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();
in.close();
pipe.closeAlphabets();
}
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();
}
private void trainingIter(int[] instanceLengths, 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;
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);
double upd =
(double) (options.numIters * numInstances - (numInstances * (iter - 1) + (i + 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);
}
// System.out.println("");
// System.out.println(" "+numInstances+" instances");
System.out.print(numInstances);
in.close();
}