IFC.zip_weka_weka svm_人工智能/神经网络/深度学习

/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * WLSVM.java * Copyright (C) 2005 Yasser EL-Manzalawy * */ /* * An implementation of a custom Weka classifier that provides an access to LibSVM. * Available at: http://www.cs.iastate.edu/~yasser/wlsvm */ package wlsvm; import weka.classifiers.Classifier; import weka.core.*; import weka.filters.*; import weka.filters.unsupervised.attribute.*; import weka.classifiers.*; import libsvm.*; import java.util.*; public class WLSVM extends Classifier implements WeightedInstancesHandler { protected static final long serialVersionUID = 14172; protected svm_parameter param; // LibSVM oprions protected int normalize; // normalize input data protected svm_problem prob; // LibSVM Problem protected svm_model model; // LibSVM Model protected String error_msg; protected Filter filter = null; public WLSVM() { String[] dummy = {}; try{ setOptions(dummy); } catch (Exception e) { e.printStackTrace(); } } /** The filter used to make attributes numeric. */ //protected NominalToBinary m_NominalToBinary = null; /** * Returns a string describing classifier * * @return a description suitable for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "An implementation of a custom Weka classifier that provides an access to LibSVM."+ "Available at: http://www.cs.iastate.edu/~yasser/wlsvm"; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(13); newVector.addElement(new Option("\t set type of SVM (default 0)\n" + "\t\t 0 = C-SVC\n" + "\t\t 1 = nu-SVC\n" + "\t\t 2 = one-class SVM\n" + "\t\t 3 = epsilon-SVR\n" + "\t\t 4 = nu-SVR", "S", 1, "-S <int>")); newVector .addElement(new Option( "\t set type of kernel function (default 2)\n" + "\t\t 0 = linear: u'*v\n" + "\t\t 1 = polynomial: (gamma*u'*v + coef0)^degree\n" + "\t\t 2 = radial basis function: exp(-gamma*|u-v|^2)\n" + "\t\t 3 = sigmoid: tanh(gamma*u'*v + coef0)", "K", 1, "-K <int>")); newVector.addElement(new Option( "\t set degree in kernel function (default 3)", "D", 1, "-D <int>")); newVector.addElement(new Option( "\t set gamma in kernel function (default 1/k)", "G", 1, "-G <double>")); newVector.addElement(new Option( "\t set coef0 in kernel function (default 0)", "R", 1, "-R <double>")); newVector .addElement(new Option( "\t set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)", "C", 1, "-C <double>")); newVector .addElement(new Option( "\t set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)", "N", 1, "-N <double>")); newVector.addElement(new Option( "\t whether to normalize input data, 0 or 1 (default 0)", "Z", 1, "-Z")); newVector .addElement(new Option( "\t set the epsilon in loss function of epsilon-SVR (default 0.1)", "P", 1, "-P <double>")); newVector.addElement(new Option( "\t set cache memory size in MB (default 40)", "M", 1, "-M <double>")); newVector.addElement(new Option( "\t set tolerance of termination criterion (default 0.001)", "E", 1, "-E <double>")); newVector.addElement(new Option( "\t whether to use the shrinking heuristics, 0 or 1 (default 1)", "H", 1, "-H <int>")); newVector.addElement(new Option( "\t whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)", "B", 1, "-B <int>")); newVector.addElement(new Option( "\t set the parameters C of class i to weight[i]*C, for C-SVC (default 1)", "W", 1, "-W <double>")); return newVector.elements(); } /** * Sets type of SVM (default 0) * * @param svm_type */ public void setSVMType(int svm_type) { param.svm_type = svm_type; } /** * Gets type of SVM * * @return */ public int getSVMType() { return param.svm_type; } /** * Sets type of kernel function (default 2) * * @param kernel_type */ public void setKernelType(int kernel_type) { param.kernel_type = kernel_type; } /** * Gets type of kernel function * * @return */ public int getKernelType() { return param.kernel_type; } /** * Sets the degree of the kernel * * @param degree */ public void setDegree(double degree) { param.degree = degree; } /** * Gets the degree of the kernel * * @return */ public double getDegree() { return param.degree; } /** * Sets gamma (default = 1/no of attributes) * * @param gamma */ public void setGamma(double gamma) { param.gamma = gamma; } /** * Gets gamma * * @return */ public double getGamma() { return param.gamma; } /** * Sets coef (default 0) * * @param coef0 */ public void setCoef0(double coef0) { param.coef0 = coef0; } /** * Gets coef * * @return */ public double getCoef0() { return param.coef0; } /** * Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) * * @param nu */ public void setNu(double nu) { param.nu = nu; } /** * Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) * * @return */ public double getNu() { return param.nu; } /** * Sets cache memory size in MB (default 40) * * @param cache_size */ public void setCache(double cache_size) { param.cache_size = cache_size; } /** * Gets cache memory size in MB * * @return */ public double getCache() { return param.cache_size; } /** * Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) * * @param cost */ public void setCost(double cost) { param.C = cost; } /** * Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR * * @return */ public double getCost() { return param.C; } /** * Sets tolerance of termination criterion (default 0.001) * * @param eps */ public void setEps(double eps) { param.eps = eps; } /** * Gets tolerance of termination criterion * * @return */ public double getEps() { return param.eps; } /** * Sets the epsilon in loss function of epsilon-SVR (default 0.1) * * @param loss */ public void setLoss(double loss) { param.p = loss; } /** * Gets the epsilon in loss function of epsilon-SVR * * @return */ public double getLoss() { return param.p; } /** * whether to use the shrinking heuristics, 0 or 1 (default 1) * * @param shrink */ public void setShrinking(int shrink) { param.shrinking = shrink; } /** * whether to use the shrinking heuristics, 0 or 1 (default 1) * * @return */ public double getShrinking() { return param.shrinking; } /** * whether to train a SVC or SVR model for probability estimates, 0 or