interaction_calc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def different_cases(number):\n",
    "    lists = []\n",
    "    for num in range(1, number+1):\n",
    "        lists.append(math.factorial(number)/math.factorial(num))\n",
    "    total =sum(lists) \n",
    "    return total, (\"{:,}\".format(total)), lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6,235,301.0'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_,a, lists = different_cases(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[120.0, 60.0, 20.0, 5.0, 1.0]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'number of independant variables')"
      ]
     },
     "execution_count": 4,
     "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": [
    "max_num = 5\n",
    "x=list(range(1,max_num))\n",
    "y = [math.log(different_cases(num)[0]) for num in x]\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.ylabel(\"log\")\n",
    "plt.xlabel(\"number of independant variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total =sum(lists)\n",
    "# print('total number of variables interaction are : ',total)"
   ]
  },
  {
   "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",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}