kmeans_observer.get_clusters() - Code Metrics - Inspection of "[.] Merge branch '0.8.dev' to 'master'." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 9cffad...7a46cc )

by Andrei

created 2018-04-05 13:23 UTC

kmeans_observer.get_clusters() A

↳ Parent: kmeans_observer

Complexity

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Importance

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cc	1
dl	0
loc	10
rs	9.4285
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"""!

@brief Cluster analysis algorithm: K-Means
@details Based on book description:
         - J.B.MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. 1967.

@authors Andrei Novikov ([email protected])
@date 2014-2018
@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 numpy;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import matplotlib.pyplot as plt;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.animation as animation;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import pyclustering.core.kmeans_wrapper as wrapper;

from pyclustering.core.wrapper import ccore_library;

from pyclustering.cluster.encoder import type_encoding;
from pyclustering.cluster import cluster_visualizer;


class kmeans_observer:
    """!
    @brief Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm.
    
    @see kmeans
    
    """
    
    def __init__(self):
        """!
        @brief Initializer of observer of K-Means algorithm.
        
        """
        self.__evolution_clusters   = [];
        self.__evolution_centers    = [];
        self.__initial_centers      = [];


    def __len__(self):
        """!
        @brief Returns amount of steps that were observer during clustering process in K-Means algorithm.
        
        """
        return len(self.__evolution_clusters);


    def notify(self, clusters, centers):
        """!
        @brief This method is called by K-Means algorithm to notify about changes.
        
        @param[in] clusters (array_like): Allocated clusters by K-Means algorithm.
        @param[in] centers (array_like): Allocated centers by K-Means algorithm.
        
        """
        self.__evolution_clusters.append(clusters);
        self.__evolution_centers.append(centers);


    def set_evolution_centers(self, evolution_centers):
        """!
        @brief Set evolution of changes of centers during clustering process.
        
        @param[in] evolution_centers (array_like): Evolution of changes of centers during clustering process.
        
        """
        self.__evolution_centers = evolution_centers;


    def get_centers(self, iteration):
        """!
        @brief Get method to return centers at specific iteration of clustering process.
        
        @param[in] iteration (uint): Clustering process iteration at which centers are required.
        
        @return (array_like) Centers at specific iteration.
        
        """
        return self.__evolution_centers[iteration];


    def set_evolution_clusters(self, evolution_clusters):
        """!
        @brief Set evolution of changes of centers during clustering process.
        
        @param[in] evolution_clusters (array_like): Evolution of changes of clusters during clustering process.
        
        """
        self.__evolution_clusters = evolution_clusters;


    def get_clusters(self, iteration):
        """!
        @brief Get method to return allocated clusters at specific iteration of clustering process.
        
        @param[in] iteration (uint): Clustering process iteration at which clusters are required.
        
        @return (array_like) Clusters at specific iteration.
        
        """
        return self.__evolution_clusters[iteration];



class kmeans_visualizer:
    """!
    @brief Visualizer of K-Means algorithm's results.
    @details K-Means visualizer provides visualization services that are specific for K-Means algorithm.
    
    """
    
    __default_2d_marker_size = 15;
    __default_3d_marker_size = 70;
    
    
    @staticmethod
    def show_clusters(sample, clusters, centers, initial_centers = None, **kwargs):
        """!
        @brief Display K-Means clustering results.
        
        @param[in] sample (list): Dataset that were used for clustering.
        @param[in] clusters (array_like): Clusters that were allocated by the algorithm.
        @param[in] centers (array_like): Centers that were allocated by the algorithm.
        @param[in] initial_centers (array_like): Initial centers that were used by the algorithm, if 'None' then initial centers are not displyed.
        @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'figure', 'display').
        
        Keyword Args:
            figure (figure): If 'None' then new is figure is creater, otherwise specified figure is used for visualization.
            display (bool): If 'True' then figure will be shown by the method, otherwise it should be shown manually using matplotlib function 'plt.show()'.
            offset (uint): Specify axes index on the figure where results should be drawn (only if argument 'figure' is specified).
        
        @return (figure) Figure where clusters were drawn.
        
        """

        visualizer = cluster_visualizer();
        visualizer.append_clusters(clusters, sample);
        
        offset = kmeans_visualizer.__get_argument('offset', 0, **kwargs);
        figure = kmeans_visualizer.__get_argument('figure', None, **kwargs);
        if (figure is None):
            figure = visualizer.show(display = False);
        else:
            visualizer.show(figure = figure, display = False);
        
        kmeans_visualizer.__draw_centers(figure, offset, visualizer, centers, initial_centers);
        kmeans_visualizer.__draw_rays(figure, offset, visualizer, sample, clusters, centers);
        
        if (kmeans_visualizer.__get_argument('display', True, **kwargs) is True):
            plt.show();

        return figure;


    @staticmethod
    def __draw_rays(figure, offset, visualizer, sample, clusters, centers):
        ax = figure.get_axes()[offset];
        
        for index_cluster in range(len(clusters)):
            color = visualizer.get_cluster_color(index_cluster, 0);
            kmeans_visualizer.__draw_cluster_rays(ax, color, sample, clusters[index_cluster], centers[index_cluster])


    @staticmethod
    def __draw_cluster_rays(ax, color, sample, cluster, center):
        dimension = len(sample[0]);
        
        for index_point in cluster:
            point = sample[index_point];
            if (dimension == 1):
                ax.plot([point[0], center[0]], [0.0, 0.0], '-', color=color, linewidth=0.5);
            elif (dimension == 2):
                ax.plot([point[0], center[0]], [point[1], center[1]], '-', color=color, linewidth=0.5);
            elif (dimension == 3):
                ax.plot([point[0], center[0]], [point[1], center[1]], [point[2], center[2]], '-', color=color, linewidth=0.5)


    @staticmethod
    def __draw_center(ax, center, color, marker, alpha):
        dimension = len(center);
        
        if (dimension == 1):
            ax.plot(center[0], 0.0, color=color, alpha=alpha, marker=marker, markersize=kmeans_visualizer.__default_2d_marker_size);
        elif (dimension == 2):
            ax.plot(center[0], center[1], color=color, alpha=alpha, marker=marker, markersize=kmeans_visualizer.__default_2d_marker_size);
        elif (dimension == 3):
            ax.scatter(center[0], center[1], center[2], c=color, alpha=alpha, marker=marker, s=kmeans_visualizer.__default_3d_marker_size);


    @staticmethod
    def __draw_centers(figure, offset, visualizer, centers, initial_centers):
        ax = figure.get_axes()[offset];
        
        for index_center in range(len(centers)):
            color = visualizer.get_cluster_color(index_center, 0);
            kmeans_visualizer.__draw_center(ax, centers[index_center], color, '*', 1.0);
            
            if initial_centers is not None:
                kmeans_visualizer.__draw_center(ax, initial_centers[index_center], color, '*', 0.4);


    @staticmethod
    def __get_argument(argument_name, default_value, **kwargs):
        if (argument_name in kwargs):
            return kwargs[argument_name];
        
        return default_value;


    @staticmethod
    def animate_cluster_allocation(data, observer, animation_velocity = 500, movie_fps = 1, save_movie = None):
        """!
        @brief Animates clustering process that is performed by K-Means algorithm.

        @param[in] data (list): Dataset that is used for clustering.
        @param[in] observer (kmeans_observer): EM observer that was used for collection information about clustering process.
        @param[in] animation_velocity (uint): Interval between frames in milliseconds (for run-time animation only).
        @param[in] movie_fps (uint): Defines frames per second (for rendering movie only).
        @param[in] save_movie (string): If it is specified then animation will be stored to file that is specified in this parameter.

        """
        figure = plt.figure();

        def init_frame():
            return frame_generation(0);

        def frame_generation(index_iteration):
            figure.clf();

            figure.suptitle("K-Means algorithm (iteration: " + str(index_iteration) + ")", fontsize=18, fontweight='bold');

            clusters = observer.get_clusters(index_iteration);
            centers = observer.get_centers(index_iteration);
            kmeans_visualizer.show_clusters(data, clusters, centers, None, figure=figure, display=False);

            figure.subplots_adjust(top=0.85);

            return [figure.gca()];

        iterations = len(observer);
        cluster_animation = animation.FuncAnimation(figure, frame_generation, iterations, interval=animation_velocity,
                                                    init_func=init_frame, repeat_delay=5000);

        if (save_movie is not None):
            cluster_animation.save(save_movie, writer='ffmpeg', fps=movie_fps, bitrate=3000);
        else:
            plt.show();



class kmeans:
    """!
    @brief Class represents K-Means clustering algorithm.
    @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
    
    CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.
    
    K-Means clustering results depend on initial centers. Algorithm K-Means++ can used for initialization 
    initial centers from module 'pyclustering.cluster.center_initializer'.
    
    @image html kmeans_example_clustering.png "K-Means clustering results. At the left - 'Simple03.data' sample, at the right - 'Lsun.data' sample."
    
    Example #1 - Trivial clustering:
    @code
        # load list of points for cluster analysis
        sample = read_sample(path);
        
        # create instance of K-Means algorithm
        kmeans_instance = kmeans(sample, [ [0.0, 0.1], [2.5, 2.6] ]);
        
        # run cluster analysis and obtain results
        kmeans_instance.process();
        clusters = kmeans_instance.get_clusters();
    @endcode
    
    Example #2 - Clustering using K-Means++ for center initialization:
    @code
        # load list of points for cluster analysis
        sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
        
        # initialize initial centers using K-Means++ method
        initial_centers = kmeans_plusplus_initializer(sample, 3).initialize();
        
        # create instance of K-Means algorithm with prepared centers
        kmeans_instance = kmeans(sample, initial_centers);
        
        # run cluster analysis and obtain results
        kmeans_instance.process();
        clusters = kmeans_instance.get_clusters();
        final_centers = kmeans_instance.get_centers();
    @endcode
    
    @see center_initializer
    
    """
    
    def __init__(self, data, initial_centers, tolerance = 0.001, ccore = True, **kwargs):
        """!
        @brief Constructor of clustering algorithm K-Means.
        @details Center initializer can be used for creating initial centers, for example, K-Means++ method.
        
        @param[in] data (array_like): Input data that is presented as array of points (objects), each point should be represented by array_like data structure.
        @param[in] initial_centers (array_like): Initial coordinates of centers of clusters that are represented by array_like data structure: [center1, center2, ...].
        @param[in] tolerance (double): Stop condition: if maximum value of change of centers of clusters is less than tolerance then algorithm stops processing.
        @param[in] ccore (bool): Defines should be CCORE library (C++ pyclustering library) used instead of Python code or not.
        @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'observer').
        
        Keyword Args:
            observer (kmeans_observer): Observer of the algorithm to collect information about clustering process on each iteration.
        
        @see center_initializer
        
        """
        self.__pointer_data = numpy.matrix(data);
        self.__clusters = [];
        self.__centers = numpy.matrix(initial_centers);
        self.__tolerance = tolerance;
        
        self.__observer = None;
        if 'observer' in kwargs:
            self.__observer = kwargs['observer'];
        
        self.__ccore = ccore;
        if (self.__ccore is True):
            self.__ccore = ccore_library.workable();


    def process(self):
        """!
        @brief Performs cluster analysis in line with rules of K-Means algorithm.
        
        @remark Results of clustering can be obtained using corresponding get methods.
        
        @see get_clusters()
        @see get_centers()
        
        """

        if (len(self.__pointer_data[0]) != len(self.__centers[0])):
            raise NameError('Dimension of the input data and dimension of the initial cluster centers must be equal.');

        if (self.__ccore is True):
            self.__process_by_ccore();
        else:
            self.__process_by_python();


    def __process_by_ccore(self):
        """!
        @brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).

        """
        results = wrapper.kmeans(self.__pointer_data, self.__centers, self.__tolerance, (self.__observer is not None));
        self.__clusters = results[0];
        self.__centers = results[1];

        if self.__observer is not None:
            self.__observer.set_evolution_clusters(results[2]);
            self.__observer.set_evolution_centers(results[3]);


    def __process_by_python(self):
        """!
        @brief Performs cluster analysis using python code.

        """

        maximum_change = float('inf');
        stop_condition = self.__tolerance * self.__tolerance;

        if (self.__observer is not None):
            initial_clusters = self.__update_clusters();
            self.__observer.notify(initial_clusters, self.__centers.tolist());

        while (maximum_change > stop_condition):
            self.__clusters = self.__update_clusters();
            updated_centers = self.__update_centers();  # changes should be calculated before assignment

            if self.__observer is not None:
                self.__observer.notify(self.__clusters, updated_centers.tolist());

            if (len(self.__centers) != len(updated_centers)):
                maximum_change = float('inf');

            else:
                changes = numpy.sum(numpy.square(self.__centers - updated_centers), axis=1);
                maximum_change = numpy.max(changes);

            self.__centers = updated_centers.tolist();


    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
        
        @see process()
        @see get_centers()
        
        """
        
        return self.__clusters;
    
    
    def get_centers(self):
        """!
        @brief Returns list of centers of allocated clusters.
        
        @see process()
        @see get_clusters()
        
        """

        if isinstance(self.__centers, list):
            return self.__centers;
        
        return self.__centers.tolist();


    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 Euclidean distance to each point from the each cluster. Nearest points are captured by according clusters and as a result clusters are updated.
        
        @return (list) updated clusters as list of clusters. Each cluster contains indexes of objects from data.
        
        """
        
        clusters = [[] for _ in range(len(self.__centers))];
        
        dataset_differences = numpy.zeros((len(clusters), len(self.__pointer_data)));
        for index_center in range(len(self.__centers)):
            dataset_differences[index_center] = numpy.sum(numpy.square(self.__pointer_data - self.__centers[index_center]), axis=1).T;
        
        optimum_indexes = numpy.argmin(dataset_differences, axis=0);
        for index_point in range(len(optimum_indexes)):
            index_cluster = optimum_indexes[index_point];
            clusters[index_cluster].append(index_point);
        
        clusters = [cluster for cluster in clusters if len(cluster) > 0];
        
        return clusters;
    
    
    def __update_centers(self):
        """!
        @brief Calculate centers of clusters in line with contained objects.
        
        @return (numpy.matrix) Updated centers as list of centers.
        
        """
        
        dimension = self.__pointer_data.shape[1];
        centers = numpy.zeros((len(self.__clusters), dimension));
        
        for index in range(len(self.__clusters)):
            cluster_points = self.__pointer_data[self.__clusters[index], :];
            centers[index] = cluster_points.mean(axis=0);

        return numpy.matrix(centers);


Push — master ( 9cffad...7a46cc )

kmeans_observer.get_clusters() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1			"""!
2
3			@brief Cluster analysis algorithm: K-Means
4			@details Based on book description:
5			- J.B.MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. 1967.
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2018
9			@copyright GNU Public License
10
11			@cond GNU_PUBLIC_LICENSE
12			PyClustering is free software: you can redistribute it and/or modify
13			it under the terms of the GNU General Public License as published by
14			the Free Software Foundation, either version 3 of the License, or
15			(at your option) any later version.
16
17			PyClustering is distributed in the hope that it will be useful,
18			but WITHOUT ANY WARRANTY; without even the implied warranty of
19			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
20			GNU General Public License for more details.
21
22			You should have received a copy of the GNU General Public License
23			along with this program. If not, see <http://www.gnu.org/licenses/>.
24			@endcond
25
26			"""
27
28
29			import numpy;
			0 ignored issues – show Configuration introduced 2018-02-27 17:16 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
30
31			import matplotlib.pyplot as plt;
			0 ignored issues – show Configuration introduced 2018-03-03 12:02 UTC by Report Bug Copy Issue Report The import `matplotlib.pyplot` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
32			import matplotlib.animation as animation;
			0 ignored issues – show Configuration introduced 2018-03-03 12:02 UTC by Report Bug Copy Issue Report The import `matplotlib.animation` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
33
34			import pyclustering.core.kmeans_wrapper as wrapper;
35
36			from pyclustering.core.wrapper import ccore_library;
37
38			from pyclustering.cluster.encoder import type_encoding;
39			from pyclustering.cluster import cluster_visualizer;
40
41
42			class kmeans_observer:
43			"""!
44			@brief Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm.
45
46			@see kmeans
47
48			"""
49
50			def __init__(self):
51			"""!
52			@brief Initializer of observer of K-Means algorithm.
53
54			"""
55			self.__evolution_clusters = [];
56			self.__evolution_centers = [];
57			self.__initial_centers = [];
58
59
60			def __len__(self):
61			"""!
62			@brief Returns amount of steps that were observer during clustering process in K-Means algorithm.
63
64			"""
65			return len(self.__evolution_clusters);
66
67
68			def notify(self, clusters, centers):
69			"""!
70			@brief This method is called by K-Means algorithm to notify about changes.
71
72			@param[in] clusters (array_like): Allocated clusters by K-Means algorithm.
73			@param[in] centers (array_like): Allocated centers by K-Means algorithm.
74
75			"""
76			self.__evolution_clusters.append(clusters);
77			self.__evolution_centers.append(centers);
78
79
80			def set_evolution_centers(self, evolution_centers):
81			"""!
82			@brief Set evolution of changes of centers during clustering process.
83
84			@param[in] evolution_centers (array_like): Evolution of changes of centers during clustering process.
85
86			"""
87			self.__evolution_centers = evolution_centers;
88
89
90			def get_centers(self, iteration):
91			"""!
92			@brief Get method to return centers at specific iteration of clustering process.
93
94			@param[in] iteration (uint): Clustering process iteration at which centers are required.
95
96			@return (array_like) Centers at specific iteration.
97
98			"""
99			return self.__evolution_centers[iteration];
100
101
102			def set_evolution_clusters(self, evolution_clusters):
103			"""!
104			@brief Set evolution of changes of centers during clustering process.
105
106			@param[in] evolution_clusters (array_like): Evolution of changes of clusters during clustering process.
107
108			"""
109			self.__evolution_clusters = evolution_clusters;
110
111
112			def get_clusters(self, iteration):
113			"""!
114			@brief Get method to return allocated clusters at specific iteration of clustering process.
115
116			@param[in] iteration (uint): Clustering process iteration at which clusters are required.
117
118			@return (array_like) Clusters at specific iteration.
119
120			"""
121			return self.__evolution_clusters[iteration];
122
123
124
125			class kmeans_visualizer:
126			"""!
127			@brief Visualizer of K-Means algorithm's results.
128			@details K-Means visualizer provides visualization services that are specific for K-Means algorithm.
129
130			"""
131
132			__default_2d_marker_size = 15;
133			__default_3d_marker_size = 70;
134
135
136			@staticmethod
137			def show_clusters(sample, clusters, centers, initial_centers = None, **kwargs):
138			"""!
139			@brief Display K-Means clustering results.
140
141			@param[in] sample (list): Dataset that were used for clustering.
142			@param[in] clusters (array_like): Clusters that were allocated by the algorithm.
143			@param[in] centers (array_like): Centers that were allocated by the algorithm.
144			@param[in] initial_centers (array_like): Initial centers that were used by the algorithm, if 'None' then initial centers are not displyed.
145			@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'figure', 'display').
146
147			Keyword Args:
148			figure (figure): If 'None' then new is figure is creater, otherwise specified figure is used for visualization.
149			display (bool): If 'True' then figure will be shown by the method, otherwise it should be shown manually using matplotlib function 'plt.show()'.
150			offset (uint): Specify axes index on the figure where results should be drawn (only if argument 'figure' is specified).
151
152			@return (figure) Figure where clusters were drawn.
153
154			"""
155
156			visualizer = cluster_visualizer();
157			visualizer.append_clusters(clusters, sample);
158
159			offset = kmeans_visualizer.__get_argument('offset', 0, **kwargs);
160			figure = kmeans_visualizer.__get_argument('figure', None, **kwargs);
161			if (figure is None):
162			figure = visualizer.show(display = False);
163			else:
164			visualizer.show(figure = figure, display = False);
165
166			kmeans_visualizer.__draw_centers(figure, offset, visualizer, centers, initial_centers);
167			kmeans_visualizer.__draw_rays(figure, offset, visualizer, sample, clusters, centers);
168
169			if (kmeans_visualizer.__get_argument('display', True, **kwargs) is True):
170			plt.show();
171
172			return figure;
173
174
175			@staticmethod
176			def __draw_rays(figure, offset, visualizer, sample, clusters, centers):
177			ax = figure.get_axes()[offset];
178
179			for index_cluster in range(len(clusters)):
180			color = visualizer.get_cluster_color(index_cluster, 0);
181			kmeans_visualizer.__draw_cluster_rays(ax, color, sample, clusters[index_cluster], centers[index_cluster])
182
183
184			@staticmethod
185			def __draw_cluster_rays(ax, color, sample, cluster, center):
186			dimension = len(sample[0]);
187
188			for index_point in cluster:
189			point = sample[index_point];
190			if (dimension == 1):
191			ax.plot([point[0], center[0]], [0.0, 0.0], '-', color=color, linewidth=0.5);
192			elif (dimension == 2):
193			ax.plot([point[0], center[0]], [point[1], center[1]], '-', color=color, linewidth=0.5);
194			elif (dimension == 3):
195			ax.plot([point[0], center[0]], [point[1], center[1]], [point[2], center[2]], '-', color=color, linewidth=0.5)
196
197
198			@staticmethod
199			def __draw_center(ax, center, color, marker, alpha):
200			dimension = len(center);
201
202			if (dimension == 1):
203			ax.plot(center[0], 0.0, color=color, alpha=alpha, marker=marker, markersize=kmeans_visualizer.__default_2d_marker_size);
204			elif (dimension == 2):
205			ax.plot(center[0], center[1], color=color, alpha=alpha, marker=marker, markersize=kmeans_visualizer.__default_2d_marker_size);
206			elif (dimension == 3):
207			ax.scatter(center[0], center[1], center[2], c=color, alpha=alpha, marker=marker, s=kmeans_visualizer.__default_3d_marker_size);
208
209
210			@staticmethod
211			def __draw_centers(figure, offset, visualizer, centers, initial_centers):
212			ax = figure.get_axes()[offset];
213
214			for index_center in range(len(centers)):
215			color = visualizer.get_cluster_color(index_center, 0);
216			kmeans_visualizer.__draw_center(ax, centers[index_center], color, '*', 1.0);
217
218			if initial_centers is not None:
219			kmeans_visualizer.__draw_center(ax, initial_centers[index_center], color, '*', 0.4);
220
221
222			@staticmethod
223			def __get_argument(argument_name, default_value, **kwargs):
224			if (argument_name in kwargs):
225			return kwargs[argument_name];
226
227			return default_value;
228
229
230			@staticmethod
231			def animate_cluster_allocation(data, observer, animation_velocity = 500, movie_fps = 1, save_movie = None):
232			"""!
233			@brief Animates clustering process that is performed by K-Means algorithm.
234
235			@param[in] data (list): Dataset that is used for clustering.
236			@param[in] observer (kmeans_observer): EM observer that was used for collection information about clustering process.
237			@param[in] animation_velocity (uint): Interval between frames in milliseconds (for run-time animation only).
238			@param[in] movie_fps (uint): Defines frames per second (for rendering movie only).
239			@param[in] save_movie (string): If it is specified then animation will be stored to file that is specified in this parameter.
240
241			"""
242			figure = plt.figure();
243
244			def init_frame():
245			return frame_generation(0);
246
247			def frame_generation(index_iteration):
248			figure.clf();
249
250			figure.suptitle("K-Means algorithm (iteration: " + str(index_iteration) + ")", fontsize=18, fontweight='bold');
251
252			clusters = observer.get_clusters(index_iteration);
253			centers = observer.get_centers(index_iteration);
254			kmeans_visualizer.show_clusters(data, clusters, centers, None, figure=figure, display=False);
255
256			figure.subplots_adjust(top=0.85);
257
258			return [figure.gca()];
259
260			iterations = len(observer);
261			cluster_animation = animation.FuncAnimation(figure, frame_generation, iterations, interval=animation_velocity,
262			init_func=init_frame, repeat_delay=5000);
263
264			if (save_movie is not None):
265			cluster_animation.save(save_movie, writer='ffmpeg', fps=movie_fps, bitrate=3000);
266			else:
267			plt.show();
268
269
270
271			class kmeans:
272			"""!
273			@brief Class represents K-Means clustering algorithm.
274			@details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
275
276			CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.
277
278			K-Means clustering results depend on initial centers. Algorithm K-Means++ can used for initialization
279			initial centers from module 'pyclustering.cluster.center_initializer'.
280
281			@image html kmeans_example_clustering.png "K-Means clustering results. At the left - 'Simple03.data' sample, at the right - 'Lsun.data' sample."
282
283			Example #1 - Trivial clustering:
284			@code
285			# load list of points for cluster analysis
286			sample = read_sample(path);
287
288			# create instance of K-Means algorithm
289			kmeans_instance = kmeans(sample, [ [0.0, 0.1], [2.5, 2.6] ]);
290
291			# run cluster analysis and obtain results
292			kmeans_instance.process();
293			clusters = kmeans_instance.get_clusters();
294			@endcode
295
296			Example #2 - Clustering using K-Means++ for center initialization:
297			@code
298			# load list of points for cluster analysis
299			sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2);
300
301			# initialize initial centers using K-Means++ method
302			initial_centers = kmeans_plusplus_initializer(sample, 3).initialize();
303
304			# create instance of K-Means algorithm with prepared centers
305			kmeans_instance = kmeans(sample, initial_centers);
306
307			# run cluster analysis and obtain results
308			kmeans_instance.process();
309			clusters = kmeans_instance.get_clusters();
310			final_centers = kmeans_instance.get_centers();
311			@endcode
312
313			@see center_initializer
314
315			"""
316
317			def __init__(self, data, initial_centers, tolerance = 0.001, ccore = True, **kwargs):
318			"""!
319			@brief Constructor of clustering algorithm K-Means.
320			@details Center initializer can be used for creating initial centers, for example, K-Means++ method.
321
322			@param[in] data (array_like): Input data that is presented as array of points (objects), each point should be represented by array_like data structure.
323			@param[in] initial_centers (array_like): Initial coordinates of centers of clusters that are represented by array_like data structure: [center1, center2, ...].
324			@param[in] tolerance (double): Stop condition: if maximum value of change of centers of clusters is less than tolerance then algorithm stops processing.
325			@param[in] ccore (bool): Defines should be CCORE library (C++ pyclustering library) used instead of Python code or not.
326			@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'observer').
327
328			Keyword Args:
329			observer (kmeans_observer): Observer of the algorithm to collect information about clustering process on each iteration.
330
331			@see center_initializer
332
333			"""
334			self.__pointer_data = numpy.matrix(data);
335			self.__clusters = [];
336			self.__centers = numpy.matrix(initial_centers);
337			self.__tolerance = tolerance;
338
339			self.__observer = None;
340			if 'observer' in kwargs:
341			self.__observer = kwargs['observer'];
342
343			self.__ccore = ccore;
344			if (self.__ccore is True):
345			self.__ccore = ccore_library.workable();
346
347
348			def process(self):
349			"""!
350			@brief Performs cluster analysis in line with rules of K-Means algorithm.
351
352			@remark Results of clustering can be obtained using corresponding get methods.
353
354			@see get_clusters()
355			@see get_centers()
356
357			"""
358
359			if (len(self.__pointer_data[0]) != len(self.__centers[0])):
360			raise NameError('Dimension of the input data and dimension of the initial cluster centers must be equal.');
361
362			if (self.__ccore is True):
363			self.__process_by_ccore();
364			else:
365			self.__process_by_python();
366
367
368			def __process_by_ccore(self):
369			"""!
370			@brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
371
372			"""
373			results = wrapper.kmeans(self.__pointer_data, self.__centers, self.__tolerance, (self.__observer is not None));
374			self.__clusters = results[0];
375			self.__centers = results[1];
376
377			if self.__observer is not None:
378			self.__observer.set_evolution_clusters(results[2]);
379			self.__observer.set_evolution_centers(results[3]);
380
381
382			def __process_by_python(self):
383			"""!
384			@brief Performs cluster analysis using python code.
385
386			"""
387
388			maximum_change = float('inf');
389			stop_condition = self.__tolerance * self.__tolerance;
390
391			if (self.__observer is not None):
392			initial_clusters = self.__update_clusters();
393			self.__observer.notify(initial_clusters, self.__centers.tolist());
394
395			while (maximum_change > stop_condition):
396			self.__clusters = self.__update_clusters();
397			updated_centers = self.__update_centers(); # changes should be calculated before assignment
398
399			if self.__observer is not None:
400			self.__observer.notify(self.__clusters, updated_centers.tolist());
401
402			if (len(self.__centers) != len(updated_centers)):
403			maximum_change = float('inf');
404
405			else:
406			changes = numpy.sum(numpy.square(self.__centers - updated_centers), axis=1);
407			maximum_change = numpy.max(changes);
408
409			self.__centers = updated_centers.tolist();
410
411
412			def get_clusters(self):
413			"""!
414			@brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
415
416			@see process()
417			@see get_centers()
418
419			"""
420
421			return self.__clusters;
422
423
424			def get_centers(self):
425			"""!
426			@brief Returns list of centers of allocated clusters.
427
428			@see process()
429			@see get_clusters()
430
431			"""
432
433			if isinstance(self.__centers, list):
434			return self.__centers;
435
436			return self.__centers.tolist();
437
438
439			def get_cluster_encoding(self):
440			"""!
441			@brief Returns clustering result representation type that indicate how clusters are encoded.
442
443			@return (type_encoding) Clustering result representation.
444
445			@see get_clusters()
446
447			"""
448
449			return type_encoding.CLUSTER_INDEX_LIST_SEPARATION;
450
451
452			def __update_clusters(self):
453			"""!
454			@brief Calculate Euclidean distance to each point from the each cluster. Nearest points are captured by according clusters and as a result clusters are updated.
455
456			@return (list) updated clusters as list of clusters. Each cluster contains indexes of objects from data.
457
458			"""
459
460			clusters = [[] for _ in range(len(self.__centers))];
461
462			dataset_differences = numpy.zeros((len(clusters), len(self.__pointer_data)));
463			for index_center in range(len(self.__centers)):
464			dataset_differences[index_center] = numpy.sum(numpy.square(self.__pointer_data - self.__centers[index_center]), axis=1).T;
465
466			optimum_indexes = numpy.argmin(dataset_differences, axis=0);
467			for index_point in range(len(optimum_indexes)):
468			index_cluster = optimum_indexes[index_point];
469			clusters[index_cluster].append(index_point);
470
471			clusters = [cluster for cluster in clusters if len(cluster) > 0];
472
473			return clusters;
474
475
476			def __update_centers(self):
477			"""!
478			@brief Calculate centers of clusters in line with contained objects.
479
480			@return (numpy.matrix) Updated centers as list of centers.
481
482			"""
483
484			dimension = self.__pointer_data.shape[1];
485			centers = numpy.zeros((len(self.__clusters), dimension));
486
487			for index in range(len(self.__clusters)):
488			cluster_points = self.__pointer_data[self.__clusters[index], :];
489			centers[index] = cluster_points.mean(axis=0);
490
491			return numpy.matrix(centers);
492

annoviko / pyclustering

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kmeans_observer.get_clusters() A

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