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.
234 lines
7.6 KiB
C#
234 lines
7.6 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.Learning
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{
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using System;
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using System.Collections.Generic;
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using Accord.MachineLearning;
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using System.Collections;
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using Accord.Compat;
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using System.Threading.Tasks;
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using System.Linq;
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/// <summary>
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/// Base class for tree inducing (learning) algorithms.
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/// </summary>
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///
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[Serializable]
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public class DecisionTreeLearningBase : ParallelLearningBase, IEnumerable<DecisionVariable>
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{
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private DecisionTree tree;
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private int maxHeight;
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private int maxVariables;
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private int join = 1;
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private IList<DecisionVariable> attributes;
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private int[] attributeUsageCount;
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/// <summary>
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/// Gets or sets the maximum allowed height when learning a tree. If
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/// set to zero, the tree can have an arbitrary length. Default is 0.
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/// </summary>
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///
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public int MaxHeight
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{
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get { return maxHeight; }
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set
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{
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if (value < 0)
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{
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throw new ArgumentOutOfRangeException("value",
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"The height must be greater than or equal to zero.");
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}
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maxHeight = value;
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}
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}
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/// <summary>
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/// Gets or sets the maximum number of variables that
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/// can enter the tree. A value of zero indicates there
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/// is no limit. Default is 0 (there is no limit on the
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/// number of variables).
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/// </summary>
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///
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public int MaxVariables
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{
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get { return maxVariables; }
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set
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{
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if (value < 0)
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{
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throw new ArgumentOutOfRangeException("value",
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"The height must be greater than or equal to zero.");
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}
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maxVariables = value;
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}
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}
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/// <summary>
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/// Gets or sets the collection of attributes to
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/// be processed by the induced decision tree.
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/// </summary>
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///
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public IList<DecisionVariable> Attributes
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{
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get { return attributes; }
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set { attributes = value; }
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}
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/// <summary>
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/// Gets or sets how many times one single variable can be integrated into the decision process. In the original
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/// ID3 algorithm, a variable can join only one time per decision path (path from the root to a leaf). If set to
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/// zero, a single variable can participate as many times as needed. Default is 1.
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/// </summary>
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///
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public int Join
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{
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get { return join; }
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set
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{
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if (value < 0)
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{
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throw new ArgumentOutOfRangeException("value",
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"The number of times must be greater than or equal to zero.");
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}
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join = value;
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}
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}
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/// <summary>
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/// Gets or sets the decision trees being learned.
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/// </summary>
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///
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public DecisionTree Model
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{
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get { return tree; }
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set { tree = value; }
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}
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/// <summary>
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/// Gets how many times each attribute has already been used in the current path.
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/// In the original C4.5 and ID3 algorithms, attributes could be re-used only once,
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/// but in the framework implementation this behaviour can be adjusted by setting
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/// the <see cref="Join"/> property.
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/// </summary>
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///
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protected int[] AttributeUsageCount
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{
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get { return attributeUsageCount; }
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set { attributeUsageCount = value; }
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}
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/// <summary>
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/// Initializes a new instance of the <see cref="DecisionTreeLearningBase"/> class.
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/// </summary>
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public DecisionTreeLearningBase()
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{
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this.ParallelOptions = new ParallelOptions();
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this.attributes = new List<DecisionVariable>();
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}
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/// <param name="attributes">The attributes to be processed by the induced tree.</param>
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//
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public DecisionTreeLearningBase(DecisionVariable[] attributes)
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{
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this.attributes = new List<DecisionVariable>(attributes);
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}
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/// <summary>
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/// Adds the specified variable to the list of <see cref="Attribute"/>s.
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/// </summary>
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///
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public void Add(DecisionVariable variable)
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{
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this.attributes.Add(variable);
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}
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/// <summary>
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/// Returns an enumerator that iterates through the collection.
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/// </summary>
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/// <returns>An enumerator that can be used to iterate through the collection.</returns>
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public IEnumerator<DecisionVariable> GetEnumerator()
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{
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return attributes.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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/// <returns>An <see cref="T:System.Collections.IEnumerator" /> object that can be used to iterate through the collection.</returns>
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IEnumerator IEnumerable.GetEnumerator()
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{
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return attributes.GetEnumerator();
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}
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/// <summary>
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/// Computes the split information measure.
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/// </summary>
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///
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/// <param name="samples">The total number of samples.</param>
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/// <param name="partitions">The partitioning.</param>
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/// <param name="missing">An extra partition containing only missing values.</param>
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///
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/// <returns>The split information for the given partitions.</returns>
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///
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public static double SplitInformation(int samples, IList<int>[] partitions, List<int> missing = null)
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{
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double info = 0;
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for (int i = 0; i < partitions.Length; i++)
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{
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if (partitions[i] == null)
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continue;
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double p = partitions[i].Count / (double)samples;
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if (p != 0)
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info -= p * Math.Log(p, 2);
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}
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if (missing != null)
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{
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double p = missing.Count / (double)samples;
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if (p != 0)
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info -= p * Math.Log(p, 2);
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}
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#if DEBUG
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int totalSum = partitions.Sum(x => x == null ? 0 : x.Count);
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int missingSum = missing == null ? 0 : missing.Count;
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Accord.Diagnostics.Debug.Assert(samples == (totalSum + missingSum));
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#endif
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return info;
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}
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}
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}
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