opennlp.tools.ml.model.DataIndexer.getNumTimesEventsSeen java code examples

Common ways to obtain DataIndexer

private void myMethod () {
 DataIndexer d =new OnePassDataIndexer()
TrainingParameters parameters;Map reportMap;DataIndexerFactory.getDataIndexer(parameters, reportMap)
Smart code suggestions by Codota
}

public NegLogLikelihood(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.numOutcomes = indexer.getOutcomeLabels().length;
 this.numFeatures = indexer.getPredLabels().length;
 this.numContexts = this.contexts.length;
 this.dimension   = numOutcomes * numFeatures;
 this.expectation = new double[numOutcomes];
 this.tempSums    = new double[numOutcomes];
 this.gradient    = new double[dimension];
}

 /**
  * Evaluate the current model on training data set
  * @return model's training accuracy
  */
 @Override
 public double evaluate(double[] parameters) {
  int[][] contexts  = indexer.getContexts();
  float[][] values  = indexer.getValues();
  int[] nEventsSeen = indexer.getNumTimesEventsSeen();
  int[] outcomeList = indexer.getOutcomeList();
  int nOutcomes     = indexer.getOutcomeLabels().length;
  int nPredLabels   = indexer.getPredLabels().length;
  int nCorrect     = 0;
  int nTotalEvents = 0;
  for (int ei = 0; ei < contexts.length; ei++) {
   int[] context  = contexts[ei];
   float[] value  = values == null ? null : values[ei];
   double[] probs = new double[nOutcomes];
   QNModel.eval(context, value, probs, nOutcomes, nPredLabels, parameters);
   int outcome = ArrayMath.argmax(probs);
   if (outcome == outcomeList[ei]) {
    nCorrect += nEventsSeen[ei];
   }
   nTotalEvents += nEventsSeen[ei];
  }
  return (double) nCorrect / nTotalEvents;
 }
}

public AbstractModel trainModel(DataIndexer di) {
 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();
 display("...done.\n");
 /* Create and return the model ****/
 return new NaiveBayesModel(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);
}

numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;

public NegLogLikelihood(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.numOutcomes = indexer.getOutcomeLabels().length;
 this.numFeatures = indexer.getPredLabels().length;
 this.numContexts = this.contexts.length;
 this.dimension   = numOutcomes * numFeatures;
 this.expectation = new double[numOutcomes];
 this.tempSums    = new double[numOutcomes];
 this.gradient    = new double[dimension];
}

Assert.assertEquals(5, indexer.getNumEvents());
Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList());
Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new String[]{"ppo=other"}, indexer.getPredLabels());
Assert.assertArrayEquals(new String[]{"other", "org-start", "org-cont"}, indexer.getOutcomeLabels());

Assert.assertEquals(5, indexer.getNumEvents());
Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList());
Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new String[]{"ppo=other"}, indexer.getPredLabels());
Assert.assertArrayEquals(new String[]{"other", "org-start", "org-cont"}, indexer.getOutcomeLabels());

public NegLogLikelihood(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.numOutcomes = indexer.getOutcomeLabels().length;
 this.numFeatures = indexer.getPredLabels().length;
 this.numContexts = this.contexts.length;
 this.dimension   = numOutcomes * numFeatures;
 this.expectation = new double[numOutcomes];
 this.tempSums    = new double[numOutcomes];
 this.gradient    = new double[dimension];
}

Assert.assertEquals(5, indexer.getNumEvents());
Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList());
Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new String[]{"ppo=other"}, indexer.getPredLabels());
Assert.assertArrayEquals(new String[]{"other", "org-start", "org-cont"}, indexer.getOutcomeLabels());

 /**
  * Evaluate the current model on training data set
  * @return model's training accuracy
  */
 @Override
 public double evaluate(double[] parameters) {
  int[][] contexts  = indexer.getContexts();
  float[][] values  = indexer.getValues();
  int[] nEventsSeen = indexer.getNumTimesEventsSeen();
  int[] outcomeList = indexer.getOutcomeList();
  int nOutcomes     = indexer.getOutcomeLabels().length;
  int nPredLabels   = indexer.getPredLabels().length;
  int nCorrect     = 0;
  int nTotalEvents = 0;
  for (int ei = 0; ei < contexts.length; ei++) {
   int[] context  = contexts[ei];
   float[] value  = values == null ? null : values[ei];
   double[] probs = new double[nOutcomes];
   QNModel.eval(context, value, probs, nOutcomes, nPredLabels, parameters);
   int outcome = ArrayMath.argmax(probs);
   if (outcome == outcomeList[ei]) {
    nCorrect += nEventsSeen[ei];
   }
   nTotalEvents += nEventsSeen[ei];
  }
  return (double) nCorrect / nTotalEvents;
 }
}

Assert.assertEquals(5, indexer.getNumEvents());
Assert.assertArrayEquals(new int[]{0, 1, 2}, indexer.getOutcomeList());
Assert.assertArrayEquals(new int[]{3, 1, 1}, indexer.getNumTimesEventsSeen());
Assert.assertArrayEquals(new String[]{"ppo=other"}, indexer.getPredLabels());
Assert.assertArrayEquals(new String[]{"other", "org-start", "org-cont"}, indexer.getOutcomeLabels());

 /**
  * Evaluate the current model on training data set
  * @return model's training accuracy
  */
 @Override
 public double evaluate(double[] parameters) {
  int[][] contexts  = indexer.getContexts();
  float[][] values  = indexer.getValues();
  int[] nEventsSeen = indexer.getNumTimesEventsSeen();
  int[] outcomeList = indexer.getOutcomeList();
  int nOutcomes     = indexer.getOutcomeLabels().length;
  int nPredLabels   = indexer.getPredLabels().length;
  int nCorrect     = 0;
  int nTotalEvents = 0;
  for (int ei = 0; ei < contexts.length; ei++) {
   int[] context  = contexts[ei];
   float[] value  = values == null ? null : values[ei];
   double[] probs = new double[nOutcomes];
   QNModel.eval(context, value, probs, nOutcomes, nPredLabels, parameters);
   int outcome = ArrayMath.maxIdx(probs);
   if (outcome == outcomeList[ei]) {
    nCorrect += nEventsSeen[ei];
   }
   nTotalEvents += nEventsSeen[ei];
  }
  return (double) nCorrect / nTotalEvents;
 }
}

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(DataIndexer di) {
 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();
 display("...done.\n");
 /* Create and return the model ****/
 return new NaiveBayesModel(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);
}

public AbstractModel trainModel(DataIndexer di) {
 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();
 display("...done.\n");
 /* Create and return the model ****/
 return new NaiveBayesModel(finalParameters, predLabels, outcomeLabels);
}

numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;

numTimesEventsSeen = di.getNumTimesEventsSeen();
numUniqueEvents = contexts.length;
this.prior = modelPrior;

Javadoc

Returns an array indicating the number of times a particular event was seen.

Popular methods of DataIndexer

getContexts
Returns the array of predicates seen in each event.
getNumEvents
Returns the number of total events indexed.
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.
index
Performs the data indexing. Make sure the init(...) method is called first.
init
Sets parameters used during the data indexing.

Popular in Java

Reactive rest calls using spring rest template
findViewById (Activity)
getContentResolver (Context)
compareTo (BigDecimal)
Compares this BigDecimal with the specified BigDecimal. Two BigDecimal objects that are equal in val
Color (java.awt)
The Color class is used encapsulate colors in the default sRGB color space or colors in arbitrary co
Component (java.awt)
A component is an object having a graphical representation that can be displayed on the screen and t
IOException (java.io)
Signals that an I/O exception of some sort has occurred. This class is the general class of exceptio
BigInteger (java.math)
Immutable arbitrary-precision integers. All operations behave as if BigIntegers were represented in
SortedMap (java.util)
A map that has its keys ordered. The sorting is according to either the natural ordering of its keys
Servlet (javax.servlet)
Defines methods that all servlets must implement.A servlet is a small Java program that runs within

How to use getNumTimesEventsSeenmethodin opennlp.tools.ml.model.DataIndexer

Best Java code snippets using opennlp.tools.ml.model.DataIndexer.getNumTimesEventsSeen (Showing top 19 results out of 315)

How to use
getNumTimesEventsSeen
method
in
opennlp.tools.ml.model.DataIndexer