Example usage for java.io DataInput readDouble

Introduction

In this page you can find the example usage for java.io DataInput readDouble.

Prototype

double readDouble() throws IOException;

Source Link

Document

Reads eight input bytes and returns a double value.

Usage

From source file:edu.umn.cs.spatialHadoop.osm.OSMEdge.java

@Override
public void readFields(DataInput in) throws IOException {
    edgeId = in.readLong();/* w  w w.  j  a  v a2s  . c o  m*/
    nodeId1 = in.readLong();
    lat1 = in.readDouble();
    lon1 = in.readDouble();
    nodeId2 = in.readLong();
    lat2 = in.readDouble();
    lon2 = in.readDouble();
    wayId = in.readLong();
    tags = in.readUTF();
}

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From source file:hip.ch3.seqfile.writable.StockPriceWritable.java

@Override
public void readFields(DataInput in) throws IOException {
    symbol = WritableUtils.readString(in);
    date = WritableUtils.readString(in);
    open = in.readDouble();
    high = in.readDouble();//from ww w .  j  a va2  s .c  om
    low = in.readDouble();
    close = in.readDouble();
    low = in.readDouble();
    close = in.readDouble();
    volume = in.readInt();
    adjClose = in.readDouble();

}

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From source file:ml.shifu.shifu.core.correlation.CorrelationWritable.java

@Override
public void readFields(DataInput in) throws IOException {
    this.columnIndex = in.readInt();
    this.sum = in.readDouble();
    this.sumSquare = in.readDouble();
    this.count = in.readDouble();

    int length = in.readInt();
    this.xySum = new double[length];
    for (int i = 0; i < length; i++) {
        this.xySum[i] = in.readDouble();
    }//from www.  ja  va  2  s. co m

    length = in.readInt();
    this.xxSum = new double[length];
    for (int i = 0; i < length; i++) {
        this.xxSum[i] = in.readDouble();
    }

    length = in.readInt();
    this.yySum = new double[length];
    for (int i = 0; i < length; i++) {
        this.yySum[i] = in.readDouble();
    }

    length = in.readInt();
    this.adjustCount = new double[length];
    for (int i = 0; i < length; i++) {
        this.adjustCount[i] = in.readDouble();
    }

    length = in.readInt();
    this.adjustSumX = new double[length];
    for (int i = 0; i < length; i++) {
        this.adjustSumX[i] = in.readDouble();
    }

    length = in.readInt();
    this.adjustSumY = new double[length];
    for (int i = 0; i < length; i++) {
        this.adjustSumY[i] = in.readDouble();
    }
}

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From source file:ml.shifu.shifu.core.dtrain.dataset.PersistBasicFloatNetwork.java

private double[] readDoubleArray(DataInput in) throws IOException {
    int size = in.readInt();
    double[] array = new double[size];
    for (int i = 0; i < size; i++) {
        array[i] = in.readDouble();
    }/*from   w w w. j av  a2  s.  c o m*/
    return array;
}

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From source file:ml.shifu.shifu.core.dtrain.dataset.PersistBasicFloatNetwork.java

public BasicFloatNetwork readNetwork(final DataInput in) throws IOException {
    final BasicFloatNetwork result = new BasicFloatNetwork();
    final FlatNetwork flat = new FlatNetwork();

    // read properties
    Map<String, String> properties = new HashMap<String, String>();
    int size = in.readInt();
    for (int i = 0; i < size; i++) {
        properties.put(ml.shifu.shifu.core.dtrain.StringUtils.readString(in),
                ml.shifu.shifu.core.dtrain.StringUtils.readString(in));
    }//from www .ja v a 2  s . co  m
    result.getProperties().putAll(properties);

    // read fields
    flat.setBeginTraining(in.readInt());
    flat.setConnectionLimit(in.readDouble());

    flat.setContextTargetOffset(readIntArray(in));
    flat.setContextTargetSize(readIntArray(in));

    flat.setEndTraining(in.readInt());
    flat.setHasContext(in.readBoolean());
    flat.setInputCount(in.readInt());

    flat.setLayerCounts(readIntArray(in));
    flat.setLayerFeedCounts(readIntArray(in));
    flat.setLayerContextCount(readIntArray(in));
    flat.setLayerIndex(readIntArray(in));
    flat.setLayerOutput(readDoubleArray(in));
    flat.setOutputCount(in.readInt());
    flat.setLayerSums(new double[flat.getLayerOutput().length]);
    flat.setWeightIndex(readIntArray(in));
    flat.setWeights(readDoubleArray(in));
    flat.setBiasActivation(readDoubleArray(in));

    // read activations
    flat.setActivationFunctions(new ActivationFunction[flat.getLayerCounts().length]);
    int acSize = in.readInt();
    for (int i = 0; i < acSize; i++) {
        String name = ml.shifu.shifu.core.dtrain.StringUtils.readString(in);
        if (name.equals("ActivationReLU")) {
            name = ActivationReLU.class.getName();
        } else if (name.equals("ActivationLeakyReLU")) {
            name = ActivationLeakyReLU.class.getName();
        } else if (name.equals("ActivationSwish")) {
            name = ActivationSwish.class.getName();
        } else if (name.equals("ActivationPTANH")) {
            name = ActivationPTANH.class.getName();
        } else {
            name = "org.encog.engine.network.activation." + name;
        }
        ActivationFunction af = null;
        try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
        } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
        } catch (final InstantiationException e) {
            throw new PersistError(e);
        } catch (final IllegalAccessException e) {
            throw new PersistError(e);
        }
        double[] params = readDoubleArray(in);
        for (int j = 0; j < params.length; j++) {
            af.setParam(j, params[j]);
        }
        flat.getActivationFunctions()[i] = af;
    }

    // read subset
    int subsetSize = in.readInt();
    Set<Integer> featureList = new HashSet<Integer>();
    for (int i = 0; i < subsetSize; i++) {
        featureList.add(in.readInt());
    }
    result.setFeatureSet(featureList);

    result.getStructure().setFlat(flat);
    return result;
}

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From source file:com.ibm.bi.dml.runtime.matrix.data.MatrixBlock.java

/**
 * //  w  ww .  j ava  2 s.  co m
 * @param in
 * @throws IOException
 * @throws DMLRuntimeException 
 */
private void readSparseToDense(DataInput in) throws IOException, DMLRuntimeException {
    allocateDenseBlock(false); //allocate block
    Arrays.fill(denseBlock, 0);

    for (int r = 0; r < rlen; r++) {
        int nr = in.readInt();
        for (int j = 0; j < nr; j++) {
            int c = in.readInt();
            double val = in.readDouble();
            denseBlock[r * clen + c] = val;
        }
    }
}

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From source file:com.ibm.bi.dml.runtime.matrix.data.MatrixBlock.java

/**
 * /*from w  w  w.  ja  va 2  s. c  o  m*/
 * @param in
 * @throws IOException
 */
private void readUltraSparseBlock(DataInput in) throws IOException {
    allocateSparseRowsBlock(false); //adjust to size
    resetSparse(); //reset all sparse rows

    if (clen > 1) //ULTRA-SPARSE BLOCK
    {
        //block: read ijv-triples
        for (long i = 0; i < nonZeros; i++) {
            int r = in.readInt();
            int c = in.readInt();
            double val = in.readDouble();
            if (sparseRows[r] == null)
                sparseRows[r] = new SparseRow(1, clen);
            sparseRows[r].append(c, val);
        }
    } else //ULTRA-SPARSE COL
    {
        //col: read iv-pairs (should never happen since always dense)
        for (long i = 0; i < nonZeros; i++) {
            int r = in.readInt();
            double val = in.readDouble();
            if (sparseRows[r] == null)
                sparseRows[r] = new SparseRow(1, 1);
            sparseRows[r].append(0, val);
        }
    }
}

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From source file:com.ibm.bi.dml.runtime.matrix.data.MatrixBlock.java

/**
 * //  ww  w .  j a v  a2s.  c  o  m
 * @param in
 * @throws IOException
 * @throws DMLRuntimeException 
 */
private void readUltraSparseToDense(DataInput in) throws IOException, DMLRuntimeException {
    allocateDenseBlock(false); //allocate block
    Arrays.fill(denseBlock, 0);

    if (clen > 1) //ULTRA-SPARSE BLOCK
    {
        //block: read ijv-triples
        for (long i = 0; i < nonZeros; i++) {
            int r = in.readInt();
            int c = in.readInt();
            double val = in.readDouble();
            denseBlock[r * clen + c] = val;
        }
    } else //ULTRA-SPARSE COL
    {
        //col: read iv-pairs
        for (long i = 0; i < nonZeros; i++) {
            int r = in.readInt();
            double val = in.readDouble();
            denseBlock[r] = val;
        }
    }
}

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From source file:com.ibm.bi.dml.runtime.matrix.data.MatrixBlock.java

/**
 * /*w  ww . j  av  a  2 s  .c o  m*/
 * @param in
 * @throws IOException
 * @throws DMLRuntimeException 
 */
private void readDenseBlock(DataInput in) throws IOException, DMLRuntimeException {
    allocateDenseBlock(true); //allocate block, clear nnz

    int limit = rlen * clen;

    if (in instanceof MatrixBlockDataInput) //fast deserialize
    {
        MatrixBlockDataInput mbin = (MatrixBlockDataInput) in;
        nonZeros = mbin.readDoubleArray(limit, denseBlock);
    } else if (in instanceof DataInputBuffer && MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) {
        //workaround because sequencefile.reader.next(key, value) does not yet support serialization framework
        DataInputBuffer din = (DataInputBuffer) in;
        MatrixBlockDataInput mbin = new FastBufferedDataInputStream(din);
        nonZeros = mbin.readDoubleArray(limit, denseBlock);
        ((FastBufferedDataInputStream) mbin).close();
    } else //default deserialize
    {
        for (int i = 0; i < limit; i++) {
            denseBlock[i] = in.readDouble();
            if (denseBlock[i] != 0)
                nonZeros++;
        }
    }
}

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From source file:com.ibm.bi.dml.runtime.matrix.data.MatrixBlock.java

/**
 * //from   w  ww .ja  va2s .c  o m
 * @param in
 * @throws IOException
 */
private void readSparseBlock(DataInput in) throws IOException {
    allocateSparseRowsBlock(false);
    resetSparse(); //reset all sparse rows

    if (in instanceof MatrixBlockDataInput) //fast deserialize
    {
        MatrixBlockDataInput mbin = (MatrixBlockDataInput) in;
        nonZeros = mbin.readSparseRows(rlen, sparseRows);
    } else if (in instanceof DataInputBuffer && MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION) {
        //workaround because sequencefile.reader.next(key, value) does not yet support serialization framework
        DataInputBuffer din = (DataInputBuffer) in;
        MatrixBlockDataInput mbin = new FastBufferedDataInputStream(din);
        nonZeros = mbin.readSparseRows(rlen, sparseRows);
        ((FastBufferedDataInputStream) mbin).close();
    } else //default deserialize
    {
        for (int r = 0; r < rlen; r++) {
            int nr = in.readInt();
            if (nr == 0) {
                if (sparseRows[r] != null)
                    sparseRows[r].reset(estimatedNNzsPerRow, clen);
                continue;
            }
            if (sparseRows[r] == null)
                sparseRows[r] = new SparseRow(nr);
            else
                sparseRows[r].reset(nr, clen);
            for (int j = 0; j < nr; j++)
                sparseRows[r].append(in.readInt(), in.readDouble());
        }
    }
}

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