chameleon - Code Metrics - Inspection of "[pyclustering.cluster] Chameleon draft." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

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by Andrei

created 2017-02-27 15:48 UTC

chameleon A

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Complexity

Total Complexity

Size/Duplication

Total Lines	39
Duplicated Lines	0 %

Importance

Changes

Metric	Value
dl	0
loc	39
rs	10
c	0
b	0
f	0
wmc	4

3 Methods

Rating	Name	Size	Complexity
A	get_clusters()	9	1
A	__init__()	7	2
A	process()	2	1

"""!

@brief Cluster analysis algorithm: CHAMELEON
@details Implementation based on article:
         - G.Karypis, E.Han, V.Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. 1999.
         
@authors Andrei Novikov ([email protected])
@date 2014-2017
@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_sqrt, knearest;



class chameleon:
    """!
    @brief Class represents clustering algorithm CHAMELEON.
    @details CHAMELEON finds the clusters in the data set by using a two phase algorithm. During the first phase,
             CHAMELEON uses a graph partitioning algorithm to cluster the data items into a large number of relatively
             small sub-clusters. During the second phase, it uses an agglomerative hierarchical clustering algorithm
             to find the genuine clusters by repeatedly combining together these sub-clusters.
    
    Example:
    @code
        NO CODE
    @endcode
    
    """
    
    def __init__(self, data, knearest, ccore = False):
param = 5

class Foo:
    def __init__(self, param):   # "param" would be flagged here
        self.param = param
        self.__data = data;
        self.__clusters = [];
        self.__knearest = knearest;
        
        self.__graph = [ [] for _ in range(len(data)) ];
        self.__ccore = ccore;
        
    
    
    def process(self):
        self.__graph = knearest(self.__data, self.__knearest);
    
    
    
    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
        
        @see process()
        
        """
        
        return self.__clusters;



1			"""!
2
3			@brief Cluster analysis algorithm: CHAMELEON
4			@details Implementation based on article:
5			- G.Karypis, E.Han, V.Kumar. CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. 1999.
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2017
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_sqrt, knearest;
			0 ignored issues – show Unused Code introduced 2017-02-27 15:49 UTC by Report Bug Copy Issue Report Unused euclidean_distance_sqrt imported from pyclustering.utils Loading history...
29
30
31			class chameleon:
32			"""!
33			@brief Class represents clustering algorithm CHAMELEON.
34			@details CHAMELEON finds the clusters in the data set by using a two phase algorithm. During the first phase,
35			CHAMELEON uses a graph partitioning algorithm to cluster the data items into a large number of relatively
36			small sub-clusters. During the second phase, it uses an agglomerative hierarchical clustering algorithm
37			to find the genuine clusters by repeatedly combining together these sub-clusters.
38
39			Example:
40			@code
41			NO CODE
42			@endcode
43
44			"""
45
46			def __init__(self, data, knearest, ccore = False):
			0 ignored issues – show Comprehensibility Bug introduced 2017-02-27 15:49 UTC by Report Bug Copy Issue Report `knearest` is re-defining a name which is already available in the outer-scope (previously defined on line `28`). It is generally a bad practice to shadow variables from the outer-scope. In most cases, this is done unintentionally and might lead to unexpected behavior: param = 5 class Foo: def __init__(self, param): # "param" would be flagged here self.param = param Loading history...
47			self.__data = data;
48			self.__clusters = [];
49			self.__knearest = knearest;
50
51			self.__graph = [ [] for _ in range(len(data)) ];
52			self.__ccore = ccore;
53
54
55
56			def process(self):
57			self.__graph = knearest(self.__data, self.__knearest);
58
59
60
61			def get_clusters(self):
62			"""!
63			@brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
64
65			@see process()
66
67			"""
68
69			return self.__clusters;
70
71

annoviko / pyclustering

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chameleon A

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3 Methods

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