def eliminate(factor, eliminationVariable):
"""
Question 4: Your eliminate implementation
Input factor is a single factor.
Input eliminationVariable is the variable to eliminate from factor.
eliminationVariable must be an unconditioned variable in factor.
You should calculate the set of unconditioned variables and conditioned
variables for the factor obtained by eliminating the variable
eliminationVariable.
Return a new factor where all of the rows mentioning
eliminationVariable are summed with rows that match
assignments on the other variables.
Useful functions:
Factor.getAllPossibleAssignmentDicts
Factor.getProbability
Factor.setProbability
Factor.unconditionedVariables
Factor.conditionedVariables
Factor.variableDomainsDict
"""
# autograder tracking -- don't remove
if not (callTrackingList is None):
callTrackingList.append(('eliminate', eliminationVariable))
# typecheck portion
if eliminationVariable not in factor.unconditionedVariables():
print "Factor failed eliminate typecheck: ", factor
raise ValueError, ("Elimination variable is not an unconditioned variable " \
+ "in this factor\n" +
"eliminationVariable: " + str(eliminationVariable) + \
"\nunconditionedVariables:" + str(factor.unconditionedVariables()))
if len(factor.unconditionedVariables()) == 1:
print "Factor failed eliminate typecheck: ", factor
raise ValueError, ("Factor has only one unconditioned variable, so you " \
+ "can't eliminate \nthat variable.\n" + \
"eliminationVariable:" + str(eliminationVariable) + "\n" +\
"unconditionedVariables: " + str(factor.unconditionedVariables()))
"*** YOUR CODE HERE ***"
unconditioned = factor.unconditionedVariables()
unconditioned.remove(eliminationVariable)
result = Factor(unconditioned, factor.conditionedVariables(),
factor.variableDomainsDict())
for assignment in result.getAllPossibleAssignmentDicts():
s = 0
for factorAssignment in factor.getAllPossibleAssignmentDicts():
f = True
for variable in unconditioned:
if factorAssignment[variable] != assignment[variable]:
f = False
for variable in result.conditionedVariables():
if factorAssignment[variable] != assignment[variable]:
f = False
if f:
s += factor.getProbability(factorAssignment)
result.setProbability(assignment, s)
return result
