protected override void UpdateModel(ISymbolicExpressionTree tree)
{
var model = new SymbolicTimeSeriesPrognosisModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter);
model.Scale(Content.ProblemData);
Content.Model = model;
}
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);
var tree = LinearModelToTreeConverter.CreateTree(
variableNames: Enumerable.Repeat(problemData.TargetVariable, nFeatures).ToArray(),
lags: Enumerable.Range(0, timeOffset).Select(i => (i + 1) * -1).ToArray(),
coefficients: coefficients.Take(nFeatures).ToArray(),
@const: coefficients[nFeatures]
);
var interpreter = new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(problemData.TargetVariable);
var model = new SymbolicTimeSeriesPrognosisModel(problemData.TargetVariable, tree, interpreter);
var solution = model.CreateTimeSeriesPrognosisSolution((ITimeSeriesPrognosisProblemData)problemData.Clone());
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);
}
protected override void UpdateModel(ISymbolicExpressionTree tree) {
var model = new SymbolicTimeSeriesPrognosisModel(Content.ProblemData.TargetVariable, tree, Content.Model.Interpreter);
model.Scale(Content.ProblemData);
Content.Model = model;
}
