On Sun, Jun 29, 2014 at 1:25 AM, Phil Steitz <phil.steitz@gmail.com> wrote:
> On 6/25/14, 12:12 AM, Luc Maisonobe wrote:
> > Le 25/06/2014 06:05, venkatesha murthy a écrit :
> >> On Wed, Jun 25, 2014 at 12:21 AM, Luc Maisonobe <luc@spaceroots.org>
> wrote:
> >>
> >>> Hi Venkat,
> >>>
> >>> Le 23/06/2014 21:08, venkatesha murthy a écrit :
> >>>> On Tue, Jun 24, 2014 at 12:08 AM, Luc Maisonobe <luc@spaceroots.org>
> >>> wrote:
> >>>>> Hi all,
> >>>>>
> >>>>> While looking further in Percentile class for MATH1120, I have
found
> >>>>> another problem in the current implementation. NaNStrategy.FIXED
> should
> >>>>> leave the NaNs in place, but at the end of the KthSelector.select
> >>>>> method, a call to Arrays.sort moves the NaNs to the end of the small
> >>>>> subarray. What is really implemented here is therefore closer to
> >>>>> NaNStrategy.MAXIMAL than NaNStrategy.FIXED. This always occur in
the
> >>>>> test cases because they use very short arrays, and we directly
> switch to
> >>>>> this part of the select method.
> >>>> Are NaNs considered higher than +Inf ?
> >>>> If MAXIMAL represent replacing for +inf ; you need something to
> >>>> indicate beyond this for NaN.
> >>> Well, we can just keep the NaN themselves and handled them
> >>> appropriately, hoping not to trash performances too much.
> >>>
> >> Agreed.
> >>
> >>>> What is the test input you see an issue and what is the actual error
> >>>> you are seeing. Please share the test case.
> >>> Just look at PercentileTest.testReplaceNanInRange(). The first check in
> >>> the test corresponds to a Percentile configuration at 50% percentile,
> >>> and NaNStrategy.FIXED. The array has an odd number of entries, so the
> >>> 50% percentile is exactly one of the entries: the one at index 5 in the
> >>> final array.
> >>>
> >>> The initial ordering is { 0, 1, 2, 3, 4, NaN, NaN, 5, 7, NaN, 8 }. So
> >>> for the NaNStrategy.FIXED setting, it should not be modified at all in
> >>> the selection algorithm and the result for 50% should be the first NaN
> >>> of the array, at index 5. In fact, due to the Arrays.sort, we *do*
> >>> reorder the array into { 0, 1, 2, 3, 4, 5, 7, 8, NaN, NaN, NaN }, so
> >>> the result is 5.
> >>>
> >>> Agreed. just verified by putting back the earlier insertionSort
> function.
> >>
> >>> If we use NaNStrategy.MAXIMAL and any quantile above 67%, we get as a
> >>> result Double.POSITIVE_INFINITY instead of Double.NaN.
> >>>
> >>> If we agree to leave FIXED as unchanged behaviour with your
> insertionSort
> >> code; then atleast MAXIMAL/MINIMAL should be allowed for transformation
> of
> >> NaN to +/Inf
> > I'm fine with it, as long as we clearly state it in the NaNStrategy
> > documentation, saying somethig like:
> >
> > When MAXIMAL/MINIMAL are used, the NaNs are effecively *replaced* by
> > +/ infinity, hence the results will be computed as if infinities were
> > there in the first place.
>
> 1  this is mixing concerns. NaNStrategy exists for one specific
> purpose  to turn extend the ordering on doubles a total ordering.
> Strategies prescribe only how NaNs are to be treated in the
> ordering. Side effects on the input array don't make sense in this
> context. The use case for which this class was created was rank
> transformations. Returning infinities in rank transformations would
> blow up computations in these classes. If a floating point
> computations need to transform NaNs, the specific stats / other
> classes that do this transformation should perform and document the
> behavior.
>
> Phil
>
OK. Agreed realized it later. Hence i have not touched NaNStrategy in my
patch(MATH1132) . Now i have added a separate transformer to transform NaNs
but it uses NanStrategy. Please let me know on this as this trasnformation
itself
can be used in different places.
>
> >>>>> When I implemented the method, I explicitly avoided calling
> Arrays.sort
> >>>>> because it is a general purpose method that is know to be efficient
> only
> >>>>> for arrays of at least medium size. In most cases, when the array
is
> >>>>> small one falls back to a nonrecursive method like a very simple
> >>>>> insertion sort, which is faster for smaller arrays.
> >>>> Please help me understand here; even java primitive Arrays.sort does
> >>>> an insertion sort for less than 7 elements
> >>>> (Refer sort1(double x[], int off, int len))
> >>>> So what is it that the custom insertion sort that does differently or
> >>>> is advantageous. Is it the value 15 elements?
> >>> I don't see a reference to 7 elements, neither in the Java6 nor in the
> >>> Java 7 doc
> >> Please take a look at the sort1 method where there is a first block in
> the
> >> code which clearly mentions len < 7
> >> /**
> >> * Sorts the specified subarray of doubles into ascending order.
> >> */
> >> private static void sort1(double x[], int off, int len) {
> >> // Insertion sort on smallest arrays
> >> if (len < 7) {
> >> for (int i=off; i<len+off; i++)
> >> for (int j=i; j>off && x[j1]>x[j]; j)
> >> swap(x, j, j1);
> >> return;
> >> }
> >> :
> >> :
> >> : code continues for the else part
> >>
> >> Also the grepcode url
> >> <
> http://grepcode.com/file/repository.grepcode.com/java/root/jdk/openjdk/6b14/java/util/Arrays.java#Arrays.sort1%28double[]%2Cint%2Cint%29
> >
> >> indicates the same
> > OK, you were refering to a specific implementation. I was thinking in
> > the general case.
> >
> >> (and in any case the doc explicitly states the algorithms
> >>> explained are only implementation notes and are not part of the
> >>> specification).
> >>>
> >> Yes its a part of comments anyways.
> >>
> >>> However, the most important part for now is the fact that we control it
> >>> and may be able to implement different NaN strategies. What we have
> >>> currently fails.
> >>>
> >>> I agree on this and hence here is my take:
> >> Leave FIXED asis and use the earlier insertionSort code (just change
> the
> >> name to sort rather than hardcoding it as insertionsort) to handle the
> case
> >> you were mentioning
> > If we leave FIXED it as it is done know, even with insertionSort we do
> > not really control what happens. It is deterministic as the pivoting and
> > if/else structure is well defined (both in the selection part and in the
> > final sort for subarrays), but it is untrackable so we can't document
> it.
> >
> >> Continue to use MAXIMAL/MINIMAL for +/Inf transformation and that way
> we
> >> have covered both Inf and nan cases.
> > OK.
> >
> >> Use REMOVED as default for all Percentile Estimation Types. (mostly
> >> influenced by R here perhaps)
> > This would be fine with me. I have concerns with FIXED only, the other
> > strategies all seem quite realistic.
> >
> > Does anybody else has an advice for FIXED? What are the use case?
> >
> > best regards,
> > Luc
> >
> >> best regards,
> >>> Luc
> >>>
> >>>>> In the select
> >>>>> operation, we know we have small subarrays at the call point. Going
> >>>>> back to the former insertionSort would recover the good behavior
for
> >>>>> small arrays, but would in fact not be sufficient to really
> implement a
> >>>>> NaNStrategy.FIXED. I guess it would be simple to make it behave
like
> >>>>> NaNStrategy.MAXIMAL but I did not try yet.
> >>>>>
> >>>>> My point here is rather: can we really and should we really implement
> >>>>> NaNStrategy.FIXED? Looking at how it is used elsewhere, it needs
to
> >>>>> store the original position of the NaNs. It is quite cumbersome.
> >>>>>
> >>>>> I wonder what is the use case for NaNStrategy.FIXED at all.
> >>>>>
> >>>>> Going further and looking at the use cases for other NaNStrategy
> values,
> >>>>> the NaNs are replaced by +/ infinity before sorting, which is OK
for
> >>>>> ranking as we only rely on the indices, but we use the values
> themselves
> >>>>> in Percentile. So sometimes, we end up with computing interpolations
> >>>>> like 0.5 * (x[k] + x[k+1]) or similar. If x[k] is a finite number
and
> >>>>> x[k+1] has been changed to +infinity, we get +infinity, instead
of
> the
> >>>>> NaN we should have retrieved without replacement. So here again,
I'm
> not
> >>>>> sure we are doing the right thing.
> >>>>>
> >>>>> What do you think?
> >>>>>
> >>>>> best regards,
> >>>>> Luc
> >>>>>
> >>>>> 
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> >>>>>
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> >>>>
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> >
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