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 ***"
conditioned = factor.conditionedVariables()
unconditioned = factor.unconditionedVariables()
unconditioned.remove(eliminationVariable)
factor_ = Factor(unconditioned, conditioned, factor.variableDomainsDict())
for assignment in factor_.getAllPossibleAssignmentDicts():
prob = 0
for i in (factor_.variableDomainsDict())[eliminationVariable]:
tmp = assignment.copy()
tmp[eliminationVariable] = i
prob += factor.getProbability(tmp)
factor_.setProbability(assignment, prob)
return factor_
def eliminate(factor, eliminationVariable):
"""
Question 2: 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 ***"
new_unconditioned = factor.unconditionedVariables()
new_unconditioned.remove(eliminationVariable)
new_Factor = Factor(new_unconditioned,factor.conditionedVariables(), factor.variableDomainsDict())
for j in new_Factor.getAllPossibleAssignmentDicts():
probabilitySum = 0
for i in new_Factor.variableDomainsDict().get(eliminationVariable):
j[eliminationVariable] = i
currentProb = factor.getProbability(j)
probabilitySum += currentProb
del j[eliminationVariable]
new_Factor.setProbability(j, probabilitySum)
return new_Factor
def eliminate(factor, eliminationVariable):
"""
Question 2: 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()))
new_unconditioned = factor.unconditionedVariables()
new_unconditioned.remove(eliminationVariable)
new_Factor = Factor(new_unconditioned, factor.conditionedVariables(),
factor.variableDomainsDict())
for j in new_Factor.getAllPossibleAssignmentDicts():
probabilitySum = 0
for i in new_Factor.variableDomainsDict().get(eliminationVariable):
j[eliminationVariable] = i
currentProb = factor.getProbability(j)
probabilitySum += currentProb
del j[eliminationVariable]
new_Factor.setProbability(j, probabilitySum)
return new_Factor
def joinFactors(factors):
"""
Question 3: Your join implementation
Input factors is a list of factors.
You should calculate the set of unconditioned variables and conditioned
variables for the join of those factors.
Return a new factor that has those variables and whose probability entries
are product of the corresponding rows of the input factors.
You may assume that the variableDomainsDict for all the input
factors are the same, since they come from the same BayesNet.
joinFactors will only allow unconditionedVariables to appear in
one input factor (so their join is well defined).
Hint: Factor methods that take an assignmentDict as input
(such as getProbability and setProbability) can handle
assignmentDicts that assign more variables than are in that factor.
Useful functions:
Factor.getAllPossibleAssignmentDicts
Factor.getProbability
Factor.setProbability
Factor.unconditionedVariables
Factor.conditionedVariables
Factor.variableDomainsDict
"""
# typecheck portion
setsOfUnconditioned = [
set(factor.unconditionedVariables()) for factor in factors
]
if len(factors) > 1:
intersect = reduce(lambda x, y: x & y, setsOfUnconditioned)
if len(intersect) > 0:
print "Factor failed joinFactors typecheck: ", factor
raise ValueError, (
"unconditionedVariables can only appear in one factor. \n" +
"unconditionedVariables: " + str(intersect) +
"\nappear in more than one input factor.\n" +
"Input factors: \n" + "\n".join(map(str, factors)))
"*** YOUR CODE HERE ***"
conditionedList = list()
unconditionedList = list()
for factor in factors:
tempUnconditioned = factor.unconditionedVariables()
for var in tempUnconditioned:
if var not in unconditionedList:
unconditionedList.append(var)
for variable in factor.conditionedVariables():
if (variable
not in unconditionedList) and (variable
not in conditionedList):
conditionedList.append(variable)
conditionedList = [
var for var in conditionedList if var not in unconditionedList
]
_newFactor = Factor(unconditionedList, conditionedList,
Factor.variableDomainsDict(factors[0]))
for assignment in _newFactor.getAllPossibleAssignmentDicts():
for factor in factors:
product = 1
for x in factors:
product *= x.getProbability(assignment)
_newFactor.setProbability(assignment, product)
return _newFactor
def joinFactors(factors):
"""
Question 3: Your join implementation
Input factors is a list of factors.
You should calculate the set of unconditioned variables and conditioned
variables for the join of those factors.
Return a new factor that has those variables and whose probability entries
are product of the corresponding rows of the input factors.
You may assume that the variableDomainsDict for all the input
factors are the same, since they come from the same BayesNet.
joinFactors will only allow unconditionedVariables to appear in
one input factor (so their join is well defined).
Hint: Factor methods that take an assignmentDict as input
(such as getProbability and setProbability) can handle
assignmentDicts that assign more variables than are in that factor.
Useful functions:
Factor.getAllPossibleAssignmentDicts
Factor.getProbability
Factor.setProbability
Factor.unconditionedVariables
Factor.conditionedVariables
Factor.variableDomainsDict
"""
# typecheck portion
setsOfUnconditioned = [
set(factor.unconditionedVariables()) for factor in factors
]
if len(factors) > 1:
intersect = reduce(lambda x, y: x & y, setsOfUnconditioned)
if len(intersect) > 0:
print "Factor failed joinFactors typecheck: ", factor
raise ValueError, (
"unconditionedVariables can only appear in one factor. \n" +
"unconditionedVariables: " + str(intersect) +
"\nappear in more than one input factor.\n" +
"Input factors: \n" + "\n".join(map(str, factors)))
"*** YOUR CODE HERE ***"
newfactor = factors[0]
while len(factors) >= 2:
factors.pop(0)
joinfactor = factors[0]
oldfactor = newfactor
olduncon = oldfactor.unconditionedVariables()
oldcon = oldfactor.conditionedVariables()
joinuncon = joinfactor.unconditionedVariables()
joincon = joinfactor.conditionedVariables()
newuncon = olduncon.union(joinuncon)
newcon = oldcon.union(joincon)
for con in newuncon:
if con in newcon:
newcon.remove(con)
newfactor = Factor(newuncon, newcon, newfactor.variableDomainsDict())
for assignment in newfactor.getAllPossibleAssignmentDicts():
newfactor.setProbability(
assignment,
oldfactor.getProbability(assignment) *
joinfactor.getProbability(assignment))
return newfactor
def joinFactors(factors):
"""
Question 3: Your join implementation
Input factors is a list of factors.
You should calculate the set of unconditioned variables and conditioned
variables for the join of those factors.
Return a new factor that has those variables and whose probability entries
are product of the corresponding rows of the input factors.
You may assume that the variableDomainsDict for all the input
factors are the same, since they come from the same BayesNet.
joinFactors will only allow unconditionedVariables to appear in
one input factor (so their join is well defined).
Hint: Factor methods that take an assignmentDict as input
(such as getProbability and setProbability) can handle
assignmentDicts that assign more variables than are in that factor.
Useful functions:
Factor.getAllPossibleAssignmentDicts
Factor.getProbability
Factor.setProbability
Factor.unconditionedVariables
Factor.conditionedVariables
Factor.variableDomainsDict
"""
# typecheck portion
setsOfUnconditioned = [
set(factor.unconditionedVariables()) for factor in factors
]
if len(factors) > 1:
intersect = reduce(lambda x, y: x & y, setsOfUnconditioned)
if len(intersect) > 0:
print "Factor failed joinFactors typecheck: ", factor
raise ValueError, (
"unconditionedVariables can only appear in one factor. \n" +
"unconditionedVariables: " + str(intersect) +
"\nappear in more than one input factor.\n" +
"Input factors: \n" + "\n".join(map(str, factors)))
#do not remove above
un_var = set([])
con_var = set([])
dom_list = {}
# go through the list of factors and store all of the unconditioned variables,
# conditioned variables, along with the variable domains
for fac in factors:
un_var.update(fac.unconditionedVariables())
con_var.update(fac.conditionedVariables())
dom_list.update(fac.variableDomainsDict())
# remove from the conditioned variables all of the unconditioned variables
# because we are able to turn these into into unconditioned varaibles
con_var = con_var.difference(un_var)
#build the new factor
newFac = Factor(un_var, con_var, dom_list)
pairs = [[]]
for doms in newFac.variableDomainsDict().iterkeys():
t = []
for tmp in newFac.variableDomainsDict()[doms]:
for i in pairs:
t.append(i + [tmp])
pairs = t
#add the probabilities to the factor
for dic in newFac.getAllPossibleAssignmentDicts():
prob = 1
for fac in factors:
prob = prob * fac.getProbability(dic)
newFac.setProbability(dic, prob)
return newFac
