ordering_analyser.__init__() - Code Metrics - Inspection of "[pyclustering.cluster.optics] Tiny refactoring." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( 6fa201...1acbc9 )

by Andrei

created 2016-07-26 12:10 UTC

ordering_analyser.init() A

↳ Parent: ordering_analyser

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
dl	0
loc	8
rs	9.4285
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 = 0.0;
        
        for distance in self.__ordering:
            if (distance >= radius):
                if (cluster_start is False):
                    cluster_start = True;
                    amount_clusters += 1;
                
                else:
                    if ((distance < previous_distance) and (cluster_pick is False)):
                        cluster_pick = True;
                    
                    elif ((distance > previous_distance) and (cluster_pick is 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 ( 6fa201...1acbc9 )

ordering_analyser.init() A

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 = 0.0;
133
134			for distance in self.__ordering:
135			if (distance >= radius):
136			if (cluster_start is False):
137			cluster_start = True;
138			amount_clusters += 1;
139
140			else:
141			if ((distance < previous_distance) and (cluster_pick is False)):
142			cluster_pick = True;
143
144			elif ((distance > previous_distance) and (cluster_pick is True)):
145			cluster_pick = False;
146			amount_clusters += 1;
147
148			previous_distance = distance;
149
150			else:
151			cluster_start = False;
152			cluster_pick = False;
153
154			return amount_clusters;
155
156
157			class optics_descriptor:
158			"""!
159			@brief Object description that used by OPTICS algorithm for cluster analysis.
160
161			"""
162
163			def __init__(self, index, core_distance = None, reachability_distance = None):
164			"""!
165			@brief Constructor of object description in optics terms.
166
167			@param[in] index (uint): Index of the object in the data set.
168			@param[in] core_distance (double): Core distance that is minimum distance to specified number of neighbors.
169			@param[in] reachability_distance (double): Reachability distance to this object.
170
171			"""
172
173			## Reachability distance - the smallest distance to be reachable by core object.
174			self.index_object = index;
175
176			## Core distance - the smallest distance to reach specified number of neighbors that is not greater then connectivity radius.
177			self.core_distance = core_distance;
178
179			## Index of object from the input data.
180			self.reachability_distance = reachability_distance;
181
182			## True is object has been already traversed.
183			self.processed = False;
184
185			def __repr__(self):
186			"""!
187			@brief Returns string representation of the optics descriptor.
188
189			"""
190
191			return '(%s, [c: %s, r: %s])' % (self.index_object, self.core_distance, self.reachability_distance);
192
193
194			class optics:
195			"""!
196			@brief Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure).
197			@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.
198			Clustering-ordering information contains information about internal structures of data set in terms of density and proper connectivity radius can be obtained
199			for allocation required amount of clusters using this diagram. In case of usage additional input parameter 'amount of clusters' connectivity radius should be
200			bigger than real - because it will be calculated by the algorithms.
201
202			Example:
203			@code
204			# Read sample for clustering from some file
205			sample = read_sample(path_sample);
206
207			# Create OPTICS algorithm for cluster analysis
208			optics_instance = optics(sample, 0.5, 6);
209
210			# Run cluster analysis
211			optics_instance.process();
212
213			# Obtain results of clustering
214			clusters = optics_instance.get_clusters();
215			noise = optics_instance.get_noise();
216
217			# Obtain rechability-distances
218			ordering = ordering_analyser(optics_instance.get_ordering());
219
220			# Visualization of cluster ordering in line with reachability distance.
221			ordering_visualizer.show_ordering_diagram(ordering);
222			@endcode
223
224			Amount of clusters that should be allocated can be also specified. In this case connectivity radius should be greater than real, for example:
225			@code
226			# Import required packages
227			from pyclustering.cluster.optics import optics;
228			from pyclustering.samples.definitions import FCPS_SAMPLES;
229			from pyclustering.utils import read_sample;
230
231			# Read sample for clustering from some file
232			sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN);
233
234			# Run cluster analysis where connvectivity radius is bigger than real
235			radius = 2.0;
236			neighbors = 3;
237			amount_of_clusters = 3;
238
239			optics_instance = optics(sample, radius, neighbors, amount_of_clusters);
240
241			# Obtain results of clustering
242			clusters = optics_instance.get_clusters();
243			noise = optics_instance.get_noise();
244			@endcode
245
246			"""
247
248			def __init__(self, sample, eps, minpts, amount_clusters = None):
249			"""!
250			@brief Constructor of clustering algorithm OPTICS.
251
252			@param[in] sample (list): Input data that is presented as a list of points (objects), where each point is represented by list or tuple.
253			@param[in] eps (double): Connectivity radius between points, points may be connected if distance between them less than the radius.
254			@param[in] minpts (uint): Minimum number of shared neighbors that is required for establishing links between points.
255			@param[in] amount_clusters (uint): Optional parameter where amount of clusters that should be allocated is specified.
256			In case of usage 'amount_clusters' connectivity radius can be greater than real, in other words, there is place for mistake
257			in connectivity radius usage.
258
259			"""
260
261			self.__sample_pointer = sample; # Algorithm parameter - pointer to sample for processing.
262			self.__eps = eps; # Algorithm parameter - connectivity radius between object for establish links between object.
263			self.__minpts = minpts; # Algorithm parameter - minimum number of neighbors that is required for establish links between object.
264			self.__amount_clusters = amount_clusters;
265			self.__ordering = None;
266
267			self.__initialize(sample);
268
269
270			def process(self):
271			"""!
272			@brief Performs cluster analysis in line with rules of OPTICS algorithm.
273
274			@remark Results of clustering can be obtained using corresponding gets methods.
275
276			@see get_clusters()
277			@see get_noise()
278			@see get_ordering()
279
280			"""
281
282			self.__allocate_clusters();
283
284			if ( (self.__amount_clusters is not None) and (self.__amount_clusters != len(self.get_clusters())) ):
285			analyser = ordering_analyser(self.get_ordering());
286			radius = analyser.calculate_connvectivity_radius(self.__amount_clusters);
287			if (radius is not None):
288			self.__eps = radius;
289			self.__initialize(self.__sample_pointer);
290			self.__allocate_clusters();
291
292
293			def __initialize(self, sample):
294			"""!
295			@brief Initializes internal states and resets clustering results in line with input sample.
296
297			"""
298
299			self.__processed = [False] * len(sample);
300			self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))]; # List of OPTICS objects that corresponds to objects from input sample.
301			self.__ordered_database = []; # List of OPTICS objects in traverse order.
302
303			self.__clusters = None; # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
304			self.__noise = None; # Result of clustering (noise).
305
306
307			def __allocate_clusters(self):
308			"""!
309			@brief Performs cluster allocation and builds ordering diagram that is based on reachability-distances.
310
311			"""
312
313			for optic_object in self.__optics_objects:
314			if (optic_object.processed is False):
315			self.__expand_cluster_order(optic_object);
316
317			self.__extract_clusters();
318
319
320			def get_clusters(self):
321			"""!
322			@brief Returns list of allocated clusters, where each cluster contains indexes of objects and each cluster is represented by list.
323
324			@return (list) List of allocated clusters.
325
326			@see process()
327			@see get_noise()
328			@see get_ordering()
329			@see get_radius()
330
331			"""
332
333			return self.__clusters;
334
335
336			def get_noise(self):
337			"""!
338			@brief Returns list of noise that contains indexes of objects that corresponds to input data.
339
340			@return (list) List of allocated noise objects.
341
342			@see process()
343			@see get_clusters()
344			@see get_ordering()
345			@see get_radius()
346
347			"""
348
349			return self.__noise;
350
351
352			def get_ordering(self):
353			"""!
354			@brief Returns clustering ordering information about the input data set.
355			@details Clustering ordering of data-set contains the information about the internal clustering structure in line with connectivity radius.
356
357			@return (ordering_analyser) Analyser of clustering ordering.
358
359			@see process()
360			@see get_clusters()
361			@see get_noise()
362			@see get_radius()
363
364			"""
365
366			if (self.__ordering is None):
367			self.__ordering = [];
368
369			for cluster in self.__clusters:
370			for index_object in cluster:
371			optics_object = self.__optics_objects[index_object];
372			if (optics_object.reachability_distance is not None):
373			self.__ordering.append(optics_object.reachability_distance);
374
375			return self.__ordering;
376
377
378			def get_radius(self):
379			"""!
380			@brief Returns connectivity radius that is calculated and used for clustering by the algorithm.
381			@details Connectivity radius may be changed only in case of usage additional parameter of the algorithm - amount of clusters for allocation.
382
383			@return (double) Connectivity radius.
384
385			@see get_ordering()
386			@see get_clusters()
387			@see get_noise()
388
389			"""
390
391			return self.__eps;
392
393
394			def __expand_cluster_order(self, optics_object):
395			"""!
396			@brief Expand cluster order from not processed optic-object that corresponds to object from input data.
397			Traverse procedure is performed until objects are reachable from core-objects in line with connectivity radius.
398			Order database is updated during expanding.
399
400			@param[in] optics_object (optics_descriptor): Object that hasn't been processed.
401
402			"""
403
404			optics_object.processed = True;
405
406			neighbors_descriptor = self.__neighbor_indexes(optics_object);
407			optics_object.reachability_distance = None;
408
409			self.__ordered_database.append(optics_object);
410
411			# Check core distance
412			if (len(neighbors_descriptor) >= self.__minpts):
413			neighbors_descriptor.sort(key = lambda obj: obj[1]);
414			optics_object.core_distance = neighbors_descriptor[self.__minpts - 1][1];
415
416			# Continue processing
417			order_seed = list();
418			self.__update_order_seed(optics_object, neighbors_descriptor, order_seed);
419
420			while(len(order_seed) > 0):
421			optic_descriptor = order_seed[0];
422			order_seed.remove(optic_descriptor);
423
424			neighbors_descriptor = self.__neighbor_indexes(optic_descriptor);
425			optic_descriptor.processed = True;
426
427			self.__ordered_database.append(optic_descriptor);
428
429			if (len(neighbors_descriptor) >= self.__minpts):
430			neighbors_descriptor.sort(key = lambda obj: obj[1]);
431			optic_descriptor.core_distance = neighbors_descriptor[self.__minpts - 1][1];
432
433			self.__update_order_seed(optic_descriptor, neighbors_descriptor, order_seed);
434			else:
435			optic_descriptor.core_distance = None;
436
437			else:
438			optics_object.core_distance = None;
439
440
441			def __extract_clusters(self):
442			"""!
443			@brief Extract clusters and noise from order database.
444
445			"""
446
447			self.__clusters = [];
448			self.__noise = [];
449
450			current_cluster = [];
451			for optics_object in self.__ordered_database:
452			if ((optics_object.reachability_distance is None) or (optics_object.reachability_distance > self.__eps)):
453			if ((optics_object.core_distance is not None) and (optics_object.core_distance <= self.__eps)):
454			if (len(current_cluster) > 0):
455			self.__clusters.append(current_cluster);
456			current_cluster = [];
457
458			current_cluster.append(optics_object.index_object);
459			else:
460			self.__noise.append(optics_object.index_object);
461			else:
462			current_cluster.append(optics_object.index_object);
463
464			if (len(current_cluster) > 0):
465			self.__clusters.append(current_cluster);
466
467
468			def __update_order_seed(self, optic_descriptor, neighbors_descriptors, order_seed):
469			"""!
470			@brief Update sorted list of reachable objects (from core-object) that should be processed using neighbors of core-object.
471
472			@param[in] optic_descriptor (optics_descriptor): Core-object whose neighbors should be analysed.
473			@param[in] neighbors_descriptors (list): List of neighbors of core-object.
474			@param[in\|out] order_seed (list): List of sorted object in line with reachable distance.
475
476			"""
477
478			for neighbor_descriptor in neighbors_descriptors:
479			index_neighbor = neighbor_descriptor[0];
480			current_reachable_distance = neighbor_descriptor[1];
481
482			if (self.__optics_objects[index_neighbor].processed != True):
483			reachable_distance = max(current_reachable_distance, optic_descriptor.core_distance);
484			if (self.__optics_objects[index_neighbor].reachability_distance is None):
485			self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
486
487			# insert element in queue O(n) - worst case.
488			index_insertion = len(order_seed);
489			for index_seed in range(0, len(order_seed)):
490			if (reachable_distance < order_seed[index_seed].reachability_distance):
491			index_insertion = index_seed;
492			break;
493
494			order_seed.insert(index_insertion, self.__optics_objects[index_neighbor]);
495
496			else:
497			if (reachable_distance < self.__optics_objects[index_neighbor].reachability_distance):
498			self.__optics_objects[index_neighbor].reachability_distance = reachable_distance;
499			order_seed.sort(key = lambda obj: obj.reachability_distance);
500
501
502			def __neighbor_indexes(self, optic_object):
503			"""!
504			@brief Return list of indexes of neighbors of specified point for the data.
505
506			@param[in] optic_object (optics_descriptor): Object for which neighbors should be returned in line with connectivity radius.
507
508			@return (list) List of indexes of neighbors in line the connectivity radius.
509
510			"""
511
512			neighbor_description = [];
513
514			for index in range(0, len(self.__sample_pointer), 1):
515			if (index == optic_object.index_object):
516			continue;
517
518			distance = euclidean_distance(self.__sample_pointer[optic_object.index_object], self.__sample_pointer[index]);
519			if (distance <= self.__eps):
520			neighbor_description.append( [index, distance] );
521
522			return neighbor_description;

annoviko / pyclustering

Push — master ( 6fa201...1acbc9 )

ordering_analyser.__init__() A

Complexity

Size

Duplication

Importance

1. Missing Dependencies

2. Missing __init__.py files

Duplication Side-by-Side

Filter issues like

ordering_analyser.init() A

2. Missing init.py files