Example usage for java.io ObjectOutput writeObject

Introduction

In this page you can find the example usage for java.io ObjectOutput writeObject.

Prototype

public void writeObject(Object obj) throws IOException;

Source Link

Document

Write an object to the underlying storage or stream.

Usage

From source file:com.act.reachables.ActData.java

public void serialize(String toFile) {
    try {/*from   www . j  a  v  a 2 s .  co m*/
        OutputStream file = new FileOutputStream(toFile);
        OutputStream buffer = new BufferedOutputStream(file);
        ObjectOutput output = new ObjectOutputStream(buffer);
        try {
            output.writeObject(_instance);
        } finally {
            output.close();
        }
    } catch (IOException ex) {
        throw new RuntimeException("ActData serialize failed: " + ex);
    }
}

From source file:org.jfree.data.xy.junit.VectorSeriesCollectionTest.java

/**
 * Serialize an instance, restore it, and check for equality.
 *//*from w w  w .j av  a 2s.  c o m*/
public void testSerialization() {
    VectorSeries s1 = new VectorSeries("Series");
    s1.add(1.0, 1.1, 1.2, 1.3);
    VectorSeriesCollection c1 = new VectorSeriesCollection();
    c1.addSeries(s1);
    VectorSeriesCollection c2 = null;

    try {
        ByteArrayOutputStream buffer = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(buffer);
        out.writeObject(c1);
        out.close();

        ObjectInput in = new ObjectInputStream(new ByteArrayInputStream(buffer.toByteArray()));
        c2 = (VectorSeriesCollection) in.readObject();
        in.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
    assertEquals(c1, c2);
}

From source file:org.jfree.chart.demo.ChartPanelSerializationTest.java

/**
 * A demonstration application showing how to create a simple time series chart.  This
 * example uses monthly data.//from   w  ww  . ja  v a2 s  .  c o m
 *
 * @param title  the frame title.
 */
public ChartPanelSerializationTest(final String title) {

    super(title);
    final XYDataset dataset = createDataset();
    final JFreeChart chart = createChart(dataset);
    final ChartPanel chartPanel1 = new ChartPanel(chart);
    chartPanel1.setPreferredSize(new java.awt.Dimension(500, 270));
    chartPanel1.setMouseZoomable(true, false);

    ChartPanel chartPanel2 = null;
    try {
        ByteArrayOutputStream buffer = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(buffer);
        out.writeObject(chartPanel1);
        out.close();

        ObjectInput in = new ObjectInputStream(new ByteArrayInputStream(buffer.toByteArray()));
        chartPanel2 = (ChartPanel) in.readObject();
        in.close();
    } catch (Exception e) {
        e.printStackTrace();
    }

    setContentPane(chartPanel2);

}

From source file:org.apache.camel.component.bean.BeanInvocation.java

public void writeExternal(ObjectOutput objectOutput) throws IOException {
    if (methodBean == null) {
        methodBean = new MethodBean(method);
    }/*  www .  j  a v a 2s  .co m*/
    objectOutput.writeObject(methodBean);
    objectOutput.writeObject(args);
}

From source file:org.jfree.data.xy.junit.DefaultOHLCDatasetTest.java

/**
 * Serialize an instance, restore it, and check for equality.
 *//*w w  w  . java2  s  .c  o m*/
public void testSerialization() {
    DefaultOHLCDataset d1 = new DefaultOHLCDataset("Series 1", new OHLCDataItem[0]);
    DefaultOHLCDataset d2 = null;

    try {
        ByteArrayOutputStream buffer = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(buffer);
        out.writeObject(d1);
        out.close();

        ObjectInput in = new ObjectInputStream(new ByteArrayInputStream(buffer.toByteArray()));
        d2 = (DefaultOHLCDataset) in.readObject();
        in.close();
    } catch (Exception e) {
        System.out.println(e.toString());
    }
    assertEquals(d1, d2);
}

From source file:org.jfree.data.xy.junit.DefaultHighLowDatasetTest.java

/**
 * Serialize an instance, restore it, and check for equality.
 *///from   w ww  .j  ava 2s . c o m
public void testSerialization() {
    DefaultHighLowDataset d1 = new DefaultHighLowDataset("Series 1", new Date[] { new Date(123L) },
            new double[] { 1.2 }, new double[] { 3.4 }, new double[] { 5.6 }, new double[] { 7.8 },
            new double[] { 99.9 });
    DefaultHighLowDataset d2 = null;

    try {
        ByteArrayOutputStream buffer = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(buffer);
        out.writeObject(d1);
        out.close();

        ObjectInput in = new ObjectInputStream(new ByteArrayInputStream(buffer.toByteArray()));
        d2 = (DefaultHighLowDataset) in.readObject();
        in.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
    assertEquals(d1, d2);
}

From source file:org.jaqpot.algorithms.resource.Standarization.java

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {/*  www  .j a va 2  s.  c o  m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST)
                    .entity("Dataset is empty. Cannot train model on empty dataset.").build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());

        LinkedHashMap<String, Double> maxValues = new LinkedHashMap<>();
        LinkedHashMap<String, Double> minValues = new LinkedHashMap<>();

        features.stream().forEach(feature -> {
            List<Double> values = request.getDataset().getDataEntry().stream().map(dataEntry -> {
                return Double.parseDouble(dataEntry.getValues().get(feature).toString());
            }).collect(Collectors.toList());
            double[] doubleValues = values.stream().mapToDouble(Double::doubleValue).toArray();

            Double mean = StatUtils.mean(doubleValues);
            Double stddev = Math.sqrt(StatUtils.variance(doubleValues));

            maxValues.put(feature, stddev);
            minValues.put(feature, mean);
        });
        ScalingModel model = new ScalingModel();
        model.setMaxValues(maxValues);
        model.setMinValues(minValues);

        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        response.setIndependentFeatures(features);
        response.setPredictedFeatures(features.stream().map(feature -> {
            return "Standarized " + feature;
        }).collect(Collectors.toList()));
        return Response.ok(response).build();
    } catch (Exception ex) {
        LOG.log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}

From source file:com.splicemachine.derby.stream.function.TableScanQualifierFunction.java

@Override
public void writeExternal(ObjectOutput out) throws IOException {
    super.writeExternal(out);
    out.writeBoolean(optionalProbeValue != null);
    if (optionalProbeValue != null)
        out.writeObject(optionalProbeValue);
}

From source file:com.conwet.silbops.model.SubscriptionTest.java

@Test
public void shouldExternalize() throws Exception {

    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    ObjectOutput output = new ObjectOutputStream(baos);

    output.writeObject(subscription);
    output.close();/*from w w w.j a  v  a 2  s  . co m*/

    ObjectInput input = new ObjectInputStream(new ByteArrayInputStream(baos.toByteArray()));

    assertThat((Subscription) input.readObject()).isEqualTo(subscription);
}

From source file:org.jaqpot.algorithm.resource.Standarization.java

@POST
@Path("training")
public Response training(TrainingRequest request) {
    try {/*from  w  ww.j  a  va2s  . co m*/
        if (request.getDataset().getDataEntry().isEmpty()
                || request.getDataset().getDataEntry().get(0).getValues().isEmpty()) {
            return Response.status(Response.Status.BAD_REQUEST).entity(
                    ErrorReportFactory.badRequest("Dataset is empty", "Cannot train model on empty dataset"))
                    .build();
        }
        List<String> features = request.getDataset().getDataEntry().stream().findFirst().get().getValues()
                .keySet().stream().filter(feature -> !feature.equals(request.getPredictionFeature()))
                .collect(Collectors.toList());

        LinkedHashMap<String, Double> maxValues = new LinkedHashMap<>();
        LinkedHashMap<String, Double> minValues = new LinkedHashMap<>();

        features.stream().forEach(feature -> {
            List<Double> values = request.getDataset().getDataEntry().stream().map(dataEntry -> {
                return Double.parseDouble(dataEntry.getValues().get(feature).toString());
            }).collect(Collectors.toList());
            double[] doubleValues = values.stream().mapToDouble(Double::doubleValue).toArray();

            Double mean = StatUtils.mean(doubleValues);
            Double stddev = Math.sqrt(StatUtils.variance(doubleValues));

            maxValues.put(feature, stddev);
            minValues.put(feature, mean);
        });
        ScalingModel model = new ScalingModel();
        model.setMaxValues(maxValues);
        model.setMinValues(minValues);

        TrainingResponse response = new TrainingResponse();
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        ObjectOutput out = new ObjectOutputStream(baos);
        out.writeObject(model);
        String base64Model = Base64.getEncoder().encodeToString(baos.toByteArray());
        response.setRawModel(base64Model);
        response.setIndependentFeatures(features);
        response.setPredictedFeatures(features.stream().map(feature -> {
            return "Standarized " + feature;
        }).collect(Collectors.toList()));
        return Response.ok(response).build();
    } catch (Exception ex) {
        LOG.log(Level.SEVERE, null, ex);
        return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(ex.getMessage()).build();
    }
}