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
On Sat, Jun 27, 2015 at 12:16 AM, Pierre CHANSON 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... ;)
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
Moose-dev mailing list Moose-dev@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
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@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@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
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@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@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@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@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
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@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@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@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
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@free.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@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@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@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
On Tue, Jun 30, 2015 at 3:55 PM, Pierre CHANSON 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
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,
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@gmail.com:
On Tue, Jun 30, 2015 at 3:55 PM, Pierre CHANSON 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
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@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
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
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
Moose-dev mailing list Moose-dev@iam.unibe.ch https://www.iam.unibe.ch/mailman/listinfo/moose-dev
-
Alexandre Bergel -
phil＠highoctane.be -
Pierre CHANSON -
Serge Stinckwich -
stepharo