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-mlpy for use of k-medoid : D. Albanese, R. Visintainer, S. Merler, S. Riccadonna, G. Jurman, C. Furlanello. mlpy: Machine Learning Python, 2012. arXiv:1202.6548 [bib]
diff --git a/doc/doc_kmedoids.py b/doc/doc_kmedoids.py
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-"""!
-
-@brief Cluster analysis algorithm: K-Medoids.
-@details Implementation based on papers @cite book::algorithms_for_clustering_data, @cite book::finding_groups_in_data.
-
-@authors Andrei Novikov (pyclustering@yandex.ru)
-@date 2014-2019
-@copyright GNU Public License
-
-@cond GNU_PUBLIC_LICENSE
- PyClustering is free software: you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- PyClustering is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
-
- You should have received a copy of the GNU General Public License
- along with this program. If not, see .
-@endcond
-
-"""
-
-
-import numpy
-
-from pyclustering.cluster.encoder import type_encoding
-
-from pyclustering.utils import medoid
-from pyclustering.utils.metric import distance_metric, type_metric
-
-import pyclustering.core.kmedoids_wrapper as wrapper
-
-from pyclustering.core.wrapper import ccore_library
-from pyclustering.core.metric_wrapper import metric_wrapper
-
-
-class kmedoids:
- """!
- @brief Class represents clustering algorithm K-Medoids.
- @details The algorithm is less sensitive to outliers tham K-Means. The principle difference between K-Medoids and K-Medians is that
- K-Medoids uses existed points from input data space as medoids, but median in K-Medians can be unreal object (not from
- input data space).
-
- Clustering example:
- @code
- from pyclustering.cluster.kmedoids import kmedoids
- from pyclustering.cluster import cluster_visualizer
- from pyclustering.utils import read_sample
- from pyclustering.samples.definitions import FCPS_SAMPLES
-
- # Load list of points for cluster analysis.
- sample = read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)
-
- # Set random initial medoids.
- initial_medoids = [1, 500]
-
- # Create instance of K-Medoids algorithm.
- kmedoids_instance = kmedoids(sample, initial_medoids)
-
- # Run cluster analysis and obtain results.
- kmedoids_instance.process()
- clusters = kmedoids_instance.get_clusters()
-
- # Show allocated clusters.
- print(clusters)
-
- # Display clusters.
- visualizer = cluster_visualizer()
- visualizer.append_clusters(clusters, sample)
- visualizer.show()
- @endcode
-
- Metric for calculation distance between points can be specified by parameter additional 'metric':
- @code
- # create Minkowski distance metric with degree equals to '2'
- metric = distance_metric(type_metric.MINKOWSKI, degree=2)
-
- # create K-Medoids algorithm with specific distance metric
- kmedoids_instance = kmedoids(sample, initial_medoids, metric=metric)
-
- # run cluster analysis and obtain results
- kmedoids_instance.process()
- clusters = kmedoids_instance.get_clusters()
- @endcode
-
- Distance matrix can be used instead of sequence of points to increase performance and for that purpose parameter 'data_type' should be used:
- @code
- # calculate distance matrix for sample
- sample = read_sample(path_to_sample)
- matrix = calculate_distance_matrix(sample)
-
- # create K-Medoids algorithm for processing distance matrix instead of points
- kmedoids_instance = kmedoids(matrix, initial_medoids, data_type='distance_matrix')
-
- # run cluster analysis and obtain results
- kmedoids_instance.process()
-
- clusters = kmedoids_instance.get_clusters()
- medoids = kmedoids_instance.get_medoids()
- @endcode
-
- """
-
-
- def __init__(self, data, initial_index_medoids, tolerance=0.001, ccore=True, **kwargs):
- """!
- @brief Constructor of clustering algorithm K-Medoids.
-
- @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
- @param[in] initial_index_medoids (list): Indexes of intial medoids (indexes of points in input data).
- @param[in] tolerance (double): Stop condition: if maximum value of distance change of medoids of clusters is less than tolerance than algorithm will stop processing.
- @param[in] ccore (bool): If specified than CCORE library (C++ pyclustering library) is used for clustering instead of Python code.
- @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'metric', 'data_type', 'itermax').
-
- Keyword Args:
- - metric (distance_metric): Metric that is used for distance calculation between two points.
- - data_type (string): Data type of input sample 'data' that is processed by the algorithm ('points', 'distance_matrix').
- - itermax (uint): Maximum number of iteration for cluster analysis.
-
- """
- self.__pointer_data = data
- self.__clusters = []
- self.__medoid_indexes = initial_index_medoids
- self.__tolerance = tolerance
-
- self.__metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE))
- self.__data_type = kwargs.get('data_type', 'points')
- self.__itermax = kwargs.get('itermax', 200)
-
- self.__distance_calculator = self.__create_distance_calculator()
-
- self.__ccore = ccore and self.__metric.get_type() != type_metric.USER_DEFINED
- if self.__ccore:
- self.__ccore = ccore_library.workable()
-
- #self.__verify_instance()
-
-
- def process(self):
- """!
- @brief Performs cluster analysis in line with rules of K-Medoids algorithm.
-
- @return (kmedoids) Returns itself (K-Medoids instance).
-
- @remark Results of clustering can be obtained using corresponding get methods.
-
- @see get_clusters()
- @see get_medoids()
-
- """
-
- if self.__ccore is True:
- ccore_metric = metric_wrapper.create_instance(self.__metric)
- self.__clusters, self.__medoid_indexes = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, self.__itermax, ccore_metric.get_pointer(), self.__data_type)
-
- else:
- changes = float('inf')
- iterations = 0
-
- while changes > self.__tolerance and iterations < self.__itermax:
- self.__clusters = self.__update_clusters()
- update_medoid_indexes = self.__update_medoids()
-
- changes = max([self.__distance_calculator(self.__medoid_indexes[index], update_medoid_indexes[index]) for index in range(len(update_medoid_indexes))])
-
- self.__medoid_indexes = update_medoid_indexes
-
- iterations += 1
-
- return self
-
-
- def predict(self, points):
- """!
- @brief Calculates the closest cluster to each point.
-
- @param[in] points (array_like): Points for which closest clusters are calculated.
-
- @return (list) List of closest clusters for each point. Each cluster is denoted by index. Return empty
- collection if 'process()' method was not called.
-
- An example how to calculate (or predict) the closest cluster to specified points.
- @code
- from pyclustering.cluster.kmedoids import kmedoids
- from pyclustering.samples.definitions import SIMPLE_SAMPLES
- from pyclustering.utils import read_sample
-
- # Load list of points for cluster analysis.
- sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
-
- # Initial medoids for sample 'Simple3'.
- initial_medoids = [4, 12, 25, 37]
-
- # Create instance of K-Medoids algorithm with prepared centers.
- kmedoids_instance = kmedoids(sample, initial_medoids)
-
- # Run cluster analysis.
- kmedoids_instance.process()
-
- # Calculate the closest cluster to following two points.
- points = [[0.35, 0.5], [2.5, 2.0]]
- closest_clusters = kmedoids_instance.predict(points)
- print(closest_clusters)
- @endcode
-
- """
-
- if len(self.__clusters) == 0:
- return []
-
- medoids = [ self.__pointer_data[index] for index in self.__medoid_indexes ]
- differences = numpy.zeros((len(points), len(medoids)))
- for index_point in range(len(points)):
- differences[index_point] = [ self.__metric(points[index_point], center) for center in medoids ]
-
- return numpy.argmin(differences, axis=1)
-
-
- def get_clusters(self):
- """!
- @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
-
- @see process()
- @see get_medoids()
-
- """
-
- return self.__clusters
-
-
- def get_medoids(self):
- """!
- @brief Returns list of medoids of allocated clusters represented by indexes from the input data.
-
- @see process()
- @see get_clusters()
-
- """
-
- return self.__medoid_indexes
-
-
- def get_cluster_encoding(self):
- """!
- @brief Returns clustering result representation type that indicate how clusters are encoded.
-
- @return (type_encoding) Clustering result representation.
-
- @see get_clusters()
-
- """
-
- return type_encoding.CLUSTER_INDEX_LIST_SEPARATION
-
-
- def __verify_instance(self):
- pass
-
-
- def __create_distance_calculator(self):
- """!
- @brief Creates distance calculator in line with algorithms parameters.
-
- @return (callable) Distance calculator.
-
- """
- if self.__data_type == 'points':
- return lambda index1, index2: self.__metric(self.__pointer_data[index1], self.__pointer_data[index2])
-
- elif self.__data_type == 'distance_matrix':
- if isinstance(self.__pointer_data, numpy.matrix):
- return lambda index1, index2: self.__pointer_data.item((index1, index2))
-
- return lambda index1, index2: self.__pointer_data[index1][index2]
-
- else:
- raise TypeError("Unknown type of data is specified '%s'" % self.__data_type)
-
-
- def __update_clusters(self):
- """!
- @brief Calculate distance to each point from the each cluster.
- @details Nearest points are captured by according clusters and as a result clusters are updated.
-
- @return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data.
-
- """
-
- clusters = [[self.__medoid_indexes[i]] for i in range(len(self.__medoid_indexes))]
- for index_point in range(len(self.__pointer_data)):
- if index_point in self.__medoid_indexes:
- continue
-
- index_optim = -1
- dist_optim = float('Inf')
-
- for index in range(len(self.__medoid_indexes)):
- dist = self.__distance_calculator(index_point, self.__medoid_indexes[index])
-
- if dist < dist_optim:
- index_optim = index
- dist_optim = dist
-
- clusters[index_optim].append(index_point)
-
- return clusters
-
-
- def __update_medoids(self):
- """!
- @brief Find medoids of clusters in line with contained objects.
-
- @return (list) list of medoids for current number of clusters.
-
- """
-
- medoid_indexes = [-1] * len(self.__clusters)
-
- for index in range(len(self.__clusters)):
- medoid_index = medoid(self.__pointer_data, self.__clusters[index], metric=self.__metric, data_type=self.__data_type)
- medoid_indexes[index] = medoid_index
-
- return medoid_indexes
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