/// <summary>
/// Creates a new <see cref="GridSearch{TModel, TRange, TLearner, TInput, TOutput}"/> combined with
/// <see cref="CrossValidation{TModel, TInput, TOutput}"/> algorithms.
/// </summary>
///
/// <typeparam name="TModel">The type of the machine learning model whose parameters should be searched.</typeparam>
/// <typeparam name="TLearner">The type of the learning algorithm used to learn <typeparamref name="TModel"/>.</typeparam>
///
/// <param name="ranges">The range of parameters to consider during search.</param>
/// <param name="learner">A function that can create a <typeparamref name="TModel"/> given training parameters.</param>
/// <param name="loss">A function that can measure how far model predictions are from the expected ground-truth.</param>
/// <param name="fit">A function that specifies how to create a new model using the teacher learning algorirhm.</param>
/// <param name="folds">The number of folds in the k-fold cross-validation. Default is 10.</param>
///
/// <example>
/// <code source="Unit Tests\Accord.Tests.MachineLearning\GridSearchTest.cs" region="doc_learn_strongly_typed" />
/// </example>
///
/// <returns>A grid-search algorithm that has been configured with the given parameters.</returns>
///
public static GridSearch <CrossValidationResult <TModel, TInput, TOutput>, CrossValidation <TModel, TInput, TOutput>, TInput, TOutput> CrossValidate <TModel, TLearner>(
GridSearchRange[] ranges,
Func <GridSearchParameterCollection, DataSubset <TInput, TOutput>, TLearner> learner,
ComputeLoss <TOutput, TModel> loss,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit, // necessary to auto-detect TModel,
int folds = 10)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
GridSearch <CrossValidationResult <TModel, TInput, TOutput>, CrossValidation <TModel, TInput, TOutput>, TInput, TOutput> gs = null;
gs = Create <CrossValidationResult <TModel, TInput, TOutput>, CrossValidation <TModel, TInput, TOutput> >(ranges,
learner: (p) => new CrossValidation <TModel, TInput, TOutput>()
{
K = folds,
Learner = (ss) => learner(p, ss),
Fit = (teacher, x, y, w) => fit((TLearner)teacher, x, y, w), // necessary to auto-detect TModel
Loss = (expected, actual, r) => loss(expected, actual, r.Model),
},
fit: (teacher, x, y, w) =>
{
IParallel parallel = teacher as IParallel;
if (teacher != null)
{
parallel.ParallelOptions = gs.ParallelOptions;
}
return(teacher.Learn(x, y, w));
},
loss: (actual, expected, model) => model.Validation.Mean
);
return(gs);
}
