EMSE6992_Assignments/HW0.ipynb

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    "# Assignment 0\n",
    "\n",
    "### In class exercise ( no submission is required)\n",
    "\n",
    "---\n",
    "\n",
    "Welcome to EMSE6992 (https://bsharvey.github.io/).  In this class, we will be using a variety of tools that will require some initial configuration. To ensure everything goes smoothly moving forward, we will setup the majority of those tools in this in class activity. While some of this will likely be dull, doing it now will enable us to do more exciting work in the weeks that follow without getting bogged down in further software configuration. This will not be graded, however it is essential that you complete it timely since it will enable us to set up your environments.  \n",
    "\n",
    "\n",
    "## Programming expectations\n",
    "\n",
    "All the assignments and labs for this class will use Python and, for the most part, the browser-based Jupyter notebook format you are currently viewing. Knowledge of Python is not a prerequisite for this course, **provided you are comfortable learning on your own as needed**. While we have strived to make the programming component of this course straightforward, we will not devote much time to teaching prorgramming or Python syntax. Basically, you should feel comfortable with:\n",
    "\n",
    "* How to look up Python syntax on Google and StackOverflow.\n",
    "* Basic programming concepts like functions, loops, arrays, dictionaries, strings, and if statements.\n",
    "* How to learn new libraries by reading documentation.\n",
    "\n",
    "There are many online tutorials to introduce you to scientific python programming. [Here is one](https://github.com/jrjohansson/scientific-python-lectures) that is very nice.\n",
    "\n",
    "## Getting Python\n",
    "\n",
    "You will be using Python throughout the course, including many popular 3rd party Python libraries for scientific computing. [Anaconda](http://continuum.io/downloads) is an easy-to-install bundle of Python and most of these libraries. We recommend that you use Anaconda for this course.\n",
    "\n",
    "All the labs and assignments in this course are Python-based. This page provides information about configuring Python on your machine.\n",
    "\n",
    "Also see: http://docs.continuum.io/anaconda/install\n",
    "\n",
    "**Step 0** \n",
    "The Jupyter notebook runs in the browser, and works best in Google Chrome or Firefox. \n",
    "\n",
    "## Recommended Method: Anaconda\n",
    "\n",
    "The Anaconda Python distribution is an easily-installable bundle of Python and many of the libraries used throughout this class. Unless you have a good reason not to, we recommend that you use Anaconda.\n",
    "\n",
    "### Mac/Linux users\n",
    "1. Download the [appropriate version](http://continuum.io/downloads) of Anaconda\n",
    "1. Follow the instructions on that page to run the installer\n",
    "1. Test it out: open a terminal window, and type ``python``, you should see something like\n",
    "```\n",
    "Python 2.7.5 |Anaconda 1.6.1 (x86_64)| (default, Jun 28 2013, 22:20:13) \n",
    "```\n",
    "If `Anaconda` doesn't appear on the first line, you are using a different version of Python. See the troubleshooting section below.\n",
    "\n",
    "1. Test out the IPython notebook: open a Terminal window, and type `ipython notebook`. A new browser window should pop up. p\n",
    "1. Click `New Notebook` to create a new notebook file\n",
    "1. Update IPython to the newest version by typing `conda update ipython` at the command line\n",
    "1. Open the command prompt (type conda list --> pip install{see libraries below})\n",
    "\n",
    "\n",
    "### Windows Users\n",
    "1. Download the [appropriate version](http://continuum.io/downloads) of Anaconda\n",
    "1. Follow the instructions on that page to run the installer. This will create a directory at `C:\\Anaconda`\n",
    "1. Test it out: start the Anaconda launcher, which you can find in `C:\\Anaconda` or, in the Start menu. Start the IPython notebook. A new browser window should open. \n",
    "1. Click `New Notebook`, which should open a new page.\n",
    "1. Update IPython to the newest version by opening a command prompt, and typing `conda update ipython`\n",
    "\n",
    "If you did not add Anaconda to your path, be sure to use the full path to the python and ipython executables, such as `/anaconda/bin/python`.\n",
    "\n",
    "1. Open the Anaconda command prompt (type conda list --> pip install{see libraries below})\n",
    "\n",
    "## Installing additional libraries\n",
    "Anaconda includes most of the libraries we will use in this course, but you will need to install a few extra ones:\n",
    "\n",
    "1. [BeautifulSoup](http://www.crummy.com/software/BeautifulSoup/)\n",
    "1. [Pattern](http://www.clips.ua.ac.be/pattern)\n",
    "1. [Seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/)\n",
    "1. [MrJob](http://pythonhosted.org/mrjob/)\n",
    "1. [PyQuery](https://pythonhosted.org/pyquery/)\n",
    "\n",
    "The recommended way to install these packages is to run `pip install BeautifulSoup mrjob pattern seaborn pyquery` on the command line. If this doesn't work, you can download the source code, and run `python setup.py install` from the source code directory. On Unix machines, either of these commands may require `sudo` (i.e. `sudo pip install...` or `sudo python`)\n",
    "\n",
    "## Opening IPython Notebooks\n",
    "To view an IPython notebook, you must first start the IPython notebook server in the directory where the file lives. Simply navigate to this directory at the command prompt, and type `ipython notebook`. This will open a browser window, listing all the `ipynb` files in that directory.\n",
    "\n",
    "## Updating from older Anaconda versions\n",
    "You can easily update to the latest Anaconda version by updating conda, then Anaconda as follows:\n",
    "\n",
    "```\n",
    "conda update conda\n",
    "conda update anaconda\n",
    "```\n",
    "\n",
    "## Troubleshooting\n",
    "\n",
    "**Problem**\n",
    "When you start python, you don't see a line like `Python 2.7.5 |Anaconda 1.6.1 (x86_64)|`. You are using a Mac or Linux computer\n",
    "\n",
    "**Reason**\n",
    "You are most likely running a different version of Python, and need to modify your Path (the list of directories your computer looks through to find programs). \n",
    "\n",
    "**Solution**\n",
    "Find a file like `.bash_profile`, `.bashrc`, or `.profile`. Open the file in a text editor, and add a line at this line at the end: `export PATH=\"$HOME/anaconda/bin:$PATH\"`. Close the file, open a new terminal window, type `source ~/.profile` (or whatever file you just edited). Type `which python` -- you should see a path that points to the anaconda directory. If so, running `python` should load the proper version\n",
    "\n",
    "**Problem**\n",
    "You are running the right version of python (see above item), but are unable to import numpy. \n",
    "\n",
    "**Reason**\n",
    "You are probably loading a different copy of numpy that is incompatible with Anaconda\n",
    "\n",
    "**Solution**\n",
    "See the above item to find your `.bash_profile`, `.profile`, or `.bashrc` file. Open it, and add the line `unset PYTHONPATH` at the end. Close the file, open a new terminal window, type `source ~/.profile` (or whatever file you just edited), and try again.\n",
    "***\n",
    "\n",
    "**Problem**\n",
    "Under Windows, you receive an error message similar to the following: \"'pip' is not recognized as an internal or external command, operable program or batch file.\"\n",
    "\n",
    "**Reason**\n",
    "The correct Anaconda paths might not be present in your PATH variable, or Anaconda might not have installed correctly.\n",
    "\n",
    "**Solution**\n",
    "Ensure the Anaconda directories to your path environment variable (\"\\Anaconda\" and \"\\Anaconda\\Scripts\").  See [this page](http://superuser.com/questions/284342/what-are-path-and-other-environment-variables-and-how-can-i-set-or-use-them) for details.\n",
    "\n",
    "If this does not correct the problem, reinstall Anaconda.\n",
    "\n",
    "\n",
    "## Hello, Python\n",
    "\n",
    "The Jupyter notebook is an application to build interactive computational notebooks. You'll be using them to complete labs and homework. Once you've set up Python, please <a https://github.com/bsharvey/EMSEDataAnalytics/blob/master/EMSE6992_Assignments/HW0.ipynb download=\"HW0.ipynb\">download this page</a>, and open it with Jupyter by typing\n",
    "\n",
    "```\n",
    "jupyter notebook <name_of_downloaded_file>\n",
    "```\n",
    "\n",
    "## Portfolios and Setup\n",
    "\n",
    "For the rest of the lab/assignment, use your local copy of this page, running on Jupyter and bring up a shell.\n",
    "\n",
    "Download portfolio template from Github\n",
    "https://github.com/bsharve/bsharve.github.io\n",
    "\n",
    "Download assignments and labs from Github\n",
    "https://github.com/bsharvey/EMSEDataAnalytics\n",
    "\n",
    "Useful github commands\n",
    "https://gist.github.com/hofmannsven/6814451\n",
    "\n",
    "Create a github account\n",
    "\n",
    "Create a username.github.io repository\n",
    "Upload bsharve.github.io zip file to to username.github.io repository\n",
    "\n",
    "Create a {assignments_labs} repository\n",
    "Upload EMSEDataAnalytics zip file to to {assignments_labs} repository\n",
    "\n",
    "Download and Install Github Desktop \n",
    "https://desktop.github.com/\n",
    "\n",
    "Download and Install Atom IDE\n",
    "https://atom.io/\n",
    "\n",
    "//useful github command line comands\n",
    "git init\n",
    "git branch -m master gh-pages\n",
    "git add --all\n",
    "git status\n",
    "git commit -m \"first commit\"\n",
    "git remote add origin https://github.com/bsharve/bsharve.github.io.git\n",
    "//git remote remove origin\n",
    "git push -u origin gh-pages\n",
    "\n",
    "\n",
    "Notebooks are composed of many \"cells\", which can contain text (like this one), or code (like the one below). Double click on the cell below, and evaluate it by clicking the \"play\" button above, for by hitting shift + enter"
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   "metadata": {
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     "text": [
      "Item is  10\n",
      "Item is  20\n",
      "Item is  30\n",
      "Item is  40\n",
      "Item is  50\n"
     ]
    }
   ],
   "source": [
    "x = [10, 20, 30, 40, 50]\n",
    "for item in x:\n",
    "    print (\"Item is \", item)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Libraries\n",
    "\n",
    "We will be using a several different libraries throughout this course. If you've successfully completed the [installation instructions](https://github.com/cs109/content/wiki/Installing-Python), all of the following statements should run."
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version:        1.12.1 (need at least 1.7.1)\n",
      "SciPy version:        0.19.0 (need at least 0.12.0)\n",
      "Pandas version:       0.20.1 (need at least 0.11.0)\n",
      "Mapltolib version:     2.0.2 (need at least 1.2.1)\n",
      "Scikit-Learn version: 0.18.1 (need at least 0.13.1)\n",
      "requests version:     2.14.2 (need at least 1.2.3)\n",
      "NetworkX version:       1.11 (need at least 1.7)\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'beautifulSoup'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-366480ab963e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;31m#BeautifulSoup is a library to parse HTML and XML documents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mbeautifulSoup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"BeautifulSoup version:%6.6s (need at least 3.2)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'beautifulSoup'"
     ]
    }
   ],
   "source": [
    "#IPython is what you are using now to run the notebook\n",
    "import jupyter\n",
    "#print (\"Jupyter version:      %6.6s (need at least 1.0)\" % jupyter.__version__)\n",
    "\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "print (\"Numpy version:        %6.6s (need at least 1.7.1)\" % np.__version__)\n",
    "\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "print (\"SciPy version:        %6.6s (need at least 0.12.0)\" % sp.__version__)\n",
    "\n",
    "# Pandas makes working with data tables easier\n",
    "import pandas as pd\n",
    "print (\"Pandas version:       %6.6s (need at least 0.11.0)\" % pd.__version__)\n",
    "\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "print (\"Mapltolib version:    %6.6s (need at least 1.2.1)\" % matplotlib.__version__)\n",
    "\n",
    "# SciKit Learn implements several Machine Learning algorithms\n",
    "import sklearn\n",
    "print (\"Scikit-Learn version: %6.6s (need at least 0.13.1)\" % sklearn.__version__)\n",
    "\n",
    "# Requests is a library for getting data from the Web\n",
    "import requests\n",
    "print (\"requests version:     %6.6s (need at least 1.2.3)\" % requests.__version__)\n",
    "\n",
    "# Networkx is a library for working with networks\n",
    "import networkx as nx\n",
    "print (\"NetworkX version:     %6.6s (need at least 1.7)\" % nx.__version__)\n",
    "\n",
    "#BeautifulSoup is a library to parse HTML and XML documents\n",
    "import beautifulSoup\n",
    "print (\"BeautifulSoup version:%6.6s (need at least 3.2)\" % BeautifulSoup.__version__)\n",
    "\n",
    "#MrJob is a library to run map reduce jobs on Amazon's computers\n",
    "import mrjob\n",
    "print (\"Mr Job version:       %6.6s (need at least 0.4)\" % mrjob.__version__)\n",
    "\n",
    "#Pattern has lots of tools for working with data from the internet\n",
    "import pattern\n",
    "print (\"Pattern version:      %6.6s (need at least 2.6)\" % pattern.__version__)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If any of these libraries are missing or out of date, you will need to [install them](https://github.com/cs109/content/wiki/Installing-Python#installing-additional-libraries) and restart IPython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook integrates nicely with Matplotlib, the primary plotting package for python. This should embed a figure of a sine wave:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11c6a09e8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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G3xgUnTvJZK22AO4kr1cvg470889zs72EqxyMVas4KW6iU3zCBPYnb71lY7vs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd80cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#this line prepares IPython for working with matplotlib\n",
    "%matplotlib inline  \n",
    "\n",
    "# this actually imports matplotlib\n",
    "import matplotlib.pyplot as plt  \n",
    "\n",
    "x = np.linspace(0, 10, 30)  #array of 30 points from 0 to 10\n",
    "y = np.sin(x)\n",
    "z = y + np.random.normal(size=30) * .2\n",
    "plt.plot(x, y, 'ro-', label='A sine wave')\n",
    "plt.plot(x, z, 'b-', label='Noisy sine')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.ylabel(\"Y axis\")           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If that last cell complained about the `%matplotlib` line, you need to update IPython to v1.0, and restart the notebook. See the [installation page](https://github.com/cs109/content/wiki/Installing-Python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hello Numpy\n",
    "\n",
    "The Numpy array processing library is the basis of nearly all numerical computing in Python. Here's a 30 second crash course. For more details, consult Chapter 4 of Python for Data Analysis, or the [Numpy User's Guide](http://docs.scipy.org/doc/numpy-dev/user/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 3 row x 4 column array of random numbers\n",
      "[[ 0.80596117  0.58085638  0.2137176   0.55817447]\n",
      " [ 0.71444621  0.56043767  0.33289552  0.11722417]\n",
      " [ 0.71265339  0.78697654  0.81669209  0.65359971]]\n",
      "Add 1 to every element\n",
      "[[ 1.80596117  1.58085638  1.2137176   1.55817447]\n",
      " [ 1.71444621  1.56043767  1.33289552  1.11722417]\n",
      " [ 1.71265339  1.78697654  1.81669209  1.65359971]]\n",
      "Get the element at row 1, column 2\n",
      "1.33289552396\n",
      "Get the first row\n",
      "[ 1.80596117  1.58085638  1.2137176   1.55817447]\n",
      "Get every 2nd column of the first row\n",
      "[ 1.80596117  1.2137176 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function print>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Make a 3 row x 4 column array of random numbers\")\n",
    "x = np.random.random((3, 4))\n",
    "print (x)\n",
    "print\n",
    "\n",
    "print (\"Add 1 to every element\")\n",
    "x = x + 1\n",
    "print (x)\n",
    "print\n",
    "\n",
    "print (\"Get the element at row 1, column 2\")\n",
    "print (x[1, 2])\n",
    "print\n",
    "\n",
    "# The colon syntax is called \"slicing\" the array. \n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])\n",
    "print\n",
    "\n",
    "print (\"Get every 2nd column of the first row\")\n",
    "print (x[0, ::2])\n",
    "print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the maximum, minimum, and mean of the array. This does **not** require writing a loop. In the code cell below, type `x.m<TAB>`, to find built-in operations for common array statistics like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the `x.max` function again, but use the `axis` keyword to print the maximum of each row in x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a way to quickly simulate 500 coin \"fair\" coin tosses (where the probabily of getting Heads is 50%, or 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.random.binomial(500, .5)\n",
    "print \"number of heads:\", x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat this simulation 500 times, and use the [plt.hist() function](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) to plot a histogram of the number of Heads (1s) in each simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Monty Hall Problem\n",
    "\n",
    "\n",
    "Here's a fun and perhaps surprising statistical riddle, and a good way to get some practice writing python functions\n",
    "\n",
    "In a gameshow, contestants try to guess which of 3 closed doors contain a cash prize (goats are behind the other two doors). Of course, the odds of choosing the correct door are 1 in 3. As a twist, the host of the show occasionally opens a door after a contestant makes his or her choice. This door is always one of the two the contestant did not pick, and is also always one of the goat doors (note that it is always possible to do this, since there are two goat doors). At this point, the contestant has the option of keeping his or her original choice, or swtiching to the other unopened door. The question is: is there any benefit to switching doors? The answer surprises many people who haven't heard the question before.\n",
    "\n",
    "We can answer the problem by running simulations in Python. We'll do it in several parts.\n",
    "\n",
    "First, write a function called `simulate_prizedoor`. This function will simulate the location of the prize in many games -- see the detailed specification below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "simulate_prizedoor\n",
    "\n",
    "Generate a random array of 0s, 1s, and 2s, representing\n",
    "hiding a prize between door 0, door 1, and door 2\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "nsim : int\n",
    "    The number of simulations to run\n",
    "\n",
    "Returns\n",
    "-------\n",
    "sims : array\n",
    "    Random array of 0s, 1s, and 2s\n",
    "\n",
    "Example\n",
    "-------\n",
    ">>> print simulate_prizedoor(3)\n",
    "array([0, 0, 2])\n",
    "\"\"\"\n",
    "def simulate_prizedoor(nsim):\n",
    "    #compute here\n",
    "    return answer\n",
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, write a function that simulates the contestant's guesses for `nsim` simulations. Call this function `simulate_guess`. The specs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "simulate_guess\n",
    "\n",
    "Return any strategy for guessing which door a prize is behind. This\n",
    "could be a random strategy, one that always guesses 2, whatever.\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "nsim : int\n",
    "    The number of simulations to generate guesses for\n",
    "\n",
    "Returns\n",
    "-------\n",
    "guesses : array\n",
    "    An array of guesses. Each guess is a 0, 1, or 2\n",
    "\n",
    "Example\n",
    "-------\n",
    ">>> print simulate_guess(5)\n",
    "array([0, 0, 0, 0, 0])\n",
    "\"\"\"\n",
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, write a function, `goat_door`, to simulate randomly revealing one of the goat doors that a contestant didn't pick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "goat_door\n",
    "\n",
    "Simulate the opening of a \"goat door\" that doesn't contain the prize,\n",
    "and is different from the contestants guess\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "prizedoors : array\n",
    "    The door that the prize is behind in each simulation\n",
    "guesses : array\n",
    "    THe door that the contestant guessed in each simulation\n",
    "\n",
    "Returns\n",
    "-------\n",
    "goats : array\n",
    "    The goat door that is opened for each simulation. Each item is 0, 1, or 2, and is different\n",
    "    from both prizedoors and guesses\n",
    "\n",
    "Examples\n",
    "--------\n",
    ">>> print goat_door(np.array([0, 1, 2]), np.array([1, 1, 1]))\n",
    ">>> array([2, 2, 0])\n",
    "\"\"\"\n",
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function, `switch_guess`, that represents the strategy of always switching a guess after the goat door is opened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "switch_guess\n",
    "\n",
    "The strategy that always switches a guess after the goat door is opened\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "guesses : array\n",
    "     Array of original guesses, for each simulation\n",
    "goatdoors : array\n",
    "     Array of revealed goat doors for each simulation\n",
    "\n",
    "Returns\n",
    "-------\n",
    "The new door after switching. Should be different from both guesses and goatdoors\n",
    "\n",
    "Examples\n",
    "--------\n",
    ">>> print switch_guess(np.array([0, 1, 2]), np.array([1, 2, 1]))\n",
    ">>> array([2, 0, 0])\n",
    "\"\"\"\n",
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last function: write a `win_percentage` function that takes an array of `guesses` and `prizedoors`, and returns the percent of correct guesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function\n",
    "--------\n",
    "win_percentage\n",
    "\n",
    "Calculate the percent of times that a simulation of guesses is correct\n",
    "\n",
    "Parameters\n",
    "-----------\n",
    "guesses : array\n",
    "    Guesses for each simulation\n",
    "prizedoors : array\n",
    "    Location of prize for each simulation\n",
    "\n",
    "Returns\n",
    "--------\n",
    "percentage : number between 0 and 100\n",
    "    The win percentage\n",
    "\n",
    "Examples\n",
    "---------\n",
    ">>> print win_percentage(np.array([0, 1, 2]), np.array([0, 0, 0]))\n",
    "33.333\n",
    "\"\"\"\n",
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, put it together. Simulate 10000 games where contestant keeps his original guess, and 10000 games where the contestant switches his door after a  goat door is revealed. Compute the percentage of time the contestant wins under either strategy. Is one strategy better than the other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many people find this answer counter-intuitive (famously, PhD mathematicians have incorrectly claimed the result must be wrong. Clearly, none of them knew Python). \n",
    "\n",
    "One of the best ways to build intuition about why opening a Goat door affects the odds is to re-run the experiment with 100 doors and one prize. If the game show host opens 98 goat doors after you make your initial selection, would you want to keep your first pick or switch? Can you generalize your simulation code to handle the case of `n` doors?"
   ]
  }
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