private static alglib.ndimensional_pgrad CreatePGrad(AutoDiff.IParametricCompiledTerm compiledFunc) { return((double[] c, double[] x, ref double func, double[] grad, object o) => { var tupel = compiledFunc.Differentiate(c, x); func = tupel.Item2; Array.Copy(tupel.Item1, grad, grad.Length); }); }
private static alglib.ndimensional_pfunc CreatePFunc(AutoDiff.IParametricCompiledTerm compiledFunc) { return((double[] c, double[] x, ref double func, object o) => { func = compiledFunc.Evaluate(c, x); }); }
public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int maxIterations, bool updateVariableWeights = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue, bool updateConstantsInTree = true) { List <AutoDiff.Variable> variables = new List <AutoDiff.Variable>(); List <AutoDiff.Variable> parameters = new List <AutoDiff.Variable>(); List <string> variableNames = new List <string>(); AutoDiff.Term func; if (!TryTransformToAutoDiff(tree.Root.GetSubtree(0), variables, parameters, variableNames, updateVariableWeights, out func)) { throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree."); } if (variableNames.Count == 0) { return(0.0); } AutoDiff.IParametricCompiledTerm compiledFunc = func.Compile(variables.ToArray(), parameters.ToArray()); List <SymbolicExpressionTreeTerminalNode> terminalNodes = null; if (updateVariableWeights) { terminalNodes = tree.Root.IterateNodesPrefix().OfType <SymbolicExpressionTreeTerminalNode>().ToList(); } else { terminalNodes = new List <SymbolicExpressionTreeTerminalNode>(tree.Root.IterateNodesPrefix().OfType <ConstantTreeNode>()); } //extract inital constants double[] c = new double[variables.Count]; { c[0] = 0.0; c[1] = 1.0; int i = 2; foreach (var node in terminalNodes) { ConstantTreeNode constantTreeNode = node as ConstantTreeNode; VariableTreeNode variableTreeNode = node as VariableTreeNode; if (constantTreeNode != null) { c[i++] = constantTreeNode.Value; } else if (updateVariableWeights && variableTreeNode != null) { c[i++] = variableTreeNode.Weight; } } } double[] originalConstants = (double[])c.Clone(); double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); alglib.lsfitstate state; alglib.lsfitreport rep; int info; IDataset ds = problemData.Dataset; double[,] x = new double[rows.Count(), variableNames.Count]; int row = 0; foreach (var r in rows) { for (int col = 0; col < variableNames.Count; col++) { x[row, col] = ds.GetDoubleValue(variableNames[col], r); } row++; } double[] y = ds.GetDoubleValues(problemData.TargetVariable, rows).ToArray(); int n = x.GetLength(0); int m = x.GetLength(1); int k = c.Length; alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(compiledFunc); alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(compiledFunc); try { alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state); alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations); //alglib.lsfitsetgradientcheck(state, 0.001); alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, null, null); alglib.lsfitresults(state, out info, out c, out rep); } catch (ArithmeticException) { return(originalQuality); } catch (alglib.alglibexception) { return(originalQuality); } //info == -7 => constant optimization failed due to wrong gradient if (info != -7) { UpdateConstants(tree, c.Skip(2).ToArray(), updateVariableWeights); } var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (!updateConstantsInTree) { UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights); } if (originalQuality - quality > 0.001 || double.IsNaN(quality)) { UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights); return(originalQuality); } return(quality); }