def setUp(self):
self._num_points = 10
self._pop_size = 5
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
self._distances = distance_matrix(self._data, self._data)
def setUp(self):
self._num_points = 10
self._pop_size = 5
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
self._distances = distance_matrix(self._data, self._data)
def setUp(self):
self._num_points = 10
self._pop_size = 5
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
self._distances = distance_matrix(self._data, self._data)
popGen = SimplePopulationGenerator(self._pop_size)
self._population = popGen.generate(self._data)
def setUp(self):
self._num_points = 10
self._pop_size = 20
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
self._distances = distance_matrix(self._data, self._data)
popGen = SimplePopulationGenerator(self._pop_size)
self._population = popGen.generate(self._distances[0])
def setUp(self):
self._num_points = 30
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
tuner = GeneticAlgorithmParameterEstimation(NUM_DATASETS, NUM_POINTS)
params = {
"num_epochs": [1000],
"num_elites": [0, 1, 2],
"generator": ["SimplePopulationGenerator"],
"generator_population_size": [40],
"selector": ["TournamentSelection"],
"selector_tournament_size": [10],
"crossover": ["OrderCrossover"],
"crossover_pcross": [0.9],
"mutator": ["InversionMutation"],
"mutator_pmutate": [0.2]
}
elite_results = tuner.perform_grid_search(params)
elite_results
gen = TSPGenerator(NUM_POINTS)
data = gen.generate()
all_fitness = []
for i, row in elite.iterrows():
params = row.to_dict()
sim = Simulator(**params)
sim.evolve(data)
all_fitness.append(sim.get_min_fitness()[::10])
df = pd.DataFrame(np.array(all_fitness))
gen = TSPGenerator(NUM_POINTS)
data = gen.generate()
all_fitness = []
for i, row in elite_results.iterrows():
def setUp(self):
self._num_points = 30
gen = TSPGenerator(self._num_points)
self._data = gen.generate()
