/// <summary>
/// Instanciate a new synapse. You'll hardly need to call this as the preferred way to create neurons is using the neuron, layer or even network classes.
/// </summary>
/// <param name="source">The source neuron feeding this synapse.</param>
/// <param name="target">The target neuron fed by this synapse.</param>
public Synapse(ConfigNode node, Neuron source, Neuron target)
{
node.AttatchToHost(this);
_sourceNeuron = source;
_targetNeuron = target;
_weight = _config.RandomInitialWeight();
}
public SingleOutput(Neuron neuron)
{
InitializeComponent();
this.neuron = neuron;
UpdateData();
//neuron.OnOutputChanged += new ValueChangedEventHandler(OnNeuronOutputChanged);
}
private double _weightBuffer = 0; //Buffer for MomentumTerm Optimization
#endregion Fields
#region Constructors
/// <summary>
/// Instanciate a new synapse. You'll hardly need to call this as the preferred way to create neurons is using the neuron, layer or even network classes.
/// </summary>
/// <param name="source">The source neuron feeding this synapse.</param>
/// <param name="target">The target neuron fed by this synapse.</param>
/// <param name="weight">The weight of the synapse.</param>
public Synapse(ConfigNode node, Neuron source, Neuron target, double weight)
{
node.AttatchToHost(this);
_sourceNeuron = source;
_targetNeuron = target;
_weight = weight;
}
/// <summary>
/// Instanciate new event arguments.
/// </summary>
/// <param name="neuron">The refering neuron.</param>
public NeuronEventArgs(Neuron neuron)
{
this.neuron = neuron;
}
protected void AddOutputNeuron(Neuron neuron)
{
SingleOutput so = new SingleOutput(neuron);
so.Location = new Point(0,17*items.Count);
items.Add(so);
Controls.Add(so);
}
protected void RemoveOutputNeuron(Neuron neuron)
{
}
/// <summary>
/// Connect input neurons of a specified 'ring' to a given neuron.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="neuron">The neuron to connect to.</param>
/// <param name="width">The width of the input grid (needed for transforming (x,y) to linear position).</param>
/// <param name="height">The height of the input grid (needed for transforming (x,y) to linear position).</param>
/// <param name="shift">The radial shift. 0 means the biggest radius, 1 the second biggest etc.</param>
protected void ConnectRing(Layer input, Neuron neuron, int width, int height, int shift)
{
ConnectVerticalLine(input,neuron,width,shift,shift,height-1-shift);
ConnectVerticalLine(input,neuron,width,width-1-shift,shift,height-1-shift);
ConnectHorizontalLine(input,neuron,width,shift,shift+1,width-2-shift);
ConnectHorizontalLine(input,neuron,width,height-1-shift,shift+1,width-2-shift);
}
/// <summary>
/// Connect input neurons in horizontal lines to new hidden neurons.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="hidden">The neuron to connect to.</param>
/// <returns>The set of new hidden neurons.</returns>
protected Neuron[] AddHorizontalLines(Layer input, Layer hidden)
{
Neuron[] horizontal = new Neuron[_height];
for(int i=0;i<_height;i++)
{
horizontal[i] = new Neuron(_network.Node, hidden);
ConnectHorizontalLine(input,horizontal[i],_width,i,0,_width-1);
}
return horizontal;
}
/// <summary>
/// Connect input neurons of a specified oblong to a given neuron.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="neuron">The neuron to connect to.</param>
/// <param name="width">The width of the input grid (needed for transforming (x,y) to linear position).</param>
/// <param name="x">X-Coordinate of the top left cornet.</param>
/// <param name="y">Y-Coordinate of the top left cornet.</param>
/// <param name="dx">Width of the oblong.</param>
/// <param name="dy">Height of the oblong.</param>
protected void ConnectArea(Layer input, Neuron neuron, int width, int x, int y, int dx, int dy)
{
for(int i=x;i<x+dx;i++)
for(int j=y;j<y+dy;j++)
input[i+j*width].ConnectToNeuron(neuron);
}
/// <summary>
/// Connect input neurons of a specified horizontal line to a given neuron.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="neuron">The neuron to connect to.</param>
/// <param name="width">The width of the input grid (needed for transforming (x,y) to linear position).</param>
/// <param name="y">The constant Y-Coordinate of the line.</param>
/// <param name="xmin">The left end X-Coordinate.</param>
/// <param name="xmax">The right end X-Coordinate.</param>
protected void ConnectHorizontalLine(Layer input, Neuron neuron, int width, int y, int xmin, int xmax)
{
for(int i=xmin;i<xmax+1;i++)
input[i+y*width].ConnectToNeuron(neuron);
}
/// <summary>
/// Connect input neurons in vertical lines to new hidden neurons.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="hidden">The neuron to connect to.</param>
/// <returns>The set of new hidden neurons.</returns>
protected Neuron[] AddVerticalLines(Layer input, Layer hidden)
{
Neuron[] vertical = new Neuron[_width];
for(int i=0;i<_width;i++)
{
vertical[i] = new Neuron(_network.Node, hidden);
ConnectVerticalLine(input,vertical[i],_width,i,0,_height-1);
}
return vertical;
}
/// <summary>
/// Connect input neurons in squares to new hidden neurons.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="hidden">The neuron to connect to.</param>
/// <returns>The set of new hidden neurons.</returns>
protected Neuron[] AddSquares(Layer input, Layer hidden)
{
if(_width < 2 || _height < 2)
return new Neuron[] {};
int widthhalf = (int)Math.Ceiling(_width/2d);
int heighthalf = (int)Math.Ceiling(_height/2d);
Neuron[] area = new Neuron[widthhalf*heighthalf];
widthhalf -= widthhalf % 2;
heighthalf -= heighthalf % 2;
int item = 0;
for(int i=0;i<=widthhalf;i+=2)
{
for(int j=0;j<=heighthalf;j+=2)
{
area[item] = new Neuron(_network.Node, hidden);
ConnectArea(input,area[item++],_width,i,j,2,2);
}
for(int j=_height-2;j>heighthalf;j-=2)
{
area[item] = new Neuron(_network.Node, hidden);
ConnectArea(input,area[item++],_width,i,j,2,2);
}
}
for(int i=_width-2;i>widthhalf;i-=2)
{
for(int j=0;j<=heighthalf;j+=2)
{
area[item] = new Neuron(_network.Node, hidden);
ConnectArea(input,area[item++],_width,i,j,2,2);
}
for(int j=_height-2;j>heighthalf;j-=2)
{
area[item] = new Neuron(_network.Node, hidden);
ConnectArea(input,area[item++],_width,i,j,2,2);
}
}
return area;
}
/// <summary>
/// Connect input neurons in rings to new hidden neurons.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="hidden">The neuron to connect to.</param>
/// <returns>The set of new hidden neurons.</returns>
protected Neuron[] AddRings(Layer input, Layer hidden)
{
Neuron[] ring = new Neuron[Math.Min(_width,_height)/2];
for(int i=0;i<ring.Length;i++)
{
ring[i] = new Neuron(_network.Node, hidden);
ConnectRing(input,ring[i],_width,_height,i);
}
return ring;
}
/// <summary>
/// Connect this neuron forward to another (specified) neuron.
/// </summary>
/// <param name="target">The target neuron.</param>
/// <param name="weight">The weight of the synapse.</param>
/// <returns>The new connection synapse.</returns>
public Synapse ConnectToNeuron(Neuron target, double weight)
{
Synapse s = new Synapse(_node.BuildSibling(), this, target, weight);
_targetSynapses.Add(s);
target._sourceSynapses.Add(s);
if(TargetSynapseAdded != null)
TargetSynapseAdded(this,new SynapseEventArgs(s));
return s;
}
/// <summary>
/// Connect input neurons of a specified vertical line to a given neuron.
/// </summary>
/// <param name="input">The input layer.</param>
/// <param name="neuron">The neuron to connect to.</param>
/// <param name="width">The width of the input grid (needed for transforming (x,y) to linear position).</param>
/// <param name="x">The constant X-Coordinate of the line.</param>
/// <param name="ymin">The top end Y-Coordinate.</param>
/// <param name="ymax">The bottom end Y-Coordinate.</param>
protected void ConnectVerticalLine(Layer input, Neuron neuron, int width, int x, int ymin, int ymax)
{
for(int i=ymin;i<ymax+1;i++)
input[x+i*width].ConnectToNeuron(neuron);
}
/// <summary>
/// Remove all connections from this neuron forward to another (specified) neuron.
/// </summary>
/// <param name="source">The neuron feeding this neuron.</param>
public void DisconnectToNeuron(Neuron source)
{
for(int i=0;i<_targetSynapses.Count;i++)
{
Synapse s = _targetSynapses[i];
if(s.TargetNeuron == source)
{
_targetSynapses.Remove(s);
s.TargetNeuron._sourceSynapses.Remove(s);
if(TargetSynapseRemoved != null)
TargetSynapseRemoved(this,new SynapseEventArgs(s));
i--;
}
}
}
/// <summary>
/// Instanciate new event arguments.
/// </summary>
/// <param name="neuron">The refering neuron.</param>
public NeuronEventArgs(Neuron neuron)
{
this.neuron = neuron;
}
