opennlp.model.DataIndexer java code examples

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}

public LogLikelihoodFunction(DataIndexer indexer) {
 // get data from indexer.
 if (indexer instanceof OnePassRealValueDataIndexer) {
  this.values = indexer.getValues();
 } else {
  this.values = null;
 }
 this.contexts = indexer.getContexts();
 this.outcomeList = indexer.getOutcomeList();
 this.numTimesEventsSeen = indexer.getNumTimesEventsSeen();
 this.outcomeLabels = indexer.getOutcomeLabels();
 this.predLabels = indexer.getPredLabels();
 this.numOutcomes = indexer.getOutcomeLabels().length;
 this.numFeatures = indexer.getPredLabels().length;
 this.numContexts = this.contexts.length;
 this.domainDimension = numOutcomes * numFeatures;
 this.probModel = new double[numContexts][numOutcomes];
 this.gradient = null;
}

 numSequences++;
outcomeList  = di.getOutcomeList();
predLabels = di.getPredLabels();
pmap = new IndexHashTable<String>(predLabels, 0.7d);
numEvents = di.getNumEvents();
outcomeLabels = di.getOutcomeLabels();
omap = new HashMap<String,Integer>();
for (int oli=0;oli<outcomeLabels.length;oli++) {
 omap.put(outcomeLabels[oli], oli);
outcomeList = di.getOutcomeList();

 numSequences++;
outcomeList  = di.getOutcomeList();
predLabels = di.getPredLabels();
pmap = new IndexHashTable<String>(predLabels, 0.7d);
numEvents = di.getNumEvents();
outcomeLabels = di.getOutcomeLabels();
omap = new HashMap<String,Integer>();
for (int oli=0;oli<outcomeLabels.length;oli++) {
 omap.put(outcomeLabels[oli], oli);
outcomeList = di.getOutcomeList();

public LogLikelihoodFunction(DataIndexer indexer) {
 // get data from indexer.
 if (indexer instanceof OnePassRealValueDataIndexer) {
  this.values = indexer.getValues();
 } else {
  this.values = null;
 }
 this.contexts = indexer.getContexts();
 this.outcomeList = indexer.getOutcomeList();
 this.numTimesEventsSeen = indexer.getNumTimesEventsSeen();
 this.outcomeLabels = indexer.getOutcomeLabels();
 this.predLabels = indexer.getPredLabels();
 this.numOutcomes = indexer.getOutcomeLabels().length;
 this.numFeatures = indexer.getPredLabels().length;
 this.numContexts = this.contexts.length;
 this.domainDimension = numOutcomes * numFeatures;
 this.probModel = new double[numContexts][numOutcomes];
 this.gradient = null;
}

public AbstractModel trainModel(int iterations, DataIndexer di, int cutoff, boolean useAverage) {
 display("Incorporating indexed data for training...  \n");
 contexts = di.getContexts();
 values = di.getValues();
 numTimesEventsSeen = di.getNumTimesEventsSeen();
 numEvents = di.getNumEvents();
 numUniqueEvents = contexts.length;
 outcomeLabels = di.getOutcomeLabels();
 outcomeList = di.getOutcomeList();
 predLabels = di.getPredLabels();
 numPreds = predLabels.length;
 numOutcomes = outcomeLabels.length;
 
 display("done.\n");
 
 display("\tNumber of Event Tokens: " + numUniqueEvents + "\n");
 display("\t    Number of Outcomes: " + numOutcomes + "\n");
 display("\t  Number of Predicates: " + numPreds + "\n");
 
 display("Computing model parameters...\n");
 MutableContext[] finalParameters = findParameters(iterations, useAverage);
 display("...done.\n");
 /*************** Create and return the model ******************/
 return new PerceptronModel(finalParameters, predLabels, outcomeLabels);
}

public AbstractModel trainModel(int iterations, DataIndexer di, int cutoff, boolean useAverage) {
 display("Incorporating indexed data for training...  \n");
 contexts = di.getContexts();
 values = di.getValues();
 numTimesEventsSeen = di.getNumTimesEventsSeen();
 numEvents = di.getNumEvents();
 numUniqueEvents = contexts.length;
 outcomeLabels = di.getOutcomeLabels();
 outcomeList = di.getOutcomeList();
 predLabels = di.getPredLabels();
 numPreds = predLabels.length;
 numOutcomes = outcomeLabels.length;
 
 display("done.\n");
 
 display("\tNumber of Event Tokens: " + numUniqueEvents + "\n");
 display("\t    Number of Outcomes: " + numOutcomes + "\n");
 display("\t  Number of Predicates: " + numPreds + "\n");
 
 display("Computing model parameters...\n");
 MutableContext[] finalParameters = findParameters(iterations, useAverage);
 display("...done.\n");
 /*************** Create and return the model ******************/
 return new PerceptronModel(finalParameters, predLabels, outcomeLabels);
}

trainingDataFeatNameIndices = di.getContexts();
trainingDataFeatValues = di.getValues();
this.cutoff = cutoff;
predicateCounts = di.getPredCounts();
numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = trainingDataFeatNameIndices.length;
this.prior = modelPrior;
labels = di.getOutcomeLabels();
outcomeList = di.getOutcomeList();
featNames = di.getPredLabels();
prior.setLabels(labels, featNames);

contexts = di.getContexts();
values = di.getValues();
this.cutoff = cutoff;
predicateCounts = di.getPredCounts();
numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;
outcomeLabels = di.getOutcomeLabels();
outcomeList = di.getOutcomeList();
numOutcomes = outcomeLabels.length;
predLabels = di.getPredLabels();
prior.setLabels(outcomeLabels,predLabels);
numPreds = predLabels.length;

contexts = di.getContexts();
values = di.getValues();
this.cutoff = cutoff;
predicateCounts = di.getPredCounts();
numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;
outcomeLabels = di.getOutcomeLabels();
outcomeList = di.getOutcomeList();
numOutcomes = outcomeLabels.length;
predLabels = di.getPredLabels();
prior.setLabels(outcomeLabels,predLabels);
numPreds = predLabels.length;

contexts = di.getContexts();
values = di.getValues();
this.cutoff = cutoff;
predicateCounts = di.getPredCounts();
numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;
outcomeLabels = di.getOutcomeLabels();
outcomeList = di.getOutcomeList();
numOutcomes = outcomeLabels.length;
predLabels = di.getPredLabels();
prior.setLabels(outcomeLabels, predLabels);
numPreds = predLabels.length;

Javadoc

Object which compresses events in memory and performs feature selection.

Most used methods

getContexts
Returns the array of predicates seen in each event.
getNumTimesEventsSeen
Returns an array indicating the number of times a particular event was seen.
getOutcomeLabels
Returns an array of outcome names.
getOutcomeList
Returns an array indicating the outcome index for each event.
getPredCounts
Returns an array of the count of each predicate in the events.
getPredLabels
Returns an array of predicate/context names.
getValues
Returns the values associated with each event context or null if integer values are to be used.
getNumEvents
Returns the number of total events indexed.

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How to useDataIndexer in opennlp.model

Best Java code snippets using opennlp.model.DataIndexer (Showing top 10 results out of 315)

How to use
DataIndexer
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opennlp.model