optics.__calculate_connectivity_radius() - Code Metrics - Inspection of "[pyclustering.cluster.optics] Improvement of clust..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( b517ee...6fa201 )

by Andrei

created 2016-07-26 10:30 UTC

optics.__calculate_connectivity_radius() B

↳ Parent: Project

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	6
dl	0
loc	32
rs	7.5384
c	0
b	0
f	0

"""!

@brief Cluster analysis algorithm: OPTICS (Ordering Points To Identify Clustering Structure)
@details Based on article description:
         - M.Ankerst, M.Breunig, H.Kriegel, J.Sander. OPTICS: Ordering Points To Identify the Clustering Structure. 1999.

@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 pyclustering.utils import euclidean_distance;

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


class ordering_visualizer:
    """!
    @brief Cluster ordering diagram visualizer that represents dataset graphically.
    
    """
    
    @staticmethod
    def show_ordering_diagram(analyser):
        ordering = analyser.cluster_ordering;
        indexes = [i for i in range(0, len(ordering))];
        
        plt.bar(indexes, ordering);
        plt.show();


class ordering_analyser:
    """!
    @brief Analyser of cluster ordering diagram.
    
    """
    
    @property
    def cluster_ordering(self):
        """!
        @brief (list) Returns values of dataset cluster ordering.
        
        """
        return self.__ordering;
    
    
    def __init__(self, ordering_diagram):
        """!
        @brief Analyser of ordering diagram that is based on reachability-distances.
        
        @see calculate_connvectivity_radius
        
        """
        self.__ordering = ordering_diagram;
    
    
    def __len__(self):
        """!
        @brief Returns length of clustering-ordering diagram.
        
        """
        return len(self.__ordering);
    
    
    def calculate_connvectivity_radius(self, amount_clusters):
        """!
        @brief Calculates connectivity radius of allocation specified amount of clusters using ordering diagram.
        
        @return (double) Value of connectivity radius.
        
        """
        
        maximum_distance = max(self.__ordering);
        
        upper_distance = maximum_distance;
        lower_distance = 0.0;
        
        radius = None;
        
        if (self.extract_cluster_amount(maximum_distance) <= amount_clusters):
            while(True):
                radius = (lower_distance + upper_distance) / 2.0;
                
                amount = self.extract_cluster_amount(radius);
                if (amount == amount_clusters):
                    break;
                
                elif (amount > amount_clusters):
                    lower_distance = radius;
                
                elif (amount < amount_clusters):
                    upper_distance = radius;
        
        return radius;
    
    
    def extract_cluster_amount(self, radius):
        """!
        @brief Obtains amount of clustering that can be allocated by using specified radius for ordering diagram.
        @details When growth of reachability-distances is detected than it is considered as a start point of cluster, 
                 than pick is detected and after that recession is observed until new growth (that means end of the
                 current cluster and start of a new one) or end of diagram.
        
        @param[in] ordering (list): list of reachability-distances of optics objects.
        @param[in] radius (double): connectivity radius that is used for cluster allocation.
        
        
        @return (unit) Amount of clusters that can be allocated by the connectivity radius on ordering diagram.
        
        """
        
        amount_clusters = 1;
        
        cluster_start = False;
        cluster_pick = False;
        previous_distance = 0;
        
        for distance in self.__ordering:
            if (distance >= radius):
                if (cluster_start is False):
                    amount_clusters += 1;
                    cluster_start = True;
                
                elif ( (cluster_start is True) and (distance < previous_distance) and (cluster_pick is False) ):
                    cluster_pick = True;
                
                elif ( (cluster_start is True) and (distance > previous_distance) and (cluster_pick is True) ):
                    cluster_start = True;
                    cluster_pick = False;
                    
                    amount_clusters += 1;
                
                previous_distance = distance;
            
            else:
                cluster_start = False;
                cluster_pick = False;
        
        return amount_clusters;


class optics_descriptor:
    """!
    @brief Object description that used by OPTICS algorithm for cluster analysis.
    
    """
    
    def __init__(self, index, core_distance = None, reachability_distance = None):
        """!
        @brief Constructor of object description in optics terms.
        
        @param[in] index (uint): Index of the object in the data set.
        @param[in] core_distance (double): Core distance that is minimum distance to specified number of neighbors.
        @param[in] reachability_distance (double): Reachability distance to this object.
        
        """
        
        ## Reachability distance - the smallest distance to be reachable by core object.
        self.index_object = index;
        
        ## Core distance - the smallest distance to reach specified number of neighbors that is not greater then connectivity radius.
        self.core_distance = core_distance;
        
        ## Index of object from the input data.
        self.reachability_distance = reachability_distance;
        
        ## True is object has been already traversed.
        self.processed = False;
        
    def __repr__(self):
        """!
        @brief Returns string representation of the optics descriptor.
        
        """
        
        return '(%s, [c: %s, r: %s])' % (self.index_object, self.core_distance, self.reachability_distance);


class optics:
    """!
    @brief Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure).
    @details OPTICS is a density-based algorithm. Purpose of the algorithm is to provide explicit clusters, but create clustering-ordering representation of the input data. 
             Clustering-ordering information contains information about internal structures of data set in terms of density and proper connectivity radius can be obtained
             for allocation required amount of clusters using this diagram. In case of usage additional input parameter 'amount of clusters' connectivity radius should be
             bigger than real - because it will be calculated by the algorithms.

    Example:
    @code
        # Read sample for clustering from some file
        sample = read_sample(path_sample);
        
        # Create OPTICS algorithm for cluster analysis
        optics_instance = optics(sample, 0.5, 6);
        
        # Run cluster analysis
        optics_instance.process();
        
        # Obtain results of clustering
        clusters = optics_instance.get_clusters();
        noise = optics_instance.get_noise();
        
        # Obtain rechability-distances
        ordering = ordering_analyser(optics_instance.get_ordering());
        
        # Visualization of cluster ordering in line with reachability distance.
        ordering_visualizer.show_ordering_diagram(ordering);
    @endcode
    
    Amount of clusters that should be allocated can be also specified. In this case connectivity radius should be greater than real, for example:
    @code
        # Import required packages
        from pyclustering.cluster.optics import optics;
        from pyclustering.samples.definitions import FCPS_SAMPLES;
        from pyclustering.utils import read_sample;
        
        # Read sample for clustering from some file
        sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN);
        
        # Run cluster analysis where connvectivity radius is bigger than real
        radius = 2.0;
        neighbors = 3;
        amount_of_clusters = 3;
        
        optics_instance = optics(sample, radius, neighbors, amount_of_clusters);
        
        # Obtain results of clustering
        clusters = optics_instance.get_clusters();
        noise = optics_instance.get_noise();
    @endcode
    
    """
    
    def __init__(self, sample, eps, minpts, amount_clusters = None):
        """!
        @brief Constructor of clustering algorithm OPTICS.
        
        @param[in] sample (list): Input data that is presented as a list of points (objects), where each point is represented by list or tuple.
        @param[in] eps (double): Connectivity radius between points, points may be connected if distance between them less than the radius.
        @param[in] minpts (uint): Minimum number of shared neighbors that is required for establishing links between points.
        @param[in] amount_clusters (uint): Optional parameter where amount of clusters that should be allocated is specified.
                    In case of usage 'amount_clusters' connectivity radius can be greater than real, in other words, there is place for mistake
                    in connectivity radius usage.
        
        """
        
        self.__sample_pointer = sample;     # Algorithm parameter - pointer to sample for processing.
        self.__eps = eps;                   # Algorithm parameter - connectivity radius between object for establish links between object.
        self.__minpts = minpts;             # Algorithm parameter - minimum number of neighbors that is required for establish links between object.
        self.__amount_clusters = amount_clusters;
        self.__ordering = None;
        
        self.__initialize(sample);


    def process(self):
        """!
        @brief Performs cluster analysis in line with rules of OPTICS algorithm.
        
        @remark Results of clustering can be obtained using corresponding gets methods.
        
        @see get_clusters()
        @see get_noise()
        @see get_ordering()
        
        """
        
        self.__allocate_clusters();
        
        if ( (self.__amount_clusters is not None) and (self.__amount_clusters != len(self.get_clusters())) ):
            analyser = ordering_analyser(self.get_ordering());
            radius = analyser.calculate_connvectivity_radius(self.__amount_clusters);
            if (radius is not None):
                self.__eps = radius;
                self.__initialize(self.__sample_pointer);
                self.__allocate_clusters();


    def __initialize(self, sample):
        """!
        @brief Initializes internal states and resets clustering results in line with input sample.
        
        """
        
        self.__processed = [False] * len(sample);
        self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))];     # List of OPTICS objects that corresponds to objects from input sample.
        self.__ordered_database = [];       # List of OPTICS objects in traverse order. 
        
        self.__clusters = None;     # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
        self.__noise = None;        # Result of clustering (noise).


    def __allocate_clusters(self):
        """!
        @brief Performs cluster allocation and builds ordering diagram that is based on reachability-distances.
        
        """
        
        for optic_object in self.__optics_objects:
            if (optic_object.processed is False):
                self.__expand_cluster_order(optic_object);
        
        self.__extract_clusters();
    
    
    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, where each cluster contains indexes of objects and each cluster is represented by list.
        
        @return (list) List of allocated clusters.
        
        @see process()
        @see get_noise()
        @see get_ordering()
        @see get_radius()
        
        """
        
        return self.__clusters;
    
    
    def get_noise(self):
        """!
        @brief Returns list of noise that contains indexes of objects that corresponds to input data.
        
        @return (list) List of allocated noise objects.
        
        @see process()
        @see get_clusters()
        @see get_ordering()
        @see get_radius()
        
        """
        
        return self.__noise;
    
    
    def get_ordering(self):
        """!
        @brief Returns clustering ordering information about the input data set.
        @details Clustering ordering of data-set contains the information about the internal clustering structure in line with connectivity radius.
        
        @return (ordering_analyser) Analyser of clustering ordering.
        
        @see process()
        @see get_clusters()
        @see get_noise()
        @see get_radius()
        
        """
        
        if (self.__ordering is None):
            self.__ordering = [];
        
            for cluster in self.__clusters:
                for index_object in cluster:
                    optics_object = self.__optics_objects[index_object];
                    if (optics_object.reachability_distance is not None):
                        self.__ordering.append(optics_object.reachability_distance);
            
        return self.__ordering;
    
    
    def get_radius(self):
        """!
        @brief Returns connectivity radius that is calculated and used for clustering by the algorithm.
        @details Connectivity radius may be changed only in case of usage additional parameter of the algorithm - amount of clusters for allocation.
        
        @return (double) Connectivity radius.
        
        @see get_ordering()
        @see get_clusters()
        @see get_noise()
        
        """
        
        return self.__eps;
    
    
    def __expand_cluster_order(self, optics_object):
        """!
        @brief Expand cluster order from not processed optic-object that corresponds to object from input data.
               Traverse procedure is performed until objects are reachable from core-objects in line with connectivity radius.
               Order database is updated during expanding.
               
        @param[in] optics_object (optics_descriptor): Object that hasn't been processed.
        
        """
        
        optics_object.processed = True;
        
        neighbors_descriptor = self.__neighbor_indexes(optics_object);
        optics_object.reachability_distance = None;
        
        self.__ordered_database.append(optics_object);
        
        # Check core distance
        if (len(neighbors_descriptor) >= self.__minpts):
            neighbors_descriptor.sort(key = lambda obj: obj[1]);
            optics_object.core_distance = neighbors_descriptor[self.__minpts - 1][1];
            
            # Continue processing
            order_seed = list();
            self.__update_order_seed(optics_object, neighbors_descriptor, order_seed);
            
            while(len(order_seed) > 0):
                optic_descriptor = order_seed[0];
                order_seed.remove(optic_descriptor);
                
                neighbors_descriptor = self.__neighbor_indexes(optic_descriptor);
                optic_descriptor.processed = True;
                
                self.__ordered_database.append(optic_descriptor);
                
                if (len(neighbors_descriptor) >= self.__minpts):
                    neighbors_descriptor.sort(key = lambda obj: obj[1]);
                    optic_descriptor.core_distance = neighbors_descriptor[self.__minpts - 1][1];
                    
                    self.__update_order_seed(optic_descriptor, neighbors_descriptor, order_seed);
                else:
                    optic_descriptor.core_distance = None;
                    
        else:
            optics_object.core_distance = None;

    
    def __extract_clusters(self):
        """!
        @brief Extract clusters and noise from order database.
        
        """
     
        self.__clusters = [];
        self.__noise = [];

        current_cluster = [];
        for optics_object in self.__ordered_database:
            if ((optics_object.reachability_distance is None) or (optics_object.reachability_distance > self.__eps)):
                if ((optics_object.core_distance is not None) and (optics_object.core_distance <= self.__eps)):
                    if (len(current_cluster) > 0):
                        self.__clusters.append(current_cluster);
                        current_cluster = [];
                        
                    current_cluster.append(optics_object.index_object);
                else:
                    self.__noise.append(optics_object.index_object);
            else:
                current_cluster.append(optics_object.index_object);
        
        if (len(current_cluster) > 0):
            self.__clusters.append(current_cluster);
                

    def __update_order_seed(self, optic_descriptor, neighbors_descriptors, order_seed):
        """!
        @brief Update sorted list of reachable objects (from core-object) that should be processed using neighbors of core-object.
        
        @param[in] optic_descriptor (optics_descriptor): Core-object whose neighbors should be analysed.
        @param[in] neighbors_descriptors (list): List of neighbors of core-object.
        @param[in|out] order_seed (list): List of sorted object in line with reachable distance.
        
        """
        
        for neighbor_descriptor in neighbors_descriptors:
            index_neighbor = neighbor_descriptor[0];
            current_reachable_distance = neighbor_descriptor[1];
            
            if (self.__optics_objects[index_neighbor].processed != True):
                reachable_distance = max(current_reachable_distance, optic_descriptor.core_distance);
                if (self.__optics_objects[index_neighbor].reachability_distance is None):
                    self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
                    
                    # insert element in queue O(n) - worst case.
                    index_insertion = len(order_seed);
                    for index_seed in range(0, len(order_seed)):
                        if (reachable_distance < order_seed[index_seed].reachability_distance):
                            index_insertion = index_seed;
                            break;
                    
                    order_seed.insert(index_insertion, self.__optics_objects[index_neighbor]);

                else:
                    if (reachable_distance < self.__optics_objects[index_neighbor].reachability_distance):
                        self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
                        order_seed.sort(key = lambda obj: obj.reachability_distance);


    def __neighbor_indexes(self, optic_object):
        """!
        @brief Return list of indexes of neighbors of specified point for the data.
        
        @param[in] optic_object (optics_descriptor): Object for which neighbors should be returned in line with connectivity radius.
        
        @return (list) List of indexes of neighbors in line the connectivity radius.
        
        """
              
        neighbor_description = [];
        
        for index in range(0, len(self.__sample_pointer), 1):
            if (index == optic_object.index_object):
                continue;
            
            distance = euclidean_distance(self.__sample_pointer[optic_object.index_object], self.__sample_pointer[index]);
            if (distance <= self.__eps):
                neighbor_description.append( [index, distance] );
            
        return neighbor_description;

Push — master ( b517ee...6fa201 )

optics.__calculate_connectivity_radius() B

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing init.py files

1			"""!
2
3			@brief Cluster analysis algorithm: OPTICS (Ordering Points To Identify Clustering Structure)
4			@details Based on article description:
5			- M.Ankerst, M.Breunig, H.Kriegel, J.Sander. OPTICS: Ordering Points To Identify the Clustering Structure. 1999.
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2016
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			from pyclustering.utils import euclidean_distance;
29
30			import matplotlib.pyplot as plt;
			0 ignored issues – show Configuration introduced 2016-07-26 10:32 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...
31
32
33			class ordering_visualizer:
34			"""!
35			@brief Cluster ordering diagram visualizer that represents dataset graphically.
36
37			"""
38
39			@staticmethod
40			def show_ordering_diagram(analyser):
41			ordering = analyser.cluster_ordering;
42			indexes = [i for i in range(0, len(ordering))];
43
44			plt.bar(indexes, ordering);
45			plt.show();
46
47
48			class ordering_analyser:
49			"""!
50			@brief Analyser of cluster ordering diagram.
51
52			"""
53
54			@property
55			def cluster_ordering(self):
56			"""!
57			@brief (list) Returns values of dataset cluster ordering.
58
59			"""
60			return self.__ordering;
61
62
63			def __init__(self, ordering_diagram):
64			"""!
65			@brief Analyser of ordering diagram that is based on reachability-distances.
66
67			@see calculate_connvectivity_radius
68
69			"""
70			self.__ordering = ordering_diagram;
71
72
73			def __len__(self):
74			"""!
75			@brief Returns length of clustering-ordering diagram.
76
77			"""
78			return len(self.__ordering);
79
80
81			def calculate_connvectivity_radius(self, amount_clusters):
82			"""!
83			@brief Calculates connectivity radius of allocation specified amount of clusters using ordering diagram.
84
85			@return (double) Value of connectivity radius.
86
87			"""
88
89			maximum_distance = max(self.__ordering);
90
91			upper_distance = maximum_distance;
92			lower_distance = 0.0;
93
94			radius = None;
95
96			if (self.extract_cluster_amount(maximum_distance) <= amount_clusters):
97			while(True):
98			radius = (lower_distance + upper_distance) / 2.0;
99
100			amount = self.extract_cluster_amount(radius);
101			if (amount == amount_clusters):
102			break;
103
104			elif (amount > amount_clusters):
105			lower_distance = radius;
106
107			elif (amount < amount_clusters):
108			upper_distance = radius;
109
110			return radius;
111
112
113			def extract_cluster_amount(self, radius):
114			"""!
115			@brief Obtains amount of clustering that can be allocated by using specified radius for ordering diagram.
116			@details When growth of reachability-distances is detected than it is considered as a start point of cluster,
117			than pick is detected and after that recession is observed until new growth (that means end of the
118			current cluster and start of a new one) or end of diagram.
119
120			@param[in] ordering (list): list of reachability-distances of optics objects.
121			@param[in] radius (double): connectivity radius that is used for cluster allocation.
122
123
124			@return (unit) Amount of clusters that can be allocated by the connectivity radius on ordering diagram.
125
126			"""
127
128			amount_clusters = 1;
129
130			cluster_start = False;
131			cluster_pick = False;
132			previous_distance = 0;
133
134			for distance in self.__ordering:
135			if (distance >= radius):
136			if (cluster_start is False):
137			amount_clusters += 1;
138			cluster_start = True;
139
140			elif ( (cluster_start is True) and (distance < previous_distance) and (cluster_pick is False) ):
141			cluster_pick = True;
142
143			elif ( (cluster_start is True) and (distance > previous_distance) and (cluster_pick is True) ):
144			cluster_start = True;
145			cluster_pick = False;
146
147			amount_clusters += 1;
148
149			previous_distance = distance;
150
151			else:
152			cluster_start = False;
153			cluster_pick = False;
154
155			return amount_clusters;
156
157
158			class optics_descriptor:
159			"""!
160			@brief Object description that used by OPTICS algorithm for cluster analysis.
161
162			"""
163
164			def __init__(self, index, core_distance = None, reachability_distance = None):
165			"""!
166			@brief Constructor of object description in optics terms.
167
168			@param[in] index (uint): Index of the object in the data set.
169			@param[in] core_distance (double): Core distance that is minimum distance to specified number of neighbors.
170			@param[in] reachability_distance (double): Reachability distance to this object.
171
172			"""
173
174			## Reachability distance - the smallest distance to be reachable by core object.
175			self.index_object = index;
176
177			## Core distance - the smallest distance to reach specified number of neighbors that is not greater then connectivity radius.
178			self.core_distance = core_distance;
179
180			## Index of object from the input data.
181			self.reachability_distance = reachability_distance;
182
183			## True is object has been already traversed.
184			self.processed = False;
185
186			def __repr__(self):
187			"""!
188			@brief Returns string representation of the optics descriptor.
189
190			"""
191
192			return '(%s, [c: %s, r: %s])' % (self.index_object, self.core_distance, self.reachability_distance);
193
194
195			class optics:
196			"""!
197			@brief Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure).
198			@details OPTICS is a density-based algorithm. Purpose of the algorithm is to provide explicit clusters, but create clustering-ordering representation of the input data.
199			Clustering-ordering information contains information about internal structures of data set in terms of density and proper connectivity radius can be obtained
200			for allocation required amount of clusters using this diagram. In case of usage additional input parameter 'amount of clusters' connectivity radius should be
201			bigger than real - because it will be calculated by the algorithms.
202
203			Example:
204			@code
205			# Read sample for clustering from some file
206			sample = read_sample(path_sample);
207
208			# Create OPTICS algorithm for cluster analysis
209			optics_instance = optics(sample, 0.5, 6);
210
211			# Run cluster analysis
212			optics_instance.process();
213
214			# Obtain results of clustering
215			clusters = optics_instance.get_clusters();
216			noise = optics_instance.get_noise();
217
218			# Obtain rechability-distances
219			ordering = ordering_analyser(optics_instance.get_ordering());
220
221			# Visualization of cluster ordering in line with reachability distance.
222			ordering_visualizer.show_ordering_diagram(ordering);
223			@endcode
224
225			Amount of clusters that should be allocated can be also specified. In this case connectivity radius should be greater than real, for example:
226			@code
227			# Import required packages
228			from pyclustering.cluster.optics import optics;
229			from pyclustering.samples.definitions import FCPS_SAMPLES;
230			from pyclustering.utils import read_sample;
231
232			# Read sample for clustering from some file
233			sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN);
234
235			# Run cluster analysis where connvectivity radius is bigger than real
236			radius = 2.0;
237			neighbors = 3;
238			amount_of_clusters = 3;
239
240			optics_instance = optics(sample, radius, neighbors, amount_of_clusters);
241
242			# Obtain results of clustering
243			clusters = optics_instance.get_clusters();
244			noise = optics_instance.get_noise();
245			@endcode
246
247			"""
248
249			def __init__(self, sample, eps, minpts, amount_clusters = None):
250			"""!
251			@brief Constructor of clustering algorithm OPTICS.
252
253			@param[in] sample (list): Input data that is presented as a list of points (objects), where each point is represented by list or tuple.
254			@param[in] eps (double): Connectivity radius between points, points may be connected if distance between them less than the radius.
255			@param[in] minpts (uint): Minimum number of shared neighbors that is required for establishing links between points.
256			@param[in] amount_clusters (uint): Optional parameter where amount of clusters that should be allocated is specified.
257			In case of usage 'amount_clusters' connectivity radius can be greater than real, in other words, there is place for mistake
258			in connectivity radius usage.
259
260			"""
261
262			self.__sample_pointer = sample; # Algorithm parameter - pointer to sample for processing.
263			self.__eps = eps; # Algorithm parameter - connectivity radius between object for establish links between object.
264			self.__minpts = minpts; # Algorithm parameter - minimum number of neighbors that is required for establish links between object.
265			self.__amount_clusters = amount_clusters;
266			self.__ordering = None;
267
268			self.__initialize(sample);
269
270
271			def process(self):
272			"""!
273			@brief Performs cluster analysis in line with rules of OPTICS algorithm.
274
275			@remark Results of clustering can be obtained using corresponding gets methods.
276
277			@see get_clusters()
278			@see get_noise()
279			@see get_ordering()
280
281			"""
282
283			self.__allocate_clusters();
284
285			if ( (self.__amount_clusters is not None) and (self.__amount_clusters != len(self.get_clusters())) ):
286			analyser = ordering_analyser(self.get_ordering());
287			radius = analyser.calculate_connvectivity_radius(self.__amount_clusters);
288			if (radius is not None):
289			self.__eps = radius;
290			self.__initialize(self.__sample_pointer);
291			self.__allocate_clusters();
292
293
294			def __initialize(self, sample):
295			"""!
296			@brief Initializes internal states and resets clustering results in line with input sample.
297
298			"""
299
300			self.__processed = [False] * len(sample);
301			self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))]; # List of OPTICS objects that corresponds to objects from input sample.
302			self.__ordered_database = []; # List of OPTICS objects in traverse order.
303
304			self.__clusters = None; # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
305			self.__noise = None; # Result of clustering (noise).
306
307
308			def __allocate_clusters(self):
309			"""!
310			@brief Performs cluster allocation and builds ordering diagram that is based on reachability-distances.
311
312			"""
313
314			for optic_object in self.__optics_objects:
315			if (optic_object.processed is False):
316			self.__expand_cluster_order(optic_object);
317
318			self.__extract_clusters();
319
320
321			def get_clusters(self):
322			"""!
323			@brief Returns list of allocated clusters, where each cluster contains indexes of objects and each cluster is represented by list.
324
325			@return (list) List of allocated clusters.
326
327			@see process()
328			@see get_noise()
329			@see get_ordering()
330			@see get_radius()
331
332			"""
333
334			return self.__clusters;
335
336
337			def get_noise(self):
338			"""!
339			@brief Returns list of noise that contains indexes of objects that corresponds to input data.
340
341			@return (list) List of allocated noise objects.
342
343			@see process()
344			@see get_clusters()
345			@see get_ordering()
346			@see get_radius()
347
348			"""
349
350			return self.__noise;
351
352
353			def get_ordering(self):
354			"""!
355			@brief Returns clustering ordering information about the input data set.
356			@details Clustering ordering of data-set contains the information about the internal clustering structure in line with connectivity radius.
357
358			@return (ordering_analyser) Analyser of clustering ordering.
359
360			@see process()
361			@see get_clusters()
362			@see get_noise()
363			@see get_radius()
364
365			"""
366
367			if (self.__ordering is None):
368			self.__ordering = [];
369
370			for cluster in self.__clusters:
371			for index_object in cluster:
372			optics_object = self.__optics_objects[index_object];
373			if (optics_object.reachability_distance is not None):
374			self.__ordering.append(optics_object.reachability_distance);
375
376			return self.__ordering;
377
378
379			def get_radius(self):
380			"""!
381			@brief Returns connectivity radius that is calculated and used for clustering by the algorithm.
382			@details Connectivity radius may be changed only in case of usage additional parameter of the algorithm - amount of clusters for allocation.
383
384			@return (double) Connectivity radius.
385
386			@see get_ordering()
387			@see get_clusters()
388			@see get_noise()
389
390			"""
391
392			return self.__eps;
393
394
395			def __expand_cluster_order(self, optics_object):
396			"""!
397			@brief Expand cluster order from not processed optic-object that corresponds to object from input data.
398			Traverse procedure is performed until objects are reachable from core-objects in line with connectivity radius.
399			Order database is updated during expanding.
400
401			@param[in] optics_object (optics_descriptor): Object that hasn't been processed.
402
403			"""
404
405			optics_object.processed = True;
406
407			neighbors_descriptor = self.__neighbor_indexes(optics_object);
408			optics_object.reachability_distance = None;
409
410			self.__ordered_database.append(optics_object);
411
412			# Check core distance
413			if (len(neighbors_descriptor) >= self.__minpts):
414			neighbors_descriptor.sort(key = lambda obj: obj[1]);
415			optics_object.core_distance = neighbors_descriptor[self.__minpts - 1][1];
416
417			# Continue processing
418			order_seed = list();
419			self.__update_order_seed(optics_object, neighbors_descriptor, order_seed);
420
421			while(len(order_seed) > 0):
422			optic_descriptor = order_seed[0];
423			order_seed.remove(optic_descriptor);
424
425			neighbors_descriptor = self.__neighbor_indexes(optic_descriptor);
426			optic_descriptor.processed = True;
427
428			self.__ordered_database.append(optic_descriptor);
429
430			if (len(neighbors_descriptor) >= self.__minpts):
431			neighbors_descriptor.sort(key = lambda obj: obj[1]);
432			optic_descriptor.core_distance = neighbors_descriptor[self.__minpts - 1][1];
433
434			self.__update_order_seed(optic_descriptor, neighbors_descriptor, order_seed);
435			else:
436			optic_descriptor.core_distance = None;
437
438			else:
439			optics_object.core_distance = None;
440
441
442			def __extract_clusters(self):
443			"""!
444			@brief Extract clusters and noise from order database.
445
446			"""
447
448			self.__clusters = [];
449			self.__noise = [];
450
451			current_cluster = [];
452			for optics_object in self.__ordered_database:
453			if ((optics_object.reachability_distance is None) or (optics_object.reachability_distance > self.__eps)):
454			if ((optics_object.core_distance is not None) and (optics_object.core_distance <= self.__eps)):
455			if (len(current_cluster) > 0):
456			self.__clusters.append(current_cluster);
457			current_cluster = [];
458
459			current_cluster.append(optics_object.index_object);
460			else:
461			self.__noise.append(optics_object.index_object);
462			else:
463			current_cluster.append(optics_object.index_object);
464
465			if (len(current_cluster) > 0):
466			self.__clusters.append(current_cluster);
467
468
469			def __update_order_seed(self, optic_descriptor, neighbors_descriptors, order_seed):
470			"""!
471			@brief Update sorted list of reachable objects (from core-object) that should be processed using neighbors of core-object.
472
473			@param[in] optic_descriptor (optics_descriptor): Core-object whose neighbors should be analysed.
474			@param[in] neighbors_descriptors (list): List of neighbors of core-object.
475			@param[in\|out] order_seed (list): List of sorted object in line with reachable distance.
476
477			"""
478
479			for neighbor_descriptor in neighbors_descriptors:
480			index_neighbor = neighbor_descriptor[0];
481			current_reachable_distance = neighbor_descriptor[1];
482
483			if (self.__optics_objects[index_neighbor].processed != True):
484			reachable_distance = max(current_reachable_distance, optic_descriptor.core_distance);
485			if (self.__optics_objects[index_neighbor].reachability_distance is None):
486			self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
487
488			# insert element in queue O(n) - worst case.
489			index_insertion = len(order_seed);
490			for index_seed in range(0, len(order_seed)):
491			if (reachable_distance < order_seed[index_seed].reachability_distance):
492			index_insertion = index_seed;
493			break;
494
495			order_seed.insert(index_insertion, self.__optics_objects[index_neighbor]);
496
497			else:
498			if (reachable_distance < self.__optics_objects[index_neighbor].reachability_distance):
499			self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
500			order_seed.sort(key = lambda obj: obj.reachability_distance);
501
502
503			def __neighbor_indexes(self, optic_object):
504			"""!
505			@brief Return list of indexes of neighbors of specified point for the data.
506
507			@param[in] optic_object (optics_descriptor): Object for which neighbors should be returned in line with connectivity radius.
508
509			@return (list) List of indexes of neighbors in line the connectivity radius.
510
511			"""
512
513			neighbor_description = [];
514
515			for index in range(0, len(self.__sample_pointer), 1):
516			if (index == optic_object.index_object):
517			continue;
518
519			distance = euclidean_distance(self.__sample_pointer[optic_object.index_object], self.__sample_pointer[index]);
520			if (distance <= self.__eps):
521			neighbor_description.append( [index, distance] );
522
523			return neighbor_description;

annoviko / pyclustering

Push — master ( b517ee...6fa201 )

optics.__calculate_connectivity_radius() B

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

Filter issues like

2. Missing init.py files