"""! @brief Module for representing clustering results. @authors Andrei Novikov (pyclustering@yandex.ru) @date 2014-2020 @copyright BSD-3-Clause """ import math from enum import IntEnum class type_encoding(IntEnum): """! @brief Enumeration of encoding types (index labeling, index list separation, object list separation). """ ## Results are represented by list of indexes and belonging to the cluster is defined by cluster index and element's position corresponds to object's position in input data, for example [0, 0, 1, 1, 1, 0]. CLUSTER_INDEX_LABELING = 0 ## Results are represented by list of lists, where each list consists of object indexes from input data, for example [ [0, 1, 2], [3, 4, 5], [6, 7] ]. CLUSTER_INDEX_LIST_SEPARATION = 1 ## Results are represented by list of lists, where each list consists of objects from input data, for example [ [obj1, obj2], [obj3, obj4, obj5], [obj6, obj7] ]. CLUSTER_OBJECT_LIST_SEPARATION = 2 class cluster_encoder: """! @brief Provides service to change clustering result representation. @details There are three general types of representation: 1. Index List Separation that is defined by `CLUSTER_INDEX_LIST_SEPARATION`, for example `[[0, 1, 2], [3, 4], [5, 6, 7]`. 2. Index Labeling that is defined by `CLUSTER_INDEX_LABELING`, for example `[0, 0, 0, 1, 1, 2, 2, 2]`. 3. Object List Separation that is defined by `CLUSTER_OBJECT_LIST_SEPARATION`, for example `[[obj1, obj2, obj3], [obj4, obj5], [obj5, obj6, obj7]`. There is an example how to covert default Index List Separation to other types: @code from pyclustering.utils import read_sample from pyclustering.samples.definitions import SIMPLE_SAMPLES from pyclustering.cluster.encoder import type_encoding, cluster_encoder from pyclustering.cluster.kmeans import kmeans # load list of points for cluster analysis sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1) # create instance of K-Means algorithm kmeans_instance = kmeans(sample, [[3.0, 5.1], [6.5, 8.6]]) # run cluster analysis and obtain results kmeans_instance.process() clusters = kmeans_instance.get_clusters() print("Index List Separation:", clusters) # by default k-means returns representation CLUSTER_INDEX_LIST_SEPARATION type_repr = kmeans_instance.get_cluster_encoding() encoder = cluster_encoder(type_repr, clusters, sample) # change representation from index list to label list encoder.set_encoding(type_encoding.CLUSTER_INDEX_LABELING) print("Index Labeling:", encoder.get_clusters()) # change representation from label to object list encoder.set_encoding(type_encoding.CLUSTER_OBJECT_LIST_SEPARATION) print("Object List Separation:", encoder.get_clusters()) @endcode Output of the code above is following: @code Index List Separation: [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] Index Labeling: [0, 0, 0, 0, 0, 1, 1, 1, 1, 1] Object List Separation: [[[3.522979, 5.487981], [3.768699, 5.364477], [3.423602, 5.4199], [3.803905, 5.389491], [3.93669, 5.663041]], [[6.968136, 7.755556], [6.750795, 7.269541], [6.593196, 7.850364], [6.978178, 7.60985], [6.554487, 7.498119]]] @endcode If there is no index or object in clusters that exists in an input data then it is going to be marked as `NaN` in case of Index Labeling. Here is an example: @code from pyclustering.cluster.encoder import type_encoding, cluster_encoder # An input data. sample = [[1.0, 1.2], [1.2, 2.3], [114.3, 54.1], [2.2, 1.4], [5.3, 1.3]] # Clusters do not contains object with index 2 ([114.3, 54.1]) because it is outline. clusters = [[0, 1], [3, 4]] encoder = cluster_encoder(type_encoding.CLUSTER_INDEX_LIST_SEPARATION, clusters, sample) encoder.set_encoding(type_encoding.CLUSTER_INDEX_LABELING) print("Index Labeling:", encoder.get_clusters()) @endcode Here is an output of the code above. Pay attention to `NaN` value for the object with index 2 `[114.3, 54.1]`. @code Index Labeling: [0, 0, nan, 1, 1] @endcode """ def __init__(self, encoding, clusters, data): """! @brief Constructor of clustering result representor. @param[in] encoding (type_encoding): Type of clusters representation (Index List, Object List or Labels). @param[in] clusters (list): Clusters that were allocated from an input data. @param[in] data (list): Data that was used for cluster analysis. @see type_encoding """ self.__type_representation = encoding self.__clusters = clusters self.__data = data @property def get_encoding(self): """! @brief Returns current cluster representation. """ return self.__type_representation def get_clusters(self): """! @brief Returns clusters that are represented in line with type that is defined by `get_encoding()`. @see get_encoding() """ return self.__clusters def get_data(self): """! @brief Returns data that was used for cluster analysis. """ return self.__data def set_encoding(self, encoding): """! @brief Change clusters encoding to specified type (Index List, Object List, Labeling). @param[in] encoding (type_encoding): New type of clusters representation. @return (cluster_encoder) Return itself. """ if encoding == self.__type_representation: return self if self.__type_representation == type_encoding.CLUSTER_INDEX_LABELING: if encoding == type_encoding.CLUSTER_INDEX_LIST_SEPARATION: self.__clusters = self.__convert_label_to_index() else: self.__clusters = self.__convert_label_to_object() elif self.__type_representation == type_encoding.CLUSTER_INDEX_LIST_SEPARATION: if encoding == type_encoding.CLUSTER_INDEX_LABELING: self.__clusters = self.__convert_index_to_label() else: self.__clusters = self.__convert_index_to_object() else: if encoding == type_encoding.CLUSTER_INDEX_LABELING: self.__clusters = self.__convert_object_to_label() else: self.__clusters = self.__convert_object_to_index() self.__type_representation = encoding return self def __convert_index_to_label(self): clusters = [float('NaN')] * len(self.__data) index_cluster = 0 for cluster in self.__clusters: for index_object in cluster: clusters[index_object] = index_cluster index_cluster += 1 return clusters def __convert_index_to_object(self): clusters = [ [] for _ in range(len(self.__clusters)) ] for index_cluster in range(len(self.__clusters)): for index_object in self.__clusters[index_cluster]: data_object = self.__data[index_object] clusters[index_cluster].append(data_object) return clusters def __convert_object_to_label(self): positions = dict() clusters = [float('NaN')] * len(self.__data) index_cluster = 0 for cluster in self.__clusters: for data_object in cluster: hashable_data_object = str(data_object) if hashable_data_object in positions: index_object = self.__data.index(data_object, positions[hashable_data_object] + 1) else: index_object = self.__data.index(data_object) clusters[index_object] = index_cluster positions[hashable_data_object] = index_object index_cluster += 1 return clusters def __convert_object_to_index(self): positions = dict() clusters = [[] for _ in range(len(self.__clusters))] for index_cluster in range(len(self.__clusters)): for data_object in self.__clusters[index_cluster]: hashable_data_object = str(data_object) if hashable_data_object in positions: index_object = self.__data.index(data_object, positions[hashable_data_object] + 1) else: index_object = self.__data.index(data_object) clusters[index_cluster].append(index_object) positions[hashable_data_object] = index_object return clusters def __convert_label_to_index(self): clusters = [[] for _ in range(max(self.__clusters) + 1)] for index_object in range(len(self.__data)): index_cluster = self.__clusters[index_object] if not math.isnan(index_cluster): clusters[index_cluster].append(index_object) return clusters def __convert_label_to_object(self): clusters = [[] for _ in range(max(self.__clusters) + 1)] for index_object in range(len(self.__data)): index_cluster = self.__clusters[index_object] if not math.isnan(index_cluster): clusters[index_cluster].append(self.__data[index_object]) return clusters