{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "As every year since 2015, I'm signed up to participate in the qualifications round of [Google Hashcode](https://hashcode.withgoogle.com), a programming competition for students and professionals from Europe, the Middle-East and Africa. While trying to prepare for the competition, I tackled the suggested training problem that aims at cutting pizza slices out of a large pizza. \n", "\n", "In this post, I'll try to outline a solution to the problem based on one of the input files, the `medium.in` input. Along the way, I plan to report on the following topics:\n", "\n", "- visualizing your data\n", "- formulating the problem as a greedy optimization task\n", "- knowing your data structures \n", "- profiling your code\n", "- using Pythran to optimize your code execution time\n", "\n", "\n", "Let's get started." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing your data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The large pizza we will use as input consists of Tomatoes as well as Mushrooms, which is encode in a grid like the following: \n", "\n", "```\n", "TTTTT\n", "TMMMT\n", "TTTTT\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's load the data for our example grid:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def read_input_pizza(filename):\n", " \"\"\"Reads the input of a Pizza problem.\n", "\n", " returns:\n", "\n", " R: number of Rows of pizza grid\n", " C: number of Cols of pizza grid\n", " L: Lowest number of each ingredients per slice\n", " H: Highest number of cells per slice\n", " pizza: the pizza grid (1 == tomato, 0 == mushroom)\n", " \"\"\"\n", " lines = open(filename).readlines()\n", " R, C, L, H = [int(val) for val in lines[0].split()]\n", " pizza = np.array([list(map(lambda item: 1 if item == 'T' else 0, row.strip())) for row in lines[1:]])\n", " return R, C, L, H, pizza" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "R, C, L, H, pizza = read_input_pizza('files/medium.in')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also plot the pizza:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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J83JVYVAVmdnGqINylL6qajP+WaJhYxVKUrL2C60xfhWaUbp73spuFQNVTDD9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\nZH6uYilLhp1kmvFkyG8NmFD8mW4TbdfnGL/XzC42s+eZ2Yl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.imshow(pizza)\n", "ax.set_title(f'medium.in shape is {pizza.shape}, max. score {pizza.size}\\nmin. ingredients is {L}, max. pizza slice is {H}');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can read in the above plot, the pizza has a 200 by 250 shape, will need slices that have each at least 4 tomatoes and 4 mushrooms on it and a maximum size of 12 cells. Also, as far as I can tell from that plot, the pizza is made of evenly distributed ingredients.\n", "\n", "Let's now try to come up with an algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A greedy optimization approach " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While brainstorming this problem with a friend, we came up with a greedy approach for solving the problem. It can be described by the following Python pseudo-code:\n", "\n", "```python\n", "slices = []\n", "\n", "while True:\n", " # randomly choose an available location for the start of the slice\n", " location = get_random_available_location(pizza)\n", "\n", " # select a random shape and try to fit it at the selected location\n", " slice_shape = get_random_shape(shapes)\n", " if satisfy_constraints(location, slice_shape, pizza):\n", " slices.append(slice(location, slice_shape))\n", " \n", " # since we sliced the pizza, we need to update the available cells \n", " update_pizza(pizza, this_slice) \n", " \n", " # should we exit while loop?\n", " if no_more_possible_slices() or iteration_limit_reached():\n", " break\n", " \n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The way the problem is described above has two properties:\n", "\n", "- it is greedy (if the slice fits, we put it there)\n", "- it is an optimization problem (since we loop until there's nothing left to do)\n", "\n", "I believe this is generally a good way to solve the problems that come up at Hashcode competitions, thinking about past editions..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A first version " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With that being said, we can transform the pseudo-code into real code. Below is my first version.\n", "\n", "Note that:\n", "\n", "- the possible shapes are hard-coded\n", "- we only try one of the possible shapes at the location and move on if it doesn't work" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import tqdm\n", "from random import randint\n", "\n", "def get_random_available_location(slice_mask):\n", " \"\"\"Returns a randomly selected empty location on the slice mask.\"\"\"\n", " nonzero_elements = np.nonzero(1 - slice_mask)\n", " n = len(nonzero_elements[0])\n", " index = randint(0, n - 1)\n", " return nonzero_elements[0][index], nonzero_elements[1][index]\n", "\n", "def satisfy_constraints(location, shape, slice_mask, pizza, L, H):\n", " \"\"\"Validates whether the given location and slice shape can be applied to the pizza.\"\"\"\n", " r, c = location\n", " dr, dc = shape\n", " if slice_mask[r:r+dr, c:c+dc].size == dr * dc:\n", " if np.all(slice_mask[r:r+dr, c:c+dc] == 0):\n", " if dr * dc <= H:\n", " tomatoes = np.sum(pizza[r:r+dr, c:c+dc])\n", " mushrooms = dr * dc - tomatoes\n", " if tomatoes >= L and mushrooms >= L:\n", " return True\n", " return False\n", "\n", "def cut_slice(location, shape, current_slices, slice_mask):\n", " \"\"\"Adds a given slice defined by location and shape to existing slices and\n", " updates the slice mask.\"\"\"\n", " r, c = location\n", " dr, dc = shape\n", " slice_mask[r:r + dr, c:c + dc] = 1\n", " current_slices.append((r, c, dr, dc))\n", "\n", "def score(pizza_slices):\n", " \"\"\"Computes score of given pizza_slices list.\"\"\"\n", " s = 0\n", " for pizza_slice in pizza_slices:\n", " s += pizza_slice[2] * pizza_slice[3]\n", " return s\n", "\n", "def write_output_pizza(filename, pizza_slices):\n", " \"\"\"Writes an output file with the required format.\"\"\"\n", " with open(filename, 'w') as f:\n", " f.write(f\"{len(pizza_slices)}\\n\")\n", " for slice in pizza_slices:\n", " r, c, dr, dc = slice\n", " f.write(f\"{r} {c} {r+dr-1} {c+dc-1}\\n\")\n", "\n", "def greedy1(fname):\n", " \"\"\"Greedy approach to the pizza slice problem.\n", " We try one random shape at one random point each turn.\"\"\"\n", " R, C, L, H, pizza = read_input_pizza(fname)\n", " possible_shapes = [(4, 2), (2, 4), (3, 3), (5, 2), (2, 5), (11, 1), (1,11), (2, 6), (6, 2), (3, 4), (4, 3)]\n", " slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " pizza_slices = [] # stores the slices we made\n", " for _ in tqdm.tqdm(range(20000)):\n", " location = get_random_available_location(slice_mask)\n", " shape = possible_shapes[randint(0, len(possible_shapes) - 1)]\n", " if satisfy_constraints(location, shape, slice_mask, pizza, L, H):\n", " cut_slice(location, shape, pizza_slices, slice_mask)\n", " print('\\n')\n", " print(f\"score: {score(pizza_slices)}\")\n", " write_output_pizza(fname.split('.')[0]+'.out', pizza_slices)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 20000/20000 [00:19<00:00, 1026.98it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "score: 28923\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "greedy1('files/medium.in')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the output from the algorithm." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, ax " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def show_output(fname):\n", " \"\"\"Reads an output file, assuming it's valid and plots the result.\"\"\"\n", " input_fname = fname.split('.')[0] + '.in'\n", " R, C, L, H, pizza = read_input_pizza(input_fname)\n", " lines = open(fname).readlines()\n", " N = int(lines[0].strip())\n", " slice_mask = np.zeros_like(pizza)\n", " pizza_slices = []\n", " for i in range(1, N+1):\n", " r, c, dr, dc = [int(val) for val in lines[i].strip().split()]\n", " dr += 1 - r\n", " dc += 1 - c \n", " slice_mask[r:r + dr, c:c + dc] = i\n", " pizza_slices.append([r, c, dr, dc])\n", " \n", " fig, axes = plt.subplots(figsize=(9, 4), ncols=2, sharex=True, sharey=True)\n", " axes[0].imshow(slice_mask)\n", " axes[0].set_title(f'coloring by slice number (1 to {slice_mask.max()})')\n", "\n", " axes[1].imshow((slice_mask > 0).astype(np.int))\n", " axes[1].set_title('coloring by empty (green) / occupied (yellow)')\n", "\n", " plt.suptitle(f'solution score: {score(pizza_slices)}')\n", " plt.tight_layout(rect=[0, 0, 1, .95])\n", " \n", " axes[1].axis((-.5, C-.5, R-.5, -.5))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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SXKHbdxzHMyWnhLaDZmyFFYDw6UavNkOVi4vjdWKf2icfsx9yms06\nD+eT+7bPilLr6lzgFM5S6RCGOv3KZo+y++r8d97P1we1d5aAEkn+n/8rT4NhpW8oxhRxbjbPTLN9\noorPt52/F93kr5GZ8y+3GXAHL2F1hO8uAICSJS0cy53O/Cl4B0OWDuKPUb0yvuyoiDDCqfPNl0yL\nFctXYFTZRPy9KDhhsE6ASclTtmao+NKqKy01934+9o43RNcIWgEIBZrq8WxTdpfzfhXdMhF3d4z+\noqrxTSsUowz/LHImXx4eENBS3YhTC+Vnhgvrx6+Nn/YnaALV9WnSbRcl1UfY8TJB0PmoAj7iSKeS\nrTQjcQi31S2Pmuo5jysIKC5yQmirbFQHG4/sD+Br3zYFLVvgp6Oc696Z9pGnnU6qgji4rHQ2nu6f\nG2abbQ2498+2k/fD5lb8c4tHJOfuM8JzaMiO4ZzcrxsZxezW5SozjZK3Bqy/jTWvVi3fbTpESV5s\nmUNT5krJbPlgX3UetfqYldKU5e0ponmt/4roUVVqkCBkARpIX4guvsyOJM+AIjqjFPfegk8/jUdg\nq6oJOxcnWD/BRWesuXg+YaTjo6WLyXXJRHBDWL9ugc/0e6KzX7rXLN6oZr1AhPRyPCQkJCQkJCQk\nJFQJOaFpsxjfe+fMeG7Cx5c9oFx/cbv90ckVEFHqEz2aSRbezDVytANoe7d/qoZl1/B2LQ/OXEWE\ngraFGeu7KrCTIgfz4Vr+a8ybFNnGL1daVGotkSKje9s+dHLFhqiUPOusttEzIBcPuWsRT6me0aM7\nSzqDqiCKWTQOdM2QVZHIuDpqB2XiNGtX1XWO4zjpaC8zHj1KRG0BvASgJYBKAE8zxh4motsBXAxg\npWg6kjHmtWOq+qxZ07HMtm1LfS5f5nXiDjKFrr54ABrN9K4DgLqwoxjtkj5OHDnRynwPgzN/X5T6\n/Go3IaD09jqKewTS5+2Ptce3CHVSNyWvHk/RULlxY9o+bNs/b4964PUqt3zTJlIf+c2a8Q+VPOKj\nYuXKgNZO/Pz8il5Zn/psmeDeWzw1lY9vVnjwZcZJ+esNd64veXJflEwDiod5k0j7Me9ete9fh5vU\n5stFY3tq91140kwM/mVzeENwHzZT5r3RGwDQ4TT/GOjhRTuPD5ubILNKHGYtk5QJUfzMgnyTghy3\no6ZjCMKdIiOOvuLsI+5IXr/o02wT5iMZx3WIYlZV9ZFJFwSd843zO5COpq0cwLWMsR+JqB6AaUT0\nudg2mjH2r/SHp0GeIhN+QE1NAKh5RJlW1yXPcY3ACV/tDTTkFSaLL4gn6Wzx+dNw7eyZeLBzT2C8\nHSjwyeZiZfuTZq3A2O7O/GBdztMrczTwZy69TNhzt4CJ36kRs6JHs0GUNB+qCFkVtHcv323Fzwuh\nRWrT7Hheb7PkceG/2JNHjS6+YSBwA1BzDfDVFf8EzgDObjsIC29xCh5t7zLTNOY3beJZV7FqtVEf\nFoUnRU/Eq9Ko5fXpgbw+PUL37fYEv452jreEKETJXWWS2yrqvmHITvhRJ0vT8wjLh6baJxukIxTE\n0S5MEFelOTG9lybjSzdlRlz7mgrE2dScRxbaGGNLAa6yYoxtJKJZAKKpYTRZcCufENv9I3widEQw\nuvrYfOtAtPvHBPT7qVLZJqicU2ry1nQaBIAl1/lpENLLbn/+H1baDv7/+a7tsfmqqtVW5HftjE4v\nrUTFH7NTmsx0UNUdLbxXfb/Lpc/58Apt7kCFr/aoA+BXTzuLZxb4l9I66IP0gk7chd1NyevVDZ1e\ntVOKqEqk9TFUqKqudZD2Og4hjNc2tX9sHNumXyq5Dn3lfX2wwTzwIxU5ra+YrFaYaCdM0kDo7iuP\nwYRMCUAq7VW20onEdWzVtY+Sr0y1X5SUG3Fdz7i1nboJk03HlGlNmGmEddTI2lh82oioCEBf8OKC\ngwBcQUTnAPgBXBvnSWBFRJcAuAQAamF3nH7rx8CtfNtDnw/xHMNK1+EhRKuWDaL6/ZTdNQAPjg1o\ncJd7hXlVBcuUpqp3aVUW8Mt7BgT7sakE5c2f8IRcdY6a69nmZs6D/PidrvU/vmX21SG/h1dr2eA7\nryZr/f7RNFmmkKglKifN3XoCFwZrv5ueNLLi3W74zCe0svkJzuexoI0l4C7xtM3r5TXt3//BC8p+\nry+yU9As/ftAtBrlfTYen88F4SETe6sHJ1h16QA0q3axoWrc7zYVOr40uegHF1c+N9PtOrgnQdWk\nGXXiDhIkTASvuITmdE3kcRP1mpv0rcJEixpVk+Zn4o9TA2pC2kIbEdUFMBbAcMbYBiJ6AsCd4Olq\n7wTwIIAL3Psxxp4G8DQA1KfG4bWQ5EF3LNJuWz63zHfb5U0m4MD3rwVgZ8H/aPGPUo1SZ6b4o9vs\nJWnaOK92K8Tsl4XK4mV7feezbbVH63/5CDrDnIuyr9CRv9qO5Z/2qo+iKbZPXKoG48neLlOpPhTC\nzYq/yVo4/SoP6ZTcUrFtiJwSYmKqkHm+jy/98ivtcbd6IaSsQRpc3M5bTzTF487FNvdL9/SKeI4/\ndpH3npxUyAUjlWYNgG1iT4NtR++Dmh/Fl9tu/hgePND+FO+9sjR5VhWTxrCfeTbY6V+a36VjNUg8\n4yWdd1smTDQ6PlF+ud9UaRN0/PF0tQ7u4+r4zsWpNcqUv53fclj7TGEiLJqmVNEh6H4GPUNxCHxx\n3uMgQTSq36cpaQltRFQDXGB7lTH2XwBgjC2Xtj8D4IN0jqHCLYgVtG/L189fGPehIjP0N6ej/Vdr\nbA1GXJqd57u2x5zXpIfhNf7PpFyXKaved2qvWlynNjFnisqNG5Xr8zVrYwZde6vg+4LRznOs87Y6\nt9uj878HAKyrlJe/dza62P54VedDtMYYxvqz5CTL3MTu1qYBQPdp4us9zfk1Lz3e7HiXjRiuXF/X\nQPDvcNoMLpSKuJ1DbvU3pVsayV0Rv5d7Lmnd4sx7FdSP268qzv5VZNvJP12n+yhaPN1jZtK8GtZn\nkJYr2/cMUPsB+hHHeNOJHiUAzwKYxRgbJa1vJfzdAGAogpyIJD7oaddt7BxTrU8Zu6h4pbTs9WE6\nus1e+PNYH/+lY9WrZa1aCkP3n80ncf8r03JCOyNy5YdO15k9C8e3sbV4+eE+89oUX87Nlw/ATkFx\nZXt18lo/qIbUn4gatcyjflQc7KzMkf+Nt04oAKw7hwtBDV9Kr1arUqPXw12JQ40VIZoHI+WSFtZz\nMC/2njNPpgUuXe1OkG+WyaRuOvHkouCZLdJJMWGiATM5TlRfu7DjmWoZwwTIeBPZqsfgRyaFQ3tc\nen7y6WjaBgE4G8AvRGSd0UgApxNRH3DzaBmAS3U6c/vSqCYPnQCEqmbufaKM0Y0TI9XrXHzjQHS2\nikL6sHLYAKx8jn8+64pP0dUhBv8PADehzh7dH52v8Qo5E/YUmou/aQ/LcQ4lT3LBonjYFFzcyaVJ\nGif+9WjmMO8NetA8IMLyZfOW6/Iy7x6XpsaSVe5pr2zf8Yz0hJn84k7K9RUlc1J+WxaXt+dmVupn\nmy3ZtPCgE8sU6uBg/TG6mdWvXLm+ICBcaP4d+vdt9R0D0f62zH4nj53pdIf9IkZBvKrQmZxyUaBR\nOeLHnc5CR+CUo1FV23SP5d5H3i+KI78JJuOMQ7A2wdRk7t4navBMOtc0ilbS1Gewqn9sZTxPG2Ps\nOwCk2KSVky2TWL/4ZQqwxZsjaqj9sfLLtqnPu0l53AAg71Db7FpsGXtL4aw7GhPH/2ab797rIZzm\nh/k0NqT5Y3yCHbOICzCnFDoFn/w/+e2ce/+AtGpphlHzY9t/6u9FA4Bn/NvOfqUvAO7D1/ksW6Np\nWlopF2A7tqdqlXoYqF6vug8NXpEE89vNxiAL4ADwxsIJGHqprZ30E8LO/H0RXu1WiG1D9nHcvzAs\nARZwCqWyL1uCPlHSWpiaM+NK8aDq133cuCMYw4SwTE/GUR3jdfwEc5kgE6GJsJmOP5373pto8oJ+\nGPiRrXuWUxURcpU3FjonstPaDsQTXToH7jOuR7PU58tKZ+OMFpL2rTT6WD7txWtLzvmnc5LvFGES\nrNzfftDyvtP7gozr0UxLK2qxdnxL/v+mlo7UHS840mvwz+chIBBAYtk73cMbSbQ8cRZ6TcvDbwc6\nExxXblYnk1X5sFWUeLWhc17tC6AvjvzOmTujE9S5Nzad0h/NpzlNh3XHqM2/qXQ0RwSnoAkjivbX\nj8G/bMbHI/TblzyhnyalcfZdU3ZJVBObiTnN1PFep984BLkoZsggzVLYseJy3o+S2iLd4JU48paZ\nRK7K/mjpCDtxmt2jCojZIGeEtqCJ38O+wq9oCo9OcwcgyBq1srsHKD8X3cyFHCtqNJAsxTcseMvy\nn9pkrztUrCvz36/fT5X46UB7Ob8FT8pbsXyF9rHl6NUS//RlDk4d8NfU50KRvqHsrHb2OBSJY6PQ\n8sRZsfTjZvUgT2YaAEDrSd4ghyX91QERMosHN7AXBg9E/fnRgjb88vz5RiULlr7TPeX4HwVVWhgA\nuGeeMz3JyA5O4UyOGt3yF3WS5t3/6/zerbpUrWmUfV2rI8W9twDgz32U1BBB+bj8tGlhUXmq41U1\nUR3b/YTEuM22psStMYy7HxNzYjr+bKZENfmme739npswoV1HONfVtnrPMfM+bVWCnUAWGH3XaQCA\nhn9s8msembw6dRzLfloYNx1vDNdwWVq58sF24tKCr3hFBAC8hNVJRZoj1cMvyjIIhynumBgHo2DQ\nuOsAACcdIE/eTGg1bWFk7/HBjniLb7QFmq0tuHCh8uvTYdUlTsGh6dPB99a0PJifVi0bLLjNum78\nWufXr4/xg+ojv77cKv7vmYVbW1rwsbNaBftBK36pWlNVAlMU7ZFuOz8fJ5U/XFxEEWwykcbClDi0\nOVGFOtNnIJ0giWzjJ9hn6/vmXpfudyLnhTZT3MlXO95nv/yjCDIAN4daSWIBoA7UiWK7XDEZJc/t\nnSp/FVby6sHOPYEbRZ8iavS9sbY2avTsMY72ozt3x53zpuL/OuyDMJb+3RZmCl/Vr96gg5FWNE1k\nPzY53UjT40q09rc0hou3NfQI4uvP1IuKdGvV8vbsjrw9nW0qf86M9g8I16gBPGp008nZKz2moveP\nBKuEwds/8R8sxRfy78SGc8xM3AmZSbWhO4G4fZZ0Jv2gtiohQtdcVjWRfJknKHedDkH7RE0TojKL\nVqUPoF+bXEjtIRMkUGd6rNGdZRISEhISEhISEqqMnNC0deq9CWM+tiMancXK483GbyXiTYf87l1S\nnytmpRFVkCZbT7R9iWq/Y14GKe+76anM9CgegMbPqc2BVqRhGOULvQ5UhfdK62LyacsGXX/gSdb+\n2HuHb5uS/+ztWrMdxRf9APQX0cz9g0s5AQAmcX9Mv+CDhW/bJsS2f/WaEOX6qcf+mBltltuHLePs\na+fGw+S3q/bYMZPJX+HZcuDPBOmOQ9bOBGnw4jIV55omyAQd7WlQKha/7ar2QccJu+dVcY2r+pmX\nzynjKT9yhVvnqpON+nHPIU6hbZ7Is9ZBwzctHXgZKjnjbu1UOaryQ7npqODLad4dY8AKQNj8Vy4M\nHzN8P98s/5mkYpVcjaCLZ/sLC77Dn8y7DgDOCyoxlUEW3cTNzF1hp7kwNoUKQWzth95zBoBGx1Sd\n4G8J4LZPG6dig6qGmFpwLH3RmfC3y7k/otWoCcpUO0ry8tHyL5J526ofLPm0JZjnlfJbl43kvrop\nONIlat6wTI3DPRadddkSjuPwmQszCWbD3Kxj3tXNhWdFU+t+F3Xy2qVLzgttz3eVkqWeLf5PsaPT\n/tHRnkBmj+I+Sp3/7u/wXSB1d2G7/YH77OX5/3A6ore/tepzSeV37gAAqJid3dzvVmoRizr/4ylM\nNh/Iy3Nt+qQj6iqKwFt5wFScsp+tsSvlOYExYy9JSotQL7zNfba/1+zRej5qebvzIt6NxnFhqnLL\nFgC2pqz4omBfRD+KL/oB7SY7A1owuQ4W7Bcc1GIJ025aXq1IurzcuRhWd1Qn1Ydf3VF31GhV88et\nDQD0RedH1UmCqzthSV5Nk8AGOTtXpZ+SH9kWUiz8/L/kCTYOTU+6wQ9R9g8TGuL0fdTxQYwjBUzU\n7UFtsv0MAtGvTU4KbX4TTcOXJ2Ljaf2x8TTv5FzvjcxG5tWRBZTuXo3J+jP7o8Grk1B8wQ+peqBy\nXdA1K7ag8bF6zvMWoztz81bpC1wT1wWZ0cRFRQ76MA3y6HK1fb/yu/AgjwsP7ejXHPldgBZ/55N3\nRak6EKT4Fi7MZ1LUuKzUG9QRlrMPAHYcsTfqPuxcV+MzLhz6CW1xs/4je5wNsAINjp4NXMSX13xQ\n7LMXjJ9bC4dA/mx4e9+I0SnewvPVlaBJxC83VqYnGB0tkVvjkK4QWFVO21FJR3NnKmxlW0uoGodF\n1MCQqr6/foEtYWNIR4j1O7bJcZzrq1HKjzkz6jqy81tCCgB0OS+aoCKngpCRNTMyC97aA9C0emXT\nj00mih+bGz8/tigc9utGHIYvAQBf9FIUcPfJkOEWwqzoW38aO5bcUbpWfjHA1hj2SlPetXzZBmfg\n1vuZqn88fi/leovSl+ztXc7hbgJT/2wd38B86HKu2iXBU3HED8scKih9xCu0drlq56jBWzJj97Sj\nBN2YmqLSTb6aDeIYR5znEtXfyjSqM0wYVo0jXcEokwJW1GcyW4Js3L6PmfgRlhNCGwAM/Hk7AKlO\npkBO8goAn/aKV6Nm+bIteGsPtL91YiovnMMs60N+wwahbdxsPWFf1H7XFrba3Dch5dMWlfVncc1j\ns2nW5wloNcopnHIHddtJ/eK3bR+xFhO5YLN8EDfjbf3YTohb+8hoZlp31QSOXqqQmgt3C2+UBpY5\n1K01u2Z8sLC4/KqBuP1RxY+Bq6Q2r9qfWzxi3wNLqyaz7mz7h0rDl73CsyWI+bFkBB9L6wfSr/8Z\nVZvmR0Ebp/DY49YlKF+8JNZjVBeiJvJMF7e5z8REFmUCd5vL4kjVoDLxmghCmdCy5Kp2EPAKYFHN\nqmHr/cz4qv1U23XNqrkmvAOZEcRMyBmhzZQa39ihFjsOXurZ7qdRs7AiLxcey41ptbDN02bh/9kT\ndNs79SfGTmd4b6psfnp1obPo+qnnmQlt24aE52mLi48Wc8Hh5DlH8hV5+ZH7mt43YKPUb/s7NLQs\nLm1NHIT5sslCWBRMon0PmrHVs25879q4f559bc5+Qr+4+/qz+gOvudc1xZCb+gNneds76psCeLVb\nIQb/stlTdzRlutfUiMv5AwGg1agJKa2a/L0486pBWv3tjMQ14aVzDD/CJug4hbRM45eENQqmkZdR\n+4pK3Ml03Zg8T2GRqOkc271OR8DyyxFoKjj6BTH4jSMqOSm0dTlfaBgYA9JMjC5PcjJXXZOZtAVy\nwXeLVOF3HzIVNZppojk/VTsAACAASURBVCYrVncWvxBmkd+oEWYdBuSLikgVa9XlqlTI9VLnvb5n\nQEsvHU7/2ah9JnELYVH4ag87yIL6BgdApKtVy+vdDcX/FIJrb661rZxRdUmdM03cWqiqIIqGIVfM\nrabEOe5MXgO3cBJVAM8FwTIqcWvjwq5FnAJulPuWk0JbGI/P/w5Xzzs5tWyVxKmL9Rk75gEz/sS3\nvWvF0teZbQel6oECQH4Le5sqajRMi2FNyNuO4Rq4Cbfs5ylDdbFt8URBxyIUCJ//8rllWmO2okaD\nNG1OPzbvBLtymLq+ZLMnvaZB0zqlFatWSxUPgnNGUwF/7P0CCCq/VOfya481yDuUF6LN79kVAPD+\nZ68DAPpM8aqrlozrgdZDf/OsD6LkWW6iLZlqVw+w0tocpI6/8KVicqNUypIwZOEU8AqolgBa+WXb\n1DWQWXCr8zjt/qGnlXxZaNfObrvratZk4tT+mBwzrkk4KGo1itZQpakIMivLwRymfkjpmFJN/Ayj\nCEi6Gpuwc9c5R11NrekzE+SPF9Ucr4OuW4Ju1LCp5i1uATcnhLZa3Qkrt/MJv8tUoHTf7alt7tQT\nFg6T6DsNHduWXWNPIDfwDBoeHxvEYGGsWJc5ITEIt4nKhIVv90KHET41JYW2y+HHttis/1Fl/oEN\nZ9+nFtrcLBnXw7HceuhvnvJkFpUbN4K+agNCGwAAG7zYOFVF7fG21Lz1oOUBLeNF5ccWxg0dJKf9\nEcFt3YJYLuD2tcQ16naVM3531B0G9PxMcw25YLzuyz/oV38mHNFNx5BJ0pngTIUu0/OK4zoERTm6\ncUcUy8JblOPlgkO/7j6Z1nZlAl1fy3S17DkhtPlR8pRTsiq+NLqwokvQxLBlqD1h7j7Oa3Yt6FiE\nj44tAhCuwbIS3lrYOeZWqJqnsOqOujUohfdOQM0P+fW5pMSpknm62D+VBgAsH6BKrKqHla7DLw2H\njEqjBgD5X9sCdcUh6Tur3zHXXzN5W8d+KU2bH25N0oq/Sdf6b23R/DGvMCRr1FZcMVD5GQCa/ztz\nglTdtybj6bf4/Zg9uj8w2tum0xivrxxNzKwZ98n53ynXD2tvB8NsOqU/TrhWSuVzChB3NZRcItNB\nCCp0/a6iOJYHHcdUI2LiG5RJ86yJpkRHsxXHPQ4TTE2FHbcGM0wbqqvxjBs/barJfplAR8Mat/Y8\nJ4S2P2cxlO4jBQIQae1XUMi1K4VX2EJH+aLFDk1btrDMXIBt5tLe92l/NWDxJeaCa1BELH3VJnDf\nY/5oI9rxZXbYMmW7gpZcWzWi/4mebeXL0tdexeo/lwP0+8mpDVSVrVp9IddMXn23pKG80P5YczVL\ntWvybPTULaoo6M6XleH3h2zzcekL/QLN9Lrm0F2NoJQfQcTtrB0n6QgicUdzBglW6UZO6mzPxejG\nbKMjzAUFD7jXZUr7qnuMXLs/aQttRFQGYCOACgDljLG9iagxgDcBFAEoA3AKY0zf+5ux8DaGuB2j\na4vl4nf48oK39nDv4owYPdmzOTL5TZx5xipWrzHuIx3Tl0MLOBj481hnUEatD7yRjVYVAapVM5Uy\nI1O4/cDO+YNrvl7qavuayRG0NQc7Bdk7D/0LAOCPK+wI407XTsJHi3/ER4t/xDHtnecbZhK9Y/gL\nAIDHuvgnoJVxa9M2nNHf8bn+a96gACvnWvE5agF/w8edlOvrD5mDa2fPBG5yb5kJQF0xYd05XAhs\n+JIQ9CKkrmE/8f6fO0DkZnMVb7j6kWGpzyeOGoiWD6uf15Qv2ynebdaPDNq7l3/i3WpGJkyaUaP1\ngrZHJQ7tSzqTZJS0Km4hNJOCsU6EoqlQ7DajBh1Pd1y67XQ0dVH97ILOy68/97Gi3EtTf7swN4eg\n+xJFKI1L03YIY2yVtHwjgC8ZY/cR0Y1i+YagDv5WyvNEyRNj7YU1HG0W3jIQbe/iL3/LZNX8sQkY\nu4hPgicV8smx5ehoAk35F9xbv+CwCPWUYiSKNo1q1gQAsG3bcP/dZ6bWN3pxosc36J9l/HpdX6RX\n9kmFu4SVW5tWPlhKY9KjEABQ8FXmomRlM2TrD4Md8Vi5uizSppO9SV7rvuU0g69633o++Q+Lo9vY\nCW6XX+XV8PqlCbE0a3kiMhLX8X8F7W3BtHy+1+E/E1i+mfnNmjnWd7t+ATBG+g5+3RqVaZSbWPGu\nKncfcHKHnwB87d1wC09zUt3RMbPJL2+5vXvfqAJNppyiTYlTm6YSsOI4T9VxVMeI63hhmAgeJoKR\niYBrIhD6CSvZ8K8zDeCIShT/yKjXIFPm0RMAHCw+vwjgG4QIbXGwVESR6vJ23/8AAK5srxe5tvu4\nyXbSWFfy2AavToJTFGjqWNJNXmppkFTBBn8e501TUuv96FUR5ooyW61d+bu2f26bUXc7PH6/Irdw\ngFN2+G8DAHDhZdtnRfYqhb+WCYvG9kThSTPT66QKsJJOW+U65OTTfx63L/48rgnuDkpfc5z9MZ1n\nxY2lmb7sR1tD3e5kqdzU1bEdKgHRJxUTDYqfZiGK9kqFqSkzE2axTAsOQfcp6rYoY8i2cK6DbhSw\nzv5xnm9VaqajEJwbQQ8G4DMimkZEl4h1LRhjSwFA/G/u3omILiGiH4johx2KxLYJCQkJ1ZHk3ZaQ\nkJAp4tC0DWKMLSGi5gA+JyKtDJiMsacBPA0A9alxLE5srU7ULB5qIZRI+S2aY9qAZ3Hc73+NfGw5\n+CHMPFuxeg1o714AAopkA1hzvjNFxu6rMpOA1u3Dtv1q/dQKa/YV8rj43+DV8CSuFStXeta9sVB9\nzU5rGz2opNO1Zgll3aZQHVZfbN+jgq3Ox7jJM9xnTOXDBgBLrhuIws/M/RkBoPKgvkaas8tKZwOj\n9Irbx4HKh635CepXw/TvCh3L6/f3JqiuTmTi3RbV4T/uNBU60Zq65uCqIGokq2qfqFGkmUj9YPo8\npGOWDuovSj44neOajNfSylW1yTVsHZCZCNu0hTbG2BLxfwURjQOwL4DlRNSKMbaUiFoBCM5j4YPl\nv6Y9lgE8IahWCoOACFU5b5cNl/Aqf/WRSQ93Chd+EaNy3VGZzn8XCXIDSlRFNW+Z5LfacHp/1IK3\nLJhp8IHKf80v0WuUoAq3+djh/N8hnnxeC2/m471ttPCfxAQ0Pc5p5paFtjCafM9LMvxfmw8BAKf8\nO54o5xWX+/fT/PHwa2v9gJDd1d585xkAwOl72lmaK1avAUTi4Y4jeMT2jpYNlX3OedD2lzQVnlXU\nHN+Sfzgw7a5ygqhCmM5+Uc1w6U48URyyw/YNw3Ryj+PYpoKZKUE+dXEJu1F83Ez7i4N0nxmT1CBR\noo3jTOOS3yqkoSAtoY2I6gDIY4xtFJ+PAPAPAO8BOBfAfeL/u6Z9lzynLt5dfIF++oxtR/sLQDU/\nNkvDASAV8ODGCoDYmbGcyPM+apRa1/Sp6Gkm3Oho1ObPkazsTzRH8WVTAnP5FU6qCwBY1J8nE5aD\nBhxYAnxI1HKzCV4BpRlmYeXAdWFDDyVVHL69sxqD7MPmZs0F+gJjQu4QxwQf9yQZp9BR3Yjbny7d\nviytkU5ggK7GK0qEsc5+OkTJsZdtv7FcJl1NWwsA44hPegUAXmOMfUJEUwGMIaILASyARsIM3XQK\nFnKC05TQlMU5LMwkamltLNp9Gp7QtvHzTqGo0feNPW3WDuLmNbYtO74zOuZQCz+N2o4jnAJ6jc+c\nAnUq1ccTZmOrSpZcJ0Wv/iumnGVfOs2GOHSRspmlTbPyugEIzNuWSvUhyHY6jZFtPgLgrPZgJVyO\nI9lytklHk5OJFCGmRJ3wTfoMEpyyJTiaHDeqljIX7m8YmXD4N9Xe6o4hLPJXdeyqIEjgNiUtoY0x\nNheAp4o2Y2w1gENN+xv6m+zr9LG60W/A55u7YuhvKzGuhx1pOPe1PujwqFNTUvOjgNQZCvPo6vdE\naopHJqCgrXPCnIjOmAi1P1BBW6B8oXpCzQaNXgzWgFmpPjqiah9eWagAggULi8r943lJmFRu8MPS\nqFl1R1N993Sa0xfcptYaNoHX59JdBN2Z5sMltMVA6Yu2trHLuT+GtnfnEFTVHY2Lsjvt56PTTlYN\nQTfJp/U/arqFsGOZ+lHlWiSijk9dNojiuxS3qTOONCdBaTpUyzp9qpYzIThFyW8XtBwncZ5vTlRE\nsHjshRMcy60fcGosll3NJ0O/RJ2m5XisVB/5Chc2PyFs9kN+plDvBNt5uJ4Waq6qJudBinXf8n8d\nbzQ3S5a+IPKmbeKf277tLfxuBSQEpfmwTKL5X7cGTmvt285NHBqTHve4+mhbiB53LU3dq8JJdYFJ\ndbHs9IBOLO1V+hbNas+zC3h5qQvb7R/S0k6uDHD/xvJ5/BkpHSmZrC+QTdV20EzJM/b64ov5D6nN\nJ8k58cwDQKo7OklWg9rr9m8i8OmkyTAdj2rfuH3bVHnT/PrV0dLopJtQ7RcnqiSxmc41tjMR1Qf0\nyNbqkmkmKWQyfR9ySmgzxXrx1xmr/9K3BKSON5k7Rw/+ZTMG40vHuq/2qJP67KhR6aLt3U5BU9YJ\ndrxxYqrsVVDJq5Ln+zn+WxSfPw2VB/UFAOSN/8n/BDTZcZjdf40veECBO/LPEsLqfevMRwcAGw9Y\n5VnX6PvGaIQ/nCsvaIy1g9Z4zKHZpOb4lth2kLpUl8V9H76Y+pxKUDzEvvft7vAxjx7B/QGHF4m2\n13mbyCXMig+dirXnScL7eba/W4N5fwaO0U0qavRF77bDfvWWCDul0N/XIK9PD3uMF9vabNnM7b6n\np8wS13QWMKZ7S50h79JE0Z6kG7HnRlczEiYImUSTqjRWcSawVU2yJiZLXXObpaHMJS2lqdbUxDSp\nEjL92ljtdISiqOMM6ldXS+33/Pv94KoqrXS1Ftp0cBcPP3tMdp23N57mp6lzZel3+zMB3DswJlSl\nqrLJnH9J0YbXqQVqUxP0sqF1xadNRvtZPwYa/25ri8J+GFh+bGMWTQSG83VuwWf1IF7JzUpvcuDD\n6UWPNn4uvkAQPwraFuaE6T9MkN4VyTXNSpTou0yQS4ISoCf0VNWYo2hNVdq+TPvmVcX1yBXTvz2G\n2Vrtc0ZoW/thF9QG92lrdExppD6unT1TWWvRlMoD+irXf3G5YuUBQN63wdotK8u/Kj+ZCcXnT8OS\ncbaWw12jEwDeW8w1H8e3cUZVBhX7NkXW/qx9VtHgvC6OxUYvTEwFTOgw+2X7+nc+27627uhdv6hd\ny3RX0CpYo3PSLG8mmg9WxKcFqvGNZgx3AI1eUAtmlmZVxu0jOHsUvz5WOpljZ64FhKb4g56NYv0R\nkEmyHSiRLnE4csc1wcTtzxaXFsyteQnSaGXSF8nUBGmqTdPR5Li3qdabjrOqoo1NMDHTh7kTBGnx\ncpkoz27OCG0yYZNtnJR/0Q75Z3JTk1+dSAurPioAPNFHmjTr1EHl5s2OiFY/6r2h1iItPVyd4iQq\n7mjVMNzm2yBUgsQlJWoH/4evPw1bT7DLLMl56kqe5OuLh0XX+s15zX7oC3V+qVgRmGPNi6RbpFOz\n1U3pY+56p2kU94zIF73qpWqgugMjLOQ8fbplVOTAkyf+xf9bgmWdsZOx6KaBKLx3Am4Yy69BEezn\nKjNppLNPVU4mJsJikM+OrmBi6isXFdP+0o0CDTpemBCWjkAZ9TzjMDXGQS4ITjrm9SgBEmHRzpnU\n4uWk0ObH41f/m38QdQ1vuN5b4NvNbR2d/l+4L/w4az7wph/RrR0aGz7pHWTaTeb+dAv22+zZZiKE\nBSELXIB/cmATPljMtX7F79t9yyZRWdNmgpWPTYU7arTwpJkYK6qrDZm5DgDwcc+GqDlebQotH9zP\nsw5QJxG22HGwN0kxAKuUqpLiSwIinl04/N1c+GnoZFQBCCVP2Pek+DLnvb5pzgzxaQbUONdfd0+4\nK0JBx6LQNg7mmDWvbqTjAG8SoRrUT6YxcQ5Xta3qtBPp9OnuN6ogEzXNhXwd/QSJKDnaogjocfln\nRv0BoeOXpjOGTLkBVEly3TiRTaLlAe1kVJPr3PsHoNmPZpVjKpavwNBCPlkVFLUz2teE/IZcu1Ox\nbr1W+3+WebVyZ/7UQ9HSS53/eQuvb7qpjWOZvjd/gVA/tfn5mdPV62tPS1/Isxzln1jHj/FFr3pp\n96kiv1Gj8EYhBDnwu3Hnc6s4ZC9UHOKTADiLLHunOwBgRcU8z7ZF25sAAD7tVd9Ryg0AUN/+2HL0\nBGz7rAjbTi1CzSPKUuvL55ZhZydK+oe48ktlOs2Cu+90okpNjxVEulGoOmRCyMuUAJ1Jc6ZOW9OI\n3KpC91kKEyKt85PP2/xeVjOfNh3+0XEv9P95R2q5/887MGnPGkZ96KTLSGnVpGCAvF7d8MTQblIZ\nK67dsvyW8lHfoVkpfWQ/dLkqPKp1/Znc1NbiK+eyX9Jahx+b0LSNKpsIpExL0W6pHAgA2Ne09bdO\ncx2bNtOz74g5v0Q6Zrpwh35J8FmYXq3SINwatbw9uSCDPbuj8udZjqjPMPb6eB+lRi3/6/C8aTKm\nIuYHPZ17lL5kC4hd/2VWpiwTbP6rV3Ne5+1dIx2IKpouapoPnTaqyD3dqDjdsfn5VZmYqeKY6HX7\nSMc0Gjeqa5cpgS7svschzGZamxtF61yVxKnhrlZCmy713rQFHnepH3m5KqLv0sHynVp4iySI3GJ/\nbAezvHRR0DGHPtBpDwDe5LkWbgf5AxcHB0XIwQfpUL7UG3GY38RbVSKOpG3Fl0xNmVllPu7Z0Hcf\nquEtUcV2bAfAkwGf9B4XSO8fNxQA0GGk8zpeNdv2P3ukczdPX1YAAgDMe92TAxvY4V0VxLPFHTzr\njvyVV/YoaNUShW+o/RpV98GEDWdIPyhefTutvqo7QZqzOAIeqgqTXFiZHoNOG10TYtg5xHVf4tLE\n6h4rndQZ7vYmgQZ+x4lbgxx0PeMy+aqQ+9Y1j+r6FCckJCQkJCQkJGSRnNK0Lbjd1ih1eGu1ss3U\nM5zL+QpXqkfnf5+qdpALWKk+LJ82GZPanToc32YfLBqr9i8rDPBh63TdJPT+0VvaC6p1ghl7mfkO\n2gEGIhBBM2r09ceOcCw3w0TMebA/9hnTH0+d+AwAru3bcEZ/QHo+lu3vNO0WX56d3HQr3uVaMHeC\n4ijkF3eSltLvL6FqiKINCEramiuYJqJN91hRneZNHM3D+lMRp2+aqq8wn7BsPRNxpXyR+0oIJqeE\nNpmKmX+kkpDK+PksdTwDSj++tud5V24+0JkvzTK/1H+NC1BW9GhjmPv5OCdVJ2//+rlj2Qp+AIBN\nJ3N/nrpv2T48Vpkh4Dt1qaHj6njXBbDqfTsqtulx3mjYMCEsldUe6qz2OrVEdcbm4aPI3Xpw19IM\nbHuwf1CAIyXFwXsh/5twf7Qm39s+ZasHrU2ZQquCDqd7TemyT5ud6mNfTzuZlxd+DwA4u63zR5Fs\nAi3o0J6vm2dWP3RX9V+T1/sR1S8rKJ+VKSbRqWHpL3RzskUZWxSfqnT31+07k5hcR5MISFP/RdPc\ndemiGymqm9tONxo33TFGJWeFtpxATrvRy+szNO/eAZBSS6EDfFI8xIgqorQC5NputznvvmvshsfY\n2ku/BMJ+uBMIF7S3SyotfLiuu3mKVid6i6Qf20adPiNbWH5n+S7PfpUgNu9e22+vw032zS/9934o\n/Zp/7nJFuPBxzh/evB8vdW2raGnG+X/4C0rH1nE+nyeJOJvFNwzE4hv4j6E6Zfb2xTcMRPvXFqDl\nibMw+5W+mP1KXwDfO/r44lQegOHQeG+xS2wt/i/f0OYv3gAWN34+kTW2mml0E8IJ8wnKhtbDNGFs\nuu0sojqxZ8Ph3hQ/gcUkJYapr2SmtK5R0uH4aSjdqVCiBJyYCHBxfp9yWmjz06ppTXhEAIvvZW9F\njW48Nb7EqpZTvI72Z/XFfEK74G7nxNbkmRDt1jD16rAqDnERFA079DdZ42l/HtfDm65k2dUDXZ/D\nk9BGMYfO+7AjMKJjarn1A/Hku0uHDiMn8moQ5wCnDLY1uargg1xGTvVhgqUB35kwyXOlEiriFA7i\n0MK5iWNCM9GOmewXF0HnaDK2KMEYUc8x14JV/ISmXBibCVHH69yvGqb8aHe7PUGqo/zMzFuA1xSa\nLcoH98Nx53ANk5U+Ir+Jt92YRVwIM8n3tf0oO93Ebp9MRX7TJqhYpfYJDEM2gabGFFLgW6VNs7CE\nNiC+qFAZK3IVCJ/c/cydQaZNuVxU3vj0xm/VHgWAlxCuVVNd94oSb3ZZOQHy8139+3uh39GOZRIK\nz8Iv1HkD2bSZoTkTK2b+4VlnmUdlPlrsvca9/+38UZaOeT3X0Yl2y6bJJR10TVFRTEvuvtMRWKNo\ngHRN1pm4FyY5v6I8U3GOP45n1zQhrl871bLOsUzHo3ss3ePpklNCm5UQNkzQ8jMjPdi5J3C/ep/l\nV/IJosWjXs2Je7LXrX4gm8cs7Em1qVYfujR5ZiKumv17WtoVtx/bXKkEVMczwh88WYgo8M7Lxsga\nNSszvjvZan7DBspkxJ2u9RfQVvxNraFtMvNP5frqxtwHuEDfcYT3+bP9IFV4tyl9JQNw+7Il6KEr\nsATlMItipgnyzzEdRyY0cla/6WoPTVNGBO0fdwqJoDx4pibWnd1ZP4r20o/q+iMojJwS2izmPOhv\nguwwzp54874zf4DnvdGb93MaL7lTcJi3anb3aUGX5Qf74wjeblY/3RoOZphOqCqaPamnvbAy8b9+\nuWLjIYp1X/+IgraFKF8YXm5LRk7iK5euCqPlw1zY3vZZEf9/TFFqm67ZTSdYIIxfznnEXjiH/zu+\nzT6+fmxLrh+YkpWWXB+c+Lf1P9M3xVrPjDsgZv5JLZTt236lFyzQBSsAABVftkXeobZ7wsphXIAM\ne86ObrNXSiO82yc8sXAh/M+3/AufyiSHag03Z9H1nYkygbgnvHQmvjijAlXE0a9uH6b+SlGFyKj3\nzy0MR+kn7vsk338dU35V5Kcz/dEQJZglStCB/IMqHXeAalfGKpfwE8LWn6UWJhsgXPgo+Goalv5d\nTNp9nJN3iylbsPvSbVwIHa0/zvlj9kCdL3kQQNOn0jMt5X/9Y2qiVAmyFtbEW3HUPiiYuRQr31Pb\n45od7zWbZZr8zjz5a6tPbYf7itl26aW8WrU8+1T+6dS+uX3YZPOoFv17O/v71r8eKgBgkl8dz3Cs\n8lINno/cBdjgxcq6qjc8/RLXXLv5kmu5l147EK0eVAtdctSoXwBCyfPeYxafH5x0OSF9THzpTPr0\nS0TqdvjWJW7Tnc6k6j6HbPvMyYSZdnVMv1EDLqK2z3bErWmKGPm+B2m8g/qQ+wk7XlQiC21E1BXA\nm9KqjgBuBdAQwMWwPctHMsaMkjZ0unaSMqdZxbr1qNzfPvn5d3g1F/mGFrClYuKT2TGd9yv72IVR\n0FKtyYgL7htmnpdr2XC1dqflQ1XjYB9HHrp75vGAgpEd9k1p1RzVBBSVBaKw9rXGABqj/hDbb8zt\nx3Z8Gy60PlRmXz/58/AiZ59ymg81zjQgfvzlVBFRorBO1n53Csre5MJi0bvq/QvvjXa/X3CZW88p\nPV3Zjr5y1rW9u8M4x/LIDsGpRABg0Uj7WS08LPsBIJkgKN1HFIf0IDLhV5YJovafbaEqzohIWaMV\n93Mg9xUkdIQd31ToiyvqNFNavLg0dlFSoqjJcCACY+wPAH0AgIjyASwGMA7A+QBGM8b+FbXvXOGk\nWStca95LfRrbvXngvqq6o61GqSciWRC1AhA2fOzK9/aK/bFoSm0AwPwye92qS+3AhRZj9XzydPzY\nwjDVqJmYROOm36TNqc9T++Sj9GGn5rSF+J3hl4tMl7UfdrE/rwtv3+iYUqP+LV+2ZYofHBYlt/Fq\n7Z3OdKVqUQQJZMa4H8yF/ezUId/25hpQWWhLaaUFft+dhGgETURR/O50tA465q240o9Upfk1zmPG\nQVAai3TN3lXlVxbFuT8X/f10BHHTaxSXefRQAHMYY/OJ/DPoh6ET6Sn7sbX387k+z/64/ci9AQCN\nSngi0y2ueViOfGwnCrB/N920FLcaWZCqvdqZoqKqEolWHMid+HXyZFUFqy5RR8W2/MI2abaeVC/1\neflR6n5UiWllU2gmKHnGjtI9+vN9UHzx1JTg1+Xq/2fvusOsJvb2mz0LS5HeYVm2UwWUvujFdq8N\nCxYUvXZBBLvXXhAUu6jYUdSrH6JYsAteu1KkKCJYtrALLL33trv5/pgzJ5Nkkswkk3POwr7Pw7Mp\nk8mkcObNr7w/g4xaSRivJqkJzKOZ/Dqb4WkkfrSZHV5Bd5rNDBgfBEHBs6zRWLZDEX6z1azwG0ek\nekILQvpUI1nIksg9jnemoSh4RIJH8IJY22SOU4Uw/g/IJo+ohirSdj6Aqcz6NZqmXQwStX+zrus2\nv4+maSMAjACASLPGKHyckKv8y0mgPy9jcPNl/Am/6WvmeK5rOwwE/HPHGKg1rfRBznkfNBfwrli7\nLvgJkxyPlP4M4GfclkWqN6B9urK+rVmjPJRMkYsvs1qZ4oUbi/kSKE/mOlvGYrhF/DytXaRWnCBb\npeBSazLM1+ZVmoCgv4io+C6Q+29y3/eeZidudT5JTCmxeIL9bctol4qZC8yTn0yCAM+FpTK70cka\nIyM3cTDASlBUkoogwelB+gwDQVzSMtfo5/qCWkbdLGAi70e8nklg0qZpWm0ApwO4I7rpBQD3A9Cj\nf58AcLn1OF3XJwGYBABpWelxkzynWaN6QQ/Tdm22UeZHJpbNL2IxSOf5D0RnYUpEaG4XgKOu3mde\nPRMAyVS0ZTPOjP69xYmM2a2DrDVRdJxNZpk1+LYMFNPe80vClj1MxrhsBlnPvp3cK9Y6ZoJduxkA\nkD9craWIl6XcGQ6k5nyg6PSoS75dW9OuilWrHc9B3zOKtHn86hVOSQUAsON8Yk1s8LY61zZ1iTqh\nOrtD2d+23j3qxO+gigAAIABJREFUxO23zWlCiTcBU5156mUtcbMSebV1gx8i5df1pcpd6fZhwGsf\n9Pyi4/J7nAr5E5E+VF6/n/ACGaiwtJ0M4Bdd19cBAP0LAJqmvQzgU5nOIp1JPFDln/Y4H6tFTQS1\nZy7wbuSA8jsIqam1A2i+xB75w7MkAA7WhCuJ67f+ScsAABvONSZTnnQJGwwPANsutFcJAIDaZ0bj\n7l7i7nZF3mnme+zpnubIgfx4z9NS5xzykndAuhUiQewAgK8dyGacvXJuFjW2osa8R737avCOQZRS\n2zm3yzxvMYr+eySK/kukW1KQuNJPxo8p50fV8p4eClYcChk9NRGEmZ3nZ3LnkUaRa42Hq8zrXiUq\nHkqFbEk8oVJDTtR16RajF2/IxGOG9duWoqCPYWBco5qmsWojQwAsUXCOGtSgBjWoQQ1qUINDGoEs\nbZqm1QPwTwBXMZsf1TStJ4h7tMyyTzn2Dpaz3Ex45lncmdXX5A6lNTCndyHrWlqa7Ti3WJz3y4k1\n5Ox0dXVJ3fD7hB7EVTVNbb/7Tulj25b2ubuZaki6/f7rA3rYtmlzfrNts4K64UyR+QlEVVSTjBWT\nFUGkFT+z+L65n4Ga/f7aZ3zbTOkkFhtYsWo1Tv/DXJ7s4y6cWmgwXMlW92ibJ2Zj7Y3Eglz3n0Z2\n9Lbjc9HoFLGUcy+c2LYnVt5tuN7bP8B3dfZZVIk+iyqNdfxo7Bxqbz+/Z0TJ+KoLZLMsAbsYKrVQ\n+I3FCVtklwdVcXss/MQgicYehhGzFjb8PtewYu9kXMtuYQAiMZpBYD2HmxtaxkUvM75ApE3X9d0A\nmlm2XSTbT1rZ7lgCAjrncdsUvW4X4wSAvEudydT6jzqh5RkWbbNnjEVDW01NfdKi5/oZ4xotnx26\n8i5zjFn78f7ieti6o7GsUfmYdQDAlkuMmLUR90WXLwGa/Ne/mO+d7SyyfWXALZnuhHf5uAHocO8c\nrKbM2oKMa7cBl5hdghXlqwAA2eBXbaD6b7FxibphGTjGxSnEyve6AQDanyNvtM48bzF2DzHeS3Y5\n0bCSsLIHnOMjM++eg5OXbsVX/MeftChcXM8zuN2PK5I3EYWl72Xtz88EqMp1xIvPU6nzpnKCF+2P\ntlEZqK/ifid7/GMY2oaqIBMCQK+j2ldEyJtvtnYVKTS+0FJIqRfvkT5254xs0/ph0Rg1HtjkgF1D\nsh3biUK07mikq71KwfvRMKu2TOkgWwzbKZl+h6YUq/vvSMh5WakRK6i+mVf25cp7GAvT/eqD6Z0s\na4lCpGHDRA+h2iHMySVI3zITvWophSCxXckSNyeCZMv6ZPfJEMEw7m+yxbcmyztjRdKSNivyLrWX\nuEnNzAAy7TUKK8qMMkx7zjSsJ3U/PPjlBvyg9GFnC0fW7c4WNae6ozxXaPGThiVtyPT+yL3R2UJ1\n8tKtuKChwdJZkdtaXxqVMlq8MAfrPxIjsmHg81XmWqantDtS+TmWvdUTtUKSvxV1h7JZo6KuYieX\nqBNIvWF+8kTOzYkTZI4HnNxwXgQlqPXrUIBfPTkv15zbOWSC9d3Ok0jSEI+g+jCvz0/WbliZvqo1\nDZOCtDXpegD4gywbWaP2uDIA6P/bgdjygjPUnH96F35mpkpsmE9csRvuj7pko0ULyu5nCROZtDLv\nEXM9dhj6u71yggvYeze3Ry3h4yhSGvAtUVU7EmMZY0FdoR+vssfg/bTXkJd4NOdwAKQs2Oi7+5vK\nbLUNgRsUP2WQ1WEf9UfuDeQkphJsY8mfDmPEiM7yaYfzdzgYjutNN7vqt99onJt9/9j3jlt3lIGb\nRIgbrPV7G/3fwU3I8rvvBn4ny37dpEEQTzJnHX+Q2B1Z0qLafRukL9XgvTdu95ZnOQvj3XIidrxx\nhpUlKxoiIEPOVMuvqEZSkDYeivrs82zDWtRUgY2ZSsFW0/a2Q/7gukPPTu+PIX9swK3HMuomf5jJ\nII+IOcXpAeYSRa3P/JNbw3P52AJgnn2yr1wqV1rKzZomgy2X2i12TV5X03dYSDVpnpnJp5NVqWw8\nvc5fuPv9ovBFe0xdKuyVH5yQd4nYeFo/qc5tW7l9O3f7vlPtSS1uONitaSxEfszjadmgMVVObkYe\nGfOaBEWTKJzO6zTZBxVQ9YtEiqn6hR8LT1jWr6D9hq1359VXkNg51e9J0pC2EYXLMCnfOe5rzc0F\nvr/uVaL04QGBSc6Wz/Kk602KYPOn+QCApoO9a49S9XpR5P7710AWNTd3KMXGM+RjDMOAaM3RHj9b\ncm4+AGDR/6WWNYCU8No4YgCaT5rDtarxSBsAzOn/MlmIeaPJ+0fr1F5XHE22KQau+do5Dyj/KnHB\nulRO2IETVH08sVZJK9j7WN3gFYyuOvjdL1SMI6xrkbHY+JlUg4w5GZ6dKFRZkax9sVnKsiSet99q\ntfM6Xua6VCZ8qDin7DmShrSJYspS5gv+Lfe2DbCXG8eW9q8yAPxC2W2H/BFbnlZuJmdD4Rz7xVrV\nKo+NxjYdS/7U/tUQyqXlubr/oqE7fsHiI8MXQV1/DXGHze1BiMLuIf3Q1u0AC1j32v2l/In/nixv\ny4q1CgVgrkTBwok4tXjBnTB3+nKkbVv+ZfZ4yHhg2SPkfcm+LbmtjSz+Ny8qEfIfY1tGPtHLpv9v\nvHDXskVghXXHZxs/SvyghxpYITKxyIr0+nWX8Y6Tdc+FBeu5whB49UsUZeLiRM+j4hgVSIQUTJjw\n675NxPVXO9KWc4H4f0q3IPVIF2KVqvzDbJUqeYwlZtVnshWBVVLEDbJyI8P+Wg3gfdv2qa+708PB\nS7fg065NbNs7LjDH3C3/nLSloMfYJF0CwC1rlZc1SuVUXl/xk7Gxt1Gvk5I2K95cOcu27aL2A5E/\n0v6BseytnjGLGgAse9RYzua8n9SaRisjsNg+jFiyGk41rFYThr0GQDwz2Qo2Rq1FtEwYHF6zTVfy\n70ezV+ag7dwGaBvV50tU9nCiocpa4DShJms2nAqEaT0JIikianXixV+F5eoT2eemhcbbTuFEXp3u\ng8yHhNu53UikSuuibFtRXTcZJCVpY+Nh0j4LVoPowr+MDEdREVMRmMkdQc4t8iRPO4IEfOu/ymua\nuAWus4HudaPKHu3Hz46V5kp/iBzr9LLtG3WAu90Jf3iHIMasapFvZex83lgxxrjWOmXu+wEgY6xx\n39zqdiYLds7IdpWW8Uu4WBRPoOTL3fJ7RWEpAGByflbgc7qh5HGeq3QGZ1v1gsof8LCyR1XFbwXV\n4PLq19pe1OoWZsJHmPp4Ivv9ZEMmEl7udBXXIPpM4mU9DPqOJCVpk0XZ+AHIvEu9VYy1cMgg8q0l\nILxxI35DDjZ8TDTWIqjC/F7R6mCr+HISa24yk5E0bJQbqAtOb8d3d6Z935q7fd+gta79TS83rEjn\nFJ3pf2AcsCSMh8LnzbFihZN7gzrH1x3X27P//Cv816/loeDH0bZtObw6nQHAS0qgljbX466xi0JT\nXUNRsO5QgLWaGvfx795yHwWHKsKKEfPbZxDrnYyYbSLkIPy6RxMpXeGWsevXxZ7s8Pu+qHjPRN7/\nsO9p0pO2y/4mbqkHJxXYXGaA3I///hN74z8je6M2M3nQCUk0XofCalWLdMwFOuai8m+ifUUtWoBh\n1QqCwkksiapEg7cDdqhpAIAT2zkkJGiWdd3dAqP1MctQ6PN/Fx4KJarYZWyzPddx7n3IyJFYSRib\nqcuDdX/mmXNQ9Ew/FD1DqgsM/MioMvD6qS9FY7qAWFzXueTPjS56eH7BlrVKhPAulexYcZ/xvm+7\nz/wx0RHy1nKtyvoCHnwQlXCQ7cu6LWjGJRtkbj1XPC0hqvqTjUsKEsck8lxlx+s2jqB9U4ulqJUy\nTMIaliVZFKJSK/FG0pM2LxT8th9ln/L3tTzjL0Ay5Inn4qTldTLv9rbmbf8iBw1PLvFspwJtJljI\n4AR+u5bPckijBwlzgpNFTevjrHVnltVwRtawaFIC4ykuv5MQgA73zsan9zZBJI9kGEeYameVRc6u\nQwDIH8UXVabVLVqfZNT4CvOHovmkOdG/xjatNylRBfo3Cn2BuWQVdUsC5O/LV5yFqqOPAPAV91zp\ncw8zrZf33+lv0JD/oLHCSsD3ndIHOIXp36O+LcUXXRsHGkd1g1fGmQrpAdmszHhNnkEkHlRZESlE\n5UtE+nbTMOMlefjVFOM91zB/29zeLS/S6xXj57UvkZm/Th84sv2TdmJi50lJ2kxxbAwR4VnVCqLz\n/ObLnKwY79q2kDg3Euv26oqjfI5SDmyJpC2bXBoGRJNZTbFl4GZbvBuPONX6zlzs7MAxaxz7jURd\nvDT7laJkCrHU5T4lrtpfeSwTR6aoPFlYAr9TokkDF+Udb9re8RZ+1utDt/THussM66VXtms8wSYg\nUMTi4RwIPwBUHcX+8JRy22TcF8yabE0+yL41ee6bSshMoqJyB7xj4ol4ndNJUkJk0heJkVORuOE3\nQ1e0T5UIOr5kcq3KJFOwiIcL2Xp80P5SAh1dgxrUoAY1qEENalCDuCApLW0qYcoYPTFx4xBFi9OZ\nagarjMXOj5nNc5UufRS+Zq60EESn7MZi4jqc2Ns9iN0rhm1IOkkGSO3QHvcu+wjjsp3rdDq5wSq+\nykAFo66XegJf1NXJ6tr0Nf/Wm6q9e/H88p9s2+8pP820vmngFtM6K7Ny3YUfcXr+1vGc07u0QPYF\ni2Ju3CCoGsSPXUz5/lfudidMzs9CpHkzRJqLH1O5Ucy0vPZGcyxcrZ3Ehd/s5YPT6hYUYVh1woSK\n8ap0jcm6Nr32W2P/ZPoSva4gWnJuYxK1OKmI72ItpireW1X3xCueL8g5VD63pCBtW5aSBIO9p5mz\n/Op8Il7gnU7I1lqc24eS9YYnl6D2TLVZgG4QTT5wk/pgM0bZGC4AKH2IEJOsO+QmtPSHZmP19C6k\nXNcWs5hwomAiqgcxqAAzm1BjdfkXvmwknOQzAfw2iY2jyZ/P/2kkgKS2M3avPdsiZzLIfWy5N4lX\nHGBJ2C6GUNZ3kCWxZhyngZQH88o4pqCJHdas1OqAwsX1AAQjG05uHtatF5bkhNt4grRzI3BBEijY\nSTcZ3HfJMIZ4QCRJRZS4yLq7RfXdRCAbAxiUjPl5V5OCtMlg2+e5AIBGp4gF7XmBBlkXPUcyAPNG\n2yUPwsBP66KT3RewJS6UcrIMcyevC31MBb+J17lUiUiuXfOrspjETqU/aJBfJ8taDH0JgWm6lAm6\nnyeWxbrxKnLPe40dgOYv8YnwqA72+Mdmdp3cpAS1qOXMr2PZUwclffYCAPLm2+sVONUA7ryQ/HQs\n2KBujIc6VJMN60QabwLBszolyiKYTOel90RGUy1IdrEI+Yi37IpKuH3UuH3M+Ek8SQYkDWmblJ8N\nnOa8X6TuaPkdBcAP/H3b72gVW37kv+SvqDWMzRpl646uuo24c9o9Mjsm9QGYLX0UNNA60rUjmgPY\n8niVaT/VMaNuRCvsGZKtuO0SiaI3DMtg3sVqi6m7ofip/kK1KTPn1TWtL9ksfo7U1vb7XbF2nc0d\naoVsZQlRpPxISJh4+keAc/1EfsSeWW4w1Oc3OpvuIh1zTev7BvE/sE5YQv5PfNWNL9dysMDLDSSa\nuSmzz481jGdlELHiiYrZihKGZJw0EyU/IZqxG+SeOb0/fjNXgyKe99fq2vaCrBs7jGtJGtIGyLlD\neVChh3YwgI1h231WP+w+qx/qfUAsiKUPDwAYbyS16rFk1A1ardqm9dxLiXvXj4BI4Qt9AcyMWdWE\njokWVeeVfJJF3Uf4sXMsLozWQE3lawoDAPSBxn9MbdYiz4xR1iW69WLDqtr4DfnYrRazzdewoWBr\nbDnSrGlsuXLTZqRmZmD5uUZbttB7autWKD0NKD/P8tFxPbsSzKzoVIuVPlMrBY094zsCnTYpoDoD\n7VCFlVi6WVNkJ06VZNHPpB1vi6hsdqUskQtKdEVdoryPIhH3aTyet98PHTcIkTZN014FMBjAel3X\nu0W3NQXwDoBMAGUAhuq6vkXTNA3A0yBKTLsBXKrretzMLmOXEcIyJruXR0tnWIViASBSvgEVa8Ri\ncGgMj1vMTpcmjLvToqmVdfsce+UBS59sLBst+xW05Nf3N9vdst+DbKu1ldxXK2kLij93t0GOZdgl\nffbaSk8B5soH60ex+4nVsvSsw2DDWXKklMX2C4zki4Zvicd8sbAmAMgG/lPwkiAAYMyqwab1mFAx\ngNaXhe+7rH/SsljJtI1jyTua/db60M97KELFpB7vGBwn8Va3Pp0sfmEhzHhAketnxyAzjiD3RoXl\nUFVSiOz1xgvss0om66+ope11AM8CeIPZdjuAr3Vdf1jTtNuj67cBOBlAXvRfPwAvRP+6gi1wzSvB\nk8zYO5h1aYYowhYAj5VR0mEmH7dkepc2YqEfIHFvha8Y5Z/yryQJHvF0ifpBWd895g3HGos0ju3B\n0nnkTQZwzV3mezOhzE78bspUX+WAIqV+fQAgdUft9epDQeunXazVN4n1UVVSZlrXUt1/ZrysptUx\nAYFFolxrKpBMk5UTVGYPWvvjEclkhJuFR8QFGMTF7gcs4YzXOyai1SeLoEkQoWWP6rr+g6ZpmZbN\nZwA4Jrr8XwDfgZC2MwC8oeu6DmCupmmNNU1ro+u6s3KrBHgJCG51R1f/x26xoWj7OJmgUurXR8db\niQJ9lWPr+KDsne4AgOzr+OW5Uno0QdVvf3L3BUWtr/jSIDR7774RVzgeu/I9s6J/+3OWOLQ0o6TP\nXhS/aVijci8ilihqVav6ur1QP/u+zERbrAms3q8aspa1/OGM2TFK2vaf1AdXXsWvBWvDqO2xxcpN\n5qA91h3qhb2D+2L9JQbJzTj3d1zbYSDbm3BfTvhkFXnXMVOsfd0Txd3o1RFuAqE8iMTJJRPp8hqL\naPyWH7jFHomMS6Qv3jndCGA8LGphwi8pBvwJR7ud248bmj02rHurSlaGRZCYtlaUiOm6vkbTtJbR\n7e2AaE4/QXl0m4m0aZo2AsAIAKiDeo4nea1jh9jy2uvNBMzVKuAT95fOByy1Eu/J6gO0cQlqcgCt\nTWlF3rU/o7y/WeLkZHQhMX3viPevF/QQbmu1qG24OmohutrcLtEK/icu2W5a/8Ihabbl88az3/6U\nnLVQFFaXKM+qxlqIAQAjnPXnALsVWSSObcbk57nbT2/XxxZPh1GGe9SeKeqM5S5JQLLQK7wJ3Yar\nBwAfiveZ6PdSBqK/bW4usTAmESdrip+JUzZWR/Y8QbSynAhBokmPnwk8LNItQnqc3NhB+hQFj/Cy\n64n6GBHJOJUdm+x9CiMRgVfp2Ranruv6JACTAKCh1tRfIUwAP5/fFZHOQOWfRQBILNvmywYAl5H9\ndTaRrt++7zHbsaMeD1bCanfbquhfprBEYQu06LnL4QhxOFnTSDkskm23StBdlQgYtTIBWvrotu+G\nost4sbjAeCDyrUGgIg0bOrYreYyQNV5d2mRz5bOad1RIpvIYM5GMfEfGPLqoEADwXF4+gL3C57Bm\n0lpLpgmN84U5Ma1BHmT1B5MJqn7bALP1yapDVl0RxOqVDJAlM4mAKPFzs+IlmuRaIUIsg8rcqCD6\nKrLF3RCEtK2jbk9N09oAoNHH5QBYn1Y6gNW2oxloabXR6X5DOqESQNHEqG7adYZuWhiWtYqvMpT3\nSZMF8hAfgsImIGwZSFxim64wT4jW732e5WLtDQVYewOxZrZ+yn6vqft0zYedYRVpaH/OEqT06Gxs\nsJd8DYyri4oRK6pbBLyQl2trk1LH2bpUtddMTF5eQYL7h2eorT+76nZnlzwPLX4lcYKrLjVc4lnD\nfkPVLkL+T28n6BpNclizRkWhHdEVgLsQdbIiv/tuAM1M23gTSrwsa0ER74mcd4/8xBGxhNCNoKi0\nRsrAaqmR1U1zsjaqfl4q7kOY7shEIWgSRrzcox8DuATAw9G/HzHbr9E07W2QBIRtquLZRMErVzTq\ntaNMrqSywXWBh4D2bK0oRF2hDtB6G3FbBxqlIetjMsmmfm3Egp24ZDuwBJjZzbDcFE4ifQ7oSgjH\npoFbsP9/HZAC4vtLHUekGaqOPgJOBaqWPTrAsYj2zcXGZPZEblfH8Vux5iaDXLSZEB+5lIrlK20l\nrN4YOBkAMC77SEwf8k/ucfWm/wwUhT483JnVF2XjCeHNBP9+N/rJPAlvO2qTyXXf7mG5e7n/xN6O\n+4hllaBoawvb/vonkf/0k1fYs0uvECSjPDfq0rHAYV9wsnFB9Om2XjQAjd/k35+yB8SSM7RKswZi\nSj3yaVG1e7fQ8YciRAVBZQPLRYL5rRIbvOOcCI6XtUfkuvzGLcVbQsNpn9s9joe1TuQ8Xs8zqOVK\ndB97Lp4FNiwZF3q9ov/PvPqyHqfiXRSV/JgKknTQXNO0cgBjQMjaNE3TrgCwAgBVgPocRO6jGETy\n47LAoxTA1ov4E4XTxBIGdp7bD8D/4nY+bfZvwm3NGa5AnU/FdM7WjyrA9Y8TQtIS4mTEVnYJiDqM\nwgFNQHBLJNk+zBr7xpfRCAuRJk1iy5VbiGW54nj/0jTC5/3uF9P/j8Yhn48lYreWeFSkGGYsPn64\nZ5L5IQlV2ZFhjCEsyATrx2tCd+s/zPsjcj2JdtE6ZauGjXhmn9LzUcgSYFUQzR4d5rDreE5bHcBo\nmUHo+/ajsrgUU1YS8U4qaBoPpJ6wImZZAQCMNwo4umWlhoXM8xY77kupUwdrjzlgc/OpAi3Q7YU2\nZ/Lj7XhxeJGOuUy1CG/Um+5cRoznDuWBWjYp8kcE068LgqfKDKJ78xGnBuqLre1JLXs7ricJMqc+\nEXVrhxBC4Ab6w3TSaRdy9z82lG/51Rca1mFav7RBnMIJ4onCxfXiPrFQuAVvJ8I9JWLpS7TbzOn8\nTkRNZrxeljgRJFNyhcp3WiSzNN7XqoLUh/H/PqkqIshg91nkq7zBnySGq/Gbc7DjPHsWId3W4B1/\n4qgs9AWMjEUAC0nF8b2Q8jBZJq5VuwgXWwd0dg+7oO3rUXfYpRlH4eljT0TFyvLYvpK3egIgcg05\nF5CXxmpp8wtao1UUnSZuQreFJFGjkK8oYsLgpeayUJ/1sLsErcj7zwIgNVUoa9EJldtJ1urYZQtx\nyVSz1daagLDtKLsWX+unZyPSNZq52bUjs8cnidJIPs/qATs9Gpqx/yS7e7/2DD5pfeby82zbaHks\nDLbtcoV2wOzWr1r8l1wHDCjRPXvBCABAxvhAsfzVDrIuRxWTmaqJRoQYerWxXpfT2FRbdFRm0arq\nnx4XNBPT6fwiGZ+qRWZVaqL5Ic1BryHIs1Bx/1K8m9SgBjWoQQ1qUIMa1CDRqLaWNgoq9SECbRbD\ncgcb1hQ/btDUrxdi2F8kKXba4Pi5c2WRmkmyY3kxbCLJB6weGgDkjea7L1MaGAHzVTt2GDs6Ors0\n86/2jqsTsZ6ldiDJyhXLV7q2azjVYm19xLNr0n9mhl2Ytq+l1Nk8j9gtF9SeSSpK4FJx3T0nNyjP\n0gb4i+1s+io5ZkLZHOBe877L75GvBHH0YrNb/8fuYjpy1TFrVAX8ut6CukFVW1bCQlC5ClFdMaf7\nGVTWQWWsmlP2qEj/XseGZeXzOsYKlbGdbkkOfuD3nvkZe9KSNir1UTrVPJFlDXMOvpdxgWbdMYdo\niZ0tekQpP7jegosbkcn7YoY/9PkiuSQbrKK/edeSe93slfjF8EU65zFr3lpn1MUKAEt6qalboVrq\no3Ip0UhbczNf8oMmH1hBpEyAF/K4u7lYP9p8jpbPuRDwlAh/84/+6qDWIBjcBGi9jhHtP0zNs+pA\n5Ch4LtZ4j0Gmvapxqr5eL/LlRbT8Zv56HZsMSTrxRlKRNj8JCGX3m7/4M+8RJx53f3ABd7uf4uJu\n+OtUs6L9KRhpWt89hJCoOpv3M1vtFi2vBAS9oIdURqkbNg23W1J6DTf+g9jqeApi2+fE8tb0ZmMb\nKwHyadcm1kNsaDG7sWl9C5Mm03ZuA7SFOZ5qNYDOC8mr/mcvYrmzZ5JSCATeSeDvA6RQiGgSxeuM\ndMcl5/HzebQ5zs/YGr+2+bIBaCpwLivOH9UXxdFKE6d/2B96U+PdzLv4F+Aix0MBAKnpRkJPRfkq\nl5aHBvxMImEEYidrMLfXuILGdVn7CjIWmX5ltfj8SGRYiVGiMn29IDIumQ+XIHIiQeRXEp3Qkxyk\nrUE9VPaxqLZ/q0ZpfuvFjNSBQMkgURDy4aoZ7AhW180vLg1gJbrqmG/MG34Hvjm8Ppa9ZbyAjb71\n3T3q/2AkD+wd7t0+7XujRNi+QWuhHdE1KV1iVqsvAHwz8DkA3ppo5e+TLMr0s5di35eZse0RQRdt\nIFRV2qQ3Hs053KExQZ1P5wEneJcHM360zEQ5hSFtALn+qdFIhobvRV3pwzguawA3ZEatiO95nj7p\nkd99N2bODCcLLqzJI8ikn2zEkpK9RCUrWJ+RSvKpEiqSTxKRJc07p9c4RMcZRBPR6TmrINbJQdpc\nQN2hH6+KWhCiH+0nXEusUzKWNVvfkhY1qzL71E5tAQARxojCsxbGxn4Qo/B+cm9yb5gLQLzmpR8M\naW4m9K9ikPCxPWMeQUIWSnY2j+3bcfRGYHzQ0alD0fBaAID8yxfY9lF3aPrcqADuRYehvL94pumW\nz/Jw6dDuLi2cpWd4WP9RJ7Q8Qz5jtPD5vsAyIH/UPCAqrjt2Gf2oif6NKp2MyQ5f0y6esP6AhxFL\nJAqnjE3RTM9DAYmSbhGFCqISb4gQG1miq/o6/RJtHpl1Inwy/ScHaduxW9qyVu+Dn0mQfaa9DJUt\naDyKZQ+LB09nCxC6e5eRMY//l3usm7UMUfdfiJzDzClkPI2W2asg8GQ+rDUfAaJObwWV+QB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eVYzBcMMVlRcrvyvW64aKHFjf0ekHG+87tFExBUwjFB4Q7DPbp1a320a7kV9fsSPbk9M7NwmItV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\nZl6pbSrleBWMUsxtJZ/XgBL+VmReK85YxYKZc30BW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_output('files/medium.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see in the plot on the left, a random colored pattern emerges from our algorithm, since we try to slice at random patterns every time.\n", "\n", "But how can we improve our existing solution? One possibility is to improve its performance. Although the algorithm runs in a couple of seconds for this grid size, it will be slow on the largest grid. So let's profile it to see what takes the most time." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%load_ext line_profiler" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 20000/20000 [00:14<00:00, 1354.32it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "score: 28839\n", "Timer unit: 1e-06 s\n", "\n", "Total time: 14.6957 s\n", "File: \n", "Function: greedy1 at line 47\n", "\n", "Line # Hits Time Per Hit % Time Line Contents\n", "==============================================================\n", " 47 def greedy1(fname):\n", " 48 \"\"\"Greedy approach to the pizza slice problem.\n", " 49 We try one random shape at one random point each turn.\"\"\"\n", " 50 1 26742.0 26742.0 0.2 R, C, L, H, pizza = read_input_pizza(fname)\n", " 51 1 5.0 5.0 0.0 possible_shapes = [(4, 2), (2, 4), (3, 3), (5, 2), (2, 5), (11, 1), (1,11), (2, 6), (6, 2), (3, 4), (4, 3)]\n", " 52 1 197.0 197.0 0.0 slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " 53 1 2.0 2.0 0.0 pizza_slices = [] # stores the slices we made\n", " 54 20001 289406.0 14.5 2.0 for _ in tqdm.tqdm(range(20000)):\n", " 55 20000 13496188.0 674.8 91.8 location = get_random_available_location(slice_mask)\n", " 56 20000 193251.0 9.7 1.3 shape = possible_shapes[randint(0, len(possible_shapes) - 1)]\n", " 57 20000 649720.0 32.5 4.4 if satisfy_constraints(location, shape, slice_mask, pizza, L, H):\n", " 58 2690 17756.0 6.6 0.1 cut_slice(location, shape, pizza_slices, slice_mask)\n", " 59 1 88.0 88.0 0.0 print('\\n')\n", " 60 1 1273.0 1273.0 0.0 print(f\"score: {score(pizza_slices)}\")\n", " 61 1 21035.0 21035.0 0.1 write_output_pizza(fname.split('.')[0]+'.out', pizza_slices)\n", "\n" ] } ], "source": [ "r = %lprun -r -f greedy1 greedy1('files/medium.in')\n", "r.print_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I was very surprised at this point: the function that takes the most time is the one selecting one random empty locaiton on the grid! Why is that? Well, it turns out at each iteration we are going over 200x250 = 50000 points to check whether they are zero or one. This takes a lot of time. We could do better with different data structures:\n", "\n", "- first, a set of available locations for fast lookup\n", "- second, a list of available locations for fast random indexing\n", "\n", "Also:\n", "\n", "- let's programmatically generate all possible shapes for the given grid as a function of its minimum number of ingredients and maximum slice size\n", "- and let's loop at a given location over all of these possible slice shapes before choosing the best one\n", "\n", "With this in mind, here's an upgraded version of the previous function." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# An upgraded version" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import sympy\n", "from random import shuffle\n", "\n", "def generate_all_shapes(L, H):\n", " \"\"\"Generates all possible tuples that satisfy the problem's shape constraints.\"\"\"\n", " possible_shapes = []\n", " for size in range(2*L, H+1):\n", " factors = sympy.factorint(size)\n", " if len(factors) == 1:\n", " prime = list(factors.keys())[0]\n", " max_exp = list(factors.values())[0]\n", " for exp in range(0, max_exp+1):\n", " factor1 = prime ** exp\n", " factor2 = prime ** (max_exp - exp)\n", " possible_shapes.append((factor1, factor2))\n", " elif len(factors) == 2:\n", " prime1, prime2 = list(factors.keys())\n", " max_exp1, max_exp2 = list(factors.values())\n", " for exp1 in range(0, max_exp1+1):\n", " for exp2 in range(0, max_exp2+1):\n", " factor1 = prime1 ** (max_exp1 - exp1) * prime2 ** (max_exp2 - exp2)\n", " factor2 = prime1 ** (exp1) * prime2 ** (exp2)\n", " possible_shapes.append((factor1, factor2))\n", " else:\n", " raise NotImplementedError\n", " return possible_shapes\n", "\n", "def get_random_available_location_set2(random_locations, empty_cells):\n", " \"\"\"Selects a random location that is still in empty_cells.\"\"\"\n", " while len(random_locations) > 0:\n", " candidate = random_locations.pop()\n", " if candidate in empty_cells:\n", " return candidate\n", " else:\n", " return None\n", "\n", "def update_empty_cells(location, shape, empty_cells):\n", " \"\"\"Updates the empty cell dictionary.\"\"\"\n", " r, c = location\n", " dr, dc = shape\n", " for rr in range(r, r+dr):\n", " for cc in range(c, c+dc):\n", " empty_cells.discard((rr, cc))\n", " \n", "def greedy5(fname, iters=30000):\n", " \"\"\"Greedy approach to pizza slicing.\n", " Fifth iteration: randomize the locations in which we try to add stuff.\"\"\"\n", " R, C, L, H, pizza = read_input_pizza(fname)\n", " possible_shapes = generate_all_shapes(L, H)\n", " slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " pizza_slices = [] # stores the slices we made\n", " empty_cells = set(tuple(args) for args in np.transpose(np.nonzero(1-slice_mask)).tolist())\n", " random_locations = list(x for x in empty_cells)\n", " shuffle(random_locations)\n", " current_score = 0\n", " for _ in tqdm.tqdm(range(iters)):\n", " location = get_random_available_location_set2(random_locations, empty_cells)\n", " if location is None:\n", " break\n", " selected_shapes = []\n", " for shape in possible_shapes:\n", " if satisfy_constraints(location, shape, slice_mask, pizza, L, H):\n", " selected_shapes.append(shape)\n", " if len(selected_shapes) > 0:\n", " current_score = score(pizza_slices)\n", " shape = max(selected_shapes, key=lambda shp: current_score + shp[0]*shp[1])\n", " cut_slice(location, shape, pizza_slices, slice_mask)\n", " update_empty_cells(location, shape, empty_cells)\n", " print('\\n')\n", " print(f\"score: {score(pizza_slices)}\")\n", " write_output_pizza(fname.split('.')[0] + '.out', pizza_slices)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 51%|█████ | 15193/30000 [00:03<00:03, 4484.51it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "score: 39850\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "greedy5('files/medium.in')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our score is greatly improved by the new algorithm, especially since we now try every possible solution. What does the pizza tiling now look like?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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NSpXQtoTwCXUDE3NXGj8OD5lpcs6YX4k9ic1Nc7Wa4s2VBzqWu7RYp0W6fcJp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\nCW1qDEvohagLxVJJux6VPBwmAq0mKxecl2oED3W2N2K2+EX5e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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_output('files/medium.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks a lot more compact although it's not completely yellow. Also, it runs much faster now. How much faster?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 53%|█████▎ | 15817/30000 [00:08<00:07, 1814.87it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "score: 39889\n", "Timer unit: 1e-06 s\n", "\n", "Total time: 7.95831 s\n", "File: \n", "Function: greedy5 at line 45\n", "\n", "Line # Hits Time Per Hit % Time Line Contents\n", "==============================================================\n", " 45 def greedy5(fname, iters=30000):\n", " 46 \"\"\"Greedy approach to pizza slicing.\n", " 47 Fifth iteration: randomize the locations in which we try to add stuff.\"\"\"\n", " 48 1 48290.0 48290.0 0.6 R, C, L, H, pizza = read_input_pizza(fname)\n", " 49 1 294.0 294.0 0.0 possible_shapes = generate_all_shapes(L, H)\n", " 50 1 80.0 80.0 0.0 slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " 51 1 3.0 3.0 0.0 pizza_slices = [] # stores the slices we made\n", " 52 1 101737.0 101737.0 1.3 empty_cells = set(tuple(args) for args in np.transpose(np.nonzero(1-slice_mask)).tolist())\n", " 53 1 16380.0 16380.0 0.2 random_locations = list(x for x in empty_cells)\n", " 54 1 159669.0 159669.0 2.0 shuffle(random_locations)\n", " 55 1 3.0 3.0 0.0 current_score = 0\n", " 56 15909 162496.0 10.2 2.0 for _ in tqdm.tqdm(range(iters)):\n", " 57 15909 85501.0 5.4 1.1 location = get_random_available_location_set2(random_locations, empty_cells)\n", " 58 15909 20380.0 1.3 0.3 if location is None:\n", " 59 1 241.0 241.0 0.0 break\n", " 60 15908 21161.0 1.3 0.3 selected_shapes = []\n", " 61 318160 433763.0 1.4 5.5 for shape in possible_shapes:\n", " 62 302252 4161306.0 13.8 52.3 if satisfy_constraints(location, shape, slice_mask, pizza, L, H):\n", " 63 17074 29113.0 1.7 0.4 selected_shapes.append(shape)\n", " 64 15908 24676.0 1.6 0.3 if len(selected_shapes) > 0:\n", " 65 3472 2565112.0 738.8 32.2 current_score = score(pizza_slices)\n", " 66 3472 25487.0 7.3 0.3 shape = max(selected_shapes, key=lambda shp: current_score + shp[0]*shp[1])\n", " 67 3472 21099.0 6.1 0.3 cut_slice(location, shape, pizza_slices, slice_mask)\n", " 68 3472 67069.0 19.3 0.8 update_empty_cells(location, shape, empty_cells)\n", " 69 1 78.0 78.0 0.0 print('\\n')\n", " 70 1 1492.0 1492.0 0.0 print(f\"score: {score(pizza_slices)}\")\n", " 71 1 12884.0 12884.0 0.2 write_output_pizza(fname.split('.')[0] + '.out', pizza_slices)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "r = %lprun -r -f greedy5 greedy5('files/medium.in')\n", "r.print_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimization using Pythran " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see in the above timing, the two most intensive functions are the `score` function and the `satisfy_constraints` function.\n", "\n", "It turns out that with a little Pythran-fu, we can optimize these to run faster. [Pythran](http://serge-sans-paille.github.io/pythran-stories/pythran-tutorial.html) is a cool C++ compiler that knows how to handle Python and a lot of Numpy array stuff. By annotating the types we want, it can compile an optimized module from our functions.\n", "\n", "Let's demonstrate this on the two above functions." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "pizza_slices = [(305, 223, 7, 2), (172, 21, 14, 1), (538, 255, 14, 1), (861, 247, 7, 2), (634, 248, 2, 7), (518, 828, 14, 1)]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Disabling color, you really want to install colorlog.\n", "WARNING: Pythran support disabled for module: omp\n" ] } ], "source": [ "%load_ext pythran.magic" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%pythran\n", "#pythran export score_pythran((int, int, int, int) list)\n", "def score_pythran(pizza_slices):\n", " \"\"\"Computes score of given pizza_slices list.\"\"\"\n", " s = 0\n", " for pizza_slice in pizza_slices:\n", " s += pizza_slice[2] * pizza_slice[3]\n", " return s" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "723 ns ± 4.13 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%timeit score(pizza_slices)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "634 ns ± 11.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n" ] } ], "source": [ "%timeit score_pythran(pizza_slices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same for the constraints call:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%pythran\n", "#pythran export satisfy_constraints_pythran((int, int), (int, int), int[][], int[][], int, int)\n", "def satisfy_constraints_pythran(location, shape, slice_mask, pizza, L, H):\n", " \"\"\"Validates whether the given location and slice shape can be applied to the pizza.\"\"\"\n", " r, c = location\n", " dr, dc = shape\n", " if slice_mask[r:r+dr, c:c+dc].size == dr * dc:\n", " if (slice_mask[r:r+dr, c:c+dc] == 0).all():\n", " if dr * dc <= H:\n", " tomatoes = pizza[r:r+dr, c:c+dc].sum()\n", " mushrooms = dr * dc - tomatoes\n", " if tomatoes >= L and mushrooms >= L:\n", " return True\n", " return False" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "63.7 µs ± 295 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit satisfy_constraints((3, 4), (4, 1), np.ones((R, C), dtype=np.int), np.ones((R, C), dtype=np.int), L, H)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "54.2 µs ± 248 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%timeit satisfy_constraints_pythran((3, 4), (4, 1), np.ones((R, C), dtype=np.int), np.ones((R, C), dtype=np.int), L, H)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equipped with this, we can now rewrite our algorithm like so:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def greedy5_pythran(fname, iters=30000, disable_progress_bar=False):\n", " \"\"\"Greedy approach to pizza slicing.\n", " Fifth iteration: randomize the locations in which we try to add stuff.\"\"\"\n", " R, C, L, H, pizza = read_input_pizza(fname)\n", " possible_shapes = generate_all_shapes(L, H)\n", " slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " pizza_slices = [] # stores the slices we made\n", " empty_cells = set(tuple(args) for args in np.transpose(np.nonzero(1-slice_mask)).tolist())\n", " random_locations = list(x for x in empty_cells)\n", " shuffle(random_locations)\n", " current_score = 0\n", " for _ in tqdm.tqdm(range(iters), disable=disable_progress_bar):\n", " location = get_random_available_location_set2(random_locations, empty_cells)\n", " if location is None:\n", " break\n", " selected_shapes = []\n", " for shape in possible_shapes:\n", " if satisfy_constraints_pythran(location, shape, slice_mask, pizza, L, H):\n", " selected_shapes.append(shape)\n", " if len(selected_shapes) > 0:\n", " current_score = score_pythran(pizza_slices)\n", " shape = max(selected_shapes, key=lambda shp: current_score + shp[0]*shp[1])\n", " cut_slice(location, shape, pizza_slices, slice_mask)\n", " update_empty_cells(location, shape, empty_cells)\n", " return score(pizza_slices)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 39%|███▉ | 11779/30000 [00:00<00:00, 29440.63it/s]\n" ] }, { "data": { "text/plain": [ "39770" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "greedy5_pythran('files/medium.in')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This now runs almost instantly. Let's check the profiling again:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Timer unit: 1e-06 s\n", "\n", "Total time: 1.71979 s\n", "File: \n", "Function: greedy5_pythran at line 1\n", "\n", "Line # Hits Time Per Hit % Time Line Contents\n", "==============================================================\n", " 1 def greedy5_pythran(fname, iters=30000, disable_progress_bar=False):\n", " 2 \"\"\"Greedy approach to pizza slicing.\n", " 3 Fifth iteration: randomize the locations in which we try to add stuff.\"\"\"\n", " 4 1 30197.0 30197.0 1.8 R, C, L, H, pizza = read_input_pizza(fname)\n", " 5 1 469.0 469.0 0.0 possible_shapes = generate_all_shapes(L, H)\n", " 6 1 758.0 758.0 0.0 slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " 7 1 2.0 2.0 0.0 pizza_slices = [] # stores the slices we made\n", " 8 1 120025.0 120025.0 7.0 empty_cells = set(tuple(args) for args in np.transpose(np.nonzero(1-slice_mask)).tolist())\n", " 9 1 17072.0 17072.0 1.0 random_locations = list(x for x in empty_cells)\n", " 10 1 153681.0 153681.0 8.9 shuffle(random_locations)\n", " 11 1 2.0 2.0 0.0 current_score = 0\n", " 12 16045 38475.0 2.4 2.2 for _ in tqdm.tqdm(range(iters), disable=disable_progress_bar):\n", " 13 16045 70258.0 4.4 4.1 location = get_random_available_location_set2(random_locations, empty_cells)\n", " 14 16045 18906.0 1.2 1.1 if location is None:\n", " 15 1 13.0 13.0 0.0 break\n", " 16 16044 19013.0 1.2 1.1 selected_shapes = []\n", " 17 320880 370831.0 1.2 21.6 for shape in possible_shapes:\n", " 18 304836 562662.0 1.8 32.7 if satisfy_constraints_pythran(location, shape, slice_mask, pizza, L, H):\n", " 19 17050 21997.0 1.3 1.3 selected_shapes.append(shape)\n", " 20 16044 20851.0 1.3 1.2 if len(selected_shapes) > 0:\n", " 21 3455 183416.0 53.1 10.7 current_score = score_pythran(pizza_slices)\n", " 22 3455 18621.0 5.4 1.1 shape = max(selected_shapes, key=lambda shp: current_score + shp[0]*shp[1])\n", " 23 3455 15084.0 4.4 0.9 cut_slice(location, shape, pizza_slices, slice_mask)\n", " 24 3455 56096.0 16.2 3.3 update_empty_cells(location, shape, empty_cells)\n", " 25 1 1365.0 1365.0 0.1 return score(pizza_slices)\n", "\n" ] } ], "source": [ "r = %lprun -r -f greedy5_pythran greedy5_pythran('files/medium.in', disable_progress_bar=True)\n", "r.print_stats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have such a fast function, we can run further improve our algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Studying different ways of iterating through the pizza " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next idea here is to vary the way we sort the pizza locations before trying to slice the pizza. Instead of doing a random search as we did so far, let's explore more straightforward sortings: by row or column, decreasing as well as increasing." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def greedy5_pythran(fname, iters=30000, sorting_func=lambda item: item[0], disable_progress_bar=False):\n", " \"\"\"Greedy approach to pizza slicing.\n", " Fifth iteration: randomize the locations in which we try to add stuff.\"\"\"\n", " R, C, L, H, pizza = read_input_pizza(fname)\n", " possible_shapes = generate_all_shapes(L, H)\n", " slice_mask = np.zeros_like(pizza) # stores the sliced cells as 1 for easy lookup\n", " pizza_slices = [] # stores the slices we made\n", " empty_cells = set(tuple(args) for args in np.transpose(np.nonzero(1-slice_mask)).tolist())\n", " random_locations = list(x for x in empty_cells)\n", " random_locations = sorted(random_locations, key=sorting_func)\n", " current_score = 0\n", " for _ in tqdm.tqdm(range(iters), disable=disable_progress_bar):\n", " location = get_random_available_location_set2(random_locations, empty_cells)\n", " if location is None:\n", " break\n", " selected_shapes = []\n", " for shape in possible_shapes:\n", " if satisfy_constraints_pythran(location, shape, slice_mask, pizza, L, H):\n", " selected_shapes.append(shape)\n", " if len(selected_shapes) > 0:\n", " current_score = score_pythran(pizza_slices)\n", " shape = max(selected_shapes, key=lambda shp: current_score + shp[0]*shp[1])\n", " cut_slice(location, shape, pizza_slices, slice_mask)\n", " update_empty_cells(location, shape, empty_cells)\n", " write_output_pizza(fname.split('.')[0] + '.out', pizza_slices)\n", " return score(pizza_slices)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 49%|████▊ | 14595/30000 [00:00<00:00, 36472.30it/s]\n" ] }, { "data": { "image/png": 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MCDGMK5XbRnR1jqDpx1RdK1NtM23xGKuIiIiIiIiIiA6gFZm2LTZfia+7bp3C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\nFbvr+tERal6fjWz9QgQ7fdJM2whmXpD+fSyA6cz8cSLaFMAvsHwhwt8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "greedy5_pythran('files/medium.in', sorting_func=lambda item: item[0])\n", "show_output('files/medium.out')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 19%|█▉ | 5736/30000 [00:00<00:01, 14335.55it/s]\n" ] }, { "data": { "image/png": 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FUBfaUVPaKQB+41D3OQB+Zdo+BMCcXf02MjwTwByAhN5O6/xm5+JmAC8x1WV2CAcAbAXw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\nhcGmmHN+sq6YSdSOMd/tD16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Zi7lw6+oVPK9VNOVv1uaydi4Y4uA8BDGIu9Gz1z7/JfQX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\nsuXr+58KlccPKz2J2GEyXLJd7F8ax9f3P6WkXD87dIlslV2EnhPzW49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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "greedy5_pythran('files/medium.in', sorting_func=lambda item: -item[0])\n", "show_output('files/medium.out')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 49%|████▉ | 14641/30000 [00:00<00:00, 36587.97it/s]\n" ] }, { "data": { "image/png": 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+w+bvTlk38HvtXFuOLgx/wcy9UvbZ+5uO2Pubjpp1Lb7Sxbz6rCBl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Fi8bFv/MA5b/6Cer9JYS7HjLL2wLi9OqkFTxQgs+4gOHiB+oBDPEWmjI+vB6kTMVDnGiQEh8pob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\neUUcyyskZ7MHhLLfq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "greedy5_pythran('files/medium.in', sorting_func=lambda item: item[1])\n", "show_output('files/medium.out')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 16%|█▌ | 4826/30000 [00:00<00:01, 16077.76it/s]\n" ] }, { "data": { "image/png": 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1GWdMHNGmwzYBodRh/0JHh337v0zOXsT1dNgLrwU5dZyZisXWYs0+yWBpdlP0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "greedy5_pythran('files/medium.in', sorting_func=lambda item: -item[1])\n", "show_output('files/medium.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is really interesting: the best scores are obtained when cutting the pizza from left to right as well as from top to bottom. Does it also work with oblique directions?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 18%|█▊ | 5326/30000 [00:00<00:01, 13304.85it/s]\n" ] }, { "data": { "image/png": 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Flo8AMJ+0/4QxPAHAPABTL0/o9idG2l8N4DmRfUUFQgPAPQAer8/XXbGxvRPAV9KubcKx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "greedy5_pythran('files/medium.in', sorting_func=lambda item: -item[0] -item[1])\n", "show_output('files/medium.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is our best score so far! Can we get even better? We should, since we see that the bottom part of the pizza is not well filled, with some border artifacts appearing.\n", "\n", "At this point, an idea that often comes up for this sort of optimization problems is to try to improve an existing sliced pizza with some local changes, an idea known as \"hill climbing\" (see this Norvig Pytude about [gesture typing](https://github.com/norvig/pytudes/blob/master/ipynb/Gesture%20Typing.ipynb) for a good example)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Improving an existing solution " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say we have an existing solution and we want to improve it. One possible way is to remove a set number of slices from the pizza, for example the 3 closest parts to a hole in the pizza, and try to fill it again while trying out every possible combination." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "from random import choice\n", "\n", "def remove_slice(this_slice, empty_cells, pizza_slices, slice_mask):\n", " \"\"\"Removes a slice that was sliced from the pizza.\"\"\"\n", " pizza_slices.remove(this_slice)\n", " r, c, dr, dc = this_slice\n", " for rr in range(r, r+dr):\n", " for cc in range(c, c+dc):\n", " empty_cells.add((rr, cc))\n", " \n", " slice_mask[r:r + dr, c:c + dc] = 0\n", "\n", "def improve_pizza(fname, slices_to_remove=3, iters=1000):\n", " \"\"\"Reads an output of pizza slices and tries to improve it by removing slices.\"\"\"\n", " input_fname = fname.split('.')[0] + '.in'\n", " R, C, L, H, pizza = read_input_pizza(input_fname)\n", " possible_shapes = generate_all_shapes(L, H)\n", " lines = open(fname).readlines()\n", " N = int(lines[0].strip())\n", " slice_mask = np.zeros_like(pizza)\n", " pizza_slices = []\n", " for i in range(1, N + 1):\n", " r, c, dr, dc = [int(val) for val in lines[i].strip().split()]\n", " dr += 1 - r\n", " dc += 1 - c\n", " slice_mask[r:r + dr, c:c + dc] = i\n", " pizza_slices.append((r, c, dr, dc))\n", " # find empty pizza cells and make a set out of it\n", " empty_cells_list = np.transpose(np.nonzero(slice_mask == 0)).tolist()\n", " empty_cells = set(tuple(args) for args in empty_cells_list)\n", " print(f\"starting score {score_pythran(pizza_slices)}\")\n", " for _ in tqdm.tqdm(range(iters), disable=True):\n", " # copy params\n", " empty_cells_original = empty_cells.copy()\n", " pizza_slices_original = pizza_slices.copy()\n", " slice_mask_original = slice_mask.copy()\n", "\n", " # select a random location on the pizza grid from one of the already vacant spots\n", " removal_location = choice(empty_cells_list)\n", " # remove existing slices closest to the grid\n", " sorted_slices = sorted(pizza_slices, key=lambda item: abs(item[0] + item[2] / 2 - removal_location[0]) + \\\n", " abs(item[1] + item[3] / 2 - removal_location[1]))\n", " for this_slice in sorted_slices[:slices_to_remove]:\n", " remove_slice(this_slice, empty_cells, pizza_slices, slice_mask)\n", " # try to place new slices by trying out all possible solutions\n", " candidates = list(empty_cells_original)\n", " shuffle(candidates)\n", " for location in candidates:\n", " selected_shapes = []\n", " for shape in possible_shapes:\n", " if satisfy_constraints_pythran(location, shape, slice_mask, pizza, L, H):\n", " selected_shapes.append(shape)\n", " if len(selected_shapes) > 0:\n", " current_score = score_pythran(pizza_slices)\n", " shape = max(selected_shapes, key=lambda shp: current_score + shp[0] * shp[1])\n", " cut_slice(location, shape, pizza_slices, slice_mask)\n", " update_empty_cells(location, shape, empty_cells)\n", "\n", " if score_pythran(pizza_slices) <= score_pythran(pizza_slices_original):\n", " empty_cells = empty_cells_original\n", " pizza_slices = pizza_slices_original\n", " slice_mask = slice_mask_original\n", " else:\n", " print(f\"improved score: {score_pythran(pizza_slices)}\")\n", " write_output_pizza(fname.split('.')[0] + '.out', pizza_slices)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "starting score 48433\n", "improved score: 48434\n", "improved score: 48442\n", "improved score: 48446\n", "improved score: 48449\n", "improved score: 48452\n", "improved score: 48458\n", "improved score: 48464\n", "improved score: 48468\n", "improved score: 48470\n", "improved score: 48471\n", "improved score: 48473\n", "improved score: 48483\n", "improved score: 48484\n", "improved score: 48494\n", "improved score: 48496\n" ] } ], "source": [ "improve_pizza('files/medium.out', slices_to_remove=3, iters=1000)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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2SdhH3E36DQCfj2m/A9SDthja9moA/5uw79MA/Db0+UAAc3H7j5nD8QDmAJj685huf1So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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "show_output('files/medium.out')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe the result of our hill-climbing algorithm here: it added a few optimisations to some of the pizza slices at the bottom of the field, shown in the left plot as patches of yellow color (slices added last). If we let it run for a long time, maybe it would add some more of these patches." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this blog post, we went from an idea to the implementation of a fast greedy solution to the training problem of the 2018 Google Hashcode. We then improved it using an iterative hill-climbing algorithm. I think this nicely shows how all the moving parts of a Hashcode problem fit together.\n", "\n", "To conclude, here's a high level summary of the tools that I found useful while tackling the pizza cutting problem:\n", "\n", "- know your data structures and their speeds: when I realized that I needed [sets](https://docs.python.org/3.6/library/stdtypes.html?highlight=set#set) (for fast membership testing) and lists (for fast lookup of index $n$) I got a 10x speedup\n", "- use a debugger for development: during the writing of the above code (something that is not shown in this Jupyter notebook), the Pycharm debugger proved invaluable\n", "- profile your functions using line_profiler and troubleshoot your hotspots\n", "- use Jupyter Notebook for interactive exploration and plotting \n", "- use pythran or numba if you need to make things faster\n", "- use tqdm to display progress bars! this gives you great feedback about how your algorithm is doing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*This post was entirely written using the IPython notebook. Its content is BSD-licensed. You can see a static view or download this notebook with the help of nbviewer at [20180207_PreparingHashcode2018.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/flothesof/posts/master/20180207_PreparingHashcode2018.ipynb).*" ] } ], "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": "3.6.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "125px", "width": "253px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 2 }