WeeklyAssignment2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Weekly Assignment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from random import uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Read data\n",
    "data = pd.read_csv(\"diet_and_atp.tsv\", header=[0], sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterations = 1000000\n",
    "p_stand = 0.5\n",
    "c_stand = 0.5 #Stablish starting points for each of the coefficients\n",
    "f_stand = 0.5\n",
    "all_my_standings = []\n",
    "\n",
    "for i in range(iterations):\n",
    "    p_step = uniform(-data[\"Protein\"].var()/2,data[\"Protein\"].var()/2)\n",
    "    c_step = uniform(-data[\"Carbohydrates\"].var()/2,data[\"Carbohydrates\"].var()/2) #The steps are balanced for each variable, so that they aren't too big or too small\n",
    "    f_step = uniform(-data[\"Fat\"].var()/2,data[\"Fat\"].var()/2)\n",
    "    p_candidate = p_stand + p_step\n",
    "    c_candidate = c_stand + c_step\n",
    "    f_candidate = f_stand + f_step\n",
    "    \n",
    "    error_stand = sum((p_stand*data[\"Protein\"] + c_stand*data[\"Carbohydrates\"] + f_stand*data[\"Fat\"] - data[\"ATP\"])**2)\n",
    "    error_move = sum((p_candidate*data[\"Protein\"] + c_candidate*data[\"Carbohydrates\"] + f_candidate*data[\"Fat\"] - data[\"ATP\"])**2)\n",
    "    if error_stand > error_move: #Move only if error decreases\n",
    "        p_stand = p_candidate\n",
    "        c_stand = c_candidate\n",
    "        f_stand = f_candidate\n",
    "    else:                        #Otherwise, move randomly, depeding on how big is the new error with respect to the previous one\n",
    "        random_number = uniform(0,1)\n",
    "        if random_number <= error_stand/error_move:\n",
    "            p_stand = p_candidate\n",
    "            c_stand = c_candidate\n",
    "            f_stand = f_candidate\n",
    "\n",
    "    all_my_standings.append((p_stand,c_stand,f_stand)) #Record the position for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "proteins = [cosa[0] for cosa in all_my_standings]\n",
    "carbohydrates = [cosa[1] for cosa in all_my_standings] #Separate each coefficiente variables\n",
    "fats = [cosa[2] for cosa in all_my_standings]\n",
    "bins = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22074148584612766\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sum(fats)/len(fats))\n",
    "plt.hist(fats,bins)\n",
    "plt.xlabel(\"f coefficient value\")\n",
    "plt.ylabel(\"frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1939650016004373\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sum(proteins)/len(proteins))\n",
    "plt.hist(proteins,bins)\n",
    "plt.xlabel(\"p coefficient value\")\n",
    "plt.ylabel(\"frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8705452323043181\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sum(carbohydrates)/len(carbohydrates))\n",
    "plt.hist(carbohydrates,bins)\n",
    "plt.xlabel(\"c coefficient value\")\n",
    "plt.ylabel(\"frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.11495278580279\n"
     ]
    }
   ],
   "source": [
    "p = sum(proteins)/len(proteins)\n",
    "c = sum(carbohydrates)/len(carbohydrates)\n",
    "f = sum(fats)/len(fats)\n",
    "error = sum((p*data[\"Protein\"]+c*data[\"Carbohydrates\"]+f*data[\"Fat\"] - data[\"ATP\"])**2)\n",
    "print(error)"
   ]
  },
  {
   "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",
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