def time_pathfinder_read(self):
get_example_model('pathfinder')
def time_munin_read(self):
get_example_model('munin')
def time_asia_read(self):
get_example_model('asia')
#Step 1: Generate some data
# Use the alarm model to generate data from it.
from pgmpy.utils import get_example_model
from pgmpy.sampling import BayesianModelSampling
alarm_model = get_example_model('alarm')
samples = BayesianModelSampling(alarm_model).forward_sample(size=int(1e5))
print(samples.head())
#Step 2: Define a model structure
# Defining the Bayesian Model structure
from pgmpy.models import BayesianModel
model_struct = BayesianModel(ebunch=alarm_model.edges())
print(model_struct.nodes())
#Step 3: Learning the model parameters
# Fitting the model using Maximum Likelihood Estimator
from pgmpy.estimators import MaximumLikelihoodEstimator
mle = MaximumLikelihoodEstimator(model=model_struct, data=samples)
# Estimating the CPD for a single node.
print(mle.estimate_cpd(node='FIO2'))
print(mle.estimate_cpd(node='CVP'))
# Estimating CPDs for all the nodes in the model
def setup(self):
self.alarm = get_example_model('alarm')
self.munin = get_example_model('munin')
def setup(self):
self.alarm = get_example_model('alarm')