org.numenta.nupic.encoders.ScalarEncoder Java Examples

public SimpleLayer() {
    params = getParameters();
    
    ScalarEncoder.Builder dayBuilder =
        ScalarEncoder.builder()
            .n(8)
            .w(3)
            .radius(1.0)
            .minVal(1.0)
            .maxVal(8)
            .periodic(true)
            .forced(true)
            .resolution(1);
    encoder = dayBuilder.build();
    
    spatialPooler = new SpatialPooler();
    
    temporalMemory = new TemporalMemory();
    
    Map<String, Object> anomalyParams = new HashMap<>();
    anomalyParams.put(KEY_MODE, Mode.PURE);
    anomaly = Anomaly.create(anomalyParams);
    
    configure();
}
 

public static MultiEncoder createEncoder() {
    MultiEncoder encoder = MultiEncoder.builder().name("").build();
    ScalarEncoder se = ScalarEncoder.builder()
        .n(50)
        .w(21)
        .minVal(0)
        .maxVal(100)
        .periodic(false)
        .clipInput(true)
        .name("value")
        .build();
    encoder.addEncoder("value", se);
    
    DateEncoder de = DateEncoder.builder()
        .timeOfDay(21, 9.5)
        .name("timestamp")
        .build();
    encoder.addEncoder("timestamp", de);
    
    return encoder;
}
 

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    Parameters params = getParameters();
    System.out.println(params);

    // Toggle this to switch between resetting on every week start day
    isResetting = true;

    //Layer components
    ScalarEncoder.Builder dayBuilder =
        ScalarEncoder.builder()
            .n(8)
            .w(3)
            .radius(1.0)
            .minVal(1.0)
            .maxVal(8)
            .periodic(true)
            .forced(true)
            .resolution(1);
    ScalarEncoder encoder = dayBuilder.build();
    SpatialPooler sp = new SpatialPooler();
    TemporalMemory tm = new TemporalMemory();
    CLAClassifier classifier = new CLAClassifier(new TIntArrayList(new int[] { 1 }), 0.1, 0.3, 0);

    Layer<Double> layer = getLayer(params, encoder, sp, tm, classifier);

    for(double i = 0, x = 0, j = 0;j < 1500;j = (i == 6 ? j + 1: j), i = (i == 6 ? 0 : i + 1), x++) {  // USE "X" here to control run length
        if (i == 0 && isResetting) {
            System.out.println("reset:");
            tm.reset(layer.getMemory());
        }

        // For 3rd argument: Use "i" for record num if re-cycling records (isResetting == true) - otherwise use "x" (the sequence number)
        runThroughLayer(layer, i + 1, isResetting ? (int)i : (int)x, (int)x);
    }
}
 

public LayerImpl(Parameters p, ScalarEncoder e, SpatialPooler s, TemporalMemory t, CLAClassifier c) {
    this.params = p;
    this.encoder = e;
    this.spatialPooler = s;
    this.temporalMemory = t;
    this.classifier = c;

    params.apply(memory);
    spatialPooler.init(memory);
    TemporalMemory.init(memory);

    columnCount = memory.getPotentialPools().getMaxIndex() + 1; //If necessary, flatten multi-dimensional index
    cellsPerColumn = memory.getCellsPerColumn();
}
 

@Test
public void testRunThroughLayer() {
    Layer<Double> layer = null;
    try {
        Parameters params = QuickTest.getParameters();

        //Layer components
        ScalarEncoder.Builder dayBuilder =
            ScalarEncoder.builder()
                .n(8)
                .w(3)
                .radius(1.0)
                .minVal(1.0)
                .maxVal(8)
                .periodic(true)
                .forced(true)
                .resolution(1);
        ScalarEncoder encoder = dayBuilder.build();
        SpatialPooler sp = new SpatialPooler();
        TemporalMemory tm = new TemporalMemory();
        CLAClassifier classifier = new CLAClassifier(new TIntArrayList(new int[] { 1 }), 0.1, 0.3, 0);

        layer = QuickTest.getLayer(params, encoder, sp, tm, classifier);

        for(double i = 1, x = 0;x < 10000;i = (i == 7 ? 1 : i + 1), x++) {// USE "X" here to control run length
            if (i == 1) tm.reset(layer.getMemory());
            QuickTest.runThroughLayer(layer, i, (int)i, (int)x);
        }
    }catch(Exception e) {
        e.printStackTrace();
        fail();
    }

    System.out.println("actual = " + Arrays.toString(layer.getActual()) + ",  predicted = " + Arrays.toString(layer.getPredicted()));
    //assertArrayEquals(layer.getActual(), layer.getPredicted());
}
 

/**
 * Searches through the specified {@link MultiEncoder}'s previously configured 
 * encoders to find and return one that is of type {@link ScalarEncoder},
 * {@link RandomDistributedScalarEncoder}, {@link AdaptiveScalarEncoder},
 * {@link LogEncoder} or {@link DeltaEncoder}.
 * 
 * @param enc   the containing {@code MultiEncoder}
 * @return
 */
private Optional<Encoder<?>> getNumberEncoder(MultiEncoder enc) {
    for(EncoderTuple t : enc.getEncoders(enc)) {
        if((t.getEncoder() instanceof RandomDistributedScalarEncoder) ||
            (t.getEncoder() instanceof ScalarEncoder) ||
            (t.getEncoder() instanceof AdaptiveScalarEncoder) ||
            (t.getEncoder() instanceof LogEncoder) ||
            (t.getEncoder() instanceof DeltaEncoder)) {
            
            return Optional.of(t.getEncoder());
        }
    }
    
    return Optional.empty();
 }
 

@Setup
public void init() {
    SDR = new int[2048];

    //Layer components
    ScalarEncoder.Builder dayBuilder = ScalarEncoder.builder()
        .n(8)
        .w(3)
        .radius(1.0)
        .minVal(1.0)
        .maxVal(8)
        .periodic(true)
        .forced(true)
        .resolution(1);
    encoder = dayBuilder.build();
    pooler = new SpatialPooler();

    memory = new Connections();
    Parameters params = getParameters();
    params.apply(memory);

    pooler = new SpatialPooler();
    pooler.init(memory);

    temporalMemory = new TemporalMemory();
    TemporalMemory.init(memory);
}
 

public SimpleLayer(Parameters p, ScalarEncoder e, SpatialPooler s, TemporalMemory t, Anomaly a) {
    this.params = p;
    this.encoder = e;
    this.spatialPooler = s;
    this.temporalMemory = t;
    this.anomaly = a;
    
    configure();
}
 

/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    Parameters params = getParameters();
    System.out.println(params);

    // Toggle this to switch between resetting on every week start day
    isResetting = true;

    //Layer components
    ScalarEncoder.Builder dayBuilder =
        ScalarEncoder.builder()
            .n(8)
            .w(3)
            .radius(1.0)
            .minVal(1.0)
            .maxVal(8)
            .periodic(true)
            .forced(true)
            .resolution(1);
    ScalarEncoder encoder = dayBuilder.build();
    SpatialPooler sp = new SpatialPooler();
    TemporalMemory tm = new TemporalMemory();
    CLAClassifier classifier = new CLAClassifier(new TIntArrayList(new int[] { 1 }), 0.1, 0.3, 0);

    Layer<Double> layer = getLayer(params, encoder, sp, tm, classifier);

    for(double i = 0, x = 0, j = 0;j < 1500;j = (i == 6 ? j + 1: j), i = (i == 6 ? 0 : i + 1), x++) {  // USE "X" here to control run length
        if (i == 0 && isResetting) {
            System.out.println("reset:");
            tm.reset(layer.getMemory());
        }

        // For 3rd argument: Use "i" for record num if re-cycling records (isResetting == true) - otherwise use "x" (the sequence number)
        runThroughLayer(layer, i + 1, isResetting ? (int)i : (int)x, (int)x);
    }
}
 

public LayerImpl(Parameters p, ScalarEncoder e, SpatialPooler s, TemporalMemory t, CLAClassifier c) {
    this.params = p;
    this.encoder = e;
    this.spatialPooler = s;
    this.temporalMemory = t;
    this.classifier = c;

    params.apply(memory);
    spatialPooler.init(memory);
    TemporalMemory.init(memory);

    columnCount = memory.getPotentialPools().getMaxIndex() + 1; //If necessary, flatten multi-dimensional index
    cellsPerColumn = memory.getCellsPerColumn();
}
 

public static Layer<Double> getLayer(Parameters p, ScalarEncoder e, SpatialPooler s, TemporalMemory t, CLAClassifier c) {
    Layer<Double> l = new LayerImpl(p, e, s, t, c);
    return l;
}
 

/**
 * Test addition of encoders one-by-one.
 */
@Test
public void testSerialAdditions() {
	setUp();
	initME();
	
	ScalarEncoder dow = ScalarEncoder.builder()
			.w(3)
			.resolution(1)
			.minVal(1)
			.maxVal(8)
			.periodic(true)
			.name("day of week")
			.forced(true)
			.build();
	me.addEncoder("dow", dow);
	
	ScalarEncoder myval = ScalarEncoder.builder()
			.w(5)
			.resolution(1)
			.minVal(1)
			.maxVal(10)
			.periodic(false)
			.name("aux")
			.forced(true)
			.build();
	me.addEncoder("myval", myval);
	
	runScalarTests(me);
	
	List<String> categoryList = new ArrayList<String>();
	categoryList.add("run");
	categoryList.add("pass");
	categoryList.add("kick");
	CategoryEncoder myCat = CategoryEncoder.builder()
			.radius(2)
			.w(3)
			.categoryList(categoryList)
			.forced(true)
			.build();
	
	me.addEncoder("myCat", myCat);
	
	runMixedTests(me);
}
 

public static Layer<Double> getLayer(Parameters p, ScalarEncoder e, SpatialPooler s, TemporalMemory t, CLAClassifier c) {
    Layer<Double> l = new LayerImpl(p, e, s, t, c);
    return l;
}