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Quick guide to getting CaMML started (updated 17-02-2013) :

for the latest src see:
for docs & discussion see:

== Installing/Running CaMML ==
1. Checkout from Github
> git clone git://

2. Get library/jar files
- For CaMML to compile cleanly, we need to download the Netica BNet inference library.
  If running Linux, simply run from the CAMML dir
  Otherwise grab the libraries for you platform direct from
  NOTE: If your platform is not supported (x86_64 linux not currently supported by netica) just grab
        the .jar file anyway so things compile.  Netica inference is disabled by default.

- CaMML includes .jars from several open source packages, to get the latest versions try:
	Tetrad :
	Weka :
	junit :

3. Compile/test CaMML
> cd CaMML
> ant
> export LD_LIBRARY_PATH=`pwd`/lib      # Tell ant where to find the netica binaries
> ant test

# Note: A bunch of tests will fail if netica libs are not found.

4. Run     
> ./

== Using CaMML ==

There are several plausible ways to use CaMML, common approaches include:
 - Using the CaMML GUI
 - Using the weka wrapper
 - Using the CDMS GUI
   - Interacting Manually
   - Writing CDMS .fp scripts
 - (advanced) Using a JVM based scripting language (e.g., jython)

One or the other may be easiest depending on the task.
The 'CaMML GUI' and 'weka wrapper' are probably the easiest places to start for
most users.

CaMML has a stand-alone Java-based GUI (added 02/2013) that should be suitable for
most basic learning tasks. The GUI has a data viewer, basic network viewer (based
on Netica libraries), and support for learning DBNs.

- To run the GUI, use:
  > camml_gui.bat (Windows)

Note that the -Djava.library.path field in the camml_gui.bat file may need to be
edited if the Netica libraries are not in the /jar/ folder - the GUI will run, but
viewing learned networks will be unavailable.

At present, this GUI only supports data files that are discrete and contain no
missing values.

== Weka Wrapper
CaMML can be used like a standard weka classifier from the command line.
It can also be added to the weka GUI, but that's beyond the scope of this doc.

- To get a list of options, use:
  > ./

- To train/test a model using weka's default x-fold validation, use:
  > ./ -t Camml/camml/test/iris.arff

Loading Data: 
	Click File->Load Data from the main menu.
	This is fairly straightforward.  Choose a filename and tell
	CaMML the type of file you are loading.  If you don't tell
	CaMML the file type it gets confused.
	The most useful types are:
	.cas : Same format as the C version of CaMML
	.arff : Load a weka file
	.arff (discretize) : As above, but converts file to a format
	 the CaMML can work with (ie. all discrete, no missing).
	Delimited Text : Try to automatically convert a plain text
	 file into a dataset.
	A tabular view of the data should pop-up onto the screen.

Running CaMML:
	Right-clicking on the data allows us to run a function on it.
	The most common option is to run 'camml' using all the default
	options.  We generally get better results (though slowly) using
	the 'cammlDualCTL' function which runs CaMML using hybrid models
	of local structure.
	Right-click data.
	Choose "Apply Function"
	Choose "camml", hit ok
	The GUI becomes unresponsive while CaMML is running, but 
	progress can be seen in the text window you launched CaMML
	from.  When CaMML is done a results window appears.

	CaMML produced a heirachy of networks. The heirachy
	is MMLECs -> SECs -> DAGs -> TOMs.	
	The results window initially shows only the best MMLECs
	along with their posterior and MML score.
	To view a representative model of the MMLEC, right-click the [n SECs] item, 
	 apply-function, ShowNet
	To save a representative model of the MMLEC, right-click the [n SECs] item, 
     apply-function, SaveNet
	To go to the lower level of the heirachy, right-click the [n SECs] item, 
	 and select "Extract". A table of all the SECs in the MMLEC will appear.
	You can save, show or extract the DAGs from each SEC the same way you
	 would for a MMLEC.

== CDMS Envinronment
	The CDMS Environment contains Values of various types. A complete
	list (sorted by type) is available on the left side of the GUI.
	To add something to the environment (eg. a dataset or learned model)
	click on in, choose add/copy and name it.  It should appear under the
	appropriate type.  If  you double click on its name it pops-up, and
	you can also refer to it by name in scripts.

== CDMS Scripting
	The CDMS scripting language is based on functional programming and
	is quite powerful, though underdocumented.
	Sample scripts can be found in CAMML/Camml/scripts
	NOTE: The "{" and "}" characters are used for comments.


Causal MML. Learns Causal Bayesian Networks in Java.







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