public static double CalculateQualityForImpacts(ISymbolicRegressionModel model, IRegressionProblemData problemData, IEnumerable <int> rows)
{
var estimatedValues = model.GetEstimatedValues(problemData.Dataset, rows); // also bounds the values
var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
OnlineCalculatorError errorState;
var r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
var quality = r * r;
if (errorState != OnlineCalculatorError.None)
{
return(double.NaN);
}
return(quality);
}
public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
: base(model, problemData)
{
foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType <SymbolicExpressionTreeTopLevelNode>())
{
node.SetGrammar(null);
}
Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
ResultCollection estimationLimitResults = new ResultCollection();
estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
RecalculateResults();
}
public static double CalculateQualityForImpacts(ISymbolicRegressionModel model, IRegressionProblemData problemData, IEnumerable<int> rows) {
var estimatedValues = model.GetEstimatedValues(problemData.Dataset, rows); // also bounds the values
var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
OnlineCalculatorError errorState;
var r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out errorState);
var quality = r * r;
if (errorState != OnlineCalculatorError.None) return double.NaN;
return quality;
}
public SymbolicRegressionSolution(ISymbolicRegressionModel model, IRegressionProblemData problemData)
: base(model, problemData) {
foreach (var node in model.SymbolicExpressionTree.Root.IterateNodesPrefix().OfType<SymbolicExpressionTreeTopLevelNode>())
node.SetGrammar(null);
Add(new Result(ModelLengthResultName, "Length of the symbolic regression model.", new IntValue()));
Add(new Result(ModelDepthResultName, "Depth of the symbolic regression model.", new IntValue()));
ResultCollection estimationLimitResults = new ResultCollection();
estimationLimitResults.Add(new Result(EstimationLimitsResultName, "", new DoubleLimit()));
estimationLimitResults.Add(new Result(TrainingUpperEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestUpperEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TrainingLowerEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestLowerEstimationLimitHitsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TrainingNaNEvaluationsResultName, "", new IntValue()));
estimationLimitResults.Add(new Result(TestNaNEvaluationsResultName, "", new IntValue()));
Add(new Result(EstimationLimitsResultsResultName, "Results concerning the estimation limits of symbolic regression solution", estimationLimitResults));
RecalculateResults();
}
