/*
    * 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.fitting;
  
  import java.util.Collection;
  
  import org.hipparchus.analysis.MultivariateMatrixFunction;
  import org.hipparchus.analysis.MultivariateVectorFunction;
  import org.hipparchus.analysis.ParametricUnivariateFunction;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer;
  
  /**
   * Base class that contains common code for fitting parametric univariate
   * real functions <code>y = f(p<sub>i</sub>;x)</code>, where {@code x} is
   * the independent variable and the <code>p<sub>i</sub></code> are the
   * <em>parameters</em>.
   * <br>
   * A fitter will find the optimal values of the parameters by
   * <em>fitting</em> the curve so it remains very close to a set of
   * {@code N} observed points <code>(x<sub>k</sub>, y<sub>k</sub>)</code>,
   * {@code 0 <= k < N}.
   * <br>
   * An algorithm usually performs the fit by finding the parameter
   * values that minimizes the objective function
   * <pre><code>
   *  &sum;y<sub>k</sub> - f(x<sub>k</sub>)<sup>2</sup>,
   * </code></pre>
   * which is actually a least-squares problem.
   * This class contains boilerplate code for calling the
   * {@link #fit(Collection)} method for obtaining the parameters.
   * The problem setup, such as the choice of optimization algorithm
   * for fitting a specific function is delegated to subclasses.
   *
   */
  public abstract class AbstractCurveFitter {
  
      /** Empty constructor.
       * <p>
       * This constructor is not strictly necessary, but it prevents spurious
       * javadoc warnings with JDK 18 and later.
       * </p>
       * @since 3.0
       */
      protected AbstractCurveFitter() { // NOPMD - unnecessary constructor added intentionally to make javadoc happy
          // nothing to do
      }
  
      /**
       * Fits a curve.
       * This method computes the coefficients of the curve that best
       * fit the sample of observed points.
       *
       * @param points Observations.
       * @return the fitted parameters.
       */
      public double[] fit(Collection<WeightedObservedPoint> points) {
          // Perform the fit.
          return getOptimizer().optimize(getProblem(points)).getPoint().toArray();
      }
  
      /**
       * Creates an optimizer set up to fit the appropriate curve.
       * <p>
       * The default implementation uses a {@link LevenbergMarquardtOptimizer
       * Levenberg-Marquardt} optimizer.
       * </p>
       * @return the optimizer to use for fitting the curve to the
       * given {@code points}.
       */
      protected LeastSquaresOptimizer getOptimizer() {
          return new LevenbergMarquardtOptimizer();
      }
  
      /**
       * Creates a least squares problem corresponding to the appropriate curve.
       *
       * @param points Sample points.
       * @return the least squares problem to use for fitting the curve to the
      * given {@code points}.
      */
     protected abstract LeastSquaresProblem getProblem(Collection<WeightedObservedPoint> points);
 
     /**
      * Vector function for computing function theoretical values.
      */
     protected static class TheoreticalValuesFunction {
         /** Function to fit. */
         private final ParametricUnivariateFunction f;
         /** Observations. */
         private final double[] points;
 
         /** Simple constructor.
          * @param f function to fit.
          * @param observations Observations.
          */
         public TheoreticalValuesFunction(final ParametricUnivariateFunction f,
                                          final Collection<WeightedObservedPoint> observations) {
             this.f = f;
 
             final int len = observations.size();
             this.points = new double[len];
             int i = 0;
             for (WeightedObservedPoint obs : observations) {
                 this.points[i++] = obs.getX();
             }
         }
 
         /** Get model function value.
          * @return the model function value
          */
         public MultivariateVectorFunction getModelFunction() {
             return new MultivariateVectorFunction() {
                 /** {@inheritDoc} */
                 @Override
                 public double[] value(double[] p) {
                     final int len = points.length;
                     final double[] values = new double[len];
                     for (int i = 0; i < len; i++) {
                         values[i] = f.value(points[i], p);
                     }
 
                     return values;
                 }
             };
         }
 
         /** Get model function Jacobian.
          * @return the model function Jacobian
          */
         public MultivariateMatrixFunction getModelFunctionJacobian() {
             return new MultivariateMatrixFunction() {
                 /** {@inheritDoc} */
                 @Override
                 public double[][] value(double[] p) {
                     final int len = points.length;
                     final double[][] jacobian = new double[len][];
                     for (int i = 0; i < len; i++) {
                         jacobian[i] = f.gradient(points[i], p);
                     }
                     return jacobian;
                 }
             };
         }
     }
 }