[1806] | 1 | /*
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| 2 | * SVM.NET Library
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| 3 | * Copyright (C) 2008 Matthew Johnson
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| 4 | *
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| 5 | * This program is free software: you can redistribute it and/or modify
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| 6 | * it under the terms of the GNU General Public License as published by
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| 7 | * the Free Software Foundation, either version 3 of the License, or
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| 8 | * (at your option) any later version.
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| 9 | *
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| 10 | * This program is distributed in the hope that it will be useful,
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| 11 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 12 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 13 | * GNU General Public License for more details.
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| 14 | *
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| 15 | * You should have received a copy of the GNU General Public License
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| 16 | * along with this program. If not, see <http://www.gnu.org/licenses/>.
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| 17 | */
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| 18 |
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| 19 |
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| 20 | using System;
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| 21 | using System.Collections.Generic;
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| 22 |
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| 23 | namespace SVM
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| 24 | {
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[2415] | 25 | /// <summary>
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[1806] | 26 | /// Class containing the routines to train SVM models.
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[2415] | 27 | /// </summary>
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[1806] | 28 | public static class Training
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| 29 | {
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[2415] | 30 | /// <summary>
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| 31 | /// Whether the system will output information to the console during the training process.
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| 32 | /// </summary>
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| 33 | public static bool IsVerbose
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| 34 | {
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| 35 | get
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| 36 | {
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| 37 | return Procedures.IsVerbose;
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| 38 | }
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| 39 | set
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| 40 | {
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| 41 | Procedures.IsVerbose = value;
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| 42 | }
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| 43 | }
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| 44 |
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[1806] | 45 | private static double doCrossValidation(Problem problem, Parameter parameters, int nr_fold)
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| 46 | {
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| 47 | int i;
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| 48 | double[] target = new double[problem.Count];
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[2415] | 49 | Procedures.svm_cross_validation(problem, parameters, nr_fold, target);
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| 50 | int total_correct = 0;
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| 51 | double total_error = 0;
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| 52 | double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
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| 53 | if (parameters.SvmType == SvmType.EPSILON_SVR || parameters.SvmType == SvmType.NU_SVR)
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[1806] | 54 | {
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[2415] | 55 | for (i = 0; i < problem.Count; i++)
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[1806] | 56 | {
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[2415] | 57 | double y = problem.Y[i];
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| 58 | double v = target[i];
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| 59 | total_error += (v - y) * (v - y);
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| 60 | sumv += v;
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| 61 | sumy += y;
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| 62 | sumvv += v * v;
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| 63 | sumyy += y * y;
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| 64 | sumvy += v * y;
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[1806] | 65 | }
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[2415] | 66 | return(problem.Count * sumvy - sumv * sumy) / (Math.Sqrt(problem.Count * sumvv - sumv * sumv) * Math.Sqrt(problem.Count * sumyy - sumy * sumy));
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[1806] | 67 | }
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[2415] | 68 | else
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| 69 | for (i = 0; i < problem.Count; i++)
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| 70 | if (target[i] == problem.Y[i])
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| 71 | ++total_correct;
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| 72 | return (double)total_correct / problem.Count;
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[1806] | 73 | }
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| 74 | /// <summary>
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| 75 | /// Legacy. Allows use as if this was svm_train. See libsvm documentation for details on which arguments to pass.
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| 76 | /// </summary>
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| 77 | /// <param name="args"></param>
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| 78 | [Obsolete("Provided only for legacy compatibility, use the other Train() methods")]
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| 79 | public static void Train(params string[] args)
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| 80 | {
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| 81 | Parameter parameters;
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| 82 | Problem problem;
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| 83 | bool crossValidation;
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| 84 | int nrfold;
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| 85 | string modelFilename;
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| 86 | parseCommandLine(args, out parameters, out problem, out crossValidation, out nrfold, out modelFilename);
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| 87 | if (crossValidation)
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| 88 | PerformCrossValidation(problem, parameters, nrfold);
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| 89 | else Model.Write(modelFilename, Train(problem, parameters));
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| 90 | }
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| 91 |
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| 92 | /// <summary>
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| 93 | /// Performs cross validation.
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| 94 | /// </summary>
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| 95 | /// <param name="problem">The training data</param>
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| 96 | /// <param name="parameters">The parameters to test</param>
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| 97 | /// <param name="nrfold">The number of cross validations to use</param>
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| 98 | /// <returns>The cross validation score</returns>
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| 99 | public static double PerformCrossValidation(Problem problem, Parameter parameters, int nrfold)
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| 100 | {
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[2415] | 101 | string error = Procedures.svm_check_parameter(problem, parameters);
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| 102 | if (error == null)
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| 103 | return doCrossValidation(problem, parameters, nrfold);
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| 104 | else throw new Exception(error);
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[1806] | 105 | }
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| 106 |
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| 107 | /// <summary>
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| 108 | /// Trains a model using the provided training data and parameters.
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| 109 | /// </summary>
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| 110 | /// <param name="problem">The training data</param>
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| 111 | /// <param name="parameters">The parameters to use</param>
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| 112 | /// <returns>A trained SVM Model</returns>
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| 113 | public static Model Train(Problem problem, Parameter parameters)
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| 114 | {
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[2415] | 115 | string error = Procedures.svm_check_parameter(problem, parameters);
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[1806] | 116 |
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[2415] | 117 | if (error == null)
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| 118 | return Procedures.svm_train(problem, parameters);
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| 119 | else throw new Exception(error);
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[1806] | 120 | }
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| 121 |
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| 122 | private static void parseCommandLine(string[] args, out Parameter parameters, out Problem problem, out bool crossValidation, out int nrfold, out string modelFilename)
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| 123 | {
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| 124 | int i;
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| 125 |
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| 126 | parameters = new Parameter();
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| 127 | // default values
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| 128 |
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| 129 | crossValidation = false;
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| 130 | nrfold = 0;
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| 131 |
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| 132 | // parse options
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| 133 | for (i = 0; i < args.Length; i++)
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| 134 | {
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| 135 | if (args[i][0] != '-')
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| 136 | break;
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| 137 | ++i;
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| 138 | switch (args[i - 1][1])
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| 139 | {
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| 140 |
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| 141 | case 's':
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| 142 | parameters.SvmType = (SvmType)int.Parse(args[i]);
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| 143 | break;
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| 144 |
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| 145 | case 't':
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| 146 | parameters.KernelType = (KernelType)int.Parse(args[i]);
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| 147 | break;
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| 148 |
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| 149 | case 'd':
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| 150 | parameters.Degree = int.Parse(args[i]);
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| 151 | break;
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| 152 |
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| 153 | case 'g':
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| 154 | parameters.Gamma = double.Parse(args[i]);
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| 155 | break;
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| 156 |
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| 157 | case 'r':
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| 158 | parameters.Coefficient0 = double.Parse(args[i]);
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| 159 | break;
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| 160 |
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| 161 | case 'n':
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| 162 | parameters.Nu = double.Parse(args[i]);
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| 163 | break;
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| 164 |
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| 165 | case 'm':
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| 166 | parameters.CacheSize = double.Parse(args[i]);
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| 167 | break;
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| 168 |
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| 169 | case 'c':
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| 170 | parameters.C = double.Parse(args[i]);
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| 171 | break;
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| 172 |
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| 173 | case 'e':
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| 174 | parameters.EPS = double.Parse(args[i]);
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| 175 | break;
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| 176 |
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| 177 | case 'p':
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| 178 | parameters.P = double.Parse(args[i]);
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| 179 | break;
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| 180 |
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| 181 | case 'h':
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| 182 | parameters.Shrinking = int.Parse(args[i]) == 1;
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| 183 | break;
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| 184 |
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| 185 | case 'b':
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| 186 | parameters.Probability = int.Parse(args[i]) == 1;
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| 187 | break;
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| 188 |
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| 189 | case 'v':
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| 190 | crossValidation = true;
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| 191 | nrfold = int.Parse(args[i]);
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| 192 | if (nrfold < 2)
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| 193 | {
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| 194 | throw new ArgumentException("n-fold cross validation: n must >= 2");
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| 195 | }
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| 196 | break;
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| 197 |
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| 198 | case 'w':
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[2415] | 199 | parameters.Weights[int.Parse(args[i - 1].Substring(2))] = double.Parse(args[1]);
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[1806] | 200 | break;
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| 201 |
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| 202 | default:
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| 203 | throw new ArgumentException("Unknown Parameter");
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| 204 | }
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| 205 | }
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| 206 |
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| 207 | // determine filenames
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| 208 |
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| 209 | if (i >= args.Length)
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| 210 | throw new ArgumentException("No input file specified");
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| 211 |
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| 212 | problem = Problem.Read(args[i]);
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| 213 |
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| 214 | if (parameters.Gamma == 0)
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| 215 | parameters.Gamma = 1.0 / problem.MaxIndex;
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| 216 |
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| 217 | if (i < args.Length - 1)
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| 218 | modelFilename = args[i + 1];
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| 219 | else
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| 220 | {
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| 221 | int p = args[i].LastIndexOf('/') + 1;
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| 222 | modelFilename = args[i].Substring(p) + ".model";
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| 223 | }
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| 224 | }
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| 225 | }
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| 226 | } |
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