Submit

nskuda.ipynb

@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPython version:      %6.6s 7.17.0\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": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make a 2 row x 3 column array of random numbers\n",
+      "[[ 0.20354485  0.87353642  0.79226415]\n",
+      " [ 0.26457656  0.23486214  0.8240387 ]]\n",
+      "Add 5 to every element\n",
+      "[[ 5.20354485  5.87353642  5.79226415]\n",
+      " [ 5.26457656  5.23486214  5.8240387 ]]\n",
+      "Get the first row\n",
+      "[ 5.20354485  5.87353642  5.79226415]\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": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function ndarray.any>"
+      ]
+     },
+     "execution_count": 3,
+     "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": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "1346269\n"
+     ]
+    }
+   ],
+   "source": [
+    "prev = 1\n",
+    "cur = 1\n",
+    "print(prev)\n",
+    "print(cur)\n",
+    "while cur < 1e6:\n",
+    "    temp = cur\n",
+    "    cur += prev\n",
+    "    prev=temp\n",
+    "    print(cur)"
+   ]
+  },
+  {
+   "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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean= 0.518\n",
+      "standard deviation= 0.4996758949559204\n"
+     ]
+    }
+   ],
+   "source": [
+    "n,p,t = 1, .5, 1000\n",
+    "res = np.random.binomial(n, p, t)\n",
+    "print('mean=',np.mean(res))\n",
+    "print('standard deviation=',np.std(res))"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f089eb8ea00>"
+      ]
+     },
+     "execution_count": 27,
+     "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": [
+    "import matplotlib.pyplot as plt\n",
+    "xmax, emax = 5,1\n",
+    "count = 50\n",
+    "x = np.random.normal(0,xmax, count)\n",
+    "e = np.random.normal(0,emax, count)\n",
+    "y = np.zeros(count)\n",
+    "for i in range(count):\n",
+    "    y[i] = 0.5 * x[i] + e[i]\n",
+    "plt.scatter(x, y)"
+   ]
+  },
+  {
+   "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": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}