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// Accord Statistics Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2017
// cesarsouza at gmail.com
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
//
namespace Accord.MachineLearning
{
using Accord.Math;
using Accord.Statistics;
using Accord.Compat;
using System.Threading;
using System.Threading.Tasks;
/// <summary>
/// Common base class for supervised learning algorithms for
/// <see cref="IBinaryClassifier{TInput}">binary classifiers</see>.
/// </summary>
///
/// <typeparam name="TModel">The type for the model being learned.</typeparam>
/// <typeparam name="TInput">The type for the input data that enters the model.</typeparam>
///
public abstract class BinaryLearningBase<TModel, TInput> :
ISupervisedBinaryLearning<TModel, TInput>
where TModel : IBinaryClassifier<TInput>
{
/// <summary>
/// Gets or sets a cancellation token that can be used to
/// stop the learning algorithm while it is running.
/// </summary>
///
public virtual CancellationToken Token { get; set; }
/// <summary>
/// Gets or sets the classifier being learned.
/// </summary>
///
public TModel Model { get; set; }
/// <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>
///
public TModel Learn(TInput[] x, double[] y, double[] weights = null)
{
return Learn(x, Classes.Decide(y), weights);
}
/// <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>
///
public TModel Learn(TInput[] x, int[] y, double[] weights = null)
{
return Learn(x, Classes.Decide(y), weights);
}
/// <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>
///
public TModel Learn(TInput[] x, int[][] y, double[] weights = null)
{
return Learn(x, Classes.Decide(y.GetColumn(0)), weights);
}
/// <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>
///
public TModel Learn(TInput[] x, bool[][] y, double[] weights = null)
{
return Learn(x, y.GetColumn(0), weights);
}
/// <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>
///
public abstract TModel Learn(TInput[] x, bool[] y, double[] weights = null);
}
}