Add full graph all outputs

Editor/ClusterEditor.cs

@@ -51,8 +51,8 @@ namespace NanoBrain.Unity {
 
         private bool showSynapses = true;
         private bool showActivation = true;
-        protected bool breakOnWake = false;
+        //protected bool breakOnWake = false;
-        protected bool trace = 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();
+            // EditorGUILayout.Space();
-            breakOnWake = EditorGUILayout.Toggle("Break on wake", breakOnWake);
+            // breakOnWake = EditorGUILayout.Toggle("Break on wake", breakOnWake);
-            if (breakOnWake && this.view.currentNucleus is Neuron currentNeuron) {
+            // if (breakOnWake && this.view.currentNucleus is Neuron currentNeuron) {
-                if (currentNeuron.isSleeping == false)
+            //     if (currentNeuron.isSleeping == false)
-                    Debug.Break();
+            //         Debug.Break();
-                // trace = EditorGUILayout.Toggle("Trace", trace);
+            //     // trace = EditorGUILayout.Toggle("Trace", trace);
-                // currentNeuron.trace = trace;
+            //     // currentNeuron.trace = trace;
-            }
+            // }
         }
 
         protected void SynapsesInspector(ref bool anythingChanged) {

Editor/ClusterView.cs

@@ -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();
-        }
-    }
 }

Editor/Dag.cs

@@ -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();
+        }
+    }
+}

Editor/Dag.cs.meta

@@ -0,0 +1,2 @@
+fileFormatVersion: 2
+guid: a755ac8461bd0c714a852df47331048e

Samples/Braitenberg/Brains/Braitenberg 1.asset

@@ -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