I did some experimentation in relation to this and came up with the following test case.*Constructor approach to reusing moments.*
Mean mean = new Mean();
SecondMoment m2 = new SecondMoment(mean);
ThirdMoment m3 = new ThirdMoment(mean, m2);
FourthMoment m4 = new FourthMoment(mean, m2, m3);
Variance var = new Variance(m2);
Skew skew = new Skew(variance, m3);
Kurt kurt = new Kurt(variance, m4);
One problem with this approach, is now order of computation for each statistic object is a big concern. The responsibility of correct ordering would have to be placed in univariate with Mark's statistics objects. It would be better to place that responsibility in the statistic objects themselves
Maybe we could make composite statistic objects that can compute more than one metric. The composite would conform to the same statistic interface and would be adaptable into individual metrics. Also, the responsibility of computation ordering would be hidden in the composite, removed from univariate.
Brent Worden
http://www.brent.worden.org/
FourthMoment m4 = new FourthMoment();
Mean m = new Mean(m4);
Variance v = new Variance(m4);
Skewness s= new Skewness(m4);
Kurtosis k = new Kurtosis(m4);for (int i = 0; i < testArray.length; i++){
m4.increment(testArray[i]);
m.increment(testArray[i]);
v.increment(testArray[i]);
s.increment(testArray[i]);
k.increment(testArray[i]);
}
assertEquals(mean,m.getValue(),tolerance);
assertEquals(var,v.getValue(),tolerance);
assertEquals(skew ,s.getValue(),tolerance);
assertEquals(kurt,k.getValue(),tolerance);
Previously, the class hierarchy looked like this (--> = extends)
mean (m1)
^
|
var --> m2,
^
|
skew --> m3
^
|
kurt --> m4I adjusted the mean,var,skew,kurt classes to be in the following hierarchy instead
mean >delegates to> m1 ^ ^ | | var >delegates to> m2, ^ ^ | | skew >delegates to> m3 ^ ^ | | kurt >delegates to> m4
For incrementals I then made Each of the kurt, skew, var and mean classes delegate to an internal copy of its corresponding moment. Because FourthMoments encapsulate all the functionality of the lower moments, I can then use one instance of the FourthMoment and hand it into the statistics constructors, when this happens tehs tatistic no longer increments the internal moment that was handed in the constructor. I created code to make sure this moment is no longer incremented internally and must be incremented externally like in the above example ( this also means that it only needs to be done once per iteration instead of once per statistic, a large savings in duplicated computation). This works at any level in the moment hierarchy, for example
FirstMoment m1 = new FirstMoment(); Mean m = new Mean(m1);
or
SecondMoment m2 = new SecondMoment(); Mean m = new Mean(m2); Variance v = new Variance(m2);
or
ThirdMoment m3 = new ThirdMoment(); Mean m = new Mean(m3); Variance v = new Variance(m3); Skewness s= new Skewness(m3);
or
FourthMoment m4 = new FourthMoment(); Mean m = new Mean(m4); Variance v = new Variance(m4); Skewness s= new Skewness(m4); Kurtosis k = new Kurtosis(m4);
Finally, I can always forget doing this approach and fall back to just:
Mean m = new Mean(); Variance v = new Variance(); Skewness s= new Skewness(); Kurtosis k = new Kurtosis();
:which is less efficient, but requires no ordering restrictions. Its a start towards both flexibility and efficiency by providing various combinations of the stats for different usecases.
-Mark
-- Mark Diggory Software Developer Harvard MIT Data Center http://www.hmdc.harvard.edu
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