private static CrossValidationValues <TModel> cloneValue(CrossValidationValues <TModel> value, bool includeModel)
{
if (includeModel)
{
return(new CrossValidationValues <TModel>((TModel)value.Model.Clone(), value.TrainingValue, value.ValidationValue)
{
Tag = value.Tag
});
}
else
{
return(new CrossValidationValues <TModel>(null, value.TrainingValue, value.ValidationValue)
{
Tag = value.Tag
});
}
}
/// <summary>
/// Computes the cross validation algorithm.
/// </summary>
///
public CrossValidationResult <TModel> Compute()
{
if (Fitting == null)
{
throw new InvalidOperationException("Fitting function must have been previously defined.");
}
var models = new CrossValidationValues <TModel> [folds.Length];
if (RunInParallel)
{
Parallel.For(0, folds.Length, i =>
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(i, trainingSet, validationSet);
});
}
else
{
for (int i = 0; i < folds.Length; i++)
{
int[] trainingSet, validationSet;
// Create training and validation sets
CreatePartitions(i, out trainingSet, out validationSet);
// Fit and evaluate the model
models[i] = fitting(i, trainingSet, validationSet);
}
}
// Return cross-validation statistics
return(new CrossValidationResult <TModel>(this, models));
}
/// <summary>
/// Starts the model training, calling the <see cref="IterationFunction"/>
/// on each iteration.
/// </summary>
///
/// <returns>True if the model training has converged, false otherwise.</returns>
///
public bool Compute()
{
double lastError = Double.PositiveInfinity;
for (int i = 0; i < MaxIterations; i++)
{
CrossValidationValues <TModel> value = IterationFunction(i);
double currentError = value.TrainingValue;
// If the storage mode is set to all models, the history should store
// all created models alongside with the validation and training errors.
if (Mode == ModelStorageMode.AllModels)
{
// Create a copy of the model information and of the created model. We
// have to clone it because it will keep changing in further iterations.
CrossValidationValues <TModel> clone = cloneValue(value, includeModel: true);
History[i] = clone;
// Check if we should store the value as current maximum or minimum
if (MinValidationValue.Value == null || MinValidationValue.Value == null)
{
// If this is the first iteration, store the first model as max/min
MinValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
MaxValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
else
{
// Store information only if the model is better
if (value.ValidationValue < MinValidationValue.Value.ValidationValue)
{
MinValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
if (value.ValidationValue > MaxValidationValue.Value.ValidationValue)
{
MaxValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
}
}
else // if (Mode == ModelStorageMode.MinimumOnly || Mode == ModelStorageMode.MaximumOnly)
{
// Create a copy of the model information and of the created model. We
// will not include the model at this step because we will be storing it
// only if it is a minimum.
CrossValidationValues <TModel> copy = cloneValue(value, includeModel: false);
History[i] = copy;
// Check if we should store the value as current maximum or minimum
if (MinValidationValue.Value == null || MinValidationValue.Value == null)
{
// If this is the first iteration, store the first model as current maximum and minimum
CrossValidationValues <TModel> clone = cloneValue(value, includeModel: true);
MaxValidationValue = MinValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
else
{
// Store information only if the model is better
if (value.ValidationValue < MinValidationValue.Value.ValidationValue)
{
CrossValidationValues <TModel> clone = cloneValue(value, includeModel: true);
MinValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
if (value.ValidationValue > MaxValidationValue.Value.ValidationValue)
{
CrossValidationValues <TModel> clone = cloneValue(value, includeModel: true);
MaxValidationValue = new KeyValuePair <int, CrossValidationValues <TModel> >(i, clone);
}
}
}
// Check for convergence
if (Math.Abs(currentError - lastError) < Tolerance * Math.Abs(lastError))
{
return(true); // converged
}
}
// Maximum iterations reached
return(Tolerance == 0 ? false : true);
}
