public static NeuronRelation SetupNeuronRelation(Neuron neuronOfCurrLayer, int currIndex, Neuron neuronOfNextLayer, int nextIndex)
{
var relation = new NeuronRelation
{
_id = Interlocked.Increment(ref _idSource),
_neuronFromPreviousLayer = neuronOfCurrLayer,
_neuronFromNextLayer = neuronOfNextLayer,
_prevIndex = currIndex,
_nextIndex = nextIndex
};
relation.Weight = (decimal)_random.NextDouble(); // - 0.5m;
// relation.Weight = 0.5m;
neuronOfCurrLayer.RelationsToNextLayer.Add(nextIndex, relation);
neuronOfNextLayer.RelationsToPrevLayer.Add(currIndex, relation);
return(relation);
}
public Perception(int[] neuronsInLayer)
{
layers = neuronsInLayer.Select(Layer.GetNeuron).ToArray();
for (int i = 0; i < layers.Length; i++)
{
Layer currLayer = layers[i];
if (i + 1 < layers.Length)
{
Layer nextLayer = layers[i + 1];
for (int j = 0; j < layers[i].Count; j++)
{
var relation = new NeuronRelation();
currLayer[j].NeuronRelationsPrev.Add(relation);
for (int k = 0; k < nextLayer.Count; k++)
{
nextLayer[k].NeuronRelationsPrev.Add(relation);
}
}
}
}
}
public Perceptron(int[] neuronsInLayer, decimal learningRate)
{
_learningRate = learningRate;
_layers = neuronsInLayer.Select(Layer.GetNeuron).ToArray();
for (int i = 0; i < _layers.Length; i++)
{
Layer currLayer = _layers[i];
if (i + 1 < _layers.Length)
{
Layer nextLayer = _layers[i + 1];
for (int j = 0; j < _layers[i].Count; j++)
{
for (int k = 0; k < nextLayer.Count; k++)
{
var relation = NeuronRelation.SetupNeuronRelation(currLayer[j], j, nextLayer[k], k);
}
}
}
}
}