using System;
using System.Collections.Generic;
using System.Globalization;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using HeuristicLab.Algorithms.DataAnalysis.Experimental;
using HeuristicLab.Problems.DataAnalysis;

namespace Main {
  class Program {
    static void Main(string[] args) {

       var xs = HeuristicLab.Common.SequenceGenerator.GenerateSteps(-3.5, 3.5, 0.02, includeEnd: true).ToArray();
       var ys = xs.Select(xi => (1.0 / (Math.Sqrt(2 * Math.PI)) * Math.Exp(-0.5 * xi * xi))).ToArray();
       var ws = xs.Select(_ => 1.0).ToArray();
 
       alglib.hqrndstate state;
       alglib.hqrndseed(1234, 5678, out state);
       var ys_noise = ys.Select(yi => yi + alglib.hqrndnormal(state) * 0.01).ToArray();
 
       SBART.SBART_Report rep;
       SBART.CalculateSBART(xs, ys_noise, ws, 2, "y", new string[] { "x" }, out rep);


      // var provider = new HeuristicLab.Problems.Instances.DataAnalysis.VariousInstanceProvider();
      // var problemData = provider.LoadData(provider.GetDataDescriptors().First(dd => dd.Name.Contains("Poly")));
      var provider = new HeuristicLab.Problems.Instances.DataAnalysis.RegressionRealWorldInstanceProvider();
      var problemData = provider.LoadData(provider.GetDataDescriptors().First(dd => dd.Name.Contains("Chem")));

      var gam = new GAM();
      gam.MaxIterations = 10;
      gam.MaxInteractions = 1;
      gam.Problem.ProblemData = problemData;
      gam.Start();

      var solution = (IRegressionSolution)gam.Results["Ensemble solution"].Value;

      Console.WriteLine("RMSE (train) {0}", solution.TrainingRootMeanSquaredError);
      Console.WriteLine("RMSE (test) {0}", solution.TestRootMeanSquaredError);
    }
  }
}