def test_Genome_eval(self):
coils = GAutils.get_coils_from_excel()
subject = Genome.Genome()
subject.evaluate(subject.sequence, Steel.calculate_max_penalty(), coils)
self.assertTrue(subject.penalty < 1 and subject.penalty > 0)
self.assertTrue(subject.fitness < 1 and subject.fitness > 0)
self.assertTrue(subject.fitness + subject.penalty == 1)
def main():
"""
This is the main method that runs the algorithm and provides the excel
:return: NONE
"""
if os.path.isfile(CONST_EXCEL_FILE_NAME_TO_READ):
coils = GAutils.get_coils_from_excel()
improvements_values = []
for i in range(20):
improvements_values.append(GAutils.testing_the_algorithm(coils, i))
temp = str(GAutils.CONST_EXCEL_FILE_NAME_TO_WRITE).split("\\")
temp = str(temp[7]) + str(improvements_values.index(max(improvements_values))) + ".xlsx"
print("The best improvement is in file: " + temp)
else:
print("Excel file not found, system shutdown")
exit()
def test_Genome_eval(self):
coils = GAutils.get_coils_from_excel()
subject = Genome.Genome()
subject.evaluate(subject.sequence, Steel.calculate_max_penalty(),
coils)
self.assertTrue(subject.penalty < 1 and subject.penalty > 0)
self.assertTrue(subject.fitness < 1 and subject.fitness > 0)
self.assertTrue(subject.fitness + subject.penalty == 1)
def main():
"""
This is the main method that runs the algorithm and provides the excel
:return: NONE
"""
if os.path.isfile(CONST_EXCEL_FILE_NAME_TO_READ):
coils = GAutils.get_coils_from_excel()
improvements_values = []
for i in range(20):
improvements_values.append(GAutils.testing_the_algorithm(coils, i))
temp = str(GAutils.CONST_EXCEL_FILE_NAME_TO_WRITE).split("\\")
temp = str(temp[7]) + str(
improvements_values.index(max(improvements_values))) + ".xlsx"
print("The best improvement is in file: " + temp)
else:
print("Excel file not found, system shutdown")
exit()
def test_calculate_transition_penalty(self):
# we calculated the transition penalty between the 2 first coils outside the program: 0.1885
coils = GAutils.get_coils_from_excel()
population = Population.Population(coils)
population.createInitial(GAutils.CONST_POPULATION_SIZE)
# coils[0] is pf type Steel and holds the attributes for the coils
temp = coils[0].calculate_penalty(coils[1])
temp = float("%.4f" % temp)
self.assertAlmostEqual(temp, 0.1984)
def test_get_chromosome_by_index(self):
coils = GAutils.get_coils_from_excel()
population = Population.Population(coils)
population.createInitial(GAutils.CONST_POPULATION_SIZE)
pop = population.getPop()
self.assertEqual(pop[1].getSequence(),
(population.get_chromosome_by_index(1)).getSequence())
self.assertNotEqual(
pop[1].getSequence(),
(population.get_chromosome_by_index(2)).getSequence())
def test_get_best_sol(self):
coils = GAutils.get_coils_from_excel()
population = Population.Population(coils)
population.createInitial(GAutils.CONST_POPULATION_SIZE)
pop = population.getPop()
max_fit = pop[0].getFit()
index = 0
for i in range(GAutils.CONST_POPULATION_SIZE):
if pop[i].getFit() > max_fit:
index = i
max_fit = pop[i].getFit()
self.assertEqual(index, (population.get_best_solution())[0])
def test_set_get_pop(self):
coils = GAutils.get_coils_from_excel()
population1 = Population.Population(coils)
population1.createInitial(GAutils.CONST_POPULATION_SIZE)
pop1 = population1.getPop()
population2 = Population.Population(coils)
population2.createInitial(GAutils.CONST_POPULATION_SIZE)
pop2 = population2.getPop()
self.assertNotEqual(pop1, pop2)
population2.setPop(pop1)
pop2 = population2.getPop()
self.assertEqual(pop1, pop2)
def test_fitness_prob(self):
coils = GAutils.get_coils_from_excel()
population = Population.Population(coils)
population.createInitial(GAutils.CONST_POPULATION_SIZE)
population.updateGenesRange()
self.assertAlmostEqual(population.getFitnessProb(), 1)
def test_is_improvement_exist(self):
coils = GAutils.get_coils_from_excel()
self.assertTrue(GAutils.testing_the_algorithm(coils) > 0)