def _mutateSelectively(xVecParam, minMaxTuple, indexP, stepParam=20):
valToret = 0
xCurrent = [x for x in xVecParam]
xBest = xVecParam
listOfMax = [10, 10, 5, 6, 5, 10]
listOfMin = [0, 0, 1, 0, 1, 0]
# distance=(listOfMax[indexP]-listOfMin[indexP])/stepParam
#
# range1 = int((xCurrent[indexP]-listOfMin[indexP])/distance)
# range2 = int((listOfMax[indexP]-xCurrent[indexP])/distance)
# #print "r-1 {}, r-2 {}, dist = {}".format(range1, range2, distance)
# for cnt in range(-range1, range2+1):
# xCurrent[indexP]=xVecParam[indexP]+cnt*distance
# if not checker.checkConstraints(xCurrent):
# continue
# else:
# if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(xCurrent, minMaxTuple):
# xBest=xCurrent
maxRangeOI = float(listOfMax[indexP])
minRangeOI = float(listOfMin[indexP])
stepOfI = float((maxRangeOI - minRangeOI) / stepParam)
cnt = minRangeOI
while cnt <= maxRangeOI:
xCurrent[indexP] = cnt
if checker.checkConstraints(xCurrent):
if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(
xCurrent, minMaxTuple):
xBest = [xtemp for xtemp in xCurrent]
cnt = cnt + stepOfI
valToret = [_ for _ in xBest]
return valToret
def _mutateSelectivelyWithModifiedRange(xVecParam, minMaxTuple,indexP, stepParam=1):
valToret = 0
xCurrent=[x for x in xVecParam]
xBest=xVecParam
listOfMax=[10, 10, 5, 6, 5, 10]
listOfMin=[ 0, 0, 1, 0, 1, 0]
fractionedMax = [float(x)*float(stepParam*10)/float(100) for x in listOfMax]
fractionedMin = [float(x)*float((stepParam*10)/100) for x in listOfMin]
#print "{}--{}".format(fractionedMax, fractionedMin)
maxRangeOI = float(fractionedMax[indexP])
minRangeOI = float(fractionedMin[indexP])
stepOfI = maxRangeOI - minRangeOI
#print "stepOfI... ", stepOfI
cnt = minRangeOI
run = 0
while cnt <= maxRangeOI:
xCurrent[indexP] = cnt
if checker.checkConstraints(xCurrent):
if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(xCurrent, minMaxTuple):
xBest = [xtemp for xtemp in xCurrent]
cnt = cnt + stepOfI
run = run + 1
valToret = [ _ for _ in xBest]
#print "run ..." , run
run = 0
return valToret
def _mutateSelectively(xVecParam, minMaxTuple,indexP, stepParam=20):
valToret = 0
xCurrent=[x for x in xVecParam]
xBest=xVecParam
listOfMax=[10, 10, 5, 6, 5, 10]
listOfMin=[ 0, 0, 1, 0, 1, 0]
# distance=(listOfMax[indexP]-listOfMin[indexP])/stepParam
#
# range1 = int((xCurrent[indexP]-listOfMin[indexP])/distance)
# range2 = int((listOfMax[indexP]-xCurrent[indexP])/distance)
# #print "r-1 {}, r-2 {}, dist = {}".format(range1, range2, distance)
# for cnt in range(-range1, range2+1):
# xCurrent[indexP]=xVecParam[indexP]+cnt*distance
# if not checker.checkConstraints(xCurrent):
# continue
# else:
# if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(xCurrent, minMaxTuple):
# xBest=xCurrent
maxRangeOI = float(listOfMax[indexP])
minRangeOI = float(listOfMin[indexP])
stepOfI = float((maxRangeOI - minRangeOI)/stepParam)
cnt = minRangeOI
while cnt <= maxRangeOI:
xCurrent[indexP] = cnt
if checker.checkConstraints(xCurrent):
if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(xCurrent, minMaxTuple):
xBest = [xtemp for xtemp in xCurrent]
cnt = cnt + stepOfI
valToret = [ _ for _ in xBest]
return valToret
def _mutateSelectivelyWithModifiedRange(xVecParam,
minMaxTuple,
indexP,
stepParam=1):
valToret = 0
xCurrent = [x for x in xVecParam]
xBest = xVecParam
listOfMax = [10, 10, 5, 6, 5, 10]
listOfMin = [0, 0, 1, 0, 1, 0]
fractionedMax = [
float(x) * float(stepParam * 10) / float(100) for x in listOfMax
]
fractionedMin = [
float(x) * float((stepParam * 10) / 100) for x in listOfMin
]
#print "{}--{}".format(fractionedMax, fractionedMin)
maxRangeOI = float(fractionedMax[indexP])
minRangeOI = float(fractionedMin[indexP])
stepOfI = maxRangeOI - minRangeOI
#print "stepOfI... ", stepOfI
cnt = minRangeOI
run = 0
while cnt <= maxRangeOI:
xCurrent[indexP] = cnt
if checker.checkConstraints(xCurrent):
if calcNormEnergy(xBest, minMaxTuple) <= calcNormEnergy(
xCurrent, minMaxTuple):
xBest = [xtemp for xtemp in xCurrent]
cnt = cnt + stepOfI
run = run + 1
valToret = [_ for _ in xBest]
#print "run ..." , run
run = 0
return valToret
def doMaxWalkSat(maxTries=10000, maxChanges=50, threshold=0.95, p=0.5):
''' This is the main place holder fo the MaxSatWalk() Algo '''
solutionVecAndEnergyToRet = []
minMaxTuple = getBaselineMinMaxForO2()
####
#print purpose
output = ""
printCounter = 0
cnt_Exc = 0
cnt_Que = 0
cnt_Dot = 0
cnt_Pls = 0
xCurrent = []
xBest = []
for cntTry in range(maxTries):
#solution = start
vec = getValidVector()
#energy = calcNormEnergy(vec, minMaxTuple)
if cntTry == 0:
xBest = [_ for _ in vec]
#bestEnergy = calcNormEnergy(vec, minMaxTuple)
for cntChange in range(maxChanges):
if calcNormEnergy(vec, minMaxTuple) >= threshold:
_printEndMsg(cntTry, cntChange)
solutionVecAndEnergyToRet = [
vec, calcNormEnergy(vec, minMaxTuple)
]
return solutionVecAndEnergyToRet
indexToChange = random.randint(0, 5)
oldSol = [_ for _ in vec]
#oldEnergy = calcNormEnergy(vec, minMaxTuple)
if p < random.random():
## when probailility is > 0.5 then we randomly select a variable and mutate it
#print "!"
#print "Now we will mutate randomly "
mutatedVec = _mutateRandomly(vec, indexToChange)
#energy = calcNormEnergy(mutatedVec, minMaxTuple)
#solutionVecToRet = mutatedVec
#print "Energy !!! ", solution
else:
## when probailility is <= 0.5 then we mutate all the variables in the vector
## one by one ,see which gives the maximum energy, and keep it as solution
#mutatedVec = _mutateSelectively(vec, minMaxTuple, indexToChange)
mutatedVec = _mutateSelectivelyWithModifiedRange(
vec, minMaxTuple, indexToChange)
xCurrent = [_ for _ in mutatedVec]
if calcNormEnergy(xBest, minMaxTuple) < calcNormEnergy(
xCurrent, minMaxTuple):
## the jump was better than the best soultion in hand
xBest = [_ for _ in xCurrent]
output += "?"
cnt_Que = cnt_Que + 1
elif calcNormEnergy(xCurrent, minMaxTuple) < calcNormEnergy(
oldSol, minMaxTuple):
xBest = [_ for _ in oldSol]
output += "+"
cnt_Pls = cnt_Pls + 1
#elif calcNormEnergy(xCurrent, minMaxTuple) == calcNormEnergy(oldSol, minMaxTuple):
# print "." ,
# cnt_Dot = cnt_Dot + 1
elif calcNormEnergy(xBest, minMaxTuple) > calcNormEnergy(
xCurrent, minMaxTuple):
output += "!"
cnt_Exc = cnt_Exc + 1
else:
output += "."
cnt_Dot = cnt_Dot + 1
solutionVecAndEnergyToRet = [
xBest, calcNormEnergy(xBest, minMaxTuple)
]
printCounter = printCounter + 1
if printCounter % 40 == 0:
say("count={} ?={} !={} +={} .={} |".format(
printCounter, cnt_Que, cnt_Exc, cnt_Pls, cnt_Dot))
say(output + '\n')
output = ""
cnt_Que = 0
cnt_Exc = 0
cnt_Pls = 0
cnt_Dot = 0
_printEndMsg(cntTry, cntChange)
return solutionVecAndEnergyToRet
def doMaxWalkSat(maxTries=10000, maxChanges=50, threshold=0.95, p=0.5):
''' This is the main place holder fo the MaxSatWalk() Algo '''
solutionVecAndEnergyToRet=[]
minMaxTuple = getBaselineMinMaxForO2()
####
#print purpose
output = ""
printCounter = 0
cnt_Exc = 0
cnt_Que = 0
cnt_Dot = 0
cnt_Pls = 0
xCurrent = []
xBest = []
for cntTry in range(maxTries):
#solution = start
vec = getValidVector()
#energy = calcNormEnergy(vec, minMaxTuple)
if cntTry==0:
xBest= [_ for _ in vec]
#bestEnergy = calcNormEnergy(vec, minMaxTuple)
for cntChange in range(maxChanges):
if calcNormEnergy(vec, minMaxTuple) >= threshold:
_printEndMsg( cntTry, cntChange )
solutionVecAndEnergyToRet = [vec, calcNormEnergy(vec, minMaxTuple)]
return solutionVecAndEnergyToRet
indexToChange = random.randint(0,5)
oldSol = [ _ for _ in vec ]
#oldEnergy = calcNormEnergy(vec, minMaxTuple)
if p < random.random():
## when probailility is > 0.5 then we randomly select a variable and mutate it
#print "!"
#print "Now we will mutate randomly "
mutatedVec = _mutateRandomly(vec, indexToChange )
#energy = calcNormEnergy(mutatedVec, minMaxTuple)
#solutionVecToRet = mutatedVec
#print "Energy !!! ", solution
else:
## when probailility is <= 0.5 then we mutate all the variables in the vector
## one by one ,see which gives the maximum energy, and keep it as solution
#mutatedVec = _mutateSelectively(vec, minMaxTuple, indexToChange)
mutatedVec = _mutateSelectivelyWithModifiedRange(vec, minMaxTuple, indexToChange)
xCurrent = [ _ for _ in mutatedVec]
if calcNormEnergy(xBest, minMaxTuple) < calcNormEnergy(xCurrent, minMaxTuple):
## the jump was better than the best soultion in hand
xBest= [ _ for _ in xCurrent ]
output += "?"
cnt_Que = cnt_Que + 1
elif calcNormEnergy(xCurrent, minMaxTuple) < calcNormEnergy(oldSol, minMaxTuple):
xBest= [ _ for _ in oldSol ]
output += "+"
cnt_Pls = cnt_Pls + 1
#elif calcNormEnergy(xCurrent, minMaxTuple) == calcNormEnergy(oldSol, minMaxTuple):
# print "." ,
# cnt_Dot = cnt_Dot + 1
elif calcNormEnergy(xBest, minMaxTuple) > calcNormEnergy(xCurrent, minMaxTuple):
output += "!"
cnt_Exc = cnt_Exc + 1
else:
output += "."
cnt_Dot = cnt_Dot + 1
solutionVecAndEnergyToRet = [xBest, calcNormEnergy(xBest, minMaxTuple)]
printCounter = printCounter + 1
if printCounter % 40 == 0:
say("count={} ?={} !={} +={} .={} |".format(printCounter, cnt_Que, cnt_Exc, cnt_Pls, cnt_Dot))
say(output + '\n')
output = ""
cnt_Que = 0
cnt_Exc = 0
cnt_Pls = 0
cnt_Dot = 0
_printEndMsg(cntTry, cntChange )
return solutionVecAndEnergyToRet
