Examples of optimize()

Examples of org.teiid.query.optimizer.relational.RelationalPlanner.optimize()

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        if (command.getType() == Command.TYPE_QUERY && command instanceof Query && QueryResolver.isXMLQuery((Query)command, metadata)) {
          result = XML_PLANNER.optimize(command, idGenerator, metadata, capFinder, analysisRecord, context);
        } else {
          RelationalPlanner planner = new RelationalPlanner();
          planner.initialize(command, idGenerator, metadata, capFinder, analysisRecord, context);
          result = planner.optimize(command);
        }
      } catch (QueryResolverException e) {
        throw new TeiidRuntimeException(e);
      }
        }
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Examples of org.toubassi.femtozip.dictionary.DictionaryOptimizer.optimize()

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    protected void buildDictionary() throws IOException {
        File dir = new File(path);
        List<String> files = Arrays.asList(dir.list());
        DocumentList documents = new FileDocumentList(path, files);
        DictionaryOptimizer optimizer = new DictionaryOptimizer(documents);
        byte[] dictionary = optimizer.optimize(maxDictionarySize  > 0 ? maxDictionarySize : 64*1024);
       
        FileOutputStream fileOut = new FileOutputStream(modelPath);
        fileOut.write(dictionary);
        fileOut.close();
    }
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Examples of tv.floe.metronome.deeplearning.neuralnetwork.optimize.VectorizedNonZeroStoppingConjugateGradient.optimize()

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        if ( optimizationAlgorithm == OptimizationAlgorithm.CONJUGATE_GRADIENT ) {
         
       VectorizedNonZeroStoppingConjugateGradient g = new VectorizedNonZeroStoppingConjugateGradient(opt);
       g.setTolerance(1e-5);
       g.setMaxIterations(numEpochs);
             g.optimize(numEpochs);

     } else {
/*
       VectorizedDeepLearningGradientAscent g = new VectorizedDeepLearningGradientAscent(opt);
       g.setTolerance(1e-5);
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Examples of tv.floe.metronome.deeplearning.neuralnetwork.optimize.util.CustomConjugateGradient.optimize()

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    logreg.labels = y;
   
   
    LogisticRegressionOptimizer opt = new LogisticRegressionOptimizer( logreg, learningRate );
    CustomConjugateGradient g = new CustomConjugateGradient(opt);
    g.optimize();
   
   
    Matrix predict = logreg.predict(x);
    //log.info(predict.toString());
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Examples of uk.ac.ucl.panda.indexing.io.IndexWriter.optimize()

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       }

   
   
    int numIndexed = writer.docCount();
    writer.optimize();
    writer.close();
                long end = new Date().getTime();
    System.out.println("Indexing " + numIndexed + " files took " + (end - start)+ " milliseconds");
                ExtraInformation EI= new ExtraInformation(index, field);
                EI.addExtraInformation();
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