private void collectWeightsPresentForGraph (UnrolledGraph unrolled, BitSet[] weightsPresent) { for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next (); if (clique.tmpl == this) { int assn = clique.lookupAssignmentNumber (); addPresentFeatures (weightsPresent[assn], clique.fv); } } }
private void collectWeightsPresentForGraph (UnrolledGraph unrolled, BitSet[] weightsPresent) { for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next (); if (clique.tmpl == this) { int assn = clique.lookupAssignmentNumber (); addPresentFeatures (weightsPresent[assn], clique.fv); } } }
private void collectWeightsPresentForGraph (UnrolledGraph unrolled, BitSet[] weightsPresent) { for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next (); if (clique.tmpl == this) { int assn = clique.lookupAssignmentNumber (); addPresentFeatures (weightsPresent[assn], clique.fv); } } }
private void collectSomeUnsupportedWeights (InstanceList training, BitSet[] weightsPresent) { for (int ii = 0; ii < training.size(); ii++) { Instance inst = training.get (ii); UnrolledGraph unrolled = new UnrolledGraph (inst, new Template[] { this }, new ArrayList (), true); for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet vs = (UnrolledVarSet) it.next (); Factor f = vs.getFactor (); Factor nrmed = f.normalize (); for (AssignmentIterator assnIt = nrmed.assignmentIterator (); assnIt.hasNext ();) { if (nrmed.value (assnIt) > SOME_UNSUPPORTED_THRESHOLD) { addPresentFeatures (weightsPresent [assnIt.indexOfCurrentAssn ()], vs.fv); } assnIt.advance (); } } } }
private void collectSomeUnsupportedWeights (InstanceList training, BitSet[] weightsPresent) { for (int ii = 0; ii < training.size(); ii++) { Instance inst = training.get (ii); UnrolledGraph unrolled = new UnrolledGraph (inst, new Template[] { this }, new ArrayList (), true); for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet vs = (UnrolledVarSet) it.next (); Factor f = vs.getFactor (); Factor nrmed = f.normalize (); for (AssignmentIterator assnIt = nrmed.assignmentIterator (); assnIt.hasNext ();) { if (nrmed.value (assnIt) > SOME_UNSUPPORTED_THRESHOLD) { addPresentFeatures (weightsPresent [assnIt.indexOfCurrentAssn ()], vs.fv); } assnIt.advance (); } } } }
private void collectSomeUnsupportedWeights (InstanceList training, BitSet[] weightsPresent) { for (int ii = 0; ii < training.size(); ii++) { Instance inst = training.get (ii); UnrolledGraph unrolled = new UnrolledGraph (inst, new Template[] { this }, new ArrayList (), true); for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext();) { UnrolledVarSet vs = (UnrolledVarSet) it.next (); Factor f = vs.getFactor (); Factor nrmed = f.normalize (); for (AssignmentIterator assnIt = nrmed.assignmentIterator (); assnIt.hasNext ();) { if (nrmed.value (assnIt) > SOME_UNSUPPORTED_THRESHOLD) { addPresentFeatures (weightsPresent [assnIt.indexOfCurrentAssn ()], vs.fv); } assnIt.advance (); } } } }