CrossValidate <TInput, TOutput, TRange, TModel, TLearner>(
TRange ranges,
Func <TRange, DataSubset <TInput, TOutput>, TLearner> learner,
ComputeLoss <TOutput, TModel> loss,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit, // necessary to auto-detect TModel,
TInput[] x, TOutput[] y, int folds = 10)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
return(GridSearch <TInput, TOutput> .CrossValidate(ranges, learner, loss, fit, folds));
}
/// <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>
/// <typeparam name="TInput">The type of the input data. Default is double[].</typeparam>
/// <typeparam name="TOutput">The type of the output data. Default is int.</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>
/// <param name="x">The input data to be used during training.</param>
/// <param name="y">The output data to be used during training.</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 <TInput, TOutput, 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,
TInput[] x, TOutput[] y, int folds = 10)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
return(GridSearch <TInput, TOutput> .CrossValidate(ranges, learner, loss, fit, folds));
}
/// <summary>
/// Creates a new <see cref="GridSearch{TModel, TRange, TLearner, TInput, TOutput}"/> algorithm.
/// </summary>
///
/// <typeparam name="TModel">The type of the machine learning model whose parameters should be searched.</typeparam>
/// <typeparam name="TRange">The type that specifies how ranges of the parameter values are represented.</typeparam>
/// <typeparam name="TLearner">The type of the learning algorithm used to learn <typeparamref name="TModel"/>.</typeparam>
/// <typeparam name="TInput">The type of the input data. Default is double[].</typeparam>
/// <typeparam name="TOutput">The type of the output data. Default is int.</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="x">The input data to be used during training.</param>
/// <param name="y">The output data to be used during training.</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 <TModel, TRange, TLearner, TInput, TOutput> Create <TInput, TOutput, TRange, TModel, TLearner>(
TRange ranges,
CreateLearnerFromParameter <TLearner, TRange> learner,
ComputeLoss <TOutput, TModel> loss,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit,
TInput[] x, TOutput[] y)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
return(GridSearch <TInput, TOutput> .Create(ranges, learner, loss, fit));
}
/// <summary>
/// Creates a new <see cref="GridSearch{TModel, TRange, TLearner, TInput, TOutput}"/> algorithm.
/// </summary>
///
/// <typeparam name="TInput">The type of the input data. Default is double[].</typeparam>
/// <typeparam name="TOutput">The type of the output data. Default is int.</typeparam>
/// <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="x">The input data to be used during training.</param>
/// <param name="y">The output data to be used during training.</param>
/// <param name="weights">The weights of each instance in the trianing data.</param>
///
/// <example>
/// <code source="Unit Tests\Accord.Tests.MachineLearning\GridSearchTest.cs" region="doc_create" />
/// </example>
///
/// <returns>A grid-search algorithm that has been configured with the given parameters.</returns>
///
public static GridSearchResult <TModel, TInput, TOutput> Create <TInput, TOutput, TModel, TLearner>(
GridSearchRange[] ranges,
CreateLearnerFromParameter <TLearner, GridSearchParameterCollection> learner,
ComputeLoss <TOutput, TModel> loss,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit,
TInput[] x, TOutput[] y, double[] weights = null)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
return(GridSearch <TInput, TOutput> .Create(ranges, learner, loss, fit).Learn(x, y, weights));
}
/// <summary>
/// Creates a new <see cref="GridSearch{TModel, TRange, TLearner, TInput, TOutput}"/> algorithm.
/// </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>
///
/// <example>
/// <code source="Unit Tests\Accord.Tests.MachineLearning\GridSearchTest.cs" region="doc_create" />
/// </example>
///
/// <returns>A grid-search algorithm that has been configured with the given parameters.</returns>
///
public static GridSearch <TModel, TLearner, TInput, TOutput> Create <TModel, TLearner>(
GridSearchRange[] ranges,
CreateLearnerFromParameter <TLearner, GridSearchParameterCollection> learner,
ComputeLoss <TOutput, TModel> loss,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : ISupervisedLearning <TModel, TInput, TOutput>
{
return(new GridSearch <TModel, TLearner, TInput, TOutput>()
{
ParameterRanges = new GridSearchRangeCollection(ranges),
Learner = learner,
Fit = fit,
Loss = loss
});
}
/// <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);
}
/// <summary>
/// Creates a new <see cref="CrossValidation{TModel, TLearner, TInput, TOutput}"/> algorithm.
/// </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>
/// <typeparam name="TInput">The type of the input data. Default is double[].</typeparam>
/// <typeparam name="TOutput">The type of the output data. Default is int.</typeparam>
///
/// <param name="k">The number of folds in the k-fold cross-validation. Default is 10.</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="x">The input data to be used during training.</param>
/// <param name="y">The output data to be used during training.</param>
///
/// <example>
/// <code source="Unit Tests\Accord.Tests.MachineLearning\CrossValidationTest.cs" region="doc_learn" />
/// <code source="Unit Tests\Accord.Tests.MachineLearning\CrossValidationTest.cs" region="doc_learn_hmm" />
/// <code source="Unit Tests\Accord.Tests.MachineLearning\DecisionTrees\DecisionTreeTest.cs" region="doc_cross_validation" />
/// </example>
///
/// <returns>A grid-search algorithm that has been configured with the given parameters.</returns>
///
public static CrossValidation <TModel, TLearner, TInput, TOutput> Create <TModel, TLearner, TInput, TOutput>(int k,
CreateLearnerFromSubset <TLearner, TInput, TOutput> learner,
LearnNewModel <TLearner, TInput, TOutput, TModel> fit,
ComputeLoss <TOutput, SetResult <TModel> > loss,
TInput[] x, TOutput[] y)
where TModel : class, ITransform <TInput, TOutput>
where TLearner : class, ISupervisedLearning <TModel, TInput, TOutput>
{
return(new CrossValidation <TModel, TLearner, TInput, TOutput>()
{
K = k,
Learner = learner,
Fit = fit,
Loss = loss,
});
}
