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I've been teaching myself F# for a while now. I've read Programming F# by Chris Smith (great book) and I've written a few small scripts for getting the job done here and there.
But IMO the best way to learn a new programming language—and more importantly, the idioms that come with it—is to read a good open source codebase written in that language. Naturally, writing code in that language is crucial, but in the beginning, you're basically struggling with your own ignorance about how things should be done. You could perform certain tasks one way or the other, but it takes experience to realize the flaws and virtues of each. Even after you've gotten a firm grasp of how things work, reading the code of people who have an even firmer one helps a great deal.
Most would agree that the most insightful parts of any learn-a-programming-language book are the code examples, and reading a well-written open source codebase is the next level of that.
So are there any out there for F#?
Ref this question.
IMO, F# PowerPack is the best code base there.
Here are a few additional links that you may find interesting:
If you download F# for Visual Studio 2008, it also comes with sources of the entire F# library. This is sometimes a bit difficult code and it uses some internal tricks in a few places, but it is sometimes very good resource for learning (for example, Map type is a great example of a tree data structure).
There are some official F# Samples and some F# Community Samples (which includes my 3D fractal, example of working with quotations and a few shorter examples).
You can also look at the source code of samples from my Real-World Functional Programming book. Especially later chapters contain relatively complex sample applications (parallel simulations of animas, rectangle drawing application, etc.) The code has quite a lot of comments, so this may be useful for learning F#.
I would say that the WPF F# control codebase at http://wpffsharp.codeplex.com/ is a good place to start. One of the least trivial aspects of F# is UI and this is an excellent start to UI in F#. Also, since the code base is written by someone also learning F#, you have the benefit of seeing the iterations they go through.
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I have the skills with C++, ruby,rails, and some other scripting languages. I want to start experimenting and learn concepts in Data mining and machine learning.
I want to be well equipped with the programming knowledge required for those concepts.
Which language has best support for DM and ML? puthon, Java, C++? Is there anything coming up in Javascript?
Thanks
Use whatever you are most comfortable with.
At least if it has the basics around. JavaScript and PHP are not very good in this domain. They just don't scale well for numerical computations. Python, R, Java and Scala are most popular. Theres Matlab, but there is not much happening there anymore. There is Julia which has a similar syntax but which is much more alive and shows some promise, if it weren't for colum-major,1-indexed arrays and the matlab-like syntax. Some use Lua, others Mathematica...
There are many many factors that play a role.
For example, scripting languages like Python and R are really slow - but these two also interface very well to C libraries (and Fortran!) so if you nostly use them as a "driver" and the libraries do all the work then they can be very usable. Just make sure to not assume every module is fast...
I think that perhaps your question is a bit off target. The languages themselves don't generally have the support: it's packages that interface with those languages, such as Apache Spark (interface to their ML package), Intel's MKL (vector and matrix operations optimized for Xeon Phi), SciKit (Python interface), etc.
That said, I see the most active support for languages that drive at distributed processing. In my ambit, Java/Spark is currently the front-runner, but one or two major releases can change the market considerably -- see the buzz on Tensor, for instance, or the staying power of BeautifulSoup.
For experimentation, start with your comfort zone. There are plenty of good tools that interface well with Ruby and C++, as well. As long as you're using this to learn the underlying concepts, I believe that you'll do best with a language you already know: that gives you one less area of frustration in your learning curve.
Anony-Mousse and Patricio have given you very good points with which I totally concur. I'm working in Python and Scala, with Java and Spark just underneath.
Python has a very strong support from the Data Science Community, you have very good packages like Pandas and Python has a very good integration with Spark
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Just for kicks, I'm trying to create an application that can simplify, factor, and expand algebra equations. Programming the rules seems as if it will be straight forward if I can get the equations into a good workable format. Parsing the equations is proving to be a hassle, Currently working with Python but I'm not against having to learn something new.
Are there any libraries (for any language) that would make this project pretty simple, or is that a pipe dream?
[Tagging this with Haskell because I have a feeling that's where the 'simple' is]
Yes, Haskell has many many libraries that make writing parsers reasonably easy. Parsec is a good start, and it even has clones in other languages, including Python (that article also links to pyparsing which looks like it might also work).
This answer of mine is an example (note, it's probably not top-notch Parsec or Haskell): it's indicative of the power of Haskell's parsing libraries, precisely 4 lines of code implement the whole parser.
You could also browse old questions and answers to get a feel for the various libraries and techniques, e.g. parsec, parsing+haskell and parsing+python.
The best way to work out your line of attack for the larger project would be to start small and just try stuff until you're comfortable with your tools: choose a library and try to implement a relatively simple parser, like parsing expressions with just numbers, + and *, or even just parsing numbers and + with bracketing... something small (but not too small; those two examples each have non-trivialities, the first has operator precedence and the second has recursive nesting). If you don't like the library much, try a different library.
It's been done in just about every language.
Python has a library for parsing algebraic equations and symbolic mathematics all ready to go:
http://code.google.com/p/sympy/
I'd recommend reusing, unless your purpose is to learn how to write such a thing.
Python or matlab would be my suggestions. Are you planning on storing the whole equation in a string, and then split it up, to factor and simplify?
Give some more information, kindof a cool project.
This is an old question, but I'd like to suggest you MathParseKit.
This is a C++ library that given a string like "2*3/4" gives you a Tree of functions/variable/constants that defines the expression.
You can solve it, but you can even change it and put it again in string format.
You can find it at:
https://github.com/B3rn475/MathParseKit
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There are lots of parsers and lexers for scripts (i.e. structured computer languages). But I'm looking for one which can break a (almost) non-structured text document into larger sections e.g. chapters, paragraphs, etc.
It's relatively easy for a person to identify them: where the Table of Contents, acknowledgements, or where the main body starts and it is possible to build rule based systems to identify some of these (such as paragraphs).
I don't expect it to be perfect, but does any one know of such a broad 'block based' lexer / parser? Or could you point me in the direction of literature which may help?
Many lightweight markup languages like markdown (which incidentally SO uses), reStructured text and (arguably) POD are similar to what you're talking about. They have minimal syntax and break input down into parseable syntactic pieces. You might be able to get some information by reading about their implementations.
Define the annotation standard, which indicates how you would like to break things up.
Go on to Amazon Mechanical Turk and ask people to label 10K documents using your annotation standard.
Train a CRF (which is like an HMM, but better) on this training data.
If you actually want to go this route, I can elaborate on the details. But this will be a lot of work.
Most of the lex/yacc kind of programs work with a well defined grammar. if you can define your grammar in terms of a BNF like format (which most of the parsers accept similar syntax) then you can use any of them. That may be stating the obvious. However you can still be a little fuzzy around the 'blocks' (tokens) of text which would be part of your grammar. After all you define the rules for your tokens.
I have used Parse-RecDescent Perl module in the past with varying levels of success for similar projects.
Sorry, it may not be a good answer but more sharing my experiences on similar projects.
try: pygments, geshi, or prettify
They can handle just about anything you throw at them and are very forgiving of errors in your grammar as well as your documents.
References:
gitorius uses prettify,
github uses pygments,
rosettacode uses geshi,
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There are a lot of programing languages these days. Fine. Not interesting for this question.
There are quite a few programming methodologies, like modular programming, Object Oriented, Agile, etc. Now, is there somewhere a good diagram on the Internet that shows how these methodologies are related to one another? Preferably something with a timeline showing when the methodology started to appear?
(Thus, not the programming languages but the methodologies...)
you can find one here for programming languages. this one looks at paradigms
and here is a timeline of developement methodologies:
Structured Programming, Object Oriented Design, Functional programming - all of these are the way code can be written.
WaterFall, Agile - is the process by which development can be done. Development is not the only thing when it comes to software development. Programming is one of the mandatory component of the process. The process can have design/testing/refactoring/maintenance.
And both of the above are complementary to each other (i.e. one can do structured programming and be modular and use some of the agile principles).
I don't know as to when it started (and I guess it should not matter).
You might come up with your own style of process, which can work better in your own scenario.
EDIT: In summary, people started with structured programming, used it very well. It had its own limitations & things became object oriented. OO has its own limitations, as some gurus say and they see functional programming as the way to fix it.
It all depends on what suits your scenario & what serves you better.
There is no silver bullet, as experts say.
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I find myself to be a visual person and sometimes it's easier to learn something if I can draw diagram/pictures out of it. My question is which tool do I use if I want to map a programming framework (for example .net) visually and see the relationship between all entities (such as classes and members).
You might try NDepend. The great Scott Hanselman discusses it more here. Tons of visual dependency formats too. It sounds like its only for .NET though.
I find doxygen is useful for generating all kinds of dependency information when faced with a new project. It apparently handles "C++, C, Java, Objective-C, Python, IDL (Corba and Microsoft flavors), Fortran, VHDL, PHP, C#, and to some extent D". It uses Graphviz to generate graphical dependency charts. You can include full source code, with hyperlinks from everything that was recognised. If you are lucky there will be some documentation that doxygen understands in there already. You can then navigate around the code quickly, learning what all the relationships are.
The tool NDepend proposes both an interactive Dependency Graph and an interactive Dependency Matrix. Also, the tool is integrated in VisualStudio 2012, 2010 and 2008. Disclaimer: I am one of the developers of the tool
Whether you need to exhibit Call Graph, Coupling Graph, Inheritance Graph, Dependency Graph, very large Graph, pinpoints Dependency Cycles and more, NDepend will generate some visual diagrams. Here are some screenshots:
A decent first-cut might be to write a simple PERL script to parse out dependencies and then pipe that data to Graphviz for visualization.
I'm not sure if you are asking only about .NET or other frameworks, my experience is mostly with Java but I'm sure similar tools exist for .NET.
On the level of classes you can get an auto-generated visualization using UML tools that can typically reverse engineer source code into a diagram. Netbeans is free and has Java source to UML reverse engineering features.
Class diagrams however are very low level, they tell you little (directly) about the larger architectural themes. At that point tools like Structure101 can be valuable in discovering architectural properties that you didn't realize were there. They have a trial version and can also deal with C and C++.
I second the Doxygen comment by Nick. I am using Doxygen for C#.NET and it generates class diagrams, inheritance diagrams, etc. Here is an informative blog post.