Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
NJTree	32	26	15	0.00%	Show methods

Class NJTree

Class NJTree	Line # 32	Total Statements 26	Complexity 15	TOTAL Coverage 0.00%
NJTree(AlignmentViewport,ScoreModelI,SimilarityParamsI)   NJTree(AlignmentViewport,ScoreModelI,SimilarityParamsI)	4343	1.01	1.01	0.0 0.00%
findMinDistance() : double   findMinDistance() : double	5252	10.010	6.06	0.0 0.00%
findNewDistances(BinaryNode,BinaryNode,double) : void   findNewDistances(BinaryNode,BinaryNode,double) : void	8383	6.06	3.03	0.0 0.00%
findClusterDistance(int,int) : void   findClusterDistance(int,int) : void	110110	9.09	5.05	0.0 0.00%

 

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1		/*
2		* Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
3		* Copyright (C) $$Year-Rel$$ The Jalview Authors
4		*
5		* This file is part of Jalview.
6		*
7		* Jalview is free software: you can redistribute it and/or
8		* modify it under the terms of the GNU General Public License
9		* as published by the Free Software Foundation, either version 3
10		* of the License, or (at your option) any later version.
11		*
12		* Jalview is distributed in the hope that it will be useful, but
13		* WITHOUT ANY WARRANTY; without even the implied warranty
14		* of MERCHANTABILITY or FITNESS FOR A PARTICULAR
15		* PURPOSE. See the GNU General Public License for more details.
16		*
17		* You should have received a copy of the GNU General Public License
18		* along with Jalview. If not, see <http://www.gnu.org/licenses/>.
19		* The Jalview Authors are detailed in the 'AUTHORS' file.
20		*/
21		package jalview.analysis;
22
23		import jalview.api.analysis.ScoreModelI;
24		import jalview.api.analysis.SimilarityParamsI;
25		import jalview.datamodel.BinaryNode;
26		import jalview.viewmodel.AlignmentViewport;
27
28		/**
29		* This class implements distance calculations used in constructing a Neighbour
30		* Joining tree
31		*/
		0% Uncovered Elements: 48 (48) Complexity: 15 Complexity Density: 0.58
32		public class NJTree extends TreeBuilder
33		{
34		/**
35		* Constructor given a viewport, tree type and score model
36		*
37		* @param av
38		* the current alignment viewport
39		* @param sm
40		* a distance or similarity score model to use to compute the tree
41		* @param scoreParameters
42		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
43	0	public NJTree(AlignmentViewport av, ScoreModelI sm,...
44		SimilarityParamsI scoreParameters)
45		{
46	0	super(av, sm, scoreParameters);
47		}
48
49		/**
50		* {@inheritDoc}
51		*/
		0% Uncovered Elements: 18 (18) Complexity: 6 Complexity Density: 0.6
52	0	@Override...
53		protected double findMinDistance()
54		{
55	0	double min = Double.MAX_VALUE;
56
57	0	for (int i = 0; i < (noseqs - 1); i++)
58		{
59	0	for (int j = i + 1; j < noseqs; j++)
60		{
61	0	if (!done.get(i) && !done.get(j))
62		{
63	0	double tmp = distances.getValue(i, j)
64		- (findr(i, j) + findr(j, i));
65
66	0	if (tmp < min)
67		{
68	0	mini = i;
69	0	minj = j;
70
71	0	min = tmp;
72		}
73		}
74		}
75		}
76
77	0	return min;
78		}
79
80		/**
81		* {@inheritDoc}
82		*/
		0% Uncovered Elements: 10 (10) Complexity: 3 Complexity Density: 0.5
83	0	@Override...
84		protected void findNewDistances(BinaryNode nodei, BinaryNode nodej,
85		double dist)
86		{
87	0	nodei.dist = ((dist + ri) - rj) / 2;
88	0	nodej.dist = (dist - nodei.dist);
89
90	0	if (nodei.dist < 0)
91		{
92	0	nodei.dist = 0;
93		}
94
95	0	if (nodej.dist < 0)
96		{
97	0	nodej.dist = 0;
98		}
99		}
100
101		/**
102		* Calculates and saves the distance between the combination of cluster(i) and
103		* cluster(j) and all other clusters. The new distance to cluster k is
104		* calculated as the average of the distances from i to k and from j to k,
105		* less half the distance from i to j.
106		*
107		* @param i
108		* @param j
109		*/
		0% Uncovered Elements: 15 (15) Complexity: 5 Complexity Density: 0.56
110	0	@Override...
111		protected void findClusterDistance(int i, int j)
112		{
113		// New distances from cluster i to others
114	0	double[] newdist = new double[noseqs];
115
116	0	double ijDistance = distances.getValue(i, j);
117	0	for (int l = 0; l < noseqs; l++)
118		{
119	0	if ((l != i) && (l != j))
120		{
121	0	newdist[l] = (distances.getValue(i, l) + distances.getValue(j, l)
122		- ijDistance) / 2;
123		}
124		else
125		{
126	0	newdist[l] = 0;
127		}
128		}
129
130	0	for (int ii = 0; ii < noseqs; ii++)
131		{
132	0	distances.setValue(i, ii, newdist[ii]);
133	0	distances.setValue(ii, i, newdist[ii]);
134		}
135		}
136		}