diff --git a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot index 5b4b48a9b..a8bce3417 100644 --- a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot +++ b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.dot @@ -6,17 +6,17 @@ 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="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e58139fb"] ; +3 [label="area error <= 48.975\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="worst symmetry <= 0.208\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#e5813913"] ; +5 [label="mean area <= 469.25\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 ; 7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139ff"] ; 5 -> 7 ; -8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#e581392c"] ; +8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#e581392c"] ; 2 -> 8 ; 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139ff"] ; 8 -> 9 ; @@ -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 radius <= 17.74\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="mean texture <= 13.745\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#e5813955"] ; +17 [label="mean fractal dimension <= 0.054\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 ; @@ -48,10 +48,10 @@ 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="fractal dimension error <= 0.013\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"] ; +25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ; 24 -> 25 ; -26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ; +26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ; 24 -> 26 ; } \ No newline at end of file diff --git a/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png b/doc/pub/DecisionTrees/ipynb/DataFiles/cancer.png index 2ceb5e1f8..22ae432a0 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/DataFiles/ride.dot b/doc/pub/DecisionTrees/ipynb/DataFiles/ride.dot index bb1938a28..50aaa7638 100644 --- a/doc/pub/DecisionTrees/ipynb/DataFiles/ride.dot +++ b/doc/pub/DecisionTrees/ipynb/DataFiles/ride.dot @@ -1,41 +1,6 @@ digraph Tree { node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ; edge [fontname=helvetica] ; -<<<<<<< HEAD -0 [label="X[0] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ; -1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ; -0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; -2 [label="X[8] <= 0.5\ngini = 0.48\nsamples = 10\nvalue = [4, 6, 0]", fillcolor="#39e58155"] ; -1 -> 2 ; -3 [label="X[11] <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [1, 4, 0]", fillcolor="#39e581bf"] ; -2 -> 3 ; -4 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3, 0]", fillcolor="#39e581ff"] ; -3 -> 4 ; -5 [label="X[6] <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1, 0]", fillcolor="#e5813900"] ; -3 -> 5 ; -6 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0, 0]", fillcolor="#e58139ff"] ; -5 -> 6 ; -7 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]", fillcolor="#39e581ff"] ; -5 -> 7 ; -8 [label="X[3] <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2, 0]", fillcolor="#e5813955"] ; -2 -> 8 ; -9 [label="X[12] <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1, 0]", fillcolor="#e5813900"] ; -8 -> 9 ; -10 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0, 0]", fillcolor="#e58139ff"] ; -9 -> 10 ; -11 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]", fillcolor="#39e581ff"] ; -9 -> 11 ; -12 [label="X[12] <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1, 0]", fillcolor="#e581397f"] ; -8 -> 12 ; -13 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]", fillcolor="#39e581ff"] ; -12 -> 13 ; -14 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0, 0]", fillcolor="#e58139ff"] ; -12 -> 14 ; -15 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4, 0]", fillcolor="#39e581ff"] ; -1 -> 15 ; -16 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5ff"] ; -0 -> 16 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -======= 0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ; 1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; @@ -45,5 +10,4 @@ edge [fontname=helvetica] ; 1 -> 3 ; 4 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]", fillcolor="#8139e5ff"] ; 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; ->>>>>>> origin/master } \ No newline at end of file diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb index 8c95110d3..3c8225c59 100644 --- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb +++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb @@ -101,11 +101,40 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2nd degree coefficients:\n", + "zero power: -0.28499417022929907\n", + "first power: 0.05490242306549711\n", + "second power: -0.0002577121418274489\n" + ] + }, + { + "data": { + "image/png": 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\n", 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ci6Ywhgkh9gBX6r8RQsQJId4DkFKeAp4GNutbor5PoWg0WBv9WjytVgvF4jx5FAq3WBxSytGVHBpqJ+0W4D6b3+8D77tINIXC+9H1Rq1WbtOVjSxRFofCeaiZ4wqFt2NYC8ri8Gq8YT0OI2+vXr2IjY0lLs6haRk1xitGVSkUilJyi3JZ8dcKcou0cN4diztiwsTCPxZiCa2ZBvBJ86E97Tmbd5YPt31Y4fjlkZcT2SzSCVIr6oKz1uMwSE5OpmXLlk4pyx5KcSgUXsarP7/K9LXTrb9XFK4gmGAmrppIboD9tSEqo11mO+Yzn/Rz6Yz7quKQ0tjWsfw24bc6y2wPMbMWrjUHkDOqdrulpaUxYsQILrroIn766Sf69evHuHHjmDFjBunp6SxYsACAyZMnk5+fT2BgIB988AFdunThlVdeYfv27bz//vts376d0aNHk5KSQlBQUIV6MjMzGT16NEeOHGHgwIEV1uN47bXXKCwsZMCAAbz55puYzWZCQkK4//77Wb16Na1bt+azzz5j3bp11vU4AgMD2bRpE6Ctx7F8+XKKiopYtGgRXbt2deJVrBtKcSgUXkZGjjZnI65tHD3Ce+Bn0kZSje49muLA4qqyViD0WCgATfyaMCamNNRbTlEOi3cuttbV0Ni7dy+LFi3i/fffp1+/fnz66ads3LiRpKQknn32WT7++GM2bNiAj48P3333HU888QRffvklkydP5vLLL2fp0qXMmjWL//3vf3aVBsDMmTO55JJLSEhIYOXKlcybNw/Qwnl8/vnn/Pjjj/j6+vLPf/6TBQsWcPfdd5OTk0NcXByvvPIKiYmJzJw5k7lz5zJ37lxmz55dxrXUsmVLfv31V958801mz57Ne++951B0XCEEw4cPRwjBhAkTGD9+vNOvr1IcCoWXIfXe8Dt73clDFz3EetN6LFjYMG0DhaZC6yzmhIQEEhMTq5wRnpeWxy/8QtGpIm4yaSG2J0+eTHFQMdxWWpdLzqMay8CVNOb1ODZu3Ei7du1IT09n2LBhdO3alcGDB1d9wWqIUhwKhZdRIfCo3q2RdjCNQgqtEVVTU1OrDSVidI4XFRWRmJColZOWBqGV1NVAaMzrcRjh4iMiIhg5ciQpKSlOVxxqVJVC4WUYVoBAa1SMxr1jx45ERkaSmJhIYmIiMTExJCYmVl2Y/oT7+/hb80VGRtK+fXuXyV8faKjrceTk5FjLycnJYfXq1fTs2dPh6+IoyuJQKLwU69uobnHs3rMbk1/pu54jQQsNiyOiZQQXx19szXcs6xhtX27rUleVN/PYY48xZswYnnnmGa699lrr/vLrcQwZMoTBgwdbXU22zJgxg9GjR9OjRw8uvvhiu+txWCwWfH19eeONN+jUqZN1PY5nnnmGiIgIPv/8c6B0PQ7bzvHacOLECUaOHAlAcXExd9xxByNGOD+guFvW43A3aj0ORX1m0qpJzN08l9dGvMakAZNY57sOWSwZXDgYk2/NnASFJwr5qfVPmEPNXPD6Bdb9Z/LO8NC3D3Gk2xG2P7vdabKr9TiqpqGsx6EsDoXCyyhvBRghR2oa4BBA+Gt5SrJK2DV2V5lj05jG/pT98GwtBVU0WpTiUCi8DMMLYHVVGXqkFtMifJv5csHcCziXcq7M/tyzuWR9lUWTnCZ1kLRxoNbjqIhSHAqFl2J0jtdFcQC0e7Ad7R4su/7Zkb1HyPoqC2FxzSS9hoRaj6MialSVQuFlWEdVCVFmuGytghxWguH2ElIpDkXNUYpDofAyygxYqUOAw6owmbQCTVI1AYqao+4ahcJLEYg6dYxXWbYTrRdF40MpDoXCy5BImuQ2ocn9Tfgo5CMALE6Ii56UlERsbCxJSUms/WEtgOrjUNQKpTgUCi9DSkmfv/vQLrUdUYVRAKSb0+tcbkJCgjVMycuvvgyoPg5n4g3rcezevZvY2Fjr1qRJE1599dVayVQVSnEoFF6GROJTog14/CPgD55u8zSBHwbWuVzbMCVTp04FVB+Ht1BecdSWLl26WMOQbN26laCgIOtMcmeihuMqFF6I0aAPvXUo//r4X04pMz4+3hqmJDMjk+1sd6ni+EH84JJyL5eXV3lcrceh8f333xMdHU2nTp1qlM8RPPa6IYToIoTYZrOdE0I8VC7N5UKIszZpEjwlr0LhLqSU1gZdmF3jSmroneN79+7l4YcfZteuXezatcu6Hsfs2bN59tln6dq1Kxs2bOC3334jMTGRJ554AtCUyd69e1m6dCnjxo1zaD2OHTt2MHLkSA4ePAiUXY9j27ZtmM1mq7Iy1uPYsWMHl112GTNnzuSWW24hLi6OBQsWsG3bNgIDNevSWI9j4sSJzJ49G9Ci49q6oozt4osvriDfZ599xujRo51+bcGDFoeUcjcQCyCEMANHgKV2km6QUl7nTtkUCk8ikZgs+judi17tTGbXD8etzjJwJY15PQ6AwsJCkpKSHArtXhu8xVU1FNgnpTzgaUEUCm/ALM2ACy2OBj4BsDGvxwFamPc+ffrQqlWrmorvEN7SMzYKWFjJsYFCiFQhxNdCiB6VFSCEGC+E2CKE2JKR0TCXw1Q0DqQstThcpjjMDVtxVEdDXY/DYOHChS5zU4EXKA4hhB8QDyyyc/hXoJOUMgZ4HVhWWTlSyneklHFSyrjw8HDXCKtQuAFJaR8HZtfU0dhnjj/22GM8/vjjXHjhhda3eai4Hse0adOsCqA8M2bMYP369fTo0YMlS5bYXY+jd+/eDBs2jGPHjgFY1+Po2bMna9euta7maKzHERsbS15eXp3OLScnhzVr1lTpBqsrHl+PQwhxA/CglHK4A2nTgDgpZZUDnNV6HIr6zLivxpH1dhb/+uZftPt3Oy6Yc0H1mWpITkEOmwM2YxEWrrBc4bRy1XocVdNQ1uPwhteN0VTiphJCtBa6E1AI0R9N3kw3yqZQeASrq8rJoUYMDN96Y7U4FHXDo53jQohgYBgwwWbfAwBSyreBW4CJQohiIA8YJT1tIikULqbFXy0Ys3oMAPvS9nE+5zu9DsNVBbC81XIO5x2m/Zvtuf4f1zu9rvqOWo+jIh5VHFLKHCCs3L63bb7PBea6Wy6FwpNEbYiyfl+2ZRlXcZVL6jkYdpCOmR0JTQ+lG914///eV4rDDmo9joooO1Wh8DJMxdpjuSZyDVe/cbVL6hAIJkyYwPj7x5MZpXl/R906yiV1KRoeSnEoFF6GEbG276i+1hAhTq9DCPL98tnfYT/dBmidpH379HVJXYqGh1IcCoWXYQ117qKhuFC6LK2U0lqPLFHdhwrHUIpDofAyrIrDhU+nMapKIktHbpW4rj5P8dRTT1njPNlj2bJl7Ny5040SNQyU4lAovA3jxd+VisPG4jBmkTdGi0MpjtqhFIdC4WUYFoerwo1AWYujobmqZs2aRefOnbnkkkvYvXs3AO+++y79+vUjJiaGm2++mdzcXH766SeSkpJ49NFHiY2NZd++fXbTKSqiFIdC4WWIEr1RN7muITcsDrBRUHVfnbZW2C5pW1e2bt3KZ599xrZt21i1ahWbN28GtEizmzdvJjU1lW7dujFv3jwuvvhi4uPjefHFF9m2bRvR0dF20ykqohSHQuFluNPisK3HUxaH7ZK2dWXDhg2MHDmSoKAgmjRpYh2V9scff3DppZfSq1cvFixYwI4dO+zmdzRdY0cpDoXCy3DHqKqyFWofnlIctkvauoqxY8cyd+5ctm/fzowZM8jPz69TusaOUhwKhQuQJZI1g9awMHAhK8JXMN9vPm8FvsXyD5ZXme/4x8eJ3hQNwN8H/naHqCzYqa1O98u0X5gXPo9Ro0bR882e3PjZjRSVFLm8/vj4eLZt2+aUOSuDBw9m2bJl5OXlkZWVxfLl2vXOysqiTZs2FBUVWVfjg4rhzCtLpyiLUhwKhQvI3Z2L70++tMlvQ8jJENoXtadbfje+mvFVlfmOvl26eNDSVHsLYjqPbi21iX+/hfwGQFhuGNEnoxm4aSA7Mnbw1e6v2JFRv1w1ffr04fbbbycmJoarr77augrf008/zYABAxg0aFCZtbtHjRrFiy++yIUXXsi+ffsqTacoi7esAKhQNChkkeb2SfdLZ+/dewlYEECfvD7cPPLmqvMVa/mmj5rOxSMqriPtTLaO38q+0/sA2PjZRpJfSmbiwYmc738+XVt2ZdfJXVikh3rM68D06dOZPn16hf0TJ06ssG/QoEFlhuNOnDjRbjpFWZTiUChcgNFfcF7387jt3dvYZdnF8fePE9s71qF8J0NP0i+un0tlDPQNpGdETwB6/rsno/uP5reBv9G6RWuCfIMA6qXiULge5apSKFyAtaNZ7+A2ZmdLSzUd0Ho7bTG5v8G2juIqKReSRKEoh1IcCoULMBSHtTF2cJKdcVwKWWauhTuwKrcSWXaCYA1Rysa7ccb/oxSHQuEK9LhPhuKwxoOqxpAwLJISUVJmroVbsFFutbU4AgICyMzMVMrDS5FSkpmZSUBAQJ3KUX0cCoULMBSAVXE4OslOVzgesTjMdbc42rdvz+HDh8nIyHC6fArnEBAQQPv27etUhlIcCoWTKbYUs3H/RkII4Xjuceb9Oo+wk2E0oxk/H/iZs7+erTRvh9wO+OHn2T4OC5iE5oyoqeXg6+tLVFRU9QkV9RqPKw4hRBqQhfauVSyljCt3XABzgGuAXGCslPJXd8upUNglJwduugkOHbLuWtk2i8R2EbzES/x56k8eXv4w/0z7J7dyK19s/4JFyxdVWtz8rPm0ox0WYSFg0kNw7El3nAUAoiACeAq5bz9iWyq0BHnHaMgMcpsMdmnXDr78Epo08awcCiseVxw6Q6SUJys5djVwgb4NAN7SPxUKz5OSAqtXl9mVEQimNq0BaJNt4Z5foXuGZkEMOGwitIrXnqYF2pv+A1ssDN18DIpdI7Z9zgEgC4sRuXkAWA4egIPulMEOf/4JmzbBVa5Ze11Rc7xFcVTFDcDHUrOZfxZCNBNCtJFSHvO0YAoFFt2l1K8ffPghAKafv+LFewcC0Dq8C/Oe28G+l7I5tC+X/PQIeg26n/Xr1zN48GBWrlxJXl6etbiSAl8AHp75LQHt3BWsSkMcKoERmeTQmklLP+X6lgfY+q8TXHLt/W6Vowzjx8OPP4LqbPcqvEFxSGC1EEIC/5NSvlPueDvgkM3vw/q+MopDCDEeGA/QsWNH10mrUNgjJAS6dwcgcGGqdXfyyc0M6X49otXfwEH8TnYg9YOdBBXHkbqviFbFQ8oUY0Yb7SK6XgAd6jbypab4RZaQbf6akJIQWp9uQ+vTbZi5aCaTH+3uVjnKEBLiuboVleINiuMSKeURIUQEsEYIsUtKub6mhegK5x2AuLg49XqicA/Gm7DN0FkjbMjvrX8n7m2ty87cRLMe+tGPfsX6jPBK3FBSSMzB7rU2AMxBZiwLLMx6ahZ3n7qbDukdGHndSLfLYRdlcXgVHlccUsoj+me6EGIp0B+wVRxHgA42v9vr+xQK70QfUhvcJ5j4G7SIr23vbwsSSrKrX9g7JDYE3xa+rpSwUuJvjyf+9njmR8+HdOjdq7dH5FB4Nx5VHEKIYMAkpczSvw8HygflTwL+JYT4DK1T/Kzq31B4DfYsDiOsiM30Wt8wXzo93smNgtUNY/VBw3ryGO6eBKlwCE9bHK2ApfpkIx/gUynlN0KIBwCklG8Dq9CG4u5FG447zkOyKhQVsac4SioqjvqGVXFUF1vLXShXlVfhUcUhpfwbiLGz/22b7xJ40J1yKRR1wvBGub+bwmkYExA9bnEovJJ6/E6kUHgBdiwOq+Kox0+X1eLw0HKyVpSryiupx7e2QuEF2HGhNAhXlVCuKkXl1ONbW6HwImzfjI0wU/XYVWVtGaofBKZohCjFoVDUBQdHVdU3rH0cylWlsEM9vrXdS1JSErGxsSQlJZX57o76FN7Pb5uvYInfEr5u9jVhH4UBcOrMqQrp6sv/avRx+E3wY9bEWXW69+vLOStqgJSywW19+/aVziYmJkYCMiYmpsx3V+GOOhROYOVKWYy/TCa5zPY938vrBl1XIXl9+V+fGfmM9Vxu9rm5Tvd+nc75mmukBCmXL695XkWNALZIB9tYZXE4SGK7ZCAXAAAgAElEQVRiIjExMSQmJpb57o76FF6MlJzz0zozCkwFrPznSu679j5uevQmgu8KrpC8vvyvP93yExu6bgDgyiFX1unery/nrHAcIRvgaIW4uDi5ZcsWT4uhaAQcWvohfTdO4ouXl5Ptn831j19vPfbUZU8x4/IZHpSu9lz36XVEvRbFzb/cTPQr0XR4qEP1mVwiyHWwciUsX659V7gMIcRWWW49pMrw9MxxhaJe81feEQp8dMPdDJHNIgEI9Qvl+i7XV57RyzEJExahDw9z/2KEFWmAL7j1GaU4FIo6IJGYLJriaB7cnP2T93tYIucghLAqju1fbGfTmU1ljvs39WfYhGEEhXh4dUCFR1CKQ6GoA1JaMEnd4mhAPYaBPoHWIblBvwQR9EtFBbHs+DLuePEO1wqihuN6JUpxVEP+gXyO/u8olnz79rop0ETbiW0JaO/eRXcU3oGUpRaHMDecRu6JS59g4xcbrb9zW+Ry9MKjAAT8FUD7Q+0pSC9wn0DKVeVVOKQ4hBBRUsr91e1riBx66RBHXq96+Q9ZJIl+IdpNEim8CYnELLVRVQ1JcfRu1ZvQLqEc4AAA7S9qzzUrrwHgo3EfwYea0qyMzJWZnFh4osL+iFsjaHlDS5fIrHAfjlocXwJ9yu1bDPR1rjjeh7HwTvht4TQZ0KTMsbM/nuXkkpMOLc6jaJg0VIsDKBMyxeRX6oeznmcVneb7HtlH7q7cCvvP/XSuZopDuaq8kioVhxCiK9ADaCqEuMnmUBOgUfhmjPARLa5uQZuxbcocMwWZOLnkpPcEglO4HYlESL1xq8+xqexgqwiFr813vTGvyuIwXLvRL0XjG+FL8eli9v57L5a8Wg7RUq4qr6K67rwuwHVAM+B6m60PcL9rRfMS9Ps8ISGB6dOnlwm9MP3/pgNwYP8Bu1mNUAu2+apKl5SUROaqTNa2XstXvl/xbei3fOX7FWtbrCV1eCqWYm8YF1mKvfNrSOElHDkXmWPi7Xe15WNy8nLcJZpbEKZSZfH7zt+t1yIjIwOAU5kVQ6oYGDGuRr4ykoFPDmTe3nkA5OflN6h7pNHiyPRyYKCjU9G9YXNmyJEdd+6QySTLK7lSBgQElAm9cA3XyGSS5bMtnrWb1wi1YJuvqnQxMTHyz3v/rBC+wthy/spx2nk5A3vnV19CajiCI+ey6rEXrf/Pi81edKN0rufkqpPye76XySTL231ut16LCT0myGSS5b+7/7vSvD+2+1EmkyzDCZeAbOXfSiaTLFeYV9TsHomP10KOLFvmxDNT2AMXhBzJFEJ8L4T4A0AI0VsI8X9O1WDeiv6S36FjB6ZOnVom9EJYmBbMru+F9rt6jFALtvmqSpeYmGhdcW1p26Wsn7SehO4JWFp50UQsG+ydX0MKL+HQuVi0t/LjzfbT+cPObpLMPYRdHUb2gmym95hO9GPR1mvRuYt2nu3bta88s36vtmvfjsjISCb8cwIAQQFBDeoeabQ4ol2AdUB/4DebfX84qp0qKbMDkAzsBHYAk+2kuRw4C2zTtwRHynamxfHH7X/IZJLl8YXHKxw7+sFRmUyy3Dlmp9Pq23nXTplMsjz20THrvp87/yyTSZbZf2Y7rR6Fc1g5ZbZMJlm+3+kVT4viNhY8uEAmkyzfueWdStNsbLVRJpMs84/lSymlLDpTJJNJlutD19esMsPiWLq0LiIrHIAaWByOjqoKklKmiLIjHIrrqLOKgYellL8KIUKBrUKINVLKneXSbZBSei5IjT5gytbfa2Dd50RLwN7qca6oR+EkdIvDCEPeGHCkc7zCc6MPHPD4+h4Kp+Co4jgphIgGJIAQ4hbgWF0qllIeM8qQUmYJIf4E2qFZIF5DlYvymMqlcQa6cigztNP46mXPXNZvWZz7+VylxwMvCKTFlS3cKJHzOPXtKfL+zgMguFcwzS5pZjed8d/LRqTVrS+QVZxy+efGuJ9rrDjUcFyvxFHF8SDwDtBVCHEE2A/8w1lCCCEigQuBX+wcHiiESAWOAo9IKXc4q97qyCvKIzMnE4Bdp3bx18G/yhy3nNKenIysDDYe3Fghf20oydJe1Xaf3s2eg3sAKC7RjLsq3/DcjJSS1KGpFJ+u2vC8KO0iAjrVr5HbuXty+X3E79bfwlcwKGMQPk3tPC5649moLA7diqjyhancC5D1Rai2U5686N5XOKg4pJR/A1cKIYIBk5Qyy1kCCCFC0CYYPiSlLP/6+ivQSUqZLYS4BlgGXFBJOeOB8QAdO3Z0imy3Lb6Ni9IuYhCDeCL5CX489mOZ40O2DyGBBL7f9z1Pf/C0U+qc+fdMBjOYJ394kvUZ6wGYd2Ye53EeWXlZhBDilHrqiiyWmtIQ0HZC2wrH0xelU5xZTNGponqnOIoyiwDwjfDFkmuhJLuE4jPFdhWHNBSHaEQWh6l6V5WhVIy0tbY4FF6JoyFHppb7DVqn9VYp5bbaVi6E8EVTGguklEvKH7dVJFLKVUKIN4UQLaWUJ+2kfQfNKiIuLs4pd+ffp/9moBwIQJfwLlp3vg2dj2ujS8IDwxnUYZAzqqRlgDartnNEZ0o6aK9nhmvgVO4p2tCm0rzuoNhSzLd7v+XM6TO0ox0WfwspE1MqpItYF4Ffph8b0zYyPGY4ZlM9mh2n64DA6ECKMorI25uHpbASxaC/QTcqxaHfj0UlRRw8e9BuGkuJdj0OZx3GhKlUyUjYPHJzaVlmQfSD0TQf0ryyypwnuMJpOOqqitO35frv64DfgQeEEIuklC/UtGKh3X3zgD+llC9XkqY1cEJKKYUQ/dE8ppk1rau2SCmtkU9fuOoFwq4NK3M8vUk6O9/eyZBOQ5h0zySn1Ll92XYyf8vkueHP0TJeUyKfJHyiyeMFM9QX7VjEHUvuIDgvmBWsIFfmctfSuyqke+vsW3SlK1O+nsKsLrO4ufvNHpC2dtj6540Z07LQ/rWX1s7xRqQ4dOthT8YeOr3ayW6arwu+JoAAur3VjXy/fAAWhywmLDuMnGVlJ0qu2buG27bdVnWlylXlVTiqONoDfaSU2QBCiBnASmAwsBWoseIABgF3AduFEIbV8gTQEUBK+TZwCzBRCFEM5AGjpBsd/RZpKQ0nYadz3F2jqqTQ9lksnm+cTuRogeu6Ne0GgNnfzJ297qyQrkWw1ilukibSc9LdJ6AzMPzzJoHw0/5jS1El1954kRaNp2G7IOwCTnOaUL9QOjSxvzKgSb+B2zZtS5Gv5vp7dvKzRB2KsqYJPx7O7atvJy8nz/VCK5yKo4ojArCNoVwEtJJS5gkhahVbWUq5kdLxQpWlmQvMrU35zmDEtyMYsHcAAD//8jPXXn1t2QR64/7zqp+Z1noaQ4YMISUlhbvvvhuAjz/+mLvvvpuBAweyadMm3nrrLQAmTpxYYZ+R9468O2hKU1I2p3DNdVo00hKL5g9JSUmh99DetTqXpKQkJk+eDMCcOXOIj4+vVTl+e/345LVPaJrVFAB/H3/m3zS/QvlBJm39BiEF0tuGg1XBH+l/8J/F/+FRHmX9gfUElgTSmc6su2gdOX45pE9KZ9Iszbo8t+UcofO02J8llrqOTq8/hAWHcZrTBPwZwNzoucTHx5OUlMQLj7/AfzL+A5ngZ/EDYM9De6wBEpOSkkhYkWCd+Dd72mwASgrL9phLKfnu0u8o+LmA1k2upC/Lqm4oFG7HUcWxAPhFCPGV/vt64FO9s9yrhs86k/5b+lu/v/jFi1z7VFnFYfLVHojzC87nkROPwGfQl77wlHZ8IhPhKfid3wkmmEd4RDtgb5+RV+eD+R9wzUxNcRSVaG9sX3z+Bfc9fl+tziUhIYG0tDTr99oqjtAtobQ/VTpjOLUklSu4okL5/z70b87jPEzS5FWjwapj7f61HDurjTQvMZWQ2jaVzkc6E1IQQkhBCL8s/QVmaWlPrz5tzfd3k1RPiOsRDEv7VOYp672UkJBA8M5gQgm1pjsYeLBMcMSEhARSU1NJSEgAoDCzEICigqIy5VvyLPj+6IsvvmSf7kExIfjWo3uoMeBQyBEp5dNoI5bO6NsDUspEKWWOlLKin6KBIHT/9ayYWUx9fmqF400va0rhkEL+CPiDPwL+IDMqk10huyiOLaY4ttj6vfmw5hTHFlvT2dtn5M2MyuSX5r9w139L+w18fDX9fuutt9b6XBITE4mMjCQyMrJOoR4M//83Ud8wqcMkoj+Ktlt+x0htZJuQAov0vIvNUaQsjXYbdCqIgY8O5MiHR1jYZSEAbduWjiAz3IpL+i/hr7j17hfWU+i6oGVYS+u9lJiYSHQn7V7YFriNSR0m0WxBs9I5H5QN4ZKYmEjzllqHuL+vf5niy4+8cjwyksJdVGtxCCHMwA4pZVdgi+tF8h6MdRZee+c1uvXvVuG4T4gPw9cOZzjD7ea/kiur/F3Zvgpy+Ghy9Lmw/JIojhMfH19rK8MWQ3F0ie3C80uer7T832b/xlnO1jtXlUQi9JZxYP+BxNwUox34FdgNzUJLJwIaDVxWYBZBQY1n7W3D4rjl5lvoEt8F0P7/AbkD+HP0nwy7fhgPff5QhXzl75Fw33AKrinAx1S2Gao4ZLcejchrJFSryqWUJcBuIYRzJkfUI4w3T6Ph9jTeMKrKqgOquyTGjGEp6pWrCrC7hrjduQu6a94iLFZl0yioJJKBEaBT+Dh2LYzRWcYLmpVykwSVxeF9ONrH0RzYIYRIAaxj6aSUdX+F9WKMG9pk9uyNa8xK9gbFYTSc9mJ32WIcN0lT/bI4bFxVtudovQdsvG7Gm3GJqaQxqY3KFUeRfm/4OnY1TCYjZk+5cizlXVVCDcf1MhxVHE+6VAovxV4D4km8YTiu9aGu7pLYWBz1qo8DadfisMYls3GjGN8bm8VhPA95+/JIX1Q61NqIW2YMGqkOw5K3DnnXUa4q78fRkCPrXC2IN2K1ODzsqrLGQfKGly5DB1TTTpaxOOrR22KlFof+dmx7LlbFYbI0qqZN+GvX5UzyGc4kn6lw3BTo2PNiDUdiKb3OJZYS5vw4h/6Ujmic09/EVFmMb12EVjgVR0OOXAS8DnQD/NBeAXKklE1cKJvHMRRHmUi1nsCYZ+gNFofhqqouFIRtH4dXaDzHqMzisCoR21Ox6ePwaUQWR9i1YbR9oC1FJ4sqHDMFmmj7z4qxy+xhuP9sFUfKkRRe2vgSn/O5dd/rA0wMyNnF5XUTW+FEHHVVzQVGAYvQQo/cDTSs5c7s4DUWh/CePg6rxVHNJamvFgfYd1FaLQ5bV5Wl1OJoPGoDfJv70vmtuj/+9lxV2YXZpYrbSGcxkWup1TxjhYtwVHEgpdwrhDDro6w+EEL8BjzuOtE8j3VUlYc7x41WqaaKQ0rJXxP+Ijs1u9ZVmwJNRL8QTZP+unFpdHFU1+9TX/s4pH2Lw3oP2PwFjbWPw1kYVqut4iiRJRVGWZmlGUs9slobA462iLlCCD9gmxDiBSHElBrkrXdIKVkzYA0heVoI85p0jiclJREVFUVUVBRJSUl2j8fGxjJ9+nRiY2PtpqmAXn3WHVl8Zf6KVQmrKi3XtryCgwUce/cYWSlZtd7OrjvLz4k/W8s8maEFJj55qkKA4rIi643Ccwuf48h3R6pMe+SNIySHJrPIfxHP/PMZx6+LCwjYG8DML2YCcOxY6Vplxj3w98m/mXrRVFYGrOTAWweAxqs4yt/LNbqnKVXG4SfDWX3FapKSknhk/CN88OYHZdJ98OYH7Es95HT53c25zedY23Itq0yrrNt3fb8j6auq24yqOPTyIZJDkvnC/wsSJyW67dlx1OK4C01R/AuYghZgvP6EO60hJdkl+KZoXXF7W+2la9OuDuetLrSHEXZh9+7d5OfnOxT+I61zGtG7ogmwBBBAABve3MA1idfYLde2PGNcfQYZzD9/Pp/M/4S7/nEXe/busebz8/OjsLDQ+vuC87XlTvbs3cOlXMpoRrP5583WSY4H0g7Qn/7s37+/SpmbXtqUzBVaIOPTy09bw7DYI2NJBiJbEE44H7/3MalFqXUKi1IXQn8tDZmx4sAKhjEMgI7NO3KOcwgEg7cPJrggGIB8n3z+avMXD+WHu11WT1P+Xq7JPQ3g09qH402P0/psa8zJZhJOJeCf6Y9fiV+FtPu/z3DFKbiV09+dxpRpIgibyaK/wn+n/7fW4YAyFmUgcgQRRDDvf/Pc9uw4GnLkABAK+EspZ0opp0op97pUMg9i68ceP2F8jVxV1YX2MMIuTJ061Rp+oTp+uPEHrnn8Gj7poIVXH9BvQKXl2pZnnIf0k9z70r00GdCEe1+6l7zIPE6GnyQvMo9rH7m2zO97X7rXmuZciDa8sl/fftYyO3XUwmifF31elTJ3fKwjf170JwDdOlecdV8GG0/WiGEjHL4urkCUaJbDd9HfMfStodb9bZtqHb4tDragCZrbbmrkVFaFj2Xzx38yPqvBd/lVoPy9XJN7GsAUYOKuSVpoHZMwkZiYSNvW2nXe1WoX5746x6E2Wii87t16uOYk3IjxPH7T5BvGhI3htFmLdTZtyrRahwOybauuu/o6tz07VVoc+poZM9AsDZO+qxh4XUrpmSfbHeijZbICs5AmWSM3RHWhPWyPz5o1y6EyBYI8/zzueuAumA6dOlRcA8FevUafSKeoTgyIH1BpOntyxMfHc/S9o/x1/190bF8aNKBFcy1cesuIltXKHdRae7NqHd66ynS2fTcj40fy4IQHqy3bZehKLPrC6LLXSX93iL8mnowvM7BgYfOOzZgXzYexYxvlgkP27mVH72nQlEWxWYsqLKTg+uuvx3LQAlvAv70/8fHxfDBFW6qndUTV91B94GSW5t41XWIiLD6M4ieLIQNW/bCKgdcNxGKyMOvALHZs2MHjlzrWfWyrOG658RYmjXPOukDVUZ2ragrauhn9pJT7AYQQ5wFvCSGmSClfcbWAbmPPHnjsMQBkQSAwHovQNIhpzFiwYz67C9P5OyEESFoG3IhcvQZGTqs+47kWwF2Io4dh5PPVJi+PONAdGAbfrYWR2o0sD0UD5yNSU2FkhUUby+bf1wPoBFt+hZHLK0+44xagnVb+m/+Db36vPK2rOdoJ6ITcuxtGjrTuFoe6ACOQP6zDkt8ZMCFG3waHdJddI1QcdcUkTCC0PiKTNCFLJLJAH3Dgo2lwY2VFLxiJXmd2ndhFe9pzJPcIvx3/jbOms4QTzu3zb7em+Tr2a14Y9UKtFEdli425guoUx13AMNulWqWUfwsh/gGsBhqO4jhzBpYtA0DSHE1xaHerWLVKW0bKQ4gxQAjsP/kXUcDRzEMc/n1Z9RkLI/HhLnLyz/KdI+nL15uVh5lhHD112FrfWb97tYPpJ2Br1WXKDvpb4okM2FR5WslQDMVh+X0H1EJWp9FJD/Z8OhO22coxFBhB7mEzhvkhkmwUZ+v6/0bsbkxCu44lphJMJSYWTF5A/vZ8mtO8tGXSn8H6NqTbHoZlHds2lsnjJ1PUoYiiBUVIKZGnJJZUC+Hnwq3r7ziEjUI99c0pirOKaXNvG3ybu3a6ZHWKw7eS9b0z9PXCGw7nnw9L9IYgU8D9pTO2TR9/AuZgj4nm+/dMyE7l854Wpu2DNdEmnh9Zfb7o42beexv+biG57+6a13tlagnTl8LaKBOz9KEQ930niAPMffrAnJuqLuDNNDgE9L4QZo+qPN20UPhL+yrvvgdu9FykfvHBPjgAstsF8EqpYjBv9oXnIBtt8IA5yIKYrx/394crrvCEuPWaIF/NlWlMEO3wZulqgjK0bGgbWdIALDpdH4QGhtKnTR+4B20DTiefJvWKVMwWc40mzNpaHCeXneTkspO0vLGlxxVHYS2P1Tv+5jSJ+jpVwTKYW7mVYt1VJa67HgKaeky2qXsDET+/QrcjWidz2+C2DI0aWk0uaGVqBUCwf7BD6cvT/Xh3ANoEtbHmj2waqR2LuQJGxlRdwKKPtM+wiDJun/LI57cCWQCk/xFBjtCUdPMhzWk9xs1v8l99rH2Gh5WRucU1Fs5rfpjCE9pt33xoc7g6zF4JCgeJCI7g4xs/xvScydqoHht0jLxOeVz2n8uAUleVV0x+rSv6Odob3m9EFDZbzFVaV6fyTvH0uqc5na91rI88O5KmNGX/xfvJCdfiz17gc0HZkVsuoDrFESOEOGdnvwACXCCPx8jMzeSjVK2ha326NbdyKxZhIdg3mAAfz57qiPNHMOL8EZwQJ/jz3T8ZGjmUSXdX3wmWtTWLrQlbOT/8fL67+7sa15vun87Ot3dyecfLefBurcN63459HFp5iGaBzarJTWlsuur80zbHs3/NJvtXbcJi+qfptPpHK/eGfNFlkeayD6/J30THRxvdygIu566Yu9jgt4GSAq1VvWrGVbQY1qI0gclQHJ6QzrlYlZ+dwGaG4vAp8alywuzinYt59ZdXrb+vzLuSpjQloU8Ch1seBmBEkxHOE7oSqlQcUkqXxm4TQowA5qBdyveklM+XO+4PfAz0BTKB26WUaa6QJap5FB/coE088jnoA3MgLDSMjfdsxN/Hv5rcbsJOhNaqMG7UWkf3NdupT7+nHSrTuHuqcdkacnac1pHALoEA7L53N7JIIi3SrYrDGI7bcKe3eh+2/2/5kOyl4XYajquqKsVRnasqr0jrbB0aNZR/9P4Hrd7VvArPDX+O4g7aCLWI4AjnyVwJDocccTb6yoJvAMOAw8BmIUSSlNJ2DfN7gdNSyvOFEKOA/wK3Vyyt7rQMasnY2LEA5AblkkIKTQKbENs61hXV1QprY+3g25e1wa9lI2ivPqsZ7chzbNTroMURfls4oRdqE/D+Gv+XplDc/abpYCwuhROxaUhNfuUufANyVVlf5Oy8CBkK02wxV2lxGEqlR3gPxsaOZZPPJgoo4OZeNxMYGegCqe3jycejP7BXSvm3lLIQ+Ay4oVyaGwDdUc5iYKioNixr7Ti35RzrA9eT7J/Mj11+BCA7r/YxnlyC/oB9vfJra3gC2xAn5UM+bFy3EYDTZ047VHz5sCXGDZ6+LJ1k/2SS/ZM58JIWZsMRxWG4e86bfx4v3/gyveJ68cj0R8p8frb0M06f1uR7+Y3SNIXFWl+COxuM4/OP0+EzrYM245T9mcr2Qrso6oZtQ/r2u2+Xvb66xREx/yLm/t9cT4hXK2zvk78e/Itk/2TaL2sPwIn0ExXSrdugrVxx/onzmbJkSqXl+u/2Z9HsRVxz3TWsNq2m4IAW/PHFl150633pScXRDm3MjcFhjDGZdtJIKYuBs4DdHkkhxHghxBYhxJaMjFqEJ5BgybcgCgW+euT/n/J+qnk5LsS6gE5uHmlpaSQkJFhDnKSlpfHyyy9bw44AvPfuewAcOHzAofJtw5YAhMSGkGPOwYQJUSgQhQIzZgpMBTS5qPqI+tk9ShXvyf0n+eP6P3jJ76Uyn6N/H82hPO02+MjvI+sxi9n9b5qnVp6yfv/2zLd205S/Roq60/zK5gCkk84LC18oc32bh5fGC9u1bJdH5KsNtvdJxpcZ1mcn1y+XVemrKqSb+e5Mss3a83LJzksqLTd4WzAts1vib/HHT2pzy9JI44V3X3DrfdlgDHIp5TtSyjgpZVx4eM3jBoX2DeXS3Es598U5xnccz/0d7yfm/WpGDbkZ483sQtOFvOP/DrMLZjO7YDbv+L/DO/7vsLDlQuYFzWN2wWx+HfQr/yr6FwCdIivONLdH+bAlAZ0CKFlUwqO9HuXcF+c498U5Hu31KAWLCmh2SfWd43G3xDHn1jkA+Jf4Yyoy4Sf9ynwGmAIw611pPvhYjxlzaIqLi2t2keqA4dqbddMsWt9mfzSXvdAuirrRfUF3zn1xjud6P8cDDz9Q5vre3PoM+ztpI92iOkR5UswaUeY+0fs27r32XuL/E88Foy6okO7JZ56k6GNtfROzpfKuZeMeXRW9ivEdx/P9o98zp/ccJj882a33pfDUxBohxEDgKSnlVfrvxwGklM/ZpPlWT7NJCOEDHAfCZTVCx8XFyS1btrhOeA+R/Xs2W2Jqfl6t7m5Ft4+qiRflIjKWZrDjph2E3RBGr2W97KZJ6ZZC7q5c+u3sR3A3bSjuKv9VBBUGMeD0AAKbucd3+8fNf3ByyUlm3DqDIf8cwlOXP+WWehVVMHo0H20y0+nAfewftZ9xC8d5WqIas7HFRopPF7Py85XM/nM2Lw9/mSkDK7qjinKK+DHkRwp8Criq6Cq7ZX304Ed0erMT+2/az7gvnXsthBBbpZRxjqT1WOc4sBm4QAgRBRxBWyjqjnJpkoAxwCbgFmBtdUqjIRPSO4QBewdQeLwGU2jMENontPp0LsLkrxm1hccKOf29/b6Wkmx9vozNSC1jNE1JSQ1m0dYR26VgG2OYdK9ECBDGUoueFaUCFgts3w6FVT+PslCzmi0njwNgPnwENm+ukM5UoJ2gyWKyexyAU2e1z7zsytP06gUBrp1C4DHFIaUsFkL8C/gWrdv3fSnlDiFEIrBFSpkEzAM+EULsBU6hKZdGTWB0IIHR7hs9UVeM9aezUrJIvTK1yrS2QzE9suqhzVKwCm/CS0dWzZwJ5VxDfzZtx+Zmg5GYsJjzyYhYTd+8zzETyOZfvoTzwDT7JUh5qUJxAhPwvRa3q39/u68uMvIOoDfs3A79/21frj17tEgYLsSTFgdSylXAqnL7Emy+5wO3ulsuhfNoenFTWo1pRcGhqpf+DO4ZTEBU6VuSMezQUuK+Rty6FKywVL+musJtCGNeg/uMT8fYq68sERUFYdqYnU0nRnPegT7WJD90DCbGZMJsgZT22r0c2jYS4ipGlxZSwFZtxUMZ19eu1SsK9L7FkCCIq8Sr5O/6eWceVRyKho/J30S3D2vev2JYHG5VHMpV5X0IgTB8VN6mOAyv+dNPw51afDWfaK0j/0z7MzQ73IwrWlxhHaX5yCWP0KxpM255fCL4VYBCu5sAABiqSURBVIx9JwDEDwCUbNqEyadivCk57kPYDvSJgY9frXDcXSjFofBKrIrDnbEmbFxVyuLwJvQ+MG+bPW6vu1XfFXZRGCWLSxjYaiBHLUcBSLwyEZNv1QNZS0wl2iTAYov91tlQnh4eD9tghuMqGhbK4lAYGBaHo6F23IY1ioJNyBRduQl/7dNSYCkN0+NA6JwSfSBApedqPA4uDQZVPUpxKLwSq+Jw4wo+xsNqPLwKL0AIrK2lt41ZsKM4DItDBNgoDh1H4rtZ9KCOJcWV3INKcSjqSmXhL2zDkDgjBEFdw2yUz2/8Lh8ixRZDcQwbNqxMeJXy+WorW/l8eybv4ex6baijxaRcVd6ESW8tz/v2PNaKtawestrDEpXD1uKQ2vcV364AIH1BOlC6tk91GCP6von4huXvLmdty7WsFdqWbEomcmEkABmZtYiO4UyklA1u69u3r2wMxMTESEDGxMTY3W/vmDPrqW1+43dAQECl5S5uslgmkyxbBrS0prGXr7aylc+3KXKTTCZZJpMsQx8Llc+uf7ZW56pwMnfeKef1iJRfBXxl/X9WmVZ5WiqNW2+VEqT87DPrrvnt5stkkuXgpoPlMpZZZZ4bOtehIhN7JlrzPNTmIet3222F3wp52ZDLnH46aNMgHGpjlcVRj6ks/EViYiKRkZFERkY6JQRBXcNslM9v/J46dWql5RoWR7t27aznYS9fbWUrn88Yivtg/INkBWUpi8OLKA5I48bHbmTUzdo0rgA/L1kKyE7nuGFxmKJNpDyewj2d7uGeTvfQYX6HCmnt8d/R/+Vg2EEArrpSmz2+LXAb93S6hzXT1jDyppFcP+16Oo7y7NowHgs54koaasiRxsTi5otpeaYl0Tui6dDdsYeuLvzU/icKjxSSPD+ZxL2JPDf0OaZdMs3l9Sqq4a67eG/nfO6Ph6vaXsW08dOQAZKwtDB6t+rtWdluuQW+/BI+/xxuuw2AhW0W0uZ4G5qva07M4JrHugt9LpQ5r87hvPTzSLo6ifiv4/mt92+8dd9bAGTkZnAm/wz/vfK/PDboMaeeTn0JOaJQVIphcRQeLSS/SX616f1a+2HyqYMBbfS/6p2TalSV9+Cvx7lce2At05hGUVERfd/py9GpRwkPrnlAU6djxzqt7eJpvSJ6Wfs5zmVri6+eKzrHnlN7yqTrHt69VuU7C6U4FF6JoTgODTvEoTLR9+3T5KIm9NnUp9p0ldanu6qMTkzlqvIervsLJoQO4VRrbba1WZopthSTmZfpWcVRxXBck7l2LzHrxq4j5bUUik4UMaH3BHI25HBV56sY/a/R1jShfqG0CW1Te7mdgFIcCq9kff/1DP5pMG1C2mAWVYw9lJpVkrUlq24V6qMfDYWlLA4vQQia58PbLcciR93FujvXWfsRPO5mt6c4jD4OU+0Uh6/ZlwD/AIooopm5GTnk0CSoCZ3DOtdZXGeiFIfCK0kansQbF71B2uQ0OjWrfD0RaZGsM69DFtetEbGNU6XwQqS0rjppkiaQVLk2t9tkKodVcdTS4tAy68UX1m3pZ1eiFIfCKzEJ7WnZdnwb6TnpDuVJOZRSa99ycYnmSD+ep4W+Vq4q70MIoTWiFi30uFvD0VSFPYujDorDmGFuKbSU+e1NKMWh8Ep8TNqteePnN1abdo1pDT4WHwa9M4hin9qtGLgibwXBBLPoz0UQQNXuMYX7KKfAhVkgLVKLIOuNrioj5EgdGnsjr2FxKMWhUDjIIxc/wvu/ve+QO0KaJVggrnUcxX61Uxw+QnsUYtvGEtA0gPgu8bUqR+EiZGkjKoskJoupyntDSu2ecEWja5SNRWJBIEssUKTdd4bFYTbV/sXDanEUKItDoagRD8Q9wANxDwBaeJDJkycDMGfOHOLj48vs8zH7QBGceeEMmXmZBAcHW9MZ+RMSErj22mtZuXJlmc9PP/2UnJwc0BdxW3fvOsxBytrwWvS/5pPXP+GHLj/Q++6KczmKzxazvvN6TOkmCq8sZPia4U6rXpZI1nZbi3mPGZhCsWkSPrf6ABsBCENbl2P9xvWc1/O82lWin2PGIi2syIFDB+hClzpK7ly8sNtFoShLQkICaWlppKWlkZCQUGFffqE2z+PwwcNkZGSUSWekTU1N5eWXX67wmZaWRkZGhvVNUT0RXkY5V1XLG7QhueFZ4Sx+fbHdLLm7cjGla39k7g+5ThWn6FSRrjQ0fCzau3eJKLFuu1ruYs7bc2pdR9g1YRSLYkooIY88FuxaUGe5nY2yOBReT2JiotW6sA1bYuzzz/SHLHgq6CnyS/LxMfvQJ7TP/7d3/9FRlXcex9/fJDSUCAISESSaKNItVhMxh7riWu2BqqwN1d11lW2Vdj1uz9Eeuu6eri5dpDnHPae1aNtVW6lyxK1H665apmCtaH9ti4pRwy9F1AKrkR9Blh+hAsnku3/cO8MkmQkZJsnNzHxe58zJzL3P3HmeXJhv7vM89/uw8dqNANw18i7Wj17PxAkT+WD7B11+btu2jY54B2WHg/8Kxzu4LoNj6qNTaX6+mYm7JnLdNdelLZOaknzUCaP69fPTpTt/6qzv8vjeNVRUVDB37lxWrlyZU6qfqlureH3y612usoeaSFKOmNldwOcJOgjeBb7s7nvTlNsKHCCYZd/R19vhlXKkuLxy7iscXH8w5+OUnVTGjF0zFDyGknnzYNkyWLoUvvxlAB479TEmfDCBEatGMH3m9B5v2fs/e2m+uBmAYeOGMaN1Rr9V53DLYV6c9GKXbW03r+XKe+f322dEJR9SjqwCbnf3DjP7NnA78C8Zyl7q7rsHr2qSb85ZeQ77/rAv5+OMnDZSQSMPJNLCJO696S71qqC/F39Kd7zSXO7ZyFORBA53T02o/xLw11HUQwrD8KrhDL92iGRMlf6V5n6aRFqYjDd9pqyBNBiBo2QIznoaaEMhVH4F+EWGfQ48Z2avmtlNvR3EzG4ysyYza2ptjXiRExHpXyld6skrjgxBYbCvOMqOM71IPhuwFpvZ82a2Ic1jTkqZBUAHkGnawEXuPg24ArjZzC7O9HnuvsTd6929vrJyCGTMFJGBEf6Bn66ramfbThb/fnHy9eH2w3zrN9/ql4/9qP0j7njhjh7bi7GrasBa7O4z3f1TaR7LAcxsHnAl8HeeYYTe3VvCn7uAp4GeI2EiUrjSdFUlrzjSdFU99eZT/OrdXyVfe9xZ9NtF7GjbkXNVfrftdzyx/oke208q79+ZW/kgklBpZpcD3wAa3D3tRGszqzCzkYnnwOeADYNXSxEZMlL+tkyOcaS54jgcP0xJ59GvtVIP7rloj7fnXIXux0449ePjcj52vonqGuteYCSwysyazexHAGY20cyeCcuMB35vZmuBNcBKd382muqKyFCRDBxHegaOeGecYfFhydelnaVYp/VLJt1O7+xy7IRiTGsWSeBw98nuXuXudeHjq+H2D9x9dvj8j+5eGz7Odvc7o6ir5J9YLEZNTQ01NTUsWLCAurq65M9YLBZ19SQbvcyqin8xzor/WJHcHovF+Nk3f8ai/1rUpfzS+5cmM83mpAWWLFnSY/Mbb27M/dh5pvhGdaTgpaYj6Z5mJDUVieSRlK6qTXWbks9jdx39Q2DhwoWM3zq+x1urd1fTsfP4kl+mKtnU/evyMB/nf/nprx/K+dj5RoFDCk5jYyPV1dVUV1dz6623Ultbm/yZSyoIGRpenvUyv6gLZvBf9YWrktsbGxsZO3IsALE/i7HqX1exc9ROIPPNgtno7AiuWl4+7WX2L9/P2ed+g09zA9d95Zqcj51vlKtKCk5DQ0MyMy7AnXfe2eWn5JE0XVVmllypsfac2uT2hoYGPox9COvg3PPPZd6d83jigWAWVK4rRMLRezgqT6kM/n2FOaT+/MILcz52vtEVh4jkFcMy3wSYmDwV/kmcWEO+P24ETB6j+2B4Ea4WqSsOERn6UsY4Sqzk6NrwYXqRfav3sfvp3YxZMybYEE5+6u2ej6yrkAgciT+3o16BMEIKHCKSV8yMeEkQMRJf5pu/upmD6w8ymtEAxE8M9h8rIWI2Ep/lpeGx0iwdWyzUVSUiQ1e6MQ6OjnE8sOYBrnj0CrZ/sB2An8/6OYuvXMyey/cA/dxV1ZnhikOBQ0RkCErpFpo0alIyIGzetZln33mWjkPBdNuHz32YFfUrmHjyxOBtJQMwxtE9ThRh4FBXlYjklfv/8n5eXPkivAQ31t7IvLnzGP69IK3+T675CSdOOJELJl0AkLwy6YznfgOguqqOUuAQkaErzZfyiGEjqD6pmvd4j8kjJlM1vorV7avppJNZn5xF2cijX2uJMQ4OHd/HuzvxA3FKRpTQ3hZO2dLguAKHiOSB1auhvDz50jaOBk5kW+M2tjVuS25fd/s3eDr231x99dWcd955uAdf9h9+/kOarl9A/WenZPWx6++uZM+6EQBMYhIAh1p3BMvZ7ggz7uqKQ0RkCEkEi2XLgkdoLLXs4JvE+Xhy2xhe5ez77mEaJG/Oe23G33DGe0GwOO2RP8Aj/57Vx+/lmR7bztj5DMxLWXd8ePGtPqnAISJD19e+BkeOwKGufU2jgar37mfNmjV0dHRQVlZG9fTpvM+lNDc3U1dXR1VVFS9MWs64/5vKJW9cwodTP8XJ9adn9/mPDuuyFO2rkx/mmqrhMPP6YMPpp8P55+fWxjykwCEiQ9eUKfDAA2l3VYWPdNsT7L6pyXs+xi74N5jbMwlib/yx30LKjKzxs2ZSdf8XszpGIdJ0XBEpWKl5rY5nSm7391hp8Y1npKPAISIFq9e8Vsfg7tBtFq8CR0CBQ0QKVuoVR+pYRZ+kufWjpExfmaDAISIFzOiZ16qv0uW3KinVVyYocIhIgcs2X1XL/hYuXXYp5/+w52wpBY5AJL8FM1tkZi1m1hw+Zmcod7mZvWVm75jZbYNdTxHJb8czOP7cu8/xm62/YdPOTT32jR+V3aysQhXldNx73P27mXaaWSlwHzALeB94xcxi7v7GYFVQRPJbaldVfH+c9j3tDBs7rNf3dHR2UH6knGtO6bkk7OTKyQNSz3wzlO/jmA684+5/BDCzx4E5gAKHiPRJ6hXHlgVb2LJgC4e/cJjLnr4sbflYLMYjdz/C8j8sp7yjvMf+5nXNTGDCgNY5H0TZYXeLma0zs6VmNibN/lOB91Jevx9uS8vMbjKzJjNram1t7e+6ikgeMowXp7zInvI9tNEGQMtzLRnLL1y4kFHbRlHeUc6RkiPsYx/t1s6BsgNsYxvffS5jJ0lRGbDAYWbPm9mGNI85wA+BM4E6YDuwONfPc/cl7l7v7vWVlZW5Hk5ECoCZ8dqZr7Hl4S384JQfAHDaaadlLN/Y2MhJo08C4IWaF/h69df56Gcf4U8699Tew/xvzx+Ueg91A9ZV5e4z+1LOzH4MrEizq4Wu2QMmhdtERPrEwlWXPnPxZ7jxyRt5fcbrjB09NmP5hoYGDvz2ADTDlE9OYcvPt3TZJ4GoZlWldhJeBWxIU+wV4CwzqzGzjwHXArHBqJ+IFAYLU567e3LlPj/GOhreEe4vHcia5beoBse/Y2Z1gANbgX8AMLOJwIPuPtvdO8zsFuCXBKdwqbtvjKi+IpLHHMdKwshxjMUAE4HDy4p3oaZjiSRwuPuXMmz/AJid8voZSJMQX0SkDxJdVe6esnLfMd6USE0ylOecRky/GhEpWMmuKlK6qtKkEtlyxxb2/movACe/fXKwUd+OGelXIyIFK/WKI9lV1S1uxA/Fuyw/W05w/8ahU45zofIioMAhIgUrccWx+0+72f2n3QC0d7Sz/cD2ZJnOtmDQw8qNScsn8cTGJ3ho80Ncd8V1g1/hPKHAISIFq8SCgY2Gxxs4c8eZPMiDvLHzDS68+8JkmYpDFaxgBQf9IJNfClOKTICSEiU0zESBQ0QK1txPzWXr3q10eidjK4L7N0q8hDFlYygfHnRJVZRUANBpnYwpC5JYHNx7kBN2nBBNpfOAHWtOcz6qr6/3pqamqKshIkNI24Y2ms5pYitb+V7t92hubgbgyO4jrK5czX72s7B2IQBr166ltrY2WaYYmNmr7l7fl7K64hCRopAYHB9ePpzGxsajO8L7OkpKS5LbFy5c2LWMdKHAISLFIZxUVVNdw6cbPp3cnFinY8y4McxomAEovcixaPRHRIpCpum4iSuO5H45JgUOESkOiW+7bilHElccVqrA0VcKHCJSHDIkOUzeSa5vwz7Tr0pEikLGJIdhbipdcfSdBsdFpDiEcSHeFqf1qaOrhB7ZfiR4oj+j+0yBQ0SKQkl5EBnaW9vZ+Fc9V2hI7JdjU+AQkaJQPrGc6m9V09bc1nOnwSnXnzLodcpXChwiUjSqF1YDEIvFmD9/PgcPHgSgoqKC79/wfRrQ/Rt9oZQjIlJ06urqWLt2bZdtxZZipLtsUo6oU09Eik5jYyPV1dVUVlZSWVlJdXW1UoxkIZIrDjP7KfCJ8OVoYK+716UptxU4QDBhrqOv0VBXHCIi2RnySQ7d/W8Tz81sMbCvl+KXuvvuga+ViIj0RaSD4xYsz3UN8Nko6yEiIn0X9RjHXwA73f3tDPsdeM7MXjWzm3o7kJndZGZNZtbU2traW1EREcnBgF1xmNnzQLqJ0QvcfXn4/DrgsV4Oc5G7t5jZycAqM9vk7r9LV9DdlwBLIBjjyKHqIiLSiwELHO4+s7f9ZlYGXA2c38sxWsKfu8zsaWA6kDZwiIjI4Iiyq2omsMnd30+308wqzGxk4jnwOWDDINZPRETSiDJwXEu3biozm2hmz4QvxwO/N7O1wBpgpbs/O8h1FBGRbgryznEzawW2HefbxwHFNv1XbS58xdZeUJuzdbq7V/alYEEGjlyYWVNfb4IpFGpz4Su29oLaPJCino4rIiJ5RoFDRESyosDR05KoKxABtbnwFVt7QW0eMBrjEBGRrOiKQ0REsqLAISIiWVHgCJnZ5Wb2lpm9Y2a3RV2fgWJmW81svZk1m1lTuG2sma0ys7fDn2OirmcuzGypme0ysw0p29K20QI/CM/7OjObFl3Nj1+GNi8ys5bwXDeb2eyUfbeHbX7LzC6Lpta5MbMqM/u1mb1hZhvNbH64vSDPdS/tHfzz7O5F/wBKgXeBM4CPAWuBqVHXa4DauhUY123bd4Dbwue3Ad+Oup45tvFiYBqw4VhtBGYDvwAMuAB4Oer692ObFwH/nKbs1PDfeDlQE/7bL426DcfR5gnAtPD5SGBz2LaCPNe9tHfQz7OuOALTgXfc/Y/ufgR4HJgTcZ0G0xxgWfh8GfCFCOuSMw8yKO/ptjlTG+cAj3jgJWC0mU0YnJr2nwxtzmQO8Li7H3b3LcA7BP8H8oq7b3f318LnB4A3gVMp0HPdS3szGbDzrMAROBV4L+X1+/R+QvJZujVOxrv79vD5DoI8YYUmUxsL/dzfEnbLLE3pgiy4NptZNXAe8DJFcK67tRcG+TwrcBSfi9x9GnAFcLOZXZy604Nr3IKeo10MbQz9EDgTqAO2A4ujrc7AMLMTgCeBr7v7/tR9hXiu07R30M+zAkegBahKeT0p3FZwPGWNEyCxxsnOxCV7+HNXdDUcMJnaWLDn3t13unvc3TuBH3O0m6Jg2mxmwwi+RB9196fCzQV7rtO1N4rzrMAReAU4y8xqzOxjBCnfYxHXqd/1ssZJDLghLHYDsDz9EfJapjbGgOvDGTcXAPtSujnyWrf++6s4up5NDLjWzMrNrAY4i2DpgrxiZgY8BLzp7nen7CrIc52pvZGc56hnCgyVB8GMi80EMw8WRF2fAWrjGQSzLNYCGxPtBE4CXgDeBp4HxkZd1xzb+RjBJXs7Qb/u32dqI8EMm/vC874eqI+6/v3Y5v8M27Qu/BKZkFJ+Qdjmt4Aroq7/cbb5IoJuqHVAc/iYXajnupf2Dvp5VsoRERHJirqqREQkKwocIiKSFQUOERHJigKHiIhkRYFDRESyosAhkiMzi4dZSTea2Voz+ycz6/X/lplVm9ncwaqjSH9S4BDJ3UfuXufuZwOzCNK53HGM91QDChySl3Qfh0iOzKzN3U9IeX0GQTaCccDpBDdoVYS7b3H31Wb2EvBJYAtBBten05UbpCaIZEWBQyRH3QNHuG0v8AngANDp7ofM7CzgMXevN7NLCNZQuDIsPyJducFtiUjflEVdAZECNwy418zqgDgwJcdyIpFT4BDpZ2FXVZwgK+sdwE6glmBM8VCGt/1jH8uJRE6D4yL9yMwqgR8B93rQD3wisN2DlNdfIlimGIIurJEpb81UTmTI0RiHSI7MLE6QnXQY0EEwyH23u3eG4xVPEmQ1fRa42d1PCNdV+CVBJteHgRXpyg12W0T6QoFDRESyoq4qERHJigKHiIhkRYFDRESyosAhIiJZUeAQEZGsKHCIiEhWFDhERCQr/w8n1ZCPZ2JaPwAAAABJRU5ErkJggg==\n", 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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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -566,11 +1053,20 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -658,11 +1154,78 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 8, + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -749,11 +1312,73 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 9, + "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", @@ -857,11 +1482,37 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 10, + "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", @@ -1061,11 +1712,32 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 11, + "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", @@ -1119,11 +1791,20 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from __future__ import division, print_function, unicode_literals\n", "\n", @@ -1200,11 +1881,20 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 13, + "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", @@ -1237,10 +1927,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# Quadratic training set + noise\n", @@ -1253,11 +1941,24 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 15, + "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": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1274,11 +1975,20 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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t7VWkN3r7qxO0s+MarB9/DP36QV4e1NW1vcPKygrZ1tgEh31ZeLvn0aW5ru3biHNcK1UGhscq0oapU6dSVlbGli1b2k19t2HDBoYPH87UqVN58MEHbWphehJIw1JRod6oJEtVFZzUmKFNGhA+KMBE3JaE323tVaQ3evurE7Sz4xqsjY2g44vI8i8k+kB81lmwdq365VOEUFxcTFlZGe+88w4TJkwI2XfzzTfTtWtX7rnnHptal96otyvJE4hn+/JQBuuAgrebKbzImnNVVFQwfvx4ayq3ALe1V5HeGOmvdmtnWmYJMINq/0JTk0ot4ATsHr4YxsiRIwF45513Qra//PLLrFq1ijlz5tC9e3c7mpYQFRUVZGRkUF1d3aFn8nFYN3IsgXi2JjIpB976t/6QAKN9yKkzBEXDbe1VpAanaqdZ/TUl2iml7JBLIUgZZ/FFWOId027JzJSyslKmCkBqX5u7+OyzzyLvSOSap2JJEp/PJ3v06CFHjx7duq2xsVEOHTpUnnjiibK5uTmh63bWWWfJrKwsmZubK7t27SoHDRokr7rqKlldXZ10mwM8/fTT8phjjgnZtmbNGun1ek07x1133SVPOeUUmZmZGXKNUkVlpZTz54feupWVUnbqJKXXq/2NdlsD1dIBGmfVUlhYqOv6deok5ZcMlhLkur99GfcYu3GrdioUTsJO7ey4HtacHBg4EE45BQYMgL59tSVom+jbl8YefdnbfSAHhpyCCJSLcxxHHKGdIzNThQMoIiKEYOTIkVRXV6Pdp/DAAw9QU1PDokWL8Hq9Cdd99913U1dXx759+1izZg0DBgxg5MiRvPDCC2Y133KOOeYY5syZY8uI6sDr7Lvv1v4GPAKRRsEqIhOIZ8vvlcEkYMRw62JYJ02aZFndVuC29irSGyP91W7t7LgG67Bh8M038P77sHEjbNumLWHbsndvo/ueb8itCSr3zTdUPfo+C67fSNXzEY777DNKgVKPRxmryWKGP7SyEjp1Aq9X+1tZmXydJjBy5Ej27dvHhg0b2LFjB3PnzmXChAmMHj3alPoBBgwYwLx585g8eTLTpk1DSsnBgwe59dZbGTRoED169OC8887jyy+/bD2mpKSE6dOnM27cOHJzcxk2bBirVq0CoKqqiqlTp/L111+Tm5tLbm5uyEwoo0ePpmvXrnTr1o3LL7884dHQ1157LePHj6dXr15Jff5EiCaugVGwXq/KIKCH4mLoeUQG4wCa20IC4r0aLC0tNfSgMm7cuOQammLc1l5FajDa71OFkf5qu3Ym45518qLntVY04rq3a2tte71k13mTJWpIgFlEek9hM6+99poE5DPPPCOvu+46mZ2dLb/66itDdUQKCZg7d267cq+++qoE5Oeffy4nTZokL7zwQrl9+3bZ0NAgf/Ob38jjjjtONjY2ttaRm5srX331VdnU1CT//Oc/y+zsbPnNN99IKaOHBAT6HiC3b98ujz32WDlv3rzWMhdeeKHs1q1b1OW5555r1+5Zs2ZZFhIQrUvEur/1dCNUSEAbJ5+sPeK9/37r9Yv3alBppyIdcVP/c6p2dtwsAUkQN0Gup+M6pl2L3cMXI3Daaafh8Xh44okn+M9//sNtt93G4MGDLTnXUUcdBcD333/P0qVL+fbbb+nTpw8As2bNYtGiRbz99tv86Ec/AmDChAmMGTMGgCuvvJLHHnuMpUuXcuedd+o6X58+fZgwYULIIIKVK1ea+ZGSItasLLHSsziwGzmbjAwEIP0eViuSiwsh0H7r3IHb2qtIb8L7q5O10xaDVQjRA3gSGAvsAmZKKZdGKJcNPABcAmQC/wGmSim3WNm+uAlyk4g/VKQPXbt25Qc/+AFvvvkmffv25a677rLsXJs3bwbA43+YOumkk0L2NzU18d1337WuDxw4MGT/wIEDW+uIhtfrpaWlpXW9S5cujk2QHs9wcqth6jjtzMhAQmseViuSi7vN+HNbexVpzCuvIH/4Q+jdW1vv0oWTdh5g86Emmsjk4KEuHHHOAcj0x6hnZlLcpQvFBw7A79q20aULHDjQlo85fFtmJvTuzSDon0xz7fKwPgI0An2AU4CXhRAfSik/DSv3S6AYOAnYB5QDDwE/sbJxcRPkKoNVoZPTTjuNTz75hAULFpCXl2fZeZYtW0a/fv0YOnQoAF988QW9AyIUgY0bN7Zbv+CCC4A2o9co559/Pm+++WbU/WVlZVx55ZUJ1W2URAynqipXTCjgLO3MyGApMMnvYbUiufjSpUtdNZDJbe1VpClVVXD++SyVktbeumsXXYAurYV2wcGw43btal+Xnm27dtEDov8o6SDl77aFEF2AS4G7pZT1Usp/Ay8BV0coPgh4RUr5vZTyMLAMGJaKdhYXw8yZUQRXhQQodNDU1ERFRQVFRUVcc801lpzju+++Y9asWSxZsoQHHniAPn36MGnSJG688Ua2bNGcaXv37uWFF16gvr6+9bgXX3yR1atX09LSwl/+8heqq6u54oorAOjbty87duxg//79htqyatUq6uvroy7BxmpTUxOHDx+mubkZn8/H4cOHaWhoMOGKaAQMp7lzQ19pRaKqCm64QTOwwke/OglHamdmJishZNBVTO1MACeFmujBbe1VpCkVFSAlbuqtdnhYhwLNUsqaoG0fAmdFKPsk8IAQogDYC1wJrLK+iaG087zo8LC6xFujsJB7772Xb775hueeew4hhGn1zp07l9/97ncIIejZsydnnHEGlZWVnHbaaQAsXryY+fPnU1JSwvbt28nPz2fUqFGMHTu2tY7rrruO+++/n4svvpijjz6a5cuXM2jQIADOPvtsxowZw6BBg2hpaeGf//ynaW0PMGXKFJ555pnW9U6dOjFgwIB2nl+9RLrf9Lz2D8RrHT7clhzCrNhLC3Cedvo9rMEGazCJ6GD4MUuXtot4cDRua68iTfG/dlqKNhoMwMivVKzAF/N+7UKxw2DNBcJdN/uASO9LvwC+A7YALcDHwE3RKhZClAKlAP37JxUq0UrEAOSRHkYECkgJYcZIrKBlRcdmz549vPLKK3z00Uf84Q9/4JZbbmmd9coMglNMRaNz587MmzePefPmRS3Tq1cvFi1aFHFfZmYmy5cvb7e9ubmZwsLC1vXZs2fHbUs0lixZwpIlSxI+Pphk7rdArGvAWBXC0SmtnKedGRmMB1Y0tc/DGu17GTFiRPt6Yhwzf/54VqxYob9NNjN+vLvaq0gNsfq9LRQXc2DQMCZ+8ymP05dGcug9JJ/cxlpoaNBy2efnQ61/HUK21e1poK4ODpPDPvLpRi2daKBnD8juGnZsTg7078+e//u/nck02Q6DtR7oGratKxBp9MYjQDbQEzgA/ArNS3B6pIqllOVosVoUFRWZEvkeefCGYJ0QbTk7wwxWK0bKKtzBK6+8wqRJkzjiiCO4+eabWbhwod1NMpV169bZ3YR2JHO/Bce6ZmTAtdfC5MmOvV+dp50ZGZqVG8HDGu17idWHIh3jxNyVsXBbexWpwYnaua+pC6XAT3iRdd7TmXutFs6jh0+DHi6DtbMghnZ+I8SmZNprh8FaA2QIIYZIKb/wbzsZCB80ANqggruklHsAhBAPAXOEEL2klBGifONj9BVV1MEbXq8m0i0t7WJarRgpq3AHV1xxRWssqCI1JHO/WTFIyEKcp50ZGZRARIM1ke8l0jEnnqjjQAdRogRf4RK65rZQAszzeF2hnSk3WKWUB4QQz6OJ58/QhPVi4IwIxd8FJgshKtDGqt0IbE1GcI2+Ooz6pQSM1JYWLWWDnmMUCgegJ6zATRi53xKNdXUCjtTOzEwKgLoIBmsiOhjpmLy8AsemT4tEQYG72qtIX3I7+cgDHrjBwwlXOl877UprdSPwFLAD2A3cIKX8VAgxClglpcz1l7sVeBAtHisL+AQtr2BCJPrqMNKXIhobAZA+n+5jFAq3Exg85rRck+H3WyRx7SCx5c7SzowMLR4hQgwrRNHOOH0o/Bi3GX9ua68iNThSO1tatPt3ild7V4OztdMWg9X/mmpChO1vog0sCKzvRhvdagqWvKoPSqSuUChSQ6zQnkjiCjB7thb/7/Ppf2B1WrYPx2lnbS3lQOmvfgU339wWGhBIHF5f335bgKoqXRe1vLzcVXGhbmuvIr0I0TSfT7t//W+Mna6daTU1qyWv6lNosDoxN6RCkWriPe2HewOffRaeeaZNcD0efQ+sTvEqOIGI2llVBa++SjVQunWr8UrPOgvWro17Uaurq5M2AFOpnWa0V6GwgnBN29XXp92/foPV6dqZdhnwzU5qTZSQALMJdIDgdYUiHYn0ejqYgDfQ69X+glYuILjnnKNPQOOdJ91op53+xOPliVbY1KTropaXJ3wGIPXamWx7FQqrCNe0Q3Ut2v3rzy3vdO1MO4PVdFLkYQ10gOB1t+Go2B0XoK5XZMJFtaREM0IWLGh7yxw8y9XkyW3ls7O111tGMoQEn0cRREkJZGZqWQISITNT10VNdtR9qrVTZQlQOJVwTevcyUcJULbY4wrtTKuQgFhECzSOFz5Q/XYLReOsb1+gAxw61LbuJrxeL01NTWQFHtsUcWlubiYjQ92i4YS/nobIr5+C79lEQoFUto84FBfz8UMVTPrjAvbl7KNb7betCcYPkcNe8sn3JxMHtOThXi989RUAHz+8luE6LmoyE1RA6rUz2fYqFFYRrmnishZmA6WLvGx+zAXaKaXskEthYaHUS2WllJ06Sen1an8rKyNvC1BWJiXazGRycM6WkH1WUlnZdl63sXXrVrlz5067m+Eqdu/eLTdt2mR3M0JwYv+bP1+7T0H7O3++tecDqqUDNM6qRa92BjTS49kSopGxtPOF26ta+1D4vmhs2bJFV3vitTVVfdeM9io6Hk7UztruA+UWkAP52hXaqUICiBxvES0Go6oKfv5zWEg+ZUBzQ0vKXs+72cPTo0cPamtr2bVrF42Njep1dwyklBw8eJBdu3bRu3dvu5sTQllZGWVlZXY3IwT16t4eAhrp8xWFaGQs7Zx/bxZlwF0cTUODvtfzRUVFSbc1ldppRnsVHQ8namenrBaKABKYOMAO1PtG2n7wGhq0WVZ79oThwyOncamo0AKQJ9KVAezl916f479kJ5CdnU3//v3Zs2cPGzdupEWlA4tJdnY2ffr0IScnx+6mhODE0c/q1b09tKW62orXC5s2aUZptBRYFRVw2JdFKfApefzeq+8HcmsiGQhsxG3tVaQGJ2pndoaPrcDDt3kovNj52qkMVrQvadEiuOkmzSswfbr2AxjpR7CkRAs+9h3SnNNzZ7c4/kt2CtnZ2Rx55JEceeSRdjdF0cGINlGH0/KodiQCDwozZtzHe+/NYPFiLQVOLO38S1YWNEAWjTz8sL7v5L777mPGjBkWfhJzcVt7FWlMSwv3ATN+4YGC0F1O1E5lsPrZvVvznAYnxg1O4RL85a1eDS+ec4AuB6H08jZPoRO/YIXCTAIpe5zmLXDy7CwdmeJi8Hq30twcGgIQTTuXLM2i/FJoytvDz4O6UCztdJvH0m3tVaQGR2qnT/OwPvCwl9PGu0A7kwmAdfJiZNCVlLEHCkTaRyCAev36uMebCQ4M3FakD07sf9HuPasGY6EGXYVgRDvffWlruz6ktFORDjix/zV26yklyD6eHa7QTjXoyk94/rFYM+eEDBTwx2LanWR89uzZCCEQQuDxeOjevTs//OEPueuuu9i+fbsl56ypqWH27Nns3bs3ZPuSJUsQQlBfX2/6OaWUzJ8/n6OPPppOnTrx4x//mA8++MD08yjcQ7R7Tw3GSg033VSoWzv//U77tHbxtLOwsNDK5puO29qrSF+aG30UAo0+ryu0UxmsQUSbBSvml+c3WJ3wBXfr1o2qqioqKyv561//yk9+8hP+9Kc/MXz4cNatW2f6+WpqarjnnnvaGaxWsnDhQubOncvtt9/OihUryM3N5ZxzzrHMKFc4n2j3XqyHUIV5lJeX69bO4rPaG6zxtNNtM0e5rb2K9CXT66McEB6PK7RTxbDqINYo5CcX+/jBFc4YqZyRkcHIkSNb188991xuuOEGfvzjHzNx4kTWr1+P1z8Fmxs5fPgwCxcuZObMmdxjYvpeAAAgAElEQVR0000AFBcXM3DgQB5++GHmzZtncwsVdhDr3os2GEthHnl5eVH3hX83p49oM1gXLGj7vmJpZ6z6nYjb2qtIXzJoIQ+4824vZ5zrAu1MJp7AyYvROCyj4I9HKfKsszTuKtp5w5k1a5bs2bNnxGNWrVolAblq1SoppZSHDh2St912mzzqqKNkVlaWPOmkk+TLL78ccsyAAQPkjBkz5Jw5c2SfPn1kly5d5KRJk+TevXullFKuWbOmtS2BZcCAAVJKKZ9++mkJyI8++kiec845snPnzvK4446Ty5cvT+qzr169WgLy888/D9l+7bXXyhEjRiRVt0If0fpfOoGKYQ1hyJAh+gu3tLT2Ia/Hp0s7DdUfg1T1XbPaq+hYOFI7O3eWQ0DK+vqUnC5Z7VQhAUkifT5bYlaNUFJSQkZGBm+99RYAl112GUuWLOHOO+9kxYoV/PCHP+Siiy5qFwv6l7/8hddff53Fixdz//338/LLL/Ozn/0MgBEjRnDvvfcC8Pzzz1NVVcULL7wQcvykSZO46KKLeOGFFxgyZAgTJ05k8+bNrft9Ph/Nzc0xl+B8rQEP8ZAhQ0LOc8IJJ7B+/XrzLphCodBNTU2N/sKetp8cj69Jl3Yaqt8BuK29ijSmpYUa0OJxXIAyWJMky9PiqKDkSOTk5NCrVy++//57Vq9ezcsvv8zf//53brjhBsaOHcsTTzzBGWecwW9/+9uQ4w4dOsTLL7/M+PHjKS0t5ZFHHmH58uV8/vnndO3aleOOOw6AU089lZEjR3LqqaeGHH/zzTczbdo0xo4dy5IlS/D5fKxcubJ1/5w5c8jMzIy5HHPMMa3la2tryc3NbRfW0L17dw4ePEhjY6PZl06hUMRh9uzZCR3XydOoSzsTrd8u3NZeRRrj8zEbQh4knYyKYU0QOXIkvPUWz05pYcg1Doz1CEPzxsPrr79O3759OfPMM2lubm7dP3r0aJYsWRJyzJgxY8jNzW1dv+SSS5BS8u6773LCCSfEPefYsWNb/+/ZsydHHHFEiIe1tLSUcePGxawjOzs77nkUqSPQjxSKRJHdu0NtLYvubOD0C3Idr50KhRk4UjsDbzCVwepedE0A4P+CJ1/lgyQENxWTDRw+fJjdu3fTp08ftmzZwvbt28nMzGxXLtxzecQRR4Ssd+7cmdzcXLZt26brvPn5+SHrWVlZHD58uHW9b9++7c4RjhCi9f/u3btTX19PS0tLSFtra2vp3LkzWVntRyArFAprCfYo6tIz/306/cZG0DHpXTSPpVMnalEeVoVrCHhYXRISoAzWMHTP8BD4glsSn+kqVbNJrFmzhubmZoqLi3njjTfo168fL774YtzjduzYEbJ+8OBB6uvrTZtadc6cOdxzzz0xywwYMICNGzcCcPzxx9PS0sKXX37ZGo4AWmzr8ccfb0qbFAqFMYYOHUpNTY1+PQs8WAaF8MTSzkD9wTh2Jh4it1ehcBw+HwBDgZogx5CTUQZrGJGSWEcSwsL33wdgnd9gTURA9Z4rGfbu3cvtt9/OscceyznnnIMQgvvuu4/c3Ny4Rt5rr71GfX19a1jACy+8gBCCoqIigFaPZrDX1AhGQwLOOOMMunbtyt///nd+/etfA5oRvWLFCmdNd9eBCSRFtyKvr8KdBOLSdWun/0F4nd9gjaedwXHvAVKhnYkSqb0KheO002+wrnRJOAAog7UdgSTWAfGMNiDgvcAsTv4vPREB1XsuvTQ3N7dmAqirq2PdunU89thjHDx4kP/93//F6/UyZswYzj33XMaMGcPtt9/OsGHD2L9/Px988AGHDx9mwYIFrfV16tSJCy+8kNtuu41t27Zx2223cckll/CDH/wAoNXLWVZWxsSJE+ncuTPDhw/X3d6CggIKCgp0l8/JyeGOO+5g7ty5dO/eneOPP577778fn8/HtGnTdNejSJz33nvP7iYoHEZdXR1gQDsbGrR//AZrPO0M1B+M2dppJpHaq1A4Tjv9zrY6ZbC6F8MTAITNdGVEQM2ebGDfvn0UFxcjhKBr164ce+yxXHXVVUybNo2+ffsCWkzo888/z/z581m0aBGbNm2iR48enHLKKe2MvokTJ5KXl8d1111HfX09F110EY899ljr/gEDBnDvvffy4IMP8tBDD3HUUUe1vr63ijvuuAOfz8eCBQvYvXs3RUVFvPbaa/Tp08fS8yoUisiUlpaybt0643rmN1jjaWeg/mCcMFFLNCK1V6FwHH5nW2lLC27prcKRI9dMoKioSFZXV1tWf2AwkPzXv+D884HUDAJoPa/F39vAgQO57LLLWnOtKhSQuv4XjWTuMbPuTyHEOillUeI1OJuUaWevXpqFmptLw556mg82ktE5i+weuVBfr1mw3bvDrbeCCSE/dvddRXpjd/9rp3/19ZCXB507w4EDxo5NkGS1U3lYkyVo0FXwVGZOHcGqULiV8FjHRYtg925995iTB+m4nRkzZnDffffpK1xV1fb/rl2t/2b7Fw4CuwgpM+P667kPTDFaU4Gh66FQpIBI2lm3xccMYEZTE7F6q5O0UxmsyRJksAZw0hesUHQUgmMdGxrg5z8HKfXdY04epON2jMShJzIlYAHA8uWuMVgNXQ+FIgVE0s6uPs1g7Stip7RyknYqgzVZ/HEgwTjpC04Uq2NRFS7nBz+A2lrt/5wcyM/X1gMDasK36SkT57hb6hq4pgUOk8M+Xz7dfLVk0YA4BJ0vzIEB0eu6pa6BiS057CWf/JZaCu5tgAd1tiF4W0MDQ6Bt+jUFM2bM0F84TnB/+MtSAcwAuPRSY42yEUPXQ6FIAcFx4h6P38/m05xtN3k76T7W7gGOymBNkCkDB8LGjRE9rD17ap0i4P1x0ghWhSIZphQVQXU1fP55ys+dTYw887X+JcaxA4PWxZ7E29EV8uOXSh8KCgrYunWrvsLFxUy56CL48ENtXYiQh4O6xhy+2pNPN2rpw/d04TAFnTqx1SXeVTB4PRRpw5QpU2w7d/AgxZ49Ydo08DZqzrZjDu8jVm910gBH2wxWIUQP4ElgLFrU0kwp5dIoZUcAi4ARwAFgvpTygVS1NRLlRUURDdaqKpg+Xdvs8WixIm7zrioU0Sjfv9/W8yeT3todqbFj40TdNDpAq/yf/4y4PRBK1eDRtPOj4us54c1yqmfNMqOZKcPKAWsK91JeXp6S80QbPxMYY1NVpT0netFsl8qu3ePWGTw+x07sTMD1CNAI9AGuBB4TQgwLLySE6AX8L1AG9ASOBV5NYTsjE5jpKiwkIBAO4PNpHtbdu1PfNIXCMk480fJTyAhLImU6KI7TTbNmdQrXzgNN2sQkNVu2mFJ/qlCzXCnsIvDQd/fd2t/gMY4BKiqguRkEmu3yWaN71NMWD6sQogtwKXCilLIe+LcQ4iXgauCOsOK3AK9IKZ/zrzcAqX8fGca6ffsAKAzzsAbHe2RkwKZNWqdxwtOJQpEs6/wDSgp79GibYtPkGNbth/M5tF2LT20kh0598zkyO7TMgcx8dn5RSyYNCCCvRw5d+1sURxu8raGB/Z9/vte6Kxwdp+rm7NmzqTAwmCqQozQw80+AcO3ce1DrX7NffpmKBx80q7mWY/R6KNKDaP3eTPSMnwncZ5mHW0DCgsZ6LrCsRSYjpUz5ApwKHAzbdiuwIkLZN4AHgEpgB7AC6B+l3lKgGqju37+/tBL8jh35zDPt9lVWSjl1qpRZWVJ6vVJ26qRtS4TKSinnz287vvW8CoUNtPa/O+6w7ByVldo9E+/eKSuTMiNDSo8nuXvMKEC17EC6Ke3SzggEa+dCcYeUIDdOnZ/QeZR2KpxEKvqfEe0c7N0oJchvRX/XaKddIQG5QHgw3D4gL0LZo4BrgF8C/YFvgL9EqlRKWS6lLJJSFvXu3dvE5sYgwqCr4mLo31/bFfykYxQ97n2FwhaEdRGhgSD/uXNjp6t6/33t/vL5Er/HXIYlugnJaWepiQOigrXzsNQ8rNNe+ofheuzUTjOvh0JhBCPaGbBdbpd7XKOddg26qge6hm3rCkSahPkQ8IKU8l0AIcQ9wC4hRDcp5T5rm6mDCAYrmJMKIpJ7X6FwBBbPPx0vyL+qCp56Sot1BO0Vchpk43CkbhYVmTvpV0A7mw9ngYQz+x9huA47tdPs66FQGEGvdh7tj2E9kRzXaKddHtYaIEMIMSRo28nApxHKfkTomApnRQhHyMMK+p90YhEQbq9XpcdSOAyLDdZ4VFS0PSsKAddemxZx4o7UTbM9igHtPPtczcN6+5ntxpTFxU7tVB5WhZMJaGcgS8DPunV3jXba8qsjpTwAPA/MEUJ0EUKcCVwM/ClC8aeBS4QQpwghMoG7gX87wrsKUT2soAnvzJmJ/5CaYfQqFJZgYUiAHoINkpwcmDzZ1uakBKfqZl5epIiE5Cguhv86TzNY8xIYcGWndlpxPRQKs2gddOXRnG3H7v/S3gYZwM6JA24EnkIbELAbuEFK+akQYhSwSkqZCyClfEMIcSfwMtAZ+DcwyaY2tyeGwWoGTsl/plCEYLOH1UnJrFOM43TTsiT5/iwUW6+6KqHD7dJONWmAwskEtPOTZT54ALYOHWp3k3Rjm8EqpdwDTIiw/U20wQXB2x4DHktR04zxxz/CvffqTqXT0AgHZQ6ZvfLJbUxgmsoAKleWwk5S7GGNlAw7HR/mnKibFRUVjB8/3vyKs7O1+jdtoleUZOhOxLLroVAYJOYkAl1a4AGoOHQIt/RWNTVrIlRVUe3xaPGrX3+t+zAJZPkXarX1hH/2zz4b1qxxvnorOhTV//M/WsR+Cj2sgRHfgQGMKjzGWZSXlxsy0HTPBOX3sD784XredNH3b/R6KNKDVM+AFlc3/eNvyvfudY3Bau97PbdSUUGhz4fR9L8iwpIwKm2AwgYK+/bV+n0KPawqW4azWbFihaHyhYWF+pKn+w3WRX3PcNX3b/R6KNID3f3eJOLqpj+cccUxx6SsTcmiDNZECEQtG8TU6SRV2gCFHQSyYiThYa2qggUL9OfGVNkynM2kSRYNKfBr7N3fV7rq+7fsehikoqKCjAz1ErUjYUQ74+qmX8snffut2c20DNWbE6G4mNLzzoMvvqC8d2/49ltdMawi2RjWhgbtf1DhAApbKP3XvwAoT9DDqvf1fnjsVZoOsHIF48aNM1Q+kPapvLw8dkG/wfqTgl7MeMw933/49SgpKaGqqoqsrCw8Hg89e/bkzDPPZPr06aZ53JYsWcK8efP48ktrRnzv2LGDW2+9lbVr17J792769u3Lddddxx133IGwOWOIW9Dd76OgRzsN6abfwzouPz+h9thCMtNkOXkpLCzUMVFY4hBlmrXw6QBN5fe/V9MLKmyltf/97ncJHT9/vjZtIGh/50eYdVPP9IKW3mdxwKapWVO1OEY7X3tN6yijR1t6Xqs566yz5Ny5c1vXN27cKO+66y6ZkZEhn3/+eVPO8fTTT8tjjjkmZNuaNWuk1+s1pf6vvvpKLliwQH799dfS5/PJjz/+WA4YMEDed999ptSfDiTb/+Jpp95pWQP32Udl/9EqGzky4TYZJVntVCEBJpLIdICGXo/anEpIoWglwb6o5/V+rNirqiq44QbtODVlsTMww8MWUTv9HlaxenXUY4yElqSKeNdjwIABzJs3j8mTJzNt2jSklBw8eJBbb72VQYMG0aNHD84777wQb2lJSQnTp09n3Lhx5ObmMmzYMFatWgVAVVUVU6dO5euvvyY3N5fc3Fwqgm6aZcuWccwxx9CtWzcuv/xy6uoiTYwWm8GDB3PHHXcwaNAghBCceOKJTJw4MeQ8CmuJp53xYlbDtfPmaZqHVbz1lvWNNwllAZmI0cEhhg1cZbAqnEKCRoqehO7RhDlwv5SVqUFYTkJznCRHRO30G6zy9NPblU/EOZAq9F6PiRMnsmXLFjZs2MCUKVNYv349b731Ftu3b+f0009n3LhxNDU1tZZ/8skn+eUvf8nevXu58847ueSSS9i4cSPFxcU8/vjjDB48mPr6eurr6ynx3zQtLS28+uqrfPjhh9TU1PD+++/zYNBEDOPGjSM/Pz/qsnTp0oht9/l8VFRUcPLJJyd+oRSGiKedsQzaSNrpa9ZiWOWoUSn7DMmiLCATMTo4xPDoZxUrpHAKSTw8xZsBLpowB+6XgD0ghDsG4XR0ohk1RoionX6Dden27e3KOzlzhN7rcdRRRwHw/fffs3TpUh599FH69OlDVlYWs2bNYtu2bbz99tut5SdMmMCYMWPIyMjgyiuvpKioSNe5Fi5cSG5uLn369GHChAkh6ZVWrlzJ3r17oy7RBpDdcsst1NbWcuutt+r6rApziKWdsQzaSNqZnaF5WJfu2mV9w01CDboyEaODQwIiHQiiDvYkRaxDeVgVTkHnw1PUvhyHSJMCBN8vGRlw7bXalKxOH4TT0Vm5cmXSI+MjaucnmsG68rvvmHTqqSEDUqeTw3kt+XSjlpyWBnreC5TnUJ+ZT9POWjp7GsjuGmEgawrQez02b94MgMev6yeddFLI/qamJr777rvW9YEDB4bsHzhwYGsd0fB6vfTu3bt1vUuXLgmFBARzyy23sGrVKlavXk23bt2SqksRnUS0M9pkKpG0c9oJPvglrNyzx0FTh8ZGGawmo2f2neCOGC7SMUcCKoNV4RR09EWzE/6rbAHOxAwPK0TQzhde0Or3+eCDD4C2VIA5wClBRcUekHugS9A2uSdKruvycvCP2LYCvddj2bJl9OvXj6H+qTG/+OKLEOMynI0bN7Zbv+CCC4A2o9co559/Pm+++WbU/WVlZVx55ZWAFgZw/fXXU1VVxdq1a+nbt29C51TEJyXa+aoWErA07EHJySiDNUFGjBgBGH8KitQRZ85s2x/pVZcyWBVOYUTv3rBzZ1wPa1UVzJ6tObZ8vgh9OUHScTpWpzN+/HhDyfJ1a+eaNVr9QKD2WL1Od8DU8uWWGqzxrsd3333HE088wZIlS1i2bBl9+vRh0qRJ3HjjjSxatIh+/fqxd+9e1qxZw5gxY8jN1WbcffHFF1m9ejUlJSX87W9/o7q6mj/96U8A9O3blx07drB//366du2qu62BgVvxaG5u5uqrr2b9+vVUVFTQq1cv3edQaAT6fTxSpp3+tFbjP/gAt0x1oQzWBFm3bl1CT0ExDVLau+43bdI6cHExKoZVYTvr/vu/4dFHYz48Be6LgOB6PCrWtCNTatD4062dEyfCmjUEao80lClYEaMNdWqnmpdeaqi9Rol0PebOncvvfvc7hBD07NmTM844g8rKSk477TQAFi9ezPz58ykpKWH79u3k5+czatQoxo4d21rHddddx/3338/FF1/M0UcfzfLlyxk0aBAAZ599NmPGjGHQoEG0tLTwz3/+09TP9J///Ie//vWvZGdnh4QmjBo1SrfRm+6se/hh+P3v4Qc/gOzsiLnaG+oaGLAHHiWHfeSTHxbyEjdXu5587oFtfnuitKkpyMhwNspgTYJ4xmckosWtBnPNNbB9O/zrX7B4MTzzjF/QlYdVYTc6ZroK3BcBY/WcczSPgV49TDTuVWEPJQk8iejSTr/hV1JeDi0tHN5eS+32BiTQSA69h4ROvnKYHHY05pN9qJaWQw00+H/0j+9bS6fa7W0/1hZ6V6H99dCT+qlz587MmzePefPmRS3Tq1cvFi1aFHFfZmYmy5cvb7e9ubk5ZH327Nlx2xKJs846y5RsEGlLVRWMGtXq1YxGFnBkhO1iD7An/mmCvyG97q2SvXvhrLNg7VrHC64yWJNAj/EZTqw4vGCvg8fjTz0R/EqglzJYFTYTPMw0CuH3hVFj1czYLYX1FBQUGB7Io1s7S0spmDGDuro6OgEfBD3MDIqlnR5NO71emPsLmPnpVfDccwl9PqMkcj0UHZyKirjGKhgIazHx+AKgrqnJnLgDi9FlsAohHgeuB/pJKbeG7TsO+Bh4XEr5C/Ob6EwCyaErK6VpI/mCvQ5SasIbkrrnM2WwKuxFlJUBIP0e1kje0GQGRyXy1kJhL0aNM6PaGVx/wtr5eeq0UxmrinaUlLQakzLsL0QPbTFigMYLmYlGHUBmpititvR6WKvQDNbTgBfD9v0R2A/MMrFdrsHoIJBYrzvDvQ6LFsHu3UFl1yuDVeEQhIjpDU10cFQiby0U9lJeXm44jhX095FA/UlpZ7nXcPsSJdHrEQs1o5TLCe6w/fpR37k3O7+oJYuGduEtAozFovq3NdY1sHVPDnv9sa8FPRrIzopzXEMD5dnZlD76qCs8A3oN1sDcXSEGqxDiQuB84OdSylqT29bhiPe6M65nSg26UjiEr77xMPsf1oxkVamr3EV1dbXpBlp4/cOHlyannd7UGaxWXw+Fu9lw4wP8Yu2lvP5VUNjKtaHZghIhG9geJWQmFtWlpZS6RGj1Gqw1aCG/pwU2CCEygfuBT4Ay85vW8dDzujPc6xDiVVCDrhQO4Q/3Cl5vsSYLgEpd5S7Ky8str3/BgiS1M4UGq9XXQ+FuZt3j4fVm52inm/qrLgtIasMD3wKKhGh18/0SGApMl1LGjyZWGJ66NXy+7C++Ugarwhk0NntCsgCowVHpSyJZAozWn6x2bt+ZOoPV6uuhcDcNDtNON/VXI1kC3gIuAI4TQuwB7gZelFKutqRlHRCjrzvDPbKfqRhWhUPwZgi8Pn1ZAFSaqo5NoqmSjNSfrHZu3pY6g9Xq66FwN94MD94W52inm/qrEYO1yv/3NODHaCETM0xvUQfHiMs+fCDBCcOUwapwBr+6w8PgTvGFVKWp6vgEpha1uv5ktLNff2/bSAyLsfp6KNzN3N96KGxyjna6qb8aMVjfAXzAz4AzgT9IKb+2pFUuoKzMeNiu0aelcK/C0E1q0JXCXspOOw3eeYchx3mYeWX88ipNVcenqKiIrVu3xi/ox6h2FhUVsXz51qS088h/pM7DavR6KNKDsmHD4NNPOWGYhxPOj18+Vdrppv6q22CVUu4XQnwGjAK2A7+1rFUuwOgo0ESflkK8CpuVh1VhL6WDB8M77+jOWKHSVHV8jP7YGdXO5cu3Jq+dL6TOYHXLj78itZT26weffhpzlsBgUqWdbuqvRi2gd/x/Z0opVXZkA0R6WjKMyhKgsJvATFc6+2LA0zV3rgoH6Kjcd999ltY/d+59yWtnCrMEWH09FC5Fx7TWwaRKO93UX3V7WP1prEqAauAZqxrkFgKpIPR6C0x5WlIGq8Jmyr/8EoBSAzmBI8UeqoFYHQejHhqj2tmt29bktTOFBqubPFaK1FH+3XcAlBr4HU+FdrqpvwopI03oFaGgEDPRwgDOkFKmKHw9cYqKimR1dbVl9Qeye+m9fmBCR3vxRcQllxg+r0JhFq39/m9/g//+b13HhPd7I+ExTjRshRDrpJRFdrfDKjqkds6ahZgzx/B5FQqzaO33b7wBZ5+t6xilnaHE9LAKIXoA5wInAbcB95tlrPrrfhIYC+xCCzNYGqN8FvAhkCelPMqMNqSaRBOiBzreJV7lYVU4g+dfEBx5VPz+HElg9Q4mUBkGIuM07SwsLGTdunVmV9uu/mS086qtqfOwWn09FO7mz0s9HJPjHO10U3+NFxJwLrAU2AH8EbjDxHM/AjQCfYBTgJeFEB9KKT+NUv42YCeQZ2IbHE9wx6tWBqvCISxd5uFfL8Y3IsMF9tlnte0ZfuWJ9YpXZRiIiqO0MxUzXSVCsHY2CDXTlcIZPPGUh3eec452uqm/xrSApJR/kVIKKWUfKeVtZs1oJYToAlwK3C2lrJdS/ht4Cbg6SvlBwFXAAjPO7yZCOl6zMlgVzqDZ59E1ACZ4hqKMDHjqKVi8WBu7NWVKbNE2OrtROuBE7czLs9aHkGj9IdrZkjqD1erroXA3TQ7TTjf1V7ssoKFAs5SyJmjbh8CwKOUfAu4EDsWqVAhRKoSoFkJU79y505yW2kxwx/NkKINV4QyER+gyIoNHul57rWY8BJb+/WN7GFSGgYg4TjvHjRtnqLxREq0/WDtTOejK6uuhcDfC43GUdrqpvxqZOMBMcoH9Ydv2EeGVlRDiEsArpXxBCFESq1IpZTlQDtrAAXOaai/BCbAvzhG8dIvdLVIoYNKVHn51g7F8mFVV8MwzxkZ7Jxr33YFxnHbW1NTEL5QEidYfrJ1XbPOy4CFz2xUNq6+Hwt1Mud7D0Kudo51u6q92uezqga5h27oCIbld/a+/fg/8IkXtcjYqrZXCIfz35cKwIak8pqbgOO20ei5yM+qXntR5WN00N7si9VxzrcdR2umm/mqXh7UGyBBCDJFSfuHfdjIQPmhgCDAQeNOfEiIL6CaE2A6MlFJuTE1z25Oq1CjBAwfejDDoyompKxQdF3n++bBqVcyHp6qqtgECkyeH9kvlMU0apZ06CdbOnREMVqWdilQiR4yA996Lqp2B/tizJ+ze3b5fKu20yWCVUh4QQjwPzBFC/AxtpOvFwBlhRT8Bjg5aPwN4GBiBNuq1wxMycECGdnSV9keRavbu8ZEPfL5ecMIF7fdXVWkpBhsatPWnnlKj+83EidrpVA9riHb6UqedbvJYKVJHfb0kF/joY8FJhaH7Av2xoUGbEMvjgezs1Pymu6m/2vmO+UagE1rKrL8AN0gpPxVCjBJC1ANIKZullNsDC7AH8PnXTclY4HRiDboyZbpXhUInVVXw7ruad+z2mR6qqtqXCfTJAE1Nql9agKO0c+jQoWZWZ1r9wdopMkI9rFZqp9XXQ+E+qqrg6y+0qVlLp7bXzkB/DMze6vOl7jfdTf3VrpAApJR7gAkRtr+JNrAg0jEVgCMmDSgs1B6RzEq4G+31VPDAgfHdBMN/3rbPlOleFQqdVFTA074q8oAjmkVEz2mgTwY8rJmZql+ajdO0c+XKlYbKG9XOePXr0c7L93t5eGHbPiu10+j1UHR8KirgT3IDnQBfk6OKa5kAACAASURBVKeddgbrZsDDmqrfdDf1V9sMVrfz3nvvmVZXvNdTrbEr/w71sAYLsorDUlhNSQnc6R/bc0GGJ6KYFhfDmjXRY1gVHY+6urr4hYIwqp2x6tetnU+Helit1E6j10PR8dG08zAAhZnttTO4P0aLYbUKN/VXZbA6AN2zUkQI1laB2IpUEdzPfn+vh2FR+p3qk+lFaWmppVM7xqpft3ZGyMNqVT+1+noo3EdwP3viKQ+nROh3dummm/qrypPkAHTPSqHSWilSTFUVLFhAu5irYScKexqkcBxW/9jFql+3dqZw4gC3/PgrrCOabgKcMsJZv+Nu6q/OunJpiu4ca6LNSIh0I0Qi1o2jUMQi8Lr17ru1vyF9SD08KfzMmDHDtvp1a2eQwapHC5PRTauvh8LZxNRNcJx2uqm/qpAAh6DndcBHn7R19NGj46e8CI/vWrTImtgYlc+wYxJzJLVQHlaFRkFBga3169HODV+1GazxtDNSXCzo1zgj10NpZ8cjbpiKwwxWq+9fM1EGq4tY935bR48Zr+Un+MZpaICf/xykNDfvoMoF23GJOZI6zsQB6kc4fbDTw6qXTz5rM1jjaWe4wfHss6HTYsbTOL3tVdrZMYmbgcJh2qk8rGnAlClTUn7OEUVtHT1avFZwhw++cTweTYCD87uZcUPoHvSgcCSxBDLSSOopRxwBO3ZE9bCqH+H0o6CggK1bt+oub1Q7jdYfiWEneWGp9n887ezZM9TgAGMap7e9SjvdjZ50asH7puTmQn19zJmu7NBOM+6vVKEM1gQpLy9P+TlPPrWto0fqzJE6fHCqjOnTzc87qHLBuhc9Ahn+urV80CDNYI0iuupHOP2orq42VN6odhqtPxLHD2vzsOrRzuDwKQj1sMbTOL3tVdrpXnSnUwuivHv3mAarXdppxv2VKpTB6iaCvFqROnKkDj9zZlvZ4cPNf92gcsG6l4QEMjAPfBQPq/oRTj9qamosjYMzpf6gQVd6tHP3bk07AxjROL3tVdrpXhLSzsA0VlEMVru00+r710yUwZoggVQQgVlbUkKcYO14Hd6qPG8q76Y7SUQg1/mTTBdG6YvqRzj9mD17NhUG5pA0qp1G649InLRWZmqnkfYq7XQnCWmnf/o/p2mnKfdXihAy4DHpYBQVFUkrXd3C72FK6fX77DPEsGExz6sGvCiMYLS/tPb7d9+FoiJL2+ZUhBDrpJQd9sN3SO1cvRpxzjkxz6u0U2GEhLVz2zbo29fStjmVZLXTWfkVFLHRkQ6juDg0DEDlYVXEIry/6EaltVL4KS0tdX79OiYOMEs7rb4eCmeQsHY6LK2Vm/qrCglwEwaNBDViW2EZDhNdhX0UWexpN6V+gzNdJaOdVl8PhctxmHa6qb8668opYmOwo8dM/K5QJIPysCr8dBQPazDJaKebPFYKG3CYweqm/uqsK6eIjcGOrmee7fDXXukeQpDun183DhNdhX3k5eU5v36DBmsy2tmli7XXw6ko7dSJw7TT6vvXTFRIgJsw2NHjjTqMlHswOFdruoUQpEMIhWkDS5SHVeHH6qTjptRv0GBNRjszM7dSVdXxtCMWSjsN4DCD1S2TBoDysLqLBDp6rMDw8Ndey5endwhBRw+hCPyo3H239jcpT4jDRFdhH1anxDGl/pRqZ0WH0454KO00gMO00y0prUAZrAlTXV2d+hkiTPZqhb/2uvTS+K/BOjJ6XgO6GTN+VKoHDqQa4oquej2YPiQyc5UR7TRlVkGDHtZ4xNJOKO9w2hEPpZ3xqc7OdqR22jFrZ6KokIAESemEAQEMPpnFe4UR6bWXFbNhuYWOnPS+qgo2bYIM/x1v5EclpB8FJleP8fCUDq8HFW2sWLHCUHmj2mm0/ogkkCUgce1ckXb9XWln9GNbr0lAMx2mnabcX6lCStkhl8LCQtnh2LJFAlL72mJTWSllp05Ser3a38rKFLRP4UiC+0JWlpRTp4b2h8pKKefPj9xHwvtR7RFDpAT53l83RD3f/PlaedD+zp9vwYeyEaBaOkDjrFqMaucVV1xhqLxRTKn/s89Spp1WXw9F6jBTO5szsqQEWVVxOOr57NDOVPbXZLVThQQkSGlpqa50EBUVFWRkmOTINuBh7egxRQr9BPeFlhbo3z80OXqs2KzgYxsaYOqO7ZQCk68RUV9ZdfTXg4pQxo0bZ6i8Xu1MtP6IGPCwJqudprRX4QjM1M7rm5soBcae53GUdrqpv6aNwVpSUkJ2djZ5eXl069aNwYMHc/XVV7fOa22UxYsXs3jx4pBtS5Ys4dhjjzWjuZEJMlizsrI4xz/VYCRS0fFVnKI1mH1dY/WFeD/Owcd6vbCMOhYDDU2eqD/kgdeDc+eqcIB0YNKkSYbKR9JOM+uPSLDBOnQoFBRAr17aUlAQsu2W+wr4tGUo31HAtpZezFjYvkyk4+jVC449lkn19XGbo7TTGpysnU8iWQwcbnSWdppyf6WItIphvfvuu/n1r38NwLfffsvixYsZOXIkf/vb37jkkktsbp0OgmJfSktLWb9+fdSiVscUqThFa7DiusbqCwFRDZwv/MEm+NiePeH667XtGZki5kNQcbHqD+mCEALtbZ+D6//739v+/+KLmEWzgeOCN+z3L3rYvRtx/fVIgCheZKWd1uAa7czyOEo7rb5/zSRtPKzhDBgwgHnz5jF58mSmTZuGlJKDBw9y6623MmjQIHr06MF5553Hl19+2XpMSUkJ06dPD3Ghr1q1CoCqqiqmTp3K119/TW5uLrm5uSHpIpYtW8YxxxxDt27duPzyy6mrqzPe6CAPa69eveIWT3iuYx2okANrsOq6RusLep7oA8cG//7++jce9SOrALD8x86U+tesSb4OnUjQ8lxFQWmnNThaO6e09eFFDwhHaadbjFVIY4M1wMSJE9myZQsbNmxgypQprF+/nrfeeovt27dz+umnM27cOJqamlrLP/nkk/zyl79sXb/kkkvYuHEjxcXFPP744wwePJj6+nrq6+sp8T9GtbS08Oqrr/Lhhx9SU1PD+++/z4MPPthax7hx48jPz4+6LF26VCvooPxtKk7RGuy4rnofbIJfs90zJ3oMqyK9aNUnJ9d/6aXJ16GTpXHOp7TTGhytnf/xtf4/fbqzQkGsvn/NJK1CAiJx1FFHAfD999+zdOlSvv32W/r06QPArFmzWLRoEW+//TY/+tGPAJgwYQJjxoxpPb6oqIilS5dy5513xjzPwoULWz2vEyZMCMlDuHLlSn2NdZDB2pHTmNiJk69rsMciEMPqpPYp7GHlypWWxsGZUn9pads72VNOgdpabSQMQE4O5Oe3bQtfj1Qm0rYDB6CujpV9+zIpxqAyJ9/jbsbJ1/X/KtoM1oD31ynts/r+NRPbDFYhRA/gSWAssAuYKaVsZ+oLIW4DrgEG+Ms9KqX8g1nt2Lx5MwAevzF40kknhexvamriu+++a10fOHBgyP6BAwe21hENr9dL7969W9e7dOmSWEhAAhMHmDadXARUnKI1mHFdrfjegz0W8eKwFNbgFN0MxhUe1mDef19XMcP30P33w4wZLJ04MW5RpZ3WkOx1ter38qxRbQar07zqysOqj0eARqAPcArwshDiQynlp2HlBDAZ+Ag4BnhVCPGdlPKvZjRi2bJl9OvXj6FDhwLwxRdfhBiX4WzcuBGAESNGtK5fcMEFQJvRa5Tzzz+fN998M+r+srIyrrzyyhAPa5AN3Y7ATdezZ9v81kaD0K00dBWxCb/2Rr8LqwZ1FBfDqd4MREszZcsERapf2IEjdDOY8ePHG0o+HtBOq+qPR1VV5Psh+D6DBO6hzEwAxv71Bc4+4o9KO20gGe20cjDcyNN8jAAkgkccNsjO7PvLUpJJ4proAnRBE92hQdv+BCzUceyDwEPxyoUnvz7rrLPk3LlzW9c3bdokf/Ob30iv1yv/8Y9/SCmlnDRpkrzsssvk5s2bpZRS1tbWyueff17W1dW11pGbmytff/112dzcLJcuXSqzs7Pl119/LaWU8pVXXpF5eXly3759redZs2aN9Hq9IW2ZNWuWHD16tDTKW28caE1+nZFxlxwx4mx56NAhefhwWyLi4GTFGRlSejzGkxCrSQfsI/zal5UZ/y4sTT59xBFaxdu2mVipu8CmiQNSoZsygYkDXnrpJUPljWJG/ZWVslU7I91H4ffd1KkJ3EOPPy4lyGmesUo7bSBZ7bRUN+vrtYo7dzaxUnOw+v4NJlnttCsocijQLKWsCdr2ITAs1kFCCAGMAsK9CYH9pUKIaiFE9c6dO9vtnzt3Lnl5eXTt2pUf//jHfPnll1RWVnKpP0B+8eLFHHfccZSUlJCXl8fw4cP5+9//jgh6FX/ddddx//33061bN+bMmcPy5csZNGgQAGeffTZjxoxh0KBB5Ofns3btWkMXJR5r32z7upqbf8t7762hU6dOHHdcWxKW4JGSPl9bDjgjryHUKFb7CL/2y5cb/y4sHXwQGFGaQHiKImks0U1/mZjaGYsSi99vmlF/8H0T6T4Kv+8ggXvI72E9wddbaacNJKudluqmzx8S4KBxKAGsvn/NxK6QgFzaZ7bbB+TFOW42WmaDpyPtlFKWA+UARUVFIbkaKnQoR+fOnZk3bx7z5s2LWqZXr14sWrQo4r7MzEyWR0hn0tzcHLI+e/bsuG2JxI9L2jp7p06y3SuLwJzHgRzZWVmwaBHs3m3s1X68/HLB50tF2EA6hSeEX/tLL4U334z/XQRj6eADBwtvGmCJbkJs7YxHQUFBYjH5Kaw/+L4Jv48i6ebkydpi6B7yG6y3sAyv989KO1NMstqZrrpp9f1rJnYZrPVA17BtXYGoV00IcRNaTNYoKWWDhW3TRcDrKlOYw2xkcZtXK5KxOnq0NlhVCBg/Hn71q9Bp5BYs0Hcj6rlxU5X82s4k23aIfeDaP/ustj58eGIiGj74wKzPInbvBkAqD6sdOFI3jf7YGdVOM35Mg/t8sIYE64vHA4WFcN11bfsDcZC6tNNvsH43+icsHp2+2mmXkWyGdkYatGXK5/H5EAD79+O0rKduMVYB22NYhwRte5YosVjA/wCbgcF6z2E0DksPwXGw+OOhUkpzc9Tzzp/fFq8KUmZmtsXsWBGTamm8jw3nCcfOOF4j566s1K5JvDJmfZbW/rd7d+KVuBzsj2G1TDdlAtpZVlZmqLxR7TRav5HzBusLSClE6D1i6N75xz+kBFl2yikx29GRtdPu8Q+O1c5du+yxGXRg1v2lh2S10xb/tJTyAPA8MEcI0UUIcSZwMdoAghCEEFcC84ExUsqvU9vSUCoqKlqndrWF4NcJYd6JkpLQ3S0tbTE7VsSkpipJs11Jtu2M49V77oAH5e67tb/RklFb8lkc+Gqro+NU3QzOKe22+gP6EnhhIGXoPWLo3snK0trrfwsR75wdUTvtHv/gWO30+eKXsQmr718zsfNX50agE7AD+Atwg5TyUyHEKCFEfVC5eUBP4F0hRL1/edyG9tpP8GvYMIO1uBgeeUR7K+XxQHZ2m0BZJVzXXANTplj7qknP9HdWYOdsNIFzezza0rNn5HJ6xdSSz6JCAuzCcbpZXl5uRbUpqT+gL9dfr2lm+D1i6N7xhwSUn3BC3PN2VO20exavkhLIyNDkKSMj+vlTrp0ONlitvn9NJRn3rJMXK0ICgvn/7Z1/sB5Xed8/j+690pUlGWGnjSuIf9AiSFyCQZq0igsSNXGTIg+UTNuMGJTixBJOjJMamIlTlKg4Y2HPqHFqnFjXiRWLVKF/gFts4sLQYBOQPKlkIMElltuCHXORMZZsSc7V1Y97+se+e+/q1ftjd9+zu+e87/czsyPdfc+efc6+53z3ec+P59BQ9/78fc+c6fh5t2GOPMMfefE97BIqTdq+a9dCWLJuz7iJ72G+/h07NlhGEUNDUwLqOopq5/r16wulL6qdRfMve9+BtfNLX3IO3PqVK3veY9i1s0m79+1zbvHiZGrH4sXdbaj9e5ieDnZKgK/2lYdBtXPkt2aNljRmVRvddvrInh90EnmnX6d5Fhd86EPwjW8kqzd77FwYDE3tRrN/fxKSZW4uObo94yKrWgcpS3tAdUA9rGKeslFPQsk/pZ929l181eph3d7a7rsTo6CdTe7itWcPnD6dDECm0+K6fad1aue1V4bbw1pX+/KBHNZYKTnEsH8/vOMdC0L45S8Xa4xpCJjxVs3pNUySFeeTJ+HOO5PzX/xi8m8qvKMUeqUf2WgPc3PJlIBez7jqrQjbX5zzaA6raJHuEhhr/nnItsuxMfjkJzs4jq0Gsnr8/NdqdvfBPCEDpZ3F2b8fdu9emC03NtZ7CL9O7bx/PFyHNYT2lRc5rCXZtWtXswaUdFj37ElEF5J/9+zJv4VdtgGOjSWhsy65pPu9snHxnDt32u1nPpOIbpNhq0IkfVGlzuo73wnbtzcX9qa9R+j3F40zNndGPaxinrVr1zI9PZ07fVHtLJp/FTz66MKPyLk5+NVfTcImZbXzXZdM8JPA2m99i6y17e3srrvg61/vfT9pZ3EefRTSkOdmcP311T2Potp51s2xC+Cii6oxaABCaF95kcNaki1Nj8t4nMSdV/iyDdA5+PznEzMeeKDzNdlhl5deWuglgGRoqz3PXkNko0J78OuqnFXI9+zb7dl62lg0h3pYxTxFX3ZFtTOEl+mGDcmP9FR25+YWFuqk2vng+AR/CUy/8Y3nXNvezr7+9UQzT52SdvqkXas2b67uXoW1c9yxZRZY0W+Pj/oJoX3lRW+dWHGuf5oObN68EMYlbdR5VkxmpwKMjSX+ytmz/VdZrlsHt94Kd9wBu3bBtdcm/6bvrKZXlTZJOicuG1KlzpW9F1+c1INe0w7a7VmUhr1WD6tosXPnzqjzz8O6dck0gPHxc6OwZLXz704nc1h3Pv/8/HXtuplOq8mzQl3a2Z3YtHPvn4S701UI7Ssv6mEtSRoKorGe1pI9rOvWJQLZPvzfa15V+1SAG26At7wFfv3Xi20ZumXL+fO+Kt0OL2B69WrXsWhh//7k+0vX7t11V/d7Zu2ZOnsWgC0BCq9ohqI9NEW1M5QeoC1bkmkA3bST8QmYhelTp4DOupn2+qU9rNLO4kSpnf9nLtn3+MQJQlszF0r7yoMc1pJs3boViM9hhfMbdT/hy/YiAFx6aXfx9mHPKDDocJ6vSA9zc0lPQZ9Y5/NsbfXsy2EVKUV7aIpqZ0g9QL2089o3Lob3ws7WsG8n3Uyv9eVoSjuLaaePRWqltHNujq0AL7wQnMMaUvvqhxzWWPEciDhtvOnwVLYxt88NSnsEfInlKK507fZM8+BjsUWp+2enoWhKgGixZs0aDh48GG3+g5K2va99boI1wJrDhzlI7zbmQztHUTehvHb6WqRW6v4BbxwQevvKIoc1Vq64ImkE4+MwOQkzMws/5dvP5Uhz5iy8YWacy5hkkhnOLD3L+FiSbt3kJC+PzXB27CxjY+NMvLd7XmdOJXmNLR5nYnl/u2YXTfKGIzNcSZLm9PJ81/Utc95zDeWVfabjwPi/zJ/XVUdnODybnDszM874tZMwVsyGdePjHF06ydzcDBOcZfzanOVJefzx0XpLiq7EvNOVD1JHaMXsBDcDv7/kAqD4kH0RB3SUIwSUnQrha5FaqfsH7LCG3r6yyGGNiewM82PHvGY9DpwTcGPm3M8nWgengBN0Zbx19EuXsqR1zHMi33XDwPwzhfOedy+Wto55Sj6vJf2TdGf9enjssdF5S4qurKh45XPV+Q9K6gidnEta87JTCz/u8vakFnVAm55S1DRleqjL9Ix2e069RiQ7ErDDGnr7yiKHNSa6LScVom5On1YcHQHAxo0bOXToULT5D0rqCM3NTsAcvPf031HU2qIOaNNTiurEl3Ndpse723Mq/AwDdlhDb19Z5LDGRMVxS/IEyuo1c7HT9XlmOmavKzIzsuz96qbbc+1ma9nn0enaXs9ooOc3MTFacXREV6p+2YX+Mk0docf+52LYBk8tMnbsSEIfvfhiPueoqAM6SISAmOK3Tk0lmzScPZusyL/nnt5b0/Zzbrv1zHa6rtdzKvwMA3ZYQ29fWcyVjOcZOmvXrnUHDhyoLH9rLTqp+/nN3/eyy5KtVyYnYeVKOHp0YQur9nN50mTOzRw+ytHDszhglkleZiWv4iiTzPLqSyZZekn3vGaPzzJ7GiaW9U5Xxq7267J2nmKSv/f6lSw/VS6vwjYUyOuZYys5c+QoS0jOzTLJMVbyxkuOspTOz/5k67l3StPr2U8fmeQlVrKSo6y6KEnTfm7J4uS6ExMreeHpoyxmFoPzv9sO97PDh5P6t29fuG+5ijGzg865tU3bURVFtXP79u2F9iMvqp1F8/d138I4B4sWsR3YzGUsZrZnW29vxycmVnL6haNcsGihjfrUy/Tv2VPdNSEEvczq2Q+PMK+F6Tvooi7voL561sWGbtf1er90fIYX9ijP889j3/9+Uk0C005f7SsPg2qnelhL0rij/93vVpb1XTtg27bk12O6GNy55BfubTcnway7cd6c1ApZCnwj88v4inA04Bw+cSPce++557o9y+yzz/O8s/yn9ms/klx7uMszWg78deazVTme33D+vBV10rh2VsXjj8+3jyt45tzPDve/fLl3gzqzBLg887cdqenGBVkMrOr0wWE6Ps9lraNfurzXLQUmM6ft6YX/d3yGfZ7jfK3X/P/SyGEV55EdnhobS5zWM2fq300lz9ylGOIQbt4Mu3cv7Avea3eUQeamlQk/FsPzE2FTde9MXb0/A9NaY7C9USPyEeLUqXaK2li2TL2uK/tZTwKb/x9N+0IOq+hA+/wogD176rWhiYUBVa2cXbcOvvzlJO9+89ryzE3rtXJ1FHe+Ec2yevXqSufBVZ2/NzZswMbHWX3mDE91SVKHo9jE3P5B5t3nzTdP/v3s6PZ5VfZ3JLD5/9G0L+SwlmbNmjUA0QTcLUq2523//oWtBB94oB7nsduk9iqcyv37E4f8/vuT+1XhIBfpyeyVtp8jX3WP6bDXe1Gchx9+uFD6onWoaP6NsW4dfOUrPPxbv4U9/TTMzjLDJH9zeGH+5cUX0Xuuo4f5oy/NTvLdoyu5sHXPC149yasvS+Zknn5lliUT/myYPQXHTk3y3IkSZcxxPyt4nfXIq9e8017X+fp+1nz/+7B0KQcfeSSo3oRo2hdyWEvzxBNPNG1CbTSxqrTT8HYVva5pnidPLmzkFPLK2aZX+I5SvRf5OH78eKH0RetQ0fwbZd06jn/iE9Byys+bk/6R/HPSy/I37Tr5+eT8Oece9qydrXN1lbEM3eb418UTZskXENiLJab2pQ3BRV9S53FsrPw81v37YceOc/c+6EU6vH3bbQuOaSdnbVDSPFNn1az+ubpF8PFdCOGTLb3iDEWQv2+y9ko7w0Ha2ZmY2pfCWpWk8bBWNd93kKF4Xz2jVfawnjqV7ET6gQ8ki6R65dv0LjFN3r+p+hcSCms1GNLOYtdKO/0h7WyWgbXTOTeUx5o1a1yVkIyCVHqPkO47CLff7tzYmHOQ/Hv77eXz2rcvuX7fPn/2Fclz3z7nli5NyrF06WB2+CxLFc+lEzHWP98AB1wAGlfVUVQ7b7nllkLpi9ahovn7um9ZfNnrnLTTx33rzKsXoWqnz/raj0G1U3NYRWGK/kodJFRTOz4XFWXLkXcuk685pD57PGLbalEMF6tWdYyWGU3+vullr7RT2hkaMbUvOayiEGUaeIjhlvrtE93NVl8vEJ+Lp5peiCVGmw9/+MNR5++bbvZKO6WdIRJT+5LDWpIbbrihaRMaoWwDDy1Afa+wWf3CRvl4gfjsOfGZVz9Gtd6L7qxatYrp6enc6YvWoaL5N003e6Wd0s4Qial9yWEtydTUVNMmNEKdDbxKupUjz0vFxwvEZ89Jnb0wo1rvRXeKLtAqWoeqXABWBd3slXZKO0MkpvYlh1UUIsQhqqKkw1Z33XX+rlN1vlR89pyE1gsjRodDhw5VOg+u6vx9081eaac/pJ3+iKp9DbJia5ADuAh4EHgFeAbY1CWdAXcAL7aOO2iF4+p1VB0l4MCBA+7AgQOV3qMTBLrSsBd1rcLMc688K1U75VFnGUKmqXofEjQcJSA07Vy/fn2h9EXrUNH8u1GXdvqy1zlp5zARqnb6rK/9GFQ7mxTdPwX+K7Ac+GfAy8CVHdJtBZ4CXgu8BvjfwAf75a+wVmHgM5SJj3uVCRNTZxlCJ7b6VwUBOKzSzojuWxZp53ARW/2rgkG1s5GdrsxsGfDzwDbn3Ann3FeBzwHv75D8F4GdzrnnnHPfA3YC/642Y8VAdJrXVHTnlkHu1U6Z3U6q2CWmE1U9FzE8hKid2unqXHzZK+3Mh3RzMGJqX03NYV0NnHHOHcqc+yawvkPaK1ufZdNdWaFtwiPt85ouvri6uHed5lC1h1kpM4+sjrlZigcochKcdq5dW+2mX1Xn7xtf9ko7+yPdHJyY2ldTDuty4FjbuZeBFV3SvtyWbrmZWauLeR4z2wJsAbj00kv9WSvmKRr4ul3kqox7134v6CxmRSfZ17FYQvEARU6C0071sJ5LN3ulnf41Tbo5ODG1r6Yc1hPAhW3nLgSO50h7IXCiXXABnHNTwBQk+2H7MVWklP012y5yVf7izt5rxw5/Ypbmmw4/ZcXXx/7UwxLyRlROcNq5YsUKjh/vdHs/VJ2/bzrZK+2sRjulm4MTU/tqymE9BIyb2eudc0+3zr0ZeLJD2idbn/1ln3SiYnz8mq0ztItvMev00gE/Q1LDEPJG1EJw2ll10PFYgpqn+Nw0IIu083ykm4MTU/tqxGF1zr1iZp8FPm5mvwxcBbwb+OkOyfcAt5jZn5GssvswcHdtxop5fIlYXXHvfItZtwUERV5EvXoURj0eoOhPiNr56KOPct111/nOHq2OvgAADwpJREFUtrb8fdPJXmlnddop3RyMmNpXkxsH/ApwP/ADkhiBNzrnnjSztwGPOOeWt9LtAl4H/HXr7z9snWuUmHaH8EWMv2Z9ilm3l07eF9EwLBAYxXofIEFp59TUVKEXXpmdsWJ5oUJne6Wd0s5QtTOm9mUdpjMNBWvXrnWhVpBBMDMAQvzefMzlDOEeRe+f16YdO2DbtqRHYWwMbrsNbr21epuFX8zsoHMunqW1BZF21o+0U9o5CgyqndqaVXihjl/AVd8jj3i29zoUeQlogYAYRjZt2sTevXujzd83Re2NXTvzamBWO7PX5HE8pZ3VEVP7ksNakjQUxNTUVMOWhEH7HKU9e/z/mq8yhEkZQS96TYzDgu2o3ot2Nm7cWCh90TpUNP+mKWpvzNpZh26CtLNKYmpfjex0NQzcd9993HfffU2bUSu9dhTJ7oIyNga7dydDONdc428HkjI7reSl26IA39fEzijWe9GbTZs2FUpftA4Vzb9pOtk7rNop3cxPqNoZU/tSD6vIRb9fxdlfwM8+C/fd5//XvO9f2dlhqTJDTkWvyT7DsTG4/nrYvDnO3gIhUsys0nmhVefvm3Z7h00769bN9J7SzmqIqX3JYRW5yDOklA0Q/cAD1cw3Su+Z/iovK1idXiJFBb3oSyD7DM+ehV27kucU44pXIVKqftnF8jJNabd3mLSzCd1MbZZ2VkNM7UsOq8hFkV/FVc438rV4oNNL5NZbiy+oKhL6JX2GJ0+Cc8mh7QRF7Ozdu7fSYcWq8/dNu73DpJ15dDO9V68yFA2ZJe2sjpjal+awilykQnrbbfknyXcSskHxNf+p35yuVNy3bUs+u/HGweeTpc9w61ZYsqSaubhC1M3DDz8cdf6+abd3mLQzz1zYVDs/9jF4+9vBxxojaWd1xNS+FIe1JE3F9As5lmAd+AzP0qsXIBv3D8AMJif9DUE1HROxLKNe/0BxWAdF2tkMvrSzn3bt2JE4q3Nzyd8TE/DYY/WH0QqNUa9/oDisjfHWt761aROCow4h8Tlk1mtYquohqFi3E1S9F+1cd911PPTQQ7nTF61DRfNvmjL2xqSd/bRrwwZYtGjBYT171u/QvbTTLzG1LzmsJTl48GDTJgRFnVvn1SFYqbjv2ZOEmTlzRkNQoHovzieNL5mXonWoaP5NU9TeYdTOe+6Bm25KnNUlS6SbEK52xtS+5LAKL1QZ1L9O2ns61q1LwqfEOAQlRB1sqNgbqTp/3xS1dxi0s103t2yBN71JuhkDMbUvOazCC8OwdV63no5Yh6CEqINVq1Zx/PjxaPP3TVF7Y9dO6WbcxNS+FCWgJGY2P4laFF8JGyLZno7ZWdi+3d9OM8OC6r1op+jLrmgdiuVlmlLU3ti1s9PWst129RplQtXOmNqXHFbhjarCsdRF2tORLhj40pf8bo8oxDBS9d7ooe293o8y9sasndlQV+PjcP/9/reWFdURU/uSwypGlvb9vdOejne+c8FpHaW9roUoQ5UhsOrI3zex2VuGrHZme4g/8IGF3aiknXEQU31VHNaSKJZg3PRamVvnqt3YUP1THNZBkXbGjbSzHKp/g2uneljFSNJr15duc8rae2SFEIoS0E5s9hZF2jlcxFRfFSVAjCT9Vua2r3BVz4EQndm+fXvU+fsmNnuLIu0cLmKqr+phFR0Z9l/ERVfm+tiHW4hhZPXq1VHn75P9++ELX1g9tLoJ0s5hI6b2pR7WkuzatatpEypjVH4RF4kTGHusRF8Mc70X5Vi7di3T09O50xetQ0Xzb4pUN2dm1vJ7vzc9tLoJ0s4yhKqdsbQvkMNampi2MyvKMOy84htf+3DHzjDXe1GOoi+7onUolpdpqpswLd3MIO1MCFU7Y2lfoCkBogPZuHqj/Iu4nZhjJQpRFTt37ow6f1+kumm2U7rZhrQzXGJpXyCHtTRTU1NRBdwtQuw7r4jqGOZ6L8pRtIemaB2KpQco1c2rrx7u6QCiHKFqZyztCxSHtTSKJShGEdU/xWEdFGmnGEVU/xSHVQghRIOsWbMm6vx9E5u9YrSJqb7KYRVCCFGaqoc5QxxG7UVs9orRJqb6KodVCCFEaVasWBF1/r6JzV4x2sRUX2t3WM3sIjN70MxeMbNnzGxTj7QfNbNvmdlxM/uOmX20TluFECIUQtXOjRs3VpV1Lfn7JjZ7xWgTU31tIg7rPcAp4EeBq4DPm9k3nXNPdkhrwGbgr4B/CHzRzP7WOffp2qwVQogwCFI7Dx065DvLWvP3TWz2itEmpvpaaw+rmS0Dfh7Y5pw74Zz7KvA54P2d0jvn7nTOPeGcO+Ocewr478DV9VkshBDNE7J2Vr0XeUx7nUN89orRJqb6WmtYKzN7C/A159wFmXMfAdY7567rc60BTwC7nHP3dkmzBUi3k/jHwLe8GB4mPwL8sGkjKmKYywYqX+y8wTlX68Qvaac3hr1uqnxxM+zlG0g7654SsBw41nbuZSBPAbaT9Ajv7pbAOTcFTAGY2YFhjpU4zOUb5rKByhc7ZlZdkNLuSDs9MMxlA5UvdkahfINc73VKgJk9amauy/FV4ARwYdtlFwLH++R7E8l8rHc552Z92iyEEE0j7RRCiN547WF1zm3o9XlrHta4mb3eOfd06/SbgU6LBtJrrgd+A3i7c+45X7YKIUQoSDuFEKI3tS66cs69AnwW+LiZLTOzq4F3A5/qlN7M3gfcDvyMc+7/FbxdPNFwyzHM5RvmsoHKFzu1l0/a6Y1hLhuofLGj8vWg1kVXkMQSBO4HfgZ4EfgN59ze1mdvAx5xzi1v/f0d4LVAdijrT5xzH6zVaCGEaBhppxBilKndYRVCCCGEEKII2ppVCCGEEEIEjRxWIYQQQggRNNE6rHn31baEO8zsxdZxRyuQdtAUKF9te4b7pMi+6K30i83s22YW/Grngnu+v9XMvmJmJ8zseTP7tTptLUOBurnEzO5tleuImT1kZq+p296imNlNZnbAzGbN7I/7pP33ZnbYzI6Z2f1mtqQmM0sj7ZxPJ+0MDGnnfLrotLMO3YzWYeXcfbXfB/yBmV3ZId0W4D0kIWB+ErgO2FqXkQOQt3zpnuGvBn4WuMnMfqE2K8uTt3wpHwVeqMMwD+Qqm5n9CPA/gF3AxcA/Ar5Yo51lyfvd/RqwjqTdrQKOAnfXZeQATAO/Q7LAqStm9i9IwkZdA1wGvA74j5VbNzjSzgRpZ3hIOxNi1M7qddM5F90BLCP50ldnzn0K+ESHtPuALZm/fwl4vOky+Cpfh2v/M3B302XwWT7gCuDbwM8BzzVtv6+ykYQd+lTTNldYvj8A7sz8/S7gqabLUKCsvwP8cY/P9wK3Z/6+BjjctN0evz9pZ2CHtHP+vLQz0KNK3Yy1h3U1cMY5dyhz7ptAp18qV7Y+65cuJIqUb57WcN3b6BFMPBCKlu9u4DeBmaoN80CRsv1T4IiZ7TOzH7SGfS6txcryFCnfHwFXm9kqM7uApEfhkRpsrItO2vKjZnZxQ/bkQdrZAWlnEEg7Fxhm7Sytm7E6rEX21V7e+iybbnngc7HK7hu+nT57hgdC7vKZ2b8CxpxzD9ZhmAeKfHevBX6RZPjnUuA7wJ9Wat3gFCnf08DfAt9rXfPjwMcrta5eOmkL9G+nTSLt7Mx2pJ1NI+1cYJi1s7RuxuqwFtlXuz3thcAJ1+qLDpTC+4ZbXHuG5yqfJdtR3gncXJNdPijy3c0ADzrn/pdz7iTJPJ6fNrNXVWzjIBQp3z3AEpI5ZstIdmoall4C6Kwt0KOdBoC0sw1pZzBIOxcYZu0srZuxOqyHaO2rnTnXbV/tJ1uf9UsXEkXKl90z/BoXx57hecv3euBy4C/M7DBJo/0HrdWFl9dgZxmKfHd/BWRf/iE7AilFyncVyVymIy1H4G7gp1oLJoaBTtryvHPuxYbsyYO0M4O0MyiknQsMs3aW182mJ+gOMLH30yRDAMuAq0m6la/skO6DJJPOX0Oy2u5J4INN2++xfO8DDgM/3rTNvssHjAOXZI73kqxEvIRkqKvxcgz43f1zktWfVwETwO8Cf9G0/R7Ltxv4DPCqVvl+E/he0/bnKN84MAnsIFkUMQmMd0j3s6229xPASuDPybG4p+lD2jmfTtoZ2CHtnE8XnXbWoZuNF3KAh3MR8N+AV4BngU2t828jGbZK0xnJ0MiR1nEnrS1pQz4KlO87wGmSbvb0uLdp+32Vr+2aDQS+0rVo2YAbSeYpHQUeAn6saft9lY9kOOu/AD8AXgK+CvxU0/bnKN92kh6b7LGdZK7cCeDSTNpbgOdJ5pntBpY0bb/H70/aGeAh7ZxPK+0M6KhDN611sRBCCCGEEEES6xxWIYQQQggxIshhFUIIIYQQQSOHVQghhBBCBI0cViGEEEIIETRyWIUQQgghRNDIYRVCCCGEEEEjh1UIIYQQQgSNHFYhhBBCCBE0cliFEEIIIUTQyGEVQ4+ZLTWz58zsWTNb0vbZH5rZWTP7habsE0KIEJF2ipCQwyqGHufcDPDbwI8Bv5KeN7MdwC8BH3LOfboh84QQIkiknSIkzDnXtA1CVI6ZjQHfBP4+8Drgl4HfBX7bOffxJm0TQohQkXaKUJDDKkYGM9sIPAT8OfAO4JPOuZubtUoIIcJG2ilCQA6rGCnM7AngLcCngU2urQGY2b8BbgauAn7onLu8diOFECIwpJ2iaTSHVYwMZvZvgTe3/jzeLrgtjgKfBP5DbYYJIUTASDtFCKiHVYwEZnYtyZDWQ8Bp4F8Db3LOfbtL+vcAd6mXQAgxykg7RSioh1UMPWb2T4DPAl8D3gd8DJgDdjRplxBChIy0U4SEHFYx1JjZTwB/BhwC3uOcm3XO/V/gj4B3m9nVjRoohBABIu0UoSGHVQwtZnYp8AWSuVU/55w7lvn4NmAGuLMJ24QQIlSknSJExps2QIiqcM49SxLwutNn08AF9VokhBDhI+0UISKHVYgMrSDZE63DzGwScM652WYtE0KIcJF2iqqRwyrEubwf2J35ewZ4Bri8EWuEECIOpJ2iUhTWSgghhBBCBI0WXQkhhBBCiKCRwyqEEEIIIYJGDqsQQgghhAgaOaxCCCGEECJo5LAKIYQQQoigkcMqhBBCCCGCRg6rEEIIIYQImv8PxryQjggyipwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", @@ -1322,11 +2032,20 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 17, + "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", @@ -1460,11 +2179,20 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 18, + "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", @@ -1489,11 +2217,24 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 19, + "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", @@ -1549,11 +2290,37 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 20, + "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": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", @@ -1577,11 +2344,36 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 21, + "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", @@ -1593,11 +2385,37 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 22, + "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": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "log_clf = LogisticRegression(random_state=42)\n", "rnd_clf = RandomForestClassifier(random_state=42)\n", @@ -1611,11 +2429,36 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 23, + "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", + "/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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression 0.864\n", + "RandomForestClassifier 0.872\n", + "SVC 0.888\n", + "VotingClassifier 0.912\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -1634,10 +2477,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", @@ -1652,11 +2493,17 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 25, + "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))" @@ -1664,11 +2511,17 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 26, + "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", @@ -1678,11 +2531,20 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 27, + "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", @@ -1788,11 +2650,80 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 28, + "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: 0.90\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.93333333 0.8 0.93333333 1. 1. 0.92857143\n", + " 1. 0.92857143 0.92857143 1. ]\n", + "Test set accuracy with Random Forests and scaled data: 0.98\n" + ] + }, + { + "data": { + "image/png": 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+/bIrGDZ8eCnCbzUESPktpVbMHtw0YFdJvUgS27FAg/Otl6Oams8461d3MvnqH9GuQtw86TneeOd9Lj51JC++/i4PPP4qXxm8K+POGE0EPPXiLH4y/k4A7n30Jb46ZDem33kBQfDIM2/w4BOvlfiIsq+yspLf/f5KvjXyIGpqavjOyWPZs18/xl3ycwYOGsyob43m5LHfY+zJJ9Jvj7506bItt952OwDXXH0ls2fPYvyl4xh/6TgAJj/0MNtvv30pD6lEWsfwMx+KKN6oUNI3gf8E2gE3RsRljZWv2HL76LD70UWLx5rf8mlXljoEK8DwfQfzwgvTNyk7bf653WLn7zT5dQgAzPz1wS80MuFl0RX1HFxEPAg8WMw2zKyFtZLhZz5KfpHBzMqLSG50LwdOcGZWMPfgzCyzyuUigxOcmRXG5+DMLKuEPOGlmWWXe3Bmllk+B2dm2eRzcGaWVcmzqOWR4ZzgzKxgZZLfnODMrHB+ksHMsqmM5oNzgjOzgtTOB1cOnODMrEDlMx+cE5yZFaxM8psTnJkVSL7IYGYZ5fvgzCzTnODMLLPKJL85wZlZ4dyDM7Ns8sP2ZpZVyYSX5ZHhymNaTjNrVSqkvJamSNpW0kRJH0uaJ2mDXw4vaaCkJyStlLRQ0plN1e8enJkVrBmHqFcBa4AdgP7AA5JmRERV3fbUDfgrcBZwN9Ae6NlU5U5wZlYQNdPD9pI6AkcAe0XESuApSfcBJwLn1Sv+U2BKRNyWrq8G3miqjQ0OUSV1amzZqCMys0yoUH4L0E3S9JzllJxqdgPWRsTMnG0zgH4NNPklYJmkZyQtkjRZ0k5NxdlYD64KCJIbl2vVrgfQZOVmlk0FXGRYEhGDN7BvK+DDettWAFs3ULYnMBD4BvAq8GtgAjC8scY3mOAiYsfGftDM2iaRXEltBiuB+qPBTsBHDZRdBUyMiGkAkn4JLJHUOSJWbKiBvK6iSjpW0gXp656SBuXzc2aWTQUMURszE6iUtGvOtn1IRo/1vUIycqwVDZRZP86mCki6EvgayYk/gE+Aa/Kp3MwySMl8cPksjYmIj4F7gXGSOkoaDowBbm2g+J+BwyT1l7QZcDHwVGO9N8ivBzcsIv4N+DQNahnJJVoza6Ok/JY8nAZsASwiOad2akRUSRohaWVtoYj4O3AB8EBati+wwXvmauVzm8g/JVWQdgkldQU+yyt0M8scQV438eYj7TAd2sD2J0kuQuRu+yPwx0LqzyfBXQXcA2yXntg7GvhlIY2YWbaUy6NaTSa4iLhF0gvAAemmoyLiteKGZWatVQHDz5LL90mGdsA/SYapfn7VrI1rriFqseVzFfVCkpN/3UlutvtvSecXOzAza72U51Jq+fTgTgIGRMQnAJIuA14CxhczMDNrvbI04eU/6pWrTLeZWRuUXEUtdRT52WCCk/Q7knNuy4AqSVPS9QOBaS0Tnpm1OiqfCS8b68HVXimtIrm5rtZzxQvHzMpB2Q9RI+KGlgzEzMpDJoaotST1AS4D9gQ2r90eEbsVMS4za8XKpQeXzz1tN5E86CrgEOBO4I4ixmRmrVy53CaST4LbMiKmAETE7Ii4iCTRmVkbJEG7CuW1lFo+t4msTh+2ny3ph0A1Dc+4aWZtRLkMUfNJcGcBHYEfk5yL6wyMLWZQZta6lUl+y+th++fTlx/xr0kvzayNEvl952lr0NiNvhNpZFrgiDi8KBGZWeuWkdlErmyxKFIDvrATTz/f4s3aJugy5PRSh2AFWP3Wu81ST9mfg4uIv7VkIGZWHgS0K/cEZ2a2Ia3gDpC8OMGZWcEyl+AkdYiI1cUMxsxav2TK8vLIcPnM6DtU0qvA2+n6PpL+UPTIzKzVaqYvfi5+nHmU+S9gFLAUICJmkHwRtJm1Uc34vahFlc8QtSIi5tXrktYUKR4za+UEVLaG7JWHfBLcfElDgZDUDjgDmFncsMysNSuT/JZXgjuVZJi6E7AQeDTdZmZtkJSBR7VqRcQi4NgWiMXMykSZ5Le8ZvS9jgaeSY2IU4oSkZm1eq3hCmk+8hmiPprzenPgMGB+ccIxs9ZO0Coms8xHPkPUOtOTS7oVeKpoEZlZ69ZK7nHLx8Y8qtUL2KG5AzGz8qFW8Y0LTcvnHNxy/nUOroLki6DPK2ZQZtZ6ZeZrA5Xc3bsPyfcwAHwWERucBNPM2oZySXCNPqqVJrMHI6ImXZzczAxJeS2lls+zqC9LGlD0SMysLCRfG5jfUmqNfSdDZUSsBQYA0yTNBj4mGYJHRAxsoRjNrJXJwpMMU4GBwOgWisXMykBzXmSQtC1wA3AgsAQ4PyL+u5Hy7YEZwNYR0bOp+htLcILk2+wLitjMMq8ZO3BXAWtIbj3rDzwgaUZEVG2g/DnAYvL88vnGEtx2kn66oZ0R8R/5NGBmWSMqmuE+OEkdgSOAvSJiJfCUpPtIvn95vVvRJPUCTgB+ClyXTxuNJbh2wFZQJnf0mVmLEAX14LpJmp6zfm1EXJu+3g1YGxG506/NAL66gbr+AFwArMq38cYS3D8iYly+FZlZGyGozP8k3JKIGLyBfVsBH9bbtoIGhp+SDgPaRcRESfvl23iT5+DMzHIV2INrzEqgU71tnYCP6rSXDGV/DXyz0AYaS3BfL7QyM2sbmuk2kZlApaRdI+LtdNs+QP0LDLsCuwBPpjcPtwc6S3of+FJEzN1QA419s/2yjY/bzLKsOfJbRHws6V5gnKTvk1xFHQMMq1f0NWDHnPVhwJUkt7EtbqyNVnCvsZmVE5EkjnyWPJwGbAEsAiYAp0ZElaQRklYCRMTaiHi/diGZ8OOzdL3RL8DyN9ubWWHUfE8ypCPFQxvY/iTJRYiGfuYxoMmbfMEJzswKlDzJUB7XIJ3gzKxg5ZHenODMbCOUSQfOCc7MCtU65nrLhxOcmRWk9ipqOXCCM7OC+SKDmWWT8BDVzLLJQ1QzyzT34Mwss8ojvTnBmVmBBLRzD87MsqpM8psTnJkVSqhMBqlOcGZWMPfgzCyTkttEyiPDOcGZWWHkHpyZZZgf1TKzTEomvCx1FPlxgjOzgvkqqpllVpmMUMvmmdlW6eEpf2XvfrvTb4++/ObXV6y3f/Xq1Zxw/DH026MvI4bty7y5cwH426OPMGzoIAb3/yLDhg7isf/9ewtH3nZ9Y9gXmDHxYl6b9AvO/u431tu/0+e78OA1ZzD1jvOZct2Z9Nh+m3X7LjtzDC/cfSEv3XMRv/3ZkS0ZdqujPP8rtaIlOEk3Slok6bVitVFKNTU1/OTHP2LS5Id46ZXXuev2Cbzx+ut1ytx04w102aYLVW/O4owzz+LCC84FoGvXbtz9P5OZ/vKrXHfjzYw9+cRSHEKbU1Eh/vO8oxlz+tUMOOJSjjp4EHv0/lydMuPPOozbHpjK0GPGc/m1DzHujNEAfGmfXny5f2+GHH05g466jEH9dmbEoF1LcRglV3sOLp+l1IrZg7sJOLiI9ZfUtKlT6dOnL71696Z9+/Ycdcyx3D95Up0y90+exLdP/A4Ahx9xJI/9/W9EBP0HDKB79+4A7NmvH5+uWsXq1atb/BjamiF77cLs+UuYW72Uf66t4a4pLzJqv73rlNmj9+d5fOpbADw+bSaj9vsiABHQof1mtN+skg7tK6msbMeiZR+2+DG0ChIVeS6lVrQEFxFPkHxBaya99141PXv+68u2e/ToSXV19fpldkzKVFZW0qlzZ5YuXVqnzMR776H/gIF06NCh+EG3cd2378yChcvXrVcvXE6P7TrXKfPqzGrG7N8fgDH770OnrbZg284def6VOTwx/W3mPHIZcx6+nEefeYO35ixs0fhbE+W5lFrJz8FJOkXSdEnTFy9ZXOpwWtTrVVVcdMG5XHn1n0odiqXO/91ERgzqy7MTzmXEoL5UL1xOTc1n9N6xG7v32oG+B11En4MuZL+huzF8QJ9Sh1sStd+LWg49uJJfRY2Ia4FrAQYNGhwlDidv3bv3YMGC+evWq6sX0KNHj/XLzJ9Pz549Wbt2LR+uWEHXrl0BWLBgAcccdRjX33gLvfu0zV+UlvbeohX03KHLuvUeO3ShevGKOmX+sXgFx559PQAdt2jPoV/vz4qVqxh7+DCmvjqXj1etAWDK01Xsu3cvnn5pdssdQCtS+tSVn5L34MrV4CFDmDXrbebOmcOaNWu4647bGTlqdJ0yI0eN5rZbbwbg3nvu5qtf2x9JfPDBBxw+eiT/ftkVDBs+vBTht0nTq+bRd6ft2Ll7VzarbMdRBw3kgcdeqVOm6zYd181We87Yg7h50nMAzH9/OSMG9aVduwoqKysYMXBX3pzzfosfQ6tRJmPUkvfgylVlZSW/+/2VfGvkQdTU1PCdk8eyZ79+jLvk5wwcNJhR3xrNyWO/x9iTT6TfHn3p0mVbbr3tdgCuufpKZs+exfhLxzH+0nEATH7oYbbffvtSHlLm1dR8xlm/upPJV/+IdhXi5knP8cY773PxqSN58fV3eeDxV/nK4F0Zd8ZoIuCpF2fxk/F3AnDvoy/x1SG7Mf3OCwiCR555gwefyOQNAnlpDcPPfCiiOKNCSROA/YBuwELgFxFxQ2M/M2jQ4Hj6+elFiceKo8uQ00sdghVg9Vt38tknizYpO33hiwPilkmP5VV2aJ9tXoiIwZvS3qYoWg8uIo4rVt1mVmLl0YHzENXMCpOcXiuPDOcEZ2aF8XxwZpZlZZLfnODMrFDyFz+bWXaVSX7zjb5mVph87/HNJwdK2lbSREkfS5on6fgNlDtH0muSPpI0R9I5+cTqHpyZFa75enBXAWuAHYD+wAOSZkREVQMtngS8AvQBHpY0PyJub6xy9+DMrGDNMeGlpI7AEcDFEbEyIp4C7gPWmyAxIn4dES9GxNqIeAuYBDT5nKMTnJkVTMpvAbrVzhaULqfkVLMbsDYiZuZsmwH0a7xtCRgB1O/lrcdDVDMrTGH3wS1p5FGtrYD6s4auALZuos5LSDpnf26qcSc4MytYMz3JsBLoVG9bJ+CjDbYrnU5yLm5ERDQ5DbaHqGZWEFHQELUxM4FKSblfbrEPGxh6ShoLnAd8PSIW5BOrE5yZFaw5bhOJiI+Be4FxkjpKGg6MAW5drz3p28DlwDci4p1843SCM7PCNd+El6cBWwCLgAnAqRFRJWmEpJU55S4FugLTJK1Ml2uaqtzn4MysYM014WVELAMObWD7kyQXIWrXe21M/U5wZlawMnlSywnOzDZCmWQ4JzgzK4gnvDSz7PKEl2aWZWWS35zgzKxQnvDSzDKsTPKbE5yZFaaVfGl9XpzgzKxwZZLhnODMrGC+TcTMMsvn4MwsmwQVTnBmll3lkeGc4MysILUTXpYDJzgzK1iZ5DcnODMrnHtwZpZZflTLzDKrPNKbE5yZFSjPb8xqFZzgzKxgfpLBzLKrPPKbE5yZFa5M8psTnJkVSs32tYHF5gRnZgUppycZ/M32ZpZZ7sGZWcHKpQfnBGdmBfNtImaWTb7R18yyqpwuMjjBmVnBPEQ1s8xyD87MMqtM8psTnJlthDLJcE5wZlYQQdk8qqWIKHUM60haDMwrdRxF0A1YUuogrCBZ/cx2jojtNqUCSX8leX/ysSQiDt6U9jZFq0pwWSVpekQMLnUclj9/ZtngZ1HNLLOc4Mwss5zgWsa1pQ7ACubPLAN8Ds7MMss9ODPLLCc4M8ssJ7giknSwpLckzZJ0XqnjsaZJulHSIkmvlToW23ROcEUiqR1wFXAIsCdwnKQ9SxuV5eEmoGQ3plrzcoIrnqHArIh4JyLWALcDY0ockzUhIp4AlpU6DmseTnDF0wOYn7O+IN1mZi3ECc7MMssJrniqgR1z1num28yshTjBFc80YFdJvSS1B44F7itxTGZtihNckUTEWuB0YArwBnBnRFSVNipriqQJwLPA7pIWSPpeqWOyjedHtcwss9yDM7PMcoIzs8xygjOzzHKCM7PMcoIzs8xygisjkmokvSzpNUl3SdpyE+raT9L96evRjc12ImkbSadtRBuXSDo73+31ytwk6cgC2trFM4BYfU5w5WVVRPSPiL2ANcAPc3cqUfBnGhH3RcQVjRTZBig4wZmVmhNc+XoS6Jv2XN6SdAvwGrCjpAMlPSvpxbSntxW6norzAAAClElEQVSsm5/uTUkvAofXViTpZElXpq93kDRR0ox0GQZcAfRJe4+/ScudI2mapFck/TKnrgslzZT0FLB7Uwch6QdpPTMk3VOvV3qApOlpfaPS8u0k/San7X/b1DfSsssJrgxJqiSZZ+7VdNOuwNUR0Q/4GLgIOCAiBgLTgZ9K2hy4DvgWMAj43Aaq/y/g8YjYBxgIVAHnAbPT3uM5kg5M2xwK9AcGSfqKpEEkj6T1B74JDMnjcO6NiCFpe28AuU8O7JK2MRK4Jj2G7wErImJIWv8PJPXKox1rgypLHYAVZAtJL6evnwRuALoD8yLiuXT7l0gm2HxaEkB7kkeP9gDmRMTbAJL+ApzSQBv7AycBREQNsEJSl3plDkyXl9L1rUgS3tbAxIj4JG0jn2dv95J0KckweCuSR9tq3RkRnwFvS3onPYYDgb1zzs91TtuemUdb1sY4wZWXVRHRP3dDmsQ+zt0EPBIRx9UrV+fnNpGA8RHxp3pt/GQj6roJODQiZkg6GdgvZ1/95wgjbfuMiMhNhEjaZSPatozzEDV7ngOGS+oLIKmjpN2AN4FdJPVJyx23gZ//G3Bq+rPtJHUGPiLpndWaAozNObfXQ9L2wBPAoZK2kLQ1yXC4KVsD/5C0GfDtevuOklSRxtwbeCtt+9S0PJJ2k9Qxj3asDXIPLmMiYnHaE5ogqUO6+aKImCnpFOABSZ+QDHG3bqCKM4Fr01k0aoBTI+JZSU+nt2E8lJ6H+wLwbNqDXAmcEBEvSroDmAEsIpkyqikXA88Di9P/58b0LjAV6AT8MCI+lXQ9ybm5F5U0vhg4NL93x9oazyZiZpnlIaqZZZYTnJlllhOcmWWWE5yZZZYTnJlllhOcmWWWE5yZZdb/B4mnnN0dEjJnAAAAAElFTkSuQmCC\n", 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Ohw4dqFWrFsYYp0wSt0ovPSmlVJrk5GQ++OAD/P39WbJkCSVLlmTChAnUqFHD0aE5lJ5RKKUUsGnTJp555hl27twJwOOPP87kyZOpUqWKgyNzPKc7o/CLqARt50F0oqNDUUoVIK+//jo7d+7E29ubn376ifnz52uSSON0icIzqSjsPAepFkeHopRyYiLC5cuXbcvTpk3j1VdfZffu3Tp9wXWcLlEopdStOnDgAPfffz9dunSxTVvg4+PD+PHj8fT0dHB0dx5NFEqpQiMhIYE333yToKAgVq9eTVhYGMeOHXN0WHc8p0sU+6qcgZXdoJS7o0NRSjmRFStWEBgYyNixY0lKSuLpp5/mwIEDeHt7Ozq0O55dE4UxpoMx5oAx5rAxZlQWz1c3xqwxxvxtjNlpjLnhhcF492QIrghuTpfjlFIOICI8/fTTtGvXjsOHD+Pv78+6deuYNWsW5cuXd3R4TsFun7bGGFfgI+BBwB94whjjf12z14D5ItIQ6AF8bK94lFKFkzGGmjVrUqxYMd555x3+/vtvWrZs6eiwnIo976MIBQ6LyFEAY8xcoDOwN10bAUql/VwaOGXHeJRShURYWBinT5/mwQcfBGDkyJH06tVLLzPlkT2v31QBTqZbjkhbl95bwFPGmAjgZ+D5rDZkjBlojNlqjNlqj0CVUgVDTEwML730Eo0bN6ZPnz5cuHABAHd3d00St8DRF/qfAGaLSFXgIeArY0ymmETkExFpkteCVkqpgk1EWLRoEf7+/kyePBmAJ598kiJFijg4soLBnpeeIoFq6Zarpq1Lrz/QAUBENhpjPAAv4J/sNuqZWAR2/AP1vbRDWynF8ePHGTJkCMuWLQOgSZMmzJw5k0aNGjk4soLDnp+0W4C6xhhvY0xRrJ3VS69rcwJoC2CM8QM8gHM5bdQv8i64fz5c1hIeShV2IkLXrl1ZtmwZpUqVYtq0aWzatEmTRD6zW6IQkRRgCPArsA/r6KY9xpixxphOac1eBgYYY3YA3wF9xdkmyFBK3XYWi7WEjzGGiRMn0r17d/bv38/gwYNxddVpkvOb001c1KRIddlaZgQc6A/lijk6HKXUbXT+/HlGjbLekvXpp586OBrnUqimQo0vmgRBFcDV6UJXSuWRiPDll1/i6+vLZ599xpw5c4iIiHB0WIWG033a7qt6FlZ1h9JawkOpwmDfvn3cd9999O3bl6ioKFq3bs2OHTuoWrWqo0MrNJwuUSilCgcR4fXXXyc4OJjff/8dLy8vvvzyS1avXo2vr6+jwytUNFEope5IxhgiIyNJTk5mwIABHDhwgN69e2OMcXRohY7TdWYbHyNywLliVkrlzqlTp4iKiiIoKAiAqKgoDhw4QIsWLRwcmfMrVJ3ZSqmCJzU1lWnTpuHn50ePHj1ISkoCwMvLS5PEHUAThVLKobZv306zZs14/vnnuXz5MrVr184wRalyvFwlCmNMUWNMHXsHkxvVz5WFl1ZDbJKjQ1FK3YLLly/zwgsvEBISwtatW6latSoLFy5k6dKleHl5OTo8lc4NE4UxpiOwC1iRttzAGLPI3oFlp0JMCfhqLySlOioEpdQtEhHuvfdepk6dijGGl156ib179/Loo49qZ/UdKDdnFGOBpsAlABEJA+6IswullHMyxvDiiy8SGhrK1q1b+eCDDyhZsqSjw1LZyE312GQRuXRdltdhR0qpXEtKSmLSpEm4uroyfPhwAHr37s1TTz2ltZmcQG4SxT5jTDfAxRjjDQwFNtk3rOwd97oA77UGT60zr5Qz+OOPP3j22WfZu3cv7u7u9O7dm0qVKmGM0SThJHJz6WkI0BiwAAuBROAFewaVk6hScdA7ADzsOZWGUupWRUVF8fTTT3Pvvfeyd+9e6taty7Jly6hUqZKjQ1M3KTeJor2IjBSRhmmPUcCD9g5MKeWcRIQvvvgCX19fvvjiC4oWLcqbb77Jzp07uf/++x0dnsqD3CSK17JYNya/A1FKFRxff/0158+fp02bNuzcuZO33noLDw8PR4el8ijb6zfGmPZYpymtYoyZlO6pUlgvQymlFADx8fFER0dTuXJljDF8/PHHbNmyhZ49e+pw1wIgpwv9/wC7gQRgT7r1McAoewallHIev/zyC4MHD6ZWrVqsWLECYww+Pj74+Pg4OjSVT7JNFCLyN/C3MeYbEUm4jTEppZxAZGQkw4YNY8GCBQCULFmS8+fP613VBVBu+iiqGGPmGmN2GmMOXn3YPbJsND5aDSpMgwtXHBWCUoVaamoqU6dOxc/PjwULFlC8eHE++OADtm3bpkmigMrNGNPZwDhgItbRTv3QG+6UKpQsFgutWrViw4YNADzyyCNMmTKF6tWrOzgyZU+5OaPwFJFfAUTkiIi8hg6PVapQcnFxoV27dlSrVo0lS5awaNEiTRKFQG7OKBKNMS7AEWPMs0AkoEVZlCoERIT58+fj5uZG165dARg5ciQvvfQSJUqUcHB06na54Qx3xpimwF6gLDAeKA28JyIb7B9eFvHoDHdK3RZHjhxh0KBB/Pbbb1SoUIEDBw5QtmxZR4el8uhWZri74RmFiPyV9mMM0Ctth1XysjOl1J0vMTGR999/n/Hjx5OQkEDZsmUZP348pUuXdnRoykFyTBTGmBCgCrBeRKKMMfWBkUAboOptiE8pdRutXbuW5557jv379wPQq1cvJk6cSMWKFR0cmXKkbDuzjTHvAN8APYHlxpi3gDXADqDebYlOKXXbpKamMmjQIPbv34+Pjw+rV69mzpw5miRUjmcUnYFgEblijCkHnAQCReTo7QlNKWVvFouFhIQEPD09cXV1Zfr06axbt44RI0bg7u7u6PDUHSLbzmxjzHYRaZRu+W8RaXjbIsuGdmYrlT927drFs88+i6+vL7NmzXJ0OMrO7NWZXcsYs/DqPgDvdMuISJe87PBWeV0uDnN2QzdfnZNCqTyIi4tj7NixTJo0iZSUFMLDw7l48aKOaFLZyumTtut1y9PsGUhu1YgqBy+vhYdra6JQ6ib9+OOPDBkyhBMnTmCMYdCgQYwfP54yZco4OjR1B8upKOCq2xmIUsp+UlJS6N69OwsXWi8KNGjQgJkzZxIaGurgyJQzyE0JD6WUk3Nzc6N06dKUKFGCyZMns2XLFk0SKtdueGf2LW3cmA7AFMAV+ExE3s2iTTfgLayFBneIyJM5bbNGuXJyvO8CGHsPlChqh6iVKhj++st6r2zTpk0BOH/+PFeuXKFqVb0FqjC6lc7sXCcKY4y7iCTeRFCuwEHgASAC2AI8ISJ707WpC8wH2ojIRWNMRRH5J8ft6qgnpXJ06dIlRo8ezcyZM/H19SUsLIyiRfVLVWF3K4nihpeejDGhxphdwKG05WBjzIe52HYocFhEjopIEjAX670Z6Q0APhKRiwA3ShJKqeyJCN9++y2+vr7MmDEDV1dXOnXqRGpqqqNDU04uN30UU4GHgfMAIrIDuC8Xr6uC9Sa9qyLS1qVXD6hnjNlgjNmUdqlKKXWTDh06RLt27ejZsydnz56lRYsW/P3337z77rsUK1bM0eEpJ5eb8aUuInL8ugnS8+srihtQF2iNtXbUOmNMoIhcSt/IGDMQGAho8RClrpOcnEybNm2IiIigXLlyTJgwgX79+uHiomNVVP7ITaI4aYwJBSSt3+F5rH0PNxIJVEu3XDVtXXoRwF8ikgyEp02xWhdrf4aNiHwCfALWPopc7FupAk9EMMZQpEgRxo8fz5o1a5gwYQIVKlRwdGiqgMnNfBQVsV5+uj9t1UpgiIhE3eB1blgTSlusCWIL8KSI7EnXpgPWDu4+xhgv4G+ggYicz3a72pmtCrmzZ8/yyiuvUK9ePV5//XVHh6OchF07s4EUEekhIl5pjx43ShIAIpICDAF+BfYB80VkjzFmrDGmU1qzX4Hzxpi9WCvTDs8pSQD4RVSCtvMgOtcDsJQqECwWi20k09dff82kSZOIiYlxdFiqEMjNGcUR4AAwD1goIg79y2xSpLpsLTMCDvSHctpJpwqHHTt28Oyzz7Jp0yYAOnTowEcffUStWrUcHJlyFnY9oxCR2sA4oDGwyxiz2BjTIy87U0rdnOTkZF555RUaN27Mpk2bqFy5MvPnz+fnn3/WJKFum1wNixCRP0VkKNAIuIx1QiOllJ25ubnx999/Y7FYeP7559m3bx+PP/44141CVMqucnPpqQTWG+V6AH7AEqz9DX/l+EI7KV6zqMQtiYD6XuCmw/9UwXPixAlSU1Px9vYGrPdIREdH06RJnq4aKAXYuYSHMeYY8CPW5PBHXnaSn3TUkyqokpOTmTJlCm+++SbNmzdnxYoVeuag8o29Ji66qpaIWPKycaVU7mzcuJFnn32WnTt3AlCuXDni4+MpXry4gyNTKodEYYz5QEReBn4wJvNNbo6a4U6pguTixYuMGjWKTz75BABvb28++ugjHnzwQQdHptQ1OZ1RzEv7946Y2U6pgiYxMZEGDRpw4sQJihQpwvDhwxkzZgyenp6ODk2pDHKa4W5z2o9+IpIhWRhjhgA6A55St8Dd3Z3+/fuzatUqpk+fjr+/v6NDUipLuenM3i4ija5b97eINLRrZNnQUU/KWSUkJPDOO+/g4+PDk09a5+dKSUnB1dVVO62V3dmlM9sY0x3rkFhvY8zCdE+VBC5l/Sr784u8C+6fr3dmK6eyYsUKBg0axOHDh6lYsSKPPvooxYoVw80tN+NJlHKsnP5KN2Odg6Iq8FG69TFYi/cppW7gzJkzvPTSS3z33XcA1K9fnxkzZugcEcqp5NRHEQ6EY60Wq5S6CampqcycOZNXX32V6OhoihUrxptvvsmLL76o05Iqp5PTpaffRaSVMeYikL4jwwAiIuXsHl0W4osmQVAFcNX+CXXnSk1N5cMPPyQ6OpqHHnqIadOm2e60VsrZZNuZbYxxERFL2mRFmYiIQybi1Tuz1Z0qJiaG1NRUypQpA8D69es5e/YsXbp00c5q5XB2qR6b7m7saoBrWmJoDjwD6O2iSqURERYuXIifnx8vv/yybf0999xD165dNUkop5eb6zeLsU6DWhv4AutUpd/aNSqlnMSxY8fo1KkTXbt2JTIykt27d5OQkODosJTKV7lJFJa0Oa27AB+KyItAFfuGpdSdLTk5mffeew9/f3+WLVtGqVKlmDZtGn/++SceHh6ODk+pfJWbQdwpxpjHgV7AI2nritgvJKXubPHx8TRr1oxdu3YB0KNHDyZNmkTlypUdHJlS9pGbRPE0MAiYICJHjTHewHf2DUupO5enpydNmjQhPj6ejz/+mHbt2jk6JKXs6oYlPACMMW5AnbTFwyKSYteoclCjXDk53ncBjL0HSuh4dGV/IsKcOXOoXbs299xzDwDR0dEULVpUb5xTTsOuc2YbY1oCh4FZwOfAQWNMi7zsLD9UiCkBX+2FJIeMzlWFzL59+7jvvvvo27cvAwcOJCkpCYDSpUtrklCFRm4uPU0GHhKRvQDGGD/gK0DnZVQF1pUrVxg/fjwTJkwgOTmZChUqMHr0aIoU0e45VfjkJlEUvZokAERknzFGr/moAmv58uUMHjyYo0ePAjBgwADeffddypVzSDECpRwuN4liuzFmBvB12nJPHFgU8LjXBXivNXjqNzuV/2JjY+nVqxdRUVEEBAQwY8YMWrRw2JVWpe4IuZmPwgMYCtyTtuoPrPdTOOSuIi3hofJbamoqFovFdlnp22+/JSIighdffFEvNakC41Y6s3NMFMaYQKA2sEdEDuUxvnyliULlp23btvHMM8/QuXNnXn/9dUeHo5Td2GXUkzHmVazlO3oCK4wxT+cxPqXuOJcvX+aFF14gNDSUbdu28dVXX5GcnOzosJS6I+U0PLYnECQijwMhwHO3JySl7EdE+P777/H19WXq1KkYY3jppZfYvn27XmZSKhs5dWYnikgcgIicM8boBBDKqcXExNC9e3d++eUXAJo2bcqMGTNo0KCBgyNT6s6WU6KolW6ubAPUTj93toh0sWtkSuWzEiVKkJiYSOnSpXn33XcZOHAgLi76/UepG8lp4qK2Ob1QRFbZJaIbaFKkumwtMwIO9Idyemesytm6deuoXLkydevWBeD48eN4eHhQqVI0a1dIAAAgAElEQVQlB0em1O11K53ZOc2Z7ZBEoFR+iIqKYsSIEXzxxRe0bduWFStWYIyhRo0ajg5NKaej592qQLFYLHz++ef4+PjwxRdfULRoUVq2bElqqtYGUyqv7JoojDEdjDEHjDGHjTGjcmjX1RgjxhitH6XybM+ePbRu3Zr+/ftz4cIF2rZty65du3jzzTdxc8tNEQKlVFZyVWYcwBjjLiKJud6wMa7AQeABIALYAjyRvm5UWruSwE9AUWCIiGzNcbt6w53KQnR0NFWrViU2NpaKFSsyadIknnzySZ2vWqk09i4zHmqM2QUcSlsONsZ8mItth2Kdu+KoiCQBc4HOWbR7G3gP0ImG1U27+kWndOnSjBw5kmeffZb9+/fTs2dPTRJK5ZPcXHqaCjwMnAcQkR3Afbl4XRXgZLrlCK6ba9sY0wioJiI/5bQhY8xAY8xWY0yOZxuq8IiMjOSxxx7j66+/tq0bM2YM06dPp2zZsg6MTKmCJzeJwkVEjl+37pZ7BtNu4JsEvHyjtiLyiYg0yetpkyo4UlJSmDJlCr6+vvzwww+8+eabto5qPYNQyj5ykyhOGmNCATHGuBpjhmHte7iRSKBauuWqaeuuKgkEAGuNMceAZsBS7dBW2dmyZQtNmzZl2LBhxMbG8sgjj/D777/j6urq6NCUKtBykyieA14CqgNnsX6g56bu0xagrjHGO22iox7A0qtPiki0iHiJSE0RqQlsAjrdqDNbFT5xcXEMGTKEpk2bsn37dqpXr86SJUtYtGgR1apVu/EGlFK35IaJQkT+EZEeaR/qXmk/R+XidSnAEOBXYB8wX0T2GGPGGmM65TVgr8vFYc5uSEjJ6yaUk3Fzc2PlypW4uLgwfPhw9u7dS6dOef4TUkrdpNxMXPQpkKmRiAy0V1A50RIehcORI0coU6YM5cuXB6yXnTw8PAgMDHRwZEo5J7sOjwVWAqvSHhuAikCu76dQ6mYkJiYybtw4AgICGDlypG19SEiIJgmlHOSGt6uKyLz0y8aYr4D1dotIFVpr167lueeeY//+/YB1hFNqaqp2VivlYHkp4eENOKz05rmSsdDLH4rqh0dB8c8//9CnTx/uu+8+9u/fj4+PD6tXr2b27NmaJJS6A9zwjMIYc5FrfRQuwAUg27pN9naiwkWY1MZRu1f5LCoqCj8/Py5cuIC7uztjxoxhxIgRuLu7Ozo0pVSaHBOFsd7BFMy1+x8sktviUErlgpeXF507dyYiIoKPP/6YOnXqODokpdR1cjPqabeIBNymeG5IiwI6t7i4OMaOHUvHjh259957AUhISMDd3V3vrFbKjuw96inMGNMwLxtXKr0ff/wRf39/JkyYwKBBg7BYLAB4eHhoklDqDpbtpSdjjFvaTXMNgS3GmCNAHNb5s0VEGt2mGJWTO3nyJC+88AKLFi0CoGHDhsycOVPnq1bKSeTUR7EZaAToLbAqT1JSUpg6dSpvvPEGcXFxlChRgnHjxjF48GCdSEgpJ5JtH4Ux5m8RueMuOfkXv0v2NpsCCx+B0joyxt6Sk5OJiIggIeHmpwuxWCxERkZisVjw9PSkbNmymiCUsjMPDw+qVq1KkSJFMqy/lT6KnP7XVjDGvJTdkyIyKS87vFWeSUVh5zlItThi94VOREQEJUuWpGbNmrnqR0hJScHFxcV2Wenuu+/GGEOZMmXsHapShZ6IcP78eSIiIvD29s637eaUKFyBElj7JFQhlZCQkKskISJcuHCBkydPUrFiRe6++24AnURIqdvIGEP58uU5d+5cvm43p0RxWkTG5uvelFO6UZJISEjg+PHjxMTEABAbG4uI6EgmpRzAHv/vckoUd+T/8n1VzsCSblBK+ycczWKxcObMGU6fPo2I4ObmRtWqVSlfvrwmCaUKkJzGJ7a9bVHchHj3ZAiuCG46tNKRkpOT2bNnD6dOnUJEKF++PPXr18fLyyvfk4SrqysNGjQgICCAf//731y6dMn23J49e2jTpg0+Pj7UrVuXt99+m/QDNH755ReaNGmCv78/DRs25OWXbzjz7h3jiSeeICgoiMmTJ+eqfYkSJewSh4gwdOhQ6tSpQ1BQENu3b8+y3ZUrV2jVqpVtato70dNPP03FihUJCMj+HuKcjvfLL7+kbt261K1bly+//NK2/v777+fixYt2jd2hRMSpHtRD1O2zd+/eLNdbLBbZv3+/7Nq1Sy5fvmzXGIoXL277uXfv3jJu3DgREYmPj5datWrJr7/+KiIicXFx0qFDB5k2bZqIiOzatUtq1aol+/btExGRlJQU+fjjj/M1tuTk5Hzd3lWnT5+W2rVr39Rr0r9P+emnn36SDh06iMVikY0bN0poaGiW7aZNmyb/93//l+vtWiwWSU1Nza8wc+X333+Xbdu2Sf369bNtk93xnj9/Xry9veX8+fNy4cIF8fb2lgsXLoiIyOzZs21/l3eCrP7fAlslr5+7eX2hox6aKG6vq39wFotFsBaHzPfHjaT/AJw+fbo899xzIiLy2WefSa9evTK0PXz4sFStWlVERHr16iWzZs264fZjYmKkb9++EhAQIIGBgbJgwYJM+/3++++lT58+IiLSp08feeaZZyQ0NFRefPFFqVGjhly8eNHWtk6dOnLmzBn5559/pEuXLtKkSRNp0qSJrF+/PtO+r1y5Ytt3gwYNZPXq1SIiEhgYKB4eHhIcHCzr1q3L8JozZ87II488IkFBQRIUFCQbNmzIEG9MTIy0adNGGjZsKAEBAbJ48WIREYmNjZWHHnpIgoKCpH79+jJ37lwRERk5cqT4+flJYGCgvPzyy5liHDhwoHz77be25Xr16smpU6cytWvevLmEh4fnGEN4eLjUq1dPevXqJf7+/nLs2DH59ddfpVmzZtKwYUN57LHHJCYmRkRE/vvf/0qTJk2kfv36MmDAALFYLFn+/m5WeHh4jokiu+P99ttvZeDAgVm2u3DhQo7bvN3yO1HooHZ1Q/Hx8Rw/ftzRYZCamsqqVavo378/YL3s1Lhx4wxtateuTWxsLJcvX2b37t25utT09ttvU7p0aXbt2gWQq0sIERER/Pnnn7i6upKamsqiRYvo168ff/31FzVq1KBSpUo8+eSTvPjii9xzzz2cOHGC9u3bs2/fvgzb+eijjzDGsGvXLvbv30+7du04ePAgS5cu5eGHHyYsLCzTvocOHUqrVq1YtGgRqampxMbGZnjew8ODRYsWUapUKaKiomjWrBmdOnVi+fLl3H333fz0008AREdHc/78eRYtWsT+/fsxxmS4rHdVZGRkhrnJq1atSmRkJJUrV7atS0pK4ujRo9SsWTPHGAAOHTrEl19+SbNmzYiKimLcuHGsXLmS4sWL89577zFp0iTeeOMNhgwZwhtvvAFAr169WLZsGf/+978zxPbNN9/w/vvvZ4q5Tp06LFiwINvfX06yO97s1oN1dF9iYiLnz5+3zcpYkGiiUNmKjY3l4sWLxMXFARAWFka1atUoW7bsbe2svnLlCg0aNCAyMhI/Pz8eeOCBfN3+ypUrmTt3rm05N0N6H3/8cdtcGd27d2fs2LH069ePuXPn0r17d9t29+7da3vN5cuXiY2NzdCXsH79ep5//nkAfH19qVGjBgcPHqRUqVLZ7nv16tXMmTMHsPbflC5dOsPzIsKrr77KunXrcHFxITIykrNnzxIYGMjLL7/MyJEjefjhh2nZsiUpKSl4eHjQv39/Hn74YR5++OEbHntWoqKiMtwrk10MADVq1KBZs2YAbNq0ib1799KiRQvAmnCaN28OwJo1a5gwYQLx8fFcuHCB+vXrZ0oUPXv2pGfPnnmKOb9VrFiRU6dOFchE4XQ9wp6JRWDHP5CiN9zZ0+LFi/Hz8+Py5csAtg7AcuXK3fYRTcWKFSMsLIzjx48jInz00UcA+Pv7s23btgxtjx49SokSJShVqhT169fP9PzNSH+c19+ZXrx4cdvPzZs35/Dhw5w7d47FixfTpUsXwDoqbNOmTYSFhREWFkZkZKTdOpzT++abbzh37hzbtm0jLCyMSpUqkZCQQL169di+fTuBgYG89tprjB07Fjc3NzZv3sxjjz3GsmXL6NChQ6btValShZMnT9qWIyIiqFKlSoY2xYoVy/AeZRcDZHzvRIQHHnjA9h7t3buXWbNmkZCQwKBBg1iwYAG7du1iwIABWVYH+Oabb2jQoEGmx2OPPZbn9y+7473R+5CQkECxYsXyvN87mdMlCr/Iu+D++XBZp+22l8jISHr06EFERARFixbFz8+P6tWrO3y2OU9PT6ZOncoHH3xASkoKPXv2ZP369axcuRKwnnkMHTqUESNGADB8+HD+97//cfDgQcD6wT1jxoxM233ggQdsyQeuXXqqVKkS+/btw2Kx2AoaZsUYw6OPPspLL72En5+f7Rtlu3bt+PDDD23tsrqM1LJlS7755hsADh48yIkTJ/Dx8cnxfWjbti3Tp08HrJfjoqOjMzwfHR1NxYoVKVKkCGvWrLFdNjx16hSenp489dRTDB8+nO3btxMbG0t0dDQPPfQQkydPZseOHZn216lTJ+bMmYOIsGnTJkqXLp3hshNYz8JSU1NtH+bZxXC9Zs2asWHDBg4fPgxYy9AfPHjQth0vLy9iY2OzvYzUs2dPW5JJ/8jrZaecjrd9+/b89ttvXLx4kYsXL/Lbb7/Rvn17wJrwzpw5Y7v0VuDktXPDUY/GbtVEvD4UOR9/8z08KltJSUkZOgsnTpwoU6dOlT179jgwKqvrR/M8/PDDMmfOHBER2blzp7Rq1Urq1asntWvXlrfeeivDcfz444/SqFEj8fX1FT8/Pxk+fHim7cfExEjv3r2lfv36EhQUJD/88IOIWDuwa9WqJU2bNpXBgwdn6Mz+/vvvM2xjy5YtAsjs2bNt686dOyfdunWTwMBA8fPzk2eeeSbTvrPrzM6pw/XMmTPSqVMnCQgIkODgYPnzzz8zvE/nzp2TZs2aSUBAgPTt21d8fX0lPDxcli9fLoGBgRIcHCxNmjSRLVu2yKlTpyQkJEQCAwMlICAgQ/xXWSwWGTRokNSqVUsCAgJky5YtWcb19NNPy4oVK3KMIavjWrVqlTRp0kQCAwMlMDBQlixZIiIiY8aMkVq1asm//vUv6du3r7z55ptZ7vdm9OjRQ+666y5xc3OTKlWqyGeffSYi1kES06dPv+Hxzpo1S2rXri21a9eWzz//3LZ+y5Yt0qVLl1uOL78U+lFPmijy34YNGyQwMND24ZtedsNjlbretm3b5KmnnnJ0GA4xdOhQWblypaPDsMnvROF0l57iiyZBUAVwdbrQ7zgXLlzgmWeeoUWLFuzatYuPP/7Y+u1BqTxo1KgR99133x19w529BAQE0LbtHXmPcr644VSodxqdCvXWiQhff/01L7/8MufOnaNIkSKMGDGCMWPGZOqM27dvH35+fg6KVCmVF1n9v7VXmXFVAJ09e5YnnniCNWvWANCqVSumT5+uyUAplS29flPIlClThtOnT+Pl5cXs2bNZs2aNJgmlVI70jKIQWLFiBY0aNaJ8+fK4u7vz/fffU7ly5QJ5Y5BSKv/pGUUBdvr0aZ544gnatWvHyJEjbesDAgI0SSilck0TRQGUmprKxx9/jK+vL3PnzqVYsWL4+Pg47YgmLTPu2DLj+/fvp3nz5ri7uzNx4sRs24kIbdq0sd3NfycaM2YM1apVu+F79c4771CnTh18fHz49ddfbeuXL1+Oj48PderU4d1337Wt79GjB4cOHbJb3A6X13G1jnpUL1tW5MVVIjGJNz+4uBDYtm2bhISE2CqzduzY0VbRMy/uhPsotMx47tirzPjZs2dl8+bN8uqrr8r777+fbbtly5bJsGHDbmrbKSkptxreTdm4caOcOnUqx/dqz549EhQUJAkJCXL06FGpVauWpKSkSEpKitSqVUuOHDkiiYmJEhQUZLshde3atfKf//zndh3GDRX6+ygqxJSAr/ZCUuEbq30jx44dIzQ0lC1btlClShV++OEHfvzxx3wrK2BWGbs8bkbz5s1tFTu//fZbWrRoQbt27QBriY9p06bZvulNmDCBMWPG4OvrC1jPTJ577rlM24yNjaVfv34EBgYSFBTEDz/8AGT8hr5gwQL69u0LQN++fXn22Wdp2rQpI0aMoGbNmhnOcurWrcvZs2c5d+4cXbt2JSQkhJCQEDZs2JBp3wkJCbZ9N2zY0DYarV27dkRGRtKgQQP++OOPDK85e/Ysjz76KMHBwQQHB/Pnn39mOp62bdvSqFEjAgMDWbJkCWAtj9GxY0eCg4MJCAhg3rx5AIwaNQp/f3+CgoJ45ZVXMsVYsWJFQkJCKFKkSJa/k6u++eYbOnfubFt+5JFHaNy4MfXr1+eTTz6xrS9RogQvv/wywcHBbNy4kW3bttGqVSsaN25M+/btOX36NACffvopISEhBAcH07VrV+Lj43Pcf240a9YsU/mR6y1ZsoQePXrg7u6Ot7c3derUYfPmzWzevJk6depQq1YtihYtSo8ePWzvbcuWLVm5ciUpKSm3HOOdyK6d2caYDsAUwBX4TETeve75l4D/ACnAOeBpEXF8PWsnVbNmTfr160fJkiX573//S8mSJR0dUr7SMuNWt7vMeG5t2LCBmTNn2pY///xzypUrx5UrVwgJCaFr166UL1+euLg4mjZtygcffEBycjKtWrViyZIlVKhQgXnz5jFmzBg+//xzunTpwoABAwB47bXXmDVrlq3S7lVr1qzhxRdfzBSLp6dnpgSaW5GRkbbqtpCxnPj1Zcb/+usvAFxcXKhTpw47duzI9DdZENgtURhjXIGPgAeACGCLMWapiOxN1+xvoImIxBtjngMmAN3tFVNBc+zYMZ5//nleeeUVWrVqBcAnn3xit+qu0tYxfRxaZjyjO7HMOFjv9E//5WTq1Km2YoonT57k0KFDlC9fHldXV7p27QrAgQMH2L17t+13mpqaavvGv3v3bl577TUuXbpEbGysrQBfevfdd1+WydQRrpYZ10Rxc0KBwyJyFMAYMxfoDNj+54jImnTtNwFP3Wijx70uwHutwTPn0+CCLDk5mUmTJvHf//6XK1euEBUVxcaNGwFuewnw2+FqmfH4+Hjat2/PRx99xNChQ/H392fdunUZ2mZVZjw4ODhP+81rmfHXXnsNuFZm3MPDI0/7z6v0Jb6LFClCzZo1M5QZ//nnn3nttddo27Ytb7zxBps3b2bVqlUsWLCAadOmsXr16jzt183NDYvFgouLC2vXrmXlypVs3LgRT09PWrdubXsPPTw8bElWRKhfv77t7ze9vn37snjxYoKDg5k9ezZr167N1MYeZxQ5lRPXMuP5rwpwMt1yRNq67PQHfsnqCWPMQGPMVmPM1qhScdA7ADwK5y0g69evp2HDhowaNYorV67Qo0cPFi5c6OiwbgstM251u8uM55aPjw9Hjx61xVC2bFk8PT3Zv38/mzZtyvY1586dsyWK5ORk9uzZA0BMTAyVK1cmOTnZ9h5d7+oZxfWPvCYJsJYZnzt3LomJiYSHh3Po0CFCQ0MJCQnh0KFDhIeHk5SUxNy5c22z9oH19xcQEJDn/d7R8toLfqMH8BjWfomry72Aadm0fQrrGYX7DbdbSOfMvnDhgvTv3982mql27dq20T72dKeNehLRMuO3u8z46dOnpUqVKlKyZEkpXbq0VKlSRaKjozO1Gzt2rHz66aciIpKQkCAdOnQQX19f6dy5s7Rq1UrWrFmTIc6r/v77b2nZsqUEBQWJv7+/fPLJJyIi8vHHH0vNmjUlJCREhgwZYnv/b8Xw4cOlSpUqYoyRKlWq2EqXL1myRF5//XVbu3HjxkmtWrWkXr168vPPP9vW//TTT1K3bl2pVauWbfSdiPV3EhIScsvx5Zf8HvVkt6KAxpjmwFsi0j5teXRaYnrnunb3Ax8CrUTknxtut5AWBTx//jy+vr5ER0czatQoRo8efVtOc7UooMqt06dP07t3b1asWOHoUG67yZMnU6pUKdtAC0dzpqKAW4C6xhhvIBLoATyZvoExpiEwE+iQmyRR2Ozfvx9vb2/c3d0pX74833zzDdWrV7cN91TqTlK5cmUGDBjA5cuXc+yML4jKlClDr169HB2G3ditj0JEUoAhwK/APmC+iOwxxow1xly9sPc+UAL43hgTZoxZaq94nEl8fDxjxowhKCiICRMm2Na3a9dOk4S6o3Xr1q3QJQmAfv364eZWcPtN7XpkIvIz8PN1695I9/P99ty/M1q+fDmDBg0iPDwcgKioKAdHpJQq7JzuzuzGR6tBhWlw4YqjQ8lXp06dolu3bjz44IOEh4cTGBjIhg0bmDJliqNDU0oVcgX3XMmJHDx4kCZNmhATE4OnpydvvfUWw4YNu2HJBKWUuh00UdwB6tatS0hICMWLF+fDDz+kRo0ajg5JKaVsnO7SU0Fw+fJlhg0bZrsRzBjD0qVLWbp0qSaJPFq6dGmGss+F1dq1ayldujQNGjTA19c3U5G/xYsXExQUhJ+fH4GBgSxevDjD8xMnTsTX15cGDRoQEhJiKxVyJ/m///u/OzKuq9atW0ejRo1wc3NjwYIF2bbbtm0bgYGB1KlTh6FDh9rK41+4cIEHHniAunXr8sADD9huAF22bBlvvPFGttuzq7zegOGohzPfcGexWGT+/PlSuXJlAaR9+/aODumGMt244/Vhxkd2vtyVsd2Lq+wb6E2wWCySmprqsP3bqzS5iMiaNWukY8eOImItw+7j4yPr168XEZGwsDCpXbu2HD16VEREjh49KrVr15YdO3aIiMj06dOlXbt2tpvpoqOjs7wB71bcalnx5ORkCQwMvKn30J7vd1bCw8Nlx44d0qtXr0w3ZqYXEhIiGzduFIvFIh06dLDd2Dd8+HB55513RETknXfekREjRoiI9e+2QYMGEhcXd8MYCn2ZcWd19OhROnbsSLdu3Th9+jTNmjXjvffec3RYd7xjx47h6+tL3759qVevHj179mTlypW0aNGCunXrsnnzZgBmz57NkCFDgKzLcB87dgwfHx969+5NQEAAJ0+e5LvvviMwMJCAgIAMMwBev/+WLVvSqFEjGjVqZCsN0aNHD1sVVrDWJVqwYAGpqakMHz6ckJAQgoKCbNVU165dS8uWLenUqRP+/v5A9mW4Z82aRb169QgNDWXAgAG248pN2fL0ihUrZiumCNazhVdffRVvb28AvL29GT16NO+//z4A//vf/5g+fbpteGupUqXo06dPpu0ePnyY+++/n+DgYBo1asSRI0dYu3ZthoKCQ4YMYfbs2YC1qvHIkSNp1KgR77//PqGhoRne38DAQIBsy42nt3r1atu3dci+FPn1peDj4uJ4+umnCQ0NpWHDhrby4Nn9fm9FzZo1CQoKwsUl+4/X06dPc/nyZZo1a4Yxht69e9vO7pYsWWJ73/v06WNbb4yhdevWLFu27JZjvGl5zTCOejjbGUViYqKMHz9ePDw8BJAyZcrIjBkzHPqN9mY4+owiPDxcXF1dZefOnZKamiqNGjWSfv36icVikcWLF0vnzp1FROSLL76QwYMHi4hIt27dZPLkySJi/QZ76dIlCQ8PF2OMbNy4UUREIiMjpVq1avLPP/9IcnKy3HfffbJo0aJM+4+Li5MrV66IiMjBgwelcePGIiKycOFC6d27t4hYf8dVq1aV+Ph4mTlzprz99tsiYi1j0bhxYzl69KisWbNGPD09bd/mRUTOnz8vItZv/vXr15eoqCiJjIyUGjVqyPnz5yUpKUnuuece23E98cQT8scff4iIyPHjx8XX1zdTvOnPKC5cuCCNGjWS06dPi4hIw4YNJSwsLEP7sLAwadiwoURHR0uZMmVy9TsJDQ2VhQsXioi1BElcXFyG/YqIDB48WL744gsREalRo4a89957tueCg4Nt78O7774rb7/9tiQlJUnz5s3ln3/+ERGRuXPnSr9+/TLt+4033pCpU6falqOiomw/jxkzxvZcnz59pGPHjrYzmNGjR8tXX30lIiIXL16UunXrSmxsbLa/3+vdc889EhwcnOmxYsWKbN+nrEq9XLVlyxZp27atbXndunW296906dK29RaLJcPy119/LUOGDMl2n1fl9xmFdmbb2cmTJxk7diyJiYn07NmTDz74gEqVKjk6LKfi7e1t+9ZZv3592rZtizGGwMBAjh07lql9VmW4L168SI0aNWzzDGzZsoXWrVtToUIFAHr27Mm6det45JFHMmwrOTmZIUOGEBYWhqurq61f6cEHH+SFF14gMTGR5cuXc++991KsWDF+++03du7cabs2HR0dzaFDhyhatCihoaG2b/OQdRnuM2fO0KpVK8qVKwdYy5lf3WduypYD/PHHHwQHB3Po0CGGDRvGXXfdlYd3PWsxMTFERkby6KOPAuS6Mu7V0utgvSlv3rx5jBo1innz5jFv3rwcy42nd/r06QylKXIqRZ6+FPxvv/3G0qVLbVO5JiQkcOLECe6+++4sf7/Xu37yqNvFGJOhivHVUua3myYKO7h48SJlypTBGEPt2rWZMmUKderUoW3bto4O7dadG5K7dr0DrI984O7ubvvZxcXFtuzi4nJTM4qlLw2enUWLFvHf//4XgM8++4xly5ZRqVIlduzYgcVisX0wenh40Lp1a3799VfmzZtHjx49AOsZ+ocffphp7oS1a9dm2H9OZbizk9uy5S1btmTZsmWEh4fTrFkzunXrRoMGDfD3989Udn3btm3Ur1+fUqVKUaJECY4ePUqtWrVu+D5d72qJ8atyKsvevXt3Hn/8cbp06YIxhrp167Jr165sy42nV6xYsQzbzqkUefp9igg//PBDpsq8b731Vpa/3+u1bNmSmJiYTOsnTpzI/fff/H3DVapUISIiwracvmR5pUqVOH36NJUrV+b06dNUrFjR1s5Rpcy1jyIfWaAGrQwAABd+SURBVCwWPv/8c+rUqcPXX39tW//MM88UjCThJG5UhhsgNDSU33//naioKFJTU/nuu+9o1aoVjz76qK1UdZMmTYiOjqZy5cq4uLjw1VdfkZp6bQre7t2788UXX/DHH3/QoUMHANq3b8/06dNJTk4GrPfIxMXFZdp/dmW4Q0JC+P3337l48SIpKSm2aVkhd2XL0/P29mbUqFG2vrBXXnmFd955x3YWduzYMf73v//ZZgEcPXo0g/+/vTOPjqLO9vjnGsGwo+DCABKBSNZOIosERhCQgLKMCMMmghwCBAYzgrI4wsgQj0Z8gCDhIT4YgggovCfwxkEEjYojAaIswx5GUfCJMCyRzShy3x9VqXRCd9KJpLP9Puf0OV3Vv6rf7dvVdeu3fe8f/sAPP/wAWClV888uqlWrFo0aNXL6zbOzs7l06RJNmjRh//79ZGdnc+7cOT744AOvdjVr1oyAgACSkpKclkZBcuPuhIaGcuTIEWfbFylysH6XV1991ZlZtHPnToACf193tmzZ4lHOvDhBAixdrNq1a5Oeno6qsmzZMieNbO/evUlNTQUgNTU1T3rZ0pIyL3eBov4PNWDZXvixbOWm3bdvH/fffz8jRozgzJkzbNjgMbWGwQ/MnTuXtLQ0IiMjadmyZZ7umhwaNGhAcnIynTp1IioqipYtW+b5Q+YwduxYUlNTiYqK4uDBg3meUuPi4vj444954IEHqFq1KgDx8fGEhYVxzz33EBERwejRoz22erp3786VK1cIDQ1lypQpTpdYw4YN+dOf/kSbNm1o3749QUFBTga7efPmkZGRgcvlIiwszGNujfwkJCTwySefcPToUaKjo3nppZfo1asXISEh9OrVi5kzZxIdHQ3AmDFj6NSpE61btyYiIoL77rvP44DsG2+8wbx583C5XLRr144TJ07QuHFj+vfvT0REBP379ycmJqZAuwYMGMDy5cvp378/AFWrVmXNmjVMnjyZqKgooqOjPQ4sP/jgg3mSVSUlJXHvvffSvn37AnXQpk2bxs8//4zL5SI8PJxp06YBBf++xWXHjh00atSI1atXM3r0aMLDw53PcnwNsGDBAuLj42nevDnNmjXjwQcfBKwc5ps2bSI4OJjNmzczZcoU55i0tDR69Ojxq20sMsUd3CitV8sbG1uDo6cvFTqg4w8uXryoU6ZM0RtvvFEBve222/TNN9/MkxOhPFMW8lFUNs6fP6+q1rTOnj17OgPHBouHH35YDx8+XNpm+J0TJ05o586dfSprpseWIQ4fPkx4eDjJycn88ssvJCQkcPDgQQYPHlwhU5Ia/MP06dOJjo4mIiKCu+6665oB9spOcnKyx6mzFZ1vvvmGWbNmlUrdZjD7V9CkSRMCAwOJiopi4cKFTveBwfBryJmZY/BMixYtCk0XWxFp3bp1qdVd7loUp2pdgMfCoGqA3+u+cuUK8+fP5/Tp04A1G+e9994jIyPDBAmDwVBhKXeB4ptbz8LszlCzql/r3b59O23atOGJJ57Is4q3SZMmFTphicFgMJS7QOFvsrKyGDduHG3btmXnzp3ceeedHmfHGAwGQ0XFBAovqCqrVq0iJCSElJQUAgICmDRpEvv376dXr16lbZ7BYDD4DRMovLB7924GDRrEiRMnaNeuHV988QUvvfTSdZlnbTD4i4CAAGcGVa9evTh37pzz2b59++jcuTMtWrQgODiYpKQkZ0EawIYNG2jVqhVhYWHExMQ4C/PKEjt37mTEiBGlbYZXTp8+TadOnahZs6Yj7ugJb9LiqkpiYiLNmzfH5XLxxRdfAJZAZM4iT39gAoUb7qsyo6OjGT9+PK+//jpbtmxxtIYqOyJ/yfPyxqJFn+cpN2rU//rRyqLhbTVuRai/WrVq7Nq1i71793LLLbeQkpICwOXLl+nduzdTpkzh0KFD7N69m88++4wFCxYAlobSuHHjWL58Ofv37ycjI4PmzZtfV9uKIr/ijRdeeIHExES/1lkUAgMDSUpKKnQmW3JyMl26dCEzM5MuXbo4uVU2bNhAZmYmmZmZLFq0iDFjxgBw66230qBBg0IVhK8XJlDYpKWlERERkWfV5+zZs4mPjy9QLthQsvgqM759+3ZiY2OJiYmhXbt2HDp0CLBuwk8//TQRERG4XC5HAsNd+nr16tXs2rWLtm3b4nK56NOnj/NElx9P0uALFy5k4sSJThl3yfPly5fTpk0boqOjGT16tBMUatasyVNPPUVUVBRbt25lxowZzoroUaNGOU/2O3bswOVyER0dzcSJEx35Bm9y5gURGxvrSI6vWLGC9u3bExcXB0D16tWZP3++c4OaOXMmzz77rLPaOSAgwLlJuXPhwgWGDx9OZGQkLpfLkRxxFypcs2YNjz/+OHCt/HdQUFCeVk5wcDDff/+9T5Lq58+fZ8+ePY52lbdrYOnSpfTu3ZvOnTs7Ujovv/yy47vnnnvOOac36ffiUqNGDX77298Wqs/lTVp83bp1DB06FBGhbdu2nDt3zllD8vDDDxcoW3JdKe5KvdJ6hVa/XbXzKtVzP/q0QrEwvv/+ex06dKgCCjiy1QaL/Cs8YXqelzdeey0jT7mRI9cXq35fZcazsrKcBDWbNm3SRx55RFVVFyxYoH379nU+y5H2zi99HRkZqR999JGqqk6bNk3/+Mc/erTHkzT4yZMntVmzZk6Z7t2765YtW3T//v3as2dP/emnn1RVdcyYMZqamqqqqoC+9dZb15xXVXXIkCG6fr3lr/DwcP3ss89UVXXy5MkaHh6uqupVzjw/NWrUUFVLbr1fv366YcMGVVUdP368vvLKK9eUr1u3rmZlZXmUJPfEpEmT8vjqzJkzeepVVV29erUOGzZMVa+V/05MTNQlS5aoqmp6erojve2LpPqHH37o/M6q3q+Bv/71r9qwYUPHxxs3btSRI0c6Cax69OihH3/8sap6/n3z8+STT3qUHM9JNuQJdxl8T3iTFu/Ro4fjB1XVzp07644dO1RV9fjx4xoREeHxfJVeZrz6T1Vhzyn45WrhhQvg6tWrLF68mMmTJ3P27Fluuukmpk6dmufJ0FA28EVmPCsri2HDhpGZmYmIOKJ8mzdvJiEhwZnCnCPfDbnS11lZWZw7d46OHTsC1hPd73//e4+2eJIGb9u2LU2bNiU9PZ3g4GAOHjxI+/btSUlJ4fPPP3cWSl2+fNlRAg0ICKBv377OedPS0pg5cyaXLl3izJkzhIeHO4qlsbGxAAwePNhJWuNNztxdxjynzpzkRaGhoY6M9/Vi8+bNrFq1ytm++eabCz3GXf57wIABzJgxg+HDh7Nq1SrnN/FFUv27775zZOLB+zUA0LVrV+e3f//993n//fcdPaoLFy6QmZlJhw4dPP6+9erVy2P/nDlzfHNOMckvLe4Nf0qOl7tAcT346quvGDJkiCM6FhcXR0pKynXvg62IqD5XeCFg1KiWjBrV8rrU6YvM+LRp0+jUqRPvvPMOR48e5f777y/0vIVNTDh27Jgzwy0hIYGQkBCv0uADBw7k7bffJiQkhD59+iAiqCrDhg3jxRdfvObcgYGBzs3yxx9/ZOzYsWRkZNC4cWOmT59eqOS4qmc58/zkjFFcunSJbt26kZKSQmJiImFhYXm6WcHKwlizZk1q165NeHj4NZLkRcH9RleQ5HhsbCxHjhzh1KlTrF27lqlTpwK+Sarnlxwv6BrILzn+zDPPMHr06Dzn81X6ffz48aSlpV2zf+DAgXkE/IqCN2nxhg0bcuzYMaecuxy5PyXHK2Xne+3atTl8+DB33HEHq1at4r333jNBopyTlZXl/IFyUnCC9ST52muvOQHlzJkz1xxbp04dbr75Zic5zRtvvEHHjh1p3LixIyedkJDgVRocoE+fPqxbt46VK1c6uSm6dOnCmjVrOHnypFP3119/fU39OTej+vXrc+HCBaeVULduXWrVqsW2bdsA8jy5+ypnnkP16tWZN28es2bN4sqVKzz66KN8+umnbN68GbBaHomJiUyaNAmAiRMn8sILLziJfK5evepRrbZr167OADngjO3cfvvtHDhwgKtXrzpP6J4QEfr06cOECRMIDQ11nt59kVTPLznu7RrIT7du3ViyZAkXLlwA4Ntvv+XkyZMF/r7uzJkzx6PkeHGDBHiXFu/duzfLli1DVUlPT6dOnTpOQid/So6Xu0BxoOEJ2Nwfat9UeGE3Nm7cSHZ2NgD16tVj/fr1HDx4kAEDBhgBvwrApEmTeOaZZ4iJickzsyU+Pp4777wTl8tFVFQUK1as8Hh8amoqEydOxOVysWvXLv785z9fU8abNDhYXS6hoaF8/fXXTk7osLAwnn/+eeLi4nC5XHTt2tWjmF3dunUZOXIkERERdOvWLY+mz+LFixk5ciTR0dFcvHjRkRz3Vc7cnZiYGFwuFytXrqRatWqsW7eO559/nhYtWhAZGUnr1q2dQXiXy8Urr7zCoEGDCA0NJSIigi+//PKac06dOpWzZ88SERFBVFSU86SdnJxMz549adeuncdMde7kSI67Z8HzRVI9JCSErKwsJ6GQt2sgP3FxcQwePJjY2FgiIyPp168f58+fL/D3/TUEBQUxYcIEli5dSqNGjZwutfj4eDIyMgDv0uIPPfQQTZs2pXnz5owcOdKZlQb+lRwXdZs3XR6QFqJ6yHebjx07RmJiImvXriUpKclp2hp848CBA3lSTxr8i3u/fI5q6ty5c0vZqrLDnDlzqFWrFvHx8aVtit/p0KED69at8zgu5Ol/KyKfq2qr4tRV7loUvnLlyhVmz55NaGgoa9eupWbNmnkGMg2G8sC7777rLJjbsmWLedDJx5gxY/KMYVUWTp06xYQJE3yaPHA9qJAtivT0dBISEti9ezcAffv2Ze7cuU7/pcF3TIvCYCh/XO8WRYWb9bRt2zbatWuHqhIUFMT8+fNLJ3VgBUJVzTiOwVBOKImH/woXKNq0aUO3bt2IiYlh6tSpVK9evbRNKtcEBgZy+vRp6tWrZ4KFwVDGUVVOnz5d6ErwolLuup5qBFXVi+uOQ3h9uPEGMjMzGT9+PLNnz+buu+8GrKl8Rnbj+vDzzz9z/PjxQuf1GwyGskFgYCCNGjWiSpUqefZXqq6n0G/vgAfeJnvPEJIXzeXFF18kOzubwMBAZ/65CRLXjypVqlyz2tdgMFQuSvSOKiLdReSQiBwRkWtWo4jITSLylv35NhEJ8uW8H/x0CNd9bZg+fTrZ2dkMHz7c4zxrg8FgMPx6SqzrSUQCgMNAV+A4sAMYpKr73cqMBVyqmiAiA4E+qjrA4wlt6t1QQ8/oJcBamblw4UI6dOhQIt/BYDAYKgpldR1FG+CIqn6pqj8Bq4D8OUR/B6Ta79cAXaSQEdOzeonAG6rwwp9nsGvXLhMkDAaDoYQpyRZFP6C7qsbb248B96rqOLcye+0yx+3tf9ll/p3vXKOAUfZmBLC3RIwuf9QH/l1oqcqB8UUuxhe5GF/k0kJVaxXnwHIxmK2qi4BFACKSUdzmU0XD+CIX44tcjC9yMb7IRUQyintsSXY9fQs0dttuZO/zWEZEbgTqAKdL0CaDwWAwFJGSDBQ7gGARuUtEqgIDgfX5yqwHhtnv+wEfanlb2GEwGAwVnBLrelLVKyIyDtgIBABLVHWfiMzASsm3HlgMvCEiR4AzWMGkMH59ItuKg/FFLsYXuRhf5GJ8kUuxfVHuVmYbDAaDwb+YJcwGg8FgKBATKAwGg8FQIGU2UJSU/Ed5xAdfTBCR/SKyR0Q+EJEmpWGnPyjMF27l+oqIikiFnRrpiy9EpL99bewTEc95YCsAPvxH7hSRNBHZaf9PHioNO0saEVkiIiftNWqePhcRmWf7aY+I3OPTiVW1zL2wBr//BTQFqgK7gbB8ZcYCC+33A4G3StvuUvRFJ6C6/X5MZfaFXa4W8AmQDrQqbbtL8boIBnYCN9vbt5W23aXoi0XAGPt9GHC0tO0uIV90AO4B9nr5/CFgAyBAW2CbL+ctqy2KEpH/KKcU6gtVTVO1BbCsm2MjP9voL3y5LgCSgJeAiqyN7osvRgIpqnoWQFVP+tlGf+GLLxSobb+vA/yfH+3zG6r6CdYMUm/8DlimFulAXRFpUNh5y2qgaAgcc9s+bu/zWEZVrwBZQD2/WOdffPGFOyOwnhgqIoX6wm5KN1bVd/1pWCngy3VxN3C3iPxDRNJFpLvfrPMvvvhiOjBERI4Dfwee8I9pZY6i3k+AciLhYfANERkCtAI6lrYtpYGI3ADMBh4vZVPKCjdidT/dj9XK/EREIlX1XKlaVToMApaq6iwRicVavxWhqldL27DyQFltURj5j1x88QUi8gDwLNBbVbP9ZJu/KcwXtbBEIz8SkaNYfbDrK+iAti/XxXFgvar+rKpfYcn+B/vJPn/iiy9GAG8DqOpWIBBLMLCy4dP9JD9lNVAY+Y9cCvWFiMQAr2EFiYraDw2F+EJVs1S1vqoGqWoQ1nhNb1UtthhaGcaX/8harNYEIlIfqyvqS38a6Sd88cU3QBcAEQnFChSn/Gpl2WA9MNSe/dQWyFLV7wo7qEx2PWnJyX+UO3z0xctATWC1PZ7/jar2LjWjSwgffVEp8NEXG4E4EdkP/AJMVNUK1+r20RdPAa+LyHisge3HK+KDpYisxHo4qG+PxzwHVAFQ1YVY4zMPAUeAS8Bwn85bAX1lMBgMhutIWe16MhgMBkMZwQQKg8FgMBSICRQGg8FgKBATKAwGg8FQICZQGAwGg6FATKAwlDlE5BcR2eX2CiqgbJA3pcwi1vmRrT6625a8aFGMcySIyFD7/eMi8hu3z/5LRMKus507RCTah2OeFJHqv7ZuQ+XFBApDWeSyqka7vY76qd5HVTUKS2zy5aIerKoLVXWZvfk48Bu3z+JVdf91sTLXzgX4ZueTgAkUhmJjAoWhXGC3HLaIyBf2q52HMuEist1uhewRkWB7/xC3/a+JSEAh1X0CNLeP7WLnMPinrfV/k70/WXJzgPyHvW+6iDwtIv2wNLfetOusZrcEWtmtDufmbrc85hfTzq24CbqJyH+KSIZYuSf+Yu9LxApYaSKSZu+LE5Gtth9Xi0jNQuoxVHJMoDCURaq5dTu9Y+87CXRV1XuAAcA8D8clAHNVNRrrRn3clmsYALS39/8CPFpI/b2Af4pIILAUGKCqkVhKBmNEpB7QBwhXVRfwvPvBqroGyMB68o9W1ctuH/+3fWwOA4BVxbSzO5ZMRw7PqmorwAV0FBGXqs7DktTupKqdbCmPqcADti8zgAmF1GOo5JRJCQ9DpeeyfbN0pwow3+6T/wVLtyg/W4FnRaQR8D+qmikiXYCWwA5b3qQaVtDxxJsichk4iiVD3QL4SlUP25+nAn8A5mPlulgsIn8D/ubrF1PVUyLypa2zkwmEAP+wz1sUO6tiyba4+6m/iIzC+l83wErQsyffsW3t/f+w66mK5TeDwSsmUBjKC+OB74EorJbwNUmJVHWFiGwDegB/F5HRWJm8UlX1GR/qeNRdQFBEbvFUyNYWaoMlMtcPGAd0LsJ3WQX0Bw4C76iqinXX9tlO4HOs8YlXgUdE5C7gaaC1qp4VkaVYwnf5EWCTqg4qgr2GSo7pejKUF+oA39n5Ax7DEn/Lg4g0Bb60u1vWYXXBfAD0E5Hb7DK3iO85xQ8BQSLS3N5+DPjY7tOvo6p/xwpgUR6OPY8le+6Jd7AyjQ3CChoU1U5b0G4a0FZEQrCyt10EskTkduBBL7akA+1zvpOI1BART60zg8HBBApDeWEBMExEdmN111z0UKY/sFdEdmHlpVhmzzSaCrwvInuATVjdMoWiqj9iqWuuFpF/AleBhVg33b/Z5/sUz338S4GFOYPZ+c57FjgANFHV7fa+Ittpj33MwlKF3Y2VH/sgsAKrOyuHRcB7IpKmqqewZmSttOvZiuVPg8ErRj3WYDAYDAViWhQGg8FgKBATKAwGg8FQICZQGAwGg6FATKAwGAwGQ4GYQGEwGAyGAjGBwmAwGAwFYgKFwWAwGArk/wE6nvb8/HAplAAAAABJRU5ErkJggg==\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", @@ -1871,10 +2802,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", @@ -1884,11 +2813,20 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.986013986013986" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)\n", @@ -2002,11 +2940,28 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Number of features of the model must match the input. Model n_features is 30 and input n_features is 2 ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mada_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mplot_decision_boundary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mada_clf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m 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SAMME.R\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m pred = sum(_samme_proba(estimator, n_classes, X)\n\u001b[0;32m--> 669\u001b[0;31m for estimator in self.estimators_)\n\u001b[0m\u001b[1;32m 670\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# self.algorithm == \"SAMME\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m pred = sum((estimator.predict(X) == classes).T * w\n", + "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;31m# The weights are all 1. for SAMME.R\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m pred = sum(_samme_proba(estimator, n_classes, X)\n\u001b[0;32m--> 669\u001b[0;31m for estimator in self.estimators_)\n\u001b[0m\u001b[1;32m 670\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# self.algorithm == \"SAMME\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m pred = sum((estimator.predict(X) == classes).T * w\n", + "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py\u001b[0m in \u001b[0;36m_samme_proba\u001b[0;34m(estimator, n_classes, X)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 282\u001b[0m \"\"\"\n\u001b[0;32m--> 283\u001b[0;31m \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0;31m# Displace zero probabilities so the log is defined.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m 828\u001b[0m \"\"\"\n\u001b[1;32m 829\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tree_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 830\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 831\u001b[0m \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;34m\"input n_features is %s \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m % (self.n_features_, n_features))\n\u001b[0m\u001b[1;32m 388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 30 and input n_features is 2 " + ] + } + ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "\n", @@ -2092,10 +3047,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", @@ -2191,10 +3144,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2272,7 +3223,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/doc/src/DecisionTrees/cancer.py b/doc/src/DecisionTrees/Programs/cancer.py similarity index 100% rename from doc/src/DecisionTrees/cancer.py rename to doc/src/DecisionTrees/Programs/cancer.py diff --git a/doc/src/DecisionTrees/cart.py b/doc/src/DecisionTrees/Programs/cart.py similarity index 100% rename from doc/src/DecisionTrees/cart.py rename to doc/src/DecisionTrees/Programs/cart.py diff --git a/doc/src/DecisionTrees/decisiontree.py b/doc/src/DecisionTrees/Programs/decisiontree.py similarity index 100% rename from doc/src/DecisionTrees/decisiontree.py rename to doc/src/DecisionTrees/Programs/decisiontree.py diff --git a/doc/src/DecisionTrees/dtcancer.py b/doc/src/DecisionTrees/Programs/dtcancer.py similarity index 100% rename from doc/src/DecisionTrees/dtcancer.py rename to doc/src/DecisionTrees/Programs/dtcancer.py diff --git a/doc/src/DecisionTrees/gini.py b/doc/src/DecisionTrees/Programs/gini.py similarity index 100% rename from doc/src/DecisionTrees/gini.py rename to doc/src/DecisionTrees/Programs/gini.py diff --git a/doc/src/DecisionTrees/moons.py b/doc/src/DecisionTrees/Programs/moons.py similarity index 100% rename from doc/src/DecisionTrees/moons.py rename to doc/src/DecisionTrees/Programs/moons.py diff --git a/doc/src/DecisionTrees/rfcancer.py b/doc/src/DecisionTrees/Programs/rfcancer.py similarity index 100% rename from doc/src/DecisionTrees/rfcancer.py rename to doc/src/DecisionTrees/Programs/rfcancer.py diff --git a/doc/src/DecisionTrees/ride.py b/doc/src/DecisionTrees/Programs/ride.py similarity index 100% rename from doc/src/DecisionTrees/ride.py rename to doc/src/DecisionTrees/Programs/ride.py