Examples of com.carrotsearch.hppc.IntArrayList.toArray()

Class com.carrotsearch.hppc.IntArrayList

Examples of com.carrotsearch.hppc.IntArrayList.toArray()

com.carrotsearch.hppc.IntArrayList.toArray()
{@inheritDoc}
The returned array is sized to match exactly the number of elements of the stack.

                    newDocumentIndices.add(labelsDocumentIndices[i]);
                }
            }
            context.allLabels.documentIndices = newDocumentIndices
                .toArray(new BitSet [newDocumentIndices.size()]);
            context.allLabels.featureIndex = newFeatureIndex.toArray();
            LabelFilterProcessor.updateFirstPhraseIndex(context);
        }
        else
        {
            context.allLabels.documentIndices = labelsDocumentIndices;

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        // Mapping from allTokens
        context.allTokens.wordIndex = wordIndexes;


        context.allWords.image = normalizedWordImages
            .toArray(new char [normalizedWordImages.size()] []);
        context.allWords.tf = normalizedWordTf.toArray();
        context.allWords.tfByDocument = 
            wordTfByDocumentList.toArray(new int [wordTfByDocumentList.size()] []);
        context.allWords.fieldIndices = fieldIndexList.toArray();
        context.allWords.type = types.toArray();
    }

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        }


        // Store the results to allPhrases
        context.allPhrases.wordIndices = phraseWordIndexes
            .toArray(new int [phraseWordIndexes.size()] []);
        context.allPhrases.tf = phraseTf.toArray();
        context.allPhrases.tfByDocument = phraseTfByDocumentList
            .toArray(new int [phraseTfByDocumentList.size()] []);
    }


    /**

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            if ((flag & (ITokenizer.TF_COMMON_WORD | ITokenizer.TF_QUERY_WORD | ITokenizer.TT_NUMERIC)) == 0)
            {
                featureIndices.add(stemsMfow[i]);
            }
        }
        preprocessingContext.allLabels.featureIndex = featureIndices.toArray();
        preprocessingContext.allLabels.firstPhraseIndex = -1;


        // Further processing only if there are words to process
        clusters = Lists.newArrayList();
        if (preprocessingContext.hasLabels())

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            wordsType[stemImagesOrder[stemImagesOrder.length - 1]] |= ITokenizer.TF_QUERY_WORD;
        }


        // Convert lists to arrays and store them in allStems
        context.allStems.image = stemImages.toArray(new char [stemImages.size()] []);
        context.allStems.mostFrequentOriginalWordIndex = stemMostFrequentWordIndexes
            .toArray();
        context.allStems.tf = stemTf.toArray();
        context.allStems.tfByDocument = stemTfByDocumentList
            .toArray(new int [stemTfByDocumentList.size()] []);
        context.allStems.fieldIndices = fieldIndexList.toArray();

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            {
                acceptedFeatures.add(i + wordCount);
            }
        }


        context.allLabels.featureIndex = acceptedFeatures.toArray();
        updateFirstPhraseIndex(context);
    }
    
    static void updateFirstPhraseIndex(PreprocessingContext context)
    {

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            public boolean onPortNumber(int portNumber) {
                ports.add(portNumber);
                return false;
            }
        });
        return ports.toArray();
    }


    public boolean iterate(PortCallback callback) throws NumberFormatException {
        StringTokenizer st = new StringTokenizer(portRange, ",");
        boolean success = false;

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