Estimations

estimations-single.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math  \n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy import stats\n",
    "\n",
    "no_of_experiments = 10000000\n",
    "num_bins = 100\n",
    "\n",
    "tasks = {\n",
    "    \"Task 1\": [4,7, 12],\n",
    "}\n",
    "\n",
    "data = pd.DataFrame(tasks, index=[\"optimistic\", \"likely\", \n",
    "                                  \"pessimistic\"])\n",
    "points = [0] * no_of_experiments\n",
    "\n",
    "for column in data:\n",
    "    a, b, c = data[column][0], data[column][1], data[column][2]\n",
    "    alpha = ((4*b) + c - (5*a))/(c - a)\n",
    "    beta = ((5*c) - a - (4*b))/(c - a)\n",
    "    r = np.random.beta(alpha, beta, no_of_experiments)\n",
    "    p = (r*(c-a)) + a\n",
    "    points += p\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "n, bins, patches = plt.hist(points, num_bins, \n",
    "                            range = (0, np.max(points)),\n",
    "                            color = \"skyblue\", lw=1, \n",
    "                            edgecolor=\"steelblue\", \n",
    "                            weights=[1/no_of_experiments]*\n",
    "                            no_of_experiments)\n",
    "plt.show() "
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}