using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using Microsoft.Win32.SafeHandles;

namespace HeuristicLab.Problems.GeneticProgramming.GlucosePrediction {
  [StorableClass]
  [Item("Model", "")]
  public sealed class Model : NamedItem, IRegressionModel {
    [Storable]
    private readonly ISymbolicExpressionTree tree;
    [Storable]
    private string targetVariable;
    [Storable]
    private string[] variablesUsedForPrediction;

    [StorableConstructor]
    private Model(bool deserializing) : base(deserializing) {
    }

    private Model(Model original, Cloner cloner) {
      this.tree = cloner.Clone(original.tree);
      this.variablesUsedForPrediction = original.variablesUsedForPrediction;
      this.targetVariable = original.targetVariable;
    }

    public Model(ISymbolicExpressionTree tree, string targetVariable, string[] variablesUsedForPrediction) {
      this.tree = tree;
      this.variablesUsedForPrediction = variablesUsedForPrediction;
      this.targetVariable = targetVariable;
    }

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

    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
      return Interpreter.Apply(tree.Root.GetSubtree(0).GetSubtree(0), dataset, rows);
    }

    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
      return new Solution(this, problemData);
    }


    public string TargetVariable {
      get {
        return targetVariable;
      }
      set {
        throw new NotSupportedException();
      }
    }

    public event EventHandler TargetVariableChanged;


    public IEnumerable<string> VariablesUsedForPrediction {
      get { return variablesUsedForPrediction; }
    }
  }
}