ch12_update_class.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xjMlgLlGonn"
   },
   "source": [
    "# 12장 파이썬 모델링 라이브러리\n",
    "* 모델 fitting\n",
    "* 모델 scoring\n",
    "* 데이터 cleaning\n",
    "* 모델링 도구\n",
    "  * statsmodels\n",
    "    * https://www.statsmodels.org/stable/index.html\n",
    "  * scikit-learn(사이킷런)\n",
    "    * https://scikit-learn.org/stable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOoe49rBITKr"
   },
   "source": [
    "## 12.1 판다스와 모델 코드의 인터페이스\n",
    "* 모델 개발 과정\n",
    "\n",
    "1. 데이터 로딩: 판다스를 사용하여 데이터를 로드하고 데이터프레임으로 변환\n",
    "\n",
    "2. 데이터 전처리: 판다스를 사용하여 데이터를 전처리\n",
    "  * 피처 엔지니어링(feature engineering)\n",
    "    \n",
    "3. 모델 훈련: 판다스 데이터프레임을 모델 훈련에 사용\n",
    "\n",
    "4. 모델 평가: 판다스를 사용하여 모델 성능을 평가\n",
    "\n",
    "5. 모델 예측: 판다스 데이터프레임을 사용하여 모델 예측\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### feature engineering\n",
    "* 기계 학습 모델의 성능을 향상시키기 위해 데이터의 원시 특징을 변환하는 프로세스\n",
    "* 데이터를 더욱 정보량이 풍부하고 예측하기 쉽게 만드는 데 도움이 도움이 됨.\n",
    "* 피처 엔지니어링 기술\n",
    "  * 결측값 처리: 결측값은 모델 성능에 부정적인 영향을 줌.\n",
    "    * 결측값을 평균, 중앙값 또는 최빈값으로 대체\n",
    "    * 결측값을 포함하는 행을 삭제\n",
    "    * 결측값을 예측하는 모델을 훈련\n",
    "\n",
    "  * 이상치 처리: 이상치는 모델 성능에 부정적인 영향을 줌\n",
    "    * 이상치를 처리하는 방법\n",
    "      * 이상치를 제거\n",
    "      * 이상치를 변환\n",
    "      * 이상치를 예측하는 모델을 훈련\n",
    "\n",
    "  * 피처 스케일링: 피처 스케일링은 모든 피처가 동일한 범위에 있도록 보장하는 프로세스\n",
    "    * 모델이 모든 피처에 동일한 가중치를 부여하는 데 도움\n",
    "    * 피처 스케일링 기술\n",
    "      * 표준화: 피처의 평균을 0으로, 표준 편차를 1로 조정\n",
    "      * 정규화: 피처의 값이 0과 1 사이에 있도록 조정\n",
    "\n",
    "  * 피처 변환: 피처 변환은 피처의 분포를 변경하는 프로세스\n",
    "    * 이는 모델이 피처 간의 관계를 더 잘 학습하는 데 도움\n",
    "    * 피처 변환 기술에\n",
    "      * 로그 변환: 피처의 값을 로그로 변환\n",
    "      * 제곱근 변환: 피처의 값을 제곱근으로 변환\n",
    "      \n",
    "  * 피처 선택: 피처 선택은 모델에 사용할 피처를 선택하는 프로세스\n",
    "    * 이는 모델의 성능을 향상시키고 과적합을 줄이는 데 도움이 됨\n",
    "    * 피처 선택 기술\n",
    "      * 상관 관계 분석: 상관 관계가 높은 피처를 제거\n",
    "      * 통계적 유의성 검정: 통계적으로 유의미하지 않은 피처를 제거\n",
    "      * 머신 러닝 알고리즘: 피처 중요도를 평가하는 머신 러닝 알고리즘을 사용하여 피처를 선택    "
   ]
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   "source": [
    "## 교차 검증(Cross-validation)\n",
    "* 교차 검증은 머신러닝 모델의 성능을 평가하는 방법 중 하나\n",
    "* 교차 검증은 데이터셋을 여러 개의 폴드(fold)로 나누고, 각 폴드를 한 번씩 평가 데이터로 사용하는 방법\n",
    "* 예를 들어, 데이터셋을 5개의 폴드로 나누고 교차 검증을 사용하여 모델의 성능을 평가하면 다음과 같다.\n",
    "  * 데이터셋을 5개의 폴드로 나눈다\n",
    "  * 첫 번째 폴드를 평가 데이터로 사용하고, 나머지 4개의 폴드를 학습 데이터로 사용하여 모델을 훈련시킨다.\n",
    "  * 훈련된 모델을 사용하여 평가 데이터에 대한 예측을 만든다.\n",
    "  * 예측과 실제 값을 비교하여 모델의 성능을 평가함\n",
    "  * 두 번째 폴드를 평가 데이터로 사용하고, 나머지 4개의 폴드를 학습 데이터로 사용하여 모델을 훈련시킴.\n",
    "  * 훈련된 모델을 사용하여 평가 데이터에 대한 예측을 만든다. 예측과 실제 값을 비교하여 모델의 성능을 평가한다.\n",
    "  * 이 과정을 모든 폴드에 대해 반복한다.\n",
    "\n",
    "* 교차 검증은 다음과 같은 장점이 있다.\n",
    "  1) 모델의 성능을 정확하게 평가할 수 있다.\n",
    "  2) 과적합을 방지할 수 있다.\n",
    "  3) 하이퍼파라미터 튜닝에 사용할 수 있다.\n",
    "\n",
    "* 교차 검증은 다음과 같은 단점이 있다.\n",
    "  * 시간이 오래 걸린다.\n",
    "  * 계산 자원이 많이 필요\n",
    "\n"
   ]
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   "metadata": {
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   "cell_type": "markdown",
   "metadata": {
    "id": "2Qho82NBB2pK"
   },
   "source": [
    "* DataFrame -> Numpy Array 변환\n",
    "  * df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
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     "output_type": "stream",
     "text": [
      "   x0    x1    y\n",
      "0   1  0.01 -1.5\n",
      "1   2 -0.01  0.0\n",
      "2   3  0.25  3.6\n",
      "3   4 -4.10  1.3\n",
      "4   5  0.00 -2.0\n",
      "\n",
      "[[ 1.    0.01 -1.5 ]\n",
      " [ 2.   -0.01  0.  ]\n",
      " [ 3.    0.25  3.6 ]\n",
      " [ 4.   -4.1   1.3 ]\n",
      " [ 5.    0.   -2.  ]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# ex\n",
    "data = pd.DataFrame({\n",
    "    'x0': [1, 2, 3, 4, 5],\n",
    "    'x1': [0.01, -0.01, 0.25, -4.1, 0.],\n",
    "    'y': [-1.5, 0., 3.6, 1.3, -2.]})\n",
    "print(data)\n",
    "print()\n",
    "print(data.to_numpy())\n",
    "print(type(data.to_numpy())) #numpy 객체는 한 가지 데이터타입(모두 숫자형)으로 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CKnE7DnaSk-2"
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eqQ7q0aJCd3J"
   },
   "source": [
    "* Numpy array -> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "executionInfo": {
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     "status": "ok",
     "timestamp": 1715637521837,
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   one   two  three\n",
       "0  1.0  0.01   -1.5\n",
       "1  2.0 -0.01    0.0\n",
       "2  3.0  0.25    3.6\n",
       "3  4.0 -4.10    1.3\n",
       "4  5.0  0.00   -2.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ex\n",
    "df2 = pd.DataFrame(data.to_numpy(), columns=['one', 'two', 'three'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C76TYgPTHJj9"
   },
   "source": [
    "* 전체 넘파이 ndarray에서 일부 배열만 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1715637530650,
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    "id": "q8g1oxe7uiH2",
    "outputId": "940839d8-3cad-4d76-c6ce-f1b6191c1573"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    0.01 -1.5 ]\n",
      " [ 2.   -0.01  0.  ]\n",
      " [ 3.    0.25  3.6 ]\n",
      " [ 4.   -4.1   1.3 ]\n",
      " [ 5.    0.   -2.  ]]\n",
      "[[ 1.    0.01]\n",
      " [ 2.   -0.01]\n",
      " [ 3.    0.25]\n",
      " [ 4.   -4.1 ]\n",
      " [ 5.    0.  ]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# ex\n",
    "df3 = data.copy() # 데이터프레임 객체 복사\n",
    "print(df3.to_numpy()) # 파이썬 객체  ndarray 2차원 배열 . 15개 저장\n",
    "\n",
    "## 입력 데이터 추출 => 2차원 배열\n",
    "df_X = df3.to_numpy()[:, :2]\n",
    "print(df_X)\n",
    "print(type(df_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 275,
     "status": "ok",
     "timestamp": 1715637534954,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "o56c3nn5vQMv",
    "outputId": "5a4c4650-d50b-4e9f-fd81-a2e4f280c022"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.5  0.   3.6  1.3 -2. ]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# 출력 데이터 추출\n",
    "df_y = df3.to_numpy()[:, -1]\n",
    "print(df_y)\n",
    "print(type(df_y)) # 1차원 벡터"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MXX7L3ThJPys"
   },
   "source": [
    "# 7.5 범주형 데이터(p.325)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ZDPyFxZOOoo"
   },
   "source": [
    "* 모델링을 위한 더미 변수 생성하기\n",
    "  * 통계나 머신러닝 도구를 사용하다 보면 범주형 데이터를 원-핫 인코딩(one-hot encoding)이라고 부르는 더미 변수(가변수, dummy variable)로 변환\n",
    "  * 범주를 열로 갖는 DataFrame을 생성하는데, 각 열에는 해당 범주의 발생 여부에 따라 0과 1의 값을 갖는다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1QDaXnOlRnnp"
   },
   "source": [
    "* pd.Categorical() 함수\n",
    "  *  판다스 시리즈나 데이터프레임에서 범주형 데이터 타입을 생성하는 데 사용\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "height": 206
    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1715637543315,
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    "outputId": "dca3efbb-cdd7-4c3f-af76-bfe38132a4d8"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype   \n",
      "---  ------    --------------  -----   \n",
      " 0   x0        5 non-null      int64   \n",
      " 1   x1        5 non-null      float64 \n",
      " 2   y         5 non-null      float64 \n",
      " 3   category  5 non-null      category\n",
      "dtypes: category(1), float64(2), int64(1)\n",
      "memory usage: 377.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# ex\n",
    "data = pd.DataFrame({\n",
    "    'x0': [1, 2, 3, 4, 5],\n",
    "    'x1': [0.01, -0.01, 0.25, -4.1, 0.],\n",
    "    'y': [-1.5, 0., 3.6, 1.3, -2.]})\n",
    "\n",
    "data['category'] = pd.Categorical(['a', 'b', 'a', 'a', 'b'],\n",
    "                                  categories=['a', 'b'])\n",
    "data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_0bcUW3SqLg"
   },
   "source": [
    "* pd.get_dummies() 함수\n",
    "  * 판다스에서 범주형 데이터를 원-핫 인코딩된 피처로 변환하는 데 사용.\n",
    "  \n",
    "  * 인자\n",
    "    * prefix: 이 옵션은 원-핫 인코딩된 피처 이름에 사용할 접두사를 지정\n",
    "    * prefix_sep: 이 옵션은 접두사와 원-핫 인코딩된 피처 이름 사이에 사용할 구분자를 지정\n",
    "    * drop_first: 이 옵션은 범주형 데이터의 첫 번째 레벨을 삭제할지 여부를 지정\n",
    "    \n",
    "  * 원-핫 인코딩은 범주형 데이터를 일련의 이진 피처로 나타내는 기술\n",
    "\n",
    "* 원-핫 인코딩은 다음과 같은 다양한 작업에 유용.\n",
    "  * 머신러닝: 원-핫 인코딩된 피처를 사용하여 머신러닝 모델을 학습할 수 있다.\n",
    "  * 데이터 분석: 원-핫 인코딩된 피처를 사용하여 범주형 데이터에 대한 통계 분석을 수행할 수 있다.\n",
    "  * 데이터 시각화: 원-핫 인코딩된 피처를 사용하여 정보성이 풍부하고 시각적으로 매력적인 시각화를 만들 수 있다.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1715637554597,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "qvODX9oNGfrd",
    "outputId": "b7bf9045-4131-408b-ee1f-dcdf0c732f48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x0    x1    y category\n",
      "0   1  0.01 -1.5        a\n",
      "1   2 -0.01  0.0        b\n",
      "2   3  0.25  3.6        a\n",
      "3   4 -4.10  1.3        a\n",
      "4   5  0.00 -2.0        b\n",
      "\n",
      "   d_a  d_b\n",
      "0  1.0  0.0\n",
      "1  0.0  1.0\n",
      "2  1.0  0.0\n",
      "3  1.0  0.0\n",
      "4  0.0  1.0\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>y</th>\n",
       "      <th>d_a</th>\n",
       "      <th>d_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x0    x1    y  d_a  d_b\n",
       "0   1  0.01 -1.5  1.0  0.0\n",
       "1   2 -0.01  0.0  0.0  1.0\n",
       "2   3  0.25  3.6  1.0  0.0\n",
       "3   4 -4.10  1.3  1.0  0.0\n",
       "4   5  0.00 -2.0  0.0  1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ex\n",
    "print(data)\n",
    "print()\n",
    "dummies = pd.get_dummies(data.category, prefix='d',\n",
    "                         dtype=float)\n",
    "print(dummies)\n",
    "print(type(dummies))\n",
    "\n",
    "# data_with_dummies = data.drop('category', axis=1).join(dummies)\n",
    "data_with_dummies = pd.concat([data.drop('category', axis=1), dummies], axis =1)\n",
    "\n",
    "data_with_dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VR39TonHXu1D"
   },
   "source": [
    "## 12.3 statsmodels 소개\n",
    "* https://www.statsmodels.org\n",
    "* 다양한 종류의 통계 모델 피팅, 통계 테스트 수행, 데이터 탐색, 시각화를 위한 파이썬 라이브러리\n",
    "* pip install statsmodels\n",
    "* import statsmodels.api as sm\n",
    "* import statsmodels.formula.api as smf\n",
    "* statsmodels 모델\n",
    "  * 선형 모델, 일반 선형 모델, 로버스트 선형 모델\n",
    "  * 선형 혼합 효과 모델\n",
    "  * 분산분석ANOVA 메서드\n",
    "  * 시계열 처리 및 상태공간 모델\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8321,
     "status": "ok",
     "timestamp": 1715637572844,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "Q4jfHaSuXuPq",
    "outputId": "63468724-fde4-4e05-a959-331544970aa3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\python39\\lib\\site-packages (0.14.1)\n",
      "Requirement already satisfied: numpy<2,>=1.18 in c:\\python39\\lib\\site-packages (from statsmodels) (1.26.4)\n",
      "Requirement already satisfied: scipy!=1.9.2,>=1.4 in c:\\python39\\lib\\site-packages (from statsmodels) (1.12.0)\n",
      "Requirement already satisfied: pandas!=2.1.0,>=1.0 in c:\\python39\\lib\\site-packages (from statsmodels) (2.2.1)\n",
      "Requirement already satisfied: patsy>=0.5.4 in c:\\python39\\lib\\site-packages (from statsmodels) (0.5.6)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\python39\\lib\\site-packages (from statsmodels) (23.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\python39\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\python39\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\python39\\lib\\site-packages (from pandas!=2.1.0,>=1.0->statsmodels) (2024.1)\n",
      "Requirement already satisfied: six in c:\\python39\\lib\\site-packages (from patsy>=0.5.4->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# 라이브러리 설치\n",
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nFtbdwlZabe"
   },
   "source": [
    "### 12.3.1 선형 모델 예측하기 : OLS\n",
    "* 선형 회귀 모델 말고리즘 :  최소제곱법(OLS; Ordinary Least Squares)\n",
    "* API 모듈 임포트해 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 2103,
     "status": "ok",
     "timestamp": 1715637854762,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "Zitokn-EaJPf"
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8IUtEz_zlJP"
   },
   "source": [
    "* np.c_[a1, a2, ..., aN]\n",
    "  * 여러 개의 배열을 하나의 배열로 결합(concatenate)해야 하는 경우\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 306,
     "status": "ok",
     "timestamp": 1715637861195,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "sgnRMDXR0Cqt",
    "outputId": "f6c468ff-9e0d-45f4-b22b-a1f988491f96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]]\n",
      "(1, 2)\n",
      "[[1 2]]\n",
      "\n",
      "[[5 6]]\n",
      "\n",
      "[[1 2 5 6]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ex numpy ndarray 생성\n",
    "a = np.array([[1, 2]]) #2차원 리스트차원(1,2)\n",
    "print(a)\n",
    "print(a.shape) #(1, 2)\n",
    "# a = a.reshape((2,1)) # 넘파이의 차원 변경\n",
    "print(a)\n",
    "print()\n",
    "\n",
    "b = np.array([[5, 6]])\n",
    "print(b)\n",
    "print()\n",
    "\n",
    "c = np.c_[a,b]\n",
    "print(c)\n",
    "c.shape # (1,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V0F0p1N34HIl"
   },
   "source": [
    "* np.dot(A, B) 함수\n",
    "  * 두 개의 벡터를 곱하여 내적을 계산할 때\n",
    "  * 두 개의 행렬을 곱하여 행렬 곱셈을 수행할 때\n",
    "  * 행렬과 벡터를 곱하여 행렬-벡터 곱셈을 수행할 때"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 280,
     "status": "ok",
     "timestamp": 1715637864561,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "Lq0xeYKX4m4p",
    "outputId": "78c4b1b4-dcfe-42df-f922-207c6ace4341"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17 39]\n"
     ]
    }
   ],
   "source": [
    "# 두 개의 벡터를 곱하여 내적을 계산\n",
    "a = np.array([1, 2])\n",
    "b = np.array([3, 4])\n",
    "c = np.dot(a, b)\n",
    "# print(c)  # 출력: 11\n",
    "\n",
    "# 두 개의 행렬을 곱하여 행렬 곱셈을 수행\n",
    "a = np.array([[1, 2], [3, 4]])\n",
    "b = np.array([[5, 6], [7, 8]])\n",
    "c = np.dot(a, b)\n",
    "# print(c)  # 출력: [[19 22]\n",
    "         # [43 50]]\n",
    "\n",
    "# 행렬과 벡터를 곱하여 행렬-벡터 곱셈을 수행\n",
    "a = np.array([[1, 2], [3, 4]]) #(2,2)\n",
    "b = np.array([5, 6]) # (1,2)\n",
    "c = np.dot(a, b)\n",
    "print(c)  # 출력: [17 39]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QWx0p2tE2xFl"
   },
   "source": [
    "### [예제] p.541\n",
    "* 무작위 데이터로 생성\n",
    "* 가설: 회귀모형 \n",
    "* OLS을 이용하여 선형 회귀 모델 생성 \n",
    "* 선형회귀모형의 성능 평가 표\n",
    "* 모형을 이용하여 새로운 값에 대해 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 281,
     "status": "ok",
     "timestamp": 1715637870693,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "sEsBQNbIGfrg",
    "outputId": "53713072-97c5-4a30-a1cf-ea3e80f732c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.12946849,  0.78016552, -0.24137971],\n",
       "        [ 0.30291036, -1.00404855,  0.21331418],\n",
       "        [-0.32852189,  0.2130076 ,  1.45297189],\n",
       "        [-0.35147471,  0.17731515, -0.45670683],\n",
       "        [ 1.2432688 ,  1.04796487, -0.25808129]]),\n",
       " array([-0.12522947, -0.42710027,  0.00800298, -0.79873057,  0.03748031]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Numpy data 배열 생성\n",
    "# 시드를 설정하면 난수 생성기가 항상 같은 순서로 난수를 생성\n",
    "rng = np.random.RandomState(12345)\n",
    "\n",
    "def dnorm(mean, variance, size=1): #정규 분포(m, var)을 따르는 난수를  생성하는 helper 함수\n",
    "    if isinstance(size, int):\n",
    "        size = size,\n",
    "    return mean + np.sqrt(variance) * rng.standard_normal(*size)\n",
    "\n",
    "N = 10\n",
    "# 3개의 열과 10개의 행으로 구성된 행렬 X : (10, 3)\n",
    "# X : 입력 데이터\n",
    "X = np.c_[dnorm(0, 0.4, size=N),\n",
    "          dnorm(0, 0.6, size=N),\n",
    "          dnorm(0, 0.2, size=N)]\n",
    "# eps : 바이어스 데이터\n",
    "eps = dnorm(0, 0.1, size=N)\n",
    "\n",
    "# W : 가중치 데이터\n",
    "W = [0.1, 0.3, 0.5]\n",
    "\n",
    "# 선형 회귀 모델을 생성.\n",
    "# X 행렬은 독립 변수이고, y 변수는 종속 변수이며, W 리스트는 회귀 계수\n",
    "# eps는 오차항\n",
    "y = np.dot(X,W) + eps\n",
    "\n",
    "X[:5] # 2차원 배열\n",
    "y[:5] # 벡터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 290,
     "status": "ok",
     "timestamp": 1715637877198,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "Ya1_D7P429NA",
    "outputId": "95594f01-8a2c-48db-9d93-a49ce19f5d85"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10,)\n"
     ]
    }
   ],
   "source": [
    "# Note\n",
    "dnorm(0, 0.4, size=N).shape  # (10,) 1차원 벡터(입력데이터)\n",
    "X.shape # (10, 3) 2차원 배열\n",
    "print(y.shape) #(10,) 1차원 벡터(출력 데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1715637881865,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "mu-SUptPGfrg",
    "outputId": "5c7337a8-db60-423e-9d5d-6f74b095b209"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -1.29468492e-01,  7.80165522e-01,\n",
       "        -2.41379712e-01],\n",
       "       [ 1.00000000e+00,  3.02910364e-01, -1.00404855e+00,\n",
       "         2.13314182e-01],\n",
       "       [ 1.00000000e+00, -3.28521889e-01,  2.13007603e-01,\n",
       "         1.45297189e+00],\n",
       "       [ 1.00000000e+00, -3.51474705e-01,  1.77315154e-01,\n",
       "        -4.56706833e-01],\n",
       "       [ 1.00000000e+00,  1.24326880e+00,  1.04796487e+00,\n",
       "        -2.58081288e-01],\n",
       "       [ 1.00000000e+00,  8.81267227e-01,  6.86625215e-01,\n",
       "         5.55087220e-02],\n",
       "       [ 1.00000000e+00,  5.87601006e-02, -1.55046159e+00,\n",
       "         1.35332899e-01],\n",
       "       [ 1.00000000e+00,  1.78191913e-01, -2.88027991e-01,\n",
       "         2.34237990e-01],\n",
       "       [ 1.00000000e+00,  4.86372577e-01,  1.29282145e+00,\n",
       "         4.20505005e-04],\n",
       "       [ 1.00000000e+00,  7.88314544e-01, -3.39714657e-01,\n",
       "         6.00970009e-01]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# case1) 선형 모델 sm을 X데이터에 Intercept와 함께 피팅하기\n",
    "# import statsmodels.api as sm\n",
    "dfX = sm.add_constant(X)  # 기존 행렬 X에 Intercept 열을 더하기\n",
    "dfX\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 299,
     "status": "ok",
     "timestamp": 1715637886893,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "wdhJRVWiGfrg"
   },
   "outputs": [],
   "source": [
    "# sm.OLS클래스는 최소제곱법 선형회귀에 피팅하기\n",
    "# model = sm.OLS(y, X)\n",
    "model_1 = sm.OLS(y, dfX) # 선형회귀모형\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1715637889651,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "_6kKfoFhGfrg"
   },
   "outputs": [],
   "source": [
    "# fit 메서드로 모형 추정. 추정 결과는 별도의 RegressionResults 클래스 객체로 출력된다\n",
    "results_1 = model_1.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1715637891369,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "J9rOMcfD_sY3",
    "outputId": "439e5d2c-974d-48d2-dd6e-f1a605d78530"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.35063984,  0.47301471,  0.23457149,  0.43693635])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 399,
     "status": "ok",
     "timestamp": 1715637895075,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "gsynmFonCyqB",
    "outputId": "36f716dd-005e-4763-d6dc-c05978457b4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.20911385, -0.07742504, -0.17078324, -0.12387928, -0.33302187,\n",
       "        0.24747314, -0.16451198,  0.31799096, -0.08248255,  0.17752601])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_1.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1715637899478,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "PyReiOyxGfrg",
    "outputId": "e6a47bf9-94e7-4613-accc-e896ef1e2b1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.756\n",
      "Model:                            OLS   Adj. R-squared:                  0.634\n",
      "Method:                 Least Squares   F-statistic:                     6.186\n",
      "Date:                Tue, 14 May 2024   Prob (F-statistic):             0.0288\n",
      "Time:                        17:27:43   Log-Likelihood:                 1.5100\n",
      "No. Observations:                  10   AIC:                             4.980\n",
      "Df Residuals:                       6   BIC:                             6.190\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.3506      0.107     -3.262      0.017      -0.614      -0.088\n",
      "x1             0.4730      0.177      2.679      0.037       0.041       0.905\n",
      "x2             0.2346      0.105      2.234      0.067      -0.022       0.491\n",
      "x3             0.4369      0.172      2.534      0.044       0.015       0.859\n",
      "==============================================================================\n",
      "Omnibus:                        1.648   Durbin-Watson:                   2.551\n",
      "Prob(Omnibus):                  0.439   Jarque-Bera (JB):                0.783\n",
      "Skew:                           0.142   Prob(JB):                        0.676\n",
      "Kurtosis:                       1.659   Cond. No.                         2.58\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python39\\lib\\site-packages\\scipy\\stats\\_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  k, _ = kurtosistest(a, axis)\n"
     ]
    }
   ],
   "source": [
    "# RegressionResults 클래스 객체는 결과 리포트용 summary 메서드와 예측을 위한 prediction 메서드를 제공한다.\n",
    "print(results_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 353,
     "status": "ok",
     "timestamp": 1715637903965,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "i872eNS7-5Ig",
    "outputId": "62dd944f-addd-40a1-bda2-900fa5545478"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col0</th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.129468</td>\n",
       "      <td>0.780166</td>\n",
       "      <td>-0.241380</td>\n",
       "      <td>-0.125229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.302910</td>\n",
       "      <td>-1.004049</td>\n",
       "      <td>0.213314</td>\n",
       "      <td>-0.427100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.328522</td>\n",
       "      <td>0.213008</td>\n",
       "      <td>1.452972</td>\n",
       "      <td>0.008003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.351475</td>\n",
       "      <td>0.177315</td>\n",
       "      <td>-0.456707</td>\n",
       "      <td>-0.798731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.243269</td>\n",
       "      <td>1.047965</td>\n",
       "      <td>-0.258081</td>\n",
       "      <td>0.037480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col0      col1      col2         y\n",
       "0 -0.129468  0.780166 -0.241380 -0.125229\n",
       "1  0.302910 -1.004049  0.213314 -0.427100\n",
       "2 -0.328522  0.213008  1.452972  0.008003\n",
       "3 -0.351475  0.177315 -0.456707 -0.798731\n",
       "4  1.243269  1.047965 -0.258081  0.037480"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# case 2) # 2.데이터프레임 생성\n",
    "#import statsmodels.formula.api as smf\n",
    "# numpy array -> DataFrame\n",
    "data = pd.DataFrame(X, columns=['col0', 'col1', 'col2'])\n",
    "data['y'] = y\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 280,
     "status": "ok",
     "timestamp": 1715637908454,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "0tRXsgCw_LLM"
   },
   "outputs": [],
   "source": [
    "results_2 = smf.ols('y ~ col0 + col1 + col2', data = data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1715637912480,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "KPRhwRaj_bf3",
    "outputId": "84469bf8-9fff-46ae-f730-fa6942624974"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -0.350640\n",
       "col0         0.473015\n",
       "col1         0.234571\n",
       "col2         0.436936\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_2.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 435,
     "status": "ok",
     "timestamp": 1715637915525,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "htaRf43rC_Y6",
    "outputId": "680d0a6a-e484-407e-d663-690bee262dcc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.209114\n",
       "1   -0.077425\n",
       "2   -0.170783\n",
       "3   -0.123879\n",
       "4   -0.333022\n",
       "5    0.247473\n",
       "6   -0.164512\n",
       "7    0.317991\n",
       "8   -0.082483\n",
       "9    0.177526\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_2.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1715637918643,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "zbgs5wl6_4X8",
    "outputId": "8572b028-8463-4721-99c7-ec6ecd4a8fab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python39\\lib\\site-packages\\scipy\\stats\\_stats_py.py:1971: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  k, _ = kurtosistest(a, axis)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 14 May 2024</td> <th>  Prob (F-statistic):</th>  <td>0.0288</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:42:07</td>     <th>  Log-Likelihood:    </th> <td>  1.5100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>   4.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     6</td>      <th>  BIC:               </th> <td>   6.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   -0.3506</td> <td>    0.107</td> <td>   -3.262</td> <td> 0.017</td> <td>   -0.614</td> <td>   -0.088</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>col0</th>      <td>    0.4730</td> <td>    0.177</td> <td>    2.679</td> <td> 0.037</td> <td>    0.041</td> <td>    0.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>col1</th>      <td>    0.2346</td> <td>    0.105</td> <td>    2.234</td> <td> 0.067</td> <td>   -0.022</td> <td>    0.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>col2</th>      <td>    0.4369</td> <td>    0.172</td> <td>    2.534</td> <td> 0.044</td> <td>    0.015</td> <td>    0.859</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.648</td> <th>  Durbin-Watson:     </th> <td>   2.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.439</td> <th>  Jarque-Bera (JB):  </th> <td>   0.783</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.142</td> <th>  Prob(JB):          </th> <td>   0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 1.659</td> <th>  Cond. No.          </th> <td>    2.58</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.756   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.634   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     6.186   \\\\\n",
       "\\textbf{Date:}             & Tue, 14 May 2024 & \\textbf{  Prob (F-statistic):} &   0.0288    \\\\\n",
       "\\textbf{Time:}             &     17:42:07     & \\textbf{  Log-Likelihood:    } &    1.5100   \\\\\n",
       "\\textbf{No. Observations:} &          10      & \\textbf{  AIC:               } &     4.980   \\\\\n",
       "\\textbf{Df Residuals:}     &           6      & \\textbf{  BIC:               } &     6.190   \\\\\n",
       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &      -0.3506  &        0.107     &    -3.262  &         0.017        &       -0.614    &       -0.088     \\\\\n",
       "\\textbf{col0}      &       0.4730  &        0.177     &     2.679  &         0.037        &        0.041    &        0.905     \\\\\n",
       "\\textbf{col1}      &       0.2346  &        0.105     &     2.234  &         0.067        &       -0.022    &        0.491     \\\\\n",
       "\\textbf{col2}      &       0.4369  &        0.172     &     2.534  &         0.044        &        0.015    &        0.859     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  1.648 & \\textbf{  Durbin-Watson:     } &    2.551  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.439 & \\textbf{  Jarque-Bera (JB):  } &    0.783  \\\\\n",
       "\\textbf{Skew:}          &  0.142 & \\textbf{  Prob(JB):          } &    0.676  \\\\\n",
       "\\textbf{Kurtosis:}      &  1.659 & \\textbf{  Cond. No.          } &     2.58  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.756\n",
       "Model:                            OLS   Adj. R-squared:                  0.634\n",
       "Method:                 Least Squares   F-statistic:                     6.186\n",
       "Date:                Tue, 14 May 2024   Prob (F-statistic):             0.0288\n",
       "Time:                        17:42:07   Log-Likelihood:                 1.5100\n",
       "No. Observations:                  10   AIC:                             4.980\n",
       "Df Residuals:                       6   BIC:                             6.190\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -0.3506      0.107     -3.262      0.017      -0.614      -0.088\n",
       "col0           0.4730      0.177      2.679      0.037       0.041       0.905\n",
       "col1           0.2346      0.105      2.234      0.067      -0.022       0.491\n",
       "col2           0.4369      0.172      2.534      0.044       0.015       0.859\n",
       "==============================================================================\n",
       "Omnibus:                        1.648   Durbin-Watson:                   2.551\n",
       "Prob(Omnibus):                  0.439   Jarque-Bera (JB):                0.783\n",
       "Skew:                           0.142   Prob(JB):                        0.676\n",
       "Kurtosis:                       1.659   Cond. No.                         2.58\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 270,
     "status": "ok",
     "timestamp": 1715637923107,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "somz9yrx75AT",
    "outputId": "b659f860-68cf-4436-fc23-2260e19c4ab3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.1254404])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RegressionResults 클래스 객체의 predict 메서드를 사용하면 새로운 xnew 값에 대응하는 y 값을 예측할 수 있다.\n",
    "\n",
    "# 새로운 데이터 : 2차원 배열\n",
    "results_1.predict([[0,  -0.1295, -1.2128,  0.5042]] ) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QxptULJ2IZJO"
   },
   "outputs": [],
   "source": [
    "(array([[-0.1295, -1.2128,  0.5042],\n",
    "        [ 0.3029, -0.4357, -0.2542],\n",
    "        [-0.3285, -0.0253,  0.1384],\n",
    "        [-0.3515, -0.7196, -0.2582],\n",
    "        [ 1.2433, -0.3738, -0.5226],\n",
    "        [ 0.8813, -0.0281, -0.369 ],\n",
    "        [ 0.0588,  0.8485, -1.1826],\n",
    "        [ 0.1782,  0.7598, -0.0684],\n",
    "        [ 0.4864, -0.4566, -0.3363],\n",
    "        [ 0.7883,  1.2252, -0.0593]]),\n",
    " array([ 0.4279, -0.6735, -0.0909, -0.4895, -0.1289, -0.045 ,  0.0876,\n",
    "        -0.5046, -0.5458,  0.2653]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "executionInfo": {
     "elapsed": 342,
     "status": "ok",
     "timestamp": 1715637926976,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "ivyS3py6EsXj",
    "outputId": "d78a5387-748e-4d7f-f26f-fe752f0e3403"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col0</th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.1295</td>\n",
       "      <td>-1.2128</td>\n",
       "      <td>0.5042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col0    col1    col2\n",
       "0 -0.1295 -1.2128  0.5042"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#results_2.predict(data[:5])\n",
    "new_data = pd.DataFrame([[-0.1295, -1.2128,  0.5042]], columns=['col0','col1','col2'])\n",
    "new_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 303,
     "status": "ok",
     "timestamp": 1715637930753,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "6SsWT8yvG8US",
    "outputId": "8ea06253-69c8-4e58-9eb8-db316f4ac0fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.47608\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_2.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "executionInfo": {
     "elapsed": 1427,
     "status": "ok",
     "timestamp": 1715637948344,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "sSMUQPXYMxHk",
    "outputId": "de41358d-cfd4-4ded-d9e8-7c790401a70d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.20911385 -0.07742504 -0.17078324 -0.12387928 -0.33302187  0.24747314\n",
      " -0.16451198  0.31799096 -0.08248255  0.17752601]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 잔차 벡터\n",
    "print(results_1.resid)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a plot of the residuals\n",
    "plt.scatter(range(len(results_1.resid)), results_1.resid)\n",
    "\n",
    "plt.title(\"Residuals\")\n",
    "plt.xlabel(\"Data point\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 563
    },
    "executionInfo": {
     "elapsed": 759,
     "status": "ok",
     "timestamp": 1715637988385,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "xIr244z7KTnc",
    "outputId": "638783e5-a5e7-4a81-b375-3de2bd15ec22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The residuals appear to be normally distributed.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create the normal probability plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.title('Normal Probability Plot of Residuals')\n",
    "\n",
    "# Plot the residuals\n",
    "plt.plot(sorted(results_1.resid), range(1, len(results_1.resid) + 1))\n",
    "\n",
    "# Add the reference line\n",
    "plt.plot([min(results_1.resid), max(results_1.resid)], [min(results_1.resid), max(results_1.resid)], color='red')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Check for normality\n",
    "if all(abs(point - line) < 2 * np.std(results_1.resid) for point, line in zip(sorted(results_1.resid),\\\n",
    "                                                          np.linspace(min(results_1.resid), max(results_1.resid), len(results_1.resid)))):\n",
    "    print(\"The residuals appear to be normally distributed.\")\n",
    "else:\n",
    "    print(\"The residuals do not appear to be normally distributed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "executionInfo": {
     "elapsed": 946,
     "status": "ok",
     "timestamp": 1715637999405,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "HLY_IcCwJvrY",
    "outputId": "aeed8de4-3137-4627-df7d-cf371263cce6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     0.430190\n",
      "1    -0.531576\n",
      "2    -0.132103\n",
      "3    -0.166424\n",
      "4    -0.028407\n",
      "        ...   \n",
      "95    0.024139\n",
      "96   -0.328237\n",
      "97    0.333616\n",
      "98   -0.031432\n",
      "99    0.108113\n",
      "Length: 100, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 잔차 벡터\n",
    "print(results_2.resid)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a plot of the residuals\n",
    "plt.scatter(range(len(results_2.resid)), results_2.resid)\n",
    "plt.title(\"Residuals\")\n",
    "plt.xlabel(\"Data point\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLKySKhL0Djp"
   },
   "source": [
    "### [문제] 간단한 선형회귀분석 문제(Next Class!)\n",
    "* scikit-learn 패키지을 사용하여 가상의 회귀분석 문제 이해하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o24S-Rpb7bIg"
   },
   "source": [
    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P/mFuOMTH6/fBL4IWf9uPPAlKjwHgJ0q/PEFMU5e57rj72L++QVcopW7IO56xSfIb57PL5VuVOy+8KG445k+NC4T+PN9vwNH8oLiHR/C+DtExpBrBkrPNd+ADMfxyTi5Z4g0j7tPAZxtR831hZrSYU9CzBo1ylrLhVeU9gEL9KddvwWFrkSwLAHppR0ofpieSRo7IqTnkVp5YoUa+XvIJu664RwOTF+bwBQSkOxu+KNA9WQJyukY6ct6DIthnsRnt2CU/B4ptgENKk+loKW7ECnkUbOIECzdtRXDe7pRv01alLuRbyonDX0uamoT0bEvXxiryNmMaPvHZjh2daAwSm88RwQYXUFjaVAh42+r5htwOjlQ1+c/OfD+h9OR/jD9axpjQ8cxRv+ctWGcCl+Sd1nVB1C4Joi4Xp6ElPnhRTNwxhZik9fzpwhDCHw7DvbzWlTo7ofmhBZqarqrqtCxR4vS55ugGKhHhtckLpI21wbRXN+Mk9eVUH11LYmz94h4ac55jJkNgs2A6j16qA72o2oryfutGUQguXCNYxV3JOCI0Ks8RMQiOdc1nEFGRLTwU8TY3mxAw6gaB16TGGN/UY82mcsuzTdp59AJQ03DgoimyNUo7+yForYU+tEsVAmvFohIu0/41QM5Mmp/SWy/AZVPtUFG0nqaW4vtjb8kwjRMRSeJDKptB9C+xd3pIfUHEdLSJECVBugtV0mdEk/OdGH/kA5rWYvkQMLTE2Foh8uy3PkFZKBdqA+IqPWqM6wwNPZhSh7aboSVO0bHYPlYjqS0FwOu4CFfSbqEfZ51rwP2m0mQTZJnSA3wXsd3wqCM5I/4pxd0MiQR2TbLFGbkCVibpIzA4+9d9hmMzyNL5pGmQtpxmJTF/g34z1wiJL/7Tci/cgGOf92AlaX+CswlckXBN+/1XIFPiAiFl1B2icF75wWn735gXGJ4QZDS/T98xSUkXcI/e15Ueu4LzzKiEgWrr8dWFLxUfPr9Tv8mIruaCm73sR779DmNyZLpEfQ3N1LeYUFEw0d0W3GvIPpFUe8W38Jv/4EveKSdbzpIIaRHgHz0wk5nq6uQsipwAyiIgckSL681OBOat1VicIUKijgFFLdikJCagqTVaqhTk8TVLKjXqB0q96Q0YYJWHZr7rVCsJoJGYvUJSR7JQvmWAA2Lp8dJXFKMu2nFVcsoelrbRI+S92RD26lm9FhlPpMLpeBgGzfBNE4ati+rkPVCB6o2cDC2NsCYWIKyb3l7zrkP9KhtAUqOiitO6FSBPT90ZQXjIPpEj25cYjoyv70dOeskj3YxqYfm4Aicq+JJ40me9foUHLF0UifJu0/ssE7PkE5UOTqq06UbV5GQXilyn7Jc/yWwHJNmTNyU46FEBdE5pAFfItHu75kTkfImzrlWL+mTFeJAgcekMhEHESHcKhXil1Hb64IqmDdPAu6mgyicCIYZCKuVVMJa0I8Wz0mzIkEnhBEBXvlsMzgimmaWF6JxfzImDtehi9hmwkoO1oATdf0RvPN7rCjsJc/rHuogIuX5NB7tguMZDTJ9jvWCeidzp1ByttyvIxMal8c4Mo+0GDSPx28Skw258TYUFI1AfYR0BjbGw3FFj5rdzcA+IvK3iakdzCMtZXPzuLzDXpMNSf1is0zAqQj/TUUwOBspT3PJgeteCY8094FB6BDqrymQpFR5THQNjrtDGG3ZZzA+byytkBbEFqk2vnEBf06EXMyopwDz9tRS5r2jghikHlW319f1u8D8b55IHCeBp3D2RhTD895fESL+aVylhPQfN7mv4ymcXafN4+t1F5n3EM//7u0ZFwj2mxsJIe0vhD3jR9PclReutPLd9+nQBILeN5TIl4IIPPMbDWh+zQInibCwPnBeBjK+noEUr0VmJbhhhuF1PYbOu4Zp5DyVjrgPnEjalRFk8tUiuTmMulwNDETgqYg4VySqkZ5KxPxjSow15sEYaNUOIratDgUSlB4NYhivUrlrFkwtTxLWyg44WoAK+g+GcdyaJKyrfHuQaOjDxKsxpZ6rj6dhnTDDPGqC8TzJ9wflmD4rR0mAdKBvC5wxEq+TfddyDsRNG6ycHArxfPMrm1Bpr0L7Njksl6cwbRuH5aYMcroG8oUUPxEkme6zNhjf6oFhYAxTcmKzW7PwxIOks7aOxEU8REAU4s0XZVCv8Vw7nECXJDxhxBNHz4vDE0JgN0L7/TbIXjiKKrdX14dAQpp2uqq/N4Skw40oT5WTPClA5UU1DhzSiMODwhWioog+7EDZUY+hRR6EFo0uuA+G0NbRBSMR8mQPqrRCFD6b5V3ufRNf7LzS46cnr2LaScUmyYPpSYy8OhxGR8D7OWlHxkHK5oTZAVW2HPogQprCXTPiZK8BBmI3Qmc0j3RGhaE9IuEIafEZHLYJWN8n9v9YITJWBS5f9O3MUq4jTcsTXU/cT5gL9RHpHF2ognqJ6s/Fln0G43MDFdJLwe/feoX/TbuZ/GXmpze+wv/HDc8wnv9d+0vzf1Po/vW3rrt2zHr+N9Wn+d/Tfzfq+d+5Ql3QMI/zIsHrHl5c5/+j2hVHSW6c5q9vJPF1b173p+e+xE9LRYmeR59D3A0MTSN6bak4BPnNnU7iLoWmsfczesZvIS9c+O670kjyWTyJMg8mdLn8+u808WPuCE+d5Cu+/ji//1czYkAAxOMqXr/KO2+R/Y/e45u+s57PbRnjna4jPJjhx3qr+aKdO/jaX02LYUvNNH/y++v5CgO9vuffQbhOnmF9BX9Syvx8uTXNv/dKBf/NrFx+xw8a+Nb2Vq+t4cWn+PVPdfET4uGR4pyZ4WeCGmWYzyTBzHAt/+T69fz69U/yuSQPqn/Syh83nOMnPpxx5V2IdJh+p4F/ipxfdMD7mVu1FUJ4k0k8MBA07X6+ny8l9y490CSce2LoPf69i1f56Y/EOFAuNpE4ElsUdwPiHONbBVt7j5+h5zqv8idffJx//MVBkkoRIDx3KX9yStwPgvP9E/z+H3Tx74W4wbShgl///ZNe8ZgaquWLyo/zY55F6tYUf+bHufzj2vfE8jLGN9G0vCjsBMDJXzXU8hX1g/yUO80kGGsheU3KYVA+OsPvp+XXID48iY+Qhv94hpTWBZzvn+Qb/u5J/vGsHfyOnaX8/pddeS/k37sT/BTJvxnhAcKJP2WGP9dSSq4l2sLPT/LnqB34XiNcW/AlwHkzxibB/nbsrOYbqO32DfLv/XrKo8yFKF+/dz+nN0Ja/+ScRJ0XiBn+6nAXX/GUxL1mzvENPzjJS5rjzBh//B9pujXw50JUzZ4stuwzGJ8X7pBHeqW3V1f0vsLTIy16WuHlDSaIxy4Lc1y0J4E90uKwj6sS440Jwb20QTzSotd9zncccgCC3UfyNwmPtMtj7zG0w2s/tEfa36PtTzjH+GOBLrsApiLvtZ1NR1JR9m8+H2vwwdKdjYKRQvQdW/ioBHdei7SKGWhH6pHhN9Ygeo9qeIjXzyTX3xZ4HWkvwvBIh4uvJ5J6DSuPmOCkMxyEiVT+gy8cdiumfdaTXPvtPMj+PxsdCumDa81i6yN0MqX75a5rKIrVAcSlVUFbGuUyXCHTIYC3dAnTTyCAN9EXYfLmvjjvj9FM6lGQ24PNvhM3HRYYjjZDb6GfrqdDZDyYc8Bm45BaoRW8xEEJc/Kin0f6phXmmwqkSE3KpJ5RYaIhvW4YHulZGyw35PMffwlEOB5p+1skngdVaPf0fhKbTS21ov5UCzIjLqLhe9QD43GNpfwgS1gErp9s53U4aZbyR5PfLuhgSmpxfVBFDAtNlHXhUpc3SoBr2k9pkLv/HJR7etG743a9ZWMw7hx3aPm7FXhg54PgDx/H9W+8jOu5/4F7931R/M2b2KzduO+hS3B8Q1x27UubseKnD+LWC3QNZXFbgjWn40p3QgZ6n4XrutdMjvvOOsx53o9uHeEsyLsG8f2kpnc/p7AtrPtMBannNT/BN+eFcrDfBHFMw+kQlwtinN1psCYP8n2/wyfuc+kydv0BhoZIEPvlL4IfGQ+SnjfgHPkdln050tpVhvuXkw6B19rOnKAZcJ/M+zX4XQL9Utk8YU3TlZpctXjkqVXQHe8Wlj3r7WhxLXvms7V0kN+OeW+ap59GVeXT2Py1LGx/lq616177VYOWgVEMHNGI+3SrQn2r67z2aEX0okmAIpxx7+GijMOSrqorT0LOt5JguZyEEt88ONwC3bH20CKaEoaInucejydYrpIW0ZRldKKh53V9Jub6EqsMKaLDhY55F8q6x7JxnFDw5RE9qjehl48MTTopw+KfnwYSdYYyLRMJl3SwKLd7lD3XVt89ilMRiei7iRjSRtEJ4wzG3c+SeaQ/T/h5sgWvuHsCnyvo80MwDztBygseJtQz9dRhYO+xRuQkyuC40Izi543Y1N2L8jVBWlRh4lQD8GIvGrNVkNFJWLvLYNyoQ2+F+2tznnAwHdmKun/L8vCouvj4A6MwAS+9opuImqhbcdivmOFMTIFS5vL4WItDeMeoNybqyVXeCJ7It7OCevE/swheKQvyA35u3AZDaTYMK4qR6ily7KPoeQOoeoMu4yaGLQbqTQw2YdON+GXDEfeXIuesMOwrQAMO4pdS6f9BDwqKhqHakeo3jtVlewrk1LaE9VGQUAh24DtRNyyoNzbyyZJSUI90+yqfL4j64vu1TXf53ULKb6lU+Q2FK/7JUXmz3dgw3DEB1XMZUI2HaQu+UBvqUIX84qc/weqMIB5k95jxiCbjcjB3FqHu8lps8qoL3W+YYpC1T4fC1WKwG2L3ur+rw9hjm5Dkpdw/xuT5MUxBhZ2H65EZyYOHLPsMxucDJqT98J1cSPAdPvF5I2BHwX8oSKTYLxlwvFcPk7iaRE7hTiJ2w/Cx0DWhdX3CxB/QCV/ZO7EzkjWhbyMLoloMkIKzwWwBkn0noUWB0CH5sATnxeW17ipoY7rbjvKAn/qmDfwE8DBJz0V7HIMwS/Lj3+VIEZZSDIHDCuMpYnvzkw3zsf1bKYj/lG2PuzKEk7PpyF8XqY/SQ0Qu8hmsJ8ow+HBL6E4pnWzZexwGOll40eWX2MilKch9J3tGywd6lA2p0EJEfkRlkwrpITXO70uPsExzsLx1Es60fKRIvGGxj+pxYjTIF1WDrTT0WSZk2WcwPh/cvUJ6fpULKYKsfBEOftcOb5WQuxraWfBZmSP4WHEGg8FgMBiMP22YR5rBYDAYDAaDwYiCOzTZkMFgMBgMBoPB+HzBhDSDwWAwGAwGgxEFTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFHAhDSDwWAwGAwGgxEFTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFHAhDSDwWAwGAwGgxEFTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFHAhDSDwWAwGAwGgxEFTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFGw7EcE8W/GIrCNtMEwziFG/pdQyJeJoWHAWaDfr8FPz04hVrUWquXBz+VuOjB3bwyWLXkXyIbhQxpoT38YVjzm4czoefEf0DPmwLIvJ4d/HsE2ooWmzYibf1BAlaRAjBgeLbbzOhx/89ewxSQgeWWsGBoa21kDjL/hsOz+lXjgC2Lg5xT7+Wb8w49/DqPtAaxNfRDhpxLBbkLPMSNs98Ri5ZceQMyn0A23jdQRm7mwZDYTDsI9X/oV3v9Ega+uXnlH7slgMBiMu4M/4wni338ScOM9qO61ISnxfjFkAc42DtPkDJBWhe5SNWRiOIXjAJlnAMVhhcWuINeSA3MOGFsKUDlZglNHcxAvHuKPA+YTBlj/So0n0pMQT3XnnA3657Nh3HYKLdmBz6T3MJF7lJ1UQHNMh/xEMXwROC60oW4ESN34NJ5OAwZLszEcKh6+hBt/CbizdUjbI4dutAopYlhAZh0k9eSQe6q/ySHUHD4ObnU59lakY/pYAQpG8xfyYI4DR3JS5qnvSTpaLQ4krFa68njOjuHGImhsofIuNPZLehjencYfxH1PBPvictB4OBNKMSwYVPQ2DAGJSl/DI8zaMHrahOSafmjSiP2FwD5phWyVCkIfb7QZqaUIL8194Cb1qM7VQtU5iqp1YiBFKp094Wyw2uRQ0bKySILZjH3cAINxKnD6k/Idk3kQuh1JYmiYXNAi9XlZVGnGYDAYjM8vd4+QvmmF+YIJQ6cNOHNBjcaz0Tdo9oFKbD2dEYFo4mAdaIa234r4dWugFIXcx1eGoH//afQOFIM2y/7XpV7eOnTZ4rFzXz0yVwmBRL0Oo26TBvJ5MWKHoWIrhreEEtI2GF7MRsM99fjlkcxFCT5PbP1lyD6bSeL9BM6FEw8/pOPP3STSarmHCKQCdtyGmEQVlO7wS0TU7Q5P1HEf6FH7Y9IJEfYUyKltcXUmPIRhgm8ezFphOKqF3q5C+sNi58k2Ct1bTmj6e+c7I5HbRBCo4L+HCH4v/etKo7rEdozuUYthoTF3pKH48l7/eN0woHJbnZ+g5S73kI7FIByJaqxxC/BrRuhOWZFe+0u0bCNXCZrmoq0fM2E6lpw/G4Ok7DK8WKB2dfpgRnNqMeAnpO0wdTeg+ewU6aCkI3G5GC5iu6DD0Hg+Ws5rkO6VLp5wMHfXQP9R4nwZc/ExxvsH4XxOB912VUibMR9NRTF0GK0Is4aYJb1k+qyekE6y+TIHxZokV1wisFMGg8Fg/Olw14yRdtinEbM6CzmpcbDPioF3DBlU2Rq0H2tH/Z5ylJe6tpInSaP+iJJIukAokbG/DOoLQ5iwi0EUuQLUL2d+vQ1tHXTrwvCk66eAUG90ayXqbClI/tAAw1krHHPibyHgJoehE+4jbofLsCk1DVsbjXCQ32P8XO0+OMzQHyxGXm4BCnLzkFfdDOM18bdAcHZMnKpFdmo2KhvF+75yHIPvnsHJARORleFDvbw0/p2/moYyLR3pwpaEyVYSn12V0F8RD5QiVoWcfe3obdTM51v5t6kUSgZn1qJslwZacu2ut13yfHE4YBnQoe3YcRzXeaS3uBkuUYugR4VPgjKY6E6Hyse1LVtdSERwL9prq8Tn3Y4EhwVYU4WqraG7CPaBauR1ylDc3Yc+cp2+E/VIGS1DUauZyNwgLIuHercWveQc7b6FMuLe8h8jx6QmQRXU1GRIKSqE4lUdLMrtHudnIummA/L74sTjlhIO9is9qNyYitQtxag5IuZX7yDOXTyD4UuRWCqDwWAw/tS4a4S0/GE1klbJobgvhOi7HRAhYuj0FkXCRgQhZmbgFA8LxoJoppseZhKWkrkTO3dRoVCCjGDDNOZsGDpUDI0lB7puHXQntEj/xADN9jJo+4dgngwuzWSJGchcOQ7dJQU20/sRYTkyeh6nqtMFQR+UOQt0RcXoU5SR+/ait78PLWlWVBfUYPimeIwUsnikbFJDRf7L+Fu3IBK3XekReX3j1+UjLzMLT+c9jSdUrm5LHBGYeQdJfI61IH+1ECSJ47LBuxNBt9dJ6ivlSNpEO0daaEichE7RoiHXzE4F16kD9zWfZy49AN3ZUQzsUYdO87BJgMLH8+uLbaAWDZfSceBQPlS+wy7mODiIQHXcdEtkO86dNgJbMqB2R3KZEpu3pMN2bBgTYlA0OG+R/yUqgnQ6QyP39RovCTJiXznIoN717CqvjjLdCtMW/X6CwWAwGJ9j/iRX7YhPzUE+htGwvwY1+ypRvKsYlfvI33vKULCrAAX7h2ATjxWQJyFnHQddpwUJ315oZA90nsdoZ35Y411TPM4rL813vR6O9RnvK4HjigF132vAxIZ6DBwtRAoVOMuIYNtahXYiqHNkZjTvzobmVHDPWcwyIkJkMVCEuJ8v3AUD2iYzULKLCEBRiCmfKcfu5UMwnL1z3jq50oGT38tF1+wTQhrmKI3QfKsSQzfEA2BEO8nP2n7aRVlAvjoHa2ZJ54NznSdstTqMDuwE3loQ16E90uOwf+Nl3BicvyEJ6sP1b3Tio9+K+wKks0c6Rd4dJ9emraYe9AK0jQb17XoR/yAR+JPD6PK5VtvPh2El0nbqunigBNylZlS+RDp769RI8RPcZuhf6cTx147j+Pwbgng8QUQzTg/D5O6bkU7cGSKulbsykCwGSXLTDN2ebKSmFqPGN65kM8UWo5gUlNDeeAVUqeKfPigUnl0QEv8jNSij5XV3DyxiKEWxSg3ZRb1fHJr3F2DTNnL84eGI3ogwGAwGgxEQOkb6bmLaUMGvX9/Ej4n7i+b6Sb5ifQV/8rq4H4iLTeS+wY+b6ivl13//JD8t7rsY45vWr+ebLoq7Aq6wogOtfGs73Rr4iqfW8xUGzzOd/IShiT/+zlV+5pYYFIhbTn7GKf4dACHd3HGbfo/v2lvE79hZxFfs3c9X7HxS/G2aP/l973hIp7fruPUt7lD/8wSEtH2Kr9CSZ9S18g3fJ/csqeZPmsj9f7CDr/4JCT9QFF5+mlv5x8lx782nxRR/8nvknm+Sewp547qG13OKjLV4xlXk9zP89Ptj/MR1V8IJ57046HWeHzdO89c3vsL/xw26Y+anN77ET5uFX24fH57hW9+cINYQIR+StN+8g2+9SJ7T2MDnfqeWP3FRfDqP9PLHyV81NPBFWd/kc3fuIOcV8ft//h4/PZ/uUvbsYmnKppQt0XuW8ienxN2g8Rch+esMVW7m8bVnCYR7krQgZbWapMuOkuP8hPgTg8FgMP50+dPxSHMOcGGOKRYmxV2ywHYzwDniK3H7pBmmU3q0HdYInsaa16foEguSwxb8h3bEI3VjFrKepV5SqaEdMiRlV6Fwo7jSQjDmnMIEt7CfL16N4kMd6O7WoeVwPQ7mBp4+RT2iMhLbCc8x0XNTJH2AzNXhDIcQh3YUlUNzVIfeTi1yUsn9G8WxtMJ45TBYrkAyrLC5PbCzVlguy6BSiqm9WhlwyITgoRyoI3lUjMqKMpQdbEbPaTOmliVAFb8wXED2qHTezfOlzbh/HzGl3D7YO/4Ft777TcSvoT9YYWjUeuRv4K35YBkKtqWRvN+Kgn0+bz6kWJWB8m1JJA8C4B6e4TFvgLusQ/FeM7K6e1G+To74NA16a+JhqChC8yXRGx5wmIRrPoDurVOuMdL9OtTvck80XBxcWHMb5FAoSXnpr0UNfWMkvDXqgnn1DKzBhjDRCYOexMolVxDhHCStbrgmLpsn7eGXGYEU5JdqhDHovZ2FwgRjBoPBYPxpc9ctfyesrlCrinz2vN0E3aEaHJ9QQPnVtQsrOAQlDmu/XYj0FeRPzoKe/bUYJEIuTlgRQYGE1UlQPfgQVCviIf/EgqGfd2H4q1XQbvUc7GGH8dgZyLPzfV6vc7Bdc0K5iso/n1UvOBuGjtTh+OUZomvIfVYSUeA6CQ67FdP09XcsB+s0+d0TeQ7qW/OJdPUn2KoUC79Jrdphx9Cep1CLKnT/OB9JseR5jpSi8sIm6F4tR4oQsQCrjggrSwwj460W5NA0lCKC1RDsozo0HJ2AIk2JmXEL4rZqUJVNhP54DwrOJqH7eTUc9Fku5+P8vnR/8RlsibYbFpidKqSsCihZRW7gI81xcBcexH3v5BELESHXtlkm4FSokBAvLeLCg1znVDPqescwgxgiKkkHwX2tT+wkz2nHgNjM9SlMe2nHOOT8sAXqXzegeVyNvfsyofSJA3eNdIiWJSPlRhtSjyXjlOfKL7QjFuMRb7ryCLk+d9OKq9cd5NnGMGWbhPEy6cyQLcd31Q7KpB6agyNwriLlgTyHg8TREZsAJV2+RIg7sdc15egIMTbf8YEJtuVqJPkaqhsJm6G2UXuwEyY6Kl+lgnqNuLRhUNzlW1xR5ZHAK31w57VIq2DL3zEYDAbDmz8dIR0m1GMVyJslcEWPSu0I5KvzUL4nY2F89FzodaTptZ1zQIzc9/quhtyUPYL6LR4SQ2Lx6oiX9hLxEtIc9WBScWTGVEw61n5YE0RIE+bsML95AvpTRlgdCVibnYPCbekeS5SFJ6SF5/9kGlarBRPXlMjKTYF8McuKScQrc8UUzPdslp4kRj8o0qLDoD3eR2h9DMtbehhX12PkcGZQkecaK/0vuIUvQta/Gw98SQwWWbJ1pKVEf6i0IulhvSYT1mqWXPecIniwpzBhjYE6dSEW3AcGNNc3Q/+BDEmJCmBlHGJGTMCuepRtiEecMhlK0kGQxwZY/s6PADYRAfTjQ/ArKwSaDh0qDHTkhDU/ITTeQlqw05s2TFweg+niKIyjVihIZ9Z4IYUJaQaDwWB48acnpO10klU7DJflWJuWCG+/tA2jnUNIPnoemrQg/qwAH7QI5vWlUJHVdFALx25fcUEb8qcw9dx5lAtDBQITrZB2eQsNmAIRnOS5E1VqrF29FkmrZCE80uEQQDTNWaHfVwODlxc/BSnJHl7bqIW0y1Pedt9BtPwwE6plDpi7K1E8lOrhKQ8XV/y7HuvFQFHwF/YzHS/jE3wT9+Ff8MnVdZBrN/t/HVByHWkx72ZbpL3l4RB2WrnWgq7rOAMnyWc/76ywjrYS2pF6ZATsNQQSzHdISHOkkzTUheZaPVJ87/WBHmVDKrQ87/3RJAHbMLSHezA2lxnw7YwUlmMFKDpqARcbTzoSD0G1bg1SvvoE1I8lQ7WC3GUxHT4Gg8FgfG75nAhpDubOIpR22JB1ZAAHNgb3KQYmTJEgNKpmZO5O9fKICR9ouRXs63hBxIXvq/UARC2kg3DbhHQ4RCtQruiQveMMCj0+qgLOCG1aJWYO+3j23bhF1qzca7gMtR9hGMJjVYI4C2g9dJWOFyAO6XB5puf27cSKrIVxK/Qz5SfNUv5o8tsFHUxJLegnQjoqC10qMRfWdcIU0rYhNPdehczvgVxefusj+ch81N1d5WAbN8HqIB2rtCpoS0MtBRiuaPfhdoheyWsuVb3DYDAYjLuVu2SyIWmAz4vLWdG1m2FED/1wQv8QrJ5rGccSWTvnNXD0NkInHonLqYnbotYiDjacxAelPEb8awl5RBVA/N8Bbsv6wBIoM1BCBLYlNgN7D9Wjfn7ToqWzF7pgIvq3Z3DjhQ9xz0/d46LXIP6nD4I/fBz2cSFAQJmWiYRLvh8UcW313aM4Fa2IdqOMw23I/QAE+eS3G2UmqiqfxuavZWH7szux+zn382rQMjCKgSMeH8IprUJ9K137uxftIUX0XcQdrXcYDAaD8VnirvNI316oB6wGca8OoPhRMUgKwTsVwCP9m6fR+7NiJElqQ5cHq+7yWmya99K5+HjcgDMfKbD5uaPQbAosaalHuisxytflAXBc0mMQWchfxwmeZWtxhB5A0SNt3UXOC7AGcEBoWupUQbz4gXBPgtyL3sM5wtAOU2sxys56ToL0x3G2DtkvTSNrW5L3sJ5ZG8YvWeHcuBcdu1OiG3ohEMQ7T8cnO5xEn4ZeP1ySqNPKB3odOnHuLEknMcgfWha6kHyqBZleN3OFq4JNIF0ySB7vy4VelgeP4dyEjzF5fgxTWIuSJg0yfBPjxhA0O/SQ5XqXT3cez8g2Y29bcWTDf2iaHVGi71j4w0UYDAaD8fmHCWkvxBU2nsl3ffgkEKRBNn8AJIe1MsDSQlc0mI5XQxXii3bR4YCxsQ7Tz2qRs0oMCgsqHivg3OMxzCJcbhjRY1aicEsU8sRrsmEcVGk52FmQiaRP2dVpH9XjxOi0uCfBI1koj+Z5qd39uxwpDy/yAcfbkNb9EH7puWqHHxxsl6YgX6fy8RxboX9+EKqmcqjvtPF/mlAh3ZeCkUMZnx9POoPBYDAWDRPSDAaDwWAwGAxGFPxJfiKcwWAwGAwGg8FYLExIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFHAhDSDwWAwGAwGgxEFTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQXLfkQQ/2ZQZjng3mXiThDmODhm5hDzhTCODQZngX6/Bj89O4VY1Vqolge/HnfTgbl7Y7As6i6QDcOHa1D3CyOmHlgL9apYMfzOYxtpg2GcQ4z8L6GQR5COdzzNlhoO5mMv4OXBm5j9YgKSV0aQB7Zh1B3qw69vAgmJD0J+rxgeAtt5HY6/+WvY5hR46MtyLNJqo4Mzo2ff/43h3zgQ++VkRPLYi4KmWbUWv7oyC8XffBUrvyCGRwk33oMXXhrCzT8ooEpSIEYMD4VtRAtNmzHi8+5GuCsG/HB/HYb+93/Bg/+/R6CIxOA4K4ZeeRO/no1B3F8pEEnVsFTQPN5zSA/L72Pw4COknN2BOAj33N+D8ZvLoPwbFR74VAopg8GIlD+5T4Rzk0NoPqLH2KwcCSvlkInhFMd1E0yXHFhb3YuW7Sqv3/yYs8PYWITK0yrUd7cgc5UYHhIHzCcMsP6VGk+kJyGeVpZzNuifz4Zx2ym0ZMe7DpNizgFTSwHKTiqgOaZDfqIYHgWmxlSUxegwWpEihgSGVvDVvTYkJd4vhizA2cZhmpwB0qrQXar2SjOO9ElkvonosMJiV5BryYXnMZLnqZwswamjOQj85HcozUgDPtw7iAmnuO/Fx5g8b0VihRblqSTuS4DlWAEKRvNDPDuBPIPVMg1FksrVoDuIMNykgbxzFFXrXIcI0M6dg0Q+Vg65hPHaT2mwdb8SutEq+Oa67Xwb2o4ZYX24HB3V6ViaJ5RgzoKeogKYciXyjQht/ZFhTC+XKnkcbBcMMC4rQUdbIZKCFk4JZo2o21jpn2Yh4BwOOInklcudMHVo0PyBCjufq0LGbxqQtkcumZbB4M7WRXWeH3aSVm+cw/Qtcd+TWRvGL3HIOaQNr166okelblKyfFO7twycgaO4A7pclRgWGMekFQ6lCkqaPzYDyrKHkflWC3JWuH4Pm5tGNO+qhHW3v51I1ituaFm5xkGVGLREhccHPSjYbkK+RPy5SSP0p8cwI5X+NydhvDwFrC7B0X0Zwcu2L9f0KHvGGF2aMRiMT4W7RkhzHwyh7efnAKUC0xeMsK3Kw959+UhaIq+W/awWpXtOQklEdGMoEU2ZNZOKvhjDae3o3+MtIBewYfhQHbps8di5r36hUfMTQnYYKrZieEsoUWiD4cVsNNxTj18eyQxeQd8wQdfYjEEraXSICOFU6aiqqEK6GAfz0VQUIzwhTbEPVGLr6YzQom8eDtaBZmj7rYhftwZKMZ8+vjIE/ftPo3egGElk3/+6n2KaUQQxCsh9xdylZqTutuDAQDtylGJYMMh9ja3NaB4ag2w5MM2txfZ9e1GcuhADqTS1j+rQcJQIl0Q11ghqhKTZuAH6C3Eof7UPxY/SEGJ7qcWAn5AmnbvWGrSNko6NSBax58I14o7wDAgo4gSbeP9ABHkcJiReFosTqiQlZMtC5JvYGYiRk06u2yM3a4W+thja9zOgPXoAGZLpb8dwbSk0Z4EkId0UyKlt8eg4BUgzT2hH6hjpSC1fi6ez0wUxaB/XobaoDSrhPGJLpdkYpp23B4/7pSV3kyN57WE3VNSN2xCTSISlOzxEHkQETatZYqs+vSbBrmpVaL9QBXWYXk37W+ScgyqJeLnSzXrQJ79sw9Ae6sJY7FqkPywK8JvjMPSbELdbh97SFMhuGFC5bRgZAUShYOtHBmGltSdRx6qNJSjbnQmVUFcEshN3vTIGeJQRN0L9clYOTX9v0E6z7VQdmi/GI5GUzQVIZ+2sAaaUevTvI53JUPGPtE4kz8gtky3YNYWWjdEpyB5JhiqePEuIezIYjM8ed4eQvjGEytwaOCoGoMslrahbHK1qx9vVgURsmDgsMBytRcMIESpNWiI6wvDF0ftX56JhJgtPx01gKjEfJc/mIEWy4pNqwF1h5m3FSBVEwcewvKUHJLwv81DPamsxykgjkYIYPFGhQV666KH0ZdYIbWY1pl7sR8szLtVhe6MSuU1y1A/UI4M0HrdfSEsjXOftLJwSRa30dT+FNKPYjNANjOEP4q4n3Acj6BkhDfQbOuSH9PLZMbTnKdRiL/obc6Ck97tGGsiCHmCLCtz705ApyUOQ+xmXVwVP0zkLdNsL0OZQI/+ZNXBJFhtGO4eQEkwUunF7qWVyyK/cZiFNRIGpuwY1rzuh3pK60HkSOgLJOCCIgzA7QCL2CzrU7O/EzKaDaNyX6UrLqJCyKQn8vKie53nE3VdIc3aY32hCTaMZqu2ZSPKsRojY3L4r3ZWmSySkHZcN6Ds7JWmr9tEeGG7ko72XCOlwXy0EjFeY6UZEqPloLor7n0DLkAbpNO+DiEKO3C93txWFb5DODi1PtKzSN0fzb6cisxM34XciXNeve8SzDvQJW2Ih7ZgcQvsPaqC/EY/0bTlI8hTxD27G7m1JITsfDAbjs8ddMdmQc8wgZk0hSra4RCGWKaHepAZ3wogJV0jkOKwwdpKK8FulGHKqkLxiBuY3BzF02QbHTQe4OfE4X+xGNO8sgOHRFvzynw9Ac6QX9dkKjB7OQ94ODbT9JtglzjW/3oa2DvemJ80TkJK5Ezt3laO8tAQZwYYcEOE+dKgYGksOdN066E5okf6JAZrtZeR+QzBPOsQDXTjOD0I/m4/CbDG9CMrsQuTPDmF41PvY2wbtoHR6PrO4DZiAmRlIjp7w4U6mmYAyHTlKIlwvJ2B7Kb3HwlbV2IfR0XBENGHyDI6fVaGqQhTRlFU5KC9ywujMhPaYDi2H63EwN7SUsvY3oO1GujB8qOzZ3WJ88r3FDvXY7tuEtG0FKKjWeqQZ2V7pxPHXjuP42xYidUIQ6H059axeMsFyLUi5oCyLh3p3O069pUP9HnfaPY3EWTuwNQcZEQgD+yU9tEVbsePEBOQrVIhxTuDcaQtspGw6fB6EHqvzfGZha0YNOX9r7TCRRxFwD32DY8Xwz93XcdldSGTxSCF1kor8l/G3C3YjbG4RvYTIV+cglSN2zT3hfS+yHeg8j9GBCER0SJKg9PLc+sNdakPdsRgUHq1yiWgf6DwFz3p14mwPbGkZ2OwuT8vkUGdkAOeHce6GGBYFTtJxxGplZGOyfVDKb8/odXliJjI3kj/WlePgPu88K6ci2nUYg8G4y7hrx0hbTxQg780c9B3LJ01X+DgmjTDoujBoUyI9OwdPJCuQsEIBuZy0OjetGDMOwjBgwPDkZjS+oUG6uzEiYsLyZjMa3pYjn4q4ZUZ0/moaCelZyFnjaiY5mxknj7Vj6MEq6HbQgQuUwN7VhbDA3hfHFQOatcOQ55WTjkSStzdViFM7Go4OQlHdD+1WMR7ntUirAFrOk/i7a2fOCG1aJXD0PDRpsog90sKr3MMGTNNX7uS+NpsDcqUScs4O6/QMEYo70XgoEwvSnTDajNRSq+iJFMN8iNQjfbvSzI1jpA5bm6bxNGnYvEaMCuNOrZhRkuc87POcvlzRIXuHCSU+w0Bsb5Qh+//JCeGNX4B67Qp2G5He2Uue2bOZDddL6EMob6jNiJ6BMSwMDPHlIWwuzoxofDK1xScrTNg9PywliKeRlD/TWQP6+i2QbcnBzi0Z4jANArWzi+cw+OYQRs5PQF07QOx4QSkujGV2He/yeOrxxNG3BXsPO838PIKu8wyJGcjaosCU+y2IxNCOsF7Lh8qDCLAeI533kSQUb/CxRvcYXYl5CwER4m6AfHeqj23Ttx9mpLQSG9wQQJnbyLnPNsC04mk0/jOpc9zZKlyzC5j30C/YT9QeaTr85kgdNP3TSPf1/M8Tnp2ajqSi7B5/j/T8Pefj/wRixscwhbUoadIgQ4wOR+q3rbVWZPnWFTfH0Xd6GsrEHBzsLBSGsLkJ+dYnHBtiMBifLaiQvuu4dZU/8Xfr+QrDtBgQJtMT/Jh1mp9xivsEp/UEX7F+Pd90UQyQxMlfHT7Jj015nEh47+X1/PqWMXEvEGN8k9/1XWFFB1r51na6NfAVT/k+j5OfMDTxx9+5ys/cEoMCccvp9Uz8rQm+6zvr+VztOV6I8q0Z/r2WXP7xF0/yU+K1xlpI3P9uP990oJTfsXMHX9Q74fohHK6fJGlWwZ+8Lu4H4mITvz7EcVN9pfz675/kvXPyU0izpcSd/i3vLcRj5j2+iYTRNHfFv5VvePEpiWd34bzYyu/YvINvvUgieWuKP9dSze/4QYN47n6+aP3jfKtZPNjNrWn+vVfIcd/dIeTpju9W810mj6sL+dFEUpJcf2aGn/m9K/i28XuSZ+SZn3r5PZIzbqb5k98X8+26Z3mc4a9evMpPh5MpzhBxl7yvlE1J4Gfbnud5xN0jLecRzn2Kr9CSPNKR/P1+Eb+jpJo/aXqP7/rBDr76JyT8QJH/eZ8FZsb4Ez8/J2mLQRHs+km++s0pfuZ9UpduLuJb3eUvRD0xberiq7/zTf6bxF5zv5PLV7cM8lfn89UjrX0Jo14JB6EO9Kq/XfdsMom74dRz1BbDMFk3wj0DlHmBeRtq4veX03LcwJ+JOFMYDMad5K70SNsGKlHwdgZ63eNPw0FqooeAhKdqjk4SmgAXnwyVUg46DkEm8bpS0qs7K756Xub2jrmuvzC2l0K9PCakHu5AVqoKquVBvHThwJF7zsJ7ghYdr/pGF3p0J2G8R43yH1R5jQ8OyyNNrsst87imGymviZBmdGKVEgp3PATvm+iRjneN1eVuWnHVMgHTxVHBaxZzywFz6gGM7lG7riNwZ9PMeVGH9rN/wP0SeewNXbnDBNNlK5CYhLx93ShPDeD2okNbWpvRMzot2A43q0L6c2Uo35IApzhBLJBH2n5ei4qfOrGz4UCAVRdo+nQh+VQLMj1OtHTnoeh6FU7RVTdo+juM0H6rGYpO0Rss5IcZmW6vY+xqpMS8h9GP7g/Da+l+9mkoVqcgz2sinxR0zGwBis+mQ3esCinzaevKt6578pH5qMuP99B/fQy2/3kMZ+jqL7EKqOLdbkZiM9en4IhNgFL0NDvsVkyT9BNYI7HCCJ1wSVfTMWdC11nucV+Jci6FnxfV5ZFVNXbjwCYIcTdlj6Be0YXUg3Hzk2YFwvEmhvJI//YMbjQC92s3w9McZzpexh/T/x4zVi36bPd7e0CloG9QRk0Yu+KA/NEn8GJDmCt4BIEOz3DO12sEuqzgD3qQ8A8tKHbPLbENQbOrBs4KUi6/dk5IjywfOw2PIOXbs16RSGduliNlLrRFC281++KQmeS+vgP2a6S8Ztejha5QIpWfAdsRH4R2wAGbZQoz8gSsFSbaivUu80gzGJ8r7j4hTV8jVlhR4tU4h0ZY9aPjOEzXgTiJ2d6BeOgbzyDe1Ir210xwKh6CymMFiqDMTzAiDfuxM5Bn5yPFd4b4NSeUq2gD5NNocDYMHanD8ctEWMgUXsv0CUICRGzEcq4hFZ7Ic1Df6j3UJZBYC0tI203QHarB8QkFlF/1mJ0flDis/XYh0mkjwFnQs78Wg/NpTp5ldRJUD5J0XBEP+ScWDP28C8NfrYJ2q+cL5U8hzYjAN9tioFpFh/mEZxuLxS9vhMl6DehDHvbuWkvEcIAGmw5PGZ8mtui5uoyU8PAJCyDiHJNmWJFAOidy/9VKokSY3NqrQku371jdwALJf1mzwMdK4xLvpeYkZMyawH27A/VEELkuGaaQJjY7pJtCSmmGxPAdOom0CLbSARTPkrQ8ljw/TEdgKYQ0xmH/xgTufScP9wx2ku5LNlZkAR9pBoDq3XjgS9TULZjgiD3TMhRBHeiLY1yHusYzmCJ/xylUiL/PFU7tyz10iw63me+4iKzd3YGS2OOofTUOJT8uRIpX/hLsFphuKkm+D6MydwolZ0mHRvzJr3MudmjhsGHCNkOErAlXP3QNo7o6aUHyDyTyni6Nt78LVrlS6DDOl2/aARPiPg2nRF3oCx2KNzGXjJRVAWxeIj+5Dwxorm+G/poCSUoV1qYlhu7UEB7K2E06juKQumBCelKPsly2/B2DcTdxdwlpwQNiQMpLpJLxb+UihjZIJuq1IRV4nHIt1F9LR1K0lVeApag8EcZxzvl4jgVcgkHwdG3xaJX8lUV4AtgDQay9lY7ew5shm7yK6RkbLJeJaDzdg+GN4V8nGPS56NrFAb00dJ1a7Qjkq/NQvsdDoJBGL9Q60nc0zW7HOtLELjgi5aTSRsib9wsXvPH02DlyLI0+FdW9TdCd5qDybaxto9C9JffzyNn6i5F9NhMDTfmuNzUf9KB4uwk57uOCiTj36h4B1qCOBCqiC95ci46jxRLLU4Ypjh3ERs+PoKu1DY60AyjJfgLpq4McTz3RRyrQxu10re7htEJfU4D2j3ajpbWYdLrDFNJuguQbhdYdYzcVUHvGyUd4Cbb7yTSsViJ8rymRlZsCeUghfWNeNC8b7cMf//UvcF/pCnwiius48aglW0fa0+bchOwQkA7tpANKulazRHlzw90k4tjigCrVY56C2DnvPMuRTrUSMrkCMdeMmF6jwd5sFeQrSSebCGK53BF2JyrSOtGXgPWXkA5WFEawjGAovIQ09Vp/TOxjwgwzaYeM5y1wPijH9Fk5SpiQZjDuGu4eIU3Xbd7dBVUDqWAW+YqSYiWio+C1ZDTSWeZKGbhJA6p39SDhsM5rEtMCHKyn2tDea/Ja43ceQdwooR2pR0YAnUVXF2g6qIXDb8k2Ki6ewtRz51HuXvM3AKEaDfv5ZtR0mBYmjE1bYYtVQblSBfWaZKhWJyFZlYzp1zahMswPsgiN9ivtMFyWS3hgXEuxJYuTGAMiTDz0FxCBPOZu7kSaeSEIStIh8vXMCo2qHukh1qf1hbMZ0XmwGrq4g97eSwJ99iJ7OQaKPKcjhQEVYxUy6Dw9fRTSMTG/0YDm161w3kP2ZSrkVO5Fvvu1ezARd9OKoWN1qDmWEkTkhYCK/1dq0RNbgvoCIholxUdoIc2N61D0/CDW1ndAs5Ec47BA/8MitMsOov+wxFrgRJy1HerBF7YfRPEGj1/n6JrklWhecQD9xPbaIhHSs1YYjmqhvwzJt1C+66ELzNHVU2pg8HoDk4KUZBUSiDgUhFpIIU2HcXRi7jsb8J//A7jvKxNwfvkv8Mfj/sM9BFuV6rwL9+C8JxtHQkghvQBdC7r24AnYFGuh9q0bSL4NnbDi6VcHxImmUgSyhzA7XITFCWkHrCN9aG5q819Kkw6Nqp1GIR1CKAbN4zCj53A7Bq+pUN7qMSk9BHQyc3a1gdxVTupiJdnSkbKapN1jKcIwQpk9/LRnMBifDe4OIS2s21wK87YOVPl6AwUPGgdzZxFKO2zIOjKAAxtD1GqOYdRs0iDOR/wJY+Z6N3s3juESRgMZtHGYdYCLCeLVFQmr0SCiyHIzDkplfEDv4uIaH0/C9PQJ6eMxPldEECS3Anuk71iaUei45hNnMCXl5RNEgQOFYa0j7U2ozoIk4uoENWdBGlml9zjgT+ywzqxBVVN5ZEuchbLRsGw4CHYLLHNJId7qhBJIDgzv3wSNvAXn96UvDF2Z1KMgtweb/UQZEUKjNsQ9Jn7xUgKX0zRCj3QIospTShhpPDvYiU/+9YtAeh65dh9uGGloMlaUevQYg6x5LnTqLySjpT98gedFBEI6KGFdJ0whHeSrl7YLOgwh02v1ko8/MGKMdGgQzio7IW0yAEuVTp4EuCb9Kmnu/nNQ7ulF745I1qliMBi3m7tiHWmM61F3lojpfdnYtGWT11Yz5LFKbCypLudIq3k3EmxohA8h1zldrkJSYmAR7SbAG9nbSAryPddOJVvJk4toFJYyzSjyJOSs46Dr5PCETzzLq1swEO460kuBTIWMzBRwN1JQcqge9Z7bkXb0dkYoot2EMQlLCipGQxIfSkTfDuTC0IFAIpqyYOeBh2rcUcLIg1u/+B3+/C/JH2uSsewXHwJfWen6wY0yHZkrxyXXPC+vJZ1GIryiEtHzyCF3j5m+E9A3KMGQkbqjugRZX9uMp5/did3PLTxvffcoRrvrvdJA00gE5zGyhRTRdxMxiAMdcsRgMD5L3H2TDZcEDuaOAhSfVqPFa2hHA7Dvl2jZFpGPyQX1NPnO4vfD5Tmvu7wWm8QVC9zQr7+d+UiBzc8dhWZT4PtT72pXYoSeEwmW6jouj3QN4oK+viUE80j/5mn0/oykm6S+uMNpFsRjGHi8dnCEoQo/HsPaLb7rzYrr/a4uwdF9Gf6ezQ96kLd9GEl+a/tysI2bYHUko6TpwPy6tiGhz6ZTBfaizlrQ88NmmGSkEyYGCcxP4MrC3qZCpCyqA+by/ll3jaIqVQzygRtvQ0HRCNRHWlCVroSMDu2oLUIzDob3mXdJqJ12QbVE3kPBI30hByOHMrzTKhSh8iACgnnFhRU26Lra0UweFbyiVpRE+2bCjehdDb5qB7UHqSFaop0UL80bhOAEqmPc5SwGWft0KFwtBrvhzND9XR3GHtvks541nU9B151WYefhemRGouSFNLMgP9phOQwG447zJyqkXdgv6HH8zRGYJmcQl5iOzG9vR866KJu3D/Qoez0BWt/luJYYxwcmTMeroQrxpbFQ2M/3YExZGPzrgGEhrrDxTL7/7H1PZm0wfwAkryHCSAy6U0SUZrNWDPUP4qrPSgULKPDEs76ridwl3DCix6xE4ZZP89WwA8bGOkw/qw0614G7ZsTJXgMMtKMhVyF9az62fyslqOc5ODYMd0wgWXI1jshxnNVC8+95aN8eYVouZR7YTdD3EdsWd/1JRtZzGVBFmmZzdlguO6FabFm1D6Hymaveq3ZIYL9ihjMxBd7TTsKzk88dVEjvtqM8muGFDAbjU+FPWkgzGAwGg8FgMBjRcneMkWYwGAwGg8FgMD5jMCHNYDAYDAaDwWBEARPSDAaDwWAwGAxGFDAhzWAwGAwGg8FgRAET0gwGg8FgMBgMRhQwIc1gMBgMBoPBYEQBE9IMBoPBYDAYDEYUMCHNYDAYjJDQL3tyc+IOg8FgMASW/Ygg/s0Igm1Ei5pDPTDaHsDa1AcRK4bfDdhH9fjZwEXYYhKQvDLcmHOwvPFDHPjJEC7HPAj1XysQ1gfSHBYYjusxfNWOuJWPQPEZTij72Ta8/D9MuP7HBPz1V+ThPR+Fs0C/X4Ofnp1CrGotVMuj/txehNhhOvEzGP6XDfiLh5DwxTt1Xwlsw6jb34WLv3Xgga8kQ/EFMTwEtvM6HH/z17DNKfDQlyNIcwq9Z7UWv7oyC8XffBUrw7zn0uKAub8L71yLQaxiJR6IJA52I5r3/ANe/99zWPl/hYj/HAfHzBxivrCIPHaY0VP7IzQMXcZ/SVTjkeg/C0ng8OHb/x2akj1ogxo7H1sphoeH/ZIer518B//fbIRlbc6K4f/+K1jm7sEDf6VAbASuH9tIHTQv/Qrvf6LAV1evRIwYHhKO3LP7FxhcqjpslgPuDeOJlyLPKRHWT/Rz8nP3xmDZbXCr2U7XoeZnF+Egbchfrwo33zmYX92Df+qzRHje3YgNw4c0aD9/E7N/oULyl8K20kXguqf29PuYfeCr+Opf3ol7fr5hXzaMhAtapD4vg260Kugnb8OBNiyGd6fxB3HfE842DhOXg8bDmVF/zpiK5xOjwEMZm5H5aDysrxYg7918nDqag6AfQb9hhVWmmv+ctu2NMmSPZPqfN+uAg4uBfLnER4RnzWgrLYYl9xRasqP45DpteGJv04fEb5LnI425SvwesYM0tpuq5ZHn6ZwN+uezYdwW5TNGwpwDVss0FEkqyImQGz60CZrlOoxWBI6xfbQHTd2DsH4kBiQQ8VNajsyHlzBdZ42o21gJeecoqtaJYRQqCBxOkodyyCVuZz+lwdb9yujKUaB7SkA7v80jMiR6f3ta4OMPjBi7TpLl2UZot3mUMhJ3DjLIfFvuGxZYkISkFeRvIrT0P8iD9pHgeSCIwFdM4L6mRvoaknf0mnMmaDeUQRYq/tS+XsyG9nI+Wt7QID3Y5/eDEamdEluznD6O4wNGWB1xUKXlYGdBJpLE+5uPpqIYIZ47ACHLGucAt0zun/Y3SQdkVyWsu/2fgbusQ2lpJ6a+rILiHhKwrhwde9JJORGJts6es6CnqACmMOowbnIIzUf0GJuVI2Elib8YTnFcN8F0yYG11b1o2a4K/sn1OTuMjUWoPK1CfXcLMsP+NDrp3J0wwPpXajyRnuT6jH64+U7y29RSgLKTCmiO6ZCfKIZHARXkThkp87FWGPbVYUiWisIXyqE8HWbb40PYbdbtgnaoegcxQaoyfz7G5HkrEiu0KE+NtnC64WCsTUOlZJ1uh/kNA85dl1QKsI2bSDmNQdY+HQpXi8FhYmpMRVlMdGWZ4Q2teu4O7CboDvdh+mFS0q+QRnDZZlTtK4Z6KUsYqchM3Q1oHrISEUDMdFaF9IoqVG0UG9qYpRMh8evyUfwoEaP3+IoNOwwVOvQkFkctoinxqfnIukIqop/GQE0qorj7FeIvIrOkoaiuwPGP1mLzxkTcT8NI5WvsHoJ1Yz1+eSRTqLxilkk/M/fvg2j4Mam8xX0kleDo/gxXhRebgAQixC3CD95wH+hR8xMDpohWjlOoEH+f+IObT6wwnrVAuacPvTtUYmA0cLD016J5wE7Seg2UglfpY4y/oYdJXo7eE8VIIg2O/AGfSpBUnkNHmqG/PI0Yz/iRBsdmm4YT6djbWY4UWcxSmsM8ttNa1B0bAxLVWCMKwI/HDdBfSICmnzZ0cijETk4gOCIenjoE1Hf2IoMKP4LjSg9qtpdi5g1yjbAbaB+oQJ4laeY2WCqUXX/5QBr2YzVoG50R94EsIiQK17j+jl+5CMsOeE9/lJs0KBwnwo+TaizKxX99cWCM1gFngbVpYrkg2C7oMKTUYuRQBimvKiQ/Iv7ghjOj5wfNGIx5AnsPFiOFRnKZChkZRhTsaIbzrRbk0LxYFhNcTLlxTsE2CahyM6EO8cCOKwa0t/TAdJ10AIjdx6zOQ1V1vhiHSOyUg7m1GKWTeeg+3IukWCKq+2tQVDSBjmNEiAbyzAYQHB+P92HQWQZdZz5oSfYraz5w1wZR/b0uoKKbiD8PG1meDBWp9ufrGg9kq4msP1ss7omQTj4XIwryQA9P6nrzmyegPyV2GLYUouSZdKjcUVymgDxEOXMjS8yE5mimuLeA/awWpXscUBPbbwwloikkz02jNii3HUBGwDJKvYl16LLFY+e+elFsy5FCOoOGTQWwujtoYec7qdcmbZCllmNzJCKatMf6PtJJVG5GTnYSiQGH6QvtKN0/hRJq64fKYCUdRtO3y7HTt+3xrUcoJD8s4zNQPJyAeDHcr82605BynrFrN9QOEldfh9GlZqR2AgeUIQonqRd0+/WYeVjpnf83x9F32omyfyb18cOyILYWj5RnUjGcSmw8DOeBG46kr8ynvDomzZiYVWBtEokLKRsxzBG9ZNwdQnrOAt33ytCW2oLzpenEIMth6cxGwQ/ux0B3/qIE5wKkV9j4FKpv7EX/CS2UtBK+ZkBlQS5qDp5C/ZYQBSYiSAM10IczNqleph2jl4gIIJUaKb8hRAMR/q010LwxjZKmvnmh4iZoRRSbhMLWUygUdynceS16ulXYXeoS0cGQPZyP+mP54p4LOobSMUcKaJBI0/O0//w0SW0Jrx/lBknzs3VQPboYEU2RISm3Hu254i6BPt+TnSqU/98uES0JqTwz9zUi5fIUYhKJkCaVkf18M5rOKpH57EGorxswqGvDOSLKLUToLDXKLRq0bxF3KKQibs7VQZZ7EE+H2dBNXNCD26KbF9EU+aNZyEhrxvBFOxHSoXKXDuupRUOfFc574qAgx9MsdditmCYV9NrdHdBsDJLJy+JJB7SddDk8oI3nTaK0qMdKDJLCTgRrQ+MJjMmI7d7ksPbbGuwtUru8bHcCEnf1bi16d4v7ImYixodJnALGXZaCwpocmLYNw1oNl4ilLCcdHyIBh3/ehikhzAYz+X9QHxDp0BpqKqG/PwXJFww4+bUE5K8LkGeTelTu6ENKhw591DNGPYxEDBcXfTzfWQwbxznoj8lQ1Z8veqDlSNpehao386A/X4KUQHWglOAg8SrudCKldrMgosNBlkgEM2eHLVgFIsDBeqIGNW9Okb8X7NOFA/Zr05jBWpS3arxt0A31Nu8sguGxRrQ0lZO63tVhKCgaQ8ertJMsHhctdHjbUVJ+RuJQ3k3q2DWhnocg5Hkp9Mtz8PS/NaP6aD5Kns1BikcZdqFExv4ymImwmrC7hTRB7rJN8+ttaHuXBoRRPwm2Uok6WwqxRwMMZ5OQly6+OQlFvBr5qUaklg4ihQjpFFLfKh9LInlN84RAhHxAZkmHobsOmmNA5u5U7/bbthm7tyV5i85PC5sRuoEx6bfGHxjJ/+VwhpozQOqF4oJhpO624IC7M00hQlzXbw3aVkYNKUMTJ2pR2mpEzJpM5GzwFPFx4BSFSPezK8ZiuEs80gqseVaDljT1vEEkpWYAHUaYbuQvGOdiII3IYD+H/I4cl4imrMpB4dN1KBsxYe+WjMCNaMSQBio7FYOBepmlB8Q/QkEa/efLkNNdjOnFTgKaNaHtsB6KHe0ofFQMk4CKyop9BnCk0Uua9/R6EkfET5b4dwCWUc9ZMMjvS91bthlQvc+ErMNabJrSoW1YrB5tUrJGhpjJdmzdpUD7eQ3UaTuhfm0rhj/cjvzsYuwmAk+23AHDZT2GxTMCMdPxMv7wlZ1YkRWNkXIwd9ShR1YIXQXtQPrAkY4LEbaCOPXIh+QN+ZAdGsLwsykeHulBDJ9PQXp1KBFNIZ2QZ15Ezgh55i0RDF2ZtUJfW4xmsxKqr65F+sNuf64HD5KGMkDP1/5WJZ46DOztPQUtFQhUXFTnoujFp6GOGYPtPuqlnw4tRBeDZOPJYfIsqYXSnGF0bj1FM4GIKiuRF4Xf3omsVfTtE+kYdQ6JP0rgMEP3YiXOqA6iv5EO7SKd5d4mFLcAqc9kYfPX0l3DS0QsIz1EVJWg3v16eZkc6txCqI91wfgBEdJByvKSQsr0vFeN5tvRZkxs3Iv+rWHaDsFxugc9yEfL10PVtDKotmvRvY2Uw1gnrKPDMF6cwoziIWxOy0CSxFAeTzjSOWm+8jRafpYO16EeHYazOwN3GELhsMJ4ohm1x8x46Ml0JK+wwvzmIIaWPYF0pZyIJolhKxQ6dv77NTBvbMEvn3N1Gh2TRvQdzkPdb1RQfzsPJc/4dyYXRDNF7KBl7sTOdbQ+sAevn0geDR2qRMP1HOi6C5FC3z6c7oJmuwUqIuAzU9ORkhgiHQT1YIa+gzoWCIKth4GcdLw2khJMhHR+Kem4iMGfOZTpyFHqsfV0hsTwknJUiX9FD8kn3zeyS4EsHinfyoCaCGlVZX3YXmxG9NwdQpp6iXK9vZ+O6zYgVgVVuG38YCcch7+I+97JI1KPcgMfaY6De+ibWFm6RnjPQcM5jiP/d1fEHNkn/8hjwp+oEjZyKBKBYa/K0IV7/GZ6RTfKU4M3CksDBxMVbLZMaIvXYmpACy3pLdMhEXH/5l01xqdVofdsqCqEVOLiXxFz004qYzUygsxlclzSo/mnzTBcUiGziFT46Tvx9IoJDJ4yQN9qg2pfFfbmpiwIHocJzRUNmP5yAmwXbYjbU4ztKvJcShXiLzdD95Z4nAfxpCFJJ82Q7SbZ8dHAMqlx4bcB20A1SvuVOPCaxGv1i3q06VyKNC5lOwrTFgqCbIMGvzzYg6Z/zEP7rNhpoWOkT3REMO4yCkh5zD88QqRQCC4Nin94YsWZ14xQFPUixx3HZUrklO5G145pZJzvRbrwICGEqA/JW6qQcbQLNfuJNXxCbGuaCOJV8Yhx2DBFPeRpe9FdSv1pIqTxzFzZg+xDSd7jakvDbTaJSPjb8oXO/Q0DLCeGBRElNV7cE+qNr33lKtTP9aN3gzs/Sd23qx7qZ20wHWtAxY42bG/qRvEa18VkseS5Zp3wGlXBOTFDrP/+EPfzQ/4EsnJrUH3UAPXhHKgET20zmifz0ZjmI6qIaDRf5qBYk+TXmbYNNKDubAa0IzmIuahD2cETcKZmITVYF4h05LtaiexbXggZdQrcJB2KQw04M0uE5Jo4WCW8q7JYK3p2lsKU04H653IgJ4JUW56LaSKmZR9YgIdJR59cx/eezlk65EjhI2pJHU+ewyFU+JFBRa9B14VBmxLp2YVo7FQgYYUCcpLndE7GmPEkGo4YMDy5GY2e493nSPq+2YyGt+XI/9EAypcZ0flKDxLSs5CzJh3FR9JRSDr6J4+1o/pEHHQ7ksQTXaR8uxzl8yJJLBd06JOfc8MbOhSoWTsMeV49BrYkiR5o0pnYWoX2LTRO7WjY3QBFdT+0ITtCKQtiWLD1BrT9Yw2sqTRGAXP7s8Fvz+BG7iW4Jol9EbL+3XjgS8LOPDI5sZPJYXR1TM0P8xKYtWH8khUzyp2h5zKtIDYs2cVQQOFZrEid3nywHabJGcRkHvTIbwVURAcY/LQCB9tZA0zk7pufOwrNpjDFEGPpoZMN7zac7w/ytd/dwbdedIoh4fG79pf437Sbhb9//9Yr/G+qT/O/F/ZcTOhy+fXfaeDPTbmuO/NuE5/79Qr+5JSwy/MXm/j164v4/S/v50t37uB3lBznJ8SfPj3G+Kb16/mmi+KuB9OGCn79UxV8Q3sr3/DiU/z675/kp8XfFnDyY+07+CdLuvjBvmr+m+TfCY9EEa4hcZ7z/ZN8QzlJA5IPdCv9yUn+6vx50/zJ76/nKwzkrFtOfuajGd55S/zJA6flBF9dUsrv1723cH2axl9vJU8Vgl938U+tf4rv+rW4TyHnPv6Tc+SJPJg+xzd8J5ev/RXNRPKsLbn8k+VNJI9dP7vytMn/ftdP8hXrSd5fpzvu55nir75ZS/K+iK/YW8Hv2Cw+YxCozV1/S7hIREwZqvknv1PLn3HH04OxlvX8+hbvGDvDLQo0Pz6c4N/79RQ/MxPsJI88FHH++jhfuvlx/pvfJXn2civf2r6fL5KyvVvT/HuvVLtsg5aT71bzXSaPdPJIc+fMDD8j2I3Ljv3SU8yHwfngwPYekkB57YNg8yGOe0/rnwfeNiMihD3FV2hpegVKs2n+nK6VP3lxSrKceOH0KUsfneH3f/1xvuK1CX6Ghjuv8idffJzPJXFz5a4rH596sYFv+IErPxqGfdLYk1sz/MRQK7//u4+TcriD398+yE/MiL8RBNv7u/3is7Tyx43e15r6VS2fS+z2eHsF/829J/kpz7gGTH+xXO4l9zK1EnshdbDXZf1tUcDcyj9Orveexz2mXi/l1784SM4Qkbrn70mdQNPs9YXCNWOidf1+/sxHYkCge/oyPcGPWad5z6LktJ4geR7KRp381eGT/JjY1rh572UJu/JDqgy4wooOuO2sga94yjf+Tn7C0MQff+eqy1aCQeuJUHWKb9p62f9CHCXbEOFc0paSuHa90sTv/x6xze+18mcu0nbFVb8EbrOWAjM/vfEV/j9uiLs3TvPXN+r534m7S4pUvUCf/3ukfIi7UnW6L86PQmWIB8I9g9vgfFn+CamrSb1Q1Pvpq5m7lbtnsiHl2jDa3pzAx1dG4fhvJSiMcAWCuNJv4o/f+BfcwATwC9Lb7N/stYxdUlEfTq3Wo+ulp1B9Ngbq5/dC+2bVwgQUAdID3+PhqfJlvA/XjckuL/c81Ps9AFRvxtmf9WFKLvHK2wdh5Y7RMVg+liMp7cXoV/BIzEBJaQ4wYIH+tBjmho6Re6US7fad6P1ncv1l5OmW16EyuxhZL3WgeF2A9L05jNoiA5K7e6F52BVkfbUYBYfkOEUnY7mCYH27C20217M+lLEbmY96X0/2aD72Zhux9bQNLxa5whzT08DXM0KPq1y9CYWr29B82ozi1a7cMJPe+dObyue9i45xHTRHppDT1DfviU2p6EVLRxFKaw3op8N4aGCaCgnCrx4sV/iFcZ84hDFnmqNqqJbTSaFbQw7toCz78nXYv3Ect4S9Bz3eikhA8sR8rBLVlkx0dLvHqvqTnugRuzk7xo7VouaYGXEBh9z4Y4pdi+270kOOh3cjW12I9tOeo+qlvcOWY6WonK7CqePpLm+Xwwjtt0qh6+xD8fxQA9crYSH9hXiokb5dhp7zJjiyM+dtyHHRCCMckPe14aoQEsYYY8/JZqHgbLBcdkJB0jNe6k2DuAKJwzYB6/sWUiaNME0CMbOAukBqDoLU0I5kFG7KwhMpKiglh3bEI70o0ORHH4gROcnzwT1MYHkG6t/sxlDvcVRuHYJ5VT4OVP4SfT5jqlVPlkATzhCdZdQzWY4XnRYMnc7Aix7zJeiqDHQxFjyWj/JS/xygS51V6+So+ucqpJOTMt6oRMFOE8p+dBD5PmXfE9sb1ai2laD3kKsO6ta0oWj7Vgzu6Ua954oqvpAymowhWK+RvBDmD3CwWsYg+3AG1bsHoaAraNjplGefkdKx6dC80YKelhrk9TpJfTGN6eWbUd59EBlhTjAU4Dhwy5OQ4pOswlhv8e95SLm2jk+Aiye/0QlqThlUm0i97EOMVIssrJBE/l0W4zVBz39oRzxSN2YhK5WuuiQ1tEOGpOwqePu1AzBHMnrWKb2Kihe+QztIXGlkhTcySsS56yFqr+KfC3h4sz3mJGS0utLFLtVmeSJ4lK2418OTLLx5HlFBrvVu232ZHbyAW9/dgJXu80at4PE7/HGcPIZTh/azf8D9IetQunKHCabLVtLOJiFvX4A3yEJbQtLpcA1M4lAOzmFDEqlDrKQoB5qv6DthUPJNqFg/cTetuHodUDxC7Cs+cFnzg5blipSAU68Z4XF3CelVGaQCzxD+tA1UIveZUbQMaKAOQzS4WIP4n07g+gsf4s/27fR7jUOJ35APjSoGU2eHkfEtj1nc4fKXf4E/u3oDdOjqrY6X8cf0v0f8muv4zwtfxL1fUiGHiHCbZQJOBRFv8WE29hFAJ/w5P5mG1WrB6NtBRqyRSk/f2IWPv9UI3fxrZFKotxxAR2wzaoZNyFsnNkCPqLzF1qQZQ7OkEhRFNEVFxCx3xAwrEdLuJpY23uXhjq8VcTjsSN+wdmFYRkBUeCI7BdqjQzCRRl0NE4YGcpD5vKsScVzowfGb6dB2umaUc3PuyY0ypJT24m1aSdHdh3PQXhq38Hx0reLaLkzMcqRpmsKZ8gLoY+OgWleMTctn4JCTiipCm7j1wgXSaft7odIWxkwP3kCc1JhpcWUa5DbiVJEYI1JBWhwKofGl8bdfsSAmsx1VKzzSVZgg14JTPhPk/KBjqm+FfvW7OOyYuGiFekvywqQlkmZJ64jAnLADj7rj7dGIisQX7EX6s6RDcEKF+lzS3NMVXg6dQ/6RfuTFkU7M6qQAQtQb7t+H0VzfjEGHEsmrF1Y/oTleTNLonLvh9+Tdh7C5OBNJ5ND4r+Ugf0MXGnYVIGF1OhJJQ/jQY0ScPJaDksxClF8njWI3abxUPkJ6eTKydhOh8hzpCHqU6/I9dtiuy8XxuD7QMdG1DThDGkHEknwmdYIL0kBen4IjNgEKIvSEYSierClHR7W4zJs8CZml9VByQyhGJnICTUwMAl3f+6RNibWyaYzZZvDxFVJ3XGpDxe5hYR1lOREKKV9V03lMVB95Q4co9NehYSoHR0nnyT2WV/lMC3pB6ukjg3jC3Wn1gk4arEWzswT9h1Lm7UW2rhzdR7+A0oouGL9+IPDSf6TT0NjxMRoOlsFMh6H9uxHW+L3oPZYDldvGLzVjqENF0tCHeDUKa8km7kYDRx07HcdhInkX57HKDkW2u5jYlP+wPdJ9xUPfeAbxpla0v2Yi7cBDpG7x6PjKikkeniPX9bNQscNJO74JUFdokJGdjxRP4V9K6qxrTiilOgOkwzh0pA7HL8+QexBh57FMnzCJmA4fiCX5Mb2w0o6APAf1ra5VV/xQqlG1LwM5HsPoyqvFPwiy2BQk/hX5Y4L8vSrIRN1o+dJmfOG7pD4dvQEI9ekNOEd+h3t2BhfRlFv/+jv82Vdc4wdpnfwJvon7vvsv+MO/3YA8qxiaR6ww22KgovEONiarVPw3GLIU5LV2oCRVXJowHKhz5FXqHDEBX1bhoUe87SsgFkd4zhEqwGmVwlbuWBL+jLqlxb/vLuZMaN5QhunDIxGtqOEaK/074Lvi2Ggp6MoR24aQ/qoWm2W0p+cg4ncM1vER9Iykh1iTdBz2b0zg3ne+gbnBd/Cf/5qM+O/cwI1G4H6PXrJ9ydaRdmC4NhuaAYfQ2ClXqqDeoIY6VQ3l5QbknXVNlADpeHhOmqCCDA+Tgk1FJqlSJQU9KWy2UT2GY3JQuM4jjTkTtFvqIGvsRdUGEk4aUmPjVjTEdqCf9G5ldIw09daGMVHN7hMvxwcmTJNGzr2OdVBuDkGzpRaK1rdRhWYUjOegb7eEv2XWCsNRLfTXiOBc470MkbCsmb0cvW94rHBAl2KyOKESlwmSxvWMlmfPQ5MWuILzm2wo+cZChKQ3zYtp+hGD0zEopI3lcgt69hegWVyz2HGBPOfzBjzdPYJiiUtwHxiENW2nV1EBKAaKCM86p0HfsQANoxc+eejjmXU+VoiMVUTUSkyYtfUXI/tsJgaa8gUPIz7oQfF2E3Lcs9bp0lFE0EqWI9LBG+ruwvHRKUGcbMreifxUTxuSvmc0cMKk0cB5RyfW1nTYoMouRJnnmPsw1pEW1tQlrZT/Ous0/jWIe3XAwztPjudIe+t1aPhlyI2wxrOzBSPPqUh9NYUZ0gkz0Q71qwbIfxBGWbygx+D1BKSkkM4iibfU5Dh6j4aVfejdvmBB9lEDTHEZyHyUpNCsDeZJ0s1VEgHi++w034moHXCLampTpEPrFiqCE2BOKs3CK2sUOl558DUDDJenSCcoBzkb42CZTULxFmmLd92T6Am/Z3Xd01ocmZ1xNvrWwiR0RuKUa6H2mRwaET7pI0Wo+JuyfdpHf0Nb1NrgAmK9FagNcVwbw8QykharPO7rUwcI5cVhw4R1ApY50sHZpPRrGyShXml32+r5t/hzIFz18jdx78i/4I+bXPWzX1291OtIB8lPmgftqwbQ/kzoFj8sBA1TBxWtJ9cs1N3myyaYLxhJvUDHZ5N/H2PrSC8Fd4dHmgihobcsSNqWueBpEApvhJCC9vFhUo/8dB3++MK/wJ6+BvFUjNBZ0/vbYKJuZMqtaViXx2G6XiN4OpIfWYuk1KeRkfgxEdLiMQFZiT/fQLrgvx3Hf+IbuBfvYOY3AP9QslfhDryOtKtQ9cwW+ntRJJEj4+AIRg+Kux7Y3xf/kCD+UVFwzk5h8Ggd2s468RAR3769Xtf6uUrkrPNYtUSmhqZ3L5qPlqKgxRWU8LVGdDzvMWkrSuQPq133kajw/VhOvSE1qBwZRuoyE7K+pRF/8CFWhZx97fB/kUrSmiPPdz3RJfjcLItHkrC4vR3G1lr0XOCw5nlSYdJOgxcy3B/MWxEp4momyq2FyOzNg9lOhPSqJKR4rFksf4AOClHh/gAdDdnDOdCIr0Z9EZ71lioMEU2RQ7VaBWPtVqQeVSFJqcLatBSSLqSTtqUw6NAR6onUoQE1Ow1w0hpGRtK/uz681XWod5U0Tv6r8kYOd82InqNt4oQ17w6UINhPTKOwvzfgRyjoxNoD1+kHiawoIUJ6Hql1pD0hHcspcxfq9vQgRVLwq6D0UQahTN0fuk56DRoGpuYnGnI2GeJXtKH0EF0xJREPPZaMrG+vheKSAXSxrlDQt3GF1OaPncDgSjWS16xFioQXTBXvXTPFp+Ys5FeMHDGXarBpl8q/o0TtO57Ud+KusNKHR5Fy/Pswun5QhymJD6/E3KOEQhE8kTgizop3W5Hzaj1698lda6fv6CJiolc8wh8nqfvbK2ok7imDYlUGeThxNwyspANZ8FoyGo9WoZykGzdpQHVuHhIO60gHwLfuoHCwnmpDe68JDh+PtoBtFLq3lNCO1CND6nSC44NBNB3UwiGRZvQZlCt9Tozc0EJzcww9h5txhnTCPNded+O1BrsYRtfJL9zQ5vPWh9hbShbUQTq3fnxpDWljj+OT8c0AnRi/KTukiKbc85Uvgj/8L/jPn/49VggOiRuYu0pM0nNduGDrSFOh2jlGbDJAxkjA2ScwrGtGXX+KX9mQk/K2ZpV0i08/LNXQOwbuv9Wj3aMDG5T4ZKgflaF5dyr0K5KgUtE6MAXJX8tCybZyaEgiUZ1hEg9nLI67wyMt9q5wcKGyEIZ2vJSAxiEN0mM5mDuLUNphQ9aRARyQXOPWtUqHu/fpt4oHHcNmmUbcqoUF4f0I5knzYKajE3/EF/Hn38lDzGgnPvnXL2LuK9/wWgJNeJVqlvJHuyoeU1IL+vd5fKErCoQe/dtZOHWENHPh9O4liM5bIeFNIw1W2+EeGK/TV4cLa7/SsWJTfl8EIz3o61OYJlo6ZkMVWp4XxbUE9OMjTz6vB7c6iKfVRjpKR7tgmpb48hh9rSnPR31jTgCBGcQzGMZYXG8vh8sG/3MnHe7j+l0a1z0Ny4uRqnTZw5DoORDWT+9Q4cBAC3IknBe2881o1pkwHZsApY8d02d1LM/HAboigxi2OKL0DodZjqRZIo+0UKcMI8NzbVcJhDLUCeRvS/ISCJ55Ik3geHIODrKQHbAgducB9YBa55TzQ3/8Ce86Xswt8qudUedvFHGdx4Hh/ZugkbfgPKk3XanLwXg4DZUOHxHnxWLu6YFjGDWbNIg76u01t54oQF7vZvQOFIc3NtmTsNIxSPzDnCuwaI90CKK9flgeaYrwlo+I418A9wabf+KJML76P/AF9/H0Gi/Ae/4K6WwbTpzBlGtyizdCR9yBwkg/cBVl2bgdeSR5TfoBmb8rRduNLLS8EWRYFcOL2/B1/dvAihzUd2sgP90AbUcbtNUFqBtVuz6d69n9jCXKYk7aTz3TcZxUq+twvyhmY7OyIdvwIT7RnBHGM9NJNtQDF1BEuwnr09W/wy1SqJd9iRyeqsLcLz7Esi97t9bKtEwkXNLBotyO8tJyr62+exSnFimi50lULlRC99zhAVGe7zvi01He2I7uo93oPdaOlkP1qCeb9qgOvZ0t0Ir7rk2Lls5eclwvdEFENEWWmiOsS5yS/URgcahMx840OSyxGdjrdZ96tHSQ+wQU0SGIDd1IUfjDx3H9Gy+TzdWRCy6iFxCWtyL2kP+YGGAzoO0YEdGtGRj+gRbGG2K4B8q0naRMWCDfstfrOd3PqlsyEb1Ibtcn4G8HwoRd7zI6nydREFpELyAPkU4yZRKSVoVhh5G8e4zoa4iMzyxh1k8UpfwzOlg2nDZrTTIR0R/i1neTwxPRlC9txoqfAp8I9TLZfEU0RZ6EnHUcdJ0cnvAp/+XVLRgYXcRXYj/jyIhccX4i7jBCcveOkf40oL1JnSpir640QTwJdCwVnQng86GNiLFbYHaqhNezQu9+siTiHi3ttZbKdDgvMUs/MK5ni3R8YXQ4MLSvBs49wT2LjrN1yH5pGlk+nkXXWLcxTK0uwdF94ifOvXC97ai7vBabHvU5U1zve23RUq/h6Uo/T4+0OakYT5hHoajpQPFqGRyna7C14yF0+329zgHjoWw02LOQ6RPf+bVP15WjY89SdNSo17ULqhBeXT8WVY7oPduhGmiX9MiHjeCRNiEnyGtzSjCPtEmpxauHpGyGYid2mQu9LE/IwwXo2q+DsNyTgJx9OhQKQ4ikWKoyFM11Ats8bk7CSL/6uXEvOnYHGMp1YwiaHXrIcn2+Wue2P1kWDnYWSnhoOZiObEXdv/nbbjhljQ7tKNhtRHqrDnTuBp1PUPw82Sed8qpAKxAFeVY69tvMKaAOq3zTjycVoPi0Gi1Hq5DuHtqxqwHY90u0bIuifqDl5GBcCG92kPpp3IAzHylCrjFM6/iuxEV65INAr18j78VAUWQ++fDbLDov6V+An4Z60xcFQTzIgcenByFQ2RDLFQK0Q/ZTGuS+JkPeBu9KT5hPRdedDlYeA0DzxXe+AyM6mJCOhBtG9JiVKAwwcSVS7KN6nBidFvckeCQL5Ut0L09RHQnmjjR0PRhpQ8DB8tZJONN8ZpXfDq7pUdabgJb517mfD+xXzHAmpogrPdCxlHrYVhcifd4DQgQZESUxayKYCb7kWKF/fhCqpnKoI0n8RZWjKO/pC2fBkG4KKaUZwSf0knJjuqmE+uE7/Y6T5O/4BJDktoFouYNl8TOA48oQjvcYYCTiIi4xHTmFO12TIO8QdMLm8TdHBHFD75/57e1RraIi8AGp215PgNa9OsttIqIJ3lFgPVGGvr/SQiM55DIwHMnLk7PpyPec6C5BuEveRQWdn9U/iKvu+VN+KPDEs3dn2aJCWr86ssUaGNIwIc2466CTyE6+OQVVthq2o+1AhXZx3kkGg8Fg3F0I45o/JH/4r80/vzqXFMFW7GIwooAJacZdh/1UDXYcGoJdpkZVkxaFa1iPmsFgMBgMxp2HCWkGg8FgMBgMBiMK7o5VOxgMBoPBYDAYjM8YTEgzGAwGg8FgMBhRwIQ0g8FgMBgMBoMRBUxIMxgMBoPBYDAYUcCENIPBYDAYDAaDEQVMSDMYDAaDwWAwGFHAhDSDwWAwGAwGgxEFy35EEP9m2I1oe+l1mH47h4TEByG/VwwPCztMr/4MRlsMYleuxAMxYnBQyDknfgbD/7IBf/EQEr4Y2beebSN10Lz0K7z/iQJfXb0SYd0yWmzDqKvW4ldXZqH4m69i5RfE8M89HCz9/wBN6zuYilFh7UMPIKxc4iwwHDyAuqHL+C+JajwSwXe8bafrUKf/NT76zwT89Vfk4d3vLoJ+Gv9nAxdhm1PgoS8v8vlImW3e9yP8/P+dwgNfVePB/yKG3zYcsF4w4wb/JSgiLK+fGe50WXaQsnBcj+GrdsStfASKiL7jzMF8bA/+oWccjj9XIjnc8icBLVc1P7sIu+MBqJIUt7e+ZEQGZ0bPi/8E/f9xIOYrf40H5be/bHHjPdizvwfjN5dB+TcqPPB5q2gZdwz2QRZfHKSR2aSBvHMUVevEsIAQkXWiFsenkqHOzEFOohVtpcWw5J5CS3a8eIwEc6QxtkxDkaSCnDTMw4c2QbNch9GKFPGACLigRerzMuhGqxDF2ZExZ4J2QxlkYaWND7McECsTd4LAkeNkQY6b4+BwOMkfMZAv9z7OdqoZPVYZ7hf3PeFsozC87UDe0V6UrwsjHj7Y+suQfTYTp47mwCtnOQccsyQ2cjlkfhWxDYbSbAxvC2EPEjhIJ2lTtfzO5OunwKKez7P80DQndtlM7BK+dklshYNMIl9E5uywjM9AuUa8jgfcFQNqj+jBbdiLA0UppJwu4DjfjIIKK0reakHOCjHQTUj7JXG/5oBiucLPfu2X9DC8O40/iPuecLZxmLgcNB7OxJJ8DT9kWeZgPdWG9l4TpuguKb8xq3NQVlkIdSBTniVlgfMvlwKz5vDqxgBYXy1A3rv5/uUvFD62wp2tQ9qeEHbnMEPf2Iy+y06SlcSGYpOQU/oiClMjj/fngTtml3NW9BTlwSRlI5wVxtdGMDYrFYuPMXl+jNjpWpQ0aZARSTZd06PsGSMypcoygxEmd6eQvjGEytwaqI5GIeikuGGBBUlIEgqSGc2pxf6Nsie0ch6fABe/FkmrZDA1pqIshgrhBBgqtmJ4y0JFYDutRd2xMSBRjTVKVwPz8bgB+gsJ0PTrkJ9I7ng0FcWIUkhfakbqboQWJHYTdIeaMbZiLbnhBFQvNKIqLdKGQTptuA/0qPmJAVNEQ8QpVIi/T/zBzSekEjxrgXJPH3p3qMTAAFwzovlwLQY/WovNGxMlRbGLh7C5OBNJvm02bcznSGMuX/jBfl6L0oqTUFb3onG7ikgrN0QsvNWM5tfHMH2PAqp4t1wiYv36FKbJ86Tv6RaEt32gEltPZ/g35LMW9PyQxPe6uJ9UgqP7M8Rj7H72EAzHpAXTy5OgWk52ws3XzyiOSSMGXzPAcJnIsJVq5BfsRM46jzQI8/m4y3rUthpgjw9cfgKWWcHma3DiQwUeWr1wvpuPrwxBfxYof3UAxY+KgR4EzPMbBlRuG0aGROPLfTCEto7jMF1PwNq0KO33Hjk8zJfgsqO6xHaM7lGLYYsleD1n6y8mHccM9B0uhEr0INtHtCj6IXDgtAZq33gTaD1Q+2MDrOL+YsqCLwHzwgPucg9qDg/C4VnXinlc2NlPnpOEhbK7OQt024swVtCPlmdEaXiN5HdBAxIOvw1NmsSD/ymwWLu0DaHuqBnxid4lgiP1veHiWtSf0CBdHsJGgpQ7SUinllvm05GmnefRKcgeSSb1PXmYSK/JYEhw9wnpORsM1bmoO8uRyjFCIU0b1sPNODOrgnqNUhRUNox2DsG8pR4jpFctl2pgbprR9oNS6K7IkfJkFlLdDbLiCezMTYF1Xgj7C2k/OHL93GLo01rw9r50IQ7BhLTtLBF6R42wUm/ubAySnq3C3mdSFjxo4QgSsXGY3iM2BLNGaDObcX9rL8rXiM9CoSL2aDOMVlL5EDEZszoPVdX5SJl3xwVpfIN5/4TKqg6qDnJeqhgWAuvpHphlaqi/lgSa3NzkEBoOH4dtTRW0z6u9PIQBIZWm6ZVqVL7qRP7howE7Dtw1izBsQ7WC3IgOFWgxQpaSie2PEVv71VXBEyM0yLdKIvSISTcM3AcGNBxogzlhMzY9LDYsxK6N3UNw7NKhn9iB7C4W0tTbnP3DaZR11CN/tRycjaRpRTWmin+Jlm1iOkT7fKT8tO0oxtBGMZ2EwOCiMBCCOKtNQMt50oh7FAM3wu+dQP62JG9B7LBg6ASkPdIi1tN6WORrkfKYy34d4z2oo3aVUYa9O7w93C4csAz04YxNyuNmx+irBkw/3Y5eIljCsv3FlOWAojfydOYcDjjn6BsbDoMvBqgb6dCP1ga0nXVAETuD6eWbUf4PVch5eCFTwhHSfsxZod+dh+YH6vHLI5mu80LZ3Xgb0oo4tFyogtqjLjMdSUWZXYuRQxnhpf/nhqWzS1d5U3mlvXfY0gppB2kz2n9QA/2NeKRvy0ESdVC4eXAzdpNyLWNCmrEUUCF9NzFlqOarW5r40vXr+aaLYuBi+HUX/9T6x/mGd51iwBjfJHHtaUMFv/77J/lpcd+TsZb1/PqWMfLXNH/y++v5CoPUURQnOTaXX/+dJn7s92IQYf585ww/8xHZxN9mfrWff/zrFfzJD137/Mx7fNN31vO5ugkxgHCxiV+/nlxP3JXCaWzg13+dHHNLDCCMtTzOr//HM/yMuM9/dIbf//XH+YrXp1z7t2b494S4dvET8+dJp01Irp/kK0gat5rF/XC4NcEf3+mdlkI6vfyeuBec6Ysn+NrvPsl/M+tJfsc/dvEn373KT7uz2Jfrg3w1eS63DXjl9e9neCd5fr/8d17lz+ha+dZ2763pQBH/5OYi/oSVHhTKHhZwvtvAP77+Kb7LIgaEka9LglnP/2bjS/x0JHkTlKv8CZJvO167Ku67EGzwKWJL4n50z+dRfj58jz8xn+77+aIo7HLq9VJ+/fdO8qLF+xGwzAv2TMrldXFfikD2+5Nz5CkCEWX58mUJyvJUXxF59uP8VY96avqdBv6przfw7/k8wLSxid/x9Sf53J2l/P6XvcuDazvOn7seoCw4x/hWWqe1vMfPiHGbMTXxuV/fwe//yX6+dGc130Cu0fDiUwHr30BMvU7yj9afnhnstrtbTldd+5HPw1hoe9DAn/MKdvLnfhIq7+4QkuX1Ov8f1S8J4b/Z+Ar/HzfE4CVjaexSKE8+ZV4Im68XQtSX4ZQ7H4QyF8xuorgmg+HL3bVqh82A5vPpqMr1fD2/GEgPuKNN8HLmpS7yihf1aOvowvCkuC+BbaAapf1KHDhKet++E27o+brjOP7acRgu2UmAFYPHhqDYVY6cVa5DIFejpDQT1u5BmOfEsDCYGNUDqclI8PCwJDyiBk6Z51/DWk91YWj5bpRni68zl8mhLipD5mQnBsddQVFz007uo0bCSnE/HJbRMaTi357cE2SK0BwH2yU9tEV5qH6dQ8Y/DeDUWyPo3Z+J5JsmdO3Pw6aNW1F5zAJOPEVghRrpacDUdYcY4EGs1NhngkyFjF3bkZW5HbufK0d5Kdmey4LymhmO1HxkCUMOwmTOipMdJI+2EjuUGGJwd8Hh45tAnMw7n2R03PDNP4CObo8WV/mZEjysjlg18r+dhaw8YrOl+VF57aevmaDckBx8bOfkMLpIHdHmuf18eGH4QiAC2S+1J/FPf+RQ0KFer/vcj2za6gIU7CpA26iX5UqyFGVZmduBvm9No323675021FzBikv5mGtzwPEp1Wh9+wI+o61o36PWBa8tkKkB/D2caND0E1moqxIPf+WTZ5agrKvW2BVlaD9mBYaco2SJ0MMCfOBu9SMykNGErcMqP3qHTP0r3QKde3xARNpBTx4NAflG08Kb6VsNKlpnULfCr6VhKpvu94gfvZYgQe0f4+V73wT94ghS8vS2GW8MkAeJsZDIf5Jsb7dNX9d7YhH7ixXQrXciuGf+8ZDi7Itm5C3qxg9l8VjGYw7yN0jpOmQjpeGkV6RA+US1RaO001oOJ8CzQ/zoZISS5HwWD5pMEqQEUBA2QY0KDimQP1rLciRarmF810NT6EwBMEBG6kUVCs8qxhSpSnI/qwVVq/aPxhExL5P/klUer0SdVVq5Do3XPuO6xZXheaZDnIFqeA4WD8M+2bgruih2V2Gmm6PBmp2BrbYJPIs4v4iUMrJPW66Jvh5Ql+dN7foYXKSzsY/90FXSxrvRPFlY6wSSVvzoWnsw8jIL1H/THgdMefkEOpKC1BcoUFtv1kM9WBZPFQkzdxC2/YmaXDHiT2Rzo7fa84bFpguWWGXaG9c56Vj7/MSr43pePxLFthuhm6oomJNHmmA/x7xa8T9RZOETQUpMPWSvHD3S2jZfVUPVdHmqAQvxTFKhNHRGOztPY+3a2Ro/l4d9NfjXOMco4KWCyWe3pAk7vsjT85C1b6DgpDzEobVLRgYvR2vglUo7B8ltutzP7JpGnvRe6wX5WF0+JeiLDsudaKucRCy4qPCfenW/eJDGHpDnHwoAR2ypH2eCKAdrq3ssAFWn3IqQCcMkzLMzZH7TNOrkbh5GT4RbkT8Wj+S6NiGAXepDUX/aEPeiRF0bzGjemczDFc8njs2FfnutN2V7jNUJB6Zjb/EgaQxtL1InqOoEm2Xk3CgvxeFn4VO7pKX13BYGrsMqPJ92ibVkyXz19Vs8vhFpkbVqX7Ul9LOs2c8NGg/TTtyOhSuFo+NCCrOm1FDbXeXFsPhN3cMhsDt6cDeBmwDDRh+ci9aqAgVxd+isBlQUzsM9cF+ccJSBNCJF3YbJi6PwXRxFMazpIxni7/5QoSQ+Vglqi2Z6OjOR5KfUnKRnpgg/uUmAao0QG+5Spo/ItbEUPuHVqIm1yJ5iRvxhMR0oN+Cq0SvzWuTG0RoQ4m1yd5NTTBkj+Zjb7YRW0/b8GKRK8wxPQ18PYNUx6HhJo3oeX0QV+0KKB8rRpLtBNo6xB9lxch0nkPna+fITjKyniPXXMbB1F0Lo1MJWSwwdXEQJy66Dvdj1oZxImZnhJ0E7DykRabg7ScNt2/nhnPCsSwZmdUdUD8sF8doir9JQeypodYkaU/Uw9Jmc42ytcfuRuajHo0OPe8l13n+HSzqOTvu8pia12K7X6NPHmmwEx+TlF12+BJuCSEP4r538hAn/E0Y78P1Fz4Ud0iB/6m7Eb6BjzTHwV2gf38Rsv7deOBL9G+R357BjdxLmJ9A8d1vYmXpQus90/EyPvmFuLNhHeTazXC/ZFFt70Df8ja0F+VBGyvDzDSHtd9uQceuwKI1GHSyaIXufmhOaF0rRqyqQsceLUqfb4JioB4Zvp5fuxG6n4/hD0GXWbNh9LwcyqQ2aLuNGKOiPFeDg9kLnSzZo5nBxRMVhHQFGZkccvFejitD6Bt2jau3EbP3zHsbsUvlsi6U7SI/JOajqiYHSTIrDKSDNyX3noQlhbBCwugYLB/LkZT2YsCVEqIqyz5zHOTrtiNntQ7DHkI4Zhm52PIYwbYck1Y4llMBLL6xuTmM2iIDkruJ+HlYOBzWV4tRcEiOUx7jij3T46EMUhYeSyedqx6MXq5CilsEzVkwSsqa4hE6cZOWdTpHIeQ7AAHbqTrUDKiwl9iKMB784QPoILZYursU9s4+FNODUh8ipd8N6VyM9GDQ4j8GWLkmXUzfKZx7vQ1CTG5Owkgn0MqTUbLvADLcbwu9GIf9G/8ilkfgz/btxIosV4VNy80f078JvOD+3bu8epUrr3IZorwGw6cs+8XnK+swR+oP4ff5srz0dkk9yknoQ/v+6fl6zGGfFsoAfWMp2T5Qu5wjdum2Y2KDxOT8EcuiwzaBKUccEtYkQRm0/HuiQsbfVrEx0oyouTsmGxKxUfkSsLcxhzREZN89eS3CyUXzzJrRvKsUVs+Z2fMEmFBzpQcFu5thIVVAUuJarE1LRcrX1FA/qsJUZyq6EukECXhPlhAmN/YBuXtRvEGsOm5aYXEooFK6GiD7FQumibSTrSAViY8g4MbbUFA0AvWRDmg2xpNGWo8aEgfsi2TSlh1De7aiJtFnMqNwnhX1p1qQSS8lTuIa2UAEzx4i2GYt0P+wCM04uDBRJ1Da+OA7Mcj2RhkaltVLTyAJgOMDEyZIOj20WuUSAw4T2qr78HFaDkp8BKV9XI+uI10443wI6tQ1CxWoMCnMipTqAyjZlIyEeDHNLxkw+K4c6c8lw3q4Dl2WGdIYWDAlU5E8kCHuETXWpCRDvToFyY8ohZnqQSc72Yag2dWA6UQlnA/uRON+0pAIgiTE5BnyTM1FldJ2GOZkPCqkHYd/Ny+QacP4h6+IDaUgorHQUAsNqhX3ejXCtNG/gD/3aZhdDb6058sl3rPnG2Pf/bAI5/noZNHuBvTJCnGgwGOCrQgVc9wqYh/LqF12QeU5YUhsWIVlCe3+E4qkJ/hywhKKdb1jpETGkM4VyXv3PUmH2GZzQE7KLUcUsqsz5kaFnYfrkenOQtrRJtLRJa5D2ACFxNVmmYBToZq30UURTVmetWKosxlNQzYoVEkek7FD4J60JZWfXmGB0oEjeVGA4rNqtLRpkB5vh/FIKSov56H30Fo4OSVSEt0d2SCTDedsGG5sxujqEpR9K8nPVmie2D8w4sS4E2vXPYH01d5XsV824SqR18n0lVes7+oUkUAF7wBQ7SmAF/ZdQnlBCHuVV1o+G4H7PTql/kiXVxcSv/mVeZfIh0d9sRAfl1j/z51iuV9yu7TBfBlIXhfItiRshNil4WgdmvutUKxWQrU6HYlSQ6Z8Ee1ygpbz9w8EtptJPcpy2fJ3jMVxVwhp87ECNHh6A29Nw3rFjphE2uvMwt5jhUHFhheCeNHCUdqCA1uk+s3hiUUXNgw1dsH8QApycumsYFdFYC0Wz6W9aVJlTNMPAZyOQWGFBhnLLejZX4DmR1yNuONCMwqeN+Dp7hEUS4gWujzQyV7XMmJxienIzNuOnDUeVUIYgsTckYbiKwdxar4RJVXWW6RhOpwE3dnyhfNmbTC+1QPDwBim5Cqkb83H9m+lEKEi/h6lkKaieDrev6MQFI6k0+4CrzVFhev2p6PvWH5Y3u1wZ3nbL1vgFGxJDJBAuPflfJwXV1qZ55oBmu/poayhq4LIYT1RjWKDHGU1B5G/2hFYRIWyw0iE9L9umPcWe+77C1yfhlJAumEWrkMEuq8n2n0Nl2fMA7/jCKKYxTLv5QgF6PPpVIEbOBHJZZn97DQLTzxIGtl1dF12CSTsIOiSk2K59RIOUc/uD0NIiyzper1LUZYdVpjPm3DOOk3EZQLUGzOgdg+X8oUzQbulDrLGXlRtIMeQjoexcSsaYjvE1VWCp4P9Qg+ajg3C6kjA2i0ZyMvNnF92jxJSSHtCBNtQYzVqR1SoP1Hvta6whbQlBaOB16Omw8aoy9NPNNJl0yxAko8A98P3TY6IZEeXQju7xmSx7LhE7q2gHucIhbTX9V14xsE3Pn7xIyz5OtJ07f1bC29xFhDbz13EJsNc3SkUQjl3C2nawf2YaIcJM8yjJhjPk3r/QTmmz8qDrsDDYITirhgjnbLLNV5qfmsuB121MqeG7ocvooX1jl8cQnJTXwARHSlKZGbG40xHFwYn6RhWOdTP6ZDvfh0srmGp3FqIzOt6mOnYK3kSUh5x/UyRP0B9hSrcH0BkylalI3+fVnju9tpybxEdJskpTwOjE5jymKA49b4J2LYWyeK+QKwS6bkaaGkat9ajPNuz4Y0e+cOiiKaqKFxkASZria+WpaDjs2t2pCFtm47IBDekwvZd19qH+NUuEW0/34bK0gKUtZogNTpTtpw0sOLfFPvZZhQ32ZH3MyIehHHtMqi2t6D3WQ7tpc2kgXEd5wsdS1r3w6W0w6UnNmu3MBZzZfoErn/jZVzXnMHCW37a0NPJTR6br4gWcMDaX4NNm9o88kNEroQ6SSktfD3wE9HU27orG822TTjQTey0qQQJb1ej1HcC6WKgQxgm6bACPYYuWL1sIWYJykMg4tflo3jXTuws9hz/6doyFCZYiP15z5gIwiLLMjeuQ8G3NBi67wnXZNrsZFiPbkXeYaNk2aDjVzW9e4H+UtcY6Z2lMMQ2ouN59xKFwYnfUIj6Vlqfa6HZ5S2iI0ZG6uXn8qGmcyl8JmXTD+EEhsPUWzVI25CNykafCW0/rkbBrhoYbOKhwaDDIzzLBtmk3uz4swbxwvHZQCMpc6Tc2Rc70XsJWFK7JDhsJvTVZCO1wkCksycyKFZlQCU5Kd0B86s1KKMTEM+GP3ZetSYH8vN12Jq6CXmlpajTDcJ8UwF13l60nOiDbl9OeE4ZBiMId9eqHQHhYO4sQBopLHXBCtmKLBz85wPiuNglgA4R+bEOjlg5HB9OCV4s5ZoUCc9mHGKIKHTPetZ7jOG1nD0JG5ETt7OBlm3IQfkKKgpEqTFrxFB/AsoXOwudflK9ukyYXV2wqwyV+2tQQ7aG0w4kzQ6jQdx3bRpUlhcJxxZLCVXbELS1zR6Nl2sFFGFMpRjW9bYVmDbhBPnbPavbU/TS8dkvPqsGd8PjxTsRFN4TmQITn1aOg9sUMFlsfqIs/luNOFW80GVzXDbgTMxO6I4UQ+3jyVBma3HqzSrJj1ZQW512pqCqcQntMACxX/4i+MPvLAxDGH8H3IUHcW9YjbqIMLnpm7jnwn+IYzpXYNlDvwPX6CmsA7AsHimpAZqph/PR/rw6YvtznNVDN5mPqlJxlQeZCjkVVVCdbYPhiuuYpUD2cCaeVoyg5nmPD4zEkg5XoH6sn/0GtuO2jmbUFG3FVio4GxeEqe28Dm3HjuO4zvN810brDHrrxax6Ej4OnHutDZZtVajaqHR5Z5cnIZ+ks6y/AX2B0pl0+qvoJLFXXZu2Il0c4vRp4b/Cg1CHBIR0hLdkIF0YM+sjGr9Ny34SVKH6vV8i5ePCJXw8GM5Enhv46PiHuCfdt0C6VuGQ7/si5v5tkROC/vIv8Ge/uICPfivu//YM/vCLL+Le1PDdr0ttl/LEDORIrsIiR3q1dmGVKi/kSNnxIjKXWzAVwcRr+aYDGBkdxegonYxIbHJfOQqz05G0KvhQFfspDTalpqHg1WD2wmCI0KEddx3C2o+e61o6+bFXdvCPf30HXzscyUqjUoS5ZubvJ/iuksf53B+f4aduTfPnXt7B7/jxSX7iI/F3L1zrY7qvOb9u9BR5DrrO6bvk3+828OeiWcsy3PV4p9/ju17cwVf8pIHcq4LvMkWTTtJp4/RdizVaSDpOXLzKTy/ielJrlUZCwLWDoyL8daT9CDNff//WK/xv2hcWlZXcF9aX9V1j1sxPz4f7/u65Lq1rC7xurdTvHizxeth0bfX1vuv8Wk/wOzzX4PZFqC+8z6Fl8CnP9dgl8LUl50wIu3RO8RPvTvBTH7nWHo+cKf7k96K0l4gJVs/N8Gf+kdRRvusmh0rnoIhl4c3oni3ichlgfeCQ1wm0rjC1450neO/V0QNw4zR/3aNs/Gajnv+d+NPv2j3DX+Kvv+VxI3GN6Pmt+jS/sIx3sPIa7Df6s/d1PcsqjY9nHHz3XSy9XUZXzy6iPg1EIDsZquafXP8kX9o78emvHc74zHNXjJG+s9Cxgz4Tl3xwXDGg+Ud64G8bcWDrgouCzhiv3D8I5cF+tLjXcBVwjf0yLC9GKgm2XdDBnFSMJ8yjUNR0oHi1DI7TNdja8RC6TxQjKRIvDh1r2qHCQEdO+GPUooamTTtUA+3SS/h9BqCvpIuePwfVjlTv9JhfsSMdVZ3lAbzF4vk/HsPaLT5fs3PP1l9dgqP73J89DsUixvzd0Xy9jdwYgmaHHrJc3/wg6XmapKcyCwc7CxH2eh7uiXQb26GjX7ics8KwrwANXhPpfLAPobLIhvIBUrbEIDp20jVBOHBOhrQlWYRxD0mQccTu8eYeK4QsjuBl2XeiM25aoK/1nbAYCWJZcM8fiRBhjPT7heF/Il0Y094FbM/0WilJ+ELpR/no++dCqKTqgDkTmjfUwepzHvfBCHos6WjvrYI6zDdcUkiNQf7ss/R2GaieFcZcT84gJvMgdDt8SxZ981yEustrselR79VEPv6ArrwDqAoaUe/RJodEsBML8gN83ZTBCAcmpP0wo4cIn5TuYqT4CVq6lF0zDMhA4TPpUElUqHT8a/ORKWxq8hZr9itmOBNThM8F0wrBekoP2+pCpM+/xqIfE7EiZk1SZOOSqeAaUvtPgrst0MZ3CGpW6YQJnXBVh+lnA72uDMIHepQNqdASxfCHzz0OK4yn+oJMpAsNZzNjKjYlsgmwdwD7qB4nRqfFPQkeyUL5lqUY1Rm6LDsmjRh8zTXRGVGm8wIcLG+dhDMtHylRpLnjrBZ1Nwuh9XJQBIFODhydgXJDgAmoAXHAesGGuNQI6+EwuTuF9J20yzsMFdK77V6dbAYjUpiQZjAYDAbjDnC3CmkGgxEYJqQZDAaDwWAwGIwo+Jys2sFgMBgMBoPBYNxZmJBmMBgMBoPBYDCigAlpBoPBYDAYDAYjCpiQZjAYDAaDwWAwooAJaQaDwWAwGAwGIwqYkGYwGAwGg8FgMKKACWkGg8FgMBgMBiMKmJBmLArO4QA3J+4wGAwGg8Fg/Amx7EcE8W/GIrCNtMEwziFG/pdQyCP5VrEF+v0a/PTsFGJVa6FaHvxc7qYDc/fGYFnUXSAbhg/XoO4XRkw9sBbqVbFieDRw+PDt/w5NyR60QY2dj60Uw8PDfkmP106+g/9vNgF//RU5wk61OSuG//uvYJm7Bw/8lQKxEaSFbaQOmpd+hfc/UeCrq1ciRgwPCUfu2f0LDF61I27lI1AsJtkocxw4fllY+Ug7K3P3LCbPKRws/f8ATes7mIpRYe1DDwRP71kHHHwMYm7DZ5Jtp+tQ87OLcMQ8iL9eFW6+czC/ugf/1GeJ8Lw7i/18M/7+8P+M3L58cKeR3fEAVEmKqK/DuD0sJn8cozpoDv53GG0PYG3qgwi3KrGf1eKFv2+D8Td/if+6IfzzAsHNclh2bxiliNRVjt/PIWaRlYFQNva/jstzK5ESqmxwpP5xkvrnXnF/KeHM6HnxH9Az5sTK/+urWPkFMTwUtmHUVWvxqyuzUPxNBOfdJXWXJEJa/RP0/8eBmK/8NR6MRNtEy3z+OLDsy8khNdFngT+5Lxty4z2o7rUhKfF+MWQBzjYO0+QMkFaF7lI1ZGI4heMAmWcAxWGFxa4g15KTysYBY0sBKidLcOpoDuLFQ/xxwHzCAOtfqfFEehLiqY3M2aB/PhvGbafQkh34THoPE7lH2UkFNMd0yE8Uw6PA1JiKshgdRitSxJAgkPtaTh/H8QEjrI44qNJysLMgE0nksSnmo6koRpjX8sFBhO2majl0o1WQPJtUqNwyOWS+ZemmEc27KmHd7Z9m3GUdSks7MfVlFRT3kIB15ejYkw4xusAFLVKflwW+ZyDmLOgpKoApN0Q+UYjwHjrSDP1lB+RKJbzqn09sMI2a4VijQW9TPlS+duUDbUCL9gxCVduLlm1KMTQ0DtJRMXyYAPXX0pEkfpHY1l+G7LOZIWyUnHuhGQXP66Go7oVuu0oMjRza8XPK5JDHWmHYV4chWSoKXyiH8nQB8t7NDxkPX6yvRnfe7YU8W20PrIkpyMrOQZIlWvtywGqZhiJJJdgLd7YOaXuClA2Kwwx9YzP6LjtJ/UQ6Z7FJyCl9EYWpn53U+TSwj/agqXsQ1o/EgATS0S8tR+bDIQpbBISVP24cJrRVaGFUZGHvwWKkWJqRWorIbWQ0/PPsRLA3dZ4h1qmAKn6+9iMQYWw1wXhFgfyjHdCkhbCVWSt69uWh7f1itJwshzrcJJwcRtsohydS05FC20hKuHXvNT0qn9HCnNuCgX0edfcisL5Rgy5rMtSZOchJmkDzhjKgcxRV68QDwmHOBC05TxbpeYQlr7vspOy/cQ7Tt8R9T2ZtGL/EIeeQFpmrxLBomSP5X5Qn3e6Rds742gjGZv8gBnjyMSbPj2EKa1HSpEFGJA8drib6rECF9F3BxSZ+/fr1PlsFf/K6+HsETBsq+PXfP8lPi/uhcfJXDQ186c5Sfv/LrXxru2trePEpfv1TXfyEeJT/daf4M/Wl/I7y/fzgh2IQZeYMX0vi33RR3CdnnPz+er7CECJGt6aE4x5/cTB03K+/x3f9YAef+50d/I7v5PK5P2jiz3nEYayFpF/LmLgXDCc5Npfc8wQ/MUN2b83wE69V8I9/p4kf+73rCMlrOa/yZ3QLaTWfZuVP8k+WnOCvioe58pVcS9z1xfn+Cb5i8zdJ2kyJIW7CTDM3v5/hnbfEvwPd89Y0P2Zo5feXkzSjef3zc/xV+szzRHhPKa6f4xu+Q/OQpIGYfsFxpf/6p5r494IcPzVc67LPIY90Inl15sfeeROu7U+9To77egU/GEn5mn6PP0Hy+Lhhgnclm5OfGmrgn3KX01vv8U2i3fvF45aTn5lxijsiJD8mLl7lpz3CIy+7dwjybA3uMh3CpinOXx/nq3fu4EsPNC2UDVqfrH+KXEN83lDXuTXBd33ncb7idY88//AkX/H1x/kGo09a/gnhfLeBf/ypBv6Mh+3OWI7zFeuL+BOe9XCkkDrEy0QD5I/zYiu/4+tP8rkkf3eQrfpNMX+uk7xxl4VgeTt9jq/9LqnzWs7wV32zMQzbCoq7/l6fS2wkzFI0RW2K1HueduaD00zSt4Q8q25MLPsuJn6+w7u8hhl/p6mJ1Bu5fKt5ae34Pa27Phybr4siQ+K8T7Pukro3QbgXSef33G1eIKYG+dq9DV5tNN2a/nEH/2RWA39OyMwQ7Z6nXYcDibNTIlunf/0eP2adFtvpJWhr7yB3zRhp+hpKubsdI6dHPLZ6ZN2RzooMqmwN2o+1o35POcpLXVvJkyrgESXp7wdCiYz9ZVBfGMKEXQyiyBVCD9v8ehvaOujWheFJ108Bod7o1krU2VKQ/KEBhrNWOAKNTZ41QptbibG0RvT196K3vw8taVZUF9Rg+KZ4TLg4zkF/TIaqinyXB3qZHEnbq1AV2wP9eYfrGClkKmTs2o2dz+6eT6/yTAUmLjiR8u3NCNfHKUtMhoqzIyYmlE+Cg/WEBgW7CshWhsr9NaiZ3ypRVlqKoiItjIGiTL3NO59CnWUtyptImnVrsTNGj4KiNpg58ZjFQL36A3UoyK2BNVuHU435UIXxbtb2RjVK++XIIfnXXNMMwyVPQ1pAuekAylJNGLJMiyEEkleK5eTfi3rRztrQ9bbV9VsQHKPNqDxkQ8rDNhiEtxDiD6GIVyM/lUNz7SBcd5FB+VjSQl4vC/Iyd3YKpu5SpKYWo0aMa9srJ3Dm3UGc+H+sJHc/G9jHDWiupjZWAM1RA8zu7CDPFomvU7a6ENpjvWivrXKVjeeykPiRDbKN5di5LswrXT6Dzsl8FGZ7vKVYlYPCpzno3zyHcLPt88bEBT24LZnIEN/CUOSPZiEjzQzjReny4wVngWF/sasuKa30qkNKdwepQ0Rk68rRe3YEfSR/e8mmjeAtkkB8OgpzFDAeG4VNDFoSbpigq8hGkUGBg2/0hfZEUxwmNFfUwbYhH/I3STock64PZGsKcTBbgeGL3mVVsZy0jpPD6HKX6dfN4i9BuGZA9R495GvkGB04CfMNMTwM6HBBnfteZGveX4C01E0o7nfVSDGhxuBcM5LynYe8XJL3uXkoPqiHOVRB+pTqLsdlA3SvdOJ4b+f8885vAyYqP0jcXMcGRJmJ8rQp6Do5POFup8m2c4MCjhsyyJfiVYAvJL2GW/JIeqVh6x7tfJxPnDXh3JAJ1rtwzhV98X1X4Jiegkr5EOTLb0fOhsBBKtYTZzDl8wrFPkqMFelwunaDIojmd///7Z0PUFRXlv+/s5lpZqxtxymYpLZZZ+jEEsoU6JR0WbHVDegvQlLSkxnARNAdkKkAzgJxQmMFZH6jWCO40cYKf6YCuCOQDWD9AkwltDuKE3+0FdNWRdoairbGtGuWntGlV0NnzNKj9fbe915Dd9P/BYPZ86l60O++//fde+655557nrzCxCMXJykZO1mjyYe9HRgY68WwtHEu9+0wHipH/U0dOk7kI2UJd7Voh367FeqXdMjwHDpjOC8MofduLlo8GlhVVj5yD5Vg+FIV0rc8pDx8jFVErshx2DMMHDdgfFMVTm0Nv/fjPNOFLuSicWOoe2adne0NOPG8C4ol07BdGmaN5gSm4p7E5vXpSFQFV05cF5mCdPWHaPwXLaRd5Q7DeznoPb8TKdHmGR+uN/XBcLgNFnU6tCtVsF0xYuhMDDasU7F3z96/v1u774CpsQw1Y1o0vlsKDc8ypw2mngPIqZuEOlWHnJ/mSumeiErziLzCStrH7E9yJuvQaNi7UMAxaEXvGWmbP+ynD6L8iB26Ex3IZw2Z86oR7ftyYF2eA12GBtpkye0gIKJEsaCXCUbxLljdCa26M5Ss47UpBTgJ5DJBHtFw90PCeaYG2w44UXXyBCqWA7bBSuS9aMURox7aB3RYtQ8a0HBNi/3vZMwd9uU+qk4uZWLEdzhDzDdZJ/5zabLvzDtxiW5oUMZ86X7VU61v4It//R6+9UEOlsppTJvDbX0nq2/897ehOFWE73xX3DBvJDGlT3HIiOGXUmaUaefVIQxfSIG2MgzZo0iErlyH4eeHkf5+I3QeCnlQHEwJq26GmSsvS2bdKVxOOybuTGN1xmZxPTR2mI1WKJj8CduNIhi3WH3sMKDl/Wlon9VAfduGkfcGEJuxAUmsPfUqUx64rvWi5pU+KMv60J2lFjuKXFFtL8uBeUki0rJ2o2irlD6DqDRPwO04+flVVvsT8pHzUibimVamuGJAx/vyRj84r3Sg/NWzUFefQuNW1n4x5b+rsRAGpEK3dfOsO2QAYtfkInk4FSX3G3GuXMtkaykqDskbQ3FnGDV5NXC+egp9R9i1ReNVIQoLPkd3TyESA133S5JdylU6pJ4J5FZZiv3yr6hRLV0YGcLyK+P/aFHTY0PpPn349WsR88hYpOeTWKaE5DK1tZ5bGvaVo3BXIcr3sd97SyQrRLXR2xKgZIJ1jYv12qyI/9Fsr21/2wVcassVO36hSPE4rrQ4V6psXIkK0QA7rw7g4Cv1GF9Xh8HjTInmsplbhbdWoKWnATqFBYaiLOhPz1paYpbwZos1qJ49Oz65jf1buiTCqqHcgMxsGwzHB2DjJ+CW1R4DDNdzkbneR7lkip7lohV2P71g+2A9Dp5PR90vdYj5uAMlz29lvX1DcAvFXTPam1j3ggl6BX+WOxZ0cGvgnhoYAljxFUts6NqZxe4vCbqfsrzepMRAWTb0Rw2o2VOCmqOsQ+PnmtN3p9hfdh0vYcmUEfZ+nKJmEiF3mNJ7sgaFO8vRPhaL/PoT6PhFFaqOd6P70G5olo2j/2g5srekocHHss8tDQdfOQDz07UYbM6Eo68ZXYMWOJaooS1qRN/bR5C/4jo6qrpglY+Z4Qe5HuWsFLk/YGlfD9xYzsA7i4cKUW/VoO7dFlGJ5ihXZqCiqQ8NWTGwsAYti9WN0Da9FLFBEe/hH9OhhhnNr/N31it2IBc3V+D4hzdwU14cV+Rk9tTDg0ZoXquFLoHlJeskql+sRdWaXtYxCcPKGQTXZT4CYEIs6/Bp5szXZUoQtzq9w+comL3zfqWOle9+GBpNsIt10wX7eVY3309ExY+0EVnIHx6P4zsNP8cTHzy3YFYcxTo9flurwvDrOcjZweQFW4rfmkRmTytyH9RfNBixWlZXTqC1jdXx1kbUHaoTl4bjHaJlWr8lTAPCmBFdV5jMYB1XhTjJWbYy8iUciy6Hl4XLAzDsZc9/xCjO+2lsO4KqvftxorMDtS8kwXGpHQeLtmJ9Qa93Z9fFFPm2chR3uJDzdh9KnzChjdVd06cuUVHVn+hjcjgT32QdS8M5n7KfkI7dHvJHHLVlcnQpU9jnzHPx5L5DvGb5OwqUvN2N/VyJ5jyuQf6hDrTu0cDRX4bsnR0hRwhjeMGKCWCgCILtdDuMy4pQ6jZAsXZWU1CCjOttGJqRAwvMXSt6a0ug31OIrXuZfJ95ViZ/DrP0Qe8xCuUylr8eI44zy2F5dLbVHNIiHqsKMD6cEOs12m77fTsa5NG4Bs/3vkwF9TJWTn/jcw+tDShh7VsO06+6xuR9v6rILh6LHu6HW/BatVAs+p1VCvV9HwmTofx/wiFc/x7Rtyv4fhN9xX58oPz5YklpBfvdPkn1Qtk2X3+gaWF84JjQ+cEnwlSo5+R+Up4+R6Lv5Fohu2FEmODp96aEj0Q/535hQj6X6Nf8k2rh2P5i0Y+voNvt6e0H7ldnbBKqX35GWLtxh1DdMiT5S8u4z+X2r+r08b2b+N0BIfvHB4TOljLhuarZexAJ6DMn+QY/W8WuZW4SdrxcL4x4nTaAD5WlSXjGxzds4v+x9+LpV+7vmn8ZEeo3evubTpmPCdkbq4Wzt+WEcP22/PjHcZ/xHnZsKL/06T+eFfo/npj15+aw+wjHr9Cfr7rvuxH9cH3K6LS1XzjWPSJ8MvOcgZn244/nhW/eetWv2brg119QPLaAla8mof2tY0L1K6yuv9IknP24X6gvleYn+Lv/+eOm8F+V/yxMWuRVwSJMbnpL+K9b/Ld/n0oxfzeze36jWihwbw9Ypuci+tNmVgo9f5wS68mOl48J/Vb56fh5NjYFP4+vX39LvzAarq/il4pn3j44/nwu/cJl5Y1x4aM/TPj1LZ1hTrvA5PFvioVnNz4nznk5xuvT/gL/73liRDj2GnsfL0tL5VtyWxWOj/Q9Jid+wmQ3O+5Zt7zm93xbvtcwy9b0jVFh/IbHvBDGlIn7HYdq6yaFj97zlQUTQv8roeWevzotpm0rE+pl+eM3zyZHhHZebsXGKjgh5Q/DUw5O24aEA1yOsLpR9jrTH1i7GMhHWjwuQPs9++z+5cD8yK5pYeRX2cKxD9l7M9Wz87HrmOVNth5hh9d9zCN+ypT43t74yL0Wut0L4KsdEPGawcqidM1tr9bP6Cj1wwvw7PPEI+PakVJ0Do2QLLguhxX9hwqw7UIVTh3RQRWsp+smUPQHf/Dh+Ct2xCSoEMeHo3yPkYdaXXds+MQ6DvPHl2Aam0DMPSeQqvI7I3eua0csUjdlIjNVzXpz/lw7FEjMqkCivBaU+9OsJ8vux/18jyWisOc0kt9tR/0PK2H6ugalrzXg9B6fYfkf5KIinEgbogW8FK9OW2E8k45Xi2eHn3lUBnHUWbSEzj0XDxNV2aFExa8roGUHpb9bjrydZpT831rkrgxsMuC+wZX23eg+lCG+3xP6ZhRs34qhvSdQF8zncFkckmCE7VNAI0Y1ccFmHYXixhQqi4YQ9wTLI1Z+uEuOF0u00L/biK7GGuR0T7Pcn8Tkss0oPVGLdLd7Sjhwy//9WCSu8SkFCjWSVsi/PXBet2D8LrtnVtaU7F4VT6VDJ2+bwV8tlcsgJ8azjPpx7YhNTUdmhgbqBKVf1w7FSh0qVsorwWDXnGZlbfquIsRIiq9rBysjThc3RjJUWOo+lt+3/HMWyZotlqQiMUEkvUnKlVCuKZJF+d+AN3+O2GR3Uh9u/gw+LgZ+uPIBewNroJw5bhz38Bm+dukWkKlGSpYChmEzdidrpHrkNGP4jAK66kbsT7PB8LZROi5MuBtNzaAaVT0N0kjTU/vRuqwZxUXFcLT1oZDvlPok4vl/EVaWz3VhyDp3hrwqWSuPjE1ghMkaMe/vXBflEpRJ2L1vP9L9WmOl/HJ7rX1t3048nslfFE8fxzfeBL742Q1p48vP4Ylid+Z4umhwPFw4/vMsbmVfhhgOah3Lz4bN4Ydq8zyWMXs/kqvIX7+/BvcP+5z7vgOjJw+g5qQFSxOSWN1LhiqMC5qXrMb2XdowIygwebyrBed2yaucy37cFO5b0VFcCddrp9G9ib1U1paYjmxF8ckT6HuB7yBZ7iac3LLsR14OGmB4TI/uzh/C2ZSH8qNxYsSh2Ehk0F0mR5anzGk7lCtYW+PraMXyznppgoknNeKX8dHKWGie95GNfIDfj6jmYTmn+UihGJVHSvPr2pGYj7SMDVi9XOXftSNWi8Ji+XcomPxhl2WiIzxzsyIhA/ub0rlkFWWk5bgR3CHTH/EJ7LlPWfEJE1Wx7tPfsrEcU2F1Ujil5AFlF5MnZkc+dKkxGD3azxLSZ9oMh8UEK7uPzStiYRtsQJ/9b2fyOCA8csclM0avOqFcuQGv1geI4LFMxcpKH1qqJ2fqgtMxiUQFf3bWbshpXojtHMtTdz5xN06fAWoRuZ1y2sdZmV+K+OTEsOqmG/Wzu1HxCETteGQUadENQv6piE1E7qsVML5Yj94rTAkIJwyNcxxdh2rQOR4H1dOroX1qthgmFiVigiu68voMHy7F6h/lQ8vl+KpMVKQdQO9reTAmaJCsikP8qkSoEzdj50amRH7BlMzftCPu6dlmTyIemjI90rNykeIpDItdsH86DZU/Aemyw3j0IDrHptjDsutw5U/e5HTYmIoXB/US1qhOcncED5Q61DXlSgX/MSYQs/V4UjGBrUz51W1iSrS4U/jYL3Sg386EiGISo/YpSShebkZZ0bAYR1mZkIiUpzWsY8N29tVtuQvIqYOon9DheCdrCGRFT/ViI7pRjuyjQ9jQyjpBUrIHfNLgARimd+PUoZQZxZ9P4Dlx/JsoLmuHaeN+aAM9zPJcHGn9HPW1JbCwBnXpn0ywxVah+6RudnIfawCNreq5k0RjNcg/wBZ5NSqmJzDc1oJO1jhxXzBNMms85E1QFDLlaMRL0XVjvr4ZLz5uRlNTJ8zTcXhyBS9j7iMVKGSCecStnHoxW0bjUyug36RD7hqPzGGC3fWpHdPL/WUYK4OnDTjYPYop1ljGeYbp+8LByhcQt1zBjp9k2z1ZCt0vWpD7lLzqiUqDin2svGWzdycnlVbKPxiKJSlI+Dv2Y5z9Xi5Nup1fkvGtfRfhNF1hPyWlb8p0Q1TIgirRjLv/8RnwZJKo9N0daoPznJqd6zN88e83WUoy0qtPIabJgMLtDew5WO7dVUP72ins5wpTJNy3Y/iIAZdW7Ubj8USvzq0ytRTdxp1wXDOieUyNxiKNh6KngDqtENufMOMTJleSVOy6gXzsZUrl//7hyvBF/M2pn+MJ0U+Zrw/idqrbb/kGvuhkyipTkJeIivVF3P5xsrSNdzqeZIp1g1ux9uC7m/H4B5vZPqwD0ymnhYOoRNvwDXY/0vX5NTvh+PvZTtG9wzYoxO2SIv/FFaZIJzNZV9SI0x7Ki194bOJ7HkrfQnDHxmTlaqSvkMvEY0rWRqyG7f/b4BAVaTXS/7EUOvtchVJ08elWo/Ukk+G8TOxpRH5lNrZV1uK3R/34zgfAMdYrhf5jCmf8Ki0SPNqYgG3dxxasztTCOWDAMaMdcXFqrF6fMKusrSpEor2HyS553ZPvbUbR86wcJ2WisCwRpVs8Va9S6B122JVMDnqUczfcJ/rgkbOs+8ekCrtm7LekdN5+2O08dCiT0pMTmPTxT1hd1MpkXZj1jil5QarIDLEZu1F4shANjVqp88LdLA7Xw7ypFrXhGBoeFKUWFUfY/ztGtPSwB87SyUYcF8bHTOx/LpgeCvUqPSrsVoy7mB7weOyDl2fVBlS1bUDSGo+2KhSsnRs6fhCGUzbErVKxe/IuZ4EwM12Bl5WQ17k/hWmfd76okS3TjyC+Qy7zw/SU93DYHKw9QtluPlRzVvAKBnRvShh5I/jQDT/31G1/55eGMap/5xVrjR0wd6jE3/B9MMQhmld6hPHbk8InH38kfPRBv9DZckyonBniCs7khz1C58CIGJbG/71L97TjnZmAdiKT5n5hyCo/z18mhFE+lOoenvSED/G80j+blz5DRFKe+RsykvIsnDBfU7YRoedXleLwUOWveoQR05DQ/jvv+/Uk1HuaM6wXiukJYVzM9yah6Tf9wsgfHqzMhiyj09L9+xtpE8vD62e9QlTxPJ9zPj/DfRHh75xu+Du2sbJ4w+cGfa45zZ+BD8PzvBuWSoi/4eO5cLeBXuGzOb+D85f33xL+3GIRPmv5Z/G/Z1poPIZ8I8k7VjaG6nYIz2yuFs76PNSc0GE+8Pzxm8fctSicMnbrjHBzE3tWn0VybZnresHzZcbtZebYIHlr6RX+XHlG8B+10Y9rB9/fJ6/5NW++L43/ev7m+K5zpv/Ih9J5PZddCTyW6p8wmbfTI/RmMHxdO3jou9usHn84IvT/Rpb9ft/zpDD06jNCduNHkkve7Y+EYzxE4XvsfQRz7bjRL1SWtgvjvpn1F1aufrxD6LTJ61HWyynbqDDUJ+VDj3HUJ6xnhPD6Gyx0J9/O666/ffj9e4SMFfEnK+a41oSPVxvJ39vNT4RRJkN4+NfZbQFcNFhbNdJXL4am5C48TQOjPi6kwVw75kN2yfuxaxz4QJaP99g1N7L7rhryltuTo0LPW3PLubi8wd1gK73D7oaCtxt+36vc7rndTOYDMb/k9yuWF66f8Lp1TKguzRayf1ImFMxxd128PBqTDe0mdJ00we45eY67anALzfciNPvzIOaH+aRCPRpmnOLdSw2K09JguBikK7QyF43FiTC+bYFHoDHR8pC0IoDTvozz2hDqd6Wh8j2fCRoiCqie8Olhz/kCTGj416NK+CQDeSlrNUM5OYAD1ccw8KEVdqigeWE3dOvkA0IQu46H10qC83wPhi6OY/ym/7xRx3rbd/mEzoyV8vPEKBFzuR5pW5rnTjbj1oLY2dEGaYho9rmdfxpGy671KB+cm2cxX1chLi54HnErT2F2L1w/2i9O+Nn/Ixd6y9oxFec7cjDLtMPErunvPSkQtzydPau8Gg7Xe1G4pRwD0CCXT358KQWTbduQc9gUMDyZ65pRCrG2h0/Q819GD5wLdDTjjg1DR/KQ9tqA34mBcyzB/B34sRY9EHdG0bWPl0E/9eytNtT/sgQlrT4h2hI0yF9nZnUkD/rDzWh7ZwgmOxCfkon8tCDuPHNIxjdevoH/HroF0T3j5aSQ1mjOkr//NvCv/4b//v7OGReGe//+Gb72/ci+2BkRChUyePQV7iLlKd8YYuiwgLgw8X4N1q/LQvkRn/z9ZSXL9xoMhBM7jbtHfMAn/80uMy4xwRCtznz/JPxVnJjZhtv/KW/7ElE8pYO+qRsN+2YnvLkXceLtGu7eEAbL1EhMMOHg86lI46HQftEiTvacVKqRmZfuZyTNTSwyjvwWetU5HCxgxx06B1Xlb9H4fAihEZuOuuOFSPS1Li5JQcWpbuRH/fEtJ0yHc7D1+DjUW6R8yFw2DP0LhegNFHKVT/w7WSO2I7weepUttjRUZiPtpY65E53d3J2A5WQ50jYZ5sp7jm/I2HmWP+rUfGgu1Uvy8+QAhsedUD6dCW04E02XqKDNZjKLhy5sqkNpVkrQKCEzzJvsAiauc+uzBikr5Lbt03ExEowmdXaUTyQ2BbkFRdiZ5xFi1r2kx8E8xuR8BKLLaTejryYLqWW+7Ybc7vk9lxOWt6Wy0nA+SJvkS0IKdMvk+rW9AMWH2jFkmURcai6qjvWh7wSf1C3v+yggK9SLG2u7kM16aJ7Wx4k+1mv7MevZir3FaWH0rR3CM2ufZb24B+lqB+ht+iL2pqSJBZ69wNCTCaSend9elucHQ4IQlkX69ifCOLcgBzHWRmrZ5h+D6QljwklAorSkBM2zkEwJZ19nz/mrEVZC3PAJHSzN1yrrxYNc0xN/12eIE0e2Ce1WeT0iwiujAa0fvPcfpFzMEPX7Co+Iy59MuFYd0WJa2StMVkYyoc3HSipaXcM9nr+XHUIPtxxGmnei9W2bUNYQoTwJZLUL+/r8eedadSX85UVg67OXtdpNpBZp3/z2WQ/HIj1hOiZU7t4hFPxTpVD9erXXUvYKS6/qD88iHQ5RvecAFulwifQ41nZuc5fLGcKRgYEJqw4Gus9Q1mw3D2CRDkZIi3RIoj0ufNk13sHq/czHg+QPcvH24g/i5lkm+CRNb5kxs/BJnc+5P6gSPmHLVy/mq730JMA5bw4JlZvXCs+83Dl/9XgeeDQs0isL0dpaisl36mFo5WHM8mCwZ6LvpE9sR9ab5M7tDwePEF/yIoX6iZIlHhPGQqBShghhJ1pTYkOG/4nI4P1YDGIi2Z9YnHCLf7jvcaHiiD4oXw/jrr6bjG/gBu5BjZiw4xQnI/aUGn/NlsPfif66kcQ5ViPObXiMOO8k39l5kyer+OTVUPDnXQMc7pwJ93fzH/o8/OE/g8srL2Yna3If8tlj3sAXeM57cidP55MUL16Gk//Wn5W/DcH9nvkxfIKj+/yyNZtbud/8tsc1L+OxNyOLM61avxPaJVYot1TNhJ9zL42t3eg4rAvPIr1gqKGW5k5Gz2Ktl+HA5U/YPr1KKN0+0/OINiHwaOSCE4bsStx+BPrnY9B3WI+DZQWoFD8ms0H0j/ZCpUXGE1fQMRaP7R5yQ1wOdODSaX3guUSPODHfgRQvf5HwNa5Ny78JWGBIrcHStwdRGGxywWUDUossyChK9Rri+/yqEb1//iG6/4Up+H6VFRcsbQU4OLYaaSu959x+fmUAZ2/HYfNPj0OfFngY0HI8Fe0JD/79+cjPE/je3dEBYjZVobUoxf9EgltG6Hf0QpHtnWfizOLLNkwpMlHblu8nSokL5qNbcfA/MpHhm2fXTBi9CawuCJxn3LUjr8gEbVMHKtYp4bxoQOEett7WjYqAX5AL/KyOSwOwuOKgCXJNT1xXmpFXcA6ao42o0KqguG/DwL481COyCUSzhFdGXVc6UPDLUazekug9u/vOFQycn0Lcpt04vi898PV5Ge9Q4/RxXRT3GBpe/mqU3RgsCCsuzQyOwXJsvb7bzwcIfJEmo/01bTbqw0LjuuOCgsfr5nl3Mgmnw32/twZQ/nw7sD1D+nqojChPbuei79f5UPsrqvfNMKw7CJvPca5r59Bl1aKluwKaqBtSaXLh3yzAB1MWFidMh7JQ75grL2ZkzZpScTLZA+sYkdYRlxXGd6eh3Z4CZbT1K+LjHDDu3YYDqMCJ2lwkLmM5JMvAtBPdKE0OJAMDI9bBizqcO5QeOA+DyHvTGStTAnUB5L2MWCds2H2pwk9sk+ixn2vGuLoU6QlcjrZDHckHd0T4cS1QD7ZAF5nHRniy6z4PQjAB5XI5wpbLhIb15ejPbsTv982NDS+e80y63/LAI2pNsy5XyG8IeBCo3XDZr8B8fQoxGbXo2OH71oLoNXIbrc47gjp3bPCwcGCgbCusL12Afn3kZfRhQ4q0Fw6YTp6F8sVcKRxVIJgwsFwDkjwjMjwknNfMmIzVQB3GDNlgOC50YVSVzwSKnPAVhn+dr7NrACYmCJYmaKHL3znrv/0wuGVGL7v+Od6IK9XQbs3F9hfC9L2bgw29e/oQf3iBrQ13bDA7WIfhqYW5iK2nBH1/1xD+zHsZF3uX/Xe13pFJ/BFuyLuFgId8mo7A8iaGIJuCal2kkXWcsF20Y2lq8K+9Rcejqkg/RK71osSoRuMeTeTtAD/2fBJaCiJUE6OplzyC0pkBDJwZEpWa+FU66F7KhNbja7iR4DzP6u2fctCyfQFt+y7WxjJ9e+HaWC5Hh6A+Vhrh1yO5Im2E5gKTvxHeWDiyy3J8PQpPuqCtlYxcztN6pFXbkB/I8ONgbUsf0wnk1bkkIfOn6VIUmEcKrkgXwPGzEEbNRQIp0gRBfHWYiUPs5/PTPjGKvfH9lPX/dkiRJoiHCx892IoaSzrqTjYgQ2mGIa8Epi0t6GCdNc8wmcTighRpgiAIgiCILxnXWBdqaoeB9SlQXLUj7sXd2L3FO9Y8sfggRZogCIIgCIIgouDRiNpBEARBEARBEIsMUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgpIkSYIgiAIgiCIKCBFmiAIgiAIgiCigBRpgiAIgiAIgogCUqQJgiAIgiAIIgq+JjDk3wRBEATxwNgH9ag85UTc8lgov3DANjmFpXFqxH7LBefNCUx+Jxd1R3RQy/sTBEE8qpBFmiAIgphHbBh5dxKb9zWi8VAdqp6PhXXMicRdtag71IDGYg1sSiXi5L0JgiAeZUiRJgiCIOaPq+dgXLEbOasU4uq4xQioNkDzlLTucrkQn6CGUlwjCIJ4tCHXDoIgCGL+cDnhvKeEcglfsaIjKw/Nq+pw7nCGpDzz7eyXUtKrv1wcw2j45QBsdgvM1zWoO9OAjGXyNoIgiDAgizRBEAQxfyjcSjTDPg6zHUhJTZm1QPPti0GJ5sSmQ398N5JvOYFVKUgkMzlBEBFCijRBEASxIDjHzDBDhQ2rVHLKwuK4aoXjvrwSLnYbrtxl/5MToX5MSooMF+xjdvaXIIj/jZAiTRAEQSwIbv/oxOVywoLiwMibBozclFfDRFL2AW3ik1JCxIyjd1cvRiNV4AmC+EpAijRBEASxAFhx5ffs36oUpCxilwnbH4fZ30Rono6VEgiCICKAFGmCIIjFyl0remtLoN9TiK17u2Cd8R9wYPgwSx+0y+sLiH0YDXv0qNmbg7xDw+zKEq6rXdDv0mPgU7ZyZxgHt6QidZ8RTqcFva3NaD5UjzZ+e0xRbW/tgPGaf+cH17UBNJTp0XCoBDl7O2C6yNYra9j18lD+tnWBXSZsGL/Ar+DCcFMh9LU1KMwuR9fYQ3LUuGOF8WQDy8c85LHFcMEpbyAI4lGBonYQBEEsSlwwHc6DOa0DpfdbsL6sF/mtl1CRyjZd70VedgPiak+jMWuuJdV5wYDiVu6wECZrStG6Vzs3JN19G3qLDMCBRmgu5CHnyFLsH2yBTgWYj6ai5O10KdLFEguadxSi43o+Wi5WQBOur/EtI8orxpH/a3aM0oau7TkwXGPn7MmAuYAp6chHx7kKpIR1PgcGymqAfdL9hYVzGDVpehiXaLG/uxG65Uy17uHPuRqNF/TQhjUp0gJD6jC0kTw3w37mIMr3WZBSW4fSjETELpYJmARBRARZpAmCIBYjTjPMjnzoUmMwaupnCelIWiFtclhMsEKF1Sv8uyMo11eg+2R3+Is/JZrzqRmmH+Tjh8vtMButQHIaUp7gG2bdNsRIF4oUFO3NBVJViOebw8R2fgjq8lKmRLMVlx32a+z/lnRol8cj8dkM5NflhqlER4nVAiP7p321SlSiOUu/xT8V0wvzmLS+ELiuNDMlegDTO/SoyiIlmiAeZcgiTRAEsZi5Y4R+Sw2GsxpxoVYLhWipXo/yU7louaiPyAoaNWMdyNrVDHX1IBpfVM1YxJ3F3RgsShR3cV1oQPaYbmY9Yphyub6gAxr3NYIQyOI+ed0K1+OJULnD77kJYHGXrM9OlJ4cROEqnuLOW5VH2iy2U+WoGZyU19zwqB0TUKxUI+7rcpJMXFYdGrN9P4TugHHvVtScZ/0Pdq9q+ROPMesq0LhH479DQxDE4oUr0gRBEMTiZHKgTFi7dq1w4INpKeHeqHBs41phbdWQMCWlLDjjHdvYPRQLPTek9anfVYv3VP+htM4ZbXxWOGaWV6Lgk3d2sHNuE9r/ICdEzKTQ/0/FQv+EvBqSKeHs6ywfNx4TPronJwnjQvs2nlYvjPxFTgoJex9rPc8RCr4/u8baAxFcgyCIxQq5dhAEQSxiJq6b2F8NUlbI4/+fjsN8l6V4fuTEB26x5ZPXwl6OmBB4mpsD4x/zWYOJUMuGYtsYd4jQzIa1c5lhHMyExseCGwr7eQNqanthdTlgMVlZygYezlnEdakZ5ads0sqC4ILrC/ZvlRrxbqv+mAn9/AMyxTnQ+lq15w0lVGI+KaGIERMIgniEIUWaIAhiEROj5NrrNKbFOMUuWAb7RP9oTZCPnETsI10ZwEdahCl+y7kSPwkn/3DJfTtsf+Tp7ntiCvF7XZgoy49Q+bSgv6YLxt9bYLOOYPgCS1qTICm1dy1oPmpF+npft4j5JBYp61OA2044+XOw5xpoa4Nzy37UbV/I66qx+SXuomOG9bqcxHCM2YJ0ZgiCWKyQjzRBEMRihofAO2xAn0OJlMcmMHLZCsfdh+gfzWH30PWLA+iciIcm3omlG/ORONaFLqsSmuVOTK4owf68FCgjuh8XrG+X48ClWMRNxSB9VwqsJwwYX56JOAegLS+BbmUkHsNRRO1w2WA8ehCdt1SIn3IiLqsUJS8kRvgc0UTtcMF2uhktb5ngTNVC7bBjKjkHr+ZpEPuw3ilBEPMCKdIEQRCLlfsu2D+dgHK5WlLuXCY0rC9Hf3Yjfr+PWzWJWaJQpOeF6MLfEQTx1YBcOwiCIBYplqZnkZWdg5r3pM+gOM8NoBdq5GZoSImegxJJGRlIWiavPjTiodmniSjsH0EQXxWA/wEAw6v+zqbj9AAAAABJRU5ErkJggg==)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1715638234175,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "dhAwVvsy7dCq",
    "outputId": "a09f3686-a79f-45c7-c7c0-385239c4c5ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(42.3855)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X, y, w = make_regression(\n",
    "    n_samples=100, n_features=1, bias=100, noise=10, coef=True, random_state=0\n",
    ")\n",
    "\n",
    "X[:10]\n",
    "y[:10]\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "executionInfo": {
     "elapsed": 464,
     "status": "ok",
     "timestamp": 1715638277374,
     "user": {
      "displayName": "김유희",
      "userId": "00390714496880607784"
     },
     "user_tz": -540
    },
    "id": "pRAwIq9eAQ3d",
    "outputId": "ccd73f0e-5a90-4fbe-cac6-fc6d1a372e1c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시각화\n",
    "xx = np.linspace(-3, 3, 100)\n",
    "y0 = w * xx + 100\n",
    "plt.plot(xx, y0, \"r-\")\n",
    "plt.scatter(X, y, s=100)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"regression line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mUZI0mDGDRxm"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}