NanoBrain/NanoBrain-unitypackage
NanoBrain/NanoBrain-unitypackage
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Cleanup

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This commit is contained in:
Pascal Serrarens 2026-02-11 17:22:20 +01:00
parent 42bc32c734
commit a62d1cc5d9
4 changed files with 55 additions and 100 deletions
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13
MemoryCell.cs
View File

@ -34,18 +34,7 @@ public class MemoryCell : Neuron {
public override void UpdateStateIsolated() {
// A memorycell does not have an activation function
Vector3 result = this.bias;
int n = 0;
//Applying the weight factgors
foreach (Synapse synapse in this.synapses) {
result += synapse.weight * synapse.nucleus.outputValue;
if (lengthsq(synapse.nucleus.outputValue) != 0)
n++;
}
if (this.average)
result /= n;
float3 result = Combinator();
if (initialized)
// Output the previous, memorized value

139
Neuron.cs
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@ -58,12 +58,6 @@ public class Neuron : Nucleus {
public AnimationCurve curve;
public float curveMax = 1.0f;
#region Parameters
public bool average = false;
#endregion Parameters
public AnimationCurve GenerateCurve() {
switch (this.curvePreset) {
case CurvePresets.Linear:
@ -84,14 +78,6 @@ public class Neuron : Nucleus {
}
}
public virtual void Deserialize(Neuron nucleus) { }
#endregion Serialization
#region Runtime state (not serialized)
#region Activation
public static class Presets {
private const int samples = 32;
public static AnimationCurve Linear(float weight) {
@ -136,9 +122,7 @@ public class Neuron : Nucleus {
}
}
#endregion Activation
#endregion Runtime state
#endregion Serialization
// this clone the nucleus without the synapses and receivers
public override Nucleus ShallowCloneTo(Cluster newParent) {
@ -166,9 +150,7 @@ public class Neuron : Nucleus {
clone.combinator = this.combinator;
clone.curve = this.curve;
clone.curvePreset = this.curvePreset;
Debug.Log($"clone preset {clone.name} = {clone.curvePreset}");
clone.curveMax = this.curveMax;
clone.average = this.average;
}
public static void Delete(Nucleus nucleus) {
@ -196,52 +178,20 @@ public class Neuron : Nucleus {
}
public override void UpdateStateIsolated() {
float3 result = CombinatorAction();
this.outputValue = Activation(result);
// switch (this.type) {
// case Type.Neuron:
// UpdateSum();
// break;
// case Type.Pulsar:
// UpdateProduct();
// break;
// default:
// UpdateSum();
// break;
// }
// Vector3 sum = this.bias;
// int n = 0;
// //Applying the weight factgors
// foreach (Synapse synapse in this.synapses) {
// sum += synapse.weight * synapse.nucleus.outputValue;
// // Perhaps synapses should be removed when the output value goes to 0....
// if (lengthsq(synapse.nucleus.outputValue) != 0) {
// n++;
// this.stale = 0;
// }
// }
// if (this.average && n > 0)
// sum /= n;
// // Activation function
// float3 result = Activation(sum);
// if (this.stale > staleValueForSleep)
// this.outputValue = new float3(0, 0, 0);
// else
// this.outputValue = result;
float3 result = Combinator();
this.outputValue = Activator(result);
}
private Func<float3> CombinatorAction => combinator switch {
CombinatorType.Sum => UpdateSum,
CombinatorType.Product => UpdateProduct,
CombinatorType.Max => UpdateMax,
_ => UpdateSum
#region Combinator
protected Func<float3> Combinator => combinator switch {
CombinatorType.Sum => CombinatorSum,
CombinatorType.Product => CombinatorProduct,
CombinatorType.Max => CombinatorMax,
_ => CombinatorSum
};
public float3 UpdateSum() {
public float3 CombinatorSum() {
Vector3 sum = this.bias;
foreach (Synapse synapse in this.synapses)
sum += synapse.weight * synapse.nucleus.outputValue;
@ -249,7 +199,7 @@ public class Neuron : Nucleus {
//this.outputValue = Activation(sum);
}
public float3 UpdateProduct() {
public float3 CombinatorProduct() {
float3 product = this.bias;
foreach (Synapse synapse in this.synapses)
product *= synapse.weight * synapse.nucleus.outputValue;
@ -257,7 +207,7 @@ public class Neuron : Nucleus {
//this.outputValue = Activation(product);
}
public float3 UpdateMax() {
public float3 CombinatorMax() {
float3 max = this.bias;
float maxSqrLength = lengthsq(max);
@ -274,32 +224,49 @@ public class Neuron : Nucleus {
return max;
}
protected float3 Activation(float3 input) {
float3 result = Vector3.zero;
switch (this.curvePreset) {
case CurvePresets.Linear:
result = input;
break;
case CurvePresets.Sqrt:
result = normalize(input) * System.MathF.Sqrt(length(input));
break;
case CurvePresets.Power:
result = normalize(input) * System.MathF.Pow(length(input), 2);
break;
case CurvePresets.Reciprocal: {
float magnitude = length(input);
if (magnitude > 0)
result = normalize(input) * (1 / magnitude);
break;
}
default:
float activatedValue = this.curve.Evaluate(length(input));
result = normalize(input) * activatedValue;
break;
}
#endregion Combinator
#region Activator
protected Func<float3, float3> Activator => this.curvePreset switch {
CurvePresets.Linear => ActivatorLinear,
CurvePresets.Sqrt => ActivatorSqrt,
CurvePresets.Power => ActivatorPower,
CurvePresets.Reciprocal => ActivatorReciprocal,
_ => ActivatorCustom
};
protected float3 ActivatorLinear(float3 input) {
return input;
}
protected float3 ActivatorSqrt(float3 input) {
float3 result = normalize(input) * System.MathF.Sqrt(length(input));
return result;
}
protected float3 ActivatorPower(float3 input) {
float3 result = normalize(input) * System.MathF.Pow(length(input), 2);
return result;
}
protected float3 ActivatorReciprocal(float3 input) {
float magnitude = length(input);
if (magnitude == 0)
return new float3(0, 0, 0);
float3 result = normalize(input) * (1 / magnitude);
return result;
}
protected float3 ActivatorCustom(float3 input) {
float activatedValue = this.curve.Evaluate(length(input));
float3 result = normalize(input) * activatedValue;
return result;
}
#endregion Activator
public virtual void ProcessStimulus(Vector3 inputValue, string thingName = null) {
this.stale = 0;
this.bias = inputValue;

2
Pulsar.cs
View File

@ -49,6 +49,6 @@ public class Pulsar : Neuron {
}
// Activation function
this.outputValue = Activation(product);
this.outputValue = Activator(product);
}
}

1
Selector.cs
View File

@ -13,7 +13,6 @@ public class Selector : Neuron {
curve = this.curve,
curvePreset = this.curvePreset,
curveMax = this.curveMax,
average = this.average
};
return clone;
}