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GH-390 : MachineLearning.KMeans: Balanced clustering.
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Sources/Accord.MachineLearning/Clustering/KMeans/BalancedKMeans.cs
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| // 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 | ||
| { | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using Accord.Math; | ||
| using Accord.Math.Distances; | ||
| using Math.Optimization; | ||
| using Statistics; | ||
| using System.Threading.Tasks; | ||
|
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| /// <summary> | ||
| /// Balanced K-Means algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <remarks> | ||
| /// The Balanced k-Means algorithm attempts to find a clustering where each cluster | ||
| /// has approximately the same number of data points. The Balanced k-Means implementation | ||
| /// used in the framework uses the <see cref="Munkres"/> algorithm to solve the assignment | ||
| /// problem thus enforcing balance between the clusters. | ||
| /// </remarks> | ||
| /// | ||
| /// <example> | ||
| /// How to perform clustering with Balanced K-Means. | ||
| /// | ||
| /// <code source="Unit Tests\Accord.Tests.MachineLearning\Clustering\BalancedKMeansTest.cs" region="doc_learn" /> | ||
| /// </example> | ||
| /// | ||
| /// <seealso cref="KMeans"/> | ||
| /// <seealso cref="BinarySplit"/> | ||
| /// <seealso cref="GaussianMixtureModel"/> | ||
| /// | ||
| [Serializable] | ||
| public class BalancedKMeans : KMeans | ||
| { | ||
|
|
||
| /// <summary> | ||
| /// Gets the labels assigned for each data point in the last | ||
| /// call to <see cref="Learn(double[][], double[])"/>. | ||
| /// </summary> | ||
| /// | ||
| /// <value>The labels.</value> | ||
| /// | ||
| public int[] Labels { get; private set; } | ||
|
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| /// <summary> | ||
| /// Initializes a new instance of the Balanced K-Means algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <param name="k">The number of clusters to divide the input data into.</param> | ||
| /// <param name="distance">The distance function to use. Default is to | ||
| /// use the <see cref="Accord.Math.Distance.SquareEuclidean(double[], double[])"/> distance.</param> | ||
| /// | ||
| public BalancedKMeans(int k, IDistance<double[]> distance) | ||
| : base(k, distance) | ||
| { | ||
| } | ||
|
|
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| /// <summary> | ||
| /// Initializes a new instance of the Balanced K-Means algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <param name="k">The number of clusters to divide the input data into.</param> | ||
| /// | ||
| public BalancedKMeans(int k) | ||
| : base(k) { } | ||
|
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|
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| /// <summary> | ||
| /// Learns a model that can map the given inputs to the desired outputs. | ||
| /// </summary> | ||
| /// <param name="x">The model inputs.</param> | ||
| /// <param name="weights">The weight of importance for each input sample.</param> | ||
| /// <returns>A model that has learned how to produce suitable outputs | ||
| /// given the input data <paramref name="x" />.</returns> | ||
| public override KMeansClusterCollection Learn(double[][] x, double[] weights = null) | ||
| { | ||
| // Initial argument checking | ||
| if (x == null) | ||
| throw new ArgumentNullException("x"); | ||
|
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| if (x.Length < K) | ||
| throw new ArgumentException("Not enough points. There should be more points than the number K of clusters."); | ||
|
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| if (weights == null) | ||
| { | ||
| weights = Vector.Ones(x.Length); | ||
| } | ||
| else | ||
| { | ||
| if (x.Length != weights.Length) | ||
| throw new ArgumentException("Data weights vector must be the same length as data samples."); | ||
| } | ||
|
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| double weightSum = weights.Sum(); | ||
| if (weightSum <= 0) | ||
| throw new ArgumentException("Not enough points. There should be more points than the number K of clusters."); | ||
|
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| if (!x.IsRectangular()) | ||
| throw new DimensionMismatchException("data", "The points matrix should be rectangular. The vector at position {} has a different length than previous ones."); | ||
|
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| int k = this.K; | ||
| int rows = x.Length; | ||
| int cols = x[0].Length; | ||
|
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| // Perform a random initialization of the clusters | ||
| // if the algorithm has not been initialized before. | ||
| // | ||
| if (this.Clusters.Centroids[0] == null) | ||
| { | ||
| Randomize(x); | ||
| } | ||
|
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| // Initial variables | ||
| int[] labels = new int[rows]; | ||
| double[] count = new double[k]; | ||
| double[][] centroids = Clusters.Centroids; | ||
| double[][] newCentroids = new double[k][]; | ||
| for (int i = 0; i < newCentroids.Length; i++) | ||
| newCentroids[i] = new double[cols]; | ||
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| Object[] syncObjects = new Object[K]; | ||
| for (int i = 0; i < syncObjects.Length; i++) | ||
| syncObjects[i] = new Object(); | ||
|
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| Iterations = 0; | ||
|
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| bool shouldStop = false; | ||
|
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| var m = new Munkres(x.Length, x.Length); | ||
| double[][] costMatrix = m.CostMatrix; | ||
|
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| while (!shouldStop) // Main loop | ||
| { | ||
| Array.Clear(count, 0, count.Length); | ||
| for (int i = 0; i < newCentroids.Length; i++) | ||
| Array.Clear(newCentroids[i], 0, newCentroids[i].Length); | ||
| for (int i = 0; i < labels.Length; i++) | ||
| labels[i] = -1; | ||
|
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| // Set the cost matrix for Munkres algorithm | ||
| for (int i = 0; i < costMatrix.Length; i++) | ||
| for (int j = 0; j < costMatrix[i].Length; j++) | ||
| costMatrix[i][j] = Distance.Distance(x[j], centroids[i % k]); | ||
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| //string str = costMatrix.ToCSharp(); | ||
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| m.Minimize(); // solve the assignment problem | ||
|
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| for (int i = 0; i < x.Length; i++) | ||
| { | ||
| if (m.Solution[i] >= 0) | ||
| labels[(int)m.Solution[i]] = i % k; | ||
| } | ||
|
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| // For each point in the data set, | ||
| Parallel.For(0, x.Length, ParallelOptions, i => | ||
| { | ||
| // Get the point | ||
| double[] point = x[i]; | ||
| double weight = weights[i]; | ||
| // Get the nearest cluster centroid | ||
| int c = labels[i]; | ||
| if (c >= 0) | ||
| { | ||
| // Get the closest cluster centroid | ||
| double[] centroid = newCentroids[c]; | ||
| lock (syncObjects[c]) | ||
| { | ||
| // Increase the cluster's sample counter | ||
| count[c] += weight; | ||
| // Accumulate in the cluster centroid | ||
| for (int j = 0; j < point.Length; j++) | ||
| centroid[j] += point[j] * weight; | ||
| } | ||
| } | ||
| }); | ||
|
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| // Next we will compute each cluster's new centroid | ||
| // by dividing the accumulated sums by the number of | ||
| // samples in each cluster, thus averaging its members. | ||
| Parallel.For(0, newCentroids.Length, ParallelOptions, i => | ||
| { | ||
| double sum = count[i]; | ||
| if (sum > 0) | ||
| { | ||
| for (int j = 0; j < newCentroids[i].Length; j++) | ||
| newCentroids[i][j] /= sum; | ||
| } | ||
| }); | ||
|
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| // The algorithm stops when there is no further change in the | ||
| // centroids (relative difference is less than the threshold). | ||
| shouldStop = converged(centroids, newCentroids); | ||
|
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| // go to next generation | ||
| Parallel.For(0, centroids.Length, ParallelOptions, i => | ||
| { | ||
| for (int j = 0; j < centroids[i].Length; j++) | ||
| centroids[i][j] = newCentroids[i][j]; | ||
| }); | ||
| } | ||
|
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| for (int i = 0; i < Clusters.Centroids.Length; i++) | ||
| { | ||
| // Compute the proportion of samples in the cluster | ||
| Clusters.Proportions[i] = count[i] / weightSum; | ||
| } | ||
|
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| this.Labels = labels; | ||
|
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| ComputeInformation(x, labels); | ||
|
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| return Clusters; | ||
| } | ||
|
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| } | ||
| } |
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