public Synapse(int inputindex, Neuron target, double weight)
{
this.Source = null;
this.Target = target;
this.Weight = weight;
this.LastDiff = 0;
this.InputIndex = inputindex;
}
public Synapse(Neuron source, Neuron target, double weight)
{
this.Source = source;
this.Target = target;
this.Weight = weight;
this.LastDiff = 0;
InputIndex = -1;
}
public NeuralLayer(int numNeurons, int inputs, NeuralNetwork net)
{
Neurons = new Neuron[numNeurons];
NumInputs = inputs;
for(int i = 0; i < numNeurons; i++)
{
Neurons[i] = new Neuron(inputs, net);
}
//Neurons[0].Debug = true;
}
/// <summary>
/// Add a neuron to the network. If a neuron with the same name has
/// already been added to the network, a <code>DuplicateNeuronException</code>
/// is thrown. This need not be the same neuron, but all neurons that
/// are part of this network must have unique names, with respect to
/// the network.
/// </summary>
/// <param name="neuron">The neuron to add</param>
private void AddNeuron(Neuron neuron) {
if(neurons.ContainsKey(neuron.Name)) {
throw new DuplicateNeuronException("Network already contains " + neuron.Name);
}
neurons[neuron.Name] = neuron;
}
/// <summary>
/// Connect two neurons. This method is used to
/// build up connections within the network. The only connection established
/// by default is the connection to the bias node.
/// </summary>
/// <param name="baseNeuron">A base neuron to connect</param>
/// <param name="dendrite">A dendrite neuron to connect</param>
public void Connect(Neuron baseNeuron, Neuron dendrite) {
Axon axon = new Axon();
axon.Base = baseNeuron;
axon.Dendrite = dendrite;
baseNeuron.AddOutputAxon(axon);
dendrite.AddInputAxon(axon);
axons.Add(axon);
}
/// <summary>
/// Connect a neuron to the bias neuron.
/// </summary>
/// <param name="neuron">The bias neuron</param>
private void ConnectToBias(Neuron neuron) {
Axon biasAxon = new Axon();
biasAxon.Base = bias;
biasAxon.Dendrite = neuron;
bias.AddOutputAxon(biasAxon);
neuron.AddInputAxon(biasAxon);
axons.Add(biasAxon);
}
/// <summary>
/// Adds a "hidden layer" neuron. Hidden layers get their inputs from either
/// input neurons or other hidden layers. They then pass those values to the
/// next output or hidden layer.
/// </summary>
/// <param name="neuron">The new neuron to add</param>
public void AddInternalNeuron(Neuron neuron) {
neuron.ActivationFunction = factory.MakeFunction();
AddNeuron(neuron);
ConnectToBias(neuron);
}
/// <summary>
/// Add input neuron. Input neurons have their value "set" to a given value
/// and then propagate that value to any neurons they are connected to.
/// </summary>
/// <param name="neuron">The new input neuron</param>
public void AddInputNeuron(Neuron neuron) {
AddNeuron(neuron);
inputNeurons[neuron.Name] = neuron;
}
/// <summary>Add output neuron.</summary>
/// <param name="neuron">The output neuron</param>
public void AddOutputNeuron(Neuron neuron) {
neuron.ActivationFunction = factory.MakeFunction();
AddNeuron(neuron);
outputNeurons[neuron.Name] = neuron;
ConnectToBias(neuron);
}
/// <summary>
/// Create a new instance of a network. By default a bias node
/// is added to the network and automatically connected to any
/// neurons added to the network (except input neurons).
/// </summary>
public Network() {
bias = new Neuron("Bias neuron");
}
/// <summary>
/// Adds a "hidden layer" neuron. Hidden layers get their inputs from either
/// input neurons or other hidden layers. They then pass those values to the
/// next output or hidden layer.
/// </summary>
/// <param name="neuron">The new neuron to add</param>
public void AddInternalNeuron(Neuron neuron)
{
neuron.ActivationFunction = factory.MakeFunction();
AddNeuron(neuron);
ConnectToBias(neuron);
}
/// <summary>
/// Add input neuron. Input neurons have their value "set" to a given value
/// and then propagate that value to any neurons they are connected to.
/// </summary>
/// <param name="neuron">The new input neuron</param>
public void AddInputNeuron(Neuron neuron)
{
AddNeuron(neuron);
inputNeurons[neuron.Name] = neuron;
}
/// <summary>
/// Create a new instance of a network. By default a bias node
/// is added to the network and automatically connected to any
/// neurons added to the network (except input neurons).
/// </summary>
public Network()
{
bias = new Neuron("Bias neuron");
}
void ProcNeuro(Neuron& n);
private void RefreshDgvNeurons()
{
if (!DoRefreshDgvNeurons)
{
return;
}
if (Network.Topology.GetAllNeuronsCount() > 50)
{
if (MessageBox.Show(this, "There is more than 50 neurons to show. Are you sure you want to see all neurons with details?", "More than 50 neurons",
MessageBoxButtons.YesNoCancel, MessageBoxIcon.Question) != DialogResult.OK)
{
cbHideDgvNeurons.Checked = true;
return;
}
}
int selectedRowIdx = -1;
int selectedColumnIdx = -1;
if (dgvNeurons.SelectedCells.Count == 1)
{
selectedRowIdx = dgvNeurons.SelectedCells[0].RowIndex;
selectedColumnIdx = dgvNeurons.SelectedCells[0].ColumnIndex;
}
dgvNeurons.Rows.Clear();
dgvNeurons.Columns[2].Visible = Network.Topology.IsNetworkUsingBiases;
for (int layerNo = 0; layerNo < Network.Topology.Layers.Count; layerNo++)
{
Layer layer = Network.Topology.Layers[layerNo];
String[] tab = new String[dgvNeurons.ColumnCount];
tab[0] = (layerNo + 1).ToString();
for (int neuronNo = 0; neuronNo < layer.Neurons.Count; neuronNo++)
{
Neuron neuron = Network.Topology.Layers[layerNo].Neurons[neuronNo];
tab[1] = (neuronNo + 1).ToString();
tab[2] = neuron.IsBias ? "yes" : "no";
tab[3] = layer.LayerActivationFunction.ToString();
int idx = 4;
for (int inputNo = 0; inputNo < neuron.Inputs.Count; inputNo++)
{
if (!neuron.IsBias)
{
Input input = neuron.Inputs[inputNo];
if (layerNo == 0)
{
tab[idx++] = "none";
tab[idx++] = Tools.DoubleToString(input.Value, howManyDigitsShowAfterDotPlaces);
}
else
{
tab[idx++] = Tools.DoubleToString(input.Weight, howManyDigitsShowAfterDotPlaces);
tab[idx++] = Tools.DoubleToString(input.Value, howManyDigitsShowAfterDotPlaces);
}
}
else
{
tab[idx++] = "none";
tab[idx++] = "none";//"1";
}
}
tab[tab.Length - 1] = Tools.DoubleToString(neuron.GetOutputValue(), howManyDigitsShowAfterDotPlaces);
dgvNeurons.Rows.Add(tab);
}
}
if (selectedColumnIdx != -1 && selectedRowIdx != -1)
{
dgvNeurons[selectedColumnIdx, selectedRowIdx].Selected = true;
}
}