def get_model(self):
"""
Returns an instance of Bayesian Model or Markov Model.
Varibles are in the pattern var_0, var_1, var_2 where var_0 is
0th index variable, var_1 is 1st index variable.
Return
------
model: an instance of Bayesian or Markov Model.
Examples
--------
>>> reader = UAIReader('TestUAI.uai')
>>> reader.get_model()
"""
if self.network_type == 'BAYES':
model = BayesianModel()
model.add_nodes_from(self.variables)
model.add_edges_from(self.edges)
tabular_cpds = []
for cpd in self.tables:
child_var = cpd[0]
states = int(self.domain[child_var])
arr = list(map(float, cpd[1]))
values = np.array(arr)
values = values.reshape(states, values.size // states)
tabular_cpds.append(TabularCPD(child_var, states, values))
model.add_cpds(*tabular_cpds)
return model
elif self.network_type == 'MARKOV':
model = MarkovModel(self.edges)
factors = []
for table in self.tables:
variables = table[0]
cardinality = [int(self.domain[var]) for var in variables]
value = list(map(float, table[1]))
factor = DiscreteFactor(variables=variables,
cardinality=cardinality,
values=value)
factors.append(factor)
model.add_factors(*factors)
return model
def get_model(self):
"""
Returns an instance of Bayesian Model or Markov Model.
Varibles are in the pattern var_0, var_1, var_2 where var_0 is
0th index variable, var_1 is 1st index variable.
Return
------
model: an instance of Bayesian or Markov Model.
Examples
--------
>>> reader = UAIReader('TestUAI.uai')
>>> reader.get_model()
"""
if self.network_type == 'BAYES':
model = BayesianModel(self.edges)
tabular_cpds = []
for cpd in self.tables:
child_var = cpd[0]
states = int(self.domain[child_var])
arr = list(map(float, cpd[1]))
values = np.array(arr)
values = values.reshape(states, values.size // states)
tabular_cpds.append(TabularCPD(child_var, states, values))
model.add_cpds(*tabular_cpds)
return model
elif self.network_type == 'MARKOV':
model = MarkovModel(self.edges)
factors = []
for table in self.tables:
variables = table[0]
cardinality = [int(self.domain[var]) for var in variables]
value = list(map(float, table[1]))
factor = DiscreteFactor(variables=variables, cardinality=cardinality, values=value)
factors.append(factor)
model.add_factors(*factors)
return model
# Represent Markov Network Model
from pgmpy.models import MarkovModel
model = MarkovModel([('A', 'B'), ('B', 'C')])
model.add_node('D')
model.add_edges_from([('C', 'D'), ('D', 'A')])
# --- Define factors to associate with model
from pgmpy.factors import Factor
factor_a_b = Factor(variables=['A', 'B'], cardibality=[2,2], values=[90. 100, 1, 10])
factor_b_c = Factor(variables=['B', 'C'], cardibality=[2,2], values=[10. 80, 70, 20])
factor_c_d = Factor(variables=['C', 'D'], cardibality=[2,2], values=[50. 120, 10, 10])
factor_d_a = Factor(variables=['D', 'A'], cardibality=[2,2], values=[20. 80, 50, 40])
# --- Add factors to model
model.add_factors(factor_a_b, factor_b_c, factor_c_d, factor_d_a)
model.get_factors()
" [<Factor representing phi(A:2, B:2) at aidfoaiudn;aksndf> "
" [<Factor representing phi(B:2, C:2) at aidfoaasdfsdfn;aksndf> "
" [<Factor representing phi(C:2, D:2) at aidfoaiud234234sndf> "
" [<Factor representing phi(D:2, A:2) at aawjdfblasjiudn;aksndf> "
# Cluster Graph -- "Cluster Graph"
########################################################
#
# ________ _______ ________
# |__f1__|---|__A__|---|__f3__|
values=cpd2)
factor5 = DiscreteFactor(['safety', 'safety_received'],
cardinality=[3, 3],
values=cpd2)
factor6 = DiscreteFactor(['lug_boot', 'lug_boot_received'],
cardinality=[3, 3],
values=cpd2)
# for any given x (where x can also be continuous) this reduces to a table with k scaled probabilities #
# print(factor3)
# factor3.reduce([('doors_received', 1)])
# # factor.normalize()
# print(factor3)
# add the factor to the network #
model.add_factors(factor1)
model.add_factors(factor2)
model.add_factors(factor3)
model.add_factors(factor4)
model.add_factors(factor5)
model.add_factors(factor6)
# print(model.nodes())
# print(model.edges())
# can also use VE #
inference = BeliefPropagation(model)
print("Class variable prior:\n{}\n".format(
inference.query(variables=['class'])['class']))
print("Class variable posterior after noisy observation:\n{}\n".format(
d1 = to_pydot(g1)
d1.set_dpi(300)
d1.set_margin(0.5)
Image(d1.create_png(), width=300)
from pgmpy.models import MarkovModel
from pgmpy.factors.discrete import DiscreteFactor
# 마르코프 랜덤 필드(마르코프 모델) 모델 정의
model = MarkovModel([('A', 'B'), ('B', 'C'), ('C','D'), ('D','A')])
factor1 = DiscreteFactor(['A', 'B'], [2,2], [30, 5, 1, 10], state_names={'A': [0,1], 'B':[0,1]})
factor2 = DiscreteFactor(['B', 'C'], [2,2], [100, 1, 1, 100], state_names={'B': [0,1], 'C':[0,1]})
factor3 = DiscreteFactor(['C', 'D'], [2,2], [1, 100, 100, 1], state_names={'C': [0,1], 'D':[0,1]})
factor4 = DiscreteFactor(['D', 'A'], [2,2], [100, 1, 1, 100], state_names={'D': [0,1], 'A':[0,1]})
model.add_factors(factor1, factor2, factor3, factor4)
print('모델의 타당성: ', model.check_model())
import numpy as np
pf_value = model.get_partition_function()
print('\n분할 함수의 값: ', pf_value)
infer = VariableElimination(model) # 추론 객체 생성
phi_ABCD = infer.query(['A', 'B', 'C', 'D']) # 전체 분포 phi(A,B,C,D)
print('phi(A,B,C,D)')
print(phi_ABCD)
P_ABCD = phi_ABCD.values/pf_value # 확률 = (팩터의 곱)/(분할 함수의 값)
PABCD = np.reshape(P_ABCD, -1)
for val in PABCD: # 확률의 출력
print(val, '\n')
