SVM_Intro.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM_Intro.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOfNllFCv174Sat/aEW5gZd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tawfiqam/MI564/blob/main/SVM_Intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6BgImDZjNSo"
      },
      "source": [
        "#Support Vector Machines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agaKyoH0jNVr"
      },
      "source": [
        "SVM separates data points using a hyperplane with the largest amount of margin. That's why an SVM classifier is also known as a discriminative classifier. SVM finds an optimal hyperplane which helps in classifying new data points. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0UHxVF2jV4g"
      },
      "source": [
        "SVM constructs a hyperplane in multidimensional space to separate different classes. SVM generates optimal hyperplane in an iterative manner, which is used to minimize an error. The core idea of SVM is to find a maximum marginal hyperplane(MMH) that best divides the dataset into classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EBWLsMuPvSQ4"
      },
      "source": [
        "Next, we'll define support vectors (where the name of the classifier originates), then discuss hyperplanes and margins."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDUkKADPm0ny"
      },
      "source": [
        "##Support Vectors\n",
        "Support vectors are the data points, which are closest to the hyperplane. These points will define the separating line better by calculating margins. These points are more relevant to the construction of the classifier.\n",
        "\n",
        "Look at the figure below.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iibHuBO1m47D"
      },
      "source": [
        "##Hyperplane\n",
        "A hyperplane is a decision plane which separates between a set of objects having different class memberships."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnS_XV2Am-D6"
      },
      "source": [
        "##Margin\n",
        "A margin is a gap between the two lines on the closest class points. This is calculated as the perpendicular distance from the line to support vectors or closest points. If the margin is larger in between the classes, then it is considered a good margin, a smaller margin is a bad margin."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZmAXk3vjXXA"
      },
      "source": [
        "![Slide2.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGQXfbCWkRrc"
      },
      "source": [
        "Figure 1. Showing support vectors, hyperplanes, and margins. Note how SVM uses support vectors (marked as red stars and green triangles) are used to anchor a hyperplane. The bigger the margin between the classes, the better. The margin represents the amount of distance between the two classes. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNsOh32OxWvI"
      },
      "source": [
        "##How SVM works\n",
        "\n",
        "The main objective is to segregate the given dataset in the best possible way. The distance between the either nearest points is known as the margin. The objective is to select a hyperplane with the maximum possible margin between support vectors in the given dataset. SVM searches for the maximum marginal hyperplane in the following steps:\n",
        "\n",
        "(1) Generate hyperplanes which segregates the classes in the best way. Left-hand side figure showing three hyperplanes black, blue and orange. Here, the blue and orange have higher classification error, but the black is separating the two classes correctly.\n",
        "\n",
        "(2) Select the right hyperplane with the maximum segregation from the either nearest data points as shown in the right-hand side figure.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3kD4NPTLkR-W"
      },
      "source": [
        "![Slide3.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCALQBQADASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9U6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK5LSfip4X1z4g6z4IstWgn8TaRBHc3tgp+eKNwpUn/AL6H511tABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVzfjj4i+G/hvpZ1HxLq9vpNp2aYnLfRRkn8BUSnGnFym7JG1GjVxFRUqMXKT2SV2/RI6SivL9F/aP8AAvjDQde1DwvrcOuzaRaSXc1pErJIVVSeA4HXFa/wX+KEPxg+H9h4ngs5LCO6LDyJSCy7SR2rKGIpVJckJXdr6dtj0sRk+YYSlOtiKMoKElF8ys05JtKz11Sb2O5oooroPHCiiigAorjfiN8YPCHwmhs5fFetQ6Ql2xSDzEdi5AyeFBrqdN1G21fT7e+s5luLS4jWWKVOjKRkEVEakJScYu7W/kdU8LiKdGGInTahO9pNOztvZ7O3WxZoooqzlCiiigAooooAKKKKACiivMdD/aK8F+IPjdrXwrs9RD+KtJtFvJ4uNhU7TtU92AYEjtg0AenUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV84eOv20dG0Pxdd+HfDHh7VPGV7YsVu5NNjDRxn0znnmuWviaOGSdWVrns5Xk+PzmpKlgaTm4q72SS7tuyXzZ9H0V8s/ET9qzVZvhPoPirwxYXGiXdzrSaXd2mq24EkZym4YIPZuor6Xt9Uhj020nvLiGBpolbMjhQSVBOM0UcTTruSg/ht+KujfMMixuWUqdXExtzynG27vC17/fpZu5fopkU0c8YkjkWSNhkOpBB/Gore/trxmWC4hnZfvCNwxH1xXTc8HleumxYoquuoWslwbdbmFpx1iEgLD8OtWKYmnHdBRXz14y/bG0Hwf8YI/A8ml3VwguorOfU48eTFLJtwpOe24V9CKwdQynKkZBrno4iliE3Slezsz18dlGOyynRq4uk4Rqx5ot9V3/HqLRRRXQeOFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWP4wvNU0/wrq1zolot/rEdrI1nbMwUSTbTsBPpnFbFFAH5MfBn4K/F7wp+174s8T2mqrqnxG0tLLU9b0mSXENzb3iRtLbhu/lebxn/AJ5iv1lRiyKSu0kZKnt7V8q/Cj/lIT8cP+wHpX/oiCvqygAooooAKKKKACikZgoyTge9LQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFeeeIPiNe6X8YvDPhC3topbXUrS4ubiZmIePYBtwO+cn8q2pUpVm1Dom/kldmNWtCik59Wl827I9DooorE2CiiigAr4q8R6DafGn9uR9B8Vq11ouh2W+106Vv3cpUFt23oRk8+uK+1a+dvjt+zPrPi/wAeWHxB8A68nhzxjaRrExmyIZ1BP3sA84OCMYI615OYUp1FTnGPMoyu13Vn99t7H33B2Pw+BxdeNer7KVSlOEKmvuSlaz01S3V1tc6zxx8IfBvhDwj4r1zQ/Dtho+o/2Lc2/mWMKxAoY2yCq4BPPWvDPhhr3ijw/wDsgeHJ/DGtaX4dka4lW61TVDlYIt5yVXBLHrwATXpHhL4W/GXU7XxKfH3jHTdRN5pU1hZWNihSASOjASSYRehI7E8Vxmrfsg+K774I+C/DsGs6bD4l8M30l7GJFMlnOWYEBgV7Y/u9686tTqylKrSptJwtbZ/Gn0fbW1z7HL8RgaFJYLMsfCq/bU5OT5px5VCpp7yV0pNJ6WTd7NJoy/2ff2gPFsvxxtfBGteMdO8daXqFo86ahY27ReVIqsQnzIp5wO2Oay/CXxu+J/i74yXVle+LdM8MS22rLbjwrqFsUee3DAExyFME7cnlq7D4a/sw/EHR/jTovjzxTqugXLwW0kFxDpUH2cR5UhCiqgBOTyTiqWt/ssfFD4g+ONIfxh4n0S78P6XqBvIry3tgNQZQ+5UZ9gJ7DluMVhThjeWldSupPS9tLq13f1te/Y9+rieGPr1aUZ0FGVGKcuVO00535I+zUW3aKdlB2s4vcx/i58bPiV/wvLV/D1r4q074f2Vgsf8AZsOq2zNFqRwD/rAjDJJxyccV9k6DNcXOi2Et2Ua6eBGlaM5UuVGSPYmvlr4zfs1/FP4n6xf6T/wkuh3Hgu5njeH7fbB721jUKCqPsJHIJwGA5r6U0Pw7P4V8D2ui6dP59zY2X2e3muScM6rhS3tn9K9PAKvB1XVTtfS+732V3+Gj7H5/xNUyqtluAjgpU1UStJQX92PvSlyxd273i+azvaVj4p+PHizwX8UP2gvEml+LfEFrp+i+H9FltLEXG7Y946NhhgHlS4/Kvav2F/iKPGnwZi0ua5W5vNAnewZweXQHcjfTDfpSfBv9kTRdC0fU7n4g6XpPibxJqN/Ldy3LR+aqKx4RSwz/APrq78Jf2eNT+EHxu8S63osthB4I1iFQunRlhJA4A6Lt24yPXvXHg8PiqNeNaolad+a26b1V/TY+mzrNchxmT1clw1WV6EYOm3bklKGk+S2t580nrvZH0BRRRX05+FhRRRQAUUUUAFFFFAGX4oXVG8N6ouiGFdZa2kFm1wf3YmKnYW4PAbFflp8Of2UfGOi/teeKH0jxVv8AipoVlaeIZ9Qnkb7Lcy3DxfaYSMfc2SyBeOy1+r9fJ3w2/wCUjXxa/wCxWsf/AG3oA+rLZpWt4jOqpMUBdUOQGxyB7ZqWiigAooooAKKKZNMlvG0krrGi9WY4AoHvoh9FNV1kUMp3KwyCO9OoEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRXnHgf4mXviLx94k0G9s47e3sZilnMhyZQv3s+4yPzqXQfiRdax8S9a0E20aaVYxBku93LOM7h9BxXmU8yw9VU3F/G2lp1V7/AJHpzy3EU3NNfDFSevR2t+Z6FRWHpPjjQddvpLPT9Vtru5jzujjfJ460tt400K8uJoIdUtpJYY/NkVX+4ozyT26GuxYijJJqa18103OR4etFtOD08mbdFYGl+PNA16Qw6bq1rdz/ADYjR+eOvFVdP8aWtjodvc6/qem288gZi1vKTEwBP3SQCePao+tUN1NW73VvvK+q1lo4O/azv16fI6miqek6xZa7Yx3un3Md3ayDKSxHINXK6U1JXRzyi4txkrNGR4u+1f8ACK6x9h4vfskvk4/v7Dt/Wvln/gnnc6RD4L8UiSS3j1/+1pPtW8gTFcDHXkrnP45r69r538dfsT+EvFPii41/RtT1Lwhf3RLXP9kylFlY98AjH4V5WJpVo4iGJox5rJpq9t9bo+7yDMMvWXYvKMwqOlGs4SU1Hms4N6SSabTv02Zz/wC3NcWlz4H8LPZyQyp/b8IdoWBG/cmc474xXM/Hex8M+LvijaaTd3Pi7xJqkGmQk6D4dnSKKy+XJkYvIgywr1i4/ZH8NzfDfTPCC6nfrDZ6iNTe8kfzJZ5htyWyeMhQKT4lfsjeHviF4sj8Rwa1qnh7VWhSC7m02YobpVGBuwRg4rzq+DxFRzfInzOL37Rs/wAe626H1+U59k2Xww+H+sTXsvbWnytfG4craTuk0n8LutE3Zs8F/Z51Lxn4r+BPxQ8MaLrF3a6jZ3Zh0yTULkCSFTgGHfuIDHoMHGTVn9mu3t/C/jDVfDk17rvhf4iPo84ks9XuRNaTy7HKyo+9vun5u3Svb/C/7IHhfwp4L8UeGbbUtQay12RJmkaQ+bBImCrK2ck7hmk+Hf7H/hnwRqOo6lf6rqfibU7yyksBealKWeGF1KsEOTgkMamngcRGVJyV3GNm7+u3VPVa7Hp43irJK8MxjTqNRrSUopU7NvlirybfK43Tbi1e+sZXZ86fCDSW+Hnxk0G2+IN74i0nxjdX7m31NbsXFhqKMcKp+c4znGMelfdXjbxRbeC/COr67duEg0+1kuGz32qSB+JGK8V8DfsY+GvCPi6w1y+1zVvEf9myebYWepTM8Vs2cgqCT04/KvUPi98MoPi94HvPDN3qNzplrdMplltfvFQQdvXocV2YTD4jDYSdOKtLW2vlZXe1/T8z5TijNsqzzNcLU9q3TSSqS5WklzXfJF3kkl0u0ntofm5J8RLfXPhH4vtbrwvr1x4l1nWP7ZttchtCYYgrKVG7OcAJ1Ar9Df2c/iInxP8Ag94d1rcDcG3EFwuckSJ8pz9cZ/Guu0Dwfp/h/wAG2nhqCPdptva/Y9jD7ybcHP1yfzrlPgn8E7D4IaXqunaZqVze2V9ePeLDcAAQFv4V56UYHA1sFVtzc0HFLa1mtvW6udHEvE+V8QZfVpU6LpVIVOaGrlzRa5ZLZcllGLttpoekUUUV7p+RhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfKfwo/wCUhPxw/wCwHpX/AKIgr6sr5T+FH/KQn44f9gPSv/REFfVlABRRRQAVHJcRQsFeVEJ6BmAqSvOfjP8ABez+L2h/Zjql9oepwg/ZtQsJSjI3+0ARuHtWdSU4wbgrvtsduCp4etiI08VU9nB7ytzW87XVzzb9tz4vSfDf4Y2K6Xe+Vqd/qESI0L/Msaks547fLj8a9t8D+MrPxl4O0bXIp4gmoWkdxgOOCygkfhmvyE+N3h/xH4I8dX/hfxFrcmtz6c+FlM7Spg8jG7ocdRXrP7I/we8S/HK8uEPjG+0fw5pLIs8FtcuJWzyFRcgAEDrXxlDN61TGygqd76ct9mj+lc08OsswvC9DE1MYoxptzdTlb5oztZJXv0VvmfqFHIkq7kZXX1U5FOrJ8LeGbLwfodrpOn+Z9mt12q0zl3b3Zj1PvWtX20b213P5hqKEZtU3ePRvTT01CiimecmSN659M0yB24Zxnn0pN67gu4bvTPNfMniT4+f2b+2TofhBbv8A4lbWLWkibvk+0SFWBPuNhH417J8Rln0uTT/Elk5d9Nf/AEiFW4eBvvceoIFeZPHQVOpUgr8js/1fy/Q+lxWQ4jBSw0cRp7eCnH53svnb8TuaKhs7qK+tYriBxJDKodGHQg1NXpJqSuj5ppp2YUUUUxBRRRQAUUUUAFeHSE3/AO15GAu9LDw8CW/us7uP5Cvca8O+HbnVf2mPiPcNyLGxs7ZT9WlJr1MFpCvPtB/i0v1PKx3vToQ7zX4Jv9D3GiiivLPVCiiigAoorxP41fHbUvCPiSw8H+ENKXW/FV8m8Rs2EgU5AY+vIPFZVKkaaV+ui8zixmMo4Ci69d2ivm23skurZ7ZRXhOh+JvitHpviC38Z6TaWlt/Zk00Go6dJjypAhIU8DmpP2dvHqWnwTh1zxVrbbEnl8y91CYscA9MnmojWjJtPSyvrp1seZSzmlVrQpOEo8yk/eXLbltvf13PcqK5fwl8T/CnjuaWLQNds9UliGXSB+R+BqK7+LXg+x8SJoFx4hsotZdgi2Zf5yx6D0zW3NHTXfY9T67hXTVVVY8rdr3Vr9r9zraK5TxN8VPCXg3UIrHW9fs9OvJcbIZnwxz9K6eCeO6hjmicSRSKGVlOQQehpqSlszaNalOcqcZJyW6vqvVdCSivlhvjV8WPFXjPxXp3hWz0h7LRZijG6IVtoA6ccmvVv2f/AItXfxa8KXN3qNgLHULK4a1n8s5jdh1K8Vz0cRCv8N9r69jxcNnmFxWIWGipJtySbWjcd7PyPUaKKK6T6AKKKKACiiigAooooAK+Tvht/wApGvi1/wBitY/+29fWNfJ3w2/5SNfFr/sVrH/23oA+saKKKACiiigClqGtWGksgvbyC0L/AHfOkCZ+ma+fP21Pi/ZeFfgjfwaVqsD6jqc0drEbaYM6fNvLDB/2MfjXsHxO+FPh34ueHZNH8Q2QuITzHMh2ywt/eRuoNflD+0V8LF+DnxMvvC8Gsf2zbwhZY5OdyKwyFYeoBr5vOcXXwtFqMVyy0vfVX8j9r8NeHsrzzMqc6teSq0Xzum4XjJRa+1fva6a9Ln6cfAX4s6T4z+EPhjVLnU7WG6azSKdZZ1VvMRQr8E+or0yw1S01SNpLO5iuo1OC8LBhn6ivyl/ZH+BVn8dvGtxp+p6w9jYafF9pltIWIlnXIBA9BkjJr9SfB/g3R/AWg22jaHZR2NhbrtWNByfcnufet8qxdbGUVOcUorS992vI8nj7h/LOHcxqYfDV5TqyfNy8towjLVLmvq/Rf5G3RRRXun5UFMaaONlVnVWbhQTgn6VBNqlnbyGOW7hikH8LyBT+RNfJv7UXx9j8E/Hb4a2FreA2dnP9o1BY2BVlfCqDj2JNceKxUMLT9pPa6X3s+kyLIsVn2L+qYda8spbfyxb/ABdkvNn15RVEa5pzKGF/alWGQfOXn9auKwkUMpyp5BFdd09j56UJR+JWHUUUUyAooooAKKKKACiiigD57m1KTwqde8TwwM76b4gnjmRVJaSKQIDgY56VYj0+bwu98DZvqN3/AMI809xCc5lkbdkE/TFe7GzgKsphjKs25htGCfU07yY/M8zYvmY27sc49K+Zjk8oqyqemmzaab+en3eZ9M85Vrez9dd0rWXy1+/yPk/wTcaY3xE8FT2Oo2l1dMzpcQ6XpvkQQ5xgM+AWY89R2rp7PwvZw/Blr210syeZf79TNvHiea2DneuepGO1fQcOl2dvzFawx87vlQDn1rP8TeHX1zQZ9Ps76bR5W5juLQ7WRvw6j1FcscjdKlO7UnaVkl1fL/M3/Lrd636HZUz1VqsLJxScbtvonJ/ZS/m7dOp4npeqeCNY+K3h6XwfZwRNHY3K3EttbeSpGwYU8DLDmm+CfDum+ILz4eW2pWEd5BHa3jiG4i3IDk4JBGPzr0bwf8L7zR9eTWNc1065ewI0dsEtlt44g3DHaCQWPHPtXbXMllpcP2icw20can942FwO+K0weV1KiU8RFJuV+WytvF7JtdO7IxebUqX7vDycko25ru+01u0n9rstjgPgrZw6WviqxtLf7JZQavIsMCrhUGxOF9q9Ak1azhvorJ7mNbuQEpDu+Y4614344+OiRNPZeGowCxIe9ZeM+qjv9a8kj17UIdYj1X7VJLfxuJBK7EkkdvpX3OX5POlh405u1lZf10PicfmkK9d1Ire1/wCup9lUVzvhTxjaeJPC0esK4RFQmYE/cYDnNfN/hu8+IH7TXiLWNR07xVdeDvC+nztb2v2EndIw9cEZ9efWvMrVJUqnslG8u3oebjsyjg/ZxhBznU+GKtrbVu70SS7n1NqesWWiwCa+uorSJmCh5mCgk9BVpWEihlOVYZBHevkz4+eFfEPhH4K2OneJNdbxJcRazEbe+lUiQxFlwGz3znv0r1Txx+0Jovwt/snSJ9M1TVtRezjmaLT4PMCIVwCTn1BrJYiPvOeiTS+9JnFHOowrVIYyPslCMZO7u7yclbS6+zpbe57DRXmfg/8AaB8K+LvBOpeJo5ZrK00zIvIblAskRAzgjNZnw5/aW8P/ABF1qbTodO1TTNsL3EdxfQBI5I1BJYEE9hmtvbU7qPNq1f5Hes3wEvZJVl+8+Hz1t+enqev0V4p4d/ar8MeJvGUOh2un6oIJ5zbQ6k0A+zu/TG4HueK9H+JF7Pp3gHxBdWsrQXENjM8ciHDKwQkEH1qHXh7J1o6pG+HzDDYyM5Yeako72Okor8+9P8Ya4vw7XxMPjFfL4jR8p4flmdi5D4AJ3c5HPSvuH4c6lqmseB9FvdaiWHVJ7ZXnVemSOv4jBp0a3tb6Wtb8fQ8XKM/p5tU9nGm4+7zLVPS9tbN2fk9To6KKK3PqgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPlP4Uf8pCfjh/2A9K/wDREFfVlfKfwo/5SE/HD/sB6V/6Igr6soAKKKKACoL6WWGxuJIYzLMsbFEH8TY4H51PRQNOzTPk/wCGv7FtlrniTWvGfxPRNW1fVp5Z00sHMVsrsSNxP3mAPpxWt8Jv2a9S/Z9+NM+o+Gpjf+CdYhaGe2kOJbN8hlOf4lGCM+9fTVFeXDLcNT5XGNpRd79b9b+p91iONc5xarUq9XmpVI8jh9hRXw8q6OPRrXvcKKKK9Q+ECvmr9p34A+JfElre+KfBHi7UtE1SGMyT6eLp0t5wBkkYPyt+HNfStecftBWPiLWfhbq2j+Frb7RrOqAWcTE4WEN96Rj2AA/WuLGUoVaElNN+m/yPp+G8diMBmlGeHnGN5JNySceVvXmT0tbX8tT8fbrxFq9xrx1WfUbmXVhJu+2NKTLuHfd1zX6H/ssfATxRq2i6f4x8d+MNU1WG9gE1npIvHeHYwyGkyeTj+HFJpn/BO3wjF8NJNKvL6aXxTKBJ/a6DCxvj7oTP3efXnivXf2YfC/iXwD8N08J+J4QLvRp3t7e4jOY57cH92y/gK+Uy3K6tCunildNX30v5n77xxx3l+b5TKnkdRRlCXLK8UpODW9NvaN9Haz9Do/hzdSaLd6j4VumPmae++1Zv47ds7cfTH613dcH8SbGbS7jT/FVkhafTWxcIvWSA/eH4YFdrZ3kWoWkNzA4khlUOjDuDX0eCbouWEl9jb/C9vu2+R/N+NSqqOLj9vfykt/v3+ZPRRSZ7d69U8sWiiigAooooAK8R+A//ABMPiR8WNUzuDa19kB9o1zj/AMer209DXiX7Kqm78M+KtVI/5CPiC6mz68IP6V6eH93C15f4V97v+h5eI97FUI/4n9yt+p7dRRRXmHqBRRRQAV8oeLtWtfhb+1vb6/4hUwaRqVp5cN5IMojEEde2K+r6w/FngjQvHWniy17TLfU7ZTlUuEDbT6j0rmrU5ScZw3i76+lv1PFzbAzx9CMaUrThKMo32vF318jjfEXxY8JeKtC1zR9H1u21K/fTLiby7dt2FEbZJNfOmj6/o2j/ALOXhiLVtHg1lrnV5EtkvJCltHIGHzS8HK9OMV9S+F/g34N8Gx3iaPoFnZfbIzDO0cYBdCMFSfTmpG+EfhGTwqPDkmh2smihzIto0YKqx7j0NYVKFSd5XV2kvulf8vxPFxWWZjjmqtaUFNRnHRNr3uW2++zv67Hyd8OZre3/AGmfD32RfD9sptJPMXwwx8jlWyG+Vfm/CqWop4e+Hvjg6hHLofjfS9S1dWVskapZyeYM/wAJwAfftX1t4d+CfgnwnqFpfaT4es7G7tc+VNDGAwzweaSy+CPgfT/EZ1238N2MeqeYZfPEQyGP8Q96iGGqQ5LW0bf3tM8X/VjFvDum5Q5nPmT1tG8UtFaz21TWvkfL3xqtNC0XxxrvjCK60PxPDMsaXvh/VeLqElFA8o7TyBg5r65+H2oWuqeCdEurK3a0tZbVGjgfqgx0rJ1z4K+CfEviAa3qXh2yu9TyGM8kYJYjoT612kMKW8SRRIscaAKqqMAAdhXRhqUqKkpde3z/AM/kfS5blVbBY6viZOPLO+i3u3e7b29E7X1Vj4R0Xwn4W8T/ABU8f/8ACS+L5vC6x3ZEaxTeX5wOM555+leu/sc69dND4l8PQSC/8O6XckWOohMebk8jPf1r07WP2evh9r2p3GoX3hmzuLu4ffLIyDLN612Xh3wxpXhLTU0/R7CDTrNORDboFXPrx3rDC4aVFpytoradfX0PIy/h/EYXHLEtxSUpyur80lJv3XfS2q27GpRRRXpH6AFFFFABRRRQAUUUUAFfJ3w2/wCUjXxa/wCxWsf/AG3r6xr5O+G3/KRr4tf9itY/+29AH1jRRRQAUUUUAMmZlicou5wpKqe57CvnXwH+yPpk3j7WPHnj3yfEGu6hdSTQWUi7ra1jYnapB++QuBkivo2iuarh6daUZVFfl27Hs4DOMbldKtTwc+T2qSk18Vuye6T623Pni0/ZZtvh38Z9P8eeBGSwtpWaPU9IJ2xtG3JMfpyB8tfQ9FFOjh6eHuqasm7izLNsZmzpyxs+eUI8qk92ltd9bX3etgoooroPHPGP2hP2b9H+NGlPdLeTaJ4gt4z5GoW7EA4GdrgEbhx+Ffk94ot7rT/EF/Z3d59untJmgNwHLBtpxkE9uK/aT4jQ6vc+CdXt9BRX1eeBorfe21QzcZJ9gTXh/wANP2GfA3hnwTd6d4ktl8Q61qMf+lahIMNEx6+V/dwe/evks1yuWMrR9hGztq+nl8z+hOAOPKPDWXVVmlRzjzKNOCSckt5O72jtpfV7dTyT9i/9miz8baBZePfE2sTarbmQra6WkreWrIesnPP+7X3bGixoqIoVVGAo6CvEf2bfg3rHwHfxF4akuhqXhqWcXWmXPR0BzvR19eRz3xXuFexluHWHw8Y8tpdfX+tj8643zmpnOc1aqr+0o3/d20Si9UrdGtpdbrUKKKK9U+ACimvIse3cwXcdoyep9KdQAUUUUAFFFFABRRRQAUVma94j0/wzYtd6jcpbxL/ePJ9gK8I8c/HDUNe8y00cNp9meDNn944/pXXQwtTEP3Vp3OatiIUV7256n44+LGkeDVeEP9t1HHFvEen+8e1fEv7ZmufEL4qfDkL4Vu7uHxBBqMEtnb2MhjCjd82cdeAK7lmZ2ZmYszHJZjkk0BivIOK+ipYGlTi11fX/ACPFqYqdSSfTscH8D9b17XvhzYTeKL2C98QwsYL3yU2GKRf4HH97GOfeu8rgvFBbwD4oTxTCCNFv2W31iFBwjE4S4/DPzeyiu8DKyqysGRgGVh0IPINdsNFyvdHLLfmXU7f4W+K10XUp9Ku5NumaopgkJ6RswwG/Wuc+F/xEm/Zd1PXfDHi3RNQbT57p7qzvrKLzBKD6dMjGO9ZZ5r6H+E/xBtNY8Kyxa1PDHNpaZkmuCAvlgfeJP0P5V85muClKaxVJ2aVn10MZ4WeLnSlSqclSm24u11ro01pe/qjx/wCPHxEHxX+CtnrNppN7YWv9sxRwLdR4eVAy/PgdBnI/CmfGL4k6x4Z+Imn6fe3mreHfDv8AZkJivtDtA9zcSbfulty/KD27c1pXn7W198UPHVt4T+Cnhb/hNbW2ukTVvE1yfJ0uzjDfOEfB8x8ZwMDmvp5YRJHH50aM4AzxkA98V81LDSe0t2n9yt5epeIyPEYhzqPEWnJQTajb4XJ7KS0fNaya23Piz4I+RH4K+Ksuv6XqWqWEjK1xbuhS5kjOCX/3gOavfA3xVeyeMrrS/Dzav4i8CW+mzNLBrVvsktso37pDk9envmvscRIN2EUbuvHWiOFIshEVAeu0YojheVx12Vtt99/LXb8TmocNToewUa/8Nt3UbN3k5WvfZ7NO6fkfEXw/8UPp3xO0jT/hwNdl0me8L6joeqW/7i3y2GdW3Hp2OB0r63+KCPL8OfEiqpZzp84Cryc+Wa6ZYY42LLGqsepCgGnEBhgjIqlh7UJUW97/AI9vJep6+V5TPLoVYSqcynskrKOnRXe+7tZdkj88PCviD4dQ/CmXRdS8G32oeNZUkiiuorTkyNkId+cjGR2r7D/Zx0nxBovwl0W08SCRL9EOI5iS6Jk7Q34V6OtpArAiGMH1CCpqujR9k3JvVpLTRaeXfzPPyjh95ZVjVlVUuWPKrRUbp21k7vmemgUUUV0n2QUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVz+u+ONJ8Oa1pml30xiudQEjRHA2qqIzszEngAKea6Cvjf9sjxD8GfiV4TsG8a+I9d0CLR9Xe0W80eNllMgRlkiOOqMu4fjUSbW39dP1RcVff+v6se6/CH9pTwP8b9b13SfDF7cS3ujyMkq3MPliZQ20yxHPzpnjd716nXzB+zJ8QPhL8TPHTXHw/1S+urjQdAi0mKyuLbyo4LVWTkf3mLKMn3NfT9bSVkv6+fz3MYtu9z5T+FH/KQn44f9gPSv/REFfVlfKfwo/5SE/HD/sB6V/6Igr6sqCwooooAKpaxrFnoGmz6hfzrbWcA3SSucBRkD+ZFY/jT4jeG/h3bw3HiTVrfSLeZtqTXLbVJ9M18v/tmftKeENY+DF3onhbxLZ6tf6pMkMkdlLuZIwdxY46cqtcGKxlLC05ScldLa59ZkPDmOzzGUKFOlL2dSSTmouyV9Xe1tEfYFrdR3trFcQtvhlQOjDuCMg1LXzZ+z/8AtT+Bbj4QeGk8Q+KtP03V7W1W1ngupgr5jAXcR74zXvHhPxno3jrSxqWhX8WpWBbatxAcox9jWlDFUsRGLhJNtXtc482yPH5RWq08RRlGMJOPM4tJ2dtHa2pt0UUV1nz4UUUjMFUk9AMmgBaKxfCvi7TPGVjcXmlTi4gguZLV2HaRDhh+dbVKMlJXWxrUpzozdOorSW6ZFc28d3byQyqHjkUqykcEGuI+HdxJod9qPhW6Yl7FvMtGb+OBumPpj9a7yuF+JFpLpMlh4ps0LXGmtidF6yQHG4H6YFeZjk6XLi4/Y384vf7t/kd+Caq82Fl9vb/Etvv2+Z3G9RwWGfrXzN8Vfj4/hX9qzwJ4Vjutlg8LRXiA/LumK7CfpsP51pftDfA/W/iRpJ8UeBPFt9our+T5v2ZbgrbXKgZ7fdb35r8w9a1/WbzxDJf6jqM9zq0UmDdPIWfcp6hq8bNszqYflgoWTaad9GlqfuPh5wTgM8jVxUsSptQlCUOVqUJTTSbvo0tWmuvZo/cPzF/vD86dXxh+yb8DfFvizStL8beM/GepXemzqs9lpcN0WWRezSE9v9nFfZygKAB0FfQYSvPE01UlDlT2Px/iDKsPkuMlgqGJVZx0k0mkmumu9uvQWiiiu0+aKesXg07Sru6JwIYmfP0FeUfsl2BsfgnpLcn7TLLcZPfc3/1q7b4tX40v4Y+J7snaIdPmbP8AwE1k/s92Dab8FfCFu331sVLcYySSf616kfdwE33mvwT/AMzype9mEF2hL8XH/I9Doooryz1QooqvqGoW2k2M95e3EdraQIZJZpmCoigZJJPQUAWK8Q+PH7VXh/4OXVt4e06zuPGXxB1D5NO8K6T89xKx6GTGfLTPViDj0rzLxB+0f40/aU1u68I/ACEW2iwuYNT+I17GTaW/OGW1X/lrIB33DHFeufAX9mHwp8B7W4u7MTa54sv/AJ9T8S6mfMvLtz1yx6L6AUAVv2d/DnxYjbWPE3xU123a+1fa1r4ZsE/0bSYxyFD5+dznk4HSvaaKKACiiigAooooAKKKKACiiigAooooAKKKKAOO8Y/FLRPA2v6Ppeqzi2bUVmkE8hCxxJHGzszE9sKa434K/tO6B8bPEGq6Tp+k6vpE9qrXFpJqlt5SahbK+z7RAc/Mm7jNeVftZal8Mfil4Ss4PH/gDx1rVtpWsmKBdD0xjcCRQSXGDzCwBGe+a679nrx54K+I3jaa90Hwb4v8MX2m6PHp0H/CRaYbOBbVWXCRAk5OQCfxpU7t3f8AWl7/AJBU91K39a2t+f4H0VXyd8Nv+UjXxa/7Fax/9t6+sa+Tvht/yka+LX/YrWP/ALb0wPrGiiigAoorlPiJ8TNE+FuijVvEEk1vp+7Y08cZdUP+16VEpRpxcpOyR0YfD1cVVjRoRcpy0SWrb8je1rWLXQNJutRvZPKtLWMyyv6KOppuha1aeJNFsdVsJBNZXsKXEMg/iRgCD+Rr5B/ae/bC8E+Jfg5rOi+FNXN5qupBbfaq4KITlm/TH40z9l39r7wV4X+Dej6L4s1c2WqafutwhXdmIH5Mc+mK8f8AtXD/AFn2POuW173632P0b/UHN/7F/tH6vP2ntOXk5Xfl5b81rX30PtCiuU+HfxM0P4paO+qeH5ZriwV9gnkj2K5/2fWurr2IzjUipRd0z85xGHq4WrKhXi4zjo09GvUKKKKs5woormPCvxA0zxfrXiLS7Fi1zodyLa6GejHP/wASalyjFpN6s3hQq1ITqQjeMLNvtd2V/m7HT0UUVRgFQy3kED7ZZ442xnDsAamry/44/ATRvjZoogu7m40vVIFP2bULRyrIfRh/EvtWVWU4wbpq77Xsd+BpYatiI08ZUdOm95KPNbztdX+88i/bb+N0nw7k8C2emXgE76kt9MIXyfLiwMHHrv8A0r6Z0nxFp+raXaX0F3CYbiJZUPmDoRn1r8YPid4f1Hwf441bw/qWpDVbjS7h7Y3CSF1O0449K+l/2M/2cl+L9l/wkviDX7ptFsJ/KTSbeYhpGHPznPC+2Oa+Nwea162MnCNO7l0vtbc/pPiPgDKcu4awuJq4zljS5m5qF3U9o7xSV1bsrv1sfo1HIsqB0YMp5DDoadUFjYwabZw2ttGIreFQiIvRQOgqevtvU/l+Vrvl2Ciiub8XePtH8G25a+uFM+Mpbocu34VpGMpvlirszlJRV5M6J3WNSzEKoGST0FeXeOvjlYaJ5lno4W/vhwZc/u0P9a8v8bfFbV/GLPCrmx049LeM8sP9o964npXvYfLkverfcePWxzfu0vvNDXNe1DxJetdajdPcynpuPC+wHaqFFFe2koqyPKbbd2FFFFMRDeWVvqVlcWd5Es9pcRtFNE3RkIwRXGeA7q48Oapc+CtTlMstqvnaXcuebm2Ofl/3lIb8MV0firxZovgbRZtX8Qanb6Vp0Qy01w4GfYDufavn/wAW6x49/aPWCXwDpsvhDRNOLy2niXUFMd5csQPkhTsrYAzk9awqTUWravsbU4uS127nqXxM+Onh34a3EWmnzdf8T3B222g6aPMndu27H3R71yWi/C/xf8XNWtNY+LV6+n+GFkWRfA2lSlI5Y85xcv8AxEjqu2ui+AvgHwh4X8MjU9AtZJtYuWKalqWoHzLxpx99XYjj1x6EV6fU+zdZXqPTsv17lc6pu0N+59bfD/S/D+j+EdNtfC9hbaZoiRAW9raIESNfQAV0VeA/Anx1/Zt8dAvJP9HuDut2Y8K/dfxr36vlcTQeHqOL26H0FCqq0FIKKKK5ToCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvBP2qNU13Q9N8Pv4LXVJvGU15s07T7K0E1pdNtJZbvj5IsZO7sQK97r49+N37QnxA0vx1q/hu6Nr8JfDNuWSDxjq1i93HdL5ZbdEwZAh3fLznvWcuiXr939f8A0gur/q51P7M1/4xv/iBrh+J8l7pvjtbPH9hWtuI9Hhtg64e2f8A5asTjLZ6k8V9M180fsT6Dba14Lbx7e+NL3x74j1F57WbVLm4WSNY0mIURIB8isFVsEnrX0vW8rqyfQwju2up8p/Cj/lIT8cP+wHpX/oiCvqyvlP4Uf8AKQn44f8AYD0r/wBEQV9WVBYUUUUAY/izwjo/jjQ7jSNcsIdS0+4Xa8My5H19jX5K/tQfDDQ/hP8AF3UvDvh27lvLONVlEcgy0JYbtme+M1+wEhYRsUGXx8oPc14Z8Of2XtK0fx5rXjvxWY9e8UajdyTxCRcw2cbMdqIp6kLxmvn82wDxyhCCV76y7I/XvD/i2HCs8TiMTUk6fL7tJfbm3o+ysk7v89j4h/Yp+C/hj4yfEK8tvEkzyJp0Au009eBcYYA7j6AkcV+pGlaTZ6Hp8Fhp9tHZ2cChIoYV2qqjoAK8Wk/Zl07wt8ZNN+IPg0rpU7Oyappw4huI2GSy/wB1sgHvXulaZVgngqcqc4rmvv3XQ5OP+Jo8TY2li8PVk6Tivcf/AC7ltJW2d979U/kFFFFe4flhT1bU4dF025vrgO0FuhkcRrubA64HevBPFv7a/wAMrXw5qpsddE2opA4itzHhjJjAGM+tfQxAYEEZFfAf/BRL4XeD/DKaT4k022Fh4i1K4ZJoYAFjnUDLOVx94HH514+aVq+GoOtRast7/ofo/AuWZTnWaU8vzKM+ab91xatpq1JNbO26en4lX9in9qDw78P/AA94j0rxnqn2Hzrz7ZbOwyGZ9xk/XFfZ/wANvjN4X+LSXMnhm7kv4LfAkm8shMnsD61+TnwZ8E6Z4i+LfhrQPFi3Fhpl/OiycbGKsMqckcA+tfsJ4V8J6R4J0O10jRLCHTtOtkCRwwrgAf1NeVkNfEVqXLJrkhp5n3/ixleTZbjvb0YzeIr+9uvZpLRva7bttfTfyNeorm3jvLeSCVA8UilWVhwQalor6xpNWZ/PSbTujw7xLN4g0bwH4u8F6Fbvd68sRj0kZxmOTIDE+i45+teZeEf+Cd3ha3+HdzYa/ezXXim7UOdRh4S2fH3VXuMnnnmvoX4kWcumSWHiezQtcaa/79V6yQHG4foK7KzvItQtIbmBxJDKodGHQg187RwdGdWVCuubkXu3/le3zW3yR99R4nzPK8Gv7MqeyVSXNNx+JzjbRv8Al+1brd3ueOfsseC/E/wv8FXng3xLGsv9l3TfYb6M/JPbt93HoRjp717VRRXu0aSoU1Ti9EfJ5lj6maYypjasUpTd3bRX6u3S71CiiitjzDyv9qC+ax+A/jAocSS2Twp9WGBXc+CbEaZ4P0a1UYEVpGuP+AivMP2t5v8Ai1cFoBn7dq1paY9nYg17HYxiKyt0HRY1H5AV6lT3cDTXeUn+EUeVT97H1H2jFfe5Mnoor5i+Kf7Wl9rXiq4+HXwQ0qPxz46B8u71HdnS9GzxunkHBYc/JkE4615Z6p6j8cf2h/Bf7P3h9dR8U6jsurg7LHS7YeZeXsnZIo+rEnH514Dp/wAIfiP+2PfQa58XvtXgb4ahhLY+ALOUi4vVzlXvpOOv/PPaOO9ehfBH9kWw8F+IT488f6pJ8QvihcDMut36/urQH/lnbRnPlqPqTX0RQBmeG/DOleD9FtNI0TT4NM0y1QRwWtsgVEUdABWnRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeD/ALU2leJ9QsPD/wDwgllrL+NPtW2wvtPk8u0tcg7nuztIMWMjbxkkciuc/Zs0/wAXaR8SdZtvio+o33xDax8xdRjbOkSWu8YW1XHyEHG5SScgniuR+OPxG+LNn421fTtfn1TwD8PIXY2viDwvpr3tzPH5Z5eUHbCQ3qp6Vu/sD6X4JvPAt34i0rxC/ivxfeT3Eepanf3QlvfLEzCMSL/BlQjYx1NFHrLy/P8ALzHWWii+/wCWvz8j6sr5O+G3/KRr4tf9itY/+29fWNfJ3w2/5SNfFr/sVrH/ANt6BH1jRRRQAVX1DT7bVbOa0vII7m2mUpJFKoZWB6girFI2cHHJpb7lRk4tOLsz8o/2yfhn4Z+H/wAZm0Xwfbyo08SzT2CfMsUjgEKgA6EHpUf7Hnw18NePPjQuieMreUiCJ5IrJ/lEsykfI49MZ49q/QLwX+znomi/EbWvHutY1rxNqE7PDJMMx2kfRVQHvjAzR46/Z10XXvH2jeO9E26J4q024EjXEK/JdJggrIO5wetfF/2LP2/1mytzX5fL/PrY/p1eJuG/sv8AsP2k+b2PL7e+vtLdt+W+nNv1t1PV9P0+20qzhtLO3jtbaFQkcMShVVQMAACrFFFfa7aI/mGUnJtyd2FR3EhhgkkVGkKqWCL1OOwqSigFufN3iX9u74feF9Qv9L1G31qw1W13Rvbz2JUq4zjv0zXyz+zT+1Zp3w6+KfjLXPFEl1/ZniGR7mQ28ZkYTbyVyM9MMwr3/wD4KGeE/CS/DGPxBfWKL4k+0JbWd1FhXbIJIb1XivgPSvCVzo+seHrnxLpV5aaJqEqMsskZQTxEjJQkcjkV+fZni8ZQxcYuSfJqtO+mp/X3A/D/AA7mvD1erGjOHt1ySTkm24e9+7dl1126eVz9XPhD+0j4b+N19cQeGrLVZILdcy3lxaGOFT2Xd6+1esVgeBfC+g+EPC9hp3huzhstJSJTCkIwCCOpPcn1rfr7qgqipr2rTl5bH8q5pPBTxc/7PpyhSWyk7y9W7LXy6eYVQ16a6t9FvZLGD7TeLE3kxZxubHAzV+itnqrHmwlyyUmr2Pl74Q/sS6BpdjqWq/EGKPxL4m1je1yHH7m33nJCerD+9+ldF+z/APAPU/2f/HHiKw0+6OoeC9WVZ7bzW/e2sik/IR3yG68dK9/orz6eX4ek4ShGzj16+d+9z7DGcX5xmEcRSxVXnhWSTi/hVnePKvs8ttLdN7hVe+v7fTbWS5upkggjGWkkOAK4zxz8XNJ8IK8ETi+1HHEERyFP+0e1eAeLPHGreMrkyahcHygcpbx8Iv4V9Hh8DUre9LRH59WxcKWi1Z6T46+PTSeZZ+HVwPum9f8A9lH9a8durqe+uZLi5meeeQ5aSQ5JqKivpKNCnQVoI8OpWnVd5MKKKK3MQoorifiZ8YvDHwnt4v7au2l1O4+W00izHmXdy3YKg5/GlKSirydkNRcnZHbY+UkkBVGSxOAB6mvHfFn7QiXWtTeGPhtpZ8ceJ1OyWaE/6DZH1lkwRx6cViDwj8Qv2ggs3jGWXwF4Ic7k8PWMn+m3a+k7/wAIPptr2Xwj4O0TwDocOj+HtNg0vT4RgRwry3ux6k1jedT4dF36/wBev3G1o099X+B5h4V/Z5bUtch8U/E/Vj418So2+C0YbbCx9o48nJHrmvZ1+VVCgIqjCqowAPaiitIwjDYzlJy3PPtcH/CufGC+IIht8P6w6wanGOFt5ycJP7A5APsteg8cEHIIyCO4PQ1W1LTbXWdNutPvohPZ3UbRTRt3UjB/GuP8C6vNoNxe+EtcuQLnTF8yzvJmwLq1JO05/vKQwPsBS+F26MPiV+qO7jleCVJYmKSxsHRh1BHQ19T/AAx8aR+NPDcUzEC8h/dTpnncB1/GvlXrgjkHkV2vwi1rUtI8YQCwtpLuKfEdxFGCQF/vH6da5MbQVak31R04Ws6VS3Rn1JRSUtfIn0gUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzvjGRLzwlrb2dvb6peW9rMYYGjEwMwQlVK9znHFdFXxp8WPD2u6n8YdTl+B+meINE8dCSNNU1i6DRaDKMAkyCRSJiF4xGy81L1fL3KXfsez/ALPXwp0jw1oVj4rbw1D4b8WatZj+1YbZXiR5CQxLRkkBsjtXsleQfBLw78ZtF1TUH+J3inw74gsZIgLWPRdOltnjkyMli8jZGM16/Wst9P8AhjKPn/w58p/Cj/lIT8cP+wHpX/oiCvqyvlP4Uf8AKQn44f8AYD0r/wBEQV9WVBYUUUUAFFFFABUF9dLY2NxcOcJDG0hJ9ACah1qO+m0u5TTZY4L8ofJkmUsgbtkAjivgz9oD9p/4y/D+HU/Cninw9punpfRtDFqVrG+yVD/EjbiM47ds15+MxsMFDnqJ28kfY8N8M4ribEfV8JOCkmrqUrO3VpdbdbH1l+z/APGKH40+D7vWECJJBfTWxRP7isQh/EV6dX5H/s5/tHeMvgy17o3hfTodYbVpUItJYmkbzACBsCnvmv0q+DF98Qta0H+0vH9tYaXdzgNDp1mh3RL/ALZLHn27VwZXmSxlOMWm5LfTT7z6vjvgmpw3jKteEoRoSf7tc3vNabR30fXY9Grybxt8ANM+JnxS07xP4nf+0NN0iHZYaWR+78wkFnf15AwK9Zor2qlKFZctRXW5+Z4LH4nLqjq4WfJJpxut0no7PpdaXWtjyf40fs6+Hfi5Y2kxiXSdf09lew1O2UK0RBBCkDqvHSvUNPS4jsbdbp1kuVQCRlGAWxyasVxPxY+KOn/CfQbHVNRwYbq+islBbHzPnB/Ss3GlQcq219/kdcK+PzWNHLU3U5W+Rbtc26Xk7Xttc7aik60tdJ4hFcW8d1byQyqHjkUqynuDXEfDyeXQNR1HwpdOSbNvNs2bq8Bzgfhj9a7yuH+JNnLp32HxNZoWudLfMyr1kgONw/QV5eNi6fLio7w384vf7t/kepgpKpzYWW09vKS2+/b5ncUVBY3kOo2cNzA4khlUOjDuDU9emmpK62PMacXZhRRRTEeI/tKsLy/+HOkEbvt3iK3O312sD/WvR/iF8SPDXwl8J3XiHxXq9toukWiZe4uZAoJxwqg/eY9gOTXzl+1j8YYtF+JXgbTfCunnxv4v0i4kuf8AhHdOlBlWRtnlGUjOxcqckir/AMPv2Vdc+IPii1+IHx81KLxR4ihPm6d4ZtsjStI5yAEyfMcf3iSOOlepidMNQj5N/fJr9Dy8Mr4nES80vuin+pzTal8T/wBt6TytOXUfhZ8FpeHvmUxaxrkeeRGG/wBTGR3KnPrX0x8K/hF4U+C/hS28PeEdJh0vToRztGZJW7u7nlmPqa6+GFLeNI4kWONBtVEGAB6AU+vLPUCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOX17XYde8N+Jbbw9fW1/rFnbTRCKF1kaG48slFYc4OccGuW+CPw6t9E0HTPEWr6Hb6f44urJYtTuo02PK2cncBgdRnpXhXxf8Ahj4k+IXxg1B/hb4X1LwF4mtZY0u/iFLMYreZCoJVbdh+/wADjK9CK9c+BPwr+KPgDWNQuPHnxQbx5ZTwBILZrEQeTJuB3Ag88ZGPeim7x5tm/wCtOuv6CqRtK26/rf0Paq+Tvht/yka+LX/YrWP/ALb19Y18nfDb/lI18Wv+xWsf/begZ9Y0UUUAFFFFABRRRQAUUUUAFFeFftTfGyX4P2PhI2suy4v9WjjlGf8Alhhg5+mSte429xHdW8c0TB4pFDqw6EHkGsIVoTqSpLeNr/M9XEZZiMNg6GOqL3K3Ny/9utJ/meffEb4K6P8AFTxNoN94iJvNM0ctLFpv/LOWYkYZ/UAZ4961viH8KfDXxO8KyaBrmmxTWW3EJRQrQMOjIexFdfRQ6FJ814/Fv5hHNMbD2PJVa9j8Fnbl1u2rdW931MLwP4dl8I+E9L0Wa8a/NjAtutxIMM6qMAn3xW7XIfFr4gQfC74e6x4mnVXWxiDiNv4ySBj9a2vCuvReKfDemaxB/qb63S4T6MoP9acZQjL2KeqX4GdahiatJ5jUj7s5NX/vWTf5mrRTZJFhjZ3YIijJZjgCvKvHXx0s9I8yz0QLf3Y4M55iT/GuylRnWlywR5NSrCkryZ6F4g8Tab4XsmutRukt0AyFJ+ZvYDvXg/jj43aj4g32ulK2m2XIMmf3jj+lcFrWuX/iK9a71G5e5mb+8eB7AVRr6PD4CFL3p6v8DxK2MnU0jogZizFmJZjyWY5Jooor1DzwoopVUyNhRk+1ACVU1bVrDw/pc+papeQ6dp9upaW6uHCIgHqTXmnxA/aE0nwzrH/CN+GbKbxv4xfhdL0s70gPYzSAEIPrisPS/gPrPxE1KDXvi/qo1iVD5lt4YsWKWFr7PgkufxrF1Lvlpq7/AARsqdleeiIZvjH4t+M11Lpnwm0/7Do2Sk/jPVIisSjv9nTjefQgmuv+GXwF8O/De8l1eV5vEniu45udd1Q+ZKzHrsGMKPwr0S1tYNPtYrW0gjtbWJdscMKBUUDsAKlojT15pu7/AK2QpVNOWKsgLFjknJooorYyCisDUPGVnpvia10WT/WzDmTPCN2Fb5+XisqdWFRyUHfldn6nPSxFKtKcacruLs/JhXg37UHwD8SfGSHS73w54hNjeaacx6W/ypOc5+8CMHtzX0r4V8F6t4xuhFp1szRg4e4YYjX8f6V794F+EGleEVS4nA1DUevnSD5VP+yK5MXiKNOLhPV9kerhqNSUlKOnmeS/DP4Jaz4g0vTZ9dU6ZCtvGskf/LR2VQDj0HHWvoLw74W0zwtZrbadapAn8TAfMx9Sa1aWvna+KqV/ienY9qlh4UdlqFFFFch0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABTVRUztULk5OBTqKACiiigD5T+FH/KQn44f9gPSv/REFfVlfKfwo/wCUhPxw/wCwHpX/AKIgr6soAKKKKACiiigAr44/ba0PX/jL4z8K/DjwtYteXSZvb2bb8lsDgKXbsMMT15r7HqvFp9tDeTXccEa3MwAkmCjcwHQE+1ceLw/1uk6Ldk9/Q+l4ezl8P4+OYwgpzgny32UmrJvva7du9j85/wDhnTxX+yZ8U/CXi87fEHh+G4jW7u7aEkQlhtYOvJAyeGr9HYZkuIkkjYPG43KynIIPem3VrDfW7wXESTwuMNHIoZT9QadDClvEkUahI0G1VHQAdqxweBhgXKNJ+69bdmehxJxRiOKFQq46K9tTTi5LTmje6uujWu2juPooor0j4o81+Nnxevfg7osesL4VvvEWmjP2iSwcBrcf3mGDx718B/tVftaW3x5s9BsdH0260q0sJWuJluJA3mScbSMAdOfzr9QriOKa3kSdFkhZSHVwCpHcEV+SvxE+GV/8WPjb41g+HHhx59MsbmU7bUfulCk5OTwCew9q+Rz54iMFCnK6npy21010P6J8J4ZNWxE8RjKHLUw65vaubUbS920k3ZPXR/qfT/w1/wCCgFrq9hoXh+DwZq2t+IGhSB1tZAxkcAAsBtJxX2Ho91c32l21xd2psbmSMNJblwxjJ/hyOtfFn/BN+z0e3t/Fdpd6Ult4qsZkEktxGRMsZ3fLg/dwRX2/Xp5TOvWw6q1p3v0ttb9T4XxAw2V5dnFTAZbhnT5Hdycm+bmSeivZR10Co54UuYXikUPG4Ksp6EGpKK9rfRn5je2qOD+Htw+galqPhS5Y5tG82zZurwN0A+mP1rvK4f4k2EtgLLxNZIWu9LfdKq/8tID99T69BXOfGb9pzwV8EvDVnqOrXj3+p6ig/svQ9OUzX1+5HCxxKCxGeN2MCvLwTdGUsJL7O3+F7fdt9x6eMtWUcXH7W/8AiW/37/Nnp2sazYeHtLudS1S8g0/T7VDJPdXMgjjjUdWZjwBXyTrXx8+IH7VWq3Phn4FQtoPg6NjDqPxIv4SEPYrYoceY3X5/mHSmaL8C/iF+1lqlv4k+OTSeHPA6Os+m/DvT5ivmDqrXsinLEj+EEe4r630PQdO8M6VbaZpNjBp2n26BIba2jCIijsAK9U8s+U/hP+z/AOGfgf8AtGeHNJ0IXN7qC6Pc3eq6xfymW5v5nwPMkY8dVOAAAM19d14foe7U/wBrPxHOBuisdCtYR/sszy5/p+Ve4V6eO932UO0F+N3+p5eA9720+85fhaP6BRRRXmHqBRRRQAVxvgv4seH/AB5r2uaRpVz5t3pEvlTg9D/tL6jOR+FYX7RPxMX4YfDXUL2Nwuo3Sm2tFzzvYY3D6ZzXwv8AAn4nT/DX4nWGsTSk2lzJ5N9k8Mjnlj9Mk1wVsUqVSMPvPgs64np5TmOHwfR/H5J6L/N+R+m9FR29xHdW8U8TB4pFDqw7gjINSV3n3id9UFFFFAwooooAKKKKACiiigAooooAKKKKACiiigAr5O+G3/KRr4tf9itY/wDtvX1jXyd8Nv8AlI18Wv8AsVrH/wBt6APrGiiigAooooAKKKKACvJfj7oPxKutGGo/DfXEtNQtlJk02eNWW4H+yT0avWqqatqEek6Xd3szBYreJpWLHjgZrGtTVSDi215rRnpZdi54LFQr04Rm0/hklKL8mn/w/Y/Hf42fFfx38QvEFvb+OJpV1LRy8K28kXlGJsjdkeuQK+if2bviF8fPjLJaabo+srp/h3T0SCbVLi3BVEUYCqf4mwKzfC/7Jfiv9pbxJ4o8da1cv4e0/UriafT/ALRH+8m3MSh29QmO/evZv2ItH8Q/CfVvFnw28UWbWl3byC8s5Mfu548kMyN0Iyy/TNfA4HDYh4qM6spKE+t9Xba/r/wx/XHFGeZNHIamFwFKjPEYZRbp8qcafM1zuCas+VvW232j6s0u1lsdPt4J7h7uaNArzyY3SMByxx61yvxW8UeJfB/hmTVPDXh7/hJbiD5pbFZQkhQDkrnqfau0qtqGpWuk2r3N5PHbQL1eRgBX6DKDnHki7M/j+hiIUa6r1YKaTu4u6T8tGmvkfmf+0f8AtnX/AMY/BL+Ev+Eek0BhdLJcmSTLnaGGwjAxyf0re+CP7depeBvA+ieD18Kya7d2aeRDJHNguowFGMdq4L9paMfGL9orV7fwpYweVGqxF4E2KxX7zv7k1S/Z809vCfxA1DR9Yslt9TaAmFpV5GCM7T7/ANK+FwsMdWzKKlUcVKXJz8umnTtc/rzMqfDOH4QfJg4ylCmsR7D2j5lzK3M3fm5bb+SPq3Vvi14p8faeh1aBdFjfn+z7eTOB23t3Nc8BjpS0V+6UaUaMFTj0P4RxFb6xVlV5VG7vZXsvJXbdl5thRRRWxzhRUdxcRWdrLc3E0dtbRDdJNMwREHqSeBXimtfHnV/HWqTeH/hDpP8Abt0jeXceJbxSunWnqVbjzCP9kmolOMNy4wctj0rx98SPDXwv0n+0fE2qRafE3+qgzunnP92OPqx+leTLN8Sf2hCwRZ/hp8P5P42GNUv0+h/1YI9V711HgL9n3TvD+rf8JH4rv5PG/jBzuOoagMxQH0ijxtA+oNersxbr+HtWXLOp8ei7f5v/ACNOaMPh1ff/ACOX+H/wz8NfC3SRp/hvTI7NDzLcP888zd2dzyTXT0UVukoqyMW3J3YUUUUxBVfUdQi0nT7i9mOI4ELHPc9hU5IXk8CuM+LGl+I72wtLDT9D1K5t5h5rzQWrurDsMgVw47ErCYeVXqtvXoeZmWLeCwk68VdpaLz6Hjeqaxcanq02oM5E7yb1b+76V9o/s8+CdP8Ail4Vttf1C68xI28mS0iPO9QPvH3BFfIH/CvfFX/Qs6x/4Ay//E19A/sd6h4p8E+OpdGv9C1WDSNUT5pJrSRUikXOGyRgZ4H4V+b4HGVsPVdm/f39e5+Z8JYuthcz5cTBuNXR3T36P79Pmfamn6bbaVapbWkCW8CDCogwKs0Vxfxf+Idv8MPAOp65KVM0UZS3jJ+/KeFH54r2JSsnKTP6Hr1qeFpSrVHaMVd+iN3S/Fmk61qmoabY38Nze6ewS5hjYFoiRkA1r1+bPwJ+MN34F+LUetX07NaarN5eobjxhzwx/wB3Ofwr9IoJkuYY5omDxyKHVh0IIyDXLh8Qq8W+qPm+Hc+hntCdRLllFtNeXR/d+JJRRRXWfWBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfKfwo/5SE/HD/sB6V/6Igr6sr5T+FH/ACkJ+OH/AGA9K/8AREFfVlABRRRQAUUUUAFFFFABRRRQAUUUUAV7+1+3WU9v5jReahQuvUZGMiuf+Hvw30H4Y6DHpOg2SW0IO6SXGZJnPVnbqSa6iiocIuSk1qjojiK0KMsPGTUJNNro2tr97XdjkLf4W6DYfEBvGNjbCy1ia3NtctD8qXCkgguO7DHX3NdfRRRGEYX5Va4VsRWxHK60nLlSSv0S2XougU13WNGd2CooyWY4AHrXJ/E74r+FPg74Ym1/xdrNvo+mx8K0zfPK39yNBy7H0AJr5kW3+KX7bbbrsX/wq+DTP8sK/utY1yP/AGs5MMZHbCtz7VZznSfEr9qjVvHnia7+HXwK0qDxl4kXMWoeIZTu0jSR0JeQELI4/uq2eOla3wQ+Afh3wX8RtQ1TxNZw618RRbxn+2Jt7x7Pmz9nRyRGM+gB6V7N8OPhh4X+Enhm30Dwno9vo2mQgARwL8zn+87Hlm9ySaz/AIl2NxYLY+JrBC13pb7pUXrJCcb1Pr0FeZjr01HFQ3hv5xe/3b/I9LBWqOWGltPbyktvv2+Z3NFVtOv4dUsYLu3cSQzIHRh3Bqwa9KLUkmtmec04tp7nh/wd3X3x2+LN6/PlTW1ojey7yR+te414j+zUP7Qv/iRq+P8Aj58SXEQbGNwRV5/8er1jxV4ktPB/h3UNZv3CWtnC0znPUAZwPevUzJ2xDT6KK+6KR4+XyjTwntZuyvJ/LmbNCO8gmmkhjmjeWPG+NXBZc9MjtU1fAfwP+Pt7afHe61bV7lhYeIp/JnVmOyMk4jOOwGa++1YMoIOQeRXh0K8a8W0ceR53RzyjOrSVuWTVvLo/mhaKKK6T6M/Pv9rr4h3Xjv4kPpdtHO2l6MPJQCNsPKfvN09MD8K8L+w3BGPs83/ftv8ACv1ok8P6XNIzyabZu7HJZoEJP44pv/CN6R/0CrL/AMB0/wAK8mpgZVJOTlv5H5FmHA1bMcVUxdXFayd/h27Lfojxj9kH4kz+M/h3/ZV+Jf7R0dvILSKRvjPKnJ79R+Fe81Ws9Ns9P3fZbSC23fe8mNUz9cCrNelTi4QUZO9j9Ly3DVcHhKeHrT55RVr2te234BRRRWh6QUUjMEUsxCqBkk8AU2OVJow8brIjdGU5B/GgPIfRRRQAUUUUAFFFFABRRRQAUUUUAFfJ3w2/5SNfFr/sVrH/ANt6+sa+Tvht/wApGvi1/wBitY/+29AH1jRRRQAUUUUAFFFFABUVxbxXkDwzRrLE4wyMMgipaKBptO6GQwpbxJFEixxoNqoowAB2AqN7K3kukuWhjNxGpVZdo3AHqM+lZ/iTxXpnhSya51G6SBR91M5dvoOprwbxx8a9T8Sb7bTN2m2HILD/AFkg9z2rsoYSpiPhWnc5quKjR1b1PUfHHxg0rwmr29uw1DUOQIozlVP+0a8C8UeNNW8YXZl1C5JT+GBOEX8O/wCNYZyzFiSzE5LE5JoXqK+lw+Ep4fVavueDWxE62+3Y5Xwvodpo/iXVzaxbdwDPIQNzseSSa2tQ0Gx1LULO+ngU3to26KdRhh7E9xVfw7mSfVZiPvXJAPsCa2K8nJaVOWBT5VaUpyt6zbX6WPdznGYlZi6ntHzqEYt31fuJNfPVNBRRWT4q8WaL4G0aTVvEGp2+k6en/La4cLuPoo6sfYV9E3bVnzKV9EayqWYADJ9BXnXxL+Ovh34bzLpqrN4i8UTHbb6DpQ824du2/Gdg92xXCyeN/iH8fpJLTwPbSeBvBTHZN4k1KLF5dL38iMj5QfUrXo3wz+DPhj4U27HSbVrrVZebjWL5jLdTt3JY5x+GKw55VP4e3f8AyNuWMPj37f5nn1r8JfGfxquI9U+K1+dI0MHfbeDtJlwgHbz5ASWPsGr27RdH0/w3pcOm6TZQabp8K7Y7e3QKoH9fqaudeT1oq401DVb9yJTctOgUUUVoZhRTfMQyGMOpkUZKZ5A9cU5QWYKoLMxwFUZJ+goFe+wVf0PQdQ8SXq2unWz3MpPO0fKv1Pau+8C/BDUNe8u71fdp9ieRF/y0cf0r3jQfDeneGbJLXTrVLeNR1UfMfqe9eXiMfCl7sNX+B6NHBzqay0R534F+BlnozR3mtMt/eL8ywj/Vof6mvVFhjjUIsaqq8BQoAFNubqGzt5J7iVIIYxueSRgqqPUk9KdHKk0ayRusiMMqynII9Qa+cq1p1pc02e1TpwpLliL5af3V/KvH/in8QNRsdds7PQo2kWxmWS6aMffbIPl/XH869irgfG1rCvivwrbwxKjTXjzS7V+9gLyfWvn83jVnhuWlPl1Wq31aWnzPfymVKOJvVhzaPTpom9fkdfoWsQa/pNtf2zbopk3D2PcfnXwz+2V8Wk8YeM4/DdjOG03RyRKVbIec9fqAMV93WdlBp8IhtolhiBJCIMAZrg779n34ealeT3dz4XtJbidy8kjM+WY9T96uyrSq1aSg2r9T8/4ny3FZvhnhcDNQjJ6819ui0T+Z+Ye5SMbh+dfoD+yD8Wl8deAxot5cK+saP+6YFvmki/hb+Y/Cu0/4Zw+G3/Qp2f8A30//AMVW74R+E/hLwHfTXmgaJBptzMnlvJEWJZR25JrDD4WrRnzXVj4/h3hXMcjxqrurFwatJK+q+7e51tFQQ31tcXEsEVxFJPDjzIlcFkz0yOoqevVP1ZNPYKKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8p/Cj/lIT8cP+wHpX/oiCvqyvlP4Uf8AKQn44f8AYD0r/wBEQV9WUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVmeI/EuleENFutX1vULfS9MtUMk11dSCONFHqTQBp189/HP8Aa4074f65H4K8D6VJ8Q/iXdfLDoWmHclqem+6kHyxKD2Yg155qvxo+I/7Xl/PoHwYSTwl8Ow5h1D4hahCUluV6FLGNhk5GfnK46YNe8/A39nXwZ8ANCey8N2Be/uPnvtZvWM17eyd3klbLHJ5xnA9KAPLfhj+ybqXiXxRb/EP456rH428ZKfNs9HUH+ytI7hIoujsOm5w3TrX08qrGoVQFVRgKBgAU6igApk0STxPHIodGGGUjgin0maAOD+H8r+HNY1LwrcMdsDfaLFm/ihbt+GP1rupn8uGRz0VSf0riviXYzWMdl4lskLXekvvkVeskJxvX36CtXxN4ggj8BalrEMgaAWTzq49NpNebgE6dR4J/Zty/wCF7fc9Pkj0cfJVKaxq+18X+Jb/AHrX7zzj9kgGb4VzX55/tDVLm7DeoYqM/wDjteZftxfFEQ2dl4JsZv3k2Lm+2nov8C/jhsivZP2YdPOm/A3wsh/5aW5l6Y6sayfGX7KHg3x14mv9d1Nr1728ffIVmOB7AZ4FeznSnWxFaNPrJ/dc/NcwweOxmSU8JgrKU0rtu2lrv79vQ/OsFlIKkqw5BHUV+kX7M3xQHxM+GdnLcSh9VsP9Fu1J5JAGG+hH8q5f/hiP4fel9/3/AD/jXd/Cr4D+Hfg/eX1xoTXIa8RUlWaUsvyk4OPXmvBwuHrUZ3drM+b4X4dzbI8b7Spy+zkrSs/ue3f82ekUUUV65+uhTVkViwVgxU4OD0rG8aeKrTwT4X1LW75wlvZwtKcn7xAyFHua+QP2Y/j5fXXxe1S1129d7XxFKXi8xiVil/hUemRgVz1K8ac4wfU+dzDPMNl2LoYOrvVf3dm/V6H21RRRXQfRBRRUV1dRWVtLcTyLFBEhd5GOAqgZJNAm7K7PEv2tfip/wr/4dyafaTeXq+sA28W08rH/ABt7cZwa5b9iv4sP4i8N3HhPUrnzL/TcvbtI2WkhJz36kHP4V8yfH74nS/FP4jX+orIx063P2azQngRqev4nJ/Guf+Gfjq6+G/jbS9ftWINtKPNX+/GeHX8s14EsV/tHOvhWnyPwGvxbJcQLFRf7mPuW7x6v79fuP1WorO8Pa5a+JdDsdUspFltbuJZUZTkcjp+HT8K0a9/fU/fYyU4qUXdMKKKKCgooooAKKKKACiiigAr5O+G3/KRr4tf9itY/+29fWNfJ3w2/5SNfFr/sVrH/ANt6APrGiiigAooooAKKK5Xxl8R9H8GW7faZxNddFtojlyff0/GrhCVR8sVdkylGCvJ2R080yW8bSSuscajLMxwBXk/jr47Wum+ZZ6Eq3tyODcN/q0+nqa8w8afE7WPGcjJJKbSwz8ttCcZH+0e9cgBjgcCvfw+XKPvVtX2PGrY5y92np5l3V9YvtevGutQuXup2OdzngfQdqp0UV7aSSsjy229WFIW8tWY9FUn9KWq+oTeRp9zJjO2M1lWqKlTlUfRN/ci6UPa1I011aX3mf4TXGjhs58yV3/M1sY+VmJCooyzMcBR6k9q848SfF/wx8KPC+n/21eebqc0Y+z6TYqZbq4Y9AEUEjPqRiuJXwf8AEL4/stx4wuJvAXgljuj8P6fJi9u17edID8oPoGFeRlL9ll+HppXlyR09Unqetmn73H16jdlzy/B9Da8X/tCC51aTw18NNKbxv4oB2STRHFjZnpukl4U49AaZ4T/Z6fUNWj8TfE/Vz428SZ3R2jDbp9n/ALMcXAbHqwNeneE/B+ieA9Hj0rw9pkGlWKDGyFcM/uzdWP1JrYr1VTcneo7+XT/g/M8t1FFWhp+YLhY1RVVI0GFRQAqj0AHSiiitzEKKKKACoby8i06zmup22wwqXY1bs7OfUbpLe1he4nc4WOMZNcN+0MZvB62Xh6a5jGozJ593bxsD5Sn7qsfXrXnY7GQwVGVST16LuzzM0xqy7BzxT6bebey/rocH4f8AHU3/AAny6jcz+XbXUoilL/dSMnrgelfoT8Pfhbonh20gvV2andyKHF0/zLgj+EdK/L/cv95fzr71/Y1+LS+MPBLeHL2dW1TRwETc2WkhP3T7kYOfwr4DBY+r71GUtJO/z6nxvA2b82Kng8S7ud5Rb79V89/kfRVFFU9X1W20PS7rULyQRWtrG0sjnoFAya9M/c5SUU5SeiPm79tr4o/2D4XtvCVjMVvdT/eXOw4ZIR2/4FyPwrd/Y7+KR8afD/8AsS8l36poxERLHLPCfut+eR+FfF/xW8eXPxK8earr1wW2TSFYIychIx91R+p/Gtb4D/EmX4X/ABI03VC5WwlcW94vYxscE/hya8BYr/aPadNvkfgFLiuX+sTxbf7l+5/270f36n6dVw2qf6d8VtIh6raWkkrY7FsgfyrtLW5jvLeKeJg8Uih1YdCCMiuJ8Iv/AGt4+8TaiPmih2WSN2O35jj/AL6rsxvvSpUu8l+Hvfof0ngtI1aq6Rf/AJNp+p3dFFFemeaFYPjrxfZ+A/Cepa7fMFgs4WkwT99sfKo9ycCt6vi39t34tJqGpW3gnT7nMVrie/2twX6qh+nB/GufEVVRpuR89n2aRyfATxL+LaK7t7f5nD/BL49X+i/G6bWtWumNhr0/lXauxIQMcRn225H5V+hEcizRq6MGRgGVh0IPevyC3L/fA/Gv0P8A2TviyPiN8PY7K7mVtY0nFvMu7LOn8L/TnH4V5uBraunJ+f8Amfm/AmdyqVKmX4iV3K8o37/aX6/ee4UUUV7R+0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8p/Cj/lIT8cP+wHpX/oiCvqyvlP4Uf8AKQn44f8AYD0r/wBEQV9WUAFFFFABRRRQAUUUUAFFFFABRUdxcRWsLzTSJDDGCzySMFVQOpJPQV8s+Pv2ovEfxQ8TXngD4AWEOvaxC3laj4vu1P8AZWlc4JDH/WuPRQwoA9Q+On7S/hH4D2UEWpSy6t4kvTs07w5pa+de3b9gEH3R/tNge9eOeHP2fPHX7TWrWviz49zf2f4dRxNpvw4sJiLeIdQ14yn96/T5csvWvS/gT+ynoHwjvpvE2sXc3jT4i3w3X3ijVv3k248lYQeIUznATFe5UAVNK0my0LTrew020gsLG3QRw21tGI441HQKoGAKt0UUAFFFFABXxf8AtHftEaho/wAZNKttDu3Fl4elDXMcb4WeQkblbHUAAfnX0v8AGj4iQ/DD4e6nrbsv2hU8u2Q/xykHaK/MG+vJ9RvJ7u5kMtxO5kkdjksxPJrysdWcUoReu5+T8dZ5PBwp4LDytNtSduiT0+9/kfrB4e1ux8ZeGrPU7Vlnsb6ASL3BUjpXjPj/AFBvBPwv+IfhqeRgsGnzTae7HlonBG0e4x+tcV+w98UftmnXvgq+mzLbf6RZbj1Q/eUfTA/OvSP2qvDNnrHw6kuLi7/s+RHWHzh/ErnlT69K1p1F7Shjkv4bTdv5b+8v1XofomSZlHPMpkotJ1YtelRKy9NdPRndfB/Tf7H+F/hmz5/c2Ma89eldhWd4ce2k0HT2s2D2vkJ5bDoRgVo16lSoq03VW0nf7z0KdN0YRpNfCrfcFFFeQ3n7RGlWfxvt/ABjXa8e17zd924PSPH5fnWEpxhbme5zYrGUMGoOvLl5mor1ex69RRRVnafH37cXxRybHwTYzel1fbT7/Ip/In8a+SbG9n028gu7aQxXMDrJE69VYHINfePjP9jXQ/HHinUddvvEWp/ar6XzHUKhC8YwM9qxf+GC/DH/AEMWqf8AfCV4dbDV6tRzt+J+EZ3w3nua5hPF8itf3feWiW3X5+p7N8FfiJB8Tvh5pmsxkC4KeVcx/wByRRyP5H8a7qvM/gv8DbH4K22oW+nateahBeMHMdzgKjDuAO54/KvTK9inzci59z9ly3619UprGK1RKz1vquvz3CqesaTba9pd3p16hltLqJoZUDFdysMEZHPSrlFaHoSipJxkrpnjf/DIvwu/6F9v/AuX/wCKo/4ZF+F3/Qvt/wCBcv8A8VXslFYewpfyr7jxf7Dyv/oFh/4Cv8jH8J+E9M8D6Ba6Lo8DW2nWoIiiZ2faCSTyST1JrYoorZJJWR7NOEaUVCCslokuiCiiimWFFFFABRRRQAUUUUAFfJ3w2/5SNfFr/sVrH/23r6xr5O+G3/KRr4tf9itY/wDtvQB9Y0UUUAFV7+/g0uzlurqQQ28S7nc9AKsVBe2cOoWkttcRrJDKpVlYZBBpq19RO9tDw7x18eJrzfZ+HgYYeQ144+Zv90dq8hmmkup3mmkaaZzlpJCSx/Guh8feEZfBniS4smBNsxL27+qHt+HSucr7PD06VOCdJaM+XrTqTk1U3QUUUV0mAUUqqW6DPevKPH37Qmk+HNWPhzwxZS+NfGT/ACppmnfNHCfWWThVH45qZSjBXkVGLk7I9L1fV7Dw/ps2o6rewadYQjc9xcyBEA+p6n2FeKXPxs1v4nXxtfAOiyf8IpE+y/8AE2ooY0kXptt0PLHPcjHFP0n4Da38Q9Wg8Q/F7Vhq0sZ8y38LWLFdPtvQOBjzCPfIr1bxJBBp3hhLS0ijtLWN0SO3hQKij0AHArws4qVFl2In8KUJeu34HtZTTg8ww8N25x9N195zPw9+B3hjwLqM2uiJ9b8SXbGWTVtS/eypnnZGDkIB/s4r0RmLMSSSfU00Daqj0GKWvZo0o0acacVZJJHkVakq1SVSTu22wooorYyCiitHQfDuo+JrxbbTbV7iQnlgPlX3JpNqKuxpNuyM0kLya7bwT8KNX8YMkzIbDT88zyjBYf7Ir1HwL8DrDQ9l3rBXUb0ciP8A5ZJ+Hf8AGvUERY0CIoRVGAqjAFeJiMxS92j956tHAt+9V+457wl4C0jwbaiOxt1M38dxIMux+vb8KxfEnwL8D+LtZm1XVtBhvL+b78zs2T+RrvaK+eqfvnepr6noVcLh68FTq01KK6NJr8Txv/hR3wk/4SIaH/YFp/afk+f9n3vu2evWuw8IfB3wh4D1RtQ0LRYdPvGQxmSNmJKntya83HghJP2pJL46jdealgl2FzxyzAx/7vFe+V42X1ZYj2rqU1Hlm0rdl+p24vI8swE6M8NTjzOKlflSab7aBWH408IWPjzw3eaJqRk+xXS7JBE5RiPTIrI0X4seH9e8far4QtLktq+nIHlXHynOcgHuR3+tdnXrXjNNbo4YzoY2nKMWpR1i+vk0zwD/AIYn+HX/ADxvf/Al/wDGj/hib4dEY8m9/wDAl/8AGvf6Kx+q0f5EeT/q9lP/AEDQ+4wLWzs/h94LFvC7/YtMtiEaVyzbVBPJPWs74V6bJY+Eop5123F7I11ID1BY8foBVL4hzN4g1LTfCls2WunE14V/ggU8g/73I/Cu6hhW3hSJBhEUKB7CuOnati3JfDTVvm7N/crfefZckcLgY0oq3PrbtGOi+93+5D6KKK9U84K891r4A+AvEWrXWp6j4egur66bfLMzvljjGevtXoVFTKMZaSVzmr4ahiko14KSXdJ/meYf8Mz/AA2/6Fe2/wC+3/xroPBfwn8KfD28ubrw/pEenT3CCOVo2Y7lByBya6+ipVOEXdRRz08twVGaqUqMVJbNRSf5BRRRWh6IUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfKfwo/5SE/HD/sB6V/6Igr6sr5T+FH/ACkJ+OH/AGA9K/8AREFfVlABRRRQAUUUUAFFFFABRRRQB4T8evgL4r+PHiTTdKufGs2gfDNIy2paTpRaK71CTIxG0owVjxnIDc8cV6p4D+Hvhz4Y+GrTQPC+kWui6TaqFjt7WMIOO5x95j3J5NdFRQAUUUUAFFFFABRRXzXrf7VsWl/HyHwyrRHwzG32O4uCBkTk43Z/uj+tZVKsadubqeXj8yw2WqEsTK3O1Fer/RdT0D45fAmP42w6bBc67daVbWTM/kwRqyyMccnPpj9a8l/4YD0j/obb7/wGT/Gvq1WDKCDkHkGlrOeHpVJc0lqcGM4dyvH1niMTR5pvrd/5nzj4A/Y2s/h94w0zxBZeLL55rKUOYjAiiRe6kg9DXRftVf6X4V8L6XgN9v8AEVlCykZBTcd3869srxP48/6Z8RPhPpxG5JtYadl7Yj2HP616+VUoU8THlWiu/uTf6Gc8twmV4GpRwcORSa6vdtLq2db8Ppn8M6tfeErljtgxPYM38cJH3R9MfrXf1xPxL06e3trPxDYJm+0l/NZV6yQ/xr+QrrNM1CHVtPt7y3cSQzIHVh6GvncG3RlLCS+zrH/C9vuen3H3eMSrRjio/a0l/iW/37/ec78UvHlt8NvAuqa9cFc28R8lG/jkI+VfxNfmBdeI9Qu/EUmuNcP/AGm1x9q8/PzB85BzX1/+2NpvjbxtqGmaBoWg3l7o9uv2iWaDbtkkPRTk/wAOP1r5p/4UR8Qf+hT1D8l/+Krlxkp1KnKk7I/m/jWvjcwxyoUKUnCls1F6y6v9D9Cfgv8AESD4nfD3S9ajZfPZPKuYx/BKoGR/I/jXc18h/sf6P458A+KL3RtY0C9s9Dv0MvmzbdkUqjrwe/A/CvryvWw9R1KaclqfreQY6rj8vp1a8XGa0ldW1XX57hRRRXQfQhXzH4o/aq/sf9oG08PRyxnwvCws7uTAOZm/jDegyPyr1P8AaA+J0fwt+HN/qKvjUJ1NvZrnB8xhgN+Gc1+Z1xPJdXEs0rlpZHLux6lick15eMxDptRhvuflfGPEdTLKlHDYSVppqUvRbJ+vXyP14jkWWNXQhkYZDDoQadXiX7KPxW/4WN8O4rS8mD6xpOLecE/M6fwv9McfhXttehTmqkVNdT9FwGMp5hhaeKpP3ZK/+a+QUUUVod4UUUUAFFFFABRRRQAUUUUAFFFFABXyd8Nv+UjXxa/7Fax/9t6+sa+Tvht/yka+LX/YrWP/ALb0AfWNFFFABRRRQBxPxW8Dr4y8Ov5Sj+0LYeZC3c46r+NfLzK0bMjqUkU7WU9QR1Ffa9fOvx68Hw+Gb2TxGrxWmlTc3MkjBEifrkk+vNe7l2Is/Yy+R5OOo3XtY/M8xrmvH/xI8N/C7RzqXiXU47GI8RW4+aeZuyogyST9K811j49ar481afw78ItLXXLuNjHdeI7xSmn2nYlSRl2HsCK1vAX7POmaDrC+JfFt/L458ZH5jqGpDdDbk9oIjlUA7EAV7PtHLSnr59P+CeXyKOtT7uv/AADmll+I/wC0REDF9o+GngCQ/fbH9p38ftj/AFYI9wa9W+H3wz8NfC3S/sPhrTUsg3M103z3E7d2kkPzMT7mupZi5yxyaSqjTUXzPV9/62JlUbVlogrF8V5aztUH8VwvX8a2qxfFCmSPT1zgNcrk9+hrx891y2su6S+9pHr5J/yMKT7Nv7k2bbfeNJSt9402vee54a2FoVTI4RFLuxwFUZJ/Cui8JeAdY8aTBbG3KW+cPdSDCL/jXv8A4I+E+j+DVWbYL3UMc3Ey5wf9kdq4cRjKeH0er7HZRw062q0R5f4F+B99rfl3eslrGzPIhH+scf0r3bQ/D+n+G7NbXTrZLaEf3RyfcnvXzz+0P+05c/Dbx9omiaOY5o7eRZtVGASUJ4jB7HrmvonQ9atfEWj2ep2MgmtLqJZY3HdSM18rUx0sVNwb26G2X43BVsRWwtB3nTspf8D02fmXqKKKxPfCiiigDymP/k5Cf/sCR/8Aob10nxc+IFv8M/AOqa7Mw82GMrbof45SPlX865uP/k5Cf/sCR/8Aob186ftrfEW68R+LLfwpZRXBsdL/AHk5WNsPMe3TnAH6187ha3saVeXX2kji40zT+ycBCvH43Tio+rX6bnifgX4laj4P+JFp4tMry3QuPNueeZUY/Op+or9P9D1i28QaPZ6lZyLNa3USyxuvQgivyW+w3P8Az6z/APflv8K+1v2JfiRPqfh288Iagswn04+batKjAGI/wgkdiP1rTA1lGfs29/zPxPgTNZ0MVPA178tTVX/m/wCCvyPp6aaO3heWV1jjQbmZjgAetcIfiFqmvNIPDWhyXtuDsF9cMI4ifUc5I/Cl8cTS+JtfsfClvIyQSL9o1B1/5454TP8AtYNdvaWsNjaxW8EaxQxqFRFGAAK6JSq4qrKnSnywjo2rXb7K97JdfP0P6OjGlhaUZ1Y805apO9ku7tu308vU5/wd4Tk0P7TfajOL3Wbxt1xcY4A7Ivoo/rXTUVR1zWLXw/o95qd7IIrS0iaaVz2VRk130aUMPTUIbL+rs87EYh1JSrVX/kkvySPCP2sfjtefDDT9M0vQbhI9bupBO54JjiU9CP8AawRXrXwv8eWnxI8D6Xr1o2RcRDzU7pIOGB/H+dfmv8UfHl18SvHWqa9cnieQiFM5CRjhQP5/jXtv7FfxX/4R3xRN4Rv5tthqZ32u48JNjp/wLgV5tLFuVd3+F6I/FMr4uliM+nCpL9zU92PlbZ/Pr6+R9zUUUV7J+zhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHyn8KP+UhPxw/7Aelf+iIK+rK+U/hR/wApCfjh/wBgPSv/AERBX1ZQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeWftGfFRPhX8Oru7hkA1W8Bt7NM87yPvfQf1r815JpJpnmkdnldt7Ox5LZzmvu/wDaC/Zx8UfGbxZBfQ69a2ul2sPl29pKp+Rj95uB3wPyry7/AIYL8Sf9DFYf98N/hXh4qnWrVNI6I/DuLMtznOMdelQfsoaR217vfr+Vj3L9lX4sD4kfD2K1vJd+s6UFt7jcfmdcfK/44P5V7XXzJ8Df2YfFXwi8cQ6x/wAJDazWLIY7q2jVv3q9uo6ivpuvTw7n7NKorNH6bw/PGvAQhmEHGpHTXqls/wDPzCvEfiE39rftKfDrT+SbS0vLwjPAx5fP6V7dXiDr/aP7W6MOf7N8P8+3msf/AImvdy/SVSfaEvxVv1O/MNYU4d5x/B3/AEPbZI1mjZHUMjDBUjgiuD8AyN4Z1rUfCk7Hy4m+02LN/FE3VR9MfrXfVxPxK02eGC08RWCk3+ksZCq9ZIjjep9eBXy+Oi6ajiobw384v4l+vqj6jBSVRyws3pPbyktn+noztqKqaVqUOr6bb3tu2+GdA6sPerdelGSklKOzPOlFxbjLdBRRRVEhRRRQB80/tLfArx58YvE1m+mXenRaHZw4ihuJnVvMJO5iAp7Y/KvHP+GHfiD/AM/Wjf8AgQ//AMRX3zRXFPB06knKV7s+Ix3B+W5hiJ4rEczlLf3v+AfJ3wD/AGcfiH8I/H9vqs15pbaXKhivIYZ3LOnbAKjkE5r6xoorelSjRjyx2PfyvKqGT0Hh8M3y3vq7hRRRWx7AUUUUAFFFFABRRRQAUUUUAFFFFABXyd8Nv+UjXxa/7Fax/wDbevrGvk74bf8AKRr4tf8AYrWP/tvQB9Y0UUUAFFFcR8YvBviLx74GvNF8MeKJvB+pXJVTqlvEJJI48/MF5GCRnntQBw3xx/a28H/Bq8h0GBbjxb45vPlsfDGiIJrqVj0Lc7UX1LGvLY/2c/iP+1Nt1X47asdB8MsTJafD/Q5mWNf7pu5RtMjD+6QRXtXwP/Zm8E/AW1ml0Sya+8QXYzf+ItSbz7+8Y/eLytlsE87c4FesU07aoD4suvBFv8NZn8NWenwaXZ2R2RW9rGI49vZgB6ioq+h/jh4F/t7SBq9pHuvrMfMqjl4+4/DivncHIyK+xwtZV6aktz5jEUnRm0LRRRXWcwVieK8/Z7Eg4IuV5/OtrNWtf8BarceEf7ZmtzbafDcR/PIMMwJwSorwM+koZZWk3sr/AHNP9D3cji5ZjRilu7femv1EtLG51S8W3s4HuZ3OFSMZNezeBfgMsfl3viJg79Vs0Pyj/eP9K9J8I+C9I8J2ajTbdd7qC1wwy7/jXQ1liMxlUuqWi79RUMCoa1NWQ2lnBYW6wW0KQQrwscagAfhXNfFDx7afDXwRqWvXZGLeMiKPODJIR8qj/Paurrxr9ob4I6v8aodLtLXXl0vT7Vmkkt2j3CR+MN+HP514NaU1BuCuwzKpiaWDqSwcOapb3V599e25+e/iHXrzxRrl9q1/IZby8laaVj6k5xX1x+xH8WvtVnc+B9RmzLADPYM56p/En4cYrE/4YH1PHHimHP8A1wrY8I/sV694P8T6brNj4ujiuLOYSBlg5wO3414lGliKVRT5fXY/EslyfP8AK8wjjHRbu/e1Wqe/X5+p9cUUi5wM8mlr6A/oEKKKKAPKY/8Ak5Cf/sCR/wDob16PcaDpl3M0s+n2s0rdXkiUk/iRXnEf/JyE/wD2BI//AEN69Wrxst2rf9fJHrZpGMvYqSv7kfyMz/hGNH/6Bdn/AN+F/wAKmt9K0/Sy81vZ29scfM8cYU4/AVdrmviNqj6T4N1KWI4neMxREf3m4FelWqRoUpVX9lN/ceXQw0atWNOMVdtLYyfhjG+qPq/iGcZk1C4KxH0iX7oHtnNd3WT4U0pdD8OafZIMCGID8Tyf51rVjgqTo4eEZb7v1er/ABOvGVVWxE5R22XotF+AV5h+0F8PfEfxO8EjQfD9/a6f50oN01yzLvQfwgqD15zXp9FdUoqcXF9Tx8XhoYyhPD1b8slZ20dj4W/4YQ8Z/wDQZ0f/AL7k/wDiKsaf+xD480m/tr6y17SILu2kWWKRXkyrKcg/c9a+4aK4/qNHt+J8XHgfJou6jK/+JlPR47uHSrSO/kSW9WJVmkj+6zAckVcoorvPvYrlSQUUUUFBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfKfwo/5SE/HD/sB6V/6Igr6sr5T+FH/KQn44f9gPSv/REFfVlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXifw/A1T9pP4iX/ACRa2NnZjPQEGUn+de2V4h8BQbz4k/FnUT8yyayIEb/ZQHj/AMer08LpQry/upffJHl4vWvh4/3m/uiz2+myRrLGyOoZWGCD3FOorzD1DgvAsjeGNe1HwrMx8lD9psC3eJuqj6EfrXe1xXxK06aC3s/ENkha90l/NKr1ki43r+QrqtK1KDWNNt722cPBOgdWFeVg/wBxKWEf2dY/4X/k9PuPUxn76McWvtaS/wAS/wA1r95booor1TywooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5O+G3/KRr4tf9itY/8AtvX1jXyd8Nv+UjXxa/7Fax/9t6APrGiiigAooooAKKKKAGuokVlYZVhgg9xXy/8AFnwS3g/xI7wIRp14TJEeynutfUVc7488Jw+MvDtxYyDEuN8L91cdK7sHiPq9S72e5yYmj7aGm6PkmtPw/wCGdS8U3gttNtWnfPzPjCr7k16L4L+A99qEwn19jZ2yn/URn55Px7CvcdG0Ox8P2a2un20dtCv8KDGfc+pr2sRmEKfu09X+B5dHBzqaz0RwHgX4Jad4f2XeqbdSvuoVlzHGfYd66D4raKdc+HeuWMa/O1sSm0dCpBGPyrrqZNGs0TxsMqwKkexr5bGc2NpzhVd+ZNfefQ4RrB1IVKa+Fp/cc38M9aXxF4A0HUA24zWcbNznDbRkV09eV/B2T/hF9Z8SeC5zsaxuWu7NW/it5CSMew4/OvVK87LqzrYWDl8S0fqtH+KPQzGkqOKmofC9V6PVfgwooor0jzQooooAKKKKACiiigDymP8A5OQn/wCwJH/6G9erV5TH/wAnIT/9gSP/ANDevVq8fLdq3/XyR7GZb0f+vcQriPiZm7bQNMH3bzUI94/2VIJ/nXb1w3ijN58RvC1v/DCs0zfkuP5VtmGuHcP5nFffJIxy/Sup/wAqk/uTf5ncKu1Qo6AYpaKK9I80KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+U/hR/ykJ+OH/YD0r/0RBX1ZXyn8KP8AlIT8cP8AsB6V/wCiIK+rKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGv91vpXi37K/8ApnhnxTqbcyXniG7bd6qNmP617JerI1nOIhmUowX644rz39n3wTqXgD4a2ml6xEkWpGaSedY23Dcx9e/SvRpTjHCVVfVuP63/AEPOqwlLF0pW0Sl+lv1PSaKKK849EbIiyIyMAysMEHvXB+BpG8L+IdR8LTHEAJurAnvG3VfwI/Wu+riviXps0dpaeILJSb7SH87avWSLjev5CvMx0XBRxMN4fjH7S/X1R6eCkpuWGntP8JfZf6ejO1oqppOpQ6xptte27b4ZkDqRVuvRjJSSlHZnnSi4txlugoooqiQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvk74bf8pGvi1/2K1j/AO29fWNfJ3w2/wCUjXxa/wCxWsf/AG3oA+saKKKACiiigAooooAKKKKACiiigAooooA8w+LmkX2i3mneOdFiM1/pJKXdsnW5tWxvX6ggEfjXf6Br1l4m0e11PTpluLO5jEkbr6Ed/erzKJFKsMqwwQe9eNajBffAnxBcapZwyXngXUJd93axjLadITzIg/uHuPpXhVr5fXlif+XU/i/uv+b0e0u2j7nu0bZhRjhn/Fh8P95fy+q3j31XY9noqppeq2mt6fDfWFxHdWkyh45ozlWBq3XtxkpJSi7pniSi4txkrNBRRRVEhRRRQAUUUUAeUx/8nIT/APYEj/8AQ3r1avKY/wDk5Cf/ALAkf/ob16tXj5btW/6+SPYzLej/ANe4hXDXZEnxdsV/uWDn8ya7ms9tDs21pdVMX+nLF5Ik/wBn0rsxNGVZQUekk/uOPDVo0XNy6xa+80KKKK7DjCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPlP4Uf8pCfjh/2A9K/9EQV9WV8p/Cj/AJSE/HD/ALAelf8AoiCvqygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACmyRrKjI4DKwwVPQinUUAYPg/wy3hTT5rIXJntzM0kKkY8tT/D+efzreoorKlTjRgqcFZI1q1JVpupN3bCiiitTIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5O+G3/ACka+LX/AGK1j/7b19Y18nfDb/lI18Wv+xWsf/begD6xooooAKKKKACiiigAooooAKKKKACiiigApk0STxtHIgeNhhlYZBHpT6KAPLrX4d6v8PfFEd14OeM6BeTZvdGnbCRZ6yRHt9K9PdvLjZj0UZp1VdUvH0/Tri5jt3uniQuIY/vPjsK4aOHpYKE3Tuo723S72X6fcd1fE1cZKPtNZbX6v1fV+f3nkekeKPFfxRubm+0LxBp3hzTra5a3jtLi38+aXaeS43Ltz2FemweJLSPWYtDnuVfV/s/nsirgFRgFvavn3xTqHws8XafdXsOmT2fjO5GVsrNWS9S49No7568V0P8AamqeAfE3h7WvEOl6hfvcaItrcTWNsZpFnGMhgOn1r5XB5g6F3OSnrHmmpOUbO/TTkd7aLofV4rLo1klGDho+WDiou6S63vNeb+R6Brvxo8L+HorOa6vD9mubiS1WZFyokTG4E/iKSX41eF18KP4hjvGl06OdbeRlX5o2Ocbh2HFeKx3i6Do/hDVNS069MA8Q3U4tJLYtcOjFMER9SeKm8WeZqHhPxj4mn0m6sNKvtQtXtrW6hKSShC+WKe+4ce1Yf23i+Wcvduo3tZ6e4pXeu13a25sskwl4RalrK17rX3+WyVt7a/I908I/E7w744kvI9Jv1me1AaVWG0hTn5h7cVD4a+LXhfxdrU+laXqSXN3ECcAcOB1KnuK8xuob74nX+tX+gaRe6VbR6JJYxveQG3aeVhwqj0GOvvU/hO4m8Yax4NttP8PX2iR+HwzX011bmJAcKPLQ/wAecHmvSp5piJTpx0ab/lfvrmavHXSy953vf01PPnleGjGctVZbXXuO1/e01u9Fa33mn/wmHhBPinb+JB4kiklvLQafHYRx5YOrEks2ePvdMdqseF/2htF8QeOL3RfMbyi0cdkyxHdIxLBt3pjA/Op/g/4F02TwTH/aOjrHcm+a6YXEW1/MB+Vq0fC+kSW/xg8WXRsjHayWdqI5vLwrMDJkA+vIzU4dY6LoVFKMVUlzSSTe8bu93pqv62dYj6j+/pyjKTpxsm2ltJJWsuz+71076+EzWcwtyFn2Hyy3TdjivONB+Kkh+Edz4j1DYdRsxJDLHjbunXooHqcivTq8Gk8J6q3xUufDC2En/CK3F4uuyXJU+XvBH7rPvt6e9elmNSvSqQdG/vpx9G7Wl8tdTy8tp0K0ZxrW920vkviXzujsz8UrDwL4e0dfGOpRrrV3F5jxwpz65254GP5V0eqfELQNG8MR+ILrUYk0qQAxzA5356AepryDx7aXmgfE3VbzVoNSOg6naxwpcaRZmeZlUtmLP8A59Oc1c1iT+zZPCGvv4V1BPC+m+Yg0uOEvNCxxsmMeOefyxXmxzKvD2kXZcrsrp3ir8vNLXVW16X9NT1JZbh6ipVFe89XZpJ6X5Y6aO/u9fv0Ol8WfHzw7pfgd9c029W6kmSRbZSuR5qjO1/7vUfnWpofxm8N6p4LbxHLe/Z7OErHK0q7f3pA+VR3OTxWPq98PGnwt8Wz2Xhq500XEE3kR3EGya5JT7+zGQT0/CsjxZp9zouj+ANZn0i41HTtJ+a8sreLdIjFVCvsH3ipGacsZjISlV504csLe67K7act72W7Xa3qTHB4OpCNFwcZ8zXxK+kbqO1rv4V5/cegaf8UvDWpeGv7eh1KP+zFkWKSVuPLYnADDt1pdD+KHhrxFdanBY6pDKdNTzLh84RUxndn0968n1XRrzxL4Z8T6r/Yt1b6frWpWphsJoisrxhlDOyfw5H8q7X4jeE9As/CMkk2gXVwk0MVrP/ZKYnSFTngAcgZJx3rWGPxsoOraKjGN3dNXvzWa7LRPro/IyqYDBRkqd5c0pWVmnb4W09rtXa6ao6Xw58TfDni3XLvStJ1GO9u7ZBI/l8qVPcHvXU14/wDAue/uLrUI3tbqXRrWNY7HUdRszbXLDJzGQeqgHr7mvYK9jL8RPFYeNWe7v0t19X997Pc8jMcPTwuIdKnsrbu/T0X3W02CiiivRPMCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCjrmsW/h/Rr7VLssLWyge4l8tSzbEUscAdTgdK+Zk/4KTfBqRVYTeIsMMj/iSTf4V9SsqyKVZQykYIIyDVL+wdM/6B1p/wB+F/woA/Of4e/ttfDLRv2yPir4yuJda/sfWNK0+3tdmlSmUtHFErbk6qMqeT1r6B/4eSfBv/nt4i/8Ek3+FUPhVplk/wDwUA+N0DWdu0K6LpbLGYVwD5EHI4r6m/sHTP8AoHWn/fhf8KAPmj/h5J8G/wDnt4i/8Ek3+FH/AA8k+Df/AD28Rf8Agkm/wr6X/sHTP+gdaf8Afhf8KP7B0z/oHWn/AH4X/CgD5o/4eSfBv/nt4i/8Ek3+FH/DyT4N/wDPbxF/4JJv8K+l/wCwdM/6B1p/34X/AAo/sHTP+gdaf9+F/wAKAPmj/h5J8G/+e3iL/wAEk3+FH/DyT4N/89vEX/gkm/wr6X/sHTP+gdaf9+F/wo/sHTP+gdaf9+F/woA+aP8Ah5J8G/8Ant4i/wDBJN/hR/w8k+Df/PbxF/4JJv8ACvpf+wdM/wCgdaf9+F/wo/sHTP8AoHWn/fhf8KAPmj/h5J8G/wDnt4i/8Ek3+FH/AA8k+Df/AD28Rf8Agkm/wr6X/sHTP+gdaf8Afhf8KP7B0z/oHWn/AH4X/CgD5o/4eSfBv/nt4i/8Ek3+FH/DyT4N/wDPbxF/4JJv8K+l/wCwdM/6B1p/34X/AAo/sHTP+gdaf9+F/wAKAPmj/h5J8G/+e3iL/wAEk3+FH/DyT4N/89vEX/gkm/wr6X/sHTP+gdaf9+F/wo/sHTP+gdaf9+F/woA+aP8Ah5J8G/8Ant4i/wDBJN/hR/w8k+Df/PbxF/4JJv8ACvpf+wdM/wCgdaf9+F/wo/sHTP8AoHWn/fhf8KAPmj/h5J8G/wDnt4i/8Ek3+FH/AA8k+Df/AD28Rf8Agkm/wr6X/sHTP+gdaf8Afhf8KP7B0z/oHWn/AH4X/CgD5o/4eSfBv/nt4i/8Ek3+FH/DyT4N/wDPbxF/4JJv8K+l/wCwdM/6B1p/34X/AAo/sHTP+gdaf9+F/wAKAPmj/h5J8G/+e3iL/wAEk3+FH/DyT4N/89vEX/gkm/wr6X/sHTP+gdaf9+F/wo/sHTP+gdaf9+F/woA+aP8Ah5J8G/8Ant4i/wDBJN/hR/w8k+Df/PbxF/4JJv8ACvpf+wdM/wCgdaf9+F/wqKPS9FmhMyWdi8Q6usSEce+KAPm3/h5J8G/+e3iL/wAEk3+FH/DyT4N/89vEX/gkm/wr6WXQ9KkUMun2bKehEKf4Uz+yNH84xfYrLzQM7PJTOPXGKAPmz/h5J8G/+e3iL/wSTf4Uf8PJPg3/AM9vEX/gkm/wr6X/ALB0z/oHWn/fhf8ACs/R5PDHiD7UdMGl6h9lmNvP9mWOTypAASjY6MARwfWgD54/4eSfBv8A57eIv/BJN/hR/wAPJPg3/wA9vEX/AIJJv8K+l/7B0z/oHWn/AH4X/Cj+wdM/6B1p/wB+F/woA+aP+Hknwb/57eIv/BJN/hUdz/wUg+D8lvKkN14hhlZSEkbQpmCnHBx3x6V9N/2Dpn/QOtP+/C/4VFdeHLCa1mjis7a3lZCqTLAmUJHDDI6igD4Q+DP/AAVF02T4iN4E+ItlN9puLoR6Zr2n2LxR3KO2Iw8BLMh7Ft2OOlfoBDMtxCkqHKOoZfoRmvFfhH+yP4G+Feu33iR7eTxN4wvpGkn17WAklxgkkIgVQqqO2Bn3r2zpwKYC0UUUgCiiigAooooAKKKKACqesapDoek3uo3Ic29nA9xII1LNtRSxwB1OB0q5SMoZSrDIPBBoA+Wv+HkHwk/59vF3/hO3H+FfPfgj9tT4d6X+2p8RfGs0PiI6PqegWlnAqaNM04dPJ3bo+qj5TgnrX6O/2Lp//Phbf9+V/wAK+VPhvp9q3/BRT4swm1gMQ8L2LBPKXAP7jnpQBpf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8zf8PIPhJ/z7eLv/AAnbj/Cj/h5B8JP+fbxd/wCE7cf4V9M/2Lp//Phbf9+V/wAKP7F0/wD58Lb/AL8r/hQB8vL/AMFEPgys3nDTPFAmznzB4am3fnip2/4KPfCJutp4tP18O3H+FfTX9i6f/wA+Ft/35X/Cj+xdP/58Lb/vyv8AhSSS2Q3JvdnyB4m/bo+D3ibXNA1J/wDhMLdtInM6xL4bnKyZxwfTpTvHn7dvwg8daCNLc+L7JBMk3mL4bnb7vbHoa+vf7F0//nwtv+/K/wCFH9i6f/z4W3/flf8ACuaWFoyU04/Hv56JfkkdMcVWg4SjLWG3lrf82fMkf/BR34RxoFFr4twBj/kXbj/ClH/BR74RrnFr4tH/AHLtx/hX01/Yun/8+Ft/35X/AAo/sXT/APnwtv8Avyv+FdRynzKP+CkHwjHS18Wj/uXbj/Cj/h5B8I+v2Xxbn/sXbj/Cvpr+xdP/AOfC2/78r/hR/Yun/wDPhbf9+V/woA+Zv+HkHwk/59vF3/hO3H+FJ/w8g+Eec/ZfFuf+xduP8K+lo9P0mSaSJLWzaWPBdBGhK56ZGKWHTdLuNxjtLOQK21tsSnB9OlAHzQ3/AAUe+ETdbTxafr4duP8ACg/8FH/hGwwbXxaR/wBi7cf4V9KzafpNu0ay21nG0jbUDRoNx9BxzUv9i6f/AM+Ft/35X/CgD5l/4eP/AAj24+y+Lcen/CO3H+FH/Dx/4R4x9l8W4/7F24/wr6Ie88MR+IE0NpNLXWXh+0Lp58vzzHkjeE67eDz7Vpf2Lp//AD4W3/flf8KAPmX/AIePfCPGPsvi3Hp/wjtx/hR/w8g+EZ4Nr4tI/wCxduP8K+mv7F0//nwtv+/K/wCFH9i6f/z4W3/flf8ACgD5lX/go98I1GFtfFoHt4duP8K8W+I//BUSH4ffESz1ax07UNe+HF4Fhnt7rR5bC6spe7iVyRIOR8u0fWv0D/sXT/8Anwtv+/K/4V5F4w/ZP8HfEb4nW3i/xb9o8QQ2KKun6HchBY2rqc+YEVQS2fUmgDtvhD8WtB+NvgXT/Fvhtrk6VermP7XCYpB6gqa7SobOzt9Pt0gtYI7eBBhY4kCqB6ACpqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4rRfhD4e8P/FLxF8QLSCRPEWvW0Nreyl8oyRKqpgduEFdrRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXwL46jk8PftDXnwQsNakh+HnjG8i1PUbhJWLadMzYexWTPyecFUAHnrivvmvFrj9lTwvf+B/EmgXt7qF3ea9qJ1W41yR1F7HdDHlyIwXA2YG0YxU/aT6fnqtP1+Q/stdfy0ev6fM7TS/iL4Z0/wASJ4LtpZor+zjEYieF9gVVH8ZGDxXxZ4c+L2kL8ctH+Lx8SWs02veIn8LtpH28ExWLFI4XMW7giRpSTj0r7h1rwLDrngu80Ga9njnurFrF9ViCi6AKbd4bH3se1cVqn7LvgXUvhYPA6actnapB5UWowRoLuJxkiUPt++CSc1SvGfPvb8bvX8NPmTZShybX/DTT8dfkefeKv2hPGnhv45x/CgQ6VLreuXEV7ol+Ubyk03difzV3cyqFYjkZyOK58/HXxPofhfxLcaRoVnZLH4zfSL3VNM0+WdbaERwH7RLErFmJLsCwwAFFemSfsn6DdLJe3uuave+J/t9vfweIpJE+1weTt8uJCF2iPK8jHO41p6f+z3/wjem6vD4e8Xaxo97qmqvqs91H5LlneNEZCrIQVxGD0zkmpSsrf1vG34Jp/eW3d3t/VpX/ABat9x1/wn8Ujxl4JsdTGuab4jEmR/aOlKVhlwcfdLEqR0IJ7V2FcX8JfhTpHwd8JLoOjtNLEZnuZp5yN80rnLOQAAM8cAAcV2laSs3oZxulZhRRRUlBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXFaT8IfDmi/FTW/iHa2zr4m1izjsLu4L5Vok27QB2+4tdrRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8b/ALanibUfgL4s0j4h+DZ2bxFrFu+kajp2HlDWwwReeWDx5G9mJ/2hmvXfh/rXg74B/Dvwtoh1K71FNQj89dRSN7g3cj4LSMwzjOR+VdXN8F9E1H4i6n4w1V59XvLyw/syO1u9rQWsB3b1jUDjfu+YnPQVY+Ffwm0z4U+Drbw1aXNzqunWcjtaf2jtkeCNjny1OB8oOcd+aVO8Y8r/AKV3p+vq32QT96V1/Wm/6elvM+U/2ufGWneMviJq9ovie10W8+G+kL4hsIJbwQPdX7byse0kF8eWnH+1Xd/E79rDUvhx4D8D/E2KK11TwJrlmLea2AK3KXrjELBs42F8huOAM5r2HwX8A/C3hG88R3s9t/b+o69fvf3d5q0UcsmWVVEanbwg28D3NcZoP7HHhHS/s9pfahqmuaBZx3Mdjod/IjWtqJxhyoCg5GOMnjtSV+RR+f53XzvZPpZMvTn5n6fLSz+Vr263Zy//AAtDxvo/xCtLG90HRtd8Vf8ACHTawn2GFllln/elLdGLH5ThR7kmu1/Zy+KV/wDEBdQj1XxLY6lqMaLLLpJsZLK9sGJIKyRSMW28YDYGcGp/DX7MeleG7mwvI/EmuXGpWGkvo1tfTSoZY4WLlTnb95d5wfYVt/D34IWvgvxhqPizUNZvvEniW8tksm1C+WNWWBCxVAI1UHlm5OTzWmnN5a/nJr8Gr/mZWdvPT8kn+tvyPTKKKKkoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/Z)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TCaaB2nyHY_"
      },
      "source": [
        "Figure 2. SVM focuses on finding the best margins to segregate the data. This is done by finding the hyperplane with the largest margin."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nDf7NNbokST7"
      },
      "source": [
        "##Dealing with non-linear planes\n",
        "\n",
        "Some problems can’t be solved using linear hyperplane, as shown in the figure below (left-hand side).\n",
        "\n",
        "In such situation, SVM uses a kernel trick to transform the input space to a higher dimensional space as shown on the right. The data points are plotted on the x-axis and z-axis (Z is the squared sum of both x and y: z=x^2=y^2). Now you can easily segregate these points using linear separation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubThO4ASsj_Z"
      },
      "source": [
        "![Slide4.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DrDWmkQyYks"
      },
      "source": [
        "Figure 3. Shows that sometimes the data is distributed in a such a way that a linear function does not represent. How do we best represent these data? Use the kernel trick and represent the data in three dimensions (x,y, and z). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoTsWE5pskPk"
      },
      "source": [
        "#SVM Kernels\n",
        "The SVM algorithm is implemented in practice using a kernel. A kernel transforms an input data space into the required form. SVM uses a technique called the kernel trick. Here, the kernel takes a low-dimensional input space and transforms it into a higher dimensional space. In other words, you can say that it converts nonseparable problem to separable problems by adding more dimension to it. It is most useful in non-linear separation problem.\n",
        "\n",
        "All you need to know is that the kernel trick allows us to expand the number of planes in which the data is presented. But if you want more details, you can find them [here](https://www.datacamp.com/community/tutorials/svm-classification-scikit-learn-python).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrcbcDhuy3Lk"
      },
      "source": [
        "#Example: Cancer Dataset\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pk57gzTXzGux"
      },
      "source": [
        "##Classifier classes\n",
        "This data has two types of cancer classes: malignant (harmful) and benign (not harmful). These are the two classes in this dataset. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E6vlxAnzNle"
      },
      "source": [
        "##Classifer features\n",
        "\n",
        "There are 30 features in this dataset. All thirty are used to determine the type of cancer (harmful or benign)\n",
        "\n",
        "1) mean radius\n",
        "\n",
        "2) mean texture\n",
        "\n",
        "3) mean perimeter\n",
        "\n",
        "4) mean area\n",
        "\n",
        "5) mean smoothness\n",
        "\n",
        "6) mean compactness \n",
        "\n",
        "7) mean concavity \n",
        "\n",
        "8) mean concave points \n",
        "\n",
        "9) mean symmetry \n",
        "\n",
        "10) mean fractal dimension \n",
        "\n",
        "11) radius error \n",
        "\n",
        "12) texture error \n",
        "\n",
        "13) perimeter error \n",
        "\n",
        "14) area error \n",
        "\n",
        "15) smoothness error\n",
        "\n",
        "16) compactness error \n",
        "\n",
        "17) concavity error  \n",
        "\n",
        "18) concave points error \n",
        "\n",
        "19) symmetry error \n",
        "\n",
        "20) fractal dimension error \n",
        "\n",
        "21) worst radius \n",
        "\n",
        "22) worst texture \n",
        "\n",
        "23) worst perimeter \n",
        "\n",
        "24) worst area \n",
        "\n",
        "25) worst smoothness \n",
        "\n",
        "26) worst compactness \n",
        "\n",
        "27) worst concavity \n",
        "\n",
        "28) worst concave points \n",
        "\n",
        "29) worst symmetry \n",
        "\n",
        "30) worst fractal dimension"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdXRch_oy2Yz"
      },
      "source": [
        "#Import scikit-learn dataset library\n",
        "from sklearn import datasets\n",
        "\n",
        "#Load dataset\n",
        "cancer = datasets.load_breast_cancer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Glqcn4u_0U78",
        "outputId": "514019ec-bef9-4363-dfd2-d511513b59e2"
      },
      "source": [
        "# print the names of the 30 features\n",
        "print(\"Features: \", cancer.feature_names)\n",
        "\n",
        "# print the label type of cancer('malignant' 'benign')\n",
        "# the labels represent the two classes\n",
        "print(\"Labels: \", cancer.target_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features:  ['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
            "Labels:  ['malignant' 'benign']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNv0_pWn0VAs",
        "outputId": "c6e0d08b-01f9-4312-d27f-4b3e6b1a4fb1"
      },
      "source": [
        "#what is the shape of the dataset\n",
        "cancer.data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOUW45Kw0VCn",
        "outputId": "b96b8871-1fda-48ce-fc89-a030fcff31c2"
      },
      "source": [
        "#Let's take a look at the first two records\n",
        "# print the cancer data features (top 5 records)\n",
        "print(cancer.data[0:2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01\n",
            "  1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02\n",
            "  6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01\n",
            "  1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01\n",
            "  4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 1.326e+03 8.474e-02 7.864e-02 8.690e-02\n",
            "  7.017e-02 1.812e-01 5.667e-02 5.435e-01 7.339e-01 3.398e+00 7.408e+01\n",
            "  5.225e-03 1.308e-02 1.860e-02 1.340e-02 1.389e-02 3.532e-03 2.499e+01\n",
            "  2.341e+01 1.588e+02 1.956e+03 1.238e-01 1.866e-01 2.416e-01 1.860e-01\n",
            "  2.750e-01 8.902e-02]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERUKrpH30yMg",
        "outputId": "3ebaf5e3-c89c-443b-8e9b-3649cc99c3aa"
      },
      "source": [
        "#and the target (the classes)\n",
        "print(cancer.target[0:2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbXpotId0-7L"
      },
      "source": [
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.3,random_state=1) # 70% training and 30% test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dd8xmGaN1Cjj"
      },
      "source": [
        "#Import svm model\n",
        "from sklearn import svm\n",
        "\n",
        "#Create a svm Classifier\n",
        "classifier = svm.SVC(kernel='linear',probability=True) # Linear Kernel\n",
        "\n",
        "#Train the model using the training sets\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1Ro69Bv1xTe",
        "outputId": "4062b83a-25b2-4b65-cafc-7f37438f9e31"
      },
      "source": [
        "#import the metrics class\n",
        "#get the confusion matrix\n",
        "from sklearn import metrics\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 57,   6],\n",
              "       [  2, 106]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sROpPMiS2Cku",
        "outputId": "cc30b90e-e3a6-45db-cbe9-c6eaad29f624"
      },
      "source": [
        "#Getting all the metrics\n",
        "print('{:<15s}{:<20s}'.format('Accuracy:',str(metrics.accuracy_score(y_test,y_pred))))\n",
        "print('{:<15s}{:<20s}'.format('Precision:',str(metrics.precision_score(y_test,y_pred,average='binary'))))\n",
        "print('{:<15s}{:<20s}'.format('Recall:',str(metrics.recall_score(y_test,y_pred, average='binary'))))\n",
        "print('{:<15s}{:<20s}'.format('f1 score:',str(metrics.f1_score(y_test,y_pred, average='binary'))))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:      0.9532163742690059  \n",
            "Precision:     0.9464285714285714  \n",
            "Recall:        0.9814814814814815  \n",
            "f1 score:      0.9636363636363636  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zf044CpD2ogH"
      },
      "source": [
        "This looks pretty good!! But let's take a look at the ROC AUC. It also shows a really good model with ROC AUC = 0.983. To review what the ROC is, please see our [first notebook](https://github.com/tawfiqam/MI564/blob/main/Logistic_Regression_Classifier_Intro.ipynb) on logistic regression classifiers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "gKnEWA7V2KdH",
        "outputId": "890e3b18-d190-4ad5-ff02-2dc615364d2f"
      },
      "source": [
        "#source: https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size':20,'figure.figsize':(10,8)})\n",
        "y_pred_proba = classifier.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
        "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
        "plt.plot(fpr,tpr,label=\"Our Model, AUC=\"+str(auc))\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3anUXz6Cu6D0"
      },
      "source": [
        "#Advantages\n",
        "\n",
        "SVM Classifiers offer good accuracy and perform faster prediction compared to Naïve Bayes algorithm. They also use less memory because they use a subset of training points in the decision phase. SVM works well with a clear margin of separation and with high dimensional space. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpBFcAvlu_DJ"
      },
      "source": [
        "#Disadvantages\n",
        "SVM is not suitable for large datasets because of its high training time and it also takes more time in training compared to Naïve Bayes. It works poorly with overlapping classes and is also sensitive to the type of kernel used."
      ]
    }
  ]
}