klee50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython version:      %6.6s 8.3.0\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "import json\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "# Pandas is good with data tables\n",
    "import pandas as pd\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
    "import bs4\n",
    "# Nltk helps with some natural language tasks, like stemming\n",
    "import nltk\n",
    "# Bson is a binary format of json to be stored in databases\n",
    "import bson\n",
    "# Mongo is one of common nosql databases \n",
    "# it stores/searches json documents natively\n",
    "import pymongo\n",
    "print (\"IPython version:      %6.6s\", IPython.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a 2 row x 3 column array of random numbers\n",
      "[[0.8898503  0.61022724 0.09357716]\n",
      " [0.30709654 0.98176825 0.03636273]]\n",
      "Add 5 to every element\n",
      "[[5.8898503  5.61022724 5.09357716]\n",
      " [5.30709654 5.98176825 5.03636273]]\n",
      "Get the first row\n",
      "[5.8898503  5.61022724 5.09357716]\n"
     ]
    }
   ],
   "source": [
    "#Here is what numpy can do\\n\",\n",
    "print (\"Make a 2 row x 3 column array of random numbers\")\n",
    "x = np.random.random((2, 3))\n",
    "print (x)\n",
    "\n",
    "#array operation (as in R)\n",
    "print (\"Add 5 to every element\")\n",
    "x = x + 5\n",
    "print (x)\n",
    "\n",
    "# get a slice (first row) (as in R)\n",
    "print (\"Get the first row\")\n",
    "print (x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.any>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
    "#       on possible completions.\n",
    "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
    "x.any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aIPsbgIu69+lFwOXj3P4RX/RJnsbg/4CrquqGofHXAK8ELqju5BkHcWuHw+lA2YdcBfxB\ntzzt2Z/H4HzqV5Ls6vLdleQXmf7sVNUTVfWjqvox8Nc8dapgarIfYH/ZDdzQnSr4EvBjBveNmZrc\nsOj7dBXw+8C1Q9NXQvZNwJPLf8+495dJXKCYli8GFzyuBD603/gZDG6pPLPf+Av5yYs8DzO5izwH\nyn7S0PKbgeu75bP5yYs8X5q2132/Obt46mLs1GcH1gwtX8TgHPHU7DOL5H498Kfd8gsYnDbItORe\nan/p3qtf2G9s6rMzOFf/sm75dODObnks+/ph/x8+TV/AbzG4KHIPcHf3dRaDizePDo19fOg5lzC4\niv8g3VXzKcv+KeDebvwfGFygfXKH+2iX/avA7LRl32/OcNFPfXbgb7ts9zC4r9Nw8U98n1kk99HA\n33X7zF3Ay6cp91L7C/BJ4PULPGeqs3fjdzL4hbQD+PVu/lj2df8yVpIad8Sfo5ek1ln0ktQ4i16S\nGmfRS1LjLHpJapxFL0mNs+glqXEWvSQ17v8ApNqW0K2lWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a2433ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "heads = np.random.binomial(500, .5, size=500)\n",
    "histogram = plt.hist(heads, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Task 1\n",
    "## write a program to produce Fibonacci numbers up to 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the Fibonacci numbers up to 1,000,000:\n",
      "0\n",
      "1\n",
      "1\n",
      "2\n",
      "3\n",
      "5\n",
      "8\n",
      "13\n",
      "21\n",
      "34\n",
      "55\n",
      "89\n",
      "144\n",
      "233\n",
      "377\n",
      "610\n",
      "987\n",
      "1597\n",
      "2584\n",
      "4181\n",
      "6765\n",
      "10946\n",
      "17711\n",
      "28657\n",
      "46368\n",
      "75025\n",
      "121393\n",
      "196418\n",
      "317811\n",
      "514229\n",
      "832040\n"
     ]
    }
   ],
   "source": [
    "num1 = 0\n",
    "num2 = 1\n",
    "\n",
    "print('Here are the Fibonacci numbers up to 1,000,000:')\n",
    "print (num1)\n",
    "while num2 < 1000000:\n",
    "    print(num2)\n",
    "    num1, num2 = num2, num1 + num2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
    "## Calculate the mean and standard deviation of that sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulate 1000 tosses of a fair coin using np.random.binomial:\n",
      "Simulated mean of 0.49 and standard deviation of 0.500054.\n",
      "Expected proportion of heads in 1000 tosses of a fair coin is 0.50 with standard deviation of 0.50.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print(\"Simulate 1000 tosses of a fair coin using np.random.binomial:\")\n",
    "\n",
    "#Head - 0, Tails - 1\n",
    "coin = 0\n",
    "\n",
    "#number of coins landing on heads\n",
    "heads = 0\n",
    "for i in range(1000):\n",
    "    #n = 1, p = 0.5\n",
    "    coin = np.random.binomial(1,0.5)\n",
    "    heads += coin\n",
    "\n",
    "mean = heads/1000\n",
    "#Formula for the standard deviation of a sample\n",
    "sd = np.sqrt((heads*(1-mean)**2+(1000-heads)*mean**2)/(1000-1))\n",
    "\n",
    "print(\"Simulated mean of %.2f and standard deviation of %f.\" %(mean, sd))\n",
    "print (\"Expected proportion of heads in 1000 tosses of a fair coin is 0.50 with standard deviation of %.2f.\" %(np.sqrt(0.5*0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3\n",
    "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
    "### use numpy.random.normal to generate gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generate gaussian distribution using np.random.normal:\n"
     ]
    },
    {
     "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": [
    "import matplotlib.pyplot as plot\n",
    "import numpy as np\n",
    "\n",
    "print (\"Generate gaussian distribution using np.random.normal:\")\n",
    "\n",
    "#Lists of x and y points\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "#Number of data points - 100\n",
    "for i in range(100):\n",
    "    value = np.random.normal(0,5)\n",
    "    x.append(value)\n",
    "    e = np.random.normal(0,1)\n",
    "    y.append(0.5*value+e)\n",
    "    \n",
    "plot.xlabel(\"x-Gauss(0,5)\")\n",
    "plot.ylabel(\"y=0.5*x+e\\ne-Gauss(0,1)\")\n",
    "plot.title(\"Scatterplot of Gaussian Distribution\")\n",
    "plot.scatter(x,y)\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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