mirror of
https://github.com/hanzoai/jube-fork.git
synced 2026-10-30 18:33:33 +00:00
richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
84 lines
3.2 KiB
C#
84 lines
3.2 KiB
C#
// Accord Machine Learning 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 System;
|
|
using System.Linq;
|
|
using System.Collections.Generic;
|
|
using Accord.MachineLearning;
|
|
|
|
/// <summary>
|
|
/// Parameters for learning a binary decision model. An object of this class is passed by
|
|
/// <see cref="OneVsRestLearning{TBinary, TModel}"/> or <see cref="OneVsOneLearning{TBinary, TModel}"/>
|
|
/// to instruct how binary learning algorithms should create their binary classifiers.
|
|
/// </summary>
|
|
///
|
|
/// <typeparam name="TBinary">The type of the binary model to be learned.</typeparam>
|
|
/// <typeparam name="TInput">The input type for the binary classifiers.</typeparam>
|
|
///
|
|
public class InnerParameters<TBinary, TInput>
|
|
{
|
|
/// <summary>
|
|
/// Gets or sets the binary model to be learned.
|
|
/// </summary>
|
|
///
|
|
public TBinary Model { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets or sets the input data that should be used to train the classifier.
|
|
/// </summary>
|
|
///
|
|
public TInput[] Inputs { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets or sets the output data that should be used to train the classifier.
|
|
/// </summary>
|
|
///
|
|
public bool[] Outputs { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets or sets the class pair that the classifier will be designated
|
|
/// to learn. For <see cref="OneVsRest{TModel, TInput}"/> classifiers, the first element
|
|
/// in the pair designates the class to be learned against all others.
|
|
/// </summary>
|
|
///
|
|
public ClassPair Pair { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Initializes a new instance of the <see cref="InnerParameters{TBinary, TInput}"/> class.
|
|
/// </summary>
|
|
///
|
|
/// <param name="model">The binary model to be learned.</param>
|
|
/// <param name="inputs">The inputs to be used.</param>
|
|
/// <param name="outputs">The outputs to be used.</param>
|
|
/// <param name="pair">The class labels for the problem to be learned.</param>
|
|
///
|
|
public InnerParameters(TBinary model, TInput[] inputs, bool[] outputs, ClassPair pair)
|
|
{
|
|
Model = model;
|
|
Inputs = inputs;
|
|
Outputs = outputs;
|
|
Pair = pair;
|
|
}
|
|
}
|
|
} |