N-Queens Problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import mlrose_hiive as mlrh\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [7, 7]\n",
    "\n",
    "random_state = 2020\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fitness_iteration(curve, title, x=\"Iteration\", y=\"Fitness\"):\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)\n",
    "    plt.title(title)\n",
    "    length = len(curve)\n",
    "    plt.plot(range(length), curve, label=y, lw=2)\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Queens Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Problem definition\n",
    "length = 35\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "# Define alternative N-Queens fitness function for maximization problem\n",
    "def queens_max(state):\n",
    "    global eval_count\n",
    "    eval_count += 1\n",
    "    # Initialize counter\n",
    "    fitness_cnt = 0\n",
    "\n",
    "    # For all pairs of queens\n",
    "    for i in range(len(state) - 1):\n",
    "        for j in range(i + 1, len(state)):\n",
    "            # Check for horizontal, diagonal-up and diagonal-down attacks\n",
    "            if (state[j] != state[i]) \\\n",
    "                and (state[j] != state[i] + (j - i)) \\\n",
    "                and (state[j] != state[i] - (j - i)):\n",
    "\n",
    "                # If no attacks, then increment counter\n",
    "                fitness_cnt += 1\n",
    "\n",
    "    return fitness_cnt\n",
    "\n",
    "# Initialize custom fitness function object\n",
    "q_fitness = mlrh.CustomFitness(queens_max)\n",
    "# q_fitness = mlrose.Queens()\n",
    "prob = mlrh.QueensOpt(length=length,\n",
    "                      fitness_fn=q_fitness,\n",
    "                      maximize=True)\n",
    "experiment_name = \"queen_prob\"\n",
    "output_directory = \"queen\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIMIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running mimic\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.08], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[50], iteration:[50], done:[True], time:[17.96], fitness:[566.0000]\n",
      "\t[32. 29. 26. 10.  5.  9. 31.  2.  3. 23.  2. 20. 10.  6. 34.  9. 31.  1.// 22. 27.  9. 26. 15. 11. 14. 21. 28. 16. 19. 27.  0. 22. 34. 15.  2.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[50], iteration:[50], done:[True], time:[17.26], fitness:[566.0000]\n",
      "\t[32. 29. 26. 10.  5.  9. 31.  2.  3. 23.  2. 20. 10.  6. 34.  9. 31.  1.// 22. 27.  9. 26. 15. 11. 14. 21. 28. 16. 19. 27.  0. 22. 34. 15.  2.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[49], iteration:[50], done:[True], time:[17.81], fitness:[568.0000]\n",
      "\t[ 9. 22. 19. 17. 22. 16. 32. 24. 12.  2.  2.  4. 10.  7.  3. 11.  7. 32.// 34.  0. 33.  8. 14. 20.  0. 15.  5. 12.  3.  1.  8.  5. 14. 17. 17.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.07], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[46], iteration:[50], done:[True], time:[18.15], fitness:[572.0000]\n",
      "\t[24. 17. 30. 24.  7. 13. 20. 16. 15. 28. 11.  5. 34. 31. 33. 25. 14.  4.// 17. 19. 22. 11.  1.  7.  4. 18. 32.  9. 29. 30. 19. 23. 13.  1. 23.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.06], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[50], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[50], iteration:[50], done:[True], time:[19.44], fitness:[569.0000]\n",
      "\t[ 8.  5. 16. 26. 31. 16. 14.  2. 11. 14.  1. 23.  2. 11. 34. 30. 28. 29.// 29.  3. 23.  9. 31. 22.  0. 14. 28.  9. 21. 21.  4.  2. 20. 10. 13.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.14], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[50], iteration:[50], done:[True], time:[25.47], fitness:[571.0000]\n",
      "\t[30.  6. 24.  6. 29. 32.  4. 18. 20. 28. 33. 14. 16.  4. 14. 11. 30.  4.//  1. 12. 19. 13.  2. 15.  5. 31. 17. 14.  7. 15. 19. 34.  0. 21. 24.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.14], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[44], iteration:[50], done:[True], time:[24.90], fitness:[574.0000]\n",
      "\t[32. 22. 24.  7. 20. 31. 20.  3.  8. 19. 10. 12. 26.  2.  4.  1. 10. 23.//  6. 29. 13. 14.  9. 28. 31.  4. 17. 18. 30. 27. 15. 34.  2.  7. 26.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.14], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[45], iteration:[50], done:[True], time:[25.28], fitness:[576.0000]\n",
      "\t[30.  6. 24.  5. 22. 32.  4. 18.  9. 28. 33. 26.  1.  4. 14. 11. 30.  4.//  1. 29. 13. 13.  2.  8.  5. 31. 20. 26.  7. 12. 19. 34.  0. 21. 24.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.14], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[48], iteration:[50], done:[True], time:[25.86], fitness:[574.0000]\n",
      "\t[30. 17. 30. 18.  5. 31. 19. 31.  0. 29. 33.  6. 23. 11. 15. 25. 30. 10.// 24. 10. 19. 16.  1.  8.  5. 27. 20. 14. 33. 15. 26. 34.  0. 29.  3.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.14], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[100], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[46], iteration:[50], done:[True], time:[26.64], fitness:[573.0000]\n",
      "\t[11. 19. 22. 10.  0. 29. 30. 22. 29. 14.  4.  7. 28. 31.  1. 30. 25. 13.//  2.  8. 33. 20.  6. 27. 24. 25.  9. 33.  6.  9. 32. 23.  7. 10. 13.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.29], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[47], iteration:[50], done:[True], time:[39.57], fitness:[575.0000]\n",
      "\t[19.  0. 34. 10. 21.  2.  9. 22.  0. 26.  8. 22. 32.  8. 16. 33.  1.  5.// 27.  4.  6. 11. 31. 23. 20.  9. 17.  6. 34. 24. 29. 23. 13.  1. 17.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n"
     ]
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      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.29], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[43], iteration:[50], done:[True], time:[40.12], fitness:[580.0000]\n",
      "\t[28.  2. 10. 28. 16. 24. 13. 29. 12. 26.  8.  4. 22. 34. 18. 33.  9. 32.//  8.  4. 34. 32. 19. 23. 21.  0. 31. 13. 14. 17. 29.  7. 19. 27.  3.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.28], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[40], iteration:[50], done:[True], time:[39.47], fitness:[581.0000]\n",
      "\t[11. 16. 33. 15.  9. 24.  4. 16. 10. 25. 17. 29.  5. 21.  1. 32. 12.  8.//  2. 22. 26. 34.  0.  7. 29. 13.  0. 18. 20. 30. 13. 28. 10. 19. 14.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
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      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.28], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[30], iteration:[50], done:[True], time:[39.37], fitness:[579.0000]\n",
      "\t[ 8. 12. 16.  6.  7. 24. 19.  4. 32. 26.  5.  1. 30.  1. 13. 17. 20. 29.// 31. 25. 18. 18. 31. 23.  6.  9. 34. 21. 15.  4. 15. 32. 13. 22. 20.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
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      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.8], max_iters:[50]\n",
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      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[200], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[31], iteration:[50], done:[True], time:[41.09], fitness:[576.0000]\n",
      "\t[20.  3. 32. 14. 15.  0.  1.  8. 29. 13. 23.  1. 32. 26. 10. 33.  0.  9.// 11. 21. 18. 26. 19. 31. 31. 16.  6. 12. 29.  6. 33.  4. 17. 27. 17.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.1], max_iters:[50]\n",
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      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
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      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[47], iteration:[50], done:[True], time:[79.37], fitness:[579.0000]\n",
      "\t[18.  5. 30. 30. 19.  0. 14. 32. 19. 21.  5. 20. 22.  3. 27. 20.  1. 33.// 31. 10. 26.  0. 32.  3. 12. 28.  2.  8. 29. 13. 13. 17. 24.  1. 12.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
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      "*****************\n",
      "*** Run START ***\n",
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      "\n",
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      "\t[22.  8. 28.  2. 21. 33. 18. 28.  3. 23.  4. 16.  9. 26. 12. 19.  5. 13.// 27. 27. 20. 32.  9. 29. 25. 23. 16.  1. 30. 17. 11. 24. 15. 34. 10.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
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      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.5], max_iters:[50]\n",
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      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[30], iteration:[50], done:[True], time:[82.96], fitness:[582.0000]\n",
      "\t[ 7. 25. 13. 20. 34. 31.  4. 10. 23. 32. 31. 12. 16. 25. 30. 33. 26.  8.//  1. 14.  9. 32.  5.  7. 11. 22. 14.  0.  6. 29. 15. 23. 28. 19. 24.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.69], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[19], iteration:[50], done:[True], time:[84.51], fitness:[581.0000]\n",
      "\t[31. 26. 16. 18. 15.  5. 24. 28.  9.  5. 13. 34. 31. 33. 22.  0.  0.  8.// 15. 19.  6.  3. 24. 27.  1. 30. 25. 29. 25. 12. 10. 21. 26.  4. 33.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.69], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[500], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[19], iteration:[50], done:[True], time:[86.48], fitness:[579.0000]\n",
      "\t[ 6. 34. 29. 31. 18.  9. 32. 20.  4. 25. 11. 28. 21. 13. 27. 17. 15.  2.//  5. 16.  3. 29.  8. 34. 22.  0. 18. 32.  2. 11.  3. 10. 23. 14. 14.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.37], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[44], iteration:[50], done:[True], time:[161.13], fitness:[585.0000]\n",
      "\t[33. 22. 27. 14.  0.  9.  5. 15. 30.  4. 12. 20.  7. 19. 28.  1. 32. 34.//  3.  8. 21. 24. 31. 16.  4. 32. 11. 17. 19. 21. 13. 25.  2. 31. 10.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.39], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[37], iteration:[50], done:[True], time:[154.21], fitness:[586.0000]\n",
      "\t[ 2. 19. 15. 29. 18. 11. 22. 26. 33. 32. 21. 31. 27.  5. 12.  6. 30. 13.// 24.  4.  9.  3. 34.  4. 25. 20. 25. 10.  8.  7. 23. 34. 16.  7.  0.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.35], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[24], iteration:[50], done:[True], time:[148.29], fitness:[585.0000]\n",
      "\t[31.  9.  5. 12. 24. 22.  2. 29. 26. 11. 25. 10.  6.  3. 33.  9.  0. 18.//  1. 32. 30. 28. 34.  4. 19.  1. 14. 27. 13. 33.  2. 17. 14. 24. 21.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.37], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[10], iteration:[50], done:[True], time:[152.79], fitness:[582.0000]\n",
      "\t[ 9. 14. 20. 29.  6. 33. 33.  4. 19. 19. 26. 17.  1. 27. 10. 30. 28.  2.//  3.  5.  0. 22. 10. 21. 31.  0. 25.  6. 32. 18. 23. 15. 16. 24.  1.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.35], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[1000], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[8], iteration:[50], done:[True], time:[161.43], fitness:[583.0000]\n",
      "\t[20. 11. 13. 18. 22. 32.  7. 24.  1. 22.  4. 19. 23.  3.  8. 10. 30. 26.//  6. 31.  3.  6. 29. 25. 34. 12. 15. 33.  0. 16. 15.  9.  2. 14.  5.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.74], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.1], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[39], iteration:[50], done:[True], time:[292.83], fitness:[587.0000]\n",
      "\t[21. 12. 12. 22.  5. 18.  0. 30. 33. 25. 28. 20. 18.  6.  3.  7. 31. 31.//  1. 27. 32. 19.  6. 14. 26. 29. 16. 34. 15.  4.  2.  8. 17. 23. 29.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.73], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.25], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[32], iteration:[50], done:[True], time:[275.32], fitness:[587.0000]\n",
      "\t[ 9.  2. 24. 15.  1. 25. 34. 18. 27. 33.  3.  6.  0. 31.  8.  4. 22. 20.//  2. 29. 22. 12.  5. 21. 28. 24. 33.  1.  3.  7. 17. 23. 14. 10. 30.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.74], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.5], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[9], iteration:[50], done:[True], time:[269.79], fitness:[585.0000]\n",
      "\t[15.  7. 14. 20. 21. 33. 28.  5. 19.  0.  4. 32.  1. 15. 25. 16. 30.  3.//  6. 23.  2. 30. 26.  3. 11. 22. 34. 27. 31. 34. 13.  2.  8. 11. 18.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.69], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.7], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[1], iteration:[50], done:[True], time:[288.05], fitness:[583.0000]\n",
      "\t[ 7. 33. 14.  9. 26. 17. 29. 21.  5.  8.  4. 28.  3. 26. 18. 34. 27.  2.// 15. 22. 30.  6. 23. 13. 24. 20. 34. 32. 12.  0. 22.  1. 14. 16.  8.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.72], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "use_fast_mimic:[True], pop_size:[2000], keep_pct:[0.8], max_iters:[50]\n",
      "runner_name:[mimic], experiment_name:[queen_prob], attempt:[6], iteration:[50], done:[True], time:[304.81], fitness:[581.0000]\n",
      "\t[ 3. 20. 17.  2. 21. 27. 34. 14. 19. 32. 18.  4.  6. 23.  0.  3. 28. 12.// 12.  1. 32. 26. 20.  7. 33. 15.  7. 10. 25.  9. 13. 31. 33. 11.  5.]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 3037.4606365829823\n",
      "Saving: [queen/queen_prob/mimic__queen_prob__run_stats_df.csv]\n",
      "Saving: [queen/queen_prob/mimic__queen_prob__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "mim = mlrh.MIMICRunner(problem=prob,\n",
    "                       experiment_name=experiment_name,\n",
    "                       output_directory=output_directory,\n",
    "                       seed=random_state,\n",
    "                       population_sizes=[50, 100, 200, 500, 1000, 2000],\n",
    "                       keep_percent_list=[0.1, 0.25, 0.5, 0.7, 0.8],\n",
    "                       iteration_list=[50],\n",
    "                       use_fast_mimic=True)\n",
    "mim_stats, mim_curve = mim.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Population Size</th>\n",
       "      <th>Keep Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>292.834724</td>\n",
       "      <td>587.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>275.321363</td>\n",
       "      <td>587.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>154.208697</td>\n",
       "      <td>586.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>161.129296</td>\n",
       "      <td>585.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>76.400956</td>\n",
       "      <td>585.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>269.790202</td>\n",
       "      <td>585.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>148.292036</td>\n",
       "      <td>585.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>161.427105</td>\n",
       "      <td>583.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>288.054391</td>\n",
       "      <td>583.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>82.957977</td>\n",
       "      <td>582.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Time  Fitness  Population Size  Keep Percent\n",
       "51  292.834724    587.0             2000          0.10\n",
       "53  275.321363    587.0             2000          0.25\n",
       "43  154.208697    586.0             1000          0.25\n",
       "41  161.129296    585.0             1000          0.10\n",
       "33   76.400956    585.0              500          0.25\n",
       "55  269.790202    585.0             2000          0.50\n",
       "45  148.292036    585.0             1000          0.50\n",
       "49  161.427105    583.0             1000          0.80\n",
       "57  288.054391    583.0             2000          0.70\n",
       "35   82.957977    582.0              500          0.50"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Population Size', 'Keep Percent']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./queen/queen_prob/mimic__queen_prob__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIMIC - Total Function Evaluations: 23027\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 10\n",
    "max_iters = 25\n",
    "keep_pct=0.25\n",
    "pop_size = 1000\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, mimic_curve = mlrh.mimic(prob,\n",
    "                                             max_attempts=max_attempts,\n",
    "                                             max_iters=max_iters,\n",
    "                                             random_state=random_state,\n",
    "                                             pop_size=pop_size,\n",
    "                                             keep_pct=keep_pct,\n",
    "                                             curve=True)\n",
    "print(\"MIMIC - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(mimic_curve,\n",
    "                       \"Queens - Mimic: Population Size: {}, Keep Percent: {}\".format(pop_size, keep_pct))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ga\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.18], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[42], done:[True], time:[5.55], fitness:[577.0000]\n",
      "\t[ 0 21 28 24  1 32 13 21  7  3 34 14 30  7 27 23  7 19 33  6 25 10  2  5//  5  4 22  8 29  9  3 24 12  1 26]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.15], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[17], iteration:[100], done:[True], time:[11.33], fitness:[589.0000]\n",
      "\t[ 8 24 34 13 22  4 34  3 23 26  2 20 10 29 16 12 28  7  0 14 27 32  6 29// 17  4 11  3 33 30 19  1 18 21  9]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.13], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[7], iteration:[100], done:[True], time:[10.90], fitness:[590.0000]\n",
      "\t[32  7  9 24 18 10  3 30  2 29 23 20 30 17 26 11 31 19  1  6 28 14 12  5// 25 33 21 16 16 27 22 34 13  8 25]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.10], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[100], done:[True], time:[10.13], fitness:[590.0000]\n",
      "\t[21 12 24 13  5 18 33  4  2 16 14  3  8 28 26  0  6 19  1 29 11 26  6 31// 27  7 32 34  9 15 25  4 20 10 23]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[100], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.11], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[100], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[4], iteration:[100], done:[True], time:[10.61], fitness:[588.0000]\n",
      "\t[26 18  8 22 17 10 25 31 29 19  3 33 21  4 26  1  6 13 32 27 14 20  2 34//  5 28 30 16  7 30 15 11  0 17 31]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.63], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[69], done:[True], time:[34.19], fitness:[579.0000]\n",
      "\t[33 17  8 24 11  4 19 21 14 28 30  5  1  6 22  7  5 18 23  1 10 13 26 32// 34  4 13 31 32 15  3 28  0 21 29]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.43], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[48], done:[True], time:[23.29], fitness:[577.0000]\n",
      "\t[29 14 25  9 19 29 32  1 18  2  2 10 26 30  5  7 17 13  6  8 28 26 12 21// 10 34  0  3  4 24 30  6 34 31 11]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.40], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[30], done:[True], time:[14.10], fitness:[577.0000]\n",
      "\t[ 9 13 27 33 22 25 14 16  9  4  4 18 28 32  3 26 26 30  7  0 25  0 21  5//  7  2 23 33 11  9  8 15 12 29  1]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.44], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[52], done:[True], time:[23.61], fitness:[580.0000]\n",
      "\t[24  6 23 14  7 30 34 32  2  4  9 18 16 26 32 21 25  4 15 12  3 13 30 15//  2  7 17  0 29 27 34 20 13  4 24]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[500], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.42], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[500], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[51], done:[True], time:[23.15], fitness:[579.0000]\n",
      "\t[29 12  8 16 22  7 29 14 32  9  5 20 27 33 10 25  0 30 15  1  2  4 15 26// 29 23 34  1 16  1  0  3 10  6 24]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.83], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.1], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[33], done:[True], time:[29.86], fitness:[576.0000]\n",
      "\t[25  0 14 16 22 16 21 13 31  5  4  7 16 27 32  2 33 26  4  3 33  6 15 18// 25  1 30  9 28  6  2 11 34 19 24]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.82], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.25], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[25], done:[True], time:[22.32], fitness:[578.0000]\n",
      "\t[ 5  0 12  6 32 28 15  4 20 33  8 28 30 13 27 17  1  2 31  3  6 16  5 30// 25 29 22  7 34  2 24  6 26 22  4]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pop_size:[1000], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.82], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.5], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[45], done:[True], time:[39.00], fitness:[578.0000]\n",
      "\t[ 7 22 29 11  4 28 18 20 34 21  0 20  8 30 32 33  0  8 33  6 34  1 15 33// 23  3 26  2 25  3 15 14  9 25  6]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.78], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.75], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[52], done:[True], time:[43.26], fitness:[579.0000]\n",
      "\t[30  8  3 24 15 11  1  4  2 24  2 30 10  7  1 34 28 22  9  5 28 17 12 23// 29 34 25 16 32  6 14 20 10 21 29]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "pop_size:[1000], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.84], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "pop_size:[1000], mutation_prob:[0.9], max_iters:[100]\n",
      "runner_name:[ga], experiment_name:[queen_prob], attempt:[21], iteration:[33], done:[True], time:[28.18], fitness:[577.0000]\n",
      "\t[18 27 17 30  6 26 14 24  1  3 14 15  8 31  0 34  0 23 30 17 34 28 33  2//  9 25 12 15 25 18 22  9 13 20  5]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 329.6645150919794\n",
      "Saving: [queen/queen_prob/ga__queen_prob__run_stats_df.csv]\n",
      "Saving: [queen/queen_prob/ga__queen_prob__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "ga = mlrh.GARunner(problem=prob,\n",
    "                   experiment_name=experiment_name,\n",
    "                   output_directory=output_directory,\n",
    "                   seed=random_state,\n",
    "                   max_attempts=20,\n",
    "                   iteration_list=[100],\n",
    "                   population_sizes=[100, 500, 1000],\n",
    "                   mutation_rates=[0.1, 0.25, 0.5, 0.75, 0.9])\n",
    "ga_stats, ga_curve = ga.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Population Size</th>\n",
       "      <th>Mutation Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.903841</td>\n",
       "      <td>590.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.127351</td>\n",
       "      <td>590.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.330267</td>\n",
       "      <td>589.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.613477</td>\n",
       "      <td>588.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.609953</td>\n",
       "      <td>580.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>43.263337</td>\n",
       "      <td>579.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.147037</td>\n",
       "      <td>579.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.191525</td>\n",
       "      <td>579.0</td>\n",
       "      <td>500</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>38.999550</td>\n",
       "      <td>578.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22.315213</td>\n",
       "      <td>578.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time  Fitness  Population Size  Mutation Rate\n",
       "5   10.903841    590.0              100           0.50\n",
       "7   10.127351    590.0              100           0.75\n",
       "3   11.330267    589.0              100           0.25\n",
       "9   10.613477    588.0              100           0.90\n",
       "17  23.609953    580.0              500           0.75\n",
       "27  43.263337    579.0             1000           0.75\n",
       "19  23.147037    579.0              500           0.90\n",
       "11  34.191525    579.0              500           0.10\n",
       "25  38.999550    578.0             1000           0.50\n",
       "23  22.315213    578.0             1000           0.25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Population Size', 'Mutation Rate']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./queen/queen_prob/ga__queen_prob__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genetic Alg - Total Function Evaluations: 10429\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 20\n",
    "max_iters = 25\n",
    "mutation_prob=0.6\n",
    "pop_size = 400\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, gen_curve = mlrh.genetic_alg(prob,\n",
    "                                                   max_attempts=max_attempts,\n",
    "                                                   max_iters=max_iters,\n",
    "                                                   random_state=random_state,\n",
    "                                                   pop_size=pop_size,\n",
    "                                                   mutation_prob=mutation_prob,\n",
    "                                                   curve=True)\n",
    "print(\"Genetic Alg - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(gen_curve,\n",
    "                       \"Queens - Genetic Alg: mutation_prob: {}, pop_size: {}\".format(mutation_prob, pop_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulated Annealing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sa\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[2.86], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[2.78], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.05], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[2.73], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[3.03], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[3.02], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1698], done:[True], time:[2.95], fitness:[594.0000]\n",
      "\t[22 12 17 11  7 19 31 28 24  3 16 10 29  2  4 33 20  8  5  1 32 18 27  6// 26 34 27 13 23  9 15  0 25 21 30]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[16], iteration:[2500], done:[True], time:[4.07], fitness:[594.0000]\n",
      "\t[25 21 30 20 17 15 29 26 32 18 24  5  7  2  0 13 32 22  8 10  4 28 19 34// 23 12 27 33 16 14  1 31  6  3 11]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[1109], done:[True], time:[1.85], fitness:[592.0000]\n",
      "\t[12  8  3  5 24 26 21 32 22 15 19  6  8 31  0 28 10 20 16 27  2 18  1 13// 17 25 30 15 33  4 29  9 23 14 11]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[0.5], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[15], iteration:[2500], done:[True], time:[4.48], fitness:[588.0000]\n",
      "\t[18 25 19 24 27  9  1 12 15 29  3  0 22 11  2  2 31 26 32  1 13  4  7 31// 17 21  4 30  8 23  5 28 16  6 33]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[28], iteration:[2500], done:[True], time:[3.95], fitness:[592.0000]\n",
      "\t[25 14 11 20 10 13 18  0 34  3 31  6 26 33 15 22 12 17  4 30  1 23 32  7// 28 15  5 26 19 27 13  8 29  2 16]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[19], iteration:[2500], done:[True], time:[4.13], fitness:[592.0000]\n",
      "\t[ 0 11 19  2 27 32  8  9 28 21  7 15 10 16  5 31 22  3  6 30 34 29 18  4//  1 17 31 12 15 24 14 20 33 26 13]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[1], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[2], iteration:[2500], done:[True], time:[5.56], fitness:[581.0000]\n",
      "\t[22 16 30 13 27  7  4 19 32  5 15 18 30  6 28 21  2 26 31 20 12 29 25 23//  0 34  1 17 24  6 13 30 10  3 31]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[166], iteration:[2500], done:[True], time:[4.39], fitness:[593.0000]\n",
      "\t[18 23  2 13 10 14 22 33 31 28  3 16 32 12  8 26 29 19 30  1 34 11 25 27//  7  5  0  6 24  6 21 15 20  9  4]...\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[2183], done:[True], time:[4.02], fitness:[593.0000]\n",
      "\t[ 8 10  1  9 19  2 11 30 12 22 17 14 23 29 24 33  5  7  4  0 34 13  4  6// 15 27 21 28 16  1 26 31 25  3 32]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[10], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[1], iteration:[2500], done:[True], time:[7.19], fitness:[568.0000]\n",
      "\t[ 7 18 13 28  2 20 34 22 10 11 19 19 26 29 17 22 31 13 21 33 26  3 34 12//  0  0 10 14 14  4  0 23 27  1 33]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[201], iteration:[2091], done:[True], time:[4.09], fitness:[594.0000]\n",
      "\t[22  6 16 14 32 26  8 17 24 21 11  5  0 30  9  4 34 20 27  1 33 28 18 15// 10 29  7 25 13 19 27 31 23  2 12]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[63], iteration:[2500], done:[True], time:[4.99], fitness:[593.0000]\n",
      "\t[ 0  8 10 13 28 19  4 18 25 11  1 31  9 22 26  2 31  5 21 12  3 17  7 13// 29 20 15 33  6 23 16 30 24 34 14]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[20], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[1], iteration:[2500], done:[True], time:[7.20], fitness:[553.0000]\n",
      "\t[31  4 10  4 26 30 24 33 21 30  5 20 26 15  1 27 31 34 31 12 20 31  7 12//  0 15 15 15 30 28 34 19  3 12  4]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[109], iteration:[2500], done:[True], time:[4.26], fitness:[592.0000]\n",
      "\t[12 17 27 11 24  2 19 14 32 26 29 34  7 23 23 16  1  6  4 34 22 20 10 21// 30 18  8  3 33  9 28 31 15  0 25]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[45], iteration:[2500], done:[True], time:[4.80], fitness:[592.0000]\n",
      "\t[12 17 23 26  1  8 24  2 33  5 27 31 14 20  7  0  6 15 28 30  3 15 22 32// 29 16 25 11 34 21  4  9  3 19 13]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "schedule:[25], max_iters:[2500]\n",
      "runner_name:[sa], experiment_name:[queen_prob], attempt:[1], iteration:[2500], done:[True], time:[6.95], fitness:[563.0000]\n",
      "\t[ 0 18  4  2  3  4 29 17  9 22  6 19 18 14  1 27 31 21 32 33  5  3 33 12// 23 16  5 14 15 20 16 11  7  8  8]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 94.88185020099627\n",
      "Saving: [queen/queen_prob/sa__queen_prob__run_stats_df.csv]\n",
      "Saving: [queen/queen_prob/sa__queen_prob__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "sa = mlrh.SARunner(problem=prob,\n",
    "                   experiment_name=experiment_name,\n",
    "                   output_directory=output_directory,\n",
    "                   seed=random_state,\n",
    "                   max_attempts=200,\n",
    "                   iteration_list=[2500],\n",
    "                   temperature_list=[0.05, 0.1, 0.5, 1, 10, 20, 25],\n",
    "                   decay_list=[mlrh.GeomDecay, mlrh.ExpDecay, mlrh.ArithDecay])\n",
    "sa_stats, sa_curve = sa.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>schedule_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.022774</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>exponential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.025773</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>geometric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.066191</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>geometric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.949320</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>arithmetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4.092280</td>\n",
       "      <td>594.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>geometric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.856756</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>geometric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.730146</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>arithmetic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.779787</td>\n",
       "      <td>594.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>exponential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4.989198</td>\n",
       "      <td>593.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>exponential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4.023431</td>\n",
       "      <td>593.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>exponential</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time  Fitness  Temperature schedule_type\n",
       "9   3.022774    594.0         0.10   exponential\n",
       "7   3.025773    594.0         0.10     geometric\n",
       "13  4.066191    594.0         0.50     geometric\n",
       "11  2.949320    594.0         0.10    arithmetic\n",
       "31  4.092280    594.0        20.00     geometric\n",
       "1   2.856756    594.0         0.05     geometric\n",
       "5   2.730146    594.0         0.05    arithmetic\n",
       "3   2.779787    594.0         0.05   exponential\n",
       "33  4.989198    593.0        20.00   exponential\n",
       "27  4.023431    593.0        10.00   exponential"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Temperature', 'schedule_type']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./queen/queen_prob/sa__queen_prob__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated Annealing - Total Function Evaluations: 1816\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 200\n",
    "max_iters = 2500\n",
    "init_temp = 0.1\n",
    "schedule = mlrh.ExpDecay(init_temp)\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, sa_curve = mlrh.simulated_annealing(prob,\n",
    "                                                             max_attempts=max_attempts,\n",
    "                                                             max_iters=max_iters,\n",
    "                                                             random_state=random_state,\n",
    "                                                             schedule=schedule,\n",
    "                                                             curve=True)\n",
    "print(\"Simulated Annealing - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(sa_curve,\n",
    "                       \"Queens - Simulated Annealing: schedule: {}, init_temp: {}\".format(schedule.__class__.__name__, init_temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Randomized Hill Climbing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running rhc\n",
      "*****************\n",
      "*** Run START ***\n",
      "*****************\n",
      "restarts:[10], max_iters:[2500], current_restart:[0]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[0.00], fitness:[555.0000]\n",
      "\t[32  8  3  3 27  3  7 29 32 24 10  9 11  3  5 28 20  8 20  1  1 15  5 10//  9 21 29 26  6 22 21  4  6 34  3]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[0]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[921], done:[True], time:[1.33], fitness:[592.0000]\n",
      "\t[32 23  2 14 27 25 31 11 32 19 12  9 33  3 30 28 13  8 24  0  1 15  5 10// 33 21 29 26  6 22 18  4  7 34 16]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[1]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[1.36], fitness:[544.0000]\n",
      "\t[ 9  9 12 16 27 34 31 34  7 27 27 26 17 24  4 15 33 34 18 22 24 10  8  7// 33  4 24 27  4 24 15 34 12 15 26]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[1]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[778], done:[True], time:[2.47], fitness:[587.0000]\n",
      "\t[ 1 21  9 16 19 34 29 25 32  0 26 15 14 24  4 20 33  3  6 13 23 10 11  7// 33  4  2 27  6 24 28 30 12  5 17]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[2]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[2.50], fitness:[551.0000]\n",
      "\t[23 24 18 22 14 29 18 20 34 15 13 16 30 20  0 12  0  7 17  8 34  0 17 19// 29 26 12 27 22 13  9  2 10 31  0]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[2]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[471], done:[True], time:[3.43], fitness:[586.0000]\n",
      "\t[33 24 28 11 14 23 20 29 32  6 13  5 27  4 20 22 16  7 17 30  2  0 34 19// 29 26 12 31 22 25 18 14  3 31 26]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[3]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[3.48], fitness:[569.0000]\n",
      "\t[ 7 23 11 30 20 25 23 12  7  8  4  7 31 21  4  1 27  0  4 25 10 34 22 17// 11 28 13 24 32  9 12 32 28 31  3]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[3]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[626], done:[True], time:[4.68], fitness:[589.0000]\n",
      "\t[27 10 20  5 20 25 23 12 29  8 16  7 34 21  4  1 27  0 13 26 30 33 22 17// 11 28 15 24 19  9  2 32 14 31  3]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[4]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[4.73], fitness:[555.0000]\n",
      "\t[21 11 20 29 34 13 21 26  4 11 14  4 16 27 19 20  0 19 26  1 16 23 22 21//  7  6 31 15 32  5 10 33 33  1 14]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[4]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[444], done:[True], time:[5.54], fitness:[588.0000]\n",
      "\t[28 11 20 29 34 30 21 26 29 17 14  4 13 27  9 12  0  2 25  1 16 30 22  7// 22  6 31 15 32  5 19 10 23  8 24]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[5]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[5.60], fitness:[555.0000]\n",
      "\t[33  8 22 32 34  8 21 34  3 12 28 18 31 19 32  3 27 14 28 11 19 20  5 29// 13  0  0 32 13 28  0  6 31 27 28]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[5]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[607], done:[True], time:[6.67], fitness:[587.0000]\n",
      "\t[33  1 22 32 15  8 21 23  4 12  7 25 31 19  9  3  6 14 11 20 30 34  5 29// 13  0  0 32 26  5 10 18 16 27 28]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[6]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[6.73], fitness:[550.0000]\n",
      "\t[16 28 34 27 10 25  3 11  2  7 29  8 28 17 32 23 20  3  0 31 30  6 31 27// 32 24 18 22 29  7 20 28 30 17 23]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[6]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[495], done:[True], time:[7.89], fitness:[589.0000]\n",
      "\t[16  5 23 32 30 25  3 34  2 15 29 14  8  4 12 22 20 28  0 33  1  6 31 10// 32 24 18 13 29  7 18 11 27 17  9]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[7]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[7.97], fitness:[547.0000]\n",
      "\t[10 17 21  0 12 15 24 17 17 28 11  7  9 28 15 33  7  3 28 10 11 19 11  9// 28 12 13 34 29  8 25 14  8 13 23]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[7]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[692], done:[True], time:[9.63], fitness:[586.0000]\n",
      "\t[10 21 16  1 12 30 27  4 17  8 24  3 25 28 31 33  7  2 18  9 11 19 32  9//  5  0 13 34 29 22 26 14  6 27 23]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[8]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[9.72], fitness:[555.0000]\n",
      "\t[31 25  2 14  5  6  0 14 13 22 15 25  3 16 27 30 22 29 27 29 33 23 31 29//  6  4 17 12  9 22 33 14 22 12  5]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[8]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[802], done:[True], time:[11.69], fitness:[589.0000]\n",
      "\t[31 25 17 15 30  6  0 23 13 11  2 25  3 16 27  9 33 10  5 29  8 17 31 20//  1  7 24 12 34  4 10 14 22 19 21]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[9]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[11.80], fitness:[563.0000]\n",
      "\t[12 34 25 26 13 14 24  8 10 11 18 33  6 30 18 21 33  9 12  5 16 17 31  9// 29 33  4 27 28  3  6  2 10 32 13]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[9]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[824], done:[True], time:[14.04], fitness:[590.0000]\n",
      "\t[12 34 16 22 33 26 24  8 14 11 18  2 15 30 34  4  1  9 31  5 21 17 32  0// 29 33 19  7 28  3  6  2 10 27 13]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[10]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[0], iteration:[0], done:[False], time:[14.15], fitness:[554.0000]\n",
      "\t[30 10  7 32  7 16 15 23 23 34  4  2  3  8 17 16  8 22 18 33 11 22 31 21// 32 30  8 34 27 31  0 14  0 21  4]...\n",
      "\n",
      "restarts:[10], max_iters:[2500], current_restart:[10]\n",
      "runner_name:[rhc], experiment_name:[queen_prob], attempt:[201], iteration:[452], done:[True], time:[15.14], fitness:[587.0000]\n",
      "\t[24 28 12 32  7 16 26 23 25  1  2 15 10  6 33 33 13 22 18 33 11 22 31 17// 32  3  8 34 27 31 19 14  5 21  4]...\n",
      "\n",
      "***************\n",
      "*** Run END ***\n",
      "***************\n",
      "Run time: 15.234209634945728\n",
      "Saving: [queen/queen_prob/rhc__queen_prob__run_stats_df.csv]\n",
      "Saving: [queen/queen_prob/rhc__queen_prob__curves_df.csv]\n"
     ]
    }
   ],
   "source": [
    "rhc = mlrh.RHCRunner(problem=prob,\n",
    "                    experiment_name=experiment_name,\n",
    "                    output_directory=output_directory,\n",
    "                    seed=random_state,\n",
    "                    max_attempts=200,\n",
    "                    iteration_list=[2500],\n",
    "                    restart_list=[10])\n",
    "rhc_stats, rhc_curve = rhc.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Restarts</th>\n",
       "      <th>current_restart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.332992</td>\n",
       "      <td>592.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14.044269</td>\n",
       "      <td>590.0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.678264</td>\n",
       "      <td>589.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.692395</td>\n",
       "      <td>589.0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.891547</td>\n",
       "      <td>589.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.544039</td>\n",
       "      <td>588.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.671673</td>\n",
       "      <td>587.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>15.137302</td>\n",
       "      <td>587.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.470676</td>\n",
       "      <td>587.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.431813</td>\n",
       "      <td>586.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time  Fitness  Restarts  current_restart\n",
       "1    1.332992    592.0        10                0\n",
       "19  14.044269    590.0        10                9\n",
       "7    4.678264    589.0        10                3\n",
       "17  11.692395    589.0        10                8\n",
       "13   7.891547    589.0        10                6\n",
       "9    5.544039    588.0        10                4\n",
       "11   6.671673    587.0        10                5\n",
       "21  15.137302    587.0        10               10\n",
       "3    2.470676    587.0        10                1\n",
       "5    3.431813    586.0        10                2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show=10\n",
    "columns = ['Time', 'Fitness', 'Restarts', 'current_restart']\n",
    "pd.set_option('display.max_rows',show)\n",
    "df=pd.read_csv(\"./queen/queen_prob/rhc__queen_prob__run_stats_df.csv\")\n",
    "df[columns].sort_values(by=['Fitness'], ascending=False).head(show)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randomized Hill Climbing - Total Function Evaluations: 14401\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_attempts = 500\n",
    "max_iters = 2500\n",
    "restarts = 10\n",
    "global eval_count\n",
    "eval_count = 0\n",
    "best_state, best_fitness, rhc_curve = mlrh.random_hill_climb(prob,\n",
    "                                                         max_attempts=max_attempts,\n",
    "                                                         max_iters=max_iters,\n",
    "                                                         random_state=random_state,\n",
    "                                                         restarts=restarts,\n",
    "                                                         curve=True)\n",
    "print(\"Randomized Hill Climbing - Total Function Evaluations:\", eval_count)\n",
    "plot_fitness_iteration(rhc_curve,\n",
    "                       \"Queens - Randomized Hill Climbing: restarts: {}\".format(restarts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitness vs Iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0SfcMgVjcsXm3PytLQbTFX79q40bWTf5/xLZtTWxzVdVAwzXDjz/5mGufv5YdFfX3Nt2wZwOg4RMiIqlQGALvb9qdeNyuuOXDcM/LL7Pntdfqfa74iCPq3f7i2hebXHnCMAZ0qjvsQkREalMYAlUxr4m0b9e2FERbvpk0vs+rlZaecQbdrp5Y67ni/v3rPWZfbB8AY/uP5dtDv13vPh2LO3JI20PSWFIRkfykMMTrtAlQ2iY7b0cwnrCge3faDByY0jFBb9FDSg7hiM711x5FRCQ1udFbJMuq/TSMRrLzdgSdZZrTczQIQ90TFBE5eApDvA40AAURy8rrxxNLMaXec1Tzi4qIpI/CEKj2wzBq2QnDA1l9oirurTyhcYQiIgdP9wyBeBCGLVwz3PCrX7N36VKq1q4FYLvbw/88fSWfVH3S5LFrdq0BVDMUEUkHhSE1NcOCaMuFYfXWrWy9++5a25YWbeSFtS806zyHlh6azmKJiISSwpCae4YtWTN0e73loqJdutD791OJlJYyb/eT8CZ88Ygvct5h5zV5jo7FHTms02GZLqqISN5TGJKde4bx4D5haXvaHnssAFWLvIV8+3boy8geI1usLCIiYacONGSpZhiEYVFNBxj1EBURyQ6FIUlDK1rwnmEQhlZUE3waOygikh0KQ7Iz6D6YdSZ5oH0iDLXShIhIi9I9Q+DptzcC0IIVQ975+E1KgPd2f8Cvn77K27b1HUBjB0VEWprCEFi50Vu1IuZa7jWfWfkU5wCbYjt4fu3ztZ7r2b5nyxVEREQUhlAzDdtln+nbYq8Zr/CGVhza7XCmnT45sb1rSVcGdxncYuUQERGFIQBx51UJi6Itt5ZhMLSic2l3ju59Sou9roiI1KUONICfhbTk1KRW6c0t2pz5SEVEJDMUhkAL3iqs4YdhtKhNNl5dRESSKAwB51cNIy1ZNUzUDBWGIiLZpnuGpK+ZdFflLv7w+h/YVrGtyX0vnu/tU9Cm5OBeVEREDprCEHB+Q+nBhuH81fO5/537m9yvy07H173OpJT07H1wLyoiIgdNYUhNzfBgm0mDdQhP7HkiFxx+QYP7Fa7aAPwfAD0uufSgXlNERA6ewpCaoRUHe8cwmE7t8E6H84UBX2hwv30Vb/MhUHzkkbXmJhURkexQBxpqepMebDNpZdwLw6amU6uZl1RBKCKSCzIahmZWbmZvmtkSM1vkbxthZq8E28zseH+7mdlUM1tpZm+YWcst6JcYW3FwaZjqEkzxCn/5pkKFoYhILmiJZtJTnXObk37+NfBz59y/zexs/+cxwFjgCP/PCcCf/L8zLsjCg13OMNUlmFxl3RUrREQke7Jxz9ABHfzHHYF1/uPzgXudN+jvFTPrZGY9nXPrM12gxD3DA2wnrd62jeVTb8TeeYJvAkcs/g/ru6xtcP+qtd5zul8oIpIbMh2GDphrZg74s3PuTuB7wFNmditeM+1n/H17AauTjl3jb6sVhmZ2OXA5wKGHHpqeQgbjDA/w+B0PP0LRzH9xZmLL62zn9SaPK+jW7QBfUURE0inTYXiSc26dmR0CzDOzd4CLge8752ab2ZeBvwL/Rf1ZVGemND9Q7wQYNWpUWmZSC8YZHujQiviuXQAsPszoeOppnPipE4hEGp/02woKKP385w/o9UREJL0yGobOuXX+3xvNbA5wPDABmOTv8g/gL/7jNUCfpMN7U9OEmlH+QvcH3Js0uAf4dh9j0lU/o1vb7mkqmYiItISM9SY1s3ZmVho8Bs4AluEF3Of83U4DVviPHwX+2+9VeiKwoyXuF6ZDsBxTdbTpzjMiIpJ7Mlkz7AHM8TulFAD3O+eeNLPdwO/MrADYh3//D3gCOBtYCewBvp7BstXi3MFNx+b8oRKVBQpDEZHWKGNh6Jz7ABhez/YXgGPr2e6AiZkqT2NqhlYcWBoGzaRVBVAUURiKiLQ2mo6N5KEVDe/z7tZ3ear8KeIunthmVTEOfXo5vV57m1IgVhgl2kTHGRERyT0KQ5KHVjSchje9ehOLNy6ute249+KcMbsmHOlQmoniiYhIhikMSW1u0p2VOwH46lFfpWtJVwC673oL+De7+3Vn/dhjuGJci93mFBGRNFIYktrivsFUa+MGjaNfx34AbF8xh/X8m14jTuK4STdnuJQiIpIpWrWCpN6kjTSTBpNw11qRIrh/GNHbKCLSmulbnNSaSYOaYWG0sOa4WMw7Lqq3UUSkNdO3ODU1w8aGVtS7VmE8aF/V2ygi0prpniE1mVawYSmsfd77wSJw8vcS+9TXTOriXs0Q1QxFRFo1hSE1NcN2j10O2z7wNkYKE2EYd3Gq49UAFEZqmkmJefcMTTVDEZFWTWFI0j3DfTu8B8d8Fdr3SDyfWLQ3UlR7zcOgA01UA+1FRFozhSEk0tCc3+z5+V9A2y6Jp4Mm0v3nHXVBzVC9SUVEWjV9i1MzHVuiprdfs2dVvAqoZxLuuIZWiIjkA32Lk7SCcFAz3C8M6x1jSE0HGg2tEBFp3fQtTs0MNIkH+022nbhnWKdmqKEVIiL5QPcMARfUDeO1a4bOOZ5d/SzLtywH6gtDDa0QEckHCkNqKng1zaRezbBsQxmTnp2U2K99Yftax7m4OtCIiOQDhSHU3DRMzDXqheHmfZsB6N2+Nyf1OolzDzu39nGx2vuLiEjrpDAkaCZ12H69SatiXi/S4YcM5/oTr697XCI8G5nUVEREcp7a9/CaSa1m6H1ixu6GepEmJMYZqmYoItKaKQzxOspEqTvGMLFSRfIUbLUO1DhDEZF8oG9xvFuGEeoOqwjCsKGaYWIGGvUmFRFp1UJ9z7AqFueJN9fjHETqqRmW7ywH6g6p2Pfee1S8t4KK91fWOUZERFqfUIdhZXWcSbOWAFAcVAitpmY4e8VsoHYYxvfto3zceNzevYltVrzf+EMREWlVQh2G0Yhx7vBPA3Bq3yKYS61aXklBCXur9zK239jEttjOnbi9e7HiYkpPP51Ih1I6nHVWSxddRETSKNRh2KYwyu8vOcb7Yc9WLwwjNbPPBL1Je5f2ThzjKr37iAXdutHrtt+0aHlFRCQzdLMr4GrPM1rtqom7OFGLUhCp+T+Dq/AC0orUNCoiki8UhoH9pmILBtzXWcPQrxlacQNjD0VEpNVRGAb2m4qtwQV9VTMUEck7CkPwmkjXLPQe+82k2yq2AVAcqV0DjO/bB0BEYSgikjcUhgAfPAsPfNV7HPVmm/nFy78AwKz2vKO7n3/ee6BZZ0RE8oa+0QF2rKl5fNr/ALCneg8AZ/Y7s9auVuCFpbXRPUMRkXyhMISaRXpHToBhXwZqpmI777Dzau0adKBpd8IJLVc+ERHJKIUhQLza+ztpCEVD85LWdKBRzVBEJF8oDKGmZpgchnEvDPfvTRqvVG9SEZF8ozCERmuGDY8zVBiKiOSLUE/HlpAIw5r/G2zdtxXwwrByzVqqN24AoHrDRm9X1QxFRPKGwhDq1Aw3792ceCqy5mPe/8IFNdO1+axNmxYrnoiIZJbCEJJmn/Hejg17NtQ8t3YDOEekQweKDzsMgILu3Wl7vHqTiojkC4Uh1KkZxvwONUO7DU30Hm07ahR9/jgtK8UTEZHMUgcaSApDb17SmD9pd9SiNR1mdI9QRCRvKQyhTs2w2v85YhHiFV4YRtR7VEQkbykMoWacob98U9y/h1gQKVDNUEQkBMJ9zzAeg83vwcdvej/vd88walGq1q8DNOOMiEg+C3cYVu2FP55Y87O/YkW185pJu2/Yx5Y7/gxoMV8RkXwW7jC0CHQ/0ntc0hkO/y+gpmbY7eO9iV07jB3b4sUTEZGWEe4wLGoLE1+tszm4Z1jo96vpcM45lAw9uiVLJiIiLUgdaOoRNJMW+mPx1XlGRCS/KQzrETSTFlZ5P2tSbhGR/KYwrEcw6L4g5s1Hqkm5RUTym8KwHokwrPbCUMMqRETym8KwHkEz6YBX1wBghYXZLI6IiGSYwrAewSr3FZ3aAhBp1y6bxRERkQxTGNYjWOU+EveaSdsMGZLN4oiISIYpDOsRhGHUGQBWEM1mcUREJMNSCkMz+7WZdTCzQjN7xsw2m9lXM124bAmaSSP+OEOiCkMRkXyWas3wDOfcTuAcYA0wEPh/GStVllXEvAV9g2ZSKwj3RD0iIvku1TAMulOeDcx0zm3NUHlyws6KnQBEg5phRK3JIiL5LNUqz2Nm9g6wF7jKzLoD+zJXrOy5/+37mb1iNgCmmqGISCikVOVxzl0LjAZGOeeqgE+A8zNZsGxZtGFR4nFb8wbbm+4ZiojktVQ70HwJqHbOxczseuDvwKczWrIsCe4X/v6031Po/LdHYSgiktdSvRn2P865XWZ2MnAmcA/wp8wVK3uCYRVFkSJczJuJRjVDEZH8lmoYxvy/vwD8yTn3CJCXs1cnwjCqMBQRCYtUw3Ctmf0Z+DLwhJkVp3KsmZWb2ZtmtsTMFvnbHvB/XuI/vyRp/5+Y2Uoze9fMzjyQCzpYyWGIH4aoA42ISF5L9Vv+y8BZwK3Oue1m1pPUxxme6pzbHPzgnBsXPDaz3wA7/MeDgfHAELz7kU+b2UDnXIwWVBH37hkWR4tVMxQRCYlUe5PuATYCJ/ubqoEVB/PCZmZ4ITvT33Q+MMs5V+Gc+xBYCRx/MK/RXJv3bmbFNu+yIi+WEduyxXtCYSgiktdS7U36M+DHwE/8TYV4PUqb4oC5ZlZmZpfv99xngQ3OuSBUewGrk55f42/bvyyXm9kiM1u0adOmVIqfsre2vJV43HbJysTjaMeOaX0dERHJLaneM7wQOA9vfCHOuXVAaQrHneScGwmMBSaa2SlJz11CTa0QwOo53tXZ4NydzrlRzrlR3bt3T7H4qYk7b8qZU3qfQtR/5R7XX49pBhoRkbyW6rd8pXPO4YeTmaW0wJ8fmjjnNgJz8Js9zawAuAh4IGn3NUCfpJ97A+tSLF9aBIv6FlgBrtq/X6jOMyIieS/VMHzQ703aycy+DTwN3NXYAWbWzsxKg8fAGcAy/+n/At5xzq1JOuRRYLyZFZtZf+AI4LXUL+XgVbtqAKKRKC7mPdbyTSIi+S+lao9z7lYz+zywExgE3OCcm9fEYT2AOV4/GQqA+51zT/rPjad2EynOueVm9iDwFl4HnYkt3ZM0uWaIXzMkqpqhiEi+S/mb3g+/pgIwef8PgOENPHdZA9tvBG5M9TXSLeZnbyQSSRpWofuFIiL5LtXepBeZ2Qoz22FmO81sl5ntzHThWloQhlGLQjyoGaqZVEQk36VaM/w1cK5z7u1MFibbEs2kkaQONGomFRHJe6m2AW7I9yCE2jXDRDOpOtCIiOS9VKs9i8zsAeBhoCLY6Jz7Z0ZKlSXVcb83qUWh2nusZlIRkfyXahh2APbgDY8IOCCvwjBRM4xENS+piEiIpBqGf3HOvZi8wcxOykB5siq4Z+g1k6pmKCISFqneM/x9ittateSaITFvajZ1oBERyX+NftOb2WjgM0B3M/tB0lMdgLyqMsVdnNsX3w7AsLueZ89r7wDqQCMiEgZNVXuKgPb+fskTc+8ELs5UobJh/SfrASiscvSa7wdhSQmFhx6azWKJiEgLaDQMnXMLgAVmNt0591ELlSkrgvuFHWkL7ALgiOefJ9o+pTnJRUSkFWuqmfR259z3gD+YWX3LKZ2XsZK1sGCS7p6FXYFdRLt1UxCKiIREU82k9/l/35rpgmRbPO51mCmKeX2KIkVF2SyOiIi0oKbCcBMkmkvzWtCTtDjurTFsCkMRkdBoamjFw8EDM5ud4bJkVdBMWlzth2FxcTaLIyIiLaipMLSkxwMyWZBsCzrQFMVUMxQRCZumwtA18DjvBM2kiTAsVhiKiIRFU/cMh/vrFhpQkrSGoQHOOdcho6VrQUHNsNAPw0iRmklFRMKiqXGGoZl+JdGBRs2kIiKhk+rcpHkvcc/Qn59bHWhERMJDYegLepMWeMMNVTMUEQkRhaGvbs1QYSgiEhYKQ1/ceVXCo172JuzWDDQiIuGhMPQFzaTtdlQAEGnbNpvFERGRFqQw9AXNpObPM9Bp/PhsFkdERFqQwtAXDK2IxL25BaywMJvFERGRFqQw9AVhaDE/DKOhGWIpIhJ6CsJKezQAACAASURBVENf0Ewa1AxRGIqIhIbC0FenmVRhKCISGgpDX3Xc601qMW+IhcJQRCQ8FIa+xD1D5zeTFjQ1h7mIiOQLhaEvGHSvDjQiIuGjMPRVx6vBOXWgEREJIYWhL+ZiRILli6NRzCyr5RERkZajMPTF4jGiwYoVEb0tIiJhom99X7WrJuKHoTrPiIiEi8LQF3fxmuWbdL9QRCRUFIa+6ng1h633bhrGd+/OcmlERKQlKQx9lbFKCv2aYfGgQdktjIiItCiFoa8iVkGhN+6e4sMGZLcwIiLSohSGvqp4VaJmaEXF2S2MiIi0KIWhryJWUdOBpqgou4UREZEWpTD0VcYqKfCbSa1YNUMRkTBRGPoWrnqJcc/785MWaZV7EZEwURj6jnh/L20rvMcFXbtltzAiItKiFIaAc4421TVvRefx47JYGhERaWkKQ7yp2Ih7TaSlZ51FpG3bLJdIRERaksIQqIpVEaxRYRGtViEiEjYKQ7xhFRYs34TCUEQkbBSGeMMqEmsZavkmEZHQ0Tc/sP6T9ZAIQ9UMRUTCRmEIPPL+I4maoVa4FxEJH4Xh/kxviYhI2OibH29h38Q9Q9UMRURCR2EIxOKxmh90z1BEJHQUhqhmKCISdgpDIOZiid6kpnuGIiKho29+VDMUEQk7hSF+zTCge4YiIqGjMEQ1QxGRsFMYonuGIiJhp29+VDMUEQk7hSH73zPUWyIiEjYZ/eY3s3Ize9PMlpjZoqTt15jZu2a23Mx+nbT9J2a20n/uzEyWLVk8nlwzbKlXFRGRXFHQAq9xqnNuc/CDmZ0KnA8Mc85VmNkh/vbBwHhgCPBp4GkzG+hccrUtM+Iunnise4YiIuGTjW/+K4FbnHMVAM65jf7284FZzrkK59yHwErg+JYoULWr1j1DEZEQy3QYOmCumZWZ2eX+toHAZ83sVTNbYGbH+dt7AauTjl3jb6vFzC43s0VmtmjTpk1pKWTcxWtWutc9QxGR0Ml0M+lJzrl1flPoPDN7x3/NzsCJwHHAg2Y2gPrv1rk6G5y7E7gTYNSoUXWePxAxF0uEoWnQvYhI6GS0GuScW+f/vRGYg9fsuQb4p/O8BsSBbv72PkmH9wbWZbJ8gXg8qWaoZlIRkdDJWBiaWTszKw0eA2cAy4CHgdP87QOBImAz8Cgw3syKzaw/cATwWqbKlyzmYjXVUnWgEREJnUw2k/YA5phX0yoA7nfOPWlmRcDfzGwZUAlMcM45YLmZPQi8BVQDE1uiJynsd89QNUMRkdDJWBg65z4AhtezvRL4agPH3AjcmKkyNST5nqEm6hYRCR+1CVK7ZmiqGYqIhI7CED8MEzN16y0REQkbffOzXzOpaoYiIqGjMGT/QfcKQxGRsFEYUntohe4ZioiEj8KQ/Qfd6y0REQkbffOje4YiImGnMCToTepTGIqIhI7CEK9mGPGXNLSo3hIRkbDRNz/79yaNZrUsIiLS8hSG+DVDLeEkIhJaCkNUMxQRCTuFIbXvGWrQvYhI+CgM8WqGwRthUdUMRUTCJvRh6JzzwjCoGWrQvYhI6IT+m786Xg1AxH8rNLRCRCR8Qv/NXxGrAKDQ+W+FaoYiIqET+m/+ynglANHgrVDNUEQkdEL/zV8Z88KwIGgmjYT+LRERCZ3Qf/MHYZioGWqcoYhI6IQ+DIN7hgUuqBlqnKGISNiEPgxf3/g6ANFg3QrVDEVEQif0Ybiveh8A8Zg3xEIz0IiIhE/owzDoTXpIm+6AZqAREQkjhWHQgSax0n3o3xIRkdAJ/Td/EIaagUZEJLxC/80f9CaNJlatCP1bIiISOqH+5o+7OOs/WQ+QtIRTqN8SEZFQCvU3/77qfTyz6hkASt/8EAAz9SYVEQmbgmwXINu6tulK5zadofB9qKqieNCgbBdJRERaWKjDsG1hW+aPm4+Lx3nnx0MAiHbpkuVSiYhISwt1M2nAVVUBYEVFaiYVEQkhhSHgKrwepVZcnOWSiIhINigMAVfpjTW0oqIsl0RERLJBYUhyzVBhKCISRgpDYMfj/wIgUqgwFBEJI4UhULV+HQAuHm9iTxERyUcKQ4CYF4Jdv/nNLBdERESyQWEIOOfXCDVJt4hIKOnbHxI1Q9O8pCIioaRvf4DgXmFEC/uKiISRwpCajjMW0ewzIiJhpDAE1QxFREJOYQi4eMx7oJqhiEgoKQwB4g4Ai6pmKCISRgpDgKBmaHo7RETCSN/+gEvUDPV2iIiEkb79AWLBPUO9HSIiYaRvf5JmoFEYioiEUkG2C5ATNAONSM6oqqpizZo17Nu3L9tFkRzXpk0bevfuTWFh4UGfS2EIGmcokkPWrFlDaWkp/fr1w0zDnaR+zjm2bNnCmjVr6N+//0GfT1UhNAONSC7Zt28fXbt2VRBKo8yMrl27pq0FQWEIqhmK5BgFoaQinb8nCkM0A42ISNgpDEEz0IhILWbG1772tcTP1dXVdO/enXPOOQeA6dOnc/XVVwMwZcoUzIyVK1cm9v/tb3+LmbFo0SIA+vXrx+bNmwH4+OOPGT9+PIcddhiDBw/m7LPP5r333mupS5MGKAyhZpyhZqAREaBdu3YsW7aMvXv3AjBv3jx69erV4P5Dhw5l1qxZiZ8feughBg8eXGc/5xwXXnghY8aM4f333+ett97ipptuYsOGDem/CGkW9SYF9i5dCmgGGpFc0+/af2XkvOW3fKHJfcaOHcu//vUvLr74YmbOnMkll1zC888/X+++F1xwAY888gjXX389H3zwAR07dqy3u/+zzz5LYWEhV1xxRWLbiBEjDvxCJG1C/+0f9CQFKOzdO4slEZFcMn78eGbNmsW+fft44403OOGEExrct0OHDvTp04dly5Yxc+ZMxo0bV+9+y5Yt49hjj81UkeUgqGZYXZ14WNC1axYLIiL7S6UGlynDhg2jvLycmTNncvbZZze5fxCeTz31FM888wx33313C5RS0kU1Q/9+oRUXZ7kkIpJrzjvvPCZPnswll1zS5L7nnnsu9913H4ceeigdOnSod58hQ4ZQVlaW7mJKGigMg6nY1JNURPbzjW98gxtuuIGhQ4c2uW9JSQm/+tWvuO666xrc57TTTqOiooK77rorsW3hwoUsWLAgLeWVA5fRMDSzcjN708yWmNkif9sUM1vrb1tiZmcn7f8TM1tpZu+a2ZmZLFtCzG8mLVCLsYjU1rt3byZNmpTy/uPHj2fkyJENPm9mzJkzh3nz5nHYYYcxZMgQpkyZwqc//el0FFcOgjnnMndys3JglHNuc9K2KcBu59yt++07GJgJHA98GngaGOicizV0/lGjRrlgHM+Bqt66lRWfOYlop04MfOXlgzqXiBy8t99+m6OOOirbxZBWor7fFzMrc86Nas55cqmZ9HxglnOuwjn3IbASLxgzylWrZigiEnaZDkMHzDWzMjO7PGn71Wb2hpn9zcw6+9t6AauT9lnjb6vFzC43s0VmtmjTpk0HX8K47hmKiIRdpsPwJOfcSGAsMNHMTgH+BBwGjADWA7/x961vYtA6bbjOuTudc6Occ6O6d+9+0AV01X5vUoWhiEhoZTQMnXPr/L83AnOA451zG5xzMectL38XNU2ha4A+SYf3BtZlsnxATQcahaGISGhlLAzNrJ2ZlQaPgTOAZWbWM2m3C4Fl/uNHgfFmVmxm/YEjgNcyVb5AYpyhwlBEJLQy2WukBzDHX2+qALjfOfekmd1nZiPwmkDLge8AOOeWm9mDwFtANTCxsZ6kaRNM0l2gMBQRCauM1Qydcx8454b7f4Y45270t3/NOTfUOTfMOXeec2590jE3OucOc84Ncs79O1Nlq1XORM1QvUlFxLNhwwa+8pWvMGDAAI499lhGjx7NnDlz0v4606dPZ926mrtB3/rWt3jrrbeadY5JkybRq1cv4knzLCcvMSWpyaWhFVmxZ/Fi74FWrBARvGWWLrjgAk455RQ++OADysrKmDVrFmvWrEn7a+0fhn/5y1/qXfqpIfF4nDlz5tCnTx+ee+65tJcvTEJfHdpb5oVhbNv2LJdEROqY0jFD593R4FP/+c9/KCoqqrXMUt++fbnmmmsAiMViXHvttcyfP5+KigomTpzId77zHebPn8+UKVPo1q1bYnWKv//975gZZWVl/OAHP2D37t1069aN6dOn8+KLL7Jo0SIuvfRSSkpKePnllxk7diy33noro0aN4sknn+SnP/0psViMbt268cwzz9Qp67PPPsvRRx/NuHHjmDlzJmPGjKmzz/vvv8+ll15KLBZj7Nix3Hbbbezevfvg38M8o+pQxHsLuiX94otIeC1fvrzRKdX++te/0rFjRxYuXMjChQu56667+PDDDwF4/fXXuf3223nrrbf44IMPePHFF6mqquKaa67hoYceoqysjG984xtcd911XHzxxYwaNYoZM2awZMkSSkpKEq+xadMmvv3tbzN79myWLl3KP/7xj3rLEqyzeOGFF/L4449TVVVVZ59JkyYxadIkFi5cqGnfGhH6mmHQgSbSrl2WCyIidTRSg2spEydO5IUXXqCoqIiFCxcyd+5c3njjDR566CEAduzYwYoVKygqKuL444+nt78u6ogRIygvL6dTp04sW7aMz3/+84BXs+zZs2eDrwfwyiuvcMopp9C/f38AunTpUmefyspKnnjiCX77299SWlrKCSecwNy5c/nCF2ove/Xyyy/z8MMPA/CVr3yFyZMnH9wbkqdCH4becEewSH1j/kUkbIYMGcLs2bMTP0+bNo3NmzczapQ31aVzjt///veceWbttQTmz59PcdJScNFolOrqapxzDBkyhJdfTn3uY+ccfk/8Bj355JPs2LEjsaLGnj17aNu2bZ0wlNSomdRfwomIhlaIiLfM0r59+/jTn/6U2LZnz57E4zPPPJM//elPiSbJ9957j08++aTB8w0aNIhNmzYlwrCqqorly5cDUFpayq5du+ocM3r0aBYsWJBoft26dWudfWbOnMlf/vIXysvLKS8v58MPP2Tu3Lm1ygpw4oknJsJ91qxZKb0HYaQwdEEYqmYoIt4ySw8//DALFiygf//+HH/88UyYMIFf/epXgDf8YfDgwYwcOZKjjz6a73znO1QHE/7Xo6ioiIceeogf//jHDB8+nBEjRvDSSy8BcNlll3HFFVcwYsQI9u7dmzime/fu3HnnnVx00UUMHz6ccePG1Trnnj17eOqpp2rVAtu1a8fJJ5/MY489Vmvf22+/ndtuu43jjz+e9evX07FjhjoltXIZXcIp09KxhNPqqyay+z//ofe0P1B6+ulpKpmIHCgt4ZRee/bsoaSkBDNj1qxZzJw5k0ceeSTbxUqbdC3hFPp7hsGqFZgqySKSf8rKyrj66qtxztGpUyf+9re/ZbtIOSn0YejiwQw0CkMRyT+f/exnWbp0abaLkfOUAHG/mTiit0JEJKyUAH7NUGEoIhJeoU8A59cMTWEoIhJaSoBgCSeNMxQRCa3Qh6GLawYaEantxhtvZMiQIQwbNowRI0bw6quvAge2xFJD+vXrx+bNmxvd56abbmr2eZtavun8889n9OjRzT7vwZoyZQq33norADfccANPP/10i5ehMaHvTZoYWqGV7kUEby7Pxx9/nMWLF1NcXMzmzZuprKwEvCWWWtJNN93ET3/607Sdb/v27SxevJj27dvz4YcfJuY+bWn/+7//m5XXbUzowzAYWkET8wCKSMsbes/QjJz3zQlvNvjc+vXr6datW2Ke0W7duiWeGzNmTGKJpfbt2zNx4kSefvppOnfuzE033cSPfvQjVq1axe233855553H9OnTWbRoEX/4wx8AOOecc5g8eXKdpZYuuOACVq9ezb59+5g0aRKXX3451157LXv37mXEiBEMGTKEGTNm8Pe//52pU6dSWVnJCSecwB//+Eei0Sh33303N998Mz179mTgwIG15khNNnv2bM4991x69OjBrFmz+MlPfgJ4M+F06NCBRYsW8fHHH/PrX/+aiy++uNnLUvXs2ZO77rqLO++8k8rKSg4//HDuu+8+2rZtW6scl112Geeccw4XX3wx/fr1Y8KECTz22GNUVVXxj3/8gyOPPJJNmzbxla98hS1btnDcccfx5JNPUlZWVuvzSKfQN5MGQytMNUMRAc444wxWr17NwIEDueqqq1iwYEG9+33yySeMGTOGsrIySktLuf7665k3bx5z5szhhhtuaNZr/u1vf6OsrIxFixYxdepUtmzZwi233EJJSQlLlixhxowZvP322zzwwAO8+OKLLFmyhGg0yowZM1i/fj0/+9nPePHFF5k3b16jzbjBkk+XXHIJM2fOrPXc+vXreeGFF3j88ce59tprE9ubsywVwEUXXcTChQtZunQpRx11FH/961+bvP5u3bqxePFirrzyykRT6s9//nNOO+00Fi9ezIUXXsiqVaua9Z42V+hrhokONJqBRiTnNFaDy5T27dtTVlbG888/z7PPPsu4ceO45ZZbuOyyy2rtV1RUxFlnnQXA0KFDKS4uprCwkKFDh1JeXt6s15w6dSpz5swBYPXq1axYsYKuXbvW2ueZZ56hrKyM4447DoC9e/dyyCGH8OqrrzJmzBi6d+8OwLhx43jvvffqvMaGDRtYuXIlJ598MmZGQUEBy5Yt4+ijjwa82mkkEmHw4MFs2LAhcVxzl6VatmwZ119/Pdu3b2f37t11Vveoz0UXXQTAscceyz//+U8AXnjhhcR7ctZZZ9G5c+dU3soDFvowDOZm1Qw0IhKIRqOMGTOGMWPGMHToUO655546YVhYWJhYZikSiSSaJiORSGLi7oKCAuJBvwRg3759dV5r/vz5PP3007z88su0bduWMWPG1Lufc44JEyZw880319r+8MMPN7ncE8ADDzzAtm3bEvcJd+7cyaxZs/jlL38JUKtpNXnO6uYuS3XZZZfx8MMPM3z4cKZPn878+fObLFvwGsH59y9DS1ACxDToXkRqvPvuu6xYsSLx85IlS+jbt+8Bnatfv34sWbKEeDzO6tWree211+rss2PHDjp37kzbtm155513eOWVVxLPFRYWJpaKOv3003nooYfYuHEj4C3r9NFHH3HCCScwf/58tmzZkrjnVp+ZM2fy5JNPJpZ8KisrO+AlnRpblmrXrl307NmTqqoqZsyYcUDnBzj55JN58MEHAZg7dy7btm074HOlIvQ1w5olnBSGIgK7d+/mmmuuYfv27RQUFHD44Ydz5513HtC5TjrpJPr378/QoUM5+uijGTlyZJ19zjrrLO644w6GDRvGoEGDOPHEExPPXX755QwbNoyRI0cyY8YMfvnLX3LGGWcQj8cpLCxk2rRpnHjiiUyZMoXRo0fTs2dPRo4cSSz4T76vvLycVatW1Tp3//796dChQ2LYSHMEy1J997vfZceOHVRXV/O9732PIUOG8Itf/IITTjiBvn37MnTo0HrXa0zFz372My655BIeeOABPve5z9GzZ09KS0sP6FypCP0STu+PPZvKDz9kwL8ep/iww9JUMhE5UFrCSQAqKiqIRqMUFBTw8ssvc+WVV7JkyZI6+2kJpzRx/vghKyrKcklERCSwatUqvvzlLxOPxykqKuKuu+7K6OspDBNhWP+4HBERaXlHHHEEr7/+eou9XuhvlMUTYViY5ZKIiEi2hD4MXUUFAJEGZmwQEZH8F+owdM7pnqGIiIQ7DKmu9ibqjkaxgtDfPhURCa1Qh6FqhSJSn2g0yogRIzj66KM599xz2b59e1rOW15enpj+LJ2Sl0cKJC8R9ZnPfKbO68+fP59zzjmn3vO99tprnHLKKQwaNIgjjzySb33rW+zZs6fW8lB33HEH9957b8plzNS1p0u4q0PRKN2+e01KUxmJSHgEE2QDTJgwgWnTpiUmom6NXnrppZT33bBhA1/60peYNWsWo0ePxjnH7Nmz6wyev+KKK9JdzKwKdRhG2rSh+1VXZbsYItKAt4/MzOD7o955O+V9R48ezRtvvAF4s9Ocf/75bNu2jaqqKn75y19y/vnnU15eztixYzn55JN56aWX6NWrF4888gglJSWJVR3atm3LySefnDjvvn37uPLKK1m0aBEFBQXcdtttnHrqqUyfPp2HH36YWCzGsmXL+OEPf0hlZSX33XcfxcXFPPHEE3Tp0qVZ19u+fXt2796d0r7Tpk1jwoQJiQWAzYyLL764zn5Tpkyhffv2iSWpjjnmGMrKyti0aRP33nsvN998M2+++Sbjxo1LzH9aXV3NhAkTeP311xk4cCD33ntvneWdsiXUzaQiIo2JxWI888wznHfeeQC0adOGOXPmsHjxYp599ll++MMfJiaUXrFiBRMnTmT58uV06tSJ2bNnA/D1r3+dqVOn1pnUetq0aQC8+eabzJw5kwkTJiQm6F62bBn3338/r732Gtdddx1t27bl9ddfZ/To0Q02Tf72t79lxIgRiT/r1q07oGsO1i1srqKiIp577jmuuOIKzj//fKZNm8ayZcuYPn06W7ZsAbx5Xy+//HLeeOMNOnTowB//+McDKmMmhLpmKCK5rTk1uHQKFtUtLy/n2GOPTSxV5Jzjpz/9Kc899xyRSIS1a9cmljvq378/I0aMALyliMrLy9mxYwfbt2/nc5/7HABf+9rX+Pe//w14SxRdc801ABx55JH07ds3sfTSqaeeSmlpKaWlpXTs2JFzzz0X8JaKCmqp+/v+97/P5MmTEz/369cvze9K44L/MAwdOpQhQ4YklnQaMGAAq1evplOnTvTp04eTTjoJgK9+9atMnTq1VpmzSTVDEZH9BPcMP/roIyorKxO1uBkzZrBp0ybKyspYsmQJPXr0SNTmGlrqqKE+CY3NC518roaWh8qUIUOGUFZW1uzjksu4f/mDMu//XuRSfw2FoYhIAzp27MjUqVO59dZbqaqqYseOHRxyyCEUFhby7LPP8tFHHzV6fKdOnejYsSMvvPACQK0ljU455ZTEz++99x6rVq1i0KBBmbuYFF199dXcc889tVaz+Pvf/87HH3980OdetWpVorl45syZte6hZpvCUESkEccccwzDhw9n1qxZXHrppSxatIhRo0YxY8YMjjzyyCaPv/vuu5k4cSKjR4+mpKQksf2qq64iFosxdOhQxo0bx/Tp02vVqLKlR48ezJo1i8mTJzNo0CCOOuoonn/+eTp06HDQ5z7qqKO45557GDZsGFu3buXKK69MQ4nTI/RLOIlIbtESTtIc6VrCSTVDEREJPYWhiIiEnsJQRHJOa759Iy0nnb8nCkMRySlt2rRhy5YtCkRplHOOLVu20KZNm7ScT4PuRSSn9O7dmzVr1rBp06ZsF0VyXJs2bejdu3dazqUwFJGcUlhYSP/+/bNdDAkZNZOKiEjoKQxFRCT0FIYiIhJ6rXoGGjPbBDQ+OWBqugGb03CeXJFv1wP5d026ntyXb9cUpuvp65zr3pyTteowTBczW9TcqXtyWb5dD+TfNel6cl++XZOup3FqJhURkdBTGIqISOgpDD13ZrsAaZZv1wP5d026ntyXb9ek62mE7hmKiEjoqWYoIiKhpzAUEZHQC3UYmtlZZvauma00s2uzXZ5UmVkfM3vWzN42s+VmNsnfPsXM1prZEv/P2UnH/MS/znfN7Mzslb5+ZlZuZm/65V7kb+tiZvPMbIX/d2d/u5nZVP963jCzkdktfW1mNijpM1hiZjvN7Hut7fMxs7+Z2UYzW5a0rdmfiZlN8PdfYWYTsnEtfjnqu57/M7N3/DLPMbNO/vZ+ZrY36bO6I+mYY/3f1ZX+NVsOXU+zf8dy6XuwgWt6IOl6ys1sib89vZ+Rcy6Uf4Ao8D4wACgClgKDs12uFMveExjpPy4F3gMGA1OAyfXsP9i/vmKgv3/d0Wxfx35lLAe67bft18C1/uNrgV/5j88G/g0YcCLwarbL38Tv2cdA39b2+QCnACOBZQf6mQBdgA/8vzv7jzvn0PWcART4j3+VdD39kvfb7zyvAaP9a/03MDaHrqdZv2O59j1Y3zXt9/xvgBsy8RmFuWZ4PLDSOfeBc64SmAWcn+UypcQ5t945t9h/vAt4G+jVyCHnA7OccxXOuQ+BlXjXn+vOB+7xH98DXJC0/V7neQXoZGY9s1HAFJwOvO+ca2ympJz8fJxzzwFb99vc3M/kTGCec26rc24bMA84K/Olr6u+63HOzXXOVfs/vgI0uh6Qf00dnHMvO+9b915q3oMW1cDn05CGfsdy6nuwsWvya3dfBmY2do4D/YzCHIa9gNVJP6+h8UDJSWbWDzgGeNXfdLXf5PO3oAmL1nGtDphrZmVmdrm/rYdzbj14/wEADvG3t4brCYyn9j/e1vr5BJr7mbSma/sGXi0i0N/MXjezBWb2WX9bL7xrCOTi9TTnd6w1fT6fBTY451YkbUvbZxTmMKyvDblVjTMxs/bAbOB7zrmdwJ+Aw4ARwHq8JgVoHdd6knNuJDAWmGhmpzSyb2u4HsysCDgP+Ie/qTV/Pk1p6BpaxbWZ2XVANTDD37QeONQ5dwzwA+B+M+tA7l9Pc3/Hcv16kl1C7f9YpvUzCnMYrgH6JP3cG1iXpbI0m5kV4gXhDOfcPwGccxucczHnXBy4i5qmtpy/VufcOv/vjcAcvLJvCJo//b83+rvn/PX4xgKLnXMboHV/Pkma+5nk/LX5nXrOAS71m9XwmxO3+I/L8O6rDcS7nuSm1Jy6ngP4Hcv5zwfAzAqAi4AHgm3p/ozCHIYLgSPMrL//P/jxwKNZLlNK/LbzvwJvO+duS9qefN/sQiDokfUoMN7Mis2sP3AE3g3mnGBm7cysNHiM16lhGV65g96HE4BH/MePAv/t92A8EdgRNN3lmFr/k22tn89+mvuZPAWcYWad/Sa7M/xtOcHMzgJ+DJznnNuTtL27mUX9xwPwPpMP/GvaZWYn+v8O/5ua9yDrDuB3rLV8D/4X8I5zLtH8PuDDlQAAAx1JREFUmfbPKFu9hnLhD14PuPfw/kdxXbbL04xyn4xX7X8DWOL/ORu4D3jT3/4o0DPpmOv863yXLPV+a+R6BuD1YlsKLA8+C6Ar8Aywwv+7i7/dgGn+9bwJjMr2NdRzTW2BLUDHpG2t6vPBC/L1QBXe/7a/eSCfCd69uJX+n6/n2PWsxLtnFvw7usPf94v+7+JSYDFwbtJ5RuGFzPvAH/Bn8sqR62n271gufQ/Wd03+9unAFfvtm9bPSNOxiYhI6IW5mVRERARQGIqIiCgMRUREFIYiIhJ6CkMREQk9haFICzKz3f7f/czsK2k+90/3+/mldJ5fJJ8pDEWyox/QrDAMBhg3olYYOuc+08wyiYSWwlAkO24BPuuvw/Z9M4uat7beQn+S5e8AmNkY89auvB9vMDVm9rA/ofnyYFJzM7sFKPHPN8PfFtRCzT/3Mn+Nt3FJ555vZg+Zt6bfjGDdNzO7xcze8stya4u/OyItrCDbBRAJqWvx1p07B8APtR3OuePMrBh40czm+vseDxztvKV3AL7hnNtqZiXAQjOb7Zy71syuds6NqOe1LsKbuHk40M0/5jn/uWOAIXhzN74InGRmb+FN5XWkc86Zv+CtSD5TzVAkN5yBN7fnErzluLrizbUI8FpSEAJ818yW4q2/1ydpv4acDMx03gTOG4AFwHFJ517jvImdl+A13+4E9gF/MbOLgD31nFMkrygMRXKDAdc450b4f/o754Ka4SeJnczG4E1aPNo5Nxx4HWiTwrkbUpH0OIa36ns1Xm10Nt6iqE8260pEWiGFoUh27AJKk35+CrjSX5oLMxvor+Cxv47ANufcHjM7Ejgx6bmq4Pj9PAeM8+9LdgdOoZFVMfx1Mjs6554AvofXxCqS13TPUCQ73gCq/ebO6cDv8JooF/udWDbh1cr29yRwhZm9gbf6wCtJz90JvGFmi51zlyZtnwOMxpvd3wE/cs597IdpfUqBR8ysDV6t8vsHdokirYdWrRARkdBTM6mIiISewlBEREJPYSgiIqGnMBQRkdBTGIqISOgpDEVEJPQUhiIiEnr/H/DN7NBCQOcxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_curves = {\"MIMIC\": mimic_curve, \"Genetic Alg\": gen_curve, \"Simulated Annealing\": sa_curve, \"Random Hill Climb\": rhc_curve}\n",
    "\n",
    "np.array([len(x) for x in all_curves]).max()\n",
    "\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Fitness\")\n",
    "plt.title(\"N-Queens\")\n",
    "\n",
    "for name, curve in all_curves.items():\n",
    "    plt.plot(range(len(curve)), curve, label=name, lw=2)\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function Evaluations vs Input Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_gen(input = 35):\n",
    "    length = input\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    # Define alternative N-Queens fitness function for maximization problem\n",
    "    def queens_max(state):\n",
    "        global eval_count\n",
    "        eval_count += 1\n",
    "        # Initialize counter\n",
    "        fitness_cnt = 0\n",
    "\n",
    "        # For all pairs of queens\n",
    "        for i in range(len(state) - 1):\n",
    "            for j in range(i + 1, len(state)):\n",
    "                # Check for horizontal, diagonal-up and diagonal-down attacks\n",
    "                if (state[j] != state[i]) \\\n",
    "                    and (state[j] != state[i] + (j - i)) \\\n",
    "                    and (state[j] != state[i] - (j - i)):\n",
    "\n",
    "                    # If no attacks, then increment counter\n",
    "                    fitness_cnt += 1\n",
    "\n",
    "        return fitness_cnt\n",
    "\n",
    "    # Initialize custom fitness function object\n",
    "    q_fitness = mlrh.CustomFitness(queens_max)\n",
    "    # q_fitness = mlrose.Queens()\n",
    "    prob = mlrh.QueensOpt(length=length,\n",
    "                      fitness_fn=q_fitness,\n",
    "                      maximize=True)\n",
    "    return prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_mimic(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 10\n",
    "    max_iters = 25\n",
    "    keep_pct=0.25\n",
    "    pop_size = 1000\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, mimic_curve = mlrh.mimic(prob,\n",
    "                                             max_attempts=max_attempts,\n",
    "                                             max_iters=max_iters,\n",
    "                                             random_state=random_state,\n",
    "                                             pop_size=pop_size,\n",
    "                                             keep_pct=keep_pct,\n",
    "                                             curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(rhc_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_gen(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 20\n",
    "    max_iters = 25\n",
    "    mutation_prob=0.6\n",
    "    pop_size = 400\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, gen_curve = mlrh.genetic_alg(prob,\n",
    "                                                   max_attempts=max_attempts,\n",
    "                                                   max_iters=max_iters,\n",
    "                                                   random_state=random_state,\n",
    "                                                   pop_size=pop_size,\n",
    "                                                   mutation_prob=mutation_prob,\n",
    "                                                   curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(rhc_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_sa(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 200\n",
    "    max_iters = 2500\n",
    "    init_temp = 0.1\n",
    "    schedule = mlrh.ExpDecay(init_temp)\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, sa_curve = mlrh.simulated_annealing(prob,\n",
    "                                                             max_attempts=max_attempts,\n",
    "                                                             max_iters=max_iters,\n",
    "                                                             random_state=random_state,\n",
    "                                                             schedule=schedule,\n",
    "                                                             curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(rhc_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_count_rhc(prob):\n",
    "    s = time.time()\n",
    "    max_attempts = 500\n",
    "    max_iters = 2500\n",
    "    restarts = 10\n",
    "    global eval_count\n",
    "    eval_count = 0\n",
    "    best_state, best_fitness, rhc_curve = mlrh.random_hill_climb(prob,\n",
    "                                                         max_attempts=max_attempts,\n",
    "                                                         max_iters=max_iters,\n",
    "                                                         random_state=random_state,\n",
    "                                                         restarts=restarts,\n",
    "                                                         curve=True)\n",
    "    e = time.time()\n",
    "    t_diff = e-s\n",
    "    result = (eval_count, len(rhc_curve), t_diff)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "inputs = [10, 20, 30, 40, 50, 60, 70, 80]\n",
    "\n",
    "mimic_curve_eval = [eval_count_mimic(prob_gen(x)) for x in inputs]\n",
    "gen_curve_eval = [eval_count_gen(prob_gen(x)) for x in inputs]\n",
    "sa_curve_eval = [eval_count_sa(prob_gen(x)) for x in inputs]\n",
    "rhc_curve_eval = [eval_count_rhc(prob_gen(x)) for x in inputs]\n",
    "\n",
    "all_curves = {\"MIMIC\": mimic_curve_eval,\n",
    "              \"Genetic Alg\": gen_curve_eval,\n",
    "              \"Simulated Annealing\": sa_curve_eval,\n",
    "              \"Random Hill Climb\": rhc_curve_eval}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = [(\"Inputs\", \"Function Evaluations\"),\n",
    "     (\"Inputs\", \"Iterations\"),\n",
    "     (\"Inputs\", \"Wall Clock Time (sec)\")]\n",
    "\n",
    "for i in range(3):\n",
    "    plt.xlabel(l[i][0])\n",
    "    plt.ylabel(l[i][1])\n",
    "    plt.title(\"N-Queens Problem\")\n",
    "\n",
    "    for name, curve in all_curves.items():\n",
    "        plt.plot(inputs, [x[i] for x in curve], label=name, lw=2)\n",
    "\n",
    "        plt.legend(loc=\"best\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Inputs\")\n",
    "plt.ylabel(\"Avg. Time (sec) / Iteration\")\n",
    "plt.title(\"Knapsack Problem\")\n",
    "\n",
    "for name, curve in all_curves.items():\n",
    "    avg_time_div_iterations = np.array([x[2] for x in curve]) / np.array([x[1] for x in curve])\n",
    "    plt.plot(inputs, avg_time_div_iterations, label=name, lw=2)\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}