diff options
Diffstat (limited to 'resources/libraries/python/PLRsearch/PLRsearch.py')
| -rw-r--r-- | resources/libraries/python/PLRsearch/PLRsearch.py | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/resources/libraries/python/PLRsearch/PLRsearch.py b/resources/libraries/python/PLRsearch/PLRsearch.py index ec58fbd10f..226b482d76 100644 --- a/resources/libraries/python/PLRsearch/PLRsearch.py +++ b/resources/libraries/python/PLRsearch/PLRsearch.py @@ -54,7 +54,7 @@ class PLRsearch: def __init__( self, measurer, trial_duration_per_trial, packet_loss_ratio_target, - trial_number_offset=0, timeout=1800.0, trace_enabled=False): + trial_number_offset=0, timeout=7200.0, trace_enabled=False): """Store rate measurer and additional parameters. The measurer must never report negative loss count. @@ -205,7 +205,7 @@ class PLRsearch: if (trial_number - self.trial_number_offset) <= 1: next_load = max_rate elif (trial_number - self.trial_number_offset) <= 3: - next_load = (measurement.receive_rate / ( + next_load = (measurement.relative_receive_rate / ( 1.0 - self.packet_loss_ratio_target)) else: next_load = (avg1 + avg2) / 2.0 @@ -439,7 +439,7 @@ class PLRsearch: :param lfit_func: Fitting function, typically lfit_spread or lfit_erf. :param trial_result_list: List of trial measurement results. :param mrr: The mrr parameter for the fitting function. - :param spread: The spread parameter for the fittinmg function. + :param spread: The spread parameter for the fitting function. :type trace: function (str, object) -> None :type lfit_func: Function from 3 floats to float. :type trial_result_list: list of MLRsearch.ReceiveRateMeasurement @@ -455,20 +455,21 @@ class PLRsearch: trace(u"for tr", result.target_tr) trace(u"lc", result.loss_count) trace(u"d", result.duration) - log_avg_loss_per_second = lfit_func( + # _rel_ values use units of target_tr (transactions per second). + log_avg_rel_loss_per_second = lfit_func( trace, result.target_tr, mrr, spread ) - log_avg_loss_per_trial = ( - log_avg_loss_per_second + math.log(result.duration) + # _abs_ values use units of loss count (maybe packets). + # There can be multiple packets per transaction. + log_avg_abs_loss_per_trial = log_avg_rel_loss_per_second + math.log( + result.transmit_count / result.target_tr ) - # Poisson probability computation works nice for logarithms. - log_trial_likelihood = ( - result.loss_count * log_avg_loss_per_trial - - math.exp(log_avg_loss_per_trial) - ) - log_trial_likelihood -= math.lgamma(1 + result.loss_count) + # Geometric probability computation for logarithms. + log_trial_likelihood = log_plus(0.0, -log_avg_abs_loss_per_trial) + log_trial_likelihood *= -result.loss_count + log_trial_likelihood -= log_plus(0.0, +log_avg_abs_loss_per_trial) log_likelihood += log_trial_likelihood - trace(u"avg_loss_per_trial", math.exp(log_avg_loss_per_trial)) + trace(u"avg_loss_per_trial", math.exp(log_avg_abs_loss_per_trial)) trace(u"log_trial_likelihood", log_trial_likelihood) return log_likelihood |