diff --git a/doc/LectureNotes/_build/.doctrees/Clustering.doctree b/doc/LectureNotes/_build/.doctrees/Clustering.doctree
index 104e791aa..cbeb500c1 100644
Binary files a/doc/LectureNotes/_build/.doctrees/Clustering.doctree and b/doc/LectureNotes/_build/.doctrees/Clustering.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree
index 2836f14ae..7b13e1d30 100644
Binary files a/doc/LectureNotes/_build/.doctrees/chapter1.doctree and b/doc/LectureNotes/_build/.doctrees/chapter1.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/chapter10.doctree b/doc/LectureNotes/_build/.doctrees/chapter10.doctree
index 88d74cf11..f4db55ab2 100644
Binary files a/doc/LectureNotes/_build/.doctrees/chapter10.doctree and b/doc/LectureNotes/_build/.doctrees/chapter10.doctree differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
index 13e390d92..82c23b74b 100644
--- a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
@@ -475,7 +475,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.47818493843078613 seconds\n"
+      "Runtime: 0.4697279930114746 seconds\n"
      ]
     }
    ],
@@ -604,7 +604,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.42156386375427246 seconds\n"
+      "Runtime: 0.41312265396118164 seconds\n"
      ]
     }
    ],
@@ -745,7 +745,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.8341619968414307 seconds\n",
+      "Runtime: 0.8252480030059814 seconds\n",
       " "
      ]
     }
@@ -877,7 +877,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.0038292407989501953 seconds\n"
+      "Runtime: 0.0037932395935058594 seconds\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index bbbe608f8..761ea4b64 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -461,7 +461,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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knumTuvMkSUtEG2FxE7ApySlJVgEXADtaeFxJ0iKZeFhU1YPApcBO4Dbgw1V1a5Irk5wHkOQXkuwDXgK8J8mtk65LkjS6Nr4NRVXdANzQN++KnvGb6OyekiTNoCV1gFuSNB2GhSSpkWEhSWpkWEiSGhkWkqRGhoUkqZFhIUlqZFhIkhoZFpKkRoaFJKmRYSFJamRYSJIaGRaSpEaGhSSpkWEhSWpkWEiSGhkWkqRGhoUkqZFhIUlqZFhIkhoZFpKkRoaFJKmRYSFJanRkhMX27bBxI6xY0fm7ffu0K5KkJeWoaRcwcdu3w9atcOBAZ3rPns40wEUXTa8uSVpCln/P4vLLDwXFnAMHOvNnib0fSTNs+YfF3r3jzV+ow9nYz/V+9uyBqkO9HwND0oxY/mGxfv148xficDf2S6X3I+mItfzD4qqrYPXqh89bvbozf7Ec7sa+rd6PJC3Q8g+Liy6CbdtgwwZIOn+3bevMX6zjBIe7sV+s3s9SPu6xlGuXjgRVtSSH0047rQ7LtddWrV5d1dlx1BlWr+7MH9eGDQ9fz9ywYcPi1HLttZ11JZ2/g2pczOczas1zz3vlykPPdyGPt5DaR2kTSY8A7KoFbHOnvtFf6HDYYXG4G/hei7GhHrbxG3Xdi/l8Rqm1v6bDCahxa287GOdjaGmJMSzGlQzeQCULW99CNhqj3GfUDelCns9CN3TDaho1oHofd82a4esZVnubwTifaYRWU4/O8FIDw2JcC9ngNL0Rx3mjjrqhGTUEmp5Pf22XXLLwXT/zBUVvbYPaY75eyaj/i2FtMnef+XaNLebGtO3Qmq/t5tpkUNusWTO4DXrbac2azjCoZzusvaYVTJdccqjulSs706NaymG60Nr77rcW7ijDYgzjfioc5bjCOOsbdUMzbLm5DUDvJ/Sjjx78+INqG7bBHWfXz7Bh7o3c/xjzbeT7h/nabr7eyKDh6KMP3ae/hlWrRt9I9s9rCssm4775RwnqpvYc9f+4evXgDxS9bTloOPbYzjAoqMYJqWFtdNZZgx93lMBYaE9w1MBcs6bquOMe3hZzbbVixSPbZL7XWP/76ayzmv9v/R8Khjzn06CqZjQsgHOA24HdwGUDbj8G+FD39s8BG5vWOTQsxv10P+qyTRv3cT9ljtpjuPbaR4bA3Ato1aqHzxu24RtnIzNsQzfuBvpwh/k22pOu5ZhjHjnv6KMf2d7jBm7//3XcDyuH+7w2bBjvtTC3QT/cYdWqwcEzaGj6ANb02h0W7PP1inv/X4N64IPeZ+P2kge1bf/7etBrbCHDscceqn/Ae2VmwwJYCXwN+ElgFfA3wOa+Zf4t8O7u+AXAh5rWOzAsJrkPuWnjPu4xg3HCZZyN46D7j/OJftD9mzZUvbsEFmPjkgx/My70zTmpob9tR329jfP/P5yNUn+t47wWFnMY57XR9AGsaRi00Z2v/Xp3m47azmvWHF5Pb4rDLIfFGcDOnuk3Am/sW2YncEZ3/CjgHiDzrXdgWExyH/Ji9yzGCbZx3uCDwmlYbaNu6OZ7U/Q+v8XcEM33KXixQmkxax13P/I4Hy4Wa6M0rZ7FuEPTB7CFDsOez0LDaVrBe5jDQsMiVTW5kziAJC8GzqmqV3WnXw48q6ou7VnmS91l9nWnv9Zd5p6+dW0FupeM5WnAl3pvPw1OG1bHzXDz4TyPtXDCetiQnhMZCx7aC3vugXubbh+2zifBiUfDqgfg4Dfg7kHL/hycenSnV9boATj4BfjiKLXfC995DDy26fHna9c98PW5+4xT5+EqqEDaeKz5DGrvUQxrq0Hrm6/9RzX3WgTYAKeMsvy98J0TYE1aPnl3rg0m8XoqeGjYe3Tcdn4ADrb1el9MdwL3VI393llSlyivqm3ANoAku6pqy5RLmgm2xSG2xSG2xSG2xSFJdi3kfm18YrgbOLln+qTuvIHLJDkKeCzwnRZqkySNoI2wuAnYlOSUJKvoHMDe0bfMDuAV3fEXA39ek94/Jkka2cR3Q1XVg0kupXMQeyXwvqq6NcmVdM4k3AH8d+ADSXbT2f99wQir3jaxopce2+IQ2+IQ2+IQ2+KQBbXFxA9wS5KWvuV/iXJJ0mEzLCRJjWY+LJKck+T2JLuTXDbg9mOSfKh7++eSbJxCma0YoS1em+TLSb6Q5JNJNkyjzjY0tUXPci9KUkmW5dcmR2mHJP+8+7q4Ncl1bdfYlhHeH+uTfCrJLd33yAumUWcbkrwvybe757ANuj1J/nO3rb6Q5JmNK13ImXxtDUzoUiFLcRixLf4JsLo7fsmR3Bbd5Y4HPgPcCGyZdt1Tek1sAm4BfqI7/fhp1z3FttgGXNId3wzcOe26J9gezwGeCXxpyO0vAD5O58TWZwOfa1rnrPcsTgd2V9UdVXUQuB44v2+Z84FruuN/BJyVZOpn9k5AY1tU1aeqau7HwG+kc07LcjTK6wLgt4G3AT9ss7gWjdIO/xq4uqq+C1BV3265xraM0hYFPKY7/ljgGy3W16qq+gxDrhzRdT7wh9VxI/C4JE+cb52zHhYnAnf1TO/rzhu4TFU9CNwPrGmlunaN0ha9LqbzyWE5amyLbrf65Kr6WJuFtWyU18RTgKck+b9JbkxyTmvVtWuUtvgt4GVJ9gE3AP+undJm0rjbk6V1uQ+NJsnLgC3AL067lmlIsgJ4J/DKKZcyC46isyvqTDo9zc8kObWq7ptmUVNyIfD+qvr9JGfQObfraVX10LQLWwpmvWfhpUIOGaUtSHI2cDlwXlX9qKXa2tbUFsfTudDkp5PcSWef7I5leJB7lNfEPmBHVT1QVV8HvkInPJabUdriYuDDAFX1WeBRwNpWqps9I21Pes16WHipkEMa2yLJM4D30AmK5bpvGhraoqrur6q1VbWxqjbSOX5zXlUt6AJqM2yU98ef0OlVkGQtnd1Sd7RYY1tGaYu9wFkASX6GTljsb7XK2bED+Jfdb0U9G7i/qv52vjvM9G6omtylQpacEdviHcBxwEe6x/j3VtV5Uyt6QkZsi2VvxHbYCfzTJF8G/h54fVUtu573iG3xOuC9SV5D52D3K5fpB0uSfJDOh4S13WM0bwaOBqiqd9M5ZvMCOr9OegD4V43rXKZtJUlaRLO+G0qSNAMMC0lSI8NCktTIsJAkNTIsJEmNDAtJUiPDQpLUyLCQFkn3txKe1x1/S5L/Mu2apMUy02dwS0vMm4ErkzweeAaw7M6e15HLM7ilRZTkL+hccuXMqvretOuRFou7oaRFkuRU4InAQYNCy41hIS2C7q+MbafzC2TfX8Y/MqQjlGEhHaYkq4E/Bl5XVbfR+TnXN0+3KmlxecxCktTInoUkqZFhIUlqZFhIkhoZFpKkRoaFJKmRYSFJamRYSJIa/X/fFmlmIqo/pwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -526,18 +526,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.95080408]\n",
+      " [1.93113373]\n",
       "Coefficient beta : \n",
-      " [[5.12184106]]\n",
-      "Mean squared error: 0.26\n",
-      "Variance score: 0.88\n",
+      " [[5.06182265]]\n",
+      "Mean squared error: 0.20\n",
+      "Variance score: 0.91\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.40\n"
+      "Mean absolute error: 0.37\n"
      ]
     },
     {
      "data": {
-      "image/png": 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8eXxqiOuvV/69IBQIRPIoz6NdV6xw1wLMgOq1RDyusCWdUSAQyZuk1wFo9diNAhAJ/P7vT9zWaKGY2rmFFARySYFAJClJ3cVnNdq1UQBypYFWr87nQjF5rk3llHoNiSQhyTlrJk1yX2DT7m0Tt/snkM8AAJRq7qBWqfuoSNaSmAAtjc9qRVQAqpbD68UEWZ27HNCkcyJZixpVu3596ymKrEa7NurWuXx5/oMA0P7cQV2eTlIgEElCo4toqw2+WfW2ueQS9/aREeD009M9dlLamTsoy8b5nFBqSCQJrty0S15TFK7uoP39xZtuoZ02gpKkk5QaEsla7V18lLxNb+zqDXTZZcGdcRG7e7ZTm9JU1AoEIomp7jPf3+/ep93pFZLOYUctCGMGXHRRZ5+dtVbHLmgqagUCkVQk2eCbdA67Mg9QtUaDwspOU1ErEIikIskG36QGmLnSQAsXdm8AqNBUGGosFsm9JAaYRaWBpDTUWCxSZq3ksGvbEpqtF5yWLu+XXzQKBCJ5FzeH7WpLqOWjFqB++YWjQCCSd3Fz2K62hAqfjcFZTZonbVMgEGmXz/RHnC6RURPGNRrXkAb1yy8cBQKRduQp/XHiiY0v9r77w6tffuEoEIi0w3f6I6r2QQL33BP9viz6w6tffuF4CQQk15F8hORqkuoXKsXnM/3hqn0MDtbXAt55J5ggLs3+8HHSYeqXXzhexhGQXAdggZm9GGd/jSOQ3PM5UVmcBWOS+n88OhrUajZsCFI51ZPOdfGiL0WgcQQivrWb/mingblZLaO3t7O2iUqZSGDRouh2D/UGKi1fgcAA/JzkKpJDrh1IDpEcIzm2ZcsWT8USian2Ag60nv5ot4G52d1+Jxfj6jK5jlX92eoNVFq+UkOHmNmzJA8EcCeAC8zs3qj9lRqSXEkqJdJqOumWW+IvCNPuesZx0k6Vzy7JvP1llfvUkJk9G/67GcA/AviQj+OKJCKplEgrd9RkfRCImtoaaL9rZpy7+cpnqzdQaaUeCEhOI7lP5WcAJwN4NO3jiiQmqZRInP71rrmBNm0aXyhmZCTZi3GzAFL92eoNVFo+agQHAfg1yYcB/CuAfzazn3k4rkgykhog1eiOes2a6BlCZ80af570xdhVpko5XJ/d6qIvUgiahlqkmSS7TVa6Z65fD/T0ALt3u/fz+f+yUZdRKYzctxGIFFqSd+EDA+N34a4gsHKl/3UCdJff9VQjEPFt9mxg48b67ep9Ix3opEYwOenCiEgDjSaHU398yYgCgYgPcaaC1uyckhG1EYikaedOdxBQf3zJEQUC6V5pLyxDAlOnTtxWWSlM/fElRxQIpDulubCMa1DYkiUTewOpp47kiAKBdKc400a0U2OIGhT23e92UlqRVKmxWLpTs2kjageRVWoMgPvuPSoAiBSAagTSnZpNGxF3ojlXGujYYxUEpFAUCKQ7NZtJM85Ec1G1gNWrEyli6o3ZIiEFAulOzaaNaFRjcNUCKr2BkpJmY7ZIDU0xIeLimmguShr/h7QIjLRIk85JfpQlnVFbY3BJuhZQTctCikcKBJKcsqUzBgbGv0u1NANARVJrIIjEoEAgyUlqScc8OOWUbLuEallI8UjjCCQ5ZUln5GFMQKXRWgvGiAcKBJKcvj53A2dR0hmuALB7d9DekYWBAV34xQulhiQ5RU1nXHZZdC0gqyAg4pH+yiU5SS+s7gMJXHzxxG0+GoMrytLLSgpNqSFJVlHSGa4awKuvAvvs468MjeYzAtQ+IN4oEEh3uf124BOfqN+excDKqF5WixcDb7wRf8I7kQ4pNSTdg6wPAj7TQLWielNt3VqebrhSCE0DAck7SR7rozAiqXDNDfT009nPENpqb6qidcOVwohTI/gagMtJ/i3JWWkXSCQxv/lNdG+gww7zX55aUb2spk9371+UbrhSOE0DgZk9aGZ/BOA2AD8jeQnJvdIvmhRelj1iSOD975+4Lcs0kEtUL6srrihmN1wprFhtBCQJ4AkAfwPgAgD/RnJRmgWTgstq3iFXGuiuu/IVAKq51i4uYjdcKbSm01CTvA/AYQDWArgfwAMAfgNgMYA9zGyowdvbommoS8D3NMqvvAK861312/MaAEQS1sk01HG6jw4BeMzqI8YFJB9v56DSBXzOO5SHuYFECixOG8FaRxCocHTIFoGfaZRdaaAf/lBBQKRFHQ0oM7OnkyqIlMzwcP0KX0k1eO7eDUx2/OkqAIi0xduAMpI9JB8ieZuvY0qG0mrwJOuDQN56A4kUjM+RxYsBqE2hm7h6xLTLlQY67zwFAJEEeAkEJA9F0J7wYx/HkwKJM9bA1Rjc2wt85CNpl66eZguVEvJVI7gcwFcBvBO1A8khkmMkx7Zs2eKpWJKpZmMNXLWAiizm3inbmswiodQDAcnTAGw2s1WN9jOzq81sgZktmDlzZtrFkrS0csccNfvm4GB0AKiW1tw7Ud+hTGsyi1TxMQ31HwD4JMlTAewJYF+SI2Y26OHY4lOj+fVd7QNxL+T9/f6WwGz0HcqyJrNIjdRrBGb2383sUDObA+BMAHcpCJRUq3fMzS7kld5APpfAbPQdfIyNEMmA1iOQ5LR6xzw8HL0mcHVvIJ9z7zT6DkVdk1mkCa+BwMx+aWan+TymeNTsjrk29z44GHQtrTYy4u4SmmRXVJdK2aK6o/b1aTI4KS0tVSnJaTSa2JV7r5bleIDastUig/LOmRN8lzQmzRPJkFJD0pnqu/ylS4Gzz3bfMbty7xVZDwprVDZyvHzqLiolpUCQZ3kfvOTqV3/99cFdc20Kx9XrB4jXTdR13CTPS6NeP7VBSt1FpYQUCPKqCIOX4vQSajQoDGi9x43rvCxaFByj3aCgtYOlyykQ5FURBi816mFzyy3N7/bb6XHjOi+dpm60drB0OQWCvCrC4KWoC6IZcPrp9dtGRjrvcdPs+zcLlq60ktYOlm5nZrl7zJ8/37pef39lONXER39/1iUbNzJi1tvrLmfl8eqrrX1ef78ZGfw7MlK/vaen8fGAYL+45e3tHT9OK2USyRkAY9bmNTfzi77roUBg7V20sjAyYjZrlvuC3OrnuL7v+ec3DzZxg2URgqtImzoJBE0Xr8+CFq8PjY4GaY4NG4I0zPBw/gYvJbVecNRi9z09wYpkUduru3cCQeomKuU0aZK7bGT9wDaRgulk8Xq1EeRZ2qNpO+HqDVTpydOOqNy/KwgAwTkxA264IX67g+YKEnFSIJDWvPBCdC2gkwtq1Ht7ehrv30qw1FxBIk4KBBIfCbz73RO3VTLtnYq6SA8NJXfx1lxBIk4KBNKcKw20cmWyU0NEXaSvvDLZi3ee020iGVFjcZkk3bj85pvAXnvVb8/h34xIt+uksVizj5ZFq6uDNZNUbyARyT2lhsoiqSkpXGmg5csVBERKTDWCsuh0Sgoz92phCgAipacaQVl00keerA8CSfUGEpHcUyAoi3b6yE+bVp8G+ta34geAvK+XICKxKDVUFpUG4bi9hjptDE66cVpEMqMaQZE0uwOP00fe1RjcThqoCOsliEgsCgRF0emKZQsX1geAM89Mfm6gPK2XICKxKDVUFI3uwJulYtIYE9DX554tVBO4iRSOagRF0c4deFJpIBdN4CZSGgoERdFK99CLL64PAPPm+ZkbSA3FIoWjQNAu310n496Bk8Bll03cZgY8+GDyZSryBG7q+iryO2ojaEcWXSebdQ/V3EDxqeuryASafbQdUcsq9vcHd8Y+LV8OfOYzE7dNngzs3Om3HEWSp9+fSEI0+6hveek6qVpAe/Ly+xPJCbURtCPttW+b5a9dvYF27lQQiJv319rFIhOkHghI7knyX0k+THItyT9P+5ipS7PrZKOBY6tWRdcCJkdU7rqlUbSVAXfq+ioykZml+gBAAHuHP08B8ACA4xu9Z/78+ZZ7IyNm/f1mZPDvyEgyn9vfX+np3/wRp4y9vRPf09ubXFnzJOq89fe790/r9yeSEQBj1uZ12mtjMcleAL8GcL6ZPRC1X+4bi9M0aVLzFA8ZbynKbmoUjTpvZNC9VaTkOmks9tJGQLKH5GoAmwHc6QoCJIdIjpEc27Jli49i5VOcPHXcuYZ8NYrmIf2kvL9I27wEAjPbbWbHATgUwIdIznXsc7WZLTCzBTNnzvRRrHyKylNPn16/rdlsnz4ujo1y8z4DhPL+Iu1rN6fU7gPAxQAubLRPIdoIklKdq3bluA85JNgn6nWy8Wen3UYQlZufPt1/+4Ty/tLF0EEbgY8L/0wA+4c/7wXgVwBOa/SewgeCuBck14U6qjG41cbQVsvSrqgAFfVoVl4RaUsngcBHamgWgLtJrgGwEkEbwW0ejpuNVroxuqaWBoLGXKtp+Gw39ZH2fECtppk0aEskd1IPBGa2xszmmdkHzGyumX0j7WN6V50LP/vseCt3HXCAu0cP4L5Y5nW2z6gA5WrTANR4K5JDmmKiU7UTmO3e7d6vcnHftQuYMqXxZ0ZdLAcGsr/w14qaDA+YeF4ANd6K5JQCQaei0ju1+vrco4J7e4t/sWwUoKJmSxWR3NBcQ52KSu9UmzSpfr977gnaAfKY7klKkdcrEOkiqhF0YnQ0uIDXNuwCQE9PcAE0qx/ZWr1/HtM9ItJVFAg6sXRp9LQGrrYC174iIhlTaqgTUV0hay/4P/mJgoCI5JZqBJ3o62veRqAAICI5pxpBJ1x96CsqY2lFRHKufIHA50RntV0/AeCssxQARKRQypUaqh3cVZneAUi+Z47WCxaRkihXjcA1uKvZVM2tcq0XrDSQiBRYuQJBmguxPPJIfQBYtkwBQEQKr1ypoahePJ1OdKY0kIiUWLlqBEmvUuWaH6gyWlhEpCTKFQiSmqp548bg/c88M77tmmuCAOCqHYiIFFi5AgHQ+URnJDB79sRtZsC55yZVwmh5WAReRLpO+QJBu849t/5uf9cuf2mgVlY2ExFJkALBtm1BALj22vFtBxwQbHvPe/xdiH10fRURcShXr6FWRS0U89JLwc9pDkirlWbXVxGRBrqzRnDRRfVB4K23gsZl11354sXp5+6jurhqjV8RSVl3BYI33wwCQHV30m9/O8jJT50affe9dWv6ufuku76KiMSUz0Dw0kvJ34GTwF57TdxmBnzta+PP4959p5G7T6rrq4hIi2g5HBy1oKfHxqqXd+ztbf+iuGwZ8LnPTdz2+uvAtGn1+9ZOWtcIWb8EpYhIRkiuMrMF7bw3nzWC2gtsO3fgu3cHF+vqIPDlLwe1AFcQANx35dOnu/dV7l5ESiKfgcClld4zJ50ETK7pEGUGfP/7zd9bOyDtiiuUuxeRUitOIIhzB75mTXAn/4tfjG/btq2zQWHK3YtIyZWjjcAsaFiudvPNwKc/nV4hRURypHxtBP398e/Ab799YhA46KAgMCgIiIjEks+RxQccAIyNNd5n61Zgxozx54cfDqxdC+y5Z7plExEpmXzWCJoZGpoYBB58EHjqKQUBEZE2FCsQ3HVXkC665prg+aWXBmmgefMyLZaISJGlHghIziZ5N8nHSK4lubjlD9m2DdhjD2DhwuD5gQcC27cDl1zS+H2a319EpCkfNYJdAJaY2dEAjgfwX0keHfvdX/kKsP/+wNtvB89XrABeeKG+b38tze8vIhJL6oHAzJ4zswfDn18D8DiAQ5q+8emngzTQD34QPL/wwuCCfvzx8Q6s+f1FRGLx2muI5BwA8wA84HhtCMAQAPT19Y1PBtfbCzz3HLDvvq0dTPP7i4jE4q2xmOTeAH4K4Etm9mrt62Z2tZktMLMFM2fOBG66KZjmYfv21oMAoPn9RURi8hIISE5BEARGzeyWmG9yryAWl+b3FxGJxUevIQK4FsDjZhZj1reEaI4gEZFYfNQI/gDAIgD/geTq8HFqokeI6iZaO5OogoCISJ3UG4vN7NcAOsjxNFG7mIzPBedFREqgWCOLXdRNVESkI8UPBOomKiLSkeIHAnUTFRHpSPEDgbqJioh0pPiBQN1ERUQ6ks+FaVo1MKALv4hIm4pfIxARkY4oEIiIdDkFAhGRLqdAICLS5RQIRES6nAKBiEiXUyAQEelyCgQiIl2OZpZ1GeqQfA3AE1mXIydmAHgx60LkgM7DOJ2LcToX4440s33aeWNeRxY/YWYLsi5EHpAc07nQeaimczFO52IcybF236vUkIhIl1MgEBHpcnkNBFdnXYAc0bkI6DyM07kYp3Mxru1zkcvGYhER8SevNQIREfFEgUBEpMtlFghIfpzkEySfJPl1x+t7kLwxfP0BknMyKKYXMc7FV0g+RnINyV+Q7M+inD40OxdV+51O0kiWtutgnHNB8ozwb2MtyZ/4LqMvMf6P9JG8m+RD4f+TU7Mopw8kryO5meSjEa+T5F+F52oNyQ82/VAz8/4A0APgKQCHA5gK4GEAR9fs8wUAV4U/nwngxizKmpNz8UcAesOfz+/mcxHutw+AewHcD2BB1uXO8O/iCAAPAXhX+PzArMud4bm4GsD54c9HA1iXdblTPB8fBfBBAI9GvH4qgP8DgACOB/BAs8/MqkbwIQBPmtnTZvY2gH8A8KmafT4F4Prw5+UAFpKkxzL60vRcmNndZrYjfHo/gEM9l9GXOH8XAHAZgL8E8KbPwnkW51z8KYD/aWYvA4CZbfZcRl/inAsDsG/4834ANnksn1dmdi+Alxrs8ikAf2eB+wHsT3JWo8/MKhAcAuCZqucbw23OfcxsF4BtAKZ7KZ1fcc5Ftc8jiPZl1PRchNXc2Wb2zz4LloE4fxfvA/A+kveRvJ/kx72Vzq845+JSAIMkNwK4HcAFfoqWS61eU3I7xYQ4kBwEsADACVmXJQskJwH4PoBzMi5KXkxGkB46EUEt8V6Sx5jZK1kWKiOfBbDMzL5H8vcA3EByrpm9k3XBiiCrGsGzAGZXPT803Obch+RkBNW9rV5K51eccwGSJwFYCuCTZvaWp7L51uxc7ANgLoBfklyHIP95a0kbjOP8XWwEcKuZ7TSz3wL4fwgCQ9nEORefB3ATAJjZCgB7IpiQrhvFuqZUyyoQrARwBMnDSE5F0Bh8a80+twI4O/z50wDusrAlpGSanguS8wD8CEEQKGseGGhyLsxsm5nNMLM5ZjYHQXvJJ82s7cm2cizO/5F/QlAbAMkZCFJFT3ssoy9xzsUGAAsBgOT7EQSCLV5LmR+3Ajgr7D10PIBtZvZcozdkkhoys10kvwjgDgQ9Aq4zs7UkvwFgzMxuBXAtgurdkwgaRs7Moqxpi3kuvgNgbwA3h+3lG8zsk5kVOiUxz0VXiHku7gBwMsnHAOwG8N/MrHS15pjnYgmAa0h+GUHD8TklvXEEyb9HcAMwI2wTuQTAFAAws6sQtJGcCuBJADsAfK7pZ5b0XImISEwaWSwi0uUUCEREupwCgYhIl1MgEBHpcgoEIiJdToFARKTLKRCIiHQ5BQKRGMK57j8W/vxNkn+ddZlEkqJJ50TiuQTAN0geCGAegNKN7JbupZHFIjGRvAfBVB8nmtlrWZdHJClKDYnEQPIYALMAvK0gIGWjQCDSRLi60yiClZ9eL/ECMNKlFAhEGiDZC+AWAEvM7HEEy2Rekm2pRJKlNgIRkS6nGoGISJdTIBAR6XIKBCIiXU6BQESkyykQiIh0OQUCEZEup0AgItLl/j9BNN/EGE+NMgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -730,7 +730,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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3xqSnVt5zxpK7MSY9tfKeMwGTu4g8JiJbRGSF17Z8EZktIl96fuZ5touI3C8ia0RkuYiMiGXwxhjTrFbecyaYlvvjwGk+2yYCc1R1EDDHcx/gdNxL6w0CxgMPRydMY4wJUSg9Z9KwPh8wuavqPGC7z+azgWme29OAH3htf0Ld3gc6iUj3KMVqjGltIkm6DT1nxu12/8zp2vy+aVifD7fm3lVVN3lubwYa3rWewDde+5V5th1ERMaLyBIRWVJRURFmGMaYtBavpJuG9fmIL6iqqgIaxvOmqmqpqpYWFhZGGoYxJh3FK+mmYX0+3ORe3lBu8fzc4tm+EejttV8vzzZjjAldvJJurEe2JqCmH25yfwm4zHP7MuBFr+2XenrNHA3s8irfGGNMaOI1nUAo9flwJKCmH3D6ARF5BjgBKBCRMuAWYDLwrIhcAWwAxnl2fw04A1gD7Ad+EoOYjTGtRbpMJ9BMeamu3kWGMzbDjQImd1W9sJmHxvrZV4FfRhqUMcaklbwSd4td6xrLS/uq67joX4s5b0RPfjy6KOovaSNUjTEm1nzKSzWjZ/CLJz7gkqm3MvSzD2LykjYrpDEmNVWWu2vXO5a5W8ZjZka/Vh4tXuUll0v5/YyPOfWhP3P+8tmw23eMaHRYy90Yk5pScOCRqvLnV1fR/8G/ccmy12HiRPjNb2LyWpbcjTGpKQUHHv1j3jpc993HtQuno1dcAX/5S8xey5K7MSY1pdjAo5lLvuHLv/6dSXOmoj84B3nkERCJ2etZcjfGJE4kg3tSaEm9OavKmfuXR/jb6/fhOvlkZPozkBHbS552QdUYkzgNdXOtO1A3D7Zfe4r0gf/gq+1M/9M/eOilv6JHHYXzhRegTZuYv6613I0xiZNqdfMQv2l8WraLhyY9yoOzbkeGDcP5+mvQrl1cQrXkboxJnBSrm4fSQ+fL8j1Mvu0J/j5jEo4B/cmY/SZ06hS3UC25G2MSJ4Xq5kDQ3zS+3rafm2+fzkNP3kRWty5kznkL4jz7rdXcjTGJk8i6eTiDoPxMI+Br864qbrhjJg/8eyLtOrYn4+050NPvshYxZS13Y0zrFM4gqADfNLbtreaGyc9x79TfktfG4U7s/fvH6ARaZi13Y0zrFM7F3Ba+aeyqrOWGvz7PnX+/lvxMyHhnLhx2WNTCDZW13I0xrVMUL+burqrld399gT/ddw35TiVz7tswbFhUwgyXJXdjTOsUpYu5e6vr+P3fXuSWKVdTSC1Zc+fA8OFRDjZ0EZVlROTXwM9wr6H6Ke7FOboD04HOwEfAj1W1JsI4jTEmuqJwMXdfdR2/v/slbrrnarq6qsl8520oKYlOfBEKu+UuIj2Ba4BSVR0GOIELgDuBKao6ENgBXBGNQI0xJpnsr6nj9/e8wsS//ZLu9ZVkvv0WjBiR6LAaRVqWyQByRCQDaAtsAk4CZnkenwb8IMLXMMaYpFJZU8+Nd7/EjXdOoEfdPjJnvwmlpYkOq4mwk7uqbgTuAr7GndR34S7D7FRtuARNGeC3g6eIjBeRJSKypKKiItwwjDEmrqpq6/njXc9z/eQJdNUq98XTo45KdFgHiaQskwecDfQDegDtgKCXFFHVqapaqqqlhXEeuWWMMeGorKnn5smzuP6OKylw1JP17jtJ12JvEElZ5mTgK1WtUNVa4L/AsUAnT5kGoBewMcIYjTHJLJJpexMhzHj3Vddx8+3PcMPkCXRs46TN/HeT5uKpP5Ek96+Bo0WkrYgIMBZYCcwFzvPscxnwYmQhGmOSWqotdxdGvHuqarnl1v/wh7/9kvZt25C9YF7C+7EHEknNfTHuC6dLcXeDdABTgRuA34jIGtzdIR+NQpzGmGSVatP2hhjvrspabv2/afxxyq/I6die7EUL4NBDI48jxt94Iuoto6q3qOqhqjpMVX+sqtWquk5VR6nqQFU9X1WroxWsMSYJpdq0vSHEu3N/DXde/zCT7ruGrIJ8cha9BwMHRieOGH/jsRGqxpjItDTSMxnr8UGOTN22t5r7rrmLWx7+HfTpQ9vFi6CoKHpxxPgbj00cZoyJTEsjPSNZRi9WghiZumlXJdMm/ImbZvyV/YeX0OHtN6Fz5+jGEcT0wZGwlrsxJnZSrR4PfFW2gekXTGDi9MnsG1pIh7dmRj+xQ8wXKrGWuzEmdmLcOo22Vd/u4t1Lfs+v353JrpJ2dLxuKyy7PDbfNmK8UIm13I0xsZNCy+h9tG4rS8+5lAnvzmT3sW3p+Jt9kFmfEt82/LGWuzEmdhK5jF4IFqz4hr3jLuLiVQvYc3Y/Opz/NQgp8W2jOdZyN8a0am++t4qsM87gtFUL2PuXO8l9ZhF0OSYlvm20xFruxphWa+YLixj+8wvov+Nb9k97kvaXXuJ+IAW+bQRiyd0Y0+qoKo/942VOv/4K8uoq0ddeo+2ppyQ6rKiy5G6MaVVq611Mve0xLpl8HdK2LVnvLsB5REmiw4o6S+7GmFZjb3Ud/7nqNn7++F/Y26sPee/OQaI56jSJWHI3xrQKFburmDNuAhP+N43ykaPpOvtVyMtLdFgxY8ndGJPeKstZ+/LPWfenLVzw2WK+/cG59JjxNGRlJTqymLLkboxJa4v/czVZt37BKRtX8+25hfQYX572iR0suRtj0thrM99m2PXz6bp3O9uu6kCPYytgZ1Wiw4qLiAYxiUgnEZklIp+LyCoRGS0i+SIyW0S+9PxM36KWMSY2Ipwq2OVSnv3TPxhzyVl0rNtP3U2ZdD52d0qPOA1VpCNU7wPeUNVDgWJgFTARmKOqg4A5nvvGGBO8CBayqKqp48WLr+W8W37B3p59aPfBHNodOzLyEae+Hzg7ViTfXPVeRFXDe6JIR2AZ0F+9DiIiXwAnqOomEekOvKOqh7R0rNLSUl2yZElYcRhj0tCzHdyJvUFGLozbfeB+Zbk74e9Y5m6Jj5kJOV0pL9/BqrMu4IQP32TdiWfQ76UZSPv20Ylp9vFNZ7h0toX6/QfuF4yO+8hWEflIVUv9PRZJy70fUAH8W0Q+FpF/iUg7oKuqbvLssxno2kxQ40VkiYgsqaioiCAMY0zaCbQUnp+W/YrFK9g28mhO+PBNvvzVDfSf80r0EjscPDd93e6knqs+kuSeAYwAHlbVI4B9+JRgPC16v18NVHWqqpaqamlhYWEEYRhj0o73VMH5I8FV07T84ZNo575ZR9exx1NU8Q1lj09n0P2TQSS6Mfl+4GR0SOq1YyNJ7mVAmaou9tyfhTvZl3vKMXh+boksRGNMq9MwVfC43eDIgu0fNa2/exJtnUt4fdaxHHvPh9S3bUvtgoX0uuxHsYnJd276U94Lfa76OK4pG3ZXSFXdLCLfiMghqvoFMBZY6fl3GTDZ8/PFqERqjGmd/C3Vd9aX7Hr9QlbctoPTl73H6hHH0u+NF8gsLIhdHP7mpg+1xh7HNWUj7ef+K+ApEckC1gE/wf1t4FkRuQLYAIyL8DWMMYnSzIXLuPKzVN/apRuRX65n9Ob1LB//G4Y//DdwpMDyFHFcUzai5K6qywB/V2rHRnJcY0yS8G1pzjvbXSbxTfax/BAYM7PJsT/4+DwO+7/jqHdm8OW0mQy/9NzovE48xHFN2bC7QkaTdYU0Jkn5dknEAeI40PoUJxQc477guf2jmHYLrKmq4YOLf8GY/z7Gmr6H0emV5ykY1mIv6+QT5Q/BlrpC2vQDxpjm+bY00QOJHUDr3Y+j7tsQk3JDxZfrKf/eDxnz5ccsOf1HFM/6N5ltc6L6GnERxzVlU6BIZYxJGN8eIvmlB7r/NdA6UI1Zt8BV02bhOOIIBny1ko//fB+lr01PzcQeZ9ZyN8YczLd8cNaXTWvrFe8BLve+kuHui+5bi4+Qq6qaZZdfzYgZ/+KrrkXseeUNjjjhqIiP21pYcjfGHKy5LnsNZYUY96LZtnwVO88+jxHrV7LgpHMpnvkoufkdo3b81sCSuzGtnb9EHajLXgxrx6unTKXHjb+mQBzMv+NhxtxwJRLt0aatgNXcjUlnwYyI9DcDY6C5XWKgfvceVpx2LoN/cyXru/ejYt4ijps4wRJ7mCy5G5POgpk6118r3fdCahRq6C3Z+s4CNg8expD/Pc/sc35GvxVLGHjksJi+Zrqzsowx6SyYEZH+BtbEq8tebS2rr5lI/3/cy9b2ecx/+BlOmRCjuWFaGWu5G5POgimvxLmV3mDvsuV8c2gxgx+5hwWlp1CzdBnfscQeNdZyNyad+Qzd95u44ziwBgCXiw23TKbb5Ftpl5nNKzffx2m3XE2G09qa0WTJ3Zh0Fu/EHUDN2q/Y/MML6Lv8AxYedjTtn/g3Z5YeGvsXToYJ0OLMPiqNSSdxnC88pBhUKb/3IeqGDiP/80+Z9YtbKF46j+HxSOwQ+pqsyfA+RsiSuzHpJIKFpWMVQ/2zZ1F25HF0/fUvWdVtAEtfeofzHppEu+zM+MUU6lS74byPSfaBYMndmHQSx/nCA8bggooX21P780/o9OlSnr70enp/8j7Hf3dU/GMKtd9+OO9jMnyweok4uYuI07NA9iue+/1EZLGIrBGRGZ6FPIwx4QqlRZiAwUf+Yqj9OpPym/IpnLGTD/sOY9FL73Lh45Pp0rFt/OOB0HsEhfM+JsMHq5dotNyvBVZ53b8TmKKqA4EdwBVReA1jWq9QWoQJ6tbYqLqaipdLkJtcZFTU8djFFzFk4Quc8t0jEzvS1HtN1oY5cloSzvuYDB+sXiLqLSMivYDvAbcDvxH3b+8k4CLPLtOAScDDkbyOMa1aKC3CBPaOqX1nHrsv+ymFX6/lteKxtHngXn56XIqOMg3nfQym22kcRdoV8l7geiDXc78zsFO1cTb/MqBnhK9hTOvU0H2vbt+BbUnQIjxIeTkVv7iWwudnUNmhkH/c8ADjbh5PXrtWVpH1/UBoKKclqPtl2GUZETkT2KKqH4X5/PEiskREllRUVIQbhjHpq6Ec0zhvujMxpZbm1Nez7+57qew/kI4vPscTJ1zI2rmLuXLy1a0vsfuT4AuskbTcjwW+LyJnANlAB+A+oJOIZHha772Ajf6erKpTgangXkM1gjiMSU/e5RgAZ9ukGZDkWriIXT8dT94XK3ivqJhVf/gLF1/2XXLqtya0tZpUEnyBNeyWu6reqKq9VLUIuAB4W1UvBuYC53l2uwx4MeIojWmNkuwCHQBbt7Lr4stwHHsM1Ru/5Z6f/YnCRfP42c/PICfLmfDWalJJ8O8vFv3cb8B9cXUN7hr8ozF4DWPSX6J7vnirraX6nilU9htIu2f+wxPHnMvCVxZw3T/+yOBuHQ7sl2TdARMqwb+/qMwto6rvAO94bq8DEjBKwZg0E4+eL4HmXFHF9dLL7Lvm1+R+vY4FfYt5f9LN/HTCWeT7q6v7mz64tUrwvD42QtWYZBTtoezNHa+lMsry5ew57kQcPzibLXtruO3KO2n77tv87rfn+k/sEFlrNdxzTrJh/8lCVBN/LbO0tFSXLFmS6DCMSR6zj2/aAi4YHVkrsLnjPdvBndgbZOTC8avZf8MfyH5yGrvatOPRsZfS/+bf8oMji3A4YjgQKdxzjvZ7lUJE5CNVLfX3mE35a0wyCrV2Hai80tzxvMso1U5q/1eAXj6QzOoqppV+nz2/v4EJ3y+lfZs4pIpw6/VW5/fLyjLGJKNQe1oE6qXS3PHGzIQOR+GanU3VdVlkPvEV7/Q6nNvueJaxrz/FNecfHZ/E3lKMsXpemrPkbkwyCrV2Haj16u949fXo9NeoHL8Bx+NVLOs4gN9f9xDtX3uZP13/Q/p0jvMkX+HW632fV/qQ1eCxmrsx6SGUuvP+zXDnSVT+8ytyNlWxvNtApp35c068+mLOOLxHbOrq8VwJqRXV4K3mbky6C2bSKlV44w2qrrmI7DU72Zjfi6nn/pDDz1AmX/Y7MmO5hmlD2UjrDpSNYpVwrQYPWHI3Jj1496n2bSUf+yy89QHVt9xKm2VL2dahkIe+9wu6nbCNW7pOpV2bTHDeH/2YvOOo3w9a794e64Rrfe0BS+7GJI9olS4aWsn1dfDae9SMP4Ss9bvZ3KkbU793LTmn5PKb/Ifo7NzuSX5HRvtMmsbhPT8OxD7hJnLq3SRaiNuSuzHJIlqli20fw8I6ap7PIGtjHWX57XnkrJ/T4YpLufakwXTJ2AUL3o198vOd+AwHZLSLfcJN5MjQeJafArDkbky8hNsXPVhVVfDkk9ROUjK/hfWdu/PI988l/+Rcfnf5H+iSm+3ZMTs+Cce3PJLGFzYbJVG935K7MfHi3aqrWAgvDwb0QKIPt1a8dSs8/DB19z9AxtYKPu86gH+e80MKj9nDxEM/p8vYxyEnO+BhQhbowyrJViaKiySq91tXSGPixXeof4OGVq2/ZNhSvXbtWvSee3A99m+cVZW83b+U/xx7PoMuOJMrxvSnS4cYJHRv0exymES16ojE+TysK6QxycC7Veet4et7MLViVVi0CNfddyPPP0+dI4P/DjmB/54wjhN+eCL3Ht2HDtmZgWOJRhKKZgkiiWrVEUnwTJDeLLkbE6lgEmVlObhqQF0cGBjesHxeEF/f9++HZ56h/sEHcS5bxr6c9jxx1HnMPeVHnHfmkTwxoidtMpzBxxyNZBrNEkQwHxTp0rqPE0vuxjQn2GQSTKJccD5s/4jGhO7N2bb5evS6dfDww9T/61GcO3ewtksRj3/3l6w79WwuP3UYE4Z0wxnOiNJotLqjWVMP5oMiXVr3cRJ2cheR3sATQFdAgamqep+I5AMzgCJgPTBOVXdEHqoxcRZsMgkmUR7ULdCbNv3QcLngzTfRBx6E11/DJQ5eHzSap888iy5nnMxlx/bjiD55oZ+P94cV4k6ikbS6o1mCCOaDIol6oqSCSFrudcBvVXWpiOQCH4nIbOByYI6qThaRicBE3EvvGZNagk0mwbQ6m6u34wTEfbG19lBY9R1cT87E8fUGdrTP48nRP+KNY8/mu6eO4N6j+nh1ZwxDk0FFTnefc+/eOokUzAdFEvVESQVhJ3dV3QRs8tzeIyKrgJ7A2cAJnt2m4V5+z5K7ST2BkklDS3j7UndpBRfkHeE/UXq3TDsOcW/btRJqgff2onNdsOJDRD9kUb8Spp81jk1jz+DH3xnEi8O6k5XRwrwvwZaPmnx7qAcUxu0OfJxkqXW3xq6VEYhKV0gRKQLmAcOAr1W1k2e7ADsa7vs8ZzwwHqBPnz4jN2zYEHEcxkRVoKQWSVfAFSvg0Udx/et+HHtdbOmQxzPDTuOV4jGUnnwcF47qzfBenYI7VrBxBNqvucdb0SyLqSamXSFFpD3wHHCdqu5253M3VVUR8fvpoapTgang7uceaRzGRJVvYi996OBEH2oN+Jtv4Jln0KeeQpYvp86ZweyBRzN9+HfZOySHHxXM4YUBz9Hu9KtCizXYOAK1fJs7TrRq3cnyDaCViCi5i0gm7sT+lKr+17O5XES6q+omEekObIk0SGPCEmoy8b3gWL//wMXU2cc2vb/g/OBqwDt3wqxZ8NRT6LvvIqp81vtQZp58Je8ecRInHN2PG+UODq19K3CpobnzCbYWHaiu3dxxolXrtt4ucRV2WcZTcpkGbFfV67y2/w3Y5nVBNV9Vr2/pWDZC1cREqOUE7/0DyciFs770n2z37oXXXoPp09FXX0Vqaigr7M2zhxzPq0NPoN/oYn5wRE9OPqwr2Zkh9E1v7nyi1SKOdc3d32Lc3jV/E7KWyjKRJPcxwHzgUw503v0DsBh4FugDbMDdFXJ7S8ey5G5i4qDh/j6zEvomqJamB3C2PdBy9/dBsWsXvPIKzJqFvvEGUlXFzg75/HfwGF4YcgLOUUdyzohenDm8B/ntsqJzPqmWHK12H3Uxqbmr6gKgudETY8M9rjFRk1finqALzyIRuJouIO2bWJp0V/TpKlj6ECy5qmnrdft2ePFFeO45dPZspKaG7Z0KeWXoybw6+Fi2FJfy/RF9uP+InhQVtIvO+aRyV8Bo9Haxun3QbOIwk74qy90zL9b5a9064JxvmyaGYBLHF1+4W+ivvILOn4/U11OR340XBxzNa4OPZcfhR3D60E6cUXMHQ2vfRPKbOU645xOPxJbMCdRa/03YxGGmdcppGDztj+tA6903mZ315YFkVlMDc16Gf12LLvwW2ew+3voeA3jtyHN4ffCx7Du8mO8N78Hth3fn0G65yFvfgT0xuHAYr0mpEn3hs6UPFxulGjRL7ia5RLvV2OzIUA4kBt9kNvMsqPoZzJ6Nvvkmsns3tRlOlvQdyqunHMfcAUfSeeggTjq0C3cN687gru3x7gKc8gko0fG39OGS6qWpOLLkbpJLoFZjqMnfu87r3b3ROzF88zEsr4MVoJ/WIVs+BD5ke34X3h5wDG/0O5KPig5hZOfPGZv7Ib/Ke5Wul7Qw6C7VE1Ci42/pw6W5un0yl5ISxGruJrkE6hESSc21IQFs/Bi+7Qs7xsL8xeiHixEXVGdl8mHfIczuezQLikqo6j+I7xzahZOr7uYY10yyZX9wr5nqiSbR8YfzO26ltXiruZvUEajVGE7JYPt2mD8f5s2DeZXo0v2I6zPqnV/wZZ9D+d/RFzK/Xwlre/amtHAzxx15MpcP7U9R57buckvlnbBg3YF5Yer2wdNOEIH8Ujj+xabJL1kWbAg3Sccr/ubiC6dXTaJLSUnIkruJjXASi/eCFuKE/JEH/2EHSv4uF6xaBYsXu/8tWgSffgpAXWYWq/oOYe7R43i/9zA+7XUYgwd0Y8zAAiYOKqC4dycynX4m6PJOdrOPh22L3bcV9+14XnAM5X1N9IXRQJqLL5wPl0SXkpKQJXcTfb5dECvmw/PdPAnbT0u3QZMFLTLAkXVgv+ZmYBz8ELz8sjuRL5yHLl6I7Hf3a69sl8tnvQ5l7nE/5oPeQ1nV+zAO79+FUf3yubpfPkf0ySMnK4QRotD8XO3xEkrCjnZrNtrlmmjGZzNGHsSSu4m+Bef771uu9S23dFv6Y59/HqxZBF/Vw9cO2JQPZWXw1eEAuBxONnTvweJDxvJBj6Es634I27vkUTygiFH98rmhXz6H9+oYeCm6QAksr8T9YeUtnq3EUBJitFuz0f4mEM34kqUUFow4XdOw5G5C592KFifggo5DwVXrSTZ+lpLzFmjRi7o6KHfC1i4wqzd8sQldX480fl642NxZWdGtF4tPOJGPex7C590H0i93I0fkrGRM22X8qu0MitruQX4U4vD8QAlszEyYdzZs+/BAzT2arcRgPlyCTYjRbs1Ge3bIYObBT0dxKpdZcjf+tZRkmqzo49FQhw5GQ0KqrobVq+GTRfC/22HNRtio6CaQunpgLXVOBxu6dGdZ0WA+6TKYlV37s6awN907VzJk8EiG9ujAqb06cnjPjmS/c2LTxJc/OvTzDpTAcrrCd98/+Hn+3i8IvYUWzIdLsAk72q3ZWMwO2Yp6tjSK08Vf6wppDqgsP9Aq9W59+/4BNjfBlj95o2DLflj9mXvy513doLIYXb0O1q5F6t31cZfAlrx8Vnfuy8r8/qwu6MNnXQewuSCfwe2+YUjOOobmfMWQ9psY1L0zbY6ffnCi9PeNoqFFGOw0A+F2qfP3PAj9WMFODpaI7oo2O2R0RLHbZkxmhYwmS+5xFCix+daTGzjbQf6IgwcDKbAf2Ob5t8XzbzPothwor0dqahoPU+d08m1+AV907seqvL6sKejDlwW9qeicR6/ccga1+YaBbbcwMHsjAzNW0zurHEfDei+FxzX/R9DcXOyBVhwKZoKwYBKYv4QFoSexaK2qlMxSOfZoiOIHsyX3dOP9n8N7Pc7m/qN4t2jrq2icJbHhD6vhq76/xF4N7HTCjjZQUQVbXY2J3LVNYBs4qpr+H6rKymRjXjfWdOrNVx27sSGvBxs6daMsvysZ+fX0zN5C3zabGNCmjEHZGxnYtoKujk0IPq3eJjM6OiEjx39LHFqei903qbY0tW+4icb39cXp/kCs23fgHDI6wFmrg18wpKU//FRu/SZykFSiB2hFmSX3dNBcy7QJTytU65uWJVw17i6GDS3tKmAvsAvYCez2/NwFugvqdzpw7XLg3OXCWX3wxdEd7XLZlFvA1x26sSm3gI0dCtmUW8imDgXs7tyeNv2G0KdzO/p0bkvf/Hb07dyWPvlt6T67Bxn1u/yfX0YHGlvPvvXq5lri3u9J/X73eftqruXe3DeUcJNkQywV73GgpNXQM8fnwzQardTW3voNV5q9bwkZoSoipwH34f4f/i9VnRyr10qo5lrR/lrUEHilG3/H2LniQItbgVrcCboKd0mk4XZlPezbje6F+r0O6vY4ce1djO51IHudOPcJmfvqcLj8f6DvymlHRds8trTLZ2vnTmzt3YmK9u77mzvksy8vB1e+UJizi26ZW+mRVUG3zO0ckrWUHhnldMvaRXaXkc3/seQPb751XbfbXXbxbkl51/j9XYBacL5P676BT6nF96LjmJnwQs+DPwwiuUjYcPGySYva5/jNXTwLpzXp3WsHdX+AV5andCs0LlrRSNaYtNxFxAmsBk4ByoAPgQtVdaW//cNquVeWs/Cl33LX6hFkSj1ZVJMhdWQ6lEypI0vqyaCaTKklywmZWkWm1JHhhCzP7Uyv7ZlOIZNKMqUOcWTgcNXgEBdORwai1Thw4ZAMHK4qnPX1OCULZ10djrpqnK4697Z6RV0K9YLWg9aL+++7XqBWoQYctQo1itSCw/NTal1QA1oLUgNSq0iN4qhVMqrqyayuI7OqjqzqWrKqa8moD9DVENiXmc3O7Fx25uSyM6c9O7Nz2ZXtvr0juwN7c3Koys2iukMWdR0c0MFBh+y9FGTspqBTJwpH/pbOX0ykYN8iCjK2kufcg1N8Xlcy3KNIHVnBJSa/rVuf4/lrSfm2thpe01/rW5xQcEzgBOmvjBLM8wLxjTXQCk7+nhPJRdwUboXGRZq9Z4louY8C1qjqOk8A04GzAb/JPSwLzidvYRkPPP0/QNwtWs/nlKg2uY+CoIgCqohnH0EbH3OoNm4Tzz547me4XDhd9WS5glhbMwwuhKrMLKqdWVRlZFGVmUWVsw1VmVnsy8phb/scKvPbUJmdTVWbLKqzsqjNzqA+20l9tgPNFshRHDmg7RRne6VNVg1tHdW0c+yno3MvhRk7GeT8ho7OSjq2qSPbUX/wQCPfBLf6Rcj21ysmwHJ1zWlo3TbXK6e5lpRv9z9XjfsP1B9n2+D+WP11KYxGq9f3uP4u0PoKtzXZilqhUdOKRrLGKrn3BL7xul8GHOW9g4iMB8YD9OnTJ/RX2LGMw/L3wICGA/r8w+e2Z8oQBVQEF0K9uz2OSxzu++K+rzhQkcZ91SG4HILL6cTl9Nx2OHA5HdQ7HajDQb3T/XwyBBwKTnEXpJyKOEEzQbMEMoEs0ExBshQyQTKULGc9WVJLptTSTirJl91kOerIlFoyfFvMkWioKXsn2OYmwPI3F3o0WjvefcV9W1L+yiK+/bW9yzTeQimrxGpEo7/jBnqdcPuP23wqoUulkawRStggJlWdCkwFd1km5APklcDgRTAotNa0O98rDpSMQCMpk4YDMtrTOBIU3DX5JhdWm+nS510C8U4AzQ3G8dbQyvHXbzxawmlJtbTWaSq2xMJtTbaiVqgJXaxq7qOBSar6Xc/9GwFU9Q5/+4dbc29MPA1JDiCjrXtWQRwHtjmzPRcko3i74XUkg4OSru9F0YZh+SLQqRgcme6LpA1xe29v7mKsv5JBMBfi0qzrF5Ce52RMGOLeFVJEMnBfUB0LbMR9QfUiVf3M3/7WFdIYY0IX9wuqqlonIlcD/8NdeX6sucRujDEm+mJWc1fV14DXYnV8Y4wxzfOz7IwxxphUZ8ndGGPSkCV3Y4xJQ5bcjTEmDSXFrJAiUgFsCLBbAbA1DuEko9Z87tC6z781nzu07vMP5tz7qmqhvweSIrkHQ0SWNNefM9215nOH1n3+rfncoXWff6TnbmUZY4xJQ5bcjTEmDaVScp+a6AASqDWfO7Tu82/N5w6t+/wjOveUqbkbY4wJXiq13I0xxgTJkrsxxqShpEvuInKaiHwhImtEZKKfx9uIyAzP44tFpCgBYcZEEOf+GxFZKSLLRWSOiPRNRJyxEuj8vfY7V0RURNKmi1ww5y4i4zy//89E5Ol4xxgrQfy/7yMic0XkY8///TMSEWcsiMhjIrJFRFY087iIyP2e92a5iIwI+uCqmjT/cE8PvBboD2QBnwBDfPa5CnjEc/sCYEai447juZ8ItPXc/kW6nHuw5+/ZLxeYB7wPlCY67jj+7gcBHwN5nvtdEh13HM99KvALz+0hwPpExx3F8z8eGAGsaObxM4DXcS8idzSwONhjJ1vLvXFhbVWtARoW1vZ2NjDNc3sWMFZEhNQX8NxVda6qepaX4n2gV5xjjKVgfvcAtwF3AlXxDC7Ggjn3nwN/V9UdAKq6Jc4xxkow565AB8/tjsC3cYwvplR1HrC9hV3OBp5Qt/eBTiLSPZhjJ1ty97ewds/m9lHVOmAX0Dku0cVWMOfu7Qrcn+jpIuD5e76S9lbVV+MZWBwE87sfDAwWkfdE5H0ROS1u0cVWMOc+CbhERMpwrxHxq/iElhRCzQuNErZAtgmfiFwClALfSXQs8SIiDuAe4PIEh5IoGbhLMyfg/sY2T0QOV9WdiQwqTi4EHlfVuz3rMz8pIsNUNVVWuE+IZGu5bwR6e93v5dnmdx/PWq0dgW1xiS62gjl3RORk4Cbg+6paHafY4iHQ+ecCw4B3RGQ97vrjS2lyUTWY330Z8JKq1qrqV7jXKB4Up/hiKZhzvwJ4FkBVFwHZuCfVag2Cygv+JFty/xAYJCL9RCQL9wXTl3z2eQm4zHP7POBt9Vx5SHEBz11EjgD+gTuxp0vNtUGL56+qu1S1QFWLVLUI9zWH76tqOqysHsz/+xdwt9oRkQLcZZp1cYwxVoI596+BsQAichju5F4R1ygT5yXgUk+vmaOBXaq6KahnJvpqcTNXh1fjvoJ+k2fbn3D/IYP7FzsTWAN8APRPdMxxPPe3gHJgmeffS4mOOZ7n77PvO6RJb5kgf/eCuyy1EvgUuCDRMcfx3IcA7+HuSbMMODXRMUfx3J8BNgG1uL+dXQFMACZ4/d7/7nlvPg3l/7xNP2CMMWko2coyxhhjosCSuzHGpCFL7sYYk4YsuRtjTBqy5G6MMWnIkrsxxqQhS+7GGJOG/h+rqyK36UZw/QAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -747,7 +747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.004999999999999991\n"
+      "0.005000000000000009\n"
      ]
     }
    ],
@@ -1322,7 +1322,7 @@
       "270 3344  160  110  270      Ds  7.253775  7.253775\n",
       "\n",
       "[267 rows x 6 columns]\n",
-      "0.009883615646716186\n"
+      "0.009883615646716182\n"
      ]
     }
    ],
@@ -1386,18 +1386,6 @@
     "editable": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -1421,6 +1409,8 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1486,6 +1476,16 @@
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
@@ -1512,7 +1512,7 @@
     },
     {
      "data": {
-      "image/png": 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X4GywlmcF417emL6xk1Zcbdp2CDrYP/y05aDbv/01+FA64ZhgvNfbHxT/tsPm7Z/5SXC4edny4NDzW+8H+32fivDt98H+HnRP0P6RY+DsPeBEk8K2f/gTsHoN9LwpuP2V16F/z0RWHo7CPv+nTIP7HoGn+sMRh0ZaYpHZtO3zvgsSvWOPDPb5i2ODLxpVM/bM971tccxXuJJisVgkl05bunQpM2bMICsri1gsRpUqVWjQoAFVqlTZpcctWHJISBUWzqzPoP9jsGZdkHL0+N+LMicX7h8c3J6cBMfUhW43BKee70m21f7164MkYMacYP6hBwcfRuXLJbri8Hz+H7j1Xpjywubzf/0tuMzIV98GHe8Gx8BtN+x5h55GjgnO6k1Lg73Lwx03wV9rBScb9B4YfPnIzYOmp8JNV+15l9rYVvt/XxOcCbnwpyARufqi4MN4T7Ot53+TC4MTjDa91Ei9I6DX7v29eoeMnxokmgX5QTt7dwsG3CfyfS+56ryiX8kmPvnhwMjWdcyBP0S2rkSJrPNVVKLufEmSlGhRd75m/VAzsnUdd+CCyNaVKF7hXpIkKUJ72IERSZIUtpJyFmJUTL4kSZIiZPIlSZLi8mzHcJl8SZIkRcjOlyRJUoQ87ChJkuLKj5nVhMmtKUmSFCGTL0mSFFeBWU2o3JqSJEkRMvmSJElxeamJcJl8SZIkRcjkS5IkxeXZjuFya0qSJEXI5EuSJMVV4JivUJl8SZIkRcjkS5IkxZVvVhMqt6YkSVKETL4kSVJcnu0YLremJElShEy+JElSXP62Y7jcmpIkSRGy8yVJkhQhDztKkqS48mNeZDVMJl+SJEkRMvmSJElxeZHVcLk1JUmSImTyJUmS4irwIquhcmtKkiRFyORLkiTF5ZivcLk1JUmSImTyJUmS4vI6X+Ey+ZIkSYqQyZckSYrLH9YOl1tTkiQpQiZfkiQprnyv8xUqt6YkSVKETL4kSVJcBXi2Y5hMviRJkiJk50uSJClCHnaUJElxOeA+XG5NSZKkCJl8SZKkuPxh7XC5NSVJkiJk8iVJkuIq8Ie1Q2XyJUmSFCGTL0mSFJdjvsLl1pQkSYqQyZckSYqrwOt8hcqtKUmSFCGTL0mSFFe+P6wdKpMvSZKkCJl8SZKkuBzzFS63piRJ2qMNGjSIwYMHb5heuXIlV199NZmZmVx00UVkZ2cDkJOTQ9euXcnMzKRNmzbMnz8fgFgsRt++fWnatCnNmjVjzpw5Gx5r2LBhNG3alCZNmjB16tRC1WPnS5IkxZVPUmR/YVq1ahU9evRg2LBhm80fOHAg9evXZ9KkSZx33nn06dMHgOeee44yZcowadIkevToQffu3QGYMmUK8+fPZ+LEiQwdOpTu3buTl5fH3LlzGT9+POPGjeOFF16gX79+rFixYrt12fmSJEm7jZUrV7Jo0aIt/lauXLnDj/XWW29Rs2ZNLrvsss3mT5s2jRYtWgBw9tlnM336dHJzc5k2bRotW7YE4LjjjuPXX39l8eLFvPvuuzRr1ozk5GRq1apF9erV+fTTT5k+fTpnnnkmpUqVolKlShx//PFMmzZtu3U55kuSJMUV5ZivESNGMGTIkC3md+zYkU6dOu3QY7Vu3Rpgs0OOAFlZWVSuXBmA1NRUypUrx/LlyzebD1C5cmWWLFlCVlYWGRkZW51ft27dLeZvj50vSZK022jfvj1t2rTZYn6FChW2eZ9JkyZx//33bzavdu3aDB8+vNDrTU7eegczOTmZWCy2Q/O3x86XJEnabVSoUCFuR2trMjMzyczMLPTyGRkZLFu2jKpVq5KXl8fq1aupWLEiGRkZZGdnc9BBBwGQnZ1NRkYGVapU2TAof3vza9Wqtd31O+ZLkiTFlR9LjuwvCo0aNWLs2LEATJw4kfr165OWlkajRo0YN24cALNnz6ZUqVJUr16dhg0bMmHCBPLz81m4cCELFiygbt26NGzYkKlTp7J27VqWL1/OzJkzadCgwXbXb/IlSZJKlBtvvJHu3bvTvHlzypcvT//+/QG45JJL6NWrF82bNyc9PZ1+/foB0LRpU+bOnbthMH6fPn0oXbo0Rx55JC1btqRt27bk5eXRuXNnqlSpst31J8W2dsCyGClYckiiS5AkKVLJVedFur67vmgV3bqOGBfZuhLFw46SJEkR8rCjJEmKK6qxWCWFW1OSJClCJl+SJCmugli4P/tT0pl8SZIkRcjkS5IkxZVvVhMqt6YkSVKETL520pvvQff7YPakjfPOvQrWr4e0tGD67DPgigsSU19RGfkavDgOkpLgwOpwT1eotM/G23/OgnbXwdinYZ+KCSuzSPQdClOmwd7/+9WLmgfAgLs23r5yFVzSGfp0gyMOTUSF0fjzc7+gAB56HN6dCclJcFANuPtW2LdiQsssMlt77U99Fx5/HnJyoXoVeKAH7LN34moM29jJMGL0xulVq2FpNrzzCpTbC+4dAJ9/DQUxOOow6NkFSpdKXL1h29ZrPycX+gyCOXOD+aecALdeCykpiaq06DjmK1x2vnbCgkXw4GOw6eVp16yFHxfDB+MgbQ/dql9+A8NeDjpW5ctBv0fhkaeDD1oI3qAHPwNZy5KAYn3t3q369At46E6od8SWt707E+4fDIu3/2P2xdrWnvuvToSv5sFrT0J6enB736HQ95+Jq7OobK39X3wN9w6Elx6F/avB/UNg0FNw1y0JKzN0rZsGfwC5eXBJJ7jqIthvXxj4FOTlw9hhwXa5rTc88Tx0viKxNYdpW6/9F16D5Stg/PDgS8jFnWDyO9D8jERUqeLEw447aO066NYbut2w+fzP/wNly8C13aBlh+ANeN36hJRYZA6vA5NHBh2v9euDb74V//dNMGsZvPU+PN43sTUWlZwc+M9/YdhL0Ppy6NwTFi/dePvzr8L9t0Pl/RJXY1Hb1nP/4Jpw63VBxwvgiDqbb5s9xbbaP/4NOLd50PEC6Nhhz0u8N/XUC0HafX7wKyscdxRceykkJweJz2F/3bP2f7zXfofz4eG7gravWBkkgnvv2O9BFxsFJEf2VxKUjFaG6M7+8I8WUKf25vN/XwvHHw2D7oHRj8PPS+HhJxJSYpFKSw0Ou5x6HsyeC22aBfMz9oPBvYMP4j1R1i9wQj24+WoY8zQc9Tfo2GNjAvLkg1tPxPYk23ru1zsCDv/fr3z9tgoefRaanhp5eUVuW+1f8CPk58MNPYIP53sHwl5lE1Jikft1BQx/GW7vtHHeycdBrQOC//+0BJ59BZqcmojqisb2XvtpqcFh9yYXQqV94dgjE1uviodIOl+LFy+O+1dcvDAGUlOCb7l/dtrJ0O+OYPxDqVJwzcVBJ2VPdMYpMGM83NABrro1iNv3dDWqwRP9oNaBwXi3y9vBD4uDD5uSIN5z/w8//BQcjjq2LlzYJrraohCv/Xl58M6HwWHG154KDsX1ejD6GqMwagKc9vfg9fBnX34T7P+L2kDjk6KvragU5rV/yzUw83XYvyrc/XDiai1K+bGkyP5KgkhGJ11zzTUsWLCAjIwM/vw73klJSbz11ltRlLFTHnk6eGOF4BvO2vXQ5grIzQ0OK7a5IjjU9uU3UK5cEMFD8K1oTxj7tWn7/3IQXNB64ze7c5sFbzS/rdqzBhf/YdO216gWdDpbNdl4eywWfCDvqQr73M/YDz76BG6+Ozjcdnm7xNYdlh1p/yG1oXKlYNk2mXBZl8TVHZZN29/4pGAM16R3oEfnLZf991vBoPs7boSzz4y2zqJQ2Nf+J58HJxbVOiB4jrRpCr0HJaRkFTNJsT/3horA6tWrufDCC7nzzjs59thjQ33sgiWHhPp4hfXTz9DyMpgzOZh+cSyMmQTPPhK8CLv1CV60N12VkPKKxOzP4NZ7YcxTwRvO2MkwfFQw0HZThzVK4sNxsT3qbMd53wXf6l99KtivL4yB19+EF4Zuvtzp58Ogu/fssx3//Nz/9Au4phs81Cs422tP9+f2v/MB9HsseC7sszc8OgLm/gf+9UBi6wzbb6ugcVv46N+bf7GcMg3uGRB0RPfE53281/6jI+Czr2Bon2Dc1539oUzprXdQw5ZcdV7Rr2QTXf4vum9VA45+KbJ1JUok2Uy5cuXo3bs3o0ePDr3ztbs4vyUs+hnOvTI48+eEenB9+0RXFa76RwWHUy+9KfjWV7kSDOmT6KqicUht+OeNcN3tUJAPVSpD/16Jrmr3MPh/Z7k9/MTGcY77Vy05z43GJ8OSbLj0RogVBJea6N0t0VWF74dFwWv+z4n+w08E+7/nJoda6x0BvfaA9A/iv/avvDA4y7n1FcFlVo6pC12uTmy9RcVLTYQrkuSrKCUq+ZIkKVGiTr5u/DS6U3gH1XsxsnUlyh4wKkmSJBWlgpgXRwiTW1OSJClCJl+SJCmufBzzFSaTL0mSpAiZfEmSpLg82zFcJl+SJEkRMvmSJElxebZjuNyakiRJETL5kiRJcRV4tmOoTL4kSZIiZPIlSZLiyvdsx1CZfEmSJEXI5EuSJMXl2Y7hcmtKkiRFyM6XJElShDzsKEmS4vLnhcJl8iVJkhQhky9JkhSXF1kNl8mXJElShEy+JElSXI75CpfJlyRJUoRMviRJUlxeZDVcbk1JkqQImXxJkqS4HPMVLpMvSZKkCJl8SZKkuLzOV7hMviRJkiJk8iVJkuJyzFe4TL4kSZIiZPIlSZLiMvkKl8mXJElShOx8SZIkRcjDjpIkKS4PO4bL5EuSJClCJl+SJCkuk69wmXxJkiRFyORLkiTF5c8LhcvkS5IkKUImX5IkKS7HfIXL5EuSJClCJl+SJCkuk69wmXxJkiRFyORLkiTFZfIVLpMvSZKkCJl8SZKkuEy+wmXyJUmSFCGTL0mSFFfM5CtUJl+SJEkRsvMlSZIUIQ87SpKkuPxh7XCZfEmSJEXI5EuSJMXlpSbCZfIlSZIUIZMvSZIUl5eaCJfJlyRJUoRMviRJUlyO+QqXyZckSVKETL4kSVJcjvkKl8mXJElShEy+JElSXI75CpfJlyRJUoRMviRJUlyxWKIr2LOYfEmSJEXI5EuSJMVVgGO+wmTyJUmSFCE7X5IkSRHysKMkSYrLi6yGy+RLkiQpQiZf2zBtBgx4AnJyoU5t6N0Nyu219WXffA+63wezJwXTfQbB7Lkbb1+aDZUrwbhnir7usBSm/X2HwpRpsHeFYLrmATDgrs2XuX8ILFwE/3ogiqrDsSv7flPFse2wa/s+Jzd4/s/53/P/lBPg1mshJSXKFuyaMPb/ylVwSWfo0w2OOLToaw5TYdo/bz70fgRWr4bkFLj7Fji8TsnZ/9t6/u8J7/3b4kVWw2XytRXLV8A/H4BB98Kk56FGdXjo8a0vu2ARPPjY5tdA+eeNMObp4G9IbyiVDg/0iKT0UBS2/Z9+AQ/dubGtf+54TXobJkyNouLw7Oq+/0NxbDvs+r5/4bXgMcYPh7HDguUmvxNd/bsqjP3/7kz4x7Xw/Q9FXm7oCtP+tevgilvhigvgtafhukuha+/gtpKy/7f1/C/u7/2KTmSdrzfffJPnnnuOH37Y/B3p5ZdfjqqEQvtgVvBttWaNYPqCVvD6m1u+ya5dB916Q7cbtv1YPR+E9v+Aw/5adPWGrTDtz8mB//wXhr0ErS+Hzj1h8dKNt89fAE+/BNe3j7T0XRbGvi+ubYdd3/cdzoeH74LkZFixElat3pgOFAdh7P/nX4X7b4fK+xV9vWErTPs/mAUH7g+NTgymTzt5Y+ejJOz/7b33/aE4vvfHE4tF91cSRNL56t+/P88//zwLFiygXbt2jBs3bsNtL730UhQl7JAlWVAtY+N0lcqw+vckfl+z+XJ39od/tAii6a2ZPhOWZMMl5xZdrUWhMO3P+gVOqAc3Xx18yzvqb9CxR/DC+X0NdLsP7usOe5WNvv5dsav7vji3HXZ93wOkpQZpQZMLodK+cOyR0bZhV4Tx2n/yQah3RNHWWVQK0/4FP8J++8I/+0Lbq+HyWyAvf+Pte/r+397zH4rve7+iE0nn69133+Wpp56iZ8+evPDCCwwaNIhJk4JBErHdsJtbULD1+cmbbK0XxkBqCpzbfNuPM2I0XHVh8RrvAIVrf41q8EQ/qHUgJCXB5e3gh8Xw0xK4ox9cfA4cso1O6e5sV/d9cW477Pq+/8Mt18DM12H/qnD3w0Vbc5jCeu0XV4Vpf15+0Ln4Rwt45Yng+X5ttyAR+sOevP8L8/wvru/98cRiSZH9lQSRdL5isRhJScEGrVmzJo8//jh9+vTho48+2jB/d1KtCmT/snF66TLYu3yMsmU2zhs7GT7/BtpcAdd0g3Xrg/9nLQtuX74C5v4HmpwaZeXhKEz7v5kP46Zsfr9YLPhQmjM3ePNpcwUMHhZMX31bNLXvql3Z94uXFu+2w67v+08+h+9/DOalpUKbpvDVvKKvOyxhvPaLs8K0P6NS0PE46m/B9Ol/h/x8+HFxydj/8Z7/ULzf+xWdSDpfTZs25ZJLLmHu3OA0kL/+9a8MGjSIm266aYsxYLuDk4+Dz74KBtQCvDw+GNewqVGPw4ThQez8eF8oXSr4f8b/xnl88nkwdmDTF21xUZj2JyXBfY/Aop+D6RfHQp2/QNUMmP7axkGnnS4PDjs80S/SJuy0Xdn31asU77bDru/7mZ/AA0MgLy9IESa8ASceE2kTdkkYr/3irDDtP+UEWLwEvvwmmJ71WfCcqFGtZOz/eM9/KN7v/fEU1+Rrzpw5nHvuubRq1Yr27dvz008/AbBy5UquvvpqMjMzueiii8jOzgYgJyeHrl27kpmZSZs2bZg/f/7/2h+jb9++NG3alGbNmjFnzpwN6xg2bBhNmzalSZMmTJ1auDOtIrnURMeOHTn22GPZa6+N5+see+yxvPbaawwbNiyKEnZIpX2gT3e4qRfk5sIB+wdnrHzxdTCIcszT23+MhYuCyL04Kkz7D6kdnNlz3e1QkB+MjejfK9GV77ow9n1xtqv7/soL4f7B0PoKSE6CY+pCl6sT26Yd4f7ffvsrV4LBfeCeAbBmHaSnwSP3QqlSJWP/b++9rzi/9++JunbtyqOPPsqhhx7KK6+8Qu/evXnssccYOHAg9evX54knnmDs2LH06dOHgQMH8txzz1GmTBkmTZrErFmz6N69O6NHj2bKlCnMnz+fiRMnsnDhQq6++momTZrEV199xfjx4xk3bhyrV6/m/PPP5/jjj6dixYpx60qK7Y6DrnZAwZJDEl2CJEmRSq4a7fHcw8fdFdm6ZjS+mZUrV24xv0KFClSoUPjTZ3Nycpg0aRKtWrUC4PPPP6dnz56MHTuW0047jZEjR1KtWjXy8vI4/vjj+eijj7j88su58cYbqV+/PgBnnHEGzz77LIMHD+aEE06gdevWALRv356OHTtuGD51ww3Bqc89evTg+OOP37DctniRVUmStNsYMWIEQ4YM2WJ+x44d6dSpU6EfJz09fUPHq6CggCFDhnDGGWcAkJWVReXKlQFITU2lXLlyLF++fLP5AJUrV2bJkiVkZWWRkZGx1fl169bdYv722PmSJElxRXmMrH379rRp02aL+fFSr0mTJnH//fdvNq927doMHz6cnJwcunfvTl5eHtdcc802HyM5eevD4JOTk7d6ZYZ487fHzpckSdpt7OjhRYDMzEwyMzO3mP/7779z3XXXUbFiRR577DHS0tIAyMjIYNmyZVStWpW8vDxWr15NxYoVycjIIDs7m4MOOgiA7OxsMjIyqFKlyoZB+dubX6tWre3W688LSZKkuIrr2Y5du3bloIMOYtCgQaSnp2+Y36hRI8aOHQvAxIkTqV+/PmlpaTRq1GjDheBnz55NqVKlqF69Og0bNmTChAnk5+ezcOFCFixYQN26dWnYsCFTp05l7dq1LF++nJkzZ9KgQYPt1mXyJUmS9jhfffUVb731FgcffPCGAfAZGRk8+eST3HjjjXTv3p3mzZtTvnx5+vfvD8All1xCr169aN68Oenp6fTrF1wrqGnTpsydO5eWLVsC0KdPH0qXLs2RRx5Jy5Ytadu2LXl5eXTu3JkqVapstzbPdpQkqZiJ+mzHw8bcHdm6/tPmzsjWlSgmX5IkKa6S8rM/UXHMlyRJUoRMviRJUlzFenzSbsjkS5IkKUImX5IkKS7HfIXL5EuSJClCJl+SJCk+B32FyuRLkiQpQiZfkiQpLsd8hcvkS5IkKUImX5IkKa7i/UOEux+TL0mSpAiZfEmSpLgc8xUuky9JkqQImXxJkqT4TL5CZfIlSZIUITtfkiRJEfKwoyRJistLTYTL5EuSJClCJl+SJCk+k69QmXxJkiRFyORLkiTF5UVWw2XyJUmSFCGTL0mSFJ9jvkJl8iVJkhQhky9JkhSXY77CZfIlSZIUIZMvSZIUn2O+QmXyJUmSFCGTL0mStB2O+QqTyZckSVKETL4kSVJ8jvkKlcmXJElShOx8SZIkRcjDjpIkKT4PO4bK5EuSJClCJl+SJCk+f14oVCZfkiRJETL5kiRJccUc8xUqky9JkqQImXxJkqT4TL5CZfIlSZIUIZMvSZIUn2c7hsrkS5IkKUImX5IkKa4kx3yFyuRLkiQpQiZfkiQpPpOvUJl8SZIkRcjkS5IkxefZjqEy+ZIkSYqQnS9JkqQIedhRkiTF54D7UJl8SZIkRcjkS5IkxWfyFSqTL0mSpAiZfEmSpPhMvkJl8iVJkhQhky9JkhSfF1kNlcmXJElShEy+JElSXEmO+QqVyZckSVKETL4kSVJ8Jl+hMvmSJEmKkMnXNkybAQOegJxcqFMbeneDcnttvH3sZBgxeuP0qtWwNBveeQX22xdOaglVKm+8/fJ20OLM6OrfVbva/j90ugMy9oOeN0VWeii2136A51+FkWOgdCmofSD07AIVK2y8/ecsaHcdjH0a9qkYafm7ZFf3/Qtj4JV/w/r1cHgd6H0bpKdH346dVZh9/8Z0GPIMJCdDhXJw721w4P7BbXt6+0v6az9e+1NT4e6H4ev/QpnScE4mXHxu9G3Q7i8pFosV6zCxYMkhoT/m8hXQoj2MHAo1a0D/f8Hva+DOm7e+fG4eXNIJ2mTC+S3h+x/gutth8sjQS4vErrb/D0+9AMNegszTitcbcGHa/9En0O0+eOlRqJoB46bA2x/AoHuC28dOhsHPwOIlSXw4LlZsOl+7uu+nTodBTwb3r1AObroT6h4KV10UaTN2WmHav2598OVqzNNwUA0YPgpmzIHH+5aM9m+qJL72N/Xn9ne/D1JS4J5bIb8AOv4TLmgNjU8Kv9bkqvPCf9A4ag1+KLJ1fd/plsjWlSiRHXZcsGABS5cuBWD06NH07t2biRMnRrX6HfLBLDji0ODFB3BBK3j9TdhWN/WpF6DSPhvffD79AlKSof2N0OoyGDoc8vMjKT0Uu9p+CDon738M57cq+nrDVpj2fzkPGhwbdLwAzmwI73wYfFvOWgZvvR98GBc3u7rvx0+BDucHCWByMtx1C7Q8K5raw1CY9ufnB9Orfw+m16yFUv9LtkpC+zdVEl/7m/pz+7+cB63OCjpg6WnQqAFMfTea2otaUiy6v5Jgu4cdhw0bxpAhQ8jPz6d69erUqVNnw98hhxxCjRo1truS4cOH89xzz1FQUMCJJ57Izz//zJlnnsmrr77K999/zw033BBKY8KyJAuqZWycrlIZVv+exO9rYlscfvh1BQx/GV59auO8vHw4qT50vS74lnxt9yC2bn9eJOXvsl1tf9YyuG8wPNkfRo2PpORQFab9dQ8LDjv+tAT2rwpjJkFubhIrVsbI2A8G905M7btqV/f9gh//l/R0DZ4Hxx4Jt14bSemhKEz79yobJCEX3BB0sgoKYOSQ4LaS0P4/lNTX/h+21v4jD4NxU6FeXcjJgTfeDQ5FSn+23afF448/Tr9+/TjyyCP58ccfmTdvHt988w3Tp0/n22+/BeCvf/0rL7744jYf49VXX2XixIksW7aMs88+m5kzZ1KqVCnOO+882rZtu9t1vgoKtj4/eSs54agJcNrfoUa1jfP+0WLj/9PTocM/4LlXi0/na1fan5sHN98Nt3eCjEpFV2NRKkz7jzsKru8QjGtJTg7GduxdIUZaMX+j3dXnfm4efDgbht4XPPdvvw8GPgU9OhVNvWErTPvnzYfHnoXXRwTjvJ57BW7sFRyGLAnt/0NJfe3/YWvP/27XQ7/H4JwrofK+wZfwT78omloj5xXuQ7Xdj4py5cpx6qmnkpqaSkZGBscee+xmty9atGhDJ2xbCgoKSE9PZ//99+fyyy+nVKlSG27L3w2Px1WrAnP/s3F66TLYu3yMsmW2XHbSO9Cj8+bzxk2BQw+GOn8JpmOx4vXtZ1fa/8XX8NPP0HdoML1seXCYZn1OMPC4OChM+39fE3TA2jYPppcth0eGbT7gvjja1ed+xn5wxikbByi3OAseG1F09YatMO1/fxbUO2LjAPsL28ADQ2HFbyWj/X8oqa/9P2zt+b96TZB0/vE+8OQLcOD2Dw6pBNrumK+rr76a0aNHb/P2GjVq0Lhx47iPcdZZZ3HxxReTn59Pp07BV8Cvv/6aCy+8kMzMzB0sueidfBx89hUsWBRMvzweTjt5y+V+WwU//BS8EW/q2+9h8LDgjWfd+uCMuMz4m2i3sivtr3dEcNbPmKeDv/NbBoNui8ubLxSu/VnLoP1NG8f9PPYsND8dkor5l8Ndfe43aQRTpgXP+1gM3novGENTXBSm/X87BGZ9FnQuIBjfV6NacEZrSWg/lOzXPmz7+f/yuOC9H4Lnxyuvw9mnF23NKp62m8c88MAD5Obm8t5773HKKadw2GGHUadOHcqU2cpXgW248cYbmTVrFikpKRvmpaen06lTJxo1arRzlRehSvtAn+5wUy/IzYUD9ocHegTf7Ho+GLyxAPywCCpXYotDTTd0gN4Dg8H2uXnQ9FQ47+yIG7ELdrX9xV1h2l/rQLjqQjj/WiiIwTF1i9dZXduyq/v+gtbBB1Pbq4Kzvf72V+i2e40qiKsw7T/xmODSMe1vhLQ02Ls8DOkT3L8ktB9K9msftt3+qy+Gbn2gRYeg831Dh2B86B6hhAyEj8p2LzXx448/8vXXX/PNN9/wzTff8PXXX7N48WJq1KjBlClToqpzm4riUhOSJO3Oor7URO2BD0e2ru9u2sa1PfYg2/3ecsABB3DAAQdw5pkbrxC6Zs0a5s2LdsdLkqQEMfkK1U5d56ts2bIcffTRIZciSZK059vDjthLkqSwlZSLn0bFH9aWJEmKkMmXJEmKz+QrVCZfkiRJETL5kiRJ8Zl8hcrkS5IkKUImX5IkKS7PdgyXyZckSVKETL4kSVJ8saREV7BHMfmSJEmKkMmXJEmKzzFfoTL5kiRJipCdL0mSpAh52FGSJMXlpSbCZfIlSZIUIZMvSZIUn8lXqEy+JEnSHmn27Nmcc845tGjRgmuvvZbffvsNgJUrV3L11VeTmZnJRRddRHZ2NgA5OTl07dqVzMxM2rRpw/z58wGIxWL07duXpk2b0qxZM+bMmbNhHcOGDaNp06Y0adKEqVOnFqouO1+SJCmupFh0f2G6/fbb6devHxMmTODggw/m6aefBmDgwIHUr1+fSZMmcd5559GnTx8AnnvuOcqUKcOkSZPo0aMH3bt3B2DKlCnMnz+fiRMnMnToULp3705eXh5z585l/PjxjBs3jhdeeIF+/fqxYsWK7dZl50uSJO02Vq5cyaJFi7b4W7ly5Q4/1sSJEzn44IPJzc1l6dKlVKhQAYBp06bRokULAM4++2ymT59Obm4u06ZNo2XLlgAcd9xx/PrrryxevJh3332XZs2akZycTK1atahevTqffvop06dP58wzz6RUqVJUqlSJ448/nmnTpm23Lsd8SZKk+CIc8zVixAiGDBmyxfyOHTvSqVOnHXqstLQ0vvnmGy677DJSU1O5+eabAcjKyqJy5coApKamUq5cOZYvX77ZfIDKlSuzZMkSsrKyyMjI2Or8unXrbjF/e+x8SZKk3Ub79u1p06bNFvP/SK22ZtKkSdx///2bzatduzbDhw+nTp06fPjhh7z00kt06dKFl156aauPkZy89YOBycnJxGJb9j7jzd8eO1+SJCm+CJOvChUqxO1obU1mZiaZmZmbzVu/fj1vvvkmZ5xxBgAtW7akb9++AGRkZLBs2TKqVq1KXl4eq1evpmLFimRkZJCdnc1BBx0EQHZ2NhkZGVSpUmXDoPztza9Vq9Z263XMlyRJ2uOkpqZy991388UXXwBBOnbMMccA0KhRI8aOHQsE48Lq169PWloajRo1Yty4cUBwpmSpUqWoXr06DRs2ZMKECeTn57Nw4UIWLFhA3bp1adiwIVOnTmXt2rUsX76cmTNn0qBBg+3XVjRNliRJe4rieIX7lJQUBgwYQK9evcjPz6dKlSobzmq88cYb6d69O82bN6d8+fL0798fgEsuuYRevXrRvHlz0tPT6devHwBNmzZl7ty5Gwbj9+nTh9KlS3PkkUfSsmVL2rZtS15eHp07d6ZKlSrbrS0ptrUDlsVIwZJDEl2CJEmRSq46L9L11bl3QGTr+qZnl8jWlSgedpQkSYqQnS9JkqQIOeZLkiTFV6wHKO1+TL4kSZIiZOdLkiQpQh52lCRJcRXHS03szky+JEmSImTyJUmS4jP5CpXJlyRJUoRMviRJUnwmX6Ey+ZIkSYqQyZckSYrLsx3DZfIlSZIUIZMvSZIUn8lXqEy+JEmSImTyJUmS4nLMV7hMviRJkiJk8iVJkuIz+QqVyZckSVKETL4kSVJ8Jl+hMvmSJEmKkJ0vSZKkCHnYUZIkxeWlJsJl8iVJkhQhky9JkhSfyVeoTL4kSZIiZPIlSZLiM/kKlcmXJElShEy+JElSXJ7tGC6TL0mSpAiZfEmSpPhMvkJl8iVJkhQhky9JkhSXY77CZfIlSZIUIZMvSZIUn8lXqEy+JEmSImTyJUmS4jP5CpXJlyRJUoTsfEmSJEXIw46SJCmupEQXsIcx+ZIkSYqQyZckSYrPAfehsvO1DdNmwIAnICcX6tSG3t2g3F4bbx87GUaM3ji9ajUszYZ3XoH99oWTWkKVyhtvv7wdtDgzuvp3VUlu/662/Q+d7oCM/aDnTZGVHgrbX7Lbv6ntbQuA8VNh2EuQlASlS8E/O8MRhyam3jAUps0AsRj0eAD+Wit4f/uzPWH/q+gkxWKxYt2fLVhySOiPuXwFtGgPI4dCzRrQ/1/w+xq48+atL5+bB5d0gjaZcH5L+P4HuO52mDwy9NIiUZLbv6tt/8NTLwQfSJmnFa83X9tfstu/qcJsi+9/gEtvhFefgoxK8O5MuPsheHv0Nh92t1bY/T9/Adw7ED77CjpdvmXnK4r9n1x1XtE88DYcddOAyNb12cAuka0rURIy5uuBBx5IxGoL7YNZwTe3mjWC6QtawetvBt90tuapF6DSPhvffD/9AlKSof2N0OoyGDoc8vMjKT0UJbn9u9p2gI8+gfc/hvNbFX29YbP9Jbv9myrMtkhPg3tvCzpeAEfUgWXLg9SoOCrs/n9hbNDhbtp4y8fYU/a/ilaRH3a8/fbbt5j39ttv89tvvwFw//33F3UJO2xJFlTL2DhdpTKs/j2J39fEtoiff10Bw18Ovvn9IS8fTqoPXa+Ddevh2u5BbN3+vEjK32Uluf272vasZXDfYHiyP4waH0nJobL9Jbv9myrMtti/WvAHQQel71BofHLQKSuOCrv//0izZn6y+f33pP2/hWJ9jGz3U+Sdr4oVKzJ27FiuvfZaKlSoAMDMmTM5/vjji3rVO62gYOvzk7eSE46aAKf9HWpU2zjvHy02/j89HTr8A557tXh0PqBkt39X2p6bBzffDbd32pgEFDe2f+vzS0r7N7Uj22LNWuhxP/ycDU/2K9q6itKOtPnP9rT9r6JV5J2vbt260bBhQwYOHMjNN9/MCSecwIgRI2jTpk1Rr3qnVasCc/+zcXrpMti7fIyyZbZcdtI70KPz5vPGTYFDD4Y6fwmmYzFILUanNpTk9u9K27/4Gn76Ofj2D8Hhl/x8WJ8DvW8r2rrDYvtLdvs3VdhtsXgpXH871D4IRgwMBt0XVzuy//9sT9v/WzD5ClUkH4kNGjTgsMMO484772TatGnk7+YDgE4+Dvo9CgsWBcf+Xx4Pp5285XK/rYIffoJ6R2w+/9vv4Y3pMOie4NvQyDFw9hnR1B6Gktz+XWl7vSOCM97+MOQZ+PW34jXg2vaX7PZvqjDbYsVKuLRzMP7phg4JKTNUhd3/W7On7X8VrcgG3FesWJFBgwZRu3ZtKleuvP07JFClfaBPd7ipFzS/BOZ9B7fdEHyzaXPFxuV+WASVK0Han7qwN3SAvcsHg81bXQb1Dofzzo60CbukJLd/V9te3Nn+kt3+TRVmW7w0Dn7OgjffC+b98ffrb4mtfWcVdv+XREmx6P5KAi81IUlSMRP1pSaO7hTdpSb+b/Cef6mJPfh7myRJCkWxjml2P/62oyRJUoRMviRJUlwlZSxWVEy+JEmSImTnS5IkKUIedpQkSfF52DFUJl+SJEkRMvmSJElxOeA+XCZfkiRJETL5kiRJ8Zl8hcrkS5IkKUImX5IkKT6Tr1CZfEmSJEXI5EuSJMXl2Y7hMvmSJEmKkMmXJEmKz+QrVCZfkiRJETL5kiRJcSXFjL7CZPIlSZIUIZMvSZIUn8FXqEy+JEmSImTnS5IkKUIedpQkSXF5kdVwmXxJkiRFyORLkiTFZ/IVKpMvSZKkCJl8SZKkuBzzFS6TL0mSpAiZfEmSpPhMvkJl8iVJkhQhky9JkhSXY77CZfIlSZIUIZMvSZIUn8lXqEy+JEmSImTyJUmS4nLMV7hMviRJkiJk8iVJkuKLGX2FyeRLkiQpQna+JEmSIuRhR0mSFJcD7sNl8iVJkvZoX331FUccccSG6ZycHLp27UpmZiZt2rRh/vz5AMRiMfr27UvTpk1p1qwZc+bM2XCfYcOG0bRpU5o0acLUqVM3zJ8wYQLNmjXjzDPPZOTIkYWqx+RLkiTFV4yTr7Vr13LPPfeQm5u7Yd5zzz1HmTJlmDRpErNmzaJ79+6MHj2aKVOmMH/+fCZOnMjChQu5+uqrmTRpEl999RXjx49n3LhxrF69mvPPP5/jjz+e9evXM2DAAF577TXS09Np164dJ5xwAgcffHDcmux8SZKk3cbKlStZuXLlFvMrVKhAhQoVdvjxHnjgATp06MCnn366Yd60adO48cYbATjuuOP49ddfWbx4Me+++y7NmjUjOTmZWrVqUb16dT799FM++ugjzjzzTEqVKkWpUqU4/vjjmTZtGrFYjBNPPJGKFSsC0KRJEyZPnkzHjh3j1mTnS5IkxZVUEN26RowYwZAhQ7aY37FjRzp16rRDj/XWW2+xbt06mjZtutn8rKwsKleuvGG6cuXKLFmyhKysLDIyMrY6v27dulvMT0pK2uxxMjIymDt37nbrsvMlSZJ2G+3bt6dNmzZbzI+Xek2aNIn7779/s3m1a9dm9erVDB8+vFDrTU5OJraV65nFm19QsGWvNCkpabvrsvMlSZLii3DM184cXszMzCQzM3OzeaNHj+bxxx/noosu2jCvVatWjBw5koyMDLKzsznooIMAyM7OJiMjgypVqpCdnb1h+Xjza9WqRSwWY/bs2Rvm/zk52xbPdpQkSXuc8847jzfffJNx48Yxbtw4AMaNG0e5cuVo1KjRhnmzZ8+mVKlSVK9enYYNGzJhwgTy8/NZuHAhCxYsoG7dujRs2JCpU6eydu1ali9fzsyZM2nQoAEnnXQSM2bMYPny5axdu5apU6fSsGHD7dZm8iVJkuLa067zdckll9CrVy+aN29Oeno6/fr1A6Bp06bMnTuXli1bAtCnTx9Kly7NkUceScuWLWnbti15eXl07tyZKlWqANClSxcuvfRScnNzadu2LUceeeR2158U29qBzGKkYMkhiS5BkqRIJVedF+n6Tj7vocjW9cHoWyJbV6KYfEmSpPiKd06z23HMlyRJUoRMviRJUlx72pivRDP5kiRJipDJlyRJis/kK1QmX5IkSRGy8yVJkhQhDztKkqS4HHAfLpMvSZKkCJl8SZKk+LzIaqhMviRJkiJk8iVJkuJyzFe4TL4kSZIiZPIlSZLiM/kKlcmXJElShEy+JElSXI75CpfJlyRJUoRMviRJUnwFRl9hMvmSJEmKkMnXn0ybAQOegJxcqFMbeneDcnsVfrlVq+GOfvDdDxArgFZN4aoLN7/vq/+GN9+Dxx6Ipk2Ftatt/8PPWdDuOhj7NOxTMZj30SfQ91HIz4eKFeD2TnDowZE0a4cVZjvMmw+9H4HVqyE5Be6+BQ6vs/H2lavgks7QpxsccWi09e+qkt7+TRVmW4x8DV4cB0lJcGB1uKcrVNonMfWGoTBtHj8Vhr0UtLl0Kfhn5437+dyrYP16SEsLps8+A664INo2hKmw+/f5V2HkmGB71D4QenYJ3uv2GAZfoTL52sTyFfDPB2DQvTDpeahRHR56fMeWe+RpqFIZJgyHUY/DS+Pg0y+C21ashLsegj6P7H7P4zDaDjB2MlzcCbKWJW2Yt2o1dO4JXa+Dcc/AnTdDl7sgJ6eoW7XjCrMd1q6DK24NPlBeexquuxS69t54+7sz4R/Xwvc/RFp6KEp6+zdVmG3x5Tcw7GV4cWjwmj+oRvAeUFwVps3f/wAPPgZPPAhjnoZrLw1e3wBr1sKPi2HMsOC2MU8X745XYffvR5/AUy/CMw8HbW54ItzZP/JyVYxE0vmaO3fuhv/PmDGDBx54gP79+/PZZ59FsfpC+2BW8O2tZo1g+oJW8PqbW/6kVbzlenSG264L5mf/EnQwypcLpie/A5UrBZ2Q3U0Ybc9aBm+9D4/33fw+CxdBuXLQ4NhguvZBUK4s/N+XRdumnVGY7fDBLDhwf2h0YjB92skw4K6Ntz//Ktx/O1TeL7KyQ1PS27+pwmyLw+vA5JHBa3z9eliaXbzTjsK0OT0N7r0NMioF00fUgWXLg6Ts8/9A2TJwbTdo2QHuHwLr1kfejNAUdv9+OS94f6uaEUyf2RDe+TDYJnuKpFh0fyVBJJ2vO++8E4CRI0dy3333UbVqVfbbbz969erF888/H0UJhbIkC6plbJyuUhlW/57E72sKv1xSEqSmwm29oeVlcPzRUOuAYLl2reCGDkEsvbsJo+0Z+8Hg3nBwzc3vU/OA4BvxB7OC6c//A/9dEHROdzeF2Q4LfoT99oV/9oW2V8Plt0Be/sbbn3wQ6h0RXc1hKunt31RhXxNpqcEwglPPg9lzoU2zaOsMU2HavH81OLVB8P9YDPoOhcYnB52y39cG73mD7oHRj8PPS+HhJyJtQugKs3/rHhakXz8tCabHTILc3CRWrIy2VhUfkR52HDVqFM8++ywdOnSgQ4cOjBw5crfqfBUUbH1+cvKOL9fvDvhwHPy2Ch4dEU59RSnMtv9Zub1gaB94/HlofTmMmwInHLNxTMjupDDty8uH6TPhHy3glSfg4nOCb/q742HUHVXS27+pHXmun3EKzBgffLm66tZt33d3tyNtXrMWutwJC3+Ce7sG8047OXjvK7cXlCoF11wcdFyKu+3t3+OOgus7QKc7gi8kSUmwd4UYaY6q1jZE0vnKy8ujoKCASpUqUbZs2Q3z09PTSY73qR2BR56GNlcEf6/8e/M0Zuky2Lt8jLJlNr9PtSrbXu79j4PDbwB7lYXmp8NX84q+HTsj7LZvS0FBcCji2UEwdhjccRP8+FNw6Gp3U5j2ZVSCWgfCUX8Lpk//e3AiwY+Lo621KJT09m+qMNti4SKYs3FUBec2g8VLgy9dxVFhX9+Ll8KFNwQnW4wYCBXKB/Pf+QBmbTKaJBaj2HVANn1fvPWewu3f39cEHbDXngq+kJzVKJhfnA9BbyEWi+6vBIik57PPPvvQqFEj/vvf/244BDljxgzatWtH06ZNoyhhmzpfsXFg6EuPwWdfwYJFwW0vjw++yf3Zycdte7lJ78DQ4cHzJycnmD7hmEiassPCbvu2JCXBNd3gi6+D6cnvBIdm6/wlvLaEpTDtO+UEWLwkGIwLwYdNUhLUqBZtrUWhpLd/U4XZFtm/wC33wK8rgukJb8Bfa8E+e0daamgK0+YVK+HSzsG4pofv3HwYxZJsePDRYJxXfj4MHwWZjaOrPwybvi+2a1W4/Zu1DNrfBKt/D6Yfezb44p2UhLRVSbFYdN3M7777jpUrV3L00UczZ84cVq1axamnnrpLj1mw5JBwivufd2cGp1nn5sIB+8MDPYJvL198DT3/d3ZPvOVWroK7HoZvv4ck4PRToNNlm8f2YybBlHfhX7vZpSZ2te2bOqxREh+Oi2241MTH/wcPDIacvOCkg3tuhQOqR9m6wtta+xYt3nwbzPoM+j8Ga9YFY116dIJjj9z8cU4/HwbdXfwutVDS27+pwmyLF8fCC2MhNSV4bvfqUrw7ottr87+eg8HD4JDam99v2MOwd/ng7MhpHwaHp0+oB3fcCOnpiWlLGLa1f//8vjjyNXhhTHAt0mPqQs+binZ8b3LVaA+pNG7Sd/sLheSdKd0iW1eiRNr5Kgphd74kSdrd2fkq3orZ0XhJkhS5Yh3T7H68yKokSVKETL4kSVJcScV7hNJux+RLkiQpQiZfkiQpvmJ64eDdlcmXJElShEy+JElSXI75CpfJlyRJUoRMviRJUnwGX6Ey+ZIkSYqQyZckSYrPMV+hMvmSJEmKkMmXJEmKK8ngK1QmX5IkSRGy8yVJkhQhDztKkqT4HHAfKpMvSZKkCJl8SZKkuJL8Ye1QmXxJkiRFyORLkiTF55ivUJl8SZIkRcjkS5IkxWfwFSqTL0mSpAiZfEmSpLiSHPMVKpMvSZKkCJl8SZKk+Ey+QmXyJUmSFCGTL0mSFJ9XuA+VyZckSVKETL4kSVJcnu0YLpMvSZKkCNn5kiRJipCHHSVJUnwedgyVyZckSVKETL4kFUvrY7mJLiGh5uXmJbqEhLm15omJLiHh3oj60g8mX6Ey+ZIkSYqQyZckSYrPi6yGyuRLkiQpQiZfkiQpLi+yGi6TL0mSpAiZfEmSpPhMvkJl8iVJkhQhky9JkhSfyVeoTL4kSZIiZPIlSZLiM/kKlcmXJElShEy+JElSfF7hPlQmX5IkSRGy8yVJkhQhDztKkqS4/HmhcJl8SZIkRcjkS5IkxWfyFSqTL0mSpAiZfEmSpPgKTL7CZPIlSZIUIZMvSZIUn2O+QmXyJUmSFCGTL0mSFJ/JV6hMviRJkiJk8iVJkuIz+QqVyZckSVKETL4kSVJ8XucrVCZfkiRpjzR27Fj+/ve/06pVK1q1asWAAQMAWLx4MRdddBFNmzbluuuu4/fffwdg5cqVXH311WRmZnLRRReRnZ0NQE5ODl27diUzM5M2bdowf/58AGKxGH379qVp06Y0a9aMOXPmFKouO1+SJCm+WEF0fyH6/PPP6d69O+PGjWPcuHF06dIFgLvvvpsLL7yQyZMnc8QRR/Doo48CMHDgQOrXr8+kSZM477zz6NOnDwDPPfccZcqUYdKkSfTo0YPu3bsDMGXKFObPn8/EiRMZOnQo3bt3Jy8vb7t12fmSJEm7jZUrV7Jo0aIt/lauXLnDj/X5558zduxYWrZsya233spvv/1Gbm4us2bNokmTJgCcc845TJ48GYBp06bRokULAM4++2ymT59Obm4u06ZNo2XLlgAcd9xx/PrrryxevJh3332XZs2akZycTK1atahevTqffvrpduuy8yVJknYbI0aM4PTTT9/ib8SIETv8WJUrV6ZTp06MGzeOatWqcc899/Drr79Srlw5UlNTNyyzdOlSALKysqhcuTIAqamplCtXjuXLl282/4/7LFmyhKysLDIyMraYvz0OuJckSfFFeKmJ9u3b06ZNmy3mV6hQYZv3mTRpEvfff/9m82rXrs3w4cM3TF955ZWcccYZ3HbbbVvcPykpaZuPnZy89ZwqOTmZ2Fa2y7aW35SdL0mStNuoUKFC3I7W1mRmZpKZmbnZvFWrVjF8+HA6dOgABIPjU1NT2XfffVm9ejX5+fmkpKSQnZ29Ib3KyMhg2bJlVK1alby8PFavXk3FihXJyMggOzubgw46CGDDfapUqbJhUP6m87fHw46SJCm+glh0fyEpW7YsTz31FJ999hkAzz//PGeeeSZpaWnUr1+fiRMnAsEZkQ0bNgSgUaNGjB07FoCJEydSv3590tLSaNSoEePGjQNg9uzZlCpViurVq9OwYUMmTJhAfn4+CxcuZMGCBdStW3e7tSXFtpaZFSMFSw4J7bGmzYABT0BOLtSpDb27Qbm9dm65TndAxn7Q86bN538wC/r/C8Y8HVrZodnV9q9aDXf0g+9+CE5YadUUrrowuM9Hn0DfRyE/HypWgNs7waEHR9u+7Qlr//+cBe2ug7FPwz4Vg3nFof3b8+Z70P0+mD0p/nJz/wMXd4J3X9nY/qKwPpa7Q8tPmJLMc6M2ft9c/XsSWdkwZXQulfbdfNmXXktm1PhkkoAD9odet+ax7z47X+vyFdDzvlR+XppEUlKMnrfmc/QRwVvvv6cmM+LloK7SpaFbp3wOP3T7b8vzcrd/RtWfTR6bytTXU0lKgirVCrimSw57b6Vdk8amMnlcKunpsP+BBVzZKYdyOxZEbGblChjSrxTZS5NIToarb8qhzuEFO1TTpm6teeIO13BN/0tp2LYBq5avBuDHbxbT54IBWyzXqmNTLuxxLr8uWQHAmlVrublRrx1e3x+Sk5O59uH2HHvWUaSkpvDKQ+N5/fE3Nlumas0Mhs7uy+1N7mXenO8K9bhvFIze6Zp2RuaBN0W2rkk/DAztsWbPnk2fPn1Yt24dNWvWpF+/fpQvX56ffvqJ7t2788svv1CtWjUefvhh9t57b1asWEH37t358ccfKV++PP3796dGjRqsX7+eXr168cUXX5Cenk7v3r05/PDDicVi9OvXj+nTpwNw++238/e//327ddn5+p/lK6BFexg5FGrWCDpIv6+BO2/e8eWeegGGvQSZp23sfK1bD/96Dl4YA1Uqw4ThoZQdmjDa32cQJCVDj06wZi206AD9e8LBNeGM82HgPdDgWPhuIdzwTxg3DNLTo2/r1oS1/8dOhsHPwOIlSXw4LsY+FYNO6e7e/u1ZsAiuuQ2WLYc5k7e93K8r4Mqu8NW8je0vKjva+dpUbh5c0TmVlk0LaNty81Pbv/omiVt6pTLq6VzKl4OHH03h97XQ85b8nV5f17tSqHNwjCsvLuDrb5PodHsq45/PZWkWXHlTGi8+mUvlSvDezCT6PJzK5FHbb9uOdr6+m5fEQ/eU4sHH11F2L3j28TTWrYWrb9p8XV/8XzJD+qbT55H1VKocY/obKcyakcItvXJ2aH2bevjedGr+pYBzLsxjwX+TuP+OUjwyfB0//VC4mv5sZzpfgz7ow+O3juCrGfPiLtdj5I3MeH0O77z4/g6vY2taXHcWJzY/lp6t+lK2fBkGfdiHfu2H8M2s/wKQViqNB9/sRe2janJr4zt3387XATdGtq5JPw6KbF2JEtlhx/fee2/DaaJjx47lnnvu4dVXX41q9dv1wSw44tDgAxXgglbw+ptbjjHc3nIffQLvfwznt9r8fu9/DGvXBSnJ7iiM9vfoDLddF8zP/gVycqB8OVi4CMqVCzoeALUPgnJl4f++jKZthRFG+7OWwVvvw+N9N79PcWh/PGvXQbfe0O2G+MsVFMBtfaDLVdHUtSuGv5DMvvuwRccL4G91YowfGXS81q8P9uve/0t9cnPhwSEptLsqlX9ckUrP+1NY/fvm9+95fwrjJm18a83Lg/dmJHPO2cG6Dv1rjAP3j/Hhx0mkpUGvrnlUrhQse3idGMuWB+sJW+1DYgwaHnRycnJg+bIkypXfcrnvvk2mbr0CKlUOnvzH/z2fOTNTyMuFvFwY/lga3a4rTddrSjO0Xzpr/tT+of3SmTYlZcN0fj588lEKZzQLOos1D45Rbf8Y/zcrpdA17aq09FQOrleT825pyb8+fZBeo2+h8gH7bXXZv51Uh9Mu+DuPzenH/ZP+Sc0jDtxwW9PLT2PorL48Nqcffaf25IA61Te771ntT6XrsM1fKCe3PoEpw9+hIL+A1St+Z9rLH3D6xadsuL3T0CuZMmIavy3b8csoqPiKpPPVp08fHn/8cdavX8/AgQOZMGECBx98MG+88Qa9e/eOooTtWpIF1TYZI1elcnBY4vc1hV8uaxncNxj69YSUP23ZM06B2ztCxSJ4YwlDGO1PSoLUVLitN7S8DI4/GmodADUPCJKwD2YF9/n8P/DfBUEHbXcRRvsz9oPBvYOkb1PFof3x3Nkf/tEiOMQazyPDoO6h8Pfjo6lrZ/26Ap4dlULXjttOjtJS4e33kmhyXhpz5ibTKjNIvYa9kExKCrz4RB6jns6j8n4w6ImUbT4OwIrfgo7pvhU3zqtSOcbS7CT2rwYNGwSdnFgM+g9N4dSTYqSl7Worty41FT7+IIXrLijDfz5PpnGTLbfBwXUK+OL/ksleGpz9NW1KKnm5SaxaCWNfSiUlBR54dB0PPr6OfSrFeOHp+MWu+i0YhlCh4sZ5+1aO8cuypELXtKsqVd+X/3v7C57u8QLX1uvKfz76lnvGbnnGW+mypfjx65948f7XuO7Y25g07G3um9iD0nuV5siGf+PMSxtxc8OeXHfsbYx6cBx3vtp1u+uufEAlsn/c+GJftugXKu8f9LYzrziN1NQUJj31VniNLSqxWHR/JUAkZzt+8MEHTJgwgZSUFKZNm8aoUaNIT0/n/PPP5+yzz46ihO0q2MZFdf98xui2lovF4Oa7g7E8GZXCrS0Ku9r+TZfrd0dwGO7GXvDoCOh0OQztAwOfggcfg/pHwgnHUGQfMDsjzPb/Wbm9dv/2b8sLYyA1Bc5tDj/9vO3lps2AuV/BU/2jq21nvfp6MqeeXMD+1eIvd9opMU47JZdXX0/m+q5pTBiZy/QZyaxancTM2UHHIS8viX0qBh8WF1+XSk4OLMlKYtanMPKVZI6uG+PKi7d+uHLT58zatdDzgVSWZsHQfuF3PjZ1/Mn5HH/yWt6cmEKf20vxyIh1m9XytyMLOO+SXPrfVYqkpBiNm+ZTrnyM1DSY81EKa1YnMXdO0OHMy4O9/9f+Hp1KkZuTxLLsJL74v2T+/VoadQ7P55wLt96eTde5vZp21ZIFWfzz7I2XIRjdfzwX3XEuVWtmsGRB1ob569as5/bMPhump4+ewcV3tKXOcX/huMx67H9wVQZ9sPH28vuWo/w+5ehw7/kcftKhlN+3HKXLleZfnzxIzvpcOjfoQXLylpcwKMgv4OB6tTj7mrN2aTyZiq9IOl+lS5fml19+ISMjg0qVKrFmzRrS09NZu3bthoucJcIjT8M7Hwb/X/07HLLJN/uly2Dv8jHKltn8PtWqBAOK/7zcfxcEH059hwbzly0P4vb1OdB7yy9Yu4Uw21+2THBo9ZDaQQK0V1lofjpMfTfosJQtA89uchi/+SVw4P5F17bCCLv927K7tn9bNt0uaamwdj20uSI4FLbuf/9/vG+wn//w2kRYmg3nbnLIsf1NcF/34DBtojw6LIVpHwQffqeeHOP6y/OZ+k4yt3Xa9vitHxbBL8uTqHdk0KlonVlAn4dTWLnqf4dWO+Xx9xOC29asCV7jAM8/FnQyet6fQv2jY7TKDHrqf/zSyMpVUOF/yXfWsiSqVA5u/3kp3NgjlVoHxnhyYD6lS4XX/peHpzF7RtBRqnFQAU1a5nHoEcF6T2uSz5OD0vl9NZTfZDD92jVBB+y0zHUArPg1eJxy5aGgIIkO1+dQ7/jgMdatDQ4XAtw3eD0QHHY8/Kh8Tm0SbOP8/23q1avYcEjx12VJVKocY8lPSaz4NWm7Ne2M9nefT4MW9QH4+bulfDD2Y958fvqG25OSksj707i5jAP3o0HL+owbssnAxiTIy80nJSWZN5+fzlPdR264f6Xq+7Dq19UM7hicQXVW+1M5qtHhPHj50A13z/phGftW23gGQaX99yX7p18489JGlK1QZkNnrlL1fen+/I08edtzzJgwe9caXxRKSCIVlUh6Ph07dqRt27Y0b96c2rVrc8kll9CgQQPef/99rrzyyihK2KrOVwR/AL/8Cq0uCwYW16wBL4+H007e8j4nHwf9Ht1yuXpHwDuvbFxuyDPw629bnu24Owmz/QCT3oE3psNdtwQf1JPegZPqB4cjr+kWpD9HHAqT3wkONdT5S3Rt3Zqw278tu2v7t2XT7bKpn34ODidv7UzdR+7dfPqwRjBiYNGe7VgY11+ez/WXb5xeuQp++CmJo47Y9gfJsuVJdL8nlZefymWfijDxzWQOrhWj4t7Q4LgYL41J4YRj8khJgXv6p1CmDNzZddududRUOKVBjFfGJ3P5RQXMm5/EdwuTqH90jN9WwhU3ptGyaT7Xdgj3N+0Azu+Qy/kdggFk//k8mYF90un3r3VU2BveezuFA2vGtujk/PpLEvfcVoqHnwrGYr36fBonN84jKQmOOjafyePSqFtvPckp8K+H0yldBq69eduD8VNSoN4J+bz571Rat8tj4XdJLFqYzOFH5bPwu2QG3bf9mnbGiDtfZsSdLwNQ8/ADeHj6PXzx/tcsWZBFi+vO4vu5C1n20/LN7rPu9/Vcdu8FfP3Rf/lm1n85PrMepcuW4puP/0uZvUrR5YlreW3gv1m+ZAVnX3smbTo35/LD4g9E/3D8LJpc1pgZE2ZTplxpGp9/MoOue5K507/isS7DNyz33HdDeeDiQYUecK/iLbKzHX/88UfefPNNFi5cSH5+Pvvttx+NGzfmyCOP3KXHDfNSE+/ODC4hkJsbnF7+QI/gsgBffA09H9z4obOt5Ta1rc7Xx5/CvYN2v7MdYdfbv3IV3PUwfPs9JAGnnwKdLgsOL3z8f/DAYMjJg8qV4J5b4YDq8aqJXpj7/7BGm5/tVxzavz1/dL7+ONsxa1nQqfxzCgZbtr8o7MzZjl98ncTt96Qy4YXN7/vl10nc/WAKo54OkpBR45J5eUwwvqvyftDjpjz2rxYkfw8/lsLs/0uiID+JOgcX0PPW/K1ekmRTvyyHux9M5aefg874zdfnc9JxMZ58LpnHnknh4Fqbvw0/8XAeFfeO/5g7c6mJqRNSmTI+leRk2LdSjCs65ZBRLcb8b5L518PpPPh4kHZNHhssVxCDQ48o4IqOOaSXgpz1wRmJX81NoaAAav6lgKtvyqHsdtq/4lf418OlyF4SpJCXXpPDUfUL4tYUz86c7Xj6RadwfrfWpKQkk71oOQ9d+RjZPy6jUrV96PPvHvyz+X388vOv1D/rKK64/yJS01NZs3Itg657gu/mLgSg5fVNOPvas4gVxFizci0Dr32chV8tirve5JRkrul/KceccSRp6am8/sQbvPLQhC2We+67odx73kO779mO1bZzxk2IJv08dPsLFXNeakJSsbQrl5rYE+xM52tPsTOdrz2Nna/izZ8XkiRJ8RXvnGa3488LSZIkRcjkS5IkxWfyFSqTL0mSpAjZ+ZIkSYqQhx0lSVJ8BR52DJPJlyRJUoRMviRJUlyxWPi/wFCSmXxJkiRFyORLkiTF55ivUJl8SZIkRcjkS5IkxedFVkNl8iVJkhQhky9JkhRfgWc7hsnkS5IkKUImX5IkKT7HfIXK5EuSJClCJl+SJCmumGO+QmXyJUmSFCGTL0mSFJ9jvkJl8iVJkhQhO1+SJEkR8rCjJEmKzx/WDpXJlyRJUoRMviRJUnwxLzURJpMvSZKkCJl8SZKkuGKO+QqVyZckSVKETL4kSVJ8jvkKlcmXJElShEy+JElSXI75CpfJlyRJUoRMviRJUnyO+QqVyZckSVKEkmKxmAdyJUmSImLyJUmSFCE7X5IkSRGy8yVJkhQhO1+SJEkRsvMlSZIUITtfkiRJEbLzJUmSFCE7X5IkSRGy8yVJkhQhO1+SJEkRsvMlSZIUITtfkiRJEbLzJUmSFCE7X7tgwoQJNGvWjDPPPJORI0cmupzIrV69mrPPPptFixYlupTIDRkyhObNm9O8eXP69euX6HIiN2jQIJo1a0bz5s155plnEl1OQvTt25fu3bsnuozIXXrppTRv3pxWrVrRqlUrPvvss0SXFKm3336bc845h6ZNm9K7d+9El6NiKjXRBRRXS5cuZcCAAbz22mukp6fTrl07TjjhBA4++OBElxaJzz77jDvuuIMFCxYkupTIffjhh7z//vuMGTOGpKQkrrzySt544w3OPPPMRJcWiY8//piZM2cyfvx48vLyaNasGY0aNaJ27dqJLi0yM2bMYMyYMZx66qmJLiVSsViM7777jmnTppGaWvI+Pn788UfuvPNORo8eTaVKlWjfvj3vvvsujRo1SnRpKmZMvnbShx9+yIknnkjFihUpW7YsTZo0YfLkyYkuKzKjRo3izjvvJCMjI9GlRK5y5cp0796d9PR00tLS+Mtf/sLixYsTXVZkjj/+eJ599llSU1P55ZdfyM/Pp2zZsokuKzIrVqxgwIABXHvttYkuJXLfffcdSUlJXHXVVbRs2ZLnn38+0SVF6o033qBZs2ZUrVqVtLQ0BgwYwFFHHZXoslQMlbyvLiHJysqicuXKG6YzMjKYO3duAiuKVp8+fRJdQsL89a9/3fD/BQsWMHHiRF566aUEVhS9tLQ0HnnkEYYNG0bTpk2pUqVKokuKTK9evejSpQs///xzokuJ3MqVK2nQoAF33XUX69at49JLL6VWrVqcfPLJiS4tEgsXLiQtLY0rrriC7OxsGjduzE033ZToslQMmXztpFgstsW8pKSkBFSiRPn222+5/PLL6datGzVr1kx0OZHr3LkzM2bM4Oeff2bUqFGJLicSo0ePplq1ajRo0CDRpSREvXr16NevH2XLlmXfffelbdu2vPvuu4kuKzL5+fnMmDGDBx98kFGjRvH5558zZsyYRJelYsjO106qUqUKy5Yt2zCdlZVVIg/BlVRz5syhQ4cO3HLLLbRp0ybR5URq/vz5/Oc//wGgTJkynHXWWXzzzTcJrioaEydO5IMPPqBVq1Y88sgjvP3229x3332JLisys2fPZsaMGRumY7FYiRr7td9++9GgQQP23XdfSpcuzemnn16ijngoPHa+dtJJJ53EjBkzWL58OWvXrmXq1Kk0bNgw0WUpAj///DM33HAD/fv3p3nz5okuJ3KLFi3ijjvuICcnh5ycHN566y2OPfbYRJcViWeeeYbXX3+dcePG0blzZ0477TR69OiR6LIis2rVKvr168f69etZvXo1Y8aMKTEnmgA0btyY999/n5UrV5Kfn897773H4YcfnuiyVAyVnK8sIatSpQpdunTh0ksvJTc3l7Zt23LkkUcmuixF4Omnn2b9+vU88MADG+a1a9eOCy64IIFVRadRo0Z89tlntG7dmpSUFM4666wS2QktiRo3brxh3xcUFHDhhRdSr169RJcVmaOOOoorr7ySCy+8kNzcXE4++WTOPffcRJelYigptrXBS5IkSSoSHnaUJEmKkJ0vSZKkCNn5kiRJipCdL0mSpAjZ+ZIkSYqQnS9JkqQI2fmSJEmKkJ0vSTssMzOThg0b8u233ya6FEkqdux8Sdphr7/+OjVr1mTKlCmJLkWSih07X5J2WEpKCscee2yJ+UFtSQqTv+0oaYetW7eOf//73/jrZJK040y+JO2wAQMGUKVKFX788Ud+//33RJcjScWKnS9JO+TTTz9l8uTJDB48mPLlyzNv3rxElyRJxYqdL0mFtn79em6//XbuvvtuKlasyKGHHuq4L0naQXa+JBXaoEGDqFevHqeeeioAhx56KF9//XVii5KkYsbOl6RCmTt3LpMnT6ZHjx4b5h122GEmX5K0g5Jinq4kSZIUGZMvSZKkCNn5kiRJipCdL0mSpAjZ+ZIkSYqQnS9JkqQI2fmSJEmKkJ0vSZKkCP0/vqbWG8swVXQAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 2 Axes>"
       ]
@@ -3266,15 +3266,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1.99543707 0.0761498  4.92956436]\n",
+      "[ 2.02224424 -0.14283432  5.20056289]\n",
       "Training R2\n",
-      "0.9944058125579283\n",
+      "0.9952679649399017\n",
       "Training MSE\n",
-      "0.010574954922481266\n",
+      "0.010488032968482625\n",
       "Test R2\n",
-      "0.9967910596862095\n",
+      "0.9935374069137406\n",
       "Test MSE\n",
-      "0.006369143720284525\n"
+      "0.012025581214177655\n"
      ]
     }
    ],
@@ -3378,13 +3378,19 @@
      "output_type": "stream",
      "text": [
       "Training R2\n",
-      "0.9999864830619026\n",
+      "0.9999874172097344\n",
       "Training MSE\n",
-      "6.55983997813135\n",
+      "5.124696456785513\n",
       "Test R2\n",
-      "0.9999701967624061\n",
-      "Test MSE\n",
-      "6.146706341928796\n"
+      "0.9999717079103604\n",
+      "Test MSE\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.60488784333949\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 398d4d5cf..5eac2c8e5 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -2284,15 +2284,15 @@
       "Learning rate  =  0.1\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.9111111111111111\n",
-      "\n",
-      "Learning rate  =  0.1\n",
-      "Lambda =  0.0001\n"
+      "\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.9222222222222223\n",
       "\n",
       "Learning rate  =  0.1\n",
@@ -2336,34 +2336,24 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.09166666666666666\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11944444444444445\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.1361111111111111\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1527777777777778\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  0.1\n",
-      "Accuracy score on test set:  0.16666666666666666\n",
       "\n"
      ]
     },
@@ -2371,10 +2361,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.16666666666666666\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.1111111111111111\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.05\n",
@@ -2382,19 +2382,19 @@
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n"
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.0001\n",
-      "Accuracy score on test set:  0.08611111111111111\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
       "Accuracy score on test set:  0.08888888888888889\n",
       "\n",
       "Learning rate  =  10.0\n",
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diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml
index 5432d4f8d..45026a281 100644
--- a/doc/LectureNotes/_toc.yml
+++ b/doc/LectureNotes/_toc.yml
@@ -36,4 +36,3 @@ parts:
     - file: chapter9.ipynb
     - file: chapter10.ipynb
     - file: chapter11.ipynb
-    - file: chapter12.ipynb
\ No newline at end of file
diff --git a/doc/LectureNotes/chapter12.ipynb b/doc/LectureNotes/chapter12.ipynb
deleted file mode 100644
index 9fe71f125..000000000
--- a/doc/LectureNotes/chapter12.ipynb
+++ /dev/null
@@ -1,724 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Convolutional Neural Networks\n",
-    "\n",
-    "\n",
-    "Convolutional neural networks (CNNs) were developed during the last\n",
-    "decade of the previous century, with a focus on character recognition\n",
-    "tasks. Nowadays, CNNs are a central element in the spectacular success\n",
-    "of deep learning methods. The success in for example image\n",
-    "classifications have made them a central tool for most machine\n",
-    "learning practitioners.\n",
-    "\n",
-    "CNNs are very similar to ordinary Neural Networks.\n",
-    "They are made up of neurons that have learnable weights and\n",
-    "biases. Each neuron receives some inputs, performs a dot product and\n",
-    "optionally follows it with a non-linearity. The whole network still\n",
-    "expresses a single differentiable score function: from the raw image\n",
-    "pixels on one end to class scores at the other. And they still have a\n",
-    "loss function (for example Softmax) on the last (fully-connected) layer\n",
-    "and all the tips/tricks we developed for learning regular Neural\n",
-    "Networks still apply (back propagation, gradient descent etc etc).\n",
-    "\n",
-    "What is the difference? **CNN architectures make the explicit assumption that\n",
-    "the inputs are images, which allows us to encode certain properties\n",
-    "into the architecture. These then make the forward function more\n",
-    "efficient to implement and vastly reduce the amount of parameters in\n",
-    "the network.**\n",
-    "\n",
-    "\n",
-    "As an example, consider\n",
-    "an image of size $32\\times 32\\times 3$ (32 wide, 32 high, 3 color channels), so a\n",
-    "single fully-connected neuron in a first hidden layer of a regular\n",
-    "Neural Network would have $32\\times 32\\times 3 = 3072$ weights. This amount still\n",
-    "seems manageable, but clearly this fully-connected structure does not\n",
-    "scale to larger images. For example, an image of more respectable\n",
-    "size, say $200\\times 200\\times 3$, would lead to neurons that have \n",
-    "$200\\times 200\\times 3 = 120,000$ weights. \n",
-    "\n",
-    "We could have\n",
-    "several such neurons, and the parameters would add up quickly! Clearly,\n",
-    "this full connectivity is wasteful and the huge number of parameters\n",
-    "would quickly lead to possible overfitting.\n",
-    "\n",
-    "<!-- dom:FIGURE: [figslides/nn.jpeg, width=500 frac=0.6]  A regular 3-layer Neural Network. -->\n",
-    "<!-- begin figure -->\n",
-    "<img src=\"figslides/nn.jpeg\" width=500><p style=\"font-size: 0.9em\"><i>Figure 1: A regular 3-layer Neural Network.</i></p><!-- end figure -->\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "Convolutional Neural Networks take advantage of the fact that the\n",
-    "input consists of images and they constrain the architecture in a more\n",
-    "sensible way. \n",
-    "\n",
-    "In particular, unlike a regular Neural Network, the\n",
-    "layers of a CNN have neurons arranged in 3 dimensions: width,\n",
-    "height, depth. (Note that the word depth here refers to the third\n",
-    "dimension of an activation volume, not to the depth of a full Neural\n",
-    "Network, which can refer to the total number of layers in a network.)\n",
-    "\n",
-    "To understand it better, the above example of an image \n",
-    "with an input volume of\n",
-    "activations has dimensions $32\\times 32\\times 3$ (width, height,\n",
-    "depth respectively). \n",
-    "\n",
-    "The neurons in a layer will\n",
-    "only be connected to a small region of the layer before it, instead of\n",
-    "all of the neurons in a fully-connected manner. Moreover, the final\n",
-    "output layer could  for this specific image have dimensions $1\\times 1 \\times 10$, \n",
-    "because by the\n",
-    "end of the CNN architecture we will reduce the full image into a\n",
-    "single vector of class scores, arranged along the depth\n",
-    "dimension. \n",
-    "\n",
-    "<!-- dom:FIGURE: [figslides/cnn.jpeg, width=500 frac=0.6]  A CNN arranges its neurons in three dimensions (width, heigh#t, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D out#put volume of neuron activations. In this example, the red input layer holds the image, so its width and heigh#t would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels). -->\n",
-    "<!-- begin figure -->\n",
-    "<img src=\"figslides/cnn.jpeg\" width=500><p style=\"font-size: 0.9em\"><i>Figure 1: A CNN arranges its neurons in three dimensions (width, heigh#t, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D out#put volume of neuron activations. In this example, the red input layer holds the image, so its width and heigh#t would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).</i></p><!-- end figure -->\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "A simple CNN is a sequence of layers, and every layer of a CNN\n",
-    "transforms one volume of activations to another through a\n",
-    "differentiable function. We use three main types of layers to build\n",
-    "CNN architectures: Convolutional Layer, Pooling Layer, and\n",
-    "Fully-Connected Layer (exactly as seen in regular Neural Networks). We\n",
-    "will stack these layers to form a full CNN architecture.\n",
-    "\n",
-    "A simple CNN for image classification could have the architecture:\n",
-    "\n",
-    "* **INPUT** ($32\\times 32 \\times 3$) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.\n",
-    "\n",
-    "* **CONV** (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as $[32\\times 32\\times 12]$ if we decided to use 12 filters.\n",
-    "\n",
-    "* **RELU** layer will apply an elementwise activation function, such as the $max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged ($[32\\times 32\\times 12]$).\n",
-    "\n",
-    "* **POOL** (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as $[16\\times 16\\times 12]$.\n",
-    "\n",
-    "* **FC** (i.e. fully-connected) layer will compute the class scores, resulting in volume of size $[1\\times 1\\times 10]$, where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.\n",
-    "\n",
-    "CNNs transform the original image layer by layer from the original\n",
-    "pixel values to the final class scores. \n",
-    "\n",
-    "Observe that some layers contain\n",
-    "parameters and other don’t. In particular, the CNN layers perform\n",
-    "transformations that are a function of not only the activations in the\n",
-    "input volume, but also of the parameters (the weights and biases of\n",
-    "the neurons). On the other hand, the RELU/POOL layers will implement a\n",
-    "fixed function. The parameters in the CONV/FC layers will be trained\n",
-    "with gradient descent so that the class scores that the CNN computes\n",
-    "are consistent with the labels in the training set for each image.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### CNNs in brief\n",
-    "\n",
-    "In summary:\n",
-    "\n",
-    "* A CNN architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)\n",
-    "\n",
-    "* There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)\n",
-    "\n",
-    "* Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function\n",
-    "\n",
-    "* Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)\n",
-    "\n",
-    "* Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)\n",
-    "\n",
-    "## CNNs in more detail, building convolutional neural networks in Tensorflow and Keras\n",
-    "\n",
-    "\n",
-    "As discussed above, CNNs are neural networks built from the assumption that the inputs\n",
-    "to the network are 2D images. This is important because the number of features or pixels in images\n",
-    "grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network.  \n",
-    "\n",
-    "As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks\n",
-    "are the **convolutional** and **pooling** layers stacked in pairs between the input and the hidden layer.\n",
-    "In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D\n",
-    "matrices, typically 1 for each color dimension (Red, Green, Blue). \n",
-    "\n",
-    "\n",
-    "\n",
-    "It means that to represent the entire\n",
-    "dataset of images, we require a 4D matrix or **tensor**. This tensor has the dimensions:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\n",
-    "(n_{inputs},\\, n_{pixels, width},\\, n_{pixels, height},\\, depth) .\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The MNIST dataset consists of grayscale images with a pixel size of\n",
-    "$28\\times 28$, meaning we require $28 \\times 28 = 724$ weights to each\n",
-    "neuron in the first hidden layer.\n",
-    "\n",
-    "If we were to analyze images of size $128\\times 128$ we would require\n",
-    "$128 \\times 128 = 16384$ weights to each neuron. Even worse if we were\n",
-    "dealing with color images, as most images are, we have an image matrix\n",
-    "of size $128\\times 128$ for each color dimension (Red, Green, Blue),\n",
-    "meaning 3 times the number of weights $= 49152$ are required for every\n",
-    "single neuron in the first hidden layer.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Images typically have strong local correlations, meaning that a small\n",
-    "part of the image varies little from its neighboring regions. If for\n",
-    "example we have an image of a blue car, we can roughly assume that a\n",
-    "small blue part of the image is surrounded by other blue regions.\n",
-    "\n",
-    "Therefore, instead of connecting every single pixel to a neuron in the\n",
-    "first hidden layer, as we have previously done with deep neural\n",
-    "networks, we can instead connect each neuron to a small part of the\n",
-    "image (in all 3 RGB depth dimensions).  The size of each small area is\n",
-    "fixed, and known as a [receptive](https://en.wikipedia.org/wiki/Receptive_field).\n",
-    "\n",
-    "\n",
-    "\n",
-    "The layers of a convolutional neural network arrange neurons in 3D: width, height and depth.  \n",
-    "The input image is typically a square matrix of depth 3. \n",
-    "\n",
-    "A **convolution** is performed on the image which outputs\n",
-    "a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as **filters**.\n",
-    "\n",
-    "\n",
-    "Each filter slides along the input image, taking the dot product\n",
-    "between each small part of the image and the filter, in all depth\n",
-    "dimensions. This is then passed through a non-linear function,\n",
-    "typically the **Rectified Linear (ReLu)** function, which serves as the\n",
-    "activation of the neurons in the first convolutional layer. This is\n",
-    "further passed through a **pooling layer**, which reduces the size of the\n",
-    "convolutional layer, e.g. by taking the maximum or average across some\n",
-    "small regions, and this serves as input to the next convolutional\n",
-    "layer.\n",
-    "\n",
-    "\n",
-    "\n",
-    "By systematically reducing the size of the input volume, through\n",
-    "convolution and pooling, the network should create representations of\n",
-    "small parts of the input, and then from them assemble representations\n",
-    "of larger areas.  The final pooling layer is flattened to serve as\n",
-    "input to a hidden layer, such that each neuron in the final pooling\n",
-    "layer is connected to every single neuron in the hidden layer. This\n",
-    "then serves as input to the output layer, e.g. a softmax output for\n",
-    "classification.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### Prerequisites: Collect and pre-process data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# import necessary packages\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn import datasets\n",
-    "\n",
-    "\n",
-    "# ensure the same random numbers appear every time\n",
-    "np.random.seed(0)\n",
-    "\n",
-    "# display images in notebook\n",
-    "%matplotlib inline\n",
-    "plt.rcParams['figure.figsize'] = (12,12)\n",
-    "\n",
-    "\n",
-    "# download MNIST dataset\n",
-    "digits = datasets.load_digits()\n",
-    "\n",
-    "# define inputs and labels\n",
-    "inputs = digits.images\n",
-    "labels = digits.target\n",
-    "\n",
-    "# RGB images have a depth of 3\n",
-    "# our images are grayscale so they should have a depth of 1\n",
-    "inputs = inputs[:,:,:,np.newaxis]\n",
-    "\n",
-    "print(\"inputs = (n_inputs, pixel_width, pixel_height, depth) = \" + str(inputs.shape))\n",
-    "print(\"labels = (n_inputs) = \" + str(labels.shape))\n",
-    "\n",
-    "\n",
-    "# choose some random images to display\n",
-    "n_inputs = len(inputs)\n",
-    "indices = np.arange(n_inputs)\n",
-    "random_indices = np.random.choice(indices, size=5)\n",
-    "\n",
-    "for i, image in enumerate(digits.images[random_indices]):\n",
-    "    plt.subplot(1, 5, i+1)\n",
-    "    plt.axis('off')\n",
-    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
-    "    plt.title(\"Label: %d\" % digits.target[random_indices[i]])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Importing Keras and Tensorflow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras import datasets, layers, models\n",
-    "from tensorflow.keras.layers import Input\n",
-    "from tensorflow.keras.models import Sequential      #This allows appending layers to existing models\n",
-    "from tensorflow.keras.layers import Dense           #This allows defining the characteristics of a particular layer\n",
-    "from tensorflow.keras import optimizers             #This allows using whichever optimiser we want (sgd,adam,RMSprop)\n",
-    "from tensorflow.keras import regularizers           #This allows using whichever regularizer we want (l1,l2,l1_l2)\n",
-    "from tensorflow.keras.utils import to_categorical   #This allows using categorical cross entropy as the cost function\n",
-    "#from tensorflow.keras import Conv2D\n",
-    "#from tensorflow.keras import MaxPooling2D\n",
-    "#from tensorflow.keras import Flatten\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# representation of labels\n",
-    "labels = to_categorical(labels)\n",
-    "\n",
-    "# split into train and test data\n",
-    "# one-liner from scikit-learn library\n",
-    "train_size = 0.8\n",
-    "test_size = 1 - train_size\n",
-    "X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,\n",
-    "                                                    test_size=test_size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
-    "                                              n_filters, n_neurons_connected, n_categories,\n",
-    "                                              eta, lmbd):\n",
-    "    model = Sequential()\n",
-    "    model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',\n",
-    "              activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
-    "    model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
-    "    model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))\n",
-    "    \n",
-    "    sgd = optimizers.SGD(lr=eta)\n",
-    "    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
-    "    \n",
-    "    return model\n",
-    "\n",
-    "epochs = 100\n",
-    "batch_size = 100\n",
-    "input_shape = X_train.shape[1:4]\n",
-    "receptive_field = 3\n",
-    "n_filters = 10\n",
-    "n_neurons_connected = 50\n",
-    "n_categories = 10\n",
-    "\n",
-    "eta_vals = np.logspace(-5, 1, 7)\n",
-    "lmbd_vals = np.logspace(-5, 1, 7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
-    "        \n",
-    "for i, eta in enumerate(eta_vals):\n",
-    "    for j, lmbd in enumerate(lmbd_vals):\n",
-    "        CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
-    "                                              n_filters, n_neurons_connected, n_categories,\n",
-    "                                              eta, lmbd)\n",
-    "        CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)\n",
-    "        scores = CNN.evaluate(X_test, Y_test)\n",
-    "        \n",
-    "        CNN_keras[i][j] = CNN\n",
-    "        \n",
-    "        print(\"Learning rate = \", eta)\n",
-    "        print(\"Lambda = \", lmbd)\n",
-    "        print(\"Test accuracy: %.3f\" % scores[1])\n",
-    "        print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Final visualization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "# visual representation of grid search\n",
-    "# uses seaborn heatmap, could probably do this in matplotlib\n",
-    "import seaborn as sns\n",
-    "\n",
-    "sns.set()\n",
-    "\n",
-    "train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
-    "test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
-    "\n",
-    "for i in range(len(eta_vals)):\n",
-    "    for j in range(len(lmbd_vals)):\n",
-    "        CNN = CNN_keras[i][j]\n",
-    "\n",
-    "        train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]\n",
-    "        test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]\n",
-    "\n",
-    "        \n",
-    "fig, ax = plt.subplots(figsize = (10, 10))\n",
-    "sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
-    "ax.set_title(\"Training Accuracy\")\n",
-    "ax.set_ylabel(\"$\\eta$\")\n",
-    "ax.set_xlabel(\"$\\lambda$\")\n",
-    "plt.show()\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize = (10, 10))\n",
-    "sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
-    "ax.set_title(\"Test Accuracy\")\n",
-    "ax.set_ylabel(\"$\\eta$\")\n",
-    "ax.set_xlabel(\"$\\lambda$\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The CIFAR01 data set\n",
-    "\n",
-    "The CIFAR10 dataset contains 60,000 color images in 10 classes, with\n",
-    "6,000 images in each class. The dataset is divided into 50,000\n",
-    "training images and 10,000 testing images. The classes are mutually\n",
-    "exclusive and there is no overlap between them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "from tensorflow.keras import datasets, layers, models\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# We import the data set\n",
-    "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
-    "\n",
-    "# Normalize pixel values to be between 0 and 1 by dividing by 255. \n",
-    "train_images, test_images = train_images / 255.0, test_images / 255.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
-    "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
-    "​\n",
-    "plt.figure(figsize=(10,10))\n",
-    "for i in range(25):\n",
-    "    plt.subplot(5,5,i+1)\n",
-    "    plt.xticks([])\n",
-    "    plt.yticks([])\n",
-    "    plt.grid(False)\n",
-    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
-    "    # The CIFAR labels happen to be arrays, \n",
-    "    # which is why you need the extra index\n",
-    "    plt.xlabel(class_names[train_labels[i][0]])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.\n",
-    "\n",
-    "As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "model = models.Sequential()\n",
-    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
-    "model.add(layers.MaxPooling2D((2, 2)))\n",
-    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
-    "model.add(layers.MaxPooling2D((2, 2)))\n",
-    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
-    "\n",
-    "# Let's display the architecture of our model so far.\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.\n",
-    "\n",
-    "\n",
-    "\n",
-    "To complete our model, you will feed the last output tensor from the\n",
-    "convolutional base (of shape (4, 4, 64)) into one or more Dense layers\n",
-    "to perform classification. Dense layers take vectors as input (which\n",
-    "are 1D), while the current output is a 3D tensor. First, you will\n",
-    "flatten (or unroll) the 3D output to 1D, then add one or more Dense\n",
-    "layers on top. CIFAR has 10 output classes, so you use a final Dense\n",
-    "layer with 10 outputs and a softmax activation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "model.add(layers.Flatten())\n",
-    "model.add(layers.Dense(64, activation='relu'))\n",
-    "model.add(layers.Dense(10))\n",
-    "Here's the complete architecture of our model.\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.\n",
-    "\n",
-    "Compile and train the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer='adam',\n",
-    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
-    "              metrics=['accuracy'])\n",
-    "​\n",
-    "history = model.fit(train_images, train_labels, epochs=10, \n",
-    "                    validation_data=(test_images, test_labels))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "plt.plot(history.history['accuracy'], label='accuracy')\n",
-    "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
-    "plt.xlabel('Epoch')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.ylim([0.5, 1])\n",
-    "plt.legend(loc='lower right')\n",
-    "\n",
-    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
-    "\n",
-    "print(test_acc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Recurrent neural networks: Overarching view\n",
-    "\n",
-    "Till now our focus has been, including convolutional neural networks\n",
-    "as well, on feedforward neural networks. The output or the activations\n",
-    "flow only in one direction, from the input layer to the output layer.\n",
-    "\n",
-    "A recurrent neural network (RNN) looks very much like a feedforward\n",
-    "neural network, except that it also has connections pointing\n",
-    "backward. \n",
-    "\n",
-    "RNNs are used to analyze time series data such as stock prices, and\n",
-    "tell you when to buy or sell. In autonomous driving systems, they can\n",
-    "anticipate car trajectories and help avoid accidents. More generally,\n",
-    "they can work on sequences of arbitrary lengths, rather than on\n",
-    "fixed-sized inputs like all the nets we have discussed so far. For\n",
-    "example, they can take sentences, documents, or audio samples as\n",
-    "input, making them extremely useful for natural language processing\n",
-    "systems such as automatic translation and speech-to-text.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "### A simple example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "# Start importing packages\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras import datasets, layers, models\n",
-    "from tensorflow.keras.layers import Input\n",
-    "from tensorflow.keras.models import Model, Sequential \n",
-    "from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU\n",
-    "from tensorflow.keras import optimizers     \n",
-    "from tensorflow.keras import regularizers           \n",
-    "from tensorflow.keras.utils import to_categorical \n",
-    "\n",
-    "\n",
-    "\n",
-    "# convert into dataset matrix\n",
-    "def convertToMatrix(data, step):\n",
-    " X, Y =[], []\n",
-    " for i in range(len(data)-step):\n",
-    "  d=i+step  \n",
-    "  X.append(data[i:d,])\n",
-    "  Y.append(data[d,])\n",
-    " return np.array(X), np.array(Y)\n",
-    "\n",
-    "step = 4\n",
-    "N = 1000    \n",
-    "Tp = 800    \n",
-    "\n",
-    "t=np.arange(0,N)\n",
-    "x=np.sin(0.02*t)+2*np.random.rand(N)\n",
-    "df = pd.DataFrame(x)\n",
-    "df.head()\n",
-    "\n",
-    "plt.plot(df)\n",
-    "plt.show()\n",
-    "\n",
-    "values=df.values\n",
-    "train,test = values[0:Tp,:], values[Tp:N,:]\n",
-    "\n",
-    "# add step elements into train and test\n",
-    "test = np.append(test,np.repeat(test[-1,],step))\n",
-    "train = np.append(train,np.repeat(train[-1,],step))\n",
-    " \n",
-    "trainX,trainY =convertToMatrix(train,step)\n",
-    "testX,testY =convertToMatrix(test,step)\n",
-    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
-    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
-    "\n",
-    "model = Sequential()\n",
-    "model.add(SimpleRNN(units=32, input_shape=(1,step), activation=\"relu\"))\n",
-    "model.add(Dense(8, activation=\"relu\")) \n",
-    "model.add(Dense(1))\n",
-    "model.compile(loss='mean_squared_error', optimizer='rmsprop')\n",
-    "model.summary()\n",
-    "\n",
-    "model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)\n",
-    "trainPredict = model.predict(trainX)\n",
-    "testPredict= model.predict(testX)\n",
-    "predicted=np.concatenate((trainPredict,testPredict),axis=0)\n",
-    "\n",
-    "trainScore = model.evaluate(trainX, trainY, verbose=0)\n",
-    "print(trainScore)\n",
-    "\n",
-    "index = df.index.values\n",
-    "plt.plot(index,df)\n",
-    "plt.plot(index,predicted)\n",
-    "plt.axvline(df.index[Tp], c=\"r\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 4
-}