{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b3f1635",
   "metadata": {},
   "source": [
    "# Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "478651c8",
   "metadata": {},
   "source": [
    "## What is a *Neuron* (artifical)\n",
    "\n",
    "First of all, **I'm not an Neurologist so i might say some nonsense, i only researched online**. \n",
    "\n",
    "An artifical *neuron* works similary to a biological *neuron* in the way it process information. In a brain, like yours, a *neuron* receives signals from other *neurons*, processes them and sends an *output*.\n",
    "\n",
    "An artifical *neuron* takes **multiple *inputs*** (such as numbers), applies updated values called **weights** to each *inputs*, adds a constant called **bias**, apply a specific function to normalize the value called **Activation function**, and then `returns` the *output* of the Activation function (such as: **sigmoid**, **ReLU**, etc...)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9d6072",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# Neuron 1\n",
    "class Neuron:\n",
    "    def __init__(self, input_size: int) -> None:\n",
    "        self.input_size = input_size\n",
    "        self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
    "        self.bias = random.uniform(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aff9ee6",
   "metadata": {},
   "source": [
    "# 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ca39a42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "\n",
    "# Neuron 2\n",
    "class Neuron:\n",
    "    def __init__(self, input_size: int) -> None:\n",
    "        self.input_size = input_size\n",
    "        self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
    "        self.bias = random.uniform(0, 1)\n",
    "\n",
    "    def sigmoid(x: float) -> float:\n",
    "        return 1/(1 + math.exp(-x))\n",
    "    \n",
    "    def forward(self, inputs: list) -> float:\n",
    "        assert len(inputs) == self.input_size, \"error: misnumber inputs number\"\n",
    "        total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
    "        return self.sigmoid(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6709c5c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neuron output : 0.9001175686881125\n"
     ]
    }
   ],
   "source": [
    "# 8 for 8 bits (1 Byte)\n",
    "nbits: int = 8\n",
    "neuron = Neuron(nbits)\n",
    "inputs: list = [1, 0, 1, 0, 0, 1, 1, 0] \n",
    "\n",
    "output = neuron.forward(inputs)\n",
    "print(\"Neuron output :\", output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa57ae8e",
   "metadata": {},
   "source": [
    "# 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6de25ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "\n",
    "class Neuron:\n",
    "    def __init__(self, isize: int) -> None:\n",
    "        self.isize = isize\n",
    "        self.weight = [random.uniform(0, 1) for _ in range(self.isize)]\n",
    "        self.bias = random.uniform(0, 1)\n",
    "\n",
    "    def forward(self, inputs: list) -> float:\n",
    "        assert len(inputs) == self.isize, \"error: incorrect inputs number\"\n",
    "        total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
    "        return self.sigmoid(total)\n",
    "    \n",
    "    def sigmoid(x: float) -> float:\n",
    "        return 1/(1 + math.exp(-x))\n",
    "\n",
    "    # target needs to be between 0 and 1\n",
    "    def train(self, inputs: list, target: float, learning_rate: float = 0.1):\n",
    "        z = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
    "        output = self.sigmoid(z)\n",
    "\n",
    "        error = output - target\n",
    "        d_sigmoid = output * (1 - output)\n",
    "        dz = error * d_sigmoid\n",
    "\n",
    "        for i in range(self.isize):\n",
    "            self.weight[i] -= learning_rate * dz * inputs[i]\n",
    "\n",
    "        self.bias -= learning_rate * dz\n"
   ]
  }
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