/// <summary>
/// Initializes a new instance of the <see cref="BootstrapResult"/> class.
/// </summary>
///
/// <param name="owner">The <see cref="Bootstrap"/> that is creating this result.</param>
/// <param name="models">The models created during the cross-validation runs.</param>
///
public BootstrapResult(Bootstrap owner, BootstrapValues[] models)
{
double[] trainingValues = new double[models.Length];
double[] trainingVariances = new double[models.Length];
int[] trainingCount = new int[models.Length];
double[] validationValues = new double[models.Length];
double[] validationVariances = new double[models.Length];
int[] validationCount = new int[models.Length];
for (int i = 0; i < models.Length; i++)
{
trainingValues[i] = models[i].TrainingValue;
trainingVariances[i] = models[i].TrainingVariance;
validationValues[i] = models[i].ValidationValue;
validationVariances[i] = models[i].ValidationVariance;
owner.GetPartitionSize(i, out trainingCount[i], out validationCount[i]);
}
this.Settings = owner;
this.Training = new CrossValidationStatistics(trainingCount, trainingValues, trainingVariances);
this.Validation = new CrossValidationStatistics(validationCount, validationValues, validationVariances);
this.Estimate = 0.632 * Validation.Mean + 0.368 * Training.Mean;
}
/// <summary>
/// Computes the cross validation algorithm.
/// </summary>
///
public BootstrapResult Compute()
{
if (Fitting == null)
{
throw new InvalidOperationException("Fitting function must have been previously defined.");
}
var models = new BootstrapValues[Subsamples.Length];
if (RunInParallel)
{
Parallel.For(0, Subsamples.Length, i =>
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(trainingSet, validationSet);
});
}
else
{
for (int i = 0; i < Subsamples.Length; i++)
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(trainingSet, validationSet);
}
}
// Return cross-validation statistics
return(new BootstrapResult(this, models));
}
/// <summary>
/// Computes the cross validation algorithm.
/// </summary>
///
public BootstrapResult Compute()
{
if (Fitting == null)
throw new InvalidOperationException("Fitting function must have been previously defined.");
var models = new BootstrapValues[Subsamples.Length];
if (RunInParallel)
{
Parallel.For(0, Subsamples.Length, i =>
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(trainingSet, validationSet);
});
}
else
{
for (int i = 0; i < Subsamples.Length; i++)
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(trainingSet, validationSet);
}
}
// Return cross-validation statistics
return new BootstrapResult(this, models);
}
