{
"cells": [
{
"cell_type": "markdown",
"id": "d7732643-d6e3-4652-9a4f-61f60d647f72",
"metadata": {
"id": "d7732643-d6e3-4652-9a4f-61f60d647f72"
},
"source": [
"# Estimation of tree height using GEDI dataset - Perceptron - 2024"
]
},
{
"cell_type": "markdown",
"id": "4bac193f-5126-4b3d-b188-70390d5209e9",
"metadata": {},
"source": [
"\n",
" cd /media/sf_LVM_shared/my_SE_data/exercise/\n",
" wget https://raw.githubusercontent.com/selvaje/SE_docs/master/source/CASESTUDY/Tree_Height_05Perceptron_intro_2024.ipynb \n",
" source $HOME/venv/bin/activate\n",
" pip3 install torch torchvision torchaudio\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58b12f60-2a68-458c-a85a-5c3127d7d9fc",
"metadata": {
"id": "58b12f60-2a68-458c-a85a-5c3127d7d9fc",
"outputId": "9b4a0a51-2e83-4f91-e173-c14361b50443",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/gpfs/gibbs/project/dijk/ahf38/conda_envs/geo_comp2/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"'''\n",
"Packages\n",
"\n",
"conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch\n",
"conda install -c anaconda scikit-learn\n",
"conda install pandas\n",
"'''\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy\n",
"import pandas as pd\n",
"from sklearn.metrics import r2_score\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "markdown",
"id": "8f3f045f-bf11-478c-bf5a-8e0da1c09511",
"metadata": {
"id": "8f3f045f-bf11-478c-bf5a-8e0da1c09511"
},
"source": [
"Single-layer perceptron takes data as input and its weights are summed up then an activation function is applied before sent to the output layer. Here is an example for a data with 3 features (ie, predictors):"
]
},
{
"attachments": {
"2af60855-7e5b-48c2-a340-04b5ed755e4a.png": {
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"
}
},
"cell_type": "markdown",
"id": "0aed8b2d-a09e-432e-a649-19d8a5d6c0a0",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "7c76382c-97fe-4301-8e65-424aefe8e1c4",
"metadata": {
"id": "7c76382c-97fe-4301-8e65-424aefe8e1c4"
},
"source": [
"**Question 1**: what is the difference between the Perceptron shown above an a simple linear regression?"
]
},
{
"cell_type": "markdown",
"id": "032b4914-c8fb-48ef-944a-41263fe70a12",
"metadata": {
"id": "032b4914-c8fb-48ef-944a-41263fe70a12"
},
"source": [
"Now let's see how we can implement a Perceptron:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b88a28fb-e0a2-486b-9e36-ab446370f810",
"metadata": {
"id": "b88a28fb-e0a2-486b-9e36-ab446370f810",
"tags": []
},
"outputs": [],
"source": [
"class Perceptron(torch.nn.Module): # torch.nn.Module is a base class for all neural network modules in PyTorch. Allows the Perceptron class to utilize PyTorch's neural network functionality.\n",
" def __init__(self,input_size, output_size, use_activation_fn=False): # initializes an instance of the Perceptron with three parameters\n",
" super(Perceptron, self).__init__()\n",
" self.fc = nn.Linear(input_size,output_size) # Initializes weights with uniform distribution centered in zero\n",
" self.activation_fn = nn.ReLU() # initializes an activation function, specifically the Rectified Linear Unit (ReLU)\n",
" self.use_activation_fn = use_activation_fn # If we want to use an activation function\n",
" def forward(self, x): # specifies how the input data (x) passes through the network layers during the forward pass\n",
" output = self.fc(x)\n",
" if self.use_activation_fn:\n",
" output = self.activation_fn(output) # To add the non-linearity. Try training you Perceptron with and without the non-linearity\n",
" return output"
]
},
{
"cell_type": "markdown",
"id": "a62a3727-f3e5-493c-a3b4-0a0a2da80f4f",
"metadata": {
"id": "a62a3727-f3e5-493c-a3b4-0a0a2da80f4f"
},
"source": [
"The building blocks of the Perceptron code:\n",
"- nn.Linear: Applies a linear transformation to the incoming data: y = xA^T + b\n",
"- nn.ReLU: Applies the rectified linear unit function element-wise"
]
},
{
"cell_type": "markdown",
"id": "127b95e2-d4b3-42dc-825b-eb60fdcba81c",
"metadata": {
"id": "127b95e2-d4b3-42dc-825b-eb60fdcba81c"
},
"source": [
"Before we try to solve a real-world problem let's see how it works on a simpler data. For data, I will create a simple 2D regression problem."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62f4e1f2-929e-4829-812a-c1deae2e3562",
"metadata": {
"id": "62f4e1f2-929e-4829-812a-c1deae2e3562",
"outputId": "5e8f040d-1489-42cc-f381-a70fe6dbfe2d",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Test data')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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HO2bG/IMQr1g3RyjELzT/34OpB6rkQ7Acqtpo7Izmdw/43ICmae4NUAghhBCtkiRMbVSxOZvvkp/ErJcBoF8w3KnInMG8pN9zZ+f/4mX0d1eIQtSiaRr4XIPmcw1KLz6/0U8SJSGEEEI4lcxhaqP25S/CrJeh0Ou8ptApteZxuGCFGyITomGawb/qjyRLQgghhHAySZjaqGOFa+pNln6hOFa01kXRCCGEEEII0TxJwtRGmfWG16yptDqw9o0QQgghhBCtmMxhaqPCvOIpKy2w2cukYSTcu7OLoxJCCCEuTU75Yc4WL6fSWoi/Rwc6B16DrynS3WEJIVoRSZjaqH4hM0gu3W3zdYWVvsHXuDAiIZovpayAFU3zdHcoQojzLHo5mzKeJaVkA1rNwtaKfbkfkBj2ML1C7nRrfEKI1kOG5LVRXfxHkRBwRT2vVE2iTwy5jg6+fV0blBDNjKXiZ0py7qQorTNFaV0pzhhHZcmnKGVxd2hCtHlbM18kpWQTUPWQr+qPDujsyXmHU4U/uTdAIUSrIQlTG6VpBia1f4qxkY8QYIqq2R7iGcPE6N8zNvJhN0YnhPtVln5Dac5NWCs2UL1Qrm49Q3nBXyjLfVCSJiHcqMh8jrPFK8BO8aIDuR+ilL3iRkII4RgZkteGGTQjA0KvIzFkJqXWfDQ0fIzBUqpZtHm6NZ3y/KeoSpSsF7xSlThZKlZiLv0KT7/b3RGeEG1eSskGqkZEKJv7FFtSKDSfIchT5uMKIRpHepgEmmbAzxSKrylEkiUhAHPpV9i7EQOoLPnYNcEIIeqw6OVoDtzCWPQKF0QjhGjtJGESQoiLWM2HsZ8wKXTL8fPFIIQQrhbs2RmF/d8/DRMBHh1cFJEQojWThEkIIS6meVFdAMU2E3IKFcI92vuNwtsYhq3fQQ0j8QGT8DQGujYwIUSrJFd7IYS4iIf3ROxNJgcjJu+rZAirEG5i0EyMivo7Bs14QUnxKhoGfE1RDAj7jZuiE0K0NpIwCSHERUzeU9CMsXDRjViVqonmnv4PujgqIcSFonwHMTnmv8T4jauZz2TS/EgIvpnJsR/jbQp1c4RCiNZCquQJIcRFNM0Tv7AvKMm5DWVNoipxqp7TZMQn5HVMngPdGKEQAiDEqztj2r2MVa/AosrxMPhh0OTWRgjRtOSsIoQQ9TCY4vCPXIOlfBmW8lUoVYnRozcevjdhMIa5OzwhxAWMBi+MeLk7DCFEKyUJkxBC2KBpHnj4XIOHzzXuDkUIIYQQbiJzmIQQQgghhBDCBkmYhBBCCCGEEMKGFpMwvfzyywwZMoSAgAAiIyOZOXMmR48edXdYQgghhBBCiFasxSRM69at49FHH+Xnn39mxYoVWCwWJk2aRElJibtDE0IIIYQQQrRSLabow9KlS2t9/fHHHxMZGcnOnTsZO3Zsve+pqKigoqKi5uvCwkKnxiiEEEI0RK5NQgjRsrSYHqaLFRQUABAaanthupdffpmgoKCaP7Gxsa4KTwjRRimlGt5JtGlybRJCiJZFUy3w6q6U4tprryUvL48NGzbY3K++p3ixsbEUFBQQGBjoilCFEG2AxZpNXtF/KCj5Equeg9EQTpDfrYQE/AqTrNkEVJ1/g4KC5PyLXJuEEKK5cPTa1GKG5F3o17/+Nfv27WPjxo129/Py8sLLSxayE0I4j9mSzNnMa7FaswArAFY9m9yidygs/ZaOkYvwMHVwb5CiWZFrkxBCtCwtbkjeb37zGxYtWsSaNWuIiYlxdzhtQomlmJSyc+RX5rk7FCGanbTcJ2olS7+wYrFmkp77O3eEJYQQQogm0mJ6mJRS/OY3v2HBggWsXbuWTp06uTukVi+zPIMFKd+yO28nOjoAXf27M6vDbLoFJDilzUq9Equy4G3wQdM0p7QhWiZdVWLVyzEZ/NG05vGsp9J8grKKzXb2sFJasYFK82k8PeScJYQQQrRELSZhevTRR/niiy9YuHAhAQEBpKenAxAUFISPj4+bo2t9MsrTefnw3yi3ltUkSwAni4/zf0f/yaNdH6dvcP8ma+9I4T5WZiziePFBAII9QhkbMZlxEdMwGVrMP1PhBAXlezmV/z7ZZWsBhYchhJjAm4kPug+Twc+tsZVX7ndovwrzAUmYhBBCiBaqeTymdcC7775LQUEB48ePp127djV/vv76a3eH1ip9nTS3TrIEoM7/75MzH2LRLU3S1pbs1bx78mVOFB+u2ZZvzmVx6le8f/JfTdaOaHkyS1azPe02csrWA1X1acx6Hqfz32dH2u1YdPeuw6Zpnk26nxBCCCGanxaTMCml6v1z9913uzu0VievMpcDhfvqJEvVFIoiSyEHCvc1uq0Ccy7fJH90/rh1k7NjxQfZmL280e2Ilseql3Ig60kUOqrO/CCdospjnMqf45bYqvl6jQQ87O6j4YWP1wjXBCSEEEKIJtdiEibhOlkVmQ3uY8BAZnlGo9v6OWct9uvaK9ZnLWt0O6LlSS/5CasqAZv/QnTOFX6DripdGVYtRmMIwf53ALbm22kE+9+F0SClooUQQoiWShImUYeP0bfBfXR0fIyNnzuWWpaE7RviKjmVmTIsrw0qrjyG1sA0S6sqpsKS5aKI6hcZ/Bf8faac/8pU6+8An2uICP6zW+ISQgghRNOQ2fSijhifWCK8Iu32NBkwkhg8sNFteRq80NBQdpImAwYMzaQqmnAdg+ZDQ8k0gNHg7fxg7NA0T9qHfUhZ5XYKS77FYs3AZIwmyO9GvD0HSbVHIYQQooWTu1BRh6ZpzOww2+4+V0ZdRYBH44cZ9Q0abHOuFFQlS32CBknC1AZF+l1Zz9ylCxkI9OqLpzHMZTHZomkavl5DiQ59lZiIz4gOfQUfr8GSLAkhhBCtgPQwiXoNCR1GqbWUb5LmYlZmjJoRXVUlNhMiJ3J9zE1N0k7voIFEeXUgqyKt3sRJAVdGzWiStoRjyiz5HCn4kbTSPaBptPdJJCFoGj6mYJfGEejZlxDvoeSV76TuorAAOp2DH3ZpTEIIIYRoezSlVMNjXlqJwsJCgoKCKCgoIDBQJmE7otRSyo68reRUZONn8mdI6DBCPEObtI38yhzePflP0svPYcBIdfFyo2bijrhHSQwZ1qTtCduSS7axLOXPWJWZX4bDaZg0LyZ3eIkYv8EujcdsLWBPxiPkV+xCwwhU/evQ0EgIe5bYwJtdGo+4fHL+tU0+m7ZJKZ1zpQcotGTiYwwizi8Ro2a/6qYQomk5ev6VhEk0C7rSOVy4l4OFuzDrZjr4xDE0dAy+Jn93h9ZmFFam8fWZO9CVhbpzhzSMmgc3dfqcAI8ol8allCKvfDuZpcux6CX4eXSmfcAsvIzhLo1DNI6cf22Tz6btOV28gxXpb1Jo/qXarLchgLGR99EvZKobIxOibXH0/CtD8kSzYNAM9A4aQO+gAe4Opc06mP89Slmpv9CCQlcWDuUvZFjEAy6NS9M0Qn2GEuoz1KXtCiGEM5wt2c13yc/VKXZUrhexPP0NdKwkhlzjpuiEEPWRmfRCCADOFm+qs3jwhRQ6Z4s3uzAiIYRoXZRSrMl4/3yyVP8An/WZH2HWy10bmBDCLkmYhBAA6Jgb3kc1vI8QQoj6ZVecJbviNPaWTKjUSzlZvNV1QQkhGiQJkxACgCjvPjWFFeqjYSTKp48LIxJCiNal1Jrb4D4aGqWWPBdEI4RwlCRMQggAeofMsrvukcJK7+CZrgtICCFaGT9Tw+vGKZRD+wn3UNZ0VPkyVPkKlN5wAixaB0mYhBAARPv0YWj4rwDQLjg1VPc6DYt4iEifnm6JTQghWoNwrzgivbqgYXtRay+DH138ZTmN5kbpeeh5v0FljUPl/waV/ygqczR6/p9Qeqm7wxNOJgmTEKLGgLDbmRbz/4jxHYJR88SoeRHjN4SrY14jMfQWd4cnhBAt3oToh6m6/ao/aRof9SAmg6dLYxL2Kb0UlXs7VKyk9vwzC5R/h8p7AKUs7gpPuICUFRdC1BLrN4RYvyHuDkMIIVqlWN++3NDxH6xMf5vcyuSa7X6mUMZG3kfvoIm19rfolRwq3MDevJUUW3IJ9ogkMWQyCYEjMGi2552KJlQ2HywnqL9Yhw7mbVCxCrwnuzoy4SKSMAkhhBBCuFBHv0Tu6fwB6eVHKTRn4mMMIsa3T50EqMxSxNwzfyKj4jQaGgpFXmUap0p209G3DzfHvYCHwdtN30Xbocq+bWAPA6p0PpokTK2WDMkTQgghhHAxTdNo59ODhMCxdPTrX29v0Q+p/yaz4ixAzUK31evlJZceZEX6R64LuC2zZmCvFDzooKe5KhrhBpIwCSGEEEI0M/mV6Rwr+tnmguIKxb78FZRZilwcWRtkjMDWnLMqBjBEuyoa4QaSMAkhhBBCNDNJpQcb3MeqLKSUHXVBNG2b5nNDA3voaL7XuyQW4R6SMAkhhBBCNDNK2RsCVmtPp8YhAJ/ZYOwE9S7ubgCPgeA1sZ7XRGshCZMQQjSBvIoTnC1aTWrJVqx6pbvDEUK0cLG+Da97Z8BIO5/uLoimbdMMfmhhX4DXOGoPzTOA93S0kA/RNKmj1prJT1cIIRoht/woWzJfJrfiSM02T0MA/ULvo0fwTWiavXHvQghRv1CvDnT2G8jpkj31zmPSMNAneDx+piA3RNf2aIZQtJD3UJZzYN4DGMBzMJox0t2hCReQHiYhhLhM+RWnWHbuIfIqjtXaXqkXsSP7DfbnfuymyIQQrcH0mCcI8WxXa5t2vocj2qcrk6IfdEdYbZpmikHzuQbNZ5okS22I9DAJ4QK5lXlszt5CTmUOAaYARoYNJ9pHKuq0dHty3sOqKm1WsdqX+xHdgmbiYwp1ahylljQsegk+pmg8DP5ObUsI4Tr+phDu6/wG+/JXsTd/BcWWPII8IhgQMoXeQeMxGTzcHaIQbYIkTEI4kVKKhamL+T5lEQAGzVCzbVzEGO6KvwOjrNTeIlVYC0gu2YC9CdcKnTNFK+gZcpNTYkgvWceRvDkUVFZVydLwIMZ/Gr1Cf4u3KcwpbQohXMvT6MPgsGsYHHaNu0MRos2ShEkIJ1qVuYYFKQtrvrYqa81/r8/aiLfBm1vjbnZHaKKRyq15NFSdyoCRUmumU9o/W/Q9e7Je4MKR1Qoz54p/ILt8K2Pbz5WkSTiksLKc7ZnJWJWif1g7onwD3B2SEEI0K5IwCeEkFt3CwvM9S/VRKFZmrmZ6+6sJ8JAblJbG2xhCVbUk20mTjhUfY9MnLWZrIfuy/1HTyoUUVsotWRzNe5f+Ec82edui9aiwWvjHrlV8dWIvlXrVwxwDGpNju/Pi0CmEevu6OUIhhGgepOiDEE5yquQ0hQ2swG5VVvYW7HdRRKIpeRmD6OA7Es3OaVRDIz5gUpO3fa54Cboy23xdYSWpeDEWvazJ2xatg64UD66fz+fHdtckSwA6iuXnjnHjis8pNle4MUIhhGg+JGESwkkqdMduNiqs5U6ORDhLYvhDGDQPm0lT75A78DWFN3m7xeYktHoXUPyFriqosOY0eduidVifeop1qafQ6+khtSrFqcIcvj6x1w2RCSFE8yMJkxBO0s7bsSp47X3aOzkS4SyhXt24KuYdAj3ja203ab4khj1MYthDTmm3qhKe/flTACaDn1PaFy3ft6f2YWxgjbAvT+xxTTBCCNHMyRwmIZwk3CucvkF9OFhwCL3eRQc1Irwi6BGQ4IboRFOJ8O7D9I5zya44SFFlMh4GP9r5DsVk8HZam+39r+Jo/vt29jAQ5j0AL2OI02IQLVtKSQFWZa/CI2SU2h9SLIQQbYX0MAnhRHfG3Y6fyRfDRb9qBgyYNBMPdr4PrYGnvKL50zSNCO8+dA6cSqz/WKcmSwCBnl1p73cV9Z/Cq/499QhxTu+WaB2ifAIa7GEK95EeSiGEAEmYhHCqSO8I/tr7L4wOH4lJq+rQNWBgYMgAnu/9LF0Duro5QtFSDYz4Ox38qgpKaBjQzg8YMGm+DIl6lXCfIe4MTzRz13fua7eHyYDGTV36uzAiIYRovmRInhBOFuYVxn2d7+GO+NsothTja/TF2+jcHgjR+hkN3gyO+ic9zA+TWrIKi15CgEcn2vtNxOjkHi7R8l3ZoStDImPZmXUO/aLEyahptPMN5NZuA9wUnRBCNC+SMAnhIp4GT0I9Q90dhmhl/D3i6B58r7vDEC2M0WDg4/E38uy2pSw6e6hW0jQiKo7/N+IaAj0l8RZCCJCESQghhGiT/Dw8eX3UDJ4eMIGtmUlYdJ3E8PZ0Dmz6xZaFEKIlk4RJCCGEaMOifAOYEd/b3WEIIUSzJUUfhBBCCCGEEMIGSZiEEEIIIYQQwgZJmIQQQgghhBDCBkmYhBBCCCGEEMIGSZiEEEIIIYQQwgapkieEaHPMeiU789bzc84qCsy5BJiCGBI6gSGh4/E2+rg7PCGEEEI0I5IwCSHalDJrKe+f/Bvnyk4DGqAosuSzMPVTNucs45EuLxDgEezmKIUQQgjRXEjCJIRoU75P+ZiUsrPnv1IXvKLIqcjgq+Q5/Krzn9wRmhBCtFlKKc6VHWdv/jpKLIUEeYQxMORKIr1j3R2aEJIwCeFKSimSSjMpNJcS7R1ChHew22I5XZzMj2nr2JN/GIWib1B3rm43gW4BcW6LydmKzAXsztuIQq/3dR2do0V7yapII8KrnYujE0KItsmsV/Jt8mscLtyKASMKHQ2NTdkLGR52NVPb3YtBk2n3wn0kYRLCRbZkH+KDkz9yuiS9ZtuQ0AQe6TaDeL9ol8ayOmMLb5+Yi4aGfj552Ji1k3VZ23mg801MbTfWpfG4SnLZyZrv154zJcckYRJCCBf5MfUDjhRuA0DHCoA6PwLg55wfCTCFMDbyerfFJ4Sk60K4wOqM3fxp30ecKcmotX1n7nEe3fEmZy/a7kzJpWm8fWIuClUrebCe/+//nPqak8VJLovHlTQ0B/cTQgjhCkXmXHblra5JkOqzMft7LLrZhVEJUZskTKJRrEpnffoJPj2xlXlndpNbUeLukJqdSquZ14/OA6hzQdDRKbeamXN8ocviWZK23m7iYMDAj6lrXRaPK8X5dsOoNdyx3tm/lwuiEUI0d5VWC2bd6u4wWrXjxXtsDpOuVmYt5lzZMRdFJERdMiRPXLYtmad5eudC0ssKMaChozBpBm7rPIQn+16FyVA7H6/UraxKPcKRgnQ8DUbGt0ugd3DrH/a0MfsAxZZym6/r6GzLPUpWRQERXkFOj+dAwXG7w9J0dA4UHnd6HO7ga/JnSMh4tuauqvdppoaBPoGDCfWMcH1wQohmQSnFwqR9fHziZ44UVPX+DwqL5b5uI7myfYKbo2t9LHqlg/tJD5OzKVVGaek8SkvmYrWmYTBE4Ot3M76+N2Mw+Ls7PLeShElcln25Kdy/aS66qrrp1M/ffFqUzmcnt1KhW/jrgKtr9t+adZonts0jr7IUk2ZAoXj7yDqGR3TijaE3EOTZete+SS/LxagZsCr7T9AyynNdkjBpDow3a81D0mZ0uJPsyjROFB9Ew1AzuVih6OATxw2xD7o7RCGEmyileH73j3x9ZheGC86Eu3PO8UjO1zzRawIP9Rjjxghbn2jveAf20qRanpPpegE52TdiNu+neskNXc+msOB5Sko+ITx8AUZj232YKEPyxGX596G16KiaROlCCvj69E6SS/IAOFaQwQOb51JQWQZUJVXW84nW9uwzPLTli5rEqzUK8PBtMFkCCDT5uiAaGBDcC4OdX30DBhKDW++QNE+DF7/q/GfujPsd3QP6EeXVga7+vbml46/5dde/42tq20/RhGjLVqcd4+szuwBqXd+q//v1Q2s4lJ/mlthaq1jfBCK9OqLZuC4ZMNAjcAiBHmEujqxtKch/FrP50Pmv1AV/K6yWs+TnPe6ewJoJSZjEJcurKGVT5km7SY6mafyYfACAD49twqrqT66sSrEn9xw/Z512WrzuNjaiLybNaPN1DY3Ofu2I9Y10STyTo8c00MukuLr9OJfE4i5GzUi/4GH8qvMz/LHHazzY5TkGhYzBZPBwd2hCCDeae2o7RjsnSKNm4MtTO10YUeunaRo3dPwdngbvOg/zNAwEeIQyvb30/DuT1ZpFWdn3gK35elYqKtZgsbTee7WGSMIkLlmhudxOLZsqBjTyKkvRlWJpykG7PSxGzcCScweaNshmJMjTnxs7jrf5ukJxf5dpaI6MlWsC7Xwi+GPC/Rg1Y62Lk+H8/x7vfjcdfdu7JBYhhGhODuWn14yAqI9V6RyUHqYmF+0dx6PdXmNQ6ERMmicA3gY/RoXP4KGurxLoEermCFs3c+UebCdLv6is3OH0WJormcMkLlmYlx8mzYDFThJkVYr2vsGYdSvmBoajKaUotjg26bOluq/zFDTg66S1WJS1Zk6Tv8mHJxKuZ0S4a4fADQvrzzsD/8Ky9I1VC9cqRd/gBKZEj6G9j2t6uoQQornxMjZ8W+TtwD7i0oV4RjGjw8Nc0/5BLMqMh+bpsgeJbZ7Dn3Pb/XnIb724ZP4eXkyL6c2P52z3HBk1jemxffE0GInw9iervNj2ATWI82/dT48MmoH7u0zjhtixbMg6QJGllGjvUEZF9MHT4J5fwyjvcO6Mn8mdzHRL+0II0dxc1b4HX5zaYfPapp3fRziPQTPgqXm5O4w2xdNzEOAB2KtEqOHpOdxFETU/MiRPXJbHek0gwMPL5ljvP/S5klAvXzRN45ZOQ2pVG7qYUorZcQOcFWqzEuTpzzUdhnNL3BVMiEp0W7IkhBCirju7DMWoGepdq86oaQR5+jArLtH1gQnhRAZDCL6+N2E7LTDi7T0NkynGlWE1K5IwicsS4xfMN+PvY1Rkl1qXlXY+gbw8aAb3dBtRs+3OrsNJCIrCcFFyVf3VE72uJMYvxPlBCyGEEHZ09A/lPyNvwdfkgUbVfNzqB4PBnr58OvoOglvxMhii7QoMegFPz+p7t+pCVVVpgodHH4JD/p9b4mouNKVacT3nixQWFhIUFERBQQGBgYHuDqfVSCst4GxJLn4mL3oHt6uTGAEUmyt4+/Bavj2zi1Jr1Xylzv7hPNRjDNNj+7k6ZCGEi8n51zb5bJqfYnMFC5P2sSs3GaNmYEREJ6bG9MLbKJU0ReullIXy8uWUlnyB1XoOozEKH9+b8PG5Bu18MY7WxtHzb4tKmNavX8+rr77Kzp07SUtLY8GCBcycOdPh98tFyf3KrWZSSwvwMpho7xskEzqFaCEsuhmFwsNweRdNOf/aJp+NEEK4h6Pn3xY1gaKkpIT+/ftzzz33cP3117s7HHEZvI0edA4Id3cYQggHHSjYwsashSSXHgMg2jue0RHTSQweLw88hBBCtAktKmGaOnUqU6dOdXcYQjQJpRRWpWMy2F7UVgh3Wpn+FWsyv0G7YLprRvlZ5iW/RXLJcaZ3+JUkTUIIIVq9FpUwXaqKigoqKipqvi4sLHRjNEJUOZSfwssHf+Bgfgo6Ck+Diant+/G7npMJ8vR16BjF5nLmJW1lQfI2ssoLCfT0ZXqHgdwUP5JwrwAnfweiLUguPc6azG8AUPxSYlmdX7Z6a+5SEgIHkxA40C3xtWRybRJCiJalVVfJe/nllwkKCqr5Exsb6+6QRBv3U8pebt30HvvzzqGfv/Gs1C18n7yL6WteJ7u8qMFj5FeWcM+Wd3n32HJSy/IwKys5FUV8dmo9t218i+SSHGd/G6IN2JqzFAO2ez81DGzNWeLCiFoPuTYJIUTL0qoTpmeeeYaCgoKaP8nJye4OSbRhJeZynt0zHxR1FsvWNCgwl/OHXV81eJxXDy0muTSnJuGqpqMoMJfy570NHwPgeFEaf98/j0mr/8YVK5/nkW0fsDbjIC2oDoxwopTSk+hYbb6u0EktO+XCiFoPuTYJIUTL0qqH5Hl5eeHlJatFi+bhgxPrqpIcG1M+NA325CWRVV5EhHf9w+pyKopYlX7A5ir0VqVzuCCFwwUp9AzqYDOWtRkH+fPeL2reA7An7zS78k4xK2YoT/aaKXNT2jhPQ8PnTpNBSixfDrk2CXuOFWSyKvUoZVYz3YMimdS+B57GVn275hK5lTlszdlMvjmfQFMQw8JGEO4V4e6wRAshv4FCuMjP2Sfr7V262PHCdJsJ0/HCdJvJ0oUOF5yzmTDlVhTz3L4v0VXtPqrqrxac28aA0E5MapfYYDui9eodNIyUshM1c5YupmGgT9BIF0clROtVbK7gd1u/Y236CYyahoaGRekEe/rw2tBZjI7u4u4QWySlFN+nzGNp+o8AGDQDSikWps5nYtRkZsfcjEFr1QOuRBNoUf9CiouL2bNnD3v27AHg9OnT7Nmzh6SkJPcGJoQDDA1lSud5GG3PG/FwsKKeSbO93w8pO7DoFw/o+4UBja/PbnaoHdF6DQqdiLfRr1aFvGoaGibNg2FhU9wQmbiYrssw2pZOKcWjW75lfcZJAKxKYTn/cKygspwHNn3Fwbw0d4bYYi3PWMKS9B9Q5/9nVVb084VsVmYs44fUhW6OULQELaqHaceOHUyYMKHm69/97ncA3HXXXXzyySduiko0F7uyz/Hfo9vYlH4aHcWQ8FjuSRjKqOhOWHSdTRmnSSkpINTLl3HtuuBjcu1worGRPThUkGp3HwMa/YM72ny9d3AMvkZPSq2VNvfR0Bga3tXm6wcLkm32GkBVT9PhgnMopWRYXhvmZwrknk7P8+npv1NiLcSAAUXV3CUvgw+3xz9DiGeku8Nss3KKS/ls/U7mbztAfmk5gT5ezBrSh7vHDiQi0N/d4YlLtCvnHFsyT9f7mjp/xn73yEbeHnGDawNr4cx6JT+lLbK7z/KMn5gUPRVvo7eLohItUYtKmMaPHy8T0kW9vjixi7/sWIpBM9QMWVuffoo1aSeZEdeLLZlnySovrtnf3+TJ433HcXe3IS5LCm7tPJwPTqzBquqfx6QUTGrfx+5YdW+jJzfHj+Ljk2vqTXkMaExs15don2CbxzBqBjQ0u0mTQRIlAXTw7cIfe77P/vzNnCrZj1KKOL+e9A8eg5fRx93htVlpeYXc9s7XZBeVoJ+/JhaWVfD5xl38sOsw/3v0JjqGBbs3SHFJfjp3EJNmqOlVuphVKVamHKXSapH5TJfgWNFRyqxldvep1Cs5VHiAgSGDnRqLWa+gyJKHp8Ebf1OwU9sSTU9+60SLd6Igm7/sWIqCWvN7rKrqefiiswfrJCjFlkpe3L0CFNyTMNQlcQZ6+PDG4Nv47Y7Pq57Un89XqnOThMAoXkq8vsHj/KrrFaSW5bI0dS/G8wli9d/9Q+L4c59Zdt8/NKwbqzMO2HzdqBkYHt5depcEAB4GLwaGTmBg6ISGdxYu8fy8FeQU/5IsVbPqivzSMp79ejmfPXKjm6ITl6PIXGHnEVYVHUW5JEyXpEKvaHgnoMJa7rQYSiyFrM74kt15qzGrqtEhHX17ckXUTXTx7++0dkXTalFzmISoz+cndtroEanOSGy/9//2r6XUYnt4W1MbE5XAonGPM6ldH/xMnngZTHTwCeHPfabzzdhfY3RgjpLJYORv/W7kw+EPcnWHAQwO7czE6D68Mfgu3ht2P74m+9W3JrdLJMTTz+acKqvSuSV+zGV9f0II50rKyWfz8SSsNuYtWXXFrjMpnMyQ9dhaknj/0AZH0AR5eOPvIdUVL0W0dzuH9mvn094p7ZdYCnj/5JNsz11ekywBJJce5ZPTf2Vf/gantCuanjymEC3ezqxz53uT6tFAJ0mZ1czKlOPMiOvd9IHZEOsfxisDb2rUMTRNo39IHP1D4i75vT4mT/496F5+s+MjCs2lNU81DZqGUvCHnjMYFNq5UfEJIZzjaGqWQ/sdTsmkS1SYk6MRTeW6+P68eXCdzdcNmsbNnQfJcOlL1N6nA539unKm5FRNoYcLaRjo4NOBON9OTml/dcZX5FdmoS5qu/rr78+9Q0LAYBni3AJID5No8UyGy/9nbEAjp6KkCaNpGboHtmf+mD/yRI/pDA3rSmJwPDfHjebr0b/j+o7D3R2eEMIGe1U0L+Rpcmw/0TxE+wTyZL+JQN3nfEZNo5N/GL9KkDL+l+PO+HvxMnphuOiW14ABT4MHd8Xf75Qh6JV6BbvzVtdJli5kVhXsL9jY5G2Lpic9TKLFG9+uCwfy0uuM53eEjqK9b6ATomr+/D28uTFuJDfGNZ+LcLnVzPac4xSay4jxDaNfcJzMpRLiAoM7d8DLw0SF2WJzHw+jgWFdbVfbFM3Tvd2HE+UTwNuH1nOyKBsAb6OJ6+ITeaL3eAI9pYrb5Wjv04E/9XyBH1K/Z3veNnRlxYCBgSGDuab9TNr72F7kvTGKzLm1huHVx4CR7IoUp7QvmpYkTKLFu7nrAP5z5GcqrNaLKr9pVZUV7NxvB3l6M76d7RLcLUGlbmVl6mG2ZZ8BBYPD45jUvmeLmhislOKrpI18dGIlJdZfJunG+ITxTO/rGBgqCzYKAeDv7cVtoxL5eO2OeosEaBrcMKwfQb5yc90SXR3bm2kxvThXmk+F1UJ73yB8TZ7uDqvFi/KO5r7OD3Gb9W5KLMX4mfzwdvIwOC+DI8dXeDq0n3A3GZInWrwonwA+HHsj3kZTrUIGBk3DqBkwagabBQ7+MmASXi0osbjYkYJ0rlr2b36/fT7zz+xm/tnd/HHHd1yx7A0O5tlf86k5+d+Zdbx59MdayRJASlkuj+38L/vzz7opMiGan99MHsm0AT0AMBq0Wn9P7NOVP14z1m2xicbTNI1YvxC6BkZIstTEvI3ehHmFOz1ZAvD3CCbWJwHNzlNbHZ3eQSOcHotoPE21oYWNCgsLCQoKoqCggMDAtjkMqzXLLi/h21N7qxauVYohkbHc3GUAaaWFvLBrGQfz0mv2jfEN4unEK5ka29ONETdObkUJ01a8Q5G5HP2iZ80GNPw8vPhx4qNEeDfvRSwLzaVcs/YlzMpa7+sGNPoGx/He0IdcHJloSnL+te1yP5v9yeks3HGIzMJiwgP8uHZQL/p1jJZhrEI0E8eLdvPZmb/V+5qGgR4Bg7k1/hkXRyUu5Oj5t+U+WhfiIuHefjzcayQP96o9J6edbyCLJt3H0fxMUksLCPHypV9o+xZfbWjemV31JktQNTer2FzO16d38Oue410f3CVYk3EAi41kCaq+l735Z0gvy7e7IK8QbU3f2Gj6xka7OwwhhA3dAgYwO+ZxFqbMwawqMWCkqkaeTo+Awczu+IS7QxQOkoRJtBkJwZEkBEe6O4wmsyzlcL3JUjUFfHtmV7NPmHIqijCcX3jX7n6VRZIwCSGEaFH6h4wjIXAI+/M3kF2RgqfRh95BI4n2vvRlQYT7SMIkRAtVYml4BfPs8mLMuhUPBxbEdZcI78AGkyWACC8ZxiWEEKLl8Tb6MiRssrvDEI0gRR+EaKHa+QZhbwaiUmBVis2Zpy752Eop9uaeY0XqYXbnJF9WyXZHTYjsi5fB9rMbAxqDQ7sQ6R3ktBiEEEIIIWyRHiYhWqihYfH8nHXa5uva+arqWeXFl3Tc9enHeWnfUpJKcmu2xfgG80y/KVzRLuGy47XF38Obh7tN4Y2jP9R5zYCGyWDk0e5Tm7xdIYQQQghHSA+TEC3UyMjONT1MF3YAXbztUqrkrU8/zsNbviT5gmQJIKU0n1///BUrU480JmSbboobzdO9riPE06/W9i7+0cwZ/AA9AmOc0q4QQgghREOkh0mIFqpfaAc6+oaSXJpb57XqZCnU04+RkZ0dOp5Sihf3LkFdtPwvVBWQ0IAX9y5hQrvuGLWmf9ZybcxQrm4/iD15pym2lNPeJ5Tuge2bvB0hhBBCiEshCVMrdK6wgP/u3sWCw4corqykfUAgt/frz239+uPr4eHu8EQT0TSNPydO4aHNX6KhLprPVFUy/Zl+kx0u+LAn9xzJpXk2X1dARnkh27PPMjyi0+UHbofJYGRwWFebr2eUFZJXWUKEdwBhXs17fSkhhBBCtA6SMLUyh7IyuWXeN5SazVjP30EnFxbwz43rWXT0CF9cfwMBXl5ujlI0lXHR3Zgz4mb+vncJqaUFNdsjvP15qu8kront4/CxMsoLHduvzLH9mtKe3CTePLKcnblngap0cHRkd57oOZkuAa2nVLwQQgghmh9JmFoRXSke/mFxrWSpmgKOZGfx/zZv5K8TrnRPgMIpJrTrzrjobuzMSSK9rJBwLz+GhMdjMlzasDlHe2zCXdyz83PWSR7d9lmtSn0K2Jx1nJ05Z/h01K/oHiiLdwohhBDCOaToQyuyKeksyYUFdZKlalal+PbQQYorK10cmUgqymfO/i38c+davjq+l2Jzw2soXQqDpjEkPI7psX0ZEdn5kpMlgIFhsUT72F/rKMzLj2FOGo5XH13pPL93AVal6izSa1WKcquZl/Yvdlk8QgghhGh7pIepFdmXkYFR02wmTADlFgun8nLpFyVP5F2h0mrlzz8vZd7J/WiahlHTMOs6f922kr8Pn8TsLn3dHWINo2bgmb6TeWzbtzb3earvpMtKxi7XtuzTpJcX2HxdR7EnL4kzxdnE+4e7LC4hhBBCtB3Sw9SKeBqNdaqb1cfRIgCi8Z7duox5Jw+gqBoyadZ1AMqsZv6w6UdWJh93b4AXmdShF68NmV1n2F2opy//GjSL6bH9XBpPUmmOQ/udq6dSoBBCCCFEU5AeplZkbFw8L29cb3efSD8/uoeFuSiiti25OJ9vT+yzmcRqaLy2ZwMTY7u5NK6GTI3pzVXte/Jz1ikyy4sI9/JnRGRntyTaASZvh/bzd3A/IYQQQohLJQlTK5IQHs6YjnFsTk6yOSzvwUFDMLpwSFVbtizpGFpVwe9fNqpf/lYoDuVmsicrlcSI5rXekMlgYHSU7fLerjImsjteBhMVusXmPpHegfQNkYVthRDicimlsCrl0iHXQrQk8pvRyvx76jR6R0YBYNS0Wn/fnTiAuxMHuC22tqawsgLD+c8eqEqWqv+cXycJBTct/YINqaddH2AL4O/hzX1dx9rd5zcJE52ykK4QQrR2h3IzeWzdYrp/9hpdP/1/jP72Pf6zfxvlFtsPqYRoi6SHqZUJ9vZh/o03s/bMaRYdPUJBRQVxQUHc2LsvvSNlvRpX6hwYikVVzVn6JVGCmmTp/N+VViv3r5rP6lkP0MHffpW6tuiBbuMx61b+e3IDulLnC5voeBpMPNFzMjNi5SGAEEJcqvUpp7lv5Xz0871LAOeKC3l5x1qWJx3n88k34m1qeLF7pRQHczPIKi8h2jeAHsERaBc+LBSiFZCEqRUyGgxc2bkLV3bu4u5Q2rQpHbsT4OFFsbkCVSdZ+oUCzLrO3KO7eXLQOFeG2CJomsave0zklk7DWZ56gNzKEqK9g5jUvg8BHjJ3SQghLlWZxcyjaxZh0S9esKHqmrQrK5V39v3M7weOsXucNSkn+duOVZwu/KXwTo/gCF4YehXDozo2feBCuImMY2kjKiwWFhw4xO8WL+HxRT/xyY5dFJSXuzusVs3b5MG/Rk49/5VGfclSNV0pVp876ZK4WqowL39u6TScRxOu5Pq4wZIsCSHEZfrh9BGKzBU2ixLpSvG/I7sx61abx1iefIx7V3/LmcLaVUqP5Wdz24ov2Zx+tgkjFsK9JGFqA45mZTP+vY/444/L+OHwUX46coyXVq1j9JwPWHdS5s4407S4Hnx+1c14OlBhznK+5Lg75JaXklJSYPfiKIQQonU4lJvZYIGH/IpyMktL6n3Nquv8ZetygDpJl45CV4rnty1H2VkXUoiWRIbktXJFFRXc8eW8mt4k/YKTV7nZwkMLFrH47tvpGi6lxp1lVLt4psR158czR2xWLzRqGoMjO7g4sqrhFG/u28Tu7FQAAj28uLX7AH7ddwT+Hl4uj0cIIYTzeRqMdTOd+vYz1v+wb3P6WdLLim2+TwHHC3I4kJtB37Doy4xSiOZDephauQUHDpFXVlbvjboCdF3x2c49Lo+rrbmr5yCbyRKAVSnu6DHQhRHBl8f3cM/qb9mbnVazrdBcwX8ObeWm5V9QYq50aTxCCCFc48rYLr8UJaqHAY3eoZFE+PjV+3paaaFD7aSW1N7vaH4WL+5YycPrvuNPPy9ha0aS9EKJFkESplZu5XH782KsSrH02HEXReN6SinO5OdxLCebMrPZbXEMiuzAU+cLOhgvqB5UXQ77hWET6R0W5bJ4sstKeO78cIqLp/zqSnE4L5P3D211WTxCCCFcZ0hUDInh7Wpdjy6ko/h1/xE23x/q5etQO2HeVfsppfjr9hVM+eFDPjm6k2XJR/nm5D5uXjGXe9Z8Q5nFfddnIRwhQ/JauTKLpcFe90qL8+etpBYW8dX+fexNS8fTaGRcp3hm9uqFv6enU9pTSjHv8EHe3vEzSQUFAPh6eHBz7748MWyU09q15+G+w+kf3o6PDm5na0YyACPbxXF/7yEMjYp1aSzzTu2vNTzzYrpSfH50F4/3G117LSkhhBAtnqZpfDDxOu5c/i2HczMxagZ0pWPQNHSl+NOQ8UyNT7D5/jHtOxHk6U1Bpe3iUe39AhkYUTXU/IPD2/jk6A4ArOd7tqr/3pB2mmd+XsIbo2c01bcnRJOThKmV6xsdxb7UdJvDwQyaRu8o567PtODQIZ5csrRqKaLzYaw+eYo3Nm3msxtm08sJ60O9sXUzb27/uVZdulKzmU/27mZbyjm+vv5mfD0aXl+iqY1sF8fIdnEub/diJwpy0DTsjmHPrSijqLKCIC+pRieEEK1NhI8fP0y/kzXnTvHTmaOUWsx0DQrjpu79iA0IsvteL6OJpweO55mfl9rc59lBV2DQNCqtVt47uMXmfrpSLDpzkD8OGEcHP/vtCuEuMiSvlbslsZ/duTO6UtwxKNFp7e9JS+MPPy1FV3BxGAXl5dw1bz7FlU07V+ZUXi5vbv8ZqKd6j1Icys7is327m7TNlsbP5IFmp8w5VBVB9zLJMxUhhGitjAYDEzt25bWxV/PeFTP5w6AxDSZL1W7plshLwyYTeFGBoFAvH/49ejrT4noAcCA3jbyKsgaPty711KV/A0K4iNwNtXLdwsN4esJY/rlmPUZNq0meqjsXZvftzeTuXZ3W/vMrV9l8TVeQW1rGosOHubV//yZr8+tD+2t9r3XbVfxv3x4eGjS0ydpsaaZ0TODTo7tsvm7UNMa374K3selOEdszk/n4yHa2ZiYBGmPadeLuhMEkhrdvsjaEEEK4zm3dB3B9l76sTTlJVlkJ7XwDGNu+c63qepXWhof9a2hUWC3ODFWIRpGEqQ24f+gguoWF8p9tO9iWdA4FJESEc8+QgVzXpxeak+aolJvNHMjIbHC/tadPN2nCdDovz26vGkBqcRG6Um12fs7wqI4MiujAnuzUOp9VdTL9SF/bE34v1YeHt/HSrlW1Etkfzh5m0ZmDvDRsKrd0TWyytoQQQriOt9HElI625zt1DQ63+xATqopM9A6V8uOi+ZKEqY0Y16UT47p0wqLrKKXwsLG2QlNac8qxRXHzyhruqr8UgV7eDZ6cfUymNpssQdWE348mzObBtd+xNTMZ0/lqfRal42Py4PVR0xkU0TTrQu3OTuGlXVU9jRf+TKon/P556xIGhXege3BEk7QnhGgci8WKZtAwNrCwqWidskpKOJabjafRSP+odjbXYnJUuLcf0zr24Kek+tciNGoa8QGhDImIaVQ7QjiTJExtTEMrezelzJL6Vwi/WEygY+OlT+bk8r89e9h45iygGNGxI7cnJpIQEV5rv2u6JTD/yEGbxzFqGjO693CozdYs2MuHrybdyu7sVJYnH6fcaiYhOIIZ8b3w82i6KoKfHt2JUTPUJEgXM2ga/zu2i78PndxkbQohLo3FqrNwzT6+XrqLpLQ8DJrG8H7x3D59CAN7uraKp3CPrJISXli/mqUnj9dUUQ328ubBQUN4YOCQRj1k/MuQq9iXk0ZySUGtCq1GTcPX5MmbY6512mgXIZqCJEzCaSL86l/w7mLX9+7d4D6LDh/h9z8tQeOXXoqk/AK+2reflydPYnafX44xNi6e/lHRHMjMqPM0y6BpeBiN/GrAYMe/kVZM0zQGRnSoKf3qDNsyk2wmS1D186ya19S8VOpWlp47zPdn9pFVXkKMXxA3dBrA+HZd23TvpGh9rLrOn99czLodJ2pKwehKsXX/GTbvPc2zD0zmmnF93BqjaBrpxUV8tmcP3x8+TFFlJfFBwdzWvx9XdOrM9fO+JO38cPVq+RXl/GvzBjKKi3l+3BWX3W64tx8Lp97Nh4e38cXxPeRWlOJr8uC6zn14oNdwYv2Dm+C7E8J5JGESTnNF5074e3rarYLXLiCAUXEd7R7nVG4uv/9pSZ11g6qToaeXLqNPVCQ9IqqGdBk0jU9mXMejS35g87kkjJqGpmlYdJ0wH1/mTJ1Ol9CwRn53wlHVi/M2dh9Xyq8s4+51czmYn44BDR3F8cJMVqYeY1x0F94ZeQNeTVgQQwh3Wrz2AOt2nABqVxa16lVfvfzhcob2jSMyNMAN0Ymmcjgri1u//Ybiysqa6+ehrEz+tHIl7QMCSC0rQtlYa+KTfbu5pU8/uoeF1/u6I4K8fPh94jh+nzgOs27FpBlaba9SbkUJ85K288O5PRSay4j1DePG+CFMad8PD4Pzp0SIpte87lJEq+Lt4cHT48bafN2gafxryuQGT5if79lrtwC2QdP43+49tbYFe/swd9YN/HDz7Tw2bCQPDRrKe9NmsOnuXzG4vfN6U0Rd49t3trmaPFT9/Ma37+zCiBr21LZFHCnIAKomI8MvCfr69FO8dmCN22IToql9s2wX9k7DSlUlVaLl0pXioUULayVL8EuCnFpUhLLanvdr1DS+OdR0/wY8DMZWmyydKc5m9rq3effoas6W5JBXWcqB/HM8u+c7Htn6GRVWs7tDFJdBEibhVLf078fLk68ixMen1vbYoCA+um5Wg71LAJvPJtkt4GBVio1nz9b7Wu+IKH4zZDi/Hz6KyV26uaTYhajtroSq4Y/1XRo1wKQZuK3bQJfGZM/Z4lzWpB23+W9Oofji5C6KzRUujkyIpqfrilPncuqsk1drH6U4drbhiqei+dpw9gzJhYX2K8gqbC5mritFanGRU2JrTZRS/G7Hl+SbS2setsEvD9525JxmzrHV7gpPNIKMKRFOd2Pfvszq1YstScnklpXSITCQwR06tNqnS6K2rkHhvDl6Jo9tXIhC1VywDZqGSTPw3tjriPFvPqu7b808a+ueoUa51cy+3FRGRnVySUxCOIumgclowGK1Pc/QYNDw8pDbhZZsd1oaJoMBi27756yh2RySZ9A0wi568Cnq2pV7lpPFth8u6Ci+Pbudh7tfgbfRw4WRicaSM6BwCQ+jkbGd4i/rvSPjOnIqN9fmkzGjpjEqLq4R0Qlnm9axB/1mtOOL47vZknEWTdMYHR3Prd0GEO3bvOZF6A2mS5e2nxDNmaZpjB7YmQ07T9bMWbqYritGD2xew2bFpTFqBlQD6xPaY1WKa7v3bMKIWq6MsiI+O7GNBWf3UmguJ9onkFs6D+LmzoPYk3e2wWVNSiwVnC7OomeQLNrekkjCJJq92xP715mjdCFdKe4ckOiyeMTlifEP4skB45vseIWVFaw5d5LCynLiAkMYFR3XJOvGDAxreC0Qk2agd7Assihah9uvGcr6HSfrfc1o0IgMC2D8kG4ujko0pTFxcby+ZbPdfTSqEuiLHwYZNI1xcfEMaic3+CcLs7l13acUmst+qdhbkscr+1fxfdI+psV2c+hRmlRabXkkYRLNXufQUP5v2hR+/9PSWmXFjZqGrhT/nDyppkKeaP10pXhz3ybePbCVCqsFjaph99G+Abwycipj2zdumFz3oEiGhHdkV05yvU8JDZrGtXF9CfHybVQ7QjQXfbq246+PTuOv7y6p6WUyaFVV8qLCAnnzmdl4ypC8Fq1/dDSJ0e3Yn5Fus/fjV4MHsyrpFCdyc2rOqwZNY2ZCT16aMLHJh9EfyMrgmyP7SSosIMTbhxldezA2Nr7ZLpislOKxrfNrJUs1r6E4UZjF/tzAOhV9Lxbs6Utnf7lnaQrl1jJyKjPxNHgR7hnl1KkemmpMH20LU1hYSFBQEAUFBQQGBro7HHGJTuTk8PnuPWw4UzXHZFRc/QvXOtvZ/HyyS0qJ8vcjJqj5zL1pK17dvZ539m+ps12j6uL+5aRbGBrVuIU200oLuWXNp6SVFtY8ba2+gegT0o7Pxt1OgIdXo9poa+T8a1tz+WxyC0r5Yf0Bjp7OwNPDxKgBnRk3uCseJimW0xpkFhdz2/x5nMzNxQDoUDN87LZ+/fjbFVcCsDMtlUPZmXgZTYyLiyfav2mHTSuleGHjaj49sLum/eq/B0W15+OrryfQ69LPr0op1qWc5rMjuziYm4GP0YOp8Qnc0WMA7f0a/3u1KyeZm9d+YncfD4OR/uGhHClMq3f9QQ14NOFKftVtfKPjacuKLYX8kPoV23M3YFEWACK92jO13WwGhoy4pGM5ev6VhEkIB21LPsfL69axLz2jZtuQmA78afw4+kXL8KwL6UqxOfUsB7Mz8TAamRDbmU5BIY0+bnZZCcPmvWPzCalB0xgU0YFvp9zW6LYKK8v5+vRu5p+uWmSxvW8QN3UewKz4fjJZ9zLI+dc2+WyEq1RYLCw5foxFR45QUF5B59AQbunbj4HtXTfc7sO9O3hx89p6XzNqGhM6dubDabMu6ZhKKZ7fupLPjuyqNYfIqGl4GU18dtWNDI5qeLi1PZ8e38o/9q2wWRij2jsjZvPW0WWcLcmpKaRh1AxYlc6U9n35x4DZzW7twZakxFLM68eeI6ciE526SemsDncwPnKaw8dz9PwrfexCOGDjmbPcO/+7OqfJnSmp3PTl13xx0w0McOEFx1n2Z6bz/fHD5JWV0SEgkNk9+hAXFHxJxziQncGjqxZxtjC/Ztjk37asZnJ8V/7fuGkEeF5+z8xPZ4/aHe6gK8X2zHOklhQ2+olioKc3v0oYwa8SLu1plRBCNFdeJhMze/ZiZs9ebmnfouu8t3ubzdetSrHy7ElO5uXSJSTU4eN+d/Ignx3ZVXOMC49XbrFw36r5bLnhYXw9PC879qqhgg33MQSZfJjefiifntxEvrkIo6YR4RPEA93HcV3HgVIhuJFWZHxvM1kC+D5lLgNDRhLoEdyk7UrCJEQDdKX40/IV6KrucyVdKSy6zl9WrmLxnXe4Jb6mUG6x8MTKH1ly6nitRWbf2vkzjwwcxh+HjXboJJ9UmM/NP3xFmaVqYb4LL1wrzp7kV8sX8OXVN132BSOnvBSjZsBSz1CHC+WWlzbJEIyGWHSdJUlH+OL4bpKK8wnx8uG6zn24oUt/GbInhBAXOZKTRXZZqd19DGisTTp9SQnTBwe31QybvpiOoqCynEWnD3Nz9/6XFvAFRkd2bjBdCjR58dqBNezOTaFqZlPVaIQkcxnP71xGrG84QyOkqu/lsiorW7JX20yWqii25qzjquhrm7Rt6RMUogFbk5NJKSy0eaLUleJQZhaHM7NcGldT+sv6lSw7dQKomuhd/QcFc3Zu5d1dWx06zn/2bafMYq53yJyuFD+nJbM5Nemy42zvF1DvuPALaUCUC0qVV1gt3LPma367aSHbspJJLS3kYF4GL+5cxdU/fkRqSaHTYxBCiJak0mptcB9NA7Pe8H7Vyi1mjuRl2U1mjJrGtoxzDR/LauFEfjZni/LqlGGPDwjjynbdaz1UvFiPoCj25KagLhq4Z1U6Fl3nN1u+pdJqaTCOC5VZKtmadZqNmSfIKm/biweXWUoo18vs7qOhkV2ZYXefyyEJkxANOFfg2I1vckGBkyNxjoySYuYdPVhV3KDmDF/7gvDKz5tYdup4g8f67vhBu+tPGDWN708cuuxYp8X1wNNou2PcqGlM6NCFCB+/y27DUf9v7zo2p58FqDVMUAGppYU8umGB02MQQoiWpGtIGJ4G+0VErErRL+JS5gU7NmLB3l5lFjMv71zL4G/eYuKijxi34D+MW/A+Xx3fWytxemXItfQN6QBQkzhVz0ea2bEvRwoyba7Rp6PIqyxjecoRh+K1Kp23j6xh3LL/497Nn/Lgls+5Yvlr/G77N+RUFDt0jNbG0+iN1uDPW8Pb0PSLLEvCJEQDgry9Hdov2MH9mpvVZ09V3fDXOcdrF/xRPLJ0MYezbfeiWXWd0vND8WzuoxT5FeWXHWuApxfPDr6i3teMmoa30cTTg8Zd9vEdVWqpZO7x3TYvjFal2JOTyv6cNKfHIoQQLUWglxfXJ/S22Utj1DTig4IZ0cHxSqfeJhP9wqLtrm1kVYoR7TrW+1q51cJtK77ig0PbKDZX1mxPLi7g6S1LeXX3+pptAR7efDn+Lt4bcROTO/RkWEQ8s+L68fX4e/h1j7EUmu1f30yagQN5DV8XlFI8t3sh7x1dR4mloma7rhQr0w5zx4b/Umi239PSGnkaPOkTNAiDnfRFx8rAkJFN3rYkTEI0YEx8HP6e9ieKRvr5MahDyyz6UG6xXPS8pr6LjoYCPtq70+ZxjAYDUb7+dtsyahqxAY0rxX5HwgBeG3U17S4adjcwogPzp95B92Dnr29xJC+zZp6WLQY0tmUmOz0WIYRoSZ4eMZZuIWEYLrrWGDUNXw8P5kyaccnzXB/oM9RmQSCDphHm7cs18T3rff2LY3vYnZVa5/3VX8058DNH8355WGjUDFzRvjtvDLue/429g38Mms6AsBi7ox8uPKanseEy/fvzUliYvLfeR3JWpUguyeOLU7aLZ7Rmk6JnomlavT1NGgZ6BiYS59elyduVhEmIBvh4ePDYSPuV0v4wZnSzXWzPlpTCQhYdPUxeaSmOLC6gK8WSk8fs7nNbz/4NPuW7KaHvpYZax3Vd+rDp+odZMPUOPr3yBtbM/BXfTrmNHiGuWQzQ0Yu5FEMSQojagry8mX/drfxx+GhiAgLRgEBPL+7oM4AlN95Fr/DISz7mNZ168mi/quv0hb1XBjQCPLz49Kob8DbVn9D87+guu8c2ahpfHt/bYAxRPgF0DQi3O2DMqnTGRXdr8FjfJe22W3pcR/HNGdsPMFuzjr5deKDzk/gaqx7QGjCinU9n+gYN4p74x5zSrlTJE8IB9wwaiFnX+ffmzVRYrDXrPPh6ePDM+HFc36e3u0N0WF5ZGc+sXs6Kkydqnl4Z0NANNDgUvKEJu/f0GcSik4c5XZBX71ym+/sOJiG0aZIag6YxIMI9vXo9giPxM3lSYqm0uY+OYnikVEMSQjROSm4BKXmFBPl4071deKsoS+3n4cnDA4bx8IBhTXbMPw4cyxUxnfn8yB7256TjY/JgalwCN3XvR5i3r833JRXl2y0YYVWK04W5DbavaRoP9RzNH7Z9X+/rRk2jd3A7BoY1vB5UWllBgwWO2nIBiB6B/fhbnznsL9hOWvk5PA1e9A0aTJS38+4JJGESwgGapvHg0CHc2r8fy44dJ7u0lOgAfyZ17Yavp2OLmG46c5bPdu5hb1o6nkYjE7t14Y6BiXQKbfyCro4qt5i59btvOJGbU/cCUb3BxrXYoGkkhIXbPX6Apxfzpt/KP7auZcGJQ5j1qhN+hI8fD/Ufyr19BjUq/ubCx+TBHd0H8v6hrfUuYmjUNAaEd6BXaJQbohNCtAbH0rL558K1bDv5y9De+IgQnpg2miv7dHVjZM3XoMgYBkU6vkCtUgovo8nu/FuDpjm8fuCMjn05V5LPGwfX1jxYrf67S0AE741ybFmNUC+/Wgvw1ifIs+kLG7QkJoOJASEjGOCq9lzUjhCtQoCXF7P79rmk9yileHXdRv6zdUetE+DcXXv5cs8+3ps1g3FdOjkj3DoWHDnM0Zzsel/TFCg753FdKe7uN7DBNoK9fXhl3FT+PHwCJ/Jz8DKa6BEagamFDVlsyBP9xnIkP4u1qSdrfq7V64DE+gfz9uiZbo5QCNFSHU/P5ra3v6LSXLsE9dmsPB77dDH/vGUK1wysf06OcEypuZKHVy6ktMJcNUHFxvVPV8rm/Kf6PNJzDFNjevHN6d2cKsrG3+TFlJieTGjX3eHr4IyYfixKtj0M0KhpzOyY6HBMovEkYRLCyVYcP8l/tu4A6q5ArlsVj3z/Axsevp9QX+c/Lfrm4H6bi/tpVCVNulb1RK16Amz1NWRql27M6u74RSPIy5tBUR0aG3Kz5Wk08uG42axMOc6Xx/dwpiiPUG8fru/Ul1md++BruvwV5S9UZjGzPvUUeRVlxPgHMSIqrsXNlxNCXJpXFq2jwmyxWYjgxQWrmdi3G94echt3uf6wbikbUs5WPSn0PF8p9qKkyahpdA8OZ2LMpfXodQoI46l+Ey87tuERnRkR0ZmtWafrVGM1ahpBHj7c2Xn4ZR9fXDr5TRPCyf67fVetBORCiqp5QfP2H+CBYUOcHktmSbH9lcoVRHv7ER8Wys8pVcNA4oOCubf/IG7t3U9u1C9iNBiYHJvA5NiEJj+2UoqPj2zn9X0bapW6jfYN4B/DpjChgwzJEaI1Ss8vYstx+wt8F5dXsvrgSaYlNv25py04U5DHT6ePnv9KAzPgcf7qeMHw9AHh7Xlv/CyXj5DQNI03h97M3/b+wI/n9tdKmhICo3l18PVE+gS6NKa2rsUlTHPmzOHVV18lLS2N3r1788YbbzBmzBh3hyWETXtS02yWO4WqG+Od51Kh6ea+2hTtH0B6se2kyaBpdA4J5YuZN1JhtWDVFT4mU6uYZNzSfHB4Gy/vWl1ne0ZpEfevmcenV97E6HauGcophHCdtPyGJ/MbDRqpuS1zsfTmYGXSydoPMpUGlZwfmne+q0mHl4ZNJtwFC6HXx9fkyT8HXccTvSayOfMkZmWld3B7ege3zCVMWroWlTB9/fXXPP7448yZM4dRo0bx/vvvM3XqVA4dOkTHjvUvSCaEuzWUa2hgtxR3U7qxd192p9teNE9Xipt6V5X91nXFD0ePsu3cORSKoR1imN6jB74ejhW5EJevqLKC1/eur/c1df7//7lrDT9cLQmTEK1NsG/Di6DruiLYr21P+m+MijrrD0J1knThuLxyq6XOXq4W5RPIrDhXlTYQtrSohOm1117jvvvu4/777wfgjTfeYNmyZbz77ru8/PLLbo5OAJSbLfyw6zDfbT9AZkExUUEBzBrSm+kDe+LVRsdaj+gYy6YzSXar3YyIc3xV88aYmdCTufv3cDgrq048Bk2jf1Q0U7t2Z296Ovcu+I688vKaNS0WHD7MPzes56OZsxjYXp5wOdOKc8fsXqgVcDAvg5MFOXQJCnNdYEIIp4uPCCGhXQTH0rNsrpFnMhq4qm/D6/mI+vUIjbB7TQbwMBiID3RdFVvRvLWYCQmVlZXs3LmTSZMm1do+adIkNm/eXO97KioqKCwsrPVHOE9BaTm3vf0lL8xfyf6kdNILitmXnMYL81dy2ztfUVhW7u4Q3eK+oYNsnpgNmoa/lyez+vRySSxeJhOfz7qRq7sl1OrVMmoaMxN68unM2RSUl3Pn/HkUVFQAVcUpquMvqqzkru/mk17cdtd/cIWc8tJaiy/aklVe4oJoRFOTa5OwR9M0npg2uuq/bexz34QhBDnQEyXqNz62E9G+/hhsfMJGTWNm114EeclnLKq0mIQpOzsbq9VKVFTtdU2ioqJIT0+v9z0vv/wyQUFBNX9iY13zFL+temH+Ck5k5AC/zJmszhOOp2Xzt+9WuScwNxsVH8czE8YCF61Armn4eJj4aPYsArwcW+OhKQR6efHGlKvZfO8DvHv1DN67egab73uQ/zdpKv6enny1fz8lZnO98650pSizWPhi3z6XxdtW5JaX8u6+rcxc/D/+e3Bng08/Adr5BrggMtHU5NokGjK6Rzyv3XFNTVJU/YDL02jk4auG88hVI9wZXotnNBh468rpeBiNdR5OGTSN2IAgnh46zk3RieZIU8qBq/J5c+bM4bvvviM0NJSHHnqIK664oua17Oxshg4dyqlTp5wSaGpqKh06dGDz5s2MGPHLieKll17if//7H0eOHKnznoqKCirOPyUHKCwsJDY2loKCAgIDpbpIU0rLL2LSyx/aHD4AVSehlX+6n8ggf9cF1owcycxi7u697E5Jw9NkZGLXLtzUvy9hfrZXIHeHaf/7H0eys+zu0yU0lBV33e2agNqAA9np3LbsaworK88vhKvQTFV1kerraDKgMTCiA99OvsPVoV62wsJCgoKC5PyLXJuE48wWK+sOnyYlt4AgX28m9O4iPUtN6GhuFu/u3cYPp45g0XWCvLy5pUc/Huo3lGBvmSPWFjh6bXJ4Usmbb77JM888wz333ENBQQHTpk3j+eef55lnngHAarVy9uzZxkduQ3h4OEajsU5vUmZmZp1ep2peXl54ufDJfVu250yq3WQJqnon9ialuXzc9bncAubvPMCZ7Dz8vbyY0rc7I7p0xGBwbeW3HpER/H3y5a/L4Crl1SueK2qVV71w5EK5xf0TYVuLcouZu1bMo8hcnSwBaCirhmZSKFU7aTJoGibNwJ8HXemWeEXjybVJOMrDZGRiX1lCwFkSQiN4Y8LV/L9xUym3WPD18HBZESbRsjicML3//vt88MEH3HrrrQA88sgjzJw5k7KyMv72t785LcBqnp6eDBo0iBUrVjBr1qya7StWrODaa691evvCvuZ6fvlw/XZeX7axqnwoCoOmMX/nAfrFRPPeXbMcqkbU1sQHBZOUnV9rmwLU+ZXQjQaNPhGR7gitVVp8+gg55aV1X1AGlEVHM9Z+EtEzOJK/DZ1EYrgU3hBCiKZgMhjw92yaxcZF6+RwwnT69GlGjhxZ8/WIESNYvXo1V155JWazmccff9wZ8dXyu9/9jjvuuIPBgwczYsQI/vOf/5CUlMRDDz3k9LaFfQPiO9hcnLWaQdMYEO+6m7wf9x7htWUbAWrmg1T/fTAlg8e/WMwn99/gsnhagrN5+exISqmz4LkGaHrVUhVWTXF7YqJ7AmyFtqQlYdS0+ucsKQPKokCDdydcS3xgKD1DJFkVojlRSrFp32m+XL6Lg6fTMRkMjE7szC2TBpLQUX5fhWgNHE6YwsPDSU5OJj4+vmZb7969Wb16NVdccQUpKSnOiK+Wm266iZycHP72t7+RlpZGnz59+Omnn4iLi3N628K+qCB/JvXrxvJ9x+tNmgyaxrTEBMIDXLMAnFKKd9dsRYN6F2m1KsW20+c4cC6dPjHRLompJXhr8xYb61Ocp+DGXr0ZKZPUm4y6YCBe/TRQcEVMV7yMbbM0vxDNlVKKf3+9nrnLdmIwaOh61W/z0i2HWbLlMC8+OI2rhia4OUohRGM5XCVv9OjRzJ8/v872Xr16sWrVKpYuXdqkgdnyyCOPcObMGSoqKti5cydjx451SbuiYc9fN5FeHaqeplWPAa7+u3dMFM/OusLme5taan4Rp7Jy7d6IGg0aa4+edllMzV252cwPR47arc5m0CAmIAituY7BbIEGRXaw3zOLRq/QSEmWhGiG1u0+ydxlOwFqkiUAq65QuuIv/1lCZp4swyBES+fwFfjpp59m586d9b7Wu3dv1qxZw7fffttkgYmWJ8DHi88euZGle4+xYPtBMgqKiQ6uWrh2Sr/ueJiMLovFbLU2uI+GRqUUL6iRX16ORdft7mPUDGQWF7soorZhZpde/GvHekrMlej1pPg6ivt7D3FDZEKIhny5fFetnqULKQVWi84/Pl7BQ9eNokd8/QWqhBDNn8MJ07x58/jLX/5i8/WAgAA2bdrUJEGJlsvTZGLGoF7MGOSahVhtaRcUgJ+nByWVZpv7WHSdHu1kfHm1IG9v23NpzlNKEe7nmmGVbYW/hxcfTryOu5Z/i1m31nz+1T+LO3sMYFYX9/4+CSHqd/BUWt1k6YIKowrYvPcMm/eeoVenKF565Go6RAa7OEohRGM5PCTvk08+YejQoezfv7/Oa//5z3/o06cPJpMMGRHNg5eHidlD+tosD2rQNIJ9vZnYq4uLI2u+fDw8mJrQvc4ifhfSlWJm754ujKptGBYdy4pZ93Ff78HE+gcR6ePH2A6d+Piq2fx1+EQZAilEM2UwXHQbdT5Z0lSd1Rg4ejaT+1/8irzCeqpiCiGaNYcTpgMHDtCnTx+GDBnCyy+/jK7rJCUlMXHiRJ588klee+01lixZ4sxYhbgkv75yBAntIuokTUaDhslg4LWbr8ZTkvxafjtqBN4eJptJ0x0DE+kYHOzaoNqI2IAg/jRkAhtueJBtNz/Kx1fNZkJMZ0mWhGjGhveJx3jhmn7nE6X6KudYdUVeYRnfrtrjouhEQ3SlWHniJPfN+44rP/gv13/+BZ/t2k1xZaW7QxPNjKZUQ8uN1rZw4UIefPBBoqOjOX36NCNGjOCDDz4gtgVUzZKV5tue0koz/9u8my+37iWzsBgPo4EpfRK4b+xgukeHuzu8ZulQRiZPLVnG4aysmm3eJhP3DxnEb0eNlEX9xGWR869t8tm0XHuPp/Krf3xVNQJPVS2/ANSbMFWLCg1g8eu/ckF0jZNfUsa8Lfv5YedhCkrLiQsP4YaRfZmcmIDJ6PDz9mbLbLXy2OIfWX78RM0Q6OofW/vAQL685Ubay+9jq+fo+feSE6b09HTuuOMOVq1ahZ+fHwsXLuSKK1xX/awx5KLUtlVaLJgMRgwGueFviFKKgxmZHM/Jwc/Dg5HxcbKon2gUOf/aJp9Ny7Zw/X7+8clKUAplxW6yBODlaWLDB791SWyXKykrn3ve+YacotKaKp7Vay2OTIjjzftmtPgRGv/etJm3N/9cbzVdo6bRMzKCBXfcJr38rZyj599LekTw5Zdf0rt3b3Rd5/Dhwzz88MNMnTqVxx57jLKyskYHLYQzeZpMkiw5SNM0+kRHMat3LyZ17ybJkhBC2HDt2L7Me/luErt1qNpg7zG0gpAAH5fEdbmUUjz+8SJyi0trLXlQ/d9bjiXx7rKf3RVek6iwWPhs126bPyqrUhzIyGRPWppL4xLNl8MJ0+zZs3nggQd44YUXWLVqFQkJCbzyyiusXbuWpUuX0r9/f7Zs2eLMWIUQrVRBeTkfb9/FHxYv4ZmflrPs6PEGS5wLIURzERsVwsQh3W3OX7rQFYO7uyKky7bzVAon0nOw1lMqHaoSqq837aXC3DKX5Tielc1zP62ksLgCzQLYuNQYNY2fk865NDbRfDncn5qWlsbu3bvp2rVrre0jRoxg7969PPXUU4wbN45KmSgnhLgEy4+e4HeLl1BpsVTNj9Jg3r6DxIUE8/FN1xEbHOTuEIUQokGj+nfm1c/W/LLh4sRJgdFo4L6Zw1wa16XafToVo0GzmTABFJdXciojl54xLWdpDl0p/r5sDXN37q261pz/9gy6htIUykSdn5my210o2hKHe5g2bNhQJ1mq5u3tzb///W9WrlzZZIEJIVq/A+kZ/Pb7H6i0WFBUDYOovkifKyjgzi/nUSGLCwshWoD2kUFMGp7wy43Vhffa5//70RtGEeDr7eLILo2jI9db2tSe9zdtY+7OvUBV8qSd/x9QVbDjovXurUoxLCbGxVGK5srhHqY6aw3UY+zYsY0KRgjRsBOZOSzbf4ziikriwoK5un8PAry93B3WZflw6w7QoL7SM1Zdca6gkOXHTjC9Vw/XByeEEJfoz/dNori0gi37zmAwaDU9FLqCe2YM5bapg90cYcOGdu2IVd9kd59gX2+6RIW5KKLGKzdb+ODnHTZf19BQuqpKbLWq4XjdwsMZ2KG9Q8c/mZPLJ7t2sfTYcSqsVnpGRHDngESmJnSXyrKtRMsucSJEG1JutvDMvKUsO3Aco0FD0zSsVp1//bSeF2ZeybUDerk7xEu28thJu8M+DJrGquMnJWESQrQIPt4evP6HWRw4kcbSzYcpKC6nQ2QQ08f2ISYq2G1xpecW8d26fWw9nIQCBifEMHtcP9qH1x3y3KdjFH07RnPoXEa952cNuH3cQDxMRucH3kR2p6RSXGF/ykh10qQZIcLfjzkzpztUIW/DmTM8sGAhVl3Hev7p367UVHakpDDz1ClenTpFkqZWQBImIVqIP81bxoqDJwDOX8SqTswVFgvPzFtGsK8P4xI6uTHCS6OUotJqtbuPrhTlLXRisRCibdI0jb7d2tO3m2O9E862fu8pnnxvMVarqql0d/hsBnNX7OKlX01l4qDaRSg0TeP/7r6G+975luScAjSqrjbV85omJXbn3iuGXFIMFWYLB5MzMFutdGsXTqi/bxN9d45x9DrSLjCAu4cM5Ia+vQn0bnjoZFFFBY8sXIzZaq01ArP6c/7+0GGGxHTg5n79Lids0YxIwiTqVVRazrJtRzmXmY+/rxdXDU4gLjrE3WG1Waeycll64JjN1w0a/HvFJvYlpzFvxwFyS0oJ9fNl9uA+3D5iACF+za+MraZpdA0P40R2js1ptQZNIyEywqVxCSFEa5GSVcCT7y7GbK1dCk7XFQrFn/7zE11eCKNTu9rD66KDA/j2D3fw067D/LDzCAUl5cRFBHP9iL6M7hHv8NpEVl3nw5Xb+GztLorKK4CqxGtKYgJPzRpPsIuuTQmR4TWJnz3vzJxOv/bRDh/3+0OHKTObbR5XAz7esUsSplZAEiZRx/cb9vPKF6sxm60YjQZ0pXjv+81MG96TZ++6Ck8P+WfjaisOnqhZNLA+uoIjaVkcTcuqOXFnFZXw/tptLNh1kLkP3Ey74ADXBeygOwYl8vyyVXb3ual/HxdFI4QQzY9SiopKC16epkteRHX+un02rxvVW79evZenb7uizuu+Xh7MHtGP2SMu/2b/b9+uYsHWA7W2WXXF0j1HOXQug7mP34K/C+bgtg8KZGyXeDaeOlszbO5CRk2je2Q4fdtFXdJx96SlYdC0eo8JVZ/xidxcysxmfDw8Lid00Uxc0sK1ovVbu/sEL366gkpzVfeyxaqjnx/DvGTrEV7+3P7NbVuXV1zG0ZQs0vOLmvS4JRWVDo2BvviUrStFVlEJz363vEnjaSo39O/DhK6d0ahdzdV4/nt9YdIVtA+yvfK2EEK0Vpk5Rbz2yWquvPctJtz9JhPvfZvXP11NZq7j15dNB87YnSdq1RWbDpxuinDrOJCUXidZurDds1n5fLlxr1Pars/fpk0kzM+35vpSzahp+Hp68v+unXLJCalRc+w2WuYwtXySMIkaSine/X6TzVKhSil+2HSQ1OwC1wbWApzNyuPxjxcz/vn3mf1/n3PV3z7kjje/YvuJpln0rlNEyGUv5GrVFVtOJnEmO69JYmlKJoOBd66bzp8njq+13tKwjrF8fNN13DLANcMYSivNlJnNLmlLCCEakpyWx13P/I/vVuyhrLzq3FRaXsn85Xu4+5nPScnId+g4ugPXDd1OQtUYC7YewGinRrmuFN9u3ueUtuvTLjCABffdxp1DB+Dv6QmAt8nEjQP68v39t9EtIvySjzk6Ps5m7xJUJUqDO7THyyQjc1o6+QmKGucy8zmZkmN3H03TWL3rBLdPGuSiqC7N8ZRskrLy8Pf2YmC3DngYnV/F53RmLrf9+ytKKyprDX3Ydzad+9+dx1v3zWBsr86NamNyn+68tHgNZZW2x0o35FBqJvHhzW8emslg4K7BA7hzUCJlZgsmowFPF/zclFIsOHCIj7bt4lhWNgC9oiK5f9ggrumZcMlPGoUQoqm8+N5SikrK6/QOWXVFYXEZL72/jDl/uanB4wxMiOFsRp7NXiajQWNgd+esNZSaV2i3dwsgs6DYKW3bEuHvxzMTx/H0lWMpM1vw9jA1qvdncreuRPv7k1VSUm/ipCvFA0MvrUCGaJ6kh0nUKKlo+Am7waBRUlbhgmguzeGkDG7711xu/Mf/+MMHP/DQW/OZ9KcP+Ga987v7/7lgLaUVlXUuDLpSKKV47qvlmBuoBtcQX08PXrp+Emh1u/YdPdl7GJv3r7umafh6ergsWfrrijU89eNyjp9PlgAOZ2byu0VLeHXtRqfHIIQQ9TmVnM2+Y6k2kw2rrth9+BxnU3MbPNYN4/vbnMNUfaybr0i83FDtCvH3tdvDBBDo4541BKuvN40dKudlMvHJ7OsJ8fGpNbS8etjfk2PHcGWXLo0LVjQLzfsOSrhU+7BAjA0sUGyx6sRFh7ooIsccT8nm3te/4UhyVq3t+cVlvPz1aj5dYXuxusZKzytiy9GzNi9sCsgtLmPj4TONbmtyn+58dM/1DIz7pVStl8nItQN6Ymrg5+ZhNDCkU2yjY2gtNp4+y9xdVcn0hT+56vuKD7buYEdyiusDc8CZ/DxeWr+OGV98zqwvv+D1LZtIL27aOXNCCPc5kZTV8E4O7te1Qzh/un0iGtRKXqr/+/c3jaN3J8erwl2Kqwf2sNvDZDRozBja2yltu1K38DBW3X8vf514JaPiOjKofXtuS+zP0rvv4kHpXWo1ZEieqBHo581VQ7qzYvtRmye5AF8vJgzs6uLI7Htz4QbMFqvNp2jvLN7EzJF9CPJreE2FS5WcU9DgEDmDppGUnd+odnRdsftMCrmFpTw6fjjxkaFUWCxEBPjh4+mBr6cnX2zdi6rnM9A0jRuG9CXYt+m//5bq8117MdqpbGQ0aMzdtZfBsR1cHJl9Cw4f4o/Ll6FBTez7MzP4z44dvD/9WsbGx7s1PiFE43l6OnZr5mjF2uvG9iUhNoIvV+1m66EkFIrBCbHccuUA+nd13lpRI7rHMbhLDLtOpdS5PhsNGgE+XtwxdqDT2nclf09Pbkvsz22J/d0dinASSZhELb+dPYadR5PJLSytlTRVd1s/f89kvJpRWfHcolI2HTxjN2mx6DrLdh7lxrFNfyJzZDiBrhQBjSibuv7QKV5asIbUvMKabREBfvxhxlimDegBwB+njiGtoIjVh0/WLC5Y/ff4hE48OXXsZbffGh3KyLQ7UdeqKw5lZLowooYdyszkj8uX1bnx0M8vAPzg4oWsuedeov2bX/l4IYTjBvWKxdPDSKXZ9lBuL08TA3s5Pmqgd6doXrx/alOE5zCDQePt+6/l+a9WsHzvMRTUrIXUJSqMV++8mqhgf5fGJMTlaj53vqJZiAwJ4LNnb2POgk0s/flwzWJ3id068OC1IxiU0LyGdWUXljTYw2M0GMhy0sTS7u3DiQ0L4pydniaT0cCEPpc3hnnD4dP8+uOFdeqFZxWV8NTcJehKcc3AnniaTLx123R2nklhwa5DZBQWExnox6yBvRkc30EKGFzEkfUwmtuaGZ/s2Y2tn6ICzLrOl/v38cSIUa4MSwjRxAL8vJk9eQBf/riD+p7raMCNUwbi5+Pp8tgula+XJ6/edTWP545my9GzmC06vWKj6BcXLdcl0aJIwiTqiAj25/l7JvPHWyaQXVCCv48XoYG+7g6rXqH+Dcdl1XXCAvyc0r6mafz26lH88bOfbO5zx9iBhPhf+mrmSin+uXAtKNurk7+ycB2T+3fHw2hE0zQGd4phcCfnVDxqTaYkdOP9n7fbHMZp0DQmJ3RzcVT2rTt7xm6vmK4Uy46foEdYBH6engyLiZFStkK0UA/fNJrcghKWbjiM0WhA6QrNoGG16kwd25sHbmxZD0Y6hAY1agFcIdxNrqbCJl9vTzp6X9oTLIvFyuZ9Z0jLLiAowIcxiV2c+hQsPMiPYQkd2X482eZaEgbNwKRB3S+7Dauus/HgaQ6dzcDTZGJ073gSYiNrXp+SmEBxWSX/+n4t5efLYlfHcvvYATx29eVd2A4kZzQ49ymvpIwtx5IY27PTZbXRVt0yoB+f7dxNmdlSJ2kyaBr+Xp7cmNjHTdHVz+5aKarqz/HsHH79ww8ABHh58ethw7hv0CB5kitEC2MyGXn+kWncPHUQP60/RE5+MeEh/lw9rjfd4iIbPoAQoklJwiSazOrtx/jXp6vIKypD0zSUUnh5mrh/5nDunDbEaTdtv7l2FPf839egUW+PwX2ThxAacHk9ZAfOpPOHDxaTkV+MyWBAR/H24k0M7hbDK/dfU9NzNHtEX6YOSGDZnmOk5BUS7OvN5MTuRAZd/vjsrMISh/bLdnA/8Yt2gQF8fNN1PPDtQvLLy6sqRqmqQgohPj58dONMwnybV6/q0A4dWH7yRN1epvPJ0sW/XUUVFby8fj3FlZU8PnKkq8IUotUpLzeTkpyL0WggNi4MowuXaEjoFEVCpyiXtSeEqJ8kTKJJbNxzimfe/qFm6Fh1tbaKSgvvfLMRFNx1zVCntN07Lpr3fzubFz5fTlJWfs12Hy8PfjVlGHdfNfiyjpuclc+Db86jvNICVBWPqLb7ZAqPvv0d/3vylppS7H7enlw3vOl6JSICHRtGGO7gfqK2AR3as/7R+/nh0FF2nEtBQ2N4XAxTe3RvlkPZ7howgCUnjtd9oYFJfO9s3cpt/fsT4Sf/ToS4FOXlZj75YB0/LdpFWVnVOoVh4f7ceOsIZs4egqGBNYaEEK1H87srEC2OUoo3v17/S/mbenz4/Rauv7I//k5apG5A1w58//zd7D2VSlJWPgE+XgzvGYeP5+VP3P989S4q6hmyBVVV1A4nZ7Lx4GnG9XXOonR9YqPoGB5Mcna+zXviED8fRnTv6JT22wIfDw9u6N+HG/o3r+F39RnaIYanRo/hXxs3/FISvZ6epfosPHyY+wdf3oMDIdqiygoLTz3+BUcOpdQaDpuTXcy7b64gOSmHx/7g2qpzQgj3kYVrRaOdPJfNmdTceqv5VKswW9mw66RT49A0jcQuHZgxvDcT+ndtVLIE8NP2ww0uurdsx7FGtWGPpmk8fe140GzfFD957Tg8jEanxSCalwcHD+GrG25kUteuhPn4EurbcDERg6aRWSLDNoW4FEt/3MPhg+dszh384ftdHD7YPBe3FkI0PelhEo2WX1zW4D4GTSOvqOH9mpPScrPd1626orC03KkxjOnZibfvubbBdZhE2zG0QwxDO1RVQiwoL2fwu+/arPYHVfP6ZDieEJdm8YJddl83Gg38tHg3PXs3r8WthRDOIQmTaLTo0MAG99GVol14w/s1J+3DAjmXXWDzdaNBo2NksNPjGNurM6N7dGL3mRQyCooJ8/dlUOcYTC6ceCyapyBvb67o3Jk1p07ZLTk+o4ck1kJcirS0fLujJqxWnZTkXNcFJIRwK7njEo0WExVMv27t7U6ADfTzYlT/llX6+oYx/e1W9rPqiutG9XVJLAaDxqDOMUwb0INh3TpKsiRq/G7kSDyMRgw2/q0+MHgwUf6XX61RiLbIz8/+fFuDQSMg4NLX1xNCtExy1yWaxO9vm1DvTVv1V3+840o8PVpWh+YNY/vRKzbS5o3o3VcNpmv7cBdHJURtCRERfHnjjXQLC6u13dfDg9+PGsUfRo92U2RCtFwTJ/ex+xBQ1xVXXNXbhREJd0nJLeDNJZt4/JPFPPPFElbtP4HFqjf8RtGqaErZ63RuXQoLCwkKCqKgoIDAwEsfHma16hgMmiwCacOh0+m89vla9p1IrdnWMTqEX984hvGDuroxsstXWl7JnB82893mA5RVVM1pah8ayF1XDSYqyJ81u09QWm6mc/tQrh3Vh3ZhLWvYoSuUmy0s23uUo6nZeHoYGd+zM/3j2snvURNTSrE/I4NTubn4e3kxqmNHfDwaV/ikKTX2/NuayWfT/GRlFvLAnR9QWlpRp/CDwaARFx/OOx/dh4eHFN1pzT5fv4tXFq1Do2ptScP5CqVdokL5z4PXN2qtRdE8OHr+lYSpARaLlcU/7WXB9ztJPpeLyWRg5Ihu3HLjMHoktHNyxC1TUnoeadmFhAT40K1jRKu4MS6rNJOclY+nyYiflye/ffN7jp3LwmjQ0JWq+h4VPHHjWG69cqC7w202Nh45wx/n/kRReQUmowGlFFZdMSC+PW/ePYMQPxnS0lZIUmCbfDbN06kTGbzwp3mkpeZjPH/+0nVF3/6xPPfi9YSESDGV1mz1gZM89vGiel8zGjS6Rofz7e9uaxX3OG2ZJEz1uNSLksVi5c/Pf8f2HadqTf40GjWUguefvZaxoxOcGLFobpRS3PmPLzmanGmz5Pj/PTKD8YnOWZupJTmcksktb32J1arXWUfKaNDo0T6SL39ziyz+2EZIUmCbfDbNl64rdu04zZFDKRiNBgYP7Uw3eVjqkIpKCwqFdyOX+HBUeaWF4rIKAn29mmQKwK3//pKDyRl2q5B+8OD1DJe1EFs0R8+/LWtSiYstWLirTrIEYLUqNA1e+udiBnwZR0CAt3sCFC6361gKh85mwPlFQ2vRqtZO+u9PW5s8YbJYdVbvOc53mw6QmlNIWKAv04f3YtqQnnh7Ns9f4w9Xb0cpVe+iu1ZdcfBcBluOJzEqIc7lsQkhhCMMBo3BQzszeGhnd4fSYqzeeoy5i7dz6GQ6AJ1jw7n16kFMG9vbKb0xJ1Oz+fCHrazadRyrrvA0GblmRC/uv2YYUSEBl3XMgtJy9iel293HZDCw7tApSZjaiOZ5p9UMKKX47vsdNsuKKgVms5VlKw8we9Zg1wYn3Gb9vlMYNK1mTPuFp36lqv7dHDydTn5xGcH+toebWaw6Px86S2ZeESEBvozsE4+XjSdi5ZUWHnv3e7YdTa5qWynOZeez52Qqc1fv4oPHbyA0wLcpv81G03XFygPHG1z4d/m+Y5IwCSFareKicn78fidLF+8hP7eEsIgAps4YwLRrB+Lj6+mSGKxWnZ+3n2T7rjNYrTo9u7fjinE98fZu+p6f/3y7iY+/+7lWsaTT57J58b1lHDiexpP3TWzSpOnA6XQe+L9vsVisNdebSouVhZsOsHbPST555mY6hAdd8nHNFqtD+1U6uJ9o+SRhsqGsrJL0jEK7+2iaxvHjGS6KSDQHZosVpVdVx7n4lK9xvtNJQaXZ9kl05c5j/OvLNeQWltZs8/fx4jfXjWL2uP519v/39xvYcewcQM3QgOpE/mxGHs9+spQ5v7nucr8lpzBbrXaTJaj6HkoqKl0UkRBCuFZOVhFPPPQJGWkFVM9+KCmt4D9vrWDp4t3837t3ERjk3IddKal5PPXcPM6l5mE8vxzF4p/2MufDNfz9uVkM6Nd0vSMHT6Tx8Xc/A9Qaxlb9n9+v2sfogV0YNbBpeuuUUjz30RLMZmudYXNWXVFQUsY/v1jNW7+ddcnHDvH3Iczfl5ziUpv7WHSdXjGRl3xs0TJJWXEbTKaGK99omiYVctqY8CA/UHWTpWra+T/VFfUutnbPSZ56/8dayRJAcVkFL89dzbx1e2ttLyqr4LuN+22Oobbqii2Hz3Imo3ktoOhpMhIR2PCE6M6RoS6IRgghXO+Vvy8kK6OQWlPFVVUCkZyUw5uvLHFq++XlZp54+mtS0/OBqp4m6/ly2CUllVWJVErTXTu+W7EXo505qUaDxrzlu5usvd3HU0jKzLd/fTxwhrQc+w+/62M0GLhldKLNZUU0wM/Lg6kDZFHwtkISJhs8PU0M6N/R7oR0q1VnxHCZ3N+WRAQ5VhXp+LmsOtuUUrz+7Tq773t7wSYqzJaarw8nZTjU5b/z+DmH4nIVTdO4ZaTti021WUP7uCgi96gwWziUmsnhtEwZuiFEG3IuKYfd20/XJCgX062KDWsPk5NV5LQYVq07TGZWYZ2y6FB1PbJarMxbuLPJ2jt6OsPuyAKrrjh2JrPJ2juZmtPgPgo4nX55SeE9EwYxqHOHmgeh1YwGDaPRwCt3XI2vV/NZukE4lwzJs+PWm4eze29Sva8ZjRrt24UwfKgkTK52JiWH+cv3sP1A1c9mcJ+OzJ6USHyHsAbe2Xi+Do75NhnrPos4eCaDc1kFdt9XVFrB5gNnmDCgat0qR2tYNsdal3eMGcDqgyc4dC6z1hPA6nlYf5w+lnbBlzcht7mrtFh4Z83PfLF1L8Xnhx0G+Xhzx4gBPDh2aL3/PoQQrceRgykN7qN0xdHDqYyMcE613fUbj6Fptq8PVl2xZv0RHn/kqiZpz9uB5KEpK+b5OJis+Fxmm54mE+89MItvNu/ji417SM4pwNNk5Kp+3bh7/CB6dGg+w/GUUqTkF2LRddoHBeLpwCipplJhsfDTkWNsOHMGq1L0bxfNdb17EezTupYNkYTJjsGDOvG7xybzxlvLgaqJ7AZD1YT/6KggXnn5xpoxwcI1lm08zN/mLEHTqHmSdS49jwUr9vLcw1OYMqaXU9sf1D0Wk9Fgd5VvD5ORgd1j6mzPLbI9FrrWfhcM1+vVMRIPk7HBCagDunZw6NjOUFZpRkOrU63Px9OD/z50A++v/JlvtuynqLwCgIT2ETxw5VCu6tvNHeE6ncWq8+jcRWw+mVQrUSwoK+ed1Vs4lp7F6zddI+XUhWjFHP39duY9RFl5ZYMP0yorLPZ3uATjh3bj0Mk0m20aDBoThjXdeX9Un3iMBgNW3fb1ODTAhz6doi+7DU+TidvHDuT2sQOx6joGTWtW6y4ppZi38wAfbNhOcm7VA9lAHy9uGdqfh8cNs1lMqqkcy8rm7m/nk1lSglHTUMBPR47yfxs28daMa7iiS+upLikJUwOmX53I8KGd+XHJPk6dycLL08Sokd0YPbKbQ/OcRNM5k5LD3+YsqboJveCEXJ04/f3dpSR0iqJTjPN6moIDfLh2dB8WrK9/XpGmacwa05dAv7ql5qMdLG8aFfrLfgG+3swc0Zv5NuYxGQ0aA7vG0KWd83vXLqSUYvG2Q3y2ehcnUrMB6BUbyV1XDmbSwO41FxRfTw+emDaGX08aSWZhMV4mE+EOzG1qyZYdPM7GE2frfU0Byw+dYP3x04xPaD0XEiFEbf0HxqMZNJSdIWoeHkZ69a37cK2pdO4UwcHDKVit9cegaRpxHZvu2jF9fB/+t2gbxaUVdYYBGjQNTw8j108a0GTthQT4MntcP75Zs6fe5SsA7p02DI8mulczGprfA/LXVmzkww07ag0ZLCyr4IP129l1NpUP77rOab1NRRUV3PHNPPLLygCwXnCPUmmx8Mj3i1h05+10jwh3Svuu1vx++s1QREQgd985mr/9ZRZ/fno648f2kGTJDeYt34O9BzuaRpNOKLXldzePZ2SfeICaCa7Vf4/p14nHbxxb7/u6xYTTtUO43e8hNMCH4b1ql9l+4rqx9O9ctVBi9Zyg6jHVHcKD+Mc9Uy//m7kMSile/mY1f/l8OSfTsmu2HzmXxVOf/MTbP2yu8x4Pk5EOoUGtPlkC+Gb7Prtzt4wGjW+273dhREIIVwuLCOCKq/rY7GnSNI2p1w4gINB5w5ZmTE20mSxB1bl81vSBTdZeUIAPb/35BoIDqr4no0GruTb6+njy+tPX0y6iaRdmfuKGsVw9oucF7RkwGDQ0De67ehg3X5HYpO01J0fSsvhwww6g7rKQulLsOHOO+bsOOK39hYcOk1taWitRqqao+vf1yc5dTmvf1aSHqQVTSjWrrmFn27b/bIMTSrfv/+XJflZuMT+sOcDxs5l4moyMGtSF8cO6Nfppk5eHidd/O5MdR5JZvPkgWXnFRIYEMH1ULwYlxNr8mWiaxpM3T+Dh1+ejo9c7bOHJW66oM7/Fx8uD9x+bzfKdx5i/cT+puQWEBvhy7YjeTB/WC19v16zlUW3ToTN8s3EfUHtsfHUP2EfLtzG2Tyf6d2rv0riaizM5eXZXhrfqijM5eS6MSAjhDr99choZ6fkc2JtcM5zfYNTQrYpBwzrzwK+bZu6QLZ07RXDvHaP57/82omlarWp9mgajR3Rj4oSmHcbePT6S7978Fau3HmPnwSR0XdEvoQOTR/XExwnrPnmYjPz1nincNXkIS7YeIa+4lOjQQK4e3pN2YU2bnDU33+zYj9Gg2b0v+nLrXm4ZWne5kqaw8sRJu69blWL58RP8Y8okp7TvapIwtTDZ2UXMX7CDpUv3UVhURmioP1dP6891MwcT6MQnVc2CA5UNqndZsu4g/3hvWdVislQllss3HaF9ZBD/fnY2HaKCGxWKpmkM6dmRIT0vbQ2LQQkxvPvE9bzy1RpOpPzSOxMTEcTjs8fWFHu4mIfJyNXDenL1sJ6NirspfL1hr92TtNGg8c2GfW02YQry9SazqMTm6xoQ7FN3yKYQonXx8fXk1bfv5OdNx1jx0z6yswqJjApiyvREBg/v6pJ5jHfeOpKOsaF88c1Wjp2oWjcyKjKQ2TMHMWvGIKfMofLyNDF1TC+mOnlO8YU6tw/j0VmjXNZec3AqK9dusqSAs7n5Tmu/wmK1ORSyWqW19VSHlYSpBTl3LpffPv45hUVlNeODc3KK+XzuZlasPMhbb9xOaKi/m6N0niF94kjNLLB7oz60bxy7D53j73OW1nqt+slaRnYhj704jy9fv6fJxjVfqkEJMXz1l9s5fi6b9LwiQgN86R0f1WJ6C4+cy2ywp+9IctOVjm1pZvTvyWsrNtVee+UCCpie6P7EVwjhfEaTgVHjejBqnPvW6xk/pgfjx/SgqLgcq1UnKNCnxVxvhG0B3p41VWdt8WvCqoQX6xMVya6UlHqH5EHVFIKekRFOa9/VZA5TC/KPfy2ulSxV03VFZkYBb7y53E2Rucb1kxLtdjIpVbXP3MXbbT65s+qK1MwC1m8/4aQoHaNpGt1jIxjbrzN9OkW3qIuXtwNVdy6umNeW3DC4L5EBfvUu4Gg0aMSGBDGjvyRMQgjXCvD3JjjIt0Vdb4RtU/ok2E2WjAaNa/o5L1G/JbGf3fZ1pbhrUNPNkXM3SZhaiBMnMjhyJK3eBeigKhHYtPk4WdnOWwTP3TrHhvPnhyZj0LRaN6NGg4ZB0/jTg5OJ7xDGz7tP2/ycqvfftPOUK0JulSYmdrNb1MCgaUxMbJ0lwx0R5OPNZ/fdQNeIqupTRk2r+bx6tYvk0/tuwM+raeedHUrN5LXlG3lx8Wr+t2U3+aXlTXp8IYQQzctVvbrSNSK03odzBk3Dy2TijhFNV5XwYp1DQ3n2ivFA1XWuWvV/Xde7F1O7t557gbb7GLiFOXosvcF9lFKcPJlBRHjrXAwUYNrY3iR0iqpauHb/WRQwpE9HZk8eQJfYcMwWq90nHgC6gkqzmTU/H2PBsj2cOJuNt5eJK0YkMHvqAKKbuIpPa3PT2ES+Wr+XCrOlzmdt0DT8fTyZOaKPm6JrHjqGBrPg0dvZlZTKzjMpaBoM7RRLv5im7U0sqajkD9/8xLqjpzEaNDQ0rLrO/y3dwHPTr+D6wW375yCEEK2Vp8nIR/dcz6/nLmJ/SkbNNcCi64T5+/LWLdOJDQ12agx3DRpI59BQPti2gy1JSSggISKcewYPYlbvXq2qN1NTtgbat0KFhYUEBQVRUFBAYKBrb4orKy1sWHuELRuPUVFhoUu3KKZdk0hkdJBD71++4gD/fOWHBvd79V83M2hgfCOjbdlufuK/JKXarkKmAV07RnDiTFZN5SKoWlTP08PEv5+bTZ+EtlmwAKqGeKblFKKUIjossE7VPoDdp1J47L2FFJZVnH+6VXWjHhrgy5yHZ9EjtvmsgN6a/frzhaw9etrmQ4I5t1/L+B7NY70nd55/mzv5bIQQl0spxZ7kNDYcP4PFqtOnQxQTenTGw+jaedpWXUdXyuXtNpaj519JmFwgNSWPJx+bS0Z6wS+lRc93of7291O4+tqGx3jm5BRz063v2B1q5uPjwfxvfou3E0p3tiTzl+3h//67yubrBmwvJmjQNAL8vVjw7gN4eTXPzzG/qIwDJ9IA6N0lmpBA3yY5rq4rvlm9m/8t20lGXtXQztBAX26+cgB3ThlSJ3EqqzSzdOdRdp0434PSPZarBnR3+sriosqx9Gxmvv0/m68bNI1e7SP55uFbXRiVbZIU2CafjRBCuIej51+5s3Eyi8XK0098QVZWIUBNwlP99xuvLqFdhxAGDu5k9zj/v707D6+ivPs//pk52QkJJGGVQICwyU5EBEFBFllEQUGprdW6VC2gVh/rVpG2KrXF+qu2UtzAx/1xAVSUTdkqooCEsMgSIAQIyB5CAlnOzO+PQDSSOQkhOXNO8n5d17kkZyaHb8Yw9/mcued7x8dHa8igTlqwaEOZockwpGtH96z1YUmSrhnURd+mZWj56u0y9OOCbp7TYbVe3Ugdy84rsx2mZdvKzjmlL1du1bDLO/qx6vLlnSrQP95cos+/2qQiryVJ8nhMDe3dXvffNEDRkeGVfm3btvX0Gws1e3npRe6OHM/TtNlfaePO/frb70aWWuk8MixUo3t30ugAnn539MRJ/d9/1+njbzbpWO5JNYmL0Zg+nTXqkk5B35hi0aZ0n+3dLdvWhr0/6MDxE2oYU3O7ZwIAUN1o+lDNVizfqn1Zx2Q5rLZtmobef3tlhV7rnolDSqbbnVk74cx/B17RUbf8uu/5F1wDhHhMPXX/1frD7YPU4oI4ScVh6dKU1pr6yLU66hCWzvB4TK3fnOWfYiuoqMire//+keb+d2NJWJIkr9fS5yu+18RnPlRBYVGlX3/Nlt1nhaUzbFtamrpdX67ZVunXd8Oew9ka+8wb+s/nK7XncLZOnCpQetYh/fWDxbrln+/pxMl8t0s8L3kFBTJU/vzwvIJCP1QDAEDNFdwfsQaBb75Ol8djyvuTN7k/ZVm21qzaoaIir0LKWRcoIiJUf336eq1Ly9SChRt05GiuGjWI0dChXdS+XZMadXPd+QrxmBo1uKtGDe4qr2XJNAwZhqGc3Ip1D/PHgoLn4otvtyptW9khzrJsbdqxXwtWbtFV/Sp3VezDpWk+r1aYhqEPlqzT4J7tKvX6bnhoxlwdyckrdX/PmT9t2XNQU2cv0+RfDHanuCrQqkG8iqyyzytnRISGqBFXlwAAOC8EpmpWVOh1XMDyDNsuvlJQXmCSitfv6da1hbp1bVFVJdZ4P51GFh0VruQWDbQ985Dj/xev11JK5+b+Kq9CPl62wecCdYZh6OOl6ysdmHZm+V4x3LJt7dx3pFKv7YZNmT9oQ+YPjtst29an327S/df0U0xUhB8rqzpDO7fVlLmLlVdQWOYVU49haFT3CxVZjQsXAgBQGzAlr5olt23sMzAZhnRBYlzANhioaQzD0I3X9HT8f2Kahho3iFG/nsl+rsy3/YdyfLZLt21b+89jDa7oyLByJ3dFR1bt2kHVKS1jX7k/T6HX0uY9B/1ST3WICgvV09ddKeMn6zyd4TEMXVA/VvcM6uNSdQAA1BwEpmp25fAuxVeOHN692bY0ekxP/xZVyw3p2143jb5Ykkot+GYYUr26kXr20WvLbKXtpoT6deRrxqVhSPH16lT69Ydc7Hs1cNMwNLRXh0q/vr+ZpuHzPrUzQjyBNfXyXA3u2EYzbx2ji1slljwXFRaqGy/ppnfvGqd6UZEuVgcAQM3AlLxqFhMbpUeeGKUnJ30kw5S8p5s/GEZxWOrTr62uqkBb8fNhWbbWrtmphfPX6/DhE2rYMEZDhnVRl67Na+V9T4Zh6K4b++mynsmatWCd0ncdVGREqAZc0lbDLu+o6DqV7zZXXa7q11GpW/Y6brdtaeRlle9WN6LPhXp93iodOnbirKl5pmmoblS4rru8S6Vf3996tS1/SmWd8DB1SGzkh2qq10Utm+m1ls10/OQp5RUUKq5OlMIqML0XAABUTNAEpqeeekpz585VamqqwsLCdOzYMbdLqrB+/dvr+Zdu0ftvr9TX/92qwkJLSS0TNGpMT105omtJp7vqkJ9fqMmPfaBV3+6Qx2PI67Xl8Ria/3ma+l3eXo9OGqXQ0Nr55urCNk10YZsmbpdRIUMuaa/3FqzV9j2Hzmor7zENtWgSp+GXVv4KUJ2IME1/cKzuf36Oduw7LI9pyjCkIq+lRvXr6rl7Rik+tvJXsPytRcP6uqxjS331fUaZ92YZkn5xebcadX9PTGSEYiKD834sAAACWdAsXPvEE0+oXr162rNnj1599dVKBaZAWRzQtm2/XdmZ+synmv95WpkLtRqGNOb6Xrpz/CC/1ILzk33ipJ56dYGWrdlearpZ326t9PgdV6pe3fOffmXbtlZt3q1V32fKtm11a3OBendKKtU4ozJO5hcqdeteFRR51SYxQU0TYs+71vIczzul3/77Q32/+0BJw4wznQAHdU3WX28ZHnQrkgezQDn/BiKODQC4o6Ln36AJTGfMnDlT9913X1AHJn85euSExl33fMk0wLKEhYXo/Tn3qU6dcFmWrc2bsnQ8O08NG8WoVXLwT1eqifYdOq61W/ZIttSt3QVq2qD6w0dleS1LL8/5Wm/P/04n839cD6h3pyQ9essgNY6v3n+HhV6vvlyXrk9Wfa8jOXlqFh+ra/t0Uq+2tXM6qptq2/n3XHBsAMAdFT3/Bs2UvMrIz89Xfv6Pi1MeP37cxWr8L3XtLp9hSZIKCoq0IW23Tp0s1MsvfqEf9meXbGud3FAT7h+qTl0SfbwC/K1JQoyaJFzodhkVMmXmIs0pY0Hcbzft0q1PvqM3Jv+qWqf6hXo8urJHO13ZI3jWj0LNV9vHJgAINoHVCqyKTZkyRbGxsSWPxMTa9ca/sNBbof2+W7VTT076qFRYkqQd2w/qwXve1Mb1e6qjPNRwWzMPlhmWJMlr2Tp8PE9vzltdpX/nwWMn9PKnKzXh/32ke5+frXe+WKucvIotVgz4S20fmwAg2LgamCZPnizDMHw+Vq+u/BuqRx55RNnZ2SWP3bt3V2H1ga9d+/IbGhiS5n+2rsxttm3LsmxNe35BFVeG2uDTrzaWatv+c5Zla86yDeUu7FxRX363TVc9/Kpe+nilvt64S/9dv1PPvrtEVz38qtK2Z1XJ3wFUhdo+NgFAsHF1St6ECRM0btw4n/skJSVV+vXDw8MVHh54LaL9pUVSA3Xp2lwbN+wuc2qex2MouU1jbd20z/E1LMvWlu/3aVfGIbVISqjOclFBR47lav3mvbJtqWPbJmoQX9ftksp04IjvxXYlKScvX4VFXoWFnt+paNueQ3p4+lxZll2qIYYtKe9UoSb8v1ma8/Stql8FjTGA81XbxyYAFWfbttZu3asl36XrZH6hWl0Qr+G9Oyg2mvHMn1wNTAkJCUpI4E14dXrosat17+9e15EjJ0q1ozZMQ40axarPpW217fv95X7Kf+jgcQKTy/JOFugfLy/SwuXfl7TKNg1Dl1/SRg/eNUQx0YHVUjouJkqmYcprW477RIaHKrQK1gx694u1klTmYrWWbSsvv1Aff7VBNw9lkWgACBaZuw5p3bpMyZY6d0lUUssGbpfkV8dOnNQDz89RWnpWcbdaQ7IsSy+8v1x//M1gDe8dHPcz1wRB0/QhMzNTR44cUWZmprxer1JTUyVJycnJio6Odre4ANaocaz+89ptmv3han0+N1XZx/IUFxet4SO765prU7Tm250VmhJVr17wrMFTExUVeXX/Xz7Qpm37SgVfy7a19Jtt2p11VNOn3Kjw8MBZV2jEpRfq/S/Lnu4pFa8fdXW/jlXSrW5Z2o4y11s6w7ZtLU/bSWACgCBw9Giupjw1R2tWZ5R6vmu35nrs8VGKj6/57/ts29YDz8/Rxh3Fs4C81o8fPhYWeTX5lXlqUC9aPTuUv1A7zl/QBKZJkybp9ddfL/m6e/fukqTFixerf//+LlUVHOrVq6Nbbrtct9x2+VnbevVJVmRkqE6eLCzjO4vXamreIkGtkhtWd5kB7cjRXKVv/0GhoR51aNdUERH+DSZLVm7Thi1l34djWbbSdx3UvKWbdM2Qrn6ty5cLWzbWwIva6Ms12/TzTO4xDUVHhetXVRRgiorKb3BSUIF9AKCm27PjoBZ+tFqH9merXny0rrimh1pf2NTtskrk5xfqgd+/pT27D5+1bf363bpv4v9q+iu3KSqqZk9rTd22V2npzvffGoah1z75hsDkJ0ETmGbOnKmZM2e6XUaNExkZpt/8doBe/OfZjR3OfPD/2/EDa+2aNcey8/T8i4u0ZPnmkis7kZFhuu6aFN1yU1+FePzTN2XuF+tLFl8ti2FIn36xPqACk2EY+sudwxX3zlLNWpqmIu+Pn461a9FQf7pjmBqfw/1XhUVeGYZR5jHv1KqJvtm0y/Eqk8c01KVV+U1QAKCmsixLLz39qeb871cyT59HDUkfzViuASO76f6/Xq+QUPcX8160cKN2ZRwqc5vltbVv3zHNn5em0dfW7BkDS75Ll8c0S11Z+inLsrV6827lnixQncgwP1dX+wRNYEL1GT22pwxTmvHSUuXl/rg2SFx8tO55YKgu7p3sYnXuyc3N18QH3tLerKOlpsGdPFmgt979Wvt/yNZjf7jKL2Hyh8O+GyjYtnTwcE6113GuQkM8+sNNV+i3o3rr2027lF/oVdvmDdSuecWuWFqWrU+Xb9S787/T9j2HZEjq0SFRN424SL27tCzZb9wV3bRiQ4bz69i2xvTvcp4/TeAr9Hr1340ZyjhwRHXCw9S/S2s1jK35U1cAlO/9l5Zqzv9+JUmyvKXfhC/5dJ3q1ovS3Y9f40ZppSyYnybD0FkzE86wbWn+5zU/MP10sXdf8guLCEx+QGCCJGnUdT017KpuWrVyu7KP5alh41j1uKilPH66ghKIZn/ynfbsPVrmPV62pEWLN2nUyO7qdGGzaq+lQVy09uwrHdx+yjCkhLjAfWNcr26khvRqf07fY9u2nnx1vuYu31RytdOWtHbLHq35frfuu7G/fjG0hySpT6ck3TQkRW8sWCPTNEqOk8c05LVsPXzjFUpqHFeVP1LAWfH9Lv3xzXk6nJMnz+ljMOWDxbq2dyc9PGZAlTTXABCcCvIL9f7LSxy327atz975RjeOH6TYOHfvWT52NM8xLJ1x9Fief4pxUaum8bJ8NE2SpNjoCMUGWMOnmqr2vhvGWcLDQ9X38vYacU0P9ezVulaHJUn65PNUnw0xPB5Dn81L80stIwZ2dgxLUvEnblcN7OyXWvzly1XbNHf5JkmlP2k8cxz++fYSZWQdkVQ8/e/eMf307Pir1b3NBQoN8SgiLET9urTSKw+O1Zj+gTNVsTqk7dynidNn68iJ4jcR3tPt1S3b1odfr9ef313kboEAXLVxzS7l5vhexLuoyKvvvtrqp4qcNW4SK9PHGn6maahx41g/VuSO4X0u9PlBl2kYuq5/1+Lueah2XGECHBw6fMLndq/X1v4Dx/1Sy4BL2uqDNt9p8/b9ZwUn0zSU1CxeQ/t39Est/vJ/C9b6vG/LNA3NWpym3/+yv6Ti0NS/W2v179baj1UGhhc/XyHLtsv8VNa2pY+/3aTbh1ysFg3r+784AK4rqOD0roJTFduvOg0f0U2rV+103G5Ztq4a2d2PFbkjpk6EHv/NEE16+XMZhlFq7DcNQ22bN9DNw2v2tMRAQiwFHMSUs8ipaRqKq++fqQuhoR49N2mMrujTrtQnb4Yh9b2otV748w2KCKCW4udj976jev7NJcWL8xZakmWXOZnda9navPMHFyoMLNm5p/T15kyf97h5TEPzvtvix6oABJKkNo2KOzyUt19b95vj9O3bTikpSWVeZTJNQ506N1P/AbVj/aGhl3TQtAfHKqV9YslzsdERumXExZr+0PWKiuDeJX/hChPgYNiQznrn/W8cp8JZlq3BA/13VadOVLgm//4qTbi5v9Z9v0eS1LFtUzVuEOO3GqrbR4vWaeqMRTINQ/bp426cnsJte+wfWzeqeOwPD+MUlnPS9zQbqfjqW3Zu+fsBqJkaNYtTSt+2Wrsi/ayGD5Jkekw1b91QbbtU/z255fGEmHry6ev18kuLNffTtcrPL5IkhYZ5NGx4V/32zisUGgDd/PwlpX2iUtonKu9UgU4VFCk2OoJpeC7g3Qbg4LpRF+mzBeuVnZ1X5jS4rp0T1bNHS4fvrj4JcdEaeOm5NVAIVPsOZGvWonX6JjVDJ07mK+vgccmQvMbpsHR6P1uS4T07NF3Wo/ZNv/u5uLp1FOoxVVjGm6AzvJalCxJq/px/AM4m/mm0fn/Di8o+klsqNJkeUxFRYfrD1HEBs4RIWHiIxk8crFtuvUxbt+6TbKlNm8aKrlt7GxxERYRxRclFRFTAQVz9OvrXs79U2+TGpZ43DGnAZe311OTrfN6YCt+++m6Hxv1+ht7+ZLW27TqofQeOy7AlWacfP1FylE/nVtOQosJDlbH7kF58d7nStu712aCjJosKD9Xwi9rL4+N30WOaGnFRzQjZACqnUbM4vfDRPRr5y96KON2GOjQ8RIOvTdELs+5Ry/buT8f7uTp1wtW9e5K690iq1WEJ7jPsWvQu4/jx44qNjVV2drZiYmrONCZUv63b9mvz1v0KDTWV0j1JDWvQNDg3HDico7H3vKqiIq/KOgHZUvHHOT/PAIZkGZLn9G1NIR5Ttl18BaVzm6Z65oFrVD8mqtrrDzQ/HM3RL6a+rWO5J8tcvPeh6/rrxsvdvUma868zjg38zeu1dCovXxGRYfKw5ABqsYqef5mSB1RA2zaN1bZN4/J3DGCHj57QB3PW6LNFG5Rz4qTi46I18squGj2iu+r6eR2H2YvS5LWsMsNSCUvSz8bxenUjlXPilM58Z9FPppVs2r5P9/31Q7325C9r3fzuRvXr6s0HfqGps5Zqcdr2kgYQzRJiNX54Hw3n6hKAn/B4TNUpp7ERgB8RmAAfcnPztW7tLhUUFCm5TSM1S4x3u6RK2bvvqMY/+Layc06W3I914GCOZrz1leZ/uVH//tuNqhfrvysz36Zl+FxXypDOClOGYSihXrRyTpwqs32217K1NeOAVq7L0KXdW1VpvcGgaVyM/nHbSB0+nqvdh7JVJyJMyU3iA+aeBAAAghWBCShDUZFXr728RLM/XK2CgqKS57t2a67/eXikmjSt515xlfDnv3+q4z8JS2dYtq19+4/puf8s0p8eutpv9ZzrTGBDxa2xM/YeLnPK2Rke09Dib7bWysB0RnxMHcXH+KfdPQAAtUHtmrcCVNDfp3yq999dWSosSdL6tN2693czdfhQjkuVnbut23/Q5m37HYOG17K1bMVWHT7ie6HeqtSjY/MKN8zwmIZM09Dk3w0vNQWvLJZtK+9UQVWUCAAAIInABJxly+YsfbFwQ5nTvizL1rFjefrgvW/8X1glbUnfX+4+lmVr244Dfqim2OjBXctdQzE8IkQx0REa1u9CzXz6Jg3s3U6N4uv6/B7DMNSiaVzVFQoAAGo9AhPwMwvmrZfH4/xPw7JsfTY3NWjaWIdWsAOSPxcCbNowVk9MHC7TNEq1wz7z53t/3V9LXr9X818ar8fuHKrk5g0kSWOGdPN5T45t27p6QOfqLR4AANQq3MME/MyRwydkWb6nfuWeyFdRkRUUq42ndGshwzB8BrzIyDB1bNfUj1VJg/q0V6vEBH04P1Vfr90hr2Wrx4WJGjO0uzq2KXs9kDFXdtfS1enatH1/qfuxzvx8439xmZo0YIFWAABQdQhMwM/ExdWRaZry+rhfpk6dcIWEBMcF2gbxdTWofwd9sfT7MjvTGZLGjOyhiIhQv9fWKjFBD94+qML7R4SF6oVHx2rGrJWatWidcvLyJUnJiQm6eVQvDbykXXWVCgAAaikCE/Azg4d20ZxZaxy3m6ahYSN8Tw0LNA/8brCOHDmhNesy5TENeS1bHtOU17I08PIOuuXGS90uscIiwkN197h+un1MHx06ekJhoSGKr0dXOAAAUD0ITMDPtO/QVFcM6qjFX2w8q/GDx2Oobkykxozr5U5xlRQZEaapf75ea9bt0oLFG3X0WK4aNojV8MGd1LFd06AKf2eEhniYfgcAAKodgQkowx8eHan6cXX08ew1KizwljzfsVOi/ueRq5SQ4LtbWyAyTUM9uyepZ/ckt0sBAFQhr9er9LUZOpV7Ss3aNlV8k/pulwTUKAQmoAwhIR7dPWGwbrq5n1LXZqigwKvWyY3UIinB7dIAACjx2Stf6I0/v69Dew5LKm6C0/vqizT+n79Rw9MdRgGcHwIT4EN03Qj1vay922UAAHCWd6bM0muPvV3qOdu2tfLTNfp+5Vb9e9UzatAs3qXqgJojONp8AQAAoMShrCOaOendMrdZXkvZh3P05p/f93NVQM3EFSYAZbJtWytWbNPsOd9p27b9CgsL0WX92mn06It0wQXMjwcANy16Y5nO6kz0E1aRpYVvLtP4529VWESYHysDah4CE4Cz2LatZ/8xT599tk6maZSs3zR7znf6dG6qnn5qrHr0SHK3SACoxQ7sOijTY8qyvI77FJ4qVPahHKblAeeJKXkAzjJ//np99tk6SSq12K1l2SostDRp0kfKO71oLADA/2Li65a5GPlPmaah6HpRfqoIqLkITADO8sGHq+S0NJNt28o7WaCFizb6tygAQIkBN/aV5bUct5seU72v7qnI6Eg/VgXUTAQmAKUUFBRpx46DvqbGyzQNbdiwx39FAQBKadGhmQbddFmZC48bpiFPiKlfPT7GhcqAmofABKCUsgbfsphmxfYDAFSP+1++S1fdNVimp/jt3JnzcoNm8frr/MeV3L2lm+UBNQZNHwCUEhrqUaeOF2jT91mO8+Mty1ZKCgMxALgpNCxU9/z7Dv3q8TFa+ckanTxxSs0vbKaUwV1kmnwmDlQVAhOAs9xwQy89PumjMreZpqHY2Cj1v5wFfQEgEMQ1rq/hdwxyuwygxuLjB8APCgqKlL55n7Z9n6X8U4Vul1OuSy9tq1tvvUyS5PH8eJowDCk6OkJ/e+Z6hYXxeQsAAKj5eMcDVCNvkVfvvLJMs9/+WieOn5IkRUWH66rrL9av7hoQ0KHjV7/so0t6tdbHn6zV1i37FB4eqosvbqURI7qpHm1qAQBALcEVJqCaWJalpx96X29NX1wSliQp70S+3p/xX/3p3rflLXJecDAQJCc30i9/0VvtWzdS+vq9mjltsX415l964R/zdPDAcbfLAwAAqHYEJqCarPrvNn31xaYy23Pbtq01X6dr2cLAXstoV8Yh3f2bVzTv01QVFBRJkvJPFWrunO90962vau+eIy5XCAAAUL0ITEA1+ezD1TI9zq23TdPQZx+s9mNF527q058oNzdfXm/p1Of12srJOal//HWuS5UBAAD4B4EJqCZ7dx2W5XVe/dWybGXtPuzHis7NjvQftHmTj9biXltpqZnanRm4PwMAAMD5IjAB1SSmXqTKWwO2bkykf4qphB3bD1Rov4wdB6u5EgAAAPcEbosuIMgNHNFNm1J3O243DEODRnbzX0HnKDw8tEL7hYVzGgkUXq+lFWt2aPk323Qqv0itWiToqoGdlRAX7XZpAAAELd7pANXkihFd9MHrX+nAvmPyeq1S20yPqfrx0bpydIpL1ZWve0qSQsM8Kixw7uQXERGqLt2a+7EqODl05IR+/6f3tXP3YXlMU7Zta8nKrZrx3gr9z52DNXJwF7dLBAAgKDElD6gmkVHh+turv1FyhyaSips8nGkCkdS6oaa+dmtAT8mLrhuh0WMu9jmt8LpxvRQZGea/olAmy7L1wF8+UObe4q6FXsuSZduyLFtey9Yz0xZoVWqGu0UCABCkuMIEVKMGjWL1zzfv1JYNe7Vu1Q7Ztq3OPZLUoWuijPJucAoAt/62v44dy9WCz9Lk8RRftTAMQ16vpauu6a6bftPP7RIh6dvUDG3fdchxu2kaeuOjb9SzW5L/igIAoIYgMAF+0K7TBWrX6QK3yzhnnhBTDz46UmNu6KWF89bryOETik+oqyHDOqtFywZul4fTvlq9XR6PedbUzzMsy9Z3G3brVH6hIip4bxoAAChGYAJQrpatG+q34we6XQYcFBQWqcwVks/az0tgAgDgHHEPEwAEueSkhrLKCUwJ9euobp1wP1UEAEDNQWACgCB35eUXKiw0xLFBh2EYum5Ej6C4bw4AgEBDYAKAIBcTHaHH7x0uwzDkMUuHIsOQundsphtGBm4LewAAAhmBCQBqgP6922ra0zfq0p6tZZ4OTU0axmj8zf317ONjFBbKLasAAFQGIygA1BAd2zbR0w+NkmXZKvJ6CUkAAFQBRlMAqGFM01CYyekdAICqwJQ8AAAAAHBAYAIAAAAABwQmAAAAAHBAYAIAAAAABwQmAAAAAHBAYAIAAAAABwQmAAAAAHBAYAIAAAAABwQmAAAAAHBAYAIAAAAAB0ERmDIyMnTbbbepZcuWioyMVOvWrfXEE0+ooKDA7dIAAAAA1GAhbhdQEZs3b5ZlWZo+fbqSk5O1YcMG3XHHHcrNzdXUqVPdLg8AAABADRUUgWno0KEaOnRoydetWrXSli1bNG3aNAITAAAAgGoTFIGpLNnZ2YqLi/O5T35+vvLz80u+Pn78eHWXBQCAT4xNABBcguIepp/bvn27XnjhBd11110+95syZYpiY2NLHomJiX6qEACAsjE2AUBwcTUwTZ48WYZh+HysXr261PdkZWVp6NChGjt2rG6//Xafr//II48oOzu75LF79+7q/HEAACgXYxMABBdXp+RNmDBB48aN87lPUlJSyZ+zsrI0YMAA9e7dWy+99FK5rx8eHq7w8PDzLRMAgCrD2AQAwcXVwJSQkKCEhIQK7bt3714NGDBAKSkpmjFjhkwzKGcTAgAAAAgiQdH0ISsrS/3791fz5s01depUHTx4sGRb48aNXawMAAAAQE0WFIFpwYIFSk9PV3p6upo1a1Zqm23bLlUFAAAAoKYLinltt9xyi2zbLvMBAAAAANUlKAITAAAAALiBwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOAgxO0CANRMXq9Xq+elauWna1SYX6TW3ZI0+NeXK7peHbdLAwAAqDACE4Aqd3DPYT0y9Ent2rRHnhCPJFsLXl+iVx55Sw+/cY/6XdvL7RIBAAAqhCl5AKqUt8irh4b8RXu2ZpV87S2yZNu2Ck8V6skb/qHN325zuUoAAICKITABqFJff7JauzfvlbfIOmubbdsyDOn//v6xC5UBAACcOwITgCq1Ys4qeUKcTy3eIksr5nwryzo7UAEAAAQaAhOAKnUqL1+W1/a5j7fIkuUlMAEAgMBHYAJQpVp2ai7DNJx3MKQmrRspJJSeMwAAIPARmABUqWG3XeFzuyFDoyYM81M1AAAA54fABKBKJVwQr3tfvEOSZHpKn2IM01D3gZ008u4hbpQGAABwzpgTA6DKDb9jkBq2aKD3npmt1MUbJEkJzeI1euIwjb53uELDQl2uEAAAoGIITACqxUVDuuqiIV11Ki9fRQVFqhMbJcPwcW8TAABAACIwAahWEVHhUlS422UAAABUCvcwAQAAAIADAhMAAAAAOCAwAQAAAIADAhMAAAAAOCAwAQAAAIADAhMAAAAAOCAwAQAAAIADAhMAAAAAOCAwAQAAAIADAhMAAAAAOAhxuwB/sm1bknT8+HGXKwGA2uXMeffMeRg/YmwCAHdUdGyqVYEpJydHkpSYmOhyJQBQO+Xk5Cg2NtbtMgIKYxMAuKu8scmwa9HHfZZlKSsrS3Xr1pVhGH7/+48fP67ExETt3r1bMTExfv/7Ax3HxxnHxjeOj2+BcHxs21ZOTo6aNm0q02Q2+E+5PTaVJRB+ZwIdx6h8HKPycYx8q+7jU9GxqVZdYTJNU82aNXO7DMXExPCPwgeOjzOOjW8cH9/cPj5cWSpboIxNZXH7dyYYcIzKxzEqH8fIt+o8PhUZm/iYDwAAAAAcEJgAAAAAwAGByY/Cw8P1xBNPKDw83O1SAhLHxxnHxjeOj28cH5wrfmfKxzEqH8eofBwj3wLl+NSqpg8AAAAAcC64wgQAAAAADghMAAAAAOCAwAQAAAAADghMAAAAAOCAwOSCjIwM3XbbbWrZsqUiIyPVunVrPfHEEyooKHC7tIDx1FNPqU+fPoqKilK9evXcLsd1L774olq2bKmIiAilpKRo+fLlbpcUEJYtW6aRI0eqadOmMgxDs2fPdrukgDFlyhT17NlTdevWVcOGDTVq1Cht2bLF7bIQhBizyseYdTbGLd8Yv3wLtDGMwOSCzZs3y7IsTZ8+XRs3btRzzz2n//znP3r00UfdLi1gFBQUaOzYsbr77rvdLsV17733nu677z499thjWrt2rfr166dhw4YpMzPT7dJcl5ubq65du+pf//qX26UEnKVLl2r8+PFauXKlFi5cqKKiIg0ZMkS5ublul4Ygw5hVPsas0hi3ysf45VugjWG0FQ8Qf//73zVt2jTt2LHD7VICysyZM3Xffffp2LFjbpfiml69eqlHjx6aNm1ayXMdOnTQqFGjNGXKFBcrCyyGYWjWrFkaNWqU26UEpIMHD6phw4ZaunSpLrvsMrfLQZBjzCobY1Yxxq1zw/hVPrfHMK4wBYjs7GzFxcW5XQYCTEFBgdasWaMhQ4aUen7IkCFasWKFS1UhGGVnZ0sS5xlUCcYsOGHcQnVwewwjMAWA7du364UXXtBdd93ldikIMIcOHZLX61WjRo1KPd+oUSPt37/fpaoQbGzb1v3336++ffuqU6dObpeDIMeYBV8Yt1DVAmEMIzBVocmTJ8swDJ+P1atXl/qerKwsDR06VGPHjtXtt9/uUuX+UZnjg2KGYZT62rbts54DnEyYMEFpaWl655133C4FAYQxyzfGrPPDuIWqEghjWIhrf3MNNGHCBI0bN87nPklJSSV/zsrK0oABA9S7d2+99NJL1Vyd+871+EBKSEiQx+M561O5AwcOnPXpHVCWiRMn6uOPP9ayZcvUrFkzt8tBAGHM8o0xq3IYt1CVAmUMIzBVoYSEBCUkJFRo371792rAgAFKSUnRjBkzZJo1/2LfuRwfFAsLC1NKSooWLlyo0aNHlzy/cOFCXXPNNS5WhkBn27YmTpyoWbNmacmSJWrZsqXbJSHAMGb5xphVOYxbqAqBNoYRmFyQlZWl/v37q3nz5po6daoOHjxYsq1x48YuVhY4MjMzdeTIEWVmZsrr9So1NVWSlJycrOjoaHeL87P7779fN910ky666KKST3YzMzO5f0DSiRMnlJ6eXvL1zp07lZqaqri4ODVv3tzFytw3fvx4vf3225ozZ47q1q1b8mlvbGysIiMjXa4OwYQxq3yMWaUxbpWP8cu3gBvDbPjdjBkzbEllPlDs5ptvLvP4LF682O3SXPHvf//bbtGihR0WFmb36NHDXrp0qdslBYTFixeX+Xty8803u12a65zOMTNmzHC7NAQZxqzyMWadjXHLN8Yv3wJtDGMdJgAAAABwUPMnIQMAAABAJRGYAAAAAMABgQkAAAAAHBCYAAAAAMABgQkAAAAAHBCYAAAAAMABgQkAAAAAHBCYAAAAAMABgQkAAAAAHBCYgADh9XrVp08fXXfddaWez87OVmJiov74xz9Kku69916lpKQoPDxc3bp1c6FSAEBtwdgEEJiAgOHxePT6669r3rx5euutt0qenzhxouLi4jRp0iRJkm3buvXWW3XDDTe4VSoAoJZgbAKkELcLAPCjNm3aaMqUKZo4caIGDBigVatW6d1339W3336rsLAwSdLzzz8vSTp48KDS0tLcLBcAUAswNqG2IzABAWbixImaNWuWfv3rX2v9+vWaNGkS0xsAAK5ibEJtRmACAoxhGJo2bZo6dOigzp076+GHH3a7JABALcfYhNqMe5iAAPTaa68pKipKO3fu1J49e9wuBwAAxibUWgQmIMB8/fXXeu655zRnzhz17t1bt912m2zbdrssAEAtxtiE2ozABASQkydP6uabb9add96pQYMG6ZVXXtGqVas0ffp0t0sDANRSjE2o7QhMQAB5+OGHZVmWnnnmGUlS8+bN9eyzz+rBBx9URkaGJCk9PV2pqanav3+/Tp48qdTUVKWmpqqgoMDFygEANRVjE2o7w+Z6KhAQli5dqoEDB2rJkiXq27dvqW1XXnmlioqKtGjRIg0YMEBLly496/t37typpKQkP1ULAKgNGJsAAhMAAAAAOGJKHgAAAAA4IDABAAAAgAMCEwAAAAA4IDABAAAAgAMCEwAAAAA4IDABAAAAgAMCEwAAAAA4IDABAAAAgAMCEwAAAAA4IDABAAAAgAMCEwAAAAA4+P9lfS/RXfk3ewAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# CREATE RANDOM DATA POINTS\n",
"# from sklearn.datasets import make_blobs\n",
"from sklearn.datasets import make_regression\n",
"\n",
"x_train, y_train = make_regression(n_samples=100, n_features=2, random_state=0)\n",
"x_train = torch.FloatTensor(x_train)\n",
"y_train = torch.FloatTensor(y_train)\n",
"\n",
"x_test, y_test = make_regression(n_samples=50, n_features=2, random_state=1)\n",
"x_test = torch.FloatTensor(x_test)\n",
"y_test = torch.FloatTensor(y_test)\n",
"\n",
"\n",
"#Visualize the data\n",
"fig,ax=plt.subplots(1,2,figsize=(10,5), sharey=True)\n",
"ax[0].scatter(x_train[:,0],x_train[:,1],c=y_train)\n",
"ax[0].set_xlabel('X1')\n",
"ax[0].set_ylabel('X2')\n",
"ax[0].set_title('Training data')\n",
"\n",
"ax[1].scatter(x_test[:,0],x_test[:,1],c=y_test)\n",
"ax[1].set_xlabel('X1')\n",
"ax[1].set_title('Test data')"
]
},
{
"cell_type": "markdown",
"id": "395243cd-b329-45e5-b839-50248d2df9e1",
"metadata": {
"id": "395243cd-b329-45e5-b839-50248d2df9e1"
},
"source": [
"Let's have a quick look at the distributions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "448d903d-d091-4bc8-ba38-729ec1e234a8",
"metadata": {
"id": "448d903d-d091-4bc8-ba38-729ec1e234a8",
"outputId": "a7988939-8f41-4c9c-9a53-16879d197461",
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_train = np.concatenate([x_train, y_train[:,None]],axis=1)\n",
"n_plots_x = int(np.ceil(np.sqrt(data_train.shape[1])))\n",
"n_plots_y = int(np.floor(np.sqrt(data_train.shape[1])))\n",
"fig, ax = plt.subplots(1, 3, figsize=(15, 5), dpi=100, facecolor='w', edgecolor='k')\n",
"ax=ax.ravel()\n",
"for idx in range(data_train.shape[1]):\n",
" ax[idx].hist(data_train[:,idx].flatten())\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"id": "033b5902-c7df-4dfc-9c6a-4e175a4a4c6a",
"metadata": {
"id": "033b5902-c7df-4dfc-9c6a-4e175a4a4c6a"
},
"source": [
"Now let's initialize our Perceptron model, define the type of optimizer and loss we want to use:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dcd27df5-fe85-40e7-b6b5-29a06e290c8a",
"metadata": {
"id": "dcd27df5-fe85-40e7-b6b5-29a06e290c8a",
"tags": []
},
"outputs": [],
"source": [
"model = Perceptron(input_size=2, output_size=1, use_activation_fn=False)\n",
"criterion = torch.nn.MSELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.01) #Check slides for a review on SGD"
]
},
{
"cell_type": "markdown",
"id": "fefbca67-3623-4a34-8dd0-7c6f7e333adb",
"metadata": {
"id": "fefbca67-3623-4a34-8dd0-7c6f7e333adb"
},
"source": [
"Just for curiosity, let's se how bad a naive model would perform in this task"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ae3a9f1-8bc2-4a25-9a51-66685c565a82",
"metadata": {
"id": "3ae3a9f1-8bc2-4a25-9a51-66685c565a82",
"outputId": "7f23211b-4516-491c-9897-de62d0a31c24",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_test.shape: torch.Size([50, 2])\n",
"y_pred.shape: torch.Size([50, 1])\n",
"y_test.shape: torch.Size([50])\n",
"Test loss before training 4665.2607421875\n"
]
},
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'slope: -82.026, r_value: -0.532')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"y_pred = model(x_test)\n",
"print('x_test.shape: ',x_test.shape)\n",
"print('y_pred.shape: ',y_pred.shape)\n",
"print('y_test.shape: ',y_test.shape)\n",
"before_train = criterion(y_pred.squeeze(), y_test)\n",
"print('Test loss before training' , before_train.item())\n",
"\n",
"y_pred = y_pred.detach().numpy().squeeze()\n",
"slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
"\n",
"# # Fit line\n",
"# x = np.arange(-150,150)\n",
"\n",
"fig,ax=plt.subplots()\n",
"ax.scatter(y_pred, y_test)\n",
"# ax.plot(x, intercept + slope*x, 'r', label='fitted line')\n",
"ax.set_xlabel('Prediction')\n",
"ax.set_ylabel('True')\n",
"ax.set_title('slope: {:.3f}, r_value: {:.3f}'.format(slope, r_value))"
]
},
{
"cell_type": "markdown",
"id": "854869aa-777b-4e55-804c-20abcad6d815",
"metadata": {
"id": "854869aa-777b-4e55-804c-20abcad6d815"
},
"source": [
"**Question 1.1**: Can you make sense of this model's output range?"
]
},
{
"cell_type": "markdown",
"id": "0edd1a7e-8911-43c7-97d1-f9862d776bae",
"metadata": {
"id": "0edd1a7e-8911-43c7-97d1-f9862d776bae"
},
"source": [
"Now let's train our Perceptron to model this data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21cdb3da-ca7d-42c8-bdbe-c542ac69ff70",
"metadata": {
"id": "21cdb3da-ca7d-42c8-bdbe-c542ac69ff70",
"tags": []
},
"outputs": [],
"source": [
"model.train()\n",
"epoch = 1000\n",
"all_loss=[]\n",
"for epoch in range(epoch):\n",
" optimizer.zero_grad()\n",
" # Forward pass\n",
" y_pred = model(x_train)\n",
" # Compute Loss\n",
" loss = criterion(y_pred.squeeze(), y_train)\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" all_loss.append(loss.item())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9d48f19-513c-4628-bb70-1e727f57d28e",
"metadata": {
"id": "e9d48f19-513c-4628-bb70-1e727f57d28e",
"outputId": "2ebd2d55-1bc0-4f5b-9e6a-3b9bd6eade0e",
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots()\n",
"ax.plot(all_loss)\n",
"ax.set_yscale('log') # Logarithmic scale on y-axis"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "576ba478-b5c6-42cc-a811-88a4b3ef4915",
"metadata": {
"id": "576ba478-b5c6-42cc-a811-88a4b3ef4915",
"outputId": "d70e301d-6c46-41f5-d90f-bf18328091b9",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss after Training 298.5340576171875\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"with torch.no_grad():\n",
" y_pred = model(x_test)\n",
" after_train = criterion(y_pred.squeeze(), y_test)\n",
" print('Test loss after Training' , after_train.item())\n",
"\n",
" y_pred = y_pred.detach().numpy().squeeze()\n",
" slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
"\n",
" # Fit line\n",
" x = np.arange(-150,150)\n",
"\n",
" fig,ax=plt.subplots()\n",
" ax.scatter(y_pred, y_test)\n",
" ax.plot(x, intercept + slope*x, 'r', label='fitted line')\n",
" ax.set_xlabel('Prediction')\n",
" ax.set_ylabel('True')\n",
" ax.set_title('slope: {:.3f}, r_value: {:.3f}'.format(slope, r_value))"
]
},
{
"cell_type": "markdown",
"id": "77ae39c9-c5ca-443f-bd16-a8e57bfa34eb",
"metadata": {
"id": "77ae39c9-c5ca-443f-bd16-a8e57bfa34eb"
},
"source": [
"This results is not bad, but note that we didn't use any activation function. Now let's see what happens when we add an activation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "406723ed-892d-446b-afb9-25c09aaf50fe",
"metadata": {
"id": "406723ed-892d-446b-afb9-25c09aaf50fe",
"outputId": "e4abeb2f-1a8e-474c-d395-b25f32317154",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss after Training 1798.19287109375\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Add activation and retrain the model\n",
"del model, optimizer\n",
"model = Perceptron(input_size=2, output_size=1, use_activation_fn=True)\n",
"criterion = torch.nn.MSELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)\n",
"\n",
"model.train()\n",
"epoch = 1000\n",
"all_loss=[]\n",
"for epoch in range(epoch):\n",
" optimizer.zero_grad()\n",
" # Forward pass\n",
" y_pred = model(x_train)\n",
" # Compute Loss\n",
" loss = criterion(y_pred.squeeze(), y_train)\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" all_loss.append(loss.item())\n",
"\n",
"model.eval()\n",
"with torch.no_grad():\n",
" y_pred = model(x_test)\n",
" after_train = criterion(y_pred.squeeze(), y_test)\n",
" print('Test loss after Training' , after_train.item())\n",
"\n",
" y_pred = y_pred.detach().numpy().squeeze()\n",
" slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
"\n",
" # Fit line\n",
" x = np.arange(-150,150)\n",
"\n",
" fig,ax=plt.subplots()\n",
" ax.scatter(y_pred, y_test)\n",
" ax.plot(x, intercept + slope*x, 'r', label='fitted line')\n",
" ax.set_xlabel('Prediction')\n",
" ax.set_ylabel('True')\n",
" ax.set_title('slope: {:.3f}, r_value: {:.3f}'.format(slope, r_value))"
]
},
{
"cell_type": "markdown",
"id": "31566549-7a6f-4c39-ac04-c2f797cbb503",
"metadata": {
"id": "31566549-7a6f-4c39-ac04-c2f797cbb503"
},
"source": [
"**Question 2**: what is happenng to this model? Why do we have so many predicted outputs with 'zeros'?"
]
},
{
"cell_type": "markdown",
"id": "8a5559d1-41bc-4a2c-8013-a842364fac68",
"metadata": {
"id": "8a5559d1-41bc-4a2c-8013-a842364fac68"
},
"source": [
"Let's see what happens when the data and target are normalized"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f294e85c-d49a-4d4a-a2c7-b3ee8cea230a",
"metadata": {
"id": "f294e85c-d49a-4d4a-a2c7-b3ee8cea230a",
"tags": []
},
"outputs": [],
"source": [
"#Now normalize the data\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"\n",
"data_train = np.concatenate([x_train, y_train[:,None]],axis=1)\n",
"data_train = scaler.fit_transform(data_train)\n",
"data_test = np.concatenate([x_test, y_test[:,None]],axis=1)\n",
"data_test = scaler.transform(data_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcd65c51-535b-4ddd-ad15-58a7d31e802b",
"metadata": {
"id": "bcd65c51-535b-4ddd-ad15-58a7d31e802b",
"outputId": "1f6dffbd-3342-43d8-91ad-03dfe8c06ee9",
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_plots_x = int(np.ceil(np.sqrt(data_train.shape[1])))\n",
"n_plots_y = int(np.floor(np.sqrt(data_train.shape[1])))\n",
"fig, ax = plt.subplots(1, 3, figsize=(15, 5), dpi=100, facecolor='w', edgecolor='k')\n",
"ax=ax.ravel()\n",
"for idx in range(data_train.shape[1]):\n",
" ax[idx].hist(data_train[:,idx].flatten())\n",
"fig.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cd0f06c-76a8-4f27-a23a-1bc7b85df2bf",
"metadata": {
"id": "4cd0f06c-76a8-4f27-a23a-1bc7b85df2bf",
"tags": []
},
"outputs": [],
"source": [
"x_train,y_train = data_train[:,:2],data_train[:,2]\n",
"x_test,y_test = data_test[:,:2],data_test[:,2]\n",
"\n",
"x_train = torch.FloatTensor(x_train)\n",
"y_train = torch.FloatTensor(y_train)\n",
"\n",
"x_test = torch.FloatTensor(x_test)\n",
"y_test = torch.FloatTensor(y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a2405b-a272-4751-a6d0-a4e365cbb544",
"metadata": {
"id": "63a2405b-a272-4751-a6d0-a4e365cbb544",
"tags": []
},
"outputs": [],
"source": [
"del model, optimizer\n",
"model = Perceptron(input_size=2, output_size=1, use_activation_fn=True)\n",
"criterion = torch.nn.MSELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8da01d5c-e312-4b6a-8909-98a1f2957e35",
"metadata": {
"id": "8da01d5c-e312-4b6a-8909-98a1f2957e35",
"tags": []
},
"outputs": [],
"source": [
"model.train()\n",
"epoch = 1000\n",
"all_loss=[]\n",
"for epoch in range(epoch):\n",
" optimizer.zero_grad()\n",
" # Forward pass\n",
" y_pred = model(x_train)\n",
" # Compute Loss\n",
" loss = criterion(y_pred.squeeze(), y_train)\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" all_loss.append(loss.item())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ddb9006-fdd8-4732-bc42-851991445d47",
"metadata": {
"id": "7ddb9006-fdd8-4732-bc42-851991445d47",
"outputId": "54214e32-c7fb-40ce-b38c-80b82151d9af",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots()\n",
"ax.plot(all_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c74713da-ea8c-4069-aba0-009d7d812bb4",
"metadata": {
"id": "c74713da-ea8c-4069-aba0-009d7d812bb4",
"outputId": "f98266f0-171a-480d-c982-d6b23978e062",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss after Training 0.001270745531655848\n",
"0.18696567 0.80532324\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"with torch.no_grad():\n",
" y_pred = model(x_test)\n",
" after_train = criterion(y_pred.squeeze(), y_test)\n",
" print('Test loss after Training' , after_train.item())\n",
"\n",
" y_pred = y_pred.detach().numpy().squeeze()\n",
" slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
"\n",
" # Fit line\n",
" print(y_test.numpy().min(),y_test.numpy().max())\n",
" x = np.linspace(y_test.numpy().min(),y_test.numpy().max(),len(y_test))\n",
"\n",
" fig,ax=plt.subplots()\n",
" ax.scatter(y_pred, y_test)\n",
" ax.plot(x, intercept + slope*x, 'r', label='fitted line')\n",
" ax.set_xlabel('Prediction')\n",
" ax.set_ylabel('True')\n",
" ax.set_title('slope: {:.3f}, r_value: {:.3f}'.format(slope, r_value))"
]
},
{
"cell_type": "markdown",
"id": "8ab32742-1b39-40ec-b7f2-474f5459665a",
"metadata": {
"id": "8ab32742-1b39-40ec-b7f2-474f5459665a"
},
"source": [
"Now that we know how to implement a Perceptron and how it works on a toy data, let's see a more interesting dataset. For that, we will use the tree height dataset. For simplicity, let's start with just few variables: latitude (x) and longitude (y)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4baf219-69cf-43cb-931c-cfdfef792bdb",
"metadata": {
"id": "d4baf219-69cf-43cb-931c-cfdfef792bdb",
"outputId": "db4e7126-bc86-4aca-df98-ef1b0f847cec",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(66522, 3)\n",
" x y h\n",
"0 6.894317 49.482459 2.73\n",
"1 7.023274 49.510552 10.75\n",
"2 7.394650 49.590488 21.20\n",
"3 7.396895 49.590968 20.00\n",
"4 7.397643 49.591128 24.23\n"
]
}
],
"source": [
"### Try the the tree height with Perceptron\n",
"# data = pd.read_csv('./tree_height/txt/eu_x_y_height_predictors.txt', sep=\" \")\n",
"data = pd.read_csv('./tree_height/txt/eu_x_y_height.txt', sep=\" \")\n",
"print(data.shape)\n",
"print(data.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "28b39994-e920-4aad-a50f-61adabce41f7",
"metadata": {
"id": "28b39994-e920-4aad-a50f-61adabce41f7",
"tags": []
},
"outputs": [],
"source": [
"#Normalize the data\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"data = scaler.fit_transform(data)\n",
"\n",
"# data[:,2] = 2*data[:,2]-1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2529ed9-00a6-4391-a072-00f68280a634",
"metadata": {
"id": "a2529ed9-00a6-4391-a072-00f68280a634",
"outputId": "5a490014-9ff4-4d03-c215-056ff3622e73",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(array([5.287e+03, 2.447e+03, 1.626e+03, 1.406e+03, 1.308e+03, 1.327e+03,\n",
" 1.483e+03, 1.721e+03, 1.957e+03, 2.022e+03, 2.131e+03, 2.314e+03,\n",
" 2.726e+03, 3.055e+03, 3.551e+03, 3.900e+03, 3.956e+03, 4.169e+03,\n",
" 4.049e+03, 3.536e+03, 3.120e+03, 2.561e+03, 1.923e+03, 1.473e+03,\n",
" 1.069e+03, 7.340e+02, 5.460e+02, 3.720e+02, 2.410e+02, 1.860e+02,\n",
" 1.010e+02, 7.200e+01, 7.300e+01, 4.300e+01, 1.500e+01, 6.000e+00,\n",
" 6.000e+00, 6.000e+00, 2.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
" 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
" 0.000e+00, 1.000e+00]),\n",
" array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,\n",
" 0.22, 0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42,\n",
" 0.44, 0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64,\n",
" 0.66, 0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86,\n",
" 0.88, 0.9 , 0.92, 0.94, 0.96, 0.98, 1. ]),\n",
" )"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Inspect the ranges\n",
"fig,ax = plt.subplots(1,3,figsize=(15,5))\n",
"ax[0].hist(data[:,0],50)\n",
"ax[1].hist(data[:,1],50)\n",
"ax[2].hist(data[:,2],50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c21aafd-7367-4172-9f9a-6cf04eef0f20",
"metadata": {
"id": "0c21aafd-7367-4172-9f9a-6cf04eef0f20",
"outputId": "582f295c-a215-43c7-817a-058a184a96e7",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train.shape: torch.Size([46565, 2]), X_test.shape: torch.Size([19957, 2]), y_train.shape: torch.Size([46565]), y_test.shape: torch.Size([19957])\n"
]
}
],
"source": [
"#Split the data\n",
"X_train, X_test, y_train, y_test = train_test_split(data[:,:2], data[:,2], test_size=0.30, random_state=0)\n",
"X_train = torch.FloatTensor(X_train)\n",
"y_train = torch.FloatTensor(y_train)\n",
"X_test = torch.FloatTensor(X_test)\n",
"y_test = torch.FloatTensor(y_test)\n",
"print('X_train.shape: {}, X_test.shape: {}, y_train.shape: {}, y_test.shape: {}'.format(X_train.shape, X_test.shape, y_train.shape, y_test.shape))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4608af6-afc4-4201-90df-196f7dec7b7f",
"metadata": {
"id": "f4608af6-afc4-4201-90df-196f7dec7b7f",
"tags": []
},
"outputs": [],
"source": [
"# Create percetron\n",
"model = Perceptron(input_size=2, output_size=1)\n",
"criterion = torch.nn.MSELoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4751f542-6105-459f-8de0-1386860c6dcc",
"metadata": {
"id": "4751f542-6105-459f-8de0-1386860c6dcc",
"tags": []
},
"outputs": [],
"source": [
"model.train()\n",
"epoch = 2000\n",
"all_loss=[]\n",
"for epoch in range(epoch):\n",
" optimizer.zero_grad()\n",
" # Forward pass\n",
" y_pred = model(X_train)\n",
" # Compute Loss\n",
" loss = criterion(y_pred.squeeze(), y_train)\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" all_loss.append(loss.item())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "681f2273-6361-4aff-9837-4a84f921cee6",
"metadata": {
"id": "681f2273-6361-4aff-9837-4a84f921cee6",
"outputId": "2cc7708c-efcf-486f-af3f-e36e8ca340a7",
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,ax=plt.subplots()\n",
"ax.plot(all_loss)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43652372-29f9-4e83-bd5f-cde9aff3a5bd",
"metadata": {
"id": "43652372-29f9-4e83-bd5f-cde9aff3a5bd",
"outputId": "6052d6f9-26a5-4c10-c1ed-c6343f558e49",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss after Training 0.021724332123994827\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model.eval()\n",
"with torch.no_grad():\n",
" y_pred = model(X_test)\n",
" after_train = criterion(y_pred.squeeze(), y_test)\n",
" print('Test loss after Training' , after_train.item())\n",
"\n",
" y_pred = y_pred.detach().numpy().squeeze()\n",
" slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(y_pred, y_test)\n",
"\n",
" fig,ax=plt.subplots()\n",
" ax.scatter(y_pred, y_test)\n",
" ax.set_xlabel('Prediction')\n",
" ax.set_ylabel('True')\n",
" ax.set_title('slope: {:.3f}, r_value: {:.3f}'.format(slope, r_value))"
]
},
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"source": [
"**Question 3**: As we can see, the Perceptron didn't perform well with the setup described above. Based on what we have discussed so far, what is wrong with our setup (model and data) and how can we make it better?"
]
},
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