001 /*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements. See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License. You may obtain a copy of the License at
008 *
009 * http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017 package org.apache.commons.math.stat.regression;
018
019 import org.junit.Before;
020 import org.junit.Test;
021
022 public class GLSMultipleLinearRegressionTest extends MultipleLinearRegressionAbstractTest {
023
024 private double[] y;
025 private double[][] x;
026 private double[][] omega;
027
028 @Before
029 @Override
030 public void setUp(){
031 y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
032 x = new double[6][];
033 x[0] = new double[]{1.0, 0, 0, 0, 0, 0};
034 x[1] = new double[]{1.0, 2.0, 0, 0, 0, 0};
035 x[2] = new double[]{1.0, 0, 3.0, 0, 0, 0};
036 x[3] = new double[]{1.0, 0, 0, 4.0, 0, 0};
037 x[4] = new double[]{1.0, 0, 0, 0, 5.0, 0};
038 x[5] = new double[]{1.0, 0, 0, 0, 0, 6.0};
039 omega = new double[6][];
040 omega[0] = new double[]{1.0, 0, 0, 0, 0, 0};
041 omega[1] = new double[]{0, 2.0, 0, 0, 0, 0};
042 omega[2] = new double[]{0, 0, 3.0, 0, 0, 0};
043 omega[3] = new double[]{0, 0, 0, 4.0, 0, 0};
044 omega[4] = new double[]{0, 0, 0, 0, 5.0, 0};
045 omega[5] = new double[]{0, 0, 0, 0, 0, 6.0};
046 super.setUp();
047 }
048
049 @Test(expected=IllegalArgumentException.class)
050 public void cannotAddXSampleData() {
051 createRegression().newSampleData(new double[]{}, null, null);
052 }
053
054 @Test(expected=IllegalArgumentException.class)
055 public void cannotAddNullYSampleData() {
056 createRegression().newSampleData(null, new double[][]{}, null);
057 }
058
059 @Test(expected=IllegalArgumentException.class)
060 public void cannotAddSampleDataWithSizeMismatch() {
061 double[] y = new double[]{1.0, 2.0};
062 double[][] x = new double[1][];
063 x[0] = new double[]{1.0, 0};
064 createRegression().newSampleData(y, x, null);
065 }
066
067 @Test(expected=IllegalArgumentException.class)
068 public void cannotAddNullCovarianceData() {
069 createRegression().newSampleData(new double[]{}, new double[][]{}, null);
070 }
071
072 @Test(expected=IllegalArgumentException.class)
073 public void notEnoughData() {
074 double[] reducedY = new double[y.length - 1];
075 double[][] reducedX = new double[x.length - 1][];
076 double[][] reducedO = new double[omega.length - 1][];
077 System.arraycopy(y, 0, reducedY, 0, reducedY.length);
078 System.arraycopy(x, 0, reducedX, 0, reducedX.length);
079 System.arraycopy(omega, 0, reducedO, 0, reducedO.length);
080 createRegression().newSampleData(reducedY, reducedX, reducedO);
081 }
082
083 @Test(expected=IllegalArgumentException.class)
084 public void cannotAddCovarianceDataWithSampleSizeMismatch() {
085 double[] y = new double[]{1.0, 2.0};
086 double[][] x = new double[2][];
087 x[0] = new double[]{1.0, 0};
088 x[1] = new double[]{0, 1.0};
089 double[][] omega = new double[1][];
090 omega[0] = new double[]{1.0, 0};
091 createRegression().newSampleData(y, x, omega);
092 }
093
094 @Test(expected=IllegalArgumentException.class)
095 public void cannotAddCovarianceDataThatIsNotSquare() {
096 double[] y = new double[]{1.0, 2.0};
097 double[][] x = new double[2][];
098 x[0] = new double[]{1.0, 0};
099 x[1] = new double[]{0, 1.0};
100 double[][] omega = new double[3][];
101 omega[0] = new double[]{1.0, 0};
102 omega[1] = new double[]{0, 1.0};
103 omega[2] = new double[]{0, 2.0};
104 createRegression().newSampleData(y, x, omega);
105 }
106
107 @Override
108 protected GLSMultipleLinearRegression createRegression() {
109 GLSMultipleLinearRegression regression = new GLSMultipleLinearRegression();
110 regression.newSampleData(y, x, omega);
111 return regression;
112 }
113
114 @Override
115 protected int getNumberOfRegressors() {
116 return x[0].length;
117 }
118
119 @Override
120 protected int getSampleSize() {
121 return y.length;
122 }
123
124 }