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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-size: 14pt\">ФИВТ, АПТ, Курс по машинному обучению, Весна 2017, семинар 7 </span>\n",
+    "\n",
+    "<span style=\"color:blue; font-size: 12pt\">Alexey Romanenko, </span>\n",
+    "<span style=\"color:blue; font-size: 12pt; font-family: 'Verdana'\">alexromsput@gmail.com</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear models (Линейные модели)\n",
+    "<h3> Plan </h3>\n",
+    "\n",
+    "* **Linear Models overview** \n",
+    " - Linear Model for Classification\n",
+    " - Linear Model for Regression (preview)\n",
+    " - Linear Models and regularization\n",
+    "\n",
+    "* **Gradient descent**\n",
+    " - GD, SGD, SAG, \n",
+    " - SGD with different loss function\n",
+    " - SGD regularization\n",
+    " \n",
+    "* **SVM: base overview**\n",
+    " - learning algorithm\n",
+    " - SVM realization\n",
+    " - MultiClasss SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import _pickle as pickle  # in Python 2 try import cPickle as pickle\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+    "\n",
+    "\n",
+    "sns.set_context(\"notebook\", font_scale=1.5)\n",
+    "\n",
+    "from IPython.display import Image, SVG\n",
+    "\n",
+    "from scipy import optimize\n",
+    "import matplotlib.pyplot as plt\n",
+    "%pylab inline\n",
+    "from IPython import display\n",
+    "import random\n",
+    "\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\">Warm Up: 3 datasets</h1> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2)\n",
+    "X += np.random.random(X.shape)\n",
+    "\n",
+    "datasets = [make_moons(noise=0.1), make_circles(noise=0.1, factor=0.5), (X, y)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f24f8a5ad30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pylab.rcParams['figure.figsize'] = 14, 4\n",
+    "pl = plt.subplots(1, len(datasets), sharex='col', sharey='row')\n",
+    "for i, (X, y) in enumerate(datasets):\n",
+    "    X = StandardScaler().fit_transform(X)\n",
+    "    pl[1][i].scatter(X[:, 0], X[:, 1], c=y, cmap=ListedColormap(['#FF0000', '#0000FF']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Вопрос **: Какая выборка линейно разделима (предполагает использование линейной модели классификации)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Вопросы **\n",
+    " - 1) как выглядит решающее правило для линейных моделей? Какие параметры у линейных моделей?\n",
+    " - 2) что общего между линейной моделью классификации и регрессии?\n",
+    " - 3) может ли предсказание линейной модели выходить за область значений обучающей выборки?\n",
+    " - 4) что такое реуляризация и зачем она нужна?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\"> Linear Model overview </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## $$Y = \\{+1, -1\\},~X\\in \\mathbf{R}^d$$\n",
+    "## $$y_{predict}(x) = sign(<w, x>) $$ \n",
+    "## $$margin(x, y) = y \\cdot sign(<w, x>)$$\n",
+    "## $$Q(w, X^\\ell) = \\frac{1}{n} \\sum_i^n L(y_i, <w,x_i>) \\rightarrow \\min_w$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## $$Y = \\mathbf{R},~X\\in \\mathbf{R}^d$$\n",
+    "## $$y_{predict}(x) = <w, x> $$ \n",
+    "## $$Q(w, X^\\ell) = \\frac{1}{n} \\sum_i^n L(y_i, <w,x_i>) \\rightarrow \\min_w$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Loss functions:\n",
+    "    - Hinge Loss \n",
+    "## $$L_i(x, y; w) = max(0, 1 - y\\cdot<w, x>)$$\n",
+    "    - Loistic Loss \n",
+    "## $$L_i(x, y; w) = log(1 + e^{-y\\cdot<w, x>})$$\n",
+    "    - Squared Loss\n",
+    "## $$L_i(x, y; w) = log(1 - y\\cdot<w, x>)^2$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loss Functions\n",
+    "![](http://scikit-learn.org/0.15/_images/plot_sgd_loss_functions_001.png = 200x200)\n",
+    "<img src=\"http://scikit-learn.org/0.15/_images/plot_sgd_loss_functions_001.png\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f24f03ba9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pylab.rcParams['figure.figsize'] = (7.0, 7.0) \n",
+    "x = np.linspace(-1, 2.5)\n",
+    "pylab.plot(x, list(map(lambda m: np.max([0, 1 - m]), x)), label='hinge')\n",
+    "pylab.plot(x, list(map(lambda m: np.log(1 + e**(-m)), x)), label='logistic')\n",
+    "pylab.plot(x, list(map(lambda m: (1 - m)**2, x)), label='squared')\n",
+    "pylab.ylabel('Loss')\n",
+    "pylab.xlabel('Margin')\n",
+    "pylab.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Регуляризация"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2017/seminars/09_linear_models/496/pic/Regularization.PNG\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---------\n",
+    "<h1 align=\"center\">Gradient Descent for Linear Classifiers</h1> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Вопросы **\n",
+    "* Что такое градиентный спуск (Gradient Descent?\n",
+    "* Какие недостатки у простоо GD?\n",
+    "* Что такое стохастический градиентный спуск (SGD)? SAG?\n",
+    "* Достоинства и недостатки метода SGD?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2016/seminars/05_linear/img/conv.jpg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gradient descent\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/f/ff/Gradient_descent.svg\" width=\"60%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Градинтный спуск\n",
+    "## $$w_{k+1} = w_k - \\nabla Q(w_k, X^\\ell)= w_k - \\nabla \\sum_i L(w_k, x_i)$$\n",
+    "\n",
+    "### Main Problems of gradient method\n",
+    "* multicollinearity\n",
+    "* scaling problem\n",
+    "* Plateau\n",
+    "* Zig-zagginh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plateau\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/6/60/Banana-SteepDesc.gif\" width=\"80%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zig-zagging\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/7/79/Rosenbrock.png\" width=\"70%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Как вычислять градиент\n",
+    "## $$         \\nabla_x \\langle a, x \\rangle = a.     $$\n",
+    "## $$         \\nabla_x \\|x\\|_2^2 = 2 x.     $$\n",
+    "## $$         \\nabla_x \\langle Ax, x \\rangle = (A + A^T) x, $$\n",
+    "### $where ~ A \\in R^{d \\times d}$\n",
+    "## $$         \\nabla_x \\|Ax + b\\|_2^2 = 2 A^T (Ax + b).     $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Задача ** Найти производные приведенных функций по w, в матричной форме \n",
+    "## $$f(w) = \\sum_i log(1-e^{-y_i <x_i, w>})$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Нютон (HF, BFGS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $$w_{k+1} = w_k - \\nabla^2 f(x_k) \\nabla f(x_k)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Методы второго порядка -- намного быстрее, но как правило дорогие т.к. требуют хранения гессиана.\n",
+    "\n",
+    "Некоторые методы второго порядка лишены этого недостатка, при необходимости используйте BFGS или HF Newton. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nw(X, y, w, gradf, hessf, dold):\n",
+    "    return -np.linalg.inv(hessf(X, y, w)).dot(grad_function(X, y, w))\n",
+    "\n",
+    "for X, y in datasets:\n",
+    "    X, y = expand(X), -2*(y-0.5)\n",
+    "    a = viz_opt(loss_function, grad_function, hess_function, X, y, nw) \n",
+    "\n",
+    "display.clear_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stochastic GD, Momentum, Nesterov"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Что делать если в функции большая сумма? Давайте считать градиент только по случайной подвыборке"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $$w_{k+1} = w_k -  \\nabla \\hat{f}(w_k)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $$w_{k+1} = w_k - E \\nabla \\hat{f}(x_k)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $$x_{k+1} =w_k - E \\nabla \\hat{f}(x_k-\\alpha E \\nabla \\hat{f}_{k-1})$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Достоинства и недостатки SGD\n",
+    "* Benefits:\n",
+    "     - Suitable for online learning\n",
+    "     - Learning on big and small sets\n",
+    "     - Faster than classic GD\n",
+    "\n",
+    "* Disadvantages and recommendations\n",
+    "    - Convergence problems!\n",
+    "    - Multiextremal functional and local extremums\n",
+    "        -- recommendation: jog of weights\n",
+    "    - Not very fast\n",
+    "           - recommendation: SAG version of SGD\n",
+    "$$w^{(t+1)}=w^{(t)}-\\frac{\\eta_t}{\\ell} \\nabla \\left( (\\ell-1)\\cdot Q(w^{(t-1)},X^\\ell\\setminus \\{x_i\\}) + Q(w^{(t)},x_i)\\right)$$\n",
+    "    - Sensitivity to feature scales\n",
+    "           - recommendation: scale features\n",
+    "    - Over-fitting and instability\n",
+    "           - recommendation: regularization \n",
+    "$$Q_\\tau(w) = Q(w)+\\frac{\\tau}{2}\\lVert w\\rVert^2$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------\n",
+    "<h1 align=\"center\">SVM</h1> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Вопросы **\n",
+    "* Основная идея SVM?\n",
+    "* Что такое \n",
+    "    - разделяющая гиперплоскость\n",
+    "    - опорный вектор\n",
+    "    - Margin?\n",
+    "* Как обучается SVM?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center> Svm_max_sep_hyperplane_with_margin.png </center>\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/2/2a/Svm_max_sep_hyperplane_with_margin.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Реализуем SVM своими руками\n",
+    "See <a href=\"https://github.com/ml-mipt/ml-mipt-part1/blob/master/2017/seminars/07_linear_models/my_svm.ipynb\">my_svm.ipynb</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\">Заключение</h1>  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**SGD**: \n",
+    "*Достоинства:\n",
+    "    - Быстрые\n",
+    "    - Работают\n",
+    "    - Интерпретируемы\n",
+    "    - Применимы к большим данным\n",
+    "    - Можно обучать онлайн\n",
+    "* Недостатки:\n",
+    "    - Не всегда хороши (вопросы сходимости)\n",
+    "\n",
+    "** SVM **\n",
+    "* Достоинства\n",
+    "    - Сильная обощающая способность\n",
+    "    - Выпуклая задача оптимизация (наличие решения)\n",
+    "    - Не нужны все объекты обучающей выборки для обучения\n",
+    "* Недостатки:\n",
+    "    - пока не добрались :)\n",
+    "\n",
+    "** HW **\n",
+    "    - реализовать SVM и запустить его сгенерированной обучающей выборке (см.ссылку на стартовый код выше)\n",
+    "\n",
+    "** Обратная связь ** \n",
+    "  * оцените <a href=\"https://docs.google.com/forms/d/e/1FAIpQLSdmyY3f-lwrhSGeqJPaxcXrdj0SfZzZbgRIggg-nx4EQ_eQLQ/viewform?c=0&w=1\"> семинар </a>\n",
+    "  * оставьте <a href=\"https://docs.google.com/forms/d/e/1FAIpQLSdefy8neFtoxDlXD3toHi3fWB3OW-23APTRj-GuTX8wtAJahQ/viewform?c=0&w=1\"> отзыв </a> о лекции"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/2018/seminars/8_linear_models/TextClassification.ipynb b/2018/seminars/8_linear_models/TextClassification.ipynb
new file mode 100644
index 0000000..9ee9006
--- /dev/null
+++ b/2018/seminars/8_linear_models/TextClassification.ipynb
@@ -0,0 +1,745 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "import numpy as np\n",
+    "import heapq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['alt.atheism',\n",
+       " 'comp.graphics',\n",
+       " 'comp.os.ms-windows.misc',\n",
+       " 'comp.sys.ibm.pc.hardware',\n",
+       " 'comp.sys.mac.hardware',\n",
+       " 'comp.windows.x',\n",
+       " 'misc.forsale',\n",
+       " 'rec.autos',\n",
+       " 'rec.motorcycles',\n",
+       " 'rec.sport.baseball',\n",
+       " 'rec.sport.hockey',\n",
+       " 'sci.crypt',\n",
+       " 'sci.electronics',\n",
+       " 'sci.med',\n",
+       " 'sci.space',\n",
+       " 'soc.religion.christian',\n",
+       " 'talk.politics.guns',\n",
+       " 'talk.politics.mideast',\n",
+       " 'talk.politics.misc',\n",
+       " 'talk.religion.misc']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_categories = fetch_20newsgroups().target_names\n",
+    "all_categories"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Возьмём темы из одного раздела, возможно, их будет сложнее отличать друг от друга"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categories = [\n",
+    "    'sci.electronics',\n",
+    "    'sci.space',\n",
+    "    'sci.med'\n",
+    "]\n",
+    "\n",
+    "train_data = fetch_20newsgroups(subset='train',\n",
+    "                                categories=categories,\n",
+    "                                remove=('headers', 'footers', 'quotes'))\n",
+    "\n",
+    "test_data = fetch_20newsgroups(subset='test',\n",
+    "                               categories=categories,\n",
+    "                               remove=('headers', 'footers', 'quotes'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Для векторизации текстов воспользуемся CountVectorizer, он представляет документ как мешок слов. Можно всячески варировать извлечение признаков (убирать редкие слова, убирать частые слова, убирать слова общей лексики, брать биграмы и т.д.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
+       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
+       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
+       "        tokenizer=None, vocabulary=None)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "CountVectorizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count_vectorizer = CountVectorizer(min_df=5, ngram_range=(1, 2)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1778x10885 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 216486 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse_feature_matrix = count_vectorizer.fit_transform(train_data.data)\n",
+    "sparse_feature_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_2_words = {\n",
+    "    v: k\n",
+    "    for k, v in count_vectorizer.vocabulary_.items()  # use .iteritems() for Python 2\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.model_selection import cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Обучим логистическую регрессию для предсказания темы документа"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo = LogisticRegression()\n",
+    "algo.fit(sparse_feature_matrix, train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Слова с наибольшим положительным весом, являются характерными словами темы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "circuit,  electronics,  power,  chips,  parts,  the number,  them,  used,  tv,  ve\n",
+      "msg,  medical,  my,  blood,  disease,  doctor,  health,  treatment,  your,  needles\n",
+      "space,  orbit,  nasa,  thanks for,  launch,  earth,  sorry,  moon,  spacecraft,  solar\n"
+     ]
+    }
+   ],
+   "source": [
+    "W = algo.coef_.shape[1]\n",
+    "for c in algo.classes_:\n",
+    "    topic_words = [\n",
+    "        num_2_words[w_num]\n",
+    "        for w_num in heapq.nlargest(10, range(W), key=lambda w: algo.coef_[c, w])\n",
+    "    ]\n",
+    "    print(',  '.join(topic_words))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сравним качество на фолдах с качеством на трейне и на отложенном тесте"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.8487395  0.84550562 0.83426966 0.83943662 0.82768362]\n",
+      "0.8391270024469429\n"
+     ]
+    }
+   ],
+   "source": [
+    "algo = LogisticRegression()\n",
+    "arr = cross_val_score(algo, sparse_feature_matrix, train_data.target, cv=5, scoring='accuracy')\n",
+    "print(arr)\n",
+    "print(np.mean(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Почему это неправильная кроссвалидация?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo.fit(sparse_feature_matrix, train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9803149606299213"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(algo.predict(sparse_feature_matrix), train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7928994082840237"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(algo.predict(count_vectorizer.transform(test_data.data)), test_data.target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Мы видим переобучение, это проклятие размерности"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.72829132 0.74719101 0.73033708 0.74647887 0.71186441]\n",
+      "0.7328325372866697\n"
+     ]
+    }
+   ],
+   "source": [
+    "algo = LogisticRegression(penalty='l1', C=0.1)\n",
+    "arr = cross_val_score(algo, sparse_feature_matrix, train_data.target, cv=5, scoring='accuracy')\n",
+    "print(arr)\n",
+    "print(np.mean(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo.fit(sparse_feature_matrix, train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7935883014623172"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(algo.predict(sparse_feature_matrix), train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6813186813186813"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(algo.predict(count_vectorizer.transform(test_data.data)), test_data.target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Добавление регуляризатора уменьшает отличие на трейне и тесте, но ухудшает качество. Поиграйтесь с параметрами регуляризации, чтобы получить максимальное качество."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Чтобы не делать векторизацию и обучение раздельно, есть удобный класс Pipeline. Он позволяет объединить в цепочку последовательность действий"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = Pipeline([\n",
+    "    (\"vectorizer\", CountVectorizer(min_df=5, ngram_range=(1, 2))),\n",
+    "    (\"algo\", LogisticRegression())\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "     steps=[('vectorizer', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=5,\n",
+       "        ngram_range=(1, 2), preprocessor=None, stop_words=None,\n",
+       "       ...ty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False))])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.fit(train_data.data, train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9803149606299213"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(pipeline.predict(train_data.data), train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7928994082840237"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(pipeline.predict(test_data.data), test_data.target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Значения ровно такие же как мы получали ранее, делаяя шаги раздельно."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "При кроссвалидации нужно, чтобы CountVectorizer не обучался на тесте (иначе объекты становятся зависимыми). Pipeline позволяет это просто сделать."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.83753501 0.84550562 0.82303371 0.83943662 0.83050847]\n",
+      "0.835203886828576\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipeline = make_pipeline(CountVectorizer(min_df=5, ngram_range=(1, 2)), LogisticRegression())\n",
+    "arr = cross_val_score(pipeline, train_data.data, train_data.target, cv=5, scoring='accuracy')\n",
+    "print(arr)\n",
+    "print(np.mean(arr))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В Pipeline можно добавлять новые шаги препроцессинга данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfTransformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.87114846 0.87078652 0.84831461 0.85633803 0.83898305]\n",
+      "0.8571141323991462\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipeline = make_pipeline(CountVectorizer(min_df=5, ngram_range=(1, 2)), TfidfTransformer(), LogisticRegression())\n",
+    "arr = cross_val_score(pipeline, train_data.data, train_data.target, cv=5, scoring='accuracy')\n",
+    "print(arr)\n",
+    "print(np.mean(arr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(memory=None,\n",
+       "     steps=[('countvectorizer', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
+       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
+       "        lowercase=True, max_df=1.0, max_features=None, min_df=5,\n",
+       "        ngram_range=(1, 2), preprocessor=None, stop_words=None,\n",
+       "  ...ty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False))])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.fit(train_data.data, train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.96962879640045"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(pipeline.predict(train_data.data), train_data.target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8241758241758241"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(pipeline.predict(test_data.data), test_data.target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Качество стало немного лучше"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Задание\n",
+    "\n",
+    "1. Поиграйтесь с параметрами регуляризации, параметрами CountVectorizer и TfidfTransformer, чтобы получить максимальное качество.\n",
+    "2. Постройте список важных слов и словосочетаний для каждой темы (на основе значений коэффициентов)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/2018/seminars/8_linear_models/seminar8_v0.ipynb b/2018/seminars/8_linear_models/seminar8_v0.ipynb
new file mode 100644
index 0000000..e9fda23
--- /dev/null
+++ b/2018/seminars/8_linear_models/seminar8_v0.ipynb
@@ -0,0 +1,1204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-size: 14pt\">ФИВТ, АПТ, Курс по машинному обучению, Весна 2017, семинар 8 </span>\n",
+    "\n",
+    "<span style=\"color:blue; font-size: 12pt\">Alexey Romanenko, </span>\n",
+    "<span style=\"color:blue; font-size: 12pt; font-family: 'Verdana'\">alexromsput@gmail.com</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear models (продолжение)\n",
+    "<h3> Plan </h3>\n",
+    "\n",
+    "\n",
+    "* **SVM: **\n",
+    " - learning algorithm (повторение)\n",
+    " - Kernel Trick\n",
+    " - MultiClasss SVM\n",
+    " \n",
+    "* **SVM: example of realization **\n",
+    "  - simple SVM\n",
+    "  - true SVM\n",
+    " \n",
+    "* ** Use cases **\n",
+    " - Budget optimization\n",
+    " - Intelligent email sending\n",
+    " - Man-hours forecasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "# import _pickle as pickle  # use for Python 2: import cPickle as pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from matplotlib.pyplot import plot, contourf, clabel, contour\n",
+    "\n",
+    "from matplotlib import cm\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
+    "\n",
+    "%matplotlib inline\n",
+    "sns.set_context(\"notebook\", font_scale=1.5)\n",
+    "import random\n",
+    "from IPython.display import Image, SVG\n",
+    "from scipy import optimize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------\n",
+    "<h1 align=\"center\">SVM</h1> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Вопросы **\n",
+    "* Основная идея SVM?\n",
+    "* Что такое \n",
+    "    - разделяющая гиперплоскость\n",
+    "    - опорный вектор\n",
+    "    - Margin?\n",
+    "* Как обучается SVM?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center> Svm_max_sep_hyperplane_with_margin.png </center>\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/2/2a/Svm_max_sep_hyperplane_with_margin.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## $$ L_i = \\sum_{j, j \\neq y_i} max(0, w_j^tx - w_{y_i}^tx + 1)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic Programming (QP) Problem:\n",
+    "\n",
+    "## $$ b = w_0 $$\n",
+    "\n",
+    "## $$ \\min_{i = 1, \\ldots, l} y_i (<w, x_i> - w_0)  = 1 $$\n",
+    "\n",
+    "Linear Separability\n",
+    "\n",
+    "## \\begin{cases}\n",
+    "    <w, w> \\to \\min\\limits_{w} \\\\\n",
+    "    y_i (<w, x_i> - w_0) \\geq 1, i = 1, \\ldots, l\n",
+    "\\end{cases}\n",
+    "\n",
+    "Linear Inseparability\n",
+    "\n",
+    "## \\begin{cases}\n",
+    "    \\frac{1}{2} <w, w> + C \\sum\\limits_{i=1}^{l} \\xi_i \\to \\min\\limits_{w, \\xi} \\\\\n",
+    "    y_i (<w, x_i> - w_0) \\geq 1 - \\xi_i, i = 1, \\ldots, l \\\\\n",
+    "    \\xi_i \\geq 0, i = 1, \\ldots, l\n",
+    "\\end{cases}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Model Equivalence\n",
+    "\n",
+    "$$ Q(w, w_0) = \\sum\\limits_{i=1}^{l} (1 - M_i(w, w_0))_{+} + \\frac{1}{2C} {\\|w\\|}^2 \\to \\min\\limits_{w, w_0} $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dual Form\n",
+    "\n",
+    "Before:\n",
+    "## \\begin{cases}\n",
+    "    \\sum f(x_i) \\to \\min\\limits_{x} \\\\\n",
+    "    h(x_i) \\geq 0, i = 1, \\ldots, n\n",
+    "\\end{cases}\n",
+    "\n",
+    "After:\n",
+    "## \\begin{cases}\n",
+    "    \\sum f(x_i) - \\lambda_i h(x_i) \\to \\min\\limits_{x} \\max\\limits_{\\lambda} \\\\\n",
+    "    h(x_i) \\geq 0, i = 1, \\ldots, n \\\\\n",
+    "    \\lambda_i \\geq 0, i = 1, \\ldots, n \\\\\n",
+    "    \\lambda_i = 0 \\ or \\  h(x_i) = 0 \\ (\\sum \\lambda_i h(x_i) = 0)\n",
+    "\\end{cases}\n",
+    "\n",
+    "Calculate derivatives over x and see corollary.\n",
+    "\n",
+    "\n",
+    "## \\begin{cases}\n",
+    "    -\\sum\\limits_{i=1}^{l} \\lambda_i + \\frac{1}{2} \\sum\\limits_{i=1}^{l} \\sum\\limits_{j=1}^{l} \\lambda_i \\lambda_j y_i y_j <x_i, x_j> \\to \\min\\limits_{\\lambda} \\\\\n",
+    "    \\sum\\limits_{i=1}^{l} \\lambda_i y_i = 0 \\\\\n",
+    "    0 \\leq \\lambda_i \\leq C, i = 1, \\ldots, l\n",
+    "\\end{cases}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decision Rule\n",
+    "\n",
+    "### $$ a(x) = sign \\left(\\sum\\limits_{i = 1}^{l} \\lambda_i y_i <x_i, x> - w_0 \\right) $$\n",
+    "## $$ w_0 = med \\{ <w, x_i> - y_i \\ |\\  \\lambda_i > 0 \\} $$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "--------\n",
+    "## Non-Linear SVM (Kernel Trick)\n",
+    "** Вопросы **\n",
+    " - Что такое ядро?\n",
+    " - Примеры ядер?\n",
+    " - Как строить ядра?\n",
+    " - Применение ядер для классификации нелинейных выборок\n",
+    "\n",
+    "Kernel fuction \n",
+    "## $$ K : X \\times X \\to R $$ \n",
+    "if $ K(x, x') = <\\phi(x), \\phi(x')> $, where $ \\phi : X \\to H $ and H is space with inner product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kernel_Machine\n",
+    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/1/1b/Kernel_Machine.png\" width=\"80%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Kernel trick **\n",
+    "## \\begin{cases}\n",
+    "    -\\sum\\limits_{i=1}^{l} \\lambda_i + \\frac{1}{2} \\sum\\limits_{i=1}^{l} \\sum\\limits_{j=1}^{l} \\lambda_i \\lambda_j y_i y_j \\color{red}{K(x_i, x_j)} \\to \\min\\limits_{\\lambda} \\\\\n",
+    "    \\sum\\limits_{i=1}^{l} \\lambda_i y_i = 0 \\\\\n",
+    "    0 \\leq \\lambda_i \\leq C, i = 1, \\ldots, l\n",
+    "\\end{cases}\n",
+    "\n",
+    "### $$ a(x) = sign \\left(\\sum\\limits_{i = 1}^{l} \\lambda_i y_i \\color{red}{K(x_i, x)} - w_0 \\right) $$\n",
+    "## $$ w_0 = med \\{ \\color{red}{K(w, x_i)}<w, x_i> - y_i \\ |\\  \\lambda_i > 0 \\} $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kernel and Building kernels\n",
+    "<img src='https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2017/seminars/08-linear_models/496/pic/Kernel.PNG' align=\"left\" width=\"70%\">\n",
+    "\n",
+    "<!---                             .\n",
+    "                            T\n",
+    "                           ( )\n",
+    "                          <===>\n",
+    "                           F|J\n",
+    "                           ===\n",
+    "                          J|||F\n",
+    "                          F|||J\n",
+    "                         /\\/ \\/\\\n",
+    "                         F+++++J\n",
+    "                        J{}{|}{}F         .\n",
+    "                     .  F{}{|}{}J         T\n",
+    "          .          T J{}{}|{}{}F        ;;\n",
+    "          T         /|\\F/\\/\\|/\\/\\J  .   ,;;;;.\n",
+    "         /:\\      .'/|\\\\:=========F T ./;;;;;;\\\n",
+    "       ./:/:/.   ///|||\\\\\\\"\"\"\"\"\"\"\" /x\\T\\;;;;;;/\n",
+    "      //:/:/:/\\  \\\\\\\\|////..[ ]...xXXXx.|====|\n",
+    "      \\:/:/:/:T7 :.:.:.:.:||[ ]|/xXXXXXx\\|||||\n",
+    "      ::.:.:.:A. `;:;:;:;'=== ==\\XXXXXXX/=====.\n",
+    "      `;\"\"::/xxx\\.|,|,|,| ( ) ( )| | | |.=..=.|\n",
+    "       :. :`\\xxx/(_)(_)(_) _   _ | | | |'-''-'|\n",
+    "       :T-'-.:\"\":|\"\"\"\"\"\"\"|/ \\ / \\|=====|======|\n",
+    "       .A.\"\"\"||_|| ,. .. || | | |/\\/\\/\\/ | | ||\n",
+    "   :;:////\\:::.'.| || || ||-| |-|/\\/\\/\\+|+| | |\n",
+    "  ;:;;\\////::::,='======='=============/\\/\\=====.\n",
+    " :;:::;\"\"\":::::;:|__..,__|============/||\\|\\====|\n",
+    " :::::;|=:::;:;::|,;:::::          |========|   |\n",
+    " ::l42::::::(}:::::;::::::_________|========|___|_\n",
+    "--->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example of Kernels\n",
+    "<img src=\"https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2017/seminars/08-linear_models/496/pic/KernelExample.PNG\" align=\"left\" width=\"60%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Какое ядро приведёт к линеаризации следующего датасета? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f1799368470>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f17a190d978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.vstack((np.random.uniform(size=100).reshape((50,2)), np.random.uniform(size=100).reshape((50,2)) + 1)) \n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot(x[:,0], x[:,1], 'bo')\n",
+    "y1 = np.random.uniform(size=100).reshape((50,2))\n",
+    "y2 = np.random.uniform(size=100).reshape((50,2))\n",
+    "y1[:,0] += 1\n",
+    "y2[:,1] += 1\n",
+    "plot(y2[:,0], y2[:,1], 'go')\n",
+    "plot(y1[:,0], y1[:,1], 'go')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "** Какое ядро приведёт к линеаризации следующего датасета? **"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f17a190d1d0>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f17d01255c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.random.randn(100, 2)\n",
+    "r = np.abs(np.random.randn(100)) + 4\n",
+    "fi = np.random.uniform(0.0, 2 * np.pi, size = 100)\n",
+    "y = np.vstack((r * np.cos(fi), r * np.sin(fi))).T\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot(x[:,0], x[:,1], 'bo')\n",
+    "plot(y[:,0], y[:,1], 'go')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example of RBF kernel\n",
+    "<a href='http://cs.stanford.edu/people/karpathy/svmjs/demo/'>Demo SVM</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "------\n",
+    "## Multiclass SVM\n",
+    "## $$ Y = \\{1,..., K\\}$$\n",
+    "\n",
+    "** Вопросы **\n",
+    "* Как построить SVM для мультиклассовой задачи классификации?\n",
+    "* Какие недостатки у подходов One-to-One и One-to-All?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Решающее правило\n",
+    "<img src=\"https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2017/seminars/08-linear_models/496/pic/imagemap.jpg\" width=\"80%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pixelspace\n",
+    "<img src=\"https://raw.githubusercontent.com/shevkunov/ml-mipt-part1/master/2017/seminars/08-linear_models/496/pic/pixelspace.jpeg\" width=\"80%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\"> SVM Realization </h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting cvxopt\n",
+      "  Using cached cvxopt-1.1.9-cp36-cp36m-manylinux1_x86_64.whl\n",
+      "Installing collected packages: cvxopt\n",
+      "Successfully installed cvxopt-1.1.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip3 install cvxopt  # Convex optimization package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "from itertools import product #, izip\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import datasets\n",
+    "from sklearn.svm import SVC, LinearSVC\n",
+    "\n",
+    "# import time\n",
+    "from cvxopt import matrix, solvers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len(X) = 10000, len(y) = 10000\n",
+      "len(X_train) =  8000\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.make_classification(n_samples=10000, n_features=20, n_classes=2, n_informative=20, n_redundant=0,\n",
+    "                                    random_state=42)\n",
+    "\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2,\n",
+    "                                                    random_state=42)\n",
+    "\n",
+    "print(\"len(X) = {}, len(y) = {}\".format(len(X), len(y)))\n",
+    "print(\"len(X_train) = \", len(X_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8000,)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore SVM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### QP-solver (cvxopt)\n",
+    "\n",
+    "* [Библиотека CVXOPT](http://cvxopt.org/)\n",
+    "* [Документация библиотеки](http://cvxopt.org/documentation/index.html)\n",
+    "* [Разреженные и плотные матрицы](http://abel.ee.ucla.edu/cvxopt/userguide/matrices.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def smo_svm(X, Y, C = 1.0, kernl = lambda a, b : np.dot(a.T, b), max_passes = 100, tol = 1e-12):\n",
+    "    lambd = np.zeros(len(X))\n",
+    "    b = 0.0\n",
+    "    \n",
+    "    passes = 0\n",
+    "    iters = 0\n",
+    "    while passes < max_passes:\n",
+    "        \n",
+    "        if iters > 10000:\n",
+    "            print(\"10000 iters!!!\")\n",
+    "            break\n",
+    "            \n",
+    "        num_changed_lambds = 0\n",
+    "        \n",
+    "        # for objects in Learning Sample\n",
+    "        for i in range(len(X) - 1):\n",
+    "\n",
+    "            Ei = svm_func(X[i,:], X, Y, lambd, b) - Y[i]\n",
+    "            \n",
+    "            if Y[i] * Ei < -tol and lambd[i] < C or Y[i] * Ei > tol and lambd[i] > 0.0:\n",
+    "                j = np.random.randint(i + 1, len(X))\n",
+    "                \n",
+    "                # print(\"optimizing %d %d\" % (i, j))\n",
+    "                \n",
+    "                Ej = svm_func(X[j,:], X, Y, lambd, b) - Y[j]\n",
+    "                lambd_i_old = lambd[i]\n",
+    "                lambd_j_old = lambd[j]\n",
+    "                if (Y[i] != Y[j]):\n",
+    "                    L = max(0,lambd[j] - lambd[i])\n",
+    "                    H = min(C, C + lambd[j] - lambd[i])\n",
+    "                else:\n",
+    "                    L = max(0,lambd[i] + lambd[j] - C)\n",
+    "                    H = min(C,lambd[i] + lambd[j])\n",
+    "                    \n",
+    "                if (L == H):\n",
+    "                    continue\n",
+    "                    \n",
+    "                nu = 2 * kernl(X[i,:], X[j,:]) - kernl(X[i,:], X[i,:]) - kernl(X[j,:], X[j,:])\n",
+    "                \n",
+    "                if nu >= 0.0:\n",
+    "                    continue\n",
+    "                \n",
+    "                lambd[j] = lambd[j] - (Y[j] * (Ei - Ej)) / (nu)\n",
+    "                \n",
+    "                if lambd[j] > H:\n",
+    "                    lambd[j] = H\n",
+    "                \n",
+    "                if lambd[j] < L:\n",
+    "                    lambd[j] = L\n",
+    "                    \n",
+    "                if abs(lambd[j] - lambd_j_old) < 1e-7:\n",
+    "                    continue\n",
+    "                    \n",
+    "                lambd[i] = lambd[i] + Y[i] * Y[j] * (lambd_j_old - lambd[j])\n",
+    "                \n",
+    "                b1 = b - Ei - Y[i] * (lambd[i] - lambd_i_old) * kernl(X[i,:], X[i,:])\\\n",
+    "                    - Y[j] * (lambd[j] - lambd_j_old) * kernl(X[i,:], X[j,:])\n",
+    "\n",
+    "                b2 = b - Ej - Y[i] * (lambd[i] - lambd_i_old) * kernl(X[i,:], X[i,:])\\\n",
+    "                    - Y[j] * (lambd[j] - lambd_j_old) * kernl(X[j,:], X[j,:])\n",
+    "                    \n",
+    "                if 0.0 < lambd[i] and lambd[i] < C:\n",
+    "                    b = b1\n",
+    "                elif 0.0 < lambd[j] and lambd[j] < C:\n",
+    "                    b = b2\n",
+    "                else:\n",
+    "                    b = (b1 + b2) / 2\n",
+    "                    \n",
+    "                num_changed_lambds = num_changed_lambds + 1\n",
+    "            \n",
+    "            iters += 1\n",
+    "            if iters % 10 == 0:\n",
+    "                print(\"%d iters done\" % iters)\n",
+    "            if num_changed_lambds == 0:\n",
+    "                passes += 1\n",
+    "            else:\n",
+    "                passes = 0\n",
+    "            \n",
+    "\n",
+    "    return lambd, b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def svm_func(x, X, Y, lambd, b, ind = None, kernl = lambda a, b : np.dot(a.T, b)):\n",
+    "    if ind is None:\n",
+    "        ind = range(len(X));\n",
+    "    res = 0.0\n",
+    "    for i in range(len(lambd)):\n",
+    "        res += lambd[i] * Y[ind[i]] * kernl(X[ind[i],:], x)\n",
+    "    return res + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The real_SVM\n",
+    "def the_svm(X, Y, C = 1.0, kernl = lambda a , b : np.dot(a.T, b)):\n",
+    "\n",
+    "    n_samples, n_features = X.shape\n",
+    "\n",
+    "    M = np.zeros((n_samples, n_samples))\n",
+    "\n",
+    "    for i in range(len(X)):\n",
+    "        for j in range(len(X)):\n",
+    "            M[i, j] = kernl(X[i,:], X[j,:]) * Y[i] * Y[j]\n",
+    "    \n",
+    "    P = cvxopt.matrix(M)\n",
+    "    q = cvxopt.matrix(np.ones(n_samples) * -1)\n",
+    "    A = cvxopt.matrix(Y, (1,n_samples))\n",
+    "    b = cvxopt.matrix(0.0)\n",
+    "\n",
+    "    tmp1 = np.diag(np.ones(n_samples) * -1)\n",
+    "    tmp2 = np.identity(n_samples)\n",
+    "    G = cvxopt.matrix(np.vstack((tmp1, tmp2)))\n",
+    "    tmp1 = np.zeros(n_samples)\n",
+    "    tmp2 = np.ones(n_samples) * C\n",
+    "    h = cvxopt.matrix(np.hstack((tmp1, tmp2)))\n",
+    "\n",
+    "    solution = cvxopt.solvers.qp(P, q, G, h, A, b)\n",
+    "    lambd = np.ravel(solution['x'])\n",
+    "\n",
+    "    sv = lambd > 1e-5\n",
+    "    ind = np.arange(len(lambd))[sv]\n",
+    "    lambd = lambd[sv]\n",
+    "    svec = X[sv]\n",
+    "    svec_y = Y[sv]\n",
+    "\n",
+    "    b = 0.0\n",
+    "    for n in range(len(lambd)):\n",
+    "        b += sv_y[n]\n",
+    "        b -= np.sum(lambd * sv_y * K[ind[n],sv])\n",
+    "    b /= len(lambd)\n",
+    "    \n",
+    "    return lambd, b, sv, sv_y, ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.4]])\n",
+    "y = np.array([1.0, -1.0, 1.0, -1.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 iters done\n",
+      "20 iters done\n",
+      "30 iters done\n",
+      "40 iters done\n",
+      "50 iters done\n",
+      "60 iters done\n",
+      "70 iters done\n",
+      "80 iters done\n",
+      "90 iters done\n",
+      "100 iters done\n",
+      "110 iters done\n",
+      "120 iters done\n",
+      "130 iters done\n",
+      "140 iters done\n",
+      "150 iters done\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.29588265, 0.78277329, 1.70411739, 1.21722671]), 1.0)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambd, b = smo_svm(x, y, C=100.0)\n",
+    "lambd, b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 0.] 1.0\n",
+      "[1. 0.] -1.0\n",
+      "[0. 1.] 1.0\n",
+      "[1.  1.4] -1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f17822387f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = 10\n",
+    "xx = np.linspace(-2, 2, n)\n",
+    "yy = np.linspace(-2, 2, n)\n",
+    "XX, YY = np.meshgrid(xx, yy)\n",
+    "\n",
+    "F = np.zeros_like(XX)\n",
+    "for i in range(len(xx)):\n",
+    "    for j in range(len(xx)):\n",
+    "        F[j, i] = svm_func(np.array([xx[i], yy[j]]), x, y, lambd, b)\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "contourf(xx, yy, F, 8, alpha=.75, cmap=cm.hot)\n",
+    "clabel(contour(xx, yy, F, 8, colors='black'), inline=1, fontsize=10)\n",
+    "for i in range(len(x)):\n",
+    "    if y[i] == 1.0:\n",
+    "        plot(x[i,0], x[i,1], 'bo')\n",
+    "        print(x[i,:], y[i])\n",
+    "    else:\n",
+    "        plot(x[i, 0], x[i,1], 'go')\n",
+    "        print(x[i,:], y[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.randn(20,2)\n",
+    "x[10:,0] += 3\n",
+    "y = np.hstack((np.ones(10), np.ones(10) * -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 iters done\n",
+      "20 iters done\n",
+      "30 iters done\n",
+      "40 iters done\n",
+      "50 iters done\n",
+      "60 iters done\n",
+      "70 iters done\n",
+      "80 iters done\n",
+      "90 iters done\n",
+      "100 iters done\n",
+      "110 iters done\n",
+      "120 iters done\n",
+      "130 iters done\n",
+      "140 iters done\n",
+      "150 iters done\n",
+      "160 iters done\n",
+      "170 iters done\n",
+      "180 iters done\n",
+      "190 iters done\n",
+      "200 iters done\n",
+      "210 iters done\n",
+      "220 iters done\n",
+      "230 iters done\n",
+      "240 iters done\n",
+      "250 iters done\n",
+      "260 iters done\n",
+      "270 iters done\n",
+      "280 iters done\n",
+      "290 iters done\n",
+      "300 iters done\n",
+      "310 iters done\n",
+      "320 iters done\n",
+      "330 iters done\n",
+      "340 iters done\n",
+      "350 iters done\n",
+      "360 iters done\n",
+      "370 iters done\n",
+      "380 iters done\n",
+      "390 iters done\n",
+      "400 iters done\n",
+      "410 iters done\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0.00000000e+00,  0.00000000e+00, -1.38777878e-17,  2.64223129e-01,\n",
+       "         0.00000000e+00,  0.00000000e+00,  5.84666925e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  1.24699303e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  4.86389934e+00,  0.00000000e+00]),\n",
+       " 5.430591994773475)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambd, b = smo_svm(x, y, C=10.0)\n",
+    "lambd, b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f178223f860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = 10\n",
+    "xx = np.linspace(np.min(x[:,0])-0.3, np.max(x[:,0]) + 0.3, n)\n",
+    "yy = np.linspace(np.min(x[:,1])-0.3, np.max(x[:,1]) + 0.3, n)\n",
+    "XX, YY = np.meshgrid(xx, yy)\n",
+    "\n",
+    "F = np.zeros_like(XX)\n",
+    "for i in range(len(xx)):\n",
+    "    for j in range(len(xx)):\n",
+    "        F[j,i] = svm_func(np.array([xx[i], yy[j]]), x, y, lambd, b)\n",
+    "\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "contourf(xx, yy, F, 8, alpha=.75, cmap=cm.hot)\n",
+    "clabel(contour(xx, yy, F, 8, colors='black'), inline=1, fontsize=10)\n",
+    "for i in range(len(x)):\n",
+    "    if y[i] == 1.0:\n",
+    "        plot(x[i,0], x[i,1], 'bo')\n",
+    "    else:\n",
+    "        plot(x[i, 0], x[i,1], 'go')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.randn(20,2)\n",
+    "x[10:,0] += 2\n",
+    "x[10:,:] *= 1\n",
+    "y = np.hstack((np.ones(10), np.ones(10) * -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def polynomial_kernel(x, y, p=2):\n",
+    "    return (1 + np.dot(x, y)) ** p\n",
+    "\n",
+    "def gaussian_kernel(x, y, gamma=0.1):\n",
+    "    return (np.exp(-gamma*np.dot(x-y, x-y)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 iters done\n",
+      "20 iters done\n",
+      "30 iters done\n",
+      "40 iters done\n",
+      "50 iters done\n",
+      "60 iters done\n",
+      "70 iters done\n",
+      "80 iters done\n",
+      "90 iters done\n",
+      "100 iters done\n",
+      "110 iters done\n",
+      "120 iters done\n",
+      "130 iters done\n",
+      "140 iters done\n",
+      "150 iters done\n",
+      "160 iters done\n",
+      "170 iters done\n",
+      "180 iters done\n",
+      "190 iters done\n",
+      "200 iters done\n",
+      "210 iters done\n",
+      "220 iters done\n",
+      "230 iters done\n",
+      "240 iters done\n",
+      "250 iters done\n",
+      "260 iters done\n",
+      "270 iters done\n",
+      "280 iters done\n",
+      "290 iters done\n",
+      "300 iters done\n",
+      "310 iters done\n",
+      "320 iters done\n",
+      "330 iters done\n",
+      "340 iters done\n",
+      "350 iters done\n",
+      "360 iters done\n",
+      "370 iters done\n",
+      "380 iters done\n",
+      "390 iters done\n",
+      "400 iters done\n",
+      "410 iters done\n",
+      "420 iters done\n",
+      "430 iters done\n",
+      "440 iters done\n",
+      "450 iters done\n",
+      "460 iters done\n",
+      "470 iters done\n",
+      "480 iters done\n",
+      "490 iters done\n",
+      "500 iters done\n",
+      "510 iters done\n",
+      "520 iters done\n",
+      "530 iters done\n",
+      "540 iters done\n",
+      "550 iters done\n",
+      "560 iters done\n",
+      "570 iters done\n",
+      "580 iters done\n",
+      "590 iters done\n",
+      "600 iters done\n",
+      "610 iters done\n",
+      "620 iters done\n",
+      "630 iters done\n",
+      "640 iters done\n",
+      "650 iters done\n",
+      "660 iters done\n",
+      "670 iters done\n",
+      "680 iters done\n",
+      "690 iters done\n",
+      "700 iters done\n",
+      "710 iters done\n",
+      "720 iters done\n",
+      "730 iters done\n",
+      "740 iters done\n",
+      "750 iters done\n",
+      "760 iters done\n",
+      "770 iters done\n",
+      "780 iters done\n",
+      "790 iters done\n",
+      "800 iters done\n",
+      "810 iters done\n",
+      "820 iters done\n",
+      "830 iters done\n",
+      "840 iters done\n",
+      "850 iters done\n",
+      "860 iters done\n",
+      "870 iters done\n",
+      "880 iters done\n",
+      "890 iters done\n",
+      "900 iters done\n",
+      "910 iters done\n",
+      "920 iters done\n",
+      "930 iters done\n",
+      "940 iters done\n",
+      "950 iters done\n",
+      "960 iters done\n",
+      "970 iters done\n",
+      "980 iters done\n",
+      "990 iters done\n",
+      "1000 iters done\n",
+      "1010 iters done\n",
+      "1020 iters done\n",
+      "1030 iters done\n",
+      "1040 iters done\n",
+      "1050 iters done\n",
+      "1060 iters done\n",
+      "1070 iters done\n",
+      "1080 iters done\n",
+      "1090 iters done\n",
+      "1100 iters done\n",
+      "1110 iters done\n",
+      "1120 iters done\n",
+      "1130 iters done\n",
+      "1140 iters done\n",
+      "1150 iters done\n",
+      "1160 iters done\n",
+      "1170 iters done\n",
+      "1180 iters done\n",
+      "1190 iters done\n",
+      "1200 iters done\n",
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+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.00000000e+01, 2.49800181e-16, 0.00000000e+00, 8.62379338e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 1.00000000e+01, 0.00000000e+00, 6.46810011e+00,\n",
+       "        1.00000000e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
+       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+01, 2.15569327e+00]),\n",
+       " 2.895924148545535)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Learn Kernel SVM\n",
+    "lambd, b = smo_svm(x, y, C=10.0, kernl=polynomial_kernel)\n",
+    "lambd, b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RwdOBfkxqGUSEizI7STYVNWaJQ/oydpeUsqdEz95CHaXyH0mSj1rFA97eF5Mkd3einJ2xERfdFE8kTYIgNHlms5lPPtnApEnz0enKad06jEWLOnHnnUGWDq1JOngwh379viAjo5L77nbny89i8GumzMX9p89U8NjAwySnStwVr2XN5FiaeyvnuaiuNTP5s3Te25SDCnj9nmCm3BuMnY1yB5C1JokVxy4w54cU0iokbFUwPMifieFBhDmLMsWbkd5o4jedvq7c7kCJnpp694dp1Nzj06xuJinY0VGszxP+H5E0CYLQpB08eJKRI6dw6NA53Nyc+OCDrowc2QEbG1E68U/JsszChb/zyivbMZlkpk4MZurEEDQaZQ4eVq65wIgXkqisglf6BDJnaBi2CkoWDp8t48lZRzmTJxHRTM2Knq25vYVyW2TXmCQ+O5LHWz+mkFUpY6eGkcEBTGgZRJCjWPR/M8mrrqkrtdtdoueEoZzLK5JUXNxEtv56JN8G2ERWsD4iaRIEoUkqKSll8uQFLFmyDlmWGTQogXfe6YK/v+hO9W/o9dUMG/YVGzacxsfHnlWfRvHg/Z6WDssiqqvNvDQxlcVLc3FzhY1T43i0o4+lw2o0RpPE7NVZzFqVgckMY25vzlsPhuFkp8wLEVVGM58cymPuz6mcr5JxUMPY0OaMCwuiuaMYbFuaLMucq6iq2x9pV14BafU2kbUH2mi1FzeS1WpprdX+6+5pgrKJV40gCE2KJEksX76F8ePnUlRkIDY2iI8+eoB77w21dGhN1tGjufTtu4LU1Ao63qllzbJYAvyVORjMyKyiz8BDHD5pJiFGzYbx7YkIVM76lDOZFQyedYRDGWYCPVQs65lAp3Blrd+6rKLWzOKDubzzcxr5NTJOGng1rAWvhAXi56DM98fNwCRJHDdUsLuk9OJMUkExhfU2kXVXqbjb05O2Wi23uLsT5+qKnViPJNwAImkSBKHJOH48iZEjZ7Jv3zGcnR2YO7czY8fejp2dOJX9G7Iss2TJQV588RtqaiReeyWIGa+HYKOgErT6vvmumCeHnUKnlxnS2Y+PxkTgaK+M2RVJklnwVQ6vfZpKtREGt2nGB11b4u6ovDbKZTUmFv2ey7u/plNUK+NqA6+1DOKl0EB87JXZat+SKs1mftcZ6srtfivWUV5vE9kAjYqHfX3rSu0ixCayQgMRIw1BEG56BkM5U6d+yMKFX2I2Szz2WCzvvdeVFgpeX3G9yspqGDFiC6tXn8TT045Nq6Lo2tnL0mFZhNksM3VWOrPfzcLBHpa+EsUzXfwtHVajybhQxZB3k/nleCk+riq+7BNL71jllCNepq828eH+HN7bnUlJrYzWFqZGBDM2NBBPO+Ulj5ZSXGtk76UEaU+JnsOlBoz17o+6tIns5XK7AAcH0bRBaBQiaRIE4aYlyzJr1mzn5ZfncOGCjpYtA1i48AE6d46wdGhN2smTF+jTZzlnz5ZzRwc31i6PpUWgMhey5xfU8vgzifz0aylhQWo2vtaWNi2VsS5OlmWWfX+BsR8lU1YNPaO9+LhnFL4uyppNKak08sFvOXywNxO9ETztVMyMCmFMSCBaWzFMamhZVdXsLtazu6SUX3PzSbpqE9kYV9e6WaS2Wi2edsp6fQo3D3E2sIA1a9yYPdubxER7YmNrmDSpiAEDDJYOSxBuKmfOpDJq1Cx+/vkADg52zJhxP+PG3YWDg7ji+2/Jssznnx9h1Kivqa6WeGVMIHPeCMPWVpnleHv2ldLvyePkFcj0vNOLZeOicXdRxuvrQkkNz753lq37i3FzhGWPRjO4TTNFXbEvqqhl/r4cFu7LoswEPvYq3ooOZWRIgGgU0EAkWSaxrII9Oj27iy9uIptTbxNZJxXc5u7OLe7udZvIOolNZIWbhDgrNLI1a9wYODCw7uuTJx0ufZ0jEidBACoqKpk5czHz5i3DZDLTrVsUCxY8QmioMhej3ygVFbWMHLmVFSuO4e5uy9plsfR4xNvSYVmELMvM/zCHCVNTAZg7PIxX+7ZQTMKwcXchI+YnUlwuc3+YO5/3jibIXTkzjfnltby7J5tF+7OpNEMzexXTI8MYERSAs9iq4IaqlSQO68vYXaxnj07PnsISdPWaNnipVdx/aRPZW7RaolxcsBVNG4SblEiaGtns2X8+SJkzx1skTYKiybLM5s0/8OKLb5KdXUhwsC8LFjxAjx4xlg6tyUtMLKBv32UkJpbR4VYP1n4eSUiwMjfh1OtNDHk+ic1bi/DzUbF2YmvuTnC3dFiNorTcyJiFKaz8MR8HW1jwSEtGdWiOWq2MZFGWZZYezuPlb85SZoLmjireCgtnWJA/jmI244YoM5n4TWe42NWuuJTfS/RU17s/RKOmYzNf2l5ajxTq5KSYixVC0yeSpkaWmPjnbUr/1/cFQQlSUjIZM2Y23323B1tbGyZPvodJk+7GyUnUrl+vFSuO8vzzW6isNDN2ZHPmzgzHzk6ZV3KPnyynz+NHSMmQuLe1O6snxeDnqYxz745DJTwz9yTndTK3hmhY0aMd0T7Olg6r0eToqxn2VTLfp+jQ2sLC+AiGtfDHXqPM98KNllVVzdspWSzNzKXm0vdUQISz88VZJHd32mq1NBObyApNmEiaGllsbA0nT/7/MojY2Jo/OVoQrFtVVTVvvfUpb7/9KTU1Rh58MJyFC7sRGanMsrEbqarKyJgx37B06WHc3GzYsDKOx3oqryPaZctW5vH8i8lU18DEAUHMfDoEGwUMmCuqzIz/JJVFW3Ox0cDMTiFM7BikiJ8dLs4urTiWz9itSeiN0NnHg08Togh0VE45YkNKr6xiTkoWy7LyMHJxJqlT80DaabW0cXPDzVYZawQFZRBJUyObNKnoijVNl732WpEFohEEy/nmm1954YUZpKVdICDAi/fff4A+feJEqcYNkJxcSN8+yzh5ykC7tu6sWxZFeJgyy/GqqsyMefUcS1dcwN1NxbrJcXS/QxlJ+W+Jega/eZyUAom4ADUreralXYAyOgMC5JXVMGLLWbYmF+NiAx+3imRYkL84x9wA58ormZ2SxRc5FzAD4TZqhrSMpKuvr1iTJFgtkTQ1sovrlnKYM+eP7nmvvSa65wnKkZmZy9ixc9iy5Sc0GjWvvPIfpk27D1dXUbZxI6xefYJnn91MebmJkcMDmDc7HAcHZa7XSE2ros/AQxxLNNMuXsP68e0J87f+5LGmVuKNLzJ4e20WMvDqf1ows1MIDrbKeB3IsszqEwWM3pKEzihzv5c7n7WOJthJzC5drzNlFbyZksnq8wVIXNwzaUhEFJ19fLARyZJg5UTSZAEDBhhEkiQoTm1tLfPmLWfmzP9SVVVDx47BLFrUnfj4ZpYOzSpUVxt56aVvWbz4IC4uNqxZFkv/x3wtHZbFfLW1kKefO43eAM8+4s8HI1viYGf9ScOJtHKenHmEEzkSod5qlvVM4O4QZTS6ACgor+X5rWfZlFiEkwY+io/gueAA1GJ26bqcMpQzKyWTdbmFyECcrZqnI6J50McHjXhuBYUQSZMgCA3uxx/3M2rUNJKTc/D1dWfJkm488URrUSZzg6SkFNOv7zKOHisloZWW9cujiIxwsnRYFmEySUyans47H2Tj6ADLx0cz+EE/S4fV4MxmmXfXZzNlWRpGMzzb3p93u4Tjaq+cj/kNpwp4fvMZimplOnpq+bx1NOHO1j+z2JCO6suYdS6TTRcuLiFobathSGQ093l7i0RUUBzlnE0FQWh058/n88or77B27beo1WpGj76NmTM74e4uBjI3ysaNp3nmmQ0YDCaGPeXPgnda4uho/TMqfybvQg39n0pk9z49EaFqNr7WjlahLpYOq8GlnK/kqblJ7Es04KdVsbRHPF0jvSwdVqMprjQyautZ1p4qxEEN78WG80JooBjUX4eDpQZmnstka34xAK1cXRkRHMzdXl7iYpegWCJpEgThhjMajXz44SqmTVtIeXkVt90WyKJF3WnXLsDSoVmNmhoT48Z9z4cf7sfJScMXn0TzxADrn1H5X37ZrWPAUyfIL5Tp09GHpa9E4eZs3R9xsiyzeFsury4+R2Ut9I/34aPukXg5Kadj2ZYzRYzYeJr8Gpk7PNxY1jqaSBdlzrLeCPtK9Mw8l8l3hSUAtHFz47mQEO708BDJkqB41v2JIghCo9u9+zCjRk3l5MkMPD1d+eSTnjzzTDvUYpHwDZOerqN/v885eEhHbKwr65dHExutnD136pMkmbnvZTF5RjpqNbz3fDhjewda/QDvfFENQ+cl8f0hHR5OKpb2jGZAgnLWB+qqjIz9JoUvjudjr4a5MWG8HNZCrK/5l34tLmXmuQx+LCoF4FZ3d0YEB9PB3d3q30uC8HeJpEkQhBuioKCY8ePnsXz5FgCGDbuFOXMexNtbmYP5hrJlyxmefno9paVGnnq8GR/Nj8TZWZnleDqdkadGJLH122Ka+6lYN7ENd8ZpLR1Wg5JlmS9/KmD0giRKK2W6RHiytFcUAW7K6T65/Wwxw9efIrda5lYPDcsS2hHrKs4z/5Qsy/xUXMqMsxnsKtEDcLuHByOCg2nvrpzmIYLwd4mkSRCE67Z69TeMHDmD0tJy2rYNZ9GiTtx+ewtLh2VVZFlm8uQfmDNnFw4Oaj5bFMWQJ/0tHZbFJCZV0O3Rw6RnSzzQzoNVE2Pw9bCzdFgNSl9h4tn3kln3ayHO9rC4RyTPtlfOvkOVtWZe/DaFTw7lYauCN6NCGR/eQrS6/ocuJ0tTk9PZp7vYyfcuT09GBAfTRmvdFx0E4XqIpEkQhH+turqGl156m8WL1+Li4siHHz7C8893QKMRg5gbyWg0M2zYV6xYcYyICBc2fhFNqzjrb3Dwv+zeW0qPfscpNci8PiiY6U+GoNFYd+JwMNlA/+nHSC+SuDPIjRWPxRDuqZyGKqfzK+i34jCJBonWWjUrWrcjwU2574F/a3dxKVOS0/n10szSvV5ePBccTJybm4UjE4SbX4MlTSqVqiUwDrgdiAd2y7J87188JgRI/5O71sqyPOAGhygIwnVITc2ib9/RHD2aSuvWYaxf34OICOV07GosFRW19O27hm+/PcdtHTzZtjYab2/rnlG5lo1bChk09DRmszLaicuyzAebcxj/cSomCSbfE8T0+0KwUciFCVmWWXo4jxe2naXKDGNCmvNOTDj2Cvn5b5QDOgNTzqazo1AHXJxZGh0SIpIlQfgHGnKmKQ7oCuwH/ukn/KvA3npfF92ooARBuH6bNu1kyJBJGAyVDBt2CwsWPIKjo3I6djWWoqIKHum6hAMHdTz8kCfrV8Qpdv0SwMIlObwwLgVnJ/h6egIPtfe0dEgNqsRgZMi7SXz9WzG+ripWPtqKB1ta989cn6HaxIivz7LmZAHutipWtYmlt7+PpcNqUo7py5h6NqOudfht7u6MDg0VZXiC8C80ZNK0VZblLQAqlWoD4P0PHpssy/L+hglLEIR/q7a2lgkT5vP++1/g5GTP8uWPMnhwW0uHZZUyM0vp3HkJycnlDB7YjE8/isLWVplX1yVJZtL0NN5+L5tmPiq2v9GOdhGulg6rQf2WqKf/G8fILpG5P8ydlX1i8HdVTrOHw+fL6P/FUVIrJG53d2NNu1iCnRwsHVaTcbqsgmnJ6Wy8tCltO62W0SEh3OrhYeHIBKHparCkSZZlqaH+bUEQGl9WVi79+49h//4kYmJasH59T+LilNPiuDGdPHmBLl2WkptbzfgXW/DWjDDFLPa/Wm2txNBRyaxck09kmJrvpt9KqL91r+X5YucFhs1PwmSGN+4PYfI9wWjUyvj9y7LMh/vP8+q3KRhlmBDegplRodiKZg9/y7nySqafzWB1bgEyEO/qyujQULHPkiDcADdrI4jPVSqVJ1AArAYmy7JcZeGYBEGxtm/fxZNPjqekpIxBgxJYvLgHLi7KuerdmHbtyqBHjxXo9UbmzwnnpdHK7UJoMJh47InT/PCzjttj3Ng6Mx5vrfWu55Ikmdc/T2fOmizcnVSs79eKB8KVU45XUmnkmc1JbEkqxsdexRetW9HZVzk///XIqKxixrlMVmRfwAy0slXzbFQs93h5iWRJEG6Qmy1pqgE+AnZzt8atAAAgAElEQVQABuBeYAIQDvT8qwerVKrpwDQAf1H3LAjXzWQyMXXqQubM+QR7e1uWLOnB8OHtxYdwA9m8OZGBA9ciSTKrlsbweD/lzuTlXajh4Z6/czxRoscdXqyeFIuTg/Wu56qoMjN47hk27Smipa+abQPaE+XjZOmwGs3eTD0DVx0ju0rmfi93VraNwd9BXJj5KzlV1byZksXSzFyMQLSNmuGR0Tzg44NanKcF4Ya6qZImWZbzgNH1vvWLSqXKBxapVKo2siwf+4vHTwemA7RvHy83VJyCoAR5eYUMHDiOX389SHi4P+vX96Jt2wBLh2W1liw5yMiRX+PoqGHTqnge6qTcK+xJyRV06XWIzByZEY/4s3BMhFV3iztfVEOPCQc4kmXm3lB3NgyIw8tJGY1VJEnm7d1ZTPkxHVmGGZEhTIoIRiMG/Nd0obqGt1KzWJx+nhog3EbNsIgoHvb1Fc+dIDSQmypp+h82AIuAdsA1kyZBEG6Mn37az8CBr1BQUMpjj8WydGlvtFqxCLshyLLMjBk/M336z/j42LN9Qxzt2ym3DfDe3/T06HeMklKZWU+HMunxIKue2TyUbKDH5KPk6WWG3eLPR90isLOx3gSxvvzyWp7ccIadqToCHFSsbtuau73cLR3WTa2otpa5KdksTM+mSoYgjYphLSPp3qyZ2ORXEBpYU0ia5Kv+FgShgUiSxJtvLmHatI+wsdHwwQddGTPmdqsetFqS2SwxatQ2liw5SGioE99viieipXJKsq721dZCBg45jdEEn78axdOd/S0dUoPasKuAwW8nUm2EeV3CeenOQMW8135ILeGJ1SfJr5F5xNeTZW2i8baz3vVq16vUaGReWg7vp2RSLkOARsWrYS3p7e8vmmQIQiNpCklTn0t/H7ZoFIJg5QoLS3jiiQns2LGPoCAf1q3rzW23KbcJQUOrrjYyaNAGNm1KpE1rLd9ujMWvmXLXcCz65DxjXj2HowNsntaKLrda70bJsiwz+8ssXl+Wjos9bHk8nu7R/2RXjqbLZJaY/nMGs3/NQqOCebHhvBgaKNbf/A9lJhMfpOfw7tkM9DL4qlWMCg+nr78/9hrrXeMnCDejBkuaVCqVExc3twVoDripVKrLCdB2WZYrVSpVCvCrLMtDLz1mOuDKxY1tDcDdwDhgkyzLJxoqVkFQuj17DjNgwEucP1/MI49Esnz5Y3h5KXfGo6GVllbRs+eX7NqVwX13u/PV6njc3JrCNawbT5ZlJr+Rzpx5Wfh6q/hmelvaR1lveWJ1rZnh88+y8sd8gjxVbB14Cwl+LpYOq1Hk6KsZuP4MezL1hDqrWdOmDR08rPd3fT0qTGY+yjjP22fTKZFkPNUqXg4NpX/z5jiJZEkQLKIhP6V9gfVXfe/y16FAxqX/v/67Pwl4FRgGOAJZwDvAmw0YpyAolizLzJu3jIkT5wPw1lsPMm7cXahFuUeDyc010KXzEk6eMtC3tw9ffBKDvb0yn2+jUWLYqGRWrM6nZYia72fcSpgV78FUoKul9/RT7Es0cHuYhs19OuCnkA1rtyYV8fT605TUyvT19+HjhEjcbZXR7OKfqDabWZKZx+ykVAokGa0KRoeE8ERgIM42yrywIgg3i4bc3DYDuOZ8uyzLIVd9vQZY01AxCYLwB51Oz1NPTWLr1l/w9/dkzZre3H13iKXDsmrJyYV07vwpmZmVjHo2gA/mRqDRKLMsqazMRJ8nT7PjRx0d2mjYNvk2fNytd03LqfRyuk86QkaRxMBWvnzWOwoHW+ufMag1SUzYkcb7v+Vgr4bFrSJ5NshfMWu3/q5aSeKz7DxmJqaQa5ZxUcGzwcEMDgxEK5JLQbgpiMsWgqBABw+epF+/sWRk5NOpUxhfftkXX19llAhZyu+/Z/PII8soLq5l1tRQJr1q3V3hruVCfg1dex7g6Gkz3W73Ys2kWJwdrTeB2P57MQNmnaSsGt64P4Qp9wYr4nefWlLFgGWHOKQzE+2qZm3bdiS4ifNMfSZJ4ovz+bxx6hyZZglHFQxp0YIhLVrgIRpjCMJNRSRNgqAgsizz0Udf8vLLczGZzEybdh9TptyLxor3wLkZfPvtWfr0+ZLqajOfLoxi6FPW3RXuWpLPVtKl5yEyciSGd/Vn0QvWuweTLMt8sDmHVxanYmcDa/vF0q+Vr6XDahRrTxYwfFMiZSZ4KrAZC+MjcBHlZXXMssya8wVMO5VMqknCHniieXOGBgXhba+Mkk1BaGrEGUwQFMJgKGf48GmsW/cdPj5aVq3qxYMPtrR0WFZvxYqjDB26GRsbFZu/jKfHI8rokvZnfvtdT/e+xyjWybwxOIQpT1jvjIvRJDF64Tk+/iYPP62KLQPa0iHQ+pseVBnNvLg9hY8P5eGsgRVtonky0M/SYd00JFlmU14hU04kkWSSsAX6BQQwPCgIPwexF54g3MxE0iQICnD8eBJ9+47h3Llc7rormDVr+tG8ufUP4Czt3Xf3MG7c93h42LJ1bTz/uUNr6ZAsZsu2IgY8fQqjCT59OYqhD1vvbJuuzEifGaf56VgpbVpo+Lr/rbRQwObQiQUV9F9+mFMGiQStmnVt2xPlIrpwwsVZx20FxUw+lshJo4QG6O3nx4jgYJo7Wm/zE0GwJiJpEgQrJssyn322idGjZ1FdXcuECR2ZNasTNjbWu37kZiBJEuPGfc/8+fsIDHTku03xxMU4Wzosi1nyWS4jXzqLgz18/UYrut5mvXswncuppNvEQ5zNl+gZ7cXKPjG42Fv3R60syyw7eoHRXydTaYaRwQHMiw3HQbTGRpZldhTqmHo2nQOlZaiAR3x9eT4khGAnkVAKQlNi3WdyQVCwiopKRo6cyYoVX+Ph4cL69f3o1i3K0mFZvdpaE0OGbObLL08QE+PK95viaBFo/bMMf0aWZabOymDW3Ex8vFR880ZbbrXiPZh+PqbjsWkn0FXKjL+rBXMeDEOtts7yw8vKakw8//VZVp0oQGsLG9rE8Zi/j6XDuin8UqRjytkM9pToAXjQx4eRISG0dFbuBRTh35FkmRKjES9bW6staW4KRNIkCFbozJlU+vQZTWJiFrfeGsm6dd0ICfGwdFhWr6yshj591rBjRwp3dHBj2/pWeHoqs12w0Sjx7JizLFt1gfBgNd+90Z6Wza33yvon23MZueAsKuDz3lE83c56yw8vO5ZXRr8VRzlXLnGbp4bVbdoT6iRKzX7T6ZmSnM6PRaUA3OvlxaiQEKJdXS0cmdAUVJvNpFZUcKa8nKTycpLLy0kyGKgG3FQqWnt40MrNjdZubsS7uuImWtI3GpE0CYKVWbVqGyNGTKOiopoXXridd97pjJ2deKs3tIKCch7p+jGHDuvo1sWLtctjcXJSZnlSebmJvoMT+W5nCe0TNHzz+m34elhn+2SzWWb8J6nM35iDl4uKTf1bc3eIu6XDalCyLPPR7+d55dsUaiUYF9aCN6NDsVX4ptiHS8uYejad7QUlANzp4cHo0FBauVnv7KpwfUqNRpLKy0kqK6tLkNIqK5HqHaMG/J2d8XZwoECnY3dJCbtLSurub6FW09bHh9ZubrRycyPC2Rkbhb8XG4oYSQmClaiurmHs2Dl8/PF6XF0dWb++P336xFs6LEVIT9fx0ENLSEmp4Jkn/ViyIBIbG2V+aOUX1PJIr985fNJM1w6erH09FhdH6/yoKas08ficM2zbX0y0n5ptA28l3NO6Z1p0VUaGbk5m85kivO1UrGgTz8O+1rtG7e84aShnanIGX+UXAdBeq2VMaCjt3K07eRb+PlmWyamuvjhrdOnP6ZISimT5iuMcgTCtlhYuLnV/ApycsK23PtBQW0u6wUC6wUCawUBWaSlf5+fzdX4+APZAnFZLazc3Ei4lUs1EG/sbwjo/yQRBgQYPfoH16/fQunUY69f3ICJC2QOZxnLsWB4PP/wZFy5UM+nVIGZNDVVszfm5lEo69zhEerbEM138WPJipNXuwZSZX033CQc4eV7ioZYerO0Xi7ujdZfJ/JalZ+CqY2RWytzjqWVV21iaOyp3MJZUXsH0sxmsyy1EBlq7uTE6NJTb3N0Vew4QwChJpFRU/FFaV17OGb2eyquO81KpaOXpeUWC5O3oiPovXjtudna09vamtffF7SskWeZCZSVp9RKpY3o9R/T6usd4q1S09fKqS6JiXV1xFI1a/jGRNAmCVchk0qT+eHvXMm9eFxytfPB2s/j55zR69vyC8nITC95pyZjnAi0dksX8ftBAtz5HKSqRmfpEMNMHh1jtwPG3RD29Xj9GQZnMqNsCeP/hllabHAJIksy7e7OZtDMNSYZpEcFMiQxBY6W/37+SWlHFjHMZrMzJRwLa2moYHh3LXZ6eVvuaF/6cwWgkuaLiivK61IoKTPWOUQOBajV+3t51yVGgiwtudjemZFmtUhHg7EyAszN3+V9cS1ltMpFRVvbHbFRJCTuLithZVFQXU5SLCwn1ZqOC/0bCpnQq+aqpQWvRvn28fOjQOkuHIQiNIPPS34ctGoXSrF9/iieeWI8sy6z8NIZ+j/paOiSL2fZtEf0Gn6KmFhaPjWR41wBLh9RgVv2Yz9B3z2CS4IOuEYy6rbmlQ2pQBeW1DN54hu9TdPg7qFjVJoH7vJXZVCarqpqZZzP5PDsPMxBnq2Z4ZAz3e3uLZMnKybLMhZqautK6y+V1+ZJ0xXH2QHM3t7rEKMjFhQBnZ+wtPKsjyzIlNTV1SVS6wUCWwYCx3jGuKhUJ7u51SVSCmxtahTSZaPXLL4dlWW7/V8eJmSZBaCJkWb7qgzmz3m2RMDWmjz76nTFjtuHiYsNXq+O4/x5lDiIBPvk8l+dePIu9HWx5I55ut3tbOqQGIUky01dkMHNVJm6O8HW/BB5q6WnpsBrUz2k6Bq0+QV61TBcfT5a3icbX3jobelxLbnUNs89l8klmLrVApI2a4ZHRPOTjI67MWyGjJJFeWVlXWpdcXk6iXk/ZVZMMWpWKWA8Pgi4lSC1cXGjm5HRTviZUKhVeDg54OTjQ3vfiBT6TJJFTXl6XRGUXFbFXp2OvTlf3uOZqNe18fOqSqEhnZ0U3fBFJkyA0AeXlFRiNJjw8tJe+I2aXLEGWZaZO/ZFZs36lWTN7vt0YT9vWymwjLEkyU2el8+Y7WXh7qtg2vS23xVhnl7DKajNPv5PE+l2FhPmo2TrgFmJ9rXevHbMkM+PnDGb+kolGBXNjwnglrMVNORhsSAU1tbydmsWitByqgVCNmuERkXRt1kyxpYnWpsJk4mxFBWfKyuqSpHPl5VfMwMDF5CGyXnldCxcXtHZ2TXqG0UatJsTNjZB63R3LamvJKCurS6QydTq25uez9VKTCTsgrl5JX+tLTSaa8vPwT4ikSRBuchMnzmfnzt8ID2/BqFEDufNOT2xtbRAJU+Mymcw8//xWPv30MOHhzuzYHE9YqHV3SvtfystNDH42ic1biwgPVrN9ensiA61zD6a84hp6TDzAoQwzHYO1bBoYh7ez9c62nDfU8Pj6RHZl6Al2UrGmbRtur7tYowwltUbeTctmQWoWFTK00KgYGh5BDz8/RV9lb8pkWaawtrZu5uhMeTmni4vJvaq8zhYIcHW9IjkKdHbGwUYZw2VXOztaeXnRyutiIylJlsmvrPyjrK+sjOMGA0cNhrrHeF5qMnF5NirO1RUnK20yoYxXgSA0UYmJKZw8eY4dOz5h48YdrFu3npMngxg9OuBPyvWEhlJVZWTAgHV8/XUS7dq6s31DLM18rXfgfC2ZWdX07HuA44kS97VxZ/2UOLzcrLPu/WhKGd0nHeG8Tubptn4s7hGJvRW3kv8muZin1p2iuFbmUT9vPk2IwsPOOn+3f0ZvNPFeWjbvpWRikMFPrWJsy5Y85u+PnUiWmgyzLJNZWVm39ii5vJzTpaXoryqvc1WpiHZ3vyJB8nNyQiN+13XUKhX+zs74Oztz56UmEzVmM5n1ZqOyiov5saiIH+s1mYhwdr5iNirkJi1b/KdE0iQIFrJmjRuzZ3uTmGhPbGwNkyYVMWCAAZ1Oj6urMzY2NuzefQRXV2e8vPQ8/ngCoaEZvP/+j3Tr1o2QEA+RODWCkpJKevRYxd69WTxwnwebVsXh6qrMU+e+/Xp6DzxGQZHMc90CWDCqJbZWmkR8tbeQQbNPU2WEtx8KY9xdLaz2vVZrkpj0Qxrz9uZgp4aP4iN4PjjAan/eq5WbTHyYcZ53kjPQyTLeahXjwsLoFxCAg5VeMbcWVWYz567qXpdcVkbNVcf5qdWEeXldkSB5KKis7Eay12iIdHcnst4+ZLrq6rqZqDSDgXS9nuSKCtbn5QHgDCR4eNQlUa1cXfG4Qd0DG5PonicIFrBmjRsDB17dnjqdyMh+REW5EBjYjEWLppKXV0jnzk/z9dfTCAnJobi4kvnz96LRqJkxo5NFYleSnBw9nTsvITGxjIF9fVm2OBo7O+tMEv7KspV5jBibjNkMC0ZGMLKHdXaNk2WZuWuzee2zNBxtYdVjcfSK9bF0WA0mXVfFgGWHOFBiJtJFzdq2bWmjVcY6vSqzmf9m5jInKY0iScZDpWJwSAiPN2+Ok0LKsZqS4traKzaHPVVUxHlJon6BnQ3gX7+0zsWFFs7OOCmkC9zNwiRJnK+oqNuEN7uwkJyrSiED1GraeHuTcCmRinJxsVj569/tnieSJkGwgISEME6edKj3HRMwDT8/B86f70337qO4447WPPZYHJs27aOkJIV58x6mttbEd9+dY/fuTKZNuw8XF+VuLNnQzpwpoHPnT8nOruLFUYHMmx2OWq28q5Jms8zEqWm8uyAbdzcV6ye34oF21tk1rqZWYsT7ySzfmU+gh4qvB7SjbYD1JhAbThUwbFMieiM80bwZi1pF4KqAZKHGLPFJVi5vnknlgiTjpoIngkN4IjBQET//zU6SZbKrqq7YHPa0TkfJVeNVJ6C5VktQvSTJ39kZG1Fed1OqMBovJlGXZqOydLorOhLaArH1SvpauboS4ODQKLOBouW4INzEEhOvTnZsgL0UFIxCrVYze3Y/Vq36mZ9+Sqdz50AmTLjA7t0ZdOwYgoODLQUFFSJhakC//ZZFt27LKSmp5e0ZYYx70XpLs67FYDDx+DOJfPN9CVHhara+3p4IK234UKSv5dE3TrP7pJ5bQzRs6dcBf1frfI9VG828/F0q/z2Qi5MGPm8dxVOBfop4je/X6Xl8/3HSzRLOKhgeFMRTLVooZj+am02N2UxKZeUV5XVJBgNVVx3no1KRcKm87nKS5NVIA2rhxnC2tSXey4v4S00mZFmmoKqqbm1UmsHAKYOB4wYDKy89xkOloo2nZ90mvHGurjhb8MKGSJoEwQL8/T8nJ2c58ADQBugOdMfH5zTgQuvWYRw//hOJiXo6dQpj0KAExo7dzrRp97F06WEiI70xmyU0GnFF7Ubbti2Zfv1WU1srsWxxNE8N8rN0SBaRll5F98cOknhO4qFbPFj7eizuLtY5sEzMrKDbxMOkF0n0i/dh2aPRONpa51qWpMIK+i8/zAm9RCutmrVtbiHG1Xrbp18myTLvpmYzOSkNM/BE8+YMDw7Gswmuq2iqSo3GK8rrksrLSauouKK8Tg34OTmRcFUHOxeR1FodlUpFMycnmjk5cYffxc/ZGrOZrLKyutmo7KIifi4u5ufiYuDi6yP8UpOJhEuzUWHOzo22BYBImgShEen1ZYwfPw939zxyciYBOcAs4D4gmnbtVrN3rwv/+U81HTuGsH37WSRJ5plnbsHV1Z6dO1O57bZAJk++16I/h7X6/PMjDB/+FXZ2arasieeRLl6WDskiftmto8+gExTrZMb2bs67I8KxsdIE/fuDJfSbeQJDFUy9N5hp94VYbRnmiqMXGLkliQozPBccwPzYcBwV0Oggv6aWwcfOsKNQh59axZutEujgodwNqRuaLMus8jjPipBsLrjV4FSiQf2LmbJTVx7nAIS6uV2RHAU4O2OngNek8OfsNRoi3N2JqN9koqambm1UmsFAll7PuYoKNl5qMuEEtHJ3r+vW18rNDe8GuhgikiZBaES2tjbcfntrliyZzpo1bsya1ZbExD2Ehx/ilVfsuXAhlx9+yCUh4T+EhnpQVWXi8OFcYmN96ds3nr594y39I1glWZZ5661dTJr0A56ednyzPo7bOyhrb5rLPv4sl1GvnL14+6VIhncNsHBEDWfhVzmMXZSCrQZW9Ynh8dbNLB1SgyivMTFq2zlWHMvHzQbWtYulb4CvpcNqFDsLS3ji4EkKJJm7PD15MzpazC41AFmW2afTsTw7myPNddTc98d9Fd5m6AOBzs60yvujg52Po6NVtKEWGpaHvT0ePj6087nYkMcsSeRWVJBWVka6wUBOQQG/l5bye2lp3WP81GraXmoy0crVlRhX1xuybYBImgShAZlMJmzq1d86OTnSu/fFrncDBpykc+cyunY9wObNfvj5uXLgQCRLlhxk9OhtdO7cktzcMlq3VmZ5WGORJImXXvqWBQv2ExTkxHcb44iJtv5ypauZTBIvv5bKh4vP4+WhYuPk1tzT2v2vH9gEmcwSYz9KYdHWXHxdVXw1oA13BFlnknw8r5z+XxwhuUyivYeGtW3aE+Zs/ZsyGyWJqckZvJ2ahQ3wang4TwYGikF6AzhYWsrC9HSO6PUA2A74Hwd2hN6HwhovMMEqadRqWri60sLVlXsCAiA6mkqjkYx6e0dl6nR8W1DAtwUFwMUmE9GurnWzUQlubgT+izVxImkShAYye/bHnD2bwUMP3UmvXp1wcnIEMrk466wDoLz8IJ6eEq6XFpx36BBITIwPCxb8xtdfJ/H22w+RkCCSpoZSU2Piqac2snbtKeLj3PhuUxzNA6xz8f+16HRG+j+dyM6fdMRFqtn6+q2E+lvnwLq03Ei/mYnsPKKjVXM1Wwd0INjd4a8f2MTIsszig7m89M05aiR4OSyQOdFhitikNaOyioFHzrC/1ECoRs3s1m2Id3OzdFhW57hez4fp6XVX+BO8vOgZEsKbvof/9Phcp8rGDE9QECdbW2I9PYn1vNjZVZZlCquq/tg3ymDgTFkZJ8vK4Px5ALT1mkz8XSJpEoQb7PDh0zz99GTato1hwICHeeWVd/DyqqVz51suH1G3Ke2ZM4XY2Wlwdrbj3LlizGaJ6GgfJk68WzR5aGAGQzWPPrqaH39Mo+OdWrasicfDQ3mLjc+eq6R7n0OcTZPodrsXqybG4OZsnR8NqblVdJtwkKQLEt2ivPiybwyu9tb3s5ZWGRn2VTIbE4vwtFOxoXUc3Zp5WzqsRrExr5ChR06jl+FhX1+mRkbiItqI31BnyspYmJ7OrpISAGI9POgZGkropcGnf4Uz510q/t/jAiqts/OmcPNRqVT4Ojnh6+TEbc0ull0bzWayysvrkqjsoiJ+LS7m10tNJv4OcSYRhBssNLQ5H3wwkfvv9wfgq68iqapKvOKYy1PC+/dnExPjw8yZP7Nhw2lmzuxEdLSPSJga2IULZXR9+GOOHiulVzdvvvwsBkdH5S0+3vlTCf2ePEmpQWZC/xa8OSQMjcY6y5d2nSjl0WnHKS6XeeU/gbz9UDgaK2z4cCDHQP8vjpFRKdHRU8uXbWMIdLS+mbSrVZnNvJyYyuLMXBxVMCMqil5+ymij3lhSKipYlJ7OzqIiACK0WnqFhl6xaB/g4awgPo098/8e3yUrqFHiFIQ/Y6vREK7VEq79oxRbX1NDelkZi06dusYj/yCSJkG4oTLx9IT77/enurqWZ56Zws6dqej1YTg62tKhQyAeHn+UPR06dJ59+7IZOrQd+/Y9i7OzWKDc0NLSSnjwwY9JS6vg2SH+fDQ/Ahsb5SWpiz45zwvjzqHRwPLx0Qx+0HrLQFfsvMCweUnIwCc9IxnW3vqaW8iyzPu/5TD+u1TMMrweEcy0iGBFbPR5rrySXnsOkWiSiLNV82abWwh3Vt66xIaSV13NB2lpbC8oQAZCXV3pGRpKjIfHnyalHQouXtn/LiiLXKdKAiqd6JIVVPd9QbhZaO3taWP/90vyRdIkCDdEZr3bF+u5HRzg3ntDWbWqL9u2JfPtt+eoqjLSq1cs6ek6QkM96NYtig8/7EZwsHUuuL/ZXLhQVpcwTZ0YzPRJIYq8Er3gvzmMHZ+Cr7eKr6a04Y5Y62yCALD6p3yeficJd0cVG/sncF+Y9bWaNksyL25PYeHv5/FzULGyTQKdvK3v5/wzB3QGuv52lGJJpn9AAK+Gh+MgWlbfEEZJYkVODv9NS6MGaKnR0CU2llaenn953uxQ0EwkSYLVEUmTIPwLsizzzTe/8vbbi9iyZSqenq5cTpYu369SqXj22VsB6N49mpUrj1NVZWLHjhROncpn1Kjb6u4XGp5eX02XzkvqEqY3JodaOiSL+PizXMaOT8HPR8Wvc28lMtB61xls21/E4LlncHOAn55uRxt/V0uHdMNVG808uTGJDacLiXdT892tt9HcURnNTL4rKOaxgyeplmFqZCR9A6xvBtFSDpaW8saJE2RKElqVisejoritWTPRfVBQNJE0CcI/tG/fUSZMmM+ePUdQq9X88MNG+vVrdcUxV1+FKyysoKyshsBANzp2DOGhh1o2ZsiKV11tpEePVRw/oee5oQFMnxRi6ZAsYvmqCzz34lm8PVX8OKe9VSdMvx4vpe+MU9hq4JtBba0yYdJXm+j15Sl+SS/lbk8tW26Nx91WGc1Mvsi5wDPHkrAB5sfF0enSHi7C9SmqrWV+aipb8/NRAfcGBNAzNBRnhbyuBOFaRNIkCH9TYmIKkyZ9wJYtPwHQs2c0s2c/SGzsn28SKUkSeXnlzJjxMwcO5PD4463p2DGkESMWAEwmMwMHrmfXrgz69PJh4bwIRZbkrd1YwDMjk/g/9u47PKpqa+Dw70x67yEF0gspJPSADZBuBQcBzawAACAASURBVIQr1auAXdF7sVAUEZUiYkFFvQKCgqCigiJIU1FUegtJSCON9N7LzJzz/ZHAR4nUhEnO7Pd58uCcJDNr4syZvc5ee21He4md83sQ7qveNR8HEyu4+6Wj6GX4cUIUN/uqr/wwp6Ke4f/bx/Fymfs8XFnTLcxoytLeSs3k+YRTOEoS70VH091RlDdfL72isCEnh3dTUqhSFIJNTBgTHY2faNUuCGeJpEkQLiMrK5e5c5exatVGZFnmllt8WbRoCDfddOlOQBqNBgsLE7p29WTx4qHY26u/g1VbU1FRx/jx3/DTT0nc3s+RNcvDVNsd7lK+/6GQCVPisbWBbW90IzrQ1tAhtZr4jGqGzThCdT2s/1c4Q4OdDR1Si0ssrGHo8gNk1Cg84evF0shgTIzgQoCsKDyfkMrbp07jZSLxQbceBNuq97V8o8RVVDDn2DGS9HpsgPHBwdzm5SVK8QThAiJpEoR/UFJSxsKFy1m6dA319VoiInxZsOAW7ror9IpnKlxdbXj88d6tHKnQnLS0Uu6+ezlxcRUMGejEN59HYGGh/k5iF9qyrZj7H4zD0gK2vtaNXqHqvXKcnlfLkOcPUlylsHxEKGMim58Fbs/2ZpVz16qjFDcovB7qz6wgH6OYOW2QZSYfO8na7AJCTTW837M3npbiQtT1qNBqWZqWxtc5OShAnw4dGB0YiL256OIqCM0RSZMgXKC2to6lS9ewcOGnlJVV0amTG6+9disTJ0aL/ZPaiT/+SGfUqC8oKmpg2uPeLJkfaJRtxXf+WsKoCbGYaGDzvGhuilBfmdoZeSX1DPrvAbJLFd4aFsiUHp6GDqnF/ZRYzJh1sTTIsCIqlMk+6nuOzanU6Rh9KI7thaVE2dvzYZcuRrN2qzUoisLm/HzeTEykTFHw0WgY06ULoU7G0XFREK6VSJoEoYlOp2PVqo3MnbuU7OxinJ3tWLJkGE880RtLS/EB3V589tlhHn10I4oCH78XwqOTjbOj1u97yrjnX8dRFPjh1Sj6R6t3QFRaqWXIf/eRWigzu58P02/uZOiQWtzKQ7k8sikRcw1s7BnJXR1cDR3SDVFQ38Dw3/ZxWKunn4sLi8PDsTKStVutIbW6mteTkjhYXo4FMCoggEEdOxrFfl6CcL1E0iQYPUVR2LhxF7NmvcXJk1lYWVkwa9ZtPP/8LTg6Wl3+DoQ2Qa+XmTFjO2+99SdOTmZ8uyacAbepN1G4lL37y7lz9FG0OvhuTiRDeqpvXc8ZVbU67nhuL7HZMk/GePHaQHW1klcUhfm7M3lpVxrO5hKbe3Wlr5N6ZwzPdaq6liF/HCBVJzPCw4NXQkLE4P4a1ej1fJKezuqsLPRAtIsLY4ODcREljoJwxUTSJBi1338/yIsvvs3evccwMdHw6KO9mDOnP15e6l33oUYVFXVMmLCBzZsTCQ215cf14QQHqbed9qUcPlrJsJFHqK2Dr2ZHcHdf9c5I1DfIjJobx95TeiZGd2DpHerqjKiXFZ7ZksyH+3LwsZbY1rsnnW3V2/XwXEfLKxn652EKZIWHfXx42t9fVf9vbxRFUfilqIj58fEUKAodNBpGh4cT7are84IgtBaRNAlG6fjxRGbOfJctW34H4L77wnnjjUGEhoq9Ptqb9PRS7r5rOSfiKhh8uxNfrw7H0dE4yylj46oYfPdhKiphzYww7rtVva9nnV5m/IJ4dhwu5e5QF1aODEWjUc+g+txNa7s4NG5a62VpHJvW/lJUyoh9x6hSYEZQEBM6djR0SO3S6dpaFqaksLu4GFPgDh8fhvv6YiHKGwXhmoikSTAq6enZzJnzAWvW/IiiKPTv78+iRUPo3Vt8KLdHe/ZkMHLk5xQVNfDUo968s9A4Gz4AnEysZtBdhygpU1gxPZTxt3cwdEitRpYVHn47ie/2FDHA35Gv7w/HTEVNWmq1eu5eE8uuU2X0c3ZgoxFtWvt1TgGTDscD8GZ4OMPc1dcBsbU1yDKrsrL4JC2NBqCzoyPjgoPxtDGOWUpBaC2tljRJkhQEPA/0ASKBPxRF6X8Fv+cAvAuMADTAZmCaoijFrRWroH5FRaW88cYnLFu2joYGHdHRASxceAtDhwaJko92atWqwzzyyEZkGT56N5jHpngbOiSDST1Vy8A7D1JQpPDh08FMHqbermqKojD9k1RWbc+jl58JmyZEYmmmnivndVo9I788wa5TZdzbwYX13cONZtPaD9JOMy0uBVsJ3omKJkZ0c7tqe0tLeTU2ltOyjLMkMapzZ3q5u4vPOUFoAa050xQB3AHsBa6m6f9XQCgwFZCBRcBG4NaWDlBQv+rqGt5553MWL15BRUUNfn4deP31Wxk3rgsasaC4XdLrZWbO3MHixXtwcjLjm8/DGdjfeAdXGZl13H7HfnLyFd5+LJAn7lF38vjamgze/e404V4ato7rg52FegomGnQyY76KZ1tKKXe6O/N1jwjMjeA8pSgKLyWmMT8lE3eNxAfduhNmZ2fosNqVwvp6FqemsrWgAA1wu7c39/j7Y22qnveHIBhaa76bflQUZROAJEkbgMuuOpQkqS8wFOinKMrvTceygX2SJA1SFGVnK8YrqIhWq2XFiu949dX3ycsrxdXVnvfeu4NHH+2FhYoGWcamsrKe8eO/YfPmREJCbNn8lfE2fADIzqnn9uH7ycxWmD/Zn//cp75W2+da+v1pXvk8HT9XDdsnxOBirZ6SNa1eZuzX8WxOLGaImxMbjCRh0skyj8YmsTIrj0BTDe/36EUnK9G19ErpZJmvcnJYmpJCDRBqYsLorl3xFUmnILS4Vhs9KooiX8OvDQfyzyRMTfezX5KktKbviaRJuCRFUfjmm2289NISkpNzsLGxZM6c/kyffjP29qK1ant2bsOHQQMaGz44Oaln0Hy18gsaGHjHPk5lyrw8wZeZ43wNHVKrWr09j2eWpeDhILFzUi+87dXTFEGnl5m0IYHvE4oY4OLI9z0jjaIkr0av5/5D8WwuKKabmQnv9IrBxfxqClOM27Hycl45fpxUvR47SWJicDC3eHqiEaV4gtAq2tol987AyWaOJzR9TxD+0a5de5kxYyEHDyZjamrCk0/G8PLL/enQwdbQoQnXac+eDEaN+oLCwnqjb/gAUFTUwKA79pKYKvP8vzrx6r/9DB1Sq9r4ZyFTlpzEyVpix6QeBDqrZyZCLytM/j6Rr04UcrOTPT/0isTaCBKmkgYtdx2I5e/SCvo6OfFORAQ2opTsipRrtbx76hTf5uaiADd5eHBfQAB2IuEUhFbV1s5QTkBZM8dLgYDL/bIkSXOBVwA8PdXbalc435EjCcyY8Tbbt/8FwNixXXjttYEEBbkYODKhJaxefYRHHtmIXq+w7J1gHp+q7jU7l1NaqmXIXfs4kSjz9AhvFk0NUPUi712HS7n/9TgszWDrxG5EqugiiCwrPPpDIl8cyyfG2YQtvaKwNYLEIau2jsG795Ook7nD3Z3XO3fGzAhKEa+XrChsystjSVIS5YqCn0bDmKgogh0dDR2aIBiFtnh2Vpo5Jv3D8fN/UVHmAnMBevaMvOzPC+1bamomL7/8PuvWbQFg8OBAFiwYTI8exj2oVgu9XmbWrB28+eYeHB3N+ObzMAYNcDZ0WAZVUaFj+L17ORKn5+E7PHn3cXV3f9wbX869c44BsGlcNDGd1LPptKIoPPVTMisO5dHDyYSfe/XB3qwtfiS3rLjKaobsOUiOXmFSx448FxgoysmuQGJVFW8kJXGkogJLYHRgILd7e2Mqkk1BuGHa2hm6FGhuisiR5megBCOUn1/E669/wscff4VOp6dHjyAWLryFQYMCDR2a0EIqK+uZOHEDP/xwkpAQW35cH05IsPE2fACortZz5+hY9h3RM2lQBz5+JkRVm7leKDatijtmHqVOCxvuj2BgoHo6JCqKwn+3pvLR/hyiHDRs79XHKPZh+rOknLv+PkqZojA9IIAHfXwMHVKbV63TsSw9nTWnTyMD3d3cuD8wECdLsUZXEG60tpY0naT51uKdaWw7LhixyspqlixZxVtvraS6uo7AQE/eeONWxoyJEO3DVSQjo4y77/qU2BOi4cMZtbV67rk/lj1/l/Ovfm6sfC5U1QlTak4tQ54/RGmNwupRnRkRrp5ya0VRmLnjFO/+fZpwew07e/fB2Vz9r+8f8oq4/+AJtMAbnTtzj4eHoUNq0xRFYUdhIQsSEihSFLw0GkZHRBDpIsrOBcFQ2lrStBV4WZKkWxRF2QMgSVJPGtczbTVoZILBNDQ08Mkn3/Daax9SWFhOhw5OvPnmIB5+uCdmKtrUUoC//spk5MjPKSio58lHvHhnYRBmZsadENfXy9w3MY5fdpdx700urJkRhqmJev8m2UX1DJp+gLxyhffuCOKBbuoaXM/9JZ1Ff2QRYqthV+8Y3CzUv3h/eWYOjx5PwlKC9yO7cKsY+F9SZk0N85OT+bO0FDPgLl9fhvn4YG4EDUIEoS1rtaRJkiRrGje3BfAG7CVJGt10e4uiKDWSJKUAuxVFmQKgKMrfkiRtAz6XJOk5/n9z2z1ijybjI8sy69dv4aWX3iEtLQ87Oyvmzbud//znJmxt1dNuWGj0+edHePjhxoYPH74dzBMPi7VpWq3M2Afj2bq9hGG9nPlqdgRmKu4aWFyhZcj0faQXybx6ux/T+nY0dEgtav7uDOb9lkGAjYZfYmLwsFT3eUxRFF5PzmBOUjrOGon3u3Yjyl4969JaWr1ez8qsLD5NT0cLhDs5MS44mA7Wxl2aLAhtRWvONLkD31xw7MxtfyC96fEvvHQyFngHWAlogM3AtFaLUmhzFEVh+/Y/mTFjEUePnsLMzJRnnunL7Nn9cHOzMXR4QguTZZlZs3ayaNEfouHDOXQ6mYlTE9i4uYjbuzry3SsRWJirN2GqrNExfPpe4nNknu3bkZf7q2vfqSV/ZjF7Zxo+1hK/xPTG20rdCZNeUZh2IpllGTn4mEh80KMX/mLw/4/+LClh3okT5MgyLpLEfWFh9HBzU3WjF0Fob1pzc9t0GrveXepn/Jo5VgY81PQlGJkDB2J58cW3+fXX/UiSxMSJ0cybNxB/f/UsAhf+X1VVY8OHTZtOEhxsy+avRMMHaGxFPfmJRL7+rpBbIh34YV4XrCzUW5pTW6/nnjknOJCu56HuHiwZFqiqweL7e0/z3M+peFtJ/BrTG19rdS/ir9PrmXT0JBtyC4k00/BezxjcLdSdJF6rvLo63kxNZUdhIRpgcMeO3O3nh6URtJ4XhPZGvCuFNiEpKZ3Zs99jw4btAAwfHsyCBYOJjvY0cGRCa8nIKOOeu5dzPLacgf0d+ebzCKNv+ACNM62PPZPEF+vy6d3VhJ/mdsHGSr0Jk1Ync//r8fx2rIxR4a787x51dQX834Ecpv2UgoelxC8xvQiwUc/GvM0p1+q490Asu0vK6eHgwNLISOyNoDPg1dLKMl9mZ/Nhaiq1QKC9PRNCQuhoq559yARBbUTSJBhUbm4hr766jOXLN6DXy/Tu3ZFFi4bQv7+/oUMTWtHff2cyYkRjw4fHp3rx3pui4QM0JkzPvJDCp6ty6RZhws9z+mBvo97TtCwrTH4rkR/3FjM40Ikvx4SrqsnFqsO5PPpDEm4WErtiehJiq+5Z1Jy6eobu3scJrcwgV1cWhoVhIZoXXORIeTmvHDtGmixjL0mMCQnhJg8PsV+VILRx6v00Ftq08vJK3nxzJe++u5qamnpCQrxZsOBWRo4MV1VZjnCxL744ytSp36PXK3ywJJgnHxENH6AxYXrx5VO8/3E2kaEatr8ag5Odeq/QK4rCtA+TWbMrn76d7Pl+fCQWKmpysfZYPpO/T8TZXGJnTA/C7dS9HjOpqobBfxwgU69wv5cXM4ODMRHn8vOUNDTwzqlTbMzLA+BWT09GBgRgK2biBKFdEEmTcEPV1dWzbNk63njjY0pKKvH0dObdd4fx0EPdMDUVVyTVTJZlZs/eycKFf+Dg0NjwYfDtouHDGXPnp7P4vSxCAzXsfK0Prg7qbkX98qo0Pvwhh6iOGn6a1AUbc/W8/785UcADGxJwMJPYEdOdKHt1l1ztL63gjr+PUCwrPOnnx6O+vuLi1zlkReHb3FzeSU6mUlEI0GgYEx1NoIODoUMTBOEqiKRJuCH0ej1r1vzInDnvkplZiIODDQsWDGbatD5YW6t7cCic3/AhKMiGH9eH0zlU3Vfer8aCtzKYtzCDAB8Nu16PoYOTut8TS77J4o0vMwly17BtfAxOVuq50r4poYjxX8djYwrbeneju4OdoUNqVT8XFHPfgVjqFHglJITRXl6GDqlNSais5PWkJI5XVmIN3B8URH8vL0zEhuyC0O6IpEloVYqi8NNPu5k5801OnMjAwsKM5567mRkzbsPFRd31/UKjzMzGhg/Hjpdze7/Ghg/OzuoZJF+vdz/MYtarafh4S/wyvzferuruMrZ8Sw7P/S8VbyeJHRN74WGnnue7JamYMetOYKGBrb270dtJ3XsSfXE6j8lHT2IKvB0RwUA3N0OH1GZU6nR8mJbGuuxsZKCXuztjAgNxFF0EBaHdEkmT0Gr+/vsoL774Nn/8cQiNRsNDD3Vn7twB+Pg4Gjo04QY5t+HDY1O8WLpYNHw410fLs/nPjFS8OkjseqMXvh3U3Yr6m90FPPJuEi62Ejsm9sTPST2d5HaklDBqbSymGtjcK5qbndVdevVWaibPJ5zCUZJ4Lzqa7o7ivA6NFwq3FhSw6ORJShSFjhoNoyMjCXcWpciC0N6JpElocQkJqcya9R4bN+4C4J57OjN//iAiIjoYODLhRlqz5ihTpnyPTifz/ltBPPmIt1jncI6Vn+fyxH+ScXeV2DW/J0He6p55/flAMRMWxGNrAdsmdifMXT3lmbvTyrh3zXEANvWMor+reveVkxWF5xNSefvUabxMJD7o1oNg0SYbgLSaGt5ISmJfWRnmwL1+fgzx8cFMlOIJgiqIpEloMadP5zF37jI+++x7ZFnm5pt9WLhwCLfc4mvo0IQbSJZlXnppFwsW/I6Dgxlfr45kyEBxlfVcX36dz9SnEnF2lNg5vwedfdSTQDTnzxPljJobi4kGfhzflR7e6lnn82dGOXeuPopOho09uzDYTb2v9QZZZvKxk6zNLiDEVMMHPXvjaanu2dErUavX82lGBp9lZqIDIp2dGRccjJuVemZSBUEQSZPQAkpLy1m4cDlLl66hrq6B8HAfFiy4hbvv7ixmFoxMVVU9kyZ9y8aNCaLhwz/4dlMhDzySgL0d7JjfnS7+6r5KfzSlkjtnHUGrh43jutDPXz1lXPtPVzB81RHqZfimRwR3dHAxdEitplKnY/ShOLYXlhJlb8+HXbrgKFpls7uoiNfi48mXZdwkidHh4XR1dRWffYKgQiJpEq5ZbW0d77+/lgUL/kdZWRUdO7oyb96tPPBAV0xUtEGlcGWyssq55+5POXqsnAG3ObLhC9Hw4UKbtxYx9sE4rCzh59e60T1YPTMuzUk6XcOQFw5TUQdrR4dxZ6h6korDOZUMXXmEah2s7x7OCA/1NkEoqG9g+G/7OKzV08/FhcXh4VgZ+aa1uXV1LExJ4ZeiIkyAoZ06caevL5amYlglCGol3t3CVdPpdKxevYlXXnmP7OxinJxsWbx4KE8+GYOViloHC1du794sRoxYTX5+PY9O9uT9t4JFw4cLbN9Vwn0TT2BmCj/N60qfcHU3CsgqqGPQ9AMUVip8dHcw46LUs6YxNq+KwSsOU65V+KJrGGO83A0dUqs5VV3LkD8OkKqTGeHhwSshIZga8RodrSzz+enTfHTqFPVAsIMDE0JC8LIRM+qCoHYiaRKapSgKlZXVlJVVUFpaQVlZJWVlleTmFrJ06WckJGRhZWXBjBm38uKLt+LoKGq3jdWXXx5j8uTv0Gplli4O4qlHRcOHC+3eU8aIsceRJPjh1Shui1JPiVpzCkobGDx9P1klCgsG+/NYb29Dh9RiEgqqGbj8ECUNCiuiQpnQUT3J4IWOlFcy7M/DFMgKD/v48LS/v1G/tw+UljI3NpZMWcZBkhgfGkqfDh2M+m8iCMZEJE0qpSgKtbV1TcnO+YnPhbdLS8vP+V4lpaWllJfXIMtys/et0Wh4+OGevPLKALy91b0PifDPZFnm5Zd3MX9+Y8OHTesjGTpIvYvgr9Vfe8u5876j6PSw8ZUuDOqu7r9RebWOYc/tIzFP5oVbOjHjNvU0gkkurmHgpwcprFf4qEswk308DR1Sq/mlqJQR+45RpcCMoCAmdOxo6JAMpqihgSWpqWzOz0cC+nl5McLfHxuxpksQjIpImtqwhoaG8xKdxoSm4goSoTLKyqpoaNBd1ePZ2Fji6GiDl5c1EREuODpa4uhoiZOT1dn/dnS05JZbfAkJcW2lZy20B9XVDUyatIHvv08gMNCGzV+Jhg/NOXi4guGjjlBXD9+8FMEdMepZ09Ocmjo9d70Uy5FMPY/09GThkABDh9Ri0kpruf2TA+TWKbwXEcRjvuqZPbvQ1zkFTDocjwK8GR7OMHf1lh9eil5R+CYnh3eTk6kGgk1MGBMdjZ+9uFgoCMZIJE2tSK/XU1FRdU6yc3Hi0/ztUsrKqqipqb+qxzM3N8XJyQ4nJ1P8/T0uSnjOT36scHJq/PfMMTMz417YK1yZcxs+9L+1seGDi4u44nqhY7FVDLnnCFXV8OXMcEbeot5GAQANWpnR8+LYc6Kc+yPdWHZ3iGrKljLL6hjw8X5O1yosDgtgmr96Z10+SDvNtLgUbCV4JyqaGCf17jl1KXEVFbx87BjJej02wLjgYPp5eaFRyWtaEISrJ5KmK1BbW0dubiG5uYUUF5edU8b2/4nQ+bdLKS2toqKi5qoeR6PR4Ohog6OjKWFhLs0kOM0nO2eSIUtLU9UMUoS2ad++LO69t7Hhw8MPevLBkmDMzY13Ufg/iT9ZzeC7D1FarrDq+c7c31/dV+r1eoVJixLYeqCE4cHOfH5fGCYadZyLsivqGfDxfjJqFF4P9ee5QB9Dh9QqFEXhpcQ05qdk4q6R+KBbd8Ls1N3dsTkVWi1L09L4OicHBYjp0IHRAQE4WFgYOjRBEAzMqJOmysrqs8lQbm4hOTkF5OYWnXO7kNzcfMrLq6/4Pu3trXF0tMHPzw5HR/fzEpx/SnbOfNnZWYikR2izYmPzGDhwBbW1et5dFMS0x0XDh+ZUVem4a9QhCosVPnk2hH8P8TB0SK1uwfoMvt5dyK2+DmwYG4G5qToSaZ1eZtSK/Zyqlnk52JfZwepZn3Wht05lMT8lk0BTDe/36EUnI9yYNaeujin793NalvHRaBjTpQuhRjrTJgjCxVSbNNXV1bNr194LkqLCc24XUFVVe8n7cHa2o1MnG3r39sDT0w5PTztcXa0vSnbO3La3t8DUVJS4CepTUlLDiBGrqK7Ws35VOPffp+6Zk+vx4pxTpGXJvHh/Jx6508vQ4bS6E2lVzPsiHS9HiU0TIrE2V8858O2/TrO/RM94L3deDfEzdDitZk9JGTMTTuGhkfi0VwxuRjirklRVxSOHDlGsKAzu2JGRAQFG3VpdEISLqTZpSkhIZdCgKRcdlyQJNzcHAgMd8PLqeDYZavyyxcvLHk9POzw8bLGwUO2fRxCumE6nZ+zYrzl1qprZz/uIhOkSftldyrJPc4gI0fDqA/6GDqfV6fQyD80/glYP/7srEicV7dN2srCaOTtP0cFCYmlksGpnVQvrG7h/3zEUYEFUtFEmTAfLynjq6FGqgTGBgQzu1MnQIQmC0AapNitwd7fh0Ud7XZQMubvbiIYHgnAVZs7cwY4dqdw51Jl5L6k/EbhWlZU6Jj92HBMTWDWtKxZGsNZr8ddZHEzXMym6A3eGqqczoF5WmPx9IvUyLIsMx8VcPcnguWRFYeLRBHL0Cs/4+9PTUd37hzVnV2Ehz8fFIQNTwsKI6aDefbcEQbg+qk2avL3tmTv3dkOHIQjt2tq1x3jrrT8JDbVl7YpwNCpZ3N8ann8plYzTCrPH+9AzVP0tiQ8lVTJndRqeDhLv3hFk6HBa1NK9p/k7q4J/eboxylO9XQ/np2SwvbCUW52dmeyjzgYXl/J1Tg5vJCVhDjwRFUWEs7r3UBME4fqoNmkSBOH6HDqUzdSp32Fvb8qmL8NxcBCni3+y45cSPlmZS5fOGl6e4GfocFpdda2e8fOOoNPD5yO74GytnpmYlOIaZm9PxdVc4oPIYEOH02p+LSrllcR0OppIzA8LM6pW2oqi8HFGBsvS03GQJJ7s1k3svSQIwmWJUZAgCBcpKKhi5MjV1NfLfPN5OKEh1oYOqc2qqNAx5fHYprK8bkZRlvffT1JIypeZfnNHBgWq5+q8LCtM2ZhIrR4+iw7DzcLc0CG1iry6esbuP44GWBDVFUcz9SS9l6MoCgtSUliXnU0HjYanevakg7U4vwmCcHkiaRIE4TxarZ4xY74iK6uW1+f4c9dwV0OH1KY9NzuVrByFORN96R6s/n1tNv5ZyP9+yiW6k4Y3BgUYOpwW9dGBHH5PL2ekhyv/UmlZnl5RGH8kgQJZ4bnAQLo6OBg6pBtqU14e67Kz8ddoeDwmBkcjbHwhCMK1Uf8lUUEQrsp//rOV339P5757XZn1nPGtc7ga23aW8OmqXKLDNcwef/V7+Kz/dQBRjyzHdOhOoh5ZzvpfB7RClC0np6ieqW/FY2kGX47ogYVK9mMCSCut5cWtyTiZSSxTcbe8V5PS+bW4jAEuLjzQsaOhw7mhsmprmZ+YiDXwcK9eImESBOGqiJkmQRDOWrHiEB9+uI/ICHtWfdxZtQPHllBermPqE7GYmsKqp7thbnZ1CcT6Xwcwbv6cs7dj0wLP3h474NcWjbUlyLLCg4tPUlyl8OFdwYS72xg6pBajKAoPb0ykWg8fdQnFw1Kdg+nthSW8Yd265AAAIABJREFUnpyBr4mG1zob1/tbJ8vMTEigFnioc2dcjXDzXkEQro9ImgRBAODvvzN54okfcHIyY+OXYdjaitPDpfx3ZgqncxXmPuBH16CrL8ubv25Cs8cXrJ/QJpOm974/zY7DpdwZ4szjvdW1ae+nB3PZdaqMO92dmeitzpbT2bX1jD8QixmwKLorDka0jgng08xMjlVU0NPNjT6irbjQDlRrtWRXV5PT9FWr13Onry8eYg2ewYhRkSAI5ORUcN99X6DTyXy9OpLAAHEV9lK2bi9m5Rd5dA03Yda4aythjM/w+4fjV1/m19qOpVYxY3kq7nYSK0eqa4Yis6yO57Yk4WAGn3QJNchzW589gPkpE4iv8iPcNp1ZQWsZ691yibNOlhl7JJ5iWWFmUBARRtYp7lh5OR+np+MmSUwICVHV61do/+p0OnJqasiprj6bJGWWlVGtKBf9bHxBAS/FxOBkaWmASAWRNAmCkaur0zJq5Apyc+tYMj+QQQPU0w2tNZSVaZn6xAnMzGD1M90wu8Z1PeG+6cSmBTZzPON6Q2xRtfV6xs87TIMOVo2IxN1WPR3lFEXhkU2JVOpgRVQo3lY3vixvffYAxh05p0yzMvDs7ZZKnF5KTGNPSTlD3NwY5+3dIvfZXlTrdLxw7BgK8EBUFDZGNsMmtB1avZ68mpqziVF2dTUZpaVUNJMc2UsSnZydcbSxwanpK6u4mMPp6by5fz8v9e0rXssGIJImQTBiiqLw5JOb2be/hIljO/Cfp4xrYfi1+M+MVHLyFV570I+oANtrvp9Z49aet6bpjJlj115PeC3uhU9PEZ8j83Qfb4aHuBg6nBa16kge21JKGermxEOdPAwSw/yUfyjTTJnQIknTT/nFLErNItBUw6uhhplJM6RFKSnkyDJDO3Ui1MnJ0OEIRkAnyxTU1p4tq8uuria9pIQyWebC9MhGkvBycjqbGDlZW+NoY4O56cXDcxdbW+q0WuKzs/kgNpb/REdjbmJyY56UAIikSRCM2ocf7mPlysN07+bI/5aKspXL+ennYlatzaN7pAkv3n99nQXPrFtasH4C8Rm+hPtmMHPs2ja1nmnLvmI+2JRNhJeGRUPU1V48u6Ke/2xOxM4U/hdluGQivsrvH45ff5lmZm0dkw6dwAJYGN0N22YGYmq2s7CQ7/PyCDIx4V5/f0OHI6iMrCgU1dWdTYxyz/lXvuBnLSUJdweH/0+Omr4sr2K2SJIk+gQFUdvQQGphIZ/Gx/NYRAQmGvV0MW3rjOsMKgjCWb/9lsazz27Bzc2C79d2xspKXLG6lNJSLQ8/2VSWN+3ay/LONXbAr20qSTpXfmkDDy06gbkpfDmyO1Zm6nl9KIrCYz8kUa6Fj7uE4GNluPUB4bbpxFY2U6Zpe31lmg2yzOg/DlAqK8wJCaGznfr3EDtXfn09c+LjMQcmde+OqRhYCtdIURTK6uvPK6vLqa4mu6oK3QU/awa42NldlBxZmZu3yIUZSZLoFxZGvU7HseJi1iYlMckIZ5ANRSRNgmCEMjLKGDNmDZIE334Rhk8nsaj0cp55MYXcAoU3HvIn0v/ay/LaA0VRmPzWSQoqFd4ZHkiUh7qe75fHC9icWMztLo484uNp0FhmBa09b03TGTODrq9Mc0bCKQ406LnT3Z3RnoZ9jjearCi8dPIklYrC+OBgPG3U0x5faF0VDQ3nldXlVFeTVVFBwwU/ZwI42NqeV1bnZGODraVlqycwJhoNAyMi2P733+zJy8Pe3JwRAeqqBGirRNIkCEampqaBkSNWUFTUwLJ3grn1ZkdDh9Tm/fBTEV+sy6dnlAkv3N/J0OG0umU/5LBlfwlDgpyY1kdd69zyKuuZ9sNJrE1gebThr9CeWbe0IGUC8VW+hNtmMPM6u+d9n1vIO2mnCTHVMMcIu8WtOX2avaWlRDo7089LXe3xhZZRo9WS09SU4UxZXWZ5ObUXNGWQAAdra7wvmDmys7REY8DZS3NTUwbGxLBl7162ZGZib27O7Ua2WbUhiKRJEIyIoihMnbqRI0fLmPpvTx6bIgYUl1NSouXRp+MwN4fV07pjaqLuMp+49Gqe+yQZF1uJVaM6o9GoZ8CtKApPbk6mpEFhaUQQ/tZto7X+WO9fW6xT3qnqWh46HIeVBAu7dsfayNYxJVVV8W5qKg6SxINGtoGvsdnvns9Wn0xybarxrLZheKYPvQvO34OrXq8n94KZo4yyMqqa6VjnqNHg5ux8XnLkYG3dZtcMWZmbM7hXL37av5/1KSnYmZvTy93d0GGpmnGdTQXByL311p+sWxdL3972fLAkWAworsC0F5LJK1RYOCWAcF91l/nUN8iMf/UQdVpYPyYCT7sb34K7NX1zopDv4ou41dmBJ/3U13q7Xi8zes9ByhV4vXNngm3VVVZ5OfV6PdMPH0YLTIqIwN5cPe3xhfPtd89neXjC2dvZttUsD08gtawcy5Om5FRXk15aSrl8YUsGsJUkOjYlR45NZXWONjaYtcNOdPZWVgzp3p0thw6xIj4eG1NTwp3FtiGtRSRNgmAktm1LZsaMbXh5WfLt2ggsLNrm1bO2ZOOPhaz9qoDeXU2YPkb9pQ+zVp7i+GmZR3t5cm+Yq6HDaVGF1Q08uTEBS03jnkwaFV4wmB6fwhGtnhEeHtzrYZgW6ob0bloa6bJMPy8volzV9foVGullmcK6Or7zOtXs938NyIHtjf9tJUl4OjqeN3PkaG2Nhcr2N3Kxs2NgdDTbjx3jw+PHeaFHD3yNrPHLjSKSJkEwAikpxYwduw5TUw3frQnH00NdMwitoaiogUenxWNhAaueUn9Z3o5DJbz97WlCPTQsGRZk6HBa3NObkylqUFgSHkiwrbWhw2lxX+UU8GFGDuGmGmYFBxs6nBvur5IS1pw+TUeNhtGBF3cjbClXUhImXD9ZUSg5p513TnU1OTU15FRVoQcY1vzvSe4wLDr6bMc6Y+Hl5ES/iAh+iYvj7cOHmdWrFx2s1XeeM7RWS5okSQoH3gf6AmXAcuBVRVH0l/gdPyCtmW99pSjK2FYIUxBUr7KynnvvXUFZmZaVy0KJ6WVv6JDahaefT6GgSOHNhwMIU3lZXlF5A/9eEIuZCXw5shs25u2vTOVSvo8v5KsThfR1sucZf/XNGCZV1fDwkXhsJFjQrQdW7bDM6HqUNjQwMzYWE+Df3bph0UrP/59KwgCROF0jRVEob+pYd25L7+zKSrQX/Kwp4NTUzvt0eQm1Thf2tAOnGhu8jHQTY383N24KDuav5GTeOniQl2JicLAQF0hbUqskTZIkOQE7gXjgXiAQWAJogJeu4C6eA/4853ZRS8coCMZAlmUeeOBb4uMrefoxbx6aZFyth6/Vt5sKWb+hgD5h9vz3PnV3y1MUhalvJ5JbrrBoSADdvdRV1lFco+Xx7+Kx0MDK6FBMVFaWV6vXc9+fB6lUYGFYGAFG1l5bURReTUqiRFEY6e/fqmVJW30ymz3+s0+mSJquQJVWe163ujPtvOsuaMqgARwu6FZ3pmPdmXW4qbn5/OaUcNFjRGVe36bj7V2Ytze1DQ0cycjgzX37mH3TTUbXDKY1tdZf8jHAChilKEoFsEOSJHtgriRJbzYdu5RERVH2tlJsgmA0Xn99Nxs3JtD/VkeWzG+9khU1KSxs4PFn4rG0gFXPd8bERF2D7Ast35rLpr+KGeDvyHM3qy9B/M+WFPLrFRZ2DqCzrfoSimfiUjihlRnj6cmdHYxv4P59Xh67iooIdnBgqE/rDphzbaqbPZ5jXdOqj9ve1Op05yVGOTU1ZJaVUd1MO29HjQYPF5fzkiN7K6vLtvMObEpSj/tkUmpdg1ONNVGZPmePG7Nufn7UNjRwMjeXZSdO8EyXLu2yyUVb1FpJ03Bg2wXJ0XpgEdAP+LGVHlcQhCY//JDAK6/8gq+vNV+vDsfMTN1rclrKU88lU1issOTRQEI7qbsmPDGrhmc/TMLJWmL1fepqLw6w/3QFXxzLp6eTCdMD1FeWt+Z0Hp9m5hJlZsKLQepbh3Y5GTU1LEhMxAaYHBbW6s09PKttyLa9OHHyqlH3eeJK5VRX8/6hQxQ307HOXqOh05l23k0d6xysrTG9jsF8YEEHkSQ1Q5Ik+oaEUKfVklRUxIqTJ3kkPFyVzW9utNZKmjoDv5x7QFGUTEmSapq+d7mk6TNJkpyBAmAdMFtRlNpWiVQQVCgrq5yJE7/GysqE79eG4eZmPAtir0dhYQN79xVxc4Q9z4xU3yD7QlJOAqEeJszsG0onB0tDh9PiehWnsi7GmkiLcEzb6F4r1yq+sppHj53EvmkdU2ut42mrtLLM9EOHqAOmhoXhYtn6r9/hmT7nrWk6Y5iRl4RBY8K06OBBahUFL0dHnGxtcW5q5e1obY25KBG7oTSSRL+wMOqOH+dwYSHrk5MZFyy2GblerfUqdqKx+cOFSpu+90/qgQ9pbBhZAfQHXqRxTdS9l3tQSZLmAq8AeHqqqy5fEK5GR49TvDy7Mx3d9XSLFu+FK+VmEk/sNnsqjoepviwPIMTDhP2Tb1ZtZ0BJkhjr1tvQYbS4ap2e+/48RI0Cb0dE4GOEXbI+ycggUa8nxt2d3jeoLPHMuqWffTLJsa7Bq8aaYaJ7HjnV1bzZlDDdHBJCZy+xaXpbYGpiwqDISLb9/Te/5eTQy92dYEdHQ4fVrrVm6n/xdsuNJazNHW/8BUXJBZ4659BvkiTlA8skSeqqKMrRSz6goswF5gL07On9j48jCGonSRLPPyn2KbkW9rndsHczdBQ3QEbj6VStCRPHL/lx0W4pisITJ5I4qZOZ4O3NYDdjeLGe72h5OZ9mZOAuSYy7we3Vexd0MPok6VxnEqYaReGm4GCRMLUxFmZmDIqJoezYMeT0dOja1dAhtWut9WlZCjSXzjrQ/AzUpWxo+rf7dUUkCMZCdxTqig0dRftTchSSjOQDpSlhIlelz/dMwlSrvuf3WVYen5/Op4eZCdNbcT+itqpKp+P5Y8dQgAeio7FW2Ual7UnuBQlTmLe3oUMSmmFtYYFX78YZ98Sj6ryYdKO0VtJ0ksa1S2dJktQJsGn63tVQLvhXEIR/ohMnxMtRmjo4Ked2cipR39/tzPOT5X84dbbjhElRLn5OFx1TYcJ0vKKKJ48n4ihJvNG9J2YqW6d1JRYkJ5Mnywzz8SFElBoZTG7TGqYaRaGvSJjahbKmWaarTZwURSG9ooL1ycmtEVa70lpn3K3AUEmSzl1McT9QC+y+yvsa3fTvoZYITBDas9TUEtLSSikoqEKrvWCf6DMJk5hluqQzC2HP/Ht2sK2yWabSSh0V1bqzHfHOJk8ZR9t1wgSN/+9qtXpyK+tJL609ewxAOXbEkKG1mkqdjvv+OkwdMC8iAm8rK0OHdMNtKyjgh/x8gk1MuNvPz9DhGK1zZ5j6BgcTLhKmduNyiVO19sIthRvPrc6WlhwqLKS4rq5V42vrWmtN08fANOA7SZIWAQE0rjV6+9w25JIkpQC7FUWZ0nR7LmBH48a2FcBtwPPAd4qiHG+lWAWhXfjhhwSWLt1LUVEN/fr5YW1txogRYcTEdBIJ0xWQZYWPV+QgywoajUT3aFv69HZAKj2GkhiNWpoKHUqq5I0vM3B1MCOnuJ4u/ra8eH8nHG3N/r8sr51bdzyfL47m42BpiqlGwsZcw4SoDtxakd6YPKlslklRFB45nkSKTubBTp0Y4Gp86xXz6up4NSEBC2BSjx6q64bYXuTV1PDmwYNUKwp9g4JEwtQOlXXtiuPRoyQePUroBWucPoqL44GQENytrcmvqSGrqorU8nIK6+rQyjIldXU3pFNlW9UqSZOiKKWSJA0EPqCxvXgZ8A5NTRouePxz+6SeBJ4DptK4OW4msBh4ozXiFIT2ZPbsnXz00T307u3N7t3pHD6cw8qVh8nIKONfo0CpLRLtRC/hmRdSyMtvINDfEidHMxYsySS0UxXPTrHAS0V/t+mfpPCvfu7cEulAg1bmo805DHjuGI/dpOeR28yR8roZOsTrNmP7Kb66PxwbMxPq9TK708v5YF82R0zredyrF2rbkuyTzBzW5xTQ1d6eaf7+hg7nhpMVhZdOnqRSUZgQEoKHEXYLbAvyampYdOAA1YpCn6Agwjuqf1sGtfqnxMnJ3JxlcXE4mJtT3tCAo7k5fnZ23O7tzdSwMCxNTVEUxWjHGq3WPU9RlHjg9sv8jN8Ft9fTuAmuIAjn0Gr19OrVEUVRMDc3ZfDgIEJDXdm9O50d2/YzMMYbFxexIPqfVFTo2Huggu0bo3ByMqO4WMugAU5899UJ3lpkz+zxWlzs2//fr6RCi42lCXfGuODbofFq4IpQe34/XsbKb+PoYRNGz3Z+YTi7op5unrb06eRw9liIizW96/J5P0VHlGU5/V0vtbNF+3K4vJJnYpNx1kgsDg83ynVMn2dlsa+sjGgXF27z9DR0OEYp/4KEKUIkTO1ec4lTmJMTcaWlPN2lC/ZmZphdsP+bMSdM0HprmgRBaEFmZibcfLMPzz+/jS++aCyx8vFxZOxoGUWBT1flNLs4/kqt3zCAqD7LMXXcSVSf5azfMKClQm8T7O1NubWvA+99dBoAFxczuvukMKl7FDnF9fx8oMTAEbYMZ3szBnV34ukPkskuqj97/CbbNHr5mfLJgVzqdbIBI7x+3vYW+DpaEvPJIXamNv5/s086wa1uptzpEsjKrLzrei+0JeVaHaP/OkID8EZEJB5GWBZzsrKS906dwkmSeCA01KgHbIZybsIUExgoEiYVKbugPC/MyYlurq64WFpiZmKCrChnvwCjf/+JpEkQ2rja2saFmVOm9GDGjFvZv/80L7ywjcP7f8XMTIO7s0xxie6aT2brNwxg3ENziI0LRK83ITYukHEPzVFd4jT1QU/+3l/B6Ikn2LvzIJIk0dnHhn/d5s5P+4pVM9B+eoQ30YG29Jt+hGc+TKYs4TCmJhK6Mm/K63RYmLb/0/57dwbzRG9vPtyXw7iv4/kqq4H6qih2FZXibmGmig92RVGYfOwkaXqZh318uMXFxdAh3XB1ej3TjxxBB0yMjMTO3NzQIRmdMwlTVVPCFNmpk6FDElpYWdeuZxtDOFlaEu7sTJ1OB4BGks5+qeUz8npIav0j9OzprRw8+LihwxCE67J48R7i4grYv/80zz7bl5AQV2pqtMSfOMoXazIJ8DXjVHotu36MxtX12gYUUX2WExt38X4vUZGpHPt76vU+BYM7lVaLLCv4+1mRm1fPlm0lfPvtKVxMnJkyzJMZK07x4BAPnry3fdet/XaslKJyLR2czPH3sESnV3jn29Os35XDHUEdOFlUw7K7gunmZXf5O2ujYvOqOJJbRYirFRISZiYSiYcTWJ7WgE5nQ7S9La+F+uNg1pr7tt8YS9NO80xcCj0dHPg0OtooGx/MT05mXXY2/b28GB8SYuhwjE5BTQ0LmxKm3oGBdBEJk6o5NpXp7cvPx8PaGl+7f/6sUFuZ3iO//XZIUZSel/s5kTQJQhuVnFxMv34rOHz4cY4cyWXTppNYW5txyy0+3H5bNZqGUk7EVxMUaIW727VfgTV13Ileb3LxcVMd2tLB1/MUDG7hkgz+2ldBQlINwwc749vJkq5+efjrI1m+JZfSKh3RATY8dnf7Tpg++jGb7/cUYW4mEe5rQ2mljmBvKx6NKkarV0hNCCLMzRp7y/abTPyUWMziPZnYmpvgYWeOo6Up7hVFDOxgSg/LnuTV1eNhaWHoMFtEYlUN0b/tx04j8XVMH9ws1PG8rsYfxcU8ERuLj0bDCzffjLnJxecoofUU1NaycP9+kTAZEcem2SaX0FAsTU2xNTOjXq8/u47y1+xszDUawpyccFXZlgdXmjS1309QQVC5rKxyevXyxsPDjuHD7RgyJIjPPjvMmi9+pyDLhsemeHNTH4fL39FlhHdOb3amKbxzxnXftyFVVur4eGUOx//uhYWFhh+3FnH8QCo7TsPdIQ3MnxKAXq9gYtL+r5at2JrL6hfCiPCz4VRuLScza9i/L4lFP8P8vn1xVcF459ODOTwZ482YSHdyK+vZl1XBkWOFrEi2xzmwFn9rdXyI65vK8uqBhWHhRpkwlTQ0MPvECUyBf3frJhKmG+y8hCkgQCRMRuJMY4jixESCo6NZn5xMcV0dYU5OaCSJ9MpKnMzN+TMvj+e6djXK2W/je8aC0E7cdFMnLCxMePfdv8jOrsDERMPUB02Z/mwwy1fnkp5R2yKPM+u5tc0enzm9+ePtRW2dTGSYDSmnarGw0DB6hDuPjLfAXe7Ic/9LJTGrRhUJkywr3N7NiY1/FgEQ4GnFHTEu3NfDnLhTtszZldbua9FlWaFvJwd2ppZSVqvF086CEbpspgVZYG9qwrNxKVRodYYOs0V8kJbNX6UVDHVzY5Cbm6HDueEUReGVxERKFYV7AwLodIkSIaHlFdbWsqgpYeoVEEAXHx9DhyTcQGcaQ6zbtw+tLHOnry9Wpqb8kp1NTzc3RgUGYmViQnJ5uYEjNQyRNAlCG2VpacasWf1ITCxiw4Y49v35C7W1em7uIWFrY0JmVv3l7+QKjB39K+s+m0dUZCqmpjqiIlNZ99k8xo7+tUXu31Dc3cwZOsiZBUsy+f6HQmqyj9Cxsjv/Hd2Jgd0c2XWk1NAhtgiNRuLBIR78GVfO0x8ks+dEGWQcpYtJDz64K5gD2ZXUtfOOeRqNxJQeHtTpZN7ck8WWbQcorpdxkbuzMCyQ9Jo6avR6Q4d53VKra5mZkIKLRmJmcLChwzGIDbm5/FZcTKijI4PFDMcNVdg0w1SpKPQMCCBKJExGqaxrV3L1emzLy/Gzt6evhwce1takV1YC4GxpSVl9y4w/2htRnicIbczixXvQ6fRkZVUwb97tPPxwT1Z/tp20VImFC2NxdTGjtk7mtlscW+wxx47+td0nSecqLtbi5GTK0491xMxMYveuZE4e0xBpW8TdfV05dqoaO6v2f/r79Wgp3YJsCfe14Z3Hg9j0VxGffRvPagn6d8jn5+QSvOzMsTJrv+VNOr2MXgFXG3Ne7u/Lt3FFbEvTsjvXmQpdEhYaiQ4W5qpYzzQtLplaBV7t3BkXI+wUV9TQwKKkJGwliYc6d0ajooXmbZ2sKLx14EBjwuTvT7RImIyarbc3Bbm5JB49im1QEOYaDXV6PU/+/jt+dnbc7edn6BANQsw0CUIbsnr1Eb788hjdu3uh0Uj06vUxhw7l8ObCLjw8yYkHJ3hw600ObFwXaehQ26z1G/J54r9JuPn9ycYfC3looicjhpghFXuy4udcYp4+hK2lCS+Obd+DgqpaHY+9l0RqTh0AoZ2seewuL/59kzl9XPz58ng+N/vY8/6d7XvGYtbOND7an83hnEp8HS2Zbl/MfR3N6eFgh5u5GRG2NnzetbOhw7xuVTod2wtKCLO1ZZi7u6HDMYiDZWXUA4P8/HA2wj2pDKlKq6VUluno7Ey0r6+hwxEMLKhDByqdnfmkvJyvU1Nxt7Li/qAgFvTpw/PduuFohGstQcw0CUKbodfL7NiRwlNP9WHo0GCGDg1m6tQePPXkV6z7UsPaT4O49y5XQ4fZpmm1MvPfyuSLT8OY8oAnm34qwp50zM0lRt/qxoyxvmTk1+HmYGboUK/bq19kMLCbEz1C7DhdWMfPB0pIOJFBn0ATpvTwZEoPT0OHeN30ssLB7Eo0EpyuqMfHwZKRkkJBhQ/R9rb8y0s9ycWeknJ0wE3Ozqpq5Xs14prKfwLt7Q0cyfXZ757PVp9Mcm2q8ay2YXimD70LOhg6rEuqbGgAwFYkqwJgZ2XF4OpqOsbE0KDXY21qSnxJCXpFwdHCAisTE1wsLY3uXCVmmgShjTAx0TBuXBRHj+ZSXFwDQNeunuzZ3Z+uERasXptn4Ajbvq++LcCnowXRXWzp29ue5atz+e5nLS/O0TD7szTKqrT4drDE2rL9lqudEeBpSc+QxkXy//04leTsWkw08PUfNry8M83A0bUME43Ey/19MdFI+DpaklpSy/PHa5lwNIG0mlpKGrSGDrHF/FpcBkBvx5Yru21vYisqkOCS+8O0dfvd81kenkC2bTWyBNm21SwPT2C/e76hQ7ukKm3je8nKrP1fUBJahqVGg4ulJVlVVew4fZrYkhISSkv5/ORJ1qekEFtSYugQbziRNAlCGxIV5UFRUQ0zZ+4gO7sCdI37JjzykCe//lFGZaU6OoS1lqEDnenobcHLr6UxakIcz04254PXrNn9dldkGbbuV89JvnuQHZ9uyeXzHXnUNcgsejiQN8dYsWRYIMfzq8gqrzN0iC1iQIAT47q442Rpynt3BqOTIczWmvU5BWzKLzJ0eC3ml6IyzICuDte/jUB7pFcU4srL8bC2xtK0/RbBbPXJbPb4z/9wvK2obEqaLETSJDSpVxRWJiSwOyeHjjY29OnQgWE+Pszu2ZPbvLzYktG+tyW5Fu33zCQIKnJmd+1OnRz47LOR/Pe/P3PPPWuZ/G937K3q+HlnCf6+ltjZibfsP1EUBUdHU2J62lNZpadHV1vsTGqpPxGFhbkGVwczMgvU0/EnJsyeZ0d15M+4csqqdXz6xT7+fZM5sqKQVFRLJ4f2X2Zz5n1xi68D837NoEdVNvHlZmyP6UJ6bR2O7Xhwfa4yrZbD5ZV0dXDA2kj3JEqrqaEO8GvHs0wAuTbVzR7Psa65wZFcnTMzTZYiaRJo3Og2X6+ntL6e57t1u+j79Xo9HtbWBojMsNTxiSMI7dyZumBFUbC0NGPZsrvZ/csOftqSh7V5PR7u5ix+4+INaIX/J0kSZmYSD01qXMtTkHSYee/JrPs1n+yiBvYnVvDeE0EGjrJljbjJFXNTCZ1e4UhmLRv3mmCqSWHGbe27ycUZZ94XQS7WjAx35bZv47ndxZWOVpZ0tGr/SeEZvxeXI2PcpXknKirl2xFpAAAgAElEQVQA8Gvn65k8q23Itr04cfKqadsDTJE0CRfq3bUrm/fvp6y+nmqtlkqtluK6Ok5XV5NYWsqEkBBDh3jDiaRJEAwoIaGA7dtTsbMzZ/jwEDw97dDp9JiYaOh3mxv9eosK2muhKArurhr6eAaw60gZvh0seOMhf8zN1PX3tDDXMPIWNwZ1dyLtcCV2lcHU62Q6u9kYOrQWNzLcDacca4JNGxNfrSxjppId6cV6JjjR1ASivc80Dc/0YXl4wkXHh2W27QsZZ5MmI2x1LzTP0tSUEQEBvB8bi6ulJa6WlphqNLhbWTEqIEA159+rIZImQTCgBx/8noEDA/j88xRycip56aX+aDQSkv4YAIlJNXh5mouyvEs4lVZLalotPh0tz/6tlP9j787D4qqvx4+/78zAAMNO2AkQAgkh+4ZJTIxxj9Fo1VrXql+jrdXa1l8XtdalVq1brVqt1rhvsWo1bokaTWKi2fcAWdgJ+zLsDLPd3x9AxEgSkgB3Zu55PQ9P4M4MHAjM3HPP55xP/XYUg8JVZ8RxxWmxGAy+MeGnxmqn0+EmKMBIVKgfTpcbg6IQUrubCaapEKF1hAPD0f19OVxuAvyMuNwqhl3bOTXGD2db15VwX3rB/rrOSgAwwcurLCcip6UFE5AUHKx1KCekZ0re8uRSKoLaSWgP4hxvmJ4nlSbRh5mxscyMjcXmclHd3o4KWEwmajs6sDmdpOmsB1POxITQyO23f8GoUVE8+OCZ3HRTNtdd9z9ycmoYPXoYk8Z3MHUsvPZ2Fffemap1qB6rptbO1TfkYbEYMRjgpxfGcP018V1J0r5JuFwqn26o55zpkV5fZdq4p5m/Lyklp6SNs6dF8oefDmd4TACqqgKQU91GZWsnZ4yM1DjSE/P5/gbe2FFNdaudy8bH8H9T4zEaFFSAjkl8WFXDlLAQ0iyBWoc6IOrsdna2tHFSeDhmnfYz2d1u9rS0kBgS4hPJcHZNrMcnSYfqGTkuSZMI376d0ZMmAd8vka7t6GBPYyNV7e002Gw02e3EBAaSYLHwk7Q0LcMdUt7/7CSEF3K73QQGmnjggTMA+NOfPickxMwf/zib8JB6XnmthIhwE7/9VRJ+Xn6yP5juuKeQs06P5IulE/njb5N59MlSdn235eDt6/OasTncXp8wAfz55SIunRvNpn9Nxd+kcNXf8+jodKGUdlUlt1W2kBTq/RsO/n55AVdNjGXR1Hj+vamC+1cWA10v3i1OJw0Op88kTACr6rqX5kX4SJnwOOxrbcWJ9y/N82atDgf+gNEHklYxsAqbmnh9717qbTamRkezKCuL+7KzuXn8eNZXV2Pt9J0BS0cjlSYhNGAwGLjnntMOfnzNNZOZN28E/v4mJo/PYPWqCjZtbWHeKfo9kTqaqupOSg90ctcfu3avP21uBAvOjuL9ZbWMN8ykqqGTlg4nF87y/g2BN+5pxtri4LJ5XVevH/tFOmffvoM1u5o4KxrWbxzBiAi8vpfpv7tqiAn24+yMrmrZ7JQwrv3fHso3bCUx0MBXdVbmRvlW34/0M/lOP5M3a2xvxywb24o+LC0u5qKRIxnT68KOw+2mzeEgNjCQBpuNCLP3X7DrD7mkIIQHOPvsDPz9TeDcTklJG3v3t5OV6d0nwIMtLtbM04+mMyzq++Uk1y1sIi/fDcAt/9pPYaXNJ6pMafEB3H/tCOwON05X1/d3waxhfJvTBMAvlu7DbPL+vq1gs5GfT4oDoNPpJiHUTGKomS1WFx2t4/lDbiHJgb714ryivAqLAmN1nDD0JE0jdNzTpSVVVelQVVmaJwjfvv1HxywmEwdaW7HabFhtNqrb29nX2MjHxcWMiYgg2cv7EI+FVJqE8CAOh5ufX7OOm29MJDZGphgdTebo7xNLu93NiOEGDK0R3P1KEdYWJ79amKhhdANnWJg/Z0yJwM/UlQC63SpzJ4Rz/3+284daA+Niw5iW6P0nnOeOiqLJ1rWBs7n7e52fEcnHmxr4tCyfnyVEE+hDfT+Vtk72Od2cHBnpE708xyunuZkA0OW+L57A5nLhQja2FV16+pl6nJ6UxIoDByhpaSHIZMLa2YlBUciMiGBWXBx+PvScfDSSNAnhIdz2bVRUdHDpRTHcfKNvnOwPJf/WnfgfmExSdAF/e6uEL/8+UeuQBlRPwgRgMCiMTbVgNMDjX3SSc0uKhpENrLCAH74sXapW8EKbmzV1VVScMUujqAZHz9K8k3S8NK/N6aSgvZ30sDAMivdXS71Rz7jxQEmaRB9GhoUxMiyMA62tNNhsRAcGEm/R50oYSZqE8ATO7RgMCimxNn51Q4LW0Xi1G86Nx9+kcPoU3+8Hu3membSABMbE+OYLmKqqKMDNyaOZE95GpL9vndStrJN+ptzWVlSkn0lLPePGpdIkjiQpOPgHWwL0TG5VdHSxQ5ImITyFrR7Q1xPQgGnYDvu6lhSMSgri/mtHaBzQECjZzqx0EzMCU7WOZND0/C1cGBfNwljvH+hxqK8qqglTIFPHCUNOczMg/Uxako1tBfTdz3QoVVUPPi/r8VxFv4uohdCY2+0mN7cGnNsPJkxiYPjKZrZHVTlJN9+rry3dKmm3UeRyMzkyCqOPfW/HQibnaa9V9mgS3Q7tZzqUHhOl3iRpEkIjd965gilTnuHLz/ZrHYrXUlX1B1Um4WN2bocO3/y/XVlvBeAkHe/PBLCjvp5QRSFKxl1r5mClSZImIY5IkiYhNPDMMxt4+OE1pKQEMWWiXGE9Hqqqcsb5O7j97x243arW4QyZosoOnn9tA9bCsVqHMqiqWjpx+PD/a88QiOk67mey2u1Uud0Mj4jQ/RVsLbVI0qR7Pf1J4sgkaRJiiC1dmsett35CTIyZZe+NJSpKXqiOxyfL6vl6dSP5u0N0s0QN4OXPq/jl6x18tq9B61AG1S8+2kfMR83Udtq1DmXAqarKV5U1RBkUMnQ6hQp67c8kS/M0JZUmfeuw23nrm2/Yp+MLOP0lSZMQQ2jDhjIuv/wdAgKMfPruWNJGBGodkldyu1X+cl8uigL3/VwHQx+6ud0qr39ehsUMF47xvcEIPdrtLr7cX098gEK02fea0wvaOyh3qUyJGuZzvVrHQvqZPINUmvStwmrFpqqYdPxc1F+SNAkxRPLz6znvvFfo7HTx31ezmDZFpkUdr/c+rGVHrpsrT4tlbKp+rtSv3d1EcZ2bS7Jisfj77oaCXxZY6XDBBTFJWocyKL6uk/2ZAHJ6kiaZnKepVocDBfA3yUBlPTrQ0LVqYWxkpMaReD75CxFiCNTWtnHOOS9QV2fn+SdHseCcKK1D8lpOp5u7/7oHoxHuudp3NnXtj9dXVAPw80lxGkcyuJbuqQPggjjf/DtxqSqJRkXX/UwAY9ra8PPzI1RGXWuq1eEgUFGkr0yHVFWlsqaGIEX5wR5Mom+SNAkxyNrb7Zx/3n8oKGjjz39I5sb/k81rT8Sb79Swt8DNovnxpCcGaR3OkOnodPHfVZUkRSicmuq7J9sut8onOdXEBShkh/tmBeLqxlquigulOEg/v799uSgggOKjjDgWg6/JZiNI57+LetXQ1kabqnJSbKyulwr3lyzPE2IQuVxurrjiXTZsbODqy2O5/y/66b8ZDHa7m/se2Ie/P/zlKn1VmT5aV09zB1w1brhPD75YV9ZEbafK+TFxPv0iXpIxWa7sC8053W46VVX6mXSqvGdpns63PugvqTQJMUhUVeXWWz9l6dI9nH5qOIv/NVpOkk7Qy29UUVTm5tcXJpIco699XV5fUQXA1ZNiNY5kcC3N69ro+YJY3x10IbSzMaaaZcmlVFraiG+zML80mewa3/6bOpI2GQKhaz39TFnSz9QvkjQJMUgefXQtzz67kfHjQnn/jXH4+0th90TYbC7uf2g/gQFw5+XJWoczpKqtdpZvamBqQjBZMb47+EJVVZbuKMdihNOH+e4SRKGNjTHVLM7KO/hxeXDbwY/1mjj1TM4zS9KkOw6Xi5rGRoYHB0tfYT/JWZwQg+Dtt3fypz99QVJSIJ+9l0VYmFyfOFHPvVhBeZXKrxcOJy7SrHU4Q+rtldW43L4/AGJPbTv7W92cEz2MAKNvTgds3b5d6xA8QqUGP4dlyaV9Hl9+mON60LNHU6AkTbpT2diIC5madywkaRJigK1aVcQ117xHWJgfy94fS1KivpaRDYbWVicPPlpISDD88Wf6qjJB19Q8kxEuGx+jdSiD6vupeb69NK84XYYfAEM+BKLS0tbn8Yqg9iGNw5O0SqVJt6Sf6dhJ0iTEAMrJqebCC18H4IO3shiXJSM8B8LTz5VTW69y209SiArV14v77qJWtu5v5Zz0KGKCfXsJxdI99RgVWBDjm6PGxQ91ulx0ulxD9vXi2/pe2prQrt/Jca3S06RblZWV+AEjw8K0DsVrSNIkxAApL29m/vwXaWpy8Mpzmcw7Ra7eDITGRgePPFFMZLjC7y4ernU4Q+77vZl8u+eiqqWTDWXNzIkMJ9Lf+0/gmh1OajvtWO0OWp1OHG631iFp5vOaGoraf1zNabDZeHXPHmxO55DEMb+07yr1OYc5rgctkjTpUktHB1a3mzFRUZgMkgr0lzRaCDEAmpttnDv/P5SVdfDQvSO44lLfPsEdSv/41wEam1X+fn0aYRZ9PWW5XCpvfnmAsEA4f7RvV18+3luPClwQ693fp0tVWVPfyJvlNexuaaPN5cJsMDA2JIgrOtuYZdbX7zDAg/v3E2M288uUFE6PjgbAqarEWyzU2WxYOzuJNw3+z6Vn2MPy5FIqgtpJaA/iHJ1PzztYaZJBALpSbrUC0s90rAbtWUpRlCzgaWAm0AgsBu5TVfWItXhFUcKAfwIX0lUJ+wS4VVXV+sGKVYgTYbc7ufjiJezc1cRNixL40236vWo50Gpr7TzxrxJioxVuuSBR63CG3ModVsqtKjdMiyfAzzcHI/RYmucb/UzvVdbyQmkFv0hO4KExIzAbDHS43HxVZ+WR3HpOG5HOQq2DHGInRUSQHBjI8poadjQ3M7+picYpUwAI9vOj0W4n3jI0UyGza2J1nSQdqsVuB6TSpDcHpJ/puAxK0qQoSgSwAsgFLgBGAo/TlQTddZSHvwOMBhYBbuBh4ENgzmDEKsSJUFWVG25YyooVBZw/P4qnHk2XvZgG0CP/LKO1DR64ZiSWQN9OGvry2pf6WJrX2ulkRX4D48MMjAgK1DqcE7KntZ1pYSH8NOH7oR0hJrg8MZa8giLy2/oeRuDrpoSFcVNqKg/t388/OzoILiigyW7HqChEmfU1DdOTSE+T/rjdbirr6ggzGIgJ0m8/3/EYrErTL4FA4CJVVZuBLxVFCQXuVRTlke5jP6IoykzgbGCuqqrfdB8rBzYoinKGqqorBileIY7LPfd8zWuvbSd7egRvv5yFySRrgwdKRWUn/3q+jOEJCr9YkKB1OEOutcPJ/9ZUM2KYgZOTfbtR94t8K51uuCDG+3vWMiyBLK2q45v6RjKDg1CAIKORkg4bOx0usnSYIIwICqLSZsOoKNw1ahTvNDayy2gk1GJhXFSUnLhpqNXhwB8wSl+LbtQ0N2MHZsb59hYWg2Gwkqb5wOeHJEdL6KoazQU+PsLjqnsSJgBVVTcqilLUfZskTcJjvPDCZu6/fxVpaRY+fmcMFov+KiGD6cHHSrB1wl9+loFZhxsDf7C2jrZOuHrmcJ+vXh4cNe7l/UwAVyTGEmAwcFtuPlaHk1CTiTq7g1Cng/8XGsC0RP0tMz0vNhZ79yAMVVU5xd+f5NRUnG63NKFrrLG9HXOAbIuhJ9LPdPwGK2nKBL7ufUBV1VJFUdq7bztc0pQJ7OnjeF73bUJ4hM8+28dNN31EVJQ/y98fS0y0NNEOpOKSDv7zcgUjUwxce5Y+r4a91j0172ofX5rndLn5JLeGxECFqWEhWoczIC6Kj+ai+GjsbjdNDicRfiZsO3fqdn+mTreb/LY2RlosuFQVp6riVlVJmDSmqirtqkqULM3TlQMNDRiA0eHhWofidQYraYqga/jDoazdtx3P49KO9kUVRbkXuAcgPt43XnyF59mypZxLL30LPz+FT/47lox0WVoy0O5/uASHA+69bBR+OlzyWF7XyVfbrMwcHkp6lG//fq0tbaLBrnJTSoJPVNRUVSW3tZ2v6qzsbW3HoapYjEYmt9sZ5XQSPART4jxNmMlEkLGrEl+7cycHJk+W/U48gM3lwo30M+mJzW6nrqWFjLAwAnX4XHSiBvMnpvZxTDnM8YF4HKqq3gvcCzBtWuJR7y/EsSoqsrJgwcu0t7v435tjmZHt270mWti3v51X36oiK8PA5fN8u8pyOG9+VY2q+v4ACICleV2DUS+I9e6peT0+rq7n8cIyxodYOH1YBEZFodXp4q3KKtrz8rht5EhG6KyHJ9psJtpsptnhYJvDwe6KCtqcTgKMRrIiIqSnSSOyR5P+yNK8EzNYSZMV6KvuF0bflaTej4vu43j4UR4nxKCrr29n/vz/UF3dydOPpXPh+X39qooTde9Dxbhc8NfLx2A0en/l4VipqsprnxXjb4JLx8Uc/QFeTFVVlu6sIMQEp0b5xlKRN8qrWZQcz9VJ3y8rbd2+nYkzZnNHXh5bGxt1lzQBVHd28r/KSkodDjra2wkymXC43bydn096WBgLUlK0DlF3ZI8m/ZGk6cQMVtK0h0N6kBRFGQ5Y6Ltnqffj+hotnknX2HEhNGGzObjggjfZu7eVP/xmOLf8IknrkHzSrpxWlrxXw+SxRi6a7RuVh2O1vaCVnAo3F2UNIzLIt68A765uo6jNzaXx0ZiNvrFgKzUwgD2t7VTZOgk0GjEq0O5W6XS5aHU6Dy5T05vHCwqI9PNjvr8/9pQUjIqCw+1mTnw8T+zYwemJiQTIcqEh1dq9R5NZKk26oKoqldXVBCoKw4ODtQ7HKw3WM9Qy4A+KooSoqtrSfexnQAew+iiP+4uiKLNVVV0LoCjKNLr6mZYNUqxCHNV1133At9+WctklMfz9r0dtrxPH6Z4HilFV+NvlWT7R33I8vt+byfcHYHy0p3tpnpdvaNvbnRnJ3Lo7n3M37iIrJIgAg4EGazsbNm7kwvh45g7zne/1WOxubuYpsxmLyURx90l675ltrQ6HJE1DrKfS5K/TRF5vapqbaVVVsmNiMOj09fVEDdYz1HPArcD/FEV5mK6k517gH73HkCuKkg+sVlX1egBVVdcpivI58JqiKL/n+81t18oeTUIrO3dWsWTJLrKnhfDKc5kYDPJkMxjcbpXPvqgjKyWI+dn6XTqwvaAVgPkZvv8zKLB2APjM1DyAcD8/Xps8hnaXi21NrTQ6nPjZWrkzczIBOj45nR4ezttWK/EGAzXl5bQ7HFS2t1PV3s5pSUlEytjrIdfzM9+Wn8+wkBCiQ0M1jkgMloLqar7NywMgO8a3l30PpkFZD6GqqhU4HTDSNV78PuAJuifb9WLqvk9vl9FVjXoJeA3YAvxkMOIUoj8WL94CwO2/S8Zs9o0lRJ6oqtpOZydkJVt0W2WCriUUAP46mBoYa+nqpajtXibkK2o67dTZHYwPsbAgNopZZhMBRiMuVb/zie4aNYr4hAS2O5002Gz4GQyMjYzk8owMZsXFyZVvDWRGRHDlqFHYVJXPtm6lrL5e65DEAHO53azbv59V3QnTL7KymKDTavdAGLRauKqqucBpR7lPah/HGoHrut+E0FRHh4PXX99CbKyZ8+Z7/8abnqy4xAbAiHh9X3HW03l1QmhX0lRp852kaU9rG/fuK+bbhmYyLIH8OSOFqd3/qbfl5JAxLoivp9VTaGkjrc3CDaXJzK/x/SmJfgYDi1JSqLRaKbBayR0xAj+DgeTgYPxkvybNzE1IINTPjxdycvhy1y7mZGaSEef7S4P1oK2zk69zcqhpbibSYOA3U6cSb7FoHZZXkwXEQhzBBx/k0tjo4E+/G46fn7ywD6ai7qQpNVbfSZOexAd3J02dvpM03ZFXxFnRESyZMpZv6hv59e79LDK6mQdU+tv4Oq0Ounuw9we38cesrivAekic9rW2siIsjNqqKpobG3GrKp+WlBDm788FI0YQHRiodYi6NDk6mtsmT+ap7dv5Zs8e2js7mZCcrOuKv7ersFpZvXMn7arKtOhofj56tPQMDgA5CxTiCF54oWtp3vU/j9c4Et9XXCpJE/RjQzofEh9iBqDC1qlxJAOn1eXipIiu3pBTosL5euYkXlNNbN65kWK/9j4vVS5OLh3iKIfegY4OnikupsZuZ4LJxLToaGbHx3PVqFEMDw7mo6IirUPUtfSwMG6fNo0QRWFzURHr8/Nx66ns7SNUVWVnaSnLd+zApqr8LD2dG7KyJGEaIJI0CXEY+/fXs2pVEafOCScjXX/7qgy1g8vz4vSdNAHo5QJvfIjvVZpCTUYa7F1TyVRVJcrfj1UzJ/FOu52OEnefSVNhUPsQRzn0Sjs6aHQ4uHf0aGb4+XFqYSEJFguxQUFkhIdT3+k7ibO3SrBYuGvGDIYZDOSWl7MyNxeny6V1WKKf7E4nX+XksKmwEIui8PvJkzk9KUkqhgNIUk8hDuPFF7uqTIuukSrTUCgq6ZqklqL3SpOOLu4eTJp8qKfpDyOHY3er2N1u/Lt7dSwmI29HWRgR10aj2fmjx6S1+/5FmeHdS++WVVcTnpxMXUEBxTU1lLS0kNPQwPmpqdoGKACIMJu5a+ZMntm9m/21tdjsds4YN072cvJwDa2trNq6FavbzajwcG7MyiJUNi0ecFJpEqIPDoeLV17ZSESEHxdfMLSTZpa8N48JMxZjCl/BhBmLWfLevCH9+lopLmomZphCUIB+xzL30Mt1wUA/I+F+ik9VmmZEhHFKVPjBhKmHSVG4c2QGhP34MYtKk4coOu0kBQRwfXIyS6uqWFlfz1ank6aCAix+flybmckkmejlMYL8/PjthAlMiY6mqqmJ5evW0SaVQI+VX13Nx5s3Y3W7OXv4cH43YYIkTINEKk1C9OGTT/ZSXd3Jr3+ZSMAQnsQveW8el19398GPd+WMPPjxZZesHLI4hprLpVJa4WbKSN/Zr+d4qXoqNQHx4YFUNPnO8jRVVQ+7HCbwy2Lus4/izfRyCoPaSWsPYpFOpucpisIpUVGcEhVFdWcnlTExNO3fjzshQfotPJCf0ciNWVm8k5/PyvJyPl2/njOnTSNCpq95DJfbzYb8fPIqKvAHbho7lsnR0VqH5dPkmUqIPvTszTTUS/MefOzKPo8/9PiVPp00VVR24nBAqvQzAfrpaYKuJXp5te10utyYjd6/+KEnYXKr6g/2HgqeNAnjtxs4py6GixoTtApPc+utVnJaWmh0OKix2ynOyaHT5eKitDQywsO1Dk/0YlAULktPJ9zfnw+Kivhs82bOmDSJ2LA+yqViSLXZbHyVk0NtSwvDDAZ+M20asUGetcx3Y0w1y5JLqbS0Ed9mYX5pMtlefoHI+1+hhBhgZWVNLF++j+xpIUwYFzykXzt3T+phjqcMaRxDrVjGjR+ks0LTwbHjVT6yRM/udvN1nZUlFTXktLT9YEPbWIMBk473JNrc2Mjb5eWUdXQwPDCQc0eN4gaHg/NTU/mgqIiSlhatQxSHUBSF+SkpXJuZSaeqsnzbNopra7UOS9cqrFaWbthAbUsL2TEx3HPyyR6ZMC3OyqM8uA23AuXBbSzOymNjTLXWoZ0Q/T57C3EYL7+8FbcbbtBgAERWZvFhjpcMbSBDrGfc+Ii4vvdpWbJyHhNuXIzp7BVMuHExS1b6dp+XnipNCaFdY8crfaRn4h+FZfwxr4BlNQ0s3LSLZ4rL6eieQPbXZtvB9/XoPyUlzI+J4d7Ro7k0IYG5w4Yx1mQiKzKSED8/6mw2rUMUhzErLo5fjx+PAnydk8OeigqtQ9IdVVXZUVLC8h076FRVLs/I4PoxYzAbPa8PeNlhtlFY7uXbK8jyPCF6cbncvPjieiwWIz+7OGbIv/6dv3/zBz1NPe74f28OeSxD6Ugb2y5ZOY/LH+zV51U08uDHl83zvSWLOis0HZygV+EjE/ReKati19zp+BkM2N1u5m/YSaXNzkNj0jABfnrKiA+RGhREXksLmcHBBBuNKIpCmcvF+vXr6QgIIMJs1jpEcQTjoqL4w5QpPLFtG9/u20d7ZyeTU1NlpPUAUVUVh8uFw+XC7nTicDqxu1wH/y2pq6Osvp5gReGWSZNI8+BlkpWWtj6PV3j59gqSNAnRy4oVBZSWtrPomnhCQob+z6Onb+mhx68kd08KWZkl3PH/3vTpfibotTyvj56mB98+TJ/Xkit9MmkC/UzPg++X5/nKBD2jotDmchFuMOBvMPDVzElcsnk3t+zaR5uqMrJoF5UZk7UOUxMXx8fzbkUFL5aWEmIy0ex04ggMJNhu52fp6SQGD+1yaHHsUkND+fP06Ty6eTPbSkpos9s5OSMDg46Xnaqqisvtxt47yXE6DyY/fR07eJvTiaujA7uq0p9ae2Z4OIu8YJx4fJuF8uAfJ04JXr69giRNQvSi1QCI3i67ZKXPJ0mH6lmelxL74yvNuSWpfT4mt8Q3+7x019MU0r08z+Yby/MmhwWzr7WD7Ijv97V5b9o4bt61j71Ot65fdEcHB3PXqFHsaGqi3GYj2GQiyt8fk81GaH4+xZMmaR2i6IeYoCD+MmMGj2zYwL7KSjrsdk7LysLkgcvEjqYn2emp5vwgsTnCsZ5/nZ2d2FUV93F8bQUwKwr+QLTFQoDJRIDRSFD3v4EmEwEmE4Hd74f6+5MZHo7RCxLU+aXJLM7K+9Hxc7x8ewU9P38L8QM1Na0sXZrLuLGhZE+T0ddDqaiwmfgYhQD/H7/oZqUUs6toZB/HfbvPSy8SQn2r0vTChNHY3T/OfJ8ZP4rrhsdhLC7UICrPMjEsjIm9lxZNmULl9u3aBSSOWai/P8SaOlkAACAASURBVHfNnMm/c3LIq6/ni+++47STTiJgiCogbrf7BxWc3onM4So9vas7PcnO8XYY+tOV8EQGBv4ouelJeHre731b79v9DQafXdrYMyVveXIpFUHtJLQHcY4PTM+TpEmIbq+9th2HQ+WGa2J89onMEzmdbsoq3WSPDu3z9jsvf/MHPU097rjMd/u89PTr17M8z1d6mgKNRgIPc8F9WngorUMbjsfq2Y9Mnmu9V4DJxK/Hj+fVvXvZUF3NZ+vXc+b06YQE9j3QB7pG8TsOk8gcunTt0EqPw+nEYbNhV1WcxxmzH13JTnhg4PcJTXdSc7jE59Db/I3GH2wnIPqWXRPr9UnSoSRpEoKuF/DFi7/FbDZw1c9864/c05VX2HG5YMRh9mjq6Vt6aMmV5JakkJVSwh2Xvemz/Ux629w22Gwi2OQ70/NE/xyaLMVPmgTbt8sSPS9jMhi4LjOTcH9/Pi8r49ONG0mMjf0+2elOfFwdHXSqKo7j/Tp0JTshAQFHrOoc7lhP4iPJjjgRkjQJAaxdW8Leva1ccWkMkZF+R3+AGDBFJR3AkfdoumzeSp9Nkvqit5f1+LBAKlu9e6pSfwVPmkTq9u0Up0ty0JtLVVlrt7PvwAFOS0rSOhxxDAyKwsUjRxJuNvNOfj77qqoO3mYE/BWFYLOZyKMsY/tBVafX+wFGo673NxOeQ5ImIYAXXtgMaDsAQq+ONDlPj/RVZ+qSEOJPfn0HDrcbPx8/ObLaHazscBDe2UmMjNg+SFVVnuvooDk/nxlxcQSZ5PTE25yelMTU6GjsLtfBPh9f/3sW+iK/zUL3Ghs7ePfdXaSnWzh1TrjW4ejO9xvbStLUQ28rSOJDzKhAtY8MgziSdytrubi+jTX19VqH4lFMBgNXjBiBDVjXq1IhvEu42UxMUBAh/v6SMAmfI7/RQvfeemsnNpub66+SARBaKCo+/Ma2eqSzlibg+w1ufWWC3pFkh3dN5tzV0qJxJJ7norg4/IC1hYW49fiHIITwaFL/Frqmqiov/GcNRqPCtVfFaR2OLhWX2lAUGB4tSVMPveXuB5MmH5mgdyRjQywEKrC7uVnrUDxOhL8/58bFsbSqijyrlbGRkVqHJPphY0w1y5JLqbS0Ed9mYb4PjJYWoi9SaRK6tnVrBdt3NHH+/Cji+thYVQy+4uJmEuMUzP7ydAQ6rTQF66fS5GcwMCU8lPy2Njpcx7tLjO+6PDERgJXl5RpHIvpjY0w1i7PyKA9uw61AeXAbi7Py2BhTrXVoQgw4OUsRuvbCC1sAGQChFYfDzYFKldSovvdo0iudFZpICO26YFFh08fY8ezwUFzAnlbZtelQY0NCmBAayq76emo7OrQORxzFsuTSPo8vP8xxIbyZJE1Ct9ra7Lz11jYSEwM550xZBqKFsgOduN3Sz9Sb3vZpAn1VmqBXX5Ms0evT5YmJqMAqqTZ5vEpLW5/HK4L0sYWA0BdJmoRu/fe/u2lpcfJ/V8VgNOrt2r5nkMl5fZOeJt+WHd5VWd0twyD6dFZ0NOGKwrrycjplCaNHi2+z9Hk8oT1oiCMRYvBJ0iR0a/HiLSgK/N/VMgBCK7JH04/pr84EYQEmAgxQ2amP5XkjggKINCjsqa/TOhSP5G8w8LPkZFpVlY3V0hvjyeaXJvd5/JzDHBfCm0nSJHQpJ6ea774r5czTIkhNCdQ6HN0qKpFx433RWaEJRVFICDRQ0dn3Uh9foygK2VERFLncWO36qK4dq58mJGAAvs3P1+WSVW+RXRPLotwxJLVaMLgVklotLModI9PzhE+SkeNCl158cSsAN1wrAyC0JMvzfkyv54fxUcGsK23GpaoYdbA+MTs8hOW1DexuaWFOVJTW4XicWLOZM6Kj+aK2lvymJjLCZeNxT5VdEytJktAFqTQJ3ensdPLaa5uJjjaz8NxhWoeja8UlNgwGSIqWce+96SBn+JH4EDNuoFY3wyCkr+loZPy4EMKTSNIkdOfDD/Oor7dzzeUx+MveQJoqKmomKV7BzyT/Dz10W2kK0dcEvekyQe+opoaFMcJgYFttLY066XcTQnguOVMRurN4cdfeTNf/XAZAaKmz001FtcoI2aPpR/RYaUroTpoqdDJBL8bsT4rRQG6jVXp2DkNRFK5OT8cFfFNRoXU4Qgidk6RJ6EphYQMrVhQwe2YYmaP7HpUqhkbZARuqKkMgRJf4kK4lmnqZoAdwUkwU9W6VcptN61A81oLYWIIVhW9LS3G63VqHI4TQMUmahK689JIMgPAURTJuvE+qqupueh7ob68mkL6m/ggyGrkoMRGrqrK1tlbrcIQQOiZJk9ANp9PFyy9vICzMj0sujNY6HN3r2aNpRJyMfBcQH6yvnibomqAH0td0ND9LSEBBBkIIIbQlSZPQjWXL9lNRYePKS6MJCjJqHY7u9Ywbl+V5P6TS1cuhNwmh3cvzdFRpmhIWggGpNB1NclAQsyMjKWhupkR+VkIIjUjSJHTjhRc2A7DoGlma5wlkeZ7oLTLQhL8BKnTU02QxGRkbYmFPc5P06xyFjB8XQmhNkiahC+XlzXz66V6mTg5m8sQQrcMRdC3PM5kgcZi/1qF4FL0OUlMUhbgAhcrOVq1DGVLZ4SG0q1DQ3q51KB7t5MhIEgwGNlVV0epwaB2OEEKHJGkSuvDqq9twu6XK5EmKi1sYHm/AZJSnoUPpb3Fel/jIYKpsqq5GcB8cBiF9TUdkUBSuHDECB7C2slLrcAbFxup53LdpMb9ctYL7Ni1mY/U8rUMSQvQiZyvC57ndbhYv/o6gICNX/DRW63AE0NHhorJGJVX2aPoRHeULPxIfYsahQr2OKgkHh0FIr85RXRgXhxn4tqgIt4/9oWysnsfivLspbxuJGyPlbSNZnHe3JE5CeBCT1gEIMdhWriyiqKida6+MIzRUfuU9QWlZV9/KCOln6pMO50AAP9zgdpi/PpZtjg2xEKhIpak/Qv38WBgfz7uVleysr2fSsGFahzRglpVe2efx/wWFsWzaJiotbcS3WZhfmkx2jVz8E0ILg1ZpUhTlBkVR9iuKYlMUZYuiKKf34zH3Koqi9vF2zmDFKXzf8uX7Abji0hiNIxE96hu6KglBZplieKgAfwMtNhVrh36qLT1SI7qS6C9rrRpHMnT8DAayI8LY19bGlsZGrcPxeD0DId7MyaHIhxLNyrbUHx8ct4SG839LeXAbbgXKg9tYnJXHxpjqIY9PCDFISZOiKJcBzwGvAfOBHOATRVHG9ePhTcDMQ97WDUacQh8CArqqS/7+shrVU0yeGExQIHy52zd7E07EpXOjcbjgpa1VWocy5K6bHEeEn8J9+wuotOlnit4DmWkowF927aBZR0sTj0dGcDD3jBpFi6ryj61b2VlXp3VIAyLeUvzjg3Me7PO+y5NLBzcYIUSfBuss8j7gVVVV71dVdSVwLZAP3N6PxzpVVV1/yFvTIMUpdCA8vGvz1MZGp8aRiB6BgUbOOn0Yewvc7C2TqWG9XX9OPIH+8K/NRbjcvtW3cTTDLP48ND+DFif8Ia9A63CGzMmRYfwlI4Uyl8rf9u/X1SCM43FJQgJPjuu6Bvvs7t18U1GhcUQnbn7ymz8+GJ3b530rguQ5UwgtDHjSpChKGjAK+G/PMVVV3cC7dFWdhBhS4eFdS34amyRp8iQXLIgC4KN1vnGleKBEhvpx9ZnxFNe5+WRvvdbhDLlFU+OZHmHkzfIaVtfrZ7naXRkpzIwIZVlNDZ9Uy/Krozl12DBemjKFEEXhjX37WFpU5NXJZnbsShaN+StJlgIMipMkSwERzX33bCW0Bw1xdEIIGJxKU2b3v3sOOZ4HRCqKEn2Ux4crilKnKIpDUZRtiqJcNPAhCj2RpMkzLTg7CoMBln4nSdOhbr0wCYAn1x3QOJKhZzQoPHPpRBTg5pydOHSy6avJYOCNSWMIUeChvXso6+jQOiSPNyE0lDenTyfBYODTkhJe3bvXqzcJzo5dyd3TF/Hc3DO5e/oiLq6I7PN+55QmD3FkQggYnKQpovvfQy8RWg+5vS/5wB+BS4GLgQrgfUmcxImQpMkzRUf7M+ukML7Lbaa20a51OB5lbKqF0yeHs7KokV1V+trsFWB6Uig3To8np9nNU0XlWoczZNIsgTwzMZMWFW7Py/PqBGCopAQF8daMGYwyGvmuqopndu/G5vSN5/rsmlgW5Y4hqdWCwa2Q1GphUe4YmZ4nxACqbGvr9337lTQpihKmKErm0d4OedihdXLlMMe/f4CqvqGq6j9UVf1aVdWPgPOA9cDd/Yzz4PS9igrZ80J0kaTJcy08NwpVhU/W628Z2tH0VJue3qCfpKG3B85II8pf4d79BZR36GcoxFWJsVyeEMPO5maeKynROhyvEOXvz+uzZjEnMpKchgaeWreOpk7f+J3Jronl7s3Tee6budy9ebokTEIMAFVV2V1fzz937OCeTZv6/bj+Vpp+StfyuqO9wfcVpfBDPkfPx/1epK52LVD+HzBBUZSjziZWVfVeVVUVVVWVhISQ/n4Z4eNkEITnumBB15r9jyRp+pEFJ0UxYpiBN3ZWUt+uv4lqUUF+/H1+Bq1O+L2OhkIoisK/x48i2ajwfEkJb5frM2k+VkFGI0+NG8fF8fHku1w8vmEDVe0yMEEI8T27y8U3FRU8sGYNT+3aRa7VyrSwsH4/vl9Jk6qqi3uSkSO9dd+9p5fp0MpTJtCgqmptv6PrFcJxPEYIACIipNLkqUZlBDF6pIEvttbR0enSOhyPYjQq/PqSEXTYYfFmfY5m/78p8WQnhbCkooav6/Szd1OYn4lls6cRY1B4cP9+SZz6yWQwcM+oUfwqNZUqt5vHNm2ioEmG7wqhd42dnXxYWMida9fyxr59VLjdnB8byztTp/Ly5Mn9/jwD3tOkqmohsI+u6hQAiqIYuj9ediyfS1EUBfgJsENVVTmjEsclNNQMSNLkqRael0h7B3y1TT8nxf113dlxWMzwzJYinC799bcYDArPnjcKBbglZxd2HfX4ZIVYWDXn+8TprQP6GwpyPBRF4abUVO4bPZpWVeWJbdvY7iN7OQkhjk1pSwsv5eVx57p1fFbatb/ZDcnJfD5zJg+OGUNWyLGtShusfZruBa5TFOUuRVHmAS8BGcDfe+6gKMpcRVGciqLM7XVstaIotyqKcpaiKD8BPgVmdH8+IY6LyWQkJMQkSZOHOrhEb50s0TtUeLAf15yVQFmDytI9+vz5TE0M4absBPJa3DxZpK/EYUyvxOmh/HzelMSp3y6Kj+fp8eNRgOd272aVVOuE0AW3qrK9ro7Htm3jb1u2sL66mgSDgbtHjWLF7NncmpZGjNl8XJ97UJImVVXfBn5J16a2y4EJwHmqqu7udTcFMPL9gAjomp73W2Ap8AYQAizoHgohxHELD/ejsVk/V6m9yYzsUKKjFD7eVIVbZ5u59sctFyQC8NR6/Z4w/+2MEQzzV7hvXyEHOmxahzOkxoRYWH3KNGINCn/Pz+cNSZz67ZSoKF7p3svprf37+aCw0Kv3chJCHJ7N6eTrAwf465o1PLt7N/uampgVEcG/x4/n4zlz+GlCAoHGo45HOKLBqjShquoLqqqmq6pqVlV1iqqqXx1y+6ruXqhVvY5dr6pqmqqqgaqqWlRVnaOq6jEt6ROiL+HhoTQ26q+Z3hsYjQrnzY+lqlZl016ZenmoMSkWzpoawTfFTWyv1OfPJyLQj0cWjKLNBbfl6mcoRI/MYAurT5lOnEHh4fx8Xi8r0zokrzEuNJS3s7NJMBhYVlrKK3v2yCh3IXxIvc3Gu/n53LF2LUvy86lzu7k4Pp4Ppk/n+YkTmR0VhUFRjv6J+mHQkiYhPEl4eABNTQ6pZHiohedGAbLR7eH85idd48efWq/fJUbXTIpj5vBQ3q2s5cvaBq3DGXKjg4NY1Z04PVJQwGuSOPXb8MBA3p4xg0yjkXXV1Ty9axcdvfZy2hhTzX3TNvHLuau4b9omNsZUaxitEKI/CpqaeD4nhz+vX8+XBw5gVhRuSU3ly1mzuHf0aNItlgH/mpI0CV0IDw9AVaGlReaJeKIzT4skwAwfbZWlR305Z3ok6TEG3tpZRW2bPjcCNhgUnjkvAwNwS85uOnU4GGN0cBCrT5lOvEHh0YICXpXEqd8i/f15bdYsTo2KIs9q5e5vv+W9ggJWhJWxOCuP8uA23AqUB7exOCtPEichPJDL7WZTTQ0PbdnCw9u2saW2lhFGIw9kZvLlnDn8IjWVSH//Qfv6kjQJXZANbj2bxWLkjHlR5OxzU1DRoXU4HsdgUPj1JWl0OuEFnY4fB5icEMKvTkpkX6ubJ4r0mTCM6k6cEowKjxUU8Er3RChxdIFGI0+MHcsvU1JQgS/KyvhvQt/LPZcny89VCE/R7nDweWkpd69Zwwu5uRS3tHBqVBQvTZzIB7NnszAuDn/D4Kc0kjQJXTi4wa0kTR7rggVdS/Q+WidL9Ppy7VlxBJvh2S3FOHRYZelx/+mpxJgV7t9fRKnOhkL0yOiVOD1eWMjLkjj1m8lg4OYRI1g5Zw4PjxkD0X3fryJINsYVQms17e28vX8/t3/7Le8XFtKiqlyemMjH2dk8PX480yMiUAaoX6k/JGkSuiCVJs933vwoFEVGjx9OqMXEdfMTKbeq/C9Xv4lleKAfjy4YTbsLbsvJ1zoczaRbuhKnRKPCPwoLeUkSp2PibzBwbmwsGe199z341StsrqmRoRFCDDFVVdlrtfLMrl38ZeNGVpaXE6Io3JaWxoqTT+bOjAxSgoI0iU2SJqELB5OmRkmaPFVcrJmTpoWyZncjDc0y6bAvv5bx4wBcPSmW2SlhvF9Vx+c1+hsK0aMrccom0ajwRGEhL0ridMxuKE3u83jnajf/yc3ljjVreL+ggJp2qTwJMZgcbjfrqqp4ZO1aHt+xgx319YwPCeHRrCw+nzOH65KTCfPz0zRGSZqELkilyTssPDcKlws+2yjVpr5kJAVxbnYk35U2s7m8WetwNKMoXUMhjArckqvPoRA9RloCWX1KNklGhX8WFrK4pETrkLzK/JpYHskdw6hWCya3wqhWC4/kjmFp4HR+npSEG/i8rIy7Nm7kiR07pPokxABrsdv5pLiYv6xZw8t79lDkcnFWdDSvT57Mm1Onck5MDH5D0K/UHyatAxBiKEjS5B0WLhjGnfcVsfS7eq46I07rcDzSrRcm8dnGBp5eX86rF4dqHY5mJsQFc8uMRJ5cV85jhWX8OSNF65A005M4nfrNRp4sKkIFbkjR78/jWM2viWV+TewPD1rgD+np3DpiBCvq6nivooLNVit5VivhisKMpCRmJyQQExioTdBCeLmKtjZWHDjAxspK7IAFuHb4cC5PTCQhIEDr8PokSZPQBRkE4R2yMoMYmWJg+dZaOu1uzP6ecXXJk5w5NYLRcQaW7K7mkbNHEhs8eONVPd19p41gydYKHsgv4srEGFKD9HsCm2YJZPXcbOau3shT3YnTjZI4nTCz0ciC2FgWxMZS2NbGe5WVfFhezvKyMpaXlTEmIoJT4uOZOGwYJg+5Gi6Ep1JVlZyGBlYcOECu1QpAgsHAz9PSuDAuDovJs9MS+QsXuiCVJu+gKAoLz0ugtQ1W7rBqHY5HMhgUbv3pSOxOeH5ThdbhaCoswMRj542mwwW/y+l7dLSejAjqSpySjQpPFxXxfHGx1iH5lDSLhT+mp7Ny9mweGjOGqWFh5FmtPJ+by51r1vC/wkJqOmTLBCEOZXe5+KaiggfWrOGpXbvItVqZFhbGk+PG8dmcOVyZlOTxCRNI0iR0QgZBeI8LzhsGyBS9I/n5mbGEBsK/t5Rgd+q7v+LKibHMSQnjw+o6PquW35mexCnFaOBfxcX8WxKnAWc2GjkvNpZXJk9m6fTpXJ2UhAtYXlrKXRs28MSOHWyR3ichaOzs5MPCQu5cu5Y39u2jwu3m/NhY3pk6lZcnT+a0YcMwDuHI8BMlSZPQBak0eY+TZ4QSGa7w0aZKVFXVOhyPFBxo4vpzk6hqUnkvp1brcDTVeyjErbk52FwurUPSXGqvxOlZSZwG1aHVpym9qk9/7q4+1Ur1SehMaUsLL+Xlcee6dXzWPdXzhuRkPp85kwfHjCErJETjCI+PJE1CF0JDzYAkTd7AZDKwYH4M5VUqW/e3ah2Ox7p5YSKKIuPHAcbHBfObmUkUtLl5pKBM63A8QkpQAKvnZpPanTg9W1SkdUg+raf69Gp39emqpCScdFWf/txTfaqtleqT8FluVWV7XR2PbdvG37ZsYX11NQkGA3ePGsWK2bO5NS2NGLNZ6zBPiOcvIBRiAJhMRkJCTJI0eYmF50bx+tvVfLSujqmjvPOK1GAbmRDIeSdF8fH6ejaUNXPScP1O0gO4Z14qb28t56H8Yq5KjCXNot+hED1SggJYfWo2c1dt5N8lJajAr1JTUbxoOYw3SrNY+FN6Or8dMYIvamt5r7KSrd2T9yIUhRnDhzM7Pp5ombwnfIDN6eTbqirWFBZS0X1RYFZEBFcnJTErMhKDDz3fSKVJ6EZ4uB+NzXKVzxucfXok/v6w9Ls6rUPxaL/5SRIg1SaA0AATj5+fic0Nv83N1zocj5EcGMA3p2YzwmjguZISnikulmWvQ8RsNHJ+XByvTp7Mh9Onc1ViIk5gWXf16Z9SfRJerN5m4938fO5Yu5Z38vOpdbu5OD6eD6ZP5/mJE5kdFeVTCRNIpUnoSHh4KGVlDVqHIfohJMTEaXMjWf5lAyXVNlJiPXPPBq2dNjmcrAQD/82p4dGzR5IQ6t1LH07UZeNj+M/mSj4uqufj6jrOjx2mdUgeYXhgV8Xp1FUbeb674nSLVJyG1EiLhT9lZPCbtDS+7FV9yrVaiVQUTho+nDnx8QyT6pPwcAVNTaw4cIBttbW4gUhF4bqUFH6akECkv29vgSGVJqEb4eEBNDU5cLvlKqs3WHhuFAAfrZNq0+EoisKtP03H6ZLx49D18/jXggxMCvwmN5cOGQpxUE/ilGY08J+SEp4uKpKKkwYCelWfPuiuPtn5YfVpq1SfhIdxud1sqqnhoS1beHjbNrbU1jLCaOSBzEy+nDOHX6Sm+nzCBJI0CR0JDw9AVaGlRU6kvMH583uSJhkjfSRXnR5LeJDCc1tK6dT5+HGAsbEWfjtrOEVtbh7OL9U6HI+S1J04jTQZeKG0VBInjaV3V59Wzp7Ng5mZTAoNJddq5bmcHO5as4YPCgupk8l7QkPtDgefl5Zy95o1vJCbS3FLC6dGRfHSxIl8MHs2C+Pi8NfRps76+U6F7snYce+SlBjA1MnBrNpppbHVoXU4HssSaGTRgiRqWlTe2VWjdTge4e55KSQEKPy9oISCNjnp7C0pMIDVc086mDg9KYmT5nqqT69NmcIH06dz5SHVpyd37GCbVJ/EEKppb+ft/fu5/dtveb+wkBZV5fLERD7Ozubp8eOZHhGhy+W9kjQJ3QgP71orLkmT97hgwTCcTli+SXrRjuTmhQkYFHhqy345AQZCzCaeWDiGTjfcmiM/k0MlBpoPJk4vlpbyz8JC+Rl5iHSLhdu7q08PdFefcqxW/i3VJzHIVFVlr9XKM7t28ZeNG1lZXk6IonBbWhorTj6ZOzMySAkK0jpMTUnSJHRDKk3eZ+G5XY38S7+TJXpHkhoXyAWzhrGlxMW6smatw/EIPx0Xzelp4XxW08BH1fL7c6jEQDPfnHoS6SYDL5WV8YQkTh4lwGhkYXf16X/Tpv24+rRzp1SfxIBwuN18V1XFI2vX8viOHeyor2d8SAiPZmXx+Zw5XJecTJifn9ZhegSZnid042DS1ChJk7eYMM5CSpLCsq012B2Z+PvJdZ7DufUniXzwbR1PrTvArOQwrcPRnKIo/Ou8DCY8vYnf5OZwZvRsgoxGrcPyKAkBXYnT3FUbeLmsa1Pg36Wl6XLZjSfLCA7m9l6T996tqGB7QwM5DQ1E9tr3SSbviWPRYrezuqKCtSUlNKgqBuCs6GiuTkpiUpi8hvRFzkCEbkREyPI8b6MoCgvPS6CpGdbsatI6HI82d0I4E5IMvJdby4Emm9bheITMaAu3zR5OSbvKQzIUok/xAWZWn3oSo0wGXi4r4/GCAqk4eajA7urT693VpysSE+lUVT6T6pM4BhVtbby2dy93fPcdHxUX06mqXDt8OMtmzODxsWMlYToCSZqEbsjyPO90wYLuJXoyevyIFEXh1kszcLnh3xtl/HiPu+amkBSo8EhBCftb27UOxyP1TpxePXCAxyRx8ngZwcHckZHByjlz+FtmJhNCQ8lpaDjY+7S0qIh6m1w8EV1UVWV3fT3/3LGDezdtYm1lJcMMBm5PT+er2bP5fyNHkhAg+yEejSRNQjckafJOp5wcRlgofLS5Qk7kjuKK02KItCg8v7WMDoeM1gcI7h4KYXfDr2UoxGHFdSdOo00GXjtwgEclcfIKgUYjF8TF8caUKbw/bRqXd1efPi0p4c7163mqu/rkkuqTLtldLr6pqOCBNWt4atcucq1WpoWF8eS4cXw2Zw5XJiVhMUmnTn9J0iR0Q5Im7+TnZ+Dcs2MoOaCys7BN63A8WqDZyI3nD6e+VWWJjB8/6OKx0Zw5MoLPa618WOVbFcsl5fOYsHoxpk9XMGH1YpaUzzvuzxUXYGb1vBlkmgy8fuAAj0ji5FVGBQdzZ3f16f7Ro5kQGspuqT7pUmNnJx8WFnLn2rW8sW8fFW4358fG8s7Uqbw8eTKnDRuGUXoXj5kkTUI3Do4cl0EQXmfhuT0b3frWCe9guOn8BIwGeGpzvpzwdlMUhacXZOCnwG9yc2lz+kYVbkn5PC7fdje7WkbiUo3sahnJ5dvuPqHEKdbsz6ruxOmNAwd4JF9+j7xNoNHIhfHxP6g+2Q6pPm2vq5Pqkw8qbWnhpbw87ly3js9Ku/o4b0hO5vOZM3lwA7IkGgAAIABJREFUzBiyQkI0jtC7SdIkdEMqTd7rnDMiMZlg6XeSNB1NckwAPzk5mu1lLtaUyPCMHqOjg/jDnGTKOlQeyC/ROpwB8WD+lX0ef+gwx/sr1uzP6nkzGGMy8EZ5OQ9L4uS1Dq0+je+uPj27e7dUn3yEW1XZVlvLo9u28bctW1hfXU2CwcDdo0axYvZsbk1LI8Zs1jpMnyALGYVuhIZ2PWlI0uR9wsP9OHVOBCtWWjlQayMpWhpWj+Q3FyXy3ppanlp3gFNSw7UOx2PcOTeFNzaX8VhBKdckxTE62Ls3asxtTT3M8ZQT/twx3RWneSvX82Z5OSpwe3q6jCP3Uj3Vpwvj49nb2sp7FRV8VFHBpyUlfFZSwtjISE5JSGBcZCQmg1xP9wY2p5Pvqqr4prCQiu6q4ayICK5OSmJWZCQG+VsdcJI0Cd0wGg2EhflRUNxJZ6cbs1leGLzJwnOjWLHSyrMfVfDg9Wlah+PRTh4bxuRkIx/k1bGioIEzRkZqHZJHsPgb+ecFWVz0dg6XbN3Mx9OmkxrkvXvbZAUXs6tlZB/HB6aSdjBxWrWet8rL2dvayu9HjmRcaOiAfH6hjdHBwfx51Ch+N3Ikn9fU8F5FBTsbGtjd0EAgkB4ZyZiICMZERJBgsUii7EEaOzvZUV/Pjro69jY04AD8gIvj47kqKYl0i0XrEH2a4qsl92nTEtXNm2/SOgzhYW6++WOefXYjt9+WzEP3yYm3N7FaHUyZtY7iA26W3jeOhbOGaR2SR1u9o5Gzbt+OyQBfXjNZNrztpqoqt36az782lDPMX+H9qRM5Jco7q3E9PU2HenvyX7ksceWAfZ3aTjuLdu7lo+p6AM6NieHWESNIlM1Ufcbe1lY+rKxkVWUlB3r1OkUoCqOio8nsTqIiZSx1v6iqitPtxu524+j51+XC3ut9R+/bu29z9LrfoY9rstspa209+DXSDAYWpKRwSXw8kf7+Gn633m/8qlVbVFWddrT7SdIkdKW1tZOJE5+guLiNtV9MZuZJciLpTbbvbGHW6Vvw84ONT2Yzerh3L68abEu/q+Pi+3YTbIZV101lUrw0Aff498Zybv1kPwDPjBvFjSkJGkd0fJaUz+Oh/CvJbU0hK7iEO9LfHNCEqbdVdVZu27KbbQ4X/sCVw4ezKDmZUD+/Qfl6QhtVNhvrrVbWW618V1uLtdd5YpLBQHpcHGMiIhgdHk6Ql/zf905i7IdJTHretx+S0Pzgvkd4nNtmoxOwqyp2YKDPro3AtPBw5g0bxtyoKJLkosWAkaRJkiZxGGvWFDN37oukpwezbc1kLBaj1iGJY/DWf6u58vo8Mkca2PD4LEItssr4SN78qpqrH85jWLDCmuumMzpaEs0eq4qsXPLGTurtKjenJPDE2HT8pJ/jiNyqypKKGm7fuYcyl0qEQeHGtJH8LCFBfnY+SFVV8tvaDiZRGxsa6BkbYQCSQ0IOLuUbGRbW798BVVW7EpK+kpjeVZdDkpie9/t83CG3D3YSA11L4/wBs6IQaDYTYDBgNhgIMBp/8L65532DAXP3bb3fP/S2nvsHdj+25zYZEz44JGmSpEkcwe9/v5zHH/+WX/8ykacezdA6HHGMbrs9nyeeOcCFJw/j/bvHYjDIC8mRPP9JBb98ch9JEQprrssmNUKuUPYosnawcPEmdje7mRcVzrtTxxLl7x1Xz7Vkc7l4qqicB/cW0qRCmtHAzaMzOTM6WnpgfJjD7WZ3S8vBJGpH0/9v787joq72P46/zgz7voOIgOK+pKLmlrfM3NoXsyytbLlFe1mW1a1uVreu7Ztpmy2m2fZr77Zq7qKo4Ia4AAqi7DPszMz5/TFoaKaI4JeBz/Px4AHzncVP9AW+7znnfE4pBxv4ewKdgoPxMpv/Emjqh5jquhDTHOqHGJ+DIaZ+8KgXYryO+NrziNDi/TdhR0JM6yOhSUKTOIaqqloSE19k61Yrv3zTl1FnBRtdkjgBNpuDMRel8vsfJcy8Lp5Hro43uqQWb9aibKa/tYuEcBNLpw6mnb+0oD3IWm3jms+38X9bC+joa+LrAYn0DvAzuiyXUFBTw8ztWbyRmYMN6BsQwH0JCfQLlKnPbUG5zca60lJWFhezLDeXzCP2fnLHGWA84C8hxvsEA82RIzb1R3MOvo6EGNEYEpokNInjWLs2hyFD5tC+vTepKxIJDJRpXq4kP7+GgSNWsidX883MPpw3ONToklq8f83bzZPzs+gVbWLJtUMJ9ZERlYMcDs3jv2cyc3EWfm4wv19vLoySZiMNlVFWwYxtu/g8z7mX2ujwcO7u2JFYH5kO2paU1tbi0PrQqIy0vRauoKGhSSYgizZr4MD2PPzwmWRnV3DPgzuMLkecoPBwD75cmIinB1z93zQy9lYYXVKL98S18dx5cXs25zoYv2AVlirZs+wgk0nxxKiOfDKxJ3YNF6/dxNMZWbKpawN18fPhs4G9WTasP0OCAvg5P5+L16zh2YwMSmprjS5PnCKB7u4Ee3jgYzZLYBKtjoQm0aY98shZ9O8XxHsf5fHN9wVGlyNOUGI/f+a+2p1SC1z89FqsFRICjkUpxYtJnZk6NorkTDsXzE+josZ+/Ce2IRP7RLD8nwOI8VY8nL6bSeu3UGGX71FDDQ8JZMXw/ixK7EmM2cRHOTmcv2I572VnUy3fRyGEC5PQJNo0d3czH3w4GQ8PEzfdmUFBQXMtTxXNZcqkKO5Mas+WDAfXzdomIwPHYTIp3rqnGxNGhPNHZikTFm6mxuY4/hPbkP7R/iTfOZThsQF8kpvPiJUr2FNZdfwnCsAZzi+PjmDrmDN4sWcCJhQv7NrFpSuW8f3+/TjkZ1QI4YIkNIk2r3fvSJ58cjT791dz27QMo8sRjfDcUwmceUYgXywr4JmF2UaX0+KZzYr5M3owblAIP2QUcfVnW7HZJTjVF+nnwa9T+3HDgChSSuwMWraaFUWlRpflUjzNJu7u1IGdo4dxX6cO5Nk1D2zdytQVy0guKTG6PCGEOCHNEpqUUlcopb5QSu1TSmml1HUn8NzhSqnVSqlKpdRupdSdzVGjEPXde+8whg+PZdEX+Sz8bL/R5YgT5O5uYtEHvYhpp3j4vd38mFxodEktnoe7ic8f7cU/+gTy2eZ8/vn1dhwOGQGoz9PNxFsXdeOV8zpTUKMZuWo97+3ZZ3RZLifYw51ZPRPYdvZgroyOIKXWzvUbNnBHWhq7ysuNLk8IIRqkuUaaJgDxwLcn8iSlVGfgf8Bu4DxgDvCCUurGpi5QiPrMZhPz5l2Kj4+ZW+/dwb68aqNLEicoItyDLxb0x8MDJj2ziR050hjieHy8zHwzsw8D4828l5LHPT/skOmNR1BKcceQGH689jR8zYrrN6Zz7+Yd2BwyMneiOvp4syCxJ6uHJzIiJJDFhYVcmpzMk9u3U1gjU6OFEC1bc4WmK7TWicA9J/i8+4FcYLLW+jet9TPAXOAxJbvliWbWuXMos2aNp7i4lhtvS5eLRxc0aEAAb77UjRKL5pL/rKWsUhpDHE+Arxs/Pj+EXtEmXlmVw2O/ZRpdUot0TkIIq28bRA9/Ey/u3st5yWkU10hXuMY4PTiAJUP78X8De9PRzcQnublcsHIFb2VlUSnNIoQQLVSzhCatdWPfghsPfKG1rn+lsxCIAXqfdGFCHEdS0umMHp3A9z8V8e4HeUaXIxrhusntuP3m9mxKd3D9cxJ+GyI0wJ2fXxhMQriJmYuzmLVM1oUdTZdQH1beOYzzuobwU34xg1esZFuZTC9rDKUUF0WFsWXMGbzeuwseSvHK7t1cvGIZX+XlSbMIIUSL02IaQSilfIEOwLYj7tpa97n7qa1ItEVKKd555xICA925+8GdZGZVGl2SaIQX/pPAiGGBfPpHPrMW7TG6HJfQLtSTX14YRPtgxfT/7WJOcq7RJbVIgV5ufHV1Hx4Y0YGMMgeDlyXzwwFZQ9dY7iYTt8a3Z+fo4czoHEuRXfPItm1MWbGMlUVFRpcnhBCHtJjQBATVfT6ypU5x3efg472AUurxusYTOjfX2qTFibajQ4dAXnnlAsrKbExNSpfF8S7I3d3Epx/2on2UYsa7u/hprVx8NUR8lDe/PDeQcH9F0jfb+XijNEU5GrNJ8cyYBOZP6EGNA85bk8ZzO7NlVPMkBLq78XT3TmwfNYRrYiJJq7Xzz9RUklJTySgrM7o8IYRoWGhSSgUqpbof76OJavq7vzrH/WuktX5ca6201io62r+JyhFt0ZQp/bjoou4sXlrCa3NyjC5HNEJkhAeff9wfNze48pk0du+TUcOG6B7ry0//TSTAC675YitfbZVNn//OVX0j+eOmRNp5Ke7fuotrN2yjStbknJQO3l68368H60YM4OzQIJYVFTFh7VoeT08nv1oa9AghjNPQkabLcU6TO97HyTg4whR0xPHgI+4XotkppZgz5yLCwjx44NFdpG+XTmyuaPCgAN54oRvFpZpJL66lVjZxbZB+nf35/j/98XSDiYs28ctOGan7O4NiAlh75xAGh5j5MGc/Z65eQW6VXNyfrP6B/vwypC/fDepDVzcTn+/bx/mrVvLG7t1U2KTBixDi1GtQaNJav31wBOdYHydTiNa6HNjDX9cuHbx95FonIZpVZKQfs2dfQlWVg2uTdmCTC26XdP01UVx9RQSr19t57P1Mo8txGcN6BfLVzNMAuOjjVFZky8auf6edvyeLbx/GlL6RrCmyM3DZKtYUW4wuy+UppTg3MpS0MWcwt09X/JVidlYWF65Yzue5udhlOqQQ4hRqSWuaAH4ALlFKmesduwJnmNpkTEmiLZswoTdXXXUaq9cU8d8XpaGAK1JK8cYLXekUa+KZT7L5NaX4+E8SAJyTGMKif/Wm2gbnfrSeDftkrejf8XI38/5l3XluXAL7qzT/WJnCR3ulA2dTcDOZuCkumh1jhvNYlzisWvP49u1MWr6UpYWFspZMCHFKNEtoUkr1VEpNAC6uOzRQKTVBKXVmvcecqZSy1T8GzMLZXvxDpdRIpdR04GbgCS2/FYVBXnvtfKKjvXj8P5lsTJMFya4oIMCNBe/3w2yGKc+nkl8iG2k21EXDwnh/eg8sVTDmwxTS82Wq6t9RSjFteAe+ndIHLzNM2bCNB7bulBGRJuLn5sbj3TqSMWooN3SIIt3m4Na0NG7auJFtVgn0Qojm1VwjTROBT4EP627fVnf73/UeowBz3WcAtNY7gHFAZ5yjTrcC07TWbzdTnUIcV3CwN2+/fTm1tZprbs6gulqm6bmi0wcG8NSjndh3QDP1uW3y7vQJuHpUJLPv7Eq+VXPOh8lkFktTjWMZ3zWU1Umn09XPxH937uHC5DRKa2UdTlOJ9vLk7b7d2fCPgYwLD2F1SQkT163j4a1byauqMro8IUQrpVrrhcPAge312rVJRpchWpGbb/6KuXPX8tB9sTz1WCejyxGN4HBoxl2Sys+/FfNSUmfuujTG6JJcyqxF2Ux/axcJ4SaWTh1MO39Po0tq0Yora5m0aAv/21FMd38TXw8YSBc/H6PLanV+zi9iWsom0modeAFTYmO5PjYWPzc3o0sTQriAPosXr9NaDzze41ramiYhWqznnhtHx44+PPNCNqvWyKJ4V2QyKT6Y253wUMX0d3awfodM6TkR90+M5ZGr49iZ72D0R6sprKg1uqQWLdjbnW8n9+HeYTFsszo4fXkyP+dLJ8KmNjo8hPVjRjCvb3dCzIq3srO5YMVyFubkUOuQmQFCiL9XWtvwv2MSmoRoIH9/T95770q0hmuTMqiokP1YXFFUpCfvz+1NTQ1cOWs9ZZUybepEPHFtPHde3J7NuQ7GL1iFpUq+f8fiZjbx/PjOvHdJNyrsmvFrUnll916ZHtrEzEpxbYcoMsacwZPdOlKlNU9lZHDFimX8VlAg328hBAB2rUm1WJidmcnVKSn8Y/nyBj9XQpMQJ+DMMzty993D2L69jBmP7zK6HNFI48eEcu/tMWzf5eCuN3YYXY5LUUrxYlJnpo6NIjnTzgXz0yirluB0PNcltmPxDf0J81DctXkHN6WmU22XUZCm5mM283CXOHacM4ykuGh22RzctWkTUzdsYJNF2sAL0RblV1fzf/v2cf/mzYxc+gdXp6TwRmYmmy0WhoUENvh1ZE2TECeosrKWxMQX2bbNym/f9WXkP4KP/yTR4lRXOxh21nJSNtlZ8FAPrhwZaXRJLsVu10x6eguf/pHPkE5mvrtiCCE+7kaX1eLtLa3i4neSWVdsZ1hwAF8M7E2kp4fRZbVa28rKeWDrLr7eXwjA+IgI7urYkfbe3gZXJoRoLrUOBxtKS1lWVMTyoiLSy8sP3RdjVoxvH8W48BBGhQUT6O6G+rZha5okNAnRCMnJexk6dC4xMd6krkgkIEAWHLuijB0V9B++BrMZNrw+mI7t5ELqRNTaHFz/XDof/bqf3u1N/HS1NIdoiMpaOzd8mc6CtAPEeCu+GphIYqC/0WW1aksKS7h3bRoptXY8gNEREQwODmZgUBAxXl4opY77GkKIlmtvZSXLi4pYVlTE2qJCyurijSdwZngwY8NDGBceQg8/n7/8vEtoktAkmtmjj/7KzJmLueGaKN5+vbvR5YhG+uDjPK69eRuD+5tZ+vRw3N1k1vKJcDg0d72xg9e+yiEh3MTPUwbRMVjC5/ForXl2aTYP/bwbLzPM69uTidERRpfVqjm0ZmHuAR5KTSer3tTIDmZF/7AIBgYFcXpQkIxCCeECKu121paUsLyoiNX797HD9ufPdGc3E+fGtGNcRAhnhgbhYzYf87UkNEloEs2spsbGkMEvs35DCd9+2ofzxoUaXZJoBK01U27ayvxPDjDjylievkHayZ8orTWPvZ/JzPlZRAcpfp4ykJ4RvkaX5RK+2VbAVZ9soswGj3SJ499d4zHJqEezcmjNZms5iwtLWFxYwu8HCil2/HktFGtW9AuLYFBQEKcHBxPt5WVgtUIIcP6d2VVRcWjKXUpxMdV19/kpGBURytjwEMaGh9DJ98Te+JDQJKFJnAJpaXkMHDibkBAPNq0aQGiorOlwRRaLjf5DV7B7j4Ofn+nLqERZp9YYL36+h3vf3EmIr+LHyf0ZFBNgdEkuYfP+ci58bx27yh1cFBnKh/174C97DJ0yDq3ZVBeifi8oYcmBQorrXRvFmU30C4/g9KAgBgUF0U5ClBCnhNVmY3VxMcuKilh9II+99j9/LvsG+DKubsrdsJBAPEyNnyUioUlCkzhFnn32Dx588GeuuCychfN6GV2OaKQ1ay0MH51CeIhi4+tDCQ+SxfmN8e6P+7jphXR8PODrq/oyspME0IYorKhl4ieb+W1XCb0DTHw9cBAdfWSamBEcWpNmLWdxQQm/Fxaz5EARJfWuleKPCFFREqKEaBIOrdlaVsbyutGkjaWlHNzcJdikGBsVztjwYMaEhxDt1XTrZyU0SWgSp4jd7mDEiLdZuXIPC+f15IrLZF2Cq5r1UjbT/7WL8waH8M3MPrI4vJE+X5rPpKc2Y1KwaGJvLuwRZnRJLqHW7mDajzt5dVUOoR6KRYmncXaYhE6j2bUmzVLmHIkqLOGPI0JUR7OJ/uHONVESooQ4MYU1NawoKmJFcTGr8g9QUDdV1gScHhTAuAjnaNLAIH/MzfQ3WUKThCZxCmVkFNK376t4e5vZtHoA7aKkg5grcjg04y5J5effinkpqTN3XRpjdEku66e1RVzyWCrVNph3SXcm94syuiSX8dbaXG77eju1GiZFR/BMj07EesuFeEvh3ByzfogqpLTepVQns4n+EZGHQlSkp/w9EOIgm8NBqsVyaG3SlrKyQ/dFmRTj20cyNjyEc8KCCfU4NUseJDRJaBKn2GuvreKOO77j/HGhfL2ot4xSuKi8/dWcNngVpVbNqpcH0L+ztIJurBWbSznvoQ2UVGhePa8ztw+RENpQq/dYuP3TjawttuNlgns7xfJg51hZ69QC2bVmo6WM3wucjSX+yC/EUu/SKsHNRL/wSAbVhagICVGijcmrqjo05W5NYcGhNxncgTNCg5xrkyJC6OPva8i1k4QmCU3iFHM4HIwZ8z6//rqLd17vxvXXtDO6JNFIP/5cyPhL0+jaycS6l4bh5y0Xqo2VuquMMfevY79F88TZ8TxyVpy8odBADodmfup+ZnyfTk6lJspL8WTXrlzXIarZpqmIk2fXmg2lZfxeWFwXooqw1rvU6nxEiAqXECVamWq7nXWlpc6QlJfLtnrtwOPNJs6NiWJseAgjw4JaxBtBEpokNAkDZGeX0KfPy2gNaasGEhcrU2pc1X0P7eD5V/dy/bgo3pkm+3CdjB05FZwzLZmsQs09w2J4flyCBKcTUF5j5/nle3h2SSYVdugbaOKFHn1kvZOLsDkcrK+bzre4sISlR4SoLkeEqDAJUcLFaK3JqttcdnlREWuLi6isO8e9FYys63I3LjyEzr7eLe73v4QmCU3CIPPmpTB16peM/EcQv3zTF5OpZf1yEA1TU+Ng2FnLWZdmZ8FDPbhyZKTRJbm0nIJqRt+7mq37HExNjGLuhV1xM8tGwicix1LNw7/s4v31+wG4MDKUWT0S6OrnY3Bl4kTYHA5SSuuFqIIiyupdinV1M9EvIopBQUEMDAoizEM6eYqWp9xmY82hzWXzyKy3YXQPNxPnxkYzNjyEESGBeB1nc1mjSWiS0CQMorXmoovm88036bwyqzN33CLrOFxVxo4K+g9fg9kM618fTKd20gL6ZBSU1jBu2mrWZdm5tGcYH1/eE083CU4nal2OlXt+2MHSrFLcFNwW355Hu8QTcooWTYumVXuUEFVe79Ks2xEhKlRClDCA1prt5eWHRpPWl5RQW3dfgILRkWGMi3BuLtvBxRrXSGiS0CQMlJdnpXfvl6iosLNh+QC6dpF3gl3VBx/nce3N2xjc38zSp4fjLhf5J8VSbuPCR9NYklrK6IRgvpjUCz9P4+e0uxqtNV9uKeD+b7ayq9xBiIfisS4JJMVF434SmzwK49U6HKwrtR7qzresoJiKepdq3Y8IUSESokQzKamtZdWhzWX3k+f480QcGOjP2PBgxkWEMDgowKV/70hoktAkDPbpp5uYOPEThgwKYOlP/XCTi22XpLVmyk1bmf/JAWZcGcvTN3QyuiSXV1ltZ+KTW/h2VSFDOgTw/ZQ+BHvLKEljVNscvLpqLzN/3YXFBl39TDzXoyfnR4S2uHUDonFqHQ7WHgxRBSUsLzw8RPVwM9Evsp0zRAUGEiwhSjSSXWs2WSyHRpM2Wa0cnHQXZlKMaxfBuPAQRocHE+HZes4zCU0SmkQLcNVVi1iwII1bb4rmxWc64+EhwckVWSw2EoetYFe2g3endeO6sdIZ8WTV2hxMfW4b8389QI92JuaedxpnxAUZXZbLyi+v4bHfMpmzJhcHMCIkkNvj23NxVBgeLvwOsPirGoeDtSVWfq+bzre8sPjQontwhqhuYRH09vent78/Xf38XHoUQDSfCpuNTVYrqRYLGy0WNhYXUVw3mmQGhoUEMraugUP/QD9MrfSNGAlNEppEC1BUVMHQoa+yfXsZAwcE8/HbXejSWabquaK1KRZGnZ+CxQpTx0bx6m1d8PVu2YtbWzqHQzNtzk5e+mIvAJP6RPDfsZ2ICXSt+fAtyeb95Uz/aSffby8CINJTcWOHDvwzLlo2yG2lahwOkkush1qcrywsOWwkygPo5u9PL39/egcE0Nvfn3gfH2lb38Y46jrcbSwtJdViIdViIaO8HEe9x8SaFWPbRzEuPIRRYcEEureNqdMSmiQ0iRbCaq3mjju+4/331+Pra+bVWZ25bnKUTJ1xQTt3VXLF5LWsS7PTs4uJRdMH0Cve1+iyXN6KzaXc+WIq67Ls+HjAQyM6Mm14DF7uEkobKz2/gjeTc5m3NoeSWo0JOC8ylKQ4Z0er1vqOsXB259taVkFyiZXkUivJJRZSS8sOLdoH8FPQPSDwUIjq7e9PtJeX/F1qRaw2G2l1I0ipFgubi4sprnfN7wUMDAlkaFAAQ4KdH9FebbPdvYQmCU2ihVm4MJWbb/4Si8XGxEvDefOlrgQHyzoOV1Nd7eD+R3by6ps5eHvB67d147qxEoJPlsOhmfdTHjPmbueAVdMxzMTzo3twcY8w+d6ehIoaO59sOsDsNbkk51gB6Ohr4uYO8VzfIYrwVrQuQfy9KrudVEs5yaVW1pRYWJ2Xz3abg/pXgKEmRY+g4EMhqldAgLQ7dxEOrdlZXn5omt3WggOkH/H/t5PZxNCoMIYEBTA0OJDTAnxl2mYdCU0SmkQLlJlZzOTJn7F8eTaxsT58NLcrI4bLOg5X9MVX+Vx/62ZKLTDlnEjeuLMLft5tYypDcyott/HEh5m88n97sdlhVKcgXj63C70iZUTvZK3LsTI7OYePN+RRaQcPE0yIiiApPprhwYESTtsYS62NlNKDo1FW1hwoJMvuOOwxHcyK7sGh9Kobkerp74+/m/yeM1pJbe2hgJRqsbClpBhLvct5XwWnhwQxtG4EaXBQQKtq3NDUJDRJaBItlM1m56mnlvDEE78D8PD9cTz6YJx013NBuzOd0/WSN9rpnmBi0QOJ9OnoZ3RZrcK27HLueXMnPyYXYTbBrae3599nx0uXvSZQUlnLBxv28+bSXWy1Oi+SeweYSIpNYHL7SALayDoG8Vf51TWsPRiiSiysKSgm33H4dWIXNxPdQ8Oda6T8/enu54dnC9+8tKk4tMZe9+HQGnu9Y0e7bdMaBxy6fazn2A7ePuI5B2+X2WxssVrZUpjPDtvh4barm4lhUREMCQ5gaHAAvfx9Zc3aCZDQJKFJtHDLl2dx9dUfk5VVwdDTA5j/Tg86xsvmqa6mpsbBg4/u4sXX9+LlCa/c2pUbx7eTd+2bgNaa71YXcvcrW9iZ7yDUT/HU2V24cUA7zCZIjo3yAAAaMUlEQVT5/p4srTVLMkuYvSaXLzbnY9Pga4bJ7dtxS1w0/QL9jS5RGExrzZ6qauf6qBILySVW1haVHDaq4QZ08fOjV12zCV+z+bBgcKIB41iPscNhr3m8123ov61sNdg0ztc/eF/d1/Z6x1uCQAWDw4IPrUU6PShANrY+SRKaJDQJF1BSUklS0jcsXJhGQIAb89/uzvnjw4wuSzTCV98WcN3NmymxaCaNjGDO3V3x95F37JtCdY2Dl77Yy5Mf7aKsGvp1MPPKmD6MiJeprU0lz1rNuyl5zFmRSXZd67UhQQEkxUczsV04Xm1kJEEcn0NrtpfXNZqo+1hfYqHa6MKOwnzwQ/35tUmpw467eXjgphTmQx9g5ojbR9x/2OM5+mMOfu125DH+/nUPf/zhtXiZTfQL8KO7n480cmliEpokNAkXobXmww83kJT0FbW1Dj6Z15NLLgw3uizRCFnZVVx53RZWJVvo0tHEoun96ddZ3q1vKvsKq3nwnV188PN+AK7oHc6scQl0kBblTcbu0PyQUcjsNbn8sL0IDYR4KKbGtOeWuGg6+8qWCeKvah0ONlnLWV9aRo12HDd0HAwPbqaDwePvQ0f98PDn448dVMxKYQIZ8RcNIqFJQpNwMUuXZjJ+/Dyqqx0snNeT2tqJPP3c1WzZFk/P7pk8dN98rpzwu9FliuOorXXw0OO7ee6VPXh6wos3d+GW86Plj3cTWrXF2aI8OdOOtwc8eEY895/RAW9pUd6kdhdXMjd5H++s2UN+tfNaYXRYMElx0VwQGYqbdN4SQrQCEpokNAkXtHx5FuPHz6OsbAJaL/jL/Qvee0KCk4v47sdCrrlpE0UlmolnhjP3nm4E+sp0vabicGg++DmPB+duZ79FExeqeO6cHlzWK1wCahOrtjn4Yks+s9fksjSrFID23oqbOsRxY4d2tPdum3u7CCFaBwlNEpqEi1q5MpszzrgDh6PPX+47rfdONq680YCqRGPs2VvFpKlbWL7KQkKciUUP9Cexi0zXa0qWchsz52fx8hd7qLXDyI5BvHxuZ/pESRfD5rBpfxlvJufywbpcrDbnmpCLIsNIiovm7LBgWWshhHA5EpokNAkX5uY2A7v9r1ON3Nxs1BaPNqAi0Vi1tQ4efTKTZ17IxsMDnr+pM7dd1F5GQ5rY9r0V3DN7B9+vKcKkIOn0aJ44uyMhPtJVqjmUVdv4OPUAs//YyYYSZ1+xLn4mbontyLUxUYRKNy8hhIuQ0CShSbiw0067ibS0yL8el5Eml/Xjz4VMuXETBUWay0aE8fa93QjykwvLpvb96kLufnUzGfsdhPgqZo7szD8HtsPNLOtvmoPWmtV7Lcxek8snqfupdoCnCa6IjiQpLprBQQHyBoEQokVraGiSvyJCtEAPPbT8qMdnTJt/iisRTWXc6FA2rBzCP4YH8vnSAhLvXElyusXoslqdcweHsum9M5j1z07U2jW3fZvBgLeXsXh3sdGltUpKKYZ0COT9y3qQ88BwnhuXQAcfEx/s3c/Q5esZsHwZc7NyKbPZjC5VCCFOioQmIVqgK6/cwoIFX3Daaftxc7NjNqcCV2K1SmhyZe2jPfn12748Mj2OzL0Oht+Twstf7KW1jvgbxcPdxH2Xx7L9/aFMHRtF6l4HI9/dyMSFm8kqqTK6vFYr1MedacM7kH7/CH669jQu6RFGaqmdm9O20/6XZdyetp3N1nKjyxRCiEaR6XlCuIDU1DxGjXqLgoIaZr/UhVtuaG90SeIk/fxbEZNvSONAgebi4WG8O60bwf4yXa85rNlm4c7XM1i9zYqXOzxwRhzTz4jFx0NalDe3HEs1b6/dx9yVWeRWOa83RoQEkhQXzaVR4XjKtEkhhMFkTZOEJtHKbNq0n7PPfov8/Gpee74Lt/1TgpOr25dXzdU3bOX3P0qIi1F8Mr0/g3sEGF1Wq+RwaD76dT8PzEknr1QTG6J4bnQPJkiL8lPCZnfwTbpz09yfdzqnSoZ7Km6I6cDNce2I9/E2uEIhRFsloUlCk2iFtmw5wNlnz2X//mpeerYzd90aY3RJ4iTZ7ZqZz2byxDNZmM3wzPUJ3DshRi7km4m1wsZTH2fxwmfOFuVnxgfyynldOE1alJ8yGYUVzEnO5b3kHIpqNAoYHxFCUlw04yNCMcu5L4Q4hSQ0SWgSrdS2bfmcffZc9u2r4vmnE7j3jg5GlySawG9Lirl6aip5+Zrzh4Qy7/7uhAbIdL3mkrG3gnvn7OTbVYWYFNw8KJqZozoSKi3KT5nKWjufbspndnIuq/Y4m6LE+Sj+2SGeG2LbEenpYXCFQoi2QEKThCbRim3fXsDIkXPIza3ivzM7cf/dsUaXJJrA/gM1TL5xK7/8XkyHaMXC6f0Y1ivQ6LJatR+TC7n7lc2k5zkI9lE8MTKBWwZFS4vyU2zDPiuz1+Qyf/0+yu3gruDSduHcEhvNmaFBMvIqhGg2EpokNIlWbseOQkaOnMPevZU8/VhHZtwXZ3RJognY7Zr/PJ/FY09lohQ8dV0n7p/YAZNJLhqbS02tg9e+yuHfH+zEUgm925t4eUwfzu4UbHRpbU5plY2PNu5n9h872WxxANDD38QtsZ24JiaSIHcZCRRCNC1DQ5NS6grgCmAoEAVM1VrPa8DzHgceO8pd47XWP55IDRKaRFuwa1cRI0fOITu7gpn/iueR6fFGlySayJJlJVx13UZy92vGDwrh/endCQ+S6UrNaX9xDQ+/u4t3/5eH1nBpzzCeH5dAfLA0KTjVtNYsyypl9ppcPtt0gFoNPmaYFB1FUlx7BgT5G12iEKKVMDo0fQokAOuAGzmx0HQ3MO6Iu7ZqrUtPpAYJTaKtyMws5qyz3iQrq4LHH4rnsRnxRpckmkh+fg1T/rmV//1STHSkYuEDfRnRJ8joslq9tekW7nxjByu3WPB0g+lnxPHAiFh8pUW5IQ6U1fBuyj7mLM8is8I5+jQo0J+k+GiuiI7Axyz/X4QQjWd0aDJprR1KKT/AyomFptu11mEnW4OEJtGWZGWVMHLkbHbvruBfD8Tx74fjZQ1AK+FwaP77YjaPzNyN1vDENR2ZMSlWpus1M6018+talOeWaGKCFbPO6c4VfSLkZ8sgDofmfzuKmL0ml+/SC3EAQe6K62Lac0tcNN38fIwuUQjhghoampplpavW2tEcryuEOLq4uCCWLLmVhARfZj6bxcP/3k1rXa/Y1phMigenxbH4+35ERyoembebcTNSOVBcY3RprZpSisnnRJH+wXBmXBnLAatm0qdbOfOdDazPtRpdXptkMinGdw3l68l92DVtCA+fGYunCV7avZfui9cwauUGPss9QK1DLkGEEE2vJbYHClJKFSilapVS65VSlxpdkBCuoEOHQBYvTqJLFz/+83w2Dz66S4JTK3LGsCDWrxjKeWND+DmlmH63r2TxxmKjy2r1/LzdePqGTmx5ZzAXDg1laVYpA95cxy1fp1NQLsHVKHFBXjx5TieyHxzBJxN7clbHIH4rLOHylC3E/baUR9N3s6eyyugyhRCtSLN2z2vE9LzJQASwAfADbgbOBS7TWn/RgOc/Tl0jiXbt/MnNnd7o2oVwVbm5Fs4+ezbp6WXce3sMzz2dINOJWhGHQ/PCq3uY8fguHA54bHI8D18Vh9ks/49PhZ/WFnH3K5vYus9BkI/i32clkHR6NO7SotxwWw+U82ZyLu+vy6G01vmu8PmRoSTFRTMmPAST/B4UQhxFk65pUkoFAu2O9zit9bYjnndCoeko/64CVgDeWut+J/JcWdMk2rJ9+6yMGjWbrVut3HVre158prMEp1Zm5epSrrx2A9k5mrP7BTF/Rg+iQjyNLqtNqLU5eP3rHB6ft5PSSugZbeLlMb05JyHE6NIEUF5jZ2HaAWavyWFdbhkAnXxN3NwhnutjowjzkC6UQog/NXVouhF463iP01ofdlV2sqGp7jXuB54F3LXW9oY+T0KTaOv27y9j1KjZbN5s4fab2/PKLAlOrU1RUS1Tk7bx9feFRIYr5t93GqMSZW+hU+VAcQ2PzNvN2z/sQ2u4uIezRXmnEGlR3lIk77UwOzmXBRvyqHKAhwkubxfBhHbhDAj0J8bLU34vCtHGtYjNbZswND0DeEhoEuLEHDhQxjmjZpO2yULSjdG89nwX6brWymiteen1vTzw6E5sNnjkqjgemxIv0/VOoZQMK3e+nsHyzc4W5fcNj+XBEbH4eboZXZqoU1xZy/vr83hz2W7SrX82igj3VCQGBJEY6E9igB8DgvyJ9/aSICVEG9IqQlPd9LzlgJfWOvFEniuhSQingoJyzhk1m42ppdx0XTvefLmrBKdWaM1aC1dM2UDmXgdnnhbIxzN6Eh0m0/VOFa01C34/wPQ3t5FTrGkfrPjvqO5MOk1alLckWmuWZpWyLKuUdblWUjKLDu39dFCQuyIxIJDEQH8GBPqRGOhPZ19vWRMlRCtl9D5NPYGegBfwIfA6sBjI11ovqXvMmcCvwKh6x5YAnwPbAF/gJpwb3V6stf76RGqQ0CTEnwoLKxh9zmzWbyjh+ilRvPVaNwlOrVBJSS3X35rOl98UEB6q+HBaH8YOknU2p1JZpY1nFmbz3KJsqm0wPDaAV87rQmK0v9Glib9RVFHL+n1lzhCVayUls5CMssODlJ8b9A8IZECgP4l1QaqbrzduJmkAIoSrMzo0PU5dF7sjLNFan1X3mLOA34GRWuvFdcfeAUbibDrhAFKAp7XWP5xoDRKahDhccXElo895g3UpJVx7VSTvvNFdpnC1QlprXpuTw30P76CmBmZcGcsT18XjJt3dTqld+yq5b85OvlxegFJwQ2I7njqnIxF+0oTAFViqbKzfV0bKPispuWWk7M5nm8VB/SjlbYZ+AQEkBjhDVGKgH738fXGXICVEi6a1Jq+6hjRrOZus5UzbstP46XlGktAkxF+VlFQydsxs1iQXM/nKSOa9KcGptVq33srEyevZle3gjN6BLHioBzHhXkaX1eb8klLE3S9vYnOug0BvePysBG4b3F5alLug8ho7G/PKnKNR+8pYl2Nl84Fy7PUuozxMcJr/n6NRAwL96O3vi5fZbFzhQrRhZTYbm60VpFnLSLOUk2YtJ9VaSmHNYflHQpOEJiH+qrS0inFjZ7NqdRGTLo/gg7ndcXOTC7jWqLTUxk13pPPpl/mEBis+mNabcweHGl1Wm2OzO5j9TS6PvreTkgpNj3YmXhrTmzGdZeqkq6uqtZO2v9wZouqm96XllVFTb0jKTUEvf9+6EOUMVH0D/PCRICVEk7E5HGSUV5JmdQajNEsZaWXF7Co/fKqtwrkFQZ/YEPpE+tIn0peJn2yR0CShSYijs1iqGD/+Q1asyGbipeF89HYP3N0lOLVGWmvefCeXex7MoLoa7p/YgaemdsRdgvIpl19Sw7/m7Wbu984W5Rd2D+WF8Z1JkBblrUqNzcGW/HJScuuC1L4yNuZaqKzX/9cEdPfzOWyNVL8APwLcpeOiEMeitSa3qsY5cmQtPzR6tLWsjOrD8xFhHoo+7QMPhaM+kX70ivD5S2dT9a8W0D3PSBKahDg2q7Wac8/9kGXLsrjsojAWvNdTglMrtiHVOV0vY7eDoT0DWPhwT2IjZLqeETbssHLnGztYmlaKhxvcOzSWh8+UFuWtmc3uIL2gsi5EOddJrc8ppcx2+OO6+JkYEBB2KEj1D/AjxMPdmKKFOEkFNTWU2xzE+TTub42l1sam+iNH1nLSrBaKaw/PLl4m6BXlR59Iv3oByZdIP48GdS+V0CShSYjjKiur5vzzP2LJkkwuuSCMhfN64uEhwam1slpt3HzXdhZ8eoDgQMX703pxwdAwo8tqk7TWLFqSz31vbGVvsaZdoOK/53Tj6r6R0qK8jXA4NBmFlX82m9hnJWVPKSVHXBB29DWR6B9y2PS+cE9pKCJajiq7nVqt8XdzvvFTZrMxcd0W0ssr6OTjzVPdOnJ6cMDfPt/mcJBeXlkvGJWTai0iq+LwnwUFdParP7XOGZISQrwxn0RHYAlNEpqEaJDy8houuOAjfv99NxeeG8qiD3rh6SnBqbXSWvP2vH3cef92qqrh3sti+M8NnfCQUUZDlFfaefaTbGYtyqKqFoZ2COCV8zozsP3fX2CI1ktrze7iqkPNJlJyrazLLqHg8EXrxHgrEgNCDpve186zYe+qC3EySmprCXJ3jn5mVlRya1oGKRYr50eEcl9CB7r7+TJzeyY1WjOzW0fezMohzVLObfHt6enve9TXTKlay4Bfyg47Fump6BMTdNjIUc9wX3w8mn4toIQmCU1CNFhFRQ0XXfQxv/yyk/PGhvDZR73w8pJFyq1Z6qYyJk5OIX2ng9P7mflk2kDio2RtjVEy8yq5b+5OPl/qbFE+tX8UT4/uRKS0KG/ztNbstVQ7R6Nyrc4pftnF7Ks6/Pot0lMxIDDY2f68rg16rLenBClx0rTWKKVI/GMtXf28eaN3V0I83Hlw604Uiv/06MTdmzOwa3i1dxfu27IDT5OJp7p3Ym9lFbOzcon09ODOngWHXqu+qh59uP27jMOm14X7nrrffRKaJDQJcUIqK2u5+OKP+emnHYwbHcKXH0twau3Kymwk3ZPBRwv3ExSgeO/enlw8PNzostq039YXc9fLaWzKceDpBpf3iuSfA9txRlygXPyKw+yzVjs35c2pG5XKKiT7iOlMoR6KxICgQ6NRiYF+JPh4y7kkDql1OEi1lLOmxIKfm5nzI0IJrreOzqE1JqX4Zn8BM7bt4pKocK6NiaSzrw/3bdlBvLcXt3eMYVVxKQtzD3BhZBi7bdtJKbbzeqIPVXbNvMwa0kqdtx29+2I6ial0zUFCk4QmIU5YVVUtl166gB9+yODKCREseK+n0SWJZqa15r0P87h9WjqVVfDqbZ25/eIYo8tq02x2B299v48XP9lJxn5nO6juUSa+mzSITtJpTxxDQXmNM0jl1q2Tyixk5xEtlwPdoX9AEE90jWdEaJBBlYqW4vXMHD7Ym0dioD9lNjuBbmZe69P1sMeU2+x8lLOffVXV7KuuISkumngfL2bt3ENCUD7Xd/TkQJWDlzKqSfAz0c7LxLtF3nw2qTe1dgc/ZhTx6aZ8PpjQw6D/ymNr86FJKZUPZBlYQjSQa+C/L1yDnCeiIeQ8EQ0h54loCDlPREO1lXMlTmt93GkWrTY0GU0ppbXWLWv8UbQ4cp6IhpDzRDSEnCeiIeQ8EQ0l58rhpF2SEEIIIYQQQhyDhCYhhBBCCCGEOAYJTc3n30YXIFyCnCeiIeQ8EQ0h54loCDlPREPJuVKPrGkSQgghhBBCiGOQkSYhhBBCCCGEOAYJTUIIIYQQQghxDBKahBBCCCGEEOIYJDQJIYQQQgghxDFIaBJCCCGEEEKIY5DQJIQQQgghhBDHIKGpmSmlApRS/1ZKrVFKlSql8pRSXyqluhpdm2hZlFJXKKW+UErtU0pppdR1RtckjKWU6qmU+lUpVaGUylVKPaGUMhtdl2g5lFKdlVJzlFIblVJ2pdRio2sSLY9S6nKl1NdKqRylVJlSap1SapLRdYmWRSk1QSm1QilVqJSqUkqlK6UeUUp5GF1bSyChqfnFAjcB/wMmADcD7YDVSqkORhYmWpwJQDzwrcF1iBZAKRUM/AJo4CLgCWAastmgOFwv4Fxge92HEEdzL1AG3ANcCPwOfKyUusPQqkRLE4rz3LgRGA+8CzwMvGBkUS2FbG7bzJRSvoBDa11Z71gIkA3M0lrLBZAAQCll0lo7lFJ+gBWYqrWeZ3BZwiBKqRnAdCBOa22pOzYdeByIOnhMtG0Hf2/Uff0ZEKa1PsvYqkRLo5QK01oXHHHsY2Co1rqjQWUJF6CUegq4DQjWbTw0yEhTM9Nal9cPTHXHioAsIMKYqkRLdPDCR4g644H/HRGOFgLewJnGlCRaGvm9IRriyMBUZz1yHSKOrxCQ6XlIaDKEUioc6AxsMboWIUSL1R3YVv+A1jobqKi7TwghTsYw5DpEHIVSyqyU8lFKnQHcCcxu66NMAG5GF9BGPY9zbvFCowsRQrRYwUDJUY4X190nhBCNopQahXOt5PVG1yJapHLAs+7rD4D7DaylxZDQ1AhKqUCczRyOSWu97chjSqkkYDJwmda6sBnKEy3EyZwnQtQ52jt76m+OCyHEcSml4oGPga9k3az4G8MAH+B04FHgNeBWQytqASQ0Nc7lwFsNeJw67IZSFwKvAg9orb9sjsJEi9Ko80SIOsVA0FGOB3L0ESghhDimukZUP+BsRjXZ4HJEC6W1Tqn7cplSqgB4Xyn1vNZ6p5F1GU3WNDWC1vptrbU63kf95yilhuGcjvem1nqWMZWLU6kx54kQ9WzjiLVLddsU+HLEWichhDgepZQPzi0tPIDztNblBpckXMPBANXmuyxKaDoFlFK9cP6i+hHngjohhDieH4CxSin/eseuACqBJcaUJIRwRUopN+BToAswXmt9wOCShOsYXvd5t6FVtAAyPa+ZKaUicIalMuAV4HSlDg0uWLTW0rlGAKCU6gn0BLzqDg1USpUB+VpruUhue97E+SbLF0qpZ4FOOPdoekH2aBIH1Y0enFt3sz0QoJSaUHf7e611hTGViRbmDZznyV1AiFJqSL371mutq40pS7QkSqkfcW6qvhmw4wxM04BP2vrUPJDNbZudUuosnLsrH80S2YRQHKSUehx47Ch3yXnSRtUF6deAoTjXMb0NPK61thtamGgx6hb1/907wB211pmnrBjRYimlMoG4v7lbzhMBgFJqJnAJEA/YgF3AeziXltQaWFqLIKFJCCGEEEIIIY5B1jQJIYQQQgghxDFIaBJCCCGEEEKIY5DQJIQQQgghhBDHIKFJCCGEEEIIIY5BQpMQQgghhBBCHIOEJiGEEEIIIYQ4BglNQgghhBBCCHEMEpqEEEIIIYQQ4hj+H0KmEHdBap12AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1781d557b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Draw polynomial Kernel\n",
+    "n = 10\n",
+    "xx = np.linspace(np.min(x[:,0])-0.3, np.max(x[:,0]) + 0.3, n)\n",
+    "yy = np.linspace(np.min(x[:,1])-0.3, np.max(x[:,1]) + 0.3, n)\n",
+    "XX, YY = np.meshgrid(xx, yy)\n",
+    "\n",
+    "F = np.zeros_like(XX)\n",
+    "for i in range(len(xx)):\n",
+    "    for j in range(len(xx)):\n",
+    "        F[j,i] = svm_func(np.array([xx[i], yy[j]]), x, y, lambd, b, kernl=gaussian_kernel)\n",
+    "\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "contourf(xx, yy, F, 8, alpha=.75, cmap=cm.hot)\n",
+    "clabel(contour(xx, yy, F, 8, colors='black'), inline=1, fontsize=10)\n",
+    "for i in range(len(x)):\n",
+    "    if y[i] == 1.0:\n",
+    "        plot(x[i,0], x[i,1], 'bo')\n",
+    "        #print x[i,:], y[i]\n",
+    "    else:\n",
+    "        plot(x[i, 0], x[i,1], 'go')\n",
+    "        #print x[i,:], y[i]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\"> Use cases </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Budget optimization\n",
+    "<a href='https://github.com/ml-mipt/ml-mipt-part1/blob/master/2017/seminars/06-trees_ensembles_2/animated_series.pdf'>See description</a>\n",
+    "## Intelligent email sending\n",
+    "<a href='https://github.com/ml-mipt/ml-mipt-part1/blob/master/2017/seminars/06-trees_ensembles_2/animated_series.pdf'>See description</a>\n",
+    "## Man-hours forecasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\">Заключение</h1>  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "** SVM **\n",
+    "* Достоинства\n",
+    "    - Сильная обощающая способность\n",
+    "    - Выпуклая задача оптимизация (наличие решения)\n",
+    "    - Не нужны все объекты обучающей выборки для обучения\n",
+    "* Недостатки:\n",
+    "    - пока не добрались :)\n",
+    "\n",
+    "** HW ** \n",
+    "\n",
+    "** Обратная связь ** \n",
+    "  * оцените <a href=\"https://docs.google.com/forms/d/e/1FAIpQLSdmyY3f-lwrhSGeqJPaxcXrdj0SfZzZbgRIggg-nx4EQ_eQLQ/viewform?c=0&w=1\"> семинар </a>\n",
+    "  * оставьте <a href=\"https://docs.google.com/forms/d/e/1FAIpQLSdefy8neFtoxDlXD3toHi3fWB3OW-23APTRj-GuTX8wtAJahQ/viewform?c=0&w=1\"> отзыв </a> о лекции"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}