public PerceptronModelWriter (AbstractModel model) { Object[] data = model.getDataStructures(); this.numOutcomes = model.getNumOutcomes(); PARAMS = (Context[]) data[0]; IndexHashTable<String> pmap = (IndexHashTable<String>) data[1]; OUTCOME_LABELS = (String[])data[2]; PRED_LABELS = new String[pmap.size()]; pmap.toArray(PRED_LABELS); }
private void init(AbstractModel model, BufferedWriter bw) { if (model.getModelType() == ModelType.Perceptron) { delegateWriter = new PlainTextPerceptronModelWriter(model,bw); } else if (model.getModelType() == ModelType.Maxent) { delegateWriter = new PlainTextGISModelWriter(model,bw); } }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,outcomeNames.length); }
public static void main(String[] args) throws java.io.IOException { if (args.length == 0) { System.err.println("Usage: PerceptronModel modelname < contexts"); System.exit(1); } AbstractModel m = new PerceptronModelReader(new File(args[0])).getModel(); BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); DecimalFormat df = new java.text.DecimalFormat(".###"); for (String line = in.readLine(); line != null; line = in.readLine()) { String[] context = line.split(" "); double[] dist = m.eval(context); for (int oi=0;oi<dist.length;oi++) { System.out.print("["+m.getOutcome(oi)+" "+df.format(dist[oi])+"] "); } System.out.println(); } } }
@SuppressWarnings("unchecked") public QNModelWriter(AbstractModel model) { Object[] data = model.getDataStructures(); params = (Context[]) data[0]; pmap = (IndexHashTable<String>) data[1]; outcomeNames = (String[]) data[2]; QNModel qnModel = (QNModel) model; parameters = qnModel.getParameters(); }
public static void main(String[] args) throws java.io.IOException { if (args.length == 0) { System.err.println("Usage: PerceptronModel modelname < contexts"); System.exit(1); } AbstractModel m = new PerceptronModelReader(new File(args[0])).getModel(); BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); DecimalFormat df = new java.text.DecimalFormat(".###"); for (String line = in.readLine(); line != null; line = in.readLine()) { String[] context = line.split(" "); double[] dist = m.eval(context); for (int oi=0;oi<dist.length;oi++) { System.out.print("["+m.getOutcome(oi)+" "+df.format(dist[oi])+"] "); } System.out.println(); } } }
@SuppressWarnings("unchecked") public QNModelWriter(AbstractModel model) { Object[] data = model.getDataStructures(); params = (Context[]) data[0]; pmap = (IndexHashTable<String>) data[1]; outcomeNames = (String[]) data[2]; QNModel qnModel = (QNModel) model; parameters = qnModel.getParameters(); }
public static void main(String[] args) throws java.io.IOException { if (args.length == 0) { System.err.println("Usage: GISModel modelname < contexts"); System.exit(1); } AbstractModel m = new opennlp.maxent.io.SuffixSensitiveGISModelReader( new File(args[0])).getModel(); BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); DecimalFormat df = new java.text.DecimalFormat(".###"); for (String line = in.readLine(); line != null; line = in.readLine()) { String[] context = line.split(" "); double[] dist = m.eval(context); for (int oi = 0; oi < dist.length; oi++) { System.out.print("[" + m.getOutcome(oi) + " " + df.format(dist[oi]) + "] "); } System.out.println(); } } }
public PerceptronModelWriter (AbstractModel model) { Object[] data = model.getDataStructures(); this.numOutcomes = model.getNumOutcomes(); PARAMS = (Context[]) data[0]; IndexHashTable<String> pmap = (IndexHashTable<String>) data[1]; OUTCOME_LABELS = (String[])data[2]; PRED_LABELS = new String[pmap.size()]; pmap.toArray(PRED_LABELS); }
private void init(AbstractModel model, BufferedWriter bw) { if (model.getModelType() == ModelType.Perceptron) { delegateWriter = new PlainTextPerceptronModelWriter(model,bw); } else if (model.getModelType() == ModelType.Maxent) { delegateWriter = new PlainTextGISModelWriter(model,bw); } }
public GISModelWriter(AbstractModel model) { Object[] data = model.getDataStructures(); PARAMS = (Context[]) data[0]; IndexHashTable<String> pmap = (IndexHashTable<String>) data[1]; OUTCOME_LABELS = (String[]) data[2]; CORRECTION_CONSTANT = (Integer) data[3]; CORRECTION_PARAM = (Double) data[4]; PRED_LABELS = new String[pmap.size()]; pmap.toArray(PRED_LABELS); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames, int correctionConstant,double correctionParam) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,correctionParam,correctionConstant,outcomeNames.length); }
public static void main(String[] args) throws java.io.IOException { if (args.length == 0) { System.err.println("Usage: GISModel modelname < contexts"); System.exit(1); } AbstractModel m = new opennlp.maxent.io.SuffixSensitiveGISModelReader( new File(args[0])).getModel(); BufferedReader in = new BufferedReader(new InputStreamReader(System.in)); DecimalFormat df = new java.text.DecimalFormat(".###"); for (String line = in.readLine(); line != null; line = in.readLine()) { String[] context = line.split(" "); double[] dist = m.eval(context); for (int oi = 0; oi < dist.length; oi++) { System.out.print("[" + m.getOutcome(oi) + " " + df.format(dist[oi]) + "] "); } System.out.println(); } } }
private void init(AbstractModel model, DataOutputStream dos) { if (model.getModelType() == ModelType.Perceptron) { delegateWriter = new BinaryPerceptronModelWriter(model,dos); } else if (model.getModelType() == ModelType.Maxent) { delegateWriter = new BinaryGISModelWriter(model,dos); } else if (model.getModelType() == ModelType.MaxentQn) { delegateWriter = new BinaryQNModelWriter(model,dos); } }
public GISModelWriter(AbstractModel model) { Object[] data = model.getDataStructures(); PARAMS = (Context[]) data[0]; IndexHashTable<String> pmap = (IndexHashTable<String>) data[1]; OUTCOME_LABELS = (String[]) data[2]; CORRECTION_CONSTANT = (Integer) data[3]; CORRECTION_PARAM = (Double) data[4]; PRED_LABELS = new String[pmap.size()]; pmap.toArray(PRED_LABELS); }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames, int correctionConstant,double correctionParam) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,correctionParam,correctionConstant,outcomeNames.length); }
private void init(AbstractModel model, DataOutputStream dos) { if (model.getModelType() == ModelType.Perceptron) { delegateWriter = new BinaryPerceptronModelWriter(model,dos); } else if (model.getModelType() == ModelType.Maxent) { delegateWriter = new BinaryGISModelWriter(model,dos); } else if (model.getModelType() == ModelType.MaxentQn) { delegateWriter = new BinaryQNModelWriter(model,dos); } }
public AbstractModel(Context[] params, String[] predLabels, String[] outcomeNames) { init(predLabels,outcomeNames); this.evalParams = new EvalParameters(params,outcomeNames.length); }