What finally got me to switch was RStudio server edition which I run on my 12-core MacPro. I did some port-forwarding magic and now I can easily rejoin my session from anywhere. It’s great that they even have an integrated shell tool so you can jump to the system shell whenever you need to. Great software!

I should mention that it was a bit tricky to get the Rstudio server to build and run on OSx Lion. I documented the procedure on a wiki in case there are any mac users out there who want to run Rstudio server: http://sagebionetworks.jira.com/wiki/display/SYNR/Building+RStudio+from+Source+%28OSx+Lion%29

I guess you forgot to insert ‘library(ggplot2)’ in your code, because when someone runs it, there s an error in last row (chart using ggplot2 syntax)

Regards,

Leonard

You’re pretty explicit about the question here – “what proportion of hands am I likely to win?” in each of the two situations. Of course, the decision of what to do in this situation should be “which of these two options maximizes my expected profit?” Because a full house pays out more than a pair, for example, the second strategy may be more profitable. The way to handle this would be to weight the outcomes by the payouts when you take the average which would give you average dollars expected by from each strategy. Then, pick the strategy that gives you the highest expected payout.

As you say, though, all outcomes are more likely in strategy 1 than in strategy 2, so weighting the outcomes differently won’t have an impact.

Not sure how it compares, but a while back I wrote some tokenizers/token filters for Lucene that work on Wikipedia. They aren’t perfect, but if you know Lucene, it may not be too hard to extend them for your needs. Naturally, you can then feed them into Lucene’s n-gram capabilities and other filters to build up what you need.

I am one of the authors and would love to get more detailed feedback on how we can improve this.
Best,
Diederik

Thanks

I am just wondering why DBpedia didnot extract the fulltext article content, but only the abstract.

As I am still half way playing with the DBpedia data, no conclusions can be made with regards to whether it has enough info for me.

Expecting to see more efforts in this space to make Wikipedia data more accessible for programmers, especially python geeks.

I’ve struggled a bit trying to get data from Wikipedia. For example, I’d love to get a plain text data dump of all the Wikis, edit histories and discussion pages on S&P 500 companies or of all the Wikis under “Companies founded in year XYZ”

It would be interesting to see if older companies have longer Wikis or do some kind of sentiment analysis on S&P 500 companies or how frequent changes are made of what byte size.

Any tips on how I’d get started trying to get the data for this? I’m technical enough to know PHP, but I don’t know – this might be beyond me.

-David

This is very cool indeed! One way to speed up this significantly is to use Wikihadoop and use your existing Python code for the mapper. Wikihadoop, in contrast to the Cloud9 package, is able to stream the full bzipped2 XML dump files using Hadoop Streaming. I am happy to help you if you are stuck. You can find Wikihadoop at: https://github.com/whym/wikihadoop

Best,
Diederik

http://wiki.freebase.com/wiki/WEX
http://download.freebase.com/wex/latest/

If by security you meant encryption, then there are a few things I’d like to do in future AMI releases, one being adding SSL support (though it would obviously be an unsigned cert because of changing EC2 IPs), but that’ll be when I have the time!