MLN Learning Tool

Start the tool from the command line with mlnlearn.

The MLN learning tool learns the weights of an MLN given one or more training databases. The tool allows you to invoke the actual MLN learning algorithms provided by ProbCog's Python and Java implementations, i.e. PyMLNs and J-MLNs respectively, or, optionally, one or more installations of the Alchemy system developed by the University of Washington. (To tell ProbCog about the location of your Alchemy installation(s), edit src/main/python/configMLN.py).

To use more than one training database, enter a pattern in the respective input field, i.e. an expression involving a wildcard such as trainingData/*.db.

Once you start the actual algorithm, the tool window itself will be hidden as long as the job is running, while the output of the algorithm is written to the console for you to follow. At the beginning, the tools list the main input parameters for your convenience, and, at the end, the query tool additionally outputs the inference results to the console (so even if you are using the Alchemy system, there is not really a need to open the results file that is generated).

The tool features an integrated editor for *.db and *.mln files. If you modify a file in the internal editor, it will automatically be saved as soon as you invoke the learning method. The new content can either be saved to the same file (overwriting the old content) or a new file, which you can choose to name as desired. Furthermore, the tool will save all the settings you made whenever the learning method is invoked, so that you can easily resume a session.