Notebooks [rus]/ML-алгоритмы с нуля/05) Метод опорных векторов (SVM).ipynb

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        "## **Метод опорных векторов**\n",
        "Метод опорных векторов (Support Vector Machines или просто SVM) — мощный и универсальный набор алгоритмов для работы с данными любой формы, применяемый не только для задач классификации и регрессии, но и также для выявления аномалий. В данной статье будут рассмотрены основные подходы к созданию SVM, принцип работы, а также реализации с нуля его наиболее популярных разновидностей.\n"
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        "### **Линейный случай SVM**\n",
        "**В случае линейной классификации** основная идея SVM заключается в поиске гиперплоскости или линии, разделяющей классы наилучшим образом, а расположенные ближе всего к разделяющей гиперплоскости образцы называются *опорными векторами*. Лучшим разделением классов будет то, при котором максимизируется расстояние между опорными векторами и разделяющей гиперплоскостью. Это расстояние называется *зазором*. Здесь возможны 2 варианта:\n",
        "- 1) **Классификация с жёстким зазором (hard margin)**, когда все обучающие образцы должны быть правильно классифицированы и находиться за пределами полосы разделения. Такой подход работает только в случае хорошей линейной разделимости данных и является довольно чувствительным к выбросам.\n",
        "- 2) **Классификация с мягким зазором (soft margin)**, когда вводится допущение, что некоторые обучающие образцы могут нарушать условие правильной классификации или попадать в полосу разделения, но при этом вводится штраф за такие нарушения, пропорциональный параметру C (чем ниже данное значение, тем шире полоса и больше нарушений попадает в зазор). При таком подходе увеличивается обобщающая способность модели, что позволяет снизить риск переобучения.\n",
        "\n",
        "Спрогнозированными классами будет линейная комбинация весов и тестовых признаков с использованием стратегии OVR (One-Versus-Rest) или OVA (One-Versus-All), а для минимизации используется функция потерь hinge:\n",
        "\n",
        "$$min \\rightarrow C \\sum \\limits_{i=1}^{n} max(0, 1 - y_i (w^TX + b))$$\n",
        "\n",
        "В реализации scikit-learn к данной функции потерь ещё добавляется l1 или l2-регуляризация на выбор, а также имеется возможность использовать квадратичную функцию потерь с помощью параметра loss='squared_hinge'.\n",
        "\n",
        "Стоит отметить, что задачи с жёстким и мягким зазором являются задачами выпуклой квадратичной оптимизации с линейными ограничениями и относятся к задачам квадратичного программирования, для решения которых существует множество различных готовых пакетов.\n",
        "\n",
        "В данном случае используется готовая реализация на основе библиотеки Liblinear, но в ручной реализации ниже для более простого понимания будет использован градиентный спуск с одним улучшением: веса будут обновляться не на каждой итерации с учётом прогнозов всех образцов, а только с учётом прогнозов для каждого обучающего образца отдельно при условии, что размер отступа меньше 1, что получается из уравнения выше. Такой подход позволяет обеспечить сравнимую точность hinge loss без регуляризации с squared hinge loss с l2-регуляризацией в scikit-learn, а если использовать аналогичные параметры, то точность будет в целом немного выше, однако такое решение менее стабильно."
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        "![LinearSVC with margines.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAy4AAAIfCAYAAACW4ogXAAAAAXNSR0IArs4c6QAAAARzQklUCAgICHwIZIgAAAAEZ0FNQQAAsY8L/GEFAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAD4mlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4KPHg6eG1wbWV0YSB4bWxuczp4PSdhZG9iZTpuczptZXRhLyc+CjxyZGY6UkRGIHhtbG5zOnJkZj0naHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyc+CgogPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9JycKICB4bWxuczpBdHRyaWI9J2h0dHA6Ly9ucy5hdHRyaWJ1dGlvbi5jb20vYWRzLzEuMC8nPgogIDxBdHRyaWI6QWRzPgogICA8cmRmOlNlcT4KICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0nUmVzb3VyY2UnPgogICAgIDxBdHRyaWI6Q3JlYXRlZD4yMDI0LTAyLTA5PC9BdHRyaWI6Q3JlYXRlZD4KICAgICA8QXR0cmliOkV4dElkPjI5YTEwOGRhLWEyYWItNGY0Yi1iOGM0LWVkODU1ZjRjZjBhYTwvQXR0cmliOkV4dElkPgogICAgIDxBdHRyaWI6RmJJZD41MjUyNjU5MTQxNzk1ODA8L0F0dHJpYjpGYklkPgogICAgIDxBdHRyaWI6VG91Y2hUeXBlPjI8L0F0dHJpYjpUb3VjaFR5cGU+CiAgICA8L3JkZjpsaT4KICAgPC9yZGY6U2VxPgogIDwvQXR0cmliOkFkcz4KIDwvcmRmOkRlc2NyaXB0aW9uPgoKIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PScnCiAgeG1sbnM6ZGM9J2h0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvJz4KICA8ZGM6dGl0bGU+CiAgIDxyZGY6QWx0PgogICAgPHJkZjpsaSB4bWw6bGFuZz0neC1kZWZhdWx0Jz5TZW5zZWkgLSAxPC9yZGY6bGk+CiAgIDwvcmRmOkFsdD4KICA8L2RjOnRpdGxlPgogPC9yZGY6RGVzY3JpcHRpb24+CgogPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9JycKICB4bWxuczp4bXA9J2h0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8nPgogIDx4bXA6Q3JlYXRvclRvb2w+Q2FudmE8L3htcDpDcmVhdG9yVG9vbD4KIDwvcmRmOkRlc2NyaXB0aW9uPgo8L3JkZjpSREY+CjwveDp4bXBtZXRhPgo8P3hwYWNrZXQgZW5kPSdyJz8+zaccqAAA87VJREFUeF7s3QdgFNXaBuAvlSTU0Lv0oiAdlV5VEAQFFAt2FLtYwX7xBxEVKV4bXntDRQSkCEhvSm/Sew0tAUICafvve/bMZrKkbGBnspu8j3fvnjM7pG7mzHfKd4IcTkJEREREROTHgvUzERERERGR32LgQkREREREfo+BCxERERER+T0GLkRERERE5PcYuBARERERkd9j4EJERERERH6PgQsREREREfk9Bi5EREREROT3GLgQEREREZHfY+BCRERERER+j4ELERERERH5PQYuRERERETk9xi4EBERERGR32PgQkREREREfo+BCxERERER+T0GLkRERERE5PcYuBARERERkd9j4EJERERERH6PgYtNZsyYIQsXLtS1/CsxMVE+/PBDmTdvnj5CRETkv5YtWyaffvqpHDt2TB8hIn8V5HDS5Xzh/Pnz8uKLL0p8fLw0aNBAnn32Wf1K3vnf//4nDz30kAQHB8vevXulSpUq+pX859FHH5VPPvlElceMGSNPP/20KhMRkX9LTU2VQYMGqfZzwoQJUqRIEf1K/nXgwAGpVauWJCUlSYUKFWTXrl0SGRmpXyUif5PvRlx+++03GT9+vHz55Zfy3HPPyYkTJ/QreWf06NHqOS0tTbZs2aLK+RUCM8P27dt1iYiI8tLRo0dl8ODB0qNHD+nevbs88MADF12j33jjDfn888/lp59+UrMECoLDhw+roAXwMzp79qwqE5F/yneBy759+3TJBVOX8lpsbKwuZSznR2PHjpUbb7xROnbsqEa+iIgob+Hm/Nprr1Wj4NOnT5eZM2eqzr0HH3xQn+GyaNEiXRK/6PSzwzXXXCMjR46Uxo0by7vvvitly5bVrxCRP8p3gUtMTIwuuYSGhurSpXvyySfVjfjLL78sKSkp6tiCBQvUNLRhw4ZJQkKCOpaVoKAgXRJ3z0520OuDKVfoHbNqXcyKFSukS5cu0rdvX1myZIk6FhcXJx988IE89thjF33e5ORkmThxogpGhg8fLv/++68MHDhQnn/+eTl16pQ+S1RvVdOmTVVjcPLkSX1U5Mcff1THOnfuLH///bc6tnbtWpk6depFvzMiooLsyJEjMnToUNX24GEOKAzopBs3bpxqg3B9za6TDlOVzZ161atXl4iICOnQoYM+4mJunzDtOje+/vpr1W795z//kQsXLuijvoXRoPbt26uAC8EY7Ny5U15//XXVHu/Zs0cdM6Bt+uijj1Q7hbWXa9asUW0eOtgwLQ7QtuFngQ434xicPn1abrnlFmnVqpU888wzgln1aOunTJmi1sRgBgUR5QGscclP7rzzTqzZcT+qVq3qcF7oHCNHjnTExsbqs9I5AxHHt99+63jggQccffr0cTgvuo74+Hj9qsOxffv2DB/vjz/+cAwZMiTDMXxsT/v373f88MMPjtmzZzvKly/vPve7777TZ2Tt8ccfd59fq1YtxzvvvONwXqgd77//vsPZmKhztm7d6pg8ebJ6zgyOv/baaw7nRdpx3333OZyBiH7Fxfxzat68ufo+8bMyjhUtWtThvKCrc3fv3u1o0KCB+zXPR+/evdV54GwQ3cdfeeUVdczZoKqPZxy/7bbbMnz+QoUKOaZNm6bOJSIqyI4dO+aoVKmS+/qIR5UqVfSrLosXL3ZERUVlOKdixYoO5w21PiPdxo0bM5w3ZswYddx5462eDc4bdUejRo3c56HtqVGjhqNfv376jKw5Ay1HaGio+98+99xzqh174oknHCtXrlTnoB2YNWuWahNRzsy8efMcTz31lOPWW29Vz2h7zPA9Gp/DGYyoNjAyMtJ9zBl86DMdjj///NMRHR3tfs3zYfwcnAFXhuPO4E0dHz58eIbjEyZMcJQtW9Zdb9eunbp/ICJ75bvA5YYbbshwsTE/cNHbtm2bPtPhmD9/vqNOnToXnXfzzTfrMxyODRs2ZHitZcuWGep4fP755/psh+PUqVOOHj16OMLCwi46D49ff/1Vn5m1Ll26ZPpv8ejQoYPj0UcfzXAMgY3hwoULjoEDBzrCw8MznING5Z9//tFnOVTDYLxWvHhxR926dTOcjyAmNTVVNTCZ/YzMj6+++kp/VIejY8eO7uNPPvmkOrZ58+YM55sbOONx3XXXqXOJiAqyTz/91H1dDAoKUgHKs88+q191dbZdeeWVGa6fxgMBjufN9CeffOJ+vVixYu4bc7NBgwapz2X+WOZ/4xnkeFqyZEmm/xYPXO/R6VauXDn3MQRER48e1f/a1dHXqVOnDP8Oj8qVKzvOnDmjz3I4SpYs6X6tYcOGGYIWPO6++2513qZNmy56zfOxd+9edS469czHT548qY4jYDMfz6zdQnBERPbKd4FLixYtLrq4mB/333+/PjPj6IDnAyMQsGXLlkxfx+Pqq692TJ8+XZ0HuMk391hl9pgxY4Y+O2s5fQzPBy6oJ06cUP/Ws/fI/MAoh+H222/P9Bx8LPRkHT9+XJ338ccfZ3j92muvVaNSeDaOmYMxc0D08MMPq2OrV692H8vqgeCJiKigQ0eQcV3s1q2bPppu0qRJGa6da9euVTMGjDraLEAbhk6sJk2auF+rVq2aGo33DEQ8R3jwQMCEUQVvbs4x8uH573N6PPPMM/pfO1S7nNk5eHz22Wf6LEeG4Mf8KFOmjOPDDz90B2We7Vv//v3VDADzrAKjzfTsnDx8+LA63rNnzwzHM3t88MEH6lwisk++W+Ny5swZXRKJjo6WTZs2qfmsISEh6pixvgKw5sJ5oy5PPPGEyqaC8w3z589Xz+Hh4erZ0z333CP//POPys5ieOedd2T9+vW6JvLee++pY2FhYfqIiPMCq0tZMy/gR5rG5cuXq/UoZkj1jIWWgHU3yKYGV111lRQuXFgtNBwxYkSGr8/4niCz76tkyZJqbQsWKJYuXVodW7x4sXo24HvCfOIBAwboI5Lh+zOXjTnAzveZejZDauhRo0ZJw4YNVR2/N84ZJqKCDmsEDbgee64B/OGHH3TJdR3Ftd68VsVYc/nmm2+qlMZYS2hA1sd69epdlKYeHxNrZcwL03EMn//666/XR7LmmXQG7d4333xz0RpTfE1t2rRR5Z9//lk9Q4sWLdT30qtXL7WGEu2bIad2Cz+vdevWyeOPP+5uf8ztFlIb42v5v//7P9XmG4xzzW0WZNduOYNA1W4ZsC6UiOyV7wIX86LAEiVKqBv5p556Stq2bauOGQv6AAsaz507JzVq1FAXI/PFd9u2berZ86IGuGn/6quvpFChQvqICy6OBlxg0ThgMTsCAkO5cuV0KWvmdIwVK1ZU2WDefvttfcQFKZ/x8bGoELZu3aqemzVrpr6P33//XSZPnpwhpSUWfBof2/P7Klq0qMydO1ctRDTDwkWzqlWrqmfzz9HcmJg/rtEAZJYTH3vbvPDCC9KpUydVRyOR28WgRET5DW7asZ8IIAh55ZVXVNmAxCoG43pr7vQxrrf9+vVTHV/m5DCGHTt26JJLu3bt5LXXXpOoqCh9RHKVXcszhTACEbSTN9xwgz4ialE9OgiRjhnQhhj/Dov6ExMTVZuGRfTocDQYbTF4tlvo+EK7hXbSzNxu4Wdp/LvM2i3Pj5lVu9WoUSMVRCEpjSGnxDxE5Hv5LnAx95LgZtxgBBmeF9jvv/9eZSPBRdPs+PHj6tkzOwpu7JHZxLMxQOrI3bt365pIqVKl3L1N5kwt3jQG5q/R+B6uuOIK9Qy4oBqBmHFxNe/4i4t2t27dZOXKlfpIusy+L3wv+DmgN8lT+fLldcnFGLnCJl0G88/ZeB2MBqB48eLq2YBetfvuu0+Vza8xcCGigg7X41tvvVXXXNm6jOs2mNsTIwuW+QbamDnQu3dvFaDcfffdqg7oREOnkbmTzQwdeQa0Yd7ybFcza7eMIMYcEJjbLewp07NnT9XBZmb+3s3tFmYFICuleaaEwdxumdsko91C22x0+plfh6zaLXRW4hjbLKK8le8CFzPzrr/GBR7PRnCDYV6kHAaca+5JMQIZzxGHhx9+ONPhavMFGMyjLMbHQkPgOUrjCRdCTP0yFCtWTD2bAyWkssTFFucZU+OM8wDD9MZGl0jnWK1aNVWGzL4vNC5oMDJz3XXX6ZKL0YiYAxeMbBnM6SSNBgA/i8x6/cCz0SAiKujuuOMOXXJNBTZPfapbt64uuV5D0HDo0CF9JOP1GMwjCngNG09mNWXZfJ3OzbXZM3DJrN3C6A+Y0+eb2y1M9TICJ3QmGsydiuZ2CyM35rbNzNxuGfvRILjDVgOQVZsFRruVVeCG7wnT2ogob+Trvz6s9TCYgwHjwoQ5tsjVDsj1bu7lMoIbz8DFmCrlyfPGHL1HWIOyf/9+d6+M53B2ZjwbAM9GCIz1JwiWjItunTp11DNgRAjq16+vpsOZp6dl9n1l9T0BctsbPVOAPQVwbPXq1fpIeg8fmD+u8XNGDxum4xnMDZe5B81zPjQRUUGEkf3KlSvrWsaNlTGSYob9tYy1m7jWm6/XYK57Tn/yZB6pMK/XzElu2i1juhbOMQIotJfGXjUIrLDOxWCeReFtu4UOOwOmTt92223uWQqQVZsFRrtlXmcDRruF841z2GYR2S/fBS7mxfnm+brmwMXoSdq4caN6Rq8PRh0wUuHJMyAx93aZ1a5dO8MoCy5uGM0xD5Ub85azY/76IbOeMSMgi4+PV89gXIix6aPRMKCBw1oY83xhg/n7yup7AnxcLKg0oOfvzz//dF+4wWiQwPxzNvdkmRecmnvyzIFLTqNRREQFwV9//ZXlTTFmBpjbBSSXmTdvnipjrYon88fJbFqVWevWrXVJZNCgQSoo8oa53cJohLktNHi2W+avxWiLAVPKsOFjZrxtt/AxMF3a8Msvv6jNJw3mn5+5zQKj3TK3WWC0W2yziPJWvgtczD375iHyzHpIqlSpop5x0cXCQeyGbzAuUjjH+DfoActq1ATnjBw5Utcy57leJDPmOcZgBAXmC7bRg4b1MsaQNXqsAPNvjfnFWNCPBfDmj2l8X5huZjBnWsnMSy+9pBoWBEL4+OaLNb4+89Q589dp/lmbF/2be/2MRgPfBxsBIiro0LOPm25kADO0bNlSl1wdbZ999lmGG2m0b7iGZnbDbx6xMI/iZOaRRx5xf1xMC8Yu+N4wtzEYSTGu/Zm1W0Y7iFEkIwgwf1233367fPrpp7qWsaPL23YLn3fatGkyc+ZM9bNDm2Nup8ztuPlrBONrR/Y1cwBmtFvmQMfcOUpE9sh3gQuGm430jcgCYsDQMS5QRhYrQLYx9F7h5hs3++ilQXYTXNTuvPNOdQ4uwn379lVlIxtKVvCxJk2apHpqjIuheeG6Mdc2O7hYduzYUdfSM58YAQmCMWP+M742rLnBMwIvwEX3119/VQvt0VDgQj9kyBA17N21a1f3POP7779fnYtzOnfurI5lB/8eWcqwLggpNg3mhgTwteHrwZSFu+66Sx8V6dOnj7vHzdyTddNNN6nzjcX6REQFGdZb4vqNNgTTodAhZh4JAXQizZkzRyU6wXUVHWxffPHFRVkhwbyAHDMDsoM2E1ONcV3H14GOL2+gvTQWrZsDBKNzEFnGMHUZMBUMwQvaI6OzCkHI6NGj1ZoVtEn4/l9++WUV0Dz00EPqHDDWoeJj1axZU5WzgoAH05oxjQ7rW8xJCsztFqZZo83F58VaT2PdDAJB498gaDG+frRX+LjoOMzpnoCILODIh5wXKbXpoafTp0+r3eBz68KFC47ly5dftCNxds6dO6d20cfuwNiJ33kTr3bq95YzsHC0adPGcejQIX3E4di9e7cjJiZG1y7fzp073bsHZ2Xbtm1qt35sFnZYb8x17733ogtPPfB1egsbov3yyy/q50lERNZ79dVX3ddr84bJvoY2ErvZOwMQfcShNoRct25drtrOnKBtj4uL07XM/fbbbw5nEOcYN26cIz4+Xt0T1KtXz/1zwEbN3khOTnb8/vvvjo0bN+ojRJTXgvB/zj9kokxhfxtMFQP0AGJo3DwtACkinYGMrhERkT/BKDnWKWKKGZ6N6cX5GTZexjQxwMwCPIyRJ7RjyIrpOVuAiAIDAxfKFuY533zzzRk2PTNgyhd29c8sPTQREVFewPQwZAk1bzhpQNplbHJJRIEp/3e90GVB9pVly5bJgw8+6N4XB2uAMAqzcOFCBi1ERORXsGYGCWWwNghtFNa7XHnllfLJJ58waCEKcBxxoVxBFhhm/yIiokCAjGt4GNnCiCiwMXAhIiIiIiK/x6liRERERETk9xi4EBERERGR32PgQkREREREfo+BCxERERER+T0GLkRERERE5PcYuBARERERkd9j4EJERERERH6PgQsREREREfk9Bi5EREREROT3GLgQEREREZHfY+BCRERERER+j4ELERERERH5PQYuRERERETk9xi4EBERERGR32PgQkREREREfo+BCxERERER+T0GLkRERERE5PcYuBARERERkd9j4EJERERERH6PgQsREREREfk9Bi5EREREROT3GLgQEREREZHfY+BCRERERER+j4ELERERERH5PQYuRERERETk9xi4EBERERGR32PgQkREREREfo+BCxERERER+T0GLkRERERE5PcYuBARERERkd9j4EJERERERH6PgQsREREREfk9Bi5EREREROT3GLgQEREREZHfY+BCRERERER+j4ELERERERH5PQYuRERERETk93weuKSlpekSERERERGRb/g0cOnXr5+ULFlSunbtKs8++6w+KpKSkiKrV6+W1NRUfYSIiMh/sNONiMj/+TRw+eOPP+T06dMyd+5c+eCDD+Ts2bPq+ODBg6V58+by/PPPq/rlSExMlH///VfXiIiILg873YiIAoNPA5fGjRvrksuff/4pJ0+elC+++ELV0QhcjjVr1kilSpXkqquuytC4EBERXSo7Ot2SkpJk06ZN4nA49BEiIsotnwYud999ty65/PDDDzJ27FhJSEhQdc/XcwMNSc+ePSU2NlbV0bj8+uuvqkxERHSprO502717t1xxxRXSsGFDue222/RRIiLKLZ8GLrggBwenf8iZM2fKuHHjVLlLly5yzTXXqPKluPfee+Xw4cO65vLAAw/Irl27dI2IiCj3rOx0S05OVp1uR48eVXV0uI0ZM0aViYgod4IcPh63btmypaxcuVLX0i1cuFDatWuna7nz4YcfypNPPqlrGTVp0kSWL18uhQoV0keIiIi8d/z4cSlfvrx7gX5ERIRqUzB9DJ1uc+bMUccvxRNPPCH//e9/dc0lJCREli1bptpLIiLynk9HXKBHjx66lK5NmzaXHLSsX78+2/Usa9euVfOQiYiILkWZMmWkWbNmuiZy/vx5FbTAa6+9pp4vxaRJky4KWgCL/TFDIS4uTh8hIiJv+Dxwufnmm3Up3dChQ3Upd7CupXfv3mqoHUqUKKGeoXr16rok8vHHH8uUKVN0jYiIKHd83em2d+9euf/++3XtYvv27ZM77rhD14iIyBs+D1ywyLFevXq65mIEHrn1n//8R138ISgoKEMAhM9hXjNz3333qawtREREueXLTje0eeh0M7KTFS5cWD0DMmMaZs2axfUuRES54PPABQYMGKBLLtOnT9el3EH6Y8OIESNUGmQDkgBMnDhRihcvruoYckcPFhERUW75stPt3XffVdOcDeiEM2AtTffu3XVNVKplz8QzRESUOUsCl23btumSy7Rp0y5pV2KkPMbCyKefflpeeuklfTQd0ktOnjxZ2rdvrwKb2rVr61eIiIhyx1edbnv27NElUe1Xhw4ddM3lu+++k8qVK6sy1rvExMSoMhERZc/ngQv2WcFIiBnSQC5dulTXXJDMLKeFiY0aNVLZXDCUjqlimenYsaMsWLDgkof0iYiIwFedbq+//rrceOONagrzqFGj9NF00dHRMnXqVBXQvPzyyyo7JhER5czngQvm7F64cEGVkfLR8NVXX+mSyObNm6Vq1arq4o0Lt7GpJBERUV7wZadblSpV1D5mX375pYSHh+ujGSFYmT9/vgwfPlwfISKinPg8cMEOwVC0aFH59NNPVRkWLVqkS67eqCNHjsiVV16p9nfBZl9ERER5hZ1uRET+z+eBS2RkpHrGAsQHH3xQ7W5fsWLFDBtIonEoUqSIDBkyRNW5+z0REeUldroREfk/nwcujz76qIwbN05++eUXVf/f//4nhw4dkqeeekrV4e2331brVoxUx1kNpRMREdmBnW5ERP7PkhEXXOixsD4rDRs2lHvvvdc9n7hUqVLqmYiIKC+w042IyP/5PHDx1h9//KHmC0O1atXUMxERUV5gpxsRkf/Ls8DFvCEXGgMiIiJ/x043IqK8kyeBy+LFi2X16tWqXK5cOalbt64qExER+TN2uhER5Z08CVx++uknXRLp2rWrJCcn6xoREZF/YqcbEVHeypPAZdOmTbok8t1336m8+AkJCfoI5TfYsO3vv/+Wjz766JJ2oSYi8gfsdCMiylt5ErhgN+E33nhDLYR8/PHHZeDAgRIREaFfpfxm/PjxKlMP9klYtmyZPkpEFFjY6VbwrF+/XmWbS0pK0keIKC8FOdAdHiCmT58uPXr0UOWbbrpJLZIk/7dgwQL37wqZe15++WX3nglERIECnS/ffPONnDp1So0eYwd9rHkJDs6+DxDTy5o3b67KzZo1k1WrVqky+bdvv/1WBS7QuXNn6datmyoTUd7JkxEXKljatm3rThuamJgoM2bMUGUiokBSo0YNefPNN1UP/IcffihvvfVWjkELBa4rr7xSl1wdcLGxsbpGRHmFV1yyXEhIiPTu3VvXRJYvXy4xMTG6RkRE5H8wOlalShVVTk1NlSlTpqgyEeUdBi5ki/r160vt2rV1TeTXX3/VJSIiIv/Ur18/XXKtcdqzZ4+uEVFeYOBCtunbt697WgUu/sbcYSIiIn9UsWJFadGiha6JSjTD7JhEeYeBC9kG61yw3sUwc+ZMXSIiIvJPSAZUqFAhVT527JgsXbpUlYnIfgxcyFbXX3+9FC5cWBo1aiSPPvqoPkpEROSfihQpotouw9y5c3WJiOzGwIVshV6rl156SQYMGCDFixfXR4mIiPxXmzZt1KyBWrVqyRNPPKGPEpHdGLiQ7aKionRJ5MSJE5wyRkREfg3ZMbFp9qBBg6RMmTL6KBHZjYEL5Rmklhw5cqT89ddf8u+//+qjRERE/gdTxgynT5/mJthEeYCBC10Eu0LbAXnxDb/99luGOhERkT/CZpRvv/22el6xYoU+SkR2YOBCbsiW8vDDD6vdoY8fP66PWqdbt24SGRmpynFxcbJw4UJVJiIiyi27Ot1SUlLUA6ZPny7nz59XZSKyHgMXcuvVq5dMmDBBDYEPGTJEH7UOghYEL4Y5c+bImTNndI2IiChnCFieeeYZqVq1qi3Tjjt06CAlSpRQ5cTERPnzzz9VmYisx8CF3N544w1dEvn666/l77//1jXrXHvttVK2bFlVTk5O5pxhIiLKFaTWHzt2rJw7d05eeOEFfdQ6oaGhqqPPsGTJEomJidE1IrISAxdyu/HGG90XY6w3efrpp1XZSthJv1+/fromsmbNGtm/f7+uERERZQ+dbsj6BTNmzJBZs2apspUaNmwo1apVU2WHw6F21Cci6zFwoQzGjRvn3iEYIy4//fSTKlupevXqcvXVV+uasAEgIiKvXXnllfLQQw/pmshzzz0nFy5c0DXroNMtKChIlffu3SubNm1SZSKyDgMXygBzhDFX2IDNIs+ePatr1unZs6fqMcOmlJ06ddJHiYiIcvbWW29J0aJFVRnrXD744ANVtlK5cuWkdevWuiby+++/MzsmkcUYuNBFXnnlFRXAAKZtDR8+XJWtFB0dLQMHDpShQ4dKkyZN9FEiIqKcYVPIYcOG6ZrIiBEjbMmOiSnWSDRTuHBh6dKli5r+TETW4V8YXQS9Vu+8846uiYwZM8aWdSe1atVSix4NCQkJukRERJQ97GyPaWOAmQKvvfaaKlspIiJCHnjgAdXphmQzxtQxIrIGAxfKVP/+/eWaa65RZcwVfuqpp1TZDsgM8/PPP6ses/j4eH00/0t1/rcvcausOT1fPbadWyMX0hL1q3ljeewMGb37CXlje3/1+HjfENkSv1K/SkTkPzDd+P3339c1kc8//9yW7JhYp4kAxsBONyLrBDmQDiNAYKOnHj16qPJNN93E1LkWwwUfPUiGmTNnqmFxq3344YdqoSO0aNFCbr/9dlXOz7adWy3TYj6Xsymx+ohLaFCYtC3ZSzqU6quP2ANfx4+H35ND53fpIxk1LtZebin/qK4RUXZWr14tzZs3V+VmzZrJqlWrVJms0bt3b5kyZYoqowPOrt3tsREl9nRZvny5SstcqlQp/Ur+d/j8bjmWdEAczv8igqPkisj6EhXiWnOUFzacXSJLT/0hRy+47iXKF6ombUr2lIZF09ckUWDiiAtlCRf8e++9V9dcmVrsWHiIoNSwcuVKOXz4sK7lTwfOb5cfDr17UdACKY5kmX/yV/nrxER9xB5zT/yYZdAC684slLVnFugaEZH/yIvsmDBx4kRZvHix2lW/oGTH3JOwWcbseUo+3f+yTD76sfx+9BP56fBoeWfXQJl1/Bt9ln0wS+GbgyNk0pEP3UELoPzrkfEy8fAHkuywPuMcWYeBC2Xr3XffzZCpZfz48apsJQy7N2jQQNfyf3rkeSd+1qWsYcpWmvM/O6RJqjMwWaRrWVt80tWjSUTkT/IqO6Z5RsLOnTtt2cU/L8Vc2C/fHRopscnH9JGM0G5NP/aFrtkD7emuhA26drF/4/+WBScn6RoFIgYuAWzLli26ZB1kann55Zd1TeT111+3JVMLNsI0NhQ7cOBAvp5asTsh59z/6CFKSjuva9bCKI83TiYfUdMCiIj8DbJjov0Cu7JjIj3yddddp2uipqvl5/TIi05NzrG9+CduttdtyuVCe7QibqauZW1FbM7nkP9i4BKAdu3apbJ+/e9//7Ml29fgwYNtz9SC9MgdOnTQNZFp06ZJUlKSruUfubnxT3Wk6JK1ktPy38+ZiPzH5s2bdck6mCkwatQoXbMvO2a3bt1UemQ4efKkLFy4UJXzox3n1ulS9uJT43TJWmkO74JEBFKYWUCBiYFLAFq2bJl71APTqKzOr4C5wp6ZWuwYAu/cubN7mhoyjc2ZM0eVC6rgIHv+XMODXXPDiYh86eDBg2pjyC+//FI2bMh6Oo+v3HfffbZnx4yKipIbbrhB10S1W/k1O6a3WS9TbOoMy00wkuawZ+o1+R4DlwBknkZ15MgR+eeff1TZSpi7a1yMMfRtnj9slfDwcLn55pt1zZ5eOrsFOf8rG15F17IXHuzqxbNaWFAhKRlWTteyVr9IS/X1ExF5A9nVDh06pMpTp05Vi9itNnbsWF1yTd2aNWuWrlmnVatWUrZsWVVOTk5WMwYKspCg9P3ZrJSbz2PX10S+x8AlABUrVkzt0GuYMWOGSsNotdGjR7sDJvQi2ZGpBbvo16lTRwUwzz//vD6avyBFY06uLHKNhDj/s0v3svfrUuaQ5rJrmTt0jYgoZ+j8MqZRxcXFyfz581XZSnmRHRO75/fp00fXRHbs2JEvpzpXj7pKl7IXaVNa5GBnG1k5opauZQ1fNzvdAhcDlwDVsWNHKVGihCpjGtXs2bNV2UpY5/LEE0/omitTC4bfrfbwww9Lu3btVGOQHzUq1k6aFu+oaxdD/vne5Qfpmj1qF24sfSs8qfaR8VQ4pLjcV/lVKRVWQR8hIsoZNmk0p7ufO3eunDlzRtes45kdc8KECapspZo1a0rTpk1VsIYEN5hBkN9cF53+u8wKggTs62KXG8rcnWm7ZcBrN5YZoGsUiBi4BKjQ0NAM06iQO/7EiRO6Zp233norQ6aWt99+W5XtsnbtWjX0b8cUAzv1KveIepQvdIUUCo5Uj+iwstLK2TDcX+U1VbcbNuoaWusLea7GRzK4+nj1eKHmp/J8zY+lnPPrJCLKLYyAVKjg6vTAyAemjFktr7Jj3nnnndK1a1fVXudHdQs3lU6lbtO1i0WHlZNbyz+ma/aoGllP7qj0fKZtZmRwYbmr0kuqM5ACF3fOD3DmXeZr164tjzzyiCpb6ZNPPpFHH3Xtmo6F+9u3b1d586329ddfy8aNG1UZjYF5ASQRkb/izvkZodMLm0QasGje6jYEswMwAmIklkFbibbMLti+ALvq4/Ma0+XyC+xSjz1bTlxwrV/Cesw6RZpKl9L9pXBIMXXMbqnO/86lnHZn4wwNDpMo59di55RrsgZHXAJc3759dcm19sUOAwcOzJCp5cUXX1RlqxkpmWHevHkSG3vxTvNEROTfEKRg/aIhP2fHhN9++01tX4CsauiAzW+uLtpGHqk6Ql6p/bV6vFDzE+lV7uE8C1oAAUqx0JJq5gIeRUOiGbTkEwxcAlz58uWlZ8+eqsfqjjvsWSyNBfrmTC0TJ060JVVxixYtMkwxKOiZWoiIAhVmTxhTqEqVKmXLesm8yI4JDRo00CWRFStWyOHDh3WNiHKLgUs+0L59+wzD7AkJCbpkHYy43H777bomqgGwOlML9OvXT5dE7QOwZ88eXSMiokBRvHhxueWWW9TUKey3goX7dsiL7JjIjFm/fn1dc40wEdGlYeCSjyAl8u+//y7Dhg2zZRoVdiW2O1MLAjTMETfYMcWAiIh8Dx1gWJtpsKPTLa+yY2L/NSMz5oEDB1SiGSLKPQYu+cj3338vS5YsURm3EMBYDUGEZ6aWs2fP6pp1MDUuLMyV7vDYsWOyfPlyVSYiosCDPU5mzpypOt3smEblmR0Tu/lbrXTp0iqtv8GuDTiJ8hsGLvlIt27ddMm1yzyyfVlt8ODB7mlqSC/52muvqbKVihQporKKGezYw4aIiKyBHe3/+usvdSNvxzQqzBRAkGQYMWKECmCsdv3110vhwoVVGZ182MeGiHKHgUs+UrFiRWnZsqWuiUyaNEnS0tJ0zRrI1PLOO+/omis9sx2ZWrCuBxtwYt7wk08+qY8SEVGg6dKli3vdCaZR2ZEu2pwdE0GEHdkxsQklZgwYFi5caMs0NaL8hIFLPtO9e3cVTMDJkyfV1DGr9e/f3z0CggX6zz77rCpbCY0cPs+DDz6oMtIQEVFgio6Olg4dOuiaqIyRmD5mpcyyY/7999+6Zh3s54PsmNWqVZOnn37a3V4TkXcYuOQzmEaF4WgDNryKj4/XNeuMGTPG3WOGz2nHGpuoqChdctm6dasu+QY2sNqZsF6Wx05Xj/VnFklCqvVreIiICprOnTu7k72cO3fOlhT7ntkxEUjYkR1z0KBBKkEAtjMgotxh4JIPtW3b1j0KgWFoLHq0GjK1PPTQQ7rmagDsGgJHSuR3331XbSjmq3nKW+NXyehdj8m3B9+WWce/VY/fjn4k7+waKH86y0RE5DuZTaM6ceKErlkH2TGNUQ+MuNiRHdNY52LArvq+tidhs6yImykrYmeqTrdzqaf1K0SBjYFLPoSUi+Yd9e1YpA95kakFli1bJjExMarsi/TI+xK3yI+H35P4LC70y2Kny8rT1vcGEhEVJE2bNpUqVaqoMtZnIvOW1ZBcZsiQIbpmX3ZMQAa1cePGqV31169fr49ent0Jm2TMnqfkq4NvycxjX8vM41+rTrdRux6R2ce/12cRBS4GLvkUcuM3atRIZRpDnno7IGjJi0wt5vTIR44cUTsTX44FJyfpUtbmHP9B0pz/ERGR75g3Gd63b58te7sMHTrU9uyYgCQERhuJdT2Xmx455sJ++f7QOxKbfEwfyWhp7DSZfuwLXSMKTAxc8rEBAwaoecOhoaH6iPWQqQXTxsCuTC3FihVT36dhxowZlzVNDT1WObmQlijJacwGQ0TkS8iO2apVK+nYsaO88sorF61ltEJeZcc0p0eOi4uTefPmqfKlWnRqsqQ4knUtc//Ezc7xHCJ/xsClgDhz5ozq0bEaFuhjob7BrkwtyEiD9MiQmJh4yet6HM7/vMWLPxGR7916661y0003qXUvdsmL7JgREREqE6gBe9mgrb5U2+LX6FL2ElIv/XMQ5TUGLgXA4sWL1bQtLHa0Iz0yLv6emVqshlGl3r1765rI0qVL3eteciPI+Z+3QoNc09OIiMgamCr2xx9/WJ4eGTyzY86aNUuVrYS915AeGRAwYTPOS5Xs8G4WQIqDO/ZT4GLgUgDgYmjMncVIhB1zhj0ztXz11VeqbKUGDRpI9erVVRkL9H/99VdVzq1KEbV0KWvhQRESFsz8+0REVlm7dq28/fbbsmDBAjUaYTXP7JjPPfec5dkxg4KCMqzrwSJ9ZMq8FGFB3rVJIUH2TR8n8jUGLgVAXqRHxkLHZ555RtdErXWxI1OLuQEoWbKkLuXODWXu1qWsXV/2LucfD/98iIisgsximPoL8+fPl9jYWFW2kjk7Jta52JEdE+0lMqoZLjU7Zt0i6R8jO4VDiukSUeDhnVcBgKFv8zSq5cuXX9I0qtzCwkpzppbhw4erspXKli0rvXr1kieffFLuuOMOfTR3roisJ73LD9K1i7WKvklaFHfNhSYiIms0a9YsQ3rky5lG5a3MsmOi/bJajx493Il00I6dP39elXOjXclbcpzC3LLE9ZzmTAGNgUsBUb9+fZUi2YAeHathF2RzphbMH7YjUwtGmK644gpdc82Rzq0mxTrIM9XHSa9yj0iX0v3V48Yy98hDVYfJDWUG6LMCG9I547/cJCQgIrKTeRR906ZNlzyNKjc8s2O+8MILqmwlZMfs06ePPPzww3LfffdJZGSkfsV75QpVlb4VntS1i9Ut3ExuKvuArhEFpiDH5e7WZ6Pp06erXglAxhEs2CPvYRdirD1BzxXcfffd0rhxY1W20rXXXuvOLHbDDTfYsuAR0GOFBZYYYULDY0yXK+g2nl0qs4//IGdSTuojIg2LtnIGZ3dIiTDXFAki8p3Vq1dL8+bNVRmjCNi/g7yH7JQrV65UZYxGPP/882qjZSuhncI+aAbsD3bNNdfomj3Q6XYp6aDjU+Nkx7l1cjbFNbUuNChcKkfUlKqR9VQ90Jk723KTUIfyB464FCClS5dWoxEGDLtf7oZX3hg7dqztmVrgxx9/VBnV8D3+/vvv+mjB9tPh0fLrkfEZghbYeHaZ/Hfv87InYbM+QkTkH9BRaSR7OXbsmMoaabUbb7xRTTs22JEd04AMamgnMWXtUjZxLhJSQs0awNQxPDC9OT8ELVvjV8n4vc/Jm9vvcD++Pfi2nEg6pM+ggoCBSwGDVMXY8Aq9OChb3WsF6KWyO1MLYHTHsGXLFtm+fbuuFUxrzyyQLfH/6NrFkhwXVFCD6WNERP6iSJEiarNGA27q7ciOOW7cuAzZMX/66SdVttrUqVNl7ty5qtPtt99+00cLtunHvpQfD793UZCyM2G9CmY2x6/QRyi/Y+BSwGDDK8yfHTp0qNqd2I7ABZCpBWtewK5MLdiBGdPUDJMmTVKpoQuqlXFzdSlrmGIQr6cXEBH5izZt2qjpvtiUsnPnzrZsTumZHfOll16yJTsmvj+jbT548KB7mlxBheDkn7g/dS1zU49+xk2hCwgGLgUQ9joxL/zbvNn66UHI1PLyyy/rmn2ZWsxTDE6ePGnLBpz+6uiFvbqUvcTUeF0iIvIPmG48YMAA1enWqVMndwYuq5mzY2Lalh3ZMaOjozNM68Z63kvJMpZfYIpYTs6nJXDKWAHBwKUAQ08ORj6+/PJLla3FaoMHD7Y9UwsCNMxVNsyePVvi4wvmjXmql7slpzoK7qgUEfmvypUru0fuwY4slZllx7yUdSe5hancRgfjuXPnZM6cOapcEB05710mufjUM7pE+RkDlwIMw8+HDrl6KLB43eppVBj5eP/993VN5Ouvv3ZnG7NS69atVSYawNoaZKcriEqHV9Sl7EWEFNYlIiL/gwX6H330kXzxxRcqa6TV+vfv784ohjbkqaeeUmUrYVq3OasZEs0gM2hBlJDq3fS85LSCOypVkDBwKcCweN3o0YmLi5MFCxaospUw+mFeNG9HphbMFb711lt1TWTHjh2SnFzw5sJeXbS1LmUNG5MVCy2pa0RE/mfDhg2ye/duVZ4xY4Yt06jM2TGRkdOO7JhYo2mk8cc2BlinWRCVL1RNl7JXNDRalyg/Y+BSgCGzmLlHB1lMzpyxfqh19OjRtmdqqVWrljRp0kQFTkOGDJGwsIK3c/C10d2lZFh5XcscNtrkrspE5M86dOggJUqUUOXExERbgojMsmNaPUsBnW633HKLrokcOHDAluQA/qZ+kRa6lD1vZxVQYGPgUsBdd9117mlUGIWwY1NPrHPJi0wtd911l3Tp0sW2RZ3+plBwpNxb5RW1u3JmOpe+Xa6LvknXiIj8E67h5j1WsK9LTEyMrlnHMzvm+PHjVdlK9erVkxo1akjt2rVVW2le41NQXFn0GqkcUVvXMtemZC+JCOY054KAgUugSDotkui8MOOR7Lub/KCgIOnXr5+uiaxZs8aWhYfI1IJMY2BXphYzLNCfNm2arhUcJULLyGNXjJKXak6QZ2v8Vz2er/GxvFr7G7VRGRFRIGjYsKFUq+aaQuRwOOSXX35RZSt5Zsd8/fXXbcmOiS0MHnnkkQIZtABmAdxdeUiWwQs63LqWvkPXKL8Lcv7BO3TZ72FRdY8ePVQZaW7tGB3Ic2d3i2z7UuTMLn1AK1FfpP5Akcjsp/5469tvv5X169ercoUKFeTZZ59VQY2VvvrqK7n//vtVGVPHsEGkkXbSSljkiHnRGGG6+eabpV27dvoVIiLfW716tTRv3lyVmzVrJqtW5ZzeNd9IOSeS7HxAaIRIWDFX2QcwyvLee++pwAVwg9+gQQNVtgoW5zdt2tSd0QwBxSeffKLKdsB6Hkzrxoacduxl428S085JUlqiKgc5/4sIKSLhQa6p51QwcMTFn53eJrLy1YuDFojbIvL3SyLx+/SBy4MbeAMWAyYlJemaddDI2J2pBbDI0Vic/+effxbY9MhERJZJPCqydrjIooEiy59xPRYPEln1mqtDzgfKlSunskYakB0Tu81byTM75ueff25LWmZYu3atmp2ARDoFNT1yZHBhKR5aWj2KhZZi0FIAMXDxZ9u/1oUspDlvvnd8pyuXp3jx4tK3b18ZNGiQCiiMxfNWQ6YWg12ZWowdmAEB08yZM1U5r6U6/9udsEn+jvtTPTbHr5DzabqnkogoUJw7IPLPKyKxmWxujI44dMihY84HkKWycGHX2obSpUurxfpWM2fHxAJ985pNK2Fkyfj+Fi5cWGDTI1PBxsDFX2ETwLNe7HSuGgbfzPZD6kVk37ITRlxuv/12XbMnUwtSWvbp00fXXJnNDh8+rGt5Y9u51fLB7ifk64P/JzOOfakePx8eI+/uGiQLTv6qzyIiCgC7fnHe0ecQQOTUMeclpPRH5i10uKHjza51IObsmBj9sCM7JqaoValSRZUxc2Dq1Kmq7A8OnN8uK0/PkX/iZsvGs0vlXOpp/QqRbzFw8Vde7nKuONJ0wXew1wl21bdjGtWoUaNsz9RSp04dqV+/vq6JLQs7s4IL/g+H3pWzKbH6SLoUR7LMdwYuf52YqI8QEfkzh8gJL9bwoGMuN+1cNho3bmz52hZPmWXHxAi+1czJdNBeYm1oXtqTsFnG7HlKPt//uvwR8z+ZfuwL+fXIeBm16xGZdfwbfRaR7zBw8Vu5+dX4dhE9sm19+umnald9OxIgYEF+XmRq6d27t8qTD8iPj4xqeWHeiZ91KWvLY2dImvM/IiK/lpt8P2m+X4+CLJWYgnzy5El9xDqe2THffvttVbZSxYoV3WtDAZtSYvQlL8Rc2C/fHRopscnH9JGM0G4hkCHyJQYu/io4VCTEtat9tgpFO+MW3/4azT1XyH5jR3rkwYMHuzOKYU+X1157TZWthHUu5oxiCNjsSErgCetacpLsuCBJadbvDk1EdFlyk40yyLUTva8sWrRIxo0bpzqi7BhFx0yBYcOG6ZrIyJEjbWkvsXG0MU0NARoyZeaFRacmq1kB2cHUsZzOIcoNBi5+y3nxr5a+wVaWqvl+743q1aurHPkGOxoAXITNC/XtytSClJJY2BkVFZUnm1M6crE+iRd/IvJ/zrYLHWo5QcccOuh8qG7duu40/jt37rSlDRk4cGCG7JgvvviiKlupSJEi7uQAMHv27DzJjrnj3Dpdyl58apwuEV0+Bi7+rEo3kdKu3P+ZqthZpFIXXfEtpEfGInY4cuSIrFy5UpWthKlbdmdqQR78Bx54QIYOHarSahpTx+yCPPTewiZcRER+z5sOtarO9s3H05yRHvm6667TNZHffvvNlmQv5k63iRMn2pKq2MiOiTasU6dOEhERoV+xzwW9n0pOUtLsn8lA+RcDF38W7LxRvfpZkYbOR4l6+qBTdAORxkNE6j2oD/hedHS0dOzYUddErXWxYxoVMrUYAZNdmVquuOIKlZnGsHlzJik8LVQjKudFpQhawoPtb5iIiHINHWoVstnYFx1yVW/SFd9CqmLjeh4XF6fSBlvNMzsmOt2sDpjQyTZgwAAZMmSIdO7c2fbZAlAo2Ivp7E6hwQVvo0yyDgOXQFDGeZFv+rpIpx9cjyYvi5S8Wr9oHfTiGNm+zp07p4ajrYZMLU888YSu2ZepBQ4ePChjxoyRL7/8UjZu3KiPWq9T6dt0KWutons4/1j550pEAaL+IJFGLzrbqvRpx1K0ushVzus7OuS8WcN5CTDtF2tADOgAy4vsmBMmTFBlK1WuXFmKFSuma67Pa6fahRvrUvaKhJTQJaLLxzshyhKGoM076mO3Xjs293rrrbdsz9QCSESA4AWwGabVOzAbqkTUkVvKP6ZrF2tSrIN0Lp3em0dEFBBKOW9sGw9N73RrMVykXCv9onUwXaxs2bKqnJycrBKvWC2z7JhINGOHY8eOyUcffSRffPGFLFu2TB+1XruSt+Q4hbllies5zZl8ioELZatJkyZqwytclJ999lmJjCgkknRa5EKs8zlO1EaZPpaXmVrMUwwQqNmlcbF28myN/0rPcg9Jx1L91OPGMvfIwKpvSe/yg/RZeQMJBIz/fMmqj0tEBRsW6Pft21fXRFavXm1LemRzdkyk9LcjOyZs2LBBdu/ercozZ86UhIQEVbZauUJV5Y5Kz+vaxeoWbiY3lX1A14h8I8jhpMt+b/r06dKjRw9Vvummm2zZY4Rc08SQeUsOzhHZPVEkxXRRxDqcqs7fSY30TbF8AfODr776avfQN+YP27HeZfny5SovPoSFhalF++ah+IIkMS1e/jo+Ue2GbIgMLiLtSvVWU9cu1dozC2Tu8Z8yZJppWLSV3FBmgBQN9SIbEVGAwY1z8+auRCvNmjVTo7tkvW+++UZiYmJUEFO9ejWR5Pj0zrZQZ5uG9svHfv/9d7WTP2C9JoIKTIHODezZFZ8SK6lqg84gKRJaXMKCXOmPM4PZASNGjJAzZ86oeqtWreTWW29VZTvEJsfImtML5OD5HXLG+XVXjqgltQo3Utd1Il9j4ELe2fY/kUN/SUJysMzbWUR61HddIN2w5gYJA3wIc5ORrtiANTZdu3bVNWvgzwEJApBJDTDidNddd6lyQXIq+ajaCflcqsfvWUPDdE/lV7xenGn44dC7su3cal3LCB/r7kpDpGpkXX2EKH9g4JI3MLVZjaIfWSiya6JrloAZMndW7+MMYqL0Ad9AgoA///xTlZEpc9asWarsjcw6duDa6G5qJD4iOPOvFRso//DDD6qMEafnn39eZVkrqPBzXHpqmhxPOqTq5QtVkzYlezqDqdaqfimOXNgji09Okc3xK1Q9MriwNC/RVX3cCGeZ7MGpYpQzTAtzBi2rDkXKiPllZcGeIvLn9iL6Re3UBpFY32bjQpBid6YWXPD79UsfPVq7dq0t09T8ze9HP80yaIGD53eqzcdyA7soZxW0AFJrTj76MaeOEZFPqKBl968iWz6VpMTTMmNrUYm/YLrtOTDTecc/TCTNyz2yEmNc/2av89q393eR09v1CxmZs2MigMEojDfmnfzFee39RC6kJsoV0kCuDGojtaS5FJPSsiJ2pnxx4M0s9/Nq2rSp2lUf0AFnx/5r/gjtyDcHR6ifoxG0wNELe+XXI+Nl4uEPJMmR+82cl8ZOk0/2DXUHLZCYdk4Wn/pdPtr7ogpqyB4MXChn8fvUk8MRJOdTXG+ZebuLSmyix67HCF58DJlajB2C7crUgjnKjRunZ0tBAxBAA5OXDQ3jvsQtupa1lXG526vAm/Mx0pOQas+CViLK55wBgOz9TTbHRKhON7Rbs7a7Mn+5xe93jchkB9Oj//1YZPlgkR3fiexxBkO7nYHB6jdF/hkqcnavPtEFU8MeeughXRN5+umnc8yOiRvuhScnSaSjqNwsz0gbuU2aOK6Xa5y1nvKU1JDGEnNhv6w5PV//i4v16dNHl5zf9t69appaQbMs9g/ZlZD19/1v/N9qJCY38HOfffx7XbvY6ZSTqrOPnW72YOBCOcNifKcWlROkQlFXb09qWpBM2+Kx9iPxuC74DoII5Kk32JWppWfPnu68+Njky66UzP7A2x360dDm5kJ9Mtk1/S4n+LhERJct4ah6CgtJk/gkV0fbigNRcviMx54nx9J70TO1+UORo4vlRLHOsr3yG7K9kvPhfD5Y+m5JPX9KZO0Idztp8MyO+cEHH6hyVmKTj6nnq4M6SoRcPO2oicO1OfOms1lnDcOeZI0aNdI1kalTp9qWHdMfoD3CyH5OvDnHDMFQTjCiczYlVtfISgxcKFf6NUyfd7vhaKTsOWXaWCrYmg2wsEDe7kwtxYsXVwssBw0aJPfdd1+e7EqcV9IsyBSXG2lqQSoR0WVKdU0JqlM6SeqXMTpEguSXjR77imTX6YaA5OQ6ORN1tZwq1lYfdEmIqCUxxW8SSYl3Ja8xQdBizo6JxfNov7KSmOraa6asVFPPniKCCkuwI1hOJGXfAWSsAwbsaWNXhjF/gLbLm44vnJMm3rdzh897Nw0sIZvp1eQ7DFwoZxGldUGkaolkaVYp/UL4y8bizouFrhSurAu+hali77zzjq6JfPjhh7ZstIXdkGvVqqVrBUeYRbscFwkprkvZKxTi24WyRES9rjwjwUGuxurA6XBZe9iUWCS7zpJE10jI6cJN1bOn+Mh6zv8PcgY3a10HTAYOHOjOKIaZAi+88IIqZyZCX/eSJZvRfeenKRySfZbL6OhotZM+EttgXWhByoqZmotgJM2Rpks5i0v2bjZJgg4+yVoMXChnxWo43ynpaSN71jvjvLl1/dEfOxcmy/frG80yLVzPFujfv78KJAAL9LGnjJ127dqlhvqNdJP5GdJulgzLORtN/SItne2osyX1UsNibXQpa8iYk1PDTETklbD09SylC6dKu2rpN5ZTtxSTpFR9/Spa0/WcGayTcUoKLaWeM+PArdT5E7qWDgv0x4wZo2siX3/9tfz999+6llFh3bGzTzaqZ08n5aBKk1wtKufUytiTDIFLcHDBusULDfJ+1kdILs4tHe5KepATpK0m6zFwoZyFRIrUuU9XnH+chdKka+30dSazthWThHI3ikRV0EesMXbs2EvK1HK5ZsyYIR9//LEcOnTIlh2Y/UH3svfrUuaiQopK1zJ36Jp32pbsJcVCS+pa5rqWudN5UfJI+kBEdCmiymdIdXx9nXgpHO7qlT97IUT+2qmzY1bs4HrODNo/p/CUrDewDHIGFOaZCWae2TGxUD8zuDZeEVlPtsgyWS/z9FGXw46dskh+Uh1FrXO5h9bBgwdV24n9bPI7tB1I1Z+T6lFX5arTrXHxdrqUtdCgMIkOK6trZCUGLuSdih1FrnzU3Qi0r35OSkWlSKFQh3S+tpYUqmf9XicYccltphZfqF+/vi650iNv27ZN1/Kv2oUbS98KT6qLsSf0DN5X+VUpFZa7QBUjKQ9W/Y+UDHPeTGTihjJ3S/PiXXSNiOgyoVe9/sO64gw+QhxqxoABqf1jC18jUjrzaWBKpOtmtGT8cvXsqdg5TBFziJRq4jqQCXN2TIy4fPXVV6rs6eZyA503v+VkkyyQn4L+I5NklPwcNFzmB30jSUGJ0qPcg1IizLXg3xuLFi1SIz4HDhyQyZNzl74+UKEdyazdMuC1G8sM0DXvNCveWcoVcq2zzUq7kr2z3SSUfIcbUFLu6bmhBw4clBLRJaRoUfum9mBxY82aNd2Zxd5+++0MWces8u2338r69etVGVnGXnrppQIxDI8MY0hPbCzYDw0OV6MtwZfZ54E9YpLTXEFncFCwRDo/ZliQNWtriPIaN6DMYydWi2z5TCTZ1W6MXVpajpwNk/aNykvnfk9LeHgO1571o9QCfaxnQWaxJGfwEJx2XqKdwUypMwudF8YiIte+64yMsp4qhHbKWKuJhfuYfly0qEdqZicsHP8nbrb8HTtLzqa6slQ1KdZBWpfsKWXCK6m6tzDK8t5777nT+SPRTIMGDVQ5P9uZsF5+PjzmooX62DDytoqDpUZU7n8G8amn1QbKh87v1EfStS91q3QqdZuukdUYuJDPIHsJsphYDT1IgwcPVmVc+Ddt2uTOOmaV2NhYGTlypHsDzN69e0ubNjmv2SAiYuDiJ1Snm0OOHo2RQhEREh2d/dRVN+zjst4ZdJzeoQ+YYBZC46EixbJZJ+OEzjYEDcaGxuj8QptiNUypXrJkiSqXKFFCZek0plznZ1ion5ByRlJ14oWQ4FCJCikmIZc5FRkdeUnOoBWCVKdbEQnnSIutOFWMLhvyxM+ZM0flrd+zx/rdY5988skMmVpefPFFVbYSMrV06tRJ11xrbBITud8IEVHAcN5oSlCIlK9QMUPQcu7cOV3KAoKTZv8RafKKSN0HRWrfI1LnXpGGg0Vaj88xaAF0spmzY6IDzo7smFikHxnpWqcTFxcnCxYsUOX8DgFK0dBoNbUOj6Ih0ZcdtABmHBgfs3hoKQYteYCBC102jIThRj45OVl+++03fdQ66C16//33dU1k4sSJWWZq8SWkmDSG9hG0zJ49W5WJiCjwpKWlqXUgmHJsTAXOVvRVIpU6i1S5UaTyDa5MmnrxvjfM2TGxPtOO7JiYBdG9e3ddE5k7d26ByI5J+RcDF7psHTt2lLAw12K4I0eOyPLlmS9i9KUbb7xRevXqpWuuhfrGNC6rYCd9TBEzLF68OOeeOiIi8ksYfcDu8ufPn1fPdvDMjjlr1ixVttK1114rZcu6kgygg5HT7CmQMXDxtbQkkVMbRA7Pdz1QxrF8DBtcdemSng0K6YPREFht3LhxGTK1TJgwQZWt1KhRI6lRo4ZaU4PNvQoXLqxfISKiQNKqVSv3Nfz06dMqkLCaZ3bM5557zvJOt6CgIOnbt6+uiaxZsybbXfyJ/BkDF19CoLLkcZF1I0W2Om+i8UAZxw7N1SflTx06dFAL/wDTqOzoRTKCB8Prr7/uzjZmpXvvvVeeeuopqVy5sj5CRBTg0pJF4raKHFnoepxY4958Mb+KiIhQiX4M8+bNs2UaFdaDGtOOsc5l/PjxqmwldLih461cuXLy6KOPqsxmRIGIgYuvHPvbFaikZDJ1CMe2fSFy1JXZIz/C0Ld5GtXSpUtt2fDqlVdecV+A0YM0fPhwVbaSeZQFmdQw7J6UlL9H1YgoHzu2wnnRflJkzTCRLZ+6HhveE1k8SGSvPRv95pWWLVtKhQquPakw8mHHxsZos4YNc/6sNXS62TECglGXF154QW0pQBSoGLj4yq6JupCNPdYvXM9LSPVYrVo1VUaW7V9++UWVrYReK2zuZbArUwtgM0os6sQ86b/++ksfJSIKIJjOvGmcSHImIw0Yhdn9s8j+/L0mol+/frrkjNc2bMiT7JivvfaaKlvJyC4G6GxDYp34+Hh9hCgwMHDxBWzOl3hUV7KBc/RGfvnVbbelb8J07NgxlX7RathUy+5MLYDeOSMlMoIX7PVCRBRQdnvRwbQHu64HzJZvuYZpx9hfx4BON6u3uPPMjvn555/bkh0TtmzZojrd5s+fb8u6HiJfYuDiE7m4wOld5/MrZC5p3769tGvXTk3jMta9+EpqokPWvxIjq585ImtfOiob3jgmm4Yfl/HX/yw96/dX59iVqQUbylWpUkWVEcRMmTJFlf0BNt/Cxlt4JiLKnLPtOrNLl7OBtS6pF3Qlf8JaF2SOBHS4HT3qRWfkZUJ2zBtuuEGV0YYgO6YdsFjfWA+6YsUKOXHihCoTBQIGLj4RpJ+94Lxg5Hc9e/aUm2++2Z3xy5eCQ4MkJCpYDVylJjgk6VSqnD+aIkGxYXJf+0f1Wa5MLRh9sZp5igF28LdjikF2jl7YJ18eGCbDtt8lw3bcrZ4/3veS7ErYoM8gIroE+Xy2ALJjYrNGjN6j081Y92K10aNHZ8iO+dNPP6mylerVq6cW6wNGliZPxoiaf0hjpxvlgIGLLwSFiIR6kRYX5wS5enQKiv3796t1JydPntRHLk9QmEiDV8pIvcGlJKqy82eJwS494NXohroZMrV88MEHqmylihUrSosWLXTNNcUAm5rlhbVnFqggZW9ixjU+CGa+OThC/jrhxTosIqJM5f9Ot06dOqnOKDvT3GOdizk75ksvvWRLp1ufPn10SWTbtm3qkZdOJh+RHw+/J/8xdbqN2fO0bI1fpc8gcmHg4itVu+lCNrDbbgGC+bPYa+XgwYM+y9SSet4h+385LVvHnJSEgykSUsT5Fna2p8FhQVK9e1l5+eWX9ZkiI0aMsCVTC6YYGD1mWNeDjGp2u5CWKH/E/E/XMrfo1GQ5fGG3rhEROS+eEa6NCXMU4vsRdH+GqWIffvih6nyzmjk7Jj4f1p9YDWmRsTGl4bfffsuzTrdt51bLuD2DLwpSYpNjVDDz29GP9BEiBi6+U+1WkZJX60om8Fr19B6OgqBu3bq65FoMuH37dl27NKdWJcrGYcfk2KIEVS/bLkrKXhelyqWuiZTg8CAZPHhwhkwtSP1otSJFikjXrl11TdT6GmPRvl2OXNgjKY5kXcvatvjVukRE5FS1uy5ko0J7Z4zj2u29IPjnn3/kvffek71796obeqt5ZsccOXKkLQFT9+7d3Z1umBWB9S52w7Sw3478V9cyt/7MIo68kBsDF19qPESkZn+RqIoioc4bajxQrtHP9VoBg2lU5h6dSZMmXdIOwRdOpMq28Sdl99dxknI2TaIqh8mVL5SWqv2Ky8nVrgChbDvX0D4uwuZMLV9//bUtmVratm0rpUqVUp+/c+fOEh4erl+xx+lk76bixVw4oEtERE6Vrxcp30ZXMlG0hkide3SlYKhdu7YEB7tujzBjYOXKlapsJc/smC+++KIqWykqKkq1V4aZM2fK+fPndc0ep1NOyvk0V2dkdladzt+beJP3GLj42hU3i1z7nki7z10PlKvdol8seDx7dBYvXqzK3nCkiByecVY2DT8mZ7cnSUhEkFTtW0yufLG0RFUJc61tcb6DSzaPlMgK6WuHzJlawI5MLUhtOWDAABkyZIiaJ406EVFAuPIx59363SKFKzsvZhGuR0RpkSrdRZq+5qyn7/9REERHR6vMmAZsMmzHDf3YsWN1SWTixIkyZ84cXbMO2it8v4DpY3bPFjiXelqXsncwcacuUUHHwIUshR4dBBIGXIi92fAKgQoClsMz41UAU7JphDR4rayUbY8EB/ok5/PVb5aVGvdenHIZmVqM4MGuTC2VK1d2JwcA7Kpvl+JhpXQpe6XC7cmUQ0QBBkHKNaNE2n/herQa5wpmCtjaFkOXLl3cGzaeO3fOliACIy633367rolatH8psxRy65ZbbpG77rpLnnjiCXcQYxdHPt8ignyPgQtZrnXr1mp/F8AQOHbrBSyuXzc0RgUnhuQzabL7y1g1NQxTxAqVDpG6T5aSGvdHS1gx79+uWOeCi7ABmVqMvPVWS0lJUY3cW2+9ZVt65AqFqksoUq7l4KqirqkIRESUtYiICOnWLT3pDmYL2LHfCda6mLNjTpgwQZWthPaySZMmuuYK1OxSOLS4LmWvcmQtXaKCjoELWQ5zhW+99VZdEzVf+NSpUxIzP15S4tPQ5aKmfR1beE42vXVMTq05r7JGV+xWRBq8UlaK1rm09SIIHMyZWoYPH67KVpsxY4baBDM5OVmt67FDoeBIuans/bqWudbRPaViIVfufiIiyh7WaGLtIiDjVk7Xc4fzvzT936XCLv7m7Jivv/66LdkxAd8jAjRkNVu7dq0+aq3osLISEexKspOd5sW76BIVdAxcyBa1atWSBg0aqDm0jz/+uBSPjHYGKK65tIWrhsm/o07I/l/PqHTHCFQQsFTsXvSytr1Br9WwYcN0TdR+MnZkasGc4bAw1+gHUmralR65afFOcmv5xy5qBDAS07FUX7m+zF36CBER5cSz023Hjh2yb98+XUuHPUi+Pfi2vLn9DvnP9jvVI7P0vt5CdkwEMICg5bXXXlNlqy1cuFCmTJmi1vNgXU9SUpJ+xTrBEiK3Vnhc1zLXsGgrqVekua5RQRfkwLapAQJTjHr06KHK2DsDf1gUOLDmA2te4NjiBNn/82kJKxEsyacx6uK8wS4aLFVvLaYW2/sK5gdjqpqRWaxXr14+21MmO/PmzVMjL4B50uhBM+ZL28HV4+f60w5yNg1B7oVB/utE0mG1eSbSOoc4I9YrIutJ2fAq+lWiwLV69Wpp3tx149WsWTNZtYqpXQMJpmshNfLNN9/szvxl2By/Qn4+PEaVC0mUhEq4OILS5LyckzRHqjQp1kF6lx+kXs8NtFNYewJYr7lhwwZ3qn+rIFDBzARjqhjW+ZjXqFoJQd60mM8lPjVOH3HBTAF2upEZAxfKExvePCZJJ/WiQ+c9dbn2haXiTUVV5jBfQ9BiTsuMlI9WX4wRMGGuMjKpQatWrTL03FG6s6mxMuXop7Lj3Dp9JF31yCull7PRx3QCokDFwCWwYW0LggfPhevoZHl31yOSmpYiHRwDpGzQFfoVkUTHWVkcPFGOO/bLXZVelDqFm+pXvId2CtOOAZkysUeY1fDeNJLZ4HtGpkw7F+wHYqfb6ZQTsjthkySnXZCgoGCpHFFLrTsla3CqGNnu6J7j6UELOK9RMQvOydoXjsreH71LjZgbnplannvuOcszteCCj9Edw7Jly2T2jl9lWex0WXN6npxJ8W7flYLg24MjMw1aYE/iv/LNweFeba5JRGSF0qVLZ7h53717txqdOJl0RO1BUt/RJkPQApFBReUax82qvPb0AvWcW+bsmAhg7JgtgAC7ShXXSDfayalTp6qyXYLVfyHq4e9BC3732NV/9O4n5Pejn8j0Y1/KHzH/k0/2DZVP970sx5K4b5oVGLiQ7bbv3yKrQv+Sw0G75HDobomsGiLh0c6LlPPdGFLImgsVRj+M/WSQqWX8+PGqbCUM61etWVnXRP6aslD+PP6tTIn5TN7f/bhMPvqxJDns3ezL3xw6v0tiLlw8Z9zsVHKM7E7YqGtERHnj9OnT8u2338pHH30ks2fPdk9ruiKogXr2VFzKqtvwS9341zM7JvYkQ2ZOq/Xr10+XRDZu3CjTN/wkK2JnyqrTf0ls8jH9Ck06Ml7t6p+Zwxd2y1cH/k8S03Le/oFyh4EL2a5N2zYSW/aALAr/XRaFTJZ/qy2Uq4eVlWZjK0iVW4vps3wLCx0x5G2wI1PL2ZRYiWuefsOdeipM0uLTN6Zcd2ahmiJVkB3NIWgxHEjcoUtERHkDIy3r169X5UWLFsnZ066b0hDJLhX95XXGeWbH/OCDD1TZShUrVpQGTdPX0yyYulxmxHwt02ImyJg9T6k1PQmp9mwv4K8wa2L7uewzr2FzzfWnvd90m7zDwIVsh0wtffr00TXXGpTDhw/rmnWGDh3qztSCPV2sztSyPHaGpBY/J2FXn5XQBmel8J1HJbhIxilqm84uL9A9Mt42fvFe7q5MRGQV7HViTKNC6uDlC/5R5aOS+a7uiY54SZNUKVfo0pOMeGbHHDFihOXZMS+kJcqxq1eJI8SV1tlxNkQccenBGRIS/Hj4PV0rmE4mx+hS9nYmuAJd8h0GLpQn6tSpkyFDyi+//KJL1sFUsXfeeUfXRD7//HM1bcwqW+JXqufwa09LoVbOG+/wzHP7n062flMzfxUV4tpoLSdFQrzbpIyIyErmaVQ71+6XcImQNfKnHHNkHD3G4vwlQRNVuUnxDur5Ug0cONDdXqLT7cUXX1Rlq/wb/7ecCz8lhVqclZA68RJ1Z4wEl8y4znB/4jaVBrqgOp/qXYdjbLI9e/AUJAxcKM8g1SNGX+DAgQOyZs0aVbZS//79pWvXrqqMhYfPPPOMKlshswX4acfCJfG3suI4k75BDRb4FVTlC2Vc0JqVKpG1dYmIKO9gGpWRpTIoLVgcf5eTJDkvc4L+J9Plv7JcfpO5QV/K1KAxckz2SbPinS8po5gZFuhjHzLDxIkT3Sn+rbAnYbN6xmyBiA5xEhSZeTKb4xcO6VLBExFSRJeyFx3mmuZHvsPAhbKXliRyaoPI4XmuB8o45gPI0tKhQ3pP1LRp02zZ8AoNgJGpZc6cOe7Uj75WLNS147IheX1RSfy9rKSdCJfzS9JHELzZNTi/qhRRU8rlELyUDCsnNaIa6hoRUd7q3r27O9lL3PpUuTq2mxo9jpMY2S3rJMaxR1IkWe1BcnO5geq8y4UON3N2TCzUtwrWZ3pKiw2VxKmlJfVYuD6CTjfXfi8FUSlnu+SNWlGNdIl8hYELZe3wfJElj4usGymy9XPXA+XFj4ocmqtPujzY4ApzeAELELFJpdUw5P7QQw/pmshLL71kSaaW+kVa6JJLSKX0DGJpByMldX+EKhcPK62eC6oBlYY4G4EKupZRcWfwd3flIWr3fyIir6UlO6OKrSJHFrgeJ9aIpCbqFy8PNlI27wW2Zup2ebzcB/JK7a/khZqfyNBa/5M36nzv840TzdkxMeLy1VdfqbKvFQ312K9mR5Qk/lJe0o5GSNKSEvooOt0K61LBg47JWlFX61rmIoOLXPY0QboYAxfK3LG/nYHKBOcVK5MeFVz8t33hCmwuU3h4uNqYccCAAfLYY49JiRLpF0UreWZqefvtt1XZl66NvlHCg1zBCQSXTpbQOuk/zwvLSkjDwq3Vxa0gQyP5aLV3pH/F56RL6TukU6l+0rn07XJbhWfkieqjswxqiIgydWyFyNInnRHFMJEtn7keG94TWTxIZK9v9kJp27atlCvn6nUvWbKk2m0e1/siISXUDT32IfE1z+yYWOuCNS++Vj3qKl1yCa543vl/rk0hMWMgZWekKpcpVEk9F1R9Kz4tVSLq6FpGhUOKyX1VXpVCwa6fFfkOd86nzC1/ViTxqK5kIcJ5499qrK74Tnx8vBQpYv3N/CeffCKPPvqoKqMXa/v27e6sY76CBYzfHhwhSQ7XiI4jMVgSfijvDP5cfQY3dr9BunRyrbkhovyJO+fbCNOZMTMgO7XudEYBrnuJy7Fz5045deqUtGzZUh+xHmYHILmNkVkMMwZGjszh+80lZBUbv/fZDFPGkv4pLsnrXLMjgqJSpc79RWVg9fRsZwXZ9nNr5NiFA5LiSFHJr6PDy0ntwk0ksgCPSFmJIy50MUdqzkELnD/uOtdHkF5y4cKF6iKMTa+sZs7UgsbAikwtVSPrynM1P5K+FZ6UrqXvkK5VbpP6rWroV0X+mjNPBWpEROQDu73IULlnsvP/Lr/PtlatWhmClsRE30xFy45ndkys2fR1dkyMEtxdaYgUNmVzDGt6RiTC1d47EkKkyk5XggISlXyhTcle0qFUH2nvfFxdtA2DFgsxcAlkmMObFOd6JPtwuNiRedreTOXm3BzMmzdPLdA/f/68TJ06VR+1Dhbov//++7rmytSCxfq+hmkDDYu2Vhe2tiV7y703DZJixVwbbSYnJ3PkkIjIJ5zByJldupwNTHdO9d26RkxcwZoTBBTLly/XR61jzo6JTrdnn31WlX0JGR+frj5G+lZ4SrqU7i9dyt8mjTvV16+KLJy3mJ1ulCcYuAQiBClYKL/gXpElj7keix8RWfaUa23K5QrKxdsiN+fmoE2bNhIZ6ZoPGhsbK3Pn+iYBQHawwLJXr166Jio9MtIkWyk0NFRNdTRg2sjJkxenTiYiyrccKfph7fU2Sz78vAhasBcZbuT//PNP1flmNXN2THzOWbNmqbIvYeSlYdFWqsOtXclb5K6OA6Vs2bLqNXS6zZw5U5WJ7MTAJdCgp2jV667UxJ7OnxDZNFbkwAx94BIFOS+GkeV1JRs4B+f6SERERIYb+r/++kvOnDmja9YZN26cO1MLhtwnTJigylbCPHfsB1C4cGG5//77pVSpjKmTiYjypROrRVY8LzL/Hv0Y4FqTknBYn2AXrEbwDaxfMkbREbyg7bKaZ3bM5557zpLsmGZBQUHSp08fXXMFbDEx3u0gT+QrDFwCzYGZIok5XCh2TXRNI7scNdPzxWep+q264DvXXHONVKjgyiKFHh1MHbMaFuSbN6J8/fXX5fhx63e7vfPOO+WFF16Qq67KmMGFiChf2v6VyIb3Lw5SsKAewcxlzxhwBiMRrhGBHIW4Oqt8AaPoN998s66JWquJWQNWQ3ZMYzsBdLp98MEHqmylmjVrSv369dXm0e3atbMtEyiRgYFLoDm6TBeyoda+XOZFs+w1IjVu05VMVO8jUr6NrvgOenT69eunayJr1651Z0+x0iuvvOLOKIag5bXXXlNlK5UvXz5D9rRt27bpEhFRPoPg5OBsXckCUvBfbqdb1e66kI0K7X06WwAaN27sbkOQaMaOtYtI6T9sWHpmrxEjRtjS6da7d2+VzAbBmjFbgcguDFwCTU6jLYYUH8yxrdZbpNkbIhU7ixSu7Hqg3NR5U4/AxSK4+Ddp0kTXRM0dtjprN3qtzJlaPv/8c59nasnKiRMn1OfDFDX01BER5TvHV+tCNlISLn/KWOXrnYFJNpv+Fa0hUuceXfEt8zSq9evXy759+3TtEiDpztb/iSx8wPXAGtYDF69jefLJJ93ZMbGnix2dbpjaXLp0+sbJu3fvtnyaGpGBgQtlr3hdkXoPilwzyvVAuUR6ZhGrYL8eDL8DFq7bMY8WmVowVQ2wQN+KTC2Z2bJli2zdulWVZ8+ezUwtRJT/nN2jCzm44IMpVvUfFqk9QKRIVZGQSNcDU8iq3uTqeEPdApUqVcrQ6TZp0iRdyiVs7rzsGefzXyJpzoAAo1DnT4rs+EZk+eAMwZ1ndkx0gmHtiR1Onz4t3377rXz00Ueq7SKyAwOXQFO0mi7kIDRKFwJT8eLFVbrHa6+9Vk3jwrQqO4wdOzZDppbff/fNLsvZad26tTtTC3qtsNEqEVG+ggxidqrSTaTlSJH2/3M9Wo0RqXWXT9e2ZAYJZoxOt8OHD7s7pbwWv19NmUsNLiQHS98t2yu94Xy8JtsrvyEninV2zbrYMFqf7GLOjolOt6efflqVrbZnzx41sgSLFy9WsweIrMbAJdBU6qIL2QgvIVKopK4Ers6dO0vfvn1V5i27YMTFnKkFDYDVQ+BY5Ghe17Ny5UrV4BER5RtFvOx0KxStC4EJi9Xbt28v0dHRcs8990i9evX0K17S64Biit8kCRG1VNlwqlhbORN1tWvE5fwxfdTFnB0TIy4//fSTKlsJ63qqVXP9XrGu55JHmIhygYFLoMGiwtJNdSUTwWEiVz0uvtxfxR8kJSWpnPF2pEc2Z2pBYgA7MrVUr15dGjRooGuudT352aHzO2X9mUWyzvnYGr9KElJ9uIEqEfkfb5K5YKZAVEVdCVzodMNMgauvdgYZuRWHJC1BEh+ZecATV8Q1nVnOn3I9a57ZMV966SW15sVq5k63HTt2qKnPRFZi4BKIrn5epLJr19wMIkq7XovOX+l1kW1r5MiRKje+HdOokKnl5Zdf1jVXphY7MpthqB+jL3DgwAFZvdqLxawBZk/CZhmz5yn5bP+r8tvRj2Sy8/Hj4ffknV0DZdbxb/RZRJTvRF+Z84yBegNdnW8BLjw8XJdcFixYIEePHtW1HKTEiyObjGdJoXrPr7Qk17MJgiW0X4A2a/jw4apspXLlysl1112na6KmV1u9iXNeirmwTzacXSLrzyyWLfEr5Vzqaf0K2YWBS6Cqc79Ip+9FOn6rH9+JtBonUrKhPiH/QCNgjLTgZt6OIGLw4MEZMrUg9aOvJZx3XuTni8xcIvL3RpHjZ6KlxXXdxKH/LJFOEyNN+UXMhf3y3aGREpuccYqDYXnsDJl+7AtdI6J8p+4DIlV76IpJWFGRhs+40vDnI3v37pW3335bXcsnT56sj+agcBUJcqRIEBblZ6LI+e2uAn5mHjBTYNSoUbrm2l3fjvayW7du7mlqSKaD9S75zZELe+TjfS/JR87HpCMfym9H/ys/HX5fRu16RCYf/ViSHD7I5EpeYeAS0IJcuejVI//+KjGNqlGjRrpmT3pkXITNmVomTpzo80wtqzaLfDtN5PPfREZ9KTJ0rMhPizvK3rR3JN7RWKWbTEhI0GcHvkWnJkuKI/s9Gv6Jm53jOUQUwGrdeXGnW9tPRcq01CfkH5GRkXLqlGtK165du2Tjxo2qnC0dvJU5O089myGYKXlmkasSlXnCmvvuu8+dHRPrM5966ilVtlJUVJQKXgxIbJOfsmPGp8bJ1weGy9ELmae3XndmoUw+8pGukdUYuFBAwDQqI9vXkSNH1AJ2qyFTyw033KBrroX6vhwCb1xP5A7ntb6Ts42pXM51DPGYwxmQtmrVVh5//PF8tSvxtvg1upS9c6nWr2MiorxUMDrdMI0KWSMNU6ZMkZSUHLKrVWin0jiXOPu3VDr5o0RccI2YFDu3TqrFfCzhKSdEqt2abUpnZMc04HPOmnXx/i++1qZNG3f2T3S65afABbMBEtOy/37+jf8nx3PINxi4UEAoVqyYWvBowFqX8+etH5odPXp0hkwt2CTSV4oVFmlaX2TPQZGDMQhYnG24sz2PCHcGNL2ucJ3kdO7cOV2ynsP5X6rzvzTnf76W7PAuO1uKI/9MjyOigg0dYBh5gbi4OLXeJVtY49N4qLOBqCmFE7dJ1eNfSJ2Db0r52N8lLDVOpEp3kRp99cmZw4jLvffeq2sizz33nC3rTm655Ra57bbb1OezawsDO2yN9269aVzycV0iKzFwoYDRqVMn9wgEbubt2PAK61zMmVpef/11n6RHxvoWTBF78QNn4HJIpERRkRY6p0KH5iJhoa70kosWLVJzpL2aYnAZDp/fLf/d+4K8uf0OGbb9LvnP9jvl3V2D1OJDXwn28nITIq49EIiIAl1ERIR07+4MNjQkmckxO2Z4cZHmb4lc+55Io5dcmUKxcWabj0Vq361Pyt67777rzo7577//+rTTLSs1a9aUli3Tp/zZ0blosLLT7UzySV3KXnIW65LItxi4UMDApl49e/bUNZElS5bYsuGVOVPL8ePHLzs98tK1Ik++7VqUD93aiHzworNx2eWqd7nW9Txv3jyZOnWquvhPmzbNddAC286tkQn7X5VjSQf0ERfM68Xiw8Wnpugjl6dKZF1dyl5kSBFdIiIKfNhI2dhkODk52fvrOVJDl2okUq61SIn6roDGS57ZMdHphvYrt9KcwQDS1Z9JOSmJad6N/mMNKmYoIBvo8uXL9VFrnEo+Kt8fGpWh023cnsGyOX6FPuPylQhztf85CQ+O0CWyEgMXCihYpI8Nr7DepV27dmoKmdXQazVs2DBdu/T0yEedMdYbH4mM/lYk7qxI9UoiowaLPHSra6oYRmFwrEZl1/mtWrVyT1PDAs+FCxeqsi8hE8rPhz/Itpdq7okfVUaVy9W+lPMbzUGNqAZSKDjrudtERIEmKCgow34na9eutT07JoKW1157TZW9hWv/8B33qXT17+9+XEbufFCV15y+OHGA2T///KOS6GCdCxbqWzXycvD8Tvlk31DZfi7j+smTyUec7doY+evERH3k8lSOzLgRaFaiQqy/HyEGLhSAMIcW6Yl79OhxUb58qwwaNMg9BJ7b9MjJKSI/zRJ5+h2RTTtFIp2xyIO3OIOWZ9ODlCsqiNzZXWRgH1cdkKmlS5f0fQ8wNc7X612OnN/rVRav7V4urM9OzaiGcn2Zu3TtYmXCK0u/Ck/rGhFR/oHsmNhpHkEM9j3BAnareWbH/Pzzz9W0MW9gjy2MtkdIYakmDaVu0LVSVa6U1LRUmRLzmVqwnpXmzZu7p3UjeMlxXc8lSHWkyNcH/k8upCXqIxdDJsvNZy9/5KVdyd4SGVxY1zJXp3ATKRZaUtfISgxcKOBgyL1UKb0JlxPSTNqx38n48eN1yZUeec6cObrmvDgnOC/0f7lGTcw27nAFLL/MFklJFWndROTDl0W6t3X+8QXpk5wKR4r0ccYodavpAxpGlYwGAGtrMHXMlzD8741jSYd06fK0ju4p91V5XRoUbSXRYWVVY1Aloo50LX2nDKz6lkSFXLw3ARFRfoCpzli43qdPH9UxZQdzdkws0Dev2czK/sRtsu7MIqks9aSX41lpLf2kuaO7tJX+0ittsBSXMmo0BlPIMoMZEeZp3fPnz5fTp327UeOp5Biv9k5ZGXf5a2Gjw8rJgMovS0Rw5r+zKyLrSd8K1qedJhcGLhSwcCH85ptv5OOPP1YLHq2GERdzphY0AEamlu9niHw33bU3C2Aq2OhvRN78WCTGGRsg3fGbj4o8O8C1EN9baACQCtqADTixq76vpDm8yzSD3i1fqR55pfRzXuSfqT5OhtT6nzxUdZi0KXkzp4gRUb5WvHjxDNm2Dh8+rEbwrYbsmMZ2Auhw++mnn1Q5KxvPLlXPzeUm9WwWFlRImsj1aqT+cDZTiDGtu2rVqqqMdhKbcPrSeS/X28R4rN28VJUiasoT1d6XhkVbSYnQMhLu/DmUDa+iZhE8UOVNtl82YuBCAWvnzp2yYcMGVcZQdGxsrCpbCZlajHUnRqYWjLIsWOkMMpx/TQ3riExfJPLECJGl60QKhYvc7bz2j37B+Vpt9c9yrWHDhlKjRg1dE/n55599tgFn8TDvpiuUCq+gS0REdDkw5Rej9kj0gjUgVsM6lyeeeELXRF566aVss2PGJh+TIOd/hSXzZADlxNUeJaRknx0NI0sGrOvZty/zDRwvhcPh++xhOSkaGq1GVgbXGC+v1P5aHq/2rppFQPZi4EIBq1mzZlKlShVVRo8ONtqyGjK1eKZHnrX4vCQli9StLvLmRyJf/C6S6GwTml0pMm6IyC2dXUHN5UB+fAM24Dx69KiuXZ4KhapLaFCYrmXtqqKunZiJiOjyHDx4UG2ijA6oFStWSExMjH7FOm+99ZY7OyYSAyDNflbCgyJUeuGsGHtyhQRnn7q+UqVK0rRpU10TmTRpki5dvsKh3mVYw0gJ5S8MXCigmTO1bNq0SfbsufzsVznxTI/80wzXkPXmna7sYKVKiAx9UOTlh0RK+2jj+woVKqhNxdAQDBkyRNV9AcPbN5W9X9cyhx6lioXSR3yIiOjS1a1bVz0Mvryhz4pndkykKs4qs5mRReuI6Bz9HvbIOvWMdYo5uemmm9RWBoCpcVu3blXly4XPndWaE7PmxdMT3FD+wMCFAlrFihWlRYsWuiYqBSM2brQSGoBRo0apcqmKrSU1qJTa9j7I+dd0pfP+vkMzkb2HnUGNj2euYbEj0lv6OhtN0+Kd5Nbyj13UCGAkpmOpvtlmAiMiotzDKDoyjMHu3btly5YtqmylgQMHqg4wwFSxrLJjNih6nbr+L5Vf5IBk/Lq2OpbL2qDZUiy0lJQvdIU+mjWs6+nQoYNER0fLgAEDpF69evqVyxMsIXJrhcd1LXNYj1KvSHNdo/wiyOGryfI2mD59ukqBC4jifb3YiwIT0i1i2NuYs4vF7G3btlVlK2FTsV0HC0nbfvOdDdDFfQB3dBPp21VXfAxZ1JCQAHu9oGHwBUwNcEiamiCA5jTI2TRgnjMRXR4k1UCKWMAU11WrVqkyFWwYaTE2aESmTKw9CQ62tj8ZG0Oi7TJgqpoRzJhhgf6vR1yZNMOCwiXUUUiSJFFSJUWN1D9Q5Q1n4OKRBjMLaK/wfRkjL760NX6VTIv5XG2YbIaZAux0y5844kIBr0iRInL99dfrmsisWbMkMTHr3O6+MnbsWDlxaJHM/aahLJtys7Spt0oe6StyT0+R228QaXGVPtHHtm/fLu+8844KXHwZvCNIQS8W/sMzgxYiIut069bNnezl5MmTlu8yDwhSbr/9dl0Tefrpp93ZMc0aFm0t91V+Ta0RSXYgZDmrgpZaUY1kYNX/8zpoAey3Zg5akEwHazV9ASMqL9T8RN6o8728rh9v1vmRQUs+xsCF8oX27du7p1CVLFnSlhSTRgNw9tS/cnT3NBkzvI+0b3ZBenUUuc0ZuFxRUZ/oYxEREe6c+HbtwExERL6FvVy6dk0flkenm1W7zJthqrMRMGEEBtkxM1M96ip5uOpw+U+dn1QwgOcBlYdKmfBK+ozcQVuFtTXocPv999/1Ud8wOt3wHzvd8jcGLpRvIPUiAgls8IVNKu2ABgBrXgAXZaS3tBpy4zdp0kTXXOt6AmjGJxERacb6D8BMAV+mDM4K2hAkeTEgO2ZOnX2+CAYQqGFkCbBxtLGdAVFuMHChfKN27doZFurbAQ3Ayy+/rGsiI0aMUJnGrIaF+mFhrjTGGHJHrxkREQUerN1t0KCBylhpzjZmpaFDh7o3iESb9dprr6mylTArwrz+dOrUqZKS4rvNjalgYOBih6Q4kW3/E1n4oOux/FmRQ3P0i2SFY8eOyUcffWRLemRk+jIaAPRavfDCC6pspWLFiknnzp11zZW4wo4pBkRE5FvYZf6+++5zj7zYAVPFsFbS8OGHH6pNla2G9aiFCxdW5bi4OJk/f74qE3mLgYvVDjv/KJc94wxU/hJJTXQ9Eo86A5kvnQHMYFeZfArrPjCFCykm7ciPjwYAC/UNX3/9tS0jIJhiUKKEa6MYTDGwYwdmIipAks+K7PpJZMmjIosGOtuyp0T2TRVJS9YnkK/FxsaqNmTNmjX6iHX69+/vXmODBfrPPvusKlsJazS7d++uayJz586VM2ey34GfyIyBi5Xi94tsneC8yCfpAx4SY0TWv68r+Z3D+b9U10Ml3LUOpowZ06iww/zSpUtV2Uq9e/eWG264QddcmVqshiwtSP1sWLJkiZw4cULXiIguw4lVrk43BCpJp0VSzomcd15fEMhg1sCZzDcnpEuHjJHDhw+XjRs3qgXsSCNstTFjxkhISIgqo/MLCQKs1rJlS/cmygiYpkyZospE3mDgYqX903UhGwmHRC6c0pV8CCNMO74TWXCfyPwBrsfCh1yNoUXyKj3y6NGj3Q0ARly++uorVbZSw4YNpVq1aurztmvXzp0ogIjokqFN2jTedf3OzIWTIhtH646o/M6+Tjdcy41pVBiFmDdvnipb6corr5SHHnK2yRqS2xh7olkFG2/269dP10TWr1/P7JjkNQYuVorbqgs5wBqY/AjT4Fa8IHJgRsapBWgM0Wv3z5CsG8bLhJt4bOgFCFpmzHB+DRZDA/DEE0/omqhdie1Iy4zhfnwuLNg3UlwSEV2yYytyng52IVYk/oCu5FN7fhVZ8EB6pxs64Hb96Gy3rFlPiP1OcB03YP0Hpo5Z7a233pIyZcqoMta52JkdE0EMNsQ02muinDBwsVKyl/M2UyxeVI3g4NQGkaNLXI/TO+zpKdvxffajSZhKt/sXXfEtjECYp1FhY6+YmBhds465AUCmFgz7Ww2ZWswXfaztsWOKARHlU95OA8uvazSxtmfFc87A5TdnAGcafUAwt2+ayN8vWjZTonnz5lKlShVVtmsaFdqsYcOG6Zq92TGxrqZv377ukSainDBwsVKUlzsQhls4vWfv7yKLB4msGyny70eux+o3RJY+4ZrDbBVHmvPjr9aVbBxe6Pw/a4bfMQKC9S6GX3/9VZesg6la5gYA84ftGgLHppTffvutyqZmxxQDIsqnkuN1IQep1k4pUrC+JuGw64GyHTC9OSGbnd2x1gcJdixinka1adMmW7JjDhw4ULWZYGd2TGOtCxw+fNiWWQoU2Bi4WKlCO13IRnCYSKRFmyXu/0Nk98+ZD/mjAdgw2jUSYwVvs85gNMjCzRPRk2PAxX/nzp26Zh1zA4C5wk899ZQqWw3fG+YKA6YYGLvrExHlSrgrW2GOQi3sJY/fJ7L6TVdGsxXPux4or3nLNVpvGWd7FLNMl7OBjjl00FmgYsWKauTFgE2G09Ks+VwGzFJ4//30ZEF2ZceEc+fOqe8RU9TsSA5AgY2Bi5UqdhIp4trfI0vVejl/C1asS3BefPdM1uVsYCg8H8MUqvbt20vJkiXl3nvvlVq1aulXrIMGACMtBgz123ExbtasmWrwAFMMsLdLXlsaO00+2/+qvLG9v7y1Y4B8ffD/ZN2ZRfpVIvJL0a6OlxwVrqQLPnZ6m8iq153P2/UBk7gtztdecwU2VkAw4u1Uaod1mydiU0qseQHsS7Zu3TpVttKNN96YYYq1HdkxAdk/ESQ5HA71bMe0bgpcDFyshNGUxkNFitXUBzxUu8X5uFVXfAyLB71Z+K4aBgtGPIJDdSEHIZFIMaIr1kCGMexujwxcLvh+jYc1kBv/9ttv1zVXphYEE1br06ePLonaB+DQoUO6Zr+vDr4ls49/L4fOu0a5UhzJsjthk0w++pFMi/lcHSMiP1S2pUihHDZDLF5XJCp9mo9Pbf8m+1F7vIbpXHnOujbEyI6J6cdIwIKF7HYYN26cO8kLgoiffvpJla1Us2ZNqV+/vq6JLfuv5WTtmQXy7cERMnzHfarT7ZuDw9UxynsMXKwWXlyk+Vsi174n0uglkaseF2n2hkjbT0RqpM9j9bm8TlMZFCJSupmuZKNie+f/WRu4uDNtpcSLbPtC5n72iBybeo/IvLtEFj3sXdrqS4BNMI3PjUwt48ePV2UrXXHFFXL11VfrWt41AH8e/1b2JGzWtYutOj1XVp6eo2tE5FfQoYRON7RfmcFMgobP6IqPYcTjrBdrOmJxfbEgcAhy3hah/fJGkGu/MKtgk+GhQ4eqaWPIvuX6fs0P30O2r2eeSf/dvvTSS7asO8FIj+t7dCWYsWMX/6z8fvQT9diZsEGSHOdVp9uuhI3q2KSjH+qzKK8wcLELFuqXaiRSrrWrpyqsmH7BKn7wq617f/ZzpaMqWRu8mSUelb3TX5IR322QWduLye//6gYZwczO713TEnycmhkNwJAhQ3RN5PXXX7ctU0twsOv3j8QAxroXu6RJqiyLzTkYnHfiZ10iIr9TuLLIdWNcAUz9h0XqDXQ+DxJp6rxWthiedVBzuXLT6WZJB53z5rlcK13OBjrmEORYTE0XwwyK3T/Lki8fk72TnO0qOt2Qphnpmi3wyiuvqPYL0IbYlR2zVav0nzumWFu9riczy2NnZDuysuHMEll8ihtm5iUGLvlViJfrZiKQGMCiEY9CJZ2N3KuZT5WLbuB6DT17dtgyQSKDzsqpRFdP2vYTEfLvsQhVVs7sdGVg8zH0lhkNAHqtXnvtNVW2UnR0tNrHxjBt2jRbpqkZktO8S8WckHpWHBb1GhKRD6AdKdlQpEIHkYodXQlnStTzfkTiUuRq6rBFbVftu7NPmoNpdOiYs8OFU3JkzhB575ul8vvmwjLlX93piTTNWKOKtM1I3+xDmJ72zjvv6Jp92TFvuOEG9yyFkydPytKlS1XZTn+dmKhLWVtw0voMpZQ1Bi75FRqWCpiGlYOq3XXBIhhpwlS5jt85H9+6Hp2+F2nysnU9dp4wHzpui5QrkiItKifogyK/bSomqeYOnSOLdcF3cBE2NwCff/65LUPgXbp0cefFj4+Pl4MHD6qyHdKc/3mLgQsRZRAU6rwz8aLjDRnNrAqgwoqKNPuPM0hLX3fhho64Zm+6OubssO1LiUo7IccTXOtGD5wOlzWHTB1+SNtswXofrKu55pprVNmu7JhRUVFqXY9h9uzZcv68xfvcmWC2QLIj5xTfmDqGB+UNBi75WZ17RIrW0JVMoBetcvpFwlLGvGHV0FjUS5YV0wjATXXPSliw68Y67nyoLNxjSueZFOf8P9/fSKMBwGJ9wMiHef6wVSIiIlTvVdOmTVViAqx9sUtoLuZ9B9n9XiAi/1e5iy5ko2o3XbAIOtaavpax0w1ldMRFuDYZtpzeD614RJp0qJ6+t84fW4tJinkQXaVv9n3bNXbsWF2yLzumkQUUEhMTbdnDxpCWi/TW7HTLOwxc8jNMw8J0LAQnanQDN4nOR2R5kVp3ueYtFwSmi1GRQmnSuVZ6AzBnR1GJv2D9nwGG2pEmGebMmWNLphbMF77zzjvVJl92CgsKl5Jh5XQta/WLtGTgQkQXQ/tUMj3JyEVKNbEuI6cnc6ebDWtaMjCt4UG7VTjcVT9zIUQW7CmiygrOy8VNt7cw4oJtBAx2ZcfEOk3shYap1uZsY1YLzsUIXghGBilPMHDJ70IiROrcJ9LmY9cULTyuGy1S9SZ9QgEQ4sqFb+hQ45wUK+S6+CanBcu0reYbe2tupHERfuihh3TNlakFw+92wT4An3zyiW29V93LZj//u1BwpHQtc4euERF5uPo5Z3DSO+OUYqwtqe4MWBpZv6u7f0jv1Q8PcUj3uulrWf7aWcSj082aEYB3331XrXkBTHOeMGGCKlsJWxc88MADah82OwU7/6sedZWuZa1UWAV1LuUN/uQp/8N86cj0EYDQYIf0qHdG10RWH4qS/XFhroWnFnrrrbfcDQAWOr799tuqbDVkFUNqZuysj92J7VC7cGO5veLgTKeNFQ4pLvdUflld/ImIMoV90GrcpjvdfnA9Wv/XGbj01ScUAB4jAC0rJ0jZIq61Feh0m77N1Z4ouRgtyI0yZcqo6cYGu7JjGmJjY+Wbb76R1atX6yPWurHMgGynO+O1Wys8rmuUFxi4UMFQJ324G5pWSpSKRV1rX64smygli0VaPvUADcCwYcN0TWTkyJG2ZGrB5l5GphaMvCxZskSVrXZlkWtkaK0vZHD18fJU9Q/U47ka/5Xna34klSNq67OIiChTCEaKVtMVZzVIpM9Vp3VNZOXBKIk5GyoSjVEC66bdDh48WM0aAAQtdmTHBHS2IRXzhg0b5I8//pCkJO8yVl6O8oWqyZ2VXpDIYNP6Vy0iOEruqPS8s/2qpY9QXmDgQgVDqcYiVz3h6sXTbm1wRu5oFCsPtHJIkWtfzj79pY88+eST7gYAU8VefPFFVbYSdmA2kgPAzJkzJSEhPbualdA7VSKsjBpdwaNYaCnnRceankEionwHnW6mdqtmqSS5sqwr01bJyFRJTAt3pW+2EDq+3n//fV2zLzsmthIwZilgO4G5c+eqstVqRl0tL9T6TJ6t8aE8WW20eqD8Yq0JUiuqkT6L8goDFyo4sKlYu89d0w1ajZNqN70nzW4fLdLGWS9S1ZbeHCzQNzcAEydOlL///lvXrNO2bVv3fGEETDNmzFBlIiLyY9iwGut9THue9ax/RrrVPSMvdz0n1To842y/rM8aeeONN6pMlWBXdkxsvomF+oYFCxaoqWN2CHH+Vzy0tJQOr6geKOMY5T0GLlSwoOcKCzwjSrse4cXF4RBZtWqVWnOycOFCfaJ10AD06tVL10SefvppyzO1IGDq06ePromsWLFCYmJidI2IiPwWMqy1+0ykzUeq061M19HS+d5RIm0/cW3m7GRHspfRo0fbnh0TKf2rVKmiythJf/LkyapMBRcDFyrw1q5dqy7AGIr+888/1bPVxo0b5153ghEXOzK11KlTR+rVq6drYttCfSIiukxY7xJewtTp5iw7j23cuFFGjBihNmu0GqY5P/HEE7pmX3bMfv366ZIrs9n27dt1jQoiBi5U4DVu3FjKlnWtb8F0MTumUWHurnmoHZla7AiYbrnlFl0S2bt3r+zYsUPXiIgokGDx+tdffy2nTp1SswXsyPaF7JhINANILvPBBx+ospUqVqwoLVu21DWRSZMmqdEXKpgYuFCBFxwcnKFHZ+XKlXL48GFds84rr7yiAhiwK1ML1rl06NBB7UyMjcVq12Z2LyKiQFSrVi2pVi0969hvv/2mS9bBYnlzdkyM9tiRHfOmm25yz1I4efKkmilBBRMDFyKn6tWrS4MGrrnCYMc0KjQA77zzjq6JfPjhh7ZkakGGMeTlxyZfREQUuMydbhhBt6MNGThwoNpVHzBTwI7smIULF1bJAdBu3n777WrtCxVMDFyINCyYNxYeHjhwwJYNr/r37+9uALBA/9lnn1VlKxm9VoZ58+ap/V2IiCiwlCtXTq677jpdE5kyZYotyV7Gjh2ra/Zlx2zXrp0MHTpUWrRoIUHY1IYKJAYuRFp0dLSaRmWYOnWqLSmS0QAYAROSA8yaNUuVrYY1LsikhjU906ZN00eJiCiQdOvWTSIjXemSMY1q0aJFqmwldLhh5MNgR3ZMQIpkw/Lly2XPnj26RgUFAxcik86dO0uxYsVU+dy5c+rm3mpoAB566CFdE3nuuedsydSChg6NHGzZskV27dqlykREFDiioqJUmn0DMozFx8frmnVGjRple3ZMOHLkiLz33ntqkT5GmKhgYeBCZGJseHX11VerxfNIIWwHZGoxdgjGHGU7MrVgioExTQ3QCDiwqQ0REQWU1q1bS/ny5VU5OTnZloyRSC4zZMgQXbMvOyYCNSOD2sGDB22Z1k3+g4ELkYcmTZrIPffco6aO2QXpJbFg3oBMLXaktsQUg7CwMFXGOhc75ikTEZHvYZPhGjVqqMXyaMfsgDUndmfHLF68uHTs2FHXRKZPny4pKSm6RvkdAxeibMTGxspXX30lGzZs0EesM3jwYLXBF6DX6oUXXlBlKxUpUkS6dOmiayIzZ860ZV0PERH5FrJjPvbYY+59yeyAqWJ5kR2zU6dOKtMYnDlzRubPn6/KlP8xcCHKwtatW2X48OGyadMm+eOPP/RR66ABeP/993VN1MZidoyAtG/fPsO6nrlz56oyEREFJqxx+fHHH2Xp0qX6iHWQHRNp9sGu7JiY1o29XQzIjmnHuh7KewxciLKAjb2MTC3YmXjBggWqbCUssESuegMytVgtNDRUresxYAfm06dP6xoREQUSpPNHpxvWfmCh/vnz5/Ur1hkzZozt2TGRFtkYXcK6Hjs6GCnvMXAh33KkOR+prkeAi4iIyBBEzJkzRxITE3XNOqNHj86QqeWnn35SZSthPrQxTxm76mPkhYiIAk/FihXd06hwLUfbZTVMc7Y7Oyb2csG6HsOqVaskJiZG1yi/YuBCvpFwWGTdSJH5dzsfA1yPFc+LnFynTwhMrVq1klKlSqkyLsJYA2I1NADPPPOMrom89NJLtmRq6d27t9x6663q86HhIyIqMPJRpxtGPm6++WZdE1m8eLFar2k1ZMdEohmwKztmzZo1pUGDBqpcokQJSUhIUGXKvxi40OU79rcrSDnlsYAdwcz6USI7vtUHAk9wcLC6oTdgwysMw1sNqZiNBmD//v1q2N9qGHFBoGbgIn0iyvfOHxfZNC5jp9uyp0RilukTAhNS+huj6GlpaWpDZauhzRo2bJiu2Zcds0ePHipD5quvvqoSFFD+xsCFLk9assjWHDadOjBTJG6LrgSe+vXrqxSTgH1Ofv75Z8v3O8GeLtjcy4D5wwhg7IDvDXOjR44cyUwtRJR/xW4WWfGiyLEV+oB2/oTI5g9FNo7RBwKTeRrVxo0bZd++fbpmnYEDB9qeHbN06dJq82gzOzZxprzBwIUuD0ZVUrwYmvVsGAKMuQHArr12pEe+77773BtE4iL81FNPqbLV1q1bp7LRIMUk5kbbMU2NiMhWmBq2+b8iadnc4B7/x3nBX6grgadSpUrSrFkzXXNtMmw1TFPLi+yYBmQBffvtt1WCAMqfGLjQ5bng5bzZM3t1ITBhl/nrrrtOlZH6EcPwdhg7dqwuiUyZMsWWTC2NGjVyZ2rBdDFmaiGifOfCKecFLk5XsnEosNPDd+/e3Z3t6/Dhw2oBu9WQHbNXr166Zk92TNi1a5fad+3kyZOyaNEitaky5T8MXIi8hAxjyE/fvHlzlc3EDhhxuf3223XNlakFefKthHU9/fr10zVR08bsmqZGRGQLb2YKwFnrp1dZCbvMY7NGQIcbFrPbYdy4cbZnx8T3hm0MDJMnT9Ylyk8YuNDlKRStCzkoln4xCVTYZd6cbevo0aO27HeCtS5Y8wLI1DJhQg5rinwACxwbNmyoayK//PKLLhERFSRp+jlwIXB59NFH5Z577pHoaC/b7MuExAB5kR0TnW5Gx+KOHTtk8+bNqkz5BwMXujxRzhv50ChdyUa5NroQ+JBuEXOFMY932rRp+qh10AC8/PLLuiby+uuv25KpBek0jSkGWNezcuVKVSYiCnjetFtQNPCzVIWFhWUYacFUKjumUSE7ppHZzK7smOZp3YBRF6tnKZC9GLjQ5QkOE6k3UFeyUKmzSPHauhL4Tpw4odIiI/sWFrLbMY1q8ODB7gYAQctrr72mylZCz5wxxQCw1sWOHZiJiCxXqKRIeAldyUalLroQ+Iw1ixjFR3ZMq2GmwDvvvKNr9mXHxBqbyMhIVY6Li5OFCwM3wQJdjIELXb6y14g0fEYkzDWdKYNqvUXqPqgr+QMCCOw0b8A0KqvTI2OusHmh/ueff66mjVkN63owRxqwA7Md6TSJiCwX5Lz9uXKQq/MtK6WbilRoryuBD5kicROPEYi9e/fakh0TyWzszo4ZFRWl9nUxIDsmvnfKHxi4kG+UaSnS9lORjt+mPzp9L1LjNn2Cxc7uFjmyyJW68vhKkWRr59L27NlTDb8DplHZke4RG2EikAA0POb5w1bC5l7YmRjD/nXr1tVHiYgCXMmrRa5+PvORl0pdXa/lI9jvpG3btromalPKlJQUXbNOXmTHxGbK5cuXV+Xk5GTZuXOnKlPgC3JY3VXsQ9OnT1c3UXDTTTcxTSuJnN4usuUz134ynio7G55ad2ffo3YZ5s6d674AY1gau/YaWVSsglEWZIYx5uxivxX0aBGR/0JmPmQjBOyrYUdKWsolB66p+nYoCGv77MkcKecOipzd69pXJiRcpHhd75PeXAJM98U+JxhBB3SGde3qbCsthn3JsKcLYINKjPYYayitsmfPHpk5c6b07dvXneKfAh9HXChwJR4TWTcy86AFDs5xBTUW6dChg5Qo4eqpS0xMtKUXCRf8J554QtdcmVq4QzAR0WVCsBIU6nrYEbQgYPlnqMjfL4r8+5GzrfpEZNM4kaWPO+sfe5+uOZciIiLU3i4GdMDZMY3q3XffzZPsmI899hiDlnyGgQsFrn3TRFJzWCweszTncy5RaGhohk22lixZIjExMbpmnbfeekvKlCmjyljo+MEHH6gyEREFgKTTIquHicRnsWbw6GKRzR/qiu+1bNlSKlSooMoYvcf0LauhzcqL7JiU/zBwocB1wsvpFt7u7n8JsNdJrVq1VBlZuOzIuoVeq2HDnI2eNmLECG4QSUQUKLAWMyVeV7Jwcp1lbRf2OTFvMrx+/Xo1rcpqyI6JWQNgV3ZMyn8YuFDgQq+VN9KSdcEaWDSPxfroTbriiiv0UWsNHDjQ3QBgU68XX3xRlYmIyM/FepkRMvGoLvgesmM2bdpUldEZhlTJVsMaUOx/ZrArOyblLwxcKHCFeLkQHmkvLYTMJe3bZ0yZaXUjgEWNyIlvmDhxoi2ZzYiI6DKdP6ELOUhJ1AVrINlRly5dZOjQobZljMQeK3mRHZPyDwYuFLhKuEYcchTu2ofEDsiUgt2B//rrL33EOsgEc/vtt+uayNNPP+3ONkZERH4q1LU5Yo6QZcxCxYoVU4FEeHj657FjuvPo0aPdGcWwx8pPP/2kykTeYOBCgeuKnrqQjaI1Mt8Y0wLbtm2Tb775RmJjY9UmX9ix12rYAdlIwYwRFzsytRAR0WUoUU8XclColC5Yb8eOHWqXezsW6jM7Jl0OBi4UuHDxz26Dy4jSIlc/qyvWw1B7xYoVVRmbemHfIathnvKQIUN0zZWpBWteiIjIT3mzG39keZEoV+Yvq2ET5U8//VQtmF+5cqUcPpzFFgM+xOyYdKkYuFBgq9ZbpKEzOClW07XmBQ/sglyxo0izN0UKldQn2qNPnz66JLJ27Vo5dOiQrlkH85MRwAAaHkxV87WfYs7JuINn5Jdj52TJ6fOyIzFZEtICZu9aIiL/UbiyyJWP6komMEugoX1rP5Aa2Uj2Ar///rsuWYfZMelSMXChwFemuUjzt0Taf+l6tPlIpN5A24MWQFaxRo0a6ZrIpEmTdMk6mCqGIX4DFu37MlPLmVSHDNx6XF7ZdUoe2HJcuq07Kk3/OSTlFu+Tp7ef1GcREZHXyrcVaTzEtVN+cJjrEVrY2Z61FGnhbM+KuDqj7HLzzTerNMmwe/du2bx5sypbCdkxr7nmGlVmdkzyFgMXIh9DppbgYNefFnqQ1q1bp8pW6t+/v1qsjyAGufKrVKmiX7l8xUKCZEydUjKwYlHpWjJSosOCBWMteGxPtD6FJhFRvlTyapFmb4h0+Nr1aDfBNdISYf9O76VLl5bWrVvrmsjUqVMlLS1N16yBBfpjx47VNWbHJO8wcCHyMWxEaU6PjAYAa16s9uGHH6qRlrffflsNw/vS3eWLSPlCobIk7rzEJqepCwf65u4tX0y9TkREge366693J3s5efKkLF26VJWthBEXZMdEZx9GYGrXrq1fIcocAxciCyA3fuHChVX5zJkzsmqVl7v8X4Y6depIjRo1dM135pxKlOb/HJK39sRKYppDWpWIUKMtZcKC5bZyru+RiIgCW1RUlHuPFZg1a5YkJCTomnXee+89WbNmjXz22WdSsqT9U7wpsDBwIbIAeq2QHx/BS9++fd3zeAPJnsRkuWVDjNy6MUb2nk+R2pFhMrNReSkR4rpsPFq5OC8gRET5SLt27aRUKVcaZqQotmPUpXLlyhnWhhJlh/cdRBa57rrrVKria6+91r3oMRAgW9iwPbHSfOUhmRubKNGhwTK6din5p0UluaZ4hMw4mSBhzm/ngYr27I9DRET26dmzp0RERKjnzp0766NE/oGBC5GFIiPTd0jGosOdO3fqmn+afPycNP3noLy7/7QkO5zBSYWisu6aymphfqgzWEHAMqJmSXnXGciUcgY0RESUvzRo0EB1umGtppFohshf8B1JZLFjx47J6NGj5ZdffpHJkyfro/5le0Ky3LjuiNzz73E5dCFVmhcNl6XNKsrYOqWkpEeA8mTlYvKgM6AhIqL8qUiRIrokKjPmxo0bdY0obzFwIbIYFjwiQwvExMTI4sWLVdkfnE11yJBdp6TlqkOy9PQFteD+47qlZV7TitKwSLg+i4iICpq4uDj573//K99995389ttvkpTE9PeU9xi4EFkMPVdIM2lAppbExERdyxvICvZDTLw0/ueg/PfgGQlx1p+vWlzWX1NFpT4OnBU5RERkBXS6nThxQpWxQeTcuXNVmSgvMXAhskGbNm0yZGqZPn26KueFjfFJ0mnNYXlk6wk5lpQqXaIjZVWLSvJG9WgpGsKQhYiIRMLDw9UCfcOCBQskNjZW14jyBgMXIhtgh+A+ffromsiKFSvk8OHDumaPUylp8syOk9J69WFZdTZJqkWEym8Ny8nkq8tJ9cgwfRYREZFL06ZNpUqVKqqMnfT9dZ0mFRwMXIhsgg0i69evr2uiFuvbIc35+PzwWWn890H5n/M5IjhIXq1WQla1rCRdS6ZnPSMiIvLUr18/XRL5999/Zfv27bpGZD8GLkQ26t27tzu95IEDB2Tt2rWqbJWVZy5I61WHZPCOkxKbkib9yxWWtS0ry0tXlJBCAbS3DBER5Y2KFStm2ER50qRJavSFKC8wcCGyEda5YGdiwE7BNWrUUGVfO56cJg9vPSGd1h6RTeeSpUHhMJnXpIJMqFdGKhXCUnwiIiLvdOvWTQoVKqTKyJK5ZMkSVSayGwMXIpt17dpVHnvsMRkwYIAUL15cH/WNFIfIhwfPSKO/D8iPMfHuXe+XNq8kLYq5Gh0iIqLcQHbMG264QZVr1aqVYdozkZ0YuBDZDL1WVoy0LIk7Ly1XHpKhu05JfKojw673/EMnIqLLgdkCDz74oAwaNEjKlCmjjxLZi/czRAEOO93f8+8x6bb+qOxITJbWxQtlues9ERHRpeJIC+U13tUQBagLDoeM2n9amvxzUCYfT1BrV76+sozMalyBu94TERFRvsPAhciPTTuRoEZUPM05lSjN/zkkb+2JlVRnAINd79e0rCy3limszyAiIiLKXxi4EPmprQnJcufmY3Lvv8f0EZE9iclyy4YYuXVjjOw9n5Jh1/uoYKY3JiIiovyLgQuRn/ro4Bn1fHWRcElIc8iwPbHSfOUhmRubKLUjw7jrPRERERUoDFyI/NCZVId8f/SsKlePCJOm/xyUd/eflkLBQTK8Zkn5pwV3vSciIqKChYELkR/68vAZSXKIFA0Jlpd3n1LrXLDr/fprqshTlYtJKGeFERERUQHDwIXID43T08TOpKZJrchQeb9WKbmnfFFJczijGSIiIqICiIELkZ9Jcz6S01wBCgZWdiamyHM7T0r39UfVonwiIiKigoiBC5GfwR/lgmYV5burysr7tUupVMd3lS8inaMj5drihVwnERERERUwDFyI/FCNiFDpVTpKHq5YVKU6/qRuafn96nJqyhgRERFRQcTAhYiIiIiI/B4DFyIiIiIi8nsMXIiIiIiIyO8xcCEiIiIiIr/HwIWIiIiIiPweAxciIiIiIvJ7DFyIiHxg5cqVUrVqVXn22Wfl/Pnz+igRERH5CgMXIiIfePPNN+XAgQPywQcfSPPmzWX79u36FSIiIv+0bds2qVmzpjzwwANy+vRpfdR/MXAhIvKBhx9+WIKCglR58+bN0rRpU5k4caKqExER+aPx48fL7t275csvv5SGDRvK+vXr9Sv+iYELEZEP9OrVSxYtWiTlypVT9XPnzkn//v3loYce4tQxIiLyS926dZPQ0FBVxqwBzBgYO3asqvujIIeTLvs9RIVPPfWUKteuXVvuvPNOVSYi8hcJCQnyyy+/yN69e/URkfLly8uSJUvUcDwVPKtXr1Y3A9CsWTNZtWqVKhMR+QOMsvTu3TtDu3XTTTfJ999/L8WLF9dH/ENABS6DBw+WMWPG6BoRUeAoW7asbNmyRUqWLKmPUEHx+++/yy233KLKFSpUUNMKiYj8SVJSkkyePFm2bt2qj4hER0fL/PnzpVGjRvpI3mPgQkRkE8wjrl69uq5RQWGeLUBEFEgKFy6s1m1eccUV+kjeCqjAZcGCBfLVV1+pcmRkpHsuORGRv8AlFWtdcL0yhIeHy4QJE+See+7RR6ggYeBCRIFs+fLlcu211+pa3gqowIWIyJ+dOHFCbrvtNjW0bqhbt65Mnz6d61sKsHXr1rlnCyCIrVixoioTEfmTv//+W2bPni1paWmqHhwcLKNGjZLnnntO1f0BAxciIh/4999/pWPHjnLs2DF9xJUiGdlZIiIi9BEiIiL/Eh8fLwMGDFDr8QxVqlSRadOm+dX6FmDgQkTkAz169FAjK4CprJgadtddd6k6ERGRP0IK5LZt28q+ffv0EVEZxrA0w98yigH3cSEi8gHs2QLYeHLjxo0MWoiIyO/95z//cQct2M/l/fffV9nF/DFoAY64EBH5iJE1zNhBn4iIyJ/NnDlT7dlSq1Yt+eGHH9x7TvkrBi5ERERERAUURlwqV64sISEh+oj/YuBCRERERER+j2tciIiIiIjI7zFwISIiIiIiv8fAhYjIB4wNu4iIiMgaDFyIiC7T+PHj1SaT119/vXTv3l1t5mXYvHmzxMXF6RoREZH/CLRONwYuRESXacaMGZKcnCxz5sxRqSXnz5+vjk+aNEkaNGigdtQnIiLyJ//8848UK1ZMWrdurR4bNmzQr4gcOnQow6aU/oKBCxHRZWrcuLEuufz555/qedSoUeo5JSVFPRMREfkLdLKdO3dOli1bph5ff/21Or53716pV6+e1K9fX44cOaKO+QsGLkREl2nAgAG65PLzzz+r4AW9WXD33XerZyIiIn+RVafbxx9/rKY8JyUl+d3eLtzHhYjIBxo2bCibNm3SNZFy5cpJTEyMlCxZUvbs2aOG44mIiPxFamqq2njy6NGj+ohrFKZXr15y5swZufHGG9X0Z3/CERciIh/o1q2bLrkgaIGnn36aQQsREfkdjKb07dtX11z69++vghZ49dVX1bM/YeBCROQDPXr00KV0RYsWlSeffFLXiIiI/EtWnW5IKoMF+/6GgQsRkQ/gAl+qVCldcxk0aJBER0frGhERkX9BgBIVFaVr6V555RVd8i8MXIiIfABD7v369dM1F6RIJiIi8leRkZHStWtXXXNp3ry5dO7cWdf8CwMXIiIf8cwuNn36dF0iIiLyT3fccYcuufhzCn8GLkREPrJt2zZdctmxY4ds2bJF14iIiPwPsohhTaZh3bp1agNKf8TAhYjIR5D73tOvv/6qS+m2b99+UZBDRESUFxCkYCNKs6lTp+qSf2HgQkTkA9hdeOXKlaps3rDL2IkYzp49K0888YTa8wW7EiNfPhERUV769NNPJS0tTddc/LXTjYELEZEP7N69W5dEvvzySylUqJAq79q1yz3kvnXrVvnoo4/UbsTAwIWIiPLalClT1LO50w3t0759+1TZnzrdGLgQEfkAMrNAUFCQSi85fvx4lQr5oYcekgoVKqjXWrRoIX///bcqw6lTp3SJiIgobxgdb2+88YYKTMDhcMiSJUtU2Z863Ri4EBH5QNOmTeWHH35QPVeVK1eWgQMHqsBkwoQJEhycfqktV66cLvl35hYiIioYjH1catasKd98841UrFhRunTpIh06dFDH/anTjYELEZGPIKVkz549dS1zERERuiTu3isiIqK8MnPmTJVc5rbbblNBCqY3z5kzRypVqqTP8J9ONwYuREQ2Mta+QEJCgi4RERHljVatWsmgQYMkNDRUH7mYv3S6MXAhIrKRMSQP58+f1yUiIiL/5S+dbgxciIhsFBYWph7AERciIgoE/tLpxsCFiMhGixcvVtlaIDY2Vj0TERH5M3/pdGPgQkRko6efftq9sHHVqlWyY8cOVSYiIvJX/tLpFuT8IlxfBRERWS4uLk7+/fdflU4yOTlZbrnlFv0KERGRf0LK/7Vr1+qaaxf92rVr65p9GLgQEREREVGW/KXTjYELERERERH5Pa5xISIiIiIiv8fAhYiIiIiI/B4DFyIiIiIi8nsMXIiIiIiIyO8xcCEiIiIiIr/HwIWIiIgoACQmJsqHH34o8+bN00dyNmPGDFm4cKGuEQU2Bi5ERETkN2bPni0DBw6URx99VP755x99NP9YsGCBdOzYUSZNmqSPeO/ZZ5+VJ598Ujp37ixjx47VR7P2v//9T2666Sbp1KmTHDhwQB/1rcOHD0u3bt3kjTfe0Ecy2rNnjwwYMED9ThFEkXUu570VKLiPCxEREfmNhx9+WCZMmKDK2K179erVqpwfnDx5UmrWrCmnT5+W+vXrqw39cgMBwqxZs1T5sccek//+97+qnJWrrrrK/Tn+/PNPuf7661XZl9q0aSNLly5V5ZiYGClbtqwqG8y/T3w9mzZtUmXyrct9bwUKjrgQERHlJUeqSNJpkcQYkfPHRJLPOI+l6RcLnvPnz+uSSN26dXUpf1i/fr26sYQTJ06o59zAKMuNN96oetVffPFFfTRrsbGxupSx7CupqamybNkyXcv8e9q3b58uuaa6kTUu970VKBi4EBER5YWze0U2vCcyf4DIkkdFlg8WWfaMyOJBIgvvF9n8X+ed3lF98uW5cOGCjB49Wh5//HH1+PLLL/Ur3ktLS5OXX35ZTVX64osv9NHc2bt3rzzxxBPZThlKSkrSJZHixYvrUrrk5GSZPHmyPPDAA9K2bVs1Beno0Yt/TkeOHJGhQ4eqrxePRYsW6VdydvbsWfnqq6/kP//5j3z++efqY3nCdBx8L/iZmCevLF++XF566SU1hcqT+XszB2hm2X3d+LowCnXNNdeoHvacBAUF6VLGzw24ucXvsU+fPurn+NZbb110Tk7wuzB/75l9TxiFMYSGhupS9hYvXqx+hh06dJBevXrJqlWr9CveWblypTzzzDPy1FNPqel1+/fv16+IrFixQk1ru++++zL8DPH7wufE788TRoyef/559fPPjjfvb0NO7zH8fnr37q1G2Yy/VwSK33//vfocnn+D3ry38gVMFSMiIiKbpKU6HLsmOhx/3ZHzY97dDsfBOfofXhpnwOG44YYbcHeZ4bFz5059hsu8efMczhs9x6233qqed+/erV9x+eWXXzL8+zFjxjicQYPDeePuOHTokDrn2LFjjt9//93hvPlTn9fMGTw5rrzySvVvg4ODHc2bN3c4b/D0q+nw+Y3P8fTTT+ujLs4bMkfr1q3drxuPChUqOA4cOKDPcn0dlSpVynBOlSpV9KvZc94wOmrUqJHh30ZGRjo+/vhjfYbD4bzJzfC688Zav+Jw1KpVSx3Dz8bMedPp+Pbbb93/JiQkxOEMQNTX5bxRV+fk9HVXr17dffyVV17RRzNy3qQ7fvjhB8fs2bMd5cuXd5//3Xff6TMcjh07dmR4zXh07NjR4QxG9Fk5c96oZ/j3TZo0cVSuXNnhDJL1GQ5HxYoV3a+HhYU5ateu7bj55psdX3/9tSMlJUWfle65557L8DHxcAY8jlmzZukzsrdw4UJ1vvnfDxgwQL/qcNStW9d9/L333tNHHY6HHnpIHcPXZ4afm3F+0aJFHc5ASL1HPHn7/gZv3mOfffaZ+zW8V/A31rlz5wz/xhmEqXO9eW/lFwxciIiI7LTl8wzBSfLCRx1xf492nPjnY/U4vWKUI2XBQxnOceydov9x7m3bti3DzU6hQoUc7dq1U0EA4Ea3U6dOGc7BAzegZ86cUefA//3f/110jvEoV66cugnEjalxrEePHuqGyjBp0qQM/8Z44EbQDDe1xmu4iTUbOXJkhn97xRVXuMv33HOPPsvh+PTTT93Hg4KCHFFRURd9nqw8/PDD7n9rfuBm9N9//1XnrFu3LsNruFkGBGvGTXPXrl3VMVi/fn2mgYLxeP/999V5OX3dCCyM15988kl91OXUqVPqZ27+HZgfv/76qz7T4bjxxhvdx3G+Obj44osv9FlZO3funKNFixbuf+P56Nmzpz7T4YiIiMj0HDzatm3rSEhI0Gc61I24+XXceOPnjnK1atUuCoYzc8cdd7j/PW7gS5Qo4fj555/1qw5VN17/z3/+o4863H8D+Pswf56GDRu6zzceVatWdWzcuFGf4eLt+xu8eY+NGzcuw2stW7bMUA8PD1fnevveyi84VawAwPzSxo0bq6wimANJRER55MBMkcN/6YrI0ehesrv8MxJT4iY5WbyTeuDYrgovyIniXfRZTrsmihxboSu547wR0yWRwoULi/NGUaXHNY5j2kxm6XUPHjwoP/30k66JHD9+XJcuhulAmEqDqUOGP/74Q6ZNm6ZrkqFcqlQpcd54qTKmsCH7lcF506hLzrsuj/xB5sXoNWrUkN27d6v1HmAsWgfz94w1Ic4bbXHewOkjWcP3YZ5G98knn4gzmFBlfF1GWmHPqWnG943jKSkpqoxpQwZkR8tsOpsz8FLTmR555BFVz+nrjo6O1iXX9D8Dpgbh54Cfufl3YOYMgtTzrl27Mvys7r77btmyZYt7Wp75tawcOnRITcfyVLp0aTX1DL9TwHstu2lLmBL2yy+/6JqoVM9mS5Yskddff12V8fPcunWrKmfH/DMcMWKEWtvTr18/VcfPLC4uTpXB/LPC+x1wjjFtC/dPzgBFlaFChQrqGVPPbr311gzfm7fvb2/fY86AUj0bzBn2MIVszZo1ahG+t++t/IKBSwGAuZkIWObPn6/mUhIRUR45OFsXnMVSd8mZwk107WKniraRmOibdc15A39wji7nTtWqVdWNFOBG2HP+fYsWLSQ4OFitJRg+fLg0aNBAvyKq3TB4Lu6eO3euClbMihUrpm7SjBvsn3/+WT0DbrQMPXr0UJmoihQpourDhg1Tz57MN5abN2/OkNI3MjJSBTJGoIPAygh0sA7EgBtB8zqL7OAm2vw5cWOINQYG3IiD58czFp2bA0DzDfJdd90lI0eOlDvvvFMfEdWhiJtxLLhHQAk5fd3mm1lzgPfOO+9k6Jh877331DHz+WXKlFHPnoHJqVOn5JtvvnF/Dd78rGrXri1ff/21WotjtnPnTvn111+lVq1aqn7mzBn1bLjhhhtk7dq18tBDD+kjIn///bcuuTKfGSIiItRaJiOgAG++NvPPEL9Pc/Cb1e8Ngdj27dtVGYzfnfk9Cwi08LuEHTt2yI8//qjK4O3729v3mBH4mOHvFMEdfi7I0AbevrfyCwYuBQAWtxkXr9atW6tnIiKy2YVTrsxhTmejGkpCZG1Vzs7pwk3lQlh5VyVui0hqei+7t7BAu2vXrrom8sILL7hHBQD7peAG7umnn1Y3ReZ0tdu2bdMl12JiM/Twv/322xkW0A8ePFgtmsficTD3kJsXqyOYat68udx///2qjh5s9PoDbs4M5q/Tc4E2Ahn0KBs91LhJNBajI/gyesdxI/jKK6+ock6wcNsMN4/mAAHBEpiDEoiPj1fPuKE0mBdL499h4bc5HbFn2mDI6evOKnBB4GHA14zfJbKOlSxZUh8VKVeunHr2/DlOmTJFJQEwfj8IPr1xzz33ZOjNx+f1TKZgHhWCihUrqpvqjz/+2L1Q3/i8GN0wj+phNAML7LFw3eDN12b+GeN7xWJ2gze/NzB+d+b3LFSvXl3GjRunaxmDQG/f396+xzxHXAAjM0iuYebteyu/YOByCTZs2KAibc8hbF/Ax5w5c6bagMuIugEp7vCmz+xzojHBHzwu8OgRw0UcmScMXbp0UR8LQ4nI+27AH9m3336ret+s+F6IiMhEBy1wNsrVW+qNcxGu3mslOfusRlnB9B0DggnztBZAb3PPnj0vymxkvpE0By7oxUWAgZvPSpUq6aOuHnUwbr6OHTumnsGcCtfoiTZGAQA3d2C+YTPf+GJkIDvYL8SAAAZTeQwYHchuqpvBM6sW2lJzW2yMJJnbWMDIAn6GnjfAnjDiZTBGwcxy+rpDQkJ0KT1wQfYpTJkz4OMaQYH5+zFuZrP7OeJnb9xseyOn78fz3qJo0aLqGV+f8b0Y76ucfr8tW7ZUQU9OkELbGI2Ad999V5cy/73h3smY2ubJ/J7FexrveQSDRnBtvGfB2/e3t+8xz6APo5vmkSpPOf0u8gsGLrmEoddGjRpJnTp1VApEzBXFMGd2czg94Q8FwQWG2T2H3idOnCjdu3dXF388449+yJAh6oJz5ZVXZrigYcgT8zbxR1StWjXVS4ONoDDCgqF/oycB85dxMSpfvry79wYXvPbt26seE+yq+9lnn6kHhnfxQFpEIiLyoZT0G5vEsPSb/Zwkhab3mkvqpaU5xXXeuGkE8xQwQC+ucePz7LPPqmcw34yZAxdzz7cxygHGFCHjJtR8nnkkxViHYL5hMz4OpggZMpu7b8AaEExNQkciAiRMjzK74447dMk1coNpPjnx3DcG3wemERlKlCihno31IgYED0hRa+4597xBBfPPyhyEmGX3dZtvvI3PZQ4OwTzKYvz+cCNrXvth9uabb7p/jvh+b7nlFv1Kzrz5fsyMG3owvpes1uTgBh73WPi6cNOPey3zeyg75p8hOpuN96Pn7w33YFgHg939zYzfXWbvWQQUxs/e/P17+/729j3m+XPB+ys7uf1dBCoGLrlknn+JBXO4SF977bVqkSCGJLGGpHLlyipIQHRsvoih1wQNAhavIbjA0D0CDvOwrTl6x6I0DPUiWDL+iIzFWbhIYtoX5pIaQ+k4ZuyUigbI+MMxzwnGOYAeN1yoDJgfiSFfXCTwwGI4jPwQEZGPYKPJy3WJG1OitxgjKgbzpoAYKTD2CsE0L6xzMZh7zM2Bi3FzZYYbJ6On15g2g04+gzEKADgXN67mdsaYImVegG5uv8z/HrA4HOeiHTb3bBtatWql2mOD+XvOChY9m6Ez0Ty1B202YMqTGe4H5sxxrUHC5wUEDeYbV8C9gQE31JnJ7us238xmdvMM+H2OGTNG3U8Ynarmr9fz54h7DXxf+DmaAwtvmL8fjNZ5BlGezOstjMDF+D48vy4EFVjfgq+rSpUq+qh3brvtNl1yMX6GxnvMgN8tAjcwT6U37pU837OABAgG88fz9v3t7XvM/LtGUGQe2cyMN++t/ICBSy6Ze3/Mbyr8weJNjz8ARM4YDcGFDAvjDegd+uCDDzL8O4y+mBc3mofI8aY39yBhpOe7775TZQQWCDAMaBwQPBlQNj5WzZo11TMYF1Gj98Fgzn5iMOYNExGRD4Sm9/aGJ3u/s3VYims3bCXk4gW73sBalawWNpuzJmG0H+sKMmNeaJ1ZoICREqPX2RjxNwch5vYNN5IIpIwF5dgAsWHDhqps3LgB2jnj5huLmM2weSB6r7FmB9PEMD3IvIv7X3/9ddHNcE6aNWvmnu4GWB+BdTyANSJGIHb11VerZ4PRpmIWBBZmGzxv5NHRafyMcHOJ0Q3P9ji7r9voqATjxh8L5c2jLLjHwFojZJUymG+wPX+OmP2B7wcLyPG1417GuHHPCX6/xsfD14MO2XXr1qk6mN8zYIx44FwjKDZGB7B+xHNUCB8PU9eQBAC/X8+sY5nBx0Ug7hnQAUYAze8vrHnB+ViTYl5Ab/zezO9ZQDYwbFxpME+/9/b97e17zPz14z4up/eyN++tfMH5C/N7G96Icax58Ygtj9h1ifqzZq5s2bL4S1MPbDTk/GNyeOYob9q0qdrACOXrrrtO/8v0jaMaN27s6NKli/t85xvN4fzjUec4/yjdx42H883rGD58eIa84s7I2v068tg7LwLqOHLp41ifPn1UHUaNGuU+1xnpq2MLFixwHzM/nBddd9l5sVDnEhGRDyQcde/LcnrZCLW/ijeP84sGp+/nkpK+50VuYGNA87Uee7IYVnjsnWF+oM0zOG9S3cexSaShQYMG6hheNxifz3kTrI84LtpY0Xhgrw7zZpfOmy7V7hmvYzNLQBvYKZP9ZsyP559/Xp3rDGou2oRwyZIl6rWcOIMlhzMwy/Bv8XDeXOozXDz31UC77AwOHc6bW/exlStX6rPToX02/7uJEyfqV3L+uvv27es+bt5U0bxZYWYP8x43R44cURt2Znae8fjjjz/02TkbP358hn/76KOP6lccjqNHjzoKFy7sfg33OJCUlOQ+hvshA/ZVMY5n9sCmjjmZMmVKhn9TvHhxhzOQ1q86HC+++GKG13EPNn/+fMeePXvcx95991117oQJEzKca368+uqr6hyDt+9v8OY9Nm3aNPfxu+++Wx/NXnbvrfwiIEZcUhLTJDXBYcsjLYeRfHPvASJozI00zwfG8Dl6S4ysDxjKdv6cVRk9QRjFwKgMonkjmsYwqTHFK7P0d8gigfU05ujbHEWjFwJRNnpijEV85uFYcy+A8bVk9nmQVhKpK9HrAeZpAUREdJkiy7keTsUSN0pEUvq89qwUSdwihZL1SH+J+iIhrkXvuYVedPRso4ccPdjmURVkAMPiZPREY9TEGXSoNgfTlcyLgQcOHOju9TW3IcY0nnvvvVc9Az4+ercx08BgXrtiuPnmm9U0GaPdAfRMo21Fm4eH0QuPMpIKoJfb82PhtXbt2rm/L0x5wveB4+hNx3Ro81Sg7GDGAtZTPPjgg6oHHO062nmM7Jh99dVXqvccPwv8DDGVHOtRsYgcXzP+nbl33/DRRx+pkQ38fHCu+WeU09eNtRv4uPi6jLS8gN/NpEmTVCpg/Fswr2kyj6BgShFS9eLzGuca8L1guqA5Q1VO8PtAdjd875iShq/FgK8TiYUw4oP7FGPUAfclxtRFZzCqngGzSZC1Cx/LE6aMea5jygxGoPD58H7H50PKYvM9kTPgUMkq8B7C+w57FWH6Pt7v+H3h68QMF8jsPYvRLSRN8FwL7O37G7x5j+H3Y/xtmUfxspPdeyu/CEL0ost+a+1LR1VQYYca90dLyaYXv/kAw6/mizWmeCFbBd70xkIw5NHGsN8333zjvogjmMC6Fvjtt9+kb9++7gDCgEwkmPeIN5051R2GR3EB94Q/MGMxFxbs44KFrCLGtLBBgwapdIOAoVWkOgS8oXHhRxpJc65+XGiwOB9wAcZaGlxUpk6dqo4REZEPHF0i8u9HqpgSUkwOlh4gSWEXT7uCiAv7pfLJHyQ4TS/Ib/q6M3ip5yrnEbQreGB/CGPjR3SkYdoWbhizg+lIxrQ0TJXB1BzcXGUF03jwyOzmH+s4sZYDHXa4wcdNqtHO+gNMH8f9Ql5kd8KUcCwgx9pYTKFDAIMAFDfnnnB/goXpuLFHUIsb3tyuc/EGpimi49fzPYLfL27aPeH3um3bNvVvMOUN90w5rfHwBXTY4v1sTLPDPZuRkQ8/F6z/QuBvDgoNuX1/ewN/V7jX85yaWKAhcPF3KQlpjpRz9jyygyFc/MiMh/NNqY7/8ssvFx0zT/kypoHFxsY6nJG6Ooahy2uvvdZ9znfffafO+eCDD9zHwsLCHM4/XnXckzOocJ9nDLPOnj3bfey5555Tx2DMmDHu4927d1fHnH8I7mN4vPnmm+o4tGrVSh27+eab9REiIvKZLZ+7p36lzbvHEfv3aMfBdVMcO7esceza/I/j0NrfHKdXjEqfHobH3in6Hweudu3auducX3/9VR8l8l/z5s1zv2cxJTI7fH/bIyCmioVEBklIlD2P7HguMsusl8AY3jR2ekUPhrEh0w8//OCe4oUy0h0bnL8L9WxeuI9RlaxS2mGHYwOGIRHZY2jfYB4WNS/mM7J3YDjX/PWb0zIbGcw8F6UREZEP1L1PpEJ7VQxyJEuJ+JVS6eRPUvPwu1Lj6AdS8dQvUuzcWvW6Uq23yBXGDvqByzzKn1n7SeRvcvOe5fvbHgERuPgLzzUfmQ1LG0Os2DASMHfRsHr1avWMNzfmSGK32ux45vo2w9xW448E2VuwV4s5rTICJoM5GDICFzBv5GQ+zsCFiMhCQSEi9R8RqT3AeaG9eMqJW6FSIlc+JlIjY2rXQGXevd+cBZPIXyF7nrHeJaf3LN/f9mDgkguegYsxumJe3GYszjJew9xRIygw0kdu2rRJrrvuOncgA1gMBuZc5VhrkhV8nuXLl8snn3yi5v/iazB/Hea5oObjxucBbFZpMKcgNEZoPNMSEhGRD1XpJtL2U5HrRos0Hipy1RPOx5MiTV4RaTVGpPV4kfLp1+lAh4XK2FAZe5N5LlYm8le4z8K6Fqw5zg7f3/YIiMX5/gIjG507d3ZvAondbHHzjzzdGL1A0IFRDwQtWDSIhYvIDmJsVoRRGCyUQ+YRBBDdunVT08+wESQ2rsLUMCw4RCCCqVvISIZ/7w0ER8iljs8L+LfNmzdXZeRUx6J8fGws1DcykSDPN87BiAymrhkJBvCHh8WXyJrhuVESEREREVFeYOCSS9g0CZsPIWsHsocZIyxIZ4yMXr4YpcAmlwhyspsqhjSM6AXAbvf9+/dXuxTXq1dPjZYgKEJAZF7nkhWM/mBnYgRRRERERET+ioFLgEJaQ2Nneyzgx4iL8avE7qmYRkZERERElF9wjUuAwiZMxp4tGAUyghaMsowaNUqViYiIiIjyC464BDDsvv/oo4+qNSzIAIYF/y+++KJa60JERERElJ8wcCEiIiIiIr/HqWJEREREROT3GLgQEREREZHfY+BCRERERER+j4ELERERUT6Gvd6wX9sbb7yhjxAFJi7OJyIiIsrH2rRpI0uXLlXlmJgYKVu2rCoTBRqOuBARERHlU9jrbdmyZbomcuLECV0iCjwccSEiIiKywJYtW1Sg8Ouvv0qRIkXk//7v/yQtLU1mz54tTZo0kfLly+szXbZt2ybTpk2TuXPnSlJSkjz88MPSv39//WpGCEj++usv9Tl27dolpUqVkhtvvFGuueYafYbL+fPnJTIyUtdEVq9eLU2bNtW1jPbt2ydTpkyRuLg4qV27tvTu3TvDv12xYoW8+uqrUqJECXnmmWfUSA7O/fLLL2XHjh1y++23S/v27dW5K1eulO+//159v6Ghoer8qlWrqteILhkCFyIiIiLynSNHjjgiIiLQOex+rFmzxvH666+rcoMGDfSZLpMmTXKEh4dnOB8PZ6Cgz0jnDEYczgDhonPxuO+++/RZLnv37s3wujNgclSuXNkxevRofYbL4sWLHVFRURnOrVixomPZsmX6DIfjzjvvdL/WvHlzx/+3dybwVk17HF9XL9KglKQ0SCVDhkgiikKDiEKTkMqQoYeU6KkMKUXF85BkjgrJmHoaiAyPCs1RmueUCKX99u9/1n/ftfc909Utp9vv22d31l57nX32sO45///+TwsWLPB8ZSToK1asmLd9+3Zv6tSpnq+sBP1Y2rdvb/dCyF+HrmKEEEIIIXkMLC2wdrgsX77cPP7449L2lRR5BVu3bjXXX3+9WFnAwQcfbA466CBpP/jgg2bRokXSVgYMGGB85cCuGZOVlWVbxsyZM0def/31V1O7dm1zxBFHyLoyY8YMOY7Jkyfbnpj15rrrrpP3uCCoH1YUbAfu+cDCcuGFF5qlS5fanthx77fffubJJ580O3bskL4CBQqIhQZjCdlVqLgQQgghhOQx5cqVs61snn/+ebNhwwZpn3feefIKRo0aZdatW2fXjBkyZIh56623pA2lYeLEidIG27dvD5QfAMVg9uzZ4i42fPhwM3bsWOlfsWKFuGtFOeSQQ0zLli3NI488YnuMuIepwgOg3FxzzTXSXrZsmSgpoGDBgvIKNm/eLK5tAK5g3bp1Ezc0KC4HHHCA9IN+/fqZTZs2mcsuu8z2EPLXoeJCCCGEEJLHlCxZMofyosoIOPvss23LmPHjx9tWjG+++cZ8/vnndi2WCUxB/MvatWvtmpGYkmOOOcYceeSRpmPHjsFnIkYFilLPnj1lXYH1BjE3VatWtT3GjBw50rZ8wdBXPE466aTQ8aklxrUSKThPWH8GDhwoShFwY2jGjBmDsAS7RsiuQcWFEEIIIWQ3cOqpp9pWDFhLAJSDM844Q9rgf//7n23FePjhh02PHj3smgncxoDr4gWiwfguV155pbiAKVA8ihcvbteyQdC9olYVBNUrGqDvWlxAsWLFRJFyzwWcf/75thU7NwTpE5IXUHEhhBBCCNkNnHPOObYV5thjjw0pIxs3brStnMCK0aJFC7tmzJIlS2wrBqwtyfjll19sy0jmsXhobA3QeBY33gWxK+D333+XV4C4GigkyI4WpXr16ua4446za0asMYTkBVRcCCGEEEJ2A5oaOMoJJ5xgW/F5//33Ja4EcSurVq0KBdhv27bNtmIUKVLEtuLjBu4jHiYeUDQUBNX//PPPEiOjILgeqMUIVKpUKWnAfZs2bWwr5vqWTDkjJF2ouBBCCCGE7AagoKi1wiWquCC43eWLL76QGBTErUS3RRUV1IRJhlsrBkqQGx+joF6LC5IFaIxNmTJlTKFChaTtKi6parJcfvnlthUDNWII2VWouOwD4MsCgXYNGjQws2bNsr2EEEII2Z0glgVFIaMgTbFL1N2rd+/eplGjRuaBBx4wtWrVMldddZXdYkyzZs1sK8ZTTz0lcS4Y179/f9ubDRQn3T/cwJDNbObMmbKudO7c2ZQuXdquGXPTTTeZSZMmSbtevXryClzrjWuliYJg/I8++ig0npC8gIrLPsDTTz8tCgsC+vr27Wt7CSGEELK7ueCCC2wrBgLk69SpY9di4Lc5almBJQVV6pFi+IUXXghiVdq1a2fat28vbQUWGox79NFHbU+YLl262FbMbWvYsGF2LQbibdCnrmSIZUFwPhQvVLxXKleubFvJkwKg+n+nTp2CbGJICHDUUUdJm5BdgYrLPgBSGmomkLp168orIYQQQnY/cMNCemDEhKC+CRQZzdKlNGzYUFIiuwHtCtINDx06NHARgxUDiswzzzwj3hRw40IKZFhVkikut9xyizn00ENlLCwsUXCcqBfTvHlz+awKFSqYESNGhDKGdejQQRQsfCaOORFIxVy2bFlRhI4//njzyiuvpIzFISQdslA+37ZJmuBpBb504H+a12ZQ3A58eeGP/cwzzzSFCxeWfhR6QgXbo48+OsdnIogOQW+HH364tHF8eK8bhIdgOxS9wlMV/cLE/j788EPJMtKkSROadAkhhJDdCILt4ZLlZhSLsnjxYrN69WqJTcFDR7xGrTF/JzgHHA8UMUL2NLS45JIBAwaYE088UUyed911l/wBI4Dtt99+syNSs2XLFinWhNznqCbrgoC4pk2bim8rXqHI3HnnnfKUBOkT3ZSIKEiFSrR4GoOMI3i6AYUFFhbkjt+6dauMg6+sfvndfffd0gcTMLKdIMc7nv7ARIwFyhiW++67T8YRQgghJG+oUqVKUqUFwB3r9NNPl9fy5ctnlNICcA5UWsjfBRWXXPLBBx/YVqxAFIR8+Koi8weKLMFPFV80UBK6desWKuC0bt06c9ttt0lOdigXCJCDwuEWnlq6dKltGTNt2jTTvXt3UZY0xzr8WMH69evF7QvVb2FN0b45c+ZIG76wMEmDZcuWySvAGDBv3jypnqsgoA9FqqCIYbnnnnskHSMhhBBCCCGZABWXXALzreKmBUSKQSgSffr0kdznsIZAsUFgvILMIoMHDw69D9YXKDiKW5UW2T8GDRpk14xYel566SVpQ7GAgqHAAgTlSUFb94WnI4oWlIrmU48WtAKwChFCCCGEEJIJ7BUxLn2eMGZb+p5Yu0Tbpr6CkDjDn+Qz1xzocN1CEBuUEddVDEF4iDVB8SiYez/99FPphzIB31UE08HqAlcxgKwdUCSQdePxxx+XNIQuiD25//77Tc+ePYM4FLiFqRIFk+0PP/wg+4GlB0pTy5YtxRoDULEWlhuA4LuxY8eKUgKrTxS4no0ZM0baCMJDYB4hhBBCCCF/N3uFxWXxCmMWLdszy9aYQSIhsJAoZ511lunXr5+4fymoLouA9xtvvFHWv/zyyyAdIBQYKAywykD5USUE7mTq4oU0iVGQox3xNG7wvGsxQRYSKC1wGYM7GnCzd7hWHD2WeJ8DpWn06NFBukMoX4QQQgghhGQCdBXLBXDxci0rxYoVk1ek+lMQUA/lRSvlajYvAGsIYkwuueQSyfzlGrtgJQGuGxmAlSVe2kK3UJRWtEV8jMbUaDYy4Ab2wf0MRIMD8Rma5133jTzuhBBCCCGEZAJ7heLS5wZjBty6Z5ZkbmJRC4QK/65igHTFwB2rFo+ffvpJFAQoLHALcwtQaWEpDcIHeF+i7F5wR1PUCuTGvLgWF1VWgCo2qlgpSKWs6PkwPTIhhBBCCMkU9grFpbIvU1etsGeWotmGihy4bmIAlpUoSFsMli9fLq9QEKCkgJEjRwYuXmjDOqOo9cW1uCA7mVuLxQWxNcpnn31m7r33XnEnU1zFRbOOAVVcUIDKPX43LbMqT66LGSGEEEIIIX8ndBXLBVGLCwLsoxQtWlReUTASIJhf+eqrr+S1Ro0aUntl3Lhxsp6I6tUTm3/atWsn+wGo14JaLW5aZdei4ipDbnpmJAlQ3H4qLoQQQgghJNOg4pILooqLWldclyqNN9FtyCKmSoHGjnz33XeSbUwVGYDgelChQgV5Baeddppt5QSfM336dPPkk09KLRgcg3scruuX26+fA1CsUtGaL0AtNG4fIYQQQgghfydUXHLBCSecYGrXrm3XsgV7FKEEUDqQaQy0bdtW3LHOPffcQFlAoP3ll18urmOwiGBMs2bNJC6mXr16MubCCy8USw5cxFx3sHjAuoOikVCOoGxUq1bNbolV3lVQhR+KDFzDrrjiCtsbS32sVhU3ZgbHgPgdpE4mhBBCCCEkE9gr6rhkEgh0v/rqq6WQ48svvxxYWJDOGIUe88JKgfoscDVL5ir23HPPibUFikvr1q3NypUrRQGCAgNFCfE4bpxLImD9QWX9Jk2a2B5CCCGEEEIyDyoueykoHqmV7WGdgTua3kpkK4MbGSGEEEIIIfkFuortpQwaNEgsPABWIFVaYGV56KGHpE0IIYQQQkh+gRaXvRhU37/hhhvMzJkzJVYFAf/du3c3xxxzjB1BCCGEEEJI/oCKCyGEEEIIISTjoasYIYQQQgghJOOh4kIIIYQQQgjJeKi4EEIIIYQQQjIeKi6EEEIIIYSQjIeKCyGEEEIIISTjoeJCCCGEEEIIyXiouBBCCCGE7AVs27bN/Pvf/zaTJk2yPal57733zNSpU+0aIXs3VFwIIYQQkjFMmDDBdO7cWQosf/HFF7Y3/zBlyhRzzjnnmNdff932pM9tt91mbr75ZtOwYUMzdOhQ25uYZ555xlxwwQWmQYMGZtmyZbY3b1m5cqVp0qSJ6d27t+0Js3jxYtO+fXu5p1CiyO5jV+bW3gILUBJCCCEkY7j22mvN008/Le2TTz7ZfPXVV9LOD2zYsMFUqVLFbN682RxzzDFmzpw5dkt6QEEYP368tLt06WIef/xxaSfiuOOOCz7jgw8+MOeff76085IzzzzTfPLJJ9Jes2aNOfTQQ6WtuPcTx/Pdd99Jm+Qtuzq39hZocSGEEEL+JmZv/cyMWTXU3Lewvem9oLUs9y+8yry5+kmz6Ndv7Kh9i99++822jKlevbpt5Q9mzZolgiVYv369vOYGWFkaN24sT9W7d+9uexOzadMm2wq384o///zTfPrpp3Yt/jn9+OOPthVzdSO7h12dW3sLVFwIIYSQPczCX2aah3+40YxeOcR89/N0s8PbbrcYs9373czYMsW8uLyf+feS282q3xfbLX+d33//3TzyyCPmxhtvlOXZZ5+1W9Jn586d5q677hJXpREjRtje3LFkyRJz0003JXUZ+uOPP2zLmOLFi9tWNtu3bzdjx44111xzjTnrrLPEBWn16tV2azarVq0yPXv2lOPF8tFHH9ktqfn555/Nc889Z/r27WuGDx8u+4oCdxycC66J67wyffp006NHD3GhiuKem6uguSQ7bhwXrFCnnXaaPGFPRVZWlm2FPxtAuMV9bNmypVzH++67L8eYVOBeuOce75xghVH+8Y9/2FZyPv74Y7mGZ599tmnevLn53//+Z7ekx5dffmn++c9/mltuuUXc65YuXWq3GPPZZ5+JW9vVV18duoa4X/hM3L8osBh169ZNrn8y0pnfSqo5hvtz8cUXi5VN/16hKL788svyGdG/wXTmVr4ArmKEEEII2TMs3Tbf6zO/rXfP/FbB0md+G+mLLW1C2x5YeLW3/o+V9t25x1c4vEaNGkG6DC2LFi2yI2JMmjTJ8wU9r0WLFvL6ww8/2C0xxowZE3r/kCFDPF9p8HzB3VuxYoWMWbt2rffmm296vvAnn+viK0/escceK+/db7/9vFq1anm+gGe3ZoPP18/o2rWr7Y3hC2Re3bp1g+26lC1b1lu2bJkdFTuOww8/PDSmQoUKdmtyfIHRO/LII0PvPfDAA70nnnjCjvA8X8gNbfcFa7vF86pWrSp9uDYuvtDpvfjii8F7ChQo4PkKiByXL6jLmFTHXbly5aD/7rvvtr1hfCHdGzlypDdhwgTvsMMOC8a/9NJLdoTnLVy4MLRNl3POOcfzlRE7KjW+oB56f82aNb3y5ct7vpJsR3heuXLlgu0FCxb0qlWr5l100UXe888/7+3YscOOyub2228P7ROLr/B448ePtyOSM3XqVBnvvr99+/Z2q+dVr1496B80aJDt9bxOnTpJH47PBddNxxcrVszzFSGZI1HSnd8gnTk2bNiwYBvmCv7GGjZsGHqPr4TJ2HTmVn6BigshhBCyh9jwxyqv/6JOgVLy8PybvQ/nj/Pmz58fWt6b96o3YMF1wbghP3T1ftmxxe4ld2B/rrBzwAEHePXq1RMlAEDQbdCgQWgMFgigW7Zkf+b999+fY4wuZcqUESEQgqn2NWvWTAQq5fXXXw+9RxcIgi4QanUbhFiX/v37h95bqVKloH3llVfaUZ731FNPBf1ZWVle4cKFc3xOIq699trgve4CYXTOnDkyZubMmaFtEJYBlDUVms877zzpA7NmzYqrKOjy8MMPy7hUxw3FQrfffPPNtjfGxo0b5Zq798BdXnvtNTvS8xo3bhz0Y7yrXIwYMcKOSswvv/zinXrqqcF7osuFF15oR3peoUKF4o7BctZZZ3m//vqrHemJIO5uh+CN6472EUcckUMZjkebNm2C90OAL1GihDd69Gi71ZN13d63b1/b6wV/A/j7cD/n+OOPD8brUrFiRe/bb7+1I2KkO79BOnPs0UcfDW2rXbt2aH3//feXsenOrfwCXcUIIYSQPcS7a0aYX/+MuZuUy6pqGpvrTHlztKy7VMmqaRrvvM6UNGVlfeP21ebD9a9KO7f4gphtGVOkSBHjC4qSHlf74TYTL73u8uXLzauvZn/munXrbCsncAeCKw1ch5R33nnHvP3223bNhNqlSpUyvuAlbbiwIfuV4guNtuVLXZH8QW4w+pFHHml++OEHifcAGrQO3HNGTIgvaBtfgLM9icF5uG50Tz75pPGVCWnjuDStcNQ1Tc8b/Tt27JA23IYUZEeL587mK17iznTdddfJeqrjPvjgg20r5v6nwDUI1wHX3L0HLr4SJK/ff/996FpdccUVZu7cuYFbnrstEStWrBB3rCiHHHKIuJ7hngLMtWRuS3AJGzNmjF0zkurZZdq0aeaee+6RNq7nvHnzpJ0M9xr269dPYnsuu+wyWcc1++mnn6QN3GuF+Q4wRt22EJ/jKyjSBmXLxv4e4XrWokWL0LmlO7/TnWO+QimvipthDy5kX3/9tQThpzu38gtUXAghhJA9wG87fw0C7ouYEqae19b8w4SFE5dCWUXMOd6V/piYADRryzSz0/wp7dxQsWJFEaQABOGo//2pp55q9ttvP4kleOCBB0yNGjXsFmMmT55sWzmDu//73/+KsuJy0EEHiZCmAvbo0aPlFUDQUpo1ayaZqIoWLSrr9957r7xGcQXL2bNnh1L6HnjggaLIqKIDxUoVHcSBKBAE3TiLZECIdj8TgiFiDBQI4iC6Pw06dxVAV0Bu166d6d+/v2nbtq3tMeakk04SYRwB91AoQarjdoVZV8EbMGCABGcrgwYNkj53fOnSpeU1qphs3LjRvPDCC8ExpHOtqlWrZp5//nmJxXFZtGiRee2110zVqlVlfcuWLfKqNGrUyMyYMcN06tTJ9hjz+eef21Ys85lSqFAhiWVShQKkc2zuNcT9dJXfRPcNitiCBQukDfTeuXMWQNHCvQQLFy40r7zyirRBuvM73Tmmio8L/k6h3OG6IEMbSHdu5ReouOQR+MNAysZkTxZyA/643D+i3ILjQdAZvpDyM/gCxlM3/MG6PxKEEJJpbPgjO1i7uqljCvgqSSqgvBxujpI2gva37ohlDcoNCNA+77zz7Joxd9xxR2AVAKiXAgGua9euIhS56Wrnz59vW7FgYhc84X/wwQdDAfS33nqrBM0jeBy4T8jdYHUoU7Vq1TIdOnSQdTzBxlN/AOFMcY8zGqANRQZPlPUJNYREDUaH8qVPxyEI3n333dJOBQK3XSA8ugoClCUQ/b3ZunWrvEKgVNxgabwPgd9uOuJo2mCQ6rgTKS5QPBQcM+4lso6VLFnS9hpTpkwZeY1ex3HjxkkSAL0/UD7T4corrww9zcfnRpMpuFYhUK5cORGqn3jiiSBQXz8X1g3Xqgd5CgH2CFxX0jk29xrjXBHMrqRz34DeO3fOgsqVK5tHH33UroWVwHTnd7pzLGpxAbDMILmGS7pzK79AxSWPuO2222SSwkS6q8CMC0366KOPDj2JyA3IdgKTLZ6ywZyZX7n//vuluBWeerhf3IQQkmls/TNb6ShrqthWag4x5W3LF6h2/rV0su5vE5QJ160F4EHZhRdemCOzkStIuooLnuJCwYDwefjhh9ve2BN1oMLX2rVr5RW4qXD1SbRaAQCEO+AKbK7gm+pBHOqFKFBg3N8+WAeSubop0axayOKkT8CBWpLQ7wLLAq5hVACOAouXolYwl1THXaBAAdvKVlyQfQoucwr2q0qBez4qzCa7jrj2KmynQ6rzibr6FStWTF5xfHouOq9S3d/atWuL0pMKpNBWawQYOHCgbcW/b1CO1bUtijtnMacx56EMqnKtcxakO7/TnWNRpQ/WTddSFSXVvcgvUHHJA/ClohVso2bFv8LgwYPFWoI/+JkzZ9re3OGaQ/FUKr+iTxHxJYgvNUIIyVT+2JltkT/QpPdUG7hj3bTJuQHV01VoBK4LGMBTXBV88CBOcYUxV3Fxn3yrlQOoi5AKoe4415KicQiuwKb7gYuQEs93X0EMCFyT4LIDBQnuUS5t2rSxrZjlBm4+qYjWjcF5wI1IKVGihLxqvIgC5QEpat0n51EBFbjXylVCXJIdtyt462e5yiFwrSx6/yDIurEfLn369AmuI873kksusVtSk875uKhAD/RcEsXkQIDHg1wcF4R+PMh151Ay3Gv4zTffBPMxet/g/og4GDwAddF7F2/OQqHQa++ef7rzO905Fr0umF/JyO292Fuh4pIHYNLpU4WoKT0K/lChVLjm7yhuXvFU+0uE+4V51FExN4NU4MsPLgL4At4dwAwM307X9QDXDV+YifLRQylU8yu+wKKKXK9eveT64wdXXRMAFMiXXnopbvAgIYT8HRywX7bQ9LvJfjqait+csf/Iyuk+kg54WgyLiuIWBYSlQGuFwM0LcS6K+8Tc/T1S4coFgpM+6dXvbff3R60AAGPxe/j+++/bnuzAZzcA3Y1pcd8P8P2OsVCW3CfbyhlnnGHKl8+2VrnnnAgEPbuMGjUq5NpzxBFHyCtcnlwQQD9x4kRp43MBlAZXcAWHHXaYbcUE6ngkO25XmI0nPAPczyFDhoiwr+7r7vFGryMC4HFeuI6uYpEO7vnAWhdVoqK48RaquOh5RI8LSgXiW3BcFSpUsL3pcfnll9tWDL2GOscU3FsobqBu3bryClQOis5ZgAQIiru/dOd3unPMvddQilzLZjzSmVv5ASoueYD7xQ6FYdiwYaLBq++kAr9IuG9hckHrb926dQ6zPHD3B/9M+BDrF6ILTJzw7UWAZDRo0lWM1GSfDOzr+OOPlwU+nPjCQ6aK6FOIVODHasKECfKEKPq0AC4E+DKBCxye9uEPC/68CPLDH6SbMQM/SFBEYNrGNgSv4cu8Zs2aIXeABg0ayNMlPN1Q1wcoQqeffrpp37697AOxR/DpxpcGgvZSmfIJIWR3UPQf2f7/K0z6MYzrvWzh/YAC4SfG6YIHRokCm92sSfieRlxBPNxA63iKAiwl+tRZf/9cJcR1AYMgCUVKA8pRABG/P0AFN4AHVip843fABQ+88PQa3+/4XYB7kFvF/cMPP8whDKfilFNOCdzdAOIj8BsMECOiitgJJ5wgr4o+0Uf2KgRmK1FBvk6dOsE1wm8grBv6XiXZcbu/7Sr44zfUtbLgtxexRsgqpbgCdvQ6QobA+SCAHMcOAT7dB5i4v7o/HA+8INwHjO6cAWrxwFiVddQ6ANkjahXC/jp06CBJAHB/o1nH4oH9QhGPKnQAFkB3fiHmBeMRk+IG0Ot9i8aZIBsYClcqXbp0sa3053e6c8w9/ipVqqScy+nMrXyBf8PIX2Tnzp2er6BIkR9cyugyfPhwO9Lz/D8iyckeHYP84L6yI2P8iea1atXKK1WqVI5xyKev+H9Qnv+lFMrV7v8xer6wb0d4nv9HEGxr166d7U2MrwAF47G4+77uuuvk2OrXry+fg6JK7mcBbG/evHloHyjE5CtUdoQnxZV0Gwo9+UpcaDyKLQH/h8dLlvfd/9KQccAtxvXMM89I3+OPPx4a739Jhdaxb/+PWcYSQsie4vedvwV1WQbMvy5H7ZZ4yzfzZnr3zr9C3tNv4TV2T7kHhQHd70HUZFE+i9TOcJdDDz3UjvI8X0gN+lEkUqlRo4b0Ybuin4dikUq0sKIuqNXhFrvE74n7e4lilgC/uQ3i1Jtxl27duslYX6nJUYRw2rRpsi0VvrLk+YpZ6L1Y8LvqEq2rgVoavnLo+cJt0Bf9rQQtW7YMvW/UqFF2S+rjvvTSS4N+t6iiW6ww3uLWuFm1apUU7Iw3Tpd33nnHjk7NY489FnrvDTfcYLd43urVq70iRYoE23zFQ/oh92jfueeeK30AdVW0P94C+SMV48aNC72nePHinq9I262e171799B2X9j3Jk+e7C1evDjoGzhwoIx9+umnQ2PdpVevXjJGSXd+g3Tm2Ntvvx30X3HFFbY3OcnmVn6BissugEq57gRxF19jlj8CBcW+dNtll10mCoqu4w8GoHCU9rkLlIg+ffrIGHDyySfHHQfFQrn33nuD/rZt29rexGByu/uKLvvvv39oHYrUDlvxFl+C7heTu/Tu3VvGACg98cZAofnnP/8pBa2AVp7Fgh8v/AC6Sg4KkCn4wtN+fHmC++67L+hLtLz//vsylhBC9iRvrHo8UF5emv9IXGXFXYbNvycYP2l9dhG93HLTTTdJMT4oFx06dAgJcgCVzvGQBw928J2Lavj4nserAoFPherWrVvbXs9r0qSJ9OF7XEFVdHzn4/tYqVKlSuh7GAuKTaIieBQIcPj+x/L111/b3ljhwy5duuR4uIVx+J1dvny5jPv999+lQCL6USzQ/d1IBwiaHTt2lMKaKFiIIoL6m6eg+N8pp5wiv494gIlCgGD27NlSxBDvW7dunfS5QLnB7z2uD5SfzZs32y2pjxtFDrFfHNf4SCV5bIN8gPfimqDKu16fpk2b2lExcH4oQqljdcG5XHPNNcED1XT4888/vVtuuUWUXBSydO8XWLp0qShKUBDwkFRBkUp8Jh4Au6DwIvblHheWqlWrelOmTLGjEoP7gs/DfMfD4Wj1+i1btoiAjzmEh5+jbXHK7du3y98AjnPChAnS51aj16VkyZIyv6PkZn6DVHMMxwOlB/t59dVXbW9yks2t/EIW/vMvSsYyeu0v5r0NYR/R3cVxRfY3d1QMp/JLBszXcEeCOxXckQDMjUiPB3co/wtB+hBY5vr5IuMX8uZjLEDWEKQVhAsT/GT9H6vATOt/6UuaQjfFINL/4jORXQOuZzDzApgIYRbE2L59+wZ+m3DPgg9lMpCa0DV5IsUkzL3q9wxgwkUwJFInAuRbR/o9mPL1fOGaheui6RZhEtU2XAui5meYft94443g+uDcsS8F1/eFF16QLDhqjvYVlCBIDQGnWpMAPr1IAemeuwJ3uYYNGwa+qXrNCSFkT7J5xwYzfOk9Zov/CiqbE01N73xzYFZ24DzY6m0yX+73jlnpLZT1MgdUNB0r9DUH7Jfa9Xd3gt8KLEhIo4Uf8bsDty24LCUD7kjqlgZXGbjmJEuqAjceLK5rj4K4RsRywHUKv6VwN8LvYaYAN3Ckuf07sjshrgYB5HBdhwsdkjJ06tTJnH322XZENogjhTyB2BO4ffkCb67jXNIBbopwG4vOEdzfePFSuK+QB/AeuLxBVE0V45EXII4L81nd7CCfaEY+XBfEf8F1zU10oeR2fqcD/q4QuB91TdyXyfgYl2+3/mFe95WXPbF89FN29pR08LV1CTaHwKwgoA4CtiotIFqROBpzgi83AH9EBMmdeOKJsg6gCETzosOHF7EtEM7hh6mfhQC3OXPmSNv9fHwBpCLqhwp/UsSMuMeKvjfffDP4ktFUzfC9hJID5a1p06ahuB1kNFPdWM9TwR8izsNV6qL+mOoT6u7TDe5zfUoTfQ7GT5kyJVRx+a8mPSCEkF2h+D9KmTblbjcFs2K+/IvNLPNG1kAzOusB84YZKO1RWfebcVmDA6WlSIGDTNtyd/ztSgtAzRf4z6vSAhBfkUppARCMlc6dO6cU6vBbE09pAfheR8wjHgIiLiCTlBaAmJK/KyUt4khwrXFd8KARiWriKS0ADxRxH/AQEQH8u0NpATiWeHMkntICEM+BY0K8Ko5rTygtAAqJGxvkzlnMRTzUjae0gNzO73TAHKLSEobB+XmABg6CeF+eUWEcQWnue6J/uKn2h+B+WC6g6OAHxDWaafClK9DjqUsqoooL/jDx5EWtQkCrsuofp34WwBcMikTdddddobR+OJfNm2O1C9xgfQTdw/oR/WKPBnxCOQR4IqS46QzdYDUNVMRxuyBZAr5A3H1H86MTQsieolyhI82V5XuaYgWyBR0Ul9xmfjbbvJ/NDi/7O7tUwbLmmgp9TImCOYPh9zbcivyJBFZCMonczFnO7z1Dxisul5cpYp47tvQeWbpX/GsTTQVmoIK2S/SJEZQEV+h3U9iBVPuDaRXaPBQWWGOQSULRPPyupSRVekLgWiBgclerhioGUIRgWQE61lWOkEVMq8kee+yxodSFekyuAgVXr3jpDZFVxH1qoQoV3NEU1+LiXitNqei+H+iTGlfJcS1ShBCyp6l44NGm65FDTaPS7UWRiVLJ335Rmc7mlsqDzSH7h1Pv7q241fvh8kxIpoMHnuoFk2rOcn7vIXzhl+wibpA+AuOiIDgPAW86BplAhg4dGqyvX7/ejozhBum/9dZbtjcbN2vWu+++GwrERyAZeO6554I+BMul4qqrrgrGI/BMOe6446QP2VLAb7/9FoxDAJ2ChAPo85UGCY50M78sW7ZMxhQuXDjoixfYpiB7ho5DYB2CQ3EO2jdp0iQ7MnytNIgRgYHah2Xs2LHSj2BU7dNAfkIIyRR22n/5lT///NO78847vQEDBtgeQjIfZLXr0KGDN3fuXNsTH87vPQNdxfIAuEmp+xIKH15//fWhasNw90JRL8VXLoJ84chBH3WXcv0i4U8ZjZHRRAAwS8K3WIPlXVwrDyoPq7tWIlyLSzz3NPV7dfcDdy9Fj6lVq1bmk08+CdUFiEe0cqwL3M3087Af1MTRYmbAtai47mdqccF1ceNctN+1+LjWIkIIyQSy7L/8ChLIoF5F9+7dbQ8hmQ+8WkaMGBFKHBQPzu89AxWXPADuXG6GqqeeeiqHsgHh2w34QqYIgEDHKNiXuogtWbLE9OjRQ9qKxmqgyj0C11RpAPjDAQhaVB9LCO5uUa54uIqLq5CoS5UeD/apQr/rvqXH9OSTT4rygkwlih6TuoZBqUBGtEQguQGyiaCoGBQzfb/iBum5Ll86DsfnKn9a0MpNUhAtckUIIYQQQjIbKi55RP/+/SVYHoHhyHJSv359uyUGrARQHpCaEBkyMA7ViZHqOAoUnBdeeEF8JGF5uPHGG+2WGMjuhRTHiG/BfhE036xZM3kaUK9ePRmD/UOJUAFdK9Mmok2bNkHsiCvUV61aVV6xHUDpwPFAgYHSpCAA/swzzxTLEzKr3X777WL5QJYxrdirViccayrFAdfooYceEgUPVfwVXA83yB5KEvpgYUJqZKVdu3byirgWzcgByxaq1+L49DoRQgghhJC9g4yv40J2DQTGI5WwKiDJmDhxornnnnvMnXfeaZo3by59yIgGa4xrLfqrYKqhpgsUmmhKaBe4xyGtMxQ0pNyEa93VV18t25Ae+rPPPpN2Kj788ENR7E4++WTbQwghhBBC9laouJCMAjErsPxo7Arcvtw4FihV8CElhBBCCCH7FnQVIxkFFBW4nWktFldpQTrmW2+91a4RQgghhJB9CVpcSEYCdzC4iiEBAGJTECuDuBkE6xNCCCGEkH0PKi6EEEIIIX8ziEf94IMPJNEPku8QQnJCVzFCCCGE7DH+/PNP07lzZ8lWuXXrVtu7b4MkOOeee67p0KGDlAJIl3Su5aOPPiplFm677TZJ2LO7Wbx4sWnfvr0c13vvvWd79w0mTJgg541SF1988YXtJXkJLS6EEEII2WP06tXLPPDAA9IeNWqUpPff1xkzZkxwHVBqIFXtNSXVtVyxYoWUAFDwOZdeeqld2z2g7MPTTz8tbRToRs25fQX33JHR1K2zR/IGWlwIIYQQssf46KOPbMuY9evX29a+zaZNm2wr3E5Fqmvp1kED27Zts63dx48//mhbe+bzMonffvvNtoypXr26bZG8hIoLIYQQsg+wbt06M378ePPYY49JbSwU+AXTp0+P+1QcgvCIESNMy5YtpXjvfffdZ/744w+7NQxcluAmM3ToUKnF1bdvX/P555/brWHcfbiCXhQIwHBzuvfee80rr7ySQwhGEhe4V8GCMG3aNOn76aefzODBg02XLl3M1KlTpQ98+eWXUvQZxwaXqahAvyvg2FBjrGHDhsE5z5gxw7z11ltmzZo1sp6KrKws2wpfn1SkupbRz0dR5lTk5r7Hw/3MdD4vCrKJ7tixw65l8+uvv0qtOdzfdPj4449Njx49zNlnny216VBHLgqKgKNe3F133RV85pQpU2SOYN7hM5XJkydLAe558+bZnpy41+mvxCnNmjVLCmRjwT0AsJq98cYbZu7cubIeBddr7NixUuQb9wuuaqtXr7Zb8yFwFSOEEEJI/ubEE0+Ea3iwPPLII96kSZOkvf/++3srV660Iz1v4cKF3mGHHRYaj8UX8jxfULKjYvgCs1e/fv0cY7H4CpIdFWPz5s2h46hatap35JFHepdddpkdEcMXOr3ChQuH9lWuXDnv008/tSM8r23btsG2WrVqeQsWLPAqVqwY9BUrVkyO1VdgPF+ADvqxtG/f3u4lOb4w67344oueLxR6viDv+QqZt3XrVrvV83xlSj5H93v55ZeHjuuAAw7w3n77bTs6jK/seRMnTvRefvllr1evXsF7cE3SIZ1rOWzYsGA7ltKlS3unnHKKd+utt8o9jpKb+54I3Cd9X8GCBb1q1ap5F110kff888/L9VS+/fZbr3r16nK9FMylMmXKyHF269ZNrq/SqVOnYL8VKlTwfAXc8xUFuzXM7bffHozVBXPAV9ztCE/mi7v9nXfe8e68885QX//+/WUsrsuBBx4ofTinSy65xJs9e7Zsc2nRokXw3q5du9rebPD35ivPMg6vP/zwg90S47zzzgvef8QRR3gPPfSQzCHt8xUsOzIGrlfdunWD7bqULVvWW7ZsmR2Vv6DiQgghhOwDlCpVKiTc9OjRQwRKtAsUKOBt3LjRjvS8xo0bB+MgqLnC6IgRI+yoGBDmdRuWrKysoF27dm07yvOuv/760DZ3Oeigg7ydO3fKOAi3xx57bNxxEFhV+HWFxOLFi4sQ7I6FEgPloE2bNkEfzrNEiRLe6NGjZR/JmDx5snfUUUeF9okF10yB8OpuiypIWE4//XQ7OpuRI0eGrqm71KhRw45KTLrX8sEHH4w7BgsE4tdee03GKbm574koVKhQ8J7octZZZ3m//vqrjBsyZEjQr8rdjBkzgr6SJUt6y5cvl/41a9bEvbYNGjTwfvrpJxmjfPbZZ6ExmDP77beftKEM6LX55ptvQuMwV911LMOHD5exN998c45tOM9Ro0bJdkX/nrBAeVKWLl0qx+q+H0v58uW9LVu22FGeKGy6Ld754gGDOx6Klbu9UqVKQfvKK6+0o/IXVFwIIYSQfYDjjz8+JORccMEFwdPc0047zY7yvEWLFoXGdejQQZ7uQznAOqwKCp54H3roocFYKAZz5szxvv/+exH6VqxYYUd63uGHHx6M0wVWlXr16nkffPCBHeV5r7/+emgMhFlYPHR97ty5Mq5Vq1ahcbpA4MPT+nXr1sk4WH1024ABA6QvHSpXrhzar7vgaT346quv4m53F1w3F1hYEikdWFxlLxHpXss77rgjxzh3wTkqubnvifjll19C+4i3wPICcH+KFi0qfXXq1JE+WLd0HOan8txzzwX9UEJgldH1K664wo6KgXXdhuXHH3/0+vTpE6xjfgLMI3ecu5xwwgneu+++K+MAFB7dBmuGtqFIuJaXZs2aBdtuu+022+vJtdT+6AKrmOJa7xIt+HtQoJRpP6xtUNRhHcM6/i7zI4xxIYQQQvYBfMXFtmIgJuX333+XNuIAFMTBuGzcuNG88MILpkiRIrLuxjD897//NWvXrrVrxtSvX98cc8wxxheiTMeOHU25cuXsFmNGjhwpcQO+QGV7Yn2IRTn//PNtT6xP8YVUc9JJJ4WOT+MOfKFRXl1QpBj7GzhwoDnkkEOkD9mdFGTV8mUfu5YcxK34SpC56aabTO/evc3BBx9st8TiHUC8feGYH3rooeB6b9myxezcuVPaCLzH/vR9Rx99tFzvM844Q9ZB6dKlbSsx6V5LfLbL22+/LfEQhx12mKwjdTFin0Bu7nsiop/XqFEjiffp1KmT7TFBHBDuD+KOAOKVPvzwQ/Ptt9/KOsC1Ub7++mvbMsYX1mVcjRo1ZP2ll16S81BQC0cpVKiQnO/y5cttT/Z5xJs/AKmjkcoYha8BrsOSJUukDfr162eeeOIJaSOmxVeGpR0FsSfKqaeeKvMCsTbIAqfHDnQugXjzqW3bthKTpiCOC/gKk1m2bJm0wYEHHmgef/zxYK7hvqY71/cq/JMihBBCSD5n8ODBkGLiLm4chmuhiLdceOGFdmTOJ/o9e/a0WxLjPr12Y1YU15oAixBwn7jrE3PXCoMFT6t9AVe2ucybNy80Dk/108VX7CQWSOMbdNGn6VFXMSzPPvusbEOMg/bBEgF8RSA0Fm52wBeEgz6cV7qkupbRewnrA3AtEDNnzpS+3Nz3RPgCfug9sDQAxMeo69PFF18sfQBuXnAJQz8sBWotwALLm3LppZcG/XA3A65l7j//+Y/0RT8/3gIrGYD7VnQbYp/UlUz57rvvQmN8BUv61YLpWjZcV7EbbrjB9sbAXEKMi2uxweIr1nZE2FUMC+4JjsdXvoI+tQS5fxPxFvw95EdocSGEEEL2AXyh0LZygtohCp4wJ6JgwYJSJFFxn0QDWFtS4RZBLFWqlG1l42ZmQrYy4GZ3UsuHWosAsnK9/PLLpmbNmrYnG6SlRT0RBdaYdME+kWEqmtFMrRTRzFF4oq5Px91tmvErmmlNLR/uOZcpU8a2UpPqWvpynm3F8IVZefUVQnkFKH4JcnPfE5Ho82C5KlCggLT18wCuUbdu3aQNywMygQHcT1jvFPf6Fy1aVF5dy5RmiUt2DqB27dpiwQPu/AGweg0fPjyU4Q1E733082Fx1H3hOinR/S9YsMD4yp9ZtWqV7Ymhcwm4cwb7HzZsmBxPvLmU6lxRUyY/QsWFEEII2Qc44YQTQu5OCty54gm9Sp8+fcyiRYvMwoULRVi65JJL7JacQp26FSXDFQxVmHVx618gRS0EXaSEVUqUKCGvritOpUqVRChMBCrLK998801KoQ/AJefWW2+VNoRVpJlV9LzhmhYVdJV45+a61QG8H7jX0XWvS0WqaxlFhW5VCIF7HV2S3fd00c8D+pnRz0OKalUCcL8BFGB3TsLNSlGly1VmE90DzPfvv/9ezgHKDRRH3Vf0OCDox3Mfcz8bJPt8uKYp0ZTESKWsiiaUYcW99+45Q5lVRSide9u4cePgfmGeDRo0yG7JX1BxIYQQQvYBIFzVq1fPrmUDhcYFT8ddUCPliCOOMFWrVg0JoiCqqCBuJhVqZQCoWxHl4osvtq0YqAivlgpYI1Q4dAXPihUr2lZ8ohXl3SKJiRg9erTZvHmztP/zn/+YFi1aSBuoZQFxBYjnUVyFyH3irtc0KmAPGTJE4ilcITc3ikuqa+kCIVgFYVUQgMZE5Oa+p4s7P1Rx0c9TMAb1Vlzq1q1rWzHcY9Nr+M4778grKFu2rLxGzwExRYhvwTkgNsYlqrgkmkPRfeLzsc+ZM2fKOpRPVXjcBwNu/AmsLVosFPVWEOeiuFYqN/YFc0+vWby5FD0u1CrC5+NcXWtUfoOKCyGEELKPoAHHLnCfcYm6eyEAH8oNgsGbNWsmQqVWace6y1NPPSVB7bVq1TL9+/e3vWFcofT6668XxcQFlg1X8EIw+6RJk6TtKl6uEpCsSjkEQwiNUaUhFRooftBBB4lFJ1EQthv87z4Zd4VNfUpfp04deVU++eQTuV5QYBQVwtMh1bV0g+ULFy5sW2HFRY85N/c9EdHgfP1MCOAqoMezHuDYXTcruNy5uNtgtejevbv597//LeuwwCGAHVSuXDnkBgfOO+88cXPr2bOnuAzq+6LzIdEccj8b4J41aNAgcNlCsVMFip4CS4+OcZMOIGGBJiWI4s4lHJ9ae+LNpej9QkFZnMMdd9wh1iOc66effmq35iP8iUQIIYSQfQAUpcNPv7tosLGyatWqHAHE0QXF+gAChxHQHG8M9hEPBIP7wmswzhfA7JZsEIzsjsHiC3GeLzTaEeEig1pvIx7jxo0L7ad48eKhIpKJQOpk933u4qYGdpMeNGnSxPZmB+fjuJWff/45R72Z6BItSpiMVNfSvUaHHHKI7fWkkKH2T5s2Tfpyc98TsXr1aq9IkSLBeF9JkH6kzda+c889V/pcJkyYEGxHsPv2SLHLdu3aBdvdBemI3bTFIFpXKLqgWCnYtGlTkDAA9VQSgeKT0X3ognPZ5hTJRG0YX+EItk+fPl36o7Vl3MUN7nfr2CAhhOIG5/tKuPThby9ebRh3QVrw/AYVF0IIIWQfAhXIkXEI2bsgNEGojALhGcUIXSEMCwRFZL2KvueZZ57xTjrpJCnKh6KFEKDHjBljt+YEmbdQQ6Ro0aIJCxsiA1Pz5s1FEEa9CmRRckG2Jwie+MzFixfb3pwgCxkEcgj4yAT13nvv2S3JgUDauXNnEfhxvRo1aiSZonB+b775ph0Vy06lwvrdd99te2PCOIpdRrOEoX4J9gsFCu854IAD5LrqNf7iiy/syPRIdi0h3P7rX/+S/TZs2ND2et7UqVNlPKrkb9iwwfbm/r7HA9cD1xsKGz5HQVYy7Ktfv362Jxu3mKhbuFHp2LFjsF2X4447ThSCeDz66KMyt6PvqVq1qjdlyhQ7yvNat24t/SjomYh4yj7mHQq4onJ9FBT9xPXD4ma5Q3Y6ZIHDfK1Zs6Yoj1CY3Gr4uF8nnniifEbdunVtr+etXLlS6rTgfVB+FWSr69Kli+zTPT58dr169YICnvmJLPznnyQhhBBC9iGQzQguKL7wZHtygjGokYE4BPjPw23qr8Y77A7gjgNff7hy/Z0gIBoxJhdddFHC+iBRIH7B9QquTshm5StD4o6FwO28vMb4HNRSgWsRYnIUBIXDFSnqWgV29b6jVgrcxqpVq2Z7YiDhgSZXUHxlSVyc4L4GNzJcS7h8uSCA/7HHHpP2UUcdZXzlQNzJMD4R2N/8+fPlODA/cB18Zd1ujYEAeNSIQZ2VRPtC3JIbNN+1a1e5R8niqnCeWFzXsXRBXA5ixVC7KN0Mc3CfQxwNzhnHhXPROkb5DSouJNfgCwlBY/jy2NuYN2+efAHl58A1QgghJNOBkI0Fyt7EiROlr2XLlua1116TtgsKgCLWBiDmSZWYPQHic6CMalIBKJvJsvCR3QuD80muwNMtBHyhom00H32mg6dZxx57rDx1ctMPEkIIIWTPgdS9sP6ULVs2UFqA1nSJ4mbbilpsdjewXrhV/N2aKmTPQ8WF5IrBgwdL5goY6jQV4N5Cr1695Lhx/FqsihBCCCF7DmTamjJlilhb4E6l3HDDDTmyrikonqquXG766T0FMpMBuGFF0xCTPQsVlzwAZkT4UcINyc3brUDAj1ZKhauVWy1VQUEj+CmmIt1xCkybbrEkBYL8+++/L/6U7nYc39y5c2W7C4R+xa1+my7wmcV+0+G7776T1I7pKEg4TlyPZApJbo49L65XqnlBCCGE7GugDg/SUyMFMuJrsI5UxcncvxCv8cwzz0gRx2hNnj0Bjq9Tp05BKmXyN+ILWmQXQPpAZCnBpcSCLBoTJ06Ubb7w65UsWVL6y5Qp4+3YsUP6kYKvdOnSkoXETXvoC7+S8QLjr7jiCklzGI90xwFkz0AmCoxFlok6dep4n3/+ud3qea+88opsw1K/fn3JaIFMGZrh5OKLL7YjY7Rq1SoYjzaygyBzSiqQiaVatWpe4cKFJbOLr2R4kydPlkwxSF3ogowvlSpVCj4HC9JKxgP7wXEjK4uORdYOZBSJgnugY3r16uXde++9nq9Y2K0x8up6JZsXhBBCCIllxUonUxkhChWXXaRDhw6BcKrLG2+8IduQ0tDt37hxo/QjZ7r2vfbaa9IH+vTpExoPwfnaa6/N8Ued7jgoSEjj6I7FctBBB4nAD9w89VAokPPbHYu0jwC5yaGolCpVKrQdS7L850rBggVD73nrrbe8U089VdqnnHKKHRVLxRhN66fL008/bUfFQBrCKlWqxB0LRUIVxddff91r2rRpkK/dXaD4KXl5vZLNC0IIIYQQknsy3lUM7j0ICE+2uD6SuzJ+5cqVtjd9XHcvX6GQgPVLLrlE1teuXSuvCvw5ges29uOPP9qWMa+++qptxfDvjxk2bJi55pprbE+MdMchHgUuT+DEE080d911l/iIIjXgyy+/LP1uRVi4Ng0aNMiuxd7z0ksvSRvvhduW624F8H6YT1NRsmRJ24oBM/GXX34pbVSgVR5++OGg0iwC96ZPn2585UTWn3/+eXlVcB1w35TLLrvM+MqNadGihZyH+sNeddVV5r333guuv4IUj61bt7ZreXu9ks0LQgghhBCSezJecYHg+sQTTyRdkJ9c2ZXxb731lu1NH+Qed6ldu7ZthRUUoAK5m41LBVzkCUcshPLiiy+aWrVqSRvCsL4n3XEA/qBKvXr1zAMPPBDkutdYkHj55rOysmQsrhMC4gCUIuSXd/OC33fffXL8SFOYCmTychkxYoRtxYLulLFjx9pWLFc7soecdtppso74GJeoItO/f39Rol5//XVz8803214j+dZx/shXryBGZfXq1eaCCy6wPXl7vZLNC0IIIYQQknsYnL+LRK0FrrKiT+8VFAgC7777rrwCVWaiQnn9+vXN6NGj7Zox48aNk9d0xyEofMWKFdIGbtEpoAJ4PEH8qaeeEmsDBHIF1oJp06aJVUGBQpFuWsCo4A4FDECZgEIElixZErJ6oZgXFJ6RI0fKuns8CK6H1UZBUalEmUb69u1rpk6dGno/rpt77nl9vZLNC0IIIYQQknsyXnFBkR8IpMkWN6f3rowvV66c7U2fc889NxDeIUxDSFW2b99uWzGw/YMPPghl1YKbF9i6dau8KsWKFZPKsSooz549W17THaeKgQK3JqCKkl6D6DEic0bnzp3tWk70/SA3VVlhwYgHasKgIi9wFYd4NGrUyLZilXb1nMDxxx9vW/GBm5iOx+dFFZC8vl7J5gUhhBBCCPkLINCF7BpXXXVVEIDtC+K21/MeeuihoB8LAtJr1qwZ6rvxxhtl7NSpU0P927Zt83zhWQLAsX7BBRfkatzKlStD43zhWsZpkPzgwYNlHF51DLZpQHsikGVLx8+ZM8f2pgaZ0JBZS9+rS7t27ewIz/v0009D23Ct3nzzTW/s2LGSgcxXGuxIz/v4449DY9u0aWO3xMdXQIKxhx56qO3NZndcr0TzghBCCCGE5B66iuUBrVq1sq1YZXmlTJkythWja9euEgeBoHBUiwUa7B4taAQXqk8++SR48l+6dGl5TXcc9u8L/tIGvuBvvv7668BiUKFCBXl1LQjly5cPAtoT4VpZFi1aZFupgZXjpJNOsmvZuJYSfz7aVgy4jjVs2NBcfPHF5uyzzw6dO3K/u8DV648//rBrOcH4okWLShsufG6CBrA7rleieUEIIYQQQnIPFZddBMK2G5ehCgSoUaOGbcXQ+JQ777xTXNPAmjVr5NXNVgUQk9GmTRu7ZszVV18tr+mOAz169LCtmJLRuHFjaUPY1oB3l2hAeTzcWJVbbrnFTJo0ya6lpkmTJraVjbu/qlWrhgLoN23aJDE1AwYMkBiSpk2bBlnYoPCokgagZKDiLgpT3XHHHbY3jH7Wzp07RRmKxgvl5fVKNi8IIYQQQshfAGYX8tcZNWpU4A6EpVatWnaLJ8UJfQUltB2uVnB5atmypaz7yo2MnTFjRmicu/iKjowB6Y5TLrroohzj3KKSbkHF3r17297ELFmyJFRnxT3fVHzyySfB+7Dsv//+UnzKBfVo3DHRZejQoXak540fPz4o/Bhd1q5da0dl8+qrr4bGoAZLlLy6XsnmBSGEEEIIyT1UXHYRFJlEUUbEO5x++unerFmz7JYY06dP94455hivWLFi3qWXXirV9MHAgQNFoEVRRzB37tyQoIulePHiOYoupjtOQWwHBGwoSFA4EAOzZs0au9Xztm7dKkUXESPz9ddf297kjB49WhSyokWLes8++6ztTQ1iRho2bCgV7IsUKeI1b97cbskGSh2q2uOc3HNEfMz555/vbdiwwY6MMXPmTK9169ZeiRIl5D6gEv+//vUvuzUnPXv2lBiXsmXLihIYJa+uV6p5QQghhBBCckcW/vMFQ/I3sGDBAskIBvevZcuWmYoVK0o/YjlQk6Rjx46hDGgg3XG5AfVMNm/enJarWF4Bly8UpURWtHjAtQrZ1xC3glTHyAKG2KBM4O+4XoQQQggh+zpUXDKEbdu2mSJFikhsBGI3olX3lXTHEUIIIYQQkp9gcH6GgIKHVapUkXYyy0K64wghhBBCCMlPUHHJIDTr1tFHHy2viUh3HCGEEEIIIfkFuoplEEj/+9xzz0mqXsS+JCLdcYQQQgghhOQXqLgQQgghhBBCMh66ihFCCCGEEEIyHiouhBBCCCGEkIyHigshhBBCCCEk46HiQgghhBBCCMl4qLgQQgghhBBCMh4qLoQQQgghhJCMh4oLIYQQQgghJOOh4kIIIYQQQgjJeKi4EEIIIYQQQjIeKi6EEEIIIYSQjIeKCyGEEEIIISTjoeJCCCGEEEIIyXiouBBCCCGEEEIyHiouhBBCCCGEkIyHigshhBBCCCEk46HiQgghhBBCCMl4qLgQQgghhBBCMh4qLoQQQgghhJCMh4oLIYQQQgghJOOh4kIIIYQQQgjJeKi4EEIIIYQQQjIcY/4PWCX1b1r9sREAAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "McLKWX3aIVfL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**В случае линейной регрессии** SVM пытается минимизировать расстояние между зазором и образцами, что лежат за его пределами, а добавление дополнительных обучающих образцов внутрь зазора не влияет на итоговый прогноз. Проще говоря, принцип такой же как и в обычной линейной регрессии, но вместо линии используется зазор, ширина которого контролируется параметром epsilon, а минимизируемая функция потерь называется epsilon-insensitive:\n",
        "\n",
        "$$\\min \\rightarrow C \\sum\\limits_{i=1}^{n} \\max(0, |y_i - (w^T \\phi(x_i) + b)| - \\varepsilon)$$\n",
        "\n",
        "Как и в случае с классификацией, в реализации scikit-learn для регрессии SVM используется l1 или l2-регуляризация на выбор, а функция потерь также может быть квадратичной."
      ],
      "metadata": {
        "id": "ZOuomkDdJCO6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "UFZwoH0KJBze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Нелинейный случай SVM**\n",
        "**В случае нелинейной классификации** данные преобразуются в пространство более высокой размерности, где они становятся линейно разделимыми. Одним из самых простых способов является добавление полиномиальных признаков, однако в таком случае сильно увеличивается вычислительная сложность.\n",
        "\n",
        "Более интересным и эффективным способом является использование *ядерного трюка* — это приём работы с данными в исходном пространстве, при котором скалярное произведение трансформированных векторов n-й степени заменяется на их произведение в степени n, что даёт аналогичный результат. Проще говоря, такой подход позволяет получить такие же результаты, как и в случае с добавлением большого количества полиномиальных признаков без их фактического добавления:\n",
        "\n",
        "$$\\phi(a)^T \\cdot \\phi(b) = (a^T \\cdot b)^n$$\n",
        "\n",
        "$\\phi \\ - \\ $ mapping function of degree n.\n",
        "\n",
        "Ниже представлен пример ядерного трюка для полиномиальноrо отображения второй степени.\n",
        "\n",
        "$$\\phi(a)^T \\cdot \\phi(b) =\n",
        "\\left( \\begin{array}{c} a_1^2 \\\\ \\sqrt{2} a_1 a_2  \\\\ a_2^2 \\end{array} \\right)^T \\cdot\n",
        "\\left( \\begin{array}{c} b_1^2 \\\\ \\sqrt{2} b_1 b_2  \\\\ b_2^2 \\end{array} \\right) =\n",
        "a_1^2 b_1^2 + 2a_1 b_1 a_2 b_2 + a_2^2 b_2^2 = \\\\\n",
        "= (a_1 b_1 + a_2 b_2)^2 =\n",
        "\\left( \\begin{array}{c} \\left( \\begin{array}{c} a_1 \\\\ a_2 \\end{array} \\right)^T \\cdot\n",
        "\\left( \\begin{array}{c} b_1 \\\\ b_2 \\end{array} \\right) \\end{array} \\right)^2 = (a^T \\cdot b)^2$$\n"
      ],
      "metadata": {
        "id": "WxJWUBmVzQOU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Kernel trick.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "Cm5bdh4DtfWA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Функция, с помощью которой выполняется ядерный трюк, то есть определяется сходство между парами точек данных в исходном пространстве, называется ядром. В scikit-learn поддерживаются следующие ядра:\n",
        "\n",
        "- линейное — просто вычисляет скалярное произведение векторов в исходном пространстве, что эквивалентно обычному линейному классификатору:\n",
        "\n",
        "$$a^T \\cdot b$$\n",
        "\n",
        "- полиномиальное — способно улавливать более сложные зависимости между данными, создавая оптимальную разделяющую гиперплоскость в новом пространстве, однако требуется тщательный подбор параметров:\n",
        "\n",
        "$$(\\gamma a^T \\cdot b + r)^d$$\n",
        "\n",
        "- гауссовское RBF ([радиально-базисная функция](https://en.wikipedia.org/wiki/Radial_basis_function)) — хорошо подходит для случаев, когда отношение в данных имеет сложную нелинейную форму и менее подвержено переобучению, поскольку учитывает не только значения признаков, но и их распределение:\n",
        "\n",
        "$$exp(-\\gamma||a - b||^2)$$\n",
        "\n",
        "- сигмоидальное — применяет гиперболический тангенс к линейной комбинации векторов, имитируя использование двухслойной нейронной сети с сигмоидальной функцией активации, что также позволяет хорошо работать со сложными нелинейными случаями, однако это может привести к переобучению в случае появления шума и выбросов:\n",
        "\n",
        "$$tanh(\\gamma a^t \\cdot b + r)$$\n",
        "\n",
        "В данном случае $\\gamma$ — параметр, определяющий степень влияния между обучающими образцами: чем ниже данное значение, тем больше радиус влияния образцов на формирование решающей границы, что делает её более гладкой. Между тем коэффициент сдвига $r$ (coef0 в scikit-learn) влияет на гибкость ядра, что позволяет создавать более сложные решающие границы.\n",
        "\n",
        "Стоит также добавить, что ядерный трюк может применяться не только в контексте SVM, но и к другим алгоритмам — просто так исторически сложилось, что ядерный трюк ассоциируется именно с SVM."
      ],
      "metadata": {
        "id": "lEEGQp5ok0qu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**В случае нелинейной регрессии** SVM также пытается сделать данные линейно разделимыми в пространстве более высокой размерности, но только для поиска оптимальной гиперплоскости, которая будет минимизировать расстояние между зазором и образцами как и в линейном случае.\n",
        "\n",
        "В scikit-learn для работы с нелинейными случаями используются классы SVR и SVC на основе библиотеки Libsvm, в которой реализован алгоритм с поддержкой ядерного трюка и l2-регуляризацией, контролируемой параметром C."
      ],
      "metadata": {
        "id": "joH_lZ7PtfF0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Реализация на Python с нуля**"
      ],
      "metadata": {
        "id": "B9DSmOcB4L-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from mlxtend.plotting import plot_decision_regions\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, r2_score\n",
        "from sklearn.utils import check_random_state, check_array\n",
        "from sklearn.svm import LinearSVC, LinearSVR, SVC, SVR, _libsvm\n",
        "from sklearn.datasets import load_iris, load_diabetes"
      ],
      "metadata": {
        "id": "Ta5KFzf5x13Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear SVM**"
      ],
      "metadata": {
        "id": "n-E0KMg9DUqK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LinearSVM:\n",
        "    def __init__(self, regression=False, C=1.0, eps=0, learning_rate=0.001, max_iter=1000,\n",
        "                 random_state=0):\n",
        "        self.regression = regression\n",
        "        self.C = C\n",
        "        self.eps = eps\n",
        "        self.learning_rate = learning_rate\n",
        "        self.max_iter = max_iter\n",
        "        self.random_state = random_state\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        if self.regression:\n",
        "            self.bias, self.weights = self._find_weights(X, y)\n",
        "        else:\n",
        "            classes = np.unique(y)\n",
        "            n_classes = len(classes)\n",
        "            _, n_features = X.shape\n",
        "\n",
        "            self.bias = np.zeros(n_classes)\n",
        "            self.weights = np.zeros((n_classes, n_features))\n",
        "            np.random.seed(self.random_state)\n",
        "\n",
        "            for i, cls in enumerate(classes):\n",
        "                y_binary = np.where(y == cls, 1, -1)\n",
        "                self.bias[i], self.weights[i] = self._find_weights(X, y_binary)\n",
        "\n",
        "    def _find_weights(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        bias = 0\n",
        "        weights = np.zeros(n_features) if self.regression else np.random.randn(n_features)\n",
        "\n",
        "        for _ in range(self.max_iter):\n",
        "            for i in range(n_samples):\n",
        "                y_pred = X[i] @ weights + bias\n",
        "                margin = y[i] - y_pred if self.regression else y[i] * y_pred\n",
        "                condition = np.abs(margin) > self.eps if self.regression else margin < 1\n",
        "\n",
        "                if condition:\n",
        "                    if self.regression:\n",
        "                        db = -self.C * (margin - self.eps)\n",
        "                        dw = -self.C * (margin - self.eps) * X[i]\n",
        "                    else:\n",
        "                        db = -self.C * y[i]\n",
        "                        dw = -self.C * y[i] * X[i]\n",
        "\n",
        "                    bias -= self.learning_rate * db\n",
        "                    weights -= self.learning_rate * dw\n",
        "\n",
        "        return bias, weights\n",
        "\n",
        "    def predict(self, X):\n",
        "        scores = X @ self.weights.T + self.bias\n",
        "\n",
        "        return scores if self.regression else np.argmax(scores, axis=1)"
      ],
      "metadata": {
        "id": "V6K9wzq5HX3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Kernel SVM на основе Libsvm**"
      ],
      "metadata": {
        "id": "HJTSd3XSDYC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SVM:\n",
        "    def __init__(self, regression=False, C=1.0, kernel='rbf', degree=3, solver='auto',\n",
        "                 gamma='scale', epsilon=0.1, coef0=0.0, shrinking=True, probability=False,\n",
        "                 tol=0.001, cache_size=200, max_iter=-1, random_state=None):\n",
        "        self.regression = regression\n",
        "        self.C = C\n",
        "        self.kernel = kernel\n",
        "        self.degree = degree\n",
        "        self.solver = solver\n",
        "        self.gamma = gamma\n",
        "        self.epsilon = epsilon\n",
        "        self.coef0 = coef0\n",
        "        self.shrinking = shrinking\n",
        "        self.probability = probability\n",
        "        self.tol = tol\n",
        "        self.cache_size = cache_size\n",
        "        self.max_iter = max_iter\n",
        "        self.random_state = random_state\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        X = X.astype(np.float64)\n",
        "        y = y.astype(np.float64)\n",
        "\n",
        "        rnd = check_random_state(self.random_state)\n",
        "        seed = rnd.randint(np.iinfo('i').max)\n",
        "\n",
        "        if self.gamma == 'scale':\n",
        "            self.gamma = 1.0 / (X.shape[1] * X.var()) if X.var() != 0 else 1.0\n",
        "        elif self.gamma == 'auto':\n",
        "                self.gamma = 1.0 / X.shape[1]\n",
        "        else:\n",
        "            self.gamma = self.gamma\n",
        "\n",
        "        if self.solver == 'auto':\n",
        "            self.solver = 'epsilon_svr' if self.regression else 'c_svc'\n",
        "\n",
        "        libsvm_impl = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']\n",
        "        self.solver = libsvm_impl.index(self.solver)\n",
        "\n",
        "        (self.support_, self.support_vectors_, self._n_support, self.dual_coef_, self.intercept_,\n",
        "         self._probA, self._probB, self.fit_status_, self._num_iter\n",
        "\n",
        "        ) = _libsvm.fit(X, y, C=self.C, svm_type=self.solver, kernel=self.kernel, gamma=self.gamma,\n",
        "                        degree=self.degree, epsilon=self.epsilon, coef0=self.coef0, tol=self.tol,\n",
        "                        shrinking=self.shrinking, probability=self.probability,\n",
        "                        cache_size=self.cache_size, max_iter=self.max_iter, random_seed=seed\n",
        "                        )\n",
        "\n",
        "    def predict(self, X_test):\n",
        "        X_test = X_test.astype(np.float64)\n",
        "        prediction = _libsvm.predict(X_test, self.support_, self.support_vectors_, self._n_support,\n",
        "                                     self.dual_coef_, self.intercept_, self._probA, self._probB,\n",
        "                                     svm_type=self.solver, kernel=self.kernel, degree=self.degree,\n",
        "                                     coef0=self.coef0, gamma=self.gamma, cache_size=self.cache_size\n",
        "                                     )\n",
        "\n",
        "        return prediction if self.regression else prediction.astype(int)"
      ],
      "metadata": {
        "id": "XMS6pMssYEd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def decision_boundary_plot(X, y, X_train, y_train, clf, feature_indexes, title=None):\n",
        "    feature1_name, feature2_name = X.columns[feature_indexes]\n",
        "    X_feature_columns = X.values[:, feature_indexes]\n",
        "    X_train_feature_columns = X_train[:, feature_indexes]\n",
        "    clf.fit(X_train_feature_columns, y_train)\n",
        "\n",
        "    plot_decision_regions(X=X_feature_columns, y=y.values, clf=clf)\n",
        "    plt.xlabel(feature1_name)\n",
        "    plt.ylabel(feature2_name)\n",
        "    plt.title(title)"
      ],
      "metadata": {
        "id": "z9p-VvfjLy0h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Загрузка датасетов**"
      ],
      "metadata": {
        "id": "Z5RO7iNHyDij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X1, y1 = load_iris(return_X_y=True, as_frame=True)\n",
        "X1_train, X1_test, y1_train, y1_test = train_test_split(X1.values, y1.values, random_state=0)\n",
        "print(X1, y1, sep='\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORG1o-eldqR3",
        "outputId": "e5925b94-8442-4b38-d7b9-3046112a321d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
            "0                  5.1               3.5                1.4               0.2\n",
            "1                  4.9               3.0                1.4               0.2\n",
            "2                  4.7               3.2                1.3               0.2\n",
            "3                  4.6               3.1                1.5               0.2\n",
            "4                  5.0               3.6                1.4               0.2\n",
            "..                 ...               ...                ...               ...\n",
            "145                6.7               3.0                5.2               2.3\n",
            "146                6.3               2.5                5.0               1.9\n",
            "147                6.5               3.0                5.2               2.0\n",
            "148                6.2               3.4                5.4               2.3\n",
            "149                5.9               3.0                5.1               1.8\n",
            "\n",
            "[150 rows x 4 columns]\n",
            "0      0\n",
            "1      0\n",
            "2      0\n",
            "3      0\n",
            "4      0\n",
            "      ..\n",
            "145    2\n",
            "146    2\n",
            "147    2\n",
            "148    2\n",
            "149    2\n",
            "Name: target, Length: 150, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X2, y2 = load_diabetes(return_X_y=True, as_frame=True)\n",
        "X2_train, X2_test, y2_train, y2_test = train_test_split(X2.values, y2.values, random_state=0)\n",
        "print(X2, y2, sep='\\n')"
      ],
      "metadata": {
        "id": "y_DvIkE_yIlV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71f18142-c6a7-4fcc-c973-5dad15c90c0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          age       sex       bmi        bp        s1        s2        s3  \\\n",
            "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
            "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
            "2    0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
            "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
            "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
            "..        ...       ...       ...       ...       ...       ...       ...   \n",
            "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
            "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
            "439  0.041708  0.050680 -0.015906  0.017293 -0.037344 -0.013840 -0.024993   \n",
            "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
            "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
            "\n",
            "           s4        s5        s6  \n",
            "0   -0.002592  0.019907 -0.017646  \n",
            "1   -0.039493 -0.068332 -0.092204  \n",
            "2   -0.002592  0.002861 -0.025930  \n",
            "3    0.034309  0.022688 -0.009362  \n",
            "4   -0.002592 -0.031988 -0.046641  \n",
            "..        ...       ...       ...  \n",
            "437 -0.002592  0.031193  0.007207  \n",
            "438  0.034309 -0.018114  0.044485  \n",
            "439 -0.011080 -0.046883  0.015491  \n",
            "440  0.026560  0.044529 -0.025930  \n",
            "441 -0.039493 -0.004222  0.003064  \n",
            "\n",
            "[442 rows x 10 columns]\n",
            "0      151.0\n",
            "1       75.0\n",
            "2      141.0\n",
            "3      206.0\n",
            "4      135.0\n",
            "       ...  \n",
            "437    178.0\n",
            "438    104.0\n",
            "439    132.0\n",
            "440    220.0\n",
            "441     57.0\n",
            "Name: target, Length: 442, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Обучение моделей и оценка полученных результатов**\n",
        "В случае классификации SVC справился лучше LinearSVC, сделав более гибкие границы разделения классов. Как можно заметить, решающие границы SVC получились гибкими, поскольку линейная граница решений в многомерном пространстве признаков будет соответствовать сложной нелинейной границе в исходном пространстве.  \n",
        "\n",
        "Однако такой подход не всегда работает лучше, что видно на примере регрессии: часто в данных с простой зависимостью линейное ядро демонстрирует лучшие результаты."
      ],
      "metadata": {
        "id": "8vtUYjcwyOMr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LinearSVC**"
      ],
      "metadata": {
        "id": "LodMBReDklNt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_svc = LinearSVM(random_state=0)\n",
        "linear_svc.fit(X1_train, y1_train)\n",
        "linear_svc_pred_res = linear_svc.predict(X1_test)\n",
        "linear_svc_accuracy = accuracy_score(y1_test, linear_svc_pred_res)\n",
        "\n",
        "print(f'LinearSVC accuracy: {linear_svc_accuracy:}')\n",
        "print(linear_svc_pred_res)"
      ],
      "metadata": {
        "id": "JC45Y6wE4htf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "871cb3dc-1de4-42c0-bc02-2b6577bb3dd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVC accuracy: 0.9210526315789473\n",
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 2 0 0 2 0 0 1 1 0 2 2 0 2 2 1 0\n",
            " 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LinearSVC (scikit-learn)**"
      ],
      "metadata": {
        "id": "IPpLp0m_yohu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sk_linear_svc = LinearSVC(loss='squared_hinge', max_iter=10000, random_state=0)\n",
        "sk_linear_svc.fit(X1_train, y1_train)\n",
        "sk_linear_svc_pred_res = sk_linear_svc.predict(X1_test)\n",
        "sk_linear_svc_accuracy = accuracy_score(y1_test, sk_linear_svc_pred_res)\n",
        "\n",
        "print(f'sk LinearSVC accuracy: {sk_linear_svc_accuracy:}')\n",
        "print(sk_linear_svc_pred_res)\n",
        "\n",
        "feature_indexes = [2, 3]\n",
        "title1 = 'LinearSVC surface'\n",
        "decision_boundary_plot(X1, y1, X1_train, y1_train, sk_linear_svc, feature_indexes, title1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "vyq9TnChyO-2",
        "outputId": "5e258142-bf25-4bd9-d496-cee88b6920af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sk LinearSVC accuracy: 0.9210526315789473\n",
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 2 0 0 2 0 0 1 1 0 2 2 0 2 2 1 0\n",
            " 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVC**"
      ],
      "metadata": {
        "id": "b3La8FhOXY9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVM(random_state=0, gamma='auto')\n",
        "svc.fit(X1_train, y1_train)\n",
        "svc_pred_res = svc.predict(X1_test)\n",
        "svc_accuracy = accuracy_score(y1_test, svc_pred_res)\n",
        "\n",
        "print(f'SVC accuracy: {svc_accuracy:}')\n",
        "print(svc_pred_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Lfdxq39vGGQ",
        "outputId": "67366942-f612-402e-afb3-57dfe6533439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC accuracy: 0.9736842105263158\n",
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVC (scikit-learn)**"
      ],
      "metadata": {
        "id": "CcBOnxNHXhN7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sk_svc = SVC(random_state=0, gamma='auto')\n",
        "sk_svc.fit(X1_train, y1_train)\n",
        "sk_svc_pred_res = sk_svc.predict(X1_test)\n",
        "sk_svc_accuracy = accuracy_score(y1_test, sk_svc_pred_res)\n",
        "\n",
        "print(f'sk SVC accuracy: {sk_svc_accuracy:}')\n",
        "print(sk_svc_pred_res)\n",
        "\n",
        "feature_indexes = [2, 3]\n",
        "title2 = 'SVC surface'\n",
        "decision_boundary_plot(X1, y1, X1_train, y1_train, sk_svc, feature_indexes, title2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "CIZFL7Z1Brka",
        "outputId": "d0e323b2-f932-4e71-f94d-dbf40d588406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sk SVC accuracy: 0.9736842105263158\n",
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LinearSVR**"
      ],
      "metadata": {
        "id": "dfPw7dbhkpS4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "linear_svr = LinearSVM(regression=True)\n",
        "linear_svr.fit(X2_train, y2_train)\n",
        "linear_svr_pred_res = linear_svr.predict(X2_test)\n",
        "linear_svr_r2 = r2_score(y2_test, linear_svr_pred_res)\n",
        "\n",
        "print(f'LinearSVR r2 score: {linear_svr_r2:}')\n",
        "print(linear_svr_pred_res)"
      ],
      "metadata": {
        "id": "7rL1Vtv7ZIlr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dde6e7f3-ae6f-40f4-e23a-8434c55d8242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearSVR r2 score: 0.3792357370266678\n",
            "[216.15968628 220.4481875  165.81989913 123.24660088 162.79339624\n",
            " 219.32628582 114.40328867 187.29239041 135.60608051 192.94216428\n",
            " 142.8356438  186.26887666 115.93676843  92.65131502 237.37770913\n",
            " 105.70347983 140.17045763  78.03390184  93.19336622 213.55077346\n",
            " 172.96582698 141.23351563 160.93321605 136.06861865 189.99746346\n",
            " 162.00412166 119.88834206  95.16558303 169.4791898  167.9510029\n",
            " 178.92773537  94.54901318 131.32930116 143.22717983 158.62314035\n",
            " 179.63207827 161.26232493 184.99305841 137.0911429  190.80874425\n",
            "  97.79122811 174.20746847 149.93081547 163.6511185  186.25250505\n",
            "  89.10531469 134.71035596 137.85610675 143.47793077 213.7920584\n",
            " 154.13931085  96.48578013 152.68919476 164.92850238 204.49516212\n",
            " 184.36868908 186.98158124 104.04646649 155.49588864 175.27621381\n",
            " 180.18811448 150.78618876 162.95426399 136.68414224 221.99330585\n",
            " 141.62183394  91.8349318  225.76370605 194.54965172  69.11345544\n",
            "  92.30405285 151.11667643 118.33817248 133.31474617 136.09121693\n",
            " 187.03275101 135.91418347 203.47303573 220.93494444 180.79064789\n",
            " 153.63359556 202.03121138  85.14842597 208.72273567  91.56979646\n",
            " 107.38020014 130.37078711 184.64399992 135.46766588 160.00350266\n",
            "  90.34899353 133.82900713  91.48257892 159.99266924 128.72744725\n",
            " 113.3641814  212.27373852 201.97525592 141.00148555 165.9290978\n",
            " 178.10808713  91.31945385 201.40199857 116.11341335 205.99603552\n",
            " 149.74428959 212.73484188 241.66640456 118.74948785 128.32248729\n",
            " 186.72222586]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LinearSVR (scikit-learn)**"
      ],
      "metadata": {
        "id": "GzfGU2SiyyS3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sk_linear_svr = LinearSVR(loss='squared_epsilon_insensitive')\n",
        "sk_linear_svr.fit(X2_train, y2_train)\n",
        "sk_linear_svr_pred_res = sk_linear_svr.predict(X2_test)\n",
        "sk_linear_svr_r2 = r2_score(y2_test, sk_linear_svr_pred_res)\n",
        "\n",
        "print(f'sk LinearSVR R2 score: {sk_linear_svr_r2:}')\n",
        "print(sk_linear_svr_pred_res)"
      ],
      "metadata": {
        "id": "Ok80P6Pcy0m0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4032bc0d-b896-4bd4-fa29-615e99e6c463"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sk LinearSVR R2 score: 0.37814542044497423\n",
            "[215.54802943 220.69479521 163.72321514 125.35050039 168.23663006\n",
            " 222.87285934 117.76887939 183.439543   143.23792374 199.25861483\n",
            " 149.2464745  180.13546176 117.73284104  98.8129158  229.40677283\n",
            " 107.70075384 144.10708869  83.42833432 101.75722386 207.55264272\n",
            " 177.38566123 146.60127268 159.10274119 144.47851976 188.12741871\n",
            " 161.72800999 122.19555137  98.38934168 172.04469244 163.88747259\n",
            " 174.31869231  98.56101933 137.43514957 143.99818253 152.57005935\n",
            " 180.16575707 160.23942338 181.13802512 137.10299603 190.01855923\n",
            "  99.30200726 168.53506831 147.88091211 166.46196854 180.1601789\n",
            "  91.81189072 137.23909085 140.51020744 137.65220301 212.07954464\n",
            " 154.84803714  97.88215713 154.47755522 160.16521948 206.99600783\n",
            " 176.91673261 183.28777653 113.64466494 148.4110365  170.98296686\n",
            " 185.33374519 155.94941503 161.03649538 131.05262642 225.54785872\n",
            " 145.68037375  95.41892062 217.82197857 191.37652659  70.60017863\n",
            "  96.29414312 144.83768093 117.20983217 138.05827775 137.59789698\n",
            " 182.48375573 127.73687759 196.85281425 212.96526525 179.20675501\n",
            " 151.86717758 197.64279852  84.30187965 201.50000094  92.92382878\n",
            " 108.05315903 137.05696185 182.37076863 136.21049311 155.56310123\n",
            "  98.99174929 132.35640936  95.05326284 155.95524466 129.92134192\n",
            " 115.17532067 211.30425492 202.94673641 139.72699584 163.73270376\n",
            " 178.32978006 102.9526864  198.70259669 111.69518298 202.22368578\n",
            " 140.46518594 208.80233486 238.65552407 122.58886831 126.21856224\n",
            " 184.20163827]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVR**"
      ],
      "metadata": {
        "id": "WJB4CoqOXb2m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svr = SVM(regression=True)\n",
        "svr.fit(X2_train, y2_train)\n",
        "svr_pred_res = svr.predict(X2_test)\n",
        "svc_r2 = r2_score(y2_test, svr_pred_res)\n",
        "\n",
        "print(f'SVR r2 score: {svc_r2:}')\n",
        "print(svr_pred_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kw5t4ItXI6m",
        "outputId": "617026c8-ebbf-4f4f-ac8e-b0520d80bc5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVR r2 score: 0.13842687260455933\n",
            "[156.05494339 160.2561796  141.5352585  126.32530705 138.56287083\n",
            " 157.79461531 118.30051225 153.1126463  133.32684359 151.34517091\n",
            " 138.31021981 150.93156042 127.62035887 125.65861919 159.80948036\n",
            " 119.27243753 132.8868118  115.3503535  130.26443966 154.69113982\n",
            " 140.44764244 131.1864082  140.91091129 132.34658014 147.43835547\n",
            " 141.71095095 135.93497953 120.372006   141.90918325 145.01581208\n",
            " 147.05308508 115.34872572 128.98260578 139.39763958 141.37219221\n",
            " 149.93533907 144.21593809 147.00705167 128.79733929 154.94745068\n",
            " 122.27967665 145.73099063 138.72486465 142.87524309 149.4272714\n",
            " 119.3023132  129.77336124 128.95425751 135.62538778 154.4679459\n",
            " 137.44976849 114.591969   136.28102471 144.85720422 155.6502593\n",
            " 145.13807371 149.33504099 124.03772077 141.352508   147.84699051\n",
            " 148.25434815 133.39538615 143.03923607 131.48119845 154.73175072\n",
            " 132.94449565 118.51262122 149.10562494 147.22897373 130.57831287\n",
            " 116.89776562 139.90942034 125.511231   129.63878496 128.60606786\n",
            " 148.44879908 136.55938776 152.91463602 158.19071606 143.85231346\n",
            " 138.42641367 157.68211977 114.52184616 158.09339445 124.94398434\n",
            " 119.09508611 129.13111767 147.82792349 130.8519467  140.09356889\n",
            " 128.19793708 128.37423877 120.27278606 141.48530479 126.975866\n",
            " 118.21921492 156.0928224  150.71999387 132.67319998 142.29171129\n",
            " 149.6172972  127.40342245 152.28664393 129.07127394 158.18758274\n",
            " 139.54904295 151.99820233 154.43678117 124.87211774 126.92101192\n",
            " 152.02873799]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVR (scikit-learn)**"
      ],
      "metadata": {
        "id": "jxhfy11UYad-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sk_svr = SVR()\n",
        "sk_svr.fit(X2_train, y2_train)\n",
        "sk_svr_pred_res = sk_svr.predict(X2_test)\n",
        "sk_svr_r2 = r2_score(y2_test, sk_svr_pred_res)\n",
        "\n",
        "print(f'sk SVR r2 score: {sk_svr_r2:}')\n",
        "print(sk_svr_pred_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1BRMD2HYgN9",
        "outputId": "697ba94c-6b69-4842-f905-c206efc12ddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sk SVR r2 score: 0.13842687260455933\n",
            "[156.05494339 160.2561796  141.5352585  126.32530705 138.56287083\n",
            " 157.79461531 118.30051225 153.1126463  133.32684359 151.34517091\n",
            " 138.31021981 150.93156042 127.62035887 125.65861919 159.80948036\n",
            " 119.27243753 132.8868118  115.3503535  130.26443966 154.69113982\n",
            " 140.44764244 131.1864082  140.91091129 132.34658014 147.43835547\n",
            " 141.71095095 135.93497953 120.372006   141.90918325 145.01581208\n",
            " 147.05308508 115.34872572 128.98260578 139.39763958 141.37219221\n",
            " 149.93533907 144.21593809 147.00705167 128.79733929 154.94745068\n",
            " 122.27967665 145.73099063 138.72486465 142.87524309 149.4272714\n",
            " 119.3023132  129.77336124 128.95425751 135.62538778 154.4679459\n",
            " 137.44976849 114.591969   136.28102471 144.85720422 155.6502593\n",
            " 145.13807371 149.33504099 124.03772077 141.352508   147.84699051\n",
            " 148.25434815 133.39538615 143.03923607 131.48119845 154.73175072\n",
            " 132.94449565 118.51262122 149.10562494 147.22897373 130.57831287\n",
            " 116.89776562 139.90942034 125.511231   129.63878496 128.60606786\n",
            " 148.44879908 136.55938776 152.91463602 158.19071606 143.85231346\n",
            " 138.42641367 157.68211977 114.52184616 158.09339445 124.94398434\n",
            " 119.09508611 129.13111767 147.82792349 130.8519467  140.09356889\n",
            " 128.19793708 128.37423877 120.27278606 141.48530479 126.975866\n",
            " 118.21921492 156.0928224  150.71999387 132.67319998 142.29171129\n",
            " 149.6172972  127.40342245 152.28664393 129.07127394 158.18758274\n",
            " 139.54904295 151.99820233 154.43678117 124.87211774 126.92101192\n",
            " 152.02873799]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Преимущества и недостатки метода опорных векторов**\n",
        "\n",
        "Преимущества:\n",
        "- высокая точность прогнозов;\n",
        "- может быть адаптирован к обнаружению аномалий в данных;\n",
        "- возможность обработки многомерных данных без их предварительного преобразования или понижения размерности.\n",
        "\n",
        "Недостатки:\n",
        "- низкая скорость работы на данных большого размера;\n",
        "- из-за разделения данных на гиперплоскости может возникнуть чувствительность к шуму или нестабильная работа с сильно перекрывающимися классами в данных.\n"
      ],
      "metadata": {
        "id": "nc23znio7KoO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Дополнительные источники**\n",
        "Статьи:\n",
        "- «Statistical performance of Support Vector Machines», Gilles Blanchard, Olivier Bousquet and Pascal Massart;\n",
        "- «A review of optimization methodologies in Support Vector Machines», John Shawe-Taylor, Shiliang Sun;\n",
        "- «On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines», Koby Crammer, Yoram Singer;\n",
        "- «LIBLINEAR: A library for large linear classification», Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin;\n",
        "- «LIBSVM: A Library for Support Vector Machines», Chih-Chung Chang and Chih-Jen Lin.\n",
        "\n",
        "Документация:\n",
        "- [Описание методов SVM](https://scikit-learn.org/stable/modules/svm.html#);\n",
        "- [LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html);\n",
        "- [LinearSVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html);\n",
        "- [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html);\n",
        "- [SVR](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html).\n",
        "\n",
        "Видео: [один](https://www.youtube.com/watch?v=_PwhiWxHK8o), [два](https://www.youtube.com/watch?v=V7mUNS3qXVY), [три](https://www.youtube.com/watch?v=0OLR3If-qS0), [четыре](https://www.youtube.com/watch?v=jMWjN6mJiSw), [пять](https://www.youtube.com/watch?v=EESZtSOdhEQ), [шесть](https://www.youtube.com/watch?v=FrsIHCr4TZE), [семь](https://www.youtube.com/watch?v=efR1C6CvhmE), [весемь](https://www.youtube.com/watch?v=Toet3EiSFcM), [девять](https://www.youtube.com/watch?v=Qc5IyLW_hns), [десять](https://www.youtube.com/watch?v=8xbnLHn4jjQ), [одиннадцать](https://www.youtube.com/watch?v=rB-iRIpAPKo).\n"
      ],
      "metadata": {
        "id": "_Y0n1ilVpvD1"
      }
    }
  ]
}