refactor: extract Neural Net function in a Seperate Module

src/components/NN_comps/NN_IOgraph.svelte

@@ -4,6 +4,7 @@
   import { Chart } from "chart.js/auto";
 
   import { Layer, af_enum, XYDataCollector, Neuron } from "./NN_classes.ts";
+  import { neuralNetwork } from "./NN_function.ts";
   import {
     hidOutLayers_store,
     selActivaFn_store,
@@ -41,27 +42,7 @@
 
   // Hidden & Output Layer Combined
 
-  function neuralNetwork(x: number | string, layers: Layer[], func: Function) {
-    x = parseFloat(x.toString());
-    let inputs = [x];
-
-    for (let i = 0; i < layers.length - 1; i++) {
-      const layer = layers[i];
-      const outputs = [];
-      for (const neuron of layer.neurons) {
-        const weightedSum =
-          neuron.weights.reduce(
-            (sum, weight, i) => sum + weight * inputs[i],
-            0
-          ) + neuron.bias;
-        let neuronOut = func(weightedSum);
-        outputs.push(neuronOut);
-      }
-      inputs = outputs; // Set inputs for the next layer
-    }
-
-    return inputs.reduce((sum, output) => sum + output, 0); // Summing the final layer's outputs
-  }
+  
 
   // ---------------------------------------
   // -------------- Plotting ---------------

src/components/NN_comps/NN_function.ts

@@ -0,0 +1,20 @@
+import { Layer, Neuron } from "./NN_classes.ts";
+
+export function neuralNetwork(x: number | string, layers: Layer[], func: Function) {
+  x = parseFloat(x.toString());
+  let inputs = [x];
+
+  for (let i = 0; i < layers.length - 1; i++) {
+    const layer = layers[i];
+    const outputs = [];
+    for (const neuron of layer.neurons) {
+      const weightedSum =
+        neuron.weights.reduce((sum, weight, i) => sum + weight * inputs[i], 0) +
+        neuron.bias;
+      let neuronOut = func(weightedSum);
+      outputs.push(neuronOut);
+    }
+    inputs = outputs; // Set inputs for the next layer
+  }
+  return inputs.reduce((sum, output) => sum + output, 0); // Summing the final layer's outputs
+}