/// <summary>
/// Creates 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()
{
VectorBundle trainingData = new VectorBundle();
trainingData.AddPair(new double[] { 0, 0 }, new double[] { 0, 0, 0 });
trainingData.AddPair(new double[] { 0, 1 }, new double[] { 0, 1, 1 });
trainingData.AddPair(new double[] { 1, 0 }, new double[] { 0, 1, 1 });
trainingData.AddPair(new double[] { 1, 1 }, new double[] { 1, 1, 0 });
return(trainingData);
}
/// <summary>
/// Builds the internal probabilistic cluster chain and makes the "One Takes All" group operable.
/// </summary>
/// <param name="readoutUnitsResultsCollection">The collection of the collections of all readout units composite results.</param>
/// <param name="readoutUnitsIdealValuesCollection">The collection of the collections of all readout units ideal values.</param>
/// <param name="filters">The feature filters to be used to denormalize output data.</param>
/// <param name="rand">The random object to be used.</param>
/// <param name="controller">The build process controller (optional).</param>
public void Build(List <CompositeResult[]> readoutUnitsResultsCollection,
List <double[]> readoutUnitsIdealValuesCollection,
BinFeatureFilter[] filters,
Random rand,
TNRNetBuilder.BuildControllerDelegate controller = null
)
{
if (DecisionMethod != OneTakesAllDecisionMethod.ClusterChain)
{
throw new InvalidOperationException("Wrong call of the Build method.");
}
OneTakesAllClusterChainDecisionSettings decisionCfg = (OneTakesAllClusterChainDecisionSettings)_groupCfg.DecisionCfg;
//Prepare the training data bundle for the cluster chain
VectorBundle trainingDataBundle = new VectorBundle(readoutUnitsIdealValuesCollection.Count);
for (int sampleIdx = 0; sampleIdx < readoutUnitsIdealValuesCollection.Count; sampleIdx++)
{
double[] inputVector = CreateInputVector(readoutUnitsResultsCollection[sampleIdx]);
double[] outputVector = CreateOutputVector(readoutUnitsIdealValuesCollection[sampleIdx], filters);
trainingDataBundle.AddPair(inputVector, outputVector);
}
//Cluster chain builder
TNRNetClusterChainBuilder builder = new TNRNetClusterChainBuilder(Name,
decisionCfg.ClusterChainCfg,
rand,
controller
);
builder.ChainBuildProgressChanged += OnChainBuildProgressChanged;
ProbabilisticClusterChain = builder.Build(trainingDataBundle, filters);
return;
}