int getMaxTime () { return fvs.size(); } int getNumFactors () { return outputAlphabets.length; }
int getMaxTime () { return fvs.size(); } int getNumFactors () { return outputAlphabets.length; }
int getMaxTime () { return fvs.size(); } int getNumFactors () { return outputAlphabets.length; }
public void computeExpectations(ArrayList<SumLattice> lattices) { double[][][] xis; for (int i = 0; i < lattices.size(); i++) { SumLattice lattice = lattices.get(i); xis = lattice.getXis(); int numStates = xis[0].length; FeatureVectorSequence fvs = (FeatureVectorSequence)lattice.getInput(); for (int ip = 0; ip < fvs.size(); ++ip) { for (int si = 0; si < numStates; si++) { this.expectation += Math.exp(xis[ip][si][si]); } } } System.err.println("Self transition expectation: " + (this.expectation/this.numTokens)); } }
public void computeExpectations(ArrayList<SumLattice> lattices) { double[][][] xis; for (int i = 0; i < lattices.size(); i++) { SumLattice lattice = lattices.get(i); xis = lattice.getXis(); int numStates = xis[0].length; FeatureVectorSequence fvs = (FeatureVectorSequence)lattice.getInput(); for (int ip = 0; ip < fvs.size(); ++ip) { for (int si = 0; si < numStates; si++) { this.expectation += Math.exp(xis[ip][si][si]); } } } System.err.println("Self transition expectation: " + (this.expectation/this.numTokens)); } }
public int getSampleCount() { int count = 0; Iterator<?> it = instanceList.iterator(); while (it.hasNext()) { Instance instance = (Instance) it.next(); Object data = instance.getData(); if (data instanceof FeatureVectorSequence) { FeatureVectorSequence fvs = (FeatureVectorSequence) data; count += fvs.size(); } } return count; }
public void computeExpectations(ArrayList<SumLattice> lattices) { double[][][] xis; for (int i = 0; i < lattices.size(); i++) { SumLattice lattice = lattices.get(i); xis = lattice.getXis(); int numStates = xis[0].length; FeatureVectorSequence fvs = (FeatureVectorSequence)lattice.getInput(); for (int ip = 0; ip < fvs.size(); ++ip) { for (int si = 0; si < numStates; si++) { this.expectation += Math.exp(xis[ip][si][si]); } } } System.err.println("Self transition expectation: " + (this.expectation/this.numTokens)); } }
public BitSet preProcess(InstanceList data) { // count number of tokens BitSet bitSet = new BitSet(data.size()); bitSet.set(0, data.size(), true); for (Instance instance : data) { FeatureVectorSequence fvs = (FeatureVectorSequence)instance.getData(); this.numTokens += fvs.size(); } return bitSet; }
private static int initialCapacity (Instance inst) { if (inst.getData () == null) { return 8; } FeatureVectorSequence fvs = (FeatureVectorSequence) inst.getData (); int T = fvs.size (); return 3 * T; }
public BitSet preProcess(InstanceList data) { // count number of tokens BitSet bitSet = new BitSet(data.size()); bitSet.set(0, data.size(), true); for (Instance instance : data) { FeatureVectorSequence fvs = (FeatureVectorSequence)instance.getData(); this.numTokens += fvs.size(); } return bitSet; }
public BitSet preProcess(InstanceList data) { // count number of tokens BitSet bitSet = new BitSet(data.size()); bitSet.set(0, data.size(), true); for (Instance instance : data) { FeatureVectorSequence fvs = (FeatureVectorSequence)instance.getData(); this.numTokens += fvs.size(); } return bitSet; }
private static int initialCapacity (Instance inst) { if (inst.getData () == null) { return 8; } FeatureVectorSequence fvs = (FeatureVectorSequence) inst.getData (); int T = fvs.size (); return 3 * T; }
private static int initialCapacity (Instance inst) { if (inst.getData () == null) { return 8; } FeatureVectorSequence fvs = (FeatureVectorSequence) inst.getData (); int T = fvs.size (); return 3 * T; }
private THashMultiMap constructFvByWord (FeatureVectorSequence fvs) { THashMultiMap fvByWord = new THashMultiMap (fvs.size ()); int N = fvs.size (); for (int t = 0; t < N; t++) { FeatureVector fv = fvs.getFeatureVector (t); String wordFeature = binner.computeBin (fv); if (wordFeature != null) { // could happen if the current word has been excluded fvByWord.put (wordFeature, new TokenInfo (wordFeature, fv, t)); } } return fvByWord; }
private THashMultiMap constructFvByWord (FeatureVectorSequence fvs) { THashMultiMap fvByWord = new THashMultiMap (fvs.size ()); int N = fvs.size (); for (int t = 0; t < N; t++) { FeatureVector fv = fvs.getFeatureVector (t); String wordFeature = binner.computeBin (fv); if (wordFeature != null) { // could happen if the current word has been excluded fvByWord.put (wordFeature, new TokenInfo (wordFeature, fv, t)); } } return fvByWord; }
private THashMultiMap constructFvByWord (FeatureVectorSequence fvs) { THashMultiMap fvByWord = new THashMultiMap (fvs.size ()); int N = fvs.size (); for (int t = 0; t < N; t++) { FeatureVector fv = fvs.getFeatureVector (t); String wordFeature = binner.computeBin (fv); if (wordFeature != null) { // could happen if the current word has been excluded fvByWord.put (wordFeature, new TokenInfo (wordFeature, fv, t)); } } return fvByWord; }
public BitSet preProcess(InstanceList data) { // count BitSet bitSet = new BitSet(data.size()); int ii = 0; for (Instance instance : data) { FeatureVectorSequence fvs = (FeatureVectorSequence)instance.getData(); for (int ip = 1; ip < fvs.size(); ip++) { for (int fi : constraintsMap.keys()) { // binary constraint features if (fvs.get(ip).location(fi) >= 0) { constraintsList.get(constraintsMap.get(fi)).count += 1; bitSet.set(ii); } } } ii++; } return bitSet; }
public Classification classify (Instance instance) { FeatureVectorSequence fvs = (FeatureVectorSequence) instance.getData(); int numClasses = fvs.size(); double[] scores = new double[numClasses]; getClassificationScores (instance, scores); // Create and return a Classification object return new Classification (instance, this, createLabelVector (getLabelAlphabet(), scores)); }
public Classification classify (Instance instance) { FeatureVectorSequence fvs = (FeatureVectorSequence) instance.getData(); int numClasses = fvs.size(); double[] scores = new double[numClasses]; getClassificationScores (instance, scores); // Create and return a Classification object return new Classification (instance, this, createLabelVector (getLabelAlphabet(), scores)); }
public Classification classify (Instance instance) { FeatureVectorSequence fvs = (FeatureVectorSequence) instance.getData(); int numClasses = fvs.size(); double[] scores = new double[numClasses]; getClassificationScores (instance, scores); // Create and return a Classification object return new Classification (instance, this, createLabelVector (getLabelAlphabet(), scores)); }