www.pudn.com > CsharpSVM.rar > svm_train.cs, change:2004-06-06,size:8571b
using System;
using libsvm;
/* Conversion notes (Andrew Poh):
* Removed nested call of Streamreader constructor - original Java used BufferedReader
* wrapped around FileReader.
*
* Removed SupportClass because duplicated in svm library.
*
* Replaced System.Console.arraycopy() with Array.Copy - this stemmed from a lousy
* conversion of System.arraycopy().
*
* Replaced System.Math.Max() with System.Math.Max().
*
* Replaced (ArrayList)vx.addElement() in read_problem() with Add().
*/
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 System.String input_file_name; // set by parse_command_line
private System.String model_file_name; // set by parse_command_line
private System.String error_msg;
private int cross_validation = 0;
private int nr_fold;
private static void exit_with_help()
{
System.Console.Out.Write("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" + "-d degree : set degree in kernel function (default 3)\n" + "-g gamma : set gamma in kernel function (default 1/k)\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 40)\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");
System.Environment.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.Console.Out.Write("Cross Validation Mean squared error = " + total_error / prob.l + "\n");
System.Console.Out.Write("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.Console.Out.Write("Cross Validation Accuracy = " + 100.0 * total_correct / prob.l + "%\n");
}
private void run(System.String[] argv)
{
parse_command_line(argv);
read_problem();
error_msg = svm.svm_check_parameter(prob, param);
if ((System.Object) error_msg != null)
{
System.Console.Error.Write("Error: " + error_msg + "\n");
System.Environment.Exit(1);
}
if (cross_validation != 0)
{
do_cross_validation();
}
else
{
model = svm.svm_train(prob, param);
svm.svm_save_model(model_file_name, model);
}
}
[STAThread]
public static void Main(System.String[] argv)
{
svm_train t = new svm_train();
t.run(argv);
}
private static double atof(System.String s)
{
return System.Double.Parse(s);
}
private static int atoi(System.String s)
{
return System.Int32.Parse(s);
}
private void parse_command_line(System.String[] argv)
{
int i;
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/k
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 40;
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];
// parse options
for (i = 0; i < argv.Length; i++)
{
if (argv[i][0] != '-')
break;
++i;
switch (argv[i - 1][1])
{
case 's':
param.svm_type = atoi(argv[i]);
break;
case 't':
param.kernel_type = atoi(argv[i]);
break;
case 'd':
param.degree = atof(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 'v':
cross_validation = 1;
nr_fold = atoi(argv[i]);
if (nr_fold < 2)
{
System.Console.Error.Write("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];
Array.Copy(old, 0, param.weight_label, 0, param.nr_weight - 1);
}
{
double[] old = param.weight;
param.weight = new double[param.nr_weight];
Array.Copy(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.Console.Error.Write("unknown option\n");
exit_with_help();
break;
}
}
// 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((System.Char) '/');
++p; // whew...
model_file_name = argv[i].Substring(p) + ".model";
}
}
// read in a problem (in svmlight format)
private void read_problem()
{
/* UPGRADE_TODO: Expected value of parameters of constructor
* 'java.io.BufferedReader.BufferedReader' are different in the equivalent in .NET.
* 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1092"'
*/
System.IO.StreamReader fp = new System.IO.StreamReader(input_file_name);
System.Collections.ArrayList vy = new System.Collections.ArrayList(10);
System.Collections.ArrayList vx = new System.Collections.ArrayList(10);
int max_index = 0;
while (true)
{
System.String line = fp.ReadLine();
if ((System.Object) line == null)
break;
SupportClass.Tokenizer st = new SupportClass.Tokenizer(line, " \t\n\r\f:");
vy.Add(st.NextToken());
int m = st.Count / 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_Renamed = atof(st.NextToken());
}
if (m > 0)
max_index = System.Math.Max(max_index, x[m - 1].index);
vx.Add(x);
}
prob = new svm_problem();
prob.l = vy.Count;
prob.x = new svm_node[prob.l][];
for (int i = 0; i < prob.l; i++)
prob.x[i] = (svm_node[]) vx[i];
prob.y = new double[prob.l];
for (int i = 0; i < prob.l; i++)
prob.y[i] = atof((System.String) vy[i]);
if (param.gamma == 0)
param.gamma = 1.0 / max_index;
fp.Close();
}
}