com.sun.faban.driver.util.timermeter
Class CommonUtilities

java.lang.Object
  com.sun.faban.driver.util.timermeter.CommonUtilities

public class CommonUtilities
extends Object

Constructor Summary

CommonUtilities()

Method Summary

static List<MeasurementGroupsCluster>	clusterTimerValuesFromHistogram(SortedMap<Long,Integer> allMeasurementsHistogram, int clusterFormationThresholdDistance) TODO move back to AbstractTimerMeter and rename to show this method's specific nature Sketch of work for threshold==2 ("entry" := one element of allMeasurementsHistogram): if a cluster with 3 elements (i.e.
static long	computeAccuracyFromClusters(List<MeasurementGroupsCluster> clusters, boolean verbose) TODO document me please TODO replace through greatest-common-divisor scheme (or add it testwise...)
static StatisticalDescription	computeCharacteristics_detailed(long[] measurements, boolean logValues)
static SortedMap<Long,Integer>	computeHistogram(long[] measurements) TODO test me! Computes a simple sorted histogram from measurements
static void	rearrangeMeasurementsByValue(SortedMap<Integer,SortedMap<Long,Integer>> source, SortedMap<Long,SortedMap<Integer,Integer>> destination)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

CommonUtilities

public CommonUtilities()

Method Detail

rearrangeMeasurementsByValue

public static void rearrangeMeasurementsByValue(SortedMap<Integer,SortedMap<Long,Integer>> source,
                                                SortedMap<Long,SortedMap<Integer,Integer>> destination)

Parameters:

source -

destination -

clusterTimerValuesFromHistogram

public static List<MeasurementGroupsCluster> clusterTimerValuesFromHistogram(SortedMap<Long,Integer> allMeasurementsHistogram,
                                                                             int clusterFormationThresholdDistance)

TODO move back to AbstractTimerMeter and rename to show this method's specific nature Sketch of work for threshold==2 ("entry" := one element of allMeasurementsHistogram): if a cluster with 3 elements (i.e. x, x+1, x+2) appears: re-visit existing clusters and break them into one-entry-per-cluster (or, simpler: discard existing clusters and re-traverse allMeasurementsHistogram with a threshold of <1). The return of this method should be postprocessed by another method that computes the accuracy as greatest common divisor of cluster distances. However, this is tricky, as some two-classes clusters may not have any values from one class, e.g. [(100,101), (200,201), (300), (400,401)], or some classes may have been skipped altogether... Just work with "+-1" cluster distances (this does not work with "2" or other small accuracies)? Or should we "fill" non-existing classes? In fact, we cannot guarantee that all classes are present... For future work: can we make any statements about the "Nachkommastelle" from the frequencies of the classes?

Parameters:

allMeasurementsHistogram -

clusterFormationThresholdDistance - the maximum distance (incl.) from the leftmost [smalles] element of the current cluster so that the considered allMeasurementsHistogram entry can still be added to the current cluster.

Returns:

computeAccuracyFromClusters

public static long computeAccuracyFromClusters(List<MeasurementGroupsCluster> clusters,
                                               boolean verbose)

TODO document me please TODO replace through greatest-common-divisor scheme (or add it testwise...)

Parameters:

clusters -

Returns:

computeCharacteristics_detailed

public static StatisticalDescription computeCharacteristics_detailed(long[] measurements,
                                                                     boolean logValues)

Parameters:

measurements -

logValues -

Returns:

TODO test me TODO set all fields!

computeHistogram

public static SortedMap<Long,Integer> computeHistogram(long[] measurements)

TODO test me! Computes a simple sorted histogram from measurements

Parameters:

measurements - the input

Returns:

the resulting histogram