def gale1(iter=1000, pop=1600, model=IPMDFC()):
n_proc = int(1000.00 / iter)
new = gale0(model, new=[], pop=int(pop / n_proc))
while iter:
iter -= 1
new = gale0(model, new, pop=int(pop / n_proc))
return new
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))
def gale0(model=IPMDFC(), new=[], pop=int(1e4)):
"""
Recursive FASTMAP clustering.
"""
if len(new) == 0:
frontier = model.generate(pop)
else:
frontier = new
frontier.extend(model.generate(pop - len(new)))
N = np.shape(frontier)[0]
leaf = []
norm = np.max(frontier, axis=0) - np.min(frontier, axis=0)
def cdom(x, y, better=['less', 'less', 'less']):
def loss1(i, x, y):
return (x - y) if better[i] == 'less' else (y - x)
def expLoss(i, x, y, n):
return np.exp(loss1(i, x, y) / n)
def loss(x, y):
n = min(len(x), len(y)) #lengths should be equal
losses = [
expLoss(i, xi, yi, n) for i, (xi, yi) in enumerate(zip(x, y))
]
return sum(losses) / n
"x dominates y if it losses least"
return loss(x, y) < loss(y, x)
def distant(lst):
R, C = np.shape(lst)
farthest = lambda one, rest: sorted(rest, key=lambda F: aDist(F, one))[
-1]
one = lst[randi(0, R - 1)]
mid = farthest(one, lst)
two = farthest(mid, lst)
return one, two
def mutate(lst, good, g=0.15):
new = []
for l in lst:
new.append([a + (b - a) * g for a, b in zip(l, good)])
return new
def aDist(one, two):
return np.sqrt(np.sum(
(np.array(one) / norm - np.array(two) / norm)**2))
def recurse(dataset):
R, C = np.shape(dataset) # No. of Rows and Col
# Find the two most distance points.
one, two = distant(dataset)
# Project each case on
def proj(test):
a = aDist(one, test)
b = aDist(two, test)
c = aDist(one, two)
return (a**2 - b**2 + c**2) / (2 * c)
if R < np.sqrt(N):
leaf.extend(dataset)
else:
half1 = cdom(model.solve(one), model.solve(two))
if half1:
_ = recurse(
sorted(dataset, key=lambda F: proj(F))[:int(R / 2)])
else:
_ = recurse(
sorted(dataset, key=lambda F: proj(F))[int(R / 2):])
recurse(frontier)
a, b = distant(leaf)
(good, bad) = (a, b) if cdom(model.solve(a), model.solve(b)) else (b, a)
new = mutate(leaf, good, g=0.5)
return new
# 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))
