double CrossValidate(int folds)
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new NoShuffleCrossValidation <double>(folds);
var predictions = sut.CrossValidate(new RegressionDecisionTreeLearner(), observations, targets);
var metric = new MeanSquaredErrorRegressionMetric();
return(metric.Error(targets, predictions));
}
double CrossValidate(int folds)
{
var targetName = "T";
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new NoShuffleCrossValidation <double>(folds);
var predictions = sut.CrossValidate(new RegressionDecisionTreeLearner(), observations, targets);
var metric = new MeanSquaredErrorRegressionMetric();
return(metric.Error(targets, predictions));
}
double CrossValidate_Provide_Indices(int folds)
{
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var sut = new NoShuffleCrossValidation <double>(folds);
var rowsToCrossvalidate = targets.Length / 2;
var indices = Enumerable.Range(0, rowsToCrossvalidate).ToArray();
var predictions = new double[rowsToCrossvalidate];
sut.CrossValidate(new RegressionDecisionTreeLearner(), observations, targets, indices, predictions);
var metric = new MeanSquaredErrorRegressionMetric();
return(metric.Error(targets.Take(rowsToCrossvalidate).ToArray(), predictions));
}
double CrossValidate_Provide_Indices(int folds)
{
var targetName = "T";
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows(v => !v.Contains(targetName)).ToF64Matrix();
var targets = parser.EnumerateRows(targetName).ToF64Vector();
var sut = new NoShuffleCrossValidation <double>(folds);
var rowsToCrossvalidate = targets.Length / 2;
var indices = Enumerable.Range(0, rowsToCrossvalidate).ToArray();
var predictions = new double[rowsToCrossvalidate];
sut.CrossValidate(new RegressionDecisionTreeLearner(), observations, targets, indices, predictions);
var metric = new MeanSquaredErrorRegressionMetric();
return(metric.Error(targets.Take(rowsToCrossvalidate).ToArray(), predictions));
}
