static int max17048_set_rcomp(struct max17048_chip *chip)
{
int ret;
int scale_coeff;
int rcomp;
int temp;
temp = chip->batt_temp / 10;
if (temp > 20)
scale_coeff = chip->rcomp_co_hot;
else if (temp < 20)
scale_coeff = chip->rcomp_co_cold;
else
scale_coeff = 0;
rcomp = chip->rcomp * 1000 - (temp-20) * scale_coeff;
rcomp = bound_check(255, 0, rcomp / 1000);
pr_debug("%s: new RCOMP = 0x%02X\n", __func__, rcomp);
rcomp = rcomp << CFG_RCOMP_SHIFT;
ret = max17048_masked_write_word(chip->client,
CONFIG_REG, CFG_RCOMP_MASK, rcomp);
if (ret < 0)
pr_err("%s: failed to set rcomp\n", __func__);
return ret;
}
// SPECIAL boundaries check
void mode_alg::bound_check(double &tri_x,double orig_x,int dim)
{
const val_range& val_bounds=m_ppara->get_val_bnd();
bool out_up_bnd,out_low_bnd;
double low_bnd,high_bnd;
low_bnd=val_bounds[dim].min_val;
high_bnd=val_bounds[dim].max_val;
out_low_bnd=tri_x < low_bnd;
out_up_bnd=tri_x > high_bnd;
if ( out_up_bnd || out_low_bnd )
{
m_alg_stat.all_ob_num++;
/*normal_distribution<> norm_dist;
variate_generator<mt19937&, normal_distribution<> > rnd_norm(gen, norm_dist);
x=m_alg_stat.cen_ind[dim]+rnd_norm();*/
//// uniformly randomize x within [lower_bound,upper_bound]
//uniform_01<> dist;
//variate_generator<mt19937&, uniform_01<> > rnd_num(gen, dist);
//x=low_bnd+rnd_num()*(high_bnd-low_bnd);
if ( out_low_bnd )
tri_x=(orig_x+low_bnd)/2;
else
tri_x=low_bnd+(tri_x-high_bnd)/2;
bound_check(tri_x,orig_x,dim);// RECURSIVE invocation
}
}// end function bound_check
void General::next_step(CM::Board<short int> *chess_board,
int x,int y,Tim::vector<CM::Step> &next_step,int player){
//static int rank=8;
if(!bound_check(x,y))return;
CM::Step cur_step;
cur_step.add_move(x,y,0,-1);
cur_step.add_move(0,0,chess_board->get(x,y),1);
int i,j,type;
i=x;
j=y+1*player;
type=chess_board->get(i,j);
if(bound_check(i,j)&&(type!=1)&&(type==0||(type*player<0&&type*player!=-2))){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}
i=x;
j=y-1*player;
type=chess_board->get(i,j);
if(bound_check(i,j)&&(type!=1)&&(type==0||(type*player<0&&type*player!=-2))){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}
i=x+1*player;
j=y;
type=chess_board->get(i,j);
if(bound_check(i,j)&&(type!=1)&&(type==0||(type*player<0&&type*player!=-2))){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}
i=x-1*player;
j=y;
type=chess_board->get(i,j);
if(bound_check(i,j)&&(type!=1)&&(type==0||(type*player<0&&type*player!=-2))){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}
}
static int max17048_set_athd_alert(struct max17048_chip *chip, int level)
{
int ret;
pr_debug("%s.\n", __func__);
level = bound_check(32, 1, level);
level = 32 - level;
ret = max17048_masked_write_word(chip->client,
CONFIG_REG, CFG_ATHD_MASK, level);
if (ret < 0)
pr_err("%s: failed to set athd alert\n", __func__);
return ret;
}
static int max17048_get_capacity_from_soc(struct max17048_chip *chip)
{
u8 buf[2];
int batt_soc = 0;
buf[0] = (chip->soc & 0x0000FF00) >> 8;
buf[1] = (chip->soc & 0x000000FF);
pr_debug("%s: SOC raw = 0x%x%x\n", __func__, buf[0], buf[1]);
batt_soc = (((int)buf[0]*256)+buf[1])*19531; /* 0.001953125 */
batt_soc = (batt_soc - (chip->empty_soc * 1000000))
/ ((chip->full_soc - chip->empty_soc) * 10000);
batt_soc = bound_check(100, 0, batt_soc);
return batt_soc;
}
void Rook::next_step(CM::Board<short int> *chess_board,
int x,int y,Tim::vector<CM::Step> &next_step,int player){
if(!bound_check(x,y)){
CM::Step cur_step;
cur_step.add_move(x,y,0,-1);
cur_step.add_move(0,0,player*type,1);
for(int i=0;i<9;i++){
for(int j=0;j<9;j++){
if(chess_board->get(i,j)==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}
}
}
return;
}
move_straight(chess_board,x,y,1,0,player,next_step);
move_straight(chess_board,x,y,-1,0,player,next_step);
move_straight(chess_board,x,y,0,1,player,next_step);
move_straight(chess_board,x,y,0,-1,player,next_step);
}
void King::next_step(CM::Board<short int> *chess_board,
int x,int y,Tim::vector<CM::Step> &next_step,int player){
if(!bound_check(x,y))return;
CM::Step cur_step;
cur_step.add_move(x,y,0,-1);
cur_step.add_move(0,0,chess_board->get(x,y),1);
int i,j,type;
i=x;
j=y+1*player;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x;
j=y-1*player;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x+1;
j=y;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x-1;
j=y;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x+1;
j=y+1*player;;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x-1;
j=y+1*player;;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x+1;
j=y-1*player;;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
i=x-1;
j=y-1*player;;
if(bound_check(i,j)){
type=chess_board->get(i,j);
if(type*player==0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
next_step.push_back(cur_step);
}else if(type*player<0){
cur_step.moves[1].x=i;
cur_step.moves[1].y=j;
capture(chess_board,cur_step,player,type);
next_step.push_back(cur_step);
}
}
cur_step.move_num=2;
}
int mode_alg::run()
{
if ( !m_ppara )
return -1;
timer elapsed_t;
// retrieve algorithm parameters
int pop_size=m_ppara->get_pop_size();
int max_pop_size=2*pop_size;
int num_dims=m_ppara->get_dim();
int num_obj=m_ppara->get_obj_num();
int min_gen=m_ppara->get_max_gen();
double pr_val,f_val;
pr_val=m_ppara->get_pr();
f_val=m_ppara->get_f();
// my_sDE SPECIFIC
// jDE SPECIFIC
double f_low_bnd,f_up_bnd;
f_low_bnd=m_ppara->get_f_low_bnd();
f_up_bnd=m_ppara->get_f_up_bnd();
double tau_1,tau_2;
tau_1=m_ppara->get_tau_1();
tau_2=m_ppara->get_tau_2();
int out_interval=m_ppara->get_out_interval();
int trunc_type=m_ppara->get_trunc_type();
bool plot=m_ppara->get_plot_flag();
string plot_script=m_ppara->get_plot_script();
int m_cur_run;
int max_run=m_ppara->get_max_run();// run/trial number
// shared_ptr<progress_display> pprog_dis;// algorithm progress indicator from boost
// alloc_prog_indicator(pprog_dis);
// allocate original pop and trial pop
population pop(pop_size);
allocate_pop(pop,num_dims,stra_num,num_obj);
population trial_pop;
// generate algorithm statistics output file name
ofstream stat_file(m_com_out_path.stat_path.c_str());
// allocate stop condition object dynamically
alloc_stop_cond();
idx_array pop_idx(pop_size-1);
// random U(0,1) generator
uniform_01<> dist_01;
variate_generator<mt19937&, uniform_01<> > rnd_01(gen, dist_01);
// generator for random DIMENSION index
uniform_int<> dist_dim(0,num_dims-1);
variate_generator<mt19937&, uniform_int<> > rnd_dim_idx(gen, dist_dim);
individual trial_ind;
allocate_ind(trial_ind,num_dims,stra_num,num_obj);
// iteration start
for ( m_cur_run=0;m_cur_run<max_run;m_cur_run++ )
{
bool has_stag;
int stag_gen;
reset_run_stat();
m_de_stat.reset();
/*m_succ_f.clear();
m_succ_cr.clear();*/
int z;
// f,pr initialial value
for ( z=0;z<pop_size;z++ )
{
pop[z].stra[f].assign(1,f_val);
pop[z].stra[pr].assign(1,pr_val);
}
set_orig_pop(pop);
update_diversity(pop);
calc_de_para_stat(pop);
record_de_para_stat(m_cur_run);
print_run_times(stat_file,m_cur_run+1);
print_run_title(stat_file);
// output original population statistics
m_cur_gen=1;
stag_gen=0;
shared_ptr<ofstream> ppop_file;
shared_ptr<mutex> ppop_mut;
while ( false==(*m_pstop_cond) ) // for every iteration
{
m_de_stat.reset();
has_stag=true;
int rnd_dim;
double dim_mut_chance;
int i,j,k;
/*double f_i;
double pr_i;*/
if ( is_output_gen(m_cur_gen,out_interval) )
{
ppop_file=shared_ptr<ofstream>(new ofstream("Output//all_pop.out"));
ppop_mut=shared_ptr<mutex>((new mutex));
}
trial_pop.clear();
trial_pop.reserve(max_pop_size);// operator =
//// recalculate succ_f_mean and succ_f_sigma periodically
// if ( is_learn_gen(m_cur_gen,learn_p) )
//{
// int f_size=m_succ_f.size();
// int pr_size=m_succ_cr.size();
// if ( f_size && pr_size )
// calc_bi_norm_var(m_succ_f,m_succ_cr,m_bi_norm_var);
// else
// {
// }
// m_succ_f.clear();
// m_succ_cr.clear();
//}// if ( is_learn_gen(m_cur_gen,learn_p) )
for ( i=0;i<pop_size;i++ )
{
// generating three mutually different individual index using random shuffle
// initialize index vector
for ( k=0;k<pop_size-1;k++ )
{
if ( k<i )
pop_idx[k]=k;
else
pop_idx[k]=(k+1)%pop_size;// EXCLUDE i
}
random_shuffle(pop_idx.begin(),pop_idx.end());
int i1,i2,i3;
// int i4,i5;// i!=i1!=i2!=i3!=i4!=i5
i1=pop_idx[0];
i2=pop_idx[1];
i3=pop_idx[2];
/*i4=arc_idx[3];
i5=arc_idx[4];*/
//double pr_chance=rnd_01();
//if ( pr_chance<=tau_2 )
//{
// pr_i=rnd_01();
// trial_ind.stra[pr][0]=pr_i;
//}
//else
// pr_i=trial_ind.stra[pr][0];
//// scaling factor F self-adaptive update equation
//double f_chance=rnd_01();
//if ( f_chance<=tau_1 )
//{
// f_i=f_low_bnd+rnd_01()*f_up_bnd;
// trial_ind.stra[f][0]=f_i;
//}
//else
// f_i=trial_ind.stra[f][0];// keep unchanged at thsi iteration
rnd_dim=rnd_dim_idx();// choose a random dimension as the mutation target
for ( j=0;j<num_dims;j++ )
{
dim_mut_chance=rnd_01();
if ( rnd_dim==j || dim_mut_chance<=pr_val )
{
// insufficent elitist size,generate perturbation from current population rather than external elitist archive
trial_ind.x[j]=pop[i1].x[j]+f_val*(pop[i2].x[j]-pop[i3].x[j]);
//+f_i*(trial_pop[i4].x[j]-trial_pop[i5].x[j]);
// boundaries check
bound_check(trial_ind.x[j],pop[i].x[j],j);
}
else
trial_ind.x[j]=pop[i].x[j];
}// for every dimension
eval_ind(trial_ind,*m_pfunc,m_alg_stat);
int comp_res=check_dominance(trial_ind,pop[i]);
if ( worse!=comp_res )
{
if ( better==comp_res )
trial_pop.push_back(trial_ind);
else
{
trial_pop.push_back(trial_ind);
trial_pop.push_back(pop[i]);
}
}
else
trial_pop.push_back(pop[i]);
}// for every point
// evaluate pop
fill_nondominated_sort(trial_pop,pop,pop_size,trunc_type);
if ( is_output_gen(m_cur_gen,out_interval) )
{
update_search_radius();
update_diversity(pop);
calc_de_para_stat(pop);
record_gen_vals(m_alg_stat,m_cur_run);
record_de_para_stat(m_cur_run);
/*if ( run_once )
++(*pprog_dis);*/
// plot current population and external archive
output_collection(*ppop_file,pop.begin(),pop.end());
*ppop_file<<"\n"<<"\n";// output seperator
output_if(*ppop_file,pop.begin(),pop.end(),front_pred());
ppop_file->flush();
if ( plot )
thread(fwd_plot_fun,ppop_mut,m_cur_gen,
0,
0,is_final_out_gen(m_cur_gen,out_interval,min_gen),
plot_script);
// system("gnuplot plot_all_point_2d.p");
}
m_cur_gen++;
}// while single run termination criterion is not met
perf_indice p_ind,nsga2_p_ind;
d_mat best_pop;
copy_obj_if(pop.begin(),pop.end(),best_pop,front_pred());
d_mat nsga2_best_pop;
load_pop("nsga2_zdt3_best_pop.out",2,nsga2_best_pop);
zdt3_assess(nsga2_best_pop,1000,point(11,11),nsga2_p_ind);
cout<<"\n"
<<"nsga2 results:"
<<"\n"
<<"convergence metric gamma="<<nsga2_p_ind.gamma
<<"\n"
<<"frontier diversity metric delta="<<nsga2_p_ind.delta
<<"\n"
<<"dominance metric hyper-volume="<<nsga2_p_ind.hv
<<"\n";
zdt3_assess(best_pop,1000,point(11,11),p_ind);
cout<<"\n"
<<"outbound count="<<m_alg_stat.all_ob_num
<<"\n"
<<"population diversity="<<m_alg_stat.pos_diver
<<"\n"
<<"mean search radius="<<m_alg_stat.avg_radius
<<"\n"
<<"stagnation indicator stag_gen="<<stag_gen
<<"\n"
<<"convergence metric gamma="<<p_ind.gamma
<<"\n"
<<"frontier diversity metric delta="<<p_ind.delta
<<"\n"
<<"dominance metric hyper-volume="<<p_ind.hv
<<"\n";
// single run end
/*if ( !run_once )
++(*pprog_dis);*/
}// for every run
print_avg_gen(stat_file,m_alg_stat.run_avg_gen);
// stat and output average time per run by second
m_alg_stat.run_avg_time=elapsed_t.elapsed();
m_alg_stat.run_avg_time /= (max_run*1.0);
print_avg_time(stat_file,m_alg_stat.run_avg_time);
write_stat_vals();
cout<<endl;// flush cout output
return 0;
}// end function Run
int jde_alg::run()
{
if ( !m_ppara )
return -1;
timer elapsed_t;
// retrieve algorithm parameters
size_t pop_size=m_ppara->get_pop_size();
size_t num_dims=m_ppara->get_dim();
double vtr=m_ppara->get_vtr();
double f_low_bnd,f_up_bnd;
f_low_bnd=m_ppara->get_f_low_bnd();
f_up_bnd=m_ppara->get_f_up_bnd();
double tau_1,tau_2;
tau_1=m_ppara->get_tau_1();
tau_2=m_ppara->get_tau_2();
int m_cur_run;
int max_run=m_ppara->get_max_run();// run/trial number
// shared_ptr<progress_display> pprog_dis;// algorithm progress indicator from boost
// alloc_prog_indicator(pprog_dis);
// allocate original pop and trial pop
population pop(pop_size);
allocate_pop(pop,num_dims,stra_num);
population trial_pop(pop_size);
allocate_pop(trial_pop,num_dims,stra_num);
// generate algorithm statistics output file name
ofstream stat_file(m_com_out_path.stat_path.c_str());
// allocate stop condition object dynamically
alloc_stop_cond();
size_t shuffle_size=pop_size-1;
vector<int> vec_idx1(shuffle_size);
// random U(0,1) generator
uniform_01<> dist_01;
variate_generator<mt19937&, uniform_01<> > rnd_01(gen, dist_01);
// generator for random DIMENSION index
uniform_int<> dist_dim(0,num_dims-1);
variate_generator<mt19937&, uniform_int<> > rnd_dim_idx(gen, dist_dim);
// iteration start
for ( m_cur_run=0;m_cur_run<max_run;m_cur_run++ )
{
reset_run_stat();
m_de_stat.reset();
// jde SPECIFIC,initialize F value vector EVERY single run
size_t i;
for ( i=0;i<pop_size;i++ )
{
pop[i].stra[pr].assign(1,0.0);
pop[i].stra[f].assign(1,0.0);
}
set_orig_pop(pop);
update_diversity(pop);
calc_de_para_stat(pop);
record_de_para_stat(m_cur_run);
record_gen_vals(m_alg_stat,m_cur_run);
print_run_times(stat_file,m_cur_run+1);
print_run_title(stat_file);
// output original population statistics
print_gen_stat(stat_file,1,m_alg_stat);
m_cur_gen=1;
while ( false==(*m_pstop_cond) ) // for every iteration
{
m_de_stat.reset();
int rnd_dim;
double f_i;
double pr_i;
double dim_mut_chance;
size_t i,j,k;
trial_pop=pop;// operator =
for ( i=0;i<pop_size;i++ )
{
// generating three mutually different individual index other than i using random shuffle
// initialize index vector
for ( k=0;k<shuffle_size;k++ )
{
if ( k<i )
vec_idx1[k]=k;
else
// EXCLUDE i
vec_idx1[k]=(k+1)%pop_size;
}
// random shuffle
for ( k=0;k<shuffle_size;k++ )
{
// generator for random SHUFFLE VECTOR index
uniform_int<> dist_uni_shuf(k,shuffle_size-1);
variate_generator<mt19937&, uniform_int<> > rnd_shuf_idx(gen, dist_uni_shuf);
int idx_tmp=rnd_shuf_idx();
swap(vec_idx1[k],vec_idx1[idx_tmp]);
}
int i1,i2,i3;// i!=i1!=i2!=i3
i1=vec_idx1[0];
i2=vec_idx1[1];
i3=vec_idx1[2];
rnd_dim=rnd_dim_idx();
double pr_chance=rnd_01();
if ( pr_chance<=tau_2 )
{
pr_i=rnd_01();
trial_pop[i].stra[pr][0]=pr_i;
}
else
pr_i=trial_pop[i].stra[pr][0];
// scaling factor F self-adaptive update equation
double f_chance=rnd_01();
if ( f_chance<=tau_1 )
{
f_i=f_low_bnd+rnd_01()*f_up_bnd;
trial_pop[i].stra[f][0]=f_i;
}
else
f_i=trial_pop[i].stra[f][0];// keep unchanged at thsi iteration
for ( j=0;j<num_dims;j++ )
{
dim_mut_chance=rnd_01();
if ( rnd_dim==j || dim_mut_chance<=pr_i )
{
trial_pop[i].x[j]=trial_pop[i1].x[j]+f_i*(trial_pop[i2].x[j]-trial_pop[i3].x[j]);
// boundaries check
bound_check(trial_pop[i].x[j],j);
}
}// for every dimension
}// for every particle
// evaluate pop
eval_pop(trial_pop,*m_pfunc,m_alg_stat);
update_pop(pop,trial_pop);
stat_pop(pop,m_alg_stat);
update_search_radius();
update_diversity(pop);
calc_de_para_stat(pop);
record_de_para_stat(m_cur_run);
record_gen_vals(m_alg_stat,m_cur_run);
print_gen_stat(stat_file,m_cur_gen+1,m_alg_stat);
update_conv_stat(vtr);
/*if ( run_once )
++(*pprog_dis);*/
m_cur_gen++;
}// while single run stop_condition is false
// single run end
stat_run(pop,m_cur_run);// stat single run for algorithm analysis
if ( is_final_run(m_cur_run,max_run) )
print_run_stat(stat_file,m_alg_stat,max_run);
/*if ( !run_once )
++(*pprog_dis);*/
}// for every run
print_avg_gen(stat_file,m_alg_stat.run_avg_gen);
// stat and output average time per run by second
m_alg_stat.run_avg_time=elapsed_t.elapsed();
m_alg_stat.run_avg_time /= (max_run*1.0);
print_avg_time(stat_file,m_alg_stat.run_avg_time);
print_best_x(stat_file,m_alg_stat.bst_ind);
write_stat_vals();
cout<<endl;// flush cout output
return 0;
}// end function Run
