/*
    * Licensed to the Hipparchus project under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The Hipparchus project 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.
   */
  
  package org.hipparchus.optim.nonlinear.vector.leastsquares;
  
  import java.io.IOException;
  
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.linear.QRDecomposer;
  import org.hipparchus.optim.LocalizedOptimFormats;
  import org.hipparchus.optim.SimpleVectorValueChecker;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * <p>Some of the unit tests are re-implementations of the MINPACK <a
   * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
   * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files.
   * The redistribution policy for MINPACK is available <a
   * href="http://www.netlib.org/minpack/disclaimer">here</a>/
   *
   */
  public class SequentialGaussNewtonOptimizerWithQRNormalTest
      extends AbstractSequentialLeastSquaresOptimizerAbstractTest {
      
      
  
      @Override
      public int getMaxIterations() {
          return 1000;
      }
  
      @Override
      public void defineOptimizer(Evaluation evaluation) {
          this.optimizer = new SequentialGaussNewtonOptimizer().
                           withDecomposer(new QRDecomposer(1e-11)).
                           withFormNormalEquations(true).
                           withEvaluation(evaluation);
      }
  
      @Override
      @Test
      public void testMoreEstimatedParametersUnsorted() {
          /*
           * Exception is expected with this optimizer
           */
          try {
              super.testMoreEstimatedParametersUnsorted();
          } catch (MathIllegalStateException mise) {
              Assert.assertEquals(LocalizedOptimFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, mise.getSpecifier());
          }
      }
  
      @Test
      public void testMaxEvaluations() throws Exception {
          try{
          CircleVectorial circle = new CircleVectorial();
          circle.addPoint( 30.0,  68.0);
          circle.addPoint( 50.0,  -6.0);
          circle.addPoint(110.0, -20.0);
          circle.addPoint( 35.0,  15.0);
          circle.addPoint( 45.0,  97.0);
  
          LeastSquaresProblem lsp = builder(circle)
                  .checkerPair(new SimpleVectorValueChecker(1e-30, 1e-30))
                  .maxIterations(Integer.MAX_VALUE)
                  .start(new double[]{98.680, 47.345})
                  .build();
  
          defineOptimizer(null);
          optimizer.optimize(lsp);
  
              fail(optimizer);
          }catch (MathIllegalStateException e){
              //expected
          }
      }
  
  
      @Override
      @Test
      public void testHahn1() throws IOException {
          try {
              /*
              * TODO This test leads to a singular problem with the Gauss-Newton
              * optimizer. This should be inquired.
              */
             super.testHahn1();
             Assert.fail("Expected Exception with: " + this.optimizer);
         } catch (MathIllegalStateException mise) {
             // pass. Both singular problem, and max iterations is acceptable.
         }
     }
 
     @Override
     @Test(expected = MathIllegalStateException.class)
     public void testMoreEstimatedParametersSimple() {
         // reduced numerical stability when forming the normal equations
         super.testMoreEstimatedParametersSimple();
     }
 
 }