def get_extreme_points(self, number_of_generations):
"""This method should be used to find the extreme points of particular generation across all the algorithms"""
from Techniques.flatten_list import flatten
points = []
points.extend(
flatten([
d.get_frontiers_collection(number_of_generations)
for d in self.data
]))
print len(points)
objectives = [point.objectives for point in points]
maps_objectives = [[-1 for _ in objectives] for _ in objectives]
from Techniques.euclidean_distance import euclidean_distance
for i, ii in enumerate(objectives):
for j, jj in enumerate(objectives):
if maps_objectives[i][j] == -1:
maps_objectives[i][j] = euclidean_distance(ii, jj)
maps_objectives[j][i] = euclidean_distance(ii, jj)
elif i == j:
maps_objectives[i][j] = 0
print maps_objectives
max_distance = max(
[max(maps_objective) for maps_objective in maps_objectives])
indexes = [[(i, j) for j, distance in enumerate(distances)
if distance == max_distance]
for i, distances in enumerate(maps_objectives)]
index = [index for index in indexes
if len(index) > 0][-1][-1] # Hack: To handle list of lists
# indexes should always be a multiple of 2. And if there more than 2 entries in indexes just use any one.
return objectives[index[0]], objectives[index[1]]
def get_extreme_points(self, number_of_generations):
"""This method should be used to find the extreme points of particular generation across all the algorithms"""
from Techniques.flatten_list import flatten
points = []
points.extend(flatten([d.get_frontiers_collection(number_of_generations) for d in self.data]))
print len(points)
objectives = [point.objectives for point in points]
maps_objectives = [[-1 for _ in objectives] for _ in objectives]
from Techniques.euclidean_distance import euclidean_distance
for i, ii in enumerate(objectives):
for j, jj in enumerate(objectives):
if maps_objectives[i][j] == -1:
maps_objectives[i][j] = euclidean_distance(ii, jj)
maps_objectives[j][i] = euclidean_distance(ii, jj)
elif i == j:
maps_objectives[i][j] = 0
print maps_objectives
max_distance = max([max(maps_objective) for maps_objective in maps_objectives])
indexes = [[(i, j) for j, distance in enumerate(distances) if distance == max_distance] for i, distances in
enumerate(maps_objectives)]
index = [index for index in indexes if len(index) > 0][-1][-1] # Hack: To handle list of lists
# indexes should always be a multiple of 2. And if there more than 2 entries in indexes just use any one.
return objectives[index[0]], objectives[index[1]]
def get_reference_point(self, number_of_generations):
"""This method should be used to find the reference point or the nadir point"""
reference_point = [-1 for _ in self.problem.objectives]
from Techniques.flatten_list import flatten
points = flatten(
[d.get_frontiers_collection(number) for number in xrange(number_of_generations) for d in self.data])
objectives = [point.objectives for point in points]
for count, objective in enumerate(self.problem.objectives):
one_objective = [point[count] for point in objectives]
if objective.lismore is True:
reference_point[count] = max(one_objective)
else:
reference_point[count] = min(one_objective)
return reference_point
