NanoBrain/NanoBrain-unitypackage
NanoBrain/NanoBrain-unitypackage
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Add full graph all outputs

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This commit is contained in:
Pascal Serrarens 2026-05-19 16:08:06 +02:00
parent 2a88689179
commit 37261bdce6
5 changed files with 178 additions and 157 deletions
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20
Editor/ClusterEditor.cs
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@ -51,8 +51,8 @@ namespace NanoBrain.Unity {
private bool showSynapses = true;
private bool showActivation = true;
protected bool breakOnWake = false;
protected bool trace = false;
//protected bool breakOnWake = false;
//protected bool trace = false;
void InspectorHandler(SerializedObject serializedObject) {
bool anythingChanged = false;
@ -172,14 +172,14 @@ namespace NanoBrain.Unity {
SynapsesInspector(ref anythingChanged);
ActivationInspector(ref anythingChanged);
EditorGUILayout.Space();
breakOnWake = EditorGUILayout.Toggle("Break on wake", breakOnWake);
if (breakOnWake && this.view.currentNucleus is Neuron currentNeuron) {
if (currentNeuron.isSleeping == false)
Debug.Break();
// trace = EditorGUILayout.Toggle("Trace", trace);
// currentNeuron.trace = trace;
}
// EditorGUILayout.Space();
// breakOnWake = EditorGUILayout.Toggle("Break on wake", breakOnWake);
// if (breakOnWake && this.view.currentNucleus is Neuron currentNeuron) {
// if (currentNeuron.isSleeping == false)
// Debug.Break();
// // trace = EditorGUILayout.Toggle("Trace", trace);
// // currentNeuron.trace = trace;
// }
}
protected void SynapsesInspector(ref bool anythingChanged) {

158
Editor/ClusterView.cs
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@ -170,7 +170,6 @@ namespace NanoBrain.Unity {
maxValue = neuron.outputMagnitude;
DrawNucleus(this.currentNucleus, position, maxValue);
}
}
else {
@ -194,8 +193,20 @@ namespace NanoBrain.Unity {
#region Full Graph
protected void DrawFullGraph() {
if (this.currentNucleus == null) {
Vector3 position = new(150, 210, 0);
DrawAllOutputs(position);
DrawOutputs(position);
return;
}
Dag dag = GenerateGraph(this.selectedOutput);
Dag.ComputeLayout(dag);
Vector3 pos = new(50, 210, 0);
DrawEdge(new Vector3(150, 210, 0), pos);
DrawAllOutputs(pos);
// Draw edges
foreach (Dag.Edge e in dag.edges) {
Dag.Node from = dag.nodes.FirstOrDefault(x => x.id == e.fromId);
@ -246,7 +257,6 @@ namespace NanoBrain.Unity {
int ix = 0;
Dag.Node receiver = new() {
id = ix,
//title = nucleus.name,
nucleus = rootNucleus
};
dag.nodes.Add(receiver);
@ -405,7 +415,7 @@ namespace NanoBrain.Unity {
Color color = Color.black;
if (Application.isPlaying) {
//if (maxValue == 0 || !float.IsFinite(maxValue))
maxValue = 1 * synapse.weight;
maxValue = 1 * synapse.weight;
float brightness = synapse.neuron.outputMagnitude * synapse.weight / maxValue;
color = new Color(brightness, brightness, brightness, 1f);
}
@ -802,6 +812,7 @@ namespace NanoBrain.Unity {
}
protected void OnAllOutputsClick() {
//this.mode = Mode.Focus;
this.currentNucleus = null;
this.selectedOutput = null;
this.expandArray = false;
@ -810,146 +821,5 @@ namespace NanoBrain.Unity {
#endregion Interaction
}
public class Dag {
public class Node {
public int id;
public Vector2 position;
public float radius = 20f; // circle radius
public Nucleus nucleus;
}
public class Edge {
public int fromId;
public int toId;
}
public List<Node> nodes = new();
public List<Edge> edges = new();
public Node FindNode(string name, bool justBaseName = true) {
if (justBaseName) {
int colonPos = name.IndexOf(":");
if (colonPos > 0)
name = name[..colonPos];
}
foreach (Node node in this.nodes) {
string nodeName = node.nucleus.name;
if (justBaseName) {
int colonPos = nodeName.IndexOf(":");
if (colonPos > 0)
nodeName = nodeName[..colonPos];
}
if (nodeName == name)
return node;
}
return null;
}
public static Node GetNodeById(Dag dag, int id) => dag.nodes.FirstOrDefault(x => x.id == id);
public static void ComputeLayout(Dag dag) {
Dictionary<int, List<int>> adjacency = dag.nodes.ToDictionary(n => n.id, n => new List<int>());
Dictionary<int, int> outdegree = dag.nodes.ToDictionary(node => node.id, n => 0);
foreach (Edge edge in dag.edges) {
if (!adjacency.ContainsKey(edge.fromId) || !adjacency.ContainsKey(edge.toId))
continue;
adjacency[edge.fromId].Add(edge.toId);
outdegree[edge.fromId]++;
}
// Kahn's algorithm to compute topological layers (horizontal layers)
// build parent list (reverse adjacency) and parentIndegree = number of children each parent has
Dictionary<int, List<int>> parents = dag.nodes.ToDictionary(n => n.id, _ => new List<int>());
Dictionary<int, int> childCount = dag.nodes.ToDictionary(n => n.id, _ => 0);
foreach (Edge edge in dag.edges) {
if (!adjacency.ContainsKey(edge.fromId) || !adjacency.ContainsKey(edge.toId)) continue;
adjacency[edge.fromId].Add(edge.toId);
parents[edge.toId].Add(edge.fromId); // parent of 'to' is 'from'
childCount[edge.fromId]++; // outdegree
}
Dictionary<int, int> column = new();
Queue<int> queue = new(outdegree.Where(keyValue => keyValue.Value == 0).Select(keyValue => keyValue.Key));
foreach (int id in queue)
column[id] = 0;
// process parents (reverse traversal)
while (queue.Count > 0) {
int nodeId = queue.Dequeue();
int col = column[nodeId];
foreach (int parentIx in parents[nodeId]) {
if (!column.ContainsKey(parentIx) || column[parentIx] < col + 1)
column[parentIx] = col + 1;
childCount[parentIx]--; // decrement remaining unprocessed children
if (childCount[parentIx] == 0)
queue.Enqueue(parentIx);
}
}
// Any unreachable nodes -> assign next layers
int maxColumn = column.Count > 0 ? column.Values.Max() : 0;
foreach (Node node in dag.nodes) {
if (!column.ContainsKey(node.id)) {
maxColumn++;
column[node.id] = maxColumn;
}
}
// Group nodes by column (left to right)
List<List<int>> columns =
column.
GroupBy(kv => kv.Value).
OrderBy(g => g.Key).
Select(g => g.Select(x => x.Key).ToList()).
ToList();
// Same code without using Linq
// Build layers dictionary: layerIndex -> List<int> nodeIds
// Dictionary<int, List<int>> layersDict = new();
// foreach (KeyValuePair<int, int> kv in layer) {
// int nodeId = kv.Key;
// int layerIndex = kv.Value;
// if (!layersDict.TryGetValue(layerIndex, out List<int> list)) {
// list = new List<int>();
// layersDict[layerIndex] = list;
// }
// list.Add(nodeId);
// }
// // Determine sorted layer indices
// List<int> layerIndices = new(layersDict.Keys);
// layerIndices.Sort(); // ascending order
// // Build final List<List<int>> in sorted order
// List<List<int>> layers = new();
// foreach (int idx in layerIndices) {
// layers.Add(layersDict[idx]);
// }
float hSpacing = 100f;
float totalHeight = 400f;
// Place nodes: x increases with column index, y spaced within column
for (int columnIx = 0; columnIx < columns.Count; columnIx++) {
List<int> nodeList = columns[columnIx];
float spacing = totalHeight / nodeList.Count;
float margin = 10 + spacing / 2;
for (int i = 0; i < nodeList.Count; i++) {
int index = nodeList[i];
Node node = GetNodeById(dag, index);
if (node == null)
continue;
float x = hSpacing + columnIx * hSpacing;
//float y = 400 - totalHeight / 2f + i * vSpacing;
float y = margin + i * spacing;
// Debug.Log($"({li}, {i}) -> {x}, {y}");
node.position = new Vector2(x, y);
}
}
//Repaint();
}
}
}

149
Editor/Dag.cs Normal file
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@ -0,0 +1,149 @@
using System.Collections.Generic;
using System.Linq;
using UnityEngine;
using UnityEditor;
namespace NanoBrain.Unity {
public class Dag {
public class Node {
public int id;
public Vector2 position;
public float radius = 20f; // circle radius
public Nucleus nucleus;
}
public class Edge {
public int fromId;
public int toId;
}
public List<Node> nodes = new();
public List<Edge> edges = new();
public Node FindNode(string name, bool justBaseName = true) {
if (justBaseName) {
int colonPos = name.IndexOf(":");
if (colonPos > 0)
name = name[..colonPos];
}
foreach (Node node in this.nodes) {
string nodeName = node.nucleus.name;
if (justBaseName) {
int colonPos = nodeName.IndexOf(":");
if (colonPos > 0)
nodeName = nodeName[..colonPos];
}
if (nodeName == name)
return node;
}
return null;
}
public static Node GetNodeById(Dag dag, int id) => dag.nodes.FirstOrDefault(x => x.id == id);
public static void ComputeLayout(Dag dag) {
Dictionary<int, List<int>> adjacency = dag.nodes.ToDictionary(n => n.id, n => new List<int>());
Dictionary<int, int> outdegree = dag.nodes.ToDictionary(node => node.id, n => 0);
foreach (Edge edge in dag.edges) {
if (!adjacency.ContainsKey(edge.fromId) || !adjacency.ContainsKey(edge.toId))
continue;
adjacency[edge.fromId].Add(edge.toId);
outdegree[edge.fromId]++;
}
// Kahn's algorithm to compute topological layers (horizontal layers)
// build parent list (reverse adjacency) and parentIndegree = number of children each parent has
Dictionary<int, List<int>> parents = dag.nodes.ToDictionary(n => n.id, _ => new List<int>());
Dictionary<int, int> childCount = dag.nodes.ToDictionary(n => n.id, _ => 0);
foreach (Edge edge in dag.edges) {
if (!adjacency.ContainsKey(edge.fromId) || !adjacency.ContainsKey(edge.toId)) continue;
adjacency[edge.fromId].Add(edge.toId);
parents[edge.toId].Add(edge.fromId); // parent of 'to' is 'from'
childCount[edge.fromId]++; // outdegree
}
Dictionary<int, int> column = new();
Queue<int> queue = new(outdegree.Where(keyValue => keyValue.Value == 0).Select(keyValue => keyValue.Key));
foreach (int id in queue)
column[id] = 0;
// process parents (reverse traversal)
while (queue.Count > 0) {
int nodeId = queue.Dequeue();
int col = column[nodeId];
foreach (int parentIx in parents[nodeId]) {
if (!column.ContainsKey(parentIx) || column[parentIx] < col + 1)
column[parentIx] = col + 1;
childCount[parentIx]--; // decrement remaining unprocessed children
if (childCount[parentIx] == 0)
queue.Enqueue(parentIx);
}
}
// Any unreachable nodes -> assign next layers
int maxColumn = column.Count > 0 ? column.Values.Max() : 0;
foreach (Node node in dag.nodes) {
if (!column.ContainsKey(node.id)) {
maxColumn++;
column[node.id] = maxColumn;
}
}
// Group nodes by column (left to right)
List<List<int>> columns =
column.
GroupBy(kv => kv.Value).
OrderBy(g => g.Key).
Select(g => g.Select(x => x.Key).ToList()).
ToList();
// Same code without using Linq
// Build layers dictionary: layerIndex -> List<int> nodeIds
// Dictionary<int, List<int>> layersDict = new();
// foreach (KeyValuePair<int, int> kv in layer) {
// int nodeId = kv.Key;
// int layerIndex = kv.Value;
// if (!layersDict.TryGetValue(layerIndex, out List<int> list)) {
// list = new List<int>();
// layersDict[layerIndex] = list;
// }
// list.Add(nodeId);
// }
// // Determine sorted layer indices
// List<int> layerIndices = new(layersDict.Keys);
// layerIndices.Sort(); // ascending order
// // Build final List<List<int>> in sorted order
// List<List<int>> layers = new();
// foreach (int idx in layerIndices) {
// layers.Add(layersDict[idx]);
// }
float hSpacing = 100f;
float totalHeight = 400f;
// Place nodes: x increases with column index, y spaced within column
for (int columnIx = 0; columnIx < columns.Count; columnIx++) {
List<int> nodeList = columns[columnIx];
float spacing = totalHeight / nodeList.Count;
float margin = 10 + spacing / 2;
for (int i = 0; i < nodeList.Count; i++) {
int index = nodeList[i];
Node node = GetNodeById(dag, index);
if (node == null)
continue;
float x = (hSpacing * 1.5f) + columnIx * hSpacing;
//float y = 400 - totalHeight / 2f + i * vSpacing;
float y = margin + i * spacing;
// Debug.Log($"({li}, {i}) -> {x}, {y}");
node.position = new Vector2(x, y);
}
}
//Repaint();
}
}
}

2
Editor/Dag.cs.meta Normal file
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@ -0,0 +1,2 @@
fileFormatVersion: 2
guid: a755ac8461bd0c714a852df47331048e

6
Samples/Braitenberg/Brains/Braitenberg 1.asset
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@ -65,7 +65,7 @@ MonoBehaviour:
m_RotationOrder: 4
curveMax: 1
persistOutput: 0
lastUpdate: 14.822748
lastUpdate: 19.870863
_receivers: []
- rid: 4201949899492425817
type: {class: Neuron, ns: NanoBrain, asm: Assembly-CSharp}
@ -73,7 +73,7 @@ MonoBehaviour:
name: Sensor
parent:
rid: 4201950148723474519
bias: {x: 0.062121756, y: 0.062121756, z: 0.062121756}
bias: {x: 0, y: 0, z: 0}
_synapses: []
combinator: 0
_activator: 0
@ -103,7 +103,7 @@ MonoBehaviour:
m_RotationOrder: 4
curveMax: 1
persistOutput: 0
lastUpdate: 14.822748
lastUpdate: 19.870863
_receivers:
- rid: 4201949899492425781
- rid: 4201950148723474519