likelihood-data-line-1d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace (0, 10, 100)\n",
    "y = 2*x\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loglikelihood (d, mu, w1):\n",
    "        \n",
    "    ll = []\n",
    "    \n",
    "    for w in w1:\n",
    "        \n",
    "        l = 0\n",
    "        for x, y in zip (d, mu):\n",
    "            \n",
    "            l += np.log (norm.pdf (w*x, y, 1))\n",
    "            \n",
    "        ll.append (l)\n",
    "        \n",
    "    return ll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = np.linspace (-5, 5, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in log\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff84c0aae10>]"
      ]
     },
     "execution_count": 43,
     "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.plot (w1, loglikelihood (x, y, w1))"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}