/*
    * 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.optim.nonlinear.vector.leastsquares;
  
  import java.io.IOException;
  
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.geometry.euclidean.threed.Plane;
  import org.hipparchus.geometry.euclidean.threed.Vector3D;
  import org.hipparchus.linear.SingularValueDecomposer;
  import org.hipparchus.optim.SimpleVectorValueChecker;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer.Optimum;
  import org.hipparchus.util.FastMath;
  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 GaussNewtonOptimizerWithSVDTest
      extends AbstractLeastSquaresOptimizerAbstractTest {
  
      @Override
      public int getMaxIterations() {
          return 1000;
      }
  
      @Override
      public LeastSquaresOptimizer getOptimizer() {
          return new GaussNewtonOptimizer(new SingularValueDecomposer(), false);
      }
  
      @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();
  
          optimizer.optimize(lsp);
  
              fail(optimizer);
          }catch (MathIllegalStateException e){
              //expected
          }
      }
  
      @Override
      @Test
      public void testCircleFittingBadInit() {
          /*
           * This test converged to the wrong solution with this optimizer.
           * It seems that the state becomes so large that the convergence
           * checker's relative tolerance test passes.
           */
          try {
              super.testCircleFittingBadInit();
              fail(optimizer);
          } catch (AssertionError e) {
              //expected
          }
      }
  
      @Override
     @Test
     public void testHahn1() throws IOException {
         try {
             /*
              * When NOT FORMING normal equations, the optimizer diverges and hit max evaluations.
              * When FORMING normal equations, the optimizer converges,
              * but the results are very bad
              */
             super.testHahn1();
             fail(optimizer);
         } catch (MathIllegalStateException e) {
             Assert.assertEquals(LocalizedCoreFormats.MAX_COUNT_EXCEEDED, e.getSpecifier());
         }
     }
 
     @Test
     @Override
     public void testGetIterations() {
         /* this diverges with SVD and no normal equations */
         try {
             super.testGetIterations();
             fail(optimizer);
         } catch (MathIllegalStateException e) {
             Assert.assertEquals(LocalizedCoreFormats.MAX_COUNT_EXCEEDED,
                                 e.getSpecifier());
         }
     }
 
     @Test
     @Override
     public void testNonInvertible() throws Exception {
         /*  SVD can compute a solution to singular problems.
          *  In this case the target vector, b, is not in the
          *  span of the jacobian matrix, A. The closest point
          *  to b on the plane spanned by A is computed.
          */
         LinearProblem problem = new LinearProblem(new double[][]{
                 {1, 2, -3},
                 {2, 1, 3},
                 {-3, 0, -9}
         }, new double[]{1, 1, 1});
 
         Optimum optimum = optimizer.optimize(problem.getBuilder().build());
 
         Plane span = new Plane(Vector3D.ZERO, new Vector3D(1, 2, -3), new Vector3D(2, 1, 0), TOl);
         double expected = FastMath.abs(span.getOffset(new Vector3D(1, 1, 1)));
         double actual = optimum.getResiduals().getNorm();
 
         //verify
         Assert.assertEquals(expected, actual, TOl);
     }
 
 }