diff --git a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot index 518b63ff3..8b86ad966 100644 --- a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot +++ b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot @@ -6,11 +6,11 @@ edge [fontname=helvetica] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e58139ee"] ; 1 -> 2 ; -3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ; +3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139ff"] ; 3 -> 4 ; -5 [label="area error <= 51.38\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#e5813913"] ; +5 [label="mean perimeter <= 78.51\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#e5813913"] ; 3 -> 5 ; 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139ff"] ; 5 -> 6 ; @@ -30,11 +30,11 @@ edge [fontname=helvetica] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#e5813994"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ; +15 [label="worst area <= 964.4\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139ff"] ; 15 -> 16 ; -17 [label="radius error <= 0.251\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ; +17 [label="worst concave points <= 0.104\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; 17 -> 18 ; @@ -48,7 +48,7 @@ edge [fontname=helvetica] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139ff"] ; 21 -> 23 ; -24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ; +24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e58139f7"] ; 20 -> 24 ; 25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; 24 -> 25 ; diff --git a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png index f40109f5c..54946ef79 100644 Binary files a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png and b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png differ diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb index 107b9dd2d..17968cdde 100644 --- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb +++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb @@ -102,10 +102,39 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2nd degree coefficients:\n", + "zero power: -5.333127866303926\n", + "first power: 0.0468843122503785\n", + "second power: -4.8231186445526945e-05\n" + ] + }, + { + "data": { + "image/png": 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\n", 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RaCqFIFjEaFrDw8OJi4uj3YPtyLk7h1WRqwBobG3sTxHLxJ8L05KBZBHZYP/+KS4Ugkaj0dQmbGJzuJ0OGz6M6PHRAFz95tWEXRlG4U2FBFpr5v7ifhshiEgKcFQp1d2edAWwy1/yaDQajTdwNhmVjAxgsRhNrjnHUNPwt5fR/cASpdQ2oB/wjJ/lqVXUhP0QzGv79u1Lv379iImp0NVZo6nTOJuMSraw5splaujyL7/GMhKRrYBuQfyAt/ZDMImPj6d58+ZeKUujqc2ISOmVyuZXq33kUENHCHUquJ2a45uHLLPLV+dJSUlcffXVDB06lF9//ZVBgwYxZcoUZs+ezalTp1iyZAkAf/3rX8nJySEkJIR33nmH7t2788ILL7B9+3YWLFjA9u3bueWWW9i4cSOhoaGl6klLS+OWW27h2LFjDBs2rNR+CC+99BJ5eXkMGTKE1157DavVSnh4OFOnTmXlypW0atWKDz/8kJ9++smxH0JISAjr1q0DjP0QvvzyS/Lz8/nkk0/o0aOHF5+iRlM/cB4hlDIZKYuZqUbib5NRnWH//v089NBD7Nmzhz179vD++++zdu1a5s6dyzPPPEOPHj34+eef+e2334iLi+Nvf/sbYCiJ/fv38/nnnzNlyhRef/11l8oAYM6cOYwYMYKdO3cyYcIEjhw5AhhhIT766CN++eUXtm7ditVqdSihzMxMYmJi2LlzJ6NGjWLOnDlcf/31xMTEsGTJErZu3UpISAgAzZs3Z8uWLUyfPp25c42FM/Hx8aUWyfXr149LLrnEIZdSijFjxjBw4EDeeOMNnz1jjaY24DyHUJbJSI8QqoGKevK+pD7vh7B27Vratm3LqVOnuPLKK+nRowcjR44s/4FpNHUUQYoa/LJMRjatEOo09Xk/BDOsd4sWLZgwYQIbN27UCkFTbxEpUgjaZKRxSV3dDyEzM9NRTmZmJitXrqRPnz5uPxeNpq4hlGMyquGTylohVBN1dT+EkydPMmLECKKjoxk8eDBjx47l6quv9rg8jaa24zxCKGkyMtch1NQRgt4PoY6j90PQaKqXOT/MIfehXMZsG0PkDZH0/ri341xifCLpl6dzrM0xbtxxI2e+PVNqF7XwfuGE9w33qkzu7oeg5xA0Go1XWL58ObGxscTFxTF+/Hh/i+M3mq9uTu9thhI4lnqM3hQpBNPLKD83n/jr4wlaHVTqegkWRp0bhSWo+g042mRUA9H7IWhqI7GxsSQmJjrMkvWVoDNFjfwLx18ods7ZZHRg0wEAtrCFlfZ/BRSgchS2bFu1yeuMHiHUQPR+CJraSFxcnGOEUK+xW4C+7P4l05+dXuyUOalswULXjl1hG6xquYpNtk0AjDg9ggAJQGz+MeVrhaDRaLzC+PHj67WpyIG9c99/QP9Sz8N0O7VYLKQVptGSlvT/S3/a9jU8EK2TrHABnvv5OR4d9ygBluptorXJSKPRaLyJae1x0bo2CmkEgNiE1HPGhl/LDizjzS1v8uaWN8kuNDz+nvv5OdYdXVcd0hZDjxA0Go3Gm9gVglhKm32ahBnRBVqGtiSykbHh1yMjHyGnTw4A4S+HQ5axTiG7wHN3cE/RCkGj0Wi8iV0hOEJdO2NPanCqAeqMwoaNCRdPoGH/hgD8GvQreeRhFaux0U41o01GtZiasB/C3r17i3lDNWrUiPnz53skk0ZTq1m0CK64ArbvNL5v3Wx8dzqC7pqIxZqH5Am2LBtKFdDgL7cW5Tlj/A0qUcjjjxe/fsECn9+CHiHUU7y1H0L37t0dwe8KCwtp27YtEyZM8IaImlpC9oFsjr16DFtuUY+22bXNaHZ1Mz9K5Qf++U84cAA6dgBA0tMgcXWxLAHAUDaQTWsAguUkQWvTHOcVU4FGWMSCbEuEfU4XDxvm4xuoYwrhR/WjT8odLaPLPa/3QzBYtWoVnTt3pkOHDpW6TlO7OTrvKMdfKx6YMe3LNIYd8X0DVqOwRzBWnbvBISBmICy4o1S2IPvhCnWbBVLAYrNge/opaDq06GRUlLclLoU2GXmJ+rwfgsmHH37ILbfc4vVnq6nZFGYWAtDytpZ0+ncnIy2r0J8i+QezgxZmzAcQ2bSUyajCI9T4W1SikIsvLn6uc2ef34LfRwhKKSuQABwTkXFVKauinrwvqc/7IQDk5eWxfPlyt0Jwa+oY9nawyR+a0HRsUw4+frDGBm+rFszQ19bKRzR1LFwTi18mlf2uEIC/AruBRv4WpCrU5/0QwAjHPWDAAFq2bFlZ8TW1HSe/e4dnjX8iL/gXEWyAOhBofPfE/mK/JvJ8JOIHrepXk5FSqh0wFnjLn3JUB3V1PwSTDz74QJuL6ilmmIVZf5/FU089BeAYAfuS5cuX069fP5YvX+7zutxChEUd7qTzzq4ApKSmlJvdlfwqwFCo896dx/Yvt/tO1jLw9xzCfOBRyulPKKXuUUolKKUSUlNTq08yL1NX90MAY57i+++/L9ccpanD2P96k44k8eprrwKQk53j82prXDA9EQoLL3J8XbZvWbnZXcnf7oF2js/rP17vfRkrQkT8cgDjgNfsn0cDX1V0zcCBA6Uku3btKpWmKSIsLMzfIoiIfk91mR037pB44mVy+8kSOzNW4omX1SGrfV7vsmXLJDo6WpYtW+bzutyidWt5s+0ciSdeJvabWKFcZcn/eo/XJZ54eXbms14TDUgQN9plf84hDAfGK6X+CAQDjZRSi0XkNj/KpNFoKovd1P2f5/5D02uasnbeWqwWq8+rrZnB9AyTz+Q7JzNufPk+MmXJHxRiOKV269zN++JVgN9MRiLyhIi0E5Eo4GZgtVYGBno/BE1twhGq2ULRlpH10ctIBGVvUl2GrXAXc17eVj+9jDQl0PshaGoVTrF7HF5s9VQhIFVXCI6geH5YylEjFIKI/Aj86GcxNBqNBzhGCIp6P0IwH4AloArGF3NTNT9skuNvLyONRlPbcY7uaQ4QpD5qBFD2EYLF6nnTao4QtELQaDS1D7Pd0gvTvGIycoyy/GAy0grByzz55JOOOECu+OKLL9i1a1c1SqTR+BazJ+s8QqivJiNzUrlKIwSrHiHUG7RC0NQ5zNGA0xxCvTQZiXjFZKRHCLWcp59+mm7dujFixAj27t0LwJtvvsmgQYOIjo5m4sSJZGVl8euvv7J8+XIeeeQR+vXrx4EDB1zm02hqE9rt1I7TpHKVTEZ6Url68WYMlM2bN/Phhx+ydetWVqxYwaZNmwAjcuimTZtITEykZ8+evP3221xyySWMHz+e5557jq1bt9K5c2eX+TSamowUCrZ8m+OQwiKTkS/dTkWK11vR4Q8sNmNRmTXQ84V55qSyLU9voVkteDMGys8//8yECRMIDQ2lUaNGjpWHO3bs4NJLL6Vv374sWbKEnTt3urze3Xya2s/y5cvp2LEjHTt2rJaAbM4dH+e6Z82a5XGHKH11Oj+G/ciaoDWO4+yqswCsW7+uqEURitVXsm4zrUWLFm7JJDZhVc9Vxeqt6Dj4t4OeProyKa8z+VbE3bRP7QnAtu3bPK/E/gxbPtWSl//2sufleF51/SIuLo7o6Gji4uJ8VsfkyZN55ZVX2L59O7NnzyYnx3WwL3fzaWo/sbGxJCUlkZSUVC0B2Zw7Ps51z5s3z+MO0bm151C5xubwBU7/jnOcZz5+plh4def6StZtpqWmprolU+GFQgL2GsumCir4V2g3vqd/n+7BUyuf8jqTjU8PACDPmsfCZQs9rqP51c0dn3cv3+1xOZ5QIxamVTfejIEycuRIJk+ezBNPPEFBQQFffvkl06ZN48KFC7Ru3Zr8/HyWLFniCH1dMux0Wfk0dY+4uDj++te/Oj5XR32xsbGOusy6b731Vr7++muPZLAVGmaMz9p/xuJWiwluEMzIkSNJ2JzArbfcyrI9y4ggAoCWI1oiShg5ciRr1qwBcOSNGRjDmjVryMnNKVbG2FvGsmxP6SihKl3RiEZkBGaw6NFF5ZbXO683z2x9hgJbQalyqkrJZ1pMRvvGOM/1iuUfc/7hcR3/9/D/MffHucR8HUOnjp08LscTVG3yBoiJiZGEhIRiabt376Znz55+ksjg6aefZtGiRbRo0YL27dszYMAAwsLCePbZZ4mMjGTIkCFcuHCBhQsX8ssvvzB16lQaNGjAp59+ysqVK13mq2vUhPekqTpL715K07eb8s7od3h39Lsu86x6chUWLFwRewU2i3fs4E0ymrB07lLSQ9O57tHyw6x3O9aN1998nZMdT3LTwZu8Ur87LG34CU0zIola34SoIdFVKmvhlIVELYzi0O2HmPJu1cPYKKU2i0hMRfnq5QjB28yaNYtZs2aVSp8+fXqptOHDhxdzO50+fbrLfBpNTeRCzgWa0pSmYU0Z372MUbYFsMH4ruOxBXhHIYSdCQMgMCiw7HrtBNuCAYrtO1IdmCMEi7XqkV4d229W87yyVggajcZ97A1U39Z9eeHmF1xm+en2nxCb8NmNn2EJ8s40Zc6RHNbPWE+zhs1YdnP5G8+s/3Y9OVT/XJxjDUKAF0J/+8n1VCsEjcbH5Kflc+qTU0hu8T/u0N6hNP1DUz9J5SH2xVLl+tn7YC2CFNhdWwPc8O/3Qf22PBunPj5FQZox6mh8eWPC+4aXqNYMbOeFEYL5fKt5cVqdUAgiUu7m8Rr/UpvmqXzB4acPk/xCcukTFrgk5RKCIoOqXygPMd+lOwrBm++9UgrB3rs2TTje4PSy0+y5fY/je0iXEIbsG1Isj2OEYKn6qMg0GekRQiUJDg4mLS2NZs2aaaVQAxER0tLSCA4O9rcofiM/zdhwvskfmhDaKxSAlIUpFJ4vpPB8IUR6t77M3ZkcnXsUW27lDNCWIAvtHmhH+MXhZWcyiyyvE2z/M1zbaG1Rb72qmBFVrRUXaLYD3lQI5jsM6RJC9v5sx/di9TrmELxgJjOL0HMIlaNdu3YkJyeTmprqb1E0ZRAcHEy7du0qzlhXsf9Rt/x/LWl1eysA0r5Oo/B8oU96gMkvJpOyIMWja6VA6Plu2d5gjlXJ5TTMTa9sStpXaUi+9++tyZVNKszjk46h/R02HNyQ7P3ZjufgjMWLcwjaZOQhgYGBdOzY0d9iaDRl4hzawcSXIR5sOUbr1XpqayJGRrh1zfn15zn+6nHHtWUXbvxXnsmoz/I+SJ5vTB2WBm70vu2ieXOE4HiHgXZTTkHp+zPrs3rRy6i6za1+UwhKqYuAd4GWGH8Wb4jIi/6SR6PxFcWCv5n40ovEXmTE8Aha3dbKrUssQRaOv3q8YgXlhulGKYVq4D/zrUNZefPR2u/b9JpyNUIoinSq3U49oQB4SES2KKUaApuVUt+LiI4NralbuGhEfdJolaivUoFp3FRQrkY7NQ1TNuW1CQynPR+CKh4h1GaTkd9iGYnICRHZYv98AdgN6LgNGp9TXUHmzEBox5OPA/DIo4846ruQYYQviV8V7/V6zcZr1t9nlQoaZ8pUMsjcnXfdCcCuHbvKDN526tNTdPrGCKVwLOWYW7K4CgZXVoC4kunme2rRooUjCJ4778uMOGwr8E73evny5cx7bh4A73/0vpFYWDp4nzmHYPWGQrB3Hrp804UX/ux6vYdPEBG/H0AUcARo5OLcPUACkNC+fXvRaKpKdHS0YPTNJTo62id1JJ9Llq6jugptkHnN5kk88XJpk0ul66iusunYJlkculjiiZcrLr7C63XvvHmnxBMvl3O5BAcHF7tP897NdPP/4QyXeOLlacvTZT6XxLGJEk+8UfaIy92SxazPuTxXaa7Snd9TZd7XpTGXSjzx8l7j99yS0Z17uJmbJZ54mcY0WcUqiSdeFKrYM1xpWSnxxEv2mfQq1/nTxz85nvWz7Z+tcnlAgrjRFvs92qlSKhz4DHhARM6XPC8ib4hIjIjEREZ62T9PUy+Ji4sjKiqKqKgonwSZ25e2j/bz27Pvsn1wD2Q1MzY9sl1lY99l+xj05iByw3MBOHTJITYkb/Ba3bkFuaRlpgHQsmdLbnroJrqM7sKkxyaxIXkDkx6bRJfRXRzpNz10E61jWhPW3ggN0a5zu2L5nY+zmUaY63//37/pNqWbW/K4iixcVrThkunme4qMjCQyMtLt93Wti286AAAgAElEQVTLrbcAEGCtmkU8PTvd8cwiOxptT8RFEQ6X21HdRzGixwhm3DyDET1GOI0Qqm6JH3nDSDZP2QxAeGg5bsBexq9eRkqpQAxlsERElvpTFk39wZvRbl1xMP0gNrHRMKgh3Zp1o0kDw1WyU9NO5LQ2QioEBxnrMpQoDqQfYEi7IWWWVxkmfjyRmAMxjGY0J3qf4MfAH2E0PPr7o/C7PdNo2M/+ov/HQdTeKDgCxzjG/tH7i+e38+zRZxnEINLC07i+3/VuyePqWZf1/Eume/qehg4dyjnOYbV4bropsBXQ67VepGQY7ruTOk6CQ3CmwxlyU3IJKQxh9t7ZRuY9MI5xANiweWcdAtD30r7wDjQIbOCV8tzBn15GCngb2C0i8/wlh6Z+kbk7k7Sv0xyTuSpAEXlDJMHtvLdwTuyFD7toGN/d9h3bvtjGme1neGHsCzT7YzMAEv6bQEZyBgrlVdfCA+kHGCSDAOjcvDNZbd3bkrVrWlcAmjRowuC2g13maRzUGICYdjFc0+UaL0jrG7zh0puVn0VKRgoKxaC2g2jfsD0ArSJa8eO4H+m9tXfpizIyKQzdhDVghOcVO2GueFaF1TeB788RwnDgdmC7UmqrPe1vIrLCjzJp6jh7puzhwoYLxdIytmTQ8z3vheY2G3jTy8XhoujccbT/jVvE4lAg3sL0dvnXlf8i8jr3zKxp36Sx/eXtXNLuEu69+16Xeba+v5Wzu8/yzJhnaNKo4gVifsMLoSvMdxgeFM6GuzdwKPkQh5cd5s6Bd9JxThnrngICILUQ1Ose1+uMJcB0/fJKcW7hN4UgImvx3sJ2jcYtCs4Ywcla3dmKgjMFnP7iNAVnvRsm2Wzgw8+Gk74qnfzTRpiDYq6aTltN2sR7zuYi4rBlV2aG0JStPLdTh+tlDXY5Be+ErjDfYUheCOmr08k5YJj6yr13c6TnpZXSpulJ2erHCEGjqXZM//EOf+tA5u5MTn9x2uuLw0QEa6GVO5+4k8SMREe66cMORY2WRSzeDQKHOEYmlQrhYGYtTzeZPvF+d0UpH2+YjMx38tiSx0h80vU7dHGR5xW6QFmUoZiqcXGaVgiaeoXDfGP1YviInBw4fbqojtOphOaGEpoRClZoPDSEBq0DaNTuAiRnGpkK8gCjFytn0iDZRTRUD5D8/KKe8Zk0SHZzX4A0Y65BcnLKlEVyDM8olZYKyRlVltVnpKUCVuM5ePhcJdfYj7lleksAGg1oQFBkAC1G2sou0wcjhEIKvRqCoyK0QtDUK4qFUfZG3PysLOjcGVKKgslJN7CONyZgAwvT6ffLZcaJT4suU7wC9DYmlR96CLY+VAUhipD7i4KsqTsnA+vduk7RH5gHa3+Bi1zLIrwK9IIJf8JYR1ozUU1bAB9BQQFcdJFHZUgI8BhY7dbEnluuJ4Tj8J07AnhZIdSTSWWNplo5cOYAOXk5WLDw3aHvsB6zEk44Jy+cJCUjhVbh7sX9Kcbx44YysFigdWsAbM2zCbDZG2UL0NrFAvzUQMgzGm9b08bQNqwKd1aEBKQU9SibNYFgNxf/5zaD0yBBIRBZxjWnGkA+qMjmEFRzgwqosIZwxj6p39YzOaVBIZCC1WY0kaplc3BnL4ZrrjF+C17A4b5aHyaVNZrqJK8wj4FvDGRR5iIiiOCOL++g24luPMuzbD2xlYcXP0zivYkVF1QS00zQqRPs22ck7fkC6/8MTx3VrhUcdmFiGLEFfjkPAvLcczDgbk9vrbg4L3UumlRe/B5c3cyt69RPZ2H0VhgyFNaUYTKKSYDNGahvvoaBDb0iry9Q23fCxakoS6DnJqPMVJjbggAxNi9SWzZCm+pbDwBFXkZ6Ulmj8TLZ+dmcyz3naCyv6nYVLQMM+7BFLBy/cNyzgl1MJIoIVpuxKMoMl1wSX00qGwLY66iMN5CT11OZ1JJJZUf46yo4MZpeRhW9R1/iWIdQjXMINf3VarxMdQV2q2kIwg2/3kDDHKNnu+TGJVwVfBVA1RaHuZhIlDzhga8fACAzJ9N1MDf7X96DXz3IjvU7PKvbBRdvv5hBB42FaevWr3P7OlN5HF93nO8v+Z7vL/meuWFz+U+D/zA3bC7fX/I9F3431m/8tOYnr8lbEm/8Pk1FEHE+gm+mf+NRGSLC9euup1FmIwC+/f5btwLyeRMzwF3jU42Z13MeN/W/yed/s1oh1DNiY2NJSkoiKSmJ2NhYf4tTbRRkFHDfyvsAyLJmYQmxsHDRQuOk4PniMBcKwbrF6miU95/bT2xsLImJicWed1BrwxQRdTqKowuPela3CyasmOD4/PL7L7t9XVArQ56wwjAC1wUSuC6QmKwYhuQNISYrhsB1gagsRSGFPPvGs16TtyTe+H1aGlnICTC8q4L+F+SRsi/MKmT6d9MByLZkM+ffc0q9Q1NeV+neoGWHlhSqQsJzwxmwZwDh6eE+/5vVCqGe4evAbjUV5/2FC18txBJoYfKUyYCXzDbOniV5RR/b/LeNy2Bu3V/vTl6QkXFwtOtQEZ4QWBAIwMIuC7nv2fvcvi6kcwgZz2ewoMMCsh7LIuuxLF6JfIXnGz3PK5GvONKe7/Y8D//rYa/JWxJv/D6tYVbumXaP8RmrR5OytlwbFnvzWPhqIbOfmu1WQD5vclHXi9j/r/280v8VVkxcwcGmB33+N6u8br/0ITExMZKQkOBvMTS1kBNHTrC3w14uhF7g2sxrAUj/MZ3EyxJJ7JDIk9OfJP2x9MoXvHs39OoFPXoYn4Flry0jYkYESf2SmPzb5DIvXXj5QqLiozg2+xiTnpzkyW2V4sPID2l1uhVtN7Wla0xXr5RZ29iXto9ur3Rj1ZxVWMTCyPyRRWEg3OTo4aMciDpARkgG47LG+UjS6kMptVlEYirKp0cImnqBuRpZVFEHyLGzlnhhDqFYkr0uSwVlmn99XlyJak5AlrfFZV3HnLC3KfuD9eDVuvq91Ae0QtDUC8wVyo5GAoptxu7NOQTM0EgV/XWZbuYu9uf1FItj/UM9VghFbkYGHihcW6Fxkc1SzZsa+xm33E6VUh1F5FBFaZqaTf7ZfCS3eOMT0DgAS4O63y8w/8Cde+1eHSE4exmZsZEqaJMdsvhihFCPFYKJqeQ9ebemkq5vIwR31yF8BgwokfYpMNC74mh8xYl3TrD3rr2lhs+BLQIZsm8IAY3q9pIUR4/P1QgB744QHOYGq3smI28G1zPXWVTWZl6XME1GYhFj7UQVTEbFfi/1gHJbAaVUD6A3EKGUus7pVCPAezuKaHzOhU0XQMAabsUSajQW+Wn55J/KJ+dwDuF9q2+bPn/gaoTgHDffq15G9gVcFfYuzTa7sNxclRPDXNVaf/VB0T4UeD4CM2NeVTgPVMeoqFvYHRgHNAaudUq/AEz1lVAaH2BGWPhPJ9reZ8R32dR3E5k7Mqs1Voq/cGUCcI6b7/GeBK5GCJWdVPbi83cEttOTyo4RoCfK3vw96BGCEyKyDFimlBomIu4ve9TUOBxmCRe7dtULheDKBOBNk5EzboZ4cCgMb44QtJeRgyp5GRWU7kDUB9wdWKYppVYppXYAKKUuVkr9vaqVK6WuVkrtVUrtV0o9XtXyNOXg6Mg6NRRV6EHVNsozGVVpYZorLyNT57jpZeTNSWXTy8ii6q/NqFQMI09MRrb6aTJy91fzJvAEkA8gItuAm6tSsVLKCrwKXAP0Am5RSvWqSpmasqn3IwQXPT7nDXJ8MqnspsnIm5PKjhGCO6Ga6yiOSWWlvYwqi7sKIVRENpZIq+pGtIOB/SJyUETygA+BP1WxTE1Z2H/Xc+LmOAJknT9/HoCffiwdrMwM2jVr1iyfBe9yBzPYWYsWLTwOeCY2YWfcTgAKne0z9l9/t5RudD/cvdg1K55YwROtnuDruK8rLP8Uo9m0YzALRixgRrsZqJeMBikjq4Jdxez1p32RxrSu05jeYzovPf5ShfWV9W5WxK4gLMfYV8Gbbqe+DODmCxyTyvbGfNSIUQ7ZXd2Lc1rBuQK+nfIti/+4GCjxe6kPiEiFB/AN0BnYYv9+PfCNO9eWU+b1wFtO328HXnGR7x4gAUho3769aDxj9+TdEk+8XMM1Eh0dLSIi74a8K/HEy9iuY0vlj46OFkCCg4MFcFxT3ZhymIcncpz99azEEy/xxMurbV51pGcdyHKkL2m8xJGel54nq1gl8cTLt5ZvxWazlVl2zrebHGWUPGb2mlmuXAvvXljqmhWBK6SwsLDc61y9m4LsAvlefS/xxMt31u/kRNoJN59OxZj1+es3UFmOnD0iPIl8GfKlxBMvjWjkkN3VvTinHXnhSLH38Vrr1/x1G14FSBA32mV3RwgzgNeBHkqpY8ADwPQqaSI3EZE3RCRGRGIiIyOro8o6idiHze0uaucIkNWmTRsA7p12b6n8ZtCumTNn+ix4lzuYwc4iIyM9DnhWcK5oMLvohkWOzyGdQsicauxxHJZbtGOZLasosFkDW4NyTWqFmfYtOTlPbliuIz03IJf9t+4vV65rnryGhOsT+KDHByzuYvRIQ/JDKjQhuXo3kisEiOEj8vdb/u7VxYa+DODmC0qajPr07uOQ3dW9OKcVnjNGBLvDdrPgsgUsvGFh9QrvZ9xajSQiB4E/KKXCAIuIXPBC3ccA5w1P29nTNL7APrEW9884Wo03toqMaBxBBhmMHDmyVPbx48czfvx4AJ5++ulqE7M8OTzGfu/ru6znbJezxU5d/szlbHhzQ7GJyJINstikTBOMI+xEaCbBURHITuP7+yPep3mf5uWK1aJtCx7+pChy6A/WHwiwBVBQUIA1wFrmda7eTX56PgCZwZls6rKpSpvDlFdfbaCkySh+dTxBLYzw3q7uxTntUIIRfGHQjEHcF3ofXZvWrwCB7oaumFniO8A5YLOIbPWw7k1AV6VURwxFcDNwq4dlaSrA1aSy86RqXcZ5grBkQ2nuSmV65wClvVIKKfMvJSM3E1AcCssl78xeutHNKELZsFrKbtRdYbpJFhZ4YLc212DZyyjmTVbPcIwQzIdSid+36XyAtXhZ9QV34xXE2I8v7d/HAduAe5VSn4hIpXfMEJECpdSfge8wHv8CEdlZ2XI0buJqW8X64mVkb+BtFlspd0zTG8exDzGUeh7lmXDScs8CTRBshASFONI7NO3ANdHXVE5Miw0KobCw8gqhZHROb44Qaiuml1dlvLgcIz5TIdSz5+iuQmgHDBCRDACl1Gzga2AksBnwaAslEVkBrPDkWk0lMXu9zr9vH7g91kScF6WV7PG5GiGUeh7l+LGbC5wDEHq16sWFw4Y1dWrMVDq071ApOR0jhHzPRwgOhVDPerbOlApdUZkRgjmatNTP5+iuQmgB5Dp9zwdaiki2Uiq3jGs0NQhzUrm+mYw2JG9g7vK5zGCGS5ORuaLXXJymlCo9QignPHUxc5TT6MuTlcJmY+6RychclKtHCI7fdaEYz7Hvq305E3HGrWsnb5jMOMbxcsLLMLj+PUd3FcISYINSapn9+7XA+/ZJ5l0+kUxTZfac3sORc0cAsJ63YsXK9lPbSTyQCEBAbgAWLGxM3ogcqFgrdGvWjajGUT6TtzCrkM2DNpP9e3a5+YI7BTNw00C3IrR+d+A7TmecBowe+LB2w4qdNxtuc08EhXI5qVwW4hRF1VkJeLIOwIy9XyWTEfWzZ+tMs5BmdGnaxaEcz2afJS0oza1r8/KMbU0zbYb3WcnfS13HXS+jfyqlvgGG25PuFRFzL0vv7P2n8SqH0g/R89Weju+zD89mNKN5au1T/JRuLER7+fTL9KEPM7+dyY7dO7j9p9vpe7hvmWUub7Ocp1Y+ReOQxj6ROev3LLJ2ZVWYL/v3bDJ3ZRIxNKLCvDaxOeYH/tTrT/S7tl/xDCXDVyhcTyqXgTgim9qKL/Os3HyyIatdIdgKPIm1YMpRh4d7bhJoDWT3jN2s/896Ci4UsHXaVoIuCnLr2uPHjnNm4xmeGfMMb0x7g6YhTX0sbc2iQoVgDzGxU0R6YCwQ09QCks8nAxDRIILBbQfTKtRwNe3bqi9BnYw/johgo0Ed3GYw7du05874O8stc9DBQaTuT6VxX98oBLMhDu8XzoCNJbffMPhtxG9c2HjBbTOXs0JoENigVM/Z7MlbxYpNbC43ZS9vhOCIkVTSZOTBCKEqJiM9qVycAEsAVouVAgo4MOSA2ya8wgzj2YeHhNMstJkvRayRVKgQRKTQHoCuvYgcqQ6hNFXHDN8b3SqalbevZMfnOzidcJrY0bFEXmcs8Pvtjd84t+8cz495nrA+Yfxyzy9Ywiz0WdqnVHlrblhD4/ONseX7Lhywo+G1giXQ9cIqR0NbCYVQXgRQ54bbZrOBtZKTyo6eua3YqMCjOQT7RKYnJiPtdlqaiOERnPrwlGOxmbtYgi00HNDQR1LVbNydQ2gC7FRKbQQyzUQRqT2rVeoZpkIw3SwrCm5nnrc0sNB0TOlhcm6Q4Tvgzf1/S2FveMttzCrpKmsTW5EHURmLdwtVIVaxGvcWWLrscieVba4nlT3ZoMZszLf9Yxv7Gu9z+zppItgusxFEEDb7Q6zvIwSAnu/3pOsrlV9YZgmxYA31wOZXB3BXIfzDp1L4mJULVpKdXjRR2bJHS4aOHepHiXyPIPRM7knvU705FXKK3GSjQS8z/LXZGJfRs61K5Ei3MYsurzGtZMhuyReG7jPedVn3ZrPYsBZaWTF/BaqlwtbYRjOKzAVbVmxh2N2lJxfFJuT91sD4bKn6pHJ2aDacg/CPK7973Vub3uJu7nZMKld2UVxdRClFYLNAf4tRq3CrHyMiP7k6fC2ct0iblUbEwxGOI2dcDu+8+o6/xaoS5UUjXb58ObNum8Vrb73G9a9dz66bdpGx2Yi8ueG3DUWF2NusqXdP5bsV3wGQlp7mKNO5bNOFb90vVd8nyYxgWjJ6qdnb3r5jO7NmzXKZ50y64T74y8+/uFXP4djDjN41GoAjJ1xbPPOtRtiHpk80pcmdTfjg9Q+Knc+dmss7r5T+vaSvSifnfWM+Jd+Sh2pQpAQSdydWKF9JAuYH8NUfv2LBoAW8PfBtx7Fg0AI+vfxTFg1Z5Pj/qz9+RfwN8SS1TwKgbbaxC16gNZB/XvZPQgNDK11/XcVVxNzy0jyNqlsncCcCHjAUI9REBpCH4Xdx3p1rvXkMHDjQo0h/b014S57q+JTMbjdbvgj+QuKJlzH9x3hUVk2hvGik0dHRcnGbiyWeePmiwRcym9kyxzJH/syfJaZvjCPfb6N/k3jipT/9ZWSvkRJPvHzCJ44yncte2NSIzDm632ivyV5S7rNrjaikL/Oyo+6Sed4Ke0viiZcJnSe4Vc/kTpMdkSuv6n6Vy3yfzPlEnur4lHwQ+oHEEy/Tu08vFYX0qgGlrz3x7gnH+T/+X7ScXXdW5jaeKw/wgAzrPcyDJ1M6uqt5uHonIiJzms2ReOLlr9a/Gu8v6BOP6q3LuIqYW1FabYns6i54OdrpK8AtwD4gBLgbY3ObWsFdS++i7/y+fNHsC042PwlQq4J1uaK8aKRxcXG0bW30GA83PcyiqEXkPp7Lz9E/84+nnKx/9g5t506deXimEWTNGmh1lOlctjnJe/uk270ie1RUVKnopWI3A4WGhjJz5kyXeTp0MFb/3nXnXW7VEx5imF8+6fUJ9z17n8t818deT9/5fdlnMez2QxsaJqbCjoXsjzIill477trSF9rNbCsvXsnRDnuJGBpB10VdiY+O5/FnPNsA0Dm6q3lERUW5fCcAQy4ZAsDA6IEANGnWxKN66zKuIuaWl+ZpVN26gDL/CMvNpFSCiMQopbaJyMX2tN9EpL/PJXQiJiZGEhKq5vm6sNtCovZFYf3UyqUTL/WSZDWPbxd+S/CUYA73OswdO+9wmWfrH7ZydtVZLl55MSFdQ9jQcQMN2jdg2OHS9vIlbZbQ9kRbmv/UnD4jS3sheYOza86yddRWIkZE0P9n1z+t30b9xrk154iOj6bJ6IobvzdueoNuH3fj+LTj3Pq/8mMn7r1nLyfePEHre1pz4o0ThPcPZ0fmDqJ+j8LykYWRNxaPCnti4Qn2TtnLd9HfsfKKV9j8vDeCAFeOA48c4Ojco7S+uzUn3jpBSNcQhvw+pNrl0NRslFKbRSSmonzuTipnKaWCgK1KqWeBE3jkR1EDsEvtUbyYWkTJmCyuKBa6ooJJZdMDxpdxj1x6QpWksgH5zNfsxhyrGehO8orkMCfTzfUGxXBy9fSXT48psy3PLl/t/KvU1BDc/fncbs/7Zwy304uAib4Sypc4vGXqekA3UyFYy7lPZ7fTwvIbY4di8d0yBNcRWUtQ6fhLFSi6YmUHFm9clVLl37dTmt/cPO2KzlRi3tw6U1P/cDd0xWGlVKT98xzfiuRj7A2eyx5fDUcKhd+n/07W78XDO1hDrHT6dyfCo8OL5YXyRwgu3U7LalDMvL5UpK4ispYhh7sKQdnsF7gzQrArhPSV6UaChXJ/L6a5tSaMENJ/sMus9YGmCpSrEJTRHZuNMTKw2JMKgJdFpFbOupgNZK1TCCkpZG69wIk3T7g8HdK6gK6zi3zn5cxZIAyRAjh82HWZOTn2sk8hgcZPQdlc5xf7Qjc5dars8qqInLCvFcnLKbuOXENmSTkJh89XXGi2EaxM5WRVKHdIhFF//mnDDTW4eSFiD2UgaWdKX59q1G9EUfUPIR2NPRgcMncI9pMkmrpARSOEBzEC2g0SkUMASqlOwH+VUg+KyAu+FtDrVEdP19vMnw8PPojQDXidEJLpxlwA0riEZG7E9s4SeGee4xJpOxKYA6kpEBXlsljFv4EhnJjyKVaygKvgwD6IGlEqr7R4zfh/xv1wdo93789BDPAc6pefIerRMvLMBQbCbbdhbMVRAR0eAHrDii/hf3eXm7UNioZ0o5BgFELDFXtY12YO0BXbs3Nh5oYSV1wLzESUYPGTSmj5/1oS1ieMggsFKKVoOKh+hlzQeIeKFMLtwJUictpMEJGDSqnbgJVArVMItXKEsNXYpVQaRcB5sAYW0KS1YSLIzTgLZ8AW1gSatXdcImERcAwkQEH79i6LDThtgyw4TZG3VWCDXGhZOr/k2T80aw6NXJdXFqnBhRwOK6gwX8H5pnAAzjcMIqFLS5d5svcHwgX4vXNTAhoVzyNipeDMCKSgqFEMz+oOgAoLLvM5mCigEdmAuaq9FdjjIEnjxhBU/Hq50AzSjVXhKjKywvvzBUopGg7USkDjHSpSCIHOysBERFKVUrVzTbhpE/YkxLC/MF2DH3gQ4kBF94VNhvnC8tEpuHkXaUGjSYi82nFJaIqxmby0bQW/uDaVdD6WS6PPUov2kVXQbNxg6DqttAidFhj/v/gSjHXf2/h87nk6zWtLRl5GhXmH/H6Ofx+AX1vm8PifTrrM8+x7uQy6AH8ZepZNXYvnGbZ3GM98ML1YWmvzw6QJMOtDt+U2kQFvwlGQvzwI068qfvK1YzBjn2EyauFagWk0tYmKFEKeh+fKRSn1HMZ4Ow84AEwRkbOellcpauG2kRnk8U0vkKzttKA1adlpLN62GIBAAmlJSwrSCxzhKQAC7K/2fOuy7ewN2jag3V/auSWDwzurksHtUjJSyMjLIMgaRN8WZe+1AND1tBGIrFFwIwa2HugyT8MGRm+4W9Nu2FoXV+p9k4zyU1qnsKdPkVlLmgh331m+uahMTC8eF7+XYoHt9Gyupg5QkUKIVkq5alEUUJXZq++BJ0SkQCn1H+AJ4LEqlOc2psnoXNK56qjOK8Q12cZzN0Kvo8t4lTHsP7ufGZ/PcJxv8UALGmeW3qOgwFrApWO8tPjO3t7ZTlVuZCUihOWEMVANZMWI8rfPPltwln3sY3iH4Uy/Z7rLPNs+38aZXWd48eoXafbH4vHqTwSdYO/CvUSPiebmhTdXSs4y5Tc3nDnu6qTxn6u9mjWa2ki56xBExCoijVwcDUXEY5ORiKwUEdOovB5wr5vqDew9vjZz2/Dioy9WW7VV4aTV8KzpE2g8Jku+hVFNRjGqySgaH2lMZMNIjuUc41jOMQJbBXKq8BSBrQJJz01nQJ7rjWaccSeol7mbV8bUDL6a95XbshdmFPLB/A+Y8685bOq9qdxj3wwjdMTJU67NRYBDMd3/5/tLy2rXVZ989kmxgGUlg/+VR8n85jqO4GeC+frJr4ud37Fth5NYvlcIZd1LZe9RoymLmrCu8U7gm7JOKqXuUUolKKUSUlNTq1xZ28ltHZ8Tv6t8REp/YIY0nhBirDwvOFXA2QVnHceB5w6Q+V4mme9lcuC5A4609HfSWfCvBRWWHxsbS1JSEklJScTGxrrMs25EUZTTpS8udVv2gpQCGuY0JN+ST0qDFFIapJBEUrHPR9QRx/8HOMC7qe+WXaC93T106FApWU0TzvmM8457iY2NJTExscz7KknJ/HtGFpmeVr65stj5b1YYP9vqGiGUdS+VvUeNpix8phCUUj8opXa4OP7klGcWUAAsKascEXlDRGJEJCbSC54c10y7hs2XGe6Kvbr3qnJ51UHRxunG6woJC3EE4zIDnplBucoKglYe7gT12jJ4Cz/0/QGAGybe4L7s9jmHkxEnCf04lNCPQ5kfPb/Y571P7HX8/3L0y9zwYjnl29vdTlGdSstqbsEZHl4sYJm7zwEolf/IwCN8eIkxGX3NldcUO3/N1dcY96jEsRGRLynrXip7jxpNWbgV3M4nFSs1GZgGXCEiFe+sjneC2wG8ff3bdP6sM0dnHOX2V6oevdPX3PZQR5Y0SuKjs3+nxfwriLg0gv5rqjWuIP3+148//vePjNk2hh6LetDq/7Vy67qta7dy9tKzpLRM4eaUqtv1t1+7nbSv0uizrA/Nxzcvdu7Yf4+x7759tLm3Dd3+263KdQFc//H1NHm5CZPWTiJ1RirpU9Id55osbs1Xu/4AABcgSURBVELk/Eg+HvYxe6btYfUdq71Sp0bjbbwd3M6rKKWuBh4FRrmrDLxKLfM0cowQTMH9YOhTShUFuKuEp5EjhIby0rMuL3SFOd/txecTGhhKocVYrfzlri9576v3HOdu2nkT93IvghASGOK9SjUaP+EXhYCxv0ID4Hu77XW9iNxbXZWb8Xp8uj+wF7GZCsHcLN4PAcwUyjGxXJkAd86umV4SxCjPxcjWrMuTDe7L4vERjxP/WTwAg1oOInhAkXNdz0M9AYhuFc3doz10a9VoahB+UQgi0sUf9TowA53VkrVpZtPnGCH4wcOx2AihEiMrR8PtrV57NY8QekX2IqxnGIc4xNhOY7n/2vsd5w7vPMwhDnFl1yvp3Kaz9yrVaPyEv0YIfsUxQqhtJiM/jhDAs8Vp3jYZlRf+2jFC8PLzMSOKZh/IJn110RxC9n57iAu9BEFTR6iXCsHRg6wle+TUiBECRSOEmmAycqkQKtjTweMqGxiVpn6USupHpV2fze1FNZraTr1UCKaNufaNEOwJtWhS2WazX+MthVCeQ0BFezp4SOR1kZxbc46Cs6UD9FnDrbSY1MKr9Wk0/qJ+KgSzwagtcwhS3MuoNk4qe2tUY5qMcg7lcGFr8T2Mc5NzjTxenFQGCG4fTJ+lvtlHWqOpSdRLheDoZdYSLyNxtG+q2H/VjUdup942GdkdAg4+drDs6Fdu7I6m0WhKUy8VgqMHWdtGCOLHEYKnXkZeVgitp7Qm52BO0abyJbCGW4m83j97E2g0tZ16ORtmNqidPu7E83c+X+q8GSxs1qxZNSJoWNfdl/DtU9/S6JXBAKScTKl2GRTK0agffOQgK2aVjlxaMsja8uXLefzxxwEosBWUG4TN3QBtTa9qysCNAzkRd4Ibz93I2ONjufHcjXwx9gtuPHcjE49NJP5IfFVv16V8zgEAdUA5TZ1ERGrNMXDgQPEGPy/9WeKJl3jiJa5TXKnz0dHRAkhwcLAAEh0d7ZV6PeXVXnMc8v7AD3J/m/urXYahbw2VobcOdcjxZPMnS+Uxn5v5vKKjo6VPiz4ST7y82vrVUufLu7YizPzmYb4rX70v5/qio6MrLa9G40+ABHGjja2XI4QRE0aw5d4tADRuVHofAeegcTUiaJh9EuHrjvOZ2mEqf/jvH6pdBIVifbf1HL7N2H1t6JChpfKUDLIWFxdH86ZGvKHA4MByg7B5EoQuKiqKyMhIR1C/igL0VYWSAQB1QDlNXcRvwe08wVvB7QCWv7CcRjMbsX/Ifu5eX7PDDvy3x9P03DucrKmJ/PGNv/pFhkvevoR1yev4Sf2EbbaNNjPa0O2VigPIrfloDbabbRzqdogpe6dUg6QajaYk7ga3q5cjBKhdE8vm5iv+3JTLsULYlMHN5+Ztt1ONRuM76qWXEYDFYvqe+lcOt7DZFYIf3SkdO4KZXYgSz82Wb2Pfn/eRk5RTLF1OeNntVKPR+Ix6qxDMEYIqrPldV9Pd1J/jOXOE4IhnVML1NGNLBifeOFHm9eebutqaW6PR1CTqrUKwWO2tq83wtMpPy3dpBgloHIAlyL+WNUdQu5qwkXsZJiNbrpEQ2juULs8XBbNNTEnk4VUP02REEx7ggWoSUqPReEK9VQhWq93+IrBvxj6O//e4y3yBLQMZ8vsQAhr581HVHJPR8czjtKY1py6c4tyxc47zhSeNSIE5DXPY32e/I31Xk11sSdrCZQ0uq16BNRpNpam3CsFcnKZsivPrDXNGQOMAVGBRLzz/TD75J/PJScoh/OJwv8gJoGz+W6FsYrUY2ui97e/xKI/y1d6vePatZx3n+x/szzzmkXAygYfeeqjM6zUaTc2l3ioES0CRycicII1eHU3D/g0deRJiEsjYnFFmmITqwjQZYfHfxOyfB/2ZnIIcog5FARAZEsmgNoMc57ue7QpAeHB4sXQwlMF9MfdVm6wajcYz/KoQlFIPAXOBSBE5XZ11O88hmCGaCymkwFYU4lgFGQ1xQU5BsXSrslarPd+fMYxMJvaayMReE0kJSGHPoj2M7TKWh6c+7Dh/5rszbJu/jSEXDeGeqff4TU6NRuM5flMISqmLgDHAEX/Ub7FYsGHjQvYFdpzZQSc6MeDNARxqeciRZ/7x+UQTzeVvXU7iqkRH+oj2I1gzeU21KYWindKqpbryKcPt1IyA6u3Q0xqNpvrwZxPzAvAofloJ0Kl5JwCiD0fT6ZTxWUQME5INLFgosBqjgseWPcZbr73lOPq93o+s/KxqkfODxz6g05FoAI4k+0V3Fsfe3n/x+ReOQG8AG9ZtAGDvvr01KjCgRqNxH7+MEJRSfwKOiUhiRb1spdQ9wD0A7du395oMzXs0Zz/7i6XJ/4p0U9/ovowfN55jLx2j9dnWxfJ1PtWZgqwCCPKaOGWzqOhjQsIq/B38wTRbZWVmkZSZRGxsLOPHj2fJ4iVMZSr7D+4n0ZbI3r17ycnJcZzXaDQ1H58pBKXUD0ArF6dmAX/DMBdViIi8AbwBRiwjb8kX3C6YjOcyCH+kyHuodZvWZOVnERYWRlxcHF3GdWFv173851//ITvb2FD9qQtPEVwQjBRUz8DGYvcwsra4n9tvnFAtdZaLfUzZMLQhUS2KAslNunkS/Bs6d+lMdEg0Y8eO5euvv9bB3zSaWkS1B7dTSvUFVgGmzaUdcBwYLCLlBvr3ZnA7gJwjOazvsN7xffCewYR2Dy33mhXBKwjNDSX6ZDRNWjTxmixl8XHzj2mR1oLWjSbR/f03YOxYn9dZHqc+OsWum3cReUMkvT/u7UhP/SyVndfvpPl1zenzmd5uUqOpSbgb3K7aTUYish1w7EqulEoCYqrbywgoPYPixoyKuWuYraB6XFHNEYISm3+j25mUscm9+V1PKms0tZd6uw4BSrtxuuPW6VAIhdWjEEwPIwtSIxSC+YwyEzPZ/1DRHEzWXvuAryZ4Qmk0Go/wu0IQkSh/1V2qN+tGeysW18HdfIWpEAKksFrqq4iAJsZPJnt/NsnzkkudD2wSWN0iaTQaL+F3heBXaoHJSNlDX1ukZowQGo9qTM/FPclLySt1TgUpWtzYwsVVmv/f3t0HWVXfdxx/f3dhQQGlPCwQWVwQzRgZ7grrw3RiYuyDhjYLzQwdpzFxMkamjjbEh0xwmFlxp60NNemodNrQRCNJJVUMDbWtWg2TTIfBqJVF8CE87BUFZA0LLEvcJ+63f5xz7y6be5flPp27935eMzt77jnnnvv73gP3u7/f757vERkNKjohDO0hjGTIKFX+uUiT8ckeQrWXxp18rNqY8aUZUTdDRAqgskd8h0Y/gj/AE1XFnUOoCstWVJXKpLKIlK2KTgjZTConewiVOmQkIuWrshPC0EnlEbwbme4YVijJHkJ1iUwqi0j5quiEkMuQUbKYW6ENzCGohyAihVXRCSGXIaPNN2xmy+aBwm1btmzJezG3h7/0MOf3BFdOl+ocwpYtW5g7d+4Zhe5EZHSq7IQw1jhVfQqAnqoeqiee/a5eJy4IbhvZcKyBJ775RGp9c3Mzra2tNDc356Vtncc6WfTUIgC6xnUxvr8vL8fNt+bmZuLxOPF4PG+xi0g0KjshVBn+qLNhzgb6/qGP6vPPnhDWfXldavlrX/laarmlpYVYLJa3Ym693b1Uhadn8uVrGeenS7KH0NLSQn19PfX19SpkJzLKFb24XS6yLm73wANw6FBe2lA3cyMrn1pD4/5GFt70MlNmD1uPL2vtPc5bP7qFk+d18oXJfwmHD8PLL8MNNxTk9USkfJVscbtIbNoEb72Vl0PZ3eDJe/o8/wLwal6OO5SPnwzcQsJOB8kAYOrUgryWiAhUSkJYswaOH8/LoaqO3D/w1dOvr4QFhblzWqLjY1gFiSpg/Xqoq4OFCwvyWiIiUCkJYfnyvB3KHvnbVELgxptgSWH+avc9h2DVr4NierffXpDXEBEZrKInlbNh2EBCKOD0S7I0RrKYnohIoSkhnCMzS80hFPJq5VRCqFJCEJHiqIwhozyqsqqBEtg9CVpvbOXjPR+nto+tHcuCzQsYN2tcTq+TvBI61RsRESkwJYRzNHjI6NSuUxx78dgZ27vbujn+i+PMuDm3EtHJ3ocSgogUS2RDRmb2V2b2jpntNrO1UbXjXJkNJIRnW58F4GjdUTY8uoG2RW0AdPd05/w6GjISkWKLJCGY2eeApUDM3a8AHo6iHdmYNXFWKiHsbQ/uKdyeaOeJjifY37sfgJ1Hdub8OhoyEpFii2rI6A7g79y9B8Dd2yNqxzl7evnTvPHUG/A2LL1kKWyD+dPn88zyZ/jg+eAew/39/Vkdu/u9btr/rR3vd04cCGomJe/hLCJSaFENGV0GXGdmr5jZL8zsqojacc5qJ9Qye/JsAF79r+Aq5QN7DlCztwbrD2oNtbW1pX3u4Iqo6aqE7vvWPvZ/az9tq9vo/F4nAL8dW5gL30REhipYD8HMXgJmptm0OnzdKcC1wFXA02Y2z9MUVjKzFcAKgDlz5hSquecmrDF37Ggwodx1qovHmh9jWdcyADLVWxpaETUej6fWNzU10X8s6Fn8nJ9zeNxh/Gpn29xtrAjCFxEpqIIlBHf/w0zbzOwO4KdhAviVmSWAacBHaY6zHlgPQXG7AjX3nCTvm1A7pRY64LyJ59HS0kLb37TBPlh85eK0z2tpaaG5uTlVFXTlypWp9TAwb/D6jNfZPnE7HX/QQW1NbaHDEREBoptD+Hfgc8BWM7sMqAF+E1Fbzl040LbsC8s48uQRrrn2GmJNMb7/yPcBqKurS/u0pqYmmpqaznh8hvAumT/e+GM6Yh3Mf2w+E86fkPfmi4ikE1VCeBx43Mx2Ab3AremGi0pWOGTk/WGTk7dRCBNFIpHdV0WTPQSrNhIeHKPKdDG5iBRHJAnB3XuBW6J47XxIDhklE4JVB48tvIFNtiUtBh8vWR7DSvCmOCJSnvTnZzbCz+ijPzsaPEzeizl8N7NOCKcHehzJDpOhhCAixaGEkIVJiycBkOgOhnUmNQaPc+4hJIeMxqiHICLFp1pGWZj99dnU3lxLojeBjTHGzQwL2SXnFrKdDgknlTWHICJRUELIUk1tze+sS80tDOkh9B3t48C3D9B/YvgrmLsPBDWQrNo0ZCQiRaeEkE/JHsKQhHBk4xHe//v3R3yMsVPHashIRIpOCSGPkj2E5959jjsfujO1/ou//CJf5au8Mf8Ntl2xbdhjHJx2kDc3vMnpRDB+pB6CiBSLEkIezZwUVOpIJBJ09Xal1vf19QHwzox32HTlprMfqHdg8fr66/PZRBGRjDRjmYPBxeoALpl2CQCf2PUJ7u29lwX/sYCNCzey5rNrAJi6b+oZ6ztXdbJx4cbU48HLnas6OXn/SdYtWRdVeCJSadx91PwsXrzYS0ksFnPAY7GYu7vv+cYe38pWX85yHz9+fGpb/K/jvpWtfhu3nbF+6DGGHk9EJB+A13wEn7HqIeSgpaWFWCyWKk6XfDcvmnUR99xzT2pbcpJ5eu30M9YPPcbvHE9EpIjMR1EJocbGRs9UWroU7L1vLx985wPmrZ3HnG8OlOqOPxgnvibOxc0XM/fBuRG2UEQqkZm97u6NZ9tPk8p5lPyKaHxNPPU106l/MpVxc4IL11IlLkRESpCGjPJo0lWToAoSv03Q91EffR/18eEPPxyoUaR3W0RKmHoIeVT757VMuXEKiZ6g7MS2WdsgAd4XXmSmHoKIlDAlhDwbc+HAW2rVhic8lRB0jZmIlDINYhSQjQlrG/VpyEhESp8+ogoomRASfcEQkoaMRKSUKSEUUPJOat6rHoKIlL5IPqLMrMHMtpvZDjN7zcyujqIdhTZ0yEg9BBEpZVH9zboWeNDdG4Dm8HH5qQ5+9R0LituphyAipSyqjygHLgiXLwQORdSOgkgWvevp7wGg4z87ANi9e/ew+yeL5ImIRCGS0hVmdjnwAsEXMauA33f39zLsuwJYATBnzpzF772XdreS0tDQQGtrK/fNvI9rOq6ht7eXLrrY9MlNvPjOixn3j8Vi7NixI4IWi0g5G2npioL1EMzsJTPbleZnKXAHcLe71wF3Az/IdBx3X+/uje7eOH369EI1N6+SRequ+9511DxTw+r61TxU/xB3rb1r2P1V1E5EohRVD+EEMNnd3YICQCfc/YKzPa/Ui9uJiJSiyHsIZ3EI+Gy4fAOwJ6J2iIhIKKrSFbcDj5jZGKCbcI5ARESiE0lCcPf/BRZH8doiIpKevhkvIiKAEoKIiISUEEREBFBCEBGRUCTXIWTLzD4Csr1UeRrwmzw2ZzRQzJVBMVeGXGK+2N3PemXvqEoIuTCz10ZyYUY5UcyVQTFXhmLErCEjEREBlBBERCRUSQlhfdQNiIBirgyKuTIUPOaKmUMQEZHhVVIPQUREhqGEICIiQIUkBDO7yczeNbO9ZrYq6vYUipnFzexNM9thZq+F66aY2f+Y2Z7w9+9F3c5cmNnjZtZuZrsGrUsbowUeDc/7TjNbFF3Ls5Mh3jVmdjA8zzvMbMmgbfeH8b5rZjdG0+rcmFmdmW01s7fMbLeZrQzXl/N5zhRzcc+1u5f1D8Gt7vcB84AaoBX4VNTtKlCscWDakHVrgVXh8irg21G3M8cYPwMsAnadLUZgCfDfBLdqvRZ4Jer25yneNcB9afb9VPjvexwwN/x3Xx11DFnEPAtYFC5PAn4dxlbO5zlTzEU915XQQ7ga2Ovu+929F/gJsDTiNhXTUuDJcPlJYFmEbcmZu/8S6BiyOlOMS4ENHtgOTDazWcVpaX5kiDeTpcBP3L3H3duAvQT//kcVdz/s7v8XLp8E3gYuorzPc6aYMynIua6EhHAR8P6gxx8w/Bs9mjnwopm9bmbJmw7NcPfD4fKHwIxomlZQmWIs53N/Vzg88vigYcCyi9fM6oErgVeokPM8JGYo4rmuhIRQST7t7ouAzwN3mtlnBm/0oK9Z1t8zroQYgX8CLgEagMPAd6JtTmGY2UTgWeAb7t45eFu5nuc0MRf1XFdCQjgI1A16PDtcV3bc/WD4ux3YTNCFPJLsPoe/26NrYcFkirEsz727H3H30+6eAP6FgaGCsonXzMYSfDD+q7v/NFxd1uc5XczFPteVkBBeBS41s7lmVgPcDGyJuE15Z2YTzGxSchn4Y2AXQay3hrvdCvwsmhYWVKYYtwBfCb+Fci1wYtCQw6g1ZHz8zwjOMwTx3mxm48xsLnAp8Ktity9XZmbAD4C33f27gzaV7XnOFHPRz3XUs+tFmsFfQjBrvw9YHXV7ChTjPIJvHbQCu5NxAlOBl4E9wEvAlKjbmmOcGwm6zn0E46a3ZYqR4Fsn/xie9zeBxqjbn6d4fxTGszP8YJg1aP/VYbzvAp+Puv1ZxvxpguGgncCO8GdJmZ/nTDEX9VyrdIWIiACVMWQkIiIjoIQgIiKAEoKIiISUEEREBFBCEBGRkBKCSAZmdjqsMLnbzFrN7F4zG/b/jJnVm9lfFKuNIvmkhCCS2cfu3uDuVwB/RFAS5IGzPKceUEKQUUnXIYhkYGZd7j5x0ON5BFe+TwMuJrhoaEK4+S5332Zm24HLgTaCipyb0+1XpBBEzokSgkgGQxNCuO448EngJJBw924zuxTY6O6NZnY9Qf36Pw33Pz/dfsWNRGRkxkTdAJFRaiywzswagNPAZTnuJxI5JQSREQqHjE4TVNl8ADgCxAjm4rozPO3uEe4nEjlNKouMgJlNB/4ZWOfBOOuFwGEPyhJ/meBWrRAMJU0a9NRM+4mUHM0hiGRgZqcJKk2OBfoJJoe/6+6JcD7gWYIKlc8Dd7r7xLCm/QsElTl/CDyXbr9ixyIyEkoIIiICaMhIRERCSggiIgIoIYiISEgJQUREACUEEREJKSGIiAighCAiIqH/B8TDB2gz2c7LAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -517,10 +546,468 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.990 10.38 122.80 1001.0 0.11840 \n", + "1 20.570 17.77 132.90 1326.0 0.08474 \n", + "2 19.690 21.25 130.00 1203.0 0.10960 \n", + "3 11.420 20.38 77.58 386.1 0.14250 \n", + "4 20.290 14.34 135.10 1297.0 0.10030 \n", + "5 12.450 15.70 82.57 477.1 0.12780 \n", + "6 18.250 19.98 119.60 1040.0 0.09463 \n", + "7 13.710 20.83 90.20 577.9 0.11890 \n", + "8 13.000 21.82 87.50 519.8 0.12730 \n", + "9 12.460 24.04 83.97 475.9 0.11860 \n", + "10 16.020 23.24 102.70 797.8 0.08206 \n", + "11 15.780 17.89 103.60 781.0 0.09710 \n", + "12 19.170 24.80 132.40 1123.0 0.09740 \n", + "13 15.850 23.95 103.70 782.7 0.08401 \n", + "14 13.730 22.61 93.60 578.3 0.11310 \n", + "15 14.540 27.54 96.73 658.8 0.11390 \n", + "16 14.680 20.13 94.74 684.5 0.09867 \n", + "17 16.130 20.68 108.10 798.8 0.11700 \n", + "18 19.810 22.15 130.00 1260.0 0.09831 \n", + "19 13.540 14.36 87.46 566.3 0.09779 \n", + "20 13.080 15.71 85.63 520.0 0.10750 \n", + "21 9.504 12.44 60.34 273.9 0.10240 \n", + "22 15.340 14.26 102.50 704.4 0.10730 \n", + "23 21.160 23.04 137.20 1404.0 0.09428 \n", + "24 16.650 21.38 110.00 904.6 0.11210 \n", + "25 17.140 16.40 116.00 912.7 0.11860 \n", + "26 14.580 21.53 97.41 644.8 0.10540 \n", + "27 18.610 20.25 122.10 1094.0 0.09440 \n", + "28 15.300 25.27 102.40 732.4 0.10820 \n", + "29 17.570 15.05 115.00 955.1 0.09847 \n", + ".. ... ... ... ... ... \n", + "539 7.691 25.44 48.34 170.4 0.08668 \n", + "540 11.540 14.44 74.65 402.9 0.09984 \n", + "541 14.470 24.99 95.81 656.4 0.08837 \n", + "542 14.740 25.42 94.70 668.6 0.08275 \n", + "543 13.210 28.06 84.88 538.4 0.08671 \n", + "544 13.870 20.70 89.77 584.8 0.09578 \n", + "545 13.620 23.23 87.19 573.2 0.09246 \n", + "546 10.320 16.35 65.31 324.9 0.09434 \n", + "547 10.260 16.58 65.85 320.8 0.08877 \n", + "548 9.683 19.34 61.05 285.7 0.08491 \n", + "549 10.820 24.21 68.89 361.6 0.08192 \n", + "550 10.860 21.48 68.51 360.5 0.07431 \n", + "551 11.130 22.44 71.49 378.4 0.09566 \n", + "552 12.770 29.43 81.35 507.9 0.08276 \n", + "553 9.333 21.94 59.01 264.0 0.09240 \n", + "554 12.880 28.92 82.50 514.3 0.08123 \n", + "555 10.290 27.61 65.67 321.4 0.09030 \n", + "556 10.160 19.59 64.73 311.7 0.10030 \n", + "557 9.423 27.88 59.26 271.3 0.08123 \n", + "558 14.590 22.68 96.39 657.1 0.08473 \n", + "559 11.510 23.93 74.52 403.5 0.09261 \n", + "560 14.050 27.15 91.38 600.4 0.09929 \n", + "561 11.200 29.37 70.67 386.0 0.07449 \n", + "562 15.220 30.62 103.40 716.9 0.10480 \n", + "563 20.920 25.09 143.00 1347.0 0.10990 \n", + "564 21.560 22.39 142.00 1479.0 0.11100 \n", + "565 20.130 28.25 131.20 1261.0 0.09780 \n", + "566 16.600 28.08 108.30 858.1 0.08455 \n", + "567 20.600 29.33 140.10 1265.0 0.11780 \n", + "568 7.760 24.54 47.92 181.0 0.05263 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.300100 0.147100 0.2419 \n", + "1 0.07864 0.086900 0.070170 0.1812 \n", + "2 0.15990 0.197400 0.127900 0.2069 \n", + "3 0.28390 0.241400 0.105200 0.2597 \n", + "4 0.13280 0.198000 0.104300 0.1809 \n", + "5 0.17000 0.157800 0.080890 0.2087 \n", + "6 0.10900 0.112700 0.074000 0.1794 \n", + "7 0.16450 0.093660 0.059850 0.2196 \n", + "8 0.19320 0.185900 0.093530 0.2350 \n", + "9 0.23960 0.227300 0.085430 0.2030 \n", + "10 0.06669 0.032990 0.033230 0.1528 \n", + "11 0.12920 0.099540 0.066060 0.1842 \n", + "12 0.24580 0.206500 0.111800 0.2397 \n", + "13 0.10020 0.099380 0.053640 0.1847 \n", + "14 0.22930 0.212800 0.080250 0.2069 \n", + "15 0.15950 0.163900 0.073640 0.2303 \n", + "16 0.07200 0.073950 0.052590 0.1586 \n", + "17 0.20220 0.172200 0.102800 0.2164 \n", + "18 0.10270 0.147900 0.094980 0.1582 \n", + "19 0.08129 0.066640 0.047810 0.1885 \n", + "20 0.12700 0.045680 0.031100 0.1967 \n", + "21 0.06492 0.029560 0.020760 0.1815 \n", + "22 0.21350 0.207700 0.097560 0.2521 \n", + "23 0.10220 0.109700 0.086320 0.1769 \n", + "24 0.14570 0.152500 0.091700 0.1995 \n", + "25 0.22760 0.222900 0.140100 0.3040 \n", + "26 0.18680 0.142500 0.087830 0.2252 \n", + "27 0.10660 0.149000 0.077310 0.1697 \n", + "28 0.16970 0.168300 0.087510 0.1926 \n", + "29 0.11570 0.098750 0.079530 0.1739 \n", + ".. ... ... ... ... \n", + "539 0.11990 0.092520 0.013640 0.2037 \n", + "540 0.11200 0.067370 0.025940 0.1818 \n", + "541 0.12300 0.100900 0.038900 0.1872 \n", + "542 0.07214 0.041050 0.030270 0.1840 \n", + "543 0.06877 0.029870 0.032750 0.1628 \n", + "544 0.10180 0.036880 0.023690 0.1620 \n", + "545 0.06747 0.029740 0.024430 0.1664 \n", + "546 0.04994 0.010120 0.005495 0.1885 \n", + "547 0.08066 0.043580 0.024380 0.1669 \n", + "548 0.05030 0.023370 0.009615 0.1580 \n", + "549 0.06602 0.015480 0.008160 0.1976 \n", + "550 0.04227 0.000000 0.000000 0.1661 \n", + "551 0.08194 0.048240 0.022570 0.2030 \n", + "552 0.04234 0.019970 0.014990 0.1539 \n", + "553 0.05605 0.039960 0.012820 0.1692 \n", + "554 0.05824 0.061950 0.023430 0.1566 \n", + "555 0.07658 0.059990 0.027380 0.1593 \n", + "556 0.07504 0.005025 0.011160 0.1791 \n", + "557 0.04971 0.000000 0.000000 0.1742 \n", + "558 0.13300 0.102900 0.037360 0.1454 \n", + "559 0.10210 0.111200 0.041050 0.1388 \n", + "560 0.11260 0.044620 0.043040 0.1537 \n", + "561 0.03558 0.000000 0.000000 0.1060 \n", + "562 0.20870 0.255000 0.094290 0.2128 \n", + "563 0.22360 0.317400 0.147400 0.2149 \n", + "564 0.11590 0.243900 0.138900 0.1726 \n", + "565 0.10340 0.144000 0.097910 0.1752 \n", + "566 0.10230 0.092510 0.053020 0.1590 \n", + "567 0.27700 0.351400 0.152000 0.2397 \n", + "568 0.04362 0.000000 0.000000 0.1587 \n", + "\n", + " mean fractal dimension ... worst radius \\\n", + "0 0.07871 ... 25.380 \n", + "1 0.05667 ... 24.990 \n", + "2 0.05999 ... 23.570 \n", + "3 0.09744 ... 14.910 \n", + "4 0.05883 ... 22.540 \n", + "5 0.07613 ... 15.470 \n", + "6 0.05742 ... 22.880 \n", + "7 0.07451 ... 17.060 \n", + "8 0.07389 ... 15.490 \n", + "9 0.08243 ... 15.090 \n", + "10 0.05697 ... 19.190 \n", + "11 0.06082 ... 20.420 \n", + "12 0.07800 ... 20.960 \n", + "13 0.05338 ... 16.840 \n", + "14 0.07682 ... 15.030 \n", + "15 0.07077 ... 17.460 \n", + "16 0.05922 ... 19.070 \n", + "17 0.07356 ... 20.960 \n", + "18 0.05395 ... 27.320 \n", + "19 0.05766 ... 15.110 \n", + "20 0.06811 ... 14.500 \n", + "21 0.06905 ... 10.230 \n", + "22 0.07032 ... 18.070 \n", + "23 0.05278 ... 29.170 \n", + "24 0.06330 ... 26.460 \n", + "25 0.07413 ... 22.250 \n", + "26 0.06924 ... 17.620 \n", + "27 0.05699 ... 21.310 \n", + "28 0.06540 ... 20.270 \n", + "29 0.06149 ... 20.010 \n", + ".. ... ... ... \n", + "539 0.07751 ... 8.678 \n", + "540 0.06782 ... 12.260 \n", + "541 0.06341 ... 16.220 \n", + "542 0.05680 ... 16.510 \n", + "543 0.05781 ... 14.370 \n", + "544 0.06688 ... 15.050 \n", + "545 0.05801 ... 15.350 \n", + "546 0.06201 ... 11.250 \n", + "547 0.06714 ... 10.830 \n", + "548 0.06235 ... 10.930 \n", + "549 0.06328 ... 13.030 \n", + "550 0.05948 ... 11.660 \n", + "551 0.06552 ... 12.020 \n", + "552 0.05637 ... 13.870 \n", + "553 0.06576 ... 9.845 \n", + "554 0.05708 ... 13.890 \n", + "555 0.06127 ... 10.840 \n", + "556 0.06331 ... 10.650 \n", + "557 0.06059 ... 10.490 \n", + "558 0.06147 ... 15.480 \n", + "559 0.06570 ... 12.480 \n", + "560 0.06171 ... 15.300 \n", + "561 0.05502 ... 11.920 \n", + "562 0.07152 ... 17.520 \n", + "563 0.06879 ... 24.290 \n", + "564 0.05623 ... 25.450 \n", + "565 0.05533 ... 23.690 \n", + "566 0.05648 ... 18.980 \n", + "567 0.07016 ... 25.740 \n", + "568 0.05884 ... 9.456 \n", + "\n", + " worst texture worst perimeter worst area worst smoothness \\\n", + "0 17.33 184.60 2019.0 0.16220 \n", + "1 23.41 158.80 1956.0 0.12380 \n", + "2 25.53 152.50 1709.0 0.14440 \n", + "3 26.50 98.87 567.7 0.20980 \n", + "4 16.67 152.20 1575.0 0.13740 \n", + "5 23.75 103.40 741.6 0.17910 \n", + "6 27.66 153.20 1606.0 0.14420 \n", + "7 28.14 110.60 897.0 0.16540 \n", + "8 30.73 106.20 739.3 0.17030 \n", + "9 40.68 97.65 711.4 0.18530 \n", + "10 33.88 123.80 1150.0 0.11810 \n", + "11 27.28 136.50 1299.0 0.13960 \n", + "12 29.94 151.70 1332.0 0.10370 \n", + "13 27.66 112.00 876.5 0.11310 \n", + "14 32.01 108.80 697.7 0.16510 \n", + "15 37.13 124.10 943.2 0.16780 \n", + "16 30.88 123.40 1138.0 0.14640 \n", + "17 31.48 136.80 1315.0 0.17890 \n", + "18 30.88 186.80 2398.0 0.15120 \n", + "19 19.26 99.70 711.2 0.14400 \n", + "20 20.49 96.09 630.5 0.13120 \n", + "21 15.66 65.13 314.9 0.13240 \n", + "22 19.08 125.10 980.9 0.13900 \n", + "23 35.59 188.00 2615.0 0.14010 \n", + "24 31.56 177.00 2215.0 0.18050 \n", + "25 21.40 152.40 1461.0 0.15450 \n", + "26 33.21 122.40 896.9 0.15250 \n", + "27 27.26 139.90 1403.0 0.13380 \n", + "28 36.71 149.30 1269.0 0.16410 \n", + "29 19.52 134.90 1227.0 0.12550 \n", + ".. ... ... ... ... \n", + "539 31.89 54.49 223.6 0.15960 \n", + "540 19.68 78.78 457.8 0.13450 \n", + "541 31.73 113.50 808.9 0.13400 \n", + "542 32.29 107.40 826.4 0.10600 \n", + "543 37.17 92.48 629.6 0.10720 \n", + "544 24.75 99.17 688.6 0.12640 \n", + "545 29.09 97.58 729.8 0.12160 \n", + "546 21.77 71.12 384.9 0.12850 \n", + "547 22.04 71.08 357.4 0.14610 \n", + "548 25.59 69.10 364.2 0.11990 \n", + "549 31.45 83.90 505.6 0.12040 \n", + "550 24.77 74.08 412.3 0.10010 \n", + "551 28.26 77.80 436.6 0.10870 \n", + "552 36.00 88.10 594.7 0.12340 \n", + "553 25.05 62.86 295.8 0.11030 \n", + "554 35.74 88.84 595.7 0.12270 \n", + "555 34.91 69.57 357.6 0.13840 \n", + "556 22.88 67.88 347.3 0.12650 \n", + "557 34.24 66.50 330.6 0.10730 \n", + "558 27.27 105.90 733.5 0.10260 \n", + "559 37.16 82.28 474.2 0.12980 \n", + "560 33.17 100.20 706.7 0.12410 \n", + "561 38.30 75.19 439.6 0.09267 \n", + "562 42.79 128.70 915.0 0.14170 \n", + "563 29.41 179.10 1819.0 0.14070 \n", + "564 26.40 166.10 2027.0 0.14100 \n", + "565 38.25 155.00 1731.0 0.11660 \n", + "566 34.12 126.70 1124.0 0.11390 \n", + "567 39.42 184.60 1821.0 0.16500 \n", + "568 30.37 59.16 268.6 0.08996 \n", + "\n", + " worst compactness worst concavity worst concave points worst symmetry \\\n", + "0 0.66560 0.71190 0.26540 0.4601 \n", + "1 0.18660 0.24160 0.18600 0.2750 \n", + "2 0.42450 0.45040 0.24300 0.3613 \n", + "3 0.86630 0.68690 0.25750 0.6638 \n", + "4 0.20500 0.40000 0.16250 0.2364 \n", + "5 0.52490 0.53550 0.17410 0.3985 \n", + "6 0.25760 0.37840 0.19320 0.3063 \n", + "7 0.36820 0.26780 0.15560 0.3196 \n", + "8 0.54010 0.53900 0.20600 0.4378 \n", + "9 1.05800 1.10500 0.22100 0.4366 \n", + "10 0.15510 0.14590 0.09975 0.2948 \n", + "11 0.56090 0.39650 0.18100 0.3792 \n", + "12 0.39030 0.36390 0.17670 0.3176 \n", + "13 0.19240 0.23220 0.11190 0.2809 \n", + "14 0.77250 0.69430 0.22080 0.3596 \n", + "15 0.65770 0.70260 0.17120 0.4218 \n", + "16 0.18710 0.29140 0.16090 0.3029 \n", + "17 0.42330 0.47840 0.20730 0.3706 \n", + "18 0.31500 0.53720 0.23880 0.2768 \n", + "19 0.17730 0.23900 0.12880 0.2977 \n", + "20 0.27760 0.18900 0.07283 0.3184 \n", + "21 0.11480 0.08867 0.06227 0.2450 \n", + "22 0.59540 0.63050 0.23930 0.4667 \n", + "23 0.26000 0.31550 0.20090 0.2822 \n", + "24 0.35780 0.46950 0.20950 0.3613 \n", + "25 0.39490 0.38530 0.25500 0.4066 \n", + "26 0.66430 0.55390 0.27010 0.4264 \n", + "27 0.21170 0.34460 0.14900 0.2341 \n", + "28 0.61100 0.63350 0.20240 0.4027 \n", + "29 0.28120 0.24890 0.14560 0.2756 \n", + ".. ... ... ... ... \n", + "539 0.30640 0.33930 0.05000 0.2790 \n", + "540 0.21180 0.17970 0.06918 0.2329 \n", + "541 0.42020 0.40400 0.12050 0.3187 \n", + "542 0.13760 0.16110 0.10950 0.2722 \n", + "543 0.13810 0.10620 0.07958 0.2473 \n", + "544 0.20370 0.13770 0.06845 0.2249 \n", + "545 0.15170 0.10490 0.07174 0.2642 \n", + "546 0.08842 0.04384 0.02381 0.2681 \n", + "547 0.22460 0.17830 0.08333 0.2691 \n", + "548 0.09546 0.09350 0.03846 0.2552 \n", + "549 0.16330 0.06194 0.03264 0.3059 \n", + "550 0.07348 0.00000 0.00000 0.2458 \n", + "551 0.17820 0.15640 0.06413 0.3169 \n", + "552 0.10640 0.08653 0.06498 0.2407 \n", + "553 0.08298 0.07993 0.02564 0.2435 \n", + "554 0.16200 0.24390 0.06493 0.2372 \n", + "555 0.17100 0.20000 0.09127 0.2226 \n", + "556 0.12000 0.01005 0.02232 0.2262 \n", + "557 0.07158 0.00000 0.00000 0.2475 \n", + "558 0.31710 0.36620 0.11050 0.2258 \n", + "559 0.25170 0.36300 0.09653 0.2112 \n", + "560 0.22640 0.13260 0.10480 0.2250 \n", + "561 0.05494 0.00000 0.00000 0.1566 \n", + "562 0.79170 1.17000 0.23560 0.4089 \n", + "563 0.41860 0.65990 0.25420 0.2929 \n", + "564 0.21130 0.41070 0.22160 0.2060 \n", + "565 0.19220 0.32150 0.16280 0.2572 \n", + "566 0.30940 0.34030 0.14180 0.2218 \n", + "567 0.86810 0.93870 0.26500 0.4087 \n", + "568 0.06444 0.00000 0.00000 0.2871 \n", + "\n", + " worst fractal dimension \n", + "0 0.11890 \n", + "1 0.08902 \n", + "2 0.08758 \n", + "3 0.17300 \n", + "4 0.07678 \n", + "5 0.12440 \n", + "6 0.08368 \n", + "7 0.11510 \n", + "8 0.10720 \n", + "9 0.20750 \n", + "10 0.08452 \n", + "11 0.10480 \n", + "12 0.10230 \n", + "13 0.06287 \n", + "14 0.14310 \n", + "15 0.13410 \n", + "16 0.08216 \n", + "17 0.11420 \n", + "18 0.07615 \n", + "19 0.07259 \n", + "20 0.08183 \n", + "21 0.07773 \n", + "22 0.09946 \n", + "23 0.07526 \n", + "24 0.09564 \n", + "25 0.10590 \n", + "26 0.12750 \n", + "27 0.07421 \n", + "28 0.09876 \n", + "29 0.07919 \n", + ".. ... \n", + "539 0.10660 \n", + "540 0.08134 \n", + "541 0.10230 \n", + "542 0.06956 \n", + "543 0.06443 \n", + "544 0.08492 \n", + "545 0.06953 \n", + "546 0.07399 \n", + "547 0.09479 \n", + "548 0.07920 \n", + "549 0.07626 \n", + "550 0.06592 \n", + "551 0.08032 \n", + "552 0.06484 \n", + "553 0.07393 \n", + "554 0.07242 \n", + "555 0.08283 \n", + "556 0.06742 \n", + "557 0.06969 \n", + "558 0.08004 \n", + "559 0.08732 \n", + "560 0.08321 \n", + "561 0.05905 \n", + "562 0.14090 \n", + "563 0.09873 \n", + "564 0.07115 \n", + "565 0.06637 \n", + "566 0.07820 \n", + "567 0.12400 \n", + "568 0.07039 \n", + "\n", + "[569 rows x 30 columns]\n", + " malignant benign\n", + "0 1 0\n", + "1 1 0\n", + "2 1 0\n", + "3 1 0\n", + "4 1 0\n", + "5 1 0\n", + "6 1 0\n", + "7 1 0\n", + "8 1 0\n", + "9 1 0\n", + "10 1 0\n", + "11 1 0\n", + "12 1 0\n", + "13 1 0\n", + "14 1 0\n", + "15 1 0\n", + "16 1 0\n", + "17 1 0\n", + "18 1 0\n", + "19 0 1\n", + "20 0 1\n", + "21 0 1\n", + "22 1 0\n", + "23 1 0\n", + "24 1 0\n", + "25 1 0\n", + "26 1 0\n", + "27 1 0\n", + "28 1 0\n", + "29 1 0\n", + ".. ... ...\n", + "539 0 1\n", + "540 0 1\n", + "541 0 1\n", + "542 0 1\n", + "543 0 1\n", + "544 0 1\n", + "545 0 1\n", + "546 0 1\n", + "547 0 1\n", + "548 0 1\n", + "549 0 1\n", + "550 0 1\n", + "551 0 1\n", + "552 0 1\n", + "553 0 1\n", + "554 0 1\n", + "555 0 1\n", + "556 0 1\n", + "557 0 1\n", + "558 0 1\n", + "559 0 1\n", + "560 0 1\n", + "561 0 1\n", + "562 1 0\n", + "563 1 0\n", + "564 1 0\n", + "565 1 0\n", + "566 1 0\n", + "567 1 0\n", + "568 0 1\n", + "\n", + "[569 rows x 2 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -567,10 +1054,19 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -660,10 +1156,77 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 0)\t1.0\n", + " (0, 7)\t1.0\n", + " (0, 9)\t1.0\n", + " (0, 13)\t1.0\n", + " (1, 3)\t1.0\n", + " (1, 5)\t1.0\n", + " (1, 8)\t1.0\n", + " (1, 12)\t1.0\n", + " (2, 3)\t1.0\n", + " (2, 5)\t1.0\n", + " (2, 8)\t1.0\n", + " (2, 11)\t1.0\n", + " (3, 1)\t1.0\n", + " (3, 5)\t1.0\n", + " (3, 8)\t1.0\n", + " (3, 12)\t1.0\n", + " (4, 2)\t1.0\n", + " (4, 6)\t1.0\n", + " (4, 8)\t1.0\n", + " (4, 12)\t1.0\n", + " (5, 2)\t1.0\n", + " (5, 4)\t1.0\n", + " (5, 10)\t1.0\n", + " (5, 12)\t1.0\n", + " (6, 2)\t1.0\n", + " :\t:\n", + " (8, 12)\t1.0\n", + " (9, 3)\t1.0\n", + " (9, 4)\t1.0\n", + " (9, 10)\t1.0\n", + " (9, 12)\t1.0\n", + " (10, 2)\t1.0\n", + " (10, 6)\t1.0\n", + " (10, 10)\t1.0\n", + " (10, 12)\t1.0\n", + " (11, 3)\t1.0\n", + " (11, 6)\t1.0\n", + " (11, 10)\t1.0\n", + " (11, 11)\t1.0\n", + " (12, 1)\t1.0\n", + " (12, 6)\t1.0\n", + " (12, 8)\t1.0\n", + " (12, 11)\t1.0\n", + " (13, 1)\t1.0\n", + " (13, 5)\t1.0\n", + " (13, 10)\t1.0\n", + " (13, 12)\t1.0\n", + " (14, 2)\t1.0\n", + " (14, 6)\t1.0\n", + " (14, 8)\t1.0\n", + " (14, 11)\t1.0\n", + "Train set accuracy with Decision Tree: 0.73\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -751,10 +1314,72 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 < 0.000 Gini=0.408\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 2.000 Gini=0.394\n", + "X1 < 0.000 Gini=0.408\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 1.000 Gini=0.394\n", + "X1 < 2.000 Gini=0.394\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 2.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 1.000 Gini=0.407\n", + "X2 < 0.000 Gini=0.408\n", + "X2 < 1.000 Gini=0.407\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X3 < 1.000 Gini=0.367\n", + "X3 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 1.000 Gini=0.405\n", + "X4 < 0.000 Gini=0.408\n", + "X4 < 1.000 Gini=0.405\n", + "Split: [X3 < 1.000]\n" + ] + } + ], "source": [ "# Split a dataset based on an attribute and an attribute value\n", "def test_split(index, value, dataset):\n", @@ -859,10 +1484,36 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Outlook', 'Temperature', 'Humidity', 'Wind']\n", + "System entropy 0.863120568566631\n", + "Outlook\n", + "(Sunny)\n", + "(Overcast)\n", + "(Rain)\n", + "Humidity\n", + "(High)\n", + "(Normal)\n", + "1\n", + "Temperature\n", + "(Mild)\n", + "(Cool)\n", + "Wind\n", + "(Weak)\n", + "(Strong)\n", + "1\n", + "1\n", + "0\n", + "0\n", + "1\n" + ] + } + ], "source": [ "import re\n", "import math\n", @@ -1063,10 +1714,31 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.95\n", + "Test set accuracy with SVM: 0.63\n", + "Test set accuracy with Decision Trees: 0.90\n", + "Test set accuracy Logistic Regression with scaled data: 0.96\n", + "Test set accuracy SVM with scaled data: 0.96\n", + "Test set accuracy with Decision Trees and scaled data: 0.90\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:757: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n", + " \"of iterations.\", ConvergenceWarning)\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1121,10 +1793,19 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from __future__ import division, print_function, unicode_literals\n", "\n", @@ -1202,10 +1883,19 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "np.random.seed(6)\n", "Xs = np.random.rand(100, 2) - 0.5\n", @@ -1239,9 +1929,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1255,10 +1943,23 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n", + " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=42, splitter='best')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1276,10 +1977,19 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1324,10 +2034,19 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "tree_reg1 = DecisionTreeRegressor(random_state=42)\n", "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n", @@ -1476,10 +2195,19 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "heads_proba = 0.51\n", "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n", @@ -1506,10 +2234,23 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.896\n", + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.912\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -1566,10 +2307,36 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='warn',\n", + " n_jobs=None, penalty='l2', random_state=42, solver='warn',\n", + " tol=0.0001, verbose=0, warm_start=False)), ('rf', RandomFore...rbf', max_iter=-1, probability=False, random_state=42,\n", + " shrinking=True, tol=0.001, verbose=False))],\n", + " flatten_transform=None, n_jobs=None, voting='hard', weights=None)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -1594,10 +2361,35 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.896\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -1610,10 +2402,36 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "VotingClassifier(estimators=[('lr', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='warn',\n", + " n_jobs=None, penalty='l2', random_state=42, solver='warn',\n", + " tol=0.0001, verbose=0, warm_start=False)), ('rf', RandomFore...'rbf', max_iter=-1, probability=True, random_state=42,\n", + " shrinking=True, tol=0.001, verbose=False))],\n", + " flatten_transform=None, n_jobs=None, voting='soft', weights=None)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "log_clf = LogisticRegression(random_state=42)\n", "rnd_clf = RandomForestClassifier(random_state=42)\n", @@ -1628,10 +2446,35 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.912\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -1651,9 +2494,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", @@ -1669,10 +2510,16 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.904\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "print(accuracy_score(y_test, y_pred))" @@ -1681,10 +2528,16 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.856\n" + ] + } + ], "source": [ "tree_clf = DecisionTreeClassifier(random_state=42)\n", "tree_clf.fit(X_train, y_train)\n", @@ -1695,10 +2548,19 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib.colors import ListedColormap\n", "\n", @@ -1739,10 +2601,60 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 1\n", + "Error: 0.06363193176078843\n", + "Bias^2: 0.05204210433027824\n", + "Var: 0.01158982743051019\n", + "0.06363193176078843 >= 0.05204210433027824 + 0.01158982743051019 = 0.06363193176078843\n", + "Polynomial degree: 2\n", + "Error: 0.0431430298456292\n", + "Bias^2: 0.026726240851249687\n", + "Var: 0.016416788994379518\n", + "0.0431430298456292 >= 0.026726240851249687 + 0.016416788994379518 = 0.043143029845629205\n", + "Polynomial degree: 3\n", + "Error: 0.02050535492395899\n", + "Bias^2: 0.011509536687537524\n", + "Var: 0.00899581823642146\n", + "0.02050535492395899 >= 0.011509536687537524 + 0.00899581823642146 = 0.020505354923958982\n", + "Polynomial degree: 4\n", + "Error: 0.02094749255672688\n", + "Bias^2: 0.011891808374690279\n", + "Var: 0.00905568418203661\n", + "0.02094749255672688 >= 0.011891808374690279 + 0.00905568418203661 = 0.02094749255672689\n", + "Polynomial degree: 5\n", + "Error: 0.020938948021305106\n", + "Bias^2: 0.013500661482514562\n", + "Var: 0.0074382865387905475\n", + "0.020938948021305106 >= 0.013500661482514562 + 0.0074382865387905475 = 0.02093894802130511\n", + "Polynomial degree: 6\n", + "Error: 0.020696248235254684\n", + "Bias^2: 0.013918781531899311\n", + "Var: 0.006777466703355368\n", + "0.020696248235254684 >= 0.013918781531899311 + 0.006777466703355368 = 0.020696248235254677\n", + "Polynomial degree: 7\n", + "Error: 0.022964614471586854\n", + "Bias^2: 0.015504116721988908\n", + "Var: 0.007460497749597958\n", + "0.022964614471586854 >= 0.015504116721988908 + 0.007460497749597958 = 0.022964614471586868\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "\n", "import matplotlib.pyplot as plt\n", @@ -1875,10 +2787,79 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.95\n", + "Test set accuracy with SVM: 0.63\n", + "Test set accuracy with Decision Trees: 0.87\n", + "Test set accuracy Logistic Regression with scaled data: 0.96\n", + "Test set accuracy SVM with scaled data: 0.96\n", + "Test set accuracy with Decision Trees and scaled data: 0.88\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:757: ConvergenceWarning: lbfgs failed to converge. Increase the number of iterations.\n", + " \"of iterations.\", ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.93333333 0.73333333 0.93333333 1. 1. 0.92857143\n", + " 1. 0.92857143 0.92857143 0.92857143]\n", + "Test set accuracy with Random Forests and scaled data: 0.98\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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Sn248/UT/8wVz8nq9UlkfkK1LSoS1A+G/3019DcZCw7f1i0l5prt37zJ27Fjmz58PQEBAAPPmzaNatWo6R2Y5KlEoVvPTrgtcvRcLQP6cbvSrXxoPV2faVC6Ku4u6EdEjDAZYPxxO/WrqqzsCGr+vX0zKMyUmJlK7dm3Onj2Lu7s7H374IWPHjsXV1VXv0CxKJQrFKm7cj+WHwPPJ7beal6dn7RI6RmTDDAb4fRQcX27qqzkQmn8MqiLMprm4uDBq1CiWLl3KvHnzqFChgt4hWYXtX+mh2KUvNgXzICEJgApFctOtZnGdI7JRUsKmcXB4samvWh+twkklCZsjpWTJkiUsXLgwuW/o0KHs3r072yYJUCMKxQqOh0aw5nBocntiG1+cndSb3hOkhC3vw79zTH2Vu0ObaWAHV+s6mkuXLjFkyBA2bdpErly5aNGiBUWLFrWLK6szK/v/hEqWklIy+TfTtRLNfAtTr6x9lwZazY5PYN8MU9uvI7SfCU7q/I0tMRgMzJw5E39/fzZt2kTevHmZMWMGRYoU0Tu0LKNGFIpF/X78Ggcv3QXA1VnwXquKOkdko3Z+Cbu+NLUrtIFXfwJn9SdpS4KDgxkwYAB79uwBoHPnznz//fcOlSRAJQrFgmITkvjsz/+S22/UK0XpAjl1jMhG7Z0OOz42tcs111aCdc5elTLZQf/+/dm7dy+FCxfmhx9+4NVXX9U7JF2oqSfFYubuvkBYxANAK4cd2aSczhHZoH/mwNYPTO3nG0HXJeCiLj60FVLK5O9nzpxJv379OH36tMMmCVCJQrGQx8thxzR/AS8P9Qn5EYcWwp9mF8+VfBG6LwVXD91CUkxiY2OZMGECPXv2TO6rUqUK8+bNI2/evDpGpj+VKBSL+HJzMDHxZuWwNVQ57COOLoXf3jS1fWpCzxXgpqbmbMHevXsJCAjg008/ZcWKFRw7dkzvkGyKShRKpp0IvcfqQ6Zy2A/a+OLirH61kp1YrV11jXFKo2gA9FoN7va5kmh2EhkZyciRI2nQoAHBwcFUqFCBPXv2UKVKFb1Dsynqr1nJFCklk38/ldxuWrEwL6pyWJPTv8HaQSANWruwP/T+FXLk0Tcuhc2bN+Pv78+MGTNwdnZmwoQJHDlyhHr16ukdms1RVU9Kpvxx4joHQkzlsBNaq3LYZGc2w6q+ILUpOQqUh97rwDOfvnEpgJYoLl++TLVq1Zg3bx4BAQF6h2SzVKJQMiw2IYlP/zCtDvt6XVUOm+z8dljRGwwJWjvf8/D6BshVUN+4HFx4eDgFC2r/B1OmTOH5559nyJAh2WIpcGtSU09Khs3bczG5HDavp6sqh30oZA8s6wlJcVo7Twl4/TfI7VgXadmSa9eu0alTJ2rUqEFkZCQAOXPmZMSIESpJpIFVE4UQoqUQIlgIcU4IMT6Fx0sIIXYIIY4IIY4LIVpZMx7Fcm7ej+WHHeeS22Oal8c7hyqH5fI/8EtXSNQSKF7PaUnC20ffuByUlJKFCxfi6+vL2rVruXPnDkeOPHkjLSV1VkulQghnYCbQDAgFDgghNkgpg8w2ex9YKaWcJYTwBf4ASlkrJsVyvtwcTLSxHPaFwrnooVaHhbDD8EtnSNBu90quIlqSyFtK17AcVUhICO+88w4HDx4E4JVXXmH27NmUKKGWu08va44oagHnpJQXpJTxwHKg/WPbSMDL+L03cNWK8SgWciL0HqsPq3LYR1w/AUs6Qtx9re1ZQDsnkb+MvnE5qMWLF+Pv78/BgwfJly8fS5YsYePGjSpJZJA1J+eeA66YtUOB2o9tMwnYIoQYCeQEmqa0IyHEIGAQQMGCBQkMDLR0rHYpKioqy18LKSVT/43l4SoHAQWdSQo7RWBYlobxBD1ei4c8oy8TcHQCbglakkhwyc1R3w+IPnUNuJbl8ej5WtiK0NBQoqOjadCgAaNHjyZv3rzs3LlT77Dslt5ncXoAC6WUXwsh6gJLhBD+Uj4sOtdIKecAcwDKly8vGzVqlPWR2qDAwECy+rX448Q1ztw9DICLk+CbPvV5vmCuLI0hJXq8FgDcOgcLB4ExSeDujevr66lZrGrWx2Kk22uho4SEBHbv3k3jxo0BaNSoEc2aNSM6OtrhXgtrsOZ8QRhgPnHtY+wz1x9YCSCl3Ad4AOpqLRv1RDlsvVI2kSR0c+ciLGoLUTe0tlsueG0N6JgkHNHhw4epWbMmzZo1Sz4fAVCzZk0do8perJkoDgDlhBClhRBuQHdgw2PbXAaaAAghKqIlinArxqRkwvy9Fwm9ayqH/V9jBy6HjbgCi9tBpPG0mqsn9FoFxdWbU1Z58OAB48ePp1atWhw7doySJUsSHx+vd1jZktUShZQyERgBbAZOo1U3nRJCTBZCtDNu9hYwUAhxDFgGvCHN1/hVbMbNyFhmbjcrh232At6eDloOe/+aNpKIuKy1nd2hxzIoqZZ+yCq7d+8mICCAzz//HIPBwOjRozlx4oRafsNKrHqOQkr5B1rJq3nfRLPvg4AXrRmDYhlfbz6TXA5brlAuetRy0OqRqJvaSOLuRa3t5Ardf9HuK6Fkiblz5zJw4EAAfH19mTdvHnXq1NE5quzNwWsalbQ4GXaPlYdMBWwOWw4bfRsWt4dbZ7S2kwt0XQTlmukbl4Np1aoVBQoUYOLEiRw+fFgliSygd9WTYuOklEz5PSi5HLZxhUK89IIDrlf0IAKWdICbxutFhZN2j+sKrfWNywHcvn2bGTNm8P777+Ps7EyxYsW4cOECuXOrZdqzikoUSqo2n7rOPxfvAFo57HutHHB12Nj78HMnuH7c2CGgw2zwd9xbY2YFKSWrVq1ixIgRhIeHkzt3bsaMGQOgkkQWU4lCearYhCQ+MSuH7V23JGULOVg5bHw0LO0KYaayS9p+B1W66ReTA7h69SrDhw9n3bp1ADRs2JB27do941mKtTjgRLOSVgv2hnDljlYOm8fTlVGOtjpswgNY1h0u7zP1tfoKqr+uX0zZnJSSefPm4evry7p168idOzc//vgj27dvp2zZsnqH57DUiEJJ0c3IWGbueLQcNo+nm44RZbHEOFjxGlzcZepr8SnUGqhfTA5g9erVDBgwAIDWrVsze/ZsfHzUyrt6U4lCSdE3W84QFZcIaOWwPR2pHDYpAVa9Aee2mfqaTIS6w3ULyVG8+uqrtGvXju7du9O9e3eEEHqHpKCmnpQUnLp6jxUHTeWw7ztSOWxSIqwZAMFml/80HAcN3tIvpmzs1KlTNG/enNBQbTViZ2dn1q9fT48ePVSSsCEO8tevpJWUksm/mcphXy5fkIaOUg5rSIJ1QyFonanvxVHQ6F39Ysqm4uPjmTJlClWrVmXr1q188MEHeoekpEJNPSmP2HzqRnI5rLOTYEJrX50jyiIGA/w2Ck6sNPXVHgJNPwL1ydaiDhw4QP/+/Tlx4gQAgwcP5vPPP9c5KiU1akShJItLfHR12N51HKQcVkr48204ssTUV70vtPxMJQkLiomJ4e2336ZOnTqcOHGCMmXKsH37dmbPno23t7fe4SmpUIlCSbZwbwiX78QA4J3DlTebOkA5rJSweQIcmGvqC+gFrb9RScLCzpw5w7fffgvA2LFjOX78OC+//LLOUSlpoaaeFADCI+P43mx12NFNy2X/clgp4a+PYP9MU59/J2j3PTipz1CW8ODBA3LkyAFAQEAA3333HTVr1qRWrVo6R6akh/prUAD4ZqupHLZMwZz0qlNS54iywM4vYM+3pnbFttDxR3By1i+mbGTjxo2UK1eO9evXJ/cNHz5cJQk7pBKFQtDV+6w4cDm5/X4bX1yzeznsnm8h8FNTu1wL6DQfnB30HhsWFB4eTq9evWjTpg1hYWEsXLhQ75CUTMrm7wbKs0gpmfz7KQzGctiGLxTk5fKF9A3K2vbPgm2TTO3nX4aui8Elm0+1WZmUkuXLl+Pr68vSpUvJkSMH33zzDatXr9Y7NCWT1DkKB7cl6Ab7L5jKYT9ok81Xhz0wDzaNN7VLNYDuS8HVQ7+YsoHw8HD69+/Pb7/9BkDjxo356aefeP7553WOTLEENaJwYCmXw2bj5ZuP/Awbx5jaxWtDj+Xg5qlfTNlEjhw5OH78ON7e3sydO5dt27apJJGNqBGFA1v0dwiXbpvKYbP16rDHV8H6EaZ2sWrQaxW4O8B1IlZy7tw5ihQpQq5cuciVKxerV6+mWLFiFCtWTO/QFAtTIwoHdSsqju//MpXDvtm0HHlzZtM5+qD18OtgwHgipkgl6L0WPNRFXhmRlJTEV199RaVKlZgwYUJyf40aNVSSyKbUiMJBfbP1DJHGctjnC+bktexaDhv8J6zuBzJJaxesCL3XQY68+sZlp06ePEm/fv04cOAAABERERgMBpzUdSfZmvrfdUCnr91n+b+mctgPWmfTcthzf8HKPmDQEiL5y0Kf9ZCzgL5x2aH4+HgmTZpEtWrVOHDgAD4+PmzcuJFFixapJOEA1IjCwUgp+XhjUHI57EsvFKRR+Wy4OuzF3bC8JyTFa+28peD13yB3YV3Dskf37t3jxRdf5NSpUwAMHTqUzz77DC8vL50jU7KKShQOZtvpm+w9dxvQymHfb10x+637f3k/LO0GibFa27u4liS81Px5Rnh7e+Pn50d8fDxz587lpZde0jskJYupROFA4hMNfLIxKLndq3YJXiiczcphQw/Bz50hIVpr5y6qTTflcaA79FnA9u3byZcvHwEBAQDMnj0bDw+P5HWbFMeiJhcdyKK/QwgxlsN6ebjwZtMXdI7Iwq4dg587Qnyk1s5ZEPpsgPxl9I3LjkRERDBw4ECaNGlC3759SUhIACBv3rwqSTgwNaJwELej4pj+19nk9qimL5AvG5XD5oy6BIv7Quw9rSNHPi1JFMxmydCKNmzYwNChQ7l69Spubm507txZ75AUG6EShYN4vBy2T91sVA4bfoYqxz6ABGOS8PCGPuugsIPcnS+Tbt68yf/+9z9WrFgBQN26dZk3bx4VK2bz5VyUNFOJwgH8d/0+y8zKYd9vXTH7lMPeuQCL2+H2MEm45YbXfoWiVfSNy04kJiZSt25dLly4gKenJ1OnTmX48OE4O6ul1hUTlSiyOSklU343lcM2KFcg+6wOG3EZFrWDyGta2zUnvLYafKrrG5cdcXFx4Z133mH16tXMmTOH0qVL6x2SYoOyycdK5Wn+MiuHdRLwQRvf7FEOe/8qLGoL964AkOTkBj2XQ4k6Ogdm2wwGA7Nnz2bOnDnJfYMGDWLLli0qSShPpUYU2Vh8ooFPzFaH7VW7ZPYoh428oSWJuyFa29mNk37vUqW0qu9PzdmzZxkwYAC7du3C09OTdu3aUaRIkezxwUGxKjWiyMYW7wvh4i3teoLcHi6MbpYNKoCib8Pi9nDbuKChkwt0XczdfNX0jcuGJSYm8sUXX1C5cmV27dpF4cKFWbx4MUWKFNE7NMVOqESRTd2Jjuc783LYJuXsvxz2wV1Y0h7CjaMk4Qyd50P5V/SNy4YdO3aM2rVrM27cOGJjY3n99dcJCgqiU6dOeoem2BGrJgohREshRLAQ4pwQYvxTtukqhAgSQpwSQiy1ZjyO5NutZ4iM1cphSxfISZ+6pfQNKLNi78OSV+H6CWOHgI4/gm97XcOyZVJKhg8fzuHDhylRogSbNm1i4cKF5MuXT+/QFDtjtXMUQghnYCbQDAgFDgghNkgpg8y2KQe8C7wopbwrhMgm5Tj6Cr4eyS//XEpuT2hVETcXOx48xkXBL13g6mFTX/sZULmLfjHZsKQkbUl1IUTyietPPvmE3LmzwfkpRRfWfPeoBZyTUl6QUsYDy4HHP/4NBGZKKe8CSClvWjEeh/D46rD1yxagSUU7zr/xMbCsO1zZb+pr/Q1UfU2/mGxUVFQUb775Jh999BFSar8A/v7+TJ8+XSUJJVOsWfX0HHDFrB0K1H5smxcAhBB7AWdgkpRy0+M7EkIMAgYBFCxYkMDAQGvEa3eioqKeeC2O3kxk99k4AATQonA0O3fuzPrgLMApKR7/k5+Q7+7R5L5zZfoTGl0GHvu5U3otHMnBgwf5+uuvuX79Ok5OTixYsEDdsxr1e2EpepfHugDlgEaAD7BLCFFJShm3QAB5AAAgAElEQVRhvpGUcg4wB6B8+fKyUaNGWRymbQoMDMT8tYhPNDB52i5ASxS96pSgd9tK+gSXWYnxsLI3mCUJmk6ibP3RlE1h88dfC0dx9+5d3nrrLRYsWABAQEAAw4YNo1+/fjpHZhsc9ffC0qw59RQGFDdr+xj7zIUCG6SUCVLKi8AZtMShZMCS/Ze4YF4Oa6+rwyYlwpp+cMZscNnoXag/Wr+YbNC6devw9fVlwYIFuLu7M3XqVP7991/KlVN/QoplWTNRHADKCSFKCyHcgO7Ahse2WYc2mkAIUQBtKuqCFWPKtu5Ex/PdtjPJ7VFNypE/l7uOEWWQIQl+HQynfzP11R8DDcfpF5ON+vvvv7l+/Tr169fn2LFjjB8/HldXV73DUrIhq009SSkThRAjgM1o5x/mSylPCSEmAwellBuMjzUXQgQBScDbUsrb1oopO5u27Qz3jeWwpfJ72mc5rMEAG0bCydWmvjrDoclEUFcPI6UkLCwMHx8fACZNmkSFChV444031H2rFauy6jkKKeUfwB+P9U00+14CY4xfSgaduRHJL/+YVoed0NrX/sphpYSNY+DoL6a+Gv2hxScqSQCXLl1i8ODBnDx5klOnTuHt7Y2np6c6F6FkCTt7N1Ee93B12CRjPeyLZfPT1N7KYaWETe/CoQWmvqqvQauvHD5JGAwGZsyYgZ+fH5s3byYmJoZTp07pHZbiYFSisHOBweHsPnsL0FaHfb+1na0OKyVs+xD+mWXqq9QV2k4HB59OCQ4OpmHDhowcOZLo6Gg6d+7M6dOnqVevnt6hKQ7Gsf8S7VyiQTJlY/KF7nSvVYKKRb10jCgDAj+Dvd+Z2r7tocMscHLsG+fMmTOHKlWqsGfPHooUKcKaNWtYtWoVhQsX1js0xQGpRGHHdlxO5EK4sRzW3YUx9rY67O6vYednpnb5VtBpHjjrfXmP/kqUKEFcXBx9+/YlKCiIV199Ve+QFAem/iLt1N3oeNadj09uj2xSlgL2VA67byb8NdnULtsUuiwEZ8cs74yNjWX79u20atUKgJYtW3LixAn8/f11jkxR1IjCbk3bdoboBO37kvk9eb1eKV3jSZd/f4LN75napRpAt5/BxY4SnQXt3buXgIAA2rRpw/79pjWtVJJQbIVKFHbo7I1IfjYvh21VEXcXO5nTP7wE/hhrapeoCz1XgGsO/WLSSWRkJCNHjqRBgwYEBwdTvnx5nJ3t5P9RcShpnnoSQjwHlDR/jpRylzWCUlL38cbTyeWw9crkp5mvnZzgPLZCu6DuoeeqQ8+V4JZTv5h0snnzZgYNGsTly5dxcXFh/PjxvP/++7i7O+aoSrFtaUoUQojPgW7AwyuoASSgEkUW2xF8k51nwgFtddgP2thJOeypX2HdELRfG6BIZXhtDXjYWZWWBcyaNYthw4YBUL16debNm0eVKlV0jkpRni6tU08dgPJSylZSyrbGr3bWDEx5UkKSgY9/N5XDNvRxsY9y2P82wpoBIA1au5Af9FkPOfLqG5dOOnbsSLFixfj888/Zv3+/ShKKzUtrorgAOGY5ig35Zf8lzhvLYXO5u/BqOTu4B/bZrbDydTBo61BR4AXosw48Hed2nNeuXWPcuHEkJmqvQZEiRTh//jzvvPMOLi6q8FCxfWn9LY0Bjgoh/uLhzQ4AKeX/rBKV8oSImHi+3XY2uT2ycVm85JVUnmEDLgTCitfAYCzPylsa+myAXHa2xEgGSSlZuHAhY8aMISIiggIFCvD2228D4OHhoXN0ipJ2aU0UG3hyiXAlC03bdpZ7D7Q33BL5PHnjxVLs22PDieLS37CsByTGam3vEvD6b+BVVN+4ssjFixcZPHgwW7duBeCVV16he/fuOkelKBmTpkQhpVxk7UCUpzt3M5Il+y8lt9+z9XLYKwfgly6QEKO1cxeD1zdAnuKpPy8bSEpKYubMmbz77rvExMSQP39+vvvuO3r27GkfRQeKkoJUE4UQYqWUsqsQ4gTJ5SomUsrKVotMSfaJWTlsnefz0cLPhsthrx6FnztBfJTWzllIG0nkK61vXFlk9erVjBo1CoBu3boxffp0ChVyjKk2Jft61ohilPHfNtYORElZYPBNdgQby2GFjZfD3jgFSzpA3D2t7ZlfG0kUSOku19lTly5dWLt2LT179qR9+/Z6h6MoFpFq1ZOU8prx30spfWVNiI4rIcnAxxtPJ7e71SiOXzFvHSNKRXgwLGoHD+5qbY88WglsoYr6xmVlhw4don79+ly6pP05ODk5sWLFCpUklGwlTeWxQog6QogDQogoIUS8ECJJCHHf2sE5uqX/XObcTW0KJ5e7C281L69zRE9x+7yWJGK0+2Lg7gW910KRSvrGZUUPHjxg3Lhx1KpVi7179zJ58uRnP0lR7FRaq55mAN2BVUANoA9gZ2ta2xetHPZMcntE47IUzG2DyzvcvaQliajrWts1J/RarS3PkU3t2rWLAQMGcPbsWZycnBgzZoxKFEq2luZFAaWU5wBnKWWSlHIB0NJ6YSnf/XWWiBhTOWzfF0vpG1BK7oXBorZwP1Rru+SAXiuhRG1947KS+/fvM2zYMBo2bMjZs2fx8/Pj77//5uuvvyZnTsdbr0pxHGm+4E4I4QYcE0J8AVxDrTxrNeduRrFkn3k5bAXbK4eNvK4liQhjnM7u0GMZlKqvb1xWFBISwk8//YSrqyvvvfce7733Hm5udnB1vKJkUloTRW+0xDAcGA34AJ2sFZSj+/SP0yQay2Frl85HC78iOkf0mKhwbbrpznmt7eQK3ZZAmZf1jcsK7t+/j5eXtp5W5cqVmT17NrVq1aJSpex7/kVRHpfqqEAI0V4IMdxY5RQLbAXeADoCAVkQn8PZeSac7f/dBGy0HDbmjlYCeytYawtn6LIAXmihb1wWJqVkxYoVlC1bljVr1iT39+/fXyUJxeE8a/roHR5dusMdqA40AoZaKSaHlfjY6rBdqxfH/zkbKoeNvQdLOsKNk1pbOEGnn6BiW33jsrCrV6/SoUMHunfvTnh4OKtWrdI7JEXR1bMShZuUj6w8t0dKeUdKeRlQZ+8sbNm/lzlrLIfN6ebMWy1sqLAsLhJ+7gzXjho7BLT/AfyzzwyklJK5c+fi6+vLhg0b8PLy4scff2Tp0qV6h6YounrWOYpHbhggpRxh1ixo+XAc172YBL7ZaiqHHd64LIVy28gKo/ExsLQbhP5r6mvzLQT00C8mC7t+/Tq9evVi+/btALRp04ZZs2bh4+Ojc2SKor9njSj+EUIMfLxTCDEY+DeF7ZUM+u6vs9w1lsMWz5eDfi/ayNpICbGwvAdc2mvqe+ULqNFXv5iswMvLi5CQEAoUKMDSpUvZsGGDShKKYvSsEcVoYJ0Qoidw2NhXHe1cRQdrBuZIzodHsXhfSHL7vVcq4uFqA+WwifGwsrd2X4mHmk2B2oN1C8mSTp06RfHixfHy8sLT05O1a9dSrFgxChZUg2VFMfestZ5uSinrAVOAEOPXZCllXSnlDeuH5xg+3Wgqh61VOh8t/W2gHDYpAVb3hbNbTH0vvw8v2v+9quLj45k8eTJVq1Zl/Pjxyf1VqlRRSUJRUpDW+1FsB7ZbORaHtOtMOH+ZlcNOtIVyWEMSrB0E//1u6mswFhq+rV9MFnLgwAH69+/PiRMnAO0EtsFgwMlJXT+qKE+j/jp0lJhk4OONpnLYLtV99C+HNRhg/XA4tdbUV3cENH5fv5gsICYmhrfffps6depw4sQJypQpw44dO5g1a5ZKEoryDOrO7jpaduAKZ26YymHH6r06rJTw+5twbJmpr9YgaP6xNtyxUxEREdSoUYPz58/j5OTE2LFj+eijj/D09NQ7NEWxCypR6OTegwS+2RKc3B72clkKeelYDisl/DkODpvd9bba69Dyc7tOEgB58uShdu3aeHp6Mm/ePGrWrKl3SIpiV1Si0Mn3ZuWwz+XJQf/6OpbDSglbP4B/fzT1Ve4ObaaBnU7L/P777xQtWpTq1bXlzmfNmoWHh4daxE9RMsA+3wXs3IXwKBb+HZLcfq+VzuWwOz6Bv783tf1ehfYz7TJJhIeH07NnT9q2bUvfvn2Jj48HtOskVJJQlIyx6juBEKKlECJYCHFOCDE+le06CSGkEKKGNeOxFZ/+8V9yOWzNUnlpVUnHctidX8KuL03tCm3g1TngbF+DTSklS5cupWLFiixbtgxPT0/69euHs7MNXI+iKHbOau8GQghnYCbQDAgFDgghNkgpgx7bLjcwCvjHWrHYkj1nb7HttHYJilYO66dfOeze6bDjY1O7XHPoPB+cXfWJJ4NCQ0OZMGEC+/btA6BJkybMmTOH559/XufIFCV7sOaIohZwTkp5QUoZDywHUrrj/BTgcyDWirHYhMQkA1PMVoftVM2HSj46lcP+M0c7L/HQ842g6xJwscHbraYiISGBF198kX379uHt7c3cuXPZunWrShKKYkHWnF94DjBfeTYUeOQemUKIakBxKeVGIcRTr+YSQgwCBgEULFiQwMBAy0ebBbZfTiD4hjZn7u4M9XPfztTPEhUVlaHnF726hfJnZia3I7z9OP7cUAx792c4Fj117dqV3bt3M3bsWAoUKMDOnTv1DklXGf29yI7Ua2EZuk1ECyGcgG/QboSUKinlHGAOQPny5WWjRo2sGps13HuQwJjdgcnt/zUtT4eXy2Zqn4GBgaT7tTi6DAJ/MLV9apKn96+85J47U7FklcTERKZNm4aHhwcjRmiLGTds2JDAwEBefjn73WEvIzL0e5FNqdfCMqyZKMKA4mZtH2PfQ7kBfyDQOEdfBNgghGgnpTxoxbh0MWP7We5Ea6MJ3cphT66B9cMA7UQ6RQOg12qwkyRx/Phx+vfvz8GDB8mRIwddunShcOHCCCH0X/ZEUbIxa56jOACUE0KUFkK4Ad0xu1uelPKelLKAlLKUlLIUsB/Ilkni4q3oR8ph321VIevLYU//BmsGgjRo7cL+0PtXyJEna+PIgLi4OD788EOqV6/OwYMHKV68OGvWrKFw4cJ6h6YoDsFqIwopZaIQYgSwGXAG5kspTwkhJgMHpZQbUt9D9vHpH6dJSNI+xdcomZfWlYpmbQBntsCqviCTtHaB8tB7HXjmy9o4MmD//v3079+foCCtCGDYsGFMnToVLy8vnSNTFMdh1XMUUso/gD8e65v4lG0bWTMWvew9d4utQaYV2Se2zeLVYc/vgBWvgUG7Cpx8ZeD1DZDL9pfTllLy9ttvExQURLly5Zg3bx4NGjTQOyxFcTj2d+mtHUkyyCfKYSv7ZOFUT8heWNYDkuK0dp6S8PpvkNsG7neRioQELakJIZgzZw7jx4/n2LFjKkkoik5UorCiFQeu8N/1SAByuDrzTsssXB32yr+wtCskPtDaXj5akvB+LutiSKeIiAgGDBhAx44dkVKbqqtYsSJTp04lR44cOkenKI7LvtZpsCP3YxP42nx12EZlKJxVq8OGHYafO0G8toQ5uYpo0015S2bN8TNg/fr1DB06lGvXruHm5kZQUBB+fn56h6UoCmpEYTUzt5/jtrEctpi3BwNfyqIrha+fgCUdIe6+1vYsoCWJ/GWy5vjpdOPGDbp160aHDh24du0adevW5ejRoypJKIoNUYnCCkJuRTN/78Xk9visWh325n+wuD3ERmjtHHmhz3ooqPMNkZ5i6dKl+Pr6snLlSnLmzMn06dPZvXs3FStW1Ds0RVHMqKknKzAvh61eMi9tK2dBOeytc7C4HcTc1tru3tp1EkX8rX/sDDp16hR37tyhWbNmzJkzh1KlSukdkqIoKVCJwsL+PneLLeblsG2yoBz2zkVY1BaijMd1ywWvrYFiVa173HQyGAyEhIQkL9j3wQcfULlyZbp27aqurFYUG6amniwoySCZbFYO+2q156hS3MrlsBFXtJFE5FWt7eoJvVZBcdu63eeZM2do1KgRL774Infv3gXAw8ODbt26qSShKDZOJQoLWnnwsXLYFhWsejy3uNtakoi4rHU4u0OPZVCynlWPmx6JiYl88cUXVKlShd27dyOl5OzZs3qHpShKOqipJwu5H5vAV5tN5bBDG5WhiLcVy2GjwqlybCLEhGptJ1fo/ot2XwkbcezYMfr168fhw4cBeOONN/j666/Jl8/2lw5RFMVEjSgsZOaOx8phG1ixHDbmDixuT87kJOECXRdBuWbWO2Y6TZ8+nRo1anD48GFKlizJ5s2bWbBggUoSimKHVKKwgEu3o1mwJyS5Pe6VCuRws1I57IMIWNIBbp7S2sIJOs2FCq2tc7wM8vX1JSkpiZEjR3Ly5EmaN2+ud0iKomSQmnqygKl//Ed8krZ8d9USeWhXpZh1DhQXqV1xfe0YABKB6DAb/Dpa53jpEBUVxebNm+nUqRMATZs25cyZM5Qtm7mbMymKoj81osikfedvs+nU9eS21cph46Phl64QZrpdR3D54VClm+WPlU5btmzB39+fLl26sGfPnuR+lSQUJXtQiSITHl8dtmPV56haIq/lD5TwAJZ1h8t/m/pafcX1ovqek7h79y59+/alRYsWXLp0iYCAAHWfCEXJhlSiyIRVB68QdE1bU8lqq8MmxsGK3nBxl6mvxadQa6Dlj5UOa9euxdfXl4ULF+Lu7s7UqVP5559/qFy5sq5xKYpieeocRQZFxibwldnqsEMalqGot4WXwk5K0O5Md26rqa/JRKg73LLHSafvvvuON998E4D69eszd+5cype3zfWkFEXJPDWiyKCZO85zK0orhy3q7cEgS68Om5QIawZA8EZTX8Nx0OAtyx4nA3r06EGpUqWYOXMmO3fuVElCUbI5lSgy4PLtGObvMVsd1tLlsIYkWDcUgtaZ+l4cBY3etdwx0iEkJISRI0cm33muUKFCnDlzhmHDhuHkpH6FFCW7U3/lGTD1z9PWK4c1GOC3UXBipamv9hBo+hFk8ZpIBoOB77//Hn9/f2bMmME333yT/Jirq2uWxqIoin7UOYp02n/hNn+eNJXDfmDJclgp4c+34cgSU1/1vtDysyxPEv/99x8DBgxg7969AHTp0oU33ngjS2NQFMU2qBFFOjxeDtshoBjVLFUOKyVsngAH5pr6AnpB62+yNEkkJCTw6aefUqVKFfbu3UuRIkVYu3YtK1eupHDhwlkWh6IotkMlinRYcyiUU1e1clgPVyfeaWmh1WGlhL8mw/6Zpj7/ztDue8jicwBr1qxhwoQJxMfH079/f4KCgujYUf8rvxVF0Y+aekqjqLhEvjBbHXbwS2UolsdC5bC7voQ9pvl/KraDjj+CUxbcPhWQUiZPn3Xt2pVNmzbx2muv0bRp0yw5vqIotk2NKNLohx3nuBUVB0ARLw8GN7RQOeyeabDjE1P7hZbQaR44Z00O37NnD9WrV+fChQsAODk5sXDhQpUkFEVJphJFGly5E8Ncs3LYca+Ux9PNAm/k+2fDtg9N7TKNocsicHHL/L6fITIykhEjRtCgQQOOHDnCZ599ZvVjKopin9TUUxpM/fM08YlaOWyV4nloX+W5zO/04HzYNM7ULtUAuv0Crla82ZHRpk2bGDx4MJcvX8bFxYV3332XCRMmWP24iqLYJ5UonuGfC7f544SpHPbDtr44OWWyCunoUvh9tKldvDb0WA5unpnb7zPcuXOH0aNHs3jxYgCqV6/O/Pnz1fpMiqKkSiWKVCQZJJPNymHbW6Ic9sRqWG+2VlOxatBrFbjnytx+0+DatWssW7YMDw8PJk+ezOjRo3FxUb8Cim1KSEggNDSU2NjYDO/D29ub06dPWzAq2+fh4YGPj49FL4pV7xKpWHP40XLYcZkthw1aD2sHgdSmsShSCXqvBQ/vTEb6dLdv3yZfvnwIIfDz82P+/PnUrl2bcuXKWe2YimIJoaGh5M6dm1KlSmX4otbIyEhy585t4chsl5SS27dvExoaSunSpS22X3Uy+ymi4hL50qwcdlBmy2GD/4TV/UAmae2CFaH3OshhhftXoP3CLFiwgLJly7JixYrk/tdee00lCcUuxMbGkj9/fuvcCCybEkKQP3/+TI3CUqISxVPMCjxHeKRWDlvYy50hmSmHPfcXrOwDhkStnb8s9FkPOQtYINInXbx4kebNm9OvXz8iIiL4888/rXIcRbE2lSTSzxqvmUoUKbhyJ4afdpuVw7askPFy2Iu7YXlPSNKWJCdvKXj9N8ht+eUwkpKS+O677/D392fbtm3kz5+fn3/+mYULF1r8WIqiOA6VKFLw2ab/TOWwPt50CMhgOezl/bC0GyQah4HexbUk4WXB1WaNwsLCaNCgAW+++SYxMTF0796doKAgevXqpT6VKUoGXb9+ne7du1OmTBmqV69Oq1atOHPmDCEhIfj7+1vlmHFxcXTr1o2yZctSu3ZtQkJCrHKc9LBqohBCtBRCBAshzgkhxqfw+BghRJAQ4rgQ4i8hRElrxpMWB0LusPH4teT2xIyWw4Yegp87Q0K01s5dFF7fAHlKWCjSR+XLl49bt25RrFgx1q9fz7JlyyhUqJBVjqUojkBKSceOHWnUqBHnz5/n0KFDTJ06lRs3blj1uPPmzSNv3rycO3eO0aNHM27cuGc/ycqsVvUkhHAGZgLNgFDggBBig5QyyGyzI0ANKWWMEGIo8AXQzVoxPYvBIJn8mym8tlWKUb1kvvTv6Npx+LkjxEdq7ZyFtJFEPsveBS84OJiAgADy5MlDjhw5WLduHcWKFSNPnjwWPY6i6K3U+I3P3iiDQj5rnWL/jh07cHV1ZciQIcl9VapU0Z5j9ik/JCSE3r17Ex2tfSicMWMG9erV49q1a3Tr1o379++TmJjIrFmzqFevHv379+fgwYMIIejXrx+jR49+5Ljr169n0qRJAHTu3JkRI0Y8sh6bHqxZHlsLOCelvAAghFgOtAeS34mllDvMtt8PvGbFeJ5p7ZEwToTdA8DdxYlxLTNwi88bQbC4PcRq+yFHPu3EdQHLVRo9ePCASZMm8dVXX3Ho0CHmzJkDgK+vr8WOoSiO7uTJk1SvXv2Z2xUqVIitW7fi4eHB2bNn6dGjBwcPHmTp0qW0aNGCCRMmkJSURExMDEePHiUsLIyTJ08CEBER8cT+wsLCKF68OAAuLi54e3tz+/ZtChSwTvFLWlgzUTwHXDFrhwK1U9m+P5BieY4QYhAwCKBgwYIEBgZaKEST2ETJx7sfJLebl3Tm3LF/OZeOfeSICaXqkQm4JWj/+QkuOTnm+wFRp2/C6ZsWifPo0aN89dVXhIWF4eTkREREBDt27HD48xBRUVFW+b2wR9nltfD29iYyMtLqx3naMWJjY4mPj0/x8aioKAwGA5GRkdy7d4+xY8dy4sQJnJ2dOXfuHJGRkfj5+TFs2DCioqJo06YNlStXpmDBgpw7d47BgwfTokULmjRp8sT+DQYDUVFRyf0P2+7u7mn+mWJjYy37OyCltMoX0BmYa9buDcx4yravoY0o3J+13xdeeEFaw5eb/pMlx/0uS477Xdb6ZKuMik1I3w5un5fyq/JSfuilfX3ynJRXDlosvnv37skhQ4ZIQALSz89Pzpw502L7t3c7duzQOwSbkV1ei6CgoEzv4/79+xl+7rZt22SDBg1SfOzixYvSz89PSinlhx9+KN966y2ZlJQkExISpLOzc/J2YWFhcs6cObJKlSpy0aJFUkopIyMj5erVq2X79u1l3759n9h38+bN5d9//y2llDIhIUHmz59fGgyGdMWe0msHHJQZfD+35snsMKC4WdvH2PcIIURTYALQTkoZZ8V4nir0bgxzdl9Ibr/TogI53dMx2Iq4DIvaQaTxJLhrTnhtNfg8e9iaFnfv3sXf35/Zs2fj6urKhx9+yOHDh9VUk6JYUePGjYmLi0ue2gU4fvw4u3fvfmS7e/fuUbRoUZycnFiyZAlJSdpFtZcuXaJw4cIMHDiQAQMGcPjwYW7duoXBYKBTp058/PHHHD58+InjtmvXjkWLFgGwevVqGjdurPuMgTWnng4A5YQQpdESRHegp/kGQoiqwI9ASymlZeZmMuCzP03lsJV9vOlYNR3lsPevakninnGWzcUDei6HEnUsFl/evHlp3LgxQUFBzJs3j0qVKlls34qipEwIwa+//sqbb77J559/joeHB6VKlWLatGmPbDds2DA6derE4sWLadmyJTlz5gQgMDCQL7/8EldXV3LlysXixYsJCwujb9++GAza+83UqVOfOG7//v3p3bs3ZcuWJV++fCxfvtz6P+wzCG1EYqWdC9EKmAY4A/OllJ8IISajDYE2CCG2AZWAh/Wol6WU7VLbZ/ny5WVwcHBqm6TLwZA7dJ69L7m9ekhdapRKY6VT1E1Y0Apun9Xazm7QYxmUzdxNf6SUrFy5kpIlS1KnjpZwoqOj8fDwwNnZdNe7wMBAGjVqlKljZRfqtTDJLq/F6dOnqVixYqb24WhrPT2U0msnhDgkpayRkf1ZdVFAKeUfwB+P9U00+17X26gZHlsdtk3lomlPEtG3teqmh0nCyQW6Ls50kggLC2PYsGFs2LCBihUrcuTIEdzd3ZM/pSiKomQ1h74y+9cjYRwP1cpY3VycGP9KGleHfXAXlrSHm8YkI5yh83wo/0qGY5FS8tNPP+Hr68uGDRvw8vLizTfftOhSwYqiKBnhsMuMR8cl8sXm/5Lbgxo8j0/eNNw4KPY+LHkVrp8wdgjo+CP4ts9wLOfPn2fgwIHs2KFdVtKmTRtmzZqFj49PhvepKIpiKQ6bKH7ceZ4b97Uiq4K53RnaqMyznxQXBb90gatmlQrtZ0DlLhmOIyEhgUaNGhEaGkqBAgX4/vvv6datm+5VDoqiKA85ZKIIi3jAj7vMy2HLP7scNj4GlnWHK/tNfW2+haqZu5jc1dWVTz75hC1btjBt2jRdr75UFEVJiUMmis///I84Yzms/3NedKr2jCmehGQJt6oAABUaSURBVFhY0QtCzOqnW34GNfql+9jx8fFMnTqV3LlzM2bMGAD69OlDnz590r0vRVGUrOBwJ7MPXbrDhmNXk9sT2/ilvjpsYjysegPObzf1Nf0I6gxN97H//fdfqlevzqRJk5gwYQLh4eHp3oeiKFlHj2XGd+3aRbVq1XBxcWH16tVWOUZ6OVSieHx12NaVi1KrdCrlsEmJsKY/nDFbgqrRe1D/zXQdNyYmhrFjx1K3bl1OnjxJ2bJl+fPPPylYsGB6fwRFUbKI1GmZ8RIlSrBw4UJ69uz57I2ziENNPa07GsYx83LYlqmUwxqSYN0QOL3B1Fd/DDR8J13H3LFjBwMGDODChQs4OTnx9ttvM2nSJDw901BhpSiKZpJ3hp6WpkvtJt1LsVuvZcZLlSoFgJOT7XyOd5hEEROfyOebTOWwAxuUpni+p7xZGwyw4X9wYpWpr85waDIR0lGNJKXko48+4sKFC1SqVIn58+dTo0aGLoxUFCWL6bXMuC1ymEQxe+eFx8phy6a8oZTwx1tw9GdTX43+0OKTNCeJ2NhYPDw8EELw008/sWLFCt555x3c3Nwy+2MoimJjEhISGDFiBEePHsXZ2ZkzZ84AULNmTfr160dCQgIdOnQgICCA559/ngsXLjBy5Ehat25N8+bNdY4+bRwiUYRFPODHneeT22+3KE+ulMphpYRN78LB+aa+qq9Bq6/SlCTCw8MZNWoUt27dYvPmzQghKFeuHO+//74lfgxFcVxPmR56lsys9eTn55emk8nffvsthQsX5tixYxgMBjw8PAB46aWX2LVrFxs3buSNN95gzJgx9OnTh2PHjrF582Zmz57NypUrmT9//jOOoD/bmQSzoi82mcph/Yp50TmlclgpYdsk+GeWqa9SV2g7HZ4xVyilZOnSpVSsWJFly5axd+9e/vvvv1SfoyiKbdNrmXFblO0TxaFLd1l/1Lwc1jflctjAz2Cv2fLBvu2hwyxwcn5yWzNXrlz5f3vnHl1VfeXxz84lIUCIDyIKRQ1VQHmYkJCWLEFwRaBVkDrgo2MkOCpOccAwivKaDi1LhdJSB5eiUZxQddqIFohKiaI8XAgViAECDRWp8pSGCIFgAnns+eOcPAgh9xrJvTc3+7PWXTmP3/md/dvr5Ozze31/jBo1invvvZeioiJSUlLYsWPH91a9NAwjsFTLjK9evZprrrmGPn36MH36dK644oqz0k2cOJElS5YQFxdHQUHBWTLjcXFx9O/fn6ysLB599FEOHjzI0KFDiY+PJzU1tUGZ8c2bN9OtWzeWLl3Kww8/TJ8+ffxS3sYI6aanqiplTh112Fv7XcGPf9jp3IQf/w7Wza3d73UrjFkMnsbds3jxYqZMmcLJkye56KKLWLBgAffff7/JbxhGiNC1a1fefPPNBs9Vd0j36NGD7du31xyfN28eAGlpaaSlpZ1znbdaRFJSEgcOHGiqyc1CSAeK7G2HyNvvjCqI8IQx/acNfOVvfB4+/HXt/rW3wJ2Z4PGu2rp//35OnjzJ6NGjeeGFF+jatesFstwwDCN4CNlA8e2ZCub+pbaf4IGGhsN++jLkzKjd734T3P06tGl4EfOKigr27NnDddc58y9mzJhBYmIiI0eOtFqEYRghS8j2Uby0bi9fnygDICaqLY/cXG84bO5rsPLx2v2rkuHnf4Lwdg3mt337dpKTkxkyZAhFRUUAREREMGrUKAsShmGENCEZKA4dL+Wl9bXDYZ+oPxx2+5uQPal2/wcD4F/fhIhzV5E7ffo0v/zlL0lMTGTLli20bduWr776qjnNNwzDCCpCMlD8ZlUBZeW1w2HHJNYZDrtzGSx7GHDXCu8SB6lvQ2T0Ofls2rSJhIQE5syZQ0VFBRMnTiQ/P5+EhAQ/lMIwDCM4CLk+itx9x1heZzjsf43sjad6OGzBSnj7QVAniNC5D9y3HNpdfE4+8+fP58knn0RV6dGjB4sXL2bw4MH+KIJhGEZQEVI1CtWz1WF/2vcKBlYPh/18NSxNg6oKZz+mJ4xbDu0bVo9NSkrC4/Ewbdo0tm3bZkHCMFohHo+H+Ph44uLiSEhI4JNPPrmg+Y8fP75m9veDDz7Irl27vFwRGEKqRnHe4bB71zkLD1WecfYv6Q7jsiGqc821x48f59133yU11VmxbujQoezdu5crr7zSr2UwDCN4aNeuHXl5eQDk5OQwffp01q1b1yz3euWVV5ol3wtByNQoSs9UnjUc9t8GdeeqTu3hq43OEqYVzggoLroK0t6B6C41aZcvX07v3r257777znoILEgYRvAgIuf91ZXZyMjIqDkeHR19TtqmcuLECS655BIASkpKSElJISEhgX79+rFixQoATp06xW233UZcXBx9+/YlKysLgK1btzJkyBASExMZMWIEhw8fPif/oUOHsmXLFgCioqKYOXMmcXFxDBw4sGYNjMLCQsaMGUNSUhJJSUls2LChyeX5LoRMjSJj/V4OF1cPh43gkZuvgQNb4I07ofxbJ1HHrpCWDRc7AeDIkSNMmjSJpUsdOfHk5GQuv/zygNhvGEbwUVpaSnx8PGVlZRw+fJiPPnJWuoyMjGTZsmVER0dz9OhRBg4cyO23386qVavo2rUr7733HuDoQJWXlzNp0iRWrFjBZZddRlZWFjNnzmxUDPDUqVMMHDiQp556iieeeIKXX36ZWbNm8eijjzJlyhQGDRrEvn37GDFiBH/729+a3Q8hESgOF5fyYh112MeH96LjNzvhtX+BMyedgx06OzWJS7ujqrz++uukp6fzzTff0KFDB5555hkmTpyIx9O4tpNhGIFBVX1KN2HCBCZMmAB8P/VYOLvpaePGjYwbN478/HxUlRkzZrB+/XrCwsI4ePAgR44coV+/fjz22GM8+eSTjBw5ksGDB5Ofn09+fj7Dhg0DoLKyki5dujR2WyIiIhg5ciQAiYmJfPDBBwCsXr36rH6MEydOUFJSQlRUVJPL6AshESjmr9pNabmj2Hh9l2juvPIE/OFncNqVJm7fyalJxDiT7hYsWMDjjzuT7YYNG0ZGRkbNqlKGYRgNkZyczNGjRyksLGTlypUUFhaydetWwsPDiY2NpaysjJ49e5Kbm8vKlSuZNWsWKSkp3HHHHfTp04eNGzf6fK/w8PCaZjKPx0NFhTMIp6qqik2bNtVImfuLFt9Hkbf/OH/+7GDN/tODw/G8NhpKjzkHIi+GcSugc63OU1paGr169SIzM5OcnBwLEoZheKWgoIDKyko6depEcXExnTt3Jjw8nDVr1tRMwj106BDt27cnNTWVqVOnkpubS69evSgsLKwJFOXl5ezcubNJNgwfPpznnnuuZr+6ttPctOgahTMcttbhqT0r6P9RGnx71DnQNhruW8bu4gh+M/MBFi1aREREBDExMezcudOamQzDaJTqPgpw3jdLlizB4/Fw7733MmrUKPr168eAAQNq9N927NjB1KlTCQsLIzw8vOad89ZbbzF58mSKi4upqKggPT29SfLhCxcu5JFHHuGGG26goqKCm266iRdffPGClrkhWnSgyN52iNx9znDY7p6jzP5mLpR87ZwM70DFPVn89rX3mT17NqdPn6ZHjx5MmzYNwIKEYRheqV6EqD4xMTENNiXFxsYyYsSIc47Hx8ezfv36c45nZmbWbK9du7Zmu6SkpGZ77NixjB07tua+1SOp/EmLDRSlZyqZ5w6HvYIi/hw1lzYl7ozsNu3I6/8UD4ydVKP9Pn78+JoOLsMwDMN3WmygePnjvRwqLuMyjpEV+TSXnHbGJZdVRTDn0C3Mm/0QlZWVXH311WRkZLSYRcwNwzCCjRbZmf11cRmL1n7BpZzgjYinuRp38kpYOCsufYinF71BVVUVkydPJj8/34KEYbRQfB0Sa9TSHD5rkTWK3+QUEFFezOsRz9Az7CBVqoSFtYE7/5e7rhvJ2t3HSE1N5cYbbwy0qYZhNJHIyEiKioro1KmTrfniI6pKUVHRBR8+2+ICxelK+CD3c16PmEvvsK94/4sK0leVsSLzf+hx/SgEWLRoUaDNNAzje9KtWzcOHDhAYWFhk/MoKyvz+5yDQBMZGUm3bt28J/wOtLhAcbyskrcj5nHl6S+4//0yMvPKAfj9e7t44bYAG2cYxgUjPDyc7t27f6881q5dS//+/S+QRa2XZu2jEJGfiMhuEdkjItMaON9WRLLc838VkVhveXap+povC3bR+/kSMvPKaRvRhrlz57Jw4cLmKIJhGEarp9lqFCLiAZ4HhgEHgM0ikq2qdQXXHwCOqeq1InIPMA+4u7F8vz52irGOhh+D4q7llax36dWrV3MUwTAMw6B5axQ/Avao6l5VPQP8CRhdL81oYIm7/RaQIl56rYrLICoCnk0fw7rc3RYkDMMwmpnm7KP4AbC/zv4B4MfnS6OqFSJSDHQCjtZNJCITgOrZcqdLzpCf/uzbpD/b6mdXx1DPV60Y80Ut5otazBe1NPmrukV0ZqtqBpABICJbVHVAgE0KCswXtZgvajFf1GK+qEVEtjT12uZsejoI1F0irpt7rME0ItIGuAgoakabDMMwjO9IcwaKzUAPEekuIhHAPUB2vTTZQJq7PRb4SG0qpmEYRlDRbE1Pbp/DfwA5gAd4VVV3isivgS2qmg0sBl4TkT3ANzjBxBsZ3pO0GswXtZgvajFf1GK+qKXJvhD7gDcMwzAao0WKAhqGYRj+wwKFYRiG0ShBGyiaQ/6jpeKDL/5TRHaJyHYR+VBErg6Enf7Amy/qpBsjIioiITs00hdfiMhd7rOxU0T+z982+gsf/keuEpE1IvKZ+39yayDsbG5E5FUR+aeI5J/nvIjIQtdP20UkwaeMVTXofjid318APwQigG1A73ppJgIvutv3AFmBtjuAvrgZaO9u/6I1+8JN1xFYD2wCBgTa7gA+Fz2Az4BL3P3OgbY7gL7IAH7hbvcGvgy03c3ki5uABCD/POdvBf4CCDAQ+Ksv+QZrjaJZ5D9aKF59oaprVPVbd3cTzpyVUMSX5wJgDo5uWJk/jfMzvvjiIeB5VT0GoKr/9LON/sIXXygQ7W5fBBzyo31+Q1XX44wgPR+jgT+owybgYhHp4i3fYA0UDcl//OB8aVS1AqiW/wg1fPFFXR7A+WIIRbz6wq1KX6mq7/nTsADgy3PRE+gpIhtEZJOI/MRv1vkXX3wxG0gVkQPASmCSf0wLOr7r+wRoIRIehm+ISCowABgSaFsCgYiEAQuA8QE2JVhog9P8NBSnlrleRPqp6vGAWhUYfg5kqurvRCQZZ/5WX1WtCrRhLYFgrVGY/EctvvgCEbkFmAncrqqn/WSbv/Hmi45AX2CtiHyJ0wabHaId2r48FweAbFUtV9V/AH/HCRyhhi++eAB4E0BVNwKROIKBrQ2f3if1CdZAYfIftXj1hYj0B17CCRKh2g4NXnyhqsWqGqOqsaoai9Nfc7uqNlkMLYjx5X9kOU5tAhGJwWmK2utPI/2EL77YB6QAiMj1OIGi6WustlyygXHu6KeBQLGqHvZ2UVA2PWnzyX+0OHz0xXwgCljq9ufvU9XbA2Z0M+GjL1oFPvoiBxguIruASmCqqoZcrdtHXzwGvCwiU3A6tseH4oeliPwR5+Mgxu2P+W8gHEBVX8Tpn7kV2AN8C9zvU74h6CvDMAzjAhKsTU+GYRhGkGCBwjAMw2gUCxSGYRhGo1igMAzDMBrFAoVhGIbRKBYojIAgIpUikici+SKyVETaB8iO9EDd273/fFfZdX4AbYg9n9qoYYAFCiNwlKpqvKr2Bc4A/+7rhSLiuYB2pAMBCxTABOAGVZ0aQBsMo1EsUBjBwMfAteDoVYnIp25t46XqoCAiJSLyOxHZBiSLSJKIfCIi29z0HUXE436hb3a19h92rx0qImtF5C0RKRCRN9yZqZOBrsAaEVnjpl0kIlvcr/xfVRsoIre612519fzfdY93cNcA+NRd6+AcNVv3XvPd2tMOEbnbPZ6NM1Fya/WxOtcMcX2Q5+bbUUSixFlvJNfNZ7SbNta1LVNE/u6W7xZxxAA/F5Efuelmi8hrIrLRPf5QA7Y26EOjlRNo/XT7tc4fUOL+bQOswFlH43rgHSDcPfcCMM7dVuAudzsCR4oiyd2PdvOZAMxyj7UFtgDdcWaqFuPo2oQBG4FBbrovgZg6dl3q/vUAa4EbcOQe9gPd3XN/BN51t58GUt3ti3H0lDrUK+sY4AM3z8tx5CS61PVDA/55B7jR3Y5yy9cGiHaPxeDMrhUgFqgA+rnl2wq86p4bDSx3r5mNs1ZDO/f6/TiBMhZ3/YLz+TDQz4v9AvuzGoURKNqJSB7Oi2gfjiRLCpAIbHbPpeAsRgOOBMXb7nYv4LCqbgZQ1RPqSM0Px9GxyQP+iiM7Xy2C96mqHlBHLTQP5+XYEHeJSC7Ogj99cBa5uQ7Yq46wHjiBoprhwDT3nmtxgspV9fIcBPxRVStV9QiwDkjy4p8NwAK31nOxWz4BnhaR7cBqHHnoy930/1DVHW75dgIfqqoCO+qVdYWqlqrqUWANzloOdWnMh0YrJSi1noxWQamqxtc9II5Q1RJVnd5A+jJVrfSSpwCTVDWnXr5DgbqKupU08OyLSHfgcZyayjERycR58Xu75xhV3e0l3XdCVeeKyHs4ujwbRGQEjhruZUCiqpaLo5BbbV/d8lXV2a/i7LLW1+ypv9+gD43WjdUojGDiQ2CsiHQGEJFLpeH1v3cDXUQkyU3XURyp+RzgFyIS7h7vKSIdvNzzJI48OThNWKeAYhG5HPhpnfv9UGrXZa/bn5ADTHKDXLWSb30+Bu522/8vw1mu8tPGjBKRa9wawjwcddTrcKT0/+kGiZuBpqyNPlpEIkWkE06T3OZ655viQyPEsRqFETSo6i4RmQW8L84iROXAI8BX9dKdcTt/nxORdkApcAvwCk4zS6774i4EfublthnAKhE5pKo3i8hnQAFO+/0G936lIjLRTXeKs1+uc4Bnge2uzf8ARta7xzIgGad/QIEnVPVrL3alu8GguinpLzgB7R0R2YHTZFfgJY+G2I7T5BQDzFHVQ3UCIDTNh0aIY+qxhuEDIhKlqiXuy/N54HNV/X2g7fouiMhsnM7z3wbaFqNlYU1PhuEbD7kdvDtxmoBeCrA9huE3rEZhGIZhNIrVKAzDMIxGsUBhGIZhNIoFCsMwDKNRLFAYhmEYjWKBwjAMw2iU/wdzU72XYnAnWAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1957,9 +2938,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -1970,10 +2949,19 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.986013986013986" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)\n", @@ -2407,10 +3395,48 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n", + "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n", + " warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "\n", @@ -2489,10 +3515,66 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py:752: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max depth: 1\n", + "Error: 0.4457280318892852\n", + "Bias^2: 0.21134456570975685\n", + "Var: 0.23438346617952846\n", + "0.4457280318892852 >= 0.21134456570975685 + 0.23438346617952846 = 0.44572803188928534\n", + "Max depth: 2\n", + "Error: 0.4334430162221736\n", + "Bias^2: 0.21262237640992304\n", + "Var: 0.22082063981225059\n", + "0.4334430162221736 >= 0.21262237640992304 + 0.22082063981225059 = 0.4334430162221736\n", + "Max depth: 3\n", + "Error: 0.4335098763401203\n", + "Bias^2: 0.21261290566156496\n", + "Var: 0.2208969706785553\n", + "0.4335098763401203 >= 0.21261290566156496 + 0.2208969706785553 = 0.4335098763401203\n", + "Max depth: 4\n", + "Error: 0.4334769070236457\n", + "Bias^2: 0.21261834606312066\n", + "Var: 0.22085856096052509\n", + "0.4334769070236457 >= 0.21261834606312066 + 0.22085856096052509 = 0.4334769070236457\n", + "Max depth: 5\n", + "Error: 0.4335245020157543\n", + "Bias^2: 0.21261725605441378\n", + "Var: 0.22090724596134043\n", + "0.4335245020157543 >= 0.21261725605441378 + 0.22090724596134043 = 0.4335245020157542\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2552,10 +3634,59 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "[0.86666667 0.86666667 0.8 1. 1. 0.92857143\n", + " 1. 0.92857143 0.85714286 1. ]\n", + "Test set accuracy with Random Forests and scaled data: 0.99\n" + ] + }, + { + "data": { + "image/png": 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\n", 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88cfUqFHD0eE5BbslCmOMCzAbuAPwAR40xvhc0+wl4GsRaQH0B963VzxKqZLJGEPjxo0pX748b7/9Nv/88w8dOnRwdFhOxZ73UYQAh0XkKIAxZhFwD5D+kiUBKqf9XAU4Zcd4lFIlRGhoKJGRkdxxxx0AjBs3joEDB+pppnyy56mnesDJdMvhaevSew142BgTDvwEPJnVhowxjxljthpjttojUKVU8RAbG8uYMWNo1aoVgwcP5vz58wC4urpqkrgBjh7MfhD4TETqAz2Bz40xmWISkQ9EpHV+C1oppYo3EWHp0qX4+Pgwbdo0AB566CHKlCnj4MiKB3ueeooAGqRbrp+2Lr1HgR4AIrLRGFMOqAmctWNcSqli5Pjx44waNYoVK1YA0Lp1a+bNm0fLli0dHFnxYc8jii1AM2OMuzGmLNbB6uXXtDkBdAEwxngD5YConDba6mgDqDXLDuEqpZyNiNCnTx9WrFhB5cqVmTVrFps2bdIkUcDslihEJAUYBfwK7MN6ddMeY8xEY8zdac2eBYYZY3YAXwFDxNkmyFBKFTqLxQJYr2iaMmUK/fr1Y//+/YwcORIXFxcHR1f8ON3ERa3LNJStVcdC1ChHh6KUKmTnzp1j/HjrLVkffvihg6NxLjoVqlKqWBMR5s+fj5eXFx999BELFiwgPDzc0WGVGE6XKLZ5nNSjCaVKkH379tG5c2eGDBlCdHQ0nTp1YseOHdSvX9/RoZUYTpcolFIlg4jw8ssvExgYyO+//07NmjWZP38+a9aswcvLy9HhlSiaKJRSRZIxhoiICJKTkxk2bBgHDhxg0KBBGGMcHVqJ43SD2cbTiBxwrpiVUnlz6tQpoqOjCQgIACA6OpoDBw7Qvn17B0fm/HQwWynl1FJTU5k1axbe3t7079+fK1euAFCzZk1NEkWAJgqllENt376dtm3b8uSTT3Lp0iWaNGmSYYpS5Xh5ShTGmLLGmKb2DiYval6qAAt2OzoMpdQNunTpEk899RTBwcFs3bqV+vXr891337F8+XJq1qzp6PBUOrkmCmNML2AXsDJtOcgYs9TegWWnUXR1eHado7pXShUAEeHWW29lxowZGGMYM2YMe/fu5b777tPB6iIoL0cUE4E2wEUAEQkFisTRhVLKORljeOaZZwgJCWHr1q289957VKpUydFhqWzkpXpssohcvCbL62VHSqk8u3LlClOnTsXFxYXnn38egEGDBvHwww9rbSYnkJdEsc8Y0xcoZYxxB0YDm+wbVvaiKsXBwGtnVFVKFVV//PEHjz/+OHv37sXV1ZVBgwZRp04djDGaJJxEXk49jQJaARbgOyAJeMqeQeXkRK0LMPU2R3WvlMqj6OhoHnnkEW699Vb27t1Ls2bNWLFiBXXq1HF0aOo65SVRdBeRcSLSIu0xHrjD3oEppZyTiPDpp5/i5eXFp59+StmyZXn11VfZuXMnXbt2dXR4Kh/ykiheymLdhIIORClVfCxcuJBz585x2223sXPnTl577TXKlSvn6LBUPmU7RmGM6Y51mtJ6xpip6Z6qjPU0lFJKAZCQkEBMTAx169bFGMP777/Pli1bGDBggF7uWgzkNJh9FtgNJAJ70q2PBcbbMyillPP4+eefGTlyJB4eHqxcuRJjDJ6ennh6ejo6NFVAsk0UIvIP8I8x5gsRSSzEmJRSTiAiIoKnn36aJUuWAFCpUiXOnTund1UXQ3kZo6hnjFlkjNlpjDl49WH3yLLhHV4Huix2VPdKlXipqanMmDEDb29vlixZQoUKFXjvvffYtm2bJoliKi/3UXwGvAlMwXq101AceMOd25WysDPKUd0rVaJZLBY6duzIhg0bALj33nuZPn06DRs2dHBkyp7yckThJiK/AojIERF5Cb08VqkSqVSpUnTr1o0GDRqwbNkyli5dqkmiBMjLEUWSMaYUcMQY8zgQAWhRFqVKABHh66+/pnTp0vTp0weAcePGMWbMGCpWrOjg6FRhyXWGO2NMG2AvUA14C6gCTBKRDfYPL7MKjctK/LJwCKztiO6VKjGOHDnCiBEj+O2336hVqxYHDhygWrVqjg5L5dONzHCX6xGFiPyd9mMsMDCtw3r56awgJLgma5JQyo6SkpJ49913eeutt0hMTKRatWq89dZbVKlSxdGhKQfJMVEYY4KBesCfIhJtjPEFxgG3AfULIT6lVCFat24dTzzxBPv37wdg4MCBTJkyhdq19ctZSZbtYLYx5m3gC2AA8Isx5jVgLbADaF4o0SmlCk1qaiojRoxg//79eHp6smbNGhYsWKBJQuV4RHEPECgil40x1YGTgL+IHC2c0JRS9maxWEhMTMTNzQ0XFxfmzJnD+vXrGTt2LK6uro4OTxUR2Q5mG2O2i0jLdMv/iEiLQossG8bTiBzQeZOUulG7du3i8ccfx8vLi48//tjR4Sg7s9dgtocx5rurfQDu6ZYRkd756fBGuSWVgR1ndUBbqXyKj49n4sSJTJ06lZSUFMLCwrhw4YJe0aSylVOi6HPN8ix7BpJX3hE3QdevIWqUo0NRyun88MMPjBo1ihMnTmCMYcSIEbz11ltUrVrV0aGpIiynooCrCzMQpZT9pKSk0K9fP777znpSICgoiHnz5hESEuLgyJQzyEsJD6WUkytdujRVqlShYsWKTJs2jS1btmiSUHlm10RhjOlhjDlgjDlsjMlyDgtjTF9jzF5jzB5jzJe5bTOh7BUIqFXwwSpVzPz999/8/ffftuV3332Xffv28fTTT1O6dF6q9yhllWsJD1tDY1xFJCnPGzbGBTgI3A6EA1uAB0Vkb7o2zYCvgdtE5IIxpraInM1xu3rVk1I5unjxIi+88ALz5s3Dy8uL0NBQypYt6+iwlIPdyFVPuR5RGGNCjDG7gENpy4HGmJl52HYIcFhEjorIFWAR1nsz0hsGzBaRCwC5JQmlVPZEhC+//BIvLy/mzp2Li4sLd999N6mpqY4OTTm5vJx6mgHcCZwDEJEdQOc8vK4e1pv0rgpPW5dec6C5MWaDMWaTMaZHHrarlLrGoUOH6NatGwMGDODMmTO0b9+ef/75h3feeYfy5cs7Ojzl5PJyorKUiBy/ZoL0gvqKUhpoBnTCWjtqvTHGX0Qupm9kjHkMeAzQ4iFKXSM5OZnbbruN8PBwqlevzuTJkxk6dCilSum1Kqpg5CVRnDTGhACSNu7wJNaxh9xEAA3SLddPW5deOPC3iCQDYWlTrDbDOp5hIyIfAB+AdYwiD30rVeyJCMYYypQpw1tvvcXatWuZPHkytWrpxR6qYOVlPoraWE8/dU1btQoYJSLRubyuNNaE0gVrgtgCPCQie9K16YF1gHuwMaYm8A8QJCLnst2uDmarEu7MmTM899xzNG/enJdfftnR4SgnYdfBbCBFRPqLSM20R//ckgSAiKQAo4BfgX3A1yKyxxgz0Rhzd1qzX4Fzxpi9WCvTPp9TkgBoGFUNxqzJQ9hKFS8Wi8V2JdPChQuZOnUqsbGxjg5LlQB5OaI4AhwAFgPfiYhDfzNbl2koW6uO1RIeqkTZsWMHjz/+OJs2bQKgR48ezJ49Gw8PDwdHppyFXY8oRKQJ8CbQCthljPneGNM/P50ppa5PcnIyzz33HK1atWLTpk3UrVuXr7/+mp9++kmThCo0ebosQkT+EpHRQEvgEtYJjZRSdla6dGn++ecfLBYLTz75JPv27eOBBx7gmqsQlbKrXK96MsZUxHqjXH/AG1gG/J+d48rW8ZrnYVInR3WvlN2dOHGC1NRU3N3dMcYwd+5cYmJiaN06X2cNlLpheRmjOAb8gHUw+o/CCConetWTKq6Sk5OZPn06r776Ku3atWPlypV65KAKjL0mLrrKQ0Qs+dm4UipvNm7cyOOPP87OnTsBqF69OgkJCVSoUMHBkSmVQ6IwxrwnIs8C3xqT+SY3R81wp1RxcuHCBcaPH88HH3wAgLu7O7Nnz+aOO+5wcGRK/SunI4rFaf8WiZntlCpukpKSCAoK4sSJE5QpU4bnn3+eCRMm4Obm5ujQlMogpxnuNqf96C0iGZKFMWYUoDPgKXUDXF1defTRR1m9ejVz5szBx8fH0SEplaW8DGZvF5GW16z7R0Ra2DWy7OLRwWzlpBITE3n77bfx9PTkoYceAqxTlLq4uOigtbI7uwxmG2P6Yb0k1t0Y8126pyoBF7N+lf21OtoAas3SO7OVU1m5ciUjRozg8OHD1K5dm/vuu4/y5cvrTHPKKeT0W7oZ6xwU9YHZ6dbHYi3ep5TKxenTpxkzZgxfffUVAL6+vsydO1fniFBOJacxijAgDGu1WKXUdUhNTWXevHm8+OKLxMTEUL58eV599VWeeeYZnZZUOZ2cTj39LiIdjTEXgPSDAgYQEalu9+iUclKpqanMnDmTmJgYevbsyaxZs3B3d3d0WErlS7aD2caYUiJiSZusKBMRcchEvDqYrYqq2NhYUlNTqVq1KgB//vknZ86coXfv3jpYrRzOLtVj092N3QBwSUsM7YDhgN4uqlQaEeG7777D29ubZ5991rb+lltuoU+fPpoklNPLS/XY77FOg9oE+BTrVKVf2jUqpZzEsWPHuPvuu+nTpw8RERHs3r2bxMRER4elVIHKS6KwpM1p3RuYKSLPAPXsG5ZSRVtycjKTJk3Cx8eHFStWULlyZWbNmsVff/1FuXLlHB2eUgUqLxdxpxhjHgAGAvemrStjv5CUKtoSEhJo27Ytu3btAqB///5MnTqVunXrOjgypewjL4niEWAEMFlEjhpj3IGv7BuWUkWXm5sbrVu3JiEhgffff59u3bo5OiSl7CrXEh4AxpjSQNO0xcMikmLXqHJQq25FiZq0CQb5OSoEVcKICAsWLKBJkybccsstAMTExFC2bFm9cU45DbvOmW2M6QAcBj4GPgEOGmPa56ezgtAoujo8u85R3asSZt++fXTu3JkhQ4bw2GOPceXKFQCqVKmiSUKVGHk59TQN6CkiewGMMd7A54DOy6iKrcuXL/PWW28xefJkkpOTqVWrFi+88AJlyujwnCp58pIoyl5NEgAiss8YozUIVLH1yy+/MHLkSI4ePQrAsGHDeOedd6heXYsRqJIpL4liuzFmLrAwbXkADiwKGFUpDgZq3X5lH3FxcQwcOJDo6Gj8/PyYO3cu7ds77EyrUkVCXuajKAeMBm5JW/UH1vspHHJXkZbwUAUtNTUVi8ViO6305ZdfEh4ezjPPPKOnmlSxcSOD2TkmCmOMP9AE2CMih/IZX4HSRKEK0rZt2xg+fDj33HMPL7/8sqPDUcpu7HLVkzHmRazlOwYAK40xj+QzPqWKnEuXLvHUU08REhLCtm3b+Pzzz0lOTnZ0WEoVSTldHjsACBCRB4Bg4InCCUkp+xERvvnmG7y8vJgxYwbGGMaMGcP27dv1NJNS2chpMDtJROIBRCTKGJOXulBKFVmxsbH069ePn3/+GYA2bdowd+5cgoKCHByZUkVbTonCI91c2QZokn7ubBHpbdfIlCpgFStWJCkpiSpVqvDOO+/w2GOPUaqUfv9RKjc5TVzUJacXishqu0SUC58KN8nettNhdT9HdK+czPr166lbty7NmjUD4Pjx45QrV446deo4ODKlCteNDGbnNGe2QxJBbtyulIWdUY4OQxVx0dHRjB07lk8//ZQuXbqwcuVKjDE0atTI0aEp5XT0uFsVKxaLhU8++QRPT08+/fRTypYtS4cOHUhNdcjMvUoVC3ZNFMaYHsaYA8aYw8aY8Tm062OMEWOM1o9S+bZnzx46derEo48+yvnz5+nSpQu7du3i1VdfpXTpvBQhUEplJc//e4wxriKSdB3tXYDZwO1AOLDFGLM8fd2otHaVgKeAv/Oy3X31TsOyvnkNQ5UQMTExtG3blri4OGrXrs3UqVN56KGHdL5qpQpAXsqMhxhjdgGH0pYDjTEz87DtEKxzVxwVkSvAIuCeLNq9AUwC8lQSJME1GQJr56WpKgGuXoxRpUoVxo0bx+OPP87+/fsZMGCAJgmlCkheTj3NAO4EzgGIyA6gcx5eVw84mW45nGvm2jbGtAQaiMiPOW3IGPOYMWarMWZrHvpVJUBERAT3338/CxcutK2bMGECc+bMoVq1ag6MTKniJy8zgUuTAAAgAElEQVSJopSIHL9m3Q2PDKbdwDcVeDa3tiLygYi0zu+lXar4SElJYfr06Xh5efHtt9/y6quv2gaq9QhCKfvIS6I4aYwJAcQY42KMeRo4mIfXRQAN0i3XT1t3VSXAD1hnjDkGtAWW64C2ys6WLVto06YNTz/9NHFxcdx77738/vvvuLi4ODo0pYq1vCSKJ4AxQEPgDNY/6Hmp+7QFaGaMcU+b6Kg/sPzqkyISIyI1RaSxiDQGNgF3i4ieXlIZxMfHM2rUKNq0acP27dtp2LAhy5YtY+nSpTRo0CD3DSilbkiuVz2JyFmsf+Svi4ikGGNGAb8CLsAnIrLHGDMR2Coiy3PeQtbcksrAjrM6oF2ClC5dmlWrVlGqVCnGjBnDq6++SoUKFRwdllIlRl4mLvoQyNRIRB6zV1A5aV2moWytOhaiRjmie1VIjhw5QtWqValRowZgPe1Urlw5/P39HRyZUs7JLvNRpLMKWJ322ADUBvJ8P4VS1yMpKYk333wTPz8/xo0bZ1sfHBysSUIpB8nLqafF6ZeNMZ8Df9otIlVirVu3jieeeIL9+/cD1iucUlNTdbBaKQfLTwkPd8BhpTcTyl6BgFqO6l7ZwdmzZxk8eDCdO3dm//79eHp6smbNGj777DNNEkoVAbkeURhjLvDvGEUp4DyQbd0me9tX/4yWGC9GoqOj8fb25vz587i6ujJhwgTGjh2Lq6uro0NTSqXJMVEY6x1Mgfx7/4NFchv9Vuo61KxZk3vuuYfw8HDef/99mjZt6uiQlFLXyMtVT7tFxK+Q4smV8TQiBzRXOav4+HgmTpxIr169uPXWWwFITEzE1dVV76xWyo7sfdVTqDGmRX42rlR6P/zwAz4+PkyePJkRI0ZgsVgAKFeunCYJpYqwbE89GWNKi0gK0AJrifAjQDzW+bNFRFoWUozKyZ08eZKnnnqKpUuXAtCiRQvmzZun81Ur5SRyGqPYDLQE7i6kWFQxk5KSwowZM3jllVeIj4+nYsWKvPnmm4wcOVInElLKiWQ7RmGM+UdEitwpp0bVq8vxIUtg6m2ODqVESE5OJjw8nMTEPE0XkoHFYiEiIgKLxYKbmxvVqlXTBKGUnZUrV4769etTpkyZDOtvZIwip/+1tYwxY7J7UkSm5qfDG1UrtiJ8vlcTRSEJDw+nUqVKNG7cOE/jCCkpKZQqVcp2Wunmm2/GGEPVqlXtHapSJZ6IcO7cOcLDw3F3dy+w7eaUKFyAiljHJFQJlZiYmKckISKcP3+ekydPUrt2bW6++WYAnURIqUJkjKFGjRpERUUV6HZzShSRIjKxQHtTTim3JJGYmMjx48eJjY0FIC4uDhHRK5mUcgB7/L/LKVEUyf/lx2ueh0mdHB2GwjoGcfr0aSIjIxERSpcuTf369alRo4YmCaWKkZyuT+xSaFFch+jK8TCoyNz/V2IlJyezZ88eTp06hYhQo0YNfH19qVmzZoEnCRcXF4KCgvDz8+Ouu+7i4sWLtuf27NnDbbfdhqenJ82aNeONN94g/QUaP//8M61bt8bHx4cWLVrw7LO5zrxbZDz44IMEBAQwbdq0PLWvWLGiXeIQEUaPHk3Tpk0JCAhg+/btWba7fPkyHTt2tE1NWxQ98sgj1K5dGz+/7P+G5LS/8+fPp1mzZjRr1oz58+fb1nft2pULFy7YNXaHEhGnetAcUYVn7969Wa63WCyyf/9+2bVrl1y6dMmuMVSoUMH286BBg+TNN98UEZGEhATx8PCQX3/9VURE4uPjpUePHjJr1iwREdm1a5d4eHjIvn37REQkJSVF3n///QKNLTk5uUC3d1VkZKQ0adLkul6T/n0qSD/++KP06NFDLBaLbNy4UUJCQrJsN2vWLPnf//6X5+1aLBZJTU0tqDDz5Pfff5dt27aJr69vtm2y299z586Ju7u7nDt3Ts6fPy/u7u5y/vx5ERH57LPPbL+XRUFW/2+xThiXv7+7+X2hox6aKArX1V84i8UiWItDFvgjN+n/AM6ZM0eeeOIJERH56KOPZODAgRnaHj58WOrXry8iIgMHDpSPP/441+3HxsbKkCFDxM/PT/z9/WXJkiWZ+v3mm29k8ODBIiIyePBgGT58uISEhMgzzzwjjRo1kgsXLtjaNm3aVE6fPi1nz56V3r17S+vWraV169by559/Zur78uXLtr6DgoJkzZo1IiLi7+8v5cqVk8DAQFm/fn2G15w+fVruvfdeCQgIkICAANmwYUOGeGNjY+W2226TFi1aiJ+fn3z//fciIhIXFyc9e/aUgIAA8fX1lUWLFomIyLhx48Tb21v8/f3l2WefzRTjY489Jl9++aVtuXnz5nLq1KlM7dq1aydhYWE5xhAWFibNmzeXgQMHio+Pjxw7dkx+/fVXadu2rbRo0ULuv/9+iY2NFRGR119/XVq3bi2+vr4ybNgwsVgsWX5+1yssLCzHRJHd/n755Zfy2GOPZdnu/PnzOW6zsBV0otCL2lWuEhISOH78uKPDIDU1ldWrV/Poo48C1tNOrVq1ytCmSZMmxMXFcenSJXbv3p2nU01vvPEGVapUYdeuXQB5OoUQHh7OX3/9hYuLC6mpqSxdupShQ4fy999/06hRI+rUqcNDDz3EM888wy233MKJEyfo3r07+/bty7Cd2bNnY4xh165d7N+/n27dunHw4EGWL1/OnXfeSWhoaKa+R48eTceOHVm6dCmpqanExcVleL5cuXIsXbqUypUrEx0dTdu2bbn77rv55ZdfuPnmm/nxxx8BiImJ4dy5cyxdupT9+/djjMlwWu+qiIiIDHOT169fn4iICOrWrWtbd+XKFY4ePUrjxo1zjAHg0KFDzJ8/n7Zt2xIdHc2bb77JqlWrqFChApMmTWLq1Km88sorjBo1ildeeQWAgQMHsmLFCu66664MsX3xxRe8++67mWJu2rQpS5Ysyfbzy0l2+5vderBe3ZeUlMS5c+dsszIWJ5ooVLbi4uK4cOEC8fHxAISGhtKgQQOqVatWqIPVly9fJigoiIiICLy9vbn99tsLdPurVq1i0aJFtuW8XNL7wAMP2ObK6NevHxMnTmTo0KEsWrSIfv362ba7d+9e22suXbpEXFxchrGEP//8kyeffBIALy8vGjVqxMGDB6lcuXK2fa9Zs4YFCxYA1vGbKlWqZHheRHjxxRdZv349pUqVIiIigjNnzuDv78+zzz7LuHHjuPPOO+nQoQMpKSmUK1eORx99lDvvvJM777wz133PSnR0dIZ7ZbKLAaBRo0a0bdsWgE2bNrF3717at28PWBNOu3btAFi7di2TJ08mISGB8+fP4+vrmylRDBgwgAEDBuQr5oJWu3ZtTp06VSwThRbbUVn6/vvv8fb25tKlSwC2AcDq1asX+hVN5cuXJzQ0lOPHjyMizJ49GwAfHx+2bduWoe3Ro0epWLEilStXxtfXN9Pz1yP9fl57Z3qFChVsP7dr147Dhw8TFRXF999/T+/evQHrVWGbNm0iNDSU0NBQIiIi7DbgnN4XX3xBVFQU27ZtIzQ0lDp16pCYmEjz5s3Zvn07/v7+vPTSS0ycOJHSpUuzefNm7r//flasWEGPHj0yba9evXqcPHnSthweHk69evUytClfvnyG9yi7GCDjeyci3H777bb3aO/evXz88cckJiYyYsQIlixZwq5duxg2bFiW1QG++OILgoKCMj3uv//+fL9/2e1vbu9DYmIi5cuXz3e/RZnTJYpWRxtArVmODqNYi4iIoH///oSHh1O2bFm8vb1p2LChw2ebc3NzY8aMGbz33nukpKQwYMAA/vzzT1atWgVYjzxGjx7N2LFjAXj++ef573//y8GDBwHrH+65c+dm2u7tt99uSz7w76mnOnXqsG/fPiwWi62gYVaMMdx3332MGTMGb29v2zfKbt26MXPmTFu7rE4jdejQgS+++AKAgwcPcuLECTw9PXN8H7p06cKcOXMA6+m4mJiYDM/HxMRQu3ZtypQpw9q1a22nDU+dOoWbmxsPP/wwzz//PNu3bycuLo6YmBh69uzJtGnT2LFjR6b+7r77bhYsWICIsGnTJqpUqZLhtBNYj8JSU1Ntf8yzi+Fabdu2ZcOGDRw+fBiwlqE/ePCgbTs1a9YkLi4u29NIAwYMsCWZ9I/8nnbKaX+7d+/Ob7/9xoULF7hw4QK//fYb3bt3B6wJ7/Tp07ZTb8VOfgc3HPVoVbqBSM2Z1z+6o3J05cqVDIOFU6ZMkRkzZsiePXscGJXVtVfz3HnnnbJgwQIREdm5c6d07NhRmjdvLk2aNJHXXnstw3788MMP0rJlS/Hy8hJvb295/vnnM20/NjZWBg0aJL6+vhIQECDffvutiFgHsD08PKRNmzYycuTIDIPZ33zzTYZtbNmyRQD57LPPbOuioqKkb9++4u/vL97e3jJ8+PBMfWc3mJ3TgOvp06fl7rvvFj8/PwkMDJS//vorw/sUFRUlbdu2FT8/PxkyZIh4eXlJWFiY/PLLL+Lv7y+BgYHSunVr2bJli5w6dUqCg4PF399f/Pz8MsR/lcVikREjRoiHh4f4+fnJli1bsozrkUcekZUrV+YYQ1b7tXr1amndurX4+/uLv7+/LFu2TEREJkyYIB4eHvJ///d/MmTIEHn11Vez7Pd69O/fX2666SYpXbq01KtXTz766CMRsV4kMWfOnFz39+OPP5YmTZpIkyZN5JNPPrGt37Jli/Tu3fuG4ysoJf6qJ00UBW/Dhg3i7+9v++ObXnaXxyp1rW3btsnDDz/s6DAcYvTo0bJq1SpHh2FT0InC6U49qYJz/vx5hg8fTvv27dm1axfvv/++9duDUvnQsmVLOnfuXKRvuLMXPz8/unQpkvcoF4hcp0ItanQq1BsnIixcuJBnn32WqKgoypQpw9ixY5kwYUKmwbh9+/bh7e3toEiVUvmR1f9be5UZV8XQmTNnePDBB1m7di0AHTt2ZM6cOZoMlFLZ0lNPJUzVqlWJjIykZs2afPbZZ6xdu1aThFIqR3pEUQKsXLmSli1bUqNGDVxdXfnmm2+oW7dusbwxSClV8PSIohiLjIzkwQcfpFu3bowbN8623s/PT5OEUirPNFEUQ6mpqbz//vt4eXmxaNEiypcvj6enp9Ne0aRlxh1bZnz//v20a9cOV1dXpkyZkm07EeG2226z3c1fFE2YMIEGDRrk+l69/fbbNG3aFE9PT3799Vfb+l9++QVPT0+aNm3KO++8Y1vfv39/Dh06ZLe4HS6/19U66lHzpgoi83dd/4XFJcS2bdskODjYVpm1V69etoqe+VEU7qPQMuN5Y68y42fOnJHNmzfLiy++KO+++2627VasWCFPP/30dW07JSXlRsO7Lhs3bpRTp07l+F7t2bNHAgICJDExUY4ePSoeHh6SkpIiKSkp4uHhIUeOHJGkpCQJCAiw3ZC6bt06+c9//lNYu5GrEn8fRaPo6vDsOkeHUSQdO3aMkJAQtmzZQr169fj222/54YcfCqysgFlt7PK4Hu3atbNV7Pzyyy9p37493bp1A6wlPmbNmmX7pjd58mQmTJiAl5cXYD0yeeKJJzJtMy4ujqFDh+Lv709AQADffvstkPEb+pIlSxgyZAgAQ4YM4fHHH6dNmzaMHTuWxo0bZzjKadasGWfOnCEqKoo+ffoQHBxMcHAwGzZsyNR3YmKire8WLVrYrkbr1q0bERERBAUF8ccff2R4zZkzZ7jvvvsIDAwkMDCQv/76K9P+dOnShZYtW+Lv78+yZcsAa3mMXr16ERgYiJ+fH4sXLwZg/Pjx+Pj4EBAQwHPPPZcpxtq1axMcHEyZMmWy/Eyu+uKLL7jnnntsy/feey+tWrXC19eXDz74wLa+YsWKPPvsswQGBrJx40a2bdtGx44dadWqFd27dycyMhKADz/8kODgYAIDA+nTpw8JCQk59p8Xbdu2zVR+5FrLli2jf//+uLq64u7uTtOmTdm8eTObN2+madOmeHh4ULZsWfr37297bzt06MCqVatISUm54RiLIrsOZhtjegDTARfgIxF555rnxwD/AVKAKOAREXF8PWsn1bhxY4YOHUqlSpV4/fXXqVSpkqNDKlBaZtyqsMuM59WGDRuYN2+ebfmTTz6hevXqXL58meDgYPr06UONGjWIj4+nTZs2vPfeeyQnJ9OxY0eWLVtGrVq1WLx4MRMmTOCTTz6hd+/eDBs2DICXXnqJjz/+2FZp96q1a9fyzDPPZIrFzc0tUwLNq4iICFt1W8hYTvzaMuN///03AKVKlaJp06bs2LEj0+9kcWC3RGGMcQFmA7cD4cAWY8xyEdmbrtk/QGsRSTDGPAFMBvrZK6bi5tixYzz55JM899xzdOzYEYAPPvjAbtVdpYtjxji0zHhGRbHMOFjv9E//5WTGjBm2YoonT57k0KFD1KhRAxcXF/r06QPAgQMH2L17t+0zTU1NtX3j3717Ny+99BIXL14kLi7OVoAvvc6dO2eZTB3haplxTRTXJwQ4LCJHAYwxi4B7ANv/HBFZm679JuDh3DYaVSkOBvoUcKjOJTk5malTp/L6669z+fJloqOj2bhxI0ChlwAvDFfLjCckJNC9e3dmz57N6NGj8fHxYf369RnaZlVmPDAwMF/95rfM+EsvvQT8W2a8XLly+eo/v9KX+C5TpgyNGzfOUGb8p59+4qWXXqJLly688sorbN68mdWrV7NkyRJmzZrFmjVr8tVv6dKlsVgslCpVinXr1rFq1So2btyIm5sbnTp1sr2H5cqVsyVZEcHX19f2+5vekCFD+P777wkMDOSzzz5j3bp1mdrY44gip3LiWma84NUDTqZbDk9bl51HgZ+zesIY85gxZqsxZuuJWhdg6m0FGKZz+fPPP2nRogXjx4/n8uXL9O/fn++++87RYRUKLTNuVdhlxvPK09OTo0eP2mKoVq0abm5u7N+/n02bNmX7mqioKFuiSE5OZs+ePQDExsZSt25dkpOTbe/Rta4eUVz7yG+SAGuZ8UWLFpGUlERYWBiHDh0iJCSE4OBgDh06RFhYGFeuXGHRokW2WfvA+vn5+fnlu98iLb+j4Lk9gPuxjktcXR4IzMqm7cNYjyhcc91uCZ0z+/z58/Loo4/armZq0qSJ7WofeypqVz2JaJnxwi4zHhkZKfXq1ZNKlSpJlSpVpF69ehITE5Op3cSJE+XDDz8UEZHExETp0aOHeHl5yT333CMdO3aUtWvXZojzqn/++Uc6dOggAQEB4uPjIx988IGIiLz//vvSuHFjCQ4OllGjRtne/xvx/PPPS7169cQYI/Xq1bOVLl+2bJm8/PLLtnZvvvmmeHh4SPPmzeWnn36yrf/xxx+lWbNm4uHhYbv6TsT6mQQHB99wfAWloK96sltRQGNMO+A1EemetvxCWmJ6+5p2XYGZQEcROZvrdktoUcBz587h5eVFTEwM48eP54UXXiiUw1wtCqjyKjIykkGDBrFy5UpHh1Lopk2bRuXKlW0XWjiaMxUF3AI0M8a4AxFAf+Ch9A2MMS2AeUCPvCSJkmb//v24u7vj6upKjRo1+OKLL2jYsKHtck+lipK6desybNgwLl26lONgfHFUtWpVBg4c6Ogw7MZuYxQikgKMAn4F9gFfi8geY8xEY8zVE3vvAhWBb4wxocaY5faKx5kkJCQwYcIEAgICmDx5sm19t27dNEmoIq1v374lLkkADB06lNKli2/pPLvumYj8BPx0zbpX0v3c1Z79O6NffvmFESNGEBYWBkB0dLSDI1JKlXROd2e2d3gd6LLY0WEUuFOnTtG3b1/uuOMOwsLC8Pf3Z8OGDUyfPt3RoSmlSjinO1Zyu1IWdkY5OowCdfDgQVq3bk1sbCxubm689tprPP3007mWTFBKqcLgdImiOGrWrBnBwcFUqFCBmTNn0qhRI0eHpJRSNk536qk4uHTpEk8//bTtRjBjDMuXL2f58uWaJPJp+fLlGco+l1Tr1q2jSpUqBAUF4eXllanI3/fff09AQADe3t74+/vz/fffZ3h+ypQpeHl5ERQURHBwsK1USFHyv//9r0jGddX69etp2bIlpUuXZsmSJdm227ZtG/7+/jRt2pTRo0fbyuOfP3+e22+/nWbNmnH77bfbbgBdsWIFr7zySrbbs6v83oDhqIdbozIioWeu596TIsNiscjXX38tdevWFUC6d+/u6JBylenGnZozMz6yM39XxnbPrLZvoNfBYrFIamqqw/q3V2lyEZG1a9dKr169RMRaht3T01P+/PNPEREJDQ2VJk2ayNGjR0VE5OjRo9KkSRPZsWOHiIjMmTNHunXrZruZLiYmJssb8G7EjZYVT05OFn9//+t6D+35fmclLCxMduzYIQMHDsx0Y2Z6wcHBsnHjRrFYLNKjRw/bjX3PP/+8vP322yIi8vbbb8vYsWNFxPp7GxQUJPHx8bnGUOLLjCe4JkNgbUeHcd2OHj1Kr1696Nu3L5GRkbRt25ZJkyY5Oqwi79ixY3h5eTFkyBCaN2/OgAEDWLVqFe3bt6dZs2Zs3rwZgM8++4xRo0YBWZfhPnbsGJ6engwaNAg/Pz9OnjzJV199hb+/P35+fhlmALy2/w4dOtCyZUtatmxpKw3Rv39/WxVWsNYlWrJkCampqTz//PMEBwcTEBBgq6a6bt06OnTowN13342Pj7VWWXZluD/++GOaN29OSEgIw4YNs+1XXsqWp1e+fHlbMUWwHi28+OKLuLu7A+Du7s4LL7zAu+++C8B///tf5syZY7u8tXLlygwePDjTdg8fPkzXrl0JDAykZcuWHDlyhHXr1mUoKDhq1Cg+++wzwFrVeNy4cbRs2ZJ3332XkJCQDO+vv78/QLblxtNbs2aN7ds6ZF+K/NpS8PHx8TzyyCOEhITQokULW3nw7D7fG9G4cWMCAgIoVSr7P6+RkZFcunSJtm3bYoxh0KBBtqO7ZcuW2d73wYMH29YbY+jUqRMrVqy44RivW34zjKMezlbCIykpSd566y0pV66cAFK1alWZO3euQ7/RXg9HH1GEhYWJi4uL7Ny5U1JTU6Vly5YydOhQsVgs8v3338s999wjIiKffvqpjBw5UkRE+vbtK9OmTRMR6zfYixcvSlhYmBhjZOPGjSIiEhERIQ0aNJCzZ89KcnKydO7cWZYuXZqp//j4eLl8+bKIiBw8eFBatWolIiLfffedDBo0SESsn3H9+vUlISFB5s2bJ2+88YaIWMtYtGrVSo4ePSpr164VNzc327d5EZFz586JiPWbv6+vr0RHR0tERIQ0atRIzp07J1euXJFbbrnFtl8PPvig/PHHHyIicvz4cfHy8soUb/ojivPnz0vLli0lMjJSRERatGghoaGhGdqHhoZKixYtJCYmRqpWrZqnzyQkJES+++47EbGWIImPj8/Qr4jIyJEj5dNPPxURkUaNGsmkSZNszwUGBtreh3feeUfeeOMNuXLlirRr107Onj0rIiKLFi2SoUOHZur7lVdekRkzZtiWo6OjbT9PmDDB9tzgwYOlV69etiOYF154QT7//HMREblw4YI0a9ZM4uLisv18r3XLLbdIYGBgpsfKlSuzfZ+yKvVy1ZYtW6RLly625fXr19vevypVqtjWWyyWDMsLFy6UUaNGZdvnVQV9RKGD2XZ28uRJJk6cSFJSEgMGDOC9996jTp06jg7Lqbi7u9u+dfr6+tKlSxeMMfj7+3Ps2LFM7bMqw33hwgUaNWpkm2dgy5YtdOrUiVq1agEwYMAA1q9fz7333pthW8nJyYwaNYrQ0FBcXFxs40p33HEHTz31FElJSfzyyy/ceuutlC9fnt9++42dO3fazk3HxMRw6NAhypYtS0hIiO3bPGRdhvv06dN07NiR6tWrA9Zy5lf7zEvZcoA//viDwMBADh06xNNPP81NN92Uj3c9a7GxsURERHDfffcB5Lky7tXS62C9KW/x4sWMHz+exYsXs3jx4hzLjacXGRmZoTRFTqXI05eC/+2331i+fLltKtfExEROnDjBzTffnOXne61rJ48qLMaYDFWMr5YyL2yaKOzgwoULVK1aFWMMTZo0Yfr06TRt2pQuXbo4OrQbFzUqb+0G+VkfBcDV1dX2c6lSpWzLpUqVuq4ZxdKXBs/O0qVLef311wH46KOPWLFiBXXq1GHHjh1YLBbbH8Zy5crRqVMnfv31VxYvXkz//v0B6xH6zJkzM82dsG7dugz951SGOzt5LVveoUMHVqxYQVhYGG3btqVv374EBQXh4+OTqez6tm3b8PX1pXLlylSsWJGjR4/i4eGR6/t0raslxq/KqSx7v379eOCBB+jduzfGGJo1a8auXbuyLTeeXvny5TNsO6dS5On7FBG+/fbbTJV5X3vttSw/32t16NCB2NjYTOunTJlC167Xf99wvXr1CA8Pty2nL1lep04dIiMjqVu3LpGRkdSu/e+pdkeVMne6MYqizGKx8Mknn9C0aVMWLlxoWz98+PDikSScRG5luAFCQkL4/fffiY6OJjU1la+++oqOHTty33332UpVt27dmpiYGOrWrUupUqX4/PPPSU1NtW2jX79+fPrpp/zxxx/06NEDgO7duzNnzhySk5MB6z0y8fHxmfrPrgx3cHAwv//+OxcuXCAlJcU2LSvkrWx5eu7u7owfP942Fvbcc8/x9ttv247Cjh07xn//+1/bLIAvvPACI0eO5NKlS4B1StVrry6qVKkS9evXt503T0pKIiEhgUaNGrF3716SkpK4ePEiq1evzjauJk2a4OLiwhtvvGE70sip3Hh63t7eHD582Lacl1LkYP1cZs6cabuy6J9//gHI8fNN748//siynHl+kgRY62JVrlyZTZs2ISIsWLDANo3s3Xffzfz58wGYP39+hullHVXK3OkShVtSGdhR9OoH7tmzh06dOvHoo49y/vx5fv45y6k1VCGYPn06a9euxd/fn1atWmU4XXNV3RMBOHMAABXiSURBVLp1eeedd+jcuTOBgYG0atUqw3/Iq0aMGMH8+fMJDAxk//79Gb6lduvWjd9//52uXbtStmxZAP7zn//g4+NDy5Yt8fPzY/jw4Vke9fTo0YOUlBS8vb0ZP3687ZRYvXr1ePHFFwkJCaF9+/Y0btzYNoPdjBkz2Lp1KwEBAfj4+GQ5t8a1Hn/8cdavX8+xY8cICgpi0qRJ3HXXXXh5eXHXXXcxefJkgoKCAHjiiSfo3LkzwcHB+Pn50aFDhywHZD///HNmzJhBQEAA//d//8fp06dp0KABffv2xc/Pj759+9KiRYsc4+rXrx8LFy6kb9++AJQtW5YlS5Ywbtw4AgMDCQoKynJg+Y477sgwWdUbb7xBmzZtaN++fY510F5++WWSk5MJCAjA19eXl19+Gcj5882vLVu2UL9+fb755huGDx+Or6/v/7d35tFR1Vke/9yOaICwCWrbbBETISRUkhaYBPqAQLMoS4vQskizHBMIDqYFBeEILQMcjDqAOIQJONDgAlGYEZi2WTUqtgSIbI1soREER4FmyWETBe788V5eKklVUglJVZbf55w6p+q933u/W7devft+2/c6+3J9DbBw4UISEhIICwvjwQcf5NFHHwWsHOabN28mPDycLVu2MHnyZOeYjIwMevfufds2lpjSDm4E6vXwHU2LHkT1M1euXNHJkyfrHXfcoYDee++9+t577+XLiVCZqQj5KKobly5dUlVrWmefPn2cgWODxeOPP65HjhwJtBl+54cfftCuXbv6VLbaT4+tSBw5coTIyEhSUlK4efMmSUlJHDp0iKFDh1bJlKQG/zB9+nRiYmKIiorigQceKDTAXt1JSUnxOHW2qvPtt98yZ86cgNRtBrNvg+bNmxMcHEx0dDRpaWlO94HBcDvkzswxeKZly5bFpoutirRr1y5gdVe6FsXVO38C1z0BqfvGjRssWLCAc+fOAdZsnA0bNpCVlWWChMFgqLJUukBxsMlp+HhQ8QXLmB07dtC+fXueffbZfKt4mzdvXqUTlhgMBkOlCxT+Jicnh3HjxhEXF8fu3btp1qyZx9kxBoPBUFUxgcILqkp6ejqtWrUiNTWVoKAgJk2axIEDB+jbt2+gzTMYDAa/YQKFF/bu3cuQIUP44Ycf6NChA7t27eLVV18tk3nWBoO/CAoKcmZQ9e3bl4sXLzr7vv76a7p27UrLli0JDw9n5syZzoI0gPXr19O2bVtat25NbGysszCvIrF7926efvrpQJvhlXPnztGlSxdCQkIccUdPeJMWV1WSk5MJCwvD5XKxa9cuwBKIzF3k6Q9MoHDDfVVmTEwM48eP56233mLr1q2O1lB1R+Tf8r28sXjxV/nKjR79v360smR4W41bFeqvWbMme/bsYf/+/dx9992kpqYCcO3aNfr168fkyZM5fPgwe/fu5csvv2ThwoWApaE0btw43n33XQ4cOEBWVhZhYWFlaltJ5Fe8MXv2bJKTk/1aZ0kIDg5m5syZxc5kS0lJoVu3bmRnZ9OtWzcnt8r69evJzs4mOzubxYsXM3bsWADuuece7r///mIVhMsKEyhsMjIyiIqKyrfqc+7cuSQkJBQpF2woX3yVGd+xYwfx8fHExsbSoUMHDh8+DFg34RdeeIGoqChcLpcjgeEufb1q1Sr27NlDXFwcLpeL/v37O090BfEkDZ6WlsbEiROdMu6S5++++y7t27cnJiaGMWPGOEEhJCSE559/nujoaLZt28aMGTOcFdGjR492nux37tyJy+UiJiaGiRMnOvIN3uTMiyI+Pt6RHF+xYgUdO3akR48eANSqVYsFCxY4N6jXXnuNl156yVntHBQU5Nyk3Ll8+TKjRo2iTZs2uFwuR3LEXahw9erVjBw5Eigs/x0aGpqvlRMeHs7p06d9klS/dOkS+/btc7SrvF0Dy5Yto1+/fnTt2tWR0nn99dcd37388svOOb1Jv5eW2rVr85vf/KZYfS5v0uJr165l+PDhiAhxcXFcvHjRWUPy+OOPFylbUqaUdqVeoF7NGjQo0yQ4p0+f1uHDhyuggCNbbbAouMITpud7eWPRoqx85RIT15Wqfl9lxnNycpwENZs3b9YnnnhCVVUXLlyoAwYMcPblSnsXlL5u06aNfvrpp6qqOm3aNP3jH//o0R5P0uBnzpzRBx980CnTq1cv3bp1qx44cED79OmjP/30k6qqjh07VpcvX66qqoC+//77hc6rqjps2DBdt87yV2RkpH755Zeqqvriiy9qZGSkqqpXOfOC1K5dW1UtufWBAwfq+vXrVVV1/Pjx+sYbbxQqX79+fc3JyfEoSe6JSZMm5fPV+fPn89Wrqrpq1SodMWKEqhaW/05OTtalS5eqqmpmZqYjve2LpPonn3zi/M6q3q+BP//5z9q4cWPHxxs3btTExEQngVXv3r31s88+U1XPv29BnnvuOY+S47nJhjzhLoPvCW/S4r1793b8oKratWtX3blzp6qqnjp1SqOiojyer9rLjN9zKQTeOQBzu97WeW7dusWSJUt48cUXuXDhAnfddRdTp07N92RoqBj4IjOek5PDiBEjyM7ORkQcUb4tW7aQlJTkTGHOle+GPOnrnJwcLl68SOfOnQHrie73v/+9R1s8SYPHxcXRokULMjMzCQ8P59ChQ3Ts2JHU1FS++uorZ6HUtWvXHCXQoKAgBgwY4Jw3IyOD1157jatXr3L+/HkiIyMdxdL4+HgAhg4d6iSt8SZn7i5jnltnbvKiiIgIR8a7rNiyZQvp6enO5wYNGhR7jLv896BBg5gxYwajRo0iPT3d+U18kVT//vvvHZl48H4NAHTv3t357Tdt2sSmTZscParLly+TnZ1Np06dPP6+DRs2zGf/vHnzfHNOKSkoLe4Nf0qOV7pAURZ88803DBs2zBEd69GjB6mpqWXeB1sVUX25+ELA6NEPM3r0w2VSpy8y49OmTaNLly58+OGHHD9+nEceeaTY8xY3MeHkyZPODLekpCRatWrlVRp88ODBfPDBB7Rq1Yr+/fsjIqgqI0aM4JVXXil07uDgYOdm+eOPP/LMM8+QlZVF06ZNmT59erGS46qe5cwLkjtGcfXqVXr27ElqairJycm0bt06XzcrWFkYQ0JCqFu3LpGRkYUkyUuC+42uKMnx+Ph4jh49ytmzZ1mzZg1Tp04FfJNULyg5XtQ1UFByfMqUKYwZMybf+XyVfh8/fjwZGRmFtg8ePDifgF9J8CYt3rhxY06ePOmUc5cj96fkeLXsfK9bty5Hjhzhl7/8Jenp6WzYsMEEiUpOTk6O8wfKTcEJ1pPkokWLnIBy/vz5QsfWq1ePBg0aOMlp3nnnHTp37kzTpk0dOemkpCSv0uAA/fv3Z+3ataxcudLJTdGtWzdWr17NmTNnnLpPnDhRqP7cm1GjRo24fPmy00qoX78+derUYfv27QD5ntx9lTPPpVatWrz55pvMmTOHGzdu8NRTT/HFF1+wZcsWwGp5JCcnM2nSJAAmTpzI7NmznUQ+t27d8qhW2717d2eAHHDGdu677z4OHjzIrVu3nCd0T4gI/fv3Z8KECURERDhP775IqheUHPd2DRSkZ8+eLF26lMuXLwPw3XffcebMmSJ/X3fmzZvnUXK8tEECvEuL9+vXj7fffhtVJTMzk3r16jkJnfwpOV7pAsWJRudhziMlPm7jxo1cv34dgIYNG7Ju3ToOHTrEoEGDjIBfFWDSpElMmTKF2NjYfDNbEhISaNasGS6Xi+joaFasWOHx+OXLlzNx4kRcLhd79uzhT3/6U6Ey3qTBwepyiYiI4MSJE05O6NatWzNr1ix69OiBy+Wie/fuHsXs6tevT2JiIlFRUfTs2TOfps+SJUtITEwkJiaGK1euOJLjvsqZuxMbG4vL5WLlypXUrFmTtWvXMmvWLFq2bEmbNm1o166dMwjvcrl44403GDJkCBEREURFRXHs2LFC55w6dSoXLlwgKiqK6Oho50k7JSWFPn360KFDB4+Z6tzJlRx3z4Lni6R6q1atyMnJcRIKebsGCtKjRw+GDh1KfHw8bdq0YeDAgVy6dKnI3/d2CA0NZcKECSxbtowmTZo4XWoJCQlkZWUB3qXFH3vsMVq0aEFYWBiJiYnOrDTwr+S4qNu86cqAtBTVw77bfPLkSZKTk1mzZg0zZ850mrYG3zh48GC+1JMG/+LeL5+rmjp//vwAW1VxmDdvHnXq1CEhISHQpvidTp06sXbtWo/jQp7+tyLylaq2LU1dla5F4Ss3btxg7ty5REREsGbNGkJCQvINZBoMlYGPPvrIWTC3detW86BTgLFjx+Ybw6ounD17lgkTJvg0eaAsqJItiszMTJKSkti7dy8AAwYMYP78+U7/pcF3TIvCYKh8lHWLosrNetq+fTsdOnRAVQkNDWXBggWBSR1YhVBVM45jMFQSyuPhv8oFivbt29OzZ09iY2OZOnUqtWrVCrRJlZrg4GDOnTtHw4YNTbAwGCo4qsq5c+eKXQleUip911N2djbjx49n7ty5PPTQQ4A1lc/IbpQNP//8M6dOnSp2Xr/BYKgYBAcH06RJE2rUqJFve7Xqenr4WFO4ZwHXTyWSkpLCK6+8wvXr1wkODnbmn5sgUXbUqFGj0Gpfg8FQvSjXO6qI9BKRwyJyVEQKrUYRkbtE5H17/3YRCfXlvB//dBiXy8X06dO5fv06o0aN8jjP2mAwGAy3T7l1PYlIEHAE6A6cAnYCQ1T1gFuZZwCXqiaJyGCgv6oWmee04S9q63m9ClgrM9PS0ujUqVO5fAeDwWCoKlTUdRTtgaOqekxVfwLSgYI5RH8HLLffrwa6STEjphf0KsHUYPbs2ezZs8cECYPBYChnyrNFMRDopaoJ9uc/AP+iquPcyuy3y5yyP//DLvPPAucaDYy2P0YB+8vF6MpHI+CfxZaqHhhf5GF8kYfxRR4tVbVOaQ6sFIPZqroYWAwgIlmlbT5VNYwv8jC+yMP4Ig/jizxEJKu0x5Zn19N3QFO3z03sbR7LiMgdQD3gXDnaZDAYDIYSUp6BYicQLiIPiMidwGBgXYEy64AR9vuBwCda2RZ2GAwGQxWn3LqeVPWGiIwDNgJBwFJV/VpEZmCl5FsHLAHeEZGjwHmsYFIct5/ItupgfJGH8UUexhd5GF/kUWpfVLqV2QaDwWDwL2YJs8FgMBiKxAQKg8FgMBRJhQ0U5SX/URnxwRcTROSAiOwTkY9FpHkg7PQHxfnCrdwAEVERqbJTI33xhYg8aV8bX4uI5zywVQAf/iPNRCRDRHbb/5PHAmFneSMiS0XkjL1GzdN+EZE3bT/tE5Ff+3RiVa1wL6zB738ALYA7gb1A6wJlngHS7PeDgfcDbXcAfdEFqGW/H1udfWGXqwN8DmQCbQNtdwCvi3BgN9DA/nxvoO0OoC8WA2Pt962B44G2u5x80Qn4NbDfy/7HgPWAAHHAdl/OW1FbFOUi/1FJKdYXqpqhagtgWTfHJn620V/4cl0AzAReBaqyNrovvkgEUlX1AoCqnvGzjf7CF18oUNd+Xw/4Pz/a5zdU9XOsGaTe+B3wtlpkAvVF5P7izltRA0Vj4KTb51P2No9lVPUGkAM09It1/sUXX7jzNNYTQ1WkWF/YTemmqvqRPw0LAL5cFw8BD4nI30QkU0R6+c06/+KLL6YDw0TkFPBX4Fn/mFbhKOn9BKgkEh4G3xCRYUBboHOgbQkEIvILYC4wMsCmVBTuwOp+egSrlfm5iLRR1YsBtSowDAGWqeocEYnHWr8Vpaq3Am1YZaCitiiM/EcevvgCEfkt8BLQT1Wv+8k2f1OcL+pgiUZ+KiLHsfpg11XRAW1frotTwDpV/VlVv8GS/Q/3k33+xBdfPA18AKCq24BgLMHA6oZP95OCVNRAYeQ/8ijWFyISCyzCChJVtR8aivGFquaoaiNVDVXVUKzxmn6qWmoxtAqML/+RNVitCUSkEVZX1DF/GuknfPHFt0A3ABGJwAoUZ/1qZcVgHTDcnv0UB+So6vfFHVQhu560/OQ/Kh0++uJ1IARYZY/nf6uq/QJmdDnhoy+qBT76YiPQQ0QOADeBiapa5VrdPvrieeAtERmPNbA9sio+WIrISqyHg0b2eMzLQA0AVU3DGp95DDgKXAVG+XTeKugrg8FgMJQhFbXryWAwGAwVBBMoDAaDwVAkJlAYDAaDoUhMoDAYDAZDkZhAYTAYDIYiMYHCUOEQkZsissftFVpE2VBvSpklrPNTW310ry150bIU50gSkeH2+5Ei8iu3ff8lIq3L2M6dIhLjwzHPiUit263bUH0xgcJQEbmmqjFur+N+qvcpVY3GEpt8vaQHq2qaqr5tfxwJ/MptX4KqHigTK/PsXIhvdj4HmEBhKDUmUBgqBXbLYauI7LJfHTyUiRSRHXYrZJ+IhNvbh7ltXyQiQcVU9zkQZh/bzc5h8Hdb6/8ue3uK5OUA+Xd723QReUFEBmJpbr1n11nTbgm0tVsdzs3dbnksKKWd23ATdBOR/xSRLLFyT/ybvS0ZK2BliEiGva2HiGyz/bhKREKKqcdQzTGBwlARqenW7fShve0M0F1Vfw0MAt70cFwSMF9VY7Bu1KdsuYZBQEd7+03gqWLq7wv8XUSCgWXAIFVtg6VkMFZEGgL9gUhVdQGz3A9W1dVAFtaTf4yqXnPb/d/2sbkMAtJLaWcvLJmOXF5S1baAC+gsIi5VfRNLUruLqnaxpTymAr+1fZkFTCimHkM1p0JKeBiqPdfsm6U7NYAFdp/8TSzdooJsA14SkSbA/6hqtoh0Ax4GdtryJjWxgo4n3hORa8BxLBnqlsA3qnrE3r8c+FdgAVauiyUi8hfgL75+MVU9KyLHbJ2dbKAV8Df7vCWx804s2RZ3Pz0pIqOx/tf3YyXo2Vfg2Dh7+9/seu7E8pvB4BUTKAyVhfHAaSAaqyVcKCmRqq4Qke1Ab+CvIjIGK5PXclWd4kMdT7kLCIrI3Z4K2dpC7bFE5gYC44CuJfgu6cCTwCHgQ1VVse7aPtsJfIU1PvEfwBMi8gDwAtBOVS+IyDIs4buCCLBZVYeUwF5DNcd0PRkqC/WA7+38AX/AEn/Lh4i0AI7Z3S1rsbpgPgYGisi9dpm7xfec4oeBUBEJsz//AfjM7tOvp6p/xQpg0R6OvYQle+6JD7EyjQ3BChqU1E5b0G4aECcirbCyt10BckTkPuBRL7ZkAh1zv5OI1BYRT60zg8HBBApDZWEhMEJE9mJ111zxUOZJYL+I7MHKS/G2PdNoKrBJRPYBm7G6ZYpFVX/EUtdcJSJ/B24BaVg33b/Y5/sCz338y4C03MHsAue9ABwEmqvqDntbie20xz7mYKnC7sXKj30IWIHVnZXLYmCDiGSo6lmsGVkr7Xq2YfnTYPCKUY81GAwGQ5GYFoXBYDAYisQECoPBYDAUiQkUBoPBYCgSEygMBoPBUCQmUBgMBoOhSEygMBgMBkORmEBhMBgMhiL5f4HHOl75ppSOAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2625,10 +3756,55 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max depth: 0\n", + "Error: 0.2701710840726194\n", + "Bias^2: 0.2701710840726194\n", + "Var: 0.0\n", + "0.2701710840726194 >= 0.2701710840726194 + 0.0 = 0.2701710840726194\n", + "Max depth: 1\n", + "Error: 0.49653427902459785\n", + "Bias^2: 0.25822660764710104\n", + "Var: 0.2383076399564743\n", + "0.49653427902459785 >= 0.25822660764710104 + 0.2383076399564743 = 0.49653424760357534\n", + "Max depth: 2\n", + "Error: 0.5125283419757325\n", + "Bias^2: 0.2578324946286893\n", + "Var: 0.2546958327293396\n", + "0.5125283419757325 >= 0.2578324946286893 + 0.2546958327293396 = 0.5125283273580289\n", + "Max depth: 3\n", + "Error: 0.5137739896856959\n", + "Bias^2: 0.2576292851847527\n", + "Var: 0.2561447024345398\n", + "0.5137739896856959 >= 0.2576292851847527 + 0.2561447024345398 = 0.5137739876192925\n", + "Max depth: 4\n", + "Error: 0.5173774791348771\n", + "Bias^2: 0.25748329306194573\n", + "Var: 0.2598941922187805\n", + "0.5173774791348771 >= 0.25748329306194573 + 0.2598941922187805 = 0.5173774852807262\n", + "Max depth: 5\n", + "Error: 0.5191783046782572\n", + "Bias^2: 0.2574361743409599\n", + "Var: 0.26174214482307434\n", + "0.5191783046782572 >= 0.2574361743409599 + 0.26174214482307434 = 0.5191783191640342\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2688,10 +3864,77 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Random Forests and scaled data: 1.00\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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+ "text/plain": [ + "
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  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
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  • Multiclass problems and regression with SVMs
    • @@ -172,7 +175,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Nov 9, 2018

    +

    Nov 8, 2019


    @@ -196,7 +199,7 @@ MathJax.Hub.Config({

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    - © 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license + © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
    diff --git a/doc/pub/svm/html/._svm-bs001.html b/doc/pub/svm/html/._svm-bs001.html index a6a050f2f..0fcddc672 100644 --- a/doc/pub/svm/html/._svm-bs001.html +++ b/doc/pub/svm/html/._svm-bs001.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
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  • Multiclass problems and regression with SVMs
    • @@ -181,9 +184,6 @@ below. We distinguish also between linear and non-linear approaches. The latter are the most frequent ones since it is rather unlikely that we can separate classes easily by say straight lines. -

    -Note: several figures are missing. They will be added shortly. To run the codes, use the jupyter notebook -

    @@ -201,7 +201,7 @@ unlikely that we can separate classes easily by say straight lines.

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    • diff --git a/doc/pub/svm/html/._svm-bs002.html b/doc/pub/svm/html/._svm-bs002.html index 674c7129a..958e16516 100644 --- a/doc/pub/svm/html/._svm-bs002.html +++ b/doc/pub/svm/html/._svm-bs002.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
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  • Multiclass problems and regression with SVMs
    • @@ -255,7 +258,7 @@ plt.show()
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  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -196,7 +199,7 @@ $$
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  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
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  • Code Example
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  • Multiclass problems and regression with SVMs
    • @@ -212,7 +215,7 @@ When we try to separate hyperplanes, if it exists, we can use it to construct a
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  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -198,7 +201,7 @@ for our data sample.
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    • diff --git a/doc/pub/svm/html/._svm-bs006.html b/doc/pub/svm/html/._svm-bs006.html index 1b1d6b58a..c25c3501b 100644 --- a/doc/pub/svm/html/._svm-bs006.html +++ b/doc/pub/svm/html/._svm-bs006.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -194,7 +197,7 @@ $$
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    • diff --git a/doc/pub/svm/html/._svm-bs007.html b/doc/pub/svm/html/._svm-bs007.html index 87aeac4a5..34a9d0cb7 100644 --- a/doc/pub/svm/html/._svm-bs007.html +++ b/doc/pub/svm/html/._svm-bs007.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -198,7 +201,7 @@ $$
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    • diff --git a/doc/pub/svm/html/._svm-bs008.html b/doc/pub/svm/html/._svm-bs008.html index f2920efe9..07c47d633 100644 --- a/doc/pub/svm/html/._svm-bs008.html +++ b/doc/pub/svm/html/._svm-bs008.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -202,7 +205,7 @@ at all.
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    • diff --git a/doc/pub/svm/html/._svm-bs009.html b/doc/pub/svm/html/._svm-bs009.html index b0f8d21e6..96cee48e8 100644 --- a/doc/pub/svm/html/._svm-bs009.html +++ b/doc/pub/svm/html/._svm-bs009.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -214,7 +217,7 @@ We have thus defined our margin as the invers of the norm of \( \boldsymbol{w} \
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    • diff --git a/doc/pub/svm/html/._svm-bs010.html b/doc/pub/svm/html/._svm-bs010.html index 2e992831e..2c633a9a7 100644 --- a/doc/pub/svm/html/._svm-bs010.html +++ b/doc/pub/svm/html/._svm-bs010.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -226,7 +229,7 @@ Then \( dz \) is no longer arbitrary.
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    • diff --git a/doc/pub/svm/html/._svm-bs011.html b/doc/pub/svm/html/._svm-bs011.html index 343548f81..a124cbc04 100644 --- a/doc/pub/svm/html/._svm-bs011.html +++ b/doc/pub/svm/html/._svm-bs011.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -219,7 +222,7 @@ $$
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    • diff --git a/doc/pub/svm/html/._svm-bs012.html b/doc/pub/svm/html/._svm-bs012.html index 548a25426..1ee8419fa 100644 --- a/doc/pub/svm/html/._svm-bs012.html +++ b/doc/pub/svm/html/._svm-bs012.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -217,7 +220,7 @@ When \( \lambda_i > 0 \), the vectors \( \boldsymbol{x}_i \) are called support
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    • diff --git a/doc/pub/svm/html/._svm-bs013.html b/doc/pub/svm/html/._svm-bs013.html index b29599a4d..12dcf424f 100644 --- a/doc/pub/svm/html/._svm-bs013.html +++ b/doc/pub/svm/html/._svm-bs013.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -200,7 +203,7 @@ subject to \( \boldsymbol{y}^T\boldsymbol{\lambda}=0 \). Here we defined the vec
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    • diff --git a/doc/pub/svm/html/._svm-bs014.html b/doc/pub/svm/html/._svm-bs014.html index 343c13ccf..51f62274b 100644 --- a/doc/pub/svm/html/._svm-bs014.html +++ b/doc/pub/svm/html/._svm-bs014.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -210,7 +213,7 @@ Below we discuss how to find the optimal values of \( \lambda_i \). Before we pr
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    • diff --git a/doc/pub/svm/html/._svm-bs015.html b/doc/pub/svm/html/._svm-bs015.html index cf20fb265..7c70e4b72 100644 --- a/doc/pub/svm/html/._svm-bs015.html +++ b/doc/pub/svm/html/._svm-bs015.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -211,7 +214,7 @@ misclassifications.
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    • diff --git a/doc/pub/svm/html/._svm-bs016.html b/doc/pub/svm/html/._svm-bs016.html index 225fafb93..ef66d304a 100644 --- a/doc/pub/svm/html/._svm-bs016.html +++ b/doc/pub/svm/html/._svm-bs016.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -230,7 +233,7 @@ $$
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    • diff --git a/doc/pub/svm/html/._svm-bs017.html b/doc/pub/svm/html/._svm-bs017.html index 407aa7783..062a5d829 100644 --- a/doc/pub/svm/html/._svm-bs017.html +++ b/doc/pub/svm/html/._svm-bs017.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -248,6 +251,8 @@ plt.show()
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    • diff --git a/doc/pub/svm/html/._svm-bs018.html b/doc/pub/svm/html/._svm-bs018.html index 3a49a0b7d..d243909ae 100644 --- a/doc/pub/svm/html/._svm-bs018.html +++ b/doc/pub/svm/html/._svm-bs018.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -216,6 +219,7 @@ the trouble of performing the transformation
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    • diff --git a/doc/pub/svm/html/._svm-bs019.html b/doc/pub/svm/html/._svm-bs019.html index d8e7741ac..1132e1efd 100644 --- a/doc/pub/svm/html/._svm-bs019.html +++ b/doc/pub/svm/html/._svm-bs019.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -209,6 +212,7 @@ Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.
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    • diff --git a/doc/pub/svm/html/._svm-bs020.html b/doc/pub/svm/html/._svm-bs020.html index 5209535ba..5c43e031e 100644 --- a/doc/pub/svm/html/._svm-bs020.html +++ b/doc/pub/svm/html/._svm-bs020.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -203,6 +206,7 @@ well in practice.
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    • diff --git a/doc/pub/svm/html/._svm-bs021.html b/doc/pub/svm/html/._svm-bs021.html index d80b9542a..bd33102cc 100644 --- a/doc/pub/svm/html/._svm-bs021.html +++ b/doc/pub/svm/html/._svm-bs021.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -367,6 +370,7 @@ plt.show()
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    • diff --git a/doc/pub/svm/html/._svm-bs022.html b/doc/pub/svm/html/._svm-bs022.html index 39cff21f9..920afa4a8 100644 --- a/doc/pub/svm/html/._svm-bs022.html +++ b/doc/pub/svm/html/._svm-bs022.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -195,6 +198,7 @@ Convex optimization problems play a central role in applied mathematics and we r
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    • diff --git a/doc/pub/svm/html/._svm-bs023.html b/doc/pub/svm/html/._svm-bs023.html index 0952dce43..33c2fb84f 100644 --- a/doc/pub/svm/html/._svm-bs023.html +++ b/doc/pub/svm/html/._svm-bs023.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -195,6 +198,7 @@ This will make our life much easier. You don't need t write your own optimizer.
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    • diff --git a/doc/pub/svm/html/._svm-bs024.html b/doc/pub/svm/html/._svm-bs024.html index e01e79cd7..329bc43da 100644 --- a/doc/pub/svm/html/._svm-bs024.html +++ b/doc/pub/svm/html/._svm-bs024.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -236,6 +239,7 @@ sol[’primal objective’]
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    • diff --git a/doc/pub/svm/html/._svm-bs025.html b/doc/pub/svm/html/._svm-bs025.html index e8e6d6e15..f908e5a11 100644 --- a/doc/pub/svm/html/._svm-bs025.html +++ b/doc/pub/svm/html/._svm-bs025.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -190,6 +193,7 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb
  • 25
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    • +
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  • »
    • diff --git a/doc/pub/svm/html/._svm-bs026.html b/doc/pub/svm/html/._svm-bs026.html index 2c909a823..0385395bb 100644 --- a/doc/pub/svm/html/._svm-bs026.html +++ b/doc/pub/svm/html/._svm-bs026.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Data Analysis and Machine Learning: Support Vector Machines @@ -71,10 +72,11 @@ Automatically generated HTML file from DocOnce source ('How do we solve these problems?', 2, None, '___sec22'), ('A simple example', 2, None, '___sec23'), ('Back to the more realistic cases', 2, None, '___sec24'), + ('Code Example', 2, None, '___sec25'), ('Multiclass problems and regression with SVMs', 2, None, - '___sec25')]} + '___sec26')]} end of tocinfo --> @@ -137,7 +139,8 @@ MathJax.Hub.Config({
  • How do we solve these problems?
  • A simple example
  • Back to the more realistic cases
    • -
  • Multiclass problems and regression with SVMs
    • +
  • Code Example
    • +
  • Multiclass problems and regression with SVMs
    • @@ -153,9 +156,36 @@ MathJax.Hub.Config({ -

    Multiclass problems and regression with SVMs

    -This material will be added later. +

    Code Example

    +

    + +

    import numpy as np
+from scipy.optimize import minimize
+def rosen(x):
+    return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)
+def rosen_der(x):
+    xm = x[1:-1]
+    xm_m1 = x[:-2]
+    xm_p1 = x[2:]
+    der = np.zeros_like(x)
+    der[1:-1] = 200*(xm-xm_m1**2) - 400*(xm_p1 - xm**2)*xm - 2*(1-xm)
+    der[0] = -400*x[0]*(x[1]-x[0]**2) - 2*(1-x[0])
+    der[-1] = 200*(x[-1]-x[-2]**2)
+    return der
+
+
+
+x0 = np.array([1.2, 0.1, 0.8, 1.9, 1.2])
+print(x0)
+res = minimize(rosen, x0, method='nelder-mead',options={'xtol': 1e-8, 'disp': True})
+print(x0)
+
+
+res2 = minimize(rosen, x0, method='BFGS', jac=rosen_der, options={'disp': True})
+print(x0)
+
    +

      @@ -171,6 +201,8 @@ This material will be added later.
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      • +
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      • +
    • »
    diff --git a/doc/pub/svm/html/reveal.js/.gitignore b/doc/pub/svm/html/reveal.js/.gitignore index e7b4f216a..a5df3133d 100644 --- a/doc/pub/svm/html/reveal.js/.gitignore +++ b/doc/pub/svm/html/reveal.js/.gitignore @@ -1,8 +1,3 @@ -.idea/ -*.iml -*.iws -*.eml -out/ .DS_Store .svn log/*.log @@ -10,4 +5,4 @@ tmp/** node_modules/ .sass-cache css/reveal.min.css -js/reveal.min.js \ No newline at end of file +js/reveal.min.js diff --git a/doc/pub/svm/html/reveal.js/.travis.yml b/doc/pub/svm/html/reveal.js/.travis.yml index ec3b27d5d..165d9ae9f 100644 --- a/doc/pub/svm/html/reveal.js/.travis.yml +++ b/doc/pub/svm/html/reveal.js/.travis.yml @@ -1,7 +1,5 @@ language: node_js node_js: - - 4 + - 0.10 before_script: - - npm install -g grunt-cli -after_script: - - grunt retire + - npm install -g grunt-cli \ No newline at end of file diff --git a/doc/pub/svm/html/reveal.js/LICENSE b/doc/pub/svm/html/reveal.js/LICENSE index c3e6e5fd6..09623076f 100644 --- a/doc/pub/svm/html/reveal.js/LICENSE +++ b/doc/pub/svm/html/reveal.js/LICENSE @@ -1,4 +1,4 @@ -Copyright (C) 2017 Hakim El Hattab, http://hakim.se, and reveal.js contributors +Copyright (C) 2015 Hakim El Hattab, http://hakim.se Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/doc/pub/svm/html/reveal.js/README.md b/doc/pub/svm/html/reveal.js/README.md index f2ab6ca88..573b19597 100644 --- a/doc/pub/svm/html/reveal.js/README.md +++ b/doc/pub/svm/html/reveal.js/README.md @@ -1,58 +1,12 @@ -# reveal.js [![Build Status](https://travis-ci.org/hakimel/reveal.js.svg?branch=master)](https://travis-ci.org/hakimel/reveal.js) Slides +# reveal.js [![Build Status](https://travis-ci.org/hakimel/reveal.js.svg?branch=master)](https://travis-ci.org/hakimel/reveal.js) -A framework for easily creating beautiful presentations using HTML. [Check out the live demo](http://revealjs.com/). +A framework for easily creating beautiful presentations using HTML. [Check out the live demo](http://lab.hakim.se/reveal-js/). -reveal.js comes with a broad range of features including [nested slides](https://github.com/hakimel/reveal.js#markup), [Markdown contents](https://github.com/hakimel/reveal.js#markdown), [PDF export](https://github.com/hakimel/reveal.js#pdf-export), [speaker notes](https://github.com/hakimel/reveal.js#speaker-notes) and a [JavaScript API](https://github.com/hakimel/reveal.js#api). There's also a fully featured visual editor and platform for sharing reveal.js presentations at [slides.com](https://slides.com?ref=github). +reveal.js comes with a broad range of features including [nested slides](https://github.com/hakimel/reveal.js#markup), [Markdown contents](https://github.com/hakimel/reveal.js#markdown), [PDF export](https://github.com/hakimel/reveal.js#pdf-export), [speaker notes](https://github.com/hakimel/reveal.js#speaker-notes) and a [JavaScript API](https://github.com/hakimel/reveal.js#api). It's best viewed in a modern browser but [fallbacks](https://github.com/hakimel/reveal.js/wiki/Browser-Support) are available to make sure your presentation can still be viewed elsewhere. -## Table of contents -- [Online Editor](#online-editor) -- [Instructions](#instructions) - - [Markup](#markup) - - [Markdown](#markdown) - - [Element Attributes](#element-attributes) - - [Slide Attributes](#slide-attributes) -- [Configuration](#configuration) -- [Presentation Size](#presentation-size) -- [Dependencies](#dependencies) -- [Ready Event](#ready-event) -- [Auto-sliding](#auto-sliding) -- [Keyboard Bindings](#keyboard-bindings) -- [Touch Navigation](#touch-navigation) -- [Lazy Loading](#lazy-loading) -- [API](#api) - - [Slide Changed Event](#slide-changed-event) - - [Presentation State](#presentation-state) - - [Slide States](#slide-states) - - [Slide Backgrounds](#slide-backgrounds) - - [Parallax Background](#parallax-background) - - [Slide Transitions](#slide-transitions) - - [Internal links](#internal-links) - - [Fragments](#fragments) - - [Fragment events](#fragment-events) - - [Code syntax highlighting](#code-syntax-highlighting) - - [Slide number](#slide-number) - - [Overview mode](#overview-mode) - - [Fullscreen mode](#fullscreen-mode) - - [Embedded media](#embedded-media) - - [Stretching elements](#stretching-elements) - - [postMessage API](#postmessage-api) -- [PDF Export](#pdf-export) -- [Theming](#theming) -- [Speaker Notes](#speaker-notes) - - [Share and Print Speaker Notes](#share-and-print-speaker-notes) - - [Server Side Speaker Notes](#server-side-speaker-notes) -- [Multiplexing](#multiplexing) - - [Master presentation](#master-presentation) - - [Client presentation](#client-presentation) - - [Socket.io server](#socketio-server) -- [MathJax](#mathjax) -- [Installation](#installation) - - [Basic setup](#basic-setup) - - [Full setup](#full-setup) - - [Folder Structure](#folder-structure) -- [License](#license) -#### More reading +#### More reading: +- [Installation](#installation): Step-by-step instructions for getting reveal.js running on your computer. - [Changelog](https://github.com/hakimel/reveal.js/releases): Up-to-date version history. - [Examples](https://github.com/hakimel/reveal.js/wiki/Example-Presentations): Presentations created with reveal.js, add your own! - [Browser Support](https://github.com/hakimel/reveal.js/wiki/Browser-Support): Explanation of browser support and fallbacks. @@ -60,36 +14,14 @@ reveal.js comes with a broad range of features including [nested slides](https:/ ## Online Editor -Presentations are written using HTML or Markdown but there's also an online editor for those of you who prefer a graphical interface. Give it a try at [https://slides.com](https://slides.com?ref=github). +Presentations are written using HTML or Markdown but there's also an online editor for those of you who prefer a graphical interface. Give it a try at [http://slides.com](http://slides.com). ## Instructions ### Markup -Here's a barebones example of a fully working reveal.js presentation: -```html - - - - - - -
    -
    -
    Slide 1
    -
    Slide 2
    -
    -
    - - - - -``` - -The presentation markup hierarchy needs to be `.reveal > .slides > section` where the `section` represents one slide and can be repeated indefinitely. If you place multiple `section` elements inside of another `section` they will be shown as vertical slides. The first of the vertical slides is the "root" of the others (at the top), and will be included in the horizontal sequence. For example: +Markup hierarchy needs to be ``
    `` where the ``
    `` represents one slide and can be repeated indefinitely. If you place multiple ``
    ``'s inside of another ``
    `` they will be shown as vertical slides. The first of the vertical slides is the "root" of the others (at the top), and it will be included in the horizontal sequence. For example: ```html
    @@ -105,36 +37,32 @@ The presentation markup hierarchy needs to be `.reveal > .slides > section` wher ### Markdown -It's possible to write your slides using Markdown. To enable Markdown, add the `data-markdown` attribute to your `
    ` elements and wrap the contents in a ` +
    ``` #### External Markdown -You can write your content as a separate file and have reveal.js load it at runtime. Note the separator arguments which determine how slides are delimited in the external file: the `data-separator` attribute defines a regular expression for horizontal slides (defaults to `^\r?\n---\r?\n$`, a newline-bounded horizontal rule) and `data-separator-vertical` defines vertical slides (disabled by default). The `data-separator-notes` attribute is a regular expression for specifying the beginning of the current slide's speaker notes (defaults to `note:`). The `data-charset` attribute is optional and specifies which charset to use when loading the external file. +You can write your content as a separate file and have reveal.js load it at runtime. Note the separator arguments which determine how slides are delimited in the external file. The ```data-charset``` attribute is optional and specifies which charset to use when loading the external file. -When used locally, this feature requires that reveal.js [runs from a local web server](#full-setup). The following example customises all available options: +When used locally, this feature requires that reveal.js [runs from a local web server](#full-setup). ```html -
    -
    ``` @@ -164,19 +92,6 @@ Special syntax (in html comment) is available for adding attributes to the slide
    ``` -#### Configuring *marked* - -We use [marked](https://github.com/chjj/marked) to parse Markdown. To customise marked's rendering, you can pass in options when [configuring Reveal](#configuration): - -```javascript -Reveal.initialize({ - // Options which are passed into marked - // See https://github.com/chjj/marked#options-1 - markdown: { - smartypants: true - } -}); -``` ### Configuration @@ -185,26 +100,12 @@ At the end of your page you need to initialize reveal by running the following c ```javascript Reveal.initialize({ - // Display presentation control arrows + // Display controls in the bottom right corner controls: true, - // Help the user learn the controls by providing hints, for example by - // bouncing the down arrow when they first encounter a vertical slide - controlsTutorial: true, - - // Determines where controls appear, "edges" or "bottom-right" - controlsLayout: 'bottom-right', - - // Visibility rule for backwards navigation arrows; "faded", "hidden" - // or "visible" - controlsBackArrows: 'faded', - // Display a presentation progress bar progress: true, - // Set default timing of 2 minutes per slide - defaultTiming: 120, - // Display the page number of the current slide slideNumber: false, @@ -229,9 +130,6 @@ Reveal.initialize({ // Change the presentation direction to be RTL rtl: false, - // Randomizes the order of slides each time the presentation loads - shuffle: false, - // Turns fragments on and off globally fragments: true, @@ -243,15 +141,6 @@ Reveal.initialize({ // key is pressed help: true, - // Flags if speaker notes should be visible to all viewers - showNotes: false, - - // Global override for autoplaying embedded media (video/audio/iframe) - // - null: Media will only autoplay if data-autoplay is present - // - true: All media will autoplay, regardless of individual setting - // - false: No media will autoplay, regardless of individual setting - autoPlayMedia: null, - // Number of milliseconds between automatically proceeding to the // next slide, disabled when set to 0, this value can be overwritten // by using a data-autoslide attribute on your slides @@ -260,9 +149,6 @@ Reveal.initialize({ // Stop auto-sliding after user input autoSlideStoppable: true, - // Use this method for navigation when auto-sliding - autoSlideMethod: Reveal.navigateNext, - // Enable slide navigation via mouse wheel mouseWheel: false, @@ -270,18 +156,16 @@ Reveal.initialize({ hideAddressBar: true, // Opens links in an iframe preview overlay - // Add `data-preview-link` and `data-preview-link="false"` to customise each link - // individually previewLinks: false, // Transition style - transition: 'slide', // none/fade/slide/convex/concave/zoom + transition: 'default', // none/fade/slide/convex/concave/zoom // Transition speed transitionSpeed: 'default', // default/fast/slow // Transition style for full page slide backgrounds - backgroundTransition: 'fade', // none/fade/slide/convex/concave/zoom + backgroundTransition: 'default', // none/fade/slide/convex/concave/zoom // Number of slides away from the current that are visible viewDistance: 3, @@ -292,14 +176,10 @@ Reveal.initialize({ // Parallax background size parallaxBackgroundSize: '', // CSS syntax, e.g. "2100px 900px" - // Number of pixels to move the parallax background per slide - // - Calculated automatically unless specified - // - Set to 0 to disable movement along an axis - parallaxBackgroundHorizontal: null, - parallaxBackgroundVertical: null, - - // The display mode that will be used to show slides - display: 'block' + // Amount to move parallax background (horizontal and vertical) on slide change + // Number, e.g. 100 + parallaxBackgroundHorizontal: '', + parallaxBackgroundVertical: '' }); ``` @@ -316,6 +196,56 @@ Reveal.configure({ autoSlide: 5000 }); ``` +### Dependencies + +Reveal.js doesn't _rely_ on any third party scripts to work but a few optional libraries are included by default. These libraries are loaded as dependencies in the order they appear, for example: + +```javascript +Reveal.initialize({ + dependencies: [ + // Cross-browser shim that fully implements classList - https://github.com/eligrey/classList.js/ + { src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } }, + + // Interpret Markdown in
    elements + { src: 'plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, + { src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, + + // Syntax highlight for elements + { src: 'plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } }, + + // Zoom in and out with Alt+click + { src: 'plugin/zoom-js/zoom.js', async: true }, + + // Speaker notes + { src: 'plugin/notes/notes.js', async: true }, + + // Remote control your reveal.js presentation using a touch device + { src: 'plugin/remotes/remotes.js', async: true }, + + // MathJax + { src: 'plugin/math/math.js', async: true } + ] +}); +``` + +You can add your own extensions using the same syntax. The following properties are available for each dependency object: +- **src**: Path to the script to load +- **async**: [optional] Flags if the script should load after reveal.js has started, defaults to false +- **callback**: [optional] Function to execute when the script has loaded +- **condition**: [optional] Function which must return true for the script to be loaded + + +### Ready Event + +A 'ready' event is fired when reveal.js has loaded all non-async dependencies and is ready to start navigating. To check if reveal.js is already 'ready' you can call `Reveal.isReady()`. + +```javascript +Reveal.addEventListener( 'ready', function( event ) { + // event.currentSlide, event.indexh, event.indexv +} ); +``` + + ### Presentation Size All presentations have a normal size, that is the resolution at which they are authored. The framework will automatically scale presentations uniformly based on this size to ensure that everything fits on any given display or viewport. @@ -343,69 +273,6 @@ Reveal.initialize({ }); ``` -If you wish to disable this behavior and do your own scaling (e.g. using media queries), try these settings: - -```javascript -Reveal.initialize({ - - ... - - width: "100%", - height: "100%", - margin: 0, - minScale: 1, - maxScale: 1 -}); -``` - -### Dependencies - -Reveal.js doesn't _rely_ on any third party scripts to work but a few optional libraries are included by default. These libraries are loaded as dependencies in the order they appear, for example: - -```javascript -Reveal.initialize({ - dependencies: [ - // Cross-browser shim that fully implements classList - https://github.com/eligrey/classList.js/ - { src: 'lib/js/classList.js', condition: function() { return !document.body.classList; } }, - - // Interpret Markdown in
    elements - { src: 'plugin/markdown/marked.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, - { src: 'plugin/markdown/markdown.js', condition: function() { return !!document.querySelector( '[data-markdown]' ); } }, - - // Syntax highlight for elements - { src: 'plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } }, - - // Zoom in and out with Alt+click - { src: 'plugin/zoom-js/zoom.js', async: true }, - - // Speaker notes - { src: 'plugin/notes/notes.js', async: true }, - - // MathJax - { src: 'plugin/math/math.js', async: true } - ] -}); -``` - -You can add your own extensions using the same syntax. The following properties are available for each dependency object: -- **src**: Path to the script to load -- **async**: [optional] Flags if the script should load after reveal.js has started, defaults to false -- **callback**: [optional] Function to execute when the script has loaded -- **condition**: [optional] Function which must return true for the script to be loaded - -To load these dependencies, reveal.js requires [head.js](http://headjs.com/) *(a script loading library)* to be loaded before reveal.js. - -### Ready Event - -A 'ready' event is fired when reveal.js has loaded all non-async dependencies and is ready to start navigating. To check if reveal.js is already 'ready' you can call `Reveal.isReady()`. - -```javascript -Reveal.addEventListener( 'ready', function( event ) { - // event.currentSlide, event.indexh, event.indexv -} ); -``` - -Note that we also add a `.ready` class to the `.reveal` element so that you can hook into this with CSS. ### Auto-sliding @@ -429,8 +296,6 @@ You can also override the slide duration for individual slides and fragments by
    ``` -To override the method used for navigation when auto-sliding, you can specify the ```autoSlideMethod``` setting. To only navigate along the top layer and ignore vertical slides, set this to ```Reveal.navigateRight```. - Whenever the auto-slide mode is resumed or paused the ```autoslideresumed``` and ```autoslidepaused``` events are fired. @@ -448,13 +313,6 @@ Reveal.configure({ }); ``` -### Touch Navigation - -You can swipe to navigate through a presentation on any touch-enabled device. Horizontal swipes change between horizontal slides, vertical swipes change between vertical slides. If you wish to disable this you can set the `touch` config option to false when initializing reveal.js. - -If there's some part of your content that needs to remain accessible to touch events you'll need to highlight this by adding a `data-prevent-swipe` attribute to the element. One common example where this is useful is elements that need to be scrolled. - - ### Lazy Loading When working on presentation with a lot of media or iframe content it's important to load lazily. Lazy loading means that reveal.js will only load content for the few slides nearest to the current slide. The number of slides that are preloaded is determined by the `viewDistance` configuration option. @@ -489,18 +347,11 @@ Reveal.next(); Reveal.prevFragment(); Reveal.nextFragment(); -// Randomize the order of slides -Reveal.shuffle(); - // Toggle presentation states, optionally pass true/false to force on/off Reveal.toggleOverview(); Reveal.togglePause(); Reveal.toggleAutoSlide(); -// Shows a help overlay with keyboard shortcuts, optionally pass true/false -// to force on/off -Reveal.toggleHelp(); - // Change a config value at runtime Reveal.configure({ controls: true }); @@ -514,14 +365,9 @@ Reveal.getScale(); Reveal.getPreviousSlide(); Reveal.getCurrentSlide(); -Reveal.getIndices(); // { h: 0, v: 0 } } -Reveal.getPastSlideCount(); -Reveal.getProgress(); // (0 == first slide, 1 == last slide) -Reveal.getSlides(); // Array of all slides -Reveal.getTotalSlides(); // total number of slides - -// Returns the speaker notes for the current slide -Reveal.getSlideNotes(); +Reveal.getIndices(); // { h: 0, v: 0 } } +Reveal.getProgress(); // 0-1 +Reveal.getTotalSlides(); // State checks Reveal.isFirstSlide(); @@ -574,59 +420,26 @@ Reveal.addEventListener( 'somestate', function() { ### Slide Backgrounds -Slides are contained within a limited portion of the screen by default to allow them to fit any display and scale uniformly. You can apply full page backgrounds outside of the slide area by adding a ```data-background``` attribute to your ```
    ``` elements. Four different types of backgrounds are supported: color, image, video and iframe. +Slides are contained within a limited portion of the screen by default to allow them to fit any display and scale uniformly. You can apply full page backgrounds outside of the slide area by adding a ```data-background``` attribute to your ```
    ``` elements. Four different types of backgrounds are supported: color, image, video and iframe. Below are a few examples. -#### Color Backgrounds -All CSS color formats are supported, like rgba() or hsl(). ```html -
    -

    Color

    +
    +

    All CSS color formats are supported, like rgba() or hsl().

    +
    +
    +

    This slide will have a full-size background image.

    +
    +
    +

    This background image will be sized to 100px and repeated.

    +
    +
    +

    Video. Multiple sources can be defined using a comma separated list. Video will loop when the data-background-video-loop attribute is provided.

    +
    +
    +

    Embeds a web page as a background. Note that the page won't be interactive.

    ``` -#### Image Backgrounds -By default, background images are resized to cover the full page. Available options: - -| Attribute | Default | Description | -| :--------------------------- | :--------- | :---------- | -| data-background-image | | URL of the image to show. GIFs restart when the slide opens. | -| data-background-size | cover | See [background-size](https://developer.mozilla.org/docs/Web/CSS/background-size) on MDN. | -| data-background-position | center | See [background-position](https://developer.mozilla.org/docs/Web/CSS/background-position) on MDN. | -| data-background-repeat | no-repeat | See [background-repeat](https://developer.mozilla.org/docs/Web/CSS/background-repeat) on MDN. | -```html -
    -

    Image

    -
    -
    -

    This background image will be sized to 100px and repeated

    -
    -``` - -#### Video Backgrounds -Automatically plays a full size video behind the slide. - -| Attribute | Default | Description | -| :--------------------------- | :------ | :---------- | -| data-background-video | | A single video source, or a comma separated list of video sources. | -| data-background-video-loop | false | Flags if the video should play repeatedly. | -| data-background-video-muted | false | Flags if the audio should be muted. | -| data-background-size | cover | Use `cover` for full screen and some cropping or `contain` for letterboxing. | - -```html -
    -

    Video

    -
    -``` - -#### Iframe Backgrounds -Embeds a web page as a slide background that covers 100% of the reveal.js width and height. The iframe is in the background layer, behind your slides, and as such it's not possible to interact with it by default. To make your background interactive, you can add the `data-background-interactive` attribute. -```html -
    -

    Iframe

    -
    -``` - -#### Background Transitions Backgrounds transition using a fade animation by default. This can be changed to a linear sliding transition by passing ```backgroundTransition: 'slide'``` to the ```Reveal.initialize()``` call. Alternatively you can set ```data-background-transition``` on any section with a background to override that specific transition. @@ -643,16 +456,16 @@ Reveal.initialize({ // Parallax background size parallaxBackgroundSize: '', // CSS syntax, e.g. "2100px 900px" - currently only pixels are supported (don't use % or auto) - // Number of pixels to move the parallax background per slide - // - Calculated automatically unless specified - // - Set to 0 to disable movement along an axis + // Amount of pixels to move the parallax background per slide step, + // a value of 0 disables movement along the given axis + // These are optional, if they aren't specified they'll be calculated automatically parallaxBackgroundHorizontal: 200, parallaxBackgroundVertical: 50 }); ``` -Make sure that the background size is much bigger than screen size to allow for some scrolling. [View example](http://revealjs.com/?parallaxBackgroundImage=https%3A%2F%2Fs3.amazonaws.com%2Fhakim-static%2Freveal-js%2Freveal-parallax-1.jpg¶llaxBackgroundSize=2100px%20900px). +Make sure that the background size is much bigger than screen size to allow for some scrolling. [View example](http://lab.hakim.se/reveal-js/?parallaxBackgroundImage=https%3A%2F%2Fs3.amazonaws.com%2Fhakim-static%2Freveal-js%2Freveal-parallax-1.jpg¶llaxBackgroundSize=2100px%20900px). @@ -673,15 +486,15 @@ You can also use different in and out transitions for the same slide: ```html
    - The train goes on … + The train goes on …
    -
    - and on … +
    + and on …
    -
    +
    and stops.
    -
    +
    (Passengers entering and leaving)
    @@ -690,6 +503,9 @@ You can also use different in and out transitions for the same slide: ``` +Note that this does not work with the page and cube transitions. + + ### Internal links It's easy to link between slides. The first example below targets the index of another slide whereas the second targets a slide with an ID attribute (```
    ```): @@ -712,7 +528,7 @@ You can also add relative navigation links, similar to the built in reveal.js co ### Fragments -Fragments are used to highlight individual elements on a slide. Every element with the class ```fragment``` will be stepped through before moving on to the next slide. Here's an example: http://revealjs.com/#/fragments +Fragments are used to highlight individual elements on a slide. Every element with the class ```fragment``` will be stepped through before moving on to the next slide. Here's an example: http://lab.hakim.se/reveal-js/#/fragments The default fragment style is to start out invisible and fade in. This style can be changed by appending a different class to the fragment: @@ -721,7 +537,6 @@ The default fragment style is to start out invisible and fade in. This style can

    grow

    shrink

    fade-out

    -

    fade-up (also down, left and right!)

    visible only once

    blue only once

    highlight-red

    @@ -767,41 +582,33 @@ Reveal.addEventListener( 'fragmenthidden', function( event ) { ### Code syntax highlighting -By default, Reveal is configured with [highlight.js](https://highlightjs.org/) for code syntax highlighting. To enable syntax highlighting, you'll have to load the highlight plugin ([plugin/highlight/highlight.js](plugin/highlight/highlight.js)) and a highlight.js CSS theme (Reveal comes packaged with the zenburn theme: [lib/css/zenburn.css](lib/css/zenburn.css)). - -Below is an example with clojure code that will be syntax highlighted. When the `data-trim` attribute is present, surrounding whitespace is automatically removed. HTML will be escaped by default. To avoid this, for example if you are using `` to call out a line of code, add the `data-noescape` attribute to the `` element. +By default, Reveal is configured with [highlight.js](http://softwaremaniacs.org/soft/highlight/en/) for code syntax highlighting. Below is an example with clojure code that will be syntax highlighted. When the `data-trim` attribute is present surrounding whitespace is automatically removed. ```html
    - 
    
+	
    
 (def lazy-fib
   (concat
    [0 1]
-   ((fn rfib [a b]
+   ((fn rfib [a b]
         (lazy-cons (+ a b) (rfib b (+ a b)))) 0 1)))
    ``` ### Slide number -If you would like to display the page number of the current slide you can do so using the ```slideNumber``` and ```showSlideNumber``` configuration values. +If you would like to display the page number of the current slide you can do so using the ```slideNumber``` configuration value. ```javascript // Shows the slide number using default formatting Reveal.configure({ slideNumber: true }); // Slide number formatting can be configured using these variables: -// "h.v": horizontal . vertical slide number (default) -// "h/v": horizontal / vertical slide number -// "c": flattened slide number -// "c/t": flattened slide number / total slides -Reveal.configure({ slideNumber: 'c/t' }); - -// Control which views the slide number displays on using the "showSlideNumber" value: -// "all": show on all views (default) -// "speaker": only show slide numbers on speaker notes view -// "print": only show slide numbers when printing to PDF -Reveal.configure({ showSlideNumber: 'speaker' }); +// h: current slide's horizontal index +// v: current slide's vertical index +// c: current slide index (flattened) +// t: total number of slides (flattened) +Reveal.configure({ slideNumber: 'c / t' }); ``` @@ -819,26 +626,20 @@ Reveal.addEventListener( 'overviewhidden', function( event ) { /* ... */ } ); Reveal.toggleOverview(); ``` - ### Fullscreen mode Just press »F« on your keyboard to show your presentation in fullscreen mode. Press the »ESC« key to exit fullscreen mode. ### Embedded media +Embedded HTML5 `
    diff --git a/doc/pub/svm/html/reveal.js/plugin/markdown/example.md b/doc/pub/svm/html/reveal.js/plugin/markdown/example.md index 89c75345e..6f6f577a1 100644 --- a/doc/pub/svm/html/reveal.js/plugin/markdown/example.md +++ b/doc/pub/svm/html/reveal.js/plugin/markdown/example.md @@ -29,8 +29,3 @@ Content 3.1 ## External 3.2 Content 3.2 - - -## External 3.3 - -![External Image](https://s3.amazonaws.com/static.slid.es/logo/v2/slides-symbol-512x512.png) diff --git a/doc/pub/svm/html/reveal.js/plugin/markdown/markdown.js b/doc/pub/svm/html/reveal.js/plugin/markdown/markdown.js index aa08ee5ed..15e3b40b3 100644 --- a/doc/pub/svm/html/reveal.js/plugin/markdown/markdown.js +++ b/doc/pub/svm/html/reveal.js/plugin/markdown/markdown.js @@ -4,26 +4,33 @@ * of external markdown documents. */ (function( root, factory ) { - if (typeof define === 'function' && define.amd) { - root.marked = require( './marked' ); - root.RevealMarkdown = factory( root.marked ); - root.RevealMarkdown.initialize(); - } else if( typeof exports === 'object' ) { + if( typeof exports === 'object' ) { module.exports = factory( require( './marked' ) ); - } else { + } + else { // Browser globals (root is window) root.RevealMarkdown = factory( root.marked ); root.RevealMarkdown.initialize(); } }( this, function( marked ) { + if( typeof marked === 'undefined' ) { + throw 'The reveal.js Markdown plugin requires marked to be loaded'; + } + + if( typeof hljs !== 'undefined' ) { + marked.setOptions({ + highlight: function( lang, code ) { + return hljs.highlightAuto( lang, code ).value; + } + }); + } + var DEFAULT_SLIDE_SEPARATOR = '^\r?\n---\r?\n$', - DEFAULT_NOTES_SEPARATOR = 'notes?:', + DEFAULT_NOTES_SEPARATOR = 'note:', DEFAULT_ELEMENT_ATTRIBUTES_SEPARATOR = '\\\.element\\\s*?(.+?)$', DEFAULT_SLIDE_ATTRIBUTES_SEPARATOR = '\\\.slide:\\\s*?(\\\S.+?)$'; - var SCRIPT_END_PLACEHOLDER = '__SCRIPT_END__'; - /** * Retrieves the markdown contents of a slide section @@ -31,15 +38,11 @@ */ function getMarkdownFromSlide( section ) { - // look for a ' ); - var leadingWs = text.match( /^\n?(\s*)/ )[1].length, leadingTabs = text.match( /^\n?(\t*)/ )[1].length; @@ -109,13 +112,9 @@ var notesMatch = content.split( new RegExp( options.notesSeparator, 'mgi' ) ); if( notesMatch.length === 2 ) { - content = notesMatch[0] + ''; + content = notesMatch[0] + ''; } - // prevent script end tags in the content from interfering - // with parsing - content = content.replace( /<\/script>/g, SCRIPT_END_PLACEHOLDER ); - return ''; } @@ -178,7 +177,7 @@ markdownSections += '
    '; sectionStack[i].forEach( function( child ) { - markdownSections += '
    ' + createMarkdownSlide( child, options ) + '
    '; + markdownSections += '
    ' + createMarkdownSlide( child, options ) + '
    '; } ); markdownSections += '
    '; @@ -380,24 +379,6 @@ return { initialize: function() { - if( typeof marked === 'undefined' ) { - throw 'The reveal.js Markdown plugin requires marked to be loaded'; - } - - if( typeof hljs !== 'undefined' ) { - marked.setOptions({ - highlight: function( code, lang ) { - return hljs.highlightAuto( code, [lang] ).value; - } - }); - } - - var options = Reveal.getConfig().markdown; - - if ( options ) { - marked.setOptions( options ); - } - processSlides(); convertSlides(); }, diff --git a/doc/pub/svm/html/reveal.js/plugin/markdown/marked.js b/doc/pub/svm/html/reveal.js/plugin/markdown/marked.js index 555c1dc1d..70af29bf9 100644 --- a/doc/pub/svm/html/reveal.js/plugin/markdown/marked.js +++ b/doc/pub/svm/html/reveal.js/plugin/markdown/marked.js @@ -3,4 +3,4 @@ * Copyright (c) 2011-2014, Christopher Jeffrey. 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    reveal.js multiplex server.

    Generate token'); - res.end(); - }); - stream.on('readable', function() { - stream.pipe(res); - }); + fs.createReadStream(opts.baseDir + '/index.html').pipe(res); }); app.get("/token", function(req,res) { @@ -55,7 +47,7 @@ var createHash = function(secret) { }; // Actually listen -server.listen( opts.port || null ); +app.listen(opts.port || null); var brown = '\033[33m', green = '\033[32m', diff --git a/doc/pub/svm/html/reveal.js/plugin/multiplex/master.js b/doc/pub/svm/html/reveal.js/plugin/multiplex/master.js index 7f4bf4511..b6a7eb7dc 100644 --- a/doc/pub/svm/html/reveal.js/plugin/multiplex/master.js +++ b/doc/pub/svm/html/reveal.js/plugin/multiplex/master.js @@ -1,34 +1,51 @@ (function() { - // Don't emit events from inside of notes windows if ( window.location.search.match( /receiver/gi ) ) { return; } var multiplex = Reveal.getConfig().multiplex; - var socket = io.connect( multiplex.url ); + var socket = io.connect(multiplex.url); - function post() { + var notify = function( slideElement, indexh, indexv, origin ) { + if( typeof origin === 'undefined' && origin !== 'remote' ) { + var nextindexh; + var nextindexv; - var messageData = { - state: Reveal.getState(), - secret: multiplex.secret, - socketId: multiplex.id - }; + var fragmentindex = Reveal.getIndices().f; + if (typeof fragmentindex == 'undefined') { + fragmentindex = 0; + } - socket.emit( 'multiplex-statechanged', messageData ); + if (slideElement.nextElementSibling && slideElement.parentNode.nodeName == 'SECTION') { + nextindexh = indexh; + nextindexv = indexv + 1; + } else { + nextindexh = indexh + 1; + nextindexv = 0; + } + var slideData = { + indexh : indexh, + indexv : indexv, + indexf : fragmentindex, + nextindexh : nextindexh, + nextindexv : nextindexv, + secret: multiplex.secret, + socketId : multiplex.id + }; + + socket.emit('slidechanged', slideData); + } + } + + Reveal.addEventListener( 'slidechanged', function( event ) { + notify( event.currentSlide, event.indexh, event.indexv, event.origin ); + } ); + + var fragmentNotify = function( event ) { + notify( Reveal.getCurrentSlide(), Reveal.getIndices().h, Reveal.getIndices().v, event.origin ); }; - // post once the page is loaded, so the client follows also on "open URL". - window.addEventListener( 'load', post ); - - // Monitor events that trigger a change in state - Reveal.addEventListener( 'slidechanged', post ); - Reveal.addEventListener( 'fragmentshown', post ); - Reveal.addEventListener( 'fragmenthidden', post ); - Reveal.addEventListener( 'overviewhidden', post ); - Reveal.addEventListener( 'overviewshown', post ); - Reveal.addEventListener( 'paused', post ); - Reveal.addEventListener( 'resumed', post ); - -}()); + Reveal.addEventListener( 'fragmentshown', fragmentNotify ); + Reveal.addEventListener( 'fragmenthidden', fragmentNotify ); +}()); \ No newline at end of file diff --git a/doc/pub/svm/html/reveal.js/plugin/notes-server/client.js b/doc/pub/svm/html/reveal.js/plugin/notes-server/client.js index 00b277baf..628586ffb 100644 --- a/doc/pub/svm/html/reveal.js/plugin/notes-server/client.js +++ b/doc/pub/svm/html/reveal.js/plugin/notes-server/client.js @@ -41,15 +41,10 @@ } // When a new notes window connects, post our current state - socket.on( 'new-subscriber', function( data ) { + socket.on( 'connect', function( data ) { post(); } ); - // When the state changes from inside of the speaker view - socket.on( 'statechanged-speaker', function( data ) { - Reveal.setState( data.state ); - } ); - // Monitor events that trigger a change in state Reveal.addEventListener( 'slidechanged', post ); Reveal.addEventListener( 'fragmentshown', post ); diff --git a/doc/pub/svm/html/reveal.js/plugin/notes-server/index.js b/doc/pub/svm/html/reveal.js/plugin/notes-server/index.js index b95f07188..df917f112 100644 --- a/doc/pub/svm/html/reveal.js/plugin/notes-server/index.js +++ b/doc/pub/svm/html/reveal.js/plugin/notes-server/index.js @@ -1,40 +1,37 @@ -var http = require('http'); var express = require('express'); var fs = require('fs'); var io = require('socket.io'); +var _ = require('underscore'); var Mustache = require('mustache'); -var app = express(); +var app = express.createServer(); var staticDir = express.static; -var server = http.createServer(app); -io = io(server); +io = io.listen(app); var opts = { port : 1947, baseDir : __dirname + '/../../' }; -io.on( 'connection', function( socket ) { +io.sockets.on( 'connection', function( socket ) { - socket.on( 'new-subscriber', function( data ) { - socket.broadcast.emit( 'new-subscriber', data ); + socket.on( 'connect', function( data ) { + socket.broadcast.emit( 'connect', data ); }); socket.on( 'statechanged', function( data ) { - delete data.state.overview; socket.broadcast.emit( 'statechanged', data ); }); - socket.on( 'statechanged-speaker', function( data ) { - delete data.state.overview; - socket.broadcast.emit( 'statechanged-speaker', data ); - }); - }); -[ 'css', 'js', 'images', 'plugin', 'lib' ].forEach( function( dir ) { - app.use( '/' + dir, staticDir( opts.baseDir + dir ) ); +app.configure( function() { + + [ 'css', 'js', 'images', 'plugin', 'lib' ].forEach( function( dir ) { + app.use( '/' + dir, staticDir( opts.baseDir + dir ) ); + }); + }); app.get('/', function( req, res ) { @@ -55,7 +52,7 @@ app.get( '/notes/:socketId', function( req, res ) { }); // Actually listen -server.listen( opts.port || null ); +app.listen( opts.port || null ); var brown = '\033[33m', green = '\033[32m', @@ -65,5 +62,5 @@ var slidesLocation = 'http://localhost' + ( opts.port ? ( ':' + opts.port ) : '' console.log( brown + 'reveal.js - Speaker Notes' + reset ); console.log( '1. Open the slides at ' + green + slidesLocation + reset ); -console.log( '2. Click on the link in your JS console to go to the notes page' ); +console.log( '2. Click on the link your JS console to go to the notes page' ); console.log( '3. Advance through your slides and your notes will advance automatically' ); diff --git a/doc/pub/svm/html/reveal.js/plugin/notes-server/notes.html b/doc/pub/svm/html/reveal.js/plugin/notes-server/notes.html index ab8c5b17a..72d0317f1 100644 --- a/doc/pub/svm/html/reveal.js/plugin/notes-server/notes.html +++ b/doc/pub/svm/html/reveal.js/plugin/notes-server/notes.html @@ -8,7 +8,6 @@ @@ -247,7 +152,7 @@
    -
    Upcoming
    +
    UPCOMING:

    Time Click to Reset

    @@ -265,10 +170,6 @@
    -
    - - -
    @@ -281,20 +182,11 @@ currentState, currentSlide, upcomingSlide, - layoutLabel, - layoutDropdown, connected = false; var socket = io.connect( window.location.origin ), socketId = '{{socketId}}'; - var SPEAKER_LAYOUTS = { - 'default': 'Default', - 'wide': 'Wide', - 'tall': 'Tall', - 'notes-only': 'Notes only' - }; - socket.on( 'statechanged', function( data ) { // ignore data from sockets that aren't ours @@ -303,6 +195,7 @@ if( connected === false ) { connected = true; + setupIframes( data ); setupKeyboard(); setupNotes(); setupTimer(); @@ -313,28 +206,13 @@ } ); - setupLayout(); - - // Load our presentation iframes - setupIframes(); - - // Once the iframes have loaded, emit a signal saying there's - // a new subscriber which will trigger a 'statechanged' - // message to be sent back window.addEventListener( 'message', function( event ) { var data = JSON.parse( event.data ); if( data && data.namespace === 'reveal' ) { if( /ready/.test( data.eventName ) ) { - socket.emit( 'new-subscriber', { socketId: socketId } ); - } - } - - // Messages sent by reveal.js inside of the current slide preview - if( data && data.namespace === 'reveal' ) { - if( /slidechanged|fragmentshown|fragmenthidden|overviewshown|overviewhidden|paused|resumed/.test( data.eventName ) && currentState !== JSON.stringify( data.state ) ) { - socket.emit( 'statechanged-speaker', { state: data.state } ); + socket.emit( 'connect', { socketId: socketId } ); } } @@ -389,7 +267,7 @@ /** * Creates the preview iframes. */ - function setupIframes() { + function setupIframes( data ) { var params = [ 'receiver', @@ -399,8 +277,9 @@ 'backgroundTransition=none' ].join( '&' ); - var currentURL = '/?' + params + '&postMessageEvents=true'; - var upcomingURL = '/?' + params + '&controls=false'; + var hash = '#/' + data.state.indexh + '/' + data.state.indexv; + var currentURL = '/?' + params + '&postMessageEvents=true' + hash; + var upcomingURL = '/?' + params + '&controls=false' + hash; currentSlide = document.createElement( 'iframe' ); currentSlide.setAttribute( 'width', 1280 ); @@ -472,74 +351,6 @@ } - /** - * Sets up the speaker view layout and layout selector. - */ - function setupLayout() { - - layoutDropdown = document.querySelector( '.speaker-layout-dropdown' ); - layoutLabel = document.querySelector( '.speaker-layout-label' ); - - // Render the list of available layouts - for( var id in SPEAKER_LAYOUTS ) { - var option = document.createElement( 'option' ); - option.setAttribute( 'value', id ); - option.textContent = SPEAKER_LAYOUTS[ id ]; - layoutDropdown.appendChild( option ); - } - - // Monitor the dropdown for changes - layoutDropdown.addEventListener( 'change', function( event ) { - - setLayout( layoutDropdown.value ); - - }, false ); - - // Restore any currently persisted layout - setLayout( getLayout() ); - - } - - /** - * Sets a new speaker view layout. The layout is persisted - * in local storage. - */ - function setLayout( value ) { - - var title = SPEAKER_LAYOUTS[ value ]; - - layoutLabel.innerHTML = 'Layout' + ( title ? ( ': ' + title ) : '' ); - layoutDropdown.value = value; - - document.body.setAttribute( 'data-speaker-layout', value ); - - // Persist locally - if( window.localStorage ) { - window.localStorage.setItem( 'reveal-speaker-layout', value ); - } - - } - - /** - * Returns the ID of the most recently set speaker layout - * or our default layout if none has been set. - */ - function getLayout() { - - if( window.localStorage ) { - var layout = window.localStorage.getItem( 'reveal-speaker-layout' ); - if( layout ) { - return layout; - } - } - - // Default to the first record in the layouts hash - for( var id in SPEAKER_LAYOUTS ) { - return id; - } - - } - function zeroPadInteger( num ) { var str = '00' + parseInt( num ); diff --git a/doc/pub/svm/html/reveal.js/plugin/notes/notes.html b/doc/pub/svm/html/reveal.js/plugin/notes/notes.html index 4c5b799b5..0cc8cf612 100644 --- a/doc/pub/svm/html/reveal.js/plugin/notes/notes.html +++ b/doc/pub/svm/html/reveal.js/plugin/notes/notes.html @@ -8,7 +8,6 @@