CorrelationAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"22bb2bbb22b2222b2222bb2bbb22b222b2b2bbbb222b2bb2b2222bbb2bbbb222bb2bb222bbb2bb22b22bbbbbb22b2b2bb2b2222bb2bbbb22bbb2222bbbb2bb22222b2bbbb2bb222bbb2bbbb2bb22bbb222bb2b222bbb22bbbb222bbb2bbb2bb2bbb2222b222bbb2bbbb2bbb22b\\n\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all = read(\"../CoinFlips-Edvin/wuerfeln-1.txt\", String)\n",
    "\n",
    "# all = read(\"../CoinFlips-Tamino/829.txt\", String)\n",
    "\n",
    "all = read(\"../CoinFlips-Edvin/wuerfeln-3.txt\", String)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = filter(c -> c in ['b', '2'], collect(all));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correlation (generic function with 2 methods)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function correlation(data, dist = 1)\n",
    "    n = Dict()\n",
    "    for pos in 1:length(data)\n",
    "        posdist = pos + dist\n",
    "        if posdist > length(data)\n",
    "            posdist = posdist - length(data)\n",
    "        end\n",
    "        k = data[pos] * data[posdist]\n",
    "        \n",
    "        n[k] = get(n, k, 0) + 1\n",
    "    end\n",
    "    n[\"bb\"] * sum(values(n)) / ( (n[\"bb\"] + n[\"2b\"]) * (n[\"bb\"] + n[\"b2\"]))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = 1:(length(all) ÷ 2)\n",
    "y = map(x -> correlation(all, x), x)\n",
    "scatter(x, y)\n",
    "xlabel(\"dist\")\n",
    "ylabel(\"correlation\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.4.2",
   "language": "julia",
   "name": "julia-1.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}