Examples with Writable org.datavec.api.writable.Writable used on opensource projects

Example 16 with Writable

use of org.datavec.api.writable.Writable in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSeqRRDSIArrayWritableTwoReaders.

@Test
public void testSeqRRDSIArrayWritableTwoReaders() {
    List<List<Writable>> sequence1 = new ArrayList<>();
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 })), new IntWritable(100)));
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 })), new IntWritable(200)));
    List<List<Writable>> sequence2 = new ArrayList<>();
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 })), new IntWritable(300)));
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 })), new IntWritable(400)));
    SequenceRecordReader rrFeatures = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
    List<List<Writable>> sequence1L = new ArrayList<>();
    sequence1L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 100, 200, 300 })), new IntWritable(101)));
    sequence1L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 400, 500, 600 })), new IntWritable(201)));
    List<List<Writable>> sequence2L = new ArrayList<>();
    sequence2L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 700, 800, 900 })), new IntWritable(301)));
    sequence2L.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1000, 1100, 1200 })), new IntWritable(401)));
    SequenceRecordReader rrLabels = new CollectionSequenceRecordReader(Arrays.asList(sequence1L, sequence2L));
    SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rrFeatures, rrLabels, 2, -1, true);
    //2 examples, 4 values per time step, 2 time steps
    INDArray expFeatures = Nd4j.create(2, 4, 2);
    expFeatures.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 1, 4 }, { 2, 5 }, { 3, 6 }, { 100, 200 } }));
    expFeatures.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 7, 10 }, { 8, 11 }, { 9, 12 }, { 300, 400 } }));
    INDArray expLabels = Nd4j.create(2, 4, 2);
    expLabels.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 100, 400 }, { 200, 500 }, { 300, 600 }, { 101, 201 } }));
    expLabels.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 700, 1000 }, { 800, 1100 }, { 900, 1200 }, { 301, 401 } }));
    DataSet ds = iter.next();
    assertEquals(expFeatures, ds.getFeatureMatrix());
    assertEquals(expLabels, ds.getLabels());
}

Example 17 with Writable

use of org.datavec.api.writable.Writable in project deeplearning4j by deeplearning4j.

the class RecordReaderDataSetiteratorTest method testSeqRRDSIArrayWritableOneReader.

@Test
public void testSeqRRDSIArrayWritableOneReader() {
    List<List<Writable>> sequence1 = new ArrayList<>();
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 1, 2, 3 })), new IntWritable(0)));
    sequence1.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 4, 5, 6 })), new IntWritable(1)));
    List<List<Writable>> sequence2 = new ArrayList<>();
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 7, 8, 9 })), new IntWritable(2)));
    sequence2.add(Arrays.asList((Writable) new NDArrayWritable(Nd4j.create(new double[] { 10, 11, 12 })), new IntWritable(3)));
    SequenceRecordReader rr = new CollectionSequenceRecordReader(Arrays.asList(sequence1, sequence2));
    SequenceRecordReaderDataSetIterator iter = new SequenceRecordReaderDataSetIterator(rr, 2, 4, 1, false);
    DataSet ds = iter.next();
    //2 examples, 3 values per time step, 2 time steps
    INDArray expFeatures = Nd4j.create(2, 3, 2);
    expFeatures.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 1, 4 }, { 2, 5 }, { 3, 6 } }));
    expFeatures.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 7, 10 }, { 8, 11 }, { 9, 12 } }));
    INDArray expLabels = Nd4j.create(2, 4, 2);
    expLabels.tensorAlongDimension(0, 1, 2).assign(Nd4j.create(new double[][] { { 1, 0 }, { 0, 1 }, { 0, 0 }, { 0, 0 } }));
    expLabels.tensorAlongDimension(1, 1, 2).assign(Nd4j.create(new double[][] { { 0, 0 }, { 0, 0 }, { 1, 0 }, { 0, 1 } }));
    assertEquals(expFeatures, ds.getFeatureMatrix());
    assertEquals(expLabels, ds.getLabels());
}

Example 18 with Writable

use of org.datavec.api.writable.Writable in project deeplearning4j by deeplearning4j.

the class TestRecordReaders method testClassIndexOutsideOfRangeRRMDSI.

@Test
public void testClassIndexOutsideOfRangeRRMDSI() {
    Collection<Collection<Collection<Writable>>> c = new ArrayList<>();
    Collection<Collection<Writable>> seq1 = new ArrayList<>();
    seq1.add(Arrays.<Writable>asList(new DoubleWritable(0.0), new IntWritable(0)));
    seq1.add(Arrays.<Writable>asList(new DoubleWritable(0.0), new IntWritable(1)));
    c.add(seq1);
    Collection<Collection<Writable>> seq2 = new ArrayList<>();
    seq2.add(Arrays.<Writable>asList(new DoubleWritable(0.0), new IntWritable(0)));
    seq2.add(Arrays.<Writable>asList(new DoubleWritable(0.0), new IntWritable(2)));
    c.add(seq2);
    CollectionSequenceRecordReader csrr = new CollectionSequenceRecordReader(c);
    DataSetIterator dsi = new SequenceRecordReaderDataSetIterator(csrr, 2, 2, 1);
    try {
        DataSet ds = dsi.next();
        fail("Expected exception");
    } catch (DL4JException e) {
        System.out.println("testClassIndexOutsideOfRangeRRMDSI(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail();
    }
}

Example 19 with Writable

use of org.datavec.api.writable.Writable in project deeplearning4j by deeplearning4j.

the class SequenceVectorsTest method buildGraph.

private static Graph<Blogger, Double> buildGraph() throws IOException, InterruptedException {
    File nodes = new File("/ext/Temp/BlogCatalog/nodes.csv");
    CSVRecordReader reader = new CSVRecordReader(0, ",");
    reader.initialize(new FileSplit(nodes));
    List<Blogger> bloggers = new ArrayList<>();
    int cnt = 0;
    while (reader.hasNext()) {
        List<Writable> lines = new ArrayList<>(reader.next());
        Blogger blogger = new Blogger(lines.get(0).toInt());
        bloggers.add(blogger);
        cnt++;
    }
    reader.close();
    Graph<Blogger, Double> graph = new Graph<>(bloggers, true);
    // load edges
    File edges = new File("/ext/Temp/BlogCatalog/edges.csv");
    reader = new CSVRecordReader(0, ",");
    reader.initialize(new FileSplit(edges));
    while (reader.hasNext()) {
        List<Writable> lines = new ArrayList<>(reader.next());
        int from = lines.get(0).toInt();
        int to = lines.get(1).toInt();
        graph.addEdge(from - 1, to - 1, 1.0, false);
    }
    logger.info("Connected on 0: [" + graph.getConnectedVertices(0).size() + "]");
    logger.info("Connected on 1: [" + graph.getConnectedVertices(1).size() + "]");
    logger.info("Connected on 3: [" + graph.getConnectedVertices(3).size() + "]");
    assertEquals(119, graph.getConnectedVertices(0).size());
    assertEquals(9, graph.getConnectedVertices(1).size());
    assertEquals(6, graph.getConnectedVertices(3).size());
    return graph;
}

Example 20 with Writable

use of org.datavec.api.writable.Writable in project deeplearning4j by deeplearning4j.

the class CSVRecordToINDArray method convert.

@Override
public INDArray convert(Collection<Collection<Writable>> records) {
    INDArray ret = Nd4j.create(records.size(), records.iterator().next().size());
    int count = 0;
    for (Collection<Writable> record : records) {
        ret.putRow(count++, RecordConverter.toArray(record));
    }
    return ret;
}