def test_calculate_distance_stats(self):
calc_mean, calc_std, calc_radius = calculate_distance_stats(self.decomposed_cluster["traj_A"], self.matrix)
distances = [1.4142135381698608, 1.4142135381698608, 1.4142135381698608, 1.4142135381698608, 0.0]
expected_mean = numpy.mean(distances)
expected_std = numpy.std(distances)
expected_radius = numpy.max(distances)
self.assertItemsEqual((expected_mean,expected_std,expected_radius),(calc_mean, calc_std, calc_radius))
def analyze_clusters(cls, separated_decomposed_clusters, distance_matrix,
analysis):
"""
Performs the overlap analysis of separated clusters.
"""
for cluster_type in separated_decomposed_clusters:
for cluster_id in separated_decomposed_clusters[cluster_type]:
decomposed_cluster = separated_decomposed_clusters[
cluster_type][cluster_id]
analysis[cluster_id] = {
"components": decomposed_cluster.keys(),
"global": {}
}
analysis[cluster_id]["global"]["mean"], analysis[cluster_id][
"global"]["std"], analysis[cluster_id]["global"][
"max"] = calculate_distance_stats(
getAllElements(decomposed_cluster),
distance_matrix)
analysis[cluster_id]["global"]["num_elements"] = len(
getAllElements(decomposed_cluster))
for traj_id in decomposed_cluster:
analysis[cluster_id]["global"][traj_id] = {}
analysis[cluster_id]["global"][traj_id]["mean"], analysis[
cluster_id]["global"][traj_id]["std"], analysis[
cluster_id]["global"][traj_id][
"max"] = calculate_distance_stats(
decomposed_cluster[traj_id],
distance_matrix)
analysis[cluster_id]["global"][traj_id][
"num_elements"] = len(decomposed_cluster[traj_id])
if cluster_type == "mixed":
analysis[cluster_id][
"centers_mean_diff"] = calculate_mean_center_differences(
decomposed_cluster, distance_matrix)
# The overlap ranges between 0 and 1, being 0 the best value. We invert it in order to
# to get a more understandable range (1 is the best value and 0 the worst).
analysis[cluster_id]["global"][
"overlap"] = 1 - OverlapCalculator.calculate_cluster_overlap(
decomposed_cluster, distance_matrix)
def analyze_clusters(cls, separated_decomposed_clusters, distance_matrix,
analysis):
for cluster_type in separated_decomposed_clusters:
for cluster_id in separated_decomposed_clusters[cluster_type]:
decomposed_cluster = separated_decomposed_clusters[
cluster_type][cluster_id]
analysis[cluster_id] = {
"components": decomposed_cluster.keys(),
"global": {}
}
analysis[cluster_id]["global"]["mean"], analysis[cluster_id][
"global"]["std"], analysis[cluster_id]["global"][
"max"] = calculate_distance_stats(
getAllElements(decomposed_cluster),
distance_matrix)
analysis[cluster_id]["global"]["num_elements"] = len(
getAllElements(decomposed_cluster))
for traj_id in decomposed_cluster:
analysis[cluster_id]["global"][traj_id] = {}
analysis[cluster_id]["global"][traj_id]["mean"], analysis[
cluster_id]["global"][traj_id]["std"], analysis[
cluster_id]["global"][traj_id][
"max"] = calculate_distance_stats(
decomposed_cluster[traj_id],
distance_matrix)
analysis[cluster_id]["global"][traj_id][
"num_elements"] = len(decomposed_cluster[traj_id])
if cluster_type == "mixed":
analysis[cluster_id][
"centers_mean_diff"] = calculate_mean_center_differences(
decomposed_cluster, distance_matrix)
analysis[cluster_id]["global"][
"overlap"] = OverlapCalculator.calculate_cluster_overlap(
2, decomposed_cluster, distance_matrix)
def analyze_clusters(cls, separated_decomposed_clusters, distance_matrix, analysis):
for cluster_type in separated_decomposed_clusters:
for cluster_id in separated_decomposed_clusters[cluster_type]:
decomposed_cluster = separated_decomposed_clusters[cluster_type][cluster_id]
analysis[cluster_id] = {"components":decomposed_cluster.keys(),"global":{}}
analysis[cluster_id]["global"]["mean"], analysis[cluster_id]["global"]["std"], analysis[cluster_id]["global"]["max"] = calculate_distance_stats(getAllElements(decomposed_cluster), distance_matrix)
analysis[cluster_id]["global"]["num_elements"] = len(getAllElements(decomposed_cluster))
for traj_id in decomposed_cluster:
analysis[cluster_id]["global"][traj_id] = {}
analysis[cluster_id]["global"][traj_id]["mean"], analysis[cluster_id]["global"][traj_id]["std"], analysis[cluster_id]["global"][traj_id]["max"] = calculate_distance_stats(decomposed_cluster[traj_id], distance_matrix)
analysis[cluster_id]["global"][traj_id]["num_elements"] = len(decomposed_cluster[traj_id])
if cluster_type == "mixed":
analysis[cluster_id]["centers_mean_diff"] = calculate_mean_center_differences(decomposed_cluster, distance_matrix)
analysis[cluster_id]["global"]["overlap"] = OverlapCalculator.calculate_cluster_overlap(2, decomposed_cluster, distance_matrix)
def analyze_clusters(cls, separated_decomposed_clusters, distance_matrix, analysis):
"""
Performs the overlap analysis of separated clusters.
"""
for cluster_type in separated_decomposed_clusters:
for cluster_id in separated_decomposed_clusters[cluster_type]:
decomposed_cluster = separated_decomposed_clusters[cluster_type][cluster_id]
analysis[cluster_id] = {"components":decomposed_cluster.keys(),"global":{}}
analysis[cluster_id]["global"]["mean"], analysis[cluster_id]["global"]["std"], analysis[cluster_id]["global"]["max"] = calculate_distance_stats(getAllElements(decomposed_cluster), distance_matrix)
analysis[cluster_id]["global"]["num_elements"] = len(getAllElements(decomposed_cluster))
for traj_id in decomposed_cluster:
analysis[cluster_id]["global"][traj_id] = {}
analysis[cluster_id]["global"][traj_id]["mean"], analysis[cluster_id]["global"][traj_id]["std"], analysis[cluster_id]["global"][traj_id]["max"] = calculate_distance_stats(decomposed_cluster[traj_id], distance_matrix)
analysis[cluster_id]["global"][traj_id]["num_elements"] = len(decomposed_cluster[traj_id])
if cluster_type == "mixed":
analysis[cluster_id]["centers_mean_diff"] = calculate_mean_center_differences(decomposed_cluster, distance_matrix)
# The overlap ranges between 0 and 1, being 0 the best value. We invert it in order to
# to get a more understandable range (1 is the best value and 0 the worst).
analysis[cluster_id]["global"]["overlap"] = 1 - OverlapCalculator.calculate_cluster_overlap( decomposed_cluster, distance_matrix)