/// <summary>
/// Creates readout layer configuration
/// </summary>
/// <param name="testDataRatio">Specifies what part of available data to be used as test data</param>
/// <param name="numOfAttempts">Number of regression attempts. Each readout network will try to learn numOfAttempts times</param>
/// <param name="numOfEpochs">Number of training epochs within an attempt</param>
ReadoutLayerSettings CreateReadoutLayerCfg(double testDataRatio, int numOfAttempts, int numOfEpochs)
{
//For each output field we will use prediction of two networks
//First network having only Identity output neuron and associated the resilient back propagation trainer
FeedForwardNetworkSettings ffNet1Cfg = new FeedForwardNetworkSettings(new IdentitySettings(),
null,
new RPropTrainerSettings(numOfAttempts, numOfEpochs)
);
//Second network having Identity output neuron, hidden layer consisting of 5 LeakyReLU neurons
//and associated the resilient back propagation trainer
HiddenLayerSettings hiddenLayerCfg = new HiddenLayerSettings(5, new LeakyReLUSettings());
FeedForwardNetworkSettings ffNet2Cfg = new FeedForwardNetworkSettings(new IdentitySettings(),
new HiddenLayersSettings(hiddenLayerCfg),
new RPropTrainerSettings(numOfAttempts, numOfEpochs)
);
//Create default networks configuration for forecasting
DefaultNetworksSettings defaultNetworksCfg = new DefaultNetworksSettings(null, new ForecastNetworksSettings(ffNet1Cfg, ffNet2Cfg));
//Create readout units. We will forecast next High and Low prices. Both fields are real numbers.
ReadoutUnitSettings highReadoutUnitCfg = new ReadoutUnitSettings("High", new ForecastTaskSettings(new RealFeatureFilterSettings()));
ReadoutUnitSettings lowReadoutUnitCfg = new ReadoutUnitSettings("Low", new ForecastTaskSettings(new RealFeatureFilterSettings()));
//Create readout layer configuration
ReadoutLayerSettings readoutLayerCfg = new ReadoutLayerSettings(new ReadoutUnitsSettings(highReadoutUnitCfg,
lowReadoutUnitCfg
),
testDataRatio,
ReadoutLayerSettings.AutoFolds,
ReadoutLayerSettings.DefaultRepetitions,
defaultNetworksCfg
);
return(readoutLayerCfg);
}
/// <summary>
/// Creates the readout layer configuration.
/// </summary>
/// <param name="foldDataRatio">Specifies what part of available data to be used as the fold data.</param>
/// <param name="numOfAttempts">Number of regression attempts. Each readout network will try to learn numOfAttempts times.</param>
/// <param name="numOfEpochs">Number of training epochs within an attempt.</param>
ReadoutLayerSettings CreateReadoutLayerCfg(double foldDataRatio, int numOfAttempts, int numOfEpochs)
{
//For each output field we will use prediction of two networks
//First network having only Identity output neuron and associated the resilient back propagation trainer
FeedForwardNetworkSettings ffNet1Cfg = new FeedForwardNetworkSettings(new AFAnalogIdentitySettings(),
null,
new RPropTrainerSettings(numOfAttempts, numOfEpochs)
);
//Second network having Identity output neuron, hidden layer consisting of 5 LeakyReLU neurons
//and associated the resilient back propagation trainer
HiddenLayerSettings hiddenLayerCfg = new HiddenLayerSettings(5, new AFAnalogLeakyReLUSettings());
FeedForwardNetworkSettings ffNet2Cfg = new FeedForwardNetworkSettings(new AFAnalogIdentitySettings(),
new HiddenLayersSettings(hiddenLayerCfg),
new RPropTrainerSettings(numOfAttempts, numOfEpochs)
);
//Create the cluster chain configuration for the forecast and the default configuration for the forecast task.
CrossvalidationSettings crossvalidationCfg = new CrossvalidationSettings(foldDataRatio);
TNRNetClusterRealNetworksSettings networksCfg = new TNRNetClusterRealNetworksSettings(ffNet1Cfg, ffNet2Cfg);
TNRNetClusterRealSettings realClusterCfg = new TNRNetClusterRealSettings(networksCfg, new TNRNetClusterRealWeightsSettings());
TNRNetClusterChainRealSettings realClusterChainCfg = new TNRNetClusterChainRealSettings(crossvalidationCfg, new TNRNetClustersRealSettings(realClusterCfg));
TaskDefaultsSettings taskDefaultsCfg = new TaskDefaultsSettings(null, realClusterChainCfg);
//Create readout unit configurations. We will forecast next High and Low prices.
ReadoutUnitSettings highReadoutUnitCfg = new ReadoutUnitSettings("High", new ForecastTaskSettings());
ReadoutUnitSettings lowReadoutUnitCfg = new ReadoutUnitSettings("Low", new ForecastTaskSettings());
//Create readout layer configuration
ReadoutLayerSettings readoutLayerCfg = new ReadoutLayerSettings(taskDefaultsCfg,
new ReadoutUnitsSettings(highReadoutUnitCfg,
lowReadoutUnitCfg
),
null
);
return(readoutLayerCfg);
}
//Methods
/// <summary>
/// Runs the example code.
/// </summary>
public void Run()
{
//Create StateMachine configuration
//Simplified readout layer configuration using FF-network having 2 hidden layers as the classifier
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, 0, 1),
StateMachineDesigner.CreateMultiLayerFFNetCfg(10, new AFAnalogLeakyReLUSettings(), 2, 5, 400),
2,
"Hand movement",
"curved swing",
"horizontal swing",
"vertical swing",
"anti-clockwise arc",
"clockwise arc",
"circle",
"horizontal straight-line",
"vertical straight-line",
"horizontal zigzag",
"vertical zigzag",
"horizontal wavy",
"vertical wavy",
"face-up curve",
"face-down curve",
"tremble"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(readoutCfg);
//Create StateMachine configuration without preprocessing
StateMachineSettings stateMachineCfg = smd.CreateBypassedPreprocessingCfg();
//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 and verification)...");
_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/LibrasMovement_train.csv", out _);
_log.Write(string.Empty);
//StateMachine verification
_log.Write("StateMachine verification:");
_log.Write("--------------------------");
_log.Write(string.Empty);
VerifyStateMachine(stateMachine, "./Data/LibrasMovement_verify.csv", null, out _);
_log.Write(string.Empty);
return;
}
/// <summary>
/// Creates the state machine configuration.
/// </summary>
private StateMachineSettings CreateStateMachineCfg()
{
//Create neural preprocessor configuration
NeuralPreprocessorSettings neuralPreprocessorCfg = CreatePreprocessorCfg("MainRes", "MainResStruct", "AnalogPool1", "AnalogPool2");
//Create readout layer configuration.
//We use test data ratio = 0.1, number of regression attempts = 2 and number of attempt epochs = 1000
ReadoutLayerSettings readoutCfg = CreateReadoutLayerCfg(0.1d, 2, 1000);
//Create state machine configuration
StateMachineSettings stateMachineCfg = new StateMachineSettings(neuralPreprocessorCfg, readoutCfg);
return(stateMachineCfg);
}
/// <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;
}
/// <summary>
/// Runs the example code.
/// </summary>
public void Run()
{
//Create StateMachine configuration
//Simplified input configuration
InputEncoderSettings inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingPatternedSettings(1, true, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped),
false,
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings()),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(StateMachineDesigner.CreateSingleLayerRegrNet(new IdentitySettings(), 5, 400),
0.0825d,
1,
"Hand movement",
"curved swing",
"horizontal swing",
"vertical swing",
"anti-clockwise arc",
"clockwise arc",
"circle",
"horizontal straight-line",
"vertical straight-line",
"horizontal zigzag",
"vertical zigzag",
"horizontal wavy",
"vertical wavy",
"face-up curve",
"face-down curve",
"tremble"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure ESN fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureESNCfg(150,
0.25d,
5,
0.1d,
0,
0,
0,
null,
PredictorsProvider.PredictorID.FiringCount
);
//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 and verification)...");
_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\\LibrasMovement_train.csv", out _);
_log.Write(string.Empty);
//StateMachine verification
_log.Write("StateMachine verification:");
_log.Write("--------------------------");
_log.Write(string.Empty);
VerifyStateMachine(stateMachine, ".\\Data\\LibrasMovement_verify.csv", null, out _);
_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;
}
//Methods
/// <summary>
/// Runs the example code.
/// </summary>
public void Run(InputEncoder.InputSpikesCoding spikesCoding)
{
//Create StateMachine configuration
//Simplified input configuration and homogenous excitability
InputEncoderSettings inputCfg;
HomogenousExcitabilitySettings homogenousExcitability;
switch (spikesCoding)
{
/*
* Horizontal coding.
*/
case InputEncoder.InputSpikesCoding.Horizontal:
inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingPatternedSettings(1, NeuralPreprocessor.BidirProcessing.Continuous, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped, new UnificationSettings(false, false)),
//136 spiking input neurons per input field - coding at once
new InputSpikesCoderSettings(InputEncoder.InputSpikesCoding.Horizontal,
new A2SCoderSignalStrengthSettings(8), //8 neurons (spike-train length = 1)
new A2SCoderUpDirArrowsSettings(8, 8), //64 neurons (spike-train length = 1)
new A2SCoderDownDirArrowsSettings(8, 8) //64 neurons (spike-train length = 1)
),
true, //Route the input pattern as the predictors to a readout layer
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings(), true),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings(), true)
);
homogenousExcitability = new HomogenousExcitabilitySettings(1d, 0.7d, 0.2d);
break;
/*
* Vertical coding.
*/
case InputEncoder.InputSpikesCoding.Vertical:
inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingPatternedSettings(1, NeuralPreprocessor.BidirProcessing.Continuous, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped),
//17 spiking input neurons per input field- coding in 10 cycles
new InputSpikesCoderSettings(InputEncoder.InputSpikesCoding.Vertical,
new A2SCoderSignalStrengthSettings(10), //1 neuron (spike-train length = 10)
new A2SCoderUpDirArrowsSettings(8, 10), //8 neurons (spike-train length = 10)
new A2SCoderDownDirArrowsSettings(8, 10) //8 neurons (spike-train length = 10)
),
true, //Route the input pattern as the predictors to a readout layer
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings(), true),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings(), true)
);
homogenousExcitability = new HomogenousExcitabilitySettings(1d, 0.7d, 0.2d);
break;
/*
* Forbidden - no spikes coding.
*/
default:
//1 analog input neuron per input field
inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingPatternedSettings(1, NeuralPreprocessor.BidirProcessing.Continuous, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped, new UnificationSettings(false, false)),
new InputSpikesCoderSettings(InputEncoder.InputSpikesCoding.Forbidden),
true, //Route the input pattern as the predictors to a readout layer
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings(), true),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings(), true)
);
homogenousExcitability = new HomogenousExcitabilitySettings(0.25, 0.7, 0.2d);
break;
}
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, 0, 1),
StateMachineDesigner.CreateMultiLayerFFNetCfg(10, new AFAnalogLeakyReLUSettings(), 2, 5, 400),
1,
"Hand movement",
"curved swing",
"horizontal swing",
"vertical swing",
"anti-clockwise arc",
"clockwise arc",
"circle",
"horizontal straight-line",
"vertical straight-line",
"horizontal zigzag",
"vertical zigzag",
"horizontal wavy",
"vertical wavy",
"face-up curve",
"face-down curve",
"tremble"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure LSM fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureLSMCfg(272, //Total size of the reservoir
new AFSpikingAdExpIFSettings(), //Activation
homogenousExcitability, //Homogenous excitability
1, //Input connection density
0, //Input max delay
0.1d, //Interconnection density
0, //Internal synapses max delay
0, //Steady bias
new PredictorsProviderSettings(new PredictorFiringTraceSettings(0.0005, 0))
);
//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 and verification)...");
_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/LibrasMovement_train.csv", out _);
_log.Write(string.Empty);
//StateMachine verification
_log.Write("StateMachine verification:");
_log.Write("--------------------------");
_log.Write(string.Empty);
VerifyStateMachine(stateMachine, "./Data/LibrasMovement_verify.csv", null, out _);
_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 FeedingPatternedSettings(1, NeuralPreprocessor.BidirProcessing.WithReset, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped),
new InputSpikesCoderSettings(),
false,
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings()),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings())
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, CrossvalidationSettings.AutoFolds, 1),
StateMachineDesigner.CreateSingleLayerFFNetCfg(new AFAnalogIdentitySettings(), 5, 400),
1,
"Hand movement",
"curved swing",
"horizontal swing",
"vertical swing",
"anti-clockwise arc",
"clockwise arc",
"circle",
"horizontal straight-line",
"vertical straight-line",
"horizontal zigzag",
"vertical zigzag",
"horizontal wavy",
"vertical wavy",
"face-up curve",
"face-down curve",
"tremble"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure ESN fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureESNCfg(150, //Size
StateMachineDesigner.DefaultAnalogMaxInputStrength, //Max input strength
0.25d, //Input connection density
5, //Max input delay
0.1d, //Interconnection density
0, //Max internal delay
0, //Max absolute value of bias
0, //Max retainment strength
new PredictorsProviderSettings(new PredictorFiringTraceSettings(0.05, 45))
);
//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 and verification)...");
_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/LibrasMovement_train.csv", out _);
_log.Write(string.Empty);
//StateMachine verification
_log.Write("StateMachine verification:");
_log.Write("--------------------------");
_log.Write(string.Empty);
VerifyStateMachine(stateMachine, "./Data/LibrasMovement_verify.csv", null, out _);
_log.Write(string.Empty);
return;
}
/// <summary>
/// Runs the example code.
/// </summary>
public void Run()
{
//Create StateMachine configuration
//Simplified input configuration
InputEncoderSettings inputCfg = StateMachineDesigner.CreateInputCfg(new FeedingPatternedSettings(1, true, RCNet.Neural.Data.InputPattern.VariablesSchema.Groupped),
false,
new ExternalFieldSettings("coord_abcissa", new RealFeatureFilterSettings(), true, new SpikeCodeSettings(15, 1e-3, true, true, true, false)),
new ExternalFieldSettings("coord_ordinate", new RealFeatureFilterSettings(), true, new SpikeCodeSettings(15, 1e-3, true, true, true, false))
);
//Simplified readout layer configuration
ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(StateMachineDesigner.CreateSingleLayerRegrNet(new ElliotSettings(), 5, 400),
0.0825d,
1,
"Hand movement",
"curved swing",
"horizontal swing",
"vertical swing",
"anti-clockwise arc",
"clockwise arc",
"circle",
"horizontal straight-line",
"vertical straight-line",
"horizontal zigzag",
"vertical zigzag",
"horizontal wavy",
"vertical wavy",
"face-up curve",
"face-down curve",
"tremble"
);
//Create designer instance
StateMachineDesigner smd = new StateMachineDesigner(inputCfg, readoutCfg);
//Create pure LSM fashioned StateMachine configuration
StateMachineSettings stateMachineCfg = smd.CreatePureLSMCfg(new ProportionsSettings(60, 1, 1), //Proportions (it also determines total size)
new AdExpIFSettings(), //Activation
//new ExpIFSettings(null, null, null, null, null, null, null, 0), //Activation
new HomogenousExcitabilitySettings(0.75, 0.75, 0.25),
1d, //Input connection density
0, //Input max delay
0.1d, //Interconnection density
0, //Internal synapses max delay
0, //Steady bias
null,
PredictorsProvider.PredictorID.FiringFadingSum
);
//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 and verification)...");
_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\\LibrasMovement_train.csv", out _);
_log.Write(string.Empty);
//StateMachine verification
_log.Write("StateMachine verification:");
_log.Write("--------------------------");
_log.Write(string.Empty);
VerifyStateMachine(stateMachine, ".\\Data\\LibrasMovement_verify.csv", null, out _);
_log.Write(string.Empty);
return;
}
