{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.\n\nfrom sklearn.neighbors import DistanceMetric\ndistString = DistanceMetric.get_metric('jaccard')\ndistString.pairwise(([int(i) for i in 'aaa'],[int(i) for i in 'aab']))\n\nfrom haversine import haversine\nprint (haversine((45.7597, 4.8422),(48.8567, 2.3508)))\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"from sklearn.neighbors import DistanceMetric\ndistString = DistanceMetric.get_metric('jaccard')\ndef similar(a, b):\n    return distString.pairwise([a,b])\n\ndisthaversine = DistanceMetric.get_metric('haversine')\ndef haversine(lon1, lat1, lon2, lat2):\n    return disthaversine.pairwise([lon1,lat1],[lon2,lat2])\n\nhaversine(1,2,3,4)"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}