public void train(int[] instanceLengths, int[] ignore, String trainfile, File train_forest)
throws IOException {
int i = 0;
for (i = 0; i < options.numIters; i++) {
System.out.print(" Iteration " + i);
System.out.print("[");
long start = System.currentTimeMillis();
trainingIter(instanceLengths, ignore, trainfile, train_forest, i + 1);
long end = System.currentTimeMillis();
// System.out.println("Training iter took: " + (end-start));
System.out.println("|Time:" + (end - start) + "]");
}
params.averageParams(i * countActualInstances(ignore));
// afm 06-04-08
if (options.separateLab) {
LabelClassifier oc =
new LabelClassifier(
options, instanceLengths, ignore, trainfile, train_forest, this, pipe);
try {
classifier = oc.trainClassifier(100);
} catch (Exception e) {
e.printStackTrace();
}
}
}
public void loadModel(String file) throws Exception {
ObjectInputStream in = new ObjectInputStream(new FileInputStream(file));
params.parameters = (double[]) in.readObject();
pipe.dataAlphabet = (Alphabet) in.readObject();
pipe.typeAlphabet = (Alphabet) in.readObject();
// afm 06-04-08
if (options.separateLab) {
classifier = (Classifier) in.readObject();
}
in.close();
pipe.closeAlphabets();
}
// Note: Change this to pass -1 for indices in instanceLengths[] that you
// don't want to use on training (need to be careful because i is being used
// in the for loop; need new index)
private void trainingIter(
int[] instanceLengths, int ignore[], String trainfile, File train_forest, int iter)
throws IOException {
int numUpd = 0;
ObjectInputStream in = new ObjectInputStream(new FileInputStream(train_forest));
boolean evaluateI = true;
int numInstances = instanceLengths.length;
// afm -- Count the real number of instances to be considered
int numActualInstances = countActualInstances(ignore);
int j = 0;
for (int i = 0; i < numInstances; i++) {
if ((i + 1) % 500 == 0) {
System.out.print((i + 1) + ",");
// System.out.println(" "+(i+1)+" instances");
}
int length = instanceLengths[i];
// Get production crap.
FeatureVector[][][] fvs = new FeatureVector[length][length][2];
double[][][] probs = new double[length][length][2];
FeatureVector[][][][] nt_fvs = new FeatureVector[length][pipe.types.length][2][2];
double[][][][] nt_probs = new double[length][pipe.types.length][2][2];
FeatureVector[][][] fvs_trips = new FeatureVector[length][length][length];
double[][][] probs_trips = new double[length][length][length];
FeatureVector[][][] fvs_sibs = new FeatureVector[length][length][2];
double[][][] probs_sibs = new double[length][length][2];
DependencyInstance inst;
if (options.secondOrder) {
inst =
((DependencyPipe2O) pipe)
.readInstance(
in,
length,
fvs,
probs,
fvs_trips,
probs_trips,
fvs_sibs,
probs_sibs,
nt_fvs,
nt_probs,
params);
} else inst = pipe.readInstance(in, length, fvs, probs, nt_fvs, nt_probs, params);
// afm 03-06-08
if (ignore[i] != 0) // This sentence is to be ignored
continue;
double upd =
(double)
(options.numIters * numActualInstances
- (numActualInstances * (iter - 1) + (j + 1))
+ 1);
int K = options.trainK;
Object[][] d = null;
if (options.decodeType.equals("proj")) {
if (options.secondOrder)
d =
((DependencyDecoder2O) decoder)
.decodeProjective(
inst,
fvs,
probs,
fvs_trips,
probs_trips,
fvs_sibs,
probs_sibs,
nt_fvs,
nt_probs,
K);
else d = decoder.decodeProjective(inst, fvs, probs, nt_fvs, nt_probs, K);
}
if (options.decodeType.equals("non-proj")) {
if (options.secondOrder)
d =
((DependencyDecoder2O) decoder)
.decodeNonProjective(
inst,
fvs,
probs,
fvs_trips,
probs_trips,
fvs_sibs,
probs_sibs,
nt_fvs,
nt_probs,
K);
else d = decoder.decodeNonProjective(inst, fvs, probs, nt_fvs, nt_probs, K);
}
params.updateParamsMIRA(inst, d, upd);
j++;
}
// System.out.println("");
// System.out.println(" "+numInstances+" instances");
System.out.print(numActualInstances);
in.close();
}