This is a simple example of a use of maximum entropy and the OpenNLP
Maxent toolkit.  (It was designed to work with Maxent v2.5.0.)  There
are two example data sets provided, one for whether a game should be
played indoors or outdoors and another for whether Arsenal or
Manchester United (two English football clubs) will win when they play
each other, based on a few potentially salient features for either
decision.

The java classes should be helpful getting up and running with your
own maxent implementation, though the context generator is about as
simple as it gets.  For more complex examples, look at the classes in
the opennlp.tools packages, available at http://opennlp.sourceforge.net.

To play with this sample application, do the following:

Be sure that maxent-2.5.0.jar and trove.jar (found in the lib directory)
are in your classpath.

Compile the java files: 
   
> javac *.java

Note: the following will avoid the need to setup you classpath in your
environment (be sure to fix the maxent jar for the correct version
number):

> javac -classpath .:../../lib/trove.jar:../../output/maxent-2.5.0.jar *.java

Now, build the models:

> java CreateModel gameLocation.dat
> java CreateModel football.dat

This will produce the two models "gameLocationModel.txt" and
"footballModel.txt" in this directory. Again, to fix classpath issues
on the command line, do the following instead:

> java -cp .:../../lib/trove.jar:../../output/classes CreateModel football.dat

You can then test the models on the data itself to see what sort of
results they get on the data they were trained on:

> java Predict gameLocation.dat
> java Predict football.dat

or, with command line classpath:

> java -cp .:../../lib/trove.jar:../../output/classes Predict gameLocation.test

You'll get output such as the following:

--------------------------------------------------
For context: Cloudy Happy Humid
Outdoor[0.9255]  Indoor[0.0745]

For context: Rainy Dry
Outdoor[0.0133]  Indoor[0.9867]
--------------------------------------------------

For the first, the model has assigned a normalized probability of 77%
to the Outdoor outcome, so given the context "Cloudy,Happy,Humid" it
would choose to have the game outdoors.  For the second, the model
appears to be almost entirely sure that the game should be indoors.

The Arsenal vs. Manchester United decision is a bit more interesting
because there are three possible outcomes: Arsenal wins, ManU wins, or
they tie.  Here is some example output:

--------------------------------------------------
For context: home=arsenal Beckham=true Henry=false
arsenal[0.3201]  man_united[0.6343]  tie[0.0456]

For context: home=man_united Beckham=true Henry=true
arsenal[0.1499]  man_united[0.2060]  tie[0.6441]
--------------------------------------------------

In the first case, ManU looks like the clear winner, but in the second
it looks like it will be a tie, though ManU looks to have more of a
chance at winning it than Arsenal.

(For those who don't know, Beckham, Scholes, and Neville are/were ManU
players and Ferguson is the coach, while Henry, Kanu, and Parlour are
Arsenal players with Wengler as their coach.  By "Beckham=false" I
mean that Beckham won't play this game.)

Also, try this on the test files:

> java Predict gameLocation.test
> java Predict football.test

Go ahead and modify the data to experiment with how the results can
vary depending on the input to training.  There isn't much data, so
its not a full-fledged example of maxent, but it should still give the
general idea.  Also, add more contexts in the test files to see what
the model will produce with different features active.

In all the previous examples, the features we're binary values, meaning
that the feature was either on or off.  You can also use features which
have real values (like 0.07).  The features are formatted with the value
specified after an equals sign such as the "pdiff" and "ptwins" features
below.

away pdiff=0.9375 ptwins=0.25 tie
away pdiff=0.6875 ptwins=0.6666 lose
home pdiff=1.0625 ptwins=0.3333 win

Features which don't contains are not in this format are considered to 
have a value of 1.  Note feature values MUST BE POSITIVE.  Using real-valued 
features has some additional overhead so you'll need to let the model know
that it should look for these features.  For these examples, you can use
the "-real" option.

> java CreateModel -real realTeam.dat

You can then test the models on the test data:

> java Predict -real realTeam.test

You see output like:
--------------------------------------------------
For context: home pdiff=0.6875 ptwins=0.5
lose[0.3279]  win[0.4311]  tie[0.2410]

For context: home pdiff=1.0625 ptwins=0.5
lose[0.3414]  win[0.4301]  tie[0.2284]

For context: away pdiff=0.8125 ptwins=0.5
lose[0.5590]  win[0.1864]  tie[0.2546]

For context: away pdiff=0.6875 ptwins=0.6
lose[0.5578]  win[0.1866]  tie[0.2556]
--------------------------------------------------

You can see that the values of the features as well as their presence or 
absence (such as the home or away features) impact the probabilities assigned 
to each outcome.

The use of the "-real" option to indicate real-valued data.  In general you'll
need to use the classes: RealBasicEventStream, RealValueFileEventStream, OnePassRealValueDataIndexer, and TwoPassRealValueDataIndexer.

For all models, though the features appear in almost the same
orderings in the data files, this is not important. You can list them
in whatever order you like.

If you have any suggestions, interesting modifications, or data sets
for other examples to add to this sample maxent application, please
post them to the maxent open discussion forum:

  http://sourceforge.net/forum/forum.php?forum_id=18384