kmedians

Complexity

Total Complexity

24

Size/Duplication

Total Lines	165
Duplicated Lines	35.76 %

Importance

Changes

0

7 Methods

Rating	Name	Duplication	Size	Complexity
A	get_cluster_encoding()	11	11	1
A	__init__()	0	15	1
D	__update_clusters()	10	27	8
A	get_clusters()	0	10	1
A	get_medians()	2	10	1
B	process()	32	32	5
C	__update_medians()	0	28	7

"""!


@brief Cluster analysis algorithm: K-Medians

@details Based on book description:

         - A.K. Jain, R.C Dubes, Algorithms for Clustering Data. 1988.


@authors Andrei Novikov (pyclustering@yandex.ru)

@date 2014-2017

@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 <http://www.gnu.org/licenses/>.

@endcond


"""



import math;


from pyclustering.cluster.encoder import type_encoding;


from pyclustering.utils import euclidean_distance_sqrt;


import pyclustering.core.kmedians_wrapper as wrapper;



class kmedians:

    """!

    @brief Class represents clustering algorithm K-Medians.

    @details The algorithm is less sensitive to outliers than K-Means. Medians are calculated instead of centroids.

    

    Example:

    @code

        # load list of points for cluster analysis

        sample = read_sample(path);

        

        # create instance of K-Medians algorithm

        kmedians_instance = kmedians(sample, [ [0.0, 0.1], [2.5, 2.6] ]);

        

        # run cluster analysis and obtain results

        kmedians_instance.process();

        kmedians_instance.get_clusters();    

    @endcode

    

    """

    

    def __init__(self, data, initial_centers, tolerance = 0.25, ccore = False):

        """!

        @brief Constructor of clustering algorithm K-Medians.

        

        @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_centers (list): Initial coordinates of medians of clusters that are represented by list: [center1, center2, ...].

        @param[in] tolerance (double): Stop condition: if maximum value of change of centers of clusters is less than tolerance than algorithm will stop processing

        @param[in] ccore (bool): Defines should be CCORE library (C++ pyclustering library) used instead of Python code or not.

        

        """

        self.__pointer_data = data;

        self.__clusters = [];

        self.__medians = initial_centers[:];

        self.__tolerance = tolerance;

        self.__ccore = ccore;



    def process(self):

This code seems to be duplicated in your project.

        """!

        @brief Performs cluster analysis in line with rules of K-Medians algorithm.

        

        @remark Results of clustering can be obtained using corresponding get methods.

        

        @see get_clusters()

        @see get_medians()

        

        """

        

        if (self.__ccore is True):

            self.__clusters = wrapper.kmedians(self.__pointer_data, self.__medians, self.__tolerance);

            self.__medians = self.__update_medians();

            

        else:

            changes = float('inf');

             

            stop_condition = self.__tolerance * self.__tolerance;   # Fast solution

            #stop_condition = self.__tolerance;              # Slow solution

             

            # Check for dimension

            if (len(self.__pointer_data[0]) != len(self.__medians[0])):

                raise NameError('Dimension of the input data and dimension of the initial cluster medians must be equal.');

             

            while (changes > stop_condition):

                self.__clusters = self.__update_clusters();

                updated_centers = self.__update_medians();  # changes should be calculated before asignment

             

                changes = max([euclidean_distance_sqrt(self.__medians[index], updated_centers[index]) for index in range(len(updated_centers))]);    # Fast solution

                 

                self.__medians = updated_centers;



    def get_clusters(self):

        """!

        @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.

        

        @see process()

        @see get_medians()

        

        """

        

        return self.__clusters;

    

    

    def get_medians(self):

        """!

        @brief Returns list of centers of allocated clusters.

        

        @see process()

        @see get_clusters()

        

        """

This code seems to be duplicated in your project.

        return self.__medians;



    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 __update_clusters(self):

        """!

        @brief Calculate Manhattan 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 = [[] for i in range(len(self.__medians))];

        for index_point in range(len(self.__pointer_data)):

            index_optim = -1;

            dist_optim = 0.0;

             

            for index in range(len(self.__medians)):

                dist = euclidean_distance_sqrt(self.__pointer_data[index_point], self.__medians[index]);

                 

                if ( (dist < dist_optim) or (index is 0)):

                    index_optim = index;

                    dist_optim = dist;

             

            clusters[index_optim].append(index_point);

            

        # If cluster is not able to capture object it should be removed

        clusters = [cluster for cluster in clusters if len(cluster) > 0];

        

        return clusters;

    

    

    def __update_medians(self):

        """!

        @brief Calculate medians of clusters in line with contained objects.

        

        @return (list) list of medians for current number of clusters.

        

        """

         

        medians = [[] for i in range(len(self.__clusters))];

         

        for index in range(len(self.__clusters)):

            medians[index] = [ 0.0 for i in range(len(self.__pointer_data[0]))];

            length_cluster = len(self.__clusters[index]);

            

            for index_dimension in range(len(self.__pointer_data[0])):

                sorted_cluster = sorted(self.__clusters[index], key = lambda x: self.__pointer_data[x][index_dimension]);

                

                relative_index_median = math.floor(length_cluster / 2);

                index_median = sorted_cluster[relative_index_median];

                

                if ( (length_cluster % 2) == 0 ):

                    index_median_second = sorted_cluster[relative_index_median + 1];

                    medians[index][index_dimension] =  (self.__pointer_data[index_median][index_dimension] + self.__pointer_data[index_median_second][index_dimension]) / 2.0;

                    

                else:

                    medians[index][index_dimension] = self.__pointer_data[index_median][index_dimension];

             

        return medians;