public void StandardInterpreterTestEvaluation()
{
var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
EvaluateTerminals(interpreter, ds);
EvaluateOperations(interpreter, ds);
EvaluateAdf(interpreter, ds);
}
protected override void Run(CancellationToken cancellationToken)
{
var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
var modelsWithComplexity = new List <(ISymbolicRegressionModel, int)>();
Stopwatch stopwatch = Stopwatch.StartNew();
RunFFX(out var basisFunctions, out var lambda, out var coeff, out var trainNMSE, out var testNMSE, out var intercept, out var elnetData);
stopwatch.Stop();
var runtime = stopwatch.ElapsedTicks / 10000000.0; // in seconds
if (Verbose)
{
var errorTable = NMSEGraph(coeff, lambda, trainNMSE, testNMSE);
Results.Add(new Result(errorTable.Name, errorTable.Description, errorTable));
var coeffTable = CoefficientGraph(coeff, lambda, elnetData.AllowedInputVariables, elnetData.Dataset);
Results.Add(new Result(coeffTable.Name, coeffTable.Description, coeffTable));
}
int complexity(double[] modelCoeffs) => modelCoeffs.Count(val => val != 0) + 1;
for (int row = 0; row < coeff.GetUpperBound(0); row++)
{
var coeffs = Utils.GetRow(coeff, row);
var numBasisFuncs = complexity(coeffs);
ISymbolicExpressionTree tree = Tree(basisFunctions, coeffs, intercept[row]);
ISymbolicRegressionModel model = new SymbolicRegressionModel(elnetData.TargetVariable, tree, interpreter);
modelsWithComplexity.Add((model, numBasisFuncs));
}
// calculate the pareto front
var paretoFront = Utils.NondominatedFilter(modelsWithComplexity.ToArray(), coeff, testNMSE, complexity);
modelsWithComplexity = modelsWithComplexity.Distinct(new Utils.SymbolicRegressionModelSameComplexity()).ToList();
var results = new ItemCollection <IResult>();
int modelIdx = 1;
foreach (var model in modelsWithComplexity)
{
results.Add(new Result("Model " + (modelIdx < 10 ? "0" + modelIdx : modelIdx.ToString()), model.Item1));
//results.Add(new ItemCollection<IResult>(3){
//new Result("Model Complexity", new IntValue(model.Item2)),
//new Result("Model Accuracy", new RegressionSolution(model.Item1, Problem.ProblemData))
//});
modelIdx++;
}
Results.Add(new Result("Pareto Front", "The Pareto Front of the Models. ", results));
if (FilePath != "")
{
SaveInFile(paretoFront, runtime, FilePath, ",");
}
}
public void TimeSeriesPrognosisInterpreterTestEvaluation()
{
Dataset ds = new Dataset(new string[] { "Y", "A", "B" }, new double[, ] {
{ 1.0, 1.0, 1.0 },
{ 2.0, 2.0, 2.0 },
{ 3.0, 1.0, 2.0 },
{ 4.0, 1.0, 1.0 },
{ 5.0, 2.0, 2.0 },
{ 6.0, 1.0, 2.0 },
{ 7.0, 1.0, 1.0 },
{ 8.0, 2.0, 2.0 },
{ 9.0, 1.0, 2.0 },
{ 10.0, 1.0, 1.0 },
{ 11.0, 2.0, 2.0 },
{ 12.0, 1.0, 2.0 }
});
var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
EvaluateTerminals(interpreter, ds);
EvaluateOperations(interpreter, ds);
EvaluateAdf(interpreter, ds);
}
public void StandardInterpreterTestEvaluation() {
var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
EvaluateTerminals(interpreter, ds);
EvaluateOperations(interpreter, ds);
EvaluateAdf(interpreter, ds);
}
public void TimeSeriesPrognosisInterpreterTestEvaluation() {
Dataset ds = new Dataset(new string[] { "Y", "A", "B" }, new double[,] {
{ 1.0, 1.0, 1.0 },
{ 2.0, 2.0, 2.0 },
{ 3.0, 1.0, 2.0 },
{ 4.0, 1.0, 1.0 },
{ 5.0, 2.0, 2.0 },
{ 6.0, 1.0, 2.0 },
{ 7.0, 1.0, 1.0 },
{ 8.0, 2.0, 2.0 },
{ 9.0, 1.0, 2.0 },
{ 10.0, 1.0, 1.0 },
{ 11.0, 2.0, 2.0 },
{ 12.0, 1.0, 2.0 }
});
var interpreter = new SymbolicDataAnalysisExpressionTreeInterpreter();
EvaluateTerminals(interpreter, ds);
EvaluateOperations(interpreter, ds);
EvaluateAdf(interpreter, ds);
}
