jupyter_chinese/topic05-集成学习和随机森林方法.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../img/ods_stickers.jpg\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 集成学习和随机森林方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本次实验介绍了集成学习的概念及主要方法，包括 Bootstraping、Bagging、随机森林，随后计算随机森林中各个特征的重要性，找出对模型贡献较大的特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 知识点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 集成\n",
    "- Bootstraping\n",
    "- Bagging\n",
    "- 随机森林\n",
    "- 特征重要性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 集成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之前的几个实验中，介绍了不同的分类算法，以及验证、评估模型的技术。现在，假设已经为某一特定问题选中了最佳的模型，想进一步提升其准确率，就需要应用一些更高级的机器学习技术：集成（Ensemble）。集成是使用一系列学习器进行学习，并使用某种规则把各个学习结果进行整合从而获得比单个学习器更好的学习效果的一种机器学习方法。在集成中，最终的整体输出比任何单个部分的表现更重要。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "某种意义上，孔多塞陪审团定理形象的描述了上面提到的集成概念。该定理的内容为：如果评审团的每个成员做出独立判断，并且每个陪审员做出正确决策的概率高于 0.5，那么整个评审团做出正确的总体决策的概率随着陪审员数量的增加而增加，并趋向于一。另一方面，如果每个陪审员判断正确的概率小于 0.5，那么整个陪审团做出正确的总体决策的概率随着陪审员数量的增加而减少，并趋向于零。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该定理的公式为："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  \\mu = \\sum_{i=m}^{N}{N\\choose i}p^i(1-p)^{N-i} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $N$ 为陪审员总数。\n",
    "- $m$ 是构成多数的最小值，即 $m=\\operatorname{floor}(N / 2)+1$。\n",
    "- $ {N \\choose i}$ 是组合数。\n",
    "- $ p$ 为评审员做出正确决策的概率。\n",
    "- $ \\mu$ 是整个评审团做出正确决策的概率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上式可知，若 $ p > 0.5$，则 $ \\mu > p$。此外，若  $ N \\rightarrow \\infty $，则 $ \\mu \\rightarrow 1$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们看看另一个集成的例子：群体的智慧。1906 年，Francis Galton 访问了普利茅斯的一个农村集市，在那里他看到一个比赛。800 个参与者尝试估计一头屠宰的牛的重量。所有参与者的预测的平均值为 1197 磅，与牛的真实重量 1198 磅十分接近。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "机器学习领域采用类似的思路以降低误差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Leo Breiman 于 1994 年提出的 Bagging（又称 Bootstrap Aggregation，引导聚集）是最基本的集成技术之一。Bagging 基于统计学中的 Bootstraping（自助法），该方法令复杂模型的统计评估变得更加可行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bootstrap 方法的流程如下：假设有尺寸为 N 的样本 X，从该样本中有放回地随机抽取 N 个样本，以创建一个新样本。换句话说，从尺寸为 N 的原样本中随机选择一个元素，并重复此过程 N 次。选中所有元素的可能性是一样的，因此每个元素被抽中的概率均为 $ \\frac{1}{N}$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设用 Bootstrap 方法从一个袋子中抽球，每次抽一个。在每一步中，将选中的球放回袋子，这样下一次抽取是等概率的，即，从同样数量的 N 个球中抽取。注意，因为我们把球放回了，新样本中可能有重复的球。把这个新样本称为 $ X_1$。重复这一过程 M 次，创建 M 个 Bootstrap 样本 $ X_1, \\dots, X_M$。最后，我们的样本数量就从原先的 1 个扩充到了 M 个，就有充足的样本来计算原始分布的多种统计数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032611491\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看一个例子，这个例子使用之前的 telecom_churn 数据集。我们曾讨论过这一数据集的特征重要性，其中最重要的特征之一是呼叫客服次数（Customer service calls）。可视化「呼叫客服次数」这一特征，看看其分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 10, 6\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "telecom_data = pd.read_csv(\n",
    "    '../../data/telecom_churn.csv')\n",
    "\n",
    "fig = sns.kdeplot(telecom_data[telecom_data['Churn']\n",
    "                               == False]['Customer service calls'], label='Loyal')\n",
    "fig = sns.kdeplot(telecom_data[telecom_data['Churn']\n",
    "                               == True]['Customer service calls'], label='Churn')\n",
    "fig.set(xlabel='Number of calls', ylabel='Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图表明，相比那些逐渐离网的客户，忠实客户呼叫客服的次数更少。估计每组客户的平均呼叫客服数可能很有用，但由于该数据集很小，如果直接计算原样本的均值，得到的估计可能较差。因此可以应用 Bootstrap 方法，基于原样本生成 1000 个新 Bootstrap 样本，然后计算均值的区间估计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义 `get_bootstrap_samples()` 函数产生 Bootstrap 样本，定义 `stat_intervals()` 函数产生一个区间估计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bootstrap_samples(data, n_samples):\n",
    "    indices = np.random.randint(0, len(data), (n_samples, len(data)))\n",
    "    samples = data[indices]\n",
    "    return samples\n",
    "\n",
    "\n",
    "def stat_intervals(stat, alpha):\n",
    "    boundaries = np.percentile(\n",
    "        stat, [100 * alpha / 2., 100 * (1 - alpha / 2.)])\n",
    "    return boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "分割数据集，分组为忠实客户和离网客户。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "loyal_calls = telecom_data.loc[telecom_data['Churn'] == False,\n",
    "                               'Customer service calls'].values\n",
    "churn_calls = telecom_data.loc[telecom_data['Churn'] == True,\n",
    "                               'Customer service calls'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "固定随机数种子，以得到可重现的结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "使用 Bootstrap 生成样本，计算各自的均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "loyal_mean_scores = [np.mean(sample)\n",
    "                     for sample in get_bootstrap_samples(loyal_calls, 1000)]\n",
    "churn_mean_scores = [np.mean(sample)\n",
    "                     for sample in get_bootstrap_samples(churn_calls, 1000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "打印区间估计值，使用 `stat_intervals()` 函数将区间定义为 95%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Service calls from loyal: mean interval [1.4077193  1.49473684]\n",
      "Service calls from churn: mean interval [2.0621118  2.39761905]\n"
     ]
    }
   ],
   "source": [
    "print(\"Service calls from loyal: mean interval\",\n",
    "      stat_intervals(loyal_mean_scores, 0.05))\n",
    "print(\"Service calls from churn: mean interval\",\n",
    "      stat_intervals(churn_mean_scores, 0.05))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述结果表明，有 95% 的概率，忠实客户平均呼叫客服的次数在 1.4 到 1.49 之间，而离网客户平均呼叫客服的次数在 2.06 到 2.40 之间。另外，可以注意到忠实客户的区间更窄，这是合理的，因为相比多次呼叫客服的离网客户，忠实客户呼叫客服的次数更少（0、1、2 次）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "理解了 Bootstrap 概念之后，接下来介绍 Bagging。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设我们有一个训练集 X。我们使用 Bootstrap 生成样本 $ X_1, \\dots, X_M$。现在，我们在每个 Bootstrap 样本上分别训练分类器 $ a_i(x)$，最终的分类器将对所有这些单独的分类器的输出取均值。在分类情形下，这种技术即投票（voting）："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ a(x) = \\frac{1}{M}\\sum_{i = 1}^M a_i(x).$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图形象化解释了上面的公式："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032635677\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在回归问题中，通过对回归结果取均值，Bagging 将均方误差降至 $\\frac{1}{M}$（M 为回归器数量）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "回顾一下上一课的内容，模型的预测误差由三部分构成："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\mathrm{Bias}\\left(\\hat{f}\\right)^2 + \\mathrm{Var}\\left(\\hat{f}\\right) + \\sigma^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging 通过在不同数据集上训练模型的方式来降低分类器的方差。换句话说，Bagging 可以预防过拟合。由于来自不同训练数据集上的模型各不相同，它们的误差在投票过程中会相互抵消，所以 Bagging 是有效的。此外，某些 Bootstrap 训练样本很可能会忽略异常值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们看下 Bagging 的实际效果，并与决策树比较下，如下图所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032656840\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树的误差："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$0.0255 (\\mathrm{Err}) = 0.0003 (\\mathrm{Bias}^2)  + 0.0152 (\\mathrm{Var}) + 0.0098 (\\sigma^2) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging 的误差："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  0.0196 \\, (\\mathrm{Err}) = 0.0004 \\, (\\mathrm{Bias}^2)  + 0.0092 \\, (\\mathrm{Var}) + 0.0098 \\, (\\sigma^2) $$  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看到，相比决策树，Bagging 误差中的方差值显著降低了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图的例子不太可能在实际工作中出现，因为它做了一个强假定：单独误差是不相关的。对现实世界的应用而言，这个强假定太过乐观。当这个假定不成立时，误差的下降就不会那么显著。在后续实验中，我们将讨论一些更复杂的集成方法，能够对现实世界的问题做出更精确的预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 袋外误差（OOB Error）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林不需要使用交叉验证或留置样本，因为它内置了误差估计。随机森林中的决策树基于原始数据集中不同的 Bootstrap 样本构建。对第 K 棵树而言，其特定 Bootstrap 样本大约留置了 37% 的输入。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这很容易证明。设数据集中有 $ \\ell$ 个样本。在每一步，每个数据点最终出现在有放回的 Bootstrap 样本中的概率均为 $\\frac{1}{\\ell}$。Bootstrap 样本最终不包含特定数据集元素的概率（即，该元素在 $\\ell$  次抽取中都没抽中）等于 $ (1 - \\frac{1}{\\ell})^\\ell$。当 $ \\ell \\rightarrow +\\infty$ 时，这一概率等于  $ \\frac{1}{e}$。因此，选中某一特定样本的概率 $ \\approx  1 - \\frac{1}{e} \\approx 63\\%$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图可视化袋外误差（Out-of-Bag Error，OOBE）估计是如何工作的："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032680775\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "示意图上方为原始数据集。将其分为训练集（左图）和测试集（右图）。在测试集上，绘制一副网格，完美地实现了分类。现在，我们应用同一副网格到测试集上，以估计分类的正确率。右图可见，分类器在 4 个未曾在训练集中出现的数据点上给出了错误的答案。而测试集中共有 15 个数据点，这 15 个数据点未在训练集中出现。因此，该分类器的准确率为 $ \\frac{11}{15}*100\\% = 73.33\\%$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总结一下，每个基础算法在约 63% 的原始样本上训练。该算法可以在剩下的约 37% 的样本上验证。袋外估计仅仅是基础算法在 37% 样本上的平均估计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 Bagging 中，主要使用的基础分类器为决策树，因为决策树相当复杂，并能在任何样本上达到零分类误差。随机子空间方法可以降低树的相关性，从而避免过拟合。基于 Bagging，基础算法会在不同的原始特征集的随机子集上训练。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下算法使用随机子空间方法构建集成模型："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 设样本数等于 n，特征维度数等于 d。\n",
    "- 选择模型的数目 M。\n",
    "- 对于每个模型 m，选择特征数 dm<d。所有模型使用相同的 dm 值。\n",
    "- 对每个模型 m，通过在整个 d 特征集合上随机选择 dm 个特征创建一个训练集。\n",
    "- 训练每个模型。\n",
    "- 通过组合 M 个模型的结果，将生成的整体模型应用于新数据中，应用时可以基于多数投票算法（majority voting）或后验概率聚合（aggregation of the posterior probabilities）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林的算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建 N 棵树的随机森林算法如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对每个 $ k = 1, \\dots, N$，生成 Bootstrap 样本 $ X_k$。\n",
    "- 在样本 $ X_k$ 上创建一棵决策树 $ b_k$：\n",
    "  - 根据给定的标准选择最佳的特征维度。根据该特征分割样本以创建树的新层。重复这一流程，直到样本用尽。\n",
    "  - 创建树，直到任何叶节点包含的实例不超过 $ n_\\text{min}$ 个，或者达到特定深度。\n",
    "  - 每次分割，首先从 $d$ 个原始特征中随机选择 $m$ 个特征，接着只在该子集上搜索最佳分割。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最终分类器定义为："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ a(x) = \\frac{1}{N}\\sum_{k = 1}^N b_k(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分类问题使用多数投票算法，回归问题使用均值。在分类问题中，建议设定 $ m = \\sqrt{d}$，取 $ n_\\text{min} = 1$。回归问题中，一般取 $ m = \\frac{d}{3}$，$ n_\\text{min} = 5$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以将随机森林看成 Bagging 决策树的一个改动版本，因为随机森林在每个 Bagging 决策树的分割处选择了一个随机特征子空间。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林与 Bagging 和决策树的比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入所需包，配置环境："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "\n",
    "# Disable warnings in Anaconda\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import make_circles\n",
    "from sklearn.ensemble import (BaggingClassifier, BaggingRegressor,\n",
    "                              RandomForestClassifier, RandomForestRegressor)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 10, 6\n",
    "\n",
    "\n",
    "n_train = 150\n",
    "n_test = 1000\n",
    "noise = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    x = x.ravel()\n",
    "    return np.exp(-x ** 2) + 1.5 * np.exp(-(x - 2) ** 2)\n",
    "\n",
    "\n",
    "def generate(n_samples, noise):\n",
    "    X = np.random.rand(n_samples) * 10 - 5\n",
    "    X = np.sort(X).ravel()\n",
    "    y = np.exp(-X ** 2) + 1.5 * np.exp(-(X - 2) ** 2)\\\n",
    "        + np.random.normal(0.0, noise, n_samples)\n",
    "    X = X.reshape((n_samples, 1))\n",
    "\n",
    "    return X, y\n",
    "\n",
    "\n",
    "X_train, y_train = generate(n_samples=n_train, noise=noise)\n",
    "X_test, y_test = generate(n_samples=n_test, noise=noise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用单棵决策树回归。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision tree, MSE = 20.09')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtree = DecisionTreeRegressor().fit(X_train, y_train)\n",
    "d_predict = dtree.predict(X_test)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test, f(X_test), \"b\")\n",
    "plt.scatter(X_train, y_train, c=\"b\", s=20)\n",
    "plt.plot(X_test, d_predict, \"g\", lw=2)\n",
    "plt.xlim([-5, 5])\n",
    "plt.title(\"Decision tree, MSE = %.2f\"\n",
    "          % np.sum((y_test - d_predict) ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 Bagging 决策树回归。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bagging for decision trees, MSE = 15.24')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bdt = BaggingRegressor(DecisionTreeRegressor()).fit(X_train, y_train)\n",
    "bdt_predict = bdt.predict(X_test)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test, f(X_test), \"b\")\n",
    "plt.scatter(X_train, y_train, c=\"b\", s=20)\n",
    "plt.plot(X_test, bdt_predict, \"y\", lw=2)\n",
    "plt.xlim([-5, 5])\n",
    "plt.title(\"Bagging for decision trees, MSE = %.2f\" %\n",
    "          np.sum((y_test - bdt_predict) ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用随机森林。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random forest, MSE = 15.71')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=10).fit(X_train, y_train)\n",
    "rf_predict = rf.predict(X_test)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test, f(X_test), \"b\")\n",
    "plt.scatter(X_train, y_train, c=\"b\", s=20)\n",
    "plt.plot(X_test, rf_predict, \"r\", lw=2)\n",
    "plt.xlim([-5, 5])\n",
    "plt.title(\"Random forest, MSE = %.2f\" % np.sum((y_test - rf_predict) ** 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面 3 张图像和 MSE 值可以看到，10 树随机森林比单棵决策树和 10 树 Bagging 的表现要好，当然随机森林在这个例子上不稳定，多次运行结果表明随机森林和 Bagging 决策树互有胜负。随机森林和 Bagging 的主要差别在于，前者分割的最佳特征是从一个随机特征子空间中选取的，而后者在分割时将考虑所有特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们将查看随机森林和 Bagging 在分类问题上的表现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iEr/+km3PL8AZExfq8Po8tcuFyuNu165yu1G7XCGISAghOtdrc7527drFAw88QExMDNdffz0ZGRl+j1u6dClLly4F4LnnniM+Pj5oMWm1Wp/z11ntQesrnGk0GqxWa6jDCKlgjUHsB28T/VMOKo8HAJWiYMnPY/DC1yh++pWA99cTffL34MxJODL7Y8zf7dPsTM9Cf/I49CpVt0/ZJ8chwGQMZAwOknEIzhj0SvLVv39/Xn/9dYxGIzk5ObzwwgvMmzfP77FTpkxhypQp3q+rqqqCFld8fLzP+evq6oLWVzizWq0n7Pd+ULDGIHntam/idTh1/p6wG/O++nuw+/bfkP3aCxiL96GoNdjTM9l9z2ya6+uP6Xx9dRwCScZAxuAgGYfgjEGvJF9ms9n7/7Fjx/L222/T0NBAVFRUb3QvRMg4O/gdd5tkzlegNIw9lc1vfUjUpg2g1VA/ehxoZCG3ECJ89Uqdr7q6OhRFAWD37t14PB4iIyN7o2shQqr42ptpjU/0aXNGRFJ+4RUhiuj4pOh01E+YSP3YUyXxEkKEvYC8Sr3yyits27aNxsZGbrvtNmbMmIHrwGTXadOm8f3337NkyRI0Gg16vZ57770X1THMxRCir7FnZJH3u8fJ+Mtb6GurcUVEUnHBZVSfOz3UoQkhhAiRgCRf995771Efnz59OtOny5uNODE1jD2V3LGnhjoMIYQQYUK2FxJ9lrahHtO+vaiczlCHIoQQQnSZTI4QfY/bxcDnHyf6p01omhtxxCdSeukMyi+dEerIhAgatc1G5jvzsezagaLXUTHtYqqmXhjqsIQQx0CSL9HnZP15HvHLv0Z9oISDtjCfjL++RcPIk7ENGBzi6DqhKOBxy6Rw0T0eD8Pm3EX0lk3epoid29BXVVBy7U0hDEwIcSzktqPoc6I3b/QmXgfp62pJ+/RvIYqoa1L//h6jf30tY6+/lFF3/pKY774NdUiij7CuX0PErm0+bdqWZhKWLQY/deSEEOFNPn6LvqeDNxuV09HLgXRd4n8+I/39hWhtLQAYK8rInvcs29IysfXLDnF0ItxFbt+CprW1Xbu2qQGNrQW3JeLoJ3C7Sfr3P4jZ8B0evZGSq35B04iTghStEKIzcuVL9DnNA4a0a3NZIigL49pZCUv/6028DjJUV5H20aIQRST6krqxE3AdVqz6IGd0DG6zpdPnD3nid/R/Yy6x674jftUyhj56H4n//Wfbgx4Pccu/ZvCTv6f/a39EX1ke6PCFEEeQK1+iz8m/63cYKsqIyNuOtrmJ1vhEqs45n8aTx4c6tA6p/Vy1ANA0NwW/c0XBvGcXmpZmmoaORNHrg9+nCKjGk8bSMGos1g1rvLfcnVFWyi65GjqpmRiR+xPRm39A7T60Abm+vo7kLz6m4vxLGPLEA1g3rEFzYNVwzPerKP3DXOr8fMgRQgSGJF+iz/GYzGx78U3MeTswFe2jYfQ4nLHB24A9EGwZWUQeMWfHo9FQd8rEoParryxnyBMPYNq3F3VrK/bUDApvmkXNpCmdP1kAELf8a5K/+ARtcxOO2Hj2/eouWgb2cmKiUrHjD3NJ+fRDrJvWHbh1OJPGUWM6fap14/do/ST5urpaYtasIHrTem/iBWAsLyXp9bmUzn0roN+CEOIQSb5EWIr68QdSP/4LmpYW7OkZ7PvV3biiY3yOaRk0lJZBQ3s9Nn1lOf3nP4+xtBi3wUj1OdMoveK6oz6nYNb9mPcXYN6Th9rtwmUy0XDSOMovvDyosQ587lEid+R6vzYX7SNr4WvUjzsVd4Rs8dWZ2JXLyH7teXQNbZvqWvLzMJUUsvXlhTiO2DYq6DRaSmdcT+mM67v1tKahI3AbDO3mjLkjIrFuWIPWZmv3HG1ledvKXNmJRIigkORLhJ2Y775lwCtPo6+tASB66yYidm1ny6vv4DG1n/fSm9StdoY9eDeWgt3eNnPBHtR2O8XX3dzh81zRVrbMW0T8N19j2bWd2tPPatuHMIhvbpqmRkzFhe3ajaVFxC//mvKLrwpa38eLlC/+7k28DjKWFpP2wdvk3/NgiKLqnrpTJtI0eLhPmQqXyUzV5Gm4IiJRVGpUiu8iFo8lQhIvIYJIJtyLsJP6yV+9iddB5r15JP/zoxBFdEjC4i8w79vr06a1tRC34n+dPlfR6qicdhEFdz5A/bjTeunNzX8fikbTC333fZqWFr/t+urKXo6kB1Qqtj8zj9KLr6Zh+EnUjx5Hwe2/oej6X1Hxs8toyervc7jbaKJ+ys9CFKwQJwa58iXCjq6xoV2bCrDszev9YI5gyc9rd5UAaJtTE2a3adwRkdiy+mGo8l29Zk9Np+qc80MUVd/SmpBERN52nzZFpaJxxOgQRXRsPCYz+ffMbt9uMLL9mXn0n/8CxtIiPAYjVZOnYrvlDqir83MmIUQgSPIlwo7TGgP7fNsUlZqmoSNDE9Bhqs88l4RlX6Gx+86TaY1LCKvE66C82U8y5InftU24dziwJ6ex79d3h/z27eEMJUVkvrMAfXUlTmsshTfdhj0zPGqfFdx2H6bCfMxFbb+QHrWapmGjKL385yGOLHAcicns/MNcnzZriGIR4kQhyZcIO/t/8SuMRYUYDru10zR4KGWXhH6OUv3406kbMwHrD4eW5tsTk9l/06wQR+afMyaOra+8jbGoEI2tmebsQWG1tZG+vJThs+/AVFLkbYvYtZ1tz74aFglYa2o6W199h9SP/4KxtJiGkSdTftGVUq5DCNEj4fMqLMQBDWNOYduz88j44G00TY20ZA9m//W/QtEb/D9BUUj435fErVgKKhVlP7ucuomTghOcSsXOJ14k7tslxK1ejtMaQ/G1N+FISApOf35Y8raT9rd30DY30dJvAPt/+X+drly0p2f2UnTdk/numz6JF4CxvITMRW+y67HnQxSVL1e0lcJf3R3qME4MbjdpH72LddN6FJWaqnOmUXHBZWF5VVmInpDkS4QlW/Zgdj3yxy4dO+Dlp4hfuhiNo20pfdRPOZRe/vPgXY1Sq6k+dzrV504PzvmPImrj9wx6/nHvVUFrznqitv7I1pcX4jEYez2entJXV/ht19XV+G0Xx7fBT88h9rvl3oKwkdt+wlRYwL7b7gtxZEIElqx2FH2avqoC67rV3sQL2jYcjl++BLXN/0q1vizjg3d8bscCWPJ2kPTFJyGKqGdaE5P9tjt6sWiutr6OhK/+hXXdajisCrzoXcaiwnaV+DWtdmK/+/a4/FsWJza58iX6NMvOXAzVVe3adTVVGEuKaBkwuEfnV7mcxP/vv1hz1tGSlU3pFdfi6cJeesGiq69t16ZSFCJ25vo5OvwV3jSLyK0/Yi46VI/MlpJO4c139Er/Kf/4gJTPPsRYUYpbp8Oe2Y+SuW9BRFSv9C8Oidi1DV19+xWWuvpaDBVl2LJCPwdQiECR5Ev0aS39B+KItqI/4kXbFW3t8KpKV6kcDobPvoPI3M2o3W4UIH7512x/9jUcPTz3sXJGRbdrU1Qqmg9sd6O224jatB6PyUzDqLEQ5vW8nHEJ5L74Fpnvvo6hogxHbDyFN83CkZQS9L51VZWk/uOvGKrariRqnE4se/JIfepBKp9bEPT+ha/GoSNwWGPQ1/l+wHBaY2lNDP7vgxC9SZIv0ae1pmbQOGoMMWtWeDccdut01J4yEXdkz65eJH/+EVFbfvTW9VIBln176ffmS+x6NDSTwYtn/BJTUSH62mpvW3P2IMou+zlx335N5qI3MJYU4dHqsGdksfPhZ8Ji1eDROOMT2HP/Y73eb+JXX3gTr8PpigpR2214jKZej+lE1pqaQf3YU4lbsRS12wWAy2Si+uwpeExh/rPweDCWFOGKjGy3DZoQ/kjyJfq8XQ8/S/pf3iLqpxxQq6k99SxKurn/nT9RWzb5LahqKCvp8bmPVd3pZ7Pz0T+S9vf30LQ0Y0vPovCWO0HxkPn265hK21YOapwOLHvzGPjik2ydtyhk8YYztyUCBT97AGg0KGFUjqOn1K12ojZvxGWJpGn4qLBeOZg3+0kah44gdt1qFLWGiqkXUX1ueBcEjlmzgsx330BfUYbHaKRp8HDy5jwtybs4quPnFUacsBStjv1BmCPksvr/BBvqAqWNo8awY9QYn7b4//0HY2lRu2ONpUXoqipxxif0Vnh9RsX5F5Py+UeYivf7tNuGjUTR6YLTqaIQuWUTcSuX0dJ/AJVTLwpqzbD4ZYvJ+MtbbVdDdTpsWdnseOx5HMmpQeuzR9Rqyq64jrJONqoPF9q6WvoveAFjeWlbQ1MjhqoVeF56irw5T4c2OBHWZLWjEB0omnkr9iPmdjkjoyi76MoQRdQxj16Pom7/56yoNSha+Yzlj8dsYc99D9M0cCjOaCutCUlUT5xEyaNdK3HS/Q49DH5yNsMevofUzz8i+9VnOenOX6Kr9F9uo6c0TY1kLnodU3EhKsWDxtFKRN52Br3weFD6OxEl/+uTQ4nXYSw7t7VtNyZEB+RVWYgOtCansuuhZ8hc9Dr6mmpclgjKL76S6jDcF7H29EnY0zIx7y/wabdl9uvwCl5XaetrGfDS05gK96KoNTSNOIm9d/7+uKjy3nDyeH56430MFWW4TWZcUdFYzRZwBH5fw9hV3xDz/SpvWRS1x4Nlbx7Z8//IzifmdvLs7ov/ZjFGP7fIjcWFaBobejwnUtBum7GD1G5X2O31KsKLJF9CHEXTiNFse/FPoQ6jU4pez+77HyX7tT9iKCtB0epoyepP3pxnenhihaEP30fU9i3eJnNhPurmZvIeea6HUfc2BYOxFI/HgNMRd6hZpaI1yKsrI7b9RPb8P/rUozvIWNL+dnEgeHR6FJUK1ZFXYFRq8HOV9HCauhpivluJPS0DW78BQYnveFB28VUk/O8/PgtgAFoy+3U6xuLEJsmXEMeJphGj+emNDzAWF+LRG7zlMFQOB1lvv0bE9q0oGg01Z0ym9MqZXfpUHrllE5b8PJ82laIQtW0L2oZ6XH5KX4QjS2Qu2UOfxWgoxaNoaWkeQN7WZ3G5gh+/cd9ehjw5G32t/6r9HkMH22b1UPXk80n/6N12c9pasrJxWyI6fF7mwvkkLv8KfXkpzohImocMZ8fjc8N/xWEItKakUXLVTO/tR7fBSEtWdkhW74q+RZIvIY4nKhX29CyfpqGP/gbrD2u9q/oiduZiKC+l4I4HOj2doaIMjd3erl1ta+4zyZdK5WDQ8McwR+R72wzGKgaNeIjtm+cHvf+Mv/4ZQ2W538dcBiPVZ54blH49JhN77p5N/zdfxlBeikevpyVrAHkPPtnhcyJ/2kTyl5+gbWoCQNfUiHXjOvoveIE99z8alDgP0jQ1kvqP9zEWFdJw0lgqLrgseAsfAqjkmhuouOAyrD+spTU+gcZRY+V2o+iUJF9CHMfMu7YTuWOLTzkFjcNBzLrvKLz5zk6vZtSPO43WhEQMR0wKdyQkYU9JC0LEgRcb9w3G2n2gAQ77ds0Re9Bq64N+9UvbWO+33W0wUjLjekquuaHrJ1MUEpZ8Sfy3SwCoOmcalVMv6vDNvmHcaWz+04eY9+3FbbZ0ens1+ct/eBOvw1nytnc9xmOgLyth+Jy7MRe2Jchxq5YRv/wrtv3xdRR9cK4MBpIrKpqqEOz1KvouuSktxHHMsmen3zdTbWM9utr22zIdyRkTS/n0S3FGWb1trfGJFF13c9hXzweIX/Il/Wa8inq8B0YBs4ADpdtUKjcqtSPoMdg6KHJbfda5FN1wW7eukvSf/zzZ854jZsMaYjasIfvVZ+k3v5OCv2o1Lf0HdmleW4f1zVTBfavo9+bL3sQLQO12E7V1Myn//KjH59bVVpP+3ptkvLMAfQdXIIXobQG58vX666+Tk5NDdHQ0c+e2X7WjKAqLFi1i06ZNGAwGZs2aRXZ2eFfdFuJ40HDSuA63bHEkdG2LpKIbbqP6zHNJ+dfHuI1mSq+aiSMhKRjhBpSpYC9Zf56H4eBk6ApgIRAHPAl2WypOR/Drn+2/4f+I+ikHy56d3iuQzVnZ7Pv1Pd06j7a+lpi1K9G0HroNrGltJXbtSop++euAVFYvufI6rOtX+2zXpajUNBxRVy7Q/JVrUCkKkbmbe3Te+GUwUOvtAAAgAElEQVSLyVr4mve2b+KSf1N03S2UX3J1j84rRE8F5OPM5MmTmTNnToePb9q0ibKyMubNm8evf/1rFi5cGIhuhRCdaE3LoG78RNyHzZ1xRkRSMf3Sbs2nsQ0YzN77Hmbf7b/pE4kX0LZv4xGr0HCC8hU0Nw2g5LNryX75adLffQOtnw3LA8UdEcnWVxay/8bbqTrzXPZfexNbX30HZ2x8t85jKixAX9W+Jpi+qgJTYUFAYm0ZOISimbfSmpWNy2iiNT6RqklT2Pd/3UsUu8vdQeFipzX2mM+pcjlJ/+Btn/l2hqpKUv/xAWqb/xIRQvSWgFz5Gj58OBUVHRcK/OGHHzj77LNRqVQMHjyY5uZmamtriYmRPbCECLbdv3+C+jHjiVv1DR6NlrLLZtAwZkKowwq6jt5g7Q1p1P9yHAOWPoeusQGAxGWL2f3bR2k4eXy3+zGUl6KvKGO3JYn9ylE+z55+Uds/gPLmtn9Hxqx20j9zPTqtnb37TsPhtHgfMzsN9IuKIbLON6FsjophndNAy56ybsfuT8rEC2mdeTOtWzfjiEvocZ24rii9bAamgt3oGw7Nj7MnpVA085ZjPqc5f7ffrcCMpUVEbNtMw7jTjvncQvRUr0y4r6mpIT7+0Ke8uLg4ampq/CZfS5cuZenSpQA899xzPs8LNK1W63P+Omv7VV0nAo1Gg9Vq7fzA49jxPgbOa35J2TW/BNoud/v7To+3MWi5/Brc679DY2vxaXclDSXh26XexAvAWFpM9qLXKXjvsy6Pg8rRStqDd6P/MQd9Qy1pSUnkXTidDbfe2KX41Conp6T9hZTIn/AoGooaTmJI/HKsxiI0ajf19hTWF93Azqq2idx2LBRNPIVBS5aidR3YeFqrZf8Zp2A/KyMgtzGqa5qpr2imsaCS1ORBZKb1zgdkzyVXUaHTEvvJ31A3NeCKT6Bi1v2YhwzjWDfz0qVn4rFY2tVW85gtmDL7oe7kZ3y8/T0cKxmH4IxB2K12nDJlClOmTPF+XVXV+aTgYxUfH+9z/rq6wFe17gusVusJ+70fJGNw/I1B3YiTMZxzPrFrVqCvq8FtMNI8YDD16ZlkrFrW7nhNeQmNRfuJTM/o0jgkPPckESuWoaatiGlsWRljP/k7nvPiqRs3sNPnj0tdQLxlK2pV2/PTozd5/w8QbSxlYuZCIAmXp22ZZsHcS1C9YyJhRS6ooPLsEey7eSoD1Du6MiSdija1UBgRz/4mFXsKK0kqK2f8gK7NDeyputMnU3j65CMau/H76PG0LV44uIDBZCE+ezAxG7/3Oay5/0DKE5I7Pffx9vdwrGQcgjMGvZJ8xcbG+iQ51dXVxMYe+718IUJGUdA21OExmvAYjKGORhyNSsXe3zxM8TU3ELP+O1qy+tMwZgLxyxbj0WhQu90+h3sMxm79TON253oTr4N0DTZO+ngjVWcfveyATreLOHO+T7J1+P8PMuuqODm2nBbboQ+k3DOYpgNTsEzA8C5H3LkaQzlRxljcKUk0RrRQv8N/mYxwYigtZsDLT2Ms2Y9Hq6Nx5Gj23v0gil7PrkeeY8DcJ7Hs2QmKgq1fNrulAKoIA72SfI0fP56vvvqKM844g7y8PMxms8z3En1O1I8/kPXnV9FXVuAxGGgcOYY9v30YRRv+hSBPZK1pGZRd/nPv19WTppL29/ew5O/2tnnUaupHj+9eUc8OSkQodF46wqDbhkbdvgRIexoURSrLd8jtYuhjv8Wy99AuDKbiQtQOB3lznsYdEcmux55vuyoGsuWPCBsBSb5eeeUVtm3bRmNjI7fddhszZszAdWBOwrRp0xgzZgw5OTncfffd6PV6Zs2aFYhuheg12oZ6Bs79A8bSYm+bsaytavje+x4KYWRC5XISs2YFuvo6qs86r9MJ4opOx/YnX2bAK8+0XS3R66k/+RQKbv9Nt/qtHjgCa0Ee6sP2TnRbI2m84ZJOn9vqGIXbHYlG03jU45yuDGz2U7sV14kkdvW3mPbl+7SpFIWIbVtQ2214jAcSV0m6RJgJSPJ17733HvVxlUrFrbfeGoiuhAiJ5H994pN4AagUD1E/5YQoIgFg3pnLiN/fibapARWQ+c4Ciq67idKrf3nU5zmSU9n+XM+2Fto883aMjdXE7dqKob4OZ1o8thkX4DhlZKfPdboGYHeMxmxci0rVdvvT4zHjcltRq1pRqTy4XElU191LGE7NDRuGyjLUble7do3DjtpuP5R8CRFm5K9aiC7Q1vnfFFnldIKi9Mm93AzlpcSuXo4tLYO6CWeEOpzuc7sZef9taA9bzahrbCDjrwupOucCnPHBLaCqaHWseugZdPZytI3byZ6QTFxsBNGWRYCGxuaL8Xg6vgpXVfMIkRGfYjJsRFG0NDb/DHvrGajV9ahw4vYEb6X38aJ60lRSP/4rhhrfhVmt8Um4ok/sFXoivEnyFWIpn/yVhGVfoWlpxhGfwL5b7qRpxOhQhyWOUH7RlSR88zW6Bt8VL/a0jD6ZeGW98RIJ33yFvrYat05PS/+BlLy2CDraXiYMxS9d3K6MBIC2pZmEJf+m5Lqb2xqCnBy3JCZhy/YwLukrkq1foNW2JQIR5sXUNtziO1neh4bGphk0Ns3wafV4wn+z8mBSt9rB48HTQeHVwzkSkqi44FKSvvwM/YFCubaU9LbdA/rg36U4cfSdV9rjUNLnfyfjL295P7mbSvYz+JmH2frq2zjiE0McnTicrd8Ayi+4lMQlX6Kvrcaj0WLL6s+ePjjfK2L7FhK//he6prb5Rhqng8hd20h55iGqH/ljiKPrusO36zmcArjNZjIXvkbM96vQ2G20JiRR8H/30jy081uCx0KraiUx+j/exAtAq60kOvIDWmyTAFmU0RlNUyMDn38My948VG43ttR09tz/GK2dbOC+/6ZZVE75GUn/+QxXlJWyS67GHRHZS1ELcWwk+QqhhG8W+9wyATCWl5D24SLy7/p9iKISHSn81d2UX3I1cd8uoTUpheqzzu1TV4oOSvryM2/idThDwd7eD6YH6k49g5TPP0J9cCXbAYpeg7l2F4mfLUbjaNs421hWwuCnH2LL/PeCcjsqwbIbg7b9Lh9aTTl63R4czqEB7zOYYjZu5uS3/4azvhXHtb+k8aSO93Y0FuaT/uEiNE1N1I07lfKLrzymv4tBz8whdv0a79eGynKGPPEAP73+fqcT5u0Z/dh3W/cWTIhjp7bbMJYU0ZqYLInuMep77xzHEU0H25/oav3PLxKh15qUQsk1NwS9H31VBVE567FlZtE8ZGRAb6G4LRF+2z3dKbPQDSqHg6QvPyVq64/YMrIoufr6gLxg1489lfqTT8Gas857BUzRgnqOm6TP/o3a4ZuUmUqLSPn0b+y/OfCrre2uKNweI1qN74cpj2LC44kKeH/BNOiNDxnw7ucY6tt2AHBu20TpFT+n6Ibb2h1rXbeaAS89haG6EoCY9auJ/X4V2599rVu/s7raaix7drVrNxUWEPXjDzSMDeJ2WB4PxtJiXBERAdmc/HiX/pe3SFj2X3Q11biioqkbfzp7750jt3m7SZKvELKnpPnUGgJwa3XUnHZWiCIS4aDfgheJW7kUQ3UlLrOF5kFD2f7ky3jMls6f3AUlM64ndtUyjIdtOOxRq2kZF/iSBupWO8MfuJ2IHVu9V6jiVn3Dtufm40jsYeV0lYrtz84j5fOPif1hCZGJ21A/6oYJoP6bx+9T9FXlftt7qtaegc2ZQaRmp0+705mNy50alD6DQd1kI+uzJd7EC0DX3EjC0v9SeuXMdklz+vsLvYkXgNrtJmpLDtZ1q6nrxuuYpqUF9YGrlD7xOFrRBXHTc+u61WS+PR9DZTkeg4GmQcPIm/MMHpOskvTHuv47Uj/9AG1zW406ra2FhCX/xp6cRsm1N4Y2uD5Gip+EUP4dD9CclY1y4BOD22CgftypVJ13QYgjE6ESvfF7Er/6wvuGpm1pJnrzRrLnvxCwPhzxieTf+TuaBg7BEROLLSWNymkXUR6E+Wupn7xP5LaffG4Nmgvz6ffmy4HpQKOl9MrrsP+zP+ov2xIvAPq1P9RlMlM59cLA9OtHfuV9tNhOx+lKwelKobnlDCpr+lY1dePOIoylle3by0p8CpkCoCjoa6vbHatpbSXm+1Xd6teemk6rn2S8NSWNulMmdutcXaVtqKf/a38kYm8eusYGDFWVxK1dyYC5fwhKf8eDpC8/9SZeB2mcTmLWrw5RRH2XXPkKIUdSClvmv0fyv/+BOX8P1WedS+1pZ0lBwBNY4ldftJsHCGDes9PP0ceu9ozJ1E6chK6mCrclAo/RhFUb+JeDiB1b/U6KN5SXBrQfRTki9teAy0DZASoFXGYLNaefTcPJpwS038O5PZFU1jwJHNy2SBO0voLFmRqLIyYKU6Xv1SZndEz75EilwuXnFrZHo6F5UDfnuKlUFNx234FtgopQAfaEJIoDdIvan8T/fIaprKRde8Su7XDE1lOijcrj/4oyHbWLDknyFWIek5mSGUcvCClOHIrO4L9dE4Q3cpUKZ1xwa2E5O6g27zZ3XkagO0oKZxKb8C16w4H5koNBWQ+Nc0fRvGkYledMp2lkb5Vw6XtJ10HOlDhqTx6GcelaVAcq9ysqFY3DRtGa3P72aeXUizCW/Qltc7O3raXfQCqnXdztvhvGTGDzm38jYel/0NhsVE67GGdM8PYA9lemBEDb1MjA5x5BGXUyDVMvlluQh6maPI3oTRvQtNq9bYpKTeOojhdkCP80jz/++OOhDuJoGhuPvv1GT5jNZlpaDv0B1lV1Za+144/RaMRut3d+4HEsXMbAlpxK3OrlaOyHFmN4NBqqzp1O/bjTgtp3Z2OgaWwg/f0/k/zvTzCUFtM8aGin+1q29B9E7JrlPrcqHNExFN58B/aMfoEKHZfLitttwmjaj1pjx+mIob5pAjvMr1A7YVKH88tUTicRO3PRNjW2JYoqVZd/FxqbWvEYNXgMYKgpYMCOnejNVpSI9omlWl2PWl2HoligC3s/horN08yOyeehbVLw2B00Wqw0TjqPPb99BPx8AGgafhIOa8yB8YulYfQ48uY8jcdybPMTFZ2e5iEjaBx5ctCTHntqBvHfLmmXhKlbW4ko2I1l/Rpi16yk5sxzulRz7Hh1+N9DS/+BGMpL0VVXobW14IyyUjduAvn3PAjqvvuhozPdeX/IyE7p0nEqRTlsY7IwVFLS/rJwoMTHx1NVdaguT8H2sqD1Fc6sVit1dXWdH3gcC6cxSPryU1L++RG66krcEZHUjx7Hnt+0f/OzRG4la+BrGIzluF0WqsqnUVJ47CsxjzYGusoKRsyehfnAPnqKSkXzoGFsffHNThcCWHZsJeudBehqqnFHRFJ62TVUT552zHEejUrlwByxB0drPE7H0a/qxa5eTuY7CzCU7EfRG2nJ6s/OR5/HMmhwl34XissacEWomTD/KZJ/3IClqg5XUhy2cydQ8/xvQKVCpWohPuYZ9LrdoHLgdidSW387rY7wLKRc4yqn0B6LW51EY2kL9TvqGT+ghwsjwkjUjxtI/cf7qO12mgcMwRltJfk/n2EsL8Wj0aByu9ulxpXnnE/eQ8+EJN5w4O91QV9ZTsSOrbT0GxDQD1HhqjvvDxPPG9ul4+S2oxBhpvyiK6mYfgmGshKc1li/c150+mqGjJyD0XzYRt/mQhRFQ+n+XwQ8pn5vveJNvODA5sW7tpH+t3covPWuoz63eehItj3/RsBj8kdR9DQ3Duv0OE1zE/3+9PKh/TpdTURt38Kg5x+l5M8fdbm/oZ/9jX7fLkfjattfUFtejeWzpbSOGUbzdT8jzvo8ZtP33uO1mgbirHMprfwTiiK3s3pT/LLF9Ht9rrcSvvXHH2gYfhI/zf8L1px1JH/+d6K2b2n3PGPJ/t4ONew5EpKoSUgKdRh9mszsFiIMKVod9vSsDicbp2a+55N4AWi1LcQnLQlKPB1NkPdXm6kviF++BMMRG6UDmPbvQ93Y4OcZ/qVsWu9NvA5Stzox/3cl4Gi74nUErbYEiyk4P6c+xeMhZs0K0t5fiHl3YBeU+JPyzw+9iddBETu3EbNhDVXnXYA9Pct/mLI5twgCufIlwp6huJDM9/6Erq4Ge0o6+2+8PagTcfsCvaF9OQAAtcZ/4d6ecpv9F2Z19tHNi5UOp111cz5WR4UlVSpUKjcqVftVcyoVqNX+J3ufKDSNDQx/8C7Me3ahcTpI/fQD6k6ZSN6DTwWtWKeuvv1tI7XbRdRPOVROu4j9v7iVqB83+NS/c0ZGUXbxVUGJR5zY5MqXCCh9eSnD5tzNyTdezuhfXUPWmy/3aNm2KT+PEb+bRcI3X2HNWU/yfz5jxG9/jTaIhRf7gprKc3C72092b7V1bbJndxVfcz2OmDifNntiMvt/8Svv12q7DZWfQpnhqHryNOwp6e3abRlZeCK7XpG+eMIZuPR6nza32Ujz5eehKCacrvYrBF2ueJpazu9+0MeR/vOfJ3LHVjTOtt8XXWMDsauXE7tiadD69PdBwaPR0nBS2xyd1rQMdv/+CepPGktLehYtI0dT8Kt7gjY/UZzY5MqXCBiV08mwh+/1qdpv2l+Ayumg4Bj3qsx853WMR9zyMhfmk/7+2xTccX+XzmHct5eM9xeibWqgadAwiq+9uc8vH6+umEJC8mKiY9eh0bSiKGBr7k/+rgeC0l/DmAns/u0jpH3yV7RNjTisMRTeNIvW9Ez05aUMfPEJjMX7UdQamgcOYffvHg9YRf5gcFsiKLj9N2QtfA1DaRGKwUBLZjZ5s/9Ad6LeeckMIsoLSd3wHebaOjyJcbRMP5OWK6YAUF33WxJjn0CrLUStduJ0JdHQdDUez4l95da8r/0+ohpHK/HfLqFm8tSg9Fl6+bUYDpvzBdA0ZDiV5033ft1w8inkHqgFF06LcMTxR5IvETDxy7/GVJjv06Z2ubDmrAO365g229V3sM+lsbiwS8+P3JzDoOce9t5KiNmwFmvOera+9BaK3n9Nrd6maW7CYzB0WrbBl5odP72ENW4V8YnLaGnpR1nRDDzu4CU8daed1X7LGI+HoY/dT8TuHd4mU1kxaqeDHU+/6m0zlBShdrRiy8oOmz3gaidOom7CRCx5O3CbLW2xdZdKxff3P4DHdSkTm4owDTwJJfrQLVq3O5XSytcx6jei0dRhs5+ORwnfjYhVKgcR+kKa3MGNsaN9RN3HWKKiK6rOuwBHXDyp//gAtd1G84Ah7L/xtmN6XQo2XW0NmQvnte05aYmg6LqbaR42KridKgqalmbcRpPfsiIisMLvt06gttmIWbsSRaej9rSzUIK04XGgGYv3o/Zzi1Fjs6F2OPCYuv/r5oyO9tvur+CjPxl//ZPPHA4Ay65tJH35GWVXXNvteAIpatN6st6ej76qErfRSP3YU8m/44FuvPCpqKs+m7rqs4Ma59FE/fhDu4QbwLJ7Z9sG8S4XQ55+EFNhPiq3G3tyKnvvnk3TiK6VWlA5ncR/8xXGkv1UnXM+tn4DAhq/otXR1IM3taqSeqKjomm1RrI3PZFkQzNxNi2RCz/DsHknrrRE6u+6Dnt8zzaG1u4twrhyI45h/XFMGBWUBDY68j2STP9DrW7E4Y6h0Hg6S3fcQHFZA2nJgd0cvGbiZCx7dqE57Da1IzaO4iAXnG44+ZSg7nIQCJqmRoY/cBuWgj3etohd28ib/RQNY4ITe/ySL0n97EN0dTW4IiKpnjyVosOmFIjAk+QrzMSuXErWwvkYS4tQ1GrsaRnsvv8xmoafFOrQOlV1zjSS//0JuoZ6n/bWhMRjLlK4/5e3Ydm7G8NhCVRLehZF13fthUFX135umNrjIXLbTyFNvnS11Qx88UmM5Yfq2BnLSvDoDey77b6QxdVduroaNI7Wdu1qRyualmYGvPgHorb+6G2P2LOLgXOfZPOfPvR+qIiybiAj+y10+lpczkhK9/+c6orz0ZeVMOyR+zAV5qN2u0n+9z+oOnsK+ffO6bXv72jSkqOgDH7cVoY7UUV1vJn++v1M/dWDROcWoj5QQVH532qWv3YrjVkd1R1TsOhqcSta7K4jkhxF4bTH/07iylxMtc04zAZqhqWz/LVbcZm7duU2cl8lmlYndQOTO9y6LD1qC2dYPkavaftZ6jXNDE75guJTEvh23bmUNgd4gcAp05m4v5TszevQ21toiYph47QryXOZYc/h9RYVThrxLwb2X4Na7aKiahDfrbsJtzv4V63N5gafIty95bTP3/NJvAAM1VVEvf0G39yXEfD+Egt2MfqNl7A0tr1uG6oq0H5Ywi6Xjm1nTQ/ZOIST7ozBxPO6dk5JvsKIutVO1juvYzpQV0bldmMuLCB73nP89MYHYXO7piO2fgOoPnsK8cu/9lY0tyelUnjL0etAHU3zkOFsf+oVMv7yFtrGeloTkii85U6cR0z+7oi/Ug3KgVh7g8rRSuonfyUydzNuk4Wi627GNmAwqZ+875N4waFbtPt6JbLAqJtwBvbkVIxH7JHXmpiMW2/AVNT+uzEWF2Jd/x21Z0zGaMpn0IhHMRgrvI+bTIW4XWZSHvzYZ/6grqGehG++pnLKhb24VdDRpSVHkZYcxeZ9+QzJ/JxTk5Zhfq8e1avAB8CFEH1VJWdV/YW/qV7G7vBNrpKsO5k6Zj7RlnI8Hi1VDZn8e/0c73GD16wka/Fm9AcSXH1LK8kb9zDskZX87/Z7jhqbpbqKS156htji/WhcLuqTkll28+0UjWj/Qe70iR95E6+DtBoH4wb9wKa6Cwn83g8qvpl9M9+6fom+xYY9MsLv69vZ2YsYm/5v9Jq2K2QpSTuISd3H3zc/Q7B3ClCbVNiDs3j4qKIb/dcVM7gasQ8KfH+jP/zMm3h5+2q1MXjLcnJunh6ycQgnwRgDSb7CSNSmDRiL2//hGcpKMO0vwJbZPwRRdc/ee+dQMeVCkhZ/jjMmltIrZ/a4LETLgMHsfOLFY3puyRXXYty/z2eSbUu/AZT0xlUvt5vhs+8kassm7z550Vs2sfu+h9D6WfYOdGm1oFptJzXrXSKjtuJ2WyguuJHmps4LiwaDOyKS4qt+QfpH72KoqkAB7CnpFPz6HlQeNyjtN9xVeRRULicA6f0X+iReADpDHSmZH2H0s7uFtqWJpK++CJvkC0Crq+WGa+7HZC5oyx8ygEXAC4Cl7V8cxdzYcB9rVj1OQ91AAFRqJ1PHvUS09VCCGmGq4epxz7Nq+QsAjFu50pt4HS5z71762Y9+9WfSyy+QuHOb9+vEfflc+OZr/O/phXiOWKFp6WBfZJvSxKi03igy6qcMhMrJyJRV3sQL2vKz1OhdnDXgW+rsA4Ma0ZHbz/UW9ehYWN2+XZOo7/RnoW20EffdNhzxUdSOG9ilD+xxfsYeIJJmRqXtD9k4hJNgjIEkX2HEbTTi0Wq9y68PUrRaPAZjwPpROZ1E7NiKx2ikeeDQgJ33oKaRo8PmzbHm7Cm4zRZS//EBmpZm7Clp7Lvtvl5ZiRf37RIitm/xJl4A+poq0j9cxL5f30Pc6m989jwEsKe2L3/gQ+ViwPDbiE/c4n1dNUZsZPmqO8gvPL1H8R7z7YVRkzBnjmL4d//DaTCyfeJUHCYzNEFaTCIpR9z6rUtIZlniYNx7ykgdVOX3lDZnHS7U+FuTWoieH/YEbyuw7o7DeWe/gtlS4NuoBhLwuTgTGVXMsNHz+fy/bVvVDOj3HRGRRe3OZ4nKp9ZVir01mmaV//l/raiOeivQ1FCLuaT9ohRLWTHG75ezZ5zvwonte8eTkJSDVnuoYKxHUVPaNIwqWwvxIdjbUK9pQuenHppOYyd9yXcMWvQlaruTxqHp7Pzt5bgjAvcaGUr5N08lcdlmoncc+t2wJcew666jb1ae9Zdl9Hv3G8zF1bhMepoGpJCz4DZak/1vbn9Q5dkjiF+1rd183cYhacf+TYhOSfIVRhpHjcWekYVlb55Puy2jP61JganfZF27kn5/noexeD8enR5bZhZlL7wBlvBdgdVT9eNPp358zxKTY2HduA6N09muXVdXQ+PIk6k8dzrxK5aia6jDo9Fgy8pmbyfzmRr5OzFx230+0EZYahl72qdsN/Tse+zJpXU7sawef0279i8fvY9Lnnqe2P1FqF0u6tJS+fbXN9E8ou2qTZF7MFnKD+0+oNeQgu30aGI+34/WdehNoS4lie9/fTn2INZ27e44RCV3sP+sn4sOEbGVh24dJdlQq/0UYdW5cQ10YXfA9zdfRnrej1jqDt0Wcup15E0+5ai3oLRVHtC237ZXhYI73t3uuT+4LiCpajtZMZuIMNThdMdQ23wy2/LuoszdTG5t4O65mHSNpETvo7YlgdqWjreoUatiGBYXQ3K07weU1pf1JDzyI8bmtphic/bAD6W8+dSTKAFcpWcymrDZQ7Oh9toHn2LKR38nsbiYlogIvrn6Ksr1mbDV//FR1dVMfH0p5tq2DzpamwPr1n2k3fMRix555Kh9qUZezi/G7iZ7ay4mmw2HTkdJdn/eueT/cGw1hXQcwkVXxyA2xsQFg7t2Tkm+wolGw86Hn2HgC09gLClG0aixZfQj7+FnA3J6tc1G/zdfwnTg1qba7SJy13bUj91P5Yt/CkgfxxWPx7v651jKUjQNHUHC0v+g9vje03EdKOKZf8+DlF16DfErlmBLy6R68tQOy00UlzXwY2M1V1yw0efqxEFWc1WPbw8F5fZCGuR8eT/WTXvR2BzUThiEWa9jFG2xNrtOo9b+PVZjAWqVB0WBxtY0ypsvwPGHCIoMdcSv2Y7G1oo9OYadv72cgSMagK5vAdRd3R0Hg7brc480ao3356RVZ9LijMei97365/TE0D+hBWiBNBP5911I5vvfYqhqwBllouqsETT/fiKjVEf5eaeBIysWanxvKbVkJmCYkcEoU/vnFhEZZJcAACAASURBVDZcQ6XtXJxKFYr6LEoLk9m1rYwktYlxlq7NsezMyNF/JrPfN5gtZbTarVRXDWft6sdRPP5/70t2XUbMqHcxGBsBcDs1sFCP8Ygrxml787lkZS5Fp50TkDgBIrWRNNoaA3a+btHEUTLzdxxM69OB9KPkv8P/+y+ia9svLkovKmd8YzSK9uhv9dvufp6yvK2kbF5PTfZQSseczijUYAvxOISJLo9BNz6jSPIVZuyZ2Wx97T30leUoWm2XJ5Z3Rex3yzGWtL/Nod+/D11tdUD76uvivl1C2oeL0NdU4TZZqBt3Kvl3/b7D1WL+VEy/lKTFnxORd6gGljMyivILr/B+beuXzf5+t3XpfNaECFzqkXiUlahVvlc1tMQy3NKzOTBWq5U6VZCKSp7d9nHQ33WOxpoFeMxfYTT8hNOZTWPzZQw0msAI/PFhql1uVA4HitlEChCcGv6HdHccPC234DY/hOaIW2SKokJ12M/J4zHiaZ3m83NqaboVfdR7aDWlKGhxOdNpaZjDcMth8ztvGUjtDTegKa/CExuNymRkeBfiss17lNbbn0K3uxCV04WrXxotD/2KofEdP7vGFYnyYS0Znz3DmEYbE6KTafzdw7iiD1so4HYR/83XRG3Jof7kU6ieNLVL5VGiY9YxaMi/0OraEiejqY7U9LWcccbb5Of9zu9z7A03siv3JFIyPkStclBTchbpVYsA3+RL63KSVZyHknxp5wPTRW1FVsN7kdNBljj/pUDUei2pKdFdK1+TcgZ1Z5+BGjj8hmNfGodgCcYYSPIVphwB3DFe21BP8ud/J3Lrj6C0vxXRdnvkxP7jOpyheD/93ngJQ/WB/RNrazBUlOK2RFB4a9dWbmqaGkn+4hOaBwyhNT4RXX0dbqORsktmUHvmsX06HzfwU4YlfIkK35+h2x1FY9PR54OENx3NLRfT3NLB96DVoGjDd0eCVucoqmoeIib6dbSaSkCFw5lBfePPiYpYjFZTgUcx0mKbREOT763ZFtsUbPYzMBm+x6OYsbeOB/y8UWo1uNO695rg6p9O2eI30G/6f/bOOzyO8urb99TtRb1Zttw7GGMwvdn0EgIJHUJ58wEhwEsogYQSQgmE0EIJBAiE8gZIICShhl5tsNyNe5Gs3rW9Tfn+WHml9a5kSbaxgb2vy9flnZ15Zma1O/Ob55zzO6sRwhFi+04HdWDPwKKn3qLkv39HOTsGTVD0p1UEr29i+R+fwVQUxHCIKddfhmPtaiQtQdG7b1D277+z8q6Ht9mAuqT81ZTw2oIgmDi9ywfcLtA9k0D3zNTrUu9r0Jqe86dbLHTvvX1eat9mWo8/hZI3XsW2VdVxaNyknGHqbkpOfH3HcS1bxLh7foOtqQEAQxDSEsAB4iPHfO8bVfel4uVne4VXD6Km4V345aDEl2P1CibceWPKMiThdNE+59jkzNkwcbpqmT3xZRzW9JJwTc+jo+sXRGPffE7brkVDEOKY5u6RixKNzaapdXaW5Ydtc1tBiGK3fY4sN2CafyMUPpJg+Pgdc2CCQHzm4CthS8b9C+XKGGyZBL8I7OetofC9N2k79geMfPpPuFf2iiUpkcC9Ygkjnn9ym78Ns79WwlmeBwei8ZSzqHrsPtSeQg5TEAhMnUH37IMH3E6Ix/AumIehWvDN3Ge3dLYfLprbw6bLrmXUXx7B0taMbrEl23xdc/OuPrQc/fDd+fblyErVnx9MCS8A0TQxRBFTkjEsFiIjq2i+7d5deIT9YJq4vl6KZ/ECAlOm45s5+xvzOZPCoazLt9gjbIvRf7ovJbwAlGCAwg/fofnEHw3bX2z8pFcyhBeAYbi/Z8JLI9/7IFZ1GYIQQ9OL6PJdhCBI6IYbTRu1qw9wiMQpKfglqtrb61CRNyIIYQKhH3+jRyJLNcjHB3uFF8B4EO8xcd69krZjf4Bj49qs2zr7WFr0R3P9j/Hmz0NRe3NnTEPE373XkI6zfe5xRMtGUP73Z5GiUfzTZ9L443MGvD7kzfuEUY/fj62hHkOSiFZUsu6XvyU8YddYtOwMuvc/hO7ZB2Ft2IzudOXSSHZzcuLrO4wUCqK2t2YsFw2DpmNPoPnUs4iMHI3X64XdqYGsrjHp5qtxL1+M3NNrLDBpKqvveHCHWm70R+vRJ5I3/1PkSHoOT3gQPmtCPI66VTsj6DEIffd1Nv90YGNMAOfKZVT+9XEUXxea20vd2f8DlbmwMEC+52Gc9rdTuVSy3E5p0S8BA8N0kEhU0dZ5K4aRvS3V7obT/jaKUpO2TJLCOOzvf+Piy2H/AMGdOQ1ljoLug/bBuWo51vrsPVV167Z/lwHfTJrqzqao7HVUSzNawkvAN4PNG4Zuwhycugdrpw7O+0+Ixxj1+APYe45d0gwctRsZd/8dLHv0ud3evHpIiCLRyqpdfRQ5BsEOEV9Llizh6aefxjAM5syZw8knn5z2/kcffcRzzz1Hfn4ytHXMMccwZ84gPfhzDBtDtWBYMqv0DEkiOHXP3da0tfwfL+Bd8EWqSlCKRvAsqaby6UepveQXO33/vr33o/2IY8j//EPU7i50i4Xw6PFsuuL6bW5rShJGlspIUxCJlW3bN8das4GJt12f1k7JVruRavVnlFZ+itOWXtEUjw+yrvk7gYnFsiwtiR1AEJJ2DZIQRLKsoMD7e9o679gVBzhkVHU9gpDpcCqKwSxr71w0vQTTAGGr6KChW3GsWkfZq3egBDKrTOOePJp+dM6g9lFf81Ma687C7thILFpKIt5fy6Udh2fRV1gbs5lXN2Bt2Ex0xA6cLTVNBF3rt2o5R44tbLf4MgyDp556ihtvvJGCggJuuOEGZs2axYgR6WaRBxxwABdddNH27i7HEDAVBf+ee2NpakizO4hUVtF++NG78MgGxrN4QYY9g8DgQhvbi9rWQsXfnkEO+Gn80Tl4v/wcxd+NbrGQ/8n7tJ5wysBPypKEb+a+WJvq00wLIyNH0XbkCdvc/8jn/pwmvAAsHW1UPf0uH+x5CvvPeJM8eysJw0YgVsWSphPQzfX9jDY07ObQLBZKLS7y5R1XGDIYBGHbHQAUpRZIADvnBiiJ7Tjs72AYLkKRIzHN4RcDhCP747C9jyimu9jr+jcfMgqFj8TheBmb2pumYGjQbsyh+J03M4SXIUmEx06g8dSz8e+596D3Y+gOgv7hNzMfKqaqJv2/trqmmJKEqaj9bDVEDIOqx+7HWz0PMRohXlTCpkuuIrQdTdtzfLfZbvG1fv16SktLKSlJXoQPOOAAFixYkCG+cuwaNlx5A4ai4lmyACGeIFo+go1X3pBqarw7snXrky3s7KdJ19KFjL/7Zqw9lVQFn76ffJIFqAXXmq+xtDZRd9HPBxxn02XXYooi3sVfIcbjREvL2Xj5LwcVMpWzzCwAOLUosc3n8PLa45HyVuOzexEsg39ir/pqIfu+/CpqOIy/qJAPL7mIQFlp2jp2m4NwJHu+WzY22JYxNj/wDYgwA0GIYZpWNK0MRR6Mu/3OCSW5HP/A7fwHstyOaYLb+Qod3dcQiw/vJhuN7UcsvgdWy6LUDJ6mFdIdOLfPWiaCEMY0LezcTBGVjW1XUex9DrvaRiKuUFszFfnNoylpfiNjbVMU2XjFDQQnTd2Jx7RthEQi6WPVz0ORb89ZxMpGYN+8KW15dMSoHWZePeqJP1L675cRtaQHn7W1mQl33sjyR55Fc++aELjS1cmYB+7EVleDoSj49tqH2p9emat+3E3Y7l9yZ2cnBQW9T2kFBQWsW7cuY70vv/ySVatWUVZWxk9+8hMKCwuzjvfee+/x3nvvAXDXXXf1u96OQJbltPG7vTu+fezuQNdNv6NvsMrW828LkiQl8752EwLnXIRnxVJkf28emu5wEjzl9J12nJIkMerZx1PCC8ioCpWiUYq/+Ijg5ddu03S1+8Y7kx3TTBMEAStJ26ptYYwZD4u+ylw+ajRTJ41kKiPZ3DCS+vbBmx5WLvqcg5+8D3sg+XmWr15Lyfo6Xr/pIWKu3huDKApYjcFXDzZp01k5OUwwr4NiSafC7qXIUj7o7QeDKj+FKv8XhACmmU88cSy67kMUaxEEPcNLK8l4vN7hXzdkSc76PRPoxmF7DUlMmqIKAihKA4X5fyYU+SvEtaSVQ5oIMJDE+YhiPZp+KKaZKVLj+kOYsX+hyPMwTDexxAXYbJXYbCCJX2FVH0EQ2gArmr4P0fh1ZLWj2AEkYhNY3v0IgagD3SfQubKLw8bqaB5vRj9Sw5OHdew45IF+k7qObdkiHNXzsS+tBk0nPGNv2i+6DLZz1snxxScU/fkB5LZWDIeTwKFzafvZ1WmfvxgKUn7rtSh+H6YoASaG3UF03ESa73xwwOvJUK6L+Yu/SgmvLdia6ql665+0X/y/wzq/7cIwGH3F+dj6VKY6ajZi13Wafj20kPzudn/YFeyMz+AbSbjfe++9OfDAA1EUhXfffZdHHnmEW265Jeu6c+fOZe7cuanX7e3Ze7/tCAoLC9PG796dks6/QZIGcrvPuXdPmIp+3v+j9I1Xkf3daC43bUccS+P+h+20wgCvx4PU0rTN9QRfN6Hamh3qw9aX4FkXMXXRV2ktpkJVY1l/3sUkes7d7RCY4shuqpiNqe//MyW8tpDXVMfhH7+aVgAw1O+BpxmWLI7SVlyEqzDMSM96Cm2NlFp2TKuqfOf7jPD+H5K05aGoFdNsYl3LL7CqjVikNnTDSoHrI1S5FdO0EEmMZFPbOejGmmHv1+VyEQhkitsCxwe4HZmzbkKsFusPT0dcGcLwOAidehSBS36MKHZRXPBrFLkGUYyj6U8SCs+h239xlr3O6fm3hW5EsYvSot8iS737FIVGYlHo8l867PPLhiBEUORaInEIhBR00U4gGCIcDtNaVkrB+MnkVc9LrW8KAr6JU2lXLP3+Ju1rVzH+nt9gq92IYBipuUjHwvkoSxay6ncPDTvZXW1pYuxWuZFK/WbCiDScfWFq2YRbr8P96Qdp2yYcDlb85g/oqnXA68mQfg/9hOvN+s275NrqnfcJ6rr034Cga1i//Bx/S/OQCpd2t/vDrmBnfAbbLb7y8/Pp6OhIve7o6Egl1m/B5eq9GM+ZM4fnn39+e3eb4ztOy8mn03LSj5GDATSHc+dPlQsC2iCabetO9071RNPcHlbc/yTlLz+LvWYDkRGjaDjzAnTn8AWNHMo+S2ZtyOx2MBQqSt1UlLqp3tBMSyt8XTwSV2EYyOxXOBzO2ePzPsIriSL70OS3eGHlz1LLRGEupc5aIgkHXdEtodThH4PVEiUay9x+fL6T8jwJWUp/T+iIYFmwCXxAI8gPPode4MFx8WdY1F5rBlnqxGl/i1DkcBKJbRdKuB2vZIRYBUHDalk0vBPrB5fjJVzON5HEVgoMOwWRaXzZfF/aOmt+cw+jH7kHx5pVIEBgyh7UXHp1/4OaJuPuuw3Hpsx8RME0ca1chmvFEgLT+7eZsDQ3Uvj+W8QLi2k//GjMPukIFS/+NSM3UorHyJ/3UUp8CYkEzvWr2RprSzNl/3gBQ1XRvHm0H3HMNg1it0W0rCLNXgZAczhpPfqk7Rp3uNjqapESmfmRUiSMFAp+I1XjOQZmu8XX2LFjaWpqorW1lfz8fL744guuuOKKtHW6urrIy0t2Vq+urs7lg+UYHKL4jeZLdBx2FLbGeqR+uiqbQMd+B+/03DPd4aTugp9te8VBEs8rYGtZaQoCwcnTdsj4s8aW0tDsp6k9DO3Zb2KioLHHtH8zsmIJCc3C4qWn0Nw2sMeSZVJ290172MC1Oj0hPdDTuGhHzLnZ7XaULDMZLcIcusr+RVFBTdpysdpMCq8epEAY59/fQbqsM2MMSQricvyHzu4BhMuWcaXMXn0AgjA4v7nBoChr8bheQpKSuYaq6KPM+QV7FN7HZ029FhCG1caGq/sYduoaJa+/ird6HrrdQcMZ5xMZ3ds2yb5pPdaG/ntPyuHQgOKr8i+PUPLWv1C7OjBEkfKXn2XNLfcQHVmV3N6ffRZCjPUpXDAN0LOL8PJXXkCOhDEFkfK/P8e6628nNHEwTZuys/HyXzL5ll9g21yLYBoknC46D55DYNqMYY+5PXQedBgVLz+L2p3+HYwXFOX8v3YTtlt8SZLEhRdeyB133IFhGBx++OFUVlby0ksvMXbsWGbNmsVbb71FdXU1kiThdDr52c923I0lR44dRcdFh1Fw6btY4/WIyyOIvzKhz7UrVlhM3YWX7boDHCa1F16Grb4Ga3MyrGoCwYlTaD759IE3HAIVpW4q6C8UajJpzyvw5s9HFJMVZ6NGrKJ23ZW0NfffFklITAbSTT0NQ8KMHsGssaXZN9oBDBRi2Lz6ftTJt2O115NIiBhfRHCfm7muHglhmpAtqymiQafWgm1FDSUPv4bsCxEdXUrTNaeh5/fKx6h/P8ZaP0WWtvKbSxTQqWV6yQ2HkZ4XUsJrC4Jg4lIWpZpqZ2CaTLrpF3gXzk9V9HoXL2DjFdfT2dM6y5SkAfuganZHv8LLWldLyRv/RPUlxadoJH25xj54J1/f+2cAOg47kvz5nyDF0qtEo+WVvYepWohWjEzL4wQwRDHl4SeYBva6WkY//HtWPPRMv8e7LWIjRrLs0ecpeuffWBvraTviGMIThi/mtpdYeSXthx1J8btvIPc0IY8WlVB37v98t3zNvsXskJyvmTNnMnPmzLRlp5/ee2E/66yzOOuss3bErnLkSEMKBkAQ0B3O7RrHal/GmCmXY7X1XKgPAONAEfEgA3wQ9+bRcOYFaaGPgVBbmhj7wJ1YG+sxVAXfjH2pueSqXVJpFJ4wma/vfYIRzz+B0tlBcOIUGn983jcWevDkfYXbuyQlvABUtYuyyhdpaz6B/ioTa9Zdhd25AbtzDZKUQEvY8XfPoqXhlKzr7zx0Siv+Tl7h5xiGQn3NhQT8e2CaErbnnmd6+DEk0m0M6kaNROtWmVpUn9YEPRAv4J3NP8D15Xrm/vpBnK1Jde+atxJhwXr+9eRtJJxbih6mkZAPYqx3HjYlgG5IdEUreGvDxYQS25esLgsxxud/DGI/pqkxnRmuAipKMwW1p3oenmWL0qxU1M52Kv72dEp8RUaOJlIxEtfaTHsYUxDxT92TwNQ9s+67+O3XUsKrL5bmRgQtgSkrdB50BJ37v0fegi+QQ8Gk7UXVWDZekd7Ca/01NzPplmuwbd6EFI8Rd7pQg5lheEtrE7KvG80z/KRqw2Kl5aTThr39jqbm59fRceiRlLz5TzSHi8bTziVevPMeWnIMjZzDfY5vJZamBsbdcyvWxs0giIRHjWHdDbehefKGNV5R2QO9wqsHcZpB6LGxBJ+dTuOpZxOpGjOosYREgsk3XZWWNG/bXIOQiLPpf381rOPbXmIlZelho28QT/58ZDkzjKconYhiFMPIHqrUdRfLq/9CftEHON2r6Go/iIBvL77pJvATpt1AftFHiGJSbLi9i6ivuYimunOJnHse/g2LcB20EPngBHpQoOPFkbx36v8j/pkTYabGiMIVKHKEYLSAL1aeQ3NTFQf/8fGU8NpC4brNTH7oHT4/47zUsrc2/YI852YmjfiY7lAFa+oPwTC377Jd5F7PibPvIs/VgCiYGCaIfT5S0wSLsW9W4QWQ99XnWUPzSlcH6FqyZ6IgsPaG25lw141YG+oQ4wlMWSJcWUX37ANpOPPCfmdgEnkFmGT+lQ1F6alYBASBdTf+DufXSyn49APCVWNon3NchoVOvKSMZY8+h3tJNWpXB2I0wpg/3pUmHAFMRc1qjvxtJzB9rwHz6nLsOnLiK8e3D9Nk4m+vw7muN5nW0tbChNuuZ+UfHh/WkLLSkXV5dE4FG4puGtJYBR+/i612Y9oyUddxVH9JY31n0pNoiPR3IxwuDc3+QY/Z0Jzde2yw29fWjqNshIIkp+cqRaIO6hrjJA1RB9r/bGBL0+rBW2wMl74l5XbHWjx5C1LCC0BRA+QXv8bC6uMxTZHRr2h4SjSQQMKk8Iw6LghfTHvbeN7/71V8oauoaohQqAAQsKPh6Mwe1ixZuwl7Q7plQYxylq45E9hiV6JlbDcUjtr/UQrcvcUWogCGISKKBom4l4B/Ki11NwHZrXeC4ydjSDKinn4cusOV1qw6VjmK5Q8/i62uBgyDyKgxgwp5tRz3Q0r/8w9sfXLGTEFI5k9tFcoMTt2TYD8zaL0nKOKfuS+QbP9V9urfcGz1+2wbNZ46XwJ8ieH91nosZbIxlN9aju8POfGV41uH8+ul2Go3ZSy312xAbWkiPgzjRE3LPmMWDY8c8ljWxrqMJ2sA4lEaRD9x67arKvvS3RakKRhiclsDFVIU/7S9MAZRmdkf1RuaaTEiNIXClDnsyRuDaWJbvABxcy2+vWejO13JJPpQmBYjgrcoM6zbtKG5d/ssbNm+1ZjEqKnjGVHYG4IyDAHJ6iNvyj9YvHHwFWGqHGLuXg9T5K7BMGRq2/bk0xUXYO4g36vutiC+VTClLHlO7ryFKGqm+BTVLto8LRR715NfvBxR6g0tiqKB0+nD6azmqB/+jhc/+QPggj4RrVC+l6KGjGFpmTCacEX/l2Wb6sOiBvEFS4d1zooUwenN0u9VNGhtOpr6TZcSjVTi9VrpT3x1HHE05a+9iHPtqtQyQxRJeL3JBPe+oXVBGHIbM8PuYO31tzH60XuxtDVjqBYCU2ew8crtnzU2VZVPL7mRSU/+nvzudnRZpnnsBN6+7Co0Syz5W+v5XWzT18kwGPXnB/Eu+AIpFiVWUsamn11DeGxvJWvW39ogEeIx8uZ/CoZB934Hb3dFZo7di5z4yvGtQw4GEbOUUYvxOFJk8O1x+tLeeAWqZQ0Wa++NKRwcTUPNhQNslY7S1UH5i89g27yJmGrFEk+/eUXKvVimGliGOHsjKy2cdN0fKa2pwxKLEC0fQeOpZ9Pyg6Hll2wRQ42FEVyFYQKEaVkZwV+/mZP/fCfehlqkWJRoaQXzDzuRJYfORS8GV2EYlzO98tCudDK14CVssQBdbZNp3ngWZUW9ffq23HSS20d5p/56Dhf/yCjvIiTRQBRNXLZODpr+VzS3gxrfvmnjq1KQPYpfx21poaZ7Fhu79wcEfjDhZsqcvTOeRd4NuAs6+HjzjiniiRdFaAja2LSugxmuApzuqSQSThQlvddiDBtSucjEii9Q5Vg/o0FR3iYqJ62mO5be13PJFSdT+OvatNBjx/iRrLr8aFzO5PfDKvvYr+I5PJYmdENFEuK4LO3IYoxQvIAFTT+mxrffkM5PFBK9obs+mCbkFcxH0zzUrL1uwDFMWWHlXY8w/dKzsbY0IZBMincvX8z4u29h3a9uH9IxZSM0eTorHnoGKRjAUC2DzrUciNTDhNfNwr/8ilIBdEXGUBVsJIAE8aIIje3Q0hpJE+HZGPXnByl77cVeV/vmRib+9pcse/Q5NgcMlgQ6Ur+fLWOmRFiJa8BZQNfShYx94A6sDXUIpkm0vJJNl1xF9/6HbPfnkGP3ICe+cnzr8M/Ym2j5iLSwBCTzmobbLDwSnsnqpfcxYvSTyIqfWKSc2g0/R9MGZ3XhWLOSCbdfj60pOZ1hCAK6JCH1zIDFRhTh//VpzC7Ydm/Cram4+jGK1/RW/dka6yl77km6Djh00GavDc1+GqQlTDvoC6baikkkJmMiswg44vIHKdjYa8hobW5g73f+zspTDkCssjHKGabU0nvDtsqbGVN8PxYlKVSNqmqaR33FvA8epKy4kOoNzcQKJDz5NtyecM/2LvKtBpKYnphulUMcUP42JXJvSb5FbmBM8X1YlWQO3ljvl+wfe4dQvJIi+4a07SXRYIxnBfFCH8Z29FjcQrMzTJtZSLPNxpI1HTQtLmLkmKl48r9M3SvjuoXuyAz2yTdxiaMwTAlRyG5poEphZhZ0EYpt1UD6yHHUl12dqnaMVZXQeO3pzMyXgTiCEGNC6W+xq70J8X0jW1Y5xOGj/sraprFoxtDsWDRtHKbZknbvFwRQVB/FZa8RCY0mFvp/A44hxqJIiURaXpaoabiXVqN0tpPI76/DgInVthlB1IiExrCt/L3t8bfry9YPHjNLwz1mwCbQ+5tsdoapddrw59mJR2WqNzT3W1nrrZ6X4WpvbdiM9W8vsOTQo/BM7P3+UxpmUbOd4g83cuBzT+PxdYHbScfBc6g/56fpQswwGPPw77HX1aYW2Ro2U/XEgyydtf9u3Roux+DJia8c3zoMq426cy9m5DOPYm1uxBSE5JPhpb8YsLx9IKSuDuwvzifANFqPPRnNO7TE/VFP/DElvABE00QXRdpnjUXZZ2/8Pz0VtcDLkO1ZDYO8jZldHuzdHUy+/ueYsoypKHTudzANZ/dfRj5j1gMcX/Uhdosfw5SIJsrY1HolFWMXM+LWr2EZcB8pvypXVwcHvPcBtXucTqklnta7sbjggZTwAhAFk9LiNYyb+CqhruRNu7jICU4dr0NMbW/p50+jimw1/v0p4QUgiQmc1lW4bKuybY4sxihULWj6cLoOGNisn2K3fY6ulSL5DsEiujE9OiVTPPhW+1i17H7ySh4gv2Q1UcEgYVahx2eSLxdD7BQSiY+wqJkt1SApLUqd1XTpB2e+OaME/5OzUy/7SiiX82VsSvrDxdZ/WovcTqXnM3yBwc/OAgR9v8Yi3I/d9klGQ29ZjqM43x5QfDU0+xEXL2NmQTtMAxYCPS1Bpa5OFi6opm7MxIzt8pwtnHbkAxR4GhEFk05/Cf/86GJGl69ictUCRMFkU9Nkvl70E2aO2fFekIXlHqIOmFjioNTS2W8/0rjpxIYdl8dNfV32fEdMM2sfVgFwtDXjLXJS7LGlvv8AI0MBjv/TvbhaemxC2sHaWI9usdJ0Wm+RhX3TeixNjRljWxvqcH29FP+MWUM78Ry7JTnxleNbSfvcY+mafSCFH76Noah0J0da/QAAIABJREFUHHY0hm14Mx+F771J1TN/Qm1OXvBK//MKmy+6jPY5xw56DLUjM49GSiSIFXsJXX/RsI4rhZBdtfRNGrZvXIccCFB76S8y1nO6lzOq6n1USzJ0Jgo6drWeiWW3IAoxxJN1OBk4FTgW6Lnna9bsVhSSmGkeKgomBYUrCWX3BQUgFp/W00A6fXk8nu72LkltGdsOlKet6/loenH68Yg+3I6XkeVmIrF9CIWPJLMfokFR/i1YLdWIYjLpf4LtfSKdvweqAFCUMGUj3sYQArR1Tqes4kPybF9jmP9F0/5GW+cttHbcRZ77cSyWZchS81YzSiZ22xf4g2ei64Mv81eV9Vl6VmYiCsPpR6vS0f1LJKkTm3VhxrtxVeOLVZvxCOlFFVtmj7qkTs65+in4tQBuEzYCjwAPQri0APX4Cipdmcd+8oQHKXX2itSKok1cdPLtKGIUWUrOII0sW01RaT1/+891/Vpd7A64li1GyWL0qtkd1Bx8TNZtZjzzWq/w6kGKRin85P008WVYLJiKnJFyZ8oKum3wvVdz7N4Mb5ogR47dAN3lpuWk02g79uRhCy8hHmfE80+mhBeAtbWJEc8/iZAYvJO43k8CfKiqOOvy/jGRpUbELQJHFIlPHZtlrXSkeJy8Lz/Leswl5a+lhFfaNmI4rYqPqcC9yf/6y8pY98MTsh6hYfZzrsGBxYUvcAbR2AwMI1nSbxgq0dg0ugPnp4/fj/VENjQ9D3/wR/QVVrJcQ2nRFXjcL+Gwf0yB936KC26gb8shUfRR6L0Vm/XLlPACsCotjPM8DIDLupnTTr6KUeMeYsy4t5gy7gXy7Y0IgokkxrGo6yjw3oNheOjovg5Ny95UXJa6sFkym6UPRDSW9BEbCF33EghlFitIUjMWdQWCkL1TwxbCkQMxjPQQlmFItCf2JDpOYEmgg+oNyRnIvmG7U098hLFl6xDzzOTHPh64HvQ9FCIn7steVQqzC+Jp/w4u2UyhLbPCwCoHU8ILkiJ+bPFKqmZ0pO1/d6PyhScQs/zWoiXldI3L3rnB1uXLulyMpaus6IhRRCpHZawXqawiNGHgrhA5vj3kZr5yfK9xrluFtSmzx6GlqR7HulUEp+wxqHHa5hyHra4WORxKLeuuGkntT+aw7S5+SRR5NQV5DyJLLZjIJBKjaO+6kY57ryEc6MDxdRNyJIquWrD5Mp+6pXAQKRzM8DqLx7MHO7PNJpkHQXvFaJZefilxtws7mQUM/uDJyHItsuTrs6yS1SvPpmjAaK1Ka8c9WC1fYrUsIRafRiR6AFs/A4YiR6DItUhS/+JB152EowfjD/4ITUu/UeV7HkeRe2/0gqBjtSzFYX+XUPgYZHkjxQW/QZEzQzsAVik5OzF73AMU5NX3GSdzXUVuQBCC2K0fY7UszbqOblhJaJWZbwxAKHw0Tvt/sagrU2MaRvJyLYoaCa2YYOhENL03PCcQozD/t1jU1YhiEE0vIRA8nkAoeyeDYPhELOoarNYFyFJnUshGJtESPJvyAjeuKTK+1b1/4y1hu1GeLGWapRB9cQZR9/9mDa3LchxhkM7qNiXI+OJG1ClT0/a/OyH7sx9XrLwi63KAjUfMpurjhcjx9LzPaFlmiHXtTXcz/nc3YquvBdMkWjaCddfdmnOn/w6RE185vtdoTje61Y64VfNpw2ZHcw0+5NF8ypmYkoTnrf8gJaIEy0pY8MvLsLkyZ5ySOzBQF61CiESJ7TsdLFCY/3tUpTfBWpY6Kcy7k1bjbqqfvILg+gIszUGkoMncX/8vlmB6zkkivwjNnVke31R3FnmFb+F0NfXufitjzS2YJSKfPPo/BL0H0F9z6kj0EDq7JKyOl9CFOG2BIlYvu5J4bDCJ3wLR2H5EY71VepLUgoCOppcBAsFQ0sHeYXsfSQogiW1ps1OmKREMH0O3/5LUMlHswuN6AVlqRZEz868EQcdmWUAofAz5nsf6FV4AmpkM7Tgsg5l1EbDbPiLf8xhCP0n3mjaSWHyoPf4UWjt+j9v5Eqq6GtNw0h04E1EMIkttRGOzMLZKtM/zPozN2lsYoMiNeFwvE43NIqFlzp6CQEf3tUhiK07Hv1HVtQiCRqFtKS2x/u1a+guG6pVlaT0u+6JpI9C0MqSM3DiJrb9nhiH0v5PdhEQ/Zs7hqnFZlwNsnLs/E99cRPmCBSjRCGaPOfSmyzOrS+NFJXx93xMone1gmCQKi7KMmOPbTE585fheExk1mvDosXhWLElbHq4aS7SyakhjtfzgNBbNPoaERyHhBpw6NjKTxOWNdRT+7A6U9ZsR4hqJMRWEb9kf5ezMGQVFrsVnrE8eU2kxYWcZvtU+ps7Yj8oFH6d628XzC2k4/bysT8ZaIo8F865l4t6P43F1YJgW4omR5Dm+yqjSE0WD6VPeZl7joVuNsqVKMTlLFYkdSENoHJuj+axpNBgR8aBswyx1aySxlcL8O1HkesBA00pp774WTRtNMHRKSoTZbR/gcb6IKHVgGnai8Rl0+3/a+3lKmykuvDElqMx+btyi4MNm/RhJyszP24KuW2gNzwFAM7bdfimujcLleANRzJ57pRt2WtrvZDiu/KZpxRf4SeY++1nfoqzJ+PNLkg+380U6un/d7348rhdx2N9BFGPYgX2sK1nXVUd109lZ1w/EKnBb0osBDFMmEDx5gLMRaO+6hqK8u5HlOgTBIKFVYJoCFrU2bU1RNJlQ9Dq1DUcOMN6upe4nF2PbvAlrW28OV2jMeBpPOxcCRvaNRJGPbruDis8WUvXhBwhTJtN67A8wB3DW779qNMe3nZz4yvG9Z82tf2DyfXegbEjaLYRHjWH9dbfutP15/vcOLCvWp16r6+swb+6GH+qwVTqVbsZY629mTccYAu1tlLfbKHPYabzlTiKff0TRB2+j2+00nHYe0QFsNtrb9qL6owcpH2visulUWn3Y1WuxqZkzQH21iywGGV14Pw5Lcr1EfDQd3ddgmNtvAVCYfztWS6/xqiT5Kcr7HU1tj9E3FBmOHEE4chiS1IZhuDG3spTI8zyRNpMlCJmG44YpYrMtwWpd3m8ulabl0+w/hrrImYDO5vZD8VhrUJReuaPpMiYKoKAlxtDRdTWlRVdkHc8wZPyB0zDN4fcLHBrZZ94sluX9biGJ7disn6dVPapSiJHu11ksntrPNpkO+wIGFstKtAFMiTVtLE1tj2FRvwY0YvHpFOXfBtRmrGuR+6ky3E0ITtmDVXf+kcrnn0D2+4iMGEXd+Zcme8xmqYJMIQi07jmLrqo9d9tighzfDDnxleN7j+bJo+7BJ/G1JavsdpaPTqfWQtfmzRTXZIazlJoAgX+4cf8k/cLd7C+jes00pFYoF21pnkNdBx1OV08j48GiGXbMnjwuf2RPLEpTWuPnRNzF8q9PhJ6oyr7lt+N1LO09TrkRQQzR2vGHIe13a2SpsWfGa6vlch0WdQWx+Na5diK6XoIs1+JyvI5hOAmETsYwPEhSphWHICTzwnTDiyw1I/YIBkHQEQQd0xQRhN4ZikSigua2h2hPRFJXxeV15xHvCDFt4gIgRChWwtubzqWy0E6xEsclTAFMDMMFpFdomqZAIHQi/uA52/EpZcPEZv0Ch+0jdN2LP3g6upGcHenP50zAQCCGSeYMi6qsRpYzW2tZpQ7ctno6yQx32eRsn7eBzfIloXD2Sr9eRGLx6alX8UQVdtvnGWvFBumvtyuJjB7H2pvu3tWHkeNbSk585cgBYBg4Vy1HDvjxzdwXYweXdK8Mrac9Eqaj3cEsXSLjNimINDXMRQjMx+lsxDAUAv5KVn1xPXtHCsC14/s7NnafgSYE8VpWYpV9RCOldLScQGPLVDx54LLU47ZszNhOVTYgy/WgDV+kCkICsuRICYKO0E/40uP6Ky7Hv5F6Ev0d9v/S2X0lhtFP9WXkCAzDjdf9fMZ7hmEnoZUhijF0vZhO36UYphvom+Qv8NWiszF8V9PQ7CfhUWhx+PC6RfLEzp6rp0AofBSy/ByS2FtskdAqkKVWCrx34Q+eRkIbXFP2gTEpyLsdu3V+aqbKZvuCjq5ricVnEIkeiNWyOstWMghmZh6VaWK/91PMawSEgvQ344aHUKyYbMlXWr+N0DNDZJLYhtv1EpLYRShyKJHoQfSd1fQHT8NunYeq9n7PwolCljSen/0j6EHpbKf4zdcwVJXWY09GH0J+5rcCXSf/0w/wLKnGP30GHYcdmdY3M8e3n9xfM8f3HktTA6N/fh7qpvWI8TjRsgrqz7qItmMG33NwMBj63uQVSEQqR+P2peeYRStG0rnXtXQt1PAWfIauOfB1zcZtl3HvNGsfmeqmXxHriuHRmhF9k3FYnUAjE8teYUzZ26hSZiskUQwjid2QZVZkMEhSM4q8CU0rRFJDae9pWgXRjFmvZHjMaX8rJbwAFLkFr/svdPvPQ1VqkKTeCtBEohx/4Gzsto+z9jw2TDct7Q+T6f01dAKhH6EbhTjtbyIIMUTRhyy1oirJmT2rdSFdvp8Sjhy1XftRlRXYLAvSQoTJz+BpWtofJBg+DpfjdWQ53UtK0yoxzcz8NccLb2B/4DOE/U04ntRHYWgireF9iWtusmXQ1/nn4LHUIPepRk1oZYSj+1Hg/T1g4g/+EFEMUZh3D7KczLGzWecRic2mvfNmtuTAmaaDlo578LieQZHrieoKi1svoC08pd/PoeT1Vxjx/JNY2pPjlv7779Rcdi1d34HWO7KvG1MQmHTL1ThXr0BKJCh++1+U/etlVt718Hb1dM2xe5ETXzm+94y751Zsq79OvbY11lP53J/p2v8QNM+Oz9f54upbOOh3N+FtrkPQNGKl5Wy8/JeYqoqpq3S2bt9NOhtu73omFGwiKs+CrebdIvFCNH8h+T0J5sfOuZsxo+dnze2B5I02Fp8ADOComhWDAu/vsVkXIkldPWFBe8ooVNMq6PRdAmTOqNmsXyBnCXfJUjuJxHjau67G7XoFUQih6wV0+X+KbhQQDB+Dy/mvNOsJ04RYfBI7QnhtIRw5jHDksKTHWOEvEMXePDFZ6sLtfIVwZC7DtVYU/EG83U8iFWdafyTDrjqG4aHLfwEe1/MociOmaSWhFYMQobz4AgzDQTB8LMHw8QA43vgEMRqDHwG/Aw4ADAivKmHxoTextcun017HUROeocAeoyN4MF5rLaIYRtMLiCeqKMq/A0lKhs1t1i8xTAuy3BuOFcU4VnUhqrqMeHzP1HLD8NDluxJIhuYD/VijAIjhEOUvPZsSXgC25kZG/uURuvY9ML2p97cI1/LFVD12P2p7C2I0ihwKpko0JC2Be+UyRj79J2ouu2aXHmeOHUdOfOX4XiMFA1gb6zKWW1uaKHz/LZpPOXOH7zNSWMRHNz/EGMOHFI0Srho77LZI20IUI0za42rszq9RLUFCsSKaggcRCP4o6/o2x3pGlC/tV3hpWiH+wGnA0BsdO+3/xm7/CFFIji1JwaRtRGgO4eghRGN79xnXxKouSt3EbdZ5/cxgWTBMB9HY/kRj+2fs0zTttHddS77nT0hSK6apEo9PorPr6iEffyYGNuvn2G2fk+gsQ3jYxDZxHtIFmQnXktSJKPowjG21rYqjKhswDG+P9QZY5i2h4Jp7UY5ugllkaFPTsLFFSIYjcwlHDsGirgYhRqH3fixyTWpdWanHMJXkLJzRE1JMAH3u6eEDSuHQ9O/jyKr/Mn3mn7HbkvlhmmEjHD6KLt/lCEKEsuJLUsIreb5+BCNTCElSGIft0zTxNRQ8S6qz+/K1NGHfvInw6P6tHnZXpGCAcff8Bltj5nn1xbFhzYDv5/h2kRNfOb7XmJKEmUX4mIKAYem/BHxHECsfmvHmcBg94Q94C75MvXZY2hglv02NNolgdFrG+m7vImy2zFCjbqiEQsfgD56VSvDeNiYOpR6L3A0UY7fNTwmvLQiCjiy3biWcdIryb8ZiWYokRrOKLkgmtcfjUzDNgeOy8fg0mtseRhS7MU1rRrXk8DAoLvg1FnUxYpuGeR7wIQhHgnkaCFtFh0zD3m9u2hYc9jdxO/+OLDVjmg5iiXH43j+Dwv/3W+QuPzwDXAb0icoahko4euBWI6nE4nuQ77k/FfLbgiQGcdnfIhw5isgR+2D9ahlCojf3zlAkmg/bt8+Bm5S98iwjrngcxdY7EyaLEey2T/AHf4wkdSJJ6aFOIKvvmWmKxBNVA34OA5Hw5GFYrUjR9Fk5w2Idki/fN45uUPX+fMpWtNO0x4EESsen3ip+85/bFF6QPMdtIWgJ1LZWEt78YXf9+CYRoxFGPv0ojrWrMGWFlmNPouOIwbd1+zaTE185vtcYNjvhqrFYW9MrEKPlI2g/YluVW7s/DldmArYiRShwfkgwOg2H0kB5xSJC1lFEmw8n6J9CNGbHakkPbxl6MV3+SxjsjJfLupmDJ96Gx1KHKBjo+ggMs79t08Wvw/4WNmt16uadTXgZhpVQ5DA6u7PbPGQiDGLWafDkOT7DaqlO9l/MB+EFYD5wBghLgD56yDBUItHZ9PfZyVIDVut8PK7/69M1oBv7zdVYH12M2N0jYuLAoSRF2FhI5BcTdByPP3hW1nH75sD1RRCTuVqBn/4IdcUGrJ8tQm7rQivOx3fQFDad05vreOiLjzGy7h2k32QWQWxpmxSJzcY0XCClh6FN04Jpmmkh2IQ2ilB4+GH14JTphEeNxbXm67TlobETiBcOtZXXN4PkCzH2vLuYvrIOOZFgsuMVWqbuTd3tfwBRRMnSrWJrEm4PTSdn71SwhdJ/vkjp66+gdHWgOVx07XsANT+/bvd1xTdNJv/qSjzLevuLOtasxNLaQuMZ5++64/qGyImvHN971l9/G1Puugl13WrERIxocRk1l1y1wysedwVmP+aepikyIv8vTLZVY5V9aFUqIf9Y1ix/iOaWyYysXJSyoNANC6HIQWxLeFm6O6l6/TFG+xfjHteKw9F7U5GltSQSJeiGDUnsTdQ2DAvhSHqitN36Zb9u8VuIJ8bQ2b3r8l/y7Z/1Nr5WgDLgJOB+4DjgQdAPcJAYNZJIdL9+BJJBQd7vsFkWZwqlZuBZeoXXFrqBk8FQZLr+dBmRY7ae9eolGpuBzTovzVIDQNN67EpEkY6HbkBqaEFZtYnElDG0FZsQTYphJRSiasUCpHACAsBW2mZL2yRdLyEWn9Szr+R7pgnR2J7E4tNTPmKaVkZn9+UMJ2SdQhBYfeu9yTBdfS0IIqGxE1h/7S3DH3MnU/Gbv+JY2lvNqYaClC/+gsgHb9M+9zhajz2Z4nf+jdqdLl41uwPN5UZze2k68Ud0zz6o3304Vy6n8rknUs2+Fb8P9a3XiBcW03jmBTvnxLYT74IvcG4lopVwkKL336Txx+d+a/P3BktOfOXY7ZB9XZS98n9Y2lpom3scvpmzd+rTm+b2sPnRZwmvX4sUCiWb2u6kHKxvGl/nPjica9MaaEc1N8HoRCryX0TuEUKyFMeTt4qxk25nxbq9yStsQZbDRLUSgpH9IZp9dgXAEgxS/uVy9nvqd1ibe2ZuPgPOBh7tXU+SuglFDsWqrkAUuzEMD5Ho/gTD6Q28DWPbolfT+m9/M3jieN1PYVFXY5oy4cihdPr3TVtDlqM4nKtRLQ4S9IZbFbkzczgJ2A/wAxdA+KxD6fz9L/rdu8vxKg7bJ9mF5rvAAJEobdxIInP3638FIBA6AZv1cyyWFYiChmkmZ566/JemradXlKBXlACgsoxCey2d8QOQWlqwBv3JvPv3gUrSdFMiMSbVNqmt8ybyPY+gqqsRMIklJtDVfTkmFvzBHZs3mSgsYtXdjyDE4yAIO82XDwDDoOS1l8mf/wmmJNF6zEl0Hjxn0Jvb6prxvFOdsVxKJMj/9APa5x5HdMRIWo4/leK3XsPS2Y4hSUQqq1j9mz8Qq6gc1LWv/JUXUsIrtY94nPz5n+624su1chlSLLMzhOz3I0XC6M7tN3LencmJrxy7FY5Vy5hw543YmpLVafmffkjngYey/obbd/q+44XF8B3r5rF542Wolg4crgVIaoCoXsxm/7GMcCxPCa++ePPnc9B+nyJJPSE/ZNrjI8mWbaLUtXLgL+7iiI0N2P1+pESffC4/8DJwOTB5y0KDYPgEunyXI0vNaHoxppmZB+ULnoHVshi5j8DZkvdlmsmqSBDI8zyMP/hjdL1kGJ+MSXHBjVgti1L3Nou6hoSwia7QLxGFGFMrX2L0Hm/hdnYyfpqLlq69+Me6nwMi0cQIbGpT5rA9Y2kVxfiuyN6eZwvJmaJ+ZvjGAE5gq9aghiQS22cqHfdeC/K2ZgYUWjvuxm77EJv1KxKJUQRCP+wnRy5OUf6tlCtfo8hBQvEyNux3BGF3HpZoBC4lWdw6FwyriL94BoHETb0njEqn76ptHM+OxVS3YwZtkBz19L1ULfocSUuGXd3LF9G4cT31P7l42xubJvtcdSdyOJb17b62EXUXXErL8T+k8KN3iRUW03nIEZjy4EWlkMjedErQshfO7A5077UvZa/+H3I4PcUh4clD/x5YauTEV47ditGPP5gSXgByNEz+vE9wrlhKcNrwKqS+15gy61f+ltbOejo9TShFY3HbNcps67OuLkrRtAdtu9pKufdlOjvTQx6qvJwJi29FzeuGDrJHkjpICrCeiJCmVxCPTwQkElr/rZA0bTSd3Zfjcb+IKHZhGHZ0vRhBSCCKbchSF07He8njs35Gl//CIXtoqcpKVGV12rmKYpQ8xzxm2i5hv6J6rLIPUUyGFu1ylJG29zlQ89IWu4AW/wm4bV8jib03DtMAY42D+P7j6P7lhegjBhaFirIp63LTBGO2E6abSPN6fdBMUSB6wiy6Hr8YfdAzf1JPBeTcAdfK9zya1pTboTYxcey/WH/4frj+40MOh+B6SCgKjSceRud955Mvf7dnJux1dVSuWpwSXgByOEzRh2/TeMZPtpkAn79mHa4NmZXUAFGXh4bT0/t2xotLk70hh0H7YUfjrZ6PFE8XesEJ/ful7WoCe8zEP20v8hbMQzCTofGEy0PLiad+ZyIPA5ETXzl2HwwDtS2zakoOhyj68O2c+NoOEnE3HQELxYUqoNHiPwGPfSGq3Jtn0l9VoSL3zUXRKcr/DVZpEeLlMbiAZKL5CZnboYA5DjCTydc+/1kM1lsrEjuYSNvBqMpSvO7nsFiWIwqxjPwlWW7H43qJcOQIsl3OJLEDp+MNTBOC4RNTSfcWdQ2SlOmZpcgdWIRMP7HkWDqj8pbS1gyR+Dj8wR/jtL2DLDejGx7i8Ym0H3wL5sEWXI6XyLc/hCiGMA0LhmEFQSGRGEWX/38QhARCll6Mpgmh8KH4gudjPOEl//r7UdbUgGwiHhLA8sBKStUr0bQyOrquJqENx1ohjsP+AZLYSSgyB10vSYYLt/rbW5Qg8u1x1k67Hc+rfwfJZOlh+5K46GhGDtnj7dtH6Wef4ghkmswqXZ1YWpqIDNBLFUCORBESmYUKhiCw5rjTiFTtiK4HSToOPwpv9TzyvvwM1deFZncQnDCF2ouv3GH72OEIAmt+ey9l/3gBz5JqDFWl6Ydn4t9rn119ZN8IOfGVY/dBFNGzJLmbgkCkov+GvTmGTkIvor7zJ+S7/4MqdKEl7OhxN968lRk3Yb1PDpbL8Ro261e94TInMJek/9SXQN8oxxQQTgMEEIRYUiRFDwUkrJaFuBz/QhDiRGJ79fiOSajKCryuFxDFIIZpQZFrkOWBq8FkqQWn49+oSi2x+ERC4SMBBYftbTzuv6L0GH06HW/T7b+QcGQu0die6LoLaSsHfyFLO53+3vcHziUQ/CGqsh5dL0brETF268c4HW+lFRZswWpZjqqupb3zOvozXI1ED0TTKsEL7Y/dDJiUFl6BbOmdRZHUdRTm3d3ThDy7oJXlTeS5n0KSOjEMJ77AmehGPkX5v0WR6xAEA7fzFUKR/p3hTVOke/9D+Hr0DBIehXqHj4nfcPWcta4GtauT4ITJGNadb58wwv0+h5kv4TyuMdmr/AHSuiwl3F5iRdsOdbdPm0xoZDnujemzX/7KKtYefzoVO/KgBYEN1/0GS0Md3sULCI4bT2jS9G1vt4sxZYXGM87/XlQ3bk1OfOXYreja72CsjXVI8d4chkhlFa3HnbwLj+rbhRCP41q+CMNqJzhler8Ju77IPizvOopop47ZZafAlDnw0POxO3vDYXHdQUfw0NRrq2Vh9jylO4DjwKwCQQLGAY+RZggqy5uxqItQlAa8rr+mhI/Vsgiruhx/8HQK827P2uh5wPMVouS5H0MUDZz2t3E5/kNb+2/xuF5MCS8ARW7F43qBcORQEtpYorG9sNs+T52PYSiIYva+kgCGKdDon5R67bC9hcvxRkooimIISexIzmoNoE9UZT1WyxI0vSRD/Gl6KZHY7LRlstyQ7KW5FbJcj0X9OksTcpCkFooLbkaRe/PSFKUGTfeiKrV91vPhcvwHXXdlzHxGE26WLj+Jyu3qcW1CPxW320IJBvnhfTdQ2liDHAwSLa+g8ZSzaPnBadtzQANS7v0/CpzvIZfFYBJJy5BZJItHAN1ipfOAQweshBZ0nSkfv8+k9UvonjYeixZDrWvFFAQC5SNY+D9XgLhzKvliFZW0VOx8/8Ac209OfOXYrdh80c8xZJn8eZ8ixqI9rXeu+0aeeIdNf/G6QW7r/fIzvNXzCEyevt0NdPM//YCRTz2MtbEOU1aIjKxizU13gdRfmySBaCIPxUhaB6xc8ke8FXeSV9iOhpWG4FyE+Czyew7JzNL6B8DUIFHkQH0sBP0Ug4liAknqwmV/I010CIKB1bIMUewasvBKbk8qHCkIOhZ1Hfl59yPLjRnrylIzqrIeTaugO3Au0dg0bNZqTGTCkf3xeh5HkYIZ20WjHtp9U/l4w4WMKwOPfT55nmcyxNPgjtfAYlmQglgvAAAgAElEQVRNl+8SCrz3I8sNCAIktCL8gTPSihAkqQW340UEIbMqLHnOGoq8ljzPU0hSB4bhJBA6CatlUZrwSp57J6KQ6b4vCCDLgWS7J1NCEkOEtTLW1h9HS/vEYYgvE4/rL9it8xHESLJxeffFJLSJQxpl/3vupnLNstRrW0MdlS88Rfe+BxIr26HzRgAIQgSPvRpZ6pM3pZL8Po8GY7NM/dkX0nDmhf2PkUhw8N3XUrB2ObKmYQLRsWXU33QOa9R8mvc5CTFuRfH1L/JzfD/Iia8cuxeCQP35l1J//qXbXncXo3R1MPbe27Bt3oQpyQQnTaPpzJ+TrTdhNoREgkk3Xol7xRKkWAz99Vcp/+eLrLzr4WGVWYuRMKOe+CO2Le2S4jGc69cw/p5b2Xj9/YMaIx4r5813b8QzyQNOHa8jzEhrb9WhP3AqFnU5cp9WMomIQm3jXOoemMvBh9yCvHWJ3pb1tFJi8WmI0pOZxy6Gsahrh3C2AyNLbRimHUlIPxbTtOJ2vYBF2ZDqSxgInpKyuzCkVeQ5P0eVfBimSLevhM7m81i3dhpd4mh0RzIHqND5/rCEV/IYJKKxPYjFZ9DU9hh22/uIYphQ+CgMo1ckuxz/wO16GVnKYmsBJLQKNK2MkqJrUPo001bkzWh6dsPRgfzTBMGgpu1KmmJFdCf2obvRJFtj7W3hcT2N2/lKylxVkZspKriD5taHMczBu9Dnr88sClE72yn754vU/GxHtIdKR5Za0r7XKUqAvSERLKD5pNMGfNAq+c8/KFq9FNHoeRgAbBuacC5Yy/rbr8UWtCJmL0zM8T0jJ75y5BgOpsmkm67C1bchd10Ns7t9fHLz7/4/e+cdJldZvuH71OllZ7ZmN72RhB6kd0IH6SBFRGyIoKIggiAqohQRC8WfiKg0QakiTUjoIYQSkpDOJtlk++7s9Hba74/ZzO7szG6y2Q1JYO7r8pI5c+ac70xm5zzzfe/7PFt0iLrHH8L/wcJ8p4+ka3hWLGX8vX+g8YqfDHtIgTfnlc6pbNmILRyC2pG3b2e13YlEL8bjegbEEGnDy6LWw9hY8S2UiEao61B8wXnY1Fy906ZJQV2vyttCmKYHSgiKvGnpKGCaXkzNhWRbWrDdsmy9Jq65c6liAp/376Szu6Hr42kJn09j+nxc5nMkYrV8+MaBzA4GSHWn8E5az4TAGhRxBpJY2j6g8Fx99hj979e5urRjevdxkEgWdysIQhyP+8ki4WVZkOsWrScUvqx3abWwSUWSoliDpAlYloIgDHb3tzBMN+H0dAzRARQ3JGwJDvs7Ba72AIrcgsf9JMnUIXjcTyBgEo2fiqZPy++jKh8zJfAnpkhRMpobVSkt4q1tVHOmGzXohqfYgqUdeB/iU2ds9keRb9F7eeHVH9uGjhJ7l/k8Myria9GiRdx///2YpslRRx3FqacW1udomsadd95JY2MjHo+H73//+1RX75hREJ8nxFSS+kfux7++kUqnmw1f/gaZMQ3be1g7BZ4lH+Bc+0nBNgEIfLICNRpB825+rca7+P288OqPc21pG4j+NLeVWD6KZZkiCAhWoYixBKFkfuXWEk+eRDx5AhFzHWuTY1jZIrDpU7Nm2S9oTb7E7l94gwxeLMFDhZLAypye7zRMJI9Ecj9asttwMCxLLOp0HIxc7VYU3fCTzkzLz1Bp2kQkqRVZLuxmlKUevO5/EwrnZlNS+nhWtX8Tz7JmTrn5p1Rm25l9fxRhbwPJZ5DIViNYm8/9FAQwDAeR6HnY7CsQhRTZ7FQi8QvY3Fev3fZ+kajKvQ8S4dgFxOLnAjJe91MlX2+YXoysH5va91nStHqSqf3xeh4vOXmj6fWktS2tF7KwqR/icr6ModcQS5yGaeWEiSiUFqZ22wI8rmeQeiOUHPZ3iMbPIBo/H0nqoDLwq/w1e1Qw9pFggBtHJlhF6zYIu4ecEI4kv4Dsfjm/9GhlwZjvoGfcwXxy5eZd9DV/6Qgrw7n5XMYyny9GLL5M0+S+++7juuuuIxgMcs0117DPPvvQ0NB3E587dy4ul4s//vGPvPXWWzz00ENcccWna8i3syJHwliSNOpuv2I6xawrv4Vn5TIAPIB38Xus/OmtJKbPGtVzfRaxdbSXdGeWMmls8SgJNi++THvpol1THfrG/t4nbbSbKfxV7oLtGw7elymP11LVXljrkxo7nqzXD2x+tmYgkpBifPAu3LZcwXdWm0IofAWW5cAwXeQ67fqLIoHm1j2Irzq0YNkyIPfdlKLx89GNGjyuf2NT12y2XM6ywDA8CIKB1K8ea+CMkmkKgIQgaKjqWlTAMJxE42cSjZ8PSNRVfbPkOQbOBgmGweE3XE+g8RO4Dziy7zmX2oFuODEMB6KYKjm71Xdck7S2D7Hk8ASDYQQxTDuSWPgZE0UDn/txJDFJOPot0tlZvWathYJbN+ro7rkar/tRVKUR3aghEjsf03Rhs63Cpi5DEAw26XRNG0t3zw/YsuJ4i/HBu/E5P8iPz+V6hc7u69D0qehGDYrSXPAKw7AjS6G88ILcDJ3b+TyxxCn43A8Xz+DdZ5BudyF+pCAl4mTq6mk58wKyNaORblCalvB5NCenU+N6AwWRNUumIdgvwbp+y8RT8/lfw7PgbVzdfdei+1x0f+nwbTTiMjsrIxZfa9asoba2lpqaXOvtgQceyMKFCwvE13vvvcdZZ50FwP77789f//pXLMtC2FEDP3cAbM1NTLntF9hbN4Iokhw/idXX/BLdN1jh9PCofepR3KuWF2yzt7cx7q93s/yWu0blHJ9lwl84gHR1bVEgdypQRax2y4qBm8+5EO+SD1F7+orMdZeb9hNOK71/W5RFsW6MavBUJvG4C2+4Qcda5L9lsa4GYS2YikB6YgOrr74pF8q8FXxhzE0E3H3Bt6rShCRG6Oi+ZesOCIBFKr0fXve/tqhPIVcQHunNhgwiiSEEQceyJEDEMJ3IUhRRTFLodQGSlMTlnEc0/iVAIqtNQlUbC/YxTDex+MkF28bMX4hvQ1PuweziMclSEssS8uPPCTChSASZpgfDqNr8RQ4gq81C18cjqSuLnpOkOG7n/4gnTiQWPw2nfX6vmDJ7XzuWWPwkfJ5/YJgBunquKXC1b++6DZfzf9htH2CZDpKpw0hn9yQnpItn2wYSdCzB6/igQBgqcjMVvj/R0X07ofBlVAVv6LWzyMVFZbSZ2NWPio4ly22oymrEUiHgHog+XsOGF36H0hMiMWUa1mZ+mIwGG6JHs6T1FDz42fjxRvaZvOWzVpmaOhZc+hOmPPEXfPEeDK+D+LmH0HP6ITnT4TJlehmx+AqFQgSDwfzjYDDI6tWrB91HkiScTiexWAyvt7j48uWXX+bll3Pu1TfffDOVldsu70WW5YLjh/3FMxnbBdNk4qXX4ugnjmyd7cy8+Xqa7nlgVE4RaFxdtDwF4IiG8ftHR+DtTEiSNLzr9vuJnHE+8j//htwrnrK1Y2g8/xI8Ph+aG/AYOB1O/H4/TsuJR/AAEooFiqmh7HIQ7Vf9lOCDf0EOdWN4/YRPPgPj9HMYOJKm5h5WaAnq9qzEW5FkvCtJvbPvRiSQocF7F7YZ3XAM8DGITgtqDTyrq/E0GbgUGY/Hhccp4XHkuq08sgcj4UGxQIwZ+P0+nM4oHrcHV2AdFSWc8G3qJwQq4mhpDx7Zg9Oh4xE9KKaWu9be1+Mx8ufy23JXJIvzsKn3IQrtCMLQ/l0DkSSdRPomnParEcW+mRVRTAxZSC5LUSoqNCyrGoPr0fUeRGkFopDAMKvR9ONwug7GCWiZJBFNpELvQdpkkDnIaudAoSUIVsEMmGWJmMzG650wjKu0kMSPkKWF6MZFiPrTyNK7RUuukhSmrvqHGOYEMtoFmNkeZOl9THMcEKc6eAuiGMaywOd5gWTmF5jmrv2OcC6GdS4IYHfm/kfv9XtkD7G0hMftQXPm/k2jCQvNo+C06UwMvoksFX9XqmpX79+Qn1T2IQzzCURxA5p+PKZVC3yFgQrEsny4XFMQhEOwrOK4pURyDI4ZuZn4oeaSbSuXEXjsH1iKSvf5F6ONnVDwfP/xe9xywWeyP5uu3xCdiMncvWq434dN+xzIswfsRe0UF35nknGOEB7AmZZx48n//e8s37PD/m78DLIt3oMdruB+zpw5zJnTF4XR1VXabXo0qKysLDh+ODy8m8G2wvPRe6jrGou2q2tWklyzKpdBOEK81bWU6jvKOFw7zPswWng++oBx99+F2t2F4XLRdcSxtAyI9vD7/cO+7vAZ59G870HUPvMYhsNJ22lfYl1GQYvF0AQAA4eVJCyESSaTxOIxiEsoMVBiGuGwQHjfg9mw78Fg6H0WEyXGEYvFsHtkPKqADwGvIaGkcndMr/sR3M7/Iku9s3Ai0Ouv6KQFSXmSWOwIEvYMsZiJZCaJ6bk6qFhaIRYnNybTTjicG6sSV7C7NiCLxR19ghAlkVhHLFlFLK2QTJnEEnL+mja9Hoz8uZRUGFlqoabyFmRp6/6mTdMC60EksXBJayjhBWAYbsI9Mha59zXMLSjKShS5mXRmL0yzAof9P7idL1JlT6MoUwmePw/+CKwD5gO7U+BlaloiYskaNJGsVotleUhndicc/TqwpZ8rnarAz7Cpi5GkJIbpIJudBlSVrP8SxW5EsRtJXEIo/F0SqR8jSxuprfoeohjpfW9AEppQxNtoD/1+syOI6TFiaQVDdBKLJ0kmk4TDYWKxGJqokDQThBOVjHEWL7Maujrgb6iwkUAOTOtdIu3blslOJdTjAQ6nOvgSqroYSdSwLIgkJzDvja+xS+3Q71/DA/dS9+Q/88HS7rkvsv6ib9PZzxuw//hjcTH/mRzs+mMJAw/+/PUPh1gsVvJvLZlQseKxgr//nYGt+W78rLEt3oMRi69AIEB3d9+vme7ubgKBQMl9gsEghmGQTCbxeD7buWAjQY7HEUsEpQpaFjG1dR1IA2k+5ysE3n4NZ1M/Q82KAM1nXTAqx99RUDvbmXrr9djb+5YH7c0bMFUbbad9acTHT48dz7rvXNW3oUQh/Baxld5eqrIYr+dRJLF0Z5hhSBjG1v2thZJTSWq1uNVCvyxdryOrTWFLbAgcchuTqu/AKXcjipGCmq1SmKaEKJYWU4KQRpFL5yEOhmHaSaQOxaJwuUrTpqNpOd8pv/fPuF3P5JfRvNYiBA9wNfBr4AqwasA6SMQISmStAHa5G0qIL0EwMYxaOrpvHdY4ATzux3vTA3q7X8UUdttHue7QIRDFLAH/HSjyOhDMgrqqTchSB4KQwrJG7pe3LnwSk/0vYO8XLG6aIqnM0PFfnaHrCfjuwqYuBywy2jR6Ipf3PivR0f0rTOU/yLZldMSnsGLtmcQTQ/thydEI1S88nRdekLOjqP/3g3Qdc+Kwwql3dORwD/6Fb5OtqiG6x+yt9xYss0MwYvE1efJkWltb6ejoIBAI8Pbbb/Pd7363YJ/Zs2fz6quvMm3aNN555x1mzZpVrvcagsje+5Gua+jza+olU1NHumH8qJzD8HhZdvOdjP/z73F1d5JRbTSf/WWis/cflePvKNQ/fH+B8AKQU0kq570wKuJre+N1PzWo8AKIxxro6ToEKI662RyGZaex54vMCD6MIudEpW5UEE2cgmXZ6S++mjNxbAkDet/qdiOCX4iwf/11eO3NJY7ex6bV79xXgophCCU7ISUpiSiuLtpecuymg2x2OonUHBLJ4wCQpRY87n8hCmliiRPJarsiCHGcjtcL6pfyX02XAGcCD4EQgXfXf48VG8Zz4qw/41IHr40SxcSgzw2Fw/Zh0fKiIFBS5BWf08Dn/TeG4SxZ/G9ZKpY1OkJEt5w0dn6faTW3IkkhBMFCFE1cjrdIZ/YhnRnsO0QlFBmq0UoinDqApp4TCSecYEpsTuB7lnyAfUCDCeR+dDma1pGcNHWLr2u0EdCZVfV/1Lk+QsDE7prEByuuBYYvgBseuJea557E1tmOYbOTnDiZFTf+Dq0isPkXl9khGbH4kiSJiy++mJtuugnTNDniiCMYO3Ysjz76KJMnT2afffbhyCOP5M477+Tyyy/H7Xbz/e9/fzTG/pnFdDjYcOE3GPe3P2Fva8ESRNL1Y1l76ZWj+msnW13L6ut+/ZmeVlbCpQ0qpfQOUt83UgZZcjNMifaeqSx554d47ApbI74A1oZPQdKnMNb3GgCx+OnoRs6OICDX0CasoS6QpEmqpFtw0t6RwqiGWYEmdq9ejNvWNtThc5fQ7yMtiilMc/DolVJ2E5pehaaP7XV0N9D1Brp7rsIw+wrdnY6XqfDem3fQdzjmE08cTzJ1GJI4RCV0JfC93AxieP50eswgTmVozybdqEQSu/F5HkCSOtH08URi52FZ7iFfN5g3l1jC3X4wJCmJZYn0L1SzLIF0dneG/3VvUV+3mMkz7iZY5+Tj5v4WGVZvjFJf3Zssd+L3PkBb536o7y3D+9cnwTCJf+k40kfsW3BkRV6D1/0YgpghkTyCVPowtiaGKFNdh+5yIycKf4AYLtd2FyYHT/gVEyrmIva+RzNs6/HNivHqW78b1nHs6xupfeqfqJHcd7SUSeNZ8TGTf3sjK27cMvPkMjseo1Lztffee7P33nsXbDvnnHPy/62qKj/4wQ9G41SfG7rmnEjP/odSOe9FTJuN7kPn7NgROzsoPQccSsX815H0wuWL9BZ2JO7oJJJHY7e9XzBzY1nwSehYnn7tu0xI2/DUjuwcWb2Onsj3Sj430zWFkK2dSkeMLl8XTZWVjPN1sau/hnEuE7GEONzUFWiauZmYgXmKomhgmmqRUWd/TNNBJrsLhllBJHohutFArtPRJJcJ0x8Dn/vRgugiSYzjcswlkTga0/Qjip0MiSUQS4zFtA8thDS9nlj8i9RUXYGSjzdagMP2Hm1dvx1SgEUTp2K3fYjYz+QzN4s1dE3bQATBRNOqEQQB01LJZHYjFPnu5l84gAOm3sqE4MuoaoqaMVA/4Q20VZcBs/E7FyKVcIOXxC689z+A9/YnkcK92Z2vvUf83OMJ35BLrXA5nqPC91ek3g5Hh20hydQCusNXD3uMyam7kJg8Hd/ivo5cC4jtshtaRXDwF25jHGqEaveSvPDaRNC9Ap+7kWRky/MX655+LC+8Cs7Rr2RE0DVqn/4X3kUL0T0+ms+7mHTDuK2/gDLbnB2u4L5MH4bbQ/vJZ27vYezUdB51PMHXXsa36F2kTAZLEEiOn8Tay67a/It3AlLpg0gkj8VpfxNZ7kY3XHSlZrJgwxXA8G7a/enojFPt3jKxH5BrCMg1hGzt7OqHgJwLek6mDsPreqroJq1p40mkD0XTJuDzPIRN/aTomJpWjyAayFILoqgXPW8Ywd66qv6zJaW/zmSpHUkqnt2S5W5U20rSmb1xSq8gCrnzlFq2E0QDn2cdftnCVqKmyrIgld6L7vC1BHy/7ye8cqhqIz7Pg4Sjl5QcI4Cu1+cK+fuft8RkUFarQRLjSFLp5U3LgljidGKJ08h1Xwx/RsmjrmJc5WuoSp8QdDvbOGTiP3jmk+mktTGYlpx/z/LnNu24H3g5L7wApHgS17OvE730S5hVXrzuJ/PCC3I1a3b7u8jyetD7bB062yLUtDahBJQhhdSKX9zOpN/9KmdOLAjEZu7O2st+NOxrHk1c9hBqifpGmxLH42oiyZaLL2OQEG9rUzi3YTDjmu/i/ajPXd//wQJWX3XDZ66M5LNEWXyV2fmwLMR0KmdGKg2+RAWAJLHil3fgX/AGwTfmkZw4mfaTzvgMzSIK9EQuJxo7F1VdTlfaxer4bpiWytYuNda5nLR2J1kudOKpTBL1dVHpiDHTNWXI1wXkmoLHuj6eROoIXM5X8nVpml5LLHFc3uhTFMIlxY6qrsU0vXSGriPg/x2K3HeztqxcRM+WigrD9OVyHokO2G5H0+tJJI9F02tx2N9FJ4tNaUERCt+7dDqAIOicsdsvkcTiIvCcz5cb06xALiH0ABSlachx+tyPIA8iqPqjaTPpjH6ZCv892JRlvVYbfc/r+hjiyeMpaM8cJg3u/2FXime2XFI3LRvSPJ+t4QJvHT57X12qYUp0bKyjeuPiotfJrZ10vP0KkWMnUCMWd7vKUoSI8DxLwwfSFDGofPQ9Lnr6GbyhTiyng8SUXVh13a8xSwgRw+1h9XVbFun1adETryeZrUR1FP6bx9NVdPYM3ZgwkNbTz6Vq7gvYOvvqDC0gNiPX0hx4Yy6epR8WxBrZujoY+8C9fFwWXzssZfFVZqci8PrLNPzzbyihbgyni+6Dj2DDVy8duhZOEAjvfyjh/Q/99Ab6KWOYlaTSh5DRN2+SuTnqa73U482Zui6Dj6vH4alM0uVbzK7+miKRNRQ9kctJJOfgdj2PYfgRxRgVvgdKFqX3F2GCkHNArwzcjNArsixLwLIUUul9CYW3vIzBslxkMrv2dvz13aA0bRLZ7EwC/t9iVz9CFNOYuo94dipe9WOk3q5L05To6tidaROewOcYanlSQJZaMM3SS4uGMfQymFjC1mMgpqmQTO+Hboyjs/vXQJZgxW+wKcsQxCyGXkUo8m0sa2Q5nvHsBAxTyr8HmxAMJ1UdPlpad+GP3T/l1D3vpcrdgmYqrGrfkzffOZHLnVdTkSwUoAm3m3n6LoSXVLNbtRu7p/BaM5qNN1fMorFrHJ7GKOc/9ijurt7PciqBrftNZl31bSJ7zKbrsDkkp80c0fVtawxTZWXXKexZ9w9scm6mNKM7WNtxNJlsBQpDd3H2RwtWsfbSKxn7wJ9RO9sx7Q5iM3aj8fvXABBY8GafJ10/1FDZ1XVHpiy+yuw02Nc3MvGu27B19/1yVh9vRff4aN3BLDLsSg91FQuQxSwwnjGe+Uz0v8DG9gPoiBxA/TvzmPbRmxguN81nX0h63MTtPeQi6mtzTnCtXUnaO3IijMlNVDpi1No8WyzCstoMQuEZSGI3tVWXDtoNWEo/i0K6nyCzAINMdtci64jN0R2+CtN0Y7MtRhAMNG0iofD3qPD+H27nS3lR5pR6EPUqVnaeStC+EiNlYKQOYMH88zj02CsHPb5lSdjt71JrW4RuVKLrFchyT/55TasjGjsbQYgPWveVSB3eG0pdOgbKsiQy2d1Jpo7ot1Wlu+daBDIIYhrT3Hys1ZawMX4Mkz1/I+DuWxI2DZl45EBmT6rPZYuGgyx/9Rb652TMUCAyfW98obmIZk64WUBk8izGB/dgfAJ6Nh5ExdRnkOW+mr5oaBoVGw5jNgL7LX25T3j1w71iKZ4VS6n57xP07H8oa67++Q5tt7Ci80yS+nR2r/ongmDwRvOxhNvmsDU9p6FDjiR00OHYW5vRPV70ftmx8anTqXr5uaKcWH2UI+nKjC5l8VVmp6H+sX8UCC8AOZMm+MYrtJ51AVIsCqKI4Rq6q2xbs/v0PzNh7PO47Z1ohook/IGxZBFFk8nB59FvcKE8G0fu9XLzL3ybtZdeReiwOZs58qdP/1mwdVYG05hN1koCpbtIh8Jm+7Cg6H1LKKq9Egwc9oXEEsOthZToiV6Wf6TIawhW3ILd9kFRB6Vd7sRC5IWP/kRkRYR9JteCFSWVLp22YZoiomggCQaQRpKiZLJT0VJTEKUYhuFFEDJUV16NgNmbu3glulHY9JFKH0wyfQAO+0IkMYFlSWS1BjLZXZHEGKnMviSSR1NqOdHChmWOXvSOhcIrS29jdu3N1FZ1YJoq4e4DafrkO0CfMC9Fy09vQr7/bnyLFoJpEZ8+i/XfvoL63migrpYfoyq1BCpfRxB0kvGprF39Q+prc7N1YonMVOhbZFbiMQJvzsV/5LGE9z1o1K55WxDNTqapO9do0BpXt8Jkoh9irut9IB0nnk7N88/gWttnw6K5PXQce3LRvmV2HMriq8xOg5Qo7WclJRPM+sE3sLdsAEEkOWEyq39846jlYA4Hf8VKpo5/EpuaW1ZRpMKOPeXDDPK8DEK/VQJbdxcN//wboUOP2qF/yY8UXW/ANJ29GYzFDBZOXbyfWHK7kMqgLlmFUVmBPqmh5D4AstTUmz04+BKtKuX+/Wy2GFNm/Ilpu68B2SStubArfTN3uh5AkoqFqCT20NF1K6bloTr4Yxz2vlxDWe6iMvAL2jrvgcLyerp7rkNVluO0v4mmTyCROoKt+Zp2u57B5XgJUUxiGFWEIpeg68ObXU1mq3n2pRty4nM4SBJNX798iB0EWtZ/lZb1Xy35bNvJZ1L90rOoocGTEOR0iqqXn9vhxdengWmzs+zWu5hw92+wN2/EtNtpP/5Uuo4+cXsPrcwQlMVXmZ2G0MFHUlGivkHt6sC1rm95xNbZzrQbr2bZb/7v0x4iU6Y9nRdeJfkPlIo0VHq6kJKJQWbtLFRbBFGQ2BqDxq1FbWth0l23YmttYbqk8tHBB7Phu1u/vJvVppPRpuKwFQcsw6ai9b7/N00ngqAXWE4Ypo1E8pii17offBbv//0buakF0+MkO2sKnX/+GZav+P30ex8YUnhpppuVnacgChqnHn8d1dV9+ZabDGFNS0DX64glTibgK/E5E0wQTEQhhKIUR4Up8npczmfQ9QlksjPpb4+R1WaQ1WYMOr7N4XY+g99zX183pNJEtXQDbV1/wDR3/Iy+bE0dzWdfSO1T/8TR1oIlCCVzaDVfxVYdXxLSeO0fogpjyWoz2Zpu0B0NrSLI6p/sWE0HZYamLL7K7DR0HXkcgfmv4X/vHeREHENRyNTWY2trKdrXua4RtbOdbNWWF4ePBoZR2igzz+6ADRhQ1mM43Rh2e9HuweqXqB//NyS5k4MNB+2p/Vndc/GojXcwxFSKGT/5Hq71OeHgAg5uXssSj8DGy07byqMKdHbfSMD3Rxz2BSV9onSjkmjsDDR9ApnsbPzev+C0v44kd+ZukZaM2/Ui6ewemGbORFNq6cB3x00OJxIAACAASURBVAPI7bklTSkcx/HWIoJX3U7Xn28ovjZxcNf0rO6lI3UAPdkUFdUPEgwWCqdNM3OiYKHILQjEsSw7wgAjVEkMU1nxczRtAgIlosIEncCKuxGuN6FdRnPXs/Gi01h5xC4lx1VrczI58CR22yIEQUPT6wmFv19gJLsJt+s/RTYUitJCbeXltHf/BsP4dP8mtobWM8+n47gv4vvwXSrmv07l3BeQ9D5bi3R1LS3nXDjs4+5a+z8OnfAoLqUVy7Kh6ePp7P4ZhjnyvNwyZYZDWXyV2XkQRVZdfwvu5UuomP86iUnTMBWFGT8rLoQWtSxScnRyMIfDqhVnMWb8mzjtfbVNBctpp4Kxq4T0fl8XmaHaCB1wKLauTgy7I79canNsYMLU32Kz5zrsHIDP9QyiaCOV2LZLCjX/+VdB7ieAmk4z4X9zRyC+wLKcdIevRlWWU115TVE0kmHUEk+elX8cjn4TVVmDouSc8iUpgUN6jzHdF5L54V6Ef/AVXE/PzQuvgvEubyy5lqkbxYIFIJ2ZRlfPT/F4dmFX/0qCvieRpcGjfQQBfJ4nsKzir1FBMHHYF2O3LcU0i2crhUYQvmRCE4COynpqV97L+z/6EWv326do/7Ezf4bb/X4+zFtRNiJJ19PWeRf9a8AqfH9EVdaVHK+itFIdvJa2zjtHJeNxW2O4PYQOOYrQwUei+/xULHgLKZUkE6yi6avfJls5PMFkU8IcNPEh3Gru70kQMtjUVQQrbqWj+zfb4hJ2GsRUironHsK9egWp+rG0nHNRQVF/mdGnLL7K7HTEZ+xGvNfjRkylSI9pwNFcnIOZGjs6OZjDIRGv56MV32KXKY/gtHdiWAqSFSBtppEEnVQmyOpfn0b9DfMJdjZjKgqJiVPxv/cONS88jamoJKZMZ9W1N9Gwy9/ywmsTsqhR43qPddtYfDmb1pVc6lESpQWtICTwuJ5EUZpIpfft7cgrLgwX4knUpWswaitJuQ7EYX8zn+OoaXWEIt8q2F8Su1BKiAlpQhpnZD7qxWtIHbjnsK5NoLig27IEYonTMYxcfVNArsFuzMEw30IShxJgWYby0xIEE0GwMAwHopjq04E30iu8+nD2JNjvH0/SGigUX5KoUbvXhrzw2oSirMNhf7M3mgcUeRUux9yCyJ+BKHITbtezxOJnDbrPDocgsP5bV7D+G99D1LKYtuIZ4i1hQsML+OzFViGK3DxqoeM7I2Iywa5XfgvXquX5BdjA/NdZdvNdZKtHGI9RZlDK4qvMTo3pcLDh/K8z7u9/wt7eiiUIpMeMZe23fwBi6cLsbc3ajSewOnoMnuAGPLYOpjr2YFliDR3x6RBXUDLQ/J051Nd6EZMJ9rjk/IIQdVtXB1Nu/RnWv0rf1EVhyz2Ctpbug4+kct6LSAM6z5LVxR1/ktRGdfBaFLkJQQCn43Xczhfp6P41/b9iPPc8hufBZ5E3tGL4PWR3n0bnAz/BVfEahlFJLHFGkVWCKMZLZxs6gWqQF3QidYTQdw8gfy0EY4CXgA9B+0JNyXIeRdlYtE0QLJz2+SRTfR2nqfQBZLO7Ybd9NGgjgGW5ME0Xolgc7tx3DUl0vRKhv3HrILXkFZZeVOAuKz041eJ/c1HQUeSWvJWu2/UikjS0V5ggWCjyuiH32RKa24qXjEeLQTspRXGrhRfkvLdKho5vZQrAZ4WGh+7DvWp5wTZn0zrG3/v7ch3ZNqQsvsrs9HQdcxI9BxxK5SvPY6kqXUcch+nYvr9iLWSimXEFnXGFnW05ql56NtelOQDXmhWsa7qCgPgGkpCGfitlseyWR5NsLeH9DiK8977435ufb3CIVNXwwXe+WbRvwHc3aj/3dlHQsdsW43Y+Rzz5RQCUFWvx3fMoUihXbyWHosivvof5gwq67xg800/Tx6IbNahi4RIoa4F5uf+UxvcgPKVDb/21dTpggZ1l1BrfoSt0TT4MPMdgN9qBAkeko/tmghU347TPL5k1mdWmkkzvj9/zUEFkTn8Mw4c18Kt2NvDf4n3TtWOKtuman3QqiNNZuLRqGH6SqUP7PS5thdEf07SRTB1csE1duBT3I89jBn1ELz4cPIMHUje3RWlNJGk3cwHq24JFq7vZ0xMc0s5ia1jXfCxTJz9MhbMw7F3TJmJZWy/qdnZcjatLbre1t5XcvjUI2QyuxjVkg5Wfeh3ujkpZfJXZKpTuTnwfvEu6fmxuCXA7WyQYHi/tp56z+R1HiH/BWzQ8fB9KTwjd46X9xNPoOKF0DZQspnAq7cDgAbdSKllSCoiaRt03H4WO3iDqKRbaXyTCDVNZ3P4d6tTivMNRRRBY+fPbCb72PwJvzqPH7uXlM0/HtWcDfgqXHiWpuHNQEAzs9g/y4stz35N54dUfdXHpL/5+RyccuYgK/z0ocu/NYCNwO9BbLib9IIRU0TcTI/RqXIE0NmkVlRW30Nb1R3Kiy0IQSpu82m2L8Xn+AvSvIVTo7rmeHjGCx/U4Tse8XPC1pZDVJ9IdugbT8pDO7IXX/W9UeRWy0pIPOrcsiVRmL0Qh1Td+gB+B9SKwMNcYCRCpH8/6b32/xMgEli76Gnsf8Fvcztx7rRtOksnDC7zCYomTcbteQJGb89v6z/SYlkw6syfpTF/kTOCa3+F6ah5iLPeeTPvPXCI3/pCOg44tGMFA0dUwZjWTA1vXbTgUXakkTZWVvPd+GxP/MY+qWJjkccfTc+DhI/6O0XUX/1t1Kcfu8ldcajuCZSerTaC7Z/vmQG5vNH/pf0fDOTp+iTXP/Iu6Jx7B1tGK4fIQnz6TVdffPKJZzM8CZfFVZtiM//PvqZz7PLauTnSnk8Tk6ay48Q6Mz7ijsmPtGib/9kZs3X11I/aWDehuD6FD+xukWsyeeBdjq97AIfeAFSQd3ouOeLF9QOcxJ1H39GPYujoKtkvpFP6P3uvb0Abxk6t47uG78HuybI3JaSEWXvsiZrrXsT67Lx3RA7B3tDLl0XtQuzvJVtbQ9NVv033EsXQfcSzNbVES9gylQmsGq5UxjBJf6jbADWyaxNmCG2oqcxCZzl3x8Djuf/0X+ZYI9Da4ahPGwDSA0rNOALLcjCw3I4ndVPjuKRRB/ZCkOB7X8yQzZwGFy5+m6SMSu5hI7GJEMYJlyfkIH5v6AT7PowhCCt0YTyxxCk7H2wiCQSq9H7HEKUhiF7J8HYq8HkEwMOxeIs/uQvfvGtDfbEevncInR55J9SCzAu1t+/LSW3+mftrfqfXFSKUOwmEWzmBZlpvO7msJ+O9BkjqwLBvZ7AxMy4Ek9pBKH0AidSSbZv7kVetw/Pf1vPACsG3sYuYd9xeJL4DKMT7SLvD6kuwXHDesmKktJWRrZ9zKDva+/VY8zbmlXOODN+nZ9yBW/fTWEQuwdT2zeXX9bKZ5luIRx2Jsq+m7nYgNX/4m3sXvY+8305WtCNK8Fd2kA7FvWM/YB+5F7entRM52U/HOG0z8w818ctXPRnz8nZmy+CozLDxLF1Hz3BPI8dy0g5xM4lvyIZP+cDOrr71pO49u29LwyP0FwgtAiUWp/c+/C8TXlPFPMn3MkyjSJj+JJJMCXWyIHEBz/JCC12vBKprPvpAxjz+Evb01Z59RV4/S08NA3OtDVH/0MdmDp47oOkQhy5TqX+G0rUESs0zw/4dwcDLOgyK41vXNmniWfcSy2/9A7f7/ZupuH5ESBFpSR9KeKrwxxxPHocjrkKS+zkVNryEaOy//OPqNU3Cd8D/E/bVc22YLcC1kAtO3aMym6SPCxcQOPx3f+39HaWpFr60k8sOLqJJ+Rl6NlUKwEEgR9P8WRWkefD9AkiIo8tPA4Dee/nVpdtt8Kit+iyRt+vdahqI00t75O0yr78eIYVbT2nE3TserKHIzidRhdKQdNH05wMqjTBoSPpTI0LV86UyQ1xq/xvQxIuPsIRwlvr01fTrtXb8jt4QqUWqpexOu/7yGHCqu3XK0h5DjCQZbnq1zuRm5+B+cmb/5a154AUiZDP6F8/F9sIDIqARFi6S1BpxyWXgBZMY0sPKntzHu/rt7Z/Q9tJx9IdG9vjDiY9c9/lBeeG1CANwrl4342Ds7ZfFVZljUPPt4Xnj1x9m4psTeny2kROliZjFdWBA+rm5eP+GVQ5FSTKv8D80theILoO30c+macwL+d95A81eQraxmtyu+Xnx+TUNJJEu4Rg2P6cGHcduX5ScRFClDZe0yhG8AP+nbz9G8gV2f/Abq2V35feuM1awLbyQePz2/XyJ1HIKg4Xa+iCgm0I0g4ehFBR5Uni/8F+FQva8xsB7Mf6hE0sMzbTWDfiI3X4jT/jqGWYGR9hNPHossbywQf/3R9TGo6gpkeQiB1otlgWWVDsBWlJV4XU8gCDrx5HGkM1/A5/lXP+GVQ1XWU1t1CaBiWg4SycOJJc4G5IKCfhh5CPrgbD5BUJtYjyVLCHpheLbusGHYbRAb3idNEBIE/Xf0msoKZLPTCYW/N+wcTkdbcTeCnE5R+epLoyS+ygwkMX0my2++c9SPK5iDdAqX6KT+vFEWX2WGhamW/iK1pO3TWTgcfO+/Q/VzT2HJMi1nnEdy2sxhvT42a08q3n27yIIh3VBoaSEKhTezzW0H0L0+uo45KffANEnXjMHduKpgn3DtGNr22RsPgx9nS6iwryjOTBSB/Yr3VdpDBfsqUppazzt8kjipYL948mTiyZNx2Obj8z5EZeDXmKaLZPIooolzsNsWI0iF75tYl8UTfY5wbMtNY72uR/G4n0KWO7EsCU0bR2fopyTTB+ByvorQr2jeslQ0fRxdPT/CYV84pAXDJnS9nqx+KgOL792uJ/B7Hswbw9ptC0kkj0MUSws+RekTVrLchIBFNLHtaxKHQ/Lkw9HueSznh9aLKUt0HrAXlizDMGV+VeAGHPZF+ceKvB5RjNEZunFYxzFcxcvYliCQHDthWMcps/1pPf1cAm/NQ40UlgUkJ0/bTiPacdjx75hldihazvoymUDhzIApikR3n72dRrRljP/THUz/+Y+oeu1/VL/yPLN+fBm1jz88rGO0nHkB0d32wlByswqWIBKfPJ11lxQWSXeGdse0CtWNYUpsiBy4ZScSRdZ+50qSDeOxBBELiFfV8eY5F+RmJEaIYQ3iwl/C0UGsL/7lqoox5BIu8bLcRMD/O2zqChS5HZvaiNfzIG7nswWiqOD40uBu8wORxE487seR5U0mmQaqupaA/zfYbYsQBQ1BIP+/rDaets570PUJJJJHo+nF5qqG6UDTa9H1StKZWXSGroGiyrYsHtd/Chz5JSmJ0/E6hlGqCm7guNO4nC+VeMYi6PiIXWv/h13ddst4g6LIdPz9lySP2g9tUgOZGZPo/OqxLLnukq041DpUtXD2WxBAVVciiYNnNJai5egD0QakPaTGTqD95OGGqZfZ3qQmTKbl7AtJ1dVjAZrHS3ivffnk+9du76Ftd8ozX2WGRXrseJou/g5j/v0galcXhtNJbLe9igTIjoQS6qLy1ZeQk32FxUo0Qu2zj9N+8plY6mYigXqxVJWPb/sTlfNexP/+O8SnzaT9xNOwBswGLln1NbyBRqp9S7DJMQzDT1tiKqu6vrjFY47tMZvF//cwgTfmImhZlk7fjzUBmdGoUlnbczKVjmXI/SJosmkX5kMqdvqW0DL1NYjXJ1AonN3JGD40o6Lo28PnfgRZLqzvkKQULucraEZ9Ub2VYXiIxU8EsnjcT2JXl2CabiLx89D14g5Rl/MFZLlYpCjKuoJr2YQshRGEZK8Xl59o7Fy8nsdQ5DZMU0HXx9IZ+im6UYsgaIM2DihyK7JU7KAvy11E4yejyO3IckeJV/Yf40ZEMZKvFxPFHqbV3oBN3ogsZUhMeIh1q48n0vm9IY8zGJLUjt/zDySpG02vIxK7qMgzrQ8Dh/0N7LYlpO170vn3G9n0Ozykt0N6cNPYwc/fUZRWACAKcSSpB8PcvA3GJlZdeh5J08H4l19HiCYRxo5j7XeuxHQ4hz2uMtuflnO+QvtJZ+BesZRsVQ2pccMLeP+sUhZfZYZN53Gn0Hn0idhbm9H8gR2+y9GzZFFRNyHkArkdzU0kJ07Z8oNJEl1zTqBrzgmD7mJaCnM/vhV/5SomVswjKBzJ4miG4U40mzZ7/jx6W5SiQMitpCP5BVrCZ1HpmQtCknTGz5rGL5I65GBmtv4JNdRFNlhF01cvZZzvLgLGPCQpN3OVyrpZ2nk4kUyKgTVLPqH07I1BnJ6ea5CC16Mo6xEFHcOoIJ48Ek2fTHXw6l4j09yyoN22iK7wlWQyhU7vstRcaJJpAM+B8FwW61sgDDC6z5ln9gmJePKLJFJH4rC/g2H4yGT3zj9fKiIoP34jgGG6EcVCiw3DdJPO7E8qfQQ+zwOIUhjLtON0vFG8rCsYeF2P55dYg/7f4rT1hcG7HJ1MnvY0y5MnkkoM4/MIyNKGnMmtkitSd5CzzWjv+m2RABOEFNXBH6MqqxBFDbfzOTLaDDq7fj3s2qz+ZLIz0bTafAzUJnSjhqw2zKQJQWDpxRew9OyvEFkRKTKd3VGR4jEaHriXSatWMsvnZsUVX4NZlWCaBB99leOf/RBBcLH62DMITdxjew/3U8Vwucv1egMoi68yW4ckF9U67aikG8ahuT0o8cKCed3jJRssnfM3GoTTk2mOZfG5xgI7VkNCd/xouuNz2JhxE2oLokQFAvV2Vl9X6Gi9+uObqBnzJBVVr5FMwruLj+fd7C54KpME3YUzhlFmc/DYRUhiYU1ac7yBT2JRarM3UO9egyy3kkwdhmHU4rTPxaZ+XFCPJctdBLz30BO5hHReIGnYbANq1SRgOkjnaFiPCXCRlfP/6kXTJhWZZ1qWe0DR++YxLQ+ZzB7I0lyEfnV72ex0NH0yAKHIZfjcj6AojViWVLBf33X1dfApclPR83Z7hNqGf7F25TXDGp/fe19eeG1CVdbj8zxAT+Sygu0+z9+w2z7OPxZFDbu6GJ/noS2qvVOlBNODT1HtaMTMHkAieSwgY1lu4omT8Hr+hdS7lKwbAWLxM4Atm1nemRFTKWZddQnu1SsAqAXGrV7G+7//MeMf+Tf+599FzOa8+WoWLWL5F88nefE3tuOIy2xvyuKrzGee5ORpJKbPxP/+gvw2U5KI7j77cx4eK2BaNoaOVhFpbzmD9pYzAKh1wJiWNto7QK0qNGFcuO4UGtQPGVu5BFVJoxsSXdGJvPT2ZWzUJGZNbqItM4Za2/S8R5TDvhBRLK4HU5T1VFdeh6aPpSt0JYKQizEqYjLwBRBetzAfELEusWFNcqBpk+jqGdw5f7h0h6/EMALY7YsAk6w2lZ7IpQAIQpKayh9iUwcX2KapkEz3dTRYg+RBmnrppTVvUyPTXngEIxslddge6F8/OP/tLUmlZxz7m61uotQYc7VZy4u2D8Rpb+WC2T+k0pVLZLCcC3E5X6Gj62ZAJZr4EsnM/njdT2JZErH4mehGsWP/Z5Exjz+Eq1d4bcLd3sGut9yLZ/3GvPACsMVjTHz1eZZ9+SIsZfNdqWU+m5TFV5nPBSt+fjsT7r4d96rlWKJIdI/ZrP/6ZZt/4ecMQdeof+g+fB99gCWJdB1+DJ1zTqTh4fvwLFuMqShUnHwWSyfuWbJA/4O5t9Bcu5C6+nfo6ZlK09qjqbFkzESSjxnHhAkWmxxWA3INWW0qlvVyUSdibobLQFXWEay4g/nNX8FX4cApDrD7iAKVwC4grjCJ/PUEYt89G8MsbRcxPCx8nr/isC9AFFOYpoeeyFfJZAv9j7zuR4cWXpZMJrt7b9h4jkx2Vj4LcxOJRDUtG84ren3gzXns9btf4QjnRJa58D3ib8+n5+E7QBAwzdIxPLoRxG57F1VpJJneH12fgGmWnoUSxQiSWBw63Z+9Z92ZF17Qm2KgLsHjeoZYIlcMr+sTCIWvGPI4nxbBeS9S/fxTiNkslQ2T+fBr3y7upRglnGtWlvwJ42jpQAkVW9TYYz2o3Z1kSsRJlfl8UBZfZXYuDAPHhnXobi9a5ZYvGZp2B40/uG4bDuyzwbRf/JjA/NcRrFyXo+fjxTQ8dB+2jrb8zcWzbAm2iy6h7dQvDXKUOXQ255b2xmwyQW+DjCXhwEHW6jPpjCdOwOt5GHmQXETIFbxLgkY4NQ2n8v6AJ4G/AxpYq0BfXjNKwgt8nvvxuh/vl+nYSk3lT0il96Kr52f5An1V+aTk63WjgkxmD1LpfUmkjqJ//VkofDkaPdjURgQrRSZVw7JFX0Yo0ZFZ/+jf88ILQDRMXO+tIvXKAtJz9icc/TI2dTGS1FeTZlkCDttCXI55iGIGr/sxUul9iSVOwaauKAjgtiywqY3UVl+GmvgCTa2li/5djuKZR0GwsNsW58XXjkLdv/5Bw4P3oSRyTQC+pYvwbfiEv//hl9vkfKmxpUswMpUB1GQSOZYq2J51etAqBs/QLPPZp2w1UWanoeKtV9njkvPY7btfZY9LL2DGNZchJUr7LJUZPq71a/Au+SAvvACkbAZbR3vBr3olHqPmv0/BYAaKw8DreRhxkKzFTViWhGnJvNr4C9Z1HkY03oC20YEVJRdV5AYqQNgPXF96bcRj2kSpMG1BMHE63ifg+31+mz5IRI2mTaSr5zoSqWOgaJlRZX3X5by67i4eeP8Onn/jr7RsPLT4IKaJEiq2apBSWRxzF/Sev76oU1MQLGS5G1HMNWlIUrS3EUCjJ/oNMtmpmKatd9/ca2Spm4D7NQKOJSWvR9eHESO1PTFNql/6b154bSKwZjmTFrw3yItGRss5XyExflLBtmQwwPIrLiS51xT6z+vqqo3m2Qd97rMNP++UZ77K7BTIkTAT774de3uvS3kygRrqYvJvfsGqG27dvoPbQZGyWXb9598Y07SKSaaAfc7RhCYeP+j+/tXLUGLFcTMCxeakciyMlEpiuEYWvuuwv1Oy5qs/mt5AUqtBM928tvwmlKjJeMcrHHjYz4v2VZRmJLGzwF1/axHEwbtLVbXPADcSuwCH/f2CGivdCBCJnbvZc+imm0jaiQcRSpnniiK62wPthQX1piyR3XOX3FiUNUglrDCKD5XB5ZhLV88NJJInUFf9dVRxXcE+sphigu95OjuKmxJWrz8Vr/cTHGrfrJmm1xCJn7/Zc3+aiOkUconPsZLJMGbpMjpPL+EmPEIMt4dlt9zN+Ht/j7BhA1GXg1WXfpnswVNpPLiButsew1zYiCA42LjvYaw99BTqN3/YbYtl4Vq1HFtHK9E99vmc179++pTFV5mdgurnnuwTXv1wrVkBhg5S+aNcgGVx+q9/yvglixDIxUQf/clyPtZDbLzstJIv6ZmxB1mfv8iN2qK4JF/3+DBGwXdJFEoLHNNUMC0nut5AV881UOA1JoIsYokiAgNn3wwQRj4jB2AYXpRBI4n6BKlpBujougm/915kuRvTdBGJfYlMdq9RGUfHMSej/v1PqP186lIzxpE47ajecVZjmn6kIZZu86Pu1/05mL2GYZW2nGhqnUPEGWH/8S/glKNgVtMT+doOF05tOpxoPn+RvYxmd7B+9p5sK7cwrbKKNdf8kua2KOvsGaonOvCTxLKrtFx/AQu6VRzxOpQom83x3NZIsSi7XH8FrsbVyMkE6epa2k84jeYLimPNymwbynesMjsFYrb0TVowTQTTwhq+L+QOiZhMMOaxB6ia9yKYBtlgFd2HzqH5gMF9xUpR8/4i6lavKBBN9lSSSc+9RPMlpc1ek/Xjie61L4E35iIaue4svfdGZm9ryR9Lc3toP/5UEEdetaAbtSXMVx2Ewt8jo+2KYWzyeCosaG9r2Z9EfAwe78aC7YKQQpbaMIwaRoaFKKQGfVbTJhc81o0Gunr6z8RlcTpeRhDSpFKHFYRsD5e2M86j01QY//aLGHoSfZexRG44C58i9557DJnsDBz2+fklxFwCllDQyKAbFUTifRFHmcyeqEojQj+xqul+1oQGr9/6uG0OungM4+yhfMdqHyZe92PYbe8CkM7sSzR+Np96dYsg0HbK2Yz/y50o0ZwgNQWBzpm7s2HP3SmIck9n8f3hIWwfLMNy2MlcfCTsdfCnO97twOQ7bsK3tC8Kyt7RRt2TjxA66HBSw/E9LLPVlMVXmZ2CjhNPp+b5p4t+zabGTvjMtGurHW3MuOZynOsb80LH0dqMe9VyMk3NrLvoK1t2HGE901c9gS1d3I5oi0ZRYvFcnVQJVl17E7VP/ZOKBW9hSRIdx59Kz34H0fDgX3q7HVXaTz6TngMP28qrLCQU/g5VwZ+hyBsQBAvTdJLKHEgyfRRDWWCYpkp72x64PRsLOgZFUcfneYCO7pGZWMpyE5JcvJRnWZDNTqZ7iI4+VVlGsOI2FHkjgmChex4hHL2ARGrwJd/N0XTIsXxy0klsdEWYPkZknL3QXqIzdD0VvnuwqTm7g0xmFhYCTvtCBDGBaQSIxM9C1yfkX9MT/QaiGMVm+whRSGEYAVoix5LQ6rdKLwX9t+FyzkMQcsLdZluGIq+nOzx6lh9bSscJp5GuH0vd4w8jZrNsmLQrH597PvSvLzRNqi/4MfZ3Fuc/aRPfW0rsqhThY0/51Mf8aeJcW9ydq0bC1D35z3Jj0qfEiMRXPB7njjvuoLOzk6qqKq644grc7uJv9XPOOYdx43JxIZWVlVx99af/x1hm5yZbWc3Gc7/KmH8/iL21GdNuJzl+Mmt+9LPtPbRRY8Ldt+Na31i0XcpmqH//TcTzzyPnXz44AbkGn2MhY05ZAPeSs2LoR8avgi9EQJ6Wi5IZiCjSdvp5tJ1eaHmw4eLvDPNqeseeiDPxzluZsWolaUWk4/D9WfeDvtkX3RhHW+dduJ3/RZabSSSPIqvtukXHVtVEkZM8sEXLb1uLhUxP9FIsazDPAouA/4+oSp8lgyy34/M+TDJ96BCvGykqPZHiLsVwVEMU471OhA0nAwAAIABJREFU9wMVlUR3+EcIQgJJjKAbNfTow8th3IQohrDbPsgLLwBR0LHbPkQUQ5jmp9/ZF91jH6J75FISmtuivWHhfThemo9tUeHssNITY9JDz7D0My6+Bpu1Nrcwaq3MyBmR+HrqqafYbbfdOPXUU3nqqad46qmnuOCCC4r2U1WV2267bSSnKlOG9lPOpvPoE/F9uBDNHyA+czdK3n13UkrVtG1CScZxxGNAaU+n/tR5X8N2cAb2B16GTWVRpl8ifv7RzPROG5XxSnKMybvciNO9GhBIxHbhkxXXYRq9VTWWxS4/+V7B8kb1uka88S7CN/cVaVuWY6usCqKRCYVxQ72Y5sjjrnR9HLo+BqlfYX1uewOZ7ODiUJbXoshrcw8scolQNpClVhz2+cN21x85CqY5dDeiZbnQtyAgfMizyM1IUk/RdkkKocjNZLI7nq2C/e0PEdPZou22UARB1z/T60KxGbvh6DfDDpAJVtF6ZvH9u8y2YUSL8QsXLuSww3LLD4cddhgLFy4clUGVKTMYptOVW/KyDIKv/Q85GtneQxo1DPvgpcAZr5/kFnYjSYtCcBCwhFylfQ1wPGT/NgXzG/2XLi3qPfM4YtaPOGSfq6mqfXVY491ltx9SWfMKTlcTTtd6qmpfZPqufbPano/ex7VmZeHYdJ2qtz9ESI08p3L1irOIxArb+3Wjgkjs7BEfGwS6ev6fvfsOb6s8Gz/+lY72sGRb3jvOXgRIQggbwigzzEIpBAoUWlYZBVIa+EEYgTJaSilQVhmlvFBGS5lhFgIkhOyEJE6899TeOr8/5CVLTjxjO34+19XrfX04OufRE41bz7jvW/EHJhKJ6IhENPgDE2huvZH4tBFdUixPoVSG4DVgHjAZmA3yit6Toe4PgqF8wuH4/GqhsI1ggiLpo4Fv3kwimvglCwFrUtwo2f5m93W30nz4sfht6YQMRtwFE6i47GqR9HUfGtQrzG63k5wc/VVltVqx2xN/EQaDQW677TYkSeKMM85g/vz5vV5z5cqVrFy5EoAVK1Zgs9kG08Q9UqlUMddvsyZI2T0OSJKE1Wod6Wb0idTSRN6Nv0RbsgPJ5yWQlUPrORfSvOTKwV13iPrA4ZYJmtUETYA5jEFvwGq1YpANmBVmQEItgzoSjLuf89wLMZbtQuWMfR+FkixUn/JTDOYkzGYjZoOEWR/Eqk3QXo8P1ZJGiB2wQbYCJx2HNdT1GIv0CGbNJ0jtKRUyU9fR1nQxjTW/7TynorqVKkd8hm6rpYyDjdvjjmuNWwlEakk3TSD7/bdR+eIXrSsdLqrqarGb+vbx0+BxU9GaiqvZhbJB5sicEHNO/itqTS1tLiPNzjmo9AHCCg2O4EnYAwVA76OIe6N01BGRIxBUU9KwDKO6AgVhXMFCor9XE19bpXSSodoN64Dr6ao7XgmK+2Va1TupPz+363n53WzdZUXZIONRBclNMvf6GnS4ZaranPibZNaGGgkUuEmXEqSm6Kccg5U0bdcXbtDvwawy4/RJmE1mgobo67TjdW3QhjCbVJj1QVJ0LWg1T6JUtBKRUwnLc5HkT1G072CVZS0R+RiSkvpeA7bj/mGlAZA67z9Yidqv/dlpRF78D8pvNnSeF0o2UXfxmeSVVjDx+T8wu8WL8vIr8R1wcL/uZVSrYt6rAAafChPmXt//+1rDH/9Gc0sTqtYW/AVFoFKTqEVj6fthuAxHH+z102/58uW0tcWvoTj//Njs1gqFAkUvU0BPPPEEKSkp1NfXc/fdd5Ofn09mZuJK9YsWLWLRoq6h+aamga1B6AubzRZz/UTPczywWq1j5rlPveNmDJu7Piw1tdUkv/QMVXMX4s/JG/B1h6oPnE4nQaWaYHt5HL3soU3RhsfjwelygktC7QS1M0hbW+z7pW3h0fiuaCXjvbfR1tdCOIS7aBKVv7ia7amFuD1unM4IUsSDM+RE7Y1vr/GVd1Hs9MQdD32j4suSSaSZtpOiykCpbCU97dvOwAtArfZgsvyH3dsvoKI6Qq3bQ33ESzgdUpJj15qZrDWoNfHJUdVqN97VrzLp7jdQ9bJD1WNM5evGiSTLfcsR1tJqQGqADCUUpASZt/B6DMZoYWqDEbzeZD767HpWV8wknA6T2urJ3rqd6ulTaSnqf/F3g96Ix9v9ufXtB6BFF+RAWxgepCvwaqdwguHlNXx3cNf0avlumBKGLKORnMzoqFhvr8Eko4LpxiQsdVBbqmCt0xD3b9JfQamZfEsJNn0NmVozKaoMnCEnTp+asNKA0+XB4/HQ1tbW+br2RNw4XUpCyrUYrA8hKbv6KRjMosV+KXrtWkCB23ssHu9xQN/fVx33d7rD4JI67z9YPdvvDDlRqwzY/34Plj+8gHbTTiI6LTUXH0V4awsLV1yNzh5dMBnctJraM8+naslVfb6XW+ePea8CeNwaZJez1/f/iFCqIDUdXL0nOh5L3w/DZTj6YK/B17Jly3r9bxaLhdbWVpKTk2ltbSUpKfGwekpKdL4/IyOD6dOnU1ZW1mvwJQi9kmX0FaVxhzVtLWS983+U/fqmEWjU0Go4+UwaTk6Qh6suPmlkIoGGKhTxOVFxt1n431aJyZPrsemdTLKUolXFF2TWaBspbVnHJmcu4XQw2zwclBkfzEkKI96gDaMmth6g12/h2AdfRx1oX0ujILpHoP0SgeQUWi+4mKxWE8QvEUooGz1ZZgM5mUkUTHwIw30V8B/ADeSD/o+tHLXgXYJNhzNn+TJydm5G73biNZqpnjyL9395G7Ky77lIdAaZSPxT3is/Gbgm2TC4Ev9baewBdDu7/j7amMeEQie5RY+g1rTgdk6muvxSIntYf5WTmUQOSVTXOahtGkAju7FHDGxJz8ds85BvqWdmH3/YpxnWMCnjfqQe2f/V6lq06hIaW+4fVLv2Jdmgp+3OX3X+7XaUU/z/lnUGXhCt6JD+8X+pPetnhM3779SxsG8Natpx7ty5fPHFFyxevJgvvviCefPmxZ3jcrnQarWo1WocDgfbt2/njDP2850kwrCRe9mlE9aJUh0ALWcfSfKLn6JvjA2sgunZJCknEwnrCcgefEEfwXASaik2UAgFknE405k8PRNMYaxGD5naRDmdwOc5F630Kqr2hdahUCqRN7PROtpHk28CLgKSgUbwPGvizbS7ackpStBymaL875gy6XP8fiNrN5yDw5nV+V9r3R5qd3mY+tYX8AzRhewAu4FzIfheE9lvvkjRhu+Q2ssj6d1OijZ8x0Efvcnak87td1/2n4KvvrmcE49cgeF9B4oes4LN2bGjcCHdRibOuhdLUjR9SortazTGr/jXuysIhUbr61lmetrf4wKvDpIUH9CPJbod1ejrGuKP19Vg3PkjjoN6XzIjCP0xqOBr8eLFPProo3z66aedqSYAdu3axccff8xVV11FdXU1Tz/9NEqlkkgkwuLFi8nNzd3LlQUhAYUC15QZGKoqYg770jOpW/zTXh60Z7qqcnRVFUjzD40OwY9xwexUSn92KgX/+C/GxmYiSiW+vAJ+uOQ3seeF03D5pmExrEHZnmQzHFLhrJqBz5dE4hznsVzus/D5FmA2vYksSzjdZ2F572VgI1wA/B46F5Hkg7rYD7t8+BIMDP1k6iNMSfsajSoaVRVOWMPHO6+ipGlhzHmaRz1dgVeHnaB8xk9OxfrOwKuDFAmTU76Oryf1PfhS6hUkWKrWJyUcQOXBT/Ozw28heU0NKk+IkFpNQ3ERK2+7HH+3mdZDZr/YGXh1SE/bycyj3+bb8t6Klg8dg+Qh39KETW9gunE2QOL0I92YtY0YeyTF7a63OpdjRSArhaAlCannj5ckK/7MrF4eJQj9N6hvG7PZzB133BF3vLi4mOLiaAboKVOm8PDDDw/mNoLQafcNtyP5vJi2b0HyefGnZVJ50RUEU/q3MUMR8DPlrlswb9uE2mEnmJGF6fBjKf/VjcPU8n1n568uYMtJp5P2ynvojelEzjgDR4uPnlFLWdPVGIzvkxr5gaTySpT/DWN5ZDXnpdzAt3ctwz5r7+ulQuFsWu3XdP7t+tnJGN7+BOmyAD1X76qtQRZNfoV1tbGbI0yaKqakfdsZeAGYdc0cN/El9NpcuidbVXgSf2RZ6puwpCZeH2o0BJmVU5nwvyViMBjweAY3pff9U0tJ+W4HaV9uxj6rgLoTDmJW027MddWkHLYdvakFqy4+p5tCAZPTNuEOHTao+/dVpjYj4ahmb+blvYVKmXhjUihkw+64JOaYUmnHan4elaqaSMRMm/MiQqFEI5+jQygjmZYDp5H58SqU0TIByIBz2kz82QNfUyoIPY39n/rCuBLR6dl+18Oo2lpRO9rw5uSD1P/aQoVP/ZHk777q/FpX19eS8f5b2A8+hLb5g/via6qxU18VXaiuLPawlRJ+qDPgbGpsXziuZ25x/9c8tjW6aFa4MNs8OCxN2PTxuxCbvB4q7GGcHgPSYSczx5xKjkYD+OIeD1BRdhJLlqzGVt4RbPjJdO5k/u/v4LE//gFzmhdNpoc6f/y9OhZpdxc4aBruC07GrH8nYUHuJLWK6cbY8iVJ5lVoVPHBjlHtZoYpC7nbwnxlfh5U9AiyzKBaEoRq4FtiykAGjVqqzjw07trDTqGgZcEUWhZMQREKM+emZ0mu/xHty24U7ZsL5QRr8wD0TIrro562uuMzlA9End8Z92+7uT5MyBUhUOElQ9m1qL+1romCo9YkTK0XDGn5X/lN+MNtdCywVym9HJL7EGZd10iZUr2JdbWXY/cVx1+k2/2dTYbO98pQaaqx02Lzslnq/f0D8MEtl3GklMSErTuQIjKR2XMovfrmft0r0Xut2WXDs7WODKWeLONwVZgUxgoRfAljUsiaTMi65+SRe2L6cUtc8RqVx0P6++8MKvjquSD6x4Z8LFP0eLZ7yVZ2LRwfyHUBaps81DfAlvT8hLvdWlq9SA3E3SsnM4mczCS+31VHfQO0OoIc89ZbHFnfQlaPaVyAtIZa5qytZlN+LpvCqWRs38H8195E5fez+5B5bDzlRH7QtHFQZklcENa6/BqUCgcmPo25piwr8Pnji00Hg4VEImqUythiwxHZgCzHPsfSO84n55dVJJU1ogBkMyjOAI4iOkD2I4T/qSRQb8BjsfDjMUfw9YFnRQOzPorf7Tg4Rz31HOkfrkN6PQLT2g+Gozsg5SRQdFvGGAjm4XD1PuXYEqpnc1s9FXYb6gR5tfoqrWQ3h7/wMnq7A3dyMl9efjGt+Xmdr58ikwFLj9dPocaPQUo8H/tdxVl8XzE75tjhRX8nSRfb8Xp1K1nGT/hq19GJn18vr9/B6v6+XL917++ftYuvwXlVBknG/u9ITPReBZAaiP4YGqLnJIxtIvgSxqde0qLIAxhFSyQmCNvpoWgIPnTjdrolmGXrvjMwkbnFmUTe+5A5z/4Bk7337YbKSJjpWRYkcyqWv7/D4W8+j6E9/1jR6rUUf7GO5395I587fMzMi+6g7B6EtXADKn89GvVOlMoA4YiOQGAWTtdZcffy+g4jGCpEq+naCtg1KhT9d2oJ1VPnd7LLHKHhnkdY+MYHzAyuJ/eWLXBE+6klwGXgv8HIe3//PfWayYTVmpgdhn0x0N2OvclfvRkpEoGOXKPLgX8CdlDYIPArFfJlOmRZh73tqvZSQLE6nn+T18OWXfmdqTcGIrWqlNMev5uk1q6dqpk/7uLN39yHLi2TLLOBGVPz47bWBwMpELbRM3WE12um9Psj0PXYiZ9RGB/UAxgj9l7/Tfb2+h2s7oHR3t4/g0kvkGhX6txJYoe/0EUEX8K4ZJ8zF+PObSjDXVvSguYk6s4YiuzoXTo+hEfTNWd/9NoeAy8AX24BrikzyFEomP3Ve52BF4AqHKZwx0aOqW+gIXc2VS123BYPmdquHXCyrKe+6VH0um/Qqrfi8x+ML3AgiYtlS3i8R6BRl6Boz5OhUIBaVYPZ9AZO13nU+Z2Uu1IxB2aSYgHnOZdTkxfGNu/n6DbWweXALkAFmgkhCq+fQXb6wBZID3VOH5OuvV6ei+hOzQfpmhqtBvWyEIoDXLDAhTrlARyuc3G4LgBAr/uSJNNbJMstpPptfLL9cmZrMlGbg30LUMJh8p/7C5YN34M5BDdIqOc1o5vRCF91nWZpqueUz/9FyW3L93Axidqq88gvfgKNpq398mpcbYdRnHoI9BiI0yqmEp0HjqVW2gY07T5UhuM9ORruJYwtgyovJAhjVcWlv6bp6BPwp2UQ1unw5xVSfd5FOGfFT4vtb9S9BBayQkFEUuHOn8Dua28FSUJyu1C3xQdqKreLzI2rAciQeit7pMTrO4w25xX4AgeROPCK0mk3dAZeHRSKMHptV8kyPbE76ULBZGrLzyVyoQTfE80b1giq77xMvud3vd5rX3PMOhBZoYS/As8TsyYNQNEItO9JkiQHJsN7KBRudNpVpFj/hE67iSRdNfmWDZwz6z70uvhUCL2Z+Ie7yH7jZcymLZj/sR3zBVvRnVYP7wLPxp6raW3e6/Uaas5m2/o/0Vh3As2NR7Fr2+/ZufWehOdWl1+C2xVb/snvS6eq9Io+t18Q9ldi5EsYnySJkqX3oLK3omlsQDtzNq2+wdcb3OdkGX35blAo8eYX9qnQeNCcRM/VLhGlktazL6R27gKcBxyMrIrWvAsbDISNJmiOTaYa1mppLRqaAt0Acq+B2Z6fj+u/M5B3xJ+jrypHW1OFP3vk09pU/OLXaOtqSFq5HrWyKfEz6raUSqWqQ6MuIcn8JiopttSUWV/LjIl/Z0P9b9gblb2VpA3fR0d3Hwa6r+G3AGcCTwLt8a0vM6dPz8ftnMnOLXtPpBoOmdn6w1PkFz+OzlBJKJhEZekVeFxT+3QfQdifieBLGNdClmRClmQ0Oj2MseDLULKDiQ/dha62ChTgzc5n521348vf81b+2jPPR1dbhabbiJaneDINv1mKw9tjQbWkoumoReS8/jJSt+RX7olTqTvgkCF7Lh7PMeg0m1B2K3cUiWjweI/c4+OUwSCKcCTuuCISRhkKJnjEvier1Oy44wE0jfUU/flBUps+jz1BAk7s+jMcMRMOp6FUJF54ptc1owiHUXrcRPSGXgPutI/eRdPcGJ3fSFTbOhn4GbAGPPlFVF7yqwQnDU4wmMKuH+PTEY1murJd5L/4NHq3i9S0DCouu5pg8sA3NwhCIiL4EoSxKBJh4oN3YtrdVUHbvGMrk1csY+NfXtrjCFjzsScRSrKS/a9XUHo9eAsmUH75tZi1WugZfAFVS64imJyC7bOPUAaDuCdMouyqG8AxsMLOkrIJpdJFMJRHNPIAt/cE1OrdGPRfIylbCEdS8HoX4PKcssdrOQ6Yiy8nD0NlecxxX2Yu3rzCAbWvO3VzI7q6GjwFEwibzIO6ViAtg51Ll6Neei3GnduQ/H4iZgnF0WEUv+52XnAKoXA24XD8WiFZBv2XzZxw26Vog34CqTYqL76StkMOjzkv8+3XyH35GZSR9sDUFXcp5DB4ggU4TptH5ZKrBrV7eF9I/++bpH3yPspAAE9RMWVX3jDof5OejNu3MuWu36JrqIv+DZi3bWTzI88Qsozv4tLC0BLBlyCMQaYdW9FXlccd11VVoC/fjbew9zxKAPa5C7DPXdDn+9Wffh71p/fYjODoW73JDgqFB1vKPWjUJSgVPkKhdNocS/D6jwAUtDl+hd15EWpVLcFQbH6v3sgaDWVX3Ujhk4+gq65ERkHIYqFm8Xl9moKNFUataQWFAcJhJj1wB0kb1qJuayGQlkHT0SdScfk1CR+p0dZTMPFPaHV1hENGqsouxWk/KO68iN7A5kf+hnX1KuS1a6k99GDyLlxJqn8jBlWEcHA6rfZro89N1sQ9XqGA5LZtSLXRv7VNDRT/8T42PfY8gbT2dB+yTPr7b6N2d4u43ic67ditapHXW8SmuS8ROajHJLQsY/zfJyR/vhLXlBk0HXti5zT0SMl55Vly/vkCKm90NND842YMpbvY9KfnBpTnrzd5Lz7ZGXh1MJSXkvvKs/tF7Vhh9BDBVw/VfSxgvD9xuGWczsQJB8eLsdYHKY1OiMSPPMnhCI31duy6/r+O+9sHtW4Pfp1EgAbypSZgz5nSU61/wKBb3fm3RlNGsvVJ/I2zO9MryLKJQHBSwsd7acARNqDwS2jdYWj/jqwumM2PdzzBUfffRFJ1GdrWFgr/8jDmjz7g6xvv6VPgMGHiOxRP/jdaXRvhUBJpT5tI/XwbyvY+1tXVkPH2a+zOnUz9nNigVaOxc/Tx12GxdqVW0Bl+ZPWqpTTUz014v+rC2dSmTcSfKvHD9olkpWxnpjU227y6pRIS5OKUeswqaxvryXnt75RecwsAinAIVc/A+FYgDKGzjQRzUvB5cyjd/lsikdjASxEKMvX235C0eT2S30dEUpH59mtsfeAvI1dUOhLB9tmHnYFXB8Ou7aR89RktRy0aslv1thlFV504bYYgDJQIvrop83io1voG8It5bDOqVbh1Y2u901Aba31QPr2I2Vk5pPWYbmvJymLD5FxQ9P+59LcPmvVezDYPB2V6+lCmJoxGsyvuqFpVj8nwLg7XhXu813TjRDK19WyWKqmQbDQrDPjlrhGPQ957HWvZjmg+LUDjdZOxaQ3Z777A1+dfvMdrZ6dsYdqc5zBoOwKWFgwbFSgjsbsv1T4PmV/+m+8WxO6IPWrmSzGBF4De0ELB7FdY/b9Zvd63o//yLU1xgReAssIFifYLJMhPpXJ0LcyXVeroFGJjtxEcGcL/T8uO8L20LTgi/gLtMt9+Dcu61Z3TlcpwCPOOrRQ+9Si7br6z18fFkGV0NVVE1GoC6YNPKaEM+FG5438USMEgph1bhzT46m1q0SdKCwlDTARfgMJux3rTTSzeuoWAHKBpchFfLLuKoGl8lIAwGCMo3UOX0XssGot98NWdl3Pk/U9jLa9BViiwF2Tz5Z2/xDwhwQKfPuhrH+ib2zji/r+RXFGOUqOBoxfStvSyPtwhfmE8gKKPgWKKKoOZVrDpnTRZmnCHu74Qi3at7Qy8OihlmYKyDWwsiv3iNqkbOSDjP6iUXrY2nsCczHe6BV7tj1Ukrv2j1ocw97ieLT1x3UijyR53bneZUmVMUeueAm9MQJW3Frp/75cD/y/2vJDOQMMJp8YcqznzfAqf+iOa9nxuskKBc/rsuLVhPVnWrelaJ9aNvrx0j4/rYCjZTvEjy9HVViMrJbz5hey4/X6CtrQ+PT6RiFZH0JqKtjE2xUbIYKB13sJeHjUwFUuuQl+2K2bq0ZNfRNXP+/L6FoS+E8EXkHz55ehWrQKio/zWijrSfU52vbx0ZBu2j5jNWpy6wN5P3I8Ndx9oyutJfmcVgZxU2k5biKwZgrfeoglUHH0vzWt3IKskPHMmMlFSMpGBPY8+9UE4wpQl92LYXNZ5KLLzX/hdLVQvv2SPD7UG0rGoYtfTBEJWqhwLCIbr+9zOTK0ZvRSkc94R0OsSL/7X6cJMtXSdl2b8geLUt9Cpo9NLE5O/IxhO8CPrKJC/A0W3y4YMGlwXzIm5HoBCtiHL8QPmSlkXd24sNRaVmZZQ4ufuPWYxxVdsQ329B2xAIwTvUuGrS8Ig2ZHCYYLmJFoWHoX94Nip0KYTTsWXnUv26y8j+b04Zh5IzXkX73VUP2wwJjwe0Wj3+Ljog0NMWrEMY1nXCKdmUyuT713Klkef2fvje6NQUHPOhRQ++Qia1pZoeyQJx+yDcR5w8MCvm4B7ynS23fsn8l58Gr3HhcuWTsVl1xCyjO7NCMLYM+6DL1VJCZrNm+OPby5n64ZWnLl7XseyPzD4VHjc8Yt7x5Ph7IND/vgSRe9+jqHVQVhSkvTEe3zw0M048rOH5gYTD4j+347lKpEI8/76GnnfrEcZDNFWmMNXt12OL3nPa3b60geFn3zLrO1VMceUoRDqLzazpk5BRN37+qqN9ms5qfhBknWVqJU+HIEMNjacwpbGgWSij53O8pxxKseuLUPn6loX5Dfo+OHkk9hl7zg3wsysTzsDLwC15CUiQyiiRKXsNuJzN9i3ZKD+2o+huQ1npo3dixbw7bwTIDb1FiXO8zlDu400Y9fokNNv49OKJTR7BzHtljOVunkXMfP6D9A2tuLTGKmYvYDSP/2KWTvXYCgvpfnI4/AUJ8635po5hx0z5/TrltXnLyFpw/doW7oSrgaNJhp+csZeH2tZ9z26BHVC9ZXlaBrq9jgFady+lZx/voDkdeOcNoua8y8hou3aHdB03E/wFBSR89qLSF4PrfMWUn/q2cOyRMRbNJEddz445JUOBKG7cR98KZuaULjip2k0Li/yliDO4NBuZR6NIgYjbs/4LnYwXH2QWlHGlLc/x+CMTmtJ4QgpuypZeMfzvH7niiG/H8Cxzz7B7I/fQx0KAWArqcBY2swr9z26x9qVfekDw/pmVMFQ3HGV049/mwqfuff3ixMzL+58jMzk7Ri1rVQ2zSYQSjzS0l9b8o/FsLiZmZ9/jM7pwGcys+2IY1g/+SRoj4mSDPUYZsRn69eqvNS2TMRqrEOvdREI6qhrncy/Lr0b/clOLPW1NOUX4jMndV4rlpl/lD/EoVNfxWYpxeO3smrbhdjdgw+u18w5k+9nnY61vIXktHSy8mxkA815J7H3fPT95ymeQuk1t5D/r3+gbGogZDLTePypNC06ea+PVfp9MeW6OijCIZTB3nOupfzvU4oeW4G2PcO+9ftvsaz/ni0PPRWzk9EzcSo7b79vAM9KEEafcR98BQ84gFBBAerS2E9VT1oW+qzpFPr2/xEhs1qP0xf/hTqeDFcfHPDhx52BV3e2ujoKPWpQDm3ApwgFmbTuh87Aq0N6RSkLvl1H7cGH9frYvvSB+4Cj8f3nLXTO2OGfoNVGpsoGvj6MRNQeQBgYonG/To0n/ZzPj78FJJjXAAAgAElEQVQAjdNOwJSErFJR6Ov676qQjUjQAD3Wd4WCWrb/cBWBQBIZmWvxe+dQXjaFXBRgsoApNzrO5gON1k6qbTNuVxYOe/fSOVrKv7+aju0Pye3/Gyo5B+27WogtRy4icvo5tLW09Ov1aZ+7AG9OLoYeo1++rBx8PSoNWL/7isx3XkMZCKCvLO8MvCBa08D042Zsn33Yp6BPEMaicR98yXo9riuvxPzoo6jqo2svPMk2tp9yPhH1/h94CcPLZ0lJeDyiUg/LlIna60Htjc+MLgWDJFWX7TH46gtXTgHVc48g/5tPUfui93GnpLH1zCVD8nx0rc3ICgV+a+J+2xtZknp9bChkoKlhFgZDPUpl14J6u72Ixvpo7Ul76yTMZjMQv1B+xuxnKSj6CKOpgYDfREvLFFZ9uZxwqGexpqG3r1PgRNOO9H/jRujcK5j96lOY6qML7p1ZeaxecgP2+q7+LP74LYrfeB5t+w7GRFsbpGAQ9aqvqZ655w0Cw2mw6WfUbhcHP/sQlsrdgIKW4qn8cOkNhLtNp44FYy0Nz3AYjj4Y98EXgPeiiwgceyyG556jxe+n6sKf4c/MZLwUlLBarWja+rCgdj82XH3QNuESPN98gKGya0dcRKnEcfghpB448B1gvZJTCWZnoLe3xBwOWK14LjyNtBwT+spK/BkZhJJi14D1tQ/KH7sPx3eryXr7bYJmMxW/+AWBrMG9X3SVlUz/3e3oKypAocBTWMCWFSsIZA7tiE+p9wGk1gexGn5AoQji8RezvekOUud0jVMl6ock/QYm5b+DWhX9ANZoXWRmrWXBCU/xY03iwtJjkbJ952mSJWNA74fAnMX8cOFJpKxaRVino23+fFQqVddrQ5aZtPyDzsALElfvjEgSvmPmkzrH1v8nMUQG+5lw4JKbSV67tvPvpNoKjKoAmx7/81A0b58R3w/D0wcKWZYT76keJWpqaobt2jabjaamrqQ5QXnbsN1rNBMLS4e3DzRrNpN891OoahuRdRr882fR/MANoB6e3z769/5Hyh1/QVUXfW2HDTo8px9DqCgH02sfIDW0ELaa8R1+EC0P3tA5tTRirwNZJvOUq9Fu3BFz2HfwdOrfeWyfNydRP6RaV2Ayrow7NxCYQG3j0/uqacNGqWwjNfkB1KqOKcNi6hpvJBIZ2pI6Co+X7KMuRVWbIFlZN/7Zk6l7+0+gGbnM+oN5P6i37iLz7BtROmNTt4TSU6h77wnCmSMXVPaX+H7oXx8U5BzXp/PEyFc3611BHL7xF4Dp3Hp8CWr6jSfD2gdFwPNXoHZ6CevURNQqsG+NPScSIf+TTRSs3IA3xcyWS4/Fm24Z2P3mWzA9eyXTn/8UtcvH7tPmEtaoOPrGF1A7o89R6fKgfOMjdiVH2HRltKrzSL0OUrZVkb2jLO64Ykcp679fiaMwPeHjbHoDmVrzXpK7xmsJ1VPnd9KUYHoWEvfDAp2DSQn2BjhDPr5s2tj5d/ZX25j19EeYqpsJa9Rsu/BItv/8qH61byScUPw4Bt3ubkfq0Zhr+WjXtUN7I1nmFLOGlNrYwyGNCvuEDEJ6DW3Fmay77lSCjj1/FqsdXg548gMsu+oIJBnYdMXxtE0eupWEg3k/ZG/fSrYzPmee7HSxvmQNraqcwTZvnxHfD/3rg4tE8NV/bfVmGhqnj3Qz9jmjwYjbM7YSjA61vvaBzulgwZuvYamroamgiNVnnENQP8hkvLLM6Q/fw4S1a1AHo3m2slfu4L1rbqJqRuIEnH1Rcn5XJvPTH7oHrTP2w0MKhbF9UkHdcdHXfM8+UITDe9wdOWCRCBN+WIOtsoySgw/B7spCGUiwSy4g07Yjl4bQxISXqUhrIN9Sj03v7FMQ1j3o2rIrn9Reakcmei18VX8p2Udux6jr2mgQiSgoq5lLXUm0/1Irylhw5/9h7LYZYe7D/0b7g5ePfn3jHts2klLMlSRNbYg7nqRuJFBvosWZv9drpJXuYtpXn9GWlcOWI48jrEm8XtbY2oyyOYhM13SjDFRPnsH/3bmia91gQ/v/eqH2+Th/2U1klnblFEtdW8W71986qPdMTFsH8bnYklTAwenvYu1RJ9KemsF2+WgiJSNbK7M/xPdDP/vggL6dJoKvbgyVQQoZf3PbYrdj3/pA19zAkQ/8FktNdGpm8ppvmLb6O764/Y8EjXsvAt2btC3rmLD+h87AC8DSWM+iV17k8zuGZn2IMZh4dYE+DIW+6Gu+ow+y13zJ1P+8is7eSsBoovyw49l5yk+HpB1qt5PDH7wNa0UJqmCABe/8i9rZ83Fl5nT2awdPWhaGzKkU+roFgJEwed9+ju3HDXxfMIENP1mIKcNPvqWemVZ6DcC2ukto8nqosNtwNtnIbtKTZUz8Xk/4WvBNZfumJUyc/DZ6QyPBgJGmxpmUrr6WQjn6MTr/9ddjAi+IZtmf8eVnlJ99Ff5RmqgzxeRHo/LFHdeovBTJfiwuJdbyXfiTrHjSeqzBk2UOfvZhctZ8idbtJKJUcsi77/D1jffhSY/P33bEY3/E2twYcyyo07P50t9S6O/7QvQp/36DjNLYclXmlmaOfe1VvvzdvD5fZ08G9bkoaSk/9gx0772GzhGdrvIk29j9kwvID5sgcU7gUUl8PwxPH4jgq5uczBEqHDvCovPZ46ueZU996YNJzz0QFyAkV+xi/kevUvarmwZ876LXVqH2xQ9pmx0t5GSYh2QXoeuU0wlvXovk7/qSlQH/7AM6X/dWqxX/DyXMeOkxNO1JNo3N9Zib6jDmpNN40t4TbXanCAYpfOIhkrZsgEgEz4RJEA5j29U15ap1Ochb+z9qzvoZmi8/QVdTCSjw5uRRdf2tTG0pI+/Fp1HZ2wgbzajsrehqKpGCQQpVao799lM+fvQRnCY97CWzfyR8MLlIqDWgNgZ7fb/39lrwOZawZe0F6I1lBP02gsEUcpO8pH3yXyS3G4urJcHVQBUKMnPLKurOuqDPfbcvKRRz8XtzMZjKYo77fXnkfdHAgpcfRltfQ9hgxF08hR13PEC4/ceGeeMP5K/+HFX7qIAyEsFaVcbCVx9n2/2xPxwUwSDWhtgEvQAan5dZ67+gaslVfW5zdl1pwoX6Jq9zwJ/jSq8XTXMD/vRMZI120J+L7st/ydYTTyTrrVeRVWpqzv4ZoYwsxs6EY5T4fhiePhDBlyD0kba+NuFxffnuhMf7ylMwAVmhRCHH1tQLGwxDlo6i5YjjaFi3hpRVX6BtbiRoTsI5fRblV1wfc17OP1/oDLw6qDxu0j96t9/B16QVy0j9ciWK9j09xrJdhHXxaRkkvx99VQUbnvoHKau+ICJJtB56JLrqSqb97jp0jYnL76hCQdK3beKA55/jq1vO6lfbBkqWNXhc0Yzy5k3rmPjQ3ehqKlHIMiG9MWY6rfMxQMg8en/YybKaytJfUjDxz+j00dd4wJ9D7Yafkv+3p9A2Ref/pEAAzdpvKX54OTvueACA9A//0xl4daeriQ+yZKUCuZe8YZF+pl/wFE5E/vKTuL4OJQ1gnaQsU/Dko6R88wUqh4NgcjKNi07GffXN/b9WD768Akqvu23Q1xH2PyL4EsYuWca8eT22zz7Em1tAw8mLiST4ch8qIVPiqcXgIKeTGn9yBpnv/iumJl5Yq6P58GMHdd0YCgWl1y+l+sLLMG3ZgLegGG/hhLjTVJ7Ei9CV/r4Vv+68jr0V85YNnYHX3q4TNhiI6PQ0HXtS57G8l/7Wa+DVXer2H/vVtiEhyxQ98TD66q6RUJXXjaxQQI/n7MvJo/moRfu6hf3S3HAi9pZDSMt+BwXgd1+M4fG/dAZe3Rl3baejmGUwJXGCkYg2wZSupMI9cSr62uqYw97MbBpOPrNf7a0550JS//cJxtKSzmP+FBtV518Sc17S2m/Jf/FpNM1NhEwmGo87mdpzf47KYSd51eeEzFa09dVk/ucNpED0tal2Ocj5v5eomX0QbbMO6le7BKGvRPAljE2yzKT7f0/yt/9D5Yl+6WX8901+vPsR/Dl5w3LL6gt+gXHndrQtXdvkfemZVF70y0FdN6LVse2+xyh6/EF0NVVEdDqajziWmvOWDLbJcQK2dFqOOr7X/9528CFY13wdVybGm1+U8PysN/9B6mcfIfm8+DOz2X3NLQQyslC3tiAlGBFRyBEikhRzfX9KKtXnXRx3rsrVt8SiIV101ESjqsOW8hdUUjORiBG76zz8/rl9ukZ/aRrq0DTEj4QqZBlveiYaexuyUsKbV8Du65ci96Uw9QgLhazUVkRfc1arNWGpICAmuKw5+0Jsn3+ErltAFVGpaJ23MOFDS357J0q/F2PJDiS/D396JhW/uLrfI1YRg5Etf3iSgmf+jK6mkrDRRNUFl+Ka3rXYXltTyaSH7kbbLYDX1VRh3L6VpG0b0dXXElapQaXqDLw6qNwurG/9k6oxFHxJTgfFDy/HWFqCrAD3pKnsuuH3RHopli6MLBF8CWOS5ftvSF71Bar2tVIKWcZYvpsJjz8Yt9ZkqDhnzmHn0nvIffU5VE4HQWsyFZf8Gn/u3neD7U0gPZPtdz8yBK3sG8nlxLR1I4GMbLwFXYFV3ennkLz6K5I2rUfyeYlIKjzFkyj7VfxuvezX/k7uy8+gak/ZYCwtQVtTxabHX8SXk0/Alo6qIrZsly8tg9ZDDsey/ntUHjeB5FRqzrsIX0H8KJw3rxDrD6v3+Dx8SRa2nXseelUrxekPolN3fdGq1aU0t96Czz80C7C7i+gNyAkqYITVakqvuxXnzANRBvwEU/qfz8m8YS35f38SdUszYZOZupPPpPHkxUPR7H6pPeM8bJ+8h7Y5NieXp3Bi53R4yJrMzlvuouBvj6FprCei09M2byEVl12T8JoRg5Ef7/sz6uZGVC4X3tz8mPqN/RGyJrPr5jt6/e+5r74QE3hBdAo99atPkULRWpNSKAihxHUnFaGxtch86p03Ydn4Q+ffhqoKVE4n21Y8PoKtEnojgi9hTEr75P3OwKs7bV11grOHjuPAeWw9cOi/zPelnJefIeODd9DV1RA0mXFPmsaPdz8MVitIKrbd92esa1ZhXb0K9+RpNB17IrIqfmu87bMPOwOvDoaKUjL+8wa1511EzTkXkvf8Xzvr9gVNSTSecBqVl/4KRTCI5HZFRzx6WQdUueQqzJvXY9y1o3Ntj9+WTtBiReVy4jEksf2Ms6mdO4+DMu+JCbwAVFIrSabXhyX4CiVZcE+cEjct58srpG3eYSBJhOm9yHhvtNWVTF6xLHa0prqCiF5P8zEnDrrd/RHIyKLqwsvJ/tcr6GqqCBuNuCdMjgt4nLMOZPNjz6MI+KOvkz7UgwymphFMHYYKD92oHPaEx6UEwVbPtXphlRrnHkaIRxt92S6Mu3fEHTeWbEdbX4s/I37nqTCyRPAljEmBXj64I9rhr7M3lhl2biP7zX+gbv9iUrucWNetZsKf7qdpRfuIoUJB2/zDaJu/5zqQkjvRtKKMoX0DQsPJZ+KYdRDZb7yMIhigdvFP8UyO5sSS1WpC1j2vlQslWdj86DNkv/4SxtISPPlF1Jx3MWGTGWSZ6nonQUs0KDRpEmdMVyr7X5+wr3b87l4mPXgnxpLtKEIhfNm5lNy4bMAjOQC5rzwbN1qjdjoo+vMDqOxtNJx6VsJAeLjUn34ujcefimnbJoIpqXgLi3s9d7RNrbbNW0jyt1/GTZ8m2hQR0WiJaDSoXU4C1mTsBy+g7cyfgmPf1tQcKHVLc8L3o+RxoWprFcHXKDSo4Oubb77h9ddfp7q6mvvuu4/i4sRvzPXr1/P8888TiUQ47rjjWLx43w+hC/uXmnMvIvXLlTGLd8MaLS2HHjmCrRr9st5+rTPw6s5YsoM9F3yJF7Cloa+N3dUW1kbXq3Xw5RWw+4bbB9JUIDpNlTAFQY9doE2eIgot38dtDg2Hh69Ca8RgZPv/ewil3wfh8JCsrUn0bwOgcdgp+uvDpHzzBdvuf7xPo0tDJaLX4zho/j6731Cp/8nppHz9GUkb1yL5/cgKJZ6CIpR+f9zrtm3uoVRcchWmnT/imDkHf04e1n3Yx4PlmjYTX1YO+h67TP0Z2XiKEicpFkbWoF5deXl53HzzzUybNq3XcyKRCM8++yy/+93vePTRR/n666+pqorfhiwI/RGyJrPjd/dgn3Ugvows3IXF1J79M6ouHtzi9/1drxnrB/BJUHHZ1fi6/aIOq9XYD5xH2yGHD7B1A7eh4TQ8gdhNAcFgFq2Oy4f93hGtbsgWNdtnH4isSPyPoQyHSdq0npSvPxuSe+33JBXb7nuM7ctWUHfyYnZf81s2PvESO2+7G+ekaQSsyfjSMmhZcDg7b7sb74RJNJ542rBt2BlOEb2B2rMuINBtjaHflk71T5cg91JtQBhZgxr5ys3N3es5JSUlZGZmkpERzTy9cOFC1qxZ06fHCsKeuKfNZsujz0Aksk9HAsay2rMuJGXVl2jauhKCyoBz6qx+X8s580A2/+k5cl59HnVrCy0Lj6LpmBOHLDdZf4Qiekrqf0+B9TM06hLCYRt2588JR8ZOAWOAusXnk/Lt/zBt3ZRwbZIU8JP8zf9oOaJv9ePGPYWCtgVH0raga0TcNeMANj3xUucGgQHlBhuF6hafT+uCI8l865+gVEaDsbT+1T0V9p1hX/PV0tJCamrX0H9qaio7d+4c7tsK44kIvPrMWziByp9fTtY7r6GtryNkMuOaNovSa37LQL6CArZ0Sq+9dcjbORARWYfdeclIN2NQZI2GLX/4K2mffEDhEw+j7pFuQ1YocU+cMkKt248oFATSM/d+3hjjz8ymPMHOZGH02WvwtXz5ctra2uKOn3/++cybN/S7iFauXMnKlSsBWLFiBTbb8P1yValUMddvs8bXNxsPJEnCarWOdDNG1HjqA/8lV1J+wSVoS3cSsqUTsqVjYez1gcMtEzSrCZpAYXRgNmuxahO33yAbMCvMgIRaBnUk2OtzHUg/KHze6EJ41dD8ng2e93Oa5Ajpf30EyeXsPO6fOBn/zy7FqutfRvj+GmuvheEg+iBK9MPw9MFePymWLVs2qBukpKTQ3NxVrqS5uZmUlJRez1+0aBGLFnVlg25q6u8y4L6z2Wwx108UZI4H0bpV4/O5dxiXfZDZPvXf/rzHWh84nU6CSjVBBXhx49QFUHsTt9/j8eB0OcEloXaC2hnstVZbf/rBuG0TRU8+irahjrBWi+OAuey+7laQBh+EtZ14Oi4Z0j/8N5LPhzc7l7Krbybo84FveH8ojrXXwnAQfRAl+mF4+mDYpx2Li4upra2loaGBlJQUVq1axXXXXTfctxUEQRhSkssJsky4vU6j5HIyecUy9NWVnefoaqqQJWnI6vk1nnQ6jSedPiTXEgRh9BhU8LV69Wqee+45HA4HK1asoLCwkNtvv52Wlhaeeuopli5diiRJ/OIXv+Dee+8lEolwzDHHkJc39naTCIIwPqmbG5l0/zL0VeUAeHPyKLltObZP3o8JvACUkQiWdWs6ax8KgiAkMqjga/78+cyfH5//JSUlhaVLl3b+fdBBB3HQQWOnRpYgCEKHKXfdQtLWjZ1/a5samHLXLTinzkx4vjIYEMGXIAh7JLaJCYIg9EJfUYq+R33KjuOOWQcSNMfvEfVl5ogduIIg7JH4hBAEYczRNNQx4ZN3yNjwfXSUaZhIHg/KgD/uuNLvJ2hLo+H4UwhYorugIspoBvXdQ7TeSxCE/Zeo7SiMHpEI1u++wrJhLfY5c6OZ0sXUjdBD4RMPk/r5h2hbmglqdbQWF/Pfx2+C1KGvLeiaNAV/Vm5nvcoOvuxcXJOn45xxAPWnnU3ax//Fn5FN4/Enj7oah4IgjD4i+BJGBaXfx7Sl12HavhnJ7yfzP6/jnDaLbfc9Jr7MhE7mjetI/+DfqDzRgtlqv4/0rVs44r6/4Xzmmj5fp/Cz/zLl24+QvF786ZmU/vpm/NkJqm5IKsouv5aivz6Mrr1uni8rl4rLrkFWRwtc+/IKqfzF1YN/coIgjBsi+BJGhdwXnyJp41o6xrkkvx/L+u/JfflZKn/x6xFtW2/S33sL26cfoAwGcU+aSvnl1xLR6Ue6Wfu1jPfe7Ay8ukvZXYUzwfmJFL//Nge8+lc0HjcAxt070VVXsunPLxA2mePObzv0SDbMmUvq5x+hkGWajj5hyGo5CoIwPongSxgVTD9upecEowIwb9uY6PQRl/vi02S//iIqrxeApC0bMOzeyZaHnx7cVKksk7T+e7R11bTNP4xgatoQtXj/ENYbEh6PqHopGJ5A8cr/dgZeHQyVZWS9+Q+qLr4y8fX1Bhp/srjvDR0BKnsbRX9+AH1FGbJaTcuCw6n++RVi6l4QRiERfAmjQkSj6eX4KJxyDIewff5RZ+DVwbRjG5YfvsN+8IIBXVbV1sq031+PYXcJUsCP35ZOwwmniimtbmrOvYiUVV+gbW7sPBaWJKrnzujzNdQeT8LjKV99RtPRJ5D12GukOe00nHAa9nkLB93mfSIcZtrvrsW8fWvnIUPpTlQul6j1JwijkNjtKIwKdWecR7A9c3iHoDmJ2jPPH6EW9U7lcsbU2+sg+bwYu3359VfxI/dg/nELUvvuOm1TA5n/+ReGku0Dvub+xp+dS9mVN+CeMAmf2YIzI5vdJ5zIt9df1Pdr9HidddDW1zDjpitJ/vfrpH32EVPuvpWix1YMVdOHVfI3X2LYvTPmmBQIkPzdVyhCwRFqlSAIvREjX8Ko0LbgCCou/TUZ/30TtcNO0GKl7tSzsc89dKSbFidkthCyWNG2xNYdDZlMOA4ceLF5fWV8Pim1007mv/+P3TcOrsbq/qT52BNpPvp4WreW4M5OxZthQFbVAuE+Pb5q3kLSt22KO67yeFC4u6YjVV4Pqf/7lJrzLsafmT1UzR8WhrJdSMH4IEvyupE8HkJJ8fnIBEEYOWLkSxg16k8/l41P/oMf/v4WG//6Cg2nnj3STUpMqaTutHMIdvtCi0gS9lkH45o2a8CXlXspxhzpZZ3TuKZU4rFlEhpA39QsOAK/3hR3PFG2ME1rM9bvvhpAA/etlkOPjBs5Bgim2Aj1MtInCMLIESNfwuiiUIyJ1BL1p5+Lp7CYrH/9A2UwQNu8hdSdfu6grumccQCG8t0ouiUN9aVlUHNu36fUhL1z5BXSPGUmWRu+6+zrsKRC1mhQemPXg4V1erz5RSPRzH7xFk+mdcERpH65Eskfnbb2p9qouuBSseBeEEYhEXwJwgA5Zx+Ec/bQ1SwtveYWJI8b89aNSF4PAVs6lRdeTsCWPmT3EKK+uf4uFrz7d8xbN4JConX+oWgb6kn/6D8oQ6HO89zFk3HMmTuCLe27klvuovnwY7F9+gFho4nqny7Bn5M30s0SBCEBEXwJwighq9XsvP0+JJcTldOBPyNL1AgcJhG1hvJf3dTjYIRQkoWUDd8T8fvxFk5k93W3js6Ro3CItJXvYVm3BteUGdSfciayRkvrYUfTetjRI906QRD2QgRfgjDKhE3mhMk+hWGmVFJx+bU4rFba2tpGujW9Uvp9TL/1akzbNqMMh0j75APSP/w3Wx94glB7nUlBEEY38bNaEARhDMn+5wuYN69HGY5OjyrkCKaS7RT87bERblk8bW015k3r4tbSCcJ4J0a+BEEQxhDzj5vjqkEA6CviU5WMFKXPy+S7b8W0Yysqhx1/Zjb1p5xFzU+XjHTTBGFUEMGXIAi9Mm9eT+4rz6JyOQmkpFJ2+XX48wpGulnjWsgQnyYDei+9NBKKHn+QlNVfd/6tr6ki+/9epG3uoXiKJ49gywRhdBDTjoIgJGTevJ7Jy28jec0qzNs2kfr150y//To0TQ0j3bRxrepnv8Cfaos5FkyyUrv4pyPUonimHfGVHjT2NrLeenUEWiMIo48IvgRBSCj3lWdjaihCdAQj98WnR6hF+y+VvZX8v/2JSfcsxfbRuxDuPVu/t3gyu25chmPGAXhz8nFOnUnpr26g7dAj92GLB2oU7hwVhBEgph0FQUhIlaB+JSBGvoaYvnQnU+/8LfqaSiBa4Dvt0w/Ydu+fQJISPqbtkMNpO+TwfdnMfnFNnoFhd0lMqBWwWEdlrVZBGAli5EsQhIQCKbaEx70FE/ZxS/ZvhU/+sTPwApBCQZI2fI/tsw9HsFWDU3rtLbQecjgBawoRpRJvdi415y0R670EoZ0Y+RIEIaGyK67DULoTfU1V5zH3hElUXXjZCLZq/6NtrI87JgWDJH/3FU2LTh6BFg1eRKvjx3v/hLauBk1TA+7iyaJGqSB0I4IvQRAS8ufms/mRZ8h76Wk0jfV4CyZQdeFlIgHsEAsbjAmP+zKz93FLhp4/Mxv/fvA8BGGoieBLEMarSARNcyMhk7nXUYmgLY3dN9y+jxs2vjQcfwr6ylJUbnfnMW9OPrXn/HwEWyUIwnASwZcgjEPJX39G3ot/Q9PcQESnxzHzQJrueXikmzUu1Z9xHigUpH38LpLHQyA9k9KrbhgTpYKUPi8pX32GIhym+cjjxNSiIPSRCL4EYZzRNDUw4fE/dFtr1Iq2vg5VcjJtV94wom0br+pPP5f6088d6Wb0S9Labyl+7AF0NZUgy+S+8ixlV/5GFPYWhD4Qux0FYZzJev3luEXeCjmC4YfVI9QiYcwJhyl68lH01RUoZBkFoK+ppOCZP6MIBke6dYIw6ongSxDGGZXHlfC4IiS+NIW+Me7ega62Ku64rqYS89aNI9AiQRhbRPAlCONM7WnnEDQlxR33i/xdQh+FdXoiak3c8YhaQ9gg1n0Jwt4Mas3XN998w+uvv051dTX33XcfxcXFCc+7+uqr0el0KJVKJElixYoVg7mtIAiD4Jk8nYafnIHtk/fRtjQR1mjxFBZTe/t9I900YYzw5RXizStE3WOUy1tQhHvi1BFqlSCMHds4zHYAAAywSURBVIMKvvLy8rj55pt5+um913q78847SUqK/7UtCMK+V37lb6g96wKSV32BPyuHtrmHYk1Jgba2kW6aMEZsv+NBJq34PfrKcpBlfDl57Lz1blCI+o2CsDeDCr5yc3OHqh2CIOxjgbSMaJoDQRiAoC2NrQ89hcreiiIiE0xOGekmCcKYsc9STdx7770AHH/88SxatGhf3VYQBEEYRiFL8kg3QRDGnL0GX8uXL6ctwVTE+eefz7x58/p0k+XLl5OSkoLdbueee+4hOzub6dOnJzx35cqVrFy5EoAVK1ZgsyUu7jsUVCpVzPXbrL5hu9doJkkSVuvoT+g4nEQfjL0+cLhlgmY1QRMojA7MZi1WbeL2G2QDZoUZkFDLoI4Ee32uY60fhoPoA9EHHUQ/DE8f7DX4WrZs2aBvkpISHY62WCzMmzePkpKSXoOvRYsWxYyMNTU1Dfr+vbHZbDHXTxRkjgdWq3XcPvcOog/GXh84nU6CSjVBBXhx49QFUHsTt9/j8eB0OcEloXaC2hmkrS3x2qSx1g/DQfSB6IMOoh+Gpw+GPdWEz+fD6/V2/v8bN24kPz9/uG8rCIIgCIIwKg1qzdfq1at57rnncDgcrFixgsLCQm6//XZaWlp46qmnWLp0KXa7nYceegiAcDjM4Ycfzpw5c4ak8YIgCIIgCGPNoIKv+fPnM3/+/LjjKSkpLF26FICMjAz+8Ic/DOY2giAIgiAI+w1RWFsQBGGEKP0+st54BfPWjQQtVqp+fjn+7LyRbpYgCMNMBF+CIAgjQBEMMv3WqzFvXk/H0n/rujXs+N29OGcdOKJtEwRheInajoIgCCMg/cN/Y9q2ie57LrWN9eS98OSItUkQhH1DjHwJgjB8IhHyX/gr1tWrUAYDeHPy2H39UoKpaSPdshGXtOF7lOFw3HFNa/MItEYQhH1JBF+CIAybwicfJePfryOFggAYynejra9l019eRFapR7h1I8tTWIwM9Mw2FjKZR6I5giDsQ2LaURCE4REOYV2zqjPw6mAo203KFytHqFGjR+2ZF+ApLI45FkyyUne6qLcpCPs7MfIlCEK/6HftIPcfz6HyuHFOm0XNTy8motXFnacMBJC8nvjj4RCGilLG++RaxGBk64q/UPjUI+hqqwnrDdQu/imthx0z0k0TBGGYieBLEIQ+s373FcWPLEfbHC3LZV2zCsu61Wx56EmQYj9OIjo9gdQ0tE0NMceD5iSajlyEAEFbGjtvv3+kmyEIwj4mph0FQeiz3Fef6wy8ILpeybRtM7ZPPog/WaGg4pKr8KVndh4K6Qy0LDwab/HkfdBaQRCE0UmMfAmC0Gfq1pa4Y1IoiHXdappOODXuv9nnLWTzY8+T/dqLqBxtNJ5wKvaDDtkXTRUEQRi1RPAlCEKfhcwWoDLmWESpxDllRq+PCdjSKbv65mFumSAIwtghph0FQeizutPPIZhkiTnmmTCZhpMXj1CLBEEQxh4x8iUIQp81nnAaIVMSmW+/huTz4s0vpPyK65E12pFumiAIwpghgi9BEPqldeFRtC48aqSbIQiCMGaJaUdBEARBEIR9SARfgiAIgiAI+5AIvgRBEIT/387dhTT5/mEAv+YszZfMaWlqUlkGdhDkzLDMSouooIjoBXo7iIj1gkWhEmlUVlRmhEpGJrEIIkg6k4TSiN6cZqGVNjMQKU2nUzPJuft/5DC03J/fdj+5XZ+j3G7s2vXcy6/P8zQikojDFxEREZFEHL6IiIiIJOLwRURERCQRhy8iIiIiiTh8EREREUnE4YuIiIhIIg5fRERERBJx+CIiIiKSiMMXERERkUQcvoiIiIgk4vBFREREJBGHLyIiIiKJOHwRERERSaQSQgilQxARERG5C7c+85Wenq50hH8Ce2AHADsYwh7YAcAOhrAH53Tg1sMXERERkWwcvoiIiIgkUp86deqU0iGUNHv2bKUj/BPYAzsA2MEQ9sAOAHYwhD04vgPecE9EREQkES87EhEREUnkqXQAmfR6PaqqquDp6YmQkBDodDr4+vqOWFdTU4Pi4mJYrVYkJydj48aNCqR1jhcvXuD+/ftoaWnBuXPnEBUVNeq6AwcOwNvbGx4eHlCr1bhw4YLkpM5lbw+uvBd6e3uRm5uL79+/Y+rUqThy5Aj8/PxGrNu6dSsiIyMBAMHBwUhLS5Md1eHGOq4DAwPIy8vD58+f4e/vj9TUVEybNk2htM4zVg/l5eXQ6/XQaDQAgDVr1iA5OVmJqE5RUFCA6upqBAQEICcnZ8TzQggUFxfjzZs38PLygk6nc8lLcGP1UFdXh4sXL9reA/Hx8di8ebPsmE7V3t6O/Px8dHV1QaVSISUlBWvXrv1tjUP3g3AjNTU1wmKxCCGE0Ov1Qq/Xj1gzODgoDh48KL59+yYGBgbEsWPHRHNzs+yoTtPc3CxaWlpEVlaWMBqNf1yn0+mE2WyWmEwue3pw9b2g1+tFSUmJEEKIkpKSUd8PQgixY8cOmbGczp7jWlpaKgoLC4UQQjx79kxcuXJFiahOZU8PT548ETdv3lQoofPV1dWJxsZGcfTo0VGfr6qqEtnZ2cJqtYr6+nqRkZEhOaEcY/VQW1srzp8/LzmVXCaTSTQ2NgohhOjr6xOHDx8e8X5w5H5wq8uOCxYsgFqtBgBER0fDZDKNWGM0GhEaGoqQkBB4enoiISEBlZWVsqM6TUREBMLCwpSOoTh7enD1vVBZWYmkpCQAQFJSkku9tr+x57gaDAYsX74cALB48WLU1tZCuNjtsa6+v+0RExMz6tneIQaDAcuWLYNKpUJ0dDR+/PiBzs5OiQnlGKsHdxAYGGg7izVp0iSEh4ePmBEcuR/c6rLjcI8fP0ZCQsKIx00mE4KCgmxfBwUF4dOnTzKj/TOys7MBAKtWrUJKSorCaeRz9b1gNpsRGBgIAJgyZQrMZvOo6wYGBpCeng61Wo0NGzZg0aJFMmM6nD3HdfgatVoNHx8f9PT0YPLkyVKzOpO9+/vVq1f48OEDpk+fjt27dyM4OFhmTEWZTKbfXm9QUBBMJpPtfeNOGhoacPz4cQQGBmLnzp2YMWOG0pGcpq2tDU1NTZgzZ85vjztyP7jc8HXmzBl0dXWNeHzbtm2Ii4sDADx48ABqtRqJiYmy40lhTwf2fA+NRgOz2YyzZ88iLCwMMTExjo7qVI7oYbz7WwfDqVQqqFSqUb9HQUEBNBoNWltbcfr0aURGRiI0NNQpeenfEhsbiyVLlmDChAkoKytDfn4+srKylI5Fks2aNQsFBQXw9vZGdXU1Ll26hGvXrikdyyn6+/uRk5ODPXv2wMfHx2l/j8sNXydPnvzr8+Xl5aiqqkJmZuaoP2w0Gg06OjpsX3d0dNhuNh0vxurAHkOvOSAgAHFxcTAajeNu+PqvPbj6XggICEBnZycCAwPR2dn5x7M6Q685JCQEMTEx+PLly7gevuw5rkNrgoKCMDg4iL6+Pvj7+8uO6lT29DD8NScnJ+POnTvS8v0LNBoN2tvbbV+Px38DHGH4ELJw4UIUFRWhu7vbpc4EA4DFYkFOTg4SExMRHx8/4nlH7ge3uuerpqYGDx8+RFpaGry8vEZdExUVha9fv6KtrQ0WiwXPnz+HVquVnFRZ/f39+Pnzp+3P7969s/1vN3fi6ntBq9WioqICAFBRUTHq2cDe3l4MDAwAALq7u1FfX4+IiAipOR3NnuMaGxuL8vJyAMDLly8xf/78P54ZHK/s6WH4/SwGg2HcH/v/l1arxdOnTyGEQENDA3x8fNzykmNXV5ftnkej0Qir1epyv4wIIXD9+nWEh4dj/fr1o65x5H5wqw9ZPXToECwWi+3Gwrlz52Lfvn0wmUwoLCxERkYGAKC6uhq3b9+G1WrFihUrsGnTJiVjO9Tr169x69YtdHd3w9fXFzNnzsSJEyd+66C1tRWXL18GAAwODmLp0qUu1QFgXw+Aa++Fnp4e5Obmor29/bePmmhsbERZWRn279+P+vp63LhxAx4eHrBarVi3bh1WrlypdPT/bLTjeu/ePURFRUGr1eLXr1/Iy8tDU1MT/Pz8kJqaipCQEKVjO9xYPdy9excGgwFqtRp+fn7Yu3cvwsPDlY7tMFevXsX79+/R09ODgIAAbNmyBRaLBQCwevVqCCFQVFSEt2/fYuLEidDpdH/8WJrxbKweSktL8ejRI6jVakycOBG7du3CvHnzFE7tWB8/fkRmZiYiIyNtv2ht377ddqbL0fvBrYYvIiIiIqW51WVHIiIiIqVx+CIiIiKSiMMXERERkUQcvoiIiIgk4vBFREREJBGHLyIiIiKJOHwRERERScThi4iIiEii/wF2ZWXnxZiAggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "X, y = make_circles(n_samples=500, factor=0.1, noise=0.35, random_state=42)\n",
    "X_train_circles, X_test_circles, y_train_circles, y_test_circles = train_test_split(\n",
    "    X, y, test_size=0.2)\n",
    "\n",
    "dtree = DecisionTreeClassifier(random_state=42)\n",
    "dtree.fit(X_train_circles, y_train_circles)\n",
    "\n",
    "x_range = np.linspace(X.min(), X.max(), 100)\n",
    "xx1, xx2 = np.meshgrid(x_range, x_range)\n",
    "y_hat = dtree.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
    "y_hat = y_hat.reshape(xx1.shape)\n",
    "plt.contourf(xx1, xx2, y_hat, alpha=0.2)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='autumn')\n",
    "plt.title(\"Decision tree\")\n",
    "plt.show()\n",
    "\n",
    "b_dtree = BaggingClassifier(\n",
    "    DecisionTreeClassifier(), n_estimators=300, random_state=42)\n",
    "b_dtree.fit(X_train_circles, y_train_circles)\n",
    "\n",
    "x_range = np.linspace(X.min(), X.max(), 100)\n",
    "xx1, xx2 = np.meshgrid(x_range, x_range)\n",
    "y_hat = b_dtree.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
    "y_hat = y_hat.reshape(xx1.shape)\n",
    "plt.contourf(xx1, xx2, y_hat, alpha=0.2)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='autumn')\n",
    "plt.title(\"Bagging (decision trees)\")\n",
    "plt.show()\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=300, random_state=42)\n",
    "rf.fit(X_train_circles, y_train_circles)\n",
    "\n",
    "x_range = np.linspace(X.min(), X.max(), 100)\n",
    "xx1, xx2 = np.meshgrid(x_range, x_range)\n",
    "y_hat = rf.predict(np.c_[xx1.ravel(), xx2.ravel()])\n",
    "y_hat = y_hat.reshape(xx1.shape)\n",
    "plt.contourf(xx1, xx2, y_hat, alpha=0.2)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='autumn')\n",
    "plt.title(\"Random forest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图显示了决策树形成的边界凹凸不平，有大量锐角，这暗示出现过拟合现象。相反，随机森林和 Bagging 的边界相当平滑，没有明显的过拟合的迹象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，看看哪些参数有利于提高随机森林模型的准确率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林的参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn 库提供了 `BaggingRegressor` 和 `BaggingClassifier` 类，其中在创建新模型时需要注意的一些参数如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- n_estimators 是随机森林中树的数量；\n",
    "- criterion 是衡量分割质量的函数；\n",
    "- max_features 是查找最佳分割时考虑的特征数；\n",
    "- min_samples_leaf 是叶节点的最小样本数；\n",
    "- max_depth 是树的最大深度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林在现实问题中的应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用之前运营商的客户离网数据集作为例子。这是一个分类问题，使用准确率评估模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，创建一个简单的分类器作为基线。出于简单的目的，只使用数值特征构建分类器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import (GridSearchCV, StratifiedKFold,\n",
    "                                     cross_val_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    \"../../data/telecom_churn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择数值特性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for i in df.columns:\n",
    "    if (df[i].dtype == \"float64\") or (df[i].dtype == 'int64'):\n",
    "        cols.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数据集分离为输入变量和目标变量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = df[cols].copy(), np.asarray(df[\"Churn\"], dtype='int8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为验证过程进行分层分割。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于默认参数初始化分类器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(random_state=42, n_jobs=-1, oob_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在训练集上进行训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = cross_val_score(rfc, X, y, cv=skf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在测试集上评估准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy score: 91.48%\n"
     ]
    }
   ],
   "source": [
    "print(\"CV accuracy score: {:.2f}%\".format(results.mean()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得到了 91.48% 的准确率作为基线，现在，尝试改进结果，同时查看一下基本参数变化时的学习曲线。首先，从改进树的数量开始，初始化 K 折交叉验证。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建列表储存训练集和测试集上的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "temp_train_acc = []\n",
    "temp_test_acc = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行网格搜索。下方列表中候选数值较多，会增加线上环境执行时间，可以自行减少数量以加快运行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees_grid = [5, 10, 15, 20, 30, 50, 75, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ntrees in trees_grid:\n",
    "    rfc = RandomForestClassifier(\n",
    "        n_estimators=ntrees, random_state=42, n_jobs=-1, oob_score=True)\n",
    "    temp_train_acc = []\n",
    "    temp_test_acc = []\n",
    "    for train_index, test_index in skf.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        rfc.fit(X_train, y_train)\n",
    "        temp_train_acc.append(rfc.score(X_train, y_train))\n",
    "        temp_test_acc.append(rfc.score(X_test, y_test))\n",
    "    train_acc.append(temp_train_acc)\n",
    "    test_acc.append(temp_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy on CV is 92.44% with 50 trees\n"
     ]
    }
   ],
   "source": [
    "train_acc, test_acc = np.asarray(train_acc), np.asarray(test_acc)\n",
    "print(\"Best accuracy on CV is {:.2f}% with {} trees\".format(max(test_acc.mean(axis=1))*100,\n",
    "                                                            trees_grid[np.argmax(test_acc.mean(axis=1))]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更改树的数量为 50 时，得到了 92.44% 的准确率。接下来，绘制相应的学习曲线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'N_estimators')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(trees_grid, train_acc.mean(axis=1),\n",
    "        alpha=0.5, color='blue', label='train')\n",
    "ax.plot(trees_grid, test_acc.mean(axis=1), alpha=0.5, color='red', label='cv')\n",
    "ax.fill_between(trees_grid, test_acc.mean(axis=1) - test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + test_acc.std(axis=1), color='#888888', alpha=0.4)\n",
    "ax.fill_between(trees_grid, test_acc.mean(axis=1) - 2*test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + 2*test_acc.std(axis=1), color='#888888', alpha=0.2)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylim([0.88, 1.02])\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_xlabel(\"N_estimators\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图表明，当树的数量增加到一定值时，测试集上的准确率不再提升，同时训练集上的准确率达到了 100%，这意味着出现过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了避免过拟合，需要给模型加上正则化参数。下面将树的数目固定为 100，然后看看不同的 max_depth 效果如何，首先创建列表以保存训练和测试集上的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "temp_train_acc = []\n",
    "temp_test_acc = []\n",
    "max_depth_grid = [3, 5, 7, 9, 11, 13, 15, 17, 20, 22, 24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for max_depth in max_depth_grid:\n",
    "    rfc = RandomForestClassifier(\n",
    "        n_estimators=100, random_state=42, n_jobs=-1, oob_score=True, max_depth=max_depth)\n",
    "    temp_train_acc = []\n",
    "    temp_test_acc = []\n",
    "    for train_index, test_index in skf.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        rfc.fit(X_train, y_train)\n",
    "        temp_train_acc.append(rfc.score(X_train, y_train))\n",
    "        temp_test_acc.append(rfc.score(X_test, y_test))\n",
    "    train_acc.append(temp_train_acc)\n",
    "    test_acc.append(temp_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy on CV is 92.68% with 17 max_depth\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Max_depth')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc, test_acc = np.asarray(train_acc), np.asarray(test_acc)\n",
    "print(\"Best accuracy on CV is {:.2f}% with {} max_depth\".format(max(test_acc.mean(axis=1))*100,\n",
    "                                                                max_depth_grid[np.argmax(test_acc.mean(axis=1))]))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(max_depth_grid, train_acc.mean(axis=1),\n",
    "        alpha=0.5, color='blue', label='train')\n",
    "ax.plot(max_depth_grid, test_acc.mean(axis=1),\n",
    "        alpha=0.5, color='red', label='cv')\n",
    "ax.fill_between(max_depth_grid, test_acc.mean(axis=1) - test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + test_acc.std(axis=1), color='#888888', alpha=0.4)\n",
    "ax.fill_between(max_depth_grid, test_acc.mean(axis=1) - 2*test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + 2*test_acc.std(axis=1), color='#888888', alpha=0.2)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylim([0.88, 1.02])\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_xlabel(\"Max_depth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_depth 在模型中起到了正则化的作用，过拟合现象有所缓解，模型准确率提高了一些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一个值得调整的重要参数是 min_samples_leaf，它也能起到正则化作用。首先，创建列表以保存训练和测试集上的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "temp_train_acc = []\n",
    "temp_test_acc = []\n",
    "min_samples_leaf_grid = [1, 3, 5, 7, 9, 11, 13, 15, 17, 20, 22, 24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for min_samples_leaf in min_samples_leaf_grid:\n",
    "    rfc = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1,\n",
    "                                 oob_score=True, min_samples_leaf=min_samples_leaf)\n",
    "    temp_train_acc = []\n",
    "    temp_test_acc = []\n",
    "    for train_index, test_index in skf.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        rfc.fit(X_train, y_train)\n",
    "        temp_train_acc.append(rfc.score(X_train, y_train))\n",
    "        temp_test_acc.append(rfc.score(X_test, y_test))\n",
    "    train_acc.append(temp_train_acc)\n",
    "    test_acc.append(temp_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy on CV is 92.41% with 3 min_samples_leaf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Min_samples_leaf')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc, test_acc = np.asarray(train_acc), np.asarray(test_acc)\n",
    "print(\"Best accuracy on CV is {:.2f}% with {} min_samples_leaf\".format(max(test_acc.mean(axis=1))*100,\n",
    "                                                                       min_samples_leaf_grid[np.argmax(test_acc.mean(axis=1))]))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(min_samples_leaf_grid, train_acc.mean(\n",
    "    axis=1), alpha=0.5, color='blue', label='train')\n",
    "ax.plot(min_samples_leaf_grid, test_acc.mean(\n",
    "    axis=1), alpha=0.5, color='red', label='cv')\n",
    "ax.fill_between(min_samples_leaf_grid, test_acc.mean(axis=1) - test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + test_acc.std(axis=1), color='#888888', alpha=0.4)\n",
    "ax.fill_between(min_samples_leaf_grid, test_acc.mean(axis=1) - 2*test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + 2*test_acc.std(axis=1), color='#888888', alpha=0.2)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylim([0.88, 1.02])\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_xlabel(\"Min_samples_leaf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这一情形下，验证集上的准确率没有提高，但当验证集的准确率保持 92%以上时，过拟合的情况降低了 2%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面考虑 max_features 这一参数。在分类问题中，默认特征为「所有特征数的平方根」。首先，检查在这个例子中选择 4 个特征是否是最佳的，创建列表以保存训练和测试集的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "temp_train_acc = []\n",
    "temp_test_acc = []\n",
    "max_features_grid = [2, 4, 6, 8, 10, 12, 14, 16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在训练集上训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for max_features in max_features_grid:\n",
    "    rfc = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1,\n",
    "                                 oob_score=True, max_features=max_features)\n",
    "    temp_train_acc = []\n",
    "    temp_test_acc = []\n",
    "    for train_index, test_index in skf.split(X, y):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        rfc.fit(X_train, y_train)\n",
    "        temp_train_acc.append(rfc.score(X_train, y_train))\n",
    "        temp_test_acc.append(rfc.score(X_test, y_test))\n",
    "    train_acc.append(temp_train_acc)\n",
    "    test_acc.append(temp_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy on CV is 92.59% with 12 max_features\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Max_features')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc, test_acc = np.asarray(train_acc), np.asarray(test_acc)\n",
    "print(\"Best accuracy on CV is {:.2f}% with {} max_features\".format(max(test_acc.mean(axis=1))*100,\n",
    "                                                                   max_features_grid[np.argmax(test_acc.mean(axis=1))]))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(max_features_grid, train_acc.mean(axis=1),\n",
    "        alpha=0.5, color='blue', label='train')\n",
    "ax.plot(max_features_grid, test_acc.mean(axis=1),\n",
    "        alpha=0.5, color='red', label='cv')\n",
    "ax.fill_between(max_features_grid, test_acc.mean(axis=1) - test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + test_acc.std(axis=1), color='#888888', alpha=0.4)\n",
    "ax.fill_between(max_features_grid, test_acc.mean(axis=1) - 2*test_acc.std(axis=1),\n",
    "                test_acc.mean(axis=1) + 2*test_acc.std(axis=1), color='#888888', alpha=0.2)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylim([0.88, 1.02])\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "ax.set_xlabel(\"Max_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图表明，在这个例子中，max_features=12 时模型表现最好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们已经看到学习曲线如何随着基本参数的变化而变化。现在，直接使用 `GridSearch()` 来查找最佳参数。首先，初始化参数集以进行穷举搜索和拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:   33.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=42, shuffle=True),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "            oob_score=True, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'max_features': [10, 13], 'min_samples_leaf': [1, 3], 'max_depth': [5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原实验搜索参数\n",
    "# parameters = {'max_features': [4, 7, 10, 13], 'min_samples_leaf': [\n",
    "#     1, 3, 5, 7], 'max_depth': [5, 10, 15, 20]}\n",
    "# 为加速线上环境执行，优化搜索参数\n",
    "parameters = {'max_features': [10, 13], 'min_samples_leaf': [1, 3], 'max_depth': [5, 10]}\n",
    "rfc = RandomForestClassifier(n_estimators=100, random_state=42,\n",
    "                             n_jobs=-1, oob_score=True)\n",
    "gcv = GridSearchCV(rfc, parameters, n_jobs=-1, cv=skf, verbose=1)\n",
    "gcv.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "             max_depth=10, max_features=10, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "             oob_score=True, random_state=42, verbose=0, warm_start=False),\n",
       " 0.9270927092709271)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcv.best_estimator_, gcv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "调参之后，该模型的准确率达到了 92.7%。随机森林最重要的一点是它的准确率不会随着树的增加而下降，这意味着可以使用 10 棵树调整超参数，接着把树的数量从 10 增至 500 来提升准确率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林的方差和去相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林的方差可以用下式表达："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\mathrm{Var} f(x) = \\rho(x)\\sigma^2(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\rho(x) = \\mathrm{Corr}\\left[T(x_1,\\Theta_1(Z)),T(x_2,\\Theta_2(Z))\\right],$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $ \\rho(x)$ 为任意两棵树之间的样本相关性；\n",
    "- $ \\Theta_1(Z)$ 和 $ \\Theta_2(Z)$ 为在样本 $Z$ 的随机元素上，随机选择的一对树；\n",
    "- $ T(x,\\Theta_i(Z))$ 为第 $i$ 个树分类器在输入向量 $x$ 上的输出；\n",
    "- $ \\sigma^2(x)$ 为任何随机选择的树上的样本方差：$\\sigma^2(x) = \\mathrm{Var}\\left[T(x,\\Theta(X))\\right]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很容易将 $ \\rho(x)$ 误认为随机森林中已完成训练的树的平均相关性，其实并非如此。$ \\rho(x)$ 是一对随机树在输入 $x$ 上估计的理论相关性，它的值源自重复取样训练集以及之后随机选择的决策树对。用统计学术语来说，这个相关性是由 $ Z$ 和 $ \\Theta$ 的取样分布导致的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任何一对树的条件相关性等于 0，因为 Bootstrap 和特征选取是独立同分布。如果考虑单棵树的方差，它几乎不受分割参数的影响（$ m$），但分割参数在集成中是关键参数。另外，单棵决策树的方差要比集成高很多，如下图所示：（图来自《The Elements of Statistical Learning》）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032735764\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林的偏差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林、Bagging 的偏差和单棵决策树一样："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\begin{array}{rcl} \\mathrm{Bias} &=& \\mu(x) - \\mathrm{E}_Z \\, f_{rf}(x) \\\\\n",
    "&=& \\mu(x) - \\mathrm{E}_Z \\, \\mathrm{E}_{\\Theta | Z} \\, T(x,\\Theta(Z))\\end{array}$$  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从绝对值上说，随机森林和 Bagging 的偏差通常比单棵决策树要大，因为随机过程和样本空间缩减使得模型受到了限制。因此，Bagging 和随机森林在预测准确率上比单棵决策树要高的原因仅仅源自方差的降低。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  极端随机树"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "极端随机树（Extremely Randomized Trees）在节点分岔时应用了更多随机性。和随机森林一样，极端随机树使用一个随机特征子空间。然而，极端随机数并不搜寻最佳阈值，相反，它为每个可能的特征随机生成一个阈值，然后挑选最佳阈值对应的特征来分割节点，通过增加少量偏差来降低方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn 库的 `ExtraTreesClassifier()` 和 `ExtraTreesRegressor()` 类实现了该方法。如果使用随机森林或梯度提升遇到了严重的过拟合，可以试试极端随机树。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 随机森林和 k 近邻的相似性 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林和 k-近邻方法有相似之处。随机森林的预测基于训练集中相似样本的标签，这些样本越常出现在同一叶节点，它们的相似度就越高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面来证明这一点，考虑一个二次损失函数的回归问题：设 $ T_n(x)$ 为输入 $x$ 在随机森林中第 $n$ 棵树的叶节点数。算法对输入向量 $x$ 的响应等于所有落入叶节点 $ T_n(x)$ 的训练样本的平均响应。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ b_n(x) = \\sum_{i=1}^{l}w_n(x,x_i)y_i,$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  w_n(x, x_i) = \\frac{\\left[T_n(x) = T_n(x_i)\\right]}{\\sum_{j=1}^{l}\\left[T_n(x) = T_n(x_j)\\right]}$$  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "故响应的构成为："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  \\begin{array}{rcl} a_n(x) &=& \\frac{1}{N}\\sum_{n=1}^{N}\\sum_{i=1}^{l}w_n(x,x_i)y_i \\\\\n",
    "&=& \\sum_{i=1}^{l}\\left(\\frac{1}{N}\\sum_{j=1}^{N}w_n(x,x_j)\\right)y_i \\end{array}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上式表明，随机森林的响应为所有训练样本响应的加权和。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时，值得注意的是，实例 $x$ 最终的叶节点数 $ T_n(x)$ 本身，就是一个有价值的特征。例如，我们可以这么做："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 基于随机森林或梯度提升技术在样本上训练决策树的复合模型\n",
    "- 再将类别特征 $ T_1(x), \\dots, T_n(x)$ 加入样本。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ T_1(x), \\dots, T_n(x)$ 特征是非线性空间分割的结果，它们提供了关于样本之间的相似性的信息。《The Elements of Statistical Learning》一书中有一个例子，演示了随机森林和 k-近邻技术的相似性，如下图所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032755393\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 转换数据集为高维表示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林主要用于监督学习，不过也可以应用于无监督学习。使用 scikit-learn 的 `RandomTreesEmbedding()` 方法，可以将数据集转换为高维的稀疏表示，继而用于无监督学习中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先创建一些极端随机树，接着使用包含样本的叶节点索引作为新特征。例如，若第一个叶节点包含输入，就分配 1 为特征值，否则，分配 0。这被称为二进制编码（binary coding），我们可以通过增减树的数目和深度来控制特征数量和稀疏性。由于相近（邻居）的数据点倾向于落入同一叶节点，所以这一转换提供了一个隐式的对数据点密度的非参数估计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  随机森林的优势和劣势"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "优势："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 高预测准确率。在大多数问题上表现优于线性算法，准确率与 Boosting 相当；\n",
    "- 随机取样导致其对离散值的鲁棒性较好；\n",
    "- 随机选取子空间导致其对特征缩放及其他单调转换不敏感；\n",
    "- 不需要精细的参数调整。\n",
    "- 在具有大量特征和类别的数据集上很高效；\n",
    "- 既可处理连续值，也可处理离散值；\n",
    "- 不容易出现过拟合。在实践中，增加树的数量几乎总是能提升总体表现。不过，当树的数量增大到一定值后，学习曲线将趋于平稳；\n",
    "- 有理论方法可以估计特征的重要性；\n",
    "- 能够很好地处理数据缺失。即使有大部分的数据缺失现象，仍能保持较好的准确率；\n",
    "- 支持整个数据集及单棵树样本上的加权分类；\n",
    "- 决策树底层使用的相似性计算可以用于后续的聚类、离散值检测或兴趣数据表示；\n",
    "- 以上功能和性质可以扩展到未标注数据，以支持无监督聚类，数据可视化和离散值检测；\n",
    "- 易于并行化，伸缩性强。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "劣势："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 相比单棵决策树，随机森林的输出更难解释；\n",
    "- 特征重要性估计没有形式化的 p 值；\n",
    "- 在数据稀疏时（比如，文本输入、词袋），表现不如线性模型好；\n",
    "- 和线性回归不同，随机森林无法外推。不过，这样也就不会因为离散值的存在导致极端值的出现；\n",
    "- 在某些问题上容易过拟合，特别是处理高噪声数据时；\n",
    "- 处理数量级不同的类别数据时，随机森林偏重数量级较高的数据，因为这能更明显的提高准确率；\n",
    "- 所得模型较大，需要大容量的 RAM 来支撑。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 特征的重要性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们常常需要对算法的输出结果做出解释，在不能完全理解算法的情况下，至少希望能找出哪个输入特征对结果的贡献最大。基于随机森林，我们可以相当容易地获取这类信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 方法精要"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图很直观地表明，在信用评分问题中，年龄比收入更重要。基于信息增益这一概念，我们可以形式化地解释这一点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557032774721\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在随机森林中，某一特征在所有树中离树根的平均距离越近，这一特征在给定的分类或回归问题中就越重要。按照分割标准，在每棵树的每处最优分割中取得的增益（例如基尼不纯度），是与分割特征直接相关的重要度测度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们深入了解一下。通过计算袋外误差可以判定是哪个变量导致平均准确率下降。哪一变量导致的准确率下降幅度越大，该变量的重要性评分（importance score）就越高。基尼不纯度或回归问题中 MSE 的下降程度代表了每个变量对所得随机森林模型节点的同质性贡献程度，下降较高标志着基于该变量进行分割可以得到纯度更高的节点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上分析可以用下述公式表达："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ VI^{T} = \\frac{\\sum_{i \\in \\mathfrak{B}^T}I \\Big(y_i=\\hat{y}_i^{T}\\Big)}{\\Big |\\mathfrak{B}^T\\Big |} - \\frac{\\sum_{i \\in \\mathfrak{B}^T}I \\Big(y_i=\\hat{y}_{i,\\pi_j}^{T}\\Big)}{\\Big |\\mathfrak{B}^T\\Big |}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $ \\hat{y}_i^{(T)} = f^{T}(x_i)$ 是特征置换或排除之前的类型预测；\n",
    "- $ \\hat{y}_{i,\\pi_j}^{(T)} = f^{T}(x_{i,\\pi_j})$ 是特征置换或排除后的类型预测;\n",
    "- $ x_{i,\\pi_j} = (x_{i,1}, \\dots , x_{i,j-1}, \\quad x_{\\pi_j(i),j}, \\quad x_{i,j+1}, \\dots , x_{i,p})$.\n",
    "- 注意，若 $ x_j$ 不在树 $ T$ 中，那么 $ VI^{T}(x_j) = 0$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，可以给出集成中计算特征重要性的公式："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 未经正则化时的公式："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  VI(x_j) = \\frac{\\sum_{T=1}^{N}VI^{T}(x_j)}{N} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用标准差正则化后的公式："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  z_j = \\frac{VI(x_j)}{\\frac{\\hat{\\sigma}}{\\sqrt{N}}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算现实问题中的特征重要性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们考虑一个数据集，这数据集的内容是 Booking.com 和 TripAdvisor.com 上所列旅馆的一些信息，数据集的特征是不同类别（包括服务质量、房间状况、性价比等）的平均评分，目标变量为旅馆在网站上的总评分。首先导入相关库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble.forest import RandomForestRegressor\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hostel_data = pd.read_csv(\n",
    "    \"../../data/hostel_factors.csv\")\n",
    "features = {\"f1\": u\"Staff\",\n",
    "            \"f2\": u\"Hostel booking\",\n",
    "            \"f3\": u\"Check-in and check-out\",\n",
    "            \"f4\": u\"Room condition\",\n",
    "            \"f5\": u\"Shared kitchen condition\",\n",
    "            \"f6\": u\"Shared space condition\",\n",
    "            \"f7\": u\"Extra services\",\n",
    "            \"f8\": u\"General conditions & conveniences\",\n",
    "            \"f9\": u\"Value for money\",\n",
    "            \"f10\": u\"Customer Co-creation\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用随机森林训练模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor(n_estimators=1000, max_features=10,\n",
    "                               random_state=0)\n",
    "\n",
    "forest.fit(hostel_data.drop(['hostel', 'rating'], axis=1),\n",
    "           hostel_data['rating'])\n",
    "importances = forest.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "标出随机森林模型中的特征重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_to_plot = 10\n",
    "feature_indices = [ind+1 for ind in indices[:num_to_plot]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印特征重要性排名（重要性从高到低排列）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. Staff 0.182757 \n",
      "2. Value for money 0.148373 \n",
      "3. Shared space condition 0.128296 \n",
      "4. Extra services 0.116604 \n",
      "5. Customer Co-creation 0.106668 \n",
      "6. General conditions & conveniences 0.088589 \n",
      "7. Shared kitchen condition 0.074273 \n",
      "8. Check-in and check-out 0.061521 \n",
      "9. Hostel booking 0.053615 \n",
      "10. Room condition 0.039305 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7febd926ad68>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(num_to_plot):\n",
    "    print(\"%d. %s %f \" % (f + 1,\n",
    "                          features[\"f\"+str(feature_indices[f])],\n",
    "                          importances[indices[f]]))\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.title(u\"Feature Importance\")\n",
    "bars = plt.bar(range(num_to_plot),\n",
    "               importances[indices[:num_to_plot]],\n",
    "               color=([str(i/float(num_to_plot+1))\n",
    "                       for i in range(num_to_plot)]),\n",
    "               align=\"center\")\n",
    "ticks = plt.xticks(range(num_to_plot),\n",
    "                   feature_indices)\n",
    "plt.xlim([-1, num_to_plot])\n",
    "plt.legend(bars, [u''.join(features[\"f\"+str(i)])\n",
    "                  for i in feature_indices])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图表明，消费者常常更关心服务人员素质（Staff）和性价比（Value for money)，这两个因子对最终评分的影响最大。然而，这两项特征和其他特征的差别不是非常大，因此排除任何特征都会导致模型准确率的下降。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 梯度提升介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在机器学习中，有一些算法通过常被称为弱模型，这里所说的“弱模型”指的是像决策树这样简单的基本模型，也指的是那些精度相对较差的模型，也就是比随机模型好那么一点点的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先来思考一个 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 问题</i>](http://www.cis.upenn.edu/~mkearns/papers/boostnote.pdf)：是否可以从大量相对较弱和简单的模型中通过某种手段而得到一个强大的模型? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正如你想的一样，这个问题的 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 答案</i>](http://www.cs.princeton.edu/~schapire/papers/strengthofweak.pdf) 显然是肯定的。集成方法就是其中的一类，集成学习的算法有很多，先从最简单的开始：AdaBoost 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " AdaBoost 采用的是一种贪婪的学习方法。在算法学习时，会给数据样本的每个都会附一个权重值，每一个基分类器开始进行分类前，都会根据前一个分类器的分类误差来调节样本的权重值。然后通线性组合的方式把所有的基分类器集成起来得到一个强分类器。整个过程可以描述为下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034245492'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图中可知，在第一棵分类树（t=1）建立之后，会有三个蓝色的数据点被错分。因此在第二棵树建立的时候会增大这三个数据点的权值，以此类推。最终我们将三个基分类器结合起来，就得到了一个完美的分类器。如下图所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034242384' width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "整个过程要是用数学公式描述的话，整个过程如下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 首先，给每个样本一个初始的权重值：$ w_i^{(0)} = \\frac{1}{l}, i = 1, \\dots, l$\n",
    "- 遍历所有 $t = 1, \\dots, T$\n",
    "    * 训练基分类器 $ b_t$, 并令 $\\epsilon_t$ 等于分类误差。\n",
    "    * $\\alpha_t = \\frac{1}{2}ln\\frac{1 - \\epsilon_t}{\\epsilon_t}$\n",
    "    * 更新样本权重: $ w_i^{(t)} = w_i^{(t-1)} e^{-\\alpha_t y_i b_t(x_i)}, i = 1, \\dots, l$\n",
    "    * 归一化样本权重: $ w_0^{(t)} = \\sum_{j = 1}^k w_j^{(t)}, w_i^{(t)} = \\frac{w_i^{(t)}}{w_0^{(t)}}, i = 1, \\dots, l$\n",
    "- 返回 $\\sum_t^{T}\\alpha_tb_t$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当进行迭代时，可以看到误分类点权重的增加，特别是在类之间的边界上。[<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 这里</i>](https://www.youtube.com/watch?v=k4G2VCuOMMg) 有一个关于 AdaBoost 的更详细的例子。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost 分类效果确实不错，但为何这一算法如此成功却 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 缺乏解释</i>](https://www.cs.princeton.edu/courses/archive/spring07/cos424/papers/boosting-survey.pdf)。这会让一些人持怀疑态度，认为 AdaBoost 只是过度拟合而已。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "过度拟合问题也确实存在，尤其是在当数据有非常怪异的异常值时，过拟合现象较为严重。因此，在这些类型的问题中，AdaBoost 是不稳定的。为解决这些问题，1999 年，杰罗姆·弗里德曼( Jerome Friedman )提出了 AdaBoost 的泛化版本：梯度提升( Machine )，也称为 GBM 。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " CART、 bootstrap 和许多其他算法都起源于斯坦福大学的统计部门。这些算法都非常的实用，但也有一些近年来研究工作尚未得到广泛的应用。例如，[<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> glinternet</i>](https://arxiv.org/abs/1308.2719)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在关于弗里德曼的录像资料不多。不过，有一个非常有趣的 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 访谈</i>](https://www.youtube.com/watch?v=8hupHmBVvb0)，关于如何提出 CART ，以及 CART 如何解决统计学问题。Hastie 也有一个很棒的 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 讲座</i>](https://www.youtube.com/watch?v=zBk3PK3g-Fc)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于 AdaBoost 算法，虽然我们可以通过添加弱算法和逐步改进误分类数据的权值来增强集成算法。但是，这还不够，例如 GBM 不仅建立在重新加权的数据点上，而且改进了它对整体目标函数梯度的逼近。这个概念极大地打开了集成算法的研究思路。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557034239100\" width=60%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GBM 的历史"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自从 GBM 诞生以来，经过了 10 多年的时间，它才成为数据科学工具箱中必不可少的一部分。为了应用于不同的统计问题，GBM 拥有许多的扩展版本，例如：GLMboost 和 GAMboost 用于增强已有的 GAM 模型，CoxBoost 用于存活曲线，RankBoost 和 LambdaMART 用于排序问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "GBM 的许多实现也以不同的名称出现在不同的平台上。例如：随机 GBM、GBDT (梯度提升决策树)、GBRT (梯度提升回归树)、MART (多元加性回归树)等等。此外，由于 ML 社区又是各自为营，所以这使我们很难去知道 GBM 到底得到多广泛的应用，到底有多少个版本等问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同时，GBM 也在搜索排序中也得到了广泛的应用。GBM 的加入使得搜索排序被看作为一个优化损失函数问题。最初，AltaVista 是第一批引入提升排名的公司之一。但很快，这些思路就传播到了雅虎、Yandex、必应等网站。从此之后，GBM 成为了主流的算法之一，不仅用于研究，而且也广泛用于工业生产中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='200px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034236116'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "许多的机器学习竞赛，尤其是 Kaggle，在促进普及算法的应用方面发挥了重要作用。因为 Kaggle 提供了一个共同的平台，这使得数据科学家们可以在不同的数据科学问题上与来自世界各地的大量参与者竞争。人们可以在 Kaggle上用真实数据上测试新的算法，让许多算法有机会“发光”。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自 2011 年以来，在 Kaggle 的冠军访谈中，许多的获奖选手都提到自己使用了梯度提升算法。这也使得梯度提升变得流行起来。尤其是在 XGBoost 库出现之后。XGBoost 虽然不是一个新的、独特的算法，但其是一个经典 GBM 的高效实现版。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GBM 的经历与许多机器学习算法一样：从被提出到成功的实践应用和大规模使用都花了许多年的时间。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBM 算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先来看一个经典的监督学习问题，假设这里有一份数据集 $ \\left\\{ (x_i, y_i) \\right\\}_{i=1, \\ldots,n}$ , $ x $ 表示特征值，$y$ 表示目标值。构建模型就相当于构建一个函数  $f(x)$ ，用 $f(x)$ 去逼近目标值 $ y$。定义损失函数 $L(y,f(x))$ 来表示预测值$f(x)$ 与真实值 $y$ 之间的距离。通过最小化损失函数，就可以得到："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ y \\approx \\hat{f}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = \\underset{f(x)}{\\arg\\min} \\ L(y,f(x)) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"600px\" src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034233029'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "在这里先不管 $f(x)$ 是具体哪一个模型或某个具体的表达式，仅假定损失函数 $L(y,f(x))$ 是可微的。如果损失函数取的是整个样本的均值，则最小化损失函数可以表达为："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = \\underset{f(x)}{\\arg\\min} \\ \\mathbb {E} _{x,y}[L(y,f(x))]  $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般情况下， $f(x)$ 的选择有很多，这意味着寻找最优的 $f(x)$ 会变得很困难。所以要对 $f(x)$ 加以限制，将其限制在一个函数空间内。假设模型的参数为 $\\theta $，定义 $\\theta \\in \\mathbb{R}^d $，则模型可描述为  $ f(x, \\theta)$。所以现在寻找最优的 $f(x)$ 相当于要寻找一组参数 $\\theta $ ，使得损失函数 $L(y,f(x))$ 达到最小。用公式描述如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = f(x, \\hat{\\theta})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{\\theta} = \\underset{\\theta}{\\arg\\min} \\ \\mathbb {E} _{x,y}[L(y,f(x,\\theta))] $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于 $f(x)$  相对比较复杂。一般情况下，解析解不存在或无法直接求得。所以要使用迭代的方法来逼近参数。定义经验损失函数为 $ L_{\\theta}(\\hat{\\theta}) $ ，其计算公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{\\theta} = \\sum_{i = 1}^M \\hat{\\theta_i} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ L_{\\theta}(\\hat{\\theta}) =  \\sum_{i = 1}^N L(y_i,f(x_i, \\hat{\\theta}))$$  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后,寻找一个合适的优化算法来最小化 $L_{\\theta}(\\hat{\\theta})$ 。梯度下降是最简单和最常用的优化方法。不过，在使用梯度下降算法时，需要参数进行初始化 $\\hat{\\theta_0}$。并定义迭代次数 $M$。整个优化过程如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 定义参数的初始值 $\\hat{\\theta} = \\hat{\\theta}_0$\n",
    "2. 对于每次迭代 $t = 1, \\dots, M$ 重复步骤 3 到 7 :\n",
    "1. 为当前的近似 $\\hat{\\theta}$ 计算损失函数的梯度值 \n",
    "$\\nabla L_{\\theta}(\\hat{\\theta}) = \\left[\\frac{\\partial L(y, f(x, \\theta))}{\\partial \\theta}\\right]_{\\theta = \\hat{\\theta}}$\n",
    "2. 通过计算梯度来设置当前的近似\n",
    "$\\hat{\\theta_t} \\leftarrow −\\nabla L_{\\theta}(\\hat{\\theta})$\n",
    "3. 更新参数 \n",
    "$\\hat{\\theta} \\leftarrow \\hat{\\theta} + \\hat{\\theta_t} = \\sum_{i = 0}^t \\hat{\\theta_i} $\n",
    "3. 保存近似结果 \n",
    "$\\hat{\\theta} = \\sum_{i = 0}^M \\hat{\\theta_i} $\n",
    "4. 得到最终的函数 $\\hat{f}(x) = f(x, \\hat{\\theta})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过梯度下降优化模型的直观过程如下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='700px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034230172'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 函数梯度下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在上述的推导过程中，是通过不断的迭代来更新逼近函数 $ \\hat{f}(x)$ 。在函数梯度下降中，将这种逼近表述为增量改进之和，每个改进都是一个函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = \\sum_{i = 0}^M \\hat{f_i}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里假设所有的 $\\hat{f_i}(x)$ 被限制在某个函数空间中，即 $\\hat{f}(x) = h(x, \\theta)$。最终的模型可能比这个函数空间中的任何模型都要复杂。在每一步迭代中，需要选择最优系数 $\\rho_t$和 $\\theta_t$ 来使 $\\hat{f_t}(x)$ 达到最优。迭代过程如下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = \\sum_{i = 0}^{t-1} \\hat{f_i}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ (\\rho_t,\\theta_t) = \\underset{\\rho,\\theta}{\\arg\\min} \\ \\mathbb {E} _{x,y}[L(y,\\hat{f}(x) +  \\rho \\cdot h(x, \\theta))]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f_t}(x) = \\rho_t \\cdot h(x, \\theta_t)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里仅以一般形式定义了模型，如果知道了损失函数的梯度表达式，就可以通过训练数据来优化模型，使其模型的预测结果和梯度更相关。换句话说，我们将基于预测结果和这些残差的最小平方差来优化参数。因此，模型的优化可以使用以下公式描述："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\hat{f}(x) = \\sum_{i = 0}^{t-1} \\hat{f_i}(x)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ r_{it} = -\\left[\\frac{\\partial L(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x)=\\hat{f}(x)}, \\quad \\mbox{for } i=1,\\ldots,n $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\theta_t = \\underset{\\theta}{\\arg\\min} \\ \\sum_{i = 1}^{n} (r_{it} - h(x_i, \\theta))^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\rho_t = \\underset{\\rho}{\\arg\\min} \\ \\sum_{i = 1}^{n} L(y_i, \\hat{f}(x_i) + \\rho \\cdot h(x_i, \\theta_t))$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='300px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034227177'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Friedman 的经典 GBM 算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 1999 年, Jerome Friedman 提出了一种经典的 GBM 算法。它是一种监督算法，其组成部分如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据集 $ \\left\\{ (x_i, y_i) \\right\\}_{i=1, \\ldots,n}$\n",
    "- 迭代次数 $M$\n",
    "- 损失函数 $ L(y, f)$\n",
    "- 函数空间 $h(x, \\theta)$ 以及训练过程。\n",
    "- 函数空间 $ h(x, \\theta)$ 的超参数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为寻找最优 $f_t(x)$ 需要知道 $f_{t-1}(x)$ 。那么 $ f_0(x)$ 是如何计算的呢？ 一般情况下，使用一个常数 $ \\gamma$ 和 最优系数 $\\rho $ 来搜索最优的  $ f_0(x)$ 。整个 GBM 运行过程如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 使用常数值初始化 GBM $ \\hat{f}(x) = \\hat{f}_0, \\hat{f}_0 = \\gamma,  \\gamma \\in \\mathbb{R}$，$ \\hat{f}_0 = \\underset{\\gamma}{\\arg\\min} \\ \\sum_{i = 1}^{n} L(y_i, \\gamma)$\n",
    "2. 对于每次迭代 $ t = 1, \\dots, M$，重复逼近过程。\n",
    "3. 计算伪残查 $r_t$，$r_{it} = -\\left[\\frac{\\partial L(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x)=\\hat{f}(x)}, \\quad \\mbox{for } i=1,\\ldots,n$\n",
    "4. 通过伪残差 $\\left\\{ (x_i, r_{it}) \\right\\}_{i=1, \\ldots,n}$  构建基算法$h_t(x)$\n",
    "5. 通过 $ h_t(x)$ 寻找最优系数。\n",
    "$\\rho_t = \\underset{\\rho}{\\arg\\min} \\ \\sum_{i = 1}^{n} L(y_i, \\hat{f}(x_i) +  \\rho \\cdot h(x_i, \\theta))$\n",
    "6. 保存 $\\hat{f_t}(x) = \\rho_t \\cdot h_t(x)$\n",
    "7. 更新 $\\hat{f}(x) \\leftarrow \\hat{f}(x) + \\hat{f_t}(x) = \\sum_{i = 0}^{t} \\hat{f_i}(x)$\n",
    "\n",
    "8. 组成最终的模型 $\\hat{f}(x) = \\sum_{i = 0}^M \\hat{f_i}(x) $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GBM 工作原理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前面主要讲述了 GBM 的数学描述。现在通过例子来详细说明其是如何工作的。在这个例子中，需要拟合一个带噪声的函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ y = cos(x) + \\epsilon, \\epsilon \\sim \\mathcal{N}(0, \\frac{1}{5}), x \\in [-5,5]$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src=\"https://doc.shiyanlou.com/courses/uid214893-20190505-1557034099295\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是一个回归问题，所以损失函数选择均方误差损失函数。生成 300 对观测数据，并使用深度为 2 的决策树进行近似。 GBM 的参数设置如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据集 $\\left\\{ (x_i, y_i) \\right\\}_{i=1, \\ldots,300}$\n",
    "- 迭代次数 $M = 3$\n",
    "- 均方差损失函数 $L(y, f) = (y-f)^2$\n",
    "-  $ L(y, f) = L_2$ 的梯度以及残差 $r = (y - f)$\n",
    "- 基算法决策树 $ h(x)$\n",
    "- 决策树的超参数，树的深度为2。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于均方差损失函数来说，初始化常数 $\\gamma$ 和系数  $\\rho_t$ 非常简单。仅需要将 $\\rho_t$ 设置为 1，而 $\\gamma$ 设置为下式即可。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "$$ \\gamma = \\frac{1}{n} \\cdot \\sum_{i = 1}^n y_i $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行 GBM 算法，并绘制两种图形：如下图所示，蓝色的图表示当前拟合模型；绿色的图表示在伪残差上构建的每棵树。图的编号对应于迭代次数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034096158'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看到，到第二次迭代时，模型已经能够拟合出数据的基本形式。然而，在第一次迭代时，算法只拟合了 $x \\in [-5, -4]$ 之间的数据。这是由于决策树模型没有足够的深度来同时构建一个对称的分支，并且它首先关注误差较大的左边的分支上。因此，右分支只出现在第二次迭代之后。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图显示的迭代过程可知，整个过程正如预期的那。在每一步中，伪残差都在减小，而 GBM 在每次迭代中都越来越好地逼近原始数据。但是，决策树本身的构造使其不能近似连续函数，这意味着在本例中 GBM 不是最理想的。如果想要直观地理解 GBM 模型的拟合过程，可以通过一些可视化工具来进行可视化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034093292'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果想要求解一个分类问题，而不是回归问题，那应该怎么做呢？首先选择一个合适的损失函数 $L(y,f)$ 很重要。因为损失函数决定了模型将如何优化，以及最终会得到什么样的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般来说， 并不需要自己去发明一个损失函数，这些工作研究人员已经替我们做了。现在来探索用于最常见的两种目标的损失函数：回归和二分类。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 回归损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于回归问题，通常对于不同的问题，不同的数据，选择的损失函数也不同。最常见的回归损失函数是"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  $ L_2$ 损失或高斯损失函数。该损失函数假定数据服从高斯分布。如果没有额外信息，也不要求模型的鲁棒性，那么可以使用该损失函数。公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ L(y, f) = (y - f)^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $L_1$ 损失或拉普拉斯损失。事实上它定义了条件中位数。通常，中位数对离群值的鲁棒性更好，所以这一损失函数在某些情形下也表现更好。但对大偏差的惩罚不像 $ L_2$ 那么大。公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ L(y, f) = |y - f|$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $ L_q$ 损失或分位数损失。与拉普拉斯损失相似， 只不过 $L_q$ 使用分位数而不是中位数。公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\begin{equation}  L(y, f) =\\left\\{   \\begin{array}{@{}ll@{}}     (1 - \\alpha) \\cdot |y - f|, & \\text{if}\\ y-f \\leq 0 \\\\     \\alpha \\cdot |y - f|, & \\text{if}\\ y-f >0  \\end{array}\\right. \\end{equation}, \\alpha \\in (0,1)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这三种损失函数的函数图如下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034090224'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用 $L_q$ 损失函数来进行实验，继续使用前文所构建的数据。GBM 的实验参数设置如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据 $\\left\\{ (x_i, y_i) \\right\\}_{i=1, \\ldots,300}$\n",
    "- 迭代次数 $ M = 3$\n",
    "- 损失函数 $  \\begin{equation} L_{0.75}(y, f) =\\left\\{\n",
    "\\begin{array}{@{}ll@{}} 0.25 \\cdot |y - f|, & \\text{if}\\ y-f \\leq 0 \\\\     0.75 \\cdot |y - f|, & \\text{if}\\ y-f >0   \\end{array}\\right. \\end{equation} $\n",
    "- 梯度 $L_{0.75}(y, f)$, $\\alpha = 0.7$\n",
    "- 基算法 $ h(x)$ 为决策树。\n",
    "- 决策树超参数：depth = 2。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一次逼近需要 $y$ 的分位数，但这里并不知道最佳参数该取什么值，所以使用标准的线搜索来求出。模型迭代过程如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034189167'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到，使用分位数作为损失函数的 GBM 能够很好的拟合初始数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常情况下， 使用 $L_q$ 损失函数的 GBM 的结果等价于使用 $L_2$ 损失函数的 GBM 加上约 0.135 的偏置。但是如果使用的是 90% 分位数的话，那我们将没有足够的数据，因为样本类别变得极不平衡。在处理非标准问题时，需要记住这一点。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "人们为回归任务开发了许多损失函数。例如，鲁棒性极强的 [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> Huber 损失函数</i>](https://en.wikipedia.org/wiki/Huber_loss)。对于少数异常值，Huber 损失函数的工作方式是 $ L_2$，但异常值超过阈值之后，损失函数将更改为$L_1$。这会减少异常值的影响，并更加关注整体情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了更好理解损失函数，通过一个例子来说明。首先，实验数据由函数 $ y = \\frac{sin(x)}{x}$ 生成，并添加来自正常分布和 Bernulli 分布的混合噪声。图 A-D 显示的是损失函数，图 F-H 显示的是相应的 GBM 拟合过程。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='600px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034086503'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个例子中，使用样条曲线作为基算法。这也说明了，梯度提升中的基算法不一定是决策树。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图中可以清楚地看到 $L_2$ 损失函数、$L_1$ 损失函数和 Huber 损失函数之间的区别。如果我们为 Huber 损失函数选择最优的参数，则可以得到更好的拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然 Huber 损失函数的效果很好。但是，Huber 损失函数只被非常少的流行库支持。例如，XGBoost 不支持。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，让我们看看二进制分类问题 $ y \\in \\left\\{- 1,1 \\right\\}$。从技术上讲，此类问题也可以使用 $ L_2$ 损失来解决这个问题，但是这不是正规的做法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目标变量的分布 $ y \\in \\left\\{- 1,1 \\right\\}$ 要求损失函数需要使用对数似然，常见的对数似然损失函数如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 逻辑损失或伯努利损失。该损失函数不仅要优化损失，同时要类别之间区别得更开。所以它甚至会惩罚正确预测的分类。公式如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ L(y, f) = log(1 + exp(-2yf))$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- AdaBoost 损失。 与逻辑损失相似，但对错误的预测有更为严重的指数惩罚。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ L(y, f) = exp(-yf)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034017468'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在来看一个例子。使用符号函数构建实验数据，如下图所示："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='500px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034013933'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先来探索将逻辑损失作为损失函数的模型。GBM 的实验参数如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 实验数据：$\\left\\{ (x_i, y_i) \\right\\}_{i=1, \\ldots,300}, y_i \\in \\left\\{-1, 1\\right\\}$ \n",
    "- 迭代次数：$M = 3$\n",
    "- 损失函数为逻辑损失函数, 其梯度为：$ r_{i} = \\frac{2 \\cdot y_i}{1 + exp(2 \\cdot y_i \\cdot \\hat{f}(x_i)) }, \\quad \\mbox{for } i=1,\\ldots,300$\n",
    "- 基算法为决策树：$ h(x)$\n",
    "- 决策树的超参数：depth = 2。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个例子中，算法的初始化有点困难。首先，数据的类别不平衡，负类为 63%，正类为 37%。其次，损失函数的初始化没有已知的解析公式，所以只能通过搜索来查找 $ \\hat{f_0} = \\gamma$，不同 $\\gamma$ 值对应的损失函数值如下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034009601'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可知，最优初始近似是 -0.273 左右。你可能已经猜到它是负数，因为样本中负类的数量要比正类多很多。接下来对 GBM 模型进行优化，过程如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034006567'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上图所示，该算法成功地将红色点数据和蓝色点数据分离。你可以看到“较低”区域是如何分隔的，因为决策树更容易正确预测负类。此外，还可以看到模型如何拟合混合分类。很明显，模型得到了大量正确分类的观测，一些误差较大的观测则可能是数据中的噪声所导致的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 权重"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有时候，我们需要一个更具体的损失函数来解决问题。例如，在金融时间序列中，我们可能想要给予时间序列中较大的波动更大的权重。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557034003691'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有些人可能想要发明他们自己的损失函数，写出它的梯度，并仔细检查这个函数是否满足所需的属性。然而，有很大的几率会在某处犯下错误，导致计算困难，花费过量时间来排除错误。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "针对这一问题，人们提出了一种非常简单的做法：对数据或预测结果进行加权。例如，如果知道数据的某个子集对模型更重要，那么可以直接给它们分配较大的权重 $w(x, y)$。权重通常需满足以下条件："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ w_i \\in \\mathbb{R}, $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ w_i \\geq 0 \\quad \\mbox{for } i=1,\\ldots,n$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\sum_{i = 1}^n w_i > 0 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加权能够显著降低调整损失函数的时间。此外，这还可以充分发挥你的创造性。比如，简单地给损失函数或残差加上标量权重："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$  L_{w}(y,f) = w \\cdot L(y,f)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ r_{it} =   - w_i \\cdot \\left[\\frac{\\partial L(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x)=\\hat{f}(x)} \\quad \\mbox{for } i=1,\\ldots,n$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很明显，对于任意权重，我们并不知道模型的统计学性质。通常，将权重与 $y$ 值联系起来可能会变得过于复杂。例如，使用正比于 $|y|$ 的权重在 $L1$ 损失和 $L2$ 损失中是不等价的，因为梯度不考虑预测本身的值: $\\hat{f}(x)$。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先通过定义一个非对称的加权函数来为数据添加一些非常奇特的权重。函数表达式如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\begin{equation} w(x) =\\left\\{   \\begin{array}{@{}ll@{}}     0.1, & \\text{if}\\ x \\leq 0 \\\\     0.1 + |cos(x)|, & \\text{if}\\ x >0 \\end{array}\\right. \\end{equation} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557033837228'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过这些权重，我们希望得到两个属性: $x$ 的负值细节更少和形成类似于初始余弦的函数。这里使用上一个例子的 GBM 模型，得到的结果如下:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557033832894'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知，基本达到了预期的结果。首先，第一次迭代上的伪残差有着很大的误差，它们看起来几乎像原本的余弦函数，如 0.E 图所示。其次，函数图的左侧往往被忽略，而右侧的权重更大，这也与我们给予数据的权重相吻合。最后，第三次迭代得到的函数看起来和原本的余弦函数类似，同时也开始有点过拟合了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加权是一个强大但有风险的工具。可以使用它来控制模型的属性。如果你想优化损失函数，可以先尝试对观测结果进行加权。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本文中，我们有意省略了有关 GBM 正则化、随机性和超参数的问题，且在整个过程中仅使用了少 3 次迭代，这也并非偶然。如果使用 30 棵树而不是 3 棵，模型可能会因为复杂都过高而过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般情况下，良好的拟合如下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557033829216'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果模型在训练时过度关注那些异常值点，这往往也会导致其过拟合，如下图所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557033825292'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30 棵决策树的拟合结果如下图所示，在训练集上得损失为 0 ，而在测试集上得损失为 0.664。这说明模型在训练集上过拟合了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width='400px' src='https://doc.shiyanlou.com/courses/uid214893-20190505-1557033819881'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本次实验简要介绍了机器学习中一个重要的技术：集成学习，文中介绍的 Bootstraping、Bagging、随机森林都是较为成熟好用的方法。通过随机森林衡量特征的重要性，能更好的帮助大家分析自己所构建的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i class=\"fa fa-link\" aria-hidden=\"true\"> 相关链接</i>\n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 统计学习</i>](https://statweb.stanford.edu/~tibs/ElemStatLearn/)\n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 随机森林原论文</i>](https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf)\n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> GBM 教程</i>](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885826/)\n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 梯度提升树介绍</i>](http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf) \n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 梯度提升-维基百科</i>](https://en.wikipedia.org/wiki/Gradient_boosting)\n",
    "- [<i class=\"fa fa-external-link-square\" aria-hidden=\"true\"> 了解实验楼《楼+ 机器学习和数据挖掘课程》</i>](https://www.shiyanlou.com/louplus/)"
   ]
  }
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