Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
PCA	35	56	21	0.00%	Show methods

Class PCA

Class PCA	Line # 35	Total Statements 56	Complexity 21	TOTAL Coverage 0.00%
PCA(AlignmentView,ScoreModelI,SimilarityParamsI)   PCA(AlignmentView,ScoreModelI,SimilarityParamsI)	6363	3.03	1.01	0.0 0.00%
getEigenvalue(int) : double   getEigenvalue(int) : double	7979	1.01	1.01	0.0 0.00%
getComponents(int,int,int,float) : Point[]   getComponents(int,int,int,float) : Point[]	9898	7.07	2.02	0.0 0.00%
component(int) : double[]   component(int) : double[]	121121	4.04	2.02	0.0 0.00%
component(int,int) : double   component(int,int) : double	144144	4.04	2.02	0.0 0.00%
getDetails() : String   getDetails() : String	162162	18.018	1.01	0.0 0.00%
run() : void   run() : void	201201	8.08	2.02	0.0 0.00%
print(String) : void   print(String) : void	240240	1.01	1.01	0.0 0.00%
println() : void   println() : void	246246	1.01	1.01	0.0 0.00%
wrapOutputBuffer(StringBuilder) : PrintStream   wrapOutputBuffer(StringBuilder) : PrintStream	236236	2.02	1.01	0.0 0.00%
getHeight() : int   getHeight() : int	261261	1.01	1.01	0.0 0.00%
getPairwiseScores() : MatrixI   getPairwiseScores() : MatrixI	273273	1.01	1.01	0.0 0.00%
setPairwiseScores(MatrixI) : void   setPairwiseScores(MatrixI) : void	278278	1.01	1.01	0.0 0.00%
getEigenmatrix() : MatrixI   getEigenmatrix() : MatrixI	283283	1.01	1.01	0.0 0.00%
setEigenmatrix(MatrixI) : void   setEigenmatrix(MatrixI) : void	288288	1.01	1.01	0.0 0.00%
getTridiagonal() : MatrixI   getTridiagonal() : MatrixI	293293	1.01	1.01	0.0 0.00%
setTridiagonal(MatrixI) : void   setTridiagonal(MatrixI) : void	298298	1.01	1.01	0.0 0.00%

 

Contributing tests

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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.bin.Console;
26		import jalview.datamodel.AlignmentView;
27		import jalview.datamodel.Point;
28		import jalview.math.MatrixI;
29
30		import java.io.PrintStream;
31
32		/**
33		* Performs Principal Component Analysis on given sequences
34		*/
		0% Uncovered Elements: 79 (79) Complexity: 21 Complexity Density: 0.38
35		public class PCA implements Runnable
36		{
37		/*
38		* inputs
39		*/
40		final private AlignmentView seqs;
41
42		final private ScoreModelI scoreModel;
43
44		final private SimilarityParamsI similarityParams;
45
46		/*
47		* outputs
48		*/
49		private MatrixI pairwiseScores;
50
51		private MatrixI tridiagonal;
52
53		private MatrixI eigenMatrix;
54
55		/**
56		* Constructor given the sequences to compute for, the similarity model to
57		* use, and a set of parameters for sequence comparison
58		*
59		* @param sequences
60		* @param sm
61		* @param options
62		*/
		0% Uncovered Elements: 3 (3) Complexity: 1 Complexity Density: 0.33
63	0	public PCA(AlignmentView sequences, ScoreModelI sm,...
64		SimilarityParamsI options)
65		{
66	0	this.seqs = sequences;
67	0	this.scoreModel = sm;
68	0	this.similarityParams = options;
69		}
70
71		/**
72		* Returns Eigenvalue
73		*
74		* @param i
75		* Index of diagonal within matrix
76		*
77		* @return Returns value of diagonal from matrix
78		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
79	0	public double getEigenvalue(int i)...
80		{
81	0	return eigenMatrix.getD()[i];
82		}
83
84		/**
85		* DOCUMENT ME!
86		*
87		* @param l
88		* DOCUMENT ME!
89		* @param n
90		* DOCUMENT ME!
91		* @param mm
92		* DOCUMENT ME!
93		* @param factor
94		* DOCUMENT ME!
95		*
96		* @return DOCUMENT ME!
97		*/
		0% Uncovered Elements: 9 (9) Complexity: 2 Complexity Density: 0.29
98	0	public Point[] getComponents(int l, int n, int mm, float factor)...
99		{
100	0	Point[] out = new Point[getHeight()];
101
102	0	for (int i = 0; i < getHeight(); i++)
103		{
104	0	float x = (float) component(i, l) * factor;
105	0	float y = (float) component(i, n) * factor;
106	0	float z = (float) component(i, mm) * factor;
107	0	out[i] = new Point(x, y, z);
108		}
109
110	0	return out;
111		}
112
113		/**
114		* DOCUMENT ME!
115		*
116		* @param n
117		* DOCUMENT ME!
118		*
119		* @return DOCUMENT ME!
120		*/
		0% Uncovered Elements: 6 (6) Complexity: 2 Complexity Density: 0.5
121	0	public double[] component(int n)...
122		{
123		// n = index of eigenvector
124	0	double[] out = new double[getHeight()];
125
126	0	for (int i = 0; i < out.length; i++)
127		{
128	0	out[i] = component(i, n);
129		}
130
131	0	return out;
132		}
133
134		/**
135		* DOCUMENT ME!
136		*
137		* @param row
138		* DOCUMENT ME!
139		* @param n
140		* DOCUMENT ME!
141		*
142		* @return DOCUMENT ME!
143		*/
		0% Uncovered Elements: 6 (6) Complexity: 2 Complexity Density: 0.5
144	0	double component(int row, int n)...
145		{
146	0	double out = 0.0;
147
148	0	for (int i = 0; i < pairwiseScores.width(); i++)
149		{
150	0	out += (pairwiseScores.getValue(row, i) * eigenMatrix.getValue(i, n));
151		}
152
153	0	return out / eigenMatrix.getD()[n];
154		}
155
156		/**
157		* Answers a formatted text report of the PCA calculation results (matrices
158		* and eigenvalues) suitable for display
159		*
160		* @return
161		*/
		0% Uncovered Elements: 18 (18) Complexity: 1 Complexity Density: 0.06
162	0	public String getDetails()...
163		{
164	0	StringBuilder sb = new StringBuilder(1024);
165	0	sb.append("PCA calculation using ").append(scoreModel.getName())
166		.append(" sequence similarity matrix\n========\n\n");
167	0	PrintStream ps = wrapOutputBuffer(sb);
168
169		/*
170		* pairwise similarity scores
171		*/
172	0	sb.append(" --- OrigT * Orig ---- \n");
173	0	pairwiseScores.print(ps, "%8.2f");
174
175		/*
176		* tridiagonal matrix, with D and E vectors
177		*/
178	0	sb.append(" ---Tridiag transform matrix ---\n");
179	0	sb.append(" --- D vector ---\n");
180	0	tridiagonal.printD(ps, "%15.4e");
181	0	ps.println();
182	0	sb.append("--- E vector ---\n");
183	0	tridiagonal.printE(ps, "%15.4e");
184	0	ps.println();
185
186		/*
187		* eigenvalues matrix, with D vector
188		*/
189	0	sb.append(" --- New diagonalization matrix ---\n");
190	0	eigenMatrix.print(ps, "%8.2f");
191	0	sb.append(" --- Eigenvalues ---\n");
192	0	eigenMatrix.printD(ps, "%15.4e");
193	0	ps.println();
194
195	0	return sb.toString();
196		}
197
198		/**
199		* Performs the PCA calculation
200		*/
		0% Uncovered Elements: 8 (8) Complexity: 2 Complexity Density: 0.25
201	0	@Override...
202		public void run()
203		{
204	0	try
205		{
206		/*
207		* sequence pairwise similarity scores
208		*/
209	0	pairwiseScores = scoreModel.findSimilarities(seqs, similarityParams);
210
211		/*
212		* tridiagonal matrix
213		*/
214	0	tridiagonal = pairwiseScores.copy();
215	0	tridiagonal.tred();
216
217		/*
218		* the diagonalization matrix
219		*/
220	0	eigenMatrix = tridiagonal.copy();
221	0	eigenMatrix.tqli();
222		} catch (Exception q)
223		{
224	0	Console.error("Error computing PCA: " + q.getMessage());
225	0	q.printStackTrace();
226		}
227		}
228
229		/**
230		* Returns a PrintStream that wraps (appends its output to) the given
231		* StringBuilder
232		*
233		* @param sb
234		* @return
235		*/
		0% Uncovered Elements: 2 (2) Complexity: 1 Complexity Density: 0.5
236	0	protected PrintStream wrapOutputBuffer(StringBuilder sb)...
237		{
238	0	PrintStream ps = new PrintStream(System.out)
239		{
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
240	0	@Override...
241		public void print(String x)
242		{
243	0	sb.append(x);
244		}
245
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
246	0	@Override...
247		public void println()
248		{
249	0	sb.append("\n");
250		}
251		};
252	0	return ps;
253		}
254
255		/**
256		* Answers the N dimensions of the NxN PCA matrix. This is the number of
257		* sequences involved in the pairwise score calculation.
258		*
259		* @return
260		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
261	0	public int getHeight()...
262		{
263		// TODO can any of seqs[] be null?
264	0	return pairwiseScores.height();// seqs.getSequences().length;
265		}
266
267		/**
268		* Answers the sequence pairwise similarity scores which were the first step
269		* of the PCA calculation
270		*
271		* @return
272		*/
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
273	0	public MatrixI getPairwiseScores()...
274		{
275	0	return pairwiseScores;
276		}
277
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
278	0	public void setPairwiseScores(MatrixI m)...
279		{
280	0	pairwiseScores = m;
281		}
282
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
283	0	public MatrixI getEigenmatrix()...
284		{
285	0	return eigenMatrix;
286		}
287
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
288	0	public void setEigenmatrix(MatrixI m)...
289		{
290	0	eigenMatrix = m;
291		}
292
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
293	0	public MatrixI getTridiagonal()...
294		{
295	0	return tridiagonal;
296		}
297
		0% Uncovered Elements: 1 (1) Complexity: 1 Complexity Density: 1
298	0	public void setTridiagonal(MatrixI tridiagonal)...
299		{
300	0	this.tridiagonal = tridiagonal;
301		}
302		}