Fixed Regression datasets

app/Services/DatasetReader/LinearOrderDataSetReader.php

@@ -29,11 +29,6 @@ class LinearOrderDataSetReader implements IDataSetReader
                 $newLine[] = (float) $value;
             }
 
-            // if the dataset is for regression, we add a fake label of 0
-            if (count($newLine) === 2) {
-                $newLine[] = 0.0;
-            }
-
             $this->lines[] = $newLine;
         }
     }

app/Services/DatasetReader/RandomOrderDataSetReader.php

@@ -29,11 +29,6 @@ class RandomOrderDataSetReader implements IDataSetReader
                 $newLine[] = (float) $value;
             }
 
-            // if the dataset is for regression, we add a fake label of 0
-            if (count($newLine) === 2) {
-                $newLine[] = 0.0;
-            }
-
             $this->lines[] = $newLine;
         }
     }

app/Services/IterationEventBuffer/PerceptronLimitedEpochEventBuffer.php

@@ -32,20 +32,12 @@ class PerceptronLimitedEpochEventBuffer implements IPerceptronIterationEventBuff
             'weights' => $synaptic_weights,
         ];
 
-        if ($this->underSizeIncreaseCount <= $this->sizeIncreaseStart) { // Special case where we need to send each iteration separately
-            $this->underSizeIncreaseCount++;
-            $this->data[] = $newData;
-            $this->flush();
-
-            return;
-        }
-
         $lastEpoch = $this->data[0]['epoch'] ?? null;
         if ($this->data && $lastEpoch !== $epoch) { // Current Epoch has changed from the last one
-            if ($lastEpoch % $this->epochInterval === 0) { // The last epoch need to be sent
+            if ($lastEpoch == 1 || $lastEpoch % $this->epochInterval === 0) { // The last saved epoch need to be sent
                 $this->flush(); // Flush all data from the previous epoch
             } else {
-                $this->data = [];
+                $this->data = []; // We clear the data without sending it as we are saving the next epoch data
             }
 
             $lastEpoch = $epoch;

config/perceptron.php

@@ -7,12 +7,12 @@ return [
      * Beyond this number of iterations, the broadcast will be splitted every x iterations,
      * x is limited_broadcast_number
      */
-    'limited_broadcast_iterations' => 200,
+    'limited_broadcast_iterations' => 100,
 
     /**
      * How much broadcasts is sent when in limmited broadcast mode
      */
-    'limited_broadcast_number' => 200,
+    'limited_broadcast_number' => 100,
 
     'broadcast_iteration_size' => 75,
 

resources/js/components/PerceptronDecisionGraph.vue

@@ -35,6 +35,30 @@ const farRightDataPointX = computed(() => {
     return maxX;
 });
 
+function getPerceptronOutput(weightsNetwork: number[][], inputs: number[]): number[] {
+    for (const layer of weightsNetwork) {
+        const nextInputs: number[] = [];
+
+        for (const neuron of layer) {
+            const bias = neuron[0];
+            const weights = neuron.slice(1);
+
+            let sum = bias;
+
+            for (let i = 0; i < weights.length; i++) {
+                sum += weights[i] * inputs[i];
+            }
+
+            const activated = props.activationFunction(sum);
+
+            nextInputs.push(activated);
+        }
+
+        inputs = nextInputs;
+    }
+    return inputs;
+}
+
 function getPerceptronDecisionBoundaryDataset(
     networkWeights: number[][][],
     activationFunction: (x: number) => number = (x) => x,
@@ -48,12 +72,17 @@ function getPerceptronDecisionBoundaryDataset(
     if (
         networkWeights.length == 1 &&
         networkWeights[0].length == 1 &&
-        networkWeights[0][0].length == 3
+        networkWeights[0][0].length <= 3
     ) {
         // Unique, 3 weights perceptron
         const perceptronWeights = networkWeights[0][0]; // We take the unique perceptron
 
         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]);
+            }
+
             // w0 + w1*x + w2*y = 0 => y = -(w1/w2)*x - w0/w2
             const w2 = perceptronWeights[2] == 0 ? 1e-6 : perceptronWeights[2]; // Avoid division by zero
             return -(perceptronWeights[1] / w2) * x - perceptronWeights[0] / w2;

resources/js/components/PerceptronSetup.vue

@@ -127,6 +127,7 @@ watch(selectedDatasetCopy, (newValue) => {
                                 name="dataset"
                                 id="dataset-select"
                                 v-model="selectedDatasetCopy"
+                                class="cursor-pointer"
                             >
                                 <NativeSelectOption value="" disabled
                                     >Sélectionnez un dataset</NativeSelectOption
@@ -154,6 +155,7 @@ watch(selectedDatasetCopy, (newValue) => {
                                 name="weight_init_method"
                                 id="weight_init_method"
                                 v-model="selectedMethod"
+                                class="cursor-pointer"
                             >
                                 <NativeSelectOption
                                     v-for="method in ['zeros', 'random']"