Hi,
in the project we're following we need to make a couple of variants
to the sldService, so I guess I'll need to discuss them a bit to make
sure we don't break FAO users of that module.
The first thing I need, is to change the generated rules so that they
all use closed intervals, such as:
0 <= x <= 10
10 < x <= 20
20 < x <= 30
as opposed of today's result:
x <= 10
10 < x <= 20
x > 20
In order to do that without breaking current users, I was thinking
of adding a new parameter, closedIntervals=true/false to the POST
call that generates the intervals.
The second issue is dealing with odd but not uncommon data histograms
like, for example {0 0 0 0 2 4 6 8}. If you ask the current quantile
classifier to classify that with 4 intervals, you'll get {0 0}, {0 0},
{2 4} {6 8}, which would result in very confusing rules (see attached
screenshot for an example).
One idea I had is to make the quantile function detect the flat areas
and make it create separate classes for them, and then classify the
rest using the standard sized classes. The above example would then
become: {0 0 0 0} {2 4} {6 8}.
Another example: {0 1 2 3 3 3 4 5}, 2 classes -> {0 1 2} {3 3 3} {4 5},
that is, first isolate the flat area, then try to build classes of 4
elements with the rest of the data, accepting the fact that more classes
than requested may be generated.
Another possible approach, which is easier because we can handle it
in post processing, that is, when building the rules, would be to simply
kill subsequent classes that do have the same min and max. With the
above examples, the rules would become:
{0 0 0 0 2 4 6 8}
x = 0 (killing the second x = 0 interval)
0 < x <= 4
4 < x <= 8
or if you prefer, {0 0 0 0} {2 4} {6 8}
and:
{0 1 2 3 3 3 4 5}
0 <= x <= 3
3 < x <= 5
that is {0 1 2 3 3 3} {4 5}
If the latter is good for FAO as well, I can just modify the way
we build the rules and avoid touching the underlying quantile
classification function. Opinions?
Cheers
Andrea
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