def correct_ksearch(sample_path, answer_path, kmin, kmax, algorithm,
ccore_flag):
attempts = 15
testing_result = False
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
for _ in range(attempts):
ksearch_instance = silhouette_ksearch(sample,
kmin,
kmax,
algorithm=algorithm,
ccore=ccore_flag).process()
amount = ksearch_instance.get_amount()
score = ksearch_instance.get_score()
scores = ksearch_instance.get_scores()
assertion.le(-1.0, score)
assertion.ge(1.0, score)
assertion.eq(kmax - kmin, len(scores))
upper_limit = len(clusters) + 1
lower_limit = len(clusters) - 1
if lower_limit < 1:
lower_limit = 1
if (amount > upper_limit) or (amount < lower_limit):
continue
testing_result = True
break
assertion.true(testing_result)
def assert_distribution(self, data, sizes, centers, widths):
index_cluster = 0
index_cluster_point = 0
actual_means = [[0.0 for _ in range(len(data[0]))]
for _ in range(len(sizes))]
for index_point in range(len(data)):
for index_dimension in range(len(data[0])):
actual_means[index_cluster][index_dimension] += data[
index_point][index_dimension]
index_cluster_point += 1
if index_cluster_point == sizes[index_cluster]:
index_cluster_point = 0
index_cluster += 1
for index_cluster in range(len(actual_means)):
for index_dimension in range(len(data[0])):
actual_means[index_cluster][index_dimension] /= sizes[
index_cluster]
assertion.ge(
centers[index_cluster][index_dimension],
actual_means[index_cluster][index_dimension] -
widths[index_cluster])
assertion.le(
centers[index_cluster][index_dimension],
actual_means[index_cluster][index_dimension] +
widths[index_cluster])
def correct_ksearch(sample_path, answer_path, kmin, kmax, algorithm, ccore_flag):
attempts = 10
testing_result = False
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
for _ in range(attempts):
ksearch_instance = silhouette_ksearch(sample, kmin, kmax, algorithm=algorithm, ccore=ccore_flag).process()
amount = ksearch_instance.get_amount()
score = ksearch_instance.get_score()
scores = ksearch_instance.get_scores()
assertion.le(-1.0, score)
assertion.ge(1.0, score)
assertion.eq(kmax - kmin, len(scores))
upper_limit = len(clusters) + 1
lower_limit = len(clusters) - 1
if lower_limit < 1:
lower_limit = 1
if (amount > upper_limit) or (amount < lower_limit):
continue
testing_result = True
break
assertion.true(testing_result)
def templateLengthProcessData(input_sample, start_centers, expected_cluster_length, type_splitting, kmax, ccore, **kwargs):
if isinstance(input_sample, str):
sample = read_sample(input_sample)
else:
sample = input_sample
xmeans_instance = xmeans(sample, start_centers, kmax, 0.025, type_splitting, ccore, **kwargs)
xmeans_instance.process()
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()
wce = xmeans_instance.get_total_wce()
obtained_cluster_sizes = [len(cluster) for cluster in clusters]
assertion.eq(len(sample), sum(obtained_cluster_sizes))
assertion.eq(len(clusters), len(centers))
assertion.le(len(centers), kmax)
expected_wce = 0.0
metric = distance_metric(type_metric.EUCLIDEAN_SQUARE)
for index_cluster in range(len(clusters)):
for index_point in clusters[index_cluster]:
expected_wce += metric(sample[index_point], centers[index_cluster])
assertion.eq(expected_wce, wce)
if expected_cluster_length is not None:
assertion.eq(len(centers), len(expected_cluster_length))
obtained_cluster_sizes.sort()
expected_cluster_length.sort()
assertion.eq(obtained_cluster_sizes, expected_cluster_length)
def template_correct_scores(self, sample_path, answer_path):
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
scores = silhouette(sample, clusters).process().get_score()
assertion.eq(len(sample), len(scores))
for score in scores:
assertion.le(-1.0, score)
assertion.ge(1.0, score)
def correct_scores(sample_path, answer_path, ccore_flag):
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
scores = silhouette(sample, clusters, ccore=ccore_flag).process().get_score()
assertion.eq(len(sample), len(scores))
for score in scores:
assertion.le(-1.0, score)
assertion.ge(1.0, score)
def templateKmeasPlusPlusCenterInitializerIndexReturn(self, data, amount):
centers = kmeans_plusplus_initializer(data, amount).initialize(return_index=True)
assertion.eq(amount, len(centers))
for center_index in centers:
assertion.gt(len(data), center_index)
assertion.le(0, center_index)
return centers
def templateKmeasPlusPlusCenterInitializerIndexReturn(self, data, amount):
centers = kmeans_plusplus_initializer(data, amount).initialize(return_index=True)
assertion.eq(amount, len(centers))
for center_index in centers:
assertion.gt(len(data), center_index)
assertion.le(0, center_index)
assertion.eq(1, centers.count(center_index))
return centers
def templateClusterAllocationOneDimensionData(ccore_flag):
input_data = [[0.0] for _ in range(10)] + [[5.0] for _ in range(10)] + [[10.0] for _ in range(10)] + [[15.0] for _ in range(10)]
xmeans_instance = xmeans(input_data, [[0.5], [5.5], [10.5], [15.5]], 20, 0.025, splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore_flag)
xmeans_instance.process()
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()
assertion.eq(len(clusters), 4)
assertion.eq(len(centers), len(clusters))
assertion.le(len(clusters), 20)
for cluster in clusters:
assertion.eq(len(cluster), 10)
def correct_scores(sample_path, answer_path, ccore_flag, **kwargs):
data_type = kwargs.get('data_type', 'points')
sample = read_sample(sample_path)
if data_type == 'distance_matrix':
sample = calculate_distance_matrix(sample, distance_metric(type_metric.EUCLIDEAN_SQUARE))
clusters = answer_reader(answer_path).get_clusters()
scores = silhouette(sample, clusters, ccore=ccore_flag, data_type=data_type).process().get_score()
assertion.eq(len(sample), len(scores))
for score in scores:
assertion.le(-1.0, score)
assertion.ge(1.0, score)
return scores
def template_correct_ksearch(self, sample_path, answer_path, kmin, kmax,
algorithm):
attempts = 5
testing_result = False
sample = read_sample(sample_path)
clusters = answer_reader(answer_path).get_clusters()
for _ in range(attempts):
ksearch_instance = silhouette_ksearch(
sample, kmin, kmax, algorithm=algorithm).process()
amount = ksearch_instance.get_amount()
score = ksearch_instance.get_score()
scores = ksearch_instance.get_scores()
assertion.le(-1.0, score)
assertion.ge(1.0, score)
assertion.eq(kmax - kmin, len(scores))
if amount != len(clusters): continue
testing_result = True
break
assertion.true(testing_result)
def templateCollectEvolution(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer)
kmeans_instance.process()
assertion.le(1, len(observer))
for i in range(len(observer)):
assertion.le(1, len(observer.get_centers(i)))
for center in observer.get_centers(i):
assertion.eq(len(sample[0]), len(center))
assertion.le(1, len(observer.get_clusters(i)))
def templateCollectEvolution(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename);
observer = kmeans_observer();
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer);
kmeans_instance.process();
assertion.le(1, len(observer));
for i in range(len(observer)):
assertion.le(1, len(observer.get_centers(i)));
for center in observer.get_centers(i):
assertion.eq(len(sample[0]), len(center));
assertion.le(1, len(observer.get_clusters(i)));
