/// <summary>
/// Runs the example code.
/// </summary>
public void Run()
{
//Create StateMachine configuration
//Simplified input configuration
InputEncoderSettings inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingContinuousSettings(FeedingContinuousSettings.AutoBootCyclesNum),
true,
new ExternalFieldSettings("High", new RealFeatureFilterSettings()),
new ExternalFieldSettings("Low", new RealFeatureFilterSettings()),
new ExternalFieldSettings("Adj Close", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateForecastReadoutCfg(StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 2, 1000),
0.1d,
1,
"High",
"Low"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure ESN fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureESNCfg(250, 1, 0, 0.2d, 0, 0.1d, 0.75d, null, PredictorsProvider.PredictorID.Activation, PredictorsProvider.PredictorID.ActivationSquare);
//Display StateMachine xml configuration
string xmlConfig = stateMachineCfg.GetXml(true).ToString();
_log.Write("StateMachine configuration xml:");
_log.Write("-------------------------------");
_log.Write(xmlConfig);
_log.Write(string.Empty);
_log.Write("Pres Enter to continue (StateMachine training)...");
_log.Write(string.Empty);
Console.ReadLine();
//Instantiation and training
_log.Write("StateMachine training:");
_log.Write("----------------------");
_log.Write(string.Empty);
//StateMachine instance
StateMachine stateMachine = new StateMachine(stateMachineCfg);
//StateMachine training
TrainStateMachine(stateMachine, ".\\Data\\TTOO.csv", out double[] predictionInputVector);
//Forecast
ReadoutLayer.ReadoutData readoutData = stateMachine.ComputeReadoutData(predictionInputVector);
_log.Write(" Forecast next High and Low (real values are High=6.58$ and Low=5.99$):", false);
_log.Write(stateMachine.RL.GetForecastReport(readoutData.DataVector, 6));
_log.Write(string.Empty);
return;
}
//Methods
/// <summary>
/// Runs the example code.
/// </summary>
public void Run()
{
//Create StateMachine configuration
//Simplified input configuration
InputEncoderSettings inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingContinuousSettings(FeedingContinuousSettings.AutoBootCyclesNum),
new InputSpikesCoderSettings(),
true,
new ExternalFieldSettings("High", new RealFeatureFilterSettings()),
new ExternalFieldSettings("Low", new RealFeatureFilterSettings()),
new ExternalFieldSettings("Adj Close", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateForecastReadoutCfg(new CrossvalidationSettings(0.1d, 0, 1),
StateMachineDesigner.CreateSingleLayerFFNetCfg(new AFAnalogIdentitySettings(), 2, 1000),
1,
"High",
"Low"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure ESN fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureESNCfg(250, //Total size of the reservoir (number of hidden neurons within the reservoir).
2.5, //Maximum stimulation strength through hidden neuron's input synapses.
1d, //Connection density of an input field. 1 means that each input field will be synaptically connected to all neurons.
0, //Maximum delay on an input synapse. 0 means no delay.
0.1d, //Interconnection density. 0.1 means that each hidden neuron will be synaptically internally connected to 10% of other hidden neurons.
0, //Maximum delay on an internal synapse. 0 means no delay.
0d, //Maximum absolute value of the hidden neuron bias. 0 means no bias.
0d, //Maximum retainment on an hidden neuron. 0 means no retainment.
new PredictorsProviderSettings(new PredictorActivationSettings(),
new PredictorActivationPowerSettings(2d, true),
new PredictorFiringTraceSettings(0.05, 30)
)
);
//Display StateMachine xml configuration
string xmlConfig = stateMachineCfg.GetXml(true).ToString();
_log.Write("StateMachine configuration xml:");
_log.Write("-------------------------------");
_log.Write(xmlConfig);
_log.Write(string.Empty);
_log.Write("Press Enter to continue (StateMachine training)...");
_log.Write(string.Empty);
Console.ReadLine();
//Instantiation and training
_log.Write("StateMachine training:");
_log.Write("----------------------");
_log.Write(string.Empty);
//StateMachine instance
StateMachine stateMachine = new StateMachine(stateMachineCfg);
//StateMachine training
TrainStateMachine(stateMachine, "./Data/TTOO.csv", out double[] predictionInputVector);
//Forecasting
double[] outputVector = stateMachine.Compute(predictionInputVector, out ReadoutLayer.ReadoutData readoutData);
_log.Write(" Forecasted next High and Low TTOO prices (real prices were High = 3.61$ and Low=3.10$):", false);
_log.Write(stateMachine.RL.GetForecastReport(readoutData.NatDataVector, 6));
_log.Write(string.Empty);
return;
}