public void LinkageTreeTestAdd() { MersenneTwister rand = new MersenneTwister(); LinkageTree tree = new LinkageTree(Length, rand); tree.Add(solutions[0]); tree.Add(solutions[1]); PrivateObject hidden = new PrivateObject(tree); int[][][] result = (int[][][])hidden.GetField("occurances"); Assert.AreEqual(1, result[1][0][0]); // Positions 0 and 1 had value 00 exactly once Assert.AreEqual(2, result[Length - 1][Length - 2][0]); // Positions 0 and 1 had value 00 exactly once Assert.AreEqual(0, result[Length - 1][Length - 2][1]); // Positions 7 and 8 never had value 10 Assert.AreEqual(1, result[1][0][3]); // Positions 0 and 1 had value 11 exactly once }
private void TestCalculatorPerfomance(CalcateFunc calculateFunc) { var twister = new MersenneTwister(31415); var dataset = CreateRandomDataset(twister, Rows, Columns); OnlineCalculatorError errorState = OnlineCalculatorError.None; ; Stopwatch watch = new Stopwatch(); watch.Start(); for (int i = 0; i < Repetitions; i++) { double value = calculateFunc(dataset.GetDoubleValues("y"), dataset.GetDoubleValues("x0"), out errorState); } Assert.AreEqual(errorState, OnlineCalculatorError.None); watch.Stop(); TestContext.WriteLine(""); TestContext.WriteLine("Calculated Rows per milisecond: {0}.", Rows * Repetitions * 1.0 / watch.ElapsedMilliseconds); }
public void LinkageTreeTestEntropyDistance() { MersenneTwister rand = new MersenneTwister(); LinkageTree tree = new LinkageTree(Length, rand); PrivateObject hidden = new PrivateObject(tree); // No information should result in a distance of 0 Assert.AreEqual((double)0, hidden.Invoke("EntropyDistance", new object[] { 0, 1 })); foreach (var solution in solutions) { tree.Add(solution); } // Check that 0 and 1 are closer than 0 and 2 var linked = (double)hidden.Invoke("EntropyDistance", new object[] { 0, 1 }); var unlinked = (double)hidden.Invoke("EntropyDistance", new object[] { 0, 2 }); Assert.IsTrue(linked < unlinked); // Reversing the arguments should not change the result var forward = hidden.Invoke("EntropyDistance", new object[] { Length - 1, Length - 2 }); var backward = hidden.Invoke("EntropyDistance", new object[] { Length - 2, Length - 1 }); Assert.AreEqual(forward, backward); }
public void LinkageTreeTestRebuild() { // The seed matters as equal sized clusters can appear in any order MersenneTwister rand = new MersenneTwister(123); LinkageTree tree = new LinkageTree(Length, rand); foreach (var solution in solutions) { tree.Add(solution); } // Check if the clusters created contain the expected variables. var found = tree.Clusters.ToArray(); Assert.AreEqual(correctClusters.Length, found.Length); for (int i = 0; i < found.Length; i++) { found[i].Sort(); Assert.IsTrue(found[i].SequenceEqual(correctClusters[i]), string.Format("Failed On Cluster {0}", i)); } }
private void randFill(IList<bool> data, MersenneTwister rand) { for (int i = 0; i < data.Count; i++) { data[i] = rand.Next(2) == 1; } }
private void Initialize(IEnumerable<string> variableNames, int nConstants) { #region symbol declaration var add = new Addition(); var sub = new Subtraction(); var mul = new Multiplication(); var div = new Division(); var mean = new Average(); var log = new Logarithm(); var pow = new Power(); var square = new Square(); var root = new Root(); var sqrt = new SquareRoot(); var exp = new Exponential(); // we use our own random number generator here because we assume // that grammars are only initialized once when setting the grammar in the problem. // This means everytime the grammar parameter in the problem is changed // we initialize the constants to new values var rand = new MersenneTwister(); // warm up for (int i = 0; i < 1000; i++) rand.NextDouble(); var constants = new List<Constant>(nConstants); for (int i = 0; i < nConstants; i++) { var constant = new Constant(); do { var constVal = rand.NextDouble() * 20.0 - 10.0; constant.Name = string.Format("{0:0.000}", constVal); constant.MinValue = constVal; constant.MaxValue = constVal; constant.ManipulatorSigma = 0.0; constant.ManipulatorMu = 0.0; constant.MultiplicativeManipulatorSigma = 0.0; } while (constants.Any(c => c.Name == constant.Name)); // unlikely, but it could happen that the same constant value is sampled twice. so we resample if necessary. constants.Add(constant); } var variables = new List<HeuristicLab.Problems.DataAnalysis.Symbolic.Variable>(); foreach (var variableName in variableNames) { var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable(); variableSymbol.Name = variableName; variableSymbol.WeightManipulatorMu = 0.0; variableSymbol.WeightManipulatorSigma = 0.0; variableSymbol.WeightMu = 1.0; variableSymbol.WeightSigma = 0.0; variableSymbol.MultiplicativeWeightManipulatorSigma = 0.0; variableSymbol.AllVariableNames = new[] { variableName }; variableSymbol.VariableNames = new[] { variableName }; variables.Add(variableSymbol); } #endregion AddSymbol(add); AddSymbol(sub); AddSymbol(mul); AddSymbol(div); AddSymbol(mean); AddSymbol(log); AddSymbol(pow); AddSymbol(square); AddSymbol(root); AddSymbol(sqrt); AddSymbol(exp); constants.ForEach(AddSymbol); variables.ForEach(AddSymbol); #region subtree count configuration SetSubtreeCount(add, 2, 2); SetSubtreeCount(sub, 2, 2); SetSubtreeCount(mul, 2, 2); SetSubtreeCount(div, 2, 2); SetSubtreeCount(mean, 2, 2); SetSubtreeCount(log, 1, 1); SetSubtreeCount(pow, 2, 2); SetSubtreeCount(square, 1, 1); SetSubtreeCount(root, 2, 2); SetSubtreeCount(sqrt, 1, 1); SetSubtreeCount(exp, 1, 1); constants.ForEach((c) => SetSubtreeCount(c, 0, 0)); variables.ForEach((v) => SetSubtreeCount(v, 0, 0)); #endregion var functions = new ISymbol[] { add, sub, mul, div, mean, log, pow, root, square, sqrt }; var terminalSymbols = variables.Concat<ISymbol>(constants); var allSymbols = functions.Concat(terminalSymbols); #region allowed child symbols configuration foreach (var s in allSymbols) { AddAllowedChildSymbol(StartSymbol, s); } foreach (var parentSymb in functions) foreach (var childSymb in allSymbols) { AddAllowedChildSymbol(parentSymb, childSymb); } #endregion }
public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) { double[,] data = new double[rows, columns]; for (int i = 0; i < rows; i++) { for (int j = 0; j < columns; j++) { data[i, j] = twister.NextDouble() * 2.0 - 1.0; } } IEnumerable<string> variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString())); Dataset ds = new Dataset(variableNames, data); return ds; }