Le 27/6/15 00:16, Pierre CHANSON a écrit :
Hola todos,
These days I am working on compete with R. I am taking the datasets
and examples from
http://cran.r-project.org/doc/contrib/Zhao_R_and_data_mining.pdf and
make it with Roassal even better ! Or almost... ;)
Nice!
I love this idea.
Practicing on real cases is the best.
First chapter first pages, the histograms... In
Roassal they were not
working so well.
So lately I restructured the RTDistribution package. This, by default
make a classical frequency distribution, with an optimized number of
classes. The method #strategyBlock: allows to change the strategy for
the distribution.
Pierre it would be good that distribution objects - domain) are
not
bound to Roassal but should be packaged in SciSmalltalk.
Roassal should be the visualisation not the math and the domain.
I also worked on the RTHistogramSet and method
#histogram of
SequenceableCollection to improve a bit the looking. There is still
here some work to do but at least the histogram works well and looks
fine.
We worked with Alejandro on the algorithm of frequency distribution to
make it efficient compared to the classic one (Only one iteration on
datas during the algo itself).
Here are some examples, don't hesitate to try a bit and give me
feedbacks if any bugs, so I can move on the next graphs ;)
#(5 3 8 6 5 4 2 9 1 2) histogram.
#(1 2 3) histogram.
#(555 1 1 2) histogram.
((1 to: 1000) collect: [:i | i atRandom ]) histogram.
Also, I found this page and thought it was really nice animations !!
http://bost.ocks.org/mike/algorithms/
cheers,
Pierre
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