protected NeuralLink(NeuralNodeBase inNode, NeuralNodeBase outNode)
{
InNode = inNode;
OutNode = outNode;
InitValues(1);
}
public override void CreateNetwork()
{
Nodes = new NeuralNodeBase[NodeCount];
Links = new NeuralLink[LinkCount];
var curr = 0;
for (var i = 0; i < NodeCounts[0]; i++) // Input layer nodes
{
Nodes[curr++] = new InputNode();
}
FirstMiddleNode = curr; // Middle layer nodes
for (var i = 1; i < NumLayers - 1; i++)
{
for (var j = 0; j < NodeCounts[i]; j++)
{
Nodes[curr++] = new BPMiddleNode(LearningRate, MomentumTerm);
}
}
FirstOutputNode = curr; // Output layer nodes
for (var i = 0; i < NodeCounts[NumLayers - 1]; i++)
{
Nodes[curr++] = new BPOutputNode(LearningRate, MomentumTerm);
}
for (var i = 0; i < LinkCount; i++) // Create Links
{
Links[i] = new BackPropagationLink();
}
curr = 0;
int layer1 = 0, layer2 = FirstMiddleNode;
for (var i = 0; i < NumLayers - 1; i++) // Connect Layers
{
for (var j = 0; j < NodeCounts[i + 1]; j++)
{
for (var k = 0; k < NodeCounts[i]; k++)
{
Links[curr].InNode = Nodes[layer1 + k];
Links[curr].OutNode = Nodes[layer2 + j];
var link = Links[curr++];
var inNode = Nodes[layer1 + k];
var outNode = Nodes[layer2 + j];
inNode.OutLinks.Add(link);
outNode.InLinks.Add(link);
link.InNode = inNode;
link.OutNode = outNode;
}
}
layer1 = layer2;
layer2 += NodeCounts[i + 1];
}
}
protected override void createNetwork()
{
nodes = new NeuralNodeBase[nodeCount];
links = new NeuralLink[linkCount];
for (int i = 0; i < nodeCount - 2; i++) // Create Input Nodes
{
nodes[i] = new InputNode();
}
nodes[nodeCount - 2] = new BiasNode(); // Create Bias Node
nodes[nodeCount - 1] = new AdalineNode(learningRate);
for (int i = 0; i < linkCount; i++)
{
AdalineLink l = new AdalineLink(nodes[i], nodes[nodeCount - 1]); // Create links
//l.InNode = nodes[i];
//l.OutNode = nodes[nodeCount-1];
links[i] = l;
}
for (int i = 0; i < linkCount; i++) // Connect inputs to ADALINE
{
Link(nodes[i], nodes[nodeCount - 1], links[i]);
}
}
public AdalineLink(NeuralNodeBase InNode, NeuralNodeBase OutNode)
{
setValue(NeuralLink.WeightIndex, Utility.RandomRange(-1.0, 1.0));
inNode = InNode;
outNode = OutNode;
}
protected NeuralLink(NeuralNodeBase inNode, NeuralNodeBase outNode, int size)
{
InNode = inNode;
OutNode = outNode;
var numValues = size;
if (numValues < 1)
{
numValues = 1;
}
InitValues(numValues);
}
protected int OutputCount; // Number of Output Layer Nodes
public override void CreateNetwork()
{
Nodes = new NeuralNodeBase[NodeCount];
Links = new NeuralLink[LinkCount];
for (var i = 0; i < InputCount; i++) // Create Input Nodes
{
Nodes[i] = new BAMInputNode();
}
for (var i = InputCount; i < InputCount + OutputCount; i++) // Create Output Nodes
{
Nodes[i] = new BAMNode();
}
for (var i = 0; i < LinkCount; i++) // Create links
{
Links[i] = new BAMLink();
}
var currLink = 0;
for (var i = 0; i < InputCount; i++) // Connect inputs to ADALINE
{
for (var j = InputCount; j < InputCount + OutputCount; j++)
{
Links[currLink].InNode = Nodes[i];
Links[currLink].OutNode = Nodes[j];
var inNode = Nodes[i];
var outNode = Nodes[j];
var link = Links[currLink++];
outNode.InLinks.Add(link);
inNode.OutLinks.Add(link);
link.InNode = inNode;
link.OutNode = outNode;
}
}
}
public BackPropagationLink(NeuralNodeBase inNode, NeuralNodeBase outNode)
: base(inNode, outNode, 2)
{
Init();
}
public AdalineLink(NeuralNodeBase inNode, NeuralNodeBase outNode) : base(inNode, outNode)
{
this[WeightIndex] = Utility.RandomRange(-1.0, 1.0);
}
