org.numenta.nupic.util.Tuple Java Examples

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
                    null,
                    0, // n
                    0, // w
                    0, 0, 0, 0, null, null, null,
                    "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
                    fieldEncodings, 
                    25, 
                    3, 
                    0, 0, 0, 0.1, null, null, null, 
                    "consumption", "float", "RandomDistributedScalarEncoder");

    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");

    return fieldEncodings;
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getNetworkDemoFieldEncodingMap() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
            null,
            0, // n
            0, // w
            0, 0, 0, 0, null, null, null,
            "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
            fieldEncodings, 
            50, 
            21, 
            0, 100, 0, 0.1, null, Boolean.TRUE, null, 
            "consumption", "float", "ScalarEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(21,9.5)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    return fieldEncodings;
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
            null,
            0, // n
            0, // w
            0, 0, 0, 0, null, null, null,
            "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
            fieldEncodings, 
            25, 
            3, 
            0, 0, 0, 0.1, null, null, null, 
            "consumption", "float", "RandomDistributedScalarEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    return fieldEncodings;
}
 

/**
 * Constructs a new {@code BatchedCsvHeader}
 * 
 * @param lines                     List of csv strings
 * @param configuredHeaderLength    number of header rows
 */
public <T> BatchedCsvHeader(List<T> lines, int configuredHeaderLength) {
    
    if((configuredHeaderLength < 1 || lines == null || lines.size() < 1) || 
        (configuredHeaderLength > 1 && lines.size() != configuredHeaderLength)) {
        
        throw new IllegalStateException("Actual Header was not the expected size: " + 
            (configuredHeaderLength < 1 ? "> 1" : configuredHeaderLength) + 
                ", but was: " + (lines == null ? "null" : lines.size()));
    }
    
    headerValues = new Tuple[configuredHeaderLength];
    for(int i = 0;i < headerValues.length;i++) {
        headerValues[i] = new Tuple((Object[])lines.get(i));
    }
}
 

/**
 * Returns a list of {@link Tuple}s which in this case is a list of
 * key value parameter values for this {@code ScalarEncoder}
 *
 * @return	a list of {@link Tuple}s
 */
public List<Tuple> dict() {
    List<Tuple> l = new ArrayList<Tuple>();
    l.add(new Tuple("maxval", getMaxVal()));
    l.add(new Tuple("bucketValues", getBucketValues(Double.class)));
    l.add(new Tuple("nInternal", getNInternal()));
    l.add(new Tuple("name", getName()));
    l.add(new Tuple("minval", getMinVal()));
    l.add(new Tuple("topDownValues", Arrays.toString(getTopDownValues())));
    l.add(new Tuple("clipInput", clipInput()));
    l.add(new Tuple("n", getN()));
    l.add(new Tuple("padding", getPadding()));
    l.add(new Tuple("range", getRange()));
    l.add(new Tuple("periodic", isPeriodic()));
    l.add(new Tuple("radius", getRadius()));
    l.add(new Tuple("w", getW()));
    l.add(new Tuple("topDownMappingM", getTopDownMapping()));
    l.add(new Tuple("halfwidth", getHalfWidth()));
    l.add(new Tuple("resolution", getResolution()));
    l.add(new Tuple("rangeInternal", getRangeInternal()));

    return l;
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap_varyN() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
                    null,
                    20, // n
                    0, // w
                    0, 0, 0, 0, null, null, null,
                    "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
                    fieldEncodings, 
                    25, 
                    3, 
                    0, 0, 0, 0.1, null, null, null, 
                    "consumption", "float", "RandomDistributedScalarEncoder");

    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");

    return fieldEncodings;
}
 

/**
 * Return the field names for each of the scalar values returned by
    * .
 * @param parentFieldName	parentFieldName The name of the encoder which is our parent. This name
    *     						is prefixed to each of the field names within this encoder to form the
    *      					keys of the dict() in the retval.
    *
 * @return
 */
@SuppressWarnings("unchecked")
public List<String> getScalarNames(String parentFieldName) {
	List<String> names = new ArrayList<String>();
	if(getEncoders() != null) {
		List<EncoderTuple> encoders = getEncoders(this);
		for(Tuple tuple : encoders) {
			List<String> subNames = ((Encoder<T>)tuple.get(1)).getScalarNames(getName());
			List<String> hierarchicalNames = new ArrayList<String>();
			if(parentFieldName != null) {
				for(String name : subNames) {
					hierarchicalNames.add(String.format("%s.%s", parentFieldName, name));
				}
			}
			names.addAll(hierarchicalNames);
		}
	}else{
		if(parentFieldName != null) {
			names.add(parentFieldName);
		}else{
			names.add((String)getEncoderTuple(this).get(0));
		}
	}

	return names;
}
 

/**
 * Return a pretty print string representing the return values from
    * getScalars and getScalarNames().
 * @param scalarValues 	input values to encode to string
 * @param scalarNames 	optional input of scalar names to convert. If None, gets
    *                  	scalar names from getScalarNames()
 *
 * @return string representation of scalar values
 */
public String scalarsToStr(List<?> scalarValues, List<String> scalarNames) {
	if(scalarNames == null || scalarNames.isEmpty()) {
		scalarNames = getScalarNames("");
	}

	StringBuilder desc = new StringBuilder();
	for(Tuple t : ArrayUtils.zip(scalarNames, scalarValues)) {
		if(desc.length() > 0) {
			desc.append(String.format(", %s:%.2f", t.get(0), t.get(1)));
		}else{
			desc.append(String.format("%s:%.2f", t.get(0), t.get(1)));
		}
	}
	return desc.toString();
}
 

/**
 * Pretty-print a header that labels the sub-fields of the encoded
    * output. This can be used in conjunction with {@link #pprint(int[], String)}.
 * @param prefix
 */
public void pprintHeader(String prefix) {
	LOGGER.info(prefix == null ? "" : prefix);

	List<Tuple> description = getDescription();
	description.add(new Tuple("end", getWidth()));

	int len = description.size() - 1;
	for(int i = 0;i < len;i++) {
		String name = (String)description.get(i).get(0);
		int width = (int)description.get(i+1).get(1);

		String formatStr = String.format("%%-%ds |", width);
		StringBuilder pname = new StringBuilder(name);
		if(name.length() > width) pname.setLength(width);

           LOGGER.info(String.format(formatStr, pname));
	}

	len = getWidth() + (description.size() - 1)*3 - 1;
	StringBuilder hyphens = new StringBuilder();
	for(int i = 0;i < len;i++) hyphens.append("-");
       LOGGER.info(new StringBuilder(prefix).append(hyphens).toString());
   }
 

/**
 * Pretty-print the encoded output using ascii art.
 * @param output
 * @param prefix
 */
public void pprint(int[] output, String prefix) {
	LOGGER.info(prefix == null ? "" : prefix);

	List<Tuple> description = getDescription();
	description.add(new Tuple("end", getWidth()));

	int len = description.size() - 1;
	for(int i = 0;i < len;i++) {
		int offset = (int)description.get(i).get(1);
		int nextOffset = (int)description.get(i + 1).get(1);

           LOGGER.info(
                   String.format("%s |",
                           ArrayUtils.bitsToString(
                                   ArrayUtils.sub(output, ArrayUtils.range(offset, nextOffset))
                           )
                   )
           );
       }
   }
 

/**
 * Return a pretty print string representing the return value from decode().
 *
 * @param decodeResults
 * @return
 */
@SuppressWarnings("unchecked")
public String decodedToStr(Tuple decodeResults) {
	StringBuilder desc = new StringBuilder();
	Map<String, Tuple> fieldsDict = (Map<String, Tuple>)decodeResults.get(0);
	List<String> fieldsOrder = (List<String>)decodeResults.get(1);
	for(String fieldName : fieldsOrder) {
		Tuple ranges = fieldsDict.get(fieldName);
		if(desc.length() > 0) {
			desc.append(", ").append(fieldName).append(":");
		}else{
			desc.append(fieldName).append(":");
		}
		desc.append("[").append(ranges.get(1)).append("]");
	}
	return desc.toString();
}
 

/**
 * Calculate error signal.
 *
 * @param classification {@link Map} of the classification information:
 * <p>&emsp;"bucketIdx" - index of the encoder bucket
 * <p>&emsp;"actValue" -  actual value doing into the encoder
 * @return
 * {@link Map} containing error. The key is the number of steps. The value
 * is a double[] of the error at the output layer.
    */
private Map<Integer, double[]> calculateError(Map<String, Object> classification) {
	Map<Integer, double[]> error = new HashMap<Integer, double[]>();
	int[] targetDist = new int[maxBucketIdx + 1];
	targetDist[(int)classification.get("bucketIdx")] = 1;

	int iteration = 0;
	int[] learnPatternNZ = null;
	int nSteps = 0;
	for(Tuple t : patternNZHistory) {
		iteration = (int) t.get(0);
		learnPatternNZ = (int[]) t.get(1);
		nSteps = learnIteration - iteration;

		if(steps.contains(nSteps)) {
			double[] predictDist = inferSingleStep(learnPatternNZ, weightMatrix.get(nSteps));
			double[] targetDistMinusPredictDist = new double[maxBucketIdx + 1];
			for(int i = 0; i <= maxBucketIdx; i++) {
				targetDistMinusPredictDist[i] = targetDist[i] - predictDist[i];
			}
			error.put(nSteps, targetDistMinusPredictDist);
		}
	}

	return error;
}
 

private Parameters getTestEncoderParams() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
        null,
        0, // n
        0, // w
        0, 0, 0, 0, null, null, null,
        "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
        fieldEncodings, 
        25, 
        3, 
        0, 0, 0, 0.1, null, null, null, 
        "consumption", "float", "RandomDistributedScalarEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    // This will work also
    //fieldEncodings.get("timestamp").put(KEY.DATEFIELD_FORMATTER.getFieldName(), DateEncoder.FULL_DATE);
            
    Parameters p = Parameters.getEncoderDefaultParameters();
    p.set(KEY.FIELD_ENCODING_MAP, fieldEncodings);
    
    return p;
}
 

private ValueList getTestHeaderOff() {
    return new ValueList() {
        Tuple[] ta = new Tuple[] {
            new Tuple("timestamp", "consumption"),
            new Tuple("datetime", "float"),
            new Tuple("T"),
        };

        @Override
        public Tuple getRow(int row) {
            return ta[row];
        }

        @Override
        public int size() {
            return ta.length;
        }
    };
}
 

private ValueList getTestHeaderSeq() {
    return new ValueList() {
        Tuple[] ta = new Tuple[] {
            new Tuple("timestamp", "consumption"),
            new Tuple("datetime", "float"),
            new Tuple("T", "B", "S"),
        };

        @Override
        public Tuple getRow(int row) {
            return ta[row];
        }

        @Override
        public int size() {
            return ta.length;
        }
    };
}
 

private ValueList getTestHeaderReset() {
    return new ValueList() {
        Tuple[] ta = new Tuple[] {
            new Tuple("timestamp", "consumption"),
            new Tuple("datetime", "float"),
            new Tuple("T", "B", "R"),
        };

        @Override
        public Tuple getRow(int row) {
            return ta[row];
        }

        @Override
        public int size() {
            return ta.length;
        }
    };
}
 

private ValueList getTestHeaderLearn() {
    return new ValueList() {
        Tuple[] ta = new Tuple[] {
            new Tuple("timestamp", "consumption"),
            new Tuple("datetime", "float"),
            new Tuple("T", "B", "L"),
        };

        @Override
        public Tuple getRow(int row) {
            return ta[row];
        }

        @Override
        public int size() {
            return ta.length;
        }
    };
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
            null,
            0, // n
            0, // w
            0, 0, 0, 0, null, null, null,
            "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
            fieldEncodings, 
            25, 
            3, 
            0, 0, 0, 0.1, null, null, null, 
            "consumption", "float", "RandomDistributedScalarEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    return fieldEncodings;
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getNetworkDemoFieldEncodingMap() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
            null,
            0, // n
            0, // w
            0, 0, 0, 0, null, null, null,
            "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
            fieldEncodings, 
            50, 
            21, 
            0, 100, 0, 0.1, null, Boolean.TRUE, null, 
            "consumption", "float", "ScalarEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(21,9.5)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    return fieldEncodings;
}
 

/**
 * Returns the Hot Gym encoder setup.
 * @return
 */
public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap_varyDateFieldTupleValue() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
                    null,
                    0, // n
                    0, // w
                    0, 0, 0, 0, null, null, null,
                    "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
                    fieldEncodings, 
                    25, 
                    3, 
                    0, 0, 0, 0.1, null, null, null, 
                    "consumption", "float", "RandomDistributedScalarEncoder");

    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 2.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");

    return fieldEncodings;
}
 

private Parameters getTestEncoderParams() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
        null,
        0, // n
        0, // w
        0, 0, 0, 0, null, null, null,
        "timestamp", "datetime", "DateEncoder");

    fieldEncodings = setupMap(
        fieldEncodings, 
        25, 
        3, 
        0, 0, 0, 0.1, null, null, null, 
        "consumption", "float", "RandomDistributedScalarEncoder");

    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");

    Parameters p = Parameters.getEncoderDefaultParameters();
    p.set(KEY.FIELD_ENCODING_MAP, fieldEncodings);

    return p;
}
 

/**
     * Returns the Hot Gym encoder setup.
     * @return
     */
    public static Map<String, Map<String, Object>> getHotGymFieldEncodingMap_varyDateFieldKey() {
        Map<String, Map<String, Object>> fieldEncodings = setupMap(
                        null,
                        0, // n
                        0, // w
                        0, 0, 0, 0, null, null, null,
                        "timestamp", "datetime", "DateEncoder");
        fieldEncodings = setupMap(
                        fieldEncodings, 
                        25, 
                        3, 
                        0, 0, 0, 0.1, null, null, null, 
                        "consumption", "float", "RandomDistributedScalarEncoder");

//        fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
        fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
        fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");

        return fieldEncodings;
    }
 

/**
 * Test that the getWidth, getDescription, and getDecoderOutputFieldTypes
    * methods work.
 */
@Test
public void testGetMethods() {
	builder = RandomDistributedScalarEncoder.builder()
			.name("theName")
			.resolution(1)
			.n(500);
	rdse = builder.build();

	assertEquals("getWidth doesn't return the correct result", 500,
			rdse.getWidth());
	assertEquals(
			"getDescription doesn't return the correct result",
			new ArrayList<Tuple>(Arrays.asList(new Tuple[] { new Tuple("theName",
					0) })), rdse.getDescription());

	assertThat(
			"getDecoderOutputFieldTypes doesn't return the correct result",
			rdse.getDecoderOutputFieldTypes(),
			is(equalTo(new LinkedHashSet<>(Arrays.asList(FieldMetaType.FLOAT, FieldMetaType.INTEGER)))));
}
 

@SuppressWarnings("unchecked")
public void runScalarTests(MultiEncoder me) {
	// should be 7 bits wide
	// use of forced=true is not recommended, but here for readability, see scalar.py
	int[] expected = new int[]{0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1};
	Map<String, Object> d = new HashMap<String, Object>();
	d.put("dow",  3.);
	d.put("myval",  10.);
	int[] output = me.encode(d);

	assertTrue(Arrays.equals(expected, output));
	
	// Check decoding
	Tuple decoded = me.decode(output, "");
	Map<String, RangeList> fields = (HashMap<String, RangeList>) decoded.get(0);
	assertEquals(fields.keySet().size(), 2);
	
	MinMax minMax = fields.get("aux").getRange(0);
	assertTrue(minMax.toString().equals(new MinMax(10.0, 10.0).toString()));
	
	minMax = fields.get("day of week").getRange(0);
	assertTrue(minMax.toString().equals(new MinMax(3.0, 3.0).toString()));
}
 

/**
 * Test closenessScores for a periodic encoder
 */
@Test
public void testCloseness() {
	setUp();
	builder.name("day of week")
        .w(7)
        .radius(1.0)
        .minVal(0.0)
        .maxVal(7.0)
        .periodic(true)
        .forced(true);
	initSE();
	
	TDoubleList expValues = new TDoubleArrayList(new double[] { 2, 4, 7 });
	TDoubleList actValues = new TDoubleArrayList(new double[] { 4, 2, 1 });
	
	TDoubleList scores = se.closenessScores(expValues, actValues, false);
	for(Tuple t : ArrayUtils.zip(Arrays.asList(2, 2, 1), Arrays.asList(scores.get(0)))) {
		double a = (int)t.get(0);
		double b = (double)t.get(1);
		assertTrue(a == b);
	}
}
 

public Tuple encodingStep(DateTime timestamp, double value, boolean isVerbose) {
            if(valueEncoder == null) {
                List<EncoderTuple> encoderTuples = encoder.getEncoders(encoder);
                valueEncoder = encoderTuples.get(0).getEncoder(); 
            }
            
            Map<String, Object> encodingInput = new HashMap<String, Object>();
            encodingInput.put("value", value);
            encodingInput.put("timestamp",  timestamp);
            int[] encoding = encoder.encode(encodingInput);
            if(isVerbose) {
                System.out.println("ScalarEncoder Output = " + Arrays.toString(encoding));
            }
            
            int bucketIdx = valueEncoder.getBucketIndices((double)value)[0];
//            writeEncOutput(encoding);
            return new Tuple(encoding, bucketIdx);
        }
 

/**
 * Input through the Temporal Memory
 * @param sparseSPOutput
 * @param learn
 * @param isVerbose
 * @return Tuple { active cell indices, previous predicted column indices, predicted column indices }
 */
public Tuple tmStep(int[] sparseSPOutput, boolean learn, boolean isVerbose) {
    // Input into the Temporal Memory
    ComputeCycle cc = tm.compute(connections, sparseSPOutput, learn);
    int[] activeCellIndices = cc.activeCells().stream().mapToInt(c -> c.getIndex()).sorted().toArray();
    int[] predColumnIndices = SDR.cellsAsColumnIndices(cc.predictiveCells(), connections.getCellsPerColumn());
    int[] activeColumns = Arrays.stream(activeCellIndices)
        .map(cell -> cell / connections.getCellsPerColumn())
        .distinct()
        .sorted()
        .toArray();
    if(isVerbose) {
        System.out.println("          TemporalMemory Input: " + Arrays.toString(sparseSPOutput));
        System.out.println("TemporalMemory prev. predicted: " + Arrays.toString(prevPredictedCols));
        System.out.println("         TemporalMemory active: " + Arrays.toString(activeColumns));
    }
    
    return new Tuple(activeCellIndices, prevPredictedCols, predColumnIndices);
}
 

@Test
public void testReadWrite() throws Exception {

    // write numerous objects to the stream, to verify that the read buffers
    // aren't too greedy

    Tuple expected1 = new Tuple(42);
    Tuple expected2 = new Tuple(101);

    ByteArrayOutputStream baout = new ByteArrayOutputStream();
    Output output = new Output(baout);
    kryo.writeObject(output, expected1);
    kryo.writeObject(output, expected2);
    output.close();

    ByteArrayInputStream bain = new ByteArrayInputStream(baout.toByteArray());
    Input input = new Input(bain);

    Tuple actual1 = kryo.readObject(input, Tuple.class);
    assertNotSame(expected1, actual1);
    assertEquals(expected1, actual1);

    Tuple actual2 = kryo.readObject(input, Tuple.class);
    assertNotSame(expected2, actual2);
    assertEquals(expected2, actual2);
}
 

@Test
public void testCopy() throws Exception {
    Tuple expected = new Tuple(42);

    Tuple actual = kryo.copy(expected);
    assertNotSame(expected, actual);
    assertEquals(expected, actual);
}
 

private Parameters getCategoryEncoderParams() {
    Map<String, Map<String, Object>> fieldEncodings = setupMap(
        null,
        0, // n
        0, // w
        0, 0, 0, 0, null, null, null,
        "timestamp", "datetime", "DateEncoder");
    fieldEncodings = setupMap(
        fieldEncodings, 
        25, 
        3, 
        0, 0, 0, 0.1, null, null, null, 
        "consumption", "float", "RandomDistributedScalarEncoder");
    fieldEncodings = setupMap(
        fieldEncodings, 
        25, 
        3, 
        0, 0, 0, 0.0, null, null, Boolean.TRUE, 
        "type", "list", "SDRCategoryEncoder");
    
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(5, 4.0)); // Time of day
    fieldEncodings.get("timestamp").put(KEY.DATEFIELD_PATTERN.getFieldName(), "MM/dd/YY HH:mm");
    
    String categories = "ES;S1;S2;S3;S4;S5;S6;S7;S8;S9;S10;S11;S12;S13;S14;S15;S16;S17;S18;S19;GB;US";
    fieldEncodings.get("type").put(KEY.CATEGORY_LIST.getFieldName(), categories);
    
    // This will work also
    //fieldEncodings.get("timestamp").put(KEY.DATEFIELD_FORMATTER.getFieldName(), DateEncoder.FULL_DATE);
            
    Parameters p = Parameters.getEncoderDefaultParameters();
    p.set(KEY.FIELD_ENCODING_MAP, fieldEncodings);
    
    return p;
}