void make_report(int time, char *workload, int iterations, char *file)
{
/* one to warm up everything */
fprintf(stderr, _("Preparing to take measurements\n"));
utf_ok = 0;
one_measurement(1, NULL);
if (!workload[0])
fprintf(stderr, _("Taking %d measurement(s) for a duration of %d second(s) each.\n"),iterations,time);
else
fprintf(stderr, _("Measuring workload %s.\n"), workload);
for (int i=0; i != iterations; i++){
init_report_output(file, iterations);
initialize_tuning();
/* and then the real measurement */
one_measurement(time, workload);
report_show_tunables();
finish_report_output();
clear_tuning();
}
/* and wrap up */
learn_parameters(50, 0);
save_all_results("saved_results.powertop");
save_parameters("saved_parameters.powertop");
end_pci_access();
exit(0);
}
TradingCycle::TradingCycle(QVector<bid*> *_vector, long _date_start, long _date_end, int w1, int w2, int w3, int w4)
{
vector=_vector;
date_start=_date_start;
date_end=_date_end;
p = new Perceptron(_vector,w1,w2,w3,w4);
deal=NULL;
_profit=0;
save_parameters(w1,w2,w3,w4);
}
void OnxxxClicked_SAVE(WM_MESSAGE * pMsg,ESaveState save_state)
{
int i,j,k;
LISTVIEW_Handle hObj;
char buf[10];
hObj = WM_GetDialogItem(pMsg->hWin,GUI_ID_LISTVIEW_RESULT);
tget_record.act_record_lenth = LISTVIEW_GetNumRows(hObj);
for(i=0;i<LISTVIEW_GetNumRows(hObj);i++)
{
LISTVIEW_GetItemText(hObj,0,i,tget_record.sig_record[i].order,4);
LISTVIEW_GetItemText(hObj,1,i,tget_record.sig_record[i].V1,5);
LISTVIEW_GetItemText(hObj,2,i,tget_record.sig_record[i].V2,5);
LISTVIEW_GetItemText(hObj,3,i,tget_record.sig_record[i].result_r_e,5);
LISTVIEW_GetItemText(hObj,4,i,tget_record.sig_record[i].state,8);
LISTVIEW_GetItemText(hObj,5,i,tget_record.sig_record[i].time,6);
LISTVIEW_GetItemText(hObj,6,i,tget_record.sig_record[i].dir,3);
}
if(save_state == RemberToSd)
{
tget_record.year = Tim.year;
tget_record.month = Tim.month;
tget_record.date = Tim.date;
file_clear();
save_custormer();
save_parameters();
save_get_record();
}
}
void file_init(void)
{
#ifndef WIN_SIM
unsigned int i, bw;
unsigned char res;
char men;
f_mount(0, &fs); //注册到文件系统0区
res = f_open(&fsrc, "speed_old.lt",FA_OPEN_ALWAYS|FA_WRITE |FA_READ) ;
if(res)
{
while(1);
}
f_close(&fsrc);
f_mount(0, NULL);
save_custormer();
save_parameters();
save_get_record();
#endif
}
void parameter_test(Tree &T, Model &Mod, long Nrep, long length, double eps, std::vector<double> &pvals, std::string data_prefix, bool save_mc_exact){
long iter;
long i, r;
double df, C;
double distance, KL;
KL=0;
distance=0;
double likel;
Parameters Parsim, Par, Par_noperm;
Alignment align;
Counts data;
double eps_pseudo = 0.001; // Amount added to compute the pseudo-counts.
StateList sl;
bool save_data = (data_prefix != "");
std::string output_filename;
std::stringstream output_index;
std::ofstream logfile;
std::ofstream logdistfile;
std::ofstream out_chi2;
std::ofstream out_br;
std::ofstream out_brPerc;
std::ofstream out_pvals;
std::ofstream out_pvals_noperm;
std::ofstream out_qvals;
std::ofstream out_bound;
std::ofstream out_variances;
std::ofstream out_qvalsComb;
std::ofstream out_qvalsCombzscore;
std::ofstream out_covmatrix;
std::ofstream out_parest;
std::ofstream out_parsim;
std::vector<double> KLe;
std::vector<std::vector<double> > chi2_array; // an array of chi2 for every edge.
std::vector<std::vector<double> > mult_array; // an array of mult for every edge.
std::vector<std::vector<double> > br_array; // an array of br. length for every edge.
std::vector<std::vector<double> > br_arrayPerc; // an array of br. length for every edge.
std::vector<std::vector<double> > cota_array; // an array of upper bounds of the diff in lengths for every edge.
std::vector<std::vector<double> > pval_array; // an array of pvals for every edge.
std::vector<std::vector<double> > pval_noperm_array;
std::vector<std::vector<double> > qval_array; // an array of qvalues for every edge.
std::vector<std::vector<double> > variances_array; // an array of theoretical variances.
std::vector<std::vector<double> > parest_array; // array of estimated parameters
std::vector<std::vector<double> > parsim_array; // array of simulation parameters
// ci_binom ci_bin; // condfidence interval
std::vector<std::vector<ci_binom> > CIbinomial ; // vector of CIs
std::list<long> produced_nan;
long npars = T.nedges*Mod.df + Mod.rdf;
// Initializing pvals
pvals.resize(T.nedges);
// Initialize the parameters for simulation of K81 data for testing
Par = create_parameters(T);
Parsim = create_parameters(T);
// Initializing data structures
KLe.resize(T.nedges);
pval_array.resize(T.nedges);
pval_noperm_array.resize(T.nedges);
qval_array.resize(T.nedges);
chi2_array.resize(T.nedges);
mult_array.resize(T.nedges);
br_array.resize(T.nedges);
br_arrayPerc.resize(T.nedges);
cota_array.resize(T.nedges);
variances_array.resize(npars);
parest_array.resize(npars);
parsim_array.resize(npars);
// initialize to 0's
for (i=0; i < T.nedges; i++) {
pval_array[i].resize(Nrep, 0);
pval_noperm_array[i].resize(Nrep, 0);
qval_array[i].resize(Nrep, 0);
chi2_array[i].resize(Nrep, 0);
mult_array[i].resize(Nrep, 0);
br_array[i].resize(Nrep, 0);
br_arrayPerc[i].resize(Nrep, 0);
cota_array[i].resize(Nrep, 0);
}
for(i=0; i < npars; i++) {
variances_array[i].resize(Nrep, 0);
parest_array[i].resize(Nrep, 0);
parsim_array[i].resize(Nrep, 0);
}
// Information about the chi^2.
df = Mod.df;
C = get_scale_constant(Mod);
if (save_data) {
logfile.open((data_prefix + ".log").c_str(), std::ios::out);
logfile << "model: " << Mod.name << std::endl;
logfile << "length: " << length << std::endl;
logfile << "eps: " << eps << std::endl;
logfile << "nalpha: " << T.nalpha << std::endl;
logfile << "leaves: " << T.nleaves << std::endl;
logfile << "tree: " << T.tree_name << std::endl;
logfile << std::endl;
logdistfile.open((data_prefix + ".dist.log").c_str(), std::ios::out);
out_chi2.open(("out_chi2-" + data_prefix + ".txt").c_str(), std::ios::out);
out_br.open(("out_br-" + data_prefix + ".txt").c_str(), std::ios::out);
out_brPerc.open(("out_brPerc-" + data_prefix + ".txt").c_str(), std::ios::out);
out_pvals.open(("out_pvals-" + data_prefix + ".txt").c_str(), std::ios::out);
out_pvals_noperm.open(("out_pvals_noperm-" + data_prefix + ".txt").c_str(), std::ios::out);
out_qvals.open(("out_qvals-" + data_prefix + ".txt").c_str(), std::ios::out);
out_variances.open(("out_variances-" + data_prefix + ".txt").c_str(), std::ios::out);
out_parest.open(("out_params-est-" + data_prefix + ".txt").c_str(), std::ios::out);
out_parsim.open(("out_params-sim-" + data_prefix + ".txt").c_str(), std::ios::out);
out_bound.open(("out_bound-" + data_prefix + ".txt").c_str(), std::ios::out);
out_qvalsComb.open(("out_qvalsComb-" + data_prefix + ".txt").c_str(), std::ios::out);
out_qvalsCombzscore.open(("out_qvalsCombzscore-" + data_prefix + ".txt").c_str(), std::ios::out);
out_parsim.precision(15);
out_parest.precision(15);
out_variances.precision(15);
}
// uncomment the 2 following lines if want to fix the parameters
// random_parameters_length(T, Mod, Parsim);
//random_data(T, Mod, Parsim, length, align);
for (iter=0; iter < Nrep; iter++) {
std::cout << "iteration: " << iter << " \n";
// Produces an alignment from random parameters
random_parameters_length(T, Mod, Parsim);
random_data(T, Mod, Parsim, length, align);
get_counts(align, data);
add_pseudocounts(eps_pseudo, data);
// Saving data
if (save_data) {
output_index.str("");
output_index << iter;
output_filename = data_prefix + "-" + output_index.str();
save_alignment(align, output_filename + ".fa");
save_parameters(Parsim, output_filename + ".sim.dat");
}
// Runs the EM
std::tie(likel, iter) = EMalgorithm(T, Mod, Par, data, eps);
// If algorithm returns NaN skip this iteration.
if (boost::math::isnan(likel)) {
produced_nan.push_back(iter);
continue;
}
copy_parameters(Par, Par_noperm);
// Chooses the best permutation.
guess_permutation(T, Mod, Par);
distance = parameters_distance(Parsim, Par);
// estimated counts: Par ; original: Parsim
std::vector<double> counts_est;
counts_est.resize(T.nalpha, 0);
// calculate the cov matrix
std::vector<std::vector<double> > Cov;
Array2 Cov_br;
full_MLE_covariance_matrix(T, Mod, Parsim, length, Cov);
if(save_data) {
save_matrix(Cov, output_filename + ".cov.dat");
}
// Save the covariances in an array
std::vector<double> param;
std::vector<double> param_sim;
param.resize(npars);
param_sim.resize(npars);
get_free_param_vector(T, Mod, Par, param);
get_free_param_vector(T, Mod, Parsim, param_sim);
for(i=0; i < npars; i++) {
variances_array[i][iter] = Cov[i][i];
parsim_array[i][iter] = param_sim[i];
parest_array[i][iter] = param[i];
}
std::vector<double> xbranca, xbranca_noperm, mubranca;
double chi2_noperm;
xbranca.resize(Mod.df);
xbranca_noperm.resize(Mod.df);
mubranca.resize(Mod.df);
for (i=0; i < T.nedges; i++) {
r = 0; // row to be fixed
// Extracts the covariance matrix, 1 edge
branch_inverted_covariance_matrix(Mod, Cov, i, Cov_br);
get_branch_free_param_vector(T, Mod, Parsim, i, mubranca);
get_branch_free_param_vector(T, Mod, Par, i, xbranca);
get_branch_free_param_vector(T, Mod, Par_noperm, i, xbranca_noperm);
chi2_array[i][iter] = chi2_mult(mubranca, xbranca, Cov_br);
chi2_noperm = chi2_mult(mubranca, xbranca_noperm, Cov_br);
pval_array[i][iter] = pvalue_chi2(chi2_array[i][iter], Mod.df);
pval_noperm_array[i][iter] = pvalue_chi2(chi2_noperm, Mod.df);
br_array[i][iter] = T.edges[i].br - branch_length(Par.tm[i], T.nalpha);
br_arrayPerc[i][iter] = branch_length(Par.tm[i], T.nalpha)/T.edges[i].br;
// Upper bound on the parameter distance using multinomial:
// cota_array[i][iter] = bound_mult(Parsim.tm[i], Xm, length);
// and using the L2 bound
cota_array[i][iter] = branch_length_error_bound_mult(Parsim.tm[i], Par.tm[i]);
out_br << br_array[i][iter] << " ";
out_brPerc << br_arrayPerc[i][iter] << " ";
out_bound << cota_array[i][iter] << " ";
out_chi2 << chi2_array[i][iter] << " ";
}
out_chi2 << std::endl;
out_bound << std::endl;
out_br << std::endl;
out_brPerc << std::endl;
// Saves more data.
if (save_data) {
logfile << iter << ": " << distance << " " << KL << std::endl;
save_parameters(Par, output_filename + ".est.dat");
logdistfile << iter << ": ";
logdistfile << parameters_distance_root(Par, Parsim) << " ";
for(int j=0; j < T.nedges; j++) {
logdistfile << parameters_distance_edge(Par, Parsim, j) << " ";
}
logdistfile << std::endl;
}
} // close iter loop here
// Correct the p-values
for(i=0; i < T.nedges; i++) {
BH(pval_array[i], qval_array[i]);
//save them
}
if (save_mc_exact) {
for(long iter=0; iter < Nrep; iter++) {
for(long i=0; i < T.nedges; i++) {
out_pvals << pval_array[i][iter] << " ";
out_pvals_noperm << pval_noperm_array[i][iter] << " ";
out_qvals << qval_array[i][iter] << " ";
}
out_pvals << std::endl;
out_pvals_noperm << std::endl;
out_qvals << std::endl;
for(long i=0; i < npars; i++) {
out_variances << variances_array[i][iter] << " ";
out_parsim << parsim_array[i][iter] << " ";
out_parest << parest_array[i][iter] << " ";
}
out_variances << std::endl;
out_parsim << std::endl;
out_parest << std::endl;
}
}
// now combine the pvalues
for(i=0; i < T.nedges; i++) {
pvals[i] = Fisher_combined_pvalue(pval_array[i]);
//using the Zscore it goes like this: pvals[i] = Zscore_combined_pvalue(pval_array[i]);
if (save_mc_exact) { out_qvalsComb << pvals[i] << " " ;
out_qvalsCombzscore << Zscore_combined_pvalue(pval_array[i]) << " ";
}
}
// Close files
if (save_data) {
logdistfile.close();
logfile.close();
}
if (save_mc_exact) {
out_chi2.close();
out_bound.close();
out_variances.close();
out_parest.close();
out_parsim.close();
out_br.close();
out_brPerc.close();
out_pvals.close();
out_qvals.close();
out_qvalsComb.close();
out_qvalsCombzscore.close();
out_covmatrix.close();
}
// Warn if some EM's produced NaN.
if (produced_nan.size() > 0) {
std::cout << std::endl;
std::cout << "WARNING: Some iterations produced NaN." << std::endl;
std::list<long>::iterator it;
for (it = produced_nan.begin(); it != produced_nan.end(); it++) {
std::cout << *it << ", ";
}
std::cout << std::endl;
}
}
int main(int argc, char **argv)
{
int option_index;
int c;
char filename[4096];
char workload[4096] = {0,};
int iterations = 1, auto_tune = 0;
set_new_handler(out_of_memory);
setlocale (LC_ALL, "");
bindtextdomain (PACKAGE, LOCALEDIR);
textdomain (PACKAGE);
while (1) { /* parse commandline options */
c = getopt_long (argc, argv, "ch:C:i:t:uVw:q", long_options, &option_index);
/* Detect the end of the options. */
if (c == -1)
break;
switch (c) {
case 'V':
print_version();
exit(0);
break;
case 'e': /* Extech power analyzer support */
checkroot();
extech_power_meter(optarg ? optarg : "/dev/ttyUSB0");
break;
case 'u':
print_usage();
exit(0);
break;
case 'a':
auto_tune = 1;
leave_powertop = 1;
break;
case 'c':
powertop_init();
calibrate();
break;
case 'h': /* html report */
reporttype = REPORT_HTML;
sprintf(filename, "%s", optarg ? optarg : "powertop.html" );
break;
case 't':
time_out = (optarg ? atoi(optarg) : 20);
break;
case 'i':
iterations = (optarg ? atoi(optarg) : 1);
break;
case 'w': /* measure workload */
sprintf(workload, "%s", optarg ? optarg :'\0' );
break;
case 'q':
if(freopen("/dev/null", "a", stderr))
fprintf(stderr, _("Quite mode failed!\n"));
break;
case 'C': /* csv report*/
reporttype = REPORT_CSV;
sprintf(filename, "%s", optarg ? optarg : "powertop.csv");
break;
case '?': /* Unknown option */
/* getopt_long already printed an error message. */
exit(0);
break;
}
}
powertop_init();
if (reporttype != REPORT_OFF)
make_report(time_out, workload, iterations, filename);
if (debug_learning)
printf("Learning debugging enabled\n");
learn_parameters(250, 0);
save_parameters("saved_parameters.powertop");
if (debug_learning) {
learn_parameters(1000, 1);
dump_parameter_bundle();
end_pci_access();
exit(0);
}
init_display();
initialize_tuning();
/* first one is short to not let the user wait too long */
one_measurement(1, NULL);
if (!auto_tune) {
tuning_update_display();
show_tab(0);
} else {
auto_toggle_tuning();
}
while (!leave_powertop) {
show_cur_tab();
one_measurement(time_out, NULL);
learn_parameters(15, 0);
}
endwin();
printf("%s\n", _("Leaving PowerTOP"));
end_process_data();
clear_process_data();
end_cpu_data();
clear_cpu_data();
save_all_results("saved_results.powertop");
save_parameters("saved_parameters.powertop");
learn_parameters(500, 0);
save_parameters("saved_parameters.powertop");
end_pci_access();
clear_tuning();
reset_display();
clean_shutdown();
return 0;
}
void SPF::learn() {
double old_likelihood, delta_likelihood, likelihood = -1e10;
int likelihood_decreasing_count = 0;
time_t start_time, end_time;
int iteration = 0;
char iter_as_str[4];
bool converged = false;
bool on_final_pass = false;
while (!converged) {
time(&start_time);
iteration++;
printf("iteration %d\n", iteration);
reset_helper_params();
// update rate for user preferences
b_theta.each_col() += sum(beta, 1);
set<int> items;
int user = -1, item, rating;
for (int i = 0; i < settings->sample_size; i++) {
if (on_final_pass && settings->final_pass_test) {
user++;
while (data->test_users.count(user)==0) {
user++;
}
} else if (settings->svi) {
user = gsl_rng_uniform_int(rand_gen, data->user_count());
} else {
user = i;
}
bool user_converged = false;
int user_iters = 0;
while (!user_converged) {
user_iters++;
a_beta_user.zeros();
a_delta_user.zeros();
// look at all the user's items
for (int j = 0; j < data->item_count(user); j++) {
item = data->get_item(user, j);
items.insert(item);
rating = 1;
//TODO: rating = data->get_train_rating(i);
update_shape(user, item, rating);
}
// update per-user parameters
double user_change = 0;
if (!settings->factor_only && !settings->fix_influence)
user_change += update_tau(user);
if (!settings->social_only)
user_change += update_theta(user);
if (!settings->social_only && !settings->factor_only && !settings->fix_influence) {
user_change /= 2;
// if the updates are less than 1% change, the local params have converged
if (user_change < 0.01)
user_converged = true;
} else {
// if we're only looking at social or factor (not combined)
// then the user parameters will always have converged with
// a single pass (since there's nothing to balance against)
user_converged = true;
}
}
if (settings->verbose)
printf("%d\tuser %d took %d iters to converge\n", iteration, user, user_iters);
a_beta += a_beta_user;
a_delta += a_delta_user;
}
if (!settings->social_only) {
// update rate for item attributes
b_beta.each_col() += sum(theta, 1);
// update per-item parameters
set<int>::iterator it;
for (it = items.begin(); it != items.end(); it++) {
item = *it;
if (iter_count[item] == 0)
iter_count[item] = 0;
iter_count[item]++;
update_beta(item);
if (settings->item_bias)
update_delta(item);
}
} else if (settings->item_bias) {
set<int>::iterator it;
for (it = items.begin(); it != items.end(); it++) {
item = *it;
if (iter_count[item] == 0)
iter_count[item] = 0;
iter_count[item]++;
if (settings->item_bias)
update_delta(item);
}
}
// check for convergence
if (on_final_pass) {
printf("Final pass complete\n");
converged = true;
old_likelihood = likelihood;
likelihood = get_ave_log_likelihood();
delta_likelihood = abs((old_likelihood - likelihood) /
old_likelihood);
log_convergence(iteration, likelihood, delta_likelihood);
} else if (iteration >= settings->max_iter) {
printf("Reached maximum number of iterations.\n");
converged = true;
old_likelihood = likelihood;
likelihood = get_ave_log_likelihood();
delta_likelihood = abs((old_likelihood - likelihood) /
old_likelihood);
log_convergence(iteration, likelihood, delta_likelihood);
} else if (iteration % settings->conv_freq == 0) {
old_likelihood = likelihood;
likelihood = get_ave_log_likelihood();
if (likelihood < old_likelihood)
likelihood_decreasing_count += 1;
else
likelihood_decreasing_count = 0;
delta_likelihood = abs((old_likelihood - likelihood) /
old_likelihood);
log_convergence(iteration, likelihood, delta_likelihood);
if (settings->verbose) {
printf("delta: %f\n", delta_likelihood);
printf("old: %f\n", old_likelihood);
printf("new: %f\n", likelihood);
}
if (iteration >= settings->min_iter &&
delta_likelihood < settings->likelihood_delta) {
printf("Model converged.\n");
converged = true;
} else if (iteration >= settings->min_iter &&
likelihood_decreasing_count >= 2) {
printf("Likelihood decreasing.\n");
converged = true;
}
}
// save intermediate results
if (!converged && settings->save_freq > 0 &&
iteration % settings->save_freq == 0) {
printf(" saving\n");
sprintf(iter_as_str, "%04d", iteration);
save_parameters(iter_as_str);
}
// intermediate evaluation
if (!converged && settings->eval_freq > 0 &&
iteration % settings->eval_freq == 0) {
sprintf(iter_as_str, "%04d", iteration);
evaluate(iter_as_str);
}
time(&end_time);
log_time(iteration, difftime(end_time, start_time));
if (converged && !on_final_pass &&
(settings->final_pass || settings->final_pass_test)) {
printf("final pass on all users.\n");
on_final_pass = true;
converged = false;
// we need to modify some settings for the final pass
// things should look exactly like batch for all users
if (settings->final_pass) {
settings->set_stochastic_inference(false);
settings->set_sample_size(data->user_count());
scale = 1;
} else {
settings->set_sample_size(data->test_users.size());
scale = data->user_count() / settings->sample_size;
}
}
}
save_parameters("final");
}
