public double[] computeCurrentResids () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf (oldF, newF); lastResids [getIndex (oldF)] = dist; } return lastResids; }
public double[] computeCurrentResids () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf (oldF, newF); lastResids [getIndex (oldF)] = dist; } return lastResids; }
public double[] computeCurrentResids () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf (oldF, newF); lastResids [getIndex (oldF)] = dist; } return lastResids; }
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 (); } } } }
private void recomputeFactors () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf ((AbstractTableFactor) oldF.duplicate ().normalize (), (AbstractTableFactor) newF.duplicate ().normalize ()); lastResids [getIndex (oldF)] = dist; oldF.setValues (newF.getLogValueMatrix ()); clique.tmpl.modifyPotential (this, clique, oldF); } }
private void recomputeFactors () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf ((AbstractTableFactor) oldF.duplicate ().normalize (), (AbstractTableFactor) newF.duplicate ().normalize ()); lastResids [getIndex (oldF)] = dist; oldF.setValues (newF.getLogValueMatrix ()); clique.tmpl.modifyPotential (this, clique, oldF); } }
private void recomputeFactors () { lastResids = new double [factors ().size ()]; for (Iterator it = cliques.iterator(); it.hasNext();) { UnrolledVarSet clique = (UnrolledVarSet) it.next(); AbstractTableFactor oldF = (AbstractTableFactor) clique.getFactor (); AbstractTableFactor newF = clique.tmpl.computeFactor (clique); double dist = Factors.distLinf ((AbstractTableFactor) oldF.duplicate ().normalize (), (AbstractTableFactor) newF.duplicate ().normalize ()); lastResids [getIndex (oldF)] = dist; oldF.setValues (newF.getLogValueMatrix ()); clique.tmpl.modifyPotential (this, clique, oldF); } }