via a few levels of blogger indirection…

Thanks Jeremy for  this link to an old pragmatic programmers article.

The specific entry is this; “Downcasting is a smell” (I agree). It also points to a few more goodies….

It sums up stuff I have tried to say less eloquently on a few occasions. I will pass this link around to a few folks

-Chris

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I was doing some C++ coding this weekend. Of course – I am spoiled with all the tools for C# and for java. Visual Studio, Eclipse, IntelliJ, etc are fantastic at certain things. One genre of functionality that I am spoiled by is refactoring support. A while back – maybe 2 years ago no (could it already be that long ago!), I tried Ref++, but I did not have much work to do at the time, so I removed it…. In any case – great tool for doing C++ coding on Windows!

It does your pretty vanilla stuff:

• Rename
• Encapsulate member variable (make getters, setters)
• Extract method
• Introduce variable
• Push up
• Pull down
• Extract superclass

This is pretty minimal by jetbrains standards, but it is great for c++. I know, I know – emacs does all I would ever need – and I am a daily xemacs user… but not for C++ dev – I am just not that much of an emacs guru!

Totally non-related; I also want to give some credit to 2 books – both Schaum’s Outlines books.

Schaum’s Outline of Probability and Statistics
Schaum’s Outline of Probability, Random Variables, and Random Processes

These books are just fantastic references for keeping my stats work honest and my stochastic processes – properly random-ish

The work I have been playing with in C++ is porting a Bayesian EDA - a real coded shot at something like the hBOA. I have c# and java versions of Bayesian nets. First, I am improving and re-implementing the basic Boolean logic, and simple fuzzy set operations. This has been in use in the C# version for years. I am a much better programmer now Years have passed – so I am already pleased with the newer C++ version. The majority of my optimization work has done a good degree of algorithm partitioning, so I am really just plugging in strategies for selection that are based on bayes nets, in stead of standard evolutionary selection mechanisms…. In any case – Ref++ was a big win. This is all great stuff.

Not only do I get to do all this – but my work at the office has been just as compelling! Who is luckier than me ?

-Enough Blabber-

Chris

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I make it a habit to really focus on long term goals. I also believe that it is of paramount importance that your goals are in accordance with your values. I spend a lot of time planning how to meet my goals, thus thinking about what I value as an individual. One of the things that I have long believed was something that I value is innovation. I was reading a paper today from Martin Pelikan and David E. Goldberg. These are 2 innovators in the world of machine learning, in particular – Pelkin is renown for his work on estimation of distribution algorithms and Goldberg is one of the foremost figures in the world of genetic algorithms and several related and sub-fields.

In any case – something in this paper struck me:

“Innovation can be though of as a model of genetic algorithms and genetic algorithms can be thought of as a model of innovation.” 

I wonder if some part of my core values has drawn me to this aspect of evolutionary computation. The job of an evolutionary algorithm is really to innovate – to find something that is innovative enough to learn how to better handle a particular problem.

Just some random musing on innovation….

-Chris

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As I was doing some reading this week, I realized how thankful I have been for some excellent resources. I wanted to take a moment to enumerate a few machine learning resources that have been immensely helpful in the past few years of practical application of many machine learning projects.

IEEE
Computer.org
IEEE Computational Intelligence Society
MIT Journal of Evolutionary Computation
Journal of Machine Learning Research
Illinois Genetic Algorithms Laboratory (IlliGAL)

There is a LOT more out there. These resources have provided the lions share of material needed to work on cutting edge machine learning. In particular:

EDAs (Estimation of Distribution Algorithms)
MOOs (Multiobjective Optimizers)
Fuzzy Classifiers
Bayesian networks
AISs (Artificial Immune Systems)

Thanks and kudos to all the people working so hard at universities around the world and in other research areas. These people provide the work needed to bring these tools, devices and techniques into practical application.

-Chris

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Wombat Partners with Coral8 for Complex Event Processing

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Via Marco on ESP:

This is what we have been talking about – SOA should be event driven! Tobco folks take on it.

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I am happy to say that my first article for Automated Trader, The Journal of Automated and Algorithmic Trading went out this weekend. I will be happy to see my article in a glossy print publication. This first article is an introduction to using optimization algorithms for training automated trading systems. It seems I will do at least a few more articles as offshoots from this one – drilling into more detail on related and sub-topics.

Since it is clear that automated trading is the way of the world – I am glad to be writing about it. It is an area that I am really in love with. I have been working in one capacity or another on writing, optimizing and running auto-trading systems since just before my 4.5 year old son was conceived… Great stuff!

I am really a believer in the idea that machine learning, distributed computing, quantitative finance and data mining will all coalesce into my ideal realization of software learning, processing and earning at a capacity that will be huge. Give it a few years and we are there!

-Chris

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The Entrepreneurial Engineer by the infamous David E. Goldberg of Genetic Algorithm infamy looks great:!

From the description:

Entrepreneurial times call for The Entrepreneurial Engineer

In an age when technology and business are merging as never before, today’s engineers need skills matched with the times. Today, career success as an engineer is determined as much by an ability to communicate with coworkers, sell ideas, and manage time as by talent at manipulating a Laplace transform, coding a Java(r) object, or analyzing a statically indeterminate structure.

This book covers those nontechnical skills needed by today’s entrepreneurial engineers who mix strong technical know-how, business and organizational prowess, and an alert eye for opportunity. Author David Goldberg unlocks the keys to ten core competencies at the heart of what entrepreneurial engineers need to master to be effective in a fast-moving world of deals, teams, startups, and innovating corporations. You’ll discover how to:

• Feel the essence-and the joys-of engineering
• Examine personal motivation and set goals
• Master time management and organization
• Write fast and well under pressure
• Prepare and deliver effective presentations
• Understand and practice good human relations
• Act ethically in matters large, small, and engineering
• Assess technology opportunities
• Understand teams, leadership, culture, and the organization of organizations

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Been busy!!!

Here are a few quickies that are goin’ on in the world:

• Everyone (Matt, Marc) is talkin’ bout: Opentick
• B of A to eat Barclays ! – Biggest bank ever!
• Wombat still rules Wall Street & Technology: RBC Capital Markets Picks Wombat Messaging Software
• Reverse engineering hedge funds via AI
• …More on Synthetic Hedge funds
• More in exchange m & a – The Euronext & NYSE saga continues
• LSE still refuses NASDAQs love
• RBC Capital Markets implements Vhayu Velocity
• We are the robots

-Chris

PS – soon, I will write something a little more meaty…

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