public boolean train(
InstanceList ilist, InstanceList validation, InstanceList testing, TransducerEvaluator eval) {
assert (ilist.size() > 0);
if (emissionEstimator == null) {
emissionEstimator = new Multinomial.LaplaceEstimator[numStates()];
transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
emissionMultinomial = new Multinomial[numStates()];
transitionMultinomial = new Multinomial[numStates()];
Alphabet transitionAlphabet = new Alphabet();
for (int i = 0; i < numStates(); i++)
transitionAlphabet.lookupIndex(((State) states.get(i)).getName(), true);
for (int i = 0; i < numStates(); i++) {
emissionEstimator[i] = new Multinomial.LaplaceEstimator(inputAlphabet);
transitionEstimator[i] = new Multinomial.LaplaceEstimator(transitionAlphabet);
emissionMultinomial[i] =
new Multinomial(getUniformArray(inputAlphabet.size()), inputAlphabet);
transitionMultinomial[i] =
new Multinomial(getUniformArray(transitionAlphabet.size()), transitionAlphabet);
}
initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
}
for (Instance instance : ilist) {
FeatureSequence input = (FeatureSequence) instance.getData();
FeatureSequence output = (FeatureSequence) instance.getTarget();
new SumLatticeDefault(this, input, output, new Incrementor());
}
initialMultinomial = initialEstimator.estimate();
for (int i = 0; i < numStates(); i++) {
emissionMultinomial[i] = emissionEstimator[i].estimate();
transitionMultinomial[i] = transitionEstimator[i].estimate();
getState(i).setInitialWeight(initialMultinomial.logProbability(getState(i).getName()));
}
return true;
}
public void estimate() {
Alphabet transitionAlphabet = getTransitionAlphabet();
initialMultinomial = initialEstimator.estimate();
initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
for (int i = 0; i < numStates(); i++) {
State s = (State) getState(i);
emissionMultinomial[i] = emissionEstimator[i].estimate();
transitionMultinomial[i] = transitionEstimator[i].estimate();
s.setInitialWeight(initialMultinomial.logProbability(s.getName()));
// reset estimators
emissionEstimator[i] = new Multinomial.LaplaceEstimator(inputAlphabet);
transitionEstimator[i] = new Multinomial.LaplaceEstimator(transitionAlphabet);
}
}
/**
* Separate initialization of initial/transitions and emissions. All probabilities are
* proportional to (1+Uniform[0,1])^noise.
*
* @author kedarb
* @param random Random object (if null use uniform distribution)
* @param noise Noise exponent to use. If zero, then uniform distribution.
*/
public void initTransitions(Random random, double noise) {
Alphabet transitionAlphabet = getTransitionAlphabet();
initialMultinomial =
new Multinomial(
getRandomArray(transitionAlphabet.size(), random, noise), transitionAlphabet);
initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
transitionMultinomial = new Multinomial[numStates()];
transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
for (int i = 0; i < numStates(); i++) {
transitionMultinomial[i] =
new Multinomial(
getRandomArray(transitionAlphabet.size(), random, noise), transitionAlphabet);
transitionEstimator[i] = new Multinomial.LaplaceEstimator(transitionAlphabet);
// set state's initial weight
State s = (State) getState(i);
s.setInitialWeight(initialMultinomial.logProbability(s.getName()));
}
}
