com.github.chen0040.libsvm.svm_predict Maven / Gradle / Ivy
package com.github.chen0040.libsvm;
import java.io.*;
import java.util.StringTokenizer;
class svm_predict {
private static svm_print_interface svm_print_null = new svm_print_interface()
{
public void print(String s) {}
};
private static svm_print_interface svm_print_stdout = new svm_print_interface()
{
public void print(String s)
{
System.out.print(s);
}
};
private static svm_print_interface svm_print_string = svm_print_stdout;
static void info(String s)
{
svm_print_string.print(s);
}
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= SupportVectorMachine.svm_get_svm_type(model);
int nr_class= SupportVectorMachine.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)
{
svm_predict.info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+ SupportVectorMachine
.svm_get_svr_probability(model)+"\n");
}
else
{
int[] labels=new int[nr_class];
SupportVectorMachine.svm_get_labels(model,labels);
prob_estimates = new double[nr_class];
output.writeBytes("labels");
for(int j=0;j=argv.length-2)
exit_with_help();
try
{
BufferedReader input = new BufferedReader(new InputStreamReader(new FileInputStream(argv[i]), "UTF-8"));
DataOutputStream output = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(argv[i+2])));
svm_model model = SupportVectorMachine.svm_load_model(argv[i+1]);
if (model == null)
{
System.err.print("can't open model file "+argv[i+1]+"\n");
System.exit(1);
}
if(predict_probability == 1)
{
if(SupportVectorMachine.svm_check_probability_model(model)==0)
{
System.err.print("Model does not support probabiliy estimates\n");
System.exit(1);
}
}
else
{
if(SupportVectorMachine.svm_check_probability_model(model)!=0)
{
svm_predict.info("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();
}
}
}