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.
115 lines
3.7 KiB
C#
115 lines
3.7 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.Performance
|
|
{
|
|
using System;
|
|
using Accord.Compat;
|
|
|
|
/// <summary>
|
|
/// Training and validation errors of a model.
|
|
/// </summary>
|
|
///
|
|
/// <typeparam name="TModel">The type of the model.</typeparam>
|
|
///
|
|
[Serializable]
|
|
public class SetResult<TModel>
|
|
{
|
|
|
|
/// <summary>
|
|
/// Gets or sets the set name (e.g. "Training" or "Testing").
|
|
/// </summary>
|
|
///
|
|
/// <value>The name of this set.</value>
|
|
///
|
|
public string Name { get; set; }
|
|
|
|
|
|
/// <summary>
|
|
/// Gets the model.
|
|
/// </summary>
|
|
///
|
|
public TModel Model { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets the indices of the samples in this subset in the original complete dataset.
|
|
/// </summary>
|
|
///
|
|
public int[] Indices { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets the number of samples in this subset.
|
|
/// </summary>
|
|
///
|
|
public int NumberOfSamples { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets how much this subset represents, in proportion, of the original dataset.
|
|
/// </summary>
|
|
///
|
|
public double Proportion { get; private set; }
|
|
|
|
/// <summary>
|
|
/// Gets the metric value for the model in the current <see cref="Name">set</see>.
|
|
/// </summary>
|
|
///
|
|
public double Value { get; set; }
|
|
|
|
/// <summary>
|
|
/// Gets the variance of the validation value for the model, if available.
|
|
/// </summary>
|
|
///
|
|
public double Variance { get; set; }
|
|
|
|
/// <summary>
|
|
/// Gets the standard deviation of the validation value for the model, if available.
|
|
/// </summary>
|
|
///
|
|
public double StandardDeviation { get { return Math.Sqrt(Variance); } }
|
|
|
|
/// <summary>
|
|
/// Gets or sets a tag for user-defined information.
|
|
/// </summary>
|
|
///
|
|
public object Tag { get; set; }
|
|
|
|
/// <summary>
|
|
/// Initializes a new instance of the <see cref="SetResult{TModel}"/> class.
|
|
/// </summary>
|
|
///
|
|
/// <param name="model">The model computed in this subset.</param>
|
|
/// <param name="indices">The indices of the samples in this subset.</param>
|
|
/// <param name="name">The name of this set.</param>
|
|
/// <param name="proportion">The proportion of samples in this subset, compared to the full dataset.</param>
|
|
///
|
|
public SetResult(TModel model, int[] indices, string name, double proportion)
|
|
{
|
|
this.Name = name;
|
|
this.Model = model;
|
|
this.Indices = indices;
|
|
this.NumberOfSamples = indices.Length;
|
|
this.Proportion = proportion;
|
|
}
|
|
|
|
}
|
|
}
|