diff --git a/doc/source/notebooks/constrained_bo.ipynb b/doc/source/notebooks/constrained_bo.ipynb index 21ed64c..f14bbfb 100644 --- a/doc/source/notebooks/constrained_bo.ipynb +++ b/doc/source/notebooks/constrained_bo.ipynb @@ -20,9 +20,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -49,9 +47,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -105,9 +103,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Initial evaluations\n", @@ -150,20 +146,23 @@ "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:From c:\\users\\icouckuy\\documents\\projecten\\gpflowopt\\gpflowopt\\acquisition\\acquisition.py:362: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", - "[ 0.15481272 0.14080552]\n", + "[0.18194828 0.14835351]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 0.63087138]\n", - "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([ 0.25],[ 1.])\n", - "[ 0.16824172]\n", + "[0.63086542]\n", + "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([0.25],[1.])\n", + "[0.16823107]\n", "\n" ] }, { "data": { - "image/png": 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XAjIAAAgNusylFzQU59qnEsKy14BsZl+WdKmkA865M33WAgAoD+Z+FIIuc/GMJRjnO06c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\n", 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uOwOqwVRriY65Ou7FRXQe29irTFGxguzTOn5qqO1h8A80eaaqx7bbK4hoLK+iGjU4TpvWEmq7C0xUkgF3qsmAvrXiWVWmsFIVkPuONxldMPzw0BuY3DCt7PaoTAVjnZ+ws9pekUVJT+xBF8R3aV1QfhjNt6jtFvv29aGjo7Xsdpd5rzXrQRlgWKZkpbLFwtRXJk/tvQ+DwwOjtg0OD+CpvfdF2h8P1AVsrzhJ1wRv2tUtz4e6WpRrl2WlfIsSFu9dvREDA6ND3MDAIO5dvVHTqMwyuZqHrraLuMUY3e0XwMk8wVYMKpWqCnIp3Z8CvRPx4q5iYfqPjNXj2theEV3UsHt1y/NOVZIp38JWkr0T8VxfxaIa16vJQPyKMqC//cKP1WXypDog++l6U2/p2xR5xYokPn2m6cQ80iuJyTrtlWBWgMgvbF/ykxu2pCoQV2KqNxlwMygDDMukX2YCsl/pQdL0Gzupg7LucJxE9dhGewWXdotGRzi2WUVmOKZK8nZBEcBsNRlwKygDZqvKAMNyHmUyIJcqd+CM+gbnQZiicrn/OO2VY6Ja4q5ykVZ5DcoAwzLFk4uAXE7agi6rx/blof84rdL290z2MCibDcqAnlUvAPerygDDcpblNiCnSRrDsS1Zba9Iy8Rro72C4ZiiYFA2E5SBfFaVAYblrGFAdliaT8jLWvWY3MRwTHExKOcnKAPJVJWBk3MTg3J6MSA7ylQ4znL1mOziEm9k0gnDU1fegzKQz/YLgFXlsGqds5KVY4HVgCwiXwHwQQD7lFLn2ByLS9Iejlk9piSwepxeUed+L1yZnGP8ITGvYZlVZTPyUlX2B+g0h2XbFeQ1AO4G8K+Wx+EEky0VeagcZ7X/OA2SngQZjlNvDWLM/UkEZYBVZSA/q18AyVeVsx6UgXSHZasBWSn1AxE5w+YYXJDmXuNSWa0eZ30Fi4f6zo201BtPyqModM39SQdlgGFZN93tF7rm6iRXwMhDUAZOhuW0BGXbFeSaROQmADcBQP2pkxP7hJeEpIJxHqrHFF+YkMyLgVAS/PP/uNa2ivdLKigD+a0qA8mFZVf6lAEGZRPSEpSdD8hKqVUAVgFA45mzRs1+5QJmGkJzkhXjJMNxVqvHLnhiz1mJTJ4P9Z2LeS0X4qLpN6B1/FQcHnoDT+29L/Ll13ViOM4f//zf1Dm75gTjD1asKptlMiy73H6R9aAcZP6P+o1jKdeDsvMBOSwXQ7OtFgqGYwprXsuF+EDnLWioawIATG6Yhg903gIA1kIygzFFwapyckyFZZeDMmD+Sn1JB+Uw87+ukAy426ecuYBcTrWAauIN7kJPMdsqKIqLpt8wMjl6GuqacNH0G6wEZIZjistGUAYYlgF9YTmvfcpJfXPoCTv/6wzJHpeqyraXeXsAQDeAqSKyE8D/Ukrdm+QYXAizuiUdjlk9zo7W8VNDbTfJVDjO4t982tiY+5NsvwBYVQbMhuW8BOUkq8lR5n8TIRlwo6pcZ+VZi5RS1yqlZiilxiulZiUdjrOm8fUGhuMMS6KaenjojVDbTTHxWl/d2c5w7Ajbc/+R0yWxZSEPz2k0uq5wWrRuP6b1w8Kk11Ts44/uY6bJOcbl+d90gL265XkjIbwWqwGZ9GFLBenw1N77MDg8MGrb4PAAntp7XyLP/8Ses4yFY6JSXlBOIiwzKBd4QVlXWHY1KJtgan70xJn/k6jyekE5qbDMgJwBtsIxq8fJM11F2NK3CY/23IVDg/ug1DAODe7Doz13JdJ/zKoxVaMaFI6dNmhsTXIG5eRlOSinsZocd/5PshUiibAsSqUn5DSeOUvNuP0W28Nwhs2qsYvh2OTBTddBWVePWpbWy7RRMX5t0fLnlFJd2p+YjKk0/5uaB5Oa4/Lco1xK5wcHHccDnR/GTJ3I5+KxwEY7hCdISL/tnEcDzf+pqiDLoFjps3URw3Gy+J7Tz8TXhawY54+pqnJS7ResKJ+ks/1Cx3EqDRVlF1f6sbkChc6qcmqXeav0ps36JYEZ1NLt1Z3tWioJSS//oxNXpyAT/HO/7nkyiaXiDs9pZDXZx/tdxPnw4Oo6yrqryS5cYKSUqdUtgtKxCkZqA3IlWQ3OrgTjPFaPXeXipFgNgzElxVRYNh2UuTTcWK3bj8WusOsMyi5flc+1wontkOyJGpZT1WIRh/dVSemPy1wbJ8OxHroDnYtfsfmZXJmC4ZhqMdGCYbr1gi0Xo+lsu8j6iXymV7oIy4ULfviFCey5CciVuBScXRgDpZNLE6KHwZhc4gVlnWHZZFBmSB4ry/3Jurl0THAtJAeVuRYLXYK88aNOtGkMv6we6/t6DchuH5rJSZmhmHTx/o51zcWmWi9c60tu2Lqj7PbBs2cnOg7X+pNdbbtw4ZiQZgzIMaQx6EbBcGyGiZAMjA6pSUyMpisVDMYEAA0Nx7XvMw1B2XZfcqVQXOs+SYRmXf3JeTiJz3ZIdqUfOQwGZKIMKw2vcSfJJL+2MxWM8/LBNov8wUHn+0P3SX1HTpdMVJODhOMgjzUZlllNDoYhOTwGZKqK1ePRdLZZAOaqyJW41JdWiYlgzFCcPabDctz3jKlqskstF0ElEZZZTa6NLRfhpOokvfrBk2eh6jgblarj7zcZbCE4eeKd7t8FT3jNhzNm9Y786KLrhD7dJ/IldfJenOqxjf0CetpQdGUL11e6sCVNJ+ylvoJc7o1s+spHFMz7z5+Lmxe+B9NPnYS9+4/g7rU/xGM/3mZ7WLHpriID5tbFdB2rxaSb7sqyzoqyrqJDEpXkwbNnxwqz3Qu7sOhTV6C9cwp6ew5gzee+g41rNwM4GZJNVJN1tFwAblaTAX3HCJvV5LS0WqSqghxUaZWZldDw4v7O3n/+XNz24fdhxtQW1IlgxtQW3Pbh9+H958/VNMJsyks1mdViSoLOqrKOirLOarLLy8B1L+zC0i/8EabPPhV1dYLps0/F0i/8EboXdo26X8PWHcYqyq5Vk11dEi4NbXe2ZDIgl8PAnKybF74HExrHj9o2oXE8bl74Hksj0stkEMtqSDbRRsF1wykI3UE5rqyH5EWfugJNzaPH1tTciEWfuqLs/V0OyYC+dkNX2y5shOQ0tFrkJiCXYmA2a/qpk0JtTyPTITkLQZm9xeQSXb3KLlWTTYbkqC0Q7Z1TQm0H8hWSXawmu3YFPhfkNiCXYmDWa+/+I6G2U3lpDMqmQzGDMengUlCOy7WQ3NtzINR2TxpCMqvJ+rheRWZAriDPYVnHa7577Q9x9NjQqG1Hjw3h7rU/jL3vcmydmJlUWDMVOnUxOT6GYgKAlnEDRvarKyjHkbWQvOZz38FA/+hQOtB/DGs+952aj3U9JAP5qCZTBlaxSIL/j4ErZATjrVaRxVUsSplY1aIa/0Roa9UL00GdgZjKKT3jXueB3PtbivrejrvahY51k01edS/MqhbeahWVVrGwRdcKF4C+i4sAei8wkrZVLlxe0UKUSk+F9JT22ersKz5mexgjshqW01g1d+H/RZIhuRLdgTnpirXJYOx/Xz93763PKaW6qtydHNP5tsnqIw++u+LtOsNy3Pd93PexjjnY1DJwJtcx9jN9qWqdFXedxx9dxxGdxwLTITnpgHzWabsDzf+sIMeQ1cqyicuk5kHSleRyXG3BqMZUKOZ7OF+8g7iOoOxCNTnu+9fUWslx10d2hY4r73l0rJnscbWabDIku1pFTlUPcv2AQuv2YyM/LslrvzKNxtaA4Ez0Fuf53AEquLTjpZGfuOL2J8cJOjpWuTDVlzx49mzjFd4k6O5Ldq03Wed5IXnsS05VQC7lD8uuhOasHKDTVBF3baw8qawyEytRZOVvjvTTGZSjirvShashGchGUNadG3TOQ66dwGcyJLu4okWqA3I5LobltHIteKYNQ3KBqeXZ0v73RcnREZRtV5PjODynMVVBOenQbSIkZ7WanKf1kjMXkP0YlOPTeWlUE1weG5DfarLpUJzWvyeyS1dQjiqrLRceLyjHCbi2KtImsoLuarKuoKyDiZDsWhU50wHZ40pVOc0HdReDsmvjqSYPQdnkhTwYikknm9XkLLdc+PnDcpDQ60K7hqmQ7Frbhcsh2SW5W8VC5zqIUehcO9EGHWt16hxH2niTm+3VLnRJalk2It10rHpxxqzeWCtd2FrlwuR6yZXYDr9B6Vzdwk/3uslAvOOI976Nu9KF6RUubMpFBbkcVpPj8SrKNoJqWsOxX1ovm+wft8nl2dL+90HpEbftwlY1OQ0tF2llMh9ksZqss5LsUptFbgMyYL9HOSshwB+WTYZXF9s8dEgidEaV1NhM9xa7ttoNhddW3290/2nuTY7D9Al8aWU6JLt0Ep9rIdkVuWuxKMfUVypB6Fxg3BWVXk/YCSFrv5egyk12plsybAVzkx8SGYSzx38xAROVprhtFzZbLgB3L1OdVqazgUsXGNHVcqGDKxcOSdWlpru6utT3vrcB967eiCc3bNG+f1shOa9BkPLLZKU4iCc3foqXmk6Zrq4u9aP/XIvewytxpP+Rke2mvpKNWxGLU5WL84FV198Wg/JJSWQDly5XHSck6+pHNhmQg15qOnUtFh0drbh12XxcfMk87fu2NSFkpdWCqBpTbRRsnciP8eNmo6PtTkxqvnJk29Utzxs5mKa55UJH2GLbxUlJzC262y7iiPPhTlerhQu9yFYDsohcJiLbRORXIrI86OOamhqweEm3wZERkS4MxVRO1Pm/rq4Z7a0rxmw3EZR1nMAXle3l4ICT/ckMy8mds6Rrvozbm+xCSLbNWkAWkXoAXwYwH8A8ANeKSOCy8LRpLUbGxQMuUXymq8WUbnHn/3H1nRVvMxWUo7J9BT5dX90zKBckNf+4UE22HZJtV5FtVpDfBeBXSqmXlVKDAL4J4IqgD963r8/YwHgAJorG1EoUDMaZE2v+P36ip+Z9XArJgL2WC4BBWbc8VZNth2SbbAbkTgA7fP/eWdw2iojcJCKbRWRzb29hghkYGMS9qzcmMsgk8CQ9SjOTS7QxGGdW5Pl/eLgfvYdXBnoS3dVk2yFZR1DWhe0XyVaTdQXlKF7d2R45KMcNyTaryDYDcrm/1DHvAKXUKqVUl1Kqq729HXv2HMYX7lxvZBULIgouiXWLKbMizf9Dx3dgz8Flo1axCMK1kGy7N1l3USbPQTnJuUrHnGur5SKNbAbknQD8156cBWBXtQf897Y9+KNr/5HhmMiSpC7oQZkXev4fGPw5Xt59fuhw7NEdkm1WkwG32i48ea4qJx2U40i65SKtVWSbAfknAN4qImeKSAOAPwSwzuJ4rGB7BbnOdCgGkj24NLy0M5HnoaqszP8u9iW70HZh4jiU17Ccpv7kNIVkG6wFZKXUcQA3A3gMwFYADyqlfmFrPER0UhKh2JPEwaThpZ0jP2SfzfnftZAM6KkmuxqUgdFhOS+BOS1BOS0h2UYVOVVX0muZNEu9s+ujiTxX2q6cQxSHjYvVmD54VAvD39/zj7ySXsqc8/YG9W+PTtW2P90HXF0VMh19nrouHZ+VD8guSEOmiPIhK8qHuzgfKnV8wM3slfSSkIY3MlEc/gpx1sIxK8UUhIuVZCB+NRmI35/sMVlV9uSlupzExY1sVJOzfOIeA7IFDMeUlNIgbCsQe0weIBiMKSzXrrznidubDOhpu/AkEZQ9eQrLprgektPSasGATJRilQKw7SBcjumqMVEUukMy4F41WXdQthGWsxiYTVaV4xwDGJILxiXyLCli+o+Q1WOqxrVQqwvDMeXNpR0vaelL9kJy3K+y/SFZR4+ydyxLcs7yH5+z1rvsvR7dGWTSaypS7mh8vSH0B6tXd7aH+lD3xJ6ztH2YNIEVZB+GYzKpVrWX4Tg8hmPSwUQVGdBXSQb0tF140lxV9mS1smyiohz1+OJyJTmJKjIDchHDMemUl/BbC8MxpYXJkJyHoAzYD8tZYiooh6VrRZRqXA3JDMgJYDjOh7yH4VIMx0Qn6f4q2eWgDCR7Yp8ni1Vl3UE5akgOE5STXCPZZEiuGpBFpEVE5pTZ/nZjI7LA5B8Tw3H2MRSP5Wp/YPfCLqz5r8/gvPPOO8/2WFyXl/nfz1QV2aO7mgyYCcqsKrtH5wl9SbQ83++GAAAgAElEQVRcJLn8W9CQPKn5SvzGjB8Hnv8rBmQR+RCAlwD8m4j8QkTe6bt5TaDRpADDMcXBYDyWzQuAVNO9sAtL77wO02efqnlE2ZOX+b8c0yEZ0F9NBvQGZSB7VeUs0TXHuhaSTa5sMan5SnS03Ynx42YH3me1VSw+BeA8pdRuEXkXgPtF5FNKqYcBpD755bnnOM6kl0Q/UlowHI/lauUYABatWICm5mwdKA3K9PzvAi8k67oCn0fXqhce3atfAKOPj0nNo4fnNDo9P4Wla9WLKKtchFnhIsmVLbyQXO5DbnvrCtTVNYfaX7WAXK+U2g0ASqn/EpGLAHxPRGYBSHUyyEs41v3pv9Y+8xSeGY7tGTxrVqQqcnvnFAOjyazMzv9BXN3yfGJrrZoOyoD+sKxzrk9yuTjv2M+gPFqWQjJQCMqlIXlcfWfo/VTrQT7i7z8rTpbdAK4A8LbQz+SIrIdjEz1kUZ7bxvMnheE4nXp7DtgeQppkcv53mYn+ZA/bL0bLWssFEP9kviy1WwCFkOz/kHv8RE/ofVQLyH8BoE5E5nkblFJHAFwGYEnoZ7IsiT4kW+HY1VDq6rjIjCSrMoNnzQr9mDUr12GgPzuVI8MyNf9HkUQvcjlJBOU0nNRnWhZDMhBvHo5y8p7pkKwjKANA7+GVGB7uD/XYigFZKfUzpdQvATwoIn8tBRMAfBHAR2KMN1FJNejbCMdpCp8My9mX9AEnbEjeuHYzvrTsG9i7Y7+hEWVHVub/NDMZlAH3q8pJBOUsnsAHJF9NdnUJOM9Dfefiq3tex56DyzB0fEfgx4lS1X8RInIKgL8DcB6ASQC+DuDvlFLDMcYbScukWeqdXR8NdN8k3/RJhuOsBcw09i2zxaIyG719UfqRv7/nH59TSnUZGE6muDT/n/P2BvVvj05N+mkT60WuRXePcjk6l+bSPbebnnez1JfsFycLhc02YfJJlA9nuj4w3nbOo4Hm/yAXChkCcBTABABNAF6xMTkGlfQnQobjeFhVzhYb1ZjBs2ZFarmgQFI1/2eZV1FOS1VZ97eGpivKWawkA/FbLsIIW0lOui85rCAB+ScoTJDvBPBuANeKyENGRxWSravnJBWO8xAi8/Aa88LWgYZB2Qjn53/TbPUiV2M6LLvcfmEyKGc5JEcNyiZDMmCnLzmoIAF5sVLqb5RSQ0qpPUqpKwB8x/TAarF9SckkwnEeQ2MeX3MW2TzQeEGZYVkLJ+d/OimNJ/XpYCooZzUkA9Grya6FZCCZanK1dZABAEqpzWW23W9mONWdaBLrb96kgnHeHTttMJX9yXSSC2uOVgzJe5IdR1q5NP9Tdf6QbCI86LwAic71lI+cLjwvJITW7cci5aiwayWHWScZCL9WMnDyfW7qA2KQCjIVMRwny9Vqsu21rtMmq2eKE7nKZAuGixVl3dXkrM9XWaokA+aqyQzIAZkORa6GQRfw95INDMpEyUtLUNaBITm4qH3JYddKbny9wfjJe4CZ3mQG5BqSWIuRAbA2/o6yw/b5A0R5ZKqqrCso66wm65KHOSqL1WRdQZkBuQq2VLjFpSo72yz0YFgmSp6JsOxSUNZZ2MrD3JS1kAzoCcoMyBWwpcJd/L1lE8MyuciVC4WY4nJQjoshOTiXQ7KOoBwlLNdcxSJvWDVOBxdWueDZ0+b4D0hZvcIVhXPwRHPZsGpqneKsB+NSulfB0LHqhY7VLnTN04fnNGZ+LnJ1hQsg2ioXpU6+rx8NdH8GZB+G43TJU0h+//lzcfPC92D6qZOwd/8R3L32h3jsx9uMP68LSifsrB+kKJy8BdkkeGHZpaCc15B88SXzsHhJN6ZNa8G+fX24d/VGPLlhi7HnSzIkA+Eykfce0nkRm2oYkJHsFfFIrzyE5PefPxe3ffh9mNA4HgAwY2oLbvvw+wAgNyHZr9zkzdBMpJ9LQTluNdk7zqfpW7+LL5mHW5fNR1NT4TV3dLTi1mXzASATIRmIXk0GzAflXPcgJ7FChcf1cKz70qJJcuF3a/J9dPPC94yEY8+ExvG4eeF7jD1n2vj7l9nLTKSXzj7luMcZHSfwxZHkvLJ4SfdIOPY0NTVg8ZJu488dpyc5Sl9ylA8+cfuTa8ltBTlvwTjopFTrfibfjHFkuZI8/dRJobbTSRUPZhsTHQZRJuiqKOuoJttsuUiq1WLatJZQ23WLWkkGkqsmA+YqyrkLyEkuz2U7HJuoCPv36VpYzmpI3rv/CGZMHTsh7t1/ROvzEJE9lU42dLHHWmdQttly4XpI3revDx0drWO29/YcMPq8fjZCMhAtP+kOyrkJyEmvW2srHCfZJuFiWM5iSL577Q9H9SADwNFjQ7h77Q+1PQcRJSfMyhtB7msrROsIyjarya6H5HtXbxzVgwwAAwODWPO57xh7znKSDslA9GoyoC8oZz4g27igg41wbLt/WMeZyrq4EpIBPSeEeCfimV7FIum/lTSdLEOki4ll6Ur3mXRgvrTjJevV5CyGZO9EvDGrWLy018jzVeO9xqgn7wHhjzFxqsnA6DwSJSOJUuk5SJ3SPludfcXHAt3X1pXO8hiOy7EdlG0HZD/XgmBargJo8vf23L23PqeU6jL2BKRd59smq488+G7bw4jF1JrNtSQZmOMG5TjHjjjzftz5Jk8r6cQ5UTHO8UdHvjpjVi82vfcLgeZ/KxVkEbkGwO0AzgbwLqXUZh37tX3gTzocuxiMPXGqATq4UEX2+N+XSYZl238PcVUbv2sfOig4U/M/VeYP5qbDctxqcpxvI+P0JbtcSXZN3JYLINrxKU7bhSfM+8pWi8WLAK4C8C9xd+RKCGA4HosheSwTYdmVv4EkMTynmrb5n8JLIiynteUibmscQ3JwcXqTgWQyl5WArJTaCgAi4X85LoYBhuPKbPcmuxiSPS6+l7Og1u+VAdquOPN/2j3Ud661NotyvLGYCMq6TuBLW1+yFxrzEJR1hGQgejUZMJu/nL9QiIjcJCKbRWTz4NCvbQ9nDIbjYGyO2/Zye+QWfjBJD//8/+uD/Ds25eqW540F97gXGIlz7Igz96fpgiI2tW4/FvvDQJyiRdSLjARhLCCLyH+IyItlfq4Isx+l1CqlVJdSqqv+lFNMDTcShuNwGJKJ8sHE/H9Km5vfBIXl4rrGHobk0RiSg9MRkl0LysZaLJRS7zW17zxKezj22OxLdrndgihLOP9X51qrhZ+ptguevJd9cVsugHhtF8Do/8dxC2OZXwfZlCQrklkJx5Rtuv4m+CGG8sDlkAwUgrJrIRlI78l7QH76koH41fOoJ/H5xe1TttKDLCILRWQngN8B8KiIPGZjHFHx6/p42GqRTsdOG6z6k9Tz6H4+Slba53+dXG63AMy0XMRttwDYcpEGOj4MxG278ERtv7ASkJVSa5VSs5RSjUqp6Uqp99sYRxTsO9aDIdltaQikDM/pFHX+7zveFLv66KK8hmT2JWefjhP4AHtBmS0WIWThwHvJ9HfgxjnzMa2pDfsGDuKe7euxYe8LVsbCfmR3ZOG97Vft9fD/e7qVC8k6qpI2JdFuMan5SrS3rsC4+k4cP9GD3sMrcaT/kUCPNdFuoUPcdgvAXl8ykI+WC0BPbzIQvz85LOeXecsz3VXWS6a/A584+xp0TJiCOhF0TJiCT5x9DS6Z/g6tz5MWWQuFYeS54pq315sHT+w5a9RPGpkMoJOar0RH250YP242ROowftxsdLTdiUnNVwbeRxbbLYDo88GR04XV5BB0VZMBfRXlWhiQA8rCQfXGOfPRVD/603JTfQNunDPf0ojst5DkKSDmNRBT/pQG5rSE5of6zjUSlNtbV6CurnnUtrq6ZrS3rtD+XGGlOSQDelou8haUdTEdlBmQc2RaU1uo7XmS1dDIUExUkKbArDskj6vvDLW9ElfXSQYKIdlmXzKDcnA6q8mAuaujsgc5gKyEi30DB9ExYUrZ7ZSNvuS0vFcrHchs9aRT/vhDsos9zF5I1hFKj5/owfhxs8tud4WOJeAAe33JQPzeZCB/6yYDelpNTPQnMyDnyD3b1+MTZ18zqs1i4MQg7tm+3uKo7J6sVyqNIdmVUKyjXSbsPlx531C6lQYzlwKzjqDce3glOtruHNVmMTzcj97DK2OPTycXQjIQ7zgQd81kIJ8n8QHuBWUG5BpcCR86eKtVuLKKhaviVhGSYPt9abt33MNKNJngYnU5zkoX3moVUVexSJJLIRlgNTlJula7APQEZQbknNmw9wUG4oBcCsq2AzHgTigOotxYGZopCpfCcpxq8pH+R5wMxOXoDMlAvL99VpOTpbOaDMQLygzIVbgQSsg+W0HZ9vsvTYE4CP/rec3iOCi9XAnL/pP4krxcdZJrIesKyYDdlgtAXzUZYFCOKkpQZkB22Ks72zMXUipxqQ+5En9g1R2WbYdhT17eb0Rx5T0sp40LLRdA/BUX2HYRT5jfPwMyUQTlAm2tidOVEFwOgzFRdFkPy7auoqezigzYb7kAWE2OQnc1OSgGZMflqYqcdi4H4Er43iLSy8WwDEQLzC5cXlp3SAZYTU6rpIMyA3IKMCSTTrbfS2FDg+sXdSCqxHvv2j65D3Aj7EblYkgG3FnpAshPNRnQ33ZRCQMyOcH1/uMsSDoY6woFtfbDAJ0Pg4PpPVy5UlVOM1MhGYh//LG90gWQz2qy6ZCc3hknZ1hFpqiSet/YOvCXPi8Dc3bVCjJpmCMZlqMzEZIBVpPTynTLBQNyinh/wGk4CJB9SbxPXDzAMzDnV7WQ4+K8ybAcnsmQDLCanEamqskMyCnEajJVY/q9kbYDuX+8DMv5VSn4uDKXutSv7DpTIRnQt9IFwGpykkyEZAbkKhpfb3B2ZYIshWT2H+tj8j2RhQO39xo2WR4HucO14MyqcjAmQzKgr+2C1eTk6A7JDMgpxpYL8qQ5GM9ruRAXTb8BreOn4vDQG3hq733Y0scIS3aVC0dJz7UMy9UlEZKBbFSTXQ3JF18yD4uXdGPatBbs29eHe1dvxJMbtkTen86+ZAbkGlyuInvSHJRZPY4nzcEYKITjD3Tegoa6JgDA5IZp+EDnLQDAkEzOKZ2vkpxzGZbLMx2SATeqyVkMyRdfMg+3LpuPpqbC76WjoxW3LpsPALFCMqCnmsyAHEAaQjKQvqDMcBxdVvqML5p+w0g49jTUNeGi6TcwIJPzbAXm0kCY98Dsvf4sV5N1tFy41pe8eEn3SDj2NDU1YPGS7tgBGYgfkhmQA0pLSAZG/wG7GpYZjqNLe9XYr3X81FDbiVzmQmDOc1hOqpoM2LtcdZaqydOmtYTanjQG5BDSFJI9roVlBuPoslI19js89AYmN0wru50o7WwE5ryH5SSqyYCey1XnPSTv29eHjo7Wstt1iVNFrtM2ipxofL0h8pvatld3to/6sfHcFE0WwzEA9B+5HcPD/aO2DQ/3o//I7bi65Xlc3fK8lXERmZD0HPzEnrNG/eRJEnPaGbN6Y83Nx04bjFx081ou4kjics3V3Lt6IwYGRr/+gYFB3Lt6o9bnifpBgBXkiNJYTS5l+ixtBuL4snaxj9LAe6S/8O/21hUYV9+J4yd60Ht4JY70P1LxMQDwUN+5ZgdKlICkv+HLW+9y1qvJaa8ke33GOlexqCRKJZkBOQbvDZ32oOzHUOuOrFSNa1WBj/Q/MioQh90nwzJlge12DCC7gTkNJ/HFCclA/JP3bIZkE4FYBwZkDbIYlMmuLITjpNojvOdhUKYssXH+SNYDs+tLwtnsS3ahJ9m0sFVkBmSNGJQprqy0VNjoHWZQzjYZFGvnf9ie022dbJ3FwJxUNTlOSAaiLwXHkKwPA7IB/je27YmV0sOFVUZ0sH1i3dUtzzMkk1bVwkrSczwvVqKH6aBss+WCIVkPBmTDWFWmIJI6yJk+qNkOxx6GZEpKpRCT1JzP6nI8SQRlhmR3hHldDMgJYVWZKmE4NoMhmWwqF2xMz/02171Pe3XZZFBOa0gG3Lnqng1cB9mCNK+lTHoxHJvl6rgon7y53/9jiq017wGkeu1lU3Nl3PWSo8jCWsk2MSBblMQkSe5iz3EyXB8f5RvDsnsu7XjJSFBmSE4XKwFZRP5eRF4SkZ+LyFoRmWxjHC5hWM6XJMNxGr/u1I0h2R2c/ytLorps86qmaQzKujEkp4etCvITAM5RSr0dwH8DWGFpHE5iWM62LIXjNAXPNI014zj/B2TyWOBCVTkNTFSTbXx7yJAcnpWArJR6XCl1vPjP/wQwy8Y40oBhOVuy0lZBFBXn/2iSCMtJS1tQ1inqsSDOiZ66QnJegrILq1j8KYBvVbpRRG4CcBMAjGttS2pMTuJKGO5YcObZ+OS5F2LmKS3Y9es+3PH8Jqx7ZWvVxzAcE43B+T8CU8cC20vGud4O5l2J75Lp78CNc+ZjWlMb9g0cxD3b12PD3hdC7y/q6hY2r7jnSetScIfnNAIbg93XWEAWkf8A0FHmpk8rpb5TvM+nARwH8PVK+1FKrQKwCgCaOmfH/7+aEVxf2Z4FZ56Nz18wH83jxgMAZk1sxecvmA8AFUOyjXDs+sHGBi79lgyX538d4SAIHdW6IEyHZQbl0Zb+j+mYP/MaNNUXfu8dE6bgE2dfAwCpCcm6pDUkB2UsICul3lvtdhG5AcAHAVyilGLwjYhV5eR98twLR8Kxp3nceHzy3AvLBmRWjilvXJn/kwrDYZ7bZHA2cTywFZRdDckXTb9hJBx7muobcOOc+ZECchy21kj2S9N6yWFbQ6y0WIjIZQD+GsCFSql+G2PIIoblZMw8pSXUdhtcPbgQmZr/bYbhMMqN00Ro1v0tY9JB2dWQ3Dp+atnt05qitwDFuZBIVDpDMuB+NTlK37StVSzuBjAJwBMi8lMR+WdL48gsnthnzq5f9wXezuox0Rja5v9Jr6mRnzTzvw7dr0X3sSDJE/pcPInv8NAbZbfvGzgYa79pPWnPL2sn8NlaxeItSqnZSqnfKv78uY1x5AFXwdDvjuc3of/40Kht/ceHcMfzmyyNiCg94s7/WQnF1Zh4jSaCclJcCslP7b0Pg8MDo7YNnBjEPdvXWxqRWyEZcCsoxxkLr6SXIwzKeqx7ZSuWP7seO988jGGlsPPNw1j+7Pox/cesHhPpUz+YnjYKnXSHZZ3HgaSryS7Y0rcJj/bchUOD+6DUMPYcPYC/3/ptLf3HNi4iApjri7cdlOM+twvLvFHCuAJGfOte2Vp1Wbe8hOOH+s5N1QU4uIIFpZkXknUEGp3HgaT6k13pS97Stwlb+k5+Y7hhrxvhPQ7dPcl+SZ/IpyuUs4KcY6woExGlj86qsu6Ksmku9iXrZKuKDJhfmtCrKJuqKuveNwMyMShrlpfqsSctVdm0jJMoDJ1BWYek2i6yHJLjcD0ke3SFZZOhmy0WNIKtF0REwQT5ujjJ/ksd7Re62y7y0nKhm41l3/xMtluU48oJfaUYkGkMBmUKy/VeZFaPKYq4PZPVHm8qFOgKygzJ6aXjKntJh2QXscWCKmLrRXh5a6/wczWEujouck/r9mOjftL8XHHDja75n33J0bhwLEmq3cJVDMhUE0MyBfVQ37lOBVKXxkJuSioQ2xiHjv5kXSGZfcnJ0vUNcJ5DMgMyBcJqMoXhQjB1YQzkLhdCcSUmgnIcaVvlggoYkuNhDzKFoqs3LYtc+ErMJV5ATbo3mcGYqnE1FJfjH2vcnuW4vcm6zk1Jqi8ZQCK9yXkJ5HnsSWYFmUJjNZnCSKrtwrX2DnKLyxXjIHSN34VqcpJX3zMZYE2HYx0fJHQWtPJWSWZApsgYkikMEwHW2yeDMVWT5mBciiE5PBNBOS+V41J5CslssbDsg2+bi49f/G7MaJ2E3YeP4ItPPo3v/WKb7WEFxpYLN7m89FG5MBu0DYNBmMJwPRhffMk8LF7SjWnTWrBvXx/uXb0RT27YUvNx3uuK03Yx6TVlfSm4JNot/HS1XqQtHOtY9s0vL+0WDMgGBH3jvP/8ubjtg5diQsN4AEDn5BZ89oOXAgBDMuUKgy/ploZwfOuy+WhqKgSXjo5W3LpsPgAECslA4TUyJIfnD7hhwnLagrFJeQjJDMgahX2z3LzwPSPh2DOhYTw+fvG7UxWQAYZkInKH6+EYABYv6R4Jx56mpgYsXtIdOCADDMlx5SX06q4iAyfbLbIalEWp9LwwEekF8FqEh04F8Ibm4cR23nnnnVfptueee+65CLt08nUawNeZLTZe5+lKKXvXcqXQOP/X5OTrNICvM1ucnf9TFZCjEpHNSqku2+Mwja8zW/g6ieLLy/uLrzNb+Drt4yoWREREREQ+DMhERERERD55CcirbA8gIXyd2cLXSRRfXt5ffJ3ZwtdpWS56kImIiIiIgspLBZmIiIiIKBAGZCIiIiIin9wEZBH5exF5SUR+LiJrRWSy7TGZICLXiMgvRGRYRJxcOiUOEblMRLaJyK9EZLnt8ZggIl8RkX0i8qLtsZgkIrNF5CkR2Vp8zy61PSbKJs7/2cD5PzvSMP/nJiADeALAOUqptwP4bwArLI/HlBcBXAXgB7YHopuI1AP4MoD5AOYBuFZE5tkdlRFrAFxmexAJOA7gVqXU2QB+G8BHM/r/k+zj/J9ynP8zx/n5PzcBWSn1uFLqePGf/wlgls3xmKKU2qqUStd1qoN7F4BfKaVeVkoNAvgmgCssj0k7pdQPABywPQ7TlFK7lVLPF//7CICtADrtjoqyiPN/JnD+z5A0zP+5Ccgl/hTAetuDoNA6Aezw/XsnHPuDomhE5AwA7wDwY7sjoRzg/J9OnP8zytX5f5ztAegkIv8BoKPMTZ9WSn2neJ9Po1Da/3qSY9MpyOvMKCmzjesUppyITATwbwD+SinVZ3s8lE6c/zn/U/q4PP9nKiArpd5b7XYRuQHABwFcolK8AHSt15lhOwHM9v17FoBdlsZCGojIeBQmx68rpR62PR5KL87/mcf5P2Ncn/9z02IhIpcB+GsAC5RS/bbHQ5H8BMBbReRMEWkA8IcA1lkeE0UkIgLgXgBblVJftD0eyi7O/5nA+T9D0jD/5yYgA7gbwCQAT4jIT0Xkn20PyAQRWSgiOwH8DoBHReQx22PSpXiSzc0AHkOhof9BpdQv7I5KPxF5AMCPAMwVkZ0istj2mAz5XQDXA7i4+Df5UxH5PduDokzi/J9ynP8zx/n5n5eaJiIiIiLyyVMFmYiIiIioJgZkIiIiIiIfBmQiIiIiIh8GZCIiIiIiHwZkIiIiIiIfBmTKHBH5vogcEpHv2R4LERElh/M/6cKATFn09yisr0hERPnC+Z+0YECm1BKRd4rIz0WkSUROEZFfiMg5SqkNAI7YHh8REZnB+Z9MG2d7AERRKaV+IiLrAHwWwAQAX1NKvWh5WEREZBjnfzKNAZnS7jMAfgJgAMBfWh4LERElh/M/GcMWC0q7KQAmApgEoMnyWIiIKDmc/8kYBmRKu1UA/l8AXwfwd5bHQkREyeH8T8awxYJSS0Q+DOC4UuobIlIP4FkRuRjA/wZwFoCJIrITwGKl1GM2x0pERPpw/ifTRCllewxERERERM5giwURERERkQ8DMhERERGRDwMyEREREZEPAzIRERERkQ8DMhERERGRDwMyEREREZEPAzIRERERkQ8DMhERERGRDwMyEREREZEPAzIRERERkQ8DMhERERGRDwMyEREREZEPAzIRERERkQ8DMqWaiLwqIkdF5E3fz90iskhEnrY9PiIiiq9krt8rIl8VkYk1HnO7iAyVHB8+mdSYKd0YkCkLLldKTfT93Gx7QEREpN3lSqmJAM4F8E4AtwV4zLdKjg93mB0iZQUDMhEREaWGUqoHwHoA54jITBFZJyIHRORXInKj7fFRNoyzPQAiIiKioERkNoDfA/AwgAcA/ALATABnAXhCRF5WSm2wOETKAFaQKQseEZFDvh9WEIiIsucRETkE4GkAmwCsAvBuAH+tlBpQSv0UwGoA1/se86GS48PM5IdNacQKMmXBlUqp//BvEJFFlsZCRERmjJrrReR8AAeUUkd893kNQJfv3w8qpf44qQFSdrCCTERERGm0C8AUEZnk23YagB5L46EMYUAmIiKi1FFK7QDwLICVItIkIm8HsBjA1+2OjLKAAZmy4Lsl61yutT0gIiJKxLUAzkChmrwWwP9SSj1hdUSUCaKUsj0GIiIiIiJnsIJMREREROTDgExERERE5MOATERERETkw4BMREREROSTqguFNNRNUBPqJ9W+I+XOW3/ztIq3/fJnryc4EkqDvuO9byil2m2Pg4Krn3iKGjdliu1hkEb1xwLeb2DsYgJ1R4cAAG99++yKj/vlz3dgeML4sredaJJAz32iMdDdKEUGd+wMNP+nKiBPqJ+EC6ZeY3sY5KA1az+D6bNPHbN97479WPSuv7EwInLZ9/f842u2x0DhjJsyBZ3L/sr2MEijSduDf4ndtm1wzLYJL/ZgzSO3Y/qssR+c9u48gD9Yck/ZfR2c2xD4eY/MGQ58X0qHV5YuCzT/s8WCMmHNynUY6B9djhjoP4Y1K9dZGhEREZl09JxOrPra0xjoHx2eBwaGsOprT5d9TJhwTPmWqgoyUSUb124GACxasQDtnVPQ23MAa1auG9lORERuOTJnOHAV+eDchrJV5A0btwIAbvyTCzGtvQX7evtwz1c3jWwv3UfY8VF+MSBTZmxcu5mBmIgoo7yAWxqUN2zcWjYQl3tsUAzHxIBMREREVoSpInv8YbdcVbn0PkRRMCATERGRNVFCssdEEGb1mACepEdERESWuRBKj8wZdmIc5AZWkImIKPcmnnm44m1vvtKa4EjyywunUavJOp6byMOATEREuVItDAe5PwOzWUkGZQZjqoQBmYiIciFsMK61HwZls/zhVWdYZiimIBiQiYgo03QF40r7ZVA2rzTUhgnMDMQUBQMyERFllqlwXCf1vR8AACAASURBVPocDMnJYugl0xiQiYgok4KE4/edti3Qvh5/fW7N52JIJsoOLvNGRES5FDQce/etdf8kqtVElAxWkEt0L+zCohUL0N45Bb09B7Bm5TpevpiIKGVqhdVyYfcdk38X13RehfH1M3FwaD/W734ALxx6ZszjqlWTWUkmygYGZJ/uhV1Yeud1aGpuBABMn30qlt55HQAwJBMRZUS5cHxLZztmT1mCurpmAMCUhnZcO3sJAIwJyUSUfWyx8Fm0YsFIOPY0NTdi0YoFlkZERERJmDF5+Ug49tTVNeOazqssjYiIbGJA9mnvnBJqOxERZcP4+pmhthNRtjEg+/T2HAi1nYiIsuHg0P5Q24ko2xiQfdasXIeB/mOjtg30H8OalessjYiIiHQrd5Ld+t0PYHB4YNS2weEBrN/9QM3H+vEEPaJs4El6Pt6JeFzFgogo3d58pbXqShZe0PVO2PNOxJs/41q0jT+17CoWtcIxEWUHA3KJjWs3MxATEWVArZAMlIbeZ8asWBEmFLN6TJQdDMhERJRZQUKyJ06FmOGYKFsYkImIKNPChOQo+yZKo0nbo52GdmTOsOaRuIkBmYiIMs8LsrqCMoMxpVHUUFxpH1kOywzIRESUG6XBNkxgZiimNNIRioPsO2thmQGZiIhyi6GXssxkOK70XFkJylbXQRaRr4jIPhF50eY4iIgoOZz7icyatL0u0XBc+txZYPtVrAFwmeUxEBFRstaAcz+RES4EVBfGEJfVFgul1A9E5AybYyAiomRx7icyI2owbds2WPX2g3MbIo0lze0Wzvcgi8hNAG4CgKa6iZZHQ0RESfHP//VtbZZHQ+S2KOG4VjAuvV/YoJzmkOx8DVwptUop1aWU6mqom2B7OERElBD//F8/8RTbwyHKjLZtg4HDcdzHpbXdwvkKMhElq3thFxatWID2zino7TmANSvX8fLrREQOCxNCKwXcCS/2oPvK87Bo+eVon9mG3l0Hsebz38X6X+0pu48obRdpks5YT0RGdC/swtI7r8P02aeirk4wffapWHrndehe2GV7aEREFFOtcLz0jmsxfdaUwvw/awqW3nEt5r+lI9S+ssL2Mm8PAPgRgLkislNEFtscD1HeLVqxAE3NjaO2NTU3YtGKBZZGRFnEuZ/IHRNe7AEALFp+OZqaR1eFm5obsGj55SP3yRPbq1hca/P5iWi09s4pobYTRcG5n8gdR8/pxIQXe9A+s/yJsO0z23D0nM6ER2UfWyyIaERvz4FQ24mIKD0q9Q0fPacTvbsOlr1tX29fqH1lBQMyEY1Ys3IdBvqPjdo20H8Ma1auszQiIiLSqVKwXfW1pzEwMDRq28DAEO756qbA+8gSrmJBRCO81Sq4igURUXocmTMcaiULL+D6T7TbsHErAODGP7kQ09pbsK+3D/d8ddPIdv/jwo4tjRiQiWiUjWs3MxATEaVM2JAMjA3KGzZuHRWIS+8XZUxpxYBMRERElAFRQjJgpmUizeEYYA8yERERUWa4EExdGENcrCATERFRLBPPPBzq/m++0mpoJAREryTreu4sYEAmIiKi0MKG4kqPZVg2wwuqSQXlrARjDwMyERERBRInFNfaJ4OyGf7gaiIsZy0YexiQiYiIqCYT4bjc/hmUzdERlrMaiEsxIBMREVFFUYLx+07bNurfj78+N9TzMSSbl5egGxUDMhEREcVSGohr3V4rMDMkk20MyERERFRWrepxrWBc63HVgjJDMtnEgEzGdC/s4iWLiYhSKmo4vr7tWUxuvgIzJi/H+PqZGDqxC9/ueRgvHHqm7D7CtF8QJYUXCiEjuhd2Yemd12H67FNRVyeYPvtULL3zOnQv7LI9NCIiiqlWOJ495Q40jJsFkTo0jJuFa2cvwS2d7aGfx/SJgUSVMCCTEYtWLEBTc+OobU3NjVi0YoGlERERkUnXtz0LAJgxeTnq6ppH3VZX14wZk5eP3McvapsGkUkMyGREe+eUUNuJiCgbxtfPDLWdyEUMyGREb8+BUNuJiCgbhk7sCrWdyEUMyGTEmpXrMNB/bNS2gf5jWLNynaURERGRSfcfvAAAsPvQ5zE83D/qtuHhfuw+9PmR+/jxJD1yUapWsVBNDRg8a5btYVAAj2/dg+Nf/D4WL+nGtGkt2LevD/eu3ognt+4B+P+QbNtjewBE6fb463PL9g7ff/AC4GAv3vHr1bim8yqMr5+Jg0P7sX73A3jhUG/Z/VTDZd7IllQFZEqXJzdswZMbttgeBhERRfDmK61VV5GoFJIB4IVDz5Rd1q308USuSlVAPtEkODynsfYdiYiq2Wh7AETpECQkA+FWoggajFk9JptSFZCJiCif6o8Bk7aPPW3myJxhC6PJl1ohGdBfDWY4JtsYkImIKLVKQzMDsxlBQrLO5yKyjQGZiIgywwvMDMr6ecHVVFBmMCaXpCogn2gAjpwutodBRESOY1A2R2dQZigmV6UqIBMREYXBoGxOuXBbKzQzEFNaMCATEVHmTdpex5CcAAZgygpeSY+IiJxXP6DQtm0QbdsGI++j3CoYRETlsIJMRESp4g/JB+c2hHosK8lEFAQ/ThMRUWpFqSqzkkxEtaSqgnzOjOl45GN/ijue34R1r2y1PRwiIkrI3Ld24Furb8Sqrz2NDRvHzv9t2wZDV5OJiCpJ3cfoWRNb8fkL5mPBmWfbHgoRESVo+qwp+MRfXob5b+koe3uYSjKryERUjdUZQkQuE5FtIvIrEVke9HHN48bjk+deaHJoRERkUNT5v6m5AYuWX44JL/aYHB4R5Zy1gCwi9QC+DGA+gHkArhWReUEfP/OUFlNDIyIig+LO/+0z20wNjYgIgN0K8rsA/Eop9bJSahDANwFcEfTBu37dZ2xgRERkVKz5v3fXQWMDIyIC7AbkTgA7fP/eWdw2iojcJCKbRWRzb28vAKD/+BDueH5TMqMkIiLdIs//A/2DWPW1p3H0nDF3D3WSHpd6I6JqbK5iIWW2qTEblFoFYBUAdHV1qT1HD+Ce7evx86EXcMYs00Mkoix6zfYAKNr8v/cw7vnqprKrWBAR6WQzIO8EMNv371kAdlV7wLYjO/GHz37O6KCIiMi48PP/L/fgD2/457K3hV3ejdVjIqrFZkD+CYC3isiZAHoA/CGA6yyOh4iIkqFl/ue6x0RkirWArJQ6LiI3A3gMQD2AryilfmFrPERElIwo8/+JJtESiFk9Jso+HeucW72SnlLq3wH8u80xEBFR8mzM/wzHRNlk4sI/qbrUdMu4AVza8ZLtYRBRynENnHxhMCbKJpNXxExVQCYiIgqKwZgom5K4VDwDMhERZQqDMVF2JRGOAQZkIiLKAIZiomxLKhh7GJCJiCg1GISJ8idOOG7bNhjpcQzIRETkvBONDMdEeRQlHEcNxX4MyERERESUejqCsadqLBeRFhGZU2b727WNgIgoIfNaLsRH3/oVnHfeeefZHovrOP8TkW1hqsfVwvGEF3sw/y0d+NbqGwPP/xUryCLyIQD/AGCfiIwHsEgp9ZPizWsAnBt41Jq01ffj6pbnk35aIsqASc1XoqPto6ira7Y9FOe5OP8TEYU14cUeAED3ledh6R3Xoqk5+NU4q7VYfArAeUqp3SLyLgD3i8inlFIPA5A4A47q4IlmPNTHeZmIwvvo9NsZjoNzbv4nIqqkVmvFouWXhwrHQPWAXK+U2g0ASqn/EpGLAHxPRGYBUKGehYjIstbxU20PIU04/xNRZrTPbAv9mGrNHUf8/WfFybIbwBUA3hb6mYiILDo89IbtIaQJ538iSo2Dc6tXh3t3HQy9z2oB+S8A1InIPG+DUuoIgMsALAn9TEREFj219z4MDg/YHkZacP4notQ7ek4njp7TiTWf/y4G+sOtcFExICulfqaU+iWAB0Xkr6VgAoAvAvhIvCETESVrS98mPNpzFw4N7rM9FOdx/iciF4RZ+7xaFXn9r/bg7///72PP3sOB9xdkHeTzAfwdgGcBTALwdQC/G/gZiIgcsaVvE7b0bcJzzz33nO2xpATnfyJKDS8klztpb8PGrdiwcWvg+T9IQB4CcBTABABNAF5RSlm5nFHf8SY8secsG09NRJnyqO0BpIUz8z8R5dOROcOhr6ZXLSgHFeQZf4LCBPlOAO8GcK2IPBT5GYmIKC04/xORdVEvM39wbsOYn6CCVJAXK6U2F/97D4ArROT6KAMlIqJU4fxPRE6IUkmOo2ZA9k2O/m33mxlOdYOD4/DqznYbT01ElDsuzf9EeTDxzOAnkQHAm6+0GhqJm7xKchJBOUgFmYiIiIgMCBuKqz02L4E5iWpyqgKyDAoaXw93qUAiIiIi18QJxrX2mYeg7O9LNhGWk2vmICIiIiIj4TjJ/bvmyJzhkR9dUlVBrh8EJr2mbA+DiIiIKLQwwfV9p22reNvjr88N/Fx5qCb7VQvJYSrNqQrIRERERGkUJBxXC8WV7lcrLE8883DuQnIlYSrMqQrI9QMKrduP2R4GERERkVZBw3G5xwWpKFM4qQrIRHl18SXzsHhJN6ZNa8G+fX24d/VGPLlhi+1hERFRALWqx5XC8S2d7ZgxeTnG18/E0Ild2H3o87irp7fs46uFZFaRw0tVQJaBQTS8tNP2MIgS1b2wC0s/fhmamhsBAB0drbj145dh3K4D2Lh2zDK1RESUIuXC8fVtz2Jy8xWYPeUO1NU1AwAaxs3C7Cl34BZ8MlJIpnC4igWR4xatWDASjj1NzY1YtGKBpREREZFpMyYvHwnHnrq6ZsyYvBzXtz1raVT5wYBM5Lj2zimhthMRUfqNr58ZajvpxYBM5LjengOhthMRUbrdf/ACDJ3YVfa2oRO7cP/BCxIeUf4wIBM5bs3KdRjoH716y0D/MaxZuc7SiIiISJdKfcPf7nkYw8P9o7YND/fj2z0Ph9oPRcOATOS4jWs340vLvoG9O/ZjeFhh7479+NKyb/AEPSKilKi1gkS5cPvCoWfwwI7VODDYC6WGcWCwFw/sWI0XDj0T6PFhnp/GStUqFkR5tXHtZgZiIqIMe/z1uWNWtHjh0DNlA3Hp40g/KwFZRK4BcDuAswG8SynFIz8RUQ5w/rej3CV2w1xVjOJ785XWmushl4bdckvAhQ3ErB5HY6uC/CKAqwD8i6XnJyIiOzj/G1QuCIe9L4OzOV5YDXLZaSBedZjBOB4rAVkptRUARMTG0xMRkSWc//ULE4rD7o9h2Ywg1eS4+6d4nO9BFpGbANwEAE11Ey2PhoiIkuKf/+vb2iyPxj26g3G152BQ1i9sNTnMPik+YwFZRP4DQEeZmz6tlPpO0P0opVYBWAUAreOnKU3DIyIiQ0zM/42nzeb8X5REMK70nAzK+pWG2jCBmYHYHGMBWSn1XlP7JiIid3H+N8NGMC43BoZksxh63WD/r42IiIiqciEce1waC5EptpZ5WwjgLgDtAB4VkZ8qpd5vYyxERJQczv/hRQ2kbdsGA9/34NyGUPtmJZmyztYqFmsBrLXx3EREZA/nf/PCBOPSx4QJygzJlGX8noSIiMhRYarHbdsGI4Xj0n2EwXYLyiq+s3Ome2EX1vzXZ/Boz91Y81+fQffCLttDIiKiMsKG40omvNiD+W/pwLdW34gn//2T+NbqGzH/LR2Y8GJP6H0R5YXz6yCTPt0Lu7D0zuvQ1NwIAJg++1QsvfM6AMDGtbzaKxFRlngBuPvK87D0jmvR1Fxon5g+awqW3nEtAGDjI8/h6DmdYx7btm0wdF8yUZawgpwji1YsGAnHnqbmRixascDSiIiIyLRFyy8fCceepuYGLFp+OQBUrCQHxTYLyiK+q3OkvXNKqO1ERJR+7TPLX4Ww0nYiYkDOld6eA6G2ExFRenmtE727Dpa93dtersUiDK5kQVnEgJwja1auw0D/sVHbBvqPYc3KdZZGRERElYQJnpX6hY+e04lVX3saA/2jT7wb6B/Eqq89XTEcs/+Y8o4n6eWIdyLeohUL0N45Bb09B7Bm5TqeoEdElAEH5zaUXYFiw8atAIAb/+RCTGtvwb7ePtzz1U0j28vthyjvGJBzZuPazQzEREQpcWTOcKiT4KqF5EqB2P/YKOMjyiIGZCIiIodFCcmeWmsax6kWMxxTljEgExEROS5sSPaYapdgOKas40l6REREKeBKKHVlHEQmsYJMRESUElErybqemyiOiWceDnzfN19pNTiS2hiQiYiIUsQLqkkFZQZjiipMIK712KQDMwMyERFRCvmDq4mwzGBMUcUJxrX2mVRQZkAmIiJKudIwGyUwMxCTDibCcen+kwjJDMhEREQZw7BLNoQJx+87bduYbY+/Pjfw85gOyQzIRERERBRLkHBcLhRXur1WWDYdkhmQiYiIiCiyWuG4VjCu9phqQdlkSGZAJiKruhd2YdGKBWjvnILengNYs3IdL4dORJQRlcLx9W3PYnLzFZgxeTnG18/E0Ild+HbPw3jh0DNjHh+09UInBmQisqZ7YReW3nkdmpobAQDTZ5+KpXdeBwAMyUREKRDlpDwvHM+ecgfq6poBAA3jZuHa2UsAYExIrvX8JqrIvJIeEVmzaMWCkXDsaWpuxKIVCyyNiIiITLq+7VkAwIzJy0fCsaeurhnXdF5lY1hjMCATkTXtnVNCbScionS7/+AFAIDx9TPL3l5pe9IYkInImt6eA6G2ExGRW6K2Nwyd2FV2+8Gh/XGGow0DMhFZs2blOgz0Hxu1baD/GNasXGdpREREpFO5E+zuP3gBvt3zMAaHB0ZtHxwewPrdD9R8vJ+pVSwYkInImo1rN+NLy76BvTv2Y3hYYe+O/fjSsm/wBD0iohSpFVLLhdwXDj2Db+9YhQODvVBqGAcGe/HtHatGnaBnY/UKD1exICKrNq7dzEBMRJRx/rDrLf32wqFnyq5YETQY80IhREREROSsN19pDbzkm47KMC81TURERETO80JrlLWRwz6HaQzIRERERAFM2l7+1K0jc4YTHonbTATlpIKxhwGZiIiIqESlMBz0vgzNo0Nt1LCcdDD2MCATERERIVwoDrMvhmV7QTcqLvNGRET/t737D66rPu88/nksfLlWbUCiQo4s0zCEqrAu04ycNAmZiRZIaneRsTNNi5ilEWHxtNMsYibNRo6nE9rJNIphOklLZqhpQTupgTBJbSwSxximhk0IWaOWdW2MUkiaYguwEgQ2K4Rs/OwflrRHtn7cX+d87zn3/ZrxwD3313OwdXhz+N5zgZq27KVFFY3j2V4f6RLkd8zM7jSzF8xsv5ltN7MLQswBAEgWx39Um6TiNe4IR2WF+p3aI2mVu18p6SeSNgWaAwCQLI7/qBohgpVITocgv0vu/pi7n5y8+Yyk1hBzAACSxfEf1SJkqBLJ1a8aPqT3GUnfmutOM9soaaMk5RctTWomAED8Cj7+1zU0JDUTakApgdowNDHv/aNtuaJn4MN71Su2QDazxyUtn+Wuze7+yORjNks6KWnbXK/j7lslbZWk8xdf5DGMChStY8NqdW9ap6YVjRo58rr6v7KTr0sGJsVx/D/34pUc/xHEmWF8TcfluvXmj+mipvN0dOSY7r3/ST2x99D044oNZVSn2ALZ3a+d734z+7Sk6yRd4+4c+JAaHRtWq+euG5WvP1eS1LzyQvXcdaMkEcmAOP6juhVz9ni2OP787WuVzy+WJC1vPl+fv32tJOmJvYemn1NoJHMWuXqFuorFGklfkLTO3cdCzACUqnvTuuk4npKvP1fdm9YFmghID47/SLNbb/7YdBxPyecX69abPxZoIsQl1CrxuyUtk7THzJ4zs3sCzQEUrWlFY1HbAczA8R+pdVHTeUVtR3oF+ZCeu78vxPsClTBy5HU1r7xw1u0A5sfxH6Edv/RUwcssRttyM5ZZHB05puXNZ38j3NGRYzOeU8wsqE5cZwQoUv9Xdmp87J0Z28bH3lH/V3YGmggAEJfRttx09N57/5MaHz8x4/7x8RO69/4nZzwO6UcgA0Xau/1Zff1PH9BrL/9Sp065Xnv5l/r6nz7AB/QAICVKOXM72pbTt195SXc8vEevvvamTp1yDb9+THc8vEfffuWlRGZAcqrhOshA6uzd/ixBDAApVsxSi6hdg0PaNThU9nujunEGGQAA1KQQoUocpwOBDAAAalaSwUocpwdLLAAAQE2bCtdSllwU8/pIDwIZAICYLb3kzaKf89bPzr6cGOIVDdlyY5koTjcCGQCAGJQSxXM9n1hO3pmBu1AwE8TZQiADAFBB5YbxfK9JKIdDANcWPqQHAECFxBHHZ75+3O8BgDPIAABURKHh+omL576G7mP/0Vbwe3E2GYgPgQwAQJkKieP5wni2xywUy0QyEB8CObCODavVvWmdmlY0auTI6+r/yk6+oQ0AUmShOJ4rjN9/wVVa+54uNSy+UKMnfqldrzyof3njhzOeV+gZZQCVxRrkgDo2rFbPXTeqeeWFWrTI1LzyQvXcdaM6NqwOPRoAIEb/fUWTulb+NzXmmmS2SI25Jn1q5Ua9/4KrZjxuobPOrEcG4kEgB9S9aZ3y9efO2JavP1fdm9YFmggAUEmzBe5NDU/rPRf0atGi+hnbc4vyWvuerqRGAzAPc/fQMxTMzEYk/byEp/6qpF9UeJyytbe3t8913+Dg4GAJL1mV+xkD9jNbQuznr7l7U8LviTJw/F9QVe5nDNjPbKna43+qArlUZvasu2d+3QL7mS3sJ1C+WvnzxX5mC/sZHkssAAAAgAgCGQAAAIiolUDeGnqAhLCf2cJ+AuWrlT9f7Ge2sJ+B1cQaZAAAAKBQtXIGGQAAACgIgQwAAABE1Ewgm9mdZvaCme03s+1mdkHomeJgZp8ys4NmdsrMqvLSKeUwszVmNmRmL5pZb+h54mBm95nZUTM7EHqWOJnZSjP7JzM7NPlntif0TMgmjv/ZwPE/O9Jw/K+ZQJa0R9Iqd79S0k8kbQo8T1wOSPqkpKdCD1JpZlYn6RuS1kq6QlKXmV0RdqpY9EtaE3qIBJyU9Dl3v1zShyT9SUZ/PxEex/+U4/ifOVV//K+ZQHb3x9z95OTNZyS1hpwnLu5+yN3P/m7TbPigpBfd/afuPiHpIUnXB56p4tz9KUmvh54jbu7+irv/8+TfH5d0SNKKsFMhizj+ZwLH/wxJw/G/ZgL5DJ+RtCv0ECjaCkkvR24fVpX9QKE0ZvZeSe+X9OOwk6AGcPxPJ47/GVWtx/9zQg9QSWb2uKTls9y12d0fmXzMZp0+tb8tydkqqZD9zCibZRvXKUw5M1sq6TuSbnf3Y6HnQTpx/Of4j/Sp5uN/pgLZ3a+d734z+7Sk6yRd4ym+APRC+5lhhyWtjNxulTQcaBZUgJkt1umD4zZ3/8fQ8yC9OP5nHsf/jKn243/NLLEwszWSviBpnbuPhZ4HJdkn6TIzu8TMcpJukLQz8EwokZmZpL+XdMjd/yr0PMgujv+ZwPE/Q9Jw/K+ZQJZ0t6RlkvaY2XNmdk/ogeJgZhvM7LCkD0v6rpntDj1TpUx+yOazknbr9IL+h939YNipKs/MHpT0I0ltZnbYzG4JPVNMrpJ0k6SrJ38mnzOz3w09FDKJ43/KcfzPnKo//vNV0wAAAEBELZ1BBgAAABZEIAMAAAARBDIAAAAQQSADAAAAEQQyAAAAEEEgI3PM7Ptm9oaZPRp6FgBAcjj+o1IIZGTRnTp9fUUAQG3h+I+KIJCRWmb2ATPbb2Z5M/sVMztoZqvc/QlJx0PPBwCIB8d/xO2c0AMApXL3fWa2U9KXJS2R9A/ufiDwWACAmHH8R9wIZKTdX0jaJ2lc0m2BZwEAJIfjP2LDEgukXaOkpZKWScoHngUAkByO/4gNgYy02yrpzyRtk/TVwLMAAJLD8R+xYYkFUsvM/lDSSXd/wMzqJD1tZldL+nNJvyFpqZkdlnSLu+8OOSsAoHI4/iNu5u6hZwAAAACqBkssAAAAgAgCGQAAAIggkAEAAIAIAhkAAACIIJABAACACAIZAAAAiCCQAQAAgAgCGQAAAIggkAEAAIAIAhkAAACIIJABAACACAIZAAAAiCCQAQAAgAgCGQACMLN/N7O3zeytyK+7J+/rNrMfLPD8O8zsvfPc32FmpyZf97iZDZnZzWXM1lLM/gFAmp0TegAAqGGd7v54MU8wsy9K+l+TN88xs82SnnD3Z2Z5+LC7t5qZSbpe0rfN7Mfu/nwcswFAVnAGGQDS5euS1ki6QdI9kp6fI46n+Wk7JI1KukKSzGydmR00szfMbK+ZXR734ACQFgQyAKSPR/767kIPNrNFZrZB0gWS/tXMfl3Sg5Jul9Qk6XuSBswsF9O8AJAqBDIAhLNj8gzu1K9bC3hOj6THJD0k6Y8lXWlmH5rjsS1m9oakX0j6kqSb3H1I0h9I+q6773H3E5LukrRE0kfmmG1HifsHAKnEGmQACGd9set83f0vJcnMrpZ00t2/PM/Dh929dZbtLZJ+HnnNU2b2sqQV5cwGAFlBIANACrn7HWU8fVjSb07dmPwQ30pJR8ocCwAygSUWAFB7Hpb0X8zsGjNbLOlzkt6R9HTYsQCgOhDIABDOwBnXGt6exJtOrkP+r5L+RqfXJ3fq9GXdJpJ4fwCodubuCz8KAAAAqBGcQQYAAAAiCGQAAAAggkAGAAAAIghkAAAAIILrIAMJyi1a4kvOOS/0GMiYy65cOed9/7b/5QQnQdYdO3H0F+7eFHoOIG4EMpCgJeecp49c9Aehx0DG9O+4Q82tjWdtf+3w6+r+0B3JD4TM+v6Rv/n5wo8C0o8lFgCQcv19Axofm3kJ4/GxCfX3DQSaCADSjTPIAJBye3cMSpK6ezvV1NKgkeFR9fcNTG8HABSHQAaADNi7Y5AgBoAKYYkFAAAAEEEgAwAAABEEMgAAABBBIAMAAAARBDIAAAAQQSADAAAAEQQyAAAAEEEgAwAAABEEMgAAABBBiRUFjwAAEZpJREFUIAMAAAARfNU0kHEd69vV3dupppYGjQyPqr9vgK8kBgBgHgQykGEd69vVs6VL+fqcJKm5tVE9W7okiUgGAGAOLLEAMqy7t3M6jqfk63Pq7u0MNBEAANWPQAYyrKmloajtAACAQAYybWR4tKjtAACAQAYyrb9vQONjEzO2jY9NqL9vINBEAABUPz6kB2TY1AfxuIoFAACFI5CBjNu7Y5AgBgCgCCyxAAAAACIIZAAAACCCQAYAAAAiCGQAAAAggkAGAAAAIghkAAAAIIJABspgZveZ2VEzOxB6FgAAUBkEMlCefklrQg8BAAAqh0AGyuDuT0l6PfQcAACgcvgmPSBmZrZR0kZJytctCzwNAABYCIEMxMzdt0raKknn55o98DgAytSxvl3dvZ1qamnQyPCo+vsG+Dp3IGMIZAAACtSxvl09W7qUr89JkppbG9WzpUuSiGQgQ1iDDABAgbp7O6fjeEq+Pqfu3s5AEwGIA4EMlMHMHpT0I0ltZnbYzG4JPROA+DS1NBS1HUA6scQCKIO7d4WeAUByRoZH1dzaOOt2ANnBGWQAAArU3zeg8bGJGdvGxybU3zcQaCIAceAMMgAABZr6IB5XsQCyjUAGAKAIe3cMEsRAxrHEAgAAAIggkAEAAIAIAhkAAACIIJABAACACAIZAAAAiCCQAQAAgAgCGQAAAIggkAEAAIAIAhkAAACIIJABAACACL5qGgAqpGN9u7p7O9XU0qCR4VH19w3wlcQAkEIEMgBUQMf6dvVs6VK+PidJam5tVM+WLkkikgEgZVhiAQAV0N3bOR3HU/L1OXX3dgaaCABQKgIZACqgqaWhqO0AgOrFEgsAmMfbq1YU9LijI8e0vPn8WbcX8hpLDhwpejYAQDwIZCBBp5YsLji4kB6jbTl9bfcP9aUbPq4lucXT29+eOKGv7f5hQa/BnwukAv8dhxpBIAPAHEbbcgs/aNKuwSFJ0m3XfVTLG5bp1dHj+utHf3B6e4Gv0zA0UdKcAIDKIpABZE4xYVtJuwaHpkO5FJWam9AGgPIQyECVCRV3KNzxS0+V9fxlL8X7+Wj+DCE2u0MPACSDQAYS9G7eiJcUKTeEi33duMMZAFAYAhlAzYsrhItFOANAdSCQAaRetQRuXCqxf0Q2ABSOQAYyKOvBiOLxZwIACkcgAylH+AAAUFn8PzcgQXXjXrFLcB2/9BRxDABADDiDDAQQjeRSrmpBGAMAEB8CGQhsKpYLDWXiuDosveTNkp731s/Or/AkAIBKI5CBKlFsKKOySg3euN+HoAaA5BHIQILaLluub/3drervG9CuF18t+vlxnz1OKhJROH5PUCn8xxZQOAIZSFhza6N6tnRJ/+NB7d0xqLdXrZhxf8PQxKxnkcuJYyILAMcBoHBcxQIok5mtMbMhM3vRzHoLeU6+Pqfu3s5Z76vEEoull7w54xcAACgcZ5CBMphZnaRvSPq4pMOS9pnZTnd/fqHnNrU0FPw+hZw9JoQBAKgMAhkozwclvejuP5UkM3tI0vWSFgzkoyPHzlpeUcrSCsIYAIDKIpCB8qyQ9HLk9mFJvx19gJltlLRRki6++GJJ0vj4Cd17/5PTjyk2jIliAADiQyAD5bFZtvmMG+5bJW2VpNWrV/urr72pr+3+oXa98pJUpWH8iYuHEnkfAAt77D/aQo8A1BwCGSjPYUkrI7dbJQ3P9eDnX35Nv/OX9521Pc5lFMQukG7l/gwT2EDxCGSgPPskXWZml0g6IukGSTfO9eB3zy38cm3FRjEhDGA2Zx4bCGZgYQQyUAZ3P2lmn5W0W1KdpPvc/WApr0UQA0jC1LGDUAbmRiADZXL370n6XiGPrTv33ZKXSxDEAAAkg0AGqhAxDCBun7h4iLPIwBwIZCBhoeL3poang7wvgPJ9c/QjFX9N4hiYG4EMJOi83Hhsr00AA9k12893HNEM4DQCGUgRIhjAlOjxoNhYXujs8Vs/O7+kmYCsIJCBKkMEAyjWTQ1PFxzJ88UxYQycRiADCbqw7i0CGEDVmS2Ml720KMAkQHUgkJF5ZnaepCZ3f+mM7Ve6+/5AYwFA4mY7e3xmHJ8Zxg1DE7HOBFQj/vMQmWZmvy/pBUnfMbODZvaByN39YaYCgOowXxw3DE1Mx/E1HZfrof/5R2pvb29PdEAgEAIZWfdFSe3u/luSbpb0TTP75OR9Fm4sAEjWQh/MOzOOp6x933J9/rY1Wt7M+mTUDpZYIOvq3P0VSXL3/21m/1nSo2bWKsnDjgYAlVHKJd/m+kBeNI6XHDii7r+7Vfn6XMmzAWnEGWRk3XEzu3TqxmQsd0i6XtJ/CjUUAFRKpa+HPNr2/2P47VUr1NTSUNHXB9KAM8jIuj+WtMjMrnD35yXJ3Y+b2RpJN4QdDQBKV0wYF/uteaNtuekzyUdHjrG8AjWHQEamufv/kSQzO2Bm35S0RVJ+8q+rJX0z4HgAULC4vznv+KWnZqxDnorke+9/Up+/fa3y+cWxvj9QTQhk1IrflvRVSU9LWiZpm6Srkh7il+8u5ethkWpcx3t+1fjzPdfZ46WXvHnWOuTZIvnbr7yk//vwHt123UdjnROoJgQyasUJSW9LWqLTZ5B/5u6nwo4EpE9cARgivKsxZiup2GUVU45fevrQGA3lXYND2jU4pP2Dg4MVGQ6ocgQyasU+SY9I+oCkCyX9rZn9nrv/XtixAEiFx2ohIZ318F1IqWF8pqlQlvhWPdQeAhm14hZ3f3by71+VdL2Z3RRyIADFq/X4lSoXwHNd5m020VgGagGBjJoQiePotsQ/oHdsIl+xf7kBafGJi4dCj1CyrP68FhPHQC0ikAEAsSomMpOI6axG73wIYqA4BDIAoGrMF6/FxHMWIpioBcIhkIEEvftOHf/SA3T6EmPFijt6+dk8Gx/OQ60ikAEAiVsoRksJ6HLfM4sIXKA0BDKQoLp3+BcWalcxV0IoNqArGb/8jAIgkAEAiSgmPBeK6VKCmPAFUCgCGUhQ3birYWgi9BhAUKNtuQUfs1DMrm1v023XfVTLG5bp1dHj+utHf6Bdg+VfAYOfTwASgQwASFipEToV1mvb2/SlGz6uJbnFkqSWxvP0pRs+LknTkUzoAigHgQwkaNHbJ7TkwJHQYwBV7e1VK2bdPhW9t3/xquk4nrIkt1i3/85VeuaBf533tfn5A1AIAhkAUFUWitiLms6bczsBDKAS+MQCACBVRoZHi9oOAMUikAEAqdLfN6DxsZlrjMfHJtTfNxBoIgBZwxILAECq7N0xKEnq7u1UU0uDRoZH1d83ML0dAMpFIAMAUmfvjkGCGEBsWGIBAAAARBDIQInM7FNmdtDMTpnZ6tDzAACAyiCQgdIdkPRJSU+FHgQAAFQOa5CBErn7IUkys9CjAACACiKQgZiZ2UZJGyUpX7cs8DQAAGAhBDIwDzN7XNLyWe7a7O6PFPIa7r5V0lZJOj/X7BUcDwAAxIBABubh7teGngEAACSLD+kBAAAAEQQyUCIz22BmhyV9WNJ3zWx36JkAAED5WGIBlMjdt0vaHnoOAABQWQQygMzpWN+u7t5ONbU0aGR4VP19A3wtMQCgYAQygEzpWN+uni1dytfnJEnNrY3q2dIlSUQyAKAgrEEGkCndvZ3TcTwlX59Td29noIkAAGlDIAPIlKaWhqK2AwBwJgIZQKaMDI8WtR0AgDMRyAAypb9vQONjEzO2jY9NqL9vINBEAIC04UN6ADJl6oN4XMUCAFAqAhlA5uzdMUgQAwBKxhILAAAAIIJABgAAACIIZAAAACCCQAYAAAAiCGQAAAAggkAGAAAAIghkAAAAIIJABgAAACIIZAAAACCCQAYAAAAi+KppAABSoGN9u7p7O9XU0qCR4VH19w3wlepATAhkAACqXMf6dvVs6VK+PidJam5tVM+WLkkikoEYsMQCAIAq193bOR3HU/L1OXX3dgaaCMg2AhkAgCrX1NJQ1HYA5SGQAQCociPDo0VtB1AeAhkAgCrX3zeg8bGJGdvGxybU3zcQaCIg2/iQHgAAVW7qg3hcxQJIBoEMAEAK7N0xSBADCWGJBQAAABBBIAMAAAARBDIAAAAQQSADAAAAEQQyAAAAEEEgAwAAABEEMgAAABBBIAMlMrM7zewFM9tvZtvN7ILQMwEAgPIRyEDp9kha5e5XSvqJpE2B5wEAABVAIAMlcvfH3P3k5M1nJLWGnAcAAFQGXzUNVMZnJH1rtjvMbKOkjZKUr1uW5ExA7DrWt6u7t1NNLQ0aGR5Vf98AX4cMIPUIZGAeZva4pOWz3LXZ3R+ZfMxmSSclbZvtNdx9q6StknR+rtljGhVIXMf6dvVs6VK+PidJam5tVM+WLkkikgGkGoEMzMPdr53vfjP7tKTrJF3j7sQvakp3b+d0HE/J1+fU3dtJIANINQIZKJGZrZH0BUkfc/ex0PMASWtqaShqOwCkBR/SA0p3t6RlkvaY2XNmdk/ogYAkjQyPFrUdANKCQAZK5O7vc/eV7v5bk7/+KPRMQJL6+wY0PjYxY9v42IT6+wYCTQQAlcESCwBASabWGXMVCwBZQyADAEq2d8cgQQwgc1hiAQAAAEQQyAAAAEAEgQwAAABEEMgAAABABIEMAAAARBDIAAAAQASBDAAAAEQQyAAAAEAEgQwAAABEEMgAAABABF81DSAWHevb1d3bqaaWBo0Mj6q/b4CvJAYApAKBDKDiOta3q2dLl/L1OUlSc2ujerZ0SRKRDACoeiyxAFBx3b2d03E8JV+fU3dvZ6CJAAAonLl76BmAmmFmI5J+XsZL/KqkX1RonNi0t7e3z3Xf4OBgOaeQU7H/Mav1fwbsf9j9/zV3bwr4/kAiCGQgRczsWXdfHXqOUGp9/yX+GbD/tb3/QFJYYgEAAABEEMgAAABABIEMpMvW0AMEVuv7L/HPgP0HEDvWIAMAAAARnEEGAAAAIghkAAAAIIJABlLGzO40sxfMbL+ZbTezC0LPlCQz+5SZHTSzU2ZWM5e7MrM1ZjZkZi+aWW/oeZJmZveZ2VEzOxB6lqSZ2Uoz+yczOzT5Z78n9ExA1hHIQPrskbTK3a+U9BNJmwLPk7QDkj4p6anQgyTFzOokfUPSWklXSOoysyvCTpW4fklrQg8RyElJn3P3yyV9SNKf1ODvP5AoAhlIGXd/zN1PTt58RlJryHmS5u6H3H0o9BwJ+6CkF939p+4+IekhSdcHnilR7v6UpNdDzxGCu7/i7v88+ffHJR2StCLsVEC2EchAun1G0q7QQyB2KyS9HLl9WARSTTKz90p6v6Qfh50EyLZzQg8A4Gxm9rik5bPctdndH5l8zGad/l+v25KcLQmF7H+NsVm2cY3OGmNmSyV9R9Lt7n4s9DxAlhHIQBVy92vnu9/MPi3pOknXeAYvZr7Q/tegw5JWRm63ShoONAsCMLPFOh3H29z9H0PPA2QdSyyAlDGzNZK+IGmdu4+FngeJ2CfpMjO7xMxykm6QtDPwTEiImZmkv5d0yN3/KvQ8QC0gkIH0uVvSMkl7zOw5M7sn9EBJMrMNZnZY0oclfdfMdoeeKW6TH8r8rKTdOv0BrYfd/WDYqZJlZg9K+pGkNjM7bGa3hJ4pQVdJuknS1ZM/88+Z2e+GHgrIMr5qGgAAAIjgDDIAAAAQQSADAAAAEQQyAAAAEEEgAwAAABEEMgAAABBBIANAhpjZ983sDTN7NPQsAJBWBDIAZMudOn3NXABAiQhkAEghM/uAme03s7yZ/YqZHTSzVe7+hKTjoecDgDQ7J/QAAIDiufs+M9sp6cuSlkj6B3c/EHgsAMgEAhkA0usvJO2TNC7ptsCzAEBmsMQCANKrUdJSScsk5QPPAgCZQSADQHptlfRnkrZJ+mrgWQAgM1hiAQApZGZ/KOmkuz9gZnWSnjazqyX9uaTfkLTUzA5LusXdd4ecFQDSxtw99AwAAABA1WCJBQAAABBBIAMAAAARBDIAAAAQQSADAAAAEQQyAAAAEEEgAwAAABEEMgAAABDx/wDmNJvTaDGwuwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -219,18 +218,69 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " fun: array([[-3.19946868]])\n", - " message: 'OK'\n", - " nfev: 50\n", - " success: True\n", - " x: array([[-2.25 , -1.29648192]])\n" + "iter # 0 - MLL [-15.4, -16.4] - fmin [-1.21] - constraints [-0.916]\n", + "iter # 1 - MLL [-17.3, -17.7] - fmin [-1.21] - constraints [-0.916]\n", + "iter # 2 - MLL [-14.4, -12.7] - fmin [-1.21] - constraints [-0.916]\n", + "iter # 3 - MLL [-11.2, -13.4] - fmin [-1.54] - constraints [-0.778]\n", + "iter # 4 - MLL [-15.2, -14.5] - fmin [-1.54] - constraints [-0.778]\n", + "iter # 5 - MLL [-15.8, -14.6] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 6 - MLL [-17.4, -14.8] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 7 - MLL [-18.4, -14.4] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 8 - MLL [-19.3, -15.1] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 9 - MLL [-21.0, -15.2] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 10 - MLL [-22.4, -15.1] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 11 - MLL [-23.5, -15.2] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 12 - MLL [-22.8, -14.3] - fmin [-1.63] - constraints [-0.615]\n", + "iter # 13 - MLL [-23.6, -14.9] - fmin [-1.88] - constraints [-0.921]\n", + "iter # 14 - MLL [-24.7, -15.7] - fmin [-1.88] - constraints [-0.921]\n", + "iter # 15 - MLL [-25.8, -16.2] - fmin [-1.88] - constraints [-0.921]\n", + "iter # 16 - MLL [-25.5, -15.3] - fmin [-1.88] - constraints [-0.921]\n", + "iter # 17 - MLL [-26.4, -15.1] - fmin [-2.3] - constraints [-0.889]\n", + "iter # 18 - MLL [-26.5, -15.2] - fmin [-2.65] - constraints [-0.567]\n", + "iter # 19 - MLL [-25.5, -15.3] - fmin [-2.85] - constraints [-0.393]\n", + "iter # 20 - MLL [-24.7, -15.4] - fmin [-2.96] - constraints [-0.299]\n", + "iter # 21 - MLL [-26.2, -18.3] - fmin [-2.96] - constraints [-0.299]\n", + "iter # 22 - MLL [-26.5, -17.1] - fmin [-3.17] - constraints [-0.493]\n", + "iter # 23 - MLL [-29.2, -18.2] - fmin [-3.17] - constraints [-0.493]\n", + "iter # 24 - MLL [-26.6, -15.9] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 25 - MLL [-27.7, -16.5] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 26 - MLL [-29.1, -18.1] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 27 - MLL [-29.9, -18.7] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 28 - MLL [-31.0, -18.6] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 29 - MLL [-32.3, -19.6] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 30 - MLL [-34.1, -18.8] - fmin [-3.18] - constraints [-0.64]\n", + "iter # 31 - MLL [-28.8, -14.1] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 32 - MLL [-29.7, -14.2] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 33 - MLL [-26.8, -10.7] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 34 - MLL [-27.2, -11.0] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 35 - MLL [-27.2, -11.9] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 36 - MLL [-24.9, -9.19] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 37 - MLL [-19.0, -3.94] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 38 - MLL [-18.5, -3.12] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 39 - MLL [-19.8, -3.26] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 40 - MLL [-21.5, -2.7] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 41 - MLL [-22.1, -1.64] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 42 - MLL [-22.6, -0.981] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 43 - MLL [-26.5, -4.06] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 44 - MLL [-26.5, -3.61] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 45 - MLL [-25.5, -1.62] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 46 - MLL [-26.3, -1.42] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 47 - MLL [-27.8, 0.0245] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 48 - MLL [-28.4, 0.894] - fmin [-3.2] - constraints [-0.571]\n", + "iter # 49 - MLL [-25.9, 3.7] - fmin [-3.2] - constraints [-0.571]\n", + " constraints: array([-0.57055951])\n", + " fun: array([-3.19946737])\n", + " message: 'OK'\n", + " nfev: 50\n", + " success: True\n", + " x: array([[-2.25 , -1.29638397]])\n" ] } ], @@ -241,9 +291,8 @@ " gpflowopt.optim.SciPyOptimizer(domain)])\n", "\n", "# Then run the BayesianOptimizer for 50 iterations\n", - "optimizer = gpflowopt.BayesianOptimizer(domain, joint, optimizer=acquisition_opt)\n", - "with optimizer.silent():\n", - " result = optimizer.optimize([townsend, constraint], n_iter=50)\n", + "optimizer = gpflowopt.BayesianOptimizer(domain, joint, optimizer=acquisition_opt, verbose=True)\n", + "result = optimizer.optimize([townsend, constraint], n_iter=50)\n", " \n", "print(result)" ] @@ -269,19 +318,19 @@ "output_type": "stream", "text": [ "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", - "[ 0.46705724 0.49762857]\n", + "[0.44676004 0.4866224 ]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 9.14493985]\n", - "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([ 0.25],[ 1.])\n", - "[ 3.10492691e-06]\n", + "[8.70831234]\n", + "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([0.25],[1.])\n", + "[1.96119947e-06]\n", "\n" ] }, { "data": { - "image/png": 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Kykp+4MABAEDozBEcavoCRSwko5LTeeNFF+P7U6cjJ3guY9oT6vOk8FXpexXFSDtM5dI6\nJV5FohZvrV/cwDmvtPuYCHnE9v/ao9hMJl3sIhmiAsfuuAWgLwYn5k/BV0ffi2DgXCY3FO7Fi0cf\nw8HOPUm3JVuoiiAzZqHhVNwCEL8pkhW5MEvw0OH0b9LhpdafGOr/Pekgh8PdaD61xhb3ePp55oXX\n0HEdEo+EeP7dd8gVVgCr4j6dcJUFRS0yg9hHsZmMWaEhGrkY8nFQSDiZEWZ6IvBg5x68ePQxnAod\nA+dhnAodSyuOgYhItUOoGsEOce6UkwyI3wzJcJOtYLeb7DkHecfv6lDXvBlvnHrVtv2o4iJnuoMM\nuPMdyMbJ7zTVTZrVtpnqb2F2sJ4dpBLkV3/jMiysnYwbp0/FgQMHxHt3wjUqKyv59u27sPHJ+owa\noGcEFd1kWU6yFdxwk+0S52adZMD64D0jeMFNrppdiXnLZ+Krs2cY6v89JZBLLini3/zlNNv3QwJZ\nHUggG0Pk6YXZNprubyFLJFfNqsC8ZTdgZNkwHG9qx6Y1LyTNHidDTyRXV03A/ffVIDs7C5WVlSSQ\nPUZ+3hh+VeW33T4MZVFRJAOZKZT9IJIB5yMXU6snYv6CKowalY9jxzqFb4ZTieSq2ZVYtPZWZOcO\nMdz/e199EEQGM3Rch3C0x8w6gDNCv2pWBRY9PBfFY4oQCDAUjynCoofnompWheVt33H7FGRnp66Z\nTKiLkXrAmUy6ShfJMBO5EEFW5MIKbsYuZDKt5B3fRi6mVk/EkqU1KCkpQCDAUFJSgCVLazC1eqLh\nbaWKXMxbPhPZuWI3kSSQCaVRzdFWCauZd7NCORlmS77FMm/ZDcjOjd9Odm4Q85bdIHQsem71qJGZ\nWfnAT8jML8oieOhw2h8nsZJNNopbuWSrOCWU7XasrYhkq6Xg0tF1PjMllP/uruuRnZ3Q95ss6agn\nkkeOHlhXPx0kkCVDA/UIJ5DZzkS2le6GJZ1ITsfIsmFCy1ORKJKPHe80dUyEeqgiko2KX6cFsxU3\nWQRRkSy7FJxZNKHsZVfZSgzFjEi2200uHq4/74HZko6JbvLxo23C2yCBTBCEVJGcinQu8vGm9pTL\nq2ZVYNNrq/Dix+uw6bVVQtGLJ36+B729+lNfE95DFZFsBqfEMkUu0mOXUHYi9+ykSAbsdZNbT+rP\ne3Ds2DljY2r1RDy9+W7s3LUMT2++21D8QhPJmx7aht5usfOBBDIRhx8GxamEHRERu55SyNqulaiF\n3gx64TDH/v/5k6l8cqyLvKv+EH74aB1aWukpj1/wskjWsFssO+EmOxW58KJQthuruWRV3GS92SA5\n58jOzsLU6omWMsqhCWOx451WrFvyNFoPnzR83FTFQgcrVSwAeZUs3BKrquV+vSzaZX+XTkR4jLZf\nu8q+/cPqm1F723UIBM51qr3dIZzuCaFg+NAB72890oZ5V69Kuj09Qb7n5WU0UYjH+MzIsXzCzO8k\nfV1mySij2CVq7artauaGIhOqXOghywF2UnSrXuUCSH3jNWNSOZZ+/XoU5uWAsZj+vzeE3t4zKCzM\nHbCO6KRBwUOH/T1RiOrIEjGqCVUiMzDaflO1Tysu8qQvXRonjoHIQL38os/ovj9dPpmmoc4M/OAm\na9jlKJtxk1WNXNjpJgPezCl73U1+eX8jekNn4sQxEBmsV1CQo7uOaEZZ5OaTBHICVxRei5eqF+MP\ntQ/iperFqCm7zO1DIggAzg4AlbEvsyJZdEAeD4fTZpJJJGcGVqtciGYc7ZzFC7BXKIuiWuQCsDeb\nHIvXxLLVbLKblS6SDdZLBmMwnEcWhQRyDFcUXotbxt6JstxCBBhDWW4hVl0+M+NEspcjDYQ8jIhk\nO6ahTjZQr7Pt0wH5ZM45Bg0eJL1mMuFtzIhkGXVY7cIOoeyHKheAM25yLKJC2Y3Z/ABrbjLgXqWL\nZIP1TnX1DMgoAwBj9p2rpIRiqCmdi2AgO25ZzuAgFk2wP/dMECpiVSSbKfumN1CvtzuEn678DdY9\nsBmtR9oQDnOcPXN24KO4FDWTyUXOLERF8vwFVabqsNrtIsciWyj7JXIBOOcma3jFVfaCmxyL3mC9\nntN9eORXv8Xqp3ag+UQn9MbOma2ZnIrBUrfmcYZlDdddXpIjPuhu6LgOqdNOO03eewHKQCuEm/W1\nrbbl9vJgykF7PZeOjhOv2rTSyaab1v598eN1utszUzOZ8CeaSDYiApNlGY1kHEMTxjo6IYi2L1ni\nvOC908I3FHkfccMD+DSRbFQ8aSJZVJxpItmJQXyxJIrkWNdYBQGtfR9mbyIuGHNc6MZF+zsbvTnS\n2lHeRxwv728EANwz+zoUD89D68kubNi6t3/5y/sb8X9PfAd6Lc9szeRkkECOob3vJIqCAxtBSw+V\nhXILinuoQTqR3DU+nPJvZUYka0I4Gceb2lE8ZuDsSMkiGkDERTYyox/hLzrGD0krko8d60RJycA2\nHluHNRVOi2RArlDWvh8RoSwikoGIYBJxGD88MtJUlQu3hLKGCqJYDytCWfs7iAplkScIXeezfpGs\nCWI9Wk92oXTEQDFs9Fw1CqmPGOqaNyMU7o1b1nMmhHWHdrp0RO5C4pQQwY48ciqSRTE2rXnB0nEQ\n/iTdAL6NT9ajtzehPfWGsPHJepuPzDqyYxciqBq5AJyPXXgFlbPJRiYXSRbDkH2ukgKK4Y1Tr+LX\nhx9HU/cphDlHU/cprHpzG+qa/mhqe1Yfi9940cWoWzkfbzx6H+pWzkdNhbX6zARhBSfyyCIiuf65\nhrhMcuuRNqx7YHNa55myyJlNMpG8e9dBrH2kDi0tHQiHOVpaOrD2kTrs3nXQ8LZl5pGrZldi0++/\nhxebfoxNv/8eqmanLtsqM5+sYpULK9lkEsoDcaMknAjpysFpeeQw52g+0YnVT+3A1g/fE9pHOmii\nkASsThKSiNns5o0XXYyHqqcjNyurf1lPqA8PbtmJuobkjx7swM0sstddbBnfnZv5Yz3Stel0f7N0\nk4gA9ovYl44+RhOFeIzs0WP5pTXJJwoxg12Ti1gVqlWzK7Fo7a3Izj0n5nu7T2PdkqdRv/WAoW3I\nEuui2WQVJxbRcCty4QWcnGRE5OZItHIKkP683l2/3FD/TxlkRVl6zeQ4cQwAOcEsLKydbLtArqko\nx8LaySgZloeW9i6s374Pz5w6ZOs+Ce9gdx4ZGJhJTqRqVkXSQXyEf4kdzCMDI9lkM1jNI89bPjNO\nHANAdu4QzFs+07BADh46bEokT62eiPkLqjBqVD6OHevExifrhZw57W+j2gA+wP1sciJ6JeDcyi9P\nK3nH0UF8ZgbwacyYVJ50EB8gNjg3Fd625zyAWfevLE9/NGbJMLEi2qLUVJRj5ZxpKCvKj9SCLsrH\nyjnT8LXCCbbuVw+vu8eAPz6DHunatQznPFncompWBRY9PBfFY4qo/nGGYiSnaBSrk4skw4qDO3L0\nwMGnqZYnQzR2kawW9OwLxgvtF7C3ZjLg32zyzfmv9/84jZOxC7ORixmTyrHitukoHRHRKKUj8rHi\ntumYMWng03+r57U/r94+oKlLfzRmS7t+EW1ZLKydjJygvnPtV7FHOI+Z+sga85bdgOzchHq1Keof\nE/5FtlCWTWjCWFNC+fjRNqHl6TAqklPVgla1ZrKfs8luiWWrQtkoZgbw3TP7OuQMSdAoQ7Jwz+zr\ndNexcl57SvF0hrKx4+Ny6TlhuzHjIj/yu33o7ksYpRnqw/rt+2Qdli7JHGptOYlkQsOqi2x20F6y\nOsexy6tmVWDTa6vSTkFN+ANZQlkVN3nTQ9vQ2x0vRnu7T2PTQ9tMH4MRN9lILWjVBvAB/nWTY3FD\nLJsVyna6ycmmotaWz5hUjhfWLMD/PfEdvLBmAWZMKjd9XntW7WhCWaZYVkl4P//uO1i+aweOdHZE\nKmq0dToyQC+ZQx273AmRTELcHqaf15j0xwxuiORkdY615RTByFxUd5ONUr/1ANYteRqth09GKrQc\nPik0QC8VqURysjqyictVm6Ya8KabbDZv7LRYVslNbu7Q1yitJ7vSxi9Ez2tPVbHIvbCM/9WPFqR9\nn5mLvd3i2MpMZE6KRS2DHBuzSFY9w87qFn4SyFa/JxlVLETOCdFzwUjbllnZQhPAsTGL3u5Qf4m3\nTa+t0p1ApPVIG+ZdvQoAVbHwItmjx/Lz71ps+P2yBvLJHsTn9GQiydAT7FoGOTZm0dsbSlnuzszN\nBFW6iEeG0HVqcJ+ZGwmZVS5qLynH6toEjXK6D6uf2oF7Zl+nO4FI84lO3LDsyf7fGzYuMdT/u6pC\nGGM/Y4wdY4z9SeZ2Y91lIy6zE86xaqW6klHX0IgHt+xEU1tnWufaTyKWOIfoDaZTbVtzktPVPzYS\nwSDcxa6+PxaZsQuZyKyVbAW9yIWZWtDkJquBU66y25GL7W83YsX2nTh6KqJRjp7qxIoXd+Ll/Y1p\n4xeiuOogM8a+COATAE9xzi9N936jDrKqmHWRVReisp1k1T+vKG47yGbjE7KdZBkuMpC+RjI5yOoj\n2vcD4g5yIjIc5Uxyk81gd81kwL9ush3C1m5X2W03OZH6f5jvHweZc/4KAHPDcj2IWaHj5kQdRsh7\nL+A7USsTN78bs+LY6rp6yMgjA+ln20s1BbU2eK+iooICyS7iRt8vw1GWPYhPJTdZBnZXuQD86ybb\nIWbtdpXddpMTeeSVfegJDZyCesPWvQDODeAz2v8rr2oYY3cyxg4wxg6c6eh2+3CIFMgQgiS01UJE\nJDsZI0olkpNFMAD0D94jvEFc/3/6EynblCWUZaGSSJYhlFWMXACZUekiFXYJZSuVLoxiVCQnxi+0\nKahf3t8YN4DPKK4P0mOMXQBgeyZELAD/xiwSMeN6e+0zimDlKYAV4WnGBb6i8FrUlM7FsKzhaO87\nibrmzfjBWycMr+9U1AIQm5I6NnpRWVmJAwcOyCl5QJhCpO8HgCHjxvDSVfee+92ESNLDavRCZuwi\n0yMXgP0D+AC1YxdOVaZI5lhPzJ+C64u/hYKsEejoO4Hftv4CBzv3GNqmapGL2HP7hTUL+sWx0f7f\nv4pEUbwyWM8qorELP4tjL3FF4bW4ZeydKAqOBGMBFAVH4paxd+IfPzfCsWOwMolIKmiQnr8QnWQg\nGVYdZb+6yTIgN1kcp6pR6DnKE/On4Kuj70VhcBQYC6AwOApfHX0vJuZPMbRNM26ynZGL2HPbzEA9\nUiWErWhCWU8Ap3rNCjUV5ahbOR9vPHof6lbOR02F+/Wt3bgBMOMe15TORTCQHbcsGMhGTelcw9tw\nYhpqjXR55FiS1U8mvI0mlK2KZasiWZZQVkkkeylyMeTjIG4cNwH7broL79/2APbddBduHDch6Tok\nkiPECuXri7+l2/9fX/wtoW06EbkQFcqtJ8VnIXa7zNtmAP8LoJwxdoQxNt/N43GKTHGRE4kVxHYJ\nRq2Oc1lRpFB4WVE+Vs6ZpoRINouVGtqiDMsannS57EF7qRBxkY2KZL3Be4Q72NX3WxXKqrjJZqeo\ntgNZItnuAXy1l5TjB1fXYMzQAgQYw5ihBVhzTU1akeyFAXxOcHP+6yjI0n9SmGx5KlRzk/UG8KXD\n7SoWcznnpZzzLM75GM75RjePh/A+C2snxxUQB4CcYBYW1k526Yi8RXvfyZTLjYpkJ11kwJhIjh28\nR7iL3X2/m0LZr26yDOx0kxdPHdj35w7OwgNXpo8HqOgmO+kia5w5qz+mo6PP+BiURFQcwGcUT0Us\nzp4e5PYhuIbqpd5UoWSYfs4o2XIn8ULOuq55M0Lh3rhloXAv6po3O34sollkoyJ53tWr0NDQ0GD2\nuAjv4LZQloEqbrLbkYt0Qrm0QL+PL/uMsaoFVt1kO3BaJB/v+D7C4fhqYaFwL37b+gtL21UlcrH9\n7UZMfWyj4f5f/St2Ap98UODoI2e7yNSYhd20tOvnjJItJ+J549Sr+PXhx9EWOg7Ow2gLHcevDz+O\nN0692v8eVV1kwJhIFsktE/5AhlA2A7nJ+piJXACp3eTmDv0+vulT444hYN5Ntity4aRI7up+Di3t\nS9F35jA4D6PvzGGcaF9suIpFKlSLXBjB9TJvIgw5bywfvfS+/t+9LjJFhb4XHEi30TLIcfO0h/qS\nTpftNGYlw5+bAAAgAElEQVRFoZm2bmdm2Ogse1ZLvgFiZd80Ysu/Vc2qwLxlN2Bk2TB0dvUAAL5U\n/UUq8+YxEsu8WdqWhRJxZsvC+a0cnJul4ICBNy21l5Rjde3Avn/F9p3Y/najo+Xg7CgF51T5t1TI\nEut2l4PTzu/aS8qxeOpklBbkobmjC7999z1cf9F4zPzSVP+XefOLm0zIo66hEQ9u2Ymmtkih8Ka2\nTmXEMWD+Jser7dwNFzmWqlkV/ZODBAIMhQW5KCzItXWfhD0Eg2eEHaVkWHGUzcYu/OYmy4xcyHCT\nEyeJOHqqs18cA86Wg/O6k5wMWSLdrJtslNPnhTBjynisrp2G0YWRAfujC/Nx61Wfx+hCD00UIkKi\ngxyLnW6ynhNn1EFLh4jwIQfZH5CLHI/MiUMS+dWTd+jOnEcThXiPvPISXvHv34xbZmVwVSxmHWVy\nkyN4aWIRQPxRvEoTi6jgJAPqu8n7broLY4bqX3sybqIQuxy2ZALDyZJXhL+gGx0xrEwcQpOD+BvN\nUbbqKpt1lMlNjuB2lQu7JxdRaQCfCk4yINdNFsXI+W50cGYqfHWldvoxNIlkgrCOnTGLY8fFBugQ\n3sWrQlkGKlS6UC1ykQ4vRy5IJKcXyaKDM/XwlUAGvCeSvT7QkHAO1XLIsqpZ2MkTP99Dk4NkGDJc\nZStCWRRyk/Vxwk3WZuATgURyPHpTVptBdi754df3oPtM/MQgopFi3wlkwHsi2ShUC9k/UMzCGX63\n4X/6JwcJhzk6Tn6CUx3d6VckfIEbQlkFN9ltZIpkFd1ks5EL2aXgVBHJgHtucrJz/PkPDmHZ7+pw\n5JMOhDnHkU868NQ7r+PIJ8YNG98M0tNDhnMlIn7NDtyjgXqZiZkbHhqoJ0ZsubdYXjr6WAPnvNLU\nRglX0BukJ4qVQX1mHsm7PYiPBvCpO4BP5uA9VQbuAeoP3gOAj+YtM9T/+1pt+dVJJvyBmZsd1WIW\nTmFmoF4ycUxkLlbiF+Qmm8NLkQvAnJtsBj87ySpGLszga4HsFWRnNGsqylG3cj7eePQ+1K2cj5oK\nOSXpCCIRlWfVI4hUqC6UzWaTp1ZPxNOb78bOXcvw9Oa7MfmeGa4LZYpc6CMzcqGSSAbUi1yYwVMC\neZCJp06Z5iJrM8mVFUWKY5cV5WPlnGkkkhXFiciMrJrdXoLcY/+RP7jXlKuUDqeFsigiInlq9UQs\nWVqDkpICBAIMJSUFWLK0BlOrJyohkr3kJntxAN+znVcqJZRVrnJhhMGWt+Awee8FyG1KwcLayXFT\nbQJATjALC2sn2z6bXE1FORbWTkbJsDy0tHfhlT+9hy9eOr7/9/Xb9ykzo52X+eSDAqp+QmQ0sRdM\nWeJCu6CKipzT54WEhJQmkkXEmiaS0wnD+QuqkJ0dfyzZ2UHMX1CF3bsO9otkO7LJVbMrMW/5TIwc\nXYTjR9uwf+cfMWnaZf2/b3poG+q3HkDw0GEpYl37LkRd9ryPuNCNypCPg0I3Qh8eGWlKnO1suVja\nzd+znVcqk0u+Of91KaJ9Wsk7wuf6BWOOWxp34CkHWUPUdfOCi2xU8KS7OSgZlie0XBZ6zvXXr/s8\nOdkGoIGXBGEezVWWJS687CaPGqU/OULictluctXsSixaeyuKxw5HIMBQPHY4brh9Stzvi9beiqrZ\nkXFRKrjJIoi6yVYiF7Lwq5PsZOTCs1dmEhX6tLR3GV4+rDEU92MFPeeasfgLgOZkE86TSTGLVPGK\nqlkV2PTaKlRUVFQ4eEiEQ8gSy1ZiFyLIziYfO6Y/OYLecpmTi8xbPhPZufHHlNj/Z+cOwbzlM+OW\nuS2SVRzA59dcsqzBe4D5yMWN4yZg3013Ge7/Pa0yRUSybBf5isJrsXzCBvzwc5uxfMIGXFF4rdTt\nm2X99n3oCcUXx+4J9WH99n1xy/QEsRWhbNShttvJ9iqqPxXxA1WzKrDo4bkoHlPk9qEQDmCnUNYu\ntO/f9gD23XQXbhw3of81N93kjU/Wo7c3ft+9vSFsfLI+6XZkiOSRo42dU3rvy4QBfGagXHJqRM/t\n6uIr8PC1X8aYocavnZ4WyIA7TvIVhdfilrF3oig4EowFUBQciVvG3hknkt0arFfX0IgHt+xEU1sn\nwpyjqa0TD27ZGZf9TSeCzYjkZM612fepAlUE8Q/zlt2A7FzxcnGEt5EtlG8cNwFrrqnBmKEFCDCG\nMUMLsOaamjiRDJhzk0VJFMm7dx3E2kfq0NLSgXCYo6WlA2sfqcPuXQdTbseqSD5+tM3S+1SIXCQK\n5dpLyrH73vk4tOI+7L53PmovOdf3U+TCGm6I5DvG1yB7kFj/77lBelaQNbippnQugoHsuGXBQDZq\nSufijVOvWt6+VeoaGi0PhhvWGBKqPbt++z6snDMtLmbBOY97zKbnZKuMlqvWPpOWowbgycGGOz4u\nd7XKytBxHa463yPLhrm2b8J9ZAzsu2DMcTxw5V3IHRwfJ8sdnIUHrpyC5z84FLdcE8lGxZSMAXy7\ndx1MK4j1sDKAb9ND27Bo7a1xMYvE/r+3+zQ2PbQt5XZUGcBXe0k5Vtee6/tHF+ZjdW2k79/+9rk+\n1IkBfFpblZGx9+vgPSD9OT0qW7z/97yDDDjvIg/LGi603CiqVSYQcZL1nOtf7f1DSidbdVJVBLED\nillYJ1X++HhTu4NHQqiMFVe57DP6A+GSLQfccZPNYkag1m89gHVLnkbr4ZMIhzlaD5/ECz/fE/f7\nuiVPo37rgbTbUsFNXjxVv+9fPHVg3+9kzWQZqBS5kCnW053Px3rF+3/fOMh2lX/Tc93a+06iKDiw\ngbf3nYz7ffp5jbYMjuoaH3bspkDESdZzrr//m3objsoZ3KoIYiduu8ipsLtdb1rzAhY9PJdiFkQ/\nRt2nWI71tqMkZ2CWtulT/QFyGm64yWYx4ybXbz0wQAD/ZPkzpo9BppssevNQWqDfxydbrv1NnXCT\n/VYKTpaTDKQ+n594rw73T7hFKGbhCwdZBBmuW13zZoTCvXHLQuFe1DVvtrxtFbFa4cKriFQEkQW5\nyPZR/1wD1j2wGa1HjOUlicxBxFF+4r069J6N7xO7z/Th4df3GFrf726yTNwawNd6Ur+Pb+5I3fd7\nscqFCm6yzAoXgL6bvKv1Dfzw0K/R0mO8//eVQHbKVX3j1Kv49eHH0RY6Ds7DaAsdx68PP65E/piQ\nh9GKIF4jk0q+JVL/XAPmXb0KDQ0NDW4fC6EeRkRy7IU2zDlaetqw9p1n8FbfK4b3I1rpItNFstOR\niw1b96Ln9MC+/0e70/f9XotcAOoM4JMtkhPP512tb2DO7x4y3P/7JmLhNG+cejWjBLHooD0/oMVF\nYmcHdGI2QJotkiDcw0jsYlfrG9jV+saA5aIzd4nMwue1yIVsnIxcvLw/0sffM/s6FA/PQ+vJLmzY\nuhfbW941tA+vRi4AuSLVDDIjF4C5Gfg0GOdiNQDdJLd4LL9wzuK07zMiLkQHxJnNbZpx64w8Nrfi\nlluJTGSaSHYLUYHsVHvWQ6SNp2vbqdq1kXabapBeLC8dfayBc15p6M2EElz6uSD/zYsj+n93yvUy\ne3EVdQVFnEfR2r2AdZGs4aZIBuQ62mZcdlE3XzRSY3bWN1lCGXBfJAP2nN/aubznS2sN9f++ilhk\nEuQw+hu740KZHLMg/IGWW7T7Ym5WeIgKHdHIhZkZ+GSQiZGLWERn4PNqzWS3Yxd2nNui57IvBTJN\nQ50aKy5wpg7YUx0arEdkMnaLZbNl4USnrLZ7AF+qaapFkDlNtVloBr6ByBTJgBrZZDdFsqeU5KBe\neXEQUUFBjhvhNH50kVWr9U34DzvFshWhbBQawGccFdxkEbxW5QLwp0g2iqcEMkEQmQvl3wlR7BTK\nolDkwj7cFskUubAfJ+JUiXhOIBt9xO/lmIVRl81KDpliFt5ApB27FbOQOeCPsvWEHdiVZxQVyhS5\nsA+KXAyE3GRreFdFugDFLAi/QW2ayCRUGPgDUOTCLjIhckFusnNusqcEcvmFJdjyi7twc+l4KdvL\n9IFN9MiaIAivkB38HD5buh95ubMsb8uJmbvSYaebTJEL/0YuAHKTNewWyp4SyABQUlyA+++rQU2F\nO86Xao4bPZL2P16IWRCEE2QNHouSYY9IEcmA3AusUwP4RKDIhXX8FLkA/OcmA/bFLlwVyIyxLzPG\nGhljf2GMLTO6XnZ2FhbWTk77PrtyyKqJZCuQi0w43Z7trGTRc+lo27ZNyMVs/x8I5GJkwXelHosK\nbrJRKHJhHBUiF34fwAf41012TSAzxgYB+DGAGgATAcxljE00un5JYZ5dh6YETpbDIpGsPqoPOpU5\nUC8V1Fb9gdX+f/Ag+TdCbrvJFLmwD3KTB2JH5MJvQtnNq+5fA/gL5/x9znkIwBYAM42ufOx4p5SD\nMPtI2ojr5pQzRzELwk9Qe84ILPX/Z84am1LcDF5zk0WgyIUcVBXJ5CafQ4ZQdlMgjwYQ22KPRJfF\nwRi7kzF2gDF24PjxSMfR29uHJ36+x5mjTEEqAey1GAY5c85RU1GOupXz8caj96Fu5XzpefpMzyFT\nzMITmO7/w+FuHO/4vq0HJ9tNFoUiF/agQuTi5pKLsPve+Ti04j7svnc+ai9J3v/TAD7rWDmX1X5u\nC4Bz/jjnvJJzXjly5Ei0tHbgh4/WYVf9IbcPDYD3hHAqjIpkEtOpGdYYSloruqaiHCvnTENZUT4C\njKGsKB8r50wzJJJVj1kQhGwS+/++M4fR0r4UXd3PObJ/ilycQ6ZIVkEoy0A0cjFjUjlW3DYdowsj\n/f/ownysrp2WUiQDNIBPBmaEsptX3KMAYs+SMdFlSWn8cwvmfOun/eJYlQkrdnxcPuDHaWQ9lm4v\nD6YUwCSOUxPbJvWE8sLaycgJZsUtywkaG3RqJzLarEgO2UrGntqgLxDu/3tDb+H95kmOiWMNilyc\nQ1bkAshMN/me2dchZ8jA/n/x1PT9Pw3gk4PI+eymQP49gAsZY+MYY0EAcwA8L3snRlw3VR9JOzlQ\nT0N7/L+77gH88pf/gKu/cVm/aCZhkppkN2yxy0uG6Q8uTbbcLKq2aaNYveGjmIXyONL/yyJTIhcz\nJpXjhTUL8H9PfAcvrFmAGZP0b5z9IpIBZ93k4uH6/XxpgfH+nyIXzuGaQOacnwFwD4CXARwC8Azn\n/G23joew9vifSI0mklvau3RfT7Y8EdVjFk5VszACiWR18Wr/73bkQgRRkTzlKxdjxW3TUToi0v+X\njsjHitumpxTJNIBvIKlEcutJ/X4+2fJkeHEAn2qRCyO4erXlnP835/wizvl4zvm/unksfsCq66bq\n438/sX77PvSE+uKW9YT6sH77PpeOyD0oZpHZeLX/dzNyYWcuefHUyQMf/w/Jwj2zr0u5nl/cZCci\nFxu27kXP6YT+/3QfNmzdSzPwKYjadhThKE49/s9UhjWGUNfQiAe37ERTWyfCnKOprRMPbtmJugZ1\nnFdVkJGrJxeZsAM/Ri6SPeZPFguIhQbwDUQvcvHy/kasfmoHmk9E+v/mE51Y/dQOvLz/XP+vYs1k\nQL6b7AUGu30AhFy6xodNP4Zvae9CWVG+7nJCHnUNjb4WxNPPa3RkoGp7edDQQN2eS0cj50/21c0l\nMpeb81+XcrGfVvKOsAC5YMxxIbFz+rxQSjHV3NGF0YUD+3+jj/81kWym/FkioQljpcYeRNH2LUOs\nF7x3Ou4G4uX9jXGCWA9NJBsdRKn9XY0+MdDajWhsZ2fLxaZu6PR4tvNK26aIlgU5yFFUHdTk5EA9\nevxvPzIqrxi9AVK1TcfixkBUgpCJ27lkWZGLH+3W7/83bN0rdEx+iVwANAOfHjIjF6rnkkkg+xCz\nj6bp8b8zqFKe0E5kDdZL15aNZpEpakHYiddKwekJ5e1vN2LF9p04eirS/x891YkV23fi2ZZ3hY+H\nIhf6+EUkA5kRuWCci335bpJXMIZXfOHeuGVGLpBGBaOqbpYZJ1D1agd+xaj4tTrIzI42LbsChdGY\nRbr2na4ti9xwaFGLl44+1sA5rzS8IuE6oy8p5Hc/M1n5x7KyLvZmBIio2BEVU6JiDZATuQDkClWz\nyBLrZm8gROtWi9bFFo1cAOZu6JLh1Ll98XnNhvp/UlEuU1N2GV6qXow/1D6Il6oXo6bsMinblTVx\nCEHYTToRL8tFBshJ9gOqP5YVuchPzJ+Cb1/4Myyf+Dy+feHPMDF/Sv9rTkUuvDRNtV/cZIpc6KPa\neZ0RAlnVzGZN2WVYdflMlOUWRuoO5xZi1eUzB4hkVZ1twj288IRApZrIsZBI9gcqC2UjInli/hR8\ndfS9KAyOAmMBFAZH4auj740TyYD9kQvAmWmq/ZJNdmMGvljsLgfnds1klc7rlFdZxlg+Y2y8zvLP\n2XdImcOiCdOQMzi+4eYMDmLRhGlStk8usrpkQg7ZSYy6yNVVE7DlF3ehoqKiwuZD8jxe6f9VuqDG\nkk4kX1/8LQQD2XHLgoFsXF/8rQHvNSuSVZqmGqABfHr4zU2WhR3ndF7uLHy2dL/h/j+pQGaMfQ3A\nOwB+wxh7mzF2VczLm6wdJgEAJTn6jnWy5WYgkewsNIFFPEZdZKsxCyD9d19dNQH331eDkmL1q3u4\njRf7fxWFcqp6yQVZI4SWm4lcAM5NU20Uilzo4yeRrGLkIi93FkqGPYKswcbbTCoHeTmACs755wHc\nDuA/GGOzo6+JnxUewcmYRUuPvihIttwsJJIzFy+UenOKO26fguzsrPRvJAAP9/+qiWRA303u6Duh\n+95kyzUochGPCiKZIhcDUS1yMbLguwgEcoXWSSWQB3HOmwGAc/5/AK4HsIIxthCAd0pfKMy6QzvR\ncya+I+o5E8K6QzsHvJdyyM5QU1GOupXz8caj96Fu5XzUVNg/4YXfUcVFHjVy4CQIRFI83f+r6ibH\n8tvWXyAU7o1bFgr34retv0i7LbdrJuvhBze5anYlNv3+e3ix6cfY9PvvoWq28UI3FLkYiEqRi8GD\nxMeepBLIXbH5s2hnWQVgJoBLhPdEDKCu6Y9Y9eY2NHWfitQd7j6FVW9uQ13TH6XvS1UXWSVBWlNR\njpVzpqGsKD8yaLIoHyvnTBM+JqMxC6s5ZC8M1JONFZF87Hin7MPxM77o/1UTyrGRi4Ode/Di0cdw\nKnQMnIdxKnQMLx59DAc79xjentcjFzMmleM/f/1t7Ny9DE9vvhtTqycKrZ+IFZFcNbsSi9beiuKx\nwxEIMBSPHY5Fa28VFslec5NF8HLk4sxZ8dlUk9ZBZoxdDqAbQBbn/GDM8iwAczjn/2HyOE1jtg4y\nIC4QVXRsZTwuV0lUaYI0J3jusXdPqM+1yUnqVs7XnWq7qa0TNQ9uFNqWSvWQjbZlO6tOyKqJDBhv\nw4l/Ay2DnJ2dhcrKShw4cEDpqICbqNj/a3WQraBSDWWZwt2LNZNnTCrHitumI2fIuf6/tzeEtY/U\nYfeugynWTI8Zkbrp999D8djhA5a3Hj6JeVf9s/D2ZEY/zDjtVDM5Hi2DHAjkGu7/k15pOOdvcs7/\nDOAZxtg/sgg5AH4E4G6hIyOkIEO0q+QkL6ydHCeOASAnmIWFtdYugmYpGZYntJwwjqyYBWC8DSfe\nfOyqP4QfPlqHllb1bn5Vw6/9v0qOcqoBfKJ4sWbyPbOvixPHAJCdHcT8BVWG95EMM5GLkaOLhJan\ngwbwDUR25ELkXO7qfg4t7UvRd8b438WIFTMJwFgAvwPwewBNAK41vAdFUMk5dRtVRLJqgrSlvUto\neSqomoUa6InkOd/6KRoaGhpcOiSv4Yv+PxHVhLIsvDSAr3i4fj8/qjjflQF8x4+2CS03AkUuBiIz\ncgGIPYnp6n4O7zdPMtz/G1GNfQB6AOQAyAbwAedcDYVlI34f/a+CSJYpSGWwfvs+9IT64pb1hPqw\nfvs+U9sjkRyPGy4yQH8Hi/i6//erSPbCAL7Wk/r9vLbc6QF8mx7aht7ueBHa230amx7aZvkYvDSA\nz2tVLgD7bniNCOTfI9JBXgXgOgBzGWO/ln4kJsm0CRdkZqO7xoddFcqyBalV6hoa8eCWnWhq64wM\nmmzrdC0PTciFRLJplOn/O89kS72oaqjiJsuMXABqDuCLFcobtu5Fz+mE/v90HzZs3dv/u5Pl4Oq3\nHsC6JU+j9fBJhMMcrYdPYt2Sp1G/9YCU/avgJovgtcgFIP9cTjpIr/8NjFVyzg8kLPtbVQbpAfYN\n1AP8O1gvEbciKDUV5VhYOxklw/LQ0t6F9dv3+U6QJruJkyHajLZpI+3YiamhjQ7WA+QO2NPQ/hZ7\nXl7WwDk3Pjw9Q1Gq/y8v4RX//s3+32UO+NFQZRBfpgzgmzGpHPfMvg7Fw/PQerILG7buxcv79fsh\nM6IwGTLzwWbw0gA+JwbvAc6ezxef12yo/08rkFXCqkAG/CGS7Yx/UFZbPnYKZEBeJYt0AvmKwmtR\nUzoXw7KGo73vJOqaN+ONU68aPk4NmRUtAHMimQSy90gUyBoklI2hmlAWdTQBEsnJcKLKxYwp4/HA\nlVNQ9pl8NH3aiYdf34PnPziU9P1uV7lIJPacNiqQSQ15EDsFuwrZZL/RXh70/CP+KwqvxS1j70RR\ncCQYC6AoOBK3jL0TVxTaN17LaDsXbbNe/1sQ8dgVu1ABL0UuAJqBTwQvRS5qLynHD66uwZihBQgw\nhjFDC7DmmhrcOG5C0nVUGMAXixa/EDm3fSGQ7c4h+33AXiJuZ5P9SqwwU1GkpXJ2a0rnIhjIjlsW\nDGSjpnSu8H6ciHIQmYUdF1Y/ZpO9MoAvHarNwGcFNwfwGa1ysXjqwJKsuYOz8MCVU1KuZ0YkA/bc\n9JrBFwJZBLMRAtVEshOxDxLK8vGqmzwsa2AB/VTLZWGXi0z4E7uEsgp4yU22OoDPCOQm62OHm1xa\noF+Sr+wzAyfWSsRKlQu3hXLGCWRCHBLKRHvfSaHlbkBtlNDws0iW6SaLYmfkAiA3WVWR3NyhX5Kv\n6dNOw9u34ia7JZR9I5BFYhbkIptDE8okRDKPuubNCIV745aFwr2oa95sansiMQuRdk5tk9CwY0IC\nlYSyDLw4A58efnOTZSAzcvGj3folWf9tx6uO1EwG3BHKvhHITpHpIlmDhLK3sNpu3zj1Kn59+HG0\nhY6D8zDaQsfx68OPm6piYQYSyYRZyE1Oj2puspnIBSDXTXYT1SIX299uxIrtO3H0VGSOgKOnOrFi\n+05sfztidDhRM1nDSZHsizJvsdhd8k0jk0q/GYHKw9nLd2+qws3XXo5BAYazYY5nX30T3/9NPQDv\n1ULWEKmJDIi38VRt8q31i6nMm8cYMm4ML111r+k6q4DcMlKqlIIDvFUOzmzNZBH8VA7uzp/8HW64\n4UoMGsRw9izHCy+8jg3rdwpvx4lScKKRGsB83WTA/Pm84tIXqcybnbgtSFWDHGX7+O5NVfj6dZ/H\n4EEBMMYweFAAX7/u8/juTVXS9yUqWq0gKsZFb0qpPfoTq49pZaGKkwx4y02mAXzG+YeHvoZZsyow\neHC07x8cwKxZFbhn4TThbdlZ5UJDdJpqwLqbbKej7DuB7EQWWUMlkayKo01CWT43X3s5GIu/QDDG\ncPO1lwMg9z4V1Bb9iwpZRpVyyYDcbLIoNIBPPl+97Trdvv+GG8y3OVWnqVZRKGf8lVWGSFZFKA8d\n10FC2YcMCuhfGJIt9xJ2u8gAiWQ/Q27yQGS5yX6qmexVNzkwSF+fDBpkre93SiS7JZRlndu+FMii\nE4fIcOBUEcmAOm4yQEJZBmfD+h1TsuVWcTJmYQYSyUQiJJIH4hU32YnIBeDNAXzhs/r91tmz1vt+\nJyIXgLibDFiLXWjIEMq+FMiA/bPr6UEiOTkklM3z7KtvInEwLeeRgXoiqNQ+3YDan79RYUICFUWy\nm26yCBS5GMiLT+3V7fv/+6lXlKpykQ433GQNK66ypwRyoKcv/ZtMIivHqVrkQjVIKIvz/d/U41d7\n/4AzZ8PgnOPM2TB+tfcP/VUsvI6Zyhlm2za1Pf/jtpusmkgG3HOTqWayNX6y/Bm88PM9OHvmLDjn\nOHvmLF74+R78ZPkzANyfWMQpN1mGUAbEb4Y9VeatIFjMrxn1dfRcOtrwOqLT+sq8gKokUFUR7bHQ\n4DJ5yCz1pqFyyTcNs+36g0VLqcybx9DKvIlgpoSUrFJwKpWBi0WGgLe7FBzgrXJwbpeCkynUnSgH\nB5grCQdYKwunsedLa9Ut88YYu4Ux9jZjLMwYE75I5fzpqOH3upFH1lBJlKo0gE+D3DzCKqq1aSI9\nZvt/FmKOlJDys5MMyBHuFLmIx+1pqlWbWMQIZtxkQK6jnA63LLw/AfgbAK+IrHTh58Zi02urUDWr\ngkSySVQTFBS5cBaR9qhyTWTC05jq/y8tLcbue+fjprzLhHZm5oIqK5fsZ5EMqBm5yNSayYA3Ixcq\nC2VXBDLn/BDn3NQVsXhMERY9PNdzIlkloUxuMuEnVGvLRGqs9P+jC/OxunYabsq7zDNussoiWZab\nLIrf3WQ3kSmSnXSTVRTKyodAGWN3MsYOMMYOHD8eOamyc4OYt+wGAGJxC1FkZ2RVEsmAekKZRLJ5\n7Mpze8VFVqkdE/LQ6/9zgllYPHUyAHMTEogiSySrLJStQiI5HopciLvJgPnYBWCPULZNIDPG/ocx\n9iedn5ki2+GcP845r+ScV44cee7DjywbJnxMZkq/+V0kA2qJCyuRi5qKctStnI83Hr0PdSvno6ZC\n7Xq+hFxUaseZjt39f2lBXv//vSKSAbXdZKs4NbGInlCuvaQcu++dj0Mr7sPue+ej9pJI30+RC/dE\nMuC8mwycE8oyxLJtAplz/iXO+aU6P9tkbP94U3v//+2MWgCZI5JVEhiiIrmmohwr50xDWVE+Aoyh\nrCgfK+dMI5Gsg2j784qLDJBIVgW7+//mjq6430UvqpRLHohXJhYB4t3k2kvKsbp2GkYXRvp+LYaj\niVjL+fgAACAASURBVGTAfTfZTVSIXLghlAHrrrLyEQs9ertD2LTmhbhlJJLloJLAEBHJC2snIyeY\nFbcsJ5iFhbWTZR9WSsjFdh+V2jAhn55QH360e5/ua15xk1UWyV6LXCyeqt/3azEcDbtF8tTqiXh6\n893YuWsZnt58N6ZWT+x/LdMjF4A5NxmQK5RFz3e3yrzNZowdAfAFAC8yxl42um7rkTase2Az6p9r\nGPAaiWQ5qOQmG41clAzLE1puB15ysf3sIgMkklXGSv9/9FQnVmzfie1vJ28jZtxkUfwskgF3c8mi\nkYvYuE0sesvtilxMrZ6IJUtrUFJSgECAoaSkAEuW1sSJZIDcZLNuMiBHKANi57tbVSy2cs7HcM6H\ncM6LOeczZG2bRLI8VBIZ6URyS3uX0HI7UMXFtgsnRbIMVGq/xDns7P9jcSJyYZVMGLxnt5vc9Gmn\n7vLEGE4sst3k+QuqkJ0d396ys4OYv6BqwHv9IpIBa26y20LZCJ6LWMSWeUsGieTk1JRdhpeqF+MP\ntQ/iperFqClLXVNUJZGRyk1ev30fekLxU5H3hPqwfrv+o1g7UMHFthunRLKsusgqtV/COnr50lTY\nHbnw0uC9vNxZ+Gzpflw05gg+W7ofebmz0q7jhVzyw6/vQfeZgX1/shiOhkyRPGpUvtByv0UunI5d\nAM4IZc8JZCC+zFsySCQPpKbsMqy6fCbKcgsjEYDcQqy6fKanRDKgL5TrGhrx4JadaGrrRJhzNLV1\n4sEtO1HX4NwEFG672KJtUuWnFzJRrf0S1sgJZmHpF40/lXFCJKseucjLnYWSYY8ga/BYMBZA1uCx\nKBn2iC9E8vMfHMKy39XhyCcdCHOOI590pI3haMiKXBw7pu9iJ1uuQW6yNTcZsFcoe1IgA8bKvJFI\njmfRhGnIGRzfkHIGB7FowrS066ooMjShrInluoZG1Dy4EVfc9yhqHtzoqDgG1HCxncBrLjKgVq6e\nsE7x8Dyhi6qXcsl2COWRBd9FIJAbtywQyMXIgu8aWt9tkZxOKD//wSFM/s1P8dmnHsbk3/wUv+n6\no9A+rLrJG5+sR29vvIbo7Q1h45P1abdDIjmCikLZswKZh8MDYhZVsyqw6bVVePHjdY5MSe01SnL0\nBXiy5YmoLDJixbJbE46o4GKLYvamzIsiGVDzRo8QJxzmmDGpPO6CmqwWbixeyCUD8t3kwYNGCy3X\nw02RDKg9scjuXQex9pE6tLR0IBzmaGnpwNpH6rB710FD2/GbSFZBKMsQy4Mtb8ElBg0ehEUPzwUA\n1D/XgKpZFVj08Fxk50a+FC2rDAB1f2kxvN1hjSG0lxv/YvPeC0gVZJ98UGDbRbylpwNluYW6y0UY\nOq5D+cfzZv4mMp4I1DU0Ki2IZbLj43LpAtYJSCR7n8GDAlhx23QAwMv7GzFjUjlW1E7rHySrZZUB\nDHjUPuTjoJB4+vDISCFxpolks0JQ49nOK6WJ0jNnjyJr8EARduasfTPRpkL7bkRvKC4Yc1zopkX7\nOxsVS5pIFhFosSLZqCDWQxPJMsWqCNp+ZYn1gvdOW6olnfcRN3XTEov2dxe9WdLwrIMMxGeR5y27\noV8cJ75u53TUgHeiFusO7UTPmfiG0nMmhHWHdgpvy48iI9GF9uLU12baopX25oST7EURTthPzpAs\n3DP7OgDAPbOvM1QLVyPTIhfHO76PcLg7blk43I3jHd8X2o4swa7hRCk4wHvTVLuJn9xkDbOusqcF\nMnAui5wsk6wt91oe2Q7qmv6IVW9uQ1P3qUgEoPsUVr25DXVNYnktDT+K5ES8LJadgkQy4RbFw/Pi\n/k0kWY1cDS+IZMB65KKr+zm0tC9F35nD4DyMvjOH0dK+FF3dzwlvSwWRDKgdubCKn0QyoI5QBsTO\nefVVXRq0Kadjp57Wex3wlki2y0Wua/ojvrzrR/j89pX48q4fmRbHGpkgkjX8LJattjcSyYQbtJ7s\nivs3kVS1cDW8NEW1FaHc1f0c3m+ehHePjMH7zZNMiWMNL4tkEaFs18QiRvBTKTgNKyIZkCuUjeBp\ngRw75fSmNS+gtzuU9HUNGrQnn0wSyRoqC2WzN2okkgkv0XO6Dxu27gUAbNi6Fz2n+wa8nq4WroZX\nIheA2pOLWMEpkQyQmyyCam4y4JxQ9pxAPnvmLDjn6Dj5CU73hHD/+tuw6bVVAIB1D2xG65E2hMPc\ntSmpveAi20EmimRAbaHsBiSSCTs5czYMzjnaO7vRG+rD9xZ8BS+sWQAAWP3UDjSfiFSQaT7RidVP\n7cCe/xYTXV4RyYD7Qlm2iwxYE8kUubAPOwYOWhXJwDmhbJdYZpw7Z1dbpSBYzK8Z9fUBFSuAiFuc\nTBDr0XOp8fI2IlUtNGSJJq8JTy+JetmolkM32wZltDknRKwVMf7HG7/XwDmvlHg4hM3k543hV1V+\nG1OrJ2Lx/V9BzpBzg/J6Tvdh9VM78PL+5O1OROCIiiczLqbVKheJ2CFYjWCXSDd7M2HmxkV08JYZ\nQSZDEALuVbnQsEOsy7qRAIyd5+/+y2JD/b9aV3SDpKpYYRSvRC28Jji9Juhl4hc32SttjpzkzGT+\ngqo4cQzEV7RIhh8nFonFbUdZNhS50MevbrKsGwiZjrInBXK6ihVGsbP8m2puIuEcqohkN9ugVycS\nIdRn1Kh83eXJKlnEInrh9MoAvlj8JJQpcqGPH0UyYI9QtiKWPanijFSskI2bVS284uhpZLKLrOF1\nkSyjzZFIJuzg2LFO3eXJKlkkInrRFH38roKbDDgnlO2OdliJolCVC/uwo8qFhiyRrGFWKHtSIBut\nWGEEr0QtvAaJZO9HLrwmkkkoZwYbn6xHb29C/98b6q9oYRQVIxd2CmUvu8pOimSA3GQRvOAma4gK\nZU8K5PrnGgxXrHCbTI5akEiO4LZIttIGvSSSAXKTM4Hduw5i7SN1aGnpQDjM0dLSgbWP1OG1X74l\nfEG10002E7kA7BPKgLfF8rSSdyhykQQV3GS7sEMoG8WTVSzswM6qFpla0ULDaxERO1DhRslKO7Ta\n9pwWrulEOVWx8B5aFQsjmBEeqlW5AORXukiG1ZiE04Lbyg0EVbmwF7uFuoybioaNS/xbxcJrqCCO\n3MSrwl4mbrvIQGYM2tOgyEVmY0Zs2B25UM1NjsWKs+yGG201ckFusn3YLdCddJR97SBXzarAvGU3\nYGTZMBxvasemNS8kjWFQXWT7ISfZ/ZslN11kQN9JvqLwWtSUzsWwrOFo7zuJuubNeOPUq5b3paEn\nzslB9h4iDjIATK2eiPkLqjCqOB+tJ7uwYevelHWSY1HRTQacc5S9BLnJyXHTTTYq0vvP01H5OHas\nExufrMfuXQcN78fMjUXGO8jaZCLFY4oQCDAUjynCoofnompWhe777R6s57YwUgEvi3tZuO0ku51H\nTuSKwmtxy9g7URQcCcYCKAqOxC1j78QVhddK2we5yZnH1OqJWLK0BiUlBQgwhtIR+Vhx23TMmGTs\nSYaKbjJgT7ULr0MD+JLjpptspMpF3HkaYCgpKcCSpTWYWj3R8H40R9kOV9m3qk3GZCIq4nUXlkSy\n+7gpkhPd3JrSuQgGsuOWBQPZqCmda2k/epBIzhzmL6hCdnZ8/29kMpFY7KyZDJhzLwHnYhdegiIX\nqVE1cqF3nmZnBzF/QZWpfckWyp4SyBd+biw2vbYqqQsci6zJRJJBLrJ5Ml0ku+0iA+q0xWFZw4WW\nW4XcZO9yUXkJnt58tyF3KelkIkXpJxOJxUyVCyfdZBLK57BS5QLwd81kwH03WY9k52my5UaRJZTV\nuEoKkC4qoeHGZCKEcYaO68h4oew2bk0iEusit/ed1H1PsuVEZmP0EWyyyUSOHeu0fQAfYM5NJqEs\nB3KTU+OWUNaLXKQ6T2VgNX7hOYEMGItKyJxMRDW8HrOIhUSyu7g50x4A1DVvRijcG7csFO5FXfNm\nKdsn/IeRR7DJJhPZ+GQ9AHMOk91uMmA+dgGQUI6F3OT0qOAmpztPZWLmnB8s/SgcIl1UQqtWYbSK\nRTKqqybgjtunYNTIfBw73oknfr4Hu+oPmT7uvPcCSjxiV4mh4zp8JfqN0DU+rEzMwWyb/OSDAtM3\nODs+Lsf08xr7q1XYWcWC8B/pHsFqo+DTjY4veO90StExY1I57pl9HYqH58VVwhARN0M+DgqJJ00k\nm612oYlkqngR+Q7M3jRo37/ITcvp80JCN0Vd5zPhpxMd44dIy9lqItnpahfa/nZHf7dSxUIUke/O\nU2XeKisr+YEDBwAALa0dmPOtnwIQq0CRisRSb9VVE3D/fTXIzs7qX9bb24cfPlrXL5Kp5Js8Mk0k\nqyKQNcy0Sytt0M0s8NrPP0Nl3jxGbP/fevgk5l31z9JcMD2RPGNSOVbcNh05Q871/z2n+7D6qR39\n5eJEXUDRx/GAtbJwAAllDZXLwblZCi4Wt8rCOe1m765f7t8yb729fXji53v6fxepYSzCHbdPiRPH\nAJCdnYU7bp9iabuqCSNV8Jvg9xpm2qWVmxqnJw8h/EFv92lsemgbAHkXdL3Hr/fMvi5OHAMDK2HY\nnU0GrOWTAYpeaKicTVYhcgG4m09WEc8ptZbWjjgHV8OqSNZbf9TIJCMskywnrEOD98xTU1GOupXz\n8caj96Fu5XzUVIgLUKdFMkGI0Hr4JNYteRr1Ww/0LzNSb9UosSK5eLh+xYvE5U5kkwFr+WSAhDLg\nTjZZBCsD+KZWT8TTm+/Gzl3LDFd7SYYbQlnmeSwLTwnkxj+3YM63fpo0AyzbST52PMkIyyTLncbP\nwoREshg1FeVYOWcayoryEWAMZUX5WDlnmmMi2SzkIhNG+fObH2PeVf8cJ45jke0mt57s0n092XIz\nbrKT1S40SChbE8qibrITA/iu/sbnsPj+r1iacEOPTBfKnhLIRui5dLSwUE72/id+vge9vX1xyxLj\nHWahmEV6/O4myxysubB2MnKCCY+Dg1lYWDvZ1PZE26efb9YI7yDzwvqzn/4WPafj+/+e033YsHVv\n0nVE3WTAndgFQEIZcHYWPjvdZL04kJUJNxLJVKHsW5Umw03eVX8IP3y0Di2tHQiHuW68w8yEIYQY\nfhfKMigZpv84ONlyIzglkslFJmQi68K6e9dB/OiH/42Wlg6EOUfzic64AXqpcMJNBuQJ5UzGD25y\nsjiQ1Qk3Esk0oezZMm9G0ERyqioX6YT0rvpDlsq62Y2VUlteI/ZzynAs9b43rzqhLe1dKCsa2Bm2\ntOs/DiYIvxM8dNjyxXz3roP9JadEB0RpIlm0JBwg7jZ+eGSkpWoXVBrOekk4N8vBtZ7sQumIgf1/\na5s9/b8b5eG0fTkp0H0tkDXsqnJhFaqJbJ5EcZtM2IrePBjdrmqs374PK+dMi4tZ9IT6sH77Pkvb\nFW2jmXTDRqiPzIuqNoDPjFAWzZSK1k4GrNdPBkgoa5/bjFAWrZus/X2NCuVUInnD1r26JQk3bN0r\ntW5yIn4Xyq5ELBhjP2SMvcMYe4sxtpUxVmhkvbPZ4iM8icxAi2Ek/sjarurUNTTiwS070dTWiTDn\naGrrxINbdqKuwXqtYSfy8hSzyBzM9v9WkHkBNztVtddiF5kcvbAauxBBRuTi5f2NWP3UDjSf6NSN\nA9lREi4WN6eutlOcuzJRCGNsOoDdnPMzjLEfAADn/B/TrZdbPJZfOGex6dyv7FnxNMxMFqJBk4Z4\nBzvcZC8N1hRpq2bao5MTh9BEIe5htv8vyBrFrxl+s+n9Vs2uxLzlMzFydJG0GbvMig4z5bzMTDIC\n0EQjVnFyghG7JxcB7JlgJBY3B9YZFepKTxTCOd/BOT8T/fU1AGPs3qc2K15JcbQMSnEB7r+vBtVV\nE+zedUq8JJAyHdVvQoY1hpL+OI1XoimE87jR/1fNrsSitbeieOxwqWWwzIoNM+LGjJsMUP1kq3jN\nTU6HE26yG44yIN9VVkGd/R2AumQvMsbuZIwdYIwdONPzKQBzjq1ds+IRmcmNF12MV+YtwF/uXYxX\n5i3AjReJX0Bk3RwZEcEyhLLdN3MUs8hIDPf/oXCP6Z3MWz4T2bnxokBWGSy9WfiMQLELbxErlKuL\nr8CWa5Zj99QfYss1y1FdfEXS9ZyodGEGPwtlQI5Ytu2Kxxj7H8bYn3R+Zsa8558AnAHwdLLtcM4f\n55xXcs4rB+d8xvTx+H1WPHLsnGHouA7ceNHFeKh6OsbkFyDAGMbkF+Ch6ummRLJVREWvVaEsIpKp\nTWYudvT/wUCO6eMZObpId7nMMlhOu8luzMYHUFm4RRcV4/4Jt6AkpwgBxlCSU4T7J9ySUiQDarrJ\ngPmYkFHcFMkaZsWybQKZc/4lzvmlOj/bAIAxNg9ALYBbuYkgtKiLrPKseBSz8BZLr5mM3Kz4pxG5\nWVlYeo25STnMYkXoUv1uwk7s7v9FOX60Lely2VNVO+UmA+5OMpKpXF/8LWQPiv/eswcFccf4mrTr\nmnGTRSA3OT0i57pbVSy+DOABADdyzrvNbkdEJNs5Kx6RWZTl6btOyZbbgQyBa9ZNttNFppiF/5HV\n/4uw6aFt6O2OF6693aex6aFt/b+7XekCcD52YYVMjVwUZI3QXT4qe5jhbagwuYgemeAmi+BWHeQN\nAIYA2MkYA4DXOOd32blDrVqFHVUsVIFq0DpDU1cnxuQPFH5NXe4/jTDDsMaQ8BMZquFNWMDx/r9+\n6wEA6K9icfxoGzY9tK1/uYYKdZMB79VOdrvSxc35rw9Y9mznlbbsq6PvBAqDowYsP9bbLrQdM3WT\nRScXAcQjPFp79VPtZLO4UubNLFqZt0TcflxspcxbLDIEBwlk+5maNQkPVU+Pi1l09/Vh+a4deP5d\n4xcKs9Eau9q7aDs22l5F26QT5d6ozJv3sFrmTRTZbpfqJeG8WA5OTxinQoZonpg/BV8dfS+Cgez+\nZaFwL148+hgOdu4x5arbWQ4OoJJwibzU+hN1y7wRhJd5/t13sHzXDhzp7ECYcxzp7BAWxyoiKryN\nCnyKWRBeRPYkBKqXhLOaTXY6biEqjrV1tB+zHOzcgxePPoZToWPgPIxToWP94hgwd6Ng5wA+QO2S\ncCqTEVNNZxIUs3CG5999x5IgVs09jt2+rCciBOEHgocOS7uQW5muGhBzkzWRbCZ2YdZNViFuYZRY\nkSzqLB/s3NMviPUwM2W13VNVA6mnq06F3dNVqxq38IWDTBd0IhNwKkoksh+7XGSCSIRnu9fPZ6Kb\nbBYnnGQrDnCy7cneJqCum2wGO91klapcxOILgewXqNwbQUSgmAWhh9sXURLJxvBqdQsZEYxEzMzE\n58TkIipWunD7/E6EFJkPIbfOPjLluyUXmVAVt90mr9ZNdjqX7FWRrGGHUBYlU91kVfCNQKaYBeEV\nzDwpcKNSi9vVYQgiFW5fSL3oJjtdM9nrIhmQK5SdcpNFUNFNdvvc1vCNQPYLFLNQF3JAk0PtlnAD\ncpPVj1z4QSQD8oWyKHZGLgBrbrIdqCCS6armU0jM+Qc3nVzZ+xZpl5RDJozi9sXUbTeZRLJzyBLK\nZt1kEUgkW4MEMkEYgG440kMuMuEmbl9MZbvJojiZS3Ybu2bJE8EtN1nVyIVduWQ3z2tfXdEoh0zY\ngUxx7EURSVlkwiu4HbkA5LnJTuaSRTEjkv3kImvIdJNFyaTIhVvntPeu1ophhyiXJaLI9bSO29+h\nH8UpxSwIuyGRbL9INoMfRTIgRyhT5CI1bpzTJJAJIglui2OVMCrUveiQE/6ERLK9IpnyyANxw012\nInJhBj+IZMa5eLjfLUrHXcgbGhpQMiwPLe1dWL99H+oaGuPeI8Nxq66agDtun4JRI/Nx7Hgnnvj5\nHuyqP6T7XqsOck1FORbWTh7wmbrGhy1tV0PWtNM1ZZdh0YRpKMkpQEtPB9Yd2om6pj9K2baK2CGO\nVSjvJtK2E2kvDyZtr7EYbbtG2+b08xrTv8kgVxRei5rSuZh+TQ0OHDhgrucnXGH06L/iDQ0NGDUq\nH8eOdWLjk/XYvetg2vVEhWrV7ErMWz4TI0cX4fjRNmx6aBvqtx4we9gpL+pTqydi/oIqw5/JjOgQ\nFTh6AurGcRPwwJVTUPaZfDR92omHX9+D5z+I9BtmpqWWNR21HbPfycJqTtrMjUSqm5bEv+G/7XgV\n29823rfmfcQxY1I57pl9HYqH56H1ZBc2bN2Ll/en3oYdU1SbvfnUzu2vzp5hqP/3lN1TWpSPsqJ8\nBBhDWVE+Vs6ZhpoKuY9gq6sm4P77alBSXIBAgKGkuAD331eD6qoJUvcDRMTxyjnTdD+TSjGLmrLL\nsOrymSjLLYwcZ24hVl0+EzVll0k4QvVQxTm2Qxxbads3l45P2l7tRFbM4orCa3HL2DtRFHR/kBEh\nTklJAUpKom23pABLltZgavXEtOuJuE5VsyuxaO2tKB47HIEAQ/HY4Vi09lZUza40fdzJLuZTqydi\nydIaoc/kRIWLRCf5xnETsOaaGowZWoAAYxgztABrrqnBjeMi/QblkfVRKXKh9zf83o3TMGPKeMPb\nnvKVi7HitukoHRHp/0tH5GPFbdMxY1Lq/lkVJzn23DaKpwRygMUL/pxgFhbWTpa6jztun4Ls7Ky4\nZdnZWbjj9ilS9wMAC2snIycYvy87PpNVFk2YhpzB8Z1mzuAgFk2Y5tIR2Ydd4liF6IHVtn3H7VMM\ntVcVPqseNaVzEQxku30YhElYQv+fnR3E/AVVhtY1ekGdt3wmsnPjL+jZuUMwb/lMQ+snQ08kz19Q\nhezs+H5V5DOJYEUkP3DlFOQOjj/vcwdn4YErz/UbKlS2UBFZ2WQR9ERyqr+h0cjF4qmTkTMkof8f\nkoV7Zl+Xdl0VRLLeuZ0OT0UsRowYwS+44IIByxsaGhrSrQrghJF9VFRUVCR7zcB+hLBhX4Y/pwhO\nficGseVzKojUz2n172hjO3Dk7xl7/B9++CFOnDhBEQsPQf1/Wqj/9xfSPqeMv2Em9v+eEshmYYwd\n4Jybf0bmEehz+gv6nARhnUxpX/Q5/QV9TvdR81koQRAEQRAEQbgECWSCIAiCIAiCiCFTBPLjbh+A\nQ9Dn9Bf0OQnCOpnSvuhz+gv6nC6TERlkgiAIgiAIgjBKpjjIBEEQBEEQBGEIEsgEQRAEQRAEEUPG\nCGTG2A8ZY+8wxt5ijG1ljBW6fUx2wBi7hTH2NmMszBhTsnSKFRhjX2aMNTLG/sIYW+b28dgBY+xn\n7P+xd//RVafXfe8/W4CMFIh05MF4hJCtReYqY3MZtyIY4+U1NCHEmTFBM05sk2vZjH3re7OSFLe9\npbRObkiT1Sqk7g21vZJ6NR6l2CF1M5gxZmJraIvdhsEzKCueyp4oNiEjJBHMWEdYWGAhznP/EF/N\nV4dzpPPr+/O8X2uxZnT042xJR8/ZZ3/3sx+z75rZcNSxBMnMNprZfzezb919zB6IOiakE+t/OrD+\np0cS1v+6SZAlPStps3Nui6S/lvQvIo4nKMOSHpf0tagDqTUzWyHpU5J+VtKbJO0zs+XPmk2eAUnv\njDqIEMxJ+qfOuTdJ2i7pl1P6+0T0WP8TjvU/dWK//tdNguycG3TOzd1987ykjijjCYpz7iXn3EjU\ncQRkm6TvOOf+xjk3K+lPJFV3BmwMOee+Jmky6jiC5py74pz7i7v/Py3pJUkboo0KacT6nwqs/ymS\nhPW/bhLkPB+S9GdRB4GybZB02ff2mGL2B4XKmNkbJf09SV+PNhLUAdb/ZGL9T6m4rv8row6glszs\njKTXF3jXx5xzT9/9mI9pvrT/uTBjq6VSvk8gKcxsjaSnJH3UOff9qONBMrH+s/4jeeK8/qcqQXbO\n7Vrq/Wa2X9K7JP2US/AA6OW+zxQbl7TR93bH3duQUGa2SvOL4+eccyeijgfJxfqfeqz/KRP39b9u\nWizM7J2SDkr6OefcTNTxoCIvSHrAzLrMrFHS+yR9MeKYUCEzM0l/KOkl59y/izoepBfrfyqw/qdI\nEtb/ukmQJX1S0lpJz5rZX5rZH0QdUBDM7DEzG5P0NkmnzewrUcdUK3c32fyKpK9ovqH/8865b0Yb\nVe2Z2XFJz0nqNrMxM/tw1DEF5O2S+iT95N2/yb80s0eiDgqpxPqfcKz/qRP79Z+jpgEAAACfeqog\nAwAAAMsiQQYAAAB8SJABAAAAHxJkAAAAwIcEGQAAAPAhQUbqmNmXzWzKzL4UdSwAgPCw/qNWSJCR\nRr+r+fmKAID6wvqPmiBBRmKZ2U+Y2YtmttrMfsTMvmlmm51z/1XSdNTxAQCCwfqPoK2MOgCgUs65\nF8zsi5J+W1KTpM8654YjDgsAEDDWfwSNBBlJ968kvSDplqR/FHEsAIDwsP4jMLRYIOleK2mNpLWS\nVkccCwAgPKz/CAwJMpLuP0j6dUmfk/Q7EccCAAgP6z8CQ4sFEsvMPiDptnPuj81shaRzZvaTkn5T\n0o9LWmNmY5I+7Jz7SpSxAgBqh/UfQTPnXNQxAAAAALFBiwUAAADgQ4IMAAAA+JAgAwAAAD4kyAAA\nAIAPCTIAAADgQ4IMAAAA+JAgAwAAAD4kyAAAAIAPCTIAAADgQ4IMAAAA+JAgAwAAAD4kyAAAAIAP\nCTIAAADgQ4KMRDOzvzWzm2Z2w/fvk2a238z+Z9TxAQCql7fWXzWzATNbs8znHDaz23nPDwfDihnJ\nRoKMNNjjnFvj+/crUQcEAKi5Pc65NZL+vqStkn6thM/5z3nPD0eCDRFpQYIMAAASwzk3LunPJG02\ns3Yz+6KZTZrZd8zsH0YdH9JhZdQBAAAAlMrMNkp6RNIJSX8iaVhSu6Qfl/SsmV10zv23CENEClBB\nRhqcNLMp3z8qCACQPifNbErS/5T0VUmflvR2Sf/cOXfLOfeXkv6jpA/4Puc9ec8P7eGHjSSigow0\n6HXOnfHfYGb7I4oFABCMRWu9mb1V0qRzbtr3MS9rvj/Z83nn3PvDChDpQQUZAAAk0YSkNjNb6ho1\nfwAAIABJREFU67utU9J4RPEgRUiQAQBA4jjnLks6J+nfmNlqM9si6cOSPhttZEgDEmSkwam8OZdf\niDogAEAo9kl6o+aryV+Q9Bv5LXdAJcw5F3UMAAAAQGxQQQYAAAB8SJABAAAAHxJkAAAAwIcEGQAA\nAPBJ1EEh9913n9vQ3qHv/d11TU/NRB1OrDywZWPR9337xcshRlI7b/zx+7Wq8d6H6O3ZOf3tX11Z\ndFscvv9c06qSP7bh5u0AI6lOGD/L5e4j6Bi+f/u7rzjn1lX9hRCa++67z73hDe26fefvdCc3FXU4\nsdLUuKXo+27Ovljy1/nenTWSpO/PrtadH67Qih9KK265SNarUtf/OKz9SZFrWqXZFlNm7Q8kSa9d\ncWPR+2v1OFrOcvcTdBzf+l+3S1r/EzXFYuvWre7ChQu6NTOroweP6+zJoahDio2B84e1vqPtntuv\njk1q//bD4QdUA6dHj6qhwe65PZdzerTzwKLb4vL939y8YdmPaRqO9wz7MH6Wy91H0DF8efwTQ865\nrct/JOLCW/9zuRldnjyoqZmnow4pNh5sf06NKzvuuX12bkwvTbytrK91LLtDkjQ42q0bl1q09mKD\nMiOzoa9bpa7/cVn7k+Lm5g3KdjfK7crq2ENPLnpfLR9HS1nufoKO4y1vGCtp/U9ki8Xq5kbtP7Qn\n6jBiZaD/lG7NzC667dbMrAb6T0UUUfWuTWRLvj0p33/ck2MpnJ/lcveRlN8nwtfQ0Kz7Ww9FHUas\nXJnqVy63+KpqLjejK1P9ZX+tvsw59WXOaXfniNZ0Xdf0ppyy3Y0lvfivpVLXf9aK8jQNjyszMis7\nk1HfN55Y9L5aPo6Wstz9hBXHchKZIEvSuvZM1CHEytmTQzp68Liujk0ql3O6OjaZ+Cp7OQtfXL7/\npRLgJCTHUjg/y+XuIy6/T8TTqhXtUYcQK1MzT+vy5EHNzo3JuZxm58aqrrIXS5LDSpRLXf9ZK8rn\nT5IfO/nRhasGQTyOClnufsKKYzmJbLGQuHxSL3b29mj/oT1a157RtYmsBvpPJWLhy38SSUpyXC9o\nsUge//pf60u+KC6/5aLzmTlJ4axpSV3/k8Jrt5jelNOaruv3tFykVaktFolMkOlBBlAJ7wn30d7d\nunDhwr0NjogtepCjE5e+ZNRefpK8u3NEfZlzUYcViNbmvbq/9ZB2bO8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rPz3IFYhyI1wp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\niavWIEvMZgCoBwIyA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\n", 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ywV3HoB8atdTw9dKF27tPiTyBVaN7JAq9dOlmjRWIAKtGK3V3ugFEbhcJRlaN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\nU5z/f0bvutGZ21774tEbVkad1+ci3DP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\n", 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x1Continuous[-5. 10.]
x2Continuous[ 0. 15.]
" ], "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -55,12 +55,21 @@ "import numpy as np\n", "from gpflowopt.domain import ContinuousParameter\n", "\n", + "def branin(x):\n", + " x = np.atleast_2d(x)\n", + " x1 = x[:, 0]\n", + " x2 = x[:, 1]\n", + " a = 1.\n", + " b = 5.1 / (4. * np.pi ** 2)\n", + " c = 5. / np.pi\n", + " r = 6.\n", + " s = 10.\n", + " t = 1. / (8. * np.pi)\n", + " ret = a * (x2 - b * x1 ** 2 + c * x1 - r) ** 2 + s * (1 - t) * np.cos(x1) + s\n", + " return ret[:, None]\n", "\n", - "def fx(X):\n", - " X = np.atleast_2d(X)\n", - " return np.sum(np.square(X), axis=1)[:, None]\n", - "\n", - "domain = ContinuousParameter('x1', -2, 2) + ContinuousParameter('x2', -1, 2)\n", + "domain = ContinuousParameter('x1', -5, 10) + \\\n", + " ContinuousParameter('x2', 0, 15)\n", "domain" ] }, @@ -73,22 +82,31 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 8, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warning: optimization restart 1/5 failed\n", - "Warning: optimization restart 2/5 failed\n", - "Warning: optimization restart 3/5 failed\n", - "Warning: optimization restart 2/5 failed\n", - " fun: array([ 0.01])\n", - " message: 'OK'\n", - " nfev: 15\n", - " success: True\n", - " x: array([[ 0. , -0.1]])\n" + "iter # 0 - MLL [-13.1] - fmin [4.42]\n", + "iter # 1 - MLL [-13.4] - fmin [4.42]\n", + "iter # 2 - MLL [-10.6] - fmin [0.723]\n", + "iter # 3 - MLL [-9.09] - fmin [0.486]\n", + "iter # 4 - MLL [-7.01] - fmin [0.486]\n", + "iter # 5 - MLL [-2.69] - fmin [0.446]\n", + "iter # 6 - MLL [1.96] - fmin [0.446]\n", + "iter # 7 - MLL [4.6] - fmin [0.446]\n", + "iter # 8 - MLL [7.37] - fmin [0.4]\n", + "iter # 9 - MLL [12.6] - fmin [0.4]\n", + " constraints: array([], dtype=float64)\n", + " fun: array([0.39970302])\n", + " message: 'OK'\n", + " nfev: 10\n", + " success: True\n", + " x: array([[9.40798299, 2.43938799]])\n" ] } ], @@ -97,22 +115,25 @@ "from gpflowopt.bo import BayesianOptimizer\n", "from gpflowopt.design import LatinHyperCube\n", "from gpflowopt.acquisition import ExpectedImprovement\n", - "from gpflowopt.optim import SciPyOptimizer\n", + "from gpflowopt.optim import SciPyOptimizer, StagedOptimizer, MCOptimizer\n", "\n", "# Use standard Gaussian process Regression\n", "lhd = LatinHyperCube(21, domain)\n", "X = lhd.generate()\n", - "Y = fx(X)\n", + "Y = branin(X)\n", "model = gpflow.gpr.GPR(X, Y, gpflow.kernels.Matern52(2, ARD=True))\n", "model.kern.lengthscales.transform = gpflow.transforms.Log1pe(1e-3)\n", "\n", "# Now create the Bayesian Optimizer\n", "alpha = ExpectedImprovement(model)\n", - "optimizer = BayesianOptimizer(domain, alpha)\n", + "\n", + "acquisition_opt = StagedOptimizer([MCOptimizer(domain, 200),\n", + " SciPyOptimizer(domain)])\n", + "\n", + "optimizer = BayesianOptimizer(domain, alpha, optimizer=acquisition_opt, verbose=True)\n", "\n", "# Run the Bayesian optimization\n", - "with optimizer.silent():\n", - " r = optimizer.optimize(fx, n_iter=15)\n", + "r = optimizer.optimize(branin, n_iter=10)\n", "print(r)" ] }, @@ -120,7 +141,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "That's all! Your objective function has now been optimized for 15 iterations." + "That's all! Your objective function has now been optimized for 10 iterations." ] } ], @@ -140,7 +161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/doc/source/notebooks/mes_benchmark.ipynb b/doc/source/notebooks/mes_benchmark.ipynb index ad5b30d..a62d33f 100644 --- a/doc/source/notebooks/mes_benchmark.ipynb +++ b/doc/source/notebooks/mes_benchmark.ipynb @@ -16,9 +16,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -30,9 +28,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def branin(x):\n", @@ -47,8 +43,9 @@ " t = 1. / (8. * np.pi)\n", " ret = a * (x2 - b * x1 ** 2 + c * x1 - r) ** 2 + s * (1 - t) * np.cos(x1) + s\n", " return ret[:, None]\n", - "branin_domain = gpflowopt.domain.ContinuousParameter('x1', -2.25, 2.5) + \\\n", - " gpflowopt.domain.ContinuousParameter('x2', -2.5, 1.75)\n", + "\n", + "branin_domain = gpflowopt.domain.ContinuousParameter('x1', -5, 10) + \\\n", + " gpflowopt.domain.ContinuousParameter('x2', 0, 15)\n", "\n", "\n", "def shekel(x):\n", @@ -68,9 +65,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot(objective, domain):\n", @@ -118,11 +113,20 @@ "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From c:\\users\\icouckuy\\documents\\projecten\\gpflowopt\\gpflowopt\\acquisition\\mes.py:102: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n" + ] + }, { "data": { - "image/png": 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h7jMetej9DXAm0Qf0X1h0RaeHiD54zojnf9jdn6mwvP8A3OfuBzzqq38r0Yf6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\nKQhERHJOQSAiknMKAhGRnFMQiIjk3P8H8eGDCmGRS/QAAAAASUVORK5CYII=\n", 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\n", 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xiIg0YdSA7mytb+C1lR9FHUqsKHGIiDShOpwIqGG5u1LiEBFpQnlpIQMrSpir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\nIiKSkv8PUaZy4tiRU9gAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -158,9 +162,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -181,7 +183,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/doc/source/notebooks/multiobjective.ipynb b/doc/source/notebooks/multiobjective.ipynb index d30a582..e64cc47 100644 --- a/doc/source/notebooks/multiobjective.ipynb +++ b/doc/source/notebooks/multiobjective.ipynb @@ -20,9 +20,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -47,9 +45,9 @@ "outputs": [ { "data": { - "image/png": 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IUwcS9OTwOKR+LhaEMXKCmsIhwTCMWNuVkAhMb9klpu6+dcTvrghTDoBU+yUO\nKnHpaQ1Sh0KpnGCYy4c9jJHj1RIOCYVxxFh6Gmu/RJabonTT7IMIVCX3/RH7Uu2XOGhwICUshpFD\nKOwrZUKTSzjEeITDcFZv39/q9S2tW9nq9S1q1c4VwUOiFP69N7eQyAFrMAsCIjqppJAo5bFkhbDY\nnpxCYV9OE5lJmOCgL+T7UC7BsO0wOMttpAyONXUTgRLl8Q4IJFDSRDO3yXt/HzkG19nkuM3OO2F7\nds+vcUp6zXZZjA+0agyHq7fvP+ArpRxqCb1/YugPO3P4cHdQCR+MIw90ENFppXQSpby6iYPoLI6X\nWxgcVkowlAiHpSg5HKYIhqlD4CwGa43dXQy57DR0JzG3pabANOggovNKm3jmPNDQWSxjG5TUNZTK\ne412FeFwOqm7cm1IcR/oJLajlA/FkRYdREBldRKlfLuJg0YFpBo7jDkHwWElhcI+wiH6Sg+HJQfC\nSWJ2FmPsmwiAgAg8rrSQKJURFAcNh6nSAmNJYXBQicFQIhyWJPR7UIj3ihjBsNZQOE7//sYIim2H\nxC4tNeWIplgOAREYUGJIlMoLin2TBuNU4bHUEDhOLhOSWTF5KQfh8EBdC4bDYgRFQiIQDgERGNKf\nmJYaFEsLiePUFtRiK3kSQjhEX2nhsOvBcFjooFhiSMwFXURMwkFqgDFKfeMs7QAkaFfJj/9Fq28o\n9nXXVSE/kCIc1iPktgnxmIZcwVLLh7ioGwERmKDkyWo/KJQaFjCb0h/rkl9rKEOocFj6EUljCbmd\nUp27cl65hMQSV0ohjrJeUUACNUxcSw8PGK2WDwFqeI11UUndw5DhELMpJSSWdhA1oE0ERGAKtUxg\nawgTqOtxrOW1hfaUEA7pGi4m1PYrKSTSRUTOOEgNMKWSD14zbDBc5DJIYXm1hEKJYFi6Ut43QoXD\nnBy2bc/M/+eh09cGqGR2q7fvb/0ANm0fuKYrB60BBhEQgRmVeiqMcUo9RUZX1BQK+wiHGCf3ZX0p\nw+E8QXCW60oVGksIiaFw2gvkioAIzKG2kCjRVcxJzRMGwmH5Qr0/5L60NEU4bDMUznJbscNiiJDY\nptq7iJzyAsMIiMCcalpyOoywGF/NoVAiGCKuksNhzFA4TQ2xwmLb50ykiwjMj4AILKjGbuIgwmI4\nXZkUEA7rUUL3sNRwmEMwHCVlZ3FRbYbE2ruIwCACItCCmruJg4YDDYFxNl0JhH0EQ5QuRjjMNRiO\n0q81ZFB+LvikAAAW/0lEQVTMfblprVhmikEERKBFtXcThxEYJ+taIBzERKM+Xesehg6HJQXDYYdt\n21NMSCyhi8gyU+SGgAi0rCvdxFFGDXBdCY0M7j0EQ9QgZDgsORgOCt1NzDUkAl1AQAQC6Vo3cZwa\nQyNhcDTCIWaVY/eQcDibkN3EHJeb1rwvIstM0ZckIJrZUZI+IumZku6S9Bp3Xxpxue9Juq35cZe7\nvyxWjUAbutxNnGS5gJU6QBIAZ8OEol05jpGpX5PLafvANCHUGA77QnYT2wqJuXcRWWaKnKTqIF4q\n6TPu/jYzu7T5+bdGXO4hdz8zbmlA+wiKs2GQLAPBMBjGyERCdA9rDobDQu+bmIuau4iAJKX6yO1C\nSR9svv+gpJcnqgOIigk1asFzOahOjJFtLS/NeWlpl8JhX4j73NZjU0KnGchBqlfKMe5+T/P9tyQd\nM+Zyh5rZVjO70cyqHCDRPRetvoHJNYrF8zcKxsgKdDEc9nX5vi8i96Xc6I5gS0zN7NOSjh3xp8sG\nf3B3NzMfczUnuvseM3uWpM+a2W3uvnPM7W2QtEGS1qw9aIHKgThYdoqSEArbFXOMHBwfV6156sy1\ndmHSGuN8h13T9nLT3PZFrHWZKQeqgRQwILr7ueP+ZmbfNrM17n6Pma2RdO+Y69jT/PsNM/u8pGdL\nGhkQ3X2jpI2SdNoZK8cNpkB2CIrIGROFMGKOkYPj47GnHZXF+Jjb8tK20UEDULJU76ybJL2h+f4N\nkq4bvoCZrTazpzTfHy3pRZK+Gq1CIDKW7iEnPB+TYoyMqO3uIeHwCW1vCzq9QBypAuLbJP2EmX1d\n0rnNzzKz9Wb23uYyPyxpq5ndIulzkt7m7gx+qB4Tc6TE8y8LjJGFIhweKMdtkmvnWerGkm7kL8lp\nLtz9fkkvGfH7rZLe3Hx/g6T/HLk0IBssPUVMhMJ85DRG5jxZbWOST0eqPG3ti9iGWvdDBFKdBxHA\nlAYn7oRFtI1giNja2v8wNzl2ynLRlfMjArUgIAIFoauINhAKAQDjcCRT5LsIG8BY7CeGefC8AZBK\nmx3WNpYG57wfIpAaHUSgYCw/xXIIhMBobe5/yPJSADUhIAKVICyij1CI2tH9AYBwCIhAhdhXsZsI\nhgByxsFqgDIQEIGKDQcGAmN9CIUAkE6IU11s2bVO552wvdXrBGZBQAQ6hGWo5SMQAnlh/0MAtSEg\nAh1FWCwHoRAAAMRCQATAUtTMEAgBAEAqBEQAByAwxkUgBAAAuSAgAljWqABDaJwPYRCoy0Onr2U/\nRABVISACmAuhcXmEQQCoW9tHMJXEEUyRHAERQGvGBaLagyNBEACWxzkQgTIQEAEEN02AyjlEEgCB\nvOw9+TGt2rkidRkAUCUCIoAsEMIAxLS0bqVWb9/fynWxH2J59p78WOoSgGzx8RsAAACCanN56dK6\nla1dF4ADERABAAAASGJFDwiIAAAgohBHfZxXm50oDsAyHtsGKAsBEQAAjJTz4fbZh6ybclpemtOH\nHUCbCIgAAAAtoFN2oBy3Sc4fLuT8oQy6g4AIAAA6q+2OVI6BKJW2t0VO3UOgZgREAAAQVVtL83Lt\nBBES85XrcwbICQERAACM1YUlbyE6U10PibV3D2vd/5AjmEIiIAIAgILl3BHqakjs6v1eVBc+jEEZ\nkgREM3u1md1uZo+Z2foJlzvfzLab2Q4zuzRmjQAApNCVMTK3DkyoDlXXwlKI+9vWY5PzhwlATlJ1\nELdJeqWkL4y7gJkdJOlySS+VdKqk15nZqXHKAwAgGcbIRAiJ83vo9LVZh8M25fbhBtC2JAHR3e9w\n9+X66GdJ2uHu33D3/ZKulnRh+OoAAEgnxzEy96VvJXSGag6JJdy33J8jub/G0C0574O4VtLdAz/v\nbn4HAEDXVTFGttmJaSsAhOxYlRCkZhXyPtE9jIsD1KDv4FBXbGaflnTsiD9d5u7XBbi9DZI2SNKa\ntQe1ffUAALQm5hg5OD6uWvPUua/nvBO2a8uudW2VlbWldSu1evv+INfdD1SHbdsT5PpjCR122wyH\nuXcPgdwEC4jufu6CV7FH0vEDPx/X/G7c7W2UtFGSTjtjpS942wAABBNzjBwcH4897ajsxsfDT3pQ\n++48opXr2nvyY1q1s53FUSFDolRuUIzRBc01HIbqHrK8FLnJeYnpTZJOMbOTzGylpNdK2pS4JgAA\ncsAYGUGMJY6hDu7Stlh15ristAtYXopBqU5z8Qoz2y3phZL+wcyub37/DDPbLEnu/qikSyRdL+kO\nSde4++0p6gUAIJacx8hQnY4c90XsixVYcg2KMetqe1vTPQTmE2yJ6STufq2ka0f8/puSLhj4ebOk\nzRFLAwAgqa6OkbkuNZXCLzcdNBjGUi0/TRFUcw6HQNckCYgAAKBMpRyspuSQ2Dcc1EIFxtSdy9yX\nldZ85FKJ5aU4EAERAABkoc0uolRHSBw0LsjNEhxTh8FhIcJhCUtLJZaXIl8ERAAAMJNSuoghpA6J\no+QW+qaVezgEuirno5gCAICOabtjEyIwLK1bmf2yyJyF2n5tP9Zd6B6yvBSjEBABAMDMQk5wSwiJ\nUv77zuUo1DYjHALtISACAIDslBQSCYrLC7mdWFY6H7qHGIeACAAA5lJaJyRkkCAojhZ6u4R4TOke\nousIiAAAYG4lLTWVwnebCIpPCL0dSguHOaF7iEk4iikAAMhW26e+kJ4IFm2eAmNYPxzldsTTGGIE\n5BLDId1DlIIOIgAAWEjoiW+oiXuMfde60lHs388YXUPC4WLoHmI5BEQAALAwQuJktQbFmPcr1GPV\nlWWlwLRYYgoAAIoQYrmp1AseIZebDhoMU6UuP00RdEs+UindQ5SGgAgAAFpx3gnbtWXXuqC3ETIk\nSmH3Sxw2HLRyDYwpO58hg2GMzmFO4RCYFgERAAC0puSQKMXtJg7LJTDmshSWcNguuoeYFgERAAC0\nqoaQKMXtJo4yLqi1FRxzCYLDQi8nZZ9DYDICIgAAKFLIkCjlExSH5Rrs2lBLOKR7iJLl9Y4HAACq\nEGuCHGPCX/IBUkoR6vQVgwiHwHQIiAAAIIjaQiJBsX2xtmtXwyEwDwIiAAAIJmZIJCiWI+Z27HI4\npHuIeRAQAQBAUDEnzrHCQD/gEBZnEzsYdjkcAvMiIAIAgOBqDIl9BMXJUoTpmM+BXMMh3UPMi6OY\nAgCAKGKc/qKvHxBCHuV02GAAyu3Ip7GlDMyEQ8IhFkNABAAA0cQMiVL4U2GM08WwmLqLGrtzTDhE\nrQiIAAAgqhQhUYrbTRxUc1hMHQqlNCe+zzUcAm0gIAIAgOhih0QpXTdx0HCgKi0w5hAIBxEOn4zu\nIdqQJCCa2asl/Z6kH5Z0lrtvHXO5uyTtlfQ9SY+6+/pYNQIAkEKXxshUIVFK100cNipw5RIacwuD\ng1IEQ4lwiG5I1UHcJumVkv56isv+mLvfF7geAABy0akxMkVIlPILioMmBbO2w2POIXCUVMFQIhyi\nO5IERHe/Q5LMLMXNAwCQrS6Okf2JN0FxeaUFurakDIYS4RDdkscahvFc0hYz+7KZbUhdDAAAGalu\njEw5CY95UnVML/Xjct4J27MOh0AIwTqIZvZpSceO+NNl7n7dlFfzYnffY2Y/IOlTZvY1d//CmNvb\nIGmDJK1Ze9BcNQMAEEPMMbK08THVktO+0jqKtcohrJcQDOkeIoRgAdHdz23hOvY0/95rZtdKOkvS\nyIDo7hslbZSk085Y6YveNgAAocQcI4fHx4tW36Arls5e9OaDSrnktI+gmEYOwVAiHKLbsl1iambf\nZ2ar+t9LOk+9HfcBAOi0RcfIUiaWOUzS+0sccwkuNcppG5eypLSU1zDKlCQgmtkrzGy3pBdK+gcz\nu775/TPMbHNzsWMk/bOZ3SLpS5L+wd3/MUW9AADEEmuMLGWCmdNkPZcQU4vctmdOz7VJSnntolzm\nXt9qzNPOWOlXfeKY1GUAAAI788TdXy7x/H+pjBofc19uOijlktNxWII6m5wCYV8pwVAiHGI2846R\nqc6DCAAAMlDCPol9OeybOGww8BAWR8sxFPYRDoEDERABAOi4kkKilP5Ip+MMB6GuBsacA2FfScFQ\nIhwiLgIiAAAoMiRKeXUTh3UlMJYQCPtKC4YS4RDxERABAICk8kKiVEZQ7BsVpEoLjSWFwWGEQ2A6\nBEQAAPC4EkOilO+y0+VMClwpw2PJQXBYicFQIhwiHQIiAAB4kv7EtLSgWFI3cRo1hbQUSg2GEuEQ\naSU5DyIAAMhfqZPUUk52jjBKf/xLfd2hHnQQAQDAWKUuOZXq6yhispJDoUQwRD4IiAAAYKKSQ6L0\n5OBAWKxP6cFQIhwiLwREAACwrNJDYh9dxTrUEAr7CIfIDQERAABMpdSD14xCUCxTTcFQIhwiTwRE\nAAAwk1q6iRLLT0tQWyiUCIbIGwERAADMrKaQ2EdYzEeNobCPcIjcERABAMBcalpyOoywGF/NobCP\ncIgSEBABAMBCauwmDiIshtOFUCgRDFEWAiIAAFhYzd3EQcOBhsA4u66Ewj7CIUpDQAQAAK2pvZs4\njMC4vK4Fwj6CIUpFQAQAAK3qSjdxlK4Hxq6GwWGEQ5SMgAgAAILoWjdxlHGBqfTgSBAcjWCIGhAQ\nAQBAMF3uJk4yKWDlEh4JgdMjGKImBEQAABAcQXF6BLOyEA5RmxWpCwAAAN3BZBq1uGj1DTyfUSU6\niAAAICq6iSgZoRC1IyACAIAkCIooCcEQXUFABAAASREUkTOCIbomyT6IZvanZvY1M7vVzK41syPH\nXO58M9tuZjvM7NLYdQIAEFuXx0j26UJOeD6iq1IdpOZTkk539zMk/auk3x6+gJkdJOlySS+VdKqk\n15nZqVGrBAAgvs6PkUzMkRLPP3RdkoDo7lvc/dHmxxslHTfiYmdJ2uHu33D3/ZKulnRhrBoBAEiB\nMfIJTNQRE883oCeHfRB/TtJHRvx+raS7B37eLen5USoCACAPjJFiH0WERSgEnixYQDSzT0s6dsSf\nLnP365rLXCbpUUlXtnB7GyRtaH58+MwTd29b9DoTOVrSfamLWEDJ9Zdcu1R2/SXXLpVdf8m1S9K6\n1AXMI+YYWdH4KOmakp+vJdculV3/xNr/LGIhc6p22xeg9PrnGiODBUR3P3fS383sYkk/Kekl7u4j\nLrJH0vEDPx/X/G7c7W2UtLG57q3uvn7WmnNQcu1S2fWXXLtUdv0l1y6VXX/JtUu9+lPXMI+YY2Qt\n46NUdv0l1y6VXX/JtUtl119y7VId9c/z/1IdxfR8Sb8p6WXu/t0xF7tJ0ilmdpKZrZT0WkmbYtUI\nAEAKjJEAgJRSHcX03ZJWSfqUmd1sZu+RJDN7hpltlqRmB/1LJF0v6Q5J17j77YnqBQAgFsZIAEAy\nSQ5S4+4/OOb335R0wcDPmyVtnuMmNs5ZWg5Krl0qu/6Sa5fKrr/k2qWy6y+5dqn8+g8QeIwsfXuV\nXH/JtUtl119y7VLZ9Zdcu9TR+m30rg0AAAAAgK5JtcQUAAAAAJCZKgKimf2pmX3NzG41s2vN7Mgx\nlzvfzLab2Q4zuzR2naOY2avN7HYze8zMxh4lyczuMrPbmv1Rsjlq3wz157jtjzKzT5nZ15t/V4+5\n3Pea7X6zmSU/CMRy29LMnmJmH2n+/kUze2b8KkebovaLzezfBrb3m1PUOYqZvd/M7jWzkacIsJ6/\naO7brWb2nNg1TjJF/eeY2YMD2/4tsWscx8yON7PPmdlXm/ebXxtxmay3fyolj49S2WNkyeOjVOYY\nWfL4KDFGpsL4OIK7F/8l6TxJBzffv13S20dc5iBJOyU9S9JKSbdIOjWD2n9YvXOUfF7S+gmXu0vS\n0anrnaf+jLf9n0i6tPn+0lHPm+Zv+1LXOsu2lPRLkt7TfP9aSR9JXfcMtV8s6d2pax1T/49Keo6k\nbWP+foGkT0oySS+Q9MXUNc9Y/zmSPpG6zjG1rZH0nOb7VZL+dcRzJ+vtn3DbFTs+NrUVO0aWPD42\ntRU1RpY8Ps5QP2Nkmto7Nz5W0UF09y3eO6KbJN2o3vmghp0laYe7f8Pd90u6WtKFsWocx93vcPft\nqeuY15T1Z7nt1avhg833H5T08oS1TGuabTl4vz4q6SVmZhFrHCfX58FU3P0Lkh6YcJELJX3Ie26U\ndKSZrYlT3fKmqD9b7n6Pu3+l+X6vekftXDt0say3fyolj49S2WNk4eOjVN4YWfL4KOX9XFhWyWMk\n4+OBqgiIQ35OvZQ8bK2kuwd+3q0DN2DOXNIWM/uymW1IXcyMct32x7j7Pc3335J0zJjLHWpmW83s\nRjNLPUBOsy0fv0wzMXxQ0tOiVDfZtM+Dn2qWQHzUzI4f8fdc5fo8n8ULzewWM/ukmZ2WuphRmiVh\nz5b0xaE/1bD9Q6t1fJTKHSNz3valjZElj48SY2TuOjU+JjnNxTzM7NOSjh3xp8vc/brmMpdJelTS\nlTFrW840tU/hxe6+x8x+QL1zY32t+cQjuJbqT2JS7YM/uLub2bhD+p7YbPtnSfqsmd3m7jvbrhWS\npL+X9GF3f9jMfl69T3p/PHFNXfEV9Z7r+8zsAkkfl3RK4pqexMwOl/QxSb/u7t9JXU8uSh4fpbLH\nyJLHR4kxskCMkWl0bnwsJiC6+7mT/m5mF0v6SUkv8WbB7ZA9kgY/aTmu+V1wy9U+5XXsaf6918yu\nVW8pQpSA2EL9WW57M/u2ma1x93uaVvu9Y66jv+2/YWafV+/TmVSD3zTbsn+Z3WZ2sKQjJN0fp7yJ\nlq3d3QfrfK96+8CUItnzvA2DA4q7bzazvzSzo939vpR19ZnZIeoNfle6+9+NuEjR238RJY+PUtlj\nZMnjo1TdGFny+CgxRmari+NjFUtMzex8Sb8p6WXu/t0xF7tJ0ilmdpKZrVRv5+TkR6Schpl9n5mt\n6n+v3kEHRh5pKVO5bvtNkt7QfP8GSQd82mtmq83sKc33R0t6kaSvRqvwQNNsy8H79SpJnx0zKYxt\n2dqH1sS/TL219KXYJOn1zdHCXiDpwYHlWdkzs2P7++KY2VnqjQ9ZTJyaut4n6Q53f8eYixW9/UOp\nfXyUih8jc972pY2RJY+PEmNktjo5PnoGR+BZ9EvSDvXW1t7cfPWPUPUMSZsHLneBekf32ane8o8c\nan+FemuBH5b0bUnXD9eu3hGtbmm+bs+l9mnrz3jbP03SZyR9XdKnJR3V/H69pPc2358t6bZm298m\n6U0Z1H3AtpT0VvUmgJJ0qKS/bV4XX5L0rNQ1z1D7HzfP8VskfU7SD6WueaD2D0u6R9IjzXP+TZJ+\nQdIvNH83SZc39+02TTjiYqb1XzKw7W+UdHbqmgdqf7F6+5jdOvA+f0FJ2z/htit2fGzqKnaMnKb2\nzLd9cWPkFGNMtuPjlPUzRqapvXPjozX/EQAAAADQcVUsMQUAAAAALI6ACAAAAACQREAEAAAAADQI\niAAAAAAASQREAAAAAECDgAhUwsz+0cz+3cw+kboWAABywhgJTI+ACNTjTyVdlLoIAAAyxBgJTImA\nCBTGzJ5nZrea2aFm9n1mdruZne7un5G0N3V9AACkwhgJLO7g1AUAmI2732RmmyT9gaTDJP2Nu29L\nXBYAAMkxRgKLIyACZXqrpJsk/YekX01cCwAAOWGMBBbAElOgTE+TdLikVZIOTVwLAAA5YYwEFkBA\nBMr015L+t6QrJb09cS0AAOSEMRJYAEtMgcKY2eslPeLuV5nZQZJuMLMfl/T7kn5I0uFmtlvSm9z9\n+pS1AgAQE2MksDhz99Q1AAAAAAAywBJTAAAAAIAkAiIAAAAAoEFABAAAAABIIiACAAAAABoERAAA\nAACAJAIiAAAAAKBBQAQAAAAASCIgAgAAAAAa/x8xhYx5F1aQLAAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -111,9 +109,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Initial evaluations\n", @@ -151,68 +147,92 @@ "name": "stdout", "output_type": "stream", "text": [ - " fun: array([[ 0.88762951, 0.29191139],\n", - " [ 0.68279472, 0.59802318],\n", - " [ 0.42659568, 0.79430421],\n", - " [ 0.07354128, 0.94891563],\n", - " [ 0.82764037, 0.37153952],\n", - " [ 0.21803674, 0.89654802],\n", - " [ 0.5652733 , 0.69464852],\n", - " [ 0.00923563, 0.9738539 ],\n", - " [ 0.9334239 , 0.12286183],\n", - " [ 0.71007284, 0.54565196],\n", - " [ 0.76405389, 0.47382314],\n", - " [ 0.34539271, 0.83984545],\n", - " [ 0.48687039, 0.75355875],\n", - " [ 0.63004337, 0.63471413],\n", - " [ 0.98267516, 0.00178449],\n", - " [ 0.91892508, 0.16232397],\n", - " [ 0.85634937, 0.30878024],\n", - " [ 0.29492334, 0.86269861],\n", - " [ 0.89223755, 0.22705924],\n", - " [ 0.79104171, 0.43108073]])\n", - " message: 'OK'\n", - " nfev: 20\n", - " success: True\n", - " x: array([[-0.16140283, -0.48940691],\n", - " [ 0.0727557 , -0.15649316],\n", - " [ 0.22042083, 0.14203954],\n", - " [ 0.52761769, 0.49694149],\n", - " [-0.17070714, -0.28668133],\n", - " [ 0.38576529, 0.32936723],\n", - " [ 0.09298785, 0.03190632],\n", - " [ 0.66424394, 0.62084368],\n", - " [-0.40696469, -0.50460869],\n", - " [-0.10057392, -0.05825421],\n", - " [-0.10349761, -0.18006422],\n", - " [ 0.2923697 , 0.20539677],\n", - " [ 0.14213026, 0.11716751],\n", - " [-0.01662408, 0.02111616],\n", - " [-0.74495067, -0.68829395],\n", - " [-0.36858442, -0.45705387],\n", - " [-0.26088788, -0.29457053],\n", - " [ 0.27909045, 0.29936819],\n", - " [-0.3425574 , -0.35403933],\n", - " [-0.20960284, -0.14451762]])\n", - "[[ 0.00924157 0.97385548]\n", - " [ 0.07353906 0.9489149 ]\n", - " [ 0.21803833 0.89654648]\n", - " [ 0.29492503 0.86270261]\n", - " [ 0.34538813 0.83984479]\n", - " [ 0.42659985 0.79430552]\n", - " [ 0.48686827 0.75354473]\n", - " [ 0.56527447 0.69466632]\n", - " [ 0.63003846 0.63471877]\n", - " [ 0.68279543 0.59801444]\n", - " [ 0.71007725 0.54563413]\n", - " [ 0.76405382 0.47382832]\n", - " [ 0.82763436 0.37155876]\n", - " [ 0.8563462 0.30880294]\n", - " [ 0.88763433 0.29189238]\n", - " [ 0.89225033 0.2270049 ]\n", - " [ 0.91890866 0.16234889]\n", - " [ 0.93342994 0.12287979]\n", - " [ 0.98267631 0.00178968]]\n" + "WARNING:tensorflow:From c:\\users\\icouckuy\\documents\\projecten\\gpflowopt\\gpflowopt\\acquisition\\hvpoi.py:124: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", + "iter # 0 - MLL [-16.1, -15.6] - fmin [0.385, 0.0171] (size 5)\n", + "iter # 1 - MLL [-17.1, -15.7] - fmin [0.385, 0.0171] (size 4)\n", + "iter # 2 - MLL [-17.2, -15.6] - fmin [0.385, 0.0171] (size 5)\n", + "iter # 3 - MLL [-16.9, -15.4] - fmin [0.104, 0.0171] (size 5)\n", + "iter # 4 - MLL [-15.6, -14.2] - fmin [0.104, 0.0171] (size 6)\n", + "iter # 5 - MLL [-12.8, -12.9] - fmin [0.0173, 0.0171] (size 7)\n", + "iter # 6 - MLL [-10.4, -11.6] - fmin [0.0173, 0.0171] (size 8)\n", + "iter # 7 - MLL [-9.08, -8.63] - fmin [0.0173, 0.0171] (size 9)\n", + "iter # 8 - MLL [-6.04, -5.29] - fmin [0.0173, 0.0171] (size 10)\n", + "iter # 9 - MLL [-3.42, -2.42] - fmin [0.0173, 0.0171] (size 11)\n", + "iter # 10 - MLL [0.634, 1.23] - fmin [0.0173, 0.0171] (size 12)\n", + "iter # 11 - MLL [3.85, 5.12] - fmin [0.0173, 0.0171] (size 13)\n", + "iter # 12 - MLL [6.22, 9.49] - fmin [0.0173, 0.0075] (size 13)\n", + "iter # 13 - MLL [9.52, 13.4] - fmin [0.0173, 0.0075] (size 14)\n", + "iter # 14 - MLL [13.1, 18.1] - fmin [0.0173, 0.0075] (size 15)\n", + "iter # 15 - MLL [16.8, 22.0] - fmin [0.0173, 0.0075] (size 16)\n", + "iter # 16 - MLL [20.5, 26.1] - fmin [0.0173, 0.0075] (size 17)\n", + "iter # 17 - MLL [25.5, 29.4] - fmin [0.000251, 0.0075] (size 18)\n", + "iter # 18 - MLL [30.7, 33.5] - fmin [0.000251, 0.0075] (size 19)\n", + "iter # 19 - MLL [35.4, 38.3] - fmin [0.000251, 0.0075] (size 20)\n", + " constraints: array([], shape=(20, 0), dtype=float64)\n", + " fun: array([[8.76019513e-01, 2.95088501e-01],\n", + " [6.18804120e-01, 6.49890805e-01],\n", + " [4.18727630e-01, 7.97346364e-01],\n", + " [1.03929952e-01, 9.38953409e-01],\n", + " [7.80270729e-01, 4.47691202e-01],\n", + " [1.72680317e-02, 9.69541151e-01],\n", + " [2.37219162e-01, 8.95331698e-01],\n", + " [9.22995807e-01, 1.49785271e-01],\n", + " [5.06696960e-01, 7.43243621e-01],\n", + " [6.92444402e-01, 5.68593315e-01],\n", + " [3.15497625e-01, 8.53859475e-01],\n", + " [8.36671433e-01, 3.53351848e-01],\n", + " [9.78800658e-01, 7.50373941e-03],\n", + " [7.46198817e-01, 5.04272230e-01],\n", + " [9.44585237e-01, 8.72091101e-02],\n", + " [5.45778434e-01, 7.10310655e-01],\n", + " [8.83126953e-01, 2.48920466e-01],\n", + " [2.50661555e-04, 9.81376183e-01],\n", + " [1.78561229e-01, 9.12201485e-01],\n", + " [6.54328288e-01, 6.09221800e-01]])\n", + " message: 'OK'\n", + " nfev: 20\n", + " success: True\n", + " x: array([[-0.18500334, -0.42945409],\n", + " [ 0.07215097, -0.0420748 ],\n", + " [ 0.1856047 , 0.18694199],\n", + " [ 0.50560643, 0.44417276],\n", + " [-0.18945651, -0.13641757],\n", + " [ 0.62198831, 0.60624197],\n", + " [ 0.25806047, 0.44415823],\n", + " [-0.46055195, -0.38855214],\n", + " [ 0.17235267, 0.05851627],\n", + " [-0.09686607, -0.02277475],\n", + " [ 0.24052228, 0.3054078 ],\n", + " [-0.29630595, -0.19019709],\n", + " [-0.73494906, -0.62490677],\n", + " [-0.0533329 , -0.18336251],\n", + " [-0.52263076, -0.46790591],\n", + " [ 0.05291566, 0.10610416],\n", + " [-0.33700177, -0.32075719],\n", + " [ 0.69332471, 0.71490084],\n", + " [ 0.36196052, 0.42858939],\n", + " [-0.02203825, -0.02132529]])\n", + "[[2.52652535e-04 9.81379457e-01]\n", + " [1.72742593e-02 9.69536115e-01]\n", + " [1.03922043e-01 9.38960356e-01]\n", + " [1.78575115e-01 9.12193438e-01]\n", + " [2.37205200e-01 8.95338579e-01]\n", + " [3.15507528e-01 8.53853294e-01]\n", + " [4.18722475e-01 7.97352438e-01]\n", + " [5.06692411e-01 7.43241571e-01]\n", + " [5.45781765e-01 7.10308458e-01]\n", + " [6.18806827e-01 6.49893408e-01]\n", + " [6.92443661e-01 5.68586537e-01]\n", + " [7.46198193e-01 5.04265827e-01]\n", + " [7.80263435e-01 4.47719401e-01]\n", + " [8.36683447e-01 3.53311767e-01]\n", + " [8.76024836e-01 2.95069786e-01]\n", + " [8.83110550e-01 2.48989866e-01]\n", + " [9.23006049e-01 1.49705150e-01]\n", + " [9.44580266e-01 8.72705698e-02]\n", + " [9.78801698e-01 7.49811797e-03]]\n" ] } ], @@ -223,9 +243,8 @@ " gpflowopt.optim.SciPyOptimizer(domain)])\n", "\n", "# Then run the BayesianOptimizer for 20 iterations\n", - "optimizer = gpflowopt.BayesianOptimizer(domain, hvpoi, optimizer=acquisition_opt)\n", - "with optimizer.silent():\n", - " result = optimizer.optimize([vlmop2], n_iter=20)\n", + "optimizer = gpflowopt.BayesianOptimizer(domain, hvpoi, optimizer=acquisition_opt, verbose=True)\n", + "result = optimizer.optimize([vlmop2], n_iter=20)\n", "\n", "print(result)\n", "print(optimizer.acquisition.pareto.front.value)" @@ -256,26 +275,26 @@ "output_type": "stream", "text": [ "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", - "[ 0.58170479 0.57493089]\n", + "[0.5838501 0.59262748]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 4.39140011]\n", + "[3.88573726]\n", "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 1.00000000e-06]\n", + "[1.00000005e-06]\n", "\n", "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", - "[ 0.60083304 0.58044734]\n", + "[0.60790817 0.58374295]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 3.35582088]\n", + "[3.34354369]\n", "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", - "[ 1.00000007e-06]\n", + "[1.00000004e-06]\n", "\n" ] }, { "data": { - 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/qi+lqQs9o+ZOOJaPHXAjn5m7nI8dcCNzJxwb+bkPdt5Eb1/3gG3uzo7dW7lz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\nZgn6P8GP7zzhOnGsAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -339,14 +358,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "R: [ 1.5 1.5]\n", - "Hypervolume indicator: 1.55890890103\n" + "R: [1.5 1.5]\n", + "WARNING:tensorflow:From c:\\users\\icouckuy\\documents\\projecten\\gpflowopt\\gpflowopt\\pareto.py:264: calling reduce_min (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", + "Hypervolume indicator: 1.5606701453857534\n" ] }, { "data": { "text/plain": [ - "" + "Text(0,0.5,'Objective 2')" ] }, "execution_count": 6, @@ -356,7 +378,7 @@ { "data": { "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -364,9 +386,9 @@ }, { "data": { - "image/png": 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fHE9+YR7x+jhHnnMwZ99wCjk5bX2mv2TK+g5vrrkDfCLwnwbjrweWRkSkg7n/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\n+7S2UocrvfZgZhXuXh52jmy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\n", 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" ] }, "metadata": {}, @@ -409,7 +431,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/gpflowopt/bo.py b/gpflowopt/bo.py index 93c00f9..65e649e 100644 --- a/gpflowopt/bo.py +++ b/gpflowopt/bo.py @@ -61,7 +61,7 @@ class BayesianOptimizer(Optimizer): """ def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=True, hyper_draws=None, - callback=jitchol_callback): + callback=jitchol_callback, verbose=False): """ :param Domain domain: The optimization space. :param Acquisition acquisition: The acquisition function to optimize over the domain. @@ -107,6 +107,7 @@ def __init__(self, domain, acquisition, optimizer=None, initial=None, scaling=Tr self.set_initial(initial.generate()) self._model_callback = callback + self.verbose = verbose @Optimizer.domain.setter def domain(self, dom): @@ -154,8 +155,16 @@ def _evaluate_objectives(self, X, fxs): def _create_bo_result(self, success, message): """ - Analyzes all data evaluated during the optimization, and return an OptimizeResult. Outputs of constraints - are used to remove all infeasible points. + Analyzes all data evaluated during the optimization, and return an `OptimizeResult`. Constraints are taken + into account. The contents of x, fun, and constraints depend on the detected scenario: + - single-objective: the best optimum of the feasible samples (if none, optimum of the infeasible samples) + - multi-objective: the Pareto set of the feasible samples + - only constraints: all the feasible samples (can be empty) + + In all cases, if not one sample satisfies all the constraints a message will be given and success=False. + + Do note that the feasibility check is based on the model predictions, but the constrained field contains + actual data values. :param success: Optimization successful? (True/False) :param message: return message @@ -166,24 +175,31 @@ def _create_bo_result(self, success, message): # Filter on constraints valid = self.acquisition.feasible_data_index() - if not np.any(valid): - return OptimizeResult(success=False, - message="No evaluations satisfied the constraints") - - valid_X = X[valid, :] - valid_Y = Y[valid, :] - valid_Yo = valid_Y[:, self.acquisition.objective_indices()] - - # Differentiate between single- and multiobjective optimization results - if valid_Y.shape[1] > 1: - _, dom = non_dominated_sort(valid_Yo) - idx = dom == 0 # Return the non-dominated points + # Extract the samples that satisfies all constraints + if np.any(valid): + X = X[valid, :] + Y = Y[valid, :] else: - idx = np.argmin(valid_Yo) - - return OptimizeResult(x=valid_X[idx, :], + success = False + message = "No evaluations satisfied all the constraints" + + # Split between objectives and constraints + Yo = Y[:, self.acquisition.objective_indices()] + Yc = Y[:, self.acquisition.constraint_indices()] + + # Differentiate between different scenarios + if Yo.shape[1] == 1: # Single-objective: minimum + idx = np.argmin(Yo) + elif Yo.shape[1] > 1: # Multi-objective: Pareto set + _, dom = non_dominated_sort(Yo) + idx = dom == 0 + else: # Constraint satisfaction problem: all samples satisfying the constraints + idx = np.arange(Yc.shape[0]) + + return OptimizeResult(x=X[idx, :], success=success, - fun=valid_Yo[idx, :], + fun=Yo[idx, :], + constraints=Yc[idx, :], message=message) def optimize(self, objectivefx, n_iter=20): @@ -232,10 +248,42 @@ def inverse_acquisition(x): for i in range(n_iter): # If a callback is specified, and acquisition has the setup flag enabled (indicating an upcoming # compilation), run the callback. - if self._model_callback and self.acquisition._needs_setup: - self._model_callback([m.wrapped for m in self.acquisition.models]) - result = self.optimizer.optimize(inverse_acquisition) - self._update_model_data(result.x, fx(result.x)) + with self.silent(): + if self._model_callback and self.acquisition._needs_setup: + self._model_callback([m.wrapped for m in self.acquisition.models]) + + result = self.optimizer.optimize(inverse_acquisition) + self._update_model_data(result.x, fx(result.x)) + + if self.verbose: + metrics = [] + + with self.silent(): + bo_result = self._create_bo_result(True, 'Monitor') + metrics += ['MLL [' + ', '.join('{:.3}'.format(model.compute_log_likelihood()) for model in self.acquisition.models) + ']'] + + # fmin + n_points = bo_result.fun.shape[0] + if n_points > 0: + funs = np.atleast_1d(np.min(bo_result.fun, axis=0)) + fmin = 'fmin [' + ', '.join('{:.3}'.format(fun) for fun in funs) + ']' + if n_points > 1: + fmin += ' (size {0})'.format(n_points) + + metrics += [fmin] + + # constraints + n_points = bo_result.constraints.shape[0] + if n_points > 0: + constraints = np.atleast_1d(np.min(bo_result.constraints, axis=0)) + metrics += ['constraints [' + ', '.join('{:.3}'.format(constraint) for constraint in constraints) + ']'] + + # error messages + metrics += [r.message.decode('utf-8') if isinstance(r.message, bytes) else r.message for r in [bo_result, result] if not r.success] + + print('iter #{0:>3} - {1}'.format( + i, + ' - '.join(metrics))) return self._create_bo_result(True, "OK") diff --git a/setup.py b/setup.py index d156dcf..f2b340b 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ raise RuntimeError("Unable to find version string in %s." % (VERSIONFILE,)) # Dependencies of GPflowOpt -dependencies = ['numpy>=1.9', 'scipy>=0.16', 'GPflow==0.4.0'] +dependencies = ['numpy>=1.9', 'scipy>=0.16', 'GPflow==0.5.0'] min_tf_version = '1.0.0' # Detect if TF is installed or outdated. @@ -65,7 +65,7 @@ extras_require={'gpu': ['tensorflow-gpu>=1.0.0'], 'docs': ['sphinx', 'sphinx_rtd_theme', 'numpydoc', 'nbsphinx', 'jupyter'], }, - dependency_links=['https://github.com/GPflow/GPflow/archive/0.4.0.tar.gz#egg=GPflow-0.4.0'], + dependency_links=['https://github.com/GPflow/GPflow/archive/0.5.0.tar.gz#egg=GPflow-0.5.0'], classifiers=['License :: OSI Approved :: Apache Software License', 'Natural Language :: English', 'Operating System :: POSIX :: Linux', diff --git a/testing/unit/test_optimizers.py b/testing/unit/test_optimizers.py index bcc3986..ebec181 100644 --- a/testing/unit/test_optimizers.py +++ b/testing/unit/test_optimizers.py @@ -1,5 +1,6 @@ import gpflowopt import numpy as np +import pytest import gpflow import six import sys @@ -192,6 +193,7 @@ def test_set_domain(self): class TestBayesianOptimizer(_TestOptimizer, GPflowOptTestCase): def setUp(self): super(TestBayesianOptimizer, self).setUp() + acquisition = gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(self.domain)) self.optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition) @@ -199,31 +201,57 @@ def test_default_initial(self): self.assertTupleEqual(self.optimizer._initial.shape, (0, 2), msg="Invalid shape of initial points array") def test_optimize(self): - with self.test_session(): - result = self.optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=20) - self.assertTrue(result.success) - self.assertEqual(result.nfev, 20, "Only 20 evaluations permitted") - self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum") - self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned") + for verbose in [False, True]: + with self.test_session(): + acquisition = gpflowopt.acquisition.ExpectedImprovement(create_parabola_model(self.domain)) + optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose) + result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=20) + self.assertTrue(result.success) + self.assertEqual(result.nfev, 20, "Only 20 evaluations permitted") + self.assertTrue(np.allclose(result.x, 0), msg="Optimizer failed to find optimum") + self.assertTrue(np.allclose(result.fun, 0), msg="Incorrect function value returned") def test_optimize_multi_objective(self): - with self.test_session(): - m1, m2 = create_vlmop2_model() - acquisition = gpflowopt.acquisition.ExpectedImprovement(m1) + gpflowopt.acquisition.ExpectedImprovement(m2) - optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition) - result = optimizer.optimize(vlmop2, n_iter=2) - self.assertTrue(result.success) - self.assertEqual(result.nfev, 2, "Only 2 evaluations permitted") - self.assertTupleEqual(result.x.shape, (7, 2)) - self.assertTupleEqual(result.fun.shape, (7, 2)) - _, dom = gpflowopt.pareto.non_dominated_sort(result.fun) - self.assertTrue(np.all(dom==0)) + for verbose in [False, True]: + with self.test_session(): + m1, m2 = create_vlmop2_model() + acquisition = gpflowopt.acquisition.ExpectedImprovement(m1) + gpflowopt.acquisition.ExpectedImprovement(m2) + optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose) + result = optimizer.optimize(vlmop2, n_iter=2) + self.assertTrue(result.success) + self.assertEqual(result.nfev, 2, "Only 2 evaluations permitted") + self.assertTupleEqual(result.x.shape, (7, 2)) + self.assertTupleEqual(result.fun.shape, (7, 2)) + _, dom = gpflowopt.pareto.non_dominated_sort(result.fun) + self.assertTrue(np.all(dom == 0)) + + def test_optimize_constraint(self): + for verbose in [False, True]: + with self.test_session(): + acquisition = gpflowopt.acquisition.ProbabilityOfFeasibility(create_parabola_model(self.domain), threshold=-1) + optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose) + result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1) + self.assertFalse(result.success) + self.assertEqual(result.message, 'No evaluations satisfied all the constraints') + self.assertEqual(result.nfev, 1, "Only 1 evaluations permitted") + self.assertTupleEqual(result.x.shape, (17, 2)) + self.assertTupleEqual(result.fun.shape, (17, 0)) + self.assertTupleEqual(result.constraints.shape, (17, 1)) + + acquisition = gpflowopt.acquisition.ProbabilityOfFeasibility(create_parabola_model(self.domain), threshold=0.3) + optimizer = gpflowopt.BayesianOptimizer(self.domain, acquisition, verbose=verbose) + result = optimizer.optimize(lambda X: parabola2d(X)[0], n_iter=1) + self.assertTrue(result.success) + self.assertEqual(result.nfev, 1, "Only 1 evaluation permitted") + self.assertTupleEqual(result.x.shape, (5, 2)) + self.assertTupleEqual(result.fun.shape, (5, 0)) + self.assertTupleEqual(result.constraints.shape, (5, 1)) def test_optimizer_interrupt(self): with self.test_session(): result = self.optimizer.optimize(KeyboardRaiser(3, lambda X: parabola2d(X)[0]), n_iter=20) self.assertFalse(result.success, msg="After 2 evaluations, a keyboard interrupt is raised, " - "non-succesfull result expected.") + "failed result expected.") self.assertTrue(np.allclose(result.x, 0.0), msg="The optimum will not be identified nonetheless") def test_failsafe(self):