//:this(),多态，继承自构造函数，先执行neuron（）的构造函数
public Neuron(IEnumerable <Neuron> inputNeurons) : this()
{
foreach (var inputNeuron in inputNeurons)
{
var synapse = new Synapse(inputNeuron, this);
inputNeuron.OutputSynapses.Add(synapse);
this.InputSynapses.Add(synapse);
}
}
public static Network ImportNetwork()
{
var dn = GetHelperNetwork();
if (dn == null)
{
return(null);
}
var network = new Network();
var allNeurons = new List <Neuron>();
network.LearnRate = dn.LearnRate;
network.Momentum = dn.Momentum;
//Input Layer
foreach (var n in dn.InputLayer)
{
var neuron = new Neuron
{
Id = n.Id,
Bias = n.Bias,
BiasDelta = n.BiasDelta,
Gradient = n.Gradient,
InputValue = n.InputValue,
OutputValue = n.OutputValue,
Error = n.Error
};
network.InputLayer.Add(neuron);
allNeurons.Add(neuron);
}
//Hidden Layers
foreach (var layer in dn.HiddenLayers)
{
var neurons = new List <Neuron>();
foreach (var n in layer)
{
var neuron = new Neuron
{
Id = n.Id,
Bias = n.Bias,
BiasDelta = n.BiasDelta,
Gradient = n.Gradient,
InputValue = n.InputValue,
OutputValue = n.OutputValue,
Error = n.Error
};
neurons.Add(neuron);
allNeurons.Add(neuron);
}
network.HiddenLayers.Add(neurons);
}
//Export Layer
foreach (var n in dn.OutputLayer)
{
var neuron = new Neuron
{
Id = n.Id,
Bias = n.Bias,
BiasDelta = n.BiasDelta,
Gradient = n.Gradient,
InputValue = n.InputValue,
OutputValue = n.OutputValue,
Error = n.Error
};
network.OutputLayer.Add(neuron);
allNeurons.Add(neuron);
}
//Synapses
foreach (var syn in dn.Synapses)
{
var synapse = new Synapse {
Id = syn.Id
};
var inputNeuron = allNeurons.First(x => x.Id == syn.InputNeuronId);
var outputNeuron = allNeurons.First(x => x.Id == syn.OutputNeuronId);
synapse.InputNeuron = inputNeuron;
synapse.OutputNeuron = outputNeuron;
synapse.Weight = syn.Weight;
synapse.WeightDelta = syn.WeightDelta;
inputNeuron.OutputSynapses.Add(synapse);
outputNeuron.InputSynapses.Add(synapse);
}
return(network);
}
