Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
SparseMatrixTest	33	157	32	0.8689320786.9%	Show methods

Class SparseMatrixTest

Class SparseMatrixTest	Line # 33	Total Statements 157	Complexity 32	TOTAL Coverage 0.8689320786.9%
testConstructor() : void   testConstructor() : void	3939	7.07	1.01	1.0 1.0100%
testTranspose() : void   testTranspose() : void	5353	11.011	1.01	1.0 1.0100%
testPreMultiply() : void   testPreMultiply() : void	7171	19.019	1.01	1.0 1.0100%
testPreMultiply_tooManyColumns() : void   testPreMultiply_tooManyColumns() : void	104104	3.03	1.01	0.6666667 0.666666766.7%
testPreMultiply_tooFewColumns() : void   testPreMultiply_tooFewColumns() : void	122122	3.03	1.01	0.6666667 0.666666766.7%
testPostMultiply() : void   testPostMultiply() : void	140140	31.031	1.01	1.0 1.0100%
testSign() : void   testSign() : void	210210	6.06	1.01	0.0 0.00%
testTred_matchesMatrix() : void   testTred_matchesMatrix() : void	226226	13.013	3.03	1.0 1.0100%
assertMatricesMatch(MatrixI,MatrixI,double) : void   assertMatricesMatch(MatrixI,MatrixI,double) : void	256256	12.012	6.06	0.72727275 0.7272727572.7%
testGetValue() : void   testGetValue() : void	282282	5.05	3.03	0.0 0.00%
testTqli_matchesMatrix() : void   testTqli_matchesMatrix() : void	305305	15.015	3.03	1.0 1.0100%
getSparseValues(int,int,int) : double[][]   getSparseValues(int,int,int) : double[][]	349349	10.010	5.05	0.8888889 0.888888988.9%
testPreMultiply_sparseProduct() : void   testPreMultiply_sparseProduct() : void	380380	7.07	1.01	1.0 1.0100%
testFillRatio() : void   testFillRatio() : void	404404	2.02	1.01	1.0 1.0100%
testTred_reproducible() : void   testTred_reproducible() : void	417417	13.013	3.03	1.0 1.0100%

 

Contributing tests

Contributing tests

Select all

	Test contribution	Test	Result	Actions
	0.26213592	jalview.math.SparseMatrixTest.testTqli_matchesMatrixjalview.math.SparseMatrixTest.testTqli_matchesMatrix	1PASS
	0.2524272	jalview.math.SparseMatrixTest.testTred_reproduciblejalview.math.SparseMatrixTest.testTred_reproducible	1PASS
	0.2524272	jalview.math.SparseMatrixTest.testTred_matchesMatrixjalview.math.SparseMatrixTest.testTred_matchesMatrix	1PASS
	0.23786408	jalview.math.SparseMatrixTest.testPostMultiplyjalview.math.SparseMatrixTest.testPostMultiply	1PASS
	0.097087376	jalview.math.SparseMatrixTest.testPreMultiplyjalview.math.SparseMatrixTest.testPreMultiply	1PASS
	0.058252428	jalview.math.SparseMatrixTest.testTransposejalview.math.SparseMatrixTest.testTranspose	1PASS
	0.03883495	jalview.math.SparseMatrixTest.testPreMultiply_sparseProductjalview.math.SparseMatrixTest.testPreMultiply_sparseProduct	1PASS
	0.03883495	jalview.math.SparseMatrixTest.testConstructorjalview.math.SparseMatrixTest.testConstructor	1PASS
	0.014563107	jalview.math.SparseMatrixTest.testFillRatiojalview.math.SparseMatrixTest.testFillRatio	1PASS
	0.014563107	jalview.math.SparseMatrixTest.testPreMultiply_tooManyColumnsjalview.math.SparseMatrixTest.testPreMultiply_tooManyColumns	1PASS
	0.014563107	jalview.math.SparseMatrixTest.testPreMultiply_tooFewColumnsjalview.math.SparseMatrixTest.testPreMultiply_tooFewColumns	1PASS

 

Source view

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.math;
22
23		import static org.testng.Assert.assertEquals;
24		import static org.testng.Assert.assertFalse;
25		import static org.testng.Assert.assertTrue;
26		import static org.testng.Assert.fail;
27
28		import java.util.Random;
29
30		import org.testng.annotations.Test;
31		import org.testng.internal.junit.ArrayAsserts;
32
		86.9% Uncovered Elements: 27 (206) Complexity: 32 Complexity Density: 0.2
33		public class SparseMatrixTest
34		{
35		final static double DELTA = 0.0001d;
36
37		Random r = new Random(1729);
38
		100% Uncovered Elements: 0 (7) Complexity: 1 Complexity Density: 0.14 1PASS
39	1	@Test(groups = "Functional")...
40		public void testConstructor()
41		{
42	1	MatrixI m1 = new SparseMatrix(
43		new double[][]
44		{ { 2, 0, 4 }, { 0, 6, 0 } });
45	1	assertEquals(m1.getValue(0, 0), 2d);
46	1	assertEquals(m1.getValue(0, 1), 0d);
47	1	assertEquals(m1.getValue(0, 2), 4d);
48	1	assertEquals(m1.getValue(1, 0), 0d);
49	1	assertEquals(m1.getValue(1, 1), 6d);
50	1	assertEquals(m1.getValue(1, 2), 0d);
51		}
52
		100% Uncovered Elements: 0 (11) Complexity: 1 Complexity Density: 0.09 1PASS
53	1	@Test(groups = "Functional")...
54		public void testTranspose()
55		{
56	1	MatrixI m1 = new SparseMatrix(
57		new double[][]
58		{ { 2, 0, 4 }, { 5, 6, 0 } });
59	1	MatrixI m2 = m1.transpose();
60	1	assertTrue(m2 instanceof SparseMatrix);
61	1	assertEquals(m2.height(), 3);
62	1	assertEquals(m2.width(), 2);
63	1	assertEquals(m2.getValue(0, 0), 2d);
64	1	assertEquals(m2.getValue(0, 1), 5d);
65	1	assertEquals(m2.getValue(1, 0), 0d);
66	1	assertEquals(m2.getValue(1, 1), 6d);
67	1	assertEquals(m2.getValue(2, 0), 4d);
68	1	assertEquals(m2.getValue(2, 1), 0d);
69		}
70
		100% Uncovered Elements: 0 (19) Complexity: 1 Complexity Density: 0.05 1PASS
71	1	@Test(groups = "Functional")...
72		public void testPreMultiply()
73		{
74	1	MatrixI m1 = new SparseMatrix(new double[][] { { 2, 3, 4 } }); // 1x3
75	1	MatrixI m2 = new SparseMatrix(new double[][] { { 5 }, { 6 }, { 7 } }); // 3x1
76
77		/*
78		* 1x3 times 3x1 is 1x1
79		* 2x5 + 3x6 + 4*7 = 56
80		*/
81	1	MatrixI m3 = m2.preMultiply(m1);
82	1	assertFalse(m3 instanceof SparseMatrix);
83	1	assertEquals(m3.height(), 1);
84	1	assertEquals(m3.width(), 1);
85	1	assertEquals(m3.getValue(0, 0), 56d);
86
87		/*
88		* 3x1 times 1x3 is 3x3
89		*/
90	1	m3 = m1.preMultiply(m2);
91	1	assertEquals(m3.height(), 3);
92	1	assertEquals(m3.width(), 3);
93	1	assertEquals(m3.getValue(0, 0), 10d);
94	1	assertEquals(m3.getValue(0, 1), 15d);
95	1	assertEquals(m3.getValue(0, 2), 20d);
96	1	assertEquals(m3.getValue(1, 0), 12d);
97	1	assertEquals(m3.getValue(1, 1), 18d);
98	1	assertEquals(m3.getValue(1, 2), 24d);
99	1	assertEquals(m3.getValue(2, 0), 14d);
100	1	assertEquals(m3.getValue(2, 1), 21d);
101	1	assertEquals(m3.getValue(2, 2), 28d);
102		}
103
		66.7% Uncovered Elements: 1 (3) Complexity: 1 Complexity Density: 0.33 1PASS
104	1	@Test(...
105		groups = "Functional",
106		expectedExceptions =
107		{ IllegalArgumentException.class })
108		public void testPreMultiply_tooManyColumns()
109		{
110	1	Matrix m1 = new SparseMatrix(
111		new double[][]
112		{ { 2, 3, 4 }, { 3, 4, 5 } }); // 2x3
113
114		/*
115		* 2x3 times 2x3 invalid operation -
116		* multiplier has more columns than multiplicand has rows
117		*/
118	1	m1.preMultiply(m1);
119	0	fail("Expected exception");
120		}
121
		66.7% Uncovered Elements: 1 (3) Complexity: 1 Complexity Density: 0.33 1PASS
122	1	@Test(...
123		groups = "Functional",
124		expectedExceptions =
125		{ IllegalArgumentException.class })
126		public void testPreMultiply_tooFewColumns()
127		{
128	1	Matrix m1 = new SparseMatrix(
129		new double[][]
130		{ { 2, 3, 4 }, { 3, 4, 5 } }); // 2x3
131
132		/*
133		* 3x2 times 3x2 invalid operation -
134		* multiplier has more columns than multiplicand has row
135		*/
136	1	m1.preMultiply(m1);
137	0	fail("Expected exception");
138		}
139
		100% Uncovered Elements: 0 (31) Complexity: 1 Complexity Density: 0.03 1PASS
140	1	@Test(groups = "Functional")...
141		public void testPostMultiply()
142		{
143		/*
144		* Square matrices
145		* (2 3) . (10 100)
146		* (4 5) (1000 10000)
147		* =
148		* (3020 30200)
149		* (5040 50400)
150		*/
151	1	MatrixI m1 = new SparseMatrix(new double[][] { { 2, 3 }, { 4, 5 } });
152	1	MatrixI m2 = new SparseMatrix(
153		new double[][]
154		{ { 10, 100 }, { 1000, 10000 } });
155	1	MatrixI m3 = m1.postMultiply(m2);
156	1	assertEquals(m3.getValue(0, 0), 3020d);
157	1	assertEquals(m3.getValue(0, 1), 30200d);
158	1	assertEquals(m3.getValue(1, 0), 5040d);
159	1	assertEquals(m3.getValue(1, 1), 50400d);
160
161		/*
162		* also check m2.preMultiply(m1) - should be same as m1.postMultiply(m2)
163		*/
164	1	MatrixI m4 = m2.preMultiply(m1);
165	1	assertMatricesMatch(m3, m4, 0.00001d);
166
167		/*
168		* m1 has more rows than columns
169		* (2).(10 100 1000) = (20 200 2000)
170		* (3) (30 300 3000)
171		*/
172	1	m1 = new SparseMatrix(new double[][] { { 2 }, { 3 } });
173	1	m2 = new SparseMatrix(new double[][] { { 10, 100, 1000 } });
174	1	m3 = m1.postMultiply(m2);
175	1	assertEquals(m3.height(), 2);
176	1	assertEquals(m3.width(), 3);
177	1	assertEquals(m3.getValue(0, 0), 20d);
178	1	assertEquals(m3.getValue(0, 1), 200d);
179	1	assertEquals(m3.getValue(0, 2), 2000d);
180	1	assertEquals(m3.getValue(1, 0), 30d);
181	1	assertEquals(m3.getValue(1, 1), 300d);
182	1	assertEquals(m3.getValue(1, 2), 3000d);
183
184	1	m4 = m2.preMultiply(m1);
185	1	assertMatricesMatch(m3, m4, 0.00001d);
186
187		/*
188		* m1 has more columns than rows
189		* (2 3 4) . (5 4) = (56 25)
190		* (6 3)
191		* (7 2)
192		* [0, 0] = 2*5 + 3*6 + 4*7 = 56
193		* [0, 1] = 2*4 + 3*3 + 4*2 = 25
194		*/
195	1	m1 = new SparseMatrix(new double[][] { { 2, 3, 4 } });
196	1	m2 = new SparseMatrix(new double[][] { { 5, 4 }, { 6, 3 }, { 7, 2 } });
197	1	m3 = m1.postMultiply(m2);
198	1	assertEquals(m3.height(), 1);
199	1	assertEquals(m3.width(), 2);
200	1	assertEquals(m3.getValue(0, 0), 56d);
201	1	assertEquals(m3.getValue(0, 1), 25d);
202
203		/*
204		* and check premultiply equivalent
205		*/
206	1	m4 = m2.preMultiply(m1);
207	1	assertMatricesMatch(m3, m4, 0.00001d);
208		}
209
		0% Uncovered Elements: 6 (6) Complexity: 1 Complexity Density: 0.17 1PASS
210	0	@Test(groups = "Timing")...
211		public void testSign()
212		{
213	0	assertEquals(Matrix.sign(-1, -2), -1d);
214	0	assertEquals(Matrix.sign(-1, 2), 1d);
215	0	assertEquals(Matrix.sign(-1, 0), 1d);
216	0	assertEquals(Matrix.sign(1, -2), -1d);
217	0	assertEquals(Matrix.sign(1, 2), 1d);
218	0	assertEquals(Matrix.sign(1, 0), 1d);
219		}
220
221		/**
222		* Verify that the results of method tred() are the same for SparseMatrix as
223		* they are for Matrix (i.e. a regression test rather than an absolute test of
224		* correctness of results)
225		*/
		100% Uncovered Elements: 0 (17) Complexity: 3 Complexity Density: 0.23 1PASS
226	1	@Test(groups = "Functional")...
227		public void testTred_matchesMatrix()
228		{
229		/*
230		* make a pseudo-random symmetric matrix as required for tred/tqli
231		*/
232	1	int rows = 10;
233	1	int cols = rows;
234	1	double[][] d = getSparseValues(rows, cols, 3);
235
236		/*
237		* make a copy of the values so m1, m2 are not
238		* sharing arrays!
239		*/
240	1	double[][] d1 = new double[rows][cols];
241	11	for (int row = 0; row < rows; row++)
242		{
243	110	for (int col = 0; col < cols; col++)
244		{
245	100	d1[row][col] = d[row][col];
246		}
247		}
248	1	Matrix m1 = new Matrix(d);
249	1	Matrix m2 = new SparseMatrix(d1);
250	1	assertMatricesMatch(m1, m2, 0.00001d); // sanity check
251	1	m1.tred();
252	1	m2.tred();
253	1	assertMatricesMatch(m1, m2, 0.00001d);
254		}
255
		72.7% Uncovered Elements: 6 (22) Complexity: 6 Complexity Density: 0.5
256	9	private void assertMatricesMatch(MatrixI m1, MatrixI m2, double delta)...
257		{
258	9	if (m1.height() != m2.height())
259		{
260	0	fail("height mismatch");
261		}
262	9	if (m1.width() != m2.width())
263		{
264	0	fail("width mismatch");
265		}
266	66	for (int row = 0; row < m1.height(); row++)
267		{
268	541	for (int col = 0; col < m1.width(); col++)
269		{
270	484	double v2 = m2.getValue(row, col);
271	484	double v1 = m1.getValue(row, col);
272	484	if (Math.abs(v1 - v2) > DELTA)
273		{
274	0	fail(String.format("At [%d, %d] %f != %f", row, col, v1, v2));
275		}
276		}
277		}
278	9	ArrayAsserts.assertArrayEquals(m1.getD(), m2.getD(), delta);
279	9	ArrayAsserts.assertArrayEquals(m1.getE(), m2.getE(), 0.00001d);
280		}
281
		0% Uncovered Elements: 9 (9) Complexity: 3 Complexity Density: 0.6 1PASS
282	0	@Test...
283		public void testGetValue()
284		{
285	0	double[][] d = new double[][] { { 0, 0, 1, 0, 0 }, { 2, 3, 0, 0, 0 },
286		{ 4, 0, 0, 0, 5 } };
287	0	MatrixI m = new SparseMatrix(d);
288	0	for (int row = 0; row < 3; row++)
289		{
290	0	for (int col = 0; col < 5; col++)
291		{
292	0	assertEquals(m.getValue(row, col), d[row][col],
293		String.format("At [%d, %d]", row, col));
294		}
295		}
296		}
297
298		/**
299		* Verify that the results of method tqli() are the same for SparseMatrix as
300		* they are for Matrix (i.e. a regression test rather than an absolute test of
301		* correctness of results)
302		*
303		* @throws Exception
304		*/
		100% Uncovered Elements: 0 (19) Complexity: 3 Complexity Density: 0.2 1PASS
305	1	@Test(groups = "Functional")...
306		public void testTqli_matchesMatrix() throws Exception
307		{
308		/*
309		* make a pseudo-random symmetric matrix as required for tred
310		*/
311	1	int rows = 6;
312	1	int cols = rows;
313	1	double[][] d = getSparseValues(rows, cols, 3);
314
315		/*
316		* make a copy of the values so m1, m2 are not
317		* sharing arrays!
318		*/
319	1	double[][] d1 = new double[rows][cols];
320	7	for (int row = 0; row < rows; row++)
321		{
322	42	for (int col = 0; col < cols; col++)
323		{
324	36	d1[row][col] = d[row][col];
325		}
326		}
327	1	Matrix m1 = new Matrix(d);
328	1	Matrix m2 = new SparseMatrix(d1);
329
330		// have to do tred() before doing tqli()
331	1	m1.tred();
332	1	m2.tred();
333	1	assertMatricesMatch(m1, m2, 0.00001d);
334
335	1	m1.tqli();
336	1	m2.tqli();
337	1	assertMatricesMatch(m1, m2, 0.00001d);
338		}
339
340		/**
341		* Helper method to make values for a sparse, pseudo-random symmetric matrix
342		*
343		* @param rows
344		* @param cols
345		* @param occupancy
346		* one in 'occupancy' entries will be non-zero
347		* @return
348		*/
		88.9% Uncovered Elements: 2 (18) Complexity: 5 Complexity Density: 0.5
349	3	public double[][] getSparseValues(int rows, int cols, int occupancy)...
350		{
351		/*
352		* generate whole number values between -12 and +12
353		* (to mimic score matrices used in Jalview)
354		*/
355	3	double[][] d = new double[rows][cols];
356	3	int m = 0;
357	29	for (int i = 0; i < rows; i++)
358		{
359	26	if (++m % occupancy == 0)
360		{
361	0	d[i][i] = r.nextInt() % 13; // diagonal
362		}
363	131	for (int j = 0; j < i; j++)
364		{
365	105	if (++m % occupancy == 0)
366		{
367	43	d[i][j] = r.nextInt() % 13;
368	43	d[j][i] = d[i][j];
369		}
370		}
371		}
372	3	return d;
373
374		}
375
376		/**
377		* Test that verifies that the result of preMultiply is a SparseMatrix if more
378		* than 80% zeroes, else a Matrix
379		*/
		100% Uncovered Elements: 0 (7) Complexity: 1 Complexity Density: 0.14 1PASS
380	1	@Test(groups = "Functional")...
381		public void testPreMultiply_sparseProduct()
382		{
383	1	MatrixI m1 = new SparseMatrix(
384		new double[][]
385		{ { 1 }, { 0 }, { 0 }, { 0 }, { 0 } }); // 5x1
386	1	MatrixI m2 = new SparseMatrix(new double[][] { { 1, 1, 1, 1 } }); // 1x4
387
388		/*
389		* m1.m2 makes a row of 4 1's, and 4 rows of zeros
390		* 20% non-zero so not 'sparse'
391		*/
392	1	MatrixI m3 = m2.preMultiply(m1);
393	1	assertFalse(m3 instanceof SparseMatrix);
394
395		/*
396		* replace a 1 with a 0 in the product:
397		* it is now > 80% zero so 'sparse'
398		*/
399	1	m2 = new SparseMatrix(new double[][] { { 1, 1, 1, 0 } });
400	1	m3 = m2.preMultiply(m1);
401	1	assertTrue(m3 instanceof SparseMatrix);
402		}
403
		100% Uncovered Elements: 0 (2) Complexity: 1 Complexity Density: 0.5 1PASS
404	1	@Test(groups = "Functional")...
405		public void testFillRatio()
406		{
407	1	SparseMatrix m1 = new SparseMatrix(
408		new double[][]
409		{ { 2, 0, 4, 1, 0 }, { 0, 6, 0, 0, 0 } });
410	1	assertEquals(m1.getFillRatio(), 0.4f);
411		}
412
413		/**
414		* Verify that the results of method tred() are the same if the calculation is
415		* redone
416		*/
		100% Uncovered Elements: 0 (17) Complexity: 3 Complexity Density: 0.23 1PASS
417	1	@Test(groups = "Functional")...
418		public void testTred_reproducible()
419		{
420		/*
421		* make a pseudo-random symmetric matrix as required for tred/tqli
422		*/
423	1	int rows = 10;
424	1	int cols = rows;
425	1	double[][] d = getSparseValues(rows, cols, 3);
426
427		/*
428		* make a copy of the values so m1, m2 are not
429		* sharing arrays!
430		*/
431	1	double[][] d1 = new double[rows][cols];
432	11	for (int row = 0; row < rows; row++)
433		{
434	110	for (int col = 0; col < cols; col++)
435		{
436	100	d1[row][col] = d[row][col];
437		}
438		}
439	1	Matrix m1 = new SparseMatrix(d);
440	1	Matrix m2 = new SparseMatrix(d1);
441	1	assertMatricesMatch(m1, m2, 1.0e16); // sanity check
442	1	m1.tred();
443	1	m2.tred();
444	1	assertMatricesMatch(m1, m2, 0.00001d);
445		}
446		}