Fixed graph showing wrong decision border for regression

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -90,7 +90,7 @@ class ADALINEPerceptronTraining extends NetworkTraining
         return $condition;
     }
 
-    private function iterationFunction(array $inputs, int $correctOutput)
+    private function iterationFunction(array $inputs, float $correctOutput): float
     {
         $output = $this->perceptron->test($inputs);
 

app/Models/NetworksTraining/GradientDescentPerceptronTraining.php

@@ -86,7 +86,7 @@ class GradientDescentPerceptronTraining extends NetworkTraining
         return $condition;
     }
 
-    private function iterationFunction(array $inputs, int $correctOutput)
+    private function iterationFunction(array $inputs, float $correctOutput): float
     {
         $output = $this->perceptron->test($inputs);
 

resources/js/components/IterationTable.vue

@@ -33,9 +33,7 @@ const rowBgDark = computed(() => {
             <th>Époch</th>
             <th>Exemple</th>
             <th
-                v-for="(weight, index) in allWeightPerIteration[
-                    allWeightPerIteration.length - 1
-                ]"
+                v-for="(weight, index) in allWeightPerIteration[0]"
                 v-bind:key="index"
             >
                 X<sub>{{ index }}</sub>
@@ -60,7 +58,10 @@ const rowBgDark = computed(() => {
             <td>{{ iteration.error.toFixed(2) }}</td>
         </tr>
 
-        <tr v-if="props.trainingEnded" class="bg-red-400 dark:bg-red-900 text-center">
+        <tr
+            v-if="props.trainingEnded"
+            class="bg-red-400 text-center dark:bg-red-900"
+        >
             <td colspan="100%">
                 <strong>Entraînement terminé :</strong>
                 {{ props.trainingEndReason }}

resources/js/components/PerceptronDecisionGraph.vue

@@ -75,12 +75,12 @@ function getPerceptronDecisionBoundaryDataset(
         networkWeights[0][0].length <= 3
     ) {
         // Unique, 3 weights perceptron
-        const perceptronWeights = networkWeights[0][0]; // We take the unique perceptron
+        const perceptronWeights = [...networkWeights[0][0]]; // Copy of the unique perceptron weights
 
         function perceptronLine(x: number): number {
             if (perceptronWeights.length < 3) {
                 // If we have less than 3 weights, we assume missing weights are zero
-                perceptronWeights.push(getPerceptronOutput(networkWeights, [x])[0]);
+                return getPerceptronOutput(networkWeights, [x])[0];
             }
 
             // w0 + w1*x + w2*y = 0 => y = -(w1/w2)*x - w0/w2
@@ -167,6 +167,7 @@ function getPerceptronDecisionBoundaryDataset(
     <Chart
         v-if="props.cleanedDataset.length > 0 || props.iterations.length > 0"
         class="flex bg-primary dark:bg-transparent!"
+        type="scatter"
         :options="{
             responsive: true,
             maintainAspectRatio: true,