linear_regression_1d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prescribe one-dimensional training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [[0], [1], [2], [3], [4]]\n",
    "y = [0, 0.3, 0.75, 1, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the linear model by least squares extimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_regr = linear_model.LinearRegression()\n",
    "lin_regr.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot trainin data an predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "X_eval = np.linspace(0,4,1000)\n",
    "X_eval = X_eval.reshape(-1,1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(X_eval,lin_regr.predict(X_eval), label=\"linear regression predictor\")\n",
    "plt.plot(X,y, 'rs', markersize=12, label=\"trainin set\")\n",
    "plt.title(\"Simplistic test of linear regression on 1d input\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}