public static 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\n");
exit_with_help();
}
}
if (i >= argv.length) exit_with_help();
try {
BufferedReader input = new BufferedReader(new FileReader(argv[i]));
DataOutputStream output = new DataOutputStream(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);
}
predict(input, output, model, predict_probability);
input.close();
output.close();
} catch (FileNotFoundException e) {
exit_with_help();
} catch (ArrayIndexOutOfBoundsException e) {
exit_with_help();
}
}
public static void main(String argv[]) throws IOException {
int i, predict_probability = 0;
svm_print_string = svm_print_stdout;
// 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;
case 'q':
svm_print_string = svm_print_null;
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 (model == null) {
System.err.print("can't open model file " + argv[i + 1] + "\n");
System.exit(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) {
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();
}
}
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 {
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;
}
/*
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");
*/
}
