double CrossValidate(int folds)
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new StratifiedCrossValidation <double>(folds, 42);
var predictions = sut.CrossValidate(new ClassificationDecisionTreeLearner(), observations, targets);
var metric = new TotalErrorClassificationMetric <double>();
return(metric.Error(targets, predictions));
}
double CrossValidate(int folds)
{
var targetName = "Pass";
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new StratifiedCrossValidation<double>(folds, 42);
var predictions = sut.CrossValidate(new ClassificationDecisionTreeLearner(), observations, targets);
var metric = new TotalErrorClassificationMetric<double>();
return metric.Error(targets, predictions);
}
double CrossValidate_Provide_Indices(int folds)
{
var(observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var sut = new StratifiedCrossValidation <double>(folds, 42);
var rowsToCrossvalidate = targets.Length / 2;
var indices = Enumerable.Range(0, rowsToCrossvalidate).ToArray();
var predictions = new double[rowsToCrossvalidate];
sut.CrossValidate(new ClassificationDecisionTreeLearner(), observations, targets, indices, predictions);
var metric = new TotalErrorClassificationMetric <double>();
return(metric.Error(targets.Take(rowsToCrossvalidate).ToArray(), predictions));
}
double CrossValidate_Provide_Indices(int folds)
{
var targetName = "Pass";
var parser = new CsvParser(() => new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new StratifiedCrossValidation<double>(folds, 42);
var rowsToCrossvalidate = targets.Length / 2;
var indices = Enumerable.Range(0, rowsToCrossvalidate).ToArray();
var predictions = new double[rowsToCrossvalidate];
sut.CrossValidate(new ClassificationDecisionTreeLearner(), observations, targets, indices, predictions);
var metric = new TotalErrorClassificationMetric<double>();
return metric.Error(targets.Take(rowsToCrossvalidate).ToArray(), predictions);
}
