IIabm/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "85\n",
      "63\n",
      "51\n",
      "26\n",
      "30\n",
      "4\n",
      "7\n",
      "1\n",
      "17\n",
      "81\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.randint(0.5, 3.5)\n",
    "p_remove = 0.9\n",
    "np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
    "rng = np.random.default_rng(0)\n",
    "for _ in range(10):\n",
    "    print(rng.integers(0,100))\n",
    "np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = 0.8\n",
    "list_succ_firms = [1, 1]\n",
    "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]\n",
      "[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]\n",
      "[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]\n",
      "[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [s / sum(size) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.2, 0.3]:\n",
    "    damp_size = [math.exp(beta*s) for s in size]\n",
    "    print([s / sum(damp_size) for s in damp_size])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]\n",
      "[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]\n",
      "[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]\n",
      "[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "size = [18,20,21,22,23]\n",
    "p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]\n",
    "print(p)\n",
    "for beta in [0.1, 0.5, 0.8]:\n",
    "    p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "import multiprocess as mp\n",
    "\n",
    "print(mp.cpu_count())"
   ]
  }
 ],
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