protected static double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
IDataset dataset, IEnumerable <int> rows)
{
//optimization: constant nodes return always the same value
ConstantTreeNode constantNode = node as ConstantTreeNode;
if (constantNode != null)
{
return(constantNode.Value);
}
var rootSymbol = new ProgramRootSymbol().CreateTreeNode();
var startSymbol = new StartSymbol().CreateTreeNode();
rootSymbol.AddSubtree(startSymbol);
startSymbol.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
var tempTree = new SymbolicExpressionTree(rootSymbol);
// clone ADFs of source tree
for (int i = 1; i < sourceTree.Root.SubtreeCount; i++)
{
tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
}
return(interpreter.GetSymbolicExpressionTreeValues(tempTree, dataset, rows).Median());
}
private static ISymbolicRegressionSolution CreateSymbolicSolution(List <IRegressionModel> models, double nu, IRegressionProblemData problemData)
{
var symbModels = models.OfType <ISymbolicRegressionModel>();
var lowerLimit = symbModels.Min(m => m.LowerEstimationLimit);
var upperLimit = symbModels.Max(m => m.UpperEstimationLimit);
var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
var progRootNode = new ProgramRootSymbol().CreateTreeNode();
var startNode = new StartSymbol().CreateTreeNode();
var addNode = new Addition().CreateTreeNode();
var mulNode = new Multiplication().CreateTreeNode();
var scaleNode = (ConstantTreeNode) new Constant().CreateTreeNode(); // all models are scaled using the same nu
scaleNode.Value = nu;
foreach (var m in symbModels)
{
var relevantPart = m.SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0); // skip root and start
addNode.AddSubtree((ISymbolicExpressionTreeNode)relevantPart.Clone());
}
mulNode.AddSubtree(addNode);
mulNode.AddSubtree(scaleNode);
startNode.AddSubtree(mulNode);
progRootNode.AddSubtree(startNode);
var t = new SymbolicExpressionTree(progRootNode);
var combinedModel = new SymbolicRegressionModel(problemData.TargetVariable, t, interpreter, lowerLimit, upperLimit);
var sol = new SymbolicRegressionSolution(combinedModel, problemData);
return(sol);
}
public override IList <string> GetFirsts()
{
var result = new StringList {
StartSymbol.ToString()
};
return(result);
}
public static ISymbolicExpressionTree ToTree(this HashNode <ISymbolicExpressionTreeNode>[] nodes)
{
var root = new ProgramRootSymbol().CreateTreeNode();
var start = new StartSymbol().CreateTreeNode();
root.AddSubtree(start);
start.AddSubtree(nodes.ToSubtree());
return(new SymbolicExpressionTree(root));
}
public static IClassificationSolution CreateLinearDiscriminantAnalysisSolution(IClassificationProblemData problemData)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable <string> allowedInputVariables = problemData.AllowedInputVariables;
IEnumerable <int> rows = problemData.TrainingIndices;
int nClasses = problemData.ClassNames.Count();
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Linear discriminant analysis does not support NaN or infinity values in the input dataset.");
}
// change class values into class index
int targetVariableColumn = inputMatrix.GetLength(1) - 1;
List <double> classValues = problemData.ClassValues.OrderBy(x => x).ToList();
for (int row = 0; row < inputMatrix.GetLength(0); row++)
{
inputMatrix[row, targetVariableColumn] = classValues.IndexOf(inputMatrix[row, targetVariableColumn]);
}
int info;
double[] w;
alglib.fisherlda(inputMatrix, inputMatrix.GetLength(0), allowedInputVariables.Count(), nClasses, out info, out w);
if (info < 1)
{
throw new ArgumentException("Error in calculation of linear discriminant analysis solution");
}
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int col = 0;
foreach (string column in allowedInputVariables)
{
VariableTreeNode vNode = (VariableTreeNode) new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
vNode.VariableName = column;
vNode.Weight = w[col];
addition.AddSubtree(vNode);
col++;
}
var model = LinearDiscriminantAnalysis.CreateDiscriminantFunctionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter(), problemData, rows);
SymbolicDiscriminantFunctionClassificationSolution solution = new SymbolicDiscriminantFunctionClassificationSolution(model, (IClassificationProblemData)problemData.Clone());
return(solution);
}
public InteractiveSymbolicTimeSeriesPrognosisSolutionSimplifierView()
: base()
{
InitializeComponent();
this.Caption = "Interactive Time-Series Prognosis Solution Simplifier";
constantNode = ((ConstantTreeNode) new Constant().CreateTreeNode());
ISymbolicExpressionTreeNode root = new ProgramRootSymbol().CreateTreeNode();
ISymbolicExpressionTreeNode start = new StartSymbol().CreateTreeNode();
root.AddSubtree(start);
tempTree = new SymbolicExpressionTree(root);
}
public static ISymbolicExpressionTree CreateTree(
IEnumerable <KeyValuePair <string, IEnumerable <string> > > factors, double[] factorCoefficients,
string[] variableNames, double[] coefficients,
double @const = 0)
{
if (factorCoefficients.Length == 0 && coefficients.Length == 0 && @const == 0)
{
throw new ArgumentException();
}
// Combine both trees
ISymbolicExpressionTreeNode add = (new Addition()).CreateTreeNode();
// Create tree for double variables
if (coefficients.Length > 0)
{
var varTree = CreateTree(variableNames, new int[variableNames.Length], coefficients);
foreach (var varNode in varTree.IterateNodesPrefix().OfType <VariableTreeNode>())
{
add.AddSubtree(varNode);
}
}
// Create tree for string variables
if (factorCoefficients.Length > 0)
{
var factorTree = CreateTree(factors, factorCoefficients);
foreach (var binFactorNode in factorTree.IterateNodesPrefix().OfType <BinaryFactorVariableTreeNode>())
{
add.AddSubtree(binFactorNode);
}
}
if (@const != 0.0)
{
ConstantTreeNode cNode = (ConstantTreeNode) new Constant().CreateTreeNode();
cNode.Value = @const;
add.AddSubtree(cNode);
}
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
startNode.AddSubtree(add);
return(tree);
}
protected IEnumerable <double> CalculateReplacementValues(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
IDataset dataset, IEnumerable <int> rows)
{
//optimization: constant nodes return always the same value
ConstantTreeNode constantNode = node as ConstantTreeNode;
BinaryFactorVariableTreeNode binaryFactorNode = node as BinaryFactorVariableTreeNode;
FactorVariableTreeNode factorNode = node as FactorVariableTreeNode;
if (constantNode != null)
{
yield return(constantNode.Value);
}
else if (binaryFactorNode != null)
{
// valid replacements are either all off or all on
yield return(0);
yield return(1);
}
else if (factorNode != null)
{
foreach (var w in factorNode.Weights)
{
yield return(w);
}
yield return(0.0);
}
else
{
var rootSymbol = new ProgramRootSymbol().CreateTreeNode();
var startSymbol = new StartSymbol().CreateTreeNode();
rootSymbol.AddSubtree(startSymbol);
startSymbol.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
var tempTree = new SymbolicExpressionTree(rootSymbol);
// clone ADFs of source tree
for (int i = 1; i < sourceTree.Root.SubtreeCount; i++)
{
tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
}
yield return(interpreter.GetSymbolicExpressionTreeValues(tempTree, dataset, rows).Median());
yield return(interpreter.GetSymbolicExpressionTreeValues(tempTree, dataset, rows).Average()); // TODO perf
}
}
public static ISymbolicExpressionTree CreateTree(string[] variableNames, int[] lags, double[] coefficients,
double @const = 0)
{
if (variableNames.Length == 0 ||
variableNames.Length != coefficients.Length ||
variableNames.Length != lags.Length)
{
throw new ArgumentException("The length of the variable names, lags, and coefficients vectors must match");
}
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
for (int i = 0; i < variableNames.Length; i++)
{
if (lags[i] == 0)
{
VariableTreeNode vNode = (VariableTreeNode) new Variable().CreateTreeNode();
vNode.VariableName = variableNames[i];
vNode.Weight = coefficients[i];
addition.AddSubtree(vNode);
}
else
{
LaggedVariableTreeNode vNode = (LaggedVariableTreeNode) new LaggedVariable().CreateTreeNode();
vNode.VariableName = variableNames[i];
vNode.Weight = coefficients[i];
vNode.Lag = lags[i];
addition.AddSubtree(vNode);
}
}
if ([email protected](0.0))
{
ConstantTreeNode cNode = (ConstantTreeNode) new Constant().CreateTreeNode();
cNode.Value = @const;
addition.AddSubtree(cNode);
}
return(tree);
}
/// <summary>
/// Transforms the tree model to a symbolic regression model
/// </summary>
/// <returns>A new symbolic regression model which matches the tree model</returns>
public SymbolicRegressionModel CreateSymbolicRegressionModel()
{
var rootSy = new ProgramRootSymbol();
var startSy = new StartSymbol();
var varCondSy = new VariableCondition()
{
IgnoreSlope = true
};
var constSy = new Constant();
var startNode = startSy.CreateTreeNode();
startNode.AddSubtree(CreateSymbolicRegressionTreeRecursive(tree, 0, varCondSy, constSy));
var rootNode = rootSy.CreateTreeNode();
rootNode.AddSubtree(startNode);
return(new SymbolicRegressionModel(TargetVariable, new SymbolicExpressionTree(rootNode), new SymbolicDataAnalysisExpressionTreeLinearInterpreter()));
}
public ISymbolicExpressionTree ExtractTree(int treeIdx)
{
var rf = RandomForest;
// hoping that the internal representation of alglib is stable
// TREE FORMAT
// W[Offs] - size of sub-array (for the tree)
// node info:
// W[K+0] - variable number (-1 for leaf mode)
// W[K+1] - threshold (class/value for leaf node)
// W[K+2] - ">=" branch index (absent for leaf node)
// skip irrelevant trees
int offset = 0;
for (int i = 0; i < treeIdx - 1; i++)
{
offset = offset + (int)Math.Round(rf.innerobj.trees[offset]);
}
var constSy = new Constant();
var varCondSy = new VariableCondition()
{
IgnoreSlope = true
};
var node = CreateRegressionTreeRec(rf.innerobj.trees, offset, offset + 1, constSy, varCondSy);
var startNode = new StartSymbol().CreateTreeNode();
startNode.AddSubtree(node);
var root = new ProgramRootSymbol().CreateTreeNode();
root.AddSubtree(startNode);
return(new SymbolicExpressionTree(root));
}
public static ISymbolicExpressionTree CreateTree(IEnumerable <KeyValuePair <string, IEnumerable <string> > > factors,
double[] factorCoefficients,
double @const = 0)
{
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int i = 0;
foreach (var factor in factors)
{
var varName = factor.Key;
foreach (var factorValue in factor.Value)
{
var node = (BinaryFactorVariableTreeNode) new BinaryFactorVariable().CreateTreeNode();
node.VariableValue = factorValue;
node.VariableName = varName;
node.Weight = factorCoefficients[i];
addition.AddSubtree(node);
i++;
}
}
if ([email protected](0.0))
{
ConstantTreeNode cNode = (ConstantTreeNode) new Constant().CreateTreeNode();
cNode.Value = @const;
addition.AddSubtree(cNode);
}
return(tree);
}
/// <summary>
/// Takes two parent individuals P0 and P1.
/// Randomly choose a node i from the first parent, then for each matching node j from the second parent, calculate the behavioral distance based on the range:
/// d(i,j) = 0.5 * ( abs(max(i) - max(j)) + abs(min(i) - min(j)) ).
/// Next, assign probabilities for the selection of a node j based on the inversed and normalized behavioral distance, then make a random weighted choice.
/// </summary>
public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, T problemData, IList <int> rows, int maxDepth, int maxLength)
{
var crossoverPoints0 = new List <CutPoint>();
parent0.Root.ForEachNodePostfix((n) => {
// the if clauses prevent the root or the startnode from being selected, although the startnode can be the parent of the node being swapped.
if (n.Parent != null && n.Parent != parent0.Root)
{
crossoverPoints0.Add(new CutPoint(n.Parent, n));
}
});
var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
var allowedBranches = new List <ISymbolicExpressionTreeNode>();
parent1.Root.ForEachNodePostfix((n) => {
if (n.Parent != null && n.Parent != parent1.Root)
{
if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
{
allowedBranches.Add(n);
}
}
});
if (allowedBranches.Count == 0)
{
return(parent0);
}
var dataset = problemData.Dataset;
// create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
var rootSymbol = new ProgramRootSymbol();
var startSymbol = new StartSymbol();
var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol); // this will change crossoverPoint0.Child.Parent
double min0 = 0.0, max0 = 0.0;
foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows))
{
if (min0 > v)
{
min0 = v;
}
if (max0 < v)
{
max0 = v;
}
}
crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore correct parent
var weights = new List <double>();
foreach (var node in allowedBranches)
{
var parent = node.Parent;
var tree1 = CreateTreeFromNode(random, node, rootSymbol, startSymbol);
double min1 = 0.0, max1 = 0.0;
foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows))
{
if (min1 > v)
{
min1 = v;
}
if (max1 < v)
{
max1 = v;
}
}
double behavioralDistance = (Math.Abs(min0 - min1) + Math.Abs(max0 - max1)) / 2; // this can be NaN of Infinity because some trees are crazy like exp(exp(exp(...))), we correct that below
weights.Add(behavioralDistance);
node.Parent = parent; // restore correct node parent
}
// remove branches with an infinite or NaN behavioral distance
for (int i = weights.Count - 1; i >= 0; --i)
{
if (Double.IsNaN(weights[i]) || Double.IsInfinity(weights[i]))
{
weights.RemoveAt(i);
allowedBranches.RemoveAt(i);
}
}
// check if there are any allowed branches left
if (allowedBranches.Count == 0)
{
return(parent0);
}
ISymbolicExpressionTreeNode selectedBranch;
double sum = weights.Sum();
if (sum.IsAlmost(0.0) || weights.Count == 1) // if there is only one allowed branch, or if all weights are zero
{
selectedBranch = allowedBranches[0];
}
else
{
for (int i = 0; i != weights.Count; ++i) // normalize and invert values
{
weights[i] = 1 - weights[i] / sum;
}
sum = weights.Sum(); // take new sum
// compute the probabilities (selection weights)
for (int i = 0; i != weights.Count; ++i)
{
weights[i] /= sum;
}
#pragma warning disable 612, 618
selectedBranch = allowedBranches.SelectRandom(weights, random);
#pragma warning restore 612, 618
}
Swap(crossoverPoint0, selectedBranch);
return(parent0);
}
/// <summary>
/// Takes two parent individuals P0 and P1.
/// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
/// </summary>
public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
T problemData, List <int> rows, int maxDepth, int maxLength, DoubleRange range)
{
var crossoverPoints0 = new List <CutPoint>();
parent0.Root.ForEachNodePostfix((n) => {
if (n.Parent != null && n.Parent != parent0.Root)
{
crossoverPoints0.Add(new CutPoint(n.Parent, n));
}
});
var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
var allowedBranches = new List <ISymbolicExpressionTreeNode>();
parent1.Root.ForEachNodePostfix((n) => {
if (n.Parent != null && n.Parent != parent1.Root)
{
if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
{
allowedBranches.Add(n);
}
}
});
if (allowedBranches.Count == 0)
{
return(parent0);
}
var dataset = problemData.Dataset;
// create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
var rootSymbol = new ProgramRootSymbol();
var startSymbol = new StartSymbol();
var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
List <double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows).ToList();
crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
ISymbolicExpressionTreeNode selectedBranch = null;
// pick the first node that fulfills the semantic similarity conditions
foreach (var node in allowedBranches)
{
var parent = node.Parent;
var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
List <double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows).ToList();
node.Parent = parent; // restore parent
OnlineCalculatorError errorState;
double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
if (range.Start <= ssd && ssd <= range.End)
{
selectedBranch = node;
break;
}
}
// perform the actual swap
if (selectedBranch != null)
{
Swap(crossoverPoint0, selectedBranch);
}
return(parent0);
}
public override int GetHashCode()
{
return(StartSymbol.GetHashCode() ^ EndSymbol.GetHashCode());
}
protected override string GetName(NodeFactory factory) => $"Region {StartSymbol.GetMangledName(factory.NameMangler)}";
public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable <string> allowedInputVariables = problemData.AllowedInputVariables;
IEnumerable <int> rows = problemData.TrainingIndices;
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
}
alglib.linearmodel lm = new alglib.linearmodel();
alglib.lrreport ar = new alglib.lrreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
int retVal = 1;
alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
if (retVal != 1)
{
throw new ArgumentException("Error in calculation of linear regression solution");
}
rmsError = ar.rmserror;
cvRmsError = ar.cvrmserror;
alglib.lrunpack(lm, out coefficients, out nFeatures);
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int col = 0;
foreach (string column in allowedInputVariables)
{
VariableTreeNode vNode = (VariableTreeNode) new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
vNode.VariableName = column;
vNode.Weight = coefficients[col];
addition.AddSubtree(vNode);
col++;
}
ConstantTreeNode cNode = (ConstantTreeNode) new Constant().CreateTreeNode();
cNode.Value = coefficients[coefficients.Length - 1];
addition.AddSubtree(cNode);
SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
solution.Model.Name = "Linear Regression Model";
solution.Name = "Linear Regression Solution";
return(solution);
}
private static ITimeSeriesPrognosisSolution CreateAutoRegressiveSolution(ITimeSeriesPrognosisProblemData problemData, int timeOffset, out double rmsError, out double cvRmsError)
{
string targetVariable = problemData.TargetVariable;
double[,] inputMatrix = new double[problemData.TrainingPartition.Size, timeOffset + 1];
var targetValues = problemData.Dataset.GetDoubleValues(targetVariable).ToList();
for (int i = 0, row = problemData.TrainingPartition.Start; i < problemData.TrainingPartition.Size; i++, row++)
{
for (int col = 0; col < timeOffset; col++)
{
inputMatrix[i, col] = targetValues[row - col - 1];
}
}
// set target values in last column
for (int i = 0; i < inputMatrix.GetLength(0); i++)
{
inputMatrix[i, timeOffset] = targetValues[i + problemData.TrainingPartition.Start];
}
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
}
alglib.linearmodel lm = new alglib.linearmodel();
alglib.lrreport ar = new alglib.lrreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
int retVal = 1;
alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
if (retVal != 1)
{
throw new ArgumentException("Error in calculation of linear regression solution");
}
rmsError = ar.rmserror;
cvRmsError = ar.cvrmserror;
alglib.lrunpack(lm, out coefficients, out nFeatures);
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
for (int i = 0; i < timeOffset; i++)
{
LaggedVariableTreeNode node = (LaggedVariableTreeNode) new LaggedVariable().CreateTreeNode();
node.VariableName = targetVariable;
node.Weight = coefficients[i];
node.Lag = (i + 1) * -1;
addition.AddSubtree(node);
}
ConstantTreeNode cNode = (ConstantTreeNode) new Constant().CreateTreeNode();
cNode.Value = coefficients[coefficients.Length - 1];
addition.AddSubtree(cNode);
var interpreter = new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(problemData.TargetVariable);
var model = new SymbolicTimeSeriesPrognosisModel(problemData.TargetVariable, tree, interpreter);
var solution = model.CreateTimeSeriesPrognosisSolution((ITimeSeriesPrognosisProblemData)problemData.Clone());
return(solution);
}
void ISymbolNode.AppendMangledName(NameMangler nameMangler, Utf8StringBuilder sb)
{
sb.Append("__");
StartSymbol.AppendMangledName(nameMangler, sb);
}
public override bool Equals(object obj)
{
Bracket <T> context = obj as Bracket <T>;
return(StartSymbol.Equals(context?.StartSymbol) && EndSymbol.Equals(context?.EndSymbol));
}
public void DeriveExpressions()
{
var formatter = new InfixExpressionFormatter();
var parser = new InfixExpressionParser();
Assert.AreEqual("0", Derive("3", "x"));
Assert.AreEqual("1", Derive("x", "x"));
Assert.AreEqual("10", Derive("10*x", "x"));
Assert.AreEqual("10", Derive("x*10", "x"));
Assert.AreEqual("(2*'x')", Derive("x*x", "x"));
Assert.AreEqual("((('x' * 'x') * 2) + ('x' * 'x'))", Derive("x*x*x", "x")); // simplifier does not merge (x*x)*2 + x*x to 3*x*x
Assert.AreEqual("0", Derive("10*x", "y"));
Assert.AreEqual("20", Derive("10*x+20*y", "y"));
Assert.AreEqual("6", Derive("2*3*x", "x"));
Assert.AreEqual("(10*'y')", Derive("10*x*y+20*y", "x"));
Assert.AreEqual("(1 / (SQR('x') * (-1)))", Derive("1/x", "x"));
Assert.AreEqual("('y' / (SQR('x') * (-1)))", Derive("y/x", "x"));
Assert.AreEqual("((((-2*'x') + (-1)) * ('a' + 'b')) / SQR(('x' + ('x' * 'x'))))",
Derive("(a+b)/(x+x*x)", "x"));
Assert.AreEqual("((((-2*'x') + (-1)) * ('a' + 'b')) / SQR(('x' + SQR('x'))))", Derive("(a+b)/(x+SQR(x))", "x"));
Assert.AreEqual("EXP('x')", Derive("exp(x)", "x"));
Assert.AreEqual("(EXP((3*'x')) * 3)", Derive("exp(3*x)", "x"));
Assert.AreEqual("(1 / 'x')", Derive("log(x)", "x"));
Assert.AreEqual("(1 / 'x')", Derive("log(3*x)", "x")); // 3 * 1/(3*x)
Assert.AreEqual("(1 / ('x' + (0.333333333333333*'y')))", Derive("log(3*x+y)", "x")); // simplifier does not try to keep fractions
Assert.AreEqual("(1 / (SQRT(((3*'x') + 'y')) * 0.666666666666667))", Derive("sqrt(3*x+y)", "x")); // 3 / (2 * sqrt(3*x+y)) = 1 / ((2/3) * sqrt(3*x+y))
Assert.AreEqual("(COS((3*'x')) * 3)", Derive("sin(3*x)", "x"));
Assert.AreEqual("(SIN((3*'x')) * (-3))", Derive("cos(3*x)", "x"));
Assert.AreEqual("(1 / (SQR(COS((3*'x'))) * 0.333333333333333))", Derive("tan(3*x)", "x")); // diff(tan(f(x)), x) = 1.0 / cos²(f(x)), simplifier puts constant factor into the denominator
Assert.AreEqual("((9*'x') / ABS((3*'x')))", Derive("abs(3*x)", "x"));
Assert.AreEqual("(SQR('x') * 3)", Derive("cube(x)", "x"));
Assert.AreEqual("(1 / (SQR(CUBEROOT('x')) * 3))", Derive("cuberoot(x)", "x"));
Assert.AreEqual("0", Derive("(a+b)/(x+SQR(x))", "y")); // df(a,b,x) / dy = 0
Assert.AreEqual("('a' * 'b' * 'c')", Derive("a*b*c*d", "d"));
Assert.AreEqual("('a' / ('b' * 'c' * SQR('d') * (-1)))", Derive("a/b/c/d", "d"));
Assert.AreEqual("('x' * ((SQR(TANH(SQR('x'))) * (-1)) + 1) * 2)", Derive("tanh(sqr(x))", "x")); // (2*'x'*(1 - SQR(TANH(SQR('x'))))
{
// special case: Inv(x) using only one argument to the division symbol
// f(x) = 1/x
var root = new ProgramRootSymbol().CreateTreeNode();
var start = new StartSymbol().CreateTreeNode();
var div = new Division().CreateTreeNode();
var varNode = (VariableTreeNode)(new Variable().CreateTreeNode());
varNode.Weight = 1.0;
varNode.VariableName = "x";
div.AddSubtree(varNode);
start.AddSubtree(div);
root.AddSubtree(start);
var t = new SymbolicExpressionTree(root);
Assert.AreEqual("(1 / (SQR('x') * (-1)))",
formatter.Format(DerivativeCalculator.Derive(t, "x")));
}
{
// special case: multiplication with only one argument
var root = new ProgramRootSymbol().CreateTreeNode();
var start = new StartSymbol().CreateTreeNode();
var mul = new Multiplication().CreateTreeNode();
var varNode = (VariableTreeNode)(new Variable().CreateTreeNode());
varNode.Weight = 3.0;
varNode.VariableName = "x";
mul.AddSubtree(varNode);
start.AddSubtree(mul);
root.AddSubtree(start);
var t = new SymbolicExpressionTree(root);
Assert.AreEqual("3",
formatter.Format(DerivativeCalculator.Derive(t, "x")));
}
{
// division with multiple arguments
// div(x, y, z) is interpreted as (x / y) / z
var root = new ProgramRootSymbol().CreateTreeNode();
var start = new StartSymbol().CreateTreeNode();
var div = new Division().CreateTreeNode();
var varNode1 = (VariableTreeNode)(new Variable().CreateTreeNode());
varNode1.Weight = 3.0;
varNode1.VariableName = "x";
var varNode2 = (VariableTreeNode)(new Variable().CreateTreeNode());
varNode2.Weight = 4.0;
varNode2.VariableName = "y";
var varNode3 = (VariableTreeNode)(new Variable().CreateTreeNode());
varNode3.Weight = 5.0;
varNode3.VariableName = "z";
div.AddSubtree(varNode1); div.AddSubtree(varNode2); div.AddSubtree(varNode3);
start.AddSubtree(div);
root.AddSubtree(start);
var t = new SymbolicExpressionTree(root);
Assert.AreEqual("(('y' * 'z' * 60) / (SQR('y') * SQR('z') * 400))", // actually 3 / (4y 5z) but simplifier is not smart enough to cancel numerator and denominator
// 60 y z / y² z² 20² == 6 / y z 40 == 3 / y z 20
formatter.Format(DerivativeCalculator.Derive(t, "x")));
Assert.AreEqual("(('x' * 'z' * (-60)) / (SQR('y') * SQR('z') * 400))", // actually 3x * -(4 5 z) / (4y 5z)² = -3x / (20 y² z)
// -3 4 5 x z / 4² y² 5² z² = -60 x z / 20² z² y² == -60 x z / y² z² 20²
formatter.Format(DerivativeCalculator.Derive(t, "y")));
Assert.AreEqual("(('x' * 'y' * (-60)) / (SQR('y') * SQR('z') * 400))",
formatter.Format(DerivativeCalculator.Derive(t, "z")));
}
}