+++ /dev/null
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 206,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "import string\n",
- "import re\n",
- "import random\n",
- "import collections\n",
- "import copy\n",
- "import os\n",
- "\n",
- "from IPython.display import clear_output\n",
- "\n",
- "from enum import Enum\n",
- "Direction = Enum('Direction', 'left right up down upleft upright downleft downright')\n",
- " \n",
- "delta = {Direction.left: (0, -1),Direction.right: (0, 1), \n",
- " Direction.up: (-1, 0), Direction.down: (1, 0), \n",
- " Direction.upleft: (-1, -1), Direction.upright: (-1, 1), \n",
- " Direction.downleft: (1, -1), Direction.downright: (1, 1)}\n",
- "\n",
- "cat = ''.join\n",
- "wcat = ' '.join\n",
- "lcat = '\\n'.join"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 180,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# all_words = [w.strip() for w in open('/usr/share/dict/british-english').readlines()\n",
- "# if all(c in string.ascii_lowercase for c in w.strip())]\n",
- "# words = [w for w in all_words\n",
- "# if not any(w in w2 for w2 in all_words if w != w2)]\n",
- "# open('wordsearch-words', 'w').write(lcat(words))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 181,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# ws_words = [w.strip() for w in open('wordsearch-words').readlines()\n",
- "# if all(c in string.ascii_lowercase for c in w.strip())]\n",
- "# ws_words[:10]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 182,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "ws_words = [w.strip() for w in open('/usr/share/dict/british-english').readlines()\n",
- " if all(c in string.ascii_lowercase for c in w.strip())]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 183,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "62183"
- ]
- },
- "execution_count": 183,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len([w for w in ws_words if len(w) >= 4])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 184,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def empty_grid(w, h):\n",
- " return [['.' for c in range(w)] for r in range(h)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 185,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def show_grid(grid):\n",
- " return lcat(cat(r) for r in grid)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 186,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n",
- "..........\n"
- ]
- }
- ],
- "source": [
- "grid = empty_grid(10, 10)\n",
- "print(show_grid(grid))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 187,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def indices(grid, r, c, l, d):\n",
- " dr, dc = delta[d]\n",
- " w = len(grid[0])\n",
- " h = len(grid)\n",
- " inds = [(r + i * dr, c + i * dc) for i in range(l)]\n",
- " return [(i, j) for i, j in inds\n",
- " if i >= 0\n",
- " if j >= 0\n",
- " if i < h\n",
- " if j < w]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 188,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def gslice(grid, r, c, l, d):\n",
- " return [grid[i][j] for i, j in indices(grid, r, c, l, d)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 189,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def set_grid(grid, r, c, d, word):\n",
- " for (i, j), l in zip(indices(grid, r, c, len(word), d), word):\n",
- " grid[i][j] = l\n",
- " return grid"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 190,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "..........\n",
- "..........\n",
- "...t......\n",
- "....e.....\n",
- ".....s....\n",
- "......t...\n",
- ".......w..\n",
- "........o.\n",
- ".........r\n",
- "..........\n"
- ]
- }
- ],
- "source": [
- "set_grid(grid, 2, 3, Direction.downright, 'testword')\n",
- "print(show_grid(grid))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 191,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'..e.....'"
- ]
- },
- "execution_count": 191,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "cat(gslice(grid, 3, 2, 15, Direction.right))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 192,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<_sre.SRE_Match object; span=(0, 4), match='keen'>"
- ]
- },
- "execution_count": 192,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "re.match(cat(gslice(grid, 3, 2, 4, Direction.right)), 'keen')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 193,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<_sre.SRE_Match object; span=(0, 3), match='kee'>"
- ]
- },
- "execution_count": 193,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "re.match(cat(gslice(grid, 3, 2, 3, Direction.right)), 'keen')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 194,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "re.fullmatch(cat(gslice(grid, 3, 2, 3, Direction.right)), 'keen')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 195,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "re.match(cat(gslice(grid, 3, 2, 4, Direction.right)), 'kine')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 196,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def could_add(grid, r, c, d, word):\n",
- " s = gslice(grid, r, c, len(word), d)\n",
- " return re.fullmatch(cat(s), word)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 197,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<_sre.SRE_Match object; span=(0, 4), match='keen'>"
- ]
- },
- "execution_count": 197,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "could_add(grid, 3, 2, Direction.right, 'keen')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 198,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "could_add(grid, 3, 2, Direction.right, 'kine')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 199,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<Direction.up: 3>"
- ]
- },
- "execution_count": 199,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "random.choice(list(Direction))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 207,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def fill_grid(grid, words, word_count, max_attempts=10000, min_word_len=4):\n",
- " attempts = 0\n",
- " added_words = []\n",
- " w = len(grid[0])\n",
- " h = len(grid)\n",
- " while len(added_words) < word_count and attempts < max_attempts:\n",
- " clear_output(wait=True)\n",
- " print(\"Added\", len(added_words), '; attempt', attempts)\n",
- " attempts += 1\n",
- " r = random.randrange(w)\n",
- " c = random.randrange(h)\n",
- " word = random.choice(words)\n",
- " d = random.choice(list(Direction))\n",
- "# print(word, r, c, d, \n",
- "# len(word) >= min_word_len, \n",
- "# not any(word in w2 for w2 in added_words), \n",
- "# could_add(grid, r, c, d, word), \n",
- "# not present(grid, word)[0]\n",
- "# )\n",
- " if (len(word) >= min_word_len\n",
- " and not any(word in w2 for w2 in added_words) \n",
- " and could_add(grid, r, c, d, word)\n",
- " and not present(grid, word)[0]):\n",
- " grid2 = copy.deepcopy(grid)\n",
- " set_grid(grid2, r, c, d, word)\n",
- "# print(present_many(grid2, [word]))\n",
- " if collections.Counter(p[0] for p in \n",
- " present_many(grid2, added_words + [word])).most_common(1)[0][1] == 1:\n",
- " set_grid(grid, r, c, d, word)\n",
- " added_words += [word]\n",
- " attempts = 0\n",
- " return grid, added_words"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 201,
- "metadata": {},
- "outputs": [
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-201-6ab66457c812>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mempty_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfill_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mws_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<ipython-input-200-42497f37ad7f>\u001b[0m in \u001b[0;36mfill_grid\u001b[0;34m(grid, words, word_count, max_attempts, min_word_len)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m# print(present_many(grid2, [word]))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m if collections.Counter(p[0] for p in \n\u001b[0;32m---> 26\u001b[0;31m present_many(grid2, added_words + [word])).most_common(1)[0][1] == 1:\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mset_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0madded_words\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<ipython-input-123-ff8574b12cf0>\u001b[0m in \u001b[0;36mpresent_many\u001b[0;34m(grid, words)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDirection\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mwordlen\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwordlens\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mword\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwordlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mpresences\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<ipython-input-188-a5adda74be28>\u001b[0m in \u001b[0;36mgslice\u001b[0;34m(grid, r, c, l, d)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;32m<ipython-input-187-e0e67252b972>\u001b[0m in \u001b[0;36mindices\u001b[0;34m(grid, r, c, l, d)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0minds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
- "source": [
- "g = empty_grid(20, 20)\n",
- "g, ws = fill_grid(g, ws_words, 40)\n",
- "len(ws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(show_grid(g))\n",
- "print(len(ws), 'words added')\n",
- "print(wcat(ws))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def present(grid, word):\n",
- " w = len(grid[0])\n",
- " h = len(grid)\n",
- " for r in range(h):\n",
- " for c in range(w):\n",
- " for d in Direction:\n",
- " if cat(gslice(grid, r, c, len(word), d)) == word:\n",
- " return True, r, c, d\n",
- " return False, 0, 0, list(Direction)[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def present_many(grid, words):\n",
- " w = len(grid[0])\n",
- " h = len(grid)\n",
- " wordlens = set(len(w) for w in words)\n",
- " presences = []\n",
- " for r in range(h):\n",
- " for c in range(w):\n",
- " for d in Direction:\n",
- " for wordlen in wordlens:\n",
- " word = cat(gslice(grid, r, c, wordlen, d))\n",
- " if word in words:\n",
- " presences += [(word, r, c, d)]\n",
- " return set(presences)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "for w in ws:\n",
- " print(w, present(g, w))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def interesting(grid, words, words_limit=40, direction_slack=1):\n",
- " dirs = set(present(grid, w)[3] for w in words)\n",
- " return len(words) > words_limit and len(dirs) + direction_slack >= len(delta)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "interesting(g, ws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def interesting_grid(width=20, height=20, words_limit=40, max_words=80, direction_slack=1):\n",
- " boring = True\n",
- " while boring:\n",
- " grid = empty_grid(width, height)\n",
- " grid, words = fill_grid(grid, ws_words, max_words)\n",
- " boring = not interesting(grid, words, words_limit=words_limit, direction_slack=direction_slack)\n",
- " return grid, words"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "g, ws = interesting_grid()\n",
- "print(show_grid(g))\n",
- "print(len(ws), 'words added; ', len(set(present(g, w)[3] for w in ws)), 'directions')\n",
- "print(wcat(ws))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def datafile(name, sep='\\t'):\n",
- " \"\"\"Read key,value pairs from file.\n",
- " \"\"\"\n",
- " with open(name) as f:\n",
- " for line in f:\n",
- " splits = line.split(sep)\n",
- " yield [splits[0], int(splits[1])]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def normalise(frequencies):\n",
- " \"\"\"Scale a set of frequencies so they sum to one\n",
- " \n",
- " >>> sorted(normalise({1: 1, 2: 0}).items())\n",
- " [(1, 1.0), (2, 0.0)]\n",
- " >>> sorted(normalise({1: 1, 2: 1}).items())\n",
- " [(1, 0.5), (2, 0.5)]\n",
- " >>> sorted(normalise({1: 1, 2: 1, 3: 1}).items()) # doctest: +ELLIPSIS\n",
- " [(1, 0.333...), (2, 0.333...), (3, 0.333...)]\n",
- " >>> sorted(normalise({1: 1, 2: 2, 3: 1}).items())\n",
- " [(1, 0.25), (2, 0.5), (3, 0.25)]\n",
- " \"\"\"\n",
- " length = sum(f for f in frequencies.values())\n",
- " return collections.defaultdict(int, ((k, v / length) \n",
- " for (k, v) in frequencies.items()))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "english_counts = collections.Counter(dict(datafile('count_1l.txt')))\n",
- "normalised_english_counts = normalise(english_counts)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "wordsearch_counts = collections.Counter(cat(ws_words))\n",
- "normalised_wordsearch_counts = normalise(wordsearch_counts)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "normalised_wordsearch_counts = normalise(collections.Counter(normalised_wordsearch_counts) + collections.Counter({l: 0.05 for l in string.ascii_lowercase}))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def weighted_choice(d):\n",
- " \"\"\"Generate random item from a dictionary of item counts\n",
- " \"\"\"\n",
- " target = random.uniform(0, sum(d.values()))\n",
- " cuml = 0.0\n",
- " for (l, p) in d.items():\n",
- " cuml += p\n",
- " if cuml > target:\n",
- " return l\n",
- " return None\n",
- "\n",
- "def random_english_letter():\n",
- " \"\"\"Generate a random letter based on English letter counts\n",
- " \"\"\"\n",
- " return weighted_choice(normalised_english_counts)\n",
- "\n",
- "def random_wordsearch_letter():\n",
- " \"\"\"Generate a random letter based on wordsearch letter counts\n",
- " \"\"\"\n",
- " return weighted_choice(normalised_wordsearch_counts)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cat(sorted(random_english_letter() for i in range(100)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cat(sorted(random_wordsearch_letter() for i in range(100)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "random_wordsearch_letter()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def pad_grid(g0):\n",
- " grid = copy.deepcopy(g0)\n",
- " w = len(grid[0])\n",
- " h = len(grid)\n",
- " for r in range(h):\n",
- " for c in range(w):\n",
- " if grid[r][c] == '.':\n",
- " grid[r][c] = random_wordsearch_letter()\n",
- " return grid"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "padded = pad_grid(g)\n",
- "print(show_grid(padded))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(show_grid(g))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "for w in ws:\n",
- " print(w, present(padded, w))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def decoys(grid, words, all_words, limit=100):\n",
- " decoy_words = []\n",
- " dlen_limit = max(len(w) for w in words)\n",
- " while len(words) + len(decoy_words) < limit:\n",
- " d = random.choice(all_words)\n",
- " if d not in words and len(d) >= 4 and len(d) <= dlen_limit and not present(grid, d)[0]:\n",
- " decoy_words += [d]\n",
- " return decoy_words"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def decoys(grid, words, all_words, limit=100, min_word_len=4):\n",
- " decoy_words = []\n",
- "# dlen_limit = max(len(w) for w in words)\n",
- " while len(words) + len(decoy_words) < limit:\n",
- " d = random.choice(all_words)\n",
- " if d not in words and len(d) >= min_word_len and not present(grid, d)[0]:\n",
- " decoy_words += [d]\n",
- " return decoy_words"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = decoys(padded, ws, ws_words)\n",
- "ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "for w in ws + ds:\n",
- " print(w, present(padded, w))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "g, ws = interesting_grid()\n",
- "p = pad_grid(g)\n",
- "ds = decoys(p, ws, ws_words)\n",
- "print(show_grid(g))\n",
- "print(show_grid(p))\n",
- "print(len(ws), 'words added; ', len(set(present(g, w)[3] for w in ws)), 'directions')\n",
- "print('Present:', wcat(sorted(ws)))\n",
- "print('Decoys:', wcat(sorted(ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "g, ws = interesting_grid(width=10, height=10, words_limit=5, direction_slack=6)\n",
- "p = pad_grid(g)\n",
- "ds = decoys(p, ws, ws_words)\n",
- "print(show_grid(g))\n",
- "print()\n",
- "print(show_grid(p))\n",
- "print(len(ws), 'words added; ', len(set(present(g, w)[3] for w in ws)), 'directions')\n",
- "print('Present:', wcat(sorted(ws)))\n",
- "print('Decoys:', wcat(sorted(ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_original = ['regards', 'perfect', 'instants', 'refined', 'coddle', 'fickler', 'gambol', 'misprint', 'tapes', 'impugns', 'moonshot', 'chump', 'brick', 'siren', 'faddish', 'winced', 'kielbasy', 'market', 'puckered', 'trains', 'welts', 'cackles', 'foaming', 'proceed', 'gliding', 'guts', 'uric', 'oaks', 'molested', 'curled', 'boor', 'solaria', 'gristle', 'bombing', 'loamier', 'ensuing', 'cunt', 'sunder', 'revel', 'coaster', 'grunts', 'mucking', 'typesets', 'carnal', 'whimsy', 'scoff', 'coccyxes', 'meanly', 'sprain', 'minuend', 'ringlet', 'fest', 'winced', 'shinier', 'dicier', 'thirds', 'olives', 'garoting', 'pastrami', 'tranquil', 'tamped', 'sunup', 'crumbled', 'throw', 'ridges', 'chaplets', 'curlier', 'lugs', 'collies', 'adapting', 'demeanor', 'deepen', 'lanyard', 'tiller', 'transfix', 'wariness', 'times', 'mitts', 'dowses', 'creels', 'curds', 'quashed', 'orgasmic', 'ibex', 'retraces', 'casino']\n",
- "ds = random.sample(ds_original, 15)\n",
- "ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "ds_original"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "print('grid = ', g)\n",
- "print('padded_grid = ', p)\n",
- "print('present_words = ', ws)\n",
- "print('decoy_words = ', ds)\n",
- "print('Directions: ', [(w, '`' + str(present(g, w)) + '`') for w in ws])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# with open('example-wordsearch.txt', 'w') as f:\n",
- "# f.write('10x10\\n')\n",
- "# f.write(show_grid(p))\n",
- "# f.write('\\n')\n",
- "# f.write(lcat(sorted(ws + ds)))\n",
- "# with open('exmaple-wordsearch-solution.txt', 'w') as f:\n",
- "# f.write('10x10\\n')\n",
- "# f.write(show_grid(g))\n",
- "# f.write('\\n')\n",
- "# f.write(lcat(sorted(ws)) + '\\n\\n')\n",
- "# f.write(lcat(sorted(ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "cts = collections.Counter()\n",
- "for w in ws:\n",
- " _, r, c, d = present(g, w)\n",
- " inds = indices(g, r, c, len(w), d)\n",
- " for i in inds:\n",
- " cts[i] += 1\n",
- " print(w, r, c, len(w), d, inds)\n",
- "[i for i in cts if cts[i] > 1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true,
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# for i in range(100):\n",
- "# print(i)\n",
- "# g, ws = interesting_grid()\n",
- "# p = pad_grid(g)\n",
- "# ds = decoys(p, ws, ws_words)\n",
- "# with open('wordsearch{:02}.txt'.format(i), 'w') as f:\n",
- "# f.write('20x20\\n')\n",
- "# f.write(show_grid(p))\n",
- "# f.write('\\n')\n",
- "# f.write(lcat(sorted(ws + ds)))\n",
- "# with open('wordsearch-solution{:02}.txt'.format(i), 'w') as f:\n",
- "# f.write('20x20\\n')\n",
- "# f.write(show_grid(g))\n",
- "# f.write('\\n')\n",
- "# f.write(lcat(sorted(ws)) + '\\n\\n')\n",
- "# f.write(lcat(sorted(ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "g, ws = interesting_grid(width=100, height=100, words_limit=1, max_words=2000, direction_slack=1)\n",
- "p = pad_grid(g)\n",
- "ds = decoys(p, ws, ws_words, limit=2500)\n",
- "# print(show_grid(g))\n",
- "# print()\n",
- "# print(show_grid(p))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(len(ws), 'words added; ', len(set(present(g, w)[3] for w in ws)), 'directions')\n",
- "# print('Present:', wcat(sorted(ws)))\n",
- "# print('Decoys:', wcat(sorted(ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true,
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "with open('huge-wordsearch.txt', 'w') as f:\n",
- " f.write('{}x{}\\n'.format(len(g[0]), len(g)))\n",
- " f.write(show_grid(p))\n",
- " f.write('\\n')\n",
- " f.write(lcat(sorted(ws + ds)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def read_wordsearch(fn):\n",
- " lines = [l.strip() for l in open(fn).readlines()]\n",
- " w, h = [int(s) for s in lines[0].split('x')]\n",
- " grid = lines[1:h+1]\n",
- " words = lines[h+1:]\n",
- " return w, h, grid, words"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def do_wordsearch_tasks(fn, show_anyway=False):\n",
- " width, height, grid, words = read_wordsearch(fn)\n",
- " used_words = [w for w in words if present(grid, w)[0]]\n",
- " unused_words = [w for w in words if not present(grid, w)[0]]\n",
- " lwp = sorted([w for w in words if present(grid, w)[0]], key=len)[-1]\n",
- " lwps = [w for w in used_words if len(w) == len(lwp)]\n",
- " lwa = sorted(unused_words, key=len)[-1]\n",
- " lwas = [w for w in unused_words if len(w) == len(lwa)]\n",
- " g0 = empty_grid(width, height)\n",
- " for w in words:\n",
- " p, r, c, d = present(grid, w)\n",
- " if p:\n",
- " set_grid(g0, r, c, d, w) \n",
- " unused_letters = [l for l, u in zip((c for c in cat(cat(l) for l in grid)), (c for c in cat(cat(l) for l in g0)))\n",
- " if u == '.']\n",
- " unused_letter_count = collections.Counter(unused_letters)\n",
- " makeable_words = []\n",
- " for w in unused_words:\n",
- " unused_word_count = collections.Counter(w)\n",
- " if all(unused_word_count[l] <= unused_letter_count[l] for l in unused_word_count):\n",
- " makeable_words += [w]\n",
- " lwm = sorted(makeable_words, key=len)[-1]\n",
- " lwms = [w for w in makeable_words if len(w) == len(lwm)]\n",
- " if show_anyway or len(set((len(lwp),len(lwa),len(lwm)))) == 3:\n",
- " print('\\n{}'.format(fn))\n",
- " print('{} words present'.format(len(words) - len(unused_words)))\n",
- " print('Longest word present: {}, {} letters ({})'.format(lwp, len(lwp), lwps))\n",
- " print('Longest word absent: {}, {} letters ({})'.format(lwa, len(lwa), lwas))\n",
- " print('{} unused letters'.format(len([c for c in cat(cat(l) for l in g0) if c == '.'])))\n",
- " print('Longest makeable word: {}, {} ({})'.format(lwm, len(lwm), lwms))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "[w for w in ws_words if len(w) == 16]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "# all_lines = []\n",
- "# for fn in sorted(os.listdir()):\n",
- "# if re.match('wordsearch\\d\\d\\.txt', fn):\n",
- "# all_lines += open(fn).readlines()\n",
- "# all_lines += ['\\n'] * 2\n",
- "\n",
- "# open('all-wordsearches.txt', 'w').writelines(all_lines)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 209,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Added 6 ; attempt 19\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "6"
- ]
- },
- "execution_count": 209,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "g = empty_grid(10, 10)\n",
- "g, ws = fill_grid(g, ws_words, 200, min_word_len=4, max_attempts=20)\n",
- "len(ws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 210,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Added 1149 ; attempt 9427\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "IOPub message rate exceeded.\n",
- "The notebook server will temporarily stop sending output\n",
- "to the client in order to avoid crashing it.\n",
- "To change this limit, set the config variable\n",
- "`--NotebookApp.iopub_msg_rate_limit`.\n"
- ]
- }
- ],
- "source": [
- "g = empty_grid(100, 100)\n",
- "g, ws = fill_grid(g, ws_words, 2000, min_word_len=5)\n",
- "len(ws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 216,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1149, 1149, [('scofflaw', 1)])"
- ]
- },
- "execution_count": 216,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pm = present_many(g, ws)\n",
- "pold = [w for w in ws if present(g, w)[0]]\n",
- "len(pm), len(pold), collections.Counter(p[0] for p in pm).most_common(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 219,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "with open('huge-unpadded-wordsearch.txt', 'w') as f:\n",
- " f.write('{}x{}\\n'.format(len(g[0]), len(g)))\n",
- " f.write(show_grid(g))\n",
- " f.write('\\n')\n",
- " f.write(lcat(sorted(ws)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 218,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- "t...rd...oncc.t.srnclennut.cainoaap...od..bfrolics.k....o.rosseforp...brazzesb.laopa.eseifidoc.elgee\n",
- "v...ee..o..ooe.eslsth.ke...drarnwan.l.es..a...sc.liatesrohc.sdellennekgi..s.u.af.albtkn...clawsrigrn\n",
- "o..bdbyc....krsindekyasa..etredsdodi.l.re.l..tgo.nobscuredhh..securere..al.c.nsfn.iace.i.coupledoanl\n",
- "t.mei.si..maximaltedynn.sdstss.doem.iebe.dm.n.nng.gnippohca....m....dt..eso.dnes.a.operpetuatingbo.i\n",
- "eegggclassroomss...ral.t.pssp.ewbidtlelpd.yedoic.g.....s.dnm..otopaetn.t.lsmov..sfh.s.piling....i..e\n",
- "dahh.n...h..pecnarpeeti.yeeredsrte.ere.e..mdepkrn.np....iucsun..ae...eotisaie..eis.ey.rknittings..br\n",
- "rt.eenickinglfy...l.rdig..ie.neet.cev.ha.eo.naceo.wispsenme.eie.gr..lmncertrntdxs.bdpsao.tokenismda.\n",
- "...dla.g..i.oar.cc.e.onchnn.ktihttfi.s.lgg.lwqitcp.en.rpttmt.ldnn.ti.i.bkii.hgeamyseprzk.r..mld.e.l.\n",
- ".vwkibp.a.n.wlo.ifg..g.ugtw.afglrtr.i..nwpa.auleae.crorpoaaek.ueo.ssoai.ds.a.ryognhkaaec..euye.ce.ad\n",
- "..ai.nmseph.msvsta..st.qh.ow.iiirden..iornp.pes.ec.eboigarxcrld.tslp.r.ahsw.sitsra.ortde..svt.a.ndne\n",
- "b.rl.inacae.aeatel.yortuetl.l.fn.irr.rooe..e.s..dcv.vuytireypii.i.a.c.rlessdnoluvy.pcsggsidaim.aoect\n",
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- "tgibps.idceott.nsi.ittnrntnalaesfsnnseysword.c..lrdedoygst.lrnggisi.xaoinoscvrcharsa.cngmesiamn.kkdu\n",
- "tnkye.e.uusel..cat.lh.aaa.legabnaei.r...kinderou.y.pler.lksleeg.e.eevtteleaeezzaipddpis.nmntfgfo.csp\n",
- "eietd..mpqankffh.yaie.gnt.....use.gallivants..ic.l.uydpuner.uyttdysinadlcrove....ee.tslu.geuijnrcipt\n",
- "rbred..peri.usoe.srbayetimikespl.debrutrepnu.n.rkl.mb.npsera.fosm.dimieiscovsrd.vtyr.om...l..fioisiu\n",
- "nbisl.e.or.a..rs.eoastrireitliugselttit.....g..e.u.pr..iadmsbnemil.gcrt..siepeclrrapteswovasidfvcgro\n",
- "uupcer.o..y.d.gtstulte.ne...rtsicitnamoraincoatk.bfearedkhayyeutycacyc.hwlgrc.oeematr...vended.ei.i.\n",
- "trxrm.msinimefe.tani.i.ih..i.....edutingamsnoynac..decay..ceagd.ara.a.celnie.vsndwo...pinup...l.dntd\n",
- "sgeosdrenblurbscalda.o.nn.n..cimonoxatkinetic..e....narks.k.rl..pl.r.teaihe.eer.pwryly...tubes.e..g.\n",
- "..saesuohdraug..ie.v.m.gig.ycnangiop.tawdrier..w....scallopedsesiwp.atstcl.ddo.astelfael..bumblers..\n",
- ".t.krandomises..rr.a..gripestsivitcellocsbawsshapeliest..gnitontoofhylluftsaobwsocilac..located..s..\n"
- ]
- }
- ],
- "source": [
- "print(show_grid(g))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 221,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Attempt 249 ; most common is [('scofflaw', 1)]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "(True, [('scofflaw', 1)])"
- ]
- },
- "execution_count": 221,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "padded = False\n",
- "padding_attempts = 0\n",
- "while not padded and padding_attempts < 10**6:\n",
- " clear_output(wait=True)\n",
- " padding_attempts += 1\n",
- " pg = pad_grid(g)\n",
- " pm = present_many(pg, ws)\n",
- " print('Attempt', padding_attempts, '; most common is', collections.Counter(p[0] for p in pm).most_common(1))\n",
- " if collections.Counter(p[0] for p in pm).most_common(1)[0][1] == 1:\n",
- " padded = True\n",
- " \n",
- "padded, collections.Counter(p[0] for p in pm).most_common(1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 222,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "249"
- ]
- },
- "execution_count": 222,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "padding_attempts"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 230,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "851"
- ]
- },
- "execution_count": 230,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dws = decoys(pg, ws, ws_words, limit=2000, min_word_len=5)\n",
- "len(dws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 231,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "print(show_grid(pg))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 232,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2000"
- ]
- },
- "execution_count": 232,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(dws) + len(ws)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 233,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "with open('huge-wordsearch.txt', 'w') as f:\n",
- " f.write('{}x{}\\n'.format(len(pg[0]), len(pg)))\n",
- " f.write(show_grid(pg))\n",
- " f.write('\\n')\n",
- " f.write(lcat(sorted(ws + dws)))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 234,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1149, 1149)"
- ]
- },
- "execution_count": 234,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "pm = present_many(g, ws)\n",
- "pold = [w for w in ws if present(g, w)[0]]\n",
- "len(pm), len(pold)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 235,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1149, 1149)"
- ]
- },
- "execution_count": 235,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ppm = present_many(pg, ws)\n",
- "ppold = [w for w in ws if present(pg, w)[0]]\n",
- "len(ppm), len(ppold)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 236,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "print(show_grid(g))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
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- "file_extension": ".py",
- "mimetype": "text/x-python",
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- "pygments_lexer": "ipython3",
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-}
projects / ou-summer-of-code-2017.git / blobdiff