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Machine learning resources for .NET developers

Machine learning for .NET

Greetings friends and welcome to this article on Machine learning libraries for .NET developers.  Machine learning is a hot topic right now and for good reason.  Personally, I haven't been so excited about a technology since my computer used my 2800 baud modem to dial into a BBS over 17 years ago.  The thought that my computer could communicate with another computer was so fascinating to me.  That moment was the very moment that would forever change my life.  I learned a lot about DOS by writing batch scripts and running other programs that allowed me to visit and then run a BBS system.  It eventually lead me to QBasic.  I wanted to learn to write BBS door games and QBasic was included as a part of a standard DOS installation back then.

Fast forward 17 years and I'm still in love with computers, programming, and the concept of communication between machines.  The magic never disappeared.  So when i first learned about the concept of Machine learning, I felt like that 13 year old kid again.  The idea that a machine can learn to do things that it has not been programmed to do is now a passion of mine.  The concepts of Machine learning have an extreme learning curve, however, I believe that we as humans can do anything that we put our mind to.  So I began looking around for tutorials on machine learning.  I found many great tutorials and books, however, most of them involved using python.  I have nothing against python.  As a matter of fact, I find it ironic that I started with BASIC and now in this moment of "rebirth" I'm beginning to use python which looks a lot like BASIC in many ways.  The fact of the matter remains, I'm a .NET developer.  I've spent the last 9 years in the .NET framework and I love the technology.  C# is an awesome programming language and it's hard to imagine life without Visual Studio.  What can I say, the IDE has spoiled me.

While I scoured the internet looking for tutorials related to Machine learning resources for .NET developers, I wished that there was a one resource that would assist me in my search for resources to help me achieve my goal.

Well that's what this article is all about.  In this article, I will introduce you to some .NET libraries that will assist you in your quest to learn about Machine learning.

NND Neural Network Designer by Bragisoft

The Neural Network Designer project (NND) is a DBMS management system for neural networks that was created by Jan Bogaerts.  The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back.  The chat bots can even scrape the internet for information to return in their output as well as to use for learning.  The project includes a custom language syntax called NNL (neural network language) that you can use in configuring your machine learning project.  The source code is designed so that the libraries can be used in your own custom applications so you don't have to start from scratch with such a complex set of technologies.  The project is actually an open source project in which I am a part of.  Some of the possibilities offered by this awesome project include predictions, image and pattern recognition, value inspection, memory profiling and much more.  Stop by the Bragisoft NND website and download the application to give it a try

 Screen shots of the neural network designer by Bragisoft

A DBMS for neural networks

A DBMS for neural networks

 

Mind map rand forrest

Machine learning

The chat bot designer and other tools

GUIs and debuggers

Accord.net

Here is a description from the Accord.NET project website 

Accord.NET is a framework for scientific computing in .NET. The framework builds upon AForge.NET, an also popular framework for image processing, supplying new tools and libraries. Those libraries encompass a wide range of scientific computing applications, such as statistical data processing, machine learning, pattern recognition, including but not limited to, computer vision and computer audition. The framework offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.

 The most impressive parts of this library has got to be the documentation and sample applications that are distributed with the project.  This makes the library easy to get started using.  I also like the ability to perform operations like Audio processing (beat detection and more), Video processing (easy integration with your web cam, vision capabilities and object recognition).  This is an excellent place to start with approaching Machine learning with the .NET framework.  Here are a two videos that should whet your appetite.

Hand writing recognition with Accord.NET

 

Here is an example of head tracking with Accord.NET (super cool)

 

AIMLBot Program# AILM Chat bot library

AIMLBot (Program#) is a small, fast, standards-compliant yet easily customizable implementation of an AIML (Artificial Intelligence Markup Language) based chatter bot in C#. AIMLBot has been tested on both Microsoft's runtime environment and Mono. Put simply, it will allow you to chat (by entering text) with your computer using natural language.  The project is located here.

Math.NET

Machine learning algorithms are extremely math heavy.  Math.NET is a library  that can assist with the math that is required to solve machine learning related problems.

Math.NET Numerics aims to provide methods and algorithms for numerical computations in science, engineering and every day use. Covered topics include special functions, linear algebra, probability models, random numbers, interpolation, integral transforms and more.

DotNumerics

DotNumerics is a website dedicated to numerical computing for .NET. DotNumerics includes a Numerical Library for .NET. The library is written in pure C# and has more than 100,000 lines of code with the most advanced algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, these libraries are the translation from Fortran to C# of LAPACK, BLAS and EISPACK, respectively.

You can find the library here. 

ALGLIB

ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Pascal, VBA) and several operating systems (Windows, Linux, Solaris). ALGLIB features include:

Accessing ‘R’ from C#–Lessons learned

Here are instructions to use the R statistical framework from within c#

ILNumerics

You can check out the library at http://www.ilnumerics.net

NuML.net http://numl.net

A nice site about the basics of machine learning in c# by Seth Juarez . NuML.NET is a machine learning library for .NET developers written by Seth Juarez.  I've recently tried this library and I'm impressed!  Seth has stated publicly that his intention behind the numl.net library is to abstract the scary math away from machine learning to provide tools that are more approachable by software developers and boy did he deliver!  I've been working with this library for a little more than an hour and I've written a prediction app in c#.  You can find his numl.net library source on github.

Encog Machine Learning Framework

Here is what the official Heaton Research website has to say about Encog:

Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.

Encog is available for Java, .Net and C/C++.

Jeff Heaton knows a great deal about machine learning algorithms and he's created a wonderful library called Encog.  I was able to write a neural network application that solved the classic XOR problem in 20 minutes after installing the library.  What really amazes me is that he has an Encog Library for JavaScript which includes live samples on his website of Javascript + encog solving problems like the Traveling Salesman Problem and Conway's game of life, all in a browser!  This library can even use your GPU for the heavy lifting if that's your choice.  I would highly recommend that you at least check out his site and download the library to look at the examples.  You can find the Encog library here

 

Conclusion

This concludes my article on Machine learning resources for the .NET developer.  If you have any suggestions regarding a project that you know of or you are working on related to Machine learning in .NET, please don't hesitate to leave a comment and I will update the article to mention the project.  This article has shown that we as .NET developers have many resources available to us to use to implement Machine learning based solutions.  I appreciate your time in reading this article and I hope you found it useful.  Please subscribe to my RSS feed.  Until next time..

Buddy James



Comments (6) -

Seth Juarez
Seth Juarez
3/4/2013 11:11:59 AM #

Hey! I also made something: http://numl.net.

Buddy James
Buddy James
3/9/2013 4:22:53 AM #

Seth,

Thank you for contributing.  I'm going to add your project to my list.

The code looks great.  The site design is really awesome too!  Kudos!

Buddy James

terrell26
terrell26
3/21/2013 12:44:32 PM #

You seem to know a great deal about this subject

Buddy James
Buddy James
3/21/2013 5:33:28 PM #

I appreciate the compliment.  I'm very passionate about machine learning and I'm constantly learning.

Thanks again!

Buddy James

Don Syme
Don Syme
7/2/2013 4:46:37 AM #

Great links!

For F# developers (or C# developers adding an F# project to their solution) see also  

    http://fsharp.org/machine-learning

Buddy James
Buddy James
7/4/2013 8:37:45 PM #

Thanks for reading @Don.  I hear great things about F# and machine learning.  F# is on my list of languages to learn.  Thanks again!

Buddy

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About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (http://www.refactorthis.net), a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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Learning ILAsm, the backbone of .NET

Who cares about ILAsm or x86 assembly language anyway? I'm sure a lot of you are wondering why anyone would care about learning ILAsm.  It's not like you ever see it unless you disassemble an application.  ILasm or MSIL is the human readable translation of Microsoft .NET intermediate language.  ILAsm is a lot like classic assembly language.  It is a low level programming language that allows you to write programs one instruction at a time with a very minimal syntax.  I've explained the benefits of learning assembly language in my previous post, Why Learn Assembly Language.  In a nutshell, if you learn .NET at the low level of IL, you will have an understanding of what makes any .NET language tick.  You will have the knowledge to disassemble any .NET binary and debug your software at the instruction level.   This post is my first tutorial on writing code in ILAsm.  I hope you'll join me and become proficient in this language of kings.  You will have an edge over the competition and it will change the way you look at high level coding.  Enough talking, let us code! How to compile ILasm If you are a .NET developer, you most likely have an ILasm compiler on your computer which by the way is appropriately named ILasm.exe.  Simply launch the Visual Studio Command Prompt, navigate to the folder that you create your IL files and issue the following command. ilasm.exe is fine if you like to write code in notepad and drop out to a command prompt to write your code.  I myself prefer an IDE.  Visual Studio is an amazing IDE but for some reason the people over at Microsoft didn't see it fit to add support for ILasm into the product.  Never fear!  There is a free, open source alternative that is nearly identical to Visual Studio and it allows you to create and compile IL projects with syntax highlighting that works on Linux, Windows, and Mac OSX!  What is this application you ask? MonoDevelop   "Hello... World?" I know, I know, it's a tired, worn out cliche but far be it from me to interrupt the order of the programming gods and illustrate a programming language without starting with the infamous "Hello World!" example. //import the mscorlib assembly to give us access to Console and other basic classes .assembly extern mscorlib{} //define our assembly .assembly HelloAssembly { //define the version of this assembly .ver 1:0:0:0 } //define the executable module .module helloworld.exe //defin our main method .method static void main() cil managed { //set up the stack. In ILAsm, all values are placed on the stack and then manipulated. //here we will allocate memory for one value to be on the stack at a given time .maxstack 1 //define the main entry point to the application .entrypoint //load the emphamis phrase on the stack ldstr "Hello ILAsm!" //print the string from the stack to the console call void [mscorlib]System.Console::WriteLine(string) //return to end the program ret } If you have never seen asm or ILasm, I can imagine how strange this code snippet may look.  As I've stated before, ILasm is a very cryptic, low level language.  Let's breakdown the application. We start by importing the mscorlib library which contains much of the base .NET classes.  As the comments state, this library gives us access to the System.Console object.   Next, we define the assembly for our program.  All .NET executables are called assemblies.  Here we name our assembly as well as set a version number.  After we define our assembly, we define the executable module.  This is required in any ILasm application.   Now it's time to define our main method that performs the loading and printing of the string.  We start by defining the maxstack, that is, the maximum number of values that can be held in memory at a given time.  In ILAsm, you push values on the stack, perform operations on the values or use them as parameters to methods.  Since we have a maxstack of one, that means we can have only one piece of data to work with at any given time.  We use ldstr to load a string onto the stack.  If we were to load another string on the stack directly after the first ldstr call, the application will simply push the first value out of memory and the new string will be available to access. Finally, we call the WriteLine method on the System.Console object and we tell it to use the current string on the stack as it's input source.   So now you can load a string onto the stack and display it.  It's pretty interesting, although very limited as well.  How about we work with more than one value?  Let's try adding two numbers! Sum it up //reference to mscorlib .assembly extern mscorlib {} //define our assembly .assembly MiniCalculator { //the assembly version number .ver 1:0:0:0 } //create the required module .module MiniCalculator.exe //define our main method .method static void main() cil managed { //we plan to work with two integers this time .maxstack 2 //the main entry point to our application .entrypoint //load a string of instructions on the stack ldstr "OK. Class is in session. Who can tell the class what is the sum of 2 + 2? That's right, the answer is " //display the instructions to the user call void [mscorlib]System.Console::Write (string) //put the number 2 on the stack. Currently the previously loaded string, and the //number 2 are both on the stack. ldc.i4 2 //when we move another integer to the stack, this pushes the string off //now we have two instances of the number 2 on the stack ldc.i4 2 //add will add the two numbers on the stack and store the result add //lets tell the computer to look for an int on the stack and print it to the console call void [mscorlib]System.Console::Write (int32) //return to exit the application ret } We start the application as before by importing mscorlib, defining our assembly and module, and creating the method to perform our work.  We then load a string on the stack and use Console's WriteLine method to display the string.  We then load two integers onto the stack, pushing the string off of the stack.  We call add whichs adds the two integers and stores the result on the stack.  We use Console's WriteLine once again to display the answer. This concludes part one of my ILasm tutorial series. Please check back soon for my next installment in which we will tackle data types, loops, and classes! Until next time.. ~/Buddy James kick it on DotNetKicks.com  


Why Learn Assembly Language?

Here is my post from http://www.codeproject.com/Articles/89460/Why-Learn-Assembly-Language "Assembly language? Isn't that the hard to read instructions on how to assemble your brand new computer desk?"... No.. What is Assembly Language? x86 Assembly is a programming language for the x86 class of processors (specifically the 32bit x86 processors IA-32 - http://en.wikipedia.org/wiki/IA-32). The instruction set defined by the IA-32 architecture is targeted towards the family of microprocessors installed in the vast majority of personal computers on the planet. Assemblylanguage is machine specific and considered a "low level" language. This means that the code and syntax is much closer to the computer's processor, memory, and I/O system. A high level language is designed with keywords, libraries, and a syntax that introduces a high level of abstraction between the language and the hardware. Background I thought assembly was a dead language, why waste the time? Though it's true, you probably won't find yourself writing your next customer's app in assembly, there is still much to gain from learning assembly. Today, assembly language is used primarily for direct hardware manipulation, access to specialized processor instructions, or to address critical performance issues. Typical uses are device drivers, low-level embedded systems, and real-time systems (EDIT:Thanks Trollslayer). The fact of the matter is, the more complex high level languages become, and the more ADT (abstract data types) that are written, the more overhead is incurred to support these options. In the instances of .NET, perhaps bloated MSIL. Imagine if you knew MSIL. This is where assembly language shines. (EDIT)Assembly language is as close to the processor as you can get as a programmer so a well designed algorithm is blazing -- assembly is great for speed optimization. It's all about performance and efficiency. Assembly language gives you complete control over the system's resources. Much like an assembly line, you write code to push single values into registers, deal with memory addresses directly to retrieve values or pointers. To write in assembly is to understand exactly how the processor and memory work together to "make things happen". Be warned, assembly language is cryptic, and the applications source code size is much much larger than that of a high-level language. But make no mistake about it, if you are willing to put in the time and the effort to master assembly, you will get better, and you will become a stand out in the field. So why should you care?     Points of Interest Wirth's Law I remember dialling into a BBS on my 486 with my brand new 2400bps modem. Fast-forward 14 years and now we are only limited by our imagination. With all of these amazing technological breakthroughs, there is a glaring anomaly; a paradox. This is referred to as Wirth's law. Wirth's law states that software is getting slower more rapidly than hardware becomes faster. There's no one reason why this is the case, but I'd like to think that the further we as developers drift away from the lower level details of software development, we write less than stellar (inefficient code). Hold the phone! I'm not calling anyone stupid. It's just that these new languages and supercharged processors have abstracted us so far from the machine, that we no longer have to be concerned with things like garbage collection, variable initialization, memory address pointers, etc. All of these features and more are now standard in today's languages/runtimes/IDEs. The result is a new breed of developers that rely on superior hardware power for performance rather than striving to write concise, cohesive, efficient code. My Eyes are Open! I realize now that learning assembly language will teach me about the inner workings of the computer. I'll learnhow the CPU/CPU registers work with memory addresses to achieve the end result one instruction at a time. This doesn't mean that I'm going to begin coding everything in assembly, however, I will learn which data types to use and when. I'll learn how to write smaller, faster, more efficient routines. I will understand software development at a level that most of my peers don't. I'm even opening up to the possibility of looking into writing my own compiler. So if you are serious about getting a leg up on the competition in your field, I'd recommend trying to learnassembly language. Resources on Learning Assembly How To Use Debug - http://www.codeproject.com/KB/miscctrl/Debug.aspx Introduction to x86 Assembly Language http://www.c-jump.com/CIS77/ASM/Assembly/lecture.html kick it on DotNetKicks.com  


About the author

My name is Buddy James.  I'm a Microsoft Certified Solutions Developer from the Nashville, TN area.  I'm a Software Engineer, an author, a blogger (http://www.refactorthis.net), a mentor, a thought leader, a technologist, a data scientist, and a husband.  I enjoy working with design patterns, data mining, c#, WPF, Silverlight, WinRT, XAML, ASP.NET, python, CouchDB, RavenDB, Hadoop, Android(MonoDroid), iOS (MonoTouch), and Machine Learning. I love technology and I love to develop software, collect data, analyze the data, and learn from the data.  When I'm not coding,  I'm determined to make a difference in the world by using data and machine learning techniques. (follow me at @budbjames).  

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