using System;
using System.Linq;
using System.Threading;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Algorithms.GeneticAlgorithm;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Random;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace HeuristicLab.Problems.GaussianProcessTuning.Tests {
  [TestClass]
  public class UnitTest {
    [TestMethod]
    public void TestGrammar() {
      var g = new Grammar();
      var r = new MersenneTwister();
      var trees = (from i in Enumerable.Range(0, 1000)
                   select
                     HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.ProbabilisticTreeCreator.Create(r, g, 100, 10))
        .ToArray();
    }

    [TestMethod]
    public void TestInterpreter() {
      var interpreter = new Interpreter();
      var g = new Grammar();
      g.Symbols.Single(s => s is MeanMask).Enabled = true;
      g.Dimension = 1;
      var r = new MersenneTwister(31415);
      var problemData = new RegressionProblemData();

      for (int i = 0; i < 1000; i++) {
        var t = HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.ProbabilisticTreeCreator.Create(r, g, 50, 7);
        double negLogLikelihood;
        HeuristicLab.Algorithms.DataAnalysis.IGaussianProcessSolution solution;
        interpreter.EvaluateGaussianProcessConfiguration(t, problemData, out negLogLikelihood, out solution);
        Console.WriteLine();
      }
    }

    [TestMethod]
    public void TestGeneticAlgorithm() {
      var prob = new Problem();
      prob.DimensionParameter.Value.Value = 1;
      var ga = new GeneticAlgorithm();
      ga.Engine = new SequentialEngine.SequentialEngine();
      ga.PopulationSize.Value = 100;
      ga.MaximumGenerations.Value = 10;
      ga.Problem = prob;

      var signal = new AutoResetEvent(false);
      Exception ex = null;

      ga.ExceptionOccurred += (sender, args) => {
        ex = args.Value;
        signal.Set();
      };
      ga.Stopped += (sender, args) => {
        signal.Set();
      };

      ga.Prepare();
      ga.Start();

      signal.WaitOne();
      if (ex != null) throw ex;

    }

    /*
    [TestMethod]
    public void TestInterpreterPrediction() {
      var interpreter = new Interpreter();
      var g = new Grammar();
      g.Symbols.Single(s => s is MeanMask).Enabled = true;
      g.Dimension = 1;
      var r = new MersenneTwister(31415);
      var problemData = new RegressionProblemData();

      double[] means;
      double[] stdDev;
      for (int i = 0; i < 10; i++) {
        var t = HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.ProbabilisticTreeCreator.Create(r, g, 50, 7);
        interpreter.EvaluateGaussianProcessConfiguration(t, problemData, problemData.Dataset, problemData.TestIndices, out means, out stdDev);
      }
    }
     */
  }
}