// Expected value using row rearrangement
//[[Rcpp::export]]
arma::mat Expected(const arma::mat& grid,
const arma::mat& value,
const arma::cube& disturb,
const arma::vec& weight) {
// Passing R objects to C++
const std::size_t n_grid = grid.n_rows;
const std::size_t n_dim = grid.n_cols;
const std::size_t n_disturb = disturb.n_slices;
// Computing the continuation value function
arma::mat continuation(n_grid, n_dim, arma::fill::zeros);
arma::mat d_value(n_grid, n_dim);
for (std::size_t dd = 0; dd < n_disturb; dd++) {
d_value = value * disturb.slice(dd);
continuation += weight(dd) * Optimal(grid, d_value);
}
return continuation;
}
int main()
{
int choice;
//以菜单的形式显示出来
while(1)
{
system("cls");//清屏
manu();
scanf("%d",&choice);
switch(choice)
{
case 1:FIFO();break;
case 2:LRU();break;
case 3:Optimal();break;
case 0:exit(0);
default:printf("您的输入有误，请重新输入\n");
}
system("pause");
}
return 0;
}
int main()
{
int icount = 0; //指示
int gencount = 0;
int tim; //指示TIME
int res = 0; //指示RESPONSE
FILE *fp;
fp = fopen("TimeSequence.txt","r+t");
while(res < RESPONSE)
{
for(gencount = 0; gencount < GENENUM; gencount++)
{
for (tim = 0; tim < TIME; tim++)
{
fscanf(fp, "%lf", &data[res][gencount][tim]);
}
}
res = res + 1;
}
fclose(fp);
int flag = 0;
srand((unsigned)time(NULL));
grpGA grp;
pgrpGA pgrp = &grp;
grp.generationCount = 0;
grp.generationMax = 5000; //遗传算法迭代次数
grp.probCross = 0.9;
grp.probMutat = 0.1;
grp.size = SIZEGROUP; //种群大小
genGA ind[SIZEGROUP];
pgenGA pind[SIZEGROUP];
for(icount = 0; icount < SIZEGROUP; icount++)
{
pind[icount] = &ind[icount];
}
int u = 0;
for(u = 0; u < GENENUM; u++)
{
printf("%d node learn\n", u);
FILE *fp1;
fp1 = fopen("Individuals.txt","r+t");
icount = 0;
while(icount < SIZEGROUP)
{
for (gencount = 0; gencount < GENENUM; gencount++)
{
fscanf(fp1, "%d", &populationMirror[icount][gencount]);
}
icount = icount + 1;
}
fclose(fp1);
genExpect = 0;
for(res = 0; res < RESPONSE; res++)
{
for(gencount = 0; gencount < GENENUM; gencount++)
{
genWigh[res][gencount] = 0;
}
}
for(gencount = 0; gencount < GENENUM; gencount++)
{
genTest[gencount] = 0;
genWighMean[gencount] = 0;
elite[gencount] = 0;
eliteWeight[gencount] = 0;
}
eliteFitness = 0;
do
{
Initial(pind);
ErrorCmp(pind, data, populationMirror, genTest, genWigh, genWighMean, genExpect, &u);
Fitness(pind);
Optimal(pind, elite, &eliteFitness, populationMirror, genWighMean, eliteWeight);
flag = Termination(pgrp);
if(flag)
{
break;
}
BinaryTourment(pind, pgrp, populationMirror);
Crossover(pind, pgrp, populationMirror);
Mutation(pind, pgrp, populationMirror);
}while(1);
ResultOutput(pind, elite, pgrp, eliteWeight);
}
return 0;
}
