| 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 | using System.Diagnostics;
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| 23 |
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| 24 | namespace SVM
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| 25 | {
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| 26 | //
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| 27 | // Kernel evaluation
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| 28 | //
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| 29 | // the static method k_function is for doing single kernel evaluation
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| 30 | // the constructor of Kernel prepares to calculate the l*l kernel matrix
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| 31 | // the member function get_Q is for getting one column from the Q Matrix
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| 32 | //
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| 33 | internal abstract class QMatrix
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| 34 | {
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| 35 | public abstract float[] get_Q(int column, int len);
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| 36 | public abstract float[] get_QD();
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| 37 | public abstract void swap_index(int i, int j);
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| 38 | }
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| 39 |
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| 40 | internal abstract class Kernel : QMatrix
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| 41 | {
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| 42 | private Node[][] _x;
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| 43 | private double[] _x_square;
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| 44 |
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| 45 | // Parameter
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| 46 | private KernelType kernel_type;
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| 47 | private int degree;
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| 48 | private double gamma;
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| 49 | private double coef0;
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| 50 |
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| 51 | public override void swap_index(int i, int j)
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| 52 | {
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| 53 | do { Node[] _ = _x[i]; _x[i] = _x[j]; _x[j] = _; } while (false);
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| 54 | if (_x_square != null) do { double _ = _x_square[i]; _x_square[i] = _x_square[j]; _x_square[j] = _; } while (false);
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| 55 | }
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| 56 |
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| 57 | private static double powi(double baseValue, int times)
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| 58 | {
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| 59 | double tmp = baseValue, ret = 1.0;
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| 60 |
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| 61 | for (int t = times; t > 0; t /= 2)
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| 62 | {
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| 63 | if (t % 2 == 1) ret *= tmp;
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| 64 | tmp = tmp * tmp;
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| 65 | }
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| 66 | return ret;
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| 67 | }
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| 68 |
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| 69 | private static double tanh(double x)
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| 70 | {
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| 71 | double e = Math.Exp(x);
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| 72 | return 1.0 - 2.0 / (e * e + 1);
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| 73 | }
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| 74 |
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| 75 | public double kernel_function(int i, int j)
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| 76 | {
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| 77 | switch (kernel_type)
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| 78 | {
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| 79 | case KernelType.LINEAR:
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| 80 | return dot(_x[i], _x[j]);
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| 81 | case KernelType.POLY:
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| 82 | return powi(gamma * dot(_x[i], _x[j]) + coef0, degree);
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| 83 | case KernelType.RBF:
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| 84 | return Math.Exp(-gamma * (_x_square[i] + _x_square[j] - 2 * dot(_x[i], _x[j])));
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| 85 | case KernelType.SIGMOID:
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| 86 | return tanh(gamma * dot(_x[i], _x[j]) + coef0);
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| 87 | case KernelType.PRECOMPUTED:
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| 88 | return _x[i][(int)(_x[j][0].Value)].Value;
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| 89 | default:
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| 90 | return 0;
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| 91 | }
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| 92 | }
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| 93 |
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| 94 | public Kernel(int l, Node[][] x_, Parameter param)
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| 95 | {
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| 96 | this.kernel_type = param.KernelType;
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| 97 | this.degree = param.Degree;
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| 98 | this.gamma = param.Gamma;
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| 99 | this.coef0 = param.Coefficient0;
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| 100 |
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| 101 | _x = (Node[][])x_.Clone();
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| 102 |
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| 103 | if (kernel_type == KernelType.RBF)
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| 104 | {
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| 105 | _x_square = new double[l];
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| 106 | for (int i = 0; i < l; i++)
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| 107 | _x_square[i] = dot(_x[i], _x[i]);
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| 108 | }
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| 109 | else _x_square = null;
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| 110 | }
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| 111 |
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| 112 | public static double dot(Node[] x, Node[] y)
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| 113 | {
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| 114 | double sum = 0;
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| 115 | int xlen = x.Length;
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| 116 | int ylen = y.Length;
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| 117 | int i = 0;
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| 118 | int j = 0;
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| 119 | while (i < xlen && j < ylen)
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| 120 | {
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| 121 | if (x[i].Index == y[j].Index)
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| 122 | sum += x[i++].Value * y[j++].Value;
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| 123 | else
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| 124 | {
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| 125 | if (x[i].Index > y[j].Index)
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| 126 | ++j;
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| 127 | else
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| 128 | ++i;
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| 129 | }
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| 130 | }
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| 131 | return sum;
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| 132 | }
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| 133 |
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| 134 | public static double k_function(Node[] x, Node[] y, Parameter param)
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| 135 | {
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| 136 | switch (param.KernelType)
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| 137 | {
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| 138 | case KernelType.LINEAR:
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| 139 | return dot(x, y);
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| 140 | case KernelType.POLY:
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| 141 | return powi(param.Gamma * dot(x, y) + param.Coefficient0, param.Degree);
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| 142 | case KernelType.RBF:
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| 143 | {
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| 144 | double sum = 0;
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| 145 | int xlen = x.Length;
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| 146 | int ylen = y.Length;
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| 147 | int i = 0;
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| 148 | int j = 0;
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| 149 | while (i < xlen && j < ylen)
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| 150 | {
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| 151 | if (x[i].Index == y[j].Index)
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| 152 | {
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| 153 | double d = x[i++].Value - y[j++].Value;
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| 154 | sum += d * d;
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| 155 | }
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| 156 | else if (x[i].Index > y[j].Index)
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| 157 | {
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| 158 | sum += y[j].Value * y[j].Value;
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| 159 | ++j;
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| 160 | }
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| 161 | else
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| 162 | {
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| 163 | sum += x[i].Value * x[i].Value;
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| 164 | ++i;
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| 165 | }
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| 166 | }
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| 167 |
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| 168 | while (i < xlen)
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| 169 | {
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| 170 | sum += x[i].Value * x[i].Value;
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| 171 | ++i;
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| 172 | }
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| 173 |
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| 174 | while (j < ylen)
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| 175 | {
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| 176 | sum += y[j].Value * y[j].Value;
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| 177 | ++j;
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| 178 | }
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| 179 |
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| 180 | return Math.Exp(-param.Gamma * sum);
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| 181 | }
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| 182 | case KernelType.SIGMOID:
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| 183 | return tanh(param.Gamma * dot(x, y) + param.Coefficient0);
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| 184 | case KernelType.PRECOMPUTED:
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| 185 | return x[(int)(y[0].Value)].Value;
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| 186 | default:
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| 187 | return 0;
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| 188 | }
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| 189 | }
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| 190 | }
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| 191 |
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| 192 | // An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918
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| 193 | // Solves:
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| 194 | //
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| 195 | // min 0.5(\alpha^T Q \alpha) + p^T \alpha
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| 196 | //
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| 197 | // y^T \alpha = \delta
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| 198 | // y_i = +1 or -1
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| 199 | // 0 <= alpha_i <= Cp for y_i = 1
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| 200 | // 0 <= alpha_i <= Cn for y_i = -1
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| 201 | //
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| 202 | // Given:
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| 203 | //
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| 204 | // Q, p, y, Cp, Cn, and an initial feasible point \alpha
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| 205 | // l is the size of vectors and matrices
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| 206 | // eps is the stopping tolerance
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| 207 | //
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| 208 | // solution will be put in \alpha, objective value will be put in obj
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| 209 | //
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| 210 | internal class Solver
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| 211 | {
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| 212 | protected int active_size;
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| 213 | protected short[] y;
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| 214 | protected double[] G; // gradient of objective function
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| 215 | protected const byte LOWER_BOUND = 0;
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| 216 | protected const byte UPPER_BOUND = 1;
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| 217 | protected const byte FREE = 2;
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| 218 | protected byte[] alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE
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| 219 | protected double[] alpha;
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| 220 | protected QMatrix Q;
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| 221 | protected float[] QD;
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| 222 | protected double eps;
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| 223 | protected double Cp, Cn;
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| 224 | protected double[] p;
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| 225 | protected int[] active_set;
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| 226 | protected double[] G_bar; // gradient, if we treat free variables as 0
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| 227 | protected int l;
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| 228 | protected bool unshrinked; // XXX
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| 229 |
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| 230 | protected const double INF = double.PositiveInfinity;
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| 231 |
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| 232 | protected double get_C(int i)
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| 233 | {
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| 234 | return (y[i] > 0) ? Cp : Cn;
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| 235 | }
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| 236 | protected void update_alpha_status(int i)
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| 237 | {
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| 238 | if (alpha[i] >= get_C(i))
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| 239 | alpha_status[i] = UPPER_BOUND;
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| 240 | else if (alpha[i] <= 0)
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| 241 | alpha_status[i] = LOWER_BOUND;
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| 242 | else alpha_status[i] = FREE;
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| 243 | }
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| 244 | protected bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; }
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| 245 | protected bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; }
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| 246 | protected bool is_free(int i) { return alpha_status[i] == FREE; }
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| 247 |
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| 248 | // java: information about solution except alpha,
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| 249 | // because we cannot return multiple values otherwise...
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| 250 | internal class SolutionInfo
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| 251 | {
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| 252 | public double obj;
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| 253 | public double rho;
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| 254 | public double upper_bound_p;
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| 255 | public double upper_bound_n;
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| 256 | public double r; // for Solver_NU
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| 257 | }
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| 258 |
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| 259 | protected void swap_index(int i, int j)
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| 260 | {
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| 261 | Q.swap_index(i, j);
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| 262 | do { short _ = y[i]; y[i] = y[j]; y[j] = _; } while (false);
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| 263 | do { double _ = G[i]; G[i] = G[j]; G[j] = _; } while (false);
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| 264 | do { byte _ = alpha_status[i]; alpha_status[i] = alpha_status[j]; alpha_status[j] = _; } while (false);
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| 265 | do { double _ = alpha[i]; alpha[i] = alpha[j]; alpha[j] = _; } while (false);
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| 266 | do { double _ = p[i]; p[i] = p[j]; p[j] = _; } while (false);
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| 267 | do { int _ = active_set[i]; active_set[i] = active_set[j]; active_set[j] = _; } while (false);
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| 268 | do { double _ = G_bar[i]; G_bar[i] = G_bar[j]; G_bar[j] = _; } while (false);
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| 269 | }
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| 270 |
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| 271 | protected void reconstruct_gradient()
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| 272 | {
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| 273 | // reconstruct inactive elements of G from G_bar and free variables
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| 274 |
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| 275 | if (active_size == l) return;
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| 276 |
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| 277 | int i;
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| 278 | for (i = active_size; i < l; i++)
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| 279 | G[i] = G_bar[i] + p[i];
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| 280 |
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| 281 | for (i = 0; i < active_size; i++)
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| 282 | if (is_free(i))
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| 283 | {
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| 284 | float[] Q_i = Q.get_Q(i, l);
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| 285 | double alpha_i = alpha[i];
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| 286 | for (int j = active_size; j < l; j++)
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| 287 | G[j] += alpha_i * Q_i[j];
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| 288 | }
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| 289 | }
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| 290 |
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| 291 | public virtual void Solve(int l, QMatrix Q, double[] p_, short[] y_,
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| 292 | double[] alpha_, double Cp, double Cn, double eps, SolutionInfo si, bool shrinking)
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| 293 | {
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| 294 | this.l = l;
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| 295 | this.Q = Q;
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| 296 | QD = Q.get_QD();
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| 297 | p = (double[])p_.Clone();
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| 298 | y = (short[])y_.Clone();
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| 299 | alpha = (double[])alpha_.Clone();
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| 300 | this.Cp = Cp;
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| 301 | this.Cn = Cn;
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| 302 | this.eps = eps;
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| 303 | this.unshrinked = false;
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| 304 |
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| 305 | // initialize alpha_status
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| 306 | {
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| 307 | alpha_status = new byte[l];
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| 308 | for (int i = 0; i < l; i++)
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| 309 | update_alpha_status(i);
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| 310 | }
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| 311 |
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| 312 | // initialize active set (for shrinking)
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| 313 | {
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| 314 | active_set = new int[l];
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| 315 | for (int i = 0; i < l; i++)
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| 316 | active_set[i] = i;
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| 317 | active_size = l;
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| 318 | }
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| 319 |
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| 320 | // initialize gradient
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| 321 | {
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| 322 | G = new double[l];
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| 323 | G_bar = new double[l];
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| 324 | int i;
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| 325 | for (i = 0; i < l; i++)
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| 326 | {
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| 327 | G[i] = p[i];
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| 328 | G_bar[i] = 0;
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| 329 | }
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| 330 | for (i = 0; i < l; i++)
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| 331 | if (!is_lower_bound(i))
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| 332 | {
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| 333 | float[] Q_i = Q.get_Q(i, l);
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| 334 | double alpha_i = alpha[i];
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| 335 | int j;
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| 336 | for (j = 0; j < l; j++)
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| 337 | G[j] += alpha_i * Q_i[j];
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| 338 | if (is_upper_bound(i))
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| 339 | for (j = 0; j < l; j++)
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| 340 | G_bar[j] += get_C(i) * Q_i[j];
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| 341 | }
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| 342 | }
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| 343 |
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| 344 | // optimization step
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| 345 |
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| 346 | int iter = 0;
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| 347 | int counter = Math.Min(l, 1000) + 1;
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| 348 | int[] working_set = new int[2];
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| 349 |
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| 350 | while (true)
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| 351 | {
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| 352 | // show progress and do shrinking
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| 353 |
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| 354 | if (--counter == 0)
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| 355 | {
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| 356 | counter = Math.Min(l, 1000);
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| 357 | if (shrinking) do_shrinking();
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| 358 | Debug.Write(".");
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| 359 | }
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| 360 |
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| 361 | if (select_working_set(working_set) != 0)
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| 362 | {
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| 363 | // reconstruct the whole gradient
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| 364 | reconstruct_gradient();
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| 365 | // reset active set size and check
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| 366 | active_size = l;
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| 367 | Debug.Write("*");
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| 368 | if (select_working_set(working_set) != 0)
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| 369 | break;
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| 370 | else
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| 371 | counter = 1; // do shrinking next iteration
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| 372 | }
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| 373 |
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| 374 | int i = working_set[0];
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| 375 | int j = working_set[1];
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| 376 |
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| 377 | ++iter;
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| 378 |
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| 379 | // update alpha[i] and alpha[j], handle bounds carefully
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| 380 |
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| 381 | float[] Q_i = Q.get_Q(i, active_size);
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| 382 | float[] Q_j = Q.get_Q(j, active_size);
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| 383 |
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| 384 | double C_i = get_C(i);
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| 385 | double C_j = get_C(j);
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| 386 |
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| 387 | double old_alpha_i = alpha[i];
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| 388 | double old_alpha_j = alpha[j];
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| 389 |
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| 390 | if (y[i] != y[j])
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| 391 | {
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| 392 | double quad_coef = Q_i[i] + Q_j[j] + 2 * Q_i[j];
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| 393 | if (quad_coef <= 0)
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| 394 | quad_coef = 1e-12;
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| 395 | double delta = (-G[i] - G[j]) / quad_coef;
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| 396 | double diff = alpha[i] - alpha[j];
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| 397 | alpha[i] += delta;
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| 398 | alpha[j] += delta;
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| 399 |
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| 400 | if (diff > 0)
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| 401 | {
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| 402 | if (alpha[j] < 0)
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| 403 | {
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| 404 | alpha[j] = 0;
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| 405 | alpha[i] = diff;
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| 406 | }
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| 407 | }
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| 408 | else
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| 409 | {
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| 410 | if (alpha[i] < 0)
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| 411 | {
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| 412 | alpha[i] = 0;
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| 413 | alpha[j] = -diff;
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| 414 | }
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| 415 | }
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| 416 | if (diff > C_i - C_j)
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| 417 | {
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| 418 | if (alpha[i] > C_i)
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| 419 | {
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| 420 | alpha[i] = C_i;
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| 421 | alpha[j] = C_i - diff;
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| 422 | }
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| 423 | }
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|---|
| 424 | else
|
|---|
| 425 | {
|
|---|
| 426 | if (alpha[j] > C_j)
|
|---|
| 427 | {
|
|---|
| 428 | alpha[j] = C_j;
|
|---|
| 429 | alpha[i] = C_j + diff;
|
|---|
| 430 | }
|
|---|
| 431 | }
|
|---|
| 432 | }
|
|---|
| 433 | else
|
|---|
| 434 | {
|
|---|
| 435 | double quad_coef = Q_i[i] + Q_j[j] - 2 * Q_i[j];
|
|---|
| 436 | if (quad_coef <= 0)
|
|---|
| 437 | quad_coef = 1e-12;
|
|---|
| 438 | double delta = (G[i] - G[j]) / quad_coef;
|
|---|
| 439 | double sum = alpha[i] + alpha[j];
|
|---|
| 440 | alpha[i] -= delta;
|
|---|
| 441 | alpha[j] += delta;
|
|---|
| 442 |
|
|---|
| 443 | if (sum > C_i)
|
|---|
| 444 | {
|
|---|
| 445 | if (alpha[i] > C_i)
|
|---|
| 446 | {
|
|---|
| 447 | alpha[i] = C_i;
|
|---|
| 448 | alpha[j] = sum - C_i;
|
|---|
| 449 | }
|
|---|
| 450 | }
|
|---|
| 451 | else
|
|---|
| 452 | {
|
|---|
| 453 | if (alpha[j] < 0)
|
|---|
| 454 | {
|
|---|
| 455 | alpha[j] = 0;
|
|---|
| 456 | alpha[i] = sum;
|
|---|
| 457 | }
|
|---|
| 458 | }
|
|---|
| 459 | if (sum > C_j)
|
|---|
| 460 | {
|
|---|
| 461 | if (alpha[j] > C_j)
|
|---|
| 462 | {
|
|---|
| 463 | alpha[j] = C_j;
|
|---|
| 464 | alpha[i] = sum - C_j;
|
|---|
| 465 | }
|
|---|
| 466 | }
|
|---|
| 467 | else
|
|---|
| 468 | {
|
|---|
| 469 | if (alpha[i] < 0)
|
|---|
| 470 | {
|
|---|
| 471 | alpha[i] = 0;
|
|---|
| 472 | alpha[j] = sum;
|
|---|
| 473 | }
|
|---|
| 474 | }
|
|---|
| 475 | }
|
|---|
| 476 |
|
|---|
| 477 | // update G
|
|---|
| 478 |
|
|---|
| 479 | double delta_alpha_i = alpha[i] - old_alpha_i;
|
|---|
| 480 | double delta_alpha_j = alpha[j] - old_alpha_j;
|
|---|
| 481 |
|
|---|
| 482 | for (int k = 0; k < active_size; k++)
|
|---|
| 483 | {
|
|---|
| 484 | G[k] += Q_i[k] * delta_alpha_i + Q_j[k] * delta_alpha_j;
|
|---|
| 485 | }
|
|---|
| 486 |
|
|---|
| 487 | // update alpha_status and G_bar
|
|---|
| 488 |
|
|---|
| 489 | {
|
|---|
| 490 | bool ui = is_upper_bound(i);
|
|---|
| 491 | bool uj = is_upper_bound(j);
|
|---|
| 492 | update_alpha_status(i);
|
|---|
| 493 | update_alpha_status(j);
|
|---|
| 494 | int k;
|
|---|
| 495 | if (ui != is_upper_bound(i))
|
|---|
| 496 | {
|
|---|
| 497 | Q_i = Q.get_Q(i, l);
|
|---|
| 498 | if (ui)
|
|---|
| 499 | for (k = 0; k < l; k++)
|
|---|
| 500 | G_bar[k] -= C_i * Q_i[k];
|
|---|
| 501 | else
|
|---|
| 502 | for (k = 0; k < l; k++)
|
|---|
| 503 | G_bar[k] += C_i * Q_i[k];
|
|---|
| 504 | }
|
|---|
| 505 |
|
|---|
| 506 | if (uj != is_upper_bound(j))
|
|---|
| 507 | {
|
|---|
| 508 | Q_j = Q.get_Q(j, l);
|
|---|
| 509 | if (uj)
|
|---|
| 510 | for (k = 0; k < l; k++)
|
|---|
| 511 | G_bar[k] -= C_j * Q_j[k];
|
|---|
| 512 | else
|
|---|
| 513 | for (k = 0; k < l; k++)
|
|---|
| 514 | G_bar[k] += C_j * Q_j[k];
|
|---|
| 515 | }
|
|---|
| 516 | }
|
|---|
| 517 |
|
|---|
| 518 | }
|
|---|
| 519 |
|
|---|
| 520 | // calculate rho
|
|---|
| 521 |
|
|---|
| 522 | si.rho = calculate_rho();
|
|---|
| 523 |
|
|---|
| 524 | // calculate objective value
|
|---|
| 525 | {
|
|---|
| 526 | double v = 0;
|
|---|
| 527 | int i;
|
|---|
| 528 | for (i = 0; i < l; i++)
|
|---|
| 529 | v += alpha[i] * (G[i] + p[i]);
|
|---|
| 530 |
|
|---|
| 531 | si.obj = v / 2;
|
|---|
| 532 | }
|
|---|
| 533 |
|
|---|
| 534 | // put back the solution
|
|---|
| 535 | {
|
|---|
| 536 | for (int i = 0; i < l; i++)
|
|---|
| 537 | alpha_[active_set[i]] = alpha[i];
|
|---|
| 538 | }
|
|---|
| 539 |
|
|---|
| 540 | si.upper_bound_p = Cp;
|
|---|
| 541 | si.upper_bound_n = Cn;
|
|---|
| 542 |
|
|---|
| 543 | Debug.Write("\noptimization finished, #iter = " + iter + "\n");
|
|---|
| 544 | }
|
|---|
| 545 |
|
|---|
| 546 | // return 1 if already optimal, return 0 otherwise
|
|---|
| 547 | protected virtual int select_working_set(int[] working_set)
|
|---|
| 548 | {
|
|---|
| 549 | // return i,j such that
|
|---|
| 550 | // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|---|
| 551 | // j: mimimizes the decrease of obj value
|
|---|
| 552 | // (if quadratic coefficeint <= 0, replace it with tau)
|
|---|
| 553 | // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|---|
| 554 |
|
|---|
| 555 | double Gmax = -INF;
|
|---|
| 556 | double Gmax2 = -INF;
|
|---|
| 557 | int Gmax_idx = -1;
|
|---|
| 558 | int Gmin_idx = -1;
|
|---|
| 559 | double obj_diff_min = INF;
|
|---|
| 560 |
|
|---|
| 561 | for (int t = 0; t < active_size; t++)
|
|---|
| 562 | if (y[t] == +1)
|
|---|
| 563 | {
|
|---|
| 564 | if (!is_upper_bound(t))
|
|---|
| 565 | if (-G[t] >= Gmax)
|
|---|
| 566 | {
|
|---|
| 567 | Gmax = -G[t];
|
|---|
| 568 | Gmax_idx = t;
|
|---|
| 569 | }
|
|---|
| 570 | }
|
|---|
| 571 | else
|
|---|
| 572 | {
|
|---|
| 573 | if (!is_lower_bound(t))
|
|---|
| 574 | if (G[t] >= Gmax)
|
|---|
| 575 | {
|
|---|
| 576 | Gmax = G[t];
|
|---|
| 577 | Gmax_idx = t;
|
|---|
| 578 | }
|
|---|
| 579 | }
|
|---|
| 580 |
|
|---|
| 581 | int i = Gmax_idx;
|
|---|
| 582 | float[] Q_i = null;
|
|---|
| 583 | if (i != -1) // null Q_i not accessed: Gmax=-INF if i=-1
|
|---|
| 584 | Q_i = Q.get_Q(i, active_size);
|
|---|
| 585 |
|
|---|
| 586 | for (int j = 0; j < active_size; j++)
|
|---|
| 587 | {
|
|---|
| 588 | if (y[j] == +1)
|
|---|
| 589 | {
|
|---|
| 590 | if (!is_lower_bound(j))
|
|---|
| 591 | {
|
|---|
| 592 | double grad_diff = Gmax + G[j];
|
|---|
| 593 | if (G[j] >= Gmax2)
|
|---|
| 594 | Gmax2 = G[j];
|
|---|
| 595 | if (grad_diff > 0)
|
|---|
| 596 | {
|
|---|
| 597 | double obj_diff;
|
|---|
| 598 | double quad_coef = Q_i[i] + QD[j] - 2 * y[i] * Q_i[j];
|
|---|
| 599 | if (quad_coef > 0)
|
|---|
| 600 | obj_diff = -(grad_diff * grad_diff) / quad_coef;
|
|---|
| 601 | else
|
|---|
| 602 | obj_diff = -(grad_diff * grad_diff) / 1e-12;
|
|---|
| 603 |
|
|---|
| 604 | if (obj_diff <= obj_diff_min)
|
|---|
| 605 | {
|
|---|
| 606 | Gmin_idx = j;
|
|---|
| 607 | obj_diff_min = obj_diff;
|
|---|
| 608 | }
|
|---|
| 609 | }
|
|---|
| 610 | }
|
|---|
| 611 | }
|
|---|
| 612 | else
|
|---|
| 613 | {
|
|---|
| 614 | if (!is_upper_bound(j))
|
|---|
| 615 | {
|
|---|
| 616 | double grad_diff = Gmax - G[j];
|
|---|
| 617 | if (-G[j] >= Gmax2)
|
|---|
| 618 | Gmax2 = -G[j];
|
|---|
| 619 | if (grad_diff > 0)
|
|---|
| 620 | {
|
|---|
| 621 | double obj_diff;
|
|---|
| 622 | double quad_coef = Q_i[i] + QD[j] + 2 * y[i] * Q_i[j];
|
|---|
| 623 | if (quad_coef > 0)
|
|---|
| 624 | obj_diff = -(grad_diff * grad_diff) / quad_coef;
|
|---|
| 625 | else
|
|---|
| 626 | obj_diff = -(grad_diff * grad_diff) / 1e-12;
|
|---|
| 627 |
|
|---|
| 628 | if (obj_diff <= obj_diff_min)
|
|---|
| 629 | {
|
|---|
| 630 | Gmin_idx = j;
|
|---|
| 631 | obj_diff_min = obj_diff;
|
|---|
| 632 | }
|
|---|
| 633 | }
|
|---|
| 634 | }
|
|---|
| 635 | }
|
|---|
| 636 | }
|
|---|
| 637 |
|
|---|
| 638 | if (Gmax + Gmax2 < eps)
|
|---|
| 639 | return 1;
|
|---|
| 640 |
|
|---|
| 641 | working_set[0] = Gmax_idx;
|
|---|
| 642 | working_set[1] = Gmin_idx;
|
|---|
| 643 | return 0;
|
|---|
| 644 | }
|
|---|
| 645 |
|
|---|
| 646 | private bool be_shrunken(int i, double Gmax1, double Gmax2)
|
|---|
| 647 | {
|
|---|
| 648 | if (is_upper_bound(i))
|
|---|
| 649 | {
|
|---|
| 650 | if (y[i] == +1)
|
|---|
| 651 | return (-G[i] > Gmax1);
|
|---|
| 652 | else
|
|---|
| 653 | return (-G[i] > Gmax2);
|
|---|
| 654 | }
|
|---|
| 655 | else if (is_lower_bound(i))
|
|---|
| 656 | {
|
|---|
| 657 | if (y[i] == +1)
|
|---|
| 658 | return (G[i] > Gmax2);
|
|---|
| 659 | else
|
|---|
| 660 | return (G[i] > Gmax1);
|
|---|
| 661 | }
|
|---|
| 662 | else
|
|---|
| 663 | return (false);
|
|---|
| 664 | }
|
|---|
| 665 |
|
|---|
| 666 | protected virtual void do_shrinking()
|
|---|
| 667 | {
|
|---|
| 668 | int i;
|
|---|
| 669 | double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) }
|
|---|
| 670 | double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) }
|
|---|
| 671 |
|
|---|
| 672 | // find maximal violating pair first
|
|---|
| 673 | for (i = 0; i < active_size; i++)
|
|---|
| 674 | {
|
|---|
| 675 | if (y[i] == +1)
|
|---|
| 676 | {
|
|---|
| 677 | if (!is_upper_bound(i))
|
|---|
| 678 | {
|
|---|
| 679 | if (-G[i] >= Gmax1)
|
|---|
| 680 | Gmax1 = -G[i];
|
|---|
| 681 | }
|
|---|
| 682 | if (!is_lower_bound(i))
|
|---|
| 683 | {
|
|---|
| 684 | if (G[i] >= Gmax2)
|
|---|
| 685 | Gmax2 = G[i];
|
|---|
| 686 | }
|
|---|
| 687 | }
|
|---|
| 688 | else
|
|---|
| 689 | {
|
|---|
| 690 | if (!is_upper_bound(i))
|
|---|
| 691 | {
|
|---|
| 692 | if (-G[i] >= Gmax2)
|
|---|
| 693 | Gmax2 = -G[i];
|
|---|
| 694 | }
|
|---|
| 695 | if (!is_lower_bound(i))
|
|---|
| 696 | {
|
|---|
| 697 | if (G[i] >= Gmax1)
|
|---|
| 698 | Gmax1 = G[i];
|
|---|
| 699 | }
|
|---|
| 700 | }
|
|---|
| 701 | }
|
|---|
| 702 |
|
|---|
| 703 | // shrink
|
|---|
| 704 |
|
|---|
| 705 | for (i = 0; i < active_size; i++)
|
|---|
| 706 | if (be_shrunken(i, Gmax1, Gmax2))
|
|---|
| 707 | {
|
|---|
| 708 | active_size--;
|
|---|
| 709 | while (active_size > i)
|
|---|
| 710 | {
|
|---|
| 711 | if (!be_shrunken(active_size, Gmax1, Gmax2))
|
|---|
| 712 | {
|
|---|
| 713 | swap_index(i, active_size);
|
|---|
| 714 | break;
|
|---|
| 715 | }
|
|---|
| 716 | active_size--;
|
|---|
| 717 | }
|
|---|
| 718 | }
|
|---|
| 719 |
|
|---|
| 720 | // unshrink, check all variables again before sealed iterations
|
|---|
| 721 |
|
|---|
| 722 | if (unshrinked || Gmax1 + Gmax2 > eps * 10) return;
|
|---|
| 723 |
|
|---|
| 724 | unshrinked = true;
|
|---|
| 725 | reconstruct_gradient();
|
|---|
| 726 |
|
|---|
| 727 | for (i = l - 1; i >= active_size; i--)
|
|---|
| 728 | if (!be_shrunken(i, Gmax1, Gmax2))
|
|---|
| 729 | {
|
|---|
| 730 | while (active_size < i)
|
|---|
| 731 | {
|
|---|
| 732 | if (be_shrunken(active_size, Gmax1, Gmax2))
|
|---|
| 733 | {
|
|---|
| 734 | swap_index(i, active_size);
|
|---|
| 735 | break;
|
|---|
| 736 | }
|
|---|
| 737 | active_size++;
|
|---|
| 738 | }
|
|---|
| 739 | active_size++;
|
|---|
| 740 | }
|
|---|
| 741 | }
|
|---|
| 742 |
|
|---|
| 743 | protected virtual double calculate_rho()
|
|---|
| 744 | {
|
|---|
| 745 | double r;
|
|---|
| 746 | int nr_free = 0;
|
|---|
| 747 | double ub = INF, lb = -INF, sum_free = 0;
|
|---|
| 748 | for (int i = 0; i < active_size; i++)
|
|---|
| 749 | {
|
|---|
| 750 | double yG = y[i] * G[i];
|
|---|
| 751 |
|
|---|
| 752 | if (is_lower_bound(i))
|
|---|
| 753 | {
|
|---|
| 754 | if (y[i] > 0)
|
|---|
| 755 | ub = Math.Min(ub, yG);
|
|---|
| 756 | else
|
|---|
| 757 | lb = Math.Max(lb, yG);
|
|---|
| 758 | }
|
|---|
| 759 | else if (is_upper_bound(i))
|
|---|
| 760 | {
|
|---|
| 761 | if (y[i] < 0)
|
|---|
| 762 | ub = Math.Min(ub, yG);
|
|---|
| 763 | else
|
|---|
| 764 | lb = Math.Max(lb, yG);
|
|---|
| 765 | }
|
|---|
| 766 | else
|
|---|
| 767 | {
|
|---|
| 768 | ++nr_free;
|
|---|
| 769 | sum_free += yG;
|
|---|
| 770 | }
|
|---|
| 771 | }
|
|---|
| 772 |
|
|---|
| 773 | if (nr_free > 0)
|
|---|
| 774 | r = sum_free / nr_free;
|
|---|
| 775 | else
|
|---|
| 776 | r = (ub + lb) / 2;
|
|---|
| 777 |
|
|---|
| 778 | return r;
|
|---|
| 779 | }
|
|---|
| 780 |
|
|---|
| 781 | }
|
|---|
| 782 |
|
|---|
| 783 | //
|
|---|
| 784 | // Solver for nu-svm classification and regression
|
|---|
| 785 | //
|
|---|
| 786 | // additional constraint: e^T \alpha = constant
|
|---|
| 787 | //
|
|---|
| 788 | sealed class Solver_NU : Solver
|
|---|
| 789 | {
|
|---|
| 790 | private SolutionInfo si;
|
|---|
| 791 |
|
|---|
| 792 | public override void Solve(int l, QMatrix Q, double[] p, short[] y,
|
|---|
| 793 | double[] alpha, double Cp, double Cn, double eps,
|
|---|
| 794 | SolutionInfo si, bool shrinking)
|
|---|
| 795 | {
|
|---|
| 796 | this.si = si;
|
|---|
| 797 | base.Solve(l, Q, p, y, alpha, Cp, Cn, eps, si, shrinking);
|
|---|
| 798 | }
|
|---|
| 799 |
|
|---|
| 800 | // return 1 if already optimal, return 0 otherwise
|
|---|
| 801 | protected override int select_working_set(int[] working_set)
|
|---|
| 802 | {
|
|---|
| 803 | // return i,j such that y_i = y_j and
|
|---|
| 804 | // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha)
|
|---|
| 805 | // j: minimizes the decrease of obj value
|
|---|
| 806 | // (if quadratic coefficeint <= 0, replace it with tau)
|
|---|
| 807 | // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha)
|
|---|
| 808 |
|
|---|
| 809 | double Gmaxp = -INF;
|
|---|
| 810 | double Gmaxp2 = -INF;
|
|---|
| 811 | int Gmaxp_idx = -1;
|
|---|
| 812 |
|
|---|
| 813 | double Gmaxn = -INF;
|
|---|
| 814 | double Gmaxn2 = -INF;
|
|---|
| 815 | int Gmaxn_idx = -1;
|
|---|
| 816 |
|
|---|
| 817 | int Gmin_idx = -1;
|
|---|
| 818 | double obj_diff_min = INF;
|
|---|
| 819 |
|
|---|
| 820 | for (int t = 0; t < active_size; t++)
|
|---|
| 821 | if (y[t] == +1)
|
|---|
| 822 | {
|
|---|
| 823 | if (!is_upper_bound(t))
|
|---|
| 824 | if (-G[t] >= Gmaxp)
|
|---|
| 825 | {
|
|---|
| 826 | Gmaxp = -G[t];
|
|---|
| 827 | Gmaxp_idx = t;
|
|---|
| 828 | }
|
|---|
| 829 | }
|
|---|
| 830 | else
|
|---|
| 831 | {
|
|---|
| 832 | if (!is_lower_bound(t))
|
|---|
| 833 | if (G[t] >= Gmaxn)
|
|---|
| 834 | {
|
|---|
| 835 | Gmaxn = G[t];
|
|---|
| 836 | Gmaxn_idx = t;
|
|---|
| 837 | }
|
|---|
| 838 | }
|
|---|
| 839 |
|
|---|
| 840 | int ip = Gmaxp_idx;
|
|---|
| 841 | int iN = Gmaxn_idx;
|
|---|
| 842 | float[] Q_ip = null;
|
|---|
| 843 | float[] Q_in = null;
|
|---|
| 844 | if (ip != -1) // null Q_ip not accessed: Gmaxp=-INF if ip=-1
|
|---|
| 845 | Q_ip = Q.get_Q(ip, active_size);
|
|---|
| 846 | if (iN != -1)
|
|---|
| 847 | Q_in = Q.get_Q(iN, active_size);
|
|---|
| 848 |
|
|---|
| 849 | for (int j = 0; j < active_size; j++)
|
|---|
| 850 | {
|
|---|
| 851 | if (y[j] == +1)
|
|---|
| 852 | {
|
|---|
| 853 | if (!is_lower_bound(j))
|
|---|
| 854 | {
|
|---|
| 855 | double grad_diff = Gmaxp + G[j];
|
|---|
| 856 | if (G[j] >= Gmaxp2)
|
|---|
| 857 | Gmaxp2 = G[j];
|
|---|
| 858 | if (grad_diff > 0)
|
|---|
| 859 | {
|
|---|
| 860 | double obj_diff;
|
|---|
| 861 | double quad_coef = Q_ip[ip] + QD[j] - 2 * Q_ip[j];
|
|---|
| 862 | if (quad_coef > 0)
|
|---|
| 863 | obj_diff = -(grad_diff * grad_diff) / quad_coef;
|
|---|
| 864 | else
|
|---|
| 865 | obj_diff = -(grad_diff * grad_diff) / 1e-12;
|
|---|
| 866 |
|
|---|
| 867 | if (obj_diff <= obj_diff_min)
|
|---|
| 868 | {
|
|---|
| 869 | Gmin_idx = j;
|
|---|
| 870 | obj_diff_min = obj_diff;
|
|---|
| 871 | }
|
|---|
| 872 | }
|
|---|
| 873 | }
|
|---|
| 874 | }
|
|---|
| 875 | else
|
|---|
| 876 | {
|
|---|
| 877 | if (!is_upper_bound(j))
|
|---|
| 878 | {
|
|---|
| 879 | double grad_diff = Gmaxn - G[j];
|
|---|
| 880 | if (-G[j] >= Gmaxn2)
|
|---|
| 881 | Gmaxn2 = -G[j];
|
|---|
| 882 | if (grad_diff > 0)
|
|---|
| 883 | {
|
|---|
| 884 | double obj_diff;
|
|---|
| 885 | double quad_coef = Q_in[iN] + QD[j] - 2 * Q_in[j];
|
|---|
| 886 | if (quad_coef > 0)
|
|---|
| 887 | obj_diff = -(grad_diff * grad_diff) / quad_coef;
|
|---|
| 888 | else
|
|---|
| 889 | obj_diff = -(grad_diff * grad_diff) / 1e-12;
|
|---|
| 890 |
|
|---|
| 891 | if (obj_diff <= obj_diff_min)
|
|---|
| 892 | {
|
|---|
| 893 | Gmin_idx = j;
|
|---|
| 894 | obj_diff_min = obj_diff;
|
|---|
| 895 | }
|
|---|
| 896 | }
|
|---|
| 897 | }
|
|---|
| 898 | }
|
|---|
| 899 | }
|
|---|
| 900 |
|
|---|
| 901 | if (Math.Max(Gmaxp + Gmaxp2, Gmaxn + Gmaxn2) < eps)
|
|---|
| 902 | return 1;
|
|---|
| 903 |
|
|---|
| 904 | if (y[Gmin_idx] == +1)
|
|---|
| 905 | working_set[0] = Gmaxp_idx;
|
|---|
| 906 | else
|
|---|
| 907 | working_set[0] = Gmaxn_idx;
|
|---|
| 908 | working_set[1] = Gmin_idx;
|
|---|
| 909 |
|
|---|
| 910 | return 0;
|
|---|
| 911 | }
|
|---|
| 912 |
|
|---|
| 913 | private bool be_shrunken(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4)
|
|---|
| 914 | {
|
|---|
| 915 | if (is_upper_bound(i))
|
|---|
| 916 | {
|
|---|
| 917 | if (y[i] == +1)
|
|---|
| 918 | return (-G[i] > Gmax1);
|
|---|
| 919 | else
|
|---|
| 920 | return (-G[i] > Gmax4);
|
|---|
| 921 | }
|
|---|
| 922 | else if (is_lower_bound(i))
|
|---|
| 923 | {
|
|---|
| 924 | if (y[i] == +1)
|
|---|
| 925 | return (G[i] > Gmax2);
|
|---|
| 926 | else
|
|---|
| 927 | return (G[i] > Gmax3);
|
|---|
| 928 | }
|
|---|
| 929 | else
|
|---|
| 930 | return (false);
|
|---|
| 931 | }
|
|---|
| 932 |
|
|---|
| 933 | protected override void do_shrinking()
|
|---|
| 934 | {
|
|---|
| 935 | double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) }
|
|---|
| 936 | double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) }
|
|---|
| 937 | double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) }
|
|---|
| 938 | double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) }
|
|---|
| 939 |
|
|---|
| 940 | // find maximal violating pair first
|
|---|
| 941 | int i;
|
|---|
| 942 | for (i = 0; i < active_size; i++)
|
|---|
| 943 | {
|
|---|
| 944 | if (!is_upper_bound(i))
|
|---|
| 945 | {
|
|---|
| 946 | if (y[i] == +1)
|
|---|
| 947 | {
|
|---|
| 948 | if (-G[i] > Gmax1) Gmax1 = -G[i];
|
|---|
| 949 | }
|
|---|
| 950 | else if (-G[i] > Gmax4) Gmax4 = -G[i];
|
|---|
| 951 | }
|
|---|
| 952 | if (!is_lower_bound(i))
|
|---|
| 953 | {
|
|---|
| 954 | if (y[i] == +1)
|
|---|
| 955 | {
|
|---|
| 956 | if (G[i] > Gmax2) Gmax2 = G[i];
|
|---|
| 957 | }
|
|---|
| 958 | else if (G[i] > Gmax3) Gmax3 = G[i];
|
|---|
| 959 | }
|
|---|
| 960 | }
|
|---|
| 961 |
|
|---|
| 962 | // shrinking
|
|---|
| 963 |
|
|---|
| 964 | for (i = 0; i < active_size; i++)
|
|---|
| 965 | if (be_shrunken(i, Gmax1, Gmax2, Gmax3, Gmax4))
|
|---|
| 966 | {
|
|---|
| 967 | active_size--;
|
|---|
| 968 | while (active_size > i)
|
|---|
| 969 | {
|
|---|
| 970 | if (!be_shrunken(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
|
|---|
| 971 | {
|
|---|
| 972 | swap_index(i, active_size);
|
|---|
| 973 | break;
|
|---|
| 974 | }
|
|---|
| 975 | active_size--;
|
|---|
| 976 | }
|
|---|
| 977 | }
|
|---|
| 978 |
|
|---|
| 979 | if (unshrinked || Math.Max(Gmax1 + Gmax2, Gmax3 + Gmax4) > eps * 10) return;
|
|---|
| 980 |
|
|---|
| 981 | unshrinked = true;
|
|---|
| 982 | reconstruct_gradient();
|
|---|
| 983 |
|
|---|
| 984 | for (i = l - 1; i >= active_size; i--)
|
|---|
| 985 | if (!be_shrunken(i, Gmax1, Gmax2, Gmax3, Gmax4))
|
|---|
| 986 | {
|
|---|
| 987 | while (active_size < i)
|
|---|
| 988 | {
|
|---|
| 989 | if (be_shrunken(active_size, Gmax1, Gmax2, Gmax3, Gmax4))
|
|---|
| 990 | {
|
|---|
| 991 | swap_index(i, active_size);
|
|---|
| 992 | break;
|
|---|
| 993 | }
|
|---|
| 994 | active_size++;
|
|---|
| 995 | }
|
|---|
| 996 | active_size++;
|
|---|
| 997 | }
|
|---|
| 998 | }
|
|---|
| 999 |
|
|---|
| 1000 | protected override double calculate_rho()
|
|---|
| 1001 | {
|
|---|
| 1002 | int nr_free1 = 0, nr_free2 = 0;
|
|---|
| 1003 | double ub1 = INF, ub2 = INF;
|
|---|
| 1004 | double lb1 = -INF, lb2 = -INF;
|
|---|
| 1005 | double sum_free1 = 0, sum_free2 = 0;
|
|---|
| 1006 |
|
|---|
| 1007 | for (int i = 0; i < active_size; i++)
|
|---|
| 1008 | {
|
|---|
| 1009 | if (y[i] == +1)
|
|---|
| 1010 | {
|
|---|
| 1011 | if (is_lower_bound(i))
|
|---|
| 1012 | ub1 = Math.Min(ub1, G[i]);
|
|---|
| 1013 | else if (is_upper_bound(i))
|
|---|
| 1014 | lb1 = Math.Max(lb1, G[i]);
|
|---|
| 1015 | else
|
|---|
| 1016 | {
|
|---|
| 1017 | ++nr_free1;
|
|---|
| 1018 | sum_free1 += G[i];
|
|---|
| 1019 | }
|
|---|
| 1020 | }
|
|---|
| 1021 | else
|
|---|
| 1022 | {
|
|---|
| 1023 | if (is_lower_bound(i))
|
|---|
| 1024 | ub2 = Math.Min(ub2, G[i]);
|
|---|
| 1025 | else if (is_upper_bound(i))
|
|---|
| 1026 | lb2 = Math.Max(lb2, G[i]);
|
|---|
| 1027 | else
|
|---|
| 1028 | {
|
|---|
| 1029 | ++nr_free2;
|
|---|
| 1030 | sum_free2 += G[i];
|
|---|
| 1031 | }
|
|---|
| 1032 | }
|
|---|
| 1033 | }
|
|---|
| 1034 |
|
|---|
| 1035 | double r1, r2;
|
|---|
| 1036 | if (nr_free1 > 0)
|
|---|
| 1037 | r1 = sum_free1 / nr_free1;
|
|---|
| 1038 | else
|
|---|
| 1039 | r1 = (ub1 + lb1) / 2;
|
|---|
| 1040 |
|
|---|
| 1041 | if (nr_free2 > 0)
|
|---|
| 1042 | r2 = sum_free2 / nr_free2;
|
|---|
| 1043 | else
|
|---|
| 1044 | r2 = (ub2 + lb2) / 2;
|
|---|
| 1045 |
|
|---|
| 1046 | si.r = (r1 + r2) / 2;
|
|---|
| 1047 | return (r1 - r2) / 2;
|
|---|
| 1048 | }
|
|---|
| 1049 | }
|
|---|
| 1050 |
|
|---|
| 1051 | //
|
|---|
| 1052 | // Q matrices for various formulations
|
|---|
| 1053 | //
|
|---|
| 1054 | class SVC_Q : Kernel
|
|---|
| 1055 | {
|
|---|
| 1056 | private short[] y;
|
|---|
| 1057 | private Cache cache;
|
|---|
| 1058 | private float[] QD;
|
|---|
| 1059 |
|
|---|
| 1060 | public SVC_Q(Problem prob, Parameter param, short[] y_) : base(prob.Count, prob.X, param)
|
|---|
| 1061 | {
|
|---|
| 1062 | y = (short[])y_.Clone();
|
|---|
| 1063 | cache = new Cache(prob.Count, (long)(param.CacheSize * (1 << 20)));
|
|---|
| 1064 | QD = new float[prob.Count];
|
|---|
| 1065 | for (int i = 0; i < prob.Count; i++)
|
|---|
| 1066 | QD[i] = (float)kernel_function(i, i);
|
|---|
| 1067 | }
|
|---|
| 1068 |
|
|---|
| 1069 | public override float[] get_Q(int i, int len)
|
|---|
| 1070 | {
|
|---|
| 1071 | float[][] data = new float[1][];
|
|---|
| 1072 | int start;
|
|---|
| 1073 | if ((start = cache.get_data(i, data, len)) < len)
|
|---|
| 1074 | {
|
|---|
| 1075 | for (int j = start; j < len; j++)
|
|---|
| 1076 | data[0][j] = (float)(y[i] * y[j] * kernel_function(i, j));
|
|---|
| 1077 | }
|
|---|
| 1078 | return data[0];
|
|---|
| 1079 | }
|
|---|
| 1080 |
|
|---|
| 1081 | public override float[] get_QD()
|
|---|
| 1082 | {
|
|---|
| 1083 | return QD;
|
|---|
| 1084 | }
|
|---|
| 1085 |
|
|---|
| 1086 | public override void swap_index(int i, int j)
|
|---|
| 1087 | {
|
|---|
| 1088 | cache.swap_index(i, j);
|
|---|
| 1089 | base.swap_index(i, j);
|
|---|
| 1090 | do { short _ = y[i]; y[i] = y[j]; y[j] = _; } while (false);
|
|---|
| 1091 | do { float _ = QD[i]; QD[i] = QD[j]; QD[j] = _; } while (false);
|
|---|
| 1092 | }
|
|---|
| 1093 | }
|
|---|
| 1094 |
|
|---|
| 1095 | class ONE_CLASS_Q : Kernel
|
|---|
| 1096 | {
|
|---|
| 1097 | private Cache cache;
|
|---|
| 1098 | private float[] QD;
|
|---|
| 1099 |
|
|---|
| 1100 | public ONE_CLASS_Q(Problem prob, Parameter param) : base(prob.Count, prob.X, param)
|
|---|
| 1101 | {
|
|---|
| 1102 | cache = new Cache(prob.Count, (long)(param.CacheSize * (1 << 20)));
|
|---|
| 1103 | QD = new float[prob.Count];
|
|---|
| 1104 | for (int i = 0; i < prob.Count; i++)
|
|---|
| 1105 | QD[i] = (float)kernel_function(i, i);
|
|---|
| 1106 | }
|
|---|
| 1107 |
|
|---|
| 1108 | public override float[] get_Q(int i, int len)
|
|---|
| 1109 | {
|
|---|
| 1110 | float[][] data = new float[1][];
|
|---|
| 1111 | int start;
|
|---|
| 1112 | if ((start = cache.get_data(i, data, len)) < len)
|
|---|
| 1113 | {
|
|---|
| 1114 | for (int j = start; j < len; j++)
|
|---|
| 1115 | data[0][j] = (float)kernel_function(i, j);
|
|---|
| 1116 | }
|
|---|
| 1117 | return data[0];
|
|---|
| 1118 | }
|
|---|
| 1119 |
|
|---|
| 1120 | public override float[] get_QD()
|
|---|
| 1121 | {
|
|---|
| 1122 | return QD;
|
|---|
| 1123 | }
|
|---|
| 1124 |
|
|---|
| 1125 | public override void swap_index(int i, int j)
|
|---|
| 1126 | {
|
|---|
| 1127 | cache.swap_index(i, j);
|
|---|
| 1128 | base.swap_index(i, j);
|
|---|
| 1129 | do { float _ = QD[i]; QD[i] = QD[j]; QD[j] = _; } while (false);
|
|---|
| 1130 | }
|
|---|
| 1131 | }
|
|---|
| 1132 |
|
|---|
| 1133 | class SVR_Q : Kernel
|
|---|
| 1134 | {
|
|---|
| 1135 | private int l;
|
|---|
| 1136 | private Cache cache;
|
|---|
| 1137 | private short[] sign;
|
|---|
| 1138 | private int[] index;
|
|---|
| 1139 | private int next_buffer;
|
|---|
| 1140 | private float[][] buffer;
|
|---|
| 1141 | private float[] QD;
|
|---|
| 1142 |
|
|---|
| 1143 | public SVR_Q(Problem prob, Parameter param)
|
|---|
| 1144 | : base(prob.Count, prob.X, param)
|
|---|
| 1145 | {
|
|---|
| 1146 | l = prob.Count;
|
|---|
| 1147 | cache = new Cache(l, (long)(param.CacheSize * (1 << 20)));
|
|---|
| 1148 | QD = new float[2 * l];
|
|---|
| 1149 | sign = new short[2 * l];
|
|---|
| 1150 | index = new int[2 * l];
|
|---|
| 1151 | for (int k = 0; k < l; k++)
|
|---|
| 1152 | {
|
|---|
| 1153 | sign[k] = 1;
|
|---|
| 1154 | sign[k + l] = -1;
|
|---|
| 1155 | index[k] = k;
|
|---|
| 1156 | index[k + l] = k;
|
|---|
| 1157 | QD[k] = (float)kernel_function(k, k);
|
|---|
| 1158 | QD[k + l] = QD[k];
|
|---|
| 1159 | }
|
|---|
| 1160 | buffer = new float[2][];
|
|---|
| 1161 | buffer[0] = new float[2 * l];
|
|---|
| 1162 | buffer[1] = new float[2 * l];
|
|---|
| 1163 | next_buffer = 0;
|
|---|
| 1164 | }
|
|---|
| 1165 |
|
|---|
| 1166 | public override void swap_index(int i, int j)
|
|---|
| 1167 | {
|
|---|
| 1168 | do { short _ = sign[i]; sign[i] = sign[j]; sign[j] = _; } while (false);
|
|---|
| 1169 | do { int _ = index[i]; index[i] = index[j]; index[j] = _; } while (false);
|
|---|
| 1170 | do { float _ = QD[i]; QD[i] = QD[j]; QD[j] = _; } while (false);
|
|---|
| 1171 | }
|
|---|
| 1172 |
|
|---|
| 1173 | public override float[] get_Q(int i, int len)
|
|---|
| 1174 | {
|
|---|
| 1175 | float[][] data = new float[1][];
|
|---|
| 1176 | int real_i = index[i];
|
|---|
| 1177 | if (cache.get_data(real_i, data, l) < l)
|
|---|
| 1178 | {
|
|---|
| 1179 | for (int j = 0; j < l; j++)
|
|---|
| 1180 | data[0][j] = (float)kernel_function(real_i, j);
|
|---|
| 1181 | }
|
|---|
| 1182 |
|
|---|
| 1183 | // reorder and copy
|
|---|
| 1184 | float[] buf = buffer[next_buffer];
|
|---|
| 1185 | next_buffer = 1 - next_buffer;
|
|---|
| 1186 | short si = sign[i];
|
|---|
| 1187 | for (int j = 0; j < len; j++)
|
|---|
| 1188 | buf[j] = si * sign[j] * data[0][index[j]];
|
|---|
| 1189 | return buf;
|
|---|
| 1190 | }
|
|---|
| 1191 |
|
|---|
| 1192 | public override float[] get_QD()
|
|---|
| 1193 | {
|
|---|
| 1194 | return QD;
|
|---|
| 1195 | }
|
|---|
| 1196 | }
|
|---|
| 1197 |
|
|---|
| 1198 | internal static class Procedures
|
|---|
| 1199 | {
|
|---|
| 1200 | //
|
|---|
| 1201 | // construct and solve various formulations
|
|---|
| 1202 | //
|
|---|
| 1203 | private static void solve_c_svc(Problem prob, Parameter param,
|
|---|
| 1204 | double[] alpha, Solver.SolutionInfo si,
|
|---|
| 1205 | double Cp, double Cn)
|
|---|
| 1206 | {
|
|---|
| 1207 | int l = prob.Count;
|
|---|
| 1208 | double[] minus_ones = new double[l];
|
|---|
| 1209 | short[] y = new short[l];
|
|---|
| 1210 |
|
|---|
| 1211 | int i;
|
|---|
| 1212 |
|
|---|
| 1213 | for (i = 0; i < l; i++)
|
|---|
| 1214 | {
|
|---|
| 1215 | alpha[i] = 0;
|
|---|
| 1216 | minus_ones[i] = -1;
|
|---|
| 1217 | if (prob.Y[i] > 0) y[i] = +1; else y[i] = -1;
|
|---|
| 1218 | }
|
|---|
| 1219 |
|
|---|
| 1220 | Solver s = new Solver();
|
|---|
| 1221 | s.Solve(l, new SVC_Q(prob, param, y), minus_ones, y,
|
|---|
| 1222 | alpha, Cp, Cn, param.EPS, si, param.Shrinking);
|
|---|
| 1223 |
|
|---|
| 1224 | double sum_alpha = 0;
|
|---|
| 1225 | for (i = 0; i < l; i++)
|
|---|
| 1226 | sum_alpha += alpha[i];
|
|---|
| 1227 |
|
|---|
| 1228 | if (Cp == Cn)
|
|---|
| 1229 | Debug.Write("nu = " + sum_alpha / (Cp * prob.Count) + "\n");
|
|---|
| 1230 |
|
|---|
| 1231 | for (i = 0; i < l; i++)
|
|---|
| 1232 | alpha[i] *= y[i];
|
|---|
| 1233 | }
|
|---|
| 1234 |
|
|---|
| 1235 | private static void solve_nu_svc(Problem prob, Parameter param,
|
|---|
| 1236 | double[] alpha, Solver.SolutionInfo si)
|
|---|
| 1237 | {
|
|---|
| 1238 | int i;
|
|---|
| 1239 | int l = prob.Count;
|
|---|
| 1240 | double nu = param.Nu;
|
|---|
| 1241 |
|
|---|
| 1242 | short[] y = new short[l];
|
|---|
| 1243 |
|
|---|
| 1244 | for (i = 0; i < l; i++)
|
|---|
| 1245 | if (prob.Y[i] > 0)
|
|---|
| 1246 | y[i] = +1;
|
|---|
| 1247 | else
|
|---|
| 1248 | y[i] = -1;
|
|---|
| 1249 |
|
|---|
| 1250 | double sum_pos = nu * l / 2;
|
|---|
| 1251 | double sum_neg = nu * l / 2;
|
|---|
| 1252 |
|
|---|
| 1253 | for (i = 0; i < l; i++)
|
|---|
| 1254 | if (y[i] == +1)
|
|---|
| 1255 | {
|
|---|
| 1256 | alpha[i] = Math.Min(1.0, sum_pos);
|
|---|
| 1257 | sum_pos -= alpha[i];
|
|---|
| 1258 | }
|
|---|
| 1259 | else
|
|---|
| 1260 | {
|
|---|
| 1261 | alpha[i] = Math.Min(1.0, sum_neg);
|
|---|
| 1262 | sum_neg -= alpha[i];
|
|---|
| 1263 | }
|
|---|
| 1264 |
|
|---|
| 1265 | double[] zeros = new double[l];
|
|---|
| 1266 |
|
|---|
| 1267 | for (i = 0; i < l; i++)
|
|---|
| 1268 | zeros[i] = 0;
|
|---|
| 1269 |
|
|---|
| 1270 | Solver_NU s = new Solver_NU();
|
|---|
| 1271 | s.Solve(l, new SVC_Q(prob, param, y), zeros, y,
|
|---|
| 1272 | alpha, 1.0, 1.0, param.EPS, si, param.Shrinking);
|
|---|
| 1273 | double r = si.r;
|
|---|
| 1274 |
|
|---|
| 1275 | Debug.Write("C = " + 1 / r + "\n");
|
|---|
| 1276 |
|
|---|
| 1277 | for (i = 0; i < l; i++)
|
|---|
| 1278 | alpha[i] *= y[i] / r;
|
|---|
| 1279 |
|
|---|
| 1280 | si.rho /= r;
|
|---|
| 1281 | si.obj /= (r * r);
|
|---|
| 1282 | si.upper_bound_p = 1 / r;
|
|---|
| 1283 | si.upper_bound_n = 1 / r;
|
|---|
| 1284 | }
|
|---|
| 1285 |
|
|---|
| 1286 | private static void solve_one_class(Problem prob, Parameter param,
|
|---|
| 1287 | double[] alpha, Solver.SolutionInfo si)
|
|---|
| 1288 | {
|
|---|
| 1289 | int l = prob.Count;
|
|---|
| 1290 | double[] zeros = new double[l];
|
|---|
| 1291 | short[] ones = new short[l];
|
|---|
| 1292 | int i;
|
|---|
| 1293 |
|
|---|
| 1294 | int n = (int)(param.Nu * prob.Count); // # of alpha's at upper bound
|
|---|
| 1295 |
|
|---|
| 1296 | for (i = 0; i < n; i++)
|
|---|
| 1297 | alpha[i] = 1;
|
|---|
| 1298 | if (n < prob.Count)
|
|---|
| 1299 | alpha[n] = param.Nu * prob.Count - n;
|
|---|
| 1300 | for (i = n + 1; i < l; i++)
|
|---|
| 1301 | alpha[i] = 0;
|
|---|
| 1302 |
|
|---|
| 1303 | for (i = 0; i < l; i++)
|
|---|
| 1304 | {
|
|---|
| 1305 | zeros[i] = 0;
|
|---|
| 1306 | ones[i] = 1;
|
|---|
| 1307 | }
|
|---|
| 1308 |
|
|---|
| 1309 | Solver s = new Solver();
|
|---|
| 1310 | s.Solve(l, new ONE_CLASS_Q(prob, param), zeros, ones,
|
|---|
| 1311 | alpha, 1.0, 1.0, param.EPS, si, param.Shrinking);
|
|---|
| 1312 | }
|
|---|
| 1313 |
|
|---|
| 1314 | private static void solve_epsilon_svr(Problem prob, Parameter param,
|
|---|
| 1315 | double[] alpha, Solver.SolutionInfo si)
|
|---|
| 1316 | {
|
|---|
| 1317 | int l = prob.Count;
|
|---|
| 1318 | double[] alpha2 = new double[2 * l];
|
|---|
| 1319 | double[] linear_term = new double[2 * l];
|
|---|
| 1320 | short[] y = new short[2 * l];
|
|---|
| 1321 | int i;
|
|---|
| 1322 |
|
|---|
| 1323 | for (i = 0; i < l; i++)
|
|---|
| 1324 | {
|
|---|
| 1325 | alpha2[i] = 0;
|
|---|
| 1326 | linear_term[i] = param.P - prob.Y[i];
|
|---|
| 1327 | y[i] = 1;
|
|---|
| 1328 |
|
|---|
| 1329 | alpha2[i + l] = 0;
|
|---|
| 1330 | linear_term[i + l] = param.P + prob.Y[i];
|
|---|
| 1331 | y[i + l] = -1;
|
|---|
| 1332 | }
|
|---|
| 1333 |
|
|---|
| 1334 | Solver s = new Solver();
|
|---|
| 1335 | s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y,
|
|---|
| 1336 | alpha2, param.C, param.C, param.EPS, si, param.Shrinking);
|
|---|
| 1337 |
|
|---|
| 1338 | double sum_alpha = 0;
|
|---|
| 1339 | for (i = 0; i < l; i++)
|
|---|
| 1340 | {
|
|---|
| 1341 | alpha[i] = alpha2[i] - alpha2[i + l];
|
|---|
| 1342 | sum_alpha += Math.Abs(alpha[i]);
|
|---|
| 1343 | }
|
|---|
| 1344 | Debug.Write("nu = " + sum_alpha / (param.C * l) + "\n");
|
|---|
| 1345 | }
|
|---|
| 1346 |
|
|---|
| 1347 | private static void solve_nu_svr(Problem prob, Parameter param,
|
|---|
| 1348 | double[] alpha, Solver.SolutionInfo si)
|
|---|
| 1349 | {
|
|---|
| 1350 | int l = prob.Count;
|
|---|
| 1351 | double C = param.C;
|
|---|
| 1352 | double[] alpha2 = new double[2 * l];
|
|---|
| 1353 | double[] linear_term = new double[2 * l];
|
|---|
| 1354 | short[] y = new short[2 * l];
|
|---|
| 1355 | int i;
|
|---|
| 1356 |
|
|---|
| 1357 | double sum = C * param.Nu * l / 2;
|
|---|
| 1358 | for (i = 0; i < l; i++)
|
|---|
| 1359 | {
|
|---|
| 1360 | alpha2[i] = alpha2[i + l] = Math.Min(sum, C);
|
|---|
| 1361 | sum -= alpha2[i];
|
|---|
| 1362 |
|
|---|
| 1363 | linear_term[i] = -prob.Y[i];
|
|---|
| 1364 | y[i] = 1;
|
|---|
| 1365 |
|
|---|
| 1366 | linear_term[i + l] = prob.Y[i];
|
|---|
| 1367 | y[i + l] = -1;
|
|---|
| 1368 | }
|
|---|
| 1369 |
|
|---|
| 1370 | Solver_NU s = new Solver_NU();
|
|---|
| 1371 | s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y, alpha2, C, C, param.EPS, si, param.Shrinking);
|
|---|
| 1372 |
|
|---|
| 1373 | Debug.Write("epsilon = " + (-si.r) + "\n");
|
|---|
| 1374 |
|
|---|
| 1375 | for (i = 0; i < l; i++)
|
|---|
| 1376 | alpha[i] = alpha2[i] - alpha2[i + l];
|
|---|
| 1377 | }
|
|---|
| 1378 |
|
|---|
| 1379 | //
|
|---|
| 1380 | // decision_function
|
|---|
| 1381 | //
|
|---|
| 1382 | private class decision_function
|
|---|
| 1383 | {
|
|---|
| 1384 | public double[] alpha;
|
|---|
| 1385 | public double rho;
|
|---|
| 1386 | };
|
|---|
| 1387 |
|
|---|
| 1388 | static decision_function svm_train_one(
|
|---|
| 1389 | Problem prob, Parameter param,
|
|---|
| 1390 | double Cp, double Cn)
|
|---|
| 1391 | {
|
|---|
| 1392 | double[] alpha = new double[prob.Count];
|
|---|
| 1393 | Solver.SolutionInfo si = new Solver.SolutionInfo();
|
|---|
| 1394 | switch (param.SvmType)
|
|---|
| 1395 | {
|
|---|
| 1396 | case SvmType.C_SVC:
|
|---|
| 1397 | solve_c_svc(prob, param, alpha, si, Cp, Cn);
|
|---|
| 1398 | break;
|
|---|
| 1399 | case SvmType.NU_SVC:
|
|---|
| 1400 | solve_nu_svc(prob, param, alpha, si);
|
|---|
| 1401 | break;
|
|---|
| 1402 | case SvmType.ONE_CLASS:
|
|---|
| 1403 | solve_one_class(prob, param, alpha, si);
|
|---|
| 1404 | break;
|
|---|
| 1405 | case SvmType.EPSILON_SVR:
|
|---|
| 1406 | solve_epsilon_svr(prob, param, alpha, si);
|
|---|
| 1407 | break;
|
|---|
| 1408 | case SvmType.NU_SVR:
|
|---|
| 1409 | solve_nu_svr(prob, param, alpha, si);
|
|---|
| 1410 | break;
|
|---|
| 1411 | }
|
|---|
| 1412 |
|
|---|
| 1413 | Debug.Write("obj = " + si.obj + ", rho = " + si.rho + "\n");
|
|---|
| 1414 |
|
|---|
| 1415 | // output SVs
|
|---|
| 1416 |
|
|---|
| 1417 | int nSV = 0;
|
|---|
| 1418 | int nBSV = 0;
|
|---|
| 1419 | for (int i = 0; i < prob.Count; i++)
|
|---|
| 1420 | {
|
|---|
| 1421 | if (Math.Abs(alpha[i]) > 0)
|
|---|
| 1422 | {
|
|---|
| 1423 | ++nSV;
|
|---|
| 1424 | if (prob.Y[i] > 0)
|
|---|
| 1425 | {
|
|---|
| 1426 | if (Math.Abs(alpha[i]) >= si.upper_bound_p)
|
|---|
| 1427 | ++nBSV;
|
|---|
| 1428 | }
|
|---|
| 1429 | else
|
|---|
| 1430 | {
|
|---|
| 1431 | if (Math.Abs(alpha[i]) >= si.upper_bound_n)
|
|---|
| 1432 | ++nBSV;
|
|---|
| 1433 | }
|
|---|
| 1434 | }
|
|---|
| 1435 | }
|
|---|
| 1436 |
|
|---|
| 1437 | Debug.Write("nSV = " + nSV + ", nBSV = " + nBSV + "\n");
|
|---|
| 1438 |
|
|---|
| 1439 | decision_function f = new decision_function();
|
|---|
| 1440 | f.alpha = alpha;
|
|---|
| 1441 | f.rho = si.rho;
|
|---|
| 1442 | return f;
|
|---|
| 1443 | }
|
|---|
| 1444 |
|
|---|
| 1445 | // Platt's binary SVM Probablistic Output: an improvement from Lin et al.
|
|---|
| 1446 | private static void sigmoid_train(int l, double[] dec_values, double[] labels,
|
|---|
| 1447 | double[] probAB)
|
|---|
| 1448 | {
|
|---|
| 1449 | double A, B;
|
|---|
| 1450 | double prior1 = 0, prior0 = 0;
|
|---|
| 1451 | int i;
|
|---|
| 1452 |
|
|---|
| 1453 | for (i = 0; i < l; i++)
|
|---|
| 1454 | if (labels[i] > 0) prior1 += 1;
|
|---|
| 1455 | else prior0 += 1;
|
|---|
| 1456 |
|
|---|
| 1457 | int max_iter = 100; // Maximal number of iterations
|
|---|
| 1458 | double min_step = 1e-10; // Minimal step taken in line search
|
|---|
| 1459 | double sigma = 1e-3; // For numerically strict PD of Hessian
|
|---|
| 1460 | double eps = 1e-5;
|
|---|
| 1461 | double hiTarget = (prior1 + 1.0) / (prior1 + 2.0);
|
|---|
| 1462 | double loTarget = 1 / (prior0 + 2.0);
|
|---|
| 1463 | double[] t = new double[l];
|
|---|
| 1464 | double fApB, p, q, h11, h22, h21, g1, g2, det, dA, dB, gd, stepsize;
|
|---|
| 1465 | double newA, newB, newf, d1, d2;
|
|---|
| 1466 | int iter;
|
|---|
| 1467 |
|
|---|
| 1468 | // Initial Point and Initial Fun Value
|
|---|
| 1469 | A = 0.0; B = Math.Log((prior0 + 1.0) / (prior1 + 1.0));
|
|---|
| 1470 | double fval = 0.0;
|
|---|
| 1471 |
|
|---|
| 1472 | for (i = 0; i < l; i++)
|
|---|
| 1473 | {
|
|---|
| 1474 | if (labels[i] > 0) t[i] = hiTarget;
|
|---|
| 1475 | else t[i] = loTarget;
|
|---|
| 1476 | fApB = dec_values[i] * A + B;
|
|---|
| 1477 | if (fApB >= 0)
|
|---|
| 1478 | fval += t[i] * fApB + Math.Log(1 + Math.Exp(-fApB));
|
|---|
| 1479 | else
|
|---|
| 1480 | fval += (t[i] - 1) * fApB + Math.Log(1 + Math.Exp(fApB));
|
|---|
| 1481 | }
|
|---|
| 1482 | for (iter = 0; iter < max_iter; iter++)
|
|---|
| 1483 | {
|
|---|
| 1484 | // Update Gradient and Hessian (use H' = H + sigma I)
|
|---|
| 1485 | h11 = sigma; // numerically ensures strict PD
|
|---|
| 1486 | h22 = sigma;
|
|---|
| 1487 | h21 = 0.0; g1 = 0.0; g2 = 0.0;
|
|---|
| 1488 | for (i = 0; i < l; i++)
|
|---|
| 1489 | {
|
|---|
| 1490 | fApB = dec_values[i] * A + B;
|
|---|
| 1491 | if (fApB >= 0)
|
|---|
| 1492 | {
|
|---|
| 1493 | p = Math.Exp(-fApB) / (1.0 + Math.Exp(-fApB));
|
|---|
| 1494 | q = 1.0 / (1.0 + Math.Exp(-fApB));
|
|---|
| 1495 | }
|
|---|
| 1496 | else
|
|---|
| 1497 | {
|
|---|
| 1498 | p = 1.0 / (1.0 + Math.Exp(fApB));
|
|---|
| 1499 | q = Math.Exp(fApB) / (1.0 + Math.Exp(fApB));
|
|---|
| 1500 | }
|
|---|
| 1501 | d2 = p * q;
|
|---|
| 1502 | h11 += dec_values[i] * dec_values[i] * d2;
|
|---|
| 1503 | h22 += d2;
|
|---|
| 1504 | h21 += dec_values[i] * d2;
|
|---|
| 1505 | d1 = t[i] - p;
|
|---|
| 1506 | g1 += dec_values[i] * d1;
|
|---|
| 1507 | g2 += d1;
|
|---|
| 1508 | }
|
|---|
| 1509 |
|
|---|
| 1510 | // Stopping Criteria
|
|---|
| 1511 | if (Math.Abs(g1) < eps && Math.Abs(g2) < eps)
|
|---|
| 1512 | break;
|
|---|
| 1513 |
|
|---|
| 1514 | // Finding Newton direction: -inv(H') * g
|
|---|
| 1515 | det = h11 * h22 - h21 * h21;
|
|---|
| 1516 | dA = -(h22 * g1 - h21 * g2) / det;
|
|---|
| 1517 | dB = -(-h21 * g1 + h11 * g2) / det;
|
|---|
| 1518 | gd = g1 * dA + g2 * dB;
|
|---|
| 1519 |
|
|---|
| 1520 |
|
|---|
| 1521 | stepsize = 1; // Line Search
|
|---|
| 1522 | while (stepsize >= min_step)
|
|---|
| 1523 | {
|
|---|
| 1524 | newA = A + stepsize * dA;
|
|---|
| 1525 | newB = B + stepsize * dB;
|
|---|
| 1526 |
|
|---|
| 1527 | // New function value
|
|---|
| 1528 | newf = 0.0;
|
|---|
| 1529 | for (i = 0; i < l; i++)
|
|---|
| 1530 | {
|
|---|
| 1531 | fApB = dec_values[i] * newA + newB;
|
|---|
| 1532 | if (fApB >= 0)
|
|---|
| 1533 | newf += t[i] * fApB + Math.Log(1 + Math.Exp(-fApB));
|
|---|
| 1534 | else
|
|---|
| 1535 | newf += (t[i] - 1) * fApB + Math.Log(1 + Math.Exp(fApB));
|
|---|
| 1536 | }
|
|---|
| 1537 | // Check sufficient decrease
|
|---|
| 1538 | if (newf < fval + 0.0001 * stepsize * gd)
|
|---|
| 1539 | {
|
|---|
| 1540 | A = newA; B = newB; fval = newf;
|
|---|
| 1541 | break;
|
|---|
| 1542 | }
|
|---|
| 1543 | else
|
|---|
| 1544 | stepsize = stepsize / 2.0;
|
|---|
| 1545 | }
|
|---|
| 1546 |
|
|---|
| 1547 | if (stepsize < min_step)
|
|---|
| 1548 | {
|
|---|
| 1549 | Debug.Write("Line search fails in two-class probability estimates\n");
|
|---|
| 1550 | break;
|
|---|
| 1551 | }
|
|---|
| 1552 | }
|
|---|
| 1553 |
|
|---|
| 1554 | if (iter >= max_iter)
|
|---|
| 1555 | Debug.Write("Reaching maximal iterations in two-class probability estimates\n");
|
|---|
| 1556 | probAB[0] = A; probAB[1] = B;
|
|---|
| 1557 | }
|
|---|
| 1558 |
|
|---|
| 1559 | private static double sigmoid_predict(double decision_value, double A, double B)
|
|---|
| 1560 | {
|
|---|
| 1561 | double fApB = decision_value * A + B;
|
|---|
| 1562 | if (fApB >= 0)
|
|---|
| 1563 | return Math.Exp(-fApB) / (1.0 + Math.Exp(-fApB));
|
|---|
| 1564 | else
|
|---|
| 1565 | return 1.0 / (1 + Math.Exp(fApB));
|
|---|
| 1566 | }
|
|---|
| 1567 |
|
|---|
| 1568 | // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
|
|---|
| 1569 | private static void multiclass_probability(int k, double[,] r, double[] p)
|
|---|
| 1570 | {
|
|---|
| 1571 | int t,j;
|
|---|
| 1572 | int iter = 0, max_iter=Math.Max(100,k);
|
|---|
| 1573 | double[,] Q=new double[k,k];
|
|---|
| 1574 | double[] Qp= new double[k];
|
|---|
| 1575 | double pQp, eps=0.005/k;
|
|---|
| 1576 |
|
|---|
| 1577 | for (t=0;t<k;t++)
|
|---|
| 1578 | {
|
|---|
| 1579 | p[t]=1.0/k; // Valid if k = 1
|
|---|
| 1580 | Q[t,t]=0;
|
|---|
| 1581 | for (j=0;j<t;j++)
|
|---|
| 1582 | {
|
|---|
| 1583 | Q[t,t]+=r[j,t]*r[j,t];
|
|---|
| 1584 | Q[t,j]=Q[j,t];
|
|---|
| 1585 | }
|
|---|
| 1586 | for (j=t+1;j<k;j++)
|
|---|
| 1587 | {
|
|---|
| 1588 | Q[t,t]+=r[j,t]*r[j,t];
|
|---|
| 1589 | Q[t,j]=-r[j,t]*r[t,j];
|
|---|
| 1590 | }
|
|---|
| 1591 | }
|
|---|
| 1592 | for (iter=0;iter<max_iter;iter++)
|
|---|
| 1593 | {
|
|---|
| 1594 | // stopping condition, recalculate QP,pQP for numerical accuracy
|
|---|
| 1595 | pQp=0;
|
|---|
| 1596 | for (t=0;t<k;t++)
|
|---|
| 1597 | {
|
|---|
| 1598 | Qp[t]=0;
|
|---|
| 1599 | for (j=0;j<k;j++)
|
|---|
| 1600 | Qp[t]+=Q[t,j]*p[j];
|
|---|
| 1601 | pQp+=p[t]*Qp[t];
|
|---|
| 1602 | }
|
|---|
| 1603 | double max_error=0;
|
|---|
| 1604 | for (t=0;t<k;t++)
|
|---|
| 1605 | {
|
|---|
| 1606 | double error=Math.Abs(Qp[t]-pQp);
|
|---|
| 1607 | if (error>max_error)
|
|---|
| 1608 | max_error=error;
|
|---|
| 1609 | }
|
|---|
| 1610 | if (max_error<eps) break;
|
|---|
| 1611 |
|
|---|
| 1612 | for (t=0;t<k;t++)
|
|---|
| 1613 | {
|
|---|
| 1614 | double diff=(-Qp[t]+pQp)/Q[t,t];
|
|---|
| 1615 | p[t]+=diff;
|
|---|
| 1616 | pQp=(pQp+diff*(diff*Q[t,t]+2*Qp[t]))/(1+diff)/(1+diff);
|
|---|
| 1617 | for (j=0;j<k;j++)
|
|---|
| 1618 | {
|
|---|
| 1619 | Qp[j]=(Qp[j]+diff*Q[t,j])/(1+diff);
|
|---|
| 1620 | p[j]/=(1+diff);
|
|---|
| 1621 | }
|
|---|
| 1622 | }
|
|---|
| 1623 | }
|
|---|
| 1624 | if (iter>=max_iter)
|
|---|
| 1625 | Debug.Write("Exceeds max_iter in multiclass_prob\n");
|
|---|
| 1626 | }
|
|---|
| 1627 |
|
|---|
| 1628 | // Cross-validation decision values for probability estimates
|
|---|
| 1629 | private static void svm_binary_svc_probability(Problem prob, Parameter param, double Cp, double Cn, double[] probAB)
|
|---|
| 1630 | {
|
|---|
| 1631 | Random rand = new Random();
|
|---|
| 1632 | int i;
|
|---|
| 1633 | int nr_fold = 5;
|
|---|
| 1634 | int[] perm = new int[prob.Count];
|
|---|
| 1635 | double[] dec_values = new double[prob.Count];
|
|---|
| 1636 |
|
|---|
| 1637 | // random shuffle
|
|---|
| 1638 | for (i = 0; i < prob.Count; i++) perm[i] = i;
|
|---|
| 1639 | for (i = 0; i < prob.Count; i++)
|
|---|
| 1640 | {
|
|---|
| 1641 | int j = i + (int)(rand.NextDouble() * (prob.Count - i));
|
|---|
| 1642 | do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
|
|---|
| 1643 | }
|
|---|
| 1644 | for (i = 0; i < nr_fold; i++)
|
|---|
| 1645 | {
|
|---|
| 1646 | int begin = i * prob.Count / nr_fold;
|
|---|
| 1647 | int end = (i + 1) * prob.Count / nr_fold;
|
|---|
| 1648 | int j, k;
|
|---|
| 1649 | Problem subprob = new Problem();
|
|---|
| 1650 |
|
|---|
| 1651 | subprob.Count = prob.Count - (end - begin);
|
|---|
| 1652 | subprob.X = new Node[subprob.Count][];
|
|---|
| 1653 | subprob.Y = new double[subprob.Count];
|
|---|
| 1654 |
|
|---|
| 1655 | k = 0;
|
|---|
| 1656 | for (j = 0; j < begin; j++)
|
|---|
| 1657 | {
|
|---|
| 1658 | subprob.X[k] = prob.X[perm[j]];
|
|---|
| 1659 | subprob.Y[k] = prob.Y[perm[j]];
|
|---|
| 1660 | ++k;
|
|---|
| 1661 | }
|
|---|
| 1662 | for (j = end; j < prob.Count; j++)
|
|---|
| 1663 | {
|
|---|
| 1664 | subprob.X[k] = prob.X[perm[j]];
|
|---|
| 1665 | subprob.Y[k] = prob.Y[perm[j]];
|
|---|
| 1666 | ++k;
|
|---|
| 1667 | }
|
|---|
| 1668 | int p_count = 0, n_count = 0;
|
|---|
| 1669 | for (j = 0; j < k; j++)
|
|---|
| 1670 | if (subprob.Y[j] > 0)
|
|---|
| 1671 | p_count++;
|
|---|
| 1672 | else
|
|---|
| 1673 | n_count++;
|
|---|
| 1674 |
|
|---|
| 1675 | if (p_count == 0 && n_count == 0)
|
|---|
| 1676 | for (j = begin; j < end; j++)
|
|---|
| 1677 | dec_values[perm[j]] = 0;
|
|---|
| 1678 | else if (p_count > 0 && n_count == 0)
|
|---|
| 1679 | for (j = begin; j < end; j++)
|
|---|
| 1680 | dec_values[perm[j]] = 1;
|
|---|
| 1681 | else if (p_count == 0 && n_count > 0)
|
|---|
| 1682 | for (j = begin; j < end; j++)
|
|---|
| 1683 | dec_values[perm[j]] = -1;
|
|---|
| 1684 | else
|
|---|
| 1685 | {
|
|---|
| 1686 | Parameter subparam = (Parameter)param.Clone();
|
|---|
| 1687 | subparam.Probability = false;
|
|---|
| 1688 | subparam.C = 1.0;
|
|---|
| 1689 | subparam.WeightCount = 2;
|
|---|
| 1690 | subparam.WeightLabels = new int[2];
|
|---|
| 1691 | subparam.Weights = new double[2];
|
|---|
| 1692 | subparam.WeightLabels[0] = +1;
|
|---|
| 1693 | subparam.WeightLabels[1] = -1;
|
|---|
| 1694 | subparam.Weights[0] = Cp;
|
|---|
| 1695 | subparam.Weights[1] = Cn;
|
|---|
| 1696 | Model submodel = svm_train(subprob, subparam);
|
|---|
| 1697 | for (j = begin; j < end; j++)
|
|---|
| 1698 | {
|
|---|
| 1699 | double[] dec_value = new double[1];
|
|---|
| 1700 | svm_predict_values(submodel, prob.X[perm[j]], dec_value);
|
|---|
| 1701 | dec_values[perm[j]] = dec_value[0];
|
|---|
| 1702 | // ensure +1 -1 order; reason not using CV subroutine
|
|---|
| 1703 | dec_values[perm[j]] *= submodel.ClassLabels[0];
|
|---|
| 1704 | }
|
|---|
| 1705 | }
|
|---|
| 1706 | }
|
|---|
| 1707 | sigmoid_train(prob.Count, dec_values, prob.Y, probAB);
|
|---|
| 1708 | }
|
|---|
| 1709 |
|
|---|
| 1710 | // Return parameter of a Laplace distribution
|
|---|
| 1711 | private static double svm_svr_probability(Problem prob, Parameter param)
|
|---|
| 1712 | {
|
|---|
| 1713 | int i;
|
|---|
| 1714 | int nr_fold = 5;
|
|---|
| 1715 | double[] ymv = new double[prob.Count];
|
|---|
| 1716 | double mae = 0;
|
|---|
| 1717 |
|
|---|
| 1718 | Parameter newparam = (Parameter)param.Clone();
|
|---|
| 1719 | newparam.Probability = false;
|
|---|
| 1720 | svm_cross_validation(prob, newparam, nr_fold, ymv, null);
|
|---|
| 1721 | for (i = 0; i < prob.Count; i++)
|
|---|
| 1722 | {
|
|---|
| 1723 | ymv[i] = prob.Y[i] - ymv[i];
|
|---|
| 1724 | mae += Math.Abs(ymv[i]);
|
|---|
| 1725 | }
|
|---|
| 1726 | mae /= prob.Count;
|
|---|
| 1727 | double std = Math.Sqrt(2 * mae * mae);
|
|---|
| 1728 | int count = 0;
|
|---|
| 1729 | mae = 0;
|
|---|
| 1730 | for (i = 0; i < prob.Count; i++)
|
|---|
| 1731 | if (Math.Abs(ymv[i]) > 5 * std)
|
|---|
| 1732 | count = count + 1;
|
|---|
| 1733 | else
|
|---|
| 1734 | mae += Math.Abs(ymv[i]);
|
|---|
| 1735 | mae /= (prob.Count - count);
|
|---|
| 1736 | Debug.Write("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + mae + "\n");
|
|---|
| 1737 | return mae;
|
|---|
| 1738 | }
|
|---|
| 1739 |
|
|---|
| 1740 | // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
|
|---|
| 1741 | // perm, length l, must be allocated before calling this subroutine
|
|---|
| 1742 | private static void svm_group_classes(Problem prob, int[] nr_class_ret, int[][] label_ret, int[][] start_ret, int[][] count_ret, int[] perm)
|
|---|
| 1743 | {
|
|---|
| 1744 | int l = prob.Count;
|
|---|
| 1745 | int max_nr_class = 16;
|
|---|
| 1746 | int nr_class = 0;
|
|---|
| 1747 | int[] label = new int[max_nr_class];
|
|---|
| 1748 | int[] count = new int[max_nr_class];
|
|---|
| 1749 | int[] data_label = new int[l];
|
|---|
| 1750 | int i;
|
|---|
| 1751 |
|
|---|
| 1752 | for (i = 0; i < l; i++)
|
|---|
| 1753 | {
|
|---|
| 1754 | int this_label = (int)(prob.Y[i]);
|
|---|
| 1755 | int j;
|
|---|
| 1756 | for (j = 0; j < nr_class; j++)
|
|---|
| 1757 | {
|
|---|
| 1758 | if (this_label == label[j])
|
|---|
| 1759 | {
|
|---|
| 1760 | ++count[j];
|
|---|
| 1761 | break;
|
|---|
| 1762 | }
|
|---|
| 1763 | }
|
|---|
| 1764 | data_label[i] = j;
|
|---|
| 1765 | if (j == nr_class)
|
|---|
| 1766 | {
|
|---|
| 1767 | if (nr_class == max_nr_class)
|
|---|
| 1768 | {
|
|---|
| 1769 | max_nr_class *= 2;
|
|---|
| 1770 | int[] new_data = new int[max_nr_class];
|
|---|
| 1771 | Array.Copy(label, 0, new_data, 0, label.Length);
|
|---|
| 1772 | label = new_data;
|
|---|
| 1773 | new_data = new int[max_nr_class];
|
|---|
| 1774 | Array.Copy(count, 0, new_data, 0, count.Length);
|
|---|
| 1775 | count = new_data;
|
|---|
| 1776 | }
|
|---|
| 1777 | label[nr_class] = this_label;
|
|---|
| 1778 | count[nr_class] = 1;
|
|---|
| 1779 | ++nr_class;
|
|---|
| 1780 | }
|
|---|
| 1781 | }
|
|---|
| 1782 |
|
|---|
| 1783 | int[] start = new int[nr_class];
|
|---|
| 1784 | start[0] = 0;
|
|---|
| 1785 | for (i = 1; i < nr_class; i++)
|
|---|
| 1786 | start[i] = start[i - 1] + count[i - 1];
|
|---|
| 1787 | for (i = 0; i < l; i++)
|
|---|
| 1788 | {
|
|---|
| 1789 | perm[start[data_label[i]]] = i;
|
|---|
| 1790 | ++start[data_label[i]];
|
|---|
| 1791 | }
|
|---|
| 1792 | start[0] = 0;
|
|---|
| 1793 | for (i = 1; i < nr_class; i++)
|
|---|
| 1794 | start[i] = start[i - 1] + count[i - 1];
|
|---|
| 1795 |
|
|---|
| 1796 | nr_class_ret[0] = nr_class;
|
|---|
| 1797 | label_ret[0] = label;
|
|---|
| 1798 | start_ret[0] = start;
|
|---|
| 1799 | count_ret[0] = count;
|
|---|
| 1800 | }
|
|---|
| 1801 |
|
|---|
| 1802 | //
|
|---|
| 1803 | // Interface functions
|
|---|
| 1804 | //
|
|---|
| 1805 | public static Model svm_train(Problem prob, Parameter param)
|
|---|
| 1806 | {
|
|---|
| 1807 | Model model = new Model();
|
|---|
| 1808 | model.Parameter = param;
|
|---|
| 1809 |
|
|---|
| 1810 | if (param.SvmType == SvmType.ONE_CLASS ||
|
|---|
| 1811 | param.SvmType == SvmType.EPSILON_SVR ||
|
|---|
| 1812 | param.SvmType == SvmType.NU_SVR)
|
|---|
| 1813 | {
|
|---|
| 1814 | // regression or one-class-svm
|
|---|
| 1815 | model.NumberOfClasses = 2;
|
|---|
| 1816 | model.ClassLabels = null;
|
|---|
| 1817 | model.NumberOfSVPerClass = null;
|
|---|
| 1818 | model.PairwiseProbabilityA = null; model.PairwiseProbabilityB = null;
|
|---|
| 1819 | model.SupportVectorCoefficients = new double[1][];
|
|---|
| 1820 |
|
|---|
| 1821 | if (param.Probability &&
|
|---|
| 1822 | (param.SvmType == SvmType.EPSILON_SVR ||
|
|---|
| 1823 | param.SvmType == SvmType.NU_SVR))
|
|---|
| 1824 | {
|
|---|
| 1825 | model.PairwiseProbabilityA = new double[1];
|
|---|
| 1826 | model.PairwiseProbabilityA[0] = svm_svr_probability(prob, param);
|
|---|
| 1827 | }
|
|---|
| 1828 |
|
|---|
| 1829 | decision_function f = svm_train_one(prob, param, 0, 0);
|
|---|
| 1830 | model.Rho = new double[1];
|
|---|
| 1831 | model.Rho[0] = f.rho;
|
|---|
| 1832 |
|
|---|
| 1833 | int nSV = 0;
|
|---|
| 1834 | int i;
|
|---|
| 1835 | for (i = 0; i < prob.Count; i++)
|
|---|
| 1836 | if (Math.Abs(f.alpha[i]) > 0) ++nSV;
|
|---|
| 1837 | model.SupportVectorCount = nSV;
|
|---|
| 1838 | model.SupportVectors = new Node[nSV][];
|
|---|
| 1839 | model.SupportVectorCoefficients[0] = new double[nSV];
|
|---|
| 1840 | int j = 0;
|
|---|
| 1841 | for (i = 0; i < prob.Count; i++)
|
|---|
| 1842 | if (Math.Abs(f.alpha[i]) > 0)
|
|---|
| 1843 | {
|
|---|
| 1844 | model.SupportVectors[j] = prob.X[i];
|
|---|
| 1845 | model.SupportVectorCoefficients[0][j] = f.alpha[i];
|
|---|
| 1846 | ++j;
|
|---|
| 1847 | }
|
|---|
| 1848 | }
|
|---|
| 1849 | else
|
|---|
| 1850 | {
|
|---|
| 1851 | // classification
|
|---|
| 1852 | int l = prob.Count;
|
|---|
| 1853 | int[] tmp_nr_class = new int[1];
|
|---|
| 1854 | int[][] tmp_label = new int[1][];
|
|---|
| 1855 | int[][] tmp_start = new int[1][];
|
|---|
| 1856 | int[][] tmp_count = new int[1][];
|
|---|
| 1857 | int[] perm = new int[l];
|
|---|
| 1858 |
|
|---|
| 1859 | // group training data of the same class
|
|---|
| 1860 | svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm);
|
|---|
| 1861 | int nr_class = tmp_nr_class[0];
|
|---|
| 1862 | int[] label = tmp_label[0];
|
|---|
| 1863 | int[] start = tmp_start[0];
|
|---|
| 1864 | int[] count = tmp_count[0];
|
|---|
| 1865 | Node[][] x = new Node[l][];
|
|---|
| 1866 | int i;
|
|---|
| 1867 | for (i = 0; i < l; i++)
|
|---|
| 1868 | x[i] = prob.X[perm[i]];
|
|---|
| 1869 |
|
|---|
| 1870 | // calculate weighted C
|
|---|
| 1871 |
|
|---|
| 1872 | double[] weighted_C = new double[nr_class];
|
|---|
| 1873 | for (i = 0; i < nr_class; i++)
|
|---|
| 1874 | weighted_C[i] = param.C;
|
|---|
| 1875 | for (i = 0; i < param.WeightCount; i++)
|
|---|
| 1876 | {
|
|---|
| 1877 | int j;
|
|---|
| 1878 | for (j = 0; j < nr_class; j++)
|
|---|
| 1879 | if (param.WeightLabels[i] == label[j])
|
|---|
| 1880 | break;
|
|---|
| 1881 | if (j == nr_class)
|
|---|
| 1882 | Debug.Write("warning: class label " + param.WeightLabels[i] + " specified in weight is not found\n");
|
|---|
| 1883 | else
|
|---|
| 1884 | weighted_C[j] *= param.Weights[i];
|
|---|
| 1885 | }
|
|---|
| 1886 |
|
|---|
| 1887 | // train k*(k-1)/2 models
|
|---|
| 1888 |
|
|---|
| 1889 | bool[] nonzero = new bool[l];
|
|---|
| 1890 | for (i = 0; i < l; i++)
|
|---|
| 1891 | nonzero[i] = false;
|
|---|
| 1892 | decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2];
|
|---|
| 1893 |
|
|---|
| 1894 | double[] probA = null, probB = null;
|
|---|
| 1895 | if (param.Probability)
|
|---|
| 1896 | {
|
|---|
| 1897 | probA = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 1898 | probB = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 1899 | }
|
|---|
| 1900 |
|
|---|
| 1901 | int p = 0;
|
|---|
| 1902 | for (i = 0; i < nr_class; i++)
|
|---|
| 1903 | for (int j = i + 1; j < nr_class; j++)
|
|---|
| 1904 | {
|
|---|
| 1905 | Problem sub_prob = new Problem();
|
|---|
| 1906 | int si = start[i], sj = start[j];
|
|---|
| 1907 | int ci = count[i], cj = count[j];
|
|---|
| 1908 | sub_prob.Count = ci + cj;
|
|---|
| 1909 | sub_prob.X = new Node[sub_prob.Count][];
|
|---|
| 1910 | sub_prob.Y = new double[sub_prob.Count];
|
|---|
| 1911 | int k;
|
|---|
| 1912 | for (k = 0; k < ci; k++)
|
|---|
| 1913 | {
|
|---|
| 1914 | sub_prob.X[k] = x[si + k];
|
|---|
| 1915 | sub_prob.Y[k] = +1;
|
|---|
| 1916 | }
|
|---|
| 1917 | for (k = 0; k < cj; k++)
|
|---|
| 1918 | {
|
|---|
| 1919 | sub_prob.X[ci + k] = x[sj + k];
|
|---|
| 1920 | sub_prob.Y[ci + k] = -1;
|
|---|
| 1921 | }
|
|---|
| 1922 |
|
|---|
| 1923 | if (param.Probability)
|
|---|
| 1924 | {
|
|---|
| 1925 | double[] probAB = new double[2];
|
|---|
| 1926 | svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB);
|
|---|
| 1927 | probA[p] = probAB[0];
|
|---|
| 1928 | probB[p] = probAB[1];
|
|---|
| 1929 | }
|
|---|
| 1930 |
|
|---|
| 1931 | f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]);
|
|---|
| 1932 | for (k = 0; k < ci; k++)
|
|---|
| 1933 | if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0)
|
|---|
| 1934 | nonzero[si + k] = true;
|
|---|
| 1935 | for (k = 0; k < cj; k++)
|
|---|
| 1936 | if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0)
|
|---|
| 1937 | nonzero[sj + k] = true;
|
|---|
| 1938 | ++p;
|
|---|
| 1939 | }
|
|---|
| 1940 |
|
|---|
| 1941 | // build output
|
|---|
| 1942 |
|
|---|
| 1943 | model.NumberOfClasses = nr_class;
|
|---|
| 1944 |
|
|---|
| 1945 | model.ClassLabels = new int[nr_class];
|
|---|
| 1946 | for (i = 0; i < nr_class; i++)
|
|---|
| 1947 | model.ClassLabels[i] = label[i];
|
|---|
| 1948 |
|
|---|
| 1949 | model.Rho = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 1950 | for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
|
|---|
| 1951 | model.Rho[i] = f[i].rho;
|
|---|
| 1952 |
|
|---|
| 1953 | if (param.Probability)
|
|---|
| 1954 | {
|
|---|
| 1955 | model.PairwiseProbabilityA = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 1956 | model.PairwiseProbabilityB = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 1957 | for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
|
|---|
| 1958 | {
|
|---|
| 1959 | model.PairwiseProbabilityA[i] = probA[i];
|
|---|
| 1960 | model.PairwiseProbabilityB[i] = probB[i];
|
|---|
| 1961 | }
|
|---|
| 1962 | }
|
|---|
| 1963 | else
|
|---|
| 1964 | {
|
|---|
| 1965 | model.PairwiseProbabilityA = null;
|
|---|
| 1966 | model.PairwiseProbabilityB = null;
|
|---|
| 1967 | }
|
|---|
| 1968 |
|
|---|
| 1969 | int nnz = 0;
|
|---|
| 1970 | int[] nz_count = new int[nr_class];
|
|---|
| 1971 | model.NumberOfSVPerClass = new int[nr_class];
|
|---|
| 1972 | for (i = 0; i < nr_class; i++)
|
|---|
| 1973 | {
|
|---|
| 1974 | int nSV = 0;
|
|---|
| 1975 | for (int j = 0; j < count[i]; j++)
|
|---|
| 1976 | if (nonzero[start[i] + j])
|
|---|
| 1977 | {
|
|---|
| 1978 | ++nSV;
|
|---|
| 1979 | ++nnz;
|
|---|
| 1980 | }
|
|---|
| 1981 | model.NumberOfSVPerClass[i] = nSV;
|
|---|
| 1982 | nz_count[i] = nSV;
|
|---|
| 1983 | }
|
|---|
| 1984 |
|
|---|
| 1985 | Debug.Write("Total nSV = " + nnz + "\n");
|
|---|
| 1986 |
|
|---|
| 1987 | model.SupportVectorCount = nnz;
|
|---|
| 1988 | model.SupportVectors = new Node[nnz][];
|
|---|
| 1989 | p = 0;
|
|---|
| 1990 | for (i = 0; i < l; i++)
|
|---|
| 1991 | if (nonzero[i]) model.SupportVectors[p++] = x[i];
|
|---|
| 1992 |
|
|---|
| 1993 | int[] nz_start = new int[nr_class];
|
|---|
| 1994 | nz_start[0] = 0;
|
|---|
| 1995 | for (i = 1; i < nr_class; i++)
|
|---|
| 1996 | nz_start[i] = nz_start[i - 1] + nz_count[i - 1];
|
|---|
| 1997 |
|
|---|
| 1998 | model.SupportVectorCoefficients = new double[nr_class - 1][];
|
|---|
| 1999 | for (i = 0; i < nr_class - 1; i++)
|
|---|
| 2000 | model.SupportVectorCoefficients[i] = new double[nnz];
|
|---|
| 2001 |
|
|---|
| 2002 | p = 0;
|
|---|
| 2003 | for (i = 0; i < nr_class; i++)
|
|---|
| 2004 | for (int j = i + 1; j < nr_class; j++)
|
|---|
| 2005 | {
|
|---|
| 2006 | // classifier (i,j): coefficients with
|
|---|
| 2007 | // i are in sv_coef[j-1][nz_start[i]...],
|
|---|
| 2008 | // j are in sv_coef[i][nz_start[j]...]
|
|---|
| 2009 |
|
|---|
| 2010 | int si = start[i];
|
|---|
| 2011 | int sj = start[j];
|
|---|
| 2012 | int ci = count[i];
|
|---|
| 2013 | int cj = count[j];
|
|---|
| 2014 |
|
|---|
| 2015 | int q = nz_start[i];
|
|---|
| 2016 | int k;
|
|---|
| 2017 | for (k = 0; k < ci; k++)
|
|---|
| 2018 | if (nonzero[si + k])
|
|---|
| 2019 | model.SupportVectorCoefficients[j - 1][q++] = f[p].alpha[k];
|
|---|
| 2020 | q = nz_start[j];
|
|---|
| 2021 | for (k = 0; k < cj; k++)
|
|---|
| 2022 | if (nonzero[sj + k])
|
|---|
| 2023 | model.SupportVectorCoefficients[i][q++] = f[p].alpha[ci + k];
|
|---|
| 2024 | ++p;
|
|---|
| 2025 | }
|
|---|
| 2026 | }
|
|---|
| 2027 | return model;
|
|---|
| 2028 | }
|
|---|
| 2029 |
|
|---|
| 2030 | // Stratified cross validation
|
|---|
| 2031 | public static void svm_cross_validation(Problem prob, Parameter param, int nr_fold, double[] target, Dictionary<int,double>[] confidence)
|
|---|
| 2032 | {
|
|---|
| 2033 | Random rand = new Random();
|
|---|
| 2034 | int i;
|
|---|
| 2035 | int[] fold_start = new int[nr_fold + 1];
|
|---|
| 2036 | int l = prob.Count;
|
|---|
| 2037 | int[] perm = new int[l];
|
|---|
| 2038 |
|
|---|
| 2039 | // stratified cv may not give leave-one-out rate
|
|---|
| 2040 | // Each class to l folds -> some folds may have zero elements
|
|---|
| 2041 | if ((param.SvmType == SvmType.C_SVC ||
|
|---|
| 2042 | param.SvmType == SvmType.NU_SVC) && nr_fold < l)
|
|---|
| 2043 | {
|
|---|
| 2044 | int[] tmp_nr_class = new int[1];
|
|---|
| 2045 | int[][] tmp_label = new int[1][];
|
|---|
| 2046 | int[][] tmp_start = new int[1][];
|
|---|
| 2047 | int[][] tmp_count = new int[1][];
|
|---|
| 2048 |
|
|---|
| 2049 | svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm);
|
|---|
| 2050 |
|
|---|
| 2051 | int nr_class = tmp_nr_class[0];
|
|---|
| 2052 | int[] label = tmp_label[0];
|
|---|
| 2053 | int[] start = tmp_start[0];
|
|---|
| 2054 | int[] count = tmp_count[0];
|
|---|
| 2055 |
|
|---|
| 2056 | // random shuffle and then data grouped by fold using the array perm
|
|---|
| 2057 | int[] fold_count = new int[nr_fold];
|
|---|
| 2058 | int c;
|
|---|
| 2059 | int[] index = new int[l];
|
|---|
| 2060 | for (i = 0; i < l; i++)
|
|---|
| 2061 | index[i] = perm[i];
|
|---|
| 2062 | for (c = 0; c < nr_class; c++)
|
|---|
| 2063 | for (i = 0; i < count[c]; i++)
|
|---|
| 2064 | {
|
|---|
| 2065 | int j = i + (int)(rand.NextDouble() * (count[c] - i));
|
|---|
| 2066 | do { int _ = index[start[c] + j]; index[start[c] + j] = index[start[c] + i]; index[start[c] + i] = _; } while (false);
|
|---|
| 2067 | }
|
|---|
| 2068 | for (i = 0; i < nr_fold; i++)
|
|---|
| 2069 | {
|
|---|
| 2070 | fold_count[i] = 0;
|
|---|
| 2071 | for (c = 0; c < nr_class; c++)
|
|---|
| 2072 | fold_count[i] += (i + 1) * count[c] / nr_fold - i * count[c] / nr_fold;
|
|---|
| 2073 | }
|
|---|
| 2074 | fold_start[0] = 0;
|
|---|
| 2075 | for (i = 1; i <= nr_fold; i++)
|
|---|
| 2076 | fold_start[i] = fold_start[i - 1] + fold_count[i - 1];
|
|---|
| 2077 | for (c = 0; c < nr_class; c++)
|
|---|
| 2078 | for (i = 0; i < nr_fold; i++)
|
|---|
| 2079 | {
|
|---|
| 2080 | int begin = start[c] + i * count[c] / nr_fold;
|
|---|
| 2081 | int end = start[c] + (i + 1) * count[c] / nr_fold;
|
|---|
| 2082 | for (int j = begin; j < end; j++)
|
|---|
| 2083 | {
|
|---|
| 2084 | perm[fold_start[i]] = index[j];
|
|---|
| 2085 | fold_start[i]++;
|
|---|
| 2086 | }
|
|---|
| 2087 | }
|
|---|
| 2088 | fold_start[0] = 0;
|
|---|
| 2089 | for (i = 1; i <= nr_fold; i++)
|
|---|
| 2090 | fold_start[i] = fold_start[i - 1] + fold_count[i - 1];
|
|---|
| 2091 | }
|
|---|
| 2092 | else
|
|---|
| 2093 | {
|
|---|
| 2094 | for (i = 0; i < l; i++) perm[i] = i;
|
|---|
| 2095 | for (i = 0; i < l; i++)
|
|---|
| 2096 | {
|
|---|
| 2097 | int j = i + (int)(rand.NextDouble() * (l - i));
|
|---|
| 2098 | do { int _ = perm[i]; perm[i] = perm[j]; perm[j] = _; } while (false);
|
|---|
| 2099 | }
|
|---|
| 2100 | for (i = 0; i <= nr_fold; i++)
|
|---|
| 2101 | fold_start[i] = i * l / nr_fold;
|
|---|
| 2102 | }
|
|---|
| 2103 |
|
|---|
| 2104 | for (i = 0; i < nr_fold; i++)
|
|---|
| 2105 | {
|
|---|
| 2106 | int begin = fold_start[i];
|
|---|
| 2107 | int end = fold_start[i + 1];
|
|---|
| 2108 | int j, k;
|
|---|
| 2109 | Problem subprob = new Problem();
|
|---|
| 2110 |
|
|---|
| 2111 | subprob.Count = l - (end - begin);
|
|---|
| 2112 | subprob.X = new Node[subprob.Count][];
|
|---|
| 2113 | subprob.Y = new double[subprob.Count];
|
|---|
| 2114 |
|
|---|
| 2115 | k = 0;
|
|---|
| 2116 | for (j = 0; j < begin; j++)
|
|---|
| 2117 | {
|
|---|
| 2118 | subprob.X[k] = prob.X[perm[j]];
|
|---|
| 2119 | subprob.Y[k] = prob.Y[perm[j]];
|
|---|
| 2120 | ++k;
|
|---|
| 2121 | }
|
|---|
| 2122 | for (j = end; j < l; j++)
|
|---|
| 2123 | {
|
|---|
| 2124 | subprob.X[k] = prob.X[perm[j]];
|
|---|
| 2125 | subprob.Y[k] = prob.Y[perm[j]];
|
|---|
| 2126 | ++k;
|
|---|
| 2127 | }
|
|---|
| 2128 | Model submodel = svm_train(subprob, param);
|
|---|
| 2129 | if (param.Probability &&
|
|---|
| 2130 | (param.SvmType == SvmType.C_SVC ||
|
|---|
| 2131 | param.SvmType == SvmType.NU_SVC))
|
|---|
| 2132 | {
|
|---|
| 2133 | for (j = begin; j < end; j++)
|
|---|
| 2134 | {
|
|---|
| 2135 | double[] prob_estimates = new double[svm_get_nr_class(submodel)];
|
|---|
| 2136 | target[perm[j]] = svm_predict_probability(submodel, prob.X[perm[j]], prob_estimates);
|
|---|
| 2137 | confidence[perm[j]] = new Dictionary<int, double>();
|
|---|
| 2138 | for (int label = 0; label < prob_estimates.Length; label++)
|
|---|
| 2139 | confidence[perm[j]][submodel.ClassLabels[label]] = prob_estimates[label];
|
|---|
| 2140 |
|
|---|
| 2141 | }
|
|---|
| 2142 | }
|
|---|
| 2143 | else
|
|---|
| 2144 | for (j = begin; j < end; j++)
|
|---|
| 2145 | target[perm[j]] = svm_predict(submodel, prob.X[perm[j]]);
|
|---|
| 2146 | }
|
|---|
| 2147 | }
|
|---|
| 2148 |
|
|---|
| 2149 | public static SvmType svm_get_svm_type(Model model)
|
|---|
| 2150 | {
|
|---|
| 2151 | return model.Parameter.SvmType;
|
|---|
| 2152 | }
|
|---|
| 2153 |
|
|---|
| 2154 | public static int svm_get_nr_class(Model model)
|
|---|
| 2155 | {
|
|---|
| 2156 | return model.NumberOfClasses;
|
|---|
| 2157 | }
|
|---|
| 2158 |
|
|---|
| 2159 | public static void svm_get_labels(Model model, int[] label)
|
|---|
| 2160 | {
|
|---|
| 2161 | if (model.ClassLabels != null)
|
|---|
| 2162 | for (int i = 0; i < model.NumberOfClasses; i++)
|
|---|
| 2163 | label[i] = model.ClassLabels[i];
|
|---|
| 2164 | }
|
|---|
| 2165 |
|
|---|
| 2166 | public static double svm_get_svr_probability(Model model)
|
|---|
| 2167 | {
|
|---|
| 2168 | if ((model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR) &&
|
|---|
| 2169 | model.PairwiseProbabilityA != null)
|
|---|
| 2170 | return model.PairwiseProbabilityA[0];
|
|---|
| 2171 | else
|
|---|
| 2172 | {
|
|---|
| 2173 | Debug.Write("Model doesn't contain information for SVR probability inference\n");
|
|---|
| 2174 | return 0;
|
|---|
| 2175 | }
|
|---|
| 2176 | }
|
|---|
| 2177 |
|
|---|
| 2178 | public static void svm_predict_values(Model model, Node[] x, double[] dec_values)
|
|---|
| 2179 | {
|
|---|
| 2180 | if (model.Parameter.SvmType == SvmType.ONE_CLASS ||
|
|---|
| 2181 | model.Parameter.SvmType == SvmType.EPSILON_SVR ||
|
|---|
| 2182 | model.Parameter.SvmType == SvmType.NU_SVR)
|
|---|
| 2183 | {
|
|---|
| 2184 | double[] sv_coef = model.SupportVectorCoefficients[0];
|
|---|
| 2185 | double sum = 0;
|
|---|
| 2186 | for (int i = 0; i < model.SupportVectorCount; i++)
|
|---|
| 2187 | sum += sv_coef[i] * Kernel.k_function(x, model.SupportVectors[i], model.Parameter);
|
|---|
| 2188 | sum -= model.Rho[0];
|
|---|
| 2189 | dec_values[0] = sum;
|
|---|
| 2190 | }
|
|---|
| 2191 | else
|
|---|
| 2192 | {
|
|---|
| 2193 | int i;
|
|---|
| 2194 | int nr_class = model.NumberOfClasses;
|
|---|
| 2195 | int l = model.SupportVectorCount;
|
|---|
| 2196 |
|
|---|
| 2197 | double[] kvalue = new double[l];
|
|---|
| 2198 | for (i = 0; i < l; i++)
|
|---|
| 2199 | kvalue[i] = Kernel.k_function(x, model.SupportVectors[i], model.Parameter);
|
|---|
| 2200 |
|
|---|
| 2201 | int[] start = new int[nr_class];
|
|---|
| 2202 | start[0] = 0;
|
|---|
| 2203 | for (i = 1; i < nr_class; i++)
|
|---|
| 2204 | start[i] = start[i - 1] + model.NumberOfSVPerClass[i - 1];
|
|---|
| 2205 |
|
|---|
| 2206 | int p = 0;
|
|---|
| 2207 | for (i = 0; i < nr_class; i++)
|
|---|
| 2208 | for (int j = i + 1; j < nr_class; j++)
|
|---|
| 2209 | {
|
|---|
| 2210 | double sum = 0;
|
|---|
| 2211 | int si = start[i];
|
|---|
| 2212 | int sj = start[j];
|
|---|
| 2213 | int ci = model.NumberOfSVPerClass[i];
|
|---|
| 2214 | int cj = model.NumberOfSVPerClass[j];
|
|---|
| 2215 |
|
|---|
| 2216 | int k;
|
|---|
| 2217 | double[] coef1 = model.SupportVectorCoefficients[j - 1];
|
|---|
| 2218 | double[] coef2 = model.SupportVectorCoefficients[i];
|
|---|
| 2219 | for (k = 0; k < ci; k++)
|
|---|
| 2220 | sum += coef1[si + k] * kvalue[si + k];
|
|---|
| 2221 | for (k = 0; k < cj; k++)
|
|---|
| 2222 | sum += coef2[sj + k] * kvalue[sj + k];
|
|---|
| 2223 | sum -= model.Rho[p];
|
|---|
| 2224 | dec_values[p] = sum;
|
|---|
| 2225 | p++;
|
|---|
| 2226 | }
|
|---|
| 2227 | }
|
|---|
| 2228 | }
|
|---|
| 2229 |
|
|---|
| 2230 | public static double svm_predict(Model model, Node[] x)
|
|---|
| 2231 | {
|
|---|
| 2232 | if (model.Parameter.SvmType == SvmType.ONE_CLASS ||
|
|---|
| 2233 | model.Parameter.SvmType == SvmType.EPSILON_SVR ||
|
|---|
| 2234 | model.Parameter.SvmType == SvmType.NU_SVR)
|
|---|
| 2235 | {
|
|---|
| 2236 | double[] res = new double[1];
|
|---|
| 2237 | svm_predict_values(model, x, res);
|
|---|
| 2238 |
|
|---|
| 2239 | if (model.Parameter.SvmType == SvmType.ONE_CLASS)
|
|---|
| 2240 | return (res[0] > 0) ? 1 : -1;
|
|---|
| 2241 | else
|
|---|
| 2242 | return res[0];
|
|---|
| 2243 | }
|
|---|
| 2244 | else
|
|---|
| 2245 | {
|
|---|
| 2246 | int i;
|
|---|
| 2247 | int nr_class = model.NumberOfClasses;
|
|---|
| 2248 | double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
|
|---|
| 2249 | svm_predict_values(model, x, dec_values);
|
|---|
| 2250 |
|
|---|
| 2251 | int[] vote = new int[nr_class];
|
|---|
| 2252 | for (i = 0; i < nr_class; i++)
|
|---|
| 2253 | vote[i] = 0;
|
|---|
| 2254 | int pos = 0;
|
|---|
| 2255 | for (i = 0; i < nr_class; i++)
|
|---|
| 2256 | for (int j = i + 1; j < nr_class; j++)
|
|---|
| 2257 | {
|
|---|
| 2258 | if (dec_values[pos++] > 0)
|
|---|
| 2259 | ++vote[i];
|
|---|
| 2260 | else
|
|---|
| 2261 | ++vote[j];
|
|---|
| 2262 | }
|
|---|
| 2263 |
|
|---|
| 2264 | int vote_max_idx = 0;
|
|---|
| 2265 | for (i = 1; i < nr_class; i++)
|
|---|
| 2266 | if (vote[i] > vote[vote_max_idx])
|
|---|
| 2267 | vote_max_idx = i;
|
|---|
| 2268 | return model.ClassLabels[vote_max_idx];
|
|---|
| 2269 | }
|
|---|
| 2270 | }
|
|---|
| 2271 |
|
|---|
| 2272 | public static double svm_predict_probability(Model model, Node[] x, double[] prob_estimates)
|
|---|
| 2273 | {
|
|---|
| 2274 | if ((model.Parameter.SvmType == SvmType.C_SVC || model.Parameter.SvmType == SvmType.NU_SVC) &&
|
|---|
| 2275 | model.PairwiseProbabilityA!=null && model.PairwiseProbabilityB!=null)
|
|---|
| 2276 | {
|
|---|
| 2277 | int i;
|
|---|
| 2278 | int nr_class = model.NumberOfClasses;
|
|---|
| 2279 | double[] dec_values = new double[nr_class*(nr_class-1)/2];
|
|---|
| 2280 | svm_predict_values(model, x, dec_values);
|
|---|
| 2281 |
|
|---|
| 2282 | double min_prob=1e-7;
|
|---|
| 2283 | double[,] pairwise_prob=new double[nr_class,nr_class];
|
|---|
| 2284 |
|
|---|
| 2285 | int k=0;
|
|---|
| 2286 | for(i=0;i<nr_class;i++)
|
|---|
| 2287 | for(int j=i+1;j<nr_class;j++)
|
|---|
| 2288 | {
|
|---|
| 2289 | pairwise_prob[i,j]=Math.Min(Math.Max(sigmoid_predict(dec_values[k],model.PairwiseProbabilityA[k],model.PairwiseProbabilityB[k]),min_prob),1-min_prob);
|
|---|
| 2290 | pairwise_prob[j,i]=1-pairwise_prob[i,j];
|
|---|
| 2291 | k++;
|
|---|
| 2292 | }
|
|---|
| 2293 | multiclass_probability(nr_class,pairwise_prob,prob_estimates);
|
|---|
| 2294 |
|
|---|
| 2295 | int prob_max_idx = 0;
|
|---|
| 2296 | for(i=1;i<nr_class;i++)
|
|---|
| 2297 | if(prob_estimates[i] > prob_estimates[prob_max_idx])
|
|---|
| 2298 | prob_max_idx = i;
|
|---|
| 2299 | return model.ClassLabels[prob_max_idx];
|
|---|
| 2300 | }
|
|---|
| 2301 | else
|
|---|
| 2302 | return svm_predict(model, x);
|
|---|
| 2303 | }
|
|---|
| 2304 |
|
|---|
| 2305 | private static double atof(string s)
|
|---|
| 2306 | {
|
|---|
| 2307 | return double.Parse(s);
|
|---|
| 2308 | }
|
|---|
| 2309 |
|
|---|
| 2310 | private static int atoi(string s)
|
|---|
| 2311 | {
|
|---|
| 2312 | return int.Parse(s);
|
|---|
| 2313 | }
|
|---|
| 2314 |
|
|---|
| 2315 | public static string svm_check_parameter(Problem prob, Parameter param)
|
|---|
| 2316 | {
|
|---|
| 2317 | // svm_type
|
|---|
| 2318 |
|
|---|
| 2319 | SvmType svm_type = param.SvmType;
|
|---|
| 2320 | if (svm_type != SvmType.C_SVC &&
|
|---|
| 2321 | svm_type != SvmType.NU_SVC &&
|
|---|
| 2322 | svm_type != SvmType.ONE_CLASS &&
|
|---|
| 2323 | svm_type != SvmType.EPSILON_SVR &&
|
|---|
| 2324 | svm_type != SvmType.NU_SVR)
|
|---|
| 2325 | return "unknown svm type";
|
|---|
| 2326 |
|
|---|
| 2327 | // kernel_type, degree
|
|---|
| 2328 |
|
|---|
| 2329 | KernelType kernel_type = param.KernelType;
|
|---|
| 2330 | if (kernel_type != KernelType.LINEAR &&
|
|---|
| 2331 | kernel_type != KernelType.POLY &&
|
|---|
| 2332 | kernel_type != KernelType.RBF &&
|
|---|
| 2333 | kernel_type != KernelType.SIGMOID &&
|
|---|
| 2334 | kernel_type != KernelType.PRECOMPUTED)
|
|---|
| 2335 | return "unknown kernel type";
|
|---|
| 2336 |
|
|---|
| 2337 | if (param.Degree < 0)
|
|---|
| 2338 | return "degree of polynomial kernel < 0";
|
|---|
| 2339 |
|
|---|
| 2340 | // cache_size,eps,C,nu,p,shrinking
|
|---|
| 2341 |
|
|---|
| 2342 | if (param.CacheSize <= 0)
|
|---|
| 2343 | return "cache_size <= 0";
|
|---|
| 2344 |
|
|---|
| 2345 | if (param.EPS <= 0)
|
|---|
| 2346 | return "eps <= 0";
|
|---|
| 2347 |
|
|---|
| 2348 | if (svm_type == SvmType.C_SVC ||
|
|---|
| 2349 | svm_type == SvmType.EPSILON_SVR ||
|
|---|
| 2350 | svm_type == SvmType.NU_SVR)
|
|---|
| 2351 | if (param.C <= 0)
|
|---|
| 2352 | return "C <= 0";
|
|---|
| 2353 |
|
|---|
| 2354 | if (svm_type == SvmType.NU_SVC ||
|
|---|
| 2355 | svm_type == SvmType.ONE_CLASS ||
|
|---|
| 2356 | svm_type == SvmType.NU_SVR)
|
|---|
| 2357 | if (param.Nu <= 0 || param.Nu > 1)
|
|---|
| 2358 | return "nu <= 0 or nu > 1";
|
|---|
| 2359 |
|
|---|
| 2360 | if (svm_type == SvmType.EPSILON_SVR)
|
|---|
| 2361 | if (param.P < 0)
|
|---|
| 2362 | return "p < 0";
|
|---|
| 2363 |
|
|---|
| 2364 | if (param.Probability && svm_type == SvmType.ONE_CLASS)
|
|---|
| 2365 | return "one-class SVM probability output not supported yet";
|
|---|
| 2366 |
|
|---|
| 2367 | // check whether nu-svc is feasible
|
|---|
| 2368 |
|
|---|
| 2369 | if (svm_type == SvmType.NU_SVC)
|
|---|
| 2370 | {
|
|---|
| 2371 | int l = prob.Count;
|
|---|
| 2372 | int max_nr_class = 16;
|
|---|
| 2373 | int nr_class = 0;
|
|---|
| 2374 | int[] label = new int[max_nr_class];
|
|---|
| 2375 | int[] count = new int[max_nr_class];
|
|---|
| 2376 |
|
|---|
| 2377 | int i;
|
|---|
| 2378 | for (i = 0; i < l; i++)
|
|---|
| 2379 | {
|
|---|
| 2380 | int this_label = (int)prob.Y[i];
|
|---|
| 2381 | int j;
|
|---|
| 2382 | for (j = 0; j < nr_class; j++)
|
|---|
| 2383 | if (this_label == label[j])
|
|---|
| 2384 | {
|
|---|
| 2385 | ++count[j];
|
|---|
| 2386 | break;
|
|---|
| 2387 | }
|
|---|
| 2388 |
|
|---|
| 2389 | if (j == nr_class)
|
|---|
| 2390 | {
|
|---|
| 2391 | if (nr_class == max_nr_class)
|
|---|
| 2392 | {
|
|---|
| 2393 | max_nr_class *= 2;
|
|---|
| 2394 | int[] new_data = new int[max_nr_class];
|
|---|
| 2395 | Array.Copy(label, 0, new_data, 0, label.Length);
|
|---|
| 2396 | label = new_data;
|
|---|
| 2397 |
|
|---|
| 2398 | new_data = new int[max_nr_class];
|
|---|
| 2399 | Array.Copy(count, 0, new_data, 0, count.Length);
|
|---|
| 2400 | count = new_data;
|
|---|
| 2401 | }
|
|---|
| 2402 | label[nr_class] = this_label;
|
|---|
| 2403 | count[nr_class] = 1;
|
|---|
| 2404 | ++nr_class;
|
|---|
| 2405 | }
|
|---|
| 2406 | }
|
|---|
| 2407 |
|
|---|
| 2408 | for (i = 0; i < nr_class; i++)
|
|---|
| 2409 | {
|
|---|
| 2410 | int n1 = count[i];
|
|---|
| 2411 | for (int j = i + 1; j < nr_class; j++)
|
|---|
| 2412 | {
|
|---|
| 2413 | int n2 = count[j];
|
|---|
| 2414 | if (param.Nu * (n1 + n2) / 2 > Math.Min(n1, n2))
|
|---|
| 2415 | return "specified nu is infeasible";
|
|---|
| 2416 | }
|
|---|
| 2417 | }
|
|---|
| 2418 | }
|
|---|
| 2419 |
|
|---|
| 2420 | return null;
|
|---|
| 2421 | }
|
|---|
| 2422 |
|
|---|
| 2423 | public static int svm_check_probability_model(Model model)
|
|---|
| 2424 | {
|
|---|
| 2425 | if (((model.Parameter.SvmType == SvmType.C_SVC || model.Parameter.SvmType == SvmType.NU_SVC) &&
|
|---|
| 2426 | model.PairwiseProbabilityA != null && model.PairwiseProbabilityB != null) ||
|
|---|
| 2427 | ((model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR) &&
|
|---|
| 2428 | model.PairwiseProbabilityA != null))
|
|---|
| 2429 | return 1;
|
|---|
| 2430 | else
|
|---|
| 2431 | return 0;
|
|---|
| 2432 | }
|
|---|
| 2433 | }
|
|---|
| 2434 |
|
|---|
| 2435 | }
|
|---|
