using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace GradientBoostedTrees {
  [StorableClass]
  [Item("RegressionTreeModel", "Represents a decision tree for regression.")]
  // TODO: Implement a view for this
  public class RegressionTreeModel : NamedItem, IRegressionModel {

    [StorableClass]
    public class TreeNode {
      public readonly static string NO_VARIABLE = string.Empty;
      [Storable]
      public readonly string varName; // name of the variable for splitting or -1 if terminal node
      [Storable]
      public readonly double val; // threshold
      [Storable]
      public readonly TreeNode left;
      [Storable]
      public readonly TreeNode right;

      public TreeNode(string varName, double value, TreeNode left = null, TreeNode right = null) {
        this.varName = varName;
        this.val = value;
        this.left = left;
        this.right = right;
      }
    }

    [Storable]
    public readonly TreeNode tree;

    [StorableConstructor]
    private RegressionTreeModel(bool serializing) : base(serializing) { }
    // cloning ctor
    public RegressionTreeModel(RegressionTreeModel original, Cloner cloner)
      : base(original, cloner) {
      this.tree = original.tree;
    }

    public RegressionTreeModel(TreeNode tree)
      : base() {
      this.name = ItemName;
      this.description = ItemDescription;

      this.tree = tree;
    }

    private static double GetPredictionForRow(TreeNode t, Dataset ds, int row) {
      if (t.varName == TreeNode.NO_VARIABLE)
        return t.val;
      else if (ds.GetDoubleValue(t.varName, row) <= t.val)
        return GetPredictionForRow(t.left, ds, row);
      else
        return GetPredictionForRow(t.right, ds, row);
    }

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

    public IEnumerable<double> GetEstimatedValues(Dataset ds, IEnumerable<int> rows) {
      return rows.Select(r => GetPredictionForRow(tree, ds, r));
    }

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

}