From d2be9952f3589275ccad41d36ac8f03ec2a9c5ff Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20GUEZO?= <loicguezo@gmail.com>
Date: Sun, 18 Jan 2026 18:28:35 +0100
Subject: [PATCH] refactor(network.py): annotate forward and backward functions

---
 network.py | 45 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/network.py b/network.py
index 1f661fd..4b96802 100644
--- a/network.py
+++ b/network.py
@@ -1,5 +1,6 @@
 import math
 import random
+from typing import Optional, Callable
 
 
 def sigmoid(x: float) -> float:
@@ -29,6 +30,50 @@ class Neuron:
         # Initialize a shift to the activation threshold with a random bias
         self._bias: float = random.uniform(-1., 1.)
 
+    def forward(self, x: list[int], fn: Optional[Callable[[float], float]] = None) -> float:
+        """
+        Execute the neuron's forward pass.
+
+        :param x: Description
+        :param activate: Description
+        """
+
+        if len(x) != self._input_size:
+            raise ValueError(
+                f"Input vertex dimension {len(x)} mismatches the "
+                f"stored size {self._input_size}")
+
+        self._z: float = sum(welement * xelement for welement,
+                             xelement in zip(self._weight, x)) + self._bias
+
+        return fn(self._z) if fn is not None else self._z
+
+    def backward(self, dz_dw: list[float], dcost_dy: float, learning_rate: float,
+                 fn_deriv: Optional[Callable[[float], float]] = None) -> list[float]:
+        # Check dimension consistency
+        if len(dz_dw) != self._input_size:
+            raise ValueError(
+                f"Input vertex dimension {len(dz_dw)} mismatches "
+                f"stored size {self._input_size}")
+
+        # Local gradient: dy/dz (defaults to 1.0 for linear identity)
+        dy_dz: float = fn_deriv(self._z) if fn_deriv is not None else 1.
+
+        # Compute common error term (delta)
+        # dC/dz = dC/dy * dy/dz
+        delta: float = dcost_dy * dy_dz
+
+        # Update weights: weight -= learning_rate * dC/dz * dz/dw
+        for i in range(self._input_size):
+            self._weight[i] -= learning_rate * delta * dz_dw[i]
+
+        # Update bias: bias -= learning_rate * dC/dz * dz/db (where dz/db = 1)
+        self._bias -= learning_rate * delta * 1.0
+
+        # Return input gradient (dC/dx): dC/dz * dz/dx (where dz/dx = weights)
+        # This vector allows the error to flow back to previous layers.
+        return [delta * w for w in self._weight]
+
     def __repr__(self) -> str:
         jmp: int = int(math.sqrt(self._input_size))
         text: list[str] = []