using HeuristicLab.Algorithms.DataAnalysis.FastFunctionExtraction;
using HeuristicLab.Common;
using HeuristicLab.Problems.DataAnalysis;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using System;
using System.Linq;

namespace UnitTests {

    [TestClass]
    public class FFXModelTests {
        private static readonly IDataset defaultDataset = new Dataset(new string[] { "x1", "x2", "x3", "y" }, new double[,] {
            { 1, 1, 3, 7 },
            { 2, 3, 1, 12 },
            { 3, -3, 0, 0 },
            { 4, -2, 1, 8 },
            { 5, -8, 5, 2 }
        });

        [TestMethod]
        public void Simulate_MultipleNominatorTerms() {
            double[] expr(double[] x1, double[] x2) {
                return Enumerable.Range(0, x1.Length).Select(i =>
                    (-0.5 + 1.5 * Math.Pow(x1[i], 2) + 0.5 * Math.Abs(x2[i]))
                ).ToArray();
            }


            var data = new RegressionProblemData(defaultDataset, new string[] { "x1", "x2" }, "y");
            var basisFunctions = new (double, IBasisFunction)[2];

            // model: -0.5 + 1.5 * x1^2 + 0.5* abs(x2)
            double intercept = -0.5;
            basisFunctions[0] = (1.5, new SimpleBasisFunction("x1", 2, NonlinearOperator.None, true));
            basisFunctions[1] = (0.5, new SimpleBasisFunction("x2", 1, NonlinearOperator.Abs, true));
            var model = new FFXModel(intercept, basisFunctions);

            var expectedVals = expr(
                data.Dataset.GetDoubleValues("x1").ToArray(),
                data.Dataset.GetDoubleValues("x2").ToArray()
            );
            var calculatedVals = model.Simulate(data, data.AllIndices);
            for (int i = 0; i < calculatedVals.Length; i++) {
                Assert.IsTrue(expectedVals[i].IsAlmost(calculatedVals[i]));
            }
        }

        [TestMethod]
        public void Simulate_MultipleDenominatorTerms() {
            double[] expr(double[] x1, double[] x2, double[] x3) {
                return Enumerable.Range(0, x1.Length).Select(i =>
                    (-0.5 + 1.5 * Math.Abs(x1[i])) / (1 + 1.5 * Math.Pow(x2[i], 2) - 0.3 * x3[i])
                ).ToArray();
            }

            var data = new RegressionProblemData(defaultDataset, new string[] { "x1", "x2", "x3" }, "y");
            var basisFunctions = new (double, IBasisFunction)[3];

            // model: (-0.5 + 1.5 * abs(x1)) / (1 + 1.5 * x2^2 - 0.3 * x3)
            double intercept = -0.5;
            basisFunctions[0] = (1.5, new SimpleBasisFunction("x1", 1, NonlinearOperator.Abs, true));
            basisFunctions[1] = (1.5, new SimpleBasisFunction("x2", 2, NonlinearOperator.None, false));
            basisFunctions[2] = (-0.3, new SimpleBasisFunction("x3", 1, NonlinearOperator.None, false));

            var model = new FFXModel(intercept, basisFunctions);

            var calculatedVals = model.Simulate(data, data.AllIndices);
            var expectedVals = expr(
                data.Dataset.GetDoubleValues("x1").ToArray(),
                data.Dataset.GetDoubleValues("x2").ToArray(),
                data.Dataset.GetDoubleValues("x3").ToArray()
            ).ToArray();
            for (int i = 0; i < expectedVals.Length; i++) {
                Assert.IsTrue(expectedVals[i].IsAlmost(calculatedVals[i]));
            }
            // TODO
        }
    }
}