def runGALE(iterationsP, minBP, maxBP):
from galeForModel import GALE
from galeForModel import distFromHellScore
from modelForGALE import IPMDFC
import utility
modelObj=IPMDFC()
galeOutput = GALE(modelObj, iterationsP)
#print("Final gale out put ", galeOutput)
for item in galeOutput:
print("item length is " , len(item))
print("item is ", item)
objectivevals = modelObj.solve(item)
print("objective values are ", objectivevals)
fromHellScore = distFromHellScore(objectivevals)
print("Distance from hell score is: ", fromHellScore)
finalScore = utility.getNormalizedScoreForGale(minBP, maxBP, fromHellScore)
print("Final score (after normalization ...)", finalScore)
def runGALE(iterationsP, minBP, maxBP):
from galeForModel import GALE
from galeForModel import distFromHellScore
from modelForGALE import IPMDFC
import utility
modelObj = IPMDFC()
galeOutput = GALE(modelObj, iterationsP)
#print("Final gale out put ", galeOutput)
for item in galeOutput:
print("item length is ", len(item))
print("item is ", item)
objectivevals = modelObj.solve(item)
print("objective values are ", objectivevals)
fromHellScore = distFromHellScore(objectivevals)
print("Distance from hell score is: ", fromHellScore)
finalScore = utility.getNormalizedScoreForGale(minBP, maxBP,
fromHellScore)
print("Final score (after normalization ...)", finalScore)
for cc in collect: final.extend(cc)
ret = gale0( modelObjP ,new=final,pop=len(final))
#print('Time Taken: ', time()-t)
# true = DTLZ2(n_dec=30, n_obj=3).get_pareto()
#m = measure(model=DTLZ2(n_dec=30, n_obj=3))
#conv = m.convergence(ret)
#print("Convergence:",conv)
# set_trace()
return ret
def distFromHellScore(objListParam):
import math
square = lambda val: math.pow(val,2)
sq_root = lambda val: math.sqrt(val)
dist_from_hell =0
for f in objListParam:
dist_from_hell += square(f)
dist_from_hell = sq_root(dist_from_hell)
return dist_from_hell
if __name__=="__main__":
iterations = 100
modelObj=IPMDFC()
galeOutput = GALE(modelObj, iterations)
#print("Final gale out put ", galeOutput)
for item in galeOutput:
print("item length is " , len(item))
print("item is ", item)
objectivevals = modelObj.solve(item)
print("objective values are ", objectivevals)
print("Distance from hell score is: ", distFromHellScore(objectivevals))
# true = DTLZ2(n_dec=30, n_obj=3).get_pareto()
#m = measure(model=DTLZ2(n_dec=30, n_obj=3))
#conv = m.convergence(ret)
#print("Convergence:",conv)
# set_trace()
return ret
def distFromHellScore(objListParam):
import math
square = lambda val: math.pow(val, 2)
sq_root = lambda val: math.sqrt(val)
dist_from_hell = 0
for f in objListParam:
dist_from_hell += square(f)
dist_from_hell = sq_root(dist_from_hell)
return dist_from_hell
if __name__ == "__main__":
iterations = 100
modelObj = IPMDFC()
galeOutput = GALE(modelObj, iterations)
#print("Final gale out put ", galeOutput)
for item in galeOutput:
print("item length is ", len(item))
print("item is ", item)
objectivevals = modelObj.solve(item)
print("objective values are ", objectivevals)
print("Distance from hell score is: ",
distFromHellScore(objectivevals))
