static void
update_limit(struct um_user *user, jobj_t obj)
{
update_online(user, obj, max);
update_online(user, obj, idle);
update_online(user, obj, numerator);
update_online(user, obj, denominator);
updata_flow(user, obj, max);
updata_flow(user, obj, numerator);
updata_flow(user, obj, denominator);
updata_rate(user, obj, max);
updata_rate(user, obj, avg);
}
static void
dhcpcd_if_cb(DHCPCD_IF *i, _unused void *data)
{
DHCPCD_CONNECTION *con;
char *msg;
const char *icon;
bool new_msg;
/* We should ignore renew and stop so we don't annoy the user */
if (g_strcmp0(i->reason, "RENEW") &&
g_strcmp0(i->reason, "STOP") &&
g_strcmp0(i->reason, "STOPPED"))
{
msg = dhcpcd_if_message(i, &new_msg);
if (msg) {
g_message("%s", msg);
if (new_msg) {
if (i->up)
icon = "network-transmit-receive";
//else
// icon = "network-transmit";
if (!i->up)
icon = "network-offline";
notify(_("Network event"), msg, icon);
}
g_free(msg);
}
}
/* Update the tooltip with connection information */
con = dhcpcd_if_connection(i);
update_online(con, false);
}
static void
dhcpcd_status_cb(DHCPCD_CONNECTION *con, const char *status,
_unused void *data)
{
static char *last = NULL;
const char *msg;
bool refresh;
WI_SCAN *w;
g_message("Status changed to %s", status);
if (g_strcmp0(status, "down") == 0) {
msg = N_(last ?
"Connection to dhcpcd lost" : "dhcpcd not running");
if (ani_timer != 0) {
g_source_remove(ani_timer);
ani_timer = 0;
ani_counter = 0;
}
online = carrier = false;
gtk_status_icon_set_from_icon_name(status_icon,
"network-offline");
gtk_status_icon_set_tooltip_text(status_icon, msg);
dhcpcd_prefs_abort();
while (wi_scans) {
w = wi_scans->next;
dhcpcd_wi_scans_free(wi_scans->scans);
g_free(wi_scans);
wi_scans = w;
}
dhcpcd_unwatch(-1, con);
g_timeout_add(DHCPCD_RETRYOPEN, dhcpcd_try_open, con);
} else {
if ((last == NULL || g_strcmp0(last, "down") == 0)) {
g_message(_("Connected to %s-%s"), "dhcpcd",
dhcpcd_version(con));
refresh = true;
} else
refresh = g_strcmp0(last, "opened") ? false : true;
update_online(con, refresh);
}
g_free(last);
last = g_strdup(status);
}
// Stochastic Gradient Descent (SGD) Optimization
int LR::train_sgd( int max_loop, double loss_thrd, float learn_rate, float lambda, int avg)
{
int id = 0;
double loss = 0.0;
double loss_pre = 0.0;
vector< vector<float> > omega_pre=omega;
float acc=0.0;
vector< vector<float> > omega_sum(omega);
//最多迭代(最大循环 * 训练样本类别个数)次
while (id <= max_loop*(int)samp_class_vec.size())
{
if (id%samp_class_vec.size() == 0) // 完成一次迭代，预处理工作。
{
int loop = id/(int)samp_class_vec.size(); //check loss
loss = 0.0;
acc = 0.0;
calc_loss(&loss, &acc); //计算损失
cout.setf(ios::left);
cout << "Iter: " << setw(8) << loop << "Loss: " << setw(18) << loss << "Acc: " << setw(8) << acc << endl;
//如果损失值小于阈值，停止迭代
if ((loss_pre - loss) < loss_thrd && loss_pre >= loss && id != 0)
{
cout << "Reaching the minimal loss decrease!" << endl;
break;
}
loss_pre = loss; //保留上次损失值
if (id) //表示第一次不做正则项计算
{
for (int i=0;i!=omega_pre.size();i++)
for (int j=0;j!=omega_pre[i].size();j++)
omega[i][j]+=omega_pre[i][j]*lambda; //lambda为权重衰减项
}
omega_pre=omega;
}
// update omega
//随机选择样本
int r = (int)(rand()%samp_class_vec.size());
sparse_feat samp_feat = samp_feat_vec[r]; //随机样本的特征
int samp_class = samp_class_vec[r]; //随机样本的类别
update_online(samp_class, samp_feat, learn_rate, lambda);
if (avg == 1 && id%samp_class_vec.size() == 0)
{
for (int i = 0; i < feat_set_size; i++)
{
for (int j = 0; j < class_set_size; j++)
{
omega_sum[i][j] += omega[i][j];
}
}
}
id++;
} //迭代结束
if (avg == 1)
{
for (int i = 0; i < feat_set_size; i++)
{
for (int j = 0; j < class_set_size; j++)
{
omega[i][j] = (float)omega_sum[i][j] / id;
}
}
}
return 1;
}
