def frame_generation(index_iteration):
figure.clf()
figure.suptitle("Clustering genetic algorithm (iteration: " +
str(index_iteration) + ")",
fontsize=18,
fontweight='bold')
visualizer = cluster_visualizer(4, 2, [
"The best pop. on step #" + str(index_iteration),
"The best population"
])
local_minimum_clusters = ga_math.get_clusters_representation(
observer.get_population_best()['chromosome'][index_iteration])
visualizer.append_clusters(local_minimum_clusters, data, 0)
global_minimum_clusters = ga_math.get_clusters_representation(
observer.get_global_best()['chromosome'][index_iteration])
visualizer.append_clusters(global_minimum_clusters, data, 1)
ax1 = plt.subplot2grid((2, 2), (1, 0), colspan=2)
ga_visualizer.show_evolution(observer, 0, index_iteration + 1, ax1,
False)
visualizer.show(figure, shift=0, display=False)
figure.subplots_adjust(top=0.85)
return [figure.gca()]
def show_clusters(data, observer, marker='.', markersize=None):
"""!
@brief Shows allocated clusters by the genetic algorithm.
@param[in] data (list): Input data that was used for clustering process by the algorithm.
@param[in] observer (ga_observer): Observer that was used for collection information about clustering process.
@param[in] marker (char): Type of marker that should be used for object (point) representation.
@param[in] markersize (uint): Size of the marker that is used for object (point) representation.
@note If you have clusters instead of observer then 'cluster_visualizer' can be used for visualization purposes.
@see cluster_visualizer
"""
figure = plt.figure()
ax1 = figure.add_subplot(121)
clusters = ga_math.get_clusters_representation(
observer.get_global_best()['chromosome'][-1])
visualizer = cluster_visualizer(1, 2)
visualizer.append_clusters(clusters, data, 0, marker, markersize)
visualizer.show(figure, display=False)
ga_visualizer.show_evolution(observer, 0, None, ax1, True)
def templateTestObserverCollecting(self, amount_clusters, iterations,
global_optimum, local_optimum, average,
**kwargs):
observer_instance = ga_observer(global_optimum, local_optimum, average)
self.assertEqual(0, len(observer_instance))
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
ga_instance = genetic_algorithm(sample,
amount_clusters,
20,
iterations,
count_mutation_gens=2,
coeff_mutation_count=0.25,
observer=observer_instance,
**kwargs)
ga_instance.process()
self.assertEqual(observer_instance, ga_instance.get_observer())
expected_length = 0
if global_optimum is True:
expected_length = iterations + 1
self.assertEqual(expected_length, len(observer_instance))
self.assertEqual(
expected_length,
len(observer_instance.get_global_best()['chromosome']))
self.assertEqual(
expected_length,
len(observer_instance.get_global_best()['fitness_function']))
expected_length = 0
if local_optimum is True:
expected_length = iterations + 1
self.assertEqual(expected_length, len(observer_instance))
self.assertEqual(
expected_length,
len(observer_instance.get_population_best()['chromosome']))
self.assertEqual(
expected_length,
len(observer_instance.get_population_best()['fitness_function']))
expected_length = 0
if average is True:
expected_length = iterations + 1
self.assertEqual(expected_length, len(observer_instance))
self.assertEqual(expected_length,
len(observer_instance.get_mean_fitness_function()))
if global_optimum is True:
clusters = ga_math.get_clusters_representation(
observer_instance.get_global_best()['chromosome'][-1])
self.assertEqual(amount_clusters, len(clusters))
return sample, observer_instance
def get_clusters(self):
"""!
@brief Returns list of allocated clusters, each cluster contains indexes of objects from the data.
@return (list) List of allocated clusters.
@see process()
"""
return ga_math.get_clusters_representation(self._result_clustering['best_chromosome'], self._count_clusters)
def templateTestObserverCollecting(self, amount_clusters, iterations, global_optimum, local_optimum, average):
testing_result = False
observer_instance = None
sample = None
for _ in range(3):
observer_instance = ga_observer(global_optimum, local_optimum, average)
assert len(observer_instance) == 0
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
ga_instance = genetic_algorithm(sample, amount_clusters, 20, iterations, count_mutation_gens=2,
coeff_mutation_count=0.25, observer=observer_instance)
ga_instance.process()
assert observer_instance == ga_instance.get_observer()
expected_length = 0
if (global_optimum is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance)
assert expected_length == len(observer_instance.get_global_best()['chromosome']);
assert expected_length == len(observer_instance.get_global_best()['fitness_function']);
expected_length = 0
if (local_optimum is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance);
assert expected_length == len(observer_instance.get_population_best()['chromosome']);
assert expected_length == len(observer_instance.get_population_best()['fitness_function']);
expected_length = 0
if (average is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance);
assert expected_length == len(observer_instance.get_mean_fitness_function());
if (global_optimum is True):
clusters = ga_math.get_clusters_representation(observer_instance.get_global_best()['chromosome'][-1])
if amount_clusters != len(clusters):
continue
testing_result = True
break
assert testing_result == True
return sample, observer_instance
def templateTestObserverCollecting(self, amount_clusters, iterations, global_optimum, local_optimum, average):
testing_result = False
observer_instance = None
sample = None
for _ in range(3):
observer_instance = ga_observer(global_optimum, local_optimum, average)
assert len(observer_instance) == 0
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
ga_instance = genetic_algorithm(sample, amount_clusters, 20, iterations, count_mutation_gens=2,
coeff_mutation_count=0.25, observer=observer_instance)
ga_instance.process()
assert observer_instance == ga_instance.get_observer()
expected_length = 0
if (global_optimum is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance)
assert expected_length == len(observer_instance.get_global_best()['chromosome']);
assert expected_length == len(observer_instance.get_global_best()['fitness_function']);
expected_length = 0
if (local_optimum is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance);
assert expected_length == len(observer_instance.get_population_best()['chromosome']);
assert expected_length == len(observer_instance.get_population_best()['fitness_function']);
expected_length = 0
if (average is True):
expected_length = iterations + 1
assert expected_length == len(observer_instance);
assert expected_length == len(observer_instance.get_mean_fitness_function());
if (global_optimum is True):
clusters = ga_math.get_clusters_representation(observer_instance.get_global_best()['chromosome'][-1])
if amount_clusters != len(clusters):
continue
testing_result = True
break
assert testing_result == True
return sample, observer_instance
