mirror of
https://github.com/hanzoai/jube-fork.git
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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.
318 lines
10 KiB
C#
318 lines
10 KiB
C#
// Accord Machine Learning Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.MachineLearning.DecisionTrees.Rules
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{
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using System.Collections.Generic;
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using System.Text;
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using Accord.Statistics.Filters;
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using System;
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using System.Globalization;
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using Accord.Math;
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using Accord.MachineLearning.DecisionTrees.Learning;
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/// <summary>
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/// Decision rule set.
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/// </summary>
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///
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/// <remarks>
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/// <para>
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/// Decision rule sets can be created from <see cref="DecisionTree"/>s using their
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/// <see cref="DecisionTree.ToRules()"/> method. An example is shown below.</para>
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/// </remarks>
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///
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/// <example>
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/// <code source="Unit Tests\Accord.Tests.MachineLearning\DecisionTrees\C45LearningTest.cs" region="doc_iris" />
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/// <code source="Unit Tests\Accord.Tests.MachineLearning\DecisionTrees\C45LearningTest.cs" region="doc_missing" />
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/// </example>
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///
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/// <seealso cref="DecisionTree"/>
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/// <seealso cref="C45Learning"/>
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///
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public class DecisionSet : MulticlassClassifierBase<double[]>, IEnumerable<DecisionRule>
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{
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HashSet<DecisionRule> rules;
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/// <summary>
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/// Obsolete. Please use <see cref="ClassifierBase{TInput, TClasses}.NumberOfClasses"/> instead.
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/// </summary>
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///
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[Obsolete("Please use NumberOfClasses instead.")]
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public int OutputClasses { get { return NumberOfClasses; } }
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/// <summary>
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/// Initializes a new instance of the <see cref="DecisionSet"/> class.
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/// </summary>
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///
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public DecisionSet()
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{
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this.rules = new HashSet<DecisionRule>();
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="DecisionSet"/> class.
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/// </summary>
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///
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/// <param name="rules">A set of decision rules.</param>
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///
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public DecisionSet(IEnumerable<DecisionRule> rules)
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{
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this.rules = new HashSet<DecisionRule>(rules);
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}
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/// <summary>
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/// Creates a new <see cref="DecisionSet"/> from a <see cref="DecisionTree"/>.
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/// </summary>
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///
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/// <param name="tree">A <see cref="DecisionTree"/>.</param>
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///
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/// <returns>A <see cref="DecisionSet"/> that is completely
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/// equivalent to the given <paramref name="tree"/></returns>
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///
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public static DecisionSet FromDecisionTree(DecisionTree tree)
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{
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var rules = new List<DecisionRule>();
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foreach (var node in tree)
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{
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if (node.IsLeaf && !node.IsRoot && node.Output.HasValue)
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{
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rules.Add(DecisionRule.FromNode(node));
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}
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}
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return new DecisionSet(rules)
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{
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NumberOfClasses = tree.NumberOfClasses,
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NumberOfOutputs = tree.NumberOfOutputs
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};
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}
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/// <summary>
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/// Computes the decision output for a given input.
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/// </summary>
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///
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/// <param name="input">An input vector.</param>
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///
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/// <returns>The decision output for the given
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/// <paramref name="input"/>.</returns>
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///
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public double? Compute(double[] input)
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{
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double r = Decide(input);
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return r == -1 ? (double?)null : r;
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}
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/// <summary>
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/// Adds a new <see cref="DecisionRule"/> to the set.
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/// </summary>
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///
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/// <param name="item">The <see cref="DecisionRule"/> to be added.</param>
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///
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public void Add(DecisionRule item)
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{
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rules.Add(item);
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}
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/// <summary>
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/// Adds a collection of new <see cref="DecisionRule"/>s to the set.
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/// </summary>
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///
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/// <param name="items">The collection of <see cref="DecisionRule"/>s to be added.</param>
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///
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public void AddRange(IEnumerable<DecisionRule> items)
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{
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foreach (var rule in items)
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rules.Add(rule);
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}
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/// <summary>
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/// Removes all rules from this set.
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/// </summary>
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///
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public void Clear()
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{
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rules.Clear();
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}
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/// <summary>
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/// Gets the number of rules in this set.
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/// </summary>
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///
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public int Count
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{
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get { return rules.Count; }
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}
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/// <summary>
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/// Removes a given rule from the set.
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/// </summary>
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///
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/// <param name="item">The <see cref="DecisionRule"/> to be removed.</param>
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///
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/// <returns>True if the rule was removed; false otherwise.</returns>
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///
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public bool Remove(DecisionRule item)
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{
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return rules.Remove(item);
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}
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/// <summary>
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/// Returns a <see cref="System.String"/> that represents this instance.
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/// </summary>
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///
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/// <returns>
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/// A <see cref="System.String"/> that represents this instance.
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/// </returns>
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///
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public override string ToString()
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{
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return toString(null, null, CultureInfo.CurrentUICulture);
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}
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/// <summary>
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/// Returns a <see cref="System.String"/> that represents this instance.
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/// </summary>
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///
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/// <returns>
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/// A <see cref="System.String"/> that represents this instance.
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/// </returns>
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///
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public string ToString(Codification<string> codebook, CultureInfo cultureInfo)
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{
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return toString(codebook, null, cultureInfo);
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}
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/// <summary>
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/// Returns a <see cref="System.String"/> that represents this instance.
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/// </summary>
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///
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/// <returns>
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/// A <see cref="System.String"/> that represents this instance.
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/// </returns>
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///
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public string ToString(Codification<string> codebook)
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{
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return ToString(codebook, CultureInfo.CurrentUICulture);
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}
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/// <summary>
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/// Returns a <see cref="System.String"/> that represents this instance.
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/// </summary>
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///
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/// <returns>
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/// A <see cref="System.String"/> that represents this instance.
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/// </returns>
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///
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public string ToString(Codification<string> codebook, string outputColumn, CultureInfo cultureInfo)
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{
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return toString(codebook, outputColumn, CultureInfo.CurrentUICulture);
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}
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private string toString(Codification<string> codebook, string outputColumn, CultureInfo cultureInfo)
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{
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var rulesArray = new DecisionRule[this.rules.Count];
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rules.CopyTo(rulesArray);
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Array.Sort(rulesArray);
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StringBuilder sb = new StringBuilder();
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if (codebook != null)
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{
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foreach (DecisionRule rule in rulesArray)
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sb.AppendLine(rule.ToString(codebook, outputColumn, cultureInfo));
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}
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else
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{
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foreach (DecisionRule rule in rulesArray)
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sb.AppendLine(rule.ToString(cultureInfo));
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}
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return sb.ToString();
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}
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/// <summary>
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/// Returns an enumerator that iterates through a collection.
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/// </summary>
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///
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/// <returns>
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/// An <see cref="T:System.Collections.IEnumerator"/> object
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/// that can be used to iterate through the collection.
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/// </returns>
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///
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public IEnumerator<DecisionRule> GetEnumerator()
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{
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return rules.GetEnumerator();
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}
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/// <summary>
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/// Returns an enumerator that iterates through a collection.
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/// </summary>
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///
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/// <returns>
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/// An <see cref="T:System.Collections.IEnumerator"/> object
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/// that can be used to iterate through the collection.
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/// </returns>
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///
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System.Collections.IEnumerator System.Collections.IEnumerable.GetEnumerator()
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{
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return rules.GetEnumerator();
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}
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/// <summary>
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/// Computes a class-label decision for a given <paramref name="input" />.
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/// </summary>
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/// <param name="input">The input vector that should be classified into
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/// one of the <see cref="P:Accord.MachineLearning.ITransform.NumberOfOutputs" /> possible classes.</param>
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/// <returns>A class-label that best described <paramref name="input" /> according
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/// to this classifier.</returns>
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public override int Decide(double[] input)
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{
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if (input.HasNaN())
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{
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int[] outputs = new int[NumberOfClasses];
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foreach (DecisionRule rule in rules)
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{
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if (rule.Match(input))
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outputs[(int)rule.Output] += 1;
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}
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return outputs.ArgMax();
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}
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else
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{
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foreach (DecisionRule rule in rules)
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{
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if (rule.Match(input))
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return (int)rule.Output;
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}
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}
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return -1;
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}
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}
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}
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