public void TestEncoderPassesUpToTopLayer()
{
Parameters p = NetworkTestHarness.GetParameters();
p = p.Union(NetworkTestHarness.GetDayDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.RANDOM, new MersenneTwister(42));
p.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Net.Network.Network n = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer <IInference>("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true))
.Add(Net.Network.Network.CreateLayer <IInference>("2", p)
.Add(Anomaly.Create(p)))
.Add(Net.Network.Network.CreateLayer <IInference>("3", p)
.Add(new TemporalMemory()))
.Add(Net.Network.Network.CreateLayer <IInference>("4", p)
.Add(new SpatialPooler())
.Add((MultiEncoder)MultiEncoder.GetBuilder().Name("").Build())));
Region r1 = n.Lookup("r1");
r1.Connect("1", "2").Connect("2", "3").Connect("3", "4");
Assert.IsNotNull(r1.Lookup("1").GetEncoder());
}
public void Setup()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.LIKELIHOOD);
an = (AnomalyLikelihood)Anomaly.Create(@params);
}
/**
* Creates a {@link Network} containing 2 {@link Region}s with multiple
* {@link PALayer}s in each.
*
* @return a multi-region Network
*/
internal Network.Network CreateMultiRegionNetwork()
{
Parameters p = NetworkDemoHarness.GetParameters();
p = p.Union(NetworkDemoHarness.GetNetworkDemoTestEncoderParams());
return(Network.Network.Create("Network API Demo", p)
.Add(Network.Network.CreateRegion("Region 1")
.Add(Network.Network.CreateLayer("Layer 2/3", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory()))
.Add(Network.Network.CreateLayer("Layer 4", p)
.Add(new PASpatialPooler()))
.Connect("Layer 2/3", "Layer 4"))
.Add(Network.Network.CreateRegion("Region 2")
.Add(Network.Network.CreateLayer("Layer 2/3", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(new PASpatialPooler()))
.Add(Network.Network.CreateLayer("Layer 4", p)
.Add(Sensor <FileInfo> .Create(FileSensor.Create, SensorParams.Create(
SensorParams.Keys.Path, "", ResourceLocator.Path(typeof(Resources), "rec-center-15m.csv")))))
.Connect("Layer 2/3", "Layer 4"))
.Connect("Region 1", "Region 2"));
}
/**
* Creates a basic {@link Network} with 1 {@link Region} and 1 {@link PALayer}. However
* this basic network contains all algorithmic components.
*
* @return a basic Network
*/
internal Network.Network CreateBasicNetwork()
{
Parameters p = NetworkDemoHarness.GetParameters();
p = p.Union(NetworkDemoHarness.GetNetworkDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.MIN_THRESHOLD, 22); // 22 18
p.SetParameterByKey(Parameters.KEY.ACTIVATION_THRESHOLD, 16); // 18
p.SetParameterByKey(Parameters.KEY.STIMULUS_THRESHOLD, 0.0); // 0.0
p.SetParameterByKey(Parameters.KEY.CELLS_PER_COLUMN, 1); // 1
Region r = Network.Network.CreateRegion("Region 1");
PALayer <IInference> l = new PALayer <IInference>("Layer 2/3", null, p);
l.SetPADepolarize(0.0); // 0.25
l.SetVerbosity(0);
PASpatialPooler sp = new PASpatialPooler();
string infile = "rec-center-15m.csv";
// This is how easy it is to create a full running Network!
return(Network.Network.Create("Network API Demo", p)
.Add(r.Add(l.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(sp)
.Add(Sensor <FileInfo> .Create(FileSensor.Create,
SensorParams.Create(SensorParams.Keys.Path, "", ResourceLocator.Path(typeof(Resources), infile)))))));
}
private Net.Network.Network GetLoadedHotGymHierarchy()
{
Parameters p = NetworkTestHarness.GetParameters();
p = p.Union(NetworkTestHarness.GetNetworkDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.RANDOM, new MersenneTwister(42));
Net.Network.Network network = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer("2", p)
.Add(Anomaly.Create())
.Add(new TemporalMemory()))
.Add(Net.Network.Network.CreateLayer("3", p)
.Add(new SpatialPooler()))
.Connect("2", "3"))
.Add(Net.Network.Network.CreateRegion("r2")
.Add(Net.Network.Network.CreateLayer("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(new TemporalMemory())
.Add(new SpatialPooler())
.Add(FileSensor.Create(Net.Network.Sensor.FileSensor.Create, SensorParams.Create(
SensorParams.Keys.Path, "", ResourceLocator.Path("rec-center-hourly.csv"))))))
.Connect("r1", "r2");
return(network);
}
private Net.Network.Network GetLoadedDayOfWeekNetwork()
{
Parameters p = NetworkTestHarness.GetParameters().Copy();
p = p.Union(NetworkTestHarness.GetDayDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.RANDOM, new XorshiftRandom(42));
Sensor <ObservableSensor <string[]> > sensor = Sensor <ObservableSensor <string[]> > .Create(
ObservableSensor <string[]> .Create, SensorParams.Create(SensorParams.Keys.Obs, new object[] { "name",
PublisherSupplier.GetBuilder()
.AddHeader("dayOfWeek")
.AddHeader("number")
.AddHeader("B").Build() }));
Net.Network.Network network = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(new SpatialPooler())
.Add(sensor)));
return(network);
}
public void TestAnomalyCumulative()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_WINDOW_SIZE, 3);
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_USE_MOVING_AVG, true);
Anomaly anomalyComputer = Anomaly.Create(@params);
object[] predicted =
{
new[] { 1, 2, 6 }, new[] { 1, 2, 6 }, new[] { 1, 2, 6 },
new[] { 1, 2, 6 }, new[] { 1, 2, 6 }, new[] { 1, 2, 6 },
new[] { 1, 2, 6 }, new[] { 1, 2, 6 }, new[] { 1, 2, 6 }
};
object[] actual =
{
new[] { 1, 2, 6 }, new[] { 1, 2, 6 }, new[] { 1, 4, 6 },
new[] { 10, 11, 6 }, new[] { 10, 11, 12 }, new[] { 10, 11, 12 },
new[] { 10, 11, 12 }, new[] { 1, 2, 6 }, new[] { 1, 2, 6 }
};
double[] anomalyExpected = { 0.0, 0.0, 1.0 / 9.0, 3.0 / 9.0, 2.0 / 3.0, 8.0 / 9.0, 1.0, 2.0 / 3.0, 1.0 / 3.0 };
for (int i = 0; i < 9; i++)
{
double score = anomalyComputer.Compute((int[])actual[i], (int[])predicted[i], 0, 0);
Assert.AreEqual(anomalyExpected[i], score, 0.01);
}
}
/**
* Creates a basic {@link Network} with 1 {@link Region} and 1 {@link PALayer}. However
* this basic network contains all algorithmic components.
*
* @return a basic Network
*/
internal Network.Network CreateBasicNetwork()
{
Parameters p = NetworkDemoHarness.GetParameters();
p = p.Union(NetworkDemoHarness.GetNetworkDemoTestEncoderParams());
// This is how easy it is to create a full running Network!
var sineData = SineGenerator.GenerateSineWave(100, 100 * 20, 10, 1)
.Select(s => s.ToString(NumberFormatInfo.InvariantInfo));
string[] header = new[]
{
"sinedata",
"float",
""
};
object[] n = { "sine", header.Union(sineData).ToObservable() };
SensorParams parms = SensorParams.Create(SensorParams.Keys.Obs, n);
Sensor <ObservableSensor <string[]> > sensor = Sensor <ObservableSensor <string[]> > .Create(ObservableSensor <string[]> .Create, parms);
return(Network.Network.Create("Network API Demo", p)
.Add(Network.Network.CreateRegion("Region 1")
.Add(Network.Network.CreateLayer("Layer 2/3", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(new Algorithms.SpatialPooler())
.Add(sensor))));
}
public void TestComputeAnomalyScoreNoActiveOrPredicted()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Anomaly anomalyComputer = Anomaly.Create(@params);
double score = anomalyComputer.Compute(new int[0], new int[0], 0, 0);
Assert.AreEqual(0.0, score, 0);
}
public void TestComputeAnomalyScoreNoMatch()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Anomaly anomalyComputer = Anomaly.Create(@params);
double score = anomalyComputer.Compute(new[] { 2, 4, 6 }, new[] { 3, 5, 7 }, 0, 0);
Assert.AreEqual(1.0, score, 0);
}
public void TestSerializeAnomalyLikelihoodForUpdates()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.LIKELIHOOD);
AnomalyLikelihood an = (AnomalyLikelihood)Anomaly.Create(@params);
// Serialize the Anomaly Computer without errors
SerialConfig config = new SerialConfig("testSerializeAnomalyLikelihood", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
byte[] bytes = api.Write(an);
// Deserialize the Anomaly Computer and make sure its usable (same tests as AnomalyTest.java)
AnomalyLikelihood serializedAn = api.Read <AnomalyLikelihood>(bytes);
Assert.IsNotNull(serializedAn);
//----------------------------------------
// Step 1. Generate an initial estimate using fake distribution of anomaly scores.
List <Sample> data1 = AnomalyLikelihoodTest.GenerateSampleData(0.2, 0.2, 0.2, 0.2).Take(1000).ToList();
AnomalyLikelihoodMetrics metrics1 = serializedAn.EstimateAnomalyLikelihoods(data1, 5, 0);
//----------------------------------------
// Step 2. Generate some new data with a higher average anomaly
// score. Using the estimator from step 1, to compute likelihoods. Now we
// should see a lot more anomalies.
List <Sample> data2 = AnomalyLikelihoodTest.GenerateSampleData(0.6, 0.2, 0.2, 0.2).Take(300).ToList();
AnomalyLikelihoodMetrics metrics2 = serializedAn.UpdateAnomalyLikelihoods(data2, metrics1.GetParams());
// Serialize the Metrics too just to be sure everything can be serialized
SerialConfig metricsConfig = new SerialConfig("testSerializeMetrics", SerialConfig.SERIAL_TEST_DIR);
api = Persistence.Get(metricsConfig);
api.Write(metrics2);
// Deserialize the Metrics
AnomalyLikelihoodMetrics serializedMetrics = api.Read <AnomalyLikelihoodMetrics>();
Assert.IsNotNull(serializedMetrics);
Assert.AreEqual(serializedMetrics.GetLikelihoods().Length, data2.Count);
Assert.AreEqual(serializedMetrics.GetAvgRecordList().Count, data2.Count);
Assert.IsTrue(serializedAn.IsValidEstimatorParams(serializedMetrics.GetParams()));
// The new running total should be different
Assert.IsFalse(metrics1.GetAvgRecordList().Total == serializedMetrics.GetAvgRecordList().Total);
// We should have many more samples where likelihood is < 0.01, but not all
int conditionCount = ArrayUtils.Where(serializedMetrics.GetLikelihoods(), d => d < 0.1).Length;
Assert.IsTrue(conditionCount >= 25);
Assert.IsTrue(conditionCount <= 250);
}
public void TestIsLearn()
{
Parameters p = NetworkTestHarness.GetParameters().Copy();
p = p.Union(NetworkTestHarness.GetDayDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.COLUMN_DIMENSIONS, new int[] { 30 });
p.SetParameterByKey(Parameters.KEY.SYN_PERM_INACTIVE_DEC, 0.1);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_ACTIVE_INC, 0.1);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_CONNECTED, 0.4);
p.SetParameterByKey(Parameters.KEY.MAX_BOOST, 10.0);
p.SetParameterByKey(Parameters.KEY.DUTY_CYCLE_PERIOD, 7);
p.SetParameterByKey(Parameters.KEY.RANDOM, new MersenneTwister(42));
p.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Net.Network.Network n = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer <IInference>("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true))
.Add(Net.Network.Network.CreateLayer <IInference>("2", p)
.Add(Anomaly.Create(p)))
.Add(Net.Network.Network.CreateLayer <IInference>("3", p)
.Add(new TemporalMemory()))
.Add(Net.Network.Network.CreateLayer <IInference>("4", p)
.Add(new SpatialPooler())
.Add((MultiEncoder)MultiEncoder.GetBuilder().Name("").Build()))
.Connect("1", "2")
.Connect("2", "3")
.Connect("3", "4"));
n.Lookup("r1").Close();
n.SetLearn(false);
Assert.IsFalse(n.IsLearn());
Region r1 = n.Lookup("r1");
Assert.IsFalse(n.IsLearn());
ILayer layer = r1.GetTail();
Assert.IsFalse(layer.SetIsLearn());
while (layer.GetNext() != null)
{
layer = layer.GetNext();
Assert.IsFalse(layer.SetIsLearn());
}
}
/**
* Creates a basic {@link Network} with 1 {@link Region} and 1 {@link PALayer}. However
* this basic network contains all algorithmic components.
*
* @return a basic Network
*/
internal Network.Network CreateBasicNetworkCla()
{
Parameters p = NetworkDemoHarness.GetParameters();
p = p.Union(NetworkDemoHarness.GetNetworkDemoTestEncoderParams());
// This is how easy it is to create a full running Network!
return(Network.Network.Create("Network API Demo", p)
.Add(Network.Network.CreateRegion("Region 1")
.Add(Network.Network.CreateLayer("Layer 2/3", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(new Algorithms.SpatialPooler())
.Add(Sensor <FileInfo> .Create(FileSensor.Create,
SensorParams.Create(SensorParams.Keys.Path, "", "rec-center-hourly.csv"))))));
}
public void TestSerializeAnomalyLikelihood()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.LIKELIHOOD);
AnomalyLikelihood an = (AnomalyLikelihood)Anomaly.Create(@params);
// Serialize the Anomaly Computer without errors
SerialConfig config = new SerialConfig("testSerializeAnomalyLikelihood", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
byte[] bytes = api.Write(an);
// Deserialize the Anomaly Computer and make sure its usable (same tests as AnomalyTest.java)
Anomaly serializedAn = api.Read <Anomaly>(bytes);
Assert.IsNotNull(serializedAn);
}
public void TestSerializeAnomaly()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Anomaly anomalyComputer = Anomaly.Create(@params);
// Serialize the Anomaly Computer without errors
SerialConfig config = new SerialConfig("testSerializeAnomaly1", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
byte[] bytes = api.Write(anomalyComputer);
double score = anomalyComputer.Compute(new int[0], new int[0], 0, 0);
score = anomalyComputer.Compute(new int[0], new int[0], 0, 0);
Assert.AreEqual(0.0, score, 0);
score = anomalyComputer.Compute(new int[0], new int[] { 3, 5 }, 0, 0);
Assert.AreEqual(0.0, score, 0);
score = anomalyComputer.Compute(new int[] { 3, 5, 7 }, new int[] { 3, 5, 7 }, 0, 0);
Assert.AreEqual(0.0, score, 0);
score = anomalyComputer.Compute(new int[] { 2, 3, 6 }, new int[] { 3, 5, 7 }, 0, 0);
Assert.AreEqual(2.0 / 3.0, score, 0);
// Deserialize the Anomaly Computer and make sure its usable (same tests as AnomalyTest.java)
Anomaly serializedAnomalyComputer = api.Read <Anomaly>(bytes);
score = serializedAnomalyComputer.Compute(new int[0], new int[0], 0, 0);
Assert.AreEqual(0.0, score, 0);
score = serializedAnomalyComputer.Compute(new int[0], new int[] { 3, 5 }, 0, 0);
Assert.AreEqual(0.0, score, 0);
score = serializedAnomalyComputer.Compute(new int[] { 3, 5, 7 }, new int[] { 3, 5, 7 }, 0, 0);
Assert.AreEqual(0.0, score, 0);
score = serializedAnomalyComputer.Compute(new int[] { 2, 3, 6 }, new int[] { 3, 5, 7 }, 0, 0);
Assert.AreEqual(2.0 / 3.0, score, 0);
}
public void TestSerializeCumulativeAnomaly()
{
Parameters @params = Parameters.Empty();
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_WINDOW_SIZE, 3);
@params.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_USE_MOVING_AVG, true);
Anomaly anomalyComputer = Anomaly.Create(@params);
// Serialize the Anomaly Computer without errors
SerialConfig config = new SerialConfig("testSerializeCumulativeAnomaly", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
byte[] bytes = api.Write(anomalyComputer);
// Deserialize the Anomaly Computer and make sure its usable (same tests as AnomalyTest.java)
Anomaly serializedAnomalyComputer = api.Read <Anomaly>(bytes);
Assert.IsNotNull(serializedAnomalyComputer);
Object[] predicted =
{
new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 },
new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 },
new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }
};
Object[] actual =
{
new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }, new int[] { 1, 4, 6 },
new int[] { 10, 11, 6 }, new int[] { 10, 11, 12 }, new int[] { 10, 11, 12 },
new int[] { 10, 11, 12 }, new int[] { 1, 2, 6 }, new int[] { 1, 2, 6 }
};
double[] anomalyExpected = { 0.0, 0.0, 1.0 / 9.0, 3.0 / 9.0, 2.0 / 3.0, 8.0 / 9.0, 1.0, 2.0 / 3.0, 1.0 / 3.0 };
for (int i = 0; i < 9; i++)
{
double score = serializedAnomalyComputer.Compute((int[])actual[i], (int[])predicted[i], 0, 0);
Assert.AreEqual(anomalyExpected[i], score, 0.01);
}
}
public void TestSearchAndListPreviousCheckPoint()
{
Parameters p = NetworkTestHarness.GetParameters();
Net.Network.Network network = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer("1", p)
.Add(Anomaly.Create())
.Add(new TemporalMemory())
.Add(new SpatialPooler())));
IPersistenceAPI pa = Persistence.Get(new SerialConfig(null, SerialConfig.SERIAL_TEST_DIR));
ArrayUtils.Range(0, 5).ToList().ForEach(i =>
((Persistence.PersistenceAccess)pa).GetCheckPointFunction <Net.Network.Network>(network)(network));
Dictionary <string, DateTime> checkPointFiles = pa.ListCheckPointFiles();
Assert.IsTrue(checkPointFiles.Count > 4);
Assert.AreEqual(checkPointFiles.ElementAt(checkPointFiles.Count - 2).Key,
pa.GetPreviousCheckPoint(checkPointFiles.ElementAt(checkPointFiles.Count - 1)));
}
public void TestMultiLayerAssemblyNoSensor()
{
Parameters p = NetworkTestHarness.GetParameters().Copy();
p = p.Union(NetworkTestHarness.GetDayDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.COLUMN_DIMENSIONS, new int[] { 30 });
p.SetParameterByKey(Parameters.KEY.SYN_PERM_INACTIVE_DEC, 0.1);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_ACTIVE_INC, 0.1);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.SetParameterByKey(Parameters.KEY.SYN_PERM_CONNECTED, 0.4);
p.SetParameterByKey(Parameters.KEY.MAX_BOOST, 10.0);
p.SetParameterByKey(Parameters.KEY.DUTY_CYCLE_PERIOD, 7);
p.SetParameterByKey(Parameters.KEY.RANDOM, new MersenneTwister(42));
p.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Net.Network.Network n = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer <IInference>("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true))
.Add(Net.Network.Network.CreateLayer <IInference>("2", p)
.Add(Anomaly.Create(p)))
.Add(Net.Network.Network.CreateLayer <IInference>("3", p)
.Add(new TemporalMemory()))
.Add(Net.Network.Network.CreateLayer <IInference>("4", p)
.Add(new SpatialPooler())
.Add((MultiEncoder)MultiEncoder.GetBuilder().Name("").Build()))
.Connect("1", "2")
.Connect("2", "3")
.Connect("3", "4"));
Region r1 = n.Lookup("r1");
r1.Lookup("3").Using(r1.Lookup("4").GetConnections()); // How to share Connections object between Layers
//r1.Observe().Subscribe(new Subscriber<Inference>()
//{
// public void onCompleted() { }
// public void onError(Throwable e) { e.printStackTrace(); }
// public void onNext(Inference i)
// {
// // UNCOMMENT TO VIEW STABILIZATION OF PREDICTED FIELDS
// System.Out.println("Day: " + r1.GetInput() + " - predictive cells: " + i.GetPreviousPredictiveCells() +
// " - " + Arrays.toString(i.GetFeedForwardSparseActives()) + " - " +
// ((int)Math.Rint(((Number)i.GetClassification("dayOfWeek").GetMostProbableValue(1)).doubleValue())));
// }
//});
const int NUM_CYCLES = 400;
const int INPUT_GROUP_COUNT = 7; // Days of Week
Map <String, Object> multiInput = new Map <string, object>();
for (int i = 0; i < NUM_CYCLES; i++)
{
for (double j = 0; j < INPUT_GROUP_COUNT; j++)
{
multiInput.Add("dayOfWeek", j);
r1.Compute(multiInput);
}
r1.Reset();
}
r1.SetLearn(false);
r1.Reset();
// Test that we get proper output after prediction stabilization
//r1.Observe().Subscribe(new Subscriber<Inference>()
//{
// public void onCompleted() { }
// public void onError(Throwable e) { e.printStackTrace(); }
// public void onNext(Inference i)
// {
// int nextDay = ((int)Math.Rint(((Number)i.GetClassification("dayOfWeek").GetMostProbableValue(1)).doubleValue()));
// Assert.AreEqual(6, nextDay);
// }
//});
multiInput.Add("dayOfWeek", 5.0);
r1.Compute(multiInput);
}
public void TestHalt()
{
Parameters p = NetworkTestHarness.GetParameters();
p = p.Union(NetworkTestHarness.GetDayDemoTestEncoderParams());
p.SetParameterByKey(Parameters.KEY.RANDOM, new MersenneTwister(42));
p.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MODE, Anomaly.Mode.PURE);
Net.Network.Network n = Net.Network.Network.Create("test network", p)
.Add(Net.Network.Network.CreateRegion("r1")
.Add(Net.Network.Network.CreateLayer <IInference>("1", p)
.AlterParameter(Parameters.KEY.AUTO_CLASSIFY, true))
.Add(Net.Network.Network.CreateLayer <IInference>("2", p)
.Add(Anomaly.Create(p)))
.Add(Net.Network.Network.CreateLayer <IInference>("3", p)
.Add(new TemporalMemory()))
.Add(Net.Network.Network.CreateLayer <IInference>("4", p)
.Add(Sensor <FileSensor> .Create(FileSensor.Create, SensorParams.Create(
SensorParams.Keys.Path, "", ResourceLocator.Path(typeof(Resources), "days-of-week.csv"))))
.Add(new SpatialPooler()))
.Connect("1", "2")
.Connect("2", "3")
.Connect("3", "4"));
Region r1 = n.Lookup("r1");
int seq = 0;
r1.Observe().Subscribe(
// next
i =>
{
if (seq == 2)
{
isHalted = true;
}
seq++;
},
//error
e => { Console.WriteLine(e); },
//completed
() => { Console.WriteLine("onCompleted() called"); }
);
//r1.Observe().Subscribe(new Subscriber<Inference>() {
// int seq = 0;
// public void onCompleted()
// {
// // System.Out.println("onCompleted() called");
// }
// public void onError(Throwable e) { e.printStackTrace(); }
// public void onNext(Inference i)
// {
// if (seq == 2)
// {
// isHalted = true;
// }
// seq++;
// // System.Out.println("output: " + i.GetSDR());
// }
//});
new Thread(() =>
{
while (!isHalted)
{
try { Thread.Sleep(1); } catch (Exception e) { Console.WriteLine(e); }
}
r1.Halt();
}).Start();
// (new Thread()
// {
// public void run()
// {
// while (!isHalted)
// {
// try { Thread.Sleep(1); } catch (Exception e) { e.printStackTrace(); }
// }
// r1.Halt();
// }
//}).Start();
r1.Start();
try
{
r1.Lookup("4").GetLayerThread().Wait();
}
catch (Exception e)
{
Console.WriteLine(e);
}
}