www.pudn.com > asm_java.zip > AgentParam.java
package asm;
import java.awt.Frame;
/**
* Title: Artificial Stock Market
* Description: 人工模拟股市（来源：SFI的Swarm版本）的Java版本
* Copyright: Copyright (c) 2003
* Company: http://agents.yeah.net
* @author jake
* @version 1.0
*/
public class AgentParam extends Frame {
public int numfcasts; /*"number of forecasts maintained by this agent"*/
//public int condwords; /*"number of words of memory required to hold bits"*/
public int condbits; /*"number of conditions bits are monitored"*/
public int mincount; /*"minimum number of times forecast must be used to become active"*/
public int gafrequency; /*"how often is genetic algorithm done?"*/
public int firstgatime; /*"after how many time steps is the genetic algorithm done"*/
public int longtime; /*" unused time before Generalize() in genetic algorithm"*/
public int individual;
public double tauv;
public double lambda;
public double maxbid;
public double bitprob;
public double subrange; /*" fraction of min-max range for initial random values"*/
public double a_min,a_max; /*" min and max for p+d coef"*/
public double b_min,b_max; /*" min and max for div coef"*/
public double c_min,c_max; /*" min and max for constant term"*/
public double a_range,b_range,c_range; /*" derived: max - min" */
public double newfcastvar; /*" variance assigned to a new forecaster"*/
public double newfcastspec;
public double addvar;
public double initvar; /*" variance of overall forecast for t<200"*/
public double bitcost; /*" penalty parameter for specificity"*/
public double maxdev; /*" max deviation of a forecast in variance estimation"*/
public double poolfrac; /*" fraction of rules in replacement pool"*/
public double newfrac; /*" fraction of rules replaced"*/
public double pcrossover; /*" probability of running Crossover()."*/
public double plinear; /*" linear combination "crossover" prob."*/
public double prandom; /*" random from each parent crossover prob."*/
public double pmutation; /*" per bit mutation prob."*/
public double plong; /*" long jump prob."*/
public double pshort; /*" short (neighborhood) jump prob."*/
public double nhood; /*" size of neighborhood."*/
public double genfrac; /*" fraction of 0/1 bits to generalize"*/
public double gaprob; /*" derived: 1/gafrequency"*/
public int npool; /*" derived: replacement pool size"*/
public int nnew; /*" derived: number of new rules"*/
public int nnulls; /*" unnused bits"*/
//int *bitlist; /*" dynamic array, length condbits"*/
//double *problist; /*" dynamic array, length condbits"*/
public int npoolmax ; /* size of reject array */
public int nnewmax ; /* size of newfcast array */
public int ncondmax; /* size of newc*/
public AgentParam() {
numfcasts=100;//规则数目
initvar=1.0; //初始化方差
a_min=0;
b_min=-10;
c_min=-1;
a_max=4;
b_max=10;
c_max=5;
subrange=1;//系数的一个变化范围，sub只能取[0,1]的数值
condbits=60;//规则if部分的二进制位数
nnulls=0;//无用空位的个数
bitcost=0.02;//位部分的适应度对总是硬度的贡献权重
newfcastvar=0.6;//新规则的方差
newfcastspec=0.4;//新规则条件部分出现2的概率[0,1]取值
firstgatime=2;//第一次执行遗传算法的允许时间
gafrequency=1000;//执行遗传算法的频率
mincount=1;//被激活唤醒的最小次数
individual=0;//是否仅仅采用最好规则进行方差计算？布尔值
tauv=50;//更新规则表现时候的权重系数global_mean = b*global_mean + a*ftarget;
lambda=0.9;//需求函数x=(E(p+d)-(1+r)p)/lambda*variance的系数
addvar=10;//改造的需求函数x=(E(p+d)-(1+r)p)/(lambda*variance+addvar)为了防止variance对结果英想过大
maxbid=10;//最大持股量
initvar=50;//初始方差
maxdev=100;//最大的离差
pcrossover=0.3;//交叉概率
pmutation=0.01;//变异概率
plong=0.6;//大变异的概率
pshort=0.3;//小变异的概率
plinear=0.4;//线性交叉的概率
prandom=0.3;//随机交叉的概率
longtime=200;//长时间没有被激活
genfrac=0.1;//位2的平均长度
nhood=3;//邻域的长度
poolfrac=0.3;//淘汰规则池的比例
newfrac=0.2;//新规则的比例
npoolmax=10;
nnewmax=10;
ncondmax=10;
reinit();
}
public void reinit(){
//重新初始化一些参数
a_range = a_max - a_min;
b_range = b_max - b_min;
c_range = c_max - c_min;
gaprob = 1.0/(double)gafrequency;
if (1.0+bitcost*(condbits-nnulls) <= 0.0){
//system.Out("wrong with the bitcost,condbits,nnulls");
return;
}
npool = (int)(numfcasts*poolfrac + 0.5);
nnew = (int)(numfcasts*newfrac + 0.5);
// Record maxima needed for GA working space
if (npool > npoolmax) npoolmax = npool;
if (nnew > nnewmax) nnewmax = nnew;
}
}