Example usage for org.apache.commons.lang3 ArrayUtils toPrimitive

Introduction

In this page you can find the example usage for org.apache.commons.lang3 ArrayUtils toPrimitive.

Prototype

public static boolean[] toPrimitive(final Boolean[] array) 

Source Link

Document

Converts an array of object Booleans to primitives.

This method returns null for a null input array.

Usage

From source file:org.carbondata.scan.executor.util.QueryUtil.java

/**
 * Below method will be used to get the dimension block index in file based
 * on query dimension/*w  w w . j a v a 2 s . c  o  m*/
 *
 * @param queryDimensions                query dimension
 * @param dimensionOrdinalToBlockMapping mapping of dimension block in file to query dimension
 * @return block index of file
 */
public static int[] getDimensionsBlockIndexes(List<QueryDimension> queryDimensions,
        Map<Integer, Integer> dimensionOrdinalToBlockMapping,
        List<CarbonDimension> customAggregationDimension) {
    // using set as in row group columns will point to same block
    Set<Integer> dimensionBlockIndex = new HashSet<Integer>();
    for (int i = 0; i < queryDimensions.size(); i++) {
        dimensionBlockIndex
                .add(dimensionOrdinalToBlockMapping.get(queryDimensions.get(i).getDimension().getOrdinal()));
    }
    for (int i = 0; i < customAggregationDimension.size(); i++) {
        dimensionBlockIndex
                .add(dimensionOrdinalToBlockMapping.get(customAggregationDimension.get(i).getOrdinal()));
    }
    return ArrayUtils.toPrimitive(dimensionBlockIndex.toArray(new Integer[dimensionBlockIndex.size()]));
}

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From source file:org.dbgl.gui.ProfilesList.java

public int[] getSelectionIndices() {
    if (type == ProfilesListType.TABLE)
        return table.getSelectionIndices();
    else {/*from  w  w  w.j a  v a2s  .  c  om*/
        GalleryItem[] items = gallery.getSelection();
        if (items.length == 0)
            return new int[0];
        List<Integer> indices = new ArrayList<Integer>();
        for (GalleryItem i : items) {
            int index = group.indexOf(i);
            if (index != -1)
                indices.add(index);
        }
        return ArrayUtils.toPrimitive(indices.toArray(new Integer[0]));
    }
}

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From source file:org.dbgl.gui.SettingsDialog.java

private void updateColumnSettings() {
    int[] sort = conf.getIntValues("gui", "sortcolumn");
    boolean[] ascs = conf.getBooleanValues("gui", "sortascending");
    java.util.List<Integer> sortColumnIDs = new ArrayList<Integer>(sort.length);
    java.util.List<Boolean> sortColumnAscs = new ArrayList<Boolean>(sort.length);

    for (int i = 0; i < sort.length; i++) {
        if (visibleColumns[allColumnIDs.indexOf(sort[i])].getChecked()) {
            sortColumnIDs.add(sort[i]);//from w w  w. j a v a  2 s .  co  m
            sortColumnAscs.add(ascs[i]);
        }
    }
    if (sortColumnIDs.isEmpty()) {
        sortColumnIDs.add(allColumnIDs.get(getFirstVisibleColumn()));
        sortColumnAscs.add(true);
    }

    conf.setIntValues("gui", "sortcolumn", ArrayUtils.toPrimitive(sortColumnIDs.toArray(new Integer[0])));
    conf.setBooleanValues("gui", "sortascending",
            ArrayUtils.toPrimitive(sortColumnAscs.toArray(new Boolean[0])));

    java.util.List<Integer> visColumns = new ArrayList<Integer>();
    for (int i = 0; i < MainWindow.columnNames.length; i++)
        if (visibleColumns[i].getChecked())
            visColumns.add(allColumnIDs.get(i));

    java.util.List<Integer> orderedVisColumns = new ArrayList<Integer>(visColumns);
    Collections.sort(orderedVisColumns);

    java.util.List<Integer> colOrder = new ArrayList<Integer>();
    for (int id : visColumns)
        colOrder.add(orderedVisColumns.indexOf(id));

    conf.setValue("gui", "columnorder", StringUtils.join(colOrder, ' '));
}

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From source file:org.dbgl.util.FileUtils.java

public static int[] findRelatedEntryIds(IInArchive zArchive, File dirToBeExtracted) {
    List<Integer> result = new ArrayList<Integer>();
    for (int i = 0; i < zArchive.size(); i++)
        if (areRelated(dirToBeExtracted, new File(zArchive.getEntry(i).getName())))
            result.add(i);/*from   www .j ava2 s .c om*/
    return ArrayUtils.toPrimitive(result.toArray(new Integer[0]));
}

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From source file:org.dbgl.util.ImportThread.java

public void preFinish() throws IOException {
    if (customFields != null) {
        for (int i = 0; i < Constants.EDIT_COLUMN_NAMES; i++) {
            if (!customFields[i].equalsIgnoreCase("Custom" + (i + 1))) {
                settings.getSettings().setValue("gui", "custom" + (i + 1), customFields[i]);
            }//from w  w  w.  j  a  v a  2 s  .c o  m
        }
    }
    if (sevenzip) {
        entryIdsToBeExtracted = ArrayUtils.toPrimitive(sevenzipDstFileMap.keySet().toArray(new Integer[0]));
        SevenzipExtractFilesCallback extractCallback = new SevenzipExtractFilesCallback(this, zArchive,
                sevenzipDstFileMap);
        zArchive.Extract(entryIdsToBeExtracted, entryIdsToBeExtracted.length,
                IInArchive.NExtract_NAskMode_kExtract, extractCallback);
        for (int i = 0; i < entryIdsToBeExtracted.length; i++) {
            int id = entryIdsToBeExtracted[i];
            FileUtils.fileSetLastModified(sevenzipDstFileMap.get(id), zArchive.getEntry(id).getTime());
        }
        zArchive.close();
    }
}

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From source file:org.deeplearning4j.clustering.cluster.ClusterUtils.java

public static void deriveClusterInfoDistanceStatistics(ClusterInfo info) {
    int pointCount = info.getPointDistancesFromCenter().size();
    if (pointCount == 0)
        return;//from  w w w. j a  va2s  . co  m

    double[] distances = ArrayUtils
            .toPrimitive(info.getPointDistancesFromCenter().values().toArray(new Double[] {}));
    double max = MathUtils.max(distances);
    double total = MathUtils.sum(distances);

    info.setMaxPointDistanceFromCenter(max);
    info.setTotalPointDistanceFromCenter(total);
    info.setAveragePointDistanceFromCenter(total / pointCount);
    info.setPointDistanceFromCenterVariance(MathUtils.variance(distances));
}

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From source file:org.deeplearning4j.examples.recurrent.encdec.CorpusIterator.java

@Override
public MultiDataSet next(int num) {
    int i = currentBatch * batchSize;
    int currentBatchSize = Math.min(batchSize, corpus.size() - i - 1);
    INDArray input = Nd4j.zeros(currentBatchSize, 1, rowSize);
    INDArray prediction = Nd4j.zeros(currentBatchSize, dictSize, rowSize);
    INDArray decode = Nd4j.zeros(currentBatchSize, dictSize, rowSize);
    INDArray inputMask = Nd4j.zeros(currentBatchSize, rowSize);
    // this mask is also used for the decoder input, the length is the same
    INDArray predictionMask = Nd4j.zeros(currentBatchSize, rowSize);
    for (int j = 0; j < currentBatchSize; j++) {
        List<Double> rowIn = new ArrayList<>(corpus.get(i));
        Collections.reverse(rowIn);
        List<Double> rowPred = new ArrayList<>(corpus.get(i + 1));
        rowPred.add(1.0); // add <eos> token
        // replace the entire row in "input" using NDArrayIndex, it's faster than putScalar(); input is NOT made of one-hot vectors
        // because of the embedding layer that accepts token indexes directly
        input.put(//from  w  w  w.  ja v a  2s.c om
                new INDArrayIndex[] { NDArrayIndex.point(j), NDArrayIndex.point(0),
                        NDArrayIndex.interval(0, rowIn.size()) },
                Nd4j.create(ArrayUtils.toPrimitive(rowIn.toArray(new Double[0]))));
        inputMask.put(new INDArrayIndex[] { NDArrayIndex.point(j), NDArrayIndex.interval(0, rowIn.size()) },
                Nd4j.ones(rowIn.size()));
        predictionMask.put(
                new INDArrayIndex[] { NDArrayIndex.point(j), NDArrayIndex.interval(0, rowPred.size()) },
                Nd4j.ones(rowPred.size()));
        // prediction (output) and decode ARE one-hots though, I couldn't add an embedding layer on top of the decoder and I'm not sure
        // it's a good idea either
        double predOneHot[][] = new double[dictSize][rowPred.size()];
        double decodeOneHot[][] = new double[dictSize][rowPred.size()];
        decodeOneHot[2][0] = 1; // <go> token
        int predIdx = 0;
        for (Double pred : rowPred) {
            predOneHot[pred.intValue()][predIdx] = 1;
            if (predIdx < rowPred.size() - 1) { // put the same vals to decode with +1 offset except the last token that is <eos>
                decodeOneHot[pred.intValue()][predIdx + 1] = 1;
            }
            ++predIdx;
        }
        prediction.put(new INDArrayIndex[] { NDArrayIndex.point(j), NDArrayIndex.interval(0, dictSize),
                NDArrayIndex.interval(0, rowPred.size()) }, Nd4j.create(predOneHot));
        decode.put(new INDArrayIndex[] { NDArrayIndex.point(j), NDArrayIndex.interval(0, dictSize),
                NDArrayIndex.interval(0, rowPred.size()) }, Nd4j.create(decodeOneHot));
        ++i;
    }
    ++currentBatch;
    return new org.nd4j.linalg.dataset.MultiDataSet(new INDArray[] { input, decode },
            new INDArray[] { prediction }, new INDArray[] { inputMask, predictionMask },
            new INDArray[] { predictionMask });
}

---

From source file:org.deeplearning4j.examples.recurrent.encdec.EncoderDecoderLSTM.java

private void output(List<Double> rowIn, boolean printUnknowns) {
    net.rnnClearPreviousState();/*from   ww w.  j  av  a  2 s .  co m*/
    Collections.reverse(rowIn);
    INDArray in = Nd4j.create(ArrayUtils.toPrimitive(rowIn.toArray(new Double[0])),
            new int[] { 1, 1, rowIn.size() });
    double[] decodeArr = new double[dict.size()];
    decodeArr[2] = 1;
    INDArray decode = Nd4j.create(decodeArr, new int[] { 1, dict.size(), 1 });
    net.feedForward(new INDArray[] { in, decode }, false, false);
    org.deeplearning4j.nn.layers.recurrent.LSTM decoder = (org.deeplearning4j.nn.layers.recurrent.LSTM) net
            .getLayer("decoder");
    Layer output = net.getLayer("output");
    GraphVertex mergeVertex = net.getVertex("merge");
    INDArray thoughtVector = mergeVertex.getInputs()[1];
    LayerWorkspaceMgr mgr = LayerWorkspaceMgr.noWorkspaces();
    for (int row = 0; row < ROW_SIZE; ++row) {
        mergeVertex.setInputs(decode, thoughtVector);
        INDArray merged = mergeVertex.doForward(false, mgr);
        INDArray activateDec = decoder.rnnTimeStep(merged, mgr);
        INDArray out = output.activate(activateDec, false, mgr);
        double d = rnd.nextDouble();
        double sum = 0.0;
        int idx = -1;
        for (int s = 0; s < out.size(1); s++) {
            sum += out.getDouble(0, s, 0);
            if (d <= sum) {
                idx = s;
                if (printUnknowns || s != 0) {
                    System.out.print(revDict.get((double) s) + " ");
                }
                break;
            }
        }
        if (idx == 1) {
            break;
        }
        double[] newDecodeArr = new double[dict.size()];
        newDecodeArr[idx] = 1;
        decode = Nd4j.create(newDecodeArr, new int[] { 1, dict.size(), 1 });
    }
    System.out.println();
}

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From source file:org.dmlc.xgboost4j.demo.util.DataLoader.java

public static DenseData loadCSVFile(String filePath)
        throws FileNotFoundException, UnsupportedEncodingException, IOException {
    DenseData denseData = new DenseData();

    File f = new File(filePath);
    FileInputStream in = new FileInputStream(f);
    BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));

    denseData.nrow = 0;//from w  ww  .j  av  a  2 s .c  o  m
    denseData.ncol = -1;
    String line;
    List<Float> tlabels = new ArrayList<>();
    List<Float> tdata = new ArrayList<>();

    while ((line = reader.readLine()) != null) {
        String[] items = line.trim().split(",");
        if (items.length == 0) {
            continue;
        }
        denseData.nrow++;
        if (denseData.ncol == -1) {
            denseData.ncol = items.length - 1;
        }

        tlabels.add(Float.valueOf(items[items.length - 1]));
        for (int i = 0; i < items.length - 1; i++) {
            tdata.add(Float.valueOf(items[i]));
        }
    }

    reader.close();
    in.close();

    denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
    denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));

    return denseData;
}

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From source file:org.dmlc.xgboost4j.demo.util.DataLoader.java

public static CSRSparseData loadSVMFile(String filePath)
        throws FileNotFoundException, UnsupportedEncodingException, IOException {
    CSRSparseData spData = new CSRSparseData();

    List<Float> tlabels = new ArrayList<>();
    List<Float> tdata = new ArrayList<>();
    List<Long> theaders = new ArrayList<>();
    List<Integer> tindex = new ArrayList<>();

    File f = new File(filePath);
    FileInputStream in = new FileInputStream(f);
    BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));

    String line;/* ww w  . j a v  a  2  s  .  c  o  m*/
    long rowheader = 0;
    theaders.add(rowheader);
    while ((line = reader.readLine()) != null) {
        String[] items = line.trim().split(" ");
        if (items.length == 0) {
            continue;
        }

        rowheader += items.length - 1;
        theaders.add(rowheader);
        tlabels.add(Float.valueOf(items[0]));

        for (int i = 1; i < items.length; i++) {
            String[] tup = items[i].split(":");
            assert tup.length == 2;

            tdata.add(Float.valueOf(tup[1]));
            tindex.add(Integer.valueOf(tup[0]));
        }
    }

    spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
    spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
    spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
    spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));

    return spData;
}

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