# This file is part of libDAI - http://www.libdai.org/
#
# libDAI is licensed under the terms of the GNU General Public License version
# 2, or (at your option) any later version. libDAI is distributed without any
# warranty. See the file COPYING for more details.
#
# Copyright (C) 2009 Joris Mooij [joris dot mooij at libdai dot org]
# This example program illustrates how to construct a factorgraph
# by means of the sprinkler network example discussed at
# http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
# using the SWIG python wrapper of libDAI
import dai
C = dai.Var(0, 2) # Define binary variable Cloudy (with label 0)
S = dai.Var(1, 2) # Define binary variable Sprinkler (with label 1)
R = dai.Var(2, 2) # Define binary variable Rain (with label 2)
W = dai.Var(3, 2) # Define binary variable Wetgrass (with label 3)
# Define probability distribution for C
P_C = dai.Factor(C)
P_C[0] = 0.5 # C = 0
P_C[1] = 0.5 # C = 1
# Define conditional probability of S given C
P_S_given_C = dai.Factor(dai.VarSet(S,C))
P_S_given_C[0] = 0.5 # C = 0, S = 0
P_S_given_C[1] = 0.9 # C = 1, S = 0
P_S_given_C[2] = 0.5 # C = 0, S = 1
P_S_given_C[3] = 0.1 # C = 1, S = 1
# Define conditional probability of R given C
P_R_given_C = dai.Factor(dai.VarSet(R,C))
P_R_given_C[0] = 0.8 # C = 0, R = 0
P_R_given_C[1] = 0.2 # C = 1, R = 0
P_R_given_C[2] = 0.2 # C = 0, R = 1
P_R_given_C[3] = 0.8 # C = 1, R = 1
# Define conditional probability of W given S and R
SRW = dai.VarSet(S,R)
SRW.append(W)
P_W_given_S_R = dai.Factor(SRW)
P_W_given_S_R[0] = 1.0 # S = 0, R = 0, W = 0
P_W_given_S_R[1] = 0.1 # S = 1, R = 0, W = 0
P_W_given_S_R[2] = 0.1 # S = 0, R = 1, W = 0
P_W_given_S_R[3] = 0.01 # S = 1, R = 1, W = 0
P_W_given_S_R[4] = 0.0 # S = 0, R = 0, W = 1
P_W_given_S_R[5] = 0.9 # S = 1, R = 0, W = 1
P_W_given_S_R[6] = 0.9 # S = 0, R = 1, W = 1
P_W_given_S_R[7] = 0.99 # S = 1, R = 1, W = 1
# Build factor graph consisting of those four factors
SprinklerFactors = dai.VecFactor()
SprinklerFactors.append(P_C)
SprinklerFactors.append(P_R_given_C)
SprinklerFactors.append(P_S_given_C)
SprinklerFactors.append(P_W_given_S_R)
SprinklerNetwork = dai.FactorGraph(SprinklerFactors)
# Write factorgraph to a file
SprinklerNetwork.WriteToFile('sprinkler.fg')
print 'Sprinkler network written to sprinkler.fg'
# Output some information about the factorgraph
print SprinklerNetwork.nrVars(), 'variables'
print SprinklerNetwork.nrFactors(), 'factors'
# Calculate joint probability of all four variables
P = dai.Factor()
for I in range(SprinklerNetwork.nrFactors()):
P *= SprinklerNetwork.factor(I)
P.normalize() # Not necessary: a Bayesian network is already normalized by definition
# Calculate some probabilities
denom = P.marginal(dai.VarSet(W))[1]
print 'P(W=1) =', denom
print 'P(S=1 | W=1) =', P.marginal(dai.VarSet(S,W))[3] / denom
print 'P(R=1 | W=1) =', P.marginal(dai.VarSet(R,W))[3] / denom