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.
Spanish Website: http://bn4r.rubyforge.org/es
Rubyforge Project: http://rubyforge.org/projects/bn4r
rgl-0.2.3 ( Ruby Graph Library ), http://rgl.rubyforge.org
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 ( http://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 networked in different formats.
Finally, a set of objects and methods are given to automaticly fill BayesNetNode probabilities tables.
Install the gem ( bn4r-0.9.0.gem )
gem install bn4r
Include the bn4r
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])
# 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 > is_there_a_burglary 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 > is_the_alarm_on 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 < is_there_a_earthquake
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 can be found at http://bn4r.rubyforge.org/rdoc or can be generated using rdoc tool under the source code with: rdoc README lib
Thanks to Núria Bel ( http://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 http://www.math.rochester.edu/people/grads/rld/bayesnets that was the inspiration of the project.
Also thanks to all the ruby community.
This work is developed by Sergio Espeja ( http://www.upf.edu/pdi/iula/sergio.espeja, sergio.espeja at gmail.com ) mainly in Institut Universitari de Lingüística Aplicada of Universitat Pompeu Fabra ( http://www.iula.upf.es ), and also in bee.com.es ( http://bee.com.es ).
It is free software, and may be redistributed under GPL license.
Please contact me in http://rubyforge.org/projects/bn4r.