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endual:
https://blog.csdn.net/chenxbxh2 ...
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ysj570440569:
Maven多模块spring + springMVC + JP ...
Spring+SpringMVC+JPA
修改两个类:svm_predict,svm_train,前者是预测的,后者是训练的。
工程图示:
根据自己要做实验的数据,修改了一部分返回值的代码:
svm_predict.java进行修改为
package libsvm.bean; import libsvm.*; import java.io.*; import java.util.*; public class svm_predict { private static int correctCount ; private static int totalCount ; public int getTotalCount() { return totalCount; } public int getCorrectCount() { return correctCount; } private static double atof(String s) { return Double.valueOf(s).doubleValue(); } private static int atoi(String s) { return Integer.parseInt(s); } private static void predict(BufferedReader input, DataOutputStream output, svm_model model, int predict_probability) throws IOException { int correct = 0; int total = 0; double error = 0; double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; int svm_type=svm.svm_get_svm_type(model); int nr_class=svm.svm_get_nr_class(model); double[] prob_estimates=null; if(predict_probability == 1) { if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) { System.out.print("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+svm.svm_get_svr_probability(model)+"\n"); } else { int[] labels=new int[nr_class]; svm.svm_get_labels(model,labels); prob_estimates = new double[nr_class]; output.writeBytes("labels"); for(int j=0;j<nr_class;j++) output.writeBytes(" "+labels[j]); output.writeBytes("\n"); } } while(true) { String line = input.readLine(); if(line == null) break; StringTokenizer st = new StringTokenizer(line," \t\n\r\f:"); double target = atof(st.nextToken()); int m = st.countTokens()/2; svm_node[] x = new svm_node[m]; for(int j=0;j<m;j++) { x[j] = new svm_node(); x[j].index = atoi(st.nextToken()); x[j].value = atof(st.nextToken()); } double v; if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC)) { v = svm.svm_predict_probability(model,x,prob_estimates); output.writeBytes(v+" "); for(int j=0;j<nr_class;j++) output.writeBytes(prob_estimates[j]+" "); output.writeBytes("\n"); } else //一般进入到这 { v = svm.svm_predict(model,x); output.writeBytes(target+"----"+v+"\n"); /**--如果要取得哪几类分错可以在这里写代码--**/ } if(v == target) { ++correct; } error += (v-target)*(v-target); sumv += v; sumy += target; sumvv += v*v; sumyy += target*target; sumvy += v*target; ++total; } //end while if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) { System.out.print("Mean squared error = "+error/total+" (regression)\n"); System.out.print("Squared correlation coefficient = "+ ((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/ ((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))+ " (regression)\n"); } else { System.out.print("Accuracy = "+(double)correct/total*100+ "% ("+correct+"/"+total+") (classification)\n"); } correctCount = correct ; totalCount = total ; } private static int predictReturnResult(BufferedReader input, DataOutputStream output, svm_model model, int predict_probability) throws IOException { int correct = 0; int total = 0; double error = 0; double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; int svm_type=svm.svm_get_svm_type(model); int nr_class=svm.svm_get_nr_class(model); double[] prob_estimates=null; if(predict_probability == 1) { if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) { System.out.print("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+svm.svm_get_svr_probability(model)+"\n"); } else { int[] labels=new int[nr_class]; svm.svm_get_labels(model,labels); prob_estimates = new double[nr_class]; output.writeBytes("labels"); for(int j=0;j<nr_class;j++) output.writeBytes(" "+labels[j]); output.writeBytes("\n"); } } while(true) { String line = input.readLine(); if(line == null) break; StringTokenizer st = new StringTokenizer(line," \t\n\r\f:"); double target = atof(st.nextToken()); int m = st.countTokens()/2; svm_node[] x = new svm_node[m]; for(int j=0;j<m;j++) { x[j] = new svm_node(); x[j].index = atoi(st.nextToken()); x[j].value = atof(st.nextToken()); } double v; if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC)) { v = svm.svm_predict_probability(model,x,prob_estimates); output.writeBytes(v+" "); for(int j=0;j<nr_class;j++) output.writeBytes(prob_estimates[j]+" "); output.writeBytes("\n"); } else { v = svm.svm_predict(model,x); output.writeBytes(v+"\n"); } if(v == target) ++correct; error += (v-target)*(v-target); sumv += v; sumy += target; sumvv += v*v; sumyy += target*target; sumvy += v*target; ++total; } //end while if(svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR) { System.out.print("Mean squared error = "+error/total+" (regression)\n"); System.out.print("Squared correlation coefficient = "+ ((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/ ((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))+ " (regression)\n"); } else { System.out.print("Accuracy = "+(double)correct/total*100+ "% ("+correct+"/"+total+") (classification)\n"); } return correct ; } //打印帮助 private static void exit_with_help() { System.err.print("usage: svm_predict [options] test_file model_file output_file\n" +"options:\n" +"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n"); System.exit(1); } public void main(String argv[]) throws IOException { int i, predict_probability=0; // parse options for(i=0;i<argv.length;i++) { if(argv[i].charAt(0) != '-') break; ++i; switch(argv[i-1].charAt(1)) { case 'b': predict_probability = atoi(argv[i]); break; default: System.err.print("Unknown option: " + argv[i-1] + "\n"); exit_with_help(); } } if(i>=argv.length-2) exit_with_help(); try { BufferedReader input = new BufferedReader(new FileReader(argv[i])); DataOutputStream output = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(argv[i+2]))); svm_model model = svm.svm_load_model(argv[i+1]); if(predict_probability == 1) { if(svm.svm_check_probability_model(model)==0) { System.err.print("Model does not support probabiliy estimates\n"); System.exit(1); } } else { if(svm.svm_check_probability_model(model)!=0) { System.out.print("Model supports probability estimates, but disabled in prediction.\n"); } } predict(input,output,model,predict_probability); input.close(); output.close(); } catch(FileNotFoundException e) { exit_with_help(); } catch(ArrayIndexOutOfBoundsException e) { exit_with_help(); } } }
主要增加了返回测试正确与总的数据,如果你想要获取是哪些类分错了,也是可以添加代码的,这里没有标注。
svm_train.java 修改后:
package libsvm.bean; import libsvm.*; import java.io.*; import java.util.*; public class svm_train { private svm_parameter param; // set by parse_command_line private svm_problem prob; // set by read_problem private svm_model model; private String input_file_name; // set by parse_command_line private String model_file_name; // set by parse_command_line private String error_msg; private int cross_validation; private int nr_fold; private static svm_print_interface svm_print_null = new svm_print_interface() { public void print(String s) {} }; private static void exit_with_help() { System.out.print( "Usage: svm_train [options] training_set_file [model_file]\n" +"options:\n" +"-s svm_type : set type of SVM (default 0)\n" +" 0 -- C-SVC\n" +" 1 -- nu-SVC\n" +" 2 -- one-class SVM\n" +" 3 -- epsilon-SVR\n" +" 4 -- nu-SVR\n" +"-t kernel_type : set type of kernel function (default 2)\n" +" 0 -- linear: u'*v\n" +" 1 -- polynomial: (gamma*u'*v + coef0)^degree\n" +" 2 -- radial basis function: exp(-gamma*|u-v|^2)\n" +" 3 -- sigmoid: tanh(gamma*u'*v + coef0)\n" +" 4 -- precomputed kernel (kernel values in training_set_file)\n" +"-d degree : set degree in kernel function (default 3)\n" +"-g gamma : set gamma in kernel function (default 1/num_features)\n" +"-r coef0 : set coef0 in kernel function (default 0)\n" +"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n" +"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n" +"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n" +"-m cachesize : set cache memory size in MB (default 100)\n" +"-e epsilon : set tolerance of termination criterion (default 0.001)\n" +"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n" +"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n" +"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n" +"-v n : n-fold cross validation mode\n" +"-q : quiet mode (no outputs)\n" ); System.exit(1); } /** * 做交叉验证 */ private void do_cross_validation() { int i; int total_correct = 0; double total_error = 0; double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; double[] target = new double[prob.l]; svm.svm_cross_validation(prob,param,nr_fold,target); if(param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR) { for(i=0;i<prob.l;i++) { double y = prob.y[i]; double v = target[i]; total_error += (v-y)*(v-y); sumv += v; sumy += y; sumvv += v*v; sumyy += y*y; sumvy += v*y; } System.out.print("Cross Validation Mean squared error = "+total_error/prob.l+"\n"); System.out.print("Cross Validation Squared correlation coefficient = "+ ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/ ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))+"\n" ); } else { for(i=0;i<prob.l;i++) if(target[i] == prob.y[i]) ++total_correct; System.out.print("Cross Validation Accuracy = "+100.0*total_correct/prob.l+"%\n"); } } private void run(String argv[]) throws IOException { parse_command_line(argv); read_problem(); error_msg = svm.svm_check_parameter(prob,param); if(error_msg != null) { System.err.print("ERROR: "+error_msg+"\n"); System.exit(1); } if(cross_validation != 0) { do_cross_validation(); } else { model = svm.svm_train(prob,param); svm.svm_save_model(model_file_name,model); } } /** * 支持向量机训练入口处 * @param argv * @throws IOException */ public void main(String argv[]) throws IOException { svm_train t = new svm_train(); t.run(argv); } //就是把字符串s转换成double类型 private static double atof(String s) { double d = Double.valueOf(s).doubleValue(); if (Double.isNaN(d) || Double.isInfinite(d)) { System.err.print("NaN or Infinity in input\n"); System.exit(1); } return(d); } //就是把字符串s转换成Int类型 private static int atoi(String s) { return Integer.parseInt(s); } //解析命令行 private void parse_command_line(String argv[]) { int i; svm_print_interface print_func = null; // default printing to stdout param = new svm_parameter(); // default values param.svm_type = svm_parameter.C_SVC; param.kernel_type = svm_parameter.RBF; param.degree = 3; param.gamma = 0; // 1/num_features param.coef0 = 0; param.nu = 0.5; param.cache_size = 100; param.C = 1; param.eps = 1e-3; param.p = 0.1; param.shrinking = 1; param.probability = 0; param.nr_weight = 0; param.weight_label = new int[0]; param.weight = new double[0]; cross_validation = 0; // parse options for(i=0;i<argv.length;i++) { if(argv[i].charAt(0) != '-') break; if(++i>=argv.length) exit_with_help(); switch(argv[i-1].charAt(1)) { case 's': //支持向量机的类型 param.svm_type = atoi(argv[i]); break; case 't': //支持向量机核函数 param.kernel_type = atoi(argv[i]); break; case 'd': //支持向量机 param.degree = atoi(argv[i]); break; case 'g': //支持向量机 gamma重要参数,double类型 param.gamma = atof(argv[i]); break; case 'r': //支持向量机 param.coef0 = atof(argv[i]); break; case 'n': param.nu = atof(argv[i]); break; case 'm': param.cache_size = atof(argv[i]); break; case 'c': //支持向量机C重要参数 param.C = atof(argv[i]); break; case 'e': param.eps = atof(argv[i]); break; case 'p': param.p = atof(argv[i]); break; case 'h': param.shrinking = atoi(argv[i]); break; case 'b': param.probability = atoi(argv[i]); break; case 'q': print_func = svm_print_null; i--; break; case 'v': cross_validation = 1; nr_fold = atoi(argv[i]); if(nr_fold < 2) { System.err.print("n-fold cross validation: n must >= 2\n"); exit_with_help(); } break; case 'w': //添加权值 ++param.nr_weight; { int[] old = param.weight_label; param.weight_label = new int[param.nr_weight]; System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1); } { double[] old = param.weight; param.weight = new double[param.nr_weight]; System.arraycopy(old,0,param.weight,0,param.nr_weight-1); } param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2)); param.weight[param.nr_weight-1] = atof(argv[i]); break; default: System.err.print("Unknown option: " + argv[i-1] + "\n"); exit_with_help(); } //end switch } // end if svm.svm_set_print_string_function(print_func); // determine filenames if(i>=argv.length) exit_with_help(); input_file_name = argv[i]; if(i<argv.length-1) model_file_name = argv[i+1]; else { int p = argv[i].lastIndexOf('/'); ++p; // whew... model_file_name = argv[i].substring(p)+".model"; } } // read in a problem (in svmlight format) private void read_problem() throws IOException { BufferedReader fp = new BufferedReader(new FileReader(input_file_name)); Vector<Double> vy = new Vector<Double>(); Vector<svm_node[]> vx = new Vector<svm_node[]>(); int max_index = 0; while(true) { String line = fp.readLine(); if(line == null) break; StringTokenizer st = new StringTokenizer(line," \t\n\r\f:"); vy.addElement(atof(st.nextToken())); int m = st.countTokens()/2; svm_node[] x = new svm_node[m]; for(int j=0;j<m;j++) { x[j] = new svm_node(); x[j].index = atoi(st.nextToken()); x[j].value = atof(st.nextToken()); } if(m>0) max_index = Math.max(max_index, x[m-1].index); vx.addElement(x); } prob = new svm_problem(); prob.l = vy.size(); prob.x = new svm_node[prob.l][]; for(int i=0;i<prob.l;i++) prob.x[i] = vx.elementAt(i); prob.y = new double[prob.l]; for(int i=0;i<prob.l;i++) prob.y[i] = vy.elementAt(i); if(param.gamma == 0 && max_index > 0) param.gamma = 1.0/max_index; if(param.kernel_type == svm_parameter.PRECOMPUTED) for(int i=0;i<prob.l;i++) { if (prob.x[i][0].index != 0) { System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n"); System.exit(1); } if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index) { System.err.print("Wrong input format: sample_serial_number out of range\n"); System.exit(1); } } fp.close(); } }
也没有什么修改,就是添加了public ,主要修改的还是svm_predict.java这个类。
调用的方法:主要的修改参数已经写好了,demo用的测试数据和训练数据是一样的
package com.endual.paper.main; import java.io.IOException; import libsvm.bean.svm_predict; import libsvm.bean.svm_train; /** * Libsvm版本3.12 * @author endual * 导入训练文件,然后训练,导入预测文件,然后预测 * */ public class EndualMain { public static void main(String[] args) throws Exception { /** 建立模型 **/ String[] arg_train = {"-s","0", //默认的支持向量机类型 "-t","2", //默认的核函数类型RBF "-d","2", //set degree in kernel function (default 3) "-g","10", //set gamma in kernel function (default 1/num_features) "-r","0", //set coef0 in kernel function (default 0) "-c","1", //set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) "-n","0.5", //set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) "-b","0", //whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) "file_data\\data\\data.txt", "file_data\\model\\data_model.txt"} ; svm_train train = new svm_train(); train.main(arg_train) ;//进行训练 /** 预测数据 **/ String[] arg_predict = {"-b","0", //predict probability estimates, 0 or 1 (default 0) "file_data\\data\\data.txt", "file_data\\model\\data_model.txt", "file_data\\out\\data_out.txt"} ; svm_predict predict = new svm_predict() ; predict.main(arg_predict) ; int correctCount = predict.getCorrectCount() ; //获取到预测准确的个数 int totalCount = predict.getTotalCount() ; //获取到总的预测个数 System.out.println("||----------------------------------------||"); System.out.println("|| 预测准确的个数="+correctCount); System.out.println("|| 总的预测的个数="+totalCount); System.out.println("||-------------------------------------- ||"); } }
运行结果:
..* optimization finished, #iter = 28 nu = 0.46153846153846156 obj = -4.025964170236124, rho = -0.6825924901648381 nSV = 13, nBSV = 3 ..* optimization finished, #iter = 38 nu = 0.8888888888888888 obj = -8.5069246595277, rho = -0.15000782032015664 nSV = 18, nBSV = 8 ..* optimization finished, #iter = 28 nu = 0.3333333333333333 obj = -2.7925423354383563, rho = -0.7960877909831303 nSV = 12, nBSV = 2 .* optimization finished, #iter = 15 nu = 0.5454545454545454 obj = -3.9300105118677906, rho = 0.618192908080933 nSV = 11, nBSV = 3 ..* optimization finished, #iter = 13 nu = 0.8 obj = -2.3219227628258747, rho = -0.3342767053776095 nSV = 5, nBSV = 2 .* optimization finished, #iter = 15 nu = 0.4 obj = -2.7316267112794113, rho = -0.7446069747755875 nSV = 10, nBSV = 2 Total nSV = 23 Accuracy = 100.0% (23/23) (classification) ||----------------------------------------|| || 预测准确的个数=23 || 总的预测的个数=23 ||-------------------------------------- ||
发表评论
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snmp
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