public static void main(String[] argv) throws IOException {
int i;
// parse options
for (i = 0; i < argv.length; i++) {
if (argv[i].charAt(0) != '-') break;
++i;
switch (argv[i - 1].charAt(1)) {
case 'b':
try {
flag_predict_probability = (atoi(argv[i]) != 0);
} catch (NumberFormatException e) {
exit_with_help();
}
break;
case 'q':
i--;
Linear.disableDebugOutput();
break;
default:
System.err.printf("unknown option: -%d%n", argv[i - 1].charAt(1));
exit_with_help();
break;
}
}
if (i >= argv.length || argv.length <= i + 2) {
exit_with_help();
}
BufferedReader reader = null;
Writer writer = null;
try {
reader =
new BufferedReader(
new InputStreamReader(new FileInputStream(argv[i]), Linear.FILE_CHARSET));
writer =
new BufferedWriter(
new OutputStreamWriter(new FileOutputStream(argv[i + 2]), Linear.FILE_CHARSET));
Model model = Linear.loadModel(new File(argv[i + 1]));
doPredict(reader, writer, model);
} finally {
closeQuietly(reader);
closeQuietly(writer);
}
}
/** <b>Note: The streams are NOT closed</b> */
static void doPredict(BufferedReader reader, Writer writer, Model model) throws IOException {
int correct = 0;
int total = 0;
double error = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
int nr_class = model.getNrClass();
double[] prob_estimates = null;
int n;
int nr_feature = model.getNrFeature();
if (model.bias >= 0) n = nr_feature + 1;
else n = nr_feature;
if (flag_predict_probability && !model.isProbabilityModel()) {
throw new IllegalArgumentException(
"probability output is only supported for logistic regression");
}
Formatter out = new Formatter(writer);
if (flag_predict_probability) {
int[] labels = model.getLabels();
prob_estimates = new double[nr_class];
printf(out, "labels");
for (int j = 0; j < nr_class; j++) printf(out, " %d", labels[j]);
printf(out, "\n");
}
String line = null;
while ((line = reader.readLine()) != null) {
List<Feature> x = new ArrayList<Feature>();
StringTokenizer st = new StringTokenizer(line, " \t\n");
double target_label;
try {
String label = st.nextToken();
target_label = atof(label);
} catch (NoSuchElementException e) {
throw new RuntimeException("Wrong input format at line " + (total + 1), e);
}
while (st.hasMoreTokens()) {
String[] split = COLON.split(st.nextToken(), 2);
if (split == null || split.length < 2) {
throw new RuntimeException("Wrong input format at line " + (total + 1));
}
try {
int idx = atoi(split[0]);
double val = atof(split[1]);
// feature indices larger than those in training are not used
if (idx <= nr_feature) {
Feature node = new FeatureNode(idx, val);
x.add(node);
}
} catch (NumberFormatException e) {
throw new RuntimeException("Wrong input format at line " + (total + 1), e);
}
}
if (model.bias >= 0) {
Feature node = new FeatureNode(n, model.bias);
x.add(node);
}
Feature[] nodes = new Feature[x.size()];
nodes = x.toArray(nodes);
double predict_label;
if (flag_predict_probability) {
assert prob_estimates != null;
predict_label = Linear.predictProbability(model, nodes, prob_estimates);
printf(out, "%g", predict_label);
for (int j = 0; j < model.nr_class; j++) printf(out, " %g", prob_estimates[j]);
printf(out, "\n");
} else {
predict_label = Linear.predict(model, nodes);
printf(out, "%g\n", predict_label);
}
if (predict_label == target_label) {
++correct;
}
error += (predict_label - target_label) * (predict_label - target_label);
sump += predict_label;
sumt += target_label;
sumpp += predict_label * predict_label;
sumtt += target_label * target_label;
sumpt += predict_label * target_label;
++total;
}
if (model.solverType.isSupportVectorRegression()) //
{
info("Mean squared error = %g (regression)%n", error / total);
info(
"Squared correlation coefficient = %g (regression)%n", //
((total * sumpt - sump * sumt) * (total * sumpt - sump * sumt))
/ ((total * sumpp - sump * sump) * (total * sumtt - sumt * sumt)));
} else {
info("Accuracy = %g%% (%d/%d)%n", (double) correct / total * 100, correct, total);
}
}
