{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import random\n", "import collections" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def pancake_sort(items, debug=False):\n", " if len(items) <= 1:\n", " if debug: print('{} -> {}: {}'.format(items, items, []))\n", " return items, []\n", " elif len(items) == 2:\n", " if items[0] < items[1]:\n", " if debug: print('{} -> {}: {}'.format(items, items, []))\n", " return items, []\n", " else:\n", " if debug: print('{} -> {}: {}'.format(items, list(reversed(items)), [2]))\n", " return list(reversed(items)), [2]\n", " else:\n", " largest = max(items)\n", " largest_index = items.index(largest)\n", " flips = []\n", " if largest_index == len(items) - 1:\n", " items1 = items\n", " elif largest_index == 0:\n", " items1 = list(reversed(items))\n", " flips = [len(items)]\n", " else: # largest_index > 0\n", " items1 = list(reversed(list(reversed(items[:largest_index+1])) + items[largest_index+1:]))\n", " flips = [largest_index + 1, len(items)]\n", " if debug: print('{} -> {}: {}'.format(items, items1, flips))\n", " sorted_items, sorting_flips = pancake_sort(items1[:-1], debug=debug)\n", " return sorted_items + items1[-1:], flips + sorting_flips" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def enflip(items, flips, burnt=False, debug=False):\n", " if debug: i0 = items\n", " for flip in flips:\n", " if burnt:\n", " items = [-i for i in reversed(items[:flip])] + items[flip:]\n", " else:\n", " items = [i for i in reversed(items[:flip])] + items[flip:]\n", " if debug: print('{} -{}-> {}'.format(i0, flip, items))\n", " if debug: i0 = items\n", " return items\n", "\n", "def unflip(items, flips, burnt=False, debug=False):\n", " return enflip(items, reversed(flips), burnt=burnt, debug=debug)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def pancake_adjacent(higher, lower, sorted_items):\n", " if sorted_items.index(higher) == sorted_items.index(lower) - 1:\n", " return True\n", " else:\n", " return False" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def pancake_chunks(items):\n", " atoms = [[i] for i in items]\n", " sorted_items = list(sorted(items))\n", " return coalesce(atoms)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def coalesce(chunks):\n", " items = sorted(merge_chunks(chunks), key=abs)\n", " i = 0\n", " while i < (len(chunks) - 1):\n", " last_index = items.index(chunks[i][-1])\n", " next_index = items.index(chunks[i+1][0])\n", " if chunks[i][-1] > 0 and chunks[i+1][0] > 0 and last_index + 1 == next_index:\n", " chunks = chunks[:i] + [chunks[i] + chunks[i+1]] + chunks[i+2:]\n", " elif chunks[i][-1] < 0 and chunks[i+1][0] < 0 and last_index - 1 == next_index:\n", " chunks = chunks[:i] + [chunks[i] + chunks[i+1]] + chunks[i+2:]\n", " else:\n", " i += 1\n", " return chunks" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def chunk_bases(chunks):\n", " return [c[-1] if c[-1] > 0 else c[0] for c in chunks]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def merge_chunks(chunks):\n", " return [i for c in chunks for i in c]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def chunk_count_to_item_count(chunks, cpos):\n", "# print(chunks, cpos, chunks[:cpos])\n", " return len(merge_chunks(chunks[:cpos]))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def chunk_index(chunks, item):\n", " \"\"\"Return the index of the first chunk containing item\"\"\"\n", " return [i for i, c in enumerate(chunks) if item in c][0]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def enflip_chunks(chunks, flips, debug=False):\n", " if debug: c0 = chunks\n", " for flip in flips:\n", " chunks = [[-i for i in reversed(c)] for c in reversed(chunks[:flip])] + chunks[flip:]\n", " if debug: print('{} ={}=> {}'.format(c0, flip, chunks))\n", " if debug: c0 = chunks\n", " return chunks\n", "\n", "def unflip_chunks(chunks, flips, debug=False):\n", " return enflip(chunks, reversed(flips), debug=debug)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def burnt_pancake_step_case1(chunks, all_chunks, items, largest, largest_burntdown, debug=False):\n", " largest_burntdown_index = chunk_index(chunks, largest_burntdown)\n", " if largest_burntdown == largest: # case 1(c): largest pancake is facedown, move to bottom of stack\n", " cflips = [largest_burntdown_index + 1, len(chunks)]\n", " flips = [items.index(largest_burntdown) + 1, len(merge_chunks(chunks))]\n", " done_chunks = enflip_chunks(chunks, cflips, debug=debug)\n", " else:\n", " largest_burntdown_partner = max(i for i in chunk_bases(chunks) if abs(i) > largest_burntdown)\n", " largest_burntdown_partner_index = chunk_index(chunks, largest_burntdown_partner)\n", " if largest_burntdown_partner_index > largest_burntdown_index: # case 1(a): partner is lower than this\n", " chunks1 = enflip_chunks(all_chunks, [largest_burntdown_partner_index + 1], debug=debug)\n", " new_lb_pos = chunk_index(chunks1, -largest_burntdown)\n", " done_chunks = enflip_chunks(chunks1, [new_lb_pos], debug=debug)\n", " flips = [chunk_count_to_item_count(all_chunks, largest_burntdown_partner_index + 1), \n", " chunk_count_to_item_count(chunks1, new_lb_pos)]\n", " else: # case 1(b): partner is higher than this\n", " chunks1 = enflip_chunks(chunks, [largest_burntdown_index + 1], debug=debug)\n", " new_lbi_pos = chunk_index(chunks1, -largest_burntdown_partner)\n", " done_chunks = enflip_chunks(chunks1, [new_lbi_pos], debug=debug)\n", " flips = [chunk_count_to_item_count(chunks, largest_burntdown_index + 1), \n", " chunk_count_to_item_count(chunks1, new_lbi_pos)]\n", " return coalesce(done_chunks), flips" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def burnt_pancake_step_case2(chunks, all_chunks, debug=False):\n", " items = merge_chunks(chunks)\n", " \n", " if items == list(reversed(sorted(items))): # invoke -I special case\n", " if debug: print(\"2: -I\")\n", " n = len(items)\n", " flips = [f for fp in [[n, n-1] for _ in range(n)] for f in fp if f != 0]\n", " done_items = enflip(items, flips, burnt=True, debug=debug)\n", " done_chunks = pancake_chunks(done_items)\n", " elif items == sorted(items): # items are in reverse order, upside down\n", " if debug: print(\"2: rev\")\n", " flips = [len(items)]\n", " done_items = enflip(items, flips, burnt=True, debug=debug)\n", " done_chunks = pancake_chunks(done_items)\n", " else:\n", " candidates = chunk_bases(chunks)\n", " largest_unsorted = min(candidates)\n", " next_largest_unsorted = min(i for i in candidates if i > largest_unsorted)\n", " largest_unsorted_index = chunk_index(chunks, largest_unsorted)\n", " next_largest_unsorted_index = chunk_index(chunks, next_largest_unsorted)\n", "# print(largest_unsorted, next_largest_unsorted, largest_unsorted_index, next_largest_unsorted_index)\n", " while next_largest_unsorted_index > largest_unsorted_index:\n", " largest_unsorted = next_largest_unsorted\n", " largest_unsorted_index = next_largest_unsorted_index\n", " next_largest_unsorted = min(i for i in candidates if i > largest_unsorted)\n", " next_largest_unsorted_index = chunk_index(chunks, next_largest_unsorted)\n", " if debug: print(\"2: general, lu = {}, nlu = {}\".format(largest_unsorted, next_largest_unsorted))\n", " chunks1 = enflip_chunks(chunks, [largest_unsorted_index + 1])\n", " done_chunks = enflip_chunks(chunks1, [next_largest_unsorted_index], debug=debug)\n", "# cflips = [largest_unsorted_index + 1, next_largest_unsorted_index]\n", " flips = [chunk_count_to_item_count(chunks, largest_unsorted_index + 1), \n", " chunk_count_to_item_count(chunks1, next_largest_unsorted_index)]\n", "# done_chunks = enflip_chunks(chunks, cflips, debug=debug)\n", " return coalesce(done_chunks), flips" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def burnt_pancake_step(chunks0, items, debug=False):\n", " chunks = chunks0\n", " largest = max(abs(i) for c in chunks for i in c)\n", " while chunks[-1][-1] >= largest:\n", " chunks = chunks[:-1]\n", " largest = max(abs(i[-1]) for i in chunks)\n", " largest_burntdown = max(merge_chunks(chunks))\n", " if debug: print('<<', chunks, chunks0, items, largest, largest_burntdown)\n", " if largest_burntdown > 0:\n", " return burnt_pancake_step_case1(chunks, chunks0, items, largest, largest_burntdown, debug=debug)\n", " else:\n", " return burnt_pancake_step_case2(chunks, chunks0, debug=debug)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def burnt_pancake_sort(items, fudge_rate=0, debug=False):\n", " flips = []\n", " flip_limit = len(items) * 3\n", " items0 = items\n", " chunks = pancake_chunks(items)\n", " while (any(i for i in items if i < 0) or sorted(items) != items) and len(flips) < flip_limit:\n", " chunks, these_flips = burnt_pancake_step(chunks, items, debug=debug)\n", " if debug: print('Got chunks:', chunks)\n", " items = merge_chunks(chunks)\n", " flips += these_flips\n", " if random.random() < fudge_rate:\n", " if debug: c_old = chunks\n", " its = [abs(i) for i in merge_chunks(chunks)]\n", " eits = sorted(i for i in items0 if i not in its)\n", " chunks = coalesce(pancake_chunks(its + eits))\n", " items = its + eits\n", " if debug: print('!! Fudge: Converting {} to {}'.format(c_old, chunks))\n", " return items, flips" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def equiv_case(base_unsorted, flips, burnt=False, max_value=10000):\n", "# new_sample = random.sample(list(range(1, max_value)), k=len(base_unsorted))\n", " valid = False\n", " while not valid:\n", " new_sample = random.sample(list(range(1, max_value)), k=len(base_unsorted))\n", " valid = len(new_sample) == len(base_unsorted)\n", " sample = sorted(new_sample)\n", " return unflip(sample, flips, burnt=burnt)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def burnt_sorted(pancakes):\n", " return pancakes == sorted(pancakes)\n", "\n", "def unburnt_sorted(pancakes):\n", " simple_pancakes = [abs(p) for p in pancakes]\n", " return simple_pancakes == sorted(simple_pancakes)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def inverted_count(pancakes):\n", " return sum(1 for p in pancakes if p < 0)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def cache_flips(start, flips, burnt=False):\n", " positions = [{'pos': start}]\n", " stack = start\n", " for f in flips:\n", " stack = enflip(stack, [f], burnt=burnt)\n", " positions += [{'pos': stack, 'move': f}]\n", " return positions" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def show_cached_flips(cache):\n", " rows = len(cache[0]['pos'])\n", " middle_row = (rows) // 2\n", " for r in range(rows):\n", " for c in cache:\n", " if r == middle_row and 'move' in c:\n", " print(' -{}-> '.format(c['move']), end='')\n", " elif 'move' in c:\n", " print(' ', end='')\n", " if c['pos'][r] > 0:\n", " print('{:2d} '.format(c['pos'][r]), end='')\n", " else:\n", " print('{:2d}*'.format(abs(c['pos'][r])), end='')\n", " print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Approach to developing test cases:\n", "\n", "1. Find a random pancake stack.\n", "2. Find the burnt pancake sort of that stack: `burnt_flips`\n", "3. Find an equivalent case for those flips: `pancakes`\n", "4. Find a bunch of fudged burnt sorts of the `pancakes`: `fudged`\n", "5. Find a bunch of random fudged pancake sorts: `padding`\n", "\n", "To assemble the test case, join together:\n", "* the `burnt_flips`\n", "* some `fudged`\n", "* enough `padding` to make a round number." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ln = 50\n", "n_equivs = 100\n", "fudge_rate = 0.3\n", "\n", "start = [i for i in random.sample(list(range(1, ln+1)), k=ln)]\n", "test_flips = {}\n", "_, test_flips['burnt_flips'] = burnt_pancake_sort(start)\n", "test_flips['pancakes'] = equiv_case(start, test_flips['burnt_flips'], burnt=True)\n", "test_flips['fudged'] = [burnt_pancake_sort(start, fudge_rate=fudge_rate)[1] for _ in range(n_equivs)]\n", "test_flips['padding'] = [burnt_pancake_sort(random.sample(list(range(1, ln+1)), k=ln), fudge_rate=fudge_rate)[1] for _ in range(n_equivs)]\n", "len(test_flips)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "99" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data = [test_flips['burnt_flips']]\n", "test_data.extend(random.sample(test_flips['fudged'], k=random.randint(50, 70)))\n", "test_data.extend(random.sample(test_flips['padding'], k=(99-len(test_data))))\n", "len(test_data)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "random.shuffle(test_data)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "55" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(1 for f in test_data\n", " if unburnt_sorted(\n", " enflip(test_flips['pancakes'], f, burnt=False)))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(1 for f in test_data\n", " if burnt_sorted(\n", " enflip(test_flips['pancakes'], f, burnt=True)))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[61]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i+1 for i, f in enumerate(test_data)\n", " if burnt_sorted(\n", " enflip(test_flips['pancakes'], f, burnt=True))]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# random.shuffle(test_data)\n", "# with open('07-flips.txt', 'w') as tdf:\n", "# tdf.write('burgers: {}\\n'.format(' '.join(str(i) for i in test_flips['pancakes'] if i > 0)))\n", "# for i, c in enumerate(test_data):\n", "# tdf.write('{:02}: {}\\n'.format(i+1, ' '.join(str(i) for i in c if i > 0)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example cases" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'burnt_flips': [5, 6, 2, 1, 2, 5, 3, 2, 3, 2, 3, 2],\n", " 'pancakes': [8, 7, 5, 4, 11, 9],\n", " 'fudged': [[5, 6, 1, 5, 4, 3, 4, 3, 4, 3, 4, 3],\n", " [5, 6, 1, 5, 4, 3, 4, 3, 4, 3, 4, 3],\n", " [5, 6, 2, 1, 2, 5],\n", " [5, 6, 1, 5],\n", " [5, 6, 1, 5, 4, 3, 4, 3, 4, 3, 4, 3]],\n", " 'padding': [[1, 6, 5],\n", " [5, 6, 1, 4, 1, 2],\n", " [1, 6, 5, 0, 4, 5, 2, 4, 2, 1, 2, 1],\n", " [2, 6, 2, 5, 2, 3, 1, 2],\n", " [2, 6, 4, 0, 1, 4, 1, 3, 1, 2]]}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ln = 6\n", "n_equivs = 5\n", "fudge_rate = 0.7\n", "\n", "start = [i for i in random.sample(list(range(1, ln+1)), k=ln)]\n", "test_flips = {}\n", "_, test_flips['burnt_flips'] = burnt_pancake_sort(start)\n", "test_flips['pancakes'] = equiv_case(start, test_flips['burnt_flips'], burnt=True, max_value=ln*2)\n", "test_flips['fudged'] = [burnt_pancake_sort(start, fudge_rate=fudge_rate)[1] for _ in range(n_equivs)]\n", "test_flips['padding'] = [burnt_pancake_sort(random.sample(list(range(1, ln+1)), k=ln), fudge_rate=fudge_rate)[1] for _ in range(n_equivs)]\n", "test_flips" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# test_flips = {'burnt_flips': [4, 5, 2, 1, 2, 3, 1],\n", "# 'fudged': [[4, 5, 2, 1, 2, 3],\n", "# [4, 5, 1, 3, 2, 1, 2, 1],\n", "# [4, 5, 1, 3, 2, 1, 2, 1],\n", "# [4, 5, 1, 3],\n", "# [4, 5, 2, 1, 2, 3, 1]],\n", "# 'padding': [[2, 5, 1, 2],\n", "# [2, 5, 2, 1, 3],\n", "# [1, 3, 1, 2, 1],\n", "# [2, 5, 4, 1, 2, 3],\n", "# [4, 5, 3, 4, 3, 1]],\n", "# 'pancakes': [4, 2, 6, 7, 5]}" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "test_flips = {'burnt_flips': [3, 5, 3, 2, 3, 2],\n", " 'pancakes': [9, 18, 22, 15, 13],\n", " 'fudged': [[3, 5, 2, 3],\n", " [3, 5, 2, 3],\n", " [3, 5, 2, 3],\n", " [3, 5, 2, 3],\n", " [3, 5, 2, 3]],\n", " 'padding': [[4, 5, 3, 4, 2, 3],\n", " [3],\n", " [3, 5, 4, 2, 3, 4, 2],\n", " [2, 5, 2, 3, 2],\n", " [3, 5, 3]]}" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 5, 3, 2, 3, 2]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bf = [f for f in test_flips['burnt_flips'] if f > 0]\n", "bf" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[9, 13, 15, 18, 22]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enflip(test_flips['pancakes'], test_flips['burnt_flips'], burnt=True)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-9, -13, -15, 18, 22]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enflip(test_flips['pancakes'], test_flips['fudged'][0], burnt=True)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[9, 13, 15, 18, 22]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "enflip(test_flips['pancakes'], bf, burnt=True)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22 13 9 15 13 9 \n", "18 18 15 15 9 9 13 \n", "22 -3-> 9 -5-> 9 -3-> 13 -2-> 13 -3-> 15 -2-> 15 \n", "15 15 18 18 18 18 18 \n", "13 13 22 22 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], bf))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 \n", "18 \n", "22 \n", "15 \n", "13 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], bf)[:1])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22 \n", "18 18 \n", "22 -3-> 9 \n", "15 15 \n", "13 13 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], bf)[:2])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22* 13* 9* 15* 13* 9 \n", "18 18* 15* 15 9 9* 13 \n", "22 -3-> 9* -5-> 9 -3-> 13 -2-> 13 -3-> 15 -2-> 15 \n", "15 15 18 18 18 18 18 \n", "13 13 22 22 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], bf, burnt=True))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 \n", "18 \n", "22 \n", "15 \n", "13 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][0], burnt=False)[:1])" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22 \n", "18 18 \n", "22 -3-> 9 \n", "15 15 \n", "13 13 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][0], burnt=False)[:2])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22 13 15 9 \n", "18 18 15 13 13 \n", "22 -3-> 9 -5-> 9 -2-> 9 -3-> 15 \n", "15 15 18 18 18 \n", "13 13 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][0], burnt=False))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22* 13* 15 9*\n", "18 18* 15* 13 13*\n", "22 -3-> 9* -5-> 9 -2-> 9 -3-> 15*\n", "15 15 18 18 18 \n", "13 13 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][0], burnt=True))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22* 13* 15 9*\n", "18 18* 15* 13 13*\n", "22 -3-> 9* -5-> 9 -2-> 9 -3-> 15*\n", "15 15 18 18 18 \n", "13 13 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][1], burnt=True))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22 13 15 9 \n", "18 18 15 13 13 \n", "22 -3-> 9 -5-> 9 -2-> 9 -3-> 15 \n", "15 15 18 18 18 \n", "13 13 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][3], burnt=False))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 22* 13* 15 9*\n", "18 18* 15* 13 13*\n", "22 -3-> 9* -5-> 9 -2-> 9 -3-> 15*\n", "15 15 18 18 18 \n", "13 13 22 22 22 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['fudged'][3], burnt=True))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 15 13 18 22 13 9 \n", "18 22 9 9 13 22 22 \n", "22 -4-> 18 -5-> 18 -3-> 13 -4-> 9 -2-> 9 -3-> 13 \n", "15 9 22 22 18 18 18 \n", "13 13 15 15 15 15 15 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['padding'][0]))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 9 18 13 15 22 13 \n", "18 9 15 13 13 22 \n", "22 -2-> 22 -5-> 22 -2-> 22 -3-> 15 -2-> 15 \n", "15 15 9 9 9 9 \n", "13 13 18 18 18 18 \n" ] } ], "source": [ "show_cached_flips(cache_flips(test_flips['pancakes'], test_flips['padding'][3]))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_data = [test_flips['burnt_flips']]\n", "example_data.extend(random.sample(test_flips['fudged'], k=4))\n", "example_data.extend(random.sample(test_flips['padding'], k=5))\n", "len(example_data)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "random.shuffle(example_data)\n", "with open('07-example.txt', 'w') as tdf:\n", " tdf.write('burgers: {}\\n'.format(' '.join(str(i) for i in test_flips['pancakes'] if i > 0)))\n", " for i, c in enumerate(example_data):\n", " tdf.write('{:02}: {}\\n'.format(i+1, ' '.join(str(i) for i in c if i > 0)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }