Package uk.org.ogsadai.tuple

Examples of uk.org.ogsadai.tuple.SimpleTuple

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  public class SimpleTupleFactory implements TupleFactory{

    private static Random mRandom = new Random(1000);

    public Tuple createTuple() {
            return new SimpleTuple(
                    Arrays.asList(
                            new Object[] {
                                    mRandom.nextInt(),
                                    mRandom.nextInt(),
                                    mRandom.nextInt()}));
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        List<Double> elements = new ArrayList<Double>();
        for (int i = 0; i < values.length; i++)
        {
            elements.add(Double.parseDouble(values[i]));
        }
        return new SimpleTuple(elements);
    }
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          result.add(ControlBlock.LIST_BEGIN);
          result.add(new MetadataWrapper(metadata));
         
          //Iterational classifier needs more data, just repeat things 500 times for test
          for (int i=0; i < 500; i ++) {
            result.add(new SimpleTuple(Arrays.asList("sunny", "false", 85, 85, "no")));
            result.add(new SimpleTuple(Arrays.asList("sunny", "true", 80, 90, "no")));
            result.add(new SimpleTuple(Arrays.asList("overcast", "false", 83, 78, "yes")));
            result.add(new SimpleTuple(Arrays.asList("rain", "false", 70, 96, "yes")));
            result.add(new SimpleTuple(Arrays.asList("rain", "false", 68, 80, "yes")));
            result.add(new SimpleTuple(Arrays.asList("rain", "true", 65, 70, "no")));
            result.add(new SimpleTuple(Arrays.asList("overcast", "true", 64, 65, "yes")));
            result.add(new SimpleTuple(Arrays.asList("sunny", "false", 72, 95, "no")));
            result.add(new SimpleTuple(Arrays.asList("sunny", "false", 69, 70, "yes")));
            result.add(new SimpleTuple(Arrays.asList("rain", "false", 75, 80, "yes")));
            result.add(new SimpleTuple(Arrays.asList("sunny", "true", 75, 70, "yes")));
            result.add(new SimpleTuple(Arrays.asList("overcast", "true", 72, 90, "yes")));
            result.add(new SimpleTuple(Arrays.asList("overcast", "false", 81, 75, "yes")));
            result.add(new SimpleTuple(Arrays.asList("rain", "true", 71, 91, "no")));
          }
         
          result.add(ControlBlock.LIST_END);
       
          return result;
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        ColumnMetadata col = new SimpleColumnMetadata("class", TupleTypes._STRING, 0,0,0);
        TupleMetadata metadata = new SimpleTupleMetadata(Collections.singletonList(col));
        MockInputPipe inputExpected = new MockInputPipe(new Object[] {
            ControlBlock.LIST_BEGIN,
            new MetadataWrapper(metadata),
            new SimpleTuple(Arrays.asList("yes")),
            new SimpleTuple(Arrays.asList("no")),
            new SimpleTuple(Arrays.asList("yes")),
            new SimpleTuple(Arrays.asList("maybe")),
            new SimpleTuple(Arrays.asList("yes")),
            new SimpleTuple(Arrays.asList("no")),
            new SimpleTuple(Arrays.asList("no")),
            new SimpleTuple(Arrays.asList("no")),
            ControlBlock.LIST_END
        });
        MockInputPipe inputPredicted = new MockInputPipe(new Object[] {
                ControlBlock.LIST_BEGIN,
                new MetadataWrapper(metadata),
                new SimpleTuple(Arrays.asList("yes")),
                new SimpleTuple(Arrays.asList("yes")),
                new SimpleTuple(Arrays.asList("yes")),
                new SimpleTuple(Arrays.asList("yes")),
                new SimpleTuple(Arrays.asList("no")),
                new SimpleTuple(Arrays.asList("no")),
                new SimpleTuple(Arrays.asList("no")),
                new SimpleTuple(Arrays.asList("maybe")),
                ControlBlock.LIST_END
            });
        EvaluateActivity activity = new EvaluateActivity();
        activity.addInput(EvaluateActivity.INPUT_EXPECTED, inputExpected);
        activity.addInput(EvaluateActivity.INPUT_PREDICTED, inputPredicted);
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        ColumnMetadata col = new SimpleColumnMetadata("class", TupleTypes._STRING, 0,0,0);
        TupleMetadata metadata = new SimpleTupleMetadata(Collections.singletonList(col));
        MockInputPipe inputExpected = new MockInputPipe(new Object[] {
            ControlBlock.LIST_BEGIN,
            new MetadataWrapper(metadata),
            new SimpleTuple(Arrays.asList("yes")),
            new SimpleTuple(Arrays.asList("no")),
            ControlBlock.LIST_END
        });
        MockInputPipe inputPredicted = new MockInputPipe(new Object[] {
                ControlBlock.LIST_BEGIN,
                new MetadataWrapper(metadata),
                new SimpleTuple(Arrays.asList("yes")),
                ControlBlock.LIST_END
            });
        EvaluateActivity activity = new EvaluateActivity();
        activity.addInput(EvaluateActivity.INPUT_EXPECTED, inputExpected);
        activity.addInput(EvaluateActivity.INPUT_PREDICTED, inputPredicted);
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        v2.add("40 60 70");
        PowerSetActivity activity = new PowerSetActivity();
        MockInputPipe input = new MockInputPipe( new Object[] {
                ControlBlock.LIST_BEGIN,
                new MetadataWrapper(metadata),
                new SimpleTuple(Arrays.asList(new Object[] {1, v1})),               
                new SimpleTuple(Arrays.asList(new Object[] {2, v2})),
                ControlBlock.LIST_END
        });
       
        MockOutputPipe output = new MockOutputPipe(new Object[] {});
        activity.addInput("data", input);
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        values.add(c);
        double d = b*b + mRandom.nextDouble();
        values.add(d);
        double e = mRandom.nextDouble()*200;
        values.add(e);
        return new SimpleTuple(values);
    }
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                    else
                    {
                        lst.add(node.toString());
                    }
                }
                Tuple tuple = new SimpleTuple(lst);
                LOG.debug("Tuple: " + tuple);
                output.write(tuple);
            }

            if (includeListMarkers)
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        tupleElements.add(b);
        tupleElements.add(c);
        tupleElements.add(d);
        tupleElements.add(e);
       // tupleElements.add(f);
        return new SimpleTuple(tupleElements);
    }
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        classification = KNNclassify(data,featuresMatrix,classType,NumberOfNeighbour);

        LOG.debug("classification: " + classification);
          List elements = new ArrayList();
          elements.add(classification);
          Tuple tupleC = new SimpleTuple(elements);
        try{
          mOutput.write(tupleC);
        }
        catch (PipeTerminatedException e)
        {
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Related Classes of uk.org.ogsadai.tuple.SimpleTuple

  • eu.admire.gateway.engine.ogsadai.EncodeTupleList
  • eu.admire.ogsadai.activity.block.GroupByActivityTest$GroupTupleFactory
  • eu.admire.ogsadai.activity.block.SortActivityTest$SimpleTupleFactory
  • eu.admire.ogsadai.activity.ConvertToTupleActivity
  • eu.admire.ogsadai.activity.datacache.SimpleTupleFactory
  • eu.admire.ogsadai.activity.datamining.ConversionActivity
  • eu.admire.ogsadai.activity.datamining.ConversionActivityTest
  • eu.admire.ogsadai.activity.datamining.LargeItemSetActivity
  • eu.admire.ogsadai.activity.datamining.MaximalSequenceActivity
  • eu.admire.ogsadai.activity.datamining.PowerSetActivity
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