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In weight calculation use unweighted variance from last regression

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This fixes a positive feedback where weights could reach inf.
Also change the SD_TO_DIST_RATIO constant to get close to the original
response.
This commit is contained in:
Miroslav Lichvar
2011-04-13 18:43:25 +02:00
parent 2a0c35646c
commit 165e6805ab
3 changed files with 18 additions and 9 deletions
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14
sourcestats.c
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@@ -105,6 +105,9 @@ struct SST_Stats_Record {
/* This is the estimated residual variance of the data points */
double variance;
/* This is the estimated unweighted variance of the data points */
double uvariance;
/* This array contains the sample epochs, in terms of the local
clock. */
struct timeval sample_times[MAX_SAMPLES * REGRESS_RUNS_RATIO];
@@ -190,7 +193,7 @@ SST_CreateInstance(unsigned long refid, IPAddr *addr)
inst->estimated_offset_sd = 86400.0; /* Assume it's at least within a day! */
inst->offset_time.tv_sec = 0;
inst->offset_time.tv_usec = 0;
inst->variance = 16.0;
inst->variance = inst->uvariance = 16.0;
inst->nruns = 0;
return inst;
}
@@ -364,7 +367,7 @@ find_min_delay_sample(SST_Stats inst)
time. E.g. a value of 4 means that we think the standard deviation
is four times the fluctuation of the peer distance */
#define SD_TO_DIST_RATIO 1.0
#define SD_TO_DIST_RATIO 1.4
/* ================================================== */
/* This function runs the linear regression operation on the data. It
@@ -382,7 +385,7 @@ SST_DoNewRegression(SST_Stats inst)
int degrees_of_freedom;
int best_start, times_back_start;
double est_intercept, est_slope, est_var, est_intercept_sd, est_slope_sd;
double est_intercept, est_slope, est_var, est_uvar, est_intercept_sd, est_slope_sd;
int i, j, nruns;
double min_distance;
double sd_weight, sd;
@@ -405,7 +408,7 @@ SST_DoNewRegression(SST_Stats inst)
/* And now, work out the weight vector */
sd = sqrt(inst->variance);
sd = sqrt(inst->uvariance);
if (sd > min_distance || sd <= 0.0)
sd = min_distance;
@@ -418,7 +421,7 @@ SST_DoNewRegression(SST_Stats inst)
inst->regression_ok = RGR_FindBestRegression(times_back + inst->runs_samples,
offsets + inst->runs_samples, weights,
inst->n_samples, inst->runs_samples,
&est_intercept, &est_slope, &est_var,
&est_intercept, &est_slope, &est_var, &est_uvar,
&est_intercept_sd, &est_slope_sd,
&best_start, &nruns, &degrees_of_freedom);
@@ -433,6 +436,7 @@ SST_DoNewRegression(SST_Stats inst)
inst->offset_time = inst->sample_times[inst->last_sample];
inst->estimated_offset_sd = est_intercept_sd;
inst->variance = est_var;
inst->uvariance = est_uvar;
inst->nruns = nruns;
stress = fabs(old_freq - inst->estimated_frequency) / old_skew;