/* ===========================================================
* JFreeChart : a free chart library for the Java(tm) platform
* ===========================================================
*
* (C) Copyright 2000-2012, by Object Refinery Limited and Contributors.
*
* Project Info: http://www.jfree.org/jfreechart/index.html
*
* This library is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation; either version 2.1 of the License, or
* (at your option) any later version.
*
* This library is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public
* License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301,
* USA.
*
* [Oracle and Java are registered trademarks of Oracle and/or its affiliates.
* Other names may be trademarks of their respective owners.]
*
* --------------------
* RegressionTests.java
* --------------------
* (C) Copyright 2002-2008, by Object Refinery Limited and Contributors.
*
* Original Author: David Gilbert (for Object Refinery Limited);
* Contributor(s): -;
*
* Changes
* -------
* 30-Sep-2002 : Version 1 (DG);
* 17-Oct-2002 : Fixed errors reported by Checkstyle (DG);
*
*/
package org.jfree.data.statistics;
import org.jfree.data.xy.XYDataset;
import org.jfree.data.xy.XYSeries;
import org.jfree.data.xy.XYSeriesCollection;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
/**
* Tests for the {@link Regression} class.
*/
public class RegressionTest {
/**
* Checks the results of an OLS regression on sample dataset 1.
*/
@Test
public void testOLSRegression1a() {
double[][] data = createSampleData1();
double[] result1 = Regression.getOLSRegression(data);
assertEquals(.25680930, result1[0], 0.0000001);
assertEquals(0.72792106, result1[1], 0.0000001);
}
/**
* Checks the results of an OLS regression on sample dataset 1 AFTER
* converting it to an XYSeries.
*/
@Test
public void testOLSRegression1b() {
double[][] data = createSampleData1();
XYSeries series = new XYSeries("Test");
for (int i = 0; i < 11; i++) {
series.add(data[i][0], data[i][1]);
}
XYDataset ds = new XYSeriesCollection(series);
double[] result2 = Regression.getOLSRegression(ds, 0);
assertEquals(.25680930, result2[0], 0.0000001);
assertEquals(0.72792106, result2[1], 0.0000001);
}
/**
* Checks the results of a power regression on sample dataset 1.
*/
@Test
public void testPowerRegression1a() {
double[][] data = createSampleData1();
double[] result = Regression.getPowerRegression(data);
assertEquals(0.91045813, result[0], 0.0000001);
assertEquals(0.88918346, result[1], 0.0000001);
}
/**
* Checks the results of a power regression on sample dataset 1 AFTER
* converting it to an XYSeries.
*/
@Test
public void testPowerRegression1b() {
double[][] data = createSampleData1();
XYSeries series = new XYSeries("Test");
for (int i = 0; i < 11; i++) {
series.add(data[i][0], data[i][1]);
}
XYDataset ds = new XYSeriesCollection(series);
double[] result = Regression.getPowerRegression(ds, 0);
assertEquals(0.91045813, result[0], 0.0000001);
assertEquals(0.88918346, result[1], 0.0000001);
}
/**
* Checks the results of an OLS regression on sample dataset 2.
*/
@Test
public void testOLSRegression2a() {
double[][] data = createSampleData2();
double[] result = Regression.getOLSRegression(data);
assertEquals(53.9729697, result[0], 0.0000001);
assertEquals(-4.1823030, result[1], 0.0000001);
}
/**
* Checks the results of an OLS regression on sample dataset 2 AFTER
* converting it to an XYSeries.
*/
@Test
public void testOLSRegression2b() {
double[][] data = createSampleData2();
XYSeries series = new XYSeries("Test");
for (int i = 0; i < 10; i++) {
series.add(data[i][0], data[i][1]);
}
XYDataset ds = new XYSeriesCollection(series);
double[] result = Regression.getOLSRegression(ds, 0);
assertEquals(53.9729697, result[0], 0.0000001);
assertEquals(-4.1823030, result[1], 0.0000001);
}
/**
* Checks the results of a power regression on sample dataset 2.
*/
@Test
public void testPowerRegression2a() {
double[][] data = createSampleData2();
double[] result = Regression.getPowerRegression(data);
assertEquals(106.1241681, result[0], 0.0000001);
assertEquals(-0.8466615, result[1], 0.0000001);
}
/**
* Checks the results of a power regression on sample dataset 2 AFTER
* converting it to an XYSeries.
*/
@Test
public void testPowerRegression2b() {
double[][] data = createSampleData2();
XYSeries series = new XYSeries("Test");
for (int i = 0; i < 10; i++) {
series.add(data[i][0], data[i][1]);
}
XYDataset ds = new XYSeriesCollection(series);
double[] result = Regression.getPowerRegression(ds, 0);
assertEquals(106.1241681, result[0], 0.0000001);
assertEquals(-0.8466615, result[1], 0.0000001);
}
/**
* Creates and returns a sample dataset.
* <P>
* The data is taken from Table 11.2, page 313 of "Understanding Statistics"
* by Ott and Mendenhall (Duxbury Press).
*
* @return The sample data.
*/
private double[][] createSampleData1() {
double[][] result = new double[11][2];
result[0][0] = 2.00;
result[0][1] = 1.60;
result[1][0] = 2.25;
result[1][1] = 2.00;
result[2][0] = 2.60;
result[2][1] = 1.80;
result[3][0] = 2.65;
result[3][1] = 2.80;
result[4][0] = 2.80;
result[4][1] = 2.10;
result[5][0] = 3.10;
result[5][1] = 2.00;
result[6][0] = 2.90;
result[6][1] = 2.65;
result[7][0] = 3.25;
result[7][1] = 2.25;
result[8][0] = 3.30;
result[8][1] = 2.60;
result[9][0] = 3.60;
result[9][1] = 3.00;
result[10][0] = 3.25;
result[10][1] = 3.10;
return result;
}
/**
* Creates a sample data set.
*
* @return The sample data.
*/
private double[][] createSampleData2() {
double[][] result = new double[10][2];
result[0][0] = 2;
result[0][1] = 56.27;
result[1][0] = 3;
result[1][1] = 41.32;
result[2][0] = 4;
result[2][1] = 31.45;
result[3][0] = 5;
result[3][1] = 30.05;
result[4][0] = 6;
result[4][1] = 24.69;
result[5][0] = 7;
result[5][1] = 19.78;
result[6][0] = 8;
result[6][1] = 20.94;
result[7][0] = 9;
result[7][1] = 16.73;
result[8][0] = 10;
result[8][1] = 14.21;
result[9][0] = 11;
result[9][1] = 12.44;
return result;
}
}