/// <summary>
/// Checks whether the network configuration is suitable for the specified type of the network output.
/// </summary>
/// <param name="outputType">The type of the network output.</param>
/// <param name="netCfg">The network configuration.</param>
public static void CheckNetCfg(TNRNet.OutputType outputType, INonRecurrentNetworkSettings netCfg)
{
switch (outputType)
{
case TNRNet.OutputType.Probabilistic:
{
if (netCfg.GetType() != typeof(FeedForwardNetworkSettings))
{
throw new ArgumentException($"Incorrect network configuration. It must be the Feed forward network configuration.", "netCfg");
}
if (((FeedForwardNetworkSettings)netCfg).OutputActivationCfg.GetType() != typeof(AFAnalogSoftMaxSettings))
{
throw new ArgumentException($"Feed forward network must have the SoftMax output activation.", "netCfg");
}
if (((FeedForwardNetworkSettings)netCfg).TrainerCfg.GetType() != typeof(RPropTrainerSettings))
{
throw new ArgumentException($"Feed forward network must have associated the RProp trainer.", "netCfg");
}
}
break;
case TNRNet.OutputType.Real:
{
if (netCfg.GetType() != typeof(FeedForwardNetworkSettings))
{
throw new ArgumentException($"Incorrect network configuration. It must be the Feed forward network configuration.", "netCfg");
}
}
break;
default:
break;
}
return;
}
//Constructor
/// <summary>
/// Creates an instance ready to start building trained non-recurrent network
/// </summary>
/// <param name="networkName">Name of the network to be built</param>
/// <param name="networkSettings">Network configuration (FeedForwardNetworkSettings or ParallelPerceptronSettings object)</param>
/// <param name="foldNum">Current fold number</param>
/// <param name="numOfFolds">Total number of the folds</param>
/// <param name="foldNetworkNum">Current fold network number</param>
/// <param name="numOfFoldNetworks">Total number of the fold networks</param>
/// <param name="trainingBundle">Bundle of predictors and ideal values to be used for training purposes</param>
/// <param name="testingBundle">Bundle of predictors and ideal values to be used for testing purposes</param>
/// <param name="binBorder">If specified, it indicates that the whole network output is binary and specifies numeric border where GE network output is decided as a 1 and LT output as a 0.</param>
/// <param name="rand">Random generator to be used (optional)</param>
/// <param name="controller">Regression controller (optional)</param>
public TrainedNetworkBuilder(string networkName,
INonRecurrentNetworkSettings networkSettings,
int foldNum,
int numOfFolds,
int foldNetworkNum,
int numOfFoldNetworks,
VectorBundle trainingBundle,
VectorBundle testingBundle,
double binBorder = double.NaN,
Random rand = null,
RegressionControllerDelegate controller = null
)
{
_networkName = networkName;
_networkSettings = networkSettings;
_foldNum = foldNum;
_numOfFolds = numOfFolds;
_foldNetworkNum = foldNetworkNum;
_numOfFoldNetworks = numOfFoldNetworks;
//Check num of output values is 1
if (trainingBundle.OutputVectorCollection[0].Length != 1)
{
throw new InvalidOperationException($"Only single output value is allowed.");
}
_trainingBundle = trainingBundle;
_testingBundle = testingBundle;
_binBorder = binBorder;
_rand = rand ?? new Random(0);
_controller = controller ?? DefaultRegressionController;
return;
}
/// <summary>
/// Creates new network and associated trainer.
/// </summary>
/// <param name="settings">Non-recurrent-network settings</param>
/// <param name="trainingInputVectors">Collection of training input samples</param>
/// <param name="trainingOutputVectors">Collection of training output (desired) samples</param>
/// <param name="rand">Random object to be used</param>
/// <param name="net">Created network</param>
/// <param name="trainer">Created associated trainer</param>
public static void CreateNetworkAndTrainer(INonRecurrentNetworkSettings settings,
List <double[]> trainingInputVectors,
List <double[]> trainingOutputVectors,
Random rand,
out INonRecurrentNetwork net,
out INonRecurrentNetworkTrainer trainer
)
{
if (IsFF(settings))
{
//Feed forward network
FeedForwardNetworkSettings netCfg = (FeedForwardNetworkSettings)settings;
FeedForwardNetwork ffn = new FeedForwardNetwork(trainingInputVectors[0].Length, trainingOutputVectors[0].Length, netCfg);
net = ffn;
if (netCfg.TrainerCfg.GetType() == typeof(QRDRegrTrainerSettings))
{
trainer = new QRDRegrTrainer(ffn, trainingInputVectors, trainingOutputVectors, (QRDRegrTrainerSettings)netCfg.TrainerCfg, rand);
}
else if (netCfg.TrainerCfg.GetType() == typeof(RidgeRegrTrainerSettings))
{
trainer = new RidgeRegrTrainer(ffn, trainingInputVectors, trainingOutputVectors, (RidgeRegrTrainerSettings)netCfg.TrainerCfg);
}
else if (netCfg.TrainerCfg.GetType() == typeof(ElasticRegrTrainerSettings))
{
trainer = new ElasticRegrTrainer(ffn, trainingInputVectors, trainingOutputVectors, (ElasticRegrTrainerSettings)netCfg.TrainerCfg);
}
else if (netCfg.TrainerCfg.GetType() == typeof(RPropTrainerSettings))
{
trainer = new RPropTrainer(ffn, trainingInputVectors, trainingOutputVectors, (RPropTrainerSettings)netCfg.TrainerCfg, rand);
}
else
{
throw new ArgumentException($"Unknown trainer {netCfg.TrainerCfg}");
}
}
else if (IsPP(settings))
{
//Parallel perceptron network
//Check output
if (trainingOutputVectors[0].Length != 1)
{
throw new InvalidOperationException($"Can't create ParallelPerceptron. Only single output value is allowed.");
}
ParallelPerceptronSettings netCfg = (ParallelPerceptronSettings)settings;
ParallelPerceptron ppn = new ParallelPerceptron(trainingInputVectors[0].Length, netCfg);
net = ppn;
trainer = new PDeltaRuleTrainer(ppn, trainingInputVectors, trainingOutputVectors, netCfg.PDeltaRuleTrainerCfg, rand);
}
else
{
throw new InvalidOperationException($"Unknown network settings");
}
net.RandomizeWeights(rand);
return;
}
//Constructor
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="networkName">The name of the network to be built.</param>
/// <param name="networkCfg">The configuration of the network to be built.</param>
/// <param name="networkOutput">The type of output of the network to be built.</param>
/// <param name="trainingBundle">The bundle of input and ideal vectors to be used for the network training.</param>
/// <param name="testingBundle">The bundle of input and ideal vectors to be used for the network testing.</param>
/// <param name="rand">The random generator to be used (optional).</param>
/// <param name="controller">The build process controller (optional).</param>
public TNRNetBuilder(string networkName,
INonRecurrentNetworkSettings networkCfg,
TNRNet.OutputType networkOutput,
VectorBundle trainingBundle,
VectorBundle testingBundle,
Random rand = null,
BuildControllerDelegate controller = null
)
{
_networkName = networkName;
NonRecurrentNetUtils.CheckNetCfg(networkOutput, networkCfg);
_networkCfg = networkCfg;
_networkOutput = networkOutput;
NonRecurrentNetUtils.CheckData(_networkOutput, trainingBundle);
_trainingBundle = trainingBundle;
NonRecurrentNetUtils.CheckData(_networkOutput, testingBundle);
_testingBundle = testingBundle;
_rand = rand ?? new Random(0);
_controller = controller ?? DefaultNetworkBuildController;
return;
}
/// <summary>
/// Determines if given settings object is ParallelPerceptronSettings
/// </summary>
/// <param name="settings">Non-recurrent-network settings</param>
public static bool IsPP(INonRecurrentNetworkSettings settings)
{
return(settings.GetType() == typeof(ParallelPerceptronSettings));
}
/// <summary>
/// Determines if given settings object is FeedForwardNetworkSettings
/// </summary>
/// <param name="settings">Non-recurrent-network settings</param>
public static bool IsFF(INonRecurrentNetworkSettings settings)
{
return(settings.GetType() == typeof(FeedForwardNetworkSettings));
}