diff --git a/2-Regression/1-Tools/notebook.ipynb b/2-Regression/1-Tools/notebook.ipynb
index 692123f3..97b3be44 100644
--- a/2-Regression/1-Tools/notebook.ipynb
+++ b/2-Regression/1-Tools/notebook.ipynb
@@ -2,59 +2,55 @@
"cells": [
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"# Welcome to your notebook"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 54,
- "source": [
- "print('Hello notebook')"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Hello notebook\n"
]
}
],
- "metadata": {}
+ "source": [
+ "print('Hello notebook')"
+ ]
},
{
"cell_type": "code",
"execution_count": 55,
+ "metadata": {},
+ "outputs": [],
"source": [
- "import matplotlib.pyplot as plt\r\n",
- "import numpy as np\r\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
"from sklearn import datasets, linear_model, model_selection"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "X = the set of attributes - age, sex, bmi, bp, and 6 medical measurements\r\n",
+ "X = the set of attributes - age, sex, bmi, bp, and 6 medical measurements\n",
"y = the target - a quantitative measure of disease progression one year after baseline"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 56,
- "source": [
- "X, y = datasets.load_diabetes(return_X_y=True)\r\n",
- "print(X.ndim, X.shape)\r\n",
- "print(X[0])"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"2 (442, 10)\n",
"[ 0.03807591 0.05068012 0.06169621 0.02187235 -0.0442235 -0.03482076\n",
@@ -62,82 +58,82 @@
]
}
],
- "metadata": {}
+ "source": [
+ "X, y = datasets.load_diabetes(return_X_y=True)\n",
+ "print(X.ndim, X.shape)\n",
+ "print(X[0])"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "`.shape` is a numpy property which says how deep the sets go and how many in each.\r\n",
- "\r\n",
- "(442,10) means 442 arrays of 10 items each\r\n",
- "\r\n",
- "Each 'level' of array is called an axis in numpy, so we have 2 axes here, \r\n",
- "the first has 442 entries and the second has 10 \r\n",
+ "metadata": {},
+ "source": [
+ "`.shape` is a numpy property which says how deep the sets go and how many in each.\n",
+ "\n",
+ "(442,10) means 442 arrays of 10 items each\n",
+ "\n",
+ "Each 'level' of array is called an axis in numpy, so we have 2 axes here, \n",
+ "the first has 442 entries and the second has 10 \n",
"(i.e. all 442 have 10, no variation allowed)"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "X is 442 entries of the 10 attributes\r\n",
- "\r\n",
+ "X is 442 entries of the 10 attributes\n",
+ "\n",
"y is the 442 results"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 57,
- "source": [
- "origX = X\r\n",
- "X = origX[:, np.newaxis, 2]\r\n",
- "print(X.shape)\r\n",
- "print(X[0])"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(442, 1)\n",
"[0.06169621]\n"
]
}
],
- "metadata": {}
+ "source": [
+ "origX = X\n",
+ "X = origX[:, np.newaxis, 2]\n",
+ "print(X.shape)\n",
+ "print(X[0])"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "This picks the second attribute from each set.\r\n",
- "\r\n",
- ": - leave outermost dimension alone\r\n",
- "\r\n",
- "np.newaxis - wrap inner one in a new array\r\n",
- "\r\n",
- "2 - pick the third attribute (bmi, presumably)\r\n",
- "\r\n",
- "without np.newaxis, we end up with 1 array of all the bmi's - not what we want.\r\n",
- "\r\n",
- "this way we get 442 arrays of 1 entry, the bmi.\r\n",
- "\r\n",
+ "metadata": {},
+ "source": [
+ "This picks the second attribute from each set.\n",
+ "\n",
+ ": - leave outermost dimension alone\n",
+ "\n",
+ "np.newaxis - wrap inner one in a new array\n",
+ "\n",
+ "2 - pick the third attribute (bmi, presumably)\n",
+ "\n",
+ "without np.newaxis, we end up with 1 array of all the bmi's - not what we want.\n",
+ "\n",
+ "this way we get 442 arrays of 1 entry, the bmi.\n",
+ "\n",
"I have no idea why instructions said to overwrite original `X` so I saved it as `origX`, I want to check that the numbers match below"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 58,
- "source": [
- "print(X.ndim, X.shape)\r\n",
- "print(X[0], X[1])\r\n",
- "print(origX[0, 2], origX[1, 2])"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"2 (442, 1)\n",
"[0.06169621] [-0.05147406]\n",
@@ -145,145 +141,146 @@
]
}
],
- "metadata": {}
+ "source": [
+ "print(X.ndim, X.shape)\n",
+ "print(X[0], X[1])\n",
+ "print(origX[0, 2], origX[1, 2])"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Yay it matches! Maybe I really do understand what's going on here 😀"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Split into training and test data"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 59,
+ "metadata": {},
+ "outputs": [],
"source": [
"X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 60,
- "source": [
- "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(296, 1) (296,) (146, 1) (146,)\n"
]
}
],
- "metadata": {}
+ "source": [
+ "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Now we see if the model can detect a link between bmi and diabetes"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 61,
- "source": [
- "model = linear_model.LinearRegression()\r\n",
- "model.fit(X_train, y_train)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
+ "execution_count": 61,
"metadata": {},
- "execution_count": 61
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "model = linear_model.LinearRegression()\n",
+ "model.fit(X_train, y_train)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "We now have a trained model, based on 2/3 of the initial data.\r\n",
- "\r\n",
+ "We now have a trained model, based on 2/3 of the initial data.\n",
+ "\n",
"Time to see how accurate it is"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 62,
+ "metadata": {},
+ "outputs": [],
"source": [
"y_pred = model.predict(X_test)"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Just the inputs, no outputs, and this is data the model hasn't seen before"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"What does y_pred look like? Presumably the same 'shape' as y_test, which is the actual results"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 63,
- "source": [
- "print(y_pred.shape)\r\n",
- "print(y_test.shape)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(146,)\n",
"(146,)\n"
]
}
],
- "metadata": {}
+ "source": [
+ "print(y_pred.shape)\n",
+ "print(y_test.shape)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Let's eyeball the first 10 entries, just for fun"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 64,
- "source": [
- "print(y_pred[:10])\r\n",
- "print(y_test[:10])"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"[102.22273764 110.70519001 89.02781173 146.51998889 176.67981953\n",
" 189.87474543 120.13013708 119.18764237 80.54535937 97.5102641 ]\n",
@@ -291,317 +288,307 @@
]
}
],
- "metadata": {}
+ "source": [
+ "print(y_pred[:10])\n",
+ "print(y_test[:10])"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Let's plot it on a graph"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 65,
- "source": [
- "plt.scatter(X_test, y_test, color = 'black')\r\n",
- "plt.plot(X_test, y_pred, color = 'blue', linewidth = 3)\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
+ "image/png": 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",
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""
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",
- "image/svg+xml": "\r\n\r\n\r\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "plt.scatter(X_test, y_test, color = 'black')\n",
+ "plt.plot(X_test, y_pred, color = 'blue', linewidth = 3)\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Are the actual predicted values directly on the line? My prediction: yes"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 66,
- "source": [
- "plt.scatter(X_test, y_test, color = 'black')\r\n",
- "plt.scatter(X_test, y_pred, color = 'green')\r\n",
- "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
+ "image/png": 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",
- "image/svg+xml": "\r\n\r\n\r\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "plt.scatter(X_test, y_test, color = 'black')\n",
+ "plt.scatter(X_test, y_pred, color = 'green')\n",
+ "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Note that there is one green dot for every black one, on the X axis. The value on the Y axis is the machine's prediction (i.e. on the line), based on bmi alone"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Presumably the point of test data is to see if the prediction matches the reality, but we didn't analyse that here"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"## Challenge question"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Let's use blood sugar level (attribute 10)"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 67,
- "source": [
- "X = origX[:, np.newaxis, 9]\r\n",
- "print(X.shape)\r\n",
- "print(X[0])"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(442, 1)\n",
"[-0.01764613]\n"
]
}
],
- "metadata": {}
+ "source": [
+ "X = origX[:, np.newaxis, 9]\n",
+ "print(X.shape)\n",
+ "print(X[0])"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Split into test and train"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 68,
- "source": [
- "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\r\n",
- "print(X_train.shape, y_train.shape)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(296, 1) (296,)\n"
]
}
],
- "metadata": {}
+ "source": [
+ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n",
+ "print(X_train.shape, y_train.shape)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Train"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 69,
- "source": [
- "bloodSugarModel = linear_model.LinearRegression()\r\n",
- "bloodSugarModel.fit(X_train, y_train)"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
]
},
+ "execution_count": 69,
"metadata": {},
- "execution_count": 69
+ "output_type": "execute_result"
}
],
- "metadata": {}
+ "source": [
+ "bloodSugarModel = linear_model.LinearRegression()\n",
+ "bloodSugarModel.fit(X_train, y_train)"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Predict"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 70,
+ "metadata": {},
+ "outputs": [],
"source": [
"y_predict = bloodSugarModel.predict(X_test)"
- ],
- "outputs": [],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 71,
- "source": [
- "plt.scatter(X_test, y_test, color = 'black')\r\n",
- "plt.scatter(X_test, y_pred, color = 'green')\r\n",
- "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
+ "image/png": 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rlo8iIChb+pBZB2dICNHkyey/6NiRaaupqZnuhoh8gHxhc8+NVhiEe36ZHq9cyfZjfeyMnExru3MnkY8PUWSk4fVy2xGo696o+nCNgBbZ0oesnld8PNHvvxNVr05UpgzRDz/kvC8jr/PXF1TkG7ZMTreCLtzzi2NLGT16/77+ddLS2s4UCN37E8aUzNa0thMnEnXoYFjzzA8UPu5DLQIeESBkSx+y6zmQy4lOnSLq3ZvI2Znoyy+J7tzJdLf09zcf5K8vyMgOhU8U7nmM/EBJi4wkkskyzvuSltZ2hpTgEEpAKsHjKqH5TLpxI+tO0aQkosaNiebN07+Ovuvl2tw117RI7sMqAqZl6z3T90Jn9kV/9oxo+nROudymDdHu3aRVGSuz8FlUmtCzL6HEEcKwOmkCPjfz14swDFG45zHyWhNNTWVt+auvMl43TXOfDUKfbgTXO4SBLcm+6W/k48PmgMmTic6fz7zd99kzoqJFmWKoC7o0XPNa5mQxxyLXtMjffttCQBQBxTLUsvNDXdQPH5h2WqcOUcmSRIsXE715Y1I30vDiBdHs2USwe07wPUoodoHQbWDadRdmi0FQ+QWGhLuY8jcXkNdpfL/9Fnj/Hvjuu4zXnd9qPmzMFbm2y/8NOD+G+asGWPWTFUJCgK1bATMzTlcgkwFjxgDHjwMpKcb3p3hxYNMmoH9/3alxdaWEdejugCRKAj44APc6A3IB8cnxCDgakCNh3GZmfVGjRjxkMnODaWmDgoLg5+eH0NBQEBFCQ0Ph5+dnUh+yI02ApSUwYABw4QKweTNw9SpQsiQwYgRw82bG2xMBZ84An30GVKwIvHwJeI4cDAxuBXidAz44pa3r45g7z62ILEKf1M/NVtA197y0uR85QuTpSfT8ufHbqDvRigU0ITunBJ3Jrm7fJpo/n6hWLWZyDBlCtHcva5HGYO5coiZNjMtCiVkCocenrE06PSZU2kIIsGQNPgeubePG7ADOCNlhcsupmV14ONGcOXz/mzcn2rFD+1onJHCKiJo1OYPl0qWUFtSWZnNv8g2h+QzR5p4PAdEsk/fIC7ZMRuYPYzF7NlGPHobXCQnhFLdNmhA5OnIu8z//NByAk5pK1K4d0ddfG9737dtElqVPs+1/aH1CgBWh4h8En39J8qV7tvsz7tzh67b+clCGNn5DgtnYe57TPpnERKLNm4kaNGC20oIFRFeucN5+d3fOWLlvn24zW2BwIDl1mU9osERky+RDiMK9ECIpiahhQ9ass4qEBNbqDhwwbv2XL4l+/ZUFt709UdeuROvXczGP9IiIYIHz99/a/8XGsgBydSUaMPkiWc+1V9ncZwpk1mQpweIuASWzVeudNImo85CbRjFF9Dp/XV2NnlHk1sxOLidatIjfemX788+Mt1u9mmjo0GztiohsgijcCyHGjyfq1Cn7gl0OHGABb2rBibdviTZt4khLBwei1q2Za//ihWqds2dZg3z0iH/L5WwSkck49F+5ri7OtYtLAAEvCKiXLVpvYiJRkSJExaY2Z6E+2UlDwKdniugTzK6uriZp4zk5s3v/nmjVKs2UBo8f88Dv5cUmqD/+YIVAF/74g6hXr2zrjohshCjcCxn++ovL3enSlLOCHj3YRJNZxMWx3bd/f+ZnN2zIrI5Hj4iWL2e77+3bPCiVL2+cOSkwMJAsLbsT8JqAHlnWerdvJ2raVEEJnWFGsHxLaDbLIFNEl2DOa4YUEacrGDeO0xd068bXMz2VNTmZaNs2PufixYm++Ybo1SvNdQ4eZJrlx4r8EkCYExCFeyHCvXusBV+8mP37DgtjE8nDh1nfV2IiCw0/P9aUy5dXmQq+/ZZo/frNRr+QgYGBVLRoRwKekrPzXNq0KfMvb8eOTCmULZMRhtUluNwnuN8kNJtNmGU8xzuvYhtSU4n272fqq5sb01ZDQozb9to1omHDuP7rgAEc9EbEM6s6dXKuzzmJ/BJAmFMQhftHhsyGfMfFEVWpwlPwnMLChSw4sjPD44EDKsGubGZmiwmoY9ILGRrKpodRozIXyBMWxjOKuDi+B+btAgh1fyBMLEJwv0lmLb6lTdeNuxd6c9as9M+RQKzoaHZoly7NSdrWrs18mcOoKC7eIpMR1avHQVK+vtnSzTTkh8RwBQGicP+IkNmQb7mcaNAgon79cja1bmIi22137Mj6vp4941TAJUoQ7dnDy65dUwr4nQTcJiCMgB8IaEYuTYtkKBijo4lateICGLGxpvVn7lwif3/V72qNn5H7534kzBbIa25N8ir1lqZPN/76phdg/iv9sz2c/+ZN7rOzMzOU/vsv++5/Sgo7uitW5Hsyc6amrySzyE1tOj+Yx3ISonDPp9CZsEoZIVryEAu5ofWNCvles4a1VlMFWmZw/DgzXEw9liohlzk5O88lO7sEmjFDW8MEPlcIdlvivPPTCFaXCNavCTXWED7rQJhuoVcwJiYSDR7M/HtjC36nprJ2evky/05OZgdwRIRqndeviSpXJpo2LXMCNO3eTrcgfFk+0+H8yck8uLZsyZTNWbP0xzFkR+KvuDiWFP7+bLLp25fo9OnMDyK5qU2Lmrso3HMd+rSXNK1urEzTVDG6tN59Xb7M9tWcSB6lD/37sz1XH9ILFf+VyhS6jQkIJuAgWVnpzpHOL+RvpEy1C4AwTnFN2o0jeJ1mR2flzeQ2yF8nl14u5wAeX1+iW7cyPp8jRzjbohIXLhB5lX6rJRhfv2bT15Qppgs3YbZAGO9FKH6O4POvyeH8ERHMUffxYc56UBAPZPqQXYm/5HIiMzMOTnv7ls0/pUqxA3zdOtMZVLmpTYs29wIu3PNj2lJ9GoV0olTjZcTwWmkCPr0mScT5Q0qWJNq6NXf7Hx7OA4ouwalLqGB8EYLTOoWZpZdBDSowMJCsrV2I0+1+wevOUvHbYRFDqBJIaDORUOqAQS79hg3ssD12zPD59OnDRauV6Dv+MpnVX6lTMEZEcNrkyZNNE/BF/D8l2L0gFLlOqPOT0Zr7lStEn3/OWvPgwUSXLhl3PK9vahA+7Uzo7GeQzmkMXF01WTSpqRyN3K4dX99p04iePjVuX/khpXNBQaEW7vk1bak+7QVVoLO/E348nibka9ViU0ZqKgu10aM1951bD/OKFRzWnl7ApZkfRpfiFAGdviDYvCLUXESAnVHaWmBgIBUr1oyA11S0aGdyneequialDjCLxe45uX42jt68McylP3qUGUQbN+o+j8hIjqpVT7RlXeEooeVUTpw13UJLMEZGElWrxgFPGQl4uZw1bke3eLIc0oFQZROh6+cGn8XERKItW5gu6u3NDKLXr/UfIzGRGVIrVrDfpVQpxYzP5jVTOWfB5JmCOkqW1J8q+u5dfgadnZkPf/Kk4WtS0LXp3EShFu4aWQ6n2hFmSvJF2lJD2ouhmcbPP5OGuaZ2bc2peW6+OCkpzMwICtJcnpY2uOgV7qf7DYJ/ZRYwJmprSs7+51u+Ut3HbgMJ5f4mDKtL7qVDqHFjdsQSsR9g+3YWcE5OKi79nj3M/pgzR1vwLF/O6yuRnKx2jW3D9QrGyEg25UycqF+YRUfzoFOvHrNxAoMDyazIQ8IXVXXOIsPDOZbA05OoRQs+F125d54+ZX76V18RNWpEZGvLfHZlvxs3JnLtP0ZjYMqK5l6jRsb02nfveHApV45nNmvWGCgKU4C16dxEoRbuaYLmKw+CNIEffosYgkMoVa7ML0HnzmxD/vJLooAADtFes4ZDsw8fZvvr/fusORmycZqCrAhhuZyFuvJFDghQCZfcnvKePcuCKDpatcxrfhVC7Z8J1pHcR5f7hFFlSBgvZOp8x40jsq54hE0y080J/+tFsIwmfO1MPktK0C+/sGlg9GhV0isivlcHDqi49EWL8vWqU0d1H+VydpIeP67abulSxbWt+Cehwl8GBWNUFAu+CRO0BfzNm0Rly7IjUplMLSaGyMZGU2DL5URnzhB9+ikPSCNGEN24ofo/Pp6LcyxaRNSzJwcbubszI+ibb9j+X7n+C5LYvCXU+Zk8J7WlwODAbJ21tmhB9M8/xq2bmkp06BC/V25uPLt58sTkQ4owAoaEu8D/5y1q165Nly5dypF9+y73RWhMKP+IKQ6cmQRcGQrrsmexbWEbODgA0dGaLSbG8G9zc8DJiZujo+q7Mb+dnAArK0AQOF1sQEAAwsLC4OPjg/nz52ullNWF8HCgdm1g9Wpuu3fz8jVrAD8/CXTdU0EQIJfLM3cRM4CfH2BtDSxfzql8x3yVgLiSm5DSYjJg8R7YtRZ40Bld/Tbi+t/LTT7fpCTAstRZTkGcZAtc/BLwPQ6UPgih1lrIZ8kRFQUEBAC7dnFq44EDAYlaQuvUVE5pu3Ej8NtvvOyLLzi97Q8/AA8e8D3Ztw/o3Bnw9I1BhMdWpDjeAxouAwDYmNtgdZfV6FdFs89v3gBt2gBNmwJLl/J+tm4FRo8GFi8GBg1SrXvyJDBlCnD2LPDhA6+3YgU/Y19+yamU37wBzp3jdvYscOcOUKkSUL++qjk4AOvXA7/+CiRJ3yC8/CwkV1wPWMZq9BUAAo4GICwmDD6OPpjfar5W/41B9+6cUrhHD9O2e/QIWLkS2LABaNyYr0nLlnyNRGQdgiBcJqLaOv8r6MI96EYQ/Pb4IT5ZlS/bOtEXrV7uxZldldCjB79spUoZtz8iID4+4wEg/W/1Zamppg8IymV2dkC3bkCLFsDs2dyn9+/5hb99W9nL9gAOafRbJpMhJCTE2MtmEqKiADc3wN4eKFsWWLUKuG8VlCZUvO1lKHPuEE7uLItVq4Bhw0w/htesBng+9yz/sI4CuvgBF0dCNmYoQsaFpK136RIwahQL9p9+AmrW1N5XSgrQpAkLTyVGj2Zhu2MHb/vzz8DEGW+Q1GEIXjntzlAwvn3LAr5ePR789+wBtm8HqlfXXG/JEuDff3lQ+f13vl716vG9vXCB+2RlpSnIa9bkwZOI/1+1ivfftSvg7w/0OeuLsHehPB9SE5oyR5nGtckKBg8GmjUDPv88c9vHxQGBgTyQEfE9GjCAn2cRmYch4Z7nJhnKYbMMkX62TGQk0YwZzATo35/zmuQGEhI4c+Ki3buo6LiuhP5tyW3gSBoy8xx9/z0zD0aO5KRZnTqxTbVSJZ6OK00xjo4c/FOjBk+Zu3UjattW0x7PwT+fkKVlO5o/fx89fsxOQ/XoTf+V/szQmcVMHf+V/nr7rQvv37PNWXlMfcmn5HI2XQC8vqkRpGsvbNE8twArgtUbWvGPdt3A1FQuJO3hwSYRXTl2lFRJgG3yxYppXrsffmD6nylmuNu3VdunP6ZczmYN9WNYWLCtvHFjviZ//aWbcfLuHUcdV6vGEaiLF2sWGcc0W0KnEQS3W4SW03KkYtKYMWyuyirkcmYude/OPoJx4zgHjojMAVmxuQPwBnAcwG0AtwCMVSx3AXAEwAPFp7NiuQDgRwAPAQQDqJnRMfKa5x4dzTU93d05YlLpnMtJZMYeumsXc5zDw/nlfviQaXFHj7Lj7fffiZYsIapdO1RDiLi5vadq1dih6OhIJJEwq8TOOYppeT4nCWV3EapuJNT+kWp12EeLF7PfYds25oBfvMgvodLvIJfzf15eHBkbHs7c6zVr9J+zXM6DKcAsH0O53tNj/HiiImWPEaqt4PMaIyXPigdp2TL927x5w36UIkU4bW36DJm//64pbB8/5uuovuzTT/k8M+rrqVM8QIwbx/6QUaOYtvrHH/xcqe+zVCmmXV66pH8wJCIKDlZFn/bowfdB/RxCQzkXvsQ2klBuJ8HjGqHbgBypdTpjBgdLZSdCQphO6ubGOX0OHMi+LKaFBYaEe4ZmGUEQPAF4EtEVQRDsAVwG0A3AYABviGiBIAhTFMJ9siAIHQGMBtARQD0APxBRPUPHyEmzjCmIjWUb5pIlQJ06bMOtWzdnjqXhC1CDvqn048c8Rd+9mz+NwT//sKkAAEqUAL7esA0LLk9C6Nun8LaqiKdP3wPkCNzpDjxpBZQ6BFi+g/DOBePqzdZraoqK0jxOgwZsVggJYfvwsGFA6dL6zUy//ALMmQNUq8bmBW9vw+dx8iTQqVM04uJKAZACeA1gPoB/UaTIL3j1qoTB7a9dY3t2cjKbW+rU4eUNGwIREcDDh/x71So2uT16BLi7A2FhbDLZsYPNIS1asM25c2fAxYW3IQJ+/BGYOxeYMAFwdgYOHVL5QZQYPx4YOZLNNNHRXKpQFz58AP76i69RSAgwfDhfz+LFVcc7e5b9G0ePsj3fu80uTL/6GeK/vwUMaAe43dfrH8gsli4Fnj4Fli3Llt0h6IbKbOdlXQbtP6zHxZ0NEB+v8j04OGTPsQoystUsA2AXgDYA7oGFPgB4Arin+P4rgE/V1k9bT1/La809PeLjmdLl5cWmjlOnsv8YaSyeKfbMAS9+llD3R0K3gXT7tqYGk5DA5pcff8zcsdatU9McSx4iTFPMGJQUvy/LEbxP8f/epwgdRtLLl9r7iY/n/CIuLkzXe/KEGSH//ccBLYGBbMoAmCExfDhR7958DevWZeaIhwebI9Q1WUHglLL/+x9vM3Eiz6R++onNEbzOAgIqEVBVsd1TAroS8JIWL/47Q1pdaioHOTm6xZNd/SDCoBYEEJWoFElRUTwrKVOGj7V1K3PllfluiEiLS1+pEs9UNM1gt0gqfaGxrGNH1b08doy30YWHD/maubvz9UpfEi8xka9v7dqs+f/4I5trlPjp2F/MlpkpyZFAvd9+40Cq7IC+Weum64F06hQ/B87OPOvKzajrjxHILiokAF8AYQAcAESrLReUvwHsBdBY7b+jAGrr2JcfgEsALvn4+OTWtdBARpGriYk8nS9RgqhZM54WZ1dSJg3+/VdFCW3Hq3jh4ApGzZvzC+/kxDzprBzbZ3EppiQqBU+tVUwrVPZhFgi9exCcHxDApps2bTi74Nu3XIatZEkOUjEUiRgdzeaJ2bMPGBS4CQlcLELZn6FDWaj+8gtnnpw6lU0SKkH5HwE3CUhIJ1CV7SgB2wn4nSSS5dSz5zVavpwF+t9/E504QTT/z31k9WV9rr+q2M56hkfafX/1ipc1bswDkNKunZDAVMUlS1jwqPPJVe0tAUkEXCYLixG0du0WWn1mK1n6XiLUXkU+S3zp0wmXNQLOkpO5KEm7dmyamDhR2/78+jXTHT09OSHanj26TRd//837ySls25ZxqcWMIJfz+bn2G0uos4JQZg/TW3WYkZ4+ZYpvkSI82O3Zk7lMnwUd2SLcAdiBTTI9FL+j0/3/lkwQ7uotLzR3U2zeyckc3ViuHFH9+qylZlXI6zv+d9v30NdfazpPAXa8ubtzut1Zs7gP6Ysq6EJSEgtoOD8gyE4Qqq3XFEotp2i8YJhiTg17biNnZ3Y0e3ur1v38c+OShY0c+R8JwlUCpAQY5rRv3kwklfL+583TvK5793IAU0wMkVSq3JcLAZEEmBHwrdq5tCIu1jGEgAlkbb2IRo/mvORdunBtV/Nitwnm73UODk2b8kxN98DBrX59vvZTpvCAO2YMkZ1dOkcvrhIwnRzrNCTreTY8M/P+j1DrF5JW2UZ+35ym58/ZmevlxUFWmzZp52e5dk2VcmDYMLa/G8LUqTyrym4oFSAMaENWZf4zaUbw7h07kefNU3He02ZtjiGEPp9oPP+6HMAJCZxConZtVi6WLNGMZSjsyLJwB2AO5tZNUFv2UZtl0jTn0vs5EVWLAIJfDfJZ4qt3m5QUdpBVqcJmku3bs+YAMjRzSEnhBE1KodGhAwexBAXxi9y6Nb/4Pj4c2LJgATtWlcFEiYlcx9TXlzU+jy97q3KzlN9BqLSVk1cphVK3AST5SsWWeflSU7AtX86aoaMjOxl37VIF5qSHj49MoUmPShPugP5Aqp07VS99//6838hIngEog4v8/f3TCXflfncq+lhV41hscdQEPm+iOifPS1xCr/G3pCmctVu7dsxIqlpV3zpvFDOLA8S5cxTLXe/y/seUYAEPIjP3R+TkRPTFF9qOe2WK3ebNeXCfP187l5A+tGzJs6vshIYCMqwOwfOiXgUoNZXZQmvXsmmtShUO1mrUiKNoN29mJ7OrK5Fjmx8I06xNipyVyzlg7rPPdAd6FVZkSbgrTC4bASxPt3wRgCmK71MAfK/43gnAAcV29QFcyOgYeSHchdkCP2DmsZxCtv4Sgusdgt0L+vxznoaqR12qIzWVX8LatTnXdVBQ9k8Zo6PZBhwYyNpyYCALGWXyqGPH+Jj37/Pxx43jF0mpBSvb4sVsK9d4UafaEdxvknnXMbTin+3k4aFa//Bh3neFCmwvPnyYBw9vb9YwX73ivC1NmrB5YuhQNlepnz/nzSlPXPrOI03YGsr6d/Agz05cXfk8Wrbkc1JHq/GtCCNcOPJ1HPg3HBV9f6xXuKemMqtEQyCbv2d2iXksSYveSlsudX5K9dqF0Nq1bCIqWpRNMnfvqrYtVYrvR2QkUfHi9Yl9AY0I6ERAPwJGElpMIzT8nlBhG6HGbwSzuLTt0z9XMTE8kJcsyea3zZsNs2jSIyWF/QDq9MisIjmZyPvbyoQJnoQxJdkv43I/TQi/ecPsllmz2Gzi5MT9/+wz9gdcuKCikR46xP6WTp3YLJPVyNn0KRrS+ycKE7Iq3BsrXpZgANcUrSMAV4XJ5QGAfwC4kGow+BnAIwA3MjLJUC4Id7150wc1Y0em2kNWLKAJrVjBmrK9PdvaFy5kLSG9KUYuZ6HUqBEL4rVrTXsp9UEuZ/vmF19o//fiBU9Nq1dngTtlCmdmjI9n7drDg5cPH85h9zVqEFlbK/jwve6TS5+vCV9Uo2JTWpKDSzz9+y/v9+ZNTeE3Y4bm+Z46xU7R2rU5MRQR20UXL+ZlHh6smZ0+rdTcQcB3BGzKUHNX4sQJFu7KPqhnPwwMDiSLbywIXyuE+2yQxTcWZFvfloBBim3Kpx3L1dWVoqK06Y6QJPNn60mE4bXJbKYN73emwMvdbxJANHzuGSLS9AsAROfOafZZVxoJa2sbchjRllBtHcHqDVNMWwQQPK6SZclzNGQIDzgPHrBpx9mZqH77J1R0bDeTMpempPDAcOQI9+3qVXZuHzrEAm/TJh6gFi9mM9DXX7OTcvBg9p106MDmqFq1uMyhtzf3Jb3DGxbvONUyiFB9LcH1DtnZsWCdOpVncboc8CEh/ByXLKnpnFbez6xmak1M5IGwQQOewS5YkL0D3McAQ8K94EeoBgXBz88P8fGqCFUbGxsMWjwIv+10R/J7R6D9V7w8HX0sPh44cQLYv5/D0lNTgY4dgU6dOITa1pb3R8R0vXnzmFY3ZQpH8llaZtA3NTqYegTksmXA5s3Af/8Z3sfNm0yZ+/ln1bL9+4EOHTTXS0gArl8HLl5UtadPOWoQ4H6/fAls2QKUKaOK3HR0BC5fVkXvyuXAH38AU6cCNWoACxdyhCXA4ftbt/I+IiJiER29Gikp2wFsBjAINjYXsXr16gzTDfz9N4e6FyvG1MVNm4B27QC3790QFR8FPKsP/H4WmM2hmHYSO3yYm4iUlCTFHr6CIFRD0aKfIDzcMW2/jo7AvXvAyDnBOHLjKmJbfw4fRx/EJsUiKiEKeF4L2BEIDGwNLHums2+LFzPdMX3ovDKNRGhoFFxcRsHefhIS5OZ4W2kBkiuvAcwTgBOzIXlTAdMDpJg7sF3atjIZULN9MPY+DUJyggWnV0i2hTTFEbXdm8FVKkNcHLRabCxfHxsb/g4AVavyM6lsdnaav/Uts7AAbt1iiuXZs8Ddu2onZx4LQABsIoAYX6DzCDjK7iJy+UmDdM5Fi5iuOW4cMGkSR93mJC5f5ujXXbuYrjp6tHZ0cEFEoY5QNZRIq1qTZ+Q2eIRR2oNczrSsJUvYhm1vz9PR5cs1U6GeOcPmjOLF+T+9WfH0TE1nbjhERYpknGjp3Tui775jNkGPHtyvgQN5etyhA6eLNVRDMyaGzS/p7cetWvFsoFcv1bKaNTXLqyUksJbk6sqap7q2JJcTXb9O1KXLDZJKVfbn777bo90JHde4Uyd2vjk6xpONzS4CiCSSCELpfayxKx2iXxVlh1yj76h8+Zca5/Dpp5epRg3V70WLeP8pKazhXb2qOiZmSghdhrF5rtYvGuYTZXNwYA1V+XvSJE5SNmQI54F3L/5OaxsXFyILq2SCkKrXiQvw9nb1g7jCVNryVILLPbKqcIzGjGEzx/79PHsMCSFatWobeXuXJYBnoi1a3DeJJiuXs4N26VL23aTvk4UFP9vWsu8JfdoRvnbmZ1T6gfC1Jbk2d9W77z17WFPv3j1vkoW9fs2zLS8vZj398Uf2zKbzK1Cos0Lqy5sOCTk7G1+GLT3eveOp77Bh7PgrXZoF3cGDLPwuX+YH3MODBaE6J5lIzaE7w4xze0+3IEx0J6nTc9q7V/9xo6OZGufmxo7Nmzc1/1e3zzs7M+Pi2DE2A6hPhb3mVaMWPe+Tpyc/Bf368dR67162o3bsqE35s7JJomIBTdIGw5UnttHIkdyXRYu0HaxKJ5hye5ksipyc5hPgo0WPTE5mgQkQNWz4WLcwLL1Pe5ntSxo1ip12urZR1hbt0kW1rFIldjTrE7jqTT0tQYkSqu8zZ7JfIG1dn5NMJfWvRFZfVaCVJ7ZRXBybrqZN43VKlVLRaWNj2awxYAAR/GoSXO4Raq4m9OpNaDeW0HAhoepGatWK/TpKc4mrayxJJBeIHckrCZhBAFH37tfp0iUut6fL/hwaymbDZs20z9HTk814v/9OGjEWgiAQqoCrYM0CwfoVobyHTt/Jw4c8KJcty+9AXiM5mf1mTZuyovXNN8axyz42FGrhrk9z9/RsTSVLZs8x5HJmPnz7LWsL9vb8oK9cycLy009ZAM6ZoyoIkRbENNmRNTXXuwSzeEKT+TqP8eYNC11XVxYIxgR3vHjB9tZq1Yhci8aSWdNFhJEVCV2HEGzDyazBz7T69B8UF8c2/CVLtM8rJIRpoFqCr+wuMm8/lQLWHqYLF1h4lijBtLXXr3m7mzeJzp/nABjWwA8SkGKUUM2O1rMnxykEBbED3MWFZzcXLvBsR7me4BTCNuXJTmnL+k+6mOZzkMs574uPD9unletUrkzk/rkfD9CzQPiiStosrOi4rvTppyyU+/fn9dM73WNj1frbvX+G7JGEBKLixRsRUJ+A7gSMJGAZD7xWp6h6dVYmzMx02M3TtWHDmF0TE6P/+dF+d+4TUFbDdxIXp8rPtGBB9qXEzk5cu8bn6+TE786FC3ndo+xDoRbu+vKmDxlyjgYMyJljRkVxQM7AgcxNr1iRc6nIZCz4p01TlEBTvsiel9JeOrsGQRovXEQEr+/iwmaAzCZZ8pzUlrVB5Qvuco8wvniaAAkJYWbIkSO6t5ctk7Ez0wThWqECO1u1IzmNaQ8JWEPADdWyctu5eAaI0LMPmc81p43XAmnDBt37kHzplpYQbfDCSeToqJarHTzo7dtHtPFaIPks8SU04usz+VfNxOUpKew0bNpUc//FihFhZCVmXVXaQnAIY83b6wzB6TEtXcozrSNHmF2kjnfveNAvXZrIxSOWpNW2pBWSMcQe0Z6JNiPgDAFW9Pvv+mckpUqxCaZrV3aMe3vzAODszM9nq1Y8CE2axNdoyxaigIDDZGVVlVTVsy6QpWUTCgwMJLmcqcAyGZuWjC2xl5eIjGRyhEzGMQsZ1aD9GFCohTuRbrbMoEEc2p7TSE1lTWH2bH6p1F84adU/2XasYGhYjWNTiZcXa5yTJvHL5+dHtHT/ziyxC9JmCgNaE4oEa/SjUiWeSh88yDb8R48MbD+iOkGSqCk8Wk4jDGpO6OhP0pobMhDalwmYR8AqAq5o/V+9OpGb2ygCWhNwQSG4uhJwk6x9lpJju6UEEFlXOkybrgfSzz/zNbK3V+2jRHW1/dZaxedc9GLasrJlie7dU53bmzds6wdYOCrx4gVP5729WRisX89+jPv32a+h8/xkxwl9PiGfJSXS9rNggSat8/p17sPw4by/uDgutmFTcydhhpnB+6vSpu0IGK+zD5068YCiz9+jRGoqKw/BwcywWb+e/ThjxnAkbuPGREWKvCNBiCNA5TeoVk11rMGDmS578yYrNdkVwZ2TSEnh2IqWLVmhmTVL06f0MaHQC3ddKF0646i/nMCrV2y6aNRI+6XceDWIXrwgDWfg+fPZUwdWI92Bso0vTjY1dusUELt2aQoHzXKFtux8TL+d9APBNpyk9q+ocWNDAp4UQrsveXq2J1dXnpEo/Qma63UlwIKsrb1JJouifv00/y9TRlUvtEgRFpqSrr3Yh6HjuGvWaEaDXrvGDsCxY9mO/tVXHAzWqxdP4/38uEB1eiidvxr77/SFznvTqxf7QeRyPr6bG/9WR3w8p3vo21fbZi6XM89+5kwiQUjVcV6p9O23e3NMsMrlbL5RnxV4e/OA1a8fC8kKFfh6WVqqNOPu3Tl19TffsGlu3z6+luHh+SeVwM2bTDl2cuKZ1JkzH8cApYQo3NPh5UuOtMzr9KIaNle15urKfOpp09is49JnEtt0J3gyd7ruD4TmM8m5xwzavJm1rkuXmJ3w7p3uhzOjAeLFC9Zg0velYUNminy96ihZz/DgYJZPBrFGrE9wu92mChWI7OxY2CqXm5n9SkAcAR8I2EGAiuUyYYJqUJs5k2j58p3k4hJAwEmSSN5Qs2YPaMySk2Tm9kTnMatXZy10zBjFMmUiNPXWfjQ1a8b3vndvFs5WVkQjF5zi0oCK9bxKvaWfftIdxJaayn4U9ePaOnzg32bx5LOojNagW6IEp0zu14/t9Pr8JfHx7Ahv357ZMaNG6b6+rVrdo2LFmhAgkFT6kpYu3an3GcuOWqVyOZtplMc3pOXGx3Pq5NOn2U+xYgU/P4MH87lVrcrPtJkZO3Jr1uT7MGwY2+5XrmSt+tw5dgJnh9nEGE7927dsjipViv0q69drp4TIjzAk3As8z10Xdu7k8nQHDuTaIXXiyy+B8+e5ZN6vv2r/X68ep3rdsQOA7ATQfjzw9zpAbg5U2A7Eu6N3SX9ERUGjpaQArq7a7aX8Fk5F/I1o4RHc3STwb9obn9ZtC1dXTlWr5C1fvJhxqmPz4rcglZ3Bh8e1gJc6yh2BU+1Wq8bfb9wAOvW5jacPfIEUG0iGlkPt14sQ/6QrKlYE/vxTtV3x4pyWV9mKFgUWbLiMLRvsQJHldB6rRw/u9/v3nFIXzg+BuCKA7wngflegwWKg3iTQMsKzZ8zdT0rSsaM+3WFd5TDWdNVMl/v+PZeKW7oUePKEl12/ztzy6dOB48e5jB/AaYw7d+bvyipVZcpwGb4ff2RuuhJyOadJPnuWU/hu3ardpcmTuWxghQqaHPunT4FatYBXr3SXrdMX42FMvIESt24xZzwqivnwffsCX31l1KYGkZzM/Q4PN9xev+bUv56eGTdl3InGNdBRic1QOmS5nOXCihXA1aucbtnfH/Dyyvo55wQKNc9dFyZOJJo7N1cPqQWl+UEqZV758OGcBS86mnm6Tk6swbduTWTm/lCludk95+LQnb7Qm4sjIYHo2TO27R47xpSwX37h/U6YwI7eTp146lymjL4shya2YucJSCXzdtPJ1TM2bbmDA88o0mYO/dppbOfiEUu1anFkJRFriY8esUP6q6/YiWlnZ1wfjh7lkPi0ZV95cKoFEME9mFBZQk+fct4TgE1zEps3XLDE6g2vV/IwodMXVHx2bSJiLXTCBL5GrVpxtG+7dqrkVXI57+fCBZ6JKdM/tGzJTurevfn3xo28fmQkmyemT+ccMunPwc6OfS2OjlxdyxBHe9s2ZmXpQ1aKpcfEcIEUd3dOvZyczDO7GTMy3DRbkZrKpsxr1/jerl3Lz/GoUcyGatiQZ0ZWVux3KVuW6Z59+3L/nbrMJ3Tvx9HoejJQ6sPdu0zPdXZm09rJk/nPZAPRLKOJBg1YEOQF7t0jqlOHr3yXLqoAoCNHNHN9v3/PNEZPT6KazcPIom9/TbYLiHwrRNL165l74J4/Z57+11+zALW2Zhpd48Y8oKjb/bOtfd6IA2JGVtRYHhKiv59JSTz4Gdqvem4cgMjW9k+C5wE2ZXUZSii7m+BxjCSSSNV6ZXeRpGdHfuFngVA5iE1NvbsTqgRq7O9//2PBUro0Dzjq9uIrV1i4KO9BSgoP1On7aFv7L/LwidFaXqwYDx7792tWe0pI4FiDnj31C3hl3nt90BfjYSjHj1zOA5GnJ+cNUueGL1vGZq/8CLmcB+LffmPbucYzYRZPKHqZzZqz9Weg1IeYGDYvlSvHzuQ1azJ2VucWROGuhoQEzlZnTOra7MTCnbuZDSHlfOT9Jl3S+D8ujhNnpQ92UhYOcS0aS1blThI+b0IuvSdrCIjixVmg/P237nJwCQlsA1XmI/f2Zk20Y0cWnDNmsH2/Rw8WNu7uTJn79ltOBFWsmKb9+flzzaCh/v05x8eBA2o2b2Nb/aXk4sLHVz/3iAi2vRvc1uY1WVc6rP9/v5qcO6j675rLx3lr5rHv9AUnEZvsyP6EolfIzCWUunZlQaHcrmxZ7Tq7kydzI2JH4c6dOhytas25SBwNGcLXS1c+FnV8+MD76t5dt+25cWP91FUi0zX3q1fZ0V+rlnYOHSLWmgcNMtznnIZcztf52DGeUXz5JQeDeXjwrKduXe7jwoVEu3cTOY2vpUExVTbXefqjbPUhNZX9W8r0xZMm5U0UrjpE4a6GU6f44c0tBAcT1WsXQrB9TWg5lVBmL6HGGp1sl6ZNWYPThfXrN5OLy9cEPCRLy7P05ZenyMeH7+CAAapQcjs7Zn/Urs0h5LVr82BWqxYXv5g5k9vIkbzMxoZfiDFjWOA8fqw9E/D35wda3QHdqxebka5fZw2yWDHe3w8/sLb3338c9FOzJpvA9Ak7iV0EPX7MpiKAmRbqlNGqVVlgunjEkrTWOoJVVJrmrb4fe3s2lahHxGq1rp8zT354bcL44vzSD6/F/1UO4v9KHSDLQZ/QxmvM5f7uO6bLffutqo8AC0H1qFejmtstKhbQ2KTn58MHPk63bpoCPimJlQF9mUuJ9Md4pHeqqtea/fVX/UyW7du5H7kBuZwdqgcO8LM9bBibYJydWTFp3JiZTMuWscB9+lT3DNa1uSthWjqW2DQYTKFgDB4+VJnrPvmE89bnhclGFO5qWLAgd6aWV66o0g84dZnP9MFPBrFtd0IxnXa/2bNZG/Bf6U/SidK0AJxW41upvaRSAvqTINyl0qVfa9jLGzRg84pOISphYeDtzdr7kiWszRvKP6NEYiIH4Uyfzr/37GEThfq2KSnMdx4wgP0F1Zs+4wyUrnfT+mA5oIdJwvDWLQ40cXdn4eriEUuObZenzX4AJS+eWSilS/P9hUOY5r4qbyaMLkXwr0go93eGx5WapWjw5rPcrN4Q2o8htJpCsI6gWbOMu+5KpgtgSdbWh6lWrbA0AX/lCtMPjd2HLrZMaiqbMTw8eADPKKPiP/+wnyA7MjoqoUxbvWsXD6QDBrBCYmenqj41ahSzaE6cYMXBFCGqlUJhHAhVDJumTEFsLPuzKlXiYLBVq0wr/J5ViMJdDV27srMup6AMxff0ZI0jLk4tAOiLqlwkwzKaC2b07qGRj+XkSSJ3nxBCgLamgQ4gjBVYQLUeQnD+TacgKV2aI1krVWJh7uSk+X/HjjydffzYtPN69Yo18fXr+dOQz+K3c1vJvMMUnf2z9L1I6DDKJOHo0IJpl/378/1T/2/cOCP3Y/+U4HaL4HlR+z/XW1ShAmvolpaG91OiBJtK0vPt1ZunJ5HHqF6E/m0Jvsc4H7rrXULD76n4jIbUqxfvZ9cu/YJKW+s2J6l0N9Wo8ZQ+fGAhkpWaphcv8gypfn3Og2TsNrLyUZmKuUhMZE75n3+yEtOnDzu2rayYP9+hA5v6fv+duebZVW0pK05lUyCX8zvRrRtr8+PHs3af0zAk3AsVFZIIKFKEKU7ZTW06exb45hum/E2eDAwdClhb83++y30RGhOqWvmDA3C7FyxvDYdddH306gUMGMC0NmuH98AEH+DKMOBBByDREfjgBHxw5O+OoYDXOcDxIXCfMGnQHCxapN2fHTuYjmdurloWHQ0cOcJpgQ8cYPqjMoVx48ZMdTOEy5eZtlmrFqDvdiUmAkV7fI/o/V8DZXcD5XcB+38CUqw11vPxYZrj9u0ZXlqdsLHhlMxKSOwiII9111zJ+RHwthTgch/oMQBCaHPQhZFAjEy1zrC6sHxXDv7FNuHMGeDCBdVfxYtHIyoqGB8+EIBmmesoAHT6AkiyB47wjVq2DDh1SkFxBTBqFFCyJNPwlG3Bgu8RHR0NQKLWrMB1cRgODsDw4aptiDT3oau9fg0cPKjqWs+e/JnRdkTA/fvAs2cAfE4BJAHK7QIa8znJHGUIGReC+HhOGXz7NlM8lZ8hIZzeuGJFbhUq8Ge5cropjNmF7KCDmorQUGDlSmDtWqYzjx4NtGkDSCTZfyxDVMhCJdzv3QPatuWLn104dQqYO5cf/KlTdedxN8S1bezQD5s384Pw8KHaRtIPQGrGSbAbN+ZjdukCtGrFg4sSixYB48cDUqn2dnI5cOUKC/r9+/mFbNmShX2HDsw1B1iAKvnzhw/zwAVwnm4iTX69umA0BpUq8UCxcaNp26lj5sZDWBTWAwkp8cCbEsCPj9P+E7r6gfb/wIIo1RKwfwb06A9sOJHp43l6xsDMzBFPn2a+zwDnwa9eXZPfP2IE51uXSIBFixYCSAUgT9fMAMwEwHnvGzTg9Q01QeB79dtvfI9cXfmZdXHRva6+/URFAX36AKgSBNz6H9B2EmDxHoioCERWgG9yJ7x8yZx+dQFeoQIvy6i+QU5BmW8/LCwMPj4+mD9/vkmCXV/dhYyQkMB1GVas4O+jRgGDBvGgnJX9qkPkuSvw++/Mfsgq5HL21jdvztPr337LOJIuvZ3yh8Pbads2noo2asSOTY3pvUUMmy8muapSAw9qziYdHaYAJycu0abrvwsX2L4aFcX2zTNn2G6+fj3b3pVMGX1mBg8PtiemX/7997yP9NWKBMv3hPLbOfvkBE9C1Q2EOj+l+RiCg7X35ejILVts3LYvtZbZ2upYz/cowTqSvL6pkcZ9t7Fh56yV1THi3C1VCBAIMCegD1lantPYhz7WFZsDwojL73UmQDerx8mJfQbK340acWRn0aKdiOvFapsTIiJ43bZtjYuiPHOGqa1NmrAD3BS8fs227pUrdVA8i13ge9tqMrkPGUb37xe8cnfZkfpDLif691/2dTk7sw/h+127srxfIsNmmTwX7JSLwn3oULY3ZxZyOXvmlWX11q83rhBAfDyzdBYtYt5y8eL8Qnfpwjzlf/5RpV7tMWkh24Y/68COQMtodgL+rychwJIfhABLwqBmTK3MhPCTSPhF79+fc6p88w2/vH/8wX25dIn9EuPHs1BwdlZt+/Ilv8ANG2rb80eNYqfqukubuXTdbBD6dCO43CfzGU5pD+6jR+w0U27Xp4/mflz+N1V33yXJ9Nln/F0pjNF0rup/NUdrZlqtWkz3i4wkhUAHAT7Eic7CiYt+7yUHB14/fS59dQDuBLxV24+yOVFgoOZxlc+lOvvG3DyZuPD2e+LMmHvJzOwX6tv3Cg0bprqH7dvrF/AvXzItsFgxdkzrs+/L5cw2OXSIC8z4+fG+XV35HjdsqEpbDBBZDm+lERCUGaGUnxEezukWRoxIF0DYp5tJQVBKpKYy6yk2VpXLRrCII5Q6SPi0Ezt6M7FfIsPCvVCZZSpU4GlSjRr61xm5aiRWP16NVNtUSOOk8Cvph5+/WIn9+3kq++4dh5v36QOdZcaIgEePuFSdst25wyaI+vVVrUQJXj8mRtO08eoVm1mEar+AzFOBqBLAk47aB7J7BS8PS5Qs7gQzM7aHx8Ror2ZlxWXP0kMQuJRdhQpA+fLclN89PTXD2c+cARo14lJ1uo5x9izbFpXbBN0Iwud/f47kd07AL9eB3j1h7nsJaz9Zh+TL/fD118C0acDYsRxy37Ytb9e3r+7we12w8LqBpGdVdP/pdQbubdfhj/5r0LIlL/r1VzZ7GELdukBEBIe9a1+zGwD4eJa+l5EYUgtFx3fBlPZDMKJpd60ych4eg/D69UAArTWWy2QyhISEAGB/Q69e7OuwseFnpEsX4MULfk7r17+Fq1c/x/PniXB3r4MOHUbAza0Oli7lfTk5KVItgFMblCsH+PqySW3fPmDbNjbLzZ7NpoDUVLZ9q9vClZ82NtqmlIoVAQ8P4M0b4JNPeL+HDgHztu/A4hsTsmROkMs5TUZKCqciMPQ9o/9N+Z6czPf3+HG+FibB6TFgEwnIzQC5Gcq7VDWqr0Ts+zIzU32+SXwFSJIBaRIwoB3gyjZZAQLks+RGd0m0uYMFZ4kS/KDqq/04ctVIrHq+ClA6IQnAra5wO78CRR19MGMGO6DUbdgxMWzHPHWKbdeXL/NyMzO2M5Ypw46kuDiVAI+M5M+3b9np6uammQNm82agcmUWRsplF94ewE+nf8er69Vhfq83nKTFMGKIHQYMUNUxBfgFPncOCAzk+qoA8Omn3IdVq7SFc7FimrU0Q0LYPli+PFC6tMrWDrCNNjVVex8VKwIzZrCQdnFROJCjQ4EV9wCf/4BuQ4F4F9gcDEQp6oCgIKBKFa7b2qMHC6g7dzT3OWX1Uazc/AjvLnUGYosZd5OHNAR8zsLG3AaLGm6Ec3hPbNvGuYSUWLJEMzfK3Lnch+bNeWC5dIkHgsTEGLx+vQopKQcB1AHAjkNJ7TWQXxrOGxe9AiHeA9KEorC2kqJIEcDdnZ32u3crjzAOwGsAr2Fl9R6LFn2NESN6pjm6r11jgf7FF6x0/Pkn8Ndf/J+yxu3SpWxfVwqN9u2B/v1Z4EZGsp8E4Fqzhw4Zd6mUaN6cBbmDg27hdO+eKmdO1658XjIZPxtZEcByuaawU35m9buZGb9XV64onL8moGZNdnxWr87+ATMzYNjeQXid8ByQpADSZP6UpMDT0R3/DD5oVJ90+by0SBYKKB3TxkIU7uBkTj/+yGwRfTCbZIZUu1RALgB3uwMnZwCvKwNl/saPo3ohIoKF+IkT+vfh6MiCS1firvTNxUU3Q6XfxMvYfe424toM0qkZEbFQ2LSJC1L7+PDL3rcvCxclwsNZ26pcmYX46dP8Il+8qFrH2Rlo0oSdzPfvc5+ePzd8LS0suFB0+fIqDbFuXRbQ1aoB/1lOA+JdgLMTNTes9Ac+XO4DS0vW9hs21H+MffuAx4+5SPgffxjujz4Ilu9BsuOcOEyBkpUj8fimm8Z6JUqokoEBzDKqXh24fv0W9u0rCrncFYIQB4tafyHxgzlw8zNeseKfgNwM1hIHVHdsjefPWetOSclcf41B2bJ8n9zcWKt+9Ej3zCyzsLNjwV26NM9iTp4ELEueR2LNpXC3d0LEul8xaxY78rMijJXOW1NBxLPb69dZ+z5xgpPvGYK1NQ9iLVpwq1LFeOeuqYnHjEV27VcU7mAmi6UlT1H1QZgtAAKAFzWAP7fz9Oudt9Z6EgnTBzt14oemaFHWfDLzsKZH0I0gDPv1Z3zYuhYYXQGA9k0nYg06OZlf7IMHgXXreOCqWpU19fbt+SW6cwf43/9YW23ViimQO3eartUo4e7OL2h4eNbPNcchOwFAAEKbmbTZjBmcMXLhQv49dCgL+9ErtwN3eqpW7DgSsHsJSFKxf+AuDeHVpAkPSuXKaQs3iYTNe2/fcnvzhgfXmUyCQdu2zFJ6/ZoFmS5TmCG8ecODtjFITuZnITSUZ23Kz/Xr1VayewE4hQJOIcDNT1Gr5VN8M9Ebvr6sxatnucwq5HJ+tu7eZcF94gQP8IYglaoEd/PmLLzt7bOvT9nBasmp/YrCHWyTnDGDp126kJQEWA9pAHlUHeBZfW4JLoDXeQhFLmDfjBmoW5c17pyE73JfhL4NA+Yq7G42rznFr9wcQrIZiMwAmEEikcPcXAJBmowkiodcSIKQag1KtNPap5IKB/DD/+EDm270oX591sB1pSFOjxo1mPu+Zg3/btCAtXJTUb0636MmTXh2Ubmy6fsAeObx5o3ih+dl4GU15tqHNgHii2iuXHo/0HAJHO3kcNhzHAMHsmmtbl2eodWqxXzlN2/Ydr37RBhr5XVWAe89AZICnUZpTaVfveJZzZs3pg34L1/yNXjwgAdkpbnq1Ss+L7mctWldcHDgWaOSounqyoK3SBHNpjQbqf9WxmMA/JwsX87mq9Q+nfDS+ijwuA1gHg/EFgV2BEGweofWTRwQGgqEhbG2rxT06T9lMu6XEqmpPLt5+JBnksePc8tIDDVtqhLelSvz+WWHMvWxw5Bw12N91th4LYDOAF4TUWXFstkAhgNQPmrTiGi/4r+pAIaCSbpjiMhEK2DWoGs0/F+5frhyhZ1+AD9IT59qOj2vXwecXf5ElPs+oNQRoNk3gOs9IJXwRXF/dOhg3LGyOqKHxYQBEgJK/APYh3MgzvN6wPO6oKR4AKcBnIEgXMKAhVWxOe43yBVTO4JKy2/uwvz5TZtY80tKYuFx4wYHGrVqxeaHTp34d4cOKm1eeU0A4PffFQ5etRcpPp6v1+XLbKNWF+Zp34fVBd6WBLYb5yG9do2dv61ba5pItFB2N5tZJrnD6kNJuO0/hFXLndClC/+dJtgBILwWf6pr2+p4Xxwofh4xFnG4c55t2Fevchs+nPnJS5ZwGzMG6Pj1WQxr1QKo9Cdg9Rb46S6sGqzH/B7jNHZ7+TIPDLqEDxFrpkpnprpjMzGRBzaATV0LF/IAK5OpAmDGjmWhqPTtODjwQPLwIccpdOvGfPY7d7jfcXE8A4iI4EHixg3V79evuVlYsKB3ddU02WHpPtX3bgP5vai2HuR9Fof38MivDIxSavyPHvHs8MQJ9t0YiwYNWHA3a8Y+nGLFWCNXvmOnYsIQ6uiDMq7z0dwtZ4KPChoy1NwFQWgKIBbAxnTCPZaIFqdbtyKALQDqAigG4B8AZYko1dAxMqO56xKsAHTascYW2YXvhrfGwoUqwZWayg+Ukr1SuzZrILrYMiv9V+o8fk7Y4tIcLedHsdb5icJ59xbAD6UB1FM1s0qA230gzh3wCAaqbQTK7YHM3R0PvgzBf/+x7XrJEs1jDBzIAU5F1BTZ1FQWHD//rLtfY8ey469JE91+grg4ZhEtX57xOQYFsfY2aVLG66pj0spj2PJmAp7NPwrZt7UQUPd7jG/bG+/fsyB9905TS8wQTk8Ay3dAx26I++kJhg7VZOvYOSYi1etfJLT6ArISqfjU9jf8sbwG5MNrISwmDE7XZ6B4+BcIPuOpIcjnzgViY4GRI7UF+J07fP2UbBR1Zoo6SykoiGcLW7bwQAyw8FeycmbMYLbMggXAZ5/xYHTsGEe9XrrExwd4QPfw0H8J3rzh2cqmTSrHuRISl8eQvynJP4rc4OcsrqgJF1gdcsD5CSTOz1ClWGl42hVHeDgPCCkpujX/O8kHsfCWPxIsQthciux5xwoSshzEBMAXwE2137MBTNSx3lQAU9V+HwLQIKP9m8pz1xdY4LrQlfm3tX/Wy2Vu0oSTUB04wGlN793jQA1j+OrqSKspOtWOMKpMGlfVVJ6q3nMbWZHg9EiVW6aKdn4MBFgShtYjOD/Ue74ABxk9fsznqCweAXCemS1buGza+PHMax49WvX/oUPMw1f+trLiQKNevYjWrePEYxs3ci6b9MeUVttCGFmB0HSO1n8lSnBWypUrOYukl5fpnHR7+7U0atS/BBCFhamuX0oKkd83p43bj8U7QpV1ab/79ye6cSPdOmp8brM6v1HfcaqCqklJHO+weDHnxp8/XzPnTPHiXBd17FhOLvXvv5zK2FicOMGZGtev52yhyiygN26o1omI4IyJRYvyesriFr/8oupHy5Z8v6KiOF3z1q2cBbJKFU7QVbkyr+fjw9z4e/f43tuUCjD5vgD8jBQrRiRYviMIyZxts8MoDS63elCf97eV6du/9tKuXUQ//sjZFrt1U2wP4nzsZXdnmgtekIGs8twFQfAFsJc0NffBAN4BuATgKyJ6KwjCTwDOEVGgYr3fARwgor907NMPgB8A+Pj41Ao1ISeABo0owQl4XldlJ39Wj+NGkq0BkgAQOPRcAWtrdnK5u7NNU+nUio7m/5yduTk5qb7rap12NASs3gAvawAHfuRjeZ0DvM7h6PRvULcuzwQyg6AbQZj2TwDCZpxH8Qk98fzv00yzTo+xApBQA7jfCbjXFQjXPYDrQ/XqbA5RYutWdkY1bQpMmcIaf+/e7IxetYo10owQF8e5U9b9EYlws7OIv9oFEFLx/d/78FXnrmjVSj/bqFkznlU8ewbMmmXSqeiE1Ok5Up3vAk9aGbeBkAKJ9Xt0HxyG41Gb8eavuUDTb4Bm84FEW+C7WDh1XoAxNaekaeK3bvGmbduyn6JiRTZjXb3K1zer2L+fTWcAkwF27tS8Z0pcuMBlGy0sePZVvTrPxmrWBIKDtdc3N7+H5OTzMDevh+Rk3aULNeB2Byh2HHhQE0gAXr+uDzc3bdOTXA78+y/TedcERgFFbgJVNgOV/gCsVZ5hG3MbjVmvNblivM8WWL9og9On2bwXY3aXqbQ+p4ESxwCnMACmc8ELMrLsUNUh3D0ARII1yG8AeBLREFOEuzpMNctI5khAICBVCix5oXCUyYFyu4F6P3LdTInivAiQLI7Cnxtc8OIFPzRnz7Idun59Ns00bMhJrACVsNfV1AeDY3euIinWBkhwBj44s9NTD0qVYi5ynTq6B4r0ATBK9O3L3OVJk9wQFRWlWGoHDozpBMG8M+D4DlR6L1B2L+DzH2yszbGs2TrgVm+sX890wnr1eOr/8iXz31MNGskYn33GgkKdNVG7tnbCsDJlIvDkSQJSUnwAAK1b38fAgWUxcCD/P3gwX+NjxzhgycpKk6nj6so25Fev2I783Xcqp2H9+uk547kDs6aLkeJ0G7g2GAhryuabaEXUWYXtmN6zZ5oppWxZDkRq04bNKOHh7PCLjMyawy8hgW3uK1YwzfXECRbSgwczM0oXkpPZPLNhg+nHK1LkPbp0sUejRvy8ensDZcuGISVlIICTamsOhK1tV8TGqvwYSmpuUBArCG5u/Pz8GNcQzyXpvOtyAZL3vpCHNATufQI4PAWeNgJeV4Zl8bsY879aaNRI8U4GZQ8XvCAj280y+v5DLpll0kwis0GYbkEYVpfQfjRZVN9GgstDDtkvcYTQZB6Zd/yaJNJUrdDrFy+4+MDEiZxOwNaWC0OMGMHT23v3DOeN1jANzQJhqi1ZTSpD3/61l44fZ3NGzZqGp692dkQWFmzu8PTk/C2NGnFhjP79Veln27S5S4JwRW3b5ySRLKZvv91Lm64bzq199y5Xny9enEP+x4/XLIYB6Mhrk1GziiLLEufJ3iWSJJKziuWfE1CBOGRec/0WLTjFQZ3WoRrL67d/ktbP1FS+Zp6eRMePczqGEyfY5KHKUZ+sv092LwjWEQQQCVWDqOPsFeTUdR6h9P5MmRbSWosANi9V3UBoO15nBZ/btzmdREQE5+xp08akx1kDcjlX1PL1ZfNXaCgvj41V9enkSU4XvHix7pw/hpqT07cEtCVOqUAEPCbgFVlYBNO8eapKUxs2EJUv/1Kr2IeFRW/yrXqN38Expcixw2IqViKafH05R9HNm3wOERFEMzceJIuefoRG33FKDV19arSA8HljQoCVVum77MjrUtCBHDDLeBJRuOL7eAD1iKivIAiVAGyGyqF6FEAZymaHqiFnJgBM2bUYz+4Ug2NkW8QcHguAA33q1VO1mjWBnTtV2eK8vUth6NAVcHRsjzNnWLuPj2etU9nq1NE0tRjLlpHLVUEXx47xtPX9e0AikcPM7DaSki7CySkcQ4Y0RLduzfH6NWururMlXoe5uTVsbb3w4YMN5HLds4H0ZiVBYHOBOhwc2AkJsJkqIoJZGVWrMuvh/XsDN8HmNaexTZfKV4mAAKb0qWc9TMOX5QHb1xDWnsVAv3dYPqkOBg5kbXfbNlVGSiWIAIlkv+JXfQAuBjqmQIvpQKVt7HDetwJmZhJYPvgUcZFGEsB1wa8yXO+/ROTxSK2/xo5lzdnDg6mm331n+u4fPODUz+fPszO2dGmOED15knnfGaFFC3Z6lyypisaeosgQvGIF01XXrDkNuP8LfPgM6NcZsLoJ/CMFbjbG6NEnsGMH89YfPGCne9GiQZg+XZVRsUyrGfhn7VCg+DkO8PO8CjOv6/ikRhPYvKuG+/eZkaOejjkNbsFo2Ow+npT6FeFWR4GFb4BxvmnmGl0aeU5xzAsKsmSWEQRhC4DmANwAvAIwS/G7Ong0DwEwQk3YBwAYAiAFwDgiOpBRB7OLLaPrpn/xBVPFOnbkl0bZgoNTkJx8E0RnAZwHcB7W1k8xePBA7N+/H2FhYShWrA569lwEM7OmOHuWBXTZsipTToMG/CKZOv1OSQG+/fYg5s07g+TkRgAaA9BOat26NfDPP/xil9NjFl13aQtm7FuK56/j4SEtj/5lR6OaY/M089Ht23oEbD7E1KlMgXNzUzU7Ox4QR4x4DaAIgG0A/oc0+kSHkcABNYqPYwgQ46u9875dgePzgFdVNZcXvQK8rGlSP318AH9/zgVTvjwL0nfv+LutLbB4MaepINLOa/LyJQvOhw+53btnOOLZEBo1UqUlSJ8GQPmZlMTUSC1TnMt9wOURkGoOpJrBLNoaNcvXQ1KSbpu+TkiSAbe7gE0kzBM94ZxaHhERfN41awJubndx4sQ8JCUdA8BRbzY2Nhi0eBA2vN2A+LnP04S7yILJHAp1EFOVKmyjrJ3u9GWycggLc4EGtRAuAC5CKeyB87CxiUtL7J+YyDkrlHb7s2f55Umv3RsTsSeTlUBYmCuATgB6AKhmcP3Ro1XHkMl4QNGawSQ4wiKsE+rFzcXt06WQZqZXwMmJB6PUVNaSX7zgF9EgzOKBlGwMQcxJuN4FnJ8A5nEccBNZAUjIIOqs6kYgeGDu9C8bMHAgR7vu3ct+ijJlmNuePkeLubnm9/BwHU7qnn0BmyhAkgIHcxscGroPEgnPbBcv5udk5kymYKbB4zrwqhoXIAmvBTxtCMR4A17nMHtgWzRuzNtzYJMvdBElZDIZ5u+ZjwF1u4DGyiAr6ihq5JlEoRXu0dHsGHrzRrMiEQBIJBJon7s72KKkFPZ1AETDxuYm5s3rgnr1OCJTPaLv6VOkmXHOngVu3mTnpbrA9/VV8bCPHGHu+bp1LwFEA9gLYB+A/wCkQBAExMfLcewYmyg0wsAzARcXLhLg7q5KUKbUigF2sE6blm4jy2jA9yTwohazgJSJu6QfgOIXALNEjloUYTIkxS9BXnofYBUNHFoGAHAv/h52FvZ4947jCJTtjtlmzDw5DWExYXA4swzNfFph1xpV6O7586y5T5rETl1lUJJ6gNLr18zc0cWYQeuv0yopCRCQPF0OPz8uHOPszOa53r3ZPHT7Nj+L16+n20fzWUCDJZAVcdMyqeh+xwBBECCXy+HkxDx3J6dMXkwRhVe4HzgAfP8927rTQ59WgSoAWgFwBBAtAPvLAg/qw99/Pc6f5yCUihU17fdlyqgiCBMSVNr9mTOaGQmVqFkTePDgN7x//xRs7XJVfLpBKvWApWVxpKSwQI6L40FKX7pdfZgzB5g4UXsWkZrKpp7ff+eglZgYDp559ozt7GVrvgBaTQNSrIB9irSSljFAzd9g4XUbSbfbArf6ZHh8NzdmXehM6mQWDxS5BctPJmBak2nwSeyg5Q8wGeV3AC4PAeu3EKzeQWKejNS/V6f9La29FpW9fXDzYTRSb/TK4sGyEUWvwrHqvzgweyxq12YlJCUFWP3fdny1YyE+xNgBcUWA7VsheNxE88aWcEgpkybE1at3VavGeY7U0wu8fMnU1B49mNra9M/yeBp/D7g+APhnAVBmP1BpG+yf9YLNneF49YoVkcGDOer0wgU2OzVqxMnCXjrtwsSunYAxJZntcqcH8LIG6jWLwYQhMnTooMrrYkhzDwkJEYV7NqDQVmKaPp0oIED3f9oFiBWBQtO0i1O7NlcxJN6+5SK/gwcTOThrF4ewsjKVkXGagCkEtCMrq4a0bNlOev9ek6lTooRmYYjoaGZl2DdfTSh6mYBUvfsfO5bo7FmiO3eIPvkkmKTS5wRcIBeXAJo27QgVKcJBL0oUndCRYPOKMKgZn/+osoQm8/Tu//vvuVCzio3BxR46duRglv79iXbu5Mr2GttKP5DELCntd+/eRE2bErVurRlQ9v49B53xekkGriMRJIkE+2day81cQ8jW4YPpTJn81pwek2P7JbRjB9F//3FVrehoDk5q2pTZNfHxqmt36BCRuzvR5s1qz72SgTK6NKHBYp3HadqUmS/79+suVG1plUze31VMY2j9fHwbrVnDRa7t7bnoyLp1RL/88qfWO2ZjY0OBgcx2cXTMvkLYhRUorJWYWrTgB1QfAgMDSSaTkSAIJJPJyG6GHQu0CcUI9ZcSqmwilDpA0uJXyNeXqYtmZhwN6FX6LUlKnCBU/JPgpTsi0tKSaM4cLnOmXi0nLo7pbH36XCFr60MEvCSpNJzq1AmlxYt5/Q8fVOsPHcqRe1r9Dw4kyxHNCS73+ZjFzpNQ5BZZ2yUaEBKHCehJgBdJJJdo+PAzWvuUDm7NAn5EdcIMKUnaTdK5rypViBwcuFRgZCRvv3s3kbc3R63eucOVfWrVUtvOPFZjH1KzVKpTh/dTooRq+ZEjRLNmsXDq3ZvIw6MXwW4/wV2NEmr+iJwb+5LgFMr3of6SnBGqkgwGFSOb1Pkpwfs/QvkdhBq/ESr+QfA9Ruaed8jDg0gQeD0bGyIUCSaU2UOo8xOhwjbeR8nDBKs35OXFEZzz5hEdPMi0ww8fuIRkgwYccf377xzdevKk6t4+e6Y7mriI97u07/qUIXV4enLlJl14+5YoMJDLNjo4EFWu/IJcXAII8CSZTJYm2IlE4Z4dKJTCPSmJhbEpD48wWxFqPrEIoeFC5kjbPyWA64j27Uu0YgW/MF7zqyjKyH2i8aKYFXlI7dqx4JNItF8ke3t+MX/6icP3g4M5NcD581yLdeRILm1nY0NUvz7z0rt1I6pTR7OvYWFEn31G5Fwkjlw/G0eYKSHZMhltuh5Ie/cSubgYJ3BsbPbQ8uV8/MREFu4W31gQenfXu03z5pq/K1XiWdLq1cy9HjfOSIFnHksS20iaM4cHh5s3iebOTbeOZTRZlT/O30vvIXzlofqv6EWynWNHqLUqS0LX3PwrAloTUJOAamRp2Znseo0gNJ9JcAwxfZ/WkWnfy5XjWqf16xNZ2b0j1P6BMKKaamYYAPJf6Z92X5OTeVB0GzyC0HwGodJW1X5d7pN1pcPUuzdrx61b834dHJgX36OH5jN35Qpz5kuV0uxf8eJ8r9RjOYKCVP8NGsRpCPShQgXDJQaViIvjWJJ+/XhG16gR1+x98oT/F4V71mFIuBdYm/ulS2w3vHnT+G30VUfxMquKoCbXERzMHN4bN4Czl99x0qkiNwHb18zScHoCOLzA3j6HERvLPPH37zmJ0+vXzFe+oSuNgBrMzNhmKQjpMhzqgIUFRzEWLcrc6l27gL//5v++/JJbuXLMlPCq3x+w6Qnc7665E7tDGNGvHc6eZZt7SpELSHS5AtzuBSS4aR1THRUqMGNDvZ/m5lxBqmRJdtZeuaK2QbUNQL0fgEftgKP6ieDeNW7j6dWKQJfhQLQvcCpAe6Xi5zlbpr7lFf4C7hwAEAcgDhYWjvj007GQyeoYlUbBJNi8BprN5SyY7b5ilma8M6xXvMGXX6oyTcbHn8IHaRIgKQPYRgHl1gGvgyCLt08rvaeEBhNq78+A8xNYVTwKP+/lcIltilu3OPXB48ccF2BhwUU89EYf2z+Dp/8QLOo/SIuVkprKrLLly5kAMHcuO09nzGC+ffrKZQ0asP3eULGV9EhK4gjlHTv4OfX25mfjzBnen4jMoVDa3JWFfk2BKRFxPkt8CWN9CX27EFpOI1TaQnC7RYJ5AlWqxFr+/PlspggJ0R3tGh7OmtXUqZzcycGBTRrt2nHypN9+42jNw4dN0xxLluSZhq0tT/VtbYlg+0JlvslCO3xY97VLSeH/lAWs1Vv9+kSCTRSho78qEdd0C4LHVd3HsXzL5gv1ZdIPHHms/O1xTXu7Ev+ovkuSCIgi4A4BJwnYRnZ2G2nWLNX6Vasqv28iwJ+APyhDuz6I/Rxl9ug+/sQihNkg14WutGIFUatWqgLUwGTiYtchxL6WC8RFtLfR3r2stavDf68/SedICZ6XSDKsEfnvVWn4cjlr+CtX8qzO2PsnOD+mRp0f0/LlbLePjeXkcaWrviafpapI54U7d1PLljwDVTftEPHzacjcmRGSkzn6GOBns3x5Ngddvmw4KlyENlAYzTK9e3MItalQz1anK5xfCf+V/oSAdM7XANDwH0bT1aucLXHiRH4RihVjwd2wIVc+//lnzhCYfkqaksLT3d9/54GpWjU2z6hPtdeuZUfatGm8T4AHhMaN2RQjk/HA8sMPRBcvsi323TsiTPDk7JXpbN651QT7l6rflTcTfE6atg+JIT+CovXsw5/9mhEqmxGgnUmTSLV+q1bq24cQcImA88Q+kKfUvz/Rr7+yEE1JIR7M+7fhDIcDWvM9H1ZX5zW1GzCAPnxgE8bu3Xx/ZTKZoh/VCVhCwAsC7pC5+W0qVYqfk8mTOWVEmqIxzZr3P8WBLP1aUN9xV6hrV87g6etLVLq05nFHjGBH6tmzRPaN1/Ny81i22xsa3K0jCR1HcpbRACuymW9Dm64H0h9/cNbOfv04ZYfy3dqyxfR3Kz0cHdkZfP48n3fp0nxOEybwwJOamvVjFHQYEu4F0ixDxNO+kyeZjpcT8PX1RahDqIo2GQPgKCB7J9OaYgNcaFpp0lG2mzeZT1yliqpVrcqmFAsLDjIaO1ZVMDk9evUChgxBWoUoIp6aq/Punzxh6uV181/wzuEssPs3YFALQHYaAOBtUQXN7wRj0yZOR0DSD3j/Vk8mM3BBi9hY5jtHakfh5z7sDgOxbfm7+03AIhYo2x04Hg1AWVy0KIAWEIRW8PUdmlYMxNeXqXgq/ArgX1hZXcJvv81Ev36a5gsNsx2BoztTrLg96ATc7gnITgH/LNTq5q+/AhcvnsOGDVuRnCwBYAXAFoLQFURVsuNKoFgxTsxmacmfVyPOcEyC0xMuUg4AKRZAVDlsaRqMW7eAefN07MgsHnC9Dzvfu1g0uC/Kl1elw5g2jZ/bunU5+jsrSE+FJOJ979gBbN/OVM/u3ZnG2ayZdqyKiEJmlgkMDqTiMxoSbMPJZ6l+zTurEARBp2YoCELGGyuQmkr06BGbZubOJfrf/3iKml6rsrdX+92rDxWf0YjGLjtBkyczI8jenrWefv2YVaN0jhIxVe7wYaKeI9XMGM4PCVU2kbR6UJoG+/Ah96VrV7VjGaBAZtQaN+bZxZ49zOYoPqMhwfWOah2v04S+XUgyzYHQWsHGafxtrs8ostQkiZwT3uY1wSGMNWP3GwTPi8yM0bGNs3Mc2dltIuA7cnRcSn36XKFly5hSmt5RrdHqLWfmjN1zkpqlkl29LYTG35LENpLGLjuh8/nSSLD3tTPh80aEzsPJvunv1LYtzxTS9l91I6HVFF4nwJIwrA6h40j6/HM2zVhbM0FBuX7RosbVQEjPSDOFLXP/PtHChUT16vGsdNAgngWpM88KO1BYzDJpU9kenxLKb8/RLHKqKbZmk8lkJvVX3QS06XogbdvGL45+gZJEqP0zmXcdQzPWH6LoaDYZ3LjBNvrhw9mWbGPDL8WYMcyE2LiRyMk9jrzmVSUMbcA2bD3HaNOGB4c1aziz5b17PPiYzuHX0zyuEszfZ7xekeC8F+BZaLrYUuqtf3/O7njhAtGtW+ybiYhgoebU+bs0ppZ6M/M9Q1ZfVSR0+oIzYQ6rm/aMy+XMcjl2jNlYbfreJUnJE1wswyKGUPwsSWtuoAZDtlOR4YMIbSaRhfc1clngqmlenK1dFCMujtldy5dr9sfLiwt/rFvHz6C630BXLElmee5hYay4NG/O2/XuTfTHH2xyLMwwJNwLlFkmbdp8YDkQ0hzwPgOYJcDBzhITm34Ja2tOHWBlBZO+S6XaxwoKCoKfnx/i1dLf2djYpOWhyQhaeWHCq0Ny6EfIQ5rAxobDvocM4ajAEj8ozmv7JuCdFyD7F3hRB5LwepDHcYZEZ2eOlC1blpuyHubJk/prktasmY7Nko9QqvFlPLpelOucArBwiETSOzfALA74ZCiwew2QnFGJ+zsAVgDYCBsbwsqVaxAQ8BmeP2eW0ddfc6bMJk2APn3YHLZtm272Rmoqm6PUWVBRURwBevD6Vey/cgXv31jDKlEGN3klJEQ7aeX2yXZU28CMrRhvWMSWhm08pyaoVElVwi/c+igCX0zDc1yEzMkHHct0xIbrGxCfmAD8ch1oPRkWFY6CiJAsT07btaFEXj/9xLn3HR2BH37gTJiNG3OKgxcvuFBI7dpAYOA4REUdBHAfLNsZWY1QVWZN3bED+O8/rr3aowfQtSun2yhMKDTpB9KKeESU56pMKVZckSnFBtMbzkNCAtLahw8Zf1f+lkp1C/64uAiEhT1AYuJb2NgIqFGjAipUKGHUwDFsfz+8TgoBJCnA4cVAWBPA6TGcmm7Bg98DNKq7p53Xo1bAHzsBaRJg9gEw+4ByRUohPFyVujcraNcOmDyZ+6hLwFWsyDZQHx+mOyphZsapZgcMALy8gA5d4xD1Ui3Lpc8pQHZc0c6yHTiyPHD+C+DS2Kx3XB2yk1z554MjbM40Qnx8GQhCCfj4JODlS5e0JFjTpgHz5zPdz8yMk2pt2AD88gv7LLJSaEMX3rzhrJFr17Jg2r6dB5a+fYGDB7kf6nAtGoe3pX6FPLIU8KQFkORg1HHs7bnZ2en+3Hr/N7zHcyDelSmjw+sCAmAnsUPCu4QMawcDXG/10CGmuj58yH6hx485rXDt2kz7vHQJ+PrrPwDUBudsugIu2nYJwGXI5Q/g7Jz19AMxMZxEbccOTqtRty4L+u7dmSJc0FFohLs+nnpWKrcQcfpUfYLf2AEi/fd9t4/y4JPgwtkLAcD2JZBiDXO5I1JSVANCTGo4UqWxCoGeAJgnAGYfYG0t4JPKbXUOIgBr7Lt363d8NmkCnDrF3+vVY2309m3NdX79FRg2jNMHnz/PTlp1J5yzM6fqPXyYX6409OsA2EQAZycANz/TPrgkCZBzpW0XF34R0x/bKFTeDJQ+CDg/BuI8gEdtgdu9YOZ5F79Ma4RevdgRd+MGV3YqXhx4/pw3LVFCNavZtYud2T16cA703r15dqbM9+/j44P58+cbNSvTh59+4jiEI0dYqHXrppnQq25djhvQSsfi/BDwPgsED+DKRQNbA08b8O+X1YEK2+HR8AieLt2G+HhVbIWuzy92TAQS7YAkO6BqIOCpyARGAOaoDmloFrp7N7B6NQtVgN+RPXuAceN4Nrh0KSsAqtwyLgBqggV9bUil9WBv74XoaFYSWrXiQUGZYC+ziI/ngXLHDi5PWLEi388ePXjfBRGFRrgbKuKR39KJZjQQpaaqBoLNV3di8oFZ+JAALo6RYgVLcsYX1cejnkczrYHkYuhNHH9wDu/v1gZeVc+R/stkLCRTUkzc0OcUp+R91B5otBBoPgt3+n9As2Y83TYa1ddx8JgkmUvgvSnFQUTJtoD9c+BN2bRV7e25DN7Zs5wgTSoFvv2Wc65/9x0H7HTowHnMwzntOFq3vo9//12EpKTzAO4CSDbJ7KYL8fGc790Qhg3j4KAhp5oA8W7A49bAxS/5T68zwJDGsLG0Vj3jMV4wvz0YHg+/grncCf378wyqTBnd+9f33CEawHLNRUrzSXqcPAlMn65SDJRQLw341VdAsWJb8OWXw3SaLtu06QcPD2DCBNb+L13iZ7d2bc3m5ZU5gZ+UxHUAtm9XBU0pBX2FCqbvL7+i0LFljOGp5zVMLSFm7HkFBgeS9fRiGiH5Eu/z5OzznCz69+KSgCOqERyfkLR6EHUecpPc3Ih8fJito9xGydAxM0vH1tHTmjXjBGK+vrnsuLR4x2kims8gfO1CmGJP+KIq2X06kJyc5hGwVe+28+dz8AzA6Q+UDkkrKyKJJIKAIAJuERBHwGUCfidn59l04oRhR2BCAtG1a5ywa/p0dmxrHV92nNC9P1mNbEi/ndtCjx7x9XN15Zw63t8q0lsMrc/BWx5XCS73yML7Go1ccIp8FpfSeBbkck43MG4cB7DVq8dO1YiIjJ87TFMkzYNm08f8unaNGTT6oGRdlSlDNGnSUaPZMs+fMxtm5kxOPOfuzvlxOnbkZbt3q7j2pkAZNDVmDDuAK1QoOEFTKCxsmY8N2T0QyeVEbgNGE+yea9as/GQQSWaZ84vc63+q5daRZN/0N7p7l7efM4eDrpQPfFwcB0KNHm2coC1alHPhjPr+FFlNLE+YIeXkY+3GEszi9G5nZcWCACAaOJDI1uEDSWuv1VhHqLKVpD0HE7oM5Yhg17ua+/E+RRhTQiWwZqkLKl8CIuibb/bT4MGax1Ye19GRW40aLLj4/6kEtCKgIgENCBhOwEpq0IAjK93cWFiULMkU1qpVOY+LlVU6miG4fq37sMGEmRJCyUOEtuN1slIePuSMo3ZOCSQUDWa65WcdCLNB1t/Y0oQfj1OzZnzc77/XPcgkJ3ME6aef8jl17Ur011+qZHTpnzvX5q5agh3Qz/x68oSVgYywbx9fj08+UeWTUUdGbBm5nFkyO3YwrbZtW6ZEFivG5zR3Lp+nelbTjJCaWrCCpkThng+Q0zOKhw/54YflW03BZ/GOswlWX6sdsu96l1B+O02YQGlC79AhDklXYt06FmJjx7Lg9/ZmLWrvXqKlS1nbrF3bgNB3u0WCVYz+/yWJVKkSf1fOENq21VzHte9ECgwO1HkNi01rRmjyDcEhlNMCtB9NmORKGKcuqBYT8H2asAI4GZu/P2vKACc8e/SIaYlbt6od3+uYZtoDE1vp0pw1s39/IhQ/y5GgFjGEWr+kCff0haHlcu5b2n6azyTvbytrPDOXLikSxznzvXn8WPdzERPD97BlSxaMfn5Ep05paqwZURbT480bFszGICGBs1e6urLyoM5Rz0ziMLmcz/XPP4m+/prPy9GRB5sePTg99OHDHPlqzL6Cg4lmz+aBuWhRvu5HjhjH4c8PKJDC3VBwRH5DTlZxT0zkB1rJQResYghN5xCmOLD22ut/LPDVhY75exbsxc+Reemj9Mknqv/KleN9KVMbqLcePZjLrJ6OWImUFIUAg5F8+A6jCCOqk/fCsjRnju51Zs/mNAqWlix0DV7bmRLCgFacptnyLcFhOwFdCHAmIJIA7zQzA8DBMcrrB/BUHVCZAUrVvKQ5QBa5Tih+hmB7goADBPxNnLPGeEFv5v6IA8MGNSNMdtSpuaekEI0axZk2Q0OZ8z5wIA+wc+eysFZHWBgLOVdXzud+RjODs9a6331HVLEip1eeMYP3T2Ta+5SSwhx+UzTdkBB+fkqW5MA2ouzLCpmaSvTgAadE+OorNhHa2/OxevfmGc6xYxzQZwjqQVOurqzw5PegKUPC/aN0qGaVY57bSHNiBX8GXP2c2S7m8bC1kWJg7V6wtuaKSTY2SPtuzOeVK8CYMcwyMTNj5kHFXtsx8diXSLjcE7j0BZBqDvN6a2FusRnxFZ8DFgASHJll8bwGLG7UR9IrrqxUtSqXEXR1ZcaDEqVKcSWeZ8+Y5fHsGVda8vbmKlFPn2pXu//pJ+aBL1zIDJUHD4y/Xnv3ct1OZQqF05wpAd26McWtQQMu/aZ0tKUvlv5uu4C3/7UCMBhcfBwAqkIme4d5u+djQLV+QKlDcK52GjXN+uPon2W1+pAGp8fA/3oDb0sBUWWAl2WAO2UAaKdE7NyZz1UiYXbK48ecafPlS/27l5Q5gm6tPTCwXVWUKcNMnbg4dgSqUwTv3we++YbZIOPGcU1dBzV25Pv3XCt4+XLmnX/1FV+v9BkdAR5qrl5lSuOWLcwaGjCAuf6uGZScVcLenp8DR0fj1lfi8GHue9myfJ/fvs2ZSkxyOV+zS5dU7do1TtGg7rCtUUNVOUodT58ys2nHDr5W7dqxA1690lR+QIFjy2RUviu/IY2nHusOPGkFRFRg+mNEBZhHV0ayKnYE9vbMRJHJOEdIfDy3hATV5/Pn6YoW64OQAuuKx1HNuwzOHTwOuMQDJeIB2wQgNR54nACE1wQwCMuXM2c4fRFlS0ugaVMW7lWqMC30zh0WCvfvq9azsGBmQ2KiimqojqFDgWJNDmPtkxl4TpfgJa2Jhm9W488faujtfo0azB7p1QsYNIgFW7duHECTkMDURvXC5HZ2vF1QUBCGD/dDQkIRAE/0X5/aq2Be9CEsT32L1i0s8emnPAg9eABs2PE8LYAKRYIB1/uA3Ax4UUex/B2ApQCCADyEofcoLo4pl48fA3/+dxm7z97E+3AP4HFbgCQ6t6lXj4VJmzZM6bNg1iju3WMhf/iwSsirC5vUVBZKS5dyQNG4cRwMp08gpaQwNXPjRi5L2bw5C/rOnfnepx84lYWsvb150PXx0X959SExkUv/TZ3K/V+wwLii8llFSgpw966mwL9xg981dYFfvbpmf9SDpk6f5jw3PXtybEdeB00VOOGeUeHd/AZDtMeHo0Lw+DFr33fuqNrduyysKlTQbNeusTYcEQG0b8+UtOBgzq/96BEHxfTrx9qQ+qAwatTXePMmHoANAGvFpzuAIbC1ZRqaei7wcuVY4zSmbqunJ0dF2tqy8N+/P3PXadIkfnGCgzmg6N493et9/z0POE+fcoTili2GaZT29uvQqVN17LoZiYSbaoW9fU6xNh6njHbZDgeH1+jduwb2WC7DK8tTAEmBm32AG/2AWA+g7BbgymZwUA5DKpUixWROKENZ07Z9e54JtW3LwllfdGvZsixYKlZkit/Jk8D48VwEPb0AP3eOheg//7CAHzOGj6EP797xjGHTJk4MV63lA5xx9kdisaOcox4qavF3n/bDli084GcWgsDne/8+9/OTT7I/eCwjJCdzXnx1gX/7Ns8M1QV+1aocQ5LfgqYKnHD/2DT3zPDviXjaqxT2e/ZwsQN9WLKEX3ClhqfVh6AgfL7scyQ3SU7LYikJ/BVW8YPg6WmJR494vcWLOYjm1CnmH9ety8FOTZoAtWrxy3fvHmswZ84A/5yMR3SEYbVr0iQeZC5e1FMwOzfhdodnTaX3A3VWAh7BwMN2wN4GADhzorV1cdTtvhCnznpDHl4ZqLCDo16LnQT2yQEdBVcy8x4RcfH2Hj04WrZfPxY2KSnAtm07MXnydHz4YAugGoCeANpnuM8WLXig9fFhYZ+czPfxxx9V63TowMW0U1JUx1P/npzMM41z5xQbOD/ioDQ3tq3JHGUovi0ECxdy2oHMQpl+4PJl1uBlMk5nUNaAlSw3kJjIGr26wL9/nyOM1QV+yZLMpc/LoKksCXdBENYC6AzgNRFVVixzAfAHAF8AIQB6E9FbQRAEAD8A6AggHsBgIsowe0lBt7kD+qe3Ga3rbVsKdUP/wp411eDtzeaRmzf5hXBwYA1WaSqJiODglQoV+EFTavtlygB/3Q/CkJ1DkERJgFwA9qwGrg7TOK6NDdCyJQvypk052lDfYKHsZ9qgdaMvsH0LAMC5SDyKONvo1LznzAEmTlSldADYLNC6NX9PTOSXaft2YOtWVVDRxwwLC46SNTNjW7C6MFXCxka1jrk5EBHxAqmpiQCSAaSkfZrZA3KXRMgphf0AStORETA31zxmo0ascVpa8nGVx1Z+9tnek4PEpEmA7wnAnG2BAgS0Py/Hl18CnTpl/rqo55ZJSuIBaMECwM8PCAjIOOArN5GQwLMZdYH/+DFQubJKs3/3jt/DfftyL2gqq8K9KYBYABvVhPv3AN4Q0QJBEKYAcCaiyYIgdAQwGizc6wH4gYh01ELTRGYiVLM7NDy/QENghjYG9vwKvCkLd88EmJM9fHwAf3/gf/9TpRlQIjaWteo7dzTNPCEhQKr9E6Skylkg6ICyJJ9S+Khrcbo0u5QUYEvwNsQlJHJk6DOFg7HUIVhJrdGgeFNcusSOvo8POwEkoG6bRrgRfg8JicmwEhzgQh54+SgKcrkUgDkAcwiCBdzcPGFhYY/kZGg1XVbC9EK2bl1tZ3lg4GqwfhQPIEH12SYecGSHPHyPA5ZxAFSRzSdOsEB5+9a0M54zh00spUqxRmpnZ9ic2OB8CDp35tlGZqErcdiLFzzTO3WK/QYfPgRh+vT8+Z7HxrKZVF3gP33KM6fERJ4Bx8SwcFcK+ho19BMBDCl8+pBls4wgCL4A9qoJ93sAmhNRuCAIngBOEFE5QRB+VXzfkn49Q/vPiRqqHyvSXqhTk4GjC3ih9APs6uzAvys/Qw39/kcNJCczm+bUKTbnHDyY8TaCwBpIlSrMKlBqcek1OuWyEfuHsGYnSQFIYIEjTQZiZPC5sRJhYTxtL1WKbegAAMdQIEaWYV9sbXkWUaYMt9Kl+dPLi7X+BQtYuz91SjUDAFRMkFq12I7777/Azp3GXTMVbsCq+GnIG59BkucJwPEpILApbZDzIOxfuD9TwoaIBcLgwdzvbds4N4+6b0T5ffz4AB0+Emugmg0nw0u2Abr4AQ78agkQIJ+lGklu3uRaqP/+ywN3kybsKL52jduFC7pTRzg5sWCytwccikbgiXAUqY73AZdHgPNjWBV5gdWfzsXpVf1QtSrXWDUEQwLMUFbIkyeB/v2j8eLFFcjlIwHwNDC/z9DfvVMlTlO2hw9V//v6spC3q3YYi8J6ICE1Lu2/zKRKyQnhHk1ETorvAoC3ROQkCMJeAAuI6D/Ff0cBTCYig5K7sAp3XbOP/jdGAdcHcKZI2wig8XdAtU0QrN5rvLzpER/P9ux//2XBceECU9yaNuUX+4vgSnhrfhu43R34cwdQZh/QfSCcBBmCml7RcObeucOCPr0zt0IFtuVKFAQPDc0uwQl40AHYsTmtT4LwHkQGeGMu92Hu8QQj27aDVKpKqRsZyVPglBRmLtSooWply7JwJ2LnsZUV54ZRd8RNn84UzZ9+Ui27fRuo9MV84PxozrAoO8GpkwU5F+G+PhhcTksHt88uHPD5D/A6i6IVQhCycAcsLQ3cWD14944TkkkkwB9/GKbU6TM9Wk+zRlSKtrdVX3K8GzdYyJ86xRqxvz/PDuRydsArhf1ff2myn8qW5YH5RdIdnHlxAu8i7GHxriKs3ldAcoI1EhJ4vbFjWdNXavwlSqgG24x8TRml/JXJSiMsrDOA6QDWAvgGQGy+9a3pw9u3rGipC/yQEPBzVX4n0GoaYM3MBVOTHOaocFf8fktEzqYId0EQ/AD4AYCPj08tXQ7Sggztl7cmpNLRkJt3A5U6yM4+2ak0lkL6m/72LTs1T51igR4czE4ypfOzUSPWCpVw+94NUQlRwA8PgGqbgKbfABKCq7UrIr/WTBtJxOwTdWGvNPO8eGHKWSYDeAzgAVDvIWvsd7vzX937A9WC+HizdD+D4eGsBam3V694ZqEU9OPHA33GXcU5WXeExYTBy7oMYhZcx6XzVrCyYibN5s3sjwgvthRUaxNQ9FradQUAyTsJpCvuIDm5F3x8EjB27GJ8dX0F8NYXSHBlh+qzesCzBsDTBrB5VxNVqzIFs2FD/iyuw/StrrUWo7qQbjmADi2c8dNPuvnnWtvrGPxRFZlKjhcczKaXM2dYyH/xhW764b17rI0fO6ZaZmnJzsTq1bmVKgWsXMlO1+HDeZBQcvrDwgB3d17n8odtiLO9zhk7SxwH7Jnwr3yWMxLuKlacB4DvAbQEMBHAnyDKf6w4UyB87c405FdVFUnweMBOPwPLcD+iWSb/gRk/rwD0AeAPrvP5K5wb7kRipzCtl3dh/Y0oEtkzTTN//Ji50ErNvF49w1zhNK59kg1godq3roeJiIW4UqirN2MKUFhYBCMp6S8AhwDcBGAHdOwB3O4NhLTglWZKAQkfV59w14WYGNY0lcL+z+3J+BCnKK5ZbQMzX+KKwqfcG8RGuKBnT+Czz/gamdUQgC7gQC4lkgDsAdoVI4wdy0wSwLC9+eawEFy6pFmr1sZGxblv0AC4a74FIw8O4/v4sgqweS/MG6zB2u/Lo3/VrJkUsmKrvX6dhfy5c1ysZMQIbd8NwA7OP/4AZsx4g2fP4pGaehDOzs9Qq9ZnCItywv2rRQAA1pUPo33jovisVVXY2fH9OXyY6YKvXwMQUplt0+RboAbb5pTPXEbCXZsV1wjAT7C0jMflyw1RqZKRFywfIrvSk+eEcF8EIErNoepCRF8LgtAJwCioHKo/ElHdjPZf2IT7/ftAuXLLAAwAcAHASgAHAMghCAI2XtuEyX+sxotbpWAb3h624R2QEmePxo1VTJYaNUwrGOw23017Oi8X4BRXFZtbXdMS4urFP6RSthUq7d8ODkz/unOH6ZfTp3Ok4oMHvG3fvnMANAXQQvN4zo/YoVt7JdByBmDzRufMwRT4LvdF6MWKwGZtcr2XFw96SpOOn18dPHe5pFXU3NXVFYkfViDW7QRkrQ5hfqv5AIzXkInYrqoU9GfOADfuxoE8rgKeV5kn32EMUOWPLNUWyE5cu8ZC/vx5LtDi56ct5FXBYHUBfAWgDiRF10FodxCp58cAd3sAQgpA2tMQNzcgseJavC+zBvC8DJipPMjGau66TFPW1vb43/+OYv/+OhgwAJg9WzNS92NBdqUnz1LKXwBbAISD59jPwGRgVwBHATwA8A9YuAM82f0ZwCMwG7h2RvunTOaW+diQnEy0fTtR69acv8TBYSVxtkIQIBBQhYCRZGOzhzw9iYoXJ+rbl2jlSs7noi+PhzE5QVJSiJzq1ib06kJoNZlQdQPB8yLBPDYt74lEwrk42rXjmpjLl3NWv/v3VUmUIiI4j4mLC2eKfP5c8zivXxP98guRldV/BLwlTpnbg4BKBFlnQqkDfLxiFzh5lu0rKl/rJY0Ywcc7dIjzn5iShlWYLXCOlk87EyoHEZwecU6dmVK6fZvrx06cyEXA7ew+EPCSgP0EzCOgJ5lVLkfmsywJzWcSms7VyPuTlWRvmGpPGNiS0HIqp+3VkyQsr3HlCmdtLFaM8/go86ikpBAVK9acgG4EBBCwmQAddXe79yOMqE4us4oTET8DR44QLVpE1LDTYxKK3OKMoB5XCdXWkXnHSTTt98Npyccyyi2j7/l+9YpoyBDu96ZNH2fq3uxIJoiCllsmv8AYOubz58CaNdxKlmSHVteuwJIlBzF//n9ITq4Hnm5GQCo9i6FDy2LKlIZaVWl0TcURjHSajRmsrKrAz2853Nyapmnh9+4p0xWkAg6hgNtDwO4BEPEACH+Au3f3oUQJ/Zz2N2+YlrZqFTsEp01TRTpGRDAbZds2DlLq0AHw8DiJ1at7ICHhTdo+rK1tYOlyFjHVFoPqbIIkVop+rpMwsM53Ws7c+Hi28aZ35pYsqW2r1pjebjoAVN4K1NigU0MmAn78cSfmz9+PiAgvWFs3RKJFBcg/2AOWMUDJf4BuHMiUVQ07J6qC5QRSU9nEt2kTpzVQQiIB5PLHAG6ptdvAtDvM1LnkD4CAZjzLAQE0W1uWBN0IwtQDc/H0oQNc3rVELWEI4p+Ww/Xr7Dxv1oxTHijt+TKZaVGq584xG8jGhh3o1apl8kJ8pChwEar5AYYCqT79tB+OHWNhePw4C/Pq1ZnB8e+/LARLlwaKFLmLK1d+QGTk35DJLPXS6rSmcMmWsHpXDWb/VEXsHW8AFRWtDAAppNIXaNHCS4NG2K1bJcjlD8FGZhUMhc7HxHAiqhUrONglIIDNM1FRLND//JOn9R06MO++QweV3T/9wFe16jjs3dsRRBUBpGpcr/Tn/Patbmfuy5fsqFMX+Dsjv8W2V98Ab0sAG44B42WAWRL8a/tjZaeVGd5HyRwJKM6FE6nZvQI8bgIw3bGVHvmtKlhqKptAbt3SbPfucaIxZVHtDx+YOfPhAyAIM/DmzSIAaomMxgFw0nGAaICWGS9L5HI2961Zw/1SsnZiY1WCXtnUc+voO7c1a4CZM5lBNXduziQjy48QhXsOwNfXF6EOoZr220POcH45Hm5uM/DgAVPdvL05mKFaNZXzs2FD0x6+NC3wTUkgaD8HDdm/ABweAE8fAHgItpA9APAYgpCslWNHMKAOpX8G3r/naMHlyzkCccYM7u/ff7NAP3eOs+T17g107Jhx0iciwNr6PBITfwIQqPGfKbS2+HhVkJay7f7vPpIjfbjodoOlQPO5vF8jNWRTNWxTgueyI0jFVMjl+oW4mxsLcfVWoYIq4Zo6Ll0Chg9/juvXCUTfAvgdQBJQBTqd0q5nXBF53DTfiS6be0QEO32Vwv7aNWbhlC2rKfCrVdNO2hUZybPKPXu4fOLAgSrabkGFKNxzAEJVBfPCHMDzusBFf+BWHyDFGra2LMCVzs+6dXUzEoxFGtNFLuFaoY5hHApO0ChqrIQugWlMPp7YWODnnzlPTdu27Cy9fZsF+tmzvEwp0E0JDT9+HGjZ8j54dpGq8V9Wk71J5khAqVLW3J1CATOemRireZuiYeentBdyOSsN6YX43buctleXEM9Mqtq5cw9iwQILJCSUhovLavTq9Q7rLv+ikaPI/JQ51o1fZ/I1yMihqkRCAp+busC/fp2Fe3ot39eXB6Yvv2QT3s8/A7fNcn+QzS0UqhqquQXpRCk7yfp8QrCOIJTbSWg7niT96lFyctb2nd6J5DrPVbPmpaK5znM1uoKOoWo7cXFcicjDg6hNG6JOnW6RldUxAqLJxmY/jRp1it6/z/z5NGtG5Oo6XuPYyqavlJsx1yUwkB1Suq6NegGMDPdrpGNLJpNl+RxMRWoql6jbu5eLTgwaxJWvbG251F67dlwm7rffiM6e1S7mkV04d46oQweuePT55+fIx6d0lgvlZKVYh7JAx7ZtXA+1UycmITg6EjVtygVPGjYkEiSpZFbvF8LXztleKCc/AKJDNfshzFEzcxA0gmJM4W2nhy7t0LyWOYSuAif9UkCpXSIYRpsJRq4aidWPVyPVNhXSOCmGeI9EZfyI775je7a3NxARkYzExH0g2gpgL4A4mJubY9060zUzADhxggNdZszYDH//4ZnWevVpzYMWD8KGtxtyxbadk6mmiTgA6PZtTU38zh2mmVasqKmJV6xoeqGM7MC5c0w/vHOHfTCDBxu2hxuCsZq7KUhv1vnz6D0kvyoFWL8F/KtqBVJ97BA19xxAdmiMOverRzt0be6aJdqURqm/6RaEjv4kODwjgMvYdetGtHkzkYuLnuO7uppUik2pDcP3GLl+OoGphVkojWhIa87p+rTG9MFYKIs+HzjAs6XPPyeqW5fLwnl6MlV27Fii1auJTp/OnjJ0OYEzZ7jWrUzGfc1MzdHsKrNnCMJsgRBgRfCvzKUY8yklNbNAQayhmtfIqbqogiDoFCDK+p+ZRdpg1L0/wTGEIP1AKPc3ufYfozGV13VsZTPaBKS8NoObEJwfEGZIs3xtcuq6GEL6wcjf39/oayCXEz19SnTwINGSJczJrlePhXjRosy7HzOG6NdfuWD1mzc5dho5itOn2ZTn60u0Zo1pQj43hHtOKWH5BYaEu2iWyQJygg2RU4VI0sxIRxYARW4A5fYAVhyGqm5GMsSq0QWdzlslA2XLTuCdN1DpT8D1PjxlsXg854hGFkdjkdsFWvSagQYNwv79qqyQ8+bNR4sW/dLMKEqzyu3bnEBLaUJRN6nkdWm2nMB//3HE66NHHLE8YEDGEdQ5YZZJj/xGSc1uFCqzTFam/vkBhhyfWUGaAzhdk06Uaqzn6upqUHtP33RpzmlRoyMrEDoPJzRYRCi7i+B6hywtiUqUYEfg6NFEK1ZwZOqTJxwVmdvXRR90m2A8qUiRz2j5cqLhw9lh5+RE5O5O1Lw5R/auXEl08iRRZGSOdCvf499/iVq25GjntWsNa/K5obkTZU8kaH4FCotZJrcFQE4hJwYoVAFhWjrhPg2EKtA6toWFhcY1tLCw0Cv0ddmbDU2Fk5OZ5bBvH9GyZUT+/iwMvLyIrKyIKlUi6t6daMoUFg7//cch7XJ57gzccjlReDgR0IqA0QT8QsApAqIIeE3AcRo5kujnn4lOnOC+idDGyZNELVoQlSpFtG4d6WSQ5ZZwL8goNMI9t6lqH9MsQSaTsYAfB869Mo4Fu65ro+u8TBk4M+uPiI0lunaN6M8/iebNIxo4kKh+fSJnZ9aQ69Yl6t+faO5coq1bia5e5W109lePtqZcFxDIy6sWTZ16hH78kWjECKLGjTlvjqsrkZnZaQJ+JuBLApoT4J7mWBZhPE6cYCpsqVJE69erhHxgcCAJVjGEyU4FTpvOTRQa4Z6bTrePbZaQHf3NDFsmO6bCcjknLTt9mrXAqVOJevYkqlKFyMIimQThGQFHCVhFwHiSlvqEzEdXIsww48FlojtZDm1PjXtvJTOz1QScICCCgCiSSP6jli3v048/Eh09SvTyJR9P30xFFO6Zw/HjzD8vXZpoxLzTZD3XnmD5ljDZscBxz3MThUa456bmnhcBLVnFxzTTMBY+Pr4EeBPQmoCRBCwnyPYTnB8SpAkEqyiC1RuC9ykSqv6qMLW0JMBDQ2Cnvy55wc4p6JDLiY4dI7IseY7gco8gSUwT7gWJwZKbMCTcCxRbJjfDw3MyoEWE8dB5H2aBg8pSLIAPjlyuUACLZx3pGtLDxsYG1tbWiNJRmcQUdk5BLeKeVQizJcCT5kBwf6DLCEDKieuymqytMMIQW6ZApdXp168fVq9eDZlMBkEQIJPJcizvh4+Pj0nLTUFQUBB8fX0hkUjg6+uLoKCgLO+zoELn9Y5RfJolAXYRadHD0jipUftUKgc26TKi2djYcKk7I6BUNEJDQ0FECA0NhZ+fn3gvAcicfICSxzm9slSVkdTHMevvjgg16FPpc7N9lEFMJtqwjTWJ6Nuvv79/lk0qBdEso4vdI60uJYs5FloOXf+V2kFI+pogCDkWUVvYkVMBgIURKCw299xGVgW2rvX1CYX0NuDMOEM/JgewsQgMDCRzc3ON8zI3Nyf/lf4G2TJpSdlMoHiaAtFmbxgFmXuemxCFex7DFC1On1DIqgD62DRJYwfOrJ5XTg16H9v1FvFxQhTueQxTtDh9QiGrWmB+0CRzYqaTHeeVE+aqgjpTEpG/IAr3PIYpWpwuoaBPgH1Mmnt2mKZ0RsPmYw25IPo4ROQviMI9j5FV56sp2Qizqw/ZjewwTenSxv39/XWu6+/vnwtnJUJE3kIU7vkAWdXiskMLzEtNMjtMUx+b5i5CRE5DFO4i8hxZNU3pm2UY8knogmgqEVGQYEi4F6ggJkAMAMpJmHJt06/bsWNHo4OC9AWjAdA6vlSqOzBJ13IxsEhEoYI+qW9MAxAC4AaAa1CMIABcABwB8EDx6ZzRfsSUv/kfJmWF1Mc9z0Iglr7jwwTNXTThiChoQE6ZZRTC3S3dsu8BTFF8nwJgYUb7+RgTh+UX5JaZwZRrmxMZFfUdXyqV5oijVoSIjwG5LdzvAfBUfPcEcC+j/XyMKX/zA3JzpmLKtTVFm87q8XVp8PqugZjGV0RBgyHhnlWbOwE4LAjCZUEQ/BTLPIgoXPH9JQAPXRsKguAnCMIlQRAuRUREZLEbjJxM5pUfERAQoJEBE+CkVwEBAdl+rLy+tvqOI5PJMGjQoDQbu1QqxaBBg8TsiyJE6JP6xjQAxRWfRQBcB9AUQHS6dd5mtB/R5p455NfiJPo0ZDs7u2y3uZsSA1DYZnYiCj6QG1RIALMBTEQemmWIChfVLbd9DKakD0ifqdHMzEzLyZodlaDyAye+ID9zBfncCgJyRLgDsAVgr/b9DID2ABZB06H6fUb7EnnumUN+nqnkx+yLOXG98vM9yCoK8rkVFOSUcC+pMMVcB3ALQIBiuSuAo2Aq5D8AXDLalyjcM4/8qlml75cuAZwdJhFTtfHsvl4FmaFVkM+toMCQcC9QZfZE5A/oKncoCAJ0PWsyE8rWGXusnCqtqAsFudxiQT63goJCU2ZPRP6ALhYPEUEQBI1lppSt04fcLK2oC3nNIspJFORzKwwQhbuIbEdYWJjO5USUI0K4X79+CAkJgVwuR0hISK7SIOfPn5+lWqv5GQX53AoF9NlrcrOJNveChcJmq82vfo/sQEE+t4IAiDZ3EbmJvLaDixBRWCDa3EXkKvLaDi5ChAiImrsIESJEfKwQNXcRIkSIKGQQhbsIESJEFECIwl2ECBEiCiBE4S5ChAgRBRCicBchQoSIAoh8wZYRBCECQKiRq7sBiMzB7nwMEK8BQ7wO4jVQorBeBxkRuev6I18Id1MgCMIlfdSfwgLxGjDE6yBeAyXE66AN0SwjQoQIEQUQonAXIUKEiAKIj1G4r87rDuQDiNeAIV4H8RooIV6HdPjobO4iRIgQISJjfIyauwgRIkSIyACicBchQoSIAoh8J9wFQXARBOGIIAgPFJ/OetY7KAhCtCAIe9MtLyEIwnlBEB4KgvCHIAgWudPz7IUJ12GQYp0HgiAMUlt+QhCEe4IgXFO0IrnX+6xBEIT2ir4/FARhio7/LRX39qHiXvuq/TdVsfyeIAjtcrXj2YzMXgdBEHwFQUhQu/e/5HrnsxFGXIemgiBcEQQhRRCEXun+0/l+FAroq+KRVw3A9wCmKL5PAbBQz3qtAHQBsDfd8j8B9FV8/wWAf16fU05dBwAuAB4rPp0V350V/50AUDuvzyMT5y0F8AhASQAWAK4DqJhunZEAflF87wvgD8X3ior1LQGUUOxHmtfnlAfXwRfAzbw+h1y8Dr4AqgLYCKCX2nK970dhaPlOcwfwCYANiu8bAHTTtRIRHQXwXn2ZwBWYWwL4K6PtPwIYcx3aAThCRG+I6C2AIwDa5073cgx1ATwkosdElARgK/haqEP92vwFoJXi3n8CYCsRJRLREwAPFfv7GJGV61CQkOF1IKIQIgoGIE+3bUF8P4xGfhTuHkQUrvj+EoCHCdu6AogmohTF72cAimdn53IRxlyH4gCeqv1Of77rFNPyGR/RS5/ROWmso7jXMeB7b8y2Hwuych0AoIQgCFcFQTgpCEKTnO5sDiIr97QgPQ8mwywvDioIwj8Aiur4K0D9BxGRIAgFlquZw9ehHxE9FwTBHsB2AAPA01YRBR/hAHyIKEoQhFoA/hYEoRIRvcvrjonIPeSJcCei1vr+EwThlSAInkQULgiCJ4DXJuw6CoCTIAhmCk3GC8DzLHY3x5AN1+E5gOZqv73AtnYQ0XPF53tBEDaDp7cfg3B/DsBb7beue6hc55kgCGYAHMH33phtPxZk+joQG5wTAYCILguC8AhAWQAfYy3LrNxTve9HYUB+NMvsBqD0ag8CsMvYDRUP9XEASo+5SdvnMxhzHQ4BaCsIgrOCTdMWwCFBEMwEQXADAEEQzAF0BnAzF/qcHbgIoIyC9WQBdhTuTreO+rXpBeCY4t7vBtBXwSIpAaAMgAu51O/sRqavgyAI7oIgSAFAEISS4OvwOJf6nd0w5jrog873I4f6mf+Q1x7d9A1sMzwK4AGAfwC4KJbXBvCb2nqnAEQASADb0toplpcEv9APAWwDYJnX55TD12GI4lwfAvhcscwWwGUAwQBuAfgBHxFrBEBHAPfBLIkAxbK5ALoqvlsp7u1Dxb0uqbZtgGK7ewA65PW55MV1ANBTcd+vAbgCoEten0sOX4c6ChkQB57B3VLbVuv9KCxNTD8gQoQIEQUQ+dEsI0KECBEisghRuIsQIUJEAYQo3EWIECGiAEIU7iJEiBBRACEKdxEiRIgogBCFuwgRIkQUQIjCXYQIESIKIP4P9+4R8FUde1AAAAAASUVORK5CYII=",
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",
- "image/svg+xml": "\r\n\r\n\r\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "plt.scatter(X_test, y_test, color = 'black')\n",
+ "plt.scatter(X_test, y_pred, color = 'green')\n",
+ "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "I am ... not quite sure what happened here :)\r\n",
- "\r\n",
+ "I am ... not quite sure what happened here :)\n",
+ "\n",
"Does this mean the machine couldn't figure out any correlation? Surely it's still supposed to give a straight line, even if it's not tightly correlated?"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Ah, typo 😊"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 72,
- "source": [
- "y_pred = bloodSugarModel.predict(X_test)\r\n",
- "plt.scatter(X_test, y_test, color = 'black')\r\n",
- "plt.scatter(X_test, y_pred, color = 'green')\r\n",
- "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "display_data",
"data": {
+ "image/png": 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",
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"text/plain": [
""
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",
- "image/svg+xml": "\r\n\r\n\r\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "y_pred = bloodSugarModel.predict(X_test)\n",
+ "plt.scatter(X_test, y_test, color = 'black')\n",
+ "plt.scatter(X_test, y_pred, color = 'green')\n",
+ "plt.plot(X_test, y_pred, color = 'blue', linewidth = 1)\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"That is more like it"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"Ok, this is pretty cool, but surely Excel could do this 20 years ago? I remember making a graph and telling Excel to 'Add trendline', and nobody called it machine learning back then..."
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"And I'm still not sure why we are using test data, we didn't actually test it"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
"I am probably jumping the gun a bit, but what happens if you include more than one property in the set?"
- ],
- "metadata": {}
+ ]
},
{
"cell_type": "code",
"execution_count": 73,
- "source": [
- "X = origX[:, :4]\r\n",
- "print(X.shape)\r\n",
- "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\r\n",
- "print(X_train.shape, y_train.shape)\r\n",
- "multiModel = linear_model.LinearRegression()\r\n",
- "multiModel.fit(X_train, y_train)\r\n",
- "y_predict = multiModel.predict(X_test)\r\n",
- "\r\n",
- "plt.scatter(X_test, y_test, color = 'black')\r\n",
- "plt.scatter(X_test, y_predict, color = 'green')\r\n",
- "plt.plot(X_test, y_predict, color = 'blue', linewidth = 1)\r\n",
- "plt.show()"
- ],
+ "metadata": {},
"outputs": [
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"(442, 4)\n",
"(296, 4) (296,)\n"
]
},
{
- "output_type": "error",
"ename": "ValueError",
"evalue": "x and y must be the same size",
+ "output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
@@ -613,54 +600,67 @@
]
},
{
- "output_type": "display_data",
"data": {
+ "image/png": "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",
+ "image/svg+xml": "\r\n\r\n\r\n",
"text/plain": [
""
- ],
- "image/png": "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",
- "image/svg+xml": "\r\n\r\n\r\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
],
- "metadata": {}
+ "source": [
+ "X = origX[:, :4]\n",
+ "print(X.shape)\n",
+ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n",
+ "print(X_train.shape, y_train.shape)\n",
+ "multiModel = linear_model.LinearRegression()\n",
+ "multiModel.fit(X_train, y_train)\n",
+ "y_predict = multiModel.predict(X_test)\n",
+ "\n",
+ "plt.scatter(X_test, y_test, color = 'black')\n",
+ "plt.scatter(X_test, y_predict, color = 'green')\n",
+ "plt.plot(X_test, y_predict, color = 'blue', linewidth = 1)\n",
+ "plt.show()"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {},
"source": [
- "Ah right, obviously you can't make a graph out of it because there's no X axis.\r\n",
- "But the model happily accepted it, so maybe you could still make predictions.\r\n",
- "\r\n",
+ "Ah right, obviously you can't make a graph out of it because there's no X axis.\n",
+ "But the model happily accepted it, so maybe you could still make predictions.\n",
+ "\n",
"But enough jumping the gun for one day."
- ],
- "metadata": {}
+ ]
}
],
"metadata": {
- "orig_nbformat": 4,
+ "interpreter": {
+ "hash": "c7d6cb708d9496164cad24676295f59deddd15f42781117113af2b6c8d53f583"
+ },
+ "kernelspec": {
+ "display_name": "Python 3.9.7 64-bit (windows store)",
+ "name": "python3"
+ },
"language_info": {
- "name": "python",
- "version": "3.9.6",
- "mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
- "pygments_lexer": "ipython3",
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
"nbconvert_exporter": "python",
- "file_extension": ".py"
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3.9.6 64-bit (windows store)"
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
},
- "interpreter": {
- "hash": "c7d6cb708d9496164cad24676295f59deddd15f42781117113af2b6c8d53f583"
- }
+ "orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}