Bayesian Networks for Ruby ( bn4r )

bn4r is a bayesian networks library on ruby that provides the user with classes for create bayesian networks and diverse algorithms for solve them.

Its algorithms implementation are based on: S.Russell, P.Norving, "Artificial Intelligence, A Modern Approach", 2nd Edition.

Website: bn4r.rubyforge.org

Spanish Website: http://bn4r.rubyforge.org/es

Rubyforge Project: rubyforge.org/projects/bn4r

Dependencies

Design principles

The library consists on the object BayesNet thinked to be filled with BayesNetNode, these objects are defined in bn.rb. BayesNet object is a especialization of RGL::DirectedAdjacencyGraph ( rgl.rubyforge.org ).

The file bn_algorithms.rb has the implementation of the inference algorithms that can be used to solve BayesNet structures.

Files bn_export.rb and bn_import.rb have methods for import and export bayesian networkd in different formats.

Finally, a set of objects and methods are given to automaticly fill BayesNetNode probabilities tables.

Play a bit …

  1. Install the gem ( bn4r-0.9.0.gem ) 
         gem install bn4r
  2. Include the bn4r 
         require 'bn4r'
  3. Create your first bayes net 
         # Create BayesNet
     bn_aima = BayesNet.new

     # Create nodes for the Bayes Net (BayesNetNodes)
     burglary = BayesNetNode.new("Burglary")
     earthquake = BayesNetNode.new("Earthquake")
     alarm = BayesNetNode.new("Alarm")
     john_calls = BayesNetNode.new("JohnCalls")
     mary_calls = BayesNetNode.new("MaryCalls")

     # Add nodes ( vertex ) to the BayesNet
     bn_aima.add_vertex(burglary)
     bn_aima.add_vertex(earthquake)
     bn_aima.add_vertex(alarm)
     bn_aima.add_vertex(john_calls)
     bn_aima.add_vertex(mary_calls)

     # Add relations ( edges ) between nodes in the BayesNet
     bn_aima.add_edge(burglary,alarm)
     bn_aima.add_edge(earthquake,alarm)
     bn_aima.add_edge(alarm,john_calls)
     bn_aima.add_edge(alarm,mary_calls)

     # Assign probabilities to each node
     burglary.set_probability_table([], [0.001, 0.999] )
     earthquake.set_probability_table([], [0.002, 0.998] )

     alarm.set_probability_table([burglary,earthquake], [0.95, 0.05, 0.94, 0.06, 0.29, 0.71, 0.001,0.999] )

     john_calls.set_probability_table([alarm], [0.90,0.10,0.05,0.95])
     mary_calls.set_probability_table([alarm], [0.70,0.30,0.01,0.99])
  4. Solve it! 
         # John and Mary are calling ...
     john_calls.set_value(true)
     mary_calls.set_value(true)

     # Why?
     is_there_a_burglary = bn_aima.enumeration_ask( burglary, [john_calls, mary_calls] )
     puts "Call the police!" if is_there_a_burglary[0] > is_there_a_burglary[1]

     is_the_alarm_on = bn_aima.enumeration_ask( alarm, [john_calls, mary_calls] )
     puts "Run home, your alarm is distubing the neigborhood!" if is_the_alarm_on[0] > is_the_alarm_on[1]

     is_there_a_earthquake = bn_aima.enumeration_ask( earthquake, [john_calls, mary_calls] )
     puts "Calm yourself, there isn't a earthquake ;)" if is_there_a_earthquake[0] < is_there_a_earthquake[1]
  5. See how your bayes net looks like 
         # In .dot format
     bn_aima.to_dot

     # In Microsoft Belief Networks (.xbn) format
     # (download for free in: http://research.microsoft.com/adapt/MSBNx )
     bn_aima.to_xbn

Documentation

Documentation can be found at bn4r.rubyforge.org/rdoc or can be generated using rdoc tool under the source code with: 

  rdoc README lib

Credits

Thanks to Núria Bel ( www.upf.edu/pdi/iula/nuria.bel ) for her work in this project without her it cannot be done.

Thanks to Ryan Dahl for his work in www.math.rochester.edu/people/grads/rld/bayesnets that was the inspiration of the project.

Also thanks to all the ruby community.

Copying

This work is developed by Sergio Espeja ( www.upf.edu/pdi/iula/sergio.espeja, sergio.espeja at gmail.com ) mainly in Institut Universitari de Lingüística Aplicada of Universitat Pompeu Fabra ( www.iula.upf.es ), and also in bee.com.es ( bee.com.es ).

It is free software, and may be redistributed under GPL license.

Support

Please contact me in rubyforge.org/projects/bn4r.

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