Cleanup

MemoryCell.cs

@@ -34,18 +34,7 @@ public class MemoryCell : Neuron {
 
     public override void UpdateStateIsolated() {
         // A memorycell does not have an activation function
-        Vector3 result = this.bias;
+        float3 result = Combinator();
-        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;
 
         if (initialized)
             // Output the previous, memorized value

Neuron.cs

@@ -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 Serialization
-
-    #endregion Runtime state
 
     // 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();
+        float3 result = Combinator();
-        this.outputValue = Activation(result);
+        this.outputValue = Activator(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;
     }
 
-    private Func<float3> CombinatorAction => combinator switch {
+    #region Combinator
-        CombinatorType.Sum => UpdateSum,
+
-        CombinatorType.Product => UpdateProduct,
+    protected Func<float3> Combinator => combinator switch {
-        CombinatorType.Max => UpdateMax,
+        CombinatorType.Sum => CombinatorSum,
-        _ => UpdateSum
+        CombinatorType.Product => CombinatorProduct,
+        CombinatorType.Max => CombinatorMax,
+        _ => CombinatorSum
     };
 
-
+    public float3 CombinatorSum() {
-    public float3 UpdateSum() {
         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) {
+    #endregion Combinator
-        float3 result = Vector3.zero;
+
-        switch (this.curvePreset) {
+    #region Activator
-            case CurvePresets.Linear:
+
-                result = input;
+    protected Func<float3, float3> Activator => this.curvePreset switch {
-                break;
+        CurvePresets.Linear => ActivatorLinear,
-            case CurvePresets.Sqrt:
+        CurvePresets.Sqrt => ActivatorSqrt,
-                result = normalize(input) * System.MathF.Sqrt(length(input));
+        CurvePresets.Power => ActivatorPower,
-                break;
+        CurvePresets.Reciprocal => ActivatorReciprocal,
-            case CurvePresets.Power:
+        _ => ActivatorCustom
-                result = normalize(input) * System.MathF.Pow(length(input), 2);
+    };
-                break;
+
-            case CurvePresets.Reciprocal: {
+    protected float3 ActivatorLinear(float3 input) {
-                    float magnitude = length(input);
+        return input;
-                    if (magnitude > 0)
-                        result = normalize(input) * (1 / magnitude);
-                    break;
-                }
-            default:
-                float activatedValue = this.curve.Evaluate(length(input));
-                result = normalize(input) * activatedValue;
-                break;
     }
+
+    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;

Pulsar.cs

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

Selector.cs

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