def GetBestHerustic(cnf, assignment):
BestAssign = GST.DeepCopy(assignment)
BestC = GST.testAssignments(cnf,assignment)
for i in range(len(assignment)):
#node change
assignment[i] *= -1
tempC = GST.testAssignments(cnf,assignment)
if(tempC > BestC):
BestC = tempC
BestAssign = GST.DeepCopy(assignment)
assignment[i] *= -1 #reverse node change and continue loop with next node
return BestAssign, BestC
def GetBestAssignment(cnf, assignment):
BestAssign = GST.DeepCopy(assignment)
BestC = GST.testAssignments(cnf, assignment)
for i in range(len(assignment)):
# change node
assignment[i] *= -1
tempC = GST.testAssignments(cnf, assignment)
if tempC > BestC:
BestC = tempC
BestAssign = GST.DeepCopy(assignment)
# reverse node change, continue loop through with next node
assignment[i] *= -1
return BestAssign, BestC
def HillClimbing(cnf, assignment):
CurrentEval = GST.testAssignments(cnf, assignment)
NextAssignment, NextEval = GetBestHerustic(cnf, assignment)
if(NextEval<=CurrentEval):
return CurrentEval
else:
return HillClimbing(cnf, NextAssignment)
def HillClimbing(cnf, assignment):
CurrentEval = GST.testAssignments(cnf, assignment)
NextAssignment, NextEval = GetBestAssignment(cnf, assignment)
if NextEval <= CurrentEval:
# print(CurrentEval)
return CurrentEval
else:
# print("dig")
return HillClimbing(cnf, NextAssignment)
def Solve(cnf, s):
AveC=[]
HighC = -1
for i in range(s):
Best = -1
MyAssignment = GST.GenerateRandomAssignment(len(cnf[0]))
newC = HillClimbing(cnf, MyAssignment)
if newC > Best:
Best = newC
if Best > HighC:
HighC = Best
if(Best == len(cnf)):
print("It is Satisfiable")
AveC.append(Best)
return sum(AveC)/len(AveC), HighC
else:
print("It is Unsatisfiable")
AveC.append(Best)
return sum(AveC)/len(AveC), HighC
def Check(cnf, model, Tracker):
CurrentUnit = createAssignments(len(cnf[0]), model)
newC = GST.testAssignments(cnf, CurrentUnit)
Tracker.compare(newC)
return newC
def testAssignments(cnf, myAssign):
fitness = []
for assignment in myAssign:
fitness.append(GST.testAssignments(cnf,assignment))
return fitness
