mirror of
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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.
211 lines
7.2 KiB
C#
211 lines
7.2 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 © Darko Jurić, 2013
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// https://code.google.com/p/accord/issues/detail?id=27
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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.Boosting.Learners
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{
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using System;
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using Accord.Compat;
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using Accord.MachineLearning.DecisionTrees;
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/// <summary>
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/// Simple classifier that based on decision margins that
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/// are perpendicular to one of the space dimensions.
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/// </summary>
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///
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/// <seealso cref="ThresholdLearning"/>
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/// <seealso cref="AdaBoost{TModel}"/>
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///
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/// <example>
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/// <para>
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/// The <see cref="DecisionStump"/> classifier is mostly intended to be used as a weak classifier
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/// in the context of an <see cref="AdaBoost{TModel}"/> learning algorithm. Please refer to the
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/// <see cref="AdaBoost{TModel}"/> class for more examples on using the classifier in this scenario.
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/// A simple example is shown below:</para>
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/// <code source="Unit Tests\Accord.Tests.MachineLearning\Boosting\AdaBoostTest.cs" region="doc_learn" />
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///
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/// <para>
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/// It is also possible to use the <see cref="DecisionStump"/> as a standalone classifier through
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/// the <see cref="ThresholdLearning"/> algorithm. An example is given below:</para>
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/// <code source="Unit Tests\Accord.Tests.MachineLearning\Boosting\DecisionStumpTest.cs" region="doc_learn" />
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/// </example>
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///
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[Serializable]
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public class DecisionStump : BinaryClassifierBase<double[]>
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{
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// TODO: Consider merging this class with DecisionNode or KDTreeNode
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private double threshold;
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private int featureIndex;
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private ComparisonKind comparison;
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/// <summary>
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/// Initializes a new instance of the <see cref="DecisionStump"/> class.
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/// </summary>
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public DecisionStump()
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{
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}
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/// <summary>
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/// Gets the decision threshold for this linear classifier.
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/// </summary>
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///
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public double Threshold
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{
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get { return threshold; }
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set { threshold = value; }
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}
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/// <summary>
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/// Gets the index of the attribute which this
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/// classifier will use to compare against
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/// <see cref="Threshold"/>.
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/// </summary>
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///
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public int Index
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{
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get { return featureIndex; }
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set { featureIndex = value; }
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}
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/// <summary>
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/// Gets or sets the comparison to be performed.
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/// </summary>
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///
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public ComparisonKind Comparison
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{
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get { return comparison; }
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set { comparison = value; }
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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 bool Decide(double[] input)
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{
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double value = input[featureIndex];
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switch (comparison)
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{
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case ComparisonKind.Equal:
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return value == threshold;
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case ComparisonKind.NotEqual:
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return value != threshold;
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case ComparisonKind.GreaterThanOrEqual:
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return value >= threshold;
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case ComparisonKind.GreaterThan:
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return value >= threshold;
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case ComparisonKind.LessThan:
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return value < threshold;
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case ComparisonKind.LessThanOrEqual:
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return value <= threshold;
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default:
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throw new InvalidOperationException();
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}
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}
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#region Obsolete
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/// <summary>
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/// Initializes a new instance of the <see cref="DecisionStump"/> class.
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/// </summary>
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///
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/// <param name="inputs">The number of inputs for this classifier.</param>
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///
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[Obsolete("Please use the default constructor instead.")]
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public DecisionStump(int inputs)
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{
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}
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/// <summary>
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/// Gets the direction of the comparison
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/// (if greater than or less than).
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/// </summary>
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///
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[Obsolete("Please use the Comparison property instead.")]
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public int Sign
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{
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get
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{
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switch (Comparison)
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{
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case ComparisonKind.Equal:
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return 0;
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case ComparisonKind.GreaterThanOrEqual:
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case ComparisonKind.GreaterThan:
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return 1;
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case ComparisonKind.LessThan:
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case ComparisonKind.LessThanOrEqual:
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return -1;
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default:
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throw new InvalidOperationException();
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}
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}
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}
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/// <summary>
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/// Computes the output class label for a given input.
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/// </summary>
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///
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/// <param name="inputs">The input vector.</param>
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///
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/// <returns>
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/// The most likely class label for the given input.
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/// </returns>
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///
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[Obsolete("Please use the Decide() method instead.")]
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public int Compute(double[] inputs)
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{
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return Decide(inputs) ? +1 : -1;
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}
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/// <summary>
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/// Teaches the stump classifier to recognize
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/// the class labels of the given input samples.
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/// </summary>
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///
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/// <param name="inputs">The input vectors.</param>
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/// <param name="outputs">The class labels corresponding to each input vector.</param>
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/// <param name="weights">The weights associated with each input vector.</param>
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///
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[Obsolete("Please use the ThresholdLearning class instead.")]
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public void Learn(double[][] inputs, int[] outputs, double[] weights)
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{
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new ThresholdLearning()
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{
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Model = this
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}.Learn(inputs, outputs, weights);
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}
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#endregion
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}
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}
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