def test_case3(self):
"""
Case 3. Reference front: [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]], front: [[1.0, 1.0, 1.0]]
Expected result: the distance to the nearest point of the reference front is 0.0. Example with three objectives
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]))
front = np.array([[1.0, 1.0, 1.0]])
result = indicator.compute(front)
self.assertEqual(0.0, result)
def test_should_gd_return_0(self):
solution1 = Solution(1, 3)
solution1.objectives = [1, 0, 1]
solution2 = Solution(1, 3)
solution2.objectives = [0, 1, 0]
reference_front = [solution1, solution2]
gd = GenerationalDistance(reference_front)
value = gd.compute(reference_front)
self.assertEqual(0.0, value)
def test_case1(self):
"""
Case 1. Reference front: [[1.0, 1.0]], front: [[1.0, 1.0]]
Expected result: the distance to the nearest point of the reference front is 0.0
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0]]))
front = np.array([[1.0, 1.0]])
result = indicator.compute(front)
self.assertEqual(0.0, result)
def test_should_gd_return_the_closest_point_case_a(self):
solution1 = Solution(1, 3)
solution1.objectives = [1, 1, 1]
solution2 = Solution(1, 3)
solution2.objectives = [2, 2, 2]
reference_front = [solution1, solution2]
gd = GenerationalDistance(reference_front)
value = gd.compute([solution1])
self.assertEqual(0, value)
def test_case5(self):
"""
Case 5. reference front: [[1.0, 1.0], [2.1, 2.1]], front: [[1.5, 1.5]]
Expected result: the distance to the nearest point of the reference front is the euclidean distance
to the nearest point of the reference front ([1.0, 1.0])
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0], [2.1, 2.1]]))
front = np.array([[1.5, 1.5]])
result = indicator.compute(front)
self.assertEqual(np.sqrt(pow(1.0 - 1.5, 2) + pow(1.0 - 1.5, 2)), result)
self.assertEqual(np.sqrt(pow(2.0 - 1.5, 2) + pow(2.0 - 1.5, 2)), result)
def test_case6(self):
"""
Case 6. reference front: [[1.0, 1.0], [2.1, 2.1]], front: [[1.5, 1.5], [2.2, 2.2]]
Expected result: the distance to the nearest point of the reference front is the average of the sum of each point
of the front to the nearest point of the reference front
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0], [2.1, 2.1]]))
front = np.array([[1.5, 1.5], [2.2, 2.2]])
result = indicator.compute(front)
distance_of_first_point = np.sqrt(pow(1.0 - 1.5, 2) + pow(1.0 - 1.5, 2))
distance_of_second_point = np.sqrt(pow(2.1 - 2.2, 2) + pow(2.1 - 2.2, 2))
self.assertEqual((distance_of_first_point + distance_of_second_point) / 2.0, result)
def test_get_short_name_return_the_right_value(self):
self.assertEqual("GD", GenerationalDistance([]).get_short_name())
def test_should_constructor_create_a_non_null_object(self) -> None:
indicator = GenerationalDistance([])
self.assertIsNotNone(indicator)
for r, _, fl in os.walk(output_directory):
for file in fl:
if 'FUN' in file and prb in r:
with open(os.path.join(r, file), 'r') as f:
line = f.readline()
while line:
tokens = line.split()
fitness = [float(x) for x in tokens]
s = Solution(None, fitness)
population.append(s)
line = f.readline()
population = non_dominated_population(population,
maximize=False,
filter_duplicate=False)
# save to file
pf_file = os.path.dirname(output_directory).join(prb + ".pf")
with open(pf_file, 'w') as f:
for s in population:
f.write("".join([str(v) + "\t" for v in s.fitness]))
f.write("\n")
# Generate summary file
generate_summary_from_experiment(
input_dir=output_directory,
quality_indicators=[
GenerationalDistance(reference_front=output_directory),
EpsilonIndicator(reference_front=output_directory),
HyperVolume([0, 0])
])
from jmetal.core.quality_indicator import GenerationalDistance, InvertedGenerationalDistance, HyperVolume import numpy as np import os file_folder_orgin = os.path.abspath(os.path.join(os.getcwd(), "..")) var_file = file_folder_orgin + "/FUN.NSGAII.OvertakeProblem" solution_file = file_folder_orgin + "/VAR.NSGAII.OvertakeProblem" var = np.loadtxt(var_file) solution = np.loadtxt(solution_file) # print(var, solution) indicator1 = GenerationalDistance(solution) GD = indicator1.compute(solution) print(GD) indicator1 = InvertedGenerationalDistance(solution) GD = indicator1.compute(solution) print(GD)