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);
}
public void TestCopy()
{
Map <string, NamedTuple> classifierInput = new Map <string, NamedTuple>();
NamedTuple classifiers = new NamedTuple(new[] { "one", "two" }, 1, 2);
object layerInput = new object();
int[] sdr = new[] { 20 };
int[] encoding = new int[40];
int[] activeColumns = new int[25];
int[] sparseActives = new int[2];
HashSet <Cell> activeCells = new HashSet <Cell>(); activeCells.Add(new Cell(new Column(4, 0), 1));
HashSet <Cell> previousPrediction = new HashSet <Cell>(); previousPrediction.Add(new Cell(new Column(4, 0), 2));
HashSet <Cell> currentPrediction = new HashSet <Cell>(); currentPrediction.Add(new Cell(new Column(4, 0), 3));
Classification <object> classification = new Classification <object>();
double anomalyScore = 0.48d;
object customObject = new Net.Network.Network("MI Network", NetworkTestHarness.GetNetworkDemoTestEncoderParams());
ManualInput mi = new ManualInput()
.SetClassifierInput(classifierInput)
.SetLayerInput(layerInput)
.SetSdr(sdr)
.SetEncoding(encoding)
.SetFeedForwardActiveColumns(activeColumns)
.SetFeedForwardSparseActives(sparseActives)
.SetPredictiveCells(previousPrediction)
.SetPredictiveCells(currentPrediction) // last prediction internally becomes previous
.SetActiveCells(activeCells)
.SetClassifiers(classifiers)
.StoreClassification("foo", classification)
.SetAnomalyScore(anomalyScore)
.SetCustomObject(customObject);
ManualInput copy = mi.Copy();
Assert.IsTrue(copy.GetClassifierInput().DictEquals(classifierInput));
Assert.IsFalse(copy.GetClassifierInput() == classifierInput);
Assert.IsTrue(copy.GetLayerInput() == layerInput);
Assert.IsTrue(Arrays.AreEqual(copy.GetSdr(), sdr));
Assert.IsFalse(copy.GetSdr() == sdr);
Assert.IsTrue(Arrays.AreEqual(copy.GetEncoding(), encoding));
Assert.IsFalse(copy.GetEncoding() == encoding);
Assert.IsTrue(Arrays.AreEqual(copy.GetFeedForwardActiveColumns(), activeColumns));
Assert.IsFalse(copy.GetFeedForwardActiveColumns() == activeColumns);
Assert.IsTrue(Arrays.AreEqual(copy.GetFeedForwardSparseActives(), sparseActives));
Assert.IsFalse(copy.GetFeedForwardSparseActives() == sparseActives);
Assert.IsTrue(copy.GetPredictiveCells().SetEquals(currentPrediction));
Assert.IsFalse(copy.GetPredictiveCells() == currentPrediction);
Assert.IsTrue(copy.GetActiveCells().SetEquals(activeCells));
Assert.IsFalse(copy.GetActiveCells() == activeCells);
Assert.IsTrue(copy.GetPreviousPredictiveCells().SetEquals(previousPrediction));
Assert.IsFalse(copy.GetPreviousPredictiveCells() == previousPrediction);
Assert.IsTrue(copy.GetClassifiers().Equals(classifiers));
Assert.IsFalse(copy.GetClassifiers() == classifiers);
Assert.IsTrue(copy.GetClassification("foo").Equals(classification));
Assert.AreEqual(copy.GetAnomalyScore(), anomalyScore, 0.0); // zero deviation
Assert.AreEqual(copy.GetCustomObject(), customObject);
}