/** * Copyright (c) 2000-2005 Chih-Chung Chang and Chih-Jen Lin All rights reserved. * * Redistribution and use in source and binary forms, with or without modification, are permitted * provided that the following conditions are met: * * 1. Redistributions of source code must retain the above copyright notice, this list of conditions * and the following disclaimer. * * 2. Redistributions in binary form must reproduce the above copyright notice, this list of * conditions and the following disclaimer in the documentation and/or other materials provided with * the distribution. * * 3. Neither name of copyright holders nor the names of its contributors may be used to endorse or * promote products derived from this software without specific prior written permission. * * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, * OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF * THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ package libsvm; public class svm_model implements java.io.Serializable { private static final long serialVersionUID = 7974831813044169852L; public svm_parameter param; // parameter public int nr_class; // number of classes, = 2 in regression/one class svm public int l; // total #SV public svm_node[][] SV; // SVs (SV[l]) public double[][] sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l]) public double[] rho; // constants in decision functions (rho[k*(k-1)/2]) public double[] probA; // pariwise probability information public double[] probB; // for classification only public int[] label; // label of each class (label[k]) public int[] nSV; // number of SVs for each class (nSV[k]) // nSV[0] + nSV[1] + ... + nSV[k-1] = l public double[] labelValues; // actual label values for all support vectors (only used for // displaying) }