/// <summary>
/// Overridden.Constucts the network topology.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
for (var i = 0; i < InputNodesCount; i++)
{
nodes[i] = new BidirectionalAssociativeMemoryInputNode();
}
for (var i = InputNodesCount; i < InputNodesCount + OutputNodesCount; i++)
{
nodes[i] = new BidirectionalAssociativeMemoryOutputNode();
}
for (var i = 0; i < linksCount; i++)
{
links[i] = new BidirectionalAssociativeMemoryLink();
}
var k = 0;
for (var i = 0; i < InputNodesCount; i++)
{
for (var j = InputNodesCount; j < InputNodesCount + OutputNodesCount; j++)
{
nodes[i].LinkTo(nodes[j], links[k]);
k++;
}
}
}
/// <summary>
/// Overridden.Initializes network data after construction.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[0];
links = new NeuroLink[0];
networksCount = 0;
orthogonalBAMEnergy = -inputLayerNodesCount * outputLayerNodesCount;
bestError = double.PositiveInfinity;
}
/// <summary>
/// Overridden.Method that constructs network topology.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
var cnt = 0;
for (var i = 0; i < InputNodesCount; i++)
{
nodes[cnt] = new InputNode();
cnt++;
}
firstMiddleNode = cnt;
for (var i = 1; i < (layersCount - 1); i++)
{
for (var j = 0; j < nodesInLayer[i]; j++)
{
nodes[cnt] = new BackPropagationMiddleNode(LearningRate, momentum);
cnt++;
}
}
firstOutputNode = cnt;
for (var i = 0; i < OutputNodesCount; i++)
{
nodes[cnt] = new BackPropagationOutputNode(LearningRate, momentum);
cnt++;
}
for (var i = 0; i < LinksCount; i++)
{
links[i] = CreateLink();
}
cnt = 0;
var l1 = 0;
var l2 = firstMiddleNode;
for (var i = 0; i < (layersCount - 1); i++)
{
for (var j = 0; j < nodesInLayer[i + 1]; j++)
{
for (var k = 0; k < nodesInLayer[i]; k++)
{
nodes[l1 + k].LinkTo(nodes[l2 + j], links[cnt]);
cnt++;
}
}
l1 = l2;
l2 += nodesInLayer[i + 1];
}
}
/// <summary>
/// Overridden.Constructs network topology.
/// </summary>
/// <remarks>Creates nodes, links and connects nodes using created links.</remarks>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
for (var i = 0; i < InputNodesCount; i++)
{
nodes[i] = new InputNode();
}
nodes[NodesCount - 2] = new BiasNode(1);
nodes[NodesCount - 1] = new AdalineNode(LearningRate);
for (var i = 0; i < LinksCount; i++)
{
links[i] = new AdalineLink();
}
for (var i = 0; i < LinksCount; i++)
{
nodes[i].LinkTo(nodes[NodesCount - 1], links[i]);
}
}
/// <summary>
/// Overridden.Constructs network topology.
/// </summary>
/// <remarks>Creates nodes, links and connects nodes using created links.</remarks>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
for (var i = 0; i < InputNodesCount; i++)
nodes[i] = new InputNode();
nodes[NodesCount - 2] = new BiasNode(1);
nodes[NodesCount - 1] = new AdalineNode(LearningRate);
for (var i = 0; i < LinksCount; i++)
links[i] = new AdalineLink();
for (var i = 0; i < LinksCount; i++)
nodes[i].LinkTo(nodes[NodesCount - 1], links[i]);
}
/// <summary>
/// Overridden.Method that constructs network topology.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
var cnt = 0;
for (var i = 0; i < InputNodesCount; i++)
{
nodes[cnt] = new InputNode();
cnt++;
}
firstMiddleNode = cnt;
for (var i = 1; i < (layersCount - 1); i++)
for (var j = 0; j < nodesInLayer[i]; j++)
{
nodes[cnt] = new BackPropagationMiddleNode(LearningRate, momentum);
cnt++;
}
firstOutputNode = cnt;
for (var i = 0; i < OutputNodesCount; i++)
{
nodes[cnt] = new BackPropagationOutputNode(LearningRate, momentum);
cnt++;
}
for (var i = 0; i < LinksCount; i++)
links[i] = CreateLink();
cnt = 0;
var l1 = 0;
var l2 = firstMiddleNode;
for (var i = 0; i < (layersCount - 1); i++)
{
for (var j = 0; j < nodesInLayer[i + 1]; j++)
for (var k = 0; k < nodesInLayer[i]; k++)
{
nodes[l1 + k].LinkTo(nodes[l2 + j], links[cnt]);
cnt++;
}
l1 = l2;
l2 += nodesInLayer[i + 1];
}
}
/// <summary>
/// Overridden.Constructs network topology.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
linksCount = NodesCount*rowsCount*columsCount;
kohonenLayer = new NeuroNode[rowsCount, columsCount];
links = new NeuroLink[LinksCount];
for (var i = 0; i < NodesCount; i++)
nodes[i] = new InputNode();
var curr = 0;
for (var row = 0; row < rowsCount; row++)
for (var col = 0; col < columsCount; col++)
{
kohonenLayer[row, col] = new SelfOrganizingNode(learningRate);
for (var i = 0; i < NodesCount; i++)
{
links[curr] = new SelfOrganizingLink();
nodes[i].LinkTo(kohonenLayer[row, col], links[curr]);
curr++;
}
}
}
/// <summary>
/// Overridden.Initializes network data after construction.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[0];
links = new NeuroLink[0];
networksCount = 0;
orthogonalBAMEnergy = -inputLayerNodesCount*outputLayerNodesCount;
bestError = double.PositiveInfinity;
}
/// <summary>
/// Overridden.Constucts the network topology.
/// </summary>
protected override void CreateNetwork()
{
nodes = new NeuroNode[NodesCount];
links = new NeuroLink[LinksCount];
for (var i = 0; i < InputNodesCount; i++)
nodes[i] = new BidirectionalAssociativeMemoryInputNode();
for (var i = InputNodesCount; i < InputNodesCount + OutputNodesCount; i++)
nodes[i] = new BidirectionalAssociativeMemoryOutputNode();
for (var i = 0; i < linksCount; i++)
links[i] = new BidirectionalAssociativeMemoryLink();
var k = 0;
for (var i = 0; i < InputNodesCount; i++)
for (var j = InputNodesCount; j < InputNodesCount + OutputNodesCount; j++)
{
nodes[i].LinkTo(nodes[j], links[k]);
k++;
}
}