classical_ML_algortithms.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "collapsed_sections": [
        "ewnPbgQGVc_U",
        "rG_VjQQcVnL7"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rksTtScVPyxV",
        "outputId": "ed76f095-d678-48c3-84d4-db40bcc2eeb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset preprocessing\n",
        "\n",
        "Image (224x224) -> PCA"
      ],
      "metadata": {
        "id": "ewnPbgQGVc_U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RgfziBDSPp-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a87f8bf9-e48b-459f-854c-04f2632fd385"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading... category : Patacon-True\n",
            "loaded category:Patacon-True successfully\n",
            "loading... category : Patacon-False\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loaded category:Patacon-False successfully\n",
            "Splitted Successfully\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "from skimage.transform import resize\n",
        "from skimage.io import imread\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "Categories=['Patacon-True','Patacon-False']\n",
        "flat_data_arr=[] #input array\n",
        "target_arr=[] #output array\n",
        "datadir='/content/drive/MyDrive/patacon-730'\n",
        "#path which contains all the categories of images\n",
        "for i in Categories:\n",
        "\n",
        "    print(f'loading... category : {i}')\n",
        "    path=os.path.join(datadir,i)\n",
        "    for img in os.listdir(path):\n",
        "        img_array=imread(os.path.join(path,img))\n",
        "        img_resized=resize(img_array,(224,224,3))\n",
        "        flat_data_arr.append(img_resized.flatten())\n",
        "        target_arr.append(Categories.index(i))\n",
        "    print(f'loaded category:{i} successfully')\n",
        "flat_data=np.array(flat_data_arr)\n",
        "target=np.array(target_arr)\n",
        "df=pd.DataFrame(flat_data) #dataframe\n",
        "df['Target']=target\n",
        "x=df.iloc[:,:-1] #input data\n",
        "y=df.iloc[:,-1] #output data\n",
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=77,stratify=y)\n",
        "print('Splitted Successfully')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca_dims = PCA()\n",
        "pca_dims.fit(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "yccdTmT43k_l",
        "outputId": "0dfcee98-76d1-41c3-9327-64ea11589e0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features = [i/100 for i in range(10, 100, 1)]\n",
        "cumsum = np.cumsum(pca_dims.explained_variance_ratio_)\n",
        "cumsums = [np.argmax(cumsum >= feat) + 1 for feat in features]\n",
        "plt.title('Número de Componentes vs. Porcentaje de Características conservadas')\n",
        "plt.xlabel('Número de Componentes')\n",
        "plt.ylabel('Porcentaje de Características conservadas')\n",
        "plt.plot(cumsums, features) #número de componentes vs cuántas características de la imagen puede conservar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6a63fc7f40>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "values = {fraction : feats for fraction, feats in zip(features, cumsums)}"
      ],
      "metadata": {
        "id": "4e6WQ8nX4eRN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comps = values[0.95] #el 96% de las características estará en los componentes\n",
        "comps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDb5N3K05MqX",
        "outputId": "4bafc5c6-8ab1-436b-c35a-1a6a8d4aa0d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "420"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "pca = joblib.load(\"/content/pca_model.pkl\")"
      ],
      "metadata": {
        "id": "dYcBfOVmW011"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pca = PCA(n_components=comps)\n",
        "\n",
        "x_train_pca = pca.fit_transform(x_train)\n",
        "x_test_pca = pca.transform(x_test)"
      ],
      "metadata": {
        "id": "xDeM0Srg3O1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(pca, 'pca_model.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HW3zz2qg-Xwe",
        "outputId": "587495d4-049f-4218-a4ce-c65a08691cc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pca_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "explained_variance = pca.explained_variance_ratio_\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(range(comps), explained_variance, alpha=0.5, align='center', label='individual explained variance')\n",
        "plt.ylabel('Explained variance ratio')\n",
        "plt.xlabel('Principal components')\n",
        "plt.legend(loc='best')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "HGAuN_Ofg0HZ",
        "outputId": "afc9244b-34cf-47bf-e467-e3942db2db4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## lazypredict\n",
        "\n",
        "F1 score for multiple ML algorithms"
      ],
      "metadata": {
        "id": "rG_VjQQcVnL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lazypredict"
      ],
      "metadata": {
        "id": "AFtiixEuQVv0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ff586be-9650-4edf-882c-34293ba3001e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: lazypredict in /usr/local/lib/python3.10/dist-packages (0.2.12)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from lazypredict) (8.1.7)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.5.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from lazypredict) (4.66.1)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.3.2)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.10/dist-packages (from lazypredict) (4.1.0)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (from lazypredict) (2.0.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightgbm->lazypredict) (1.23.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm->lazypredict) (1.11.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->lazypredict) (2023.3.post1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->lazypredict) (3.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->lazypredict) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from lazypredict.Supervised import LazyClassifier\n",
        "\n",
        "clf = LazyClassifier(verbose=4,ignore_warnings=True, custom_metric=None)\n",
        "models,predictions = clf.fit(x_train_pca, x_test_pca, y_train, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f_Labu-aQf8u",
        "outputId": "31d6f864-e6d9-4c3e-d1b4-e54490dec2f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  3%|▎         | 1/29 [00:03<01:38,  3.52s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'AdaBoostClassifier', 'Accuracy': 0.7226027397260274, 'Balanced Accuracy': 0.7226027397260274, 'ROC AUC': 0.7226027397260274, 'F1 Score': 0.722443231825383, 'Time taken': 3.5235252380371094}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  7%|▋         | 2/29 [00:07<01:37,  3.62s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'BaggingClassifier', 'Accuracy': 0.7534246575342466, 'Balanced Accuracy': 0.7534246575342466, 'ROC AUC': 0.7534246575342466, 'F1 Score': 0.7507941776113034, 'Time taken': 3.6874186992645264}\n",
            "{'Model': 'BernoulliNB', 'Accuracy': 0.660958904109589, 'Balanced Accuracy': 0.660958904109589, 'ROC AUC': 0.660958904109589, 'F1 Score': 0.6609549276943105, 'Time taken': 0.06476879119873047}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            " 14%|█▍        | 4/29 [00:09<00:55,  2.22s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'CalibratedClassifierCV', 'Accuracy': 0.7602739726027398, 'Balanced Accuracy': 0.7602739726027398, 'ROC AUC': 0.7602739726027398, 'F1 Score': 0.760093896713615, 'Time taken': 2.7062764167785645}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 21%|██        | 6/29 [00:10<00:30,  1.31s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'DecisionTreeClassifier', 'Accuracy': 0.7465753424657534, 'Balanced Accuracy': 0.7465753424657534, 'ROC AUC': 0.7465753424657534, 'F1 Score': 0.7465634529673939, 'Time taken': 0.5435912609100342}\n",
            "{'Model': 'DummyClassifier', 'Accuracy': 0.5, 'Balanced Accuracy': 0.5, 'ROC AUC': 0.5, 'F1 Score': 0.3333333333333333, 'Time taken': 0.04014778137207031}\n",
            "{'Model': 'ExtraTreeClassifier', 'Accuracy': 0.6815068493150684, 'Balanced Accuracy': 0.6815068493150684, 'ROC AUC': 0.6815068493150686, 'F1 Score': 0.6814134375109985, 'Time taken': 0.04401421546936035}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 38%|███▊      | 11/29 [00:11<00:09,  1.87it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'ExtraTreesClassifier', 'Accuracy': 0.7671232876712328, 'Balanced Accuracy': 0.7671232876712328, 'ROC AUC': 0.7671232876712328, 'F1 Score': 0.7649621212121213, 'Time taken': 0.5926737785339355}\n",
            "{'Model': 'GaussianNB', 'Accuracy': 0.6027397260273972, 'Balanced Accuracy': 0.6027397260273972, 'ROC AUC': 0.6027397260273972, 'F1 Score': 0.5589583333333333, 'Time taken': 0.04770469665527344}\n",
            "{'Model': 'KNeighborsClassifier', 'Accuracy': 0.5856164383561644, 'Balanced Accuracy': 0.5856164383561644, 'ROC AUC': 0.5856164383561644, 'F1 Score': 0.5185260891487129, 'Time taken': 0.0653681755065918}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/semi_supervised/_label_propagation.py:231: RuntimeWarning: invalid value encountered in divide\n",
            "  probabilities /= normalizer\n",
            " 41%|████▏     | 12/29 [00:11<00:07,  2.17it/s]/usr/local/lib/python3.10/dist-packages/sklearn/semi_supervised/_label_propagation.py:231: RuntimeWarning: invalid value encountered in divide\n",
            "  probabilities /= normalizer\n",
            " 45%|████▍     | 13/29 [00:11<00:06,  2.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'LabelPropagation', 'Accuracy': 0.6506849315068494, 'Balanced Accuracy': 0.6506849315068493, 'ROC AUC': 0.6506849315068493, 'F1 Score': 0.6021373230029389, 'Time taken': 0.1333768367767334}\n",
            "{'Model': 'LabelSpreading', 'Accuracy': 0.6506849315068494, 'Balanced Accuracy': 0.6506849315068493, 'ROC AUC': 0.6506849315068493, 'F1 Score': 0.6021373230029389, 'Time taken': 0.17397260665893555}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 48%|████▊     | 14/29 [00:12<00:05,  2.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'LinearDiscriminantAnalysis', 'Accuracy': 0.7636986301369864, 'Balanced Accuracy': 0.7636986301369864, 'ROC AUC': 0.7636986301369864, 'F1 Score': 0.7636958586960346, 'Time taken': 0.36899495124816895}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            " 55%|█████▌    | 16/29 [00:12<00:04,  2.61it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'LinearSVC', 'Accuracy': 0.773972602739726, 'Balanced Accuracy': 0.773972602739726, 'ROC AUC': 0.773972602739726, 'F1 Score': 0.7738771295818276, 'Time taken': 0.7173483371734619}\n",
            "{'Model': 'LogisticRegression', 'Accuracy': 0.7808219178082192, 'Balanced Accuracy': 0.7808219178082192, 'ROC AUC': 0.7808219178082192, 'F1 Score': 0.7808116349988272, 'Time taken': 0.11237859725952148}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 59%|█████▊    | 17/29 [00:13<00:03,  3.11it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'NearestCentroid', 'Accuracy': 0.7636986301369864, 'Balanced Accuracy': 0.7636986301369864, 'ROC AUC': 0.7636986301369864, 'F1 Score': 0.7636958586960346, 'Time taken': 0.15634679794311523}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 66%|██████▌   | 19/29 [00:13<00:02,  3.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'NuSVC', 'Accuracy': 0.7842465753424658, 'Balanced Accuracy': 0.7842465753424658, 'ROC AUC': 0.7842465753424659, 'F1 Score': 0.7835127978817299, 'Time taken': 0.41762256622314453}\n",
            "{'Model': 'PassiveAggressiveClassifier', 'Accuracy': 0.7363013698630136, 'Balanced Accuracy': 0.7363013698630136, 'ROC AUC': 0.7363013698630136, 'F1 Score': 0.7362240289069556, 'Time taken': 0.125258207321167}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 69%|██████▉   | 20/29 [00:13<00:02,  4.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'Perceptron', 'Accuracy': 0.7294520547945206, 'Balanced Accuracy': 0.7294520547945205, 'ROC AUC': 0.7294520547945206, 'F1 Score': 0.7293727049824611, 'Time taken': 0.10241961479187012}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 72%|███████▏  | 21/29 [00:13<00:01,  4.06it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'QuadraticDiscriminantAnalysis', 'Accuracy': 0.75, 'Balanced Accuracy': 0.75, 'ROC AUC': 0.75, 'F1 Score': 0.7342906647719483, 'Time taken': 0.2734673023223877}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 79%|███████▉  | 23/29 [00:16<00:03,  1.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'RandomForestClassifier', 'Accuracy': 0.7808219178082192, 'Balanced Accuracy': 0.7808219178082192, 'ROC AUC': 0.7808219178082192, 'F1 Score': 0.7807293377763177, 'Time taken': 2.0852982997894287}\n",
            "{'Model': 'RidgeClassifier', 'Accuracy': 0.7636986301369864, 'Balanced Accuracy': 0.7636986301369864, 'ROC AUC': 0.7636986301369864, 'F1 Score': 0.7636958586960346, 'Time taken': 0.10049295425415039}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 86%|████████▌ | 25/29 [00:16<00:01,  2.58it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'RidgeClassifierCV', 'Accuracy': 0.7671232876712328, 'Balanced Accuracy': 0.7671232876712328, 'ROC AUC': 0.7671232876712328, 'F1 Score': 0.7671123621862539, 'Time taken': 0.2928125858306885}\n",
            "{'Model': 'SGDClassifier', 'Accuracy': 0.7602739726027398, 'Balanced Accuracy': 0.7602739726027397, 'ROC AUC': 0.7602739726027398, 'F1 Score': 0.7599924850875956, 'Time taken': 0.1087486743927002}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 90%|████████▉ | 26/29 [00:17<00:01,  2.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'SVC', 'Accuracy': 0.773972602739726, 'Balanced Accuracy': 0.773972602739726, 'ROC AUC': 0.7739726027397261, 'F1 Score': 0.771875, 'Time taken': 0.41861963272094727}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 97%|█████████▋| 28/29 [00:19<00:00,  1.41it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'XGBClassifier', 'Accuracy': 0.8013698630136986, 'Balanced Accuracy': 0.8013698630136986, 'ROC AUC': 0.8013698630136986, 'F1 Score': 0.8012859623597879, 'Time taken': 2.1439504623413086}\n",
            "[LightGBM] [Info] Number of positive: 584, number of negative: 584\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004994 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 107100\n",
            "[LightGBM] [Info] Number of data points in the train set: 1168, number of used features: 420\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:20<00:00,  1.41it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Model': 'LGBMClassifier', 'Accuracy': 0.8184931506849316, 'Balanced Accuracy': 0.8184931506849316, 'ROC AUC': 0.8184931506849316, 'F1 Score': 0.818132675245314, 'Time taken': 1.3987326622009277}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions.index, x=\"F1 Score\", data=predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 858
        },
        "id": "F_bK7tMi5jJH",
        "outputId": "9bf66efa-ab80-40ee-b64d-94b07155f19e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ],
            "image/png": 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TAPj6+uarX41GQ0ZGBhqNhlu3bhEWFkbNmjWpU6cO8CTp1Wq1+Pn5vVTcDx48IDg4GBsbG+Li4lizZg1BQUEcOHCAUqVK8fjxY4KDg3F3d2fBggUYGxtz9epVEhISAHKtf1bXrl2pWrUqEyZMYOrUqTg6OmJra6sk209bs2YNc+fOpW/fvkycOJG///5bSU7HjRun03bs2LF069aN4ODgQv3mECGEECKLJKei2PP09MTT05OTJ08yb948ypYtq9RlzUba2dnpHJOZmcnT38xbqpTuf+qjR4/WeV+5cmVWr16t3G8aExOTY795FRISohOLq6srTZs25eTJk/j6+nL9+nUSExMZM2YMarUaeHIbQpbc6p9la2urtKtdu7aylP/0ZjB4kvQuWbKEQYMGMWbMGAB8fHwwMjJi9uzZDBw4kHLlyintu3fvzuDBg19qDIQQQoiXIcv6oti7evUqf/zxByqVit9++y1Px3Tq1AlHR0fl9exO+nHjxrFjxw62b9/O0qVLsbGxYdCgQUpSmkWlUr1UzMeOHaN79+64u7vj4OBA06ZNAbhx4wbw5H7asmXLMm3aNL755pts8eVW/7LOnj1LUlISbdu2JSMjQ3l5e3uTkpLCX3/9pdO+WbNmBXJeIYQQIq8kORXFmlarZdq0aVSvXp3//Oc/bN++nXPnzin1NjY2ANmSyoULF7Jjxw5GjBiRY79Vq1bF2dmZBg0a0LJlS5YvX05MTAxr164FUO5fjY6OznfM58+fZ9iwYdjY2DBnzhwiIiLYtm0bgPL4KUtLS9asWUOZMmUYP348Pj4+BAUFceXKlTzVv6z4+HgA3n33XZ3kvXXr1jleb4UKFV7pfEIIIUR+ybK+KNZ27tzJ6dOn2bBhA40aNWLfvn1MmzaNr7/+GkNDQzw8PAA4fvw4PXr0UI7Lunf02ZnA57G2tqZcuXJK+8aNG6NSqfj555/x9vbOV8w//PADZcuWZdGiRcoTBXJ6hmqDBg348ssvSUlJ4dSpU3zxxRcMHz6cH374IU/1L8PS0hKAsLAwbG1ts9W/9dZbL923EEIIURBk5lQUW/Hx8cyZM4d3331XSRanTZvG//73PzZs2ABAo0aNcHZ2Zvny5dl2w+fH/fv3iY+PV+63rFy5Mm3atGHr1q1cvXo1W/uEhATOnj2bY18pKSkYGRnp3BKwb9++557bxMQEf39/evToQVRUlDK7mtf6/HB1dcXU1JS7d+/i7Oyc7fX0/aZCCCFEUZCZU1FsZT266OOPP1bK6tWrR+/evVmyZAnt2rWjUqVKzJ8/n759+9K5c2f69OmjPIT/9u3bbN26FWNjY4yMjHT6vnnzJufOnUOr1RITE0N4eDgqlYr3339fafPpp5/Sp08fevToofMQ/v/+979s3LiRDz74AFdX12xx+/j4sG7dOj777DNatWrF2bNn2bNnj06bo0ePsmPHDlq2bEnlypW5f/8+GzduxM3NjdKlS+da/7KynnYwd+5c7t69S5MmTTA0NOTWrVscPnyY0NBQ2ZUvhBCiSElyKoql06dPs2vXLj777DOsra116j766CO+/fZbQkJCWLRoEdWrV2fnzp2Eh4eza9cuwsLCUKlUVK1aFV9fXxYsWIC5ublOHwsWLFB+LleuHPXq1WPdunU0btxYKbe2tmbr1q2sXbuWb7/9VvlWqNq1azNo0CC6d++eY+z+/v6MGzeOjRs3snPnTtzc3Fi5ciVt2rRR2lSrVg0DAwMWLVrEgwcPsLKywtfXV9lBn1v9qxgwYACVKlVizZo1bNy4kVKlSlGtWjWaNWuWLYkXQggh9E2lffp5O0IIUcAuXLgAQJUr58m4//K3XgghhCh4pSrYYN2lF8nJyYW6cpb1t8DZ2TnXtnLPqRBCCCGEKDYkORVCCCGEEMWG3HMqhNALw3LWuTcSQgihV8Xxd7Mkp0IIvbBs0a6oQxBCCJEDTWZmUYegQ5b1hRCFLi0tjeTk5KIOo0RLTk4mMjJSxrmQyTjrh4yz/iQnJ3Pxzz8pTvvjJTkVQuhFcfrFVxJptVqSk5NlnAuZjLN+yDjrj1arJT09vajD0CHJqRBCCCGEKDYkORVCCCGEEMWGJKdCCL1QqVRFHUKJplKpMDU1lXEuZDLO+iHj/GaT3fpCiEJnbGxcqN88IsDU1BQHB4eiDqPEk3HWDxnngqXVaFAZvD7zkZKcCiH04sF34WTERxd1GEII8UYpVc6O8q0HFnUY+SLJqRBCLzLio0mPvVXUYQghhCjmXp85XiGEEEIIUeJJciqEEEIIIYoNSU6FEEIIIUSxIcmpEHoQGhqKWq2mV69e2epmzpxJQEBAvvvcu3cvXbp0wd3dHTc3N9q1a8fkyZN58OAB6enpeHh4MHny5Oce/+GHH9K8eXOdb2A5ffo0Q4cOxcvLCycnJ5o2bcq4ceO4cOFCvuMTQgghXoYkp0Lo0enTpzl16tQr97N69WrGjx9Po0aNWLhwIQsXLuS9997j4sWL3Lt3DyMjI9q0acP3339PWlpatuMfP37MsWPHCAwMVJ4juGnTJnr37k1ycjKTJ09mzZo1jB8/nsTERAYMGPDKMQshhBB5Ibv1hdATMzMzateuzbJly/Dw8HilvjZs2MC7777LxIkTlTJ/f38GDRqERqMBoGPHjkRERPDTTz/RsmVLneO/++47UlNTCQwMBODy5cvMmjWLTp06MXv2bJ0HXwcGBnLkyJFXilcIIYTIK5k5FUKPhg0bxsmTJzlz5kyO9Tt37kStVhMXF6dT3qlTJ51ENCEhARsbmxz7MPj/D1pu1KgRdnZ2HDhwIFubAwcOULduXdRqNQDr169HpVIxYcKEHL+RpXnz5nm7QCGEEOIVSXIqhB41b94cBwcHli5d+kr9ODo6snXrVrZv305sbGyObVQqFe3bt+fIkSP8+++/SvmDBw/49ddf6dixo1L2+++/4+TkhLW19SvFJYQQQrwqSU6F0LOhQ4dy/Phxzp8//9J9fPrpp1haWjJlyhR8fX1p0aIFn3/+OVFRUTrt3n77bZKTkzl8+LBS9u2336LRaJQlfYCYmBgqV6780vEIIYQQBUWSUyH0rFWrVtStW/eVZk/r1q3L/v37WbVqFX369MHc3JwNGzbw9ttvc+nSJaVdvXr1qF27ts7S/v79+3F3d8+WjOa0nC+EEELomySnQuiZSqViyJAhHD16lD///POl+zE2Nsbf35/Jkyeze/duvvzyS1JSUrIlvYGBgZw4cYL4+HiioqI4d+6czpI+QKVKlbhz585LxyKEEEIUFElOhSgC7dq1w97enmXLlumUly5dGoD09HSd8oSEhFz79PPzo169evz999865YGBgaSnp3Po0CG++eYbSpUqRdu2bXXaNGnShIsXL/Lw4cOXuBohhBCi4EhyKkQRMDAwYMiQIRw+fJgrV64o5ZUqVQLg2rVrStnff/9NdHS0zvH379/P1mdKSgrR0dFUqFBBp7xq1aq4urqyf/9+9u3bh6+vL1ZWVjptgoKC0Gg0fPHFFznGe/To0fxcnhBCCPHS5DmnQhSRjh07snTpUk6dOkWVKlUAaNiwIXZ2dsyaNYuxY8fy+PFjVq1alS2Z7NixI82bN8fX1xcbGxtiYmLYuHEj8fHx9O3bN9u5AgMD+fzzz9FqtQwdOjRbfb169fjkk0/47LPPiImJ4b333qNSpUrExMRw4MABTp8+zW+//VYo4yCEEEI8TWZOhSgihoaGDB48WKfMyMiIsLAwSpcuzciRI1m5ciWTJk1SZlSzjBgxgnv37jF79mz69evH7NmzKVOmDGvXrs32wH2A9u3bY2hoiJmZ2XO/KrVXr15s3LiR0qVLM2PGDPr27av0u2bNmoK7cCGEEOIFVNqnv1hbCCEK2IULFwCwidxDeuytIo5GCCHeLEYVq1Kp25Tn1iclJXHp0iXq16+PmZlZocWR9bfA2dk517YycyqEEEIIIYoNSU6FEEIIIUSxIcmpEEIIIYQoNmS3vhBCL0qVsyvqEIQQ4o3zOv7uleRUCKEX5VsPLOoQhBDijaTVaFAZvD6L5a9PpEKI11ZaWhrJyclFHUaJlpycTGRkpIxzIZNx1g8Z54L1OiWmIMmpEEJP5Kl1hUur1ZKcnCzjXMhknPVDxvnNJsmpEEIIIYQoNiQ5FUIIIYQQxYYkp0IIvVCpVEUdQommUqkwNTWVcS5kMs76IeP8ZpPd+kKIQmdsbIypqWlRh1GimZqa4uDgUNRhlHgyzvoh41xwXred+iDJqRBCT/75YT6p8beKOgwhhHhjlC5XlWotxxZ1GPkmyakQQi9S42+RfP9aUYchhBCimHu95nmFEEIIIUSJJsmpEEIIIYQoNiQ5FUIIIYQQxYYkp2+40NBQXF1dC/08p06dQq1Wc+HChTwfExoaypkzZ7KVq9VqwsPD89xPVFQUarVaeTk7O9O2bVuWLFlCSkpKnvt5nejrcxVCCCEKmmyIEnrh6OhIREQEtWrVyvMxYWFhmJmZ4ebmplMeERFB5cqV8x3DmDFj8PDwIDk5mcOHD7N06VLu37/PjBkz8t1Xcde1a1f8/f2LOgwhhBAi3yQ5FXpRtmxZXFxcCqSvl+2nevXqyrFeXl5cu3aNPXv2MG3aNAz08Ay4lJQUTExMCv08ALa2ttja2urlXEIIIURBkmV98UJXrlxh4MCBuLi44O7uzkcffcSdO3d02iQmJjJu3DhcXV3x8vJiwYIFfPXVV6jVaqVNTsv6O3bsoEOHDjRo0AAPDw969OjB+fPnAZRj58yZoyzHnzp1Sql7dln/6NGjdO/enYYNG9K4cWOCgoKIjIx84bXVr1+flJQU4uLilLKEhASmTZuGr68vTk5OdO7cmePHj+scp9VqCQsLw8fHB1dXVz766CN++eUXnRiz4ly1ahVz587Fx8cHLy8v5fjw8HDatGmDk5MTLVq0YO3atTrnuHv3LiNHjsTb2xtnZ2cCAgKYNWtWnutzWta/ffs2H330Ee7u7ri4uDBw4ECuXLmi0yYgIIAZM2awadMmmjdvjru7O8OGDdMZIyGEEKIwycypeK7o6Gh69+5N1apVmTt3LqmpqSxcuJDevXuzd+9eypYtC8CkSZM4efIkH3/8MVWqVGHbtm38+eefL+z7999/Z/LkyQwYMAB/f39SUlI4f/48iYmJwJOl+27duhEUFERgYCAAtWvXzrGvb775hjFjxtCiRQvmz5+PkZERZ86cISYm5oXfMHLnzh3KlClDuXLlAEhLS6N///48ePCAUaNGUalSJfbu3UtwcDA7d+5UEuYNGzYQFhbGoEGD8PT05OTJk0yZMiXHc6xfv56GDRsyc+ZMMjIyAJg5cybbt29nyJAhNGzYkDNnzjBv3jxKly5Njx49ABg/fjz37t1jypQplC9fnujoaC5evKj0m1v9sx4/fkxQUBAGBgZMnz6d0qVLs3z5cuWztLOzU9r++OOP3Lx5k6lTpxIfH09ISAifffYZCxcufG7/QgghREGR5FQ819q1a8nIyOCrr77CysoKeDLb2KFDB3bt2kVQUBBXr17l+++/54svvuCdd94BwM/Pj3bt2r2w7/Pnz2NlZcWECROUsmbNmik/Zy2/29nZvXAZX6vV8sUXX+Dj48PSpUuV8pzut9RoNGRkZCj3nH733XeMGjUKQ0NDAPbt28fly5fZs2ePkgj7+flx8+ZNli1bxuLFi8nMzGTVqlV07tyZcePGAeDr60t8fDw7duzIdk5LS0vCwsKU74f+559/2LhxI9OnT6dbt24AeHt7k5KSwtKlS+nWrRsGBgZcuHCBMWPG0L59e6WvrPEFcq1/1s6dO7lz5w4HDhxQ7vtt3LgxzZs3Z926dUycOFFnTJcvX46xsTHwZMZ15cqVaDQavdz+IIQQ4s0mf2nEc50+fRoPDw8lMQWoVasW9erV448//gBQlulbtGihtDEwMKB58+Yv7NvBwYGHDx8yceJETpw4QXJy8kvFeO3aNe7evct7772Xa9vRo0fj6OhIo0aNmDBhAm3atOGDDz5Q6k+cOEHdunWpUaMGGRkZysvb21u5zrt37xIbG0tAQIBO309f/9OaNm2qJKYAv/zyCwCtW7fOdo7Y2Fiio6OBJ+Pz1VdfsXnzZm7evJmt39zqn3X69Gnq1KmjsyHNysoKb29v5bPM0rhxYyUxhSefeXp6Og8ePMj1PEIIIcSrkuRUPFdCQgIVKlTIVl6+fHkePXoEQGxsLEZGRpibm+u0sba2fmHfXl5ezJkzh7/++ouBAwfi6enJ+PHjefjwYb5izGpvY2OTa9tx48axY8cO1qxZQ4sWLThw4ABbt25V6uPj44mMjMTR0VHntXz5cu7evatcb07XV758+RzP+Wx5fHw8Wq0WT09PnXP0798fQElOFy5ciKenJ4sWLaJ169a0bduW7777Tuknt/pn5eWzzGJhYaHzPitRTU1NfW7/QgghREGRZX3xXJaWljnOlj148IAaNWoAULFiRdLT00lMTNRJUPOygaZTp0506tSJuLg4Dh8+TEhICKVKldLZ2JObrFnde/fu5dq2atWqODs7A+Dh4UGXLl1YtGgRb7/9NmZmZlhaWqJWq5k5c+Zz+6hYsSKQ/fqeN6v49KwpPBlTlUrF5s2bMTIyytbe3t4eeJJsh4SEoNFouHjxIsuXL2f06NEcPHiQqlWr5lr/LEtLS65fv56t/MGDB1haWj73eoUQQgh9k5lT8Vzu7u6cPHlSZ2bt2rVrXLlyBXd3dwCcnJwAOHz4sNJGo9Fw5MiRPJ/H2tqarl274uPjw7Vr15RyIyOjXGfratasia2tLTt37szz+QAMDQ35+OOPiY+PZ9u2bcCTez9v3bqFjY0Nzs7O2V7w5BFNFStW1LlegB9++CFP583asf/w4cMcz5G1ySyLgYEBDRo0YNSoUWRkZGRbws+tPou7uzv/+9//dMb30aNH/PLLL8pnKYQQQhQHMnMqyMzM5ODBg9nK+/Tpw86dOxkwYABDhw4lNTWVRYsWYWdnx7vvvgtAnTp1aNWqFZ9//jnJyclUrlyZbdu2kZKSkm3W8GlLlizh4cOHNGnShPLly/O///2Pn3/+mX79+iltatasyeHDh2nUqBGmpqbY29tnS95UKhUTJkxgzJgxfPjhh3Tq1AljY2POnTuHs7PzC+999fb2xt3dnbVr19KrVy/eeecdtm7dSp8+fRgwYAA1atQgMTGRyMhI0tPTGTt2LIaGhgwePJhZs2ZRoUIFPDw8OHXqFL/++itArhuG7O3t6dWrF+PHj2fgwIE0bNiQ9PR0bty4walTp1i2bBmJiYkMHDiQTp06YW9vT3p6Ohs2bMDCwgIHB4dc63PSuXNn1q5dS3BwMKNGjVJ265cqVYq+ffu+MGYhhBBCnyQ5FaSmpjJy5Mhs5XPmzGHDhg3MmTOHcePGYWBggI+PDxMnTtRJEmfNmsWMGTOYM2cOxsbGvPvuu9SpU4dNmzY995zOzs6sW7eOb7/9lsePH2Nra8vAgQMZOnSo0mbq1KnMmjWLDz74gJSUFNavX4+Hh0e2vtq3b4+JiQkrVqxgzJgxlC5dGgcHB1q1apXrtY8YMYL+/fuzb98+OnfuzPr16wkNDWXFihXExsZiZWWFg4MDPXv2VI4JCgoiISGBzZs3s2HDBry8vPj4448ZPXp0tntvczJlyhTs7e2JiIhg6dKllClTBnt7e9q2bQtA6dKlqVu3Lhs2bCA6OhoTExOcnJwIDw/H2tqatLS0F9bnpGzZsmzYsIHZs2fzn//8B41Gg5ubGxs3btR5jJQQQghR1FRarVZb1EGIkqdXr14YGBiwYcOGog5FLxYtWsSaNWs4deqU3r4F6nWR9aQDk8vhJN+/lktrIYQQBcW0Qk3qdF30wjZJSUlcunSJ+vXrY2ZmVmixZP0tyLpN7kVk5lS8skOHDhEdHU3dunVJTk5m//79nD59Wue5oyXJ33//zd69e3F1dcXIyIjffvuN8PBwevToIYmpEEII8YokORWvzMzMjD179nDjxg3S09OpWbMmc+fOpWXLlkUdWqEwMTHh7NmzbNmyhX///ZdKlSoxcOBAPvzww6IOTQghhHjtSXIqXpmfnx9+fn5FHYbeVKlShfXr1xd1GK+d0uWyP+JKCCFE4Xldf+9KciqE0ItqLccWdQhCCPHG0Wo0qF6zr55+vaIVQryW0tLSXvorakXeJCcnExkZKeNcyGSc9UPGueC8bokpSHIqhNATeTBI4dJqtSQnJ8s4FzIZZ/2QcX6zSXIqhBBCCCGKDUlOhRBCCCFEsSHJqRBCL170dbbi1alUKkxNTWWcC5mMs37IOL/ZZLe+EKLQGRsbY2pqWtRhlGimpqY4ODgUdRglnoyzfsg4F5zXcbe+JKdCCL249ONckh7eKuowhBDijWFmVZX6AR8XdRj5JsmpEEIvkh7e4vGDv4s6DCGEEMXc6zXPK4QQQgghSjRJToUQQgghRLEhyakQQgghhCg2JDnVk9DQUNRqtfLy8PCgR48eHDt2TK9xdOrUiYkTJ+rtfKdOndK57qdfcXFxeovjRaKioggNDSUmJua59f/5z39o3rw5Tk5ONGnShIEDB3Lw4EGlzcSJEwkMDNRXyAq1Wk14eLhO2Zw5c/D19aVevXrMnDmTnTt3FqvxFkIIIV5ENkTpkYmJCevWrQPg3r17rFixgiFDhrBp0ybc3NyKOLrCFRISQs2aNXXKLCwsiigaXbdv3yYsLIxmzZpRqVIlnbpz584xaNAgrK2t+eCDD6hduzaPHz/m2LFjjBs3jho1alCvXr0iihwiIiKoXLmy8v6XX34hPDycSZMm0bBhQ2xsbDA1NSUiIqLYjLcQQgjxIpKc6pGBgQEuLi7K+4YNG+Lv78/u3btLfHJap04dnJ2dC6y/zMxMNBoNRkZGBdbns1JTUxk1ahS2trZs3bqVsmXLKnUBAQH06NGjyBO+p/97Arh27RoAffr0weCp59pZW1u/8rm0Wi3p6ekYGxu/cl9CCCHE88iyfhGqVKkS1tbW3LlzB3gymzpp0iRatGhBgwYNaN26NQsWLCAtLU3nOLVazerVqwkNDcXb2xsPDw8mTZpEUlKSTrszZ87QuXNnnJ2dCQwMfO4tBN999x2dOnXC2dkZX19fQkJCSE1NVeqzluZ//vlnRo4ciaurK82aNWPfvn0ArF+/nmbNmtGkSRMmT56cLd7cPHz4kEmTJuHh4UGDBg3o3r07v//+u06boKAggoOD2bVrF23atMHZ2ZnLly8DcPToUbp27UqDBg3w9PTk008/1RmL9PR0vvjiC5o1a4aTkxO+vr4MGTKExMRETp06RZ8+fQDo0qWLcssBwLfffkt0dDRjxozRSUyz1KtXT2fW8ml5/Sx37NhBhw4daNCggXKrx/nz5/Nc//SyflBQEJ999hkA9evXR61Wc+rUqRyX9dPS0liwYIFyq0K7du2UzzNL1q0Kx44d4+2338bZ2Zkff/wxx+sVQgghCorMnBahf//9l0ePHvHWW28BEB8fj5WVFZMmTcLCwoIbN24QGhpKbGwsISEhOsdu2rQJd3d3Zs+ezY0bN5gzZw7ly5dn3LhxAMTGxjJw4EDUajWLFi0iISGB6dOnk5SURP369ZV+Dh8+zEcffUSHDh0YO3Ys165dY+HChURHR7NkyRKdc06bNo13332X999/n23btjF+/HguX77MX3/9xfTp07l16xazZ8+matWqDBkyROdYjUZDRkaG8t7AwAADAwMyMzP54IMPuHXrFuPGjaNChQps2LCB/v37s3XrVpycnJRjLl68yO3btxk5ciQWFhbY2dlx8OBBRo8eTefOnfnwww+JjY1l/vz5JCQksHDhQgBWrlzJ1q1bGTduHHXq1CE+Pp4TJ06QlpaGo6MjU6dOZcaMGdluPfj9998xNDTE29s7359tXj7L33//ncmTJzNgwAD8/f1JSUnh/PnzJCYm5qn+WZ9++inbtm1j3bp1REREAFC7dm1u376dre3IkSM5c+YMw4cPp1atWhw7doyPP/4YCwsL/P39lXb37t3j888/Z+jQodjZ2T03GRdCCCEKiiSnepaVoN27d4+5c+dSpkwZZeZOrVYzYcIEpa2bmxumpqZMnDiRqVOn6nz9Y8WKFZk/fz4ATZs2JTIykkOHDinJ6bp161CpVKxevRpzc3MAbG1t6devn048YWFhuLi46PRlamrK1KlTuXLlijKLCNC2bVtGjBgBQIMGDfj+++85cOAA33//vbK8/ttvv3Hw4MFsyen777+v875Lly7MnDmTo0ePcv78eb788kv8/PwA8PX1pXXr1qxcuZLQ0FDlmEePHrFjxw7s7OyAJ8vMc+bMoX379sycOVNnbAYPHsywYcOoU6cOFy5cwNfXl169eilt2rRpo/xcu3ZtIPutBzExMVhbW2NiYkJ+5eWzPH/+PFZWVjrtmjVrpvycW/2zateurSSPzy73P+3kyZP8+OOPhIeH4+vrC4CPjw+xsbGEhobqJKePHj1i9erVNGzYMK+XLoQQQrwSSU71KCkpCUdHR+W9oaEhy5YtU2brtFot69atY9u2bURFReksrd+6dYu6desq75+dzatVqxYHDhxQ3v/3v//Fw8NDSUwBvLy8sLKyUt7/+++/XLp0SSf5AWjfvj1Tp07ljz/+0ElOfXx8lJ/Nzc2xtramUaNGOvd91qhRg1OnTmW79i+++IJatWop77PugTx9+jRly5ZVElMAIyMjWrVqxf79+3X6qFu3rpKYAly/fp3bt2/zySef6MzKNmnSBAMDAy5evEidOnVwcHAgPDxcSbycnJx07scsDHn5LB0cHHj48CETJ06kY8eOSgKbJbf6l3XixAmsrKzw9PTUGTdvb2+mTZtGZmYmhoaGAFhZWUliKoQQQq8kOdUjExMTNm7ciFar5caNG8yfP58JEyawb98+bGxsWLduHV988QWDBg3Cw8MDCwsLLly4wIwZM3SSG8i+093IyEjnfsbY2FiqV6+eLYanN8YkJiai1WopX768Thtzc3OMjY159OhRtvKnGRsb5xpHllq1auW4ISohISHb+QEqVKiQ7fwVKlTQeR8fHw/A8OHDsx0PEB0dDcDQoUMxMDBg165dhIWFYW1tTa9evRg+fDgqlSrHY+HJPcG//vorqamplC5d+rntcpKXz9LLy4s5c+awfv16Bg4cSOnSpWnTpg2ffPIJVlZWuda/rPj4eB4+fKjzD6WnxcbGYmtrC2QfcyGEEKKwSXKqRwYGBkqC1qBBA+zt7Xn//fdZunQp06dP5+DBgwQEBDB27FjlmL//frnvIq9YsSIPHjzIVv70phhzc3NUKlW2518mJiaSlpaGpaXlS507PywtLXOM8/79+9nO/2wimZWgTZ06lQYNGmTrw8bGBniSRH/44Yd8+OGH3Lx5k6+//prQ0FDeeust3nnnnefG1qRJE3bs2MGvv/76wuX0nOT1s+zUqROdOnUiLi6Ow4cPExISQqlSpZg1a1ae6l+GpaUl1tbWrFq1Ksf6p/8B86LkXQghhCgMslu/CDk7O9OhQwd27txJbGwsKSkp2R6N9OwO6rxq0KABp06d0tk88+uvv/Lw4UPlfZkyZahfv77Ow+ThyS51AHd395c6d364u7vz+PFjjh8/rpRlZGTwww8/5Hr+mjVrYmtry61bt3B2ds72evaZpQDVq1dnzJgxWFlZKY9dyhrzZ2en27Zti52dHQsWLODx48fZ+rpy5YoyO/us/H6W1tbWdO3aFR8fHyWu/NTnh7e3N3FxcRgZGeU4bvKoKCGEEEVJZk6L2LBhw/jmm29Yt24d3t7erF+/no0bN1KjRg327t3LzZs3X6rfvn37snnzZj744AM++OADEhISCA0NzbYcPGLECIYPH864ceN4++23uX79OgsXLqRNmzY695sWlmbNmtGgQQM+/vhjxo4dq+zWv3fvXranBTxLpVIxceJExo0bR1JSEs2aNcPU1JQ7d+5w7NgxRo8ejb29PcOGDcPR0REHBwdMTU05cuQIjx49wtPTE3hyn6yhoSFff/01pUqVwtDQEGdnZ0qXLs2iRYsYNGgQ7733Hv369VMewn/8+HG2bdvG9u3bde6DzZKXz3LJkiU8fPiQJk2aUL58ef73v//x888/K5vWcqt/WT4+PjRv3pxBgwYxaNAg1Go1ycnJXL16lZs3b+psLhNCCCH0TZLTIlazZk3at2/Pli1bOHr0KPHx8UpS1qZNG6ZMmZJt53te2NjYsHr1aj7//HNGjhxJtWrVmDp1qvJ4pSwtWrRg8eLFLF26lGHDhmFlZcX777+vsxxdmAwNDVm1ahVz5sxh7ty5yqaxr776SucxUs/Trl07LCwsWLFihTIzWaVKFfz8/JT7Jd3c3Pj2229Zs2YNmZmZ2NvbM2/ePGVTmbW1NVOnTuXLL79k7969ZGRkcOXKFeDJrvddu3axatUqVq5cyf379zEzM8PZ2ZkFCxY899uhhg8fnutn6ezszLp16/j22295/Pgxtra2DBw4kKFDh+ap/lUsWbKEVatWsWXLFm7fvo25uTl16tShc+fOr9y3EEII8SpUWq1WW9RBCCFKrgsXLgCQ9tdqHj94uXuohRBC5F/Z8rVw7/ziVcikpCQuXbpE/fr1MTMzK7RYsv4W5OXbIuWeUyGEEEIIUWxIciqEEEIIIYoNSU6FEEIIIUSxIRuihBB6YWZVtahDEEKIN8rr+ntXklMhhF7UD/i4qEMQQog3jlajQVXIX9ld0F6vaIUQr6W0tDSSk5OLOowSLTk5mcjISBnnQibjrB8yzgXndUtMQZJTIYSeyFPrCpdWqyU5OVnGuZDJOOuHjPObTZJTIYQQQghRbEhyKoQQQgghig1JToUQeqFSqYo6hBJNpVJhamoq41zIZJz1Q8b5zSa79YUQhc7Y2BhTU9OiDqNEMzU1xcHBoajDKPFknPVDxvnlvY67858lyakQQi/+ODqHx4/+KeowhBCixCprWQ33ZuOLOoxXJsmpEEIvHj/6h0cP/i7qMIQQQhRzr/e8rxBCCCGEKFEkORVCCCGEEMWGJKdCCCGEEKLYkHtOhXiN7d27l/Xr13P9+nW0Wi2VKlXCzc2NMWPGYGFhga+vLy1btmTmzJk5Hv/hhx9y8eJFfvzxR+WRLadPnyY8PJxz586RmJiItbU1TZo0oW/fvjg7O+vz8oQQQryBZOZUiNfU6tWrGT9+PI0aNWLhwoUsXLiQ9957j4sXL3Lv3j2MjIxo06YN33//PWlpadmOf/z4MceOHSMwMFBJTDdt2kTv3r1JTk5m8uTJrFmzhvHjx5OYmMiAAQP0fYlCCCHeQDJzKsRrasOGDbz77rtMnDhRKfP392fQoEFoNBoAOnbsSEREBD/99BMtW7bUOf67774jNTWVwMBAAC5fvsysWbPo1KkTs2fP1nn4dWBgIEeOHNHDVQkhhHjTycypEK+phIQEbGxscqwz+P8PYG7UqBF2dnYcOHAgW5sDBw5Qt25d1Go1AOvXr0elUjFhwoQcv5WlefPmBRi9EEIIkTNJToV4TTk6OrJ161a2b99ObGxsjm1UKhXt27fnyJEj/Pvvv0r5gwcP+PXXX+nYsaNS9vvvv+Pk5IS1tXWhxy6EEEI8jySnQrymPv30UywtLZkyZQq+vr60aNGCzz//nKioKJ12b7/9NsnJyRw+fFgp+/bbb9FoNMqSPkBMTAyVK1fWW/xCCCFETiQ5FeI1VbduXfbv38+qVavo06cP5ubmbNiwgbfffptLly4p7erVq0ft2rV1lvb379+Pu7t7tmQ0p+V8IYQQQp8kORXiNWZsbIy/vz+TJ09m9+7dfPnll6SkpLB06VKddoGBgZw4cYL4+HiioqI4d+6czpI+QKVKlbhz544+wxdCCCGykeRUiBLEz8+PevXq8fffut9hHxgYSHp6OocOHeKbb76hVKlStG3bVqdNkyZNuHjxIg8fPtRjxEIIIYQuSU6FeE3dv38/W1lKSgrR0dFUqFBBp7xq1aq4urqyf/9+9u3bh6+vL1ZWVjptgoKC0Gg0fPHFFzme7+jRowUVuhBCCPFc8pxTIV5THTt2pHnz5vj6+mJjY0NMTAwbN24kPj6evn37ZmsfGBjI559/jlarZejQodnq69WrxyeffMJnn31GTEwM7733HpUqVSImJoYDBw5w+vRpfvvtN31cmhBCiDeYJKdCvKZGjBjBkSNHmD17NnFxcZQrVw61Ws3atWvx9PTM1r59+/aEhIRgbGxMQEBAjn326tULtVpNeHg4M2bM4PHjx1hbW+Pp6cmaNWsK+5KEEEIISU6FeF316tWLXr165bm9tbU1f/75Z67tGjVqRKNGjV4lNCGEEOKlyT2nQgghhBCi2JDkVAghhBBCFBuSnAohhBBCiGJD7jkVQuhFWctqRR2CEEKUaCXl96wkp0IIvXBvNr6oQxBCiBJPq9GgMni9F8Zf7+iFEK+FtLQ0kpOTizqMEi05OZnIyEgZ50Im46wfMs4v73VPTEGSUyGEnmi12qIOoUTTarUkJyfLOBcyGWf9kHF+s0lyKoQQQgghig1JToUQeqFSqYo6hBJNpVJhamoq41zIZJz1Q8b5zSYbooQQhc7Y2BhTU9OiDqNEMzU1xcHBoajDKPFknPVDxvnVaDSZGBgYFnUYL02SUyGEXhz/aTaPHt4q6jCEEKJEs7Sqim/TiUUdxiuR5FQIoRePHt4iLu5qUYchhBCimJN7ToUQQgghRLEhyakQQgghhCg2JDkVQgghhBDFhiSn4o0WGhqKq6trjnVRUVGo1WoOHjyo56jyJj4+nlmzZtG6dWucnZ3x8vKiR48erF27FoBDhw6hVqs5ffp0jsc/fPgQJycnFi9erJSlpaWxdu1aOnfujKurKw0aNKBjx46EhoaSkJCgj8sSQgjxhpMNUUI8h42NDREREdSoUaOoQ8kmIyODvn37kpiYyODBg6lZsyb379/nzJkzHDlyhH79+tGsWTPKli3LgQMHaNSoUbY+Dh06RHp6Oh07dgQgNTWVQYMGce7cOXr16sWoUaMwNjbm0qVLbNiwgcTERD755BN9X6oQQog3jCSnQjyHsbExLi4uRR1GNikpKZw5c4YrV66wceNGGjdurNR16NABjUYDQOnSpWndujUHDx5k8uTJlCql+7/7/v37cXR0pGbNmgAsXryY06dPEx4ejre3t9LO09OTnj17cubMGT1cnRBCiDedLOsL8Rw5LesHBAQwY8YMNm3aRPPmzXF3d2fYsGHExcXpHJuQkMC0adPw9fXFycmJzp07c/z4cZ02R48epX///nh5eeHm5kbXrl356aefdNrs3LkTtVrN2bNn6d+/Py4uLsyZM4dHjx4BULFixWxxGxj83//WHTt2JC4ujl9++UWnTUxMDKdPn1ZmTVNSUtiyZQstW7bUSUyzlC5dGi8vr7wMmxBCCPFKJDkVIp9+/PFHfvzxR6ZOncrkyZP5/fff+eyzz5T6tLQ0+vfvz9GjRxk1ahTLly+nVq1aBAcHc+XKFaVdVFQUzZs3Z86cOYSGhuLm5sbgwYM5depUtnOOHTsWT09PVqxYQadOnahfvz4GBgZMmTKFX3/9lbS0tBxj9fT0pGLFiuzfv1+n/MCBAwC0b98egIsXL5KUlISfn98rj48QQgjxKmRZX4h80mq1LF++HGNjYwBu377NypUr0Wg0GBgYsG/fPi5fvsyePXuoXbs2AH5+fty8eZNly5YpG5B69+6t9KnRaPDw8ODq1ats27YNDw8PnXN2796dwYMH65RNnDiRuXPn0q9fP4yMjGjQoAHt2rWjR48eyhK+gYEB7du3Z8eOHaSmplK6dGngyZJ+kyZNqFSpEgD37t0DwM7OrqCHSwghhMgXmTkVIp8aN26sJKYAtWrVIj09nQcPHgBw4sQJ6tatS40aNcjIyFBe3t7eXLhwQTnu7t27TJgwAT8/PxwcHHB0dOT48eNcv3492zmbNWuWraxv374cOXKEGTNm0KZNG27cuMHnn39O//79lftOAQIDA/n33385cuQIADdu3ODPP/9UlvSfplKpXnpchBBCiIIgM6dC5JOFhYXO+6xENTU1FXjyiKfIyEgcHR2zHWtoaAg8mSkdOnQoiYmJfPTRR1SvXh1TU1OWLFlCdHR0tuMqVKiQYywVK1akW7dudOvWjfT0dKZOncrOnTs5cuQILVq0AKBBgwbUqFGD/fv307ZtW/bv34+xsTFt2rRR+rGxsQHI8dxCCCGEPklyKkQBs7S0RK1WM3PmzOe2uXnzJpGRkSxdupSWLVsq5SkpKS99XiMjI/r168fOnTv5+++/leQUnsyerlq1isTERPbv30+zZs0wNzdX6p2cnDAzM+Pnn3+ma9euLx2DEEII8apkWV+IAubt7c2tW7ewsbHB2dk52wv+b5bVyMhIOe727ducPXs2T+d4+PAhGRkZ2cpv3LgBZN/FHxgYSFpaGosWLeL69evZlvRNTEzo0aMH33//PSdPnszWb2pqKr/++mueYhNCCCFehcycijdeZmZmjt8CVb58+Zfq75133mHr1q306dOHAQMGUKNGDRITE4mMjCQ9PZ2xY8dSs2ZNbG1tmT9/PhqNhqSkJJYsWaIsr+fm5MmTzJs3j3fffZcGDRpQqlQpLl26xMqVK6lcuTKtWrXSaW9vb4+joyObNm3C3Nw8x3tYR44cyYULFxg8eDC9evXC29sbIyMjLl++rDw6Sx4nJYQQorBJcireeKmpqYwcOTJbeU5leWFsbMz69esJDQ1lxYoVxMbGYmVlhYODAz179lTahIaGMmPGDEaOHImdnR1Dhw7l5MmTXLx4MddzNGzYkDZt2nD48GHWrVtHamoqtra2dOzYkcGDB1O2bNlsx7z99tv8+eeftG7dWmdDV5bSpUsTHh7O5s2b2bt3L1u2bEGj0VC9enU6depE3759X2o8hBBCiPxQabVabVEHIYQoubKeUPDP9RXExV0t4miEEKJks7auTYe3l+a5fVJSEpcuXaJ+/fqYmZkVWlxZfwuybm97EbnnVAghhBBCFBuSnAohhBBCiGJDklMhhBBCCFFsyIYoIYReWFpVLeoQhBCixCsJv2slORVC6IVv04lFHYIQQrwRNJpMDAwMizqMlybL+kKIQpeWlkZycnJRh1GiJScnExkZKeNcyGSc9UPG+dW8zokpSHIqhNATeWpd4dJqtSQnJ8s4FzIZZ/2QcX6zSXIqhBBCCCGKDUlOhRBCCCFEsSHJqRBCL1QqVVGHUKKpVCpMTU1lnAuZjLN+yDi/2WS3vhCi0BkbG2NqalrUYZRopqamODg4FHUYJZ6Ms37IOOfP6747/1mSnAoh9OL747OJS7hV1GEIIUSJYm1RlVa+JetRfZKcCiH0Ii7hFvfjrhZ1GEIIIYo5uedUCCGEEEIUG5KcCiGEEEKIYkOSUyGEEEIIUWxIclrChYaGolarc3ytWrUqX/2cOXOmwOKaOHHic+PKegUFBRXY+V5FXFwcs2fPpk2bNjg7O+Pm5kbv3r3Zvn07mZmZwJPxcXV11XtsAQEBzJgxQ6ds7dq1NGvWjPr16zNs2DBOnTqFWq3mwoULeo9PCCGEyC/ZEPUGMDExYd26ddnK7ezs8txHWFgYZmZmuLm5FUhMw4YNo3v37sr7ZcuWce3aNebNm6eUlS1btkDO9Spu3rxJnz59yMzMpH///jg6OpKWlsbJkycJCQmhXLlytGzZssjiCwsLw8LCQnl/48YNZs+ezQcffEDz5s0pV64cFStWJCIiglq1ahVZnEIIIUReSXL6BjAwMMDFxUUv50pJScHExCTXdtWqVaNatWrKe2tra+7cufPCOPPad0EaN24cmZmZfP3111SqVEkpb9q0Kb179yYxMVGv8Tzr2ecAXr9+Ha1Wy/vvv0/VqlWV8oL6/IviMxBCCPFmkWX9N9wPP/yAWq3myJEjStnDhw/x8/NjzJgxAKjVagDmzJmjLLmfOnVKqVu1ahVz587Fx8cHLy8vAM6ePcuQIUPw9fXFxcWFTp06sXv37jzHFRUVhVqtZufOnUyZMgUPDw+6du0KQFpaGgsWLKB58+Y4OTnRrl079u3bl62Ps2fP0qdPH1xcXHB3d2fs2LE8ePBAp82qVato1aoVzs7OeHp60q9fP27devIsztOnT3P+/HmCg4N1EtMslStXVsbmWUlJScyYMYM2bdrQsGFDAgICmDp1arZk9vDhw3Tu3BlXV1caNWpE586dOXbsWJ7rn17WnzhxIkOGDAGgZcuWyvjltKyv1WoJDw+nTZs2ODk50aJFC9auXasTW9atCufPn6dbt244OzuzadOmHK9XCCGEKCgyc/qGyMjIyFZWqlQpWrZsyTvvvMOUKVPYt28f1tbWTJ8+HYBPP/0UgIiICLp160ZQUBCBgYEA1K5dW+ln/fr1NGzYkJkzZyrnuXPnDm5ubvTo0QNjY2POnDnDlClT0Gq1vPvuu3mOe8GCBfj7+zN//nw0Gg0AI0eO5MyZMwwfPpxatWpx7NgxPv74YywsLPD39weeJKZBQUH4+/uzcOFCkpOTWbRoEcOGDSMiIgKA3bt3s3jxYj766CNcXFxITEzkjz/+4N9//wXgt99+A8DPzy/vA/3/paSkkJmZyejRo7G2tiY6OpoVK1YwbNgwNmzYAMA///zDyJEj6dChA2PHjkWj0XD58mUePXqUp/pnDRs2jFq1ajFv3jzCwsKoWLEi1apV46+//srWdubMmWzfvp0hQ4bQsGFDzpw5w7x58yhdujQ9evRQ2qWnpzN27Fj69evH6NGjsbKyyvdYCCGEEPkhyekbICkpCUdHx2zlmzZtolGjRkyZMoWOHTsydepU2rZtyzfffMOXX36JpaUl8H9LwnZ2djkuD1taWhIWFqbzHcgdOnRQftZqtTRu3JiYmBgiIiLylZzWq1ePmTNnKu9PnjzJjz/+SHh4OL6+vgD4+PgQGxtLaGiokpzOnz8fJycnnbjq1q1LYGAgx44dw9/fn/Pnz6NWqwkODlb6f/r+0ZiYGODJDGl+PZ3kw5N/HLz11lv07NmT69evY29vT2RkJOnp6fznP/9R7q99OhHOrf5Z1apVw97eHoD69evz1ltv5djun3/+YePGjUyfPp1u3boB4O3tTUpKCkuXLqVbt24YGDxZVElPT2f06NG0b98+32MghBBCvAxJTt8AJiYmbNy4MVt5zZo1ATA3NyckJIT+/ftz7NgxevToka/ZwqZNm+okpgCPHj0iNDSUw4cPExMTo+xqz+/MW7NmzXTenzhxAisrKzw9PXVmg729vZk2bRqZmZmkpaVx5swZxo8fr5wXoEaNGtjZ2XHhwgX8/f1xcHBg8+bNhISE0KpVKxo2bIiRkVG+4nuR3bt3s3btWm7evElSUpJSfuPGDezt7VGr1RgaGjJu3Djef/99GjdujLm5udIut/qX9csvvwDQunXrbGO4evVqoqOjqVKlilKelfALIYQQ+iDJ6RvAwMAAZ2fnF7Zxd3encuXK3L59m969e+er//Lly2crmzhxImfPnmX48OHUrl2bsmXLsmXLFr799ttX6js+Pp6HDx/mOBMMEBsbi0qlIjMzk5CQEEJCQrK1iY6OBqBz5878+++/bNu2jbVr12Jubs4777zDuHHjMDExUe4zjY6Opnr16vmK+/vvv2fChAl069ZNWQ6PjY1l+PDhpKamAmBvb8+KFStYuXIlI0aMwMDAAF9fX6ZOnUrlypVzrX9Z8fHxaLVaPD09c6x/Ojk1NTWlTJkyL30uIYQQIr8kORUALFmyhPj4eGrUqMH06dNZv359ttnQ53m2XWpqKkePHmXixIk6zyrdvHlzvuN6tm9LS0usra2f+4xWa2trMjIyUKlUBAcH5/iYp3LlygFPkva+ffvSt29fYmJiOHDgAPPnz6dcuXIMHz6cJk2aAHD8+PF8J6cHDx6kfv36Os8gzbqH9WlNmzaladOmPH78mJ9++omQkBAmTZqkPPort/qXYWlpiUqlYvPmzTnOFGfdGgDZx18IIYQobJKcCs6cOUN4eDjTpk3DwcGB7t27s27dOvr166e0MTIyUmb8cpOWloZGo9FJfB4/fsyPP/74yrF6e3vz5ZdfYmRkRL169XJsY2xsjIuLC9euXct1xjhLpUqVGDBgAPv37+fatWsANGrUiAYNGrBixQpatWqFjY2NzjHR0dEkJCTkuGM/JSUlW+KX0xMFspQtW5b27dtz/vx59u/fn+/6/Mh6osLDhw8JCAh4pb6EEEKIgibJ6RtAo9Fw7ty5bOXly5enfPnyTJgwAV9fX2VzzJAhQ1iwYAF+fn7Kg9tr1qzJ4cOHadSoEaamptjb2z/3Ifnm5uY4OzuzevVqrK2tKVWqFKtWraJs2bLExcW90rX4+PjQvHlzBg0axKBBg1Cr1SQnJ3P16lVu3rypbJ4aP348ffv2ZdSoUXTo0AELCwvu3r3LL7/8QufOnfHw8GDq1KlYWFjg4uKChYUFZ86c4fLlyzq71efNm0dQUBDvvfeezkP4f//9dzZt2sQXX3yRY3Lq7e3NjBkzWLp0Ka6urhw7doxff/1Vp83WrVs5d+4cfn5+VKxYkaioKPbu3YuPj0+e6l+Wvb09vXr1Yvz48QwcOJCGDRuSnp7OjRs3OHXqFMuWLXul/oUQQohXIcnpGyAlJUVJPJ/WpUsXDA0NSUhI0NkRP3ToUI4ePcr48eOJiIigVKlSTJ06lVmzZvHBBx+QkpLC+vXr8fDweO4558+fz9SpU5k4cSJWVlYEBQWRlJTEV1999crXs2TJElatWsWWLVu4ffs25ubm1KlTh86dOytt3Nzc2Lx5M6GhoUyaNIn09HRsbW3x9PRUluhdXV3Ztm0b27dvJzk5mapVqzJp0iTleaoA1atXZ9euXaxevZotW7YQHR2NsbExDg4OfPLJJzRv3jzHGLt3705UVBQbN25Uniwwf/583n//faVN1vNlQ0JCePjwIRUrVqRDhw6MHDkyT/WvYsqUKdjb2xMREcHSpUspU6YM9vb2tG3b9pX7FkIIIV6FSqvVaos6CCFEyZX18P/IWyu4H3e1iKMRQoiSpYJ1bbq1X/rSxyclJXHp0iXq16+PmZlZAUamK+tvQV5ut5NviBJCCCGEEMWGJKdCCCGEEKLYkORUCCGEEEIUG7IhSgihF9YWVYs6BCGEKHFK4u9WSU6FEHrRyndiUYcghBAlkkaTiYGBYVGHUWBkWV8IUejS0tJITk4u6jBKtOTkZCIjI2WcC5mMs37IOOdPSUpMQZJTIYSeyFPrCpdWqyU5OVnGuZDJOOuHjPObTZJTIYQQQghRbEhyKoTQC5VKVdQhlGgqlQpTU1MZ50Im46wfMs5vNtkQJYQodMbGxpiamhZ1GCWaqakpDg4ORR1GiSfjrB8yzvmn0WZioCoZ955KciqE0ItdJ2dzP+FWUYchhBAlTgWLqrzrWXKeiCLJqRBCL+4n3OJu/NWiDkMIIUQxJ/ecCiGEEEKIYkOSUyGEEEIIUWxIciqEEEIIIYoNSU7Fc4WGhqJWq5WXh4cHPXr04NixYzrt1Go14eHhL+zr0qVLqNVqTp06VWjxarVadu3aRc+ePXF3d8fJyYk2bdowe/ZsYmJiAIiKikKtVnPw4MFCiyMnoaGhuLq66pT9+eefvP/++zRs2BC1Wk1CQgIBAQHMmDGjQM+d27hcvHgRtVrNnj17cjw+MzMTb29vxo8fX6BxCSGEEDmRDVHihUxMTFi3bh0A9+7dY8WKFQwZMoRNmzbh5uYGQEREBJUrVy7KMNFqtYwdO5Zvv/2Wzp07M2jQIMqWLcvVq1fZunUrt27dYunSpUUWX9euXfH399cp+/zzz8nMzGTlypWYmJhQpkwZwsLCsLCwKLDz5nVc7O3tOXDgAJ06dcrWx6+//sqDBw/o2LFjgcUlhBBCPI8kp+KFDAwMcHFxUd43bNgQf39/du/erSSnT9cXlc2bN3PgwAFmzpxJly5dlPImTZrQrVs3jh8/XoTRga2tLba2tjpl165do2fPnnh6eiplBfVcv5SUFExMTPI8LoGBgaxYsYL4+HjKlSun09f+/fspX7483t7eBRKbEEII8SKyrC/ypVKlSlhbW3Pnzh2lLKdl/WXLluHj44OrqysjRozgwYMH2fpKTExk3LhxuLq64uXlxYIFC/jqq69Qq9U67RISEpg2bRq+vr44OTnRuXPnbMnmmjVrcHR01EnAshgaGmabtXza7t276dGjB02aNKFx48YEBQVx/vx5nTZ3795l5MiReHt74+zsTEBAALNmzcpz/dPL+qdOnUKtVvPw4UOWLVuGWq0mKCgIIMdl/bNnz9KnTx9cXFxwd3dn7NixOuOZdavCzp07mTJlCh4eHnTt2jVf49KxY0fS09Oz3e6QmprK999/T/v27TE0LBkPdxZCCFG8ycypyJd///2XR48e8dZbbz23zcaNG1m8eDEDBgzA29ubX375hcmTJ2drN2nSJE6ePMnHH39MlSpV2LZtG3/++adOm7S0NPr378+DBw8YNWoUlSpVYu/evQQHB7Nz507UajV3797l1q1bDBky5KWuKSoqinfeeYdq1aqRlpbGgQMH6NWrF3v37sXe3h6A8ePHc+/ePaZMmUL58uWJjo7m4sWLSh+51T/N0dGRiIgI+vfvT/v27enatStly5bNse3Zs2cJCgrC39+fhQsXkpyczKJFixg2bBgRERE6bRcsWIC/vz/z589Ho9Hka1yqV6+Os7Mz+/fvp0ePHkr50aNHefz4sSzpCyGE0BtJTkWuMjIygCf3nM6dO5cyZcrQp0+fHNtm3UPZqVMnJkyYAICfnx8PHjzQ2XBz9epVvv/+e7744gveeecdpV27du10+tu3bx+XL19mz5491K5dW2l38+ZNli1bxuLFi5XNTnZ2di91fSNGjFB+1mg0+Pj4cP78eXbt2sWYMWMAuHDhAmPGjKF9+/ZK26y481L/tLJly+Li4oKhoSG2trYvvC1i/vz5ODk5ERYWpnzHdN26dQkMDOTYsWM6M8L16tVj5syZyvv//ve/QN7HpWPHjoSEhBAdHa0cc+DAAapVq0bDhg3z1IcQQgjxqmRZX7xQUlISjo6OODo60rx5cw4dOsScOXOoWbNmju3v3r3LvXv3aNWqlU55mzZtdN5fuHABgBYtWihlBgYGNG/eXKfdiRMnqFu3LjVq1CAjI0N5eXt7K31kyUre8uvvv/9m+PDheHt7U79+fRwdHbl+/To3btxQ2jg4OPDVV1+xefNmbt68ma2P3OpfRnJyMmfOnKFt27ZkZmYq116jRg3s7OyyXX+zZs1y7Cev49K+fXsMDAz45ptvAHj8+DFHjx4lMDDwla5DCCGEyA9JTsULmZiYsGPHDrZv387cuXOpWLEiEyZM4N69ezm2j42NBcDa2lqnvEKFCtnaGRkZYW5urlP+7HHx8fFERkYqCXLWa/ny5dy9exd4ch8soHMfbF49fvyYAQMGcOfOHSZOnMimTZvYsWMH9erVIzU1VWm3cOFCPD09WbRoEa1bt6Zt27Z89913ea5/GQkJCWRmZhISEpLt+u/cuUN0dLRO+/Lly+u8z++4VKxYEQ8PD/bv3w/A999/T2pqqizpCyGE0CtZ1hcvZGBggLOzMwANGjTA3t6e999/n6VLlzJ9+vRs7StWrAhAXFycTvn9+/eztUtPTycxMVEnQX32OEtLS9Rqtc5y9bNsbW2pVq0ax48fZ/To0fm6vnPnznH37l1WrlxJvXr1lPLExESd3fU2NjaEhISg0Wi4ePEiy5cvZ/To0Rw8eJCqVavmWv8yzM3NUalUBAcH07Jly2z1z+6qf3aG9GXGpWPHjkyaNIlr166xf/9+HB0dnztLLoQQQhQGmTkV+eLs7EyHDh3YuXOnMkv6NFtbWypWrMj333+vU37o0CGd905OTgAcPnxYKdNoNBw5ckSnnbe3N7du3cLGxgZnZ+dsryz9+vXj4sWL7Nq1K1tMGo2Gn376KcfrSUlJAcDIyEgpO3PmDLdv386xvYGBAQ0aNGDUqFFkZGRkW8LPrT4/zMzMcHFx4dq1azle+4s2pWXJ77i0bt2a0qVLs27dOk6ePCmzpkIIIfROZk5Fvg0bNoxvvvmGdevWMW7cOJ06Q0NDBg8ezMyZMylfvjw+Pj6cOHEi2zdD1alTh1atWvH555+TnJxM5cqV2bZtGykpKTozgO+88w5bt26lT58+DBgwgBo1apCYmEhkZCTp6emMHTsWgJ49e/LHH38wefJkzpw5Q4sWLTAzM+PatWts3bqVKlWq0LRp02zX4uLigpmZGdOnT2fw4MHExMQQGhqqLInDk1nUgQMH0qlTJ+zt7UlPT2fDhg1YWFjg4OCQa/2rGD9+PH379mXUqFF06NABCwsL7t69yy+//ELnzp3x8PB44fH5HZeyZcvSrFkzIiIiUKlUOhu8hBBCCH2Q5FTkW82aNWnfvj1btmwhODg4W31QUBAJCQls3ryZLVu24OXlxeeff86gQYN02s2aNYsZM2YwZ84cjI2Neffdd6lTpw6bNm1S2hgbG7N+/XpCQ0NZsWIFsbGxWFlZ4eDgQM+ePZV2KpWK+fPn4+vry/bt2zlw4ABpaWlUqVKFgIAABgwYkOO1VKhQgcWLFzNnzhyGDRtGjRo1mD59Ol9++aXSpnTp0tStW5cNGzYQHR2NiYkJTk5OhIeHY21tTVpa2gvrX4WbmxubN28mNDSUSZMmkZ6ejq2tLZ6enlSvXj3X419mXDp27MihQ4fw8PDQSdKFEEIIfVBptVptUQchRJZevXphYGDAhg0bijoUUUCynipwMnoFd+OvFnE0QghR8tiWq80HrV/uK7qTkpK4dOkS9evXx8zMrIAj+z9ZfwueviXveWTmVBSZQ4cOER0dTd26dUlOTmb//v2cPn2apUtf7n8wIYQQQrz+JDkVRcbMzIw9e/Zw48YN0tPTqVmzJnPnzs1xZ7oQQggh3gySnIoi4+fnh5+fX1GHIYQQQohiRJJTIYReVLB4uee9CiGEeLGS9vtVklMhhF686zmxqEMQQogSS6PNxEBlWNRhFAh5CL8QotClpaWRnJxc1GGUaMnJyURGRso4FzIZZ/2Qcc6/kpKYgiSnQgg9kafWFS6tVktycrKMcyGTcdYPGec3mySnQgghhBCi2JDkVAghhBBCFBuSnAoh9EKlUhV1CCWaSqXC1NRUxrmQyTjrh4zzm0126wshCp2xsTGmpqZFHUaJZmpqioODQ1GHUeLJOOuHjHPelaRd+lkkORVC6MXK378gOvFWUYchhBAlhp15VYIbTyjqMAqcJKdCCL2ITrzFzUdXizoMIYQQxZzccyqEEEIIIYoNSU6FEEIIIUSxIcmpEEIIIYQoNt7o5DQ0NBRXV9cc66KiolCr1Rw8eFDPUeVNQEAAarUatVqNo6MjXl5eBAUFsXbtWpKSknTanjp1CrVazYULFwo9roIeN7VaTXh4eIH09TISEhIIDQ3l6tXn3yv5/fffo1ar6du3b6HHExQURHBwcIH2+aL/D4QQQgh9kw1Rz2FjY0NERAQ1atQo6lCeq02bNgwYMIDMzEzi4uI4deoUixYtYsuWLaxbtw5bW1sAHB0diYiIoFatWoUeU0GPW0REBJUrVy6Qvl5GQkICYWFh1KlTh9q1a+fYZt++fQD89ttvxMTEUKlSJX2G+Mq6du2Kv79/UYchhBBCAG/4zOmLGBsb4+LigpWVVVGHoiMlJUX5uUKFCri4uODu7k6rVq2YMmUKmzdvJiYmhkmTJintypYti4uLC2ZmZoUeW0GPm4uLCzY2NgXSV2F4/PgxR48exdvbG41GwzfffFPUIeWbra0tDRo0KOowhBBCCECS0+fKaXk6ICCAGTNmsGnTJpo3b467uzvDhg0jLi5O59iEhASmTZuGr68vTk5OdO7cmePHj+u0OXr0KP3798fLyws3Nze6du3KTz/9pNNm586dqNVqzp49S//+/XFxcWHOnDkvjNvBwYGePXvyyy+/cO3aNSDnZf0dO3bQoUMHGjRogIeHBz169OD8+fNKvUajYc2aNbRr1w4nJyd8fHz46KOPSExMBP5vKfj8+fN069YNZ2dnNm3a9MJxW7t2Lf7+/ri6ujJx4kTS0tK4dOkS3bt3x8XFhS5dunDlyhWd63l2WT9rWfvgwYO0adMGV1dX+vTpwz///KNz3Lx58+jYsSOurq74+fkxZswY7t27p9Mmt76ioqJo0aIFACNHjlRuo4iKilL6+O6770hNTWXEiBE4Ojoqs6j5OU9+Yn7alStXUKvVnDhxQqc8MzMTPz8/5b+Vu3fvMnLkSLy9vXF2diYgIIBZs2Yp7Z9d1k9PT+eLL76gWbNmODk54evry5AhQ5TPXgghhChMsqyfTz/++CM3b95k6tSpxMfHExISwmeffcbChQsBSEtLo3///jx48IBRo0ZRqVIl9u7dS3BwsJJswpPEp3nz5gwYMAADAwN++uknBg8ezLp16/Dw8NA559ixY+nWrRvBwcF5+pYdX19fwsPD+e9//0vNmjWz1f/+++9MnjyZAQMG4O/vT0pKCufPn9dJPj777DMiIiLo27cvPj4+/Pvvvxw9epSkpCTMzc2BJ0nM2LFj6devH6NHj37hbOnhw4epU6cOM2bM4NatW8yePRsjIyPOnTtHv379qFChAvPmzWPkyJF88803GBg8/99Nly5dIi4ujnHjxpGZmcns2bP5+OOPiYiIUNo8ePCA4OBgbGxsiIuLY82aNQQFBXHgwAFKlSqVp75sbGwICwtjxIgRjBkzRvlcnp7J3bdvH1WqVMHNzY2OHTsye/Zsrl27lm3cCzLmLGq1moYNG/L111/j4+OjlP/888/cu3eP9957D4Dx48dz7949pkyZQvny5YmOjubixYvPHd+VK1eydetWxo0bR506dYiPj+fEiROkpaU99xghhBCioEhymk9arZbly5djbGwMwO3bt1m5ciUajQYDAwP27dvH5cuX2bNnj3KPop+fHzdv3mTZsmUsXrwYgN69eyt9ajQaPDw8uHr1Ktu2bcuWnHbv3p3BgwfnOcase01jY2NzrD9//jxWVlZMmPB/3yrRrFkz5efr16+zZcsWRo8erbP5pk2bNjr9pKenM3r0aNq3b6+UPT2r+Kxly5Yp4/bbb7+xbds2Vq9eTdOmTYEn4zBkyBD+97//Ua9evef2k5iYyO7du7G2tgYgKSmJSZMmcffuXeXaQ0JClPaZmZm4urrStGlTTp48ia+vb577ql+/PgDVq1fHxcVFJ47Y2FhOnTrFwIEDUalUtG/fnjlz5rBv3z5GjhxZaDE/rWvXrnz22Wc8evQIS0tLAL7++mtcXV2Ve4wvXLjAmDFjdD6nd95557nje+HCBXx9fenVq5dS9uxnL4QQQhQWWdbPp8aNGysJFkCtWrVIT0/nwYMHAJw4cYK6detSo0YNMjIylJe3t7fOsvrdu3eZMGECfn5+ODg44OjoyPHjx7l+/Xq2cz6dOOaFVqsFQKVS5Vjv4ODAw4cPmThxIidOnCA5OVmn/uTJk2i1Wrp06ZLrufK6kebZcatRowYGBgZ4enrqlAFER0e/sK969eopSR6g/CPg7t27StmxY8fo3r077u7uODg4KAnwjRs38t3X83zzzTdkZmYSGBgIQKVKlWjcuDH79+8v1Jif1qFDB0qVKqWcMy4ujiNHjuh8dg4ODnz11Vds3ryZmzdv5npdDg4OHDt2jNDQUM6fP49Go8n1GCGEEKKgSHKaTxYWFjrvsxKu1NRUAOLj44mMjMTR0VHntXz5ciUR0Wg0DB06lD/++IOPPvqI9evXs2PHDpo2bZrj0mmFChXyFWNMTAwAFStWzLHey8uLOXPm8NdffzFw4EA8PT0ZP348Dx8+BODhw4eUKlWK8uXLv/A8pqamlClTJk8xPTtuRkZGmJiY6CSsRkZGwP+NZX76evq48+fPM2zYMGxsbJgzZw4RERFs27Ytx75z6+tF9u3bh729PXZ2diQkJJCQkEBAQAD//PMP//3vfwst5qeZmZkRGBjIjh07ANi7dy9GRka0a9dOabNw4UI8PT1ZtGgRrVu3pm3btnz33XfP7XPo0KF88MEH7Nq1i65du+Lj40NYWJjyjx4hhBCiMMmyfgGztLRErVYzc+bM57a5efMmkZGRLF26lJYtWyrlT+/EfxU///wzQLZl6Kd16tSJTp06ERcXx+HDhwkJCaFUqVLMmjULKysrMjIyePDgwQsT1OfNzBa1H374gbJly7Jo0SLl3tXbt28X6Dlu3rypzIQ3btw4W/2+ffto2LBhnvt7lZi7du1KREQEly9fZufOnbRr107nHw02NjaEhISg0Wi4ePEiy5cvZ/To0Rw8eJCqVatm68/Y2JgPP/yQDz/8kJs3b/L1118TGhrKW2+99cLbAYQQQoiCIMlpAfP29ubYsWPY2Ng893mXWTNhWbNn8CQROXv27Cs/H/TSpUts2bIFX1/fPPVlbW2tPCkga3e/p6cnKpWKr7/+Ol/3uhYXKSkpGBkZ6STPOe2iz4vnzaTu27cPlUpFWFiYskEsy6pVq/jmm2+YNGkShoaGhR6zs7Mz9evX5/PPP+fKlSt8+umnObYzMDCgQYMGjBo1StnYl1Ny+rTq1aszZswYIiIilP8+hBBCiML0xienmZmZOX6bUW5L2s/zzjvvsHXrVvr06cOAAQOoUaMGiYmJREZGKrvba9asia2tLfPnz0ej0ZCUlMSSJUvy/TzP+/fvc+7cOTQaDXFxcZw8eZIdO3Zga2ur86igZy1ZsoSHDx/SpEkTypcvz//+9z9+/vln+vXrB4C9vT3du3dn8eLFPHr0CC8vL1JSUjh69CgffvhhsX/IvI+PD+vWreOzzz6jVatWnD17lj179rxUXxUrVsTCwoIDBw7w1ltvYWxsjFqtZv/+/TRq1Ehn5jvL48ePGTZsGL/88gt+fn56iblr167MmDEDe3t73N3dlfLExEQGDhxIp06dsLe3Jz09nQ0bNmBhYYGDg0OOfQ0bNgxHR0ccHBwwNTXlyJEjPHr0SOf+YCGEEKKwvPHJaWpqarad1UCOZXlhbGzM+vXrCQ0NZcWKFcTGxmJlZaU8fzSrTWhoKDNmzGDkyJHY2dkxdOhQTp48+cJH/Dzr0KFDHDp0iFKlSmFubk6dOnUYPXo0Xbt2feED952dnVm3bh3ffvstjx8/xtbWloEDBzJ06FClzdSpU3nrrbfYvn0769atw8rKisaNG+f5HtOi5O/vz7hx49i4cSM7d+7Ezc2NlStXvtSOcwMDA0JCQliwYAH9+vUjLS2Nr7/+muvXrzNw4MAcj2natCnW1tbs27cvz8npq8bcqlUrZsyYoTw+Kkvp0qWpW7cuGzZsIDo6GhMTE5ycnAgPD9fZoPU0Nzc3vv32W9asWUNmZib29vbMmzcPb2/vPMUihBBCvAqVVnY5CPHa27FjB59++ilHjx597ka4opJ1b+7XsSu5+ehqEUcjhBAlR3XL2kwLCHulPpKSkrh06RL169cv1G+SzPpb4OzsnGvbN37mVIjXWVRUlPIM3Xbt2hW7xFQIIYTIL0lOhXiNhYWFsX//fuUrYYUQQojXnSSnQrzGZs+ezezZs4s6DCGEEKLASHIqhNALO/MXP7ZKCCFE/pTU36uSnAoh9CK48YSiDkEIIUocjTYTA1Xenqn9upCvLxVCFLq0tDSSk5OLOowSLTk5mcjISBnnQibjrB8yznlX0hJTkORUCKEn8tS6wqXVaklOTpZxLmQyzvoh4/xmk+RUCCGEEEIUG5KcCiH0QqVSFXUIJZpKpcLU1FTGuZDJOOuHjPObTTZECSEKnbGxMaampkUdRolmamqKg4NDUYdR4sk464eMc95ptBoMVCVrrlGSUyGEXsw5vZ5/EmOKOgwhhCgxqplXYnyjPkUdRoGT5FQIoRf/JMbw96Ooog5DCCFEMVey5oGFEEIIIcRrTZJTIYQQQghRbEhyKoQQQgghig1JToVehYaGolarlZeHhwc9evTg2LFjOu3UajXh4eEv7OvSpUuo1WpOnTpVaPFqtVp27dpFz549cXd3x8nJiTZt2jB79mxiYp5s7omKikKtVnPw4MFCiyMnoaGhuLq66pT9+eefvP/++zRs2BC1Wk1CQgIBAQHMmDFDr7EJIYQQL0s2RAm9MzExYd26dQDcu3ePFStWMGTIEDZt2oSbmxsAERERVK5cuSjDRKvVMnbsWL799ls6d+7MoEGDKFu2LFevXmXr1q3cunWLpUuXFll8Xbt2xd/fX6fs888/JzMzk5UrV2JiYkKZMmUICwvDwsKiiKIUQggh8keSU6F3BgYGuLi4KO8bNmyIv78/u3fvVpLTp+uLyubNmzlw4AAzZ86kS5cuSnmTJk3o1q0bx48fL8LowNbWFltbW52ya9eu0bNnTzw9PZWygnpWYEpKCiYmJgXSlxBCCPE8sqwvilylSpWwtrbmzp07SllOy/rLli3Dx8cHV1dXRowYwYMHD7L1lZiYyLhx43B1dcXLy4sFCxbw1VdfoVarddolJCQwbdo0fH19cXJyonPnztmSzTVr1uDo6KiTmGYxNDTMNmv5tN27d9OjRw+aNGlC48aNCQoK4vz58zpt7t69y8iRI/H29sbZ2ZmAgABmzZqV5/qnl/VPnTqFWq3m4cOHLFu2DLVaTVBQEECOy/pnz56lT58+uLi44O7uztixY3XGM+tWhZ07dzJlyhQ8PDzo2rXrc69XCCGEKCgycyqK3L///sujR4946623nttm48aNLF68mAEDBuDt7c0vv/zC5MmTs7WbNGkSJ0+e5OOPP6ZKlSps27aNP//8U6dNWloa/fv358GDB4waNYpKlSqxd+9egoOD2blzJ2q1mrt373Lr1i2GDBnyUtcUFRXFO++8Q7Vq1UhLS+PAgQP06tWLvXv3Ym9vD8D48eO5d+8eU6ZMoXz58kRHR3Px4kWlj9zqn+bo6EhERAT9+/enffv2dO3albJly+bY9uzZswQFBeHv78/ChQtJTk5m0aJFDBs2jIiICJ22CxYswN/fn/nz56PRaF5qLIQQQoj8kORUFImMjAzgyT2nc+fOpUyZMvTpk/O3XGTdQ9mpUycmTJgAgJ+fHw8ePGDPnj1Ku6tXr/L999/zxRdf8M477yjt2rVrp9Pfvn37uHz5Mnv27KF27dpKu5s3b7Js2TIWL16sbHays7N7qesbMWKE8rNGo8HHx4fz58+za9cuxowZA8CFCxcYM2YM7du3V9pmxZ2X+qeVLVsWFxcXDA0NsbW1feFtEfPnz8fJyYmwsDDle6vr1q1LYGAgx44d05kRrlevHjNnzszPpQshhBCvRJJToXdJSUk4Ojoq7w0NDVm2bBk1a9bMsf3du3e5d+8erVq10ilv06aNTnJ64cIFAFq0aKGUGRgY0Lx5c9asWaOUnThxgrp161KjRg0lSQbw9vZm7969OufISt7y6++//2bBggWcPXtWZ7n8xo0bys8ODg589dVXGBoa4uPjQ/Xq1XX6yK3+ZSQnJ3PmzBnGjx9PZmamUl6jRg3s7Oy4cOGCTnLarFmzVz6nEEIIkR+SnAq9MzExYePGjWi1Wm7cuMH8+fOZMGEC+/btw8bGJlv72NhYAKytrXXKK1SokK2dkZER5ubmOuXPHhcfH09kZKROgpzF0NAQeHIfLKBzH2xePX78mAEDBmBtbc3EiROpXLkypUuXZsqUKaSmpirtFi5cyMKFC1m0aBHTp0/H3t6eMWPG0Lp16zzVv4yEhAQyMzMJCQkhJCQkW310dLTO+/Lly7/0uYQQQoiXIcmp0DsDAwOcnZ0BaNCgAfb29rz//vssXbqU6dOnZ2tfsWJFAOLi4nTK79+/n61deno6iYmJOgnqs8dZWlqiVqtfuFxta2tLtWrVOH78OKNHj87X9Z07d467d++ycuVK6tWrp5QnJibq7K63sbEhJCQEjUbDxYsXWb58OaNHj+bgwYNUrVo11/qXYW5ujkqlIjg4mJYtW2arL1eunM77l505FkIIIV6W7NYXRc7Z2ZkOHTqwc+dOZZb0aba2tlSsWJHvv/9ep/zQoUM6752cnAA4fPiwUqbRaDhy5IhOO29vb27duoWNjQ3Ozs7ZXln69evHxYsX2bVrV7aYNBoNP/30U47Xk5KSAoCRkZFSdubMGW7fvp1jewMDAxo0aMCoUaPIyMjg5s2b+arPDzMzM1xcXLh27VqO1/6iTWlCCCGEPsjMqSgWhg0bxjfffMO6desYN26cTp2hoSGDBw9m5syZlC9fHh8fH06cOJHtm6Hq1KlDq1at+Pzzz0lOTqZy5cps27aNlJQUnRnAd955h61bt9KnTx8GDBhAjRo1SExMJDIykvT0dMaOHQtAz549+eOPP5g8eTJnzpyhRYsWmJmZce3aNbZu3UqVKlVo2rRptmtxcXHBzMyM6dOnM3jwYGJiYggNDVVuFYAns6gDBw6kU6dO2Nvbk56ezoYNG7CwsMDBwSHX+lcxfvx4+vbty6hRo+jQoQMWFhbcvXuXX375hc6dO+Ph4fFK/QshhBCvQpJTUSzUrFmT9u3bs2XLFoKDg7PVBwUFkZCQwObNm9myZQteXl58/vnnDBo0SKfdrFmzmDFjBnPmzMHY2Jh3332XOnXqsGnTJqWNsbEx69evJzQ0lBUrVhAbG4uVlRUODg707NlTaadSqZg/fz6+vr5s376dAwcOkJaWRpUqVQgICGDAgAE5XkuFChVYvHgxc+bMYdiwYdSoUYPp06fz5ZdfKm1Kly5N3bp12bBhA9HR0ZiYmODk5ER4eDjW1takpaW9sP5VuLm5sXnzZkJDQ5k0aRLp6enY2tri6elZIJuuhBBCiFeh0mq12qIOQojC1KtXLwwMDNiwYUNRh/JGynqKwsr7B/n7UVQRRyOEECVHLcu3CGv+8Sv1kZSUxKVLl6hfvz5mZmYFFFl2WX8Lnr597nlk5lSUKIcOHSI6Opq6deuSnJzM/v37OX36NEuXLi3q0IQQQgiRB5KcihLFzMyMPXv2cOPGDdLT06lZsyZz587NcWe6EEIIIYofSU5FieLn54efn19RhyGEEEKIlyTJqRBCL6qZV8q9kRBCiDwrqb9XJTkVQujF+EZ9ijoEIYQocTRaDQaqkvXY+pJ1NUKIYiktLY3k5OSiDqNES05OJjIyUsa5kMk464eMc96VtMQUJDkVQuiJPLWucGm1WpKTk2WcC5mMs37IOL/ZJDkVQgghhBDFhiSnQgghhBCi2JDkVAihFyqVqqhDKNFUKhWmpqYyzoVMxlk/ZJzfbLJbXwhR6IyNjTE1NS3qMEo0U1NTHBwcijqMEk/GWT9knPOuJO7Wl+RUCKEXc37fy63E+0UdhhBClBhVzSswvvHbRR1GgZPkVAihF7cS7/P3o5iiDkMIIUQxV7LmgYUQQgghxGtNklMhhBBCCFFsSHIqhBBCCCGKDUlOxSsLDQ1FrVbTq1evbHUzZ84kICCgCKJ6NWvXruXYsWM51qWlpbF27Vo6d+6Mq6srDRo0oGPHjoSGhpKQkFDgsSQkJBAaGsrVq1cLtN+goCCCg4NzbdeoUSNCQ0ML9NxCCCHE88iGKFFgTp8+zalTp/Dw8CjqUF7Z+vXradasGf7+/jrlqampDBo0iHPnztGrVy9GjRqFsbExly5dYsOGDSQmJvLJJ58UaCwJCQmEhYVRp04dateuXWD9fvrppxgYyL9PhRBCFC+SnIoCYWZmRu3atVm2bFmRJacpKSmYmJgU6jkWL17M6dOnCQ8Px9vbWyn39PSkZ8+enDlzplDPn5v8jEFBJrpCCCFEQZFpE1Fghg0bxsmTJ1+YoCUkJDBt2jR8fX1xcnKic+fOHD9+XKfN0aNH6d+/P15eXri5udG1a1d++uknnTY7d+5ErVZz9uxZ+vfvj4uLC3PmzAHg7t27jBs3Dg8PDxo0aECvXr24ePGizvGHDx9WluUbNWpE586dlWX8gIAAbt++zaZNm1Cr1ajVanbu3ElKSgpbtmyhZcuWOolpltKlS+Pl5ZWva81aWj948CBt2rTB1dWVPn368M8//wAQFRVFixYtABg5cqQST1RUFFFRUUpsU6ZMwcPDg65duwLw8OFDJk2apIxB9+7d+f3333M899N++OEH2rZti7OzM126dOH8+fPP/SyFEEKIwiAzp6LANG/eHAcHB5YuXUp4eHi2+rS0NPr378+DBw8YNWoUlSpVYu/evQQHByvJJjxJyJo3b86AAQMwMDDgp59+YvDgwaxbty7brOzYsWPp1q0bwcHBmJqa8ujRI3r27ImZmRn/+c9/MDc3Z8OGDfTt25fvvvuO8uXL888//zBy5Eg6dOjA2LFj0Wg0XL58mUePHgEQFhbG4MGDcXNzY8CAAQBUq1aNixcvkpSUhJ+fX65jkddrBbh06RJxcXGMGzeOzMxMZs+ezccff0xERAQ2NjaEhYUxYsQIxowZo1y/jY0N9+7dA2DBggX4+/szf/58NBoNmZmZfPDBB9y6dYtx48ZRoUIFNmzYQP/+/dm6dStOTk45xnzp0iU++ugjmjZtyqRJk4iKimLUqFGkpaXler1CCCFEQZHkVBSooUOH8uGHH3L+/HkaNGigU7dv3z4uX77Mnj17lCVlPz8/bt68ybJly1i8eDEAvXv3Vo7RaDR4eHhw9epVtm3bli057d69O4MHD1beL1myhISEBLZv30758uUB8PLyok2bNoSHhzN+/HgiIyNJT0/nP//5D2XLllXiyOLg4ICxsTEVKlTAxcVFKT958iQAdnZ2uY5DXq8VIDExkd27d2NtbQ1AUlISkyZN4u7du9ja2lK/fn0AqlevrhNPlnr16jFz5kzl/eHDhzl//jxffvmlcl2+vr60bt2alStXPndz06pVq7Czs2Pp0qUYGhoCT2aDJ0+enOv1CiGEEAVFlvVFgWrVqhV169Zl6dKl2epOnDhB3bp1qVGjBhkZGcrL29ubCxcuKO3u3r3LhAkT8PPzw8HBAUdHR44fP87169ez9dmsWbNs5/Dw8MDS0lLp38DAgMaNGyvnUKvVGBoaMm7cOH788UcSExPzdY0qlSrXNnm9VniSXGYlpvB/94LevXs3T/E8OwanT5+mbNmyOgm3kZERrVq14o8//nhuP//9739p3ry5kpgCtG3bNk8xCCGEEAVFZk5FgVKpVAwZMoQxY8bw559/6tTFx8cTGRmJo6NjtuOyEiKNRsPQoUNJTEzko48+onr16piamrJkyRKio6OzHVehQoVs5zh37lyO56hWrRoA9vb2rFixgpUrVzJixAgMDAzw9fVl6tSpVK5c+bnXZmNjA5BjHM/Ky7VmsbCw0HlvZGQEPHkyQF5kzRBnSUhIyFYGT8Yq69aFnMTGxmY7rmzZspQuXTpPcQghhBAFQZJTUeDatWtHaGgoy5Yt00n2LC0tUavVOkvQz7p58yaRkZEsXbqUli1bKuUpKSl5OrelpSV+fn6MHDkyW52xsbHyc9OmTWnatCmPHz/mp59+IiQkhEmTJrFu3brn9u3k5ISZmRk///yzsvHoRXHkdq0F5dmZXEtLSx48eJCt3f3797G0tHxuPxUrVsx23OPHj/OcJAshhBAFQZb1RYEzMDBgyJAhHD58mCtXrijl3t7e3Lp1CxsbG5ydnbO94P9mC7NmDwFu377N2bNn83Rub29v/v77b2rVqpWt/6c3IWUpW7Ys7du3p0OHDvz9999KuZGRUbakzMTEhB49evD9998r958+LTU1lV9//TXP15pX+Z1JdXd35/HjxzpPBsjIyOCHH37A3d39ucc1aNCAI0eOkJmZqZQdPHgwX7EKIYQQr0pmTkWh6NixI0uXLuXUqVNUqVIFgHfeeYetW7fSp08fBgwYQI0aNUhMTFQ2KI0dO5aaNWtia2ur7DxPSkpiyZIlypJ6bvr168e+ffvo3bs3ffr0oXLlysTFxfHf//6XSpUq0a9fP7Zu3cq5c+fw8/OjYsWKREVFsXfvXnx8fJR+atasycmTJzlx4gQWFha89dZblCtXjpEjR3LhwgUGDx5Mr1698Pb2xsjIiMuXL7Np0yaaN2+Ol5dXnq41rypWrIiFhQUHDhzgrbfewtjYOMdEO0uzZs1o0KABH3/8MWPHjlV269+7d48lS5Y897jBgwfTpUsXhg8fTo8ePYiKiiI8PFyW9YUQQuiVJKeiUBgaGjJ48GCmTJmilBkbG7N+/XpCQ0NZsWIFsbGxWFlZ4eDgQM+ePZU2oaGhzJgxg5EjR2JnZ8fQoUM5efJktmeV5qRcuXJERESwaNEi5s2bx8OHDylfvjwNGzakVatWwJMNUUeOHCEkJISHDx9SsWJFOnTooHMrwJgxY5g2bRoffvgh//77LyEhIXTu3JnSpUsTHh7O5s2b2bt3L1u2bEGj0VC9enU6depE375983yteWVgYEBISAgLFiygX79+pKWlcfjw4ReO/apVq5gzZw5z584lKSkJR0dHvvrqq+c+RgqePKVg8eLFzJs3jxEjRlCnTh0WLlzIwIED8xWvEEII8SpUWq1WW9RBCCFKrqynE6yK/Z2/H8UUcTRCCFFy1LKsRGjAgFfqIykpiUuXLlG/fn3MzMwKKLLssv4W5OXWNrnnVAghhBBCFBuSnAohhBBCiGJDklMhhBBCCFFsyIYoIYReVDWvkHsjIYQQeVZSf69KciqE0Ivxjd8u6hCEEKLE0Wg1GKhK1kJ4yboaIUSxlJaWRnJyclGHUaIlJycTGRkp41zIZJz1Q8Y570paYgqSnAoh9ESeWle4tFotycnJMs6FTMZZP2Sc32ySnAohhBBCiGJDklMhhF6oVKqiDqFEU6lUmJqayjgXMhln/ZBxfrPJhighRKEzNjbG1NS0qMMo0UxNTXFwcCjqMEo8GWf9kHHOm5K4GQokORVC6Mnc37/nVmJ8UYchhBAlQlXzcnzcuFVRh1EoJDkVQujFrcR4/n54v6jDEEIIUcyVvLlgIYQQQgjx2pLkVAghhBBCFBuSnAohhBBCiGJDktNi6vDhwwwYMIAmTZrg5OREQEAAU6dO5fr163nuY+LEiQQGBirvd+7ciVqtJi4uDoCoqCjUajUHDx4s8Pif9sMPP7Bp06YC7XPmzJkEBARkK79y5Qpjx47F19cXJycnvL29GTFiBL/++qvSJigoiODg4AKNJzc5jXVaWhqTJk3C09MTtVrN2rVrCQ0NxdXVtcDPn9u4dOzYkX79+j33+NmzZ+Pi4sK///5b4LEJIYQQT5MNUcXQvHnzWL16NW3atOGzzz7D2tqaf/75h6+//prRo0eze/ful+q3WbNmREREYGFhUbAB5+KHH37g4sWL9OrVq9DPM3r0aOrUqcPo0aOpVq0acXFxfPfddwwYMIDffvsNc3PzQo3heWxsbIiIiKBGjRpK2Z49e9izZw+zZ8+mWrVqVKlShczMTPz9/Qv03HkZl8DAQBYvXkxsbCwVK1bUOV6j0fDNN98QEBBAmTJlCjQ2IYQQ4lmSnBYzx44dY/Xq1QwbNoyRI0cq5Y0bN+a9997jyJEjL923tbU11tbWBREmKSkpmJiYFEhfBSE2NpYJEybg7u7OqlWrMDY2VuratGlD165dKVWq6P5zNzY2xsXFRafs2rVr2NjY8Pbbb+uU29ravvL5MjMz0Wg0PHz4ME/jEhgYyMKFC/nmm2/o27evTl+///47MTExOrPwQgghRGGRZf1i5quvvqJChQoMGzYsx/rmzZsr7d577z3c3d3x8vIiODg41yX/Z5f1syQnJ/PJJ5/g7u5OkyZNCAkJISMjI9txZ8+epX///ri4uDBnzpw8xTFx4kR27drFX3/9hVqtRq1WM3HiRKX+7Nmz9OnTBxcXF9zd3Rk7diwPHjzQiS8mJoYhQ4bQsGFD/Pz8WL16dbZr27ZtG48fP2bSpEk6CVgWT0/P5z4E/u+//2b06NH4+/vTsGFD2rdvz1dffYVGo9Fpt2rVKlq1aoWzszOenp7069ePW7du5an+2WX9gIAAvvrqK6Kjo5VxiYqKynFZPyEhgWnTpilL8p07d+b48eM6bbJuVdi1axdt2rTB2dmZy5cv53lcqlSpgqurKwcOHMjW5sCBA1hZWeHn55fj+AkhhBAFSWZOi5GMjAzOnDlD69atMTIyemHbu3fv0rt3bypXrszjx4/ZunUr3bt359ChQ1hZWeXrvAsWLMDX15dFixYRGRnJkiVLMDIyYty4cTrtxo4dS7du3QgODlYSvdziGDZsGHFxcVy7do158+YBKLO3Z8+eJSgoCH9/fxYuXEhycjKLFi1i2LBhREREKOcdNmwYMTExTJs2DXNzc1avXk10dLTOTOjvv/+OjY0NarU6X9cOcO/ePezt7enYsSNlypTh0qVLhIaGkpSUxIgRIwDYvXs3ixcv5qOPPsLFxYXExET++OMP5R7M3OqfFRYWxurVq/n9998JCwsDniz9PystLY3+/fvz4MEDRo0aRaVKldi7dy/BwcHKPxqyXLx4kdu3bzNy5EgsLCyws7PL17gEBgYyY8YMbt26RdWqVQFIT0/n0KFDtG3bNtf/JoUQQoiCIMlpMfLw4UPS0tKoXLlyrm0/+eQT5efMzEx8fHzw8vLi0KFDdOvWLV/nrVatGiEhIQD4+fmRkpLCmjVr+OCDD7C0tFTade/encGDB+crjmrVqmFtbc2dO3eyLWvPnz8fJycnwsLClO9Prlu3LoGBgRw7dgx/f39++uknLl68yNq1a/Hy8gLAw8MDf39/nSQ8JiYmT+OWEy8vL6VvrVaLu7s7KSkpbNy4UUlOz58/j1qt1tlI1bJlS+Xn3Oqf5eDgQIUKFXJc7n/avn37uHz5Mnv27KF27drAk8/o5s2bLFu2jMWLFyttHz16xI4dO7Czs1PK8jMu7dq1Y9asWezfv5+hQ4cCcPz4cR4+fEjHjh3z1IcQQgjxqmRZvxjKStRe5Ny5c/Tv3x8PDw8cHBxo2LAhSUlJ3LhxI9/na9VK9+vP2rRpQ3JyMv/73/90yps1a1ZgcSQnJ3PmzBnatm1LZmYmGRkZZGRkUKNGDezs7Lhw4QLwJOkzNzdXkkcAc3NzvL29s/WZl3HLSWpqKkuWLFGW5B0dHVm4cCGxsbHKzKeDgwORkZGEhIRw+vRp0tPTdfrIrf5lnThxgrp161KjRg1ljDIyMvD29lbGKEvdunV1EtMseR0Xa2trfHx8dJb29+/fT+XKlXF3d3+1CxFCCCHySGZOixErKytKly7NnTt3Xtjuzp07DBgwACcnJ6ZPn46NjQ1GRkYEBweTmpqa7/M+u0mqQoUKwJNNRjmVF0QcCQkJZGZmEhISoszaPi06Ohp4suSe0yau8uXL67yvVKkS165de+E5n2fu3Lls376d4cOH4+TkhLm5OYcPH2b58uWkpqZSpkwZOnfuzL///su2bdtYu3Yt5ubmvPPOO4wbNw4TE5Nc619WfHw8kZGRODo6ZqszNDTUef/s5wP5H5fAwEA+/vhjLl++TPXq1fnxxx/p3bv3Syf+QgghRH5JclqMlCpVCjc3N06ePElGRsZzd5f//PPPJCUlERYWpjwWKiMjg0ePHr3UeZ/dIHX//pPvP3/2kUIFGYe5uTkqlYrg4OAcl7/LlSsHPLkP89n4gGybppo0acKvv/7KX3/9RZ06dXI9/9MOHjxIt27ddG5ZOHbsmE4bAwMD+vbtS9++fYmJieHAgQPMnz+fcuXKMXz48FzrX5alpSVqtZqZM2fm2janBDK/49KyZUvMzMw4cOAA9erVIykpSXbpCyGE0CtZ1i9m+vfvT2xsLCtWrMix/tix/8fevcf1eP+PH38UpaiklFMOOb0VqZxKKhRympE5DnM+5NgcVmMMs5xyqsxhzsecRzaGbdkc2gwfNoc5E0VUYp3r/fvDr+vrrVA6Ts/77dbt5n29Xtfrel6vqKfX4bpCSUxMREtLSyN5/eGHHzR22OfEkSNHND4fPnwYfX196tat+8bzshuHjo5OppHU0qVLY2dnx82bN7Gxscn0ZWFhAYCNjQ3Pnj3TeIj+s2fPOHnypEZ7PXr0wMDAAD8/vyyn1MPCwkhISMjyPpKSkjQ2+6SlpWW5az1DhQoVGDx4MCqVKstRybeV54STkxP37t3D3Nw8y356m5z2S+nSpXFzc+PgwYMcOHCAunXrvtMmMyGEEOJdychpEdOyZUuGDh1KQEAA169fp1OnTpQrV47w8HB2797Ns2fPmDdvHgC+vr707t2ba9eusW7dund+uP7du3fx9fWlY8eOXLp0iVWrVvHJJ59obIbKiqOjY7biqFWrFrt37yYkJITq1atTrlw5LCwsmDJlCp988gkTJkygU6dOGBkZERkZycmTJ/H09MTBwQFXV1fq16/P5MmTmTRpEoaGhqxatQoDAwONa5iZmTFv3jwmTJhAnz59+Pjjj6latSoxMTEcPXqUAwcOEBYWluV9ODk5sXPnTmrXrk25cuXYunUrycnJGnWmT5+OkZERdnZ2GBkZcfbsWa5cuUKfPn2yVf6uunbtyvbt2xkwYACDBw+mRo0aPHv2jEuXLpGSksLEiRPfeP679Evnzp0JCQnhwYMHfPrpp7mKXwghhMgpSU6LoMmTJ2Nvb8+WLVv4/PPPSUhIwNzcHGdnZ4YMGUL16tXx8/MjMDCQESNGYGVlxdKlS5kwYcI7Xc/b25vff/+d8ePHU6JECfr27Yu3t/dbz1OpVNmK46OPPuLChQvMnj2b2NhYunXrxty5c2nUqBFbt24lICAAX19fUlJSqFixIo6OjlSvXh14MVW9fPlyZsyYoSSA/fv35/Hjxxw7dkzjOm3atGHXrl2sXr0af39/YmJiMDIyonHjxqxdu/a1b4f64osvmDFjBrNnz0ZfX59u3brRtm1bpk2bptSxt7dnx44d7Ny5k4SEBKpWrYqvry89evTIVvm70tXVZePGjQQEBLBixQqioqIwNjbG2tqavn37ZquNnPaLs7MzxsbGPH36VKb0hRBCFDgttVqtLuwghBDvr4ynCqyOusyN2MeFHI0QQrwfahmXZ5lbz1y3Ex8fz+XLl7GysqJ06dJ5EFnWMn4XZGdJmqw5FUIIIYQQRUaOpvX37dv3Thfp2rXrO50nhBBCCCGKlxwlpy+/Ez27tLS0JDkVQgghhBDZkqPk9NUNKEIIkV1VDcsVdghCCPHeeJ9/puYoOa1SpUp+xSGEeM9Nbtr27ZWEEEJkW7o6HW2t92/7UJ7cUXJyMufOnePo0aNZvs1HCFG8JScnv/YlCCJvJCQkcOnSJennfCb9XDCkn7PnfUxMIQ+S040bN+Ls7Ezfvn0ZO3YsV69eBV68EtPBwYFdu3blOkghxH+fPLUuf6nVahISEqSf85n0c8GQfi7ecpWc7t69m6+//hoXFxfmzJmj8ZfIxMQER0dHvv/++1wHKYQQQgghiodcJafr1q3D3d0df39/Wrdunam8fv36XLt2LTeXEEIIIYQQxUiuktM7d+7g6ur62nJjY2NiY2NzcwkhxHtCS0ursEN4r2lpaaGvry/9nM+knwuG9HPxlqPd+q8yMjIiJibmteXXr1/HzMwsN5cQQrwHdHV10dfXL+ww3mv6+vpYW1sXdhjvPenngiH9nD3v6279XCWnrq6u7Nixg759+2Yqu3btGjt37qR79+65uYQQ4j2x8Pfj3HsWW9hhCCHEe6GqoTGTmr1+9vq/LFfJ6YQJE+jZsyedO3emdevWaGlpsW/fPnbv3s2PP/6ImZkZXl5eeRWrEOI/7N6zWG7EyqPmhBBCvFmuxoIrVKjAnj17cHFx4YcffkCtVvPdd9/x888/06lTJ3bs2IGJiUlexSqEEEIIId5zuRo5BTA1NWXOnDnMmTOH6Oho0tPTMTExQVv7/VsDIYQQQggh8leuk9OXySipEEIIIYTIjRwlp4GBgTm+gJaWFqNHj87xeULs37+fjRs3cuvWLdRqNRUqVKBRo0Z8+umnmJqaKvVSUlLYsWMH+/fv5/r16yQlJWFubk6TJk3o1asXjRs3Vuq6ublx//59AEqWLImRkRG1a9fG3d2dnj17Urp06UxxhIeHs3LlSn777TeioqIoXbo0NjY29OjRg/bt2wPg4+PDX3/9RUhISD73iiaVSsWUKVMYMmSIcmz+/Pns37+fx48f079/f6ysrPD19eXUqVPyH0ghhBBFXq6T04xnkL36ijEtLS3UarUkp+KdrF69Gn9/fwYOHMi4ceNQq9Vcu3aNAwcO8OjRIyU5TUpKYtiwYZw7d45evXoxcuRIypQpw507d9i3bx99+/bl4sWL6OrqKm17eHgwePBg0tLSiI6OJiwsjCVLlrBt2zY2bNhAxYoVlbrnz59n6NChmJiYMGzYMGrXrs3z588JDQ1l0qRJ1KhRg3r16hV4/2QIDg6mcuXKyueTJ0+yZs0afH19sbW1xdzcHH19fYKDgzEyMiq0OIUQQojsylFyeuXKFY3PDx8+ZPjw4dSpU4dPPvkES0tLAG7evMmGDRu4ceMGK1euzLtoRbGxadMmunXrho+Pj3KsZcuWDB06lPT0dOXYkiVL+OOPP1i7di3NmzdXjjdr1owePXqwe/fuTG2XL18eOzs75XPbtm3x9PSkb9+++Pr6sm7dOuBF4jthwgQqVqzI9u3bMTAwUM5xc3OjT58+hZ7wvXwf8OLfHsCAAQM01n3nxYipWq0mJSVFI9EXQggh8lqudi3NnDmT6tWrs3DhQmxsbDAwMMDAwICGDRvi7+9PtWrVmDVrVl7FKoqRuLg4zM3NsyzLSLoSExPZvn077dq100hMX9a9e/dsJVPW1tb07duXkydPKgneDz/8QEREBJ9++qlGYpqhXr16GqOWL3v06BG+vr64u7vTsGFD2rVrx6JFi0hOTtaot2vXLjp16kTDhg1xcHCgT58+XLhwIdvlKpWKNWvWANC/f39mz54NgJWVFSqVirCwMPbs2YNKpSI6+v8e45ScnMyiRYto3bo1DRo0oEOHDhw4cEAjNh8fHzp37kxoaChdunTBxsaGn3766a19KYQQQuRGrjZEnT59mkmTJr223NHRkYULF+bmEqKYql+/Ptu3b8fCwoJWrVpl+aaxv/76i/j4eJydnfPkms7OzqxZs4b//e9/1KxZkz/++IMSJUrg5OSU47ZiYmIwNjbG19cXIyMjbt++TUBAAFFRUfj5+QHwxx9/MHXqVAYPHkzLli1JTEzkwoULPHv2LFvlr5oxYwY7duxgw4YNBAcHA1C7dm1lje3Lxo8fz9mzZxk9ejS1atUiNDSUyZMnY2RkRMuWLZV6jx494quvvmLUqFFUqlTptcm4EEIIkVdylZyWKlWK8+fPZ/mGKIBz585RqlSp3FxCFFMzZsxgzJgxTJs2DQALCwtat27NwIEDsbCwAF4kToDGGlGA9PR0jan/EiVKZOv9zBntREVFAS+WrZiYmKCnp5fj+FUqFZ999pnyuVGjRujr6+Pj48P06dPR19fnwoULGBsba9Rr1aqV8ue3lb+qdu3aSvL46nT/y06fPs1PP/3EmjVrlMS+RYsWREVFERAQoJGcPn36lNWrV2Nra5vdWxdCCCFyJVfT+h988AEHDhzgq6++4vbt20pScPv2bWbPnk1ISAgffPBBXsUqipG6desSEhLCqlWrGDBgAIaGhmzatIkuXbpw+fJljbqvJp5fffUV9evXV74OHz6crWtmbOrLTiKbnbbWr19Px44dadiwIfXr12fSpEmkpqZy79494MVSgtjYWHx8fDhx4gQJCQkabbyt/F2dOHECY2NjHB0dSU1NVb6cnJy4fPkyaWlpSl1jY2NJTIUQQhSoXI2cTpo0iZiYGDZv3syWLVuUtYDp6emo1Wo6der0xml/Id5EV1eXli1bKiN5v/76KyNGjCAoKIjAwEBlTWpkZKTGeUOHDqVbt25ERUUxatSobF/v4cOHAMoSggoVKnDq1CmSkpJyPAOwYcMG5s2bx9ChQ3FwcMDIyIiLFy8ya9YskpKSAGjevDnz589n48aNDBkyhFKlSuHh4cHnn3+OsbHxW8vfVUxMDLGxsdSvXz/L8qioKGUUuXz58u98HSGEEOJd5Co51dXVZcGCBQwZMoTQ0FAePHgAQJUqVXB1dS3UR+yI94+Liwv16tXjxo0bADRo0IDSpUtz4sQJPvroI6Ve5cqVqVy5MuHh4Tlq/9dffwX+b0q8WbNm7Nq1i1OnTr1xOj0rhw4dws3NjYkTJyrHMuJ+2YcffsiHH35IdHQ0x44dw8/Pj5IlS/L1119nq/xdlC1bFhMTE1atWpVl+cs7+/NiFFkIIYTIiTx5Q1S9evUkERV56vHjx5lG7RITE4mIiKB27doA6Onp0bt3bzZs2EBYWBgODg7vfL3Lly+zbds2nJ2dqVGjBgDt27dn8eLFLFq0iCZNmmTasX/16lWMjIyoVKlSpvYSExPR0dHROPbqbviXmZiY0KNHD44fP648LSAn5Tnh5OTEt99+i46Ojvy7FUIIUeTkSXJ67949jh8/rjFy6uLiQtWqVfOieVEMffDBB7Ru3RpnZ2fMzc15+PAhmzdvJiYmhk8++USpN2HCBP7++2+GDRtGr169aNGiBWXKlOHJkyfKWtNX3/r0+PFjzp8/T3p6OtHR0Zw+fZpdu3ZRsWJFjRHJUqVKsWTJEoYOHUr37t0ZOHCg8hD+3377jR07drBz584sk1MnJyc2btzI5s2bqVGjBvv37+fOnTsadZYtW0ZsbCzNmjXD1NSUf/75h19//ZWBAwdmq/xdtWjRgtatWzN06FCGDh2KSqUiISGB69evc+fOHebMmZOr9oUQQojcyHVyOnfuXDZu3KixOxpePIvyk08+0dhpLER2jRkzhp9//pm5c+cSHR1NuXLlUKlUrF+/HkdHR6VeqVKlWLNmDcHBwezfv59du3aRkpKCubk5jRs3ZuvWrRqvLwU4fPgwhw8fpmTJkhgaGlKnTh28vb3p0aNHpkTWzs6OvXv3smrVKlauXMnjx4+V15cuWrTotSOPo0ePJiYmhmXLlgEv3ko1bdo0Ro4cqdSxsbFhw4YN/PDDDzx//pyKFSsyZMgQZZ3s28pzY9myZaxatYpt27Zx//59pR88PT1z3bYQQgiRG1rqV987mgNr165l/vz5yusga9WqBbxYW7d+/XoOHTrEZ599luuRHiHEf9fFixcB+PbRLW7ERr+lthBCiOyoZWzCUvcuuW4nPj6ey5cvY2VllWmAJi9l/C6wsbF5a91cjZzu2LEDNzc3li5dqnHc1taWxYsXk5SUxPbt2yU5FUIIIYQQ2ZKr55zev3//jW/ncXZ2zvLtNEIIIYQQQmQlV8mpqakpV65ceW35lStXNB5LI4QQQgghxJvkalq/ffv2bNy4EQsLC/r166esVYiPj2fz5s3s2rVLY2e1EKL4qmpoXNghCCHEe+N9/pmaq+R0/PjxXL58mUWLFrFs2TLljT2PHj0iNTUVBwcHxo0blyeBCiH+2yY1cy3sEIQQ4r2Srk5HWytXk+BFUq6SU319fTZs2MDRo0c1nnPq7OxMy5YtcXNzkzfMCCFITk4mISEBfX39wg7lvZWQkMCtW7ewtLSUfs5H0s8FQ/o5e97HxBTy6CH8bdq0oU2bNnnRlBDiPZWLp9aJbFCr1SQkJEg/5zPp54Ih/Vy85Tg5ffkh4tmhpaXFN998k9PLCCGEEEKIYijHyekvv/xCqVKlKF++fLb+RyPT+kIIkJ8F+U1LSwt9fX3p53wm/VwwpJ+LtxwnpxUqVODhw4eUK1eOzp0706lTJ8zMzPIjNiHEe0JXV1fWjeUzfX19rK2tCzuM9570c8GQfs4sXa1Gu5gk6+/0+tLff/+dkJAQDh8+zPPnz2natCkffPABHh4eGBgY5EecQoj/qIxX1h2O/5d7z+IKORohhPjvqWpoxCQHx3xpuyi+vvSdktMMKSkphIaGEhISwi+//EJ6ejqurq507twZNzc3dHV137VpIcR7IuMH0rcPI7gRG1u4wQghxH9QLWNjlrZply9tF8XkNFfPINDR0aFNmzYsWbKEEydOMGvWLB4/foy3tzerV6/OTdNCCCGEEKIYypMHZCUnJ/Pbb79x7NgxLl26RKlSpahSpUpeNC2EEEIIIYqRd37OaXp6OidOnODgwYMcPXqUxMREmjdvzuzZs2nbtm2+Dg0LIYQQQoj3U46T07NnzxISEsKhQ4eIjY3F1tYWb29vOnTogImJSX7EKIQQQgghiokcJ6d9+/ZFT09P2fiUMX0fERFBRERElufUr18/d1GKQhUQEEBgYKDyWVdXFwsLCzw9PRkyZAja2oXz+rSwsDAGDBjArl27srXA+l0dO3aMLVu28NdffxEfH4+5uTnOzs4MGjQIS0tLAFQqFVOmTGHIkCH5Fser9uzZg6+vL6dOnVL+Y3jv3j2mTZvGhQsXiI+PZ9++fXz99deULl2alStXFlhsQgghxLt6p2n9xMREfvzxR44cOfLGemq1Gi0tLS5fvvxOwYmiQ09Pjw0bNgAvvv9hYWH4+/ujVqsZPnx4ocRUv359goODqVWrVr5dY+HChaxevRoPDw9mz56NiYkJd+/eZffu3Xh7e7Nv3758u/bbtGrViuDgYIyMjJRjS5cu5d69eyxbtgxDQ0Nq1KjBjBkzCu0/EEIIIURO5Tg59fPzy484RBGnra2NnZ2d8tnR0ZF//vmHH3/8sdCSUwMDA42Y8lpoaCirV6/Gy8uL8ePHK8ebNm1K9+7d+fnnn/Pt2tlhYmKSaSnNzZs3adKkCS4uLsqx2rVr58n1EhMT0dPTy5O2hBBCiNfJcXLarVu3/IhD/AeVKVOG1NRU5fPChQsJDQ0lPDwcAwMDmjZtio+PD+bm5kodtVpNUFAQ27ZtIz4+HhcXF3r37s2gQYPYuHEjDg4OADx79oyZM2dy7Ngx9PT06NGjB8bGxsybN4+rV68CWU/rq1QqJk2aRGJiItu2bSMtLQ03Nze++OILjU16Z86c4auvvuLGjRvUqFEDHx8f5s+fj5WVFXPnzgVg7dq1lC9fHi8vryzvv3Xr1q/tm19++YUNGzZw5coVkpKSqFWrFmPHjsXV1VWpExcXx/z58wkNDSU2NhYTExMaNWrE4sWLs1X+8rR+fHw87u7uAPz999989913VKlShZ9++on+/ftnmta/ceMGCxcu5PfffyctLY1mzZoxbdo0qlWrptRRqVRMnDiRp0+fsm/fPuLj4zl37txr71kIIYTIC++8W18UPxmJaMa0/o8//siIESOU8idPnjBixAjMzc2Jjo5m3bp19O/fn4MHD1Ky5Iu/aps2bSIwMJChQ4fi6OjI6dOnmTZtWqZr+fr6cvr0aSZPnkyVKlXYsWMHf//9d7bi3LJlC40bN2bu3Lncvn2b+fPnY2pqyqRJkwB49OgRw4YNw9ramiVLlvDs2TO+/PJLnj17hpWVlXKvZ8+epV27dujo6OS4r8LDw2ndujWDBw9GW1ub48ePM3z4cDZs2KAk4H5+fvz6669MnDiRKlWqEBUVxfHjx5U23lb+MnNzc4KDg/nss8+oXr06Xl5er30Jxr179+jduzd16tRh7ty5aGlpsWLFCgYOHMihQ4c0ztu4cSO2trbMmTNH4z8iQgghRH6R5FRkS3x8fKaNbR07dtSY0n95yUdaWhr29va4urpy+vRpnJ2dSUtLY9WqVXh6eiqJorOzMzExMezatUs59/r16xw5coR58+bRtWtXAFxcXOjQoUO2YjUzM8Pf3x8AV1dXLl26xOHDh5Vrrl+/nhIlSrBy5UrldbsWFhZ8/PHHShuxsbEkJydTuXLl7HaRhn79+il/Tk9Px8HBgevXr7Njxw4lOb148SKdO3fWmI3o1KmT8ue3lb9MV1cXOzs79PT0MDExeeNyh8DAQMqWLcu6desoVaoUAI0aNcLd3Z2dO3dq9EPZsmUJDAxEq5i8z1kIIUThk+RUZIuenh6bN28GXrx04e+//2bZsmVMmzZNSUpDQ0P55ptvuHbtGs+fP1fOvX37Ns7OzkRGRhIVFYWbm5tG2+7u7hrJacYrzjKmqeHFmtfWrVuzbt26t8bq5OSk8blWrVocPHhQo30HBwclMQVo0qQJxsbGmdp616QsMjKSxYsXc/LkSaKiosh4S/DLCb61tTV79+7FzMwMFxcX6tatq9HG28rf1YkTJ+jYsSMlSpRQRkONjIywtrbmr7/+0qjr6uoqiakQQogCJcmpyBZtbW2NxzU1btyYtLQ05s6dy6BBg0hMTMTLywt3d3eGDRuGqakpWlpa9OzZk6SkJACioqIAMm3iMTU11fgcFRWFjo4OhoaGGsez+xzdl3evw4vX7CYnJ2u0X6NGjUznvdy+sbExpUqV4sGDB9m65svS09MZNWoUz549Y9y4cVSvXh19fX2WLVum8bi1L774QhnBnD9/PpUqVWL48OH07ds3W+XvKiYmhg0bNihPX3jZq0sYXv3eCCGEEPlNklPxzmrWrAm8mIa/cuUKBgYGLFmyRHls0f379zXqm5mZARAdHa1x/MmTJ5nqpaSk8OzZM40E9dXz3pWZmVmWbb18rGTJkjRq1IjTp0+TmpqqrJnNjjt37nDp0iWCgoJo06aNcjwxMVGjnqGhIVOnTmXq1KlcvXqVjRs3MnPmTOrWrUuTJk3eWv6uypYtS8uWLbNMcsuUKaPxWUZNhRBCFDR5+KF4Z9euXQOgXLlyJCYmoqOjo5HMHDhwQKN+xYoVMTMz49ixYxrHjx49qvG5QYMGABr10tPT8+zRTTY2Npw+fVpj6cGZM2eIjY3VqDdo0CCioqJYsWJFlu2EhoZmeTxjpPjlUcj79++/cae7SqXC19cXeLGTPqflOdG8eXOuXbuGtbU1NjY2Gl8Z/+EQQgghCouMnIpsSU9P5/z58wCkpKTw999/880331C7dm2aNGlCcnIyGzZsYPbs2bRt25Zz587x3XffabRRokQJhg8fztdff0358uVxcHAgLCyMU6dOASgjrnXq1KFt27Z89dVXJCQkULlyZXbs2EFiYmKejOQNHDiQbdu2MWLECIYMGUJcXBxBQUGUK1dOo/2WLVsydOhQAgICuH79Op06daJcuXKEh4eze/dunj17RsuWLTO1X7NmTSpWrIi/vz/p6enEx8ezbNkyjUdqAfTu3Zu2bdtSp04dSpQowb59+9DR0VFGRd9W/q7GjRvHRx99xJAhQ+jZsyfly5fn8ePH/P777zRp0oTOnTvnqn0hhBAiNyQ5FdmSmJhIr169gBdT3hUrVqRLly6MGTMGHR0dWrZsyaRJk9i8eTN79uyhUaNGrFy5Eg8PD412+vfvT1xcHFu3bmXTpk00b96cyZMn4+3trTGF//XXXzNr1izmz5+Prq4u3bp1o06dOmzZsiXX92Jubs7q1av56quvGDduHNWqVWPq1KnMmjUr0zrXyZMnY29vz5YtW/j8889JSEhQXl/6uleV6urqEhAQwKxZsxg/fjyVKlVi1KhRnD59WmPDUaNGjdi3bx/h4eFoa2tTt25dVqxYobzx6m3l76p69ers3LmTJUuWMHPmTOLj4zEzM6Np06aoVKpctS2EEELklpY6YxuxEIVkyZIlrFu3jrCwsDe+gejjjz9GW1ubTZs25XkMt2/fpkOHDnz99dfyook8lvH0hW8fRnDjlaUTQggh3q6WsTFL27TLl7bj4+O5fPkyVlZWGi+ryWsZvwte3lz9OjJyKgrUjRs32L9/P/b29ujo6PD777+zZs0a+vTpo5GYHj58mIiICOrWrUtCQgIhISGcOXOGoKCgPInD398flUqFubk59+7dY+XKlZiZmdGuXf784xdCCCFE9khyKgqUnp4e586dY9u2bfz7779UqFCBIUOGMHbsWI16pUuX5rvvvuP27dukpKRQs2ZNFixYoLH7PTdSUlJYuHAhjx8/Rk9Pj2bNmjFlypRMu9WFEEIIUbBkWl8Ika8ypnIOx//LvWdxhRyNEEL891Q1NGKSg2O+tC3T+kKIYiu/frAKIURxkK5Wo11Mnj0tzzkVQuS75ORkEhISCjuM91pCQgKXLl2Sfs5n0s8FQ/o5s+KSmIIkp0KIAiIriPKXWq0mISFB+jmfST8XDOnn4k2SUyGEEEIIUWRIciqEEEIIIYoMSU6FEAUiL149K15PS0sLfX196ed8Jv1cMKSfizfZrS+EyHe6urro6+sXdhjvNX19faytrQs7jPee9HPBKK79XJx25L+JJKdCiALhH3aOe8+eFXYYQghRJFU1NGSig31hh1EkSHIqhCgQ954942asPIRfCCHEm8maUyGEEEIIUWRIciqEEEIIIYoMSU6FEEIIIUSRUSST0/3799O7d2/s7e2xt7end+/ehISEFGgMcXFxqFQq9uzZk2ftBQQEcP36dY3j4eHhqFQqDh06lO22wsLCUKlUypednR3u7u54e3tz4sSJTPV9fHzo3Llzru8hOwICArC3z5sF3Xv27EGlUhEdHZ0n7b2LsLAwVqxY8cY6o0aNQqVSsW/fvnyN5V3+rmSHm5sbs2bNytM2hRBCiHdV5DZEzZ49my1bttC9e3e8vLzQ0tLi8OHDTJo0ib///pvPPvussEN8J3FxcQQGBlKnTh1q166tHDc3Nyc4OJgaNWrkuE0/Pz9q1qxJUlIS9+7d4+DBgwwePJi+ffsyY8YMpZ6Xlxfx8fF5cRtv1aNHD1q2bJknbbVq1Yrg4GCMjIzypL138fvvv7N27VpGjhyZZXlsbCy//vorACEhIXTt2rUAo8sbgYGBhdrHQgghxMuKVHJ67NgxNm/ezJgxYxg7dqxy3MXFBXNzc4KCgnBycsLFxaUQo/w/ycnJlCxZEm3tdx+A1tXVxc7O7p3OrVOnDjY2NgA4ODjw0UcfsWjRIlauXIm9vT1dunQBoFq1au8cX3Zl9EXFihWpWLFinrRpYmKCiYlJnrSVXw4fPkxKSgpOTk6cOnWKJ0+eYGpqWthh5UhxfJagEEKIoqtITetv2LCBsmXLMnjw4ExlQ4YMoWzZsqxfvx6A/v37M2LECI06ly9fRqVSERYWphxbu3Yt3bt3p3HjxjRv3pwRI0Zw69atTO3v2LEDNzc3bG1t+eSTT7hz506mOhnTn6tXr6Z169Y0bNiQ2NhYbty4gbe3Ny1btsTW1paOHTuydu1a0tPTgRfTse7u7gCMHz9emY4PDw9/7VTtvn376Nq1KzY2Njg4ODBs2DDu37//1j4cN24cZmZmbN26VTn26rR+XFwc06ZNw8XFBRsbG1q2bIm3t7dGOw8fPmTKlCk4OTnRsGFD2rdvz4YNG97aF69O62csQfj1118ZP3489vb2tGrVigMHDgCwceNGWrVqRbNmzZg6dSrJycnKua9O62f01XfffcesWbNo2rQpzs7OzJs3j9TUVOW8t30/sttWQEAAgYGBxMfHK9+z/v37a/RTSEgI1atXx8fHh9TUVL7//nuN8ryM+VVz586lVatWmeqEhoaiUqmUJSTHjh3D09MTe3t7mjRpgqenJ6GhoZm+lxmuXbvGsGHDcHBwwNbWFg8PD1avXv3aOIQQQoi8VGRGTlNTUzl37hytWrWiTJkymcrLlCmDg4MDv/76K2lpadluNzIykn79+lG5cmWeP3/O9u3b6d27N4cPH8bY2BiAn3/+mS+++AJPT086duzI33//zfjx47Ns78cff6R69epMnToVbW1tSpcuzdWrV7G0tOSDDz6gTJkyXL58mYCAAOLj4xkzZgzm5uYEBgYyZswYPv30UxwcHIAXU/qPHj3KdI1vv/2WBQsW8NFHH+Ht7U1KSgqnT58mOjqaKlWqvPF+S5YsiaOjI4cOHSIlJQUdHZ1Mdfz8/Pj111+ZOHEiVapUISoqiuPHjyvlMTEx9OrVCwBvb28sLCy4c+cOd+/efWtfvM6XX35Jt27d6NmzJzt27GDKlClcuXKFa9euMXPmTO7du8fcuXOpWrXqa6fQMyxZsgR3d3eWLFnCuXPnCAgIoFq1avTp0weAR48evfH7kd22evToQWRkJCEhIUpibmBgoJwbGRnJH3/8gZeXFyqVirp16xISEpIpgc3rmDP06NGDdevWceLECY3ZhN27d2NnZ0ft2rW5e/cu48ePp1OnTkycOJH09HSuXLnC06dPX9u/I0eOpHz58syZMwcDAwPu3r1LZGTkG78nQgghRF4pMslpTEwMycnJVKpU6bV1KlWqREJCwht/sb7q888/V/6clpZGixYtaN68OYcPH1YSsG+++YYmTZrg5+cHvFhGkJSUxPLlyzO1l5KSwurVqzUSsebNm9O8eXMA1Go1jRs3JjExUVmioKuri5WVFQDVq1d/4zT+s2fPCAwMpFevXhqjWW3atMn2PVeqVImUlBSePn1K+fLlM5VfvHiRzp07061bN+VYp06dlD+vX7+eJ0+e8MMPP2BhYaHc46uy6ovXad++vZJkNWzYkCNHjnDw4EGOHDmiJNC///47hw4demty2rBhQ6ZNmwZAixYtCAsL4/Dhw0qi97bvR3bbyliioK2tneX3LCQkBLVarYxKf/DBB/j7+3P37t1MSynyMuYMtWrVonHjxuzevVtJTmNiYvjpp5+YPn06AJcuXSIlJYUvvvhCSazftCwmOjqa8PBwpk6dipubGwCOjo6vrS+EEELktSKTnOaX8+fPs3TpUi5dukRsbKxy/Pbt28CLhPXvv/9m8uTJGud5eHhkmZw6ODhkSsaSkpJYuXIlBw4cICIigpSUFKXs33//zXIk+HXOnTtHQkICH330UbbPeZVarQZA6zXv57W2tmbv3r2YmZnh4uJC3bp1NcpPnTqFo6Ojkpi+TlZ98TotWrRQ/mxoaIiJiQlNmjTRGNmtUaOGxpKM13F2dtb4XKtWLU6fPq18zsn3421tvUlISAj169enZs2awIsEf9GiRRw4cIDRo0fnW8wv69mzJ1988QWxsbEYGxtz4MABdHR06NixIwAqlYoSJUowadIkevbsSdOmTTE0NHztPZUrV44qVaqwaNEinj59SvPmzfNsDbEQQgiRHUVmzWm5cuXQ1dUlIiLitXUiIiLQ1dXN9iaZBw8eMHjwYNLS0pg5cybbtm1j165dmJqakpSUBLwYKUpNTc3UZlYjjkCWm10WLFjAmjVr6NGjB6tWrWLXrl2MGjUKQLlOdmUk0Obm5jk672WRkZHo6OhQtmzZLMu/+OILunTpwrp16/jggw9o1aqVxhrV2NjYbF0/Jxt/Xk2IdHV1M+0Q19HR0Vhzmt22Xj0vJ9+Pt7X1Ojdu3ODy5cu4ubkRFxdHXFwchoaGNGjQIMvHnuVlzC9r3749enp67N+/H3ixTtfDw0MZJbW0tGTFihU8e/aMMWPG0Lx5c0aOHMmDBw+ybE9LS4s1a9ZQs2ZNZs2aRcuWLfH09OSPP/54a58IIYQQeaHIjJyWLFmSRo0a8fvvvxMfH59pRC4+Pp7ff/+dJk2aAC+Sm5dHl4BM0/2//vor8fHxGo/KSU1N1ahnYmJCyZIlMz1L8/Hjx1nGmdVo5KFDh+jVqxfDhw9Xjr284SQnMtbBPnr06J1GrFJTUzl9+jQ2NjaULJn1t9fQ0JCpU6cydepUrl69ysaNG5k5cyZ169alSZMmGBsbZ7kW9lWvG5ktbHn5/XidjGQwICCAgICATOV///039evXz3Z77xqznp4eH3zwAXv27KFx48ZcvnxZWT6QwdXVFVdXV54/f87x48fx8/PD19dXY4PbyywtLVm2bBkpKSmcO3eORYsWMXLkSI4fP56jWQAhhBDiXRSZkVOAAQMGEBsby9q1azOVrV27ltjYWGWdaMWKFbl165YyhQ1kegB9YmIiWlpaGknaDz/8oLFLukSJElhbW3PkyBGNcw8fPpztuJOSkjSmp9PS0jh48KBGnYzyt42k2tvbo6+vz+7du7N9/ZctW7aMqKgo+vXrl636KpUKX19f4MVoILxY/3j69OnXjq4Vddn5fmTX60ZSDx48iJ2dHRs3btT4WrNmDTo6OsrTCAoi5p49e3L58mX8/PyoUaOG8h+4VxkYGNCxY0c6deqkfK/fREdHh2bNmjF8+HCeP3+erf+wCCGEELlVZEZOAdzd3enXrx+BgYFERkbSvn174MWu8B07dtCtWzflmIeHB7t27WL27Nm0adOGs2fPZkooMzZy+Pr60rt3b65du8a6desyTSePHDkSLy8vfH19ld363333XbbjdnJyYufOndSuXZty5cqxdevWTAmNmZkZRkZGHDx4EAsLC3R1dVGpVJnaMjQ0ZPTo0SxcuBC1Wo27uzvp6emEhYXRqVMn5bmm8OKRP2lpaSQnJ3Pv3j1CQkI4efIk/fv319jg9KrevXvTtm1b6tSpQ4kSJdi3bx86OjpKUjNw4EC+++47+vXrx6hRo6hatSr37t3j9u3bmdbmFkXZ+X5kV61atUhNTWXDhg3Y29tjYGDA06dPuXfvHqNGjVKevPCyVq1acfDgQaZMmVIgMderVw8bGxv++OMPJk6cqFG2fft2zp8/j4uLC2ZmZoSHh7N//36NNcAvu3LlCvPmzaNjx45UrVqV58+fs3LlSqpUqVIgz8sVQgghilRyCi/WQzZs2JBt27YxduxY5c1G3t7eGs81dXV1ZfLkyWzevJm9e/fi6urKzJkzGThwoFJHpVLh5+dHYGAgI0aMwMrKiqVLlzJhwgSNa7q7uzNz5kxWrFjBwYMHsbW1ZcmSJfTo0SPbMc+YMYPZs2ejr69Pt27daNu2rcb0qra2Nn5+fixatIiBAweSnJzMsWPHsmxv2LBhmJiYsH79evbs2UOZMmWwt7fPtMYzY8RTT08PU1NTbG1tWbduHU5OTm+Mt1GjRuzbt4/w8HC0tbWpW7cuK1asoFatWsCL9b/btm3D39+fhQsXkpCQQJUqVejbt2+2+qOwZef7kV2tW7emb9++rFq1iidPntC0aVPq1q2Lvr4+Hh4eWZ7TtWtXjhw5QlhYGFWrVi2QmNu2bculS5cyvaFKpVLx888/4+fnR2xsLGZmZnTq1Om1j0ozMzOjfPnyrFy5kocPH2JoaEiTJk1YsGABJUqUyFYsQgghRG5oqV+eFy+Cnjx5Qvfu3alVqxYrV6587TpKIYqzjz/+GENDQ1asWFHYoWRy8eJFANY8jOFmbFwhRyOEEEVTTWMjlrRxLfDrxsfHc/nyZaysrLL9BJ53kfG74OUZ4NcpUmtOs2JqakpgYCB//PEHX375ZWGHI0SRcvHiRdavX8+ZM2cYMGBAYYcjhBBC5Np/YhiyQYMGXLhwobDDEKLI+eijjzA0NMTLy+utyzmEEEKI/4L/RHIqhMja1atXCzsEIYQQIk9JciqEKBBV3/BmKiGEKO7kZ+T/keRUCFEgJjrYF3YIQghRpKWr1WgX0RfcFKQivyFKCPHfl5ycTEJCQmGH8V5LSEjg0qVL0s/5TPq5YBTXfpbE9AVJToUQBaKIP7XuP0+tVpOQkCD9nM+knwuG9HPxJsmpEEIIIYQoMiQ5FUIUCC2ZrspXWlpa6OvrSz/nM+nngiH9XLzJhighRL7T1dVFX1+/sMN4r+nr62NtbV3YYbz3pJ8LhvSzpuK2UUqSUyFEgVj8+2XCn8UXdhhCCPGfYmFYGu9mVoUdRoGS5FQIUSDCn8VzM/Z5YYchhBCiiJM1p0IIIYQQosiQ5FQIIYQQQhQZkpwKIYQQQogiQ5JTIYQQQghRZEhyWoQFBASgUqlQqVTUq1ePxo0b88EHHzBr1ixu3LiRL9d0c3Nj1qxZ2a7v4+ND586d8zyO/v37K/f+ui8fH588v25WwsPD+eKLL2jdujUNGjSgWbNmDBkyhEOHDil18qsf3kalUrFmzRqNY/Pnz8fZ2Zl69eoxZ84c9uzZg0qlIjo6usDjE0IIIXJKdusXcXp6emzYsAGAf//9l3/++Yfg4GB27NjBnDlz+PDDD/P0eoGBgRgZGWW7vpeXF/Hxef94oBkzZvD8+f/t7J45cyZ6enp89tlnyjETE5M8v+6rzp8/z9ChQzExMWHYsGHUrl2b58+fExoayqRJk6hRowb16tXL9zheJzg4mMqVKyufT548yZo1a/D19cXW1hZzc3P09fUJDg7O0fdVCCGEKCySnBZx2tra2NnZKZ9btGhB3759GT58OFOnTqVRo0ZUrVo1z66X04ceV6tWLc+u/bLatWtrfDYwMKB06dIaffGqxMRE9PT08iyGpKQkJkyYQMWKFdm+fTsGBgZKmZubG3369Cn0hO/V/rh58yYAAwYMQFv7/yZG8iKRV6vVpKSkoKurm+u2hBBCiNeRaf3/oFKlSvHFF1+QkpLCzp07leN79uzhgw8+wMbGBhcXFxYvXkxaWprGuQ8fPmTKlCk4OTnRsGFD2rdvr4zMQuZp/WvXrjFs2DAcHBywtbXFw8OD1atXK+VZTWdfvXqVIUOGYGdnR+PGjRk3bhwPHjzQqKNSqVi9ejUBAQE4OTnh4OCAr69vtkdhw8LCUKlU/PLLL4wbN45GjRoxfvx4AOLi4vjyyy9xdnamQYMGeHp68ttvv2Vq45dffqFHjx40bNgQR0dHZsyYoXH9H374gYiICD799FONxDRDvXr1NEYtX/bo0SN8fX1xd3enYcOGtGvXjkWLFpGcnKxRb9euXXTq1ImGDRvi4OBAnz59uHDhQrbLX57W79+/P7NnzwbAysoKlUpFWFhYltP6ycnJLFq0SFmq0KFDBw4cOKARW8b3NjQ0lC5dumBjY8NPP/2U9TdECCGEyCMycvofVbt2bSpUqMC5c+cAWLduHQsWLOCTTz7Bx8eHGzduKMnppEmTAIiJiaFXr14AeHt7Y2FhwZ07d7h79+5rrzNy5EjKly/PnDlzMDAw4O7du0RGRr62fkREBP369aNq1aosWLCApKQkFi9eTL9+/di/f79GkrdlyxYaN27M3LlzuX37NvPnz8fU1FSJNzu++OILunTpQlBQENra2iQnJzNo0CCePHnChAkTqFChAvv372fEiBFKkgZw6NAhvL298fT0ZOzYsURFReHv709cXByLFy8G4I8//qBEiRI4OTllO54MMTExGBsb4+vri5GREbdv3yYgIICoqCj8/PyU9qdOncrgwYNp2bIliYmJXLhwgWfPnmWr/FUzZsxgx44dbNiwgeDgYODF35P79+9nqjt+/HjOnj3L6NGjqVWrFqGhoUyePBkjIyNatmyp1Hv06BFfffUVo0aNolKlSq9NxoUQQoi8Isnpf1ilSpV4/Pgxz58/Z9myZQwdOpRPP/0UeDH9r6Ojw9y5cxkyZAjlypVj/fr1PHnyhB9++AELCwsAmjdv/tr2o6OjCQ8PZ+rUqbi5uQHg6Oj4xpjWr19Pamoqa9euxdjYGHgxitepUyf27t1L//79lbpmZmb4+/sD4OrqyqVLlzh8+HCOklM3NzcmT56sfN69ezdXrlzhu+++U5YGuLi4cOfOHZYvX87SpUtRq9XMnz+fjh07MmfOHI14hg8fjpeXF3Xq1OHhw4eYmJi801IBlUqlsT62UaNG6Ovr4+Pjw/Tp09HX1+fChQsYGxtr1GvVqpXy57eVv6p27dpK8vim5Q+nT5/mp59+Ys2aNTg7OwMv/r5ERUUREBCgkZw+ffqU1atXY2trm91bF0IIIXJFpvX/w9RqNVpaWpw7d474+Hjat29Pamqq8uXk5ERiYiLXrl0D4NSpUzg6OiqJ6duUK1eOKlWqsGjRIvbu3fvGEdMMZ86cwcHBQUlMAWrVqkW9evX4888/Neq+OiJZq1atbF3jZa8maydOnKBu3brUqFEjU19cvHgRgFu3bnH//n06dOigUadZs2Zoa2vz119/5SiGrKjVatavX0/Hjh1p2LAh9evXZ9KkSaSmpnLv3j3gxfre2NhYfHx8OHHiBAkJCRptvK38XZ04cQJjY2McHR0z9dHly5c1loIYGxtLYiqEEKJAycjpf1hkZCQ1atQgJiYGgG7dumVZLyIiAoDY2Fjq1KmT7fa1tLRYs2YNixcvZtasWcTHx1O/fn18fX1p2rRplufExcVhZWWV6bipqSlPnz7VOPbqZiIdHZ1MazLfxtTUVONzTEwMly5don79+pnqlihRQqkDMHr06CzbzOivChUqcOrUKZKSkihVqlSO4tqwYQPz5s1j6NChODg4YGRkxMWLF5k1axZJSUnAi1Hr+fPns3HjRoYMGUKpUqXw8PDg888/x9jY+K3l7yomJobY2Ngs+wggKiqKihUrAlC+fPl3vo4QQgjxLiQ5/Y+6du0aDx8+pFu3bpQtWxZ48RiojKTiZRkjpcbGxjx69ChH17G0tGTZsmWkpKRw7tw5Fi1axMiRIzl+/DhlypTJVL9s2bI8efIk0/EnT55Qo0aNHF07O7S0tDJdX6VSaUzXvyojsZs+fToNGzbMVG5ubg5As2bN2LVrF6dOnXrjdHpWDh06hJubGxMnTlSOZfVs2g8//JAPP/yQ6Ohojh07hp+fHyVLluTrr7/OVvm7KFu2LCYmJqxatSrL8pd39r/av0IIIUR+k+T0PygpKYnZs2ejq6tLjx49MDIyQl9fn8jISNq2bfva85o3b87atWt58OBBjje26Ojo0KxZM4YPH86oUaN49OgRlpaWmeo1btyYHTt28PTpUyVpvnnzJlevXqV79+45u9F34OTkRGhoKObm5lSoUCHLOjVr1qRixYrcu3ePjz/++LVttW/fnsWLF7No0SKaNGmSacf+1atXMTIyolKlSpnOTUxMREdHR+PYq7vhX2ZiYkKPHj04fvy48jionJTnhJOTE99++y06OjqF+oxWIYQQIiuSnBZx6enpnD9/HoD4+HjlIfz37t1j7ty5yqjouHHjWLBgAZGRkTRr1owSJUpw7949jh07RkBAAPr6+gwcOJDvvvuOfv36MWrUKKpWrcq9e/e4ffu2xqaiDFeuXGHevHl07NiRqlWr8vz5c1auXEmVKlVe+3zTgQMHsmfPHgYPHsyoUaNISkpiyZIlVKpU6bXLDvJS165d2b59OwMGDGDw4MHUqFGDZ8+ecenSJVJSUpg4cSJaWlr4+PgwadIk4uPjadWqFfr6+jx48IDQ0FC8vb2xtLSkVKlSLFmyhKFDh9K9e3cGDhyoPIT/t99+Y8eOHezcuTPL5NTJyYmNGzeyefNmatSowf79+7lz545GnWXLlhEbG0uzZs0wNTXln3/+4ddff2XgwIHZKn9XLVq0oHXr1gwdOpShQ4eiUqlISEjg+vXr3Llz542jzkIIIUR+k+S0iEtMTFQe/1S6dGksLCxo3rw5gYGB1KpVS6k3ePBgKlSowLp169i8eTMlS5akWrVqtGrVShnBK1euHNu2bcPf35+FCxeSkJBAlSpV6Nu3b5bXNjMzo3z58qxcuZKHDx9iaGhIkyZNWLBggbJ+81WVKlVi06ZNzJ8/n0mTJqGtrU2LFi3w8fHJ8lmheU1XV5eNGzcSEBDAihUriIqKwtjYGGtra4377NChA0ZGRqxYsUIZ0axSpQouLi4a6yzt7OzYu3cvq1atYuXKlTx+/JjSpUtjY2PDokWLXjvyOHr0aGJiYli2bBkAHh4eTJs2jZEjRyp1bGxs2LBhAz/88APPnz+nYsWKDBkyhFGjRmWrPDeWLVvGqlWr2LZtG/fv38fQ0JA6derg6emZ67aFEEKI3NBSq9Xqwg5CCPH+ynhKwvpHydyMff6W2kIIIV5W09gAf/fG+dZ+fHw8ly9fxsrKitKlS+fbdTJ+F9jY2Ly1rjxKSgghhBBCFBmSnAohhBBCiCJD1pwKIQqEhWH+TRcJIcT7qjj+7JTkVAhRILybZX45gxBCiLdLV6vRLkbPnZZpfSFEvktOTs6z16+KrCUkJHDp0iXp53wm/VwwpJ81FafEFCQ5FUIUEHkwSP5Sq9UkJCRIP+cz6eeCIf1cvElyKoQQQgghigxJToUQQgghRJEhyakQokBoFbM1UwVNS0sLfX196ed8Jv1cMKSfizfZrS+EyHe6urro6+sXdhjvNX19faytrQs7jPee9HPBKM79XNx25mdFklMhRIFY+vsdwp8lFXYYQghRZFkYlmJ8s+qFHUahk+RUCFEgwp8lcStWHgsjhBDizWTNqRBCCCGEKDIkORVCCCGEEEWGJKdCCCGEEKLIKFbJaUBAACqVSvlydHRkwIABnDlzpsBi8PHxoXPnzvl6jejoaOrXr4+9vT2JiYn5eq3CFhAQgL29fb61r1ar2bt3L3379qVx48Y0aNAADw8P5s6dy8OHDwEIDw9HpVJx6NChfIsjK1nd+99//03Pnj2xtbVFpVIRFxeHm5sbs2bNKtDYhBBCiHdV7DZE6enpsWHDBgAiIyNZvnw5AwcOZM+ePdStWzffr+/l5UV8fHy+XuP7778nNTWV1NRUfvrpJzp27Jiv1ytMPXr0oGXLlvnStlqtZuLEifzwww94enoydOhQDAwMuH79Otu3b+fevXsEBQXly7WzI6t7/+qrr0hLS2PlypXo6elRpkwZAgMDMTIyKqQohRBCiJwpdsmptrY2dnZ2yueGDRvi5ubG9u3bmT59er5fv1q1avl+jZCQEGrVqsXz58/Zv39/gSeniYmJ6OnpFci1KlasSMWKFfOl7a1bt3Lw4EHmzJnDRx99pBxv1qwZvXr14rfffsuX62ZXVvd+8+ZN+vbti6Ojo3Isr54VWJDfVyGEEMVXsZrWz0rlypUxMTEhPDyctWvX0r17dxo3bkzz5s0ZMWIEt27d0qh/7do1hg0bhoODA7a2tnh4eLB69epsl788rf+m6WBPT08+/fRT5XNkZCSTJk3CwcGBhg0b8vHHH/PXX39lOu/evXucO3eODz74gE6dOvHbb78RGxubqd6RI0fw8PDAxsaGnj178vfff9OkSRMCAgKUOmq1msDAQFq0aIG9vT3jxo3j5MmTqFQqwsLClHoqlYpVq1axYMECWrRoQfPmzZXz16xZg4eHBw0aNMDd3Z3169drxBEZGcn48eNxcnLCxsYGNzc3vv7662yXvzy1HR8fj52dHWvWrMl0v+PGjaNXr17K57i4OL788kucnZ1p0KABnp6emZLNdevWUb9+fY3ENEOJEiXeOGK7b98++vTpQ7NmzWjatCn9+/fnwoUL+XbvYWFhqFQqYmNjWb58OSqViv79+wNkOa1/7tw5BgwYgJ2dHY0bN2bixIk8efJEKc/4u7lnzx6mTZuGg4MDPXr0eO39CiGEEHml2I2cvur58+fExsZibm5OZGQk/fr1o3Llyjx//pzt27fTu3dvDh8+jLGxMQAjR46kfPnyzJkzBwMDA+7evUtkZKTS3tvKX2ZhYYGdnR3ff/897du3V47fvn2bv//+mzFjxgDw9OlT+vbtS+nSpfniiy8wNDRk06ZNfPLJJ/z444+Ympoq54aEhADQuXNn4uLiWLt2LYcOHaJ3795KnUuXLjF+/Hhat27N559/zv379/H29iY5OVkjvk2bNhEYGMjQoUNxdHTk9OnTTJs2Lct72bhxI7a2tsyZM4fU1FQA5syZw86dOxk5ciS2tracPXuWhQsXUqpUKfr06QPAlClTePToEdOmTcPU1JSIiAiNpPtt5S8rXbo0bm5uHDx4kCFDhmh8j3/55RcmT54MQHJyMoMGDeLJkydMmDCBChUqsH//fkaMGMGePXtQqVRERkZy7949Ro4cmeW13iY8PJyuXbtSrVo1kpOTOXjwIB9//DH79+/H0tIyz++9fv36BAcHM2jQIDp27EiPHj0wMDDIsu65c+fo378/LVu2ZPHixSQkJLBkyRK8vLwIDg7WqLto0SJatmyJv78/6enp79QXQgghRE4Uy+Q0I3mKjIxk3rx5pKWl4eHhgYuLi1InLS1NGQU8fPgwvXr1Ijo6mvDwcKZOnYqbmxuAxvTp28qz0qlTJxYuXMjz58+VZCIkJISyZcvi7OwMwIYNG4iLi2Pnzp1KItq8eXM8PDxYs2YNU6ZMUdo7ePAgdnZ2VK1aFYCaNWty4MABjeR05cqVWFhYEBAQgLb2i8HzMmXKaLSTlpbGqlWr8PT0ZNKkSQA4OzsTExPDrl27Mt1H2bJlCQwMVN6DfPfuXTZv3szMmTOVEUsnJycSExMJCgqiV69eaGtrc/HiRT799FONpQddu3ZV/vy28qz608vLi9u3b1OjRg0Ajh49SmpqKh06dADgwIEDXLlyhe+++47atWsD4OLiwp07d1i+fDlLly5VNjtVqlTptdd6k4z/WACkp6fTokULLly4wN69e5UR8by8dwMDA+zs7ChRogQVK1bUWLryKn9/fxo0aKDx/apbty6dO3cmNDRUY0S4Xr16zJkzJye3LoQQQuRKsZvWj4+Pp379+tSvXx93d3fCwsKYPn06Li4unD9/nkGDBuHg4IC1tTW2trbEx8dz+/ZtAMqVK0eVKlVYtGgRe/fuzTQi+rbyrHTo0IGUlBSOHj2qHPv+++9p164durq6AJw4cQIHBwfKli2rbHTS1tamadOmXLx4UTnvypUrXLt2TeNpAJ06deLPP//kwYMHyrGLFy/SqlUrJTEFcHd314grMjKSqKgoJcl+Xb0Mrq6uSqIDcPLkSQDatWunxJyamoqTkxNRUVFEREQAL9ZDrl27lq1bt3Lnzp1M7b6t/FUuLi4YGRlx8OBB5djBgwdxcHCgfPnywIv+rFu3LjVq1MgU28v9CWjcU07cuHGD0aNH4+TkhJWVFfXr1+fWrVvK36X8uPfsSEhI4OzZs7Rv3560tDTl3mvUqEGlSpUy3X+rVq3y5LpCCCFEdhW75FRPT49du3axe/dufvrpJ06fPs3HH3/MgwcPGDx4MGlpacycOZNt27axa9cuTE1NSUp68T5wLS0t1qxZQ82aNZk1axYtW7bE09OTP/74I1vlWTEzM8PBwUFJpq5cucKNGzc0EsyYmBiOHj2qJNUZX999951GArx//360tbVxdnYmLi6OuLg4WrZsiVqtVqb7AaKiojAxMdGIw8DAgFKlSmnUATLVe3kJwZuOx8TEoFarcXR01Ih50KBBAEpyunjxYhwdHVmyZAnt2rWjffv2/Pjjj0o7byt/la6uLu3ateP7779X4jh58mSm/rx06VKm/vzmm2+U/qxQoQKARlKfXc+fP2fw4ME8ePAAHx8ftmzZwq5du6hXr57ydyk/7j074uLiSEtLw8/PL9P9P3jwQPm+ZHjd91sIIYTIL8VuWl9bWxsbG5tMx3/99Vfi4+M1HruTmprK06dPNepZWlqybNkyUlJSOHfuHIsWLWLkyJEcP36cMmXKvLU8K506dWLmzJnExMRw8OBBzMzMaNasmVJetmxZXFxcGD9+fKZzM0ZX1Wo133//Penp6RrrVzMcOHCA4cOHAy8S4ujoaI3y58+fayROZmZmAJnqvbxp5mWvjjCWLVsWLS0ttm7dio6OTqb6Gesuzc3N8fPzIz09nb/++otvvvkGb29vDh06RNWqVd9anpXOnTuza9curly5wvnz59HW1qZdu3YasalUqjdOV1esWJFq1arx22+/4e3t/dp6WTl//jyRkZGsXLmSevXqKcefPXumsbs+P+79bQwNDdHS0mLEiBG0adMmU3m5cuU0Pr/ryLEQQgjxrordyOnrJCYmoqWlRcmS/5ev//DDD8r61Ffp6OjQrFkzhg8fzvPnz3n06FGOyl+WkTgdPnyYgwcP0rFjR40pdycnJ27cuEGtWrWwsbHR+FKpVACcOXOGiIgIxo4dy8aNGzW+hg0bxj///MPVq1cBsLGx4ZdfftHY4PLysgJ4kZyZmZlx7NgxjeOv1nudjB37sbGxmWK2sbHJtFlHW1ubhg0bMmHCBFJTUzNNY7+t/GXNmjXDzMyMgwcPcvDgQVxdXTE0NNToz3v37mFubp5lbBkGDhzIX3/9xd69ezNdIz09nePHj2d5/YwXH7yclJ89e5b79+9nWT8v7/1tSpcujZ2dHTdv3szy3i0sLN65bSGEECIvFLuR09fJ2Ljk6+tL7969uXbtGuvWrdN4ePmVK1eYN28eHTt2pGrVqjx//pyVK1dSpUoVqlWr9tby18kYGQ0KCuLRo0eZ3iA1cOBADhw4QL9+/RgwYACVK1cmOjqa//3vf1SoUEEpL126NIMGDco0QlunTh3Wr19PSEgIKpWKESNG8NFHHzF27Fh69uzJgwcPWLt2LaVKlVJGykqUKMHw4cP5+uuvKV++PA4ODoSFhXHq1CkAjeQ5K5aWlnz88cdMmTKFIUOGYGtrS0pKCrdv3yYsLIzly5fz7NkzhgwZwocffoilpSUpKSls2rQJIyMjrK2t31r+OiVKlKB9+/bs3buXJ0+esGjRIo3yrl27sn37dgYMGMDgwYOpUaMGz54949KlS6SkpDBx4kQA+vbty59//snUqVM5e/Ys7u7ulC5dmps3b7J9+3aqVKmCq6trpuvb2dlRunRpZs6cyfDhw3n48CEBAQHKUgEg3+49O6ZMmcInn3zChAkT6NSpE0ZGRkRGRnLy5Ek8PT1xcHDIVftCCCFEbkhy+v+pVCr8/PwIDAxkxIgRWFlZsXTpUiZMmKDUMTMzo3z58qxcuZKHDx9iaGhIkyZNWLBgASVKlHhr+Zt07tyZn376iWrVqtGwYUONsnLlyhEcHMySJUtYuHAhsbGxmJqaYmtrS9u2bUlJSeHw4cO0adMmy6UDJiYmtGzZkpCQED799FOsra1ZsmQJ/v7+jBkzhjp16jB37lwGDBigMcLYv39/4uLi2Lp1K5s2baJ58+ZMnjwZb29vjXqvM23aNCwtLQkODiYoKEhZ9pCx7KBUqVLUrVuXTZs2ERERgZ6eHg0aNGDNmjWYmJiQnJz8xvK39eemTZsoXbo0rVu31ijT1dVl48aNBAQEsGLFCqKiojA2Nsba2pq+ffsq9bS0tPD398fZ2ZmdO3dy8OBBkpOTqVKlCm5ubgwePDjLa5cvX56lS5cyf/58vLy8qFGjBjNnzuTbb79V6uTnvb9No0aN2Lp1KwEBAfj6+pKSkkLFihVxdHSkevXquWpbCCGEyC0ttVqtLuwgROE7deoUAwcOZNOmTRrrXV+1ZMkS1q1bR1hYmLwtSGRLxhMANj4qxa3YhEKORgghii5LY30WuOf/q9RfFh8fz+XLl7GysqJ06dL5dp2M3wVZ7ft5lYycFlNffvklzZs3x9jYmOvXr7N8+XKsra1p0qSJUufGjRvs378fe3t7dHR0+P3331mzZg19+vSRxFQIIYQQ+UKS02IqLi6O2bNnExsbi4GBAS4uLnz22Wcaa0n19PQ4d+4c27Zt499//6VChQoMGTKEsWPHFmLkQgghhHifSXJaTL26SSgrVapUYePGjQUQjRBCCCHEC5KcCiEKhIVhqbdXEkKIYkx+Tr4gyakQokCMbyZPAhBCiLdJV6vRLuYvQJGH8Ash8l1ycjIJCbJTPz8lJCRw6dIl6ed8Jv1cMIpzPxf3xBQkORVCFBB5al3+UqvVJCQkSD/nM+nngiH9XLxJciqEEEIIIYoMSU6FEAVCS6aq8pWWlhb6+vrSz/lM+rlgSD8Xb7IhSgiR73R1ddHX1y/sMN5r+vr6WFtbF3YY7z3p54JR3PpZNkFpkuRUCFEgNv7xhMhnKYUdhhBCFCkVDXUY0NS0sMMoUiQ5FUIUiMhnKYQ/leRUCCHEm8maUyGEEEIIUWRIciqEEEIIIYoMSU6FEEIIIUSRIcmpEEIIIYQoMiQ5FcVGQEAAKpVK+XJ0dGTAgAGcOXOmsEPLsaNHj7Jly5bCDkMIIYTIc5KcimJFT0+P4OBggoOD+fLLL4mNjWXgwIH8888/hR1ajhw9epRt27YVdhhCCCFEnpPkVBQr2tra2NnZYWdnR/v27VmxYgWpqals3779ndtMTEzMwwjzllqtJjk5ubDDEEIIIbJNklNRrFWuXBkTExPCw8MB2LNnDx988AE2Nja4uLiwePFi0tLSlPp79uxBpVJx7tw5Bg0ahJ2dHfPnzwfg4cOHTJkyBScnJxo2bEj79u3ZsGGDxvWy2/758+cZMGAAtra2uLm5sWvXLqWOj48Pe/fu5dq1a8oSBR8fH6Wsc+fOhIaG0qVLF2xsbPjpp58A+PHHH/nwww+xsbHB2dkZPz8/kpKSlHbDwsJQqVScOHGCiRMnYm9vT+vWrVm9enUe97oQQgjxevIQflGsPX/+nNjYWMzNzVm3bh0LFizgk08+wcfHhxs3bijJ46RJkzTOmzhxIr169WLEiBHo6+sTExNDr169APD29sbCwoI7d+5w9+5d5ZyctP/pp5/Sq1cvhg0bxvfff8/UqVMxNzfH1dUVLy8voqOjuXnzJgsXLgTAxMREOffRo0d89dVXjBo1ikqVKlG5cmWOHTvGuHHj6NSpExMnTuTmzZssXryYiIgIli1bpnHtGTNm8OGHHxIUFMTRo0dZuHAhKpUKV1fXPO17IYQQIiuSnIpiJzU1FYDIyEjmzZtHWloarVq1YvLkyQwdOpRPP/0UgBYtWqCjo8PcuXMZMmQI5cqVU9ro3bs3w4cPVz4vXryYJ0+e8MMPP2BhYQFA8+bNlfLnz5+zbNmybLf/4YcfMmLECABcXFy4d+8eQUFBuLq6Uq1aNUxMTHjw4AF2dnaZ7u/p06esXr0aW1tb5diECROws7PD398fAFdXV/T19Zk+fTpXr15FpVIpddu1a8fYsWOVe/jll184fPiwJKdCCCEKhEzri2IlPj6e+vXrU79+fdzd3QkLC2P69OmUKlWK+Ph42rdvT2pqqvLl5OREYmIi165d02inVatWGp9PnTqFo6Ojkpi+6ty5czlqv23bthqf27Vrx99//62xBOB1jI2NNRLTf//9l8uXL+Ph4aFRr2PHjgD8+eefGsednZ2VP2tpaVGrVi0iIyPfel0hhBAiL8jIqShW9PT02Lx5M1paWpQrV45KlSqhra3N/v37AejWrVuW50VERGh8Ll++vMbn2NhY6tSp89rrxsTE5Kh9U1PTTNdLSUkhJiYm07Vf9Wr5s2fPUKvVmdo0NDREV1eXp0+fZjr+Mh0dHZ49e/bGawohhBB5RZJTUaxoa2tjY2OT6XjZsmUBCAwMpGLFipnKXzcimsHY2JhHjx69tjyn7T958oQKFSoonx8/foyOjo7G1P/raGlpaXw2NDRES0uL6OhojePPnj0jOTlZiU0IIYQoCiQ5FQKwt7dHX1+fyMjITFPq2dG8eXPWrl3LgwcPqFy5cq7bP3LkCNbW1srnH3/8kfr161OiRAngxWjmyzvt36RMmTJYWVlx6NAhBg4cqBz/4YcfAGjcuHG22hFCCCEKgiSnQgBGRkaMGzeOBQsWEBkZSbNmzShRogT37t3j2LFjBAQEoK+v/9rzBw4cyHfffUe/fv0YNWoUVatW5d69e9y+fZvJkyfnuP3vvvsOPT09rK2t+f777/njjz9YtWqVUl6rVi12795NSEgI1atXp1y5cm8c3R0zZgyjR49m0qRJdOnShVu3brF48WI8PDw0NkMJIYQQhU2SUyH+v8GDB1OhQgXWrVvH5s2bKVmyJNWqVaNVq1bo6Oi88dxy5cqxbds2/P39WbhwIQkJCVSpUoW+ffu+U/v+/v4sWrSIoKAgTE1NmT17Ni1btlTKP/roIy5cuMDs2bOJjY2lW7duzJ0797Xxubu7s3TpUoKCgvDy8sLY2JiePXsyceLEd+wtIYQQIn9oqdVqdWEHIYR4Yc+ePfj6+nLq1CmNZ5f+l128eBGAH6LMCH+aUsjRCCFE0WJRVocpbpn3IhSU+Ph4Ll++jJWVFaVLl86362T8Lshq38er5FFSQgghhBCiyJDkVAghhBBCFBmy5lSIIsTT0xNPT8/CDiNfVDR887pdIYQojuRnY2aSnAohCsSApqZvrySEEMVQulqN9ivPqC7OZFpfCJHvkpOTSUhIKOww3msJCQlcunRJ+jmfST8XjOLWz5KYapLkVAhRIOTBIPlLrVaTkJAg/ZzPpJ8LhvRz8SbJqRBCCCGEKDIkORVCCCGEEEWGJKdCiAKhJWuq8pWWlhb6+vrSz/lM+rlgSD8Xb7JbXwiR73R1ddHX1y/sMN5r+vr6WFtbF3YY7z3p54JRHPpZdui/niSnQogCceyPp8Q8Sy3sMIQQotCVMyyJe9OyhR1GkSXJqRCiQMQ8S+VxrCSnQggh3kzWnAohhBBCiCJDklMhhBBCCFFkSHIqhBBCCCGKDElOBQBdunRBpVJx5syZbNVv0qQJAQEBObpGWFgYKpVK+bK2tqZ169bMmDGDmJiYdwk71+Li4ggICOD69etZlkdHRzN37lw8PDywsbGhUaNG9OvXj507d5KWlgZAQEAA9vb2BRk2AG5ubsyaNUvj2Pr162nVqhVWVlZ4eXkpfX7x4sUCj08IIYR4F7IhSnDt2jWuXr0KwIEDB2jSpEm+Xs/Pz4+aNWuSmprK9evXWbx4MeHh4axZsyZfr5uVuLg4AgMDqVOnDrVr19You3PnDgMGDCAtLY1BgwZRv359kpOTOX36NH5+fpQrV442bdoUeMwZAgMDMTIyUj7fvn2buXPnMmzYMFq3bk25cuUwMzMjODiYWrVqFVqcQgghRE5Icio4cOAA2traNG3alEOHDjFt2jR0dHTy7Xp16tTBxsYGeDECm5SUhJ+fH//++y9lypTJt+vm1KRJk0hLS2P37t1UqFBBOe7q6kq/fv149uxZIUZHpmcA3rp1C7VaTc+ePalatapy3M7OLk+ul5iYiJ6eXp60JYQQQryOTOsXc2q1mpCQEBwdHRk0aBCxsbH8+uuvGnWOHj1K+/btsbGx4aOPPuLChQuZ2vnll18YNGgQzZs3p1GjRvTo0YPjx49nK4YyZcqgVqtJT09XjqWnp7N8+XLc3Nxo0KAB7du3Z/v27ZnO/eOPP+jduzcNGzbEwcEBX19fYmNjNeqsWrWKtm3bYmNjg6OjIwMHDuTevXuEh4fj7u4OwPjx45XlBuHh4Zw5c4YLFy4wYsQIjcQ0Q+XKlVGpVFneT3x8PLNmzcLDwwNbW1vc3NyYPn16pmT22LFjeHp6Ym9vT5MmTfD09CQ0NDTb5S9P6/v4+DBy5EgA2rRpg0qlYs+ePVlO66vVatasWYOHhwcNGjTA3d2d9evXa8SWsVThwoUL9OrVCxsbG7Zs2ZLl/QohhBB5SUZOi7mzZ89y//59Ro8ejbOzM8bGxoSEhODm5gbA5cuXGTduHK6urvj6+hIeHs6ECRNITk7WaCc8PJzWrVszePBgtLW1OX78OMOHD2fDhg04ODho1E1PTyc1NZW0tDSuXbvG2rVrcXJywtDQUKkzf/58Nm7cyKhRo7C3t+eXX35hxowZpKam0q9fPwD++usvBg0ahIODA0uXLuXx48f4+/tz/fp1tm/fTokSJdi3bx9Lly5l3Lhx2NnZ8ezZM/7880/+/fdfatasSWBgIGPGjOHTTz9V4jQ3N2f//v0AuLi45LhPExMTSUtLw9vbGxMTEyIiIlixYgVeXl5s2rQJgLt37zJ+/Hg6derExIkTSU9P58qVKzx9+jRb5a/y8vKiVq1aLFy4kMDAQMzMzKhWrRrXrl3LVHfOnDns3LmTkSNHYmtry9mzZ1m4cCGlSpWiT58+Sr2UlBQmTpzIwIED8fb2xtjYOMd9IYQQQuSUJKfFXEhICKVKlaJdu3bo6Ojg4eHB/v37lSn2VatWUalSJYKCgihRogQApUqVYurUqRrtZCSM8CL5dHBw4Pr16+zYsSNTctqzZ0+NzyqVinnz5imfo6Oj2bx5M0OGDGHs2LEAODs7ExMTQ1BQEH369KFEiRKsWLECMzMzVqxYoSxDqFSpEkOGDCE0NBQ3NzcuXLiASqVixIgRSvsvrxO1srICoHr16hrT3w8fPgRejJDmlImJCTNnzlQ+p6amYmFhQd++fbl16xaWlpZcunSJlJQUvvjiCwwMDADNRPht5a+qVq0alpaWyj1ZWFhkWe/u3bts3ryZmTNn0qtXLwCcnJxITEwkKCiIXr16oa39YkIlJSUFb29vOnbsmOM+EEIIId6VTOsXY6mpqRw6dIiWLVsqo5YffPABCQkJHDlyBID//e9/tG7dWklMAdq3b5+prcjISD777DNcXFywtramfv36/Pbbb9y6dStT3Xnz5rFr1y527tzJokWLSElJYejQofz7778AXLhwgZSUlEzX6dChA9HR0dy+fRuAM2fO4O7urrE+1tnZGSMjI/7880/gxbrMS5cu4efnx5kzZ0hJSclFj2Xfvn376Nq1K/b29tSvX5++ffsCKLGrVCpKlCjBpEmT+OmnnzJN+b+t/F2dPHkSgHbt2pGamqp8OTk5ERUVRUREhEb9li1b5sl1hRBCiOySkdNi7MSJE0RHR9O6dWvi4uIAqFu3LmZmZoSEhNC1a1eioqIwNTXVOM/AwIBSpUopn9PT0xk1ahTPnj1j3LhxVK9eHX19fZYtW5Yp2QGoVauWsiGqYcOG1KhRA09PT/bu3Uu/fv2Uqevy5ctrnJfxOWNNaVxcXKbYAExNTZU2PD09+ffff9mxYwfr16/H0NCQrl27MmnSpDdu7slYZxoREUH16tVf34lZOHLkCJ999hm9evVSpsOjoqIYPXo0SUlJAFhaWrJixQpWrlzJmDFj0NbWxtnZmenTp1O5cuW3lr+rmJgY1Go1jo6OWZZHRERQpUoVAPT19YvUBjUhhBDFgySnxdiBAwcA8PX1xdfXV6MsJiaGJ0+eYGZmxpMnTzTKnj9/riRZ8OKRS5cuXSIoKEhjyjwxMTFbcWQ85ihjfWTG2sYnT55obEZ6/PixRnnZsmUzxZZxXtmyZQHQ1tbmk08+4ZNPPuHhw4ccPHgQf39/ypUrx+jRo18bU7NmzQD47bffcpycHjp0CCsrK41nkP7++++Z6rm6uuLq6srz5885fvw4fn5++Pr6smHDhmyVv4uyZcuipaXF1q1bs3wiQ8bSAAAtLa13vo4QQgjxrmRav5hKSEjg2LFjtGnTho0bN2p8LVq0iNTUVL7//nsaNmzIzz//rDxwHl4kXy/LSFRfTnbu37/PuXPnshXLP//8A0C5cuUAsLGxQUdHJ9N1fvjhB0xNTalRowYAjRs35tixY6Smpip1Tpw4QVxcHI0bN850nQoVKjB48GBUKhU3b97UiPnlZBtePOKqYcOGrFixgkePHmVqKyIiQnk27KsSExMzJX4Z/xHIioGBAR07dqRTp07cuHEjx+U50bx5c+DF6LONjU2mr4z1rUIIIURhkZHTYurYsWPEx8fTv3//TBuWAL799ltCQkKYMWMGH330EaNHj6ZPnz7Kw/JfntavWbMmFStWxN/fn/T0dOLj41m2bBnm5uZZXvvatWukpaWRnp7OvXv3WL58Ofr6+nTt2hV4saGoX79+rFmzBl1dXezs7AgNDSUkJIQvvvhCWf86cuRIevfuzYgRI+jfv7+yW79hw4bKWsnp06djZGSEnZ0dRkZGnD17litXrii70s3MzDAyMuLgwYNYWFigq6uLSqVCV1eXhQsX0r9/f7p3767xEP4//viDLVu2MG/evCwfJ+Xk5MSsWbMICgrC3t6e0NBQTp06pVFn+/btnD9/HhcXF8zMzAgPD2f//v20aNEiW+XvytLSko8//pgpU6YwZMgQbG1tSUlJ4fbt24SFhbF8+fJctS+EEELkliSnxVRISAiVK1fOMjEF6Nq1K19//TUGBgYsXbqUhQsXMmbMGOrUqcPixYsZMmSIUldXV5eAgABmzZrF+PHjqVSpEqNGjeL06dP89ddfmdrOWEKgpaVF+fLlsbGxYenSpcqIKMCUKVMwNDRk165drFixgipVqjBz5kx69+6t1GnQoAFr165l0aJFjB07ltKlS+Pm5sZnn32mJLD29vbs2LGDnTt3kpCQQNWqVfH19aVHjx7Ai2l/Pz8/Fi1axMCBA0lOTubYsWNYWFhQvXp19u7dy+rVq9m2bRsRERHo6upibW3N559/TuvWrbPsu969exMeHs7mzZtZs2YNzs7O+Pv7azylQKVS8fPPP+Pn50dsbCxmZmZ06tSJ8ePHZ6s8N6ZNm4alpSXBwcEEBQVRpkwZLC0ts9zoJoQQQhQ0LbVarS7sIIQQ76+MFwBcjarM49jUt9QWQoj3X3njknzklnlDb2GIj4/n8uXLWFlZUbp06Xy7TsbvgowN0W8ia06FEEIIIUSRIcmpEEIIIYQoMiQ5FUIIIYQQRYZsiBJCFIhyhvLjRgghQH4evo30jhCiQLg3LVvYIQghRJGRrlajLS87yZJM6wsh8l1ycjIJCQmFHcZ7LSEhgUuXLkk/5zPp54JRHPpZEtPXk+RUCFEg5Kl1+UutVpOQkCD9nM+knwuG9HPxJsmpEEIIIYQoMiQ5FUIUCC2ZwspXWlpa6OvrSz/nM+nngiH9XLzJhighRL7T1dVFX1+/sMN4r+nr62NtbV3YYbz3pJ8LRlHtZ7VaLQlzAZDkVAhRIP4Me8bzOHl9qRDiv8nAqCSNHQwLO4xiQZJTIUSBeB6XytPYtMIOQwghRBEna06FEEIIIUSRIcmpEEIIIYQoMiQ5FUIIIYQQRYYkp0IIIYQQosiQ5LSYCwgIQKVSZfm1atWqHLVz9uzZPIvLx8fntXFlfPXv3z/Prvcm0dHRzJ07Fw8PD2xsbGjUqBH9+vVj586dpKW92OATEBCAvb19gcTzMjc3N2bNmqVxbP369bRq1QorKyu8vLwICwtDpVJx8eLFAo9PCCGEyCnZrS/Q09Njw4YNmY5XqlQp220EBgZSunRpGjVqlCcxeXl50bt3b+Xz8uXLuXnzJgsXLlSOGRgY5Mm13uTOnTsMGDCAtLQ0Bg0aRP369UlOTub06dP4+flRrlw52rRpk+9xvE5gYCBGRkbK59u3bzN37lyGDRtG69atKVeuHGZmZgQHB1OrVq1Ci1MIIYTILklOBdra2tjZ2RXItRITE9HT03trvWrVqlGtWjXls4mJCQ8ePHhjnNltOycmTZpEWloau3fvpkKFCspxV1dX+vXrx7Nnz/L0ejn16kOqb926hVqtpmfPnlStWlU5nlff3/zoYyGEEOJlMq0v3ujo0aOoVCp+/vln5VhsbCwuLi58+umnAKhUKgDmz5+vTLmHhYUpZatWrWLBggW0aNGC5s2bA3Du3DlGjhyJs7MzdnZ2fPjhh+zbty/bcYWHh6NSqdizZw/Tpk3DwcGBHj16AJCcnMyiRYto3bo1DRo0oEOHDhw4cCBTG+fOnWPAgAHY2dnRuHFjJk6cyJMnT5TyM2fOcOHCBUaMGKGRmGaoXLmycu+vio+PZ9asWXh4eGBra4ubmxvTp0/PlMweO3YMT09P7O3tadKkCZ6enoSGhma7/OVpfR8fH0aOHAlAmzZtlP7JalpfrVazZs0aPDw8aNCgAe7u7qxfv14jtoylChcuXKBXr17Y2NiwZcuWLO9XCCGEyCsycioASE3N/OaekiVL0qZNG7p27cq0adM4cOAAJiYmzJw5E4AZM2YAEBwcTK9evejfvz+dO3cGoHbt2ko7GzduxNbWljlz5ijXefDgAY0aNaJPnz7o6upy9uxZpk2bhlqtplu3btmOe9GiRbRs2RJ/f3/S09MBGD9+PGfPnmX06NHUqlWL0NBQJk+ejJGRES1btgReJKb9+/enZcuWLF68mISEBJYsWYKXlxfBwcEA/P777wC4uLjkqC/hxQhjWloa3t7emJiYEBERwYoVK/Dy8mLTpk0A3L17l/Hjx9OpUycmTpxIeno6V65c4enTp9kqf5WXlxe1atVi4cKFBAYGYmZmRrVq1bh27VqmunPmzGHnzp2MHDkSW1tbzp49y8KFCylVqhR9+vRR6qWkpDBx4kQGDhyIt7c3xsbGOe4LIYQQIickORXEx8dTv379TMe3bNlCkyZNmDZtGh988AHTp0+nffv2fP/993z77beULVsW+L8p40qVKmU5fVy2bFkCAwM13kfcqVMn5c9qtZqmTZvy8OFDgoODc5Sc1qtXjzlz5iifT58+zU8//cSaNWtwdnYGoEWLFkRFRREQEKAkp/7+/jRo0EAjrrp169K5c2dCQ0Np2bIlDx8+BF6MkObUy0k8vEj+LSws6Nu3L7du3cLS0pJLly6RkpLCF198oayffTkRflv5q6pVq4alpSUAVlZWWFhYZFnv7t27bN68mZkzZ9KrVy8AnJycSExMJCgoiF69eqGt/WJSJSUlBW9vbzp27JjjPhBCCCHehSSnAj09PTZv3pzpeM2aNQEwNDTEz8+PQYMGERoaSp8+fXI0mujq6qqRmAI8ffqUgIAAjh07xsOHD5Vd7zkdmWvVqpXG5xMnTmBsbIyjo6PGaLCTkxNffvklaWlpJCcnc/bsWaZMmaJcF6BGjRpUqlSJixcvKklsbuzbt4/169dz584d4uPjleO3b9/G0tISlUpFiRIlmDRpEj179qRp06YYGv7fe5vfVv6uTp48CUC7du0y9dHq1auJiIigSpUqyvG86AshhBAiuyQ5FWhra2NjY/PGOo0bN6Zy5crcv3+ffv365ah9U1PTTMd8fHw4d+4co0ePpnbt2hgYGLBt2zZ++OGHXLUdExNDbGxsliPBAFFRUWhpaZGWloafnx9+fn6Z6kRERAAo60wjIiKoXr16juI6cuQIn332Gb169VKmw6Oiohg9ejRJSUkAWFpasmLFClauXMmYMWPQ1tbG2dmZ6dOnU7ly5beWv6uYmBjUajWOjo5Zlr+cnOrr61OmTJl3vpYQQgiRU5KcimxZtmwZMTEx1KhRg5kzZ7Jx48ZMo6Gv82q9pKQkfvnlF3x8fDSeVbp169Ycx/Vq22XLlsXExOS1z2g1MTEhNTUVLS0tRowYkeVjoMqVKwdAs2bNAPjtt99ynJweOnQIKysrjWeQZqxhfZmrqyuurq48f/6c48eP4+fnh6+vr/Jor7eVv4uyZcuipaXF1q1b0dHRyVSesTQAMvevEEIIkd8kORVvdfbsWdasWcOXX36JtbU1vXv3ZsOGDQwcOFCpo6Ojo4wIvk1ycjLp6ekaidHz58/56aefch2rk5MT3377LTo6OtSrVy/LOrq6utjZ2XHz5s03jhg3adKEhg0bsmLFCtq2bYu5ublGeUREBHFxcVnu2E9MTMyU+GX1xIAMBgYGdOzYkQsXLhASEpLj8pzIeGJCbGwsbm5uuWpLCCGEyGuSnArS09M5f/58puOmpqaYmpry2Wef4ezsrGyeGTlyJIsWLcLFxUV5sHvNmjU5duwYTZo0QV9fH0tLy9c+JN/Q0BAbGxtWr16NiYkJJUuWZNWqVRgYGBAdHZ2re2nRogWtW7dm6NChDB06FJVKRUJCAtevX+fOnTvK5qkpU6bwySefMGHCBDp16oSRkRGRkZGcPHkST09PHBwcAFi4cCH9+/ene/fuGg/h/+OPP9iyZQvz5s3LMjl1cnJi1qxZBAUFYW9vT2hoKKdOndKos337ds6fP4+LiwtmZmaEh4ezf/9+WrRoka3yd2VpacnHH3/MlClTGDJkCLa2tqSkpHD79m3CwsJYvnx5rtoXQgghckOSU0FiYqKSeL7so48+okSJEsTFxWnsiB81ahS//PILU6ZMITg4mJIlSzJ9+nS+/vprhg0bRmJiIhs3blQSvKz4+/szffp0fHx8MDY2pn///sTHx7N27dpc38+yZctYtWoV27Zt4/79+xgaGlKnTh08PT2VOo0aNWLr1q0EBATg6+tLSkoKFStWxNHRUWMKv3r16uzdu5fVq1ezbds2IiIi0NXVxdrams8//5zWrVtnGUPv3r0JDw9n8+bNypMD/P396dmzp1In4/mxfn5+xMbGYmZmRqdOnRg/fny2ynNj2rRpWFpaEhwcTFBQEGXKlMHS0pL27dvnum0hhBAiN7TUarW6sIMQQry/Mh7+Hx1pwdPYtLfUFkKIoqmscQlati1X2GHkufj4eC5fvoyVlRWlS5fOt+tk/C542wZskDdECSGEEEKIIkSSUyGEEEIIUWTImlMhRIEwMJIfN0KI/y75GVZwpKeFEAWisUPu324lhBCFSa1Wy/OfC4BM6wsh8l1ycjIJCQmFHcZ7LSEhgUuXLkk/5zPp54JRVPtZEtOCIcmpEKJAyINB8pdarSYhIUH6OZ9JPxcM6efiTZJTIYQQQghRZEhyKoQQQgghigxJToUQBULWauUvLS0t9PX1pZ/zmfRzwZB+Lt5kt74QIt/p6uqir69f2GG81/T19bG2ti7sMN570s8FozD6WZ2uRktbkuGiQJJTIUSB+OfXOOKfyutLhRBFT+myJajrYlTYYYj/T5JTIUSBiH+axr/RqYUdhhBCiCJO1pwKIYQQQogiQ5JTIYQQQghRZEhyKoQQQgghigxJTsV7JyAgAJVKpXzZ2NjQoUMHVq9eTXp6emGH987c3NyYNWuW8tnHx4fOnTsrn/fs2YNKpSI6Olo5ltEHp06d0mgrLi4OlUrFnj17NNp7ud/s7Ozo0qULO3fuzMe7EkIIITTJhijxXtLT02PDhg0AJCYmEhYWhr+/P2q1muHDhxdydPmjVatWBAcHY2SUecfp8uXLad68+VvbqFq1KgsXLgTg33//5ciRI0ybNo3SpUvTqVOnPI9ZCCGEeJUkp+K9pK2tjZ2dnfLZ0dGRf/75hx9//DFXyalarSYlJQVdXd08iDJvmZiYYGJikum4g4MDYWFhnDlzhiZNmryxDT09PY1+a9GiBefPn+fHH3+U5FQIIUSBkGl9UWyUKVOG1NT/e5RRcnIyixYtonXr1jRo0IAOHTpw4MABjXMyps5DQ0Pp0qULNjY2/PTTTwQEBGBvb8/Vq1fp06cPtra2dO7cmV9//VXj/PT0dJYvX46bmxsNGjSgffv2bN++PctrvCyrafe3yWpaH6Bly5bUr1+foKCgbLf1slf7TQghhMhPkpyK91Zqaiqpqak8f/6cY8eO8eOPP+Lh4aGUjx8/nuDgYAYNGsTKlStxcXFh8uTJhIaGarTz6NEjvvrqKwYOHMjq1auxsrICICUlhUmTJuHp6UlgYCAmJiaMGzeOmJgY5dz58+cTGBhIt27dWLFiBc7OzsyYMYPNmzcXTCf8f15eXpw8eZLz58+/tW5Gv8XFxbFr1y7Onj2r0W9CCCFEfpJpffFeio+Pp379+hrHOnbsqEzpnz59mp9++ok1a9bg7OwMvJjCjoqKIiAggJYtWyrnPX36lNWrV2Nra6vRXkZymlHX0tISd3d3jh8/zocffkh0dDSbN29myJAhjB07FgBnZ2diYmIICgqiT58+lChRIt/64GXu7u6oVCqCgoJYvXr1a+tdu3YtU78NHjyYLl265HeIQgghBCAjp+I9paenx65du9i1axdbt25l6tSp/Prrr0ybNg2AEydOYGxsjKOjozJSmJqaipOTE5cvXyYt7f9es2lsbJwpMYUX61pf3mRkYWGBnp4eDx8+BODChQukpKTQvn17jfM6dOhAdHQ0t2/fzoc7z5qWlhajRo3i+PHjXLx48bX1qlWrpvTb5s2bmTBhAps2bSIwMLDAYhVCCFG8ycipeC9pa2tjY2OjfG7cuDFpaWnMnTuXQYMGERMTQ2xsbKZRwgxRUVFUrFgRgPLly2dZR09PL9PGKB0dHZKSkoAXI65ZnZ/xOTY2Nuc3lgseHh7Url2b5cuXM2/evCzrlCpVSqPfmjZtypMnT1ixYgX9+vXD2Ni4gKIVQghRXElyKoqNmjVrAnD9+nXKli2LiYkJq1atyrLuy7vetbS03ul6GYnckydPqFChgnL88ePHGuW6urqkpKRonJuR2OYlbW1tRo4cyeTJk7ly5Uq2z6tZsyYpKSncuXNHklMhhBD5Tqb1RbFx7do1AMqVK4eTkxPR0dHo6OhgY2OT6SsvHhVlY2ODjo4Ohw4d0jj+ww8/YGpqSo0aNQCoWLEikZGR/Pvvv0qdEydO5Pr6WenYsSPVq1fP0c79l/tNCCGEyG8ycireS+np6crO9JSUFP7++2+++eYbateuTZMmTdDR0aF169YMHTqUoUOHolKpSEhI4Pr169y5c4c5c+bkOgYTExP69evHmjVr0NXVxc7OjtDQUEJCQvjiiy+UzVDt2rVj2bJlfP755/Ts2ZNr166xa9euXF8/KyVKlGDEiBH4+vpmWZ6YmKj0W2JiImfOnGHnzp20aNGCatWq5UtMQgghxMskORXvpcTERHr16gVAyZIlqVixIl26dGHMmDHo6OgAsGzZMlatWsW2bdu4f/8+hoaG1KlTB09PzzyLY8qUKRgaGrJr1y5WrFhBlSpVmDlzJr1791bq1K5dm7lz57J8+XK8vLxo3LgxCxcu5MMPP8yzOF7WpUsXgoKCCA8Pz1R27949pd90dHSoUqUKQ4YMYdiwYfkSixBCCPEqLbVarS7sIIQQ76+MpwOk3bHg32h5mL8QougpY1ISu87Fc+lSfHw8ly9fxsrKitKlS+fbdTJ+F7y86fZ1ZM2pEEIIIYQoMiQ5FUIIIYQQRYYkp0IIIYQQosiQDVFCiAJRumzBvKpVCCFySn4+FS2SnAohCkRdF6PCDkEIIV5Lna5GS/vdXroi8pZM6wsh8l1ycjIJCQmFHcZ7LSEhgUuXLkk/5zPp54JRGP0siWnRIcmpEKJAyFPr8pdarSYhIUH6OZ9JPxcM6efiTZJTIYQQQghRZEhyKoQQQgghigxJToUQBUJLS9Zz5SctLS309fWln/OZ9HPBkH4u3mS3vhAi3+nq6qKvr1/YYbzX9PX1sba2Luww3nvSzwUjo5/V6bLmtDiS5FQIUSDCjz4lOSa1sMMQQvxH6JYriUWbsoUdhigEkpwKIQpEckwqiY8lORVCCPFmsuZUCCGEEEIUGZKcCiGEEEKIIkOSUyGEEEIIUWRIciryTUBAAPb29rlqo3///owYMSLf4rl+/Trjxo3D1dUVGxsbXF1dGTFiBKGhoXlyzYJw9OhRVCoV4eHhAISHh6NSqTh06FAhRyaEEELknGyIEsXW3bt36dGjByqVCh8fH0xMTLh//z6hoaH8/vvvtGzZsrBDfCfm5uYEBwdTo0aNwg5FCCGEyDFJTkWxtXv3bgDWrVun8QzO7t27k56enuv209LSSE9PR0dHJ9dt5YSuri52dnYFek0hhBAir8i0vig0Cxcu5IMPPsDe3h4XFxc+/fRTHj16lGXdffv20aZNGxo2bEj//v25efOmRrlarWbNmjV4eHjQoEED3N3dWb9+/RuvHxcXh4GBQZYPh9fW/r9/GhnLAS5cuMBHH32EjY0NHTp04Oeff9Y4J2MJwt69e/Hw8MDGxoYrV64A8Msvv9CjRw8aNmyIo6MjM2bMID4+Xjk3Pj6eWbNm4eHhga2tLW5ubkyfPp1nz55pXCMlJYU5c+bQrFkzGjduzOeff86///6rUSeraX03NzdmzZrFli1baN26NY0bN8bLy4vo6GiNc69du8bHH3+MjY0N7dq1Y//+/Xh5edG/f/839qUQQgiRV2TkVBSaJ0+eMGLECMzNzYmOjmbdunX079+fgwcPUrLk//3V/Pvvv7l79y4TJ04EYMmSJQwdOpRDhw6hq6sLwJw5c9i5cycjR47E1taWs2fPsnDhQkqVKkWfPn2yvH79+vXZunUr06dPp0+fPqhUKo2k9GUpKSl4e3szePBgLCws2LZtG2PGjGHPnj2oVCql3l9//cX9+/cZP348RkZGVKpUiUOHDuHt7Y2npydjx44lKioKf39/4uLiWLx4MQCJiYmkpaXh7e2NiYkJERERrFixAi8vLzZt2qS0v2jRIrZt28bYsWOxtrbm4MGD+Pv7Z6u/f/rpJ+7cucP06dOJiYnBz8+P2bNna8QwePBgjIyMWLBgAQBBQUHExcVRrVq1bF1DCCGEyC1JTkWh8fPzU/6clpaGvb09rq6unD59GmdnZ6XsyZMnbN68WVlDaW1tTfv27dmzZw+9e/fm7t27bN68mZkzZ9KrVy8AnJycSExMJCgoiF69emWZdHbr1o1Tp04RHBxMcHAwZcqUwdHRke7du+Pu7q5RNyUlhVGjRvHRRx8B4OzsTLt27Vi5ciWLFi1S6j19+pRdu3ZRqVIl4MWI7vz58+nYsSNz5sxR6pmZmTF8+HC8vLyoU6cOJiYmzJw5UylPTU3FwsKCvn37cuvWLSwtLYmNjWXr1q0MGzZM2STm4uJCv379ePjw4Vv7W61W88033ygJ/f3791m5ciXp6eloa2uze/dunjx5wrZt27CwsACgQYMGtGvXTpJTIYQQBUam9UWhCQ0NpXfv3jRu3Bhra2tcXV0BuH37tka9OnXqaGzuqV69OvXq1eN///sfACdPngSgXbt2pKamKl9OTk5ERUURERGR5fVLlCiBv78/ISEhTJw4kSZNmnDixAm8vLxYunRppvpt27bVOLdNmzZKDBnq1q2rJKYAt27d4v79+3To0EEjtmbNmqGtrc1ff/2l1N23bx9du3bF3t6e+vXr07dvX43++Oeff0hMTNSII+O+s6Np06ZKYgpQq1YtUlJSePLkCfBi1Ldu3bpKYgpgYWFBvXr1stW+EEIIkRdk5FQUigsXLuDl5YW7uzvDhg3D1NQULS0tevbsSVJSkkZdU1PTTOebmpoSFRUFQExMDGq1GkdHxyyvFRERQZUqVV4bS506dahTpw7Dhw8nOjqaIUOGsGrVKj755BOMjY0B0NHRoWxZzXc8vxxDhvLly2t8jomJAWD06NGvjQ3gyJEjfPbZZ/Tq1Qtvb2+MjY2Jiopi9OjRSn9kXOvV/nj1mq9jZGSk8TkjUc1o/9GjR5iYmGQ6z8TEJNP3RAghhMgvkpyKQnH06FEMDAxYsmSJMuV+//79LOtmjOy9eixjRK9s2bJoaWmxdevWLHfGW1paZjsuExMTPD09+eqrr7hz546SnKakpPD06VONBPXJkyeYmZlpnK+lpaXxOeP86dOn07Bhw0zXMzc3B+DQoUNYWVkxa9Yspez333/XqJtxrSdPnlChQgXl+OPHj7N9f29ibm7O5cuXMx2Pjo6mTJkyeXINIYQQ4m1kWl8UisTERHR0dDSSuQMHDmRZ99q1a9y5c0f5fOfOHa5cuYKtrS0AzZs3ByA2NhYbG5tMXwYGBlm2+7qkLmMa/dURySNHjih/TktL4+jRo0oMr1OzZk0qVqzIvXv3sowtI8nM6I+XvdofdevWRU9PTyMOgB9//PGNMWRXgwYNuHr1Kvfu3VOOhYeHK08cEEIIIQqCjJyKfJWWlpblm4qsra3ZsGEDs2fPpm3btpw7d47vvvsuyzZMTU0ZOXIk48aNA2Dp0qVUqFABT09P4MXI6Mcff8yUKVMYMmQItra2pKSkcPv2bcLCwli+fHmW7S5fvpzLly/TuXNnateuTVJSEidOnGDr1q20adNGYymAjo4O33zzDUlJScpu/cjISIKCgt54/1paWvj4+DBp0iTi4+Np1aoV+vr6PHjwgNDQULy9vbG0tMTJyYlZs2YRFBSEvb09oaGhnDp1SqMtY2NjevfuzerVq9HT01N269+9e/eNMWRX9+7dWbFiBSNHjmTs2LEABAYGUr58+UwjwkIIIUR+keRU5KukpCTGjx+f6fj8+fOZNGkSmzdvZs+ePTRq1IiVK1fi4eGRqW79+vVp164dCxYsICoqCltbW2bOnKmxuWfatGlYWloSHBxMUFAQZcqUwdLSkvbt2782ti5dupCUlMSmTZt4+PAhJUqUoEqVKkyZMkXZjJRBR0eHRYsWMXPmTP755x8sLCxYtmxZtjYLdejQASMjI1asWKGMhlapUgUXFxdldLZ3796Eh4ezefNm1qxZg7OzM/7+/vTs2VOjrYkTJ5KWlsa3335Leno6bdu2ZeLEiUyZMuWtcbyNnp4ea9euZcaMGUyaNIkKFSrg5eXFvn37MDQ0zHX7QgghRHZoqdVqdWEHIURRFhAQwNq1azl37lxhh1LgYmNjadOmDQMHDmTMmDHv1MbFixcBKHOlMomPU/MyPCHEe0yvfElq9si8IVbkrfj4eC5fvoyVlRWlS5fOt+tk/C6wsbF5a10ZORVCKFatWkX58uWpUqUKUVFRrF27lrS0NLp3717YoQkhhCgmJDkVQii0tbX55ptvlGUOtra2bNiwQePZrUIIIUR+kml9IUS+ypjKKfewGskxMq0vhMge3XIlsWhT9u0VRa7ItL4QotiSXzJCiJxSp6vR0panhRQ38pxTIUS+S05OJiEhobDDeK8lJCRw6dIl6ed8Jv1cMDL6OTEpsbBDEYVAklMhRIGQFUT5S61Wk5CQIP2cz6SfC4b0c/EmyakQQgghhCgyJDkVQgghhBBFhiSnQogCIa9AzV9aWlro6+tLP+cz6eeCIf1cvMlufSFEvtPV1UVfX7+ww3iv6evrY21tXdhhvPeknwtGRj+r02XNaXEkyakQokDEhDwh9Yk851QIkT0lTUtSrrO8vrQ4U0NoaQAAN4RJREFUkuRUCFEgUp+kkvowpbDDEEIIUcTJmlMhhBBCCFFkSHIqhBBCCCGKDElOhRBCCCFEkSHJqXirgIAA7O3tc9VG//79GTFiRL7EEx4ejkqlUr4aNmxIp06d+Pbbb0lJeb/XOB49epQtW7ZkOu7j40Pnzp0LISIhhBAid2RDlHhvfPrppzg4OBAfH8+PP/7IggULePr0KRMnTizs0PLN0aNH+euvv/j44481jnt5eREfH19IUQkhhBDvTpJT8d6oXr06dnZ2ADg5OXHr1i02b9782uQ0MTERPT29Aoyw4FSrVq2wQxBCCCHeiUzri1xbuHAhH3zwAfb29ri4uPDpp5/y6NGjLOvu27ePNm3a0LBhQ/r378/Nmzc1ytVqNWvWrMHDw4MGDRrg7u7O+vXr3ymuBg0aEB8fT3R0NGFhYahUKn755RfGjRtHo0aNGD9+PAD3799n3LhxNG7cGDs7O4YMGcLVq1c12nJzc2PWrFl8++23uLi4YGtry6hRozLdZ3b6Qq1WExgYSIsWLbC3t2fcuHGcPHkSlUpFWFiYUm/t2rV0796dxo0b07x5c0aMGMGtW7eUch8fH/bu3cu1a9eUJQ0+Pj5K2avT+levXmXIkCHY2dnRuHFjxo0bx4MHDzTqqFQqVq9eTUBAAE5OTjg4OODr6yujsEIIIQqMjJyKXHvy5AkjRozA3Nyc6Oho1q1bR//+/Tl48CAlS/7fX7G///6bu3fvKiOZS5YsYejQoRw6dAhdXV0A5syZw86dOxk5ciS2tracPXuWhQsXUqpUKfr06ZOjuMLDw9HV1cXY2Fg59sUXX9ClSxeCgoLQ1tbm+fPn9O/fH21tbWbOnEmpUqX45ptv6NevH/v376dSpUrKuUeOHKFKlSp8+eWXxMXFsXDhQsaOHUtwcHCO+mLTpk0EBgYydOhQHB0dOX36NNOmTcsUf2RkJP369aNy5co8f/6c7du307t3bw4fPoyxsTFeXl5ER0dz8+ZNFi5cCICJiUmWfREREUG/fv2oWrUqCxYsICkpicWLFyv3aWBgoNTdsmULjRs3Zu7cudy+fZv58+djamrKpEmTctT/QgghxLuQ5FTkmp+fn/LntLQ07O3tcXV15fTp0zg7OytlT548YfPmzdSoUQMAa2tr2rdvz549e+jduzd3795l8+bNzJw5k169egEvpucTExMJCgqiV69eaGu/frA/PT2d1NRUEhISOHz4MEeOHKFDhw4a57i5uTF58mTl88aNG3nw4AEHDx6kVq1aADRt2pTWrVuzYcMGZSQS4N9//2X16tUYGhoCULFiRQYOHMivv/6Ki4tLtvoiLS2NVatW4enpqSR7zs7OxMTEsGvXLo37+fzzzzXaatGiBc2bN+fw4cP06tWLatWqYWJiwoMHD5TlDK+zfv16UlNTWbt2rZKsW1lZ0alTJ/bu3Uv//v2VumZmZvj7+wPg6urKpUuXOHz4sCSnQgghCoRM64tcCw0NpXfv3jRu3Bhra2tcXV0BuH37tka9OnXqKIkpvFgjWq9ePf73v/8BcPLkSQDatWtHamqq8uXk5ERUVBQRERFvjMPb25v69evTpEkTpk2bRrt27TKNSLZq1Urj85kzZ6hTp46SmAIYGxvj5OTEn3/+qVHXwcFBSUwBmjdvjrGxsRJ/dvoiMjKSqKgo3NzcNNp2d3fPdD/nz59n0KBBODg4YG1tja2tLfHx8Zn6NTvOnDmDg4ODxihyrVq1qFevXqb7dHJy0vhcq1YtIiMjc3xNIYQQ4l3IyKnIlQsXLuDl5YW7uzvDhg3D1NQULS0tevbsSVJSkkZdU9PM70g2NTUlKioKgJiYGNRqNY6OjlleKyIigipVqrw2lkmTJuHo6Ii+vj5VqlRBX18/y+u9LC4ujvLly2dZ79q1a2+N38TERIk/O32RUffV6fdX237w4AGDBw+mQYMGzJw5E3Nzc3R0dBgxYkSmfs2OuLg4rKyssrzPp0+fahwzMjLS+Kyjo0NycnKOrymEEEK8C0lORa4cPXoUAwMDlixZokyf379/P8u6T548yfJYvXr1AChbtixaWlps3boVHR2dTHUtLS3fGEvVqlX/X3t3HhZV+f+P/znDJusoCog7aYyA7CICKq4RqR/DRJPFNdnc0nDLXbJwR0ZNNNzNcMG1orRSUwMzrUzEEH2ruCuyyS7z/aMf5+cIsg8M+HxcF9fl3Pd9znnNC5SX97nvM7C2ti53jEgkUngtkUgUNhm9HJdEIqkw/rS0NBgZGQGoXC5KxqalpZV77l9//RU5OTlYt26dUCwWFRWVKiQrSyKRvDb/L89mExER1Tfe1qcaycvLg4aGhkLRd/To0TLHJicn49atW8LrW7duISkpCba2tgD+u00OAOnp6bC2ti719fKmndri6OiIf//9V+GpARkZGTh37hwcHR0VxiYkJCArK0t4/dtvvyE9PV2IvzK5aNmyJYyMjPDTTz8ptJ84cULhdV5eHkQikcKGsu+//x5FRUUK4zQ0NCo1k+ro6Ij4+HiF4vbGjRu4du1aqfdJRERUnzhzSpXy4sULxMXFlWq3tLTE9u3bERYWhgEDBuDSpUs4fPhwmedo3rw5goKCMGXKFADA2rVrYWJigqFDhwL4b2bU19cXM2fOxPjx42Fra4vCwkL873//Q0JCAjZs2FDr72vo0KHYtm0bAgMD8fHHHwu79dXV1TF69GiFsbq6upgwYQImTJiArKwsrFy5EjY2NsJmKDc3twpzoaamhoCAAHz++edo0aIFnJ2dkZCQgN9++w0AhBnXkqUNc+bMwYcffojk5GRs3bq11C33jh074sCBAzh27Bjat2+PZs2aoU2bNqXe55gxYxAbG4tx48YhODgY+fn5iIiIgKmpKby8vGonmURERLWAxSlVSn5+vvBc0JctX74coaGh2LVrF2JjY+Hg4ICoqCh4eHiUGmtlZYV33nkHK1aswOPHj2Fra4vFixcLj5ECgHnz5sHMzAwxMTFYv349dHV1YWZmhnfffVcp70tPTw87d+5EeHg45s+fj+LiYjg4OGDXrl0Kj5ECgAEDBqBly5ZYuHAhMjMz4erqisWLFwv97u7ulcqFv78/MjMz8fXXX2Pnzp1wcXHBjBkzMG3aNGHDlVQqxRdffIF169YhMDAQFhYWWLt2LT7++GOFcw0bNgx///03wsLCkJ6eDi8vL4SHh5d6n6ampti5c6fw/RKLxXBzc8Ps2bOVMiNNRERUXSK5XC6v7yCIVF3fvn3Ru3dvLFiwQCnnj4iIwNatW5GQkNDoPrXq8uXLAICWF41R9LCwnqMhooZC3UQDRqNN6juMRi8nJwdXr16FhYUFdHR0lHadkt8FFe0NAThzSlTnUlJScOTIEdjb20NDQwPnz59HdHQ0Ro4c2egKUyIioqpicUpUx5o0aYJLly5hz549eP78OUxMTDB+/HhMnjy5vkMjIiKqdyxOiSrh559/rrVztW7dGjt27Ki18xERETUmLE6JqE6oN+c/N0RUefw3483F7zwR1Ylmg0p/whYRUXnkxXKIxKKKB1KjwofwE5HSFRQUIDc3t77DaNRyc3ORmJjIPCsZ81w3SvKcl59X36FQPWBxSkR1gk+tUy65XI7c3FzmWcmY57rBPL/ZWJwSERERkcpgcUpEREREKoPFKRHVCZGImxqUSSQSQVtbm3lWMuaZSPm4W5+IlE5TUxPa2tr1HUajpq2tDUtLy/oOo9FjnivGHfZUUyxOiahOZHx/G0Vp+fUdBhEpkbqhFiSe7eo7DGrgWJwSUZ0oSstH0SM+foeIiMrHNadEREREpDJYnBIRERGRymBxSkREREQqg8UpNSqnTp3ChAkT0L17d1hZWcHV1RUBAQE4duwYiouL6zu8Uvz9/REYGKiUc8fGxkIqlaJfv34oKipS6Nu2bRukUqlCm1QqVfhydXVFUFAQrl27ppT4iIiIysINUdRorF69GlFRURgwYAAWLFgAIyMjPHnyBCdOnMCMGTMgkUjQs2fP+g5TwcKFCyEWK/f/iKmpqThy5AiGDh1a4Vh/f38MGjQIcrkcDx48QFRUFMaPH4/vvvsOBgYGSo2TiIgIYHFKjcTJkycRFRWFSZMmYfLkyQp9np6eGD16NNTVVe/HvVOnTkq/hrOzM6KiojBkyBCoqamVO9bU1BR2dnbCazMzMwwZMgSXLl2Cu7u7kiMlIiLibX1qJLZu3QojIyMEBweX2W9jYyM8OPvkyZMYO3YsXFxc4ODgAG9vb5w+fVphvEwmg729fanzdO3aFTKZTHj9xx9/wNfXF46OjrC3t8fgwYNx8ODBSve/els/JSUF06ZNg7u7O2xtbfHee+9hy5YtCksSUlNTIZVKcfjwYSxZsgROTk7o0aMHli1bVur2PQCEhITg1q1b+PbbbytKYym6uroAgMLCwiofS0REVB2qN5VEVEVFRUW4ePEiPDw8KjU7mpqaij59+mDcuHEQi8U4ffo0AgICsH37djg7O1f6utnZ2QgMDISjoyNWr14NTU1NXL9+HZmZmZXqL8ujR49gZmaGwYMHQ1dXF1evXoVMJkNOTg4mTZqkMDYiIgL9+vVDREQELl26BJlMhnbt2mHkyJEK48zNzdGvXz9s3LgRgwYNKncZQXFxMYqKiiCXy/Hw4UOsWLECzZo1q1JeiIiIaoLFKTV46enpKCgogKmpqUK7XC7HixcvhNdisRhisRh+fn5CW3FxMZydnXH9+nXs3bu3SkXYzZs3kZWVhenTpwubi1xcXCrdXxYXFxdhjFwuh6OjI/Ly8rBr165SxamNjQ3mzZsHAHBzc0NCQgJ++OGHUsUpAAQHB+ODDz5AXFwc3nvvvddef+XKlVi5cqXwumnTpli3bh309fXLjZuIiKi2sDilRkMkUvws5x9++AFTp04VXvv6+mLBggV48OAB1qxZg3PnzuHx48eQy+UAACsrqypdr127dtDT08OiRYvg7++P7t27w9DQsNL9ZcnPz0dUVBSOHj2K+/fvK9xOf/78uXCbHQB69OihcGzHjh0RHx9f5nm7dOkCd3d3fPnll/D09Hzt9UeNGoX/+7//AwCkpaXh66+/RkhICHbu3InOnTuXGzsREVFt4JpTavCaNm0KTU1NPHjwQKHdxcUF+/fvx/79+2FkZATgv5nS4OBg/PHHH5gyZQp27NiB/fv3o1evXigoKKjSdSUSCbZu3QpdXV3MnDkTbm5u8Pf3Fx69VFF/WVasWIHo6Gh4e3tj06ZN2L9/v7CONj9f8XPpX53N1NDQKPc9hISE4N9//8WJEydeO6Zly5awtraGtbU13N3dIZPJoKamhg0bNlSYDyIiotrA4pQaPHV1dTg4OOC3335TuI0vkUiEQktTUxMAcOvWLSQmJmL27Nnw9vZGt27dYG1tjby8PIVzamlpldoEVFhYiJycHIU2GxsbfPXVV7hw4QI2btyIp0+fYuLEiZXuf1VcXBxGjBiBgIAAuLq6wtrautaeMmBnZwc3N7cqFZqamppo27YtkpOTayUGIiKiirA4pUZh7NixePToETZu3FjuuJLZRw0NDaHt7t27uHTpksI4ExMTFBYW4vbt20JbfHy8QvH7siZNmsDd3R0jR45EampqqVnOivpfju/l2F68eFGtXfavExISgsTERPzyyy+VGp+fn4/bt2+jWbNmtRYDERFRebjmlBqF3r17IyAgAJGRkUhKSoKnpyeMjY2RlZWFCxcu4PHjx9DV1cVbb72Fli1bYtWqVSguLkZOTg4iIyNhbGyscL5evXpBR0cH8+bNw4QJE/DgwQPs2LEDWlpawpiTJ09i//796N+/P1q1aoUnT55g165dcHBwgJaWVoX9ZXF1dcW+ffvQqVMnNGvWDF9//XWVlxuUp2vXrujWrdtr16bev38ff/75J4D/1pzu3r0b6enp+PDDD2stBiIiovKwOKVG45NPPoGjoyN2796NxYsXIzs7GxKJBFZWVvj8888xcOBAiMViyGQyLFmyBFOnToWpqSmCg4MRHx+Pf/75RzhXs2bNEBkZiWXLlmHixImwsLDA8uXL4e/vL4xp164dxGIxIiIi8PTpUzRt2hQ9evTA9OnTK9Vflvnz52PhwoUICwuDtrY2vLy8MGDAAGFXfm0ICQnB+fPny+zbuXMndu7cCQAwMDBAx44dsX79evTv37/Wrk9ERFQekbxkqzIRkRJcvnwZANDq7yYoepRbz9EQkTKpG2ujue/bNT5PTk4Orl69CgsLC+jo6NRCZPQ6dZXrkt8F1tbWFY7lmlMiIiIiUhksTomIiIhIZXDNKRHVCXXDsjeBEVHjwb/nVBtYnBJRnZB4tqvvEIioDsiL5RCJRRUPJHoN3tYnIqUrKChAbi43QylTbm4uEhMTmWclY54rxsKUaorFKRHVCT4YRLnkcjlyc3OZZyVjnomUj8UpEREREakMFqdEREREpDJYnBJRnRCJuA5NmUQiEbS1tZlnJWOeiZSPu/WJSOk0NTWhra1d32E0atra2rC0tKzvMBq9xpxn7rInVcHilIjqRMYPSShKy6nvMIioDOqGOpB4dK7vMIgAsDglojpSlJaDosfP6zsMIiJScVxzSkREREQqg8UpEREREakMFqdEREREpDJYnDZwMpkM9vb2pdrDw8PRuXNn7Nu3DzKZDFKpFL6+vqXGLV26FH379q3ydf39/REYGKiU41JTUyGVShEXF1fl8yvDTz/9hHHjxqFbt27o0qUL+vbtiwULFuDmzZvCGKlUiujo6DqNKzY2FlKpFGlpaULbnTt3MHr0aNjb20MqleLq1avV/l4RERHVB26IaoRWrFiBbdu2YfHixfD29oZMJgMAXLhwAQkJCXB2dq7xNRYuXAixuPH/32blypXYvHkzPDw8EBYWBkNDQ9y+fRsHDhzAtGnTcOjQoXqLrXfv3oiJiYGBgYHQtnbtWty5cweRkZHQ19dHhw4d3pjvFRERNQ4sThuZNWvW4KuvvsLChQsxYsQIoV1HRwedOnXChg0baqU47dSpU43PUV/y8vLQpEmTCsedOnUKmzdvRkhICKZOnSq0Ozk54YMPPsAvv/yizDArZGhoCENDQ4W2GzduoGvXrujZs6fQVlvfq8rmjYiIqCY4ndKIyGQybNy4EfPnz4ePj0+p/pCQEMTHx+PixYvlniczMxOLFi1Cjx490KVLFwwdOhRnzpxRGFPWreLjx4/Dw8MD1tbWGD58OK5cuYKuXbsKM7cvi4uLg4eHB+zt7TFq1Cjcvn271Jjc3Fx8+umncHR0RLdu3fDFF1+gqKhIYcy1a9cwfvx42NnZwdHREVOmTMG9e/cUxkilUmzatAkrVqyAm5sbXFxcAADJycmYMGECnJ2dYWtrCw8PD2zevFk4bsuWLWjRogVCQkLKzFOfPn1em8OTJ09i7NixcHFxgYODA7y9vXH69GmFMZmZmZg3bx569uwJa2truLu7Y9q0aZXuf/m2fslSiCtXruDw4cOQSqXCco2yvlcpKSkIDg6Go6Mj7OzsEBAQUOp78Lq8ERERKRNnThuJL7/8EuvWrcOcOXPg5+dX5pg+ffrA0tIS69evf+36yIKCAowdOxZPnz7Fxx9/DBMTExw5cgSBgYFCMVSWxMRETJ06FX369MGnn36Ku3fvYtq0aSgoKCg19urVq0hLS0NoaChevHiB8PBwzJgxAzExMQrjVq9ejR49eiAiIgKJiYmIjIyEhoYGQkNDAQD379+Hn58f2rZtixUrViA/Px9r1qyBn58fjhw5Aj09PeFcO3bsgK2tLZYuXSoUuEFBQWjRogWWLl0KPT093L59Gw8ePAAAFBUV4eLFi3jnnXegoaFRQfZLS01NRZ8+fTBu3DiIxWKcPn0aAQEB2L59uzBz/cUXX+DXX3/FJ598gtatW+Px48cKBWxF/S8zNjZGTEwMZs2ahfbt2yMkJASamppljr1z5w4+/PBDvP322wgPD4dIJMLGjRsxZswYxMXFKRxXVt6IiIiUicVpI5CTk4OIiAh4e3tjzJgx5Y4NDg7G5MmT8ffff8PGxqZU/9GjR5GUlITDhw8Lt4N79uyJW7duYcOGDVi7dm2Z542KikKbNm0gk8mE9Y26urqYOXNmqbFZWVk4dOiQcEs6JycHc+bMwYMHD9CyZUthXLt27fDFF18IMeTl5WHr1q2YMGECJBIJtm3bhqKiImzZsgVNmzYFAFhYWGDgwIE4ePAg/P39hXNJJBKsW7dO+DzsktnGuXPnCjOM3bt3F8anp6ejoKAArVq1Kjefr/PyfxCKi4vh7OyM69evY+/evUJxevnyZQwaNAheXl7C2IEDBwp/rqj/ZZqamrCzs0OTJk1gaGgIOzu718a2bt06SCQSbN26FVpaWgAABwcH9OvXD/v27VPYOPdq3oiIiJSNt/UbgSZNmsDJyQnHjh3DH3/8Ue7YAQMGwNzcHOvXry+z/+zZszA3N0eHDh1QVFQkfLm6uuLy5cuvPe/ly5fRu3dvhY03/fr1K3Ns586dFdZKlhTBJbOWL8f6Mg8PD+Tm5uLff/8F8N8GL2dnZ6EwBYCOHTuic+fOpfLQq1cvhQKrWbNmaN26NVavXo2DBw+WunaJ6hZlDx48wKxZs9CzZ09YWlrCysoKZ86cUdjhb2lpiYMHDyI6Olp4Ty+rqL+6zp49i759+0JNTU34/hoYGMDS0hL//POPwthX80ZERKRsnDltBMRiMb788kv4+/sjKCgIu3bteu3td5FIhKCgIEyfPh1Xrlwp1f/s2TMkJibCysqqVJ+amtprY3j8+HGpzTl6enrCzNzLXt5dDkC4bZ6fn6/Q/ur5WrRoIVwL+G9NpoWFRanzN2/eHBkZGaXaXiYSiRAdHY01a9ZgyZIlyMnJgZWVFebMmQMnJyc0bdoUWlpapdavVkZxcTGCg4ORlZWFKVOmoH379tDW1kZkZCTu378vjJs/f74wg7l8+XKYmpoiICBAWC9cUX91PXv2DNu3b8f27dtL9b26hOHVvBERESkbi9NGQl9fH9HR0Rg5ciTGjx+PPXv2oG3btmWO9fT0hEwmw4YNG0rdtpZIJJBKpVi6dGmVrm9kZKTwvE0AyM7OLlVwVsWr53vy5IlwrZJYnz59Wuq4p0+fokOHDgptZc3+mZmZITIyEoWFhbh06RJWr16NoKAgnD59Grq6unBwcEB8fDyKioqgrl75vyq3bt1CYmIi1q9fj/79+wvteXl5CuP09fUxd+5czJ07F9euXcOOHTuwePFimJubo2vXrhX2V5dEIoG7u3uZRa6urq7Ca86aEhFRXeNt/UakefPm2LJlC0QiEcaNGyfMML5KLBYjKCgIP/30E65du6bQ5+rqijt37sDY2BjW1talvl7H2toaJ0+eRHFxsdB24sSJGr2f48ePK7z+4YcfoK2tDXNzcwCAo6Mj4uPjFWZJb9y4gWvXrsHR0bHS19HQ0EC3bt0QEBCA7OxsPHr0CAAwduxYPH78GBs3bizzuFOnTpXZXlKQvzwLeffuXVy6dOm1MUilUsyZMwfAfzvpq9pfFS4uLkhOToalpWWp7+9bb71Vo3MTERHVFGdOG5k2bdogOjoafn5++Oijj7Br164yxw0ePBjr169HQkICWrduLbS///77+OabbzBq1CiMGzcOHTp0QFZWFhITE1FYWIhPPvmkzPMFBgZi2LBhmDx5MoYPH4579+5hy5Yt0NLSqvbs2+3btzFnzhy89957SExMxKZNmzB69GhIJBIAwJgxYxAbG4tx48YhODgY+fn5iIiIgKmpqcImorIkJSVh2bJleO+999C2bVtkZ2cjKioKrVu3Rrt27QAA7u7u+OijjyCTyXD9+nUMHDgQzZo1Q2pqKg4cOICsrCy4u7uXOvdbb72Fli1bYtWqVSguLkZOTg4iIyNhbGysMO7DDz/EgAED8Pbbb0NNTQ2HDh2ChoaGMCtaUX91TZkyBcOGDcP48eMxfPhwtGjRAk+ePMH58+fRtWtXDBo0qEbnJyIiqgkWp42Qubk5oqKiMHbsWAQGBpb58aZqamoICAjAvHnzFNo1NTWxY8cO4Zmpjx8/RtOmTWFpaVnuWkdLS0tERERg1apVmDRpkvCYolGjRkFfX79a72PatGk4f/48pk6dCjU1Nfj4+Cg859PU1BQ7d+7E8uXLERoaCrFYDDc3N8yePVvhMVJlMTIyQosWLRAVFYWHDx9CX18fXbt2xYoVKxTW1s6YMQP29vbYvXs3Pv30U+Tm5sLY2Bg9evTA+PHjyzy3pqYmZDIZlixZgqlTp8LU1BTBwcGIj49X2HDk4OCAQ4cOITU1FWKxGObm5ti4cSM6duxYqf7qat++Pfbt24eIiAgsXrwYOTk5MDIygpOT02vXKhMREdUVkVwul9d3ENQ4/fbbbxgzZgx27tyJbt261Xc4VE9KnvLQ6p9CFD1+Xs/REFFZ1I100XykQ32HIcjJycHVq1dhYWEBHR2d+g6nUaurXJf8LihviWAJzpxSrVm0aBFcXFzQtGlTXL9+HRs2bIClpWWNb0MTERHRm4PFKdWazMxMhIWFIT09HXp6eujZsydmzZql8OxTIiIiovKwOKVas3r16voOgYiIiBo4FqdEVCfUDblujEhV8e8nqRIWp0RUJyQenes7BCIqh7xYDpGYH7xB9Y+LAYlI6QoKCpCbm1vfYTRqubm5SExMZJ6VrDHnmYUpqQoWp0RUJ/jUOuWSy+XIzc1lnpWMeSZSPhanRERERKQyWJwSERERkcpgcUpEdUIk4no2ZRKJRNDW1maeiajB4259IlI6TU1NaGtr13cYjZq2tjYsLS3rO4w6xd3lRI0Ti1MiqhMZP17Ci2fZ9R0GNRJqzfQgece+vsMgIiVgcUpEdeLFs2wUPc6s7zCIiEjFcc0pEREREakMFqdEREREpDJYnBIRERGRymBxSlUik8kglUohlUrRuXNnODo6YvDgwViyZAlSUlLqOzyluHDhAoKDg+Hi4oIuXbqgV69eCA0NxeXLl4Uxffv2xZIlS+o0roSEBEilUoU40tPTMXHiRDg5OUEqleLEiROYPXs2Bg0aVKexERERVRc3RFGVNWnSBNu3bwcAPH/+HP/++y9iYmKwd+9eLF26FEOGDKnnCGvP7t27ERYWhu7du2Pu3LkwMTHBw4cPcfToUYwbNw6///57vcVmZWWFmJgYdOzYUWjbunUrEhISsGzZMjRv3hxmZmYwNzdHTk5OvcVJRERUFSxOqcrEYjHs7OyE125ubvDx8UFAQADmzp0LBwcHtG3btv4CrCVJSUn4/PPPMWTIEISHhys83HzQoEH45Zdf6jE6QE9PT+H7AAA3b96EVCpFv379hDaJRFIr1ysoKIC6ujrEYt5wISIi5eFvGaoVWlpamD9/PgoLC7Fv3z4AgFQqRXR0tMK4bdu2QSqVCq9Lbk3/+uuvmDp1Kuzt7dG7d28cPXoUALBjxw707t0b3bp1w9y5c1FQUCAcGxsbK9zWHjduHGxtbeHh4YFz586huLgYa9asgaurK1xdXbFq1SoUFxcDAK5duwapVIqzZ88qxPbixQv07NkTy5cvF64tEokwa9asMj91p0+fPq/Nx6VLlxAUFIQePXrAzs4OQ4YMwaFDhxTGFBYWYtmyZejduze6dOmCHj16ICgoCFlZWZXqf/W2vlQqxQ8//IALFy4ISy8AlHlb/8GDBwgNDYWzszNsbGzg6+uLf/75R2FMyVKFzZs3o0+fPrCxsUF6evpr3zMREVFt4Mwp1ZpOnTrBxMQEly5dqvKxixYtgpeXF4YPH469e/di5syZSEpKQnJyMhYvXow7d+4gPDwcbdu2RVBQkMKxs2bNwocffoixY8di06ZNmDRpEry8vJCdnY1ly5bhr7/+gkwmg7m5OQYPHgypVApbW1scOHAAbm5uwnl+/fVXPHr0CB988AEA4Pfff0eXLl1gaGhY5fdz7949ODg4YOTIkdDU1MTFixcxb948yOVyeHl5AQCioqLwzTffIDQ0FG+//TaePXuGs2fPCgV4Rf2viomJwcqVK/H8+XMsXLjwtbFlZGTAx8cHOjo6mD9/PvT19bFz506MHj0aP/74I5o3by6M/fHHH9G+fXvMnTsXYrEYOjo6Vc4FERFRVbA4pVplamqKJ0+eVPm4d999F5MmTQIA2NjY4Pjx4/j2229x/PhxaGhoAADOnz+PuLi4UsWpn58ffHx8AAAmJiYYPHgw/vnnH8TExAAAevbsiZ9//hlxcXEYPHgwAMDb2xthYWHIyMgQbnsfOHAA9vb2whrOhw8fwtrauhpZAAYOHCj8WS6Xw8nJCQ8fPkRMTIxQnF6+fBk9evSAr6+vMNbDw0P4c0X9r7Kzs4OBgQFEIlGp2/0v2759OzIzM7Fv3z6hEHVxcYGHhweio6Mxc+ZMYWxhYSE2b97MopSIiOoMb+tTrZLL5WXeAq/IyzOY+vr6MDQ0RNeuXYXCFAA6dOiA+/fvl3tshw4dAADdu3dXGGNmZqZw7MCBA6Guro5jx44BANLS0vDLL79g2LBhCsdV570A/81OfvbZZ+jTpw+srKyEzUs3b94UxlhaWuLUqVOQyWT4+++/hWUHle2vrrNnz8LZ2RkSiQRFRUUoKiqCWCyGk5OTws5/AHB2dmZhSkREdYozp1SrHjx4IBSIVaGvr6/wWlNTEwYGBgptGhoaZd7SfvlYTU1NAKjwWB0dHQwaNAj79++Hr68vjhw5Ag0NDXh6egpjTExMcO/evSq/F+C/dZ6XLl3CxIkT0alTJ+jp6WHPnj34/vvvhTHBwcEQi8U4ePAg1q1bB0NDQ/j6+mLixIkQiUQV9lfXs2fP8Oeff8LKyqpUX7t27RRev3yLn4iIqC6wOKVak5ycjIcPHwq3rTU1NVFYWKgwJjNTdT5b3dvbGzExMUhKSkJsbCw8PT2hq6sr9Hfr1g1HjhxBeno6mjZtWunz5ufn4+TJk5g9ezb8/f2F9q+//lphnKamJiZPnozJkyfj1q1bOHDgAGQyGdq0aYP333+/wv7qkkgk6NmzJ6ZOnVqqr6S4L1GTIpiIiKg6eFufakV+fj7CwsKgqakJb29vAEDLli1LPZj/3Llz9RFemaytrWFhYYHPPvsM165dEzZClfD390dxcTGWLVtW5vEnT54ss72goADFxcUKSxKys7Px888/vzaW9u3bY/r06WjatClu3LhR5f6qcHV1RUpKCjp27Ahra2uFr5efpEBERFQfOHNKVVZcXIw///wTAJCTkyM8hL9kR32bNm0A/Ld5Z/v27bC2toaZmRmOHDmChw8f1mPkpXl7e2PJkiUwMzODo6OjQl/nzp3x6aefIiwsDA8fPsQHH3wgPIT/22+/xYULF3D+/PlS59TX14e1tTU2b94MQ0NDqKurY9OmTdDT00NaWpowLiQkBFZWVrC0tIS2tjZ++eUXZGRkCOtlK+qvrjFjxuDo0aPw8/PDqFGj0KpVK6SlpeGvv/6CiYkJxowZU6PzExER1QSLU6qyvLw8jBgxAsB/azfbtGkDFxcXrFu3TuHTikJCQvD06VOsX78eIpEII0aMwKhRoxAeHl5foZcyYMAALFmypNSsaQlfX1/hea1LlixBdnY2DA0N0b17d2zduvW15121ahUWLFiA2bNno2nTpvD390dOTg62bNkijHFwcMD333+PrVu34sWLFzAzM8PKlSvh6upaqf7qatasGWJiYhAREYGVK1ciPT0dzZs3h62tLQYMGFCjcxMREdWUSC6Xy+s7CKL6sn//fixcuBAnT56EkZFRfYfTKJU8AaB1YjqKHqvOmmNq2NSNDGA4omedXzcnJwdXr16FhYUFn2ShRMxz3amrXJf8LqjMIxo5c0pvpNTUVNy6dQsbNmyAp6cnC1MiIiIVweKU3kjr1q3DsWPHYG9vj9mzZ9d3OERERPT/YXFKb6Tw8HCVWvv6JlBrplffIVAjwp8nosaLxSkR1QnJO/b1HQI1MvJiOURiPouXqLHhc06JSOkKCgqQm5tb32E0arm5uUhMTHyj8szClKhx4m59IlKqixcvQi6XQ0NDg584pURyuRyFhYXMs5Ixz3WDea47dZXrgoICiEQiODg4VDiWt/WJSKlK/rHjLxjlEolEpT5+lmof81w3mOe6U1e5FolElf49wJlTIiIiIlIZXHNKRERERCqDxSkRERERqQwWp0RERESkMlicEhEREZHKYHFKRERERCqDxSkRERERqQwWp0RERESkMlicEhEREZHKYHFKRERERCqDxSkRERERqQwWp0RERESkMlicEhEREZHKYHFKRNWWkpKCsWPHws7ODm5ubli+fDkKCgoqPE4ul2PTpk3o3bs3bGxsMGLECPz555/KD7iBqk6eHz16hOXLl2PIkCGwt7dHr1698Mknn+Du3bt1FHXDU92f55dt27YNUqkUgYGBSoqy4atJnh8+fIhZs2ahe/fusLGxgaenJ44cOaLkiBuu6ub62bNnWLBgAXr37g07OzsMGjQIe/bsqYOI/6NeZ1ciokYlIyMDo0ePRocOHSCTyfDw4UOEh4cjLy8PCxYsKPfYzZs3IzIyEqGhoZBKpdi9ezfGjRuHw4cPo23btnX0DhqG6ub5ypUrOH78OD744APY2tri2bNn+PLLL+Ht7Y1jx47B0NCwDt+F6qvJz3OJx48fY/369WjevLmSo224apLnR48eYcSIETAzM0NYWBj09PSQnJxc5f9AvClqkuupU6fixo0bmD59OkxNTXH69GksWrQIampqGD58uPKDlxMRVcPGjRvldnZ28mfPnglt33zzjdzCwkL+4MGD1x6Xl5cnd3BwkK9atUpoy8/Pl/fp00e+cOFCJUbcMFU3zxkZGfLCwkKFtvv378ulUqk8OjpaWeE2WNXN88tmzJghnzlzptzPz08eEBCgpEgbtprkOTQ0VD5ixAh5UVGRkqNsHKqb60ePHsnNzc3lBw4cUGj39fWVjxo1SlnhKuBtfSKqltOnT8PFxQVNmzYV2jw9PVFcXIyzZ8++9riLFy8iOzsbnp6eQpumpiYGDBiA06dPKzPkBqm6eTYwMIC6uuLNsZYtW8LQ0BCPHj1SVrgNVnXzXOLChQs4ceIEPvnkEyVG2fBVN8/Z2dn4/vvv4ePjAzU1tTqItOGrbq6LiooAAPr6+grtenp6kMvlSon1VSxOiahabty4gbfeekuhzcDAAEZGRrhx40a5xwEodWzHjh1x79495OXl1X6wDVh181yWmzdv4unTp+jYsWNthtgo1CTPL168QFhYGIKCgmBsbKzMMBu86ub5ypUrKCwshLq6Ovz8/GBlZQU3NzesWLEChYWFyg67Qapurk1NTdGjRw9s3LgR169fR3Z2Nr777jucPXsWvr6+yg4bANecElE1ZWZmwsDAoFS7RCJBRkZGucdpampCS0tLod3AwAByuRwZGRlo0qRJrcfbUFU3z6+Sy+X47LPPYGxsjIEDB9ZmiI1CTfL89ddfIzc3F2PGjFFSdI1HdfP85MkTAMC8efMwfPhwTJo0CX///TciIyMhFos5Y12GmvxMy2QyTJs2Tfi3Qk1NDfPmzYOHh4dSYn0Vi1MiojeATCZDfHw8vvrqK+jo6NR3OI3G06dPERkZiWXLlkFTU7O+w2m0iouLAQCurq6YPXs2AKB79+54/vw5tmzZgokTJ/I/tbVELpdjzpw5+N///odVq1bByMgI586dw+effw6JRFIn/7llcUpE1WJgYICsrKxS7RkZGZBIJOUeV1BQgPz8fIXZ08zMTIhEonKPfRNVN88v27t3L9avX4+lS5fCxcWltkNsFKqb57Vr10IqlaJr167IzMwE8N+avaKiImRmZkJHR6fU2t83WU3+3QD+K0hf5uLigo0bN+LWrVuQSqW1G2wDV91cnzx5EnFxcThy5IiQU2dnZzx9+hTh4eF1UpxyzSkRVctbb71Vat1SVlYWHj9+XGqd06vHAf+tf3zZjRs30KpVK85+vKK6eS5x/PhxLFq0CFOmTMGwYcOUFWaDV90837x5E7///jucnJyEr4sXL+LMmTNwcnLCuXPnlB16g1LdPHfq1Knc8+bn59dKfI1JdXN9/fp1qKmpwdzcXKHdwsICjx49Qm5urlLifRmLUyKqll69euHcuXPCbBEAxMXFQSwWw83N7bXHOTg4QE9PD99//73QVlhYiB9//BG9evVSaswNUXXzDAAJCQmYPn06vL29MXHiRGWH2qBVN8+ffvopduzYofDVuXNn2NnZYceOHbCxsamL8BuM6ua5devWMDc3L1Xsnzt3Dk2aNKmweH0T1STXL168wLVr1xTar1y5gubNm0NbW1tpMZcQyevquQBE1KhkZGRg4MCBMDMzQ2BgoPCA58GDBys84Hn06NG4d+8ejh8/LrRt2rQJMpkMoaGhMDc3x549e3DmzBk+hL8M1c1zSkoKRowYAVNTUyxevBhi8f8/F2FoaIh27drV+XtRZTX5eX6Vv78/dHR0EBUVVRehNyg1yfPPP/+MkJAQ+Pv7o3fv3rh8+TLWrVuH8ePHY9q0afXxdlRadXOdnZ2NwYMHQ0NDAxMnToSxsTHOnDmDLVu2YPLkyQgJCVF67FwIQ0TVIpFIsH37doSFhWHixInQ1dXFsGHDSv2SKC4uxosXLxTaJkyYALlcji1btiAtLQ0WFhaIjo5mYVqG6ub5r7/+QlZWFrKysjBy5EiFsV5eXggPD6+T+BuKmvw8U+XVJM99+/bF6tWrsWHDBuzZswfGxsaYPHkyAgIC6vItNBjVzbWenh62bduGNWvWYOXKlcjKykKbNm0we/Zs+Pn51UnsnDklIiIiIpXBNadEREREpDJYnBIRERGRymBxSkREREQqg8UpEREREakMFqdEREREpDJYnBIRERGRymBxSkREREQqg8UpEREREakMFqdEREREpDL48aVERNRoxcbGYs6cOWX2TZgwAaGhoQCAM2fO4LvvvsPff/+NlJQUmJqa4ueff670dZ4/f47o6Gj8+OOPSE1NhZaWFlq2bAknJydMmDABJiYmtfJ+iN4ELE6JiKjRmzJlCtq0aaPQZm5uLvz52LFj+O6772BpaQljY+MqnbuwsBB+fn64ceMG3n//ffj5+SEnJwfJyck4duwYBgwYwOKUqApYnBIRUaPXq1cvWFtbv7Z/2rRpCAsLg4aGBgIDA5GcnFzpc584cQKJiYlYuXIlBg8erNCXn5+PwsLCasddVTk5OdDR0amz6xEpA9ecEhHRG8/ExAQaGhrVOvbOnTsAAAcHh1J9Wlpa0NPTU2hLSUnB1KlT0b17d9jY2MDDwwNr1qxRGJOYmIiPPvoIDg4OsLe3x+jRo/Hnn38qjImNjYVUKsX58+exaNEiuLi4wN3dXeg/deoUfHx8YGdnB3t7ewQEBFSp6CaqLyxOiYio0cvOzkZaWprCV21p1aoVAODQoUOQy+Xljk1KSsLw4cMRHx+P4cOHY+7cuejfv7/C+tbk5GT4+voiKSkJH330EYKDg5Gamgp/f3/89ddfpc65ePFipKSkYOLEiZgwYYIQS2BgIHR0dBAaGoqQkBBcv34dPj4+SE1NrbX3TqQMvK1PRESN3pgxY0q1Xbt2rVbO3b9/f5iZmSEyMhIHDhyAs7MzHB0d0adPHzRv3lxh7GeffQa5XI6DBw8KRS0AYWMWAERERKCwsBB79uxB27ZtAQDvv/8+3n33XaxYsQK7du1SOKdEIsG2bdugpqYG4L/NWUuXLoW3tzfCwsKEcV5eXnj33XcRFRWl0E6kalicEhFRo7dgwQKYmZkp5dxNmjTBvn378OWXXyIuLg6xsbGIjY2FWCyGj48PZs2aBU1NTaSlpeH333/HqFGjFApTABCJRACAFy9e4OzZs+jfv79QmAKAsbExBg0ahH379iE7O1thqcDw4cOFwhQAzp07h8zMTAwcOFBhhlgsFsPW1hYJCQlKyQNRbWFxSkREjZ6NjU25G6JqSl9fHzNnzsTMmTNx9+5d/Pbbb9iyZQt27doFPT09TJs2TVib+vJTAl6VlpaG3NzcMgvpjh07ori4GPfv38fbb78ttL/6FIL//e9/AIDRo0eXeY1X18ASqRoWp0RERLWodevWGDZsGAYMGID+/fvj6NGjmDZtmtKup6WlpfC6ZN3r8uXLYWRkVGr8y7OsRKqIxSkREZESSCQStG3bVtghX3Kb/t9//33tMYaGhtDW1sbNmzdL9d24cQNisRimpqblXrfkOs2bN4erq2t1wyeqN9ytT0REVANJSUll7v6/e/cuUlJShFv0hoaGcHJywoEDB3Dv3j2FsSWznWpqanBzc8NPP/2ksKv+yZMnOHbsGBwdHSu8Ld+zZ0/o6ekhKiqqzGes1uaTCoiUgTOnRET0xktKShIe53Tr1i1kZWVhw4YNAIDOnTujb9++rz327NmzkMlk6Nu3L2xtbaGjo4PU1FQcOHAABQUFmDx5sjB23rx5GDlyJLy8vDBixAi0adMGd+/excmTJ3H48GEAwMcff4xz587Bx8cHPj4+UFNTQ0xMDAoKCjBjxowK34uenh4WLVqEmTNnYujQoXjvvfdgaGiIe/fu4dSpU3BwcMCCBQtqki4ipWJxSkREb7zExESsXbtWoa3ktZeXV7nF6TvvvIPnz5/j7NmziI+PR0ZGBgwMDGBjY4OxY8eie/fuwtjOnTtj7969WLt2Lfbs2YP8/Hy0atUKnp6ewpi3334bu3fvxqpVqxAVFQW5XA4bGxusWLECtra2lXo/gwcPhrGxMTZt2oTo6GgUFBTAxMQEXbt2xdChQ6uSGqI6J5JX9MRgIiIiIqI6wjWnRERERKQyWJwSERERkcpgcUpEREREKoPFKRERERGpDBanRERERKQyWJwSERERkcpgcUpEREREKoPFKRERERGpDBanRERERKQyWJwSERERkcpgcUpEREREKoPFKRERERGpjP8HkCMjawjeeeUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = 'lazypredict_results.xlsx'  # Define the file path where you want to save the Excel file\n",
        "models.to_excel(file_path, index=True)"
      ],
      "metadata": {
        "id": "ZI-rjgfQiNxT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Gridsearchcv\n",
        "\n",
        "Choose multiple parameters and see which combinations get the best f1 scores"
      ],
      "metadata": {
        "id": "RJ-0lt5BXXc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "def predict(model, pca, url, threshold = .8):\n",
        "  img=imread(url)\n",
        "  plt.imshow(img)\n",
        "  plt.show()\n",
        "  img_resize = resize(img,(224,224,3))\n",
        "  l=[img_resize.flatten()]\n",
        "  l = pca.transform(l)\n",
        "  probability=model.predict_proba(l)\n",
        "\n",
        "  prediction = \"Patacon-True\" if probability[0][Categories.index(\"Patacon-True\")] > threshold else \"Patacon-False\" #change classes if necesarry and logic if there are more than two classes\n",
        "  print(probability[0][Categories.index(\"Patacon-True\")])\n",
        "  print(\"The predicted image is : \"+ prediction)"
      ],
      "metadata": {
        "id": "aWYKrV9KXyC1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "\n",
        "param_grid={'C':[0.1,1,10,100,1000],'gamma':[0.00001, 0.0001,0.001,0.1,1,1.5, 2],'kernel':['rbf','poly','sigmoid']}\n",
        "svc=svm.SVC(probability=True)\n",
        "model=GridSearchCV(svc,param_grid, verbose = 4)\n",
        "model.fit(x_train_pca ,y_train)"
      ],
      "metadata": {
        "id": "j-X3eQJMrok0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9d07ce22-e17e-42a8-9e3b-55ea0f6a0df4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 105 candidates, totalling 525 fits\n",
            "[CV 1/5] END ....C=0.1, gamma=1e-05, kernel=rbf;, score=0.654 total time=   1.0s\n",
            "[CV 2/5] END ....C=0.1, gamma=1e-05, kernel=rbf;, score=0.735 total time=   1.0s\n",
            "[CV 3/5] END ....C=0.1, gamma=1e-05, kernel=rbf;, score=0.692 total time=   0.9s\n",
            "[CV 4/5] END ....C=0.1, gamma=1e-05, kernel=rbf;, score=0.639 total time=   0.9s\n",
            "[CV 5/5] END ....C=0.1, gamma=1e-05, kernel=rbf;, score=0.734 total time=   1.0s\n",
            "[CV 1/5] END ...C=0.1, gamma=1e-05, kernel=poly;, score=0.500 total time=   0.8s\n",
            "[CV 2/5] END ...C=0.1, gamma=1e-05, kernel=poly;, score=0.509 total time=   0.8s\n",
            "[CV 3/5] END ...C=0.1, gamma=1e-05, kernel=poly;, score=0.491 total time=   0.8s\n",
            "[CV 4/5] END ...C=0.1, gamma=1e-05, kernel=poly;, score=0.498 total time=   0.8s\n",
            "[CV 5/5] END ...C=0.1, gamma=1e-05, kernel=poly;, score=0.498 total time=   0.8s\n",
            "[CV 1/5] END C=0.1, gamma=1e-05, kernel=sigmoid;, score=0.645 total time=   0.9s\n",
            "[CV 2/5] END C=0.1, gamma=1e-05, kernel=sigmoid;, score=0.709 total time=   0.8s\n",
            "[CV 3/5] END C=0.1, gamma=1e-05, kernel=sigmoid;, score=0.662 total time=   0.9s\n",
            "[CV 4/5] END C=0.1, gamma=1e-05, kernel=sigmoid;, score=0.639 total time=   0.9s\n",
            "[CV 5/5] END C=0.1, gamma=1e-05, kernel=sigmoid;, score=0.730 total time=   0.9s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.675 total time=   0.9s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.744 total time=   1.0s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.697 total time=   0.9s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.639 total time=   0.9s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.734 total time=   1.0s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.692 total time=   0.7s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.744 total time=   0.7s\n",
            "[CV 3/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.718 total time=   0.7s\n",
            "[CV 4/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.657 total time=   0.7s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.0001, kernel=poly;, score=0.734 total time=   0.7s\n",
            "[CV 1/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.684 total time=   0.8s\n",
            "[CV 2/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.735 total time=   0.9s\n",
            "[CV 3/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.714 total time=   0.8s\n",
            "[CV 4/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.674 total time=   0.8s\n",
            "[CV 5/5] END C=0.1, gamma=0.0001, kernel=sigmoid;, score=0.734 total time=   0.9s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.603 total time=   1.0s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.598 total time=   1.1s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.667 total time=   1.0s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.498 total time=   1.0s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.498 total time=   1.0s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.761 total time=   0.8s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.773 total time=   0.8s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.001, kernel=poly;, score=0.811 total time=   0.8s\n",
            "[CV 1/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.628 total time=   0.6s\n",
            "[CV 2/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.632 total time=   0.6s\n",
            "[CV 3/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.637 total time=   0.6s\n",
            "[CV 4/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.597 total time=   0.7s\n",
            "[CV 5/5] END C=0.1, gamma=0.001, kernel=sigmoid;, score=0.609 total time=   0.7s\n",
            "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.551 total time=   1.0s\n",
            "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.590 total time=   1.0s\n",
            "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.577 total time=   1.0s\n",
            "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.498 total time=   1.0s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.611 total time=   0.5s\n",
            "[CV 2/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.624 total time=   0.5s\n",
            "[CV 3/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.615 total time=   0.5s\n",
            "[CV 4/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.549 total time=   0.4s\n",
            "[CV 5/5] END ..C=0.1, gamma=0.1, kernel=sigmoid;, score=0.575 total time=   0.5s\n",
            "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.551 total time=   1.1s\n",
            "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.538 total time=   1.0s\n",
            "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.530 total time=   1.1s\n",
            "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.498 total time=   1.0s\n",
            "[CV 1/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.791 total time=   0.9s\n",
            "[CV 3/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.752 total time=   0.9s\n",
            "[CV 4/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.785 total time=   1.0s\n",
            "[CV 5/5] END .......C=0.1, gamma=1, kernel=poly;, score=0.798 total time=   0.9s\n",
            "[CV 1/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.577 total time=   0.5s\n",
            "[CV 2/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.598 total time=   0.5s\n",
            "[CV 3/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.547 total time=   0.5s\n",
            "[CV 4/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.549 total time=   0.4s\n",
            "[CV 5/5] END ....C=0.1, gamma=1, kernel=sigmoid;, score=0.571 total time=   0.5s\n",
            "[CV 1/5] END ......C=0.1, gamma=1.5, kernel=rbf;, score=0.534 total time=   1.0s\n",
            "[CV 2/5] END ......C=0.1, gamma=1.5, kernel=rbf;, score=0.534 total time=   1.0s\n",
            "[CV 3/5] END ......C=0.1, gamma=1.5, kernel=rbf;, score=0.526 total time=   1.0s\n",
            "[CV 4/5] END ......C=0.1, gamma=1.5, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 5/5] END ......C=0.1, gamma=1.5, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 1/5] END .....C=0.1, gamma=1.5, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .....C=0.1, gamma=1.5, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .....C=0.1, gamma=1.5, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .....C=0.1, gamma=1.5, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .....C=0.1, gamma=1.5, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ..C=0.1, gamma=1.5, kernel=sigmoid;, score=0.577 total time=   0.4s\n",
            "[CV 2/5] END ..C=0.1, gamma=1.5, kernel=sigmoid;, score=0.607 total time=   0.5s\n",
            "[CV 3/5] END ..C=0.1, gamma=1.5, kernel=sigmoid;, score=0.543 total time=   0.5s\n",
            "[CV 4/5] END ..C=0.1, gamma=1.5, kernel=sigmoid;, score=0.605 total time=   0.5s\n",
            "[CV 5/5] END ..C=0.1, gamma=1.5, kernel=sigmoid;, score=0.571 total time=   0.5s\n",
            "[CV 1/5] END ........C=0.1, gamma=2, kernel=rbf;, score=0.526 total time=   1.1s\n",
            "[CV 2/5] END ........C=0.1, gamma=2, kernel=rbf;, score=0.521 total time=   1.1s\n",
            "[CV 3/5] END ........C=0.1, gamma=2, kernel=rbf;, score=0.517 total time=   1.0s\n",
            "[CV 4/5] END ........C=0.1, gamma=2, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 5/5] END ........C=0.1, gamma=2, kernel=rbf;, score=0.498 total time=   1.1s\n",
            "[CV 1/5] END .......C=0.1, gamma=2, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=0.1, gamma=2, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .......C=0.1, gamma=2, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .......C=0.1, gamma=2, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .......C=0.1, gamma=2, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ....C=0.1, gamma=2, kernel=sigmoid;, score=0.594 total time=   0.5s\n",
            "[CV 2/5] END ....C=0.1, gamma=2, kernel=sigmoid;, score=0.598 total time=   0.5s\n",
            "[CV 3/5] END ....C=0.1, gamma=2, kernel=sigmoid;, score=0.534 total time=   0.5s\n",
            "[CV 4/5] END ....C=0.1, gamma=2, kernel=sigmoid;, score=0.588 total time=   0.4s\n",
            "[CV 5/5] END ....C=0.1, gamma=2, kernel=sigmoid;, score=0.571 total time=   0.5s\n",
            "[CV 1/5] END ......C=1, gamma=1e-05, kernel=rbf;, score=0.726 total time=   0.8s\n",
            "[CV 2/5] END ......C=1, gamma=1e-05, kernel=rbf;, score=0.786 total time=   0.8s\n",
            "[CV 3/5] END ......C=1, gamma=1e-05, kernel=rbf;, score=0.744 total time=   0.8s\n",
            "[CV 4/5] END ......C=1, gamma=1e-05, kernel=rbf;, score=0.700 total time=   0.8s\n",
            "[CV 5/5] END ......C=1, gamma=1e-05, kernel=rbf;, score=0.811 total time=   0.8s\n",
            "[CV 1/5] END .....C=1, gamma=1e-05, kernel=poly;, score=0.500 total time=   0.8s\n",
            "[CV 2/5] END .....C=1, gamma=1e-05, kernel=poly;, score=0.509 total time=   0.8s\n",
            "[CV 3/5] END .....C=1, gamma=1e-05, kernel=poly;, score=0.491 total time=   0.8s\n",
            "[CV 4/5] END .....C=1, gamma=1e-05, kernel=poly;, score=0.502 total time=   0.8s\n",
            "[CV 5/5] END .....C=1, gamma=1e-05, kernel=poly;, score=0.545 total time=   0.8s\n",
            "[CV 1/5] END ..C=1, gamma=1e-05, kernel=sigmoid;, score=0.684 total time=   0.7s\n",
            "[CV 2/5] END ..C=1, gamma=1e-05, kernel=sigmoid;, score=0.739 total time=   0.8s\n",
            "[CV 3/5] END ..C=1, gamma=1e-05, kernel=sigmoid;, score=0.701 total time=   0.7s\n",
            "[CV 4/5] END ..C=1, gamma=1e-05, kernel=sigmoid;, score=0.691 total time=   0.8s\n",
            "[CV 5/5] END ..C=1, gamma=1e-05, kernel=sigmoid;, score=0.790 total time=   0.8s\n",
            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.778 total time=   0.9s\n",
            "[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.859 total time=   0.8s\n",
            "[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.791 total time=   0.8s\n",
            "[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.773 total time=   0.8s\n",
            "[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.854 total time=   0.9s\n",
            "[CV 1/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.731 total time=   0.7s\n",
            "[CV 2/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.803 total time=   0.7s\n",
            "[CV 3/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.756 total time=   0.7s\n",
            "[CV 4/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.738 total time=   0.7s\n",
            "[CV 5/5] END ....C=1, gamma=0.0001, kernel=poly;, score=0.807 total time=   0.7s\n",
            "[CV 1/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.679 total time=   0.5s\n",
            "[CV 2/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.709 total time=   0.5s\n",
            "[CV 3/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.667 total time=   0.5s\n",
            "[CV 4/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.665 total time=   0.5s\n",
            "[CV 5/5] END .C=1, gamma=0.0001, kernel=sigmoid;, score=0.661 total time=   0.6s\n",
            "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.637 total time=   1.0s\n",
            "[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.679 total time=   1.0s\n",
            "[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.662 total time=   1.0s\n",
            "[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.687 total time=   1.1s\n",
            "[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.734 total time=   1.1s\n",
            "[CV 1/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .....C=1, gamma=0.001, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.620 total time=   0.5s\n",
            "[CV 2/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.615 total time=   0.6s\n",
            "[CV 3/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.624 total time=   0.6s\n",
            "[CV 4/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.579 total time=   0.5s\n",
            "[CV 5/5] END ..C=1, gamma=0.001, kernel=sigmoid;, score=0.579 total time=   0.6s\n",
            "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.577 total time=   1.1s\n",
            "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.585 total time=   1.1s\n",
            "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.601 total time=   1.1s\n",
            "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.588 total time=   1.1s\n",
            "[CV 1/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .......C=1, gamma=0.1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.607 total time=   0.4s\n",
            "[CV 2/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.620 total time=   0.4s\n",
            "[CV 3/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.620 total time=   0.4s\n",
            "[CV 4/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.536 total time=   0.4s\n",
            "[CV 5/5] END ....C=1, gamma=0.1, kernel=sigmoid;, score=0.554 total time=   0.4s\n",
            "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.556 total time=   1.1s\n",
            "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.585 total time=   1.1s\n",
            "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.567 total time=   1.1s\n",
            "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.562 total time=   1.1s\n",
            "[CV 1/5] END .........C=1, gamma=1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .........C=1, gamma=1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .........C=1, gamma=1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .........C=1, gamma=1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .........C=1, gamma=1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.556 total time=   0.4s\n",
            "[CV 3/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.526 total time=   0.4s\n",
            "[CV 4/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END ......C=1, gamma=1, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END ........C=1, gamma=1.5, kernel=rbf;, score=0.538 total time=   1.1s\n",
            "[CV 2/5] END ........C=1, gamma=1.5, kernel=rbf;, score=0.551 total time=   1.1s\n",
            "[CV 3/5] END ........C=1, gamma=1.5, kernel=rbf;, score=0.543 total time=   1.1s\n",
            "[CV 4/5] END ........C=1, gamma=1.5, kernel=rbf;, score=0.549 total time=   1.1s\n",
            "[CV 5/5] END ........C=1, gamma=1.5, kernel=rbf;, score=0.554 total time=   1.1s\n",
            "[CV 1/5] END .......C=1, gamma=1.5, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=1, gamma=1.5, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .......C=1, gamma=1.5, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .......C=1, gamma=1.5, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .......C=1, gamma=1.5, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ....C=1, gamma=1.5, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END ....C=1, gamma=1.5, kernel=sigmoid;, score=0.560 total time=   0.4s\n",
            "[CV 3/5] END ....C=1, gamma=1.5, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ....C=1, gamma=1.5, kernel=sigmoid;, score=0.609 total time=   0.4s\n",
            "[CV 5/5] END ....C=1, gamma=1.5, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END ..........C=1, gamma=2, kernel=rbf;, score=0.534 total time=   1.1s\n",
            "[CV 2/5] END ..........C=1, gamma=2, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 3/5] END ..........C=1, gamma=2, kernel=rbf;, score=0.543 total time=   1.1s\n",
            "[CV 4/5] END ..........C=1, gamma=2, kernel=rbf;, score=0.532 total time=   1.1s\n",
            "[CV 5/5] END ..........C=1, gamma=2, kernel=rbf;, score=0.545 total time=   1.1s\n",
            "[CV 1/5] END .........C=1, gamma=2, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .........C=1, gamma=2, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .........C=1, gamma=2, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .........C=1, gamma=2, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .........C=1, gamma=2, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ......C=1, gamma=2, kernel=sigmoid;, score=0.573 total time=   0.4s\n",
            "[CV 2/5] END ......C=1, gamma=2, kernel=sigmoid;, score=0.551 total time=   0.4s\n",
            "[CV 3/5] END ......C=1, gamma=2, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ......C=1, gamma=2, kernel=sigmoid;, score=0.597 total time=   0.4s\n",
            "[CV 5/5] END ......C=1, gamma=2, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END .....C=10, gamma=1e-05, kernel=rbf;, score=0.774 total time=   0.7s\n",
            "[CV 2/5] END .....C=10, gamma=1e-05, kernel=rbf;, score=0.812 total time=   0.7s\n",
            "[CV 3/5] END .....C=10, gamma=1e-05, kernel=rbf;, score=0.778 total time=   0.7s\n",
            "[CV 4/5] END .....C=10, gamma=1e-05, kernel=rbf;, score=0.751 total time=   0.7s\n",
            "[CV 5/5] END .....C=10, gamma=1e-05, kernel=rbf;, score=0.837 total time=   0.8s\n",
            "[CV 1/5] END ....C=10, gamma=1e-05, kernel=poly;, score=0.658 total time=   0.7s\n",
            "[CV 2/5] END ....C=10, gamma=1e-05, kernel=poly;, score=0.701 total time=   0.8s\n",
            "[CV 3/5] END ....C=10, gamma=1e-05, kernel=poly;, score=0.688 total time=   0.7s\n",
            "[CV 4/5] END ....C=10, gamma=1e-05, kernel=poly;, score=0.639 total time=   0.8s\n",
            "[CV 5/5] END ....C=10, gamma=1e-05, kernel=poly;, score=0.678 total time=   0.7s\n",
            "[CV 1/5] END .C=10, gamma=1e-05, kernel=sigmoid;, score=0.774 total time=   0.7s\n",
            "[CV 2/5] END .C=10, gamma=1e-05, kernel=sigmoid;, score=0.803 total time=   0.7s\n",
            "[CV 3/5] END .C=10, gamma=1e-05, kernel=sigmoid;, score=0.744 total time=   0.7s\n",
            "[CV 4/5] END .C=10, gamma=1e-05, kernel=sigmoid;, score=0.742 total time=   0.7s\n",
            "[CV 5/5] END .C=10, gamma=1e-05, kernel=sigmoid;, score=0.803 total time=   0.7s\n",
            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.795 total time=   1.0s\n",
            "[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.850 total time=   1.0s\n",
            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.799 total time=   1.0s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.798 total time=   1.0s\n",
            "[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.850 total time=   1.0s\n",
            "[CV 1/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.748 total time=   0.8s\n",
            "[CV 2/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.808 total time=   0.8s\n",
            "[CV 3/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.761 total time=   0.8s\n",
            "[CV 4/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.777 total time=   0.8s\n",
            "[CV 5/5] END ...C=10, gamma=0.0001, kernel=poly;, score=0.807 total time=   0.8s\n",
            "[CV 1/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.620 total time=   0.4s\n",
            "[CV 2/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.658 total time=   0.4s\n",
            "[CV 3/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.645 total time=   0.4s\n",
            "[CV 4/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.635 total time=   0.4s\n",
            "[CV 5/5] END C=10, gamma=0.0001, kernel=sigmoid;, score=0.597 total time=   0.4s\n",
            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.684 total time=   1.1s\n",
            "[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.704 total time=   1.1s\n",
            "[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.738 total time=   1.0s\n",
            "[CV 1/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ....C=10, gamma=0.001, kernel=poly;, score=0.798 total time=   0.9s\n",
            "[CV 1/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.611 total time=   0.5s\n",
            "[CV 2/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.611 total time=   0.6s\n",
            "[CV 3/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.620 total time=   0.5s\n",
            "[CV 4/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.588 total time=   0.6s\n",
            "[CV 5/5] END .C=10, gamma=0.001, kernel=sigmoid;, score=0.575 total time=   0.6s\n",
            "[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.577 total time=   1.1s\n",
            "[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.585 total time=   1.1s\n",
            "[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.601 total time=   1.1s\n",
            "[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.588 total time=   1.1s\n",
            "[CV 1/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.791 total time=   0.9s\n",
            "[CV 3/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ......C=10, gamma=0.1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.611 total time=   0.4s\n",
            "[CV 2/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.620 total time=   0.4s\n",
            "[CV 3/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.611 total time=   0.4s\n",
            "[CV 4/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END ...C=10, gamma=0.1, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.556 total time=   1.1s\n",
            "[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.571 total time=   1.1s\n",
            "[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.567 total time=   1.1s\n",
            "[CV 1/5] END ........C=10, gamma=1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ........C=10, gamma=1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ........C=10, gamma=1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ........C=10, gamma=1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ........C=10, gamma=1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END .....C=10, gamma=1, kernel=sigmoid;, score=0.567 total time=   0.4s\n",
            "[CV 1/5] END .......C=10, gamma=1.5, kernel=rbf;, score=0.551 total time=   1.1s\n",
            "[CV 2/5] END .......C=10, gamma=1.5, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 3/5] END .......C=10, gamma=1.5, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 4/5] END .......C=10, gamma=1.5, kernel=rbf;, score=0.558 total time=   1.1s\n",
            "[CV 5/5] END .......C=10, gamma=1.5, kernel=rbf;, score=0.554 total time=   1.1s\n",
            "[CV 1/5] END ......C=10, gamma=1.5, kernel=poly;, score=0.769 total time=   0.9s\n",
            "[CV 2/5] END ......C=10, gamma=1.5, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ......C=10, gamma=1.5, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ......C=10, gamma=1.5, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ......C=10, gamma=1.5, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ...C=10, gamma=1.5, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END ...C=10, gamma=1.5, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END ...C=10, gamma=1.5, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ...C=10, gamma=1.5, kernel=sigmoid;, score=0.614 total time=   0.4s\n",
            "[CV 5/5] END ...C=10, gamma=1.5, kernel=sigmoid;, score=0.554 total time=   0.4s\n",
            "[CV 1/5] END .........C=10, gamma=2, kernel=rbf;, score=0.538 total time=   1.1s\n",
            "[CV 2/5] END .........C=10, gamma=2, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 3/5] END .........C=10, gamma=2, kernel=rbf;, score=0.543 total time=   1.1s\n",
            "[CV 4/5] END .........C=10, gamma=2, kernel=rbf;, score=0.545 total time=   1.1s\n",
            "[CV 5/5] END .........C=10, gamma=2, kernel=rbf;, score=0.549 total time=   1.1s\n",
            "[CV 1/5] END ........C=10, gamma=2, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ........C=10, gamma=2, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ........C=10, gamma=2, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ........C=10, gamma=2, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ........C=10, gamma=2, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END .....C=10, gamma=2, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 2/5] END .....C=10, gamma=2, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END .....C=10, gamma=2, kernel=sigmoid;, score=0.526 total time=   0.4s\n",
            "[CV 4/5] END .....C=10, gamma=2, kernel=sigmoid;, score=0.609 total time=   0.4s\n",
            "[CV 5/5] END .....C=10, gamma=2, kernel=sigmoid;, score=0.554 total time=   0.4s\n",
            "[CV 1/5] END ....C=100, gamma=1e-05, kernel=rbf;, score=0.744 total time=   0.9s\n",
            "[CV 2/5] END ....C=100, gamma=1e-05, kernel=rbf;, score=0.765 total time=   0.9s\n",
            "[CV 3/5] END ....C=100, gamma=1e-05, kernel=rbf;, score=0.761 total time=   0.9s\n",
            "[CV 4/5] END ....C=100, gamma=1e-05, kernel=rbf;, score=0.751 total time=   0.9s\n",
            "[CV 5/5] END ....C=100, gamma=1e-05, kernel=rbf;, score=0.807 total time=   0.9s\n",
            "[CV 1/5] END ...C=100, gamma=1e-05, kernel=poly;, score=0.692 total time=   0.7s\n",
            "[CV 2/5] END ...C=100, gamma=1e-05, kernel=poly;, score=0.744 total time=   0.7s\n",
            "[CV 3/5] END ...C=100, gamma=1e-05, kernel=poly;, score=0.718 total time=   0.7s\n",
            "[CV 4/5] END ...C=100, gamma=1e-05, kernel=poly;, score=0.657 total time=   0.7s\n",
            "[CV 5/5] END ...C=100, gamma=1e-05, kernel=poly;, score=0.734 total time=   0.7s\n",
            "[CV 1/5] END C=100, gamma=1e-05, kernel=sigmoid;, score=0.731 total time=   0.8s\n",
            "[CV 2/5] END C=100, gamma=1e-05, kernel=sigmoid;, score=0.752 total time=   0.9s\n",
            "[CV 3/5] END C=100, gamma=1e-05, kernel=sigmoid;, score=0.688 total time=   0.8s\n",
            "[CV 4/5] END C=100, gamma=1e-05, kernel=sigmoid;, score=0.712 total time=   0.8s\n",
            "[CV 5/5] END C=100, gamma=1e-05, kernel=sigmoid;, score=0.742 total time=   0.8s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.795 total time=   1.0s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.850 total time=   1.0s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.799 total time=   1.0s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.798 total time=   1.0s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.854 total time=   1.0s\n",
            "[CV 1/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.761 total time=   0.8s\n",
            "[CV 2/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.773 total time=   0.8s\n",
            "[CV 5/5] END ..C=100, gamma=0.0001, kernel=poly;, score=0.811 total time=   0.8s\n",
            "[CV 1/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.624 total time=   0.4s\n",
            "[CV 2/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.645 total time=   0.4s\n",
            "[CV 3/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.637 total time=   0.4s\n",
            "[CV 4/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.631 total time=   0.4s\n",
            "[CV 5/5] END C=100, gamma=0.0001, kernel=sigmoid;, score=0.562 total time=   0.5s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.684 total time=   1.1s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.704 total time=   1.1s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.738 total time=   1.1s\n",
            "[CV 1/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ...C=100, gamma=0.001, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.607 total time=   0.5s\n",
            "[CV 2/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.603 total time=   0.6s\n",
            "[CV 3/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.624 total time=   0.6s\n",
            "[CV 4/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.588 total time=   0.5s\n",
            "[CV 5/5] END C=100, gamma=0.001, kernel=sigmoid;, score=0.579 total time=   0.6s\n",
            "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.577 total time=   1.1s\n",
            "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.585 total time=   1.1s\n",
            "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.601 total time=   1.1s\n",
            "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.588 total time=   1.1s\n",
            "[CV 1/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .....C=100, gamma=0.1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.594 total time=   0.4s\n",
            "[CV 2/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.615 total time=   0.4s\n",
            "[CV 3/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.615 total time=   0.4s\n",
            "[CV 4/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END ..C=100, gamma=0.1, kernel=sigmoid;, score=0.554 total time=   0.4s\n",
            "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.556 total time=   1.1s\n",
            "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.571 total time=   1.1s\n",
            "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.567 total time=   1.1s\n",
            "[CV 1/5] END .......C=100, gamma=1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=100, gamma=1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .......C=100, gamma=1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .......C=100, gamma=1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .......C=100, gamma=1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END ....C=100, gamma=1, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END ......C=100, gamma=1.5, kernel=rbf;, score=0.551 total time=   1.1s\n",
            "[CV 2/5] END ......C=100, gamma=1.5, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 3/5] END ......C=100, gamma=1.5, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 4/5] END ......C=100, gamma=1.5, kernel=rbf;, score=0.558 total time=   1.1s\n",
            "[CV 5/5] END ......C=100, gamma=1.5, kernel=rbf;, score=0.554 total time=   1.1s\n",
            "[CV 1/5] END .....C=100, gamma=1.5, kernel=poly;, score=0.769 total time=   0.9s\n",
            "[CV 2/5] END .....C=100, gamma=1.5, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .....C=100, gamma=1.5, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .....C=100, gamma=1.5, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .....C=100, gamma=1.5, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ..C=100, gamma=1.5, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 2/5] END ..C=100, gamma=1.5, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END ..C=100, gamma=1.5, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ..C=100, gamma=1.5, kernel=sigmoid;, score=0.605 total time=   0.4s\n",
            "[CV 5/5] END ..C=100, gamma=1.5, kernel=sigmoid;, score=0.549 total time=   0.4s\n",
            "[CV 1/5] END ........C=100, gamma=2, kernel=rbf;, score=0.538 total time=   1.1s\n",
            "[CV 2/5] END ........C=100, gamma=2, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 3/5] END ........C=100, gamma=2, kernel=rbf;, score=0.543 total time=   1.1s\n",
            "[CV 4/5] END ........C=100, gamma=2, kernel=rbf;, score=0.545 total time=   1.1s\n",
            "[CV 5/5] END ........C=100, gamma=2, kernel=rbf;, score=0.549 total time=   1.1s\n",
            "[CV 1/5] END .......C=100, gamma=2, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .......C=100, gamma=2, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .......C=100, gamma=2, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .......C=100, gamma=2, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .......C=100, gamma=2, kernel=poly;, score=0.798 total time=   0.9s\n",
            "[CV 1/5] END ....C=100, gamma=2, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 2/5] END ....C=100, gamma=2, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END ....C=100, gamma=2, kernel=sigmoid;, score=0.521 total time=   0.4s\n",
            "[CV 4/5] END ....C=100, gamma=2, kernel=sigmoid;, score=0.601 total time=   0.4s\n",
            "[CV 5/5] END ....C=100, gamma=2, kernel=sigmoid;, score=0.549 total time=   0.4s\n",
            "[CV 1/5] END ...C=1000, gamma=1e-05, kernel=rbf;, score=0.739 total time=   0.9s\n",
            "[CV 2/5] END ...C=1000, gamma=1e-05, kernel=rbf;, score=0.761 total time=   1.0s\n",
            "[CV 3/5] END ...C=1000, gamma=1e-05, kernel=rbf;, score=0.761 total time=   0.9s\n",
            "[CV 4/5] END ...C=1000, gamma=1e-05, kernel=rbf;, score=0.742 total time=   0.9s\n",
            "[CV 5/5] END ...C=1000, gamma=1e-05, kernel=rbf;, score=0.833 total time=   1.0s\n",
            "[CV 1/5] END ..C=1000, gamma=1e-05, kernel=poly;, score=0.731 total time=   0.7s\n",
            "[CV 2/5] END ..C=1000, gamma=1e-05, kernel=poly;, score=0.803 total time=   0.7s\n",
            "[CV 3/5] END ..C=1000, gamma=1e-05, kernel=poly;, score=0.756 total time=   0.7s\n",
            "[CV 4/5] END ..C=1000, gamma=1e-05, kernel=poly;, score=0.738 total time=   0.7s\n",
            "[CV 5/5] END ..C=1000, gamma=1e-05, kernel=poly;, score=0.807 total time=   0.7s\n",
            "[CV 1/5] END C=1000, gamma=1e-05, kernel=sigmoid;, score=0.654 total time=   1.0s\n",
            "[CV 2/5] END C=1000, gamma=1e-05, kernel=sigmoid;, score=0.726 total time=   1.2s\n",
            "[CV 3/5] END C=1000, gamma=1e-05, kernel=sigmoid;, score=0.658 total time=   1.1s\n",
            "[CV 4/5] END C=1000, gamma=1e-05, kernel=sigmoid;, score=0.682 total time=   0.9s\n",
            "[CV 5/5] END C=1000, gamma=1e-05, kernel=sigmoid;, score=0.712 total time=   1.2s\n",
            "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.795 total time=   0.9s\n",
            "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.850 total time=   1.0s\n",
            "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.799 total time=   1.0s\n",
            "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.798 total time=   0.9s\n",
            "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.854 total time=   1.0s\n",
            "[CV 1/5] END .C=1000, gamma=0.0001, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END .C=1000, gamma=0.0001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END .C=1000, gamma=0.0001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END .C=1000, gamma=0.0001, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END .C=1000, gamma=0.0001, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END C=1000, gamma=0.0001, kernel=sigmoid;, score=0.624 total time=   0.4s\n",
            "[CV 2/5] END C=1000, gamma=0.0001, kernel=sigmoid;, score=0.641 total time=   0.4s\n",
            "[CV 3/5] END C=1000, gamma=0.0001, kernel=sigmoid;, score=0.632 total time=   0.4s\n",
            "[CV 4/5] END C=1000, gamma=0.0001, kernel=sigmoid;, score=0.627 total time=   0.4s\n",
            "[CV 5/5] END C=1000, gamma=0.0001, kernel=sigmoid;, score=0.562 total time=   0.5s\n",
            "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.684 total time=   1.1s\n",
            "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.671 total time=   1.1s\n",
            "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.704 total time=   1.0s\n",
            "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.738 total time=   1.0s\n",
            "[CV 1/5] END ..C=1000, gamma=0.001, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ..C=1000, gamma=0.001, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ..C=1000, gamma=0.001, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ..C=1000, gamma=0.001, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ..C=1000, gamma=0.001, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END C=1000, gamma=0.001, kernel=sigmoid;, score=0.607 total time=   0.5s\n",
            "[CV 2/5] END C=1000, gamma=0.001, kernel=sigmoid;, score=0.603 total time=   0.5s\n",
            "[CV 3/5] END C=1000, gamma=0.001, kernel=sigmoid;, score=0.624 total time=   0.6s\n",
            "[CV 4/5] END C=1000, gamma=0.001, kernel=sigmoid;, score=0.588 total time=   0.5s\n",
            "[CV 5/5] END C=1000, gamma=0.001, kernel=sigmoid;, score=0.579 total time=   0.6s\n",
            "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.577 total time=   1.1s\n",
            "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.585 total time=   1.1s\n",
            "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.601 total time=   1.1s\n",
            "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.588 total time=   1.1s\n",
            "[CV 1/5] END ....C=1000, gamma=0.1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ....C=1000, gamma=0.1, kernel=poly;, score=0.791 total time=   0.9s\n",
            "[CV 3/5] END ....C=1000, gamma=0.1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ....C=1000, gamma=0.1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ....C=1000, gamma=0.1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END .C=1000, gamma=0.1, kernel=sigmoid;, score=0.594 total time=   0.4s\n",
            "[CV 2/5] END .C=1000, gamma=0.1, kernel=sigmoid;, score=0.615 total time=   0.4s\n",
            "[CV 3/5] END .C=1000, gamma=0.1, kernel=sigmoid;, score=0.615 total time=   0.4s\n",
            "[CV 4/5] END .C=1000, gamma=0.1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END .C=1000, gamma=0.1, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.556 total time=   1.1s\n",
            "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.590 total time=   1.1s\n",
            "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.571 total time=   1.1s\n",
            "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.567 total time=   1.1s\n",
            "[CV 1/5] END ......C=1000, gamma=1, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ......C=1000, gamma=1, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ......C=1000, gamma=1, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ......C=1000, gamma=1, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ......C=1000, gamma=1, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ...C=1000, gamma=1, kernel=sigmoid;, score=0.547 total time=   0.4s\n",
            "[CV 2/5] END ...C=1000, gamma=1, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 3/5] END ...C=1000, gamma=1, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END ...C=1000, gamma=1, kernel=sigmoid;, score=0.545 total time=   0.4s\n",
            "[CV 5/5] END ...C=1000, gamma=1, kernel=sigmoid;, score=0.558 total time=   0.5s\n",
            "[CV 1/5] END .....C=1000, gamma=1.5, kernel=rbf;, score=0.551 total time=   1.1s\n",
            "[CV 2/5] END .....C=1000, gamma=1.5, kernel=rbf;, score=0.560 total time=   1.1s\n",
            "[CV 3/5] END .....C=1000, gamma=1.5, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 4/5] END .....C=1000, gamma=1.5, kernel=rbf;, score=0.558 total time=   1.1s\n",
            "[CV 5/5] END .....C=1000, gamma=1.5, kernel=rbf;, score=0.554 total time=   1.1s\n",
            "[CV 1/5] END ....C=1000, gamma=1.5, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ....C=1000, gamma=1.5, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ....C=1000, gamma=1.5, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ....C=1000, gamma=1.5, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ....C=1000, gamma=1.5, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END .C=1000, gamma=1.5, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 2/5] END .C=1000, gamma=1.5, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END .C=1000, gamma=1.5, kernel=sigmoid;, score=0.517 total time=   0.4s\n",
            "[CV 4/5] END .C=1000, gamma=1.5, kernel=sigmoid;, score=0.601 total time=   0.4s\n",
            "[CV 5/5] END .C=1000, gamma=1.5, kernel=sigmoid;, score=0.558 total time=   0.4s\n",
            "[CV 1/5] END .......C=1000, gamma=2, kernel=rbf;, score=0.538 total time=   1.1s\n",
            "[CV 2/5] END .......C=1000, gamma=2, kernel=rbf;, score=0.547 total time=   1.1s\n",
            "[CV 3/5] END .......C=1000, gamma=2, kernel=rbf;, score=0.543 total time=   1.1s\n",
            "[CV 4/5] END .......C=1000, gamma=2, kernel=rbf;, score=0.545 total time=   1.1s\n",
            "[CV 5/5] END .......C=1000, gamma=2, kernel=rbf;, score=0.549 total time=   1.1s\n",
            "[CV 1/5] END ......C=1000, gamma=2, kernel=poly;, score=0.769 total time=   0.8s\n",
            "[CV 2/5] END ......C=1000, gamma=2, kernel=poly;, score=0.791 total time=   0.8s\n",
            "[CV 3/5] END ......C=1000, gamma=2, kernel=poly;, score=0.752 total time=   0.8s\n",
            "[CV 4/5] END ......C=1000, gamma=2, kernel=poly;, score=0.785 total time=   0.8s\n",
            "[CV 5/5] END ......C=1000, gamma=2, kernel=poly;, score=0.798 total time=   0.8s\n",
            "[CV 1/5] END ...C=1000, gamma=2, kernel=sigmoid;, score=0.564 total time=   0.4s\n",
            "[CV 2/5] END ...C=1000, gamma=2, kernel=sigmoid;, score=0.568 total time=   0.4s\n",
            "[CV 3/5] END ...C=1000, gamma=2, kernel=sigmoid;, score=0.521 total time=   0.4s\n",
            "[CV 4/5] END ...C=1000, gamma=2, kernel=sigmoid;, score=0.601 total time=   0.4s\n",
            "[CV 5/5] END ...C=1000, gamma=2, kernel=sigmoid;, score=0.558 total time=   0.4s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
              "                         'gamma': [1e-05, 0.0001, 0.001, 0.1, 1, 1.5, 2],\n",
              "                         'kernel': ['rbf', 'poly', 'sigmoid']},\n",
              "             verbose=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;gamma&#x27;: [1e-05, 0.0001, 0.001, 0.1, 1, 1.5, 2],\n",
              "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;sigmoid&#x27;]},\n",
              "             verbose=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(probability=True),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10, 100, 1000],\n",
              "                         &#x27;gamma&#x27;: [1e-05, 0.0001, 0.001, 0.1, 1, 1.5, 2],\n",
              "                         &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;sigmoid&#x27;]},\n",
              "             verbose=4)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.best_params_, model.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s3OXsALnij-",
        "outputId": "b25c6df6-e107-4fb2-ba18-313846c1a285"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}, 0.8193609918931808)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "filename = 'pca_svm_v2.sav'\n",
        "joblib.dump(model, filename)"
      ],
      "metadata": {
        "id": "1bn0yeVtsJJ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eeb0ffc-0f4f-4936-d9c8-7411c7adee55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pca_svm_v2.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Define the parameter grid for each model\n",
        "knn_param_grid = {'n_neighbors': [1+(2*i) for i in range(1, 10)]}\n",
        "dt_param_grid = {'max_depth': [None, 1, 5, 10, 15, 20, 50]}\n",
        "rf_param_grid = {'n_estimators': [50, 100, 200], 'max_depth': [None, 1, 5, 10, 15, 20, 50]}\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "dt = DecisionTreeClassifier()\n",
        "rf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "3yrtgsHt819-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid = GridSearchCV(knn, knn_param_grid, cv=5, verbose = 4, scoring='f1')\n",
        "knn_grid.fit(x_train_pca, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 967
        },
        "id": "j20bRevZ87LU",
        "outputId": "fc85757e-0274-47d9-8e2a-e617e6d7ba7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
            "[CV 1/5] END .....................n_neighbors=3;, score=0.718 total time=   0.2s\n",
            "[CV 2/5] END .....................n_neighbors=3;, score=0.747 total time=   0.0s\n",
            "[CV 3/5] END .....................n_neighbors=3;, score=0.756 total time=   0.2s\n",
            "[CV 4/5] END .....................n_neighbors=3;, score=0.754 total time=   0.1s\n",
            "[CV 5/5] END .....................n_neighbors=3;, score=0.757 total time=   0.0s\n",
            "[CV 1/5] END .....................n_neighbors=5;, score=0.734 total time=   0.0s\n",
            "[CV 2/5] END .....................n_neighbors=5;, score=0.762 total time=   0.0s\n",
            "[CV 3/5] END .....................n_neighbors=5;, score=0.752 total time=   0.0s\n",
            "[CV 4/5] END .....................n_neighbors=5;, score=0.713 total time=   0.0s\n",
            "[CV 5/5] END .....................n_neighbors=5;, score=0.776 total time=   0.0s\n",
            "[CV 1/5] END .....................n_neighbors=7;, score=0.760 total time=   0.0s\n",
            "[CV 2/5] END .....................n_neighbors=7;, score=0.747 total time=   0.0s\n",
            "[CV 3/5] END .....................n_neighbors=7;, score=0.712 total time=   0.0s\n",
            "[CV 4/5] END .....................n_neighbors=7;, score=0.741 total time=   0.0s\n",
            "[CV 5/5] END .....................n_neighbors=7;, score=0.748 total time=   0.0s\n",
            "[CV 1/5] END .....................n_neighbors=9;, score=0.773 total time=   0.0s\n",
            "[CV 2/5] END .....................n_neighbors=9;, score=0.766 total time=   0.0s\n",
            "[CV 3/5] END .....................n_neighbors=9;, score=0.724 total time=   0.0s\n",
            "[CV 4/5] END .....................n_neighbors=9;, score=0.723 total time=   0.0s\n",
            "[CV 5/5] END .....................n_neighbors=9;, score=0.758 total time=   0.0s\n",
            "[CV 1/5] END ....................n_neighbors=11;, score=0.748 total time=   0.0s\n",
            "[CV 2/5] END ....................n_neighbors=11;, score=0.770 total time=   0.0s\n",
            "[CV 3/5] END ....................n_neighbors=11;, score=0.697 total time=   0.0s\n",
            "[CV 4/5] END ....................n_neighbors=11;, score=0.713 total time=   0.0s\n",
            "[CV 5/5] END ....................n_neighbors=11;, score=0.769 total time=   0.0s\n",
            "[CV 1/5] END ....................n_neighbors=13;, score=0.751 total time=   0.0s\n",
            "[CV 2/5] END ....................n_neighbors=13;, score=0.740 total time=   0.0s\n",
            "[CV 3/5] END ....................n_neighbors=13;, score=0.716 total time=   0.0s\n",
            "[CV 4/5] END ....................n_neighbors=13;, score=0.709 total time=   0.0s\n",
            "[CV 5/5] END ....................n_neighbors=13;, score=0.755 total time=   0.0s\n",
            "[CV 1/5] END ....................n_neighbors=15;, score=0.722 total time=   0.0s\n",
            "[CV 2/5] END ....................n_neighbors=15;, score=0.744 total time=   0.0s\n",
            "[CV 3/5] END ....................n_neighbors=15;, score=0.708 total time=   0.0s\n",
            "[CV 4/5] END ....................n_neighbors=15;, score=0.681 total time=   0.0s\n",
            "[CV 5/5] END ....................n_neighbors=15;, score=0.746 total time=   0.0s\n",
            "[CV 1/5] END ....................n_neighbors=17;, score=0.711 total time=   0.0s\n",
            "[CV 2/5] END ....................n_neighbors=17;, score=0.741 total time=   0.0s\n",
            "[CV 3/5] END ....................n_neighbors=17;, score=0.733 total time=   0.0s\n",
            "[CV 4/5] END ....................n_neighbors=17;, score=0.684 total time=   0.0s\n",
            "[CV 5/5] END ....................n_neighbors=17;, score=0.733 total time=   0.0s\n",
            "[CV 1/5] END ....................n_neighbors=19;, score=0.709 total time=   0.0s\n",
            "[CV 2/5] END ....................n_neighbors=19;, score=0.756 total time=   0.0s\n",
            "[CV 3/5] END ....................n_neighbors=19;, score=0.706 total time=   0.0s\n",
            "[CV 4/5] END ....................n_neighbors=19;, score=0.681 total time=   0.0s\n",
            "[CV 5/5] END ....................n_neighbors=19;, score=0.742 total time=   0.0s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'n_neighbors': [3, 5, 7, 9, 11, 13, 15, 17, 19]},\n",
              "             scoring='f1', verbose=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15, 17, 19]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15, 17, 19]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_grid.best_params_, knn_grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbE4tSJr9FoS",
        "outputId": "03bb2ade-959b-450b-f6dc-36e4bb032351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'n_neighbors': 9}, 0.7487957508647198)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_grid = GridSearchCV(dt, dt_param_grid, cv=5, verbose = 4, scoring='f1')\n",
        "dt_grid.fit(x_train_pca, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "tMT9xGStCJ49",
        "outputId": "f27e0bea-fedd-48d8-8376-8f63d4c109c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n",
            "[CV 1/5] END ....................max_depth=None;, score=0.678 total time=   0.4s\n",
            "[CV 2/5] END ....................max_depth=None;, score=0.717 total time=   0.4s\n",
            "[CV 3/5] END ....................max_depth=None;, score=0.655 total time=   0.4s\n",
            "[CV 4/5] END ....................max_depth=None;, score=0.684 total time=   0.4s\n",
            "[CV 5/5] END ....................max_depth=None;, score=0.736 total time=   0.4s\n",
            "[CV 1/5] END .......................max_depth=1;, score=0.548 total time=   0.1s\n",
            "[CV 2/5] END .......................max_depth=1;, score=0.615 total time=   0.1s\n",
            "[CV 3/5] END .......................max_depth=1;, score=0.584 total time=   0.1s\n",
            "[CV 4/5] END .......................max_depth=1;, score=0.450 total time=   0.1s\n",
            "[CV 5/5] END .......................max_depth=1;, score=0.601 total time=   0.1s\n",
            "[CV 1/5] END .......................max_depth=5;, score=0.661 total time=   0.2s\n",
            "[CV 2/5] END .......................max_depth=5;, score=0.705 total time=   0.2s\n",
            "[CV 3/5] END .......................max_depth=5;, score=0.653 total time=   0.2s\n",
            "[CV 4/5] END .......................max_depth=5;, score=0.582 total time=   0.2s\n",
            "[CV 5/5] END .......................max_depth=5;, score=0.713 total time=   0.2s\n",
            "[CV 1/5] END ......................max_depth=10;, score=0.670 total time=   0.4s\n",
            "[CV 2/5] END ......................max_depth=10;, score=0.701 total time=   0.4s\n",
            "[CV 3/5] END ......................max_depth=10;, score=0.646 total time=   0.4s\n",
            "[CV 4/5] END ......................max_depth=10;, score=0.684 total time=   0.4s\n",
            "[CV 5/5] END ......................max_depth=10;, score=0.717 total time=   0.4s\n",
            "[CV 1/5] END ......................max_depth=15;, score=0.684 total time=   0.4s\n",
            "[CV 2/5] END ......................max_depth=15;, score=0.689 total time=   0.4s\n",
            "[CV 3/5] END ......................max_depth=15;, score=0.649 total time=   0.4s\n",
            "[CV 4/5] END ......................max_depth=15;, score=0.672 total time=   0.4s\n",
            "[CV 5/5] END ......................max_depth=15;, score=0.750 total time=   0.4s\n",
            "[CV 1/5] END ......................max_depth=20;, score=0.684 total time=   0.4s\n",
            "[CV 2/5] END ......................max_depth=20;, score=0.684 total time=   0.4s\n",
            "[CV 3/5] END ......................max_depth=20;, score=0.646 total time=   0.4s\n",
            "[CV 4/5] END ......................max_depth=20;, score=0.713 total time=   0.4s\n",
            "[CV 5/5] END ......................max_depth=20;, score=0.733 total time=   0.4s\n",
            "[CV 1/5] END ......................max_depth=50;, score=0.675 total time=   0.4s\n",
            "[CV 2/5] END ......................max_depth=50;, score=0.726 total time=   0.5s\n",
            "[CV 3/5] END ......................max_depth=50;, score=0.646 total time=   0.4s\n",
            "[CV 4/5] END ......................max_depth=50;, score=0.686 total time=   0.5s\n",
            "[CV 5/5] END ......................max_depth=50;, score=0.720 total time=   0.4s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={'max_depth': [None, 1, 5, 10, 15, 20, 50]},\n",
              "             scoring='f1', verbose=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None, 1, 5, 10, 15, 20, 50]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None, 1, 5, 10, 15, 20, 50]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_grid.best_params_, dt_grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMaELZYxCNVc",
        "outputId": "a6a9d2aa-35b8-48e5-e556-ece49c37956a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'max_depth': None}, 0.6939630100007109)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filename = 'pca_decision_tree.sav'\n",
        "joblib.dump(dt_grid, filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flJiVIQNMRkm",
        "outputId": "65912165-4087-41a7-b307-88df72dad6e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pca_decision_tree.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_grid = GridSearchCV(rf, rf_param_grid, cv=5, verbose = 4, scoring='f1')\n",
        "rf_grid.fit(x_train_pca, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "go1sM-16CyPG",
        "outputId": "e9e3cf3a-5f26-42d7-95a9-6dfef250289d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 21 candidates, totalling 105 fits\n",
            "[CV 1/5] END ...max_depth=None, n_estimators=50;, score=0.700 total time=   0.7s\n",
            "[CV 2/5] END ...max_depth=None, n_estimators=50;, score=0.739 total time=   0.8s\n",
            "[CV 3/5] END ...max_depth=None, n_estimators=50;, score=0.713 total time=   0.8s\n",
            "[CV 4/5] END ...max_depth=None, n_estimators=50;, score=0.683 total time=   0.8s\n",
            "[CV 5/5] END ...max_depth=None, n_estimators=50;, score=0.715 total time=   0.7s\n",
            "[CV 1/5] END ..max_depth=None, n_estimators=100;, score=0.698 total time=   1.5s\n",
            "[CV 2/5] END ..max_depth=None, n_estimators=100;, score=0.735 total time=   1.5s\n",
            "[CV 3/5] END ..max_depth=None, n_estimators=100;, score=0.694 total time=   1.5s\n",
            "[CV 4/5] END ..max_depth=None, n_estimators=100;, score=0.713 total time=   1.5s\n",
            "[CV 5/5] END ..max_depth=None, n_estimators=100;, score=0.771 total time=   1.5s\n",
            "[CV 1/5] END ..max_depth=None, n_estimators=200;, score=0.742 total time=   3.0s\n",
            "[CV 2/5] END ..max_depth=None, n_estimators=200;, score=0.718 total time=   3.0s\n",
            "[CV 3/5] END ..max_depth=None, n_estimators=200;, score=0.704 total time=   2.9s\n",
            "[CV 4/5] END ..max_depth=None, n_estimators=200;, score=0.714 total time=   3.0s\n",
            "[CV 5/5] END ..max_depth=None, n_estimators=200;, score=0.784 total time=   3.0s\n",
            "[CV 1/5] END ......max_depth=1, n_estimators=50;, score=0.626 total time=   0.2s\n",
            "[CV 2/5] END ......max_depth=1, n_estimators=50;, score=0.740 total time=   0.2s\n",
            "[CV 3/5] END ......max_depth=1, n_estimators=50;, score=0.644 total time=   0.2s\n",
            "[CV 4/5] END ......max_depth=1, n_estimators=50;, score=0.630 total time=   0.2s\n",
            "[CV 5/5] END ......max_depth=1, n_estimators=50;, score=0.687 total time=   0.2s\n",
            "[CV 1/5] END .....max_depth=1, n_estimators=100;, score=0.684 total time=   0.3s\n",
            "[CV 2/5] END .....max_depth=1, n_estimators=100;, score=0.678 total time=   0.3s\n",
            "[CV 3/5] END .....max_depth=1, n_estimators=100;, score=0.670 total time=   0.3s\n",
            "[CV 4/5] END .....max_depth=1, n_estimators=100;, score=0.611 total time=   0.3s\n",
            "[CV 5/5] END .....max_depth=1, n_estimators=100;, score=0.714 total time=   0.3s\n",
            "[CV 1/5] END .....max_depth=1, n_estimators=200;, score=0.716 total time=   0.7s\n",
            "[CV 2/5] END .....max_depth=1, n_estimators=200;, score=0.736 total time=   0.7s\n",
            "[CV 3/5] END .....max_depth=1, n_estimators=200;, score=0.658 total time=   0.7s\n",
            "[CV 4/5] END .....max_depth=1, n_estimators=200;, score=0.690 total time=   0.7s\n",
            "[CV 5/5] END .....max_depth=1, n_estimators=200;, score=0.723 total time=   0.7s\n",
            "[CV 1/5] END ......max_depth=5, n_estimators=50;, score=0.708 total time=   0.5s\n",
            "[CV 2/5] END ......max_depth=5, n_estimators=50;, score=0.759 total time=   0.5s\n",
            "[CV 3/5] END ......max_depth=5, n_estimators=50;, score=0.742 total time=   0.5s\n",
            "[CV 4/5] END ......max_depth=5, n_estimators=50;, score=0.700 total time=   0.5s\n",
            "[CV 5/5] END ......max_depth=5, n_estimators=50;, score=0.773 total time=   0.5s\n",
            "[CV 1/5] END .....max_depth=5, n_estimators=100;, score=0.732 total time=   0.9s\n",
            "[CV 2/5] END .....max_depth=5, n_estimators=100;, score=0.760 total time=   0.9s\n",
            "[CV 3/5] END .....max_depth=5, n_estimators=100;, score=0.690 total time=   0.9s\n",
            "[CV 4/5] END .....max_depth=5, n_estimators=100;, score=0.723 total time=   0.9s\n",
            "[CV 5/5] END .....max_depth=5, n_estimators=100;, score=0.795 total time=   0.9s\n",
            "[CV 1/5] END .....max_depth=5, n_estimators=200;, score=0.727 total time=   1.8s\n",
            "[CV 2/5] END .....max_depth=5, n_estimators=200;, score=0.761 total time=   1.8s\n",
            "[CV 3/5] END .....max_depth=5, n_estimators=200;, score=0.733 total time=   1.8s\n",
            "[CV 4/5] END .....max_depth=5, n_estimators=200;, score=0.709 total time=   1.8s\n",
            "[CV 5/5] END .....max_depth=5, n_estimators=200;, score=0.796 total time=   1.8s\n",
            "[CV 1/5] END .....max_depth=10, n_estimators=50;, score=0.740 total time=   0.7s\n",
            "[CV 2/5] END .....max_depth=10, n_estimators=50;, score=0.763 total time=   0.7s\n",
            "[CV 3/5] END .....max_depth=10, n_estimators=50;, score=0.726 total time=   0.7s\n",
            "[CV 4/5] END .....max_depth=10, n_estimators=50;, score=0.739 total time=   0.7s\n",
            "[CV 5/5] END .....max_depth=10, n_estimators=50;, score=0.771 total time=   0.7s\n",
            "[CV 1/5] END ....max_depth=10, n_estimators=100;, score=0.722 total time=   1.3s\n",
            "[CV 2/5] END ....max_depth=10, n_estimators=100;, score=0.722 total time=   1.4s\n",
            "[CV 3/5] END ....max_depth=10, n_estimators=100;, score=0.717 total time=   1.3s\n",
            "[CV 4/5] END ....max_depth=10, n_estimators=100;, score=0.738 total time=   1.3s\n",
            "[CV 5/5] END ....max_depth=10, n_estimators=100;, score=0.733 total time=   1.3s\n",
            "[CV 1/5] END ....max_depth=10, n_estimators=200;, score=0.728 total time=   2.7s\n",
            "[CV 2/5] END ....max_depth=10, n_estimators=200;, score=0.752 total time=   2.7s\n",
            "[CV 3/5] END ....max_depth=10, n_estimators=200;, score=0.706 total time=   2.7s\n",
            "[CV 4/5] END ....max_depth=10, n_estimators=200;, score=0.693 total time=   2.7s\n",
            "[CV 5/5] END ....max_depth=10, n_estimators=200;, score=0.815 total time=   2.7s\n",
            "[CV 1/5] END .....max_depth=15, n_estimators=50;, score=0.693 total time=   0.7s\n",
            "[CV 2/5] END .....max_depth=15, n_estimators=50;, score=0.705 total time=   0.7s\n",
            "[CV 3/5] END .....max_depth=15, n_estimators=50;, score=0.657 total time=   0.7s\n",
            "[CV 4/5] END .....max_depth=15, n_estimators=50;, score=0.685 total time=   0.7s\n",
            "[CV 5/5] END .....max_depth=15, n_estimators=50;, score=0.767 total time=   0.7s\n",
            "[CV 1/5] END ....max_depth=15, n_estimators=100;, score=0.713 total time=   1.5s\n",
            "[CV 2/5] END ....max_depth=15, n_estimators=100;, score=0.732 total time=   1.5s\n",
            "[CV 3/5] END ....max_depth=15, n_estimators=100;, score=0.727 total time=   1.5s\n",
            "[CV 4/5] END ....max_depth=15, n_estimators=100;, score=0.699 total time=   1.5s\n",
            "[CV 5/5] END ....max_depth=15, n_estimators=100;, score=0.775 total time=   1.5s\n",
            "[CV 1/5] END ....max_depth=15, n_estimators=200;, score=0.714 total time=   2.9s\n",
            "[CV 2/5] END ....max_depth=15, n_estimators=200;, score=0.727 total time=   2.9s\n",
            "[CV 3/5] END ....max_depth=15, n_estimators=200;, score=0.705 total time=   2.9s\n",
            "[CV 4/5] END ....max_depth=15, n_estimators=200;, score=0.694 total time=   3.0s\n",
            "[CV 5/5] END ....max_depth=15, n_estimators=200;, score=0.777 total time=   2.9s\n",
            "[CV 1/5] END .....max_depth=20, n_estimators=50;, score=0.720 total time=   0.8s\n",
            "[CV 2/5] END .....max_depth=20, n_estimators=50;, score=0.690 total time=   0.7s\n",
            "[CV 3/5] END .....max_depth=20, n_estimators=50;, score=0.716 total time=   0.7s\n",
            "[CV 4/5] END .....max_depth=20, n_estimators=50;, score=0.676 total time=   0.8s\n",
            "[CV 5/5] END .....max_depth=20, n_estimators=50;, score=0.771 total time=   0.8s\n",
            "[CV 1/5] END ....max_depth=20, n_estimators=100;, score=0.743 total time=   1.5s\n",
            "[CV 2/5] END ....max_depth=20, n_estimators=100;, score=0.739 total time=   1.5s\n",
            "[CV 3/5] END ....max_depth=20, n_estimators=100;, score=0.698 total time=   1.5s\n",
            "[CV 4/5] END ....max_depth=20, n_estimators=100;, score=0.680 total time=   1.5s\n",
            "[CV 5/5] END ....max_depth=20, n_estimators=100;, score=0.761 total time=   1.5s\n",
            "[CV 1/5] END ....max_depth=20, n_estimators=200;, score=0.685 total time=   2.9s\n",
            "[CV 2/5] END ....max_depth=20, n_estimators=200;, score=0.735 total time=   3.0s\n",
            "[CV 3/5] END ....max_depth=20, n_estimators=200;, score=0.718 total time=   2.9s\n",
            "[CV 4/5] END ....max_depth=20, n_estimators=200;, score=0.700 total time=   2.9s\n",
            "[CV 5/5] END ....max_depth=20, n_estimators=200;, score=0.750 total time=   3.0s\n",
            "[CV 1/5] END .....max_depth=50, n_estimators=50;, score=0.717 total time=   0.8s\n",
            "[CV 2/5] END .....max_depth=50, n_estimators=50;, score=0.731 total time=   0.8s\n",
            "[CV 3/5] END .....max_depth=50, n_estimators=50;, score=0.695 total time=   0.8s\n",
            "[CV 4/5] END .....max_depth=50, n_estimators=50;, score=0.676 total time=   0.8s\n",
            "[CV 5/5] END .....max_depth=50, n_estimators=50;, score=0.706 total time=   0.7s\n",
            "[CV 1/5] END ....max_depth=50, n_estimators=100;, score=0.709 total time=   1.5s\n",
            "[CV 2/5] END ....max_depth=50, n_estimators=100;, score=0.700 total time=   1.5s\n",
            "[CV 3/5] END ....max_depth=50, n_estimators=100;, score=0.709 total time=   1.5s\n",
            "[CV 4/5] END ....max_depth=50, n_estimators=100;, score=0.706 total time=   1.5s\n",
            "[CV 5/5] END ....max_depth=50, n_estimators=100;, score=0.762 total time=   1.5s\n",
            "[CV 1/5] END ....max_depth=50, n_estimators=200;, score=0.748 total time=   3.0s\n",
            "[CV 2/5] END ....max_depth=50, n_estimators=200;, score=0.742 total time=   3.0s\n",
            "[CV 3/5] END ....max_depth=50, n_estimators=200;, score=0.696 total time=   3.0s\n",
            "[CV 4/5] END ....max_depth=50, n_estimators=200;, score=0.677 total time=   3.0s\n",
            "[CV 5/5] END ....max_depth=50, n_estimators=200;, score=0.793 total time=   3.0s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={'max_depth': [None, 1, 5, 10, 15, 20, 50],\n",
              "                         'n_estimators': [50, 100, 200]},\n",
              "             scoring='f1', verbose=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None, 1, 5, 10, 15, 20, 50],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None, 1, 5, 10, 15, 20, 50],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
              "             scoring=&#x27;f1&#x27;, verbose=4)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_grid.best_params_, rf_grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjuCJ77iDOu-",
        "outputId": "1be9f0dd-6006-4eb7-9a04-7b39d2216fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'max_depth': 10, 'n_estimators': 50}, 0.7479328782266712)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_grid.best_score_ < knn_grid.best_score_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJLuFGqJIJpV",
        "outputId": "c35e1790-4c38-49d1-b515-c4d35409dbc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "filename = 'pca_knn.sav'\n",
        "joblib.dump(knn_grid, filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFczQncs9ufV",
        "outputId": "027bdf23-c048-441b-ecb5-cde768f563ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['pca_knn.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing..."
      ],
      "metadata": {
        "id": "zszMeACXuqQ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "u5qElM5suoO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = joblib.load(\"/content/pca_svm_v2.sav\")"
      ],
      "metadata": {
        "id": "53qsJnDquuTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "def test(clf):\n",
        "  y_pred=clf.predict(x_test_pca)\n",
        "  print(f\"The model's f1 score is: {f1_score(y_pred,y_test)}\")"
      ],
      "metadata": {
        "id": "iWXf3nvZuaY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1apG-6xukwb",
        "outputId": "9b81a259-372c-462f-f9e4-488402be1737"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model's f1 score is: 0.8285714285714286\n"
          ]
        }
      ]
    }
  ]
}