Method othed than search (and than __init__)
are just internal code structure.
+
TODO: Those method names should start with underscore then.
"""
:type min_rate: float
:type max_rate: float
:returns: Average and stdev of critical load estimate.
- :rtype: 2-tuple of floats
+ :rtype: 2-tuple of float
"""
stop_time = time.time() + self.timeout
min_rate = float(min_rate)
focus_trackers = (None, None)
transmit_rate = (min_rate + max_rate) / 2.0
lossy_loads = [max_rate]
- zeros = [0, 0] # Cosecutive zero loss, separately for stretch and erf.
+ zeros = 0 # How many cosecutive zero loss results are happening.
while 1:
trial_number += 1
logging.info("Trial %(number)r", {"number": trial_number})
self.trial_duration_per_trial * trial_number, transmit_rate,
trial_result_list, min_rate, max_rate, focus_trackers)
measurement, average, stdev, avg1, avg2, focus_trackers = results
- index = trial_number % 2
- zeros[index] += 1
+ zeros += 1
# TODO: Ratio of fill rate to drain rate seems to have
# exponential impact. Make it configurable, or is 4:3 good enough?
if measurement.loss_fraction >= self.packet_loss_ratio_target:
- for _ in range(4 * zeros[index]):
+ for _ in range(4 * zeros):
lossy_loads.append(measurement.target_tr)
if measurement.loss_count > 0:
- zeros[index] = 0
+ zeros = 0
lossy_loads.sort()
if stop_time <= time.time():
return average, stdev
next_load = (measurement.receive_rate / (
1.0 - self.packet_loss_ratio_target))
else:
- index = (trial_number + 1) % 2
- next_load = (avg1, avg2)[index]
- if zeros[index] > 0:
+ next_load = (avg1 + avg2) / 2.0
+ if zeros > 0:
if lossy_loads[0] > next_load:
- diminisher = math.pow(2.0, 1 - zeros[index])
+ diminisher = math.pow(2.0, 1 - zeros)
next_load = lossy_loads[0] + diminisher * next_load
next_load /= (1.0 + diminisher)
# On zero measurement, we need to drain obsoleted low losses
# even if we did not use them to increase next_load,
- # in order to get to usable loses with higher load.
+ # in order to get to usable loses at higher loads.
if len(lossy_loads) > 3:
lossy_loads = lossy_loads[3:]
logging.debug("Zeros %(z)r orig %(o)r next %(n)r loads %(s)r",
- {"z": zeros, "o": (avg1, avg2)[index],
+ {"z": zeros, "o": (avg1 + avg2) / 2.0,
"n": next_load, "s": lossy_loads})
transmit_rate = min(max_rate, max(min_rate, next_load))
:type focus_trackers: 2-tuple of None or stat_trackers.VectorStatTracker
:type max_samples: None or int
:returns: Measurement and computation results.
- :rtype: 6-tuple: ReceiveRateMeasurement, 4 floats, 2-tuple of trackers.
+ :rtype: _ComputeResult
"""
logging.debug(
"measure_and_compute started with self %(self)r, trial_duration "
- + "%(dur)r, transmit_rate %(tr)r, trial_result_list %(trl)r, "
- + "max_rate %(mr)r, focus_trackers %(track)r, max_samples %(ms)r",
+ "%(dur)r, transmit_rate %(tr)r, trial_result_list %(trl)r, "
+ "max_rate %(mr)r, focus_trackers %(track)r, max_samples %(ms)r",
{"self": self, "dur": trial_duration, "tr": transmit_rate,
"trl": trial_result_list, "mr": max_rate, "track": focus_trackers,
"ms": max_samples})
:type pipe: multiprocessing.Connection
:returns: Computed value tracker, actual focus tracker,
and number of samples used for this iteration.
- :rtype: 3-tuple of tracker, tracker and int
+ :rtype: _PartialResult
"""
pipe.send(None)
if not pipe.poll(10.0):
@staticmethod
def _get_result(measurement, stretch_result, erf_result):
- """Collate results from measure_and_compute"""
+ """Process and collate results from measure_and_compute.
+
+ Turn logarithm based values to exponential ones,
+ combine averages and stdevs of two fitting functions into a whole.
+
+ :param measurement: The trial measurement obtained during computation.
+ :param stretch_result: Computation output for stretch fitting function.
+ :param erf_result: Computation output for erf fitting function.
+ :type measurement: ReceiveRateMeasurement
+ :type stretch_result: _PartialResult
+ :type erf_result: _PartialResult
+ :returns: Combined results.
+ :rtype: _ComputeResult
+ """
stretch_avg = stretch_result.value_tracker.average
erf_avg = erf_result.value_tracker.average
- # TODO: Take into account secondary stats.
- stretch_stdev = math.exp(stretch_result.value_tracker.log_variance / 2)
- erf_stdev = math.exp(erf_result.value_tracker.log_variance / 2)
- avg = math.exp((stretch_avg + erf_avg) / 2.0)
- var = (stretch_stdev * stretch_stdev + erf_stdev * erf_stdev) / 2.0
- var += (stretch_avg - erf_avg) * (stretch_avg - erf_avg) / 4.0
- stdev = avg * math.sqrt(var)
+ stretch_var = stretch_result.value_tracker.get_pessimistic_variance()
+ erf_var = erf_result.value_tracker.get_pessimistic_variance()
+ avg_log = (stretch_avg + erf_avg) / 2.0
+ var_log = (stretch_var + erf_var) / 2.0
+ var_log += (stretch_avg - erf_avg) * (stretch_avg - erf_avg) / 4.0
+ stdev_log = math.sqrt(var_log)
+ low, upp = math.exp(avg_log - stdev_log), math.exp(avg_log + stdev_log)
+ avg = (low + upp) / 2
+ stdev = avg - low
trackers = (stretch_result.focus_tracker, erf_result.focus_tracker)
sea = math.exp(stretch_avg)
eea = math.exp(erf_avg)
return _ComputeResult(measurement, avg, stdev, sea, eea, trackers)
-_PartialResult = namedtuple('_PartialResult',
- 'value_tracker focus_tracker samples')
-_ComputeResult = namedtuple('_ComputeResult', 'measurement avg stdev ' +
- 'stretch_exp_avg erf_exp_avg trackers')
+# Named tuples, for multiple local variables to be passed as return value.
+_PartialResult = namedtuple(
+ "_PartialResult", "value_tracker focus_tracker samples")
+"""Two stat trackers and sample counter.
+
+:param value_tracker: Tracker for the value (critical load) being integrated.
+:param focus_tracker: Tracker for focusing integration inputs (sample points).
+:param samples: How many samples were used for the computation.
+:type value_tracker: stat_trackers.ScalarDualStatTracker
+:type focus_tracker: stat_trackers.VectorStatTracker
+:type samples: int
+"""
+
+_ComputeResult = namedtuple(
+ "_ComputeResult",
+ "measurement avg stdev stretch_exp_avg erf_exp_avg trackers")
+"""Measurement, 4 computation result values, pair of trackers.
+
+:param measurement: The trial measurement result obtained during computation.
+:param avg: Overall average of critical rate estimate.
+:param stdev: Overall standard deviation of critical rate estimate.
+:param stretch_exp_avg: Stretch fitting function estimate average exponentiated.
+:param erf_exp_avg: Erf fitting function estimate average, exponentiated.
+:param trackers: Pair of focus trackers to start next iteration with.
+:type measurement: ReceiveRateMeasurement
+:type avg: float
+:type stdev: float
+:type stretch_exp_avg: float
+:type erf_exp_avg: float
+:type trackers: 2-tuple of stat_trackers.VectorStatTracker
+"""
Code Review / csit.git / blobdiff