{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Think Bayes\n",
    "\n",
    "This notebook presents example code and exercise solutions for Think Bayes.\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import classes from thinkbayes2\n",
    "from thinkbayes2 import Pmf, Suite\n",
    "\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Subclass(Suite):\n",
    "    \n",
    "    def Likelihood(self, data, hypo):\n",
    "        \"\"\"Computes the likelihood of the data under the hypothesis.\n",
    "        \n",
    "        data: \n",
    "        hypo: \n",
    "        \"\"\"\n",
    "        like = 1\n",
    "        return like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior = Subclass([1,2,3])\n",
    "thinkplot.Hist(prior)\n",
    "thinkplot.Config(xlabel='x', ylabel='PMF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posterior = prior.Copy()\n",
    "posterior.Update(1)\n",
    "thinkplot.Hist(prior, color='gray')\n",
    "thinkplot.Hist(posterior)\n",
    "thinkplot.Config(xlabel='x', ylabel='PMF')"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}