private void AddWeightedLayer(int neuronCount, IActivation activation, bool hasBias)
{
WeightedLayer adding = new WeightedLayer(neuronCount, activation, hasBias);
weightedLayers.Add(adding);
layers.Add(adding);
}
public void TestNextLayerReferenceIsCorrect()
{
InputLayer il = new InputLayer(3);
WeightedLayer wl = new WeightedLayer(5, new UnipolarSigmoidActivation(), true);
il.ConnectTo(wl);
Assert.AreEqual(wl.NeuronCount, il.Next.NeuronCount);
}
private void ConnectLayersForward()
{
for (int i = 1; i < layers.Count; i++)
{
WeightedLayer currentLayer = (WeightedLayer)layers[i]; // cast or throw exception if it's not the right type
layers[i - 1].ConnectTo(currentLayer);
}
}
public void TestRandomizeLeavesNoZerosInBias()
{
InputLayer il = new InputLayer(3);
WeightedLayer wl = new WeightedLayer(5, new UnipolarSigmoidActivation(), true);
il.ConnectTo(wl);
wl.RandomizeWeights();
Assert.IsTrue(wl.BiasWeights.Enumerate().All(x => x != 0.0));
}
public void SetBiasForLayer(int layerIndex, double[] bias)
{
WeightedLayer weightedLayer = (WeightedLayer)layers[layerIndex];
if (bias.Length != weightedLayer.NeuronCount)
{
throw new ArgumentException("Number of bias values provided does not match neuron count");
}
weightedLayer.SetBias(bias);
}
public void SetIncomingWeightsForLayer(int layerIndex, List <double[]> incomingWeights)
{
// important: the matrices in Math.NET are column-major!
WeightedLayer weightedLayer = (WeightedLayer)layers[layerIndex];
if (incomingWeights.Count != weightedLayer.NeuronCount)
{
throw new ArgumentException("Number of weight vectors passed " +
"is different than the neuron count in this layer");
}
weightedLayer.SetIncomingWeights(incomingWeights);
}
public RecursiveNetwork(Type type, IActivation activation, bool useBiases, int inputNeurons, int hiddenNeurons, int outputNeurons)
{
IsInitialized = false;
Activation = activation;
NetworkType = type;
inputLayer = new InputLayer(inputNeurons);
hiddenLayer = new WeightedLayer(hiddenNeurons, activation, useBiases);
contextLayer = new ContextLayer(NetworkType == Type.Jordan ? outputNeurons : hiddenNeurons);
outputLayer = new WeightedLayer(outputNeurons, activation, useBiases);
hiddenLayer.ConnectFrom(inputLayer, contextLayer);
outputLayer.ConnectFrom(hiddenLayer);
}
public void SetBiasForNeuron(int layerIndex, int neuronIndex, double bias) // TODO: write test
{
WeightedLayer weightedLayer = (WeightedLayer)layers[layerIndex];
weightedLayer.SetBiasForNeuron(neuronIndex, bias);
}
public void SetIncomingWeightsForNeuron(int layerIndex, int neuronIndex, double[] weights)
{
WeightedLayer weightedLayer = (WeightedLayer)layers[layerIndex];
weightedLayer.SetIncomingWeightsForNeuron(neuronIndex, weights);
}
