/// <summary>
/// Prepares input for regression stage of State Machine training.
/// All input patterns are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="dataSet">
/// The bundle containing known sample input patterns and desired output vectors
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionStageInput PrepareRegressionStageInput(PatternBundle dataSet,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
if (_settings.InputConfig.FeedingType == CommonEnums.InputFeedingType.Continuous)
{
throw new Exception("This version of PrepareRegressionStageInput function is not useable for continuous input feeding.");
}
//RegressionStageInput allocation
RegressionStageInput rsi = new RegressionStageInput
{
PredictorsCollection = new List <double[]>(dataSet.InputPatternCollection.Count),
IdealOutputsCollection = new List <double[]>(dataSet.OutputVectorCollection.Count)
};
//Reset the internal states and statistics
Reset(true);
//Collection
for (int dataSetIdx = 0; dataSetIdx < dataSet.InputPatternCollection.Count; dataSetIdx++)
{
//Push input data into the network
double[] predictors = PushInput(dataSet.InputPatternCollection[dataSetIdx]);
rsi.PredictorsCollection.Add(predictors);
//Add desired outputs
rsi.IdealOutputsCollection.Add(dataSet.OutputVectorCollection[dataSetIdx]);
//Informative callback
informativeCallback?.Invoke(dataSet.InputPatternCollection.Count, dataSetIdx + 1, userObject);
}
//Collect reservoirs statistics
rsi.ReservoirStatCollection = CollectReservoirInstancesStatatistics();
return(rsi);
}
/// <summary>
/// Prepares input for Readout Layer training.
/// All input patterns are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="patternBundle">
/// The bundle containing known sample input patterns and desired output vectors
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public VectorBundle InitializeAndPreprocessBundle(PatternBundle patternBundle,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
//Check correctness
if (_settings.InputConfig.FeedingType == CommonEnums.InputFeedingType.Continuous)
{
throw new Exception("Called incorrect version of InitializeAndPreprocessBundle function for continuous input feeding.");
}
//Reset the internal states and also statistics
Reset(true);
//Initialize normalizers and normalize input data
List <List <double[]> > nrmInputPatternCollection = NormalizeInputPatternCollection(patternBundle.InputPatternCollection);
//Allocate output bundle
VectorBundle outputBundle = new VectorBundle(patternBundle.InputPatternCollection.Count);
//Collection
for (int dataSetIdx = 0; dataSetIdx < nrmInputPatternCollection.Count; dataSetIdx++)
{
//Push input data into the network
double[] predictors = PushInput(nrmInputPatternCollection[dataSetIdx], true);
outputBundle.InputVectorCollection.Add(predictors);
//Add desired outputs
outputBundle.OutputVectorCollection.Add(patternBundle.OutputVectorCollection[dataSetIdx]);
//Informative callback
informativeCallback?.Invoke(patternBundle.InputPatternCollection.Count, dataSetIdx + 1, userObject);
}
return(outputBundle);
}
/// <summary>
/// Prepares input for regression stage of State Machine training for the classification or hybrid task type.
/// All input patterns are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="dataSet">
/// The bundle containing known sample input patterns and desired output vectors
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionStageInput PrepareRegressionStageInput(PatternBundle dataSet,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
if (_settings.TaskType == CommonEnums.TaskType.Prediction)
{
throw new Exception("This version of PrepareRegressionStageInput function is useable only for the classification or hybrid task type.");
}
//RegressionStageInput allocation
RegressionStageInput rsi = new RegressionStageInput();
rsi.PredictorsCollection = new List <double[]>(dataSet.InputPatternCollection.Count);
rsi.IdealOutputsCollection = new List <double[]>(dataSet.OutputVectorCollection.Count);
//Collection
for (int dataSetIdx = 0; dataSetIdx < dataSet.InputPatternCollection.Count; dataSetIdx++)
{
//Push input data into the network
double[] predictors = PushInput(dataSet.InputPatternCollection[dataSetIdx]);
rsi.PredictorsCollection.Add(predictors);
//Add desired outputs
rsi.IdealOutputsCollection.Add(dataSet.OutputVectorCollection[dataSetIdx]);
//Informative callback
if (informativeCallback != null)
{
informativeCallback(dataSet.InputPatternCollection.Count, dataSetIdx + 1, userObject);
}
}
//Collect reservoirs statistics
rsi.ReservoirStatCollection = CollectReservoirInstancesStatatistics();
return(rsi);
}
/// <summary>
/// Prepares input for regression stage of State Machine training for the time series prediction task.
/// All input vectors are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="dataSet">
/// The bundle containing known sample input and desired output vectors (in time order)
/// </param>
/// <param name="numOfBootSamples">
/// Number of boot samples from the beginning of all samples.
/// The purpose of the boot samples is to ensure that the states of the neurons in the reservoir
/// depend only on the time series data and not on the initial state of the neurons in the reservoir.
/// The number of boot samples depends on the size and configuration of the reservoirs.
/// It is usually sufficient to set the number of boot samples equal to the number of neurons in the largest reservoir.
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionStageInput PrepareRegressionStageInput(TimeSeriesBundle dataSet,
int numOfBootSamples,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
if (_settings.TaskType != CommonEnums.TaskType.Prediction)
{
throw new Exception("This version of PrepareRegressionStageInput function is useable only for the prediction task type.");
}
int dataSetLength = dataSet.InputVectorCollection.Count;
//RegressionStageInput allocation
RegressionStageInput rsi = new RegressionStageInput();
rsi.PredictorsCollection = new List <double[]>(dataSetLength - numOfBootSamples);
rsi.IdealOutputsCollection = new List <double[]>(dataSetLength - numOfBootSamples);
//Reset the internal states and statistics
Reset(true);
//Collection
for (int dataSetIdx = 0; dataSetIdx < dataSetLength; dataSetIdx++)
{
bool afterBoot = (dataSetIdx >= numOfBootSamples);
//Push input data into the network
double[] predictors = PushInput(dataSet.InputVectorCollection[dataSetIdx], afterBoot);
//Is boot sequence passed? Collect predictors?
if (afterBoot)
{
//YES
rsi.PredictorsCollection.Add(predictors);
//Desired outputs
rsi.IdealOutputsCollection.Add(dataSet.OutputVectorCollection[dataSetIdx]);
}
//An informative callback
if (informativeCallback != null)
{
informativeCallback(dataSetLength, dataSetIdx + 1, userObject);
}
}
//Collect reservoirs statistics
rsi.ReservoirStatCollection = CollectReservoirInstancesStatatistics();
return(rsi);
}
/// <summary>
/// Prepares input for regression stage of State Machine training.
/// All input vectors are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="dataSet">
/// The bundle containing known sample input and desired output vectors (in time order)
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionStageInput PrepareRegressionStageInput(TimeSeriesBundle dataSet,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
if (_settings.InputConfig.FeedingType == CommonEnums.InputFeedingType.Patterned)
{
throw new Exception("This version of PrepareRegressionStageInput function is not useable for patterned input feeding.");
}
int dataSetLength = dataSet.InputVectorCollection.Count;
//RegressionStageInput allocation
RegressionStageInput rsi = new RegressionStageInput
{
PredictorsCollection = new List <double[]>(dataSetLength - _settings.InputConfig.BootCycles),
IdealOutputsCollection = new List <double[]>(dataSetLength - _settings.InputConfig.BootCycles)
};
//Reset the internal states and statistics
Reset(true);
//Collection
for (int dataSetIdx = 0; dataSetIdx < dataSetLength; dataSetIdx++)
{
bool afterBoot = (dataSetIdx >= _settings.InputConfig.BootCycles);
//Push input data into the network
double[] predictors = PushInput(dataSet.InputVectorCollection[dataSetIdx], afterBoot);
//Is boot sequence passed? Collect predictors?
if (afterBoot)
{
//YES
rsi.PredictorsCollection.Add(predictors);
//Desired outputs
rsi.IdealOutputsCollection.Add(dataSet.OutputVectorCollection[dataSetIdx]);
}
//An informative callback
informativeCallback?.Invoke(dataSetLength, dataSetIdx + 1, userObject);
}
//Collect reservoirs statistics
rsi.ReservoirStatCollection = CollectReservoirInstancesStatatistics();
return(rsi);
}
/// <summary>
/// Prepares input for Readout Layer training.
/// All input vectors are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="vectorBundle">
/// The bundle containing known sample input and desired output vectors (in time order)
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public VectorBundle InitializeAndPreprocessBundle(VectorBundle vectorBundle,
PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
//Check correctness
if (_settings.InputConfig.FeedingType == CommonEnums.InputFeedingType.Patterned)
{
throw new Exception("Called incorrect version of InitializeAndPreprocessBundle function for patterned input feeding.");
}
//Reset the internal states and also statistics
Reset(true);
//Initialize normalizers and normalize input data
List <double[]> nrmInputVectorCollection = NormalizeInputVectorCollection(vectorBundle.InputVectorCollection);
//Allocate output bundle
VectorBundle outputBundle = new VectorBundle(vectorBundle.InputVectorCollection.Count - _settings.InputConfig.BootCycles);
//Collect predictors
for (int dataSetIdx = 0; dataSetIdx < vectorBundle.InputVectorCollection.Count; dataSetIdx++)
{
bool afterBoot = (dataSetIdx >= _settings.InputConfig.BootCycles);
//Push input data into the network
double[] predictors = PushInput(nrmInputVectorCollection[dataSetIdx], afterBoot);
//Is boot sequence passed? Collect predictors?
if (afterBoot)
{
//YES
//Predictors
outputBundle.InputVectorCollection.Add(predictors);
//Desired outputs
outputBundle.OutputVectorCollection.Add(vectorBundle.OutputVectorCollection[dataSetIdx]);
}
//An informative callback
informativeCallback?.Invoke(vectorBundle.InputVectorCollection.Count, dataSetIdx + 1, userObject);
}
return(outputBundle);
}
