1. /* ===========================================================

  2.  * JFreeChart : a free chart library for the Java(tm) platform

  3.  * ===========================================================

  4.  *

  5.  * (C) Copyright 2000-2005, by Object Refinery Limited and Contributors.

  6.  *

  7.  * Project Info:  http://www.jfree.org/jfreechart/index.html

  8.  *

  9.  * This library is free software; you can redistribute it and/or modify it under the terms

  10.  * of the GNU Lesser General Public License as published by the Free Software Foundation;

  11.  * either version 2.1 of the License, or (at your option) any later version.

  12.  *

  13.  * This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;

  14.  * without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

  15.  * See the GNU Lesser General Public License for more details.

  16.  *

  17.  * You should have received a copy of the GNU Lesser General Public License along with this

  18.  * library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330,

  19.  * Boston, MA 02111-1307, USA.

  20.  *

  21.  * [Java is a trademark or registered trademark of Sun Microsystems, Inc. 

  22.  * in the United States and other countries.]

  23.  *

  24.  * --------------------

  25.  * RegressionTests.java

  26.  * --------------------

  27.  * (C) Copyright 2002-2005 by Object Refinery Limited and Contributors.

  28.  *

  29.  * Original Author:  David Gilbert (for Object Refinery Limited);

  30.  * Contributor(s):   -;

  31.  *

  32.  * $Id: RegressionTests.java,v 1.2 2005/01/11 17:50:16 mungady Exp $

  33.  *

  34.  * Changes

  35.  * -------

  36.  * 30-Sep-2002 : Version 1 (DG);

  37.  * 17-Oct-2002 : Fixed errors reported by Checkstyle (DG);

  38.  *

  39.  */

  40. 

  41. package org.jfree.data.statistics.junit;

  42. 

  43. import junit.framework.Test;

  44. import junit.framework.TestCase;

  45. import junit.framework.TestSuite;

  46. 

  47. import org.jfree.data.statistics.Regression;

  48. import org.jfree.data.xy.XYDataset;

  49. import org.jfree.data.xy.XYSeries;

  50. import org.jfree.data.xy.XYSeriesCollection;

  51. 

  52. /**

  53.  * Tests for the {@link Regression} class.

  54.  */

  55. public class RegressionTests extends TestCase {

  56. 

  57.     /**

  58.      * Returns the tests as a test suite.

  59.      *

  60.      * @return The test suite.

  61.      */

  62.     public static Test suite() {

  63.         return new TestSuite(RegressionTests.class);

  64.     }

  65. 

  66.     /**

  67.      * Constructs a new set of tests.

  68.      *

  69.      * @param name  the name of the tests.

  70.      */

  71.     public RegressionTests(String name) {

  72.         super(name);

  73.     }

  74. 

  75.     /**

  76.      * Checks the results of an OLS regression on sample dataset 1.

  77.      */

  78.     public void testOLSRegression1a() {

  79. 

  80.         double[][] data = createSampleData1();

  81.         double[] result1 = Regression.getOLSRegression(data);

  82.         assertEquals(.25680930, result1[0], 0.0000001);

  83.         assertEquals(0.72792106, result1[1], 0.0000001);

  84. 

  85.     }

  86. 

  87.     /**

  88.      * Checks the results of an OLS regression on sample dataset 1 AFTER converting it to

  89.      * an XYSeries.

  90.      */

  91.     public void testOLSRegression1b() {

  92. 

  93.         double[][] data = createSampleData1();

  94. 

  95.         XYSeries series = new XYSeries("Test");

  96.         for (int i = 0; i < 11; i++) {

  97.             series.add(data[i][0], data[i][1]);

  98.         }

  99.         XYDataset ds = new XYSeriesCollection(series);

  100.         double[] result2 = Regression.getOLSRegression(ds, 0);

  101. 

  102.         assertEquals(.25680930, result2[0], 0.0000001);

  103.         assertEquals(0.72792106, result2[1], 0.0000001);

  104. 

  105.     }

  106. 

  107.     /**

  108.      * Checks the results of a power regression on sample dataset 1.

  109.      */

  110.     public void testPowerRegression1a() {

  111. 

  112.         double[][] data = createSampleData1();

  113.         double[] result = Regression.getPowerRegression(data);

  114.         assertEquals(0.91045813, result[0], 0.0000001);

  115.         assertEquals(0.88918346, result[1], 0.0000001);

  116. 

  117.     }

  118. 

  119.     /**

  120.      * Checks the results of a power regression on sample dataset 1 AFTER converting it to

  121.      * an XYSeries.

  122.      */

  123.     public void testPowerRegression1b() {

  124. 

  125.         double[][] data = createSampleData1();

  126. 

  127.         XYSeries series = new XYSeries("Test");

  128.         for (int i = 0; i < 11; i++) {

  129.             series.add(data[i][0], data[i][1]);

  130.         }

  131.         XYDataset ds = new XYSeriesCollection(series);

  132.         double[] result = Regression.getPowerRegression(ds, 0);

  133. 

  134.         assertEquals(0.91045813, result[0], 0.0000001);

  135.         assertEquals(0.88918346, result[1], 0.0000001);

  136. 

  137.     }

  138. 

  139.     /**

  140.      * Checks the results of an OLS regression on sample dataset 2.

  141.      */

  142.     public void testOLSRegression2a() {

  143. 

  144.         double[][] data = createSampleData2();

  145.         double[] result = Regression.getOLSRegression(data);

  146.         assertEquals(53.9729697, result[0], 0.0000001);

  147.         assertEquals(-4.1823030, result[1], 0.0000001);

  148. 

  149.     }

  150. 

  151.     /**

  152.      * Checks the results of an OLS regression on sample dataset 2 AFTER converting it to

  153.      * an XYSeries.

  154.      */

  155.     public void testOLSRegression2b() {

  156. 

  157.         double[][] data = createSampleData2();

  158. 

  159.         XYSeries series = new XYSeries("Test");

  160.         for (int i = 0; i < 10; i++) {

  161.             series.add(data[i][0], data[i][1]);

  162.         }

  163.         XYDataset ds = new XYSeriesCollection(series);

  164.         double[] result = Regression.getOLSRegression(ds, 0);

  165. 

  166.         assertEquals(53.9729697, result[0], 0.0000001);

  167.         assertEquals(-4.1823030, result[1], 0.0000001);

  168. 

  169.     }

  170. 

  171.     /**

  172.      * Checks the results of a power regression on sample dataset 2.

  173.      */

  174.     public void testPowerRegression2a() {

  175. 

  176.         double[][] data = createSampleData2();

  177.         double[] result = Regression.getPowerRegression(data);

  178.         assertEquals(106.1241681, result[0], 0.0000001);

  179.         assertEquals(-0.8466615, result[1], 0.0000001);

  180. 

  181.     }

  182. 

  183.     /**

  184.      * Checks the results of a power regression on sample dataset 2 AFTER converting it to

  185.      * an XYSeries.

  186.      */

  187.     public void testPowerRegression2b() {

  188. 

  189.         double[][] data = createSampleData2();

  190. 

  191.         XYSeries series = new XYSeries("Test");

  192.         for (int i = 0; i < 10; i++) {

  193.             series.add(data[i][0], data[i][1]);

  194.         }

  195.         XYDataset ds = new XYSeriesCollection(series);

  196.         double[] result = Regression.getPowerRegression(ds, 0);

  197. 

  198.         assertEquals(106.1241681, result[0], 0.0000001);

  199.         assertEquals(-0.8466615, result[1], 0.0000001);

  200. 

  201.     }

  202. 

  203.     /**

  204.      * Creates and returns a sample dataset.

  205.      * <P>

  206.      * The data is taken from Table 11.2, page 313 of "Understanding Statistics" by Ott and

  207.      * Mendenhall (Duxbury Press).

  208.      *

  209.      * @return the sample data.

  210.      */

  211.     private double[][] createSampleData1() {

  212. 

  213.         double[][] result = new double[11][2];

  214. 

  215.         result[0][0] = 2.00;

  216.         result[0][1] = 1.60;

  217.         result[1][0] = 2.25;

  218.         result[1][1] = 2.00;

  219.         result[2][0] = 2.60;

  220.         result[2][1] = 1.80;

  221.         result[3][0] = 2.65;

  222.         result[3][1] = 2.80;

  223.         result[4][0] = 2.80;

  224.         result[4][1] = 2.10;

  225.         result[5][0] = 3.10;

  226.         result[5][1] = 2.00;

  227.         result[6][0] = 2.90;

  228.         result[6][1] = 2.65;

  229.         result[7][0] = 3.25;

  230.         result[7][1] = 2.25;

  231.         result[8][0] = 3.30;

  232.         result[8][1] = 2.60;

  233.         result[9][0] = 3.60;

  234.         result[9][1] = 3.00;

  235.         result[10][0] = 3.25;

  236.         result[10][1] = 3.10;

  237. 

  238.         return result;

  239. 

  240.     }

  241. 

  242.     /**

  243.      * Creates a sample data set.

  244.      *

  245.      * @return the sample data.

  246.      */

  247.     private double[][] createSampleData2() {

  248. 

  249.         double[][] result = new double[10][2];

  250. 

  251.         result[0][0] = 2;

  252.         result[0][1] = 56.27;

  253.         result[1][0] = 3;

  254.         result[1][1] = 41.32;

  255.         result[2][0] = 4;

  256.         result[2][1] = 31.45;

  257.         result[3][0] = 5;

  258.         result[3][1] = 30.05;

  259.         result[4][0] = 6;

  260.         result[4][1] = 24.69;

  261.         result[5][0] = 7;

  262.         result[5][1] = 19.78;

  263.         result[6][0] = 8;

  264.         result[6][1] = 20.94;

  265.         result[7][0] = 9;

  266.         result[7][1] = 16.73;

  267.         result[8][0] = 10;

  268.         result[8][1] = 14.21;

  269.         result[9][0] = 11;

  270.         result[9][1] = 12.44;

  271. 

  272.         return result;

  273. 

  274.     }

  275. 

  276. }