/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      https://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.stat.regression;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.exception.NullArgumentException;
  import org.hipparchus.linear.RealMatrix;
  import org.hipparchus.linear.RealVector;
  import org.junit.jupiter.api.BeforeEach;
  import org.junit.jupiter.api.Test;
  
  import static org.junit.jupiter.api.Assertions.assertEquals;
  import static org.junit.jupiter.api.Assertions.assertThrows;
  import static org.junit.jupiter.api.Assertions.assertTrue;
  
  
  public abstract class MultipleLinearRegressionAbstractTest {
  
      protected AbstractMultipleLinearRegression regression;
  
      @BeforeEach
      public void setUp(){
          regression = createRegression();
      }
  
      protected abstract AbstractMultipleLinearRegression createRegression();
  
      protected abstract int getNumberOfRegressors();
  
      protected abstract int getSampleSize();
  
      @Test
      public void canEstimateRegressionParameters(){
          double[] beta = regression.estimateRegressionParameters();
          assertEquals(getNumberOfRegressors(), beta.length);
      }
  
      @Test
      public void canEstimateResiduals(){
          double[] e = regression.estimateResiduals();
          assertEquals(getSampleSize(), e.length);
      }
  
      @Test
      public void canEstimateRegressionParametersVariance(){
          double[][] variance = regression.estimateRegressionParametersVariance();
          assertEquals(getNumberOfRegressors(), variance.length);
      }
  
      @Test
      public void canEstimateRegressandVariance(){
          if (getSampleSize() > getNumberOfRegressors()) {
              double variance = regression.estimateRegressandVariance();
              assertTrue(variance > 0.0);
          }
      }
  
      /**
       * Verifies that newSampleData methods consistently insert unitary columns
       * in design matrix.  Confirms the fix for MATH-411.
       */
      @Test
      public void testNewSample() {
          double[] design = new double[] {
            1, 19, 22, 33,
            2, 20, 30, 40,
            3, 25, 35, 45,
            4, 27, 37, 47
          };
          double[] y = new double[] {1, 2, 3, 4};
          double[][] x = new double[][] {
            {19, 22, 33},
            {20, 30, 40},
            {25, 35, 45},
            {27, 37, 47}
          };
          AbstractMultipleLinearRegression regression = createRegression();
          regression.newSampleData(design, 4, 3);
          RealMatrix flatX = regression.getX().copy();
          RealVector flatY = regression.getY().copy();
         regression.newXSampleData(x);
         regression.newYSampleData(y);
         assertEquals(flatX, regression.getX());
         assertEquals(flatY, regression.getY());
 
         // No intercept
         regression.setNoIntercept(true);
         regression.newSampleData(design, 4, 3);
         flatX = regression.getX().copy();
         flatY = regression.getY().copy();
         regression.newXSampleData(x);
         regression.newYSampleData(y);
         assertEquals(flatX, regression.getX());
         assertEquals(flatY, regression.getY());
     }
 
     @Test
     public void testNewSampleNullData() {
         assertThrows(NullArgumentException.class, () -> {
             double[] data = null;
             createRegression().newSampleData(data, 2, 3);
         });
     }
 
     @Test
     public void testNewSampleInvalidData() {
         assertThrows(MathIllegalArgumentException.class, () -> {
             double[] data = new double[]{1, 2, 3, 4};
             createRegression().newSampleData(data, 2, 3);
         });
     }
 
     @Test
     public void testNewSampleInsufficientData() {
         assertThrows(MathIllegalArgumentException.class, () -> {
             double[] data = new double[]{1, 2, 3, 4};
             createRegression().newSampleData(data, 1, 3);
         });
     }
 
     @Test
     public void testXSampleDataNull() {
         assertThrows(NullArgumentException.class, () -> {
             createRegression().newXSampleData(null);
         });
     }
 
     @Test
     public void testYSampleDataNull() {
         assertThrows(NullArgumentException.class, () -> {
             createRegression().newYSampleData(null);
         });
     }
 
 }