forked from annoviko/pyclustering
-
Notifications
You must be signed in to change notification settings - Fork 0
/
CHANGES
executable file
·854 lines (541 loc) · 44.4 KB
/
CHANGES
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
------------------------------------------------------------------------
CHANGE NOTES FOR 0.10.1 (STARTED Aug 17, 2020), (RELEASED: -)
------------------------------------------------------------------------
GENERAL CHANGES:
- Introduced human-readable error for genetic clustering algorithm in case of non-normalized data (Python: `pyclustering::cluster::ga`).
See: https://github.com/annoviko/pyclustering/issues/597
- Optimized windows implementation `parallel_for` and `parallel_for_each` by using `pyclustering::parallel` instead of `PPL` that affects all algorithms which use these functions (C++: `pyclustering::parallel`).
See: https://github.com/annoviko/pyclustering/issues/642
- Optimized `parallel_for` algorithm for short cycles that affects all algorithms which use `parallel_for` (C++: `pyclustering::parallel`).
See: https://github.com/annoviko/pyclustering/issues/642
- Introduced `kstep` parameter for `elbow` algorithm to use custom K search steps (Python: `pyclustering.cluster.elbow`, C++: `pyclustering::cluster::elbow`).
See: https://github.com/annoviko/pyclustering/issues/489
- Introduced `p_step` parameter for `parallel_for` function (C++: `pyclustering::parallel`).
See: https://github.com/annoviko/pyclustering/issues/640
- Optimized python implementation of K-Medoids algorithm (Python: `pyclustering.cluster.kmedoids`).
See: https://github.com/annoviko/pyclustering/issues/526
- C++ pyclustering CLIQUE interface returns human-readable errors (Python: `pyclustering.cluster.clique`).
See: https://github.com/annoviko/pyclustering/issues/635
See: https://github.com/annoviko/pyclustering/issues/634
- Introduced `metric` parameter for X-Means algorithm to use custom metric for clustering (Python: `pyclustering.cluster.xmeans`; C++ `pyclustering::clst::xmeans`).
See: https://github.com/annoviko/pyclustering/issues/619
- Introduced `alpha` and `beta` probabilistic bounds for MNDL splitting criteria for X-Means algorithm (Python: `pyclustering.cluster.xmeans`; C++: `pyclustering::clst::xmeans`).
See: https://github.com/annoviko/pyclustering/issues/624
CORRECTED MAJOR BUGS:
- Corrected bug with a command `python3 -m pyclustering.tests` that was using the current folder to find tests to run (Python: `pyclustering`).
See: https://github.com/annoviko/pyclustering/issues/648
- Corrected bug with Elbow algorithm where `kmax` is not used to calculate `K` (Python: `pyclustering.cluster.elbow`; C++: `pyclustering::clst::elbow`).
See: https://github.com/annoviko/pyclustering/issues/639
- Corrected implementation of K-Medians (PAM) algorithm that is aligned with original algorithm (Python: `pyclustering.cluster.kmedoids`; C++: `pyclustering::clst::kmedoids`).
See: https://github.com/annoviko/pyclustering/issues/503
- Corrected literature references that were for K-Medians (PAM) implementation (Python: `pyclustering.cluster.kmedoids`).
See: https://github.com/annoviko/pyclustering/pull/572
- Corrected bug when K-Medoids updates input parameter `initial_medoids` that were provided to the algorithm (Python: `pyclustering.cluster.kmedoids`).
See: https://github.com/annoviko/pyclustering/issues/630
- Corrected bug with Euclidean distance when numpy is used (Python: `pyclustering.utils.metric`).
See: https://github.com/annoviko/pyclustering/issues/625
- Corrected bug with Minkowski distance when numpy is used (Python: `pyclustering.utils.metric`).
See: https://github.com/annoviko/pyclustering/issues/626
- Corrected bug with Gower distance when numpy calculation is used and data shape is bigger than 1 (Python: `pyclustering.utils.metric`).
See: https://github.com/annoviko/pyclustering/issues/627
- Corrected MNDL splitting criteria for X-Means algorithm (Python: `pyclustering.cluster.xmeans`; C++: `pyclustering::clst::xmeans`).
See: https://github.com/annoviko/pyclustering/issues/623
------------------------------------------------------------------------
CHANGE NOTES FOR 0.10.0.1 (STARTED Aug 17, 2020), (RELEASED: Aug 17, 2020)
------------------------------------------------------------------------
GENERAL CHANGES:
- Metadata of the library is updated.
------------------------------------------------------------------------
CHANGE NOTES FOR 0.10.0 (STARTED Jan 24, 2020), (RELEASED: Aug 17, 2020)
------------------------------------------------------------------------
GENERAL CHANGES:
- Supported command `test` for `setup.py` script (Python: `pyclustering`).
See: https://github.com/annoviko/pyclustering/issues/607
- Introduced parameter `random_seed` for algorithms/models to control the seed of the random functionality: `kmeans++`, `random_center_initializer`, `ga`, `gmeans`, `xmeans`, `som`, `somsc`, `elbow`, `silhouette_ksearch` (Python: `pyclustering.cluster`; C++: `pyclustering.clst`).
See: https://github.com/annoviko/pyclustering/issues/578
- Introduced parameter `k_max` to G-Means algorithm to use it as an optional stop condition for the algorithm (Python: `pyclustering.cluster.gmeans`; C++: `pyclustering::clst::gmeans`).
See: https://github.com/annoviko/pyclustering/issues/602
- Implemented method `save()` for `cluster_visualizer` and `cluster_visualizer_multidim` to save visualization to file (Python: `pyclustering.cluster`).
See: https://github.com/annoviko/pyclustering/issues/601
- Optimization of CURE algorithm using balanced KD-tree (Python: `pyclustering.cluster.cure`; C++: `pyclustering::clst::cure`).
See: https://github.com/annoviko/pyclustering/issues/589
- Optimization of OPTICS algorithm using balanced KD-tree (Python: `pyclustering.cluster.optics`; C++: `pyclustering::clst::optics`).
See: https://github.com/annoviko/pyclustering/issues/588
- Optimization of DBSCAN algorithm using balanced KD-tree (Python: `pyclustering.cluster.dbscan`; C++: `pyclustering::clst::dbscan`).
See: https://github.com/annoviko/pyclustering/issues/587
- Implemented new optimized balanced KD-tree `kdtree_balanced` (Python: `pyclustering.cluster.kdtree`; C++: `pyclustering::container::kdtree_balanced`).
See: https://github.com/annoviko/pyclustering/issues/379
- Implemented KD-tree graphical visualizer `kdtree_visualizer` for KD-trees with 2-dimensional data (Python: `pyclustering.container.kdtree`).
See: https://github.com/annoviko/pyclustering/issues/586
- Updated interface of each clustering algorithm in C/C++ pyclustering `cluster_data` is substituted by concrete classes (C++ `pyclustering::clst`).
See: https://github.com/annoviko/pyclustering/issues/577
CORRECTED MAJOR BUGS:
- Bug with wrong data type for `scores` in Silhouette K-search algorithm in case of using C++ (Python: `pyclustering.cluster.silhouette`).
See: https://github.com/annoviko/pyclustering/issues/606
- Bug with a random distribution in the random center initializer (Python: `pyclustering.cluster.center_initializer`).
See: https://github.com/annoviko/pyclustering/issues/573
- Bug with incorrect converting Index List and Object List to Labeling when clusters do not contains one or more points from an input data (Python `pyclustering.cluster.encoder`).
See: https://github.com/annoviko/pyclustering/issues/596
- Bug with an exception in case of using user-defined metric for K-Means algorithm (Python `pyclustering.cluster.kmeans`).
See: https://github.com/annoviko/pyclustering/pull/600
- Memory leakage in the interface between python and C++ pyclustering library in case of CURE algorithm usage (C++ `pyclustering`).
See: https://github.com/annoviko/pyclustering/issues/581
------------------------------------------------------------------------
CHANGE NOTES FOR 0.9.3.1 (STARTED Dev 23, 2019), (RELEASED: Dev 23, 2019)
------------------------------------------------------------------------
CORRECTED MAJOR BUGS:
- Hotfix for the CF-tree - call method with incorrect amount of arguments.
See: https://github.com/annoviko/pyclustering/issues/570
------------------------------------------------------------------------
CHANGE NOTES FOR 0.9.3 (STARTED Oct 10, 2019), (RELEASED: Dev 23, 2019)
------------------------------------------------------------------------
GENERAL CHANGES:
- Introduced `get_cf_clusters` and `get_cf_entries` methods for BIRCH algorithm to get CF-entry encoding information (pyclustering.cluster.birch).
See: https://github.com/annoviko/pyclustering/issues/569
- Introduced `predict` method for SOMSC algorithm to find closest clusters for specified points (pyclustering.cluster.somsc).
See: https://github.com/annoviko/pyclustering/issues/546
- Parallel optimization of C++ pyclustering compilation process.
See: https://github.com/annoviko/pyclustering/issues/553
- Include folder for easy integration to other C++ projects.
See: https://github.com/annoviko/pyclustering/issues/554
- Introduced new targets to build static libraries on Windows platform.
See: https://github.com/annoviko/pyclustering/issues/555
- Introduced new targets to build static libraries on Linux/MacOS platforms.
See: https://github.com/annoviko/pyclustering/issues/556
CORRECTED MAJOR BUGS:
- Bug with incorrect finding of closest CF-entry (pyclustering.container.cftree).
See: https://github.com/annoviko/pyclustering/issues/564
- Bug with incorrect BIRCH clustering due incorrect leaf analysis (pyclustering.cluster.birch).
See: https://github.com/annoviko/pyclustering/issues/563
- Bug with incorrect search procedure of farthest nodes in CF-tree (pyclustering.container.cftree).
See: https://github.com/annoviko/pyclustering/issues/551
- Bug with crash during clustering with the same points in case of BIRCH (pyclustering.cluster.birch).
See: https://github.com/annoviko/pyclustering/issues/561
------------------------------------------------------------------------
CHANGE NOTES FOR 0.9.2 (STARTED Sep 9, 2019), (RELEASED: Oct 10, 2019)
------------------------------------------------------------------------
GENERAL CHANGES:
- Introduced checking of input arguments for clustering algorithm to provide human-readable errors (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/548
- Implemented functionality to perform Anderson-Darling test for Gaussian distribution (ccore.stats).
See: https://github.com/annoviko/pyclustering/issues/550
- Implemented new clustering algorithm G-Means (pyclustering.cluster.gmeans, ccore.clst.gmeans).
See: https://github.com/annoviko/pyclustering/issues/506
- Introduced parameter `repeat` to improve parameters in X-Means algorithm (pyclustering.cluster.xmeans, ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/525
- Introduced new distance metric: Gower (pyclustering.utils.metric, ccore.utils.metric).
See: https://github.com/annoviko/pyclustering/issues/544
- Introduced sampling algorithms `reservoir_r` and `reservoir_x` (pyclustering.utils.sampling).
See: https://github.com/annoviko/pyclustering/issues/542
- Introduced parameter `data_type` to Silhouette method to use distance matrix (pyclustering.cluster.silhouette, ccore.clst.silhouette).
See: https://github.com/annoviko/pyclustering/issues/543
- Optimization of HHN (Hodgkin-Huxley Neural Network) by parallel processing (ccore.nnet.hhn).
See: https://github.com/annoviko/pyclustering/issues/541
- Introduced `get_total_wce` method for `xmeans` algorithm to find WCE (pyclustering.cluster.xmeans).
See: https://github.com/annoviko/pyclustering/issues/508
CORRECTED MAJOR BUGS:
- Bug with incorrect center initialization in K-Means++ when candidates are not farthest (pyclustering.cluster.center_initializer).
See: https://github.com/annoviko/pyclustering/issues/549
------------------------------------------------------------------------
CHANGE NOTES FOR 0.9.1 (STARTED Apr 14, 2019), (RELEASED: Sep 9, 2019)
------------------------------------------------------------------------
GENERAL CHANGES:
- Introduced 'predict' method for X-Means algorithm to find closest clusters for particular points (pyclustering.cluster.xmeans).
See: https://github.com/annoviko/pyclustering/issues/540
- Optimization of OPTICS algorithm by reducing complexity (ccore.clst.optics).
See: https://github.com/annoviko/pyclustering/issues/521
- Optimization of K-Medians algorithm by parallel processing (ccore.clst.kmedians).
See: https://github.com/annoviko/pyclustering/issues/529
- Introduced 'predict' method for K-Medoids algorithm to find closest clusters for particular points (pyclustering.cluster.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/527
- Introduced 'predict' method for K-Means algorithm to find closest clusters for particular points (pyclustering.cluster.kmeans).
See: https://github.com/annoviko/pyclustering/issues/515
- Parallel optimization of Elbow method. (ccore.clst.elbow).
See: https://github.com/annoviko/pyclustering/issues/511
------------------------------------------------------------------------
CHANGE NOTES FOR 0.9.0 (STARTED Nov 19, 2018), (RELEASED: Apr 14, 2019)
------------------------------------------------------------------------
GENERAL CHANGES:
- CCORE (pyclustering core) is supported for MacOS.
See: https://github.com/annoviko/pyclustering/issues/486
- Introduced parallel Fuzzy C-Means algorithm (pyclustering.cluster.fcm, ccore.clst.fcm).
See: https://github.com/annoviko/pyclustering/issues/386
- Introduced new 'itermax' parameter for K-Means, K-Medians, K-Medoids algorithm to control maximum amount of iterations (pyclustering.cluster, ccore.clst).
See: https://github.com/annoviko/pyclustering/issues/496
- Implemented Silhouette and Silhouette K-Search algorithm for CCORE (ccore.clst.silhouette, ccore.clst.silhouette_ksearch).
See: https://github.com/annoviko/pyclustering/issues/490
- Implemented CLIQUE algorithms (pyclustering.cluster.clique, ccore.clst.clique).
See: https://github.com/annoviko/pyclustering/issues/381
- Introduced new distance metrics: Canberra and Chi Square (pyclustering.utils.metric, ccore.utils.metric).
See: https://github.com/annoviko/pyclustering/issues/482
- Optimization of CURE algorithm (C++ implementation) by using heap (multiset) instead of list to store clusters in queue (ccore.clst.cure).
See: https://github.com/annoviko/pyclustering/issues/479
CORRECTED MAJOR BUGS:
- Bug with crossover mask generation for genetic clustering algorithm (pyclustering.cluster.ga).
See: https://github.com/annoviko/pyclustering/pull/474
- Bug with hanging of K-Medians algorithm for some cases when algorithm is initialized by wrong amount of centers (ccore.clst.kmedians).
See: https://github.com/annoviko/pyclustering/issues/498
- Bug with incorrect center initialization, when the same point can be placed to result more than once (pyclustering.cluster.center_initializer, ccore.clst.kmeans_plus_plus).
See: https://github.com/annoviko/pyclustering/issues/497
- Bug with incorrect clustering in case of CURE python implementation when clusters are allocated incorrectly (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/483
- Bug with incorrect distance calculation for kmeans++ in case of index representation for centers (pyclustering.cluster.center_initializer).
See: https://github.com/annoviko/pyclustering/issues/485
------------------------------------------------------------------------
CHANGE NOTES FOR 0.8.2 (STARTED May 28, 2018), (RELEASED: Nov 19, 2018)
------------------------------------------------------------------------
GENERAL CHANGES:
- Implemented Silhouette method and Silhouette KSearcher to find out proper amount of clusters (pyclustering.cluster.silhouette).
See: https://github.com/annoviko/pyclustering/issues/416
- Introduced new 'return_index' parameter for kmeans_plus_plus and random_center_initializer algorithms (method 'initialize') to initialize initial medoids (pyclustering.cluster.center_initializer).
See: https://github.com/annoviko/pyclustering/issues/421
- Display warning instead of throwing error if matplotlib or Pillow cannot be imported (MAC OS X problems).
See: https://github.com/annoviko/pyclustering/issues/455
- Implemented Random Center Initializer for CCORE (ccore.clst.random_center_initializer).
See: no reference.
- Implemented Elbow method to find out proper amount of clusters in dataset (pyclustering.cluster.elbow, ccore.clst.elbow).
See: https://github.com/annoviko/pyclustering/issues/416
- Introduced new method 'get_optics_objects' for OPTICS algorithm to obtain detailed information about ordering (pyclustering.cluster.optics, ccore.clst.optics).
See: https://github.com/annoviko/pyclustering/issues/464
- Added new clustering answers for SAMPLE SIMPLE data collections (pyclustering.samples).
See: https://github.com/annoviko/pyclustering/issues/459
- Implemented multidimensional cluster visualizer (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/450
- Parallel optimization of K-Medoids algorithm (ccore.clst.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/447
- Parallel optimization of K-Means and X-Means (that uses K-Means) algorithms (ccore.clst.kmeans, ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/451
- Introduced new threshold parameter 'amount of block points' to BANG algorithm to allocate outliers more precisely (pyclustering.cluster.bang).
See: https://github.com/annoviko/pyclustering/issues/446
- Optimization of conveying results from C++ to Python for K-Medians and K-Medoids (pyclustering.cluster.kmedoids, pyclustering.cluster.kmedians).
See: https://github.com/annoviko/pyclustering/issues/445
- Implemented cluster generator (pyclustering.cluster.generator).
See: https://github.com/annoviko/pyclustering/issues/444
- Implemented BANG animator to render animation of clustering process (pyclustering.cluster.bang).
See: https://github.com/annoviko/pyclustering/issues/442
- Optimization of CURE algorithm by using Euclidean Square distance (pyclustering.cluster.cure, ccore.clst.cure).
See: https://github.com/annoviko/pyclustering/issues/439
- Supported numpy.ndarray points in KD-tree (pyclustering.container.kdtree).
See: https://github.com/annoviko/pyclustering/issues/438
CORRECTED MAJOR BUGS:
- Bug with clustering failure in case of non-numpy user defined metric for K-Means algorithm (pyclustering.cluster.kmeans).
See: https://github.com/annoviko/pyclustering/issues/471
- Bug with animation of correlation matrix in case of new versions of matplotlib (pyclustering.nnet.sync).
See: no reference.
- Bug with SOM and pickle when it was not possible to store and load network using pickle (pyclustering.nnet.som).
See: https://github.com/annoviko/pyclustering/issues/456
- Bug with DBSCAN when points are marked as a noise (pyclustering.cluster.dbscan).
See: https://github.com/annoviko/pyclustering/issues/462
- Bug with randomly enabled connection weights in case of SyncNet based algorithms using CCORE interface (pyclustering.nnet.syncnet).
See: https://github.com/annoviko/pyclustering/issues/452
- Bug with calculation weighted connection for Sync based clustering algorithms in C++ implementation (ccore.nnet.syncnet).
See: no reference
- Bug with failure in case of numpy.ndarray data type in python part of CURE algorithm (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/438
- Bug with BANG algorithm with empty dimensions - when data contains column with the same values (pyclustering.cluster.bang).
See: https://github.com/annoviko/pyclustering/issues/449
------------------------------------------------------------------------
CHANGE NOTES FOR 0.8.1 (STARTED Feb 23, 2018), (RELEASED: May 28, 2018)
------------------------------------------------------------------------
GENERAL CHANGES:
- Implemented feature to use specific metric for distance calculation in K-Means algorithm (pyclustering.cluster.kmeans, ccore.clst.kmeans).
See: https://github.com/annoviko/pyclustering/issues/434
- Implemented BANG-clustering algorithm with result visualizer (pyclustering.cluster.bang).
See: https://github.com/annoviko/pyclustering/issues/424
- Implemented feature to use specific metric for distance calculation in K-Medians algorithm (pyclustering.cluster.kmedians, ccore.clst.kmedians).
See: https://github.com/annoviko/pyclustering/issues/429
- Supported new type of input data for K-Medoids - distance matrix (pyclustering.cluster.kmedoids, ccore.clst.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/418
- Implemented TTSAS algorithm (pyclustering.cluster.ttsas, ccore.clst.ttsas).
See: https://github.com/annoviko/pyclustering/issues/398
- Implemented MBSAS algorithm (pyclustering.cluster.mbsas, ccore.clst.mbsas).
See: https://github.com/annoviko/pyclustering/issues/398
- Implemented BSAS algorithm (pyclustering.cluster.bsas, ccore.clst.bsas).
See: https://github.com/annoviko/pyclustering/issues/398
- Implemented feature to use specific metric for distance calculation in K-Medoids algorithm (pyclustering.cluster.kmedoids, ccore.clst.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/417
- Implemented distance metric collection (pyclustering.utils.metric, ccore.utils.metric).
See: no reference.
- Supported new type of input data for OPTICS - distance matrix (pyclustering.cluster.optics, ccore.clst.optics).
See: https://github.com/annoviko/pyclustering/issues/412
- Supported new type of input data for DBSCAN - distance matrix (pyclustering.cluster.dbscan, ccore.clst.dbscan).
See: no reference.
- Implemented K-Means observer and visualizer to visualize and animate clustering results (pyclustering.cluster.kmeans, ccore.clst.kmeans).
See: no reference.
CORRECTED MAJOR BUGS:
- Bug with out of range in K-Medians (pyclustering.cluster.kmedians, ccore.clst.kmedians).
See: https://github.com/annoviko/pyclustering/issues/428
- Bug with fast linking in PCNN (python implementation only) that wasn't used despite the corresponding option (pyclustering.nnet.pcnn).
See: https://github.com/annoviko/pyclustering/issues/419
------------------------------------------------------------------------
CHANGE NOTES FOR 0.8.0 (STARTED Oct 23, 2017), (RELEASED: Feb 23, 2018)
------------------------------------------------------------------------
GENERAL CHANGES:
- Optimization K-Means++ algorithm using numpy (pyclustering.cluster.center_initializer).
See: no reference.
- Implemented K-Means++ initializer for CCORE (ccore.clst.kmeans_plus_plus).
See: https://github.com/annoviko/pyclustering/issues/382
- Optimization of X-Means clustering process by using KMeans++ for initial centers of split regions (pyclustering.cluster.xmeans, ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/382
- Implemented parallel Sync-family algorithms for C/C++ implementation (CCORE) only (ccore.sync).
See: https://github.com/annoviko/pyclustering/issues/170
- C/C++ implementation is used by default to increase performance.
See: https://github.com/annoviko/pyclustering/issues/393
- Ignore 'ccore' flag to use C/C++ if platform is not supported (pyclustering.core).
See: https://github.com/annoviko/pyclustering/issues/393
- Optimization of python implementation of the K-Means algorithm using numpy (pyclustering.cluster.kmeans).
See: https://github.com/annoviko/pyclustering/issues/403
- Implemented dynamic visualizer for oscillatory networks (pyclustering.nnet.dynamic_visualizer).
See: no reference.
- Implemented C/C++ Hodgkin-Huxley oscillatory network for image segmentation in CCORE to increase performance (ccore.hhn, pyclustering.nnet.hhn).
See: https://github.com/annoviko/pyclustering/issues/217
- Performance optimization for CCORE on linux platform.
See: no reference.
- 32-bit platform of CCORE is supported for Linux OS.
See: https://github.com/annoviko/pyclustering/issues/253
- 32-bit platform of CCORE is supported for Windows OS.
See: https://github.com/annoviko/pyclustering/issues/253
- Implemented method 'get_probabilities()' for obtaining belong probability in EM-algorithm (pyclustering.cluster.ema).
See: https://github.com/annoviko/pyclustering/issues/387
- Python implementation of CURE algorithm method 'get_clusters()' returns list of indexes (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/384
- Implemented parallel processing for X-Means algorithm (ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/372
- Implemented pool threads for parallel processing (ccore.parallel).
See: https://github.com/annoviko/pyclustering/issues/383
- Optimization of OPTICS algorithm using KD-tree for searching nearest neighbors (pyclustering.cluster.optics, ccore.optics).
See: https://github.com/annoviko/pyclustering/issues/370
- Optimization of DBSCAN algorithm using KD-tree for searching nearest neighbors (pyclustering.cluster.dbscan, ccore.dbscan).
See: https://github.com/annoviko/pyclustering/issues/369
CORRECTED MAJOR BUGS:
- Incorrect type of medoid's index in K-Medians algorithm in case of Python 2.x (pyclustering.cluster.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/415
- Hanging of method 'find_node' in KD-tree if it does not contain node with specified point and payload (pyclustering.container.kdtree).
See: no reference.
- Incorrect clustering by CURE algorithm in some cases when data have a lot of identical points (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/414
- Segmentation fault in CURE algorithm in some cases when data have a lot of identical points (ccore.clst.cure).
See: no reference.
- Incorrect segmentation by Python version of syncsegm - oscillatory network based on sync for image segmentation (pyclustering.nnet.syncsegm).
See: https://github.com/annoviko/pyclustering/issues/409
- Zero value of sigma under logarithm function in Python version of pyclustering X-Means algorithm (pyclustering.cluster.xmeans).
See: https://github.com/annoviko/pyclustering/issues/407
- Amplitude threshold is ignored during synchronous ensembles allocation for amplitude output dynamic 'allocate_sync_ensembles' - affect HNN, LEGION (pyclustering.utils).
See: no reference.
- Wrong indexes can be returned during synchronous ensembles allocation for amplitude output dynamic 'allocate_sync_ensembles' - affect HNN, LEGION (pyclustering.utils).
See: no reference.
- Amount of allocated clusters can be differ from amount of centers in X-Means algorithm (ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/389
- Amount of allocated clusters can be bigger than kmax in X-Means algorithm (pyclustering.cluster.xmeans, ccore.clst.xmeans).
See: https://github.com/annoviko/pyclustering/issues/388
- Corrected bug with returned nullptr in method 'kdtree_searcher::find_nearest_node()' (ccore.container.kdtree).
See: no reference.
------------------------------------------------------------------------
CHANGE NOTES FOR 0.7.2 (STARTED Oct 19, 2017), (RELEASED: Oct 23, 2017)
------------------------------------------------------------------------
GENERAL CHANGES:
- Correction for setup failure with PKG-INFO.rst.
------------------------------------------------------------------------
CHANGE NOTES FOR 0.7.1 (STARTED Oct 16, 2017), (RELEASED: Oct 19, 2017)
------------------------------------------------------------------------
GENERAL CHANGES:
- Metadata and description of the pyclustering package is updated.
------------------------------------------------------------------------
CHANGE NOTES FOR 0.7.0 (STARTED Jun 01, 2016), (RELEASED: Oct 16, 2017)
------------------------------------------------------------------------
GENERAL CHANGES (pyclustering):
- Implemented Expectation-Maximization clustering algorithm for Gaussian Mixute Model and clustering visualizer for this particular algorithm (pyclustering.cluster.ema).
See: https://github.com/annoviko/pyclustering/issues/16
- Implemented Genetic Clustering Algorithm (GCA) and clustering visualizer for this particular algorithm (pyclustering.cluster.ga).
See: https://github.com/annoviko/pyclustering/issues/360
- Implemented feature to obtain and visualize evolution of order parameter and local order parameter for Sync network and Sync-based algorithms (pyclustering.nnet.sync).
See: https://github.com/annoviko/pyclustering/issues/355
- Implemented K-Means++ method for initialization of initial centers for algorithms like K-Means or X-Means (pyclustering.cluster.center_initializer).
See: https://github.com/annoviko/pyclustering/issues/354
- Implemented fSync oscillatory network that is based on Landau-Stuart equation and Kuramoto model (pyclustering.nnet.fsync).
See: https://github.com/annoviko/pyclustering/issues/168
- Optimization of pyclustering client to core library 'CCORE' library (pyclustering.core).
See: https://github.com/annoviko/pyclustering/issues/289
See: https://github.com/annoviko/pyclustering/issues/351
- Implemented feature to show network structure of Sync family oscillatory networks in case 'ccore' usage.
See: https://github.com/annoviko/pyclustering/issues/344
- Implemented feature to colorize OPTICS ordering diagram when amount of clusters is specified.
See: no reference.
- Improved clustering results in case of usage MNDL splitting criterion for small datasets.
See: https://github.com/annoviko/pyclustering/issues/328
- Feature to display connectivity radius on cluster-ordering diagram by ordering_visualizer (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/314
- Feature to use CCORE implementation of OPTICS algorithm to take advance in performance (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/120
- Implemented feature to shows animation of pattern recognition process that has been performed by the SyncPR oscillatory network. Method 'animate_pattern_recognition()' of class 'syncpr_visualizer' (pyclustering.nnet.syncpr).
See: https://www.youtube.com/watch?v=Ro7KbApL4MQ
See: https://www.youtube.com/watch?v=iIusOsGehoY
- Implemented feature to obtain nodes of specified level of CF-tree. Method 'get_level_nodes()' of class 'cftree' (pyclustering.container.cftree).
See: no reference.
- Implemented feature to allocate/display/animate phase matrix: 'allocate_phase_matrix()', 'show_phase_matrix()', 'animate_phase_matrix()' (pyclustering.nnet.sync).
See: no reference.
- Implemented chaotic neural network where clustering phenomenon can be observed: 'cnn_network', 'cnn_dynamic', 'cnn_visualizer' (pyclustering.nnet.cnn).
See: https://github.com/annoviko/pyclustering/issues/301
- Implemented feature to analyse ordering diagram using amout of clusters that should be allocated as an input parameter to calculate correct connvectity radius for clustering (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/307
- Implemented feature to omit usage of initial centers - X-Means starts processing from random initial center (pyclustering.cluster.xmeans).
See: no reference.
- Implemented feature for cluster visualizer: cluster attributes (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/295
- Implemented SOM-SC algorithm (SOM Simple Clustering) (pyclustering.cluster.somsc).
See: https://github.com/annoviko/pyclustering/issues/321
GENERAL CHANGES (ccore):
- Implemented feature to obtain and visualize evolution of order parameter and local order parameter for Sync network and Sync-based algorithms (ccore.nnet.sync).
See: https://github.com/annoviko/pyclustering/issues/355
- Cygwin x64 platform is supported (ccore).
See: https://github.com/annoviko/pyclustering/issues/353
- Optimization of CCORE library interface (ccore.interface).
See: https://github.com/annoviko/pyclustering/issues/289
- Implemented MNDL splitting crinterion for X-Means algorithm (ccore.cluster_analysis.xmeans).
See: https://github.com/annoviko/pyclustering/issues/159
- Implemented OPTICS algorithm and interface for client that results all clustering results (ccore.cluster_analysis.optics).
See: https://github.com/annoviko/pyclustering/issues/120
- Implmeneted packing of connectivity matrix of Sync family oscillatory networks (ccore.interface.sync_interface).
See: https://github.com/annoviko/pyclustering/issues/344
CORRECTED MAJOR BUGS:
- Bug with segmentation fault during 'free()' on some linux operating systems.
See: no reference.
- Bug with sending the first element to cluster in OPTICS even if it is noise element.
See: no reference.
- Bug with amount of allocated clusters by K-Medoids algorithm in Python implementation and CCORE (pyclustering.cluster.kmedoids, ccore.cluster.medoids).
See: https://github.com/annoviko/pyclustering/issues/366
See: https://github.com/annoviko/pyclustering/issues/367
- Bug with getting neighbors and getting information about connections in Sync-based network and algorithms in case of usage CCORE.
See: no reference.
- Bug with calculation of number of oscillations for output dynamics.
See: no reference.
- Memory leakage in LEGION in case of CCORE usage - API function 'legion_destroy()' was not called (pyclustering.nnet.legion).
See: no reference.
- Bug with crash of antmeans algorithm for python version 3.6.0:414df79263a11, Dec 23 2016 [MSC v.1900 64 bit (AMD64)] (pyclustering.cluster.antmeans).
See: https://github.com/annoviko/pyclustering/issues/350
- Memory leakage in destructor of 'pyclustering_package' - exchange mechanism between ccore and pyclustering (ccore.interface.pyclustering_package').
See: https://github.com/annoviko/pyclustering/issues/347
- Bug with loosing of the initial state of hSync output dynamic in case of CCORE usage (ccore.cluster.hsyncnet).
See: https://github.com/annoviko/pyclustering/issues/346
- Bug with hSync output dynamic that was displayed with discontinous parts as a set of rectangles (pyclustering.cluster.hsyncnet).
See: https://github.com/annoviko/pyclustering/issues/345
- Bug with visualization of CNN network in case 3D data (pyclustering.nnet.cnn).
See: https://github.com/annoviko/pyclustering/issues/338
- Bug with CCORE wrapper crashing after returning value from CCORE (pyclustering.core).
See: https://github.com/annoviko/pyclustering/issues/337
- Bug with calculation BIC splitting criterion for X-Means algorithm (pyclustering.cluster.xmeans).
See: https://github.com/annoviko/pyclustering/issues/326
- Bug with calculation MNDL splitting criterion for X-Means algorithm (pyclustering.cluster.xmeans).
See: https://github.com/annoviko/pyclustering/issues/328
- Bug with loss of CF-nodes in CF-tree during inserting that leads unbalanced CF-tree (pyclustering.container.cftree).
See: https://github.com/annoviko/pyclustering/issues/304
- Bug with time stamps for each iteration in hsyncnet algorithm (ccore.cluster.hsyncnet).
See: https://github.com/annoviko/pyclustering/issues/306
- Bug with memory occupation by CCORE DBSCAN implementation due to adjacency matrix usage (ccore.cluster.dbscan).
See: https://github.com/annoviko/pyclustering/issues/309
- Bug with CURE: always finds max two representative points (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/310
- Bug with infinite loop in case of incorrect number of clusters 'ordering_analyser' (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/317
- Bug with incorrect connectivity radius for allocation specified amount of clusters 'ordering_analyser' (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/316
- Bug with clusters are allocated in the homogeneous ordering 'ordering_analyser' (pyclustering.cluster.optics).
See: https://github.com/annoviko/pyclustering/issues/315
------------------------------------------------------------------------
CHANGE NOTES FOR 0.6.0 (STARTED: Jul 18, 2015), (RELEASED: Jun 01, 2016)
------------------------------------------------------------------------
GENERAL CHANGES (pyclustering):
- Implemented phase oscillatory network syncpr (pyclustering.nnet.syncpr).
See: https://github.com/annoviko/pyclustering/issues/208
- Feature for pyclustering.nnet.syncpr that allows to use ccore library for solving.
See: https://github.com/annoviko/pyclustering/issues/232
- Optimized simulation algorithm for sync oscillatory network (pyclustering.nnet.sync) when collecting results are not requested.
See: https://github.com/annoviko/pyclustering/issues/233
- Images of english alphabet 100x100.
See: https://github.com/annoviko/pyclustering/commit/aa28f1a8a363fbeb5f074d22ec1e8258a1dd0579
- Implemented feature to use rectangular network structures in oscillatory networks.
See: https://github.com/annoviko/pyclustering/issues/259
- Implemented CLARANS algorithm (pyclustering.cluster.clarans).
See: https://github.com/annoviko/pyclustering/issues/52
- Implemented feature to analyse and visualize results of hysteresis oscillatory network (pyclustering.nnet.hysteresis).
See: https://github.com/annoviko/pyclustering/issues/75
- Implemented feature to analyse and visualize results of graph coloring algorithm based on hysteresis oscillatory network (pyclustering.gcolor.hysteresis).
See: https://github.com/annoviko/pyclustering/issues/75
- Implemented ant colony based algorithm for TSP problem (pyclustering.tsp.antcolony).
See: https://github.com/annoviko/pyclustering/pull/277
- Implemented feature to use CCORE K-Medians algorithm using argument 'ccore' to ensure high performance (pyclustering.cluster.kmedians).
See: https://github.com/annoviko/pyclustering/issues/231
- Implemented feature to place several plots on each row using parameter 'maximum number of rows' for cluster visualizer (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/274
- Implemented feature to specify initial number of neighbors to calculate initial connectivity radius and increase percent of number of neighbors (or radius if total number of object is exceeded) on each step (pyclustering.cluster.hsyncnet).
See: https://github.com/annoviko/pyclustering/issues/284
- Implemented double-layer oscillatory network based on modified Kuramoto model for image segmentation (pyclustering.nnet.syncsegm).
See: no reference
- Added new examples and demos.
See: no reference
- Implemented feature to use CCORE K-Medoids algorithm using argument 'ccore' to ensure high performance (pyclustering.cluster.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/230
- Implemented feature for CURE algorithm that provides additional information about clustering results: representative points and mean point of each cluster (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/292
- Implemented feature to animate analysed output dynamic of Sync family oscillatory networks (sync_visualizer, syncnet_visualizer): correlation matrix, phase coordinates, cluster allocation (pyclustering.nnet.sync, pyclustering.cluster.syncnet).
See: https://www.youtube.com/watch?v=5S5mFYVihso
See: https://www.youtube.com/watch?v=Vd-ww9PcZvI
See: https://www.youtube.com/watch?v=QYPqWoyNHO8
See: https://www.youtube.com/watch?v=RA0MiC2WlbY
- Improved algorithm SYNC-SOM: accuracy of clustering and calculation are improved in line with proof of concept where connection between oscillator in the second layer (that is represented by the self-organized feature map) should be created in line with classical radius like in SyncNet, but indirectly: if objects that correspond to two different neurons can be connected than neurons should be also connected with each other (pyclustering.cluster.syncsom).
See: https://github.com/annoviko/pyclustering/issues/297
GENERAL CHANGES (ccore):
- Implemented phase oscillatory network for pattern recognition syncpr (ccore.cluster.syncpr).
See: https://github.com/annoviko/pyclustering/issues/232
- Implemented agglomerative algorithm for cluster analysis (ccore.cluster.agglomerative).
See: https://github.com/annoviko/pyclustering/issues/212
- Implemented feature to use rectangular network structures in oscillatory networks.
See: https://github.com/annoviko/pyclustering/issues/259
- Implemented ant colony based algorithm for TSP problem (ccore.tsp.antcolony).
See: https://github.com/annoviko/pyclustering/pull/277
- Implemented K-Medians algorithm for cluster analysis (ccore.cluster.kmedians).
See: https://github.com/annoviko/pyclustering/issues/231
- Implemented feature to specify initial number of neighbors to calculate initial connectivity radius and increase percent of number of neighbors (or radius if total number of object is exceeded) on each step (ccore.cluster.hsyncnet).
https://github.com/annoviko/pyclustering/issues/284
- Implemented K-Medoids algorithm for cluster analysis (ccore.cluster.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/230
- Implemented feature for CURE algorithm that provides additional information about clustering results: representative points and mean point of each cluster (ccore.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/293
- Implemented new class collection to oscillatory and neural network constructing.
See: https://github.com/annoviko/pyclustering/issues/264
- Memory usage optimization for ROCK algorithm.
See: no reference
CORRECTED MAJOR BUGS:
- Bug with callback methods in ccore library in syncnet (ccore.cluster.syncnet) and hsyncnet (ccore.cluster.hsyncnet) that may lead to loss of accuracy.
- Bug with division by zero in kmeans algorithm (ccore.kmeans, pyclustering.cluster.kmeans) when cluster after center updating is not able to capture object.
See: https://github.com/annoviko/pyclustering/issues/238
- Bug with stack overflow in KD tree in case of big data (pyclustering.container.kdtree, ccore.container.kdtree).
See: https://github.com/annoviko/pyclustering/pull/239
See: https://github.com/annoviko/pyclustering/issues/255
See: https://github.com/annoviko/pyclustering/issues/254
- Bug with incorrect clustering in case of the same elements in cure algorithm (pyclustering.cluster.cure).
See: https://github.com/annoviko/pyclustering/pull/239
- Bug with execution fail in case of wrong number of initial medians and in case of the same objects with several initial medians (pyclustering.cluster.kmedians).
See: https://github.com/annoviko/pyclustering/issues/256
- Bug with calculation synchronous ensembles near by zero: oscillators 2*pi and 0 are considered as different (pyclustering.nnet.sync, ccore.nnet.sync).
See: https://github.com/annoviko/pyclustering/issues/263
- Bug with cluster allocation in kmedoids algorithm in case of the same objects with several initial medoids (pyclustering.cluster.kmedoids).
See: https://github.com/annoviko/pyclustering/issues/269
- Bug with visualization of clusters in 3D (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/273
- Bug with obtaining nearest entry for absorbing during inserting node (pyclustering.container.cftree).
See: https://github.com/annoviko/pyclustering/issues/282
- Bug with SOM method show_network() in case of usage CCORE (pyclustering.nnet.som).
See: https://github.com/annoviko/pyclustering/issues/283
- Bug with cluster allocation in case of switched off dynamic collecting (pyclustering.cluster.hsyncnet).
See: https://github.com/annoviko/pyclustering/issues/285
- Bug with execution fail during clustering data with rough values of initial medians (pyclustering.cluster.kmedians).
See: https://github.com/annoviko/pyclustering/issues/286
- Bug with meamory leakage on interface between CCORE and pyclustering (ccore).
See: no reference
- Bug with allocation correlation matrix in case of usage CCORE (pyclustering.nnet.sync).
See: https://github.com/annoviko/pyclustering/issues/288
- Bug with memory leakage in CURE algorithm - deallocation of representative points (ccore.cluster.cure).
See: https://github.com/annoviko/pyclustering/issues/294
- Bug with cluster visualization in case of 1D input data (pyclustering.cluster).
See: https://github.com/annoviko/pyclustering/issues/296