Add files via upload

2024-08-09 12:42:22 -07:00
1 parent ae09e1caf5
commit 3fdfba4a7b
@@ -36,7 +36,7 @@
    "\n",
    "Word  = str # A word is a string of five different letters\n",
    "Score = int # A score is the number of guesses it took to get the target word\n",
-    "Reply = str # A reply is a str of 5 characters, e.g. 'G...Y'\n",
+    "Reply = str # A reply is a str of 5 characters from Green, Yellow, Miss; e.g. 'G...Y'\n",
    "\n",
    "Green, Yellow, Miss = 'GY.'\n",
    "Correct = 5 * Green # The reply for the correct guess\n",
@@ -131,8 +131,9 @@
    "    \"\"\"All the words in `targets` that give this `reply` to this `guess`.\"\"\"\n",
    "    return [target for target in targets if reply_for(guess, target) == reply]\n",
    "\n",
-    "assert consistent_targets(wordlist, 'NINNY', 'Y.G..') == ['ANNEX', 'ANNUL', 'CANON']\n",
-    "assert consistent_targets(consistent_targets(wordlist, 'NINNY', 'Y.G..'), 'ENNUI', 'YGG..') == ['ANNEX']"
+    "targets = consistent_targets(wordlist, 'NINNY', 'Y.G..')\n",
+    "assert  targets == ['ANNEX', 'ANNUL', 'CANON']\n",
+    "assert  consistent_targets(targets, 'ENNUI', 'YGG..') == ['ANNEX']"
   ]
  },
  {
@@ -144,8 +145,7 @@
    "The function `play` will play a game of Wordle and return the score (the number of guesses). The  arguments are:\n",
    "- `guesser`: a `callable` (e.g., a function) that should return the guess to make. The guesser is passed two arguments: \n",
    "  - The **reply** to the previous guess.\n",
-    "  - A list of **consistent target words**.<br>(A word is **consistent** if it\n",
-    "  gives the same replies to all the guesses made so far as the replies that were actually received.)\n",
+    "  - A list of **consistent target words**.\n",
    "- `wordlist`: The list of allowable words.\n",
    "- `target`: The target word. If none is given, the target word is chosen at random from the wordlist.\n",
    "- `verbose`: Unless false, print a message for each guess.\n",
@@ -456,7 +456,7 @@
   "source": [
    "# Metrics for Minimizing Partitions\n",
    "\n",
-    "We want partitions with **small branches**, but what exactly does that mean? Ideally, we want the partition that minimizes the average number of additional guesses it will take to finish the game (and possibly also that minimizes the number of times that we more than 6 guesses). However, just looking at a single partition (without looking ahead further) can't tell us what we want to know, so instead we can minimize one of the following proxy metrics:\n",
+    "We want partitions with **small branches**, but what exactly does that mean? An ideal goal would be to minimize the average number of additional guesses it will take to finish the game, and to eliminate the possibility of losing the game (needing more than 6 guesses). However, just looking at a single partition (without looking ahead further) can't tell us that, so instead we can minimize one of the following proxy metrics:\n",
    "\n",
    "- **Maximum**: choose the partition that minimizes the size of the largest branch.\n",
    "\n",
@@ -542,9 +542,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Here is a minimizing tree that covers the 24 `few` words. The tree says that the first guess, `\"ROAST\"`, covers 24 target words, and for 12 replies there is only one word left, but for the first 6 replies a second guess is required. Either that second guess will be right, or we will need a third guess.\n",
-    "\n",
-    "if the reply is `0` the next guess is `\"would\"`. If, say, the reply to `\"would\"` is 4, then there is only one other word left, the leaf word `'cloud'`. If the reply is 5, that means `\"would\"` was correct. I won't go through the other branches of the tree."
+    "Here is a minimizing tree that covers the 24 `few` words. The tree says that the first guess, `\"ROAST\"`, covers 24 target words, and for 12 replies there is only one word left, but for the first 6 replies a second guess is required (e.g., if the reply is `'..G..` then the second guess is `\"ABACK\"`). Either that second guess will be right, or we will need a third guess (in this example, `'QUACK'`)."
   ]
  },
  {
@@ -583,10 +581,10 @@
    "\n",
    "A tree is not a guesser, but we can easily make a guesser from a tree. A `TreeGuesser` works as follows:\n",
    "- When *initialized*, it takes a tree as input, and stores the tree under the `.root` attribute.\n",
-    "- When *called*, it sets the `.tree` attribute as follows:\n",
+    "- When *called*, it first sets the `.tree` attribute as follows:\n",
    "  - For the first turn in a game (when the reply is `None`), it resets `.tree`  to `.root`.\n",
    "  - On subsequent turns, it updates `.tree` to be the branch corresponding to the reply.\n",
-    "- When *called*, it then returns the guess for the current tree: either the `.guess` attribute  or the leaf word. "
+    "  - After setting `.tree`, it returns the guess for the current tree: either the `.guess` attribute  or the leaf word. "
   ]
  },
  {
@@ -622,8 +620,8 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "CPU times: user 24.3 s, sys: 596 ms, total: 24.9 s\n",
-      "Wall time: 26.2 s\n"
+      "CPU times: user 23 s, sys: 284 ms, total: 23.3 s\n",
+      "Wall time: 23.3 s\n"
     ]
    }
   ],
@@ -949,14 +947,12 @@
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -981,14 +977,12 @@
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1020,20 +1014,18 @@
      "first guess: \"ARISE\"\n",
      "median: 4 guesses, mean: 3.68 ± 0.86, worst: 8, best: 1\n",
      "cumulative: ≤2:5%, ≤3:43%, ≤4:87%, ≤5:97%, ≤6:99.4%, ≤7:99.9%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 2.08 s, sys: 171 ms, total: 2.25 s\n",
-      "Wall time: 2.42 s\n"
+      "CPU times: user 3.17 s, sys: 176 ms, total: 3.34 s\n",
+      "Wall time: 1.66 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1054,20 +1046,18 @@
      "first guess: \"RAISE\"\n",
      "median: 4 guesses, mean: 3.62 ± 0.86, worst: 8, best: 1\n",
      "cumulative: ≤2:6%, ≤3:47%, ≤4:88%, ≤5:98%, ≤6:99.4%, ≤7:99.9%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 1.99 s, sys: 17.6 ms, total: 2.01 s\n",
-      "Wall time: 2.03 s\n"
+      "CPU times: user 3.2 s, sys: 277 ms, total: 3.48 s\n",
+      "Wall time: 1.68 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1088,20 +1078,18 @@
      "first guess: \"RAISE\"\n",
      "median: 4 guesses, mean: 3.60 ± 0.85, worst: 8, best: 1\n",
      "cumulative: ≤2:6%, ≤3:49%, ≤4:89%, ≤5:97%, ≤6:99.5%, ≤7:99.9%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 2.08 s, sys: 31.5 ms, total: 2.11 s\n",
-      "Wall time: 2.12 s\n"
+      "CPU times: user 2.92 s, sys: 179 ms, total: 3.1 s\n",
+      "Wall time: 1.88 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1113,6 +1101,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
+    "We see that `neg_entropy` gives the best mean number of guesses, 3.60, and the best winning percentage (99.5% in 6 guesses or less).\n",
+    "\n",
    "The random guesser is also a consistent guesser. Here is a report on it (each run will give slightly different results):"
   ]
  },
@@ -1127,20 +1117,18 @@
     "text": [
      "median: 4 guesses, mean: 4.09 ± 1.04, worst: 9, best: 2\n",
      "cumulative: ≤2:4%, ≤3:29%, ≤4:69%, ≤5:92%, ≤6:98%, ≤7:99.7%, ≤8:99.9%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 1.92 s, sys: 34.5 ms, total: 1.96 s\n",
-      "Wall time: 1.96 s\n"
+      "CPU times: user 2.88 s, sys: 319 ms, total: 3.2 s\n",
+      "Wall time: 1.6 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1176,20 +1164,18 @@
      "first guess: \"ARISE\"\n",
      "median: 4 guesses, mean: 3.64 ± 0.66, worst: 6, best: 1\n",
      "cumulative: ≤2:2%, ≤3:42%, ≤4:93%, ≤5:99.5%, ≤6:100%, ≤7:100%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 6.48 s, sys: 141 ms, total: 6.62 s\n",
-      "Wall time: 6.78 s\n"
+      "CPU times: user 6.35 s, sys: 229 ms, total: 6.58 s\n",
+      "Wall time: 5.25 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1210,20 +1196,18 @@
      "first guess: \"RAISE\"\n",
      "median: 4 guesses, mean: 3.55 ± 0.64, worst: 6, best: 1\n",
      "cumulative: ≤2:2%, ≤3:48%, ≤4:95%, ≤5:99.6%, ≤6:100%, ≤7:100%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 6.62 s, sys: 94.8 ms, total: 6.71 s\n",
-      "Wall time: 6.78 s\n"
+      "CPU times: user 7.14 s, sys: 223 ms, total: 7.36 s\n",
+      "Wall time: 5.51 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGwCAYAAABcnuQpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAtNklEQVR4nO3de1hVdaL/8c9OdAuKmJgg3jW8kmhSBppQKaaO2TBntMEpHbqOmBBmaZ6OZAnoTKYTZVmNOTVezpyynE4qNCZeqESUdDBviQ6VxMlUSA0D1u+PHvevPSiwFV37a+/X86znaX3X2mt92DPKx+9aa2+HZVmWAAAADHWV3QEAAAAuBmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBoPnYHuNSqq6v11Vdfyd/fXw6Hw+44AACgHizLUnl5uUJCQnTVVbXPvVzxZearr75Shw4d7I4BAAAuQHFxsdq3b1/rPld8mfH395f045vRokULm9MAAID6KCsrU4cOHVy/x2tzxZeZs5eWWrRoQZkBAMAw9blFhBuAAQCA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEbzsTsAcCXrPP1/7Y5Qp0MZo+yOAAAXhZkZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACj8Wg2jMFjzgCAc2FmBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0rykz6enpcjgcSk5Odo1ZlqXU1FSFhITI19dXMTExKiwstC8kAADwOl5RZvLy8rR48WL17dvXbXzevHmaP3++MjMzlZeXp+DgYA0bNkzl5eU2JQUAAN7G9jLz3Xffafz48XrllVd09dVXu8Yty9KCBQs0c+ZMxcXFKSwsTEuXLtWpU6e0bNmy8x6voqJCZWVlbgsAALhy2V5mEhMTNWrUKA0dOtRtvKioSCUlJYqNjXWNOZ1ORUdHKzc397zHS09PV0BAgGvp0KHDJcsOAADsZ2uZWbFihbZv36709PQa20pKSiRJQUFBbuNBQUGubecyY8YMnThxwrUUFxc3bGgAAOBVfOw6cXFxsZKSkpSVlaWmTZuedz+Hw+G2bllWjbGfcjqdcjqdDZYTAAB4N9tmZvLz81VaWqoBAwbIx8dHPj4+ysnJ0Z/+9Cf5+Pi4ZmT+fRamtLS0xmwNAAD4+bKtzNx2223atWuXCgoKXEtERITGjx+vgoICde3aVcHBwcrOzna95syZM8rJyVFUVJRdsQEAgJex7TKTv7+/wsLC3MaaNWumwMBA13hycrLS0tIUGhqq0NBQpaWlyc/PT/Hx8XZEBgAAXsi2MlMfjz32mE6fPq1Jkybp2LFjGjhwoLKysuTv7293NAAA4CW8qsxs2LDBbd3hcCg1NVWpqam25AEAAN7P9s+ZAQAAuBiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRbC0zixYtUt++fdWiRQu1aNFCkZGRWrNmjWu7ZVlKTU1VSEiIfH19FRMTo8LCQhsTAwAAb2NrmWnfvr0yMjK0bds2bdu2TbfeeqvGjBnjKizz5s3T/PnzlZmZqby8PAUHB2vYsGEqLy+3MzYAAPAitpaZ0aNHa+TIkerevbu6d++uOXPmqHnz5vr4449lWZYWLFigmTNnKi4uTmFhYVq6dKlOnTqlZcuW2RkbAAB4Ea+5Z6aqqkorVqzQyZMnFRkZqaKiIpWUlCg2Nta1j9PpVHR0tHJzc897nIqKCpWVlbktAADgymV7mdm1a5eaN28up9Ophx56SKtWrVLv3r1VUlIiSQoKCnLbPygoyLXtXNLT0xUQEOBaOnTocEnzAwAAe9leZnr06KGCggJ9/PHH+v3vf68JEyZo9+7dru0Oh8Ntf8uyaoz91IwZM3TixAnXUlxcfMmyAwAA+/nYHaBJkya69tprJUkRERHKy8vTwoUL9fjjj0uSSkpK1LZtW9f+paWlNWZrfsrpdMrpdF7a0AAAwGs0yMzM8ePHG+Iwkn6ceamoqFCXLl0UHBys7Oxs17YzZ84oJydHUVFRDXY+AABgNo/LzNy5c7Vy5UrX+tixYxUYGKh27drp008/9ehYTzzxhDZt2qRDhw5p165dmjlzpjZs2KDx48fL4XAoOTlZaWlpWrVqlf75z39q4sSJ8vPzU3x8vKexAQDAFcrjy0wvv/yy3nzzTUlSdna2srOztWbNGv33f/+3pk2bpqysrHof6+uvv9bdd9+tI0eOKCAgQH379tXatWs1bNgwSdJjjz2m06dPa9KkSTp27JgGDhyorKws+fv7exobAABcoTwuM0eOHHE9IfTee+9p7Nixio2NVefOnTVw4ECPjvXaa6/Vut3hcCg1NVWpqamexgQAAD8THl9muvrqq11PCK1du1ZDhw6V9OO9LlVVVQ2bDgAAoA4ez8zExcUpPj5eoaGhOnr0qEaMGCFJKigocD2VBAAAcLl4XGaee+45de7cWcXFxZo3b56aN28u6cfLT5MmTWrwgAAAALXxuMw0btxYjz76aI3x5OTkhsgDAADgkQv6nJk33nhDgwcPVkhIiA4fPixJWrBggd59990GDQcAAFAXj8vMokWLlJKSohEjRuj48eOum35btmypBQsWNHQ+AACAWnlcZp5//nm98sormjlzpho1auQaj4iI0K5duxo0HAAAQF08LjNFRUXq379/jXGn06mTJ082SCgAAID68rjMdOnSRQUFBTXG16xZo969ezdEJgAAgHrz+GmmadOmKTExUd9//70sy9LWrVu1fPlypaen69VXX70UGQEAAM7L4zLzu9/9TpWVlXrsscd06tQpxcfHq127dlq4cKHuuuuuS5ERAADgvDwqM5WVlfrrX/+q0aNH6/7779c333yj6upqtWnT5lLlAwAAqJVH98z4+Pjo97//vSoqKiRJrVu3psgAAABbeXwD8MCBA7Vjx45LkQUAAMBjHt8zM2nSJE2dOlVffPGFBgwYoGbNmrlt79u3b4OFAwAAqIvHZWbcuHGSpClTprjGHA6HLMuSw+FwfSIwAADA5eBxmSkqKroUOQAAAC6Ix2WmU6dOlyIHAADABfG4zEjS559/rgULFuizzz6Tw+FQr169lJSUpG7dujV0PgAAgFp5/DTTunXr1Lt3b23dulV9+/ZVWFiYPvnkE/Xp00fZ2dmXIiMAAMB5eTwzM336dD3yyCPKyMioMf74449r2LBhDRYOAACgLh7PzHz22We69957a4wnJCRo9+7dDRIKAACgvjwuM9dcc805vzW7oKCATwMGAACXnceXme6//3498MADOnjwoKKiouRwOLR582bNnTtXU6dOvRQZAQAAzsvjMvPkk0/K399fzz77rGbMmCFJCgkJUWpqqtsH6QEAAFwOHpcZh8OhRx55RI888ojKy8slSf7+/g0eDAAAoD4u6BOAKysrFRoa6lZi9u/fr8aNG6tz584NmQ8AAKBWHt8APHHiROXm5tYY/+STTzRx4sSGyAQAAFBvHpeZHTt2aNCgQTXGb7rppnM+5QQAAHApeVxmHA6H616Znzpx4gTfmA0AAC47j8vMzTffrPT0dLfiUlVVpfT0dA0ePLhBwwEAANTF4xuA582bpyFDhqhHjx66+eabJUmbNm1SWVmZ1q9f3+ABAQAAauPxzEzv3r21c+dOjR07VqWlpSovL9c999yjPXv2KCws7FJkBAAAOC+PZ2akHz8kLy0traGzAAAAeMzjmZm1a9dq8+bNrvUXXnhB/fr1U3x8vI4dO9ag4QAAAOricZmZNm2aysrKJEm7du1SSkqKRo4cqYMHDyolJaXBAwIAANTmgj4BuHfv3pKkt956S6NHj1ZaWpq2b9+ukSNHNnhAAACA2ng8M9OkSROdOnVKkvTBBx8oNjZWktSqVSvXjA0AAMDl4vHMzODBg5WSkqJBgwZp69atWrlypSRp3759at++fYMHBAAAqI3HMzOZmZny8fHR//zP/2jRokVq166dJGnNmjW6/fbbGzwgAABAbTyemenYsaPee++9GuPPPfdcgwQCAADwhMczMwAAAN6EMgMAAIxGmQEAAEarV5nZuXOnqqurL3UWAAAAj9WrzPTv31/ffPONJKlr1646evToJQ0FAABQX/UqMy1btlRRUZEk6dChQ8zSAAAAr1GvR7N/9atfKToLine truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1244,20 +1228,18 @@
      "first guess: \"RAISE\"\n",
      "median: 3 guesses, mean: 3.52 ± 0.64, worst: 6, best: 1\n",
      "cumulative: ≤2:2%, ≤3:50%, ≤4:95%, ≤5:99.6%, ≤6:100%, ≤7:100%, ≤8:100%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 6.96 s, sys: 93 ms, total: 7.06 s\n",
-      "Wall time: 7.21 s\n"
+      "CPU times: user 6.88 s, sys: 234 ms, total: 7.11 s\n",
+      "Wall time: 5.72 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1269,7 +1251,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "**Mission accomplished!** With inconsistent guesses allowed, all three metrics  solve every target word in 6 guesses or less. \n",
+    "**Mission accomplished!** With inconsistent guesses allowed, all three metrics  solve  100% of the target words in 6 guesses or less. Again, `neg_entropy` is the best metric by a small amount, giving a mean of 3.52 guesses.\n",
    "\n"
   ]
  },
@@ -1849,7 +1831,7 @@
    "\n",
    "The [**Evil Wordle**](https://swag.github.io/evil-wordle/) variant says it works like this: *\"There's no [target] word set by default. Every time you guess, I look at all possible 5-letter words that would fit all your guesses, and choose the match pattern [reply] that results in the most possible words. My goal is to maximize the amount of guesses it takes to find the word.\"*\n",
    "\n",
-    "To play against Evil Wordle, minimizing the max branch size is a good strategy. We know the guess tree that minimizes max has a few branches that require 6 guesses. If the Evil Replier did look-ahead, perhaps they could get to one of those branches. But we know that the Evil Replier greedily picks the branch with the \"most possible words.\" So I will define `evil` to follow branches through a guess tree and produce the (guess, reply) pairs along the way. For a given tree, there is only one possible game (assuming the Evil Replier breaks ties the same way each time)."
+    "To play against Evil Wordle, minimizing the max branch size is a good strategy. We know the guess tree that minimizes max has a few branches that require 6 guesses. If the Evil Replier did look-ahead, perhaps they could force us to one of those branches. But we know that the Evil Replier greedily picks the branch with the \"most possible words.\" So I will define `evil` to follow branches through a guess tree and produce the (guess, reply) pairs along the way. For a given tree, there is only one possible game (assuming the Evil Replier breaks ties the same way each time)."
   ]
  },
  {
@@ -1903,7 +1885,7 @@
    "- Would it be possible for a smarter Guesser to create a different tree (perhaps minimizing something other than max) that only requires 4 guesses? **No.** I know that because (a) when there are two words left in a branch, it will always take two guesses to defeat the Evil Replier, and (b) I previously tried all two-word guess pairs to see which resulted in the smallest branches, and every pair had branches with multiple words.\n",
    "- Would it be possible for a smarter Evil Replier to force a game with 6 guesses? **Maybe.** We know the minimizing-max guess tree has 11 out of 2,309 leaves that require 6 guesses. If the Evil Replier did look-ahead instead of acting greedily, could it get to one of those? I think it is unlikely, but I would need to do a minimax search to prove it one way or the other.\n",
    "\n",
-    "Alas, this is all moot, because a little experimenting shows that [**Evil Wordle**](https://swag.github.io/evil-wordle/) is using a word list that contains multiple words that are not in the 2,309 word list. I suspect it is using Wordle's 12,971–word list."
+    "Alas, this is all moot, because a little experimenting shows that [**Evil Wordle**](https://swag.github.io/evil-wordle/) is using a word list that contains multiple words that are not in the 2,309 word list. I suspect it is using Wordle's complete 12,971–word list."
   ]
  },
  {
@@ -1932,14 +1914,12 @@
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -1962,7 +1942,7 @@
   "source": [
    "# Jotto\n",
    "\n",
-    "[Jotto](https://en.wikipedia.org/wiki/Jotto) is a venerable word game (invented in 1955) that is quite similar to Wordle. The difference is that the reply in Jotto is an integer from 0 to 5 giving the number of matching letters, with no indication of which letter(s) are correct nor whether they are in the right position or not. There are several variants of Jotto; here are four key questions and my answers:\n",
+    "[Jotto](https://en.wikipedia.org/wiki/Jotto) is a venerable word game (invented in 1955) that is quite similar to Wordle. The difference is that the reply in Jotto is an integer from 0 to 5 giving the number of matching letters, with no indication of which letter(s) are correct nor whether they are in the right position or not. There are several variants of Jotto; here are four key questions and my answers (designed to keep things simple):\n",
    "\n",
    "- How many letters can each word have? **Five**.\n",
    "- Does a guess have to be a word in the word list? **Yes.**\n",
@@ -1970,9 +1950,9 @@
    "- What if the reply is \"5\", but the guess is not the target? **Not allowed.**<br>*(E.g., only one of the anagrams APERS/PARES/PARSE/PEARS/REAPS/SPARE/SPEAR  is allowed in the word list.)*\n",
    "\n",
    "We can make a Jotto word list by:\n",
-    "- Starting with a list of words. (We'll use the Wordle word list.)\n",
-    "- Discarding the ones that don't have 5 distinct letters.\n",
-    "- Putting the rest into a dict of anagrams keyed by the set of letters.\n",
+    "- Starting with a word list (such as the Wordle list).\n",
+    "- Discarding words that don't have 5 distinct letters.\n",
+    "- Putting the remaining words into a dict of anagrams keyed by the set of letters.\n",
    "- Keeping only one word for each anagram."
   ]
  },
@@ -2004,8 +1984,15 @@
   "outputs": [],
   "source": [
    "wordle_reply_for = reply_for      # Save the original Wordle reply_for function\n",
-    "wordle_wordlist  = list(wordlist) # Save a copy of the original Wordle wordlist, which will be mutated\n",
-    "\n",
+    "wordle_wordlist  = list(wordlist) # Save a copy of the original Wordle wordlist, which will be mutated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
    "def jotto_reply_for(guess, target) -> Reply: \n",
    "    \"The number of letters in common between the target and guess\"\n",
    "    return len(set(target).intersection(guess))\n",
@@ -2020,7 +2007,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
@@ -2042,7 +2029,7 @@
       "7"
      ]
     },
-     "execution_count": 50,
+     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -2054,7 +2041,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
@@ -2077,7 +2064,7 @@
       "8"
      ]
     },
-     "execution_count": 51,
+     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -2095,7 +2082,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
@@ -2113,7 +2100,7 @@
       "3"
      ]
     },
-     "execution_count": 52,
+     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -2125,7 +2112,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
@@ -2147,7 +2134,7 @@
       "7"
      ]
     },
-     "execution_count": 53,
+     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -2168,7 +2155,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
@@ -2179,20 +2166,18 @@
      "first guess: \"DRAPE\"\n",
      "median: 6 guesses, mean: 6.31 ± 1.43, worst: 16, best: 1\n",
      "cumulative: ≤2:0%, ≤3:2%, ≤4:7%, ≤5:24%, ≤6:58%, ≤7:88%, ≤8:95%, ≤9:97%, ≤10:99%\n",
-      "CPU times: user 1.4 s, sys: 13.2 ms, total: 1.41 s\n",
-      "Wall time: 1.42 s\n"
+      "CPU times: user 1.66 s, sys: 40.7 ms, total: 1.7 s\n",
+      "Wall time: 1.54 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -2200,40 +2185,6 @@
    "%time report_minimizing_tree(max, inconsistent=False)"
   ]
  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "minimizing the expectation of partition sizes over 1,391 targets; inconsistent guesses prohibited\n",
-      "first guess: \"SOUTH\"\n",
-      "median: 6 guesses, mean: 6.11 ± 1.22, worst: 14, best: 1\n",
-      "cumulative: ≤2:0%, ≤3:2%, ≤4:8%, ≤5:26%, ≤6:64%, ≤7:93%, ≤8:98%, ≤9:99%, ≤10:99.5%\n",
-      "CPU times: user 1.39 s, sys: 10.9 ms, total: 1.4 s\n",
-      "Wall time: 1.41 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%time report_minimizing_tree(expectation, inconsistent=False)"
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": 56,
@@ -2243,29 +2194,27 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "minimizing the neg_entropy of partition sizes over 1,391 targets; inconsistent guesses prohibited\n",
-      "first guess: \"STARE\"\n",
-      "median: 6 guesses, mean: 6.31 ± 1.46, worst: 15, best: 1\n",
-      "cumulative: ≤2:0%, ≤3:2%, ≤4:8%, ≤5:25%, ≤6:58%, ≤7:87%, ≤8:95%, ≤9:97%, ≤10:98%\n",
-      "CPU times: user 1.4 s, sys: 8.2 ms, total: 1.41 s\n",
-      "Wall time: 1.41 s\n"
+      "minimizing the expectation of partition sizes over 1,391 targets; inconsistent guesses prohibited\n",
+      "first guess: \"SOUTH\"\n",
+      "median: 6 guesses, mean: 6.11 ± 1.22, worst: 14, best: 1\n",
+      "cumulative: ≤2:0%, ≤3:2%, ≤4:8%, ≤5:26%, ≤6:64%, ≤7:93%, ≤8:98%, ≤9:99%, ≤10:99.5%\n",
+      "CPU times: user 2.29 s, sys: 214 ms, total: 2.51 s\n",
+      "Wall time: 1.48 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
-    "%time report_minimizing_tree(neg_entropy, inconsistent=False)"
+    "%time report_minimizing_tree(expectation, inconsistent=False)"
   ]
  },
  {
@@ -2277,22 +2226,52 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "median: 7 guesses, mean: 6.65 ± 1.46, worst: 13, best: 2\n",
-      "cumulative: ≤2:0%, ≤3:2%, ≤4:7%, ≤5:20%, ≤6:44%, ≤7:75%, ≤8:91%, ≤9:97%, ≤10:99.3%\n",
-      "CPU times: user 1.49 s, sys: 7.59 ms, total: 1.5 s\n",
-      "Wall time: 1.51 s\n"
+      "minimizing the neg_entropy of partition sizes over 1,391 targets; inconsistent guesses prohibited\n",
+      "first guess: \"STARE\"\n",
+      "median: 6 guesses, mean: 6.31 ± 1.46, worst: 15, best: 1\n",
+      "cumulative: ≤2:0%, ≤3:2%, ≤4:8%, ≤5:25%, ≤6:58%, ≤7:87%, ≤8:95%, ≤9:97%, ≤10:98%\n",
+      "CPU times: user 2.43 s, sys: 177 ms, total: 2.61 s\n",
+      "Wall time: 1.62 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time report_minimizing_tree(neg_entropy, inconsistent=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "median: 7 guesses, mean: 6.65 ± 1.46, worst: 13, best: 2\n",
+      "cumulative: ≤2:0%, ≤3:2%, ≤4:7%, ≤5:20%, ≤6:44%, ≤7:75%, ≤8:91%, ≤9:97%, ≤10:99.3%\n",
+      "CPU times: user 2.53 s, sys: 217 ms, total: 2.75 s\n",
+      "Wall time: 1.61 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -2309,7 +2288,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
@@ -2320,20 +2299,18 @@
      "first guess: \"DRAPE\"\n",
      "median: 6 guesses, mean: 6.38 ± 0.93, worst: 9, best: 1\n",
      "cumulative: ≤2:0%, ≤3:1%, ≤4:3%, ≤5:13%, ≤6:52%, ≤7:93%, ≤8:99.8%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 4.54 s, sys: 27.1 ms, total: 4.56 s\n",
-      "Wall time: 4.71 s\n"
+      "CPU times: user 4.98 s, sys: 51.4 ms, total: 5.03 s\n",
+      "Wall time: 4.83 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -2341,40 +2318,6 @@
    "%time report_minimizing_tree(max, inconsistent=True)"
   ]
  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "minimizing the expectation of partition sizes over 1,391 targets; inconsistent guesses allowed\n",
-      "first guess: \"SOUTH\"\n",
-      "median: 6 guesses, mean: 6.13 ± 0.89, worst: 9, best: 1\n",
-      "cumulative: ≤2:0%, ≤3:1%, ≤4:4%, ≤5:19%, ≤6:66%, ≤7:97%, ≤8:99.9%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 4.59 s, sys: 42.4 ms, total: 4.63 s\n",
-      "Wall time: 4.85 s\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%time report_minimizing_tree(expectation, inconsistent=True)"
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": 60,
@@ -2384,24 +2327,54 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "minimizing the neg_entropy of partition sizes over 1,391 targets; inconsistent guesses allowed\n",
-      "first guess: \"STARE\"\n",
-      "median: 6 guesses, mean: 6.18 ± 0.93, worst: 9, best: 1\n",
-      "cumulative: ≤2:0%, ≤3:1%, ≤4:3%, ≤5:20%, ≤6:63%, ≤7:95%, ≤8:99.7%, ≤9:100%, ≤10:100%\n",
-      "CPU times: user 4.87 s, sys: 45.5 ms, total: 4.91 s\n",
-      "Wall time: 4.94 s\n"
+      "minimizing the expectation of partition sizes over 1,391 targets; inconsistent guesses allowed\n",
+      "first guess: \"SOUTH\"\n",
+      "median: 6 guesses, mean: 6.13 ± 0.89, worst: 9, best: 1\n",
+      "cumulative: ≤2:0%, ≤3:1%, ≤4:4%, ≤5:19%, ≤6:66%, ≤7:97%, ≤8:99.9%, ≤9:100%, ≤10:100%\n",
+      "CPU times: user 5.38 s, sys: 173 ms, total: 5.55 s\n",
+      "Wall time: 4.66 s\n"
     ]
    },
    {
     "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
      ]
     },
-     "metadata": {
-      "needs_background": "light"
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%time report_minimizing_tree(expectation, inconsistent=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimizing the neg_entropy of partition sizes over 1,391 targets; inconsistent guesses allowed\n",
+      "first guess: \"STARE\"\n",
+      "median: 6 guesses, mean: 6.18 ± 0.93, worst: 9, best: 1\n",
+      "cumulative: ≤2:0%, ≤3:1%, ≤4:3%, ≤5:20%, ≤6:63%, ≤7:95%, ≤8:99.7%, ≤9:100%, ≤10:100%\n",
+      "CPU times: user 6.17 s, sys: 207 ms, total: 6.37 s\n",
+      "Wall time: 4.87 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
     },
+     "metadata": {},
     "output_type": "display_data"
    }
   ],
@@ -2425,6 +2398,8 @@
    "|random guesser      |6.59 (13)|         | 4.09 (9) |          |\n",
    "\n",
    "\n",
+    "So for both games, the best approach is using the neg-entropy metric and allowing inconsistent guesses.\n",
+    "\n",
    "One surprising thing: allowing inconsistent guesses in Jotto does *not* improve the mean score with eiither the max or expectation metric. However, it does uniformly improve the worst score.\n",
    "\n",
    "\n",
@@ -2435,7 +2410,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
@@ -2589,7 +2564,7 @@
       "[1391 rows x 6 columns]"
      ]
     },
-     "execution_count": 61,
+     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -2652,7 +2627,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.15"
  }
 },
 "nbformat": 4,