List of reduce() Examples

Examples of reduce()

com.hazelcast.concurrent.semaphore.Permit.reduce()
com.hazelcast.mapreduce.Reducer.reduce()
This method is called to supply values to be reduced into a final reduced result.
The reduce method might be called multiple times so the eventually reduces value needs to be hold internally in a member state of the Reducer. @param value value to be reduced
com.hp.hpl.jena.rdf.model.RDFList.reduce()

Apply a function to each value in the list in turn, accumulating the results in an accumulator. The final value of the accumulator is returned as the value of reduce().
@param fn The reduction function to apply @param initial The initial value for the accumulator @return The final value of the accumulator.
dk.brics.automaton.Automaton.reduce()
Reduces this automaton. An automaton is "reduced" by combining overlapping and adjacent edge intervals with same destination.
ij.plugin.HyperStackReducer.reduce()
io.crate.executor.transport.task.elasticsearch.facet.InternalUpdateFacet.reduce()
This method is called on the collecting node. In this implementation no reduction is done here, since the statement is not known the real reduce happens in {@link UpdateFacet#reduce()}
nexj.core.persistence.Query.reduce()
Reduces the query multiplicity. TODO: Use metadata and include in the query planning pass. @return The current multiplicity.
no.uib.cipr.matrix.distributed.Communicator.reduce()
Reduces values on all processes to a single value
org.allspice.parser.Translation.reduce()
@param range The positions spanned by the object. @param parser The parser @param state The state of the parser. @return the object to replace the objects on the stack @throws SyntaxError
org.apache.accumulo.core.iterators.Combiner.reduce()
Reduces a list of Values into a single Value. @param key The most recent version of the Key being reduced. @param iter An iterator over the Values for different versions of the key. @return The combined Value.
org.apache.giraph.comm.aggregators.OwnerAggregatorServerData.reduce()
Reduce partial value of one of current worker's reducers. Thread-safe. Call only after reducers have been registered. @param name Name of the reducer @param value Value to reduce to it
org.apache.hadoop.mapred.Reducer.reduce()
Reduces values for a given key.
The framework calls this method for each <key, (list of values)> pair in the grouped inputs. Output values must be of the same type as input values. Input keys must not be altered. Typically all values are combined into zero or one value.

Output pairs are collected with calls to {@link OutputCollector#collect(WritableComparable,Writable)}.

Applications can use the {@link Reporter} provided to report progress or just indicate that they are alive. In scenarios where the application takes an insignificant amount of time to process individual key/value pairs, this is crucial since the framework might assume that the task has timed-out and kill that task. The other way of avoiding this is to set mapred.task.timeout to a high-enough value (or even zero for no time-outs).
@param key the key. @param values the list of values to reduce. @param output to collect keys and combined values. @param reporter facility to report progress.
org.apache.lucene.util.automaton.Automaton.reduce()
Reduces this automaton. An automaton is "reduced" by combining overlapping and adjacent edge intervals with same destination.
org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureReducer.reduce()
org.apache.mahout.clustering.spectral.MatrixDiagonalizeJob.MatrixDiagonalizeReducer.reduce()
org.apache.mahout.clustering.spectral.common.MatrixDiagonalizeJob.MatrixDiagonalizeReducer.reduce()
org.apache.mahout.clustering.spectral.eigencuts.EigencutsAffinityCutsJob.EigencutsAffinityCutsCombiner.reduce()
org.apache.mahout.clustering.spectral.eigencuts.EigencutsAffinityCutsJob.EigencutsAffinityCutsReducer.reduce()
org.apache.mahout.math.hadoop.similarity.RowSimilarityJob.EntriesToVectorsReducer.reduce()
org.apache.mahout.math.hadoop.similarity.RowSimilarityJob.SimilarityReducer.reduce()
org.eclipse.gmf.runtime.emf.commands.core.command.CompositeTransactionalCommand.reduce()
org.geotools.coverage.grid.GridGeometry2D.reduce()
Reduces the specified envelope to a two-dimensional one. If the given envelope has more than two dimensions, then a new one is created using only the coordinates at ( {@link #axisDimensionX}, {@link #axisDimensionY}) index.
The {@link Envelope#getCoordinateReferenceSystem coordinate reference system} of thesource envelope is ignored. The coordinate reference system of the target envelope will be {@link #getCoordinateReferenceSystem2D} or {@code null}. @param envelope The envelope to reduce, or {@code null}. This envelope will not be modified. @return An envelope with exactly 2 dimensions, or {@code null} if {@code envelope} was null.The returned envelope is always a new instance, so it can be modified safely. @since 2.5
org.xadoop.zorba.ZorbaReducer.reduce()
thaumcraft.api.aspects.AspectList.reduce()
Reduces the amount of an aspect in this collection by the given amount. @param key @param amount @return

Examples of org.apache.lucene.util.automaton.Automaton.reduce()

      
      if (random().nextBoolean()) {
        if (VERBOSE) {
          System.out.println("TEST: reduce the automaton");
        }
        a.reduce();
      }


      final CompiledAutomaton c = new CompiledAutomaton(a, true, false);


      final BytesRef[] acceptTermsArray = new BytesRef[acceptTerms.size()];

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Examples of org.apache.lucene.util.automaton.Automaton.reduce()

      
      if (random().nextBoolean()) {
        if (VERBOSE) {
          System.out.println("TEST: reduce the automaton");
        }
        a.reduce();
      }


      final CompiledAutomaton c = new CompiledAutomaton(a, true, false);


      final BytesRef[] acceptTermsArray = new BytesRef[acceptTerms.size()];

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Examples of org.apache.lucene.util.automaton.Automaton.reduce()

      if (Character.isLetter(i)) {
        initial.addTransition(new Transition(i, i, accept));
      }
    }
    Automaton single = new Automaton(initial);
    single.reduce();
    Automaton repeat = BasicOperations.repeat(single);
    jvmLetter = new CharacterRunAutomaton(repeat);
  }
  
  public void testLetterAscii() throws Exception {

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Examples of org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureReducer.reduce()

    Collection<StringTuple> keySet = new TreeSet<StringTuple>(cmp);
    keySet.addAll(mapperOutput.getKeys());
    
    for (StringTuple k: keySet) {
      List<DoubleWritable> v = outputData.get(k);
      reducer.reduce(k, v.iterator(), reducerOutput, Reporter.NULL);
    }


    return reducerOutput;
  }

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Examples of org.apache.mahout.classifier.bayes.mapreduce.common.BayesFeatureReducer.reduce()

    Collection<StringTuple> keySet = new TreeSet<StringTuple>(cmp);
    keySet.addAll(mapperOutput.getKeys());
    
    for (StringTuple k: keySet) {
      List<DoubleWritable> v = outputData.get(k);
      reducer.reduce(k, v.iterator(), reducerOutput, Reporter.NULL);
    }


    return reducerOutput;
  }

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Examples of org.apache.mahout.clustering.spectral.MatrixDiagonalizeJob.MatrixDiagonalizeReducer.reduce()

    Reducer<NullWritable, IntDoublePairWritable, NullWritable, VectorWritable>.Context
      redContext = DummyRecordWriter.build(reducer, conf, redWriter, 
      NullWritable.class, IntDoublePairWritable.class);
    
    // only need one reduction
    reducer.reduce(NullWritable.get(), mapWriter.getValue(NullWritable.get()), redContext);
    
    // first, make sure there's only one result
    List<VectorWritable> list = redWriter.getValue(NullWritable.get());
    assertEquals("Only a single resulting vector", 1, list.size());
    Vector v = list.get(0).get();

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Examples of org.apache.mahout.clustering.spectral.common.MatrixDiagonalizeJob.MatrixDiagonalizeReducer.reduce()

    Reducer<NullWritable, IntDoublePairWritable, NullWritable, VectorWritable>.Context
      redContext = DummyRecordWriter.build(reducer, conf, redWriter, 
      NullWritable.class, IntDoublePairWritable.class);
    
    // only need one reduction
    reducer.reduce(NullWritable.get(), mapWriter.getValue(NullWritable.get()), redContext);
    
    // first, make sure there's only one result
    List<VectorWritable> list = redWriter.getValue(NullWritable.get());
    assertEquals("Only a single resulting vector", 1, list.size());
    Vector v = list.get(0).get();

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Examples of org.apache.mahout.clustering.spectral.eigencuts.EigencutsAffinityCutsJob.EigencutsAffinityCutsCombiner.reduce()

      redContext = DummyRecordWriter.build(combiner, conf, redWriter, Text.class,
      VertexWritable.class);
    
    // perform the combining
    for (Map.Entry<Text, List<VertexWritable>> entry : data.entrySet()) {
      combiner.reduce(entry.getKey(), entry.getValue(), redContext);
    }
    
    // test the number of cuts, there should be 2
    assertEquals("Number of cuts detected", 4, 
        redContext.getCounter(EigencutsAffinityCutsJob.CUTSCOUNTER.NUM_CUTS).getValue());

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Examples of org.apache.mahout.clustering.spectral.eigencuts.EigencutsAffinityCutsJob.EigencutsAffinityCutsCombiner.reduce()

      comContext = DummyRecordWriter.build(combiner, conf, comWriter, Text.class,
      VertexWritable.class);
    
    // perform the combining
    for (Map.Entry<Text, List<VertexWritable>> entry : data.entrySet()) {
      combiner.reduce(entry.getKey(), entry.getValue(), comContext);
    }
    
    // finally, set up the reduction writers
    EigencutsAffinityCutsReducer reducer = new EigencutsAffinityCutsReducer();
    DummyRecordWriter<IntWritable, VectorWritable> redWriter = new

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Examples of org.apache.mahout.clustering.spectral.eigencuts.EigencutsAffinityCutsJob.EigencutsAffinityCutsReducer.reduce()

      redContext = DummyRecordWriter.build(reducer, conf, redWriter, 
      Text.class, VertexWritable.class);
    
    // perform the reduction
    for (Text key : comWriter.getKeys()) {
      reducer.reduce(key, comWriter.getValue(key), redContext);
    }
    
    // now, check that the affinity matrix is correctly formed
    for (IntWritable row : redWriter.getKeys()) {
      List<VectorWritable> results = redWriter.getValue(row);

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