/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.</returns>
/// <exception cref="System.InvalidOperationException">
/// Please set the Learner property before calling the Learn(x, y) method.
/// or
/// Please set the Learner property before calling the Learn(x, y) method.
/// </exception>
public override SplitResult <TModel, TInput, TOutput> Learn(TInput[] x, TOutput[] y, double[] weights = null)
{
if (Learner == null)
{
throw new InvalidOperationException("Please set the Learner property before calling the Learn(x, y) method.");
}
if (Loss == null)
{
throw new InvalidOperationException("Please set the Learner property before calling the Learn(x, y) method.");
}
int n = x.Length;
if (this.Indices == null || this.IndicesValidationSet == null || this.IndicesTrainingSet == null)
{
this.Indices = CreateValidationSplits(x, y);
this.IndicesValidationSet = Indices.Find(i => i == 1);
this.IndicesTrainingSet = Indices.Find(i => i == 0);
}
else
{
this.ValidationSetProportion = this.IndicesValidationSet.Length / (double)this.Indices.Length;
}
var split = new TrainValDataSplit <TInput, TOutput>(0, x, y, weights, IndicesTrainingSet, IndicesValidationSet);
SplitResult <TModel, TInput, TOutput> result = LearnSubset(split);
return(result);
}
/// <summary>
/// Creates a new split-set validation algorithm.
/// </summary>
///
/// <param name="size">The total number of available samples.</param>
/// <param name="proportion">The desired proportion of samples in the training
/// set in comparison with the testing set.</param>
///
public SplitSetValidation(int size, double proportion)
{
this.Proportion = proportion;
this.IsStratified = false;
this.Indices = Categorical.Random(size, proportion);
this.ValidationSet = Indices.Find(x => x == 0);
this.TrainingSet = Indices.Find(x => x == 1);
}
/// <summary>
/// Creates a new split-set validation algorithm.
/// </summary>
///
/// <param name="size">The total number of available samples.</param>
/// <param name="proportion">The desired proportion of samples in the training
/// set in comparison with the testing set.</param>
///
public SplitSetValidation(int size, double proportion)
{
this.Proportion = proportion;
this.IsStratified = false;
this.Indices = Accord.Statistics.Tools.RandomGroups(size, proportion);
this.ValidationSet = Indices.Find(x => x == 0);
this.TrainingSet = Indices.Find(x => x == 1);
}
public DatabaseIndex Find(Predicate <DatabaseIndex> pred)
{
return(Indices.Find(pred));
}