/**
* 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 TestSampling()
{
foreach (double sample in SineGenerator.GenerateSineWave(100, 100, 10, 1))
{
Assert.AreEqual(0, sample, 10.0);
}
}