|
1
|
|
|
"""! |
|
2
|
|
|
|
|
3
|
|
|
@brief Cluster analysis algorithm: MBSAS (Modified Basic Sequential Algorithmic Scheme). |
|
4
|
|
|
@details Implementation based on book: |
|
5
|
|
|
- Theodoridis, Koutroumbas, Konstantinos. Elsevier Academic Press - Pattern Recognition - 2nd Edition. 2003. |
|
6
|
|
|
|
|
7
|
|
|
@authors Andrei Novikov ([email protected]) |
|
8
|
|
|
@date 2014-2018 |
|
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
|
|
|
|
|
29
|
|
|
from pyclustering.cluster.bsas import bsas; |
|
30
|
|
|
|
|
31
|
|
|
|
|
32
|
|
|
class mbsas(bsas): |
|
33
|
|
|
def __init__(self, data, maximum_clusters, threshold, ccore=True, **kwargs): |
|
34
|
|
|
super().__init__(data, maximum_clusters, threshold, ccore, **kwargs); |
|
35
|
|
|
|
|
36
|
|
|
def process(self): |
|
37
|
|
|
self._clusters.append([0]); |
|
38
|
|
|
self._representatives.append(self._data[0]); |
|
39
|
|
|
|
|
40
|
|
|
skipped_objects = []; |
|
41
|
|
|
|
|
42
|
|
|
for i in range(1, len(self._data)): |
|
43
|
|
|
point = self._data[i]; |
|
44
|
|
|
index_cluster, distance = self._find_nearest_cluster(point); |
|
45
|
|
|
|
|
46
|
|
|
if (distance > self._threshold) and (len(self._clusters) < self._amount): |
|
47
|
|
|
self._representatives.append(point); |
|
48
|
|
|
self._clusters.append([i]); |
|
49
|
|
|
else: |
|
50
|
|
|
skipped_objects.append(i); |
|
51
|
|
|
|
|
52
|
|
|
for i in skipped_objects: |
|
53
|
|
|
point = self._data[i]; |
|
54
|
|
|
index_cluster, _ = self._find_nearest_cluster(point); |
|
55
|
|
|
|
|
56
|
|
|
self._clusters[index_cluster].append(i); |
|
57
|
|
|
self._update_representative(index_cluster, point); |
|
58
|
|
|
|
|
59
|
|
|
|
annoviko /
pyclustering
