Saturday, December 18, 2010

Standard Developer Toolkit - 2010 version


I don't know if it is because I am getting older but I am starting to record my thoughts online or digitally so that I can easily go back to that record. I do this by posting blog posts on blogspot and through other subversion code repositories.

One task that I have long neglected is creating a list of common developer tools. The toolkit suite contains a list of common software applications that a developer of any language or environment would need. Most of the software is open source of freely available. Obviously, different developers want or need different tools. Here is the list of packages that are essential for what I need. Yes, the list is bias with a emphasis on Java development. I marked the critical applications in bold. I would typically install of all of these tools on one machine over a long time period. Some of the tools seem similar in nature, for example, I do install Netbeans and Eclipse on the same machine. I may swap between the IDEs if there are tools built for one platform not available in the other environment.

Operating Systems:
- Use both WindowsXP/Windows7.
- Cygwin
- Use VMWare and an Ubuntu 9+ Image (only VMWare player is needed for win32)
- VMWare Fusion if you use Mac
- Ubuntu 10+ on a standalone machine (AMD64bit or Intel machine)

Programming Languages and Environments:

- Java (Java JDK from Oracle on Win32, possibly OpenJDK on Windows)
- Scala (JVM language for Java)
- Clojure (JVM language for Java)
- Python (install on both win32 and Linux)
- Perl (for some scripting)
- Haskell

Developer IDE Tools:

- Eclipse (you can easily install/setup multiple versions of Eclipse)
- Eclipse CDT for any C++ Development
- Eclipse PyDev for Python development
- IntelliJ IDEA (Great for Scala development, rapid Java development)
- Netbeans
- Emacs

Misc Developer Tools:

- Subversion client
- Git client
- Mercurial client (optional)
- Maven (for Java development)
- Ant (for Java development)

Text Editors:

- Vim (install on Cygwin if using Win32)
- Emacs (I prefer Emacs over XEmacs and use the Emacs win32 version)
- TextPad
- Notepad++ (notepad++ is open, textpad isn't, I like both)
Word Processing:

- Open Office
- Microsoft Office (I prefer older versions of the Microsoft Office suite. If you have the money, I would consider purchasing the software).

Web Browsers:

- Mozilla Firefox
- Install Firefox Firebug plugin
- Install Firefox Tamper data plugin
- Google Chrome

Network Tools:

- WinSCP (Win32)
- FileZilla (Win32)
- XChat (or XChat2)
- VMWare Player

Graphic Tools:

- Gimp

Misc Tools:

- 7zip
- WinMerge (open merging software, very useful)
- R (google R statistics application). Useful for charting data

Cygwin Installs

- Install Cygwin
- Download or make sure that these are installed: vim, find, grep, wget, gcc, g++, gtk libs, openssl libs, gnuplot

Eclipse Plugins for Java Development

- Subclipse
- Maven Plugin (m2eclipse)

Java Frameworks

- Spring Framework, Google's GWT, Apache Wicket, Hibernate, iBatis ORM, Lucene, AspectJ, ASM, Antlr

Practical AI: Hello World Bitworm Example with Numenta's Nupic (HTM for the masses)



Overview

Jeff Hawkins of Numenta and previously the creator of Palm Inc has shifted his focus from mobile computing to developing a sophisticated AI system. He has always been passionate about artificial intelligence. Jeff knew early on that the trends in AI research weren't very promising. He had concentrated his AI interest in human biology and neuroscience. Numenta has patented HTM (Hierarchical Temporal Memory) technology. According to Jeff Hawkins, you cannot mimic brain functions without including a hierarchical system of memory. The lower level has more input/output nodes than the level above it. According to Jeff, you must also take into account "temporal" memory. For example, the human brain has many parts of the brain that handle visual information. The brain may be able to detect a particular object but also factor in the time that the visual event occurred. If you are at the zoo, your brain predicts that you will see animals and animals in cages at the zoo. It is rare that you see a plane take off or land in the middle of your zoo visit. You would have had previous memories or seen pictures of the zoo in the past and parts of your brain activate other things associated the zoo. The context in time is a visit to the zoo. The memory of a zoo visit is probably in a different area of the brain than a trip to an airport. In the case of an airport, you expect to see planes landing and taking off. -- Repost from my previous blog entry.

Jeff's vision of HTM is implemented though Numenta's Nupic. Nupic is a HTM Python library and software suite that includes simple speech recognition demos, computer vision demos(picture object recognition), and other Nupic examples. Normally, you would have to pay hundreds for pattern recognition software of this quality. But all of these examples are functional and demonstrate the power of the HTM Nupic approach.

Bitworm Hello World Example

The Bitworm example provided with Nupic is a Hello World Example. But it is probably the most complex and thorough Hello World I have seen. It covers the basics but it also usable as a library or simple Nupic API. In the case of the Bitworm example, the goal is to track the movement of a bitworm through it's movement in 2D space and time. Think of the bitworm example in three dimensions. Dimension 1 is on the Y axis and is the height. In this case, the height is one. Dimension 2 is on the X axis. The X axis contains the length of the bitworm and movement along the X axis. And the third dimension is time. There are 20 time sequences trained for the bitworm examples. (OK, in reality you could think of the example in 2 dimensional space. The X and T axis are relevant).


There is one bitworm represented in the screen shot above. There are two bitworm lines from the top to the bottom of the screen. Line one is a representation of ONE bitworm and its position. In line two, the bitworm has moved in the X direction. Line two is a representation of the bitworm at TIME sequence two. There is ONE bitworm and 20 time sequences of that bitworm movement. The goal of the bitworm example is to train that movement and then predict the bitworm type based on a test set of bit worm examples.

I wrote a python example TK graphic program to render the bitworm's movement. The TK example is not provided with Nupic. The string of bits are created by the bitworm example, here is a representation of the training data set:

1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 [---- bitworm zero and time sequence 0
0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 [---- bitworm zero and time sequence 1
0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 [---- bitworm zero and time sequence 2
0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0
0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0
0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0
0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0
0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0
0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0
...

There are 420 lines of these bitworm bit vectors in the text file. Each line in the training set and the test set are representation bitworms.


The bitworm example is located at NUPIC_HOME\share\projects\bitworm

Summary to understand the bitworm example:
1. The bitworm python example does the following:
1a. The bitworm example creates the training data (File: training_data.txt)
1b. The bitworm python example creates the test data (File: test_data.txt)
1c. The bitworm example creates the bitworm categories (needed during the training process)
1d. The application trains the training test after the test and training data is created.
1e. The application validates that Nupic can learn the training data by verifying against the test data.

Think of training as training the Nupic AI software and test data as the way to check that the training worked.

2. training_data.txt and test_data.txt is a simple text file
2. Each line in the training_data.txt and test_data.txt database file consists of a bitworm (16 bits in the bitworm vector)
3. Each column or bit in the training and test file is a bit in the bitworm. There are 16 bits in the bitworm example.
4. Each line in the training and test data file is a bitworm and a representation of that bitworm at a particular time sequence.
5. There are twenty time sequences in a GROUP training set. Basically, line 1 of the training file represents a bitworm at time sequence 0. Line 2 of the training file represents a bitworm at time sequence 1, etc. At line twenty, that is the end of a time sequence group. At line twenty 22, a new bitworm example starts.
6. There are twenty EXAMPLE BITWORM time sequences.
7. There are 420 lines in the training and test data file, each line is bitworm at a particular moment in time.
9. There are zero bitworm vectors that delimit a time sequence

If you look at the file training_data.txt:

One bitworm is 16 bits:

1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 [---- bitworm zero and time sequence 0
Here is a formula to determine why there are 420 lines in the training and test file:

(1 bitworm * 20 time sequences) + (20 groups of time sequences)
+ (ZERO bitworm delimiter line * 20) = 420 lines in the file.

Example snippets from the Python Bitworm Example:

def generateBitwormData(additiveNoiseTraining = 0.0,
bitFlipProbabilityTraining = 0.0,
additiveNoiseTesting = 0.0,
bitFlipProbabilityTesting = 0.0,
numSequencesPerBitwormType = 10,
sequenceLength = 20,
inputSize = 16,
trainingMinLength = 9,
trainingMaxLength = 12,
testMinLength = 5,
testMaxLength = 8):

# Generate training data with worms of lengths between 5 and 8
trainingData = BitwormData()
trainingData['prefix'] = 'training_'
trainingData['minLength'] = trainingMinLength
trainingData['maxLength'] = trainingMaxLength
trainingData['sequenceLength'] = sequenceLength
trainingData['inputSize'] = inputSize
trainingData['numSequencesPerBitwormType'] = numSequencesPerBitwormType
trainingData['additiveNoise'] = additiveNoiseTraining
trainingData['bitFlipProability'] = bitFlipProbabilityTraining
trainingData.createData()

Train and verify the bitworm example:

# Train the network
# TrainBasicNetwork: This function trains a basic network with the given data and category files and returns the trained network
bitNet = TrainBasicNetwork(bitNet,
dataFiles = [trainingFile],
categoryFiles = [trainingCategories])
print "Bit Net (TrainBasicNetwork-1): ", bitNet

# RunBasicNetwork: Runs the network using the given data files. The output of the classifier
# node for each input pattern is stored in resultsFile.
accuracy = RunBasicNetwork(bitNet,
dataFiles = [trainingFile],
categoryFiles = [trainingCategories],
resultsFile = trainingResults)
print "Bit Net (RunBasicNetwork-2): ", bitNet
print "Training set accuracy with HTM[a] = ", accuracy * 100.0

# RunBasicNetwork: Runs the network using the given data files. The output of the classifier
# node for each input pattern is stored in resultsFile.
# Run inference on test set to check generalization
accuracy2 = RunBasicNetwork(bitNet,
dataFiles = [testFile],
categoryFiles = [testCategories],
resultsFile = testResults)
print "Bit Net (RunBasicNetwork-3): ", bitNet
print "Test set accuracy with HTM[b] = ", accuracy2 * 100.0


Modifications to the Bitworm example and moving forward:

The bitworm example is a common type of example in the AI and pattern recognition world. You are given a bit sequence. Train the bit sequence and attempt to test the sequence against other sequences that have a similar structure. In the case of the bitworm example, a bit vector of 16 bits is trained. You could modify the example to train a 16x16=256 bit vector image.

Resources:

[1]. http://www.numenta.com/

Wednesday, December 15, 2010

Practical AI: Machine Learning and Data Mining with Weka and Java



Practical AI: Machine Learning and Data Mining with Weka and Java

Most people tend to think of Artificial Intelligence with depictions of autonomous bipedal robots speaking and greeting humans in a monotone voice. The computer science field of Artificial Intelligence is far from that science fiction world that we see portrayed in the movies. The most prominent past AI research seems more like it would fit under a branch of mathematics and not computer science. AI research sub-fields include machine learning, data mining, computer vision, string match and indexing, search algorithms and neural networks. There is a new focus on not just faster search algorithms and pattern recognition, but building a relationship between computer science, neuroscience, philosophy, psychology, biology. AI is branching out beyond the algorithms and merging with those other general science and medical fields. Marvin Minsky, a prominent researcher in the AI field, called some of the previous interests like Neural Network, technology fads. Marvin Minsky's most recent book, the Emotion Machine describes some of human's behavior with a simple set of rules. The book is a useful guide for computer scientists that want to model that behavior. Jeff Hawkins of Numenta and previously the creator Palm Inc has shifted his focus from mobile computing to developing a sophisticated AI system. Numenta has patented HTM (Hierarchical Temporal Memory) technology. According to Jeff Hawkins, you cannot mimic brain functions without including a hierarchical system of memory. The lower level has more input/output nodes than the level above it. According to Jeff, you must also take into account "temporal" memory. For example, the human brain has many parts of the brain that handle visual information. The brain may be able to detect a particular object but also factor in the time that the visual event occurred. If you are at the zoo, your brain predicts that you will see animals and animals in cages at the zoo. It is rare that you see a plane take off or land in the middle of your zoo visit. You would have had previous memories or seen pictures of the zoo in the past and parts of your brain activate other things associated the zoo. The context in time is a visit to the zoo. The memory of a zoo visit is probably in a different area of the brain than a trip to an airport. In the case of an airport, you expect to see planes landing and taking off.

Basic Data Mining With Weka

In my post, I present a practical Hello World using WEKA. WEKA (Waikato Environment for Knowledge Analysis) is a suite of machine learning tools and libraries that can be used to mine data.

What is data mining and how could you use data mining techniques? Many enterprise companies are connected to large databases. These databases contain millions of records. Data mining and machine learning techniques are used to find patterns within that massive trove of data. These techniques can also used to filter out noise. Popular email filter software utilizes data mining to remove or categorize spam email.

Hello World Weka for Java Web Server Log Files

I have dozens of Apache web server log files. I wanted to find groups or clusters between some of the log files and the time that a user requested a page on my site. I used WEKA to find clusters and categorize various groups of relevant data.

12_15_10_10_14_03 [HASERR N] log.fileName1[]:Wed 22:14:03 EST 2010 [SIZE 144]
12_15_10_10_14_03 [HASERR N] log.fileName2[]:Wed 22:14:03 EST 2010 [SIZE 121]
12_15_10_10_14_03 [HASERR N] log.fileName2[]:Wed 22:14:03 EST 2010 [SIZE 156]
12_15_10_10_15_33 [HASERR N] log.fileName3[]:Wed 22:15:33 EST 2010 [SIZE 160]
12_15_10_10_15_33 [HASERR N] log.fileName3[]:Wed 22:15:33 EST 2010 [SIZE 146]
...

The timestamp with hour, minute and seconds are on the furthest column to the left. The middle column contains filename information. There are only four or five rows shown in the example. But the log files contains millions of requests. I determined the data that I want to categorize, then I converted the timestamp log file into an ARFF file format that WEKA requires. WEKA has several tools to convert generic CSV files into a ARFF format. ARFF is essentially a text database with column attributes and rows of the data that you to analyze.

Example ARFF File for Weather Data:
@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
@data
sunny,85,85,FALSE,no
sunny,80,90,TRUE,no
overcast,83,86,FALSE,yes
rainy,70,96,FALSE,yes
rainy,68,80,FALSE,yes
rainy,65,70,TRUE,no
overcast,64,65,TRUE,yes
sunny,72,95,FALSE,no
sunny,69,70,FALSE,yes
rainy,75,80,FALSE,yes
sunny,75,70,TRUE,yes
overcast,72,90,TRUE,yes
overcast,81,75,FALSE,yes
rainy,71,91,TRUE,no
Here is my arff file with web server log data. The attributes consist of the log file name and the rows contain information with a count the server wrote a line to the log file at a particular period of time.

Example arff file for server logs:
% Title: Database for application line count
@relation scan_app_data
@attribute LogXX0 real
@attribute LogXX1 real
...
@attribute LogXX36 real
@attribute LogXX37 real
@attribute LogXX38 real
@attribute LogXX39 real
@attribute timeperiod real
@attribute class { 'night_morn', 'earl_morn', 'midday', 'after', 'night', 'late_nigh' }
@data
0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,night_morn
,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,night_morn


I used the WEKA GUI tools to visualize the patterns. I simply clicked on ./weka.jar to launch the WEKA explorer.

It was interesting to focus on the groups of requests. I could see a cluster of requests combined with errors displayed to the user. After looking at the visual tools, I could easily determine what time period had the most errors and in which log file.

I just wanted to cover some basics. WEKA is mature Java software but the developer and the researcher must do some work to determine which data to feed to WEKA. In the case of the log files, I wanted to cluster log file data, errors and the time period that these events occurred. In future test, I might change my ARFF file to include the web server execution time vs the application requested to see if there is any correlation between the two.

Resources:

http://www.cs.waikato.ac.nz/ml/weka/

http://www.numenta.com/

Saturday, September 11, 2010

Anime Reviews: Cyberpunk/Horror/Adult/Thriller....



What is Anime? Google it. Anime is Japanese animation. Animation that is not necessarily targeted for kids. In fact, some Animation can be pretty rough and make a 40 year old squirm. Basically, Anime has characters and culture, stories, plots. If you like interesting stories, then you will like most of the popular Anime.

What is the difference between Manga and Anime? I am American and usually just refer to anything that moves and is from Japan as "Anime" or Japanimation. But that isn't entirely accurate. There are some Anime TV series/movies that were Manga comic books in Japan and then made into movies. Manga to Anime. Manga is Comic Book in Japanese. If someone says, "Fullmetal Alchemist is a great Manga". I guess they could refer to the comic book version of Fullmetal or the TV adaptation. But normally they are referring to the comic book. Anime can be an animated series derived from a Manga.

Popular Adult Stuff

There are many genres of Anime. I like the adult themed stuff. Horror, Cyberpunk, Sci/Fi etc. With the adult themed Anime, you get similar story lines, violence and action that you would get from a Hollywood blockbuster. The difference is that the Anime is animated. When you think Cyberpunk, think Blade Runner. Cyberpunk focuses on technology, cybernetics, robotics but culture is at a lowpoint. Popular cyberpunk Anime focuses on wealth by big corporations but poverty is rampant among the lower classes. You see this with series like Ghost in the Shell, Akira and Ergo Proxy. Why cyberpunk? It is for those that like technology, sci-fi and a bit of nihilism.

What is some popular Anime?

Ghost in the Shell (theme Cyberpunk, rating = 11/10) - Ghost in the Shell is the iconic Cyberpunk series. It is cyberpunk, alongside Blade Runner. I can't even review Ghost in the Shell. It is the best of the best and you should get as many of the TV series and movies as you can. I encourage you to watch every episode. That is my review. Ghost in the Shell is a story about cyborgs and robotics fighting crime. That is the core of the series. But my short synopsis doesn't do justice to the intricate story lines. Just watch it.

Ergo Proxy (theme Cyberpunk, 10/10) - Ergo Proxy is popular but not as familiar as Ghost in the Shell. It is Cyberpunk with a mix of fantasy, horror and sci/fi. It is a long series and has a solid, dark theme. Ghost in the Shell is a detective cyberpunk series. Ergo Proxy is more fantasy and dystopia. Most of the series involves the main character running from the oppressive society.

Monster (theme Detective, rating 8/10) - Monster is pretty basic detective/conspiracy story. A doctor is falsely accused of murder and other crimes. He goes on the lamb and tries to find the sadistic conspirator. I gave Monster a score of 8 because the stories are pretty basic and plain. But it still is an enjoyable series.

The Last Exile (theme Steampunk/Military/Flight?, rating 9/10) - What is steampunk. Somehow in Japanese Anime world, steam and diesel? can be used to fly planes and control machines. Generally these steam punk series target a fictional time period around the early 1900s. Ironically, you have steam powered robots and other machines that seem quite advanced. Anyway, The Last Exile is a steam punk/flight series that focuses on two friends that are caught up in a war. A lot of the series focuses on their relationship and how the war tears them apart. It is solid Anime. Good story, animation. At times, there is too much a focus on the drama of their lives as opposed to the war going on.

Pumpkin Scissors (theme Military/Detective?, rating 7/10)


Heroic Age (theme Science Fiction)


Other Notable Shows:

Black Lagoon (8/10, part fan service, lots of bad language)

--- Berlin Brown (2012)
-- Ron Paul 2012

Friday, September 10, 2010

Movie Review: Girl with the Dragon Tattoo (rating 7.5/10)

**Contains Spoilers**

I rated this film a little lower than the movie review sites and user ratings on
IMDB. The film suffers from two problems. First problem: It is a basic, very obvious thriller movie. In the beginning of the movie, when a character tells you, "One of these persons in the picture killed her". And the movie leads towards one of the people actually killing that person. Who cares, you already know what is going to happen. And there are only two or three suspects. So, the thriller aspect of the movie isn't that interesting. It is unfortunate that they spent all of this energy to build up the suspense of the potential killer, when we already know it is either that guy, that guy or that guy. And to be honest, the older killers aren't even that interesting anyway. I don't care if any of them were the killers. They were Nazi's and/or Nazi sympathizers. So, the thriller elements in the movie weren't that interesting to me.

Second point: Outward displays of graphic misogyny. I don't know if the writer hated women or was a passionate fighter for the female cause. Either way, women are treated like crap in this movie. There are a couple of graphic rape scenes. So graphic, it took me a hour into the movie to even get the rape scenes out of my head. There is female and male rape. If you wanted to describe the overall theme of the movie. The theme is misogyny. The killer (shocker) kills women and the main female character was apparently raped in her past and hates women. And is continually treated like crap by other men.

So there are two issues; obvious ending and misogyny. Despite those issues, the movie could be one of the best pictures of the year. It has great direction and style. I really liked the main male character and the actresses. And wow on the actress. I hope Noomi Rapace gets every international movie award you can give out. Yea, she was the girl that was raped. She will probably get an Oscar or some award. Everything is great up to a point. I wish there was more conspiracy involved. The main character sleuths work hard at their case. But, I didn't really care about the case. And who knows, maybe the book did a better job at making us away of severity of their crimes. The movie didn't do that. You get the impression that something is going on and it is bad, but you don't feel the sense that it is a major conspiracy.

"Oh, he is a crazy, serial killer. Oh, OK. Well just arrest him and be done with. And you might save some time and just arrest all of them and search their houses. Problem solved".

In Summary: Great direction, great setting and mood. Great acting. Great build up. And overall a good movie. But there was a little bit too much man on woman violence for me and I bet the plot from the book was dumbed down too much for the movie. Either way, it probably will beat out most Hollywood movies this year.

Friday, August 27, 2010

Practical AI: Using ProgramD AIML platform

Using ProgramD AIML bot as a personal assistant


http://code.google.com/p/ainotebook/

Introduction

ProgramD is a fully functional AIML bot engine that is implemented with Java. It supports multiple bots, it is easy to configure and runs in a GUI application and also under a J2EE environment.

AIML, or Artificial Intelligence Markup Language, is an XML dialect for creating natural language software agents.

When the AIML markup language is loaded for your bot, then you chat with the bot. Once you become familiar with the AIML format, you can then build a simple personal assistant bot. For Java developers, you may add Java keyword definitions or links to useful resources.

Quickstart

To use ProgramD, you can use the mirrored binary/source version provided on this site (programd.zip) or visit

http://www.aitools.org/Program_D

The version on this site includes jetty webserver configured with a programd web application.

Launch the GUI application

Navigate to:

the HOME\bin directory and click on the simple-gui-console.bat icon.

The ProgramD GUI application should launch.

At the prompt, enter the commands and dialog to interact with the bot. I entered "hello" and "java".

Configure

The bots are configured in the PROGRAMD/conf/bots.xml file.

In the screenshot below, the ../aiml/alice/.aiml and ../aiml/java/.aiml AIML files are loaded into the system.

The AIML XML dialect looks like other pattern matching formats. An input pattern is defined and the bot will respond with the matching output.

Running with jetty web servlet container

Jetty is provided in the download. It is configured for the programd webapp. Navigate to the PROGRAMD/jetty/jetty directory and invoke start.bat or java -jar start.jar. Jetty will launch. Use your web browser and navigate to:

http://localhost:8080/programd

Resources

http://aitools.org/Main_Page

http://aitools.org/Getting_Started_with_Program_D

http://aitools.org/Free_AIML_sets

Sunday, February 28, 2010

AI Notebook - AI and Tron Bot

Java OpenGL and the Google AI Tron Bot Challenge.

The Challenge

Google and the University of Waterloo a Computer Science Club (Waterloo, Ontario, Canada) have created an AI challenge. This is more algorithm AI and not Turing Strong AI. "The objective is to create an AI to play the classic game Tron, Surround, Snafu, Snake, etc." You must submit your entry by Feb 26th, 2010. Imagine Tron, there are two opposing players on a N x N (say 50x50) grid and the goal is to trap the other player. The players can move north, south, east, west in the adjacent squares but into a wall or the other player's previously occupied squares. I thought I would try my hand at the challenge. I was actually quite pleased with their handling on the contest. There are normally about 600+ active bots in the challenge. The website provides great stats on how your bot is doing. There is a graph with wins and losses and a javascript application that can replay any game. I really didn't want to talk about my particular entry, but I wanted to show you how I tested my bot with Java's OpenGL (jogl) library. The default google challenge starter pack comes with some crude console based game output. I needed something more visually appealing for my testing. I created a small testing environment in Java. I was able to test my particular bot but the bot and the OpenGL initialization and rendering are embedded with the bot. If I have more time, I hope to separate a running bots from the OpenGL rendering. It would be nice for the 3D window to bind to a socket connection and wait for input from several bot clients and render the game that way.

http://csclub.uwaterloo.ca/contest/index.php

OpenGL and jogl (Java Binding for the OpenGL API)

There is one benefit when working with Java, you can interact with a number of different mature libraries. Even though, you normally think of writing OpenGL games or graphics programs with the C++ programming language. You can just as easily use OpenGL with Java. OpenGL is a framework and API for writing 2D and 3D graphics. Most programming languages have a similar interface to OpenGL including initialization, rendering vertices and displaying the content. OpenGL provides a lot of the legwork for rendering fast through the 3D graphics pipeline on your machine, the core OpenGL libraries do not provide a framework for developing complex games or demos. OpenGL is as low-level a library as say the Socket API is for writing web applications. Also, I would strongly encourage you to use OpenGL for both 2D and 3D graphics. The framework is highly portable and the visualizations are extremely fast. I created two versions of the tron board, one in 2D and the other in 3D. Despite your fears about 3D or 2D graphics, OpenGL has always remained fairly simple for getting starting. Like a lot of other graphics frameworks, OpenGL requires a set of initialization calls and then subsequent double buffering display routines. In Java, a couple more calls are made to create a GLCanvas object and then attaching the Canvas to a Java Swing JFrame or other Swing widget.

To get started, visit the jogl website and download the jogl libraries. I am using jogl version 1.1.0. I needed to ensure that jogl.jar, gluegen-rt.jar gluegen-rt.dll, jogl_awt.dll, jogl_cg.dll, jogl.dll were in my runtime classpath. For Eclipse, I also needed to add the jogl directory to the java.library.path setting.

-Djava.library.path=${workspace_loc:pinobot}/lib/jogl

Once the libraries are included, you can start with developing your Swing JFrame/main application.

Jogl Hello World

This is the simplest Java OpenGL application you can create:

package org.berlin.pino.test.functional.jogl;

import java.awt.Color;
import java.awt.Frame;
import java.awt.event.WindowAdapter;
import java.awt.event.WindowEvent;
import javax.media.opengl.GLCanvas;

public class Basic {
public static void main(String[] args) {

Frame frame = new Frame("Hello World!");
GLCanvas canvas = new GLCanvas();
frame.add(canvas);
frame.setSize(300, 300);
frame.setLocation(400, 400);
frame.setBackground(Color.white);
frame.addWindowListener(new WindowAdapter() {
public void windowClosing(WindowEvent e) {
System.exit(0);
}
});
frame.setVisible(true);
}
} // End of the class //

Java Open Nehe Game Dev Example, Rendering a Box

package org.berlin.pino.test.jogl.box;

import java.awt.event.MouseEvent;
import java.awt.event.MouseListener;
import java.awt.event.MouseMotionListener;

import javax.media.opengl.GL;
import javax.media.opengl.GLAutoDrawable;
import javax.media.opengl.GLCanvas;
import javax.media.opengl.GLEventListener;
import javax.media.opengl.glu.GLU;

public class GLBox implements GLEventListener,
MouseListener, MouseMotionListener {

private final GLU glu = new GLU();
private float rtri = 0.0f;
private float rquad = 0.0f;

public static class Builder {

public GLCanvas buildCanvas() {

GLCanvas canvas = new GLCanvas();
canvas.addGLEventListener(new GLBox());
canvas.setSize(100, 60);
return canvas;
}

} // End of the Class //

public GL renderGLScene(final GLAutoDrawable drawable) {

final float shiftXPos = 1.2f;
final GL gl = drawable.getGL();
gl.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT); // Clear and depth buffer bit
gl.glLoadIdentity(); // Reset The View
gl.glTranslatef(-4.0f + shiftXPos, 0.0f, -7.0f);
gl.glRotatef(rtri, 0.0f, 1.0f, 1.0f);

gl.glBegin(GL.GL_QUADS); // Draw A Quad
gl.glColor3f(0.0f, 1.0f, 0.0f);
gl.glVertex3f(1.0f, 1.0f, -1.0f); // Top Right Of The Quad (Top)
gl.glVertex3f(-1.0f, 1.0f, -1.0f); // Top Left Of The Quad (Top)
gl.glVertex3f(-1.0f, 1.0f, 1.0f); // Bottom Left Of The Quad (Top)
gl.glVertex3f(1.0f, 1.0f, 1.0f); // Bottom Right Of The Quad (Top)

...
...
gl.glEnd(); // Done Drawing The Quad

return gl;

}

public void display(final GLAutoDrawable drawable) {

final GL gl = this.renderGLScene(drawable);
gl.glFlush();
}

public void displayChanged(final GLAutoDrawable drawable, boolean modeChanged, boolean deviceChanged) {

}

public void init(final GLAutoDrawable drawable) {

final GL gl = drawable.getGL();
System.err.println("INIT GL IS: " + gl.getClass().getName());
System.err.println("Chosen GLCapabilities: " + drawable.getChosenGLCapabilities());

gl.setSwapInterval(1);

gl.glShadeModel(GL.GL_SMOOTH); // Enables Smooth Color Shading
gl.glClearColor(1.0f, 1.0f, 1.0f, 1.0f); // This Will Clear The Background Color
gl.glClearDepth(1.0); // Enables Clearing Of The Depth Buffer
gl.glEnable(GL.GL_DEPTH_TEST); // Enables Depth Testing
gl.glDepthFunc(GL.GL_LEQUAL); // The Type Of Depth Test To Do
gl.glHint(GL.GL_PERSPECTIVE_CORRECTION_HINT, GL.GL_NICEST); // Really Nice Perspective Calculations
}

public void reshape(final GLAutoDrawable drawable, int x, int y, int width, int height) {

final GL gl = drawable.getGL();
System.err.println("GL_VENDOR: " + gl.glGetString(GL.GL_VENDOR));
System.err.println("GL_RENDERER: " + gl.glGetString(GL.GL_RENDERER));
System.err.println("GL_VERSION: " + gl.glGetString(GL.GL_VERSION));

if (height <= 0) {
height = 1;
}
final float h = (float) width / (float) height;

gl.glViewport(0, 0, width, height); // Reset The Current Viewport And Perspective Transformation
gl.glMatrixMode(GL.GL_PROJECTION); // Select The Projection Matrix
gl.glLoadIdentity(); // Reset The Projection Matrix
glu.gluPerspective(45.0f, h, 0.1f, 100.0f); // Calculate The Aspect Ratio Of The Window
gl.glMatrixMode(GL.GL_MODELVIEW);
gl.glLoadIdentity();
}

...
... Click event methods
...

} // End of the Class //


My Bot

I am using kind of a mishmash of broken algorithms. Basically, use my post for finding out visualizing my bot. I would go elsewhere for a good algorithm for beating the google challenge. My bot is hovering around 290 / 600. It was something I created over a couple of hours. I started out with the random, select a valid direction. Then I went to favoring an order of directions. It seems that the best algorithm is a combination of a working flood-fill pattern, a min-max strategy in close combat and possibly a good attack strategy in an open environment. I used a simple min-max strategy that found good paths with open squares. My score/evaluation was based on 1 point for the the number of open squares for a particular move. Here is a more robust set of strategies on the Waterloo forum:

"The evaluation function is simple. Partition the board into squares OUR bot can reach first and squares THEIR bot can reach first. I think of this as a Voronoi partition of the board. The score is simply count(OUR squares) - count(THEIR squares)."

http://csclub.uwaterloo.ca/contest/forums/viewtopic.php?f=8&t=190

Basic Favor Order Algorithm:

@Override
public void makeLogicMove() {
this.printThoughts();
this.printMessages();

// If dead, don't make a move //
if (this.isDead()) {
return;
}
if (this.getVerbose()) {
System.err.println("Making logic move for aibotscorer - " + this.getMoves().size());
}
final Move lastMove = this.getLastMoveNull();
if (lastMove == null) {
this.addMessages("-3000-AI: using default move, last move null");
// Revert back to the default move
super.makeLogicMove();
return;
}

final Move north = lastMove.decy();
final Move south = lastMove.incy();
final Move east = lastMove.incx();
final Move west = lastMove.decx();

final Stack stack = (Stack) this.getMoves().getMoves();
final boolean nb = validateMove(stack, north);
final boolean sb = validateMove(stack, south);
final boolean eb = validateMove(stack, east);
final boolean wb = validateMove(stack, west);
if (wb) {
this.makeMove(west);
return;
}
if (nb) {
this.makeMove(north);
return;
}
if (eb) {
this.makeMove(east);
return;
}
if (sb) {
this.makeMove(south);
return;
}
this.makeMove(this.dumbRandomMove());
return;
}

Source for the Java OpenGL Code and the Tron Bot

http://jvmnotebook.googlecode.com/files/simple_tron_opengl.tar.gz

// Packages and Code of Interest:
package org.berlin.tron.gl (GLGridApp)
package org.berlin.tron.gl.game (GL3DGridBoard)

Note: I did include the source for the bot. By the time, you figure out how to get it deployed and usable for the Google challenge, you probably would have found it better to have created your own.
Enter a comment:

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Sunday, January 17, 2010

Download and Cache Youtube/FLV files with Firefox

If you want to cache/copy youtube videos for later use, go tor you firefox cache and just copy the flv files over.

My Name\Local Settings\Application Data\Mozilla\Firefox\Profiles\rxxs25wm.default\Cache

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The filenames are like:
B7922209d01

Add .flv as your file extension and then open a valid flv player.

Look for the files that are over 2mbs. And normally you have to watch a complete youtube video, wait until it caches and then close firefox for the cache to be saved to disk.

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And another note, I watch a lot of youtube videos. Every youtube download may cost you 15mbs of bandwidth. So I would consider watching the videos from file.

I ate up 20gigs of bandwidth last month. I think my isp is considering charging me extra.