27 Jun
2015
27 Jun
'15
12:16 a.m.
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... ;)
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.
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
27 Jun
27 Jun
10:57 a.m.
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON <chans.pierre(a)gmail.com>
wrote:
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... ;)
Great PDF :-)
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.
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.
I'll try these out. I suppose you use latest Roassal.
Also, I found this page and thought it was really nice animations !!
http://bost.ocks.org/mike/algorithms/
Yes, that page is awesome.
I've been working on the SVG+HTML5+JS exporter in Roassal in order to
integrate a graph in a webpage as a seaside component (but it can be used
in other ways too) as the implementation was showing one graph on a single
page and had cross browser issues.
It works fine now. But Roassal is a moving target and integrating things
back may need some work.
Phil
cheers,
Pierre
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28 Jun
28 Jun
5:13 p.m.
Damien pollet implemented some of the algortihms
Also, I found this page and thought it was really nice animations
!! http://bost.ocks.org/mike/algorithms/
27 Jun
27 Jun
12:40 p.m.
Good job Pierre !
I was looking exactly to the same document recently and thought that I
could try to reproduce some examples from chapter 3.
Maybe we can join our effort and reproduce the examples as a Pillar chapter
somewhere ?
Regards,
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON <chans.pierre(a)gmail.com>
wrote:
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... ;)
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.
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
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
28 Jun
28 Jun
2:25 a.m.
Yes, we started to work on a chapter. Probably it will be part of AgileVisualization
cheers,
Alexandre
On Jun 27, 2015, at 7:40 AM, Serge Stinckwich
<serge.stinckwich(a)gmail.com> wrote:
Good job Pierre !
I was looking exactly to the same document recently and thought that I could try to
reproduce some examples from chapter 3.
Maybe we can join our effort and reproduce the examples as a Pillar chapter somewhere ?
Regards,
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON <chans.pierre(a)gmail.com> wrote:
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... ;)
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.
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.
<histo1.png>
<histo2.png>
Also, I found this page and thought it was really nice animations !!
http://bost.ocks.org/mike/algorithms/
cheers,
Pierre
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
--
_,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:
Alexandre Bergel http://www.bergel.eu
^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;.
5:15 p.m.
Le 27/6/15 12:40, Serge Stinckwich a écrit :
Good job Pierre !
I was looking exactly to the same document recently and thought that I
could try to reproduce some examples from chapter 3.
Yes I pass it to alex because pierre will visit us and I propose to
enhance the statistical part as well build on top of NeoCSV to improve
data collection.
Maybe we can join our effort and reproduce the
examples as a Pillar
chapter somewhere ?
Serge the histogram objects (not the drawer) should be packaged
in
SciSmalltalk.
Stef
Regards,
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON
<chans.pierre(a)gmail.com <mailto:chans.pierre@gmail.com>> wrote:
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... ;)
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.
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
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch <mailto:Moose-dev@iam.unibe.ch>
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
30 Jun
30 Jun
3:55 p.m.
Hi,
thanks,
I was looking exactly to the same document recently and thought that I
could try to reproduce some examples from chapter 3.
Maybe we can join our effort and reproduce the examples as a Pillar
chapter somewhere ?
Serge, sure here is where i put my work for now
https://github.com/stonesong/roassal-data-mining-book.git
2015-06-28 12:15 GMT-03:00 stepharo <stepharo(a)free.fr>fr>:
Le 27/6/15 12:40, Serge Stinckwich a écrit :
Good job Pierre !
I was looking exactly to the same document recently and thought that I
could try to reproduce some examples from chapter 3.
Yes I pass it to alex because pierre will visit us and I propose to
enhance the statistical part as well build on top of NeoCSV to improve
data collection.
Maybe we can join our effort and reproduce the examples as a Pillar
chapter somewhere ?
Serge the histogram objects (not the drawer) should be packaged in
SciSmalltalk.
Stef
Regards,
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON <chans.pierre(a)gmail.com>
wrote:
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... ;)
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.
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
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
_______________________________________________
Moose-dev mailing
listMoose-dev@iam.unibe.chhttps://www.iam.unibe.ch/mailman/listinfo/moose-dev
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
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4:09 p.m.
On Tue, Jun 30, 2015 at 3:55 PM, Pierre CHANSON <chans.pierre(a)gmail.com>
wrote:
Hi,
thanks,
I was looking exactly to the same document recently and thought that I
Thank you Pierre !
Pay attention that the original book is not a free book, so maybe you have
to change a little bit the examples to avoid any problems in the future.
Maybe you should ask the author about that.
Cheers,
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
could try to reproduce some examples from chapter
3.
Maybe we can join our effort and reproduce the examples as a Pillar
chapter somewhere ?
Serge, sure here is where i put my work for now
https://github.com/stonesong/roassal-data-mining-book.git
4:34 p.m.
Mmh I did not know, actually I was trying to do it as similar as possible
to have a good comparison :)
In that case we will have to adapt it change texts, and maybe change
datasets later on the evolution. Or indeed ask the author directly could be
interesting.
Thanks !
Pierre
2015-06-30 11:09 GMT-03:00 Serge Stinckwich <serge.stinckwich(a)gmail.com>om>:
On Tue, Jun 30, 2015 at 3:55 PM, Pierre CHANSON <chans.pierre(a)gmail.com>
wrote:
Hi,
thanks,
I was looking exactly to the same document recently and thought that I
Thank you Pierre !
Pay attention that the original book is not a free book, so maybe you have
to change a little bit the examples to avoid any problems in the future.
Maybe you should ask the author about that.
Cheers,
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
could try to reproduce some examples from chapter
3.
Maybe we can join our effort and reproduce the examples as a Pillar
chapter somewhere ?
Serge, sure here is where i put my work for now
https://github.com/stonesong/roassal-data-mining-book.git
7:41 p.m.
Yes, we will change the examples on some points. For now, we would like to know how do we
compare against R. What is missing in Roassal.
Cheers,
Alexandre
--
_,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:
Alexandre Bergel http://www.bergel.eu
^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;.
On Jun 30, 2015, at 11:09 AM, Serge Stinckwich
<serge.stinckwich(a)gmail.com> wrote:
On Tue, Jun 30, 2015 at 3:55 PM, Pierre CHANSON <chans.pierre(a)gmail.com
<mailto:chans.pierre@gmail.com>> wrote:
Hi,
thanks,
I was looking exactly to the same document recently and thought that I could try to
reproduce some examples from chapter 3.
Maybe we can join our effort and reproduce the examples as a Pillar chapter somewhere ?
Serge, sure here is where i put my work for now
https://github.com/stonesong/roassal-data-mining-book.git
<https://github.com/stonesong/roassal-data-mining-book.git>
Thank you Pierre !
Pay attention that the original book is not a free book, so maybe you have to change a
little bit the examples to avoid any problems in the future.
Maybe you should ask the author about that.
Cheers,
--
Serge Stinckwich
UCBN & UMI UMMISCO 209 (IRD/UPMC)
Every DSL ends up being Smalltalk
http://www.doesnotunderstand.org/
<http://www.doesnotunderstand.org/>_______________________________________________
Moose-dev mailing list
Moose-dev(a)iam.unibe.ch
https://www.iam.unibe.ch/mailman/listinfo/moose-dev
28 Jun
28 Jun
5:12 p.m.
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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3462
days inactive
3466
days old
10 comments
5 participants
participants (5)
-
Alexandre Bergel -
phil＠highoctane.be -
Pierre CHANSON -
Serge Stinckwich -
stepharo