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iIAIiIAIiMMgEmpub7cqVK664yre//W0XvVuxYoVt3LjRPvzhD9ucOXPcGVlF89SpUy59k20UWHHz0UcfdcVWKIGTJk0a5JXpcENJQILnT1uC589OM0VABERABERABERABAaZQH/Frb8iOMjL0+GGiIAEzx+0BM+fnWaKgAiIgAiIgAiIgAgMMgG2P+C+OxZWeeONN1yq5Z/8yZ+4/VadUy9jqZw7d+40tlE4dOiQ5ebm2tq1a29I5VTBlUG+QUN0OAmeP2gJnj87zRQBERABERABERABERhkAqyO+cILL9jWrVtdK4Tp06e7vXUUt87FU2ICcOzYMXvppZdsz549VllZ6VomdC7GkpKSMsgr1OGGgkDs/vJcKrIyMOISvIHx0qtFQAREQAREQAREQATiQIARuba2Ntu1a5drYM7qmEzXnDZtmn30ox91VTK7i8ZduHDByd3Ro0ddhcV58+a5dgp33XWXa62QlZUVh9XqkPEmIMHzJyzB82enmSIgAiIgAiIgAiIgAoNEgEVSrl69au+8846xuMp7773nCqcwajd58mQbM2ZMt/3tmpqarLa21hVmaWxsdCL4+OOPu2IrixcvtgkTJgzSCnWYoSQgwfOnLcHzZ6eZIiACIiACIiACIiACg0SAlTO5/+6tt95yETxG5MaNG2eMwEWj0W7lrvOpY6LHObfffrurunn//ffbrFmzOr9MPycIAQme/42S4Pmz00wREAEREAEREAEREIFBIlBeXm4smELBe+WVV+zatWvGpuUzZ860tLQ01/uut1MxVbO4uNhF8goKClxDdPbNW7p0aW/T9NwwJSDB878xEjx/dpopAiIgAiIgAiIgAiIwSARYLOXll192xVUOHDjgonef+tSnbMmSJf0SvJKSEnvzzTeNx2E0kPOeeeYZF81LT0/vUxAH6TJ0mEEiIMHzBynB82enmSIgAiIgAiIgAiIgAoNAgB/md+/ebd/73vds+/btxnRL7p/7whe+YCtXruxXiuaZM2dc9I979/bt2+eKs/zlX/6lS9Vk2mZmZuYgrFSHGCoCEjx/0hI8f3aaKQIiIAIiIAIiIAIicJMEWltbjcVRWDWTxVUOHz7sCqMwPfNP//RPbdmyZf06Q1lZmUvxZP88pniOHj3annzySVdsZf78+S4i2K8D6UXDgoAEz/82SPD82WmmCIiACIiACIiACIjATRJgOiXl7O2337Yf/vCHVlFR4aJ2bHMQa4/Qn1OwkibTNCl4P/rRj1xlzXvvvdcJ3t133+328vXnOHrN8CAgwfO/DxI8f3aaKQIiIAIiIAIiIAIicJMEKHQsjkIxY8Py69evO7G75557bM2aNTZ16tR+nYFFWdg3b8uWLfad73zHTp06ZXPmzLENGzbYpz/9adcAnX30uuul168T6EVDSkCC549bgufPTjNFQAREQAREQAREQARuksCJEyfs1VdfdYLH/XesgMnUTApeUVGR5ebm9usMFEOme27bts2+9a1vuT19KSkprsjKl7/8Zfd91KhRbj9fvw6oF91SAhI8f/wSPH92mikCIiACIiACIiACInCTBGLFUfbu3Wv8edKkSbZp0yZbvny55eXlub10AznFkSNH7MUXXzRW4mSxFu6/+8xnPuOqakrwBkLy1r5WgufPX4Lnz04zRUAEREAEREAEREAEbpJAQ0ODVVZWuj1z/JnFUZiWychdamrqgNsbxPb01dXVuXTP7Oxsmz59uuXk5Lj0TKVo3uQNG6LpEjx/0BI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIiIAIiIAIiEAcCEjx/qBI8f3aaKQIiIAIiIAIJTSAIzPjVeM2sui6wugb83GLWeh2XFTHLTDcryDbLyYpYZoZZasrQXy7X19Zm1oQ1trQG1t5uKJtvNjo9Ymmp4c/R6NCvS2cUARGILwEJnj9fCZ4/O80UAREQAREQgYQmQFmiPJVeCGzHgcAOngnsbJVZLWQqAokqHG+2YV7ElhRFrHBqxHIge0M5YgJ65apZ+cXAaiChzRDQjNFmkydELC8HEpoRQa+0oVyVziUCIjAUBCR4/pQleP7sNFMEREAEREAEEppAXb3ZWcjdgZOBvbsX308HdgaCVwfBs5jgzY/YHQsitmoRJG9yxMlVvCN5FDvKZy3WV1oZWNlFszKss6vgTYPkTZ+IKGNuxLIYYZToJfT7UYsXgc4EJHidaQzsZwnewHjp1SIgAiIgAiKQNASOnA3sxbcD234osHMVgV2uNWvoJkVz2ayIfWRtxFZC9qZB8saOiS+C60gRbW4JbH+J2S+34DsEtAnpoy3NgV3vSNHMQIrm7Clmdy+O2LK5EZsxLWK5Y+O7Lh1dBERg6AhI8PxZS/D82WmmCIiACIiACCQkAZeaCYnafjiw77zYbu8gPbOhCXvvkK5piJ7dMLAXr2iS2YeWR2zj0ojdviRiUyfiwTiOq5C5MqRkbsW6/uP3ge07FVjAtUHuOg+mkK5fGLENWNfGVWGEMYKl8Wugw+31A5MWCC4lMopjpCCKyagg9/xpiIAIDC0BCZ4/bwmePzvNFAEREAEREIGEJNDSalbfEDix++5LiOBB9Bg1o/h1N3IQsZsBydu4LGKffSBqS2Z7GFR3B+7hsbPlgb29K7At+5E6ejSw0st4IdfWRT5dERjsw1sP6fzCI1EXYaSMDbToSiwllJJ7uTrc58c0VKZ95uVEbDT2/GmIgAgMLQEJnj9vCZ4/O80UAREQAREQgYQkwIqU3M+2ZV9g//JyYLuOwZy6yFPnC0tNQzGTLBRcQaTsq49H7Q5EzeI5uCfwud+2u/WdrcSewEacraf1YSl3YH/gX38q6gSUBVhS+lntk1JL2a29Glg59h5erA4F71pzWDE0C9ecnxexCflm0wpQZAa/M7LnEyGMJy8dWwSSkYAEz/+uSvD82WmmCIiACIiACCQkgcaOSBUjZP/2SmB7jvcueFGkKaYgmrUBEbz/+cmorYdQxXNsPxLYP21ut61YXwPSNVuRNtnjwFJWodLnlx5BqibWNw5CRsnrz6DoVtWExWVe3RnY0VLs/YPctUP8Imi9QLFNg9QtRcTyk+tRTXRmWLFzlNoy9AevXiMCN0VAguePT4Lnz04zRUAEREAERCAhCTBCVcsIHgTqn38d2M5YBK+HKFkGUxXzQsF7BqmQqyFU8RzbkDL6D79ABA/ra4aMtiHK1uPoELwvPhwKXgEEjz37+jMqLgdWjGunSFLwTpZhVicGbBURgeQtg+D9yb04Pgq6TEHlzmxIn4YIiEB8CUjw/PlK8PzZaaYIiIAIiIAIJCSBWGoii5h8/efttu0gipggatVZbt6/MAjUZMjdCkgO9+A9dGfE5kyPr+DtgXR979eI4CGFtLIOlT0RaetxYClrkTL6lcfC9Y1BU3Y2QO/P2HsisH9/NTzPBaRo1ndNBcWxKXnTCszWQWo3YK/fPWsiNhM9ATVEQATiS0CC589XgufPTjNFQAREQAREIGEJsLAIq1P+6Ldhtcpy9Jtj9UoneR1RrDQWGkG644JpZveviNja2yK2CC0JxufHV3BOlAb2CtojMLK2rySwizXdY06HyI1FtG4dImtf3BSxNUgdTU+N9Fn1ksVkKLkU3P/z03Z7F+cJuini4s6KS+U5po8Li8w8/dGoLQUDDREQgfgSkOD585Xg+bPTTBEQAREQARFIaAKXUFSEzc0pOr/ZEdjxcxAdpkNSduAweUhFnD0RETI0Ov8wIne3IYo3NhsShbTFeI4q9OM7gR59TNHcjD59h/Hz+yP2I9Y3PttsIfrfbVhq9rG7o7agKOIqaPZVBKUV19jYFArkP/0ysPeQEtpt9LLjpKyomQnR5R6/r6KYyx0QXQ0REIH4EpDg+fOV4Pmz00wREAEREAERSGgCzShecgUVJPefMSd4xyB47difx2gWJWkcBIqCxyIjt0NupqH/XV/yNBhAYuvad9LsV1vbbf8ps6v1gXHvIHvUsT8d99kVYj/cqtlIH0X65ApI1+Tx/RMvCl5DY1hF9P9uDttE9LpuHJZFV9ajiuj/+ySqdeI7WzEMBYte16UnRSCJCUjw/G+uBM+fnWaKgAiIgAiIQEITYKoim5vXITWzHP3f6urRNqAZkuckCtUo05GemGmWiz54+bkoXtLP6pQ3CyW2rsu1gZ06j2geUjaPIdJYgX5411oC159uxuRwL+D8Ioonoo1j0a8O6+3PYHomq2UyBfTrPwv3IPY5D5LHVNCvfSYs5tKfVNA+j6kXiIAI9EhAgtcjmj6fkOD1iUgvEAEREAEREIGRQSAsvoI0TWQspowK97LFM1LF8/ArJnTuvEiHZBsCnrcNItaEVMqKSrOjJR2CBwEdA+mk4E2bhKqWiCpmQ0AHMng+XuuOQ4F998V21/D9Kqp1tkB2exwQPEYyn34AxVwQwZuOc48d4Hl7PLaeEAER+AABCd4HkPT7AQlev1HphSIgAiIgAiKQ3ARissWrZPph7CteVx0TLaZe1iFVtB2yR3kbjUghI2RMi6SI8fmrDTemaLL4S8bosOddfxubx66D18mvI4gKvvBGuBfv+IXAqq7GXtHNd/CYgobnq1FghXvxHrgjYrOx/09DBEQgPgQkeP5cJXj+7DRTBERABERABETAg0BLxx64WgjVJRRUqUIrBO4FpOBldaSEjstB0/KxSL3E9/6mXg50KWUXA9uBNM0taMfwBr5KKno5AlxuDKRyYk5YTfPzH4vaivkSvF6I6SkRuCkCEjx/fBI8f3aaKQIiIAIiIAIi4EGgBk3WS1DQhcVT3kUFyzMXw72APNQoFFCZmGt2G1ozLJ2DIiosnoJiKvEYDUjLrKwKI3jfeyWwPcdhmL0MFncZjQqijOD9zWeibk9eLy/XUyIgAjdBQILnD0+C589OM0VABERABERABAZAIJaSeaYcUlXMvW9m244Edg7FUzqPAlTvXDAlgnYEhvYMaH8wK0zFZLuCgQxGBJniGftiyimPQYnkz0zT5HOM4P39c+FevF6PT89E2ij34P3t01G0Z4iPePa6Bj0pAiOEgATP/0ZL8PzZaaYIiIAIiIAIiMAACMTaE+w+GthPXgvbE1RdQU867LHrPNjAPBttEJZB7D6+AQ3W0cB8KoqqDLSoCSuENl0LUKiFbRFCsctFtU22WIhJHqWT1TT/97Ptrh9gb/3wKHcRyCEjeH/7WQjeEgle5/umn0VgMAlI8PxpSvD82WmmCIiACIiACIjAAAjUQ7IuViJ6h4jZD34bWPFJhNB6yoqEO82dYvbR1WxkHrFlCyM2qZ997hqvYV9fTWCXsb/vMtJB2f6hEZLH6py53NuHFNBJefgZTdvZ+qH4RGDf/iUED6LHaprNbPbezeBrx2Ee1/OX2IO3SnvwuqGkh0RgcAhI8Pw5SvD82WmmCIiACIiACIjAAAhcRq+9o6fCoiYvbsPP5zC5J8HDU6xauWZ2GDF7AKmas6b3L2J2DhUx3ykObB/E7RT297E6JlsuMC0zDdHBoklIs0RPO7Y9KJwasQuXA/vV78N1HUX66GVW0+y6LsydVoBeeBBNpmjesyZiM5FGqiECIhAfAhI8f64SPH92mikCIiACIiACIjAAAhchUvux544RvFd2BXa8HJO7ilSn47Fi5ZLpoeA9ck/U5s3sXahi1TkPIDL48h/CFNAz2N9Xi8hh51E4HoKH4i3rkWJ55/KI25e3F+vag9TRnSfMTlcEbg4jeVGcMgOFVfLQ825hYcTuW2G2EpG7WfiZFT41REAE4kNAgufPVYLnz04zRUAEREAEREAEBkCAEbwjkC8KnovgncfkngQP7jQF6ZCrihAxw563B9aj7xykqrfB6pynSyF2aGD+8nuBHTqDvXfY38e9eJ0HUy0L0IJhHSTvcx+O2qKZ2KuH15VeMNt9uN2KT5ntw3EqsT8wFWmd0xBJXIVo3wr0wFsGuSuczHYOERcN7Hxc/SwCIjB4BCR4/iwleP7sNFMEREAEREAERGAABOqQ+niOFTSx1+1ZpETuL4HdochJt5IHl5uFVMr7IXcsarIG0bapE7sXvFjjclbn/MPu8Pisznme1Tl7EchV8yL2pY+FApmfG8E+vcBOYE2Hz5rtRRsHCl5ah+CthOAtgGBOR1rmWFT51BABEYgvAQmeP18Jnj87zRQBERABERABERgAgTCFElUrDwT2zZfabTskLGB0rTvJg8stQx+8P30gFLApkLvsrO5PFmu/wD52338lbHdQhQIrLLbS48DxFyPl8zN3h3vqiiBvWaiuebUhsCtI6WRaZzPWBr+zTKRo5iJFcyyidhl4zUDbNfS4Bj0hAiLQIwEJXo9o+nxCgtcnIr1ABERABERABESABChSLFbC3nGMmkVhP7G+cv0hFIu07T+NNgmI4O1Ak/MapG2yymVDS3jM0WhDwBYJ3N+2eoHZp7D3bvlc7IPLCPfKdXceV50T+/u2IDL4778LbC+KqwRYY4/ROx4EgrdwutnjSP1kCug8tGQYl4cHNURABIYFAQme/22Q4Pmz00wREAEREAERGFEEXAQOES7uV2tDdIsVKXPQV250elihsj8wKHnVSNU8VYY9cqioufsgUiKx3+10TSiPkxClm480yDXYH7cUe94WzDAbnx9xfetY8KS7cZapmTtDwXsXhVLOVeJVPaVmxg7gInhmT9zTEcFDhU62UNAQAREYHgQkeP73QYLnz04zRUAEREAERCCpCcQibkxZvFAV9pWruxK41EcWLqHgUYrGo6/c5HERy4GcpeIx9pvrbTAC2NKKPXIVZnsQxXOCh5TKVjw+OdNsHloXsLn5zKmI5mWhmAlSJHsb+xGxexapmVtQvOVcldkV9LLrdcDj2LCcRVO+8BD2+GF/38QCpGji3BoiIALDg4AEz/8+SPD82WmmCIiACIiACCQ1AaZkUuSOnEVbA1SlPICI2xWkU15DBK8dETKKXCqid2wf8PDtEVsyCxG97DCi1xsYiiOP3YQ9cjUQRgpkA1oS8JixFM18RAa5Jy4lJUwF7e1476Fq5j/9LBQ8Rhcpir0Nyl0U616HXnhf3RS19YgWMgrJc2mIgAgMDwISPP/7IMHzZ6eZIiACIiACIpDUBK42IHJXGfaT+zn6yu1Fi4NGRMfebzvASBgkb+60iD0CwWPbAaZUTkRKpYvkQaSGYjBy93c/bnfVOftMzcSCctEiYQZSMlmd87NI0WSxFTZB1xABERg+BCR4/vdCgufPTjNFQAREQAREIKkJuL1taEj+LqpevocKldzbxvRKRt/c6JCibKQ2MkVz9XyzTetRFGVOWCRlNPrNDcUYqOAtRkGVJzqqc86cjIblqJCpIQIiMLwISPD874cEz5+dZoqACIiACIhAUhM4gL1tP3k1TH0sxd62OqRS9hghg+wxkvfRtYjkIfVxYZHZJEgf9+mNinMkj43Nv/kLRPAQyatH2mcL0kq7G2lIwcxCKuZ67Ln7b49H7XZEHLk2VgPVEAERGF4EJHj+90OC589OM0VABERABEQgqQlQnP6J4oT2Aw2dUzN7uOoxjOQhPXMVInmP3hlBe4Ow9UBGnCN5rMb589+F6zyFwi3VSC39gIhCQPOwvlkTzDYsjdinH4zabeizx9RMpWf2cEP1sAjcQgISPH/4Ejx/dpopAiIgAiOHAHLyAuTmBa2t1t6ChmXM0+NA+COKEocRbLiKKBQSMkmC/7KISt3VwDUk/+5Lge1E64GAaZkogtLngDDNn45UzXVhf7lFs1FlE9IXz3H+IlJIEb3bASHdhahjyUU0LIeQxiJ5jNxlQzJnQO5WQeoYuVu3ImLTJ8V3XfG8Zh1bBJKdgATP/w5L8PzZaaYIiIAIjBgCTuwaG63tyhVrq6qydlbawIhmZljKuHGWMnYsfs50ojdioCTxhV5E0/ADxyB4iNz9Zkdgx8twsf2Ruw4mE9A24TYUW2Gk7NG7ozZ/RnxFqhFpmVU1aLeABupv70ZvveNmxyoCq0LFT657HPbYzZ0YsZXzzO5eFTEnnWhqjrevhgiIwDAlIMHzvzESPH92mikCIiACI4bA9fp6a7140VrKyq2ltNTa6q64a0/JGWtphYWWNnWKpU6caKPGqFpFIr8pXCPzRgjdWbO3drXbjsNm+9Ei4SJ61A1kZCJaNi7HbCME74toQ7BqfnwFL9avr/xS2Dj9QInZcQje5S6CxzYOq7E/cCpkT6mZA7mjeq0IDD0BCZ4/cwmePzvNFAEREIERQ6D10iVrPHjQmg4fsWvHjlsrongcqYjejZ4/zzIWLbTMxYstdQJy4DQSlkBNXWCnzyElE83Hf7vT7NCZwOqwn+0aetQNZKSgsEo6mp4zgvc/UMxkHZqWx3tQ8phaymvgmq8iqtfcUWwlHSmaYyCdbMSeh8bsGSi0on138b4jOr4I3BwBCZ4/PwmePzvNFAEREIERQ6C59JxdfecdaygutmtHj1vb5cvu2pmemT53jmWtXG5jN2609BnIy9NIWAKlF5CWuSewLUjN3HIwsFK0RRhIambswtMgUGOyEcFDn7mvfDxqaxfEX/Bi59Z3ERCB5CAgwfO/jxI8f3aaKQIiIAIjhsC106et7rXXrWFPsTWfOWvXa8OcvVHZYyx10iTLXLnC8h552DLmYZOTRsISOIBqlD/5r7DdwNlLKLTCFEePwb5ysyeHgvfpB1CtEn3nNERABERgIAQkeAOhdeNrJXg38tBvIiACIiAC3RC4VlJidW+8YQ27IXinS+x6TY17VSQ93Ubl5FjWqpU27tOftMzbbutmth5KFAK7UC3zn19E3ztE8GquhimPPmsvHA+5Q6VKRvA2oKhJIZqJa4iACIjAQAhI8AZC68bXSvBu5KHfREAEREAEuiHQUlZmV7fvQARvjzXtO+AKrvBlkZQUi2RkQPBW2Pinn7LMpUu7ma2HEoXAbgjev74U9pOrqjNrxJ42n7EEEbun7gsFr3BKxHKQrqkhAiIgAgMhIMEbCK0bXyvBu5GHfhMBERABEeiGAIuqNB07hgjeHrv69hZrOVsavioaRWuEFMtascIK/uRzlrV8WdgPTxUsuqE4/B9im4FfvBEK3gm0RqhGFG8gA28HSx1ldjuKqnz54xG7c0nEsjIjloaiKxo9E2hDW8lmtJdsuhYYO5Dwd7JMT0P/PvBDoJwtJy2qQGjPEPVM0hGQ4PnfUgmePzvNFAEREIERQ6AdPfAoeRS8mhd/ZU1HjobX3lFrPnPZUitABC8Le/Gi+DTKyJ5G4hFgm4FdKK6yBU3DXytGRc0KXAOqU/ZrQD7SIXLZ6C23HmL3DIqrrIHopVBMICsaPRNogNRVVgVWhobtZ8vN6hvNSfGEfPTvQw/BiQVmGRkRw99Skmaw6imH/hYUctB/P0hAgvdBJv19RILXX1J6nQiIgAiMYAJBW5u1Nze7IitVzz5njfv3W9Dejg//4ac0tknIf/wxtxcvJS/Pokjb1Eg8AldQVKUMlTT3oMn5K9sD24eIXhWieC5VM/aBnLIGmQtw+93A44zaZSDaxAbnM7Hf7g70mtu0MTKgBud8K/GrveM8o3CeZP/wz6jdlfrALqBa6fHSwE6dD+wMIqcUPMryhHFmcyB4c6ZHbNZUs4JcSB4eJ5tEGi5CiXTfuoawp2I9WlhY7P2D91IWWlgUjGUbi7D5fDKJbCLdp+G2Vgme/x2R4Pmz00wREAERGDkE8Mk7uH7dGvfus8ofP2uNxXstaG11jxFC+qwiy7n/Pid46UVFlpKLT/oaCUegFX3jmvDh+2w5euEhkvfekcDePR7YOQgIhc4FbCFyTvDYY67D8cfiA/r0PLOl2Ht318qILZ2HwiqTsPeun33vY3KHt5i1tIaGl54eRv8SDuIAFlxZHdiRE4EVg/F7x8xOQ64ZzaMQUeKYojkG0sMG7Y+uj9qyOWa5YyM2GimbiTQaIKyXEKE8gD8YvIbo8OkLeD/x/YNbHcF1zpiIojyI9nLv5nTt2UykWxvXtUrw/PFK8PzZaaYIiIAIjDgCjQcOWtXzP3WRvOv19Ra0IASBkTZlimWtXuWKrXA/HlsnaCQugdorZmfQ8PxASWDvdAiei7gg2hLpEDzDB/QAIsIP6U7wkE5420zsu0PlzBn4kM4s3b5SMxnBuoqoDtsxMFJYD7lpaQmcSFLw2JicUUFGdig1yZL5GxPaU2D8X++g9yCqlhZDfi6w+0hHBJMSHRsLCiP28fVh0ZqFRRErgEwnwuB1UtrLKxEVRgGfd/BHg9f3dghex3uHglc4IRS8OxH5XYsvVl1NxEhlItyTRFqjBM//bknw/NlppgiIgAiMOAJNR49ZzUu/dtU0Wy9VWntDg2OQkp9n6bNnO8HLufdeYxRPI3EJIDjrIklMqbsM8WpiNU2KB6UDH8jd4O8dX9xnlwnxGwshK8gJ0+xctK+TpHTMuuHb5ZrAjkFsDpwy23USe9CqEBTsSN0bBUGcNcXsQ0vDyM7kCRFEs26YnrC/8BopPmxL8W+oWvoO21Lgn9K18O8lH7iughyzhZA8tp34+N0RWwCRToSBzG671swIpdlzr7XbtkOBXYLEuhTN2PsHl5IJeR+Pa1w9P2KfvCdqK+eb5eF9lGiRykS4J4m0Rgme/92S4Pmz00wREAERGHEEmkvQD+/NN8N+eKdOWVt12A8vmpVlqRPGuyIreZs2WcYCfELTEIEeCDAF8Rqk8TQiWG/vbrf3DkF20GS9vBoTOkSSkZ0iBII/BKlZj6jOavTVmwrJYzXJRN+bF9uTthXpit/4eSg+70fuumE2GvKci3TXDWDx5U+geM3CxBA8VgRl8Rj2VfzeK4jiIR21x+vEJS2GuH7mHogspL4I+w7zIH0aI5eABM//3kvw/NlppgiIgAiMOAKtFRXWUFzsBK9h125rKS93DCJpaUbJY8PzgiefsMwli0ccG11w/wmwiMhFpO3twh6/X21FhAfROzZWb+ocwcIHfhbfYIrmOsjdUw9GbcU8Q1QnTP/s/9mG3ytdcZWrYWrmt18MbAcieT2KD5ZPqU1FlIvi87XPRI2pjIkwLnOPIe4tU1Bf3BbY0XNYNQW+hzETe/HuxbVRZNetiNg07OPUGLkEJHj+916C589OM0VABERgxBFoq601RvHYLqHu9Tes+XRJyACbrSLYNMM2CQWfe1r98EbcO2NgF8yoziFEcxjZ+fV7SNM8j/k9ffDHZ/xVKNryxUfCyM64vDAFdGBnjO+rryPl8v20Vogbq47yMe4ZHANJzc4Mi6XE+gG6vYeonskI3rd+BcGD6PZ4/Vg6jzMax6Hg/c2no3YHhDcRxgW03diDfXe8zv9C242TFVh1T/cZT03FPs41s8NU1PvuROVQRPE0Ri4BCZ7/vZfg+bPTTBEQAREYcQTam5qsrabGCV715hes6fCRkEHHhiv1wxtxbwmvCy5BO4Dfv9fu+u1tP4HWDNh71+PAZ/xFhWaf3BBGduaiyMg4tAsYToPppjV1YQ+7fShKcx7RyQZEIyl2c1FwZu50fEf6YX7HusNqoeYiW1//abu9CwnqUXxwqZTEiYhkUvA+/zFEMrFXLRFGBQSvGPvuGMF7dQ8E7wJW3ZPg4ZIoeLfPCUX+Q+vCNM1EuE6tMT4EJHj+XCV4/uw0UwREQARGHAH1wxtxtzwuF3z8TGC/eSsUvANI27tU18tp8MF/HnrAfWxNKHhLIDcTCoaH4FDUGI27iIjk0TNmB7GPcD8ik+cgNjHBmwfBWzTTbBnWXTSNFTAjloF0S1aY3I1+g99/ud0J0GUwaGR/uM6Dl4mvyaiauZKRLQjeQ4hszU6QyFYViugcRxEdRmo3o1rokVJcTy+CNwspmvchPZPFZNbgWqdOHB73ufMt0c9DR0CC589agufPTjNFQAREYOQRwKdS9cMbebd9sK/45FlEdN4OP/jvhexVsD1ATwOf8RdMM9t0BwQPH/oXzY3Y+HHD44M/i4hQYvZC6l5+F3J3yqwWKZqsOtrGFE3sncvOMJtWgAIiaFi+agEKxqBPINsAUPBK0fduK1IXGeHagiheaWUXCLhMBscXI2r5REfxkZlTI+iF1+V1w/RX9lSsqg1TNP/lN4ET2t4Ej30Un7ovvM+FEOOc7GF6YVrWkBCQ4PljluD5s9NMERABERixBNgP7/Jzz7tUTbZKYNNzjrSp6Ie3dg324q20rGXL0A8Pf5LXEIEuBM5BbN6l2GBv1h8gNmcudXlB7Fd6HL6WIL3xyXvDCNYMRMF8BYdSxb1xbLx9GeJxBd+voZQ/H+N5WK6/AFKRi757GRCzVOx9621cQhGRg9xLiOt4GXsJj3e3lxDHzUWq5gxI3volqBL5UNSWQFLZyPwK+v+VlgW2D4VI3sIxTpSx0XzgWiiwh2BqGvYbYu5SRO8eQ4oqG55nZoQ94npb13B5zkU4WyHAbJPwelgt9SKEmP0O2xH9dME88BkD1hNQMXMtBPhT9zIFFWKMe8BG7xojl4AEz//eS/D82WmmCIiACIxYAk1Hjlj15hed4LVVVxv35nGkFBS4Fgmsppm9cYOlF2LzlIYIdCHARupnITZM3Xv+92FD9Q9EdvDBn9LFCBbbAvz3TVEXwctGuwDfD/5hewI02oaIbUXD7cOlgV3EWphOaYi2zWDDbaRSLkXEbDoiSGP7iCAdQfTxhTeRYgk5Y6EYpll2NyiKWZAVFkf50scjdgcqRbKRO4WTUUD2AzyLAiTnKtAq4iLSO/EYC7KwF9w0rKMQ7SIKka7IVgmsqNlXA/nu1nArHuP1sedfBVJYD54023Y4sNfA/QT2YLayEA2ej4DNrMlIzYT83gk+3F84Be0wyCxRrvNWsB0J55Tg+d9lCZ4/O80UAREQgRFL4Nrx41bzm1cgeMXWeuGCXa9HKAJjVE6Opc8odNU0cx560Eaj+bmGCHQl4NoE1IcNsDdjL94u7EWrhBwxssNoWhRSlw7BYZGS8dizRjF66gGIFyJZNyM4VZC5E+i9V4zzMTXyMASNglcPwYtAnAoheHchinQHhHLVIopV2GybVSy7GzvR3uC7L4Z7CevQ5qGnRuWxuWspqo+Fewk7R6hcBc7GwKqRqloGwWtEZBFFaZ3gTUc6Zw5SMm/mumPnv1XfY0VoDp21GwSvvYvgLYFYT8T+yizcdw0RkOD5vwckeP7sNFMEREAERiyBa2hyXve711wEr/lsqV2vC0MXo8aMsdTJk1w/vLxHHrbR89C4TEMEuhBgVKcVqZEXUG2SRUnYJoCpmqyySPlLg2wVZGHvXWEoQ9y7Nh972ApQSZIRPX4NdDCatL8ksGffCOw9nKsS575CKUN2MffLuRRNVKscj6jdclRy/BiqOK5ENGnS+IiNwVq6G++iQuTXfxEKXiv2m13HNfU4sGa2e/hLtHtgnzfX7gHn4yCPWGVNpmgy0kjJTUv9Y6qo73WHZ7i1/6W0U2LrGlCQBqmxLkWTzHFPDKmo7Hc4ESmaOUzLRJos9y5qiIAEz/89IMHzZ6eZIiACIjBiCTSXltrVLVutYc8eazpyzNouX3Ysoti4NCo/H4K3wsY9/phlLFw4Yhnpwvsm4NIT2QwbRVfegnSdigkePvSPQzriAlSLZEXF2dMgAZn+e89i1S634hzf2Nxu2yBmAbeNxiSj81IhVnOnmH0EVTt57mWQy0k9VO2kmH4TETwWSbkKWWxhqmdPA8ddDWF85tGwiAjTLzM6BK+nKXpcBEYyAQme/92X4Pmz00wREAERGLEEWisuWsO+fU7wGnbstJaycseCzc6jyK9ikZWCp560zCVLRiwjXXjfBGLidQXpiS5FE1EwRrPgd5aGtMixSNUrQO84FuG4mRTFWLVLiti//ieqOaJXXbdy17Hk8Ygmsfce2xJsuidq81HkpbtxGC0AfvF6WCzmBArHVDNTmVGp7gYOsQ577/4HGpWzGqgiVd1B0mMi8EcCErw/shjoTxK8gRLT60VABERABKytttaaS0pcimbd629Y8+mSkAqqIlDyslausILPPW1Zy5dhbxPyrXxy6sRZBDwJuOIeEC2W6a+uQxsGBJhLsbdtD/be/XYnUkH594ieRAxPUSjZe46C92doLL4cqZXdjfMoirIdBVa2HkCKKaKCZy6GqZYBI4OdRhaONwHH24BCIp9/OOpSP/uTcsnr4FcLoo1M3eR3/s4CJBmjI64Qi/55dQKtH5OKgATP/3ZK8PzZaaYIiIAIjFgCrJrZVlPjBK968wvWdPhIyKLjU2vmsqVW8PRTTvSiCFVEeqpSMWIJ6sLjSSC254tVKXdCvvajAflxpH+eQQPyiioUVUERk94G94Qxise9cn/xaChk3b2e7RYuoUIkq0P+BNVA92A/YTOksq3LXrwiVolcHqZmrkUUbyqqRPbnbx6MZvJYNZBUtpZg9VFeG4vPTEPxlXzsSRyNapz659Xd3dFjiU5Agud/ByV4/uw0UwREQARGLIEAnzrbm5uRollsVc8+Z43791vAT6MML2BkLFpo+diDx3YJKXl5xr15GiIwVASqsR/uVDlL86OAC9IyD5cguoYoXi2EzEXXwrdpj8vJw/4/lu5nKuVnHojaYlTv7G7EBIx7CH/lGp2He/GuxVJNkWvKVMy52EN4/4qwCmhvRVti5+BxWYSmBtfByCPl7n3BQwEWFn2Zhgqf0/FVOBH7FZHGmsHiJD1U+4wdV99FIJEISPD875YEz5+dZoqACIjAyCUAkQuwgapx7z6r/PGz1li81zU752Mc6bOKLOf++5zgpRcVWUouQg0aIjBEBFy1zDeRPom0yWpUy6xD5KsRBVBaGFnrQ+5YTZP98O5GpI2Ctx5ixlYF3Q3+PYNfbJhefhktF6rR0w5FY65iLx6rgbJfX0E+irSMQ8onvvKye2+7EDsH515tYOTR7KV3IKrY69fYhN5xHSmarDLJFM35080eWAlxRNXPyeiTl91Dtc/YcfVdBBKJgATP/25J8PzZaaYIiIAIjHgCjQcOWtXzP3WRPPbCCzrKCKZNmWJZq1e5appZK1ZY6qRJI57VSAQQi0TxOwcbV8ezgXWsaMtWiN03UN1yG6pmBqxsyb879CV2eAl77+VAkhajqMpH1oYN1otQyZMVL/szGLljOiVTQBHgtjQI3ngIHiNuAykScxn98I4hKshG8C9uCauMdrf+Ivyz+lBH6uft2N83DZKnIQLJQkCC538nJXj+7DRTBERABEY8gaajx6zmpV+7apqtlyqtvQGNrjBS8vMsHU3O2S4h5957jVE8jZFHINbQnN+554wRrc4NvgebyPvVMrHv7v1qmf2UO0buJmDf3WII3e2LIE5rozZvRtieIQ3i159BwWSUjX3sXMN2pmjimhlx609Rldg5jpwJbPNbKN4CwTuGxuxVbDPZjaC64i35YTGYP38kait6KAYTO66+i0AiEZDg+d8tCZ4/O80UAREQgRFPgJU06958E8VWiq0Zzc/bqmsck2hWlqVOGO+KrORt2mQZC+aPeFbJDIBCw4hVIyo9XoXj1yOSxZTIhiakL9aj8AijaBjcjzYWaYrjUVFy6riI5aBYyGBWgeS5yrBfjZGvH78W2L7TOGk3YuQWg/+4iCLkKwd77iaj191s7LtbgubqS2ab6383sYf+d7H58fq+A0VbvoV+fbwO8mT1zG4HpDQCiVyCFM3PPhDu8aNIZoIz+wjmoHdgJra/orCthggkHAEJnv8tk+D5s9NMERABERjxBForKqyhuNgJXsOu3dZSXu6YRJCbRsljkZWCJ59AP7zFI55VMgOIVZM8i8ImTC08fQHFQeD6NdiL1gY5YTSLYxSKgKRC6hYXReyxdRCpmYNbBbL2SmBnz4eRr+ewB+9ACU7ai+AxJZMVKZegiMrHNoSCROnMpRx17Jfjuod6vIsI5N8/Hwoe+cVSXLtdB4QuZyz2DU5FKim+k3Eh9vutg/TdNiPiHufzGiKQaAQkeP53TILnz04zRUAERGDEE1A/vJH9FuCWSxYDKavEnrEzodzx+ykI3jkIXh32ot0gWExVhFRR8D6zEUVMbotYUYeYDAZJFjcpR9VJpjb+6HeB7UV7hBvO3+UkFLmZqELJYipPPhhWy2QE7FYP9tX7X89B8NBjL2CKaYcg97guRPEiEDt+MSIaE7w18yO2cgEqbSIymYVoXn9TTXs8j54QgSEkIMHzhy3B82enmSIgAiIw4gmoH97IfgtU1UDqUOGx+Dik6rDZybIwpbChI0WzlXLSOYJGeYKATEBR1cVIhWTj749B9OYj0jQYg6mM3AbK1MZvvtBu25HqeMP5O58Epywcj/1ri7AO9LvbgGqUM6YMzjo6n8bn53dQJOZ//yyM4LUj9TVg9c/eBpeNL8opv2Ipmozg3bfSbBVEbyDFYno7lZ4TgaEiIMHzJy3B82enmSIgAiIw4gmoH97IfQuwPcBppEO+ti2MmO1Cz7lytAnoUahiqCAgoxHFY685Rs6+/FjUVi/Eg4MwmMrIQicUzv/4bbu9g0jYRUQS67EXsPO60hDpysbeNAoQq2XejkjibPzc32qZg7DUXg/ByOOPXmu3rYjglVcgSorI5IAHkE6HwK6DwG4E57tXRWzmnyWF3AAAQABJREFUMBHYAV+LJoxIAhI8/9suwfNnp5kiIAIiIAL4lK9+eCPvbUC549euo6hW+SuICCJmbMrd1FFMpS8iozraJWxE5OxrT0btTkTyBmPE1nUBve/2YG1c1++KsScQW0Ndg3OeBOtmAZK56G3HapkfvjNqi1BUZTilMF5AT709x7B+CN5/7QwQGe1EB+vv72CBlXEdVTa/iCqbq1Rls7/o9LphQECC538TJHj+7DRTBERABESggwD74V1+7nkUW9njWiUErBWPkTYV/fDWrkE1zZWWtWwZ+uFhw5NGwhOIRcqYCvn32CvGSFnnCFm/LhBOx9TIv/1c1EWY+jWnny9i9c7KKhRZQfroa3sCOwFBYrESrpGVM8dnm81DNGvpHPS6g1xOHWb949ju4TLWvxdR0f/cHtihEkQhGwO7BoEm+2ZcCyuVusbtvTHh3jxESx1niPQGNG/XEIFEISDB879TEjx/dpopAiIgAiLQQaDpyBGr3vyiE7y26mrj3jyOlIIC1yKB1TSzN26w9MLCjhn6lsgEwrYIaEeACNM//DxwDcUHdD30jJjgPT34gufWBxmqQ9uEi2gaXnMFbRsgSJSj9DQ0HkdkiwVWciF6+WhinjF6QKuP+4tjDdsrscfx1HnsbUQvvJOoTnoJKbDXWgOrRF+8UxfNqvtK3aTgYc+jE7zPQvAGKVIadwA6gQiAgATP/20gwfNnp5kiIAIiIAIdBK4dP241v3nFtUtovXDBrteHnzxH5eRY+oxC1w8v56EHbTSan2skPoGY4DEF8hs/g+ChKMhAInij0actD3LFvWHPfAJ78FDpMZ6DAeUGCB5TOCl47AvHapOM5g3nERaNCazistmJDsFrhuCdQMrpm4ialmB/nuOO6+pusGomm6GT81c+FUVKanw5d7cGPSYCvgQkeL7k8IcdwOvhfxb8D9qfmbHTRlDuqaamxkrQLJffOfLy8qyoqMhyc3OdvfMxvo5fGiIgAiIgAsOPwLVTp6zud6+5CF7z2VK7XocQA8aoMWMsdfIk1w8v75GHbfS8ecNv8VrRgAnwkwN721Hw/k9Hiqbb49afTxT4v/LJaHS+Cr3nGFl66E4UOJke3/9/j6WUcnnc/8ePE7GvAV/8EE7guinT11BJkymabBhP7rtQROZfXw1sN/bpBUw9xWMfGLjGvCyzIhRaIecnHkIbCPTG0xCBRCEgwfO/UxI8f3aaKQIiIAIi0EGgubTUrm7Zag179ljTkWPWdhkhB4xoRoaNys+H4K2wcY8/ZhkLF3bM0LdkIMBqld9/Jaz2eBHpg9z71t1wMoWIWQYidyxwsgC97zYuhuQhcrcAoleQn7jiQdllhLCRe+IQXaOUpeBaR6dHEC0cWKSQx2rHV+xP76TCKGPXv2/vw97C/3gtLCJTVhHYFQbMaa/8wqDEMno3Y4LZHWiRcCf23q1HG4jpKCyjIQKJQkCC53+nJHj+7DRTBERABESgg0BrxUVr2LfPCV7Djp3WUoYcMowIcuGiWZmuyErBU09a5pIlHTP0LRkIlF0KbMfBcC/eG6hW6VIGu7mwKIRnFOSOZfvvmBWx1XMjtgJyN2MK2hWMCUWom2nD/iEXyUSErQ57/M5DtKprA0ge+tBhT98UFG4Zh0glq3MyJbQ/g9E5yiK/88NtKGoRl07aeX4l9hUeLoHgIU3zpffCBvPvN0SH5GVALPORArsCnD+BPoOr0IZi4rgI1tL5KPpZBIY3AQme//2R4Pmz00wREAEREIEOAm21tdaMVHtW0ax7/Q1rPl0SPoPwAyUva+UKK/jc05a1fBmKPuDTfteQhEgmJIH6BrMKtCTYh2qPr+2GaJxjOmHg0gp5i1NTwgIm6RCeFAgeG4vfjojdYvScm4m0zNyxCXnZLsLGQihsDVEKyT2HvXDny9Eqoi5Mo2R7AlbmnIavwkmo2pkHucJjXUWPgsiv+kazi9Vhqwk2iack8sNtCvr1ZYxGfz7I2mREObORcknpa0Xj8/oGVAnFP7OXUGXzyBkwZ0N0rIncs8C7AGyXgPWDayM2B6wTYc9hYr4btOp4EZDg+ZOV4Pmz00wREAEREIEOAqya2YZ91BS86s0vWNPhI+Ez/LSJr8xlS63g6aec6EXT0y3CT64aCU+gDaLB/WFVEJtzlyA5F9GYG19M1WSaYk42WhBMilgOZCOFBT8gHvmQlJys7oUnUYDwupuuBbb/tNkLW/AdjckbIbstLWGKJmWKYjYbEcq7UblyGfa+dSe0bo8djnUCVTJf3xHYwZLALrPiJ5gy3ZJCmAVet82M2Edvj9jCGSgSg9TPKP5ZcW9eHc5ZjnYKVdjy2gDh47rC9FCzsWCcn2M2EWLIqqEd/xQTBbHWKQLujxyUPA7W4ahH8a6ysjIrxZYA1u7Izs62DRs2WKGqM3/g3SLB+wASPSACIiACIjBQAgE+WbY3NyNFs9iqnn3OGvfvR2Npl2fmDpWxaKHlYw8e2yWkoJAW9+ZpJA+BWFXNWsiJEzxEpOjwFDymKuYgApVMgds6Ru4uIDUVRWaeezPst9ddkZnpBWbrUblyA6pY3rWKKaksGPfH+34Vgsam7LuOBPbyO2Hfu+pODeNdBA//VJYgrXUTitHcgWMVYf8i2zt0HrEqoRS8UeDO/XcUQcqehggkKgFF8PzvnATPn51mioAIiIAIxAjgr6wBctYa9+6zyh8/a43Fe1Hdr9U9xpekzyqynPvvc4KXjirJKaiSrJE8BPhH9tj+MUa2mL5IkUlNRYomUjMZieosNol+5afPB/YGUiMpeDtPBFZWhStioCEMNrx/eZksKoPoJQXvzz8WdUVlOhdNKSkL7E1E7t7FPsZiRAHPozYR0zPJkiOCdEyK8QT8c5mPNMv1iAZuwp66+Uhx7Tz4txQyZ4EWcuY5YtVCO79OP4tAIhGQ4PnfLQmePzvNFAEREAER6EKg8cBBq3r+py6Sx154QQvqumOkTZliWatXuWqaWStWWOokbEzSEIEEJbAP1UN/xOqhELyyarOrrB7aRe7evzQI17rbIvY3n0ZDd7QrQIby+5G194+DhvHuONh/94HjYH46BDkHBVLY7uC/PRa1NSiakkzC/D4r/SACnQhI8DrBGOCPErwBAtPLRUAEREAEeibQdPSY1bz0a1dNs/VSpbU3IAcNIyU/z9LR5JztEnLuvdcYxdMQgUQl8N7hwP7xl2F7CGw/dZUve7wWCNpqtCr48ia0hoCg5Y6NoIVC+Or30CD+H3/WblsgeE3Yd8dU1+4GI3KpiORR8L72RNTuRCSPgifJ646WHksWAhI8/zspwfNnp5kiIAIiIAJdCLCSZt2bb6LYSrE1nzplbdU17hVRVIpInTDeFVnJ27TJMhbM7zJTv4pA4hDYBjH7xi9CMWtG1O069r71ODoE75lHQ8HLy/mj4DHF8++eDUWxuxTPrsdkquf/93TUiZ4Erysd/Z5sBCR4/ndUgufPTjNFQAREQAS6EGitqLCG4mIneA27dltLeUc/vLQ09MPLcnvwCp58Av3w0OVaQwQSlMDuo4H960thiiYrWLqqlz1dCwSPxVH++lMQMwga6wuldhSRfV/wIHrGfXc9pXny2DhOTPAYCVT0rifgejxZCEjw/O+kBM+fnWaKgAiIgAh0IaB+eF2A6NekJHD8LKpeoj0C9+AdOBPYpTBQ/YFrHY2G4zloc7B+ccS+uAlFVpCqydGKVMxrKEazA9Uzv/9K+J0pmuxv191IgxCy1QEF7ysQxbUQRgled6T0WDIRkOD5300Jnj87zRQBERABEehCQP3wugDRr0lJoKrWjJLHCNzmt9FoHD+/P2I/wuUmovrlMvSwo5h9ZD3aJEyOWEMj+tZhfjkapB+FHG6H5B0pRQ9BFGu50l2xFhwnf4zZnInhHrxPPRC129DAXEMEkp2ABM//Dkvw/NlppgiIgAiIQBcC6ofXBYh+TUoCzSgOe+VqYPtOmv3qnXY7cAqVNNFoHK0gXYsD9p/LRMStCD0A12K76W1FEZuJ/nVse3C2Au0QLob9Ai+gLcIFNCovRwTQCR6raHYao1E9c6w7jtmquRFXPfN2pGdOw3E1RCDZCUjw/O+wBM+fnWaKgAiIgAh0JaB+eF2J6PckJMC+c0ynvFwb2Okys1PnAjuBKN4l9MO7BvnLgpTNmGI2e1rE5hWiqApSNSshckfxmneOmJ2+YKiaGdg1CCFf38QvHK8Vx6UEcr8dxwQ0NF8MMVw51+xOiN08RAPH5UYsc3T4vP4rAslMQILnf3cleP7sNFMEREAERKAHAuyHd/m551FsZY9rlcCm5xxpU9EPb+0aVNNcaVnLlqEfHvLONEQgQQm0QcrY2P0iInEnIW8XOwRvDHrWzZhsNnlCxMaPi9il6sDe2hnu2duOHnquMXrXa4bYpSFiRznMgdgVQORmjIfgTYnYkiKzpQsiNrFAe++6YtPvyUtAgud/byV4/uw0UwREQAREoAcCTUeOWPXmF53gtVVXG/fmcaQUFLgWCVmrVlr2xg2WXljYwxH0sAgMfwIIWNt1FkxBJK4ee+sYjWN0z6VoIsqWlhaxURC3vScD+8HL7fbuwcCqrzJ61+XaOiJ2udhrVwSpWzonYncuj9ic6RHLofChUEtO9h/bK3SZrV9FICkJSPD8b6sEz5+dZoqACIiACPRA4Nrx41bzm1dcu4TWCxfsen29e+WonBxLn1Ho+uHlPPSgjUbzcw0RSFYCLQhcNzSaa2T+zc3trqBKj60QIHm5ELkiBLXZBuGJB6O2WMVUkvWtoevqBwEJXj8g9fASCV4PYPSwCIiACIiAP4FraHJe97vXXASv+WypXa9DszCMUWPGWOrkSa4fXt4jD9voefP8T6KZIjDMCVDuuPeO7RS+95+B7TmBkF+symY3a09LR4omongUvK98Imq3Iy1TQwRGKgEJnv+dl+D5s9NMERABERCBHgg0l5ba1S1brWHPHms6cszaLmOTEkYUXZ5H5edD8FbYuMcfs4yFC3s4gh4WgcQnQMG7dDkUvO+/2rfgpaIYSwaieGyr8NePR+2OhRK8xH8X6Ap8CUjwfMmhThPg9fK3JP8D9zUzdtoIOnXW1NRYSUmJ+855eXl5VlRUZLm5udb5dXythgiIgAiIwPAn0Fpx0Rr27XOC17Bjp7WUlbtFR1JTLZqV6YqsFDz1pGUuWTL8L0YrFAFPAmyncLUe/fL2BfbtF9HQ/GgvETx8xMnGfrvJ+YjgQfD+9OGoLZ+nzz2e6DUtCQjQATp7QD1S/cvKyqwUf0CkN2RnZ9uGDRusUHu5P3C3JXgfQKIHREAEREAEbpZAW22tNeP/gFlFs+71N6z5dEl4yGjUKHlZK1dYweeetqzlyywyCk3D9Ae8m0Wu+cOQQBsKsDSjHcJONDP/3q8D23YosCuI6jWHRWX/uOIOj5sCuVuFAisUvAfWRWw2iqx0N1jUpQ59+Ooa8B31i2LHS0cVzsEsyuKKyKBoTCwUwH+mLBoT73+uPB+/KMhsDM/iNSxmg//5sAyksY5ODwvOpKR0R0ePJQsBCZ7/nZTg+bPTTBEQAREQgR4IsGpmG7IzKHjVm1+wpsNHwlfykyG+MpcttYKnn3KiF01Pt4g+qfVAUg8nMoGYIB0rCezlP7TbOwfMDp8PrPIKrgoC40bM4fB9MfrcfeaeUPCKIHd5Yzte0+kbj3kRaZ/7jwV24LTZwTL03+PxMCbg9eybt2QWKnHOv/m2CjFBpVxx8G8x6ZArVgmN52AlUraguFwT2KlSXB8yvBvQBD4dKaxTJiDKOT5ik/A1BumsGslLQILnf28leP7sNFMEREAERKAHAgE+nbU3NyNFs9iqnn3OGvfvt4Cf2vjpFCNj0ULLxx48tktIQVo+9+Zp3DoCjAjVXgkjQlcQEbrWEWFKxQf5THyoZpn+WIPtDke/dYtNwDNXVAZWfBh78NADb88ps3OXAmsE5zb8k6A0jUZUakxWxJbNMXtsY9SWzUaxFfzOvnidRxMkp6YusGNnzd4pbrfdx7sXvFXzzNaviNr8Gdj2khOxjH42Ruc/T/4zZZSxAr37qiGOjYigsRooB4LvlpUJ8USfvsno7zcW/f4YVRusiB7Pfx3nr8F5z1QEVgIZpuCxz2ADeLFhPAVvxmS0kEAD+an4OX8A1xdehf6bKAQkeP53SoLnz04zRUAEREAEeiKAT2oB/uzfuHefVf74WWss3mtsds7HONJnFVnO/fc5wUvHnusU7LnWuDUE+KG6AhGhfUgjZEToED5UxyJCORCDwnGIChVFbB0qO85A020KCT/Ua/SfQEygz18yO46G6BSX0grIE4SNKYcTC8zmInrHlMxZUyDTkBZGybpyLocY7joQpnzuOGZ2ChJEGbuGaBfHaKQsUrpmT4rY2vlma1CkZfWSCKQoFiYMX9fTf/nPs7k1sKMQyFe3B3aoBMdHh5MWpEhypEGwGDW7De+Hj94esYUQyHT2+sNaB2PEIob78T785R8gxag6Wo89jM3gxOeYHkoZnoqG70tw7pXYo7gG6axTJ/bv+gZjjTrG0BGQ4PmzluD5s9NMERABERCBPgg0HjhoVc//1EXy2Asv6PikmDZlimWtXuWqaWatWGGpkyb1cSQ9HQ8CMfE4dsbs7d3tthPScAgpf7EUQu7nmp5vthz7wu5dHbGlc0NZyFZqnNftqMeeOUbzyi4Gdg6Cx6hUJiR6IiSagjcB33tLgTxyOrAX3giLthy7EFjVVSwDgn7DgOuMQ4RtPqJcbLfw2H0QsVn9E6A6yFwpjrsd0cZfvYM00FOBWyP+NuMGI3iZeE8swfE+vj5idyyC9OM8OTjfYIyr4HMBErsVEvvD3+GPDjh/gIieu8bYdfL60EpiLiT2TtRo+vi9UVuEfoHdCfFgrEnHuHUEJHj+7CV4/uw0UwREQAREoA8CTUePWc1Lv3bVNFsvVVp7Az7BYaTk51k6mpyzXULOvfcao3gaQ08gFrnbiQ/0Ww+ZnShHxAbSESvaEUvRnAzxWISUuNtvi9iDLP4xrX/CMPRXNLzPyH1llOomFF5h9I4Rs1hUagxSH7EdtdciJjtwn771y3ZXlfMq7lMsdbLrVTO1kxU5Wazlv38qamshYv0ZJ84F9irE7l0I1gFEGiuqwzUybZODEUVG6ybmIYo3I2LrER18GKI3F++NwRjnEZHctjcU2LcOBnYGEc8b5K7jJGmIVPL61iJC+YWHEa3E+5Kpo11TWgdjTTrGrSMgwfNnL8HzZ6eZIiACIiACfRBgJc26N99EsZViaz51ytqqa9yMaFaWpU4Y74qs5G3aZBkLkE+mMeQEjp1h8Y+wT9tBFrNgP/pYpCS2Gnx2z4J4sIAH+7N94dGorUQD7sHadxU7jb73TWALGqb/3Y/bbSvaLnzgPnWdjvvG+/W3n4u6SF7Xp7v7fTfaOPzbr3B8nOcy998xNbOb90MGUjUZRduACOGX0JB91SA1ZD+KlNAXGaHE+Y8ikny5uwhlbOG4vpWIKH8RgsdIZUE+ijdB+jSSh4AEz/9eSvD82WmmCIiACIhAHwRaKyqsobjYCV7Drt3WUl7uZkSwmYeSxyIrBU8+gX54i/s4kp6OBwGW7//OC4gI4QM10/NYlr67wfS3dERN1kMYvvrpqK1bHO67kuR1Ryt+j8Vb8LYhavb150OBbEa0sWPL7AcuiFFHRssoVl97Mnw/fOBFHg8UowjNv/8mPH8F/hZUjyhljwOCtwR7AZ+6N+JEcwaqh+Z0U3W0x/l6YtgTkOD53yIJnj87zRQBERABEeiDgPrh9QHoFj/NSM3f/SSM2ASsf9M1WtNlfUzJ+5+fjbrIEDtbdC0C0uXlCfsrC3owlbIeFSRrEEVqxM9tYMPUwLHYMzcWexDHjkFKJSJZQzkoYP/w0zBFk2m0XGd3wwl5h4D9zRP9F7ABCyQEz0UIIf6DMfZC8H74Snh9F5Ae2pfgLaXgfQiCh/MXSvAG4xYMq2NI8PxvhwTPn51mioAIiIAI9EFA/fD6AHSLnx6o4N1JwUPEhpGbZBa8elSmvIhiKNyTthvScbYSkoeIVi4rSKKS6KIZ2JOIwjNMCxzKKGYxet/94KUw4noR6bQNEM8PSDlciym1E3PCPXhf2BS1FeiJ159xqwWPKcO/fgspw0hBPQT2rpprT390wCWtQhXNLz0SCt64PLSDUIpmf25zwrxGgud/qyR4/uw0UwREQAREoA8C6ofXB6Bb/DSLdnx7cygMrGAYK67SdVlMyUtliiYE76+RoknRS8Z2CSyCwl5zZajkeOBkYHtRpp+CVwrBa0C0LI+Ch6qRy2ZBLlDYg73YxkMshmrv1ym0V3htG4qgIJJ3APvVyhHlYqEV9o7jiKVOTkblU0a37kQq7QMDKIrDYjt8P1D863p5P6QjOsh2DNyD91eP97+IS7jKnv/L6qI7UeCF538dX6dRadRiVTQ7TeP52ZuRVTz/7KNolYBiK5kZEdenr9PL9GOCE5Dg+d9ACZ4/O80UAREQARHoiwCarKkfXl+Qbt3zsbL7/EB9DBU0uy1qgUjJaHygzmNRDaTCfenjUVuND9SMXA1l9GooKF3FPsRySAb3gr26I5SoWIrmdUSSKLmudUQBegOiiiQ5bFyF9EBI31CMK5AuStAuFEN5BdUu2UagGimkTR17J1n8hE3Il6JtwCN3huubhh5xTCntz3BFdxBB29JbBA2XOh573RahkirfD5vujdgCyORgjAZGTqsgeBDYH/wu7IMXQLqd5HU6wQREJxejZ+B6tEn4KBrDz8O9SMY/OHS65BH5owTP/7ZL8PzZaaYIiIAIiEA/CbAf3uXnnkexlT2uVQKbnnOkTUU/vLVrUE1zpWUtW4Z+eBP7eUS97GYIED/L9J9GGty7xaEw7ENZ/LKuEaGO4iosiz8PH+jXIXL3yIaIzRuksvg3cw3xmHse8vQeIkgUnDfxdYYRpK4pgnAZSt4MtI5g0ZknPxK1JajmGMXj8RZetitohfAwdfR1CCgbkVex2iVTNTHYNH0c5IuNyO9fg/YFkCD2rmNkrz8j1jZjF4rvbDuC86AnXi2ksin85xqKPiJ3cyC0dywwJ7grEEWbNH5wBI+RSL43GZ38+dthBLURAtt8LQgbneP9ODo9YtPHo4ImoqjLkaK5DBU8J6LxuUbyEZDg+d9TCZ4/O80UAREQARHoJ4GmI0esevOLTvDaqquNe/M4UgoKXIsEVtPM3rjB0gsL+3lEvcyXAIKqdgUfms/hw3sJUv5Ooj3CyTKz4/i9rMqsClGsWEQoExEhRmvY2PrB2yOuPYJrbI1oXjKOPUjJ/D6KfDCieQksGFHqbrj+gJAppkB++bGo3YHvAxGp7o7Zn8d47/h1Feu6VA35wr2iqDO1lIP7IjNQBCYX92ci9geOgYwNRDxd4/urgZ0qNdtxAI3vT5jtgfhfYPsMjEmInK2ANK6ZZ3b7kihSVHGusRFIV/j8zf6X19aOL9dwHWmypRfQeB3v0UpcayP+J4N9AidDJqfh70AzsRdyEiQ7J3vwzn+z69f8wSUgwfPnKcHzZ6eZIiACIiAC/SRw7fhxq/nNK65dQuuFC3a9Hp9MMUbl5Fj6jELXDy/noQdtNJqfa8SPAMWg/HIAuQujd+X4EF1VC1nAB/iKWvwM8WOjc5bAn4yoyCRE7gqQ8seI0D1IRZwzwIhQ/K5kcI/MdgBsEbEV0btvoJE4q1W6qqKIKPU27sA+vK+ikTgbirPAB1M4h3Iwose1d21EfrPVTS9DqI4i/fNAidlu/AEgJniTIfsrEL1dMtNsIaKW4yGR8Rgxka1FdLKU/fAoeBBZVi2dNIHnRSpqDphDZjWSl4AEz//eSvD82WmmCIiACIhAPwlcQ5Pzut+95iJ4zWdL7XpdGBIYNWaMpU6e5Prh5T3ysI2eh9CARlwI8EPzcaT2/QZ7t/agGmMFIlR1iNawSMe1ji+emB+iF82M2MeQisnv3H/HPVwsJjLQiFBcLiQOB2V0iBLByN2/vYLCKojkudRMfOtxwG3WIj3wrz4R7kUbi0jSULdNiIlQ5zUyTfRmU0Upu1frw0IrNfijAN8fHHwv5CIqyAIn8W4TwWtjOirvTXNL4ESW4soUTXLmHyG4704jeQlI8PzvrQTPn51mioAIiIAI9JNAc2mpXd2y1Rr27LGmI8es7fJlNzOKsMeo/HwI3gob9/hjlrFwYT+PqJcNhIDbc4cPyjuwt+qHr7bbdlRLfL9KYkxiIAasTpiND/C3o3jI5z8aVkfMQmSKH6SZBsg9UowWUSBiH7BvViYGch2+r2WUiyLbhmZ2FIfoqFAS2C+O66+qCex4SVief/PWwA4jRbHPgXlrwekrj6HQCqpJjsmKOCZ9zhuEF8TEjtfU2BRKOu8LUzQzR2MdECBe281G8gZhqTqECHgTkOB5ozMJnj87zRQBERABEegngdaKi9awb58TvIYdO62lrNzNjGDjUjQr0xVZKXjqSctcsqSfR9TLBkKgDqmXTHVjef2f/yGw/acDFx2JpfbFjkUhYJrhkqKIffoulNlHUZUZaCDNVLirDYFrIdCC6A73mzFFju0BKEjDWfIoQ2x9UI0U1AY0LmdzcEaB2MPORSRxzdyPuHUXiqsggrcF8su2CL0OXLPhy/UFRCNx1xdwCIWK943RLV4T72s1AuL8ndczHQVQCpDCmJU5dMLZKys9KQKeBCR4nuAwTYLnz04zRUAEREAE+kmgrbbWmktKXIpm3etvWPPpknAmjIKSl7VyhRV87mnLWr7MIgwXDWdj6Oc1D6eXXcS+uwMorU+BeQXVF4/Tr2ORu64LhbjMnYLy89hztxz7rCZgz1MUt6S6joIU9spj9I6CNx579Caj0EXumDAiNlxS5ih1lCBWgDyPa7+IdNRqpGA6wYMIjUaUi4I3EWufiq+qGrO3wIV773ZDfsvxe2+DKYJMU+Teu/+GPnDswzYUg9fESCpbI5ytYBESNGGH4NVgH6UTPKyJgse2DTMmhWm1lPCh3hs4FCx0juQnIMHzv8cSPH92mikCIiACItBPAqya2VZT4wSvevML1nT4SDizI/yTuWypFTz9lBO9KErlRZhrpjFoBBih2oZ2CFtR+v8tSMyZS70fuhBl6DdCWtgeoQ4Ru2qIUhWiRA2IhFEyGOlLQ0XDedPMPgwRXIK+axSmoWr43fvqw8Ij3Ld14IzZL7Hn8AAKhrRATtvwGKs0jmKKJta/oNDsI2gnMAbCthfpq7uPm21Hg3O2i+hRgPGU68OG3msUvI/djbYR2Ks4FIPRU0ZS9580+9W74XW5FE08TqllWmZGBtojTDW7F2mjFPTpqDaZg0I5GiKQaAQkeP53TILnz04zRUAEREAE+kkgQNihvbkZKZrFVvXsc9a4f78FNAV+KsXIWLTQ8rEHj+0SUvLyjHvzNAaPwPkOwWN/t/4I3qRc9BiDtHBP3hlEwCoQIeKevVixDbcyOM1siMRDEDyKzkoUHGEJ++EwrqBI6zlEuNgw+9m3wpTUABJ0Q8NsLHUO1k/Bm4VoVxt6ybFdxGtgdLoCrw3fmh+8HMxjhPMjuO4NlCiI8GD1gfvgyW58hBVPT6BtASOxm7dgryB+7m6dMyZA0FHhk+vbsGLoGrHfuFr9JgI3R0CC589PgufPTjNFQAREQAT6SwAiF6DSRePefVb542etsXivsdk5H+NIn1VkOfff5wQvvajIUnJhGBqDRuASUzRROZMRvJd3IkUTItOdGLgTQmDykeo3e2IYqStFemMNhIkpgCyy8v7A68bAw9mLbAME789QlGUFIkbDIbv2NHqnvbEd1wsR2sGIHK7ByV1naeP6sWdtcoHZUvT5ewAiVI8o349fC2wf0jS75YM53Hu3Cg22v/hwKLbjUF10qCKXxyB0v0YDcIr6Ifxciahqd+vMxJ7J8ehZx/vyF49GbeX84XFf3n/v6AcR6AcBCV4/IPXwEgleD2D0sAiIgAiIwOATaDxw0Kqe/6mL5LEXXsCcM4y0KVMsa/UqV00za8UKS52EkIrGoBFgkZVz5UjTRJ83V2SlJKy8eIOw4WyjOoqsZEEQ8tAsm89fQtSoAdGt7kSCshPBnHVo9P21T0edUMSqaw7a4j0OtPd4YD/6DRqWQ4S4n+4qUkt7XD/SGtew3cEmpGoibXMzBGoXZLgS112PefwbBKU1Da/LhvgWQOjY/+6z9yN6NyfiKlcOVbXKXdhH+c8vtjvBq8E9vRb+8+meENbsisA8GfbpY9bzUK2z+wXpUREYGAEJ3sB4dX61BK8zDf0sAiIgAiIQVwJNR49ZzUu/dtU0Wy9VWnsD8v4wUvLzLB1NztkuIefee41RPI3BIxBrk0BB+I/fhm0SGJVr6iII7HNGsaMMNEFs+HwzJKcN5fh7HBAJCtKXIUhM1cwZi955EKVbOd47FNg//jwUoSbIKQuT9Diw/tsXod3BJ9DIHRVDT6BX4B70wfsDZPgkitFwfiokNg/Rvvlo9L5xecRW4XoXzgyLzHRsI+3x8IP5BKugfv1nobjybyMdAfAeT8Hr+uonsWakamZibx6rn2qIQKIQkOD53ykJnj87zRQBERABERggAVbSrHvzTRRbKbZmND9vqw7LFUazsix1wnhXZCVv0ybLWDB/gEfWy/siwO2OJ5G6+CqKc7DReRlaEdbWh5E8zmXkjdUwpyDlsglCdAivvYjbw71p7X0I0mqkLH7p0bDhd35u2Fahr/XE8/mtkLO/ez4UoYBr75xa2s2JGZH7609B3HAdbKlwEpUp36LgXUCUDNefAsHLjwkeZGnOdKR3Zg19GwIK3t/zupB6SmlvDzOcu7mi8KHBEjy+d1wPRJyPbSYotazMqYK3PaLXE4NAQILnD1GC589OM0VABERABAZIoLWiwhqKi53gNezabS3lrNePD4zozEzJY5GVgiefQD+8xQM8sl7eHwLcY3ahKkABErMSCNyFS+ihdiWcmTfWbAqKpBQhSlWOlgK/+ENgxYhkMchKmehtxBp+s6hHNsUHVSlv5diKCN7/QqSLe9UCCJr1JkKQlTuRYvr/oJ8dU00pMw3Xwv1tLkUTcgi/szQITTb2HBZAYLl371Y0Et+BHn3f3BwK3lXcFzY673HErqsjRZPRO98UTUpdc3PgZLexKTxOTnYo8jzmcNh32SMHPZGwBCR4/rdOgufPTjNFQAREQAQGSED98AYILE4vZ5XJMlTWvAjZq2GhDgwK3oSCiE1DD7VSiN/P3ggFqQQOzv1e3Y1UluWHzFGMnvk49qbhezqaiFN+buUoRmGV7yMVlYKHTGAnqd2th1VCKW0sRvLlx6K2FimNw1lYDmPv5C/fDIvlnICgV3XIeddry0CK7Di0RliP6/ripqitRkrpQFJJmfpJeaxDSwZWUK3F+6UZ0otuJ/a+4CEVtwCFXNhKI7dD9tRvr+ud0O83Q0CC509PgufPTjNFQAREQAQGSED98AYILE4vZ0VMpiIyKhOLAjFFk3KWgQIrl2sRvUNfOJbj/y9U3eRetA8UKUGEaOz/z965fzd93nn+85Vky7YMso0Ag8FgEi4GjDG3JA1MS5Mmk5KWNmk7YSGzO7ft9sw5u3N2zv4wf8H8sDO7Z87szs7u2ZnZNgwk7ZCUXGamuZSmkBsXG3MzJoDBXGx8N1g2siV99/15hKgDRrEfbEuW34+OI1vSo+/zfT0i5/vW5/KGOFpQAiFR5cjO5zyyBimO2qgl3RGdqzdc+RQplrr+A8fh+4eI5UjrD0EEVaLuTiOP39nqyIqKzO422Y6U2dPw9NPmMfuRatuIesG7I/kr9mUB0mwfw17oeW3dhKgsznEse6KfjS58Bk5ecuVf4Z947iqCoHgsBtFnGvPgGCrmHpkn8jWISPVB1C8GZqJ+k4MExosABZ49SQo8e3acSQIkQAIkMEYC9MMbI7A0vTyMVM4bsFaoQzfKdz6FjxxERQ+ieNpNMwohoZE7I+4gJCrLHdkIL7ivbnBkEUy1M2HcQsTpOkServ9fIVBPQ6j0alMZrF+Nzk26JdIsK0pRd/cobA8Q4VqHOrxM8fFLMlSrSE2PVMF1E/WS3YjYdUJ4nWkSI1wvIgprauPwOn2tii5tpvLIfPXBE6mByHsEhuxaFzmaYYQ/onStiOyqJcOnaMqjAk/9AV1NB9VU12FvtQhWGl+FuN+Mn8ch9BaCZyZHQEfDgK/JHAIUePZ7QYFnz44zSYAESIAExkoABU70wxsrtMl/faLmCiKp3ZVTEHcnUItXC7F0GemOfbjQD0IcVSJiU1UBcYS0xkch8maXOBJARC8ThnbNVFGk6z8DTztzDjiPFjSWiUCkaKfQStQaroL/nUYdF0EQFSPlMN3dP+9lp9HVPqRJXrsh0oD1X4Fovam1dzg/bXCi4k6bwETwOj1ntXFQkV1RhujaAhiwQ4CPpRnMTYj4qxCNWnv5fm3CD1BTNPsg+u7zEcRDSb89rWF8+bfVbw9R4FxEcdOconsvR/49NQlQ4NnvGwWePTvOJAESIAESsCSgfngde/ai2UqtsUpQ03MduWXww9u0Ed0010mguhp+eAgRcKSNQBgX9u2I5lxAvZcReG2I4mGrZt4ReJXlEErwgps9a2wpgGM9IW18opE3FW29t1y5hQhjGDYBeEjyEbVSw3WtA1PBoeIi2Uwkuf4mdMU8CYF0HQJPxVExUjNXQOAtxc9i/BSh/jCThtbA6T+Jtm5Ez66INCICeeaOQB0YdEVr7BagIU4RhKpaOOShDlKb28xGuqwKvNLZOMfg6LuZJvleaUXUDumf2oX0Q6S3XsZ+G8ip4CCitwFG6j/6diIlVI+baUI51fL5XOYSoMCz3xsKPHt2nEkCJEACJGBJYKChQbr2vWEEXrSrS7Q2T4cvFDIWCdpNc8aWzeIvh4LgSBsBE8mDkAr3Iz0QKY79EFjJFM0ZEFMq9GYWamOViV2iRqpU8FxDBKseaYONED2XIX708XkQa0shamqQJrqwNJGimPR7u7v+gUSKpka7VChqKqOuvbAgEXVMvn5iz2L0734bnLt7XTl+XuTNj+IQp4kUTU0x1XNWEVsA5nOLcO5zE5HIGkRSyxGJLMhLCKyxGM4n+ap9xk9+EZePIfDU6F0F8pcOCLwqeALuQA2j1vwtRuQw0wTzl54DX5CRBCjw7LeFAs+eHWeSAAmQAAlYErh97px0v/2OsUsYammRWB/UA4Y3GBT/onLjhxd89hnJg/l5Jo3khXAEoqcfokEFhAcXuGoLEEDtkx/3rEEa3x3T6FIXUgcvIArX0ATRA6sAI/Bg5XBX4KGJSA3q6DTVcgYEp0b0CrAX2uFTm8bo67QTpNYQDuJ39bWbeUegauRPI05jaUIyvmd4/7tpMxVNydQmMfs/cuWsNlPRcOXwgc+dNolZAXGrTW6+83U0iUHKqX4eR3suyW6ZXTddaUYa6Cewl1B7DO3W6YLTfcccfvzk7zjeynKR721OCLylFY7MGmXNX/ItkvfJf19q4h5BpFL33gcxnpuT2CP9nWP6EKDAs99rCjx7dpxJAiRAAiRgSeD2hQvS++57JoIXudwssd5Er35vYaHkzCs1fnjFz2+TvGXLLI8wMdM0AqQpgjeQ6ncFtUp9qIfS6E8IkRT1j5uFFDk/LkZZgzQ+/PUCX39OoI7uH3/pivrA9SByp01U+iECVPPk3UnR1Bo67eI4Az9zkHK5sBidHUNIZUSWbz/2rR6CqQk1hJ34vRDirnKOI6shTNZAGGpzlUwS5mcvQdhBaKnAa0Czk/Y7Vhb3Uk3aPGgN3L+HHcImNIrRz6N2Mh3N0JTXTvDUJjr//Jkrx87hcw2LDK3zG5W404NA4K1eLCaCt+VuBA8PWgz999UDsdkJgdsBAW/qCrGf2iSmFHtUiBpDjulDgALPfq8p8OzZcSYJkAAJkIAlgUhzs9w6eEjCtbUy0NAo0Q4oJgxPfr54S0og8Gpk1osvSH5lpeURxneaNrvQNMUbnSIXr6Pt/nWRZtyrcbh2ZJwFLzAVeNqOfvE8XJBCbGgaYLIWbHxXM33ezXCH2Dh4Cgbf++NyGNYNLmrovtDwY5iWSHSRxH5AFKjAW5gUeBCDx+GNpwKvCyIiAIG3EgJvTQXqxyCKliPFsAx/z8wQAXEUaah/+/OEj596EKrweeDA+T+G9Mz/+KJHVGBpo5vRppxqt8w6pGUegpD8l8Oo97uKo6hqHu1Q9kgXXbfUkd9/1pEtaxyZg3rMANJfxzI09VRTUvXflzaS0WY4WvupEXJtHKMNfNSGoWwOUnLx/jPw/qONUo5lHXxtZhGgwLPfDwo8e3acSQIkQAIkYElgqPWGhOvrjcALHz4ig9egmDAcXJl6cHWoTVZCO3dIQVWV5RHGd5pefF5AJOU4ugsePCXGF64fxs8aYVARp6mZAdRzVSGjdPuTHql+NNH0Qh/nsCfQC1uAy0jN1EjWnl+hUQpSB78g7u55a90LjZ6q6NYUTa1T04YkX0jRhGjQ12iKptawLUJr/40QSM8/6cgydAPNhPExUiX/8mdx43c3iChbTEXtgwaWrE1OfvgtCCwIvLE0V1HR+w+ouVOB19KGbpmIjI5pgKMDvk9AJP/JdzyyGZFETXcdq9H9dUQNj0HE67+vk5fR/bRrWIomjmEayOBLlLUQkt/CPi3HPqnAo8gb025NuRdT4NlvGQWePTvOJAESIAESsCQQ7emRSFOTSdHsff8DiVxsSrwTrtBV5AXW1Ujo5V0SWFstjl6Np/FKTlMEL0Fk/Ap+aio01BvsKiIN9w1ccFbCc+zFLYkL7WX4fVbwvlfxgTEQaEMU5zQiTMr9LaQQNsKPbdQRJuyHGQ+KSOH5PKQzFiFC9BXUsP3htz3Gzy8TIq+fIVL5V/sSAk89CYcQgXzgwHmYLpbbUQOHCNpYulhqt8w/35sQeMbnTuvuRjHUB1HrG4NIhQ2hycvjEHg7t+ILDkRCx/JPNVlz14AU3Ld/HZdP8OXJWUTGOxC1HD60YYxG7TYinfZ3n3FMxHImaif5BcpwStn3OwWe/Z5S4Nmz40wSIAESIAFLAto1M9rdbQRe177XZeBMQ+Kd9OoQPwXVayS0a6cReh60aHTS1F1BxZ0aSB+DqPu/byYuhDtx8akpZfcNLH02BN0qXORqJOXbEHrLIfI47Am0ILJTi8iORph+UYcUwha814MEm8VhtFZNBZ0atf/R844ResEMEA4nEVnb86+JFE31HuyFyHvgeeMj9gQiZ//5B4igQeBpR9PRRtCU65/vThzHvP8o2SZN7lejqctXahyphpfgEqRQqv3EWEay5k5rK3f/wpWjEPO30LlTff2GD43M6v8CKheKfPvxRFOZ5Tj2LKThcmQvAQo8+72lwLNnx5kkQAIkQAKWBFzkNsYjEaRo1knn7j3Sf+IEuvZBSamiwshfWSklqMFTuwRfcbGpzbM81ENNS3YZPARvsL98De3jITYeeKGt68bFtV7kai3SD7/rkQ2IOHDYE2jrQMrenRqxt1Ajdm4sEbzRHhZbVIPUvz94LiHMh9eQ6cdRPwOaiqv+eTpUEKrY0MCydqyciHEZUaxfH01ELj/GlwvNWqKq0bV7BJgxGkeaqQq739+mRuNjaxZjBN4rwwReqpPBuQYR7VT/vYXw2StHfePqCgi8tY4sRidPZaJCbCzjJlJCr6FZkUZoX3kvYax+7znefT8cfxGOuwXptPrv68n1agsxQRtw96D8JZ0EKPDs6VPg2bPjTBIgARIgAVsCuHJ2ceXcf7xe2l/ZLf11x0XNzvUxHf4lFRJ8+ikj8PwVFeIrwlVsGoa2a+8LJy5A//p1dHFE6twDL0CxPlP/BZGnF6B/+jseE1lJw7Kz5pBGAMB8+yAE9pcKANuzhkaoRjTo5W8kBF4ZavK0G6cOFXYDqLXUbqk9qAfUEYSA126O+bDFULE3EeMWjqeef4fwhcJPPoDwQZfLu81lhh1wEZqOfBWfNf28PYGo8UKsXYPgox1G4P1jIhU0pS0C3tOBeFsFQfeDrzmmqYqmTOqXGWqJoI1d9LhjObauUTtlNiBaqfv7809gB3EVD94jYoefyxxEyKvKIWhxrt/6qkeWIVrOkb0EKPDs95YCz54dZ5IACZAACTwkgf6Tp6Rz76smkqdeeK4qKozc+fMlsGG96aYZqKmRnFI4WKdh2Ag8TZHTiAoF3sNvmPoN3oQthXaV3IMIzxHca1fJgVQ1aWM9LDRCzaOO/N5vJwTeXESoNMVR7TA6ekRuwEZA77Xhi2oPFXhaWzkX6YFqjzGWmrfRLk2/54gMaVMZkX86mLAxGIDoG4q4JmXY6000M3m0TOTrEDtrsH7tMpkUpqM9Tu05pB6/lYjgqfeeGtmPNFTAzUaTkyeRCvpvn0OkECmZNhG7e99bO2Wexho0gvem1lh+icCbjZq/lUjTVEG7fatHlkNwcmQvAQo8+72lwLNnx5kkQAIkQAIPSWDgbKN073/TdNMcamuXeBhXsRi+kmLxw+Rc7RKCW7eKRvHSMfRCW1v1jyVFs2RYiuZ6pmg+1LZp1u4QomgXYPT9qyNIkT0pchTRLO2yeHekiPjcfU2qX6AR1GbgP8FmQCNDKma60DW1HtHaekSX6i8lujoiwGyGWhCUQtitgtCoRmrnejQYmQeLhfEcydrPHvxzuNruyjXU4d1ARK/3pmvq0/L9jsydLTIfYnQBonizgo4xdB9rRLEZ0dGPUNuoETS1oriMTpr3RdBwaovhJaiRQv3i4nE0pElGCscasbuXkXanbcLeaiTx1V+5curSCMdPTsI6yuAz+RiYq8D7+hOwJVkwvtyTh+J9ZhCgwLPfBwo8e3acSQIkQAIk8JAEtJNm74EDaLZSJxGYn0e7EEbA8AQCkjNntmmyUrx9u+SvWP6QR7KbnrzQNk1WEOnQC9FORHIe1GRFU8hWI23MpJCxyYod9BFmdSCK1ohOiycguA5/Dq80CB4VXAOIaIXRlEN/+hB9StbJ3X0Lvf5P1oXdW8OG59QPb65GpiBaNDKlkTAV9Z/DEuMXH6Or42mRU82utN1Jz0y+bwgifgXqv55YJfLcZo9UIsVzLM1Nku8zmnv9DGqKaCuEnkYVNaqcl6fG34looh9ib7RNVe49Xi+ioVdQ76f2BB9A6J1txrHg96hfaig67V5ZGHBkBdIiv7HOMTYFZRaRwnuPm/xb902jeJ9AXL7yLhrqIJoXgaC/k6mdfFliMVjQ0jKRbai9U6FZjcY4c0O6So5sJUCBZ7+zFHj27DiTBEiABEjgIQkMtbZKuK7OCLzw0WMyeP26eUcnN9eIPG2yEtrxEvzwVj/kkeyn6wX2JVwEf3jHJuETpAle1aYX9w5ca65E18zvQdipwFtKm4R7CVn/ramat1AL2QWhpSmTnUiZ1Jq4FjRhaYKFxUV8bM7fQOTtXh83iDsH7fx1uJrWqSIvObBfS+aJPIUmISoYNiISV4wOmp14/6OI3r32S3izQXBoB8t7uzr6tW0/In2a2vn9rzmyCXNVbIzV4Du5lC+711pA7Tg5FE2kaHrQ3UX95jSaqJ1AbSNpGh3th8i6AZF1DuLuc4jZC/jp6k28Zwm+sHgUn+Ol8J1biojlXJiM50MUjzVS+KDzUyNz3dta/Jv68T+roE58gdKve5WMzKqGw4+DtFlNDf0jNMPZDEEeQopsAfaAI3sJUODZ7y0Fnj07ziQBEiABEnhIAlPFD09TyS4ilawekY5fI03wPARF/wCMznGBqp0UoUfvGp1/Z5jRuT7OMX4EEimziaidCjyNaqnAO39NpBF70gbhp6JFh4ofH/j7IEh0RNUwHJEpfQ/t9qjPaw3b02jzv+YRTXl0EBVMGNprSu5rv0Z92GVMTAoN8y7D/oN917b9L8B4WwW9tu1X0TEVxwDY6Gf8OkTyeXzOuyHwVFRpreGjEHfz58IvcGZCVE7E+Z2/iogpmqxopLwZqagd2FsVtPrliorJfAi5GRDf2iX0d7Ziv8BaHx9r186JWDvfc+IIUODZs6XAs2fHmSRAAiRAAg9JYKr44WnKWhipa22o/bqIaN5liIlmtHdXE2q90JyFmqzFCx1ZXIafUvyNi+HxaELxkHizbnoyZVaFtQq523dSNHuQatiGyJtGnjTtUEcRGnIUohtmfkFCdA1g/2C/aISD7o0KFt23UqRoFkE8aERMbRmOwvxbU3HfOw6x14o3epDAw1Nam7YVjUdU4D0BobgAXSyn4kgI5wSbZIqmnodJ0QQ/E7W7Ey2ciPPrw7+jVkQRNVLe2IR7CHb9W/e5qNCRMtQZLoWoewT/xiru+O1p1FJ/OLKXAAWe/d5S4Nmz40wSIAESIIGHJDBV/PCSp6npZFoH1daJ2iUIPK2NylWBB5GwGA0f9D43xzF2Cck5vJ94Air2BhBRvWtngAt/I/DUzgD1ajrU7uA2IlUaGVI7CxV4KlyGCwXd009Qi6YRvF8hXfDSSE1Hhp2OesE9iUY6amy/ZaMjiyDwOewIqHjvQRMZbbpyFcLaCDzsqwo8jSCqJUKohF+c2NGdmrMo8Oz3jQLPnh1nkgAJkAAJPCwBXNVNBT+85GkmuzqqSFDBoLVRmiamkY4AIh16r38zspAkNjn3cYgDjULpfphul9BZGlnVSJ2KOR36fBw/MdTh6f6MVL/W0oZGH2j4oW37fwGhd6EFEx8UwcN7VCCy9PU1ia6Oj6GWTz30OOwJJCPlmjJ6G1+mqOgzKZqIrmqzFz9SbocLcvsjceZUIECBZ79LFHj27DiTBEiABEhgnAioH17Hnr1otlJrrBLU9FxHbhn88DZtRDfNdRKoroYfHr7KH+PQi0Tjp9aHCA9S9Pq1tgcX+Rp50yYNQUQINJKjQkDr6TimL4GuHtTzoYOmpmj+7OCwGrx7RZ5+TvCjNXjfRbt+bdKyAnV8GmHiIAESGB8CFHj2HCnw7NlxJgmQAAmQwDgRGGhokK59bxiBF+3qEq3N0+ELhYxFgnbTnLFls/jL0a99DEPFnUbd2lDPcxb+aeevonYOdVaqH0sg7BbPE6lCZ74y6EZN5dNoAcf0JWC+CEAK7kHU4f3NWzBWRzdNF1E/E8VLirw74s5BpHYDmn78h+cTXR2N4Tm+KOAgARIYHwIUePYcKfDs2XEmCZAACZDAOBG4fe6cdL/9jrFLGGppkVhfot+9NxgU/6Jy44cXfPYZyYP5+VhGL2rkrqAr33m0fj+FlvefX4HAQ+dF9UubhZSvRRB4ayDw1MfsUURjQsFE/RxTLFNTVi82bcahPmYaEdW0xxxEQAsgcGZqRBQpdRoRnWoc9QsBTeU8Ac+9PbBJUIHXi6ie1vYNaMogsOQjDbcQtX1F+KxsgBfbjq8nujqyqU7qzwyfJYGxEqDAGyux37yeAu83LPgbCZAACZBAmgjcvnBBet99z0TwIpebJdaLdogYXrRBzJlXKhrBK35+m+QtWzamFTYg3e51tLtXX7MbiNx1o/26ChKN6pm6OQiSIJptrFsu8oOveaT6UZhIq3E0I3kP5KwiSFMZL4BtE7odNqNuTeulgqhBXIhI6KqlaIqB2rSpytGcHz4n52G/cLYJ5upo3a+R32s4Z31uHoSd2iuo0bb5YgCNVUrQsZO1YQ/8yPAJErAiQIFnhc1MosCzZ8eZJEACJEAC40Qg0twstw4eknBtrQw0NEq0I+Ek7oEBlrekBAKvRma9+ILkV1aO6ojaXj0CIffpGVf+9s24fIqOiGrobDzSNAyjI1kuhfu1MKz+d88kaqnmzXFkBiI0HPcTSLazb4Jv2RmkvJqIKCKkmtoYREQ0KfCWo+PhIxBBs4oSEdGpVtuYsA2ALxy6aJ5oTAi86yrwgCbVOBIAADGMSURBVKQUXwgYgYfumZra689l19T7PympH1GhrI1xNFVa/53G8IcK5Bxfwq6CX7Ck5jddnqXAs99pCjx7dpxJAiRAAiQwTgSGWm9IuL7eCLzw4SMyeA1GcxgOWh16AgWmyUpo5w4pqKoa1RHVn04jdh+hlurH77pSB4Ny00URkbsvjDsibwm8655CowxtlrEJLe/L5ibV3xdePe3/uABh996nCUPqi7AUUGPxZETUpGiiac1MeMrVLBX53lcTEdH8/KlX22gECD4rAxAfaouhzXk0RVNHnqZo4jzVO0+b87BraoLLWP6rKb0q7tSz8AZSprUjrXY11fTeuSHHpMBOtfTesZw/Xzs6AhR4o+M00qso8EaiwsdIgARIgAQmlUC0p0ciTU0mRbP3/Q8kcrEpcXxcPavIC6yrkdDLuySwtlqcURR3dfe6chF1dwfhZ/bah8O6IY50VtBypUFE8RB12gKB99xvIUKziAJvOCoVx3pBXoto1k/+OS6fwEqgDWmMYQiguzYCSWS4r0ZHyZefTghmNf+eCcNxDhJQGwu1GOmC393VdpEW/OgXMUbgIS1avxyYiwj6vNkiCyH0gvjceGk7Mm0/OBR49ltPgWfPjjNJgARIgATGiYB2zYx2dxuB17XvdRk405B45zuFTQXVayS0a6cReh6/X5wvyeFq73KlAVE79TPb/wlqqVBDdVeIjLDmWTNEls8T2Yzo3YtPe0xt1Qgvm7YPqUG4imYVdv/wL6hphNCLQPBpKuwXxh2Rtwg1eF9dnRB4T9YgdXNeUv194dX8Y5oR0GY1rYjYnTiPSHCtK+fw71IFXyzmmkhoDtJd8wrQ2RZfEHxvsyNVFUiB1ZpYNOzhmH4EKPDs95wCz54dZ5IACZAACYwTARdf7cdRjBOurZPO3Xuk/8QJeNUhj0tz5TDyV1ZKCWrwtNmKr7hYtDYv1ejsRn1YU0Lg7TuEerFmvDrxViNOm4MI3qqFIlsg8L6FZitaQ8bxGwK30NT0GmrtDiEi+uP3XKlH/d0DeQJdCIK5cgF4IiL6na97ZEUFef6G5vT97So+Q4fxJYH6DH4AI/mm1ntYaLQOkbw16Gq7C91JN+NLgoWMAN8Dafr8SYFnv9cUePbsOJMESIAESGC8CEDIucgD7D9eL+2v7Jb+uuOiZuf6mA7/kgoJPv2UEXj+igrxFRWlPHKyBk8Fyd8h4lSHiEEqQbIYEaev3Yk4PcGI031su9HUVLtmasrrT9GV9DR+fyBPzJ6JKMzCUEIw73rOI2vQWZNjehPQ72pqYVXy9+/EjcBr60KKL2obvzD0YwKRVzZL5DF8ZrQm9qnHHKlAp1KO6UeAAs9+zynw7NlxJgmQAAmQwDgT6D95Sjr3vmoieeqF56rhGkbu/PkS2LDedNMM1NRITim6oqQYg0gfDMOnTe0Rdv/Clc9w342GDslGGcmpmvqVl4vo3SKRbz/uyOMQeY+g/q4E3R85fkOgB/V2l64kIqJ7D8BT8FJqgVeM2qkKdJjUiOjvPOOR1Ui5y6ahwWXtyKrNVzrUJw/36q0IKsYPcAYCzCF8hrQZizYP0Tqy6TySvDRy919fjcvHaH5kYCmwewc+KsqvrCQRAf7dbWjWA69KjulHgALPfs8p8OzZcSYJkAAJkMA4Exg42yjd+9803TSH2tolHkbRDoavpFj8MDlXu4Tg1q2iUbxUQy8otT7sIkTJLw/rBaXIUaQVXkfUYPgoRBfEeQgGPgZPs+8hJWztcu3g5xiPvOGvm+6/D6AGT9NeNYL3f95GF01EYh4YwcO1uF6cb0IERlM0n3oCEZgF2XWBrrYQt8IJm4iPIFrO4XPWhc6tGqUKInq5FBEnPfelSPsNonFIHozfp/PQL1z6wEsF3l+/ji9cYF/ywM8PQOUgTVM7lGpN7J983yOPr8quz890/iyM5dwp8MZC64uvpcD7Ig/+RQIkQAIkkEYC2kmz98ABNFupkwjMz6Nd3WY1nkBAcubMNk1Wirdvl/wVcCYfxeiAKGm8mGjq8Nk5NeVGpAUX53ohrn1aNNKkqYRrIUa2bkykgrE9+/1gNVqlnQ6PIRL6Y6S8qr/gSBFRtQzQi/PlqL97boMjX0FEdBWiL3NmZccFun5u9MuDNnyuzjQh5RAm6CrwGiHwuiHwYOdmBN4yCDxNL9wAr7yVS0TmliS88qbrZ0v/zakg1i8I/scbqMPD5yiVwPMgsu5F5FNF8n/Z4ZEn8TnimH4EKPDs95wCz54dZ5IACZAACYwzgaHWVgnX1RmBFz56TAav3/HDy82FH17A1OCFdrwEP7zVozpyMtLSiRqylk609kcErxM/6sNVCDPzEjRX0YvvOYg4zcF9IHXvllEdMxtfpMJFyyGTEdGPNCKKusZ7I6J+9TJDBEuFzU7YJGxa6UjRzOyJYCmDwSFXjp8X2ft+XA6fQYomxN4tiLtBPKdD/QA1NVNTNDetFHkJTWbWLkUqsHaDhPidjkOj6WporjWx/+2nsNk4nVrgaVqr9lEyEbzvIYKHzxHH9CNAgWe/5xR49uw4kwRIgARIYJwJjLcfXnJ5Cf8tXIijG2QngoJ6wTkD0Tv90RQ6P+rwOL6cgKZpakS0HuLus0bU5d3AhbuJiLqmlX1hgQNhk4iIbkNq5rJyJ6uMwO+mqiJq94VUVeiV+wY0SQ0iw3/wXCKapwbeAYjf6Tg06qni+Agiv3/787h8hBq8mxDFarUx0ihCF9aK+QmBt+Mp1HCiqybH9CNAgWe/5xR49uw4kwRIgARIYJwJjLcfXnJ5mlqnF5gq9PSiMpmiqemE+qOphRxfTiCZatfRA9sE+Jm1dIi0IyIajbom+qnNaUpn4wdpr/OQljkDgiab0hI71F8RtZwaiXpD/RWvgNlI4k5RQpMsKxPZhtRfTddcg6imirzpOPTfm/40XnLlrQ8TtXinkdbajuY99/EDoqXg9twm1DGiBq8adbGlWZLiOx33/mHOmQLPnh4Fnj07ziQBEiABEhhnAuPthzfOy+Pb3SEABwvpH3ClF51J2zu1oyQEHsScpmPOKnakIEtTXdUL8Aiidyrw3sfPRUQw7xMoyU8JhMqi2SK/hfRCFXhPrnekfP70FHhJJGpyfhz1d7WN6HD7uUgTePYhkqdfvOiXLPloRjOz0EHETuR52CNobWwI4i5bP09JLrwfmQAF3shcRvMoBd5oKPE1JEACJEACk0MAX/OPpx/e5Cx6+h0lmXKnzVc0RTOOIj2tL8vxJdJdvahDy8ZxtRUNZuoSzUIOwLD7UhvOMkUETxv4PIkIlEaifguRvEXT3M/tNurwem66cgUG56cRCT0LP8XzV5GuiWa5eai7m4cI50pYaqxYjPTeRYn6WE2fztbPUzb+GxnPc6LAs6dJgWfPjjNJgARIgAQmiID64XXs2YtmK7XGKkFNz3XklsEPb9NGdNNcJ4HqavjhwWwti0ZSOOm9Do1q6MUtU0gTPNL9X41A1SMCpd0g/+UYbBK0B1AKgbcEdo1PQ9xpBG8jfsrmTu8IXnL/biLye6UFAvkaBB5+7go8ROsqIfAWgBvtJZK0pu89BZ793lPg2bPjTBIgARIggQkiMNDQIF373jACL9rVJVqbp8MXChmLhMD6dTJjy2bxl5dP0ArS87aqY9WgXX3DVApoN8EAGpfoPUf6CdxEkx6N4mmK5ivvQeyh4UwqgbcakaiXtiY88SoWakfR9J9DJqzA2G7gn3QYab5qEj+E+lgvPvDqFzgTPpTqgUeD+EzYqfSugQLPnj8Fnj07ziQBEiABEpggArfPnZPut98xdglDLS0S68OVNYY3GBT/onLjhxd89hnJg/n5VB7aeEKbv/SiDukabBy0eUm4z5UhpD2qwoM7hBSiJmkW7BzKEN2YCWsHL6J62dS4ZCrtX9J249hZkdc+gE0ConkdaBTSDyP44UNryWZBzG1Eeub3IfDW4744CLsICBcOEiCB0RGgwBsdp5FeRYE3EhU+RgIkQAIkkFYCt2Fy3vvueyaCF7ncLLFeGNlheAsLJWdeqfHDK35+m+QtW5bWdT7swZP2DScvibz+qSunmyDuIPbiqG3T4UPkzo/mJasqHPnO42g+sRh/++GnlqU1buakM/g/mjqr0admpBd+gijex6jDO4TW/83Da/EgzBeg9u4JdM38Cuwan1zrkcVormIiUty3DN5dLi3TCFDg2e8IBZ49O84kARIgARKYIAKR5ma5dfCQhGtrZaChUaId6MeP4YH7sbekBAKvRma9+ILkV1ZO0Aom5227Ef1pQg3SQRg///SQK6cg8FyN3iGqp8MDQeBFNKgKPmA/2AzBsMoxYkF9wjjSR6AX+3YZ+3ZCLRMg8C6jm6ZGYnVozWT5nITAq3pEZAlSMzV6x0ECJDA2AhR4Y+M1/NUUeMNp8HcSIAESIIGMIDDUekPC9fVG4IUPH5HBa9rNAqmJCIN40I9fm6yEdu6QgqqqjFiv7SIa0UVw/68h8GD8fEZ9wZCiacTdncYdjqZj4mduscjqRWjWUeXINgg9NRDnSB+BhE0EOkIinVa93LRJyO3biU3Ly0MqLaKuIaRoBgsTNZS5rKFM32bxyFOWAAWe/dZR4Nmz40wSIAESIIEJIhDt6ZFIU5NJ0ex9/wOJXGxKHAntJFXkBdbVSOjlXRJYWy2OhkymaFHaEdRw/c99cdO0Q+vw1IT9vqYd0HL5qMUrgVjQbow//K5HNlRS4E3QR2/Mb6t1lMlUW52cpym0sIyYoh/JMZ8/J5DARBGgwLMnS4Fnz44zSYAESIAEJoiAds2Mdncbgde173UZONOQOJJeNeOnoHqNhHbtNELP4/eLo1fUU3B8jMjdX+yJm7b72kkwaY9w76loYxWNAqnA+9MdHvkKInkcmUNA9y25d2ppQVuLzNkbrmTqEqDAs987Cjx7dpxJAiRAAiQwQQRchETikQhSNOukc/ce6T9xQly9gtZwCUb+ykopQQ2e2iX4iotNbd4ELWVC31b91P78lUQE70sPBE23GZ5qf/ayR7ZA6HGQAAmQQDYToMCz310KPHt2nEkCJEACJDBRBCDkXHSt6D9eL+2v7Jb+uuOiZuf6mA7/kgoJPv2UEXj+igrxFRVN1Eom9H0Pnhgm8BLadeTjqZ5LCrxdFHgjQ+KjJEAC2USAAs9+Nynw7NlxJgmQAAmQwAQT6D95Sjr3vmoieeqF5w5qi0mkK86fL4EN6003zUBNjeSUlk7wSibm7bUG72+0Bg9Cr1cbdSRO776D5aEGr0hr8BDB+xFq8DayBu8+RnyABEgguwhQ4NnvJwWePTvOJAESIAESmGACA2cbpXv/m6ab5lBbu8TDUEEYvpJi8cPkXO0Sglu3ikbxpuI4hy6a73yILpoQeCfx+w21+7s3kofIXSkClNUV6KKJ1MxvbnFkKbtoTsXt5ppJgATGQIACbwyw7nkpBd49QPgnCZAACZBA5hDQTpq9Bw6g2UqdRGB+Hu3qNovzBAKSM2e2abJSvH275K9YnjmLHsNKbnRA2DW6cvQsjLPRR+bcdVe6hkXyTPfMgMiyMkeeXCWyAebZq5Y6MmeW5mxyTHUCWlKqpaV9AyJt3a6xWxhEJ9UoHtORi95BhXmwW8BnoKTIkXx4IrI7Z4IN/5v9BCjw7PeYAs+eHWeSAAmQAAlMMIGh1lYJ19UZgRc+ekwGr9/xw8vNhR9ewNTghXa8BD+81RO8kol5+9sRkd5bLszORY6ejsvRz0UOI5J3HX54aMYoZYjcbVzsyIZlIutXeqSiDBf7MxzxI2WTY+oT0JLSwSFXzl8VOXAUXoiXXOmAr17/nVRdtcZ4ZG7C6H4TorcL5jpG4FHkTf295xl8OQEKvC9n9KBXUOA9iAwfJwESIAESSDuB6eKH19Xjyue4uD95SeQI7q8jVVNjdEmBV7UYF/owOtcoDkf2EOi5JXK5xZVjiOC+exj7f8GVTjzWD+GvH4BiRO5U4G1Y7sjTmxC9fRSfgSAieYjqcZBAthOgwLPfYQo8e3acSQIkQAIkMMEEposfnqbl9YUTjVa6YHg+gL9VyuXD+04v8oMFSNULOMYLb4KR8+0nkUAjorX7D7ry8SlXLlx1pR2RWzW7jw1L0QxAzD0yT+RJNNbZtMqR9asdmT+HQn8St4mHShMBCjx78BR49uw4kwRIgARIYIIJTBc/vAnGyLfPMAJae6c/h8+48r/eSHRRvQlhr+JupCY7oRkiK1CHuQVdVL/7dUdWoOEOBwlkOwEKPPsdpsCzZ8eZJEACJEACE01gmvjhTTRGvn9mEdDGKtGoyMcnXfnvP42b+yHU4+njIw2tuZyJKK7aZPzxix7ZRJuMkTDxsSwjQIFnv6EUePbsOJMESIAESGCSCKgfXseevWi2UmusEtT0XEduGfzwNm1EN811Eqiuhh8eCpY4SCDDCWhzFf0IH6p35S9ehcBDimbKgYCd400IvD/b6ZHNVYzgpeTFJ7OCAAWe/TZS4Nmz40wSIAESIIFJIjDQ0CBd+94wAi/a1SVam6fDFwoZi4TA+nUyY8tm8ZeXT9KKeBgSsCegkToVeAdV4O2lwLMnyZnZTIACz353KfDs2XEmCZAACZDAJBG4fe6cdL/9jrFLGGppkVhfnzmyNxgU/6Jy44cXfPYZyYP5OQcJZDoBFXgaxfsEKZp/9bO4fIT74c1V7l1/HlI0g6jD0xq8H33XIxvhh8hBAtlOgALPfocp8OzZcSYJkAAJkMAkEbgNk/Ped98zEbzI5WaJ9cJHAMNbWCg580qNH17x89skbxkM4zhIIMMJJJus1MIe4e/2J5qstKs9gvrf3ZutCS03NyiyBn6IWoO3bYsjy2CZwUEC2U6AAs9+hynw7NlxJgmQAAmQwCQRiDQ3y62DhyRcWysDDY0S7egwR/bk54u3pAQCr0ZmvfiC5FdWTtKKeBgSeHgC56+48ouPXfn0tCtn8XtLFywyIPK04YoOP2wygvkiy7SDZhVM79FcpQqeeHNDFHgJQvxvNhOgwLPfXQo8e3acSQIkQAIkMEkEhlpvSLi+3gi88OEjMnjtujmyk5MjnkCBabIS2rlDCqpwFcxBAlOEwK2wyLU2V06cc+WXR12ph9H5FYi8m1piCg03Z6bIqgWObFwu8lvrPbJ8saZqOpLnnyInyGWSwEMQoMCzh0eBZ8+OM0mABEiABCaJQLSnRyJNTSZFs/f9DyRysSlxZI9HVOQF1tVI6OVdElhbjW6DaDfoMMIxSVvDwzwEgWQt3uXrrnx03JXTTQmB13s78aZzkJq5GtG7qiUi1YjeaeSOH+2HAM6pU4oABZ79dlHg2bPjTBIgARIggUkioF0zo93dRuB17XtdBs40JI6sV7v4KaheI6FdO43Q8/j94vh8k7QyHoYE7Akka/H6Ieg6ely5iYie1uElUzTzkKI5EymawYBI0UxG7uxJc+ZUJECBZ79rFHj27DiTBEiABEhgkgi4cIWORyJI0ayTzt17pP/ECXE1/KFXyBj5KyulBDV4apfgKy4Wrc3jIAESIAESmLoEKPDs944Cz54dZ5IACZAACUwWAQg5F33l+4/XS/sru6W/7rio2bk+psO/pEKCTz9lBJ6/okJ8RUWTtTIehwRIgARIYAIIUODZQ6XAs2fHmSRAAiRAApNMoP/kKenc+6qJ5KkXnjuofeVFcufPl8CG9aabZqCmRnJKSyd5ZTwcCZAACZDAeBKgwLOnSYFnz44zSYAESIAEJpnAwNlG6d7/pummOdTWLvEwipYwfCXF4ofJudolBLduFY3icZAACZAACUxdAhR49ntHgWfPjjNJgARIgAQmmYB20uw9cADNVuokAvPzaFe3WYEnEJCcObNNk5Xi7dslfwX6ynOQAAmQAAlMWQIUePZbR4Fnz44zSYAESIAEJpnAUGurhOvqjMALHz0mg9fv+OHl5sIPL2Bq8EI7XoIf3upJXhkPRwIkQAIkMJ4EKPDsaVLg2bPjTBIgARIggUkmQD+8SQbOw5EACZBAmghQ4NmDp8CzZ8eZJEACJEACk0yAfniTDJyHIwESIIE0EaDAswdPgWfPjjNJgARIgAQmmQD98CYZOA9HAiRAAmkiQIFnD54Cz54dZ5IACZAACUw2AfrhTTZxHo8ESIAE0kKAAs8eOwWePTvOJAESIAESSBMB9cPr2LMXzVZqjVWCmp7ryC2DH96mjeimuU4C1dXww5ubphXysCRAAiRAAg9DgALPnh4Fnj07ziQBEiABEkgTgYGGBuna94YReNGuLtHaPB2+UMhYJATWr5MZWzaLv7w8TSvkYUmABEiABB6GAAWePT0KPHt2nEkCJEACJJAmArfPnZPut98xdglDLS0S6+szK/EGg+JfVG788ILPPiN5MD/nIAESIAESmHoEKPDs94wCz54dZ5IACZAACaSJwG2YnPe++56J4EUuN0ust9esxFtYKDnzSo0fXvHz2yRv2bI0rZCHJQESIAESeBgCFHj29Cjw7NlxJgmQAAmQQJoIRJqb5dbBQxKurZWBhkaJdnSYlXjy88VbUgKBVyOzXnxB8isr07RCHpYESIAESOBhCFDg2dOjwLNnx5kkQAIkQAJpIjDUekPC9fVG4IUPH5HBa9fNSpycHPEECkyTldDOHVJQVZWmFfKwJEACJEACD0OAAs+eHgWePTvOJAESIAESSBOBaE+PRJqaTIpm7/sfSORiU2IlHo+oyAusq5HQy7sksLZaHK9XxHHStFIelgRIgARIwIYABZ4NtcQcCjx7dpxJAiRAAiSQJgLaNTPa3W0EXte+12XgTENiJSrk8FNQvUZCu3Yaoefx+8Xx+dK0Uh6WBEiABEjAhgAFng21xBwKPHt2nEkCJEACJJAmAm40KvFIBCmaddK5e4/0nzghbjwuAiN0HfkrK6UENXhql+ArLhatzeMgARIgARKYOgQo8Oz3igLPnh1nkgAJkAAJpIsAhJwbi0n/8Xppf2W39NcdFzU718d0+JdUSPDpp4zA81dUiK+oKF0r5XFJgARIgAQsCFDgWUC7M4UCz54dZ5IACZAACaSZQP/JU9K591UTyVMvPHdw0Kwod/58CWxYb7ppBmpqJKe0NM0r5eFJgARIgATGQoACbyy0vvhaCrwv8uBfJEACJEACU4jAwNlG6d7/pummOdTWLvFw2KzeV1Isfpicq11CcOtW0SgeBwmQAAmQwNQhQIFnv1cUePbsOJMESIAESCDNBLSTZu+BA2i2UicRmJ9Hu7rNijyBgOTMmW2arBRv3y75K5aneaU8PAmQAAmQwFgIUOCNhdYXX0uB90Ue/IsESIAESGAKERhqbZVwXZ0ReOGjx2Tw+h0/vNxc+OEFTA1eaMdL8MNbPYXOikslARIgARKgwLP/DFDg2bPjTBIgARIggTQToB9emjeAhycBEiCBCSJAgWcPlgLPnh1nkgAJkAAJpJkA/fDSvAE8PAmQAAlMEAEKPHuwFHj27DiTBEiABEggzQToh5fmDeDhSYAESGCCCFDg2YOlwLNnx5kkQAIkQALpJkA/vHTvAI9PAiRAAhNCgALPHisFnj07ziQBEiABEsgQAuqH17FnL5qt1BqrBDU915FbBj+8TRvRTXOdBKqr4Yc3N0NWzGWQAAmQAAmkIkCBl4pO6uco8FLz4bMkQAIkQAJTgMBAQ4N07XvDCLxoV5dobZ4OXyhkLBIC69fJjC2bxV9ePgXOhkskARIgARKgwLP/DFDg2bPjTBIgARIggQwhcPvcOel++x1jlzDU0iKxvj6zMm8wKP5F5cYPL/jsM5IH83MOEiABEiCBzCdAgWe/RxR49uw4kwRIgARIIEMI3IbJee+775kIXuRys8R6e83KvIWFkjOv1PjhFT+/TfKWLcuQFXMZJEACJEACqQhQ4KWik/o5CrzUfPgsCZAACZDAFCAQaW6WWwcPSbi2VgYaGiXa0WFW7cnPF29JCQRejcx68QXJr6ycAmfDJZIACZAACVDg2X8GKPDs2XEmCZAACZBAhhAYar0h4fp6I/DCh4/I4LXrZmVOTo54AgWmyUpo5w4pqKrKkBVzGSRAAiRAAqkIUOClopP6OQq81Hz4LAmQAAmQwBQgEO3pkUhTk0nR7H3/A4lcbEqs2uMRFXmBdTUSenmXBNZWi+P1ijjOFDgrLpEESIAEpi8BCjz7vafAs2fHmSRAAiRAAhlCQLtmRru7jcDr2ve6DJxpSKxMhRx+CqrXSGjXTiP0PH6/OD5fhqycyyABEiABEhiJAAXeSFRG9xgF3ug48VUkQAIkQAIZTMCNRiUeiSBFs046d++R/hMnxI3HRWCEriN/ZaWUoAZP7RJ8xcWitXkcJEACJEACmUuAAs9+byjw7NlxJgmQAAmQQKYQgJBzYzHpP14v7a/slv6646Jm5/qYDv+SCgk+/ZQReP6KCvEVFWXKyrkOEiABEiCBEQhQ4I0AZZQPUeCNEhRfRgIkQAIkkPkE+k+eks69r5pInnrhuYODZtG58+dLYMN6000zUFMjOaWlmX8yXCEJkAAJTGMCFHj2m0+BZ8+OM0mABEiABDKMwMDZRune/6bppjnU1i7xcNis0FdSLH6YnKtdQnDrVtEoHgcJkAAJkEDmEqDAs98bCjx7dpxJAiRAAiSQYQS0k2bvgQNotlInEZifR7u6zQo9gYDkzJltmqwUb98u+SuWZ9jKuRwSIAESIIHhBCjwhtMY2+8UeGPjxVeTAAmQAAlkMIGh1lYJ19UZgRc+ekwGr9/xw8vNhR9ewNTghXa8BD+81Rl8FlwaCZAACZAABZ79Z4ACz54dZ5IACZAACWQYAfrhZdiGcDkkQAIkYEmAAs8SHKZR4Nmz40wSIAESIIEMI0A/vAzbEC6HBEiABCwJUOBZgsM0Cjx7dpyZAQTQGF0G5Lb0uD1yy70l/bhFccNHW/JwK3KCMkMKJeCg/gY3DhIggewmQD+87N5fnh0JkMD0IUCBZ7/XFHj27DgzzQRU3MVxu+62SF3suDTGz8kV3G5C6Hkcn5Q6c6XaWS3LnWWy2LMIYo++V2neMh6eBCaeAP3wJp4xj0ACJEACk0CAAs8eMgWePTvOTDOBPrdPbrg3pNE9J5/FjsiZeINccSHwpA8CzyuluKnAW+upkrXetbLQWcBIXpr3jIcngckioH54HXv2otlKrbFKUNNzHbll8MPbtBHdNNdJoLoafnhzJ2tJPA4JkAAJkMAYCFDgjQHWPS+lwLsHCP+cOgQuxS/JgdiHUhevlya3SdrddiRoDsgQUjSduymaM2Wps1Q2e74iVZ7VjORNne3lSkngoQgMNDRI1743jMCLdnWJ1ubp8IVCxiIhsH6dzNiyWfzl5Q91HE4mARIgARKYGAIUePZcKfDs2XFmmggkUzNPxE/Jq9HX5Ej8mHS73ajEuz3iiuY582SdUyMbvRvkK97HTSRvxBfyQRIggawhcPvcOel++x1jlzDU0iKxvj5zbt5gUPyLyo0fXvDZZyQP5uccJEACJEACmUeAAs9+Tyjw7NlxZpoIxCQmEXdQjsVr5cfRV8z9EOJ2+vhIo0AKZJYzWzZB4L3k/b6s9qwc6WV8jARIIIsI3IbJee+775kIXuRys8R6e83ZeQsLJWdeqfHDK35+m+QtW5ZFZ81TIQESIIHsIUCBZ7+XFHj27DgzTQRUzA24A3I4flT+Pvr/kKJ5POVKHPGI18mRTZ4N8iPfH8kGz7qUr+eTJEACU59ApLlZbh08JOHaWhloaJRoR4c5KU9+vnhLSiDwamTWiy9IfmXl1D9ZngEJkAAJZCEBCjz7TaXAs2fHmWkiMCiDog1WDseOyo9jr8hx1OClHlqR5zEC749zfigbIfQ4SIAEspvAUOsNCdfXG4EXPnxEBq9dNyfs5OSIJ1BgmqyEdu6Qgqqq7AbBsyMBEiCBKUqAAs9+4yjw7NlxZpoIaASv3+1H7Z1G8H4CgXcchgnuA1eTK7kSwE1r8H7P97voqln9wNdOxSfU9++2G0Hv0D7jB6iNZtQ+wotbgeTLDGeG8QNUX0CVunrjIIFsJxDt6ZFIU5NJ0ex9/wOJXGxKnLLHIyryAutqJPTyLgmsrRbH64V1Jv9dZPtngudHAiQwtQhQ4NnvFwWePTvOTBMBFS9ab3ckdkz+d/Tv5CiarLj4+0Eir1iKpMKpMBG8b/qek6WeR9O08ok5bJ8bNnYR5+MXpM49DquIa0bwBZwCWSgLZLlnKWwiqmWeU2pEnwfRTA4SyHYC2jUz2t1tBF7Xvtdl4ExD4pRVyOGnoHqNhHbtNELP4/eL4/NlOxKeHwmQAAlMKQIUePbbRYFnz44z00zgTPys7IvuN5G8G9KKtM1bRuQlhZ5PfOLHrdxZiLq79bLOs9YInVIInWwYGrmLIHLX4rbK6fgZORE/aSwjrsgdgYf43UKnTCo9y2WDd70xfJ+PjqIa0eMggWwn4EajEo9EkKJZJ52790j/iRPixuMiMELXkb+yUkpQg6d2Cb7iYtHaPA4SIAESIIHMIUCBZ78XFHj27DgzzQQ63S75PH4eEbxa+SB+QC64FyXmDpn0RF2apmXOdeZIFczOn/Z9XVZ6KqXYKUbSYnZcyIURuWuD99+p+Gn5ZeyAnHEbkKLZa7wANcKpAldTNEucEimD0KtBauqz3m/II54lad45Hp4EJoEAhJwbi0n/8Xppf2W39NchlRtm5/qYDv+SCgk+/ZQReP6KCvEVFU3CongIEiABEiCB0RKgwBstqftfR4F3PxM+MkUIDEHMqchpdD+XX8Z/JefdCzII+wRN4NQ6s0LcSp25ssJZLk94H5OFnoXm8WypQWtz2+RU7LTpJvph/Ndyyb084s7laA2iEzDdQ3d5/40Rej7Hh0RNpmqOCCzND2oKctSNoqIyLF34EiOM2xCitQg9IcXWh68tCoxoL8RvOegOq7WWHA8m0H/ylHTufdVE8tQLzx0cNC/OnT9fAhvWm26agZoaySnNjsj+g0nwGRIgARKYWgQo8Oz3iwLPnh1nppmAqcVzY3ILN41k9cpNpCzeNhE8r+OFrPGbi+GgzIQP3izJd/KNwEvzssft8Bq9fCf2L6hFPCJN7iXplp4R31uFnAqDtZ41stP7kklX1TTNXCd3xNfzwfQS0C8pbiHd+KLbJJ/Fj5h7/VtTjwsg1CucRaae9BFnCZrnFEmek5feBWf40QfONkr3/jdNN82htnaJh8Nmxb6SYvHD5FztEoJbt4pG8ThIgARIgAQyhwAFnv1eUODZs+PMDCOgaYmJCB46SELgJW/ZGqnSmrvd0b2I4B2RXqRm3sYt1VjtrJId3h/A8H3jXcGb6vV8bnIJqIDTLy063E6kHn8u9fET9wk8jcQudhbLY7D6qIZgX476ytnObPNZR2/IyV3wFDmadtLsPXAAzVbqJALz82hXt1m5J4AI6JzZpslK8fbtkr9i+RQ5Iy6TBEiABKYHAQo8+32mwLNnx5kZRiB5gaz3moY5/JZhSx2X5aj/3z9EXzECT1NVh+APmGqsdCrl+94XEf3ZKHM9c0zaZqrX87nJJaBfUGja8Wn3jOyPvoWGOcelCzftkqoNdXTolxYq8kqk2Nh9fMu3TVY6K010OgdRWo77CQy1tkq4rs4IvPDRYzJ4/Y4fXm4u/PBQqYsmK6EdL8EPb/X9k/kICZAACZBA2ghQ4Nmjp8CzZ8eZJJBWAqfQOfO16M+MwOtwO9BcpT/lehIRvN9BBG8DI3gpSaXnyQF3QLrdbrOfe2Kvycn4qZQL0aZBKtg3Ipo3F7WmhRB+HPcToB/e/Uz4CAmQAAlMBQIUePa7RIFnz44zSSCtBC7FL8mB2IdyGDV4p9FBsx23VGOdp0b+0Pd7JoLnd/yI9zDik4rXZD/XidTMc7HPIfCOynvx901X2FRrWOSUy1c9WyDwNkqVd7XpGJvq9dP1OfrhTded53mTAAlMdQIUePY7SIFnz44zSSCtBDRqdzZ2VmqRyvdR/BPTjEPr8JLpfMnFFaDrotZpbYQX4Pd9L8pqz6q76avJ12TDvabm6m0QyaqasqoegTHQcNBkJg+CNk9vaEiSqcK2Jd4qdbE6I/A+dj+BYf3VlNsyT0plnVNjIrJPeJ9Al9gFKV8/XZ+kH9503XmeNwmQwFQnQIFnv4MUePbsOJME0kogIhG5ie6KDfEGeS/6gdS7J4zpeR8a7A8fi9B1USM9mpqp4m4OvAG1PjHbhumqCknXhTTHJkQ32+NtxmJAbSLUD3GuZ67Mg8l9oVOYkad+PX5djsaOGYH3mXtYrrrXUq6zVOZKtVNlmuZs9j4p5Z7ylK+ftk/SD2/abj1PnARIYGoToMCz3z8KPHt2nEkCaSegEaur8WvyWeywNLhn5bp73dhFqNhREafRqgpnsXzVu0UqnRUJo3fYRWTTUAZ6vr3uTYiiq9LsXpGmWJOxzlAPuVzJMaJ2ASJcymK+M8/4yKltRiaNNgjSu76G7oN9DZNrXgDz+sedTcYyYb13vcz3zE8+xfsRCKgfXseevWi2UmusEtT0XEduGfzwNm1EN811Eqiuhh/e3BFm8yESIAESIIHJJkCBZ0+cAs+eHWeSQEYQ6EdzDjXE7sFN/dL6ZcDYRXiRmqgdF9UrbbYTkhm4qfddthljJ7tPqsB9O/bPos1nTIqmSVeNgYJj0jNDYLAE3nFVsIt4DFYRZZ6yjNi/5CJuuX3SEm+RI7C92Bf7uRHsyedGul/mLJVt3ueMwFvsWWz2eaTX8bEEgYGGBuna94YReNGuLtHaPB2+UMhYJGg3zRlbNou/nJHQBDH+lwRIgATSS4ACz54/BZ49O84kgYwioFEsbbM/qDeYZSdb6uc4ORm1zvFeTL/bL+0wutfmJK/F/klOQ+AJonr3DhW4Gr1b71kn3/Juk0rPClE2meKTqHumwlS7Z+6PvSXHkXKrXTUHcBs+tJZQRfsaZ7UReFWe1SYyS8Pz4ZTu//32uXPS/fY7xi5hqKVFYn2JVGZvMCj+ReXGDy/47DOSB/NzDhIgARIggfQToMCz3wMKPHt2nEkCGUUg0WIkka4YdxMpmmr4nikCZqJgtbqtcjxWD4F3xDSb0RTNkYamq+bjVu2pku95XxDtKjrTmSnaUTQThgr0qBuVa6i9O4ZmK0fjx+SIe8yk3Q5fXynqCGucaiNUN6KustxZaIRqtkVmh5/zePx+Gybnve++ZyJ4kcvNEuvtNW/rLSyUnHmlxg+v+Pltkrds2Xgcju9BAiRAAiTwkAQo8OwBUuDZs+NMEiCBNBNQUXs+fkF+EXsXdhFHYS1wAdbg3SlXpYbvL3q/a1IbSz2Z13TlptYSoq7ybPysEXjabEXFnw4V6xqFVIG3AhHIxZ5FTM1Mudu/eTLS3Cy3Dh6ScG2tDDQ0SrSjwzzpyc8Xb0kJBF6NzHrxBcmvrPzNJP5GAiRAAiSQNgIUePboKfDs2XEmCZBAGgkkm6uciJ+UPdGfonbtKBqt9KDy7nbKVS11HpXnPM+a7pNLPBUmvTHlhEl+cgg2D2p6rh1Su3DrQ6MYTd/UkYuU0gBuxbjNdGaYGsscNJHh+HICQ603JFxfbwRe+PARGbx23UxycpCmGygwTVZCO3dIQVXVl78ZX0ECJEACJDDhBCjw7BFT4Nmz40wSmBAC6mM34KpMwQ33Gr3xoaJOUwkLcdNGKdohMxutDkYDNJmKql0zTTpjvE7eif2rNLrnkOI4BFqxlG+jAu+bnt82thEVGSjwhi9ez1WbyKjA06pC3Xv9LEzXvR/OZqy/R3t6JNLUZFI0e9//QCIXmxJv4UEbHoi8wLoaCb28SwJrq8XxekWc7LMSGSszvp4ESIAE0kmAAs+ePgWePTvOJIEJIWC6Kbot0grj61b3BlqmRGBVXmC83JZ4lsgsZ5b4HF/W19Y9CG6yVq3BbZS30DXzGAReC+whtIOoViCqKEo1ViFF8/veF5GiuVHmeOaYKFiq16f7uWSkUs9KUzS1KyjH2Alo18xod7cReF37XpeBMw2JN1Ehh5+C6jUS2rXTCD2P3y+Ozzf2g3AGCZAACZDAuBGgwLNHSYFnz44zSWBcCWhantodXIe4u+g2yZX4FbkBgRdB9MYIPIgRTSlchLqrhWisUeQE71zuT68LfuXU6Xaiqcox2Rv7GWwRTmMftEYttbDTVEZNb1zrWWOarKz1VMsMpDlqVIwj+wm40ajEIxGkaNZJ5+490n/ihLhxfG5ghK4jf2WllKAGT+0SfMXForV5HCRAAiRAAukjQIFnz54Cz54dZ5LAuBK4ZgzLj8gp9zSahVw0rf81TVM7YnqRmJeHFE31tVvtWYX2+N80xuV+iJPp1j2xzW2Tk7FTxhbhw/hBuew2Yx9SizvdqCLcKpxFssGzXp7xfUPUR246R0LH9cM7Fd4MQs6NxaT/eL20v7Jb+uuOi5qd62M6/EsqJPj0U0bg+SsqxFdUNBXOimskARIggawlQIFnv7UUePbsOJMExoXA3ZRDdE18K/aOaY+vUbxbuI00VnoSKYYbPRuQtjlXCiH6JnJoDZhGEXU9HYic9eOmgiopqQpgPVDilMhM+MypF5vaEUzkuBS/JAdiH5qumafdM9KOW6qRK7mmdlHtBKoRvdMI3lpvtajdAMf0I9B/8pR07n3VRPLUC88dvNPAZv58CWxYb7ppBmpqJKeUn4/p9+ngGZMACWQSAQo8+92gwLNnx5kkMC4EtIGG1o/Vxo/LT2HUXR8/YcyttdnKSGOBUyaPeTaJCryN3vVomz9/pJeN22P9d1IiG+Pn5JD7qTQjYhaHX1tS4ul6Hsd6VniWyzy08C90Csft2CO9ka7jreg7xvdOPe96cUs1SqREljqPyBr43z3m3SSPoI5RjcLVMJxj+hEYONso3fvfNN00h9raJR4OGwi+kmLxw+Rc7RKCW7eKRvE4SIAESIAE0keAAs+e/f8HAAD//4hwQEMAAEAASURBVOy9iV8V973//56ZAwioCOK+4o4bImKSJmlqzdIsrU3SJdakub399t7b3/177tLbR3vb2yQmTUyaNGnaLE2zNY2oiBvuKG64gUQBgTMzv9frfTyKejhwRlA4vD/nYYFzZubMPIc8el683+/Xywmx5A6s5Ns6jiMtLS3S0NCgX3kqxcXFUlZWJuPGjZOe23FbW0Yg2wi0hW1yJjwjNf4W+b2/SXaHe9JeYrEUy1xnjlR7q+RR7xGZ785Lu33UF0MJxcfjdHBGdge7ZUtQK58HX8hRaZQg9PFqgEM7Mt2ZJne7q6XKXSkV7nKZ6kyRHCdHXDwGY+0PDsgf4+/I5qBGjoaN0opHujXDmS73OvfIardaKr0VMtmdnG5zey3LCXTi/2taP/pI2rbWSuehQxJvbtErdgsLJWfiBClcWSnF69ZJ/qKFWU7CLs8IGAEjMLQJUAP01AGXLl2SEydOSGNjo+qGMWPGyH333SczZ84c2hdyB87OATgTeHcAvL2lEUgSuBBekKPBUQiWLfK2/yfZHx5IvpTya6EUyiRnEgTLKvle7GlZ4pan3O5Wn4xLXDrDTtkV7JE3/bdkW7BdzoXnpR0Pij/RfyL5eIx3SlTcPe4+KkvdJTLWGSt5Tt6tnkLK/cnqY/9T2QxBvDPcJWfwSLcWOgvkO94TymuGO0OKnKJ0m9trWU6gu6lJ2mprVeC1bdkqXSdP6hU7ublCkVdYtVJK1z8jBcuWZjkJuzwjYASMwNAmYAIv+v0xgRedne1pBAaEQGvYqgKvBgLvj/47sq8PgTdaRstkZ7IKlqdjT8riQRJ47WGHnIegY6XsZf/3EHq7017v7RJSZ8OzstvfI+T1t+BTORIeUcGZEJ3XTjFHcqQA4nO5u0ye9p6USneFirvBEp7X3nlwvgtQMe0Ou6UTj7awXbrx4OJ1FjoFMgqPmBMbtMrp4FzV7T9q/MIFYRWvbes2af3gQ+k83JA4CdcVJwcsUcErfe5ZKVxRIY7noUhtnSO3/y7ZOxoBI2AE8Gdkq+BF/jUwgRcZne1oBAaGQDs+rFNIUbBs7IeQGi/jhWJqNVo0H/LWyhx3zsCcyA1H4Tnt8w+owHs/+FAOh4dv2OL6H7VV01mtrZBVXpVMdadcv8EA/XRZLktL2IKW0W3ycvw1reL5ED4UQD1XkYyVGc4MbR19LPYImC0c1NbRnu89GN+zmnoxvCin0c57BFXMC2hNpfQYh0eZO0smOhNljDNGcp3cwXj7rDlm0NEhcYwFUOA1b3pdOvbUJ66NQg7/CiqWS+mzG1TouXl54sRiWXPtdiFGwAgYgeFEwARe9LtlAi86O9vTCAwIAVZiKPK2B3XyanyT1OLrJTy68Lh+OfhA78hMiJb73K9JNVo0K7zlqOZNun6zAfqpKWiS7X6dto5+Hv5dGsNjaY88SSbKMmcJKmWVcpdXLRPcCXIJ84W8Dp53Lh4UIKyqsYrm4RFlUchxNpCzeO/4f9HK4sXwK8g+fHDHg7N/eXhMcCbIPGeutoxWeSt1NjDK+93pfXhNHaimngvPqbA7Eh7Vr6z8cgayGC2ns93ZMtuZha+z0C5bKqPAN4aHrZsJhPG4BJ2ogm6rlfMvvCTtO3ZIGOCPA1emFfIXl0vJ009pq2YM8+Bufv7NB7FnjIARMAJGYNAJmMCLjtgEXnR2tqcRGBACScFyMDgkH8b/isrUVjkQHpJmPHouB8LFQwveUoioH3hPySq3Ch/mx0uBMzgfQKMJvKVoGV0E45f5kB6uHMR1sNpG0VUK4bHQmS+cgyvV8y7oeXn9/p6tmHxcDC/JyfCUHA+Py7HgmLB1k4IyB8KG1awpqCBSDE9yJ0EEFUNWjur3ewylDVm1OxmehNHNHjW5ORQc1jlItmty0dCGc5nz3Lnydfc+8F+sM5qjncKhdBlD51zY8uP70r69Ts7+7gVpr90uYXe3PseTzJtTJkUPrlWBl1dWJjGYfdkyAkbACBiB20/ABF505ibworOzPY3AgBKgk+Yuf7d+kN8T1supsEnnrSgAWe3ijFUhKmBL8QH+Me9bKpYGc+Yq2aJZgxk8tmge6qNFkxW85c4yddUswEzYRVQhKVpb0ExIgTcBraUL3AX4Nx//5qkIyYc45QxZlJUUeq2o3h0PjmuF6xJEH4/H6h0riLciJKOc02DscwLirsav0RbeL3Ev+HOqRbfQe9y7tUV2pVcpUzCnaat3Au07d8m5lzZqq2bQ1qYij1vnTpsqhaur0aK5UgorKiRn8uBUyHs/M3vFCBgBI2AESMAEXvTfAxN40dnZnkZgQAnQPINtd6xEHQ9OaHXqdHBaWxwLUY1htW66O12mOVO13ZDtjommzcExgUiarCRmA1/p02RlkkxSgceK4vHwhJyT86ioXbpqBsK2yTHOaG2bvBctphSqvB46bkZdFHnd2sLYDnpdEg/ZouloCyjf71ZaQaOe00DvtzPYJRvjr+gsZDOqoR14pFoFaH5leyZbd9fHvq+tqam2s+cSBDrq6zGD94YKvHhzs3A2jytWWqoRCXTTHHP/fZJn9tsJYPa/RsAIGIHbTMAEXnTgJvCis7M9jcCgELgcXhZGJ7CCxgw6Che6JFLgTXOnqiAaTGGXvKg45txo7MEMvD8gJqH2SkxCWy8xCSXI56PhRzsECKt9LXikWlMhUKudKhUid3mrIfKmpdrs6nPJGbSLqAm2gAsFDoUdq5o61ydjpAQtmKwaslJINtm06GL6H93/pbOQ6a+L1+2ou+q/5/ybfk2//ch+9fL+/dLy9jsQeLXSfeqU+MhX4vKKiiRv1kw1WSl65GEZNXfuyAZlV28EjIARuEMETOBFB28CLzo729MIDAoBGohwvormJBR72qKJ2bs8yBm2NNI843aIGIooH+9+BlVEzn9tRdD5Z1eCzn1Uyq4POr8LZ5eDKt8uOYbqXRseN5vEJHAxN48zcQwe/1Hsh7LMTZ83xhm0EzjmPpiqbA1rtUWR78+ZOs71zXfmw1G0Wuft8uAgGdW8ZVBu5gActP8Cj2+WFHg/N4HXB/vLCDlvfe99reB1Hm0Uv5WmNRB4o0dLzpTJOoNX/MTjMmrBgj6OZC8bASNgBIzAYBAwgRedqgm86OxsTyMwIgjQwfF82AyBtV8+D7+QoyE+DKvAY9i5ozN3d0GsUYht8t+Q+nBvP7j0LUSSQesUdzuCneouuhVVRDSv6vsnBd4CCDxWAhmwPtuZKeOc7DLF2Iz4jGsVPDLvbSXquhTO/57zr1oh7W1Le16ks7FRLn76Gdw0t0lH/T6JnzunWOia6ZWUQOBVyni4aeaXlxsuI2AEjIARuAMETOBFh24CLzo729MIjAgCrCh2hV1okLwII5Pz6uCYMDhJXD6FVjFEVX2wT/43/ltt5ewPmIQQ6b3S1AZHTGa+cQbtQ/+vsgfC8foWzUQcAmf4JsA1cyXiGb7rPSELYeRyOyqc/bnGgdgmIfD++0qLJgVeapGXcFnN0crdz2M/g8vqyoF4+6w9RnfTaWmrq1OB17a5RrpOJMxrGHbuFhaoyUrphvVSsGxZ1jKwCzMCRsAIDGUCJvCi3x0TeNHZ2Z5GwAiAAMUe20hpxvKf3f+DmIct+lxvcDgnxzbT1TAD+dccCpGqlJsmXUUpcD4OPhHmv6VaSWFTBYH3z96PUbmq0rDvbGnV3IX22FcQ6M5WzXMw4GnHI9UaDQObyc4UbX192vuuxlWk2s6eSxCIX7ggnQ0N2qLZ+sGH0nm4IfGCizlOiLzClZVS+tyzUriiQhwPmY0MQrdlBIyAETACt42ACbzoqE3gRWdnexoBI3CFAEVeDfL7/rP7l/hag5/8XkUejVEoRijwno89JyvcipQcGbHwLoLMa/wtMG1hLmBq0xa2ifLBWb713g9U4JU4JTqvmPLAw+zJpvC01Pk7VEB/GnyOwPnGlFfAoPMHvAeUK9tVJyIqwlbvBOiaGW9pUYHXvOl16dhTn9iYQg7/CiqWS+mzG1TouXl54sQsOL53mvaKETACRmDgCZjAi87UBF50dranETACPQjsCerl1fgb2kp4Ghl+bcjBS7VosDLHKdNK06PeIwhFn5dqM41leDn+qlau6CjaW+UquXO5s0ie9p6E4coqZMBNURGZfG04f20P2zXjb0+wVyuZDI/nXGQ3HlzM/aOD6HxnnqyBwCt3ytVVlIY8tnonEMbjEnR2okWzVs6/8JK079ghYRAweEl3yl8MjpjBY1xCrLhYOJtnywgYASNgBG4fARN40VmbwIvOzvY0AkagBwHm91HkbUbF7aPgb9IQHunx6rVv5zpz5BHvIRV4FHestqVaOzB792J8owrGC8h/u4xHurXIWYgZvHVawZqG6IWxyAnMhsUZSMZVtIDBcYScM2PwBILdv0LAO1eRU6R5gsmMRIZV5Gahm+iA30sIudD3pX17nZz93QvSXrtdw875HFfenDIpenCtCry8sjKJjcsu854B52kHNAJGwAgMMAETeNGBmsCLzs72NAJGoAeBhNvmea24bfR/32swOqt3D3prVeDREKUU+X6p1l6YtryB/L3Nfo3GJNDkJd1a4iyW73vf0woe2xMZDp9NK+kqyrDz48EJuJYmBB5NZma4MzR6IhtjIgb7Hrbv3CXnN76slTxm4YVdXfqWuVOnSuGqKnXTLKyslJzJkwf7VOz4RsAIGAEj0IOACbweMDL81gRehsBscyNgBFIT6EQkeytEB81Afhd/UaMNUm05BoJkmjNNK23f9b4ji3pxvWSl6h/+lyrwtobb5GR4KtXhrj5X6a6Qn8b+CTN41Tp/lwMjl2xanHNMOJp2a9h7HFmJXDEHLZpIBWTVLhuD3gf7Hnbs3Sctb76lbprdZ85K0NaW4FpSLHkIOWdcQtGaNcIqni0jYASMgBG4fQRM4EVnbQIvOjvb0wgYgR4EmINHEUY3zdf9PyA2YW+PV6996yKKPAeipBKul897G9TOf5QzSp01r20lciG8IA3BEdke1Ons2f7gYIoAdUcKIW8moGJH98zvxZ6SJe5iEzo9Qdr3aQnQSbP1o49gtlIrnQg/jzcnzHzcwkLJmYhKMNw0i9etk/xFC9Mex140AkbACBiBgSVgAi86TxN40dnZnkbACPQgcBLzYVv8rVrB+xJVPFbgUq2E56ULG/9yeQozc9Vw05zsTLrJFIUmIszCOwBh94n/mQq9Q+FheGkm3TRpW+8K3SPXuPdrayaPSbHH97BlBPpDoLsJhkC1tSrw2rZsla6TV/LwclERhcijyUrp+meQh7e0P4ezbYyAETACRmCACJjAiw7SBF50drbnCCWQzH3j5VtL3LVfgkPBYXnf/0BjDfaFB+Q8HukWXR/pokmBN9edozNkqbY/jZiAHf5ODVI/JIelOWy+EsGgCXhShpm+Nd79sshZIOMQuM5qYM+VaG0M0N4Yl+6rbY1M4ouhluiZGOwJawR+b3l4I/Cm2yUbASMwLAiYwIt+m0zgRWdne45AAj3noHj5Zmpx7Zdgf3BA3o6/o66XRxFKfkFar72Y4juarax116jZyiJvIcxWSlNsJeqe2Rq2wjWSNisX9ed46EOYISIAc2dj8JiIffmVrZ83BpzHr7hQtqHBk8dhNDuNSVCbUTFIoWdr5BKwPLyRe+/tyo2AERjaBEzgRb8/JvCis7M9RwiBLunSVkHa0lO0UCh0oRJEgUFjCwoLWv2PxddUs2QjBJOwgvee/77GJBzQCl5z2kuf58yVb3kPq8Cb687V7LZ0O1Bc8xHgEQ/j4O+ooGMVNdWi6QvvWQtm+c4hR4+Vv4TAExV4rPbRwZO5fOMQNTAKj1tZSfHv4/zCMOjz/G7lvWzfgSNgeXgDx9KOZASMgBEYSAIm8KLTNIEXnZ3tOUIIMH/sUHBIWwR3hDulCS2DFBmJCTBXZjgz5G53NdwgF2ZVwHamt5cGK1v9bdqi+UX4JWbwjqc9xFJ3ifzQ+74KPAothnX3tSiiuBL8KfESj1T7aS6fX49svr1SH+6V03gkWzRZ6SvFgyJzsbtIKrwKnQNMdZz+PscYg47wMmRlp74PXTzHIIuPfwTgWdoaogQsD2+I3hg7LSNgBEY6ARN40X8DTOBFZ2d7ZjmBZMD00bBRvvQ3y7Zgm+wId8np8Mx1Vz4dlv93u3fBDbIKzpArZKo7RdsER9qH+gtofzwaHJXaYLt84P9V9qOK14EHhU/PxUoZK2Zk9ZT3pCx3l16JNcjpuVnk7xNRAl1yOGyQj+OfyFbct1QzgQwEL3Nmy0q4ea71vikMXc9z8nQ2r79vTsFJIxiKyWbUd+kk2iHtKvDY+kmBx+ouc/nG4kFh2VvFsb/vadsNDgHm4Z17aSPMVrZpVELYnYihyJ2GPLzV1XDTXCmFFRXIw5s0OCdgRzUCRsAIGIHrCJjAuw5HRj+YwMsIl208kgi0h+3a2rcj2Cl/9v8iu8Ld2uLHGk3PlY8MsvH4EE/b/yeR67ZUBcvNtv8998nG7xOul+2yFxWzP8ffl+3hdo1NuDGgnI6ZK5wKFcT3eHdpSDfn5gZK+DBwnVXXumCHvOW/IzuDXTq7x1bbnitXcnQObzFiFR73viUr3AoIsYn9DkhPtmQyyuFj/xPZG+5DG2gL5F27NpLSAoYib54zR77prYEJzEIpgrCliLQ19Ah01NdL86Y3VODFm5uFs3lcsdJSjUigm+aY+++TvJkzh97J2xkZASNgBLKQgAm86DfVBF50drZnlhM4j5mtA/5B5LrVyF+C94UW/enWkqsth6tg1V/ar5bDdMcbrq+dCE6g4lkje8J6OYY2zQt49JyZm+ZMlZXOCilHpEGZO1udLwfyWpOVRN63P/p/0kpiuuPPhQB7yH1Qg9cXePMh1sen2/zqaxRynO3jHwDejV/7AwBbNHsuVgm/6X4DMQ7Ves383bA19Ahc3r9fWt5+R+MSuk+dEv/SJT1JrwiifNZMzcMreuRhGYXwc1tGwAgYASMw+ARM4EVnbAIvOjvbM8sJHAuOy9/9LzTXrRbVqFNhU9ornqNCYa3OlC3MQCikPegwfJEVNBqaNCOvjhUtGp1cCi9pja4IxiYlMDWhyKHJSaGDMGk8BnI1Baelzq9TN8/Pws+lMTyW9vBTITirnSoVeHd5q2WaOy3t9skXk7l/W9ACui2o1dw/VjE5H9hz0a2TLZqr3Wr5fuxpDWLv+bp9PzQIXEbIeet772sFr/Noo/itCRdYb/RoyZkyWfPwip94XEYtWDA0TtjOwggYASOQ5QRM4EW/wSbworOzPbOcwOGgQT7ELFmNvwUmHfvkHB7p1ixnptzv3KuVmuXeMpmEVsSRvLrVdIQTae06p8YWTM7eFeAxmLNoJ4OTuGdXAtfDGjnRS+B68t5MlklS4SzXoPT7vHtlptu/Frx9wX55K/5HFZLHICIR5JA8ZMqvnNH8t9jPVOh5zsC1pKZ8M3syYwKdjY1y8dPPpG3bNumo3yfxc4n/3t38fPFKSiDwKmX8009Jfnl5xse2HYyAETACRiBzAibwMmeW3MMEXpKEfTUCNxA4gtmqj/yPVeDtDvfIGTl7wxbX/zjbmSXfcL+uwd1LvSU6z3X9FiPrJ4QFIIGOD9isXM2to7dkLK375a1SYjB6nb9DhdenASt4jWkPOU2uVPDQQsk2yv5W8Ggm8+v4b/H7UQMJ24HaXcKUo7c344zfT7zntVKolUsYrtgaOgS6m05LW12dCry2zTXSdeKknpyTA2OcwgI1WSndsF4Kli0bOidtZ2IEjIARyGICJvCi31wTeNHZ2Z5ZTuBkQNv/rZjB2yIJ2/8Taa94vjNPzTqq0YpX5pYhX21c2u3txcEhwLZQxlpsxgzen/335GB4KO0bMXD9QXcNhHm1pAtcv/Eg/L34z+5fqJBkOl8ywuHG7ZI/r3CXy/Pec1rBG+2M1viE5Gv29c4TiF+4IJ0NDdqi2frBh9J5uCFxUq4rFHmFKyul9LlnpXBFhTieJ+JY9MWdv2t2BkbACGQzARN40e+uCbzo7GzPLCfA2TEahmzGB/k/+G+pS2K6S65wl8lz3gYVCkXOWHNLTAdrEF/jDCANcijwNvqvyK5gd9p3WwbX0x95z2hljaHn+U5+2u2TLzJ+4RfxX+n7MF8vQK0y3WIsxE9j/6QCj+/BSqatoUOArpnxlhYVeM2bXpeOPfWJk6OQw7+CiuVS+uwGFXpuXp44Mbt/Q+fu2ZkYASOQjQRM4EW/qybworOzPbOcQFfYpbNjFAjvBO/Cdn+nmodwpoyNh8y5o0HIaBiFlOCxEh/gvxtbJ4udcok5MUycuXeEECtJyUfS8GMgYwjuyEX14015zT5kFiMSjoRHdA7vXcRbHAwPptw7aX5S5a6UJ3HfljiLM5oN3BPUy6v+67IZLZpsC23DI9WikMvDg+/zfOxZzd3jDCLvia2hQyCMxyXo7ESLZq2cf+Elad+xQ8IAhjkIQufKX1wuJZjBY1xCrLhYOJtnywgYASNgBAaPgAm86GxN4EVnZ3tmOQGKI9r7n0Gw+R5ku20PEs6MR6VRLqNKxA/oY1Gpo83+XWjvW44KHsOyabNP8Xengs553lcm36Qz7NTzKHAKtGJ0p87pdvyqxHHVvN56xDO8jXiErX6tNMH59BIeqVYyvqDaWyWL3IVw9pyQ0X1LumiyUvgl/h3vxcyFQnIS8vWq3VXydOxJWYx4CIr/bL4XqXgP+ecg5ELfl/btdXL2dy9Ie+12Ydg5n+PKm1MmRQ+uVYGXV1YmsXHWgj3k76mdoBEwAsOagAm86LfPBF50drbnCCGQbPk7yLmucIscDY9C4F2+SeBRMNA8I9fJvSNkLstlYQZcK/59hQeD2pnJxsDtAoSxM2S7FOJzjCTaR2NZVkHitR/BvaHgeof5d8GBtPdhobNAvu09ri2TM90ZyiftDje8qC28EHWs8H7ifyb7wgPKvgP3gSJbq7sgP9mZjLDzuVIBk5V7vXsQ7D79hiPZj0OJQPvOXXJ+48tayWMWXtjVpaeXO3WqFK6qUjfNwspKyZk8eSidtp2LETACRiDrCJjAi35LTeBFZ2d7jhACrIaxXZOVIBp4tKFFkzUyVmBibNFEhabEgY06PszfydbMJnWPrJN6VBsPS4O2k7JlkefJaiNjHO5z771arSpEVS+b1j4Iuj/67+jMZCOEHoVuujXdmS73OHepwKvyKmWKOyXd5je9RtdMiv+T4SnZHexRobcj2CUnpUm68CiCkJ4js1GxWySVOD4rvRTYrKbaGroEOvbuk5Y331I3ze4zZyVoS7TexkqKJQ8h54xLKFqzRljFs2UEjIARMAKDR8AEXnS2JvCis7M9jcCQIECh0YZq3QEInI/9T4T2/Q2YQWuRC9ed30xnhtzrfk1bBWkIM9mdrOIvW1oFaXryP/Ffq8DrRqsmhXm6NVEmYO5uiebffcN7QGa7s9Jt3utrFxHifgoi71BwWHaEEHj4vgv3ZCxqpXOdMlngzJPFXvnVFtBeD2QvDAkCdNJs/egjmK3USifCz+PNLXpebmGh5EycoCYrxevWSf6ihUPifO0kjIARMALZSsAEXvQ7awIvOjvb0wgMCQJsTTwaHNUZwY+Cv2lrIg0/KDJ6LgaMs4JEs4+nvSdlqbskI1ORnscaat/TYIWxBf/V/T/6lS2SfC7dGg9jnPnOfK3gPRx7UOa6c9Jt3utrSPlDJe8yiF9Ci+xXaNDs0HenuQqru2MQicBZTRqt2Br6BLqbmqSttlYFXtuWrdJ18koeXm4u8vAwUQmTldL1zyAPb+nQvxg7QyNgBIzAMCZgAi/6zTOBF52d7WkEhgQBmn3UIK9vs79Fvgw3y4lezD6SJ1uBPLYfe89q5WqMMyYrhAfFHGfv/qP7v1XgJa813dcJWsFbrOHma26hgpfuPey14UfA8vCG3z2zMzYCRiA7CZjAi35fTeBFZ2d7GoEhQeBAcBCmIu+qXT9bMy/c0Jp540kuchbKd71vC90jOYfG6lI2rITA+y9t0ezP9bBl9T7nXhW6FV4FzFAm9Wc32ybLCVgeXpbfYLs8I2AEhg0BE3jRb5UJvOjsbE8jMCgEWI3ig5b/NHTp0nmyQI1S8p1RqLjhH5w6kzlqzGPbFH9DK1g0WuktFiB5svMxE/aY9y20Jq6SMrdMGO6dDYuzh7/q/g0qeDVokuxAg2o87WVNc6ZKtVOtAm+1Vy38eagv/l7wujrREspYCAcn7KEVdJSTh/+N6UzlUL+GoX5+loc31O+QnZ8RMAIjhYAJvOh32gRedHa2pxEYFAKcH+sOu+V8eF4agiNyDl8ZgZCPx1SIkEnuRM3aK3ASQct7g33yh/hbWrlieyYjEtKta/EAq2SmOzPjeIB0x76TryU4vKlC9wTaVr+Si2lPZzQm5BhhQHHHmUS6XQ7lRXFH4xiaujCbsS1sU0HHaI6JyNnjrJ/l6w3AHbQ8vAGAaIcwAkbACNw6ARN40RmawIvOzvY0AgNKIBlQTtOU4+FxGKc0yhEIvLPhuasCj1Um5qjNdmZB6E3S9spTQZP8zf9YBd7ucDc+/J/t5bzol+nIEnexfN97Sit4DPemQMiGdSLgLGKNcmBeIWcTKYp6W6x85aHyRdOZ5zGTWOUi28zJuVoZ7W2/2/08RR0FP4X7KQS3n0aV9nQAgYcH7yeNXCZC9E9Ci+kUZ4rGM9zJuI7bzWew3o95eOde2gizlW0alcDQc67cacjDW10NN82VUlhRgTw8a+0drHtgxzUCRmBkEzCBF/3+m8CLzs72NAIDSiCZq1Yf7pV3/b8IWy9ZpemEHyZz99iSmajiTZGlsPenC+YSb7E+ryHsaE38i/++HAwPpTwvBp67jicrIWT+2fsx4hKqVOCwtS8bViKQ/jwE3lZ5xX9Nc+lY8+pN5FEcuaC33F0qz3jf1/gItquOQhvsUFqX0Y7JTL+94T750P9IDoQHtX2X7p1cvH9s0VyA4PaHvLXCGUua5+SijddWdAId9fXSvOkNFXjx5mbhbB5XrLRUIxLopjnm/vskb+bM6G9iexoBI2AEjECvBEzg9YqmzxdM4PWJyDYwAreHwFew2D+Gyt1m2P2/6f9R9qH1MtWivf8C2Puvwgzdg7Fv6uwYBQAF4V+C91XYtGggewfkDYWhy6k9fOgfi9bOUhV4NFlZ5C7QClCq9xiOz1HI8bE1qJX/6f41ZvG2Yl6tU8VxuutZ7CzSFs1qtGpOQcvmaLQ6DqV1DhVchtfz94ICryFsSHl6c5258oj3kFZm57vzpMQpSbmdPdk/Apf375eWt9/RuITuU6fEv3RJd/SKiiRv1kzNwyt65GEZhfBzW0bACBgBIzDwBEzgRWdqAi86O9vTCAwoAbZkfup/jg/yNQjM3qmteKneIFdyEaE9GkJtpayP/VCWoZLHahRbOesh8uqCnRA52+SoHEM+WwfEXa627zHzbZVXhRbNcmGL5zhnXKrDD+vnKPC2BzvkN/HfaVxCG+bVuvBIt1jxWgfBS9OZ6Wh/HWquohR3r/ucLdyCFs2TmMFLPVtY5BTJLNzX1W61fNt7TBa489Ndtr3WB4HLCDlvfe99reB1Hm0Uv7VV9/BGj5acKZM1D6/4icdl1IIFfRzJXjYCRsAIGIEoBEzgRaGW2McEXnR2tqcRGFACrMC9BjdMBnY3YdaqLzdM5tk97z2nLpBsyYuHcRiynBO2a24Ja6UxpMBrR+0uTyMA6J5Z5a2UGYhGYPte0oVzQC9iCByMguiNeEIQcQ7vYh9mK4udcvkeZxIRG8E5tqFWwdsCsf7f8V+qwPMxi8dZzVQr0aqZr0L1p7Gf4A8AK1JtZs/1k0BnY6Nc/PQzadu2TTrq90n83Dnd083PF6+kBAKvUsY//ZTkl5f384i2mREwAkbACGRCwAReJrSu39YE3vU87CcjcMcIJCtPrOD1p/K0xFksP/C+p8KEZimcHWO0AoVhS3gBAQvt2qJJZ8VReNA1kjNmdN8cam6LrLxxsRJ5q+tYcEw+97/QSmhtWKdiOd0xV7gV8pPY8yqMaDiTg8dQWeTCVtP/7P4Fvtbgp8Qj1fmRHUU7K3g/z/kXtPBWpdrMnusnge6m09JWV6cCr21zjXSdOKl7Ojk54hYWqMlK6Yb1UrBsWT+PaJsZASNgBIxAJgRM4GVC6/ptTeBdz8N+MgJ3jAAF3m/jL6gwuYQ2vL5aC28UeAPhhkn50IkH5wEvweCFeXI+KkY5MPIogMUL2wAL8LgVl0ZWoFhtpBBlW+kluEHSRIbvzcWKI1tQOTM4FpVJ/pzJasb84SFUMbf52+WT4BM1nUmVi8frKHXGqxD6fuxpdRelQKL4HSqLTFjR/Y/u/ge4s9X033N+rkJvqFzHcDyP+IUL0tnQoC2arR98KJ2Hr8w+uq5Q5BWurJTS556VwhUV4nge/jpx63+cGI6c7JyNgBEwAoNFwARedLIm8KKzsz2NwIASuNZaWAOL/1N9thauuNqiWa1thZzNu5VFMUHxxYy1en+vHAoPI67hpEY0UHDNdGao4+QMfL0Vl8ausEvdQekG+XHwmRyGaQiDu/0rrpDjIboWygIpRy5duVcuE2AMk8miMKY43YtWzQ/if5U6VPGOaz7g9bNrvJ773XvVPXO5t0zbM1kFG4gqYibn29e2rOiawOuL0sC/TtfMeEuLCrzmTa9Lx576xJtQyOFfQcVyKX12gwo9Ny9PnFh2uNEOPEk7ohEwAkYgGgETeNG4cS8TeNHZ2Z5GYEAJJFsLWbGpDbdr5lmqN2BFayxqXMxveyb2A+Es3kDktzFA+xSEJWMWdvm75ACqYMflhFb0KPA4u7fcXaY2/PPcuah+lUaq5DG4fX+wX7bB7fKT4HMVkmwtvSrwBAIPlv+VaJ38mne3zHHnaFZfpq2TJzUXb4swdqIR7qStckGrkRRwMVTqZjuz5T4IvHJ3oV5LgVOQCvcdf24HTHNeiL+klTxmJDL0PtXKRxsuW3CrUcHbEFsP852lqTaz5/pJIIzHJejsRItmrZx/4SVp37FDwgDzjwhC58pfXC4lmMFjXEKsuFg4m2fLCBgBI2AEBo6ACbzoLE3gRWdnexqBASVAd0RW7lixeQOuiazopVoTpFTKYQzCHLtvxB6QMnf2gMzUHQoOa44eW0WbcB6c40u0aPraopkPATQOMdoMSn/UfUQWw40z00oeq4QJM5nXZQtmy84KWjRRbevZoplwCR0DF8h58nX3fmGlcjauMVPXTzqIMi7iAqQdW045k0ghyRZMtrPyeBSpFMsMPB+qpjONcFf9TN1Vt6AayXtzOtWvhUxFyHmVs1JnCe/27lJH0JQb2pP9IwAhF/q+tG+vk7O/e0Haa7cLw875HFfenDIpenCtCry8sjKJjcs+V9r+gbKtjIARMAKDQ8AEXnSuJvCis7M9jcCAEmBwNQUIK05/8v+sQqgdLpiciWPrJAUIzVKmO9OkwlkOgbVYFnoLdI7sVk6EWXlsm6wN6uRF/2VU1rZpCyWD11MtZvB923tchcQsd5bO5aXa7sbn+D68Ptr9/zL+K63g3bhNz5+vCha4W6pgQQUxyiK7brhPsnUzKfDolMmq51Brx0x1fRSpDUGD5ht+CfHfEB6BKGb8Q+L+5KE1l9cz15krX3PvVgE+3Z2ms4tfCYSt/g4loiJY/WWlkqKWpjy8/uHAIBWX2/Vc+85dcn7jy1rJYxZe2JVgmTt1qhSuqlI3zcLKSsmZPPl2nZK9jxEwAkZgRBAwgRf9NpvAi87O9jQCA0qA1S2KoFZUm05gZoyh56zenEdLI4VJPh5T3MkINp8mM9wZmE2boBU0fsC/lcVKF41JWFH7vf+a7Ax26XlQGKVakxElsMKpUIF3r3evzMS59Gcl3qdZBd5G/xV9n3T7sS10CqpSDCB/2vuuzuSl276318iVD16PH/oqaG7FJKa39xms5ylM21DlZHTG/uAAWmcPavtsi7TotYxHSyurnfORe8eAc84ssp31fNisuYhHw0bEZ5zH6Tl4bTyy8maCZTlmDieqyB1KpjKDxfBWjtuxd5+0vPmWuml2nzkrQVubHi5WUix5CDlnXELRmjXCKp4tI2AEjIARGDgCJvCiszSBF52d7TnABFgxogiguyJFzuUrlSt+WB0thRAzozWEOlNXxQE+zUE/nAoRSKwLaJFshOV/QuBdviLwpmhbId0sWYEZiPUVWkMpKGv8Ldoaujfcl/awRWjTpEEJZ72+E3tCFrr9C3pmm+Tx4Li2oL7p/1H2hvvTvg/vewFaKWn7/5PYj3UmL+0OWf4iRR4NcI6B4QHMSbKyx+rbeKdE5jvzIfqn4fvxWqmkqDsIIch22KP4HTon57GloLm3VGa5CYE3H3OUMyH2xuF3yap4vf/y0Emz9aOPYLZSK50IP483t+jGbiEiNSZOUJOV4nXrJH/Rwt4PYq8YASNgBIxAxgRM4GWM7OoOJvCuorBv7jSBpADgh9dd4R4YfpxG02IccmKszHPmwHhjnroqcm4qmz+QJitOFLxsr2MFh5UntmjmI8OOM2oDYaqSvN+ctWMLIHPW3kFrKN0t061CiO2JqOLRjj8RL9C/oGcKVVagKCTfCz5Qc5V07+PBCiUPIpbv8zMN7q5Mt3nWv5Zs4W3HZGSiRbNL/zvg7wNbNGmywsokGf8BAnpHsENnD9uu/A4REKu9FM1jIepozvNd7wmtjA61XMShdDO7m5qkrbZWBV7blq3SdfJKHl5uLvLw8F8DTFZK1z+DPDwztRlK983OxQgYgeFPwARe9HtoAi86O9tzgAhQyPBDKKtIO+HeuCvYLTvD3XJKEgKPxh7zpAyzReVS6VXKHIg9Vi0odmzdOoFWODMeC4+p8HrTf1v29VFZQzodHDUTFbx1sW/Lon5W8E6j+rTD36lC8hPEI7DKlG5ZBe96Osk2U/63QrHMSreK/yuttEzwo9jbG+yTV9BquzvYgwMkHB+vPxJreY4KvB/HfqQCmmY52V4Zv55B/3+yPLz+s7ItjYARMAIDScAEXnSaJvCis7M9B4gArd+PBkd1Juvz4AvY9B/UFs0ONGnyQ22yRZOmG4uchfrB9C7MZU1DS5qtWydA232KvM2orL3gv4TKz860B53sTJZKzOBVo3XyXu+efs/gMbZgi79VZ/C+DDdrNl26N0oGkbNF84ex78tSd0m6zYfVa/y95sqkEp00qTkCYfwP/0uttLZgzo7/nXCNxoPzkXRj3RpuU0dWfSHl/ziois+VR71HVODNRRQFIxZs3UzA8vBuZmLPGAEjYARuBwETeNEpm8CLzs72HCACnCn63P9Cc75qw9pe89/GwPmPIo8te9+NrUPlaKF+QM7kQ/IAnXJWHSYhHLpUYL/mv64umpzvSgqH5MWyRZRVnrmooH7D/bqs9FaqqQerqf1ZmQq8YoQylDmzddbv8dijaiLSn/cZatskWysvIrqeZieMa+gp8GieU4zHWFTRWJXmHzRSLc5kNoT8Q8hu+QL/vewPD+gcHqMsKBUTpjQJJ0fmGV5EhS/dYhX2Xvce/e+p0lsBcWgukKl4WR5eKir2nBEwAkZg8AmYwIvO2ARedHa25wAR2AHXxhfjr6jAY0Xisn5gvfngMcxk8cPwKuS//VPOczDdWKFzaeYCeDOrTJ5Jtv4dD06gilcDN80tsiVFBShRUSuFg+ZyeSz2LbTMLsnIxZMtmjvRosmYhE+CT/ts0ZyilcIVKkC+hkohnUOH40qYo5zV1snPg79rO2yycZLNklOdqbIK+XWL3EW4xulqJJTqOvciHJ7mNFuDWpitnNb5ukSLZiKXLfHfR8J4h+KcwjLd4h9LVjlVyvcuzc2zinhKXpaHlxKLPWkEjIARGGwCJvCiEzaBF52d7TlABPiB/z+6f6Ef/EXniZIff1O/QZW7Uv415/9pZYfVDlaWbN06AZrcHIPjIgPWa8Kt0oi5PObjUQBSPHBOiy2Ai51yuc/7GlozZ2bUYsgK1BG04vJ+v+O/ixbDA1crWanOfo5TJg+5ayFAqjXvjw6Rw2mRG6ujTUGTtr3WBFuFLcicd+y5kkKLv9crMWPKnMOeJjo8BnP8tgTb5Ff+byHwtsF0p1tjH3oeJ9Pv+T53O3cp3ypUY6e6UzI9xIjannl4517aCLOVbRqVwNBzrtxpyMNbXQ03zZVSWFGBPLxJI4qLXawRMAJGYLAImMCLTtYEXnR2tucAEUgIvP+6IvDSizu+5Uq3Uv5f7J+18pDn5Kn4GKBTGdGHScZUcCbybHhW2wnZqkmBweodWwhL0I7Jlkw6mTIwO5PFahPdHyl0fht/SeqCOpVAyXbFG4+13F0qG7z1KkDGOeMGLBbixvcZrJ9ZQbscXlbToD/4b6H1dbuao7BFs+ei+yXn35Y7y+Qx71uyDNfd83p5DM5I1kAYM4ieM5Jk1hu3nsdO9z1n8L7lPqyzlPO8ubi3NoOXjldHfb00b3pDBV68uVk4m8cVKy3ViAS6aY65/z7Jmzkz3WHsNSNgBIyAEegnARN4/QSVYjMTeCmg2FO3lwA/+P6q+3/xAbZG5756by3jtJ2jAu9nEHjMYctzcq2CN8C3izl88TAubXgwQiGOiIZCzIYxHqEQFvusLkVZSVFSD5fHN/13IPB2QLi0aIh3JzIP6fhIF0gKx7HOWFnuLpN13rel3Fmk9v8D3YqbEGCdaGbEAyKK181q8Cj80YDXmovfrcRvHBspM1+MuDgbnlNh9or/qgq9dEdZ4MyXx71H9Q8Xs93ZKvK4PVs8KbhpgvN7H+6YiBC5lcWqN69vibtYnvAekxVodWY4emGGgv1WzmE47nt5/35pefsdjUvoPnVK/EuJGUevqEjyZs3UPLyiRx6WUQg/t2UEjIARMAK3TsAEXnSGJvCis7M9B4gAWwI3xf+gAi+dOYQDG3jX8TCDt0r+LfZTCLwqPqMfwgfoVOwwIJAUYhRAbA0M8EwMwodtmh7434rQ4rEZYs/2z0PBoStB3EflDKK4Q7zTeCnpEcQ9T2ZpEPe4WxJavd3USxBOpzHLxhbKJnylyGSlcqIzQcogsNgSyly5qNdLQ5V9/n79vX4/+FAOhw29nYo+Pw2zeKudahV41eoSO1Wfp8DjeXI+8lWY4OwJ69Mep68Xx8G8ZrYzS6pQCX/QW6vmNYlKuNfXriP69csIOW99732t4HUebRS/tVV5eKNHS86UyZqHV/zE4zJqwYIRzcku3ggYASMwUARM4EUnaQIvOjvbc4AIHIeL5hewfeeM0Y5wh9q7U1ywonJtORrmPEVdNKvlaW+dBjRfe92+u9MEkm6cN7tFJtpuaZDD9k5Wj1hFag6bdd6Ptv+sUFH88fWZcHdcjMxDujr2nEUbqOujAQlbT08Fp6QhPCL8/WsKm1Tg5V8VeGUqNOk0OQ6h4FH+kEDhuN2v09bjz8O/q6hNdw0TZYIsdZbIam+VPOA9ILPdWbp5R9ihrNjKvNF/Rd1O0x1nFFo+i3DOo8F5FFxP+YcRtt9ykTvba+c5c8B4MXIlK8w9Mx3MHq91NjbKxU8/k7Zt26Sjfp/Ez53TV938fPFK8HtdVSnjn35K8svLe+xl3xoBI2AEjEBUAibwopKDtzbg9T30FP34ve6ZfFvHcaSlpUUaGhr0K3coLi6WsrIyGTdunPTcjtvayj4CCZfBM7IdLXvv+u+hQrFHs7w4s5VYvO8OAs5huoGKA2MSFrrztcKSfTSG7xW1Q4gwgHsf3B4/hZkIg8xpM5IwzhGZBlOPu2CYwngLije2Y36FB4O7Ey2aMKxItmjC9H+UMyqSsOqL4ElECGz1t2nLJI1e6O7JNs1kiyajINiKytw9tkyyRZQVrkzNfE4Hp6XO3wGBVyOfhp/3Q+BNlGUq8Koh8O6HwEwIvIRw7lSh+Mv4rzDLV5v2EifBCKcC83yMs5jkTNTK6wVJVJvGSZHOUU50J6qY1lk/yEBbfRPobjotbXV1KvDaNtdI14mTupOTkyNuYYGarJRuWC8Fy5b1fTDbwggYASNgBPokQA3QUwdcQmv8iRMnpBF/cKNuGDNmjNx3330y02afb2JpAu8mJPbE7SaQnPliC9vf/E9kb7hPTSWY78WqDqsnMVQeaArxoPdNhJ0vUEdHzkjZuvMEeI98yCMKmt3Bbq3EUuA1XhF4fJ0rKfDoFrnCrdBMw8Go0PVGJPl7ti/cL3+K/xnnuVUdLVshMlOtBfgjwhPe4yrweJ5sUWWrKiuRFEaskKUTfqxQHvAPaovmX4L35WB4ONXbXH1uujNd7lFXy1VCV8spPVwtyXBPsEdei7+O420VWODobB6vKcmX4phh5/PdufJ19z6tgk6SSdpmSgdTLp438yRvZZby6gmPsG/iFy5IJz5Q0EWz9YMPpfNwQ4KAizZxiLzClZVS+tyzUriiQhwP7a72B8kR9htil2sEjMBAEzCBF52oCbzo7GzPASLAD6h8JCp5Z6VFLmgFj1UVH2YfFHejndHq8jcRFYmx+IB6K7NRA3TadpgrBOKQd51hp4q7hFtk7U2B3tyUwogunBUwT3nM/ZZWyNhKyErd7ViMfODvWC1MfWhWsh0unu34I0KyffHGcyiCGJqFWTWeY3IOsQDXMAOVyBVOhcx1y9K6ibK1kq2gSffLnch7TLcWOQvVVIaxENPdaTfl4Z2DYct+VEeZg/e34DOd6esGd1b4uErwmI8/glA8M8aiDOfHdk0axSSvkS2afNzqLGW668jW1+iaGUe3CQVe86bXpWPPlVlICjn8K6hYLqXPblCh5+ahNTYWy1YUdl1GwAgYgdtCwARedMwm8KKzsz0HiQDn7zrgaqghzirwYirwrGI3SMBv8bAUMudRrWIrImfEdqGKl27RLfLbcG+shpBhGyJn3G7HoqsljU9qYFbysg9XyzD9edJWhhU6zrDxd9JF9a4AgmmGTJdKdzkE6lKdA2VLZCo3VwovmtSQx+v+H7S1kq6aN8YkJK99qbNYfuh9X2iwQlfLG2MoKFAZM7E/PCgfIyj+ECrePQUe5xfnyVw9p6XeEpjFTEweOqOvyTgLmtBcgpMqrHa0ip6Y7RsLBonKJauZt7JYfSQjXher9d34b51iNBfVUv4xgP+9R5l9vJVzSrdvGMdccGcnWjRr5fwLL0n7jh0SBpgTvjLlkL+4XEowg8e4hBjGDDibZ8sIGAEjYASiEzCBF52dCbzo7GzPQSLAah4/+Gn7GT488UOfVewGCfYAHJbi7oB/QAXee8EHEB7pWxHpFnkX3CIp8KphKDLNTbhFDsCppD0E3TsZ5E4h+pb/trBVM93i7x1FHSdAWcHjz5zDYxWPonQxBNlDaBleglk9Om4WIEqi5+LvMX+H2bq6EyJvC0xSPg0+19nEntslv79WwVuFCt70myp4PBbjKy5CdlEoXsLDh/zi+/BM8660aDKvkFVHzhJmungstpYehMPpweCwVglpmsP2zymYm1yCa54Nh9GBiFVI5i7y94dGNxchXunRSrYz3Bla7b2dLbx9suIsiO9L+/Y6Ofu7F6S9drsw7JzPceXNKZOiB9eqwMsrK5MYZshtGQEjYASMQHQCJvCiszOBF52d7WkEspIAxQwFNtsZ2RaYqOLQ1TTxQZaVnLF8QEiwdbYlaMnQTGSCCoXVqFR9o4db5GDD/IoCLzyucQNv+n/sU+D1dT5lTpmsddeo6U+5t0jbNVPtQwGzHwKYOY/pBLDO4Ll3ab5jlVcJQTUFwq13YymKMT64uF26bVOd143PJSuxR8KjstPfBT77IPCOqBEOxeMUgcCDmC13F10xypmk7bWcTMxksRrK3EEVduEJOc5/EHiXwotaseOcIAXeDIhczm3SGGYoCb32nbvk/MaXtZLHLLywK2EGlTt1qhSuqlI3zcLKSsmZPDkTLLatETACRsAI3EDABN4NQDL40QReBrBsUyMwEgh04wM4P+zT9OZz/+8wBzmkrYFd8LrkYusf7fzphjnfnadtiJm5RaaOAxhstmwDbMZMHPPk+hM30Nf5cOZtnjNPYw0e8R6SeTA3SbWOBo3yqf+5vu+OcKecxiPVSsRIjNd8xx/Gvi/L0ALal3DrKfBSHTOT504EJ+RLsNkR7FTxi3RA3He2aLI91dV5viIE0C90FkCYf11nKNmeSpGfyWKbKfMu9wR75fPg7/p7RgdWtoLyejkjyPZUvs9ab42a3NzOWc2+rqVj7z5pefMtddPsPnNWgrY23SVWUix5CDlnXELRmjXCKp4tI2AEjIARiE7ABF50dibworOzPY1AVhKgCKLjI01Irgm8izoTyQtmEDgFXqW7QqtNrOQdhzjg9nSL7KtFczrjEjTQu1pWeVUy9Ta1aCZbAhnH8Wr8NTVbYbvjtTiOzG5nwjRmvFbw1sd+CEG2JOUB6iFk3oi/qTEHJ8OTaHi8mHK75JMMIP9Z7KfKNp1LZ3L7W/2arATyPP8Qf0tNYRgl0dt5MqfwXvdren5RcvQSMRVblcc/gi+1gpfqGhiLwiD2RCzKQlRIx6fa7LY/RyfN1o8+gtlKrXQi/Dze3KLn4BYi33HiBDVZKV63TvIXLbzt52ZvaASMgBHIJgIm8KLfTRN40dnZnkYg6wjww359sE9egynI1mAbWjTPauscqzicAePibFeykvOA+3Wt4tGenzNt/amMsTLzHcQPcAZvJlrxWJ25HYvnz9bTQ5gt+zD+V41JOADDkvPSHOntc8FhDMQtXS+fj22AO+jylMeha+evun+rgqYDFiusVKVby+Ey+pz3IxVQt6NyRS40g2G+3v/FX9T7zmon73mqxZgFto+yxfYpb522bKbarrfnaDqzMf6KzkKyTZPvlWrx2mc6M5UDf18WugtSbXbbn+tuapK22loVeG1btkrXySt5eLkwhYHIo8lK6fpnkIe39Lafm72hETACRiCbCJjAi343TeBFZ2d7GoGsIpB0NNyMnLVfxn+tdvyJkPLEnNeNF0uzlNWoxDHXbrm3DOEWF7QCRKFwDqKpHe19yeoQp8QSLYiJmITH3Ue1xW8sWv5Ypbqd6wxE625/N2Id9sjucA/aBZs06Jy2PjT56ERNj7lxjOlIt+gmOQHVTFaYfhj7HubTFqfcfAuE8i+6f6WChuIuOcuYcmM8ydbMDd56PW6xUzzoMRIUcmzJ3QwTmF/F/1crm72dG59PuotW47p/Fvtn3P/KdJtffS35u0CTm//s/oVWCq++mOIbzvaNgnEN+f409hNZiYrxUFiWhzcU7oKdgxEwAiOBgAm86HfZBF50dranEcgqAjS+YG4bP+i/6L8M50fmtqUWd7xwVnJoglHtVsl3Yt/WnEKGcTOn7fPgH3CLPArXRwoaHsMRtmbe465WQcgsPFaBaJ7B+a7buToh4eioeTY8A1fN48KWwaaAIq9TxRSNZWrDOmmC8Eu3JkjSLGaVrFGzmNkpN2dL6G/jL6jAo5FIXy2hzLH7Sex5FTYaSI6ZtMFcjCm4iPOi8Pqt/4LU4XzTLc7J0V2UwuvnOf+q9z/d9snXki6gfJ9fxH+JCuq25Espv/J96FrKCunPc/5FVuH3bCgsy8MbCnfBzsEIGIGRQMAEXvS7bAIvOjvb0whkFYFE0PwZNQN51d+E6taVIOdervLGFkXm21Eg7gsPQOB9IUfCRgTVU+CxtZMCb7oKPJqzTI5gztHLaUR+moKWlToKuiZEGbBix9D1pvC0fBlsRt7cAc3NuzG3jpUltqlyRuxe9x5UliolnYvmgeCgvOP/WQVUA4xr+J6plpqLoM7JiuizsR/pjOPtcI+kwOO9p7D/jf9/OkuZ6vySz/H6WY3VylrOT/Q8k6+l+xpF4FH8s5X3/8tASKY7h4F4zfLwBoKiHcMIGAFjmPBvAABAAElEQVQj0DcBE3h9M+ptCxN4vZGx543ACCPAD/mnIW5q/C2SmcBbhRm0Z7XlUqtBMC45j0DxNsybMQkuWQNkfhzDuFn5o5CiULiTiy2pnD1jRe9y2Ikz9cV1PBWpzIHbjtk55tY1hseuO01MWQndIyucZfJw7CG0ZpZrZl1vuXMUkDQwoYD60P9IKPJSrSJET8yAgQlbH5+IPaYzZ7cj6DspvGpwfv+tlbWtqU7v6nO8f1M5gwfh9WSs/zN4mbZosnqXg/ZdCsl/gelMf1tBr57oYH1jeXiDRdaOawSMgBG4joAJvOtwZPSDCbyMcNnGRiB7CXAOixU4ftB/0X+lXy2aUzmHh7DyJyOYbQxVku1hu1b1aDbzGQReA7LgODtHgcLWRGYAMvS73Fkk93h3a14b2wl7W+RKZ1LO/H0Y/FXFHh0qWUHk3B+FLmMBKJoYds4ZvCpvpf7c2zEH43me3yvxV1WInsWc4o2Vy0RjpqfnVelUaMvk3d5dCGWfltHpsPX3xfhGfR/+vvU265gQktN6/H7dmisl7x/FbFLUU+BTRGpFFkIy0z84MA/v3EsbYbayTaMSGHrOlTsNeXirq+GmuVIKKyqQhzcpIz62sREwAkbACCQImMCL/ptgAi86O9vTCGQVgWsmK1tgsvK/6qaYmMFL1uCuv1yKu2qnSt0U74KjIufxsmGRQycqel9BhFHoUIRwRo3P56HyyAoezWGKEcHNiiTFWbqV5HoWlbxDqAyy9ZPikTN+nPsbjePNcmbBjXQuHCnLVTDSXCUfyXO3c53BTCLF12ZUcD8OPtUZyp7vH8MsYD5MT5Y4i+UJ71E1PWEmIucEM1kMNf/C/xICr0a2hrWaiZdq/9lg8oD7gFbwlnpLNJ4j1Xb9fS5xX7uEgfe81k4IbBr88F7SLIf3MZ1Qv/F9OurrpXnTGyrw4s3Nwtk8rlhpqUYk0E1zzP33Sd7MmTfuaj8bASNgBIxAPwiYwOsHpF42MYHXCxh72giMRAKscjCAepP/B9niIyZBEjEJ/HCcmKVLxCSMRZveAtjWP+DeL7T1n+XOknG3Ke7gdt4X8qDYuybw2IiJB4QBqz+ZLBWNEBfHwxNaxeOsH6tXDAqfhTgACpo7yTFZaawP98rH/icaQM6WW953toomxdAiVC4fRAD5PGeuxJyYvpYJh9awFeY2x7Si+Q/MOrJCymphdxjXOmiuk6sieoE7X9ZA4C2G6B2PDDyKyygrmX/I2Ucy57+EwOvUa2IcA4Uq226n4B+jLyj0+hJ7l/fvl5a339G4hO5Tp8S/dElPzysqkrxZMzUPr+iRh2UUws9tGQEjYASMQOYETOBlziy5hwm8JAn7agSMgBJgNlliBq1OZ9AOhYfUhIMflLkmSCmqOOWyArb1d3mrZbY7G1WcAtR3BtftUd/8DvwPxW0c4kNbNB02aTJMwe1TANx4qsn2QOa+fYWKIMUdj83WQPJj7MKd5MhzoaD7Co/TqHBxjrI5aFHXz1zc2wJU6oodVi1LIYYmqDDtjxC6kQN/j9gGy4rm0eAoKoWN+NqoIppcS1C95O/UbPzRgDl4rJJS9GUqqJPvy4rdiSuimoKS78f22J7CtRAiuxy/09/yHpKFzvx+ubteRsh563vvawWv8ygMhVpb9S290aMlZ8pkzcMrfuJxGbVgaOT3JXnYVyNgBIzAcCFgAi/6nTKBF52d7WkEspJA8gM4A8E/C/4uB8PDmmnXjY/6XGxnWyzlaCdcKAu8BVpdyUoQI/SiKGT5aL8yk0nRRzdPmuSwujVQuYWsaLKax2raUVT0KCwp4pICbwKEJNsmb+UPB7wOijsaB9HkhvOlJ8JEMHny9lJUxnB9i9F6+qT3HcwWroSA7bv1tLMRovTTz6Rt2zbpqN8n8XPn9JBufr54JSUQeJUy/umnJL+8PPlW9tUIGAEjYAQyIGACLwNYN2xqAu8GIPajERjpBFjZYDXnElw16QB5CX6YqGFpxYNs2KJIo5Gx+LDPD/y5eNjKLgIURomKXiLmgvVKiq+BzC1MVjRZyaSY5B8WWBFMtGgWXG2DpQCLspLXwLlCmsdsCbaq2Q0rqD1XsgrJmdJVzkrN9WNlelof5jHdTaelra5OBV7b5hrpOpEQjk4Osh0LUY+FyUrphvVSsGxZz7ez742AETACRqCfBEzg9RNUis1M4KWAYk8ZASNgBG6FQFJc0LGR3zN+gY2dif+NJlhu5XxG4r5x/FGCrZg0c/kVTIO2IfYi3eIfLWYgq5ExFeti3xbmNaZb8QsXpLOhQVs0Wz/4UDoPX4m/cCGHIfIKV1ZK6XPPSuGKCnE8zGs6vTutpnsfe80IGAEjMFIJmMCLfudN4EVnZ3saASNgBG4iQEHHB01LaOwRRy2MuX90xaRRyK20HN70ZvZErwQSpjHN2pb50tXYj14310o0Z/EYrP6T2HOYMV3e+8Z4ha6Z8ZYWFXjNm16Xjj31ie0p5PCvoGK5lD67QYWem5cnTuzO5j6mvRh70QgYASMwBAmYwIt+U0zgRWdnexoBI2AErhJgxSgRr/CVGojQrKZVBV5cBV4hAhHoNFqCRylcITO15b/6RvZNvwjQyIVGMZy72+j/XnYFu9Pux0S8PAhwBqv/LPYTjYFIt0MYR9tyZydaNGvl/AsvSfuOHRIGAZJFErEi+YvLpQQzeIxLiBUXC2fzbBkBI2AEjED/CZjA6z+rG7c0gXcjEfvZCBgBIxCBQBtmFmkYwoy7z2FOcyQ8qnNljNdmc+YYCDza8TMg/X7vXpnpztSZs77s+COciu0CAl14XAwvQeDVyG/jv5PtwY60XEahwjoODp4UeBti6xH/sTTt9hRyoe9L+/Y6Ofu7F6S9drsw7JzPceXNKZOiB9eqwMsrK5PYuHHpj2evGgEjYASMwHUETOBdhyOjH0zgZYTLNjYCRsAI3EyALZmnEFy+3a9TQfF58IUcC49ftyFdKJnnttxZJo/GHpGlEBDMYBsF05qhtHgtNEChe2YbHp2ahedDosbQYsqzxb9biC24XddKkxhWVGuDOnkxvhEzeNuQtpcwc0l1DoxjmI+IBM7gPYK4hLnunFSb3fRc+85dcn7jy1rJYxZe2JVwm82dOlUKV1Wpm2ZhZaXkTJ580772hBEwAkbACPROwARe72z6esUEXl+E7HUjYASMQBoCFEQUE2wBfDW+SVsC2RpIMdFz0WAlD5Nes53Zcq97D1oAV8oSr1xjJ3pud6e/57XQHIbX0BAcUSfVy9KJmIQCodPkJHeiCtWCiMHjt+v6kkJ1b7Bf3o6/oy6ax8Jj0oo4hlRrrjNHhR0F3nyErFPw9Wd17N0nLW++pW6a3WfOStDWprvFSoolDyHnjEsoWrNGWMWzZQSMgBEwAv0nYAKv/6xu3NIE3o1E7GcjYASMQAYEaO9PQw/mrP06/htUjNK7NZYiKJ5tmqu9VbLWWyNl7tD44J+Mx6AxDKuPDB9vCBrkXHAOQQZdmBnMl2kQeDPcGRCpsyD0JmlUBmMzhvI6GZ6Srf422RnslL3hfmlCpZXzeTS/obMpDXAQ+IFcx0XysPugLHbLE5VVPN+fRSfN1o8+gtlKrXQi/Dze3KK7uYWFkjNxgpqsFK9bJ/mL0rty9ue9bBsjYASMwEgiYAIv+t02gRedne1pBIyAEZA2iIWz4VmEadfIK/5rsjvck5YKK2HjEeK9Gm6Nz8S+h1bNJWm3v10vJgPu64O98q7/F6kP9+La2KLZCSkUIAzcQ5NpQuQtc5bqeZdrBbL0dp1ipPfpgDxtCVvkeHBc9gb75FBwWBrDRjSftqPZdJRMcSbLQncB2jPnyRyIbbbRMu+PFdf+rO6mJmmrrVWB17Zlq3SdvJKHl5uLPLxCncErXf8M8vD6mOnrz5vZNkbACBiBEUTABF70m20CLzo729MIGAEQSLbCUQQEYcJgIhmIPRIMRC7ByONU0KR5a5v8N1QYpfvFYDD8aATEU+A9H3u2Tzv+dMcayNdaw1ZpDI7pdfzRf0f2odqVao2X8bLQWaCB4Gtj31RRxDDyoX6vk9dHcXcUrZrtEK+jUJWcLJM0826qMwX3ZbQGrae67t6eszy83sjY80bACBiBWyNgAi86PxN40dnZnkbACIBAwsyiC1NanQiW7sBHfRete2NhxIHsryuPbAaVtOPfjArey/6rsitMb8fPKhirRHRrXB/74ZCp4B0JjsrH/ic6Q7gT10BH0FSLApUtjaswQ8jzr0BeXCYVr1THvB3PJSuU7ajcteP3lLEWrNKxijcGgps5hTEnpr+/mZyP5eFlQsu2NQJGwAj0n4AJvP6zunFLE3g3ErGfjYAR6BcBzp0x6+0CHq3hV3IJj8vhZXxkdvUDczEs5yegFXEsHhR7MTyycdFt8mJ4UbbCpfEl/2W146eY4ExbqpV0a6TAezgDt8ZUxxrI53YHe+T3ahJTA3F3Vh000x1/hVsh/xT7sQrVQqcQsi833eZZ+5rl4WXtrbULMwJG4A4TMIEX/QaYwIvOzvY0AiOWANsyTwQn5Et/s+zBrNaJ8CTcCVu1mseqXQ7E3CwYcdAtchHMKyYh/40iIBsXhVw8jEsdctZeRqD2Fgg9tgOyoplqzXHK5EFvrQqjhe5CDT1Ptd3tfo5xAr+O/1bNYjrQvkiRmm5xdvAZ7/to1ay+Gtyebvusfc3y8LL21tqFGQEjcGcJmMCLzt8EXnR2tqcRGJEEkgHS9UG9vBf/QOrCHciAOyUXUcHruWY40+VrEHi0nWe1Z4o7OatbNhkp8Ff/I1Tw6mDicRzVzXMalUC3xhjqmvmY9xonRerS+JC7VpZAIJWgysnnh8KiwPvf+P+pwON8WjecM9OthMD7gQpVtpwWOAXpNs/615iHd+6ljTBb2aZRCQw958qdhjy81dVw01wphRUVyMOblPUs7AKNgBEwAgNBwARedIom8KKzsz2NwIgk0Iy2zAPBQan1t8vHwadyKDyExLfLOtPUE0gy2HuVWyXf857SWbMoM049jzmUv6fZSlN4Wg6CBwPP9yF/rVGOc+JLM+RmONN0Xm2JU66ujRNR1cwdQoHheyDYX4u/oQLvNKIE2HKbblG0/yT2/NUWzRzUbUfy6qivl+ZNb6jAizc3C2fzuGKlpRqRUFi1Usbcf5/kzZw5kjHZtRsBI2AE+k3ABF6/Ud20oQm8m5DYE0bACKQjQKfFz/2/q9vi9rBORU267XVWy3tOqr1quBRm76xW0k2U5iS7/T1yAEKvkW6NkL8Uu6xoVrjLZLYze0hV7pL3jvf1U/9zva+syp6GWE21mHtXBBOdKpqseD+U5bimpGtqqu1HynOX9++Xlrff0biE7lOnxL+UEMheUZHkzZqpeXhFjzwsoxB+bssIGAEjYAT6JmACr29GvW1hAq83Mva8ETACKQkwJ+2N+Jta6TmJ2buLeKRbDPV+yvsugr2rkTk2BQYso9NtPqxfo8jj7J2azqCix+odXUbp1sj8uyKnSArxdShV7pLAv4JRzInwhN7XP/hvamZc8rWeXyc6E2SJs0RjEh7w7pfZ7uysbr3tee3pvr+MkPPW997XCl7n0UbxW1t1c2/0aMmZMlnz8IqfeFxGLViQ7jD2mhEwAkbACFwhYAIv+q+CCbzo7GxPIzAiCewMdsvG+Cta6WG7Zgce6RYz077jPaGtfDPcGSpy0m1vr90ZAowNoAsqA87/5P9ZKOQZAcGZy6RIZcTDdLSaVrordJZwvjsfkQ8ld+aEh9i7djY2ysVPP5O2bduko36fxM+d0zN08/PFKymBwKuU8U8/Jfnl5UPszO10jIARMAJDk4AJvOj3xQRedHa2pxEYkQQyndViBe/pHhU8hknbGnoEWH2kkKMDKF1Rj8EopjFoFIr4zrAT0i4fRjlTVODNcKfDOXOCVmNHajzCjXewu+m0tNXVqcBr21wjXSdO6iZOTo64hQVqslK6Yb0ULFt24672sxEwAkbACKQgYAIvBZR+PmUCr5+gbDMjYAQSBJJukZuDLaj21MtZuEWmWoxLYGviMnep/ND7HoKxq7R6R9dIhqHzdVtDjwBjHyj0LoQXIPCOwQ30/FWBNxUCrxTZhmw1ZbahrWsE4hcuSGdDg7Zotn7woXQebki86OJ3HSKvcGWllD73rBSuqBDH80Qc+/2/Rs++MwJGwAjcTMAE3s1M+vuMCbz+krLtjIARUAJJF83NQY38xX8fLpqHU5JhsPkoiLmVaOfb4K1XgxEKOz4/FGfQUl7ECHySlTw+mIN3tUUzDFSs5zujYLGSZ6YqKX4v6JoZb2lRgde86XXp2FOf2IpCDv8KKpZL6bMbVOi5eXnixGIpjmJPGQEjYASMQJKACbwkicy/msDLnJntYQRGNAHOabWELbI72CPvBR/o1xa5ADHQgbpPHBKOhiL5EHcJMTDHmSP3e/fKDHeaBoKzctdT4PFnB3vRhGQC8tTG4EFXRlb/bBmB4UIgjMcl6OxEi2atnH/hJWnfsUPCIBBBEDpX/uJyKcEMHuMSYsXFwtk8W0bACBgBI9A7ARN4vbPp6xUTeH0RsteNgBG4jgDb97rDbtjon0HW2z7ZFe4RhmQzEqANrpGj8JghU2UshBqXj5a/uNONr/wu1MbMni2a2sjpxGS2M0vud78mC535UowAcApEW0Zg2BCAkAt9X9q318nZ370g7bXbhWHnfI4rb06ZFD24VgVeXlmZxMaNGzaXZidqBIyAEbgTBEzgRaduAi86O9vTCIxoAh2o2DWjktcQNlwReMch8NpQi3NlXDhG2/xY6TstZ+Q47Pe/6iVOgdU7DxW7sisCr8qtVIdGBoEnqnu3PqvUs+WQMQZ+6PNdISLZcJh3XUVxRN9Uu/hbJtC+c5ec3/iyVvKYhRd2dekxc6dOlcJVVeqmWVhZKTmTJ9/ye9kBjIARMALZTMAEXvS7awIvOjvb0wiMaAKsyHWFXSrqLsB5kZlvcTx3AaLumH9MDiLom9U9ZuUxSqEbr6ZeCRlXiBD0CTIeM3uV8lTsu8haWzwgs14UdzQOSZqGnIMpDMUphd0kdxLaQicMyeDx1Kzs2aFOoGPvPml58y110+w+c1aCtjY95VhJseQh5JxxCUVr1gireLaMgBEwAkagdwIm8Hpn09crJvD6ImSvGwEjkBGBI8FR+dj/RGr8LbIz3I363ZmM9l/uLpPnvB8hSLta3RpZZYuyksLuq/ArVBBP4l/C9p+ukDQPSQi8yTLNmSozkc/HiiHdITk5aMsIRCVAJ83Wjz6C2UqtdCL8PN7coodyCwslZ+IENVkpXrdO8hctjPoWtp8RMAJGYEQQMIEX/TabwIvOzvY0AkYgBYFdMF95Jf6aBqGfC89qZS/FZr0+tQAzeI97jyIYvVpmu7NknBNtVik5K1gf7pO3/T+pGUzCFRItmniwlZTzgpOdybLYWSRL3CWyzFsqkyD0bBmBqAS6m5qkrbZWBV7blq3SdfJKHl5uLvLwCnUGr3T9M8jDWxr1LWw/I2AEjMCIIGACL/ptNoEXnZ3taQSMQAoCNFz5dfz/VOB1hG1qt59is16fmuOUyVp3DSp4q6TcW6S5a71unOaFNrz3WQhM5vX93t+kAg+WhjftUSLFMteZqzl9D8XWyjx3rjp4UgDaMgKZErA8vEyJ2fZGwAgYgdQETOCl5tKfZ03g9YeSbWMEjEC/CdQFO+S38RdV4F0ML0oXTE0yWUmBxwreIm9hZIF3KjwlW/1teh5fBJu1RTPVeeRKroyWQlmBvL4fxL6Hr8ulwCmQHDxsGYFMCVgeXqbEbHsjYASMQGoCJvBSc+nPsybw+kPJtjECRqDfBPYF++Ut/23ZjBm8Y4hOaMUjk8UWzSe8x9CiuUpm3UKL5sHgoLzr/0XPg2HsLXikW6ziPeI9LHTxnOXMlPFOieXxpQNmr6UkYHl4KbHYk0bACBiBjAmYwMsY2dUdTOBdRWHfGAEjMBAEmsImqfXrpAatkZ8Hf9d8vEyOW6EmKxt0Bm+sM1byIpqsJGYBX9UKHp0z6fKZbo1Gbt9kZ4osc5eiRfQBRDUssjy+dMDstdQELA8vNRd71ggYASOQIQETeBkC67G5CbweMOxbI2AEbp3AxfCSsD1yZ7BLPvI/lr3hXmkJLyAo4bJm4904B8eQBGbSFaItshQxCVXuSnkytk5jEmIIQI86C7cr2C0vxynwtsj5fgg8wXnwTBi4/oB7n1R7VVIOkTcBpisenuerw2HRPZQmMu2IgmA0BLMJu/DgonMobD5gXFMk+U6+zhoOl+saDux7niPz8M69tBFmK9s0KoGh51y505CHt7oabporpbCiAnl4k3ruZt8bASNgBIzAFQIm8KL/KpjAi87O9jQCRiAFgTjy7jrCy3IC4eZ1/g7ZDtOVbWGdnJRTCBiPaypdz908iWmVrsyZLfe69yAHb4UschdqPh3FR1QBcgAtmu/476JFs0YawiNyAY++lwP5U4D3LtU8vnWx76jQZBUxBjk0HFaSP2MhtqOSyms/J80qTymgOeNY4VXIdGeaBr3HcGW2Bp5AR329NG96QwVevLlZOJvHFSst1YiEwqqVMub++yRv5syBf3M7ohEwAkYgCwiYwIt+E03gRWdnexoBI5CGgObPBcdlX3ggIfCQRde7wBslCYF3t8x35mkeXdTWzOQpncD7MYuPraJfBjUqOJOv9efrUsQmPOP9QFtFxzvjYbyS35/d7tg2rNzx0Rw2y+GwQeqDfRDYdfp9QuA5qJCWqMBbAYG3CNXJOc5sbUO9FSF9xy54iL/x5f37peXtdzQuofsU/rhx6ZKesVdUJHmzZmoeXtEjD8sohJ/bMgJGwAgYgZsJmMC7mUl/nzGB119Stp0RMAIZEehGQEIH2gQvyiVt0byMJs2kCOl5oESDpqutgxRSY+BpmePkRG7NTB77EmISToentUXzNcQk7Anqky/16ysrXQ+6a9XsZaG6eY7v1353aqNAAukOu2VPWA+Tmz+icroDLZqtwriITrRossFUHUMdtmiOkwq4hX7X+46UIwPwVlph79T1DvX3vYyQ89b33tcKXufRRvFbE2ZD3mj8fk+ZrHl4xU88LqMWLBjql2LnZwSMgBG4IwRM4EXHbgIvOjvb0wgYgSFMgHNonWGXzgAy6JytoufC88LoBopPvp5uzXCmyz3OXcjjq5Yqr1KmuFPSbX7HX2NbbEvYgopljbzov6wzkOlOajnMbJ711muFsggzeaOcUek2t9cyJNDZ2CgXP/1M2rZtk476fRI/d06P4OZj9rGkBAKvUsY//ZTkl5dneGTb3AgYASMwMgiYwIt+n03gRWdnexoBIzCECbBayKpWK6pYxzEPSNOXT/3PZX+I1jmYj1zGI93ijNpdzmqt4K2C4cpUd2q6ze/4axSv+/z9KvDeDz7U1sx0JzXXmSOPuA+pwJvvzZMSxELYGjgC3U2npa2uTgVe2+Ya6TpxUg/u5KA6XVigJiulG9ZLwbJlA/emdiQjYASMQBYRMIEX/WaawIvOzvY0AkZgGBCgyPNDX5iF95H/N9kSbNW5wPMQROkW3TTXOA/ATXOVLPGWyERnQrrN7/hrJwPOHG7VWIgvw75nDhMVyrtVwK4cBhXKOw44wxOIX7ggnQ0N2qLZ+sGH0nm4IXEEF46sEHmFKyul9LlnpXBFhTgeDHyc4eHSmiEG29wIGAEjEJmACbzI6MQEXnR2tqcRMALDgEBy7q8Z7YuHg8NquvJn/z05GB5Ke/aLnXL5nveUrIbAm+RMktHO6LTb3+kXj8HQ5u/+FzpzWBvWIqqiKe0pTUXm3yqnSgXeXd5dMt2dlnZ7ezEzAnTNjLe0qMBr3vS6dOy5MgNKIYd/BRXLpfTZDSr03Lw8cWLmZpoZYdvaCBiBbCdgAi/6HTaBF52d7WkEjMAwItAZdgqdPdmqyZm8HeFOOE62wPolYV+fvJR8yReavVS6FbLO+7bQTbMAGX05eAzldTI4JVu1grdF/hF+qW2p6c6XFbyvOfeowKvUCt7kdJvbaxkSCONxCTo70aJZK+dfeEnad+yQMAgQAxnqkfIXl0sJZvAYlxArLhbO5tkyAkbACBiBawRM4F1jkel3JvAyJWbbGwEjMCwJJF0mmc+3zd8uW4NtUhNuQXxCYjYqeVHTaa7i0lylSp0maa7i4RE1cD153MH+SoOVw36DVvDeDf4sB8KDad9ygTNfnvAeVROZ2e4sddZMu4O9mBkBCLnQR+D89jo5+7sXpL12uzDsnM9x5c0pk6IH16rAyysrk9i4cZkd37Y2AkbACGQ5ARN40W+wCbzo7GxPI2AEhiEBVvFOBCdkL3LiNkPgnYTAS9RU0DmH60kIvLulHGHrw6E1M3kLGEnBucI6xCOwQrkr2I2IiosakZDchl/z8BiDx3J3KQTeYypii51iye8j568LUQt0IL0YXtLoC/7M/LxRON5YZyzCLUZL4TCodPZkcTu+b9+5S85vfFkreczCC7u69G1zp06VwlVV6qZZWFkpOZOtgno77oe9hxEwAsOHgAm86PfKBF50dranETACw5DA1Xw+iJVmSbRock6Pi4KlAI/xcJSkCGJ0QAyP4bAY+9CFWAjOGX7if4oKZS3MZPbLObneTKYUcefMvqtyK+X+2H0aMJ/r5GqVMt11UjzuDw7ov32oDpId65oTnYmyBMdjQP0srQQWpTvMiHutY+8+aXnzLXXT7D5zVoK2NmUQKymWPIScMy6haM0aYRXPlhEwAkbACFwjYALvGotMvzOBlykx294IGIGsIpA0YeFFUeDxMZzX2fCc1Pv1Uo9g9/pwn5yWMxLHg4tidbJMEhrIlLuLZJEGuJemvVzGSVxArERDcES2o7V1V7gHURMUeBe0bXWSTNDjcVaxAtl6M9zpakjDUHVbok6arR99BLOVWulE+Hm8uUWxuIWFkjNxgpqsFK9bJ/mLFhouI2AEjIAR6EHABF4PGBl+awIvQ2C2uREwAtlHoGcFb7hfXSeaMr9CdZKirDmkDGuVS/iZ8nUMnEDH4cHMu3EINx/jjNGWzXTX3AQ3zlq/Tnag9ZMGNcfkuLZpdiEsnmI40aI5RuYgV+8uhMIvg8ib7861XL0rULubmqSttlYFXtuWrdJ18koeXm4u8vAKdQavdP0zyMNbmu422GtGwAgYgRFHwARe9FtuAi86O9vTCGQVAVZ5OMfVDlfJ9rBdqz40FuEHeAqBUXjEnJhWbbLqwrP0Ytiy2R12q0so5+ZYl+R9pEtojoOwbTz6s9iW+U78T2reciQ8CsF4IeVubNVc6ixRV84HvK8LjVtsiVgenv0WGAEjYASiETCBF40b9zKBF52d7WkEsooADTRoOMI8taNhI6o+lyTXyUND30RZ6M6XKchNYxYc57VsDX0CrEpqyDuEXjxMtGhS2NERtL+tqDzG9qBOfhP/P9nsb5E2PDjDmGrRvKUIVcHV7ip5LrYBJi7LUm024p6zPLwRd8vtgo2AERggAibwooM0gRedne1pBLKCAGesWsNWOEuehLX+ATkEk46jwTG5hEce5qhYmVnoLpB5zlwpc2dLqVOqFSAKBVvDlwDFH4UfRRvzAJPijXYzOZjVo9kM3TX3BHvkf+K/0liJ/lwtBd6/5/wcQq+6P5tn/TaWh5f1t9gu0AgYgUEiYAIvOlgTeNHZ2Z5GICsInA5Pyw5/J+ardsvucLfmwvVs0cyDk+QYWOAzNmCt+01Z4i7WD/50mLQ1fAnQcZNV2yPhEbRfbpXD4WEI/a9Qswu1lXO2M0tWuSvlHNwzX/Q3YgZvZ78u1gTeDZgsD+8GIPajETACRqB/BEzg9Y9Tqq1M4KWiYs8ZgRFAgB/k+TgQHJS/xN+TGnzIP4QP+S14pFpznDJ50PsmgrFXySKIPVbybA0/ArznrN4lYw9onFITbMG9b9BKLl/nrF6ZM1sFXhfm+D4OPlUhyP34eqrFii4rvtWo3P0s56caw5Bqu5H6HPPwzr20EWYr2zQqgaHnXLnTkIe3uhpumiulsKICeXiTRioiu24jYASMwHUETOBdhyOjH0zgZYTLNjYC2UOAH9ZpwsEZqxfjG2VLsE3b9BhgnWoVO+Pwob9MW+8e874Fp8R5qTaz54Y4gaT5ym7EHbwZ/6Pe/2aEHlwKr83XMU6h0CmUEinG1TjqxNkG+53usBMTfX7KKyyAect4Z7wKvPWxHwhjE2xdI9BRXy/Nm95QgRdvbhbO5nHFSks1IqGwaqWMuf8+yZs589pO9p0RMAJGYAQTMIEX/eabwIvOzvY0AsOaQDLwm9WbX8V/I7XB9rTXw1yz0ajssAXv+dhzssJdnnZ7e3FoEqBTKmfuNgc1stF/RaMP0p3pKAg3zuKp0EP0wmX4ciaqeMlweBe1O08mORM0QP3/b+/eouOqrzyP71NVUkkq27JkGeR7ZIJtGWxZvpAQ7CQOBBIgQ4KTWRDImjUrM2tWP/ZDP/Tqp37up37op0lPh4EGBtrQoUNndQhNEpgw+CbbGMuXxMIG27ItSxa27nWZvf+iQAa1ZG90Sirpe7QcyaradU59DtHSz/9z9t6a2CJ00fy86OCJE9Lzy1fDuISR8+cld+1aeFKytlbSq1aGeXi1D9wvVTr8nA0BBBBAQISA5/+vgIDnt6MSgbIWGCyMNlexgPd07tlJ77Gytvopbb9hAe9/VPz3cPleWQPM0YO3SzOP506EsQev5V8P995NRFGv63jWYCfS839K3te1vh7Ja3MWu9DTNvtvojqqDuHuu8n7pTWxSZZqx1W7zJPtU4FBHXLe++vXwgre0OkzkuvtDQ8m582TiiWNYR5e3cMPSdWaNZ8W8RUCCCAwhwUIeP6TT8Dz21GJQFkLDOnldh9pUw1byXk6949yaJImGnbZXlpn4VnA+28V/1U2J1rL+v3P1YM/nz8v+3Nt4by/XXhHPix8OCGFjcnYGG0IIxB6Ih2arhfyDtulmiHkFSQZ2b13VbImul3uT94XOq5WawMe+++F7VOBoTNn5Oqbb0nfgQMy0H5csl1d4cFEdbUk6+s14LXKol2PSnVz86dFfIUAAgjMYQECnv/kE/D8dlQiUNYCdi+VdVK0gPc/Qxv8tgnfT0YyehneLeEevB+mdmk3TX4RnRBshj5o4zD25vaG876nsC90TZ3oUG/VSYgtGvCaE+tkdfI27aea0fv1ror9A0FOV/Hy+r9Z/WMz8FYnmnSsxmKpiWp0Xa9iopedc4+NdF6QvkOHQsDr27NXhs+eCwZRhQ6dz9SEJisNTzwuNRuYHzjn/uPgDSOAwLgCBLxxWW7omwS8G2LiSbNB4MqVK/L++++LfZ5oSyT0YkT9k0qlpLKyUjKZjCxYsEDm6aVU1fqv7fb92bDZfVT2YV0Un8++ELpodhe69Q6r0eYP17/HSBoj+0V/Y+iiuSN5j6xMrLj+KfytLAQu5C/IuzoWY49emvv7wlthqP1EB748Wi53R18JwX5LslUWRAvkYv6iXCp06eWa1pzlmrblGdYLOJN6j+Y8DXoLwj171qDF7t2zyzfZRLL6c2eooyNcotn7m9dl6FTHKIv+rLGQl9ncKg0/eVIym1okSuqMySiCDQEEEJjTAgQ8/+kn4PntqCwzgYMHD8pTTz0l9nmirRjs5s+fL4sWLZJVq1bJnXfeKbdp84OlS5eKfX+2bBbwzhXOyb7c/vAL/zu6mne2cPYzb89+0UyEFZwfJP+T/qK/hXusPiNUTn+12Xdn82fDCt7LuVfkWOH4hIdvl14+nHwwBLxViVVhxe5k/qS8l28PHTjPF86HdTxrwpKKUqHz5qpoZbgnb1tyqyxNLJ3w9efKg9Y1M9ujA+V1TEL37pdk4Gj76Fu3IKd/alo2SsOTT4Sgl0inJZol/5A0V84v7xMBBKZegIDnNyXg+e2oLDOB3/72t/LXf/3X8uabb4aVuZqaGqm1Dnb6y9TYLdJftpL6L+gW9OyxW2+9VdatWycb9NKp1tZWWbFixaxaybNf+M/pL+nt+gv72/l3pEMHX49efpfTWDfaWKVKL7mzS/QeTD6gv7ivkXSUDp0Tx7rxdXkI2Lm1c24rt6/kfimHCu9Kj3bVHNSPsVuV3le3UC+7tPvvHkp+N4w9sMYpFwsX5e3cOzpWY7/WHpbOwoWxZbJA5svyaJm0Rpvk/tS39Z68tXrJZvWcv2SzkM1KfmhIL9Fsk8vPPCv9hw9LIa+NanQQum3V65ulXu/Bs3EJqbo6sXvz2BBAAIG5LEDA8599Ap7fjsoyEygGvHfeeUfqtamBrczdcccdsnjx4k/eif0wGdEBxAP6r+1d2gThfb2k86OPPgphr1mbH3zve9+TrVu3yrJly2bNSl5W76Cy1vk92gL/fKFTf2HvlAv6S7t9rzKq1F/YF8itiUZZEjXqyl1juETPgl+kH2zlJ1Ccf2jNVfbqyu1+nX+4v9Cm5/78dW/GLsndFLXowPLNcldyWzj/NlrjvfxReSn3z2H17kqh93PBsEKbq1Trx9porXw7eW/oqrlCL+e1Szfn9KY/Wwq5nPQfPCSXnn5G+tsOig07t+/Zll7dJLX33RsCXrqpSVILF85pLt48AgggQMDz/zdAwPPbUVlmAsWAd0C72FlAa2lpkZ07d0qT/jJV3OyHyfDwsPT19cmHH34o7733nhw7dkxOnjwZguD3v/99+cY3vhFqGxsbi2Wz4rM1XbHVnV7trHlBQ96AjlGoiCrCL+a36i/7dn+VhTqC3aw43Xqee+VM/gM5lj8WAt5ZvVS3OMTc5tpZoG/VgLdOV26bEl8KK3CXCpfCpbwv5P4pBL2JJFZEK+Se6O4QDluTLXoP5+z6/8tE732ix/rfPSKXn3s+rOTZLLyC/ryxrVIv/85s3RK6aWb0SoGKWfbzZSITHkMAAQTGEyDgjadyY98j4N2YE8+aBQLFgHdIO9mtXr1avva1r8mjjz4qtjI3dsvrZVNZvZzKmrGcPXtWbMXvxRdfDCt5X/3qV+Wb3/ymPPDAA+GevLF15f613Y9XXN2xy/Xsl31bqbNuiHZJprW9J9yV+1n+9PhtNa6/0K+jMq7qZLseuaofgxrwbbPzPV/7ZdbJwrBim4ky4bJOu/fOmrP8W+41+VPh1KcvNs5XDdIg63QV7y69D+/e5LdCh81xnjbnvjVw7Lj0/OKV0E1z5OIlyes/JtmWqq+TtN7na+MSavUfnmwVjw0BBBCYywIEPP/ZJ+D57agsM4FiwHv33Xfl9ttvD0Htxz/+cbi3bry3Mjg4GEKe1f3t3/6tnDp1KjzXAt6uXbs+FwzHew2+h0A5CFi4t0t1B3XV1u4IqxoT6Iuh3lbvjubatdvqPvn3/G+lo/D+hG/NwuHqqEm7rm6TB1PfkdsTX57w+XPlQeuk2fvGG9pspU2GdPh5trsnvPWEduutuGVxaLJS98gjUr1u7Vwh4X0igAAC4woQ8MZluaFvEvBuiIknzQaBmw14di+eXar5u9/9Tv7mb/4mXKZpjVYIeLPhvwbew2cFbPXWPizgJT++x7IY7uy5NkLjj/k/frKC98fCnz77Etf9vUEWfbyCt40VvDEyI52d0tfWFgJe3779Mnzu43l4lZU6D0+nTWqTlYbHH9N5eHeOqeJLBBBAYO4JEPD855yA57ejsswEigHvsHavsxU8u5fuscceCyMQxr6V4iWa1/T+mEuXLoWumz/72c/C13fffXeom42XaI41uJGvLQzkCjm5ph+X9Zd/+2yrQKMRQbsCahdGnYSml/jND7PQGHx9I6oz9znWedMasezV7pkv534hR/XePVv50zaQ4x60ddK8O/GVsIK3Jbk5jNYY94lz7JvMw5tjJ5y3iwACbgECnptOCHh+OyrLTGBswLOZdtu3b5cf/ehHsn79+k/eif0wsSYr/f39cubMmTAzb8+ePfLWW2+Fgedjm6zY+IS5vI0UdIWz0BfuxXo7///C59Gh1yPKEoVf6LdGm3W8wlpZnlge7uWay17l/t7tnj3rrLpPu24+lftHOZA/qAFfu0BqpB9vs7Eau5I/0IC3VRu2LJH52qSHTYR5ePxXgAACCNyYAAHvxpzGexYBbzwVvjcrBYoBb//+/WFguQ0v//rXvx7GJRTf8NiAZ1007X49a7RiYxMsFD6i98bYmITZNvC8+P5v9LOt3FwuXJaTesneofxh+cN1Ac+6AlrAWyrbNOBtTrTKJu2iuExXdEabtSRvdDfXPa/YBKY/jHTo1pYg12RIP0Y7P0ahNX9dtDA0B6lm7tp1dlP1FzsHx/IndH7eq2FMQrf+N2Are9aUx1ZubURCRmcm1urHxoTOz0s9KOuj5o9XcFNTdRhl/TrMwyvr08fBI4BACQUIeH5sAp7fjsoyEygGPFuNq9YhwjbkvKGhIazMFd+K/TCxSzRzOptqyIYS6z149pwtW7aEYLdt27YQCK3eBqHPxa0YtI7m2+Wl7Ms6R00HN+vHNV3NG71Ec3RFx2ah1UV1cke0Xueh3ScbEnfK4ki9NQB4Ngtytmp4pvCBzm/bJycKJ8Uaf/TLgCSilAbIpbJZ2/qv1UHsyxPLWDH0IN9ATRivoDP0TuRP6rD0w3pf3inRyYm6vpfVWLdAVkUrpUXDna3g3Za4TRZFi/SevtG7+m7g5Wf/U5iHN/vPMe8QAQSmRICA52ck4PntqCwzgWLAe/PNNyWdTsu8efNkoQ4TtrBW3MYGPLtU01bubN6d3Xv3la98JfxZuXKlVFRUSDLpW4kq7qtcPxfb61u7/H/IPiVteqneRFuTdlK8N7FT7tJL9ZqT66RBQ55ns2BxunAmzF97J7dHA94JuRgC3uBowJMlGvA26WDtFmlJbggrhlVRVRjv4NkfNeML2L2X2UJW78frlCP5I3ppbod+fUHj3UhYuVul8+9aEhtlRbRcw3xGKqPK8V9ojn/X5uF1PfucNls5EEYl2NBz2yqX6Ty8u7ZpN83NktFZnRWNc/tS8Dn+nwlvH4E5LUDA859+Ap7fjsoyEygGPLunbvHixeGSy1YdKLxkyZJP3on9MCneg3f+/Pkw5Nzm4VkI3LhxYxiPsFl/8aqrq5OqqqpP6ubSF1cL10abbegq2u7cy9JeODbh27d5aM3Ruo/noelg+YRvvtdxvTTwX/TSQFsxvKiBwgKfXaJpbV2s22No6qKXaNq+vpXcGVYMGz8e0D7hAfLgTQnYCq592EiFXv2wldtBPQ92L5410qnRj4VRbbhkNqUrqzZLke3zAgPt7dK9++UQ8LLd3eHePHtWSq8YsBEJ1k1z/o7tktZ/UGJDAAEE5qIAAc9/1gl4fjsqy0ygGPDatEW5rcLZZZff+c535Mtf/nQ+19iAZ/feHTlyRI4ePSrHjx8PodCarFhzlrVr14ZLN8uMYEoOt6dwRTryHWEe2qu5X8nJwh8nfN0Felec3X9nK3jfTz0i67Tpys1sxUsz92tzj/+V+98a8A6ESzX1QtpxX8YuEdyRuCd0b7SVvFs15LEhMNMEBk+ckJ5fvhrGJYzoPybltGuvbUm9dDy9amWYh1f7wP1Spff+siGAAAJzUYCA5z/rBDy/HZVlJlAMeIcOHQqrdxbUfvjDH17XRdPe0tgxCV1dXWIrfs8//7zY17aKZ3UPPvhgGLVQZgRTcrg9hR45pQFvT36v/Cr3b5MGvPka8JZqF0ULeD9IfT/cm3UzB2IrRVc0VFp7/mdzz8thvSzQVotsFWm8baEO2G6KVsm25DZ5KPldWZO4fbyn8T0EplVgUIec9/76tbCCN3T6jOR6e8PxJPXS8YoljWEFr+7hh6RqzZppPU52jgACCEyXAAHPL0/A89tRWWYCxYBnnTFtDp4NLP/xj38sNrx8vK3YaOXtt9+Wv/u7vxObn2eNWex+vJ/+9Kdil3fOxc26Jp4tnJU9eommzUM7Vjg+IcMiqZc10e3hEs1vJ7+tjTdWT/j8zz5ooxguFi7q/vbKi7nd8l6h/bNPue7vVTZ/Ty/V3JbYJk+kHtdujgyMvg6Iv8wIgSEdw3L1zbek78ABGWg/Lln9ByTbEno5eLK+XgNeqyza9ahUNzfPiOPlIBBAAIFSCxDw/OIEPL8dlWUmcLMBz36w2GqejVX4+7//e3nnnXfk6tWr4dLOP//zPw9Br8wIpuRwh2U4zL+zFbyfZ58O7fInemG7ZPIbiR26grdNNiTvlFuiWyZ6+uces9l65/OduoK3N9zzd3SSe/7SktaZawvC/LX/knoiNPz43IvyDQSmWWCk84L06dUEFvD69uyV4bPnwhFF2sApkakJTVYannhcav6Df4Ca5sNn9wgggEDsAgQ8PzEBz29HZZkJ3GzAK769gwcPylNPPRWGndt9eZs2bZK//Mu/lB07dhSfMqc+WxdFuy/OxiTs1jEJ+/TSyUs6JsFW2kYvmxy9dLJKg1atNtto1jlo9yW/FeaiLYkaZd5NDrzuL/SHmXu2Yvh87gU5UnhvQu95Mk8adT8WKHelfiDrtV0/GwIzTSCrzZuGOjrCJZq9v3ldhk51jB5iIiEW8jKbW6XhJ09KZlOLRNaxN4pm2lvgeBBAAIFYBQh4fl4Cnt+OyjIT+CIB7+c//7nYeIViwPurv/qrORvwRnsoFnQGXZe0a8jbp01Pfp//v9JROC05nVNn98fZdquu1G2I7gyDzu/S++FsJc8ztsBm6w0VhsI9fz/L/oMc0C6aE20W7lp1Hp4FvHuSd8uKxIqJns5jCEyLQF5HsGR7ekLA6979kgwc/fjSYwty+qemZaM0PPlECHoJHesSzdG5m9NyctgpAgjMCAECnv80EPD8dlSWmcDNBrziwPN9+/aFSzTtXrxebYSwdetW+Yu/+Au55557ykxgag83tMnXUQXHdeD4aMB7XwNeVgPeaHfLW7R75UYNeNY10wZe1+vQc89WDJRH80flxexLoXvnRQ2XffohYxqtWIv+jH7cHt2mXTS3S2uyJezXhq2zITDTBApZHfAxNKSXaLbJ5WeelX69x7egl4SLXhpuW/X6ZqnXe/BsXEJKx7LYvXlsCCCAwFwSIOD5zzYBz29HZZkJ3GzAG9HBw/39/fKHP/whNFmx7puZTEa+9rWvyZ/92Z/Jtm3bykxgag+3OL7gqlyTrhC4+nXtzlbvipdoVoXB1/Oj+XpZpg681g/vZiHP9mErhnty++WN/O91xfB9/e6n+xvtnvkl2aSDzr+R3BG6Z4ZB219gv97jpQ6BSQU0yBVyOek/eEguPf2M9LcdFBt2bt+zLb26SWrvuzcEvHRTk6QWLpz0JXkCAgggMJsECHj+s0nA89tRWWYCxYBnQW316tUhqD366KPS/JkudcUxCdZQ5cKFC2FMwgsvvCAXL14Mz/36178eBp5/tq7MOGI5XAtittng8ane7DJNG25+rHBC/j33ezlV6NB4l9XdFML+bKVutXwpjGFo0dU7G3LOhsBMF+h/94hcfu75sJJns/AKw8PhkCuXLpXM1i2hm2ZGO/ZWNDbO9LfC8SGAAAJTKkDA83MS8Px2VJaZQDHgHdCudcuWLZOWlhbZuXOnNOm/jhc3+2FiK3d9fX1y7ty5MOT82LFjcvLkSVm0aJE89NBDYgFv8+bNsmTJkmIZn0sgYKuDI3qP30dyVccmXNL/vRr+bqt4qSilLV2qdOLePFmgHTQXanMXG5fAhsBMFxg4dlx6fvFK6KY5cvGS5PVnj22p+jpJ65BzG5dQqz+nbBWPDQEEEJhLAgQ8/9km4PntqCwzgWLAs3EH9TpnatWqVXLHHXfI4sWLr3snFvAGBwfDYPPTp0+H0QgV2tVu3bp18r3vfe+TcDd//vzr6vhLaQVsXIPN5BvQQehZGdGdR3oXXoU2ckmHe/Eqo8qwshfHamJp3yl7m80C1kmz9403tNlKmwzp8PNsd094uwm9HLzilsWhyUrdI49I9bq1s5mB94YAAgh8ToCA9zmSG/4GAe+GqXhiuQsUA551w6ysrJSampowuDytHerGbgltU25/7DnV2tjAVuosCNofG4q+VC+dsu+n6Go3lq3kX1+1+XiF89KpM/JsRW9EA59OD9M5e4ulKfElbepSH1b29GyW/NjYIQI3KjDS2Sl9bW0h4PXt2y/DeuWAbZH+/LGQZ01WGh5/TOfh3XmjL8nzEEAAgVkhQMDzn0YCnt+OyjITKM6zs88TbRbcLNwtWLAgrO7Z/XobN24Ml3LecsstodHKRPU8Fq/AgAxIT6EnDD/v0PvwPsiflQuFCxrvhjTg1chiHc9gAW9VYqUsj5aHyzUt5LGSF+954dV9AszD87lRhQACs1+AgOc/xwQ8vx2VZSZwRQcLv//++2KfJ9qKK3gW9Gx1b968eWGlzzpo2t9ZuZtIL/7HzhXOyT7tpHkk/56cLPxJV+8uyqCGOxu/ntKPtH5Y98w7EuvloeSDOmh9naT1cs2kfrAhMNMEmIc3084Ix4MAAjNFgIDnPxMEPL8dlQggUEIBa7KS1Tl7xwrH5dXsr2S/Dlj/oPChNl35aNyjWJ9olh8ld4WB5zZ03UIfGwIzTYB5eDPtjHA8CCAwUwQIeP4zQcDz21GJAAIlFBguDMs1ve+uLX9QXsj9kxzMH9aLNQf03jtrsPL5bXm0TL6auCsEvC3JLbI0ouvp55X4zrQLMA9v2k8BB4AAAjNTgIDnPy8EPL8dlQggUEKBvkKfXNJmKntz++T/aMB7r3B0wr3XS53cFq2Wbclt8t3kA/LlxG0TPp8HEZhOAZuH1/Xsc9ps5UAYlWBDz22rXKbz8O7apt00N0tGR7tUNDLfcTrPE/tGAIHSCRDw/NYEPL8dlQggUEKBK4Urcjp/Wvbm98m/5P5VThROTrh37T8ot+qw822JrfKj1C69J695wufzIALTKTDQ3i7du18OAS/b3S12b55tqYaGMCLBumnO37Fd0itXTudhsm8EEECgZAIEPD81Ac9vRyUCCJRQoLfQGwLenhDwXp004M3ToecW8O5KbJMfpn4gdk8eGwIzVWDwxAnp+eWrYVzCyPnzkrt2LRxqsrZW0qtWhnl4tQ/cL1U6/JwNAQQQmAsCBDz/WSbg+e2oRACBEgr0F/rlcuGy7MntledzL8qRwnsT7n2RLJI10e1yV3KrfDt5n9yWWD3h83kQgekUGNQh572/fi2s4A2dPiO53t5wOEnt4luxpDHMw6t7+CGpWrNmOg+TfSOAAAIlEyDg+akJeH47KhFAoIQCI4URsfvwDuYPyYu53eHzNenT+XfDnzsKm3m3Iloh2xN3h0s0NyVbpDFq/Nzz+AYCM0Vg6MwZufrmW9J34IAMtB+XbFdXOLREdbUk6+s14LXKol2PSnUzK9Ez5ZxxHAggEK8AAc/vS8Dz21GJAAIlFLAxCblCTv5Y+KO8ln1d9umYhD/pHLxu6bnuKCzcRTrz7s7EHfKfw5iELbIoWiQ1Uc11z+MvCMwkgZHOC9J36FAIeH179srw2XPh8KKKCklkakKTlYYnHpeaDRtm0mFzLAgggEBsAgQ8Py0Bz29HJQIITIPABR1s/m7uiLyng87bC8eks9D58aDzfBhmnpYqnXk3Lww6fzj5HVkXrZVUlJKEfrAhMFMFsleuyFBHR7hEs/c3r8vQqY7RQ00kxEJeZnOrNPzkSclsapEomRSJopn6VjguBBBAYEoECHh+RgKe345KBBCYBoFBGRJruHJRg96H+Q/lXOGcXMhf1O8Oa7CrCat1KxIrZJnOwVums+8WRAvCmp6t67EhMFMFrGtmtqcnBLzu3S/JwNH20UO1IKd/alo2SsOTT4Sgl0inJUqlZupb4bgQQACBKREg4PkZCXh+OyoRQGAaBQYLg9JT6JGuQpd05i+Ee/HsMswGvRxzeWK51Ea1iYeTqQAAG/lJREFUBLtpPD/s+uYECtms5IeG9BLNNrn8zLPSf/iwFPJ5ER2Eblv1+map13vwbFxCqq5O7N48NgQQQGA2CxDw/GeXgOe3oxIBBKZRICc5scYrQ/phYc/u0UvqpZhpqZTqqFoq9INVu2k8Qez65gQ0yBVyOek/eEguPf2M9LcdFBt2bt+zLb26SWrvuzcEvHRTk6QWLry51+fZCCCAQJkJEPD8J4yA57ejEgEEEEAAgSkV6H/3iFx+7vmwkmez8ArDo11iK5culczWLaGbZqa1VSoa6Qo7pfC8GAIIzDgBAp7/lBDw/HZUIoAAAgggMKUCA8eOS88vXgndNEcuXpJ8X194/VR9naR1yLmNS6jduVNsFY8NAQQQmM0CBDz/2SXg+e2oRAABBBBAYEoFrJNm7xtvaLOVNhnS4efZ7tExIIlMRipuWRyarNQ98ohUr1s7pfvlxRBAAIGZJkDA858RAp7fjkoEEEAAAQSmVGCks1P62tpCwOvbt1+Gz308D6+yUufhZcI9eA2PP6bz8O6c0v3yYggggMBMEyDg+c8IAc9vRyUCCCCAAAJTKsA8vCnl5MUQQKCMBQh4/pNHwPPbUYkAAggggMCUCjAPb0o5eTEEEChjAQKe/+QR8Px2VCKAAAIIIDClAszDm1JOXgwBBMpYgIDnP3kEPL8dlQgggAACCEytAPPwptaTV0MAgbIVIOD5Tx0Bz29HJQIIIIAAArEI2Dy8rmef02YrB8KoBBt6blvlMp2Hd9c27aa5WTItLToP79ZY9s+LIoAAAtMtQMDznwECnt+OSgQQQAABBGIRGGhvl+7dL4eAl+3uFrs3z7ZUQ0MYkZDZslnm79gu6ZUrY9k/L4oAAghMtwABz38GCHh+OyoRQAABBBCIRWDwxAnp+eWrYVzCyPnzkrt2LewnWVsr6VUrwzy82gfulyodfs6GAAIIzEYBAp7/rBLw/HZUIoAAAgggEIvAoA457/31a2EFb+j0Gcn19ob9JOfNk4oljWEeXt3DD0nVmjWx7J8XRQABBKZbgIDnPwMEPL8dlQgggAACCMQiMHTmjFx98y3pO3BABtqPS7arK+wnUV0tyfp6DXitsmjXo1Ld3BzL/nlRBBBAYLoFCHj+M0DA89tRiQACCCCAQCwCI50XpO/QoRDw+vbsleGz58J+oooKSWRqQpOVhicel5oNG2LZPy+KAAIITLcAAc9/Bgh4fjsqEUAAAQQQiEUge+WKDHV0hEs0e3/zugyd6hjdTyIhFvIym1ul4SdPSmZTi0TJpEgUxXIcvCgCCCAwXQIEPL88Ac9vRyUCCCCAAAKxCFjXzGxPTwh43btfkoGj7aP7sSCnf2paNkrDk0+EoJdIpyVKpWI5Dl4UAQQQmC4BAp5fnoDnt6MSAQQQQACBWAQK2azkh4b0Es02ufzMs9J/+LAU8nkRHYRuW/X6ZqnXe/BsXEKqrk7s3jw2BBBAYDYJEPD8Z5OA57ejEgEEEEAAgXgENMgVcjnpP3hILj39jPS3HRQbdm7fsy29uklq77s3BLx0U5OkFi6M5zh4VQQQQGCaBAh4fngCnt+OSgQQQAABBGIV6H/3iFx+7vmwkmez8ArDw2F/lUuXSmbrltBNM9PaKhWNjbEeBy+OAAIIlFqAgOcXJ+D57ahEAAEEEEAgVoGBY8el5xevhG6aIxcvSb6vL+wvVV8naR1ybuMSanfuFFvFY0MAAQRmkwABz382CXh+OyoRQAABBBCIVcA6afa+8YY2W2mTIR1+nu3uCftLZDJSccvi0GSl7pFHpHrd2liPgxdHAAEESi1AwPOLE/D8dlQigAACCCAQq8BIZ6f0tbWFgNe3b78Mn/t4Hl5lpc7Dy4R78Boef0zn4d0Z63Hw4ggggECpBQh4fnECnt+OSgQQQAABBGIVYB5erLy8OAIIzGABAp7/5BDw/HZUIoAAAgggEKsA8/Bi5eXFEUBgBgsQ8Pwnh4Dnt6MSAQQQQACBWAWYhxcrLy+OAAIzWICA5z85BDy/HZUIIIAAAgjEK8A8vHh9eXUEEJixAgQ8/6kh4PntqEQAAQQQQKAkAjYPr+vZ57TZyoEwKsGGnttWuUzn4d21TbtpbpZMS4vOw7u1JMfDThBAAIG4BQh4fmECnt+OSgQQQAABBEoiMNDeLt27Xw4BL9vdLXZvnm2phoYwIiGzZbPM37Fd0itXluR42AkCCCAQtwABzy9MwPPbUYkAAggggEBJBAZPnJCeX74axiWMnD8vuWvXwn6TtbWSXrUyzMOrfeB+qdLh52wIIIDAbBAg4PnPIgHPb0clAggggAACJREY1CHnvb9+LazgDZ0+I7ne3rDf5Lx5UrGkMczDq3v4Ialas6Ykx8NOEEAAgbgFCHh+YQKe345KBBBAAAEESiIwdOaMXH3zLek7cEAG2o9Ltqsr7DdRXS3J+noNeK2yaNejUt3cXJLjYScIIIBA3AIEPL8wAc9vRyUCCCCAAAIlERjpvCB9hw6FgNe3Z68Mnz0X9htVVEgiUxOarDQ88bjUbNhQkuNhJwgggEDcAgQ8vzABz29HJQIIIIAAAiURyF65IkMdHeESzd7fvC5DpzpG95tIiIW8zOZWafjJk5LZ1CJRMikSRSU5LnaCAAIIxCVAwPPLEvD8dlQigAACCCBQEgHrmpnt6QkBr3v3SzJwtH10vxbk9E9Ny0ZpePKJEPQS6bREqVRJjoudIIAAAnEJEPD8sgQ8vx2VCCCAAAIIlESgkM1KfmhIL9Fsk8vPPCv9hw9LIZ8X0UHotlWvb5Z6vQfPxiWk6urE7s1jQwABBMpZgIDnP3sEPL8dlQgggAACCJRGQINcIZeT/oOH5NLTz0h/20GxYef2PdvSq5uk9r57Q8BLNzVJauHC0hwXe0EAAQRiEiDg+WEJeH47KhFAAAEEECipQP+7R+Tyc8+HlTybhVcYHg77r1y6VDJbt4RumpnWVqlobCzpcbEzBBBAYKoFCHh+UQKe345KBBBAAAEESiowcOy49PzildBNc+TiJcn39YX9p+rrJK1Dzm1cQu3OnWKreGwIIIBAOQsQ8Pxnj4Dnt6MSAQQQQACBkgpYJ83eN97QZittMqTDz7PdPWH/iUxGKm5ZHJqs1D3yiFSvW1vS42JnCCCAwFQLEPD8ogQ8vx2VCCCAAAIIlFRgpLNT+traQsDr27dfhs99PA+vslLn4WXCPXgNjz+m8/DuLOlxsTMEEEBgqgUIeH5RAp7fjkoEEEAAAQRKKsA8vJJyszMEEJhGAQKeH5+A57ejEgEEEEAAgZIKMA+vpNzsDAEEplGAgOfHJ+D57ahEAAEEEECgpALMwyspNztDAIFpFCDg+fEJeH47KhFAAAEEECitAPPwSuvN3hBAYNoECHh+egKe345KBBBAAAEEpkXA5uF1PfucNls5EEYl2NBz2yqX6Ty8u7ZpN83Nkmlp0Xl4t07L8bFTBBBA4IsKEPD8ggQ8vx2VCCCAAAIITIvAQHu7dO9+OQS8bHe32L15tqUaGsKIhMyWzTJ/x3ZJr1w5LcfHThFAAIEvKkDA8wsS8Px2VCKAAAIIIDAtAoMnTkrPq/86OvD83HnJXb0ajiNVZwPPm8IKXu2932Lg+bScHXaKAAJTIUDA8ysS8Px2VCKAAAIIIDAtAoOndOD566+HFTwbfp7ruRKOI1VfL+nbbxsNeDu/KekvfWlajo+dIoAAAl9UgIDnFyTg+e2oRAABBBBAYFoEhs+elat79kr/wUMyeOyYjFzqCsdRsbhBqtatk5pNLTJf78WrXLZsWo6PnSKAAAJfVICA5xck4PntqEQAAQQQQGBaBLLdPTL4pz+J3YvXf+iQDHdeCMdRqU1VarS5SnVzs1Tddpuk6uum5fjYKQIIIPBFBQh4fkECnt+OSgQQQAABBKZFIN/fLyOXL8vwBx/K4MkT+nV3OI6KRfVSdfsaqVyxXCoWLZJETc20HB87RQABBL6oAAHPL0jA89tRiQACCCCAwLQIFHI5KQwNhe6Z1mAlPzwcjiNRWSnJ+fMlUV0tUTotUTI5LcfHThFAAIEvKkDA8wsS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzoxLw/HZUIoAAAggggAACCCCAQAwCBDw/KgHPb0clAggggAACCCCAAAIIxCBAwPOjEvD8dlQigAACCCCAAAIIIIBADAIEPD8qAc9vRyUCCCCAAAIIIIAAAgjEIEDA86MS8Px2VCKAAAIIIIAAAggggEAMAgQ8PyoBz29HJQIIIIAAAggggAACCMQgQMDzo87IgLdw4UJZvXq12Gc7ubZFUeR/l1QigAACCCCAAAIIIIBAWQmMzQHXrl2Ts2fPypkzZ6Sjo0Pmz58v27dvl5UrV5bVeyrFwc64gGcnsq6u7pOAl8/ngwMBrxT/ObAPBBBAAAEEEEAAAQRmloDlAALejZ+TGRXwrly5Eo7cVu6ampo+WcGz0EfAu/GTyjMRQAABBBBAAAEEEJgtAsWAd+7cubCCd+rUKVbwJji5Myrg9fb2SiKRCCt4q1atktra2k8u0ZzgPfAQAggggAACCCCAAAIIzFKBYsC7cOFCCHgnT56UefPmcYnmf3C+Z0TAs5U7u5b28uXLMjw8HBL52IBXvP72P3gPfBsBBBBAAAEEEEAAAQRmoYCFu7EBz+7D++CDD8JC0I4dO7gHb5xzPmMC3unTp+XDDz+Uzs5OSaVSn9yDl81mxe7DsxPLhgACCCCAAAIIIIAAAnNDwBZ57Oq+ZDIZ7sGzSzS7urrk6tWrsmTJErGAZ4tCbNcLzIiAZyfp/PnzIeDZSl4ul5OlS5dKJpMJXxPwrj9p/A0BBBBAAAEEEEAAgdkuUOzDYYs/AwMDcunSpfDZQp9lhS1btoSgN9sdbvb9zYiANzQ0FJL4xYsXw3W11iUnnU6HlTw7scWTe7NvjucjgAACCCCAAAIIIIBAeQqMzQC24GOZoaKiIjRiXLx4sSxbtkwWLFhQnm8uxqOeEQHPVuxGRkbko48+Egt5FvCK4xFifO+8NAIIIIAAAggggAACCJSJgN2yVVVVFRoyWjNGu9qvsrKyTI6+dIc5bQFv7Fu0dG6Bzu63GxwcDJ/HPs7XCCCAAAIIIIAAAggggIDdj2erePbHLt20yzXZrheYEQHv+kPibwgggAACCCCAAAIIIIAAAh6BGRfwbDWPDQEEEEAAAQQQQAABBBCYSIAu++PrEPDGd+G7CCCAAAIIIIAAAgggMIMFCHjjn5wZF/DGP0y+iwACCCCAAAIIIIAAAgggMJkAAW8yIR5HAAEEEEAAAQQQQAABBMpEgIBXJieKw0QAAQQQQAABBBBAAAEEJhMg4E0mxOMIIIAAAggggAACCCCAQJkIEPDK5ERxmAgggAACCCCAAAIIIIDAZAIEvMmEeBwBBBBAAAEEEEAAAQQQKBMBAl6ZnCgOEwEEEEAAAQQQQAABBBCYTICAN5kQjyOAAAIIIIAAAggggAACZSJAwCuTE8VhIoAAAggggAACCCCAAAKTCRDwJhPicQQQQAABBBBAAAEEEECgTAQIeGVyojhMBBBAAAEEEEAAAQQQQGAyAQLeZEI8jgACCCCAAAIIIIAAAgiUiQABr0xOFIeJAAIIIIAAAggggAACCEwmQMCbTIjHEUAAAQQQQAABBBBAAIEyESDglcmJ4jARQAABBBBAAAEEEEAAgckECHiTCfE4AggggAACCCCAAAIIIFAmAgS8MjlRHCYCCCCAAAIIIIAAAgggMJkAAW8yIR5HAAEEEEAAAQQQQAABBMpEgIBXJieKw0QAAQQQQAABBBBAAAEEJhMg4E0mxOMIIIAAAggggAACCCCAQJkIEPDK5ERxmAgggAACCCCAAAIIIIDAZAIEvMmEeBwBBBBAAAEEEEAAAQQQKBMBAl6ZnCgOEwEEEEAAAQQQQAABBBCYTICAN5kQjyOAAAIIIIAAAggggAACZSJAwCuTE8VhIoAAAggggAACCCCAAAKTCRDwJhPicQQQQAABBBBAAAEEEECgTAQIeGVyojhMBBBAAAEEEEAAAQQQQGAyAQLeZEI8jgACCCCAAAIIIIAAAgiUiQABr0xOFIeJAAIIIIAAAggggAACCEwmQMCbTIjHEUAAAQQQQAABBBBAAIEyESDglcmJ4jARQAABBBBAAAEEEEAAgckECHiTCfE4AggggAACCCCAAAIIIFAmAgS8MjlRHCYCCCCAAAIIIIAAAgggMJkAAW8yIR5HAAEEEEAAAQQQQAABBMpEgIBXJieKw0QAAQQQQAABBBBAAAEEJhMg4E0mxOMIIIAAAggggAACCCCAQJkI/H9tilxtSYfgawAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![1.png](attachment:1.png)"
   ]
  },
  {
   "attachments": {
    "2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![2.png](attachment:2.png)"
   ]
  },
  {
   "attachments": {
    "3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![3.png](attachment:3.png)"
   ]
  },
  {
   "attachments": {
    "4.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![4.png](attachment:4.png)"
   ]
  },
  {
   "attachments": {
    "5.png": {
     "image/png": 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qaam0woHCgcKBwoHCgf9wYFhxDEUXCgcKBwoHCgcaOVAcQyM3yvvCgcKBwoHCgVQcQ1GCwoHCgcKBwoFeHCiOoRc7yofCgcKBwoHCgeIYig4UDhQOFA4UDvTiQHEMvdhRPhQOFA4UDhQOFMdQdKBwoHCgcKBwoBcHimPoxY7yoXCgcKBwoHCgOIaiA4UDhQOFA4UDvThQHEMvdpQPhQOFA4UDhQPFMRQdKBwoHCgcKBzoxYEh4xj8S6f4t07Dhg1LqBttsMbpxtxLn93nQOiH1/HGG69tPfznn3+y/nZyT/dXM+aMEHztpm2POattfybBF3cMJm+GjGP46aefEppooonSFFNMkSaccMK2jbJ9MaT022+/5XEYsnEmnXTSrozTyZzKtWMOB3799df07bffpt9//z1NN910aeqpp245uT///DP98MMP6eeff05TTTVVvqdbgU3LyYxhFwTwsTs0/vjjp8kmmyzb9xg21f/LdOjZjz/+mAYbjzpyDCFErxEB9aXgfu+rVa8VPfUXQekHY9CHH36YPvroozTjjDOm+eefPxtYtb++xuz0O4ZPEIgx//3332m22WbL1Glf49L1IfNuyGRM4OP333+fvvzyy9FGKjCZZZZZ0kwzzdRyegKaN998M+vvggsumBZYYIEMgC1vHAcuYF+w5OOPP06jRo1KU045ZZpnnnmy0x3blv/XX3+lIGvWOEI0wQQTZKqumbOEQ4FJgozZZ5+963jUkWOwGII0OeR9gHssyDWx6PguXqvXMi4ZQDPngImMET300EPpwQcfTMsvv3zadttt07zzzhvd1vr6yy+/pO+++y69/fbb6f77788Oacstt0ybbrppyRyacJpTaHQMY6NzeOmll7I+fP3119ko55prrgTk29FDAc0111yT9Zfubrfddl3LeJuIaIz9+o8//shYcs8996SbbropzT333GnrrbdOiy222Bg754FOTMYI5GGMYJfNyI5UJVQnULW5VlACjx544IH03nvvJXi0+eabdxWPOnIMnIEFiYCk0xYagBALCrD3ORwEoHAdoOdMvEczzDBDzgAmnnji7C0bAcXvvOWrr76a6bHHHkuPPvpoWnfdddO+++6bFllkkRiy1lcZA0E8//zz6bLLLksvvPBC2n333dNOO+2UsxQRDUdWWsoGzbApL50Q9UwzzTRp8sknz0rbKM+hyi/ro/MCkyuuuCJ98803OThZeuml06KLLpqj/2Zro/9sRrZw+umnpxtuuCEdeeSRmej82MCfZmtv93u8RRdffHE69dRTs10fffTRaY011mgaMLbb95h23RdffJGzxq+++ipjjPmpgAT1lX0GHr344osZj5555pm02267pV133TVnV0qT3cCjjhwDRyCalvKJoN59990MCIAhGo8vFQQSBA7g4/0nn3ySMIeDYDCrr756WnPNNdPMM8/cq67oHtdwPqIIBqVPtMQSS6SllloqO5UYs85X43JI77//fo4QX3755ewQAN7KK6+cVllllTTJJJPUOeSQ7evzzz9PiPOmuNNPP30aMWJENm4BAhrqjc6K+J977rn0yCOP5DUJTlZYYYWsg9NOO23TJbILjuSNN95I1113XRLc7LPPPpk4htJSDhTZ3AUXXJBOOumkXCY+5JBDMjaIpFUUxpbGTp566qkcKCiNCwyWXHLJtPjii6f55psvr726VryBo/BIxiBQjX0qWASTZB11t44cA6cACIDAXXfdlZ588sn06aef5lJP1MhWXHHFPFkC5UhETd6LKF955ZX01ltv5e/9JhLfa6+9MkNs5IWxYAajUnc855xzMh122GHpiCOOyHXdwYi0rFWkR5gAgafec88908iRI7u2v1G3cLvdH/4APboQZYCDDz44rb/++jk9HhscKF1/+umncwpPH+03bbHFFomet2qcwjvvvJN59Oyzz+Y+tt9++4TGBqfZav2d/H7hhRdmx6B+zs5WXXXVxOnK0LvdYBSCKxF9dwNjHn/88XTbbbelJ554Igk4tQ022CCtvfba2UEIeJs1VQz29tprr2U80hcswit8qnu+HTkGkTSAZyAAUxSl7u/VopASz8ILL5ydgaxA9G9zRUrOqXAksg202WabpW222SYNHz48bzaFY5BqcTr6jT2GTTbZJKH+IrRmTB3I91I49WSGrYQA+Pbee+8c7SmBmat1jctNxog/t99+e46IZYuivfXWWy87hpDnUOQRvUUM+dprr81ZL91GSkhzzjlny2UJgu68885szLIpm9XLLbdcpnFdd6rMu++++9LNN9+ccYJzsEGvXCeS7mZT2mbnMIcTcsqM3nLc4STqGv+DDz7IQYKy5I033pg3lHfccce8XzDHHHP0q1Ow1xztMcAjOin7hEkqLnXjUUeOIYzFJHkwGcB5552XbrnlllyD33nnnfPiZp111v9KAd3LK9tdVxrCGKWkddZZJ2FKY8bAoEQQIlGRw2qrrZYWWmihTIMVhcZapXwnnnhiOuuss9L++++fSdQonRvXoz7pLbr11lvT1VdfnWV/6KGH5oyBnIZyGSCiyPPPPz+dfPLJ2SFwelJ362pH9iLDM844IwdR9qhkGvQc1R3h1QVe/69+AJ7s8/XXX88kAMQv5ZJuNsErvBGNc9wcPttWxlIFqbOphBjPRvspp5ySS5QybM7Bvhxq1gKPlDbh0WmnnZb222+/jEcx53Z0sln/1e87cgxxM6fA+9mg5b14QBvCgFM0zes2Y6oSkigK6DthRPA8Hm/tHh5cunTmmWemO+64IzMN42zM2KRp1m/Mre5XXtqJEhmD6AWpC4pm7DuMy02pBAkQlEoY81prrZVBlJwGW1Z1yYJTiEyV07M/YG+Ljov4RZL9AXs4FSVIBgzsDjzwwLTLLruMLrH1d39d6xhK/dAjPHfABM85X2USZclu8kolQ90eAVinzDhueFR3YBOHNO69994caMIW5XF6AdTbGQ+f4NH1118/Go/oJjwy77ragByDchCnICK6++67c73s8MMPz4uMh8+apWFKNDbhEIBdZpllMqBEJsCjqr8xKA7E3gKK9K6bStIXUzkydWalLxENkuWoE7dTTuirz7HlOxEQIlN8Uh6JaIucBltWdfHVmpRKEV1EyqQ77LBDLiO1WldEhg8//HA+aSMiddJG2s8umtlGXfMfiv2we8QpyLI4V5iy1VZbZT1qxfOBrtnGrno9crhFGVwQKsipuxSqZPXZZ5/lTeRLLrkkl+WPPfbYvNfarr1wLrAIJgUeCcbgkVJuXW1AjsGZWhG/TMEk7R3wfI7iAXiKzxMrOcWzDqJHHlFKxKnw0MpDduQjW3A95skYRGn2GBiTSK1qTPoNZSJc713TqEDmYsc+IlfXADBAFtfqhxK6LpxaI3OtgzClcMpfyDMNsqNu1z8b59HN92QSIO+ctYwQT/BSJMNAwjH7jPAUbxAeIt9Jwf3eKIfGuQdokhklN4579M+xILxGvpd90g9ptt+Mp49oZIzI1dz9Zmy65h59hKzjnlavxpCt0nF96YeeKn16fqFVc5rO6Tv6C+h8HtmzUegZhmjmi4LPVX75HGvDJ0ROeKDFezprjT5r9gBR9IsP+vEa/M0Xdvgn+g8+k3fIOX4zRpDfyJhNk4lrzMl9wVN89Z58og98V7Zlo3vssUfacMMNs2124+QNFpifLAXJFuwhKSlxDPhWZxvVc5oTthnLOvHjoIMOynoRtkSeiKyqOmEuMAweCc4Dj2xgwyPP1dTVBuQY4oy/Wpl0iFBF9Tw8A7cgp3qkhpSDklDe8MLh6QjCJhOhUw5OgdMRYblG30AY+b2x6ZeS2rMwllfAFIbj2tjwM7Y5xca5fsNI9EMolIHHpayNjfAoj/7PPvvsrLSiGI6QIPoSXuP9Q+E9+QX42ExGeIKf+CFFVTajsAHSZMaBoOCh75QSXdcMjBk8OUiJBRQUXYnQGHQHsDjQ4Gie7/EYGNvXIUfgT+4a3pOxezkSm+HWYd4yF5uYUX5s1ItWMjHHc889N5NjqRtttNHoDed2/gWGeYjo1M3ZgHk74mq/LJr5OsRh/eZW1W/fWRt+RmBCTvhj3QAWcFmjUiw99bs9MQSM8YG89EN+wd+YQyev+jaGOVsX/SBvYwTY4zkyptfAADJxjTm5z1rN216k+1E0webll1+e73VaB/9d19cZ/7jnf3kdTMcgGLY+JxxloeRhX1a5jB3RbVUIFDhaxZfAIzzlQGGSQznwiFOrXj9Q3gzIMSgh2QNQ6qHAlI7HOuCAA7IToETABSMoA8Bl2BwDZaJcCBj7PpjAo+rbRhCwYQSec5AqVReMieE59cUAjdvYZCRKVQyDMoriHD3ktc07iGJKIZUL+qrT6ZdCn3DCCen444/PTx4eddRRWRD6qBp14xyq78PAvFbnW712IJ/xyXzamRMlA0yULDaSvUf6CaMHlN6TFWKkwAhAAEERMbAHwk7sOExAdo0GzxG4jpw4ZvI1RsjVa8gjUmW6YizBA9lwUvSKvDX3+N245gyIyEkztvsBFMcCiFzfDl/0ddxxx2V5CwJEdXSJw7OuVo2OiQgFOsYWAOGLf+USzb4M52HOZEAWmnkDVg4oQCKuxbPgKaClq47NqjH7jJyTZ0Nkhp9AlW6bB15UA5+YT6vX6N9cABtZ6h9P/WZecbLGmPQkMAAf4IDrXE8uQAxPvUe+18jeCSUyNl/92NdRdu5GG0zHIOBx8AYuWh8Ze4rZw3wCT4QXZISH+Ennqg1u4GXg0cYbb5zgER2DpXW0jh2DSUmFnNSQMTBMwM/zeRqP4Zk04V511VXZUVj8SiutlCMnhiXiY3wiKUygYBqlwDi1XWBOKaTvKBQnFs0puC5OMQAoxkDQ0URp8e8zKC5FtVkeJQJzNQYhiOZG9DycxVlVmzUzXk4B8dBO33hsX9QmI2q34Y2+GIp+AxDavb/VdXiJx+0AGBAFTHhprwg5DMAZc/b6krkJADzkFQ/jABqGLfoR2TuIwOkCPkeQGTJwo+TRgCWSDXIoeG4cxu97siQ7c6JTSJ+CBSU7PGck5uL6aMBRwOEa2YV10y3ZiLnTCf8+gPHQAVTVpegrXuknOTM8tVvZsL4ZXehqXNvXK6fggS1roftO1Yn8zVMjdyDBfuituZqzZo34yBki47ouHA19QfpQE2dzeEOf6NWVV16ZI26O2DqBhYiUTDjQvgKfPHCLP9aC7A2yH7YkyjVvc2HHNkAFYuyCftzf8y9lbJRao3vxHg84aQdPzAlfUPCVLuobFjiyLgC0OetYezfaYDoGx3GdtlRxwTvrVg2Bb2QKGwStbMHpN0HJ8J6j/FV9DdwIPCJfJ+bwfKDyrfK2I8dg8hTQppqnFO2ui2oosnSbglqcyMb5b4+5A3hpDgZQjFCA6kR8FulgnDRLTRcQSSUpUZU5PK6ISwSDkbIM36FgMsY668s4AAYnItNRmwN8AShAhfGGY6iOZW4UG1AgNT3ZkTky5E68NEVktBQeL1GdzVyAKr63aoxa2QFYO24aZ6MdoYsIm3ECOYZK8Rg9J484EkAtAqLQ+GxPyHMM+Av0o+nbvhGgF0GSrWCCcTAYukR2aqf0CAEIQETHAKxAQh+CDoYlwjJPxLgcCjCm3+iFIMP6OHHywhvUl3xjnowOX8IxOGZq45iO093+7g29s1aBE32378ZZNuq+MawBwNqHMFeZFF2gk3grE8BrZJ/CWgAs3tFFwQj9Y1ucA6eAHNpwFBJv8ILt4F2c/mtHL4IXja9s2riCQvNmo7JMzhu/zCf0wliIrNibQADf6YQoWAZlXssuu2wOxGSfgQvsw9xtBiuTsO1jjjkmZ2143x//G+fb7nvrsiZ6Zl6CHvMhh7r2GMhb47TJh63AI1kcnSXr0A+6w5nCS1kAO8GbaqYLZwOPlNxUbGRgbKGO1pFjIDTRFMfg5ADljhqmdBb5nWECG8pDsS2QcRBqdYGNiyAgdTMgzwsqIRGWyKiqEJQnyhJKEyJakRWKzEEWAGAAPwAX2QJAESfwklHwsIjCElSzVJvRMXbkPgBobYyP0rfbRIfAwBqVX/CrzsbwpaYcaqsmWyAjDpZT5gREZ55GZxx4jq9KB/Z+8FwjFwQYGL1oFgjIvhwUwNvYNA6j8LyLZwLwmrIrDYkw8Zvxi6I4GjrliXgbj4ASv/QFmOmO+eKfTAV4MAoEJBm1LIWx6894HJqjogIEY9GDqi418sl8ySSMjvMCTHTI+P3dCyARR2ePgiOz78bI3deo+3g3qsdJAiQ6K2hRnmFPwGJET/YamZk1OA5svWxEUCH6x7+RPZvazvsDCo6JUzB3v9N/AIzXbMj6zWkgTf+I8zbv2As0bzKxZ8KBD++JcAVOiF4I9PBUdmA97CyydDpmTij4SuYckLX630mCDvgB+IAnqrPRFWPRf1gDXAVWnFgndt3fnEI2qhXkw+7wg/xgpsyQbiB2QnfogCzANWRWrUqQdeARe6ff+iGDOlpHjoFCilg4BgJnnJjHs1JE0Z9oGFADZ0IWyYm4AHSrJr3mUYGNKIjC89ztpEfmddFFF+UsxfiIMUttKSNwIRCGyKGIigEGb9wqEjTviMbMj9E6Yy2C1m8nkYWoz5PCFDHq7q340snvFAO4K5+0anjEKNSl8RzhubSdcVBUSs1Qpfjm7BpATHHDmC+99NIMRkDXQzccA4fpM1BAInAP5lBifAdWQNAYHKR+RYjAXOlGJKysJ7JyjUYGnBMeCiBkpBwRsm7G7HrNmozpWQJzAqDmRF7RX76w8ofBmY+5IrzkGDimWG/lltEf8VMEzbmJ/Ixlf0KQ06zRecEKOQBcwKfshYcCDw5PFsVByxxszBoH0AMX/6nVtRo+Awvr9hsH71WwQBZ1No5A5EsnOABBBTuCB3QPWZPsnP056stRcQrtZC36sxZBHH1hbwAStZKDdZIjgkEIb/q6j24LfIyjJA5cYRldCV1q5Jt+NEDtd3321W/jPcZA4RjgBb1iR/hhPE3fnDr5qWDQW/hlLlWHbm2wCLlWIEVHraGO9m8AAAD//2NlAOoAABs1SURBVO3cVawjx9IH8N4wM+OGmZk3zMzMzCRFykMeoryEmZmZQeENMzNvmJnxfv71/SoaWYY5Z+0DV9NSrY/tme7qwn9Vj3fIf2ojlRyffPJJevvtt9MjjzySbrzxxvTuu++mpZdeOi266KJp3HHHTeONN176+eef008//ZTee++99Prrr6dZZ5017bTTTmnYsGFtV3nwwQfTKaecku/ddttt0/rrr58mmmiiTO1u/uWXX9Kzzz6bnnvuufTAAw9kGmOMMdIUU0yRxh9//DTmmGOmCSecME055ZRpxhlnzHwvtdRSaciQIWmUUUZpN336888/M2/4s5ftt98+zTXXXHnOscYaq+39ccE333yT3nrrrfThhx+mX3/9NVN814lX8pp//vnTnHPO2Xa6H374Ib322mvp5ZdfTmT/0EMPpTnmmCPNPffcaaqppkqTTDJJpkknnTTZI73+/fffaeaZZ85Edsall16ajjvuuKz/XXfdNa2yyippggkmyO+ZFzr22GPT8ccfn5Zddtm0++67pwUXXDDrZbTRRss2M2LEiHTGGWekc889N+2zzz6Zpp566rzuqKOOmtehgx9//DHb3ZlnnpkuvvjitPfee2fC79hjj53i2qeeeiodc8wx6dFHH0142mGHHTJPbKCZvv/555/022+/pW+//Tadeuqpmbbeeut08MEHZzuO/WZmGvzz9NNPp8ceeyx98cUXeZ4ZZpghrbjiimneeedtcPV/P3rjjTfSk08+mflktx999FGaffbZ8z3rrbde9gF78N3jjz+erEEGE088cdYz39pggw2yfZLPaaedlk4++eS0zDLLpL322istvPDCaZxxxkl8oZOD7Xz55Zfp1VdfTffee2/m//PPP09fffVVtiH2Rxd//fVXtsUVVlgh+wtfxE+7Yd6rr746vfDCC2nxxRfPMWammWZKQ4cOTWym1aBHfobYlThFr+4L3dMl/tgmf0TsXLwgW/brevZuPsO13tsTH2PDcV3M24iv7777LqHrr78+27d1+MDKK6+c7xc7Y50TTjgh+xI/YbcLLbRQvoZtFweexCJE19ttt122UbGtE2NIbbOlE8Obb76ZDZOhDh8+PP3xxx9pv/32y05HyIQTguMgt956a04YG220UVZuO4YjMVCmxMDgOTJqN4pKO/300xP67LPPssOEYuebb760xx57pE022SQHkXpht1qjU4kh+PQa1Grdnn4XBh8BstX9EhMZvfPOO+myyy5Ll19+eeaJHqeddtrEETk4uUkYnNLnvo/57cG9ZRMDI95tt92yU9Er55PU33///ayzc845Jwc0AX+aaabJQSTWapQYBG3E2SJw40likIx6khg4vIAnsJ999tmZttlmm9KJ4ZprrslJUnCREAQPe5Bgmw1JyHqSskT38MMPZ/lONtlk6cADD0wHHXRQ1st5552Xg5fr+Z29SuAHHHBAkrwCZOBbgpWcDznkkCznkEszHnrzedgugCMW4B8we/755/+1j+WWWy6tvvrqOagDUUCZUYYf85KFBEH/gAkgh9olOXqUDBC+7r///pxM3RdJhQ17b27yR8CjdQBcAd81ZG0+Qxzx/vfff0+bb755ljt/cE/YaL6w7h+A+uOPP0633XZb1iXAwGbJJmRhDfZ90kknpRNPPDEDqEGTGDjb7bffnp544olcDch0DHPHHXfMG7RJG0SCzUsvvZSFD7UIKu1GfWLYcMMNs4LKJIaYm8HecMMNmSBhfEBYxjzzzJODksQAuaCyo5OJIZzKazcGg0bthj0F8iN79MEHH2TUijcGL+BGkpAgODgkjwzXtUsMrhOkJQ9zLb/88mnJJZfMAYMDqloElDvuuCPdfffdGWzsv//+OZBw3thLo8Qg+HEywTecDE+9SQxAjaoI6lWRoJ5UDHfeeWe65ZZbstzISTJVMatmmg0B3ZoCmIB+zz335EvJxdoSE4B13XXXZdAl6QhMEqnqEFJca621MiKVNMhR1ayS32KLLfL6zdbuxOfWBBita++CXwxIf1ituhbMdRWmm266+Krtq46DSuSVV15Jk08+ebaFBRZYIKHRRx+95f0CuOpFsNe1kFxUDIJ32JLXSDDsz3USglhDxlGtsrlIDBaN+MZ+oXrVBX5i3kaMqQqtQcf2NMsss+Qqd6WVVsqX4xc4QmxA1SepDprEwGkvvPDCXDV8//33WdH77rtvbquEQAhSVqUIG5WhCbtM+diJxIAP5TYKtPDpp59m9qBfjqRFxdhQ2WFfUbox9t62kgStKBsZhPedHAyUzFshmFjP2gxdoIHQtDEgbK1CCUKrgC45kOC2xBJLZFpkkUUSEojN0SoxxFraOhIDu5BsyJAuzCuYcBjlPJQHJQv4UHPR4ZolBtd3omKgD4FasNNK4qA9SQzREmL3qlHIEChSeTUb1kRaRVptgJfBX1QdKFpJwJWkisfhNZQO8EgKghRUys6jpataEZADpTdbf2Q/pxP7FchV6Sq+GICYVoggJwhKkmWHZHPzzTfnNqdWHIoWZqD+ZnOxSXaGxCnE5wI4uI9dCejsn+zFHjYkIdAXXiXn8NVYy9xI7JBA+IZ5i3PHtfEKpADTgLJEBzBoAaqeDWtEu0llSIb0PCgSA2FALpzF5qBtgVbLB7KPERnV9YYAFRTXNHuNxADlK9XWWWedfxXQ7J743HqMFFE0EmggK8hBvxHCYqQUAsFw2uCtlWKtYd4o8zjrDrWeNcM3J2RddjBSrTJO7DyGkzPSMoG8zBqhF07UbnAcshEIGTiCnOhXi+nrr7/O36kq6JWjcBy9b0Rm5F4mMbAbgcP15hC46AFoePHFFzPqxI9ERO/mr6/o6KD+jEEC6WRisIb90jWSGKwB5bWzEUkt+tVeBQ+Bm521G9owWknAF7Rrn84aBBH6oQtoWYtIRXPVVVdlmani6Jp9u0bylhDcK8DVy7DIR/ioz9rtrXhf8W/rQsPOAvgcMMamkcA5/fTT5zayfjpeVXateDI3vlT7l1xySbaNNddcM/fjA8wVwUKRl+Lfgi2iTyT5Fof98jn2NryWZLWbtP3ITJ8e4dN9RTnFHHxe66mM36okVcN8jY1LcmKmpGkAZmE3N910U0KrrbZabqk6xwAy+GZx4EvLiY2qXMQjyQz/nRilzhhCMA5PMCOwSQpQI6U5WIrh2iCfUUBQXNPsNRKD0ktgIBwKgrzaGS5BKckFW71yBBFQtmCsnJSVoVBOrsxG0VdsNz/jgngR9LPzzjtnBSsle3JWIXjcdddd+SAxDusiKDeTS08+54iS6tprr932Nk4tIHmgQOsDMVJO7ZXTMGaJGoKDeNzj4MxZDQel6zKJQdBDggWHECA4LrlDgPTgM6hNFSF51CNDOuhmYrAXdsSG6NlB4FZbbVU6MUgoSDvoyiuvzK0Jslp11VXb6oJdCKyqNZWH5Ky1gTg7EvQBG9dKtPwlqnGyoS9VmCDBZ1QPrdousV86CGrLaN0FgISHD9g0fbEJNqPaNHwGQAFTWkvORdodkOILyrZHbTFnUgAoG0F4bTfMEfuLv4v3xH7ZuWTmPEOCFdfYnjgRgM/99UNCsNcyvIhFkhxZ0Av7VxFI3oa4RfcSlH0jicMZEwAVaxV5YKcRj9iECmS22WbrUVVWnK/+71KJgcERoCeRPPEAmUgGMtViiy2Wma+fuDfvPXXhqRTGpkeqRKYoAauRAkLh0KxyVoATxDgXilJeMNFaEtwkCYHYwbaWEmPgdIyglaI5nnaIXrmExVhlfig6DKjMniXV++67Lzs/5yFL/HDgRnssM2fxGj18h/0OttoNDswhIHZGigTlKJHd7xpyIz9IRsDS0z/00EP/5beYGHbZZZeMagUsQS0G57jiiity+5FufQcJM3DJABqMhEAWjeTBDumPjh2yCkjtKgZ2IIkLmJI4oudWQ2KIM5Ett9wyVyTQWCv7MJ9Exxb5CSRnDx7OkKQFBdRsqAKgxpCz1loMVQKbU+VC3fRh/6oLiQi/wAmSsD2NRJYR/GIer3wm0LQDUS0oCUQgZMuN0Gnxfvoyh3XZhiBGD9pdEhew6HOkWtdiMrdqR2IQxPwdQd7cRbsXZ/gyOVx00UUJSLQfya54XZGnkfkbgsc7omNJTPuN/eKxE4Pd8xFxAii1f7FtaK01yF7EAOeigLf9I4Db+ZJg32jQQ8SjYbW2rHikuhTzOjFKJQbOyIkdLumB2czGG2+cyztBFUrtxBCgCIcx2KCgXexnF9eIpCBgcwzoW39WycYQEXQi4zJiKAz6UKJyCErBu8pk3XXXzcGJITQLGoKSYIG0B5ytmJ+yBYCyQ5LijJJCIHNrNlu37LxxHSe3rzIHfYKRZBxPkuBBoufAUe6rsshLZcHAOWwxMdADo9cft7YgrG0gAMcceIOYBBBJh07jiSMyFzAlR4EpyL2QUjEYcGIViwDKDqHy+sSAH0PAoiuJz3kQ5B+tiHblv8AUZyKbbbZZchDOQem51b3W5rASqPMo78lDULefVudsbIGP8QF7u/baa/M+/KOdJbFxfPJTMWjrqhi0cZwPRVKVyAQU9l+UXUyGP3K0Hl9B0e4xP2ChPVo/Qq6R/OIw1dOHbIOd4FMVzsahYImLLHwn+Utq2oSSA/58xuaKfAqS7I2fSuremxOQ68YoJgZBm09LDOy3U4lBNaWdJODzEXImAzGInJzt8SH+wfciwaosyKjRoIeIRypSj3gD0apug54lF2saKrYjjjgi/13mn5aJIRAtZxRElFySAydW6ugRMyJCxBAqKrkMA8VrIEEllYrBxosHcPXzcl58MTpEuJzJ0xsEssYaa+SSDaqX2AQKRmx+hmw+AYkjMTxKogSKi2AVvBGyxBClm2SizFPJmKNTQT3W66tXTkcuEqbggobV0IdWGVnYG8N13kAnw2ulrqpM2YoCMHD+C2sPJUCbqhVlsnaBkpxskGB3wQUX5EqBAUv8gpnKgfECG+wKhU2FXQnG5B96FpSgcs4GSSKJBsozj8D0zDPPpPPPPz+/CkaSOZ4Qu4oKsZGs2Vb0b4EGVZBqCq/4azfiIQ2ARZLk7PZLHs2GwMvmyVkLRZBgdz5TBXh81xySE2Bhfw40VRaSiaALBElCqkWyaDTMSad48wSMJ68ERMEFioVmJYf6IZEgIAsBCB4aACq8pysJFJ/AGoL4VU4qHP4GsOBN3JCMED5R+JAYYG8qDfZFT+xRd6Ibo5gYAFE+zS7tx9qdGPxLzBGjVFJslQyAN3GV32k/shuA21OT7BQPjZJT2EXEI7FOPAqgimc2IvnEAKD23HPPeNv2tWViUN5QfhiFA1PloYABQdmgvzkMhSPv64N4Wy7+/wKo31kAghgIjZCgtvo5oUZB3nUMyLUMENUnBoZLMRKDNo6/8cnJoy/HIRAn4RjhWByWkQsWjFzAkBShZgGOQdfzVna//X1dJAZOzsEdIEIzDAwyFaDJjqwFW3IRnAQhBLEOryULgUlAI1POD+kIUFpGDBvFUz4CPABhbjKmA4mBjCUA8uSQSKASWCVrDiUpBa8jai05DkUHyAGvagefsRe2wWY5Gb5UKvGUkKDUDMELFlqaEgv0phVELgI7W2832C+0y1fsl4NDbPbTbLAzpBpQXfE9fCCPg0tO/I186ITjs2MtOoAtKl98kkezoCao8Gf2HA8E4I/tC7705v7iwJdAjVTzdM0m8CpZAwRkDCyoziJwAQLOaVzLNuhcMCR7r+4JhBwBkNz8HoS+6Uqy46P0143RF4mBDSJ6Uj1I9vZF7pIFgKuCUtHyHfsmKzqsr1AjHvGZiEfsUzzit+RsWAtgiqETQ8dlR8vEcNRRR2WUHJNBKxQYzDIADDIMSEE54/veBkpOzSA4NETIwfXOUKwZvGiBQCSSg/sgoBh6skjwggB8J3hBx643Lz4R/pEAIAgJHu6Jx/wo0fwcQLBAEoPfb3DCwTwYJDQDoXFIxFgZqcQAOUm6zhXoWIAqlvTkz/nJhiwZLVlCXq51huNzBu5sSnvFmpIB8h1bcY/EEEMw9X7TTTfNule9eHyWDiErPMf9oT+oEm8AgvaIAGaEjXp1LjasVhFxELYRyT/WjVdzRvUpiOk7swlENu2GPQrc+FTVkA95QIPthjZKPL4L2bM9clDVChwGmyQjldNZZ52VZWJ+pGUleQUCr1+PjgRDe3ROwZ7Jhr4hTA8uCEzF4R5JKMCVfjw/NSRtgEEy4Rd0HkPVAxBIIEVd402SAzJUmMg81vGIqhaJxKVSMp/9NEvisVZvX8kibL/YSgJemiXXnq6likW6GSo0FbgOBZmMqAEcPNCvdidbj2q90Tp0zy6Ag4hHEkN0MOIeFfyRRx6Z35ItW6yPoXFto9eWiUEQJbQYJkZhdBRpwxKGwIp839vEwFgFcU+/CDiynoMnFP3dmBtaFOAJ1n0cJQYFI8GdM/lONcLpXC97y6x4JWh7gGDcAxW6J5AhfiBpPEFKSALhQILGYB6QowCGBF4yZaycMIjsBFuBGJoRHGOQv2DBsMmTPZCn5OJa8iRrjiCga3uQrUDL+MNWyN99hjkgbskIqhLMOZGzB+sIMvgN/cW90QagLwgVzzGfuc1rHkTHAmEzx5eUnEkhNsN2JBNtpTJgQFAT0LV6oH9zKOP14Nlv2HBmsO4fDq/qtc+wa/KSyDi4IZAjwVkFpf0imJM5/dFVszVCR+QGKLmfPFWP9ibIaL8Wh3voEbERtiI4GdaiGwkTYi1WG/yGDJydFHWNNwkMKnYP8hmetKohYXbn4F6ysQb/78ZgUx5VHV6rfLUL2Rz7YJ/AUCdG6Itdsym2AZSQCeDFPq2LrMn3IsbWr8+2AKSo0sUjYEdXZWjNtmMcfvjh+QzOe9Wkyq0no2ViwHA4bLtJbRKNzGCASMUQP/ZxyMtAOHHR4MPA457iuoTK0OI1rvFKSV7rR/F6fyODw6heoDjGj5S/yu4y6LF+nYH0PuRBJka9nHxODqFb8iwabBh8vTxDlhKP9p3qTvARWPRWIXtthEbDnM4syFyQ46SCojaMe3xfv555grfYU7NrQrfFfdTzYY0IhFCeQ3dtKr+X8BuBdsPayMG3PjDnda+D6OCz2RxF/mMPcU/YZNzre/6JXzpyXf01cW2jVwlQMIYm8SpQD6sFGQGyftTzFby5rijTolzxhb/itTFv/T2SIHKorspwj/aIKqsne4r5y76yUe0d/X2gR4JVAQEwnUoMwUvII159HroN/fms1X4lWb6hshJ/8OlpNfEI2ImhBU+vxtFHH12qWo17vbZMDMUL++pvBqGXqc+oYvEDDwTNoGYor9P84QNBu1omnoCBXhHHgVADwXV67b6czx5jMMjYt1cG7LMw3riu7Cv0Likg/XYk8Ki2yK/RsKY2Dv3TtcQArTuv6FafuZ4Pe4fcITuIX7sGigZS8IGvMggWGnforq0F+Tok7MRDGvX89va9qgqpAugGStZK1eLp66EKUflAwuSmCmEnHmTo5pAcIXltObamCyBBqmjK6Lg3vIVvubdsMg+/9ECQNpE2XbRGIx5Fl4MPOZeJIenbW0/GgEsMmLd5wUTGkyERp0KU1heDIqAdh0P64/p52gFIZoZmR7ZC6ot99HYN+4/RCsHENY1eJQals5YF1CxAasN59NIz440Go/arXgSxafEBBn2ZGPBF98h/T6B6xa//ogAf2gBlQIGAA93Zt6pHcok2ZxFZN5JDX3yGLwSp07HWK/BVRJ59wYc12AkgGOdVkpMqsdia6gYv7C2e0uLTbE6bEvXW7svwGf5Vdo2IRypvZ3X+FwEtdvHIWWAxHrkmnkgCprRxezoGZGKgKAhGr9U5g6df4umkeiH0dMNlr5eMBDNIQi/dKwSjlwdNlFVo2fX+F68TcOp/dDi01gd1FiVAxlkO54TcoFeggLwRo9Y312OHivojYGkzCO5aLpKUwAlVN0tsRT0CFZKi4Kv6kGgckiOBpz+HQOMJPf11ctXn16rjX+y7r4ffIPlvtlVjWiOSKFvBT7dHgICojPn2QPNvCTPiUZx5ikViEqBS5Lf4RBIwc9hhh/VYhAMyMXDCOBhVikPrTuz9cEewUDXIkN0ckpLessoFakHR1ur22t3cV1/OLeALiBK9x+X06x22cUBBSIAVbOlb60YbwSG/Eh5pO/kNgiSiTO4PuUNf2hzxmwEtjh1qjwGqfIrO2Eiu9u3gmkM7N4GKOSpyXtZfQ1JAWqTI+Ynf80h6ZNzXSQsvWiMen9XK8cgr8EDnne7zN5J5yCP0Ga+Nru2vzwAs7WxtoYhHWqxiUr2MtJriiSR/R/XQE94HZGKIDciSUIS2AkQDOUIS/tar7cbwlAKH9lSMQCZQaWH5AZBSG4L5X24hdVqmkgPHiwM+gVY1plrQXtH7lBjIXFUGXfsOadt4/p/M+8tZVTKekIHC2CHA4lFoDyDgq1UVY1+qJolB0HNg6BDaoWp/JLnQLX0YfiuhGlKVOQOB0Pt6kBHE7izHobPK0GPvdB8Ivq95GkjrsR/gim+IR9rr4pHfjIlHqD6R67aoVn1Ot70BIQM6MXBIj+05lCIQ7QVIwnPPDi+7ESw8AhhrWlfAknEZqsyMurHuQDLGTvISQcgjm8i5g+SrItCyECAlD8HBdx7HCzkLuhJHt55hL7NPvCEPRKhoRtQed9by0vJQNbT6Ra6925fHZ/WFVQ2esPMLYfvuTzvCmwASh86SQhxelpFLJ67BA1uQbCUpyUFi8GM+7STy6U8ZdWKPIzuH1moxHqms2Zyn+8JPJNBOjwGdGGyW8SCHLRCFk3g/poM2u2E0HEUC8qQGtEj4Sm1tj2qMvAToUqCV9KEhMjYYN1kLuAMxIHBITqq/qx+uRaTl4eyr3QAwPMAAnUPDnm7q78TQjue++J4tAF7IjxIhYk/TeDih/kd2fcHPQFxDHBKPxCW+wm5UqxJnN8eATwyxec+Va0M4aIEiyzwVEvf25FWwYqgClwCmbQRJ9TWa6gnPg+3aSPbk629DMhjIrQPIn2Oqavyy2d8qmjK/ZYGK2a7fxPjFL/vtBsobbHaAX3JF2ovkAxiQa+Vv/9WmeMTmvPIXdqPS7rZ8Bk1iGIxGX/FcSaCSQCWBwSiBKjEMRq1VPFcSqCRQSaCLEqgSQxeFW01dSaCSQCWBwSiBKjEMRq1VPFcSqCRQSaCLEqgSQxeFW01dSaCSQCWBwSiBKjEMRq1VPFcSqCRQSaCLEqgSQxeFW01dSaCSQCWBwSiBKjEMRq1VPFcSqCRQSaCLEqgSQxeFW01dSaCSQCWBwSiB/wPERxRaHRzl2gAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![5.png](attachment:5.png)"
   ]
  },
  {
   "attachments": {
    "6.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![6.png](attachment:6.png)"
   ]
  },
  {
   "attachments": {
    "7.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![7.png](attachment:7.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V/0SpzVzO3v5IkRZtkuN0p6rqeXy+FzDNz7GsXdC0tUQi52EYa9Z7XZh4/BTa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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X, y = make_blobs(n_samples=50, centers=2,\n",
    "                  random_state=0, cluster_std=0.60)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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be8o3Xtps7N8/1fffYGVULJCUau6g8vjjj/PVV18xe/ZsrrjiilDXVWsej4fT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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
    "\n",
    "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
    "    plt.plot(xfit, m * xfit + b, '-k')\n",
    "\n",
    "plt.xlim(-1, 3.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8RFEAx/nh8/khSaEZZqoTtubmZmzZsgkbN25AU1MTKIrC7NlzUFZWjunTp/eQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "\n",
    "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
    "    yfit = m * xfit + b\n",
    "    plt.plot(xfit, yfit, '-k')\n",
    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none',\n",
    "                     color='#AAAAAA', alpha=0.4)\n",
    "\n",
    "plt.xlim(-1, 3.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "model = SVC(kernel='linear', C=1E10)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def plot_svc_decision_function(model, ax=None, plot_support=True):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    \n",
    "    x = np.linspace(xlim[0], xlim[1], 30)\n",
    "    y = np.linspace(ylim[0], ylim[1], 30)\n",
    "    Y, X = np.meshgrid(y, x)\n",
    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
    "    P = model.decision_function(xy).reshape(X.shape)\n",
    "    \n",
    "    ax.contour(X, Y, P, colors='k',\n",
    "               levels=[-1, 0, 1], alpha=0.5,\n",
    "               linestyles=['--', '-', '--'])\n",
    "    \n",
    "    if plot_support:\n",
    "        ax.scatter(model.support_vectors_[:, 0],\n",
    "                   model.support_vectors_[:, 1],\n",
    "                   s=300, linewidth=1, facecolors='none');\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3UUiutR08eCgjRowCorW2iUvmaJqGw+HMWGsb640Qm1qw1i95VVJS2qxa2+a2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\nLYQQJiZJWgghTEyStBBCmNj/Ayv7N+nDQ32sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plot_svc_decision_function(model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.44359863,  3.11530945],\n",
       "       [ 2.33812285,  3.43116792],\n",
       "       [ 2.06156753,  1.96918596]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkZOq+1wZmf72J/TbvvVqFy3ZIIoBO/EZdj1ECJGqhzt37nD27BlOnTpJZmYmNWrUzE9G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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_circles\n",
    "X, y = make_circles(100, factor=.1, noise=.1)\n",
    "\n",
    "clf = SVC(kernel='linear').fit(X, y)\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plot_svc_decision_function(clf, plot_support=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = np.exp(-(X ** 2).sum(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = SVC(kernel='rbf', C=1E6)\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WsHHjBgouZlLq06cv48dPDKrteWvGwWarZfnypZw+fYr09K7cfvvcdu/Y1pJx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\n7vTCmuy2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plot_svc_decision_function(clf)\n",
    "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
    "            s=300, lw=1, facecolors='none');"
   ]
  },
  {
   "attachments": {
    "8.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![8.png](attachment:8.png)"
   ]
  },
  {
   "attachments": {
    "9.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![9.png](attachment:9.png)"
   ]
  },
  {
   "attachments": {
    "10.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![10.png](attachment:10.png)"
   ]
  },
  {
   "attachments": {
    "11.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![11.png](attachment:11.png)"
   ]
  },
  {
   "attachments": {
    "12.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![12.png](attachment:12.png)"
   ]
  },
  {
   "attachments": {
    "13.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![13.png](attachment:13.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n",
      "Test  1: 50, 2: 50, 3: 50\n",
      "Train 1: 50, 2: 50, 3: 50\n"
     ]
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X_iris = iris.data\n",
    "y_iris = iris.target\n",
    "\n",
    "print (X_iris.shape)\n",
    "print (y_iris.shape)\n",
    "\n",
    "print ('Test  1: {}, 2: {}, 3: {}'.format(np.sum(y_iris == 0), np.sum(y_iris == 1), np.sum(y_iris == 2) ) )\n",
    "print ('Train 1: {}, 2: {}, 3: {}'.format(np.sum(y_iris == 0), np.sum(y_iris == 1), np.sum(y_iris == 2) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = X_iris[:,0]\n",
    "b = X_iris[:,1]\n",
    "c = X_iris[:,2]\n",
    "d = X_iris[:,3]\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit_transform (X_iris,y_iris)\n",
    "X_scaled = scaler.transform (X_iris)\n",
    "\n",
    "\n",
    "X_squares   =  np.vstack (([a**2], [b**2], [c **2], [d**2])).T\n",
    "\n",
    "X_multi = np.vstack ((a*b, a*c, a*d, b*c, b*d, c*d)).T\n",
    "\n",
    "transform = PolynomialFeatures(10)\n",
    "transform.fit_transform(X_iris)\n",
    "X_poly = transform.transform(X_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(X_tr_o, X_ts_o, y_tr_o, y_ts_o ) = train_test_split(X_iris, y_iris, stratify=y_iris, test_size= 0.3)\n",
    "(X_tr_sc, X_ts_sc, y_tr_sc, y_ts_sc) = train_test_split(X_scaled, y_iris, stratify = y_iris, test_size = 0.30)\n",
    "(X_tr_p, X_ts_p, y_tr_p, y_ts_p ) = train_test_split(X_poly, y_iris, stratify=y_iris, test_size= 0.3)\n",
    "(X_tr_sq, X_ts_sq, y_tr_sq, y_ts_sq ) = train_test_split(X_squares, y_iris, stratify = y_iris, test_size = 0.3)\n",
    "(X_tr_m, X_ts_m, y_tr_m, y_ts_m ) = train_test_split(X_multi, y_iris, stratify = y_iris, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = LogisticRegression()\n",
    "paramgrid = {'C': [0.01, 0.05, 0.1, 0.5, 1], 'penalty': ['l1','l2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = GridSearchCV(estimator, paramgrid, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "optimizer.fit(X_tr_o, y_tr_o)\n",
    "predict = optimizer.best_estimator_.predict(X_ts_o)\n",
    "z_o = accuracy_score(y_ts_o,predict)\n",
    "\n",
    "optimizer.fit(X_tr_sc, y_tr_sc)\n",
    "predict = optimizer.best_estimator_.predict(X_ts_sc)\n",
    "z_sc = accuracy_score(y_ts_sc,predict)\n",
    "\n",
    "optimizer.fit(X_tr_p, y_tr_p)\n",
    "predict = optimizer.best_estimator_.predict(X_ts_p)\n",
    "z_p = accuracy_score(y_ts_p,predict)\n",
    "\n",
    "optimizer.fit(X_tr_sq, y_tr_sq)\n",
    "predict = optimizer.best_estimator_.predict(X_ts_sq)\n",
    "z_sq = accuracy_score(y_ts_sq,predict)\n",
    "\n",
    "optimizer.fit(X_tr_m, y_tr_m)\n",
    "predict = optimizer.best_estimator_.predict(X_ts_m)\n",
    "z_m = accuracy_score(y_ts_m,predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for original: 0.8888888888888888\n",
      "Accuracy score for scaled: 0.9777777777777777\n",
      "Accuracy score for polynomial: 0.9555555555555556\n",
      "Accuracy score for squares: 0.9777777777777777\n",
      "Accuracy score for multi: 0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "print ('Accuracy score for original: {}'.format(  z_o) )\n",
    "print ('Accuracy score for scaled: {}'.format (  z_sc) )\n",
    "print ('Accuracy score for polynomial: {}'.format( z_p) )\n",
    "print ('Accuracy score for squares: {}'.format( z_sq) )\n",
    "print ('Accuracy score for multi: {}'.format(  z_m) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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