trying to buy a ThinkPad without paying for Windows

I got that Dell Mini 12 some time ago, and honestly it's been a pain. It's a good-looking machine, and the keyboard and screen are nice, but the Poulsbo chipset just has terrible Linux support. Like every third time I get an update, something breaks, and I haven't been able to make it suspend/resume reliably in months -- oh, and X just broke again. What I really want is a little ThinkPad.

So I just sent this email to Lenovo:

Hey, good evening,

I'd love to purchase a ThinkPad X200. I haven't found the option on your website, though, for how I can buy one without Windows? Could you point me to that link?

I'm simply not going to use Windows; I would install Linux on it as soon as I get the machine anyway, and I don't want to pay for software I won't use.

So if you can sell me a ThinkPad with no Windows, that would be fantastic, and I'll be really happy and gladly give you money and say nice things about your company.

Thanks very much!

--
-- Alex Rudnick

We'll see what they say! I might just buy the laptop anyway, not agree to the Windows EULA, and then go through the hullabaloo to refund it.

constraint programming in Python

You may be familiar with constraint programming, an approach where, instead of describing how to solve a problem, you describe what a possible solution looks like, and let a generalized solver find possible solutions. This is the sort of thing you might do with Prolog, Oz, or any number of libraries for your favorite programming language.

If your favorite programming language is Python, there are at least two different libraries for this approach! Unfortunately, they're both called "python-constraint"; this led to some confusion on my part. Here they are:

logilab-constraint. This is packaged in Debian/Ubuntu as "python-constraint". It's put out by the French company LogiLab, who contribute a bunch of Free Software useful for doing AI-flavored things. Their HMM library is pretty slick too.

python-constraint is a package by Gustavo Niemeyer, and it's got this really nice tutorial.

I mention these because my new research group is using this latter one to build a dependency grammar system based on Ralph Debusmann's XDG.

And more about that, as we get to it :)

normal distributions and R

When I'm using R to do statistical things (such as homework), I feel somewhat torn -- it's got so many nice functions that come built in, but the language itself is slightly clunky, and integrating code that I've written in R with bigger projects seems like it would be kind of a pain. That's a general problem with picking any special-purpose language, though -- I might make similar complaints about Matlab/Octave or even Prolog...

I note, though, that I haven't jumped ship to NumPy yet.

pnorm and qnorm

I just wanted to mention these fantastically easy-to-use functions that come built right in: pnorm and qnorm.

pnorm is what you use if you have a z-score and you want the probability that a value in the distribution would come up as less than that score. This is equivalent to looking up probability values in the "z" tables in the back of your stats book. pnorm(0) gives you 0.5, since half of all values are going to have a value less than 0.

qnorm does the inverse -- you give it a probability and it gives you back the z-score below which that much of the probability mass lies. So if you give it 0.5, it gives you back 0.

Both of these functions can take more parameters -- you can specify your distribution mean and stddev (so you don't have to use z-scores), for example. Type "?qnorm" for the docs!