ProBT
Boost.serialization (optional, note: compiled with ./bjam --with-serialization --build-type=complete --layout=versioned toolset=darwin architecture=x86 address-model=32)

One replay/game per line, lines such as
First_Building_Name Time_Built; Second_Building_Name Time_Built2;

See the *v*.txt files

1. Learn the possible tech trees (X) from replays (or you can generate them)
2. Learn the distributions of P(Time | X, Opening) and P(X | Opening)
3. Learn the distributions of P(Time | BuildTree) with TECH_TREES switched on
4. Infer P(Opening | Observations, Time)

See model.pdf or CIG'11 publication for 1,2,4 and AIIDE'11 publication for 3.

1. (Optional, our labels) Clusterize from clustering/launch_labeling.sh
2. Transform (transmute) arff files into txt files with scripts/transmute.sh
3. To perform evaluations, cut the txt files into learn and test files with the scripts/cut* scripts
4. Compile (make) and test either with make targets or export (DY)LD_LIBRARY_PATH=<probt/lib(+boost/lib)> && ./model lXvX.txt tXvX.txt

You will always need one runtime library: probt (probt22 in Makefile, any probt >= 2.2 should do). depending on how it was compiled, the main of model.cpp (model (Ben Weber openings), mymodel (my openings, c.f. clustering/), or tt (tech trees only) in the Makefile) will learn and exit from file (match-up) XvY.txt if given:

    ./model XvY.txt

or will learn on XvY and test on WvZ if given:

    ./model XvY.txt WvZ.txt

Example:

    ./model lZvT.txt tZvT.txt

The prediction will always be against X or W, for instance in ZvT, the predictions will be as a Terran player against a Zerg one (the learning is also of Zerg build/tech trees and openings).

1. To just compile and run the Opening prediction model with Ben Weber's openings labels:

    make model
    make run OR (with appropriate DYLD_LIBRARY_PATH) ./model XvT.txt [WvZ.txt]
   

2. To compile and run the Opening prediction model with Ben's openings labels and serialization, you need to be able to link to a boost >= 1.45 serialization dylib/dll/so:

    make model_with_serialization
    make run_with_serialization OR (with appropriate DYLD_LIBRARY_PATH) ./model XvT.txt [WvZ.txt]
    ex here: ./model PvT.txt
   

3. The same with my openings (c.f. clustering/clustering.py):

    make mymodel[_with_serialization]
    make run[_with_serialization] OR (with appropriate DYLD_LIBRARY_PATH) ./mymodel XvY.txt [WvZ.txt]
    ex here: ./mymodel PvTx.txt
   

4. To compile and run the (build/)tech trees prediction only:

    make tt
    make run_tt OR (with appropriate DYLD_LIBRARY_PATH) ./tt XvY.txt [WvZ.txt]
    ex here: ./tt PvT.txt
   

5. A lot of plotting options, see Makefile