#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis;

namespace HeuristicLab.Algorithms.DataAnalysis {
  /// <summary>
  /// Linear time series prognosis data analysis algorithm.
  /// </summary>
  [Item("Linear Time Series Prognosis", "Linear time series prognosis data analysis algorithm (wrapper for ALGLIB).")]
  [Creatable("Data Analysis")]
  [StorableClass]
  public sealed class LinearTimeSeriesPrognosis : FixedDataAnalysisAlgorithm<ITimeSeriesPrognosisProblem> {
    private const string LinearTimeSeriesPrognosisModelResultName = "Linear time-series prognosis solution";
    private const string MaximalLagParameterName = "MaximalLag";

    public IFixedValueParameter<IntValue> MaximalLagParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[MaximalLagParameterName]; }
    }

    public int MaximalLag {
      get { return MaximalLagParameter.Value.Value; }
      set { MaximalLagParameter.Value.Value = value; }
    }

    [StorableConstructor]
    private LinearTimeSeriesPrognosis(bool deserializing) : base(deserializing) { }
    private LinearTimeSeriesPrognosis(LinearTimeSeriesPrognosis original, Cloner cloner)
      : base(original, cloner) {
    }
    public LinearTimeSeriesPrognosis()
      : base() {
      Parameters.Add(new FixedValueParameter<IntValue>(MaximalLagParameterName,
                                                       "The maximal lag to use for auto-regressive terms.",
                                                       new IntValue(1)));
      Problem = new TimeSeriesPrognosisProblem();
    }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() { }

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

    #region linear regression
    protected override void Run() {
      double[] rmsErrors, cvRmsErrors;
      var solution = CreateLinearTimeSeriesPrognosisSolution(Problem.ProblemData, MaximalLag, out rmsErrors, out cvRmsErrors);
      Results.Add(new Result(LinearTimeSeriesPrognosisModelResultName, "The linear time-series prognosis solution.", solution));
      Results.Add(new Result("Root mean square errors", "The root of the mean of squared errors of the linear time-series prognosis solution on the training set.", new DoubleArray(rmsErrors)));
      Results.Add(new Result("Estimated root mean square errors (cross-validation)", "The estimated root of the mean of squared errors of the linear time-series prognosis solution via cross validation.", new DoubleArray(cvRmsErrors)));
    }

    public static ISymbolicTimeSeriesPrognosisSolution CreateLinearTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData, int maximalLag, out double[] rmsError, out double[] cvRmsError) {
      Dataset dataset = problemData.Dataset;
      string[] targetVariables = problemData.TargetVariables.ToArray();

      // prepare symbolic expression tree to hold the models for each target variable
      ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
      ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
      tree.Root.AddSubtree(startNode);
      int i = 0;
      rmsError = new double[targetVariables.Length];
      cvRmsError = new double[targetVariables.Length];
      foreach (var targetVariable in targetVariables) {
        IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
        IEnumerable<int> rows = problemData.TrainingIndizes;
        IEnumerable<int> lags = Enumerable.Range(1, maximalLag);
        double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset,
                                                              allowedInputVariables.Concat(new string[] { targetVariable }),
                                                              rows, lags);
        if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
          throw new NotSupportedException(
            "Linear time-series prognosis does not support NaN or infinity values in the input dataset.");

        alglib.linearmodel lm = new alglib.linearmodel();
        alglib.lrreport ar = new alglib.lrreport();
        int nRows = inputMatrix.GetLength(0);
        int nFeatures = inputMatrix.GetLength(1) - 1;
        double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant

        int retVal = 1;
        alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
        if (retVal != 1) throw new ArgumentException("Error in calculation of linear time series prognosis solution");
        rmsError[i] = ar.rmserror;
        cvRmsError[i] = ar.cvrmserror;

        alglib.lrunpack(lm, out coefficients, out nFeatures);

        ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();

        int col = 0;
        foreach (string column in allowedInputVariables) {
          foreach (int lag in lags) {
            LaggedVariableTreeNode vNode =
              (LaggedVariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.LaggedVariable().CreateTreeNode();
            vNode.VariableName = column;
            vNode.Weight = coefficients[col];
            vNode.Lag = -lag;
            addition.AddSubtree(vNode);
            col++;
          }
        }

        ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
        cNode.Value = coefficients[coefficients.Length - 1];
        addition.AddSubtree(cNode);

        startNode.AddSubtree(addition);
        i++;
      }

      SymbolicTimeSeriesPrognosisSolution solution = new SymbolicTimeSeriesPrognosisSolution(new SymbolicTimeSeriesPrognosisModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData.TargetVariables.ToArray()), (ITimeSeriesPrognosisProblemData)problemData.Clone());
      solution.Model.Name = "Linear Time-Series Prognosis Model";
      return solution;
    }
    #endregion
  }
}