linear_regression_single_variable.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1736,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1737,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single variable linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1738,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff68ebeb760>"
      ]
     },
     "execution_count": 1738,
     "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": [
    "dataset = pd.read_csv(\"./datasets/Salary_Data.csv\")\n",
    "dataset = dataset.sample(frac=1).reset_index(drop=True)\n",
    "dataset = dataset.to_numpy()\n",
    "X, y = dataset[:,0], dataset[:,1]\n",
    "plt.scatter(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1739,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from random import uniform\n",
    "\n",
    "def create_dataset(n_samples=25, spread=3.0, yint=0):\n",
    "    X = np.array([*range(0, n_samples)])\n",
    "    y = np.zeros(len(X))\n",
    "    for i in range(len(X)):\n",
    "        y[i] = yint + (X[i] + (uniform(-spread, spread)))\n",
    "    return X, y\n",
    "#X, y = create_dataset(50, 5, 50)\n",
    "#plt.scatter(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1740,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = len(y)\n",
    "\n",
    "def predict(h0, h1, x):\n",
    "    return h0+h1*x\n",
    "\n",
    "def predictions(h0, h1):\n",
    "    preds = np.zeros(m)\n",
    "    for i in range(m):\n",
    "        preds[i] = predict(h0, h1, X[i])\n",
    "    return preds\n",
    "\n",
    "def cost_function(h0, h1):\n",
    "    preds = predictions(h0, h1)\n",
    "    costs = np.zeros(m)\n",
    "    for i in range(m):\n",
    "        costs[i] = (preds[i]-y[i])**2\n",
    "    return (1/(m))*np.sum(costs)\n",
    "\n",
    "def derive_h0(h0, h1):\n",
    "    H = 0.0000001\n",
    "    a = cost_function(h0+H, h1)-cost_function(h0, h1)\n",
    "    return a/H\n",
    "\n",
    "def derive_h1(h0, h1):\n",
    "    H = 0.0000001\n",
    "    a = cost_function(h0, h1+H)-cost_function(h0, h1)\n",
    "    return a/H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1753,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████| 10000/10000 [00:01<00:00, 6860.54it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(25445.40349394083, 9501.425363123417)"
      ]
     },
     "execution_count": 1753,
     "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": [
    "lr = 0.001\n",
    "\n",
    "n_epochs = 10000\n",
    "\n",
    "weight0 = 0\n",
    "weight1 = 0\n",
    "\n",
    "losses = []\n",
    "for epoch in (t := trange(n_epochs)):\n",
    "    \n",
    "    temp0 = weight0-derive_h0(weight0, weight1)*lr\n",
    "    temp1 = weight1-derive_h1(weight0, weight1)*lr\n",
    "    weight0 = temp0\n",
    "    weight1 = temp1\n",
    "    losses.append(cost_function(weight0, weight1))\n",
    "    \n",
    "plt.plot(losses)\n",
    "weight0, weight1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1754,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff66adbe7c0>]"
      ]
     },
     "execution_count": 1754,
     "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": [
    "plt.scatter(X, y)\n",
    "plt.plot(X, predictions(weight0, weight1))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "b3638bd696681dbb2fed92db5d4df48a0993b580d16519ccce1c2abac5d7ec4b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}