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Adding Nu-SVMs based on LibSVM's quadratic programming solver.
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Sources/Accord.MachineLearning/VectorMachines/Learning/OneclassSupportVectorLearning.cs
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| // Accord Machine Learning Library | ||
| // The Accord.NET Framework | ||
| // http://accord-framework.net | ||
| // | ||
| // Copyright © César Souza, 2009-2015 | ||
| // 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 | ||
| // | ||
|
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| namespace Accord.MachineLearning.VectorMachines.Learning | ||
| { | ||
| using Accord.Math.Optimization; | ||
| using Accord.Statistics.Kernels; | ||
| using System; | ||
|
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| /// <summary> | ||
| /// One-class Support Vector Machine Learning Algorithm. | ||
| /// </summary> | ||
| /// | ||
| public class OneclassSupportVectorLearning : ISupportVectorMachineLearning | ||
| { | ||
| SupportVectorMachine machine; | ||
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| private double[][] inputs; | ||
| private double[] alpha; | ||
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| private double nu = 0.5; | ||
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| IKernel kernel; | ||
| readonly double[] zeros; | ||
| readonly int[] ones; | ||
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| double eps = 0.001; | ||
| bool shrinking = true; | ||
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| /// <summary> | ||
| /// Constructs a new one-class support vector learning algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <param name="machine">A support vector machine.</param> | ||
| /// <param name="inputs">The input data points as row vectors.</param> | ||
| /// | ||
| public OneclassSupportVectorLearning(SupportVectorMachine machine, double[][] inputs) | ||
| { | ||
| // Initial argument checking | ||
| if (machine == null) | ||
| throw new ArgumentNullException("machine"); | ||
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| if (inputs == null) | ||
| throw new ArgumentNullException("inputs"); | ||
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| this.inputs = inputs; | ||
| this.machine = machine; | ||
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| this.zeros = new double[inputs.Length]; | ||
| this.ones = new int[inputs.Length]; | ||
| this.alpha = new double[inputs.Length]; | ||
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| for (int i = 0; i < alpha.Length; i++) | ||
| alpha[i] = 1; | ||
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| for (int i = 0; i < ones.Length; i++) | ||
| ones[i] = 1; | ||
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| // Kernel (if applicable) | ||
| var ksvm = machine as KernelSupportVectorMachine; | ||
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| if (ksvm == null) | ||
| { | ||
| kernel = new Linear(0); | ||
| } | ||
| else | ||
| { | ||
| kernel = ksvm.Kernel; | ||
| } | ||
| } | ||
|
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| /// <summary> | ||
| /// Gets the value for the Lagrange multipliers | ||
| /// (alpha) for every observation vector. | ||
| /// </summary> | ||
| /// | ||
| public double[] Lagrange { get { return alpha; } } | ||
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| /// <summary> | ||
| /// Convergence tolerance. Default value is 1e-2. | ||
| /// </summary> | ||
| /// | ||
| /// <remarks> | ||
| /// The criterion for completing the model training process. The default is 0.01. | ||
| /// </remarks> | ||
| /// | ||
| public double Tolerance | ||
| { | ||
| get { return eps; } | ||
| set { eps = value; } | ||
| } | ||
|
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| /// <summary> | ||
| /// Gets or sets a value indicating whether to use | ||
| /// shrinking heuristics during learning. Default is true. | ||
| /// </summary> | ||
| /// | ||
| /// <value> | ||
| /// <c>true</c> to use shrinking; otherwise, <c>false</c>. | ||
| /// </value> | ||
| /// | ||
| public bool Shrinking | ||
| { | ||
| get { return shrinking; } | ||
| set { shrinking = value; } | ||
| } | ||
|
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| /// <summary> | ||
| /// Controls the number of outliers accepted by the algorithm. This | ||
| /// value provides an upper bound on the fraction of training errors | ||
| /// and a lower bound of the fraction of support vectors. Default is 0.5 | ||
| /// </summary> | ||
| /// | ||
| /// <remarks> | ||
| /// The summary description is given in Chang and Lin, | ||
| /// "LIBSVM: A Library for Support Vector Machines", 2013. | ||
| /// </remarks> | ||
| /// | ||
| public double Nu | ||
| { | ||
| get { return nu; } | ||
| set { nu = value; } | ||
| } | ||
|
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| /// <summary> | ||
| /// Runs the learning algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <param name="computeError">True to compute error after the training | ||
| /// process completes, false otherwise.</param> | ||
| /// <returns> | ||
| /// The misclassification error rate of the resulting support | ||
| /// vector machine if <paramref name="computeError" /> is true, | ||
| /// returns zero otherwise. | ||
| /// </returns> | ||
| /// | ||
| public double Run(bool computeError) | ||
| { | ||
| int l = inputs.Length; | ||
| int n = (int)(nu * l); // # of alpha's at upper bound | ||
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| for (int i = 0; i < n; i++) | ||
| alpha[i] = 1; | ||
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| if (n < inputs.Length) | ||
| alpha[n] = nu * l - n; | ||
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| for (int i = n + 1; i < l; i++) | ||
| alpha[i] = 0; | ||
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| var s = new FanChenLinQuadraticOptimization(alpha.Length, Q, zeros, ones) | ||
| { | ||
| Tolerance = eps, | ||
| Shrinking = true, | ||
| Solution = alpha | ||
| }; | ||
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| bool success = s.Minimize(); | ||
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| int sv = 0; | ||
| for (int i = 0; i < alpha.Length; i++) | ||
| if (alpha[i] > 0) sv++; | ||
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| machine.SupportVectors = new double[sv][]; | ||
| machine.Weights = new double[sv]; | ||
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| for (int i = 0, j = 0; i < alpha.Length; i++) | ||
| { | ||
| if (alpha[i] > 0) | ||
| { | ||
| machine.SupportVectors[j] = inputs[i]; | ||
| machine.Weights[j] = alpha[i]; | ||
| j++; | ||
| } | ||
| } | ||
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| machine.Threshold = s.Rho; | ||
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| if (computeError) | ||
| return ComputeError(inputs); | ||
| return 0.0; | ||
| } | ||
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| /// <summary> | ||
| /// Runs the learning algorithm. | ||
| /// </summary> | ||
| /// | ||
| /// <returns> | ||
| /// The misclassification error rate of | ||
| /// the resulting support vector machine. | ||
| /// </returns> | ||
| /// | ||
| public double Run() | ||
| { | ||
| return Run(true); | ||
| } | ||
|
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| /// <summary> | ||
| /// Computes the error rate for a given set of inputs. | ||
| /// </summary> | ||
| /// | ||
| public double ComputeError(double[][] inputs) | ||
| { | ||
| double error = 0; | ||
| for (int i = 0; i < inputs.Length; i++) | ||
| error += machine.Compute(inputs[i]); | ||
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| return error; | ||
| } | ||
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| double Q(int i, int j) | ||
| { | ||
| return kernel.Function(inputs[i], inputs[j]); | ||
| } | ||
|
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| } | ||
| } |
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