common.h

自己编写的人工智能/遗传算法

// common.h
// 

#pragma once

// const 
const double DOUBLE_MAX = 999999999999;
const double DOUBLE_MIN = -DOUBLE_MAX;

const size_t MAXGENERATION = 10000;
const size_t MAXUNCHANGE = 100;	// 当最优值持续MAXUNCHANGE代不发生变化，停止
const double MINDIFF = 0.0001;	// 所能分辨的适应值函数的最小差值
const size_t POPULATION = 50;	// 样本的大小
const size_t SAMPLELENGTH = 22;	// 每一个样本的长度

typedef char MyBit;
typedef MyBit BinBits[SAMPLELENGTH];

// action to do in next step
enum Action{STAY, MUTATE , CROSSOVER, DONE};

class MySample
{
private:

	// Binary string parse
	double BinaryParse(BinBits bits);

	// initialize samples
	void Init();

	BinBits _bits;	// binary string 
	double _fitvalue;	
	double _prop;	// probability to stay

public:

	MySample()
	{
		// TODO:
		// Init();
	};

	MySample(const MySample& rhs);

	MySample& operator = (const MySample& rhs);

	double Fitness();

	// get probability to STAY
	// double Probability() const;
 
	Action NextAction() const;

	// calulate probability of current sample
	void CalcuPro(double totalfit);

	void CrossOver(MySample& rhs);

	void Mutate();

};

// initialize samples
// void Init(MySample* samples, size_t length);

// fitness function
// double FitnessFun(double x);



// cross over
// void CrossOver(MySample& lhs, MySample& rhs);

// mutate
// void Mutate(MySample& lhs);