static void
process_reading(time_t rtc_time, struct timeval *system_time)
{
double rtc_fast;
accumulate_sample(rtc_time, system_time);
switch (operating_mode) {
case OM_NORMAL:
if (n_samples_since_regression >= N_SAMPLES_PER_REGRESSION) {
run_regression(1, &coefs_valid, &coef_ref_time, &coef_seconds_fast, &coef_gain_rate);
n_samples_since_regression = 0;
maybe_autotrim();
}
break;
case OM_INITIAL:
if (n_samples_since_regression >= 8) {
handle_initial_trim();
}
break;
case OM_AFTERTRIM:
if (n_samples_since_regression >= 8) {
handle_relock_after_trim();
}
break;
default:
assert(0);
break;
}
if (logfileid != -1) {
rtc_fast = (double)(rtc_time - system_time->tv_sec) - 1.0e-6 * (double) system_time->tv_usec;
LOG_FileWrite(logfileid, "%s %14.6f %1d %14.6f %12.3f %2d %2d %4d",
UTI_TimeToLogForm(system_time->tv_sec),
rtc_fast,
coefs_valid,
coef_seconds_fast, coef_gain_rate * 1.0e6, n_samples, n_runs, measurement_period);
}
}
void
SST_DoNewRegression(SST_Stats inst)
{
double times_back[MAX_SAMPLES * REGRESS_RUNS_RATIO];
double offsets[MAX_SAMPLES * REGRESS_RUNS_RATIO];
double peer_distances[MAX_SAMPLES];
double weights[MAX_SAMPLES];
int degrees_of_freedom;
int best_start, times_back_start;
double est_intercept, est_slope, est_var, est_intercept_sd, est_slope_sd;
int i, j, nruns;
double min_distance, mean_distance;
double sd_weight, sd;
double old_skew, old_freq, stress;
convert_to_intervals(inst, times_back + inst->runs_samples);
if (inst->n_samples > 0) {
for (i = -inst->runs_samples; i < inst->n_samples; i++) {
offsets[i + inst->runs_samples] = inst->offsets[get_runsbuf_index(inst, i)];
}
for (i = 0, mean_distance = 0.0, min_distance = DBL_MAX; i < inst->n_samples; i++) {
j = get_buf_index(inst, i);
peer_distances[i] = 0.5 * inst->peer_delays[j] + inst->peer_dispersions[j];
mean_distance += peer_distances[i];
if (peer_distances[i] < min_distance) {
min_distance = peer_distances[i];
}
}
mean_distance /= inst->n_samples;
/* And now, work out the weight vector */
sd = mean_distance - min_distance;
if (sd > min_distance || sd <= 0.0)
sd = min_distance;
for (i=0; i<inst->n_samples; i++) {
sd_weight = 1.0 + SD_TO_DIST_RATIO * (peer_distances[i] - min_distance) / sd;
weights[i] = sd_weight * sd_weight;
}
}
inst->regression_ok = RGR_FindBestRegression(times_back + inst->runs_samples,
offsets + inst->runs_samples, weights,
inst->n_samples, inst->runs_samples,
min_samples,
&est_intercept, &est_slope, &est_var,
&est_intercept_sd, &est_slope_sd,
&best_start, &nruns, °rees_of_freedom);
if (inst->regression_ok) {
old_skew = inst->skew;
old_freq = inst->estimated_frequency;
inst->estimated_frequency = est_slope;
inst->skew = est_slope_sd * RGR_GetTCoef(degrees_of_freedom);
inst->estimated_offset = est_intercept;
inst->offset_time = inst->sample_times[inst->last_sample];
inst->estimated_offset_sd = est_intercept_sd;
inst->variance = est_var;
inst->nruns = nruns;
if (inst->skew < MIN_SKEW)
inst->skew = MIN_SKEW;
stress = fabs(old_freq - inst->estimated_frequency) / old_skew;
if (best_start > 0) {
/* If we are throwing old data away, retain the current
assumptions about the skew */
inst->skew_dirn = SST_Skew_Nochange;
} else {
if (inst->skew < old_skew) {
inst->skew_dirn = SST_Skew_Decrease;
} else {
inst->skew_dirn = SST_Skew_Increase;
}
}
if (logfileid != -1) {
LOG_FileWrite(logfileid, "%s %-15s %10.3e %10.3e %10.3e %10.3e %10.3e %7.1e %3d %3d %3d",
UTI_TimeToLogForm(inst->offset_time.tv_sec),
inst->ip_addr ? UTI_IPToString(inst->ip_addr) : UTI_RefidToString(inst->refid),
sqrt(inst->variance),
inst->estimated_offset,
inst->estimated_offset_sd,
inst->estimated_frequency,
inst->skew,
stress,
inst->n_samples,
best_start, nruns);
}
times_back_start = inst->runs_samples + best_start;
prune_register(inst, best_start);
} else {
inst->estimated_frequency = 0.0;
inst->skew = WORST_CASE_FREQ_BOUND;
times_back_start = 0;
}
find_best_sample_index(inst, times_back + times_back_start);
}
