public override string FormatNodeValue(DynamicalNode node)
{
if (node is NeuralOscillatorNode)
{
double averageMagnitude = node.AverageMagnitude;
return(averageMagnitude.ToString("F4"));
}
else
{
return(base.FormatNodeValue(node));
}
}
public GFNN2LayerSystem(
IIntegrator integrator,
ToneNode soundSource,
bool enableLearning,
double middleFrequency = ToneNode.MiddleC,
int octaves = 2,
int nodesPerOctave = 120)
: base(integrator)
{
this.EnableLearning = enableLearning;
// Initialize SIRs
this.SIRs = new double[]
{
// Sub harmonics.
1.0 * 1 / 1,
1.0 * 1 / 2,
//1.0 * 1 / 3,
//1.0 * 2 / 3,
//1.0 * 1 / 4,
//1.0 * 3 / 4,
//1.0 * 1 / 5,
//1.0 * 2 / 5,
//1.0 * 3 / 5,
//1.0 * 4 / 5,
// 12-tone ET.
1.0 * 16 / 15,
1.0 * 9 / 8,
1.0 * 6 / 5,
1.0 * 5 / 4,
1.0 * 4 / 3,
1.0 * 17 / 12,
1.0 * 3 / 2,
1.0 * 8 / 5,
1.0 * 5 / 3,
1.0 * 16 / 9,
1.0 * 15 / 8,
1.0 * 2 / 1,
};
_soundSource = soundSource;
_layers = new List <List <NeuralOscillatorNode> >();
var layer1 = GenerateLayer(
"COC",
middleFrequency,
octaves,
nodesPerOctave,
-0.01,
// omega=2 pi
-1,
-1,
0,
0.1,
2.0 * Math.PI);
var layer2 = GenerateLayer(
"DCN",
middleFrequency,
octaves,
nodesPerOctave,
-0.4,
1.2,
-1,
-0.01,
0.75,
0);
// Connect sound source to layer 1.
foreach (var node in layer1)
{
node.AddIncomingNode(soundSource);
}
// Afferent layer 1 to layer 2.
// Connect each node in layer 1 to its corresponding node in layer 2.
double afferentWeightPlasticity = 0;
double afferentWeightDecay = 0;
foreach (var pair in layer1.Zip(layer2, (n1, n2) => new { L1N = n1, L2N = n2 }))
{
Debug.Assert(pair.L1N.CenterFrequency == pair.L2N.CenterFrequency);
pair.L2N.AddIncomingNode(pair.L1N, 1.0, afferentWeightDecay, afferentWeightPlasticity);
}
// Internal layer 2 to layer 2.
double internalWeight = 0.005;
double sirMatchThreshold = 0.005;
double internalWeightDecay = 1.0;
double internalWeightPlasticity = 1.0;
int connectionsAdded = 0;
int iteration = -1;
var outerJoin = layer2
.Join(layer2, n => true, n => true, (n1, n2) => new { N1 = n1, N2 = n2 })
.OrderBy(n => n.N1.CenterFrequency)
.ThenBy(n => n.N2.CenterFrequency);
foreach (var pair in outerJoin)
{
iteration++;
if (pair.N1 == pair.N2)
{
continue;
}
// Learn weights.
if (this.EnableLearning)
{
//if (iteration % 2 != 0)
//{
// continue;
//}
pair.N1.AddIncomingNode(pair.N2, internalWeight, internalWeightDecay, internalWeightPlasticity);
pair.N2.AddIncomingNode(pair.N1, internalWeight, internalWeightDecay, internalWeightPlasticity);
connectionsAdded += 2;
}
// Hardcoded weights.
else
{
// Connect layer 2 nodes to each other when they
// match a (probably) learned SIR/eigenmultiple frequency.
double frequencyRatio = pair.N1.CenterFrequency / pair.N2.CenterFrequency;
double frequencyRatioAlt = 1.0 / frequencyRatio;
foreach (double sir in this.SIRs)
{
bool isMatch = MathHelpers.ApproximatelyEqual(frequencyRatio, sir, sirMatchThreshold) ||
MathHelpers.ApproximatelyEqual(frequencyRatioAlt, sir, sirMatchThreshold);
if (!isMatch)
{
continue;
}
pair.N1.AddIncomingNode(pair.N2, internalWeight, 0, 0);
pair.N2.AddIncomingNode(pair.N1, internalWeight, 0, 0);
connectionsAdded += 2;
goto NextPair;
}
}
NextPair:
continue;
}
// Assign layers to system.
var allNodes = new DynamicalNode[] { soundSource }.Concat(layer1).Concat(layer2);
this.Nodes = allNodes;
// Remember layers.
_layers.Add(layer1);
_layers.Add(layer2);
// Create hebbian learning system.
if (this.EnableLearning)
{
_hebbianSystem = new ConnectionWeightSystem(new RungeKuttaIntegrator(), this);
}
}
