/// <summary>
/// Creates the instance and initializes it from given xml element.
/// </summary>
/// <param name="readoutUnitElem">
/// Xml data containing the settings.
/// </param>
public ReadoutUnitSettings(XElement readoutUnitElem)
{
Name = readoutUnitElem.Attribute("name").Value;
TaskType = CommonEnums.ParseTaskType(readoutUnitElem.Attribute("task").Value);
//Net settings
List <XElement> netSettingsElems = new List <XElement>();
netSettingsElems.AddRange(readoutUnitElem.Descendants("ff"));
netSettingsElems.AddRange(readoutUnitElem.Descendants("pp"));
if (netSettingsElems.Count != 1)
{
throw new Exception("Only one network configuration can be specified in readout unit settings.");
}
if (netSettingsElems.Count == 0)
{
throw new Exception("Network configuration is not specified in readout unit settings.");
}
XElement netSettingsElem = netSettingsElems[0];
//FF?
if (netSettingsElem.Name.LocalName == "ff")
{
NetType = ReadoutUnitNetworkType.FF;
NetSettings = new FeedForwardNetworkSettings(netSettingsElem);
OutputRange = ((FeedForwardNetworkSettings)NetSettings).OutputRange.DeepClone();
}
else
{
//PP
NetType = ReadoutUnitNetworkType.PP;
NetSettings = new ParallelPerceptronSettings(netSettingsElem);
OutputRange = ((ParallelPerceptronSettings)NetSettings).OutputRange.DeepClone();
}
return;
}
/// <summary>
/// Creates the instance and initializes it from given xml element.
/// </summary>
/// <param name="readoutUnitElem">
/// Xml data containing the settings.
/// </param>
public ReadoutUnitSettings(XElement readoutUnitElem)
{
RegressionAttempts = int.Parse(readoutUnitElem.Attribute("attempts").Value);
RegressionAttemptEpochs = int.Parse(readoutUnitElem.Attribute("attemptEpochs").Value);
//Net settings
List <XElement> netSettingsElems = new List <XElement>();
netSettingsElems.AddRange(readoutUnitElem.Descendants("ff"));
netSettingsElems.AddRange(readoutUnitElem.Descendants("pp"));
if (netSettingsElems.Count != 1)
{
throw new Exception("Only one network configuration can be specified in readout unit settings.");
}
if (netSettingsElems.Count == 0)
{
throw new Exception("Network configuration is not specified in readout unit settings.");
}
XElement netSettingsElem = netSettingsElems[0];
//FF?
if (netSettingsElem.Name == "ff")
{
NetType = ReadoutUnitNetworkType.FF;
NetSettings = new FeedForwardNetworkSettings(netSettingsElem);
}
else
{
//PP
NetType = ReadoutUnitNetworkType.PP;
NetSettings = new ParallelPerceptronSettings(netSettingsElem);
}
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;
}
/// <summary>
/// Creates the instance and initializes it from given xml element.
/// </summary>
/// <param name="readoutUnitElem">
/// Xml data containing the settings.
/// </param>
public ReadoutUnitSettings(XElement readoutUnitElem)
{
//Name
Name = readoutUnitElem.Attribute("name").Value;
//Task and filter
XElement taskElem = readoutUnitElem.Descendants().First();
if (taskElem.Name.LocalName == "forecast")
{
TaskType = ReadoutUnit.TaskType.Forecast;
}
else
{
TaskType = ReadoutUnit.TaskType.Classification;
}
FeatureFilterCfg = FeatureFilterFactory.LoadSettings(taskElem.Descendants().First());
//Net settings
List <XElement> netSettingsElems = new List <XElement>();
netSettingsElems.AddRange(readoutUnitElem.Descendants("ff"));
netSettingsElems.AddRange(readoutUnitElem.Descendants("pp"));
if (netSettingsElems.Count != 1)
{
throw new Exception("Only one network configuration can be specified in readout unit settings.");
}
if (netSettingsElems.Count == 0)
{
throw new Exception("Network configuration is not specified in readout unit settings.");
}
XElement netSettingsElem = netSettingsElems[0];
//FF?
if (netSettingsElem.Name.LocalName == "ff")
{
NetType = ReadoutUnitNetworkType.FF;
NetSettings = new FeedForwardNetworkSettings(netSettingsElem);
OutputRange = ((FeedForwardNetworkSettings)NetSettings).OutputRange.DeepClone();
}
else
{
//PP
NetType = ReadoutUnitNetworkType.PP;
NetSettings = new ParallelPerceptronSettings(netSettingsElem);
OutputRange = ((ParallelPerceptronSettings)NetSettings).OutputRange.DeepClone();
}
return;
}
private static void CreateNetAndTreainer(ReadoutLayerSettings.ReadoutUnitSettings settings,
List <double[]> trainingPredictorsCollection,
List <double[]> trainingIdealOutputsCollection,
Random rand,
out INonRecurrentNetwork net,
out INonRecurrentNetworkTrainer trainer
)
{
if (settings.NetType == ReadoutLayerSettings.ReadoutUnitSettings.ReadoutUnitNetworkType.FF)
{
FeedForwardNetworkSettings netCfg = (FeedForwardNetworkSettings)settings.NetSettings;
FeedForwardNetwork ffn = new FeedForwardNetwork(trainingPredictorsCollection[0].Length, 1, netCfg);
net = ffn;
if (netCfg.TrainerCfg.GetType() == typeof(QRDRegrTrainerSettings))
{
trainer = new QRDRegrTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, (QRDRegrTrainerSettings)netCfg.TrainerCfg, rand);
}
else if (netCfg.TrainerCfg.GetType() == typeof(RidgeRegrTrainerSettings))
{
trainer = new RidgeRegrTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, (RidgeRegrTrainerSettings)netCfg.TrainerCfg, rand);
}
else if (netCfg.TrainerCfg.GetType() == typeof(ElasticRegrTrainerSettings))
{
trainer = new ElasticRegrTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, (ElasticRegrTrainerSettings)netCfg.TrainerCfg);
}
else if (netCfg.TrainerCfg.GetType() == typeof(RPropTrainerSettings))
{
trainer = new RPropTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, (RPropTrainerSettings)netCfg.TrainerCfg, rand);
}
else
{
throw new ArgumentException($"Unknown trainer {netCfg.TrainerCfg}");
}
}
else
{
ParallelPerceptronSettings netCfg = (ParallelPerceptronSettings)settings.NetSettings;
ParallelPerceptron ppn = new ParallelPerceptron(trainingPredictorsCollection[0].Length, netCfg);
net = ppn;
trainer = new PDeltaRuleTrainer(ppn, trainingPredictorsCollection, trainingIdealOutputsCollection, netCfg.PDeltaRuleTrainerCfg, rand);
}
net.RandomizeWeights(rand);
return;
}
private static void CreateNetAndTreainer(ReadoutLayerSettings.ReadoutUnitSettings settings,
List <double[]> trainingPredictorsCollection,
List <double[]> trainingIdealOutputsCollection,
Random rand,
out INonRecurrentNetwork net,
out INonRecurrentNetworkTrainer trainer
)
{
if (settings.NetType == ReadoutLayerSettings.ReadoutUnitSettings.ReadoutUnitNetworkType.FF)
{
FeedForwardNetworkSettings netCfg = (FeedForwardNetworkSettings)settings.NetSettings;
FeedForwardNetwork ffn = new FeedForwardNetwork(trainingPredictorsCollection[0].Length, 1, netCfg);
net = ffn;
switch (netCfg.RegressionMethod)
{
case FeedForwardNetworkSettings.TrainingMethodType.Linear:
trainer = new LinRegrTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, settings.RegressionAttemptEpochs, rand, netCfg.LinRegrTrainerCfg);
break;
case FeedForwardNetworkSettings.TrainingMethodType.Resilient:
trainer = new RPropTrainer(ffn, trainingPredictorsCollection, trainingIdealOutputsCollection, netCfg.RPropTrainerCfg);
break;
default:
throw new ArgumentException($"Not supported regression method {netCfg.RegressionMethod}");
}
}
else
{
ParallelPerceptronSettings netCfg = (ParallelPerceptronSettings)settings.NetSettings;
ParallelPerceptron ppn = new ParallelPerceptron(trainingPredictorsCollection[0].Length, netCfg);
net = ppn;
trainer = new PDeltaRuleTrainer(ppn, trainingPredictorsCollection, trainingIdealOutputsCollection, netCfg.PDeltaRuleTrainerCfg);
}
net.RandomizeWeights(rand);
return;
}