On Wed, Jun 25, 2014 at 9:35 AM, venkatesha murthy <
venkateshamurth...@gmail.com> wrote:
Can i put a patch for this change?
>
> On Wed, Jun 25, 2014 at 12:21 AM, Luc Maisonobe <l...@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 <l...@spaceroots.org>
>> wrote:
>> >> Hi all,
>> >>
>> >> While looking further in Percentile class for MATH-1120, 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
>> >> sub-array. 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
>
>> >>
>> >> 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 non-recursive 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 sub-array 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[j-1]>x[j]; j--)
> swap(x, j, j-1);
> return;
> }
> :
> :
> : code continues for the else part
>
> Also the grepcode url
> <http://grepcode.com/file/repository.grepcode.com/java/root/jdk/openjdk/6-b14/java/util/Arrays.java#Arrays.sort1%28double[]%2Cint%2Cint%29>
> indicates the same
>
> (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 as-is 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
> Continue to use MAXIMAL/MINIMAL for +/-Inf transformation and that way we
> have covered both Inf and nan cases.
> Use REMOVED as default for all Percentile Estimation Types. (mostly
> influenced by R here perhaps)
>
> best regards,
>> Luc
>>
>> >
>> >> In the select
>> >> operation, we know we have small sub-arrays 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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>
