必须先用C++做控件），所以有句话说的好“真正的VB高手其实是C++高手”，不学VB还有一个原因，就是他破坏你学习其他软件的思路，他的语法与其他流行软件的语法不一样，学完以后会有少许误入歧途的感觉。
说说C语言：
我学过C语言，它只能编写DOS程序，功能不是很强大，但却是基础，因为大部分流行的编程软件都与C语言的语法结构类似，一定要先学。
C++：
C语言进化而来的，有强大的功能，举例吧：
软件：
微软的Office系列
Macromedia网页三剑客 - flash，dreamweaver，fireworks
Adobe出的 超有名的作图软件 - PhotoShop
3D动画软件 - 3DMax
.....
游戏：
PC平台几乎所有的游戏
星际争霸、魔兽争霸、CS、帝国时代、跑跑卡丁车、传奇、魔兽世界....
那数不胜数了，自己数吧:)
以上均出自C++之手，原因是C++靠近计算机底层，编写出来的程序对系统资源依赖较小，功能强大，运行速度快，比如你的两个朋友与你分别玩 用 VB、Java、与C++编写的“跑跑卡丁车”，你玩C++那款游戏已经跑玩结束了，发现你的两个朋友还没开始跑呢，那是相当的卡啊，所以这一系列的软件、游戏都会采用C++编写。
虽然C++很有优点，但是对于学习者来说应该是比较困难的一门语言。（再说明一点，C或C++属于语言，而VC - “Visual C++”是用来编写C或C++的工具）
Java：
不可小视的编程语言，我举不出来用它编写的应用软件和游戏，因为它是商业编程语言，我之前举的C++开发的软件都是面向普通人的，而Java开发的软件大部分是面向事业单位、公司及企业的，它的功能同样强大，结构比C++清晰，学习起来比C++简单多了，并且是跨平台运行的程序，用Java开发出来的软件可以在世界几乎所有的系统上运行（包括Linux、掌上电脑、手机等），但正因为如此，使之运行时会比C++开发的软件要慢。
没接触过编程的人恐怕很少有人知道，我不知道楼主你年龄多大了，你学编程是因为兴趣还是因为职业？如果是因为职业，建议选择学习Java。
VF：
Visual FoxPro - 微软出的数据库编程软件，和以上都不一样，没必要考虑这个，并且近几年已经被微软淘汰，因为这个软件在世界上早已被 Sql-Server 和 Oracle 等数据库编程软件所取代。
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