def template_clustering(path,
count_clusters,
chromosome_count,
population_count,
count_mutation_gens,
coeff_mutation_count=0.25,
select_coeff=1.0,
fps=15,
animation=False):
sample = read_sample(path);
algo_instance = genetic_algorithm(data=sample,
count_clusters=count_clusters,
chromosome_count=chromosome_count,
population_count=population_count,
count_mutation_gens=count_mutation_gens,
coeff_mutation_count=coeff_mutation_count,
select_coeff=select_coeff,
observer=ga_observer(True, True, True))
start_time = time.time();
algo_instance.process();
print("Sample: ", path, "\t\tExecution time: ", time.time() - start_time, "\n");
observer = algo_instance.get_observer();
ga_visualizer.show_clusters(sample, observer);
if (animation is True):
ga_visualizer.animate_cluster_allocation(sample, observer, movie_fps=fps, save_movie="clustering_animation.mp4");
def template_clustering(path,
count_clusters,
chromosome_count,
population_count,
count_mutation_gens,
coeff_mutation_count=0.25,
select_coeff=1.0,
fps=15,
animation=False):
sample = read_sample(path)
algo_instance = genetic_algorithm(data=sample,
count_clusters=count_clusters,
chromosome_count=chromosome_count,
population_count=population_count,
count_mutation_gens=count_mutation_gens,
coeff_mutation_count=coeff_mutation_count,
select_coeff=select_coeff,
observer=ga_observer(True, True, True))
start_time = time.time()
algo_instance.process()
print("Sample: ", path, "\t\tExecution time: ", time.time() - start_time, "\n")
observer = algo_instance.get_observer()
ga_visualizer.show_clusters(sample, observer)
if (animation is True):
ga_visualizer.animate_cluster_allocation(sample, observer, movie_fps=fps, save_movie="clustering_animation.mp4")
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 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
from pyclustering.cluster.ga import genetic_algorithm, ga_observer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import SIMPLE_SAMPLES
# Read input data for clustering
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
print("SIMPLE_SAMPLES.SAMPLE_SIMPLE4)", SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
print("sample", sample)
# Create instance of observer that will collect all information:
observer_instance = ga_observer(True, True, True)
# Create genetic algorithm for clustering
ga_instance = genetic_algorithm(data=sample,
count_clusters=4,
chromosome_count=100,
population_count=200,
count_mutation_gens=1)
# Start processing
ga_instance.process()
# Obtain results
clusters = ga_instance.get_clusters()
# Print cluster to console
print("Amount of clusters: '%d'. Clusters: '%s'" % (len(clusters), clusters))
