From 4441b149f93a415131b1abb8f24648b2354a6ab0 Mon Sep 17 00:00:00 2001
From: Laurent <naaturel@noreply.git.naaturel.be>
Date: Wed, 1 Apr 2026 17:40:33 +0200
Subject: [PATCH] Fix weighted sum back

---
 src/main/java/com/naaturel/ANN/Main.java             | 10 +++++-----
 .../ANN/domain/abstraction/TrainingContext.java      |  2 +-
 .../com/naaturel/ANN/domain/model/neuron/Neuron.java | 12 +++++-------
 .../training/GradientBackpropagationTraining.java    |  2 +-
 4 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/src/main/java/com/naaturel/ANN/Main.java b/src/main/java/com/naaturel/ANN/Main.java
index b551c4c..9cc16bc 100644
--- a/src/main/java/com/naaturel/ANN/Main.java
+++ b/src/main/java/com/naaturel/ANN/Main.java
@@ -24,9 +24,9 @@ public class Main {
         int nbrClass = 1;
 
         DataSet dataset = new DatasetExtractor()
-                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv", nbrClass);
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);
 
-        int[] neuronPerLayer = new int[]{1800, 2, 1800, dataset.getNbrLabels()};
+        int[] neuronPerLayer = new int[]{10, 5, 10, dataset.getNbrLabels()};
         int nbrInput = dataset.getNbrInputs();
 
         FullyConnectedNetwork network = createNetwork(neuronPerLayer, nbrInput);
@@ -36,7 +36,7 @@ public class Main {
         Trainer trainer = new GradientBackpropagationTraining();
         trainer.train(0.01F, 2000, network, dataset);
 
-        //plotGraph(dataset, network);
+        plotGraph(dataset, network);
     }
 
     private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){
@@ -78,8 +78,8 @@ public class Main {
             });
         }
 
-        float min = -5F;
-        float max = 5F;
+        float min = -3F;
+        float max = 3F;
         float step = 0.03F;
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){
diff --git a/src/main/java/com/naaturel/ANN/domain/abstraction/TrainingContext.java b/src/main/java/com/naaturel/ANN/domain/abstraction/TrainingContext.java
index 35f65af..7de9377 100644
--- a/src/main/java/com/naaturel/ANN/domain/abstraction/TrainingContext.java
+++ b/src/main/java/com/naaturel/ANN/domain/abstraction/TrainingContext.java
@@ -23,7 +23,7 @@ public abstract class TrainingContext {
     public TrainingContext(Model model, DataSet dataset) {
         this.model = model;
         this.dataset = dataset;
-        this.deltas = new float[model.neuronCount()];
+        this.deltas = new float[dataset.getNbrLabels()];
     }
 
 }
diff --git a/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java b/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
index c6ee3f8..f5356a7 100644
--- a/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
+++ b/src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java
@@ -60,14 +60,12 @@ public class Neuron implements Model {
     }
 
     public float calculateWeightedSum() {
-        float sum = bias.getWeight() * bias.getInput();
-
-        for (int i = 0; i < weights.length; i++) {
-            sum += weights[i] * inputs[i];
+        this.weightedSum = 0F;
+        this.weightedSum += this.bias.getWeight() * this.bias.getInput();
+        for(Synapse syn : this.synapses){
+            this.weightedSum += syn.getWeight() * syn.getInput();
         }
-
-        this.weightedSum = sum;
-        return sum;
+        return this.weightedSum;
     }
 
     public int getId(){
diff --git a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
index c40915f..2f7c80c 100644
--- a/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
+++ b/src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java
@@ -35,7 +35,7 @@ public class GradientBackpropagationTraining implements Trainer {
                 .afterEpoch(ctx -> {
                     ctx.globalLoss /= dataset.size();
                 })
-                .withVerbose(false,epoch/10)
+                .withVerbose(true,epoch/10)
                 .withTimeMeasurement(true)
                 .run(context);
     }