04 - OOP I - Introduction to Object Oriented Programming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OOP I: Introduction to Object Oriented Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://lectures.quantecon.org/py/oop_intro.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140500396435736"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 2.5\n",
    "x = 2.5\n",
    "id(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"int(x=0) -> integer\\nint(x, base=10) -> integer\\n\\nConvert a number or string to an integer, or return 0 if no arguments\\nare given.  If x is a number, return x.__int__().  For floating point\\nnumbers, this truncates towards zero.\\n\\nIf x is not a number or if base is given, then x must be a string,\\nbytes, or bytearray instance representing an integer literal in the\\ngiven base.  The literal can be preceded by '+' or '-' and be surrounded\\nby whitespace.  The base defaults to 10.  Valid bases are 0 and 2-36.\\nBase 0 means to interpret the base from the string as an integer literal.\\n>>> int('0b100', base=0)\\n4\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 25\n",
    "x.__doc__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "callable(x.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aa', 'b']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = ['a', 'b']\n",
    "x.__setitem__(0, 'aa')\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The empirical cumulative distribution function (ecdf) corresponding to a sample {Xi}\n",
    "n\n",
    "i=1\n",
    " is defined as\n",
    "\n",
    "Fn(x):=\n",
    "1\n",
    "n\n",
    " \n",
    "n\n",
    "∑\n",
    "i=1 1{Xi≤x}(x∈R) \n",
    "Here 1{Xi≤x} is an indicator function (one if Xi≤x and zero otherwise) and hence Fn(x) is the fraction of the sample that falls below x.\n",
    "\n",
    "The Glivenko–Cantelli Theorem states that, provided that the sample is IID, the ecdf Fn converges to the true distribution function F.\n",
    "\n",
    "Implement Fn as a class called ECDF, where\n",
    "\n",
    "A given sample {Xi}\n",
    "n\n",
    "i=1\n",
    " are the instance data, stored as self.observations.\n",
    "The class implements a __call__ method that returns Fn(x) for any x.\n",
    "Your code should work as follows (modulo randomness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import uniform\n",
    "\n",
    "samples = [uniform(0, 1) for i in range(10)]\n",
    "F = ECDF(samples)\n",
    "F(0.5)  # Evaluate ecdf at x = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F.observations = [uniform(0, 1) for i in range(1000)]\n",
    "F(0.5)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}