{"cells":[{"metadata":{},"cell_type":"markdown","source":"## <span style=\"color:red\">V1-V3</span>\n\n**In this naive sample submission, we submit the last grid as the initial grid, naively assuming therefore that there was no change. The solely purpose was to get an idea of the worst score possibly achievable and an idea of performances range.**\n\n## <span style=\"color:red\">After V3</span>\n**Since the private and public dataset are the same, we can save our submission in a dataset and submit them and even bag them. Moreover we can choose iterative optimization algorithms and process grid by chunks on our computer for days and combine all the chunks on one csv file (one notebook coming soon)**\n\n**Credits (link to original notebook)**\n\n* [sub_1111](https://www.kaggle.com/yakuben/crgl-probability-extension-true-target-problem)\n* [sub_1245](https://www.kaggle.com/maxjeblick/crgl2020-iterative-cnn-approach-with-postproces)\n* [sub_1274](https://www.kaggle.com/hasnainajmal281/iterative-cnn-approach)\n* [sub_1333](https://www.kaggle.com/jpmiller/demo-cython-generator-and-keras-cnn)\n* [sub_1402](https://www.kaggle.com/li325040229/the-game-of-life-reverse-with-random-forest?scriptVersionId=41870640)\n* [sub_1334](https://www.kaggle.com/ulrich07/quick-neighborhood-fe-mlp-keras)\n\n\n## <span style=\"color:red\">Versions</span>\n* V4: [sub_1245](https://www.kaggle.com/maxjeblick/crgl2020-iterative-cnn-approach-with-postproces)\n* V5-V6: Bagging\n* V7: [sub_1111](https://www.kaggle.com/yakuben/crgl-probability-extension-true-target-problem)"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom tqdm import tqdm","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"\"\"\"\n%%time\nsub1 = pd.read_csv(\"../input/game-of-life/sub_1111.csv\")\nsub2 = pd.read_csv(\"../input/game-of-life/sub_1245.csv\")\nsub3 = pd.read_csv(\"../input/game-of-life/sub_1274.csv\")\nsub4 = pd.read_csv(\"../input/game-of-life/sub_1333.csv\")\n\"\"\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\"\"\"\nsub = sub1[['id']].copy()\n\nfor i in tqdm(range(625)):\n    col = f\"start_{i}\"\n    sub[col] = sub1[col]*0.4 + sub2[col]*0.2 + sub3[col]*0.2 + sub4[col]*0.2\n    sub[col] = (sub[col] > 0.5).astype(int)\n#=======================================================\n\"\"\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.read_csv(\"../input/game-of-life/sub_1111.csv\")\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.to_csv(\"submission.csv\", index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}