/** Returns the <code>Alphabet</code> mapping target output labels to * integers. */ public Alphabet getTargetAlphabet () { if (targetAlphabet == null && pipe != null) { targetAlphabet = pipe.getTargetAlphabet (); } assert (pipe == null || pipe.getTargetAlphabet () == null || pipe.getTargetAlphabet () == targetAlphabet); return targetAlphabet; }
/** Returns the <code>Alphabet</code> mapping target output labels to * integers. */ public Alphabet getTargetAlphabet () { if (targetAlphabet == null && pipe != null) { targetAlphabet = pipe.getTargetAlphabet (); } assert (pipe == null || pipe.getTargetAlphabet () == null || pipe.getTargetAlphabet () == targetAlphabet); return targetAlphabet; }
public Classifier (Pipe instancePipe) { this.instancePipe = instancePipe; // All classifiers must have set of labels. assert (instancePipe.getTargetAlphabet() != null); assert (instancePipe.getTargetAlphabet().getClass().isAssignableFrom(LabelAlphabet.class)); // Not all classifiers require a feature dictionary, however. }
public Classifier (Pipe instancePipe) { this.instancePipe = instancePipe; // All classifiers must have set of labels. assert (instancePipe.getTargetAlphabet() != null); assert (instancePipe.getTargetAlphabet().getClass().isAssignableFrom(LabelAlphabet.class)); // Not all classifiers require a feature dictionary, however. }
public CRF (Pipe inputPipe, Pipe outputPipe) { super (inputPipe, outputPipe); this.inputAlphabet = inputPipe.getDataAlphabet(); this.outputAlphabet = inputPipe.getTargetAlphabet(); //inputAlphabet.stopGrowth(); }
public HMM(Pipe inputPipe, Pipe outputPipe) { this.inputPipe = inputPipe; this.outputPipe = outputPipe; this.inputAlphabet = inputPipe.getDataAlphabet(); this.outputAlphabet = inputPipe.getTargetAlphabet(); }
public NaiveBayesTrainer (Pipe instancePipe) { this.instancePipe = instancePipe; this.dataAlphabet = instancePipe.getDataAlphabet(); this.targetAlphabet = instancePipe.getTargetAlphabet(); }
public HMM(Pipe inputPipe, Pipe outputPipe) { this.inputPipe = inputPipe; this.outputPipe = outputPipe; this.inputAlphabet = inputPipe.getDataAlphabet(); this.outputAlphabet = inputPipe.getTargetAlphabet(); }
public NaiveBayesTrainer (Pipe instancePipe) { this.instancePipe = instancePipe; this.dataAlphabet = instancePipe.getDataAlphabet(); this.targetAlphabet = instancePipe.getTargetAlphabet(); }
public CRF (Pipe inputPipe, Pipe outputPipe) { super (inputPipe, outputPipe); this.inputAlphabet = inputPipe.getDataAlphabet(); this.outputAlphabet = inputPipe.getTargetAlphabet(); //inputAlphabet.stopGrowth(); }
public PRAuxClassifier(Pipe pipe, ArrayList<MaxEntPRConstraint> constraints) { super(pipe); this.constraints = constraints; this.parameters = new double[constraints.size()][]; for (int i = 0; i < constraints.size(); i++) { this.parameters[i] = new double[constraints.get(i).numDimensions()]; } this.numLabels = pipe.getTargetAlphabet().size(); }
public PRAuxClassifier(Pipe pipe, ArrayList<MaxEntPRConstraint> constraints) { super(pipe); this.constraints = constraints; this.parameters = new double[constraints.size()][]; for (int i = 0; i < constraints.size(); i++) { this.parameters[i] = new double[constraints.get(i).numDimensions()]; } this.numLabels = pipe.getTargetAlphabet().size(); }
public PRAuxClassifier(Pipe pipe, ArrayList<MaxEntPRConstraint> constraints) { super(pipe); this.constraints = constraints; this.parameters = new double[constraints.size()][]; for (int i = 0; i < constraints.size(); i++) { this.parameters[i] = new double[constraints.get(i).numDimensions()]; } this.numLabels = pipe.getTargetAlphabet().size(); }
public boolean alphabetsMatch (AlphabetCarrying object) { Alphabet[] oas = object.getAlphabets(); return oas.length == 2 && oas[0].equals(getDataAlphabet()) && oas[1].equals(getTargetAlphabet()); }
public boolean alphabetsMatch (AlphabetCarrying object) { Alphabet[] oas = object.getAlphabets(); return oas.length == 2 && oas[0].equals(getDataAlphabet()) && oas[1].equals(getTargetAlphabet()); }
public int getNumParameters () { assert (this.instancePipe.getDataAlphabet() != null); assert (this.instancePipe.getTargetAlphabet() != null); return MaxEnt.getNumParameters(this.instancePipe); }
public int getNumParameters () { assert (this.instancePipe.getDataAlphabet() != null); assert (this.instancePipe.getTargetAlphabet() != null); return MaxEnt.getNumParameters(this.instancePipe); }
private void resolveAlphabets () { Alphabet da = null, ta = null; for (Pipe p : pipes) { p.preceedingPipeDataAlphabetNotification(da); da = p.getDataAlphabet(); p.preceedingPipeTargetAlphabetNotification(ta); ta = p.getTargetAlphabet(); } dataAlphabet = da; targetAlphabet = ta; }
private void resolveAlphabets () { Alphabet da = null, ta = null; for (Pipe p : pipes) { p.preceedingPipeDataAlphabetNotification(da); da = p.getDataAlphabet(); p.preceedingPipeTargetAlphabetNotification(ta); ta = p.getTargetAlphabet(); } dataAlphabet = da; targetAlphabet = ta; }
public static void writeInstanceList(ArrayList<Pipe> pipes) throws Exception { Pipe serialPipe = new SerialPipes(pipes); DBInstanceStore saver = new DBInstanceStore(outputDatabase.value); for (String filename: inputFiles.value) { logger.info("importing from " + filename); CsvIterator reader = new CsvIterator(new FileReader(filename), "(.*?)\\t(.*?)\\t(.*)", 3, 2, 1); saver.saveInstances(serialPipe.newIteratorFrom(reader)); } saver.saveAlphabets(serialPipe.getDataAlphabet(), serialPipe.getTargetAlphabet()); saver.cleanup(); }