diff --git a/README.txt b/README.txt new file mode 100644 index 0000000..7c15d55 --- /dev/null +++ b/README.txt @@ -0,0 +1,50 @@ +Seismic-2d +========== +This is a simplification of a model originally used for global-scale +seismology. + +NET-VISA: Network Processing Vertically Integrated Seismic +Analysis. Nimar S. Arora, Stuart Russell, Erik Sudderth. Bulletin of the +Seismological Society of America (BSSA) April 2013, vol. 103 no. 2A +pp709-729. + +Files +===== + +description.odt -- a description of the model +generate.py -- generates the physics of a 2-D world and some episodes +util.py -- some geophysical utility functions +solve.py -- a sample solver that learns the physics and solves the episodes +evaluate.py -- evaluates a solution versus a reference +mwmatching.py -- utility script for max-weight max cardinality matching +training.data -- 10K episodes for training +test.data -- another 10K episodes for testing +test.blind -- the test data with the event-to-detection mapping omitted +test.solution -- the sample solution on the test data + +Overview +======== + +The model is completely described in ```description.odt``` and this should be +translated in the Probabilistic Programming Language of your +choosing. The unlabeled data in ```test.blind``` (and optionally the labeled +data in ```training.data```) comprises the observations to the model. The +query of interest is the seismic bulletin for each of the observed +episodes. + +Once all the bulletins have been produced, the script ```evaluate.py``` +can be used to produce the reports on the accuracy versus the reference +script ```test.data```. One can also compare the results versus the +baseline in ```test.solution```. + +The files ```generate.py``` and ```solve.py``` have only been provided +for convenience they shouldn't normally be used. However, if you want to +check the performance of your model on more than just the provided data +you may generate more as needed. The sample solver is based loosely on +the published greedy algorithm, and may be used as a competitive +baseline. + +Authors +======= +Nimar S. Arora, Bayesian Logic Inc., nimar.arora@gmail.com +Stuart Russell, Deptt. of Computer Science, Berkeley.