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https://issues.apache.org/jira/browse/MAHOUT-300?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12836466#action_12836466
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Jake Mannix commented on MAHOUT-300:
------------------------------------
I'm a little concerned about correctness though:
I changed the benchmark code to reset the "result" value after each
implementation test (so the result is then a multiple of the sum of the
vec_i.dot(vec_(i+1)) for the dot test, and the sum of the min distances for the
distance tests), and I get back the following, which has a little anomaly at
the beginning, for Dense dot:
{code}
INFO: DotProduct DenseVector
sum = 763.2092807960457
INFO: DotProduct RandomAccessSparseVector
sum = 588.1135274394809
INFO: DotProduct SequentialAccessSparseVector
sum = 596.8159785621963
INFO: org.apache.mahout.common.distance.CosineDistanceMeasure DenseVector
minDistance = 4762.262583384831
INFO: org.apache.mahout.common.distance.CosineDistanceMeasure
RandomAccessSparseVector
minDistance = 4762.256231037734
INFO: org.apache.mahout.common.distance.CosineDistanceMeasure
SequentialAccessSparseVector
minDistance = 4762.076824677951
INFO: org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure
DenseVector
minDistance = 960106.3770997674
INFO: org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure
RandomAccessSparseVector
minDistance = 958642.7572087944
INFO: org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure
SequentialAccessSparseVector
minDistance = 959249.2711033518
INFO: org.apache.mahout.common.distance.EuclideanDistanceMeasure DenseVector
minDistance = 67835.12813051333
INFO: org.apache.mahout.common.distance.EuclideanDistanceMeasure
RandomAccessSparseVector
minDistance = 67781.10820288945
INFO: org.apache.mahout.common.distance.EuclideanDistanceMeasure
SequentialAccessSparseVector
minDistance = 67802.77780568038
INFO: org.apache.mahout.common.distance.ManhattanDistanceMeasure DenseVector
minDistance = 767471.8978953827
INFO: org.apache.mahout.common.distance.ManhattanDistanceMeasure
RandomAccessSparseVector
minDistance = 766991.0938857986
INFO: org.apache.mahout.common.distance.ManhattanDistanceMeasure
SequentialAccessSparseVector
minDistance = 767126.9510115242
INFO: org.apache.mahout.common.distance.TanimotoDistanceMeasure DenseVector
minDistance = 4781.060847329619
INFO: org.apache.mahout.common.distance.TanimotoDistanceMeasure
RandomAccessSparseVector
minDistance = 4781.058010411256
INFO: org.apache.mahout.common.distance.TanimotoDistanceMeasure
SequentialAccessSparseVector
minDistance = 4781.051812134896
{code}
> Solve performance issues with Vector Implementations
> ----------------------------------------------------
>
> Key: MAHOUT-300
> URL: https://issues.apache.org/jira/browse/MAHOUT-300
> Project: Mahout
> Issue Type: Improvement
> Affects Versions: 0.3
> Reporter: Robin Anil
> Fix For: 0.3
>
> Attachments: MAHOUT-300.patch, MAHOUT-300.patch, MAHOUT-300.patch,
> MAHOUT-300.patch, MAHOUT-300.patch
>
>
> AbstractVector operations like times
> public Vector times(double x) {
> Vector result = clone();
> Iterator<Element> iter = iterateNonZero();
> while (iter.hasNext()) {
> Element element = iter.next();
> int index = element.index();
> result.setQuick(index, element.get() * x);
> }
> return result;
> }
> should be implemented as follows
> public Vector times(double x) {
> Vector result = clone();
> Iterator<Element> iter = result.iterateNonZero();
> while (iter.hasNext()) {
> Element element = iter.next();
> element.set(element.get() * x);
> }
> return result;
> }
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