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