protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
weights = new double[LearningConnections.ItemArray.Length];
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
process = new Process(LearningConnections.Select(c => new QuantumWeight(c.Connection)));
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
qsa = new QuantumStabilizerAlgorithm(LearningConnections.Select(c => new QuantumWeight(c.Connection)), qsaStrength, false);
lastMSE = double.MaxValue;
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
if (mode == AlgoInitializationMode.Startup)
{
foreach (var lc in LearningConnections)
{
var rule = (NoisedWeightInitializationRule)lc.Rule;
lc.Connection.Weight = (rule.Noise * RandomGenerator.Random.NextDouble()) * 2.0 - rule.Noise;
}
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
rule = (CrossEntropyRule)LearningConnections[0].Rule;
int connCount = LearningConnections.Count;
infos = Enumerable.Range(0, connCount).Select(idx => StatisticalInfo.Create()).ToArray();
evaluatedWeights = new Population(rule.PopulationSize);
nextStep = null;
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
rule = (GARule)LearningConnections.ItemArray[0].Rule;
population = new Population(rule.PopulationSize);
bestSel = new GaussianSelectionAlgorithm(rule.BestSelectStdDev);
worstSel = new GaussianSelectionAlgorithm(rule.WorstSelectStdDev, SelectionDirection.FromBottom);
nextStep = null;
}
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
base.InitializeNewRun(mode);
if (mode == AlgoInitializationMode.Startup)
{
count = LearningConnections.ItemArray.Length;
p = new double[count];
r = new double[count];
gradients = new double[2, count];
lastWeights = new double[count];
wError = stepError = 1.0;
decay = ((SCGRule)LearningConnections.ItemArray[0].Rule).WeightDecay;
}
// SCG Step 1:
nextStep = null;
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode)
{
if (mode == AlgoInitializationMode.Startup) CurrentMSE = double.MaxValue;
}
protected internal abstract void InitializeNewRun(AlgoInitializationMode mode);
private void InitializeNewAlgoRun(AlgoInitializationMode mode)
{
if (forwardLearningAlgos != null) foreach (var fwa in forwardLearningAlgos) fwa.InitializeNewRun(mode);
if (backwardLearningAlgos != null) foreach (var bwa in backwardLearningAlgos) bwa.InitializeNewRun(mode);
}
protected internal override void InitializeNewRun(AlgoInitializationMode mode) { }
