diff --git a/content/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb b/content/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb index 67b7bc6..0765372 100644 --- a/content/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb +++ b/content/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb @@ -58,7 +58,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -67,18 +78,34 @@ "data_filename = 'Advertising.csv'\n", "\n", "# Read advertising.csv file using the pandas library\n", - "df = pd.read_csv(___)\n" + "df = pd.read_csv(data_filename).loc[:7, ['TV', 'Sales']]\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " TV Sales\n", + "0 230.1 22.1\n", + "1 44.5 10.4\n", + "2 17.2 9.3\n", + "3 151.5 18.5\n", + "4 180.8 12.9\n", + "5 8.7 7.2\n", + "6 57.5 11.8\n", + "7 120.2 13.2\n" + ] + } + ], "source": [ "# Print your new dataframe to see if you have selected 7 rows correctly\n", "\n", - "print(___)" + "print(df)" ] }, { @@ -90,16 +117,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Sales')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Use a scatter plot for TV vs Sales\n", - "plt.___\n", + "plt.scatter(df['TV'], df['Sales'])\n", "\n", "# Add axis labels for clarity (x : TV budget, y : Sales)\n", - "plt.___\n", - "plt.___\n" + "plt.xlabel('TV budget')\n", + "plt.ylabel('Sales')\n" ] }, { @@ -113,15 +163,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Newspaper Budget')" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_all = pd.read_csv(data_filename)\n", + "\n", + "fig, ax = plt.subplots(1, 3, figsize=(20, 6))\n", + "\n", + "ax[0].scatter(df_all['TV'], df_all['Sales'])\n", + "ax[0].set_ylabel('Sales')\n", + "ax[0].set_xlabel('TV Budget')\n", + "\n", + "ax[1].scatter(df_all['Radio'], df_all['Sales'])\n", + "ax[1].set_ylabel('Sales')\n", + "ax[1].set_xlabel('Radio Budget')\n", + "\n", + "ax[2].scatter(df_all['Newspaper'], df_all['Sales'])\n", + "ax[2].set_ylabel('Sales')\n", + "ax[2].set_xlabel('Newspaper Budget')" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -135,7 +224,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture05/notebook/s1-exa2-challenge.ipynb b/content/lectures/lecture05/notebook/s1-exa2-challenge.ipynb index f02052c..d31d870 100644 --- a/content/lectures/lecture05/notebook/s1-exa2-challenge.ipynb +++ b/content/lectures/lecture05/notebook/s1-exa2-challenge.ipynb @@ -30,10 +30,8 @@ ] }, { - "cell_type": "code", - "execution_count": 24, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "# Instructions:\n", "Part 1 **KNN by hand for k=1**\n", @@ -77,7 +75,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -164,7 +173,7 @@ "5 8.7 48.9 75.0 7.2" ] }, - "execution_count": 25, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -172,6 +181,7 @@ "source": [ "# take a quick look of the dataset\n", "\n", + "df_adv = pd.read_csv(\"Advertising.csv\")\n", "df_adv.head(6)" ] }, @@ -184,18 +194,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 4)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m data_y = df_adv.Sales.iloc[]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "# Get a subset of the data rows 6 to 13 and only TV advertisement. \n", "# The first row in the dataframe is the first row and not the zeroth row. \n", @@ -204,17 +205,93 @@ "\n", "# Sort the data\n", "\n", - "idx = np.argsort(___).values # Get indices ordered from lowest to highest values\n", + "idx = np.argsort(data_x).values # Get indices ordered from lowest to highest values\n", "\n", - "# Get the actual data in the order from above and turn them into numpy arrays. \n", + "# # Get the actual data in the order from above and turn them into numpy arrays. \n", "\n", - "data_x = data_x.iloc[___].___\n", - "data_y = data_y.iloc[___].___" + "data_x = data_x.iloc[idx]\n", + "# data_y = data_y.iloc[idx].values" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5 8.7\n", + "6 57.5\n", + "7 120.2\n", + "8 8.6\n", + "9 199.8\n", + "10 66.1\n", + "11 214.7\n", + "12 23.8\n", + "Name: TV, dtype: float64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_x" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 0, 7, 1, 5, 2, 4, 6], dtype=int64)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8 8.6\n", + "5 8.7\n", + "12 23.8\n", + "6 57.5\n", + "10 66.1\n", + "7 120.2\n", + "9 199.8\n", + "11 214.7\n", + "Name: TV, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_x" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -225,13 +302,13 @@ "# absolute value.\n", "\n", "def find_nearest(array,value):\n", - " idx = pd.Series(___).___ # hint: use pd.idxmin()\n", + " idx = pd.Series(np.absolute(array-value)).idxmin() # hint: use pd.idxmin()\n", " return idx, array[idx]" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -269,12 +346,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "text/plain": [ + "Text(0, 0.5, 'Sales in $1000')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" ] @@ -289,7 +376,7 @@ "# Plot your solution \n", "plt.plot(x,y, '-.')\n", "# Plot the original data using black x's.\n", - "plt.plot(___, ___, 'kx')\n", + "plt.plot(data_x, data_y, 'kx')\n", "plt.title('')\n", "plt.xlabel('TV budget in $1000')\n", "plt.ylabel('Sales in $1000')\n" @@ -304,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -332,34 +419,34 @@ "\n", "# Choose sales as your response variable 'y' and 'TV' as your 'predictor variable' \n", "\n", - "x = df[[___]]\n", - "y = df[___]" + "x = df[['TV']]\n", + "y = df['Sales']" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "### edTest(test_shape) ###\n", "\n", "# Split the dataset in training and testing with 60% training set and 40% testing set \n", - "# with random state = 42\n", + "# with random state = 42 to fix the output of train and test data (if not, any time using train_test_split on the same data, the train and test data are different)\n", "\n", - "x_train, x_test, y_train, y_test = train_test_split(___, ___, train_size=___)" + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, train_size=0.6, random_state=42)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "### edTest(test_nums) ###\n", "# Choosing \n", - "k_value_min = ___\n", - "k_value_max = ___\n", + "k_value_min = 1\n", + "k_value_max = 70\n", "\n", "# creating list of integer k values betwwen k_value_min and k_value_max using linspace\n", "\n", @@ -368,12 +455,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -382,17 +469,6 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "ename": "NameError", - "evalue": "name 'k_list' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Looping over k values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mk_value\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mk_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# creating KNN Regression model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'k_list' is not defined" - ] } ], "source": [ @@ -402,13 +478,13 @@ "for k_value in k_list: \n", " \n", " # creating KNN Regression model \n", - " model = KNeighborsRegressor(n_neighbors=int(___))\n", + " model = KNeighborsRegressor(n_neighbors=int(k_value))\n", " \n", " # fitting model \n", - " model.fit(___,___)\n", + " model.fit(x_train, y_train)\n", " \n", " # test predictions \n", - " y_pred = model.predict(___)\n", + " y_pred = model.predict(x_test)\n", " \n", " ## Plotting\n", " colors = ['grey','r','b']\n", @@ -443,15 +519,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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SyLE2ChGB3HqIQBYEQRCEOJGdnc2CBQvIycnh/vvvJycnhwULFpCdnd3WQ2tGZ3S77TjI0Do55JaMWESTQRaBHBkikAVBEAQhjmRnZzNr1izmzZvHrFmz2qU4hs7pdttxkCG4QI7nSYO1zJtELDoeIpAFQRAEIY7k5eWRm5vLnDlzyM3NbSa42gsdye22g9baZyJaKIIJ5HidNNTW1lJXV4fL5SIpKSnuVSximaQnTULsIQJZEARBEOKEKagWLFjA3LlzvQK0PYvkjuB228EUjU6nM2yVhmACOV4nDVb3WCllW9iGE8gOhwOllE/JNruIgxwZIpAFQRAEIU7k5+f7CCpTcOXn57fxyALTFm73hg0beOWVV6iqqorrdu3mjwGSk5OBwBnkeJw0WJuEWMcUa8QiErHtjwjkyJCjJAiCIAhxYvbs2c2WZWdnt0tn1up2m2Ns6ZjFvn37eP3116mvr2fLli0cddRRcdu23fwxhJ6k53/SEM37Z52gB/YFsh0R63K5cLvduN1uEhMTbY9JBHJkiIMsCIIgCF2Q1na7a2trWbBggVeo1dbWxm3b1sl1phgNNbkumECOV0QmmECONWIRybai2bbQhAhkQRAEQejEBKvMADRzRrOzswO64LGiteadd97hwIED3mXxFMhTpkzhhhtuoKCgAJfLFXZynSmQa2pqfJbH66TBXyDbLfMWLmJh3VakAlkm6UWGCGRBEARB6MS0h3Juy5cv57vvviMxMdEbq4inQM7OziY3N5eFCxfy3nvvhY2KBHOQZ8+eHZeThmgiFtaJd6FErGSQWwcRyIIgCILQiWnrcm67d+/mgw8+AOCCCy6gf//+ANTV1cV1P8ceeyyTJ0/mv//9b9jJdS3dKCSaiIVVwIaqwmHXjQ61fSE8IpAFQRAEoZPTVuXcqqurWbhwIQ0NDUyePJkjjzySpKQkIP4CecmSJSxfvpyLLroobEWO1hLIGRkZgD0H2a6AjTWDLBELe4hAFgRBEIROTluUc9Nas2jRIkpLS+nfvz9nnXUWgLfyQjwjFnl5edxxxx3MnDmTK664IuzkupYUyFprr0BOS0sD7Lm+dsvUdbSIRUdtaS4CWRAEQRA6MW3VvGTFihVs3LiRpKQkZs6c6RVmpoMcT4Gcn5/PQw89xPDhw3G5XGEn1yUlJaGUoq6uzjt5LV5UVVXh8XhISUnxil07DrLdMnUdTSC3hwx8NIhAFgRBEIROTFs1L9m8eTMAZ5xxBj169PAub4mIxezZsznmmGOAJjEaanKdUsrbLMS/kkWs+OePrWOym0EOhd2ayv7YbcMdb9o6Ax8tktQWBEEQhE5MWzUvMYVnr169fJa3RMQCImsUAkbMorq6murqalJTU+M2Dv8uehBZBrmzRSzANwM/Z86cdi+OQQSyIAiCIAgtgCmATcfYpCUiFhC5AGypHHIoB7ldRyy2b4dHHgFLrep4kbd3L7lffMGc8ePJnT+f7KVLye7Xr/mKv/gFTJwY9/1HgwhkQRAEoUvx3//+l4aGBi688MK2HkqnprUFsp0mG1ZaUyCblSMaGhrweDw4HM0TrnYFfqxl3oJWsbjqKvjyy4i2aYc8IAdYAGR/+y3ZQM7HHxv3/Ve+/PJ2I5AlgywIgiB0GTweD8uXL2fVqlVxLzMm+GIKYDPra2JGLOrq6tBax21/7dlBVkqFzSG3dMQiZAZ55coWEccA+eAjhrMb77dsAj52xEEWBEEQugxW17Kqqsor1oT4orX2Hmv/Y+x0OnG5XNTX11NfX2/b8Q1HNBlkaB2BbI7L7XbjdrsDfu7aNGLxxBNNv59+Olx7bUTbDkWgaZLZBHCPASZMiNt+Y0UEsiAIgtBlsArkyspKMjMz224wnZj6+no8Ho9XDPuTmJhIfX09tbW1cRPIdh1YE9PZbimBbDYJMUlISKC6ujpoNCKWRiHz589nypQpPpPf8vLyyM/P907SDLr9sjJ46aWm+/ffDyedFHIMXQGJWAiCIAhdBn8HWWgZgsUrTFoih9yeIxYQvtRbLBlkO7WGg27/xRfB/Fs44gg48cSQ++8qiEAWBEEQugxWQfb44493yA5fHYFgE/RMIq2FbKcbW6QOsimQ41kHuaGhgcrKSpRSzUrHhatkYXeSYaCIhZ1awwEn6WkNublN92fNAqXCvMrI6Yjd9EQgC4IgCF0Gq0AeMWJEh+zw1REwRWcwgRxpLWQ7Dml7yCBXVFQARotp/0oV4apPxBKxAN9aw7NmzWpWazjgJL0lS+C774zfU1PhmmtC7jtaOmI3PRHIgiAIQpfBKsjGjh3bITt8dQTsOsh2BXIkDmlblnkL1CTEJJyDHGnEwl8g5+XlkZuby5w5c8jNzW3m2AbcvnVy3pVXgl9uOl50xG56IpAFQRCELoP/JL1wrpsQHfGOWEB4h7Q9OMjB8scQPoMcS8TCdGQXLFjA3LlzvWLUKpKbCeSiInjttaYNz5oVcr+x0tH+1kQgC4IgCF0Gq0Curq4O67oJ0RFOIEfTbjoqhzQEbSWQ4+UgW7eTn5/v48iajm1+flO14Wbbf+YZME9QjjuuxRt0dLS/NSnzJgiCIHQZrIJsxYoVPPvss15hkZ2d3SEu/XYE4i2QrQ5psPcqlkl6WmtUHCanhRLILZlBNku5WTGPU8Dtezzwz382rdzC7rGd96+9IQ6yIAiC0GWwCrJNmzaFdd2E6Ih3xMKOQxppxMLhcJCUlOTT1CRW4uEgt3QnPafTCR9+CAUFxgM9ekBOTkTbihQ77197QxxkQRAEoctgFUJTp05t5l75u25CdISrYhHpJL1IHNJIGo8kJydTW1tLdXV10JrNkRCsSQiEF7at2knPWtrtuuug0U1vKey8f+0NcZAFQRCELoPVsaytrfW6akJ8aYtGIZE6yBD/HHJbZZDDobVu2v6ePfDuu00P3nqr7e10JdrUQVZKDQaeB/oBHuBJrfVjSqmewKvAMGAbkKO1PthW4xQEQRA6B/6CrKqqKqCYEZpTV1fHwYMH6du3b9h17WaQI6liEY5oHOR4C+R4lHkLN/5w1TC8vPkm/OpXRitp4O6qKlAK9fDDRgYZ4LTTYPTo0NvporS1g1wP/FJrPRY4HrhNKTUOuAf4RGs9Cvik8b4gCIIgxEQggSzY47333uOJJ55g7969YdeNdx3kcHg8Ht+MrU3iKZBra2upq6vD5XIFdM7tdtKLS8SishJuugm2boWSElRJCd2qq+lWVQWNzUyAFp+c15FpU4Gstd6jtV7Z+Hs5sB4YCFwEPNe42nPAjDYZoCAIgtCpMAVZt27dAKMWsmCPAwcOAFBSUhJ23ZaogxwKazwhkmoU8RTI1nhFoDGEE7axNgrx4d//hsb3KyhnnQUXXRR6nS5Mu5mkp5QaBkwEvgb6aq33gCGilVJ9gjznFuAWgCFDhrTSSAVBEISOiincevToQVVVlTjIEWBOvLMjJluiDnIoIq2BbNJSAjkQ8YpYOJ1OlFJ4PB48Hk+zltbU18Of/9x0/6GHOHTJJTzxxBOkp6cza9YscDqN6hVCUNo6YgGAUioNeB24S2t9yO7ztNZPaq0na60n9+7du+UGKAiCIHQKrAIZJGIRCeaxM4WynXWTk5OZP39+s6YQ33zzDUuWLIm7QI4kfwxtI5BjrWJhXSeg2H79ddi2zfi9Z0+YNQt39+5Up6ZSn5kJWVkijm3Q5gJZKZWAIY5f0lq/0bh4n1Kqf+Pj/YH9bTU+QRAEoXNgrXdrCmSJWNjHPHaROshTpkzxaXucl5fHjTfeyMCBA+MWsYimggX4NguJlbLGyXCxOsh2XkNQsa01zJ/fdP/22yE1NWqHvSvTpgJZGSGdp4H1WutHLA+9Dfy48fcfA4tae2yCIAhC58IUYwkJCaSlpQHiINvFenIRTiBb101MTPQ2hcjJyeH+++8nJyeHV155heHDh1NbW4vWOubxResgm5Pp4uEgm5MX+/QJmAqNW8QCQuSQ8/Jg5Urj9+RkQyBDVBMYuzq2TyWUUqcApwKjgUxAA2XAJuBTrfVnUex/GnAN8K1SanXjst8AfwQWKKVuBHYAM6PYtiAIgtCOWbp0KampqRx11FGtsj+rq2lO0hOBbA9rFCKc2+p2u9Fak5CQ4BVk2dnZzJo1i3nz5jFnzhxOP/10li5d6q0+EauzGauDHA+BvHv3bgAGDBgQ8PFQsQitdVQRi2YC2eoeX389NMZPxUGOnLBHSik1BXgGGAcEmxp6n1LqO+AGrfVyuzvXWi8Jsc3T7G5HEARB6FiUl5fz4YcfAoaLN7oVarEGEsgSsbCHVSCHE5OBJujl5eWRm5vLnDlzyM3NJTs7m6SkJKqrq6mtrY1ZuLV1BrmyspLS0lISEhIINicqlIPsaaxL7HA4mk+6C0BAsb12LXzwgfG7UvCLX3gfEoEcOSHfBaXUGCAPOAJYguHuzgTOBM5q/P03wJfAkcCnjc8RBEEQhKCYE5oA3nrrLW9+syWxCrfU1FRAHGS7ROIg+7eZzsvLIycnhwULFjB37lxv3GLHjh3Nth0tbe0g79mzB4D+/fsHFbihJulFOv6A23rooabfL70URo703hWBHDnhTlMeABKBi7TWp2it/6i1fl1r/bHW+qPG3/+otT4ZuBhIBn7XwmMWBEEQOjhW57a6uprXXnutxds+S8QieqyiOFIHOT8/nwULFpCdnQ3gzSQXFhYC8amFHI8yb7Fkoc3XEixeAaEd5EjH3yxisWMH/Oc/TSvcfXdM2xfCC+TpwAKt9TvhNqS1XgS8BmTHYVyCIAhCJ6aisZvXqFGjyMjIYNeuXXz88cctus9gAjkek8Q6O7FELGbPnu0VxybZ2dlccMEFzbYdLabojDRikZCQgMvloqGhIejkOTuEyx9D6AxypBGRZgL5L38x6h8DnHIKHHtswO3LJD37hBPIGcDOCLa3vfE5giAIQoTs2rUrLpOFOgKmg5yVlcXMmTNxOBwsW7aM9evXt9g+rcLN6XSSlJSE1jrux7y0tJR9+/bFdZttjVXE1tXVhXT7wzUJMYlnu+lYHNJYYxZaa1sCOZSDHG3Ewu12w8GD8K9/NT3o5x5DUxULcZDtE04gb8eoXBGWxpJtp2FUnRAEQRAiYNu2bTz99NPeiWudHdNBTktLY9CgQZx++ukALFq0yNvSON74C7eWyCFrrXn++ed58sknKS4ujtt22xr/3HGoHLK1SUgo4tluui0Fcnl5ORUVFSQnJ9OzZ8+g65nurdkBz0o0EQtHQwNJS5fCT34CjX9PjBsH55zTbH2JWEROOIH8KjBFKfUfpdTQYCs1PvYfYBLwchzHJwiC0CX44YcfgKbJPp0dU5SaIvX4449nzJgx1NbW8tprrwXtNhYL/gK5JXLIBw4c4ODBg3g8Hj77LJrqp+0Tf5c3lEA2HzPbSQcjnu2mo41YQJOQj7ZZiDV/bHiFgVFKBXWRbUcsCgvhqaeY+vDDzP7Tnzjsxhth4cKmx+++GwJMEpSIReSEE8h/AL4CcoCtSqn1SqlFSqnnG2+LlFLrga0YFS2WYtQwFgRBECJg504jzVZaWtq2A2klrA4yGOLhoosuIjMzkz179vDRRx/FfZ/BHOR4lnozKzMArFu3jqKioqi39d133/Hhhx+2i4y0v3gM5bbajVjYFciBWlXn5eUx31Lzty0dZDvxCpNglSxCRixqauCll2D6dBg0CG6+mQHLlpHk77wfdRRceWXA/YqDHDkhBbLWugYjYvFbYBdwOHABcHXj7YLGZTsxyr2dqrWOT2N1QRCELkJDQ4PXhaqtrY1L29v2jilKTZEKhpN32WWXAbBq1aq4C0N/4WYKo3g6yKZANrcdi4ucl5fH0qVL20VUw1/E2hHI8YpYBGpVnZOTw5QpU7zrxOIgRyyQtYYDB2D/fti/nwMbNtCtooLBSUneZc1uRUVQUxPWQfYRsN99B3fdBQMHwtVXQ4DPUk3//jBrFrz9NnzzDQRx7UUgR07YI6W1rsNwkv+glBqBIYi7Nz5cBmzUWv/QckMUBEHo3Ozbt8/HUSotLaVfv35tOKKWx3SQrQIZDBfO5XLhdrupq6sL60JGginEWjKDvH37dgAuvPBCXnvtNb777jtOPvnkoO2HQ2EKtvZQii6SiEW8J+lZW1XPmjWL3Nxcn7JxEJsANKM2Qa/eVFbCihWwdCksW2bcGttKg6XV78MPh93X7QkJVCclkfLKK5CVBZmZ4HLRr7ycywsLSU9PNyITe/YYgtcfpxOmT2fTqFF85HQyfuZMTj7llLD7lUl6kRPRkWoUwiKGBUEQ4ogZrzApKyvr1ALZ4/E0yyCbKKVIT0/n4MGDlJeXx1UgB8sgxytiUV5ezsGDB0lMTGT06NEcc8wx5Ofn89lnnzFz5szwG7CgtfaK0BarbLJ8OaxbZ1yWD5MXNo9dWloaFRUVrRqxgOatqv3LxkXbKARg2LBhfPXVV6xfv57s7OymHPGiRTB3LqxZA3Gq0e1yu0l3u2HjRuPWSAaWEmDLAzQkHjYMbr4ZrrsOBgxg7+efU5yXR73NcYmDHDlypARBENoYUyAnJiZSV1fX6XPIpjju1q1bwK5jpkCuqKggKysrbvtt6SoWpns8ePBgHA4HJ510EitXruT7779n37599O3b1/a26uvrvZUOWiRys2IFTJsGdXXwxRfw9NMhVzePXWZmZtwEciRVLAK1qg7kIEcTsTjssMNISUmhuLiYffv2GSenixbBxRcbcYpAdOsGqanU19dTW1uL0+UiOdTr9XigrKypVrEdXC6YMcMQxqef7jP5LlRN5UCIQI4cW0dKKTUEuB4jjzwayAQ0RsRiE/AJ8KzWWkq8CYIgRIgpkMeMGcPatWs7vUAOlD+2Yk7cs7ajjgctXcXCzB8PGTIEMIT+pEmT+Oabb/jss8/Iycmxva1IOtdFjNYwe7YhjgGeew4eeMCYABZmPJmZmezatatVIxbWVtWmMLbeh9gEoNPpZNy4caxYsYJvv/2Wfjt2wI9+5CuOx42DqVPh+OON29ix4HTyyQcfsGzZMqZPn84p4aIOWvPKU0+xd/16LjvtNAanpRmi2eNh48aNrFq1ilGjRjFp0iRDHB97LASJ5jRrFBIGM2IhVSzsE/aTpJT6KfAwkAT41y9JBvoCJwH3KKV+qbXOjfsoBUEQOillZWUcOnSIpKQkRo4cydq1aykrK2vVMTQ0NOBwOEKWqIonwfLHJq0tkOMVsfAXyAAnnngiK1euZP369ezdu9d2dMYqGuPuIH/4IXz6adP9hgZ4/HH4wx/CjiczMxOIzyQ9M2IRzkEO1qo6Pz+/mUCOxkEGGD9+PCtWrGDnZ5+hn3wSZb6+ESPg888hSIWKSCpYoBSkpXGoe3cqR4yAMWO8DxUtWcLGmhp6nXACnHFG2E1FKpDFQY6ckFUslFLnAX8HqoAHgROA3kBi461347IHgWrg70qp5hWqBUEQhIDs2rULMC7L9+jRA2jdUm/V1dU88sgj/Pe//221fZqC1BTC/qSnpwNNQjpetGTEoqamhn379uFwOBg4cKB3uekiQ2QVLVrMQfZ44Ne/br78n/+EEMfBHE/37t2bjc+feDvIwVpVz54923s/lgwyGCc1fZxOLnziCZRZmq9XL3jvvaDi2OPxeOuWW9/zUERUxcLGdkQgtxzh6iD/CigBjtFa/5/WepnWukRrXd94K2lcdj9Gk5ADQPMeh4IgCEJAzHjFoEGDvOKjNR3k4uJiqqqqvPnZ1iCcg9wSAllr7RVipnNpjVjEWlLOdI8HDhzYzMU88cQTcblcbNiwwXYjGKsAtesgr1+/nvfffz9kG2heecWYdAZGjnbwYOP3gwfhhRcCPsV67Ow4yOZ426LVdLQOsqqt5Yr//IeskhJzcEbptNGjgz6nuLgYt9tNZmam97MUjqjqIAdAHOSWJ5xAngj8x062WGu9DaPz3qQ4jEsQBKFLYArkwYMHk5aWhtPppKqqKi7td+1gipPWrL3cFhlkc9Kb0+n0ioSEhARcLhf19fW2JzsFwzzBsMYrTNLS0rw1excvXmxrexFHLOrqcP34x0y8/np2BptwV1sL993XdP8Xv4Bf/rLp/mOPBZyUZj125slLMIGstfZ+dsN10otnq+mYHGSPB378Y3p89x0AWikann8eTjgh5NOsHfTsEmxyXaQCXybptTzhBLITiOSTW2djm4IgCAKGMNizZw9KKQYOHIhSqtVdZFOIxcPFs4vdiEU8BXKgy/5KqbjlkE0HeejQoQEfnzZtGi6Xi02bNtlyxiOOWDz7LKPy8+m3bx9DfvpTePnl5uvk5sK2bcbvvXoZbYmvvx4ajzfr10OADobWYxeuLbNVHAeqUGIlnq2mY3KQf/tbWLDAe/fDM86g4Jhjwj4tovxxIxKx6DiEE7PfA5cppTLCrIdSKhO4rPE5giAIQhh2796N1pq+fft6hZt5Cbu1csimOHG73d6yYi1NOAe5JSIWwXKx8cghu91ur1gabEYW/EhNTfVmzO2I8YgEstbov//de9fR0IC++mpj4p1JWRk8+GDT/TlzICPDuN1wQ9Pyv/yl2eatk+7CdZ2zG68AQ6wppWhoaAgdC7FB1A7yV1/BH//ovbvrkktYNnUq69atC/vUthTIUsWi5QknkP8BDAbylVLXKqWaFXFUSvVVSv0Y+AYYiDGpTxAEQQiDNX9sYjrIrS2Q/X9vScJlkJOTk3E6ndTW1sYtahJMIMej1FthYSEej4e+ffuGrNwQSUvjiCIWX3yB+vZbn0VKa7j9dqPRhdYwfz6Y+drhw+HWW5tW/tnPjAoLAO+/Dxs2+GzLKnoTEhJwOBzU19cHFGd2J+iB4eDHK4cclUPqdsNPftJ0/6yzSMnNBaVYv359yPhCfX09exu76cVTIEcasRAHueUIKZC11s8BjwCjgH8Du5VSZUqpHY23MmA38AwwEnhUax045S8IgtAJ8Xg8fPvtt1G5ndYKFiZt5SD7/x4NW7ZsYf/+/WHXCxexUEp5H4uXixxOIMcSsTDzx8HiFSbh4glW/B3kkJMILU7xt0ceyU5rPeP/+z+46SZ49NGmZQ8+aExCMxkxAi68sOn+X//qs3nrsVNKhRT6kQhk63rxEsgRRSwefdToJAiQmgpPPkmvPn0YMGAAdXV1bN68OehT9+/fj8fjISsrK6Juj8GEbbST9CSD3HKEzQtrrX8FTANewhDD6cCgxlt647IXgWmN6wqCIHQZ1q9fzxtvvEFeXl5Ez9Na+0zQMzEFcmtnkP1/j5SKigpeeuklFi5cGHI9rXXYiAXEP4fckg5yoPrHgTCFpR2BbH0vPB5PcCFUWAhvvOG9+83pp/PCtddSMGpU0zrPPAOmmJ04Ea64ovl27rqr6ffnnjOqWjRijjfd44HvvyclhNCPVCDbrYUcjogjFgUF8LvfNd1/4AFofP+OPPJIAL71c+WtRDNBDySD3JGwNaFOa71Ua32t1nowkIYRpRgIpGmtB2utf6y1XtqSAxUEQWiPmGW7InV8S0pKqK6uJi0tzSuKgTabpAexVbIwhWxJSUnILHNNTQ0ej4ekpKSQX9bxziG3VAbZ4/F4T3TsOsh2Ihb+70XQ9+bJJ73ti7cNHYprwgR6DBrES5dfTvk5AdoS/OlPPi2LvZxyChx9tPF7VRU89ZT3Icfq1Vy4aBEX3norHHEEV/zpT4xfu5bqQ4eabcZukxCTeDjIHo/H+5mzlbE14yfm+3D00XDnnd6HjzjiCAA2b94c9LhHkz+G4MJWIhbtj4grTmitq7TWexpv8enPKQiC0EEpLi4GIr9Eb3WPrR3sWjtiYXXuYhEpppDQWocU9+HyxybxLvXWUhGLPXv24Ha76dmzZ9DIiEkkEQv/9yKgqK6rMxp8NJJ/7LGkpKQwduxYGlwuFt9yi2/G9swzg3dpU8pHJPK3v8GLL8LUqYy56iomrlqFq/Gz0mvXLi554w36n3yyEcewHDvztYUr8WYSD4FsFZe2ukG+/rrRAASM1/3kk0Zr50YyMjIYNmwYDQ0NbPDLY5vEKpD9HeTWqoMsk/TsE5FAVkolKKUOV0odp5Q6tvH36KpyC4IgdAKKGrtuRep0BopXgOGcOhwOKioqbH/5xUK8IhbW54YS9+HyxyatLZCjdZDtxisgOgfZFFQBRfUbb8C+fQDU9e7NhjFjvAIZYMPmzXgefxz+/W/41a8Cl36z8qMfQe/exu87d8I118CyZb7rWARWwu7dhqgeMgTuvx+qqiJ2kOMRsYhIXJaVwR13NN2fNQuOPbbZaqFiFnV1dRQVFeFwOGy3DjcJVwc50oiF3QyyWcVCHGT72BLISqkcpVQeUIFRxu0rYGnj7xVKqU+VUjNbbpiCIAjtj/r6eg42ZjWrqqoiKpMWTCA7HA4yMozKmq0Rs4iXQLYKuIOW/Ko/dh3kjhKxCFf/2EokGWRznZCd6yyl3Xaffz4ep5Nu3brRp08fevbsSVVVFTt27oTrroOHHjJqH4ciOdkQjH54XC7Wjh/P2n/+E/btY9PVV1Np7Rx34ADMmwfnn09d43FszUl6EYnL++4Ds5thv37w+98HXG3s2LE4HA4KCgooLCykpKTEe9u8eTNaa/r06RNx3eV4VbFwOBwopdBah/2/Y0ZQlFJha1MLTYT8NCmlHMArGPWNFVAFbALKGu9nAMOB6cApSqlLgR/pWHt2CoIgdABKSkq81QW01lRXV4cVfmCIneLiYpxOZ0AHKjMzk9LSUkpLS+kVTtTESLwyyHYFsp0JetB6AjmWiIXWOmQHPX+iiVhkZmZSVFTU/DmrV8OXXxq/JySw9fTTYfNmUlJSUEoxduxYvvzyS77//nuGDRtm+zVx223GJL3t22HQILj1Vj4aPJhlBQWcP2kS9OrF7htvZOGQIVxWWcnhb79tTHgDyMtjcN++MGZMxJP0YhHIptgMKy7z831rQz/2GDRm/v3p1q0bI0eOZNOmTTxlyWNbiTReYR1jrFUszG3V1dXhdrtDHm9rDWRbERQBCCOQgZ8BMzHc4vuAz7XWPtW8lVJO4BTgwcZ1vwL+iiAIQifHjFeYVFRU2BLIZnm3AQMGBPxCbM1ayC3hILfHiIV5CT+eDnJxcbF3oqXZBCQUdh1krXV4B9kq9C69lEONQt/cx7hx4/jyyy/ZsGED55xzTlhh5PEYqYrNm/uw+bZ17N9WCVlZUO/ku3e/o6hoCPv3D+Ldd2HXrhFs2QLrBw5k1DV/hk/zYMkXxob+A2vHH8mePUP58MOwh4SCgsPZvj2Fbdv621o/EBUVSSxffgqpqakcOBBkpcoKePZr0HOM+yNHwvcz4YHg2y0rO5dNm0YGdGidTieHDo1m5crIxlpZ2Z38/FPo1q0bjTFmAL744jgaGhpwu5Oxa0p/+eXJuN1u6uoUoSLfbjd8+eUpjW3VIxtva9O/P9xyS1uPwiCcQL4B2ABka60DBoQaBfOnSqlsYDVwIyKQBUHoAvgLZLsuZLB4hUlrlnpr7QxypBGLls4gJycno5SitraWhoaGiCYxWesf23Hm7GaQre2aTYfbR1QfOAAvvdR0//bbqWoci7l+//796d69OwUFinvuKWPfvsyA+yothc2b4YcfoOktTGu8mRhVHRYvNu8PbryZnN54a+TbxpstDmu8xUI6xoVs+O9/g62TBtzedHcLIcWxQXdgSmxDCziO6UDTPEGDkwDrMbbDNJvPSfDu8+OPI9l+6zNpUscRyCOBvwcTx1a01rVKqbfx+QQKgiB0XswKFg6HA4/HEzeBHGupt9raWm/Hs1BorVs9g2w3YtGtWzccDgc1NTXU19fHPLkomEBWStGtWzcqKyupqqryCnM7RDJBD+w7yNbOdQFF9b//3VSibMIEOOEEqhurLaSkpFBWBgsXKp566sd89114Z1sQhOaE+49TA/SMYHs9G58jCILQ6TEd5IEDB7Jz505beVmPx+NtMhDOQY4mYnHw4EGeeOIJxo8fz/nnnx9y3fr6ep8ObfFykCsrK3G73QEzoXYjFmY3vUOHDlFRUeFTK9pk/vz5TJkyhezsbO+yvLw88vPzmT17dsDxBcpqmgK5srIyKoFsZ4Ie2HeQa2trQWsmrFrFqCVLSNy2jZ5ffNHUEMRqPd52GyhFRUU1mzeP5K67+vL++2BobBHHghAt4QTy18DlSql/aK1XhVpRKTUJuAL4LF6DEwRBaK94PB5KSkoAQyDt3LnTloO8b98+b93cYC5qLAJ548aN1NXVeUV4KPwFcbwm6YEh1Pv06dNsPbsRC8ArkMvLywMK5ClTppCTk8OCBQvIzs4mLy/Pe9+fUAI5NTWVoqKiiHLI1dXVlJWV4XK56G2WRguDy+XC6XQ2Zk0Dn0CAcSzHr13LqW++CUDQaZqZmew8+SqeeQAeffRaysoybI8/ED17wqhRxm3IkKbSwEuXLqWuro7jjz+epKQkKisrWb58OSkpKRzrVyJt56v/YfDGjU0LLr8cDh8TdJ/FxcV899139OzZk/Hjx0c17qKiIr7//nuysnpxxBFGeTa2boVXXwV34wXwhESjg+Dw4VHtI154PB6++OILlFKcfPLJgDFB76uvvsLlcjFt2jTb21q5ciXl5eVMnDjRW/kmEOXl5axcuZK0tDQmTZoU82toSaKY99hihBPID2II3q+UUq8AH9BUxQKMgM5o4GwMcewE/l/LDFUQBKH9cODAATweD927d/dO0LIjkPfv3w8YGdFgmF925eXlEedit23bBkTXjCIeEYuUlBSqq6spLS1tJpCtbabDOcgQPoecnZ3NggULyMnJYdasWeTm5nrFsj/hHGSIbKKe+T727t3bdukspRTJyclUVlZSU1MTVCDX7d/PmSFmrNXj5D3O5cneD/P+2BSMOWTBBdLgwTs4//wajj12NGC8DxUV5Rw4cJDERE129lBGj1b0DHK9+Pe/z8PtdnPPPceTlAQVFZo//3kxqamp/OpXvgL5z2k7Of+plzl80yZjwf8ehd+vhMMC54wLCsp5/vnFDB06lOuui0Ag79sHb74Jy5dzoKiI7WU76OnowdAdQ43OggsWNInjXr3g/fdhStuKYwCtFfPmfYbWmvvum9Y42a+aRx9dTHp6Or/4hX2B/Oyza9m+fTs//vFQhg0L/v7v2lXG008vZuDAgdx0U/sWyO2JkAJZa/2VUuoy4F/AdcCPg6yqgGLgZmk5LQhCV8DMH/fu3dvrhtoRyKbYC+X4OJ1OMjIyOHToEIcOHbJVIQEMd8qcOBaJQE5MTKSuri4uAnnFihWkpKT45JDN2MOdd97pzRPbqfdqiuhQ0ZXs7GxmzZrFvHnzmDNnTkBxDPYEciSl3kyB3LdvX98HtDacy+JiuPlm8NtfSkoKlZWVVFdXB41zZD7yCGmNY6nv159njziXXbVHUJuezYrtWSzflsXBqmTYHHx8w4bBtdfC9Ok7+fzzf5OZmcnQoYUUFhaya9cu7/Goq4PBg2+iZ8+BAbfj8Xi8JcjMkmzWqIjW2meCYq3bzVszZjD7lVdQO3cajTkuv9woSReg1EJEdZD37jViJgsXwuef03hmQE9CZEEHDYIPP4TG5iltjVLKW56tvr4ep9MZVYk36/rhmoVIm+noCHu0tNaLlFKfYpRwywYOx3COwXCSNwKfAq9preMz3VgQBKGdY+aPs7KyvALZTgb50KFDAGGzrt27d+fQoUOUlpbaFsj79u3zCtWamppm4sUfU5RkZGRQXFwclwzypEmTeOCBBxg3bhzHHXecT+zB6h7bqfpgp5JFXl4eubm5zJkzh9zcXLKzs5uJ5IaGBurr61FKBRQJsTrIPjz9tCGMwRBmb77p033OWgu5qgr+8x94/nkjEeB0grO+Bueu/8PJfbioZ3fFaEo+sdeVLiHBzVVXJXDDDTBtGjgc4PEMJD8/hdLSUj7//HPvuhkZGTQ0NFBZWUlpaSkDBwYWyNYTC/M9c7lcjSXD6nG73V7hbIppd7duhoN78slGjbHly+G3vzWalfhhSyC//Tb8+c/wxRfGCYhdRo+Gjz4y8iLtCP/6xdEK2GA1lf0RgRwdto5Wo/B9pvEmCILQ5bE6yKbTGS8HGYwc8s6dOyOqZGHGK0xqa2tDtvw1RUn37t0pLi62JaoDYa3be/bZZ/P999/z4IMPUlFR4RN7MCe12ckfQ/hmIVbxbQpj633/12kVeVYiuQJgEtBBLi6GX/+66f477xitmP/2N2jcb0pKCkVFWdx3Xzpvv20YrL4kY/hQjdjok3LEEXUMHfoRZ55ZxJ13XufzmMPh4Mwzz2Tt2rX069ePQYMGMWjQIDIyMvjvf//L8uXLQ57YBWsdnZKSQnl5OdXV1c2afSQlJaGOPx7++Ef45S+NJzz8MJx2Gpx9ts92Qraa1hrmzoXf/a75Y0rBSSfBhReyqbiY9evXM2LECG+LaDIz4ZxzjO6A7Qx/5zfSLnr+2xGB3DLI0RIEQYgC00H2j1iEE5imQLbjIENkE/X8BXJNTY0tgdytWzfv5LGGhoaIv0jr6+vxeDy4XC6ysrIYPnw4J510UrPYQyT5Y+t6wRzk/Px8HzFsZpLz8/ODCuRAmA5yuOoSJlpr9u3bB+Cbs77nHmoOVPIhF7CdxsoWjwOFeXDqqbjd8MwzZ/Ddd/Ym9YWie3fIyTFqxmZkbOeVV5bTq9eIgOtOmDCBCRMmNFtuJ8JiLTlnxSqQzc9qs+N8111G4d333zfuX3strFljdINoJKiDXFsLN90EL77YtMzhMFzpyy6DSy7xbmf34sWsTk4m4+STIUjEpj3h32461oiFCOSWQY6WIAhChGitvQ5yVlYWCQkJ3hxvTU2Nt95tIMyIhR0HGezXQrbmj82JcuFyyFZBk5SURFVVFTU1NbYFrIm5n+TkZDIzMykoKGDx4sXcd999PrEHuzWQTcI5yP6l3ICAEQu7Atmug1xeXk5tbS0pKSneY7Xq2TU88/TRvMR8DvonYt+aD28pjJSiKY7zgHyg+WvwJympjn79dnP++f054YQkJk0yKk2YcwPXrKnyeR12sSOQgx27QDWdm63rcMCzz8LRRxv54aIiQyR/8IF38AkJCSilvCdZDofDaIRy8cVGztjk9NONLEqAya0dTQD6RyOiHX+kGeRIJvsKcRbISqmHgEu01oFPYwVBEDoBZWVluN1u0tLSvEIhNTWVuro6Kisrgwpkj8fjFSPhHORIS73t3buX2tpaevToQXp6Ojt27IhKINfW1kYtkJOSkli2bBkLFy5k5syZ3HvvvZx66qne2IPprNsVyPFqNx1OINfUpLFu3RF8/PFYPvwQ+vY1dFi/fsatf3/DsTUvDBQUlFBWlkFKymAef1zxzNOaVauPBo4OMoIpQA6wAEMk53nvZ2XBjTfCVVseIO31Z2nASXnPPvz70ss55axzOfroUfzvf09TUrKfW2+9tfmkQJqc71AnZoGwEw2ynvxYCVTTOeBx7tPHcIHPOMOITHz8McyfD/fcAxiT1hITE6mtrTVOOgoL4bzzwKyCAYaT/I9/EKwHsykQI40otBX+DnJLRywaGhp81hfsEe+jlQUMi/M2BUEQ2hXWCXomaWlpHDx4kMrKSp/lVswIhhlpCEWkEYuCggIAhg0b5hU8kQhkU/BEM1HPKqKWL1/OLbfcQmZmJqWlpT6xhyOOMFoW2xXIqampKKWoqqqKuNydFX/hVlcHS5cac+g+/BBWrOiN1pdFsMXhwM8t98NltrMxxHEOMAvIZdiwp/h//y+bSy+FpNVfw9QHAGMC2uLrLqJXRinjx2tGjoRu3ZIoKQn+fpqTC6MVyLE4yIEEcrNYz2mnwb33wu9/b9y/7z445RSYOtXYdmIiKXv3ol94AX7zG2isLw7An/4Ed9/ddHYSgI7mIPs7v9FGLGSSXssiR0sQBCFCrPljEzuVLOzGK6BJIB86dKjp0nMIzHjFsGHD+OGHHwD7AjkxMTGyclt+WAXy7NmzefXVV9mwYQMHDx5kwIAB3tiD2cDDrkPtcDhITU2loqKCiooK7zGJFMOdTGDlysN4913473/B15SObFJiOHr1ggsvhNRUaOz7DDXVfM0k8pnHzMzRPNr/Twx880V4E6PKg1md4YIL2HrEEbBzp1doBhKjVmJ1kKMRyNZqHCbB8sqAMdFu8WL46itoaIAf/cho3LFiBbO++opk/woiycnwwgtG3jgM0TqwbUUwB1kiFu2LkO+GUur5CLd3QgxjEQRB6BAEcpDtVEKwO0EPjC/R1NRUKisrKS8vDykOrfnjYcOGebvohRPIZuUAM2Jh5zmB8HcOzXiItRYyRNZFzyQ9PT2sQK6rMwpI+FBWhvv/zeezdb14Ze80Pt3/S+p04IhFPHDQwFndlnDDUydwwSUJlvLH3eH2o8g76ST+U1fHHCC3dBOblkKzwmrJyfDYY9Q0Tmozj2e4FtXm8kgzyHYml4aapOc/ppBRloQEePllmDABSkth+3bDHcao2+FDnz5GabfjjrP1OqJ1YNuKeGeQxUFuGcIdrasxrvlEcnodQZFCQRCEjoe1xJuJHTfObg1kk8zMTCorKykrKwspkHfv3k1dXR09e/YkIyMjoLsXCP8MsnVZJPiLqGD56UirWIBxrPbs2RMwh1xSAg88YJQebl7CuDuRNHYdnbaFI/utIfuy8VRmjmbvXry3PXug6bxHc+hQOaDJSEqid9H3XMrr/JjnGPTmM3Bmcxczr7KSnORkFtTXk+3xkI1vItnLvHkwfHiz4xnu/YzWQXa5XCQnJ1NTU0N1dXVAgR0sNmE7g2xl6FB46qmArnB1cjJ64kS6TZ8Ot90GQeoyB6KjCcB4RSxEILcs4Y5WObAL+KnN7d0DnBnTiARBENoxWuuQEYtQDnIkEQswhGZhYSGlpaUMCdHswCzvNmzYMCC8oDKJt0A292s2NgkmkCNxkANN1HO7ITfXuGrvZ1Lbpi97OYsPOJMPOZ2P6VuxH7YAf8SYNffnPxsz8/woKirmH//4B72Tkvjpiy9C0XfGA5ddBmcG/vrLz89nwVtvkd2/Pwe/+ILijz7i7spK8nv0IPuii4yVBgyAE4yLsP6iNFzEItoMMhjHt6amhoqKipACOaoqFoG49FLjjOa992DkSJg0iXf37GHFgQPMzMlh3LhxEb+GrjpJTzLILUu4o7UGOFpr/ZmdjSmlrot5RIIgCO2YiooKbwMOq9CLd8QC7E/Ui6dAjiZi4e94mgLZGrGor6+ntrYWh8MRsjazP6ZA1mvXom//Ge9VnsIvK37Hxu2Ri8HB3faTM+hrLkl5n+NL/otj147AKz79NPzvf/Dkk3DuuT4P7S8sZMo333DqkiXQeMJDaio8+mjQ/VrL0Tn69+f73btJT0/nF7/4RbN1PR4PdXV13uoOYN9BjjRiAcbxLS4upqKiwremcyPRTNILKZABbrjBuDVS/9ZbcPBg1J0cO5oAjHcGOZYqFvPnz2fKlCk+pRHN1vCByih2JcK9G6uBaUqpEVrrH1phPIIgCO0aq3tszWzaKZllt4ueiZ1ayA0NDd4OdbEI5FiqWATLIJeWlnonGFrzx5F06ktPT6f7wYO4HvmAsw89wIecFXC9jAxIddUYNXRNUlLoNyaTkSM30bPnYm655ViOOeYC4ALj8cpKo5zYhg1sWLSIhPx8Rmzdajxmlhu79lpD/PboAW+9xfA77uCIXbt8d/7//h8MGmTr9YR7bwJ1/Qvk1lqJNmIB4aNB4cq8RewgB8C/E1+kdLRJev7CNloHPB4RiylTpvh0n7R2p+zqhJ4WDZ8BawF7f/nwFjA3lgEJgiC0J+bPn09eXp73fnFxMQUFBXxubWJAZFUs4ukg7969G7fbTVZWlne7bR2xMCcYejwe70lBNPljgIZiWPvEeKYc+iKgOE5P8/DHP8K+T79jt7sPuxlo3KZexu4DKaxcqbjssjX077+X5GQ/4ZaaChMnwo9+xK6bbuLFa6/l+wceAEt0huefhyOOgGnT4JJL6GYVx0OGwEsvGe2kbZKYmIhSCrfb7XX2rASaFBcqYtHQ0MCnn37Ktm3bfJ6Tl5fH/Pnzw44n3Oe2RRxkP5rK7wVoN22DjjpJL1YH2X87wTA/Z4GqWJjVZXJycrj//vsDtmrvqoR8N7TWrwOv292Y1noRsCjWQQmCIARj586dLFy4MOCXaUJCAjNmzGDEiPj1KvJ3WD755BMWLlzInxpn4Ju0lYNsxiuGDh3qXRbOcTQJVMUiHg4yGDGLyspKSktL6d69e8T5Y7cbHv9LHQ/cO5rShsnNHld4uImnmJf0GH0H/QYu/W1T7bYhQ+DNN42qENgTbmY8YcexxzLu++/hjjvglVeMB83Zeo3UJCXhnj2b9N/8xrsPuyilSElJ8XYt9D8egRzbUCc8VVVVDBw4kIULF3LttddG7AKGc5DjUgc5DLF89qDjOcitHbEIt/3s7GxmzZrVrDV8VyecgywIgtCu2LBhg7fVr/+toqKCdevWxXV//g7LH/7wB2bOnMnZZ5/ts15iYiJOpxO32x1QvNfW1lJXV+etHGAHa1RB68AFgkyBPHz4cO8yOw6yx+PxubQbj0YhVhHln0M2BVg4B1lro07x+PGan89OpLSh+cnE9CFbWckknuQn9C35Hq6+2igbZuwA3n3XaIfXSCQCuaqqCrKyjJJkb77psx2dkMCy44/n7z//Od3+7/8iFscmocq2BRKZoRzk6upqhg8fzo033hiVCxjuxM5OxML8bLZVxKKzOMhtEbEA42pDbm4uc+bMITc31+eKWVemY3yaBEEQGjGF1rnnnsv48eO9y3fu3MnLL7/szQjHE6vDcvrppzN8+HCfChZgOINpaWmUlZVRWVnp/dI3sVawsJvBTUxMJCUlherqaiorK5uJy4aGBnbu3An4Osh2BLJ/1jWeEQtoXgvZFGDBJpJpDZ98AnPnwhdfQKDqoiNGaB56SDFjxmGoTx+Ga64xarCZKGW4vpbPhf9rDYbp5FZZ68XNmAEnn2y0Rq6uZl9ODh98/DG9e/eOqelCKIc/0MlGqPfTFM1HH3003bt3j9gFjNZBdjgcJCUleU9OzXJxgdYNR6wRi442Sc9f2LZWo5BA27debTAb+kjMwiBmB1kpNUkpdblS6vh4DEgQBCEUZkyhZ8+eJCcne28DBgwAjEl0wdzWaDEdlnvuuYevvvqKnTt3BoxJhMpzRlrBwiRYTWGAwsJC3G43vXv39hHPVrHr8XgCbtdf+MSjikUggWyOO6CDvHs3esVKPvj7Zk6cWMEZZ5ji2JdUZyVnnPERy5ZVcPHFjV2HTzsN1qwxJtKZ/PnPcP75YV9rIEzh3sxJ7dkT/vhHeOwx9jSu09fiKkdDLA6y/2fbFPQFBQVRuYChPrNa65DHzv91tHUGuaNGLNqy1XR+fr6PGLa2hu/qhH03lGF13A9s11o/6/fYcxjNRMz7nwIXaK0j/w8rCIJgA1No+rupqampXre1vLzcds43HFaHZcSIEezZs4dXX32Vq666qpnDEupydaQ1kE26d+/Onj17KC0tZZBfpYRA+WPA6wib7l6g6gbBBHI8M8jQJJD9M8hlT77KZ7Ne4feee/iaUQG3q/Bw02F5jLx5B9W1O6irOwKwnGD07g3vvANLlkBiYtDOa1E7yH7s37+/cbe9g65jh1COcCAX1uVy4XK5qK+vx+12+1ydqK6upqCggDfffJO33norYhcwlIPsdrvRWuNyuQI65ikpKZSVlVFdXU2PHj3aJGLR0NDg7QIYrh17eyFYJ722iFgEKuVmfoa6OnY+TUdhCOQs60Kl1OXANcAq4C7gbeBU4I74DlEQBKGJUE6sKVziGbOwOixFRUUMHz6cu+++O6DD0pIOcqCJeoHyxyaRlBOzru8VKZWVRvmyBx+Ezz6DINsx6/YCPsLNFMglJQf58ktYtGgQ//3vOdx66ygGZNWR+ZPLucjzFl8T+OLjmXzAism38uT3JzJwsLHdgDEApeCkk4KK42DjA98KJaaD/O233zabgGliCuSWdJDDZX79n1NVVUVhYSG//e1vo3IBrScG/lcbgo3FxBoVaWhooL6+HqVUxEIvlpMzq/iLpHxgW9JWdZBjiQV1Rey8G0c3/nzfb/n1QAVwlta6BPibUmoZcCUQvraMIAhChNTV1VFbW4vT6QzoimZlZbFjxw6Ki4vjVsnC6rCYwvvUU0/lxBNPbLZuqGYhkZZ4MzFLvW3fvp1evXr5PBYof2xiunt2BbLVxdNao266Cf7zn6YnmA7tyScbt2HDQCnqamroWVJiZJm3bDGWJySQkJDB8uWT+eqr4xtLE9tL4Z2b8QVz+j3F8cdp+NvfICkpYDc9u1jFsb+AslYomT59OoWFhbz66qs888wzAbe1b98+gIANNSIhVAY5VNWIiooKampqfNqOV1dXc+KJJzJ16lSf9e26gA6Hg9TUVCorK6msrPT5fIZzhK2iPVD9ZrvEErHoaBUsIH4Ri3hkkIXgBD1aSimzr+nhjT89lmUAJwD5QDellFmnZjEwSyk1mMYZFlrrIK2KBEEQIsN0ENPT0wN+CbeEg2yluLgYMIR4IEIJ5EhLvJmYTuzmzZvZvHlzs8f79OkTsHRaOAfZWuINDKGUmJhIXV0d7m++IdEqjo0nGAHhL74wnGVzP8DPzDsPP0xRrzE8nv0ajy8+guLi87DLBRfA/ffD5MknASf5PGaKtmgEciiRZ61QMmvWLBYsWMDMmTMDCjVTQCYmJnpd/WixE7Gw6yDH0kXPxBTIFRUVAQVyOAfZXyBHSiwRi45WwQKaC1tpNd0+CXW0PgM0kNl4/z3LY4lAGkb8wlotP7Vx+WcYAlkDh8VprIIgdHHCxRRMgWwK2Xhj7aIXiFAZ5GgjFiNGjGDSpEkB4wVKKSZPbl4jGOxHLKyxg8TEJA4edLLmrmfZx3kUMpDKPsOp8yRQV1yGmwTqSKSORHSAKhMl9OK1ksuoeS18RzcXbkYNrePY6anceafRryMY4SothCKccLNWKLnnnnvIyMhg69at7NixgyFDmjwha/441kv5ocq2BROlwVznWLromaSlpbF///5mn9twVSmsn7FoayBbtx9LxKIjO8jRCliHw4HD4cDj8dDQ0BA0QiECOTqCHi2t9XAApdRPgb8BU7XWexuXXQG8BFyptf7IfI5S6kZgvtZaRLEgdBL279/PypUrOeWUU2L6Eo4HdgVySzjItbW1HDp0CKfT6XV1/QmVQY52kp7L5eL8AJUZwhFOIBcX17N580jWrz+Sv/3NKCO8ffsduN0uHrKuuD/iXTfD6fQwrccXnFX8AWNZz1jWMyL3bhJuvdHW8833uyUEsn8N2HvvvZe6ujoWL17Mtdde613PFMixxisg8kl6EFxUm5MKYxXI0Pz4hjt28XaQ3W63tzW5XTqig+zv/MbyGlwuF3V1ddTX14tAjjN2jtZ6DDf4V8CvlFJO4BagGljit+4ooDCuIxQEoU1ZtmwZq1atolevXkyZMqVNxxKsgoVJeno6iYmJVFVVUVlZabtrmxWtNVu3bm0mXsxJcr169Qr6BR4sYuHxeKJutRwt/mXbysuN/hlffGEUfVi3bgpaH+v3rPh+gXanlJ/wTy4+agnHr3q36YE77gCb4hhaLmIRrAbsjBkzACP3bea7zfxxrBP0ILRADubEBntOPCIW8RDI0dZABuNKiBnvqauri8iF7ojiL16NQsBXIAc79ub2ZZJeZIT9RGmt85RSq4GfK6XOwYhRDAH+rLX2vz50Ps1Fc1CUUs80Pme/1vrIxmW/A24GTAvoN1rr9wJvQRCElsZ0qALV4W1twjnISil69+5NYWEhxcXFUQnkdevW8cYbbwR9PJSDGCxiUVFRgdaa1NTUqL+k5s+fz5QpU3wmXuXl5ZGfnx+wVJMpMn74QfHqq/DMM02dmA1absb/kKxKfl7xIDfW/J10KoxaRyZnnGHUK46AWCbphRJ5wWrA/qcxf7148WJ+/OMfA/F1kENFLKLNIMfiIAe78hFNxCIagWw+LxqB3NFqIINvBllrHbODbG4rEFprbxWLSLcf6f+czobdo3UR8A8gGzgI/AmYY11BKXUuMA74ZQT7fxb4O/C83/JHtdYPR7AdQRBaCPML2IwItCXWSXrBMAVyUVFRwOoO4di6dSsAAwYMaDYZy+l0csIJJwR9bkpKCkopampqqK+v934hRRuvsGKtuJCdne3jfvqjNaxd25eXX76CzZtHE0nflDTKGcxOBg2CQWeMIzPTKGCRkGD8NH83TfRt27axYcMGhg4dytixYxk9Gs48MxXXnp9Sc/lnsHSpd9vlAwaQ/uqrEOEXtdXhNGve2iWUcAtWA3bq1Kn85S9/Ydu2bWzbto2hQ4d6YzttHbGwPkdrHdeIhf+JXWtN0oPoJ+p1RAfZ6XR6s8OmsDXzxJESbqKetcRbpNn5SP7ndEZsfaK01juBC8Ks8x4RdubTWn+ulBoWyXMEQWhdTIEcqA5va2NnoptZYSLaHLJZOu3cc89l4MCBET1XKUVqaioVFRVUVlZ6y3FFO0HPin/FhdzcXO8XV309bNgAK1catw8/hPXrx4YZq6Z37/1Mn+7iwgt7MaH4YwbfdQkZlONJTMSxbCvYePmLF2/js8++5qSTEjn1VMs+Bw+m/n//48PLLuOkL76gIi2NDQ88wElB8tuhcLlcpKSk8NFHH/H+++9z7rnneh8L52hFI9ySk5PZtm0bNTU1LF68mIsuuoi6ujr27NnD448/HrN7Fs0kvUCiuq6uDo/Hg8vlislBjTViES8H2bpPu3TESXrQFI0wPwPRCvxwtZBjiVeE+p/TFWivp1y3K6WuBZYDv9RaHwy0klLqFow8tM9sY0EQ4kdHE8ixVLKoqqqipKQEl8tFv379ohpjWlpaM4EcbQ1kK/X1MGJENuedZ1RcOPXUOfznP9nccw+sXRu0j4cPffrAtddCdjbs2vUae/Z8z5VXXsmowzLhqDsB4/juufRS2ycHoVzG1PR0lk+fztKpU1Fac+qYMXZfbjPS09MZOHAg11xzDa+99pptRyta4XbxxRdz6aWXAtCvXz8KCgp44403WLRoUdSvwcRa99c6Ka2+vp76+nocDkczwRRIVMcjfwzhBXIwBzlYHeRoiLYWckecpAeGoG8NgRxtvMLEWuVlzpw5XUYcQ/sUyLnAPIwScfOAPwM3BFpRa/0k8CTA5MmTI7iIKAiCXcx/4OXl5RHPMI83Le0g79q1i2Fbt3L6ypU4d+yAc86B006DCIRtoIl60dRA3rYNPvnEuH31FezcCR5PHsa/yDl8+mkun36ajZF8C82kSXDnnZCTA6Z+eeYZY0xJSUnw8svw/fcA1CYmUpCTY8c8BkJ3W1NKkZmZSVFREZrYJiimpaUxfPhw/vKXv0TkaEUr3M466ywefPBBfvvb31JQUMDy5cu5//774yIQlFIkJydTU1NDTU2NV+BaBan/5fBADnI88scQXCCHyyBLxCJ6TMfbfA+jdcDDZZBjPT7+VV66UhvqdveJ0lrvM39XSv0LeDfE6oIgtCBut9vrQGitKS8v9+ni1ZrU1tZSV1eHy+UK+SWcmZmJy+WivLycmpqaiCb8FC9bxo9eeYVEtxvWrYMnnzQCtyedZIjls8+GAQOaP9HhgO7doTFiAb5iwyrsS0pg2bLAjm9lpVFl4tNPoTEKbSEPyAEWYIjibL/7vjidmjFjvuPsszfx0EOX4B8/9AoapeB3v/MuXzZ1KlURiJxwLmOPHj28JyvRTJo0MU+KjjzyyIgcrViE26xZs3j33Xf55JNPOPnkkznzzDMjH3gQUlJSmgnkUII0kIMcj/yx+XyHw9EsOx/u2Jld86xOaGtHLDriJD1oLpCjFbDhMsixCORgVV66Ssyi3QlkpVR/rfWexrsXA+vacjyC0FL88MMPbNmyhTPOOCOkK9uWM4n9M5JlZWVtJpDDddEzcTgc9OrVi3379lFcXMygQYPs7aC+npFz5xri2IrbbSjWTz+Fu+8O/vwRI+DKK+ndGPcyHWStYf16xZIl03jnnTGsWgUej70h+ZKPrxjObryfD2TTty9kZhqflUsuyebII8t5+eXX2bdvHw89tKXZZ6W2tpbe+/fT4447vGq8PiODpVOnMjaCy9yhHGTAZ6JjLALZdDk/++yziBwt/46BkfDVV1+xfPlyTj75ZJYvX87mzZuZGKqjSQQEqkoR6lgGWj9eEQszO19eXu4TDQp38mM64dXV1d4qN9E0CoEmBznSiEVHdZDN8ZonOS2dQY5m+8GqvOTn54tAbmmUUq8A04EspdQu4P+A6UqpCRgRi23AT9pqfILQUjQ0NPDWW29RUVHBqFGjOOyw4L112nImcSCB3FZEMtGtd+/e7Nu3j6KiItsC2fP739Pnhx8A0C4X6rbb4LPPYPVqewP84QeYN48TgaF9B/LpN6ks7Hc07y5OZ+vWGfa2EZLZ9GE/Q8hnCDsYzE6GsIPRbGLSORvp//AvydtnfFZuuGEBQ4acQEFBAQsXLuTddy0X4rSGJUs4/5//ZOSGDT57KLnpJmobL/3bJdxleKtAjiVikZ6eTkFBAW+++SZvvfWWbUcrWgfZ/Dt79dVXWbduHWPGjOG2226jd+/ecREHgSIToQSpdUKcWckjXhELMN6b8vJyKioqvALZTm1jUyCb/xvaykHuaAI53hGLlhDIwaq8dAVxDG0skLXWPwqw+OlWH4ggtDIbNmzwOqLharu25UziYM0y2oJIBLKZQ7Y9UW/5ctS8ed676oEH4De/Me7s3g3/+x+8/77RYSPQF3hVFbW1mk84jTe4hEX7LqL4vcDtqO2SSC1TWcppfMKpfMoxrCSFIML1feCDZ8m+9loW/PWv5OTkcOutt7Jw4UJmzpzJySefDPv3w+efwyOPwNKljPTfxkUXUXnjjbBwYUQiJZyDbO06GGvEorCwkDvvvDMiRytagWx1z6ZNm0ZDQwNXXHFF3NyzQJGJUILU5XLhcrmor6/H7XZ7G+JYtxULgXLIdo5dSkoKBw8e9P59tlUGuatGLMJlkK1l3oTI6FinXILQSVi+fLn3dzvtc9tqJnF7dJDtuJARtZyuqoKrr0Y1ftGWjB1Lr1//uunxAQPghhuMWwC+/BIe/2sD777jobza3pf0UaxhJFuaLVdoRrKFU/mUE1lCN5qXAaNbNxg0CAYPNrLPH31kLPd44NlnyX75ZWZNmMC8Bx/kxsGD+c3nn+Po3x+CHYsZM4zoyAknkFRoNEKNRKTYySCbj8fyJZ2WlsaJJ57Y7IpAOEcrWoFsdc/Mz1w83bNIHWQwxGh5eTnV1dUkJibGLWIBgZuFhBuPOSYr4iDbI94CuSUc5K6OHDFBaGWKiorYtm2b974dgdxWM4mtE29qa2vbtFlIpBELsOkg3303bNwIGBUc9vzpT/SyIeRKS+HXvzbm8YGz8RaYRGo5lU+5gHc4n3cZwk5ITYXDDoPhw42f5u+pxwHHNd9IVpYhjDMz8Zlxt3Kl4XZ/8AEAeXV15H7zDXOA3J07uQrwD/DoxERWHXEEq08/nRvmz/cuj1SkaK3DXobPyspiyJAhUZfNMzHf99LSUurq6ryOYzhira7QUgQSyHY615mTT7t37x73iAU0/T9qaGgIWnLOSrwFcrQZ5I7mIJvH1HzPox1/S07S6+pEfMSUUk7gcKAHQb4RtNafxzguQei0mO5xt27dqKqqCiuQ23ImsfkF3LdvX3bs2NGmDrKdLnomPXv2xOFwcPDgQdyrV5Pw3HMwcCCcfDJMnGhUpgAjNvGPf3if979zzuGE448Pu/0334TbboM9e4Kvk5JSTc6liRx/zA72Fj7HqJH9uOqqP2E0IgXS0mhWWiIajjnGiIAsXkzeT39Kzvr1getcdOsGRxwBp5/OwSuv5J3XX6dnz54+mzJFit0Mcn19vbdRRbAvYKfTyfXXXx/1yzNJT0/3NmF54oknuPDCCxk2bFjY57VXgRwqYmGncx3Er4oFNBfIVrEealKs/1hjjVh0lUl6rRWx6KjHpz0Q0RFTSs0Bfg6Em8YuYRdBCEBdXR1r1qwBYNq0aXz00UdhBXJbziRuTwI5EgfZ6XTSs2dPDhUW4jjrLCN/a5KaCiecYJRus4jj9WPGsOH447mwMb8ciD174Pbb4Y03Aj/erx/MmKGprHyBYcO287vf/ZY1a0p5+21Ft759I6qnHDHTp5P/4x+zwO0me/VqUIoeDQ3MOniQj0aNIvuJJ7z9oWt27waai5lIHeRwgi6euFwurrrqKhYtWsS+fft47rnnmDx5MmeccUZQN1lr7X0tdh3n1iJUxCKUg2x9TjwjFv7tpu2eWFjf+3Bucyi6Yic9aN9VLLo6to+YUmo28ABQBrwA7AQCvyOCIATk22+/pba2lsGDBzNy5EhbArktZxKbX8RZWVk4nU5vS9m2cOMibdfcu3dvjli4EKdVHINRbPijj5qyu4C7Vy/eveACBgwcwr//rXjnHQj0tuTnQ6BzhMMPh7/9zegp4nAoHnpoH1VVHqqqquLSZtous63ZaWDTggU416/nipkzveIYCCoaExISUEpRX19PQ0ND2MywnSoH8aR///7cfPPNfPHFF3zxxRfe0msXXnhhwEowbrcbrTUul6vdTVIKFbGw6yC3ZMTCTv7Yf9/h3OZQSAa5/bWa7upE8o7cDBQCx2itI29RJQhdHK21N14xefLkoN2r2hPWL+Du3btz4MABDh065M34thZmkxKwLzQHKMWxX33VtODUU2HzZqMlnR9r7riD77YO5Z8PXEhjpTdbuFxwzz3w29+CVUekpqZSVVVFZWVlXNpMR0uwyEQwd1ApRVJSkvdEKJwz2ZoOsonT6WT69OmMHTuWRYsWsWfPHl544QWmTZvG6aef7rNue41XQOCIhd3WzuZxb62IRSis+47lcxBrBrmjC+SWyiDH2mq6KxNJz9jBwFsijgUhOnbt2sXevXvp1q0b48aNa9a9qj1ifkmaAhmir2RRW1vLd999F9Vrraurw+12k5CQYPtS+dgFC5qafhx1FHz4IezYYfRwfv55uOkmOOkkCua+wE8WXc1zz/2YH36wX6f32GON+XHz5vmKY/CtCBBNm+l4EUiEQWjhaIocO06eXZexJejbty833ngjp556Kg6Hgy+//JIDBw4EHF97FMjRTtID4/30eDw+f5+x4l/FIpqIRSzHOdoybx29k16sAlYiFi1HJAJ5H1L1QhCixnSPJ06ciMvlQinV7l1kU1glJyfHLJCXLl3Ka6+9xsqVKyN+rtU9tnUJd/16elqDwvPng3mJcehQuOYaKv/yL+47+XPG/r+rWLkyeKMWf1JT4dFH4auvYPz4wOtY85ytGbHwJ5AIg9DiJ5KJem3hIFtxOp2cdNJJjG98I1b7NXVpzwI51kl61nVDdeK0S1JSEi6XC7fbTV1dXdQRi1j2D9FnkDuaAPQX9BKxaH9E8o4sAC5WSiVprSP7BAtCF6eqqorvvvsOgEmTJnmXp6WlcejQISoqKnw6jrUX/CMWEL1ALikpAaCwsdZuJEQsMu+5B9XYz/mHESMYdvrpPjOHV66ESy81zGRoLrivvtq4+euOhASYMsUQyaGwunFmxKItHORYBLIdodLaGeRgTJgwgTVr1rBmzRqmT5/uFYztWSBHM0nP2k0vnvljwHvCXlpaSkVFRVQRi3g4yHV1dd5OgXboqA6yvyCOtZOeVLGIP5EcsfuB44HXlFJ3aK0LWmhMgtDpWLVqFQ0NDYwaNcqns1hHcZBTUlK8Ai/aWsjm8/bt2xfxcyMSyJ9/Dm+/DYBWio9PP51LDhzw5qafew5uvRUCGaRTpsBjj8HUqREP0QdTIB86dIjKykqfqwWtSbC4RLwFcls5yCZDhw4lMzOT0tJSCgoKGDFiBNBxBLIpCMMdT+tz4pk/NklNTfUK5NaOWDgcDhISErwOtt1tdVQBGC8HWeogtxyRXJf5DhgGnAtsUUodUEptDXCLYIqLIHR+/CfnWQnUvaq9YG0CEY+IhSlyi4qKvLm7SJ8bVmRqbTT+aGTbtGns7d+foqIi6uqMEm3XXddcHKemVjBv3k6WLYtdHBvbM95X82QgNTU1LpfBIyWcgxwozx1NBrmtBahSigkTJgC+MYv2Mr5AOBwOn9yttSSdnYhFPEu8mVhP2O0eu3g5yBBdLeSOWuattSMWIpAjJ5L/2A6Msm47Gm9lGNcm/W+t/y0gCO2YLVu2UFpaSmZmJiNHjvR5zHRE26NAtl5idTgcMQlkrbXXQfZ4PPY63Fmw7SC/9hp8843xe1ISO2+5BYD168vIzobHH2/+lOOPX8XPfvY3br89vVmkIlpMobF3716gbeIVEFwgmwIkkKAxRUpHyCBbOfroowHYsGGDd1ztWSCDr+B1u93epivB8qLW9zPeEQvwzc7bfW8TEhK8J3+xHudocsgdvcxbsPt2CRexkCoW0WP7iGmth7XgOASh07Jq1SrAyB77u4jtOWLh/wVsFciRZATNbVld471799K3b1/bz7fVRa+uDu69t+n+HXeQOu5INn+g+MtfJlBa6rt6cjI8/HA5xcVvk56e7n198cB0kE2R0RYT9CC6DHJHqWLhT2ZmJsOHD6egoIB169YxefLkdi+Qk5OTKSsro6amxitgQh1Lq6BuiYhFNA6yUoqUlBQqKytbXSBrrTusg+wvWCVi0f6QIyYILUxRkVEZcdSoUc0e60gCOSEhwdseu7KyMqJMrekAm0SaQ/ZxkL/+Gp56Cvy/RPfuxSxi/F3GVF6qf4DnL0qgsHBis+0NG2Z2w9vC22/D4MGDo25wEAj/Y2NXIM+fP58pU6b4NIHJy8sjPz8/YMOYcFjLglnpbBlkkwkTJlBQUMDq1as7hEC2Cl5TwIQaa0s7yNbIVyTHLl4COdKIhcfj8Z6st0WEKRakikX7RwSyILQwpvgNJCjbs0AOVGM1IyODqqoqysrKIhLIZrzC4XDg8XiiFsjd9+6FM84I2OZuN/15hV/wIlez+tBEeDTwts48E15+GXr1gkWLdgCGQI4nqX5lLuxGLKZMmUJOTo63tXheXp73fjR0lSoWJmPHjuW9996jsLCQoqKidi+Qre+PKZhCnWy4XC5cLhf19fXeqFNLZ5DtnPyY68R6ohSpg9xRK1hA/CMW0igk/gQ9Ykqpaxt/fVNrXW65Hxat9fMxj0wQOgENDQ3U1NR4L0P6Ew+BrLWmtLQUT2NZMyspKSlRf4FaayCbdO/enb1791JWVsbAgQNtb8sUuEOGDGHbtm0RCWSzi15ibS3dr7/eRxyX0p3XuZSXuZI8stFhpkD85jcwd25TSeRdu3YB8RfILpeLpKQk7xe9XQc5OzubBQsWkJOTw6xZs8jNzfWK5Wgw86H19fXU19d7vyQ7q4OckJDAEUccwcqVK1m1alXIrHV7wCqQTffUzqS48vJyb1OUlsogR3LszM+3/4lhpJivZf/+/YwZMybs+h05PhBvB1nKvMWfUEfsWUADy4Byy/1QqMZ1RCALAsYXDQSvYmAVyJHmek0++OADvv7664CPKaU4/fTTmTp1asTbDnQJN9qJeqaDPGjQIPbs2UNlZSUVFRW2XOiamhrq3W5y3nkHx4YN1JLIO4mX8vK4B/nvuqHU1Ye+dJiYWM/o0eu5995MrryySQhXVVVRXFyMy+WiX79+Eb0eO6SmpnpFZiST9LKzs5k1axbz5s1jzpw5UYtjMN7/5ORkqqqqqK2ttSWQg7nOgWhPGWSTCRMmsHLlStauXet9X9urQLZGLEyBHO5YJicnU15ezsGDB4GWc5C1Nr7u7Ry70047jWHDhjF8+PCY9j9+/HjWrFnDsmXLOO6448LuuyM7yJJBbv+EOmI3YIjdPY33r2/54QhC58J0hoM5KwkJCV6nsaamJmI3yOPxsHbtWgB69OjhI4K11hw8eJCPPvqIsrIyzjrrrIhyevEUyNZ2y3379mXHjh3s27fPlkAuLy9n6tKljF23jjUcxSW8wda6EbA6+HMcDjjtNKPZR2rqp6xbt5S9e9N54okmMWF+uQ4YMKBF8nlpaWlely+SSXp5eXnk5uYyZ84ccnNzyc7OjkkkmwK5pqbG+znsrA4yGCdhvXr1oqSkhG1GJ5h2K5CtJyPmGO2WVTNPvltqkp75N2Hnve3Vqxe9evWKef+HHXYYgwcPZufOnXzzzTecdNJJIdfvyOKvJSIWgUyWjnyM2pqgR0xr/azf/edafDSC0MkIlT82SUtLo7a2loqKioi/7Hbu3El1dTU9e/bk9ttvb/bPcd26dbz11lt88803lJeXc/HFF9v+RxwsYgGRNwuxTrKzCmSzoUMo6j/+mNM/+oi3uYAreZlKgh/LSZPgyivhiitgwABj2fr1g1m3binl5eXNJgsCjB49OqLXYhfrSZFdB9maOTaFsfV+NPg7wg0NDSFn/tsVyB6Px3sZPlA95bbCrIn8ySefePOX7V0gV1dX287x+v+PiKdATkhIIDExkbq6Ou+xa833VinF9OnTeeGFF/jqq6849thjQ753HbWCBcQvYuFwOLxzOzweT7OTfRHI0SNHTBBaEGvEIhhpaWmUlJRQUVHh7fZml02bNgGGyAsUoTjyyCNJS0vj1VdfZf369VRUVHDFFVfYuiwbaJJerBEL00EGm5Usdu0i67bb+bP+Jb/mTwEzxiNHGqL4yivh8MObb2Ls2LHccccdAQWfy+WKi/MVCPM9T0xMtC3Q8vPzfcSwmUnOz8+Pm0C2ZksDfWbslnmzxiviWQEkHhx99NF8+umnEcUE2gJr62i7brz/4/GMWIDvlY/ExMRWrw4xfPhwhg4dyvbt21m2bBmnnHJK0HU7ag1k8BW2ENtrcLlc1NXVUV9f30wgmyc6UsUicjpWXRRB6GDYdZCt60bCxo0bATg8kDJsZNiwYVx//fVkZGSwc+dOnnnmGW9+MRSBIhamExptxMJ0kMGGQK6tpe7SH3Fr6cPM5qFm4vjaa42eIJs2wQMPBBbHJj169KBfv37NbllZWS0m7kyBHEm8Yvbs2c2EcHZ2dlQl3kz8S72Fq+xgLg+XQW4PFSzmz59PXl6ezzIzomJtytNeBbL15CWa1s7Wbnzxwvq/qi2Om+kiAyxbtizk57Cju6NWFzkWFzxUJYvWPkbB/ibnz5/fKvuPJyKQBaEFsesgQ+QCuaSkhJKSEpKTk8NWYejTpw833XQTffv2paSkhKeffjpg3MBKIIGclpaGw+GgsrIy6Kxpf+rr66mqqkIpRWpqKn369AHCt5wuvu3/OOObB/k3N/gsdzqNjnjPPQdTpkA7My+9mO9rW3XRM/F3kO0KZLP9cTDaQ/7YLItnfiGbEZUpU6Z4W09D+xXI1kl6do+n9e8xJSUl7id4VoHcVu/tsGHDGDZsGDU1NSxbtizoeh15kh74jjsWh9fcTqD/ya0tkEP9TXY0RCALQgvSkg6y6R6PGjXK1j/X9PR0rr/+egYMGEBlZaU3nhGMQBELh8PhFXx2c8hW99h0vHr27Bmw5bTWsGwZ3HHFfsY+/Us+x/fyavfu8P778NOf2tp1mzJgwACUUgwaNKhNxxGpQHY6nbhcLrTWIU+C2kMFC2tZvPvvv98nrz169Gh69epFnz592u3l5Vgd5Hjmj02sJ/NteWJhx0Xu6A6yOW6XyxXTiU57cpBD/U12NEQgC0ILYjrIdgTyCy+8ENGlKWv+2C5JSUneiXF2HWR/ARTpRD2fLniNmDGLvXv3ArBxI9x/P4waBVOnwt9e7UMxvnnsoUPdLFtm9AnpCAwYMIC77767zb8YIhXI1sdC5ZDbg4MMvmXxZs2a5T3eLpeLn/zkJ/zkJz9pdxlpE+t7E42DHO/8MbR9xMJk6NChDB8+nNraWpYuXRpwnc7iIMcqXtuTQIbgf5MdDRHIghBH/PNXFRUVFBQU8O9//zvoc8wvpIEDB9q+NFVdXc2OHTtwOBw+WUs72HWAg7WyjXSinnWCHkBlJezbN4qvvjqeu+7qw8iRMGYMzJvn7RTdjKFDt/HxxxXY6B3QrmiJS+CR0tICua3jC/5l8ax/f2ajlPZKNBGLlnaQ20PEwsTqIvu3S4eO7yCbAjlWgR+qWUhbtJoO9TfZkWi//zkEoQPin79au3YtCxcuZOrUqUGfY34hDRkyxPalqc2bN6O1ZujQoRF/iZlCNZSD7Ha7qa+vx+FwNPvnHelEvfLycioqUnnjjWM46ijIyIA77pjIhx+exeLF/YOKYoDuiVWcdNKXXHPNCwwdar+1tdCEf1UK82eoyV12Klm0BwfZWhZv7ty53r+fjvKF7HQ6SUhI8HaLBPt1kP1/jxftxUEG43/iYYcdRl1dXUAXubMI5FjHH6xZiMfj8dZGbq0TxY7+N2kl5iOmlMpSSl2slDpLKdU+g16C0EpY81f33XcfL774Ijk5OZx99tlBn2PNINu9NBVNvMLEjoNszR/7O6CROMjbt8Mf/jCQv/zlTl59dSTffgsBOmL7kEgtl/A6rydfxda1RZx22sekpiZ02MuobU2oMm/BsFPJwm5mtiUJVRavo+BfZaQ9OchtLZChyUX++uuvKSoq8nlMIhb4PN9fIFtPIFrrSlZn+Js0sf2uKKVmAdcB52itDzQumwT8D+jZuNpypdSpWuvKeA9UEDoKVpF78sknc+SRR4Y8e+/WrRtKKaqqqvj444/DdlFraGhgy5YtQOjybsGwI5CDxSvAnkBevx7++Ed4+WWorx8SdkxOJ5x0Qj1XrZ7NpeX/pgelcP/v2ZdpiIFISqUJvkRa5s36WHt3kAOVv4u182Brk5KS4nM1JxIHuTNnkE0GDx7MiBEj+OGHH/jHP/7BqFGjOP744xk+fHiHd5DNcccrYhFMILdmvKIz/E2aRPKpuhzQpjhu5CGgB/BvoC9wHnAr8Oe4jVAQOhhm/uquu+7iySef5Jhjjgm5vsPhIDU1lW+//Za//e1vLFy4MGQXte3bt1NbW0vv3r3p0aNHxONLSUnB6XRSU1NDXV1dwEvtdgRyIIG9eTP85jfw+utGRYpgjBoFvXr9QPfum7nhhvGcf/5Auv3hAfjiUWOFQYPgrrsoLywERCDHQmefpNfRsR4/O405WjpiYa1i0V7e24svvphPPvmEtWvXsnnzZjZv3kzfvn29/7s6qkCOt4Psn0E2y2h21OPT1kQSsRgFrDXvKKWygFOAp7XWN2mtLwDygSvjO0RB6DhY81c//elPmTlzJk8//XTY/FVaWhqFhYVe1xiCX5qy0xwkFEqpsDnkQCXeTKwOslknt7gY7rgDxo2D114LLI4HDmzgr3+FkhKjucd9921g6tSvGTRoO90O7II/W86rf/97sDhrIpCjp6UEcnso89YZsP6N2XFszdyy/3PjhdPp9G63PTjIYIj2Cy+8kJ///OdMnz6d1NRU9u3bx86dO4GOG7GwlnmLx3ZCRSyEyIlEIPcC9lvuT2v8+aZl2RfA0FgHJQgdFWv+qqKiguHDh/OrX/0qbP4qLS2NE088kaOPPtpnuX8XNa11TPljk3Axi1B5yKSkJJKSknC73Rw8WM38+TBiBPztbxCgyhBZWcVcdNFbbNjQwM9+Bj0bA1n9+vUDGjvqzZkD5iz1iRPhqquAptrQIpCjJxaBHCqD3F6qWHR0rH9jdk82zPVaImIBTTGL9vbepqamcsopp3DXXXcxY8YM+vXrh1LKWzayoxGvKhbBJumJQI6NSI7aASDLcv8UwAN8ZVmmgU5vJ2zZAn/9a1uPQmifzGbXLnjzTdi9uw87d55N//79GTJkCHfcEfxZW7dOpahoJOvXZ7Jz53z69p3CoEFNsYpdu/LYty+fsWNv59tvjychIYHi4kFNXeQ8HohglvKWLSdSUjKWjRvTyMpq/viePX3ZseNsVq7sRyDz+9tvz6WqqoZ//SuRxhREMyZOhF/+soZNm/5Bt25JpKXN8Hnc/FKrX7HCaItn8vDD3tciDnLsJCQk4HQ6aWhooL6+3pZA7ihVLDoD0Qjk7t27U15e7r2aE2969OhBUVFRm3eBDIbL5eLoo4/mqKOOwu12x73ddmvR0hELEcixEclRWw9coJT6LdCAkUnO11pbLahhwN74Da99UlhouGWCEJqBjTc7HAYcxjffAEwBcoAFQDaQ573/1VfdgOMA+PJL6/MjLUgzEhjZuL9A9G+8BeOooI8MGwZ/+APk5MD+/aVs2aINgau1MXtv/XrYsIH+69dz4+LF9C4qaspknHcenHqqd1umQA7UaGX+/PlMmTLFJ5+dl5dHfn6+13W3s05XIDk5mcrKStsd2yRi0XpYj59dx3bGjBkcOHAgqjkIdjj33HOZMGECAwYMaJHtByPSv1elVIcVx9C6VSyEyInkW/UxjG/MXcBOoB/wD/PBxhJvJwJr4jlAQeh6ZGOI4xzgfnzFcvslM9MwfzdsgCuuMExgb5OQ9HSYMQOOOAIuuwzuuw/nSy8xqLCQpMayYzgc4Nc1MJSD7F9zOlBjFTvrdAWsMYtIyryJg9zyWHPEdo9lr169GDVqVEsNie7duzN27NhWb3LT1f5eWyuD3F5brbd3bL8rWuu3lVK3Arc0LnpJa/2iZZXTMeIVH8RxfILQRckGZgHzgDm0Z3GckAC33Qb33Qe9evk+Zgrcw7ZuhbffDrqN+owMXA88YMzyC/D8QALZWnN61qxZ5ObmNqv4YWedroC11Fs8HGSttWSQ40Q0DnJnpav9vQ4cOBCn08mgQYNi2k6wDLJUsYiNiI6a1vpJ4Mkgj32AUfKt0zNiBDz2WFuPQghGXV1dyC5xgUhJSYn7hJcvvviCiooKTjxxGunpobN8Bw4c4Ouvv6ZHjx4cf/zxbN6cx7//ncuJJ85hyZJcrr8+m5SUUXz77bf07t2byePHG4WGD5QYG0hNg8qKpg0OGw633AKWkk1WSktLWbp0Kd27Z3DCCdOaPZ6fn09xcTGTJ0+id+8+zR4vLCxk7dq1DBvWm3vvncyQIKWOTQd5jFUcH320EaM4/HBWV1fz8a5dHHXGGZx51lk+z9Vah52kZ605PWfOnIBfpHbW6exYHeRIMsjBJunV19fj8XhwuVxRf/laL6ebvwPey+mdKQoTKjowY8YM77L27sa3RmSpK/29Dhs2jHvvvTdmh9f8Gzx48CAFBQXe5YWNE0REIEeHHLUoGDSIkBOuhJYl1D/pu+++myeeeJrKyv0httCc+nonN9xwV8Csa7TU1Cylurqau+46OZhO9VJSolHqG3r06MH48dXMm5fDO+8YzkleXrbXUTn+eCfnnXcek5/5ORzINZ6cmQnffgv/+IcR/gXYBrzwL/jf/wikXg8dcqD1N6SmpnLHHc0F8r/+tZrdu3dz443jCWRubN9ez7PPfsOgQYMYMmRy0NdVXl5O/9276blypbHA6TRmMA4fDkDyhg1Uvvoq+/06ZAFUVVXh8XhISUkJ+g/erDkdqrGKnXU6O4EEcqjsZjgHOR7usXk5fcGCBUyZMoUZM2aglOLNN9/0KZfYGbC+VuNvuun1dSQH2fo68vPzcblc/OEPf/C+T/EQy13t7zUe8Qfzb3njxo3eMqBWOmoZvLYmYoGslLoAuAoYC6RqrUc2Lh8LXIARvQgyr10QYifUl83BgwfZv38/LpfL9mWroqIiKisr2bFjB+P8LvFHS0NDA9XV1SilSElOht27AxcHzsiA9HSfdtPffPNNs1adr776Kg8//DDHHXcco7dvh9zcpm389a/GWdvvfw/9+8OddzZNiDvhBPjww2bRhbS0NJRSVFZW0tDQ0OyfdKg6yGC/3XR5eTknWGcT5uR4xTE0VbLYu7f53N5wFSys73uwxip21ukKmCKsvLwcrTUulyvkF7NdgRyL4+l/OV0phdbaK5A603sUKjqwf3/TyXx7d5Ctr+Occ87hxRdf5OGHH272fzha5O81OkaNGsW4ceOoqqpq9pjT6ey0Ge6WJpJW0wp4Fri6cVE1YP32PAj8HlDAn+I0PkFoRqgvm+XLlwNGjeCZM2fa2t7ixYv57LPP2LVrV9wEcmWl0W19fEEBjlGjwHLZywel4NJLSfrtb0lISMDtdvPLX/6ymZM0fvx4jjvuOLJcLtLvuqvpgRkz4Oqrm+7/7GfQt6+xzO02Sq6ccw4sXw69e3tXczgcpKWlUV5eTnl5OZmZmT77C1UHGZpEa3l5eUCB7eWHHxj3/fdN9+++2+fhzMxMEhMTqayspKKiwsfBD1XBAnxrToNvYxVzmZ11ugLm+2ie0IRzKsOVeYtXBQv/y+lAp720Hiw6EM0kvbbCvHpnvo5rrrmGuXPn8t5777FmzZqYhaz8vUZHt27dbH/fCfaJxEH+KXAN8AzwS+DnGLOHANBa71VKfYnRbloEstCiBPuy2bp1KwDDLS5lOAYONEqxFQYr6BsF1Rs3cvkrrzAmwOUuH7Q2Ws+99ho/OuIIPjn+eCoqKpoJmO3btwNw3scfo3btMhb26gVPPAH+M81zciArCy66CCoqYMcOuPxyw0m2RBUyMjIoLy/n0KFDPgLZOgErmIPsdDpJT08PKrBNxr3/Pg7TOT/zTKM4sgWzyP/OnTtZs2aNT8F/870M5iAHuozrfznWzjpdgUgFsnnJtq6uDo/H06z9cbwqWFgvp//1r39Fa91pL60Hiw50tIiFGYUx37Pq6mo++eSTuJzUyN+r0J6IpMzbjRgl3G7WWpdhNAXxZzNgX5kIQpT4f9nk5eXh8Xi8ExRGjBhhe1umQN69e7d31m/UuN3w0EP0PuUU3t64EW+PjW7dYMAA8nr1Yn56OgwYYMQhLCz87jtGPP003WbMgPffh1WryHvySebfeSdlixczceVKhi1e3PSEJ54w3OJAnHoq/Oc/TeI5Lw9+/WufVYJ106utrUVrTVJSUjNhZCVczMJdWMiRjY4+AEFyiaYo/vjjj3nppZe8t2XLlgHSJCQeRCqQlVLedcyycFbikUH2v5yutUYp5XOFKFyL9o6C9bXOnTvX5/WZjVyg/TvIgDcKA0ZjCrfbzTXXXOP9PywInYVIBPLhQJ7WgYKUXvYDvUM8LggxE+zL5rXXXqOmpobMzMyICuh369aNnj17Ul9f75MHjJivvoJjjoHZs3FUV3vbfeSddx7s2EHeiy+SoxRTFi0yog+7d8PKlUZdYKW86y9bsgTOPZe8Y44h5yc/Ycpf/8ppd9/NhdZKED/6kfG8UJx3HjzwQNP9Rx6Bl1/23rXGJKyEi1eYhBPI9Y8+SoJZdmjSJJ8GIFaOO+44xowZw4gRI5rdxo4dy0Q/11mInEgFsnWdQJUs4uEgWy+n5+fn89Zbb/Hmm296L6ebl9Y7A6GiA9B0paa9C+T8/HzefPNN7rzzTubNmwfAww8/zJFHHtnpTmoEIZKIRT3h20gPBCrCrCMIMRHsy2bBggX069ePww47LOJtDho0iAMHDrBr1y76+zm7YSkrg3vv9Z04B4zr04cHLr2UnIULmfXYY4EnHk2cCAsXwvffM/r22/lPXh45GBWQcwnSHqRfP/j73+2N7be/hRUrYNEi4/5NNxkT9iZMCOogmwI5WLzCJKuxR3VhYSFHHeXXWa+igsSnnmq6/+tfN4+CWLZz+eWX23s9HZS27uhnrYMMoStYmISaqGenVFw4rK/b+rv177qzXFoPFx0YMWIEO3bsaLHOePHCLL+Xm5vLaaedRn5+PhMnTvS+DskLC52JSBzk74HpKkhrHaVUMnAqsCoeAxOEYMyePbvZP+Ds7GymTp0KEJVAjjqHvGiRITit4jg1lQ033cSTP/kJE66+2puVnjVrVvAvjnHj+OF3v+O7O+7gqlGjmAfM6tuX7AkTqD78cPb068eBoUPhlFOMMmk9e9obn8MBzz8Phx9u3K+uhosvhpKSmAWyeZzNrLAPTz2Fs9GtrOjbFy65xN54Oylt3SHM35mMxEEOJJCli158mTFjBj/72c/afTku69W7jz/+mLfeesvnc52dnd0p6lYLAkQmkF8AxgCPKqV8ntfYZvoRYABGpQtBaFXcbjc7duwAIpugZ2KWhNtlToALx549RsRhxgwjKmFywQXw/fd8d/bZeJxOvv/++2ZZ6WCkpaWxsqyMf+/bZ6zf0EDeI4/w8UMP8eStt7L+pZdg8WI4/vjIXlxGBrz1FphZ3m3b4Ec/ItPtJqWyktrCQigu9t7qdu8mpbKS7m530/Lt2+Gjj+Bvf4Pbb4fTT2fwCSfwmwcf5NyHH6bmj39sqtThdhtxjka2z5xp1D9uJebPn9/sOOfl5THfr411a+7Lmqu9//77W710lf/Jjh2BHKpZiAjk+NPabZ2jIVxURBA6E5FELP4JXAjcAcwEygGUUq8Bx2OI40Va65fiPUhBCMeOHTtoaGigf//+UXXE69u3L06nk5KSEqqrq0nZvdtoshHIHa2rgxdeMKIVTRswxGNjnrgiL4+CggIee+wxXn/9dVs1PdetW8fChQu56667mDt3rnf9K6+8kp49ezIkWLs6O4wZY4zZ7Nr10UcM/ugjvF7Pz3/uXXVs4w0wIhlBUEACMHzbNiNicu+9MH684Vbv3AlARWoqhy6+OPpxR0GoOtltua+27BAWbwc5XmXehI6FVJkQuhK2HWStdQNwPjAXSARGY3xHXgJ0A+ZhCGdB8NJabp55mT+aeAWAs66OKSUlnP3++zjHjYORIw2n9JFHmt/+/ndfcXzjjUZTjpkzvTnbiooKCgsL+de//hXUbfE/Nps2beLEE09k/fr13vWff/55vv/+e1wuFwMGDIjqtXm56CK4//7YthGOb781ytY18s1xx5HWu3Xn7bamWxvJvgJVXmkt/AVxvCIW7b0smSAIQrREErFAa12vtf4d0AfDZDoRGA/01lr/n9a6Pv5DFDoyrZW9jFogl5bCtddCr16c9Ze/cNzXX5O4bZu9544cCZ9+Ck89BX6TayorKznxxBM5++yzfZZbM3r+x2bq1KksWbKEPn36eNcfPnw4J554IoMGDYpLS1L+7//gttugTx/o1Yuqbt2oSknB07OnUVe5Vy/qMjKoSknB3b27dxn9+sG0aXDDDTB/vpG93riRktWreef88/lh7Fi0n1hyJyWRP3lym5Rps7q1IbPfrbSvUGW+WgOXy+XTrlsyyIIgCKGJuNU0QGOptzAdEAQhdNe7eFFZWcnevXtxuVyRxRAKCoxSaI2OrQ/duhllyU44AQLN+O/Xz5h4FmAim0+b6RAT3QIdm5kzZzJgwABvcwazQUhM8QorDofhgDdWwXjhn/9k79693HzzzV6H+v1Fi1i9ejXnn38+kyZNCrm5nlqzafp0Vk6ezE+vvZbeq1bBokXM/+IL6saOpaFbN+9kwNas2hCsKUNb7as9dAhLTk6mosIoMhRJFYtAGWSJWAiC0NmJSiALgonWmueff55tYVzXcePGMW/ePE4++WQ+//xzCgoKuO6660I2orCL2RxkyJAhPi5ZSL7+Gi68ECx1jxtGjiQ/K4sdRxzBzL/9DRWmikMwzDbT3bp1C/v6/HOp6enpVFVVUVVVRVpamnfiYdwEsh8ZGRns3buXQ4cOeQVyuC56VpRSHHbYYaxdu5Yte/fS+5JL4JJLmPzpp1xwwQXM7N2b9PT0Fs0B++PfgCJc9rs19tUesptWgSwOsiAIQmiCfnsrpT6N8vZJa74AoW2pqKgIK44LCgpYvnw5J598MsuXL6egoICdO3cGLg8WBRG3l379dZg+vUkcJybCiy/i2LSJJZdeyvrBgznYWOosGkwRkpaWFnZdf/fRLDNXUVFBXV0de/bsQSnF4MGDox6PSaA8eEFBAUuWLPEp9Wa3zJuJGWsxT1TAaP4xc+ZMXnvtNebNm9eqVRtac6Z9R5rVb30/I6liIRnk+NOalVYEQYiOUHbb9Ci3GarTntDJKC0tBWDAgAHcfPPNzR7Py8vj73//O++++653lv/FF1/MjBkzWL16NSNHjoxp/1prr0AO215aa3j4Yd+Wx716GSXQTjwRhVHubePGjezatYuedmsN+2E6yFaBHKhRxCOPPML999/PO++843UTL7roIi655BIqKiqorKxEa82AAQNsXRIPR6CKC/Pnz2fGjBlxEcjbtm2joaEBp9PJoUOHGD58OCeffHKrV21oTbe2PTjDdrG6vbE4yB6Px9t+WgRydLRmpRVBEKIjqIOstXZEeWu9gqdCm3Pw4EEAMjMzAz4eyGF74YUXKCwsZMOGDV4xFsv+y8rKSElJoV+/ftDQAA89BFdc0fx2+um+4njUKFi2DE480bvIbBhiux5yAEwHOTU11bss0GTFOXPmeMu5gXFs7r77bgoLCykvL497vCJQxYWHHnqI4cOH+7SbjiRiAUbL6t69e+N2u73Hrby83OtOB6raIA5a6xOtQK6pqaGhocF7q6qq8j7eEWr3tkfaui62IAjhkQyyEBOmgxxMIAdy2C644AJKS0vZunUr69ati6mixQ8//AAY8QqlFMyda9zCcfLJ8MYbhoNswWwYEnFHPQumg2wVyIEm5JmuupXjjz8ej8dDRUWFd4Le0KFDox6LP/6Z51NPPZUXX3wxJgcZDBe5qKiIrVu3MnToUD799FMWLlzIvffey3333dcsmysOWutjFcWRRCy2b9/Ogw8+GPRxITrasi62IAjhiX2GlNClMR3kHn5lzsIxYcIEAFavXh3T/s3c62GHHQZffAEBvsibcfXV8OGHzcQx4J2otnfvXurro6taGCyDbKccmPmcsrIyr0iP5wQ9/8zz2rVrgaZ20/X19bjdbhwOR0Rtb/3bTq9atYqZM2cybdo0oHk2Vxy04LSUux6pg9y7d2/69OmDw+FodnM6nYwbNy6m8XR12rIutiAI4YnKQVZKDQIGAgH/y2qtP49lUELHwXSQIxXIY8aMISkpid27d7N//36f2r928Xg8XoE8omdPuPhi8HiMB0880aj368/IkTBpkrehhz9JSUn06dOH/fv3s2fPnqgmxwXKIIO9cmDmczZv3kx9fT1ZWVlRdQYMRLCKC+eddx4ulwuttU+8IpLL50OHDsXhcFBYWEhNTQ3nnnsuq1ev9pZ4g+bZXHHQAtNS7ropkJVStk5+EhISmDVrVkz7FALTmpVWBEGIjogEslLqTOBRYEyYVSWH3EUIl0EORkJCAkceeSQrVqxg1apVrFmzptkktnB1c/fs2UNNTQ09MjPJnD3b296YHj3glVegMS4RKQMHDmT//v3s2rUrKoEcKINs9wvRbKphOrrxjFcEq7gwf/58hg8fTk1NjTdeEenl86SkJAYNGsSOHTvYtm2bN9McqklIa9Yq7ki0VO1w8z1NTEyU7HAb0x7qYguCEBrbEQul1HHAu0Am8HeMNtOfA/8CNjTefwejFbXdbT6jlNqvlFpnWdZTKfWRUmpz48/IrEmh1WhoaPAKue7du0f8/IkTJwLw7bffcswxx0Tccc+8nH/KDz/4tDfmqaeiFscQew45kINstxyYv+scz3jF7Nmzm335Zmdnc/755wOGKI8mf2xiltnbunWrVyBbHWQrbd1Zrr3TEp0AzfdUKk+0PcH+FlujiY4gCPaIxEH+DVADTNFa71ZK/QzI01rPVYYd8Tvgl8BvI9jmsxhi+3nLsnuAT7TWf1RK3dN4/9cRbFNoJcrKytBak5GRYb9Bh4UBAwaQlZVFcXExgwYNCuiaTZ8+naVLl3on41nZt28fvYqLGf/0000Lb7nF6HAXA2Yli2gFciAH2W45MH+BHE8HORgZGRkUFxdz6NAhPI0RlWgE8ogRI/jss8/YunWr9yQhmIMsDlpoWsJdNx1kEciCIAjhiUTVTAXe1lrvtixzgLf19P8ppc4FHgAus7NBrfXnSqlhfosvoqkG83PAYkQgt0uizR+bKKWYMGECH3/8MWvWrOHyyy9vlkldvHgxn332WcDnO+vruer113GYpeLGjIFHH41qLFZ69+5NYmIipaWlVFRU2Gr4YWJtMx1NdjgpKQmn00lDQwPdu3ePypmPFNPlPXToEE6nkY6KRiCb9ZpLSkoAcDqdQY9BR6of3Nq0VD7VrOsdbX1vQRCErkQkArk7sMNyvw5I9VvnS+DKGMfUV2u9B0BrvUcpFfnsLaFViDZ/bOXoo4/mk08+YdOmTbz33ns+rtmAAQPYt28fSinOPvts3y/2ujqy/vQnMvfsMe4nJsJ//gNxmNDmcDgYMGAA27Zto7CwkMMPP9z2cyNpMx0IpRRpaWmUlZW1WHtpf6y5Z1MYR1PCy+l0Mnz4cDZu3OjdrmRdI6el3PXMzExuu+22kLlwQRAEwSASgbwf6OF33791WQIQufUUJUqpW4BbIL5ZTcEe4Wog2yEtLY1Ro0bxwQcf8Mgjj/DGG2+QnZ3N8OHDuf3225k5cyY/+9nPmNSnj9HUY9kyWLoUVq4Ea4ev+fPh6KNje0EWBg4cyLZt29i1a1dUAjkS19mf9PT0VhXIpoNsbRYSjYMMNBPIQuS0pLuelZUV8zYEQRC6ApEI5E34CuJlwDlKqdFa601KqX7ApcDmGMe0TynVv9E97o8hxAOitX4SeBJg8uTJ0uK6lYk1YmEy4aijeO/3v+dZh4PpZ52FBq71eBjs8bDihReY9NJLEKom8TnnwB13xDQGf6wT9QK1iQ5WYSNQ/jhSJkyYgFKq1erMWiMWZpY8WoFs1kO2blcQBEEQOhqRXAP+H3CKUsq8zv0Yhlu8SimVj1HJojfwlxjH9Dbw48bffwwsinF7QgsRbZMQHz77jDHXXccL69Zx0YEDKLcb5XbjbGjgdK35tccTXByPGAF33mlEK+J8Kd86UW/SpEm2K2wEaxISCZMmTeKGG26IW/3jcFgFcqRtpv3JysryOsfiIAuCIAgdlUgE8j+BkwE3gNb6S2AmUAAcCewBZmmtnw+6BT+UUq8AS4HDlVK7lFI3An8EzlBKbQbOaLwvtACRduzyX//gwYMUFBTw7LPPRr7z9evhwgth+nTU8uXh109Nhexs+M1v4O23Yf9+2LIF/vIXaAGnMj09ne7du1NXV8fo0aNtd30L1Ga6vWMVyNHWQTZRSjFihHGhKZbojSAIgiC0JbYjFlrrQ8DXfsveBN6Mduda6x8Feei0aLcp2CfSjl3W9adNm8Z3333HwoULeeedd+zvtLoafvUr+Oc/oaHBu1gnJfHl5Ml8NW0aAw4/nCuuuMK3dJzLBVFMeouFI444gq+++ooVK1YwY8YMW13f4uEgtzYpKSk4nU5qa2spKyvzLouW0047jZ49e3rrXAuCIAhCR6N1FYfQrrB27ArnjPqvf++997Jw4UJuvPFGTj31VHs7rKuDSy+Ff/yjSRwrBddei9q0iZK772bQ0Ucz86qrcHXrZlSmMG+tLI7BiDoArFu3jvfff9+nwkawhhbxmKQXqbMfK0opr4tslmiLRSCnpaVx0kknkZiYGJfxCYIgCEJrE5PqUEpdqJT6i1LqMaXUpfEalNB6RNqxy1z/kUceYfLkyRx77LH2dlRfD1ddBe+/37Ts9NNhxQp47jkYMoSLLrqIK6+8st00MujZsycjR45ky5YtXHnllba6vpkO8ksvvRS1yDWd+ki6CsaKKZBjaRQiCIIgCJ2FkAJZKXWBUupzpdQpAR77N0a84g7gZ8ACpdTrLTNMwYrWGqM3S2QEciYfeeQRHnnkkbDOqInZ4ev6669n+fLl7Ny5M/yOPR64+WbfdtD33Qcffgjt/DL85MmTKSws5Oqrr2b69OlA8DbR0OQgH3vssVGL3Eid/XjgX3Ei2gyyIAiCIHQGwjnIFwLH4Jc9Vkqdj1Fhogp4EKPT3VZghlIqWK5YiAO1tbU8+uijvP565Oci/s7kI488wq9+9Svmzp0b1hkF3w5fOTk5zJw5M6Do9kFr+PnPwTqR7847Ye7cuFeeaAlGjRrFeeedR1ZWlk+76+zs7ID1ak0H+ayzzopJ5Ebq7MeKteJEYmKit6OeIAiCIHRFwgnkY4GlWusav+U3ABq4Xmt9v9b6IeAkoAa4Kv7DFEz2799PeXk5GzZsoMEyyc0O/s7knDlzePjhh/nFL37h83ggZxR8O3yVlpYyfPhwHnnkkaDrA3D//fDXvzbdv+EGeOSRDiGOweiqZ2aRl4eptuHfZjoWkWs69Xad/VixOsgSrxAEQRC6OuGqWPTDKMPmz8lAKeC1MbXWe5VS/wWmxW10QjMOHToEGGJs//799O/fP6LnW0XbnDlzvOLY+ngwIWd1TM0ayGeddRYDDh6EQJUsli6FP/yh6f7MmfDkk20y4S4WJk6cyOLFi9m0aRNlZWV079494Hr+bab9Ra7dbmhWp958TkvHLKwCWeIVgiAIQlcnnEDuARywLlBKDQF6Au/o5kHYAoxYhtBCmGW4APbs2ROxQI5WtFnRWnsFctZHH8F114V/0nnnwYsvQge8dJ+Wlsa4ceNYt24dK1asCFq1w1rBIhaRa3XqwdfZbw2BLA6yIAiC0NUJZ+WVA4P8lk1q/LkqyHP84xhCHDEdZDAEciRYRZudzHEwqqqqcLvdZFVXk2inxfP06bBwoVGurYMyefJkAFauXBk02mJtMx1K5IZj9uzZzYRwsMxzvLBmkEUgC4IgCF2dcA7yt8B5Sqk0rXVF47KLMfLHSwKsPxyjo57QQsQikOPlTJaWloLHw0WLFoE5nn79oFFE+jBypDEhr4OLriFDhtCnTx/279/P+vXrOfLII5utY20SEkjMRuPWB2L+/PlMmTLFZ1t5eXnk5+dHLaLT0tJQSqG1loiFIAiC0OUJJ5Bfwmgx/ZlS6jlgNMYkvL2Aj+2olFLAiQTOLAtxoqysjCVLljBw4ECcTicNDQ04nU5bAsmuaCsoKCAxMZGBAwcG3M7Bgwc5Nj+fQRs2GAscDqOE27SOET+PRmAqpZg8eTLvvfcey5cvDyiQW6vNdKQdEO3gcDhIT0/n0KFD4iALgiAIXZ5wEYungQ+AicCjwE+BeuBOrbX/debTMCb1fRzvQQpNHDp0iIEDB7Jw4UK2bNlCcXFxXBtJlJeX88ILL/DCCy9QX18fcJ2a1as5/aOPmhbMnt1hxDFE34jjqKOOIjExke3bt7N///5mj7dWm+mWqpNsxixEIAuCIAhdnZACWWvtAc4DrgGewKh5fJzW+rUAq2cBjwFvx3uQgkF9fT0VFRUcdthh/PKXv2ThwoXMmTMnrhUOtmzZgtaa2tpatm3bFmgQjJg7lwRTPB91FPzudzHvtzWJVmAmJSVx1FFHAYFLvrWWgwwtUyfZnKgnAlkQBEHo6oSLWJgi+aXGW6j1/gP8J07jEgJQXl4OGE7flClT+Oyzz/jXv/7FnDlz4lbdYMuWLd7fN27cyMiRI31X+MMf6LF5MwA6IQH1wgvQTlpDR4J/uTu7x2/y5MksX76clStXUlBQ4POYWWGkpR1kiE81En+OOeYYampqmr/ngiAIgtDF6FgFabs4pgDr3r0727ZtY/ny5Zx//vlxayTh8Xh8usVt2rTJt6X1ihXGhLtGKn/9a8NBbkMCdfLLy8tj/vz5IZ8XbSOOvn37MmLECBoaGiguLva5ud1uXC4Xffr0ifr12CFe1Uj8GTlyJNdee23QOs+CIAiC0FUI6yAL7QezgsWOHTt4/PHHmTlzJqNGjeKuu+6KS8xi165d1NbW0i8lhW579qC3bqX06afpoTUcPAhPPw2N0YodgwfT/7e/jcvrioVoJqzF2ojjRz/6EQcOHAj4WFpaWotHFNqiTrIgCIIgdCVEIHcgTAd5165dLFiwgDVr1lBWVsb48ePjIpA2b97MkG3buPall3C63cbC559vtl5dQgIfXnUVN7WDcmDWPPGsWbPIzc0NK3RjFZhOp5PevXvH7TVESkuWkBMEQRAEQQRyh8J0kG+++WaOO+44ioqKKCsrY8+ePXERSD/88ANnffppkzgOwgdnn41j1KiY9hVPIs0Ti8AUBEEQBCEUIpA7EKZANqsN9OvXj/Xr17Nnzx6OPvromLZdUVGBZ9Uqhu7YAYB2udg+eDDVSUmMmDKFxD59oEcPfsjIYOWBA4zPzIxpf/Ek2IS1lmioIQiCIAhC50cEcgfCOkkPYMCAAUDkHfUCsWXLFo795hvvfXXZZSy76CI2btzI+eefz6RJRofxHXl58PnnZLYTgRwqT9wSDTUEQRAEQej8SBWLDoTpIJsCuX///oAhkH2qTUTBjtWrGb92bdOCn/2M0aNHA0a5N5ODBw8C0KNHj5j2Fy/C5YlboqGGIAiCIAidG3GQOwhut5vq6mqcTifdunUDjIYUGRkZHDp0iJKSErKysqLatsfjIWPhwqbmH8ccA1OnMrqx8UVBQQF1dXUkJiZSWloKtB+BHC5PHG29Y0EQBKF9Ultby4EDBygvL6ehwb+pr9CVcTqdpKen07NnT5Ji7NEgArmDYMYrMjIyUEp5l/fv359Dhw6xZ8+eqAVy4Y4dHP3VV00Lbr8dlCItLY2BAwdSWFjI1q1bGTNmjNdBbi8Ri3C0REMNQRAEoW2ora1lx44d9OjRg2HDhpGQkODznSh0XbTWuN1uDh06xI4dOxgyZEhMIlkiFh0E/3iFiRmz2L17d9TbPvjii/RodIbp1QuuuML7mBmz2LRpE263m4qKChwOh3eiYHumpRpqCIIgCG3DgQMH6NGjB1lZWSQmJoo4FrwopUhMTCQrK4sePXoE7VdgFxHIHQSrg2zFFMh79+6Nuqtcz5dfbrpz881gaXRx+OGHA4ZANuMV3bt3x+Gw99GJdkzxIFQ+WRAEQeh4lJeXdwiDRmhbMjIyKC8vj2kbIpA7CP4l3kysE/UmT57s45CaDuqUKVOCbrdqxQoGrV8PgHY44NZbfR7v06cP3bt3p7KyknXr1gGR5Y/NShKRjClezJ49u1mcIjs7W0q8CYIgdFAaGhpISEho62EI7ZyEhISY8+kikDsI/iXeTNLT00lLS6O2tpajjz464qoNVQ895P1dXXQRDB3q87hSyhuzWLFiBRBZ/lgqSQiCIAjxRGIVQjji8RkRgdxBCOYgg6+LbK3aMGvWrNBC9NAhui9a1HT/9tsDrmbGLCobq1pEOkEvojEJgiAIgiC0MSKQ2xGh8rrBHGTwFcj+VRtCTUjTzz5LQk0NAPVjxkAQ4Tps2DASExO99yMt8RbJmARBEARBENoaEcjtiGB53cmTJ9tykD/99FP7VRs8Huofe8x713nHHRDkkoTT6WTkyJHe+5EIZKkkIQiCIAhCR0MEchsQzCk2qy7453VPOOEE6urqSEhIIDk5udn2BmRlcezXX5P82GO8WlZG9jnnQHIy2eecw4KyMvLPOAOSk31vKSkkbN0KgDs1FXXNNSHHbOaQIbKIhVSSEARBEIT4sW3bNpRSXHfddW09lE6NNAppA0yn2BSOVpc1UOe3ffv2AUa8wid4rjW8/jrp997LOVu2cE6AfWU33ggxm7P8ssvomZYWcsyjRo3yCnSzk58dwnW6EwRBEASh8/HRRx/xv//9j9WrV7Nq1SoOHjzItGnTWLJkSVsPzRYikNsAa2WHWbNmkZub6yOW/Tu/DRo0CPCLV3z1FfzqV7B0KbHM1dzfpw+Zc+eGXa9bt27ceOONOJ1OmUEsCIIgCEJIHn/8cRYtWkRycjIjR470duLtKIhAbiMCOcX+TnJ2djY5OTnMmzcPaBTIFRVw/fXw2ms+23OnpZF3wgkk3HYb2Wee2Wx/dXV1rF69mvz8fO+HNDExkVPPOYc+Q4bYGnPfvn1jfNWCIAiCIHQFfv3rX/P//t//Y8yYMezcuZPhw4e39ZAiQgRyGxHIKQ6W133llVcYOHCgUcHiV7/yFceJiXD77Wy97DKWfvgh3bdupex///PZV0NDA5s3b6a2thaAzN69OfbYY5k4caJPpnn+/PlMmTLFJ/7w/9u79/iqyjvf458fQRJKIxcRREm4CRGxbYYDWsSGhpxWPKcIMphKucWhVSxTCqd9iTBT8VKmvhhbUcfBqlUQ2zoURGxHLEJAK20ttOOlGAkQE0EUxEBigACS5/yx1g47m71DLjt77YTv+/XaryRrrWetX/bKgt9+1m89T6g2WpNriIiIJK+amhrmzJnDww8/zA033MCvfvWrqM8tJcqIESMCO3Y8KEEOQKye4mgTaOTm5lJRUcGbb75J5/POg1/+8vTKb34TfvIT6NePXpWV2Msv124bTUZGBl/+8pe57LLLok4VXV9ttIiIiCSn6upqpkyZwurVq5k1axYPPfRQ1P/npeGUIAegvpEdoj28FhoD+eKtW70SC4CBA+HXvwaz2p7fm2++mU8++aT2GNu3b699yrVnz561w8HFUl9ttIiISDK7++67gw6hQRYuXBjX/ZWXlzNu3Di2bNnCfffdx7x58xrcdsmSJRw+fLjB22dnZzN+/PjGB9kKKUEOQGNHdgiNgdz1d787vXDq1Npxi6P1/C5YsICVK1eSnZ3dqNii1UaLiIhI8ikrK2PMmDHs3r2bFStWMHny5Ea1X7JkCWVlZQ3efvr06UqQJf6aUuPrnKOyspJOVVWct3nz6RVTptR+m5uby4QJE7jhhhuYPXt2bc9v6JiNqR+OVhutJFlERJJdvHtmk92OHTsYMWIER44cYd26deTl5TV6H6WlpfEPrI1QgUoCxZopb/jw4THbHD16lM8++4zsd9/FQmMZX3MNRDwNetNNN3HixAnuvfdebrvtNoCz7juSZr0TERFpHYqLi/nwww/p378/Q4cODTqcNkc9yAnUlBrfUHnFF9966/TCGLPedejQAYCf/vSnPPjggzz//PON6v1tbG20iIiIBGPs2LFkZWWxYMEC8vLyWL9+Pd27d2/UPlSDHJsS5ARrbI1vRUUF3T/+mB7vv+8t6NABbryxzjahnt81a9awadMm7r333kbNdheiWe9ERERaj/nz59OxY0fmzp1Lbm4uGzZsaNScBapBjk0lFgkWWeN7tvKFiooKvhg+bNvYsdC1a51tQj2/QO2+27dvz7PPPhv3+EVERCR5zJkzh6VLl7J9+3ZGjRrFvn37Gty2tLQU51yDX8uWLWu5XyTJqAc5gRoz/nFI5eHDDH/77dMLopRX3H777TH3fdNNN6kHWEREpA2bOXMmaWlpzJgxg5ycHAoLC8ls4Cy5LeW1117jiSeeAKDKH6J2586dtcPPAkmdcCtBTqCm1Ph2+POf6eKPg8wFF8B118Vt3yIiItI2FBQUkJqayrRp02qT5P79+wcWz65du1i+fHmdZQcOHKizLJkTZHPOBR1DXAwbNsxt27Yt6DDirvgrX2HQa695P3z3u/DII8EGJCIiEpCioiIGDx4cdBjSCjT0b8XM/uqcGxa5XDXIyezYMfqEJ/0xRq8QERERkfhRgpzEap5/ntTqagDcwIFw1VUBRyQiIiLS9ilBTmKnwup0bMqU2qmlRURERKTl6CG9pjp8GE6ebLn9HzpE+w0bTv8cNrW0iIiIiLQcJchNdcMNsHlzo5stBoYD4eNKbAK2ApHTdIT6iz/OyuLCAJ9EFRERETmXqMQiwYYD+XhJMf7XfH95LPu/9rWWDktEREREfOpBbqrOnaERc54vPnqU4e3bk9uhAytPnCD/00+57rzzeO7ECX57/vnkduhwRpvjJ0+ys3dvKidMiGfkIiIiIlIPJchN9fzzjdp8eMRMd9dNm8aKFSuYOnUquU8/HbXN2pUrKSoq4h8vvDAOAYuIiIhIQ6jEIkFCM9vl5+czbdo0nnnmGaZOncq6devYtGlT1DaVlZUAdO7cOZGhioiIiJzTlCC3kMWLF0dNfDMyMlixYgVTpkzh6aefrk2ao21b4U8xff7557d4vCIiIiLiUYLcQoYPH14n8d20aRPjx4+nqKioTs9xqGd569atddqfOnWKqqoqzIz09PQgfgURERGRc5JqkFtIeEnFbbfdxoMPPoiZ8eKLL5Kbm8umiJrk3NzcOu0/+OADANLT02nXTp9jRERERBJFmVcLys3N5bbbbuPee+9l+PDhrFmzpjYRjtVzDFBeXs7KlSsByMrKSmjMIiIiIue6pE2QzazUzN42szfMbFvQ8TTFpk2bWLp0KT/60Y948803z1ifm5vL7bfXnR6kqqqKFStWcOTIEQYMGMC1116bqHBFREQkyZWWlmJmFBQUBB1Km5a0CbIv1zmX7ZwbFnQgjRVeQnHPPffU+zBeSHV1Nc888wyHDx/m4osvJj8/n5SUlARGLSIiItJ8L7/8Mj/4wQ/Iy8ujW7dumBnXXHPNWdu988475Ofn06NHD9LS0sjKymLhwoUcO3YsAVGfphrkFrJ169ba+mKoW1IRWW8M8Nlnn/Hss8+yf/9+LrjgAr71rW/RIcrkISIiIiLJ7pFHHmHt2rWkpaVx6aWXcujQobO2ef311xk9ejQnT55k4sSJZGRkUFhYyD333MPGjRvZuHEjqampCYg+uRNkB6w3Mwf83Dn3WOQGZnYLcAtAZmZmgsOrX2TpBBD1YTyAmpoannvuOcrKykhPT2fKlCl06tQpEWGKiIiIxN28efNYtGgRl112GXv27KFfv371bn/q1Cluvvlmjh49ytq1a7n++usBL0fKz89n9erVPPDAA9xxxx2JCD+pSyxGOueGAtcBs8wsJ3ID59xjzrlhzrlhF7aC2ea2b9/Oli1bznitXr2aoqIiUlNTmTx5Ml26dAk6VBEREWlFampqmD17NmbGhAkTqK6uDjSeESNGMGTIkAaXir7yyisUFRWRk5NTmxwDtGvXjsWLFwPw6KOP4pxrkXgjJW0PsnNun//1gJmtAa4EXg02qqbbs2cPq1atirm+ffv2TJo0iZ49eyYwKhEREWntqqurmTJlCqtXr2bWrFk89NBDrW6I2MLCQgDGjBlzxrr+/fszaNAgiouLKSkpYcCAAS0eT1ImyGbWCWjnnPvU//7rwD0Bh9UsxcXFgDeTXu/eveusMzMuv/xyLrnkkiBCExERaf3Mgo6g4eLYC1peXs64cePYsmUL9913H/PmzWtw2yVLlnD48OEGb5+dnc348eMbH2QD7NixA4BBgwZFXT9w4ECKi4spLi4+dxNkoCewxrw/9vbAr5xzLwUbUvPs2rULgJycHC699NKAoxEREZHWrqysjDFjxrB7925WrFjB5MmTG9V+yZIllJWVNXj76dOnt1iCXFFRAUDnzp2jrg8tb0xC3xxJmSA750qALwUdR7xUVVXx0Ucf0b59e/r06RN0OCIiItLK7dixgxEjRnDkyBHWrVtHXl5eo/dRWloa/8BaSKj22BJ0pyApE+S2JtR73LdvX84777yAoxEREWmDEvTwVrIoLi6mvLyc7Oxshg4dGnQ4zRbqIQ71JEeqrKyss11LU4KcAKEEWaUVIiIiEg9jx44lKyuLBQsWkJeXx/r16+nevXuj9pFMNchZWVnA6We2Iu3cuROIXaMcb0qQW1hNTQ27d+8GlCCLiIhI/MyfP5+OHTsyd+5ccnNz2bBhQ6NGw0qmGuTRo0ezaNEiXnrpJebPn19nXUlJCcXFxfTp04f+/fu3yPEjta4xQFqhDz74gOrqarp27coFF1wQdDgiIiLShsyZM4elS5eyfft2Ro0axb59+xrctrS0FOdcg1/Lli1rsd9j1KhRDB48mFdffZUXXnihdnlNTU3tyBwzZ85UDXJbofIKERERaUkzZ84kLS2NGTNmkJOTQ2FhYeAzDL/22ms88cQTgDdYAXhlEgUFBbXbhCfcKSkpPPXUU4wePZqJEycyceJEMjMz2bhxI9u2bWPkyJHMnTs3YfErQW5hoQR54MCBAUciIiIibVVBQQGpqalMmzatNklOVDlCNLt27WL58uV1lh04cKDOssge6auuuoqtW7eycOFC1q9fz6effkqfPn248847ueOOO0hNTU1E6ABYoqbsa2nDhg1z27ZtCzqMOo4cOcL9999PSkoK8+bN0wgWIiIizVBUVMTgwYODDkNagYb+rZjZX51zwyKXqwa5BYUeztPwbiIiIiKthxLkFqT6YxEREZHWRwlyC3HOaXg3ERERkVZICXIL2bdvH0ePHqVLly4a3k1ERESkFVGC3ELCyysSNWafiIiIiDSfEuQWovpjERERkdZJCXILOHr0KHv37iUlJYV+/foFHY6IiIiINIIS5BZQUlICQGZmJh06dAg4GhERERFpDCXILUDlFSIiIiKtlxLkOHPOKUEWERERacWUIMfZRx99xJEjRzj//PO58MILgw5HRERERBpJCXKc7dy5E9DwbiIiIiKtlRLkOFN5hYiIiEjrpgQ5jo4dO8bevXtp164d/fv3DzocERERaWNKS0sxMwoKCoIOpU1TghxHJSUlOOfIyMggNTU16HBEREREEm7z5s2Y2Vlfe/bsOaPtO++8Q35+Pj169CAtLY2srCwWLlzIsWPHEvo7tE/o0do4lVeIiIjIua5v374sXLgw6rq3336b5557jiFDhpCRkVFn3euvv87o0aM5efIkEydOJCMjg8LCQu655x42btzIxo0bE9YBqQQ5TsKHdxs4cGDA0YiIiIgEo2/fvtx1111R102aNAmAW265pc7yU6dOcfPNN3P06FHWrl3L9ddfD0BNTQ35+fmsXr2aBx54gDvuuKNFYw9RiUWc7N+/n6qqKtLT0+nRo0fQ4YiIiMg5pKamhtmzZ2NmTJgwgerq6qBDOsMnn3zCmjVr6NixI1OnTq2z7pVXXqGoqIicnJza5BigXbt2LF68GIBHH30U51xCYlWCHCeh3uMBAwZoeDcREZEEMmtdr3irrq4mPz+fhx9+mFmzZrFq1SrS0tLif6BmWrZsGcePH+fGG2+ka9euddYVFhYCMGbMmDPa9e/fn0GDBlFWVkZJSUlCYlWJRZyovEJEREQSrby8nHHjxrFlyxbuu+8+5s2b1+C2S5Ys4fDhww3ePjs7m/Hjxzc+SN8TTzwBwK233nrGuh07dgAwaNCgqG0HDhxIcXExxcXFDBgwoMkxNJQS5Diorq5mz549mJmGdxMREZGEKCsrY8yYMezevZsVK1YwefLkRrVfsmQJZWVlDd5++vTpTU6QX3nlFd59912GDBnC1Vdffcb6iooKADp37hy1fWh5YxL65lCCHAfvvfceNTU1ZGZmJuUtDREREWlbduzYwYgRIzhy5Ajr1q0jLy+v0fsoLS2Nf2AxPPbYY0D03uOGCNUeJ6qMVQlyHIRPLy0iIiKJlaDntpJKcXEx5eXlZGdnM3To0KDDqVd5eTmrV6+O+nBeSKiHONSTHKmysrLOdi1NCXIzOefYvXs3oARZREREEmPs2LFkZWWxYMEC8vLyWL9+Pd27d2/UPhJVg7x8+XKOHz/O9OnT6dKlS9RtsrKyAC/xjybUGRmrRjnelCA308cff0xlZSWdOnXioosuCjocEREROUfMnz+fjh07MnfuXHJzc9mwYQM9e/ZscPtE1SA//vjjwJljH4cbPXo0ixYt4qWXXmL+/Pl11pWUlFBcXEyfPn0S9qyXhnlrpvDyCg3vJiIiIok0Z84cli5dyvbt2xk1ahT79u1rcNvS0lKccw1+LVu2rNHx/eEPf6CoqIgrrrgi6sN5IaNGjWLw4MG8+uqrvPDCC7XLa2pqakfmmDlzpmqQWwtNLy0iIiJBmjlzJmlpacyYMYOcnBwKCwvJzMwMOizg9MN59fUeA6SkpPDUU08xevRoJk6cyMSJE8nMzGTjxo1s27aNkSNHMnfu3ESEDKgHuVmOHz/O+++/j5klZEw+ERERkWgKCgp45plnKCsrIycnJ2ETatTn0KFDrFq1qt6H88JdddVVbN26lXHjxrF+/XoeeOABKioquPPOO3n55ZdJTU1NQNQe9SA3Q2h4t969e9OxY8egwxEREZE2rm/fvjGnW540aRKTJk1KcESxde3alWPHjjWqzeWXX85vfvObFoqo4dSD3AwqrxARERFpe5QgN5FzTgmyiIiISBukBLmJDh48SEVFBZ/73Oe4+OKLgw5HREREROJECXITHTp0iI4dOzJgwAAN7yYiIiLShughvSYaNGgQP/zhDzl+/HjQoYiIiIhIHKkHuRnatWun0StERERE2hglyCIiItJqxBriTCQkHn8jSpBFRESkVUhJSeHkyZNBhyFJ7uTJk6SkpDRrH0qQRUREpFVIT0+nsrIy6DAkyVVWVpKent6sfShBFhERkVahW7duHDp0iIMHD3LixAmVW0gt5xwnTpzg4MGDHDp0iG7dujVrfxrFQkRERFqF1NRUMjMzKS8vp7S0lFOnTgUdkiSRlJQU0tPTyczMJDU1tVn7UoIsIiIirUZqaiq9evWiV69eQYcibZhKLEREREREwihBFhEREREJowRZRERERCSMEmQRERERkTBKkEVEREREwihBFhEREREJowRZRERERCSMEmQRERERkTDWVqZpNLOPgbIEHrI7cDCBx5Oz0zlJPjonyUfnJLnofCQfnZPk05LnpI9z7sLIhW0mQU40M9vmnBsWdBxyms5J8tE5ST46J8lF5yP56JwknyDOiUosRERERETCKEEWEREREQmjBLnpHgs6ADmDzkny0TlJPjonyUXnI/nonCSfhJ8T1SCLiIiIiIRRD7KIiIiISBglyCIiIiIiYZQgN4GZjTGzHWa2y8zuCDqec5WZlZrZ22b2hplt85d1M7OXzWyn/7Vr0HG2VWb2pJkdMLO/hy2L+f6b2Xz/mtlhZtcGE3XbFuOc3GVmH/jXyRtm9n/C1umctDAzyzCzTWZWZGbbzez7/nJdKwGo53zoOgmImaWZ2V/M7E3/nNztLw/0GlENciOZWQpQDHwN2AtsBSY5594JNLBzkJmVAsOccwfDli0Gyp1z9/kfXro65+YFFWNbZmY5QBXwtHPuCn9Z1PffzC4Hfg1cCVwMbAAGOedOBRR+mxTjnNwFVDnn7o/YVuckAcysF9DLOfc3M0sH/gqMBwrQtZJw9ZyPfHSdBMLMDOjknKsys/OA14DvAxMI8BpRD3LjXQnscs6VOOdOAM8C4wKOSU4bByz3v1+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TVRadioNewspaperSales
0230.137.869.222.1
144.539.345.110.4
217.245.969.39.3
3151.541.358.518.5
4180.810.858.412.9
\n", + "
" + ], + "text/plain": [ + " TV Radio Newspaper Sales\n", + "0 230.1 37.8 69.2 22.1\n", + "1 44.5 39.3 45.1 10.4\n", + "2 17.2 45.9 69.3 9.3\n", + "3 151.5 41.3 58.5 18.5\n", + "4 180.8 10.8 58.4 12.9" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# again, take a quick look at the data\n", "\n", @@ -110,14 +191,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Select the 'TV' column as predictor variable and 'Sales' column as response variable \n", "\n", - "x = df[[___]]\n", - "y = df[___]" + "x = df[['TV']]\n", + "y = df['Sales']" ] }, { @@ -129,19 +210,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "### edTest(test_shape) ###\n", "# Split the dataset in training and testing with 60% training set and 40% testing set \n", "\n", - "x_train, x_test, y_train, y_test = train_test_split(___,___,train_size=___,random_state=66)" + "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.6, random_state=66)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -172,9 +253,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(figsize=(10,6))\n", "\n", @@ -186,20 +280,20 @@ "for k_value in k_list: \n", " \n", " # creating KNN Regression model \n", - " model = KNeighborsRegressor(n_neighbors=int(___))\n", + " model = KNeighborsRegressor(n_neighbors=int(k_value))\n", " \n", " # fitting model \n", - " model.fit(x_train,y_train)\n", + " model.fit(x_train, y_train)\n", " \n", " # predictions\n", - " y_pred = model.predict(___)\n", + " y_pred = model.predict(x_test)\n", " \n", " # Calculating MSE \n", - " MSE = ____\n", + " MSE = np.mean((y_test - y_pred) ** 2)\n", "\n", " \n", " #Storing the MSE values of each k value in a dictionary\n", - " knn_dict[k_value] = ___\n", + " knn_dict[k_value] = MSE\n", " \n", " \n", " ## Plotting\n", @@ -226,14 +320,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot k against MSE\n", "\n", "plt.figure(figsize=(8,6))\n", - "plt.plot(___, ___,'k.-',alpha=0.5,linewidth=2)\n", + "plt.plot(knn_dict.keys(), knn_dict.values(),'k.-',alpha=0.5,linewidth=2)\n", "\n", "plt.xlabel('k',fontsize=20)\n", "plt.ylabel('MSE',fontsize = 20)\n", @@ -250,16 +357,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best k value is 9 with a MSE of 13.04548302469136\n" + ] + } + ], "source": [ "### edTest(test_mse) ###\n", "\n", "# Looking for k with minimum MSE\n", "\n", - "min_mse = min(___)\n", - "best_model = ___ # HINT YOU MAY USE LIST COMPREHENSION \n", + "key_list = list(knn_dict.keys())\n", + "value_list = list(knn_dict.values())\n", + "\n", + "min_mse = min(knn_dict.values())\n", + "position = value_list.index(min_mse) # get index of the min mse\n", + "\n", + "best_model = key_list[position] # HINT YOU MAY USE LIST COMPREHENSION \n", "print (\"The best k value is \",best_model,\"with a MSE of \", min_mse)" ] }, @@ -278,21 +398,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your answer here " - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The R2 score for your model is 0.5492900595652463\n" + ] + } + ], "source": [ "# Run this cell to calculate the R2_score of your best model\n", - "model = KNeighborsRegressor(n_neighbors=best_model[0])\n", + "model = KNeighborsRegressor(n_neighbors=best_model)\n", "model.fit(x_train,y_train)\n", "y_pred_test = model.predict(x_test)\n", "\n", @@ -315,14 +434,12 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "# your answer here" - ] + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -336,7 +453,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" +<<<<<<< HEAD + "version": "3.7.4" +======= + "version": "3.9.7" +>>>>>>> 264b3bd09f3f92989cfd72c37e8c722c1eaef304 } }, "nbformat": 4, diff --git a/content/lectures/lecture06/notebook/s1-exc1-challenge.ipynb b/content/lectures/lecture06/notebook/s1-exc1-challenge.ipynb index 38d8647..b05e941 100644 --- a/content/lectures/lecture06/notebook/s1-exc1-challenge.ipynb +++ b/content/lectures/lecture06/notebook/s1-exc1-challenge.ipynb @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -78,32 +78,32 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a new dataframe called `df_new` witch the columns 'TV' and 'sales'\n", - "df_new = df[['TV', 'sales']]" + "df_new = df[['TV', 'Sales']]" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -117,7 +117,7 @@ "source": [ "# Plot the data\n", "\n", - "plt.plot(df_new.TV, df_new.sales, '*', label='data')\n", + "plt.plot(df_new.TV, df_new.Sales, '*', label='data')\n", "plt.xlabel('TV')\n", "plt.ylabel('Sales')\n", "plt.legend()" @@ -132,26 +132,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "### edTest(test_betas) ###\n", "# Estimate beta0 by observing the value of y when x = 0\n", - "beta0 = ___\n", + "beta0 = 1.25\n", "\n", "# Estimate beta1 - Check the slope for guidance\n", - "beta1 = ___" + "beta1 = (np.mean(df_new['Sales']) - beta0) / np.mean(df_new['TV'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.08686264175323463" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beta1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Calculate prediction of x using beta0 and beta1\n", - "y_predict = ___" + "y_predict = beta0 + df_new['TV']*beta1" ] }, { @@ -163,12 +183,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the predicted values as well as the data\n", - "plt.plot(df_new.TV, df_new.sales, '*', label='data')\n", + "plt.plot(df_new.TV, df_new.Sales, '*', label='data')\n", "plt.plot(df_new.TV, y_predict, label='model')\n", "plt.xlabel('TV')\n", "plt.ylabel('Sales')\n", @@ -184,13 +227,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My MSE is: 21.855083200465696\n" + ] + } + ], "source": [ "### edTest(test_mse) ###\n", "# Calculate the MSE\n", - "MSE = ___\n", + "MSE = np.mean((y_predict - df_new['Sales']) ** 2)\n", "\n", "# Print the results\n", "print(\"My MSE is: {0}\".format(MSE))" @@ -199,7 +250,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -213,7 +264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture06/notebook/s1-exc2-challenge.ipynb b/content/lectures/lecture06/notebook/s1-exc2-challenge.ipynb index 06d708e..ef5a8a4 100644 --- a/content/lectures/lecture06/notebook/s1-exc2-challenge.ipynb +++ b/content/lectures/lecture06/notebook/s1-exc2-challenge.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -116,40 +116,40 @@ " TV\n", " Radio\n", " Newspaper\n", - " sales\n", + " Sales\n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", " 230.1\n", " 37.8\n", " 69.2\n", " 22.1\n", " \n", " \n", - " 1\n", + " 1\n", " 44.5\n", " 39.3\n", " 45.1\n", " 10.4\n", " \n", " \n", - " 2\n", + " 2\n", " 17.2\n", " 45.9\n", " 69.3\n", " 9.3\n", " \n", " \n", - " 3\n", + " 3\n", " 151.5\n", " 41.3\n", " 58.5\n", " 18.5\n", " \n", " \n", - " 4\n", + " 4\n", " 180.8\n", " 10.8\n", " 58.4\n", @@ -160,7 +160,7 @@ "" ], "text/plain": [ - " TV Radio Newspaper sales\n", + " TV Radio Newspaper Sales\n", "0 230.1 37.8 69.2 22.1\n", "1 44.5 39.3 45.1 10.4\n", "2 17.2 45.9 69.3 9.3\n", @@ -168,7 +168,7 @@ "4 180.8 10.8 58.4 12.9" ] }, - "execution_count": 16, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -180,12 +180,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create a new dataframe called `df_new`. witch the columns ['TV' and 'sales'].\n", - "df_new = df[['TV', 'sales']]" + "df_new = df[['TV', 'Sales']]" ] }, { @@ -207,37 +207,37 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Create lists to store the MSE and beta1\n", - "mse_list = ___\n", - "beta1_list = ___" + "mse_list = []\n", + "beta1_list = []" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "### edTest(test_beta) ###\n", "\n", "# This loops runs from -2 to 3.0 with an increment of 0.1 i.e a total of 51 steps\n", - "for beta1 in ___:\n", + "for beta1 in np.arange(-2, 3, 0.1):\n", " \n", " # Calculate prediction of x using beta0 and beta1\n", - " y_predict = ___ \n", + " y_predict = beta0 + df_new['TV'] * beta1 \n", " \n", " # Calculate Mean Squared Error\n", - " mean_squared_error = ___\n", + " mean_squared_error = np.mean((y_predict - df_new['Sales']) ** 2)\n", "\n", " # Append the new MSE in the list that you created above\n", - " mse_list.___ \n", + " mse_list.append(mean_squared_error)\n", " \n", " # Also append beta1 values in the list\n", - " beta1_list.___" + " beta1_list.append(beta1)" ] }, { @@ -249,9 +249,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'MSE')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0Ycc+npm6mlG9WjKyV0u/ywlrtYVL9VvPzwA+BXDO7QH0m7MG9esk8ODPerFy6x7Gf7ve73JEJECcc9z/wRKS4uN4aHQvv8sJe7WFyyYz+4WZnU/VtZbPAcysLqD+4CGM7NWSM3uk8czUNWzKL/K7HBEJgHfnb2Zm1k7uObs7aanJfpcT9moLl+uBXsA1wKXOud1e+1Dg1SDWFfEeHt2L+DjjvveW6N4XkQi3c28Jj36ynIz2jfn5kHZ+lxMRahstluecu9k5N9o590W19q+cc08Fv7zIdVyjutx7dne+zdrB25mb/C5HRH6ERz5ezr6Scv5wQR/d03KEDnufi5lNOtz3zrmfBbac6PLzIe34aFEuj368glO7tqBlQ3WlRSLN16u388HCXH55RjrpaSl+lxMxaruJ8gRgE/AW8B2aT+yoxMUZT17Yl1F//Yb731/Cy2MyMNM/oUikKCot5/73l9C5eX1uO62z3+VElNquubQEfgv0Bv4KnAXscM597Zz7OtjFRYMOzepz94huTFuZp6lhRCLMk5+tZPPuYp64sC91EuL9Liei1HbNpcI597lzbgxVF/GzgOlm9ouQVBclrj2xI/3bNuLBScvYsbfE73JE5AjMWruDCbOzuWZYBwZ3aOJ3ORGn1idRmlkdM7sAeBO4DXgWeC/YhUWT+Djjjxf1ZV9JBQ9OWuZ3OSJSi30l5fzmncV0aFpPz2k5RrVN/zIBmEXVPS4POecGO+cecc5trm3DZjbezPLMbGm1tiZmNsXM1ng/G3vtZmbPmlmWN+vywGrrjPGWX2NmY6q1DzKzJd46z5p3MeNQ+/Bb17QUfnF6Fz5evIXJy7b6XY6IHMYT3umwP13cj7pJOh12LGrruVwFdAXuAGaZWaH32mNmhbWs+xow6qC2e4Fpzrl0YJr3GeBsIN173QiMhaqgAB6garqZIcAD1cJirLfsgfVG1bIP3908vDM9WqXyuw+WUlBUVvsKIhJys7J28MacbK47saNOh/0ItV1ziXPOpXiv1GqvFOdcai3rfgPkH9Q8GpjgvZ8AnFet/XVXZQ7QyJsocyRV85nlO+d2AVOAUd53qc652a7qDsXXD9pWTfvwXWJ8HH+6qC/5+0p5YNLS2lcQkZDaW1LOr99ZTEdvII4cu1qvuQRYmnNuC4D3s4XX3pqqIc8H5Hhth2vPqaH9cPsIC71bN+QXp3fhg4W5fLJ4i9/liEg1f/h0BbkFxfzpor46HfYjhTpcDqWmmz/cMbQf3U7NbjSzTDPL3L59+9GufsxuO60L/do05P4PlpBXuD9k+xWRQ/t2zQ7+8d1GbjipIxk6HfajhTpctnmntA48HybPa88Bqj+Eug2QW0t7mxraD7ePH3DOjXPOZTjnMpo3b37MB3W0EuPj+PMl/SkureA37y7W3GMiPtuzv4x73l1Mp+b1uUunwwIi1OEyCTgw4msM8GG19qu9UWNDgQLvlNZkYISZNfYu5I8AJnvf7TGzod4osasP2lZN+wgrXVo04L6zuzN91Xbe+l5zj4n46aGPlrOloJg/XdSP5ESdDguEoIWLmb0FzAa6mVmOmV0PPAGcZWZrqLrb/wlv8U+BdVTdpPkScCuAcy4feASY670e9toAbgFe9tZZC3zmtR9qH2Hn6hM6cGKXpjz6yXKyd+7zuxyRmPTpki28My+H207rwqD2YXHnQlQwnZKpkpGR4TIzM0O+39zdxYx85hu6paXwr5tOIF4zroqEzJaCYkY9M4MOTevxzi3DSIwPl8vQkcPM5jnnMg5u17+kz45rVJeHR/ciM3sX475Z53c5IjGjstJx978XUVpeydOX9lewBJj+NcPAef1bc06flvxlyiqW59Z2b6qIBML4meuZmbWT/zu3J52aN/C7nKijcAkDZsaj5/WhUb0kfjlxAcWlFX6XJBLVlucW8sfPV3FWzzQuG9y29hXkqClcwkST+kk8fUl/1m7fy8MfL/e7HJGotb+sgjv/tYCG9RJ58sK+esZSkChcwshJ6c246ZTOvPX9Rt29LxIkT3y2ktXb9vLUxf1oUj/J73KilsIlzNw1oiv92zbi3vcWsym/yO9yRKLK9FV5vDZrA9cM68CpXUN343QsUriEmcT4OJ67fAA4uGPiAsoqKv0uSSQqbCvcz11vL6JrWgPuPVvPaAk2hUsYatukHo9f0If5G3fzzNTVfpcjEvHKKyr55VsLKCqt4IUrBuou/BBQuISpc/sdxyUZbXhh+lpmZe3wuxyRiPbXaWv4bn0+j57Xmy4tUvwuJyYoXMLYgz/rRadm9bnzXwvZubfE73JEItKMNdv521dZXDyoDRcOalP7ChIQCpcwVi8pgecuH8juojLu+vciKis1VY/I0dhWuJ87Jy4kvUUDHh7d2+9yYorCJcz1PC6V35/bk+mrqv76EpEjU/06y/M/H6iHf4WYwiUCXHl8O84f0Jqnp67mm9Whe6iZSCR71rvO8sh5vUlP03WWUFO4RAAz47Hze9O1RQp3TFzA5t3FfpckEta+XbOD577K4qJBbbhI11l8oXCJEPWSEhh75UDKKxy3vjmPknLNPyZSk827i/nlxAV0ad6Ah0f38rucmKVwiSCdmjfgTxf3Y1FOAY9o/jGRH9hfVsHNb8yjtLySv181iHpJCX6XFLMULhFmVO+W3HRKJ96cs5H35uf4XY5I2HDOcf/7S1myuYCnL+1PZ02j7yuFSwT69chuHN+xCb99fwkrtuj5LyIAb8zJ5t35OfzyjHTO6pnmdzkxT+ESgRLi43ju5wNITU7k5jfnUVBU5ndJIr76fn0+D3+0nDO6t+DOM9L9LkdQuESsFinJjL1yILm7i7ntn/Mp1wSXEqO2Fuzn1n/Mp22Tevzl0v7Exen5LOFA4RLBBrVvwmPn9+HbrB08+skKv8sRCbmS8gpufnMexaXljLtqEA3rJvpdkng0lCLCXZLRllVb9/DKt+vp1jKFy4e087skkZBwzvHgpGUs3LSbsVcM1I2SYUY9lyhw39ndOaVrc37/wVK+W7fT73JEQmL8zA289f0mbh3embP7tPK7HDmIwiUKJHgPGGvXtB63/GO+nmApUW/q8m08+slyRvVqyd0juvldjtRA4RIlGtZN5OWrMyivqOSGCZnsLSn3uySRoFiWW8AvJy6gT+uGPK0L+GFL4RJFOjVvwPNXDCRr+17unLhQU/RL1NlWuJ/rX8v8zx9Tmuk4fClcoszJ6c353U96MHXFNh77VCPIJHoUlZZz/YS57NlfxitjBtMiNdnvkuQwNFosCl0zrAPZO4t45dv1tGqYzA0nd/K7JJEfpbLScefEhSzPLeTlMRn0PC7V75KkFgqXKGRm/P6nPdlasJ/HPl1Bq4Z1+UlfjaaRyPXk5yv5Yvk2Hji3J6d319QukUCnxaJUfJzxzGX9GdSuMb96eyHfr8/3uySRYzJh1gZe/GYdV5/QnmuGdfC7HDlCCpcolpwYz0tXZ9CmcV1umDCXNdv2+F2SyFH5aFEuD360jLN6pvF/P+2JmUaGRQqFS5RrXD+JCdcOISkhnmtencu2wv1+lyRyRGas2c7/vr2Qwe2b8NzlA0iI16+rSKL/WjGgbZN6vHrNYHYVlXLtq1WjbUTC2aJNu7npjXl0bt6Al8ZkkJyoIceRRuESI/q0acgLVwxk1bY9/M/rmewv02OSJTyt3b6Xa1+bS5P6Sbx+3RBNRhmhfAkXM9tgZkvMbKGZZXptTcxsipmt8X429trNzJ41sywzW2xmA6ttZ4y3/BozG1OtfZC3/SxvXZ2oBYZ3a8FfLunHd+vzufnNqkfBioSTbYX7ufqV7zHgjeuP170sEczPnstpzrn+zrkM7/O9wDTnXDowzfsMcDaQ7r1uBMZCVRgBDwDHA0OABw4EkrfMjdXWGxX8w4kMo/u35vHz+zB91XbumLhAz4GRsFFQVMbVr3xPQXEZE64bQsdm9f0uSX6EcDotNhqY4L2fAJxXrf11V2UO0MjMWgEjgSnOuXzn3C5gCjDK+y7VOTfbOeeA16ttS4DLh7Tjdz/pwWdLt/KbdxdrmhjxXeH+Mq4e/x3rd+xj3FWD6N26od8lyY/k102UDvjCzBzwonNuHJDmnNsC4JzbYmYtvGVbA5uqrZvjtR2uPaeG9h8wsxup6uHQrl1sPQflhpM7UVRawV+mrKZ+UgIPj+6lYZ7iiz37yxgz/nuWbynk71cOYliXZn6XJAHgV7ic6JzL9QJkipmtPMyyNf3Gc8fQ/sPGqlAbB5CRkRFzf77/4vQu7Csp58Vv1lGvTjz3juqugJGQ2ltSzjWvzmVJTgHPXzGQM3ro7vto4Uu4OOdyvZ95ZvY+VddMtplZK6/X0grI8xbPAdpWW70NkOu1Dz+ofbrX3qaG5eUgZsa9Z3dnX2k5L369juSEeO48M10BIyFRVFrOda/OZeGm3fzt8gGM7NXS75IkgEJ+zcXM6ptZyoH3wAhgKTAJODDiawzwofd+EnC1N2psKFDgnT6bDIwws8behfwRwGTvuz1mNtQbJXZ1tW3JQcyMh3/Wm4sGteGv09bw1BerqLpUJRI8xaUVXPfaXDKz83nm0v56kmQU8qPnkga87/11nAD80zn3uZnNBd42s+uBjcDF3vKfAucAWUARcC2Acy7fzB4B5nrLPeycOzCB1i3Aa0Bd4DPvJYcQF2f88cK+JMYbz3+1ltLySn57Tg/1YCQo9pdV8D+vZ/L9+nyevrQ/5/Y7zu+SJAhCHi7OuXVAvxradwJn1NDugNsOsa3xwPga2jOB3j+62BgSF2c8dl4fkuLjeGnGesoqHA+cq7mcJLD2lZRz4xuZzFq7k6cu6sfo/jWOtZEooCn35T/i4owHf9aLpISqgCkpr+Sx83rrMbISELuLSrn2tbkszingqYv6ceGgNrWvJBFL4SL/xcz47Tk9SEqI4/mv1lJWUcmTF/YlXgEjP0Lenqo779dt38cLVwzUxfsYoHCRHzAz7h7RjaT4eJ6eupqS8kr+fHE/khLC6Z5biRSb8ou48pXv2L6nhPHXDOakdN3HEgsULlIjM+OOM9OpkxjHE5+tJH9fCX+/chApyZpEUI5cVt4ernz5e4pKy3nzhuMZ2K5x7StJVNCfonJYN5/amacu7sd36/K59MU55Ol5MHKElm4u4JIX51Be6fjXTScoWGKMwkVqddGgNrw8JoMNO/dxwdhZrN2+1++SJMx9tTKPS1+cTd3EeN65+QR6tEr1uyQJMYWLHJHh3Vow8cah7C+r4MKxs5iXvcvvkiRMvTF7A9dPmEuHZvV579ZhdNDsxjFJ4SJHrG+bRrx7yzAa1U3kipfnMGX5Nr9LkjBSUel4+KPl/P7DZZzevQVv33QCaXoeS8xSuMhRad+0Pu/cMoxuaSnc9EYm475Zq+lihKLScm56Yx7jZ67nuhM78uJVGdSvo/FCsUzhIketWYM6vHXjUM7u3YrHP13Jr/61UI9NjmHbCvdzyYuz+XLlNh76WS/+79yeui9KNBRZjk29pAT+9vMB9Pgqhae+WM26Hft48apBtGpY1+/SJIQWbtrNLW/Oo7C4jJfHZHB6d02ZL1XUc5FjZmbcfno6L12dwdq8vZz73EzmZefXvqJEPOccb87J5uK/zyI+znj75hMULPJfFC7yo53VM433bzuR+nXiuWzcHP41d6PfJUkQFZdWcNe/F/G7D5ZyYpdmfPyLk+h1nB5LLP9N4SIB0TUthQ9vO5GhnZpyz7tL+PW/F1FUWu53WRJg2Tv3cf4LM3l/wWbuPDOd8WMG06hekt9lSRhSuEjANKqXxKvXDOb207rwzvwczn3uW5bnFvpdlgTI1OXb+Olz37KlYD+vXjOYO8/sqhmz5ZAULhJQCfFx3D2yG/+4/nj27C/nvBdm8sbsDRquHMH2l1Xw0EfLuOH1TNo3rcfHvziJ4d1a+F2WhDmFiwTFsC7N+OyOkxnWuSm//3AZN785j4KiMr/LkqO0LLeAc5/7lldnbmDMCe155+ZhtG1Sz++yJAIoXCRomjaow/gxg/ndT3rw5co8znl2BrPX7vS7LDkCFZWOsdPXct7zMykoLmPCdUN4aHRvkhPj/S5NIoTCRYIqLs644eROvHvLMBLjjdd2aAwAAArzSURBVMtfmsP97y9hz371YsLVpvwiLh83hyc/X8lZPdOYfOcpnNq1ud9lSYTRTZQSEn3bNOKzO07hz1+s4pWZ6/lqZR6PX9BH5+7DSGWlY+LcTTz+6QoA/nxxPy4Y2BozXbSXo2e60FolIyPDZWZm+l1GTJi/cRe/eWcxWXl7uXBgG37/0x4azuqzFVsKuf/9JczfuJuhnZrwp4v66dqKHBEzm+ecy/hBu8KlisIltErKK3huWhZjv15L43pJPPiznvykTyv9lRxiRaXlPDN1Da98u56GdRO5/5we6q3IUVG41ELh4o+lmwu4593FLMstZEiHJvzfuT3p3Vp3e4fClOXbeHDSMjbvLuaywW25Z1R3GtdXD1KOjsKlFgoX/1RUOibO3cifv1jNrqJSLhnUlrtHdqN5Sh2/S4tKq7bu4cnPV/Llyjy6pjXgsfP7MLhDE7/LkgilcKmFwsV/BcVlPDdtDa/N2kByYjy3n96Fa0/sQJ0EDX8NhM27i3l6ymrenZ9DgzoJ3H5aF647qSOJ8Ro0KsdO4VILhUv4WLd9L49/uoKpK/Jo07gut5/WhQsGtiEpQb8Ej8XuolJemL6W12ZtAAdjhrXn1uFddApMAkLhUguFS/iZsWY7T01exaKcAlo3qsttp3XhokEKmSNVUFzGm3Oy+fvXa9lbUs4FA9rwq7PSadNYo8AkcBQutVC4hCfnHNNXb+evU9ewcNNujmuYzC2ndeGSjDY6XXYIWwqKGf/tev753Ub2lVZwevcW/GZUN7q3TPW7NIlCCpdaKFzCm3OOGWt28Ndpa5iXvYu01DpccXx7LhvSlhYpyX6XFxZWbd3DuG/W8eHCzTjgp31b8T8nd9LoOwkqhUstFC6RwTnHzKydvPjNWmas2UFCnDGqd0uuGtqeIR2bxNz9GaXllXy5Mo+JczcyfdV26ibGc+ngtlx/UkfdBCkhcahw0fQvElHMjJPSm3FSejPW79jHm3Oy+XfmJj5evIVuaSlcObQd5/Y7Lurv+F+5tZB/Z+bwwYLN7NxXSouUOtx1VleuHNpeF+olLKjn4lHPJXIVl1YwadFmXp+dzbLcQhLijBO7NOOnfVsxomdLGtZL9LvEgMgr3M/kZVv597wcFucUkBhvnNkjjYsz2nBKenMSNKRYfKDTYrVQuEQ+5xxLNxfy8ZJcPlm8hZxdxSTGGyenN+ecPq04rVtzmjaInBsznXMs31LItBV5TFuxjUU5BQB0b5nCJRltOW9Aa5qolyI+U7jUQuESXZxzLM4p4JMlW/hk8RY27y4Gqn4xn9C5KcM6N2NIxyY0rBtevZotBcUs2LibWWt38OWKPHIL9mMG/do04sweLTijRxrdW6bE3LUlCV8xFy5mNgr4KxAPvOyce+JwyytcopdzjkU5BczM2sGstTvI3LCLkvJK4gx6t27IwHaN6d4yhe6tUuma1oB6SaG5FFlcWsHyLYUs2LiLBRt3M3/jLrYU7AegbmI8J6c348weaZzWvYWmwpGwFVPhYmbxwGrgLCAHmAtc7pxbfqh1FC6xo6S8ggUbdzN77U5mr93Jks0FFJdVAGAG7ZrUo1taCl1aNKBFSh1apCZX/UxJpkVqnSN6GmNZRSV79pdTWFxGQXEZubuL2bCziOyd+1i/Yx/ZO4vYWrj/P8u3blSXge0bM7BdIwa0a0zPVqm6WVQiQqyNFhsCZDnn1gGY2URgNHDIcJHYUSchnqGdmjK0U1N+dVbVQ7I27SpixZY9rNq6h1XbClm5dQ/TVuZRUfnDP77qJ8WTmBBHQpyREBdHfJyRGG/EmbGvtJzC4vL/hNXBmjWoQ4em9TixSzM6NK1HeloKA9s1okWq7tWR6BKt4dIa2FTtcw5w/MELmdmNwI0A7dq1C01lEnbi4oz2TevTvml9RvVu+Z/2ykpHflEpeYUlbN9bQl7hfvL2lJC/r5TyikrKKh0VFY6yykrKKxwVzlE/KZ7U5ERS6yaSmpxAat1EUpITadUwmfZN65GSHF7XeESCJVrDpaarnT/4E9Q5Nw4YB1WnxYJdlESWuDijWYM6NIugEWYi4SJaT+rmAG2rfW4D5PpUi4hIzInWcJkLpJtZRzNLAi4DJvlck4hIzIjK02LOuXIzux2YTNVQ5PHOuWU+lyUiEjOiMlwAnHOfAp/6XYeISCyK1tNiIiLiI4WLiIgEnMJFREQCTuEiIiIBF5Vzix0LM9sOZB/j6s2AHQEsJ1LouGNPrB67jvvQ2jvnmh/cqHAJADPLrGnitmin4449sXrsOu6jp9NiIiIScAoXEREJOIVLYIzzuwCf6LhjT6weu477KOmai4iIBJx6LiIiEnAKFxERCTiFS4CY2Z/MbKWZLTaz982skd81hYKZXWxmy8ys0syifqimmY0ys1VmlmVm9/pdTyiY2XgzyzOzpX7XEkpm1tbMvjKzFd7/xu/wu6ZQMLNkM/vezBZ5x/3QsWxH4RI4U4Dezrm+wGrgPp/rCZWlwAXAN34XEmxmFg88D5wN9AQuN7Oe/lYVEq8Bo/wuwgflwF3OuR7AUOC2GPnvXQKc7pzrB/QHRpnZ0KPdiMIlQJxzXzjnyr2Pc6h6+mXUc86tcM6t8ruOEBkCZDnn1jnnSoGJwGifawo659w3QL7fdYSac26Lc26+934PsAJo7W9Vweeq7PU+Jnqvox75pXAJjuuAz/wuQgKuNbCp2uccYuCXjYCZdQAGAN/5W0lomFm8mS0E8oApzrmjPu6ofVhYMJjZVKBlDV/d75z70Fvmfqq60/8IZW3BdCTHHSOshjaN5Y9yZtYAeBe40zlX6Hc9oeCcqwD6e9eO3zez3s65o7rmpnA5Cs65Mw/3vZmNAX4KnOGi6Aai2o47huQAbat9bgPk+lSLhICZJVIVLP9wzr3ndz2h5pzbbWbTqbrmdlThotNiAWJmo4B7gJ8554r8rkeCYi6QbmYdzSwJuAyY5HNNEiRmZsArwArn3F/8ridUzKz5gdGuZlYXOBNYebTbUbgEzt+AFGCKmS00s7/7XVAomNn5ZpYDnAB8YmaT/a4pWLwBG7cDk6m6uPu2c26Zv1UFn5m9BcwGuplZjpld73dNIXIicBVwuvf/6YVmdo7fRYVAK+ArM1tM1R9UU5xzHx/tRjT9i4iIBJx6LiIiEnAKFxERCTiFi4iIBJzCRUREAk7hIiIiAadwEQkhM6vwhrQuMrP5ZjasluUbmdmtR7jtmJy9WMKTwkUktIqdc/29GWfvA/5Qy/KNgCMKF2J39mIJQwoXEf+kArsOfDCzX5vZXO+ZQAeeofEE0Nnr7fzJzBqY2TSv17PEzP4zK3Oszl4s4Ulzi4mEVl1vttlkqu6EPh3AzEYA6VRN62/AJDM7BbiXqucE9feWSwDOd84VmlkzYI6ZTYqmuewkOihcREKruFpQnAC8bma9gRHea4G3XAOqwmbjQesb8LgXPJVUTfmfBmwNQe0iR0zhIuIT59xsr/fRnKrQ+INz7sXqy3jPEanuCm/5Qc65MjPbQFUvSCSs6JqLiE/MrDsQD+ykajLM67xnh2Bmrc2sBbCHqglRD2gI5HnBchrQPsRlixwR9VxEQuvANReo6q2M8R7M9IWZ9QBmV830zl7gSufcWjOb6Q0v/gx4EvjIzDKBhVSbCt2bvXg40MybqfoB59wroTowkeo0K7KIiAScTouJiEjAKVxERCTgFC4iIhJwChcREQk4hYuIiAScwkVERAJO4SIiIgH3/7j2qgTuHOu/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "### edTest(test_mse) ###\n", "# Plot MSE as a function of beta1\n", @@ -271,17 +294,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39.00352950000301\n", + "0.10000000000000187\n" + ] + } + ], "source": [ - "# Your answer here" + "print(min(mse_list)) # It's higher\n", + "print(beta1_list[mse_list.index(min(mse_list))])" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -295,7 +328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture06/notebook/s1-exc3-challenge.ipynb b/content/lectures/lecture06/notebook/s1-exc3-challenge.ipynb index 64380d8..b4090b7 100644 --- a/content/lectures/lecture06/notebook/s1-exc3-challenge.ipynb +++ b/content/lectures/lecture06/notebook/s1-exc3-challenge.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -116,40 +116,40 @@ " TV\n", " Radio\n", " Newspaper\n", - " sales\n", + " Sales\n", " \n", " \n", " \n", " \n", - " 0\n", + " 0\n", " 230.1\n", " 37.8\n", " 69.2\n", " 22.1\n", " \n", " \n", - " 1\n", + " 1\n", " 44.5\n", " 39.3\n", " 45.1\n", " 10.4\n", " \n", " \n", - " 2\n", + " 2\n", " 17.2\n", " 45.9\n", " 69.3\n", " 9.3\n", " \n", " \n", - " 3\n", + " 3\n", " 151.5\n", " 41.3\n", " 58.5\n", " 18.5\n", " \n", " \n", - " 4\n", + " 4\n", " 180.8\n", " 10.8\n", " 58.4\n", @@ -160,7 +160,7 @@ "" ], "text/plain": [ - " TV Radio Newspaper sales\n", + " TV Radio Newspaper Sales\n", "0 230.1 37.8 69.2 22.1\n", "1 44.5 39.3 45.1 10.4\n", "2 17.2 45.9 69.3 9.3\n", @@ -168,7 +168,7 @@ "4 180.8 10.8 58.4 12.9" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -189,23 +189,23 @@ "\n", "\n", "x = df[[\"TV\"]]\n", - "y = df[\"sales\"]" + "y = df[\"Sales\"]" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# divide the data into training and validation sets\n", "\n", - "x_train, x_test, y_train, y_test = train_test_split(___,___,train_size=0.8)" + "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -213,23 +213,23 @@ "\n", "model = LinearRegression()\n", "\n", - "model.fit(___, ___)\n", + "model.fit(x_train, y_train)\n", "\n", "# Now predict on the test set\n", "\n", - "y_pred_test = model.predict(___)" + "y_pred_test = model.predict(x_test)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The test MSE is 10.1024550349862\n" + "The test MSE is 7.510000690502556\n" ] } ], @@ -237,28 +237,28 @@ "### edTest(test_mse) ###\n", "# Now compute the MSE with the predicted values and print it\n", "\n", - "mse = mean_squared_error(___, ___)\n", - "print(f'The test MSE is {___}')" + "mse = mean_squared_error(y_test, y_pred_test)\n", + "print(f'The test MSE is {mse}')" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -274,7 +274,7 @@ "\n", "fig, ax = plt.subplots()\n", "ax.scatter(x,y,label='data points')\n", - "ax.plot(___,___,color='red',linewidth=2,label='model predictions')\n", + "ax.plot(x_test, y_pred_test, color='red',linewidth=2,label='model predictions')\n", "ax.set_xlabel('Advertising')\n", "ax.set_ylabel('Sales')\n", "ax.legend()" @@ -293,19 +293,35 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test MSE is 9.261400904373627\n" + ] + } + ], "source": [ - "# your answer here" + "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.6)\n", + "\n", + "model = LinearRegression()\n", + "model.fit(x_train, y_train)\n", + "\n", + "y_pred_test = model.predict(x_test)\n", + "\n", + "mse = mean_squared_error(y_test, y_pred_test)\n", + "print(f'The test MSE is {mse}')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "MSE get worse" + ] } ], "metadata": { @@ -324,7 +340,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/content/lectures/lecture06/notebook/s2-exa1-challenge.ipynb b/content/lectures/lecture06/notebook/s2-exa1-challenge.ipynb index 5fc8f11..1fee9e7 100644 --- a/content/lectures/lecture06/notebook/s2-exa1-challenge.ipynb +++ b/content/lectures/lecture06/notebook/s2-exa1-challenge.ipynb @@ -43,6 +43,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting prettytable\n", + " Downloading prettytable-3.0.0-py3-none-any.whl (24 kB)\n", + "Requirement already satisfied: wcwidth in c:\\users\\ghtk\\anaconda3\\lib\\site-packages (from prettytable) (0.2.5)\n", + "Installing collected packages: prettytable\n", + "Successfully installed prettytable-3.0.0\n" + ] + } + ], + "source": [ + "!pip install prettytable" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "#import necessary libraries\n", @@ -174,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -187,29 +208,29 @@ "\n", "for i in cols:\n", " #Set each of the predictors from the previous list as x\n", - " x = df[___]\n", + " x = df[i]\n", " \n", " \n", " #\"Sales\" column is the reponse variable\n", - " y = df[___]\n", + " y = df[\"Sales\"]\n", " \n", " \n", " #Splitting the data into train-test sets with 80% training data and 20% testing data. \n", " #Set random_state as 0\n", - " xtrain, xtest, ytrain, ytest = train_test_split(___)\n", + " xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=0.8, random_state=42)\n", "\n", " #Create a LinearRegression object and fit the model\n", " lreg = LinearRegression()\n", - " lreg.fit(___)\n", + " lreg.fit(xtrain, ytrain)\n", " \n", " #Predict the response variable for the test set\n", - " y_pred= lreg.predict(___)\n", + " y_pred= lreg.predict(xtest)\n", " \n", " #Compute the MSE\n", - " MSE = mean_squared_error(___)\n", + " MSE = mean_squared_error(ytest, y_pred)\n", " \n", " #Append the MSE to the list\n", - " mse_list.append(___)\n" + " mse_list.append(MSE)\n" ] }, { @@ -221,9 +242,27 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------------------+--------------------+\n", + "| Predictors | MSE |\n", + "+------------------------------+--------------------+\n", + "| ['TV'] | 10.204654118800956 |\n", + "| ['Radio'] | 23.248766588129108 |\n", + "| ['Newspaper'] | 30.620733995242563 |\n", + "| ['TV', 'Radio'] | 3.1379480090683516 |\n", + "| ['TV', 'Newspaper'] | 11.062557300662819 |\n", + "| ['Radio', 'Newspaper'] | 23.204643745444592 |\n", + "| ['TV', 'Radio', 'Newspaper'] | 3.174097353976105 |\n", + "+------------------------------+--------------------+\n" + ] + } + ], "source": [ "t = PrettyTable(['Predictors', 'MSE'])\n", "\n", @@ -245,13 +284,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Your answer here" + "* Min MSE is with predictors TV and Radio, while the max is with Radio only\n", + "* " ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -265,7 +305,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture07/notebook/s2-exb1-challenge.ipynb b/content/lectures/lecture07/notebook/s2-exb1-challenge.ipynb index 68a8453..6eca5ea 100644 --- a/content/lectures/lecture07/notebook/s2-exb1-challenge.ipynb +++ b/content/lectures/lecture07/notebook/s2-exb1-challenge.ipynb @@ -84,35 +84,91 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'DataFrame' object has no attribute '___'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'poly.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Get the column values for x & y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m___\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m___\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5128\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5129\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5130\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5132\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute '___'" - ] - } - ], + "outputs": [], "source": [ "# Read the data from 'poly.csv' to a dataframe\n", - "df = pd.read_csv('poly.csv')\n", + "df = pd.read_csv(r\"poly.csv\")\n", "# Get the column values for x & y in numpy arrays\n", - "x = df[['x']].___\n", - "y = df['y'].___" + "x = df[['x']].values\n", + "y = df[['y']].values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot x & y to visually inspect the data\n", "\n", @@ -135,21 +204,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# Fit a linear model on the data\n", - "model = ____\n", - "model.___(___)\n", + "model = LinearRegression()\n", + "model.fit(x, y)\n", "\n", "# Get the predictions on the entire data using the .predict() function\n", - "y_lin_pred = model.predict(___)" + "y_lin_pred = model.predict(x)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -157,28 +226,28 @@ "# Now, we try polynomial regression\n", "# GUESS the correct polynomial degree based on the above graph\n", "\n", - "guess_degree = ___\n", + "guess_degree = 3\n", "\n", "# Generate polynomial features on the entire data\n", - "x_poly= PolynomialFeatures(degree=guess_degree).fit_transform(___)\n" + "x_poly= PolynomialFeatures(degree=guess_degree).fit_transform(x)\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "#Fit a polynomial model on the data, using x_poly as features\n", "polymodel = LinearRegression()\n", - "polymodel.fit(_,_)\n", + "polymodel.fit(x_poly, y)\n", "\n", - "y_poly_pred = polymodel.predict(___)" + "y_poly_pred = polymodel.predict(x_poly)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -201,18 +270,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# First plot x & y values using plt.scatter\n", - "plt.scatter(_, _, s=10, label=\"Data\")\n", + "plt.figure(figsize=(15, 8))\n", + "plt.scatter(x, y, s=10, label=\"Data\", color='grey')\n", "\n", "# Now, plot the linear regression fit curve\n", - "plt.plot(_,_,label=\"Linear fit\")\n", + "plt.plot(x, y_lin_pred,label=\"Linear fit\", color='darkred', alpha=0.5)\n", "\n", "# Also plot the polynomial regression fit curve\n", - "plt.plot(_, _, label=\"Polynomial fit\")\n", + "plt.plot(x, y_poly_pred, label=\"Polynomial fit\", color='darkgreen', alpha=0.5)\n", "\n", "#Assigning labels to the axes\n", "plt.xlabel(\"x values\")\n", @@ -223,52 +306,65 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "### edTest(test_poly_predictions) ###\n", "#Calculate the residual values for the polynomial model\n", - "poly_residuals = ___\n" + "poly_residuals = y_poly_pred - y\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "### edTest(test_linear_predictions) ###\n", "#Calculate the residual values for the linear model\n", - "lin_residuals = ___" + "lin_residuals = y_lin_pred - y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "#Use the below helper code to plot residual values\n", "#Plot the histograms of the residuals for the two cases\n", "\n", "#Distribution of residuals\n", - "fig, ax = plt.subplots(1,2, figsize = (10,4))\n", + "fig, ax = plt.subplots(1,2, figsize = (15,6))\n", "bins = np.linspace(-20,20,20)\n", "ax[0].set_xlabel('Residuals')\n", "ax[0].set_ylabel('Frequency')\n", "\n", "#Plot the histograms for the polynomial regression\n", - "ax[0].hist(___, bins,label = ___)\n", + "ax[0].hist(poly_residuals, bins,label = 'Polynomial residuals', color='darkred', alpha=0.5)\n", "\n", "#Plot the histograms for the linear regression\n", - "ax[0].hist(___, bins, label = ___)\n", + "ax[0].hist(lin_residuals, bins, label = 'Linear residuals', color='darkgreen', alpha=0.5)\n", "\n", "ax[0].legend(loc = 'upper left')\n", "\n", "# Distribution of predicted values with the residuals\n", - "ax[1].scatter(y_poly_pred, poly_residuals, s=10)\n", - "ax[1].scatter(y_lin_pred, lin_residuals, s = 10 )\n", + "ax[1].scatter(y_poly_pred, poly_residuals, s=10, color='darkred', alpha=0.5)\n", + "ax[1].scatter(y_lin_pred, lin_residuals, s = 10, color='darkgreen', alpha=0.5 )\n", "ax[1].set_xlim(-75,75)\n", "ax[1].set_xlabel('Predicted values')\n", "ax[1].set_ylabel('Residuals')\n", @@ -301,7 +397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/content/lectures/lecture07/notebook/s2-exc1-challenge.ipynb b/content/lectures/lecture07/notebook/s2-exc1-challenge.ipynb index e27f463..1400eb3 100644 --- a/content/lectures/lecture07/notebook/s2-exc1-challenge.ipynb +++ b/content/lectures/lecture07/notebook/s2-exc1-challenge.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -151,7 +151,7 @@ "4 0.664165 -1.373739 0.317570 -0.437413 -72.681681" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -171,22 +171,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Choose all the predictors as the variable 'X' (note capitalization of X for multiple features)\n", "\n", - "X = df.drop([___],axis=1)\n", + "X = df.drop(['y'],axis=1)\n", "\n", "# Choose the response variable 'y' for y values\n", "\n", - "y = df.___" + "y = df.y" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -197,14 +197,14 @@ "\n", "for i in X:\n", " \n", - " x = df[[___]]\n", + " x = df[[i]]\n", "\n", " #Create a linear regression object\n", - " linreg = ____\n", + " linreg = LinearRegression()\n", "\n", " #Fit it with training values. \n", " #Remember to choose only one column at a time as the predictor variable\n", - " linreg.fit(___,___)\n", + " linreg.fit(x, y)\n", " \n", " # Add the coefficient value of the model to the list\n", " linear_coef.append(linreg.coef_)\n" @@ -219,18 +219,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Here you must do a multi-linear regression with all predictors\n", "\n", "# use sklearn library to define a new model 'multi_linear'\n", - "multi_linear = ____\n", + "multi_linear = LinearRegression()\n", "\n", "# Fit the multi-linear regression on all features and the response\n", "\n", - "multi_linear.fit(___,___)\n", + "multi_linear.fit(X, y)\n", "\n", "# append the coefficients (plural) of the model to a variable multi_coef\n", "\n", @@ -246,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -306,12 +306,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -345,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/content/lectures/lecture07/notebook/s2-exd1-challenge.ipynb b/content/lectures/lecture07/notebook/s2-exd1-challenge.ipynb index 02fc649..f4be413 100644 --- a/content/lectures/lecture07/notebook/s2-exd1-challenge.ipynb +++ b/content/lectures/lecture07/notebook/s2-exd1-challenge.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -97,14 +97,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Assign the values to the predictor and response variables\n", "\n", - "x = df[['x']].___\n", - "y = df.y.___" + "x = df[['x']].values\n", + "y = df.y.values" ] }, { @@ -116,9 +116,8 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -130,7 +129,7 @@ "#Split the dataset into train and validation sets with 75% Training set and 25% validation set. \n", "#Set random_state=1\n", "\n", - "x_train, x_val, y_train, y_val = train_test_split(___)" + "x_train, x_val, y_train, y_val = train_test_split(x, y, train_size=0.75, random_state=0)" ] }, { @@ -142,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "### edTest(test_regression) ###\n", "# To iterate over the range, select the maximum degree of the polynomial\n", - "maxdeg = ___\n", + "maxdeg = 5\n", "\n", "# Create two empty lists to store training and validation MSEs\n", "training_error, validation_error = [],[]\n", @@ -157,19 +156,19 @@ "for d in range(maxdeg):\n", " \n", " #Compute the polynomial features for the train and validation sets\n", - " x_poly_train = PolynomialFeatures(d).fit_transform(___)\n", - " x_poly_val = PolynomialFeatures(d).fit_transform(___)\n", + " x_poly_train = PolynomialFeatures(d).fit_transform(x_train)\n", + " x_poly_val = PolynomialFeatures(d).fit_transform(x_val)\n", " \n", " lreg = LinearRegression()\n", " lreg.fit(x_poly_train, y_train)\n", " \n", - " y_train_pred = lreg.predict(___)\n", - " y_val_pred = lreg.predict(___)\n", + " y_train_pred = lreg.predict(x_poly_train)\n", + " y_val_pred = lreg.predict(x_poly_val)\n", " \n", " #Compute the train and validation MSE\n", " \n", - " training_error.append(mean_squared_error(___))\n", - " validation_error.append(mean_squared_error(___))\n", + " training_error.append(mean_squared_error(y_train, y_train_pred))\n", + " validation_error.append(mean_squared_error(y_val, y_val_pred))\n", " " ] }, @@ -182,9 +181,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best degree of the model is 3\n" + ] + } + ], "source": [ "### edTest(test_best_degree) ###\n", "\n", @@ -206,14 +213,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the errors as a function of increasing d value to visualise the training and testing errors\n", "\n", @@ -221,11 +240,11 @@ "\n", "#Plot the training error with labels\n", "\n", - "ax.plot(___)\n", + "ax.plot(range(maxdeg), training_error, label='Training error')\n", "\n", "#Plot the validation error with labels\n", "\n", - "ax.plot(___)\n", + "ax.plot(range(maxdeg), validation_error, label='Validation error')\n", "\n", "# Set the plot labels and legends\n", "\n", @@ -243,6 +262,46 @@ "#### Once you have marked your exercise, run again with Random_state = 0" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the errors as a function of increasing d value to visualise the training and testing errors\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "#Plot the training error with labels\n", + "\n", + "ax.plot(range(maxdeg), training_error, label='Training error')\n", + "\n", + "#Plot the validation error with labels\n", + "\n", + "ax.plot(range(maxdeg), validation_error, label='Validation error')\n", + "\n", + "# Set the plot labels and legends\n", + "\n", + "ax.set_xlabel('Degree of Polynomial')\n", + "ax.set_ylabel('Mean Squared Error')\n", + "ax.legend(loc = 'best')\n", + "ax.set_yscale('log')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -260,7 +319,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -274,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture07/notebook/s2-exd2-challenge.ipynb b/content/lectures/lecture07/notebook/s2-exd2-challenge.ipynb index 23750cd..ca14b33 100644 --- a/content/lectures/lecture07/notebook/s2-exd2-challenge.ipynb +++ b/content/lectures/lecture07/notebook/s2-exd2-challenge.ipynb @@ -63,9 +63,8 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 1, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -94,9 +93,8 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 2, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -112,9 +110,8 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 3, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -136,9 +133,8 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 16, "metadata": { - "collapsed": true, "jupyter": { "outputs_hidden": true } @@ -148,7 +144,7 @@ "### edTest(test_random) ###\n", "\n", "#Split the data into train and validation sets with 75% for training and with a random_state=1\n", - "x_train, x_val, y_train, y_val = train_test_split(___)" + "x_train, x_val, y_train, y_val = train_test_split(x, y, train_size=0.75, random_state=0)" ] }, { @@ -160,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -173,33 +169,33 @@ "training_error, validation_error, cross_validation_error = [],[],[]\n", "\n", "#Run a for loop through the degrees of the polynomial, fit linear regression, predict y values and calculate the training and testing errors and update it to the list\n", - "for d in range(___):\n", + "for d in range(maxdeg):\n", " \n", " #Compute the polynomial features for the entire data, train data and validation data\n", - " x_poly_train = PolynomialFeatures(___).fit_transform(___)\n", - " x_poly_val = PolynomialFeatures(___).fit_transform(___)\n", - " x_poly = PolynomialFeatures(___).fit_transform(___)\n", + " x_poly_train = PolynomialFeatures(d).fit_transform(x_train)\n", + " x_poly_val = PolynomialFeatures(d).fit_transform(x_val)\n", + " x_poly = PolynomialFeatures(d).fit_transform(x)\n", "\n", " #Get a Linear Regression object\n", " lreg = LinearRegression()\n", " \n", " #Perform cross-validation on the entire data with 10 folds and get the mse_scores\n", - " mse_score = cross_validate(___)\n", + " mse_score = cross_validate(lreg, x_poly, y, cv=10)\n", " \n", " #Fit model on the training set\n", - " lreg.fit(___)\n", + " lreg.fit(x_poly_train, y_train)\n", "\n", " #Predict of the training and validation set\n", - " y_train_pred = lreg.predict(___)\n", - " y_val_pred = lreg.predict(___)\n", + " y_train_pred = lreg.predict(x_poly_train)\n", + " y_val_pred = lreg.predict(x_poly_val)\n", " \n", " #Compute the train and validation MSE\n", - " training_error.append(mean_squared_error(___))\n", - " validation_error.append(mean_squared_error(___))\n", + " training_error.append(mean_squared_error(y_train, y_train_pred))\n", + " validation_error.append(mean_squared_error(y_val, y_val_pred))\n", " \n", " #Compute the mean of the cross validation error and store in list \n", " #Remember to take into account the sign of the MSE metric returned by the cross_validate function \n", - " cross_validation_error.append(___)" + " cross_validation_error.append(np.abs(np.mean(mse_score['test_score'])))" ] }, { @@ -211,20 +207,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The best degree of the model using validation is 7\n", + "The best degree of the model using cross-validation is 2\n" + ] + } + ], "source": [ "### edTest(test_best_degree) ###\n", "\n", "#The best degree with the lowest validation error\n", - "min_mse = min(___)\n", - "best_degree = validation_error.index(___)\n", + "min_mse = min(validation_error)\n", + "best_degree = validation_error.index(min_mse)\n", "\n", "\n", "#The best degree with the lowest cross-validation error\n", - "min_cross_val_mse = min(___)\n", - "best_cross_val_degree = cross_validation_error.index(___)\n", + "min_cross_val_mse = min(cross_validation_error)\n", + "best_cross_val_degree = cross_validation_error.index(min_cross_val_mse)\n", "\n", "\n", "print(\"The best degree of the model using validation is\",best_degree)\n", @@ -240,9 +245,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plot the errors as a function of increasing d value to visualise the training and validation errors\n", "\n", @@ -270,6 +288,44 @@ "#### Once you have marked your exercise, run again with Random_state = 0" ] }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the errors as a function of increasing d value to visualise the training and validation errors\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "#Plot the training error with labels\n", + "ax.plot(range(maxdeg), training_error, label = 'Training error')\n", + "\n", + "#Plot the cross-validation error with labels\n", + "ax.plot(range(maxdeg), cross_validation_error, label = 'Cross-Validation error')\n", + "\n", + "# Set the plot labels and legends\n", + "\n", + "ax.set_xlabel('Degree of Polynomial')\n", + "ax.set_ylabel('Mean Squared Error')\n", + "ax.legend(loc = 'best')\n", + "ax.set_yscale('log')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -287,7 +343,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -301,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/content/lectures/lecture08/notebook/lec8-ex1-challenge.ipynb b/content/lectures/lecture08/notebook/lec8-ex1-challenge.ipynb index 7437f99..3093f83 100644 --- a/content/lectures/lecture08/notebook/lec8-ex1-challenge.ipynb +++ b/content/lectures/lecture08/notebook/lec8-ex1-challenge.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -55,12 +55,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.09121121972586788" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### edTest(test_norm_prob) ###\n", - "prob = 1-norm.cdf(___,___,___)\n", + "prob = 1-norm.cdf(600, loc=500, scale=75)\n", "prob" ] }, @@ -73,22 +84,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# define parameters\n", - "mu = ___\n", - "sigma = ___\n", + "mu = 500\n", + "sigma = 75\n", "\n", "# the 'dummy' x for plotting\n", "x = np.arange(200,800)\n", "\n", "# calculate the normal distribution at each value of x\n", - "prob = norm.pdf(___,mu,sigma)\n", + "prob = norm.pdf(x,mu,sigma)\n", "\n", "# plot it\n", - "plt.plot(___,___);\n", + "plt.plot(x, prob);\n", "plt.title(r'$\\mathrm{N(\\mu=500, \\sigma^2=75^2)}$')\n", "plt.ylim((0,0.006))\n", "plt.show()" @@ -105,9 +129,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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s0aKhvNKhH34gYuRI7nv5ZSp26GA7js+o9vTT1PjHP9g0fDhHli61HUdZoEVDeZ2rJ06wtHt3QmvUoMnHH9uO43OajBpFkbAwlnTvzrUzZ2zHUW6mRUN5lcSEBH546inib96k7dy5BOTNazuSz8mTLx+PzJ5NTFQUS3v0wCQm2o6k3EiLhvIqv77/PpFr1tB8/HjuqFTJdhyfFRoWRpORIzmyZAlbxo61HUe5kRYN5TVO/PwzG4YOperTTxPWvbvtOD7vvpdeonzbtqzt35+z27bZjqPcRIuG8gqxV6+yuFs3CpYuTYvx423HUTjXb3zxBXlDQljUuTNx16/bjqTcQIuG8gqr+/Yl6sgRWk+fTmCBArbjKEf+IkVoM2MGF/ftY3XfvrbjKDfQoqE83qHvv2fH559Tt39/va+UByrdogW1X3uN7RMncmTZMttxVA6T3HznyvDwcBMREWE7hroF18+dY1pYGMHFivHUpk0EBAXZjqRSEX/zJjNq1SL2yhWe2bWLvIUL246kboGIbDbGhKc2T/c0lMcyxvBjz57EXL5Mm5kztWB4sIC8eWk9fTrXTp9mZZ8+tuOoHKRFQ3ms3TNmcPC772j4wQeEVq9uO47KwF116nD/wIHsmTGDA999ZzuOyiEuFQ0RaSUi+0TkoIgMSGV+kIjMdeZvFJEyyea96UzfJyItM+pTRGY503eJyBcikseZ3kREokRkm/M35FY+uPJsV0+cYFWfPpRs1Ijar71mO45yUf1Bg7jzvvv46R//4Pq5c7bjqByQYdEQEX9gPNAaqAp0FpGqKZo9B1wyxtwDjAKGO8tWBToB1YBWwAQR8c+gz1lAZaA6kA94Ptl61hlj7nP+hmblAyvPZ4zhpxdeICE2lpZffKHP+fYi/oGBtJ4xg5jLl/npxRf1aX+5kCt7GnWBg8aYw8aYWGAO0D5Fm/bAdOf1fKC5JD3YoD0wxxgTY4w5Ahx0+kuzT2PMYuMANgElb+0jKm/z++zZHF60iIYffMDt99xjO47KpNDq1Xlg6FAOLFjAvnnzbMdR2cyVolECOJ7sfaQzLdU2xph4IAoISWfZDPt0Dks9DSS/lWZ9EdkuIktEpFpqYUWkp4hEiEjEOd099jrXzpxhRa9e3FWvHrV697YdR2VRnb59KVq7Nit69eLGhQu246hs5ErRSO1RaCn3OdNqk9npyU0A1hpj1jnvtwCljTE1gLFAqiNtxpjJxphwY0x4aGhoak2UB1vRqxdx0dG0nDpVD0t5Mb+AAFpOnUrMpUusev1123FUNnKlaEQCpZK9LwmcTKuNiAQAhYCL6Sybbp8i8jYQCvz1r80Yc8UYE+28XgzkEZEiLuRXXmL/ggXs/+Yb6r/9NkWqphw2U97mzho1qDtgAHtmzNCL/nIRV4rGb0AFESkrIoEkDWwvTNFmIfDnHeQ6AiudMYmFQCfn7KqyQAWSxinS7FNEngdaAp2NMX/dc1lEijnjJIhIXSe77vfmEjcuXGD5yy9zZ82a1OnXz3YclU3qDRrEHZUr82PPnsRGR9uOo7JBhkXDGaN4BVgG7AXmGWN2i8hQEWnnNJsKhIjIQZL2DgY4y+4G5gF7SBqbeNkYk5BWn05fE4GiwIYUp9Z2BHaJyHbgU6CT0VMzco1Vr73GzQsXaPXFF/jnyWM7jsomAUFBtJwyhavHj7P+rbdsx1HZQG8joqz748cfmd+yJfUGDaLhe+/ZjqNywIpevdg6fjxdfv6Z4vXr246jMpDebUS0aCir4m7cYFpYGH7+/nTfsUOfxJdLxV69ypdhYeQJDqbb1q16SxgPp/eeUh5r4wcfEHX4MA9NnKgFIxcLLFCAhydN4uLevWwcNsx2HHULtGgoa87v3s2mjz6iardu3N2sme04KoeVbdWKKl26sGnYMC7u3287jsoiLRrKCpOYyE8vvEBggQI0+fhj23GUmzQZOZKAfPlY/tJLeosRL6VFQ1mx68svObF+PQ+OGEF+vQjTZwQXK0ajYcM4tmIFv8+ebTuOygItGsrtrp09y5p+/SjZuDFhPXrYjqPc7N6ePSlWt27SadaXLtmOozJJi4ZyuzV9+xIbHc1DEyfiXK+pfIifvz8PTZzIjfPnWafXbngdLRrKrY6uWMGemTOp+8YbhFSpYjuOsqRozZrU6t2b7RMncmrjRttxVCbodRrKbeJv3mT6vfdijKH7jh3kyZfPdiRlUezVq3xRpQr5Q0Pp+ttv+AUE2I6kHHqdhvIIv40YwaUDB2gxYYIWDEVggQI0GzOGs9u2sXXcONtxlIu0aCi3iPrjDzZ++CEVO3akzEMP2Y6jPESFDh0o26YN6wcP5mpkpO04ygVaNJRbrH79dfDzo8knn9iOojyIiNB87FhMfDyr9FnwXkGLhspxR5Yt48C331J/0CAKliqV8QLKpxQuV47733qL/fPnc3TFCttxVAZ0IFzlqPiYGKZXrw5A95079UZ1KlXxN2/yZbVq+AcF0X37dr09vmU6EK6s2fzJJ1w6cIBmY8dqwVBpCsibl2ajR3Nx7162jh1rO45KhxYNlWOuHDvGhvffp8Jjj1G2ZUvbcZSHK/foo5Rt04Zf3nmH6FOnbMdRadCioXLM6r59ITFRB7+VS0SEZqNHkxATw9o33rAdR6VBi4bKEUeXL2f//PncP3AghcqUsR1HeYnbK1Qg/J//ZM9XXxG5fr3tOCoVOhCusl1CbCzT772XxPh4ntm1Sx+upDIl9to1vqxcmbwhITy9eTN+/v62I/kcHQhXbrV59Ggu7ttH0zFjtGCoTAsMDqbJJ59wbvt2tk+aZDuOSkGLhspWVyMj2TB0KOXbtqX8I4/YjqO8VMWOHbm7WTPWv/UW18+dsx1HJaNFQ2WrNf36kRgfT9PRo21HUV5MRGg2dixx0dGs19unexQtGirbRK5fz+9z5lCnf38KlytnO47yckWqVqVm797smDKF0zo26TG0aKhsYRITWfXqq9xWogR19XRJlU0eePtt8t95Jyt799ZninsILRoqW+yaPp0zmzfTePhwAoODbcdRuURQwYI0GjaMkxs26DPFPYQWDXXLYq5cYd2bb3JXvXpU6dLFdhyVy4R1707R2rVZ078/sdeu2Y7j87RoqFu28cMPuX7mDM3GjNFnfqtsJ35+NBszhugTJ9g0fLjtOD5Pi4a6JZcPHWLzqFFU7daNu+rWtR1H5VIlGjSgcufORIwYQdTRo7bj+DQtGuqWrOnXD788eWg8bJjtKCqXazx8OIiwtn9/21F8mhYNlWXHVq7kwLffcv+bb3Jb8eK246hcrmCpUtR94w32zZvH8bVrbcfxWVo0VJYkxsez8tVXKVi6NLVff912HOUj6vTrR4FSpVj16qskJiTYjuOTtGioLNkxZQrnd+7kwREjyJMvn+04ykfkyZ+fBz/6iLNbt7Lryy9tx/FJLhUNEWklIvtE5KCIDEhlfpCIzHXmbxSRMsnmvelM3yciLTPqU0RmOdN3icgXIpLHmS4i8qnTfoeI1LqVD66y7ubly/w8eDAlGzemYseOtuMoH1PpyScp0aAB6wYOJCYqynYcn5Nh0RARf2A80BqoCnQWkaopmj0HXDLG3AOMAoY7y1YFOgHVgFbABBHxz6DPWUBloDqQD3jemd4aqOD89QQ+y8oHVrduw9Ch3LhwgaajR+sptsrtRISmY8Zw4/x5Nrz/vu04PseVPY26wEFjzGFjTCwwB2ifok17YLrzej7QXJK+TdoDc4wxMcaYI8BBp780+zTGLDYOYBNQMtk6ZjizfgUKi8hdWfzcKosu7tvH1rFjqf7ccxStWdN2HOWjitWuTViPHmwZM4aL+/fbjuNTXCkaJYDjyd5HOtNSbWOMiQeigJB0ls2wT+ew1NPA0kzkQER6ikiEiESc01sqZ7tVr79OQL58NNRfeMqyRh98QEDevEmPFVZu40rRSO34Q8o7h6XVJrPTk5sArDXGrMtEDowxk40x4caY8NDQ0FQWUVl1ZOlSjixeTP0hQwguWtR2HOXjgosVo97gwRxetIgjy5bZjuMzXCkakUCpZO9LAifTaiMiAUAh4GI6y6bbp4i8DYQCyc/ldCWHyiEJcXGseu01Ct9zD7V697YdRykAavXuTeHy5Vn12mskxMXZjuMTXCkavwEVRKSsiASSNLC9MEWbhUB353VHYKUzJrEQ6OScXVWWpEHsTen1KSLPAy2BzsaYxBTr6OacRVUPiDLGnMrCZ1ZZsP2zz7j4++80GTkS/8BA23GUAiAgKIgmn3zCxb172f6ZnhvjDhkWDWeM4hVgGbAXmGeM2S0iQ0WkndNsKhAiIgdJ2jsY4Cy7G5gH7CFpbOJlY0xCWn06fU0EigIbRGSbiAxxpi8GDpM0mP458NKtfXTlquvnz/Pz229TukULyrdtazuOUv+lfNu2lG7Rgp/ffpvr58/bjpPrSW5+sEl4eLiJ0Cd+3bLlL7/M9kmT6LZtG6FhYbbjKPU/zu/ezfQaNajxj3/QYvx423G8nohsNsaEpzZPrwhX6Tq3axfbJ06kxgsvaMFQHqtItWrUeOEFtk+cyLmdO23HydW0aKg0GWNY/dprBBUqRIN337UdR6l0NXj3XYIKFWLVa6/po2FzkBYNlaZD33/P0eXLeeCdd8gXEmI7jlLpyhcSwgPvvsuxFSs4tDDluToqu+iYhkpVfEwM06pVwy8wkO7bt+OfJ4/tSEplKCEujuk1apAYG8szu3cTEBRkO5JX0jENlWlbPv2Uy4cO0XTUKC0Yymv458lD01GjuHzoEFs+/dR2nFxJi4b6H9fOnOHX996j3COPULZly4wXUMqDlG3ZknKPPMKv773HtTNnbMfJdbRoqP+xftAg4m/coMnIkbajKJUlTUaOJP7GDda/9ZbtKLmOFg31X85s3crOqVOp2asXd1SqZDuOUllyR6VK1Ozdm51ffMGZrVttx8lVtGiovxhjWNWnD/lCQqg/ZEjGCyjlweoPHky+kBBW9emjp+BmIy0a6i/7588nct06Gr7/PnkLF7YdR6lbkrdwYRq+/z6R69axf/5823FyDT3lVgEQd+MGX1apQlChQjy9ZQt+/v62Iyl1yxITEviqVi1uXr7Ms7//rs+zd5GecqsyFDFyJFeOHqXp6NFaMFSu4efvT9PRo7l67BgRemJHttCiobh64gQbhw2jQocO3N20qe04SmWru5s2pUKHDmwcNoyrJ07YjuP1tGgo1g0YgImP58ERI2xHUSpHPDhiBCY+nnVvvmk7itfTouHjTv76K3tmziS8b18KlytnO45SOaJwuXLUfv119nz1Fac2brQdx6tp0fBhJjGRlX36EFysGPfrLzCVy9UbOJDgYsVY2acPJjEx4wVUqrRo+LA9s2ZxetMmGv3rXwQWKGA7jlI5KrBAARp++CGnNm5k79df247jtfSUWx8VGx3NF5UqcVuJEjz166+In/5+ULmfSUxkZt26XDt9mmf37SMwONh2JI+kp9yq/7HpX/8i+uRJmo0ZowVD+Qzx86PZmDFEnzjBpuHDbcfxSvpt4YOi/viD3z7+mCpdulC8fn3bcZRyqxINGlC5UyciRowg6uhR23G8jhYNH7SmXz/E35/G+ktL+ajGw4eDCGv797cdxeto0fAxx9esYf/8+dR94w0KlCxpO45SVhS8+27q9O/PvnnziFy3znYcr6JFw4ckJiSwsk8fCpQqRZ1//tN2HKWsqtu/PwVKlmTVq6/qKbiZoEXDh+z64gvObd/OgyNGkCd/fttxlLIqT/78NB4+nDNbtrBr2jTbcbyGFg0fERMVxbq33qJEw4ZUeuIJ23GU8giVO3emeP36rBs4kJgrV2zH8QpaNHzEhvfe48b58zQdPRoRsR1HKY8gIjQdM4brZ87w6wcf2I7jFbRo+ICL+/ez5dNPCevRg2K1a9uOo5RHuatOHap1786W0aO5fOiQ7TgeT4uGD1jdty8BefPSSH9JKZWqRh9+iF+ePKzWE0QypEUjlzuydCmHFy2i3qBBBBcrZjuOUh7ptuLFuX/gQA5+9x1HV6ywHcej6b2ncrGE2FimVa8OxtB9504CgoJsR1LKY8XfvMkXVaoQeNttdNu6Fb+AANuRrNF7T/mozZBVR40AABTLSURBVKNHc2n/fpqOGaMFQ6kMBOTNy4MjRnB+1y52fP657TgeS4tGLnX1xAk2DB1K+XbtKNe6te04SnmFio8/TskHH2T9oEHcuHDBdhyPpEUjl1rbvz+J8fE0HTXKdhSlvIaI0HzsWGKiolj/1lu243gkl4qGiLQSkX0iclBEBqQyP0hE5jrzN4pImWTz3nSm7xORlhn1KSKvONOMiBRJNr2JiESJyDbnb0hWP3Rud3ztWvZ+/TV1+vfXR7gqlUmh1atT85VX2D55Mqc3b7Ydx+NkWDRExB8YD7QGqgKdRaRqimbPAZeMMfcAo4DhzrJVgU5ANaAVMEFE/DPo82egBZDaPYvXGWPuc/6GZu6j+obE+HhW9upFgbvv5v4B/1PflVIuaPDuu+QPDWXFyy/rfalScGVPoy5w0Bhz2BgTC8wB2qdo0x6Y7ryeDzSXpMuO2wNzjDExxpgjwEGnvzT7NMZsNcb8cYufy2dtmziRczt20PSTT/T+UkplUVChQjT+6CNObdzIrunTM17Ah7hSNEoAx5O9j3SmpdrGGBMPRAEh6SzrSp+pqS8i20VkiYhUS62BiPQUkQgRiTh37pwLXeYe18+d4+fBg7m7eXMqdOhgO45SXq3a009T/IEHWPvGG9y8dMl2HI/hStFI7UZFKS/uSKtNZqenZwtQ2hhTAxgLfJdaI2PMZGNMuDEmPDQ0NIMuc5d1AwcSFx1Ns08/1ftLKXWLxM+P5uPGcfPCBX5++23bcTyGK0UjEiiV7H1J4GRabUQkACgEXExnWVf6/C/GmCvGmGjn9WIgT/KBcl936rff2Dl1KjV796ZI1ZRDTkqprChasyY1XniBbePHc3b7dttxPIIrReM3oIKIlBWRQJIGthemaLMQ6O687gisNEmXmi8EOjlnV5UFKgCbXOzzv4hIMWecBBGp62TXE6kBk5jIildeIf+dd/KA/iJSKls1eO898t5+OyteeYXcfAcNV2VYNJwxileAZcBeYJ4xZreIDBWRdk6zqUCIiBwEXgcGOMvuBuYBe4ClwMvGmIS0+gQQkd4iEknS3scOEZnirKMjsEtEtgOfAp2M/j8IwK7p0zm9aRMPjhhBUMGCtuMolavku+MOGv3rX5xYv569s2bZjmOd3nvKy928dImplSpxe4UKdF6/XscylMoBJjGRWfXqcfX4cZ7dty/X/zjTe0/lYuvefJObFy/SYsIELRhK5RDx86P5+PFcO3OGX95913Ycq7RoeLFTGzeyffJkavXuzZ01atiOo1SudledOtz7/PNsGTOG87t3245jjRYNL5UYH89PL7zAbcWL08DHf/ko5S4NP/yQoEKFWP7iiz57pbgWDS+1dfx4zm7bRtPRowksUMB2HKV8Qv4iRXjwo4+IXLfOZ68U16LhhaJPnuTnwYMp06oVFR9/3HYcpXxKWI8elGjQgLX9+nH9/HnbcdxOi4YXWvX66yTGxdFi3Dgd/FbKzcTPjxYTJxITFcXa/v1tx3E7LRpe5o+ffmLf3LncP3AghcuXtx1HKZ8UGhZGeN++7PryS46vXWs7jlvpdRpeJP7mTaZVr474+dF9xw59hKtSFsVeu8a0atXIExxMt61b8Q8MtB0p2+h1GrnEpuHDuXzwIC0mTNCCoZRlgcHBNB83jgt79hAxcqTtOG6jRcNLXDp4kI3DhlG5c2dKN29uO45SCij/6KNU6NCBDUOHcvnwYdtx3EKLhhcwxrD8pZfwDwqi6Sef2I6jlEqm2ZgxSECAz9zQUIuGF9jz1Vcc/eknGg0bRnCxYrbjKKWSKVCyJA2GDuXIkiXsX7DAdpwcpwPhHu7a2bN8WaUKd1SuTOd16xA/rfNKeZrE+Hhm1qnD9bNn6bF3r9ff0FAHwr3Y6tdeI/bqVR7+/HMtGEp5KL+AAB6aNInoU6dYO2CA7Tg5Sr+FPNjhJUvY+/XX3D9woD6NTykPd1fdutTu04ftn31G5Lp1tuPkGD085aFio6P5Mtk54HqKrVKeL/baNaaFheEfGEj37dsJyJvXdqQs0cNTXujnwYO5euwYLT//XAuGUl4iMDiYhydN4tL+/WwYOtR2nByhRcMDndq0ic1jxlDjxRcp0aCB7ThKqUwo8/DDVOvenU0ffcTZbdtsx8l2WjQ8TEJcHMuef57bihen8bBhtuMopbKgySefkC8khGXPPUdifLztONlKi4aH+W3ECM7v3EmL8eMJKlTIdhylVBbku+MOmo8bx5ktW4gYNcp2nGylRcODnN+zhw3vvkuFxx/nnvbtbcdRSt2Cih07ck/79vwyZAiXDh60HSfbaNHwEInx8Szt0YPAAgVoMX687ThKqVskIrSYMAH/oCB+7Nkz19xiRIuGh/ht5EhOb9pE83HjCC5a1HYcpVQ2uK14cR4cMYLjq1axY/Jk23GyhRYND3B+zx5+GTKECh06UOnJJ23HUUplo+rPP0/pFi1Y3bdvrrgTrhYNy/7rsNSECfr4VqVyGRGh5dSpiL8/S599FpOYaDvSLdGiYVnEJ5/oYSmlcrmCd99N01GjiFyzhi1jx9qOc0u0aFh0Ye9eftbDUkr5hLAePSj3yCOsGzCAi/v22Y6TZVo0LPnrsNRtt+lhKaV8gIjw8OefE5AvH0ueeYbEhATbkbJEi4Ylm4YP59TGjXpYSikfcttdd9F83DhO/forv338se04WaJFw4LTmzfzyzvvULlTJyp36mQ7jlLKjSp37kyFDh34ZcgQzu3aZTtOpmnRcLO469dZ3LUr+YsWpcWECbbjKKXcTERo8dlnBBYsyJJu3UiIi7MdKVO0aLjZ2jfe4OLvv9N62jTy3n677ThKKQuC77yThyZO5OzWrV53C3WXioaItBKRfSJyUET+51mGIhIkInOd+RtFpEyyeW860/eJSMuM+hSRV5xpRkSKJJsuIvKpM2+HiNTK6oe25ciyZWwdN45affpQukUL23GUUhZVfPxxqnbrxsYPPyRy/XrbcVyWYdEQEX9gPNAaqAp0FpGUzx59DrhkjLkHGAUMd5atCnQCqgGtgAki4p9Bnz8DLYCjKdbRGqjg/PUEPsvcR7XrxoULLO3Rg5CqVWmktzxXSgHNx46lYJkyLO7alZioKNtxXOLKnkZd4KAx5rAxJhaYA6S8BWt7YLrzej7QXJLOIW0PzDHGxBhjjgAHnf7S7NMYs9UY80cqOdoDM0ySX4HCInJXZj6sLcYYfnrhBW6cP0+bmTPJky+f7UhKKQ8QVLAgj8yaxdXISJa/9JLtOC5xpWiUAI4nex/pTEu1jTEmHogCQtJZ1pU+s5IDEekpIhEiEnHu3LkMunSPPV99xf7582kwdChFa9a0HUcp5UGK16vHA2+/zd6vv2bPrFm242TIlaKR2lVnKe/xm1abzE6/1RwYYyYbY8KNMeGhoaEZdJnzLu7fz/KXXqJko0bU6dfPdhyllAe6/803KdGgActfeonLR47YjpMuV4pGJFAq2fuSwMm02ohIAFAIuJjOsq70mZUcHiU+JoZFnTrhHxTEI19/jZ+/v+1ISikP5BcQQJuZMwFY3LWrRz8i1pWi8RtQQUTKikggSQPbC1O0WQh0d153BFaapCeOLAQ6OWdXlSVpEHuTi32mtBDo5pxFVQ+IMsacciG/NWvfeIOzW7fSeto0CpQsaTuOUsqDFSpThoc++4yTv/zCrx98YDtOmjIsGs4YxSvAMmAvMM8Ys1tEhopIO6fZVCBERA4CrwMDnGV3A/OAPcBS4GVjTEJafQKISG8RiSRpT2KHiExx1rEYOEzSYPrngEePGh1cuJAtY8ZQq08fyrdtazuOUsoLVOnShapdu7Jh6FCOrVplO06qJLc8gjA14eHhJiIiwu3rvRoZyfQaNShYujRdNmwgICjI7RmUUt4pNjqameHhxERF0W3bNiv3phORzcaY8NTm6RXh2SwxPp4funQhISaGR+fM0YKhlMqUwNtuo+033xBz+TI/PPWUx90NV4tGNtvw3ntErltHi88+446KFW3HUUp5odDq1Wk+bhzHVqzwuPENLRrZ6MjSpWx47z2qdutGtaefth1HKeXFwp59lqpPP80v77zDsZUrbcf5ixaNbBJ19Cg/PPUURcLCeOgzr7rDiVLKA4kILSZM4I5KlVjUpQvXTp+2HQnQopEt4m/eZGHHjiTGx9N+wQLy5M9vO5JSKhf4c3wj9soVjxnf0KKRDVb26cOZiAjazJjB7RUq2I6jlMpFQsPCaD5+PMdWrmT9W2/ZjkOA7QDebte0aeyYPJm6AwZwT/uU93FUSqlbV71HD05v2sSm4cO5s1YtKj/xhLUsuqdxC85u28byF1+kVNOmNHzvPdtxlFK5WLMxYyj+wAMs7dGDczt3WsuhRSOLbly8yH8ef5y8ISE8OmcOfgG606aUyjn+gYG0mz+foEKF+M9jj3Hz0iUrObRoZEFifDyLnnySq8eP0+6bbwi+807bkZRSPuC2u+6i3fz5XDl2zNrAuBaNLFjdty9Hly/noUmTKF6/vu04SikfUuKBB2g+dixHlizhl7ffdvv6tWhk0o4pU9jy6afUfu01qvfoYTuOUsoH3duzJ9Wfe45fP/iAffPnu3XdWjQyIXL9epa/9BJlWrbkwY8+sh1HKeWjRITm48dTvH59lnTrxmk33phVi4aLoo4e5T8dOlCobFkd+FZKWRcQFET7b78l/5138m27dlyNjHTLerVouCDmyhW+a9eOxNhYHlu4kLyFC9uOpJRSBBctymOLFhEXHc23bdsSGx2d4+vUopGBhLg4vv/737mwZw9tv/mGOypVsh1JKaX+EhoWxqNz53Juxw63nFGlRSMdxhiWv/gif/z4Iw9NmkSZhx6yHUkppf5HudataTp6NIcWLmTtgAE5ui49MJ+OjR9+yM6pU6k3aBDVn33WdhyllEpTrV69uLhvHxEff0zhcuW478UXc2Q9WjTSsGfWLNYPGkTVrl1pMHSo7ThKKZWhZqNHc+XoUVa88gq3lSjBPe3aZfs69PBUKo6vWcOyZ5+lVJMmPDxlCiJiO5JSSmXILyCAR+fMoVidOkSfOJEj69A9jVTkDQmhVNOmPDp7tj7jWynlVQKDg+m8fn2OXRagRSMVoWFhdFy61HYMpZTKkpy8jkwPTymllHKZFg2llFIu06KhlFLKZVo0lFJKuUyLhlJKKZdp0VBKKeUyLRpKKaVcpkVDKaWUy8QYYztDjhGRc8DRLC5eBDifjXGyk6dm01yZo7kyR3NlXlazlTbGhKY2I1cXjVshIhHGmHDbOVLjqdk0V+ZorszRXJmXE9n08JRSSimXadFQSinlMi0aaZtsO0A6PDWb5soczZU5mivzsj2bjmkopZRyme5pKKWUcpkWDaWUUi7TogGIiL+IbBWRRanMCxKRuSJyUEQ2ikgZD8n1jIicE5Ftzt/zbsz1h4jsdNYbkcp8EZFPnW22Q0RqeUiuJiISlWybDXFTrsIiMl9EfheRvSJSP8V8W9sro1xu314iUinZ+raJyBUReTVFG7dvLxdz2fr39ZqI7BaRXSIyW0Typpifrd9h+uS+JH2AvUDBVOY9B1wyxtwjIp2A4cCTHpALYK4x5hU3ZUmpqTEmrYuGWgMVnL/7gc+c/7WdC2CdMeZRN2X50xhgqTGmo4gEAvlTzLe1vTLKBW7eXsaYfcB9kPSjCTgBfJuimdu3l4u5wM3bS0RKAL2BqsaYGyIyD+gETEvWLFu/w3x+T0NESgKPAFPSaNIemO68ng80FxHxgFyerD0wwyT5FSgsInfZDmWDiBQEGgNTAYwxscaYyymauX17uZjLtubAIWNMyrs62P73lVYuWwKAfCISQFLhP5lifrZ+h/l80QBGA/2BxDTmlwCOAxhj4oEoIMQDcgE87uyezxeRUm7I9CcD/Cgim0WkZyrz/9pmjkhnmu1cAPVFZLuILBGRam7IVA44B3zpHGqcIiLBKdrY2F6u5AL3b6/kOgGzU5lu69/Xn9LKBW7eXsaYE8DHwDHgFBBljPkxRbNs/Q7z6aIhIo8CZ40xm9Nrlsq0HD1P2cVc3wNljDH3Asv5/78k3KGBMaYWSYcJXhaRxinmu32bOTLKtYWke+rUAMYC37khUwBQC/jMGFMTuAYMSNHGxvZyJZeN7QWAc7isHfBNarNTmeaWawcyyOX27SUit5O0J1EWKA4Ei0jXlM1SWTTL28uniwbQAGgnIn8Ac4BmIjIzRZtIoBSAs/tXCLhoO5cx5oIxJsZ5+zlQO4czJV/3Sed/z5J0XLduiiZ/bTNHSf53l9ntuYwxV4wx0c7rxUAeESmSw7EigUhjzEbn/XySvqxTtnH39sowl6Xt9afWwBZjzJlU5ln59+VIM5el7dUCOGKMOWeMiQP+DTyQok22fof5dNEwxrxpjClpjClD0i7nSmNMyiq9EOjuvO7otMnRXzWu5EpxDLcdSQPmOU5EgkWkwJ+vgYeBXSmaLQS6OWe51CNpl/mU7VwiUuzPY7kiUpekf/8XcjKXMeY0cFxEKjmTmgN7UjRz+/ZyJZeN7ZVMZ9I+BOT27eVKLkvb6xhQT0TyO+tuzv9+F2Trd5iePZUKERkKRBhjFpI0UPiViBwkqTp38pBcvUWkHRDv5HrGTTGKAt86/20EAF8bY5aKyAsAxpiJwGKgDXAQuA708JBcHYEXRSQeuAF0yukfAI5ewCzn0MZhoIcHbC9XclnZXiKSH3gI+Eeyada3lwu53L69jDEbRWQ+SYfG4oGtwOSc/A7T24gopZRymU8fnlJKKZU5WjSUUkq5TIuGUkopl2nRUEop5TItGkoppVymRUMppZTLtGgopZRymRYNpdxMRFb/eSW2iISISMor6pXyWFo0lHK/e4ADzut7gZ0WsyiVKVo0lHIjESkNnDDG/HnL+3uBHRYjKZUpWjSUcq/7+O8iURstGsqLaNFQyr1qAHkBRKQCSc9C0MNTymto0VDKve4D/ERkOzCEpNtYd09/EaU8h97lVik3cm5PXdMYc9V2FqWyQvc0lHIT5yFRiVowlDfTPQ2llFIu0z0NpZRSLtOioZRSymVaNJRSSrlMi4ZSSimXadFQSinlMi0aSimlXKZFQymllMv+H6Blxth8JMFdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "### edTest(test_likelihood) ###\n", "# define the data set\n", @@ -117,13 +154,13 @@ "# is what we need to determine. Consider \n", "#the values (4, 4.01, 4.02, ..., 7.99).\n", "sigma = 2\n", - "mu = np.arange(___,___,0.01)\n", + "mu = np.arange(4, 8, 0.01)\n", "\n", "# calculate the likelihood\n", - "like = norm.pdf(x[0],mu,sigma)*___*___\n", + "like = norm.pdf(x[0], mu, sigma) * norm.pdf(x[1], mu, sigma) * norm.pdf(x[2], mu, sigma)\n", "\n", "#plot it\n", - "plt.plot(mu,like,color=\"darkred\");\n", + "plt.plot(mu, like, color=\"darkred\");\n", "plt.title('Likelihood Function')\n", "plt.xlabel(r'$\\mu$')\n", "plt.show()" @@ -138,14 +175,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "5.999999999999957" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### edTest(test_mle) ###\n", "# determine which value of mu aligns with where \n", "# the maximum of the likelihood function is\n", - "mle = ___[np.argmax(__)]\n", + "mle = mu[np.argmax(like)]\n", "mle" ] }, @@ -158,10 +206,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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monthdayyearbudgetdomesticworldwide
0Dec182009425.0760.5076252783.918982
1Dec191997200.0659.3639442208.208395
2Dec182015306.0936.6622252058.662225
3Jun122015215.0652.2706251671.713208
4May42012225.0623.2795471519.479547
\n", + "
" + ], + "text/plain": [ + " month day year budget domestic worldwide\n", + "0 Dec 18 2009 425.0 760.507625 2783.918982\n", + "1 Dec 19 1997 200.0 659.363944 2208.208395\n", + "2 Dec 18 2015 306.0 936.662225 2058.662225\n", + "3 Jun 12 2015 215.0 652.270625 1671.713208\n", + "4 May 4 2012 225.0 623.279547 1519.479547" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Take a peak at the dataset\n", "movies.head()" @@ -82,19 +175,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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TlWEYhlEQZW2qGjNmjE6bNq2/xTAMwygr6uvrt6rq2PxHZqasFce0adNYvDhbZJ9hGIaRCRFpzH9UdsxUZRiGYRSEKQ7DMAyjIExxGIZhGAVhisMwDMMoCFMchmEYRkGY4jAMwzAKwhSHYRhFo76xmRseX0l9Y3N/i2L0ImW9jsMwjNKlvrGZS29eSGt7kprqBLdfOY85U+v6WyyjF7AZh2EYRWHhqm20tidJKrS1J1m4alv+k4yywBSHYRhFYd700dRUJ6gSGFSdYN700f0tktFLmKnKMIyiMGdqHbdfOY+Fq7Yxb/poM1MNIExxGIZRNOZMrTOFMQAxU5VhGIZREKY4DMMwjIIwxWEYhmEUhCkOwzAMoyBMcRiGYRgFYYrDMAzDKAhTHIZhGEZBmOIwDMMwCsIUh2EYhlEQpjgMwzCMgjDFYRiGYRSEKQ7DMAyjIExxGIZhGAVhisMwDMMoCFMchmEYRkGY4jAMwzAKwhSHYRiGURCmOAzDMIyCMMVhGIZhFIQpDsMwDKMgTHEYhmEYBWGKwzAMwyiIoioOEfkPEWkQkWUicqeIDBGRA0RkkYisEJG7RaTGHzvYv1/p908rpmyGYRhG9yia4hCR/YFPAXNVdTZQBVwEfBv4vqoeBDQDV/hTrgCaVXUG8H1/nGEYhlFiFNtUVQ0MFZFqYBiwATgRuMfvvxU4x78+27/H7z9JRKTI8hmGYRgFUjTFoarrgO8Aa3AKYwdQD2xX1XZ/WBOwv3+9P7DWn9vujx8dva6IXCUii0Vk8ZYtW4olvmEYhpGFYpqq6nCziAOAicBw4PQMh2pwSo59nRtUb1LVuao6d+zYsb0lrmEYhhGTYpqqTgZeV9UtqtoG3Av8EzDKm64AJgHr/esmYDKA3z8SeKOI8hmGYRjdoJiKYw0wT0SGeV/FScBLwOPAef6YDwH3+df3+/f4/Y+papcZh2EYhtG/VOc7QESOAS4D/hmYAOwBlgF/BH6jqjsynaeqi0TkHmAJ0A78A7jJn3eXiHzDb7vFn3ILcJuIrMTNNC7qwXMZhmEYRUJyDepF5GGcKek+YDGwGRgCHAy8G3gf8D1Vvb/4onZl7ty5unjx4v64tWEYRtkiIvWqOre75+ebcXxAVbdGtr2Jm0UsAb4rImO6e3PDMAyjcOobm1m4ahvzpo9mztS6Pr9/TsWRQWl06xjDMAyjd6hvbObSmxfS2p6kpjrB7VfO63Plkdc5LiIXish0//rtPiXIehE5t/jiGYZhGGEWrtpGa3uSpEJbe5KFq7b1uQxxoqo+D6zzr/8L+DQwB/hasYQyDMMwMjNv+mhqqhNUCQyqTjBvepd10kUnp6lKRL6GW9H9BRGpAo7DRULNBUaKyDXAAlV9suiSGoZhGMyZWsftV84raR/H10XkRNzivP2AP6vqtQAicpqqXld8EQ3DMIwwc6bW9YvCCIhjqvo4cCpwKPA5ABE5DLcewzAMw6gw8i4AVNWXgQsj217CrQI3DMMwKgyrAGgYhmEUhCkOwzAMoyBMcRiGYRgFEUtxiMj5IlLrX39FRO4VkSOKK5phGIZRisSdcXxVVVtE5DhchNWtwE+LJ5ZhGIZRqsRVHB3+/3uBn6rqfUBNcUQyDMMwSpm4imOdiNwIXAA8JCKDCzjXMAzDGEDE7fwvAP4MnKaq24F9cTmsDMMwjAojluJQ1d24Yk67RGQKMAh4pZiCGYZhGKVJ3pXjACLy77hsuJuApN+swNuLJJdhGIZRosRSHLhU6jNVte8TvxuGYRglRVwfx1pgRzEFMQzDMMqDuDOOVcACEfkjsDfYqKrfK4pUhmEYRskSV3Gs8X812PoNwzCMiiaW4lDVrwP4tCOqqm8WVSrDKGHqG5v7tfqaYfQ3caOqZgO34dZvICJbgQ+qakMRZTOMkqO+sZlLb15Ia3uSmuoEt185L6vyMAVjDFTimqpuAj6rqo8DiMgJwM+BfyqSXIZRkixctY3W9iRJhbb2JAtXbcuoFApRMIZRbsSNqhoeKA0AVV0ADC+KRIZRwsybPpqa6gRVAoOqE8ybPjrjcZkUjGEMFGJHVYnIV3HmKoDLgNeLI5JhlC5zptZx+5Xz8pqgAgXT1p7MqWAMoxwRVc1/kEgd8HXgOECAJ4FrVbW5uOLlZu7cubp48eL+FMEwsmI+DqNUEZF6VZ3b3fPjRlU1A5/q7k0MoxKZM7XOFIYxIMmpOETkB6r6GRF5AJebKg1VPatokhnGAMZmI0Y5k2/GEfg0vlNsQQyjXOhpp28RV0a5k1NxqGq9//9E34hjGKVNb3T6cUN6DaNUyWeqWkoGE1WAqlpadaOi6I1O3yKujHInn6nqzJ5cXERGATcDs3EK6CPAcuBuYBqwGrhAVZtFRIDrgTOA3cDlqrqkJ/c3jN6mNzr9uCG9hlGqxArH7fbFRW4FnlLVm0WkBhgGfAl4Q1W/JSJXA3Wq+gUROQP4d5ziOBq4XlWPznV9C8c1+gNzbBvlTlHDcUWkhcymKsElOxyR49wRwLuAy3EHtwKtInI2cII/7FZgAfAF4Gzg1+o02UIRGSUiE1R1QyEPZBjFxsJsjUonn3O8tgfXng5sAX4pIu8A6nGVBPcLlIGqbhCRcf74/XEFowKa/LY0xSEiVwFXAUyZMqUH4hmGYRjdIWeuKj9rQET2zfSX59rVwBHAT1X1ncAu4Opct8uwLdPakZtUda6qzh07dmweEQzDMIzeJp9z/A6cg7we14mHO3fFzSqy0QQ0qeoi//4enOLYFJigRGQCsDl0/OTQ+ZOA9bGewjAMw+gzcs44VPVM//8AVZ3u/wd/uZQGqroRWCsiM/2mk4CXgPuBD/ltHwLu86/vBz4ojnnADvNvGIZhlB5xs+MiIm/HhdCmzlHVe/Oc9u/A7T6iahXwYZyy+q2IXIErR3u+P/YhXETVSlw47ofjymYYhmH0HXErAP4CeDvQACT9ZgVyKg5VfR7IFPJ1UoZjFfhEHHkMwzCM/iPujGOeqh5WVEkMwzCMsiBuBcBnRMQUh2EYhhF7xnErTnlsBPbSuQDQclUZhmFUGHEVxy+ADwBL6fRxGIZhGBVIXMWxRlXvL6okhmEYRlkQV3G8IiJ3AA/gTFVArHBcwzAMY4ARV3EMxSmM94S25Q3HNYxywTLeGkZ8YikOVbXFeMaApdCqfncsWsPDyzZw+uwJXHK0Jdo0Ko98adW/AvxEVd/Isv9EYJiqPlgM4QyjLyikqt8di9bwpd8vBeCpFVsBTHkYFUe+GcdS4AEReQtYgkuTPgQ4CDgc+AvwzaJKaBhFppCqfg8v29DlvSkOo9LIV4/jPuA+ETkIOBaYAOwEfgNcpap7ii+iYRSXQkq5nj57QmqmEbw3jEojro9jBbCiyLIYRr8Rt6pfMLswH4dRycTOjmsYhuOSo6eYwjAqmri5qgyjJKlvbOaGx1dS39jc36IYRsVgMw6jbCk0jNYwjN4h1oxDRP5XREaIyCAR+auIbBWRy4otnGHkIlMYrWEYxSeuqeo9qroTV3+8CTgY+HzRpDKMGARhtFVC3jDa3sTMY0alE9dUNcj/PwO4U1XfEJEiiWQY8SgkjLa3MPOYYcRXHA+IyCvAHuDfRGQs8FbxxDKMeMQNo+0tClllbhgDlVimKlW9GjgGmKuqbcAu4OxiCmaUB5Vmtukv85hhlBKxZhwicj7wJ1Xt8PmrjgC+AWwspnBGaVOJZpv+MI8ZRqkR1zn+VVVtEZHjgFNxpWR/WjyxjHKgUqOa5kyt4xPvnmFKw6hY4iqODv//vcBPfQ6rmuKI1P9Umvmlu5jZxugr7DdZWsR1jq8TkRuBk4Fvi8hgBuiq80o0v3QXM9sYfYH9JkuPuJ3/BcCfgdNUdTuwLwN0HUelml+6i5ltBgalPKK332TpETc77m4ReQ04VUROBZ5S1UeKK1r/UEhtBsMYCJT6iN5+k6VH3KiqTwMfpbPG+G9E5CZV/VHRJOsnysn8YnWyjd6g1NemlNNvslKI6+O4AjhaVXcBiMi3gWeAAac4yoVSHyUa5UM5jOj7eqGnkZu4ikPojKzCvx6QOUfKpUMu9VGiUT7YiN4olLiK45fAIhH5vX9/DnBLcUTqX8qlQy6HUWIYM6uVNjaiNwohrnP8eyKyADgON9P4sKr+o5iC9Rfl0iHnGyWWUkddLrO4vqSUPh/DKJS8ikNEEsCLqjobWFJ8kfqXcpq2ZxslllpHXS6zuLj0tNMvtc/HMAolr+JQ1aSIvCAiU1R1TV8I1d+U+7S91Drqns7iSml03hudfql9PoZRKHF9HBOABhF5FpcZFwBVPSvfiSJSBSwG1qnqmSJyAHAXbhHhEuADqtrqV6P/GpgDbAMuVNXVhTyM4Qg66ta2JCJC3bD+zQ7Tk1lcqY3Ow51+azc7/d40h5aSUjUqh7iK4+s9uMengZeBEf79t4Hvq+pdIvIzXKjvT/3/ZlWdISIX+eMu7MF9K5Y5U+u45sxZXHPfMpKqXPdgAzPH1/Zrx5JrFper8+uNjro3qRtWQ1Ld66TSRSnH6ch7yxxaakrVqBziOsef6M7FRWQSLjHifwOfFVc28ETgEn/IrcC1OMVxtn8NcA/wYxERVdXu3HugUejIsnl3K0nVkjeH5Ov88nXUfU3z7lYEUFy+nubdral9hXTkvWEONZOX0V/kzFUlIleIyOdD75tEZKeItIjIv8a4/g+A/wck/fvRwHZVbffvm4D9/ev9gbUAfv8Of3zFE3RI331kOZfevDBWPqF8mWtLJTdRvjxEQUcNXTvq/mDe9NEMHuTatWZQerv2dU4ly05s9Bf5ZhwfB04Lvd+iqpNEZAjwCDlqcojImcBmVa0XkROCzRkO1Rj7wte9CrgKYMqUKXnEHxh0Z2SZyxxSSiaOfPb+oKMulfDowAz48LINnD57Qlq79XUodzlFABoDi3yKI6Gq4WHT7wBU9S0RGZrn3GOBs0TkDGAIzsfxA2CUiFT7WcUkYL0/vgmYDDSJSDUwEngjelFVvQm4CWDu3LkVYcbqboeUzRxSSiaOfJ1fqXWO9Y3NXPdgA63tSZ5b/Uaa76g/ZC33CECjPMmnOEaG36jqNyG1tiNn76WqXwS+6I8/AfhPVb1URH4HnIeLrPoQcJ8/5X7//hm//zHzbzh6u0OKKqKWPW184JZFnD57ApccXdgsLpfvJa5fJl/nV0qdYz6lW0qyGkaxyKc4HhGRb6jqVyLbr8OZqrrDF4C7ROQbwD/oTF1yC3CbiKzEzTQu6ub1ByS92SGFFVHLnjZ+9uQqAJ5asRUgtvLIZfIqJXNYbzJv+miqE0Jbh1KVkH43nRlGf5BPcXweuNl35i/4be/Arcu4Mu5NVHUBsMC/XgUcleGYt4Dz416zFCi1GPr6xmbuXdKEAuceMSnWKP4DtyxK2/7wsg2xFUeu0XcpmcN6HfFxVTIg83waRl5yKg6fRv1iEZkOzPKbX1LV14ouWYnTFyPqQhRTfWMzF//cyQNwz+K13HnVMXnPO332hNRMI3gfl1y+l1LM+dUbin7hqm20dyRRoKNjgClEw4hJ3HUcq4BVRZalrCj2iLpQxbRw1Tba2pOp920dGkumYHYRRAkV4uPI5XspRad2byj6UlSIhtHXxF05bkQodgdSqGKaN300g6oTqRnHoKr49vdLjp5SsFM8IJfvpZQcxQtXbWNvm5sptLblbs9cM5NSU4iG0R+Y4ugmxe5ACnXCzplax50fnRfbx1Fp1A2rSS0KSpJ9BXqmmQmQ9jmXkkI0jP4gbs3xeUCDqrb497XAYaq6KPeZA5uidyAFOmGtQ8tO8+5WEuLSliQk+wr06Exv/pIm7l3SNOCiwwyjJ+RMORLip8Cbofe7yLFq3Og5mZywxaJU0o9kozfkC6fnqMlhWoym8RDo0zQihlEOxK45Hl6M52t0mJmriBTqQ+luxFAxo8N6I4qpt+TLlSokelzYBAkwf0lT2udQamHYmSgHGY3yJW7nv0pEPkXnLOPfsCironHHojU8vGwDlx8zjdqhg/L++HvSuRYrOqy3OvyeyBfuPIGsqUKiRE1+UUVSSgsbMymIgbr40igd4iqOjwM/BL6CSzz4V3yiQSMz3R3x3bFoDV/6/VLAreT+5rWjZdsAACAASURBVL+8Le/5PelcixUd1lsKqbvyRTvP9x8xKXZUVZSwIrnh8ZUls7Axm4IY0IsvjZIg7jqOzVgKkNj0ZMT38LINXd7nC5XtSedfrOiw3lJI+eTLpqCjnefWlr2xoqr66rl6g2wKopRkNAYmORWHiPw/Vf1fEfkRGVKcq+qniiZZGdOTEV93VnL3tPOPG41VyCwqrk+hJ+RS0NHOc2zt4LSoqob1O7jh8ZWpZwnMg/kWQZbSOo5sCqKUZDQGJvlmHC/7/4uLLchAoicjvu6u5C52KG62TjqbMsmVfrw37gu5FXRUcc0cX5tycldVJfjd4rW0J5Wa6gSXHzOtoESPpRL2nG/lfinIaAxM8uWqesC/3K2qvwvvE5GySkjY15x7xKRuL8Tr7kruYkbSZKtu151OvRDZc10nl4KOKq7br5yX6mTXbd/DXc+uSV3zTw0b02QpJNFjf1NqCsKiuSqDuM7xL+KLOOXZVvFER8jnHjGpaPcJ/0CLHUmTqZPubqee6VnimpzC18k14s4k2yfePSPVVveGQmxPmzU+NeOA/ObBUuoc45rY+gKL5qoc8vk4TgfOAPYXkR+Gdo0A2jOfVdn0RURLph9ose+brZPO1anH9XHkMznlqxCY6dqFKpwpo4fH6oBLqXOMRuBB/FoqxcCiuSqHfDOO9Tj/xllAfWh7C/AfxRKqnMnWYfXmKDXTD7QvImminXSuTj2OjyNok7phNd2WPVu7ZpItuq4jTFzzYG91jr3xfehOBF4xsWiuyiGfj+MF4AURuUNV2wBEpA6YrKqlmZ+ijyi0w+rNUWqmH2ipRdLk62CjbXLNmbNo3t2aUQFdfNMztHUog6okrcZIvnYNK7rwsdUJARHaOwr/PHqjc+yt70NPaqkUg1L7DhrFI66P41EROcsf/zywRUSeUNXPFk+00qA7K3OjI/PensJn+4H2taO0u74J6Nomzbtb+cS7Z3S5x/wlTbR2uEjw1g5l/pKmNAd83EV9affrUEBRCv88eqNz7K3vQ09qqRSLUnPWG8UhruIYqao7ReRK4Jeq+jURebGYgpUCPVmZG1Y4PRml5prZ9PcPNK5vom5YTSoKq1Dn+daWvVnfx02VHr1flZ9xdHR0b9bQ07bvTZNOT2qpGEZ3ias4qkVkAnAB8OUiylNSdHdlbiaF051Raik5YsMyxVWIgayZniHuyH1s7eCs7+OmSg9kieac6i+Tipl0jHInruK4Dvgz8DdVfc7XIF9RPLFKg+6uzM0VCloIvbUWorfojkLMNyvJJ+f7j5jE7+o7Q2ffHwpvLnTknsm53x16o51LYcZoGN0lbq6q3xFas+FrkJ9bLKFKhe6uzO0tU0ShayEu/vnC1LF3frT3ZyfdUYg9bYs5U11lw7iBCMWmFGeBhtHXxK0AeDAupfp+qjpbRN4OnKWq3yiqdP1IeFSZyWmbi97q0Aq5TlClDlzhoXtDTuRs5EoXkml7XCUQPb+nbZFLSefaV4wZWG+lee8teUppMaJROcQ1Vf0c+DxwI4CqvigidwADUnGU46gymoFSyd2p5Mo9le3Z4yiBbOd3V4nl25fresX4DHsrzXtvyJMrVNkwiklcxTFMVZ+V9NrXA3bleE/DJXvLbJSvswl3qOceMYl7Fq9NdSKzJ47Mee78JU2pUNbwM/b02bOd3x0l1pO2LNYq5u7OoIohT65QZcMoJnEVx1YRORA/sBWR84ANuU8pX3pql++O2SgTuTqbTKPNO686JtWh5Tv3t4vXpmYpVQlJPWO+xIH5Rs2Zzs93Xi5Zu9uWxVzF3B3zWDHkkTzvDaNYxFUcnwBuAg4RkXXA68BlRZOqj4n+2Ls7qgyuszmy9qBLIZOY5OpsMo02o9UCqxNCW4emKYb6xmaue6CB9o5OqU6YOS6WOSrOqDnT+fmq5uV6zkwmuDjk+wyL5W/oiZmvUHJFnBnlRzn5q+JGVa0CThaR4UBCVVuKK1bfkcsmH5hYwgV/4lynuipBdQI6kjCoSrqdITdXZxNrtCkCqP/fKeNbbcm0w6JrJbKNqOOOmqPnRxffrd++h/rG5lidatQEF23LaP6p6AAgrh8mem4ucqWAz7WSvbdDcHNFnBnlRbn5VeNGVY0CPghMwy0GBAZGBcB8Jp24H2b4Oh0dSS46agoTRw3t8Q86W2eTb7S5cNU22jtcJ9bRkWT+kibWvrGbvRGlUVPASLW7o+bgvPlLmrinvok7n13D/CVNXUbkma43Z2pdmgku6uMJ559KAh0xHMXhz6q13bVNYBLL9znn8rkUspK9t7D1IAODcsssHNdU9RCwEFiK+00MGHKNojN9mMH2fDbs93ejgFMhBKPN+UuaUrONbKu6qxLCPfVNKUWSABJVwqwJI7jwyClFDScNZ8Bd+8Zu2tq7OuS7S5oCCJne8jmK64bVkPSHJ9WlMQl/zvOXNGV9zlw+l0JWsvcWpWbeKDV5yoVyyywcV3EMGagJDXONoqMfZt2wmj61Ycch6Mh+V98EqqlyqOFV3eu37+FOX/EuIfC2/Ufy8sYWlq7bwfJNDbHKunZnKh2cE5hvvOGMBMT+ccRNpAgQ0h05HcXhDl2AMbWDMyrZTM+Zy+fS1z/+UjNvlJo85UR/9R/dJa7iuE1EPgo8CKQ8v6r6RlGk6mNymUnCH2Z4hLu3LcmNT7zGOyaPom5YTSoleKGLBTORq6pbeER3bySkFkgbzYcr3s0PVbybvf9Ilq7bUdC0uNDEjnOm1qXJF8gmwLEHjeEzJx8cKzIpbiLFlj1taVX8Zk0cmfVZwiYkBWZPHMm5R0zqomQzPWc2n0sgc7b08MWg1MwbpSZPuVFOZse4iqMV+D9cgsNwPzC9GEKVEuEP89GGjSkThwKPvLSJR1/alOoQBw/q+SgrWtVtzbZd1A4dRN2wGhrW7+B3i9fSntSUSST4MKqrBAE6kpq34h2QpkjijIwLTex4zZmzuPu5NRlH6LkqAma6Tr5EinOm1qXaLGDZ+h1ZnyWTSSkcDJGrbTL5XPprpF1q5o1Sk8coHnEVx2eBGaq6Ne+RA5T6xmZufvr1LtvDWjTXgre4tt9oVbebnlqFalcTSTKy4fy5k1Oj5kz3iI5m4pZ1DZ9fSGLHu59bQ3sGb1iC3Lb/aGRS8+5WTps1ngWvbuGEg8dmlTVTlFm2zyBXxcE4JoNoW/bXSLvUzBulJo9RPOIqjgZgdyEXFpHJwK+B8TiH+k2qer2I7AvcjYvQWg1coKrN4kK1rsfVON8NXK6qSwq5Z28S7XQWrtpGUruuIshkt880ag7KqOYbkUarukUVRCaqE85kEneqGy3rCq4zD0xuYdNb3HDS6GhzcHUibX9CXFtlG4kG7d2ypy0tMmnRqm086dvjD8+vZ/yIIVx9xqFdzo9Gmc2KrJyPfgY9MSlFvxv9OdIuNfNGqcljFIe4iqMDeF5EHifdx5ErHLcd+JyqLhGRWqBeRB4FLgf+qqrfEpGrgauBLwCnAwf5v6NxSRWPLvB5CiZuhb+gc2htTyIinHjION49c1zGjja64O3hZRtyhvyG7x+u6jZrwgh+9cxqWtuSJHGdb3VVguMPHssTr26hvSNJQoTrzp5d0NqDqK/mmvuW0ZHUNAd2oaa36Ghz/pImnl3dWV345EP34x2TR+VNOZIQ6VTGAkvWpFco/u3itWmKI/xc4TUN0VnAw8s2dJnJZPJHxUnzkml/b4y0ezsiqT8inCyqqjKIqzj+4P9io6ob8GlJVLVFRF4G9gfOBk7wh90KLMApjrOBX6uqAgtFZJSITPDXKQqFVPj7xLtnxErwd8PjK9NMIVVVCYYMqqK6KtGl4ly2+4erup0ya3zKvBJWTpl+oNmc6tH7XH7MtDRfTaA0gvfB/+6UVQ0fG3Yif+z4A2OtqwC30l3V+Wqm7DuMlzZ0rjdt3tOWWjyYqf3CyiA8C5g1YURqJpdrjUU+s1O2/T0dafe2n6Q//C4WVVU5xF05fquI1AAH+03LVbUt7k1EZBrwTmARLjV7oFA2iMg4f9j+wNrQaU1+W5riEJGrgKsApkzpWcnMbJ1ANtNDrs4hk3kqcGb/9eVNVCeEi46akra+I24Kjzh29qhTHTprUkfv07BhZ9rMoiohJJNKknTTW6ZV3nGZM7WOjxx7AH9q2Mhps8bnjMCaN310Kj1KdUK49qzZKSW5fGNLuuNbSbVT9LlufOI19rR1pBRnNCIuzhqLfGansKzhVC49pbf9JP3hd7Goqsoh7srxE3Czg9W4vmWyiHxIVZ+Mce4+wHzgM75uedZDM2zrYuFX1ZtwebOYO3dud9NAUd/YzPrte6hOSJdIpGymh1zT8Gi22ebdrUwcNZT2pLrV5Ell4qihsZ202cg0q6hvbOamJ19LO+7hZRtS++dNH011VecM6PTZE3hu9Rup+wb2/mBW07KnjWdWbaNhw84uq7zjpj9fvrElFR77sydXMWX08DR5wwkarz1rdlp6lPC6kvlLmtLukciSkFESwiMvbQI6FefM8bWp8wpJl5LX7BRJ5dIb9LafpD/8LhZVVTnENVV9F3iPqi6HVGGnO4E5uU4SkUE4pXG7qt7rN28KTFC+jvlmv70JmBw6fRKwPqZ8BRHNK3XhUZOZPXFkamV4eDQfpKOYNXFkVgf3HYvWcPdzmbPNZssUu7fNLTa78rgDqB06KJbJKdOsYub42tT1wpw+e0KagiJw7Ksyc3xtqnPMZAK7+OcLU6ujIX3VfK705+F9k0YNTZPnhgUrU8+0bP2OtASNdz+3Ji09SnikGu2aTzokc0LGRxo28kJTZwju3c+tYfmmlm7Vfc81s4ymcsk1qi7E3t/bEUn9EeFkUVWVQ1zFMShQGgCq+qpXClnxUVK3AC+r6vdCu+4HPgR8y/+/L7T9kyJyF84pvqNY/o1oXqmtLXtTDuJBVcL5cycza+JIrr1/WaqDSwipsNi9bUmue6CBC4+cwrL1O7j7ubV0hMKfDp0wAkj/IdUNq2Hhqm2s274nNTNpTyo3P/06d3/smDSlkc3kFA3VfXjZBpp3t9La3rkye+roYVz1rgOZOb42NapPSOeq6rYOTflsgC55l8IpNSA9EiqXKSK6LzoaX9e8h3XNe3hqxVaOmpbeoew3Yggvb9iZMv/UDatJJZbctTe97Muwmqq090EnXzeshhealqZdM7rIsTt136PEHVUXau8vhlO5PyKcLKqqMoirOBaLyC3Abf79pUB9nnOOBT4ALBWR5/22L+EUxm9F5ApgDXC+3/cQLhR3JS4c98MxZSuYtDxOVQn++vKmVMfa2qHcsWgNVQmhPaQMkurs/ur/XmjakdZRhXmxaQcX3PgMJx0yjo8dfyDzpo9Om+GEO/KkaloHHFUOdz+3JjUjCDt4AWZNGNGlI/vuBYcDcN0DDSmlF07FoUDLHueeypR3KZoS/sCxw/n2ee9IyZet04zKcfIh41i5+c2M7bPXd6bBsSfMHMeCV7cAzkl/zX1LU5mF9xmS/hUNZj5Rwsr19NkTmDm+lidXbIltNonbcc+ZWhdrDUwh9n6r5GeUG3EVx7/ianJ8CjcIfRL4Sa4TVPVpsqcMOinD8ervUXTCM4F12/dw17Nr0mXBdeiBozhg4qghNG1/K+/1g0ilR17axIJXt3DenElp/o9TDtuPx17ZTFJdXqlwpxZdx/HShp0sXbeDhAgHjdsn7T6rtu7qYh4AMqZOD/Onho2cMmt8xlXd4yIp1qeP3SejaSjTGo+oMzrafgEXHjmFmeNr044NzD/tHZ0RXq0dSiIyc5my77CszxWORgNim00KmR3UNzZz7QMNtLUnWfT6G1nzfBVi77dKfka5ETeqaq+I3AbcpqpbiixTnxBMqesbm7ln8dq07KrgFtWd8bYJ/OH5TjfLxFFDsyqO6sgMJaCtPcnKTS1pYa5Nzbu58rgDaNiwM+/K7aAjTary8sb0Miibdr6VehaAG594jRebtnfxd0RZvW03l9680KXzqJKUOWv2xJHMHF/LXSHT24Llm7vUzojTqYVTjAOcc/hEtu1qTfPbhK8TrJHRoJE808cMZ+ubnRFQ57wzfm2TuLJGV6vnmh3ErUhYiL3fKvkZ5UZOxeH9FF8DPon7PouIdAA/UtXr+kC+vkHSx8aCS+ERJiEwalhNKqQzSialAc4/sDeSe+OlDS28tKEFAZ5bnT5q/d6jy9OOzRU2dsz00al1I9fcvyytql8+9ra5RXEfOfYAbn76dTqSynUPNnDNmbM6Hek4xZVrwWJAdNQervEhwEH71fKDLAkgA/NP4GcKqKlOMGO/2tRCQqGwVOW5ZA1vL6SORqZZWjbiKi6r5GeUG/lmHJ/B+SqOVNXXAURkOvBTEfkPVf1+sQUsNoGZJCBYMR04x1PbRfjLS5tiFSMZVCWcMHMc42oHM2viSBYs3wx0TboXXWR3x6I1aaNrgH0GV/FWW5JkUqkZ5BbvNWzYyawJI/jF317v4vwOGJxBYUXv/fSKrTzz2rbUAsBghXX4UuHw11wmnahNf2vIVxL4VXJVUmze3UpSNbVi/NgZLoPu8tAsS4lfHClTwSVwM4YgUWTwDIXU0chXkbA7zJlqlfyM8iKf4vggcEo4uaGqrhKRy4BHgLJXHFFH+XlzJjF74kgeXraBNt8bC5AMra6OEsxXqhLC7IkjUjb8+UuauPaBhrQopSjh0N2oYxzgzb0dfPxd01MhuwC1QwfxwtrtGZ3fATVVQiQgqQuBCSy8Uvv02RN45rWtqWCAK487IOWQzuXwjdr0x9YOTnXGAvz8qVUklazO3+j5Qdr18MI9ARoiWW+zzSqiJqUbn3iNJ1dsSUvzHjxD9N7hqK5MCzKzVSTsCRaNZJQT+RTHoEwZcVV1S75w3FInSJ8tkFrlrTg7f7BeI+hgEn5ldTaCPR1J5cWmHTRsWOZWYsewHJ0ws3NdQtQxHvCnho2paKlgxJ/LDl6VgJMO3S/NP5MJweW+uvZ9s9JWagd6Lgn8/OlVJH2E07VnzU5bSBg3dTuQFrWWzfn7/iMmIf5/WCFVV3X6P363eG1qf64ZULTpN+18K+0zBTeLrBtW0yVsOl9Cyt7o5C2nk1HO5FMcuQzKxa+LWSSC8MdgxB6k3Agn+QuTTCrVVZIW8ZONIDIoF2E/Sdj5fMnRU3j29W1dOvzV23Zz0U3PcP7cyWkj5kwIcNGRuVOxzBg7nNXbdtGRJLUgMOi8fvCXV9OODax4rR3qTG6hhYRRoh1qELa67c29afmmwkqvvrG5i/kobOOfM7WO4w8ey6N+VXhHstPnkmsGFDUpXXjkFJZvakitMld1s63rHuysgDhnqktQGddR3l0sp5NR7uRTHO8QkZ0ZtgswpAjy9AkLV21LmaGANIdspk45UAanHLZfKq1FTwjPRNoizudl67r6QoLj/tHYnFdpiEDt4Gp2ZrFT1VQJR00fzWtbdqWeK1xjO7pWJMymnW+llG0gN5A2ywi/vu7BhoyKLqjOd8eiNVxz37K0wIJoZ13f2MxfQm1elSXlSKa6GlGTUhACnKvKXyGO8rhkStFvOZ2Mcian4lDVqlz7y5V500czqEq6hODmQoHHl2/OuC8hzjT07pnj/EryNYT87Rw6vrZLKG34ui+s3Z6adex8K3vuyLATN8rUfYfR+MZuVF1uqIPGDu9yzKhhg7ho7mSmjB6e1jne/dxakkmlylcRjBKsHj9gzPBUWg8FVmxq4UePrXALGxMCIqla3e8/YlIX01BAw/od3LFoDV/5w9Iuz5KMtMdXfr807RoTRg5NCw3OtRgvOgMKh2AHUUxRk1scR3khZqZcKfotp5NRrsRdADigCEaj85c0sbVlb9rKcfAZY71pKkxbDkVz+ORRrNm2i7+v3MrM/WpTphkBlm/KrDQCHnlpEwuWb+bOq47J6RepG1bDG7vb6OhwHV5HMklH0pWNHTVsEI2hCvArtuzqcv723a4udzTlRzDjymRiE+A4XyP8C/NfTNv35IotnSPnDiUw5LX6qKpsz7K5ZS93ZVAaAY++tIknV2zh9ivnsXrbrsi5netoogWpsi3Gy0gWk1vdsBq/6LDr4szgnoWYmbqbot8wSplE/kMGNmNrB/Nf57yNI6fVMX7EYM45fKJboJbBhp+NpLp65D97chWrt+1Os+crXWcIwwd3ncgFTuNROUwjL29soSOZZGztYOYdsC/JwIndoRwwpusMI9d1shGdcVQlSEU4RdtkaE01NdUJqsQp2oDgeTPNXqoTbntUUYcJhylHnyv8PlOnHCaoj1LfmF4MauGqbbR7n1bgMwmOv+7BBjqSbsX6NWfO6tKpB4sFk9ppVstFMLuoErpkYO6N3FmG0R9U5IyjvrGZC2/8eyp6aFCVC0ftSMJDSzdw/MxxGetl5+L5psy+iUzs2ttBVYI0cxa4DvQjxx6QXn8iQkcSNu7cy8adneskksADL8bPB/nmW+n+jyAH16AqYcLIoTS+0VkleNTwTkX2keOmp8n2iRNmpPwGz6/dnnJgE7peW4dL3SI+ACGR6DpWOXDs8JTPBVIrTakbVsNl86al3fOyedNSr3OZfDLNDICc6ewDRaSAqmY0UxXqA7GMscZApCIVx/wlTWmKIWyCau3QtA4wF9lyMUXJpCTGjxhCR1LZ3LIXVVIrhm97ZnWse0fpiBP76wkW2SXVzQCuPG56Kv3JD/+aHlW1taWVS29eyO1XzmPm+FqqfP2SqoSkRSN9OaLs3KxCU0khg2lIR0eSMbWDQ2tnhH2H17AyZFoTIbWS/V0HjU27bngdR65OOTobCdLj56o5Hsf3UMhiwbCcpjCMgURFKo6tkQyw3SVuVx1VGgDrQjmvjppWxxdOP5Q5U+u47OaFvSJbLoLoK9Q9wy/+vpr2DucnOGravmmzGYC32pKpNS+BgupIalqepmjaDGcG6nrfQdUJzj1iEuceMYn5S5q4pz69Njl0mrpa25OpfFwBW1r2pi3OC3fK0aqCYSUg/npJdf8z1RwPFNG9S5qyfrb5lIutzzAqgYpUHKXGc6GOM1dW297ikFCUV0cSOryzpC1LFBS49O4Hj6tN2xZOwT5nah3Xvq8zwim6wluAt08ayYVHTklLU9KeSat6kurycaVqdVQJC5Zv5i8vb8pYTCvIdTV4UNfCTcs3tqQUUlLTI7eizPczk3tDlQ/Dz5ltlmPrM4xKoSKd4/GNOn2D4jLbBq+LxbTRw/jmv7yty3YhiCRLMHp4Zpt9R7KrU31HyEwTpBt/esVWrn2ggVkTR1LjR/oJ7zxfum4H1z3YQH1jM/WNzazbvidVnySgKvQmgUuvcudVx/Cfp87kgrmTU6V4w87w+sbm1HqQ8MK9MMsiiuyRlzZx6c0LU47zwJEeKI1sDnfI7tjO56w3jIFCRc44ojUnSoG/vLypS/RPb/PdCw5nztQ6vvPnV9K2p5y9ySSvb+0axpuNcBLFaG6ohvU7Uon7gponmfwN1Qnh4qOmMGviyFTd8+sebEgzBaWlwM+w/mLhqm0kQxFfCV9FMDz6j/pKIHNJ3OqqRMY69GGymaPKeX2GmdiMQqhIxRHY43MlH+xrkuo636oMmW4D4jrjM1ElndXzZozbp4tfAaA9Sc42iS4+PCbUMW6J+I22tOxN6/DvXdLZ4Tes25FWPnfiqKFpBZjCRZ6C8zPVTg8IOuzW9iQJEa47e3aqpG6qlG2EbCVxOzqSXHTUFCaOGpqxE81ljirXCCozsRmFUpGKY87UOg4bX1tQCG1f8OqmlqxKA3IrjeoEOUOIOxT+78/LqamSnCPhaKqSQFnVVCc4Y/b4tDxaN//tdU6ZNZ45U+ty1qkIO51/t3htavU5OEUUDWmNOryD0Olw6d5wzqpMHXZ9Y3Pa6H9M7eDUs4QXNQb3CR8bTrIYyBCuVpgrXUg5RlBZChSjUCpScQC8kmc1d19TJelO8sLPF9pD3XWwhiJKa4eycNUbXbYHbIlEMZ182H4cPnkU86aP7pIAsT2Ur2pz5LxM5sA1b+zOWPAq6n8I87MnXkvL1pvAzXwy5aaKduDhdCQzx9emZj1B+vhA9kIc3tecOatszVHZCDIQZ0rBYhiZqFjFcdS0fXkySzK/vmbMPq6y4OaW7icc3ltAepRcOX6j+bsOHDM8FbYaTYBYlSDlSwjK1QYmoHCG26DzzZbZN7pyPDzCj/pcJo4awsVHT41VRzycjiQcZZUtdXqm60VH4827W7ukj89VoKpsyJH12DCiVKzi+PUVR/O2r/2Jlr0d/S1Kl6p/xaQqAcNrqti+J0+VJ89fXtmcKiLVEjFjnXTIfixb3+mvEGDq6GFc9a4DMy7GU9yM4W2TRrJs/Y5Unq33HzEpzY8R7tQPGJ2ecuSwiSO7rL/IRLYcUUHq9LimmXnTR1OdcLO3IDNv2HcTnY1EFxWWA5lSsJST/EbfU7GK41sPvVwSSqOv6UjC7tb4z71y85t895HlGTvxtW/sZsGrW1KzCMWZo4IaF9CZ4iPofKt9bYyX7l9GBy4dyfKNLSllkRAhqZpaqPfq5jdT90sIfOz4A9NmJMs3tqTMUWEHe64Ip4Kjn4LVkpI+Nworp9a2JNfct4ykatk5mMs5GszoHypScdQ3NnPjU6v6W4x+I1c6+UxRXcHIvKl5d9r2TTvf6rKAL6luEeO9S5pSK8mrq6TTRCVuEV9bqK7H3c+tSc1a1JeyFRQRSfOJiEiakqlKdPpxAhNaoDxy+S0KiX4KFikqLuIqW7lcCSm8cnMwl2s0mNF/VKTiuHdJk5lyM1BTJdRUJ3gzMhOrErcwb1dkpjJj3D68uG5HasV52O/99IqtqdDesL+lPUMakcE+RQm4WcuVxx1A7dBB1A2rSSv0pKo8vGxDapSfjGi4h5dtSJt15Ipwihv9lK9YVNRvUq6j9nKMBjP6j4pUHEuKvNCuFMi1HiQTAiBda6sPq6niE++e4nS8JwAADghJREFUwfrte7hj0Zq0fee8cxLnvHMSDy/bQMP6Hbyxq7MI1bZdmfOBJUlPIzKoSpixXy2LG5tTiQNrhw5K82N89b5lvnyvi4Z6bvUbtLUnSSTSI8dOnz0h1rMWstgt32g83OFG158YxkCl4hRHfWMzr+SoR9GfjN2nhi295CgfXJ1gd5a8V5kWEirQ3pFk1D417G7rlGHa6GF84t0zqG9s5s5n16TN1G54fAWbWvbS0aFR8z8nH7ofDy3dQFuHphYOBll5gzQi4cikcKhseLQ+c3wtVeIUTlAfPTzKv+a+pbQn3TqWwK+Si7BDO1gsGJ6lZCLuaNxG7UalUHGKY+GqbSWXqyrggDHDe01xZFMauUgqjB4+OC0seP+6YYDrFCeMHJKW1Tf8OqxQEgIfOGYaHzhmWlYzTrSTzZUePRrxE46OCiZIquT1K9yxaA03PflaKpFkUpVr7ltWWOVAwzAqT3HMmz66R6k7ik2hJqZu3SPLKvMEsPXNdBPTupBDfNbEkWnKIhtB+pRZE0eycNU2Tp89IWNt8PrG5lS69lkTR2a8VjQctm5YTWrdRCHRQHcsWpOxQFbSwk8No2AqTnHMmVqXtePsbxavbqYvxBpUlaA92XmnhLgRe3WV8FZ7ugN8bfOe1OuPHX8gjy3fnKpNHk7hIZE8Vq9uauF27xN5asVWqhNuf1AbHODim55Ji/ASSKVET+vIfTisAtfe75zlQchr3Gigh5elV0gMMgLXDCo/R7Zh9DcVpzjqG5tLUmkAfaI0wJl8qquEdu+bEK84EGHfYYNpeatzltHyVnuqbsWcqXVcd9ZsHl62gVkTRqQin5p3t9Kyp42fPdkZ4ty8K93kFrR5OCNtdHW70jWUdeGqbamoraAMbfi4uHW7T589IW3V+8feNT21sNFmG4ZRGBWnOIK6F5XMxFFDWetNUKqdprH2jmSqqFOYoCPPlMYj6HRveHxlWknV6WP3SSsHW50gVSI3GOEPqpK0GUeCrjmowjW+wZnZoteJQ+AAz7RY0DCMwqg4xRFdQ1BpJAQO3q+W1dt2d9mXVHgrQ0W+cN2LbKk6ov6Gjx1/ICfMHJeWZDBqUrrzqmPSfByZ0nVEa3xfeGT2lOf5uOToKaYwDKMXqDjFsXtvvBxNA5HPnzqTedNHM39JU8b9CWDU0Bq2hqKqDvUdPsRfDBeOmoouyAsTJ3w1es9oynPDMPqeklIcInIacD1QBdysqt/q7Xus2BK/wl05E40cG1KdSC2qW55hHUsC5yj+yLEH8NX7ltKRdCP817buSuWqyueMLsY6BkuHYRilR8koDhGpAm4ATgGagOdE5H5Vfal/JSsPovU3aqoTaaVd33VwZ+nU5t3pjmsBjvWFjQASInR4tZNyTGdwRvdVuVFbWGcYpUXJKA7gKGClqq4CEJG7gLMBUxwx2K92MOu2v5WaZeyNhI6dMHNc6nW04l5VlaSq4X3590tTCiipLkcV2rX+tpUbNYzKpZQUx/7A2tD7JuDo6EEichVwFcCUKeboDDh04ki27mpNrYoOk5D0WUbz7tY0U9YFcyenOv3o2sMTDxmXqgCYqcZGOWaDNQyjZyT6W4AQ0UJwkGGBt6repKpzVXXu2LFjM5xS3gytzv6RnHP4RKp8KyXEhaYKLqvtx48/kNuvnMd7Dtsv7RzBma2i9SgGD0pQJTBkUIJzQ9X6zj1iEjVVknbdTGslAqd1VYYyroZhDGxESyS/uIgcA1yrqqf6918EUNX/yXbO3LlzdfHixQXfa9rVf+yumFmJ+hiC91UCx84Yw1Mrtqa04MffNZ1TZo1P+Qdue2Y1C17dwgkHj+UHF72Tc378NMvW72T8iMGM3mcw40YM4ePHH5haSxFODpjJx3DHojVpi/Qy+SBy+Sfi+i76ysdhGEbvIiL1qjq32+eXkOKoBl4FTgLWAc8Bl6hqQ7Zzuqs4DMMwKpmeKo6S8XGoaruIfBL4My4c9xe5lIZhGIbRP5SM4gBQ1YeAh/pbDsMwDCM7peQcNwzDMMoAUxyGYRhGQZjiMAzDMArCFIdhGIZRECUTjtsdRGQL0NjN08cAW/MeVblY++TG2ic71ja5KYX2maqq3V5BXdaKoyeIyOKexDEPdKx9cmPtkx1rm9wMhPYxU5VhGIZREKY4DMMwjIKoZMVxU38LUOJY++TG2ic71ja5Kfv2qVgfh2EYhtE9KnnGYRiGYXQDUxyGYRhGQVSk4hCR00RkuYisFJGr+1ue/kBEfiEim0VkWWjbviLyqIis8P/r/HYRkR/69npRRI7oP8mLj4hMFpHHReRlEWkQkU/77RXfPiIyRESeFZEXfNt83W8/QEQW+ba5W0Rq/PbB/v1Kv39af8rfV4hIlYj8Q0Qe9O8HVPtUnOIQkSrgBuB04DDgYhE5rH+l6hd+BZwW2XY18FdVPQj4q38Prq0O8n9XAT/tIxn7i3bgc6p6KDAP+IT/jlj7wF7gRFV9B3A4cJqIzAO+DXzft00zcIU//gqgWVVnAN/3x1UCnwZeDr0fUO1TcYoDOApYqaqrVLUVuAs4u59l6nNU9Ungjcjms4Fb/etbgXNC23+tjoXAKBGZ0DeS9j2qukFVl/jXLbgOYH+sffDP+KZ/O8j/KXAicI/fHm2boM3uAU4SkUxlogcMIjIJeC9ws38vDLD2qUTFsT+wNvS+yW8zYD9V3QCu8wTG+e0V22bedPBOYBHWPkDKDPM8sBl4FHgN2K6q7f6Q8POn2sbv3wEM9AL1PwD+H5D070czwNqnEhVHJm1uMcm5qcg2E5F9gPnAZ1R1Z65DM2wbsO2jqh2qejgwCTeDPzTTYf5/RbWNiJwJbFbV+vDmDIeWdftUouJoAiaH3k8C1veTLKXGpsDE4v9v9tsrrs1EZBBOadyuqvf6zdY+IVR1O7AA5wcaJSJBRdHw86faxu8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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# create the scatterplot to predict 'domestic'\n", "# from 'budget'\n", - "plt.scatter(___,___,marker='.')\n", + "budget = movies[['budget']].values\n", + "domestic = movies['domestic'].values\n", + "\n", + "plt.scatter(budget,domestic,marker='.')\n", "plt.xlabel('Budget (millions $)')\n", "plt.ylabel('Domestic Gross (millions $)')\n", "plt.title('Revenue vs. Budget for Major Domestic Movies since 1980')\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -106,15 +222,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.11222637] 7.282264927408114\n" + ] + } + ], "source": [ "### edTest(test_sklearn_regress) ###\n", "\n", "from sklearn.linear_model import LinearRegression\n", "regress = LinearRegression().fit(\n", - " ___,___)\n", + " budget,domestic)\n", "print(regress.coef_,regress.intercept_)" ] }, @@ -127,9 +251,104 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: domestic R-squared: 0.463
Model: OLS Adj. R-squared: 0.463
Method: Least Squares F-statistic: 4505.
Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00
Time: 14:59:59 Log-Likelihood: -27815.
No. Observations: 5222 AIC: 5.563e+04
Df Residuals: 5220 BIC: 5.565e+04
Df Model: 1
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
const 7.2823 0.875 8.322 0.000 5.567 8.998
budget 1.1122 0.017 67.117 0.000 1.080 1.145
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Omnibus: 3349.953 Durbin-Watson: 1.321
Prob(Omnibus): 0.000 Jarque-Bera (JB): 69300.215
Skew: 2.727 Prob(JB): 0.00
Kurtosis: 19.993 Cond. No. 67.1


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: domestic R-squared: 0.463\n", + "Model: OLS Adj. R-squared: 0.463\n", + "Method: Least Squares F-statistic: 4505.\n", + "Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00\n", + "Time: 14:59:59 Log-Likelihood: -27815.\n", + "No. Observations: 5222 AIC: 5.563e+04\n", + "Df Residuals: 5220 BIC: 5.565e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 7.2823 0.875 8.322 0.000 5.567 8.998\n", + "budget 1.1122 0.017 67.117 0.000 1.080 1.145\n", + "==============================================================================\n", + "Omnibus: 3349.953 Durbin-Watson: 1.321\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 69300.215\n", + "Skew: 2.727 Prob(JB): 0.00\n", + "Kurtosis: 19.993 Cond. No. 67.1\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import statsmodels.api as sm\n", "\n", @@ -141,6 +360,109 @@ "ols1.summary()" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: domestic R-squared (uncentered): 0.614
Model: OLS Adj. R-squared (uncentered): 0.614
Method: Least Squares F-statistic: 8313.
Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00
Time: 12:49:16 Log-Likelihood: -27849.
No. Observations: 5222 AIC: 5.570e+04
Df Residuals: 5221 BIC: 5.571e+04
Df Model: 1
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
budget 1.1973 0.013 91.175 0.000 1.172 1.223
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Omnibus: 3037.759 Durbin-Watson: 1.430
Prob(Omnibus): 0.000 Jarque-Bera (JB): 57408.069
Skew: 2.400 Prob(JB): 0.00
Kurtosis: 18.518 Cond. No. 1.00


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "=======================================================================================\n", + "Dep. Variable: domestic R-squared (uncentered): 0.614\n", + "Model: OLS Adj. R-squared (uncentered): 0.614\n", + "Method: Least Squares F-statistic: 8313.\n", + "Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00\n", + "Time: 12:49:16 Log-Likelihood: -27849.\n", + "No. Observations: 5222 AIC: 5.570e+04\n", + "Df Residuals: 5221 BIC: 5.571e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "budget 1.1973 0.013 91.175 0.000 1.172 1.223\n", + "==============================================================================\n", + "Omnibus: 3037.759 Durbin-Watson: 1.430\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 57408.069\n", + "Skew: 2.400 Prob(JB): 0.00\n", + "Kurtosis: 18.518 Cond. No. 1.00\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "\"\"\"" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = movies['budget']\n", + "ols1 = sm.OLS(movies['domestic'],X).fit()\n", + "\n", + "ols1.summary()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -152,14 +474,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: domestic R-squared: 0.471
Model: OLS Adj. R-squared: 0.471
Method: Least Squares F-statistic: 2328.
Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00
Time: 14:28:01 Log-Likelihood: -27774.
No. Observations: 5222 AIC: 5.555e+04
Df Residuals: 5219 BIC: 5.557e+04
Df Model: 2
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
const 1016.2111 111.905 9.081 0.000 796.830 1235.592
year -0.5042 0.056 -9.016 0.000 -0.614 -0.395
budget 1.1418 0.017 68.087 0.000 1.109 1.175
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 3333.347 Durbin-Watson: 1.314
Prob(Omnibus): 0.000 Jarque-Bera (JB): 69688.767
Skew: 2.703 Prob(JB): 0.00
Kurtosis: 20.060 Cond. No. 3.28e+05


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.28e+05. This might indicate that there are
strong multicollinearity or other numerical problems." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: domestic R-squared: 0.471\n", + "Model: OLS Adj. R-squared: 0.471\n", + "Method: Least Squares F-statistic: 2328.\n", + "Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00\n", + "Time: 14:28:01 Log-Likelihood: -27774.\n", + "No. Observations: 5222 AIC: 5.555e+04\n", + "Df Residuals: 5219 BIC: 5.557e+04\n", + "Df Model: 2 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "const 1016.2111 111.905 9.081 0.000 796.830 1235.592\n", + "year -0.5042 0.056 -9.016 0.000 -0.614 -0.395\n", + "budget 1.1418 0.017 68.087 0.000 1.109 1.175\n", + "==============================================================================\n", + "Omnibus: 3333.347 Durbin-Watson: 1.314\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 69688.767\n", + "Skew: 2.703 Prob(JB): 0.00\n", + "Kurtosis: 20.060 Cond. No. 3.28e+05\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 3.28e+05. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\"\"\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### edTest(test_ols2) ###\n", "\n", "\n", - "X = sm.add_constant(___)\n", + "X = sm.add_constant(movies[['year', 'budget']])\n", "ols2 = sm.OLS(movies['domestic'],X).fit()\n", "\n", "ols2.summary()" @@ -176,21 +599,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
OLS Regression Results
Dep. Variable: domestic R-squared: 0.471
Model: OLS Adj. R-squared: 0.471
Method: Least Squares F-statistic: 1551.
Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00
Time: 14:30:09 Log-Likelihood: -27774.
No. Observations: 5222 AIC: 5.556e+04
Df Residuals: 5218 BIC: 5.558e+04
Df Model: 3
Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err t P>|t| [0.025 0.975]
const 1023.4502 125.314 8.167 0.000 777.783 1269.117
budget 0.5594 4.536 0.123 0.902 -8.333 9.452
year -0.5078 0.063 -8.117 0.000 -0.630 -0.385
interaction 0.0003 0.002 0.128 0.898 -0.004 0.005
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Omnibus: 3331.783 Durbin-Watson: 1.314
Prob(Omnibus): 0.000 Jarque-Bera (JB): 69581.788
Skew: 2.702 Prob(JB): 0.00
Kurtosis: 20.047 Cond. No. 1.94e+07


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.94e+07. This might indicate that there are
strong multicollinearity or other numerical problems." + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: domestic R-squared: 0.471\n", + "Model: OLS Adj. R-squared: 0.471\n", + "Method: Least Squares F-statistic: 1551.\n", + "Date: Sun, 13 Mar 2022 Prob (F-statistic): 0.00\n", + "Time: 14:30:09 Log-Likelihood: -27774.\n", + "No. Observations: 5222 AIC: 5.556e+04\n", + "Df Residuals: 5218 BIC: 5.558e+04\n", + "Df Model: 3 \n", + "Covariance Type: nonrobust \n", + "===============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "const 1023.4502 125.314 8.167 0.000 777.783 1269.117\n", + "budget 0.5594 4.536 0.123 0.902 -8.333 9.452\n", + "year -0.5078 0.063 -8.117 0.000 -0.630 -0.385\n", + "interaction 0.0003 0.002 0.128 0.898 -0.004 0.005\n", + "==============================================================================\n", + "Omnibus: 3331.783 Durbin-Watson: 1.314\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 69581.788\n", + "Skew: 2.702 Prob(JB): 0.00\n", + "Kurtosis: 20.047 Cond. No. 1.94e+07\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "[2] The condition number is large, 1.94e+07. This might indicate that there are\n", + "strong multicollinearity or other numerical problems.\n", + "\"\"\"" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### edTest(test_interaction) ###\n", "\n", "#create the interaction term\n", - "interaction =___*___\n", + "interaction = movies['budget'] * movies['year']\n", "movies['interaction'] = interaction\n", "\n", "# define the X matrix\n", - "X = ___\n", + "X = sm.add_constant(movies[['budget', 'year', 'interaction']])\n", "\n", "#fit the model \n", - "ols3 = sm.OLS(___,___).fit()\n", + "ols3 = sm.OLS(movies['domestic'], X).fit()\n", "\n", "ols3.summary()" ] @@ -206,27 +734,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# define predicted values (yhat) and residuals\n", "yhat = ols3.predict()\n", - "resid = ___ - ___\n", + "resid = yhat - movies['domestic']\n", "\n", "#plot the histogram of the residuals\n", - "plt.___(resid,bins=20)\n", + "plt.hist(resid,bins=20)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# residual scatterplot \n", - "plt.scatter(___,___,marker='.')\n", + "plt.scatter(yhat,resid,marker='.')\n", "plt.hlines(0,xmin=0,xmax=500,color='red')\n", "plt.show()" ] @@ -247,12 +801,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "-27814.79344349679" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "### use ols1.params,ols1.mse_resid, and norm.logpdf as your basis\n", - "from scipy.stats import norm" + "from scipy.stats import norm\n", + "\n", + "params = ols1.params\n", + "std_dev = np.std(ols1.resid)\n", + "y_pred = params[0] + params[1] * movies['budget']\n", + "\n", + "prob = np.sum(norm.logpdf(movies['domestic'], y_pred, std_dev))\n", + "prob" ] } ], @@ -272,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/content/sections/section2/notebook/cs109a_section_2.ipynb b/content/sections/section2/notebook/cs109a_section_2.ipynb index 7868a45..19af69a 100644 --- a/content/sections/section2/notebook/cs109a_section_2.ipynb +++ b/content/sections/section2/notebook/cs109a_section_2.ipynb @@ -3136,7 +3136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb b/docs/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb index 67b7bc6..ad322a1 100644 --- a/docs/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb +++ b/docs/lectures/lecture05/notebook/s1-ex1a-challenge.ipynb @@ -58,16 +58,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Advertising.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13288/583825472.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Read advertising.csv file using the pandas library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_filename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 584\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 481\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 482\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 483\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 809\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 811\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 813\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1038\u001b[0m )\n\u001b[0;32m 1039\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1042\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 52\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 220\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 221\u001b[0m \"\"\"\n\u001b[1;32m--> 222\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 223\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 700\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 701\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 703\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 704\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Advertising.csv'" + ] + } + ], "source": [ "\n", "# Data set used in this exercise\n", "data_filename = 'Advertising.csv'\n", "\n", "# Read advertising.csv file using the pandas library\n", - "df = pd.read_csv(___)\n" + "df = pd.read_csv(data_filename)\n" ] }, { @@ -121,7 +152,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -135,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4,