protected override void UpdateModel(ISymbolicExpressionTree tree) { var model = new SymbolicRegressionModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit); model.Scale(Content.ProblemData); Content.Model = model; }
public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations) { var parser = new InfixExpressionParser(); var tree = parser.Parse(modelStructure); var simplifier = new SymbolicDataAnalysisExpressionTreeSimplifier(); if (!SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree)) { throw new ArgumentException("The optimizer does not support the specified model structure."); } var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter(); SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices, applyLinearScaling: false, maxIterations: maxIterations, updateVariableWeights: false, updateConstantsInTree: true); var scaledModel = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone()); scaledModel.Scale(problemData); SymbolicRegressionSolution solution = new SymbolicRegressionSolution(scaledModel, (IRegressionProblemData)problemData.Clone()); solution.Model.Name = "Regression Model"; solution.Name = "Regression Solution"; return(solution); }
protected override void UpdateModel(ISymbolicExpressionTree tree) { var model = new SymbolicRegressionModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter, Content.Model.LowerEstimationLimit, Content.Model.UpperEstimationLimit); model.Scale(Content.ProblemData); Content.Model = model; }
/// <summary> /// Fits a model to the data by optimizing the numeric constants. /// Model is specified as infix expression containing variable names and numbers. /// The starting point for the numeric constants is initialized randomly if a random number generator is specified (~N(0,1)). Otherwise the user specified constants are /// used as a starting point. /// </summary>- /// <param name="problemData">Training and test data</param> /// <param name="modelStructure">The function as infix expression</param> /// <param name="maxIterations">Number of constant optimization iterations (using Levenberg-Marquardt algorithm)</param> /// <param name="random">Optional random number generator for random initialization of numeric constants.</param> /// <returns></returns> public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations, bool applyLinearScaling, IRandom rand = null) { var parser = new InfixExpressionParser(); var tree = parser.Parse(modelStructure); // parser handles double and string variables equally by creating a VariableTreeNode // post-process to replace VariableTreeNodes by FactorVariableTreeNodes for all string variables var factorSymbol = new FactorVariable(); factorSymbol.VariableNames = problemData.AllowedInputVariables.Where(name => problemData.Dataset.VariableHasType <string>(name)); factorSymbol.AllVariableNames = factorSymbol.VariableNames; factorSymbol.VariableValues = factorSymbol.VariableNames.Select(name => new KeyValuePair <string, Dictionary <string, int> >(name, problemData.Dataset.GetReadOnlyStringValues(name).Distinct() .Select((n, i) => Tuple.Create(n, i)) .ToDictionary(tup => tup.Item1, tup => tup.Item2))); foreach (var parent in tree.IterateNodesPrefix().ToArray()) { for (int i = 0; i < parent.SubtreeCount; i++) { var varChild = parent.GetSubtree(i) as VariableTreeNode; var factorVarChild = parent.GetSubtree(i) as FactorVariableTreeNode; if (varChild != null && factorSymbol.VariableNames.Contains(varChild.VariableName)) { parent.RemoveSubtree(i); var factorTreeNode = (FactorVariableTreeNode)factorSymbol.CreateTreeNode(); factorTreeNode.VariableName = varChild.VariableName; factorTreeNode.Weights = factorTreeNode.Symbol.GetVariableValues(factorTreeNode.VariableName).Select(_ => 1.0).ToArray(); // weight = 1.0 for each value parent.InsertSubtree(i, factorTreeNode); } else if (factorVarChild != null && factorSymbol.VariableNames.Contains(factorVarChild.VariableName)) { if (factorSymbol.GetVariableValues(factorVarChild.VariableName).Count() != factorVarChild.Weights.Length) { throw new ArgumentException( string.Format("Factor variable {0} needs exactly {1} weights", factorVarChild.VariableName, factorSymbol.GetVariableValues(factorVarChild.VariableName).Count())); } parent.RemoveSubtree(i); var factorTreeNode = (FactorVariableTreeNode)factorSymbol.CreateTreeNode(); factorTreeNode.VariableName = factorVarChild.VariableName; factorTreeNode.Weights = factorVarChild.Weights; parent.InsertSubtree(i, factorTreeNode); } } } if (!SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree)) { throw new ArgumentException("The optimizer does not support the specified model structure."); } // initialize constants randomly if (rand != null) { foreach (var node in tree.IterateNodesPrefix().OfType <ConstantTreeNode>()) { double f = Math.Exp(NormalDistributedRandom.NextDouble(rand, 0, 1)); double s = rand.NextDouble() < 0.5 ? -1 : 1; node.Value = s * node.Value * f; } } var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter(); SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices, applyLinearScaling: applyLinearScaling, maxIterations: maxIterations, updateVariableWeights: false, updateConstantsInTree: true); var model = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone()); if (applyLinearScaling) { model.Scale(problemData); } SymbolicRegressionSolution solution = new SymbolicRegressionSolution(model, (IRegressionProblemData)problemData.Clone()); solution.Model.Name = "Regression Model"; solution.Name = "Regression Solution"; return(solution); }