Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
41 lines
1.2 KiB
41 lines
1.2 KiB
import glob
|
|
import os
|
|
|
|
import sys
|
|
sys.path.append("./PyCTBN/")
|
|
|
|
import structure_graph.network_graph as ng
|
|
import structure_graph.sample_path as sp
|
|
import structure_graph.set_of_cims as sofc
|
|
import estimators.parameters_estimator as pe
|
|
import utility.json_importer as ji
|
|
|
|
|
|
def main():
|
|
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
|
|
#import data
|
|
importer = ji.JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
|
|
#Create a SamplePath Obj
|
|
s1 = sp.SamplePath(importer)
|
|
#Build The trajectries and the structural infos
|
|
s1.build_trajectories()
|
|
s1.build_structure()
|
|
#From The Structure Object build the Graph
|
|
g = ng.NetworkGraph(s1.structure)
|
|
#Select a node you want to estimate the parameters
|
|
node = g.nodes[1]
|
|
#Init the graph specifically for THIS node
|
|
g.fast_init(node)
|
|
#Use SamplePath and Grpah to create a ParametersEstimator Object
|
|
p1 = pe.ParametersEstimator(s1, g)
|
|
#Init the peEst specifically for THIS node
|
|
p1.fast_init(node)
|
|
#Compute the parameters
|
|
sofc1 = p1.compute_parameters_for_node(node)
|
|
#The est CIMS are inside the resultant SetOfCIms Obj
|
|
print(sofc1.actual_cims)
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|
|
|
|
|