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      "metadata": {
        "id": "26ec93c9-8072-497a-bd5d-ed54e5e2fd6d"
      },
      "source": [
        "# 教師なし学習\n",
        "\n",
        "## クラスタリング\n",
        "### K-means"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acbaddaf-1427-439f-899d-80d32c0c6c01",
      "metadata": {
        "id": "acbaddaf-1427-439f-899d-80d32c0c6c01"
      },
      "source": [
        "irisの特徴量`petal_length`と`petal_width`を用いて、クラスタリング手法の一つであるK-meansを使ってクラスタリングします\n",
        "\n",
        "K-meansではクラスタの数を指定する必要があります。ここでは3つのクラスタを生成します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "3e536482-3d6d-42a4-955a-3690dc3959dd",
      "metadata": {
        "id": "3e536482-3d6d-42a4-955a-3690dc3959dd"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "%matplotlib inline"
      ]
    },
    {
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      "execution_count": 2,
      "id": "08abe37e-eef7-4fc8-aa65-086a94132258",
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        "outputId": "467767ff-18b1-469a-c49f-67ee23373fd8"
      },
      "outputs": [
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            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   species  \n",
              "0        0  \n",
              "1        0  \n",
              "2        0  \n",
              "3        0  \n",
              "4        0  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.435866284936698,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594667,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "data = datasets.load_iris()\n",
        "df = pd.DataFrame(data.data, columns=data.feature_names).reset_index(drop=True)\n",
        "target = pd.DataFrame(data.target, columns = ['species']).reset_index(drop=True)\n",
        "df = df.merge(target, left_index=True, right_index=True, )\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "9cd156d1-60e7-4d72-af8d-0618c9312fef",
      "metadata": {
        "id": "9cd156d1-60e7-4d72-af8d-0618c9312fef"
      },
      "outputs": [],
      "source": [
        "X_iris=df[['petal length (cm)', 'petal width (cm)']]\n",
        "\n",
        "model = KMeans(n_clusters=3) # k-meansモデル、n_clustersでクラスタの数を指定\n",
        "model.fit(X_iris) # モデルをデータに適合\n",
        "y_km=model.predict(X_iris) # クラスタを予測"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "78c2bc88-5c84-4aec-948d-3879f8e5999f",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.scatter(df['petal length (cm)'], df['petal width (cm)'], c = y_km)\n",
        "plt.colorbar()\n",
        "plt.xlabel('petal length')\n",
        "plt.ylabel('petal width')\n",
        "plt.title('K-means clustering')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77a3fe53-57f5-425a-80b7-203c534cc211",
      "metadata": {
        "id": "77a3fe53-57f5-425a-80b7-203c534cc211"
      },
      "source": [
        "### PCA（主成分分析）次元削減\n",
        "\n",
        "PCA（主成分分析）で次元削減を行う目的はいくつかあります。  \n",
        "主に以下のような理由です。\n",
        "\n",
        "**計算コストの削減**  \n",
        "特徴量が多いと、機械学習モデルの学習に時間がかかります、過学習のリスクも高まります。  \n",
        "PCAで特徴量を減らすことで、学習時間の短縮やモデルのシンプル化が可能になります。  \n",
        "特徴量が減れば、学習・予測の速度が向上します。  \n",
        "\n",
        "**情報の圧縮（次元削減）**  \n",
        "データには多数の特徴量（変数）があることが多いですが、それらの中には似たような情報を持っているものもあります。  \n",
        "PCAは相関の高い変数をまとめ、「できるだけ情報を失わないように」少数の新しい軸（主成分）に圧縮します  \n",
        "→ 少ない次元で効率的にデータを表現できる。  \n",
        "→ 重要な特徴を抽出できる。  \n",
        "→ 予測に本当に役立つ要素だけを取り出すことができます。  \n",
        "\n",
        "**データの可視化が容易になる**  \n",
        "圧縮した2次元・3次元データなら、散布図などで傾向を直感的に把握できます。   \n",
        "PCAを使えば、高次元のデータを2次元や3次元に射影できるので、クラスタリングの傾向や異常値の存在などが見やすくなります。  \n",
        "\n",
        "**ノイズ除去**  \n",
        "高次元データの中には、分析にほとんど寄与しない「ノイズ的な変数」も含まれています。  \n",
        "PCAでは分散が小さい成分（＝ほとんど情報を持たない軸）を落とすことで、ノイズを減らし、より本質的な構造を捉えることができます。  \n",
        "\n",
        "ここでは、irisデータの4つの特徴量を2次元にPCAを用いて削減します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "7104c1d7-d363-47e2-b610-d401d0107d45",
      "metadata": {
        "id": "7104c1d7-d363-47e2-b610-d401d0107d45"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[:, :4]\n",
        "y = df['species']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ff047ec-05f0-4e7b-9604-b705cd52329a",
      "metadata": {
        "id": "3ff047ec-05f0-4e7b-9604-b705cd52329a"
      },
      "source": [
        "PCAを実行する前に標準化します。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "81f42393-32d7-49f8-9c4a-528e952397fb",
      "metadata": {
        "id": "81f42393-32d7-49f8-9c4a-528e952397fb"
      },
      "outputs": [],
      "source": [
        "# 特徴量の標準化\n",
        "X = StandardScaler().fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9e61deff-5b30-4a95-a716-43de359c9eb1",
      "metadata": {
        "id": "9e61deff-5b30-4a95-a716-43de359c9eb1"
      },
      "outputs": [],
      "source": [
        "# PCAの実行（2次元に圧縮）\n",
        "pca = PCA(n_components=2)\n",
        "X_pca = pca.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape) # もともとの特徴量"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kng7T00mBhq",
        "outputId": "78a48a4f-1058-4a0f-e6c3-0589b4cf4173"
      },
      "id": "1kng7T00mBhq",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_pca.shape) # 特徴量が圧縮されている。次元が削減されている"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nvIjHQimKIn",
        "outputId": "db3fc51b-645e-431b-fa4d-49fb62c5d1b3"
      },
      "id": "_nvIjHQimKIn",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e79e3dda-2ad8-430b-b6c8-e7275596c363",
      "metadata": {
        "id": "e79e3dda-2ad8-430b-b6c8-e7275596c363"
      },
      "source": [
        "どのくらいデータを説明できているか、寄与率(explained_variance_ratio)を用いて確認します。\n",
        "\n",
        "PCAの説明分散（または固有値）は、各主成分に帰属させることができる分散を示します。  \n",
        "各値は各主成分の分散に等しく、配列の長さは n_components で定義された成分の数に等しくなります。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "db8ec47c-045c-4bca-a420-5e1942b4689e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "db8ec47c-045c-4bca-a420-5e1942b4689e",
        "outputId": "8c6b2ea0-b0dd-4ea8-d2d0-a1d915bc0c2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "寄与率: [0.72962445 0.22850762]\n",
            "累積寄与率: 0.9581320720000163\n"
          ]
        }
      ],
      "source": [
        "print(\"寄与率:\", pca.explained_variance_ratio_)  # 寄与率\n",
        "print(\"累積寄与率:\", sum(pca.explained_variance_ratio_)) # 累積寄与率"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9498ed5c-1815-45ef-85b0-bd1f428b37d0",
      "metadata": {
        "id": "9498ed5c-1815-45ef-85b0-bd1f428b37d0"
      },
      "source": [
        "PCA後のデータを可視化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "3767a670-5bd7-4466-b47b-218aea833145",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "3767a670-5bd7-4466-b47b-218aea833145",
        "outputId": "d62772dc-3ae9-463b-8be7-d257784fc585"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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GAhEhhBBCZIwEIkIIIYTIGAlEhBBCCJExEogIIYQQImOyuqCZYRjs2bOH3NxcKW8shBBCjBJKKTweD9OmTRu06WVWByJ79uxhxowZmR6GEEIIIYZg165dTJ8+fcBjsjoQiZaf3rVrl7TDFkIIIUaJ9vZ2ZsyYkVAbiawORKLLMXl5eRKICCGEEKNMImkVkqwqhBBCiIyRQEQIIYQQGSOBiBBCCCEyRgIRIYQQQmSMBCJCCCGEyBgJRIQQQgiRMRKICCGEECJjJBARQgghRMZkdUGz8cYwFDU1jTQ3eykszKGiogRdlx47Qgghxi4JRLJEdXU9VVVbqKtrJhg0sNt1ysoKWb36BCor52R6eEIIIURayNJMFqiurmfFis3U1u7D7bZTUuLG7bZTW7uPFSs2U11dn+khCiGEEGkhgUiGGYaiqmoLHk+A0tJcXC4buq7hctkoLc3F4wlQVbUFw1CZHqoQQgiRchKIZFhNTSN1dc1Mnuzq0xxI0zQKClzU1TVTU9OYoREKIcQoogzYtxV2PGXeKiPTIxKDkByRDGtu9hIMGjgc8X8UTqeV1lY/zc3eER6ZEEKMMjur4fUqaKmDSBAsdigog8WrYWZlpkcn+iEzIhlWWJiD3a4TCITjPu73h7HbdQoLc0Z4ZEIIMYrsrIZnVsD+WrC5wV1i3u6vNe/fWZ3pEYp+SCCSYRUVJZSVFXLggA+leuaBKKVoafFRVlZIRUVJhkYohBBZThnmTEjQA+5SsLlA081bd6l5/+tVskyTpSQQyTBd11i9+gRycx00NHjwekMYhsLrDdHQ4CEvz8Hq1SdIPREhhOhPU425HOOcDL1y7dA0cBaYjzfVZGZ8YkASiGSByso5rF+/nPLyYjo7gzQ2dtDZGaS8vJh165ZLHREhhBiIr9nMCbE64j9udYIRNI8DSWjNMpKsmiUqK+ewZMlsqawqhBDJchWaianhgLkc01vYD7rdPE4SWrOOBCJZRNc1Fi6clulhCCHE6DKlwgwm9teCtbTn8oxS4G+BonLwt8Kzl5k5I87J4HKYwUs0ofXU9RKMZIAszQghhBjdNN2c0bDnQkcDhLzmckvIa35vz4NFq+CNWyShNQtJICKEEGL0m1lpzmgUlUOoEzobzduicjh1HTgnSUJrlpKlGSGEEGPDzEqYscQMJnzNZk7IlApz5mPHU2ZOiGuAhNZA66GEVjFiJBARQggxdmg6FC/se38yCa1iRMnSjBBCiLEvmtDqP2AmsHYXTWgtKDOPEyNKAhEhhBBjXyIJrYtXm8eJESWfuBBCiPFhsIRW2bqbEZIjIoQQYvwYKKFVZIQEIkIIIcaX/hJaRUZICCiEEEKIjJFARAghhBAZI4GIEEIIITJGAhEhhBBCZIwEIkIIIYTIGAlEhBBCCJExEogIIYQQImMkEBFCCCFExkggIoQQQoiMkUBECCGEEBkjgYgQQgghMkYCESGEEEJkjAQiQgghhMgY6b6bBoahqKlppLnZS2FhDhUVJei6lulhCSGEEFlHApEUq66up6pqC3V1zQSDBna7TllZIatXn0Bl5ZxMD08IIYTIKrI0k0LV1fWsWLGZ2tp9uN12SkrcuN12amv3sWLFZqqr6zM9RCGEECKrSCCSIoahqKragscToLQ0F5fLhq5ruFw2Sktz8XgCVFVtwTBUpocqhBBCZA0JRFKkpqaRurpmJk92oWk980E0TaOgwEVdXTM1NY0ZGqEQQgiRfSQQSZHmZi/BoIHDET/txum0EgwaNDd7R3hkQgghRPaSQCRFCgtzsNt1AoFw3Mf9/jB2u05hYc4Ij0wIIYTIXhKIpEhFRQllZYUcOOBDqZ55IEopWlp8lJUVUlFRkqERCiGEENlHApEU0XWN1atPIDfXQUODB683hGEovN4QDQ0e8vIcrF59gtQTEUIIIbqRQCSFKivnsH79csrLi+nsDNLY2EFnZ5Dy8mLWrVsudUSEEEKIXqSgWYpVVs5hyZLZUllVCCGESEBaZ0TWrl3LokWLyM3NZcqUKXz5y1+mrq4unS+ZFXRdY+HCaSxdehgLF06TIEQIIYToR1oDkRdffJHLL7+cv//97zzzzDOEQiG+8IUv0NnZmc6XFUIIIcQooaneWzzSaP/+/UyZMoUXX3yRz33uc4Me397eTn5+Pm1tbeTl5Y3ACIUQQggxXMlcv0c0R6StrQ2AgoKCuI8HAgECgUDs+/b29hEZlxBCCCEyY8R2zRiGwRVXXMHxxx/Pv//7v8c9Zu3ateTn58e+ZsyYMVLDE0IIMVopA/ZthR1PmbfKyPSIRBJGbGnmsssu44knnmDLli1Mnz497jHxZkRmzJghSzNCCCHi21kNr1dBSx1EgmCxQ0EZLF4NMyszPbpxK+uWZlauXMnmzZt56aWX+g1CABwOBw6HYySGJIQQYrTbWQ3PrICgB5yTweWAcAD215r3n7pegpFRIK2BiFKK733vezz66KO88MILzJkjBb2SYRhK6pEIIUQ8yjBnQoIecJdCtOu5zQXWUuhoMB+fsQQ0qd2ZzdIaiFx++eU88MADbNq0idzcXPbu3QtAfn4+LpcrnS896lVX11NVtYW6umaCQQO7XaesrJDVq0+QCq1CCNFUYy7HOCcfCkKiNA2cBebjTTVQvDAzYxQJSWuYePfdd9PW1saSJUsoKSmJfT300EPpfNlRr7q6nhUrNlNbuw+3205JiRu3205t7T5WrNhMdXV9pocohBCZ5Ws2c0Ks/SznW51gBM3jRFZL+9KMSI5hKKqqtuDxBCgtzUXrivRdLhulpVYaGjxUVW1hyZLZskwjhBi/XIVmYmo4YC7H9Bb2g243jxNZTRbOskxNTSN1dc1MnuyKBSFRmqZRUOCirq6ZmprGDI1QCCGywJQKc3eM/wD0/qNXKfC3mI9PqcjM+ETCJBBJgGEotm7dw1NPfcjWrXswjPTN9DQ3ewkGDRyO+JNVTqeVYNCgudmbtjEIIUTW03Rzi64910xMDXnNBNaQ1/zenmc+LomqWU+67w5ipJNGCwtzsNt1AoEwLpetz+N+fxi7XaewMCflry2EEKPKzEpzi260jkig1VyOKSqXOiKjiAQiA4gmjXo8ASZPduFwWAkEwrGk0fXrl6c8GKmoKKGsrJDa2n2Ullp7LM8opWhp8VFeXkxFRUlKX1cIIUalmZXmFt2mGjMx1VVoLsfITMioIT+pfvROGnW5bOi61pU0movHE6CqakvKl2l0XWP16hPIzXXQ0ODB6w1hGAqvN0RDg4e8PAerV58giapCCBGl6eYW3dlLzVsJQkYV+Wn1I5NJo5WVc1i/fjnl5cV0dgZpbOygszNIeXkx69alfhZGCCGEyBRZmulHIkmjra3+tCWNVlbOYcmS2VJZVQghxJgmgUg/siFpVNc1Fi6clrbzCyGEEJkmSzP9iCaNHjjg61OYLZo0WlZWKEmjQgghxDBIINIPSRoVQggh0k8CkQFI0qgQQgiRXpIjMghJGhVCCCHSRwKRBEjSqBBCCJEesjQjhBBCiIyRQEQIIYQQGSOBiBBCCCEyRgIRIYQQQmSMBCJCCCGEyBgJRIQQQgiRMRKICCGEECJjpI5IFjMMlVAhtUSPE0IIIbKNBCJZqrq6nqqqLdTVNRMMGtjtOmVlhaxefUKP0vKJHieEEEJkI1mayULV1fWsWLGZ2tp9uN12SkrcuN12amv3sWLFZqqr65M6TgghhMhWEohkGcNQVFVtweMJUFqai8tlQ9c1XC4bpaW5eDwBqqq2EA4bCR1nGCrTb0kIIYTolwQiWaamppG6umYmT3ahaT3zPDRNo6DARV1dMw8++HZCx9XUNI7k8IUQQoikSI5Ilmlu9hIMGjgc8X80TqeV1lY/n3zSltBxzc3euI9LgqsQQohsIIFIlikszMFu1wkEwrhctj6P+/1h7HadWbPyEzqusDCnz2OS4CqEECJbyNJMlqmoKKGsrJADB3wo1TO/QylFS4uPsrJCzj336ISOq6go6fGYJLgKIYTIJhKIZBld11i9+gRycx00NHjwekMYhsLrDdHQ4CEvz8Hq1SdgteoJHdd9uSXRRFhJcBVCCDFSJBDJQpWVc1i/fjnl5cV0dgZpbOygszNIeXkx69Ytjy2fJHpcVKKJsJLgKoQQYqRIjkiWqqycw5IlswdNKE30OEg8Eba/BFchhBAi1SQQyWK6rrFw4bSUHZdoImy8BFchhBAiHWRpZhxJNBG2d4KrEEIIkS4SiIwjiSbCSj0RIYQQI0UCkXEm2QRXIYQQIp0kR2QcSibBVQghhEgnCUTGqUQTXIUQQoh0kqUZIYQQQmSMBCJCCCGEyBgJRIQQQgiRMRKICCGEECJjJBARQgghRMbIrplRxjCUbLsVQggxZkggMkK6BxAFBS4AWlp8SQUT1dX1VFVtoa6umWDQwG7XKSsrZPXqE6QQmRBCiFFJApER0D2A6OgI4vWGUApycmzk5toTCiaqq+tZsWIzHk+AyZNdOBxWAoEwtbX7WLFiM+vXS1VUIYQQo4/kiKRZNICord2Hpml0dAQJhQzCYQOvN4SmEQsmqqvr457DMBRVVVvweAKUlubictnQdQ2Xy0ZpaS4eT4Cqqi0Yhor7fCGEECJbSSCSRr0DiJYWH4YBDocFu91CJKJoafEzbZp7wGCipqaRurpmJk92oWk9l3A0TaOgwEVdXTM1NY0j9daEEKOdMmDfVtjxlHmrjEyPSIxTsjSTRt0DCJ8vTCAQxmrVAQ1NA6tVx+8P4/NFegQTvUuvNzd7CQYNHI74Py6n00prq5/mZu8IvCshxKi3sxper4KWOogEwWKHgjJYvBpmVqb/9ZUBTTXgawZXIUypAE3+Lh6vJBBJo+4BREdHEMMAi+XQ45oGSkE4bJCba+83mCgszMFm0zl40I/VqmO16uTk2GKP+/1h7HadwsKckXhbQojRbGc1PLMCgh5wTgaXA8IB2F9r3n/q+vQGI5kOgkTWkUAkjQoLc7Db9dhMiK6bgUd0dSX639GZkf6CidZWP52dIQ4e9KFpGhaLhsNhpaQkF7fbRkuLj/LyYioqSkb4HQohRhVlmEFA0APu0kO/jGwusJZCR4P5+Iwl6ZmhyHQQJLKSzIWlUUVFCWVlhRw44MPlsuJwWAmHDUChlCIcNnA6rbhcFlpafJSVFfYJJqqr67nsssdQSmGx6F05Ihpeb4j6+lbq6w+Sl+dg9eoTpJ6IEONNsnkeTTXmTIRz8qEgJErTwFlgPt5UM7TzD8QIw8s/MZdjHJPM4EfTzVt3qRmcvF4luSrjkMyIpJGua6xefQIrVmymocFDQYGLxkYPgUAEMGdCJk1ysmdPR49gIlpzpKmpkxtueJH2dj9z5kzE4wmyd28Hfn8YTdOIRBSapvGb33xRtu4KMd4MZYnD12we63LEf9zqhECreVwql1B2VptByL43ze9DXrA6YEIJ2HP7BkHFC5M7vxjV0joj8tJLL3HGGWcwbdo0NE3jL3/5SzpfLitVVs5h/frllJcXo5TC7XZgs/XM8ygvL2bdOrMOSHV1PcuW/YGzznqIb33rUd58cw+dnSE8niB5eQ7mzy9g3rwCZs+eyMyZ+UyYYGXSJGeG36UQYkRFlzj214LNDe4S8za6xLGzOv7zXIVmQBEOxH887AfdDm31Qzv/QGNtrTO/123mTEjYB+2fmDMhYAZBRtAMgsS4ktYZkc7OTo455hguvvhizjrrrHS+VFarrJzDkiWzB62s2rtomc1m4eBBP4FAhJ0725g5M5+8PAcTJpgBjGEoGhs7ZLeMEOPJcPI8plSYsxr7a81juy/PKAX+Fig8Gv71SGrySLqPNWcqhDrN+zUdNLsZeHQ2mrMi0SDIVZiKT0mMImkNRE477TROO+20dL7EqKHrWp9tud31rjmiaRpKgcWixZZh9u7tIDfXHqslIrtlhBiHksnz6L3Eoenm0sozK8yAwllgzkSE/WYQYs+Dw78K/1g7tPMPNFar0/wK+bpmRTTQrObsTLDTXBIqKjeDJTGuZFWyaiAQoL29vcfXeBGvaJnLZcNmsxAOG+i6GXh4vWEAlFL9JrgKIcawaJ6HdYA8j4GWOGZWmrtTisrNGYrORvO2qBxOXQf5c4Z3/v7GqmkwYSroFjBCh5JSVQS8+8wgaPFqqScyDmVVsuratWu54YYbMj2MjOhdtMzjCcYSWyMRRSRiVlxta/OjaeayjuyWEWIc6p7nYXP1fTyRJY6ZlebSSryiYvu2Dv/8/Y3Vngd5M6Fzr3meaDDiLoWjvgmOfPM+CUbGlaz6aV9zzTW0tbXFvnbt2pXpIY2Y7jVHPJ4gn3xyEJ/PrD/icFhiM6T79nVw4IC3R4KrEGIcieZ5+A+YeR3dRfM8CsoGX+LQdHNpZfZS8zZ68U/V+fs7lz0PJs6H/HlgdYFtAvgPwtbbYdNZ8Miy5JJhxaiXVYGIw+EgLy+vx9d40b3myJ497UQiCrtdR9c1dF3DYjF32eTlOZgzZxKPP36+BCFCjEfRPA97rpnnEfKaswghr/n9cJc4Unn+/s4V9pnLMZEAaBZw5A1vZ44Y1bIqEBnPojVH7HYLXm8IXTeTVQ1DEQoZWCwapaW5lJTk0tjo4a239mZ6yEKIJBlKURfw8rqvnbqAF6P3jEOiBsvzGG510qGcv7/iZ3HP1WHmjNjcZk6K1WkGJ0bQLHYWaJfiZuNIWnNEOjo6+PDDD2Pf19fXs337dgoKCpg5c2Y6X3pUqqycw+WXL2LNmhdQShEKKTQNXC4rU6e6yctzYBhKGtyJccdQig+CPtqMMPm6lfl2F3rvHR1ZbpvPwwPtTewKBQgrhVXTmGFzcF7eFI515SZ/woHyPFIhmfMPVvys97m8+80CZ/ZcCHkO5YygAM3cVdP01tgpbiZN/gaU1kDkzTff5OSTT459f+WVVwJwwQUXsGHDhnS+9Kh1yilzueuu17FaLVgs0cJnVtmyK8atlF/AM2Cbz8NtLbvxGhHydCs2XSOkFB8H/dzWspsrC6YP7b1E8zzSJZHzJ9o/pvu5djxl7pwxguDZBUYEdCugAcoMZiIH4JNnR38gIk3+BpXWkGzJkiUopfp8SRDSv4qKEo44ogivN0Renp0JE2yxIES27IrxJnoB/zjow6XpFFisuDQ9dgHf5vNkeoiDMpTigfYmvEaEQosNh66jaxoOXafQYsVrGDzQ3jT0ZZpM6l1cLdH+Ma5Cc9ajo7ErCOmqtqppXbcWM7n1g0dH9/LMUCvgjjMyN5RlorkiubkOGho8eL0hDEPh9YZoaPDIll0xboyVC/gHQR+7QgHy9EMzm1GappGnW9gVCvBB0JehEQ5Dsk30oqZUgHsaRHzmTEifZbaImTfSuafvc0cDZcDeN+DFH5u7jJIJ0sYhCUSyUPf+NJ2dQRobO+jsDMqWXTGujJULeJsRJqwUtn5yWmyaRlgp2ozwCI8sBYZaXE3TYf5XAK1bcTNl3hpBc0bEPc18bLT1ntlZbW5BfvRLsH+7mXh78INDPXVg4CBtHMqqgmbikN79abr3pBFiPIhdwPv5N2/TNDxGai7g6UyGzdetWDUzJ8QR55yhrryXfH0U/joeTnG1WafAm7eZO2iMcFcwopu1RSaUmMHIaOs90z1fRnd0JaRaDjX4y5tlJuhCz07H49wo/Jc/fgzWn0aIsWykLuDpToadb3cxw+bg46CfQk3rMbujlKLdiDDX7mS+Pc6FPNsl0kSvv/4xUypgyjHmcx2TzFLvuhVsOeZzOxpGV++Z3vkyYS94ta68l14N/kCa/HUjSzNCiKwUvYC3GxFUrzyQ6AV8hs0xrAv4SCTD6prGeXlTyNF1miNhAoaBoRQBw6A5EiZH1zkvb8qo244MDK/4WffnBlrNIMTqTF1htpHWO1/GmtO1NBU2A6tog7+QN/kKtWPcKPkJCyHGm3RfwEcyGfZYVy5XFkxnrt2JTxm0RML4lMFcu3PoW3ezxXCKq6W7MNtI6p0v07vJH8qc9Ql1js5AK41kaUYIkbWiF/Do0onHMJdO5tqdw146SSYZtswx/Lo9x7pyWeB0j/rCbHENp7hauguzjZR4+TI9mvz5iNVIKSqXOiLdSCAihMhq6bqAj2QybJSuaSkJaoDsq9bZu/hZtOR7IuNLd2G2kdBfvow9D6xu8HQlq566vmeTQSGBiBAi+6X0At5lVO9mSVW1zlQHM9HzffKsWYyscw9EQuOjmmg05+WZFebSi7Ogq4eO38wHcU6Gk26FqYsyPdKso6neWWBZpL29nfz8fNra2sZVJ96hMgwl232FSJChFKuaPjZ3s1isfXazNEfCzLU7uWXK3OxaPuldUt3aVVLdf8BM/IyWVE/kPKksPR49X9Nb5liUMi/E7mnm7pBkxzdadf9cjaD53sd6EBZHMtdvCURGwEgECNXV9VRVbaGurplg0MBu1ykrK2T16hOkAJoQ/TjUA8YgT7dg65ohaTci5Oh69iWSKsMslrW/1twi2nu7bHTL61efHHhmI1XBTO/zBdrNZMxIwKwDQsS8zZtlljZPdHyjXbYtm2WABCJZZCQChOrqelas2IzHE2DyZBcOh5VAIMyBAz5ycx2sXy/VWIXoz6hqqrdvK2w6y7yoxysgFvKagcCZf+4/5yJVwUy88zkmQdtHZvARfa4RNIuUTTo8sfGJMSGZ63cWLn6OHf0FCLW1+1ixYnNKAgTDUFRVbcHjCVBamhubXna5bJSWWmlo8FBVtYUlS2bLMo0QcWTjbpZ+K71Gt4i6BiipPli1zmT6wyQSLHQ/nxEEFGYX3eg5u9XP6D0+mTkQSCAyLAMtuYxUgFBT00hdXTOTJ7vibkEsKHBRV9dMTU2jVGkVIo50lncfioFnaIZRUj0qFcFMf+cLG5hBSLdgRNO6esiEe44v1TkqYtSSQGSIBltyGakAobnZSzBo4HDE/1E6nVZaW/00N3uH/BpCjFXZtixzKGclQp5uxaabOSvRSq9XTjqMY4daUj0qFcFMf+eLVhMN+UC3dQUhCtDN5Zro+Pyt8Oxlh3JUXF05KvtrzVyTsZ7QKnqQObAhiC651Nbuw+22U1Lixu22x5ZcqqvrEwoQgkFj2AFCYWEOdrtOIBC/1oHfH8Zu1yksTO3WRyFGu5Eo756MhCq9epoxFl09tJLqUdF6F9GdLd0NpfR49/OBmSeiaeYyjTLMqqK61ZxlsefBolXwxi2HerLYXOZ4bS7z+6DHnClRRv+vGa1RsuMp83agY0XWk0AkSb2XXFwuG7qudS255OLxBKiq2kJBgWtEAoSKihLKygo5cMAXtx9HS4uPsrJCKipKhvU6QowlI1nePVEJV3qd+pnhlUUfTn+Ygc6nW6H5n9CxB4yIWc484geUmVwbHZ9zUuI5KvHsrDaTYzedBU9caN4+ssy8X4xKEogkKdElFyClAYJhKLZu3cNTT33I1q17MAzznLqusXr1CeTmOmho8OD1hjAMhdcboqHBQ16eg9WrT5BEVSG6Saa8+0iJVXrtJz/FpmmEVVel15mV5q6WM/8MyzaYt199MvHljHT0eIn+ntMAvWspRrOCfSIcf8Oh8fXuydKb1WnOpsTLUYluE95fawY37hLzNrqkI8HIqCQ5IklKNCejpcXH6tUnsGLFZhoaPBQUuHA6rfj9YVpafEkFCIPlo1RWzmH9+uWxY1pb/djtOuXlxVJHRIg4hlLefShJrck8J+lKr8Mti56qHi/K6FpKicDkf4OIz0xM1a1gcZnVVT/4M5Rfah4/1ByV6OtEl3Sin5HNZebLdDSYj89YIjtvRhkJRJLUPSfD5bL1ebz7ksvChdOGHSAkugW4snIOS5bMlsqqQiQg2Yv+UJJak33OfLuLGTaHWelV0/pUem03Isy1O5lvj3PxHqpU9Hjpvn1X10Gf0PPx3tuB++vJAgMn3KZ627HIGhKIJCmak1Fbu4/S0r5loVtafJSXF8eWXJIJEHpvBz7mmKmsXbuFlhYfBQVOlKJbPkrfLcC6rskWXSESkMxFf9CdLHGqrw7lObqmcV7eFG5r2U1zJBy30ut5eVOyq9w8JL8deLCeLP3lqKR627HIGhKIJCmak5HMkksiAUK85ZecHDsffdRi/mJsD6Dr4HBYKSnJJTfXnvAWYOlBI0RPiV70gR5JrdGAxaFpFGoazZEwD7Q3scDpjgUIvRNhE3lO1LGuXK4smB6bSfEY5kzKXLszOyu9wtCWWqI5KtE6IoFW85ii8v7riKR627HIGuMyEBnuhTnVORnxll9aWrz861/NGAbYbDpWq45S4POF+eSTg8yaNZEJE2yD1giRHjRCxJfIRb8u4E04qTXaHTiZRNh4HYWzsdLrgIa61JJsjspQX0dkvXEXiKTqwpyqnAzDUKxd+zItLV4KClwYBoCipcXf9UtMEQ4b2GwWNA3sdp1g0KCx0cP06XkDbgEeiRLzQoxmg130h5LUOpTn9KZrWtwgJSsNdakl+txE8zmG8zoiq42rQCTVF+ZU5GTcc8+bvPzyTiIRRXt7EE0Dm81CIBDumgWJYBgQiRhYLDqgYbXq+P0hmpo6OfbYkrhbgIdSYl6WcMR4NNBFP+mdLEN8TtKyrUdL76UWf6t5v7sUyi8xZz7S8TrxlnSy7bMZzGgbbxqMm0AkG5vDVVfX8/Ofv0woFMFms6DrGkpBIBAmElFYLAqr1ZwBCYcNNE3rqpisiEQUDoe13y3AA9U78fnCOBwW3n57H1u37mHRolJZwhEijqHsZEn77pds7dESXWqpvcf86mgETwP8Y625fTdV4xtoSSdbP5v+jLbxpsm4CbuS6f0yEqKBkd8fxmLRuoIMc+eL1Wr+WKLBh9Wq4XBYiUQMQiGDSERhs1m49toT+w0S4tU78XiC/OtfB/jooxYaGjw0NXlZsWIzv/rVq4OWrBdirDKUoi7g5XVfO3UBb49qqtGk1hxdpzkSJmAYGEoRMAyaI+G4O1mG8pyEZXtBr10vwBu3Qvsn5kyFcyJYJ6R+fNElndlLzdtoEJLNn01vo228aTRuZkSyrTlcNDCaOnUCoVAEn8+sPwJaV2AChgGhUIQJE+zMn1+A1xsmHI7Q0uLj2GNLuPTS4/o9f+96Jx5PkE8+OUgkYs6yaJo5q7Jjx0GuvfZ5nE4rc+ZMzIqZIiFGSiK1PoaykyUtu1+yvaCXMuDFVeDZBSgItgOamceRM9X8Pl3jy/bPprfRNt40GzeBSDKFyEZC98CopCSXTz45SDBodAUJYLVqhELmX2YFBS6UMv+tejxBJk/O4ZprThwwMOhd76Sx0UMkorDbzd034bDC5bIyZUoOH3zQiqaR1i7BQmSbZGp9DGUnS8p3v2R7Qa/ae2D/W4AyO+9iJtsT8oFnJ+QUp2982f7Z9DbaxptmYz/U6pJtzeG6B0a5uXZmzZqIy2XFMMzlF6XMbbuHH16IUorGxg46O4OUlxezbt3gSbXde9B88kkbPl8Ii0XrmmUxsFg0pk51E4mY/+5DoQheb6jPeVLVJViIbDKUpnfRpNbFrjzKHDkJBRRDeU6/htOjJd2UAbX3mre6zfwrXtPMW91mNsHzt0IkkJ7xZfNnE89oG2+ajZsZkaEUIkun3jMWubl2cnMn4/WGCIUitLb6qago4Yknzuett/YOaSdLtN7Jj3/8NNu3m10wNc2cCZk61U1enoPOzhC6rmEY5jbh3kZ6pkiIkTDcWh8Z0auglwIChkEEhQUNR8SPlqmCXk01ZtfduFt0NbPvTMQHtgnpGd9oK3Y22sabZuNmRgQOXZjLy4vp7AwmPcuQSv11zQVz+aWgwMU115yA1aqzcOE0li49LLY0Eq8Lb38qK+ewbt1ypkzJYdo0N/PmFTB/fgF5eWYknpNjxWazoJTCYun5CzkTM0VCjISkOt1mi2hBL/8BOiMRdocCNISDNIaDNIQCeLz7ac+fl5mCXtG/3K1OUPE+s66kN3dJesbX7bOh14x3rNhZQVn2FDsbbeNNs3EzIxKVTc3hkq3QOtQttgsXTuPoo4uprd1HYWHfvwAdDjMQaW01i6hlcqZIiJEwIrU+Uq2roJf/6UsIeXah7PlYLA5sRpAJgTY6bDlsmP8tlvk7R74UfPQvfOtk6NxrLsFolq78Bw2MkDn+8kvTk3w52oqdjbbxppmmeidMZJH29nby8/Npa2sjLy8v08NJm0QKifVXjO3AAR+5uY5Bi7F1f368ZakVKxby1FMfSR0RMS4YSrGq6WOz1oelb/PK5kiYuXYnt0yZm1Wl1Q2luKd2I59++zfM8OzAaoQJ61b25c3hiSMv5ZWi4zIzbmXAI8tg75vmxTRiLgXHaFaYcgyc/3p6L67d63IYQXN5I5vrcoy28SYhmeu3BCKjgGEoli69n23bGikocGG1WsjJMX95KqVoaPBQXl7Mk09+c8CZi8FmVKSyqhhPDu2aMeI2vYvXITfT6gJe1uzfQQ5wWNv7uP2tdDgnsWvSUShNJ2AY+JTBjUWzKbM7exb9KjrG3NWSrgqeb/4KXr4GjHDXrhnMAEVFQLfAiVVw3FWpe73+JFqpNFsqmmbLOFIsmet3Fs07iv7EKwPvdB5KOC0ocPH++8088EAtRUUT+g0iBluWSkXJeiFGi2RrfRhKZbwRXTS3xWqxsrPg3/s8Hu1jY+x8Dt6681DFTmWYuRua1bzIpbqCpzJgx1NmQS4jfGhGRNPA6jaTVXc8BQt/mP6LbCL9a7Kpomky/XbGKJkRyULdZybq61v5+c9fZu9eT48y8OGwuQV35sx8lIKdO9vIz3dgseiyrCJEEhIJMBIpfDYSojMiLk3Hofe9oAcMg7l7X2V1zS+xhzrMOhVG0Kx0aoTNgCBvlrkE4D8A9lyzd8twL777tsKms8xAxOqEsPfQ61lzIOyDUCec+efMX3SjFU2DHvPzsTrM3Sup/DyEzIiMZt2XTwKBCAcP+olEVKwEfLTfjM2mEwoZNDR4CAbDZkK6287EiU7psCtEEgbrdJtM4bN0G6yPjScS4pz3/xdbqMOs2AlwcJe5E8PiBBUC7z6YdHhqK3hG62K4HOYsiG1Cz8etTrNBXabrYkhF06wkn3QWiSaURnu+TJzoJBJRKKUwDEUoFCGaAGb2oNHx+UKEw4oJE2xMmuRE17Wu8uy5eDwBqqq2DLrFVwgR31AKn6XTYH1sjmyrY3bHJ2jRip1hb1dNCmtXgTGr+dd/yNu3gudwdK+LEU+21MVIpqKpGDESiGSJ3t2BXS4bkYj5y81u12NFx4JBA8NQXR14zQqs0Sqp3f86ykQjPyHGmmQKn42UaG7LXLsTnzJoiYTxKYO5dicX2C04jPChip1GGPOPFy06aMDoup/UVfAcLXUxpKJpVpKlmSwRrztwtO+MUho2m4Vw2MDhsBAKRTAMYqXqS0rcsQJl3Y10Iz8hxppY4bN+do9Fk0NHuvBZv31sONizYqduJdbzBa0rSNC77id1MxWjpS6GVDTNSjIjkiXidQfOybHidFq7Sq9Hg45c5s0rYPbsfNxuOw6Hhdxce9xzSnl2IYane+GzeDJZ+CxuH5veMxPWnK6/8sPm96prtsSWk/qZipmVZqJnUbmZmNrZaN4WlcOp67IjAXS0zNyMMzIjkiG9a3YUFLhiTfCcTiteb5hw2GDiRCfBYCehkIGmESvD7vEEKSqaQH6+g92723G5bH0S11pafJSXF0t5diGGaLDk0HYjwly7k/n2OH9dZ0K8mYmcYnPXTKQrVySn2MwRScdMxcxKM9EzW+tijJaZm3FGtu9mQLzCYocfXkhrq4+PP24lHDbw+8Oo6DZ8q7lDxmrVmDjR1WN7LjBgxdSR7qEjxFgzGguf9anYaXSrI6LrY6qC55CM4Yqm2UIqq2axgUq1RyIGra1+DEN11Qwxf3+EQhEsFo2VKxezbNlhfQqRDbUHjRAiMdlSRyQpvSt2pruy6mgzRiuaZgsJRLKUYSiWLfsDtbX7KC3N7THNaxgG77yzH8MAp9NCMGgmpOo62O0WbDYLxx03rd8y7qkqzy5l3oWILxsqqwoxWkhBsywVb2dMlM9nBh6gmD493ywBEDawWnVycmx4vaHYVtx4ZdhTUZ5dZlaE6N9ghc96kL+2hUiYBCJp0N+sQrydMVHmzhgzcTsSMfpsx033Vtz+loykQqsQScqmPiZCjAISiKTYQLMKhYU5sZ0xLpetx/OsVvOvpWhyam/p3Irbu5hadLbGrNBqpaHBQ1XVFpYsmS3LNEJ0ibtUs+v5nn1MXF19TPbXmvdLHxMh+pBAJIUGm1W4++4vUlZW2JUj0rNSo8tlJqeCjsvV88eS7q24Ay0Z9a7QKt15xWiTjtyObT4Pf2zbi6VpOxMCrXQ6JmEUlfOz135BnvQxEaNFliwhSiCSIonMKtxyyyusWnU8l132GA0Nnj7bbYuKzEZR0cccDgsHDwY4eNBHbq6DVauOT8uMxEBLRiAVWsXolY7dLtt8Hp5872HOf/depnt2YDNChHQbB5yFqM4GvDlTyBmsj0mmO9AKkUVLiBKWp0iiswqTJjlZv3455eXFdHYGaWzsoLMzSHl5Mb///Vf4/e+/Qnl5MQcOeHn33WZ27WrD6w0TCIS55ZZXqK6uT/nYuy8ZxSMVWsVoFK3/8XHQh0vTKbBYcWl6rGvuNp8n6XMaSvH6vzZx4RvXM+fgvwhac2hzFRG05jDd8wnu4EE6Qz7ibkWUPiYiW+ysNpcK99eCzQ3uEvM2uoS4s3pEhzMigchdd93F7NmzcTqdfOpTn+L1118fiZcdUYnMKgSDBs3NXior5/Dkk9/kz38+hw0bzuTPfz6HJ5/8JpWVc6isnMOqVcfjcFjJybExc2Y+//ZvRRQW5sSWeFIdjFRUlFBWVsiBAz567+aOLguVlRVKhVYxaqSra+4HgU4+8/bd5IQ6OZhTTMjqRGk6IYsDry0XlCLXu5dAOE5QL31MRDZQhjkTEl1CtLnM5Riby/w+6DEfV8aIDSntgchDDz3ElVdeyXXXXce2bds45phjWLp0KU1NTel+6RGV7KxCdLvt0qWHsXDhtNiSi2EobrnlFYLBCHPnTqSgwIWua11LPLl4PAGqqrZgGKkr/6LrGqtXn0BuroOGBg9ebwjDUHi9IRoaPOTlOVi9+gRJVBWjRrq65ob2bWWap55Ox8RYDogz1MEUzydM8jeho3AYAWxtH0Cw/dATR7KPiTJg31bY8ZR5O4IXFDEKNNWYyzHOyYfymKJ6LyGOkLQHIrfddhuXXHIJF110EUcddRTr1q0jJyeH//3f/+1zbCAQoL29vcfXaJGqWYVkEkdTqbJyTr9LRlImXmQDQynqAl5e97VTF/AOOJsR65rbT1KqTdPwKyOhc3WXHziI1QgR0s1Gk85QBwWdjdgifgw0Qpq5G04P+6BtBwTazL4uHQ0j08dkZzU8sgw2nQVPXGjePrJsxKfaRRbzNZs5Ida+HduBjCwhpjVZNRgMsnXrVq655prYfbquc8opp/Daa6/1OX7t2rXccMMN6RxS2kRnFVas2Bw3ETXRWYVMJo5WVs5hyZLZUllVZJ1kk067d8119ApGvEaE5nCIIIqH25vZ3NGScAJraV4pbboN3QhgaE7yfM1oKkJEs3b9dWlgYEHXHWaTufadZpO5ovKkkwCT3u0TXfeXrcNiIK5CMzE1HDCXY3rLwBJiWmdEmpubiUQiFBcX97i/uLiYvXv39jn+mmuuoa2tLfa1a9eudA4v5VIxq5DpxNH+loyEyJShJJ1Gu+a2G5EeM5ReI8LecJAACjsaRUkmsOrFx2IpOIK8QBuWkBdrJIihWUDTUIBFRcCagzb5CMibBY58OPGX8NUnkwoCtvk8rGr6mDX7d3Bz8y7W7N/BqqaP+x9fFq77iyw1pcJcIvQfMJcMuxvJJcRusmr7rsPhwOHoZ7polBjurEJ0iSderZF01xMRItv0TjqN/v/g0DQKNY3mSJgH2ptY4HT3mC3QNY3z8qZwW8tumiPhWNfc5nCICGBFo9Bqw6LrWGDAc/Wg6eR95mf4n74Em7cZHYMQVnSlsKoImm5Fd5d0rbVPhIgPcoqSWo451O03Qp5uxaabMzvRYClut99k1v1l6/D4punm7NwzK8wlQ2eBuRwT9ptByEgsIfaS1lcqLCzEYrGwb9++Hvfv27ePqVOnpvOlM2o4swqSOCrEIcNJOj3WlcuVBdOZa3fiUwZNEXM5xoFGsdVGjm5J+Fw9zKzE+YV7yZl8JDpgV2GsGOg2F3reLLB3BQlDmOIe8m6fLFz3F1lsZqW5VFdUDqFO6Gw0b4vK4dR1I76El9YZEbvdzsKFC3nuuef48pe/DJhdZp977jlWrlyZzpce1aJLPNFS8a2tfux2nfLyYmlAJ8aVWNJpP4G3TdPwGIo2I/5S5rGuXBY43XwQ9PG6r52H25spslix6H3/BhvsXD3MrET7xhb44wlorf8y80DsEw49Hp3iLipPaoo7mcCrRwO+LFz3F1luZqVZ5Xc8VFa98sorueCCCzjuuONYvHgxt99+O52dnVx00UXpfulRTRJHhRg46RQg1JW4mq8f+lUWL8kzetHe3NFCGLD0OVP8cw1It5r5H8+sgEBrV6Oo4U1xDznwiq777681y8l3/6yGGBSJcUDTs2KpLu2ByDnnnMP+/ftZs2YNe/fuZcGCBTz55JN9ElhFX9ElHiHGq2jS6cdBP4Wa1idnqt2IMNfuZL7dnAUYaHfNAqc7qXMlJDrFHS2VHWg1Zx6GsEsGhhZ4AVm57i9EojTVu+hFFmlvbyc/P5+2tjby8vIyPRwhRBzpaCrX3aHkTSOWdBrqChxydD2WvNknyTPOcUCfc3UYEToMgxxdZ9XkGSwcSg+aFDUPM5RiVdPHZrBk6Zus3hwJM9fu5JYpc+N/xt37hxhBMyhKV/+QeO8ZsmKqX2ReMtdvCUSymGEoWZoRWS3VTeX6C2oGe51DF3Bfj9010PcCvt3fwQPtTXwU9NFhGBgodMCtW5hndw2rIV4qJBp49WskOqrGa5jmKgIN8O7PeBM1kXkSiIwB1dX1sWTVYNDAbtcpKyuUZFWRNaIXzHYjgkvTsKNj0bTEL5hxzjdYsNHfzEtdwMua/TtwaTqOOImoAcPApwxuLJpNmSOHN73t3NKyC59h4NYtTNB0wjDksQ8m2VmjdHQNTpnehdOsDvC1QMdu8/Hc6ebSUDhg1qqw50oxtXEomet3VtUREabq6npWrNiMxxNg8mQXDoeVQCAca3q3fr2UXBeZZSjF+tZG9oWDKMALaGjYNY1JuiW2zXTAmhzdJFo7o8dOkW4STfI8GAnzfqCT9Qcb8RsGUy029K7AJal6IkkYSlDRfbdPupa8hqR34TRNM5NhAy2Y0yFdibGuQnP3jrXUzFl5vcrcoSHLNCIO+VeRZQxDUVW1BY8nQGlpLi6XLe1N74RI1t88B/go5MMALGhYNQ0dCCiDpkgYm0bCTeVS0Sm3e5JnPCGlMFBsaNvLT5p28HHIj1cZ7ImE8BqR2HHDaYgXz1CqwkbpmkaZI4fFrjzKHDmZD0IgfuG0sLdre7AVdJs5ExLqakGRoSZqYnSRQCTLZKrpnRCJMpRic8cBDMwp1eg/U00Dq6ZhoPBEIoRVYjU5UtEpt7+S7tBVkTgSxmcY7A0FsHW9RDRw2hfuGYzYNC3hsQ8kFQFW1olXOM0IAwrQYv126P7ZSTE1MQgJRLJMIk3vgkEjLU3vhEjEB0EfByJhdMzLT28WNIIoFCRUkyORTrmDBQbRku45uk5zJEzAMDCUImAYNEdCBDGwaRpFVjtOzYKOhoaGBTAwA5VoAJN0PZF+pCLAyjrdC6dF6VZiyzJKAXrXfV2kmJoYhAQiWSbTTe+EGEybEQalsGs6BvSZgQCFARRarD1qchhKURfw8rqvnbqANzYTkMiySiKBQe+S7i2RMD5lMNXqwKXpTO7aTePUdeyaRgSF1rWkFFSKgFKxeiIzbI7k6onE0V+ApSmDmS3/ZMHeLcw88E/aIsFhvc6IitcwzZrTNesRBiNkzpbYun4/ZaiJmhhdJFk1y0jTO5Ht8nUrNl3HpqBVKSIodKWifxMTwfwL54vuybG8hpEqNBYvybM1EuL/HdjdIyAosFjZFw4RVqprZkfhNww8KHJ0nfPypgw7JyNecbKyva+x7N17KG6vx2KECOk2cuqOhLKzIX9O9tfe6K9wmmPSobwQR4GZ1CrF1ESC5F9GlpGmdyLbRfMxQiimWKw4NB2FRgQzENGBuTYnZ+ROBgZP2Nzu7xhgWSWcdGDQO8lzksXWZ8YlR7dQbLXhiM7qACEUc+3OlG3d7Z23Urb3Nc5/4zpKW+vwW3M44CxERyenYQs89z3Y/A3YdBY8sszcIput4jVMAyhaAFMWACrjTdTE6CJ1REbQQAXKej/W2urnlltekToiIit1L7qVq+kYGgQNhU8Z5Oo6V02eMeRCY6munTFYtdJ9kRDFFhs/LJie8t0p0c/JFwlzw8vfZUZrHS2uYiIaTAh3UuxtRI8mylpzwD3d3Ao7GmpvSGVVMQCpI5KFBipQBsR9bNWq45k0ySmVVUXWieZjxAIHwwwcDu9VmTSZhM101c6IJrLe1rKb5ki4T7XSXN3CdydN40jnhMFPlqTo5/RifTXF7fW0OfIxNA2HpjHF34KuDLA4zIt6xA+aMutzjIbaG/01TMuCJmpidJFAZAQMVKDsW996FE2DcNjo89hllz3G+vXLWbr0sEy/BSH6SCRwSLabbHRZJR1j7R44eboCp7l2Z9qrlR7rymWBy4GBQY7djUWz4Ij40SIB0KK/grsybIxw39obcmEXY5wEImlmGIq1a1+mpcVLQYELwzB/z7hcNqZNs/DOO/sBjX//96LYX4xm8TIrDQ0eqqq2sGTJbJkJEVlpsMChe8KmHQh0JbdaMGcFUrVVNhGZrFaq5xShWxxYjTDYbBAKAwZolq4juupwRD8Hq9Ps5Cu1N8Q4IIFImt1zz5u8/PJOIhFFe3vQ/GPHaWXqVDcWi45hABj4fGFycmyx5/UuXrZw4bSMvQchhiqasFkX8GJgbpNVqFg5eB0oc+QMe6tsotI14zKo6LbX/bVm2XPdCuiHtsAa4a6S6F1jk9obYhzJ0sXHsaG6up6f//xlQqEIFouGzaZjsej4fGF27myjvd0sCqSUuTTTmxQvE6OdrmkscubiU2bTOVCYcwAqdt8iZ27SsxL91SQZ7rFpE932as818z/ALAxmhMwv3QITph7q3TKStTeUAfu2wo6nzFvV93eREOkkMyJpEu0Z4/eHsVjM2gjmF9hsOqGQwcGDfpRSZmlsa9+YUIqXiUxLtmtsvOe/4ffg0ixElEEIzEJiaDgBq6bzht/D2XlFCZ83mSZyWdXFNrrt9fUqM//DYjdnPjQdcorB5jZrcaSq9ka8XS29z7ez+tB4IkFzTAVl5mtn844dMaZIIJIm0Z4xU6dOIBSK4POZQQWYAYnVqhMKRdA00HUdl6vnj2Kw4mUDbQUWIhVScRGP7pqZbLHi0HX8hhHLEXHqOgHDiO2aSWTJJNEuvckeO2JmVpo7YaIBQls9/OsRaP2XWXtDt5u1N4YbCCQSYOysNguTBT1mEzuXwyzdvr/WvD/btw+LMUMCkTTp3jOmpCSXTz45SDBoYLXqXbOvikhEMWmSE6fTTEwtKHDhdFrx+8O0tPjIy3OwatXxfQKOF17Y0e9WYKkxIlIhVRfx3rtmnHrPv8h775oZSO8mctHkboemUahpNEfCPNDexAKnGyDhY0e8q23vba/ll6a29kYiAcaMJWagEvSY24Wjn4HNZeawjIbtw2LMkEBkCBKZjejeMyY3186sWRNpbPQQCIS7ElQVdrvOL35RyeGHT44FFq2tfux2nfLyYpYundenqFlR0QT27u2Iu913xYrNrF+/XIIRMSzJXPAHu4jHK3PeXTK7ZpJtIpfosRlJXu0xoH7qcfRnoCUXZSQWYNhzzdkS5+RDx8TGI9uHxciSQCRJAxUm6x4A9O4Zk5trJzd3Ml5viFAoQmurn4qKEi69dCG6rrFkyew+lVUvu+yxHrVH/P4QtbX7iEQMpk51o5RZEl62+4pUSuaCP9hFPLprJhV9ZJKtSZLMsaPGYEsuTTWJBRiN/zCf73LEfx3ZPixGkMy5JSFamKy2dh9ut52SEjdutz02G1FdXR87tr+eMQAeT5CCAhfXXHOoZ4yuayxcOI2lSw+joqKEW255BY8nQGlpLi6XDV3X6OgIEQ4bGAY0Nnbw4Yct/OtfB/B4gn22+woxVP11jY2yaRphldhFPFrVNBV9ZJLp0puqjr5ZJbrksr/WTGx1l5i30SWXndVm4BAJmh1w47E6wejq9muxm0s28cj2YTGCJBBJUHQXTO/gwJyNyMXjCVBVtSUWbABUVs5h/frllJcX09kZpLGxg87OIOXlxaxb1/8SSjTRdfJkV+wvyPb2AHv3dtD996qmafh8YT755CAeT1C2+4qUSPVFPFrVdK7diU8ZtETC+JSRdIO53k3kuovOrsywOZhvdyV17KjQe8nF5jKXY2wu8/ugx3zcWZBYgFHyKXMmxX8Aev+cR3r7sBj3RtGfA5kVLziIGqj4WGXlnD7LLoPtcOme6ArmL869ezswDBUrM6CUOdNqt+sEgwaNjR6mT8+T7b5i2FK5nBKViqqmg/WM6T27ksyxWS/RJRfoWTit+7HRAKOo3Mz7WLzanEnpaDCfb3WagUqqtg8LkSD5V5ag3sFBbwPNRnRfdlm4cNqg+RvdE10BvN4wfn+4a8fNoeea/21uBfb7QzQ1dVJWVhh3u68QiUrlckrv85Y5cljsyhtyl9tkZldSNROTsHQWBkt0ycXf0rNwWshrjiPkNb/vHmBE65oUlUOo09w+HOo0vz91nWzdFSNGZkQS1D04cLlsfR5PZfGx3omu4bARS0y12TQCgUNTqUqp2FZgh8PK6tUnSKKqGLZMNolLZGyJzq6MWH+ZdBcGcxUeWnKxxZmJ6p7TUbywZ+G0QGv/9Ul61zVJxfZhIZKkqd4LqFmkvb2d/Px82trayMvLy+hYDEOxbNkfuoKD3D7T1Q0NHsrLi3nyyW8mFQj0txW4e8del8tGQ0M7mqbFlmdsNguhUCS2vGuxaNx++1K++91FqX7rYhwbbmXV4ZwzHa+dFr3rdli76nb4D5gzE6koDKYMeGSZueTijrPk0tFgBhpffbLnVl4JMESGJHP9lhmRBEV3waxYsbnf4mPJzkYMthV4/frlVFVt4f3396NpGuFwhJwcG9Om5eF22/B6w4TDEVpafBx7bAmXXnpcGj8BMR6luklcotVas6o0+0ASrdsx3MJg0V41yeR0JFufRIgMkRmRJCVaRySR80RnPLoXJjtwwEduriNWmCw6Y/Lssx9z111vEAxG4gZBA+3CASkJLzKvT7XWXsmj0byNRI/LCvu2wqazzG208ZZMQl5UqINPTvsjTYVHD39mp/sSkBE0l1ykN4zIQjIjkkZD2QXTW++twNFlnv4Kky1cOI2FC6exaFFp3AqsgwVBqQqehBiqRKu1ljsmZG9p9niiSaT9FAbr1O1EQn4e2PsOW5k4/JkdyekQY5AEIkMQDQ6GaiS3Avc38yIl4cVISrRaa7X34OgpzQ4DJpF2GgatgQ4cuoWAczIFFmtqmu7JkosYYyQQyYBEtgK3tvoH3AocZRiKrVv3xA1Mkp15ESJdEi3Pvi8cHF2l2adUxK3boYCWcBB38CB7JpWxb/K/oWta9s7sCJFBEohkQKq2Ag+25DLUmRchUi3R5nfFVnvKmuSNiH6SSIMhH27vfgI2N08ddSmq29JJj5kdv4ey+r9C+yeQNwuOOBey5b0JMUJkYTEDonVCDhzwxS0/3dLiG7QwWfe+NxMm2MjPN9eot27dw6WX/o3q6vphFWETIpUSLblemTNx9JVmj1sYrINPJh7OxkU3UDf1M32eYtM0TnvvPub+djY8eSG8ssa8vbsY3vzVSL+DzEhnATgxqkjonQGJbAVeter4fnNBui+55OXZ2b3bg98fjpV97+gIsmrVM9x99xdHrAibEANJtDy7tet21JVm75VE2mhxU0U+Tt1KvDTWyvd+y9lv345FRUC3ga6bF2J/K7x8jXnQcVeN5DsYWekuACdGFdm+m0H9La0sXTqPp576qN8ll61b93DWWQ+habB3byeRiOoq/27WNgqFImga3HHHafzlL++nvAibEEM15uqI9MNQilVNH5v9eiw9E2+1SIhbHj0ed7AdzeLsuWyqFET84JwEl+0bm8s0I1EATmRcMtdvCUQyrHd9j9ZWP5dd9tiA9UVCoQgXXPAXvN4QPl8Ym61nDxrDMAgGIyxYMJWbbz41dr6h1B8RItXGXGXVfhyqh2L0mNk55uO/8P03fgqaFUu8QMMIg4rAsg1w1DdHfNxpNZQKsWJUSub6LT/pDOveEK+iooRbbnkltsvF5bKh61rXLpdcPJ4AVVVbKChwoWng8/VthGfS0HWNPXs8TJrkZP365ZSXF9PZGaSxsYPOziDl5cUShIgRl0xwkYomeZnUX9O9ssB+dAWW/i60mg4oM4F1rEm0i3BTTWbGJzJiDM77jV6J7nIBKCnJpanJi6YpoPuxinDYwOm0AhrNzV6WLj1s2EXYhEhGvIBju79jVC+3DEXcpnttC9BqNXN2IF4wogxAM3fRjDWDFIDD6jSb9PmaR3ZcIqMkEMkiidYXaWnxcemlC/ne954gGDSw2bRYfkg4bGCxaEyenINSKpaIOtwibEIkKl5+R75uodUIE1HKLNuua6kp7jUK9OnXc+Q34IUrzMRUzdJ3ecIImTkiR5w74mNNu2S6CItxQ5Zmskj3+iLxdN/lcumlCznmmGIsFp1IJEIoZGAYBi6XlZkz8/H7w4NuARYi1aJ5ER8Hfbg0nQKLFSca9SE/LZEwOZqOQ9fN4l66TqHFitcweKC9CSN709VSS7fCp35i3kb8XTkhhnkb8ZuzJEd9a2zmSEQLwPkPQO+ft1JmA7+CMvM4MW6MwX/po1cy9UV0XeOWW05l5sx88vKcTJvmZs6cSUyfnkd7e2BI3YCFGI7e/WSiAUf3v/hbjUiP5/Qu2z5uHHcVnLjWnPlQEYgEzCZ2aGDNgQ8eNZM6d1ZneqSpFS0AZ881E1NDXjMIC3nN7+N1ERZjnvy0s0i0vkhuroOGBg9ebwjDUHi9IRoaPH2Ci8rKOaxfv5xjjzVnPdraApKIKjKmv34yERQKsABBpfAbhwpXKaWIoPAaEd4JdI6fWREwg5HL9sGiH4NjknkRnlQGkw4zu/nurzW3uY61YCRuAbhO8/tT18nW3XFItu9moWS75fbeAiyJqCITXve1c3PzLgos1h47XPyGwZ5wEA2FAUy12pmgW/AaEVoiYQLKQGGWgZ9rd47p5NU+xvN2VmVIF+ExLJnrtySrZqFku+xKIqrIBv31k3FoGnZNw68UGmBBw2tE2BcOYXTNljgwl2jGQ/JqD8lsZx1rHXeli7DoIoFImgx3lkKCCzHaRPvJfBz0U6hpseUZTdOYpFtojBjmRnOlOGCEiWBOxlrQmGw1c0rGXWfaTG1nldkIkUUkEEmDZJdWhBgLBuon41WKAt1KgcXG/kiIgDKDEkfXzpoc3QL0TV7tse11LMrEdlbp8yKyjITAKda9K67bbaekxI3bbae2dh8rVmymuro+00MUIm36qyY61+7kmsKZ3F0yn2/mT2GCbqHEYme6zRELQqJsmkZYKdqM+NvYx5R+trMqwB+JEPQdoHPiYRhFC1LzetE+L/trzYRYd8nYTowVo4LMiKRQ96643ZvMmSXarTQ0eKiq2sKSJbMlmVSMWXGriXYr5f5vjgm4NB1LP8suoVgRtHHw6ym6nfWZFWZiqrOATt2OJ9iJI9CKzzaB/5l3Pu37dww/iVcZ5kxI0NMzMdbmAmup+fqvV5ldhGWZRowg+dc2AMNQbN26h6ee+pCtW/dgGANvMEq0RHtNTWM6hy1Exg3UJyaaS9JuROLWy2k3IsywOZhvj7NUMRZ1284aCHoIdTRgCXfQMKmMPy66kY+nfjaWxLvN5xn660ifF5GlxsGfHEMzlDyPREu0Nzd70zl0IbLaQLkk7UaEHF3nvLwpYz9RtbuZlRjTT+I372/G07kX3VXI7oJ/Q2k6DkhNEq/0eRFZSmZE4hhqnkcyJdqFGM/6yyWZarVzuruACbplfBU3Az4IBfh73nw+nnYSuyYfjeq2PJKSCrTdE2PjkT4vIkMkEOmld56Hy2VD17WuPI9cPJ4AVVVb4i7TJFOiXYjx7lhXLrdMmcuNRbP5am4hBRYrzeEgj7Q3s2b/DlY1fTy8pYhRps0IE1YKWz+zHcNO4pU+LyJLpS0Quemmm/jsZz9LTk4OEydOTNfLpNxw8jySLdEuxHinaxqdRoTHO1vYFw6So1sosFhxaXpq8iJGke4F4eIZdhKv9HkRWSpt/+KCwSBnn302l112WbpeIi0SyfMIBo1+8zyi/V/Ky4vp7AzS2Ngh/V/EmGAoRV3Ay+u+duoC3pQsnfTXKG88duYdkSRe6fMislDaklVvuOEGADZs2JDwcwKBAIHAofXL9vb2VA9rUN3zPFwuW5/HE8nzSLZEuxDZyFAqtgW3MRTkJe9BdoeDhLv+Mp9hcwx7S2l/jfJg/BU3G7Ek3pmV5hZdqawqskRW7ZpZu3ZtLIDJlGieR23tPkpLe/5yjOZ5lJcXD5rnISXaxWi2zefhgfYmdoUC+JSB14igAQW6jQKLlZBSKekLE8uL6CdIt2kaHmOcFDfjUBJv9LP3GGbQl/JmgNLnRWSRrApErrnmGq688srY9+3t7cyYMWNExxDN81ixYjMNDR4KClw4nVb8/jAtLT7J8xBj3jafh9taduM1IuTpVjyRMNGFglYjjF3XyNEtKdlS2l+jvKhxVdysy2AF4YQYa5Kai1u9ejVaVzOr/r7ef//9IQ/G4XCQl5fX4ysTJM9DjFe9czYUEAKsaFgAA0VLJIxSKiVbSqW4WXwDFYQTYqxJ6s+Mq666igsvvHDAY+bOnTuc8WSNdOZ5DLczrxDp0jtnI6IMFKpriVJDV4qgUgSUwqlpw1o6ieagHOt0szMUoDkSIk+3SnEzIcaZpAKRoqIiioqK0jWWrJOOPA/pzCuyWe+cDQsaGhpKmVXANcxZkUjXYs1Ql06656CEleo6p/n6Olp68iKEEFkpbQuvO3fupKWlhZ07dxKJRNi+fTsAhx12GG63O10vm9WiFVs9ngCTJ7twOKwEAuFYxdb162XZR2RW75wNp65j1zQCysCKhgI0NCxosaWTuXZnUksnvXNQbLr5em2RMDZd48vuQha6ciUvQohxIm37tdasWUNFRQXXXXcdHR0dVFRUUFFRwZtvvpmul8xqw6nYKsRIiZezUWCxomMGCxHMnSwoRXMknPTSyUB1Q4qsNsIKtgU6JAiJUgbs2wo7njJvlZHpEQmRcmkLRDZs2IBSqs/XkiVL0vWSWU0684rRIFrLIkfXaY6ECRgGTk1nksWCjrk0YwX8KObanUlv3U2mbsi4t7MaHlkGm86CJy40bx9ZZt4vxBgyfvbEZZh05hWjRX+1LI5xuDkxJ58Sm33IW0qlbkiCdlbDMysg6AHnZLNjbjgA+2vN+09dL1VQxZghgcgISUXFViFGSrpqWUjdkAQoA16vMoMQd6mZJQxgc4G11OwL83qVWR1VqqGKMUD+FY8Q6cwrRpt01LKQuiEJaKqBljpzJqT3Z65p4CwwH2+qycz4hEgxCURGiHTmFSJ+DoqhFAHDGFLy65jka4ZIEKyO+I9bnWAEzeOEGAMkEBlBUrFViEM5KHPtTnzKoCUSxqeMISW/jkmuQrDYzZyQeMJ+0O3mcUKMAeN4ITYzpDOvENJPZUBTKqCgzExMtZb2XJ5RCvwtUFRuHifEGCCBSAZIZ14xHkRLuPcXaERzUEQvmg6LV5u7YzoazJwQq9OcCfG3gD3PfFwSVcUYIYGIECLlepdwt2oaM2wOKdmeqJmV5hbd16vMxNRAq7kcU1RuBiGydVeMIZrqnbqeRdrb28nPz6etrS1jnXiFEMnpU8K9VxO7aB7IYDMmAnMrb1ONmZjqKjSXY2QmRIwCyVy/ZUZECJEyvUu4R6unOjSNQk2jORLmgfYmFPBHmTEZnKZD8cJMj0KItJLQWgiRMomUcP8o6OPmA7v4OOjDpekUWKy4NJ2Pg35ua9nNNp8nQ6MXQmSCBCJCiJSJlXDvZ4nFCnQYEXxxmt4VWqx4DYMH2pswsnfFWAiRYhKICCFSpnsJ93g6lYEBuHXLuGh6ZyhFXcDL67526gJeCbCEiENyRIQQKRMt4f5x0E+hpvUINpRSdBgRdDTcuiXu88dS0zvZOSREYmRGRAiRMoOVcHfpOm5d73fGZKw0vYvuHJI8GCEGJ4GIECKlBirhvqpgBvPsrjHd9K73ziHJgxFiYKP7zw4hRFYaqIS7rmnc1rKb5kiYPN3Sp87IaG96l8jOoWgejFSWFUICESFEmvRXwj06YxLNn/AY5nLMXLtzTORPxHYO9dM/aizlwQiRChKICCFG3Fhuetd955AjzvsZK3kwQqSK/J8ghMiIsdr0brCdQ+1GhLl256jOgxEilSRZVQghUmiwnUNjIQ9GiFSSQEQIIVJsoJ1D0aZ/QgiTLM0IkeWkS+3oNJbzYIRIJQlEhMhi2Vadc7CgKGVBkzKgqQZ8zeAqhCkVZifaUWas5sEIkUoSiAiRpaLVOb1GhDzdik03d2JEq3OO9BT/YEFRyoKmndXwehW01EEkCBY7FJTB4tUwszJ9b1AIkRGj708MIcaBbKvOOVjJ8ofamlJT0nxnNTyzAvbXgs0N7hLzdn+tef/O6vS+USHEiJNARIgslEx1znQbPCiKsLG9ic5IeHhBkzLMmZCgB9ylYHOZyzE2l/l90GM+roy0v2chxMiRQESILBSrztlPfoVN0wirkanOOVhQ5NB0Oo0ITt0yvKCpqcZcjnFOht7vW9PAWWA+3lSTireVtQylqAt4ed3XTl3AKz1pxJgnOSJCZKFsqs45WMlyDVBdt/EkXNLc12zmhLgc8R+3OiHQah43RmVbcrIQI0FmRITIQtHqnNnQpbZ7UBRPNAjp7+/2hIMmV6GZmBoOxH887Afdbh43Bg2Wh5Nwno0Qo4wEIkJkoWyqzjlYUBRQBhN0C37DGF7QNKXC3B3jPwC9gx6lwN9iPj6lIhVvK6tkW3KyECNJAhEhslS2VOccPCiycH7eFCZYLMMLmjTd3KJrz4WOBgh5zcTUkNf83p5nPj4K64kMJpuSk4UYaZIjIkQWy5bqnNGgKJq/4DHM5Za5dmcsf2G+3TXg4wmZWQmnrj9URyTQai7HFJWP6Toi/eXhaMpgRuu7TPC30GDNo61gGiAF0sTYIoGIEFkuW6pzDhYUpSxomlkJM5aMicqqiYqXnFy29zWWvXsPxe31WIwQId1GTt2R8OmfjtmATIxPEogIIRI2WFCUsqBJ06F44fDPM0pE83A+Dvop1DSO2Pd3zn/jOpzBDjocE/HrNiYYYXIOvGMWdjt1vQQjYswYu39iCCHGhPFQV6N7Hs6BcJAvvHsPzmAHLa5ifBYHmmYh15GLJoXdxBgkMyJCiKyVbF2N0dypOJqH82J9NcXt9bQ58jE0DYemUWCxMUHv+ruxe2G3cTRrJMYuCUSEEFkp2aZ/Y6EY2LGuXBa4HBgY5NjdWDQLDl3vWSxuHBR2E+OLBCJCiKzTu65GdEurQ9Mo1DSaI2EeaG9igdONrmlZ16l4OPScInSLA6sRBput7wGjvLCbYRgEg8FMD0OkgN1uR9eHn+EhgYgQIuskU1cjum040aAl60ULu+2vBWtpz7470cJuReWjsrBbMBikvr4ew5D8lrFA13XmzJmD3W4f1nkkEBFCZJ3B+tt071+TTNCSDdugBxUt7PbMCrOQm7PAXI4J+80gZJQWdlNK0djYiMViYcaMGSn5S1pkjmEY7Nmzh8bGRmbOnNnn/71kSCAiRBYazUmXqZBM079kgpZRYwwWdguHw3i9XqZNm0ZOzigICMWgioqK2LNnD+FwGFu8ZcQESSAiRJYZC0mXw9W7rkb3v7ai/Wvm2p3Mt7v4IOjLmk7FKTXGCrtFIhGAYU/ji+wR/VlGIpFhBSKj81+0EGOUdGA1JdP0L5s6FadctLDb7KXm7SgNQrobzhS+yC6p+lmO/n/VQowR0oG1p0Sb/mVTp2IhRPJG2VylEGPXmEu6TIFE+9ck0pRPCJGdJBARIkuMyaTLFEi0f022dCoWQiRHlmaEyBLdd4rEM2qTLkdQNGhZ7MqjzJEjQcgYZBiKrVv38NRTH7J16x4MI3uXKnfs2IGmaWzfvj3TQ8lq8htNiCyRzE4RIcaj6up6qqq2UFfXTDBoYLfrlJUVsnr1CVRWzsn08MQQyYyIEFlCki6F6F91dT0rVmymtnYfbredkhI3bred2tp9rFixmerq+rS99sMPP8zRRx+Ny+Vi8uTJnHLKKXR2dgLwP//zPxx55JE4nU6OOOIIfvOb38SeN2eOGRxVVFSgaRpLliwBzGJgN954I9OnT8fhcLBgwQKefPLJ2POCwSArV66kpKQEp9PJrFmzWLt2bezx2267jaOPPpoJEyYwY8YM/vM//5OOjo60vf90k0BEiCyS6E4RIcYTw1BUVW3B4wlQWpqLy2VD1zVcLhulpbl4PAGqqrakZZmmsbGRb3zjG1x88cW89957vPDCC5x11lkopdi4cSNr1qzhpptu4r333uOXv/wl1157Lb/73e8AeP311wF49tlnaWxs5M9//jMAd9xxB7/61a+49dZbqa2tZenSpXzpS1/igw8+AOC//uu/+Otf/8r//d//UVdXx8aNG5k9e3ZsTLqu81//9V+88847/O53v6O6uppVq1al/L2PFE313nifIjt27ODnP/851dXV7N27l2nTpvHNb36Tn/70pwkXtGlvbyc/P5+2tjby8vLSMUwhstJ4r6wqxh6/3099fT1z5szB6XQm9dytW/dw1lkP4Xbbcbn6Fs7yekN0dgb585/PYeHCaakaMgDbtm1j4cKF7Nixg1mzZvV47LDDDuPnP/853/jGN2L3/eIXv+Dxxx/n1VdfZceOHcyZM4eamhoWLFgQO6a0tJTLL7+cn/zkJ7H7Fi9ezKJFi7jrrrv4/ve/zzvvvMOzzz6bUK2Ohx9+mO9+97s0N49sR+aBfqbJXL/TliPy/vvvYxgG69ev57DDDuOf//wnl1xyCZ2dndx6663pelkhxoREd4pIwCLGg+ZmL8GggcMR/5LldFppbfXT3OxN+Wsfc8wxfP7zn+foo49m6dKlfOELX+BrX/sadrudjz76iO985ztccsklsePD4TD5+fn9nq+9vZ09e/Zw/PHH97j/+OOP56233gLgwgsv5NRTT6WsrIxly5axfPlyvvCFL8SOffbZZ1m7di3vv/8+7e3thMNh/H4/Xq93VJbPT1sgsmzZMpYtWxb7fu7cudTV1XH33XdLICJECkgpeDFeFBbmYLfrBALhuDMifn8Yu12nsDD1F2GLxcIzzzzDq6++ytNPP82dd97JT3/6U/72t78BcO+99/KpT32qz3OG49hjj6W+vp4nnniCZ599lq9//euccsopPPzww+zYsYPly5dz2WWXcdNNN1FQUMCWLVv4zne+QzAYHJWByIjmiLS1tVFQUNDv44FAgPb29h5fQoi+pBS8GE8qKkooKyvkwAFf3DL+LS0+ysoKqagoScvra5rG8ccfzw033EBNTQ12u51XXnmFadOm8fHHH3PYYYf1+IomqXbvxRKVl5fHtGnTeOWVV3q8xiuvvMJRRx3V47hzzjmHe++9l4ceeohHHnmElpYWtm7dimEY/OpXv+LTn/40hx9+OHv27EnL+x4pI7Z998MPP+TOO+8ccDZk7dq13HDDDSM1JCFGpd6l4KNryA5No1DTaI6EeaC9iQVOtyzTiDFB1zVWrz6BFSs209DgoaDAhdNpxe8P09LiIy/PwerVJ6D3UwxwOP7xj3/w3HPP8YUvfIEpU6bwj3/8g/3793PkkUdyww038P3vf5/8/HyWLVtGIBDgzTffpLW1lSuvvJIpU6bgcrl48sknmT59Ok6nk/z8fH784x9z3XXXMW/ePBYsWMB9993H9u3b2bhxI2DuiikpKaGiogJd1/nTn/7E1KlTmThxIocddhihUIg777yTM844g1deeYV169al/H2PKJWkq6++WgEDfr333ns9nrN79241b9489Z3vfGfAc/v9ftXW1hb72rVrlwJUW1tbssMUYsx639+pvr7rHXXB7vfUpXvq+nxdsPs99fVd76j3/Z2ZHqoQMT6fT7377rvK5/MN+RzPPfexOvXU36uZM29TU6feqmbOvE2deurv1XPPfZzCkfb07rvvqqVLl6qioiLlcDjU4Ycfru68887Y4xs3blQLFixQdrtdTZo0SX3uc59Tf/7zn2OP33vvvWrGjBlK13V10kknKaWUikQi6vrrr1elpaXKZrOpY445Rj3xxBOx59xzzz1qwYIFasKECSovL099/vOfV9u2bYs9ftttt6mSkhLlcrnU0qVL1e9//3sFqNbW1rR9DvEM9DNta2tL+Pqd9K6Z/fv3c+DAgQGPmTt3bmxKas+ePSxZsoRPf/rTbNiwAV1PfDVIds0I0dfrvnZubt5FgcUad8bDUIqWSJirC2ew2CX/34jsMJxdM90ZhqKmppHmZi+FhTlUVJSkZSZEDC5ju2aKioooKipK6NiGhgZOPvlkFi5cyH333ZdUECKEiK97KXhHnEBESsGLsUzXtZRv0RWZlbbfVA0NDSxZsoRZs2Zx6623sn///thjU6dOTdfLCjHmSSl4IcRYkrZA5JlnnuHDDz/kww8/ZPr06T0eS3I1SAjRTbQU/G0tu2mOhMnTLdi6ZkjajYiUghdCjCppWyu58MILUUrF/RJCDI+UghdCjBWyiCzEKHWsK5cFTrdUVhVCjGoSiAgxiiVaCl4IIbKVbGMRQgghRMZIICKEEEKIjJFARAghhMgSO3bsQNM0tm/fnpXnSwfJERFCCCGyxIwZM2hsbKSwsDDTQxkxEogIIYQYNQylRvVOsVAohM1m6/dxi8WSdUU/g8FgrG1LOsjSjBBCiFFhm8/DqqaPWbN/Bzc372LN/h2savqYbT5PWl7vnnvuYdq0aRiG0eP+M888k4svvhiATZs2ceyxx+J0Opk7dy433HAD4XA4dqymadx999186UtfYsKECdx00020trZy/vnnU1RUhMvlYv78+dx3331A/KWUd955h+XLl5OXl0dubi4nnngiH330EQCGYXDjjTcyffp0HA4HCxYs4Mknnxzwfb344ossXrwYh8NBSUkJq1ev7jHmJUuWsHLlSq644goKCwtZunTpsD7HwUggIoQQIutt83m4rWU3Hwd9uDSdAosVl6bzcdDPbS270xKMnH322Rw4cIDnn38+dl9LSwtPPvkk559/Pi+//DLf/va3+cEPfsC7777L+vXr2bBhAzfddFOP81x//fV85Stf4e233+biiy/m2muv5d133+WJJ57gvffe4+677+53KaahoYHPfe5zOBwOqqur2bp1KxdffHEscLjjjjv41a9+xa233kptbS1Lly7lS1/6Eh988EG/5zv99NNZtGgRb731FnfffTe//e1v+cUvftHjuN/97nfY7XZeeeUV1q1bN5yPcXCpawicesm0ERZCCJG9BmoZP5iIYair9n6ovrLzbXVJw/vq0j11sa9LGt5XX9n5T3XV3g9VxDBSPu4zzzxTXXzxxbHv169fr6ZNm6YikYj6/Oc/r375y1/2OP7+++9XJSUlse8BdcUVV/Q45owzzlAXXXRR3Nerr69XgKqpqVFKKXXNNdeoOXPmqGAwGPf4adOmqZtuuqnHfYsWLVL/+Z//Gfd8P/nJT1RZWZkyun1Wd911l3K73SoSiSillDrppJNURUVFfx9JzEA/02Su3zIjIjAMxdate3jqqQ/ZunUPhiFl+IUQ2eODoI9doQB5urVHk0cwlz7ydAu7QgE+CPpS/trnn38+jzzyCIFAAICNGzdy7rnnous6b731FjfeeCNutzv2dckll9DY2IjX642d47jjjutxzssuu4wHH3yQBQsWsGrVKl599dV+X3/79u2ceOKJcfNK2tvb2bNnD8cff3yP+48//njee++9uOd77733+MxnPtPjczz++OPp6Ohg9+7dsfsWLlw4wKeSWpKsOs5VV9dTVbWFurpmgkEDu12nrKyQ1atPoLJyTqaHJ4QQtBlhwkph0+Mnpdo0DY+haDPCcR8fjjPOOAOlFI899hiLFi3i5Zdf5te//jUAHR0d3HDDDZx11ll9nud0OmP/PWHChB6PnXbaaXzyySc8/vjjPPPMM3z+85/n8ssv59Zbb+1zHpcrM120e485nWRGZByrrq5nxYrN1Nbuw+22U1Lixu22U1u7jxUrNlNdXZ/pIQohBPm6FWtXh+l4Qkph1TTy9dT/be10OjnrrLPYuHEjf/zjHykrK+PYY48F4Nhjj6Wuro7DDjusz5euD3x5LSoq4oILLuAPf/gDt99+O/fcc0/c48rLy3n55ZcJhUJ9HsvLy2PatGm88sorPe5/5ZVXOOqoo+Ke78gjj+S1117r0YD2lVdeITc3l+nTpw845nSRQGScMgxFVdUWPJ4ApaW5uFw2dF3D5bJRWpqLxxOgqmqLLNMIITJuvt3FDJuDdiPSp4O7Uop2I8IMm4P59vTMHpx//vk89thj/O///i/nn39+7P41a9bw+9//nhtuuIF33nmH9957jwcffJCf/exnA55vzZo1bNq0iQ8//JB33nmHzZs3c+SRR8Y9duXKlbS3t3Puuefy5ptv8sEHH3D//fdTV1cHwI9//GNuvvlmHnroIerq6li9ejXbt2/nBz/4Qdzz/ed//ie7du3ie9/7Hu+//z6bNm3iuuuu48orrxw0eEoXCUTGqZqaRurqmpk82RV3zbWgwEVdXTM1NY0ZGqEQQph0TeO8vCnk6DrNkTABw8BQioBh0BwJk6PrnJc3JW31RCorKykoKKCuro7zzjsvdv/SpUvZvHkzTz/9NIsWLeLTn/40v/71r5k1a9aA57Pb7VxzzTWUl5fzuc99DovFwoMPPhj32MmTJ1NdXU1HRwcnnXQSCxcu5N57743ljHz/+9/nyiuv5KqrruLoo4/mySef5K9//Svz58+Pe77S0lIef/xxXn/9dY455hi++93v8p3vfGfQ4CmdNNU7vMwi7e3t5Ofn09bWRl5eXqaHM6Y89dSHXHjhJkpK3Ohx1l0NQ9HY2MGGDWeydOlhGRihEGIs8fv91NfXM2fOnB75E8nY5vPwQHsTu0IBwl3LMTNsDs7Lm8KxrtwUj1gMZqCfaTLXb0lWHacKC3Ow23UCgTAuV99sbL8/jN2uU1goLeaFENnhWFcuC5zuUV1ZVfQlSzPjVEVFCWVlhRw44Iu75trS4qOsrJCKipIMjVAIIfrSNY0yRw6LXXmUOXIkCBkDJBAZp3RdY/XqE8jNddDQ4MHrDWEYCq83REODh7w8B6tXnxB32UYIIYRIFQlExrHKyjmsX7+c8vJiOjuDNDZ20NkZpLy8mHXrlksdESGEEGknOSLjXGXlHJYsmU1NTSPNzV4KC3OoqCiRmRAhhBAjQgIRga5rLFw4LdPDEEIIMQ7J0owQQgghMkYCESGEEEJkjAQiQgghhMgYCUSEEEKIIbj++utZsGDBsM/zwgsvoGkaBw8eTPg5F154IV/+8peH/drZQEq8CyGESLtUlHjPNh0dHQQCASZPnjys8wSDQVpaWiguLu7T+6s/bW1tKKWYOHHisF57OKTEuxBCiPFHGdBUA75mcBXClArQMjO573a7cbvd/T4eDAax2+2DnsdutzN16tSkXjs/Pz+p47OZLM0IIYQYHXZWwyPLYNNZ8MSF5u0jy8z70+Cee+5h2rRpGIbR4/4zzzyTiy++uM/STHS55KabbmLatGmUlZUB8Oqrr7JgwQKcTifHHXccf/nLX9A0je3btwN9l2Y2bNjAxIkTeeqppzjyyCNxu90sW7aMxsbGPq8VZRgGt9xyC4cddhgOh4OZM2dy0003xR6/+uqrOfzww8nJyWHu3Llce+21hEKh1H5gQySBiBACQynqAl5e97VTF/BiZO+KrRivdlbDMytgfy3Y3OAuMW/315r3pyEYOfvsszlw4ADPP/987L6WlhaefPJJzj///LjPee6556irq+OZZ55h8+bNtLe3c8YZZ3D00Uezbds2fv7zn3P11VcP+tper5dbb72V+++/n5deeomdO3fyox/9qN/jr7nmGqqqqrj22mt59913eeCBByguLo49npuby4YNG3j33Xe54447uPfee/n1r3+dxKeRPrI0I8Q4J63VRdZTBrxeBUEPuEshmkdhc4G1FDoazMdnLEnpMs2kSZM47bTTeOCBB/j85z8PwMMPP0xhYSEnn3wyL7/8cp/nTJgwgf/5n/+JLcmsW7cOTdO49957cTqdHHXUUTQ0NHDJJZcM+NqhUIh169Yxb948AFauXMmNN94Y91iPx8Mdd9zBf//3f3PBBRcAMG/ePE444YTYMT/72c9i/z179mx+9KMf8eCDD7Jq1aokPpH0kBkRIcaxbT4Pt7Xs5uOgD5emU2Cx4tJ0Pg76ua1lN9t8nkwPUQgzJ6SlDpyTDwUhUZoGzgLz8aaalL/0+eefzyOPPEIgEABg48aNnHvuueh6/Mvn0Ucf3SMvpK6ujvLy8h7JnIsXLx70dXNycmJBCEBJSQlNTU1xj33vvfcIBAKxYCmehx56iOOPP56pU6fidrv52c9+xs6dOwcdx0iQQESIccpQigfam/AaEQotNhy6jq5pOHSdQosVr2HwQHuTLNOIzPM1QyQIVkf8x61OMILmcSl2xhlnoJTiscceY9euXbz88sv9LsuAOSOSCjabrcf3mqbR3yZXl8s14Llee+01zj//fE4//XQ2b95MTU0NP/3pTwkGgykZ63BJICLEOPVB0MeuUIA83dpny6CmaeTpFnaFAnwQ9GVohEJ0cRWCxQ7hQPzHw37Q7eZxKeZ0OjnrrLPYuHEjf/zjHykrK+PYY49N+PllZWW8/fbbsRkVgDfeeCOlY5w/fz4ul4vnnnsu7uOvvvoqs2bN4qc//SnHHXcc8+fP55NPPknpGIZDAhEhxqk2I0xYKWz91C2waRphpWgzwiM8MiF6mVIBBWXgPwC9ZwWUAn+L+fiUirS8/Pnnn89jjz3G//7v/w44GxLPeeedh2EYXHrppbz33ns89dRT3HrrrQAJ1wwZjNPp5Oqrr2bVqlX8/ve/56OPPuLvf/87v/3tbwEzUNm5cycPPvggH330Ef/1X//Fo48+mpLXTgUJRIQYp/J1K1ZNI9TPdG+oK3E1X5ecdpFhmg6LV4M910xMDXnNBNaQ1/zenmc+nqZ6IpWVlRQUFFBXV8d5552X1HPz8vL429/+xvbt21mwYAE//elPWbNmDUBKC7tde+21XHXVVaxZs4YjjzySc845J5ZT8qUvfYkf/vCHrFy5kgULFvDqq69y7bXXpuy1h0sqqwoxThlKsarpYz4O+im09FyeUUrRHAkz1+7klilz0VP0l5sYv1JSWXVntbk7pqXOzAnR7eZMyOLVMLMytQNOo40bN3LRRRfR1tY2aH5HNpPKqkKIYdE1jfPypnBby26aI2HydAu2rhmSdiNCjq5zXt4UCUJE9phZaW7RzZLKqon6/e9/z9y5cyktLeWtt97i6quv5utf//qoDkJSSQIRIcaxY125XFkwPVZHxGOYyzFz7U6pIyKyk6ZD8cJMjyIpe/fuZc2aNezdu5eSkhLOPvvsHlVPxztZmhFCYCjFB0EfbUaYfN3KfLtLZkJESo3FpnfjnSzNCCFSRtc0yhw5mR6GEGIcyu6FNSGEEEKMaRKICCGEGDFZnA0gkpSqn6UszQghhEg7m82Gpmns37+foqKilBXzEpmhlGL//v1omtanHH2yJBARQgiRdhaLhenTp7N792527NiR6eGIFNA0jenTp2OxWIZ1HglEhBBCjAi32838+fMJhUKZHopIAZvNNuwgBCQQEUIIMYIsFktKLl5i7JBkVSGEEEJkjAQiQgghhMgYCUSEEEIIkTFZnSMS3aPc3t6e4ZEIIYQQIlHR63YitUayOhDxeDwAzJgxI8MjEUIIIUSyPB4P+fn5Ax6T1U3vDMNgz5495Obm9ih+097ezowZM9i1a5c0wxuAfE6Jkc8pMfI5JUY+p8TJZ5WY0fg5KaXweDxMmzYNXR84CySrZ0R0XWf69On9Pp6XlzdqfiiZJJ9TYuRzSox8TomRzylx8lklZrR9ToPNhERJsqoQQgghMkYCESGEEEJkzKgMRBwOB9dddx0OhyPTQ8lq8jklRj6nxMjnlBj5nBInn1VixvrnlNXJqkIIIYQY20bljIgQQgghxgYJRIQQQgiRMRKICCGEECJjJBARQgghRMZIICKEEEKIjBn1gciXvvQlZs6cidPppKSkhG9961vs2bMn08PKKjt27OA73/kOc+bMweVyMW/ePK677jqCwWCmh5Z1brrpJj772c+Sk5PDxIkTMz2crHLXXXcxe/ZsnE4nn/rUp3j99dczPaSs89JLL3HGGWcwbdo0NE3jL3/5S6aHlHXWrl3LokWLyM3NZcqUKXz5y1+mrq4u08PKOnfffTfl5eWxaqqf+cxneOKJJzI9rLQY9YHIySefzP/93/9RV1fHI488wkcffcTXvva1TA8rq7z//vsYhsH69et55513+PWvf826dev4yU9+kumhZZ1gMMjZZ5/NZZddlumhZJWHHnqIK6+8kuuuu45t27ZxzDHHsHTpUpqamjI9tKzS2dnJMcccw1133ZXpoWStF198kcsvv5y///3vPPPMM4RCIb7whS/Q2dmZ6aFllenTp1NVVcXWrVt58803qays5Mwzz+Sdd97J9NBST40xmzZtUpqmqWAwmOmhZLVbbrlFzZkzJ9PDyFr33Xefys/Pz/QwssbixYvV5ZdfHvs+EomoadOmqbVr12ZwVNkNUI8++mimh5H1mpqaFKBefPHFTA8l602aNEn9z//8T6aHkXKjfkaku5aWFjZu3MhnP/tZbDZbpoeT1dra2igoKMj0MMQoEAwG2bp1K6ecckrsPl3XOeWUU3jttdcyODIxFrS1tQHI76MBRCIRHnzwQTo7O/nMZz6T6eGk3JgIRK6++momTJjA5MmT2blzJ5s2bcr0kLLahx9+yJ133smKFSsyPRQxCjQ3NxOJRCguLu5xf3FxMXv37s3QqMRYYBgGV1xxBccffzz//u//nunhZJ23334bt9uNw+Hgu9/9Lo8++ihHHXVUpoeVclkZiKxevRpN0wb8ev/992PH//jHP6ampoann34ai8XCt7/9bdQ4qFyf7OcE0NDQwLJlyzj77LO55JJLMjTykTWUz0kIkX6XX345//znP3nwwQczPZSsVFZWxvbt2/nHP/7BZZddxgUXXMC7776b6WGlXFb2mtm/fz8HDhwY8Ji5c+dit9v73L97925mzJjBq6++OiansLpL9nPas2cPS5Ys4dOf/jQbNmxA17MyDk25ofx72rBhA1dccQUHDx5M8+iyXzAYJCcnh4cffpgvf/nLsfsvuOACDh48KDOQ/dA0jUcffbTHZyYOWblyJZs2beKll15izpw5mR7OqHDKKacwb9481q9fn+mhpJQ10wOIp6ioiKKioiE91zAMAAKBQCqHlJWS+ZwaGho4+eSTWbhwIffdd9+4CUJgeP+eBNjtdhYuXMhzzz0Xu6gahsFzzz3HypUrMzs4Meoopfje977Ho48+ygsvvCBBSBIMwxiT17asDEQS9Y9//IM33niDE044gUmTJvHRRx9x7bXXMm/evDE/G5KMhoYGlixZwqxZs7j11lvZv39/7LGpU6dmcGTZZ+fOnbS0tLBz504ikQjbt28H4LDDDsPtdmd2cBl05ZVXcsEFF3DcccexePFibr/9djo7O7nooosyPbSs0tHRwYcffhj7vr6+nu3bt1NQUMDMmTMzOLLscfnll/PAAw+wadMmcnNzY3lG+fn5uFyuDI8ue1xzzTWcdtppzJw5E4/HwwMPPMALL7zAU089lemhpV5mN+0MT21trTr55JNVQUGBcjgcavbs2eq73/2u2r17d6aHllXuu+8+BcT9Ej1dcMEFcT+n559/PtNDy7g777xTzZw5U9ntdrV48WL197//PdNDyjrPP/983H8/F1xwQaaHljX6+1103333ZXpoWeXiiy9Ws2bNUna7XRUVFanPf/7z6umnn870sNIiK3NEhBBCCDE+jJ9EASGEEEJkHQlEhBBCCJExEogIIYQQImMkEBFCCCFExkggIoQQQoiMkUBECCGEEBkjgYgQQgghMkYCESGEEEJkjAQiQgghhMgYCUSEEEIIkTESiAghhBAiY/4/qQs6nDYTmsEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "colors = ['navy', 'turquoise', 'darkorange']\n",
        "target_names =  ['setosa','versicolor','virginica']\n",
        "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
        "    plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1], color=color, alpha=.8, label=target_name)\n",
        "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
        "plt.title('PCA of IRIS dataset')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "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": "3.8.16"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}