BayesNetCreacion 1.1.1

Library capable of creating Bayesian Networks and making probabilistic inference over them, as well additional functions

BayesNetCreacion Library

This library has the objective of building Bayesian networks and making probabilistic inference over them. Also, adding some other additional features that could serve the developers that make use of this library.

This library has zero dependencies to assure it is futureproof, easier to debug, to contribute to and use.

For the most part this library works over classes like BayesNetCreacion and Node, this was chosen so that in a way it could facilitate the usage of the OOP paradigm.

For further inquiries you about the usage of the library you can consult Usage section of this repo or the testing python file to see the Burglar Alarm System example being used.

Features

• Created without using any dependencies/libraries.

• Can create Bayesian Networks, represented as a dictionary.

• Calculation of probabilistic inference given the created Bayesian Network.

• Can check if network is fully defined or not.

• Has the capability of returning a compact representation of the network as a string.

• Can return the network's factors as a dictionary.

• Is able to return the numeration of the network given evidence and a query.

Prerequisites

• Python 3.10.^

Usage

from BayesNetCreacion import BayesNetCreacion, Node #Import the library



examplenode = Node('E') #Create a Node object

example2node = Node('E2') #Create another Node object

example2node.set_parents([str(examplenode)]) #Set node as parent



bnc = BayesNetCreacion() #Instantiate the Bayes Network class



#Add both nodes into the network

bnc.add_node(examplenode)

bnc.add_node(example2node) 



#Add the probability values of each node 

bnc.add_prob({

    ('E', True): 0.001,

    ('E', False): 0.999,

    ('E2', True, 'E', True): 0.25,

    ('E2', True, 'E', False): 0.9,

    ('E2', False, 'E', True): 0.75,

    ('E2', False, 'E', False): 0.1

})



#Features:



#Get dictionary with network

bn = bnc.get_network()



#Probabilistic inference

evidence = {'E': True}

query = 'E2'



result = bnc.probabilistic_inference(query, evidence) #Calculate the inference

print(result) #Profit?



#Compact

WIP



#Factors

print(bnc.get_factors())



#Definition Check

WIP



#Enumeration

evidence = {'E': False}

query = 'E2'

result = bnc.pre_enum(query, evidence) #Calculate numeration

print(result)

Author

👤 Andrés de la Roca

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