/* Copyright 2013-2025 CS GROUP
    * Licensed to CS GROUP (CS) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * CS 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
    *
    *   http://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.
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  package org.orekit.rugged.adjustment;
  
  import java.util.ArrayList;
  import java.util.HashMap;
  import java.util.HashSet;
  import java.util.List;
  import java.util.Map;
  import java.util.Set;
  
  import org.hipparchus.Field;
  import org.hipparchus.analysis.differentiation.Gradient;
  import org.hipparchus.analysis.differentiation.GradientField;
  import org.hipparchus.optim.ConvergenceChecker;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.MultivariateJacobianFunction;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.ParameterValidator;
  import org.orekit.rugged.adjustment.measurements.Observables;
  import org.orekit.rugged.errors.RuggedException;
  import org.orekit.rugged.errors.RuggedMessages;
  import org.orekit.rugged.linesensor.LineSensor;
  import org.orekit.rugged.utils.DerivativeGenerator;
  import org.orekit.utils.ParameterDriver;
  
  /**
   * Builder for optimization problem.
   * <p>
   * Constructs the optimization problem defined by a set of measurement and sensors.
   * </p>
   * @author Jonathan Guinet
   * @author Guylaine Prat
   * @since 2.0
   */
  abstract class OptimizationProblemBuilder {
  
      /** Margin used in parameters estimation for the inverse location lines range. */
      protected static final int ESTIMATION_LINE_RANGE_MARGIN = 100;
  
      /** Gradient generator.*/
      private final DerivativeGenerator<Gradient> generator;
  
      /** Parameter drivers list. */
      private final List<ParameterDriver> drivers;
  
      /** Number of parameters to refine. */
      private final int nbParams;
  
      /** Measurements. */
      private Observables measurements;
  
      /** Sensors list. */
      private final List<LineSensor> sensors;
  
      /** Constructor.
       * @param sensors list of sensors to refine
       * @param measurements set of observables
       */
      OptimizationProblemBuilder(final List<LineSensor> sensors, final Observables measurements) {
  
          this.generator = this.createGenerator(sensors);
          this.drivers = this.generator.getSelected();
          this.nbParams = this.drivers.size();
          if (this.nbParams == 0) {
              throw new RuggedException(RuggedMessages.NO_PARAMETERS_SELECTED);
          }
          this.measurements = measurements;
          this.sensors = sensors;
      }
  
      /** Least squares problem builder.
       * @param maxEvaluations maximum number of evaluations
       * @param convergenceThreshold convergence threshold
       * @return the least squares problem
       */
  
      public abstract LeastSquaresProblem build(int maxEvaluations, double convergenceThreshold);
  
      /** Create the convergence check.
       * <p>
       * check LInf distance of parameters variation between previous and current iteration
       * </p>
       * @param parametersConvergenceThreshold convergence threshold
       * @return the checker
      */
     final ConvergenceChecker<LeastSquaresProblem.Evaluation>
                             createChecker(final double parametersConvergenceThreshold) {
 
         final ConvergenceChecker<LeastSquaresProblem.Evaluation> checker = (iteration, previous, current)
             -> current.getPoint().getLInfDistance(previous.getPoint()) <= parametersConvergenceThreshold;
 
         return checker;
     }
 
     /** Create start points for optimization algorithm.
      * @return start parameters values (normalized)
      */
     final double[] createStartTab() {
 
         // Get start points (as a normalized value)
         final double[] start = new double[this.nbParams];
         int iStart = 0;
         for (final ParameterDriver driver : this.drivers) {
             start[iStart++] = driver.getNormalizedValue();
         }
         return start;
     }
 
     /** Create targets and weights of optimization problem. */
     protected abstract void createTargetAndWeight();
 
     /** Create the model function value and its Jacobian.
      * @return the model function value and its Jacobian
      */
     protected abstract MultivariateJacobianFunction createFunction();
 
     /** Parse the observables to select mapping .*/
     protected abstract void initMapping();
 
     /** Create parameter validator.
      * @return parameter validator
      */
     final ParameterValidator createParameterValidator() {
 
         // Prevent parameters to exceed their prescribed bounds
         final ParameterValidator validator = params -> {
             int i = 0;
             for (final ParameterDriver driver : this.drivers) {
 
                 // let the parameter handle min/max clipping
                 driver.setNormalizedValue(params.getEntry(i));
                 params.setEntry(i++, driver.getNormalizedValue());
             }
             return params;
         };
 
         return validator;
     }
 
     /** Create the generator for {@link Gradient} instances.
      * @param selectedSensors list of sensors referencing the parameters drivers
      * @return a new generator
      */
     private DerivativeGenerator<Gradient> createGenerator(final List<LineSensor> selectedSensors) {
 
         // Initialize set of drivers name
         final Set<String> names = new HashSet<>();
 
         // Get the drivers name
         for (final LineSensor sensor : selectedSensors) {
 
             // Get the drivers name for the sensor
             sensor.getParametersDrivers().forEach(driver -> {
 
                 // Add the name of the driver to the set of drivers name
                 if (names.contains(driver.getName()) == false) {
                     names.add(driver.getName());
                 }
             });
         }
 
         // Set up generator list and map
         final List<ParameterDriver> selected = new ArrayList<>();
         final Map<String, Integer> map = new HashMap<>();
 
         // Get the list of selected drivers
         for (final LineSensor sensor : selectedSensors) {
 
             sensor.getParametersDrivers().filter(driver -> driver.isSelected()).forEach(driver -> {
                 if (map.get(driver.getName()) == null) {
                     map.put(driver.getName(), map.size());
                     selected.add(driver);
                 }
             });
         }
 
         // gradient Generator
         final GradientField field = GradientField.getField(map.size());
         return new DerivativeGenerator<Gradient>() {
 
             /** {@inheritDoc} */
             @Override
             public List<ParameterDriver> getSelected() {
                 return selected;
             }
 
             /** {@inheritDoc} */
             @Override
             public Gradient constant(final double value) {
                 return Gradient.constant(map.size(), value);
             }
 
             /** {@inheritDoc} */
             @Override
             public Gradient variable(final ParameterDriver driver) {
                 final Integer index = map.get(driver.getName());
                 if (index == null) {
                     return constant(driver.getValue());
                 } else {
                     return Gradient.variable(map.size(), index.intValue(), driver.getValue());
                 }
             }
 
             /** {@inheritDoc} */
             @Override
             public Field<Gradient> getField() {
                 return field;
             }
 
         };
     }
 
     /** Get the sensors list.
      * @return the sensors list
      */
     protected List<LineSensor> getSensors() {
         return sensors;
     }
 
     /** Get the number of parameters to refine.
      * @return the number of parameters to refine
      */
     protected final int getNbParams() {
         return this.nbParams;
     }
 
     /**
      * Get the parameters drivers list.
      * @return the selected list of parameters driver
      */
     protected final List<ParameterDriver> getDrivers() {
         return this.drivers;
     }
 
     /**
      * Get the derivative structure generator.
      * @return the derivative structure generator.
      */
     protected final DerivativeGenerator<Gradient> getGenerator() {
         return this.generator;
     }
 
     /** Get the measurements.
      * @return the measurements
      */
     protected Observables getMeasurements() {
         return measurements;
     }
 }