speed_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ising_genetic_new import genetic_algorithm\n",
    "from ising import monte_carlo_metropolis\n",
    "from matplotlib import pyplot as plt\n",
    "from timeit import timeit\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "betac = np.log(1.0 + np.sqrt(2)) / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = [\n",
    "    timeit(\n",
    "        f\"monte_carlo_metropolis(n=60, beta=0.01 * {betac}, h=0.0, max_steps=1_000, initial_state=1, method='checkerboard')\",\n",
    "        number=10,\n",
    "        setup=\"from ising import monte_carlo_metropolis\",\n",
    "    ),\n",
    "    timeit(\n",
    "        f\"genetic_algorithm(60, 0.01 * {betac}, 0.0, n_states=100, n_children=10, n_cycles=1_000, mutation_rate=0.01)\",\n",
    "        number=10,\n",
    "        setup=\"from ising_genetic import genetic_algorithm\",\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Comparison of average run time over 10 runs')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.bar([\"Monte Carlo\", \"Genetic algorithm\"], times)\n",
    "ax.set_ylabel(\"Time (s)\")\n",
    "fig.suptitle(\"Comparison of average run time over 10 runs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"figs/comparison_algorithms.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}