List of classify() Examples

Examples of classify()

au.csiro.ontology.classification.IReasoner.classify()
Classifies the current axioms. @return
au.csiro.snorocket.core.NormalisedOntology.classify()
Starts the concurrent classification process.
au.csiro.snorocket.core.SnorocketReasoner.classify()
cc.mallet.classify.Classifier.classify()
com.clarkparsia.modularity.IncrementalClassifier.classify()
com.digitalpebble.classification.TextClassifier.classify()
Returns the probability for each possible label*
com.flaptor.hounder.classifier.bayes.BayesClassifier.classify()
Classifies a document, indicating the calculated confidence level the document belongs to the category with. @param doc the document token data, as generated by parseDocument. @return the confidence level of the document
com.github.pmerienne.trident.ml.nlp.KLDClassifier.classify()
edu.msu.cme.rdp.classifier.Classifier.classify()
Takes a query sequence, returns the classification result. For each query sequence, first assign it to a genus node using all the words for calculation. Then randomly chooses one-eighth of the all overlapping words in the query to calculate the joint probability. The number of times a genus was selected out of the number of bootstrap trials was used as an estimate of confidence in the assignment to that genus. @throws ShortSequenceException if the sequence length is less than the minimum sequence length.
edu.stanford.nlp.ie.regexp.NumberSequenceClassifier.classify()
Classify a {@link List} of {@link CoreLabel}s. @param document A {@link List} of {@link CoreLabel}s. @return the same {@link List}, but with the elements annotated with their answers.
gannuCF.classifiers.Classifier.classify()
Method for classifying a sample extracted as an array of weights. This method should be used together with its train counterpart when you want to control the Word Space Model by yourself. @param Sample An array of weights containing a sample. @return An array with the weights of each class. You should select the greater one as the answer.
jnisvmlight.SVMLightModel.classify()
org.apache.devicemap.DeviceMapClient.classify()
org.apache.mahout.classifier.AbstractVectorClassifier.classify()
Classify a vector returning a vector of numCategories-1 scores. It is assumed that the score for the missing category is one minus the sum of the scores that are returned. Note that the missing score is the 0-th score. @param instance A feature vector to be classified. @return A vector of probabilities in 1 of n-1 encoding.
org.apache.mahout.classifier.bayes.BayesClassifier.classify()
Classify the document and return the top numResults @param model The model @param document The document to classify @param defaultCategory The default category to assign @param numResults The maximum number of results to return, ranked by score.Ties are broken by comparing the category @return A Collection of {@link org.apache.mahout.classifier.ClassifierResult}s.
org.apache.mahout.clustering.classify.ClusterClassifier.classify()
org.apache.mahout.common.Classifier.classify()
Classify the document and return the top numResults @param model The model @param document The document to classify @param defaultCategory The default category to assign @param numResults The maximum number of results to return, ranked by score.Ties are broken by comparing the category @return A Collection of {@link org.apache.mahout.classifier.ClassifierResult}s.
org.apache.mahout.df.node.Node.classify()
predicts the label for the instance @param instance @return -1 if the label cannot be predicted
org.dbpedia.spotlight.spot.cooccurrence.classification.SpotClassifier.classify()
Classify a surface form candidate. @param surfaceFormOccurrence the surface form occurrence @return a Classification object containing the proposed classification and a confidence value
org.dmd.util.parsing.Classifier.classify()
The classify function breaks down the specified string into tokens and passes back an array of classified tokens. @param s The string to be parsed. @param returnSeps Set to true if the separators should be returned as tokens.
org.encog.neural.som.SOM.classify()
{@inheritDoc}
org.fnlp.app.tc.TextClassifier.classify()
org.fnlp.ml.classifier.bayes.BayesClassifier.classify()
org.fnlp.ml.classifier.knn.KNNClassifier.classify()
org.fnlp.ml.classifier.linear.Linear.classify()
org.mindswap.pellet.KnowledgeBase.classify()
org.mindswap.pellet.taxonomy.POTaxonomyBuilder.classify()
org.mindswap.pellet.taxonomy.TaxonomyBuilder.classify()
Classify the KB.
org.semanticweb.owl.inference.OWLReasoner.classify()
org.springframework.classify.BinaryExceptionClassifier.classify()

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new Canopy(new DenseVector(2), 1, measure));
    models.add(new Canopy(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }


  @Test
  public void testClusterClassification() {

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new Cluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new Cluster(new DenseVector(2), 1, measure));
    models.add(new Cluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new Cluster(new DenseVector(2), 1, measure));
    models.add(new Cluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.107, 0.787, 0.107]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.867, 0.117, 0.016]", AbstractCluster.formatVector(pdf, null));
  }


  @Test
  public void testMSCanopyClassification() {

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new MeanShiftCanopy(new DenseVector(2).assign(1), 0, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2), 1, measure));
    models.add(new MeanShiftCanopy(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    try {
      classifier.classify(new DenseVector(2));
      fail("Expected NotImplementedException");
    } catch (NotImplementedException e) {
    }
  }

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    DistanceMeasure measure = new ManhattanDistanceMeasure();
    models.add(new SoftCluster(new DenseVector(2).assign(1), 0, measure));
    models.add(new SoftCluster(new DenseVector(2), 1, measure));
    models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.000, 1.000, 0.000]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.735, 0.184, 0.082]", AbstractCluster.formatVector(pdf, null));
  }

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new SoftCluster(new DenseVector(2), 1, measure));
    models.add(new SoftCluster(new DenseVector(2).assign(-1), 2, measure));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.000, 1.000, 0.000]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.735, 0.184, 0.082]", AbstractCluster.formatVector(pdf, null));
  }


  @Test
  public void testGaussianClusterClassification() {

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    models.add(new GaussianCluster(new DenseVector(2).assign(1), new DenseVector(2).assign(1), 0));
    models.add(new GaussianCluster(new DenseVector(2), new DenseVector(2).assign(1), 1));
    models.add(new GaussianCluster(new DenseVector(2).assign(-1), new DenseVector(2).assign(1), 2));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.274, 0.452, 0.274]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.806, 0.180, 0.015]", AbstractCluster.formatVector(pdf, null));
  }

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new GaussianCluster(new DenseVector(2), new DenseVector(2).assign(1), 1));
    models.add(new GaussianCluster(new DenseVector(2).assign(-1), new DenseVector(2).assign(1), 2));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.274, 0.452, 0.274]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.806, 0.180, 0.015]", AbstractCluster.formatVector(pdf, null));
  }


  @Test
  public void testASNClusterClassification() {

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    List<Model<VectorWritable>> models = new ArrayList<Model<VectorWritable>>();
    models.add(new AsymmetricSampledNormalModel(0, new DenseVector(2).assign(1), new DenseVector(2).assign(1)));
    models.add(new AsymmetricSampledNormalModel(1, new DenseVector(2), new DenseVector(2).assign(1)));
    models.add(new AsymmetricSampledNormalModel(2, new DenseVector(2).assign(-1), new DenseVector(2).assign(1)));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.212, 0.576, 0.212]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.952, 0.047, 0.000]", AbstractCluster.formatVector(pdf, null));
  }

View Full Code Here

Examples of org.apache.mahout.classifier.AbstractVectorClassifier.classify()

    models.add(new AsymmetricSampledNormalModel(1, new DenseVector(2), new DenseVector(2).assign(1)));
    models.add(new AsymmetricSampledNormalModel(2, new DenseVector(2).assign(-1), new DenseVector(2).assign(1)));
    AbstractVectorClassifier classifier = new VectorModelClassifier(models);
    Vector pdf = classifier.classify(new DenseVector(2));
    assertEquals("[0,0]", "[0.212, 0.576, 0.212]", AbstractCluster.formatVector(pdf, null));
    pdf = classifier.classify(new DenseVector(2).assign(2));
    assertEquals("[2,2]", "[0.952, 0.047, 0.000]", AbstractCluster.formatVector(pdf, null));
  }


}

View Full Code Here

0 1 2 3 4 5 6

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.