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))