Java Code Examples for org.numenta.nupic.Parameters#union()

private Network getLoadedHotGymNetwork() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getHotGymTestEncoderParams());
    p.set(KEY.RANDOM, new FastRandom(42));
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));

    Sensor<ObservableSensor<String[]>> sensor = Sensor.create(
        ObservableSensor::create, SensorParams.create(Keys::obs, new Object[] {"name", 
            PublisherSupplier.builder()
            .addHeader("timestamp, consumption")
            .addHeader("datetime, float")
            .addHeader("B").build() }));

    Network network = Network.create("test network", p).add(Network.createRegion("r1")
        .add(Network.createLayer("1", p)
            .alterParameter(KEY.AUTO_CLASSIFY, true)
            .add(Anomaly.create())
            .add(new TemporalMemory())
            .add(new SpatialPooler())
            .add(sensor)));

    return network;
}
 

@Test
public void testAutomaticClose() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network n = Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("4", p)
                .add(MultiEncoder.builder().name("").build())));
        //.close(); // Not necessary due to implicit call during start() or compute()
    
    Region r1 = n.lookup("r1");
    r1.start();
    
    assertTrue(r1.isClosed());
    
    try {
        r1.add(Network.createLayer("5", p));
        fail();
    }catch(Exception e) {
        assertTrue(e.getClass().isAssignableFrom(IllegalStateException.class));
        assertEquals("Cannot add Layers when Region has already been closed.", e.getMessage());
    }
}
 

@Test
public void testClose() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network n = Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("4", p)
                .add(MultiEncoder.builder().name("").build()))
                .close());
    
    assertTrue(n.lookup("r1").isClosed());
    
    try {
        n.lookup("r1").add(Network.createLayer("5", p));
        fail();
    }catch(Exception e) {
        assertTrue(e.getClass().isAssignableFrom(IllegalStateException.class));
        assertEquals("Cannot add Layers when Region has already been closed.", e.getMessage());
    }
}
 

private Network getLoadedDayOfWeekNetwork() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new FastRandom(42));
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("dayOfWeek", CLAClassifier.class));

    Sensor<ObservableSensor<String[]>> sensor = Sensor.create(
        ObservableSensor::create, SensorParams.create(Keys::obs, new Object[] {"name", 
            PublisherSupplier.builder()
                .addHeader("dayOfWeek")
                .addHeader("number")
                .addHeader("B").build() }));
    
    Network network = Network.create("test network", p).add(Network.createRegion("r1")
        .add(Network.createLayer("1", p)
            .alterParameter(KEY.AUTO_CLASSIFY, true)
            .add(Anomaly.create())
            .add(new TemporalMemory())
            .add(new SpatialPooler())
            .add(sensor)));
    
    return network;
}
 

@Test
public void testCalculateInputWidth_WithPrevLayer_NoTM() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network network = Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("1", p)
                .add(Anomaly.create())
                .add(new SpatialPooler()))
            .add(Network.createLayer("2", p)
                .add(new SpatialPooler()))
            .connect("1", "2"));
                
    Region r1 = network.lookup("r1");
    Layer<?> layer1 = r1.lookup("1");
    
    int width = layer1.calculateInputWidth();
    assertEquals(2048, width);
}
 

@Test
public void testCalculateInputWidth_WithPrevLayer_WithTM() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network network = Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("1", p)
                .add(Anomaly.create())
                .add(new SpatialPooler()))
            .add(Network.createLayer("2", p)
                .add(Anomaly.create())
                .add(new TemporalMemory())
                .add(new SpatialPooler()))
            .connect("1", "2"));
                
    Region r1 = network.lookup("r1");
    Layer<?> layer1 = r1.lookup("1");
    
    int width = layer1.calculateInputWidth();
    assertEquals(65536, width);
}
 

@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andNoTM() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network network = Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("2", p)
                .add(Anomaly.create())
                .add(new SpatialPooler())
                .close()));
    
    Region r1 = network.lookup("r1");
    Layer<?> layer2 = r1.lookup("2");
    
    int width = layer2.calculateInputWidth();
    assertEquals(8, width);
    assertEquals(8, layer2.getConnections().getPotentialRadius());
}
 

@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andSPTM() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    Network network = Network.create("test network", p)
            .add(Network.createRegion("r1")
                    .add(Network.createLayer("2", p)
                            .add(Anomaly.create())
                            .add(new TemporalMemory())
                            .add(new SpatialPooler())
                            .close()));

    Region r1 = network.lookup("r1");
    Layer<?> layer2 = r1.lookup("2");

    int width = layer2.calculateInputWidth();
    assertEquals(8, width);
    assertEquals(8, layer2.getConnections().getPotentialRadius());
}
 

@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andTM() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    Network network = Network.create("test network", p)
        .add(Network.createRegion("r1")
                .add(Network.createLayer("2", p)
                        .add(Anomaly.create())
                        .add(new TemporalMemory())
                        .close()));
    
    Region r1 = network.lookup("r1");
    Layer<?> layer2 = r1.lookup("2");
    
    int width = layer2.calculateInputWidth();
    assertEquals(65536, width);
}
 

private Network getLoadedHotGymNetwork_FileSensor() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getHotGymTestEncoderParams());
    p.set(KEY.RANDOM, new FastRandom(42));
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));

    Object[] n = { "some name", ResourceLocator.path("rec-center-hourly.csv") };
    HTMSensor<File> sensor = (HTMSensor<File>)Sensor.create(
        FileSensor::create, SensorParams.create(Keys::path, n));

    Network network = Network.create("test network", p).add(Network.createRegion("r1")
        .add(Network.createLayer("1", p)
            .alterParameter(KEY.AUTO_CLASSIFY, true)
            .add(Anomaly.create())
            .add(new TemporalMemory())
            .add(new SpatialPooler())
            .add(sensor)));

    return network;
}
 

/**
 * Test that we automatically calculate the input dimensions despite
 * there being an improper Parameter setting.
 */
@Test
public void testInputDimensionsAutomaticallyInferredFromEncoderWidth() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    
    // Purposefully set this to be wrong
    p.set(KEY.INPUT_DIMENSIONS, new int[] { 40, 40 });
    
    Network.create("test network", p)
        .add(Network.createRegion("r1")
            .add(Network.createLayer("4", p)
                .add(MultiEncoder.builder().name("").build()))
                .close());
        
    // Should correct the above ( {40,40} ) to have only one dimension whose width is 8 ( {8} )
    assertTrue(Arrays.equals(new int[] { 8 }, (int[])p.get(KEY.INPUT_DIMENSIONS)));
}
 

@Test(expected = IllegalStateException.class)
public void isClosedAddSensorTest() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    Layer<?> l = Network.createLayer("l", p);
    l.close();

    Sensor<File> sensor = Sensor.create(
            FileSensor::create,
            SensorParams.create(
                    Keys::path, "", ResourceLocator.path("rec-center-hourly-small.csv")));
    l.add(sensor);
}
 

@Test
public void testBasicSetupEncoder_UsingObserve() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    MultiEncoder me = MultiEncoder.builder().name("").build();
    Layer<Map<String, Object>> l = new Layer<>(p, me, null, null, null, null);

    final int[][] expected = new int[7][8];
    expected[0] = new int[] { 1, 1, 0, 0, 0, 0, 0, 1 };
    expected[1] = new int[] { 1, 1, 1, 0, 0, 0, 0, 0 };
    expected[2] = new int[] { 0, 1, 1, 1, 0, 0, 0, 0 };
    expected[3] = new int[] { 0, 0, 1, 1, 1, 0, 0, 0 };
    expected[4] = new int[] { 0, 0, 0, 1, 1, 1, 0, 0 };
    expected[5] = new int[] { 0, 0, 0, 0, 1, 1, 1, 0 };
    expected[6] = new int[] { 0, 0, 0, 0, 0, 1, 1, 1 };

    Observable<Inference> o = l.observe();
    TestObserver<Inference> tester;
    o.subscribe(tester = new TestObserver<Inference>() {
        int seq = 0;
        @Override public void onCompleted() {}
        @Override public void onNext(Inference output) {
            assertTrue(Arrays.equals(expected[seq++], output.getSDR()));
        }
    });

    Map<String, Object> inputs = new HashMap<String, Object>();
    for(double i = 0;i < 7;i++) {
        inputs.put("dayOfWeek", i);
        l.compute(inputs);
    }
    
    // Check for exception during the TestObserver's onNext() execution.
    checkObserver(tester);
}
 

@Test
public void testBasicSetupEncoder_UsingSubscribe() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    MultiEncoder me = MultiEncoder.builder().name("").build();
    Layer<Map<String, Object>> l = new Layer<>(p, me, null, null, null, null);

    final int[][] expected = new int[7][8];
    expected[0] = new int[] { 1, 1, 0, 0, 0, 0, 0, 1 };
    expected[1] = new int[] { 1, 1, 1, 0, 0, 0, 0, 0 };
    expected[2] = new int[] { 0, 1, 1, 1, 0, 0, 0, 0 };
    expected[3] = new int[] { 0, 0, 1, 1, 1, 0, 0, 0 };
    expected[4] = new int[] { 0, 0, 0, 1, 1, 1, 0, 0 };
    expected[5] = new int[] { 0, 0, 0, 0, 1, 1, 1, 0 };
    expected[6] = new int[] { 0, 0, 0, 0, 0, 1, 1, 1 };

    TestObserver<Inference> tester;
    l.subscribe(tester = new TestObserver<Inference>() {
        int seq = 0;
        @Override public void onCompleted() {}
        @Override public void onNext(Inference output) {
            assertTrue(Arrays.equals(expected[seq++], output.getSDR()));
        }
    });

    Map<String, Object> inputs = new HashMap<String, Object>();
    for(double i = 0;i < 7;i++) {
        inputs.put("dayOfWeek", i);
        l.compute(inputs);
    }
    
    // Check for exception during the TestObserver's onNext() execution.
    checkObserver(tester);
}
 

private Network getLoadedHotGymSynchronousNetwork() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getHotGymTestEncoderParams());
    p.set(KEY.RANDOM, new FastRandom(42));
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));

    Network network = Network.create("test network", p).add(Network.createRegion("r1")
        .add(Network.createLayer("1", p)
            .alterParameter(KEY.AUTO_CLASSIFY, true)
            .add(Anomaly.create())
            .add(new TemporalMemory())
            .add(new SpatialPooler())));
            
    return network;
}
 

@Test
public void closeTest() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    Region region1 = Network.createRegion("region1");
    Layer<?> layer1 = Network.createLayer("layer1", p);
    region1.add(layer1);

    Region region2 = Network.createRegion("region2");
    Layer<?> layer2 = Network.createLayer("layer2", p);
    region2.add(layer2);

    Network network = Network.create("test network", p);

    // Calling close on an empty Network should not throw any Exceptions
    network.close();

    // Calling close on a Network with a single unclosed Region
    network.add(region1);
    network.close();

    assertTrue("Region 1 did not close, after closing Network", region1.isClosed());
    assertTrue("Layer 1 did not close, after closing Network", layer1.isClosed());

    // Calling close on a Network with two regions, one of which is closed
    network.add(region2);
    network.close();

    assertTrue("Region 1 did not close, after closing Network with 2 Regions", region1.isClosed());
    assertTrue("Layer 1 did not close, after closing Network with 2 Regions", layer1.isClosed());
    assertTrue("Region 2 did not close, after closing Network with 2 Regions", region2.isClosed());
    assertTrue("Layer 2 did not close, after closing Network with 2 Regions", layer2.isClosed());

}
 

/**
 * Creates a {@link Network} containing 2 {@link Region}s with multiple
 * {@link Layer}s in each.
 *
 * @return a multi-region Network
 */
Network createMultiRegionNetwork() {
    Parameters p = NetworkDemoHarness.getParameters();
    p = p.union(NetworkDemoHarness.getNetworkDemoTestEncoderParams());

    return Network.create("Network API Demo", p)
        .add(Network.createRegion("Region 1")
            .add(Network.createLayer("Layer 2/3", p)
                .alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
                .add(Anomaly.create())
                .add(new TemporalMemory()))
            .add(Network.createLayer("Layer 4", p)
                .add(new SpatialPooler()))
            .connect("Layer 2/3", "Layer 4"))
       .add(Network.createRegion("Region 2")
            .add(Network.createLayer("Layer 2/3", p)
                .alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
                .add(Anomaly.create())
                .add(new TemporalMemory())
                .add(new SpatialPooler()))
            .add(Network.createLayer("Layer 4", p)
                .add(Sensor.create(FileSensor::create, SensorParams.create(
                    Keys::path, "", ResourceLocator.path("rec-center-hourly.csv")))))
            .connect("Layer 2/3", "Layer 4"))
       .connect("Region 1", "Region 2");

}
 

@Test(expected = IllegalStateException.class)
public void isClosedAddMultiEncoderTest() {
    Parameters p = NetworkTestHarness.getParameters();
    p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));

    Layer<?> l = Network.createLayer("l", p);
    l.close();

    l.add(MultiEncoder.builder().name("").build());
}
 

@Test
public void testGetAllValues() {
    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
    p.set(KEY.RANDOM, new UniversalRandom(42));
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("dayOfWeek", CLAClassifier.class));

    MultiEncoder me = MultiEncoder.builder().name("").build();
    Layer<Map<String, Object>> l = new Layer<>(p, me, new SpatialPooler(), new TemporalMemory(), Boolean.TRUE, null);

    // Test that we get the expected exception if there hasn't been any processing.
    try {
        l.getAllValues("dayOfWeek", 1);
        fail();
    }catch(Exception e) {
        assertEquals("Predictions not available. Either classifiers unspecified or inferencing has not yet begun.", e.getMessage());
    }

    TestObserver<Inference> tester;
    l.subscribe(tester = new TestObserver<Inference>() {
        @Override public void onCompleted() {}
        @Override
        public void onNext(Inference i) {
            assertNotNull(i);
            assertEquals(42, i.getSDR().length);
        }
    });

    // Now push some fake data through so that "onNext" is called above
    Map<String, Object> multiInput = new HashMap<>();
    multiInput.put("dayOfWeek", 0.0);
    l.compute(multiInput);

    Object[] values = l.getAllValues("dayOfWeek", 1);
    assertNotNull(values);
    assertTrue(values.length == 1);
    assertEquals(0.0D, values[0]);
    
    // Check for exception during the TestObserver's onNext() execution.
    checkObserver(tester);
}
 

@Test
public void testHalt() {
    Sensor<File> sensor = Sensor.create(
        FileSensor::create, SensorParams.create(
            Keys::path, "", ResourceLocator.path("rec-center-hourly-small.csv")));

    Parameters p = NetworkTestHarness.getParameters().copy();
    p = p.union(NetworkTestHarness.getHotGymTestEncoderParams());
    p.set(KEY.RANDOM, new MersenneTwister(42));
    p.set(KEY.AUTO_CLASSIFY, Boolean.TRUE);
    p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));

    HTMSensor<File> htmSensor = (HTMSensor<File>)sensor;

    Network n = Network.create("test network", p);
    final Layer<int[]> l = new Layer<>(n);
    l.add(htmSensor);

    TestObserver<Inference> tester;
    l.subscribe(tester = new TestObserver<Inference>() {
        @Override public void onCompleted() {
            assertTrue(l.isHalted());
            isHalted = true;
        }
        @Override public void onNext(Inference output) {}
    });
    
    l.start();

    try {
        l.halt();
        l.getLayerThread().join();
        assertTrue(isHalted);
    }catch(Exception e) {
        e.printStackTrace();
        fail();
    }
    
    // Check for exception during the TestObserver's onNext() execution.
    checkObserver(tester);
}