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I am trying to build a string theory solver and one of the ideas that came up was to write the code inside the z3 prover but that requires understanding the whole z3 code, I am wondering if there is a tutorial on how to do that? I have checked thoroughly but I don't seem to find anything.
You can't really integrate a custom theory with z3 without getting more or less intimately familiar with the internals, and unfortunately this process isn't all that well documented. This is hardly surprising: Z3 is a big and a research(-y) project and there are many moving parts.
Having said that, see the stack-overflow question on prior advice from Nikolaj, who is the main author of z3: SMT solver with custom theories?
This resource is a nice write-up about how to understand theory solvers are architected: http://theory.stanford.edu/~nikolaj/z3navigate.html
No matter which path you go, you'll have a lot of questions. The best place to ask them would be the "discussions" forum of the z3 GitHub site: https://github.com/Z3Prover/z3/discussions
Best of luck!
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What I want is to be able to parse a sentence and classify the words in it as a noun,verb,adjective...etc
I'm aware of Ruby Wordnet and a few other similar projects, but I want something that's a lot simpler to use. Also, I'm open to any suggestions on how I could accomplish this without even requiring a gem.
I recently used engtagger to accomplish what you're describing. It's a pure Ruby gem, so it has no dependencies, but it is pretty old and has some quirks. Still, it was fast enough and accurate enough for my purposes.
As for doing part-of-speech tagging without a gem... it turns out that it's more complicated than you might think. You could implement a Brill tagging algorithm on your own, but that's probably not the best use of your time.
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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I have found OpenCV code that uses CvGaussBGStatModelParams structure, cvCreateGaussianBGModel, and other related functions. However, I haven't been able to find any explanations for how they work and how they are to be used and what they mean.
Any help would be appreciated.
These functions are undocumented (at least as far as the manual goes). However, if you look around in the source, you will find them in src/cvaux/cvbgfg_gaussmix.cpp. In there:
This is based on the "An Improved
Adaptive Background Mixture Model for
Real-time Tracking with Shadow
Detection" by P. KaewTraKulPong and R.
Bowden
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
The windowing method is used, but not
the shadow detection. I make some of
my own modifications which make more
sense. There are some errors in some
of their equations.
That link is probably a good start for you.
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I need a simple parser generator. The language does not matter. It only has to be easy and well documented.
Have you looked at ANTLR?
I found SableCC for Java pretty easy to use, back in the days of my compiler construction classes. A tutorial is here.
Edit: Those days were a few years back. Looking at the other answers, you might be better off with ANTLR. I'll let the votes decide!
You're definitely going to want to take a look at ANTLR. It's not only well-documented but is considered one of the de-facto utilities for language designers.
If you want to work in Visual Studio and possibly use C#, look at MPPG from Microsoft, part of the Managed Language Services SDK.
http://msdn.microsoft.com/en-us/library/bb165963(VS.80).aspx
I'm fond of bison, also known as yacc. It's not terribly hard, but it requires knowing C.
If you are more a Perl person, Parse::RecDescent is not difficult either.
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I was wondering whether there're recommendable sites, that host collections of good and useful code-snippets.
Searching stackoverflow, to be honest, sometimes is priceless.
- But if you know sites like commandlinefu (just for Shell stuff), you may also want something like that for Ruby, Java, Python or C#. There're some small collections, of course. Very often these are specific sites dedicated to be just funny or "wicked cool" (the book series).
I'm just looking for practical stuff to learn from other people's experiences. The standard stuff. Not funny, not wicked. Just pragmatic and workflow-oriented. It seems no one wants to share that.
If you're simply googeling the web and put everything into a growing list of files, there's no overview. So that's not the workflow either, isn't it?
Not really snippets. But quite useful, http://github.com
With many languages, there is http://snippets.dzone.com/
Still with many languages, there is http://codesnippets.joyent.com/
For Django, there is http://www.djangosnippets.org/
After, there's a lot of blogs all other the web giving advices and snippets.
Some others:
Snipplr
RefactorMyCode
Codeproject and codeguru have lots of code snippets which are useful