def calculate_distance_stats(elements, matrix):
"""
Calculates the mean, dispersion and radius of all the distances to the central element of a set of
elements.
@param elements: The elements we are working with.
@param matrix: The used condensed matrix.
@return: Mean, std deviation and radius of all the elements with respect to their central element.
"""
cluster = Cluster(None, elements)
medoid = cluster.calculate_medoid(matrix)
# We also get a 0 distance from the medoid vs itself (it is contained in 'elements')
distances = get_distances_of_elements_to(medoid, elements, matrix)
return numpy.mean(distances), numpy.std(distances), numpy.max(distances)
def evaluate(self, clustering, matrix):
"""
Mean is approximated to medoid.
"""
update_medoids(clustering, matrix)
global_cluster = Cluster(None, clustering.get_all_clustered_elements())
global_cluster.prototype = global_cluster.calculate_medoid(matrix)
global_variance = numpy.var(get_distances_of_elements_to(global_cluster.prototype,
global_cluster.all_elements,
matrix))
variances = [self.cluster_variance(cluster,matrix) for cluster in clustering.clusters]
sum_ci = numpy.sum(variances)
Cmp = sum_ci / (len(clustering.clusters)*global_variance)
return Cmp
def calculate_mean_center_differences(decomposed_cluster, matrix):
"""
Given a mixed decomposed cluster, it calculates the mean of all center differences (giving a qualitative
view of how separated the inner subclusters are).
@param decomposed_cluster: A MIXED decomposed cluster.
@param matrix: The condensed distance matrix used.
@return: The mean of center distances.
"""
centers = []
for traj_id in decomposed_cluster:
cluster = Cluster(None, decomposed_cluster[traj_id])
centers.append(cluster.calculate_medoid(matrix))
center_distances = []
for i in range(len(centers)-1):
for j in range(i+1, len(centers)):
center_distances.append(matrix[centers[i],centers[j]])
return numpy.mean(center_distances)
