{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data\n",
    "#features: obesity, smoking habits, and exercise habits of five people\n",
    "#target:  people are diabetic or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "feature_set = np.array([[0,1,0],[0,0,1],[1,0,0],[1,1,0],[1,1,1]])\n",
    "labels = np.array([[1,0,0,1,1]])\n",
    "labels = labels.reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define hyper parameters for our neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "weights = np.random.rand(3,1)\n",
    "bias = np.random.rand(1)\n",
    "lr = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to define our activation function and its derivative \n",
    "# The derivative of sigmoid function is simply sigmoid(x) * sigmoid(1-x)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_der(x):\n",
    "    return sigmoid(x)*(1-sigmoid(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we are ready to train our neural network that will be able to predict whether a person is diabetic or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0.0011212577062124085\n",
      "0.001121221103568314\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(30000):\n",
    "    inputs = feature_set\n",
    "\n",
    "    # feedforward step1. Calculate the product between inputs and weights\n",
    "    XW = np.dot(feature_set, weights) + bias\n",
    "\n",
    "    # feedforward step2 Pass the result from step 1 through an activation function\n",
    "    z = sigmoid(XW)\n",
    "\n",
    "    # backpropagation step 1. - The first step in the back propagation section is to find the \"cost\" of the predictions. \n",
    "    error = z - labels\n",
    "\n",
    "    print(error.sum())\n",
    "\n",
    "    # backpropagation step 2 - minimize the cost\n",
    "    \n",
    "    dcost_dpred = error\n",
    "    dpred_dz = sigmoid_der(z)\n",
    "\n",
    "    z_delta = dcost_dpred * dpred_dz\n",
    "\n",
    "    inputs = feature_set.T\n",
    "    weights -= lr * np.dot(inputs, z_delta)\n",
    "\n",
    "    for num in z_delta:\n",
    "        bias -= lr * num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can now try and predict the value of a single instance. Let's suppose we have a record of a patient that \n",
    "# comes in who smokes, is not obese, and doesn't exercise. Let's find if he is likely to be diabetic or not.\n",
    "# The input feature will look like this: [1,0,0]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00470299]\n"
     ]
    }
   ],
   "source": [
    "single_point = np.array([1,0,0])\n",
    "result = sigmoid(np.dot(single_point, weights) + bias)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.49973264],\n",
       "       [11.70629716],\n",
       "       [-0.85505421]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x293c70781c0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "input = np.linspace(-10, 10, 100)\n",
    "plt.plot(input, sigmoid(input), c=\"b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(gradient, start, learn_rate, n_iter):\n",
    "    vector = start\n",
    "    for _ in range(n_iter):\n",
    "        diff = -learn_rate * gradient(vector)\n",
    "        vector += diff\n",
    "    return vector"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}