NanoBrain/NanoBrain-unitypackage
NanoBrain/NanoBrain-unitypackage
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Fixed the evaluation order

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This commit is contained in:
Pascal Serrarens 2026-01-30 12:46:29 +01:00
parent b4f8e5a4d8
commit 91c4500b0a
4 changed files with 121 additions and 20 deletions
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35
Cluster.cs
View File

@ -262,20 +262,45 @@ public class Cluster : INucleus {
UpdateState(new float3(0, 0, 0));
}
public void UpdateState(float3 inputValue) {
float3 sum = inputValue; // new(0, 0, 0);
public void UpdateState(float3 bias) {
float3 sum = bias; // new(0, 0, 0);
//Applying the weight factgors
//Applying the weight factors
foreach (Synapse synapse in this.synapses) {
sum += synapse.weight * synapse.nucleus.outputValue;
}
//this.prefab.inputs[0].UpdateState(sum);
this.inputs[0].UpdateState(sum);
//this.inputs[0].UpdateState(sum);
this.inputs[0].UpdateStateIsolated(sum);
foreach (IReceptor receptor in this.sortedNuclei) {
if (receptor is INucleus nucleus && nucleus != this.inputs[0])
nucleus.UpdateStateIsolated();
}
UpdateResult(this.output.outputValue);
}
public void UpdateStateIsolated() {
float3 bias = new(0,0,0);
UpdateStateIsolated(bias);
}
public void UpdateStateIsolated(float3 bias) {
float3 sum = bias; // new(0, 0, 0);
//Applying the weight factors
foreach (Synapse synapse in this.synapses) {
sum += synapse.weight * synapse.nucleus.outputValue;
}
//this.inputs[0].UpdateState(sum);
this.inputs[0].UpdateStateIsolated(sum);
foreach (IReceptor receptor in this.sortedNuclei) {
if (receptor is INucleus nucleus && nucleus != this.inputs[0])
nucleus.UpdateStateIsolated();
}
this.outputValue = this.output.outputValue;
}
public virtual void UpdateResult(Vector3 result) {
// float d = Vector3.Distance(result, this.outputValue);
// if (d < 0.5f) {

3
INucleus.cs
View File

@ -20,7 +20,8 @@ public interface INucleus : IReceptor {
public void UpdateState();
public void UpdateState(float3 inputValue);
public void UpdateStateIsolated();
public void UpdateStateIsolated(float3 inputValue);
#endregion dynamic state
}

59
MemoryCell.cs
View File

@ -4,27 +4,34 @@ using Unity.Mathematics;
using static Unity.Mathematics.math;
[Serializable]
public class MemoryCell : Neuron {
public class MemoryCell : Neuron, INucleus {
public MemoryCell(ClusterPrefab cluster, string name) : base(cluster, name) {}
public MemoryCell(ClusterPrefab cluster, string name) : base(cluster, name) { }
public MemoryCell(Cluster parent, string name) : base(parent, name) { }
// this.parent = parent;
// this.name = name;
// this.parent?.nuclei.Add(this);
// }
#region Parameters
// Returns the memorized value weighted by time
// return lastValue * (current time - last time)
// [SerializeField]
// public bool deltaValue = false;
#endregion Parameters
public override IReceptor ShallowCloneTo(Cluster newParent) {
MemoryCell clone = new(newParent, this.name) {
array = this.array,
curve = this.curve,
curvePreset = this.curvePreset,
curveMax = this.curveMax,
average = this.average
};
return clone;
}
#region State
private float3 _memorizedValue;
private float _memorizedTime;
public override void UpdateState() {
public override void UpdateState(float3 bias) {
// A memorycell does not have an activation function
float3 result = new(0, 0, 0);
float3 result = bias;
int n = 0;
//Applying the weight factgors
@ -40,6 +47,32 @@ public class MemoryCell : Neuron {
UpdateResult(result);
}
public override void UpdateStateIsolated() {
float3 bias = new(0, 0, 0);
UpdateStateIsolated(bias);
}
public override void UpdateStateIsolated(float3 bias) {
// A memorycell does not have an activation function
float3 result = 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;
this.outputValue = this._memorizedValue;
// Store the result for the next time
this._memorizedValue = result;
this._memorizedTime = Time.time;
}
public override void UpdateResult(Vector3 result) {
// output value is the previous value
// if (this.deltaValue) {
@ -47,7 +80,7 @@ public class MemoryCell : Neuron {
// this._outputValue = this._memorizedValue * deltaTime;
// }
//else
this.outputValue = this._memorizedValue;
this.outputValue = this._memorizedValue;
// Store the result for the next time
this._memorizedValue = result;

44
Neuron.cs
View File

@ -282,7 +282,8 @@ public class Neuron : INucleus {
}
public virtual void UpdateState() {
UpdateState(new float3(0, 0, 0));
//UpdateState(new float3(0, 0, 0));
this.parent?.UpdateState();
}
public virtual void UpdateState(float3 inputValue) {
@ -323,6 +324,47 @@ public class Neuron : INucleus {
UpdateResult(result);
}
public virtual void UpdateStateIsolated() {
UpdateStateIsolated(new float3(0, 0, 0));
}
public virtual void UpdateStateIsolated(float3 bias) {
float3 sum = 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++;
}
if (this.average && n > 0)
sum /= n;
// Activation function
Vector3 result;
switch (this.curvePreset) {
case CurvePresets.Linear:
result = sum;
break;
case CurvePresets.Sqrt:
result = normalize(sum) * System.MathF.Sqrt(length(sum));
break;
case CurvePresets.Power:
result = normalize(sum) * System.MathF.Pow(length(sum), 2);
break;
case CurvePresets.Reciprocal:
result = normalize(sum) * (1 / length(sum));
break;
default:
float activatedValue = this.curve.Evaluate(length(sum));
result = normalize(sum) * activatedValue;
break;
}
this.outputValue = result;
}
public virtual void UpdateResult(Vector3 result) {
// float d = Vector3.Distance(result, this.outputValue);
// if (d < 0.5f) {