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.
63 lines
2.7 KiB
C#
63 lines
2.7 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.Bayes
|
|
{
|
|
using System;
|
|
using System.Linq;
|
|
using System.Collections.Generic;
|
|
using Accord.Statistics.Distributions;
|
|
using Accord.Statistics.Distributions.Fitting;
|
|
|
|
#if !MONO
|
|
/// <summary>
|
|
/// Base class for Naive Bayes learning algorithms.
|
|
/// </summary>
|
|
///
|
|
/// <typeparam name="TModel">The type for the Naive Bayes model to be learned.</typeparam>
|
|
/// <typeparam name="TDistribution">The univariate distribution to be used as components in the Naive Bayes distribution.</typeparam>
|
|
/// <typeparam name="TInput">The type for the samples modeled by the distribution.</typeparam>
|
|
/// <typeparam name="TOptions">The fitting options for the independent distribution.</typeparam>
|
|
/// <typeparam name="TInnerOptions">The individual fitting options for the component distributions.</typeparam>
|
|
///
|
|
public abstract class NaiveBayesLearningBase<TModel, TDistribution, TInput, TOptions, TInnerOptions> :
|
|
NaiveBayesLearningBase<TModel, TDistribution, TInput, TOptions>
|
|
where TDistribution : IFittableDistribution<TInput, TInnerOptions>,
|
|
IUnivariateDistribution<TInput>,
|
|
IUnivariateDistribution
|
|
where TOptions : IndependentOptions<TInnerOptions>, new()
|
|
where TInnerOptions : class, IFittingOptions, new()
|
|
where TModel : NaiveBayes<TDistribution, TInput>
|
|
{
|
|
|
|
/// <summary>
|
|
/// Fits one of the distributions in the naive bayes model.
|
|
/// </summary>
|
|
protected override void Fit(int i, TInput[][] values, double[] weights, bool transposed)
|
|
{
|
|
Options.Transposed = transposed;
|
|
Model.Distributions[i].Fit(values, weights, Options);
|
|
this.optimized = true;
|
|
}
|
|
}
|
|
#endif
|
|
} |