import networkx as nx

import matplotlib.pyplot as plt

from pgmpy$.$models import BayesianNetwork

from pgmpy$.$factors.discrete import TabularCPD

import numpy as np

def construct$\_$bayesian$\_$network$()$:

# Define Bayesian network structure

bayesian$\_$network $=$ BayesianNetwork$([(\text{'}$Rain$\text{'},$ 'Maintenance'$),(\text{'}$Rain$\text{'},$ 'Train'$),(\text{'}$Maintenance$\text{'},$ 'Train'$),(\text{'}$Train$\text{'},$ 'Appointment'$)])$

# Define Conditional Probability Distributions

cpd$\_$rain $=$ TabularCPD$($variable$=$'Rain', variable$\_$card$=3,$ values$=[[0\mathrm{.}7],[0\mathrm{.}2],[0\mathrm{.}1]])$

cpd$\_$maintenance $=$ TabularCPD$($variable$=$'Maintenance', variable$\_$card$=2,$ values$=[[0\mathrm{.}4,0\mathrm{.}6,0\mathrm{.}2,0\mathrm{.}8,0\mathrm{.}1,0\mathrm{.}9],[0\mathrm{.}6,0\mathrm{.}4,0\mathrm{.}8,0\mathrm{.}2,0\mathrm{.}9,0\mathrm{.}1]],$ evidence$=[\text{'}$Rain$\text{'}],$ evidence$\_$card$=[3])$

cpd$\_$train $=$ TabularCPD$($variable$=$'Train', variable$\_$card$=2,$ values$=[[0\mathrm{.}8,0\mathrm{.}2,0\mathrm{.}9,0\mathrm{.}1,0\mathrm{.}6,0\mathrm{.}4,0\mathrm{.}7,0\mathrm{.}3,0\mathrm{.}4,0\mathrm{.}6,0\mathrm{.}5,0\mathrm{.}5],[0\mathrm{.}2,0\mathrm{.}8,0\mathrm{.}1,0\mathrm{.}9,0\mathrm{.}4,0\mathrm{.}6,0\mathrm{.}3,0\mathrm{.}7,0\mathrm{.}6,0\mathrm{.}4,0\mathrm{.}5,0\mathrm{.}5]],$ evidence$=[\text{'}$Rain$\text{'},$ 'Maintenance'$],$ evidence$\_$card$=[3,2])$

cpd$\_$appointment $=$ TabularCPD$($variable$=$'Appointment', variable$\_$card$=2,$ values$=[[0\mathrm{.}9,0\mathrm{.}1],[0\mathrm{.}6,0\mathrm{.}4]],$ evidence$=[\text{'}$Train$\text{'}],$ evidence$\_$card$=[2])$

# Add CPDs to the network

bayesian$\_$network.add$\_$cpds$($cpd$\_$rain, cpd$\_$maintenance, cpd$\_$train, cpd$\_$appointment$)$

return bayesian$\_$network

def visualize$\_$bayesian$\_$network$($bayesian$\_$network$)$:

# Visualize the Bayesian network

nx$\_$bayesian$\_$network $=$ bayesian$\_$network.to$\_$nx$()$

pos $=$ nx$.$spring$\_$layout$($nx$\_$bayesian$\_$network$)$

nx$.$draw$($nx$\_$bayesian$\_$network, pos, with$\_$labels$=$True, arrowsize$=20,$ node$\_$size$=3000,$ node$\_$color$=$"skyblue", font$\_$size$=12,$ font$\_$weight$=$"bold"$)$

plt$.$title$("$Bayesian Network"$)$

plt$.$show$()$

def inference$($bayesian$\_$network, queries$)$:

# Perform inference on the Bayesian network

for query in queries:

query$\_$variable $=$ query$[0]$

evidence $=$ query$[1]$

print$($f$"$P$(\{$query$\_$variable$\}|\{\text{'},\text{'}.$join$($evidence$.$keys$())\})=",$ bayesian$\_$network.get$\_$inference$().$query$($variables$=[$query$\_$variable$],$ evidence$=$evidence$))$

def markov$\_$chain$\_$simulation$($initial$\_$state, transition$\_$matrix, steps$)$:

# Simulate Markov chain

current$\_$state $=$ initial$\_$state

for $\_$ in range$($steps$)$:

current$\_$state $=$ np$.$dot$($current$\_$state, transition$\_$matrix$)$

return current$\_$state

if $\_\_$name$\_\_=="\_\_$main$\_\_"$:

# Question $1$: Construct and visualize the Bayesian network

bayesian$\_$network $=$ construct$\_$bayesian$\_$network$()$

visualize$\_$bayesian$\_$network$($bayesian$\_$network$)$

# Question $2$: Perform inferences

queries $=[(\text{'}$Rain$\text{'},\{\}),(\text{'}$Maintenance$\text{'},\{\text{'}$Rain$\text{'}$: 'heavy'$\}),(\text{'}$Appointment$\text{'},\{\text{'}$Rain$\text{'}$: 'none', 'Maintenance': $\text{'}$no$\text{'}\})]$

inference$($bayesian$\_$network, queries$)$

# Question $3$: Markov chain simulation

initial$\_$state $=$ np$.$array$([1,0])$

transition$\_$matrix $=$ np$.$array$([[0\mathrm{.}9,0\mathrm{.}1],[0\mathrm{.}5,0\mathrm{.}5]])$

steps $=10$

final$\_$state $=$ markov$\_$chain$\_$simulation$($initial$\_$state, transition$\_$matrix, steps$)$

print$($f$"$Probability distribution of 'attend' and 'miss' ten days later: $\{$final$\_$state$\}")$

currently getting ValueError: values must be of shape $(2,3).$ Got shape: $(2,6)$

cpd$\_$maintenance $=$ TabularCPD$($variable$=$'Maintenance', variable$\_$card$=2,$ values$=[[0\mathrm{.}4,0\mathrm{.}6,0\mathrm{.}2,0\mathrm{.}8,0\mathrm{.}1,0\mathrm{.}9],[0\mathrm{.}6,0\mathrm{.}4,0\mathrm{.}8,0\mathrm{.}2,0\mathrm{.}9,0\mathrm{.}1]],$ evidence$=[\text{'}$Rain$\text{'}],$ evidence$\_$card$=[3])$

How to fix?