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Java > Open Source Codes > JSci > tests > PolynomialTest


1 package JSci.tests;
2
3 import JSci.maths.*;
4 import JSci.maths.polynomials.*;
5
6 /**
7 * Testcase for polynomials.
8 */
9 public class PolynomialTest extends junit.framework.TestCase {
10         private final int n = 15;
11
12         public static void main(String JavaDoc arg[]) {
13                 junit.textui.TestRunner.run(PolynomialTest.class);
14         }
15         public PolynomialTest(String JavaDoc name) {
16                 super(name);
17         }
18         protected void setUp() throws Exception JavaDoc {
19                 JSci.GlobalSettings.ZERO_TOL=1.0e-6;
20                 super.setUp();
21         }
22         private static double[] createRandomArray(int n) {
23                 double[] arr = new double[n];
24                 for(int i=0; i<arr.length; i++)
25                         arr[i] = 2.0*Math.random()-1.0;
26                 return arr;
27         }
28         public void testSubtract() {
29                 RealPolynomial p = new RealPolynomial(createRandomArray(n));
30                 p = (RealPolynomial) p.subtract(p);
31                 assertEquals(RealPolynomialRing.getInstance().zero(), p);
32                 assertEquals(0, p.degree());
33         }
34         public void testLeastSquaresFit() {
35                 // generate random polynomial
36 RealPolynomial p = new RealPolynomial(createRandomArray(n));
37
38                 // generate random sample data
39 final double scale = 10.0;
40                 double[][] data = new double[2][n];
41                 data[0][0] = scale*Math.random();
42                 data[1][0] = p.map(data[0][0]);
43                 for ( int k = 1; k < data[0].length; k++ ) {
44                         data[0][k] = data[0][k - 1] + scale*Math.random();
45                         data[1][k] = p.map(data[0][k]);
46                 }
47                 RealPolynomial fitted = LinearMath.leastSquaresFit(n-1, data);
48                 assertEquals(p, fitted);
49         }
50         public void testInterpolation() {
51                 // unstable at large n
52 final int n = 5;
53                 // generate random sample data
54 final double scale = 10.0;
55                 double[][] data = new double[2][n];
56                 data[0][0] = scale*Math.random();
57                 data[1][0] = scale*Math.random();
58                 for ( int k = 1; k < data[0].length; k++ ) {
59                         data[0][k] = data[0][k - 1] + scale*Math.random();
60                         data[1][k] = scale*Math.random();
61                 }
62
63                 RealPolynomial p = PolynomialMath.interpolateLagrange(data);
64                 for ( int k = 0; k < n; k++ ) {
65                         assertEquals(data[1][k], p.map(data[0][k]), JSci.GlobalSettings.ZERO_TOL);
66                 }
67         }
68 }
69
70
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