def backtrack(csp):
"""Performs the backtracking search for the given csp.
If there is a solution, this method returns True; otherwise, it returns False.
"""
''' Initial completion check '''
complete = is_complete(csp)
if (complete):
return complete
#print len(csp.variables) + " length"
currentVariable = select_unassigned_variable(csp)
if not (currentVariable is None):
#print str(currentVariable.name)
for dv in order_domain_values(csp, currentVariable):
domainValue = dv
#print "domainValue: " + str(domainValue)
if (is_consistent(csp, currentVariable, domainValue)):
#print "assign"
csp.variables.begin_transaction()
''' assign variable '''
currentVariable.assign(domainValue)
result = backtrack(csp)
if not (result == False):
return True
csp.variables.rollback()
return False
def backtrack(csp):
"""Performs the backtracking search for the given csp.
If there is a solution, this method returns True; otherwise, it returns False.
"""
if is_complete(csp):
return True
# get the next unassigned variable
var = select_unassigned_variable(csp)
result = False
orderedDomain = order_domain_values(csp, var)
# for each value in var
for value in orderedDomain:
if is_consistent(csp, var, value):
csp.variables.begin_transaction()
var.assign(value)
if inference(csp, var):
result = backtrack(csp)
if result:
return result
csp.variables.rollback()
return None
def backtrack(csp):
"""Performs the backtracking search for the given csp.
If there is a solution, this method returns True; otherwise, it returns False.
"""
# TODO copy from p3
solution = is_complete(csp)
if solution:
return solution
nextVar = select_unassigned_variable(csp)
# solution = False here
for i in order_domain_values(csp, nextVar):
if is_consistent(csp, nextVar, i):
csp.variables.begin_transaction()
nextVar.assign(i)
if backtrack(csp):
return True
csp.variables.rollback()
return solution
def backtrack(csp):
"""Performs the backtracking search for the given csp.
If there is a solution, this method returns True; otherwise, it returns False.
"""
# TODO implement this
solution = is_complete(csp)
if solution:
return solution
nextVar = select_unassigned_variable(csp)
# solution = False here
for i in order_domain_values(csp, nextVar):
if is_consistent(csp, nextVar, i):
csp.variables.begin_transaction()
nextVar.assign(i)
if backtrack(csp):
return True
csp.variables.rollback()
return solution
def backtrack(csp):
"""Performs the backtracking search for the given csp.
If there is a solution, this method returns True; otherwise, it returns False.
"""
#print "Finding Match"
# TODO implement this
if is_complete(csp):
return True
else:
variable = select_unassigned_variable(csp)
#print variable
for value in order_domain_values(csp, variable):
#print value
if is_consistent(csp, variable, value):
#print "Found Valid"
csp.variables.begin_transaction()
variable.assign(value)
if backtrack(csp):
return True
csp.variables.rollback()
return False
def backtrack(csp): """Performs the backtracking search for the given csp. If there is a solution, this method returns True; otherwise, it returns False. """ # TODO implement this #pass ''' Initial completion check ''' complete = is_complete(csp) if (complete): return complete #print len(csp.variables) + " length" currentVariable = select_unassigned_variable(csp) if not(currentVariable is None): #print str(currentVariable.name) for dv in order_domain_values(csp, currentVariable): domainValue = dv #print "domainValue: " + str(domainValue) if (is_consistent(csp, currentVariable, domainValue)): #print "assign" csp.variables.begin_transaction() ''' assign variable ''' currentVariable.assign(domainValue) result = backtrack(csp) if not(result == False): return True csp.variables.rollback() return False
def select_unassigned_variable(csp):
"""Selects the next unassigned variable, or None if there is no more unassigned variables
(i.e. the assignment is complete).
This method implements the minimum-remaining-values (MRV) and degree heuristic. That is,
the variable with the smallest number of values left in its available domain. If MRV ties,
then it picks the variable that is involved in the largest number of constraints on other
unassigned variables.
"""
# TODO implement this
if is_complete(csp):
return True
unassignedList = []
unassignedFlag = False
min_val = sys.maxint
for variable in csp.variables:
if not variable.is_assigned():
if len(variable.domain) == min_val:
unassignedList.append(variable)
unassignedFlag = True
elif len(variable.domain) < min_val:
unassignedList = [variable]
min_val = len(variable.domain)
unassignedFlag = True
if unassignedFlag:
if (len(unassignedList)) == 1:
return unassignedList[0]
else:
max_constraint = -sys.maxint
var = None
for variable in unassignedList:
count = 0
for constraint in csp.constraints[variable]:
if not constraint.var2.is_assigned(
): #count only the unassigned variables
count += 1
if count > max_constraint:
var = variable
max_constraint = count
return var
#
# sortList = sorted(unassignedList, key=itemgetter(1))
# front = sortList[0]
# tieList = []
# tieList.append(front)
# if(len(sortList) > 1):
# for var in range(1, len(sortList)):
# if front[1] == sortList[var][1]:
# tieList.append(sortList[var])
# else:
# break
#
# #No repeat take the head
# if len(tieList) == 1:
# return front[0]
# else:
# maxVar = max(tieList, key=lambda i:len(csp.constraints[i[0]]))
# #print "maxVar: " + str(maxVar[0])
# return maxVar[0]
# #return front[0]
else: # When all the variables are assigned
return None
pass