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 }