public static void main(String[] args) {
if (args.length < 2) {
System.out.println("Usage: java LR examplefile numexamples numfeatures");
System.exit(1);
}
numfeatures = Integer.parseInt(args[2]);
exsize = Integer.parseInt(args[1]);
try {
Scanner infile = new Scanner(new FileReader(args[0]));
FileWriter outfile = new FileWriter("weights.txt");
int index = 0;
while (infile.hasNextLine()) {
System.out.println("Creating model for k-best list num " + index);
ArrayList<Double[]> trainexamples = new ArrayList<Double[]>(exsize);
int n = 0;
// get the next 100 examples
while (infile.hasNextLine() && (n < exsize)) {
String line = infile.nextLine();
StringTokenizer tok = new StringTokenizer(line);
Double[] ex = new Double[numfeatures + 1];
for (int k = 0; k < (numfeatures + 1); k++) ex[k] = Double.parseDouble(tok.nextToken());
trainexamples.add(ex);
n++;
} // end while
// train logistic regression
if (trainexamples.size() < exsize) {
System.out.println("ERROR: trainexamples has only " + trainexamples.size() + " items!");
System.exit(1);
}
trainLR(trainexamples);
// print weight vector
System.out.println("Done, printing weight vector");
for (int k = 0; k < weightvector.length; k++) {
outfile.write(weightvector[k] + " ");
}
outfile.write("\n");
index++;
} // end while next k-best list
infile.close();
outfile.close();
} // end try
catch (IOException e) {
System.out.println(e.getMessage());
}
} // end main
