def __improve_structure(self, clusters, centers):
"""!
@brief Check for best structure: divides each cluster into two and checks for best results using splitting criterion.
@param[in] clusters (list): Clusters that have been allocated (each cluster contains indexes of points from data).
@param[in] centers (list): Centers of clusters.
@return (list) Allocated centers for clustering.
"""
difference = 0.001
allocated_centers = []
for index_cluster in range(len(clusters)):
# split cluster into two child clusters
parent_child_centers = []
parent_child_centers.append(
list_math_addition_number(centers[index_cluster], -difference))
parent_child_centers.append(
list_math_addition_number(centers[index_cluster], difference))
# solve k-means problem for children where data of parent are used.
(parent_child_clusters,
parent_child_centers) = self.__improve_parameters(
parent_child_centers, clusters[index_cluster])
# If it's possible to split current data
if (len(parent_child_clusters) > 1):
# Calculate splitting criterion
parent_scores = self.__splitting_criterion(
[clusters[index_cluster]], [centers[index_cluster]])
child_scores = self.__splitting_criterion(
[parent_child_clusters[0], parent_child_clusters[1]],
parent_child_centers)
split_require = False
# Reallocate number of centers (clusters) in line with scores
if (self.__criterion ==
splitting_type.BAYESIAN_INFORMATION_CRITERION):
if (parent_scores < child_scores): split_require = True
elif (self.__criterion ==
splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH):
# If its score for the split structure with two children is smaller than that for the parent structure,
# then representing the data samples with two clusters is more accurate in comparison to a single parent cluster.
if (parent_scores > child_scores): split_require = True
if (split_require is True):
allocated_centers.append(parent_child_centers[0])
allocated_centers.append(parent_child_centers[1])
else:
allocated_centers.append(centers[index_cluster])
else:
allocated_centers.append(centers[index_cluster])
return allocated_centers
def __improve_structure(self, clusters, centers):
"""!
@brief Check for best structure: divides each cluster into two and checks for best results using splitting criterion.
@param[in] clusters (list): Clusters that have been allocated (each cluster contains indexes of points from data).
@param[in] centers (list): Centers of clusters.
@return (list) Allocated centers for clustering.
"""
difference = 0.001;
allocated_centers = [];
for index_cluster in range(len(clusters)):
# split cluster into two child clusters
parent_child_centers = [];
parent_child_centers.append(list_math_addition_number(centers[index_cluster], -difference));
parent_child_centers.append(list_math_addition_number(centers[index_cluster], difference));
# solve k-means problem for children where data of parent are used.
(parent_child_clusters, parent_child_centers) = self.__improve_parameters(parent_child_centers, clusters[index_cluster]);
# If it's possible to split current data
if (len(parent_child_clusters) > 1):
# Calculate splitting criterion
parent_scores = self.__splitting_criterion([ clusters[index_cluster] ], [ centers[index_cluster] ]);
child_scores = self.__splitting_criterion([ parent_child_clusters[0], parent_child_clusters[1] ], parent_child_centers);
split_require = False;
# Reallocate number of centers (clusters) in line with scores
if (self.__criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION):
if (parent_scores < child_scores): split_require = True;
elif (self.__criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH):
if (parent_scores > child_scores): split_require = True;
if (split_require is True):
allocated_centers.append(parent_child_centers[0]);
allocated_centers.append(parent_child_centers[1]);
else:
allocated_centers.append(centers[index_cluster]);
else:
allocated_centers.append(centers[index_cluster]);
return allocated_centers;
