/*
    * 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.util.ArrayList;
  
  import org.hipparchus.analysis.MultivariateMatrixFunction;
  import org.hipparchus.analysis.MultivariateVectorFunction;
  import org.hipparchus.geometry.euclidean.twod.Vector2D;
  
  /**
   * Class used in the tests.
   */
  class CircleVectorial {
      private ArrayList<Vector2D> points;
  
      public CircleVectorial() {
          points  = new ArrayList<Vector2D>();
      }
  
      public void addPoint(double px, double py) {
          points.add(new Vector2D(px, py));
      }
  
      public int getN() {
          return points.size();
      }
  
      public double getRadius(Vector2D center) {
          double r = 0;
          for (Vector2D point : points) {
              r += point.distance(center);
          }
          return r / points.size();
      }
  
      public MultivariateVectorFunction getModelFunction() {
          return new MultivariateVectorFunction() {
              public double[] value(double[] params) {
                  Vector2D center = new Vector2D(params[0], params[1]);
                  double radius = getRadius(center);
                  double[] residuals = new double[points.size()];
                  for (int i = 0; i < residuals.length; i++) {
                      residuals[i] = points.get(i).distance(center) - radius;
                  }
  
                  return residuals;
              }
          };
      }
  
      public MultivariateMatrixFunction getModelFunctionJacobian() {
          return new MultivariateMatrixFunction() {
              public double[][] value(double[] params) {
                  final int n = points.size();
                  final Vector2D center = new Vector2D(params[0], params[1]);
  
                  double dRdX = 0;
                  double dRdY = 0;
                  for (Vector2D pk : points) {
                      double dk = pk.distance(center);
                      dRdX += (center.getX() - pk.getX()) / dk;
                      dRdY += (center.getY() - pk.getY()) / dk;
                  }
                  dRdX /= n;
                  dRdY /= n;
  
                  // Jacobian of the radius residuals.
                  double[][] jacobian = new double[n][2];
                  for (int i = 0; i < n; i++) {
                      final Vector2D pi = points.get(i);
                      final double di = pi.distance(center);
                      jacobian[i][0] = (center.getX() - pi.getX()) / di - dRdX;
                      jacobian[i][1] = (center.getY() - pi.getY()) / di - dRdY;
                  }
  
                  return jacobian;
              }
          };
      }
 }