cluster_result_representor.get_data() - Code Metrics - Inspection of "[pyclustering.cluster] Clustering result represent..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 0ec04d...6ed467 )

by Andrei

created 2016-12-19 20:15 UTC

cluster_result_representor.get_data() A

↳ Parent: cluster_result_representor

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
cc	1
c	1
b	0
f	0
dl	0
loc	6
rs	9.4285

"""!

@brief Module for representing clustering results.

@authors Andrei Novikov ([email protected])
@date 2014-2016
@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

"""


from enum import IntEnum;


class type_cluster_result(IntEnum):
    ## 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, 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, for example [ [obj1, obj2], [obj3, obj4, obj5], [obj6, obj7] ].
    CLUSTER_OBJECT_LIST_SEPARATION = 2;


class cluster_result_representor:
    """!
    @brief Provides service to change clustering result representation.
    
    Example:
    @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();
        
        # by default k-means returns representation CLUSTER_INDEX_LIST_SEPARATION
        type_repr = type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION;
        representor = cluster_result_representor(type_repr, clusters, sample);
        
        # change representation from index list to label list
        representor.get_representation(type_cluster_result.CLUSTER_INDEX_LABELING);
        
        # change representation from label to object list
        representor.get_representation(type_cluster_result.CLUSTER_OBJECT_LIST_SEPARATION);
    @endcode
    """
    
    def __init__(self, type_representation, clusters, data):
        """!
        @brief Constructor of cluster result representor.
        
        @param[in] type_representation (type_cluster_result): Type of clusters representation (index list, object list or labels).
        @param[in] clusters (list): Current clusters representation.
        @param[in] data (list): Data that corresponds to clusters.
        
        """
    
        self.__type_representation = type_representation;
        self.__clusters = clusters;
        self.__data = data;


    @property
    def get_representation(self):
        """!
        @brief Returns current cluster representation.
        
        """
        return self.__type_representation;


    def get_clusters(self):
        """!
        @brief Returns clusters representation.
        
        """
        return self.__clusters;


    def get_data(self):
        """!
        @brief Returns data that corresponds to clusters.
        
        """
        return self.__data;


    def set_representation(self, type_representation):
        """!
        @brief Change clusters representation to specified type.
        
        @param[in] type_representation (type_cluster_result): New type of clusters representation.
        
        """
        
        if(type_representation == self.__type_representation):
            return;
        
        if (self.__type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
            if (type_representation == type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION):
                self.__clusters = self.__convert_label_to_index();
            
            else:
                self.__clusters = self.__convert_label_to_object();
        
        elif (self.__type_representation == type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION):
            if (type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
                self.__clusters = self.__convert_index_to_label();
            
            else:
                self.__clusters = self.__convert_index_to_object();
        
        else:
            if (type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
                self.__clusters = self.__convert_object_to_label();
            
            else:
                self.__clusters = self.__convert_object_to_index();
        
        self.__type_representation = type_representation;


    def __convert_index_to_label(self):
        clusters = [0] * 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 = [0] * len(self.__data);
        index_cluster = 0;
        
        for cluster in self.__clusters:
            for data_object in cluster:
                index_object = -1;
                if (data_object in positions):
                    index_object = self.__data.index(data_object, positions[data_object] + 1);
                else:
                    index_object = self.__data.index(data_object);
                    
                clusters[index_object] = index_cluster;
                positions[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]:
                index_object = -1;
                if (data_object in positions):
                    index_object = self.__data.index(data_object, positions[data_object] + 1);
                else:
                    index_object = self.__data.index(data_object);

                clusters[index_cluster].append(index_object);
                positions[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];
            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];
            clusters[index_cluster].append(self.__data[index_object]);
        
        return clusters;

1			"""!
2
3			@brief Module for representing clustering results.
4
5			@authors Andrei Novikov ([email protected])
6			@date 2014-2016
7			@copyright GNU Public License
8
9			@cond GNU_PUBLIC_LICENSE
10			PyClustering is free software: you can redistribute it and/or modify
11			it under the terms of the GNU General Public License as published by
12			the Free Software Foundation, either version 3 of the License, or
13			(at your option) any later version.
14
15			PyClustering is distributed in the hope that it will be useful,
16			but WITHOUT ANY WARRANTY; without even the implied warranty of
17			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18			GNU General Public License for more details.
19
20			You should have received a copy of the GNU General Public License
21			along with this program. If not, see <http://www.gnu.org/licenses/>.
22			@endcond
23
24			"""
25
26
27			from enum import IntEnum;
28
29
30			class type_cluster_result(IntEnum):
31			## 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].
32			CLUSTER_INDEX_LABELING = 0;
33
34			## Results are represented by list of lists, where each list consists of object indexes, for example [ [0, 1, 2], [3, 4, 5], [6, 7] ].
35			CLUSTER_INDEX_LIST_SEPARATION = 1;
36
37			## Results are represented by list of lists, where each list consists of objects, for example [ [obj1, obj2], [obj3, obj4, obj5], [obj6, obj7] ].
38			CLUSTER_OBJECT_LIST_SEPARATION = 2;
39
40
41			class cluster_result_representor:
42			"""!
43			@brief Provides service to change clustering result representation.
44
45			Example:
46			@code
47			# load list of points for cluster analysis
48			sample = read_sample(path);
49
50			# create instance of K-Means algorithm
51			kmeans_instance = kmeans(sample, [ [0.0, 0.1], [2.5, 2.6] ]);
52
53			# run cluster analysis and obtain results
54			kmeans_instance.process();
55			clusters = kmeans_instance.get_clusters();
56
57			# by default k-means returns representation CLUSTER_INDEX_LIST_SEPARATION
58			type_repr = type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION;
59			representor = cluster_result_representor(type_repr, clusters, sample);
60
61			# change representation from index list to label list
62			representor.get_representation(type_cluster_result.CLUSTER_INDEX_LABELING);
63
64			# change representation from label to object list
65			representor.get_representation(type_cluster_result.CLUSTER_OBJECT_LIST_SEPARATION);
66			@endcode
67			"""
68
69			def __init__(self, type_representation, clusters, data):
70			"""!
71			@brief Constructor of cluster result representor.
72
73			@param[in] type_representation (type_cluster_result): Type of clusters representation (index list, object list or labels).
74			@param[in] clusters (list): Current clusters representation.
75			@param[in] data (list): Data that corresponds to clusters.
76
77			"""
78
79			self.__type_representation = type_representation;
80			self.__clusters = clusters;
81			self.__data = data;
82
83
84			@property
85			def get_representation(self):
86			"""!
87			@brief Returns current cluster representation.
88
89			"""
90			return self.__type_representation;
91
92
93			def get_clusters(self):
94			"""!
95			@brief Returns clusters representation.
96
97			"""
98			return self.__clusters;
99
100
101			def get_data(self):
102			"""!
103			@brief Returns data that corresponds to clusters.
104
105			"""
106			return self.__data;
107
108
109			def set_representation(self, type_representation):
110			"""!
111			@brief Change clusters representation to specified type.
112
113			@param[in] type_representation (type_cluster_result): New type of clusters representation.
114
115			"""
116
117			if(type_representation == self.__type_representation):
118			return;
119
120			if (self.__type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
121			if (type_representation == type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION):
122			self.__clusters = self.__convert_label_to_index();
123
124			else:
125			self.__clusters = self.__convert_label_to_object();
126
127			elif (self.__type_representation == type_cluster_result.CLUSTER_INDEX_LIST_SEPARATION):
128			if (type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
129			self.__clusters = self.__convert_index_to_label();
130
131			else:
132			self.__clusters = self.__convert_index_to_object();
133
134			else:
135			if (type_representation == type_cluster_result.CLUSTER_INDEX_LABELING):
136			self.__clusters = self.__convert_object_to_label();
137
138			else:
139			self.__clusters = self.__convert_object_to_index();
140
141			self.__type_representation = type_representation;
142
143
144			def __convert_index_to_label(self):
145			clusters = [0] * len(self.__data);
146			index_cluster = 0;
147
148			for cluster in self.__clusters:
149			for index_object in cluster:
150			clusters[index_object] = index_cluster;
151
152			index_cluster += 1;
153
154			return clusters;
155
156
157			def __convert_index_to_object(self):
158			clusters = [ [] for _ in range(len(self.__clusters)) ];
159			for index_cluster in range(len(self.__clusters)):
160			for index_object in self.__clusters[index_cluster]:
161			data_object = self.__data[index_object];
162			clusters[index_cluster].append(data_object);
163
164			return clusters;
165
166
167			def __convert_object_to_label(self):
168			positions = dict();
169			clusters = [0] * len(self.__data);
170			index_cluster = 0;
171
172			for cluster in self.__clusters:
173			for data_object in cluster:
174			index_object = -1;
175			if (data_object in positions):
176			index_object = self.__data.index(data_object, positions[data_object] + 1);
177			else:
178			index_object = self.__data.index(data_object);
179
180			clusters[index_object] = index_cluster;
181			positions[data_object] = index_object;
182
183			index_cluster += 1;
184
185			return clusters;
186
187
188			def __convert_object_to_index(self):
189			positions = dict();
190			clusters = [ [] for _ in range(len(self.__clusters)) ];
191			for index_cluster in range(len(self.__clusters)):
192			for data_object in self.__clusters[index_cluster]:
193			index_object = -1;
194			if (data_object in positions):
195			index_object = self.__data.index(data_object, positions[data_object] + 1);
196			else:
197			index_object = self.__data.index(data_object);
198
199			clusters[index_cluster].append(index_object);
200			positions[data_object] = index_object;
201
202			return clusters;
203
204
205			def __convert_label_to_index(self):
206			clusters = [ [] for _ in range(max(self.__clusters) + 1) ];
207
208			for index_object in range(len(self.__data)):
209			index_cluster = self.__clusters[index_object];
210			clusters[index_cluster].append(index_object);
211
212			return clusters;
213
214
215			def __convert_label_to_object(self):
216			clusters = [ [] for _ in range(max(self.__clusters) + 1) ];
217
218			for index_object in range(len(self.__data)):
219			index_cluster = self.__clusters[index_object];
220			clusters[index_cluster].append(self.__data[index_object]);
221
222			return clusters;

annoviko / pyclustering

Push — master ( 0ec04d...6ed467 )

cluster_result_representor.get_data() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like