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"""!
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@brief Cluster analysis algorithm: CURE
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@details Implementation based on article:
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- S.Guha, R.Rastogi, K.Shim. CURE: An Efficient Clustering Algorithm for Large Databases. 1998.
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@authors Andrei Novikov ([email protected])
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@date 2014-2016
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@copyright GNU Public License
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@cond GNU_PUBLIC_LICENSE
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PyClustering is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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PyClustering is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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@endcond
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"""
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from pyclustering.utils import euclidean_distance;
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from pyclustering.container.kdtree import kdtree;
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import pyclustering.core.cure_wrapper as wrapper;
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class cure_cluster:
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"""!
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@brief Represents data cluster in CURE term. CURE cluster is described by points of cluster, representation points of the cluster and by the cluster center.
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"""
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def __init__(self, point = None):
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"""!
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@brief Constructor of CURE cluster.
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@param[in] point (list): Point represented by list of coordinates.
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"""
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## List of points that make up cluster.
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self.points = [ ];
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## Mean of points that make up cluster.
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self.mean = None;
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## List of points that represents clusters.
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self.rep = [ ];
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if (point is not None):
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self.points = [ point ];
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self.mean = point;
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self.rep = [ point ];
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## Pointer to the closest cluster.
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self.closest = None;
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## Distance to the closest cluster.
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self.distance = float('inf'); # calculation of distance is really complexity operation (even square distance), so let's store distance to closest cluster.
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def __repr__(self):
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"""!
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@brief Displays distance to closest cluster and points that are contained by current cluster.
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"""
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return "%s, %s" % (self.distance, self.points);
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class cure:
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"""!
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@brief Class represents clustering algorithm CURE.
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Example:
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@code
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# read data for clustering from some file
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sample = read_sample(path_to_data);
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# create instance of cure algorithm for cluster analysis
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# request for allocation of two clusters.
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cure_instance = cure(sample, 2, 5, 0.5, True);
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# run cluster analysis
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cure_instance.process();
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# get results of clustering
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clusters = cure_instance.get_clusters();
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@endcode
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"""
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def __init__(self, data, number_cluster, number_represent_points = 5, compression = 0.5, ccore = False):
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"""!
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@brief Constructor of clustering algorithm CURE.
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@param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
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@param[in] number_cluster (uint): Number of clusters that should be allocated.
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@param[in] number_represent_points (uint): Number of representative points for each cluster.
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@param[in] compression (double): Coefficient defines level of shrinking of representation points toward the mean of the new created cluster after merging on each step. Usually it destributed from 0 to 1.
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@param[in] ccore (bool): If True than DLL CCORE (C++ solution) will be used for solving.
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"""
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self.__pointer_data = data;
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self.__clusters = None;
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self.__representors = None;
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self.__means = None;
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self.__number_cluster = number_cluster;
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self.__number_represent_points = number_represent_points;
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self.__compression = compression;
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self.__ccore = ccore;
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if (ccore is False):
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self.__create_queue(); # queue
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self.__create_kdtree(); # create k-d tree
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def process(self):
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"""!
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@brief Performs cluster analysis in line with rules of CURE algorithm.
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@remark Results of clustering can be obtained using corresponding get methods.
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@see get_clusters()
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"""
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if (self.__ccore is True):
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cure_data_pointer = wrapper.cure_algorithm(self.__pointer_data, self.__number_cluster, self.__number_represent_points, self.__compression);
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self.__clusters = wrapper.cure_get_clusters(cure_data_pointer);
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self.__representors = wrapper.cure_get_representors(cure_data_pointer);
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self.__means = wrapper.cure_get_means(cure_data_pointer);
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wrapper.cure_data_destroy(cure_data_pointer);
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else:
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while (len(self.__queue) > self.__number_cluster):
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cluster1 = self.__queue[0]; # cluster that has nearest neighbor.
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cluster2 = cluster1.closest; # closest cluster.
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#print("Merge decision: \n\t", cluster1, "\n\t", cluster2);
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self.__queue.remove(cluster1);
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self.__queue.remove(cluster2);
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self.__delete_represented_points(cluster1);
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self.__delete_represented_points(cluster2);
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merged_cluster = self.__merge_clusters(cluster1, cluster2);
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self.__insert_represented_points(merged_cluster);
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# Pointers to clusters that should be relocated is stored here.
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cluster_relocation_requests = [];
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# Check for the last cluster
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if (len(self.__queue) > 0):
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merged_cluster.closest = self.__queue[0]; # arbitrary cluster from queue
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merged_cluster.distance = self.__cluster_distance(merged_cluster, merged_cluster.closest);
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for item in self.__queue:
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distance = self.__cluster_distance(merged_cluster, item);
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# Check if distance between new cluster and current is the best than now.
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if (distance < merged_cluster.distance):
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merged_cluster.closest = item;
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merged_cluster.distance = distance;
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# Check if current cluster has removed neighbor.
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if ( (item.closest is cluster1) or (item.closest is cluster2) ):
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# If previous distance was less then distance to new cluster then nearest cluster should be found in the tree.
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#print("Update: ", item);
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if (item.distance < distance):
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(item.closest, item.distance) = self.__closest_cluster(item, distance);
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# TODO: investigation of root cause is required.
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# Itself and merged cluster should be always in list of neighbors in line with specified radius.
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# But merged cluster may not be in list due to error calculation, therefore it should be added
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# manually.
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if (item.closest is None):
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item.closest = merged_cluster;
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item.distance = distance;
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# Otherwise new cluster is nearest.
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else:
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item.closest = merged_cluster;
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item.distance = distance;
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cluster_relocation_requests.append(item);
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elif (item.distance > distance):
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item.closest = merged_cluster;
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item.distance = distance;
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cluster_relocation_requests.append(item);
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# New cluster and updated clusters should relocated in queue
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self.__insert_cluster(merged_cluster);
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for item in cluster_relocation_requests:
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self.__relocate_cluster(item);
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# Change cluster representation
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self.__clusters = [ cure_cluster_unit.points for cure_cluster_unit in self.__queue ];
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self.__representors = [ cure_cluster_unit.rep for cure_cluster_unit in self.__queue ];
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self.__means = [ cure_cluster_unit.mean for cure_cluster_unit in self.__queue ];
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def get_clusters(self):
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"""!
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@brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
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@return (list) List of allocated clusters.
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@see process()
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@see get_representors()
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@see get_means()
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"""
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return self.__clusters;
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def get_representors(self):
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"""!
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@brief Returns list of representors of each cluster.
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@details Cluster index should be used for navigation between lists of representors.
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@return (list) List of representors of each cluster.
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@see get_clusters()
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@see get_means()
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"""
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return self.__representors;
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def get_means(self):
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"""!
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@brief Returns list of mean values of each cluster.
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@details Cluster index should be used for navigation between mean values.
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@return (list) List of mean values of each cluster.
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@see get_clusters()
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@see get_representors()
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"""
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return self.__means;
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def __insert_cluster(self, cluster):
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"""!
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@brief Insert cluster to the list (sorted queue) in line with sequence order (distance).
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@param[in] cluster (cure_cluster): Cluster that should be inserted.
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"""
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for index in range(len(self.__queue)):
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if (cluster.distance < self.__queue[index].distance):
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self.__queue.insert(index, cluster);
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return;
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self.__queue.append(cluster);
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def __relocate_cluster(self, cluster):
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"""!
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@brief Relocate cluster in list in line with distance order.
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@param[in] cluster (cure_cluster): Cluster that should be relocated in line with order.
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"""
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self.__queue.remove(cluster);
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self.__insert_cluster(cluster);
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288
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def __closest_cluster(self, cluster, distance):
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290
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"""!
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@brief Find closest cluster to the specified cluster in line with distance.
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293
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@param[in] cluster (cure_cluster): Cluster for which nearest cluster should be found.
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@param[in] distance (double): Closest distance to the previous cluster.
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@return (tuple) Pair (nearest CURE cluster, nearest distance) if the nearest cluster has been found, otherwise None is returned.
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"""
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299
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300
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nearest_cluster = None;
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301
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nearest_distance = float('inf');
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302
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303
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for point in cluster.rep:
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304
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# Nearest nodes should be returned (at least it will return itself).
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305
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nearest_nodes = self.__tree.find_nearest_dist_nodes(point, distance);
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306
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for (candidate_distance, kdtree_node) in nearest_nodes:
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307
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if ( (candidate_distance < nearest_distance) and (kdtree_node is not None) and (kdtree_node.payload is not cluster) ):
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nearest_distance = candidate_distance;
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309
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nearest_cluster = kdtree_node.payload;
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310
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311
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return (nearest_cluster, nearest_distance);
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312
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313
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314
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def __insert_represented_points(self, cluster):
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315
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"""!
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316
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@brief Insert representation points to the k-d tree.
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317
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318
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@param[in] cluster (cure_cluster): Cluster whose representation points should be inserted.
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319
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320
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"""
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321
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322
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for point in cluster.rep:
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323
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self.__tree.insert(point, cluster);
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324
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325
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326
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def __delete_represented_points(self, cluster):
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327
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"""!
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328
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@brief Remove representation points of clusters from the k-d tree
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329
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330
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@param[in] cluster (cure_cluster): Cluster whose representation points should be removed.
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331
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332
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"""
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333
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334
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for point in cluster.rep:
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335
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self.__tree.remove(point);
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336
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337
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338
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def __merge_clusters(self, cluster1, cluster2):
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339
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"""!
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340
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@brief Merges two clusters and returns new merged cluster. Representation points and mean points are calculated for the new cluster.
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341
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342
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@param[in] cluster1 (cure_cluster): Cluster that should be merged.
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343
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@param[in] cluster2 (cure_cluster): Cluster that should be merged.
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344
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345
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@return (cure_cluster) New merged CURE cluster.
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346
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347
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"""
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348
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349
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merged_cluster = cure_cluster();
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350
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351
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merged_cluster.points = cluster1.points + cluster2.points;
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352
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353
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# merged_cluster.mean = ( len(cluster1.points) * cluster1.mean + len(cluster2.points) * cluster2.mean ) / ( len(cluster1.points) + len(cluster2.points) );
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354
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dimension = len(cluster1.mean);
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355
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merged_cluster.mean = [0] * dimension;
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356
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if merged_cluster.points[1:] == merged_cluster.points[:-1]:
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357
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merged_cluster.mean = merged_cluster.points[0]
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358
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else:
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359
|
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for index in range(dimension):
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360
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merged_cluster.mean[index] = ( len(cluster1.points) * cluster1.mean[index] + len(cluster2.points) * cluster2.mean[index] ) / ( len(cluster1.points) + len(cluster2.points) );
|
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361
|
|
|
|
|
362
|
|
|
temporary = list();
|
|
363
|
|
|
|
|
364
|
|
|
for index in range(self.__number_represent_points):
|
|
365
|
|
|
maximal_distance = 0;
|
|
366
|
|
|
maximal_point = None;
|
|
367
|
|
|
|
|
368
|
|
|
for point in merged_cluster.points:
|
|
369
|
|
|
minimal_distance = 0;
|
|
370
|
|
|
if (index == 0):
|
|
371
|
|
|
minimal_distance = euclidean_distance(point, merged_cluster.mean);
|
|
372
|
|
|
#minimal_distance = euclidean_distance_sqrt(point, merged_cluster.mean);
|
|
373
|
|
|
else:
|
|
374
|
|
|
minimal_distance = min([euclidean_distance(point, p) for p in temporary]);
|
|
375
|
|
|
#minimal_distance = cluster_distance(cure_cluster(point), cure_cluster(temporary[0]));
|
|
376
|
|
|
|
|
377
|
|
|
if (minimal_distance >= maximal_distance):
|
|
378
|
|
|
maximal_distance = minimal_distance;
|
|
379
|
|
|
maximal_point = point;
|
|
380
|
|
|
|
|
381
|
|
|
if (maximal_point not in temporary):
|
|
382
|
|
|
temporary.append(maximal_point);
|
|
383
|
|
|
|
|
384
|
|
|
for point in temporary:
|
|
385
|
|
|
representative_point = [0] * dimension;
|
|
386
|
|
|
for index in range(dimension):
|
|
387
|
|
|
representative_point[index] = point[index] + self.__compression * (merged_cluster.mean[index] - point[index]);
|
|
388
|
|
|
|
|
389
|
|
|
merged_cluster.rep.append(representative_point);
|
|
390
|
|
|
|
|
391
|
|
|
return merged_cluster;
|
|
392
|
|
|
|
|
393
|
|
|
|
|
394
|
|
|
def __create_queue(self):
|
|
395
|
|
|
"""!
|
|
396
|
|
|
@brief Create queue of sorted clusters by distance between them, where first cluster has the nearest neighbor. At the first iteration each cluster contains only one point.
|
|
397
|
|
|
|
|
398
|
|
|
@param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
|
|
399
|
|
|
|
|
400
|
|
|
@return (list) Create queue of sorted clusters by distance between them.
|
|
401
|
|
|
|
|
402
|
|
|
"""
|
|
403
|
|
|
|
|
404
|
|
|
self.__queue = [cure_cluster(point) for point in self.__pointer_data];
|
|
405
|
|
|
|
|
406
|
|
|
# set closest clusters
|
|
407
|
|
|
for i in range(0, len(self.__queue)):
|
|
408
|
|
|
minimal_distance = float('inf');
|
|
409
|
|
|
closest_index_cluster = -1;
|
|
410
|
|
|
|
|
411
|
|
|
for k in range(0, len(self.__queue)):
|
|
412
|
|
|
if (i != k):
|
|
413
|
|
|
dist = self.__cluster_distance(self.__queue[i], self.__queue[k]);
|
|
414
|
|
|
if (dist < minimal_distance):
|
|
415
|
|
|
minimal_distance = dist;
|
|
416
|
|
|
closest_index_cluster = k;
|
|
417
|
|
|
|
|
418
|
|
|
self.__queue[i].closest = self.__queue[closest_index_cluster];
|
|
419
|
|
|
self.__queue[i].distance = minimal_distance;
|
|
420
|
|
|
|
|
421
|
|
|
# sort clusters
|
|
422
|
|
|
self.__queue.sort(key = lambda x: x.distance, reverse = False);
|
|
423
|
|
|
|
|
424
|
|
|
|
|
425
|
|
|
def __create_kdtree(self):
|
|
426
|
|
|
"""!
|
|
427
|
|
|
@brief Create k-d tree in line with created clusters. At the first iteration contains all points from the input data set.
|
|
428
|
|
|
|
|
429
|
|
|
@return (kdtree) k-d tree that consist of representative points of CURE clusters.
|
|
430
|
|
|
|
|
431
|
|
|
"""
|
|
432
|
|
|
|
|
433
|
|
|
self.__tree = kdtree();
|
|
434
|
|
|
for current_cluster in self.__queue:
|
|
435
|
|
|
for representative_point in current_cluster.rep:
|
|
436
|
|
|
self.__tree.insert(representative_point, current_cluster);
|
|
437
|
|
|
|
|
438
|
|
|
|
|
439
|
|
|
def __cluster_distance(self, cluster1, cluster2):
|
|
440
|
|
|
"""!
|
|
441
|
|
|
@brief Calculate minimal distance between clusters using representative points.
|
|
442
|
|
|
|
|
443
|
|
|
@param[in] cluster1 (cure_cluster): The first cluster.
|
|
444
|
|
|
@param[in] cluster2 (cure_cluster): The second cluster.
|
|
445
|
|
|
|
|
446
|
|
|
@return (double) Euclidean distance between two clusters that is defined by minimum distance between representation points of two clusters.
|
|
447
|
|
|
|
|
448
|
|
|
"""
|
|
449
|
|
|
|
|
450
|
|
|
distance = float('inf');
|
|
451
|
|
|
for i in range(0, len(cluster1.rep)):
|
|
452
|
|
|
for k in range(0, len(cluster2.rep)):
|
|
453
|
|
|
#dist = euclidean_distance_sqrt(cluster1.rep[i], cluster2.rep[k]); # Fast mode
|
|
454
|
|
|
dist = euclidean_distance(cluster1.rep[i], cluster2.rep[k]); # Slow mode
|
|
455
|
|
|
if (dist < distance):
|
|
456
|
|
|
distance = dist;
|
|
457
|
|
|
|
|
458
|
|
|
return distance;
|
|
459
|
|
|
|
annoviko /
pyclustering
