The redesign of the Readout Layer, introduction of the Cluster Chain …

…as the standard computation unit for the Readout Unit and One Takes All group. Enhanced data bundle folderization. Enhanced and revised code comments.

Demo/DemoConsoleApp/Examples/ExampleBase.cs

@@ -1,17 +1,17 @@
-using System;
-using Demo.DemoConsoleApp.Log;
+using Demo.DemoConsoleApp.Log;
 using RCNet.CsvTools;
 using RCNet.Neural.Data;
 using RCNet.Neural.Network.NonRecurrent;
 using RCNet.Neural.Network.SM;
 using RCNet.Neural.Network.SM.Preprocessing;
 using RCNet.Neural.Network.SM.Preprocessing.Input;
+using RCNet.Neural.Network.SM.Readout;
 
 
 namespace Demo.DemoConsoleApp.Examples
 {
     /// <summary>
-    /// Base class of the implemented examples
+    /// Implements the base class of the examples.
     /// </summary>
     public class ExampleBase
     {
@@ -28,10 +28,10 @@ protected ExampleBase()
         //Methods
         //Event handlers
         /// <summary>
-        /// Displays information about the verification progress.
+        /// Displays an information about the verification progress.
         /// </summary>
-        /// <param name="totalNumOfInputs">Total number of inputs to be processed</param>
-        /// <param name="numOfProcessedInputs">Number of processed inputs</param>
+        /// <param name="totalNumOfInputs">The total number of inputs to be processed.</param>
+        /// <param name="numOfProcessedInputs">The number of already processed inputs.</param>
         protected void OnVerificationProgressChanged(int totalNumOfInputs, int numOfProcessedInputs)
         {
             //Display progress
@@ -43,15 +43,15 @@ protected void OnVerificationProgressChanged(int totalNumOfInputs, int numOfProc
         }
 
         /// <summary>
-        /// Displays information about the preprocessing progress and at the end displays important NeuralPreprocessor's statistics.
+        /// Displays an information about the preprocessing progress and at the end displays important NeuralPreprocessor's statistics.
         /// </summary>
-        /// <param name="totalNumOfInputs">Total number of inputs to be processed</param>
-        /// <param name="numOfProcessedInputs">Number of processed inputs</param>
-        /// <param name="finalPreprocessingOverview">Final overview of the preprocessing phase</param>
+        /// <param name="totalNumOfInputs">The total number of inputs to be processed.</param>
+        /// <param name="numOfProcessedInputs">The number of already processed inputs.</param>
+        /// <param name="finalPreprocessingOverview">The final overview of the preprocessing.</param>
         protected void OnPreprocessingProgressChanged(int totalNumOfInputs,
-                                                    int numOfProcessedInputs,
-                                                    NeuralPreprocessor.PreprocessingOverview finalPreprocessingOverview
-                                                    )
+                                                      int numOfProcessedInputs,
+                                                      NeuralPreprocessor.PreprocessingOverview finalPreprocessingOverview
+                                                      )
         {
             if (finalPreprocessingOverview == null)
             {
@@ -72,39 +72,38 @@ NeuralPreprocessor.PreprocessingOverview finalPreprocessingOverview
         }
 
         /// <summary>
-        /// Displays information about the readout unit regression progress.
+        /// Displays information about the build process progress.
         /// </summary>
-        /// <param name="buildingState">Current state of the regression process</param>
-        /// <param name="foundBetter">Indicates that the best readout unit was changed as a result of the performed epoch</param>
-        protected void OnRegressionEpochDone(TrainedNetworkBuilder.BuildingState buildingState, bool foundBetter)
+        /// <param name="buildProgress">The current state of the build process.</param>
+        /// <param name="foundBetter">Indicates that the best network so far was found during the last performed epoch.</param>
+        protected void OnEpochDone(TNRNetBuilder.BuildProgress buildProgress, bool foundBetter)
         {
             int reportEpochsInterval = 5;
             //Progress info
             if (foundBetter ||
-                (buildingState.Epoch % reportEpochsInterval) == 0 ||
-                buildingState.Epoch == buildingState.MaxEpochs ||
-                (buildingState.Epoch == 1 && buildingState.RegrAttemptNumber == 1)
+                (buildProgress.Epoch % reportEpochsInterval) == 0 ||
+                buildProgress.Epoch == buildProgress.MaxEpochs ||
+                (buildProgress.Epoch == 1 && buildProgress.AttemptNumber == 1)
                 )
             {
                 //Build progress report message
-                string progressText = buildingState.GetProgressInfo(4);
+                string progressText = buildProgress.GetInfo(4);
                 //Report the progress
-                _log.Write(progressText, !(buildingState.Epoch == 1 && buildingState.RegrAttemptNumber == 1));
+                _log.Write(progressText, !(buildProgress.Epoch == 1 && buildProgress.AttemptNumber == 1));
             }
             return;
         }
 
         /// <summary>
-        /// Loads given file and executes StateMachine training.
-        /// This version of function requires configured NeuralPreprocessor.
+        /// Loads the specified file and executes the StateMachine training.
         /// </summary>
-        /// <param name="stateMachine">Instance of StateMachine to be trained</param>
-        /// <param name="trainingDataFileName">Name of the csv file containing training data</param>
-        /// <param name="predictionInputVector">Returned vector to be used for next prediction (relevant only in case of continuous feeding of the input)</param>
+        /// <param name="stateMachine">An instance of StateMachine to be trained.</param>
+        /// <param name="trainingDataFileName">The name of the csv file containing the training data.</param>
+        /// <param name="predictionInputVector">The vector to be used for next prediction (relevant only in case of continuous feeding of the input).</param>
         protected void TrainStateMachine(StateMachine stateMachine, string trainingDataFileName, out double[] predictionInputVector)
         {
-            //Register to RegressionEpochDone event
-            stateMachine.RL.RegressionEpochDone += OnRegressionEpochDone;
+            //Register to EpochDone event
+            stateMachine.RL.EpochDone += OnEpochDone;
             //Load csv data
             CsvDataHolder trainingCsvData = new CsvDataHolder(trainingDataFileName);
             //Convert csv data to VectorBundle useable for StateMachine training
@@ -153,13 +152,12 @@ out predictionInputVector
         }
 
         /// <summary>
-        /// Loads given file and executes StateMachine verification.
-        /// This version of function requires configured NeuralPreprocessor.
+        /// Loads the specified file and executes the StateMachine verification.
         /// </summary>
-        /// <param name="stateMachine">Instance of StateMachine to be trained</param>
-        /// <param name="verificationDataFileName">Name of the csv file containing verification data</param>
-        /// <param name="omittedInputVector">Remaining input vector from training phase (relevant only in case of continuous feeding of the input)</param>
-        /// <param name="predictionInputVector">Returned vector to be used for next prediction (relevant only in case of continuous feeding of the input)</param>
+        /// <param name="stateMachine">An instance of StateMachine to be verified.</param>
+        /// <param name="verificationDataFileName">The name of the csv file containing the verification data.</param>
+        /// <param name="omittedInputVector">Remaining input vector from training phase (relevant only in case of continuous feeding of the input).</param>
+        /// <param name="predictionInputVector">The vector to be used for next prediction (relevant only in case of continuous feeding of the input).</param>
         protected void VerifyStateMachine(StateMachine stateMachine, string verificationDataFileName, double[] omittedInputVector, out double[] predictionInputVector)
         {
             //Load csv data
@@ -175,7 +173,7 @@ protected void VerifyStateMachine(StateMachine stateMachine, string verification
                     //Continuous input feeding
                     //Last known input values from training (predictionInputVector) must be pushed into the reservoirs to keep time series continuity
                     //(first input data in verification.csv is output of the last data in training.csv)
-                    double[] tmp = stateMachine.Compute(omittedInputVector);
+                    double[] tmp = stateMachine.Compute(omittedInputVector, out ReadoutLayer.ReadoutData readoutData);
                     //Load verification data and get new predictionInputVector for final prediction
                     verificationData = VectorBundle.Load(verificationCsvData,
                                                          stateMachine.Config.NeuralPreprocessorCfg.InputEncoderCfg.VaryingFieldsCfg.ExternalFieldsCfg.GetFieldNames(),

Demo/DemoConsoleApp/Examples/FFNetBoolAlg.cs

@@ -1,20 +1,22 @@
-using System;
-using System.Globalization;
-using RCNet.Neural.Activation;
+using RCNet.Neural.Activation;
 using RCNet.Neural.Data;
 using RCNet.Neural.Network.NonRecurrent.FF;
+using System;
+using System.Globalization;
 
 namespace Demo.DemoConsoleApp.Examples
 {
     /// <summary>
-    /// This "Hello world" example shows how to use implemented FF network as the independent component.
-    /// It trains the multilayer Feed Forward network to solve AND, OR and XOR.
+    /// Trains a multilayer Feed Forward network to solve AND, OR and XOR.
     /// </summary>
+    /// <remarks>
+    /// This "Hello world" example shows how to use the feed forward network component independently.
+    /// </remarks>
     public class FFNetBoolAlg : ExampleBase
     {
         /// <summary>
-        /// Creates training data.
-        /// Input vector contains 0/1 combination and output vector contains appropriate results of the AND, OR and XOR operation
+        /// Creates the training data.
+        /// Input vector contains 0/1 combination and output vector contains appropriate results of the AND, OR and XOR operation.
         /// </summary>
         private VectorBundle CreateTrainingData()
         {
@@ -48,7 +50,7 @@ public void Run()
             //Training
             _log.Write("Training");
             _log.Write("--------");
-            //Create trainer instance
+            //Create the trainer instance
             RPropTrainer trainer = new RPropTrainer(ffNet,
                                                     trainingData.InputVectorCollection,
                                                     trainingData.OutputVectorCollection,
@@ -63,7 +65,7 @@ public void Run()
             _log.Write(string.Empty);
 
             //Training is done
-            //Display network computation results
+            //Display the network computation results
             _log.Write("Trained network computations:");
             _log.Write("-----------------------------");
             foreach (double[] input in trainingData.InputVectorCollection)

Demo/DemoConsoleApp/Examples/LibrasClassificationESNDesigner.cs

@@ -1,17 +1,17 @@
-using System;
-using RCNet.Neural.Activation;
+using RCNet.Neural.Activation;
 using RCNet.Neural.Data.Filter;
 using RCNet.Neural.Network.NonRecurrent;
 using RCNet.Neural.Network.SM;
 using RCNet.Neural.Network.SM.Preprocessing;
 using RCNet.Neural.Network.SM.Preprocessing.Input;
 using RCNet.Neural.Network.SM.Preprocessing.Neuron.Predictor;
 using RCNet.Neural.Network.SM.Readout;
+using System;
 
 namespace Demo.DemoConsoleApp.Examples
 {
     /// <summary>
-    /// Example code shows how to setup StateMachine as a pure ESN for multivariate timeseries classification using StateMachineDesigner.
+    /// Example code shows how to use StateMachineDesigner and setup StateMachine as a pure ESN for multivariate timeseries classification.
     /// Example uses LibrasMovement_train.csv and LibrasMovement_verify.csv from ./Data subfolder.
     /// The dataset is from "Anthony Bagnall, Jason Lines, William Vickers and Eamonn Keogh, The UEA & UCR Time Series Classification Repository, www.timeseriesclassification.com"
     /// https://timeseriesclassification.com/description.php?Dataset=Libras
@@ -44,9 +44,9 @@ public void Run()
                                                                                 );
             //Simplified readout layer configuration
             ReadoutLayerSettings readoutCfg = StateMachineDesigner.CreateClassificationReadoutCfg(new CrossvalidationSettings(0.0825d, CrossvalidationSettings.AutoFolds, 1),
-                                                                                                  StateMachineDesigner.CreateSingleLayerRegrNet(new AFAnalogIdentitySettings(), 5, 400),
+                                                                                                  StateMachineDesigner.CreateSingleLayerFFNetCfg(new AFAnalogIdentitySettings(), 5, 400),
+                                                                                                  1,
                                                                                                   "Hand movement",
-                                                                                                  new NetworkClusterSecondLevelCompSettings(new CrossvalidationSettings(0.25d, CrossvalidationSettings.AutoFolds, 2), StateMachineDesigner.CreateMultiLayerRegrNet(10, new AFAnalogLeakyReLUSettings(), 1, 5, 400)),
                                                                                                   "curved swing",
                                                                                                   "horizontal swing",
                                                                                                   "vertical swing",