diff --git a/doc/HandWrittenNotes/NotesSeptember10.pdf b/doc/HandWrittenNotes/NotesSeptember10.pdf
new file mode 100644
index 000000000..01d59cdda
Binary files /dev/null and b/doc/HandWrittenNotes/NotesSeptember10.pdf differ
diff --git a/doc/pub/Regression/ipynb/.ipynb_checkpoints/Regression-checkpoint.ipynb b/doc/pub/Regression/ipynb/.ipynb_checkpoints/Regression-checkpoint.ipynb
index adc9a7978..48b487be1 100644
--- a/doc/pub/Regression/ipynb/.ipynb_checkpoints/Regression-checkpoint.ipynb
+++ b/doc/pub/Regression/ipynb/.ipynb_checkpoints/Regression-checkpoint.ipynb
@@ -10,9 +10,11 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Aug 30, 2019**\n",
+ "Date: **Sep 10, 2020**\n",
+ "\n",
+ "Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
+ "\n",
"\n",
- "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
@@ -380,7 +382,159 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " A \n",
+ " A^(2/3) \n",
+ " A^(-1/3) \n",
+ " 1/A \n",
+ " \n",
+ " \n",
+ " A \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1.0 \n",
+ " 1.0 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 1.0 \n",
+ " 2.0 \n",
+ " 1.587401 \n",
+ " 0.793701 \n",
+ " 0.500000 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 1.0 \n",
+ " 3.0 \n",
+ " 2.080084 \n",
+ " 0.693361 \n",
+ " 0.333333 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 1.0 \n",
+ " 4.0 \n",
+ " 2.519842 \n",
+ " 0.629961 \n",
+ " 0.250000 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " 1.0 \n",
+ " 5.0 \n",
+ " 2.924018 \n",
+ " 0.584804 \n",
+ " 0.200000 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 264 \n",
+ " 1.0 \n",
+ " 264.0 \n",
+ " 41.153106 \n",
+ " 0.155883 \n",
+ " 0.003788 \n",
+ " \n",
+ " \n",
+ " 265 \n",
+ " 1.0 \n",
+ " 265.0 \n",
+ " 41.256962 \n",
+ " 0.155687 \n",
+ " 0.003774 \n",
+ " \n",
+ " \n",
+ " 266 \n",
+ " 1.0 \n",
+ " 266.0 \n",
+ " 41.360688 \n",
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+ " \n",
+ " \n",
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+ " 41.671089 \n",
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+ " \n",
+ " \n",
+ " 270 \n",
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+ " \n",
+ " \n",
+ "
\n",
+ "
267 rows × 5 columns
\n",
+ "
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+ ],
+ "text/plain": [
+ " 1 A A^(2/3) A^(-1/3) 1/A\n",
+ "A \n",
+ "1 1.0 1.0 1.000000 1.000000 1.000000\n",
+ "2 1.0 2.0 1.587401 0.793701 0.500000\n",
+ "3 1.0 3.0 2.080084 0.693361 0.333333\n",
+ "4 1.0 4.0 2.519842 0.629961 0.250000\n",
+ "5 1.0 5.0 2.924018 0.584804 0.200000\n",
+ ".. ... ... ... ... ...\n",
+ "264 1.0 264.0 41.153106 0.155883 0.003788\n",
+ "265 1.0 265.0 41.256962 0.155687 0.003774\n",
+ "266 1.0 266.0 41.360688 0.155491 0.003759\n",
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+ "270 1.0 270.0 41.774300 0.154720 0.003704\n",
+ "\n",
+ "[267 rows x 5 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"%matplotlib inline\n",
"\n",
@@ -519,7 +673,7 @@
"metadata": {},
"source": [
"$$\n",
- "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}^T\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}^T\\boldsymbol{\\beta}\\right)\\right\\}.\n",
+ "C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
"$$"
]
},
@@ -532,7 +686,7 @@
"\n",
"\n",
"It is also common to define\n",
- "the function $Q$ as"
+ "the function $C$ as"
]
},
{
@@ -921,7 +1075,20 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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u0rfnBL27T3Cse5Rje8coHSwQPxrgQyVKQyVGnx/lEAPsofxBkEzGsPYENZ0ZGtbU0LimhrZ1tSxZX09mSaoqB3BVH/j5Ujhx+4nXjmE/PkGyCEvf3KywF1kgko1J1mxtZc3W1pPuH80X6Xt1hAMvDVfOLciR7cmR6A/InAixvgL5vgJDTw6//iSDZE0cOsrfDMofBHUs21BP27o6EvXRjcXo9myKRvKlidvf+1kPb90R0JBJsvw3l57jWSKyENSlk2zY1MKGTS0n3Z8rBgwM5ji8d5SDL51geH+WfG+e4GCB2JESjAXQHVDszjPy+DB9lBf3MgNrimNLk2Q60zSurqFlXR1L19ex9JIGEqnFXSKq+sAfywcTt1f9MiBRgMbr6zW6F1nEMsk4KzvqWNlRB2/pOGlbrhhwqHeMwX2j9L86yvHXxhg9kCM4WCQxEBAfCmAoYPSlHKMc5yDlM46JgbclsGVJaldlaFpTQ/sldazY2EBbZ82iWKG06gN/fIQfKzkrd5ZIJWJsvnv+lnUQkdmVScbp6mqgq6sB3nnytrF8if79owzsHWXg1TGG92XJ9eQoHSwSGwyw/hL0lxh7LssYgxyk/M3A0+UVStMr0jStqaG5q4al68sziZIL6IpmVR/4o4Vy4HfsCemIJ1n7plZaNjec51kiEkW16QRrNjSxZkPTadvGRosMvDZG/6ujDOwd4UTleEFwsEhsNIQDRQoHivQ/MUI/8DLli9hkmhKsvayZ5rW11HamSa9Mk1qRIr0iTbx2bs8uVuBXRvhv3p9idWuS5R9YsmA+jUVk4aitS1J7RROrrjj9w2DoaI6jr47St+cEx14bZawnT643T6y/RDhYZPcvjuI/d9KJGMubamjIlEf9iebERPinVlZ+V/6OzcLxAgV+PqB2ICTT58RWpGl6++lvpojIuTS3Z2huz7D+zW0n3T80WuD/fXgv3buOUzvk1AyGHBjK0jISY1k+Tuw42H6jPp2gLjVpUTmDy79x+YyHvgI/X2L5roC4JWl+e/OsfKqKSHVqrkvxr9+/gcffcJQ1bXW8cmSER148wvMjeXAnfQJqhwJqBks0j8boClLUDjruzpUa4c+80VyRpS+GxNNG8w3N890cEYmYdCLODZeVp3mv76jnxsuX8kLfMPsqq/KeyBV5tuc4u4Zz7KJcYk7GjJtnoS1VH/jBy3kyo05yVZLaS2vnuzkiEnFmxhUrm7hi5evl49vfBIeO59jZM0QpdGpTcdx9xo8nVn3gx3ZkAah5c6MO1orIvFnWlGFZ0+xeXCnyBesf7jrMIy8emfg7Vwx4Yu8AuWKAu1PzfB6AputPvwqQiEiURHqEH4TOtif3A/CuS8vTLR98cj8/efko13S1cGfnCux4SK7BaN5UP8+tFRGZXZEe4Qfh65fFLVVub99Xvl7t092DjDw9QhCGDHTFqEtH+rNPRCTagR/664FfDMqrYk6+bubQU8O4w9DaOGmtmy0iERfplJuU9xRK44Ff7nIi55zYPUoYh+KG6lwbW0SqS6QD/+QRfvl2TWWE33IgpFgMGV4Wo7nl9As0i4hETRUF/sklnaa+kEIpZLAzRlu9Al9Eoi/agf/6xawmAj9RWbO6uSckVwoY6ozRVpeaj+aJiMypaAf+GUb4hSAgkXfqjzpjQcjwMqOtXoEvItFXNYFfKHnld0hTb4g5HG4NCZOmko6IVIWIB/7rt8dH+MXAaTxc3jBQufKZSjoiUg0iHfg+eYQ/UdIJaThcvj28tNz9do3wRaQKRDrwzzTCLxQDGg+Vb59YFqMmFacmNbeXGRMRmQ+RDvzgpBp+OeTtWEAyB8UayDVodC8i1SPSgR9OXkuncuJVprd8gYHhpTEw4/1XrZiXtomIzLVIrxh20tIKQVheDvlQAMCWt3fwkdtW0dGYmafWiYjMrUgHfnDKPPxS6NQedcxgy1s6aFLYi0gViXZJ55S1dIpBSP1A+bJh6TWq3YtIdYl04PspB21zw0Uyw44ljPRyBb6IVJdIB34waS2dUhgyui8HQL49hsW1HLKIVJdI1/DDU0b42b5y4BeWat69iFSfOR3hm1mzmX3DzF40s91m9tbZ3F94ypm2uf3lC5YHyyL9OScickZznXz3AT9w939hZimgdjZ3NnlaZrHkFHorgd+hwBeR6jNnyWdmjcA7gDsB3L0AFGZzn6cuj1w8VN6dL1Hgi0j1mcuSzjqgH/jvZvZLM/uvZlY3+QFmdreZbTez7f39/Re9w5PW0ikEBEeK5f20K/BFpPrMZeAngKuBv3P3NwKjwGcmP8Dd73f3re6+dcmSJRe9w2BS4vtgQBg4uQYjWaODtiJSfeYy8HuAHnd/ovL3Nyh/AMyayfPw40dLhO5km4xUItKzUUVEzmjOks/dDwEHzOzSyl03ALtmc5+TSzrxowFhCLkmIxlX4ItI9ZnrYvbvAl+rzNDZC/z2bO5s8kHbxECAe4yxZiOlwBeRKjSnge/uO4Ctc7W/kwL/WEhIjGyzSjoiUp0inXzhpKUVUkMhYejkGlXSEZHqFOnkmzzCT54ICb08S0cjfBGpRpFOvvHAjxWdZA4CK1/aMKmF00SkCkU68McH+OmR8o3hTAhmtNal5rFVIiLzI9KBP37iVXqk/PexVLmov7p1VpfwERFZkCId+OMlncyJ8u98vdFYk6S5ViN8Eak+EQ/88u/xkk6uQaN7EaleEQ/8ctB3JcoXKy/UG6sU+CJSpaoi8NsK5cXScg1Ge73KOSJSnSK9TvB4SSd+PGB1Wy25S2t5y7q2+W2UiMg8iXTgj6+WGTse0lKb4o6b15NMamlkEalO5y3pmNkXzey6uWjMTAvdwR0bK0/HjDcq7EWkek2lhv8y8Bdm1m1mf2pmW2a7UTMlCCGZA3OI18eJaUkFEali501Ad7/P3d8KvBM4RvkShbvN7I/MbOOst/AihO4kxxwD4k0a3YtIdZvykNfd97n7n1YuT/hR4IPA7llr2Qxwd1JZwCDRFOnDFSIi5zXlwDezpJm938y+BjwE7AE+NGstmwGhQzJbHuEr8EWk2p03Bc3s14CPALcATwIPAne7++gst+2ijZd0wBT4IlL1ppKCfwj8A/D77n5sltszo0KHVBbMVMMXETlv4Lv7rwBY2ceAde7+J2a2Gljm7k/OdiOnKwxdJR0RkYoLmaf4t8BbKZd3AE4AfzPjLZpBoTupMddBWxERLuxM2ze7+9Vm9ksAdx80swW9ME35oC2YavgiIhc0wi+aWRxwADNbAoTnfsr88vERPqrhi4hcSOB/Afg20GFm/xF4HPhPs9KqGRKohi8iMmHKKejuXzOzp4EbAAN+3d0X9IlXYVi+eDm1EG/QCF9EqtsFDXvd/UXgxVlqy4zzbIg5xDIxraMjIlVvKide/eO5trv7rTPXnBlWWSWTWoW9iMhURvhvBQ4A24AnKJdzFgXLVtbDV+CLiEwp8JcB48srfBT4/4Bt7v7CbDZsRmTLI3yrUf1eRGQqyyMH7v4Dd78DeAvwCvComf3urLfuIlmuHPixOo3wRUSmdNDWzNKUF0/7CNBFeYrmt2avWTPDcirpiIiMm8pB278HrqC8JPIfu/vzs96qmTI+wq9VSUdEZCoj/N8CRoGNwKfMzCv3G+Du3jhbjbtYEwdt6xT4IiJTCfy3AT93dz/vIxeYWKWkE1cNX0RkSksrfBx42sweNLM7zWzZbDdqpkwctFUNX0RkSuvhfwLAzN4AvBf4spk1AY8APwB+6u7BVHZWWXxtO9Dr7u+bdqunaLykE1cNX0Tkgi5i/qK7/6W73wS8m/Liab9B+WSsqfoUc3jh81hegS8iMm5atQ53z7r79939d91961SeY2adlKd2/tfp7HM6bKKGr8AXEZnL4vZfAf+OOVxDP1ap4cfrFfgiIhe8SLyZfR2oA1JAQHlq5k3nec77gCPu/rSZvescj7sbuBtg9erVF9q008Rz5U+XeL0O2oqIXHASuvtvUD7weiNwE/DDKTzteuBWM+sGHgTebWZfPcNr3+/uW91965IlSy60aaeJ5csj/IRq+CIi0y7pbARWUl5Ybe35Huzuf+Dune7eBdwO/NjdPzbNfU+JB44VwE3TMkVEYBolnYrPAf+2cvuvZ6gtMyoYC8AhSEEipsAXEZlW4Lv7S8DvT/O5jwKPTue5FyIYDXCcUsqI2aJZwl9EZNZMK/DN7A+ByymXhNzdPzqjrZoBYWWGTpBEgS8iwvRLOubuvzmjLZlhXnDcy4FvquiIiEw78C8xs9+gvIom7v79mWvSzAjzIQ6ECZV0RERg+oH/GFBb+VmQq2h6odysIAEx5b2IyLROvLoe6Of1i5kvyMAP8yHuTpjUCF9EBKY3wm8FWqgcsGUBBz5AoJKOiAgwvTNt/xflE65uqfwsnelGzYTyCB9ClXRERIDpn2m7xN1/w90/zAINfK8sjRwmIK7EFxGZVg3/FmC5mf0W5XLOgrwCVpArX5MlSICppCMicv4Rvpldfspd7cA/V54br9xecIJKDT9MKuxFRGBqI/yvAFcDmNld7j5xARMzq3X3sdlq3MUIKmfaklLgi4jA1Gr4kxPzk6ds+8kMtmVGjY/wXSN8ERFgaoE/edrlqem5YBctCBX4IiInmUpJZ5mZ3Qk8y+mBvyDn4AMElVk6npznhoiILBBTCfzPA1uB3wY6zewF4MXKT/vsNe3ihPnyLB2N8EVEys4b+O5+/+S/zawT2AxcCfzvWWrXRQtzlS8fCnwREWAa8/DdvQfoARbcCpmTTRy01SwdERFgAR90vVhhQdMyRUQmi27ga5aOiMhJIhv4Xgl8SyvwRUQgwoE/Pi3T0pHtoojIBYlsGo5fxDyuwBcRASIa+O4+cdA2qcAXEQGiGvgFJ3QnSEAiEckuiohcsEim4eSrXSVikeyiiMgFi2QahoWQECdIGimN8EVEgIgGvud90ghf0zJFRCCigR/mw4kafjIeyS6KiFywSKbh6zV8I6mSjogIEOHAD90JE5BUSUdEBIho4HupUsOPQ0IlHRERIKKBTwDu4DFIxjXCFxGBiAa+l8onXpUDP5JdFBG5YJFMQw9eL+ko8EVEyuYsDc1slZk9Yma7zewFM/vUbO3LS47jeMxU0hERqbjgSxxehBLwf7j7M2bWADxtZv/s7rtmekceqKQjInKqOUtDdz/o7s9Ubp8AdgMrZ2VfJSd0CGM601ZEZNy8DH/NrAt4I/DEbLz++LRMj6ETr0REKuY8Dc2sHvgm8Gl3Hz5l291mtt3Mtvf39097H+WDtpWSjlbLFBEB5jjwzSxJOey/5u7fOnW7u9/v7lvdfeuSJUumvZ/xkk55hK+SjogIzO0sHQMeAHa7+3+e1Z0FlateaVqmiMiEuUzD64HfAt5tZjsqPzfPxo48cELAzVTSERGpmLNpme7+ODAn9ZWJGn7cSGgevogIENUzbSfX8FXSEREBohr4lRF+qMXTREQmRDPwNcIXETlNNNOwMkvHNUtHRGRCJNMwLIaVM221eJqIyLhoBn7JAbA4lKf/i4hIJAO/VAgBiCUj2T0RkWmJZCIGQXmEH9eyCiIiEyIZ+Brhi4icLpKJGBTLgR/X0sgiIhMimYjjB20TSZV0RETGRTLwx0f4KumIiLwukokYVEb4cY3wRUQmRDLww8oIP6EavojIhEgm4vgIP5mKZPdERKYlkok4Pi0zlYrPc0tERBaOSAb++Ag/lYlk90REpiWSiTg+SyetEb6IyIQ5u8ThXBoP/FQ6kp9nIotWsVikp6eHXC43301Z9DKZDJ2dnSSTySk/J5KBP37iVSodye6JLFo9PT00NDTQ1dWllWwvgrszMDBAT08Pa9eunfLzIjkEHg/8jGr4IgtKLpejra1NYX+RzIy2trYL/qYUyUQMS+OzdDTCF1loFPYzYzr/jtEM/KJG+CIip4pkIoaV9fDTquGLyBl8+9vfxsx48cUXZ+T1vva1r7F582Y2b97Mddddx7PPPjux7Qc/+AGXXnop69ev59577524/+tf/zqXX345sViM7du3n/R6O3fu5K1vfSuXX345V1555Ywd5I5c4Ls7XqnhpzXCF5Ez2LZtG29729t48MEHZ+T11q5dy2OPPQs7MJkAAAu/SURBVMbOnTv57Gc/y9133w1AEAT8zu/8Dg899BC7du1i27Zt7Nq1C4ArrriCb33rW7zjHe846bVKpRIf+9jH+NKXvsQLL7zAo48+ekEzcc4lckNgD5zAHTeoUQ1fZMH6l19+alZe94E7rz3n9pGREX7605/yyCOPcOutt/L5z3+eRx99lM997nMsXbqUHTt2cNttt3HllVdy3333kc1m+c53vsMll1xCf38/n/jEJ9i/fz8Af/VXf8X111/PddddN/H6b3nLW+jp6QHgySefZP369axbtw6A22+/ne9+97ts2rSJyy677Izte/jhh9m8eTNXXXUVAG1tbRf9bzIuekPgAMIQwjiktXiaiJziO9/5DjfddBMbN26ktbWVZ555BoBnn32W++67j+eee46vfOUr7NmzhyeffJK77rqLv/7rvwbgU5/6FPfccw9PPfUU3/zmN7nrrrtOe/0HHniA9773vQD09vayatWqiW2dnZ309vaes3179uzBzLjxxhu5+uqr+bM/+7OZ6noER/glJ3TH45BJ6kxbkYXqfCPx2bJt2zY+/elPA+UR97Zt27jlllu49tprWb58OQCXXHIJ73nPewC48soreeSRRwD44Q9/OFGSARgeHubEiRM0NDQA8Mgjj/DAAw/w+OOPA+US86nON7umVCrx+OOP89RTT1FbW8sNN9zANddcww033HCRPY9i4AeVwI9BRiN8EZlkYGCAH//4xzz//POYGUEQYGbcfPPNpNPpicfFYrGJv2OxGKVSCYAwDPn5z39OTU3Naa+9c+dO7rrrLh566KGJMkxnZycHDhyYeExPTw8rVqw4Zxs7Ozt55zvfSXt7OwA333wzzzzzzIwEfuQSsZgPcAfiRiIeue6JyEX4xje+wcc//nH27dtHd3c3Bw4cYO3atRMj8vN5z3vewxe/+MWJv3fs2AHA/v37ue222/jKV77Cxo0bJ7Zfe+21vPzyy7z22msUCgUefPBBbr311nPu48Ybb2Tnzp2MjY1RKpV47LHH2LRp0zR6e7rIJWKusjSyJXRyh4icbNu2bXzwgx886b4PfehD/MM//MOUnv+FL3yB7du3s3nzZjZt2sSXvvQlAP7kT/6EgYEBPvnJT7Jlyxa2bt0KQCKR4Itf/CI33ngjl112GR/+8Ie5/PLLgfLU0M7OTn7+859zyy23cOONNwLQ0tLC7/3e73HttdeyZcsWrr76am655ZYZ6b+dqca0EGzdutVPnZs6FYdePcEPP7SDsC3Ox3903fmfICJzZvfu3WednSIX7kz/nmb2tLtvPdPjIzfCz+crFzDXCF9E5CTRC/xc+eBKTPV7EZGTRC4V85UafjypEb6IyGRzGvhmdpOZvWRmr5jZZ2ZjH/lcAEBMgS8icpI5C3wziwN/A7wX2AR8xMxmZq7RJPl8OfDjmoMvInKSuUzFNwGvuPtedy8ADwIfmOmdFMcDP6nAFxGZbC5TcSVwYNLfPZX7JpjZ3Wa23cy29/f3T2snjekEjTVJGutnZnU5EYmWeDzOli1bJn66u7snFj/r7u6e8pz8xWgul1Y4U1H9pJMA3P1+4H4oz8Ofzk7Wt9WTbK+jfmnddJ4uIhFXU1MzcYbsuJ/97GfA64H/0Y9+dD6aNuvmMvB7gFWT/u4E+mZ8L+WKDhbXQVuRhey59z83K6975f+68oKfU19fz8jICJ/5zGfYvXs3W7Zs4Y477uCee+6ZhRbOn7kM/KeADWa2FugFbgdm/GPUK1e7UuCLyJlks1m2bNkClC9c8u1vf3ti27333suf//mf873vfW++mjer5izw3b1kZv8G+CcgDvw3d39hxvdTUuCLLAbTGYnPhDOVdKrFnC6P7O7fB74/q/sYH+FraQURkZNEbu7i+AgfXftERC5QQ0MDJ06cmO9mzJroBb5G+CIyTZs3byaRSHDVVVfxl3/5l/PdnBkXuSteWcKIN8WJ12qILyKnGxkZOet9yWSSH/3oR3PdpDkTucBv/dVWWn+1db6bISKy4ESupCMiImemwBeRObVQr7K32Ezn31GBLyJzJpPJMDAwoNC/SO7OwMAAmUzmgp4XuRq+iCxcnZ2d9PT0MN3FEeV1mUyGzs7OC3qOAl9E5kwymWTt2rXz3YyqpZKOiEiVUOCLiFQJBb6ISJWwhXq03Mz6gX3TeGo7cHSGm7PQqI+LX9T7B9Hv40Lt3xp3X3KmDQs28KfLzLa7+9b5bsdsUh8Xv6j3D6Lfx8XYP5V0RESqhAJfRKRKRDHw75/vBswB9XHxi3r/IPp9XHT9i1wNX0REziyKI3wRETkDBb6ISJWIVOCb2U1m9pKZvWJmn5nv9swEM+s2s+fMbIeZba/c12pm/2xmL1d+t8x3Oy+Emf03MztiZs9Puu+sfTKzP6i8py+Z2Y3z0+oLc5Y+ft7Meivv5Q4zu3nStkXVRzNbZWaPmNluM3vBzD5VuT8y7+M5+rh430d3j8QP5cuWvwqsA1LAs8Cm+W7XDPSrG2g/5b4/Az5Tuf0Z4E/nu50X2Kd3AFcDz5+vT8CmynuZBtZW3uP4fPdhmn38PPD7Z3jsousjsBy4unK7AdhT6Udk3sdz9HHRvo9RGuG/CXjF3fe6ewF4EPjAPLdptnwA+PvK7b8Hfn0e23LB3P1/A8dOuftsffoA8KC75939NeAVyu/1gnaWPp7Nouujux9092cqt08Au4GVROh9PEcfz2bB9zFKgb8SODDp7x7O/eYsFg48bGZPm9ndlfuWuvtBKP9HCXTMW+tmztn6FLX39d+Y2c5KyWe83LGo+2hmXcAbgSeI6Pt4Sh9hkb6PUQp8O8N9UZhzer27Xw28F/gdM3vHfDdojkXpff074BJgC3AQ+IvK/Yu2j2ZWD3wT+LS7D5/roWe4b7H2cdG+j1EK/B5g1aS/O4G+eWrLjHH3vsrvI8C3KX9FPGxmywEqv4/MXwtnzNn6FJn31d0Pu3vg7iHw//D61/1F2UczS1IOwq+5+7cqd0fqfTxTHxfz+xilwH8K2GBma80sBdwO/OM8t+mimFmdmTWM3wbeAzxPuV93VB52B/Dd+WnhjDpbn/4RuN3M0ma2FtgAPDkP7bto40FY8UHK7yUswj6amQEPALvd/T9P2hSZ9/FsfVzU7+N8HzWeyR/gZspH0l8F/v18t2cG+rOO8lH/Z4EXxvsEtAE/Al6u/G6d77ZeYL+2Uf4qXKQ8KvqX5+oT8O8r7+lLwHvnu/0X0cevAM8BOymHw/LF2kfgbZTLFTuBHZWfm6P0Pp6jj4v2fdTSCiIiVSJKJR0RETkHBb6ISJVQ4IuIVAkFvohIlVDgi4hUCQW+iEiVUOCLiFQJBb5UBTP7opntu8jX+Ndm5mZ22aT7dlcW1prO6/33SWuq7zCzQ2Y21RU2RS6YAl8ir3Ka+7uA1PhSFdO0mfLZlrdUXjcNLAXO+EFiZu8ysy+f7cXc/bfdfYu7b6F8in4JuPMi2idyTgp8qQZ/DPzfwC7g8ot4nSuBe6kEfuW1dvtFnq5uZm3AD4D/y90X9fpPsrAp8CXSzOxy4Argf1C+gMVpgW9mPzmltDL+86unPHQT5bVTOsysifIHwHMX2b4a4HvA/3T3/3IxryVyPon5boDILPuPwGfd3c1sN+XwP4m7v/18L2Jmq4ABd8+a2T8DN1Iu8ew8w2OfoHyZu3qg1cx2VDb9n+7+T5MeF6f8QfSiu3/2wrsmcmEU+BJZZvZmysG8xcz+Bshw5oD+CeVrlp7q9939h5Xbm3l9NP994DcpX/P0O6c+yd3fXHnddwF3uvudZ2ni3wJJ4F9NrUciF0eBL1H2n4D3ufuPAMxsKfDLUx80lRE+J5dvHgO+BNQyzZKOmX0OuAZ4l7uXpvMaIhdKNXyJJDP7NSA9HvZQvlIRUGdmrdN4yYnAd/d85XbB3Yem0bYu4POU145/fNIxg/8xjXaJTJnWwxcRqRIa4YuIVAkFvohIlVDgi4hUCQW+iEiVUOCLiFQJBb6ISJVQ4IuIVIn/HxYks6SRh1OcAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"Masses['Eapprox'] = ytilde\n",
"# Generate a plot comparing the experimental with the fitted values values.\n",
@@ -943,8 +1110,8 @@
"source": [
"## Adding error analysis and training set up\n",
"\n",
- "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of _Scikit_Learn_ in the introductory slides.\n",
- "Since we are not using _Scikit-Learn here we can define our own $R2$ function as"
+ "We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of **Scikit-Learn** in the introductory slides.\n",
+ "Since we are not using **Scikit-Learn** here we can define our own $R2$ function as"
]
},
{
@@ -954,7 +1121,7 @@
"outputs": [],
"source": [
"def R2(y_data, y_model):\n",
- " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_model)) ** 2)"
+ " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)"
]
},
{
@@ -968,7 +1135,15 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9547578478889096\n"
+ ]
+ }
+ ],
"source": [
"print(R2(Energies,ytilde))"
]
@@ -984,7 +1159,15 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.037875961483052376\n"
+ ]
+ }
+ ],
"source": [
"def MSE(y_data,y_model):\n",
" n = np.size(y_model)\n",
@@ -1004,7 +1187,27 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A \n",
+ "1 0 inf\n",
+ "2 1 1.123190\n",
+ "3 2 0.327631\n",
+ "4 6 0.344172\n",
+ "5 9 0.044402\n",
+ " ... \n",
+ "264 3304 0.009911\n",
+ "265 3310 0.009154\n",
+ "266 3317 0.007824\n",
+ "269 3338 0.011347\n",
+ "270 3344 0.009790\n",
+ "Name: Ebinding, Length: 267, dtype: float64\n"
+ ]
+ }
+ ],
"source": [
"def RelativeError(y_data,y_model):\n",
" return abs((y_data-y_model)/y_data)\n",
@@ -1379,7 +1582,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1389,21 +1592,23 @@
"Mean squared error: 12.36\n",
"Variance score: 1.00\n",
"Mean absolute error: 2.83\n",
- "[ 0. 618.32047562 -861.13519106 1404.91549644] -11.057088709963637\n",
+ "[ 0. 618.32047562 -861.13519106 1404.91549644] -11.057088709963296\n",
"Mean squared error: 197.93\n",
"Variance score: 1.00\n",
"Mean absolute error: 11.69\n",
- "[ 0. 28.18220995 282.79902342 842.30879705] 12.946893955207997\n"
+ "[ 0. 28.18220995 282.79902342 842.30879705] 12.946893955209475\n"
]
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -1523,7 +1728,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1531,13 +1736,13 @@
"output_type": "stream",
"text": [
"Training R2\n",
- "0.9999860992358398\n",
+ "0.999985063278987\n",
"Training MSE\n",
- "6.455334759816797\n",
+ "6.991057217389305\n",
"Test R2\n",
- "0.999982201783859\n",
+ "0.9999878398124225\n",
"Test MSE\n",
- "6.614329624113382\n"
+ "4.264567442512323\n"
]
}
],
@@ -1571,7 +1776,7 @@
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"def R2(y_data, y_model):\n",
- " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_model)) ** 2)\n",
+ " return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
"def MSE(y_data,y_model):\n",
" n = np.size(y_model)\n",
" return np.sum((y_data-y_model)**2)/n\n",
@@ -1655,19 +1860,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.7/site-packages/matplotlib/__init__.py:886: MatplotlibDeprecationWarning: \n",
- "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
- " \"found relative to the 'datapath' directory.\".format(key))\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt \n",
@@ -1685,7 +1880,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -1694,7 +1889,7 @@
"dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
]
},
- "execution_count": 7,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -1718,7 +1913,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -1736,7 +1931,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1759,7 +1954,7 @@
"dtype: int64"
]
},
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -1778,12 +1973,12 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1810,22 +2005,22 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 11,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -1853,7 +2048,18 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.figure(figsize=(20, 5))\n",
"\n",
@@ -1898,7 +2104,18 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(404, 2)\n",
+ "(102, 2)\n",
+ "(404,)\n",
+ "(102,)\n"
+ ]
+ }
+ ],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
@@ -1922,7 +2139,24 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The model performance for training set\n",
+ "--------------------------------------\n",
+ "RMSE is 5.6371293350711955\n",
+ "R2 score is 0.6300745149331701\n",
+ "\n",
+ "\n",
+ "The model performance for testing set\n",
+ "--------------------------------------\n",
+ "RMSE is 5.137400784702911\n",
+ "R2 score is 0.6628996975186953\n"
+ ]
+ }
+ ],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
@@ -1961,7 +2195,18 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# plotting the y_test vs y_pred\n",
"# ideally should have been a straight line\n",
@@ -1969,6 +2214,212 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Reducing the number of degrees of freedom, overarching view\n",
+ "\n",
+ "Many Machine Learning problems involve thousands or even millions of\n",
+ "features for each training instance. Not only does this make training\n",
+ "extremely slow, it can also make it much harder to find a good\n",
+ "solution, as we will see. This problem is often referred to as the\n",
+ "curse of dimensionality. Fortunately, in real-world problems, it is\n",
+ "often possible to reduce the number of features considerably, turning\n",
+ "an intractable problem into a tractable one.\n",
+ "Later this semester we will discuss some of the most popular dimensionality reduction\n",
+ "techniques: the principal component analysis (PCA), Kernel PCA, and\n",
+ "Locally Linear Embedding (LLE). Furthermore, we will start by looking\n",
+ "at some simple preprocessing of the data which allow us to rescale the\n",
+ "data.\n",
+ "\n",
+ "Principal component analysis and its various variants deal with the\n",
+ "problem of fitting a low-dimensional [affine\n",
+ "subspace](https://en.wikipedia.org/wiki/Affine_space) to a set of of\n",
+ "data points in a high-dimensional space. With its family of methods it\n",
+ "is one of the most used tools in data modeling, compression and\n",
+ "visualization.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Preprocessing our data\n",
+ "\n",
+ "Before we proceed however, we will discuss how to preprocess our\n",
+ "data. Till now and in connection with our previous examples we have\n",
+ "not met so many cases where we are too sensitive to the scaling of our\n",
+ "data. Normally the data may need a rescaling and/or may be sensitive\n",
+ "to extreme values. Scaling the data renders our inputs much more\n",
+ "suitable for the algorithms we want to employ.\n",
+ "\n",
+ "**Scikit-Learn** has several functions which allow us to rescale the\n",
+ "data, normally resulting in much better results in terms of various\n",
+ "accuracy scores. The **StandardScaler** function in **Scikit-Learn**\n",
+ "ensures that for each feature/predictor we study the mean value is\n",
+ "zero and the variance is one (every column in the design/feature\n",
+ "matrix). This scaling has the drawback that it does not ensure that\n",
+ "we have a particular maximum or minimum in our data set. Another\n",
+ "function included in **Scikit-Learn** is the **MinMaxScaler** which\n",
+ "ensures that all features are exactly between $0$ and $1$. The\n",
+ "\n",
+ "## More preprocessing\n",
+ "\n",
+ "\n",
+ "The **Normalizer** scales each data\n",
+ "point such that the feature vector has a euclidean length of one. In other words, it\n",
+ "projects a data point on the circle (or sphere in the case of higher dimensions) with a\n",
+ "radius of 1. This means every data point is scaled by a different number (by the\n",
+ "inverse of it’s length).\n",
+ "This normalization is often used when only the direction (or angle) of the data matters,\n",
+ "not the length of the feature vector.\n",
+ "\n",
+ "The **RobustScaler** works similarly to the StandardScaler in that it\n",
+ "ensures statistical properties for each feature that guarantee that\n",
+ "they are on the same scale. However, the RobustScaler uses the median\n",
+ "and quartiles, instead of mean and variance. This makes the\n",
+ "RobustScaler ignore data points that are very different from the rest\n",
+ "(like measurement errors). These odd data points are also called\n",
+ "outliers, and might often lead to trouble for other scaling\n",
+ "techniques.\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Simple preprocessing examples, Franke function and regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MSE before scaling: 0.00\n",
+ "R2 score before scaling 0.99\n",
+ "Feature min values before scaling:\n",
+ " [1.00000000e+00 2.14316539e-03 2.98946890e-03 4.59315788e-06\n",
+ " 6.40692628e-06 8.93692432e-06 9.84389699e-09 1.37311026e-08\n",
+ " 1.91533069e-08 2.67166574e-08 2.10970993e-11 2.94280239e-11\n",
+ " 4.10487044e-11 5.72582153e-11 7.98686164e-11 4.52145730e-14\n",
+ " 6.30691223e-14 8.79741624e-14 1.22713825e-13 1.71171654e-13\n",
+ " 2.38764745e-13]\n",
+ "Feature max values before scaling:\n",
+ " [1. 0.99793718 0.99891194 0.99587862 0.99685137 0.99782506\n",
+ " 0.9938243 0.99479504 0.99576673 0.99673937 0.99177422 0.99274296\n",
+ " 0.99371265 0.99468328 0.99565486 0.98972837 0.99069511 0.9916628\n",
+ " 0.99263143 0.993601 0.99457153]\n",
+ "Feature min values after scaling:\n",
+ " [ 0. -1.74785246 -1.71827976 -1.12929894 -1.11597664 -1.10272041\n",
+ " -0.88753741 -0.87948075 -0.87154033 -0.86372458 -0.75221097 -0.74726925\n",
+ " -0.7424295 -0.73769264 -0.73305913 -0.66303761 -0.66007414 -0.65718786\n",
+ " -0.65437766 -0.65164227 -0.64898034]\n",
+ "Feature max values after scaling:\n",
+ " [0. 1.73676102 1.73659896 2.24509 2.23190303 2.21746087\n",
+ " 2.65737717 2.63704474 2.61603284 2.5944056 3.01210005 2.98798361\n",
+ " 2.96349638 2.93867424 2.91355053 3.32619552 3.30022856 3.27405512\n",
+ " 3.24769478 3.22116573 3.19448489]\n",
+ "MSE after scaling: 0.00\n",
+ "R2 score for scaled data: 0.99\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Common imports\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import sklearn.linear_model as skl\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n",
+ "\n",
+ "# Where to save the figures and data files\n",
+ "PROJECT_ROOT_DIR = \"Results\"\n",
+ "FIGURE_ID = \"Results/FigureFiles\"\n",
+ "DATA_ID = \"DataFiles/\"\n",
+ "\n",
+ "if not os.path.exists(PROJECT_ROOT_DIR):\n",
+ " os.mkdir(PROJECT_ROOT_DIR)\n",
+ "\n",
+ "if not os.path.exists(FIGURE_ID):\n",
+ " os.makedirs(FIGURE_ID)\n",
+ "\n",
+ "if not os.path.exists(DATA_ID):\n",
+ " os.makedirs(DATA_ID)\n",
+ "\n",
+ "def image_path(fig_id):\n",
+ " return os.path.join(FIGURE_ID, fig_id)\n",
+ "\n",
+ "def data_path(dat_id):\n",
+ " return os.path.join(DATA_ID, dat_id)\n",
+ "\n",
+ "def save_fig(fig_id):\n",
+ " plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
+ "\n",
+ "\n",
+ "def FrankeFunction(x,y):\n",
+ "\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
+ "\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
+ "\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
+ "\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
+ "\treturn term1 + term2 + term3 + term4\n",
+ "\n",
+ "\n",
+ "def create_X(x, y, n ):\n",
+ "\tif len(x.shape) > 1:\n",
+ "\t\tx = np.ravel(x)\n",
+ "\t\ty = np.ravel(y)\n",
+ "\n",
+ "\tN = len(x)\n",
+ "\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n",
+ "\tX = np.ones((N,l))\n",
+ "\n",
+ "\tfor i in range(1,n+1):\n",
+ "\t\tq = int((i)*(i+1)/2)\n",
+ "\t\tfor k in range(i+1):\n",
+ "\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n",
+ "\n",
+ "\treturn X\n",
+ "\n",
+ "\n",
+ "# Making meshgrid of datapoints and compute Franke's function\n",
+ "n = 5\n",
+ "N = 1000\n",
+ "x = np.sort(np.random.uniform(0, 1, N))\n",
+ "y = np.sort(np.random.uniform(0, 1, N))\n",
+ "z = FrankeFunction(x, y)\n",
+ "X = create_X(x, y, n=n) \n",
+ "# split in training and test data\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X,z,test_size=0.2)\n",
+ "\n",
+ "\n",
+ "clf = skl.LinearRegression().fit(X_train, y_train)\n",
+ "\n",
+ "# The mean squared error and R2 score\n",
+ "print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test), y_test)))\n",
+ "print(\"R2 score before scaling {:.2f}\".format(clf.score(X_test,y_test)))\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "scaler.fit(X_train)\n",
+ "X_train_scaled = scaler.transform(X_train)\n",
+ "X_test_scaled = scaler.transform(X_test)\n",
+ "\n",
+ "print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n",
+ "print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n",
+ "\n",
+ "print(\"Feature min values after scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n",
+ "print(\"Feature max values after scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n",
+ "\n",
+ "clf = skl.LinearRegression().fit(X_train_scaled, y_train)\n",
+ "\n",
+ "\n",
+ "print(\"MSE after scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test_scaled), y_test)))\n",
+ "print(\"R2 score for scaled data: {:.2f}\".format(clf.score(X_test_scaled,y_test)))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -2204,7 +2655,7 @@
"two orthogonal/unitary matrices. The [Singular Value Decompostion\n",
"(SVD) theorem](https://en.wikipedia.org/wiki/Singular_value_decomposition)\n",
"states that a general $m\\times n$ matrix $\\boldsymbol{X}$ can be written in\n",
- "terms of a diagonal matrix $\\boldsymbol{\\Sigma}$ of dimensionality $n\\times n$\n",
+ "terms of a diagonal matrix $\\boldsymbol{\\Sigma}$ of dimensionality $m\\times n$\n",
"and two orthognal matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$, where the first has\n",
"dimensionality $m \\times m$ and the last dimensionality $n\\times n$.\n",
"We have then"
@@ -2592,7 +3043,7 @@
"\n",
"We see that Ridge regression is nothing but the standard\n",
"OLS with a modified diagonal term added to $\\boldsymbol{X}^T\\boldsymbol{X}$. The\n",
- "consequences, in particular for our discussion of the bias-variance\n",
+ "consequences, in particular for our discussion of the bias-variance tradeoff \n",
"are rather interesting.\n",
"\n",
"Furthermore, if we use the result above in terms of the SVD decomposition (our analysis was done for the OLS method), we had"
@@ -2733,6 +3184,824 @@
"For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
"Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n",
"\n",
+ "## Codes for the SVD"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1. -1. 2.]\n",
+ " [ 1. 0. 1.]\n",
+ " [ 1. 2. -1.]\n",
+ " [ 1. 1. 0.]]\n",
+ "[[ 4. 2. 2.]\n",
+ " [ 2. 6. -4.]\n",
+ " [ 2. -4. 6.]]\n",
+ "[[-1.96889890e-16 8.16496581e-01 -5.77350269e-01]\n",
+ " [-7.07106781e-01 4.08248290e-01 5.77350269e-01]\n",
+ " [ 7.07106781e-01 4.08248290e-01 5.77350269e-01]]\n",
+ "[1.00000000e+01 6.00000000e+00 2.38805416e-31]\n",
+ "[[-5.76324444e-17 -7.07106781e-01 7.07106781e-01]\n",
+ " [ 8.16496581e-01 4.08248290e-01 4.08248290e-01]\n",
+ " [-5.77350269e-01 5.77350269e-01 5.77350269e-01]]\n",
+ "[[ 1.39583657e+30 -1.39583657e+30 -1.39583657e+30]\n",
+ " [-1.39583657e+30 1.39583657e+30 1.39583657e+30]\n",
+ " [-1.39583657e+30 1.39583657e+30 1.39583657e+30]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "# SVD inversion\n",
+ "def SVDinv(A):\n",
+ " ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).\n",
+ " SVD is numerically more stable than the inversion algorithms provided by\n",
+ " numpy and scipy.linalg at the cost of being slower.\n",
+ " '''\n",
+ " U, s, VT = np.linalg.svd(A)\n",
+ "# print('test U')\n",
+ "# print( (np.transpose(U) @ U - U @np.transpose(U)))\n",
+ "# print('test VT')\n",
+ "# print( (np.transpose(VT) @ VT - VT @np.transpose(VT)))\n",
+ " print(U)\n",
+ " print(s)\n",
+ " print(VT)\n",
+ "\n",
+ " D = np.zeros((len(U),len(VT)))\n",
+ " for i in range(0,len(VT)):\n",
+ " D[i,i]=s[i]\n",
+ " UT = np.transpose(U); V = np.transpose(VT); invD = np.linalg.inv(D)\n",
+ " return np.matmul(V,np.matmul(invD,UT))\n",
+ "\n",
+ "\n",
+ "X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ])\n",
+ "print(X)\n",
+ "A = np.transpose(X) @ X\n",
+ "print(A)\n",
+ "# Brute force inversion of super-collinear matrix\n",
+ "#B = np.linalg.inv(A)\n",
+ "#print(B)\n",
+ "C = SVDinv(A)\n",
+ "print(C)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The matrix $\\boldsymbol{X}$ has columns that are linearly dependent. The first\n",
+ "column is the row-wise sum of the other two columns. The rank of a\n",
+ "matrix (the column rank) is the dimension of space spanned by the\n",
+ "column vectors. The rank of the matrix is the number of linearly\n",
+ "independent columns, in this case just $2$. We see this from the\n",
+ "singular values when running the above code. Running the standard\n",
+ "inversion algorithm for matrix inversion with $\\boldsymbol{X}^T\\boldsymbol{X}$ results\n",
+ "in the program terminating due to a singular matrix.\n",
+ "\n",
+ "\n",
+ "\n",
+ "## A better understanding of regularization\n",
+ "\n",
+ "The parameter $\\lambda$ that we have introduced in the Ridge (and\n",
+ "Lasso as well) regression is often called a regularization parameter\n",
+ "or shrinkage parameter. It is common to call it a hyperparameter. What does it mean mathemtically?\n",
+ "\n",
+ "Here we will first look at how to analyze the difference between the\n",
+ "standard OLS equations and the Ridge expressions in terms of a linear\n",
+ "algebra analysis using the SVD algorithm. Thereafter, we will link\n",
+ "(see the material on the bias-variance tradeoff below) these\n",
+ "observation to the statisical analysis of the results. In particular\n",
+ "we consider how the variance of the parameters $\\boldsymbol{\\beta}$ is\n",
+ "affected by changing the parameter $\\lambda$.\n",
+ "\n",
+ "## Decomposing the OLS and Ridge expressions\n",
+ "\n",
+ "We have our design matrix\n",
+ " $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. With the SVD we decompose it as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{X} = \\boldsymbol{U\\Sigma V^T},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "with $\\boldsymbol{U}\\in {\\mathbb{R}}^{n\\times n}$, $\\boldsymbol{\\Sigma}\\in {\\mathbb{R}}^{n\\times p}$\n",
+ "and $\\boldsymbol{V}\\in {\\mathbb{R}}^{p\\times p}$.\n",
+ "\n",
+ "The matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are unitary/orthonormal matrices, that is in case the matrices are real we have $\\boldsymbol{U}^T\\boldsymbol{U}=\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ and $\\boldsymbol{V}^T\\boldsymbol{V}=\\boldsymbol{V}\\boldsymbol{V}^T=\\boldsymbol{I}$.\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Introducing the Covariance and Correlation functions\n",
+ "\n",
+ "Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about\n",
+ "the definition of the covariance and the correlation function. These are quantities \n",
+ "\n",
+ "Suppose we have defined two vectors\n",
+ "$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
+ " \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{y}] \\\\\n",
+ " \\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "where for example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this definition and recalling that the variance is defined as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\mathrm{var}[\\boldsymbol{x}]=\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})^2,\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "we can rewrite the covariance matrix as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
+ " \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] & \\mathrm{var}[\\boldsymbol{y}] \\\\\n",
+ " \\end{bmatrix}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The covariance takes values between zero and infinity and may thus\n",
+ "lead to problems with loss of numerical precision for particularly\n",
+ "large values. It is common to scale the covariance matrix by\n",
+ "introducing instead the correlation matrix defined via the so-called\n",
+ "correlation function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]=\\frac{\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}]}{\\sqrt{\\mathrm{var}[\\boldsymbol{x}] \\mathrm{var}[\\boldsymbol{y}]}}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The correlation function is then given by values $\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]\n",
+ "\\in [-1,1]$. This avoids eventual problems with too large values. We\n",
+ "can then define the correlation matrix for the two vectors $\\boldsymbol{x}$\n",
+ "and $\\boldsymbol{y}$ as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{K}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} 1 & \\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
+ " \\mathrm{corr}[\\boldsymbol{y},\\boldsymbol{x}] & 1 \\\\\n",
+ " \\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the above example this is the function we constructed using **pandas**.\n",
+ "\n",
+ "## Correlation Function and Design/Feature Matrix\n",
+ "\n",
+ "In our derivation of the various regression algorithms like **Ordinary Least Squares** or **Ridge regression**\n",
+ "we defined the design/feature matrix $\\boldsymbol{X}$ as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{X}=\\begin{bmatrix}\n",
+ "x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n",
+ "x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n",
+ "x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n",
+ "\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n",
+ "x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n",
+ "x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n",
+ "\\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ refering to the column numbers and the\n",
+ "entries $n$ being the row elements.\n",
+ "We can rewrite the design/feature matrix in terms of its column vectors as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{X}=\\begin{bmatrix} \\boldsymbol{x}_0 & \\boldsymbol{x}_1 & \\boldsymbol{x}_2 & \\dots & \\dots & \\boldsymbol{x}_{p-1}\\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "with a given vector"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{x}_i^T = \\begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \\dots & \\dots x_{n-1,i}\\end{bmatrix}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With these definitions, we can now rewrite our $2\\times 2$\n",
+ "correaltion/covariance matrix in terms of a moe general design/feature\n",
+ "matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. This leads to a $p\\times p$\n",
+ "covariance matrix for the vectors $\\boldsymbol{x}_i$ with $i=0,1,\\dots,p-1$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x}] = \\begin{bmatrix}\n",
+ "\\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & \\mathrm{var}[\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
+ "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
+ "\\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & \\mathrm{var}[\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "and the correlation matrix"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{K}[\\boldsymbol{x}] = \\begin{bmatrix}\n",
+ "1 & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & 1 & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & 1 & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n",
+ "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
+ "\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
+ "\\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & 1\\\\\n",
+ "\\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Covariance Matrix Examples\n",
+ "\n",
+ "\n",
+ "The Numpy function **np.cov** calculates the covariance elements using\n",
+ "the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have\n",
+ "the exact mean values. The following simple function uses the\n",
+ "**np.vstack** function which takes each vector of dimension $1\\times n$\n",
+ "and produces a $2\\times n$ matrix $\\boldsymbol{W}$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 \\\\\n",
+ " x_1 & y_1 \\\\\n",
+ " x_2 & y_2\\\\\n",
+ " \\dots & \\dots \\\\\n",
+ " x_{n-2} & y_{n-2}\\\\\n",
+ " x_{n-1} & y_{n-1} & \n",
+ " \\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "which in turn is converted into into the $2\\times 2$ covariance matrix\n",
+ "$\\boldsymbol{C}$ via the Numpy function **np.cov()**. We note that we can also calculate\n",
+ "the mean value of each set of samples $\\boldsymbol{x}$ etc using the Numpy\n",
+ "function **np.mean(x)**. We can also extract the eigenvalues of the\n",
+ "covariance matrix through the **np.linalg.eig()** function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.042229208919236545\n",
+ "3.965106055560731\n",
+ "[[ 0.92354285 2.88986604]\n",
+ " [ 2.88986604 10.07530538]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Importing various packages\n",
+ "import numpy as np\n",
+ "n = 100\n",
+ "x = np.random.normal(size=n)\n",
+ "print(np.mean(x))\n",
+ "y = 4+3*x+np.random.normal(size=n)\n",
+ "print(np.mean(y))\n",
+ "W = np.vstack((x, y))\n",
+ "C = np.cov(W)\n",
+ "print(C)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Correlation Matrix\n",
+ "\n",
+ "The previous example can be converted into the correlation matrix by\n",
+ "simply scaling the matrix elements with the variances. We should also\n",
+ "subtract the mean values for each column. This leads to the following\n",
+ "code which sets up the correlations matrix for the previous example in\n",
+ "a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\\times 2$ correlation matrix (since we have only two vectors)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.07953778271319319\n",
+ "1.5265483445750982\n",
+ "[[1. 0.71417833]\n",
+ " [0.71417833 1. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "n = 100\n",
+ "# define two vectors \n",
+ "x = np.random.random(size=n)\n",
+ "y = 4+3*x+np.random.normal(size=n)\n",
+ "#scaling the x and y vectors \n",
+ "x = x - np.mean(x)\n",
+ "y = y - np.mean(y)\n",
+ "variance_x = np.sum(x@x)/n\n",
+ "variance_y = np.sum(y@y)/n\n",
+ "print(variance_x)\n",
+ "print(variance_y)\n",
+ "cov_xy = np.sum(x@y)/n\n",
+ "cov_xx = np.sum(x@x)/n\n",
+ "cov_yy = np.sum(y@y)/n\n",
+ "C = np.zeros((2,2))\n",
+ "C[0,0]= cov_xx/variance_x\n",
+ "C[1,1]= cov_yy/variance_y\n",
+ "C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)\n",
+ "C[1,0]= C[0,1]\n",
+ "print(C)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that the matrix elements along the diagonal are one as they\n",
+ "should be and that the matrix is symmetric. Furthermore, diagonalizing\n",
+ "this matrix we easily see that it is a positive definite matrix.\n",
+ "\n",
+ "The above procedure with **numpy** can be made more compact if we use **pandas**.\n",
+ "\n",
+ "## Correlation Matrix with Pandas\n",
+ "\n",
+ "We whow here how we can set up the correlation matrix using **pandas**, as done in this simple code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 2.11842576 5.22468553]\n",
+ " [ 0.82757833 -0.16388067]\n",
+ " [-0.66170841 -2.44421898]\n",
+ " [ 0.78136059 2.21927291]\n",
+ " [-0.7470833 -0.67119054]\n",
+ " [-1.03156683 -2.1492145 ]\n",
+ " [ 0.78741508 3.01521106]\n",
+ " [-1.12967477 -4.76411542]\n",
+ " [ 0.83056964 3.49404968]\n",
+ " [-1.77531609 -3.76059908]]\n",
+ " 0 1\n",
+ "0 2.118426 5.224686\n",
+ "1 0.827578 -0.163881\n",
+ "2 -0.661708 -2.444219\n",
+ "3 0.781361 2.219273\n",
+ "4 -0.747083 -0.671191\n",
+ "5 -1.031567 -2.149214\n",
+ "6 0.787415 3.015211\n",
+ "7 -1.129675 -4.764115\n",
+ "8 0.830570 3.494050\n",
+ "9 -1.775316 -3.760599\n",
+ " 0 1\n",
+ "0 1.000000 0.925137\n",
+ "1 0.925137 1.000000\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "n = 10\n",
+ "x = np.random.normal(size=n)\n",
+ "x = x - np.mean(x)\n",
+ "y = 4+3*x+np.random.normal(size=n)\n",
+ "y = y - np.mean(y)\n",
+ "X = (np.vstack((x, y))).T\n",
+ "print(X)\n",
+ "Xpd = pd.DataFrame(X)\n",
+ "print(Xpd)\n",
+ "correlation_matrix = Xpd.corr()\n",
+ "print(correlation_matrix)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We expand this model to the Franke function discussed above.\n",
+ "\n",
+ "## Correlation Matrix with Pandas and the Franke function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0 1 2 3 4 5 6 7 \\\n",
+ "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "1 0.0 0.095612 0.088507 0.097649 0.092959 0.088538 0.090166 0.086293 \n",
+ "2 0.0 0.088507 0.082492 0.091076 0.087082 0.083303 0.084720 0.081374 \n",
+ "3 0.0 0.097649 0.091076 0.106048 0.101344 0.096900 0.101656 0.097608 \n",
+ "4 0.0 0.092959 0.087082 0.101344 0.097137 0.093150 0.097539 0.093887 \n",
+ "5 0.0 0.088538 0.083303 0.096900 0.093150 0.089587 0.093635 0.090350 \n",
+ "6 0.0 0.090166 0.084720 0.101656 0.097539 0.093635 0.099905 0.096237 \n",
+ "7 0.0 0.086293 0.081374 0.097608 0.093887 0.090350 0.096237 0.092896 \n",
+ "8 0.0 0.082678 0.078241 0.093817 0.090460 0.087261 0.092789 0.089752 \n",
+ "9 0.0 0.079297 0.075305 0.090261 0.087240 0.084353 0.089547 0.086790 \n",
+ "10 0.0 0.082207 0.077758 0.095018 0.091520 0.088191 0.095041 0.091831 \n",
+ "11 0.0 0.078893 0.074863 0.091457 0.088285 0.085258 0.091736 0.088803 \n",
+ "12 0.0 0.075800 0.072154 0.088122 0.085250 0.082503 0.088633 0.085956 \n",
+ "13 0.0 0.072909 0.069616 0.084998 0.082403 0.079912 0.085717 0.083277 \n",
+ "14 0.0 0.070207 0.067238 0.082068 0.079728 0.077476 0.082976 0.080755 \n",
+ "\n",
+ " 8 9 10 11 12 13 14 \n",
+ "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "1 0.082678 0.079297 0.082207 0.078893 0.075800 0.072909 0.070207 \n",
+ "2 0.078241 0.075305 0.077758 0.074863 0.072154 0.069616 0.067238 \n",
+ "3 0.093817 0.090261 0.095018 0.091457 0.088122 0.084998 0.082068 \n",
+ "4 0.090460 0.087240 0.091520 0.088285 0.085250 0.082403 0.079728 \n",
+ "5 0.087261 0.084353 0.088191 0.085258 0.082503 0.079912 0.077476 \n",
+ "6 0.092789 0.089547 0.095041 0.091736 0.088633 0.085717 0.082976 \n",
+ "7 0.089752 0.086790 0.091831 0.088803 0.085956 0.083277 0.080755 \n",
+ "8 0.086888 0.084185 0.088805 0.086034 0.083424 0.080965 0.078647 \n",
+ "9 0.084185 0.081722 0.085949 0.083416 0.081027 0.078772 0.076643 \n",
+ "10 0.088805 0.085949 0.091604 0.088651 0.085870 0.083250 0.080781 \n",
+ "11 0.086034 0.083416 0.088651 0.085937 0.083377 0.080963 0.078684 \n",
+ "12 0.083424 0.081027 0.085870 0.083377 0.081023 0.078800 0.076698 \n",
+ "13 0.080965 0.078772 0.083250 0.080963 0.078800 0.076753 0.074816 \n",
+ "14 0.078647 0.076643 0.080781 0.078684 0.076698 0.074816 0.073033 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Common imports\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "def FrankeFunction(x,y):\n",
+ "\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
+ "\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
+ "\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
+ "\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
+ "\treturn term1 + term2 + term3 + term4\n",
+ "\n",
+ "\n",
+ "def create_X(x, y, n ):\n",
+ "\tif len(x.shape) > 1:\n",
+ "\t\tx = np.ravel(x)\n",
+ "\t\ty = np.ravel(y)\n",
+ "\n",
+ "\tN = len(x)\n",
+ "\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n",
+ "\tX = np.ones((N,l))\n",
+ "\n",
+ "\tfor i in range(1,n+1):\n",
+ "\t\tq = int((i)*(i+1)/2)\n",
+ "\t\tfor k in range(i+1):\n",
+ "\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n",
+ "\n",
+ "\treturn X\n",
+ "\n",
+ "\n",
+ "# Making meshgrid of datapoints and compute Franke's function\n",
+ "n = 4\n",
+ "N = 100\n",
+ "x = np.sort(np.random.uniform(0, 1, N))\n",
+ "y = np.sort(np.random.uniform(0, 1, N))\n",
+ "z = FrankeFunction(x, y)\n",
+ "X = create_X(x, y, n=n) \n",
+ "\n",
+ "Xpd = pd.DataFrame(X)\n",
+ "# subtract the mean values and set up the covariance matrix\n",
+ "Xpd = Xpd - Xpd.mean()\n",
+ "covariance_matrix = Xpd.cov()\n",
+ "print(covariance_matrix)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We note here that the covariance is zero for the first rows and\n",
+ "columns since all matrix elements in the design matrix were set to one\n",
+ "(we are fitting the function in terms of a polynomial of degree $n$).\n",
+ "\n",
+ "This means that the variance for these elements will be zero and will\n",
+ "cause problems when we set up the correlation matrix. We can simply\n",
+ "drop these elements and construct a correlation\n",
+ "matrix without these elements. \n",
+ "\n",
+ "\n",
+ "## Rewriting the Covariance and/or Correlation Matrix\n",
+ "\n",
+ "We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\\boldsymbol{X}$ as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}\\boldsymbol{X}^T= \\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T].\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see this let us simply look at a design matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{2\\times 2}$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{X}=\\begin{bmatrix}\n",
+ "x_{00} & x_{01}\\\\\n",
+ "x_{10} & x_{11}\\\\\n",
+ "\\end{bmatrix}=\\begin{bmatrix}\n",
+ "\\boldsymbol{x}_{0} & \\boldsymbol{x}_{1}\\\\\n",
+ "\\end{bmatrix}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we then compute the expectation value"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T] = \\frac{1}{n}\\boldsymbol{X}\\boldsymbol{X}^T=\\begin{bmatrix}\n",
+ "x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n",
+ "x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\\\\n",
+ "\\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "which is just"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n",
+ " \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n",
+ " \\end{bmatrix},\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n",
+ "\n",
+ "It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$.\n",
+ "\n",
+ "\n",
+ "## Linking with SVD\n",
+ "\n",
+ "We have that the covariance matrix (the correlation matrix involves a simple rescaling) is given as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}\\boldsymbol{X}^T= \\mathbb{E}[\\boldsymbol{X}\\boldsymbol{X}^T].\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let us now assume that we can perform a series of orthogonal transformations where we employ some orthogonal matrices $\\boldsymbol{S}$.\n",
+ "These matrices are defined as $\\boldsymbol{S}\\in {\\mathbb{R}}^{p\\times p}$ and obey the orthogonality requirements $\\boldsymbol{S}\\boldsymbol{S}^T=\\boldsymbol{S}^T\\boldsymbol{S}=\\boldsymbol{I}$. The matrix can be written out in terms of the column vectors $\\boldsymbol{s}_i$ as $\\boldsymbol{S}=[\\boldsymbol{s}_0,\\boldsymbol{s}_1,\\dots,\\boldsymbol{s}_{p-1}]$ and $\\boldsymbol{s}_i \\in {\\mathbb{R}}^{p}$.\n",
+ "\n",
+ "Assume also that there is a transformation $\\boldsymbol{S}\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T=\\boldsymbol{C}[\\boldsymbol{y}]$ such that the new matrix $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal with elements $[\\lambda_0,\\lambda_1,\\lambda_2,\\dots,\\lambda_{p-1}]$. \n",
+ "\n",
+ "That is we have"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{C}[\\boldsymbol{y}] = \\mathbb{E}[\\boldsymbol{S}\\boldsymbol{X}\\boldsymbol{X}^T\\boldsymbol{S}^T]=\\boldsymbol{S}\\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T,\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "since the matrix $\\boldsymbol{S}$ is not a data dependent matrix. Multiplying with $\\boldsymbol{S}^T$ from the left we have"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{S}^T\\boldsymbol{C}[\\boldsymbol{y}] = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T,\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "and since $\\boldsymbol{C}[\\boldsymbol{y}]$ is diagonal we have for a given eigenvalue $i$ of the covariance matrix that"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\boldsymbol{S}^T_i\\lambda_i = \\boldsymbol{C}[\\boldsymbol{x}]\\boldsymbol{S}^T_i.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the derivation of the PCA theorem we will assume that the eigenvalues are ordered in descending order, that is\n",
+ "$\\lambda_0 > \\lambda_1 > \\dots > \\lambda_{p-1}$. \n",
+ "\n",
+ "\n",
+ "The eigenvalues tell us then how much we need to stretch the\n",
+ "corresponding eigenvectors. Dimensions with large eigenvalues have\n",
+ "thus large variations (large variance) and define therefore useful\n",
+ "dimensions. The data points are more spread out in the direction of\n",
+ "these eigenvectors. Smaller eigenvalues mean on the other hand that\n",
+ "the corresponding eigenvectors are shrunk accordingly and the data\n",
+ "points are tightly bunched together and there is not much variation in\n",
+ "these specific directions. Hopefully then we could leave it out\n",
+ "dimensions where the eigenvalues are very small. If $p$ is very large,\n",
+ "we could then aim at reducing $p$ to $l << p$ and handle only $l$\n",
+ "features/predictors.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
"## Where are we going?\n",
"\n",
"Before we proceed, we need to rethink what we have been doing. In our\n",
@@ -2760,6 +4029,15 @@
"information that would not be available from fitting the model only\n",
"once using the original training sample.\n",
"\n",
+ "Two resampling methods are often used in Machine Learning analyses,\n",
+ "1. The **bootstrap method**\n",
+ "\n",
+ "2. and **Cross-Validation**\n",
+ "\n",
+ "In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular\n",
+ "cross-validation and the bootstrap method.\n",
+ "\n",
+ "\n",
"\n",
"\n",
"## Resampling approaches can be computationally expensive\n",
@@ -2809,6 +4087,8 @@
"\n",
" \n",
"\n",
+ "\n",
+ "\n",
"## Statistics\n",
"The *probability distribution function (PDF)* is a function\n",
"$p(x)$ on the domain which, in the discrete case, gives us the\n",
@@ -3138,7 +4418,7 @@
"source": [
"## Covariance example\n",
"\n",
- "Suppose we have defined three vectors $\\hat{x}, \\hat{y}, \\hat{z}$ with\n",
+ "Suppose we have defined three vectors $\\boldsymbol{x}, \\boldsymbol{y}, \\boldsymbol{z}$ with\n",
"$n$ elements each. The covariance matrix is defined as"
]
},
@@ -3147,7 +4427,7 @@
"metadata": {},
"source": [
"$$\n",
- "\\hat{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n",
+ "\\boldsymbol{\\Sigma} = \\begin{bmatrix} \\sigma_{xx} & \\sigma_{xy} & \\sigma_{xz} \\\\\n",
" \\sigma_{yx} & \\sigma_{yy} & \\sigma_{yz} \\\\\n",
" \\sigma_{zx} & \\sigma_{zy} & \\sigma_{zz}\n",
" \\end{bmatrix},\n",
@@ -3180,7 +4460,7 @@
"\n",
"The following simple function uses the **np.vstack** function which\n",
"takes each vector of dimension $1\\times n$ and produces a $3\\times n$\n",
- "matrix $\\hat{W}$"
+ "matrix $\\boldsymbol{W}$"
]
},
{
@@ -3188,7 +4468,7 @@
"metadata": {},
"source": [
"$$\n",
- "\\hat{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n",
+ "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n",
" x_1 & y_1 & z_1 \\\\\n",
" x_2 & y_2 & z_2 \\\\\n",
" \\dots & \\dots & \\dots \\\\\n",
@@ -3203,8 +4483,8 @@
"metadata": {},
"source": [
"which in turn is converted into into the $3\\times 3$ covariance matrix\n",
- "$\\hat{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can\n",
- "also calculate the mean value of each set of samples $\\hat{x}$ etc\n",
+ "$\\boldsymbol{\\Sigma}$ via the Numpy function **np.cov()**. We note that we can\n",
+ "also calculate the mean value of each set of samples $\\boldsymbol{x}$ etc\n",
"using the Numpy function **np.mean(x)**. We can also extract the\n",
"eigenvalues of the covariance matrix through the **np.linalg.eig()**\n",
"function.\n",
@@ -3215,33 +4495,19 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 28,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.46093857320542087\n",
- "0.9584934057186903\n",
- "-0.26752080180948346\n",
- "[[ 0.10225672 0.0019588 -0.02602575]\n",
- " [ 0.0019588 0.98674188 0.17401382]\n",
- " [-0.02602575 0.17401382 1.04422461]]\n",
- "[0.10148464 1.19217163 0.83956695]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Importing various packages\n",
"import numpy as np\n",
"\n",
"n = 100\n",
- "x = np.random.uniform(size=n)\n",
+ "x = np.random.normal(size=n)\n",
"print(np.mean(x))\n",
- "y = np.random.exponential(size=n)\n",
+ "y = 4+3*x+np.random.normal(size=n)\n",
"print(np.mean(y))\n",
- "z = np.random.normal(size=n)\n",
+ "z = x**3+np.random.normal(size=n)\n",
"print(np.mean(z))\n",
"W = np.vstack((x, y, z))\n",
"Sigma = np.cov(W)\n",
@@ -3252,7 +4518,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -3346,8 +4612,8 @@
"metadata": {},
"source": [
"1\n",
- "1\n",
- "1\n",
+ "3\n",
+ "2\n",
" \n",
"<\n",
"<\n",
@@ -4126,9 +5392,9 @@
"\n",
"## Assumptions made\n",
"\n",
- "The assumption we have made here can be summarized as (and this is going to useful when we discuss the bias-variance trade off)\n",
+ "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
"that there exists a function $f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
- "which describes our data"
+ "which describe our data"
]
},
{
@@ -4276,14 +5542,14 @@
"\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
"\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
"variance of the estimate of the $j$-th regression coefficient:\n",
- "$\\hat{\\sigma}^2 (\\hat{\\beta}_j ) = \\hat{\\sigma}^2 \\sqrt{\n",
+ "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n",
"[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n",
"construct a confidence interval for the estimates.\n",
"\n",
"\n",
- "In a similar way, we cna obtain analytical expressions for say the\n",
+ "In a similar way, we can obtain analytical expressions for say the\n",
"expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
- "when we employ Ridge regression, and thereby a confidence interval. \n",
+ "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
"\n",
"It is rather straightforward to show that"
]
@@ -4342,113 +5608,30 @@
"matrix product is non-negative definite. \n",
"This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below. \n",
"\n",
- "\n",
- "## Cross-validation\n",
"\n",
- "Instead of choosing the penalty parameter to balance model fit with\n",
- "model complexity, cross-validation requires it (i.e. the penalty\n",
- "parameter) to yield a model with good prediction\n",
- "performance. Commonly, this performance is evaluated on novel\n",
- "data. Novel data need not be easy to come by and one has to make do\n",
- "with the data at hand.\n",
+ "## Resampling methods\n",
"\n",
- "The setting of **original** and novel data is\n",
- "then mimicked by sample splitting: the data set is divided into two\n",
- "(groups of samples). One of these two data sets, called the \n",
- "*training set*, plays the role of **original** data on which the model is\n",
- "built. The second of these data sets, called the *test set*, plays the\n",
- "role of the **novel** data and is used to evaluate the prediction\n",
- "performance (often operationalized as the log-likelihood or the\n",
- "prediction error or its square or the R2 score) of the model built on the training data set. This\n",
- "procedure (model building and prediction evaluation on training and\n",
- "test set, respectively) is done for a collection of possible penalty\n",
- "parameter choices. The penalty parameter that yields the model with\n",
- "the best prediction performance is to be preferred. The thus obtained\n",
- "performance evaluation depends on the actual split of the data set. To\n",
- "remove this dependence the data set is split many times into a\n",
- "training and test set. For each split the model parameters are\n",
- "estimated for all choices of $\\lambda$ using the training data and\n",
- "estimated parameters are evaluated on the corresponding test set. The\n",
- "penalty parameter that on average over the test sets performs best (in\n",
- "some sense) is then selected.\n",
+ "With all these analytical equations for both the OLS and Ridge\n",
+ "regression, we will now outline how to assess a given model. This will\n",
+ "lead us to a discussion of the so-called bias-variance tradeoff (see\n",
+ "below) and so-called resampling methods.\n",
+ "\n",
+ "One of the quantities we have discussed as a way to measure errors is\n",
+ "the mean-squared error (MSE), mainly used for fitting of continuous\n",
+ "functions. Another choice is the absolute error.\n",
+ "\n",
+ "In the discussions below we will focus on the MSE and in particular since we will split the data into test and training data,\n",
+ "we discuss the\n",
+ "1. prediction error or simply the **test error** $\\mathrm{Err_{Test}}$, where we have a fixed training set and the test error is the MSE arising from the data reserved for testing. We discuss also the \n",
+ "\n",
+ "2. training error $\\mathrm{Err_{Train}}$, which is the average loss over the training data.\n",
+ "\n",
+ "As our model becomes more and more complex, more of the training data tends to used. The training may thence adapt to more complicated structures in the data. This may lead to a decrease in the bias (see below for code example) and a slight increase of the variance for the test error.\n",
+ "For a certain level of complexity the test error will reach minimum, before starting to increase again. The\n",
+ "training error reaches a saturation.\n",
"\n",
"\n",
- "## Computationally expensive\n",
"\n",
- "The validation set approach is conceptually simple and is easy to implement. But it has two potential drawbacks:\n",
- "\n",
- "* The validation estimate of the test error rate can be highly variable, depending on precisely which observations are included in the training set and which observations are included in the validation set.\n",
- "\n",
- "* In the validation approach, only a subset of the observations, those that are included in the training set rather than in the validation set are used to fit the model. Since statistical methods tend to perform worse when trained on fewer observations, this suggests that the validation set error rate may tend to overestimate the test error rate for the model fit on the entire data set.\n",
- "\n",
- "\n",
- "## Various steps in cross-validation\n",
- "\n",
- "When the repetitive splitting of the data set is done randomly,\n",
- "samples may accidently end up in a fast majority of the splits in\n",
- "either training or test set. Such samples may have an unbalanced\n",
- "influence on either model building or prediction evaluation. To avoid\n",
- "this $k$-fold cross-validation structures the data splitting. The\n",
- "samples are divided into $k$ more or less equally sized exhaustive and\n",
- "mutually exclusive subsets. In turn (at each split) one of these\n",
- "subsets plays the role of the test set while the union of the\n",
- "remaining subsets constitutes the training set. Such a splitting\n",
- "warrants a balanced representation of each sample in both training and\n",
- "test set over the splits. Still the division into the $k$ subsets\n",
- "involves a degree of randomness. This may be fully excluded when\n",
- "choosing $k=n$. This particular case is referred to as leave-one-out\n",
- "cross-validation (LOOCV). \n",
- "\n",
- "\n",
- "## How to set up the cross-validation for Ridge and/or Lasso\n",
- "\n",
- "* Define a range of interest for the penalty parameter.\n",
- "\n",
- "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
- "\n",
- "* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$$\n",
- "\\begin{align*}\n",
- "\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n",
- "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n",
- "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n",
- "\\end{align*}\n",
- "$$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
- "\n",
- "* Repeat the first three steps such that each sample plays the role of the test set once.\n",
- "\n",
- "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter by computing the *cross-validated log-likelihood*. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "$$\n",
- "\\begin{align*}\n",
- "\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n",
- "\\end{align*}\n",
- "$$"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The value of the penalty parameter that maximizes the cross-validated log-likelihood is the value of choice. Or we can use the MSE or the R2 score functions.\n",
"\n",
"## Resampling methods: Jackknife and Bootstrap\n",
"\n",
@@ -4498,7 +5681,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
@@ -4639,7 +5822,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
@@ -4690,6 +5873,94 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "\n",
+ "## Various steps in cross-validation\n",
+ "\n",
+ "When the repetitive splitting of the data set is done randomly,\n",
+ "samples may accidently end up in a fast majority of the splits in\n",
+ "either training or test set. Such samples may have an unbalanced\n",
+ "influence on either model building or prediction evaluation. To avoid\n",
+ "this $k$-fold cross-validation structures the data splitting. The\n",
+ "samples are divided into $k$ more or less equally sized exhaustive and\n",
+ "mutually exclusive subsets. In turn (at each split) one of these\n",
+ "subsets plays the role of the test set while the union of the\n",
+ "remaining subsets constitutes the training set. Such a splitting\n",
+ "warrants a balanced representation of each sample in both training and\n",
+ "test set over the splits. Still the division into the $k$ subsets\n",
+ "involves a degree of randomness. This may be fully excluded when\n",
+ "choosing $k=n$. This particular case is referred to as leave-one-out\n",
+ "cross-validation (LOOCV). \n",
+ "\n",
+ "\n",
+ "## How to set up the cross-validation for Ridge and/or Lasso\n",
+ "\n",
+ "* Define a range of interest for the penalty parameter.\n",
+ "\n",
+ "* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
+ "\n",
+ "* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\begin{align*}\n",
+ "\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n",
+ "\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n",
+ "\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n",
+ "\\end{align*}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
+ "\n",
+ "* Repeat the first three steps such that each sample plays the role of the test set once.\n",
+ "\n",
+ "* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\n",
+ "\\begin{align*}\n",
+ "\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n",
+ "\\end{align*}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cross-validation in brief\n",
+ "\n",
+ "For the various values of $k$\n",
+ "\n",
+ "1. shuffle the dataset randomly.\n",
+ "\n",
+ "2. Split the dataset into $k$ groups.\n",
+ "\n",
+ "3. For each unique group:\n",
+ "\n",
+ "a. Decide which group to use as set for test data\n",
+ "\n",
+ "b. Take the remaining groups as a training data set\n",
+ "\n",
+ "c. Fit a model on the training set and evaluate it on the test set\n",
+ "\n",
+ "d. Retain the evaluation score and discard the model\n",
+ "\n",
+ "\n",
+ "5. Summarize the model using the sample of model evaluation scores\n",
+ "\n",
"## Code Example for Cross-validation and $k$-fold Cross-validation\n",
"\n",
"The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial."
@@ -4697,9 +5968,20 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 28,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -4926,7 +6208,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
@@ -4995,9 +6277,96 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polynomial degree: 0\n",
+ "Error: 0.3214960170351912\n",
+ "Bias^2: 0.3123314713548606\n",
+ "Var: 0.009164545680330616\n",
+ "0.3214960170351912 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n",
+ "Polynomial degree: 1\n",
+ "Error: 0.08426840630693411\n",
+ "Bias^2: 0.07968918676726029\n",
+ "Var: 0.004579219539673836\n",
+ "0.08426840630693411 >= 0.07968918676726029 + 0.004579219539673836 = 0.08426840630693413\n",
+ "Polynomial degree: 2\n",
+ "Error: 0.10398646080125035\n",
+ "Bias^2: 0.10077114273548984\n",
+ "Var: 0.003215318065760509\n",
+ "0.10398646080125035 >= 0.10077114273548984 + 0.003215318065760509 = 0.10398646080125035\n",
+ "Polynomial degree: 3\n",
+ "Error: 0.06547790180152357\n",
+ "Bias^2: 0.06208238634231953\n",
+ "Var: 0.0033955154592040944\n",
+ "0.06547790180152357 >= 0.06208238634231953 + 0.0033955154592040944 = 0.06547790180152363\n",
+ "Polynomial degree: 4\n",
+ "Error: 0.06844519414009438\n",
+ "Bias^2: 0.06453579006728315\n",
+ "Var: 0.003909404072811231\n",
+ "0.06844519414009438 >= 0.06453579006728315 + 0.003909404072811231 = 0.06844519414009438\n",
+ "Polynomial degree: 5\n",
+ "Error: 0.05227921801205692\n",
+ "Bias^2: 0.04818727730430296\n",
+ "Var: 0.0040919407077539514\n",
+ "0.05227921801205692 >= 0.04818727730430296 + 0.0040919407077539514 = 0.05227921801205691\n",
+ "Polynomial degree: 6\n",
+ "Error: 0.03781367141738885\n",
+ "Bias^2: 0.033657685071527485\n",
+ "Var: 0.004155986345861374\n",
+ "0.03781367141738885 >= 0.033657685071527485 + 0.004155986345861374 = 0.03781367141738886\n",
+ "Polynomial degree: 7\n",
+ "Error: 0.027609773491022314\n",
+ "Bias^2: 0.02299949826036602\n",
+ "Var: 0.004610275230656294\n",
+ "0.027609773491022314 >= 0.02299949826036602 + 0.004610275230656294 = 0.027609773491022314\n",
+ "Polynomial degree: 8\n",
+ "Error: 0.017355848195591845\n",
+ "Bias^2: 0.01033172130665515\n",
+ "Var: 0.007024126888936694\n",
+ "0.017355848195591845 >= 0.01033172130665515 + 0.007024126888936694 = 0.01735584819559184\n",
+ "Polynomial degree: 9\n",
+ "Error: 0.026605727637176654\n",
+ "Bias^2: 0.010018312644139347\n",
+ "Var: 0.016587414993037307\n",
+ "0.026605727637176654 >= 0.010018312644139347 + 0.016587414993037307 = 0.026605727637176654\n",
+ "Polynomial degree: 10\n",
+ "Error: 0.02159270458799264\n",
+ "Bias^2: 0.010516485576652856\n",
+ "Var: 0.011076219011339788\n",
+ "0.02159270458799264 >= 0.010516485576652856 + 0.011076219011339788 = 0.021592704587992645\n",
+ "Polynomial degree: 11\n",
+ "Error: 0.07160048164248561\n",
+ "Bias^2: 0.014436800088969727\n",
+ "Var: 0.05716368155351588\n",
+ "0.07160048164248561 >= 0.014436800088969727 + 0.05716368155351588 = 0.07160048164248561\n",
+ "Polynomial degree: 12\n",
+ "Error: 0.11547777218940905\n",
+ "Bias^2: 0.016285782696075054\n",
+ "Var: 0.099191989493334\n",
+ "0.11547777218940905 >= 0.016285782696075054 + 0.099191989493334 = 0.11547777218940906\n",
+ "Polynomial degree: 13\n",
+ "Error: 0.22842468702288576\n",
+ "Bias^2: 0.01975416527179247\n",
+ "Var: 0.20867052175109335\n",
+ "0.22842468702288576 >= 0.01975416527179247 + 0.20867052175109335 = 0.22842468702288582\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
@@ -5087,12 +6456,14 @@
"flexible statistical methods have higher variance.\n",
"\n",
"\n",
- "## Another Example rom Scikit-Learn's Repository"
+ "You may also find this recent [article](https://www.pnas.org/content/116/32/15849) of interest.\n",
+ "\n",
+ "## Another Example from Scikit-Learn's Repository"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
@@ -5169,6 +6540,373 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## More examples on bootstrap and cross-validation and errors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Degree of polynomial: 1\n",
+ "Mean squared error on training data: 439772.32300399\n",
+ "Mean squared error on test data: 480505.64097586\n",
+ "Degree of polynomial: 2\n",
+ "Mean squared error on training data: 113923.41075701\n",
+ "Mean squared error on test data: 133356.07161812\n",
+ "Degree of polynomial: 3\n",
+ "Mean squared error on training data: 8971.72576731\n",
+ "Mean squared error on test data: 10910.17357950\n",
+ "Degree of polynomial: 4\n",
+ "Mean squared error on training data: 298.44612441\n",
+ "Mean squared error on test data: 503.87244240\n",
+ "Degree of polynomial: 5\n",
+ "Mean squared error on training data: 3.72500207\n",
+ "Mean squared error on test data: 6.61815443\n",
+ "Degree of polynomial: 6\n",
+ "Mean squared error on training data: 3.63584439\n",
+ "Mean squared error on test data: 9.73676688\n",
+ "Degree of polynomial: 7\n",
+ "Mean squared error on training data: 0.47883212\n",
+ "Mean squared error on test data: 1.44277955\n",
+ "Degree of polynomial: 8\n",
+ "Mean squared error on training data: 0.04897868\n",
+ "Mean squared error on test data: 0.15164016\n",
+ "Degree of polynomial: 9\n",
+ "Mean squared error on training data: 0.02578971\n",
+ "Mean squared error on test data: 0.07328980\n",
+ "Degree of polynomial: 10\n",
+ "Mean squared error on training data: 0.02424626\n",
+ "Mean squared error on test data: 0.46083549\n",
+ "Degree of polynomial: 11\n",
+ "Mean squared error on training data: 0.01617683\n",
+ "Mean squared error on test data: 0.74076161\n",
+ "Degree of polynomial: 12\n",
+ "Mean squared error on training data: 0.00802245\n",
+ "Mean squared error on test data: 0.18300856\n",
+ "Degree of polynomial: 13\n",
+ "Mean squared error on training data: 0.00776962\n",
+ "Mean squared error on test data: 4.22709091\n",
+ "Degree of polynomial: 14\n",
+ "Mean squared error on training data: 0.00467325\n",
+ "Mean squared error on test data: 0.45715881\n",
+ "Degree of polynomial: 15\n",
+ "Mean squared error on training data: 0.00417156\n",
+ "Mean squared error on test data: 0.64899850\n",
+ "Degree of polynomial: 16\n",
+ "Mean squared error on training data: 0.00316867\n",
+ "Mean squared error on test data: 7.34167999\n",
+ "Degree of polynomial: 17\n",
+ "Mean squared error on training data: 0.00243942\n",
+ "Mean squared error on test data: 100.99309443\n",
+ "Degree of polynomial: 18\n",
+ "Mean squared error on training data: 0.00225951\n",
+ "Mean squared error on test data: 57.70941839\n",
+ "Degree of polynomial: 19\n",
+ "Mean squared error on training data: 0.00152885\n",
+ "Mean squared error on test data: 74.59442615\n",
+ "Degree of polynomial: 20\n",
+ "Mean squared error on training data: 0.00137813\n",
+ "Mean squared error on test data: 1718.77573858\n",
+ "Degree of polynomial: 21\n",
+ "Mean squared error on training data: 0.00120458\n",
+ "Mean squared error on test data: 9535.67732812\n",
+ "Degree of polynomial: 22\n",
+ "Mean squared error on training data: 0.00092876\n",
+ "Mean squared error on test data: 1059.53684605\n",
+ "Degree of polynomial: 23\n",
+ "Mean squared error on training data: 0.00085809\n",
+ "Mean squared error on test data: 1830.08357907\n",
+ "Degree of polynomial: 24\n",
+ "Mean squared error on training data: 0.00085551\n",
+ "Mean squared error on test data: 7686.42984972\n",
+ "Degree of polynomial: 25\n",
+ "Mean squared error on training data: 0.00083016\n",
+ "Mean squared error on test data: 6420.29569681\n",
+ "Degree of polynomial: 26\n",
+ "Mean squared error on training data: 0.00075788\n",
+ "Mean squared error on test data: 3781.44189824\n",
+ "Degree of polynomial: 27\n",
+ "Mean squared error on training data: 0.00070078\n",
+ "Mean squared error on test data: 1719.20554311\n",
+ "Degree of polynomial: 28\n",
+ "Mean squared error on training data: 0.00064450\n",
+ "Mean squared error on test data: 2636.68580306\n",
+ "Degree of polynomial: 29\n",
+ "Mean squared error on training data: 0.00061610\n",
+ "Mean squared error on test data: 61702.78847697\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:74: RuntimeWarning: divide by zero encountered in log10\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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WRyIiIiIKGCy/FliQn4QmRKIpKBXqofUwDd3qSEREREQBgeXXAs5QO/KzYrGqJR1m5wloRwutjkREREQUEFh+LbIgPwm7uxLgsjmhHlxndRwiIiKigMDya5GsFCcSY8LwuTYGem0R9BM1VkciIiIi8nssvxYRBAEL8pOwujkFpqhA5bJnRERERMOO5ddCM8bGw1BCUGHPgVq6Daar0+pIRERERH6N5ddCQXYZs8Yl4L3jowDNDbV4i9WRiIiIiPway6/F5ucn4agahbbgFLgPrYNpGlZHIiIiIvJbLL8WS4wJwZhRkfjkZCbM9uPQq/dbHYmIiIjIb7H8eoEF+UnY2pYIzRYONx98IyIiIho2LL9eYFJmDCLCglAo5EKv3g+jrd7qSERERESXxOhuh2vPKpimaXWUs7D8egFJFDFvUiLeq0+BKUhwc9MLIiIi8nHqvo/g3vFPQFetjnIWll8vcfmkJHQJwTgWnAO1eAtMtcfqSERERESDYpom1IodkJJyIcg2q+OcheXXS0SE2DAlZwTea0oF1G6opdusjkREREQ0KEbTEZgnG6GkT7c6yjlYfr3IgvwklHRHoTM4EerBtV43I0NEREQ0EGp5ASBKkFPzrY5yDpZfL5KRFIGUEWHY1J0No6UWem2R1ZGIiIiILoppmtAqCiAljYVgD7E6zjlYfr2IIAhYkJ+Edc2J0JUQqFz2jIiIiHyM0VgBs6MZSvo0q6OcF8uvl5mRGw/FbkeRMg7a0UIYJ5usjkREREQ0YL0jDzLkUXlWRzkvll8vY7dJmD0+ASvqUmACUA+ttzoSERER0YCYpgGtYgek5HFeOfIAsPx6pQX5SWjWg9EYmgP34U0wNbfVkYiIiIj6ZTSUw+w84bUjDwDLr1eKiwrG2NFR+PDEaMDVCa38c6sjEREREfVLrSgAJO8deQBYfr3Wgvwk7DkZDVdwPNwHuOwZERERebdTIw9y8ngItiCr41wQy6+Xmpgeg+jwIGzXx8BoPgqjoczqSEREREQXpDeUwexqheyFG1ucieXXS4migHl5iVhZFw9TCYKby54RERGRF9PKCwBJgTxyotVR+sTy68XmTEyEIdlQHjQOWsVOGJ0tVkciIiIiOodpfDHykDLBq0ceAJZfrxYebMPUnDj8q24kYBpQizZaHYmIiIjoHHp9CczuNshevMrDKSy/Xm7B5CTUukLQGpEFtWgjTF2zOhIRERHRWbSKAkCyQR45yeoo/WL59XJpCeEYFR+GNe0ZMLvboFXutDoSERER0WmmYUCr3Al51EQIit3qOP1i+fVygiBgQX4StjVHQQuO5YNvRERE5FX0usMwu9shp3n/yAPA8usTpo+JQ7BDQaEwDkZDGfTGI1ZHIiIiIgLwxciDbIM8coLVUQaE5dcH2BQJsyckYEVNPEzZzru/RERE5BVMQ4dWuQvyyEkQZO8feQBYfn3G/LwkdJsKjoWNh1a+HUZ3u9WRiIiIKMDptYdh9pz0+o0tzsTy6yNGRAZjXFo03mkYBega1OJPrY5EREREAU6rKAAUB+SU8VZHGTCWXx+yID8JZR0h6HRmQD24HqahWx2JiIiIApRpaFArd0IeNQmCbLM6zoCx/PqQ8WnRiIlwYFNXFszOE9CO7rE6EhEREQUo/VgR4Or0mVUeTmH59SGiKGB+fhLW1EfBCIqCygffiIiIyCK9Iw9BkJPHWR3lorD8+pg5ExIhyzIO2SdAry2CfqLG6khEREQUYExdg3pkN+TUPJ8aeQBYfn1OaJCCaWNG4J/VCYCkQD24zupIREREFGD0YwcBVycUHxt5AFh+fdKC/GS0qAqOO8dDLd0K09VpdSQiIiIKIGpFAWALgpQ81uooF43l1weNTgjH6IRwrGxOAzQ31JItVkciIiKiAGHqKrQjuyGn5kOQFKvjXDSWXx+1ID8Je08Eo8c5Gu6D62GahtWRiIiIKADoNQcBdzeUNN/Z2OJMLL8+atqYEQgNUrBdy4XZ3gC9er/VkYiIiCgAqBUFgD0EUlKu1VEGheXXRymyhDkTE/B+dSRMRwTcfPCNiIiIhpmpuaEd2Q0lNR+CJFsdZ1BYfn3Y/ElJMEwR5SGToFfvg9FWb3UkIiIi8mNazQFA7fG5jS3OZGn5/fWvf42f/vSnVkbwaTHOIEzMiMG/jiUCogT3wfVWRyIiIiI/plUUQLCHQkoaY3WUQbOs/H722Wd45513rDq931iQn4TaLgWtUeOhFm+GqbmtjkRERER+yNTc0I7ugTx6MgTRN0ceAIvKb2trK1544QXcfffdVpzer+SOjsKIyCBsaE8F1G5oVXutjkRERER+SKve5/MjDwBgSW1/9NFH8dBDD6Guru6i3xsdHdrva2JjwwYTy2ctnpOO/32/EzclhkOq2YXY6QusjjQggXadfBWvk2/gdfINQ3md3M21kMOiINocQ3ZM6sXfT+fXsKUQYnA44idOhSBKVscZ9HXyePl9++23kZCQgJkzZ2LFihUX/f7m5g4YhnnBz8fGhqGx8eSlRPQ5k9IiocgyyuRMZJTuwvFjxyHYgqyO1adAvE6+iNfJN/A6+YahvE56ay26/vko5JETEXT1A0NyTOrF30/nZ2oudJbsgJJ5GZqau6yO0+d1EkWhz5ulHh97WLVqFbZu3YobbrgBL774ItavX4+nn37a0zH8SohDwYyxcfioIQ74YtcVIiLyT6ZpwrX5r4ChQTuyC/qJGqsjUQDQqvYBmhtyum9ubHEmj9/5ffXVV0//eMWKFSgoKMDPf/5zT8fwOzNy4/Hc3lqoUZGQyj+HkjXL6khERDQMtNJt0OsOwzb1Zrj3rIR7z0oELeAzNDS8tIoCCEHhkOKzrY5yybjOr5/ITIlAiENBmZwJveYgzJ4OqyMREdEQM3s64Nr+JsQR6bBNWghb7gJo5Z8H5DrvpnnhEUgaWqbaA+3oXsijp0AQfb86WrpOxZIlS7BkyRIrI/gNSRQxKSMGa8sTMCZIh1q5E7Yx86yORUREQ8hV8A+Yrk4EzbkdgiBCGf81uA98AveeD+GYe4fV8TxGLd2Gnk1/hhAaAykqCWLkF/9FJUGMiIcgKVZH9Cta1V5Ad/v8Kg+n+O4ibXSOSZmx+MOBOqjRIyCVf87yS0TkR7T6EqiHP4Uy4RpI0SkAADE4AsqYeVAProct/waIYTEWpxx+emsteja/BjEyGWJ4LIyWY9CO7gFMo/cFgggxIh5iZOIXhTi5938jRvj02rRW0soLIAQ7IcVnWR1lSPBXgR8ZNzoKiiyh3JaNnNotMDpbIIZEWh2LiIgukWlocG3+C4TQaNgn33TW52wTroV6aD3ce1fDMfs7FiX0DFNzo2ftSxBkO4KuefD033GmrsJoq4dx4hiMlmMwTtRAb66GVrkLwBfjEaIE0Zlw1l1iKTIJQtgIv/hW/nAx3d3QqvdByZnrNz9PLL9+xG6TMDY1CmuPdyJHNqFV7IBt/NVWxyIiokvk3vcxjJZjCLr6RxAU+1mfE0OjoGTNhlq8Cbb8xRCDnRalHH6uz96AcaIGQdf8+KybO4KkQIpKgRSVctbrTc0Fo7XudCnWW45BP14OrfzzL18kKRCdib13iqNSoE2/EoDNQ1+R99Oq9gC6CjndP0YeAJZfv5OXFYNXy5qgpSZDLN/O8ktE5OOM9ka4d70HOTUfcmreeV9jm7QIavFmuPd9BMeMWzyc0DPUigKoRRugTLgW8sgJA3qPINshxaRCikk96+Om2gOjpba3EJ+o6f3fumJoZZ+hatcKKNlzYZu0CGJo1DB8Jb7l9MhDXIbVUYYMy6+fmZgRA0EAKh3ZyDy+DkZ7I8TwWKtjERHRIJimiZ6tfwMEAfbLbrvg68TwEZAzZkA9tL63tDn8a4cyo/04eja9CnFEOuzTll7y8QTFAWlEGqQRaTjz0TijvRHi4Y9xcu8GqIc3QckJ7BLcO/KwH0rufAiCf4w8AFzqzO+EB9uQmezE+qZEAIB65rd2iIjIp2iVO6FX74N9yk0QQ6P7fK1t0nWApkLdv8ZD6TzD1DV0r/sjIAgIuuLuYX1oTQyPReyiexByy7O9oyRFG9H55k/Qs+WvMDqah+283ko7WggYGhQ/2NjiTCy/fig/MwaHmgTo0WlnzzUREZHPMN3dcG17HWJ0CpRxV/X7eikyEfLoyXAfWAvT1emBhJ7hKngbRmMlHHN/ADHMM9/JFMNi4bj89i9L8OFN6HzzPwKuBKvlBRBCoiCOSLM6ypBi+fVDk7J6/3CocuTAOFENveWYxYmIiOhiuXaugNnVBsec2yGI0oDeY8tbDKjdcB9cN8zpPEM7ugfq/o+h5F4BZfQUj5//dAn+5q+hZJ8qwT9Bz+a/+H0JNl2d0Gv2Q06b6lcjDwDLr18a4QxCcmwoNp5IAASBd3+JiHyM3nQE6sG1UHLnQxqRPuD3STGjII2cCHX/GpiqaxgTDj+j4wS6N74CMXok7DO+aWkWMSwGjjm3I+SW56BkXw61+FO/L8G9Iw86FD9a5eEUll8/lZcZg721Ksy4bKjln3MbSCIiH2EaBno2/wWCIwz2qRf/cJc9bzFMVwfUog3DkM4zTENHz/o/AbqGoCvuhSB7x9JjYmg0HHO+11uCc+aeUYJfg3Gyyep4Q0otL4AQGg0x1r9GHgCWX7+VnxUL0wSqg8fAbGuA0XTU6khERDQA6qH1MBorYZ/5LQj2kIt+vxSXASkpF+69q2Fq7mFIOPzcu96FXl8Cx5zvQXTGWx3nHGJoNByzv3tGCd6Czrf+Az2f+kcJ7h15OAg5bRoEQbA6zpBj+fVTI+NCERVux+YT8YAoQS3fbnUkIiLqh9HZAteOf0JKGgv5Ep6wt+UthtndBrV48xCm8wzt2CG4C1dCzpoDJfMyq+P06csS/OveElxyqgS/CuNko9XxBk2r3AWY/jnyALD8+i1BEJCXGYs9Vd0QksZBKy+AeWrfcyIi8kquz94ADA2O2d+9pDtuUkIOxLgMuPeugmloQ5hweBldbehZ/zJEZwIcs75tdZwBO/tO8DyoJVvR+eZPfbYEqxUFEMJiIX5lcxB/wfLrx/IzY+DWDNSF5sLsPAG9oczqSEREdAFa9T5oFQWw5S2GGBF3SccSBAH2vOthdjRDK/1siBIOL9M00LPxFZjuLjiuvOecbZx9gRgaBcfs7/SW4NwvS7Cr4G2f+UeI2dMB/dghKGlT/XLkAWD59WuZKU6EOGRsa4kDJBu0Mq76QETkjUzNhZ4tf4MYEQ/bxIVDckwpZTzEmFFwFa6EaXj/d/7ce1dBrzkA+2W3QYpKsTrOJRFDo+CY9R2E3PobyJmXwb3nQ3R98KxPzAOrR3YBpnFJYzfejuXXj8mSiAnpMdhZ0Q5p5ARolTtgGrrVsYiI6Cvcuz+AebIR9jnfgyAp/b9hAARBgC3vepjtDdAqCobkmMNFqy+Fe8cKyGnToOTMtTrOkBFDIhE07w44rrgHxokadK74FdQju62O1SetvABCeBzE6JFWRxk2LL9+Lj8rBp09Go5HjIfZ3Q69tsjqSEREdAa95Rjc+1ZDzpwFOXHMkB5bTs2DGJkEd+FKr33uw+zpQM+6P0IIjYbj8tv98lvtSvp0hCx5HGJYDHrWvIieba/D1FWrY53D+KIn+PPIA8Dy6/fGjY6GIovY3hoLKA5ueEFE5EVM04Br818AxTEsGzkIgghb3nUwWmp6Ny3wMqZpomfTn2F2tyHoynsh2IKtjjRsxIg4BN+wDMq4q6Ae+ARd7z0Fo/241bHO0rvKgwHZT1d5OIXl18/ZbRJyR0ViV1kr5NR8qJU7vfJfm0REgUgr2Qq9vgT26d+AGBQ+LOeQ06ZBCI/rHa3wsg2P1INroR0thH36NyDFjrY6zrATJAWOy26D4+oHYLQfR+e/HoVa7j0jKVpFAcSIeIg+PnPdH5bfAJCXFYvm9h60RE8E3N3Qqw9YHYmIKOAZPSfh2v4WpLhMKNlzhu08gijBNmkhjKYj0Gv2D9t5LpbeeKT36x85Ccq4q62O41FK6mSELF0OMTIJPeteQs/m1yzfkMToaoNedxhyun9ubHEmlt8AMCkjBgKAHa1REOyh3PCCiMgLuLb/A6a7G/Y5t0MQhvevY8ve9AgAACAASURBVCVzFoSQKK+5+2u6u9G97iUIQeEImvdDvy9b5yOGxSD4+p/BNnEh1KKN6Hp3OfTWWkuyGN3tcO96FzBNyGn+PfIAsPwGhPAQGzKSI1BY1gI5bSq0o4UwVZfVsYiIApZWVwytZDNsE6+BFJU07OcTJBm2SQuhN5RCryse9vP1xTRN9Gz+C8yTTXBccTcER6ileawkiDLs07+BoGt+DLOrDV0rHoNastUj5zZNE1ptEbrX/RGdr/8YatEGyGnTIEYO/69Hq7H8Boi8zFhUHe9AZ9wkQHNDq9pjdSQiooBk6hpcm/8CISwGtvzrPXZeJftyCEERcBd+4LFzno9a/Cm08u2wTb4RcnyWpVm8hTxyAoKXLocUOxo9G19B98ZXhu0mldnTAfe+j9D1j5+he+WvoVXvh5I7H8E3P9X70GEA3IWXrQ5AnpGXFYN/bCjD7lYnZgY7oZVth+LHC1gTEXkr977VMFprEXTNQxBkz+1iJsg22CZcA9fnb0E/Xg5pRLrHzn2KfuIYXFtfh5Q0FrZJ13n8/N5MDIlE0KKfwL37fbh3v4+u4xVwXHnvkGz4YZom9PoSqEUboVXuAHQNYlwGHHk/hJw21aO/Dr0By2+AiIsMRlJsCArLmjEnfTrUg+tgujoh2EOsjkZEFDCM9uNw734f8ugpkEdO9Pj5ldz5cO1ZCdfuDxB8zYMePbepudCz7g8QbA445t8JQeQ3n79KECXYp9wEKSEbPetfRtc7y2G/7DYoOXMHdUfWdHVCLd0GtWgDjJZaQAmCkj0XSu48n99F71LwV14AycuMRXF1K9TkyYChQfPyXWaIiPyJaZro2fJXQJRgv+w2SzIIigO28V+DXrUHetNRj57btfV1GC11cMy/C2Kw06Pn9jVyUm7vGER8FlybX0PP+j/BdHcP6L2maUJvKEP3xlfQ8X8PwrXtdUC2w3759xH67d/BMfs7AV18Ad75DSj5WTFYue0I9rWEYmJYLNTyz4d1eR0iIvpSZ9E26DUHYL/sNoghkZblsI29Au69q+HesxJBV97nkXOqZZ9BLf4UtknXQU4e65Fz+joxOAJBCx+Ge88quHeuQGdjJYKuuBdSbOp5X2+6u764y7sRxokaQHFAyZoFZcx8SDGjPBvey7H8BpBRcWGIDLNjd2kTpqRPh3vvKhjd7cO2sDoREfUy3V1oXvO/EGNSoeReYWkWwR7SW4D3fAi9tRaSM3FYz6efqEHP5r9AisuEbcpNw3oufyMIIux510FKyELPuj+i670nYJ9xC5SxV0IQBJimCaOxEmrRBqjlnwOaG2LMKNjn3A4lfToEW5DVX4JXYvkNIIIgIC8zBlv21cGYPRXYsxJaxQ7Yxlr7BzERkb9zFfwLelc7gq/+kVfMuirjr4b7wBq4Cz9E0Pw7h+UcRkcz3Lvfh1q8BYItqHdZM1EalnP5Ozk+C8FLl6Nn4//Ate116LVFkJLHQS3aBKP5KCDboKTPgJI7PyB2yrtULL8BJi8rFut3H8Ph9mBkRSZBK/+c5ZeIaBgZ7cehHlqP8CnXwvSSYiIGhUMZMx/qgU9gTL4BYviIITu20dUKd+FKqEUbAQBK7jzY8hZzzvcSiY4wBH3tQaj7P4br87ehHdkNMSoF9lnfgZI5E4It2OqIPoPlN8BkpzgRZJdRWNKE3PTpcO9cAaOjGWJotNXRiIj8klq2HYAJ58wb0OJF+wvZJlwD9eA6uPesguPy2y/5eEZ3O9x7V0E9uB4wNCjZc2DLv55/vwwhQRBgm3AN5JGTYKrdEGNSA2Jd3qFm/fdeyKNkScTEjGjsKWuCOHoqAECrKLA4FRGR/9IqCiDGZUAOj7E6ylnEkEgoOZdDLdkCo+PEoI9jujrh2vEvdL75E6j7P4acNhUh33wWjsu/z+I7TERnPKTY0Sy+g8TyG4DyM2PR0a2i4qQDYuxoqGWfWx2JiMgv6S21ME7UeO2mQraJ1wKmCfe+1Rf9XtPdDdfu99Hxxr/DXfgB5JQJvbuEzb9zSMcoiIYaxx4C0NjRUZAlEYWlTRidPg2u7W/BaKuHGBFvdTQiIr/S+501AfLoKVZHOS8xLBZy5kyoRZtgm3QdxOCIft9jai6oB9fDvedDmK4OyKPyYJtyE6TokR5ITHTpeOc3AAXZZeSmRmJ3SSOk0dMAAGo5Rx+IiIaSaZrQygsgJWRZuq5vf+yTrgN0Fer+j/t8namrcB/4BJ1v/ASuz9+CGJuK4BsfRdDXfsTiSz6Fd34DVH5WLPaVN6O2247o+Cxo5dthy1vM+SEioiFitByD0VoL+9jvWB2lT6IzHnL6NLgPrYdt4kIIjtCzPm8aGtTiLXDvfh9m5wlICdmwXXkv5IRsixITXRre+Q1QEzNiIAAoLGmEnDEDRkstjJYaq2MREfkNrfxzQPDekYcz2fKuA9QeuA+uPf0x0zCglmxF51s/g2vzaxBCIhG08BEEXfdTFl/yaSy/ASoixIb0pAgUljb1/sEsiND44BsR0ZAwTRNqxQ5ICTkDmqO1mhSVAnlUHtwHPundJrf8c3T98xfo2fgKBFswgq55EME3LIOcPJbfISSfx/IbwPKyYnC04SRa3DZISblQyz+HaZpWxyIi8nlGcxXMtnrIXrrKw/nY8hYDrk50vvET9Kz7IyAIcFx1P4KX/AryyEksveQ3WH4DWH5mLACgsLQRSvp0mCcbYTRWWpyKiMj3aRUFgCBCHj3Z6igDJo1Ig5w2FYIjFI4FdyF46ZNQRk+BILAqkH/hr+gAFhcVjMSYkN7Rh9R8QJS/2ImIiIgGyzRNqOUFkJJyITrCrI5zUYKuvA8h33wWSsZMCCIrAvkn/soOcHmZMSiuakWnYYM8cgK0igKYhmF1LCIin2U0HYF5shFK2jSroxDRebD8Brj8rFgYpol95U2Q06fD7GqFXl9sdSwiIp+lln8OCFLvd9SIyOuw/Aa4UfFhiAyzo7CkCfLISYBs612eh4iILpppmtAqdkBKHnvOerlE5B1YfgOcKAiYlBGD/ZXNUCFDHpUPrWInTEOzOhoRkc8xjpfD7GiG4kOrPBAFGpZfQl5WDNyqgUNHW3pXfXB1QK85ZHUsIiKfo5YXAKIMOTXP6ihEdAEsv4SckZEIsksoLGmElDIOsAX3zqwREdGAmaYBrXIH5JTxEGzBVschogtg+SXIkogJ6THYU9YEU5ChjJ4M7cgumJrb6mhERD5DbyiD2dkCOZ2rPBB5M5ZfAtC75NnJLhVlx9p6dyRSe6BV77M6FhGRz9DKPwckpffhYSLyWiy/BAAYnxYNWRJQWNoIKXEMhKBwaNzwgohoQEzDgFaxE3LKBAi2IKvjEFEfWH4JABBklzFmVBQKS5q+2JJzKrSqvTDd3VZHIyLyenp9MczuNo48EPkAll86LS8rBsdbu3GsqRNyxgxAV6EdLbQ6FhGR19PKCwDZxpEHIh/A8kun5WXEQAB6V32IS4cQEsVVH4iI+mEaOrTKnZBHToKg2K2OQ0T9YPml0yJC7UhLDMfu0iYIggg5fRr06gMwezqsjkZE5LX02sMwe05CTptqdRQiGgCWXzpLXlYsjtafxIn2HiijpwCmDq2WG14QEV2IVlEAKA7IIydaHYWIBoDll86SlxkDACgsbYIYmwrIdui1xdaGIiLyUqahQa3cCXnUJAiyzeo4RDQALL90loToECREB6OwtBGCKEOKz4Rex/JLRHQ++rFDgKsTchpXeSDyFSy/dI68zFgUV7Wis0eFFJ8Fo6UGRs9Jq2MREXkdtXwHoARBThlvdRQiGiCWXzpHXlYMdMPEvvJmSIk5AAC9vsTiVERE3sXUNWhHdkFOzYcgKVbHIaIBYvmlc4xOCEdEqK13ybPY0YCkcO6XiOgr9JoDgLsLSjpXeSDyJSy/dA5REJCXGYv9FSegmSKkuAzO/RIRfYVaUQDYQyAljbM6ChFdBJZfOq/8zBi4VB2HjrT0zv02V8F0dVodi4jIK5iaG9qR3VBS8yFIstVxiOgisPzSeWWPjITDJqGwtOmLuV8TekOp1bGIiLyCVrMfUHu4ygORD2L5pfNSZBET0qOxp7QRQkwaIMrQOPdLRAQA0Mp3QLCHQkoaY3UUIrpILL90QZMyY9DepeJIUw+kEWmc+yUiAmBqLmhHCyGPngxB5MgDka9h+aULyh0VBQAoqWrtnfttOgLT3W1xKiIia2lV+wDNBTl9utVRiGgQWH7pgsJDbEiIDkZxdWvv3K9pQD9ebnUsIiJLaRUFEILCISVkWx2FiAaB5Zf6lJXiRGlNG4TYdEAQodcetjoSEZFlTLUH2tG9kEdPgSBKVschokFg+aU+ZaU40e3ScKxVgxibyrlfIgpoWtVeQHdzlQciH8byS33KTnECQO/oQ3w29MYKmJrL4lRERNbQygsgBDshxWdZHYWIBonll/oUFe5ATIQDJdWtkBOzAUOHfrzC6lhERB5nuruhVZ8aeeBfn0S+ir97qV9ZKU6UVLdCjMsEBIFzv0QUkLSjhYCucZUHIh/H8kv9ykpx4mSXioYOQIweyblfIgpIankBhJAoSHHpVkchokvA8kv9Omfu93g5TF21OBURkeeYrk7oNfshp02FIPCvTiJfxt/B1K8RkUGICLGhpLoVUmI2oKvQGyutjkVE5DHa0ULA0KGkc5UHIl/H8kv9EgQBWSlOFFe1QorrfcKZc79EFEjU8gIIodEQY9OsjkJEl4jllwYkK8WJlpMunHDJEKOSOfdLRAHD7OmAXnMQcto0CIJgdRwiukQsvzQg58z9NpTBNDSLUxERDT/1yC7A1KFwlQciv8DySwOSGBuCEIf85dyv5oLRdNTqWEREw04rL4AQPgJizCiroxDREGD5pQERBQGZyb3r/Urx2QAArZajD0Tk34zudui1RVA48kDkN1h+acCyUpxoaOlGu+GA6EyAXseH3ojIv2mVuwDTgMxVHoj8BssvDVj2yN6531N3f/X6UpiGYXEqIqLho1UUQIiIhxiVYnUUIhoiLL80YCPjQmFXpC/nftVuGCeqrI5FRDQsjK5W6HWHoaRz5IHIn1hSfn//+99j0aJFWLRoEZ577jkrItAgSKKIjOSI3vKbkAMA0Dn3S0R+SqvcCZgm5DSu8kDkTzxefrdt24YtW7bgnXfewbvvvouDBw/ik08+8XQMGqSsFCdqGjvRJYZCCB/BuV8i8ltaeQHEyERIUUlWRyGiIeTx8hsbG4uf/vSnsNlsUBQF6enpqK2t9XQMGqRT6/2W1vTO/Wr1JTBNzv0SUd+02iKoxZt9Zn1wo7MFen0p7/oS+SHZ0yfMzMw8/eMjR45g9erVeOONNwb8/ujo0H5fExsbNqhs1L8IZzAUeQ+qm7owM3siGks2w4lW2GIvfv1LXiffwOvkG7z5Opm6hqq//z/oHS2Q932IqMtvQcjYWRAE733spK1yEzphYsSUebDFDN3PrTdfJ/oSr5NvGOx18nj5PaW0tBR33XUXfvKTnyA1NXXA72tu7oBhmBf8fGxsGBobTw5BQrqQ0Qnh2FtyHNeNSwUANB7cDZsQdVHH4HXyDbxOvsHbr5NauQt6RwtsExdCq96P4+/9DuLmf8E+dSmkkRO98mGyzn2bIUaloM2MAIbo59bbrxP14nXyDX1dJ1EU+rxZask/u3ft2oXbb78dDz/8MG666SYrItAlyEpx4mh9B3oUJ4SQKM79ElGf1KINEEKiYJu6FMFLH4djwd0wNRe6P/4dut5/Clqddz04a3Q0w2go49q+RH7K4+W3rq4O9913H55//nksWrTI06enIZCd4oRhmqioa4eUkA29vgSmeeG78UQUuIz249BrDkDJmQtBlCAIIpSMGQj5xtOwz/4ezJNN6P7gGXSt/i10L9kyXasoAAAoaSy/RP7I42MPf/7zn+FyufDss8+e/tgtt9yCW2+91dNRaJDSk8IhCgJKqluRlZgDrewzGG11kJyJVkcjIi+jFm0EBBFKzuVnfVwQZdhy50PJmgX14Fq49nyIrhW/gpw2DfYpN0F0JlgTGIBaXgAxZhTEiDjLMhDR8PF4+V22bBmWLVvm6dPSEHLYZIyKD0NJVSvkidlwoXe9X5ZfIjqTqatQizdDHjUJYkjkeV8jyDbYJi6EMmYe3HtXw71/DbTKnVCyZsM2+QaIodEey2v0nIRecxBGYyVs077hsfMSkWdZ9sAb+bbsFCfW7qqGFjwRQlAE9LpiIHe+1bGIyItolbtg9pyEMqb/PxsEWzDsU5dCGXcV3IUfQD20AWrZNii5V8A2aRHEoPAhz2d0tkCvK4ZeXwK9rhhGy7HeLI4wKBkzhvx8ROQd+iy/ra2tAzqI0+kckjDkO7JGOvFRQRUq605iZEI29PpimKbplU9tE5E11KKNEMJiISWPHfB7xKBwOC67DbbxX4Nr13tQD6yBengTbOO/BtuEayDYggaVxTRNmCcbodcVQ6sr6f0zq/147ycVB6S4DNgyZkBKyIYUOxqCpAzqPETk/fosvzNmzIAgCH0+zCSKIg4dOjTkwci7ZSZHQABQUt2K0Yk50CoKYJ5shBA+wupoROQF9NZa6HWHYZv29UGt5yuGxSBo3h3QJ14L984VcO9+D+rBdbDlXQcldwEE2dbn+03TgNFSB72+uPfubl0xzK7eGzqCPRRSQhak3CsgJWRBjB4JQZQG9XUSke/ps/zm5OTg3Xff7fMAN95445AGIt8Q4lCQPCIUJdWtWJibDQDQaw9DZPklIgDqoY2AKEHJnnNJx5EiExF01f3QGyvhKvgnXNvfhHv/Gtgm3wAla/bp0moaOozmKuhf3NXV60pgujoAAEKwE1JCTm/hjc+GGJng1RtsENHw6rP8Ll++vN8DDOQ15J+yUpzYsq8ORtgECI4waHXF5zzRTUSBx9TcUEu3Qk6dPGSzulLsaAQvegRabRFcBW/D9emrcO9dDSVtKvTGSugNZYDaAwAQwkdAGpUHOSELUkI2hLBYjmQR0Wl9/tN37dq1p3+8devWsz537733AgAmTJgwDLHIF2SnOOFSdVQf74QUnwW93rsWqicia2gVOwBXJ5RheAhWThyD4Bt+iaCrfwRBlOEu/ABmZyuUzMvguOIehNz2AkJveQ5B8+6Akj0HYvgIFl8iOkufd343b96MH//4xwCA559/HrNmzTr9udra2uFNRl4vM6X3QceS6lYkJ+ZAO7ILRkezR5cmIiLv4y7aADEiHlJCzrAcXxAEyKl5kEZNBDQ3BMUxLOchIv/U553fMx90++pDb/yXNEWE2BAfFYyS6lZICV/O/RJR4NKbq2E0lEEZM3/Y/54QBJHFl4gu2oAn/ll26XyyUpwoqW4FnEmALbh3vV8iClhq0QZAkqFkzer/xUREFuiz/LLwUn+yU5zocmmobe6GFJ8FjXO/RAHLVHuglm6DnDYNgiPU6jhEROfV58xvfX09nnzyyXN+DAANDQ3Dm4x8QtYZc79zErPhqtoDo6sVYjA3PiEKNGrZdkDtgW0AO7oREVmlz/J72223nffHAPCtb31reBKRT4mOcCA63IHi6lbMm9X7cIteexgitwYlCjhq0UaIUckQ4zKsjkJEdEF9lt/777/fUznIh2WlOHHwyAkIUTmA4oBeVwyF5ZcooOiNlTCajsA+69scmSMir9bvA29vv/02ysrKAABvvPEGFi9ejJ/97Gfo6uoa9nDkG7JHOtHe6cbxNjek+Eyu90sUgNRDGwDZBiXzMqujEBH1qc/y+/LLL+ONN96AIAgoKirCs88+izvvvBMxMTH4zW9+46mM5OXOnPuVErJhtNTC6G63OBUReYrp7oJavh1KxgwItmCr4xAR9anPsYdVq1bh9ddfR2hoKF544QXMnTsX119/PUzTxMKFCz2VkbxcXGQQwkNsKK5qxaxpOXAD0OuKIaZNtToaEXmAWroN0NxQxiywOgoRUb/6HXsIDe1drmb37t2YMaN3jlMQBMhyn72ZAoggCKfX+xVjUgHJBr2+xOpYROQBpmlCPbQRYkwqpNhUq+MQEfWr3x3eTNNEd3c39u/fj+nTpwMAenp64HK5PBKQfEN2ihPN7T1o7lAhxWdAr+NOb0SBwGgog9FSAyWXy5sRkW/o8/btnDlz8OCDD0LTNKSnpyM9PR1HjhzBiy++iPnz+QcdfenU3G9pdRvyE7Lh3vkuTFcnBHuIxcmIaDi5izYAShCU9OlWRyEiGpA+7/w+9NBDGD9+PFJSUvDSSy8B6F39wW6348c//rFHApJvSIoNQbBdRnF1C6SEHAAm9DqOPhD5M7OnA1pFAZTMmRAUh9VxiIgGpM87v7Is44c//OFZH3vkkUeGNRD5JvGLud/i6jZIV+UDkgytvhhyap7V0YhomKglWwBdg8Id3YjIh/RZfs/czvh8li1bNqRhyLdlpTixp6wJ7T0mlBHp0Ou43i+RvzJNE+6ijRDjMiBFp1gdh4howPoce3j99dfx0UcfQRAEOJ3Oc/4jOtPp9X5r2nrX+206AtPdbXEqIhoOet1hmG31sPGuLxH5mD7v/K5duxbvvPMOPvroI6Snp2PJkiWYM2cORLHfFdIoAI2MC4VdkVBS1Yq8sTnA7veh15dCHjnB6mhENMTUQxsAewhkrudNRD6mzxablJSE+++/HytXrsR3v/tdrF27FosWLcJzzz2H8vJyT2UkHyFLIjKSwlFc3QppRDogSNzqmMgPGV1t0I7sgpI1G4JsszoOEdFFGfAt3MmTJ+OJJ57Aq6++igMHDuC6664bzlzko7JSnDjW2IFOTYQ4YjQ0zv0S+R21ZDNg6FDGzLU6ChHRRRtQ+XW73Vi9ejXuvvtuLF26FKmpqXj99deHOxv5oKwUJ0wAZTVtkBOyYRyvhKlyQxQif2GaBtSiTZASciA5E62OQ0R00fqc+S0oKMB7772H9evXY9KkSbjpppvw4osvwmbjt7no/NISwyFLAkqqWzEuMxvY8yH0hjLIyWOtjkZEQ0CvOQjzZCOUaTdbHYWIaFD6LL/f/e53kZiYiKVLlyIqKgq1tbVn3fH9/ve/P+wBybcosoS0hC/mfmePBQQBen0xyy+Rn1CLNkBwhEFOnWx1FCKiQemz/N54440QBAHNzc1obm72VCbycVkjnVj1WRVcUCDGpHK9XyI/YXS2QDu6B7YJ10CQ+vzrg4jIa/X5p9fChQtx+eWX93mATz/9tN/XUGDJSnFi5bajKD/WjvSEbKgH18LU3HwqnMjHqYc/BUwDyph5VkchIhq0Ph94e/7559HW1obW1tZz/jv18eeff95TWclHpCdGQBQEFFe3Qk7IBnQN+vEKq2MR0SUwDR3q4U2QksdBDB9hdRwiokHr885vSUkJZsyYAdM0L/gaQRCGPBT5tiC7jFHxoSipboU0PRvAF3O/iTlWRyOiQdKr9sHsPAHlstusjkJEdEn6LL+HDx/2VA7yM1kpTqzbdQya5IAYncy5XyIf5y7aACHYCXnURKujEBFdkn6fWGhoaMDq1avR0NAAURQRHx+P+fPnIzk52RP5yEdlpTjxcUE1KutOYmRCDtSiTTB1jQ/JEPkg42Qj9Or9sOVfD0Hk72Ei8m19zvyuXbsWX//611FcXAy73Q5ZlnHo0CHceuutWLlypacykg/KTHYCQO+SZwnZgO6G0VhpcSoiGgy1aBMgAEoOH24mIt/X5z/hf/vb3+Ktt95CQkLCWR+vr6/HD37wA25xTBcUGqQgOTYEJdWtWJSXBQDQ6oshxWdanIyILoZpaFCLP4WUMhFiaLTVcYiILlmfd34FQTin+AJAfHw8RHFAOyNTAMtKcaKspg2mPRRiZCLnfol8kHakEGZ3O2y5862OQkQ0JPpssGPGjMGjjz6KAwcOoLm5GSdOnMDhw4fx+OOPIzc311MZyUdlpTjhUnVUNXRASsiBXl8K09CtjkVEF0Et2gAhNBpS8niroxARDYk+y+9TTz2FsLAwPPzww5g/fz7mzp2LH/3oR3A4HHjsscc8FJF8VVbKF3O/VV/M/ao9MJqOWpyKiAbKaKuHfuwQlJy5EPjdPiLyE33O/DocDjzyyCN45JFHPJWH/Igz1I64yCCUVLfi6vHZAAC9vhjSiLQhO4fR3Q6t/HNoFTsgp02DbdyVQ3ZsokDnLtoICBIfdCMiv8I1a2hYZaU4sbukEQgaDyEiDlptMWwTrr2kY5qaG1rVHqgl26BX7wdMHVCCoDcegTx6MsSQyCFKTxS4TM0NrXgL5NQ8iMFOq+MQEQ2ZPsvvmjVr+nzz1VdfPaRhyP9kpTixeV8dahs7EZOQDbViJ0zDuOjjmKYBvb4UWulWqBU7AHc3hGAnlPFXQcmcBUFxoPMfP4NrxwoEzbtjGL4SosCiVe6E6eqAMoYPuhGRf+mz/L755psoLCzE+PHjz9niWBAEll/qV3bKl+v9xsVnQz38KYwT1UBcxIDeb7TWQS3dBrV0G8yOZkC2Qx49GUrmZZASc8+aQ1TGXQl138fQx18FKXrksHw9RIFCLdoIITwOUtIYq6MQEQ2pPsvvn/70J9xyyy24++67cdlll3kqE/mR6AgHosLtKKluxfwrcwAAen0JMGbcBd9zao5XLd3WuzGGIEBKGgtl6lLIqZMhKPbzvs+etxhq8Wb8/+zdeXxU9b0//tdZZyaZ7HvIAgkkELawBpBdDLIVtFRRi1pb7739+m376G2913qt9rbq9Wvtt9VH/eq1P1tvrdaFosgOosgWIOw7gYSshCSQPZnMnJlzfn8khLBkgcySTF7PxyOPmTPzOee8h8NJXvnkc87HvvdjWBb+HIIgeOQzEfk7V3UZXJfyYMp6EILAC92IyL90GX5VVcXzzz+P999/n+GX7oggCEhLDMXpwhoIgSMhBEXCdfHMTe1uNY5XjEiEacqDkIdO7dGYQ8EUCNP4pbDnfAhXyXHISWM88ZGI/J52H6HkBwAAIABJREFU+mtAlCGnT/d1KUREbtftBW+ZmZnIzMz0Ri3kp9ISQ7H3ZAUqa2wIjkuHq/gYDMPoYhxvNpS0aZDCE297X0rGXDhOboN930eQEkZCECUPfCIi/2VoLdDO7YY8ZCJEc5CvyyEicrse3e3h4sWL1y0LggCz2YywMF5VT93rOO53amw6nHm7cXnTO2jKO9hhHO/EtnG8I3p1P1FBkmHK+g5atv4R2tmdUEfMdtOnIBoYtLO7AIcN6uh7fF0KEZFH9Cj8PvTQQ6isrERgYCBEUURDQwMkSUJYWBhef/11jB8/3tN1Uj8WGx6AoAAFeSW1mD5zBAABDYe/hDQoo9txvHdCHjwBUswwOA6shpKaBUG1uG3bRP7MMHQ4TmyFGJ0KKTrV1+UQEXlEj8LvtGnTkJWVhWXLlgEANm/ejN27d2PFihV44YUX8Omnn3q0SOrfro77zSuphRicgYCl/4HI5MGoafHMbaYFQYBp6go0f/4bOI5thGni/R7ZD5G/cRUfg1FfAdMknjNE5L969PflM2fOtAdfAJg/fz5OnDiBjIwMaJrmseLIf6QlhuJyXQuu1LVAihkKOcizQ2ak6FTIqVlwHN0EvanGo/si8heOE1sgBIZDHjLB16UQEXlMj8Kv0+lEXl5e+3JeXh50XYfdbofT6fRYceQ/ro77zSut9do+TZOWA4YOe+4/vLZPov7KVV0CV9kpKCPvhiBy8k8i8l89+g7385//HCtXrsSwYcOg6zqKiorw2muv4Y033sC8efM8XSP5gYQoKywmGXkltZg6MtYr+xSDo65NfDHqHkiRyV7ZL1F/pB3fCkgq1OGzfF0KEZFH9Sj8zpo1C5s3b8aBAwcgyzLGjRuHkJAQjB49Glar1dM1kh8QRQHDEkKQV+K9nl+gw8QX+z6GZeHTnPiC6BZ0Wz2083ugpM2AYOb3dCLybz0a9qDrOj799FP8z//8D/77v/8b77//PpxOJ4Mv3Zb0xFCUX2lGfZPDa/sUTIEwTVgGV9kpuEqOeW2/RP2Jdno74HJCGcXbmxGR/+tR+P3d736HvXv34rHHHsP3vvc9HD58GK+++qqnayM/k3Z13K+Xe3+VEXMgBMfAvu9jGLrLq/sm6usMlxPayW2QEkdDCov3dTlERB7Xo/C7c+dOvP3225g3bx6ys7Px1ltvYceOHZ6ujfxMcmwQVEX0evhtnfjiAeg1F6Gd4f9b6p7hckJv9u7/U19xFuyHYauDOirb16UQEXlFj8KvYRhQFKV9WVXV65aJekKWRKTGe3/cLwDIg8dDik2D4+BnMBw2r++f+hfHwc/R9PEz0G31vi7FowzDgOP4Zoih8ZASRvm6HCIir+hR+B0+fDhefvllFBcXo6SkBC+//DLS0tI8XRv5ofTEUJRUNqLR5t37QwuCANOUFTBs9XAc3eDVfVP/YhgGtPM5gNYC7fgWX5fjUa5LedAvF0EZdQ8vBiWiAaNHd3t44YUX8OKLL+Khhx6CruuYPn06nn/+eU/XRn4oLTEUBoD/em8/AlQJkihAkkTIkgBZEiFJAmSx7VESIXd4XxJvaHfd+yKSY62QxM5/n5OiUyCnToHj2GYoI+ZAtIZ774NTv6FX5sNovALBHATHyW1Qxy6AYAr0dVkeoR3fApgCoaRN83UpRERe02X4XbJkyXXL4eGtYeHMmTP47ne/i7Vr13quMvJLqYNCMGpIOGoaWnDJ4YLLpcPpMuB06XDqRvvynZg0PBo/XNb1n25Nk78NZ+EB2A/8A5bZT97Rfsi/afn7AVGGed5TsK17BY6T22Aa/y1fl+V2en0VnEWHoI5dCEE2+bocIiKv6TL8/vKXv/RWHTRAKLKIf30wE1FRQaiqarhlG8MwoBsGnC7junDs0tseXQacun7tfd3AobNV+PpwGe4tr8eQuOBO9y8GRUEdlQ3H0Y1wjcrmxBd0HcPQ4byQCzlxNOT44ZASx0A7sRXq6PkQFP8KiI6TXwIQoWTc7etSiIi8qsvwO3nyZG/VQdROEARIggBJBKBIPVonJS4YuWcqsfqbfPxsxbgu26qZi6Cd2QH73o9gWfRvHOtI7VwV52E01UDOegAAoI5bAtsXL0E78w3U0f5zNwTDYYN2ZgfklEkc/kNEA06PLngj6ussJhmLpibjZGENThfVdNlWMAVCnbAUroun4So56qUKqT9w5u8DJAVyUiYAQI4dBikuHY5jG2G4nD6uzn20vF2AZvOrQE9E1FMMv+Q35owbhLAgE1bvyIdhdD1uWMmYAyEkBva9n3DiCwIAGLoOZ8EByIljIKiW9tfVcUtgNNVAO7fbh9W5j2HocJzYCjFmKKToFF+XQ0TkdQy/5DdURcKSuwYjv6weR/OvdNlWENsmvqi9CO3MN16qkPoy16WzMGx1kFOzrntdGjQSYuRgOI5s8ItflFzFR2HUV3JSCyIasBh+ya9MHx2H6FALVn9TAL2b3l85+erEF59z4guCsyAXkFXISWOve10QBKjjFsOor2ht0885jm+BEBgOecgEX5dCROQTDL/kV2RJxLIZQ1Ba1Yj9pyu6bHvdxBdH1nupQuqLDN0FZ0Eu5KTMW97VQR48HmJoPBxH1nU7pKYvc10pgeviaSgj50EQe3YxKRGRv2H4Jb8zOSMGCVGB+HznBThdepdtpegUyEOnwHF8M/TGrodKkP9yXTwDo6UBcuqt73AjCCLUzEXQq0vhKu6/F0lqJ7YAsgp1+Exfl0JE5DMMv+R3REHA/TNTUVljw+7j5d22N01aDsCAPXe154ujPslZsB9QzJATx3TaRh6aBSEoEvbDa/tl769uq4d2PgdK2nQIZquvyyEi8hmGX/JLY4dGIDU+GF/sLoTm7PoiJTEoEuqobDjP7YbrcqF3CqQ+w9Cd0C4cgJycCUFWO20niDLUsQuhV+bDVX7GixW6h3b6a8DlhDJqnq9LISLyKYZf8kuCIOD+WamoabDj60Nl3bZXxy2GYA6CPeejftmrR3fOVXYasDdBScnqtq2SNh2CJQSOw+u8UJn7GC4N2smvICWOgRQa7+tyiIh8iuGX/NaI5DBkDA7Dupwi2OxdT1AgqAGtE1+Un+nXYzrp9mn5+wHFAilxVLdtBVmFOuZeuMpOwlVZ4IXq3MOZvx+GrY6TWhARgeGX/Nz9M1PRaNOw9UBJt22VEbMhhMTCvu9jGLr/zOZFnTNcTjgLD0IePB6CpPRoHWXEbMAUCMeR/tH7axgGHMe3QAyNhzRopK/LISLyOYZf8msp8cEYNywSm/cXo9Gmddn22sQX5dDO7PBSheRLrrITgKMZSuqkHq8jqBaoI+fBWXgIrupSD1bnHq5LedCvFEEZnQ1BEHxdDhGRzzH8kt+7b2YKWuwubNxb1G1bOXkcpLh0OA58xokvBgAtfz+gBkAa1P2Qh47UUfcAsqlf3B9aO74FgskKZdg0X5dCRNQnMPyS30uIsmLKyBhsO1iKmgZ7l23bJ75oaegXwYbunOF0wFl4GPLgCRAk+bbWFcxWKBlz4MzfC72+0kMV9p5eXwln4aHWIT1d3MmCiGggYfilAWHp9CFw6QbW5RR221aKGgJ56FROfOHnnKUnAM0GpZOJLbqjjrkXECQ4jm5wc2Xu4zi5DRBEKCPv9nUpRER9BsMvDQjRYQGYMTYeO45cRGVt98MZTJOvTnzxD88XRz7hzN8PwWSFNGjEHa0vBoRCSZ8B7ewu6E01bq6u9wyHDdqZbyCnToIYGObrcoiI+gyGXxowlkwbDFEUsGbnhW7bitYIKCPnwXk+p0//WZvujOF0wFl0GPKQCRDE2xvy0JE6dgFg6HAc2+TG6txDy9sFaC1QR/H2ZkREHTH80oARFmTC3RMSsPfkJZRVNXbbXh09v+3P2hu9UB15k7P4KOC0Q07tfmKLrojB0ZBTs6Cd/hpGS/f/p7zF0HU4TmyFGDMUUnSKr8shIupTGH5pQFk4JRkmVcJnPen9DQyDkjYdWt5O6M21XqiOvMVZkAvBHAQpLr3X21LHLQacDjhObHVDZe7hKj4Ko76Sk1oQEd0Cwy8NKFaLgnsnJ+FQXhUulNd3214duwDQXdCOb/FCdeQNhmaHs/gI5JRJEESp19uTwgZBHjwBjhNb+8zt8RwntkAIDIc8eIKvSyEi6nN8En7Xrl2LhQsXIjs7Gx988IEvSqAB7J5JibBaFKz+Jr/btmJIDOQhk+A49RUMe5MXqiNPax3y4ICccmd3ebgVNXMR4GiG49TXbtvmnXJdKYbr4mmoo+a5JdwTEfkbr4ffiooK/P73v8eHH36Izz//HB9//DHOnz/v7TJoALOYZCyamoyThTU4XdT9Vfpq5iJAa4Hj1FdeqI48zZm/D4IlBFJsmtu2KUWnQBo0EtrxTTCcDrdt9044jm8FZBXK8Fk+rYOIqK/yevjds2cPpkyZgtDQUAQEBGD+/PnYtKnvXSlN/m3OuEEICzJh9Y58GIbRZVspMhlS4hhox7fAcHY9SQb1bYbDBmfJsbYhD+799qeOWwLDVg/t7E63bvd26LZ6OM/nQEmbDsEU6LM6iIj6sju/x88dqqysRFRUVPtydHQ0jh071uP1IyKs3baJigq6o9rIu3x9nB6ePxxvrjqKwsvNmJwR22Vb2+zlKH//eZjKchEycYGXKuwbfH2c3KnxxBE0ujRETZgNs5s/lxE5ERePpMN1YhMiZyy+7VnjeisqKgg1OzeiSXciZuYyqBH+c9z8iT+dT/6Mx6l/uNPj5PXwq+s6BEFoXzYM47rl7ly50ghd77ynLioqCFVVDb2qkTyvLxynsUPCEB1mwV++OInkyACIXfw/NMyJEGOGonr3Z7AnTunVvWH7k75wnNzJduQbCIFhqDfFo8EDn0sctQD2TX9A+d4voaTd5fbtdyYqKgiVl6rRlLsRUuIY1OnBgB8dN3/hb+eTv+Jx6h+6Ok6iKHTZWer1YQ+xsbGoqqpqX66qqkJ0dLS3yyCCLIlYNmMISqsakXu664ksBEGAKXMxjMYrcJ7f56UKyZ0MRzOcJcchp0yGIHjmW5+UOBZiRCIcR9bBMHSP7KMzzvx9MGz1vL0ZEVE3vB5+p02bhpycHFRXV8Nms2HLli2YOXOmt8sgAgBMHhGDhKhAfLazAE5X12FFShoDMSwBjqMbvB5sqPechYcB3QklZZLH9iEIAtTMJdBry+G8cNBj+7mRYRhwHN8CMWwQpEEjvbZfIqL+yOvhNyYmBj/96U/x6KOPYtmyZVi8eDHGjBnj7TKIAACiIOD+mamorLFh9/HyLtsKggg1cyH0mjK4io56qUJyFy1/HwRrBMToVI/uRx4yEUJITFvvb9cXU7pLS/Ep6FeKoYzOvq1hZEREA5FP7vO7ZMkSrFu3Dps3b8aTTz7pixKI2o0dGoHU+GB8sbsQmtPVZVs5NQtCUCTsXgw2/sJwOaEV5EKvq/D+vu1NcJWebL3Lg4fDoSCKMI1dBP1yEVylJzy6r6vq9q+DYLJCGTrVK/sjIurPOMMbDXiCIOD+WamoabDj60NlXbcVJahjFkCvzIer/KyXKuzfjJZG2A+vQ9Pff46WL99E84bfen3CEOeFg4DhgpKa5ZX9ycOmQQgMh+PIOo/vS6+vRHNeLpSMORBk1eP7IyLq7xh+iQCMSA5DxuAwrMspgs3u7LKtkj4DgiXYK8GmP9PrKtCy6300fvivcOSughieANNd34XRVIOW7f+fV3vOtYL9EIKiIEYO9sr+BEmGOnYBXOVn4byU59F9OU58CYgilIy5Ht0PEZG/YPglanP/zFQ02jRsPVDSZTtBVqGMzoar9ARclwu9U1w/YRgGnOVnYdvyBpo+fgbamW8gp2QhYPlvELDw51BHzoMp60E4iw5DO+adyW30lga4yk5BSZ3s1fGwyvCZEMxBcBz2zC9Jeu0l2A+vhXZ2B6wZd0EMDPPIfoiI/M3AuFkpUQ+kxAdj3LBIbN5fjLnjE2C1KJ22VTPmwnF4PRxH1sMy7ykvVtk3GboTzoIDcBzfDL3qAgSTFeq4xVBG3g0xIPS6tsqoe+C6lAf7/k8hxgyFHDvMo7W1DnnQIadM9uh+biTIJiij58ORuwquy0WQIpN7vU1X7UU4C3LhLDgAvbr1lzQxZijCZj6I2q7/YEFERG0Yfok6uG9mCl54dz827i3Cd+YM7bSdoAZAHTkXjiMboNddghjS9Qxx/spwNEM7/Q0cJ7bCaKqGEBIL0/RHoaTdBUE23XIdQRBgnvUEmq4Uo2Xb/0PA/f8J0RLssRqdBfshhMRAjEjy2D460/p/ZD0cR9bd8S9JruoyOC/kwlmQC72mdUy6FDMMpqkPQx4yAaI1AkpYECe1ICLqIYZfog4SoqyYMjIG2w6WYt7ERIQF3TrAAYAyKhuO45vhOLoB5plPeLFK39MbquA4vhXa2R2A1gIpbjjU6SshJY3t0QQSghoAy7yn0LzmN2j5+h1Y7v1XCKL7R2HpzXVwXTwNNXOxT24B1vpL0t1wHFkPvbYcYmhct+sYhgG9pvRaD2/tRQACpLg0mKY9AnnIRA5xICLqBYZfohssnZGC/acrsS6nECuz0zttJwaEQEmfCe3MN1An3DcgAomrMh+OY5vgvHAAgAg5dTLUMfMh3cGFZFJkMkzTvgv7zvfgOLIWpvFL3V6vs/AgYBiQU7075KEjZXQ2HMe3wH5kPSyzf3DLNoZhQL9SDOeFA9AKcmHUXQIEAVLccJhG3t3aw3vD8BEiIrozDL9EN4gOtWDm2HjsOHIR8ycnITrU0mlbdcwCaKe3w3F8M8xTVnixSu8xdB3OokNwHNsEveI8oFqgjlkAZeQ8iNbwXm1bGT4LrvKzcBz4HFLMMMiDMtxUdStn/j6IofEQwxLcut3bIVqCoYyYBe3kV9AnLIMYFAmgLfBeLoLzQi60ggMw6itaA2/8CMij50MePB5iQIjP6iYi8lcMv0S3sHjaYOw6Xo4vdl3ADxZ3HsjE4CjIqVnQTn0NU+ZiCGarF6v0LENrQV3uDjTlrIXRUAUhKAqmaY+03upNMbtlH4IgwDzjcTRfKULLV2+3jv91Uw+63lQDV3ke1AlLfT7rmTrmXminvoLj6EYoaXdBK8iF88IBGA1VgCBCGpQBeeyC1sDrwfHPRETE8Et0S2FBJswZNwhfHijFsulDENlV72/mQjjP58BxchtME9z/p3tf0Bur0fzZr9Boq4cYMxSmrAcgD57gkXG5gmKCed5TaP7sP9Hy1duwLPo3CKLU6+22Ds0wvH6Xh1sRrRFQht0F7dQ2aKe2AYIEKWEklHFLIA8e71e/NBER9XUMv0SdyJ6UiG0HS7F5fwkeyU7rtJ0UnggpaSy0E1uhjrkXgtL5RXL9hX3/pzAczYhb+Rs0WhI9vj8pbBDMMx5Hy9fvwHFgNUyTv9PrbTrz90MMT4AUFu+GCntPnbAMgAEpLh1y8jgIpkBfl0RENCBxkguiToQHmzF1ZCx2HruI+mZHl21NmYth2BuhnfnGS9V5juvSOTjP50AdswCWJPeOwe2KMmwalOGz4TiyHs7iI73alt5YDVfFuT7R63uVaA2Hedb3oaRNZ/AlIvIhhl+iLtyblQSHU8dXB0u7bCfFDoMUmwbHsU0wXP13tgHD0NGS8yGEgFComYu8vn/TtIchRiTB9vWfoDdcvuPtOAtyAQBKHwq/RETUNzD8EnUhPjIQ44ZFYtvBUrQ4ug61auZiGE3VcJ7P8VJ17uc8twd61QWYJn/HbRe13Q5BVlsng9B12L78f3f8i4RWsA9iRBLE0IE5+QgREXWO4ZeoGwunJKOpxYkdR8u7bCcljoYYkQTH0Q0wDN1L1bmP4bDBvu9TiNEpkIdN9VkdYkgMzLO/D72qAPZ9H9/2+npDFfTKAp/e25eIiPouhl+ibqQOCkFaYii25BbD6eo81AqCADVzEfTacjgLD3mxQvdwHFkHw1YH87RHejRLmycpQyZCGZUN7cRWaG1DGHqKQx6IiKgrDL9EPbBwShKq6+3Yd6qiy3bykIkQgqPhOLIehmF4qbre0+sr4Ti2GfKwuyBFp/q6HACAKesBiNGpaPnmXeh1l3q8nlaQCzFqCMTgaA9WR0RE/RXDL1EPjE6JQEJUIDbuK4beRagVRAnq2IXQqy7AdfG0FyvsHfvejwBRgmnycl+X0k6QZFjm/S9AlGD78k0Yzq7vuAG0hni96gJ7fYmIqFMMv0Q9IAgCFkxJxsXLTTh2/kqXbZW0uyAEhMJxZJ2XqusdZ+lJOAsPQR23xG2zq7mLaI2AZc4/Qb9SAvueD7ptr+XvBwDIKZM8XRoREfVTDL9EPTR5RDQigs3YsK+oy3aCpEAdnQ1X2Sm4Kgu8VN2dMXQX7DkfQgiKgjo629fl3JKcNBZq5mJoZ76Blre7y7bOgv0Qo1MhBkV6qToiIupvGH6JekgSRcyfnIjzpXXIK6ntsq0yYg6gBsBxZL2Xqrsz2unt0GvKYJryIARZ9XU5nVIn3gcpLh0tu/4HruqyW7bRay9Bv1IMhXd5ICKiLjD8Et2GGWPiYbUo2Li3m95f1QJ15N1wFh6Cq/ail6q7PUZLI+wHVkOKHwF58ARfl9MlQZRgvvuHEBQzWr58E4bWclMbraBtyMMQDnkgIqLOMfwS3QaTKmHehAQczb+C0qrGLtsqo+4BJAWOIxu9VN3tsR/8HHA0wzT1YQiC4OtyuiUGhMJ89w+h15WjZed7N91Nw5m/H1JsGkRruI8qJCKi/oDhl+g2zZ2QAFURsWlfcZftREswlOEzW2dNa+z6Ijlvc1WXQTv1FZQRcyBFJPq6nB6T40dAnXAfnOf3Qju9vf11V00Z9JpSXuhGRETdYvgluk1Wi4KZY+Ox71QFrtTd/Of3jtQx9wIAHMc2eaO0HjEMA/acDwHFDHXifb4u57ap4xZDShgF+54P4LpcCKC11xcQGH6JiKhbDL9Ed2D+pCQAwObcbnp/gyIhD50C7cw30FsavFFat1xFR+AqOwnTxPsgmoN8Xc5tEwQR5rn/DMESDNvWN2HYm+As2A8pLh1iQKivyyMioj6O4ZfoDkSEmJGVEYMdRy+i0aZ12VbNXAg4HdBOfOml6jpnuDS07P07xNB4KBlzfF3OHRPNQbDc/UMYjdVo3vg76LXlkHmXByIi6gGGX6I7tCArCQ5Nx1cHS7tsJ4UNgjx4PBwnv4ThsHmpultzHN8Ko74SpqkPQRBln9bSW1LsMJiyvgO9sgAQBMhDJvq6JCIi6gcYfonu0KAoK8amRuDLg6Wwa64u26qZiwB7E7Qz271T3C3ozbVwHP4CUlIm5MTRPqvDnZTR8yEPmwZ56DSIlmBfl0NERP0Awy9RLyyYkoxGm4Zdx8q7bCdFp0KKHwHHsc0wXF0Pk/AUR+4/AJcG89QVPtm/JwiCAMucf4JlzpO+LoWIiPoJhl+iXkhLDMXQhBBs2lcMp0vvsq2auQhGcy20c3u8VN01rqoL0M7ugjLqHoghsV7fPxERUV/B8EvUSwuzknGlvgW5Zyq7bCcNGgkxMhmOw2uh13fd1p0Mw0DLng8gWIJgGv8tr+2XiIioL2L4JeqlMUMjEB8ZiI17i2+adawjQRBgmvowDHsTmlb9Eo5TX3XZ3l2c+fugV5yHOunbENQAj++PiIioL2P4JeolURCwICsJpVWNOF5Q3WVbOS4dgctfghQzFPZdf4Vt4++gN9V4rDZDs8O+7xOIkclQ0mZ4bD9ERET9BcMvkRtkZcQgPNiEDXuLum0rWsNhWfhzmKY/CtelPDR9+h/Qzu3xSC+w4+gGGE3VME17BILI052IiIg/DYncQJZEZE9KQl5JLfLL6rptLwgC1Iy5CPz2byCGxaPl63fQ8uWbbp0FTm+8AsfRDZBTsyDHprltu0RERP0Zwy+Rm8wcG4dAs9yj3t+rxJAYBCx5Furk5XAWHUbzp/8BZ9ERt9Rj3/sxAAGmrAfcsj0iIiJ/wPBL5CZmVcbc8Qk4fO4yLl5u6vF6gijClLkYAff9CoIlBLbNf0DLN+/2ajY4Z/lZOAv2Q81cCNEaccfbISIi8jcMv0RudPfEBKiyiE37im97XSkiEQH3vQA1czG0vF1oWvUcnBdP3/Z2DF2Hfc+HEALDoY5dcNvrExER+TOGXyI3Cg5QMWNMPHJOXkJ1fcttry9IMkyTlyPgW/8BSDJs6/4PWvZ8CMPp6PE2tLyd0K8UwZT1AATZdNs1EBER+TOGXyI3mz85EYYBbD1QcsfbkGKGIvD+X0MZeTe0E1vQvPoFuCoLul3PcDTDsX8VpJhhkFOz7nj/RERE/orhl8jNIkMtmDwiGtuPXERTi3bH2xEUE8x3rYRl4dMwNDua17wI+4HVMHRnp+vYD30Bo6Wx9dZmgnDH+yYiIvJXDL9EHrBgSjLsDhe+OlTW623JCSMRuPw3kIdOhePQF2j+/DdwVd+8Xb32ErQTW6Gkz4AUNbjX+yUiIvJHDL9EHpAYbcXolAh8eaAEDs3V6+0JpkBY5jwJ8z0/gtFYjebPXoDj2EYYut7epmXv3wFJgTrp273eHxERkb9i+CXykIVTktDQrGH38XK3bVMZMgEB33kJcuIY2Pd+DNu6V6DXV8JZcgyu4qMwjV8KMSDEbfsjIiLyNwy/RB6SlhiKlPhgbNpfDFeHHtreEi3BMN/zI5hnPwnXlRI0rfolWna8ByEkBsqoe9y2HyIiIn/E8EvkIYIgYOGUZFTVtuDAmSq3b1tJuwuB33kRUkwqjKZqmKc8BEGS3bofIiIif8OflEQelDksEnERAdi4twiTR0S7/Q4MojUCloU/h9F4BWJQlFu3TURE5I/Y80vkQaIg4N7JSSiubMTJwmqP7EMQRAZfIiKiHmL4JfKwKSNjEWpVsXHv7U95TERERO7F8EvkYYosIntSEk4X1eBCeb2vyyEiIhrQGH6JvGBWZjwCTDJQy4eIAAAgAElEQVQ27C3ydSlEREQDGsMvkRdYTDLmjB+EQ2ercKm62dflEBERDVgMv0ReMm9iIiRJxKZ9HPtLRETkK7zVGZGXhASqmDE2DtsPlQEAls9OhdWi+LgqIiKigYXhl8iLHpg9FKosYmtuKQ7lVWH57FRMHxMH0c33/yUiIqJb47AHIi8yqRIenDsMv3piEuIjA/HexjN4+f2DKLrU4OvSiIiIBgSGXyIfSIiy4t8fHocfLB6By7U2/Pq9XLy/5SyaWjRfl0ZEROTXOOyByEcEQcC0UXHIHBqJz3ZewFeHSnHgTCW+M3sopo2O5VAIIiIiD2DPL5GPBZgVPHJPGl54fBKiwyz484bTeOWDQyipbPR1aURERH6H4Zeoj0iKCcIvvjsB31swHJeuNOM//5KLD7/Mg83u9HVpREREfoPDHoj6EFEQMGNsPMalRWH1jgJsO1CK3NOVeHDuUGRlxEDgUAgiIqJeYc8vUR9ktSh4dH46nntsIsKDTXhn7Sn89u+HUXa5ydelERER9WsMv0R92JC4YPzHyol4dH46Siob8as/78cnX59Hi4NDIYiIiO4Ehz0Q9XGiKGD2uEEYnx6Ff2zPx6Z9xdh3qgIr7h6GielRHApBRER0G9jzS9RPBAeo+N7CEXh25QQEWRS89fkJ/N+Pj6D8CodCEBER9RR7fon6maGDQvD845Pw9eEyrN5RgOff3Y+7JyQgMdqKAJMMy9Uvs4wAkwyzKkGW+HsuERERwPBL1C+JooC7JyRg4vBofPr1eWzJLemyvSqL10KxSUaASYK5/XmHwGySWgOzSUagWUZYeKCXPhEREZF3MPwS9WMhgSp+sDgDD88bhkabBpvdhWa7E7a2r47PbXZXh+dOVDfY21+3a65bbj80yIQpI2Jw15g4DIpkECYiov6P4ZfIDwSYFQSYlTte36XrsNldaOkQmGsbHThacAVbD5Rg0/5ipMQHY/qYOEweHoMAM791EBFR/8SfYEQESRRhtYiwWq4P0ItnDcX5wivIOXEJu46X46+bzuKjL89hQnoUpo+JR3pSKETebYKIiPoRhl8i6lJIoIp7s5Iwf3IiLpQ3YNfxcuw7dQk5JysQGWLG9NFxmDY6FpEhFl+XSkRE1C2GXyLqEUEQkBIfjJT4YKyYOxSH8qqw81g5Pt91AWt2XcCIwWGYPiYO44dFQVUkX5dLRER0Swy/RHTbVEXClJGxmDIyFpdrbdh94hJ2HSvHO1+cgsUkY0pGDKaPicPg2CBOwkFERH0Kwy8R9UpkqAVLpw/BkrsG42xRDXYeL8eu4+X4+nAZBkUFYvroOEwdGYvgQNXXpRIRETH8EpF7iIKAEYPDMWJwOJrv0bD/dCV2HivHx1+dx6rt+RiTGoEZY+IxOjUckshJN4iIyDcYfonI7QLMCmaPG4TZ4wahrKoRu46XI+fEJRw+dxkJUVZ8f9EIJMcG+bpMIiIagNj9QkQeNSjKigfnDsNrT92Ff/7WSDQ0O/Cb/zmA1TvyoTl1X5dHREQDDHt+icgrZElEVkYMRg4Jx9+/PId1e4pwOO8ynlg0AkPign1dHhERDRDs+SUir7JaFDy5JAM/WT4GTS0aXvzrAXy6/Tw0562nWCYiInInhl8i8omxQyPx4g+ycNfoOGzcW4xf/SUX58vqfF0WERH5OYZfIvKZALOCJxaOwL8+MBZ2zYX/ev8gPtp2DnaNvcBEROQZDL9E5HOjUiLwm+9nYVZmPLbkluCFP+9HXkmtr8siIiI/5PXwe/DgQSxfvhxLly7FY489hrKyMm+XQER9kMUk49F7h+PnKzKh6wb+zweH8OHWPNgd7AUmIiL38Xr4ffrpp/Hiiy9izZo1WLJkCV588UVvl0BEfVjG4HD8+vuTMXd8Ar48WIrn/7wPZ4pqfF0WERH5Ca+GX4fDgZ/85CcYPnw4ACA9PR3l5eXeLIGI+gGzKuOR7DT8+8PjIEDAq38/jPc3n4XN7vR1aURE1M8JhmEYvtixruv44Q9/iNGjR+N//+//7YsSiKgfaHE48beNZ/DFznxEhVrwowcykZkW7euyiIion/JY+N24cSP+67/+67rXUlJS8N5778HhcOCZZ55BXV0d3n77bSiK0uPtXrnSCF3vvOSoqCBUVTXccd3kHTxO/UNfOk7nS+vw7obTqKhuxsyxcXhgzjAEmDlPD9C3jhN1jsepf+Bx6h+6Ok6iKCAiwtrpuh77ybFgwQIsWLDgptebmprwwx/+EKGhoXjrrbduK/gS0cA1NCEE//m9Sfh81wVs3l+M4wXVeOze4RiTGuHr0oiIqB/xyQVvycnJ+MMf/gBVVb29eyLqx1RFwgNzhuLZlRNgMcn4w6dH8e76U6iqtaHRpsHucMGl674uk4iI+jCv/s3w1KlT2LZtG4YOHYr77rsPABAdHY0//elP3iyDiPq51PgQvPD4JHyx+wI27i3G7uOXrntfFAQosghZan1s/ZKuLUsi5LbH9vevvtb2PDzYjKyMGJgUyUefkoiIPMGr4TcjIwNnz5715i6JyE8psohvz0pFVkYMzpfVwenUobl0aE4dzrbHq1/XLbt0OJ06mmzaze+3r996XcGq7fnInpSIueMTOL6YiMhP8Ls5EfVrCVFWJER1fmHDndANA+dL67A+pwirdxRg474izB2fgHsmJSI4gMO1iIj6M4ZfIqIbiIKAtMRQpCWGouhSA9bnFGJDThG25pZgZmY87p2chPBgs6/LJCKiO8DwS0TUheTYIPyv+0bj4uUmbNxbhK8OluHrQ2W4a3QcFkxJQkxYgK9LJCKi28DwS0TUA/GRgfj+4gwsnT4EG/cXY+fRcuw8dhFZI2KwcGqy24deEBGRZzD8EhHdhshQC1Zmp2PJtMHYsr8EXx8uw95TFRg3LBKLpg5GSnywr0skIqIuMPwSEd2BUKsJD8wdioVTk/HlgRJsO1iKw+cOIGNwGBZNHYzhSaEQBMHXZRIR0Q0YfomIesFqUbBsRgrmT07C9iNl2Ly/BL/9+2GkDgrGoqmDMTY1giGYiKgPYfglInIDi0nGgqxkzJuQgF3HyrFhbzHeWHUMCVFWLJ6WjInp0RBFhmAiIl9j+CUiciNFljBnfAJmjI3HvlMVWJ9ThLfXnERMWAGyJychMdqKMKsJIVYVsuT1GeaJiAY8hl8iIg+QJRF3jY7D1JGxOJRXhXU5hXh/87UZLgUAwYEqQoNMCA8yITTIhDCrCWFB13+ZVX6bJiJyJ35XJSLyIFEUMHF4NCakR6HschOq61tQ3WBHbYMdNW1flbU2nC2uRbPdedP6FpOEsCAzwqytQTksyHwtHFtNCA82IcoHn4uIqL9i+CUi8gJBELqditnucKG20d4ejqsbWlDb4EBNox01DS0ou9yEuiYHDOP69WIjApCWEILhyWEYkRyOkEBOwUxE1BmGXyKiPsKkSogJD0BMeOezxrl0HXWNbYG43o7LdS0oqmxE7pkq7DhaDgAYFBmI4clhyEgOQ3pSKALMirc+AhFRn8fwS0TUj0iiiPBgM8KDzUB862tRUUGoqKhHUUUDThfV4HRRDXYevYhtB0shCEByTBBGJIdhxOAwDBsUCpMq+fZDEBH5EMMvEZEfEEUBQ+KCMSQuGAunJENz6ii4WIfTRTU4U1SDLbkl2LivGJIoIDU+GCMGh2NEchhS4oN51wkiGlAYfomI/JAii0hPCkN6Uhgwo3U88bnS2vae4S92XcCaXRegKiKGJYQiIzkMw5PDkBwTxPsRE5FfY/glIhoATKqEUSkRGJUSAQBoatFwtri2vWf40+35AIAAk4z0pFAMT2odL5wQbYXIGeqIyI8w/BIRDUCBZgXj06IwPq31Rml1jXacLm4NwqcKa3D43GUArWE4LTEUaYmhSE8KRVKMFZLIYRJE1H8x/BIREUKsJkzJiMWUjFgAQHV9C84W1+JsSQ3OFtfiyPnWMGxWJQxLaA3C6YmhSI4N4phhIupXGH6JiOgm4cFmTB0Vi6mjWsNwTYMdeSW1OFtSi7PFNTi+/QoAwKRIGDooGGlJYUhPDMWQuGAoMsMwEfVdDL9ERNStsCATsjJikJURAwCoa3K0huHiGpwtqcVnOwoAtF5oN3RQCNLbhkmkxAdDkXlrNSLqOxh+iYjotoUEqpg0PBqThkcDABqaHcgrqcPZkhrkFddiza4LMADIkoiU+GCkJ4YiMdqKQIsCq0VBoFmG1aJAVRiMici7GH6JiKjXggJUTEiPwoT01gvomlo0nGsLw2eLa7Eup/CmaZmB1p7ijmE40Kwg0KIg0HJt+br328IzxxkT0Z1i+CUiIrcLNCvIHBaJzGGRAACb3YmqWhuaWpxosmlobNHQZNPQZHOi0aahqW25vLq59X2bBpd+i7TcxqRIsFpkRIZYkJURg0kjohHIaZyJqAcYfomIyOMsJhlJMUE9bm8YBuyaqzUY25wdwrKGxrYA3WTTcOFSA/66+Sw+/DIPmUMjMW1UHEalhLNnmIg6xfBLRER9jiAIMKsyzKqMyJDO2xmGgeKKRuw+UY59pypw4GwVggIUZGXE4K5RcUiKsULgJB1E1AHDLxER9VuCICA5NgjJsUF4YM5QnCioxp4T5dh+uAxfHijFoMhATBsViykjYxEWZPJ1uUTUBzD8EhGRX5AlsX2ccVOLhtzTldhz4hI+3Z6PVdvzkTE4DNNGx2H8sCiYVN5lgmigYvglIiK/E2hWMHvcIMweNwgV1c3Yc+IS9py4hD+tPQWTKmFiehSmjYpDelIoRA6LIBpQGH6JiMivxYQH4L6ZKVg6YwjOldRi94lLOHCmEruPX0JEsKl1JruRsYiLCPR1qUTkBQy/REQ0IIiCgPSkMKQnheGRe9Jw+FwV9py4hPU5RVi3pwgp8cGYNioWk0fEwGrhbdOI/BXDLxERDTgmRcKUjFhMyYhFbaMde09WYM+JcvxtSx7+tiUPgWYZoVYTQqwqQgJNCLWqCLG2PQaq7e+ZVf4YJepveNYSEdGAFmo14d6sJMyfnIiSykYcy7+CmkY76hodqGu0I6+6FnVNdjhdN0+6YVIlhAZ2DMZXg3Lba23vBZr545aor+DZSEREhNbbpiXFBN1yMg7DMNDU4kRdox21Ta2huLbRgdoOIbnwUgPqGq/ArrluWl+WRESHWRAWZEJkiLnty4LI0NbH4ACF9yMm8hKGXyIiom4IggCrRYHVomBQVNdtbXYn6joE5KuPDXYnyioaUHSpAY027bp1VFlExHWB2IyoEAsiQsyICrUg0CwzHBO5CcMvERGRG1lMMiwmGbHhAde9HhUVhKqqBgCtAflKfQsu17Xgcq2t9bGuBZfrbMgvq0Oz3XnduiZVuj4Qh5gREWJBcowVkaEWr302In/A8EtERORlFpOMhCgrEqKst3y/uUW7FohvCMeni2tgd1wbWhERbEJaYhjSk0KRnhiK6DALe4mJusDwS0RE1McEmBUkmZUuxx9X1bb2Ep8tqcWJC1eQc/ISACDEqiI9sTUIpyWFIT4igGGYqAOGXyIion6k4/jjIXHBmDcxEYZhoPxKM86W1CKvpBZni2uw/3QlAMBqUdqCcGsgToi2clY7GtAYfomIiPo5QRAQHxmI+MhAzBk3CIZhoLLWhrziWpwtqcXZ4loczKsCAASYZKQlhiItMRTpSaFIirFCEkUffwIi72H4JSIi8jOCICAmLAAxYQGYMTYeAHC5ztbWK9zaO3zk/GUAgFmVMDQhpG2oRBgSogNhUiQOlSC/xfBLREQ0AESGWBAZYsG0UXEAgJoGO/KuDpMoqcU/vilobytLAgItCoLahlcEtj1aLQoCzdeet74nt78uigzM1Pcx/BIREQ1AYUEmZGXEICsjBgBQ3+xAXnEtqmptaLRpaLBpaLJpaLRpuHi5qe25E7px80x3ACAACDDLHULxtYBsVlt7koUOjQUAaOtdFq49bV2+qa3Q4b1rbRRJgCyJUGQRsiRClkUoHZclAUrba3Lba1ffZ1AfuBh+iYiICMEBKiYOj+6yjWEYsNldaGy5FowbbRoam9seO7xe22hHWVUjGm3OW85652uiIECWhfZgfDUUhwaZYVElBAcoCApQERyoIihAQXCAiqBAFcEBrcGeFw32Xwy/RERE1COCICDALCPALAO3MbmGYRho7y82AAMGOnYgGx3eNAzc1LZjm6uPumHA5dKhuXQ4XQY0pw6nS7/50aXD6TRufu3qstNoX3Y4W78qappxvtSBBpuGW3V0i4IAa4Byy4B8q7DMMdR9C8MvERERedSNwxiAvhsEO87Ep+sGGls0NDQ5UN+soaHZgfobnjc0a7hQXo+GZgds9lv3cF8dQ221KLCarx8WEmiRb3rt6uu8C4dnMPwSERER3YIoCq29uQEqBvWgveZ0oaFZQ32zA/VN1wJyY4chIk02DeXVze3PXfqtx1ADrTMBBnYYR31jQA4K6PhchdWiQJEZmLvD8EtERETkBoosITxYQniwuUftDcNAi8PVOk66pWNAdl573nItNFfUNKPR5oTN7ux0m2ZV6hCM1faAfC0ot752dXkg3qWD4ZeIiIjIBwRBgMUkw2KSEYmej6F26TqabE402DQ0NrcOvbh6h47Wiw8d7T3QFy83odGmdXrRoQC03tYuQEGAWYZJkdq/VEWE2mHZpEgwqa2vm2QJqtrxPbFtndblvhyoGX6JiIiI+hFJFBEc2HpxHRDYo3Ucmqs1ILcHZUf7XToa2l5vbmkNyfVNGhxOF+yaCw7NBbtD7/QWd52RJREmRURMeACeeWQ8ZKnvDMdg+CUiIiLyc6oiIVzp+ZCMjgzDgNNlXAvDmgsOTYe97bnd4WoLy3rr86uvay5YLX1vWAXDLxERERF1ShAEKHLrhCGwKL4up9f6Th80EREREZGHMfwSERER0YDB8EtEREREAwbDLxERERENGAy/RERERDRgMPwSERER0YDB8EtEREREAwbDLxERERENGAy/RERERDRgMPwSERER0YDB8EtEREREAwbDLxERERENGAy/RERERDRgMPwSERER0YDB8EtEREREAwbDLxERERENGAy/RERERDRgyL4u4HaJouCWNuR7PE79A49T/8Dj1D/wOPUPPE79Q2fHqbvjJxiGYXiiICIiIiKivobDHoiIiIhowGD4JSIiIqIBg+GXiIiIiAYMhl8iIiIiGjAYfomIiIhowGD4JSIiIqIBg+GXiIiIiAYMhl8iIiIiGjAYfomIiIhowPCb8Lt27VosXLgQ2dnZ+OCDD3xdDnVi5cqVWLRoEZYuXYqlS5fi6NGjvi6JOmhsbMTixYtRWloKANizZw+WLFmC7Oxs/P73v/dxdXTVjcfpF7/4BbKzs9vPq61bt/q4QvrjH/+IRYsWYdGiRXj11VcB8Hzqi251nHg+9T2vv/46Fi5ciEWLFuEvf/kLgF6eT4YfuHTpkjFnzhyjpqbGaGpqMpYsWWKcO3fO12XRDXRdN6ZPn25omubrUugWjhw5YixevNgYOXKkUVJSYthsNmPWrFlGcXGxoWma8cQTTxjbt2/3dZkD3o3HyTAMY/HixUZFRYWPK6Ordu/ebTz44IOG3W43HA6H8eijjxpr167l+dTH3Oo4bdmyhedTH7Nv3z5jxYoVhqZphs1mM+bMmWOcPn26V+eTX/T87tmzB1OmTEFoaCgCAgIwf/58bNq0yddl0Q0KCgoAAE888QS+9a1v4W9/+5uPK6KOPvnkE7zwwguIjo4GABw7dgzJyclITEyELMtYsmQJz6s+4MbjZLPZcPHiRTz77LNYsmQJ3njjDei67uMqB7aoqCg888wzUFUViqIgNTUVhYWFPJ/6mFsdp4sXL/J86mMmT56Mv/71r5BlGVeuXIHL5UJ9fX2vzie/CL+VlZWIiopqX46OjkZFRYUPK6Jbqa+vx9SpU/Hmm2/ivffew0cffYTdu3f7uixq89JLL2HixIntyzyv+qYbj9Ply5cxZcoUvPzyy/jkk09w4MABrFq1yocV0rBhw5CZmQkAKCwsxMaNGyEIAs+nPuZWx2nGjBk8n/ogRVHwxhtvYNGiRZg6dWqvfz75RfjVdR2CILQvG4Zx3TL1DePGjcOrr76KoKAghIeHY/ny5fjmm298XRZ1gudV/5CYmIg333wT0dHRsFgsWLlyJc+rPuLcuXN44okn8G//9m9ITEzk+dRHdTxOKSkpPJ/6qB//+MfIyclBeXk5CgsLe3U++UX4jY2NRVVVVftyVVVV+58Eqe84cOAAcnJy2pcNw4Asyz6siLrC86p/OHv2LDZv3ty+zPOqbzh48CAef/xx/OxnP8N9993H86mPuvE48Xzqe/Lz83H69GkAgMViQXZ2Nvbt29er88kvwu+0adOQk5OD6upq2Gw2bNmyBTNnzvR1WXSDhoYGvPrqq7Db7WhsbMRnn32Ge+65x9dlUSfGjh2LCxcuoKioCC6XC+vWreN51QcZhoGXX34ZdXV10DQNH3/8Mc8rHysvL8dTTz2F1157DYsWLQLA86kvutVx4vnU95SWluK5556Dw+GAw+HAtm3bsGLFil6dT37x60xMTAx++tOf4tFHH4WmaVi+fDnGjBnj67LoBnPmzMHRo0exbNky6LqOhx9+GOPGjfN1WdQJk8mEV155BT/60Y9gt9sxa9Ys3Hvvvb4ui24wfPhw/NM//RMeeughOJ1OZGdnY/Hixb4ua0B79913Ybfb8corr7S/tmLFCp5PfUxnx4nnU98ya9YsHDt2DMuWLYMkScjOzsaiRYsQHh5+x+eTYBiG4cGaiYiIiIj6DL8Y9kBERERE1BMMv0REREQ0YDD8EhEREdGAwfBLRERERAMGwy8RERERDRgMv0Q0YJWWlmLEiBFYunRp+9e3vvWtHk1nunLlytuaS97dnnzySZw/f77LNs888wzefffdbrf1z//8z1i9erW7SiMi6tP84j6/RER3ymw2Y82aNe3LFRUVWLx4MUaNGoXhw4f7sLKu/elPf/J1CURE/RLDLxFRBzExMUhOTkZhYSGGDx+ON998E+vXr4ckSRgyZAh++ctfIioqqr39W2+9hfPnz+N3v/sdgNZpvF988UX88Y9/xOOPP45Zs2bh6NGjqK+vx9NPP4177rkHmqbhlVdeQU5ODiRJwpgxY/CLX/wCVqsVc+fOxeLFi7F3717U1dXhBz/4AQ4dOoSTJ09ClmW89dZbiImJwdy5c/H6669j5MiRePnll3H06FE0NTXBMAy8+OKLmDBhQqefsaKiAs888wwqKysRHx+PK1eutL+Xn5+Pl156CbW1tXC5XFi5ciWWL18OAHjnnXewatUqBAYGYuLEidi2bRu++uorPPPMM6itrUVJSQlmz56Nn/zkJ3jttdeQm5sLl8uFjIwMPPfcc7BaraioqMCvf/1rlJeXQ9M0LFq0CP/yL//ioaNJRHQzDnsgIurg8OHDKC4uxtixY/GPf/wDO3fuxKpVq7B27VoMGzYMzzzzzHXtH3jgAWzfvh21tbUAgE8++QQrVqwAAJSUlGD69OlYtWoVfvazn+Hll18G0BqYKysrsWbNGqxZswa6ruPVV19t36bdbscnn3yCn/zkJ3j++efx2GOP4YsvvkBcXBw+++yz6/Z/9OhRVFZW4uOPP8aGDRtw3333ddsr/Otf/xpjx47F+vXr8dxzz+HChQsAAKfTiR//+Mf42c9+htWrV+Nvf/sb/vznP+PIkSPYuXMnVq9ejVWrVmH16tVoamq6bpstLS1Yv349nn76abzzzjuQJAmrV6/GF198gejoaLz22msAgKeffhrf/va327e1Z88ebNiw4XYPExHRHWPPLxENaC0tLVi6dCkAwOVyISwsDL/97W8RFxeHHTt24P7770dAQAAA4NFHH8Xbb78Nh8PRvn5ERARmz56NNWvWYNmyZdi1axdeeOEF1NTUQFEUzJo1CwCQkZHRHpB37NiBn/70p1AUBUDr+OGnnnqqfZvZ2dkAgMTERERGRrYPv0hKSkJdXd119Y8bNw4hISH46KOPUFJSgn379iEwMLDLz7xnzx78+7//OwAgOTkZWVlZAIDCwkIUFxfj2Wefve7f59SpUygoKMC9996L4OBgAMAjjzyCvXv3trfr2NO8fft2NDQ0YM+ePQAATdMQERGB5uZm5Obmoq6uDq+//joAoLm5GWfOnMHChQu7rJmIyF0YfoloQLtxzG9Huq5DEITrlp1O503tHnnkEfzqV7+CLMvIzs5GYGBge/gVxdY/sN24nRuXNU1rX1ZVtf351YDcme3bt+Oll17C9773Pdx9991ISUnBF1980eU6giCg48z2stz6o8DlciEoKOi6f4/Lly8jKCgIv//9769bR5Kk67Z59ReEq5/n2WefbQ/+TU1NsNvt0HUdhmHgo48+gsViAQBUV1fDZDJ1WS8RkTtx2AMRUSdmzJjx/7dzvyCRxGEYx79nEIPFtk02GAQRETbICmJTmFlUXP8FuyIYTBsWgxgWREwmZYMsYrAaFo0igiCi0ex2BcM6O15bTsHz3OMQbr6fPjPvtGfeefhxfHzM8/MzAAcHB2QymTfhFGBwcJC2tjb29/eblYfP7nt4eMjLywtxHFOpVMhmsy3NeH5+zujoKAsLC/T19XF6ekqj0fj0+UdHRwA8PDxweXkJQDqdfvMxUKvVCIKAu7s7RkZGqFarPD09Afz2RIzh4WEqlQr1ep04jikWi2xvb9PZ2cnAwADlchmAx8dH5ufnOTs7a+ndJakVbn4l6QPT09PUajXy+TxxHNPd3d3srr43NTXFycnJH50QsbS0RKlUYmJigiiK6O/vp1gstjTj3Nwca2trhGFIFEVks1mq1SpxHH94zfr6OoVCgfHxcVKpVHPm9vZ2dnd32dzcZG9vjyiKWF1dbVYaZmZmmJ2dpaOjg56enub29r3l5WVKpRKTk5M0GnipoX8AAACvSURBVA16e3ubXemtrS02NjYIw5B6vU4QBORyuZbeXZJa8eP11/9YkqQvi6KIlZUVcrncf9tdvb295fr6msXFRQDK5TI3Nzfs7Ox882SS9DXWHiTpL9zf3zM0NERXVxdjY2PfPc4/k06nubq6IggCwjDk4uKCQqHw3WNJ0pe5+ZUkSVJiuPmVJElSYhh+JUmSlBiGX0mSJCWG4VeSJEmJYfiVJElSYhh+JUmSlBg/Ac7xBXBEXY+hAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Common imports\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.utils import resample\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "# Where to save the figures and data files\n",
+ "PROJECT_ROOT_DIR = \"Results\"\n",
+ "FIGURE_ID = \"Results/FigureFiles\"\n",
+ "DATA_ID = \"DataFiles/\"\n",
+ "\n",
+ "if not os.path.exists(PROJECT_ROOT_DIR):\n",
+ " os.mkdir(PROJECT_ROOT_DIR)\n",
+ "\n",
+ "if not os.path.exists(FIGURE_ID):\n",
+ " os.makedirs(FIGURE_ID)\n",
+ "\n",
+ "if not os.path.exists(DATA_ID):\n",
+ " os.makedirs(DATA_ID)\n",
+ "\n",
+ "def image_path(fig_id):\n",
+ " return os.path.join(FIGURE_ID, fig_id)\n",
+ "\n",
+ "def data_path(dat_id):\n",
+ " return os.path.join(DATA_ID, dat_id)\n",
+ "\n",
+ "def save_fig(fig_id):\n",
+ " plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
+ "\n",
+ "infile = open(data_path(\"EoS.csv\"),'r')\n",
+ "\n",
+ "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
+ "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
+ "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
+ "EoS = EoS.dropna()\n",
+ "Energies = EoS['Energy']\n",
+ "Density = EoS['Density']\n",
+ "# The design matrix now as function of various polytrops\n",
+ "\n",
+ "Maxpolydegree = 30\n",
+ "X = np.zeros((len(Density),Maxpolydegree))\n",
+ "X[:,0] = 1.0\n",
+ "testerror = np.zeros(Maxpolydegree)\n",
+ "trainingerror = np.zeros(Maxpolydegree)\n",
+ "polynomial = np.zeros(Maxpolydegree)\n",
+ "\n",
+ "trials = 100\n",
+ "for polydegree in range(1, Maxpolydegree):\n",
+ " polynomial[polydegree] = polydegree\n",
+ " for degree in range(polydegree):\n",
+ " X[:,degree] = Density**(degree/3.0)\n",
+ "\n",
+ "# loop over trials in order to estimate the expectation value of the MSE\n",
+ " testerror[polydegree] = 0.0\n",
+ " trainingerror[polydegree] = 0.0\n",
+ " for samples in range(trials):\n",
+ " x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
+ " model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n",
+ " ypred = model.predict(x_train)\n",
+ " ytilde = model.predict(x_test)\n",
+ " testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
+ " trainingerror[polydegree] += mean_squared_error(y_train, ypred) \n",
+ "\n",
+ " testerror[polydegree] /= trials\n",
+ " trainingerror[polydegree] /= trials\n",
+ " print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n",
+ " print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n",
+ " print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n",
+ "\n",
+ "plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
+ "plt.plot(polynomial, np.log10(testerror), label='Test Error')\n",
+ "plt.xlabel('Polynomial degree')\n",
+ "plt.ylabel('log10[MSE]')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## The same example but now with cross-validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:63: RuntimeWarning: divide by zero encountered in log10\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Common imports\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "\n",
+ "\n",
+ "# Where to save the figures and data files\n",
+ "PROJECT_ROOT_DIR = \"Results\"\n",
+ "FIGURE_ID = \"Results/FigureFiles\"\n",
+ "DATA_ID = \"DataFiles/\"\n",
+ "\n",
+ "if not os.path.exists(PROJECT_ROOT_DIR):\n",
+ " os.mkdir(PROJECT_ROOT_DIR)\n",
+ "\n",
+ "if not os.path.exists(FIGURE_ID):\n",
+ " os.makedirs(FIGURE_ID)\n",
+ "\n",
+ "if not os.path.exists(DATA_ID):\n",
+ " os.makedirs(DATA_ID)\n",
+ "\n",
+ "def image_path(fig_id):\n",
+ " return os.path.join(FIGURE_ID, fig_id)\n",
+ "\n",
+ "def data_path(dat_id):\n",
+ " return os.path.join(DATA_ID, dat_id)\n",
+ "\n",
+ "def save_fig(fig_id):\n",
+ " plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
+ "\n",
+ "infile = open(data_path(\"EoS.csv\"),'r')\n",
+ "\n",
+ "# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
+ "EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
+ "EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
+ "EoS = EoS.dropna()\n",
+ "Energies = EoS['Energy']\n",
+ "Density = EoS['Density']\n",
+ "# The design matrix now as function of various polytrops\n",
+ "\n",
+ "Maxpolydegree = 30\n",
+ "X = np.zeros((len(Density),Maxpolydegree))\n",
+ "X[:,0] = 1.0\n",
+ "estimated_mse_sklearn = np.zeros(Maxpolydegree)\n",
+ "polynomial = np.zeros(Maxpolydegree)\n",
+ "k =5\n",
+ "kfold = KFold(n_splits = k)\n",
+ "\n",
+ "for polydegree in range(1, Maxpolydegree):\n",
+ " polynomial[polydegree] = polydegree\n",
+ " for degree in range(polydegree):\n",
+ " X[:,degree] = Density**(degree/3.0)\n",
+ " OLS = LinearRegression()\n",
+ "# loop over trials in order to estimate the expectation value of the MSE\n",
+ " estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
+ "#[:, np.newaxis]\n",
+ " estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)\n",
+ "\n",
+ "plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n",
+ "plt.xlabel('Polynomial degree')\n",
+ "plt.ylabel('log10[MSE]')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Cross-validation with Ridge"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.model_selection import KFold\n",
+ "from sklearn.linear_model import Ridge\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "\n",
+ "# A seed just to ensure that the random numbers are the same for every run.\n",
+ "np.random.seed(3155)\n",
+ "# Generate the data.\n",
+ "n = 100\n",
+ "x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
+ "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
+ "# Decide degree on polynomial to fit\n",
+ "poly = PolynomialFeatures(degree = 10)\n",
+ "\n",
+ "# Decide which values of lambda to use\n",
+ "nlambdas = 500\n",
+ "lambdas = np.logspace(-3, 5, nlambdas)\n",
+ "# Initialize a KFold instance\n",
+ "k = 5\n",
+ "kfold = KFold(n_splits = k)\n",
+ "estimated_mse_sklearn = np.zeros(nlambdas)\n",
+ "i = 0\n",
+ "for lmb in lambdas:\n",
+ " ridge = Ridge(alpha = lmb)\n",
+ " estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n",
+ " estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
+ " i += 1\n",
+ "plt.figure()\n",
+ "plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
+ "plt.xlabel('log10(lambda)')\n",
+ "plt.ylabel('MSE')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -5209,7 +6947,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
@@ -5322,7 +7060,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@@ -5386,7 +7124,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
@@ -5402,7 +7140,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
@@ -5487,7 +7225,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
@@ -5498,7 +7236,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
@@ -5514,7 +7252,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
@@ -5530,7 +7268,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
@@ -5596,7 +7334,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
@@ -5702,7 +7440,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
@@ -5732,7 +7470,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
@@ -5748,7 +7486,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
@@ -5764,7 +7502,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@@ -5798,8 +7536,8 @@
"metadata": {},
"source": [
"1\n",
- "7\n",
- "5\n",
+ "9\n",
+ "6\n",
" \n",
"<\n",
"<\n",
@@ -5820,7 +7558,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
@@ -5871,7 +7609,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
@@ -5905,7 +7643,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
@@ -5968,7 +7706,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
@@ -6030,7 +7768,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.6.8"
}
},
"nbformat": 4,
diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb
index e3e5908cd..8bab50290 100644
--- a/doc/pub/Regression/ipynb/Regression.ipynb
+++ b/doc/pub/Regression/ipynb/Regression.ipynb
@@ -381,10 +381,160 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "265 1.0 265.0 41.256962 0.155687 0.003774\n",
+ "266 1.0 266.0 41.360688 0.155491 0.003759\n",
+ "269 1.0 269.0 41.671089 0.154911 0.003717\n",
+ "270 1.0 270.0 41.774300 0.154720 0.003704\n",
+ "\n",
+ "[267 rows x 5 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"%matplotlib inline\n",
"\n",
@@ -888,9 +1038,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# matrix inversion to find beta\n",
@@ -909,9 +1057,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
@@ -928,10 +1074,21 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"Masses['Eapprox'] = ytilde\n",
"# Generate a plot comparing the experimental with the fitted values values.\n",
@@ -960,9 +1117,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def R2(y_data, y_model):\n",
@@ -979,10 +1134,16 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9547578478889096\n"
+ ]
+ }
+ ],
"source": [
"print(R2(Energies,ytilde))"
]
@@ -997,10 +1158,16 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.037875961483052376\n"
+ ]
+ }
+ ],
"source": [
"def MSE(y_data,y_model):\n",
" n = np.size(y_model)\n",
@@ -1019,10 +1186,28 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A \n",
+ "1 0 inf\n",
+ "2 1 1.123190\n",
+ "3 2 0.327631\n",
+ "4 6 0.344172\n",
+ "5 9 0.044402\n",
+ " ... \n",
+ "264 3304 0.009911\n",
+ "265 3310 0.009154\n",
+ "266 3317 0.007824\n",
+ "269 3338 0.011347\n",
+ "270 3344 0.009790\n",
+ "Name: Ebinding, Length: 267, dtype: float64\n"
+ ]
+ }
+ ],
"source": [
"def RelativeError(y_data,y_model):\n",
" return abs((y_data-y_model)/y_data)\n",
@@ -1398,10 +1583,35 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean squared error: 12.36\n",
+ "Variance score: 1.00\n",
+ "Mean absolute error: 2.83\n",
+ "[ 0. 618.32047562 -861.13519106 1404.91549644] -11.057088709963296\n",
+ "Mean squared error: 197.93\n",
+ "Variance score: 1.00\n",
+ "Mean absolute error: 11.69\n",
+ "[ 0. 28.18220995 282.79902342 842.30879705] 12.946893955209475\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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EdgCBOewyBJihqqlAlIjsBTqISDRQXlXXAIjINGAoliyMMR4QcyiJn5+JJGBHKutq/8rW61dRro4/7Wq14+nOT1POr5ynQ8wXBfIEt4gEA22Ada6ix0Rki4h8LiLnOxwHAgez7BbrKgt0LV9cnt1xRotIuIiEHzt2LA/fQd7y9vYmNDSUFi1aMHjwYE6dOgVAly5dsq3/yiuvMHHixGy3XamtW7dSt25dPvzww2t6nZ9++onGjRvTsGFD3nzzzcvWe+CBB6hWrRotWrS4puMZUxisXXuUX0Zuhj0nmBc6hZ3911K+bgB3t7qbl3u8XGwTBRRAshCRssAc4AlVPYPzklIDIBRny+Nf56tms7vmUH5poerHqhqmqmFVq1a95tjzS0BAABEREWzbto1KlSrxwQcfALB69ep8P3bLli2ZMWPGhfGprobD4WDs2LEsWrSI7du3880337B9+/Zs695333389NNPV30sYwqDzExl7rQ97By/mxNp+5lx/Qec7X6EajUr82rPVxneYvhlR44uLvI1WYiID85EMV1VvwNQ1aOq6lDVTOAToIOreixQO8vuQcBhV3lQNuXFQufOnTl06BAAZcuWvVD+xhtv0LhxY/r27cuuXbv+tM/f//53mjRpQr9+/RgxYsSFVsdXX31Fhw4dCA0N5eGHH8bhcGR7zGrVqhEZGXnVMa9fv56GDRtSv359fH19GT58OPPmzcu2bvfu3alUqdJVH8sYT0tMSWfKS5tJfDeOiEormN99CqXbQ8vaLZjUfxJtarbxdIgFIt/uWbh6LH0G7FDVd7KU13TdzwAYBmxzLc8HvhaRd3De4A4B1quqQ0QSRaQTzstYI4H3rzW+wd8MvtaXyNYPIy4d9+lyHA4HS5cu5cEHH/xT+caNG5kxYwZ//PEHGRkZtG3b9sKkSeHh4cyZM+eSbTt27GDmzJmsWrUKHx8fxowZw/Tp0xk5cuQlx50wYQKpqanExMRcMoptt27dSExMvGSfiRMn0rdvXwAOHTpE7dr/zetBQUGsW7fukn2MKepiDifx03OReO84zcLGs4lrvZ9KdUszrOkwRrYeWSgnKcov+flOrwfuBbaKSISr7C/ACBEJxXkpKRp4GEBVI0VkFrAdZ0+qsa6eUACPAlOAAJw3tov0ze3k5GRCQ0OJjo6mXbt29OvX70/bV6xYwbBhwy4MW37zzTdf2LZy5UqGDBlCQEAAAIMHO5Pe0qVL2bhxI+3bt79wjGrVql1y7J9++omzZ88ycOBAIiMjL0kWK1asyDV+1UuvAhb3JrgpedZtOMqWF/eSdPYgC9t+jbRKpWbNKjzR6Qk6BXXydHgFLj97Q60k+/sNC3PY5w3gjWzKw4E8vUN6JS2AvHb+nsXp06cZNGgQH3zwAePGjftTnct9+Wb3RX2+fNSoUfzzn/+87HFTUlJ47rnnmD9/Pl988QXbtm1jwIABf6rjTssiKCiIgwf/2xchNjaWWrWK/hOqxoDz/sS8b/Zz8v1D7Cm/nhUdfqRiiwAa1WrCC91eoEbZGp4O0SNsPgsPqlChAu+99x4TJ04kPT39Qnn37t2ZO3cuycnJJCYm/mlI865du/LDDz+QkpJCUlISP/74I+CcWGn27NnEx8cDcOLECWJiYv50vNdff52RI0cSHBxMy5Yt2bZtGxdbsWIFERERl/ycTxQA7du3Z8+ePURFRZGWlsaMGTP+1PoxpqhKSklnyqtbOPFOFEuCZrIibAGV25ZhUMuBvH3D2yU2UYAlC49r06YNrVu3ZsaMGRfK2rZty5133kloaCi33nrrn+a0aN++PTfffDOtW7fmlltuISwsjAoVKtCsWTNef/11brjhBlq1akW/fv2Ii4u7sN+uXbtYvHgxTzzxBMBlk4U7SpUqxeTJk7nxxhtp2rQpd9xxB82b/3ds/gEDBnD4sLMPwogRI+jcuTO7du0iKCiIzz777KqOaUx+O3gkiemjN5H4yx5mtPyA2LY7CQytwjPdnmFsh7H4evt6OkSPkstd1ijqwsLCNDw8/E9lO3bsoGnTph6KKO8kJSVRtmxZzp07R/fu3fn4449p27atp8PyiOLyOzWetWFjPH/8ZQ/RjnB+bfQ9ZZv40qhBA17o+gK1K9TO/QWKERHZqKphF5eXnFv5xcjo0aPZvn07KSkpjBo1qsQmCmOuVWamMm/Gfo6/H8OqGj+wPTicKs3L0bdZH8Z2GIt/KX9Ph1hoWLIogr7++mtPh2BMkZeYks6sNyI5tziKBSHTOVP7GEHNq/Bwx4e5scGN1sPvIpYsjDElTtTBM/wyYTuxRzayuNUcfIOF5i1CeKHrCzSo1MDT4RVKliyMMSXKqhVxbHl1F+vK/cDWpuuo1KQMvVv3ZFzHcZTxLePp8AotSxbGmBIhw5HJd5/tIW7adhbWm87JavHUalGJR65/mAEhA+yyUy5KXLJQVTspioni2pPP5L2TSanMeSmSmM1r+KXZHLyrK63aNmJCjwk0rNTQ0+EVCSUqWfj7+5OQkEDlypUtYRRxqkpCQgL+/tZbxeRs975TLH5uC+GO+WxpvIZywQHc0Lk34zuOt8tOV6BEJYugoCBiY2MpzHNdGPf5+/sTFBSUe0VTIqkqvy2OZdPbG1lSYzrHr4ujWrPrGNv7EQaGDLQ/GK9QiUoWPj4+1KtXz9NhGGPyWVpGJrMn72T7j7+xJHgOmRUzaBEWwot9/1IspzwtCCUqWRhjir9jp1KY/UIE6+Nms7nhGgJq+HFzz/6M72yXna6FJQtjTLGxeUsCv7y8iqVlv+RYrcNUblie8YPG2mWnPGDJwhhT5KkqP86KYuXUH/ktcC6O0uk0bduQlwf91S475RFLFsaYIi0pJZ1v3ohgReR0ttbZgN91pRja+0ae7PGkXXbKQ7kmCxFpBHwIVFfVFiLSCrhZVV/P9+iMMSYH0TGJzPzLbyzxnkJC9aNUrF2WZ24dx4BG9pBdXnNnPotPgBeAdABV3QIMz8+gjDEmNyuWxPL+E58xp8w7nCgfT7O2IXz+6EcMbGz3J/KDO5ehSqvq+os+/Ix8iscYY3KU7shk5vubWbjqM3ZU/4NSZb25re9gnuw73oYUz0fuJIvjItIAUAARuQ2Iy3kXY4zJe8dOJPPZiz+zOPkLTlY5TvnqZfnrXc/Qr1E/T4dW7LmTLMYCHwNNROQQEAXck69RGWPMRbb8cYxP/3cqayrMJ7OMg6ZNG/Pmva+WuJnsPCXXZKGq+4G+IlIG8FLVxPwPyxhjnFSVudMj+Xrh++yruA1vfy+G97mN8QNsXuyCdNlkISJPXaYcAFV9J59iMsYYABLPpTP51QX8FP8pZ8qfovx15Xh11AR6Nunp6dBKnJxaFuUKLApjjLnI7t0neeefH7DB7yfUX2lWvylvP/watcrX8nRoJdJlk4WqvnotLywitYFpQA0gE/hYVSeJSCVgJhAMRAN3qOpJ1z4vAA8CDmCcqv7sKm8HTAECgIXAeLXJDIwpllSV+d9t5dN5/yK29F68fIW7egzniWGPUsrLniP2lFyfsxCRqSJSMcv6dSLyuRuvnQE8rapNgU7AWBFpBkwAlqpqCLDUtY5r23CgOdAf+D8R8Xa91ofAaCDE9dPfzfdnjClCktMy+Mers3hr0TPElt5LhXIVeO/RiTxz6+OWKDzMnU+/laqeOr+iqidFpE1uO6lqHK4utqqaKCI7gEBgCNDTVW0qsAx43lU+Q1VTgSgR2Qt0EJFooLyqrgEQkWnAUGCRO2/QGFM0REWf5LV/TiRCliE+0LpOKO+M+zuVylTydGgG95KFl4hcl+VSUSU397tARIKBNsA6nMOGnE8icSJSzVUtEFibZbdYV1m6a/ni8uyOMxpnC4Q6depcSYjGGA/64cdNvP/dmxzzOUSpUt7c1+M+xtx2vz2JXYi486X/L2C1iMx2rd8OvOHuAUSkLDAHeEJVz+Twy89ug+ZQfmmh6sc4nwkhLCzM7mkYU8ilZTj4x9vTWBg1jXSfNCoFVOHt0X+nbdNWng7NXMSd5yymiUg40BvnF/ctqrrdnRcXER+ciWK6qn7nKj4qIjVdrYqaQLyrPBbI+nRNEHDYVR6UTbkxpgiLOZTAhP99nZ1p6xFvaF+zM/96+mXKlbGOmIXRZW9wi0h517+VgCPA18B04IirLEfibEJ8Buy46JmM+cAo1/IoYF6W8uEi4ici9XDeyF7vumSVKCKdXK85Mss+xpgiaO5Paxj52v3sTFuPj5cPY3uM5z9/e9sSRSGWU8via2AQsJE/X/YR13r9XF77euBeYKuIRLjK/gK8CcwSkQeBAzgva6GqkSIyC9iOsyfVWFV1uPZ7lP92nV2E3dw2pkhKS8/gb+/8H0uiviVTMqnhE8hbj7xOy+aNPB2ayYUU18cVwsLCNDw83NNhGGNc9hw4xDPvvMSBlJ0A9Kh+A/+c8BwBAQEejsxkJSIbVTXs4nJ3Jj9aqqp9ciszxpjLmb7gJyYvfIeUzLOU1jI83vtJht95k6fDMlcgp7Gh/IHSQBURuY7/9koqD9jz9saYXJ1LS+HZd95idczPoFDPqwlvjX+Fho1tpNiiJqeWxcPAEzgTw0b+myzOAB/kc1zGmCJuy/49PD35rxw/F4uXejGg+m28/OJYSvnZk9hFUU5jQ00SkcnAX1T17wUYkzGmCFNVPvp+Jp8t/QhHRjrXZVTmyRufY/DtXT0dmrkGOaZ4VXWIyADAkoUxJlcnz51i3KRX2HZgAyi0zOzIGxP+Qu2GVTwdmrlG7rQHfxGRW4HvbKRXY8zlLNu6hr9NeZ2ks6fwc/hxS837ePqv9+Dtm+t4paYIcCdZPAWUATJEJAXXcxaqWj5fIzPGFAnpjnRe/2Yy89fOhgwITAnm6WET6DWkpadDM3nIneE+7JFKY0y2ohJiGP/BXzkYtx8vFa533MRfXx1Htbr2tVHcuNUtwdV1NgTwP1+mqsvzKyhjTOGmqsxY/T3vznmP9HNpVEi9jrtDxvDgc/3xKmWXnYojdx7K+x9gPM4B/CJwTmS0BufAgsaYEuZM6hmen/o6a7esggxoltSOJ+9/irA+wZ4OzeQjd1oW44H2wFpV7SUiTYBrmnLVGFM0rY/ZwITPXudk/HH8HP7cwJ089a97qVjdhuwo7txJFimqmiIiiIifqu4Ukcb5HpkxptBIc6Tx7qIPmbn0WzRZCUyqy8h2j3Pb+I54edllp5LAnWQR65qD+3tgsYicxOaTMKbE2H9iP89MeZmovfvxcnjR+fSNjBn3MM27VMt9Z1NsuNMbaphr8RUR+Q2oAPyUr1EZYzxOVZmxcTb/nvMhaSdTqZRShUHlHuB/PupPuYp+ng7PFDB3e0O1BbrinMdilaqm5WtUxhiPOn7uOC/N/gdrwtdDmtIqoRN3D3iIfvc1tnmxSyh3ekO9hHOCovPTon4hIt+q6uv5GpkxxiOWRy3nlelvcfLQSUqnlaHv2Tu5/7VbCW5a0dOhGQ9yp2UxAmijqikAIvImsAmwZGFMMXI27Sz//vUDvlv6A5mJmdQ71ZihDR/kjr92xN/fRoot6dw5A6JxPoyX4lr3A/blV0DGmIK3LX4bf5v5BjG7DuCdVooe8UO446HhdBoY5OnQTCHhTrJIBSJFZDHOexb9gJUi8h6Aqo7Lx/iMMfko3ZHO1PBpfLLgK9KOpVH9bCD9vUcy4oNeVA8s4+nwTCHiTrKY6/o5b1n+hGKMKUgxp2J4Zf4/2LIxEk1VOsT1ZkDvEQx8rAmlvO3ZCfNn7nSdnVoQgRhjCoaq8v2O73n3+w9JOniWCsmV6Xd6BEP/dgPNwip7OjxTSNldK2NKkOPnjvPm4rdYtnItjkQHLePb07vBXdz6fijlyvl6OjxTiFmyMKYEUFV+j/md/53/Lkd3JxCQXJrecbfS76Gb6Da0tj07YXKVY7IQEW/gTVV9toDiMcbksTOpZ3h/5WTmL/uFtGPpNDjZlN6+dzHkow4E1inr6fBMEZHjXSxVdQDt5Cr+7BCRz0UkXkS2ZSl7RUQOiUiE62dAlm0viMheEdklIjdmKW8nIltd2967mliMKak2HNrA/dMe4rv5C9GjQt/oW7m709M89EUPSxTmirhzGeoPYJ6IfLchUpQAABxcSURBVAucPV+oqt9dfhcApgCTgWkXlb+rqhOzFohIM2A40ByoBSwRkUauZPUhMBpYCywE+gOL3IjbmBIrOT2ZT8M/ZcaSuSQfTiXwTDC9E++i7+sdaRlWxdPhmSLInWRRCUjgz5MdKf8d/iNbqrpcRILdjGMIMENVU4EoEdkLdBCRaKC8qq4BEJFpwFAsWRhzWZHxkbz5y9vs+GMfJAndDg4gLHQAQ19sQbkyPp4OzxRR7nSdvT+Pj/mYiIwEwoGnVfUkEIiz5XBerKss3bV8cXm2RGQ0zlYIderUyeOwjSnc0h3pfLXlK6Ys+ZrEqHNUTaxJn/gRdHmiI9f3D7Sb2Oaa5PrkjYg0EpGl5+89iEgrEfnrVR7vQ6ABEArEAf86f5hs6moO5dlS1Y9VNUxVw6pWrXqVIRpT9Ow7sY/Hvn+cD2dOIXHfOToc7MVtAc8wcmp/ut4UZInCXDN3LkN9AjwL/AdAVbeIyNdcxUCCqnr0/LKIfAIscK3GArWzVA3COcFSrGv54nJjDJCRmcHs7bP5dOlUTu5JpHxSJfrG3kHrezpx08gGeHtZkjB5w51kUVpV11/0l0nG1RxMRGqqapxrdRhwvqfUfOBrEXkH5w3uEGC9qjpEJFFEOgHrgJHA+1dzbGOKm4OnD/KvFf9i7boI0o6nExrXhfZ+g+g9uTWNm9hw4iZvuZMsjotIA1yXf0TkNpyXkHIkIt8APYEqIhILvAz0FJFQ12tFAw8DqGqkiMwCtuNMRGNdPaEAHsXZsyoA541tu7ltSrRMzWT+rvn857dPid95irKJFRgUfTuNBnZg8OONCPC1Z21N3hPVy94CcFYQqQ98DHQBTgJRwN2qGpP/4V29sLAwDQ8P93QYxuSpI0lHeGfVO6xaG07ykVSaHw2jU8YQuvytJW3a2306c+1EZKOqhl1c7k5vqP1AXxEpA3ipamJ+BGiMuTxVZdHeRXz0+8ccjkzA/0wZhkQNp37XTtz8fBMqlLFxnUz+cmda1co4LyF1BVREVgKvqWpCfgdnjIH4s/FMWjOJ5evXcvZgCo2Ot6TrmVsJfbEZ1/euZT2dTIFw5+LmDGA5cKtr/W5gJtA3v4IyxjhbE4v3L+bD5R8Ru+04Pqf9GBh1F8EtOjPoo6ZUrxzg6RBNCeLWE9yq+vcs66+LyND8CsgY4xxK/L217/H7htUkxSTT4HgLuh0fRqPHmtBvaF28rEusKWDuJIvfRGQ4MMu1fhvwY/6FZEzJpar8GvUrH678iANbj1LqpB83RQ2nTkgnBkxqRlBNm+rUeIY7yeJh4CngS9e6N3BWRJ4CVFXL51dwxpQkCecSmLx+Mr9tWElidDL1jjeh+9FbqTe6ETfdWc+mOjUe5U5vqHIFEYgxJdX51sRHqz7iQGQ8klCKG6Nup27tTvSd0pQGwfb3mPE8e3rHGA86fu447697n+UbV3MmOpngY43peeQWAkeGMGhUQ3xLWWvCFA6WLIzxAFVlyf4l/Gf1fzgYeQyvBB9na6JmR3p+2oTGITZchylcLFkYU8AutCY2reZMVDL1jjWhR9wwat4dwqAHGuDv4+3pEI25hDsP5U0EvlDVyAKIx5hiS1X5ed/PfLLmEw5FHkcSfOgfdQd1qneg+6dNaNrIWhOm8HKnZbET+FhESgFfAN+o6un8DcuY4uVo0lEmrZvEmogNF1oTPQ/dQrV7GjD4gQY2+J8p9NzpDfUp8KmINAbuB7aIyCrgE1X9Lb8DNKYoU1UW7F7A52s/5/COE3gf92VA1HACa7Sj26eNad6kkqdDNMYtbv05IyLeQBPXz3FgM/CUiDysqsPzMT5jiqxDZw4xad0k1kf8QWJ0Mo3iW3H9kcFUv6c+g+631oQpWty5Z/EOcDOwFPiHqq53bfpfEdmVn8EZUxQ5Mh3M2zWPqeumcWT7SXwSAhi8/x5q1m5D988a270JUyS586fNNuCvqnoum20d8jgeY4q06FPRTFo7iU1btpIUk0LTI23ocmww1e8PZtC9DfArZT2dTNHkTrKIAJpcNAzyaSDGbnQb45TuSGdW5Cy+3jCDYztP459QlqH7h1Otfit6T2xMSL0Kng7RmGviTrL4P6AtsAUQoIVrubKIPKKqv+RjfMYUeruO7+Lfa/5N5NbdnI1NoWVcJzqeGEDN/6nLgBH17SlsUyy4kyyigQfPP2chIs2AZ4G/A98BlixMiZSSkcJXW75i9obvSNiVSLkT13HbvpFUbtGcG95rTHBtG1bNFB/uJIsmWR/IU9XtItJGVffbDF2mpPoj7g/eX/M+u7dFk3I4nbaHu9H2bD/qPFGPG4bWsRFiTbHjTrLYLSIf4pwxD+BOV5kfkJ5vkRlTCCWmJvLJpk9YtOkXTu45S+WE6gzdfxsVOzVm4ITG1Kxa2tMhGpMv3EkWo4AxwBM471msBJ7BmSh65V9oxhQeqsrymOX8Z+1/iIo8jCNe6XLwBppqD5q+Wp8efQJtLmxTrOWYLFwP4/2gqn2Bf2VTJSlfojKmEIk/G88H6z9gRcQaTu8/R2BCPXrH3EKlGxow6MnGVCrv5+kQjcl3OSYLVXWIyDkRqWDdZE1Jk6mZLNi9gC/WTuHwjgQkoRR9Y4ZRv2xn2v67Ie3bV/N0iMYUGHcuQ6UAW0VkMXD2fKGqjstpJxH5HBgExKtqC1dZJWAmEIyzl9UdqnrSte0F4EHAAYxT1Z9d5e2AKUAAsBAYr6rq9js05ipEnYzivbXvsXHzVs4eTKbhsRZ0ixtCtTvqM3B0A8r4+Xg6RGMKlDvJ4kfXz5WaAkwGpmUpmwAsVdU3RWSCa/15V3fc4UBzoBawREQaqaoD+BAYDazFmSz6A4uuIh5jcpWakco3275hxrpZJOxOJOBkOQZH3U5g3Tb0+KIRjRraUB2mZHJn1NmpIhIA1FFVt8eCUtXlIhJ8UfEQoKdreSqwDHjeVT5DVVOBKBHZC3QQkWigvKquARCRacBQLFmYfPBH3B+8v/p9dm6LIvVIOq3jOhF25ibqPVKffrfVte6wpkRzZyDBwcBEwBeoJyKhwGuqevNVHK+6qsYBqGqciJy/6BuIs+VwXqyrLN21fHH55WIdjbMVQp06da4iPFMSnU45zccbP2ZR+GJO7ztH5VPVGRp1C9U6NqP/842pYd1hjXHrMtQrOAcMXAagqhEiUi+P48iuz6HmUJ4tVf0Y+BggLCzM7muYHP13HuxPOLD9KHoCrj94I829etLqjYZ06lbDusMa4+JOsshQ1dMX/ae52i/ioyJS09WqqAnEu8pjgdpZ6gUBh13lQdmUG3NNDp4+yOR1k1m9MZxzsSnUORFCz9ih1BraiJvGhlDW325gG5OVW0OUi8hdgLeIhADjgNVXebz5OB/ye9P177ws5V+75s6oBYQA611ddxNFpBOwDhgJvH+VxzaGNEcaM7fNZPqamRzffRq/0wHcFD2c4Nod6fF5I0JC7Aa2MdlxJ1k8DrwIpALfAD/jHEQwRyLyDc6b2VVEJBZ4GWeSmCUiDwIHgNsBVDVSRGYB24EMYKyrJxTAo/y36+wi7Oa2uUqb4jYxedUHbN+8j7Tj6bQ80oGOiQNoNKYRvYbVwdvLLjkZczlSXB9ZCAsL0/DwcE+HYQqBE8kn+GTjJ/y4ZjFJMSlUPlONPjHDqNOtNf2faUSliv6eDtGYQkNENqpq2MXl7vSGaoRzLKjgrPVVtXdeBmhMXsvUTBbuWcjHKz7n8I7jeJ3xoltsf1qU6UXnfzemRZsqng7RmCLDnctQ3wIfAZ/ifLramEJvT8Ie3l81mfWbtpB2NI16J5rQ/dhQGo9qRp+7g/Gx6U2NuSLu9ob6MN8jMSYPnE07y9TNU5m5fC6J0cmUPVue/tHDCWnbiRveD6FqNXtmwpir4U6y+EFExgBzcd7kBkBVT+RbVMZcIVVlWfQyJv/2ETHbD6OJEBbXjbBS/en0z6a06miD/hlzLdydzwKcU6mep0D9vA/HmCt34PQB3ls5mRXr15Men06tM3XpdeQWWt7dhl4j7ZKTMXnBnbGh8vppbWPyRHJ6MtO3TufLpbNIjDlHQHJpeh0YSmjrnvR9tzFVqgV4OkRjio3LJgsReU5V33It366q32bZ9g9V/UtBBGjMxVSVVQdX8e4vHxC9PRY9l0mro53o7DOIbv9oSbP21svJmLyWU8tiOPCWa/kFnL2izusPWLIwBe7g6YNMWv4Bv69bQ8bxDGqcDaJn/DA63NORbnfZyLDG5JeckoVcZjm7dWPyVXJ6Ml/9MZ0pS2Zw7lAyAaml6Rl7M53a30C/9xtRvqJNbWpMfsopWehllrNbNyZfqCrLY5YzccEHxO45jKQqrY52oFu5ofR9pzXBzWwsJ2MKQk7JorWInMHZighwLeNat/ERTL6LPhXNWz+/x9qN4egZBzXOBtH79K30HH09YQNr2fDhxhSgyyYLVbX+hsYjktKS+HTtFKYvnUNGfBoB6aXpFjeA3n0H0mtMA3z97NQ0pqC585yFMQUiUzNZuGsR78z7iBMHT+CVAW2OdKFPvVu56fXWVKphXWGN8RRLFqZQ2B6/nde+e4edO3ciKUrtM/W4wet2Bv6tG/XbVfJ0eMaUeJYsjEclnEvgrYUf8Mv6xXAmk/JpFehxajBD7hpE21sC8fKyrrDGFAaWLIxHpDnSmLJ6Bp/9/CVpCecolelN2LHeDO02nF6PNMY3wE5NYwoT+x9pCpSqsnT3ct789j2Oxx1BHBBysgWD645gyN87Ub6adbQzpjCyZGEKzM5je3hl+jvs3LcFSYdq52pwo98djHipP7WaV/B0eMaYHFiyMPnuZPJJXps5md82/4IkZ1I6ozTdkwdy7/130qxPDXtewpgiwJKFyTdpjjTeX/gVM37/moykZLzVi3ZnunPPTSPpeldjvErZzWtjigpLFibPqSrfrv2ZD+Z9xJnTx0ChQVJThoeOYuiYLpQqbQ/VGVPUWLIweWrlzj948+v3iD2+G8mEask1GBp0F/e+NpCydvPamCLLkoXJE3viDvLaF5PYemgN4oAyGWW4ocxQxj59L1VCynk6PGPMNbJkYa5J/JlTvPHZR6zcu4hMRwY+6k1n6cu4hx6kYcdAT4dnjMkjHkkWIhINJAIOIENVw0SkEjATCAaigTtU9aSr/gvAg67641T1Zw+EbbJISk1h4tQvWbTlW9IyzgLQKqM9Y28ZTfubmloPJ2OKGU+2LHqp6vEs6xOApar6pohMcK0/LyLNcM7a1xyoBSwRkUaq6ij4kE16hoMPZ33P7NXTSMxw/vrqOhrwUI/RDLjresTLkoQxxVFhugw1BOjpWp4KLAOed5XPUNVUIEpE9gIdgDUeiLHEcmQqX/+wjC+XfMKx9BgAqmRW4542o7j7/kGU8i9Mp5IxJq956n+4Ar+IiAL/UdWPgeqqGgegqnEiUs1VNxBYm2XfWFfZJURkNDAaoE6dOvkVe4miqsxbspEp8z8hJn0bAGW1LMMa3cGYh+/Gv6z1cDKmJPBUsrheVQ+7EsJiEdmZQ93srmtkO62rK+l8DBAWFmZTv14DVWXJyp18/u1n7EpbgwJ++HJjncE89fD/UKFyeU+HaIwpQB5JFqp62PVvvIjMxXlZ6aiI1HS1KmoC8a7qsUDtLLsHAYcLNOASRFVZsS6aKV9PYWvqMjIkA2/xonvNPjxz/yPUql3D0yEaYzygwJOFiJQBvFQ10bV8A/AaMB8YBbzp+neea5f5wNci8g7OG9whwPqCjru4U1XWbTzElC+nE3HuF1K9U/DyhrAqnXh+1BgaNmjg6RCNMR7kiZZFdWCuq2tlKeBrVf1JRDYAs0TkQeAAcDuAqkaKyCxgO5ABjLWeUHlHVdmw6QhfTZvFprOLOFsqEa9S0KxyC54Z8Rhtmrf0dIjGmEJAVIvnpf2wsDANDw/3dBiFlqqycVM8M6Z8T3jSj5z2S8BLoG7l+jx9y1i6tOloz0oYUwKJyEZVDbu43Po7ljCqyoYNR5k7dRHhST9wPOAoXv4QWDmIcTePpl/73pYkjDGXsGRRQmRmKuvWHOHHL5cSfvYHjpaJxas0VK9cjUcHPsigjjfh7WWjwRpjsmfJopjLcGSydsURFn+1jI3JCzhULgavclClSmUeumEkwzoPwcfbx9NhGmMKOUsWxVRaRiarlhxi2TcriUhfxIHy+/CqAJWqXccDfe/i1k634F/KHqgzxrjHkkUxk5yWwfIfYlk7ew1/8BMxFfbiVVaoVL0io/oM57YOt1Lap7SnwzTGFDGWLIqJxJR0fp8Rw6b569nk97MzSZQSKtWowMg+d3Jbu9so41vG02EaY4ooSxZF3IlTKSz7Mpptv2xgU9klxFTbi5ePUDmwIvf2voNbWt9COT+bfMgYc20sWRRRhw4nseKLaHatCGdjpaUcDNyPt59QrXYl7u51O8NaDLMkYYzJM5YsipjdO0+y7rNo9kWEs6HabxwOjsG7tBe16lZleI9bGdZsGGV9y3o6TGNMMWPJoghQVf5YFU/E1ANERW1gfc1fOdYwDu9y3tSpV4MR3W5ncKPBdk/CGJNvLFkUYukZDtbOP8Surw+y5/Q6wgN/52TIcUpVKkWDBnW4q/Md3BRyk3WBNcbkO0sWhVDSmTRWfRVDzLxYtnutYWPNFZytfgbfqr40b9yQEWF30rd+X3y9fT0dqjGmhLBkUYgciUpi3RcxHP7tAJsrriKizhrSAlIJqOlHh6atuLP1HXSr241SXvZrM8YULPvW8TBVZe/qBCKmHuTQtn1sqrGSHc02kVlWKRPoT7fmnbm9xe20r9XeBvgzxniMJQsPcaQ42DTnEPtmxbH/5A421lzB/tY78aroTcXapendohu3NL2FplWbejpUY4yxZFHQzsQms3HaQWJ/imOb30b+qLmK+NpH8KnqQ2DdKgxofiNDGg8hsHygp0M1xpgLLFkUAFXl8KqTbP4qlkMRsWysupotjdZxrlwy/jV8aVo/mCFNB3NTw5uo4F/B0+EaY8wlLFnko4zEDHbPPcLubw+z4/QONldfw57QSBwVoWygPz1CwhjaeAhd63S1YcKNMYWaJYs8pqqc3XmOrV8fInrZYTaV3kBE9TXE1z2KV9VSVAoqR7/G3RnSeAhNqjSxm9bGmCLBkkUecaQ4iF98gu0zDxN5YBcbK69hR9MIksum41fdl0bBdRnadCA3NryRKqWreDpcY4y5IpYsrlHKwRRivjvK9oUxrJF1bKm6niMtD5NRwYuytfzp36gDgxoN4Po619vzEcaYIsu+va5CZkYmZ9acYdesQ6zZtZH15dexp+42UkpnQOVS1Kpdg1tb9uemhv2pXaG2p8M1xphrZsniCqSfSufYjwksn7+JlWmr2XLdJs40PE1aWcG3ui89GnfkjpaD6BTUyYbiMMYUK5Ys3JASk8KGGRH8tOlXwgM2En/dERy+kF7Ri6A6dRjeZgADG/ejRtkang7VGGPyRZFJFiLSH5gEeAOfquqb+Xk8VSVycyTzvv2JtXHriPU7DJUgLQB8qpSnb+sejGw3kNbVW1iPJmNMsVckkoWIeAMfAP2AWGCDiMxX1e15eZyUjBTWH1rPun3r+fX3lRw7dhxHpqJ+oGUDaNQgjOHX38TNzbrYcxHGmBKlSCQLoAOwV1X3A4jIDGAIkKfJIvpkHM/MfZX0vSmQAWXTy1O1fAs69urOqJ69qVHeJhcyxpRMRSVZBAIHs6zHAh0vriQio4HRAHXq1Lnig1TwrkHwhqbUOlGToPLNaDe2PZ0718Tfx/sqwzbGmOKhqCSL7G4K6CUFqh8DHwOEhYVdsj03FU/DQ4dHUa6WPx2mNMfLx+vKIzXGmGKoqCSLWCDrAwtBwOG8PkhAvQB6L2hLWnyaJQpjjMmiqHwjbgBCRKSeiPgCw4H5+XEg7zLeBNQLyI+XNsaYIqtItCxUNUNEHgN+xtl19nNVjfRwWMYYU2IUiWQBoKoLgYWejsMYY0qionIZyhhjjAdZsjDGGJMrSxbGGGNyZcnCGGNMrixZGGOMyZWoXvGDzkWCiBwDYq5y9yrA8TwMp7ixzydn9vnkzj6jnHny86mrqlUvLiy2yeJaiEi4qoZ5Oo7Cyj6fnNnnkzv7jHJWGD8fuwxljDEmV5YsjDHG5MqSRfY+9nQAhZx9Pjmzzyd39hnlrNB9PnbPwhhjTK6sZWGMMSZXliyMMcbkqkQnCxHpLyK7RGSviEzIZruIyHuu7VtEpK0n4vQUNz6fniJyWkQiXD8veSJOTxGRz0UkXkS2XWZ7ST9/cvt8Svr5U1tEfhORHSISKSLjs6lTeM4hVS2RPzjnxdgH1Ad8gc1As4vqDAAW4ZzWtROwztNxF7LPpyewwNOxevAz6g60BbZdZnuJPX/c/HxK+vlTE2jrWi4H7C7M30EluWXRAdirqvtVNQ2YAQy5qM4QYJo6rQUqikjNgg7UQ9z5fEo0VV0OnMihSkk+f9z5fEo0VY1T1U2u5URgBxB4UbVCcw6V5GQRCBzMsh7Lpb8od+oUV+6+984isllEFolI84IJrcgoyeePu+z8AUQkGGgDrLtoU6E5h4rMTHn5QLIpu7gfsTt1iit33vsmnOPIJInIAOB7ICTfIys6SvL54w47fwARKQvMAZ5Q1TMXb85mF4+cQyW5ZREL1M6yHgQcvoo6xVWu711Vz6hqkmt5IeAjIlUKLsRCrySfP7my8wdExAdnopiuqt9lU6XQnEMlOVlsAEJEpJ6I+ALDgfkX1ZkPjHT1SOgEnFbVuIIO1ENy/XxEpIaIiGu5A87zKaHAIy28SvL5k6uSfv643vtnwA5Vfecy1QrNOVRiL0OpaoaIPAb8jLPnz+eqGikij7i2fwQsxNkbYS9wDrjfU/EWNDc/n9uAR0UkA0gGhqurC0dJICLf4OzRU0VEYoGXAR+w8wfc+nxK9PkDXA/cC2wVkQhX2V+AOlD4ziEb7sMYY0yuSvJlKGOMMW6yZGGMMSZXliyMMcbkypKFMcaYXFmyMMYYkytLFsYYY3JlycKYQkpEmorIRyIyW0Qe9XQ8pmSzZGHMNRCRYBFJzvJQFSIyzjVHwfRreW1V3aGqjwB3AGGu1w5wzf2QVtKGxjCeVWKf4DYmD+1T1dAs62OAm1Q1yp2dRaQl8M+Lih9Q1XgRuRmYAEwGUNVkIFREoq89bGPcZy0LY66AiIwRkW0iEiMij2ez/SOcE0bNF5EnRWSniHzq2me6iPQVkVUissc1HhKqulVVB130E+/aNl9VuwB3F+gbNeYi1rIwxk0icivQD+e8A1WArThnMbtAVR8Rkf5AL6As8DZwOzAa5+CMdwFdgZtxjgM0NIfj9QRuAfxwjhFkjMdYsjDGfeOAh1Q1HYgTkXRyb51HqepWABGJBJaqqorIViA4px1VdRmw7FqDNiYv2GUoY9zgmneglarudq3XBI4DabnsmpplOTPLeib2x5opQixZGOOeZkAFEakvIl44b0i/7+GYjCkwliyMcU8bYDrwDbAFOKCqH3s2JGMKjjWDjXFPKLBAVWfmVlFVg12Lx4EWWcrvy7IcnXWbMYWdtSyMcU8oEJFNuQPn5anstuW58w/l4ZxxLrMgjmkM2Ex5xhhj3GAtC2OMMbmyZGGMMSZXliyMMcbkypKFMcaYXFmyMMYYkytLFsYYY3JlycIYY0yuLFkYY4zJlSULY4wxufp/FZ4CftEaGSsAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -1519,10 +1729,23 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training R2\n",
+ "0.999985063278987\n",
+ "Training MSE\n",
+ "6.991057217389305\n",
+ "Test R2\n",
+ "0.9999878398124225\n",
+ "Test MSE\n",
+ "4.264567442512323\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"import numpy as np\n",
@@ -1638,9 +1861,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -1660,10 +1881,19 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from sklearn.datasets import load_boston\n",
"\n",
@@ -1684,9 +1914,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
@@ -1704,10 +1932,33 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "CRIM 0\n",
+ "ZN 0\n",
+ "INDUS 0\n",
+ "CHAS 0\n",
+ "NOX 0\n",
+ "RM 0\n",
+ "AGE 0\n",
+ "DIS 0\n",
+ "RAD 0\n",
+ "TAX 0\n",
+ "PTRATIO 0\n",
+ "B 0\n",
+ "LSTAT 0\n",
+ "MEDV 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# check for missing values in all the columns\n",
"boston.isnull().sum()"
@@ -1723,10 +1974,19 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# set the size of the figure\n",
"sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
@@ -1746,10 +2006,29 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# compute the pair wise correlation for all columns \n",
"correlation_matrix = boston.corr().round(2)\n",
@@ -1768,10 +2047,19 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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HZb6nEgxS5nvGeuPC5y9rb0ZlJPuQV0ZCWNbe7HlbiYiIiIiIiMxox7laHMMGDwNHebA737OttQkrO1oy0dmG+ihWdrSwqBgRERERERH5gmicq+AYNpg4VS0PduZ7aqe03dE5hz82IiIiIiIi8hWj+kXrV883fb2dci5UGphxlAe9eZ3ax+1OaSMiIiIiIiIqBqvjXBGOfcsTA0d5sFqziEsYEhERERERUSnIpzYvx77liVPV8qCk25ml4RlNaVuzoYcpfEREREREROQLVse5InbKuRQDp9E5w8BRntpam0y/aA31UdMfkJLCp7wnERERERERUTFYGeeK6I19rUxz85oyjU7JiOIY3DpOVSsAUaqfCFP4iIiIiIiIqFTlM83Na5xG5xwzjgpAlOrn9xQ+IiIiIiIiIjvymebmNY7BnWPgqEC0qX5KbSMtP6TwERERERERETnhdJqb1/w8jc7vGDhyid0iW8vam7PmVwL+SeEjIiIiIiKi0sQC0GIcgzvHwJEL9IpsHT12Cvv7B4U/WD+n8BEREREREVHp8UMBaL8GrjgGd46BIxfoFdl6ft/xzP9FP1i/pvARERERERFR6TEqAF2IsacfAldGOAZ3hququcBqMS1WbCciIiIiIiKvFLsANFcuK08MHLmgpips+bms2E5EREREREReMCr0HOuNe779YgeuyBucquYCSZIsP7dUKrb7dV4qERERERERiS1rb8bGHX3Cv23bc8TzMR5XLitPDBy54Mxw0tLzwhJKomK73+elEhERERERUa621ibdwNGZ4WRm7OrVGM/vK5cxQcIZTlVzgdXoqRSynplUTJyXSkREREREVJqsjk+9GOO1tTZhZUdLpg0N9VGs7GjxRXBGSZBQMqKU4FkhpvCVOmYcuUAUVRVJpuSCVbPPB+elEhERERERlSar41PAmzGeX1cuK/aKc6WMgSMXtLU24eixU+h+/TjSMhCSgLQsfm4pBF84L5WIiIiIiKg0KUEQ9ZSskdEkzo6kcp6rHuOV+zQuJkg4x8CRC2K9cfQciGeCRXpBI6A0gi9+n5dKRERERERE+rRZP9o6tkD2GC8IdW6ZIOEcaxy5QJTyJlIqwRc/z0slIiIiIiIie8zGeEGoc7usvRmVkewQSKmM0YuNGUcWmKXsGaW2KVHNUkv18+u8VCIiIiIiIrLPaIwXhGlcoil86jF6uU/VywcDRyaspOwZpbytXz2/cI0lIiIiIiIisiko07j0gmdBmKqXD05VM2ElZY8pb0RERERERFSqgj6mDcJUvXww48iElZQ9s5Q3O5geR0RERERE5A9BGZ+5OaYtRUGYqpcPBo5MWE3Zc6MmENPjiIiIiIiI/CFo47Mg17kNylQ9pzhVzUQhU/aYHkdEREREROQPHJ8FR9Cn6plhxpGJQqbsMT2OiIiIiIjIHzg+C46gT9Uzw8CRBYVK2WN6HBERERERkT9wfBYsQZ6qZ4ZT1XyE6XFERERERET+wPEZ0TjPA0ff+c53sHbtWgDAyy+/jM7OTixevBj333+/15suOW2tTVjZ0ZKJYDfUR7Gyo4VRTyIiIrKNfTAiovxwfEY0ztOparFYDE8++SQWLFiAkZER3HvvvXj00Udx4YUX4s4770R3dzfa29u9bELJYXocERER5Yt9MCIid3B8RuRh4OjUqVO4//778aUvfQmHDh3C/v37cckll2DGjBkAgM7OTuzatYudlhIR642zUBgREVEJYB+MiIgKhePEYPBsqto3v/lN3HPPPaivrwcAvPfee2hsbMz8ferUqThx4oRXmycXxXrj2LLzUKYw3OBQAlt2HkKsN17klhEREZEW+2BERFQIHCcGhycZR0888QQuvPBCtLW1Yfv27QCAdDoNSZIyz5FlOev/djQ21rnSzlJV6P1/6qUYRpPprMdGk2k89dKbWLrg8oK2hZ99cPc/yPsOcP+DvP9B3neyz8s+WENDrWvtLCX8DXqLx9dbPL7eC/IxLsQ4McjHtxCsHl9PAkfPPvssBgYG8KlPfQp/+MMfcO7cOfzv//4vwuFw5jkDAwOYOnWqo/cfGDjtVlNLTmNjXcH3f+DksO7jhWxLMfbdT4K8/0Hed4D7H+T9D9q+h0JSYIMTbvGyDzY4eAbptOxmc30vaL/BQuPx9RaPr/eCfoy9HicG/fh6TX18zfpgngSONm/enPn39u3b8corr+Af//EfsXjxYrz99tuYPn06urq6cMstt3ixeXJZQ300k36ofZyIiIj8g30wIiIqFI4Tg8OzGkda0WgU69atw1133YUbbrgBf/zHf4wlS5YUavOUh2XtzaiMZH9VKiMhLGtvLlKLiIiIyCr2wYiIyAscJwaHJMtyyeUcl1O6mt0q9MVK1/NDtfygpyoGef+DvO8A9z/I+x+0fedUNX/jVDVyG4+vt3h8vcdj7O04UXt8/TAmLSdFn6pG1ihV6JWCYkoVegC++wG0tTb5rk1ERERERERUPIUaJ5bS2LkcFWyqGuXa3t0vrEK/vbu/SC0iIiIiIiIi8heOnYuLgaMiEhUSM3qciIiIiIiIKGg4di4uTlUrIrMq9KI5nEsX1BW6mURERERERJQH1ufJD1dwKy5mHBWRURV6ZQ6n8uNQ5nC+8Oo7xWgqEREREREROaA3tov1xovcstLBFdyKi4Ejl8R641izoQer1u3Fmg09lk4Cba1NWNnRkomSNtRHsbKjBW2tTbpzOLfuPOhJ+4mIiIiIiMh9rM+THyVbazSZRkgaf0w9dibvcaqaC/Kp8K5XhV5vrub7J4fzbC0REREREREVCuvzOKcda6fliUwjBo0Kh4EjF+hFkB/bfdjxPFa9OZwygDUbevhDISIiIiIiKiBtnaLbb2pF68WTTV/H+jzOGWVrcTxcOJyq5gK9SPHZkZTjeayiOZzq7W3c0YdHf3HIWYOJiIiIiIjIlLokycYdfVnju4eeeMPS+G5uc4Otx2kCs7X8gYEjF1iNFNuZx6qtfyTy/L7jLKhGRERERETkAW1Ra63EWMrS+G5//6Ctx2mC3niY2VqFxcCRC4yyg7TsREbbWpuwfvV8w+ewoBoREREREZH7RNOktKyM75g14xxXU/MH1jhygTK3Uj3fNTGWwpnhZM5znURG9ebEAv442Wjn+rL+EhERERERlTorYy0r4zu98VxtdQRrNvRwHGVANNbmcSo8Bo7yYBQw0VZ/B5xHRpe1N2Pjjj7h34qdopfPinJERERERER+ZXQDHwCiFWFL47tl7c05Y0MAODOczCQbcBylT28lciocBo4cMguYuBkZbWttwtFjp/D8vuM5fxscSriyyprTrCG3qtwza4mIiIiIqDjYFxfTC/gAsLWqmjKee2HfccgGzxtNpvFwV1/mNUR+wcCRQ1YCJm5GRpdf34LLpk/GUy+9iYGTw1l/yzc6bTdrSH1h0WNnCh2zloiIiIiIioN9cX1myQCNjXUYGDht+j6x3jh6DsQNg0aKtAzh8Wdwj4qJgSOHilFzqK21CUsXXI7b/3FXznacZPko7GQNiabgidiZQudW1hIREREREdnDvrgxN5IBrBTZVtMefwb3qNi4qppDxVwW0O2glZ33s3LSs1vLyc+Fv4mIiIiIyhn74t5zcizVrzEK7hEVAjOOHBLNd3VjWUB1CmJtdQSyLOPsSCqTjrh0QZ1ukTanQSs772d20nOSNun2/hARERERkTXsi1sjmiq2dEGdpdeaFdkWCUnj22xrbWJwj4qOgSOHvFgWUJuCqFTYBybSEevrqlwJWqlPfDVVYUTCEpKpiVm3eu9ndGFZv3q+5e2reRWEIyIiIiIiY+yLm9ObKlZfV2WpOLZeke3a6gg+2jIVPQfiOX9T1zryQ3CPNZaCjYGjPLi9LKDZNLDRZBr/8dQB/PDuT2Se7+SHqz3xnR1JISyNn7jODCcN38+LC4sXQTgiIiIiIjIXtL64kwCI3lSxrTsP4jt3tplu0+wYXzZ9Mh7u6kNaUz1bmY5W7OAeaywRA0cqxY6iWkk1PH1uLJOyCEycfJT5rVbaKzrxpWQgWhHGA1+5xvC1Xl1Y3A7CERERERGRNUHpizsNgOiN0wZODmPNhh5L4yL1MVbGnRt39GVepw0aqbdd7OAeC6gTA0cfKmQUVS9ApWT8mFGCRE7bm+8c2aBcWIiIiIiIqHw4DYAYjdOUMZTV8VisN47Nzx7MlAkZHEpg87MHUVMVxtmRVM7z1bWOijUGY40l4qpqHypUpXolQKU9wcR645BlnTCzhpJh5LS9tdXieKHe40RERERERKXOaQDE6jjNynhs254jWbVlASCZkpFMjU8/01JqHcV645ba4IVirihO/sDA0YcKFUU1CviIIswiRlX5rbRX78Rn9YRIRERERERUapwGQKyO0wDz8Zhe5lJiLIWVHS0ISbl/8yKhwY5l7c05QS0WUA8WBo4+VKgoqlHAx8q2ohVhLGtvzqu9eic+OydEIiIiIiKiUuI0AGJnTJjP+LGttcmw1lGxtLU2YWVHS2bfGuqjWNnRwvIlAcK5SR8qVKV6o6UURW0IS0B11cRqZ7ff1JpZ8tFpe/2wnCMREREREVEhOS0yLRqnRcISZBlIqSI9VsZjerWMaqrCAPw7VmOd22Bj4OhDhapUbxSgstKGxsY6DAyczqu9xV7O0UixV7YjIiIiIqLy5SQAojf2qq+rwiNdvbbGLrddNwubuvqgKXMESZIQ6437eqxGwcXAkUohoqhmAR87bXDa3mIv56inkCvbERERERERWSUaezU21mVmg9h5HwB4bPfhrMyjM8NJbNl5CCs7WrCyo8V3YzUKNgaO8uA0O6aYaX7aNt/ROcc3JyGny2MSEREREREVgno81TilGjdffamjDCbR4kjK2Gf96vm235MzN8hLDBw5lG92TCF+2OpthCTkFFrzW0ZPoVa2IyIiIiIisks7Bhw4Oex4PGU09lmzoSdrDGc2Xnz0F4fw/L7jWe/hp3EelT4GjhzKJztGFHTauKMPR4+dwvLrW3Keqw4wqYtj29mGXnX+fDN63AyA+bUQHBERERERlQ+nY5jHdh+2PAY024be2AeYCCopYzijQFCsN54VNDJrF5ETDBw5lE92jCjoBADP7zuOVw6ewNmRFBrqo5jb3ICeA/GsANNDT7yBFUtmmZ4A9LbhtM0ibtckYiE4IiIiIiLyktMxTKw3LlwNTXkPs21s3NGHjTv6MkEk0djHiF4gaHt3v+5rOHOD3BIqdgNKlV4WjJXsGKMfsHIyGhxK4Pl9x3NOJImxVNbJIdYbx5oNPVi1bi/WbOhBrDduug0nbRYxyrpyoq21CSs7WjLtaaiPYmVHC6PkRERERETkCqdjGKO/a8dTRjfx1YEq9dinpips2nbRGM9o3MeZG+QWZhw5ZCU7Ri890Sgt0QrltXqR7Md2H0ZtdQRnhpOm75VPRo8XNYmKWTiciIiIiIj8wY2SGKL3cDqGMfq7djxl9l7aItjKuM6MKBBkNLbkzA1yCzOOHDLLjlF+/MqPWIksx3rjef+AlW3qRbLPjqQwPJJEJCyZvk8+GT35ZF0RERERERGJGI2l8n2P2mpx7oTZGEbv77XVkZzxlN1ZKFbKjOjd8F/W3ozKSO6wfuG8abwhT65hxlEejLJjjFIg16+ej6PHTgmLmJmJVoQzJwyjSHZKBmoqQjivJmKrIr9Vsd44EmO5c3xZk4iIiIiIiPKRz0JEZu9REZFQGQnZrqsqmnESrQjjc9fOzHqe3jhJSx1cMstQMhrDKY95vWI3BRsDR3nSS6E0S4Fcfn0LLps+OfPa2uoIhkeSSKlWP6uMhDD/iibs7x8UrqpmNuXt7EgKD3613XbbreyzqJBbTVUYt11nXribiIiIiIhIjxslMfSee3YkhYXzpmXGWLXVEciyjI07+rC9u99WgEa74rXeOElLG6gyWl16/er5pvvKch/kNQaO8mBUkd/K0vLaH7iVQE5jYx2eeeG3mecZMUqRzGdFNL1UyqrK3DRNIiIiIiIiO6yMpZy+BwD0HIhjZUcLANgaE2nHb42NdRgYOJ35v944qbY6gmhFWHecl+/q0m7UgyIywsBRHoxSKPWWVxwcSuAL39krnDamPhEpP0rOgP0AACAASURBVH71ko1trU144dV3HEWx1e+pnromarvZScaLothEREREREQAMLe5Iaesh92SGEbL3Y8m09i4oy+vMZGI3njozHASD3zlmqwxnjq7yc50M22QaG5zA3oOxB0lBBBZxcBRHowCKNofv5pyctL7URtlAz310puOotja99SeIM32Sc2NOwBERERERERasd44eg7kFsGef4W96VjKczfu6NN9Tj5jIhGjcZLZjA8r081E7yGqm5tP8ItIhKuq5cFsVbG21iasXz3fMKCi/KjVHtt9WDeT6f2Tw8L3OTOcxPrV87Fp7aLMso5qVir1G+2TmqhyP4tiExERERFRvvTGLfv7B22/V1trk6Ob2zVVYduvAYzHSUazVayyOqYDOBuE3MXAUR6sBlDMfrTqv8d64zg7Iq7CPziUgE5Q3PSEaOXEYTX409bahJUdLZltNtRHsbKjxbcR7VhvHGs29GDVur1Ys6HH1jKeRERERERUOG6XxdBbrt5IYiztaMxgNE7Kd79ivXFbx4CzQchNnKqWB6tzUc1WP6utjmDNhp5M7SG7rAR89NqgzOu1W0StVCr351MEnIiIiIiICsto3LJq3V5H4xZgfFaH3g16rWRKdjzVS2+clE+5D2VMYxVng5DbGDjKk5UAilFhtkhYwvBIEmeGkwD059nqsXri1KvU7+dMITcYpYT6eb9FKyMsXVBX7GYREREREXlKb+xkVifWSFtrE7Z391sOHCnbMRLrjeOpl2IYODlsaUyWz8ppRlPUKiMhzL+iCfv7B7mqGnmGgaMC0GYmqbN8RkaTtk5gWutXz3fUhqCcUEpxBTi9LKn6uiq0Xjy5yK0jIiIiIvKO3thJzcqNYO2NWLv9f6NMICezGvIZjxm1vdwTAcgfGDgqEL3MpFXr9jp+T7vzVosxvUyUOVPINpTiCnB6WVJbdx7Ed+5sK1KriIiIiIgKQz1u0RsvGQVTRIEdPTVVYYwlZVuZQFZmNeiNg5yMhYzGNAwaUSF4Whz7hz/8IW644QbceOON2Lx5MwDg5ZdfRmdnJxYvXoz777/fy82XBKcBDLfnrVopIG23yLRywlZOckokvpDFqUtxBTi9C5veinpERERq7H8RUTnRGy+FJOiOK0SrVIuEJUCSJIwm05las1YW/jGb1eD2OKgUxzRUXjwLHL3yyiv47//+bzzzzDP4+c9/jkcffRSHDh3Cvffeiw0bNuDZZ5/Fb37zG3R3d3vVhJJgt8q/BPdXMYv1xrH52YNZJ7bNzx7MOrE5Ofm5seRkvkptBThA/+J4wZTqAreEiIhKDftfRFRu9MZLaRnC8YjRKtXARF+7pioMKSTl1Jqd29xgOlbQ668rj7s9DirFMQ2VF8Opanv27MEnP/lJSJL9pb7+/M//HFu3bkUkEsGJEyeQSqUwNDSESy65BDNmzAAAdHZ2YteuXWhvb3fW+jKg/Ngf7uozLYzdUB/FI/+wBAMDp11tw7Y9R5BMZW88mZKxbc+RrLm4dotM+6W+kCgltNhT6IzoFc5b0TG7iK0iIqJCctoHY/+LiMqBtq8+/4omdL9+3FKtI6PgTEN9NFMjds2GHpwdyR2XPL/vOC6bPlk4NlC3S0udAeTFOKhUVrWm8mQYOPrhD3+If/3Xf8Wtt96Kz3zmMzj//PNtvXlFRQUeeOABbNq0CUuWLMF7772HxsbGzN+nTp2KEydOOGt5CbAanGhrbcLGHX2G7+VlKqISZTd63MnJz6/1hZwUszN7PzeDUHqF8xZcNcP1oCEREflTPn2woPe/iMgZv9xYFfXVew7EdW+ya8cbRuMT9XjK6HnaYFSsN47Hdh/WzWTSHi+/joOInDIMHO3YsQP79u3D448/jo6ODixYsACf//znMXfuXMsbuPvuu3HHHXfgS1/6Et56662sO2eyLDvKZmps9P+y5C+8+g627jqMxNj4yWVwKIGtuw6jvq4KC66akfP8xinVGNCpYRMKSbjr/1yZeV1jY934++88iPdPDuOCKdVY0TFb+L75Uo61Xvsap1Trfh6339SKh554I3MMACBaEcbtN7U6/gzd+OyfeikmzJ566qU3sXTB5bbey+7nbNXSBXXCtpTCd98rQd53gPsf5P0P8r4HWb59MC/6Xw0NtbZfUw74G/QWj6+3rB5fr/q0TvzkuV8K++qhkIS0IHqkHY/ojVvqJlVk9a+Nxl8fDCUy76k9NqLtb/r64qzHvBgHBRWPl7esHl/TVdXmzZuHefPm4cyZM3j66afxzW9+E+FwGMuXL8fNN9+s+7r+/n6Mjo5i9uzZqK6uxuLFi7Fr1y6Ew+HMcwYGBjB16lRLDVUrhayLR7p6c04uibEUHunqFS6pfvPVl+pmHaXTMlovnoyBgdNobKzDMy/8NisKP3ByGA8+/jqGTo84uitQUxUWRs9rqsKZY33z1ZcKp0/dfPWlup9H68WTsWLJrJw7F8q+2NXYWOfKZ693gRg4OWz7/e1+zvlwa/9LUZD3HeD+B3n/g7bvoZAU2OCEiJM+mJf9r8HBM8JBWzkL2m+w0Hh8vWXn+BayT2sk1hvH6XNjwr+l0zIqIyHT8YjeuOXWT16e8zy98df59dHMc0XHRk00hnB7HBRUPEd4S318zfpgpoEjRW1tLT7/+c/jU5/6FB588EHce++9hoGjY8eO4YEHHsC2bdsAAM899xxuvfVWfPe738Xbb7+N6dOno6urC7fccovVJpQUN+e1KisG5FNvyMht183Cpq4+aMoc4exICms29GSlXWpPfsD4/GC9lFY/zsV1M3XUL3WciIiofNnpgwW9/0VEzhSrT6udHjcyKi6hAYzf1AYA5Sm11RF87tqZwrFGRUQyfV5baxOOHjuF5/cdz3pcWyLE7BjUVkeE4yE/joOInLIcOPrVr36Fn/3sZ+ju7sbChQvx05/+1PD57e3t2L9/P26++WaEw2EsXrwYN954I84//3zcddddSCQSaG9vx5IlS/LeCT+yE5xQ5vHqUVYMAManMLl9YtcGhbTvqa7/o53r62atoELRKz7tpIYU5y8TEZHX7PTBgt7/IiJnitGnFY0ljCTG0lkL+oyOpXOeo31Pvecpll/fgsumTzas7aR3bAAgEpYwPJLM1IYtlfEQkV2SLMu6OcfvvfcennzySfz85z8HAHz2s5/FLbfcgsmTC5euKFIK6Wqik1ZlJCRcNlGJUJtpqI/i9ptacf+214TF4WqrI4hWhIUnPavF7vTaol6BwMlz7dBrq5upim4V/7PzOecryKmaQd53gPsf5P0P2r5zqtoEP/bBOFWN3Mbj6y07x7cQfVpt/zsxltJdpEdLAqB39qmpCkOSJJwZTiIkQThOymd8Ijo2wPjYS5Ik4dQ6pR1+W7251PAc4S3XpqotWrQIV199Nf7v//2/uOaaaxwVUgwa9QmxtjqCioiEsyMpw5OG1UyhwaEEHnriDeHJ0CjaDUCYGXT02Cns7x/MCp7YyWZykvlkFrAxymJausC9wmhupY7qTeHjxYGIiPLBPhgRFZLXfVq72UVq2rpGWuparVZXXtO2zWi/jY7NX6/bK3xPpR3MQKJyYRg42rVrF6ZPn16otpQ87QnxzHASlZEQ7uicY3iiMEp/VAtJEBZmC0lAtCKUU+BaqXuk/Fv7N/V8XuWkVlsdEUb+RWmqdlNarUxtM6rfZHfFs0Lh/GUiInIb+2BEVGhe9mlFfXw9olkUopIaduQzPlH+LTo2FxiszKbIpxYtkV8YBo6mT5+O3bt340c/+hGOHDmC6upqzJw5E6tWrcI111xTqDaWDKdFq0U1d7SMIu1pGcJV0QB70fzRZBoVEUm4WsHc5oacom92awXpHZ9te46YXgxYbJqIiIKEfTAiKmXaLB6rffnKSEi34LXeCmhW3lNvfPLY7sN5LTq0omM2Hnz8ddOgGMcyVOoMA0c7d+7E/fffj7vvvhstLS2QJAn79+/Ht771Lfzt3/4tFi9eXKh2lgSnRatF6Y9Tp1Tj8O9PIS2PZxTNv6IpM61Mq6YqjKrKiGH2j9WT1dmRFO7onJPVlrnNDeg5EM+JxK/saMHKjhbLKa16bTgznDSd31yoYtNu1T4iIiLKB/tgRKWH/chxdqalqccxRsesrbXJUeBIGUeJ3jPWG8/75vuCq2Zg6PRI5nM3qrFEVMoMA0dbt27FI488gmnTpmUea25uxpVXXol7772XnRaNfFYjUKc/Kidb5aSTloGeA3HMv6IJL77xLlKas1FiLI0/n50d3AGyo+tmGU3qtmpTMdds6NGNxK9fPd/yBdHO3QY1pyue2VWqq8QREVH5YR+MqLSwHznB6rS0ykgIt103y/Lx0QvKGFHGUZdNn5yzHaWkh4idQI9oHOfG6s1EfmIYODp79mxWh0Vx6aWXIpFgup2WW8u8603p2t8/iElVkZzK/cmUjP39g6bZP0ZZROq2Wk0ttRsEsjIlT0tZJWHjjj489dKbuPnqSws695pzkotD+x28/aZWtF5c3NUciYgKKQh9MGZnUDlhP3KC0RhBGVs4+c23Xzktq0armiQBsiwOLul9DkbtHBlNItYbt/3ZceEcKleGgaNwOKz7N1kO1nKsVrh1ojAK1OitqTI4lDAsaCf622XTJ+e0FchdgU2P3ZRL0fEZGU3qpogunDftw+DW+N8HTg57eufGrQAZ5Ud0x+6hJ97AiiXW70gREZW6cu+DMTuDyg37kROMZmGsXz3f8fsuv75FN3Aky8CmtYuwSmeVM7326H0+Z0dSjs9JXDiHypFh4Ijsc+NEYXSyDYVDwsr9TubNitoqmpYm4jTlUrvNWG9cd75y9+vHLd8xcEM+Uw3JPaI7domxVCDv2BERlStmZ1C5YT9ygluzMETMjrPedLaQ4O77svZmw7pJPCcRTTAMHB0+fBh/9md/lvO4LMsYHR31rFFBZ3Syra+rElbuHxxKYM2GnrxTIY3uiignYjdTLo0K3enNYfbqzo2XFznKZjQ9gXfsiIjKvw/Gcz2Vm3LpRxr10axOL3VjFobetsyOs974QfS4lYLbPCcRjTMMHO3evbtQ7SAVo5NtY2NdVuV+NTfSvI1SNtPyxInZzci73jYLvSoB5yQXhtn0BN6xIyIq/z4Yz/VUbsqhH2nURwNyy1kYjTvymYVhZSqr3nG2e24xW7yH5ySicYaBI0mShIUZAeDFF1/ERRdd5EmjaOJkq0TbN+7ow/buftx+U2vmb2s29OSc6EaTaTy2+7DhRcvoboFZAWsvUjZF2wxLQCQiITGWHTny+s4N5yR7z2x6guj7EK0Il9wdOyKifJR7H6xcsjOI1Eq9H2nUR1P+Lfqb2/ucz1RW0bklEpYwMprEqnV7bY19KiMhzG1uwN0/fBFnhpMAxhfusbMSHFG5CN9333336f1xxYoVuPXWWwEAd911F2644YbM37761a9m/lZo586Vfoq2FUq0XTlRDSdSeO3Qe2g4rwozptbiJ88dFb5uLCljOJHKvOY3vxvMvEb0nuq/z5hai4bzqvB2fCjzHlrDiRSefulNvLT/OOomVWLG1FrDfXjgZ2/gJ88d1X2+dps1VWGk5PH9UKubVIH/Z3Fpn6itHA89NTXRsvju631vhxMpfOrqS3O+Dw31UXzx01fgzy6/oMAt9Y9y+eydCvL+B23fJUnCpEmVxW6GL/ixDzY8PAq36nKLzvWfu3am767xQfsNFlo5Hd98+nhesXt8jfpoRuOCT119qaP2KbTHTi8DaDiRwtQp1bbGMrXVEYyNpZH4cFwxnEjhtSMDGDqbwJ9edkHO80MSIGM80+ijLVPxy/3vYmR0Iqg0lpTxxm8H0DilGjOm1pbVd9iPeHy9pT6+Zn0ww4wj9aod77zzju7fyBtmRYLNUisV6gi9lQi+UUaTmlmKqp0VU9R3aNZs6MHZkdztVkUjvutQ2sEVZMZZSSHW3rFrbKzDwMDpgrSPiMgPgtAHK/XsDCJFufTxzPpoXkwvFR07o/bZGcsA4+MKJcik9vy+47hs+uTMc0Wf05oNPUimcs+3KRksmk2BEzL6oyRJwn+L/k/uMyscuay9GZURw48w5zV2ilFaeX91+qqWWbqrWVu13hesJldKnB6PciP6XnF6AhFRNvbBiEpHufTxjPpoXvXfRMdORNmWlbFMrDeONRt6sGrdXsNAlNMxidnfiMqR5YwjKjyzqL+oOFxiLCWMqiuvqakK4+xIbqppTVU45zHt++uxuzKK2YlWb78vmFJt+Dq/4woy48qheCQRkdfYByMqHeXSx7PSR3O7/2aWYaTdlt64RBnraDOYnG5bvX29vxEFiWHgKJ1O4w9/+ANkWUYqlcr8GwBSKfE8V3KPlSLB2tRK0clSfTdA7y6l3uPaVE83VikwO9HqFcxc0THb8HV+xxVkJnB6AhGRMfbBiEpHOfXxjPpoXvTfjI7d+tXzcx43K6xvNYNJ2YaRZe3N2PzswZzpamEJzJSnwDEMHB05cgQf//jHMx2Vj33sY5m/MU3ae6Ko/+03taL14sm2XqO+GyDKRjJ6XM3uCihOV0zR24cFV80o6To3XEGGiIisYh+MqHSwj+ec3WNnNtaxmuVlZ0yybc8RrqpGgWcYOIrFYpl/S5KUlTbNTkth2C0SHOuNG6aQ5nNHxO4Uo3ymJJVjRgqnaBERkVXsgxGVjnLq45mNJdzm5NgZjRP0xjq11RFEK8IckxA5ZBg4amtrE3ZOZFmGJEk4ePCgZw0j+6ys6CCK6gPA3OYGS9uwe/LkyTYbjwcREVnBPhhRaSmHPp7RWALwLjDm5rHTy2D63LUzS/7zISomw8DRzTffjH379mHRokW45ZZbcNlllxWqXWRA706A3ooOP9rRh407+gCMp1c2X1SPg2+fynpez4E4AGB//6DpBcGLOxGx3ji27jqIxNj4HVUJQMslk/HeyWHL0/SIiIjKBftgROQlUX9ebyyhjCMUopvTVrYTkoC0DOH4wc74wui5ogymuc0N2N7dj407+jzNoip0thZRIUmyybIdw8PD+K//+i889dRTOHfuHJYuXYrOzk7U19cXqo05SqnOjdsnkN7fn8KDj7+eE0Vf2dGSc1LPl/K+Vopva59nR6w3jh919cFsAZloRRgrlgR3TrHZNMVyFuR9B7j/Qd7/oO17KCShoaG22M3wDb/1wQYHzyCdDtZqb0H7DRYaj6+39I6vXn/ealFphTrgJBrrmK1wptQLAiBsz/wrmnJuaus9V28sYnXs4nTMphxjL8ZIxHOE19TH16wPZho4UovH43j66aexc+dO/NEf/RF+8IMf5N9aB0rly+P2CSTWG8fD/3lQ2GlrqI9iZDSJsyPurrSivSAodwpEzxOtfGCF3mptIkZ3KspdkE+cQd53gPsf5P0P2r4zcKTPD30wBo7IbTy+7hEFPpYuuFx4fPX63nr9fCOigNPCedOw/PoW3P3DF00X4KmMhFARkSyNYYyeqzcWMVoVWnl+PmM25TtsZTtkH88R3rITODKcqqb1wQcf4IMPPsDJkyfR0GCtJk6Q6aV7bu/utx3wUE5oeh22waEEImH3i2UqqajKfuhdTKwGfvJ9rbJ9sxRZP6SKOm2DH9pORET+wj4YEWmp+4xqSj+5vq4qq8yD3vMVdoNGIQnCbKLn9x1H/INzllZtHk2mMWr+NNPn6u2TlcfdGLPZ3T5RqTENHL377rt45pln8PTTTyMcDmPp0qV4/PHH8ZGPfKQQ7Stpbp5ARCc0tZAEJFPu3wXUuyBoWVmVTUu5eDmld0K3UiTc6+CMlTbYfd3SBXWutY+IiPyPfTAi0mM2DWw0mcb9P9mHv75xNtpamxDrjWPzswcNxws1VWFIkmQp4GM2tU1bT9VrNVVh4eNWVpR2Y8yWz8rVRKXAMHC0fPlyvPnmm7jhhhvwve99D3PmzClUu8qCmycQsxOXF5njduY6K3OOrTK72FklOi5mdw2cBnXscHrnwuh1Sxdc7krbiIjI/9gHIyIjZjeVASCdljN93G17jpjeZE6MpXH59PMsBX0qIpLlTKFCEK1CCeivsqYeu7gxZrOyHaJSZhg4+tWvfoVoNIonnngCP/vZzzKPK0vBvvbaa543sJS5dQKJ9cYdzTnOh2Qx00hhd7qYlYudmlFtJe02ze4auDmFUI/TOxdMcyUiIoB9MCIyZrVvqPRxrWQRJVOy5Uwht+uq5uvMcNJ07KE308DqmE1cR2p8RoCV7RCVMsPA0XPPPVeodpQlN04gmdpGBa5Fab1kujgab5bVYycQckfn+F3WrbsOIzE2cZHSntCVbeqprY5k2iIyOJRwbQqb0zsXTHOlIGJdL6Jc7IMRFU4pXIe0baytjlgKBgHBuAFZWx0Rjj2OHjuVszKb9rO1MmbTG9uo60i1tTb57ntD5BbDwNFFF11UqHaULScnEPWFwa1MI0myFwyyY25zbpFOo6wewN6qDdu7+7F+9XzU11Xhka5e3RO6WRbT8Mj4nQi94IzeBQewP4XNabYZ01wpaAoxdZSoFLEPRlQYpXAdErUxLAGRsGSpxqlbqy/r9aFFImEJsiwj5aAqRUiSkLY5cJFlWTj2eH7f8cz/jT5bszGb3tjm3x57zbfBRiI3hYrdAMqmXBiUk7IbQaPa6ggmRcUF49ywv38w6/9m08XsZlAp77XgqhlYv3o+Nq1dhPWr5+ecnM0uZCl5/KS/rL0ZlZHsr35lJKR7wXFawLsiMjHXurY6YmlJz7bWJqzsaMlkGDXURy29jqhUmQWZiYiIvFQK1yFRG1Py+I1YdZ9x4bxpOX1cYPwm723XzYLTBZgb6qPYtHYRlrU3I2TxPaIVIUdBIwBIyzJ0ShbpshoUc/rZGo0zlPFNrDdu+32JSoXpqmpUWHZr/xhpqI9i/er5AIBV6/a68p4i6hOp2XQxq6u0qSkXxBdefccw48jKXZDBoYRuOurGHX26r7FDVPh7dEx/n0Xp0crnVg5KIf2biod1vYiIqJhK4Tqk15bRpCzsV6mzbJT/v/ybdxGJhJEas5d1FJaAxFjK9lgi3+wmJ2UzrH5mTj5bs3GG2/VSifyGgSOfcesipUxvynfJeyvU9XeMAl92VmlTv0bZD3WNo8GhBDbu6MPGHX2ZaW+VEfNbE0pbRemoSnBD7zVWxHrjeLirLyejSu9iIko93vzsQTy2+zDOjqR8E2hxGvwphfRvKi7W9SIir/EGBhnJ5zpk5bvlxvfPKGixbc+RrPdP6ASGEmMyAPvBHCkkWa6lpBYOwXHGkV1KSQerKzY76WOIyklo+SnYSOQ2TlXzmZoq8ZQyq2mhynPnX9GEbXuOYOOOPk9PYurAzpoNPYbbUk/B0hMJS5ljoJ6mtb27X/dCqARpRpPmtyZE9ZgUoilskbCEkdEkVq3bizUbegxTUM0KmYuOjSjQlkzJmbs0fkh91U6ftNOmUkj/puLSmzrKul5E5IZ8rmEUDE6vQ4/+4lBWP1v03XLr+2fUljPDyaz3txvk0Rt7KKzUUBIpVNBIoZR7UBbDAcZvKkc08/Oc9jG05SREeNOLyhkzjnwk1htHQjClKSwBq26ag7bWJvT+/lRmula0IiwMpsy6eDJ6DsRdm/KmR8J4MAgwj/DXVIV1M3oUyl0YYCL7RwkwuBX8euXgiczKCkqmkt5ynbXVEQyPJLOCOEqWk5Pi3KKLiZX9UgItSxdcbnd3XWEU/DG7Y1YK6d9UXFy+loi8lM81jILByXUo1hvPmQ4G5H633Pj+xXrjeGz3Yau7Y1syvxllnlP660YL66jLZKjLQ4wmZYQlZFagy7ePocxYEJWl4E0vKncMHPnI9u5+YVQ/EhkPumzc0YfGKdWZE96aDT3CwNHh359ypai2mZrqSKYdRgGT8bnRaZwdEacBq0/gsd44NnX1QTkMg0MJbOrqgwTAjV06O5LKBILSqm2op08pbVmzoUf3ro0oiGQUDNG7mFhdncJJnSW3BuJGwR+z7XAaElnB5WuJyCu8gUFW2L0OGWVOq79bdr5/olWVa6sjODucdKUPrEcvo98v0jKwae0iPPqLQ8JgHZCdzS4qIh6tCOOBr1zjWpvUgcEPhhI4nze9KAAYOPIRvYtLYiyVOakPnBzOBDn0nl+IoBGATFDFLIsoMZYSBmDUxbsVj+0+DG3szGGGrC2iuz92Cuxt2XkoczdDKyRBd2U0K/OlAft1ltysK6QX/KmtjphuR7R/vCNDRESFwhsY5AWzvq/631a+f9q+m9KXd1JbqNCMMoHc0FAfRaw3jp4DxtP7zFY9c5sSbGxsrMPAwGnX35/Ib1jjyEesdmKUIIdXnR7tXGAjsd64aTuMsna08l2BIR/a9tg5vqPJNGRZFs6R/+sPpxmKaOdL11ZHcpZKtRtocbuukN7cf1mWTbej3T913SoiIiKvsY4aecGoj6j+bln9/rm5qnJlJGRat8guvVqrlZEQZl082dVtad9/WXuzpePTUB/V/VwYKCbKHzOOfMRq9gngLHIeDo0HhcZXVdD3ibkXZuoAmdne3Y+5zQ26qaNW78gAMC0UqFfTyS3a9tj5PIDxoNcdnXMMp26JpnapRSvC+GjL1MzxdzLNzO20fL25/xt39FnaDqchERFRsbCOmn+I+kBLF9QVu1mO6PURF86blvXdsvr9yzcjRlu3E4BuP82uykgoc9NP9Bl6VX+ppiqM266bhbbWJtN9UQfjjDLdrZZy4EqM5EfF/l4ycOQjoovLyGjStSycqsowxpIyzKoF7e8ftBw0GRxK4MXXxUEjI9q7LUqKruFrKkKoiIwvCVpTFcbIaBqpD3NjJQAtl0xG//8OObpjUxkJYW5zQ2ZlOOXHuLKjxbSo98R7SKZBI+3Urs3PHoSclrNqOvUciNvOyhHNi9eyO91Nuy/aaYV6x8Wvd3WKfbIlIqLi4A2M4tObRl9fV4VWDzNWvGInIKn+/il9EXWN/83b8AAAIABJREFUTCC/6V5KYOfosVPofv04Nu7oQ0iCa/VB1dnkot+SWwEqhaj+qZ3nA+LPxWopB7dLPhC5wQ/fSwaOfEZ7QhZV7XfKagBqcCiRc0E0uqBZrUGkzPNuqI9ibnND1oUzMZYy3cfT58ZQGQlh4bxpePH141nblQH89tgfsrKlrBaeVtqjXolO+TGu7GjJBExivXHDi+NoUs5ZbhXI7lxo91FUDN3JahuiefFqdtLyrZyYxlcAzP0++TX93w8nWyIioqDSm0a/dedBfOfOtiK1Kj92A5JGNxCNgkaRsCTsLyrmX9GEl/Yfx8G3T2Ue03u/sOSsdqi236S+Gec20Y1KPXd05paD0PtcrK5wx5UYyY/88L1k4MjntAGcQlAyRrR3SPIJYCmFsGO9cWzbcyRrapud/RpNptH9+nHhBTGZkrG/fzDrgqNkEBlZv3q+cGU47Y+xrbXJ1uegfb2d/RwcSuRkP+mlc+vN+9amLVs9qZidmPS+C+qUYr/xw8mWiIgoqPT6QO+fHC5wS4rH6g1ENbPyAADwysETlm8OV1dFEK0IOxpTjCbT2LijD1t3HUYymfJk8RpR1rpRW70o5cCVGMmP/PC9ZOCoBKgDOH//7zEMOLjIVkZCqKwIma7OoJcxYicDSe893cqeMtquskS8elWvzc8e1L0wKxcoox/jqnV7Mxduu3WP1O9rNQNK+1qzdG6j1fU2rV1keXtm76c8rheoqqqM+DYI44eTLRERUVDp9YEumFJdhNYUh90+h7oP93BXn27/105JizPDSchyfhEfL+uNisYgbq2MaPV9uBIj+ZEfvpdcVa3ErOiYLVydYeG8aVkrV2n/v7KjBZ+7dmbOa9VqqsKGtXXaWpuwfvV8bFq7yHLQSHlPYPyi58aUO72VHRRbdh7KzIdua21CtEJ/n5ULVLTCePUJJXhz9Ngp2Jkxrv4x5zOFS0nnNtuGlcfNmL1fKQZhuMoGERFR8eitLraiY3aRWlR4dvoc6ufGeuOwvt6xuUKsYBySJNsru2kLiyusrEwX641jzYYerFq3F2s29AjrIlld4Y4rMZIf+eF7yYyjErPgqhkYOj2SV5FfbdaQk/cwy56prY7gc9fOzExt2mRwp8SOsARIIQlpg/xY7RQkowuk0j4rd09Gk2nd1eP0qH/Mba1NeGz3YWF71J+F3XRuURZUPicSs/fzQ8TbLrePEREREVmnV0x6wVUzMDBwuuDtKcSCGdptaOtpAuP1i9SLpAC5q4Bt6urTnRYWlsann5nNKCi0tCyjqjKC266bZZqpb3b8zQqRW61jabWgOVdiJD/yw/eSgaMSJCqgra2HY3TyFS0Pv3FHH7Z391v+AhrNt1baoLyvW6s61E2qwNnhJFIWJlVbmSIWkib23wuSBBw9dirrB37xR+qyihcq2q+chuXXj2dm6dVl0kvndvtEYvZ+pRiE8cPJloiIKMj8srpdIRbMEG2j50Ac869oylrERek76fVPtnf3G9YSSsnwXdBIoV5sZ9ueIzntVFaDs3LMjb47dupYWv0O+uW7SqRW7O8lA0clLp+Ln5PXWllFYXAokRVUyjdopBTW/soDv0Ta4rzs2uqJr7ZeXaK0DNdWrBORZeQUAdc7bvv7Bw3bq03nFt0p065CkQ+jE1Nba1Nmyde0PB6Am3+F/y+wxT7ZEhERUfEVYsEMvW1oF3FR6G3Xz2UAzCh9caX/5VWWVymWUCAqRQwclbh8Ln52X+tWcWs7KiMhzG1uwN0/fNHWHRV14T9lX0SFBQu5L0aUVdS0d5lE6dxGAT/R67xIve45EM8cy7QM9ByI47LpkxmYISIiIl+zEmjIN8jhNJih3W5NVbggNYm8oC3CbeUGnpPjXoolFIhKkafFsR966CHceOONuPHGG/Hd734XAPDyyy+js7MTixcvxv333+/l5gMhnyi73dfqrablFSWTpedA3HYarvYi29baZLvG0rSG7Klh4ZCESNjN8oTZlACQqKCfml7A77Hdh7Fl56Gc1djM3s8uo4AjEREVH/tfRPrMFsxQbtDl058y2oZeIWfRdhNjaVcLYzshOWyA3YCX0+Puh6LBREHgWcbRyy+/jJdeeglPPvkkJEnCF77wBXR1deF73/seHn30UVx44YW488470d3djfb2dq+aUfbyibLbfW2hUz7T8vgULifBKtE+1FZbKx4YkoBZF0/Gkd9n1yJKpWVIkvX3cWI0mcaWnQcxmpyIcikXzvq6KrRePFn3cxBdoJWAkptZSEwJnlCI4ppERHaw/0WkL9Ybx8hobh9OHWhwYyqbXtmBuc0NOVnjm589qLt4StJCXU+vWawSkcNuxo/T4846lkSF4VngqLGxEWvXrkVlZSUAoLm5GW+99RYuueQSzJgxAwDQ2dmJXbt2seOSh3wKFdt9rVmgySxwEK2QkBizt5S9k2CEaB9ivXEMj1gL9qRlCAtYA+MXz2hFGNGKsGeBEnXQaOKxNLbuPIjv3Nlm+7icHUllOiNuFIAslZRgr4M6hSiuSURkF/tfRGJ6JRckKTtz2o0bZHrBDFFwJJmSkUyV5nQ0PVbHIlZrp5phHUsi73k2Ve3yyy/HlVdeCQB46623sHPnTkiShMbGxsxzpk6dihMnTnjVhEBoa23Cyo6WzKC9oT5qa4UCO681SgUV/U2rtroSm9YuQk1V2LRtyl2ZkMX0WOV5evtgtiqFHUZFro1Y3Rc9758cBqD/OagLghsZTaaxcUdfVnq0HVZTgvVSsQvBjTRzM5yyR0R+xP4XkZheyQUlo0a7uIuW3Rtkba1NWL96PjatXYT1q+ejrbUpQNnZ5p1ubV9Nj99uTBIFlefFsX/729/izjvvxN/93d8hHA7jrbfeyvxNlmVIDibONjbWudjC0qPd/6UL6rB0weWO3svOa5cuqEN9XRW27jyIgZPDCIUkjCbTeOqlN7GiYzbu+j9XZv4m8sFQAo2NdQiFQgD076w0TqnGR1um4rlfH7NUlyhaEcb/+5d/igVXzdB9zgcuXqjrJlXg9Lkx26+zW2NJ64Ip1WhsrNP9HOz+lAaHEti66zDq66oMj52WevvvnxzGBVOqsaJjdtZ7vPDqO9i66zASYxOZTk62pWbnd//USzFhUOepl950/FvR0vtOKd9zrRdefcfwmJnheS+4+x/kfSfnvOh/NTTUutjC0sHfoLcKdXzz6QtGK8K4/aZWw7YaXeeVv7lNQv6rF3thNClj038eNOz3ifpqWlaOux/4vX2ljsfXW1aPr6eBo1dffRV333037r33Xtx444145ZVXMDAwkPn7wMAApk6davt9BwZOu9nMktLYWFfU/W+9eDJuvvrSrFTfgZPDePDx17GyowXfubMNazb0CO8enF8fxcDAacOgS0N9NPMeStDBTEN9JVovnmx4XM53OO1N5IyDoJFVkqQ/l3xFx+zMPoo+h8SY/VpQibEUHunqRevFk229rvXiyfjOnW1Zj6mP/yNdvTmfn9NtAfa/93rBy4GTw679fvS+U8r3XE2bHq/8ZoZOj1jKDiz2777Ygrz/Qdv3UEgKbHDCTV71vwYHzyCd712QEhO032ChFfL4Ou0LKtPMjPqaRtd5AJ6tSuznX2MqLRv2+/T6agorx90PeI7wFo+vt9TH16wP5lng6N1338WXv/xl3H///WhrGx9g/umf/inefPNNvP3225g+fTq6urpwyy23eNUE8ohZ8Tqz2klGNXrUU4usOj44jFhv3HAALmqTU15dpCUJWHDlNPQciOe0M1oRxvcfew3nq2r1uLXKXb7Lz4peU+wC2nrfsZAE0++KVXZqhLlRaJOIyAr2v4jEnPQFN61dZOl5ZtPXC7kqsZ/o9fuMSgc01EexfvV8r5pERA55Fjh6+OGHkUgksG7dusxjt956K9atW4e77roLiUQC7e3tWLJkiVdNII+YBQXMVjdY1t6sO4e8oT6KR39xyHab7K644DfRCgkrlsxGW2sTLps+Gdv2HMlauU095UspwOzWfqiXn9387MHMCh7KSh8K0eepVyC6piosXB2kUPPU9TqHaRmuFbC2s4pHsQNpRBQc7H8RiWmv2zVVYSTG0q6sXFau13mzlYT1+nsKvX6fUT1IK0W1iajwPAscff3rX8fXv/514d+eeeYZrzZLBWAlKGC0ukFbaxOOHjuF5/cdz/nb1CnVwsfNDA4lsGrdXsPBu7pNetPpCq0yEhIW8x41mHam3MFyuuqcdvvKBXrrrsM5nadkSsbWXYchy7Jw9TC9O2yVFRFURkKOVvtzg3I8H+7qy6kt5Wamj9VVPEplJToiKn3sfxHp0163Y71x/KirT1gmwOqiI0D+Kw8XWm11BOdGkob1N2uqwnjgK9fgrh90C/v9NVVhPPjV9pwbj4qwNBEE0manGx0PZmIT+ZNnq6pReYr1xoW1dNQXByuWX9+ChfOm5Tx+8O1TebXP6upZy9qbEba5YptbrKwAZ5bSPDiUsLSSnZnKivHXj3+u4jtGibGUbvq13oX/zHASo8m06b56qa21SbdDVOgOnNWV6IiIiKhw2lqb8IWb5iCi6RRGwhI+d+1My+8j6lcqfWOn/bWwNF6qwAvDJkGjykgIt103CwBw23WzIOoKnx1JYc2GHgDAX90wOyvQVlMVxqqb5mRlp1stR1HolXiJyBrPV1Wj8rK9u1+Y0puSJ9JOrQYH9vcP2t5+JCyZphSLMkq0dzrmNjdACknjDTegZDDlWx9JO71LaYvomFkJatRWR3JSrisjEsZSMmR5PDhlpX7pmeEktuw8hIqI/eiYlbtGaXkiQFKMO0h+yfSxM62NiIiICseta7S2XymFJBw9dgr7+wfHV7+FvTqZKRlIWVwoxo7KiITRpH5LQhJybvaFdfrfyg3blR0teOAr1wjfz25NTnVmO/tJRP7BwBHZYhQksHuit5v18f/d9mcYOj2CTV19ZvGenILP2jo8VqbDaQMeTuojaQv86dUEAiaOmZUpaPKHOdVKyrXyvsrjdha9GU2mMao/fV2X1aBaMYtA2ylg7TWr09qIiIiosPK9RoturCZTclZ/0y8roBkFjSJhCX91w+ysY6F303ji/Sb6eXYWTAH0+7xcQITIfxg4IlvMghp2TvR2avQsnDcNC66agdv/cZdp0Eh5b4WT1ce0d5uUDsWqdXstv4coQGFldS0rwRglPVi5MI+MJj1bsWPhvNyV3uwG1Yo1t5+ZPuRXTlYwJCJyA88/7vNbDSM9C+dNw/7+Qd32RitCOd8FK/s2OJSwvWBKSDJeMKdUjilRUDBwRLZYCWpYPdHrZYPMv6Ipc1HTdmjsvLfd9gDmS4BaDXbVVIVx23WzLF98lceVzpxSH8goc8jqXHGlzQ31USTGUoarY2gtnDcNy69vAQB0v34c6Q+nwc2/oiknqBbrjQuLUSttKBZm+niPgxB7rGQeEhF5oVzOP3677rixYInbQpKE9IeZ6CEJaL9yvE8X643rBmu0NyaXtTebrqwGjO+/3s1Rvax2ZaXbYq/EW+r89lug8sXAEdliJcPE6oneSTaIlQvzwnnTst7D6sVcbwqT+oRcWx1BWFMaKSwB1VXjF1WzfTCquaPtzCkBGEmCcLUPK8ymyplROhg9B+KZ9qRloOdAHJdNn5zZT+V99QJdc5sbnO0A+V65DEIKyUrmIRGRF8rh/OOn6466j5gvsxpIlZEQmi+qt7SQzPjNRxmhkIR0WsaUuigumz4ZwPgxemz3YWGwBsi+Mbmpqw9mPUal/6wXjDLih5V4S5mffgtU/riqGtnW1tqE9avn447OOXmvFKW816a1i7B+9XzTk5zZyhSRsJS5MBq9pjISwsJ50zJBLr1Vv7QrQZwZTkIKSaipCmded82V0yyveqHXlrnNDXi4q08Y0HEaNBJ9Fm2tTVjZ0WJppTjl2Bh1MhVm0wGdFEInsVhvHGs29GDVur2+WHnEyveDspllHhIReaUczj9+ue5o+4hasy+ZnNPni4QlRCtyO2GVkRAWzJum28cdX7FMthQ0kjBx8zH94T+0qw7fdt0sSyu9pWTjfqi6/+w0Q+jMcBIrO1pM++SUyy+/BQoGZhyRY8WoH2OW8ZRMyTl3zfJpp+iEnEzJSKdTuKNzDgDYivSL2jK3uSEro8ep2uoIohVh031sa20yvSukLCGr7JOI+nGzDmexOqTllr7r9M6Sl8ehHAYhheaX1f6IKHjK4fzjl+uO2U2zQ78/hQVXThOWX9C7Ll82fXLO4wAsLQyj0HuaOrNM1B+1e/w2rV2U9X+nqxA31EcdlxUot36eXX75LVAwMHBEeSlG/RizQtWik6XTduqdeJV52RURyXbKt7Ytazb05F3YujISwueunWl5H806CNVVEdNV3tSdTLP3M+uQenHhL8f0XSdTDLw+DuUwCCk0P632R0TBUg7nH79cd8xXwB2f2q/OnlGyhpX+zh2dc3JudmqvzWs29FgOGtlps3pbRnUqRUTH2ukqxImxFGK9cdt9knLs59nll98CBQOnqlHJ0jspunWyNJsCNJpMm84Pt7INp3cFlERnJym9ZlP+1EUQ9abXqTuZRu9n1iHVpnpr06mdKsf0XSd3lrw+Dla+H5RNmTLKtHwiKrRyOP/45bpjpb+pvt5a7e9op6S7mT0SknL7t0Z1KsPS+PQ6NaNjbVTOQs+Z4aSjfl859vPs8stvgYKBGUdUsry+a5bPhcdKZ0K5UDs1qSqMB7/a7ui1SgfRyipoVqb6aZ+jrAhXUxVGMgVs3NGHjTv6hKvNeVWosxzTd53cWfL6OBRjymo54Gp/RFQspX7+8ct1x+rULOV6a6W/I8qicVNaBjY/exBA9nEU7UNIAlbdNCfzHDvH2m720WgyjY07+rC9u9/yZ1mO/Ty7/PJboGBg4Ig85eXcY69PllYuPLXVEYyOpR0Fr8zmxpvRy3bSI/os/vqmOZaCb1Y6mdrnxHrj2PzsQSRV+dVnR1LY1NWXeT7g3YW/HNN3nQRLC3EcvBiEqL+vjVOqcfPVl7IjREREAPwR/FK2v23PEcPl6pUsHyv9nXz7hoBSgDuk209MpmRs23PEtB+Wlif20cmxNistIaJkYR09dkpYG0pNr38TkoBV6/YGJojih98CBQMDR+SZQsw9tnuytBPIMqvbo9QVApwFr9wIjFil91ms7GjByo6WnGLd27v7sXFHX14X3e3d/VlBI0VKRtbdNSuBjRdefQePdPXaOsblUMdBy0mwtBSPg/b7OnByOHB1C4iIKJffiiEr/dBYb1x3iXulLma0IozEWO7f1f0dJ31DdaBIe0z0gjbqQJfXN5jsFt4eTabx/L7jmf/rjR/0Mr6UTPog1jwi8hIDR+QZr6YgOWUWyNJ2RpTVzvTSd+df0ZTXnRgnK1io2Rn4G30W61fPN0yR3rijD0ePncLy61tstc9o39R/MwtsxHrj2LrrcKazZbUjUK7pu3aDpaV4HPx27iAiIndo+1q339SK1osnW36tX4shqwNIojIARllEc5sbMv826xtuWrvI9eBZPjeYrLRlWXuz6Wq+ZkR9AL0yCWavIyJnGDgiz/ht7rFZET1tZ6TnQBzzr2jKpMqqpT9cKeOy6ZMdX4ycLlsKjE+Rs7Ndq5+FXor08/uO295Xo86PnRpK27v7c+7QWe0IMH13XKkdB7+dO4iIKH+iwM9DT7yBFUtmWbpGlcJNhbbWJttBkv39g5l/GwVZlL6T3jVdFMSpm1SB0+fGcp5bUxXOajNg/waT1UBeW2uTbjaWHWarJttZbZmI7GPgiDzjtxozRoNRvc7I/v5BrF89X7iqRb6dFatz47XUU+SssvpZmK3OZXflNm2NI2B8hQ47NZQYRCgsP0wD8Nu5g4iI8ifqayXGUpb7F6XQH4j1xoWZL0bU7W9rbcLRY6eypmoB1leo1QZxrv3oDPziv9+GuisWloDbrpuV9XonN5jsBPJuu25W3llHZn0A9h2IvMXAEXnGb7VVjC4oZp0Rrzorba1N2N7dLwwcKR2PyoiEsZQMWc6dImdFrDeOkVFxYGpwKIE1G3oywQErx8IqUWBMtKqaGXYE8mc1GOSXaQB+O3cQEVH+8u1fFLo/YPdGitGy9kZqqyOZG5TKdi6bPtnydVtv5bLRZBq/OvQeVt00x/J+2NlnO5+nWSaW8tnqlYqw0gdg34HIW+H77rvvvmI3wq5z50aL3YSiqamJlsz+z5hai4bzqvB2fAjDifGCfZ+7dqbjAWi++143qRK/+d0gUqorupK9o7RRq7Y6go6PX4KX9h8X/r2hPorFH73YcZsA4CfPHRU+LgO4o3MO3jg60WYZwPH3z6LhvCrMmFpr+t5KJ2ZkVH863HAihd/8bhAN51Vh9h+dj9eODAifpxwLO2ZMrUXHxy/B1CnVeDs+hD+cHcPb8SHUTaq01H5g/HPrffMD4edm9T2A8WPxwM/ewE+eO4qX9h+31YZiy+e7r3wHlOCd+vPW7v8DP3sjJ4iZSst4Oz6U9/fcDu25o3FKNW795OW+mYpQSKV0zneDJEmYNKmy2M0gHcPDo5BtDopLXdB+g17S60spfzO7Lhv149y+ntu5diq++9iruv2t2ZdMxsmhEfz/7d17dFTl3S/w7yRDkoEECCFpag1KOWIkBaSUaiSUSzEQkgAiPYI9gPL6Uo9WemiX9bJqZbVKUdpFi0pXpYhYC4JWi6GAQlMphCxbUaECoqKoLAnGFJqETC4zs88fvHuYmezrzL7O/n7+Igmz59nPs2c/v/nt55L48cnw+RAKRXD+f+pFfJ+rrxiMxdUjMKtiKCrHDZF8z8QySgl2hnDnnJGoHDdE8VjJnLPe2Fju//fLyYQ/MwPBrjD6ZvsxftQluPqKwbq/Pxj9vUMr3iPMxfo1V2z9qsVgHHFEpnLS2ipqc7ilplUFO0NoPNJk6lMMpSdoqc7n17qtq3hMpfMRBEFyAXGl7VKldhnRO4qlvKwY/fNydO+qFsspI2nsoOcactI0gNh7R2FhHpqb2ywvAxERGUdpbUct/bKVmz1s2n1cV/zVeKRJcQ2fz88GsbhmRK+YSBAEJNaG1jhPS4w3OD+g+PdYm/e8p+uc9cbGUv8/0wd09URwvvPiCH9x199Vd4xXLbPUCCktryMi/Zg4ItdIZkv2RHKJLHHhvlA4vtMXt44XOyG9w4ZT3S5dbliv1i/yer7wxwZtUs53hnslX5S2S01M1sTSuz7UpLElcbuuNB5p6jWsW+lYblhQ0yx6kkGcFkhERGZJTPwk0tIvW/FAUikJJNenihutyGlp7YqWOzY2khvApyV+U/s/Wf4MLKy6SvU4wIVzlhu5JPc+WhJ5iTFx7KYzBf2z0dUT7vW+WuMzLz0UdML6k0RMHJErJLslux5qQYKWYCWZTkyp45ULrrR+kVfb1jVWhk95u1i1vwPxnb3ak7BkR7EkU8dOGkljNT3JIK4PQEREZhJjKSfvgKWUBJKLv9TKLb5O60jw2F3PlI6ptHvtnInDMGlsCZqb21QTD8mcM9A7ht28573oqKrcgB/BzlB0YW5xx+JFVaWG7ISm9lAwXZItXkqQkbMxcUSukMqW7FoZMdoi2ZEtckmpVL/IjxpW0GtnDgDw+RC3TkWWP0M1kNG62KPaguKiZEexJFPHXh5Jo+casnIaQKx0Ce6IiOgipXu7k/tlpfhFLv5SSuLE9rlaE2M+n0/1/8j177GJGUBb4kGpXIkbqcRKPHbs6CGpEUyJ8Voq14HSQ8F0SrZ4edQ8OQsTR+QKVowYSSZJkxgUGV3O2C/y/27twiCdX6oPn2iR/H2/HD+y+2TGBXNyo5v0Ejt7pe1otSS/Yuu2MD+A2RVDUV5WnFQde3kkjd5kkNXrkhkR3Lk18eTWchMRqVG7t8utdzRqWIEtZY29F+cG/JJJj9yAX/YeLXc+ibvJah0J3h4MqU7J19q/yyUe1tUdja5vqVYuub5Z6wiqxGOJUonPzFwj1Em8PGqenIWJI3IFK55M6f2CLRUUyUmlnOIXeaUFguW+gMqVqT0YwpoffKvX7+XWJNJDfDKlNEIp8WlYosS6bT4bjAYsyVwLdo2kcQonLVKfKNXgzq1PFd1abiIiLdTu7eVlxfjg1Lleo6Ib/tWE/3XpQMvug1L34kwf4M/0xW2YIu7eJicxzsgN+CEIAs53hqPTwJQSZlJiR3DL9Q9K/XvjkSb8eX+jpoTQ+JHFaPhXk2K5pPrmZJIX/XIy45JiieseaY3PzFwj1EmcPDqPvIWJI3KFOROHxa1xBJgzYkTPF2ytT1nMHtki9wX0g1PnZF8j1dmI571++1HN09LkqCXR1JJGUmWI3fktmadTTk6eeFmqT9Lc+lTRreUmItJCy71dalS01fdBqXtxWAD69cnAgH5+XckMMc7Q8mBATDD1y8lEV0+k166+ifTWi9IGJVLHPnyiBYuqSlVHnyf+Tc9amoD0LmqJ6x5p3QDFiDVC3TDy18uj5slZmDgiVzBiS3ajqSVHrCqn3BfQvW/3XttIJDdfXWrHDyNpmfq3cee7somr2F1JnNTR6wk83BCkWCnVJ2luHcLt1nITEWmh5d7uhPug3Hud7wzjsf83UdexYvv3RImjrZR2HjOiXvROIRPjK7Fc//dXe3utLQpcmK4XS88IKi27qBk1GldLssUtI3+dGPeSNzFxRK6RuCW73ZSColV3jDf8/V47+Klk4kwukFAbNSTXQSZ2UEprFWmhJ4mmFuiIAaeTRg/pCTzcEqTIMSPpleqTNLcO4ZYrd4bvwi4zYv3OnJRnQ+nkMfFJJI2fjXhSm3Mk3tudcP9WStTILQgtRcsIH7n3SYxpxNE2UmXVSm/yLXb6WG7AL5k0AgBBiA8IE2NGHwCpkFGMjdV2UdMzGlfPyC65neTcMvLXSXEveRcTR0RJsnLoaOORpriperGdo9IXULWEj9hBAr033PkQAAAgAElEQVQ7VzH5JRUMZfkzMH5kseSObbH0JtGUAh2xbtWCc6uDdz2Bh5uClERmJb1SfZLm1iHcck9pIzHbFm/c+S765+U4JmHu9sQnkVn42YjXeKQJDf9q6vX78SPjv/xatQyBEqURM1raUWmUUSKtiR+9/Vpi3DNqWIFsDJgb8KO7J9LrfM93hnG+80I7SC0MHvv/EsUmNeRiRrHsaslCPaOttKyjpfT5c8KINyI3YeKIKElWDh19ce+JXk9/1Nb80bLQIXAxMJIKeMX37g5FokGIeJ4AFBNHyQR/skmwDB8WVZUCgGJwLhW8r6s7Gl0kMXF3EyPoCTycFKToTbCZmfRK5UmaW4dwaxnZ1x2K4Jmdx/DI98ptKGFvbk58EpmJn414cqOHE9c0csIyBOIi3Xvf/kwy0aLUjnrWEdITE0n1a6OGFeDFvSewru5oXD1JxT1ysVnsAt+bdh+XTAKpyfDpL3tsm6olxbSOQms80qQpppJKqokLccsl15w+YpnILkwcEaXAqqGjSp2jXCcNAP84dgbd//PgSG74cIYPkgHv7+uOIjNmV5GIcLFzLy8rxt1rGxTLnPhkUQu5gOKu/301yoYMxN1rGxSDc7Wpbuc7w3hq+4UkklHtluX3oTvUu2Yzfei1uGO/nEzJQK1fTqYhZdEqmafjTkp6JXLrEO7YcssN3//ibNDKIily8jVAZCd+NuLpqQ+zliHQ+nBEHB2lNEJb7ny0riOUmOjRUi6lUTyxfbbWMmT44ne03bT7uOprpGhZuiCx7IkJr9hFuMXd59bVHcWLe09g1LCCXg895dYmkiMmftSSalLn4oYRy0R2YeKIyAXUnsBILbSYmIDJzPRBiAiI3bgjy58hG3AIQK9dPmKTNGoBsZYtdaUCqNiAQvzdpLElaG5uUw1GtQTpYQGGPgXukdkJJSz03krX55N+VCf3e7PofTreeKT3lAMRn8wZQ+4zPjg/YENppDlhPRIiJ+JnI57d9aHn4YiWxItcudVijix/Rq/dwpKZ0qjUZ2tNTkaE+PdIZrQRoK8N5UaBi8eZPOaSuCRRS+uFHdbGjyyOjgrSujaRKDbxoyepFjui3o0PooiswMQRkQvoXQdAqrMMhQXkBvzI7pMZ1xnrCTyAi4GS2hasasP05QKoRVWlsusiqQWjWreFTeUpcGKyS9C4cLhS8KK0noAZ9D4dF9fBksInc8aQG223sOoqG0sVz63rSRGZjZ+NeHbXh56HI1qSP3LlVoo5xNHcYv8pNypay5RGpT5ba9xjRNJObxsqJW7kptR1hyI4fKJFcX1MpfONTdTpSao9de8UTf+XyMuYOCJyAb3rAMh1lu3BENqDIeQG/HGv1zpHH7gYfGjZgjWxHLFJF7k1XZQCKLVgVOu2sHoDKLnFL42ahmD1U2m9T4OVzpNP5owhN+VUHG3nBG5dT4rIbPxsxLO7PvQ8HFFKvKiVWyrmyPQBvoyL0/xjRxUlO6VRaRMUqd3rEkklfHIDfl0PrcSpbkDvafh6Y1E1ydZHQf/suLJYmVQj8gImjohcQs86AGqdZXswhA07jgG4GOCt335Ude56bPCRGBjKlUOUOMJI7r20JCnkglEtZcr06Rslo2fxSzX9cjLRExJsfyqt92mw3dMOvMINazW5oYxEduBnI56d9aGnz5LrD2NHrsiRikm6esK9EjLiQzGl2KzxSJOuBBVwIY5q+FeT7FqLIqk1J+dPHY4NO47FLUngz/RhwqgvS64xpGWDkkRaEzdSr1OiNYbR8jDRyyMDifTKXL58+XK7C6FXR0e33UWwTb9+2Z49fy+fO6Dv/PP6ZuGdD1sQVsgERQTg46ZWVI4bgpKiXBTmB3q9JtMH9Av40R2KoKB/NuZPHR4XHJQU5aJy3BAUSbxW3L2jpCgXALDmhUOanm7lBvyouvYy2XMX33NWxdBo2WPF/r0oP4D3Pj0bDRr65WRiYdVVuoJZreXO8F1YFyrDB5ReNhCt57t71ceCaaW4+orB+LipFcGucFydNh5pwpoXDuG5v36A/Yc/Q17frOi5GX3tlxTlomBAjmQ5pEhdT4ntayYvf/a9du4+nw99+2bZXQySEQx2a56emy689hm0mhn1q6fP0tsfJvbVV10+CIurR0Rjkudfk57aHewK4/9UXok332uW/LsYj0kRy/j2+829NjkJRwQEsv3Rf0tp6+judeySolwMHhiIO++brx+O6vLLZetDKh4KRwTZsmuJRRNpiS20tpnU/7t2xJfQ1tGtqa214j3CXKxfc8XWr1oMxhFHRGlIy8gbIH50TypDy7W8VutTJ0HhW4neLeSNeOKpdZhz4nx8pbImlinZBTNToadu7J52QEREpJXePktrf6ilr1abRiUuDp1ILdZQem17MIT/rh2h+9hy5y33e71T7bSMaJ885hLFhbDlaG0zjgQkMg4TR5QUvV/gyXpiZynORZeSOBw4lQ5W7bVahyzL7fRhR3IFUC+3OMxZ6jOhtLhjLLkFM9dvP4p1dUdRmB/A7Iqhtn7GGHwREcljXOQsalvCJ9M2Wha3VptGlcrUb7WklNzDwn45mZrXJUr2/eUoJbwAYMG0Ut3lICJ7cKqayzhhuJ74BV4crhrsCuOdD1tQMCDH1GkrTjh3O/3z3c+x6o8HJacyKcnrm4VDEsOb/Zk+3Hx9/HBgpelSqdI6ZLmgf3avIc/9+mXjFxv/qWuItFGUyi0Ocwag+plQqtvn/vqB5HuL79jRGbLkM2YlPdealz/7Xjt3TlVzNk5Vk2ZXXORUTrq/G9k2cn11sCuMbfs/ik5dk5uSDqQ29VvttVJ/z8zwIRwRcL4z+fMX21MqaaSl7HUNH/WKQUX7D39mSsxpJa/101Zj/ZqLU9XIVMluJ0rJazzShGd2HUdXz4XROHpG24h/37T7eHQ0T27A32tet9kjehKHjucG/Ah2hhCzLqPiIoVqQ6TNetqrZcj73WsbFD8TanWrZTRWOn3G7Bo9RkRkBsZFFznt/q7WNnpiB7W+WjzXRVWlsiOOzVwWQOrv3aEI2jp6ZM9fjdIGIVrLrvS8UKxPu68TIlLHxBHplux2opS8F/eeiCaNRHo6fi3TjKwIfBPLYUTAVtA/25Kkl9JxlD4Td69tQFdPWLFutez8ofQ+bsMvWUSUThgXXeS0+7tS2+iNHbT01VrO1cxlARL//l8r6yX/n9ZrU6o9Aem1HeVoXaqAcQCRs2XYXQByH7m5zNya2zxWBKV2BL7lZcVYdcd4PHXvFKy6Y7xisDBn4jBk+eNvWeIIJaVA1QpK135La5fsrmxi3ZaXFWNRVWn0OBk+/e/jJvySRUTphHHRRU67vyu1jd7YobysGONHqic1nNSXDc4PSP5e67VpRHtKxW9634+I7MfEEemm9AWezGFFUOr0wDcxuVLQPxuLqkpRXlasObBpPNKEu9c2YPHKety9tgGNR5oMKZueoChWbN3GJtH+q2ZEWn/GnH6tERHpwbjoIqfd35XaJpmkyOETLarv6aS+bGHVVUlfm41Hmgx5kCUVv+UGpCe9OKnuiCgep6qRbtya2xxK07bmTBwWt8YRYHxQqrYTiBPIDdFWGgZ999qG6DmYNZ0t8TOhhVLdJh7PCbuqGckN1xoRkVaMiy5y2v1dqW3k+my1UcRKzDjXVNZwnDS2BK1tnbpfL07jk1qfKJlzlFqqQOk64S6FRM7DxBElhVtzG0ttnn15WTH65+Xg6e1HTOtE3Rz4Kq07INZlVp8MU9ddENtJ3PJWidTi5HLHA4DCwjw0N7elXEancPO1RkQkhXHRBU68v8u1TTJJLqUHVWacqxFrOCZzbcqtbQQA40emfq0rXSdOW2CdiC5g4ojIAbQsJjlpbAnKhgw0tRyJHbk4z9/pHbXaiJ/uUEQ2ADJ6VzYti2dm98m0rU7NeIqXzDH5JYuIyF5m7kbqpPu73Hkmk+SSSzaJU+fNKHMiKxaRTnW6nhZy14nTFliPJXUtzZyUZ2uZiKzCxBGRA6S6+KBRwZ+bn/KIAchimR1ElNz1673o6okgFL4wJjuV89Yybc2uxR/NaF87rxkOZSciSo5Z/YHT7slaRnTrKaMVI6qkpnElMjuOUBpZJe5IZ1bbOi12EsldS/3zckx/sEvkBKYujt3e3o6amhqcOnUKAHDgwAHU1taisrISq1evNvOtiVwllcUkxY5M7FDFjiyZhZ/t3p3MCHJ11i8nU3YB6/Od4WjSSJTKeYsLXTttkVAz2teua8bI654oHTEGIyVG37udek82o4/SsyNsMpSmiYnMjiPU1jAys22dFjuJ5K6lZ3Yes6lERNYyLXF06NAhzJ8/HydPngQAdHZ24v7778fatWuxY8cOvPPOO9i7d69Zb0/kKqnsyGJkUOTUpzx6yNXlzddfGberhxapnrfTdtoxo33tumbSIclJZBbGYKTG6Hu3U+/JboxrtJTN7DiivKwYk8dcIvt3M9vWabGTSK5dvjgbtLgkRPYwLXG0detWPPjggygqKgIAHD58GJdddhlKSkrg9/tRW1uLXbt2mfX2RK6itNW8GiODIqc+5dFDqS7Fp4RapXreqbSrVo1HmnD32gYsXlmPu9c2KD4BNKN97bpm3PhlgMgqjMFIjdH37lTuyXr6Mb3cGNdoKZsVUwAXTCvFf9eOkP27Wf2tFbFTMuTaZXB+wOKSENnDtDWOHn744bifP//8cxQWFkZ/LioqwpkzZ5I6dmGhtxch8/L5p/O5z5yUh5mTrlD8P1LnX5gfQLPE047C/IDu+rqlpgyPP38IXT3h6O+y+2TilpoyyWO9dvBTPLPzGL44G8Tg/AAWVl2FSWNLdL2nVnrORa0u5eosltJ56xFbFrG+fl93VHd9ydb/ruPR9mpp7cIzu46jf16O5HH1tq8WZhxTSuKxjLzunS7dzofMZ1YMVlCQm3LZ3CgdP4NG37uTvSfr7cf0sqqPMpJUmWPp7edSOc+Zk/Lw5/0fJd3fKsWJSn/TEhNbTe5aWlh1lWOvpXTB+jWX1vq1bHHsSCQCn88X/VkQhLif9Uinban1SrdtufXw8rkD8uc/u2Ko5A4fsyuG6q6vsiEDsXD6lb0WfSwbMrDXsRIXCWw+G8RjW99Ga1un4U+FjG57qTrL9AGBHD/agyHF805WKvUld/5Pbz/SK7Ds6gnj6e1HJBdq1NO+WplxzERS52/kde9kXrvvZWT4PJucMJNRMVhLSzsiEUH9P6aRdP0MGn3vTvae/MzOY7r6Mb2s6KOMJpZ585730B4Mxf1Nbz9nxPWbbNsqxT0ALIshjSJ3LU0aW+LYaykdpOs92Cli61ctBrMscVRcXIzm5uboz83NzdEh1ESUPKN3+NC6w4iTt0tVY8WuKInMqK9kpgaYsU2yHVsv29GGRG7FGIykGHnvTvaeLLc+jJHToOzoo1IlltkJO9Ul27Zq6165MYZ047VEZBTLEkejR4/GRx99hI8//hiXXnoptm/fjhtvvNGqtydKa3Z0ZG5fY8bqOjOjvuS2y3Xy2g1GYgBHpA1jMLJCMvfkwTJT3LzSj6lxSj+XTDmSiXvcEkMSeZFliaPs7GysXLkSd911F7q6ujBx4kRMnz7dqrcnIoM5IWnhhCdxWplRX3MmDpMcPm73ziNWcVP7E9mJMZj7eOX+trDqKjy29W3P9mNGceL1ohb32B1DEpE+pieO6uvro/8uLy/Hyy+/bPZbEpEF7E5aJM6db2ntwsad7wKwZrcRvcyoLz3Dx50YVKbCbe1PZAfGYO7kpfvbpLElaG3rTKv+yWpK18vMSfoWFTYyVlCLe7z84IvIjSwbcURE6cXsNWZig5fC/ABmVwyNO7bVayylGkyZVV9aho/r/RLihiST29bYckOdEpEzuO3+liqnTMfSysj7uRHH2rT7uOz1omdnMqMTllriHjv7RfbLRPowcURESTMr2PvDK+/ib299Fv25+WywV/Bi5RpLRgVTifXVeKQJd69tMD1o0fMlxC1Put20xpZb6pSInMFN9zc3SiVhYOT9PNljxZa/X04mzneGJf+f3uvFjISlUpxoZ8KQ/TKRfhl2F4CIKFbjkaa4pJEodicOQH4evBnz49V2BkmGGLSIgZ0YtDQeaUqprFL0fAkx41y1EhNpi1fW4+61DYp1YWX7p8rOOiUi93HT/c0sevoDvcdNpe818n6ezLESyy+XNAL0Xy9eSliyXybSj4kjInIUpU47NniZM3EYsvzxtzCz5sebEUxZGbTo+RJiV+CoN5i3sv1T5aVgnIhS56b7mxnMfLCSat9r5P08mWNJlV+O3uvFSwlL9stE+jFxRESOotRpxwYv5WXFWFRVGv1dQf9sLKoqNWWIsRnBlJVBi54vIXYFjnqDeSvbP1VeCsaJKHVuur+ZwcwHK6n2vUbez5M5ltZy5gb8uq8XLyUs2S8T6cc1jojIUeS2bwV6Pz2zan68GTuiqW1Tq4faeg16Fua2a7e8ZIJ5tyyoavcOhETkPm65v5nBzAcrqfa9Rt7PkzmWUowUe4z5U4frLo/Zm57YLXFtKH+mD6GwEP07+2UiZUwcEZGjSAVSADB5zCW2BS9mBFNGBZ9aF3jU+iXErsDRyESaHlbsqpLuwTgRkV7ivfffrV0YlHBPNLM/SLXvNfJ+nsyxpMqf6QMCOX60B0Mp9y/pmrBMjJXOd4aR6bswMsuIeiPyAiaOiMhRpAKpW2rKUDZkIAD7tk81OpgyKvi0eheUVCi1XSrBfLLXhJW7qqRrME5EpJfavdeIByty/YIRfa+R93O9x3LDgwi1PtmOOE4qVgoLQHafTKz5wbdMfW+idMHEERE5TmIgVViYh+bmNldvn6oWxCbLLQs8qrVdssFwKteEGUk3t7ErEUtExnPL51nt3ptqckRLf5N4LLfUHeDsBxFqdW9XHOeWWInIyZg4IiLXcOsXfTMDJbumeOmlpe2SCYZTuSa8Hki6ORFLRPHc9HnWcu9NJTmit19wU905nVrd2xXHuSVWInIy7qpGRK7h1i/6Zu4Q45ZdUMxqu1SO6/VdVcy8LonIWm76PJt979XbL7ip7pxOre7tiuPcEisRORkTR0TkGm79om9moOSWbZvNartUjuv1QNKtiVgi6s1Nn2ez7716+wU31Z3TqdW9XXGcW2IlIifjVDUicg23bmtu9hBpJ693INLbdrHrTRTmBzC7YqjkOaZyTbhhkVEzceg+Ufpw0+c59t4rtataqvT2C06vOzetv6RW93bGcW6IlYicLHP58uXL7S6EXh0d3XYXwTb9+mV79vy9fO6At89fPPeSolwUDMjBx02tCHaFUdA/G/OnDnd8IJDXNwvvfNiCcESI/i7Ln4H5U4ejpChX9fXp0PZ62k5cb6I9GAIAdHSG8M6HLSgYkNOrvlK9JkqKclE5bghmVQxF5bghmtrDSma2farXpRl8Ph/69s2y5b1JXTDYDUFQ/3/pxC33Xyd+npWI997bbhiF8WVfMrSMevsFJ9ddYn8Y7ArL9odSrL5+1ererXGcErfcI9yK9Wuu2PpVi8E44oiIXMWNT4y8PrJFpLXt9C6e6cZrwgl4XRKlD36e4+npF5xcd27cFESt7tlnE7kTE0dERBZgoKQd15uwDq9LovTBz3PynFp37A+JyCmYOCIi8iAnr5ng9PUmiIiIrJBMf6h1jUAiIj24qxoRkceIaybEbo+7cee7aDzSZHPJLvD6bmdERESA/v4wsX9vPht0VP9ORO7FxBERkccorZngBInb5hbmB7htLhEReY7ebeSd3r8TkXtxqhoRkU5OnualhRvWTIhdb6KwMA/NzW1xf3d7GxARkXMZ3cekcjw96y+5oX8nIndi4oiISAdxGLj4RE+c5gXANYkLt68hlA5tQEREzmR0H2Nln+X2/p2InItT1YiIdEiHYeBuX0MoHdqAiIicyeg+xso+y+39OxE5F0ccERHpkA7DwMUnnG6d6pUObUBERM5kdB9jZZ+V2L9zVzUiMgoTR0REOqTLMHA9ayY4Tbq0AREROY/RfYzVfZbaGoFERMngVDUiIh04DNx+bAMiIjKL0X0M+ywiSgcccUREpIPbp3mlA7YBERGZxeg+hn0WEaUDJo6IiHRy8zSvdME2ICIisxjdx7DPIiK341Q1IiIiIiIiIiKSxMQRERERERERERFJYuKIiIiIiIiIiIgkMXFERERERERERESSmDgiIiIiIiIiIiJJTBwREREREREREZEkv90FSEZGhs/uItjKy+fv5XMHvH3+Xj53gOfv5fP30rl76VzdyKvt49Xztgrr11ysX/Oxjs3F+jWXWL9q9ewTBEGwokBEREREREREROQunKpGRERERERERESSmDgiIiIiIiIiIiJJTBwREREREREREZEkJo6IiIiIiIiIiEgSE0dERERERERERCSJiSMiIiIiIiIiIpLExBEREREREREREUli4oiIiIiIiIiIiCQxcURERERERERERJJckziqq6vDjBkzUFlZiT/+8Y92F8dyCxYsQHV1NWbNmoVZs2bh0KFDdhfJdO3t7aipqcGpU6cAAAcOHEBtbS0qKyuxevVqm0tnvsTzv++++1BZWRm9Bnbv3m1zCc3z+OOPo7q6GtXV1Xj00UcBeKf9pc7dS23/m9/8BjNmzEB1dTU2bNgAwDttD0ifv5fan8hpHnnkEdx77712FyPteDGutVp9fT3mzJmDqqoqPPTQQ3YXJ608//zz0Wt31qxZGDt2LH72s5/ZXay0sm3btmg8/Mgjj9hdnLT05JNPYtq0aaitrcVvf/tb9RcILtDU1CRMnjxZOHv2rHD+/HmhtrZWeP/99+0ulmUikYhQUVEh9PT02F0Uy7z99ttCTU2NUFZWJnz66adCMBgUJk6cKHzyySdCT0+PsHjxYuG1116zu5imSTx/QRCEmpoa4cyZMzaXzHwNDQ3CTTfdJHR1dQnd3d3CwoULhbq6Ok+0v9S5v/rqq55p+9dff12YN2+e0NPTIwSDQWHy5MnCsWPHPNH2giB9/idOnPBM+xM5zYEDB4RrrrlGuOeee+wuSlrxYlxrtU8++USoqKgQTp8+LXR3dwvz589P277Tbu+9955w/fXXCy0tLXYXJW10dHQI48aNE1paWoSenh5h7ty5QkNDg93FSisNDQ1CTU2N0NbWJoRCIeF73/ue8Morryi+xhUjjg4cOIBrr70WAwcORN++fTFt2jTs2rXL7mJZ5sMPPwQALF68GDNnzsSzzz5rc4nMt3XrVjz44IMoKioCABw+fBiXXXYZSkpK4Pf7UVtbm9bXQOL5B4NBfPbZZ7j//vtRW1uLNWvWIBKJ2FxKcxQWFuLee+9FVlYW+vTpg2HDhuHkyZOeaH+pc//ss8880/bf/OY38cwzz8Dv96OlpQXhcBitra2eaHtA+vxzcnI80/5ETnLu3DmsXr0at99+u91FSTtejGuttnv3bsyYMQPFxcXo06cPVq9ejdGjR9tdrLS0fPlyLFu2DIMGDbK7KGkjHA4jEokgGAwiFAohFAohOzvb7mKllaNHj6KiogK5ubnIzMzEhAkTsGfPHsXXuCJx9Pnnn6OwsDD6c1FREc6cOWNjiazV2tqK8vJyPPHEE3j66afx3HPPoaGhwe5imerhhx/GN77xjejPXrsGEs//iy++wLXXXosVK1Zg69ateOONN/DCCy/YWELzXHHFFbj66qsBACdPnsTOnTvh8/k80f5S5z5hwgTPtD0A9OnTB2vWrEF1dTXKy8s999lPPP9QKOSp9idyip/+9KdYtmwZ+vfvb3dR0o4X41qrffzxxwiHw7j99tsxa9YsbNq0CQMGDLC7WGnnwIED6OzsRFVVld1FSSu5ubn4wQ9+gKqqKkycOBFf+cpX8PWvf93uYqWVsrIy7N+/H+fOnUNXVxfq6+vxxRdfKL7GFYmjSCQCn88X/VkQhLif092YMWPw6KOPIi8vD4MGDcLcuXOxd+9eu4tlKa9fAyUlJXjiiSdQVFSEQCCABQsWpP018P7772Px4sX48Y9/jJKSEk+1f+y5f/WrX/Vc2y9duhSNjY04ffo0Tp486am2B+LPv7Gx0XPtT2S3559/Hl/+8pdRXl5ud1HSEuNa84XDYTQ2NmLFihXYsmULDh8+jJdeesnuYqWd5557DrfeeqvdxUg77777Lv70pz/hb3/7G/bt24eMjAysX7/e7mKllfLycsyZMwcLFizAbbfdhrFjx6JPnz6Kr3FF4qi4uBjNzc3Rn5ubm6NTeLzgjTfeQGNjY/RnQRDg9/ttLJH1vH4NHD9+HK+88kr053S/Bg4ePIhbbrkFP/rRj3DDDTd4qv0Tz91LbX/ixAkcO3YMABAIBFBZWYnXX3/dM20vdf47duzwTPsTOcWOHTvQ0NCAWbNmYc2aNaivr8eKFSvsLlbaYFxrvsGDB6O8vByDBg1CTk4Opk6disOHD9tdrLTS3d2Nf/7zn5gyZYrdRUk7+/fvR3l5OQoKCpCVlYU5c+bgH//4h93FSivt7e2orKxEXV0d/vCHPyArKwslJSWKr3FF4ui6665DY2Mj/v3vfyMYDOLVV1/Ft771LbuLZZm2tjY8+uij6OrqQnt7O1566SVcf/31dhfLUqNHj8ZHH30UHXq7fft2T10DgiBgxYoV+M9//oOenh5s2bIlba+B06dP484778Qvf/lLVFdXA/BO+0udu5fa/tSpU/jJT36C7u5udHd3469//SvmzZvnibYHpM9/3Lhxnml/IqfYsGEDtm/fjm3btmHp0qWYMmUK7r//fruLlTYY15pv8uTJ2L9/P1pbWxEOh7Fv3z6UlZXZXay0cvz4cVx++eXo27ev3UVJO6WlpThw4AA6OjogCALq6+sxcuRIu4uVVk6dOoU77rgDoVAIbW1teOGFF1SnXLoivf+lL30Jy5Ytw8KFC9HT04O5c+di1KhRdtFAekkAAAV5SURBVBfLMpMnT8ahQ4cwe/ZsRCIR3HzzzRgzZozdxbJUdnY2Vq5cibvuugtdXV2YOHEipk+fbnexLFNaWoolS5Zg/vz5CIVCqKysRE1Njd3FMsX69evR1dWFlStXRn83b948T7S/3Ll7pe0nTpyIw4cPY/bs2cjMzERlZSWqq6sxaNCgtG97QPr8v//97yM/P98T7U9E3sC41nyjR4/Gbbfdhptvvhk9PT0YP348brzxRruLlVY+/fRTFBcX212MtFRRUYGjR49izpw56NOnD0aOHIklS5bYXay0UlpaisrKSsycORPhcBi33HILxo4dq/ganyAIgkXlIyIiIiIiIiIiF3HFVDUiIiIiIiIiIrIeE0dERERERERERCSJiSMiIiIiIiIiIpLExBEREREREREREUli4oiIiIiIiIiIiCT57S4AEaWvU6dOoba2Fm+99Vavv61btw7bt2+HIAiIRCKYMGECli1bhs7OTixYsAAA0NHRgTNnzmDo0KEAgOuuuw733HMPenp6MHnyZJSWluL3v/89AODJJ5/EX/7yFwDAJ598gvz8fOTl5QEAHnvsMQwZMsSKUyYiIiJyvCuvvBLDhw9HRkYGfD4fgsEgcnNzsXz5cowcORIvvvgi7rvvPtx5551YunRp9HWCIGDq1KkIBALYvn27jWdARFZi4oiILLdz507s2bMHW7ZsQU5ODrq6urB06VI8/vjj+OEPf4ht27YBAF5//XX8/Oc/j/4s2r17N0pLS/HOO+/gxIkTGDZsGJYsWYIlS5YAABYsWIDvfve7mD59uuXnRkREROQGGzduxKBBg6I/r1+/Hg899BC2bNkCALjkkkvw8ssvxyWO3njjDXR2diIQCFheXiKyD6eqEZHlmpubEQ6H0dnZCQDIzs7GAw88gKlTp2p6/ebNm/Htb38bM2bMwMaNG80sKhEREVHaC4VCOH36NAYMGBD93fDhw9G3b1+8+eab0d+99NJLmDlzph1FJCIbMXFERJa74YYb0L9/f1RUVOCmm27CypUrcfr0aYwaNUr1tR988AHeeustTJ8+HbNnz8a2bdtw9uxZC0pNRERElD4WLVqE2tpaVFRUYNq0aQCAX/ziF3H/R4y1ACAYDOLgwYOYMGGC5WUlInsxcURElsvLy8NTTz2FnTt3Yu7cuWhpacGSJUuwatUq1ddu3rwZkydPRn5+PkaNGoVLL70UW7dutaDUREREROlj48aNqKurw+9+9zt0dnbimmuuQUFBQdz/qa2txe7du9Hd3Y3du3djypQpyMzMtKnERGQXJo6IyHLr1q3Dm2++iZKSEnznO9/BqlWrsG7dOmzatEnxdR0dHdi2bRsOHjyIKVOmYMqUKWhubsazzz6Lnp4ei0pPRERElD7Kyspw33334d5778WpU6fi/lZYWIgRI0bg73//O/785z/jhhtusKmURGQnJo6IyHKdnZ341a9+hXPnzkV/995772HEiBGKr6urq8PAgQOxb98+1NfXo76+Hnv27EFHRwd27dpldrGJiIiI0lJNTQ1GjRrVa6oacGG62oYNG9DW1obhw4fbUDoisht3VSMiU3V0dGDMmDFxv9u8eTN8Ph/mzZsHn8+HSCSCr33ta/j1r3+teKzNmzfj1ltvjRsi3b9/fyxYsABPP/00amtrTTkHIiIionT3wAMPYObMmdi3b1/c76dOnYoHH3wQy5Yts6lkRGQ3nyAIgt2FICIiIiIiIiIi5+FUNSIiIiIiIiIiksTEERERERERERERSWLiiIiIiIiIiIiIJDFxREREREREREREkpg4IiIiIiIiIiIiSUwcERERERERERGRJCaOiIiIiIiIiIhIEhNHREREREREREQk6f8DB/JCQBlla9IAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.figure(figsize=(20, 5))\n",
"\n",
@@ -1798,9 +2086,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n",
@@ -1817,10 +2103,19 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(404, 2)\n",
+ "(102, 2)\n",
+ "(404,)\n",
+ "(102,)\n"
+ ]
+ }
+ ],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
@@ -1843,10 +2138,25 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The model performance for training set\n",
+ "--------------------------------------\n",
+ "RMSE is 5.6371293350711955\n",
+ "R2 score is 0.6300745149331701\n",
+ "\n",
+ "\n",
+ "The model performance for testing set\n",
+ "--------------------------------------\n",
+ "RMSE is 5.137400784702911\n",
+ "R2 score is 0.6628996975186953\n"
+ ]
+ }
+ ],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
@@ -1884,10 +2194,19 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# plotting the y_test vs y_pred\n",
"# ideally should have been a straight line\n",
@@ -1908,8 +2227,7 @@
"curse of dimensionality. Fortunately, in real-world problems, it is\n",
"often possible to reduce the number of features considerably, turning\n",
"an intractable problem into a tractable one.\n",
- "\n",
- "Here we will discuss some of the most popular dimensionality reduction\n",
+ "Later this semester we will discuss some of the most popular dimensionality reduction\n",
"techniques: the principal component analysis (PCA), Kernel PCA, and\n",
"Locally Linear Embedding (LLE). Furthermore, we will start by looking\n",
"at some simple preprocessing of the data which allow us to rescale the\n",
@@ -1971,11 +2289,42 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MSE before scaling: 0.00\n",
+ "R2 score before scaling 0.97\n",
+ "Feature min values before scaling:\n",
+ " [1.00000000e+00 2.01589441e-03 6.07977945e-04 4.06383028e-06\n",
+ " 3.09411442e-04 3.69637181e-07 8.19225275e-09 1.82733023e-06\n",
+ " 2.56371299e-07 2.24731254e-10 1.65147165e-11 4.06883937e-09\n",
+ " 9.57354406e-08 1.55868096e-10 1.36631646e-13 3.32919248e-14\n",
+ " 8.20235054e-12 1.30819291e-09 1.08106295e-10 9.47643644e-14\n",
+ " 8.30690272e-17]\n",
+ "Feature max values before scaling:\n",
+ " [1. 0.99979915 0.99873382 0.99959834 0.94963861 0.99746924\n",
+ " 0.99939758 0.92601445 0.92482025 0.99620627 0.99919685 0.90297799\n",
+ " 0.9018135 0.9006505 0.99494489 0.99899616 0.88051461 0.87937908\n",
+ " 0.87824502 0.87711242 0.99368511]\n",
+ "Feature min values after scaling:\n",
+ " [ 0. -0.48626484 -0.50728267 -0.32203291 -0.25049587 -0.3397714\n",
+ " -0.2396946 -0.16600408 -0.17030832 -0.25538849 -0.19081388 -0.12379376\n",
+ " -0.11345556 -0.12898676 -0.20454643 -0.15862786 -0.09866217 -0.08494126\n",
+ " -0.0861909 -0.10363162 -0.17048872]\n",
+ "Feature max values after scaling:\n",
+ " [0. 0.51151842 0.49084318 0.67756137 0.69883334 0.65769748\n",
+ " 0.75970297 0.76000854 0.75451167 0.74081778 0.80838297 0.77918422\n",
+ " 0.78835784 0.77166375 0.79039846 0.8403683 0.78185244 0.79443782\n",
+ " 0.79205413 0.7734808 0.82319639]\n",
+ "MSE after scaling: 0.00\n",
+ "R2 score for scaled data: 0.97\n"
+ ]
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -2039,8 +2388,9 @@
"# Making meshgrid of datapoints and compute Franke's function\n",
"n = 5\n",
"N = 1000\n",
- "x = np.sort(np.random.uniform(0, 1, N))\n",
- "y = np.sort(np.random.uniform(0, 1, N))\n",
+ "x = np.random.uniform(0, 1, N)\n",
+ "y = np.random.uniform(0, 1, N)\n",
+ "\n",
"z = FrankeFunction(x, y)\n",
"X = create_X(x, y, n=n) \n",
"# split in training and test data\n",
@@ -2053,7 +2403,7 @@
"print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test), y_test)))\n",
"print(\"R2 score before scaling {:.2f}\".format(clf.score(X_test,y_test)))\n",
"\n",
- "scaler = StandardScaler()\n",
+ "scaler = StandardScaler(with_mean=True,with_std=False)\n",
"scaler.fit(X_train)\n",
"X_train_scaled = scaler.transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)\n",
@@ -2841,10 +3191,32 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1. -1. 2.]\n",
+ " [ 1. 0. 1.]\n",
+ " [ 1. 2. -1.]\n",
+ " [ 1. 1. 0.]]\n",
+ "[[ 4. 2. 2.]\n",
+ " [ 2. 6. -4.]\n",
+ " [ 2. -4. 6.]]\n",
+ "[[-1.96889890e-16 8.16496581e-01 -5.77350269e-01]\n",
+ " [-7.07106781e-01 4.08248290e-01 5.77350269e-01]\n",
+ " [ 7.07106781e-01 4.08248290e-01 5.77350269e-01]]\n",
+ "[1.00000000e+01 6.00000000e+00 2.38805416e-31]\n",
+ "[[-5.76324444e-17 -7.07106781e-01 7.07106781e-01]\n",
+ " [ 8.16496581e-01 4.08248290e-01 4.08248290e-01]\n",
+ " [-5.77350269e-01 5.77350269e-01 5.77350269e-01]]\n",
+ "[[ 1.39583657e+30 -1.39583657e+30 -1.39583657e+30]\n",
+ " [-1.39583657e+30 1.39583657e+30 1.39583657e+30]\n",
+ " [-1.39583657e+30 1.39583657e+30 1.39583657e+30]]\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"# SVD inversion\n",
@@ -3200,10 +3572,19 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-0.042229208919236545\n",
+ "3.965106055560731\n",
+ "[[ 0.92354285 2.88986604]\n",
+ " [ 2.88986604 10.07530538]]\n"
+ ]
+ }
+ ],
"source": [
"# Importing various packages\n",
"import numpy as np\n",
@@ -3233,10 +3614,19 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.07953778271319319\n",
+ "1.5265483445750982\n",
+ "[[1. 0.71417833]\n",
+ " [0.71417833 1. ]]\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"n = 100\n",
@@ -3279,10 +3669,39 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 2.11842576 5.22468553]\n",
+ " [ 0.82757833 -0.16388067]\n",
+ " [-0.66170841 -2.44421898]\n",
+ " [ 0.78136059 2.21927291]\n",
+ " [-0.7470833 -0.67119054]\n",
+ " [-1.03156683 -2.1492145 ]\n",
+ " [ 0.78741508 3.01521106]\n",
+ " [-1.12967477 -4.76411542]\n",
+ " [ 0.83056964 3.49404968]\n",
+ " [-1.77531609 -3.76059908]]\n",
+ " 0 1\n",
+ "0 2.118426 5.224686\n",
+ "1 0.827578 -0.163881\n",
+ "2 -0.661708 -2.444219\n",
+ "3 0.781361 2.219273\n",
+ "4 -0.747083 -0.671191\n",
+ "5 -1.031567 -2.149214\n",
+ "6 0.787415 3.015211\n",
+ "7 -1.129675 -4.764115\n",
+ "8 0.830570 3.494050\n",
+ "9 -1.775316 -3.760599\n",
+ " 0 1\n",
+ "0 1.000000 0.925137\n",
+ "1 0.925137 1.000000\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
@@ -3311,10 +3730,48 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0 1 2 3 4 5 6 7 \\\n",
+ "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "1 0.0 0.095612 0.088507 0.097649 0.092959 0.088538 0.090166 0.086293 \n",
+ "2 0.0 0.088507 0.082492 0.091076 0.087082 0.083303 0.084720 0.081374 \n",
+ "3 0.0 0.097649 0.091076 0.106048 0.101344 0.096900 0.101656 0.097608 \n",
+ "4 0.0 0.092959 0.087082 0.101344 0.097137 0.093150 0.097539 0.093887 \n",
+ "5 0.0 0.088538 0.083303 0.096900 0.093150 0.089587 0.093635 0.090350 \n",
+ "6 0.0 0.090166 0.084720 0.101656 0.097539 0.093635 0.099905 0.096237 \n",
+ "7 0.0 0.086293 0.081374 0.097608 0.093887 0.090350 0.096237 0.092896 \n",
+ "8 0.0 0.082678 0.078241 0.093817 0.090460 0.087261 0.092789 0.089752 \n",
+ "9 0.0 0.079297 0.075305 0.090261 0.087240 0.084353 0.089547 0.086790 \n",
+ "10 0.0 0.082207 0.077758 0.095018 0.091520 0.088191 0.095041 0.091831 \n",
+ "11 0.0 0.078893 0.074863 0.091457 0.088285 0.085258 0.091736 0.088803 \n",
+ "12 0.0 0.075800 0.072154 0.088122 0.085250 0.082503 0.088633 0.085956 \n",
+ "13 0.0 0.072909 0.069616 0.084998 0.082403 0.079912 0.085717 0.083277 \n",
+ "14 0.0 0.070207 0.067238 0.082068 0.079728 0.077476 0.082976 0.080755 \n",
+ "\n",
+ " 8 9 10 11 12 13 14 \n",
+ "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "1 0.082678 0.079297 0.082207 0.078893 0.075800 0.072909 0.070207 \n",
+ "2 0.078241 0.075305 0.077758 0.074863 0.072154 0.069616 0.067238 \n",
+ "3 0.093817 0.090261 0.095018 0.091457 0.088122 0.084998 0.082068 \n",
+ "4 0.090460 0.087240 0.091520 0.088285 0.085250 0.082403 0.079728 \n",
+ "5 0.087261 0.084353 0.088191 0.085258 0.082503 0.079912 0.077476 \n",
+ "6 0.092789 0.089547 0.095041 0.091736 0.088633 0.085717 0.082976 \n",
+ "7 0.089752 0.086790 0.091831 0.088803 0.085956 0.083277 0.080755 \n",
+ "8 0.086888 0.084185 0.088805 0.086034 0.083424 0.080965 0.078647 \n",
+ "9 0.084185 0.081722 0.085949 0.083416 0.081027 0.078772 0.076643 \n",
+ "10 0.088805 0.085949 0.091604 0.088651 0.085870 0.083250 0.080781 \n",
+ "11 0.086034 0.083416 0.088651 0.085937 0.083377 0.080963 0.078684 \n",
+ "12 0.083424 0.081027 0.085870 0.083377 0.081023 0.078800 0.076698 \n",
+ "13 0.080965 0.078772 0.083250 0.080963 0.078800 0.076753 0.074816 \n",
+ "14 0.078647 0.076643 0.080781 0.078684 0.076698 0.074816 0.073033 \n"
+ ]
+ }
+ ],
"source": [
"# Common imports\n",
"import numpy as np\n",
@@ -4040,9 +4497,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Importing various packages\n",
@@ -4065,9 +4520,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -5230,9 +5683,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from numpy import *\n",
@@ -5373,9 +5824,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from numpy import *\n",
@@ -5520,11 +5969,20 @@
},
{
"cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -5752,9 +6210,7 @@
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
@@ -5822,11 +6278,96 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polynomial degree: 0\n",
+ "Error: 0.3214960170351912\n",
+ "Bias^2: 0.3123314713548606\n",
+ "Var: 0.009164545680330616\n",
+ "0.3214960170351912 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n",
+ "Polynomial degree: 1\n",
+ "Error: 0.08426840630693411\n",
+ "Bias^2: 0.07968918676726029\n",
+ "Var: 0.004579219539673836\n",
+ "0.08426840630693411 >= 0.07968918676726029 + 0.004579219539673836 = 0.08426840630693413\n",
+ "Polynomial degree: 2\n",
+ "Error: 0.10398646080125035\n",
+ "Bias^2: 0.10077114273548984\n",
+ "Var: 0.003215318065760509\n",
+ "0.10398646080125035 >= 0.10077114273548984 + 0.003215318065760509 = 0.10398646080125035\n",
+ "Polynomial degree: 3\n",
+ "Error: 0.06547790180152357\n",
+ "Bias^2: 0.06208238634231953\n",
+ "Var: 0.0033955154592040944\n",
+ "0.06547790180152357 >= 0.06208238634231953 + 0.0033955154592040944 = 0.06547790180152363\n",
+ "Polynomial degree: 4\n",
+ "Error: 0.06844519414009438\n",
+ "Bias^2: 0.06453579006728315\n",
+ "Var: 0.003909404072811231\n",
+ "0.06844519414009438 >= 0.06453579006728315 + 0.003909404072811231 = 0.06844519414009438\n",
+ "Polynomial degree: 5\n",
+ "Error: 0.05227921801205692\n",
+ "Bias^2: 0.04818727730430296\n",
+ "Var: 0.0040919407077539514\n",
+ "0.05227921801205692 >= 0.04818727730430296 + 0.0040919407077539514 = 0.05227921801205691\n",
+ "Polynomial degree: 6\n",
+ "Error: 0.03781367141738885\n",
+ "Bias^2: 0.033657685071527485\n",
+ "Var: 0.004155986345861374\n",
+ "0.03781367141738885 >= 0.033657685071527485 + 0.004155986345861374 = 0.03781367141738886\n",
+ "Polynomial degree: 7\n",
+ "Error: 0.027609773491022314\n",
+ "Bias^2: 0.02299949826036602\n",
+ "Var: 0.004610275230656294\n",
+ "0.027609773491022314 >= 0.02299949826036602 + 0.004610275230656294 = 0.027609773491022314\n",
+ "Polynomial degree: 8\n",
+ "Error: 0.017355848195591845\n",
+ "Bias^2: 0.01033172130665515\n",
+ "Var: 0.007024126888936694\n",
+ "0.017355848195591845 >= 0.01033172130665515 + 0.007024126888936694 = 0.01735584819559184\n",
+ "Polynomial degree: 9\n",
+ "Error: 0.026605727637176654\n",
+ "Bias^2: 0.010018312644139347\n",
+ "Var: 0.016587414993037307\n",
+ "0.026605727637176654 >= 0.010018312644139347 + 0.016587414993037307 = 0.026605727637176654\n",
+ "Polynomial degree: 10\n",
+ "Error: 0.02159270458799264\n",
+ "Bias^2: 0.010516485576652856\n",
+ "Var: 0.011076219011339788\n",
+ "0.02159270458799264 >= 0.010516485576652856 + 0.011076219011339788 = 0.021592704587992645\n",
+ "Polynomial degree: 11\n",
+ "Error: 0.07160048164248561\n",
+ "Bias^2: 0.014436800088969727\n",
+ "Var: 0.05716368155351588\n",
+ "0.07160048164248561 >= 0.014436800088969727 + 0.05716368155351588 = 0.07160048164248561\n",
+ "Polynomial degree: 12\n",
+ "Error: 0.11547777218940905\n",
+ "Bias^2: 0.016285782696075054\n",
+ "Var: 0.099191989493334\n",
+ "0.11547777218940905 >= 0.016285782696075054 + 0.099191989493334 = 0.11547777218940906\n",
+ "Polynomial degree: 13\n",
+ "Error: 0.22842468702288576\n",
+ "Bias^2: 0.01975416527179247\n",
+ "Var: 0.20867052175109335\n",
+ "0.22842468702288576 >= 0.01975416527179247 + 0.20867052175109335 = 0.22842468702288582\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
@@ -5924,9 +6465,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
@@ -6011,11 +6550,121 @@
},
{
"cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Degree of polynomial: 1\n",
+ "Mean squared error on training data: 439772.32300399\n",
+ "Mean squared error on test data: 480505.64097586\n",
+ "Degree of polynomial: 2\n",
+ "Mean squared error on training data: 113923.41075701\n",
+ "Mean squared error on test data: 133356.07161812\n",
+ "Degree of polynomial: 3\n",
+ "Mean squared error on training data: 8971.72576731\n",
+ "Mean squared error on test data: 10910.17357950\n",
+ "Degree of polynomial: 4\n",
+ "Mean squared error on training data: 298.44612441\n",
+ "Mean squared error on test data: 503.87244240\n",
+ "Degree of polynomial: 5\n",
+ "Mean squared error on training data: 3.72500207\n",
+ "Mean squared error on test data: 6.61815443\n",
+ "Degree of polynomial: 6\n",
+ "Mean squared error on training data: 3.63584439\n",
+ "Mean squared error on test data: 9.73676688\n",
+ "Degree of polynomial: 7\n",
+ "Mean squared error on training data: 0.47883212\n",
+ "Mean squared error on test data: 1.44277955\n",
+ "Degree of polynomial: 8\n",
+ "Mean squared error on training data: 0.04897868\n",
+ "Mean squared error on test data: 0.15164016\n",
+ "Degree of polynomial: 9\n",
+ "Mean squared error on training data: 0.02578971\n",
+ "Mean squared error on test data: 0.07328980\n",
+ "Degree of polynomial: 10\n",
+ "Mean squared error on training data: 0.02424626\n",
+ "Mean squared error on test data: 0.46083549\n",
+ "Degree of polynomial: 11\n",
+ "Mean squared error on training data: 0.01617683\n",
+ "Mean squared error on test data: 0.74076161\n",
+ "Degree of polynomial: 12\n",
+ "Mean squared error on training data: 0.00802245\n",
+ "Mean squared error on test data: 0.18300856\n",
+ "Degree of polynomial: 13\n",
+ "Mean squared error on training data: 0.00776962\n",
+ "Mean squared error on test data: 4.22709091\n",
+ "Degree of polynomial: 14\n",
+ "Mean squared error on training data: 0.00467325\n",
+ "Mean squared error on test data: 0.45715881\n",
+ "Degree of polynomial: 15\n",
+ "Mean squared error on training data: 0.00417156\n",
+ "Mean squared error on test data: 0.64899850\n",
+ "Degree of polynomial: 16\n",
+ "Mean squared error on training data: 0.00316867\n",
+ "Mean squared error on test data: 7.34167999\n",
+ "Degree of polynomial: 17\n",
+ "Mean squared error on training data: 0.00243942\n",
+ "Mean squared error on test data: 100.99309443\n",
+ "Degree of polynomial: 18\n",
+ "Mean squared error on training data: 0.00225951\n",
+ "Mean squared error on test data: 57.70941839\n",
+ "Degree of polynomial: 19\n",
+ "Mean squared error on training data: 0.00152885\n",
+ "Mean squared error on test data: 74.59442615\n",
+ "Degree of polynomial: 20\n",
+ "Mean squared error on training data: 0.00137813\n",
+ "Mean squared error on test data: 1718.77573858\n",
+ "Degree of polynomial: 21\n",
+ "Mean squared error on training data: 0.00120458\n",
+ "Mean squared error on test data: 9535.67732812\n",
+ "Degree of polynomial: 22\n",
+ "Mean squared error on training data: 0.00092876\n",
+ "Mean squared error on test data: 1059.53684605\n",
+ "Degree of polynomial: 23\n",
+ "Mean squared error on training data: 0.00085809\n",
+ "Mean squared error on test data: 1830.08357907\n",
+ "Degree of polynomial: 24\n",
+ "Mean squared error on training data: 0.00085551\n",
+ "Mean squared error on test data: 7686.42984972\n",
+ "Degree of polynomial: 25\n",
+ "Mean squared error on training data: 0.00083016\n",
+ "Mean squared error on test data: 6420.29569681\n",
+ "Degree of polynomial: 26\n",
+ "Mean squared error on training data: 0.00075788\n",
+ "Mean squared error on test data: 3781.44189824\n",
+ "Degree of polynomial: 27\n",
+ "Mean squared error on training data: 0.00070078\n",
+ "Mean squared error on test data: 1719.20554311\n",
+ "Degree of polynomial: 28\n",
+ "Mean squared error on training data: 0.00064450\n",
+ "Mean squared error on test data: 2636.68580306\n",
+ "Degree of polynomial: 29\n",
+ "Mean squared error on training data: 0.00061610\n",
+ "Mean squared error on test data: 61702.78847697\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:73: RuntimeWarning: divide by zero encountered in log10\n",
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:74: RuntimeWarning: divide by zero encountered in log10\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -6107,11 +6756,27 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:63: RuntimeWarning: divide by zero encountered in log10\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Common imports\n",
"import os\n",
@@ -6191,11 +6856,20 @@
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
@@ -6275,9 +6949,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -6390,9 +7062,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X = np.zeros((n, L ** 2))\n",
@@ -6456,9 +7126,7 @@
{
"cell_type": "code",
"execution_count": 41,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X_train_own = np.concatenate(\n",
@@ -6474,9 +7142,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
@@ -6561,9 +7227,7 @@
{
"cell_type": "code",
"execution_count": 43,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
@@ -6574,9 +7238,7 @@
{
"cell_type": "code",
"execution_count": 44,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"beta = ols_svd(X_train_own,y_train)"
@@ -6592,9 +7254,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"J = beta[1:].reshape(L, L)"
@@ -6610,9 +7270,7 @@
{
"cell_type": "code",
"execution_count": 46,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
@@ -6678,9 +7336,7 @@
{
"cell_type": "code",
"execution_count": 47,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -6786,9 +7442,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X = np.zeros((n, L ** 2))\n",
@@ -6818,9 +7472,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"clf = skl.LinearRegression().fit(X_train, y_train)"
@@ -6836,9 +7488,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"J_sk = clf.coef_.reshape(L, L)"
@@ -6854,9 +7504,7 @@
{
"cell_type": "code",
"execution_count": 51,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
@@ -6912,9 +7560,7 @@
{
"cell_type": "code",
"execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"_lambda = 0.1\n",
@@ -6965,9 +7611,7 @@
{
"cell_type": "code",
"execution_count": 53,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n",
@@ -7001,9 +7645,7 @@
{
"cell_type": "code",
"execution_count": 54,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"lambdas = np.logspace(-4, 5, 10)\n",
@@ -7066,9 +7708,7 @@
{
"cell_type": "code",
"execution_count": 55,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
@@ -7113,7 +7753,25 @@
]
}
],
- "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": "3.6.8"
+ }
+ },
"nbformat": 4,
"nbformat_minor": 2
}