using System;
using System.Collections.Generic;
using HeuristicLab.Data;
using HeuristicLab.Problems.DynamicalSystemsModelling;
using HeuristicLab.Random;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace AutoDiffForDynamicalModelsTest {
  [TestClass]
  public class TestOdeIdentification {
    [TestMethod]
    public void RunOdeIdentification() {
      var alg = new OdeParameterIdentification();
      var dynProb = new Problem();
      var parser = new HeuristicLab.Problems.Instances.DataAnalysis.TableFileParser();
      // var fileName = @"C:\reps\HEAL\EuroCAST - Kronberger\DataGeneration\test.csv";
      var fileName = @"D:\heal\documents\trunk\Publications\2019\EuroCAST\Kronberger\DataGeneration\test.csv";
      parser.Parse(fileName, true);
      var prov = new HeuristicLab.Problems.Instances.DataAnalysis.RegressionCSVInstanceProvider();
      var regressionProblemData = prov.ImportData(fileName);
      regressionProblemData.TrainingPartition.Start = 0;
      regressionProblemData.TrainingPartition.End = 40;
      regressionProblemData.TestPartition.Start = 41;
      var allowedInputs = new List<string>(); // empty
      var targets = new List<string>(new[] { "x1", "x2", "v" });
      foreach (var checkedItem in regressionProblemData.InputVariables) {
        regressionProblemData.InputVariables.SetItemCheckedState(checkedItem, allowedInputs.Contains(checkedItem.Value));
      }
      dynProb.Load(regressionProblemData);

      foreach (var checkedItem in dynProb.TargetVariables) {
        dynProb.TargetVariables.SetItemCheckedState(checkedItem, targets.Contains(checkedItem.Value));
      }

      dynProb.TrainingEpisodesParameter.Value.Add(new IntRange(0, 40));
      dynProb.NumericIntegrationStepsParameter.Value.Value = 1;

      var rand = new FastRandom(1234);
      var expressions = new string[] { "v", "-v", "1.0*(x2-x1)" };

      alg.CreateSolution(dynProb, expressions, 200, rand);
      Assert.AreEqual(6.8350792038369173E-20, ((DoubleValue)alg.Results["SNMSE"].Value).Value, 1E-8);
    }
    [TestMethod]
    public void RunOdeIdentificationMultipleEpisodes() {
      var alg = new OdeParameterIdentification();
      var dynProb = new Problem();
      var parser = new HeuristicLab.Problems.Instances.DataAnalysis.TableFileParser();
      // var fileName = @"C:\reps\HEAL\EuroCAST - Kronberger\DataGeneration\test.csv";
      var fileName = @"C:\reps\HEAL\EuroCAST - Kronberger\DataGeneration\s-system.csv";
      parser.Parse(fileName, true);
      var prov = new HeuristicLab.Problems.Instances.DataAnalysis.RegressionCSVInstanceProvider();
      var regressionProblemData = prov.ImportData(fileName);
      regressionProblemData.TrainingPartition.Start = 0;
      regressionProblemData.TrainingPartition.End = 61;
      regressionProblemData.TestPartition.Start = 61;
      var allowedInputs = new List<string>(); // empty
      var targets = new List<string>(new[] { "y1", "y2", "y3", "y4", "y5" });
      foreach (var checkedItem in regressionProblemData.InputVariables) {
        regressionProblemData.InputVariables.SetItemCheckedState(checkedItem, allowedInputs.Contains(checkedItem.Value));
      }
      dynProb.Load(regressionProblemData);

      foreach (var checkedItem in dynProb.TargetVariables) {
        dynProb.TargetVariables.SetItemCheckedState(checkedItem, targets.Contains(checkedItem.Value));
      }

      dynProb.TrainingEpisodesParameter.Value.Add(new IntRange(0, 30));
      dynProb.TrainingEpisodesParameter.Value.Add(new IntRange(30, 61));
      dynProb.NumericIntegrationStepsParameter.Value.Value = 1;

      var rand = new FastRandom(1234);
      var expressions = new string[] {
"1.0*(y3*exp(log(y5)*0.1)) + 1.0*(y1*y1)",
"1.0*(y1*y1) + 1.0*(y2*y2)",
"1.0*exp(log(y2)*0.1) + 1.0*exp(log(y2)*0.1)*(y3*y3)",
"1.0*(y1*y1)*exp(log(y5)*0.1) + 1.0*(y4*y4)",
"1.0*(y4*y4)+1.0*(y5*y5)"
      };

      alg.CreateSolution(dynProb, expressions, 200, rand);
      Assert.AreEqual(0.0, ((DoubleValue)alg.Results["SNMSE"].Value).Value, 1E-8);
    }
  }
}