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.
238 lines
9.2 KiB
C#
238 lines
9.2 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 © 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
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{
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using Accord.Math;
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using Accord.Math.Distances;
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using System;
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using Accord.Compat;
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using System.Threading.Tasks;
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/// <summary>
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/// Data cluster.
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/// </summary>
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///
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[Serializable]
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public class CentroidCluster<TCollection, TData, TCentroid, TCluster> : Cluster<TCollection, TData, TCluster>
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where TCollection : ICentroidClusterCollection<TData, TCentroid, TCluster>, IMulticlassScoreClassifier<TData, int>
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where TCluster : CentroidCluster<TCollection, TData, TCentroid, TCluster>, new()
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{
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/// <summary>
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/// Gets the cluster's centroid.
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/// </summary>
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///
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public TCentroid Centroid
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{
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get { return Owner.Centroids[Index]; }
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set { Owner.Centroids[Index] = value; }
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}
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/// <summary>
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/// Computes the distortion of the cluster, measured
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/// as the average distance between the cluster points
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/// and its centroid.
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/// </summary>
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///
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/// <param name="data">The input points.</param>
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///
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/// <returns>The average distance between all points
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/// in the cluster and the cluster centroid.</returns>
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///
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public double Distortion(TData[] data)
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{
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return Owner.Distortion(data, Vector.Create(data.Length, Index));
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}
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[Serializable]
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new internal class ClusterCollection : Cluster<TCollection, TData, TCluster>.ClusterCollection,
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ICentroidClusterCollection<TData, TCentroid, TCluster>
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{
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private IDistance<TData, TCentroid> distance;
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private TCentroid[] centroids;
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/// <summary>
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/// Initializes a new instance of the <see cref="KMeansClusterCollection"/> class.
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/// </summary>
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///
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/// <param name="collection">The collection that contains this instance as a field.</param>
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/// <param name="k">The number of clusters K.</param>
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/// <param name="distance">The distance metric to consider.</param>
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///
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public ClusterCollection(TCollection collection, int k, IDistance<TData, TCentroid> distance)
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: base(collection, k)
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{
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this.distance = distance;
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this.centroids = new TCentroid[k];
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collection.NumberOfOutputs = k;
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collection.NumberOfClasses = k;
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}
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/// <summary>
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/// Gets or sets the distance function used to measure the distance
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/// between a point and the cluster centroid in this clustering definition.
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/// </summary>
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///
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public IDistance<TData, TCentroid> Distance
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{
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get { return distance; }
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set { distance = value; }
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}
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/// <summary>
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/// Gets or sets the clusters' centroids.
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/// </summary>
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///
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/// <value>The clusters' centroids.</value>
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///
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public TCentroid[] Centroids
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{
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get { return centroids; }
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set
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{
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if (value == null)
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throw new ArgumentNullException("value");
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if (value.Length != this.Count)
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throw new ArgumentException("The number of centroids should be equal to K.", "value");
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for (int i = 0; i < centroids.Length; i++)
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centroids[i] = value[i];
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Owner.NumberOfInputs = Tools.GetNumberOfInputs(value);
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}
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}
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/// <summary>
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/// Calculates the average square distance from the data points
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/// to the nearest clusters' centroids.
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/// </summary>
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///
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/// <remarks>
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/// The average distance from centroids can be used as a measure
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/// of the "goodness" of the clustering. The more the data are
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/// aggregated around the centroids, the less the average distance.
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/// </remarks>
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///
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/// <returns>
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/// The average square distance from the data points to the nearest
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/// clusters' centroids.
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/// </returns>
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///
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public double Distortion(TData[] data, int[] labels = null, double[] weights = null)
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{
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if (labels == null)
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labels = Owner.Decide(data);
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if (weights == null)
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{
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double error = 0.0;
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for (int i = 0; i < data.Length; i++)
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error += Distance.Distance(data[i], centroids[labels[i]]);
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return error / (double)data.Length;
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}
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else
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{
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double error = 0.0;
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for (int i = 0; i < data.Length; i++)
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error += weights[i] * Distance.Distance(data[i], centroids[labels[i]]);
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return error / weights.Sum();
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}
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}
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/// <summary>
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/// Transform data points into feature vectors containing the
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/// distance between each point and each of the clusters.
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/// </summary>
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///
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/// <param name="points">The input points.</param>
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/// <param name="labels">The label of each input point.</param>
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/// <param name="weights">The weight associated with each point.</param>
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/// <param name="result">An optional matrix to store the computed transformation.</param>
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///
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/// <returns>A vector containing the distance between the input points and the clusters.</returns>
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///
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public double[] Transform(TData[] points, int[] labels, double[] weights = null, double[] result = null)
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{
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if (result == null)
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result = new double[points.Length];
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if (weights == null)
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{
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for (int i = 0; i < result.Length; i++)
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result[i] = Distance.Distance(points[i], centroids[labels[i]]);
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}
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else
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{
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for (int i = 0; i < result.Length; i++)
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result[i] = weights[i] * Distance.Distance(points[i], centroids[labels[i]]);
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}
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return result;
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}
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/// <summary>
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/// Transform data points into feature vectors containing the
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/// distance between each point and each of the clusters.
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/// </summary>
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///
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/// <param name="points">The input points.</param>
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/// <param name="weights">The weight associated with each point.</param>
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/// <param name="result">An optional matrix to store the computed transformation.</param>
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///
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/// <returns>A vector containing the distance between the input points and the clusters.</returns>
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///
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public double[][] Transform(TData[] points, double[] weights = null, double[][] result = null)
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{
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if (result == null)
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{
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result = new double[points.Length][];
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for (int i = 0; i < result.Length; i++)
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result[i] = new double[centroids.Length];
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}
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if (weights == null)
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{
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for (int i = 0; i < result.Length; i++)
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for (int j = 0; j < centroids.Length; j++)
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result[i][j] = Distance.Distance(points[i], centroids[j]);
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}
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else
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{
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for (int i = 0; i < result.Length; i++)
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for (int j = 0; j < centroids.Length; j++)
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result[i][j] = weights[i] * Distance.Distance(points[i], centroids[j]);
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}
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return result;
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}
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public double Score(TData input, int classIndex)
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{
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return -Distance.Distance(input, Centroids[classIndex]);
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}
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}
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}
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}
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