499 lines
17 KiB
C#
499 lines
17 KiB
C#
using System;
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using System.Collections.Generic;
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using System.Linq;
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using UnityEngine;
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using Unity.Mathematics;
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using static Unity.Mathematics.math;
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[Serializable]
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public class Cluster : Nucleus {
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#region Init
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public Cluster(ClusterPrefab prefab, Cluster parent) {
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this.prefab = prefab;
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this.name = prefab.name;
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this.parent = parent;
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this.parent?.clusterNuclei.Add(this);
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ClonePrefab();
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_ = this.inputs;
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this.sortedNuclei = TopologicalSort(this.clusterNuclei);
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}
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public Cluster(ClusterPrefab prefab, ClusterPrefab parent = null) {
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this.prefab = prefab;
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this.name = prefab.name;
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this.clusterPrefab = parent;
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if (this.clusterPrefab != null)
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this.clusterPrefab.nuclei.Add(this);
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ClonePrefab();
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_ = this.inputs;
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this.sortedNuclei = TopologicalSort(this.clusterNuclei);
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}
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private void ClonePrefab() {
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Nucleus[] prefabNuclei = this.prefab.nuclei.ToArray();
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// first clone the nuclei without their connections
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foreach (Nucleus nucleus in this.prefab.nuclei) {
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// Debug.Log($"prefab clone {nucleus.name}");
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nucleus.ShallowCloneTo(this);
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}
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Nucleus[] clonedNuclei = this.clusterNuclei.ToArray();
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// Now clone the connections
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for (int nucleusIx = 0; nucleusIx < prefabNuclei.Length; nucleusIx++) {
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Nucleus prefabNucleus = prefabNuclei[nucleusIx];
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if (prefabNucleus is not Neuron prefabNeuron)
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continue;
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Nucleus clonedNucleus = clonedNuclei[nucleusIx];
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if (clonedNucleus == null || clonedNucleus is not Neuron clonedNeuron)
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continue;
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// Copy the receivers, which will also create the synapses
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// Clusters do not have receivers...
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foreach (Nucleus receiver in prefabNeuron.receivers.ToArray()) {
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int ix = GetNucleusIndex(prefabNuclei, receiver);
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if (ix < 0)
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continue;
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if (clonedNuclei[ix] is not Nucleus clonedReceiver)
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continue;
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// Find the synapse for the weight
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float weight = 1;
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foreach (Synapse synapse in receiver.synapses) {
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// Find the weight for this synapse
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if (synapse.nucleus == prefabNucleus) {
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weight = synapse.weight;
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break;
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}
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}
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clonedNeuron.AddReceiver(clonedReceiver, weight);
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}
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}
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// Copy nucleus arrays for receptors
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for (int nucleusIx = 0; nucleusIx < prefabNuclei.Length; nucleusIx++) {
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Nucleus prefabNucleus = prefabNuclei[nucleusIx];
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if (prefabNucleus is not IReceptor prefabReceptor)
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continue;
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if (prefabReceptor.nucleiArray == null || prefabReceptor.nucleiArray.Length == 0)
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continue;
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IReceptor clonedNucleus = clonedNuclei[nucleusIx] as IReceptor;
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if (prefabReceptor == prefabReceptor.nucleiArray[0]) {
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// We clone the array only for the first entry
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NucleusArray clonedArray = new(prefabReceptor.nucleiArray.Length, "array");
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int arrayIx = 0;
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foreach (Nucleus prefabArrayNucleus in prefabReceptor.nucleiArray) {
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int arrayNucleusIx = GetNucleusIndex(prefabNuclei, prefabArrayNucleus);
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if (arrayNucleusIx >= 0) {
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Nucleus clonedArrayNucleus = clonedNuclei[arrayNucleusIx];
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clonedArray.nuclei[arrayIx] = clonedArrayNucleus;
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}
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else {
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Debug.LogError($" Could not find prefab nucleus {prefabNucleus.name} in the clones");
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}
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arrayIx++;
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}
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//clonedNucleus.array = clonedArray;
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clonedNucleus.nucleiArray = clonedArray.nuclei;
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}
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else {
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// The others will refer to the array created for the first nucleus in the array
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int firstNucleusIx = GetNucleusIndex(prefabNuclei, prefabReceptor.nucleiArray[0]);
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IReceptor clonedFirstNucleus = clonedNuclei[firstNucleusIx] as IReceptor;
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clonedNucleus.nucleiArray = clonedFirstNucleus.nucleiArray;
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}
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}
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}
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// Sort the nuclei in a correct evaluation order
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private List<Nucleus> TopologicalSort(List<Nucleus> nodes) {
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Dictionary<Nucleus, int> inDegree = new();
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foreach (Nucleus node in nodes)
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inDegree[node] = 0; // Initialize in-degree to zero
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// Calculate in-degrees
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foreach (Nucleus node in nodes) {
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if (node is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers)
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inDegree[receiver]++;
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}
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}
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Queue<Nucleus> queue = new();
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foreach (Nucleus node in nodes) {
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if (inDegree[node] == 0) // Nodes with no dependencies
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queue.Enqueue(node);
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}
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// The queue basically stores all input nuclei?
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List<Nucleus> sortedOrder = new();
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while (queue.Count > 0) {
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Nucleus current = queue.Dequeue();
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sortedOrder.Add(current); // Process the node
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if (current is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers) {
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inDegree[receiver]--;
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if (inDegree[receiver] == 0) // If all dependencies resolved
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queue.Enqueue(receiver);
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}
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}
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}
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// Check for cycles in the graph
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if (sortedOrder.Count != nodes.Count)
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throw new InvalidOperationException("Graph is not a DAG; a cycle exists.");
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return sortedOrder;
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}
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public override Nucleus Clone(ClusterPrefab parent) {
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Cluster clone = new(this.prefab, parent);
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foreach (Synapse synapse in this.synapses) {
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Synapse clonedSynapse = clone.AddSynapse(synapse.nucleus);
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clonedSynapse.weight = synapse.weight;
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}
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foreach (Neuron output in this.outputs) {
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foreach (Nucleus receiver in output.receivers) {
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int ix = GetNucleusIndex(this.clusterNuclei.ToArray(), output);
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if (ix < 0)
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continue;
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if (clone.clusterNuclei[ix] is not Neuron clonedOutput)
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continue;
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clonedOutput.AddReceiver(receiver);
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}
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}
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return clone;
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}
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public override Nucleus ShallowCloneTo(Cluster parent) {
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Cluster clone = new(this.prefab, parent) {
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name = this.name,
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clusterPrefab = this.clusterPrefab,
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};
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// This cloned the prefab with the clusternuclei,
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// but did not clone the receivers outside the cluster
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RestoreExternalReceivers(clone, this.clusterPrefab, parent);
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return clone;
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}
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protected void RestoreExternalReceivers(Cluster clone, ClusterPrefab prefabParent, Cluster clonedParent) {
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for (int nucleusIx = 0; nucleusIx < this.clusterNuclei.Count; nucleusIx++) {
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Nucleus prefabNucleus = this.clusterNuclei[nucleusIx];
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if (prefabNucleus is not Neuron prefabNeuron)
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continue;
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Nucleus clonedNucleus = clone.clusterNuclei[nucleusIx];
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if (clonedNucleus == null || clonedNucleus is not Neuron clonedNeuron)
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continue;
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// Copy the receivers, which will also create the synapses
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foreach (Nucleus receiver in prefabNeuron.receivers) {
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int ix = GetNucleusIndex(prefabParent.nuclei, receiver);
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if (ix < 0)
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continue;
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//if (clone.clusterNuclei[ix] is not Nucleus clonedReceiver)
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if (clonedParent.clusterNuclei[ix] is not Nucleus clonedReceiver)
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continue;
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// Find the synapse for the weight
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float weight = 1;
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foreach (Synapse synapse in receiver.synapses) {
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// Find the weight for this synapse
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if (synapse.nucleus == prefabNucleus) {
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weight = synapse.weight;
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break;
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}
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}
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clonedNeuron.AddReceiver(clonedReceiver, weight);
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}
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}
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}
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protected int GetNucleusIndex(Nucleus[] nuclei, Nucleus nucleus) {
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for (int i = 0; i < nuclei.Length; i++) {
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if (nucleus == nuclei[i])
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return i;
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}
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return -1;
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}
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public int GetNucleusIndex(List<Nucleus> nuclei, Nucleus nucleus) {
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int i = 0;
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foreach (Nucleus nucleiElement in nuclei) {
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//for (int i = 0; i < nuclei.Length; i++) {
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if (nucleus == nucleiElement)
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return i;
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i++;
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}
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return -1;
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}
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#endregion Init
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public ClusterPrefab prefab;
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[SerializeReference]
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public List<Nucleus> clusterNuclei = new();
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// the nuclei sorted using topological sorting
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// to ensure that the cluster is computer in the right order
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public List<Nucleus> sortedNuclei;
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//public Dictionary<string, Nucleus> nucleiDict = new();
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public List<Nucleus> _inputs = null;
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public virtual List<Nucleus> inputs {
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get {
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if (this._inputs == null) {
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this._inputs = new();
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foreach (Nucleus nucleus in this.clusterNuclei) {
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// inputs have no synapses
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if (nucleus.synapses.Count == 0)
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this._inputs.Add(nucleus);
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}
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ComputeOrders();
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}
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return this._inputs;
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}
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}
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public Dictionary<Nucleus, List<Nucleus>> computeOrders = new();
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private void ComputeOrders() {
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foreach (Nucleus input in this._inputs) {
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computeOrders[input] = TopologicalSort2(input);
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}
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}
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private List<Nucleus> TopologicalSort2(Nucleus startNode) {
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Dictionary<Nucleus, int> inDegree = new Dictionary<Nucleus, int>();
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HashSet<Nucleus> visited = new HashSet<Nucleus>();
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// Initialize in-degrees and mark all nodes as unvisited
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foreach (Nucleus node in this.clusterNuclei) {
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inDegree[node] = 0;
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}
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// Calculate in-degrees for all nodes reachable from the start node
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Queue<Nucleus> queue = new Queue<Nucleus>();
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queue.Enqueue(startNode);
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visited.Add(startNode);
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while (queue.Count > 0) {
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Nucleus current = queue.Dequeue();
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if (current is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers) {
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if (!visited.Contains(receiver)) {
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visited.Add(receiver);
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queue.Enqueue(receiver);
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}
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inDegree[receiver]++;
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}
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}
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}
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// Perform topological sort on all reachable nodes
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queue.Clear();
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foreach (var node in visited) {
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if (inDegree[node] == 0) {
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queue.Enqueue(node);
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}
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}
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List<Nucleus> sortedOrder = new List<Nucleus>();
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while (queue.Count > 0) {
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Nucleus current = queue.Dequeue();
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sortedOrder.Add(current); // Process the node
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if (current is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers) {
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if (visited.Contains(receiver)) {
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inDegree[receiver]--;
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if (inDegree[receiver] == 0) // If all dependencies resolved
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queue.Enqueue(receiver);
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}
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}
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}
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}
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// Check for cycles in the graph
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if (sortedOrder.Count != visited.Count)
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throw new InvalidOperationException("Graph is not a DAG; a cycle exists.");
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return sortedOrder;
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}
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private List<Nucleus> TopologicalSort3(Nucleus startNode) {
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Dictionary<Nucleus, int> inDegree = new();
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foreach (Nucleus node in this.clusterNuclei)
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inDegree[node] = 0; // Initialize in-degree to zero
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// Calculate in-degrees
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foreach (Nucleus node in this.clusterNuclei) {
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if (node is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers)
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inDegree[receiver]++;
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}
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}
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Queue<Nucleus> queue = new();
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queue.Enqueue(startNode);
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List<Nucleus> sortedOrder = new();
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while (queue.Count > 0) {
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Nucleus current = queue.Dequeue();
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sortedOrder.Add(current); // Process the node
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if (current is Neuron neuron) {
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foreach (Nucleus receiver in neuron.receivers) {
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inDegree[receiver]--;
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if (inDegree[receiver] == 0) // If all dependencies resolved
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queue.Enqueue(receiver);
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}
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}
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}
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Debug.Log($"Compute order for {startNode.name} length = {sortedOrder.Count}");
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// Check for cycles in the graph
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// if (sortedOrder.Count != this.nuclei.Count)
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// throw new InvalidOperationException("Graph is not a DAG; a cycle exists.");
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return sortedOrder;
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}
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public virtual Neuron defaultOutput {//=> this.nuclei[0] as Nucleus;
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get {
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if (this.clusterNuclei.Count > 0)
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return this.clusterNuclei[0] as Neuron;
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return null;
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}
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}
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protected List<Neuron> _outputs = null;
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public List<Neuron> outputs {
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get {
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if (this._outputs == null) {
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this._outputs = new();
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foreach (Nucleus nucleus in this.clusterNuclei) {
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if (nucleus is Neuron neuron) // && neuron.receivers.Count == 0)
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this._outputs.Add(neuron);
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}
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}
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return this._outputs;
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}
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}
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public bool TryGetNucleus(string nucleusName, out Nucleus foundNucleus) {
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foreach (Nucleus receptor in this.clusterNuclei) {
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if (receptor is Nucleus nucleus)
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if (nucleus.name == nucleusName) {
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foundNucleus = nucleus;
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return true;
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}
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}
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foundNucleus = null;
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return false;
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}
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public Nucleus GetNucleus(string nucleusName) {
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string nucleusName0 = nucleusName + ": 0";
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foreach (Nucleus nucleus in this.clusterNuclei) {
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if (nucleus is IReceptor receptor) {
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if (nucleus.name == nucleusName | nucleus.name == nucleusName0)
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return nucleus;
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}
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else if (nucleus.name == nucleusName)
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return nucleus;
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}
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return null;
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}
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[Obsolete("Use GetNucleus instead")]
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public IReceptor GetReceptor(string receptorName) {
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return GetNucleus(receptorName) as IReceptor;
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}
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#region Receivers
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public virtual List<Nucleus> CollectReceivers() {
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List<Nucleus> receivers = new();
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foreach (Neuron output in this.outputs) {
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foreach (Nucleus receiver in output.receivers) {
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// Only add receivers outside this cluster
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if (receiver.clusterPrefab != this.prefab)
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receivers.Add(receiver);
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//receivers.AddRange(output.receivers);
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}
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}
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return receivers;
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}
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#endregion Receivers
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#region Update
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public void UpdateFromNucleus(Nucleus startNucleus) {
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// no bias+synapse input state calculation for now...
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List<Nucleus> computeOrder = this.computeOrders[startNucleus];
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if (startNucleus.trace)
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Debug.Log($"Update from {startNucleus.name}");
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foreach (Nucleus nucleus in computeOrder) {
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nucleus.UpdateStateIsolated();
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if (startNucleus.trace)
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Debug.Log($" {nucleus.name} = {nucleus.outputValue}");
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}
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this.outputValue = this.defaultOutput.outputValue;
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this.stale = 0;
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UpdateNuclei();
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}
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public override void UpdateStateIsolated() {
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float3 sum = this.bias;
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//Applying the weight factors
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foreach (Synapse synapse in this.synapses) {
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if (lengthsq(synapse.nucleus.outputValue) > 0) {
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sum += synapse.weight * synapse.nucleus.outputValue;
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this.stale = 0;
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}
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}
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foreach (Nucleus nucleus in this.sortedNuclei)
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nucleus.UpdateStateIsolated();
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this.outputValue = this.defaultOutput.outputValue;
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this.stale = 0;
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UpdateNuclei();
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}
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public override void UpdateNuclei() {
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foreach (Nucleus nucleus in this.clusterNuclei)
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nucleus.UpdateNuclei();
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}
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#endregion Update
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}
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