我试图从日食中启动LibSVM source。
我使用的是Windows 7.如何启动它？我找不到任何示例
我已经做的是添加classpath变量。＆＃34;
C：\ Program Files（x86）\ libsvm-3.20 \ java \ libsvm.jar
＆＃34;
我有一个培训数据集。如果您使用的是Windows版本。您只需选择当前目录并通过选择svm-train.exe并通过name.train给出列车数据来指定路径 整条路径都是这样的。
C：\ Program Files（x86）\ libsvm-3.20 \ svm-train.exe name.train
现在下载包中有一个文件＆＃34; train.java＆＃34;
import libsvm.*;
import java.io.*;
import java.util.*;
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 (multi-class classification)\n"
+" 1 -- nu-SVC (multi-class classification)\n"
+" 2 -- one-class SVM\n"
+" 3 -- epsilon-SVR (regression)\n"
+" 4 -- nu-SVR (regression)\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);
}
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);
}
}
public static void main(String argv[]) throws IOException
{
svm_train t = new svm_train();
t.run(argv);
}
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);
}
private static int atoi(String s)
{
return Integer.parseInt(s);
}
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':
param.svm_type = atoi(argv[i]);
break;
case 't':
param.kernel_type = atoi(argv[i]);
break;
case 'd':
param.degree = atoi(argv[i]);
break;
case 'g':
param.gamma = atof(argv[i]);
break;
case 'r':
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':
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':
++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();
}
}
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();
}
}
所以我的问题是如何在eclipse中启动训练功能？
提前致谢
答案 0 :(得分：2)
基本上有两种不同的入门方式：
通过cmd使用预构建.exe文件（基于C代码），如：
svm-train.exe [options] [path-to-training-file] [path-the-model-should-be-stored]
将java源代码导入新生成的eclipse项目，因为提供的.jar文件未提供清单（例如，无法在命令行上使用java -jar启动）。
然后你可以像往常一样svm_train.java在eclipse中启动Run。请注意，您必须通过Run Configurations > Program Arguments提供与.exe方法相同的参数。