--- /dev/null
+{
+ "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
+}
projects / summerofcode2018soln.git / commitdiff