#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.DataAnalysis {
  /// <summary>
  /// Gaussian process least-squares classification data analysis algorithm.
  /// </summary>
  [Item("Gaussian Process Least-Squares Classification", "Gaussian process least-squares classification data analysis algorithm.")]
  [Creatable(CreatableAttribute.Categories.DataAnalysisClassification, Priority = 160)]
  [StorableClass]
  public sealed class GaussianProcessClassification : GaussianProcessBase, IStorableContent {
    public string Filename { get; set; }

    public override Type ProblemType { get { return typeof(IClassificationProblem); } }
    public new IClassificationProblem Problem {
      get { return (IClassificationProblem)base.Problem; }
      set { base.Problem = value; }
    }

    private const string ModelParameterName = "Model";
    private const string CreateSolutionParameterName = "CreateSolution";

    #region parameter properties
    public IConstrainedValueParameter<IGaussianProcessClassificationModelCreator> GaussianProcessModelCreatorParameter {
      get { return (IConstrainedValueParameter<IGaussianProcessClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
    }
    public IFixedValueParameter<GaussianProcessClassificationSolutionCreator> GaussianProcessSolutionCreatorParameter {
      get { return (IFixedValueParameter<GaussianProcessClassificationSolutionCreator>)Parameters[SolutionCreatorParameterName]; }
    }
    public IFixedValueParameter<BoolValue> CreateSolutionParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
    }
    #endregion
    #region properties
    public bool CreateSolution {
      get { return CreateSolutionParameter.Value.Value; }
      set { CreateSolutionParameter.Value.Value = value; }
    }
    #endregion

    [StorableConstructor]
    private GaussianProcessClassification(bool deserializing) : base(deserializing) { }
    private GaussianProcessClassification(GaussianProcessClassification original, Cloner cloner)
      : base(original, cloner) {
      RegisterEventHandlers();
    }
    public GaussianProcessClassification()
      : base(new ClassificationProblem()) {
      this.name = ItemName;
      this.description = ItemDescription;

      var modelCreators = ApplicationManager.Manager.GetInstances<IGaussianProcessClassificationModelCreator>();
      var defaultModelCreator = modelCreators.First(c => c is GaussianProcessClassificationModelCreator);

      // GP regression and classification algorithms only differ in the model and solution creators,
      // thus we use a common base class and use operator parameters to implement the specific versions.
      // Different model creators can be implemented,
      // but the solution creator is implemented in a generic fashion already and we don't allow derived solution creators
      Parameters.Add(new ConstrainedValueParameter<IGaussianProcessClassificationModelCreator>(ModelCreatorParameterName, "The operator to create the Gaussian process model.",
        new ItemSet<IGaussianProcessClassificationModelCreator>(modelCreators), defaultModelCreator));
      // this parameter is not intended to be changed, 
      Parameters.Add(new FixedValueParameter<GaussianProcessClassificationSolutionCreator>(SolutionCreatorParameterName, "The solution creator for the algorithm",
        new GaussianProcessClassificationSolutionCreator()));
      Parameters[SolutionCreatorParameterName].Hidden = true;
      // TODO: it would be better to deactivate the solution creator when this parameter is changed
      Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
      Parameters[CreateSolutionParameterName].Hidden = true;

      ParameterizedModelCreators();
      ParameterizeSolutionCreator(GaussianProcessSolutionCreatorParameter.Value);
      RegisterEventHandlers();
    }


    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      // BackwardsCompatibility3.3
      #region Backwards compatible code, remove with 3.4
      if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
        Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
        Parameters[CreateSolutionParameterName].Hidden = true;
      }
      #endregion
      RegisterEventHandlers();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new GaussianProcessClassification(this, cloner);
    }

    #region events
    private void RegisterEventHandlers() {
      GaussianProcessModelCreatorParameter.ValueChanged += ModelCreatorParameter_ValueChanged;
    }

    private void ModelCreatorParameter_ValueChanged(object sender, EventArgs e) {
      ParameterizedModelCreator(GaussianProcessModelCreatorParameter.Value);
    }
    #endregion

    private void ParameterizedModelCreators() {
      foreach (var creator in GaussianProcessModelCreatorParameter.ValidValues) {
        ParameterizedModelCreator(creator);
      }
    }

    private void ParameterizedModelCreator(IGaussianProcessClassificationModelCreator modelCreator) {
      modelCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
      modelCreator.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
      modelCreator.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;

      // parameter names fixed by the algorithm
      modelCreator.ModelParameter.ActualName = ModelParameterName;
      modelCreator.HyperparameterParameter.ActualName = HyperparameterParameterName;
      modelCreator.HyperparameterGradientsParameter.ActualName = HyperparameterGradientsParameterName;
      modelCreator.NegativeLogLikelihoodParameter.ActualName = NegativeLogLikelihoodParameterName;
    }

    private void ParameterizeSolutionCreator(GaussianProcessClassificationSolutionCreator solutionCreator) {
      solutionCreator.ModelParameter.ActualName = ModelParameterName;
      solutionCreator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
    }
  }
}