//Constructor
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="name">The name of the input field.</param>
/// <param name="idx">The zero-based index of the input field among other input fields.</param>
/// <param name="coordinates">The coordinates of input neurons in 3D space.</param>
/// <param name="dataWorkingRange">The output range of the input data.</param>
/// <param name="featureFilterCfg">The configuration of the feature filter.</param>
/// <param name="spikesEncodingCfg">The configuration of the spikes coder.</param>
/// <param name="routeToReadout">Specifies whether to route the input field values to readout layer.</param>
/// <param name="inputNeuronsStartIdx">The zero-based index of the first input neuron of this field among all other input neurons.</param>
public InputField(string name,
int idx,
int[] coordinates,
Interval dataWorkingRange,
IFeatureFilterSettings featureFilterCfg,
InputSpikesCoderSettings spikesEncodingCfg,
bool routeToReadout,
int inputNeuronsStartIdx
)
{
Name = name;
Idx = idx;
RouteToReadout = routeToReadout;
_featureFilter = FeatureFilterFactory.Create(dataWorkingRange, featureFilterCfg);
_iAnalogStimuli = 0;
_currentDataIdx = 0;
//Spikes encoder
_spikesEncoder = new InputSpikesCoder(spikesEncodingCfg);
//Analog neuron
int verticalCycles = _spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Vertical ? _spikesEncoder.LargestComponentLength : 1;
AnalogNeuron = new AnalogInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]), verticalCycles);
++inputNeuronsStartIdx;
//Spiking neurons
int spikingPopulationSize;
if (_spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Horizontal)
{
//Population encoding
spikingPopulationSize = _spikesEncoder.AllSpikesFlatCollection.Length;
}
else if (_spikesEncoder.Regime == InputEncoder.InputSpikesCoding.Vertical)
{
//Spike-train encoding
spikingPopulationSize = _spikesEncoder.ComponentSpikesCollection.Length;
}
else
{
//Forbidden encoding
spikingPopulationSize = 0;
}
SpikingNeuronCollection = new SpikingInputNeuron[spikingPopulationSize];
for (int i = 0; i < SpikingNeuronCollection.Length; i++)
{
SpikingNeuronCollection[i] = new SpikingInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
}
return;
}
/// <summary>
/// Builds the inner cluster chain.
/// </summary>
/// <param name="dataBundle">The data to be used for training.</param>
/// <param name="filter">The feature filter to be used to denormalize output.</param>
/// <param name="rand">The random object to be used (optional).</param>
/// <param name="controller">The build process controller (optional).</param>
public void Build(VectorBundle dataBundle,
FeatureFilterBase filter,
Random rand = null,
TNRNetBuilder.BuildControllerDelegate controller = null
)
{
rand = rand ?? new Random(0);
TNRNetClusterChainBuilder builder = new TNRNetClusterChainBuilder(Name,
_clusterChainCfg,
rand,
controller
);
builder.ChainBuildProgressChanged += OnChainBuildProgressChanged;
_clusterChain = builder.Build(dataBundle, new FeatureFilterBase[] { filter });
return;
}
//Constructor
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Name of the input field</param>
/// <param name="idx">Index of the input field</param>
/// <param name="coordinates">Input coordinates (entry point)</param>
/// <param name="dataWorkingRange">Input data range</param>
/// <param name="featureFilterCfg">Feature filter configuration</param>
/// <param name="spikeCodeCfg">Configuration of the input spike code</param>
/// <param name="routeToReadout">Specifies whether to route values as the additional predictors to readout</param>
/// <param name="inputNeuronsStartIdx">Index of the first input neuron of this unit among all input neurons</param>
public InputField(string name,
int idx,
int[] coordinates,
Interval dataWorkingRange,
IFeatureFilterSettings featureFilterCfg,
SpikeCodeSettings spikeCodeCfg,
bool routeToReadout,
int inputNeuronsStartIdx
)
{
Name = name;
Idx = idx;
RouteToReadout = routeToReadout;
_featureFilter = FeatureFilterFactory.Create(dataWorkingRange, featureFilterCfg);
//Analog neuron
AnalogNeuron = new AnalogInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
//Spiking neurons
_realSpikeCode = null;
int populationSize = -1;
switch (_featureFilter.Type)
{
case FeatureFilterBase.FeatureType.Real:
_realSpikeCode = new SpikeCode(spikeCodeCfg);
populationSize = _realSpikeCode.Code.Length;
break;
case FeatureFilterBase.FeatureType.Binary:
populationSize = 1;
break;
case FeatureFilterBase.FeatureType.Enum:
populationSize = ((EnumFeatureFilterSettings)featureFilterCfg).NumOfElements;
break;
}
SpikingNeuronCollection = new SpikingInputNeuron[populationSize];
for (int i = 0; i < SpikingNeuronCollection.Length; i++)
{
SpikingNeuronCollection[i] = new SpikingInputNeuron(new NeuronLocation(InputEncoder.ReservoirID, inputNeuronsStartIdx, InputEncoder.PoolID, inputNeuronsStartIdx, idx, coordinates[0], coordinates[1], coordinates[2]));
++inputNeuronsStartIdx;
}
return;
}
//Methods
/// <summary>
/// Prepares a set of filters for the input data standardization.
/// </summary>
/// <param name="bundle">A bundle of the input and output data vectors.</param>
protected FeatureFilterBase[] PrepareInputFeatureFilters(VectorBundle bundle)
{
//Allocation of the feature filters
FeatureFilterBase[] inputFeatureFilters = new FeatureFilterBase[bundle.InputVectorCollection[0].Length];
for (int i = 0; i < inputFeatureFilters.Length; i++)
{
//Input data is always considered as the real numbers.
inputFeatureFilters[i] = new RealFeatureFilter(Interval.IntN1P1, //Data to be standardized between -1 and 1
new RealFeatureFilterSettings(true, //We want to standardize data
true //We want to keep a range reserve for unseen data
)
);
}
//Update feature filters
foreach (double[] vector in bundle.InputVectorCollection)
{
for (int i = 0; i < vector.Length; i++)
{
//Update filter by the next known data value.
inputFeatureFilters[i].Update(vector[i]);
}
}
return(inputFeatureFilters);
}