/*
    * 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.scalar.gradient;
  
  import java.util.ArrayList;
  
  import org.hipparchus.analysis.MultivariateFunction;
  import org.hipparchus.analysis.MultivariateVectorFunction;
  import org.hipparchus.geometry.euclidean.twod.Vector2D;
  import org.hipparchus.optim.nonlinear.scalar.ObjectiveFunction;
  import org.hipparchus.optim.nonlinear.scalar.ObjectiveFunctionGradient;
  
  /**
   * Class used in the tests.
   */
  public class CircleScalar {
      private ArrayList<Vector2D> points;
  
      public CircleScalar() {
          points  = new ArrayList<Vector2D>();
      }
  
      public void addPoint(double px, double py) {
          points.add(new Vector2D(px, py));
      }
  
      public double getRadius(Vector2D center) {
          double r = 0;
          for (Vector2D point : points) {
              r += point.distance(center);
          }
          return r / points.size();
      }
  
      public ObjectiveFunction getObjectiveFunction() {
          return new ObjectiveFunction(new MultivariateFunction() {
                  public double value(double[] params)  {
                      Vector2D center = new Vector2D(params[0], params[1]);
                      double radius = getRadius(center);
                      double sum = 0;
                      for (Vector2D point : points) {
                          double di = point.distance(center) - radius;
                          sum += di * di;
                      }
                      return sum;
                  }
              });
      }
  
      public ObjectiveFunctionGradient getObjectiveFunctionGradient() {
          return new ObjectiveFunctionGradient(new MultivariateVectorFunction() {
                  public double[] value(double[] params) {
                      Vector2D center = new Vector2D(params[0], params[1]);
                      double radius = getRadius(center);
                      // gradient of the sum of squared residuals
                      double dJdX = 0;
                      double dJdY = 0;
                      for (Vector2D pk : points) {
                          double dk = pk.distance(center);
                          dJdX += (center.getX() - pk.getX()) * (dk - radius) / dk;
                          dJdY += (center.getY() - pk.getY()) * (dk - radius) / dk;
                      }
                      dJdX *= 2;
                      dJdY *= 2;
  
                      return new double[] { dJdX, dJdY };
                  }
              });
      }
  }