🏥👩‍⚕️ Brest Cancer Classification with SVM & PCA.ipynb

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  {
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   "source": [
    "# 🏥 Brest Cancer Classification using *Support Vector Machines* & *Principal Component Analysis* ⚕️\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f510266c",
   "metadata": {},
   "source": [
    "## 1. Overview\n",
    "<p style=\"text-align: justify;\">   \n",
    ".   For this project, it will be used the 'Breast Cancer Wisconsin (Diagnostic) Database', as source for the ML model for predicting if a cancer is either <strong>Benign</strong> or <strong>Malignant</strong>, based on many features further described.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "280ec095",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d4c4dc6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957abe17",
   "metadata": {},
   "source": [
    "<h5> Here it has some information describing the data: </h5>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2a4eb9d",
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532d0aaa",
   "metadata": {},
   "source": [
    "### 1.1 Principal Component Analysis (PCA)\n",
    "According to [wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis), PCA can be defined as: \n",
    "> PCA is a statistical technique for **reducing the dimensionality of a dataset**. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data .\n",
    "\n",
    "<figure>\n",
    "    <center><img src=\"https://i.stack.imgur.com/O740D.png\", height=\"250\" width=\"500\"></center>\n",
    "</figure>\n",
    "\n",
    "<center> Image source: i.stack.imgur.com </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2f6334",
   "metadata": {},
   "source": [
    "### 1.2 Support Vector Machines\n",
    "\n",
    "According to [wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis), SVM can be defined as: \n",
    "\n",
    "> Support Vector Machines are supervised learning models with associated learning algorithms. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. \n",
    "\n",
    ">SVM maps training examples to points in space so as to maximise the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.\n",
    "<figure>\n",
    "    <center><img src = \"https://upload.wikimedia.org/wikipedia/commons/7/72/SVM_margin.png\", height=\"125\" width=\"250\"></center>\n",
    "</figure>\n",
    "\n",
    " _ img source: wikipedia _"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f1eb3b",
   "metadata": {},
   "source": [
    "## 2. Preprocessing\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8315ab2e",
   "metadata": {},
   "source": [
    "* Starting the project tacking a look into the Data. The predicted variable array is stored on `cancer['target']`, and the features are stored at `cancer['data']`. \n",
    "\n",
    "* Let's take a look into the df head, to check which kind of data we are working with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d805a7c4",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
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       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
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       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
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       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
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       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
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       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b430aa",
   "metadata": {},
   "source": [
    "* To better our ML model, before decomposing it, we need to scale the columns into a standard scaler. It will help the model to better understand the influence of data points, when applying linear transformations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "052eebe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scale the components\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#create the scaler:\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df)\n",
    "\n",
    "#transform the Features\n",
    "df_scaled = scaler.transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f9ba0b",
   "metadata": {},
   "source": [
    "### 3. Machine Learning Model\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a681c865",
   "metadata": {},
   "source": [
    "### Part 1: Decomposing the model\n",
    "\n",
    "* Now, first thing to do is to reduce the features into two components of their linear transformations (So that we can plot the data and see how it is classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb753b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62007efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit transform features to decompose them into 2 components:\n",
    "pca = PCA(n_components = 2)\n",
    "pca.fit(df_scaled)\n",
    "x_pca = pd.DataFrame(pca.transform(df_scaled), columns = ['Component_1', 'Component_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "88b28538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Component_1  569 non-null    float64\n",
      " 1   Component_2  569 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 9.0 KB\n"
     ]
    }
   ],
   "source": [
    "#Check the new DF scaled!!\n",
    "x_pca.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5606de8f",
   "metadata": {},
   "source": [
    "* Bellow you can see the weights of each feature for the projections in each component. For that, the PCA model stores them in the `.components_` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb12c4f3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Component_1</th>\n",
       "      <td>0.218902</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.227537</td>\n",
       "      <td>0.220995</td>\n",
       "      <td>0.142590</td>\n",
       "      <td>0.239285</td>\n",
       "      <td>0.258400</td>\n",
       "      <td>0.260854</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.064363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227997</td>\n",
       "      <td>0.104469</td>\n",
       "      <td>0.236640</td>\n",
       "      <td>0.224871</td>\n",
       "      <td>0.127953</td>\n",
       "      <td>0.210096</td>\n",
       "      <td>0.228768</td>\n",
       "      <td>0.250886</td>\n",
       "      <td>0.122905</td>\n",
       "      <td>0.131784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component_2</th>\n",
       "      <td>-0.233857</td>\n",
       "      <td>-0.059706</td>\n",
       "      <td>-0.215181</td>\n",
       "      <td>-0.231077</td>\n",
       "      <td>0.186113</td>\n",
       "      <td>0.151892</td>\n",
       "      <td>0.060165</td>\n",
       "      <td>-0.034768</td>\n",
       "      <td>0.190349</td>\n",
       "      <td>0.366575</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.219866</td>\n",
       "      <td>-0.045467</td>\n",
       "      <td>-0.199878</td>\n",
       "      <td>-0.219352</td>\n",
       "      <td>0.172304</td>\n",
       "      <td>0.143593</td>\n",
       "      <td>0.097964</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>0.141883</td>\n",
       "      <td>0.275339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean radius  mean texture  mean perimeter  mean area  \\\n",
       "Component_1     0.218902      0.103725        0.227537   0.220995   \n",
       "Component_2    -0.233857     -0.059706       -0.215181  -0.231077   \n",
       "\n",
       "             mean smoothness  mean compactness  mean concavity  \\\n",
       "Component_1         0.142590          0.239285        0.258400   \n",
       "Component_2         0.186113          0.151892        0.060165   \n",
       "\n",
       "             mean concave points  mean symmetry  mean fractal dimension  ...  \\\n",
       "Component_1             0.260854       0.138167                0.064363  ...   \n",
       "Component_2            -0.034768       0.190349                0.366575  ...   \n",
       "\n",
       "             worst radius  worst texture  worst perimeter  worst area  \\\n",
       "Component_1      0.227997       0.104469         0.236640    0.224871   \n",
       "Component_2     -0.219866      -0.045467        -0.199878   -0.219352   \n",
       "\n",
       "             worst smoothness  worst compactness  worst concavity  \\\n",
       "Component_1          0.127953           0.210096         0.228768   \n",
       "Component_2          0.172304           0.143593         0.097964   \n",
       "\n",
       "             worst concave points  worst symmetry  worst fractal dimension  \n",
       "Component_1              0.250886        0.122905                 0.131784  \n",
       "Component_2             -0.008257        0.141883                 0.275339  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "composit = pd.DataFrame(pca.components_, columns = df.columns, index = ['Component_1', 'Component_2'])\n",
    "composit.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fcdadf",
   "metadata": {},
   "source": [
    "* Finally, let's plot the data using the `x_pca` DF and the labels stored for each row in the `cancer['target']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2fa4b309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l1FEBjqIoiqIopU6JrkWlKIpSfHgRxgLARpoXAolF3SBFOaWpAEdRFOUkCWM2RsLjIEXWBgszczDSvLJoG6YopzA1RKUoinIyxEGMhEcRIhOhZYT+CR9GwtMgdhd16xTllFWgPThLly7l9ddfZ9SoUaxcuZKXXnoJXddxOp28+uqrVKxYMWL/bt26kZwcKntevXp1hg4dWpDNUxRFOWmaYyogYjwi0RyTsQM3FXaTFEWhAAOckSNH8uOPP+LxeAB4+eWXeeaZZ2jUqBFjxoxh5MiRDBo0KLy/3+8HYNSoUQXVJEVRlAKQCZgxtgdBZBZ2YxRFyVJgAU6NGjV49913eeKJJwB48803qVy5MgCWZeFyuSL2X7VqFV6vl1tvvRXTNHnkkUdo2rRptufQdUFKSkKBtP9E6LpWrNqjxKauU8lREq6VLa/Ax/tR24Vwk+i+At1TvNufX0rCtVJCTpVrVWABTvv27dm2bVv45yPBzeLFi/nqq68YPXp0xP5ut5vbbruNnj17smnTJu644w4mT56MYcRvomVJDh0qPt+QUlISilV7lNjUdSo5Ssa1qobu7o7mnAB4EQKk9GAHryLNW5dQD0/pVzKulQKl71pVqpQcc3uhrqL65Zdf+PDDDxkxYgTly5ePeKx27drUrFkTIQS1a9cmJSWFvXv3UrVq1cJsoqIoSp5Zviewg23QnD8BNnawI9K8mNhzcxRFKQyFFuBMnDiRsWPHMmrUKFJSUqIeHzduHGvWrOH5559n9+7dpKenU6lSpcJqnqIoykkQSOtCLO+FRd0QRVGyFMoyccuyePnll8nIyOD++++nf//+vPPOOwA88cQT7Nixgx49epCWlkafPn14+OGHGTJkSLbDU4qiKIqiKPEIKaUs6kacqGDQKlbjiKVtXLO0Utep5FDXquRQ16rkKG3XKt4cHJXoT1EURVGUUkcFOIqiKIqilDoqwFEURVEUpdRRAY6iKIqiKKWOCnAURVEURSl1VICjKIqiKEqpowIcRVEURVFKHRXgKIqiKIpS6qgAR1EURVGUUkcFOIqiKIqilDoqwFEURVEUpdRRAY6iKIqiKKWOCnAURVEURSl1VICjKIqiKEqpowIcRVEURVFKHRXgKIqiKIpS6qgAR1EURVGUUkcFOIqiKIqilDoqwFEURVEUpdRRAY6iKIqiKKWOCnAURVEURSl1VICjKIqiKEqpowIcRVEURVFKHRXgKIqiKIpS6qgAR1EURVGUUkcFOIqiKIqilDoqwFEURVEUpdQxiroBiqIopUFGRoDx361i3pzt1KhZhhv6n0ONmmWLulmKcspSAY6iKMpJOnjQR9eO33Jgvxev18Th0Bg96j+Gf3INl7Q6o6ibpyinJDVEpSiKcpI+eO9v9uzOwOs1AQgGbbxekycemYaUsohbpyinJhXgKIqinKQpv24gGLSjth8+7GfrltQiaJGiKCrAURRFOUmJiY6Y2y1LkpAQ+zFFUQqWCnAURVFOUv+bG+PxRE5p1HVBk3MrU7FSQhG1SlFObQUa4CxdupT+/fsDsHnzZvr06UPfvn157rnnsO3I7lzbtnn22Wfp1asX/fv3Z/PmzQXZNEVRlHzTq8/ZdOxcF5dLJzHRQUKig1q1yvLO++2LummKcsoqsFVUI0eO5Mcff8Tj8QAwdOhQHnroIS688EKeffZZpk2bxpVXXhnef+rUqQQCAcaOHcuSJUt45ZVX+PDDDwuqeYqiKPlG0wSvvt6OAQ+04N+luzmtahLnNT8NIURRN01RTlkFFuDUqFGDd999lyeeeAKA5cuXc8EFFwBw2WWXMWfOnIgAZ9GiRVx66aUANG3alP/++y/Hc+i6ICWl+HT/6rpWrNqjxKauU8lR0q5VSkoCjZucVtTNKBIl7Vqdyk6Va1VgAU779u3Ztm1b+GcpZfjbTGJiImlpaRH7p6enk5SUFP5Z13VM08Qw4jfRsiSHDmXmc8tPXEpKQrFqjxKbuk4lh7pWJYe6ViVHabtWlSolx9xeaJOMNe3oqTIyMihTpkzE40lJSWRkZIR/tm072+BGURRFURQlnkILcM466ywWLFgAwMyZMzn//PMjHj/vvPOYOXMmAEuWLKF+/fqF1TRFURRFUUqZQgtwnnzySd5991169epFMBikffvQ6oInnniCHTt2cOWVV+J0OunduzdDhw5l0KBBhdU0RVEURVFKGSFLcB7xYNAqVuOIpW1cs7RS16nkUNeq5FDXquQobdeqyOfgKIqiKIqiFBYV4CiKoiiKUuqoAEdRFEVRlFJHBTiKohQwE7CKuhGKopxiVICjKErBEDswEu7GUaYFjjIt0BMeBLGvqFulKMopQgU4iqIUAC+OpH4IYwFC2AhhoRmzcCTeSKhHR1EUpWCpAEdRlHynOaaAyEQIO7xNCAu0QwhjdhG2TFGUU4UKcBRFyXdC24gQ3hiP+BHa5kJvj6Iopx4V4CiKku+k3QApY1UrdiLtuoXeHkVRTj0qwFEUJd/ZwXZgpyClHt4mpQNpn440WxZhyxRFOVWoAEdRlALgJJjxFXbwaqT0IGUidqALZvpnqD87iqIUBqOoG6AoSiklK2B5h2DFmoqjKIpSwNRXKUVRFEVRSh0V4CiKoiiKUuqoAEdRFEVRlFJHBTiKoiiKopQ6KsBRFEVRFKXUUQGOoiiKoiiljgpwFCXfeEFsA4JF3RBFUZRTnsqDoygnzUR3vYnmGkfoO4PA8t2FHbgJEEXcNkVRlFOTCnAU5STprvfQXOMRwn90m/tDkOWxg12KsGWKoiinLjVEpSgnxUJzjUEIX8RWIXzorpFF1CZFURRFBTiKclK8xJ1zo+0r1JYoiqIoR6khKkU5KYkgy4PYE/WItBoWQXuU/LJyxT5m/rmFpCQnHTqeSfnynqJukqIoeaACHEU5KQLT+wRGwv+Fh6mkFIALy/dI0TZNOSFSSp556k8mjF+NadoYDo2hg+fw/vCrad2mZlE3T1GUXFJDVIpykqR5JWbGO9jmeUi7ErbZCjP9c6TVuKibppyAmX9uYeKE1fh8JqZp4/OaeL0mA+75DZ/PLOrmKYqSS6oHR1HygbQuxMy4sKiboeSD78etJjMzOpDRBMybu422l9cq/EYpipJnqgdHURTlGFLKbB4svHYoinJyVA+OohQIG835LZrzG4TIwA5eiuW/F2Slom6YEoOUkgnjV/P5p/+yZ3c6hqFhmnbEPrYtuejiakXUQkVR8koFOIpSAHT3S2jOX8ITjzXnRDTHnwTTfgDKFGnblGgvPjeL78auxOsNDU2JrATUQoDDqaMJwf/euQqPx1GErVQUJS9UgKMo+U3sQnNOQojA0U3CQpKO5hyHHbi1CBunHG/XrnTGfL2CQMAKb5MSnE6Ni1pW47I2NenUuS6VKicWYSsVRckrNQdHUfKZ0FcBzujtwo8wFhZ+g5RsLft3D05n9J/CQMBGCMEtt52rghtFKYFUgKMo+c0+DbCiNkupg63yqBQ3VaokYdvRs4d1XXBGDTWcqCglVaEOUX3//fdMmDABAL/fz8qVK5kzZw5lyoT+iHz22WeMGzeO8uXLA/DCCy9Qp06dwmyiUir4EfoKkIlIux6FXdFb2g2RVm3Q1yDEscuNnViB3oXaFiVnjZtUovoZZVi/7iCWdTTQcTh1+t+kchkpSklVqAHOddddx3XXXQeEgpfu3buHgxuA5cuX8+qrr3LOOecUZrOUUkRz/IzuGQxSgLDBrkQw832waxRqO8zMDzA8T4PxF6CBLIeZ+SLYtQq1HUrOhBB8OboLA+75jX//3YOuCxISHLz6+uXUrVe+qJunKMoJEjLbpA8FY9myZQwbNoxRo0ZFbO/QoQP16tVj7969tGnThrvuuivb49i2HfGNq6jpuoZl2TnvqBQIW67CR0/g2MreAkFV3MxAiNCIbGFeJylTkWQiqIIQhduTVBoU9u/Url3ppKUFOPPMcmiaul55of7+lRyl7Vo5HHrM7UWyimr48OHcd999Uds7duxI3759SUpKYsCAAcyYMYO2bdvGPY5lSQ4dyizIpuZJSkpCsWrPqUZ3f4nmDBAZR0hseZjUjDlIqzlQ2NfJILQs3FtI5ytdCvt3yu3WcLvdpKaq65VX6u9fyVHarlWlSskxtxf6JOPU1FQ2bNjARRddFLFdSslNN91E+fLlcTqdtG7dmhUrVhR285SSTOxFiDjfSsTB8P9KaSKMuWiO30DsL6TGKYqiKIWp0AOchQsXcvHFF0dtT09Pp1OnTmRkZCClZMGCBWoujpIn0myNlJ4YjwSRVjMAhLYGL60wEh5D97yAI/lqNNfwwm2ooiiKUuAKfYhq48aNVK9ePfzzTz/9RGZmJr169eLhhx/mxhtvxOl00rJlS1q3bl3YzVNKMDvYEc31NWhbwxmEpfRg+fuBrABYGIn3AvsjhrF012dIsynSyq9imb5Qj5GsCKjMt4qiKEWhSCYZ55dg0CpW44ilbVyzZMpEc45Dc/wOMgkr0AdpXgaA0P/BSLwXISKvkZRgB6/E8r5+kue20F1vo7nGZv2sY/nuxA7cRGEvVS8t1O9UyaGuVclR2q5VvDk4qlSDUsokYAduxA7cGP2QyCTWqKwQIETGSZ9Zc32E5hob7j0C0N0fgkzBDnY76eMriqIouacyGSunDGk2Bczo7dKNHbzqJI9uo7u+ighuAITwobtGnuSxFUVRlLxSAY5yCknE8g4C3EgZ+uhL6UFaDbCDnU7y2D7AH/shbd9JHltRFEXJKzVEpZxS7GA33DQjIzAaxEGkeTl28ApOfjKwB2QlELuiHpFWg5M8tqIoipJXKsBRTjmaaITleyqfjyowvU9gJDx1zAouADeW75F8PpeiKIqSEzVEpSj5RJrtMDPewTabIu3ySLMlZsbHSKtpUTdNURTllKN6cBQlH0nrQsyM/MqnoyiKopwoFeAoSqEKojm/RnNOACzsYCds/41ArAzMiqIoyolSAY6iFBqJkTAAYSwJz9MR2idoxh+YGV8Bx1XEFfvRHFMBL9JshbTrFnqLFUVRSioV4ChKIRH6UoSxNCJXjhB+0DchjFlIs83R7cZ0jISBWT9ZwAfY/uux/I8VapsVRVFKKjXJWFEKidD/JVaiQSEy0fQlx2zJwEgYhBD+rH8mQvjRXOMQ+qLCaq6iKEqJpgIcRSkssjKx8u1I6UbKquGfhTGXqOEqAHxojkkF1jxFUZTSRAU4ilJI7GBbkC6kPL7wpoEd6HDMz/Hq30oQdgG1TlEUpXRRAY6iFBoXwYzPkPaZSOlEShfSqoGZPhIoE95Lmi2JNZQFHuxAx8JqrKIoSommJhkrSmGya2Omj88q6WCBPB04vkcnGTPzJYyEZwj15piAEzvQGWm1KOwWK4qilEg59uB88MEHET+/8cYbBdYYRTllyNNAViM6uMl62GxPMO0nLN/9WP67MdM/x/I9HXf/wmKaNv8u3c3qVfuRMt5QmqIoStGL24Pz3XffMW7cONavX8/MmTMBsCwL0zR59NFHC62BinLKklWwAzcWdSvC/vxjM4888DtB08a2JRUrJjDik2uo36BCUTdNURQlipBxvoYFAgH27NnD8OHDufvuuwHQNI0KFSrgdDoLtZHxBIMWhw5lFnUzwlJSEopVe5TY1HXKu+3bUrmq3Tf4vJFzg8qXdzPnr5txOmOt+jp56lqVHOpalRyl7VpVqpQcc3vcISqn00n16tV54YUX2L9/Pzt27GDbtm0sXbq0wBqpKErx9N23q7Cs6BVc/oDFzD+2FEGLFEVRspfjJOMHHniA/fv3U7VqKE+HEIIWLdRER0U5lezZnUEwEB3g2JZk/35vEbRIURQlezkGOPv27WPMmDGF0RZFUYqpSy87g58mriUzMxix3ZaSCy48vYhapSiKEl+Oq6hq167N7t27C6MtiqIUU1e2r0O9+uVwu4/OtfEkGHTt1oDadVKKrmGKoihx5NiDs3jxYtq2bUu5cuUQIrREdfbs2QXeMOVUk47u/hDN8QsgsAPXYPnvARKLumEKYBga33x7Ld98vZyJE9bgdhv07X8OnTqrCueKohRPcVdRlQRqFVVpYWEk9UJomxEiABDK9GvVxsz4hth1mU6cuk4lh7pWJYe6ViVHabtWeV5FdcTatWvp27cvnTt3ZsSIEcyYMSPfG6ec2oQxC6FtDwc3AEIEEPrWrMKTiqIoipI3OQY4gwcPZujQoaSkpNCjRw/efffdwmiXcgoR+iog1rcJH0JfXdjNURRFUUqBXBXbrFmzJkIIypcvT2KimhOh5DO7GpAQ4wE32GqFjqIoipJ3OQY4ZcuWZcyYMXi9Xn7++WfKlCmT01MUJU/s4BUg3Uh59OMopQbSgx1sV4QtUxRFUUqqHAOcIUOGsG3bNsqVK8d///3Hyy+/XBjtUk4pHoIZXyCtJkhphP5Z5xJM/xJwFXXjFEVRlBIox2XiSUlJ3HLLLfj9fgAyMzNJSUkp6HYppxq7BmbGF0B61oakomyNoiiKUsLlGOA8//zzzJw5k8qVKyOlRAihMhsrBUgFNkrhWLF8L2+/+RfLl++jdu0UHnioBS1UVmZFKTVyDHD+/fdfpk6diqblaj6yoihKsbfkn1306z0Rn89ESti5I53Fi3fx7vvtufyKWkXdPEVR8kGOUUvNmjXDw1OKkt+EtgLN+RmaYzyQWtTNUU4RQwbPxesNBTdH+LwmLzw3kxKc+1RRlGPk2IOzc+dO2rZtS82aNQFOeoiqW7duJCeHsg5Wr16doUOHhh+bPn0677//PoZh0L17d66//voTPo9S3Nnonv9Dc0wDTMCB7nkNM+M9pHV+UTdOKeWW/7c35vadO9Lx+Uw8Hkcht0hRlPyWY4Dzxhtv5NvJjvQEjRo1KuqxYDDI0KFDGTduHB6Phz59+tC2bVsqVaqUb+dXig9hTENzTEcIX9YWEwAj4WGCadMBdYPJybq1B/h74U4qVUrgsjY1cDjyt6RFfpj2+0Y+GbmEAwe8XH5FbW6/synly3uKullUKO9h+/a0qO0ut4HLleOfRUVRSoAcf5N1XWfIkCGsX7+eWrVqMWjQoBM+2apVq/B6vdx6662YpskjjzxC06ZNAVi/fj01atSgbNmyADRv3py///6bDh06ZNM2QUpKrARxRUPXtWLVnuLMJ3/Cxhu1XWgWyWVXo4sLCuzchXWdpPRj8TMWcxGcjkEvNFHtpI9r25IB9/zCxAlrEFro9XjcBj/+2pv69SvkQ8vzxxuvzeOtNxaQmRkEYPOmVH76YQ2z5t1CSjl3ro5RUNfq0Sda8n+DZoTbBuDxGNx513mUL6+SmZ4I9fev5DhVrlWOAc7//d//0adPH1q0aMFff/3F008/zRdffHFCJ3O73dx222307NmTTZs2cccddzB58mQMwyA9PT08dAWQmJhIenp6NkcDy5LFqmBYaStgVpD0BBM9RieNlJCe4UNaBfc+Fs51ysBI6ofQdiKEFykdBPkMM/0dpHXhSR35+3Gr+PGHNfh85tGzpQe4odf3TJneFyHEyTb+pKUe9vPGsHn4/VZ4WyBgsX+/l3ffXsD9D7XI1XEK6lp16VaXLVsOMfzDxQghsEybnr0acc+A89Tv8AkqDn//Nm86zJCXZjN3znYSEkIV7+8d0LxY9m4WpeJwrfLTCRfb9Pv9tGvXjjJlynDFFVdgmmZOT4mrdu3adOnSBSEEtWvXJiUlhb17Q2PhSUlJZGRkhPfNyMiICHiU0kUGOyNlrKEKgbTOLfT25DfN9QVC24YQoV4qIYII4cNIeAqwT+rYo0f9h9cb+Xt4ZCXQxg2HTurY+WX58r04ndE3Fb/fYuafW4qgRZGEENz/YAv+XnIbP/5yPQuX3MpzL16GYajVoiXVvr2ZXNv5O6ZP20xmZpB9+7yM+HAxjzzwe1E3TSkiOf42W5bF6tWhgoerV68+qW+H48aN45VXXgFg9+7dpKenh+fYnHnmmWzevJlDhw4RCAT4+++/adas2QmfSyne7GB77ODFSOlBSoGULqR0Y2a+RmmYf6M5pkRURw8TGaBtPqlj+32xv2RomojoMSlKlSolYJrRgZwQcFrV4pPryO02qF07hcREZ1E3RTlJX30ZCvxt++gqOJ/PYtrUTWzdolZonopyNUT11FNPsXfvXipXrsxLL710wifr0aMHgwYNok+fPgghGDJkCL/++iuZmZn06tWLgQMHcttttyGlpHv37lSpUuWEz6UUdxqW9w3swBKEMR9kGezg1SCLzxySkyLjzTGxQZ5c+Yku19Zn48a/8Pkigxm3x6B+g/Indez8Urdeec6sW45VK/dhmkdvOC63wa23l/weOqX4+WfxLgKB6ADf6dRZvWo/Z9RQdRRPNULmIunDgQMH2LJlC7Vq1SpWZRqCQatYjSOWtnHN0qowrpPm+B7d8+oxq8RCBUSlVQ8z49uTOrbXG6RX9wls3HCIzMwgTqeGrmsM//gaLrn0jJNter7ZtzeTu+/4hRXL92E4NASC51+6lGu7N8z1MdTvVMlR1NdqyEuz+fLzZQSDkT2HbrfBxJ97Urde8Qj+i4Oivlb5Ld4cnBx7cEaPHs2XX35JvXr1WLt2Lffeey9du3bN9wYquSFB2wQYYBefG5kSzQ52QxiL0RxTQOqhsRmZhJn55kkf2+NxMH5iF37/bROzZ+2katVEul/fiNNPL15z1ipWSmDcDz3Yvi2VQ4f81KtfPua8HEXJDzfe3IRvRq+ICHCcTo2mzaqo4OYUlWMPTrdu3Rg7diwulwuv10u/fv0YP358YbUvW6dSD47Ql2AkPAEiFbCR9umhm6Vdp0DOV5oV6rcXbROa/i9SVkKaFwAneYMX2zA8LyCMvwGBbV6G5X2m9AztHae0fdMszYrDtVq6ZDdPD/yD1av2YxiCjp3r8cLgy9Qcq+MUh2uVn064B6dChQroeuiPstvtLlZDVKcMsR8j8e7wihwAtE04Em8lmDYFUL+8xZZdC9uulU8Hy8SR1A/EYYQIfUvVjFloiTcRTJ9I3oOnTBAHQFahNEzsVpRzm1Zh0uRe+HwmhqGpVXGnuBwDHCkl3bp1o1mzZqxYsQLTNHn00UeB/M1yrMSnOSZx/NJiISRS+hHGTKR5RdE0TClUmmMyCF84uAEQwkRq+xHGXKR5aS6PFER3D0NzTiS0kFLD8t2LHehXEM1WlELndqts1EouApy77747/P+dO3cu0MYosQltN0LEKHgqTIS2B1Ua8NQg9PWRvXhhJkLbjCR3AY7ufgPN+WPEZ0p3vwuyAnYwfuZwRVGUkiTHAKdq1arMmDEjoqL4HXfcUaCNUiLZZnM05wSEOH7MVEOaJT1XUCaaYzJCX46062AHOgNqOWcs0mqIlJ4YQY6BtOrl8igBNOf3UQGzED4010gV4CiKUmrkOEB57733cvjwYZxOZ/ifUrik2Rpp1UIekz9FSje22RJpNyrClp0ksQ9Hcjd0zzB01zh09zs4kjtmrRRTjmcHrwKZgpRH59pI6UBaNZBW7kofINIhTp+fZE8+tFJRFKV4yFUPzv33318YbVHiMjAzPkVzfoPm/AlwYAd6YAeuK+qGnRTd/QaIfQgRSs4lhA+JH8PzPGbG50XbuGLJRTB9dGiIyTED0LAD12D5HiS77ypr1xxg5PB/WLPmAE2bVuTFNxMxjMgsy7YNc/5M4Z/5i7jnvuYF+zIURVEKQY4BTtu2bXn99depW7dueFu3bt0Ksk1KTB7swK3YgVuLuiH5RnP8GQ5ujhBCgv4v4AdOLuNvqSQrYHmH8OsPG/jog8Xs25tJy4sX8MBDLahWPXpob+GCHdxy408EAhaWJVm5fC/+4PkMe28muh4aprIs8PsMhjzXgvVr/+aSVmfQ5NzKWUdIDwVTIh3bbAn5tiJMURSlYOUY4Pzyyy/UqVOH9evXAxSLSsVKKSF1iPlxEuRi9PSU9fHwf3jrzb/CBTcnjF/N779tZNJvvaKS/T3z9B8RhTlNU/LdN7UxHBXoecOfVDvjMEsXV+KNIeezankFNM1iwvhVNDm3MkL/GyPxSO+tiY7ADnTH8j1BnAtXoNatPcCrQ+ayYMEOypZ1cevtTbnpliZomvqbpChKtBwDHKfTyQsvvFAYbVFOMXawC5rz24iilFIa2OalqLwssXm9wYjgBsCyJBkZAYZ/sJgXBrcOb/f5TNavOxR1DCnhu69TmDjuOjIyghGP2bbE5zOBIEbCQ1ET2zXnBGyzFdK8JF9fV062bU2le9dxZGQEkRIy0oO8MWw+mzcd5vmXLivUtiiKUjLk+DX59NNPZ/jw4cyaNYvZs2cze/bswmiXcgqwfPchrUZZFcXdSJmAtM/A8j5btA0Th0EcLNo2xLFh/SF0PbrHwjQl8+Zuj9jmcGg4nLF/xcuWdcWs9p2QYNCxUz2EvghErGrgXjTHDyfW+JMwcvg/+Hwmx+Zd93pNvh2zggMHYi2dVxTlVJdjD45pmmzatIlNmzaFt7Vq1aog26SUGJkIbRtSVgaZcgLPT8DM+AKhL0Xoa7JWA11A3LhbHEJzfoXmmAl2eaxAf6R5CUL/G831OULbiTQvxPLfDLJy7GNkR2zDSBiE0FcCIO06WJkvI+3cLsEueJUqJUQVEzzi9GpJET/rukaPng0Z9+0q/P6jc508HoNbbj+X5GQnQ16aQzBoY9sSj8fgyvZ1uOTS6iA2Z9MKM5vHCsaSxbsjqpIf4XTqbFh/iPLlPYXeJkVRirccA5yhQ4eyZs0a1q1bR+3atWnUqAQvS1byiURzDUd3fUaoPEAQO3gFlvd58j4xWCCtpkiraQ67HcaRdD2Ig6EhLR2E8Q92sDWa40/AF6pnqW1Cc0wimP5dVgmC3AriSLoZxP6jmYK1NRiJtxBM+xUoHoUsK1dJ5OJLqjNn9jYCgcig5e57o1c/Pf1MK/bt9fLHjM04nTr+gEXXa+tzx13N0HWNCy+qxg8T1pCZGeSq9nW48KLTEUIgzeYcnz0bQEoPdrBjQb7EmM6sW44VK/Zh25FBTiBgUa168bg2iqIULzkW2xw1ahSTJk2iSZMm/PPPP3To0IHbbrutsNqXrVOp2GZxojkmonuGIIQvvE1KF3agE5Yv98NLQluL0Fcj7WpZAU78yaKa60N016cR83VC5w0V6o7cpmMHrsXyPRPzWLGukzCmYnieRWgZxx3Lg+V7FDvQM9evq6Clpwd47OGp/DljS7jezv89dwnde8b/8rFzZzpbt6RSp04KFSsl5Oo8wpiKkfAUoUDHBNzYwYuxvK9TWJPAj1yrVSv30b3beHzHzD1yuXQua12Djz6+plDaomTvVPn7VxqUtmt1wsU2J02axOjRozEMg2AwSO/evYtNgFNyeNFdH6A5JwEmttkOy/cAyPJF3bATork+jQhuAITwozknYfmeJOdenCB6wiNoxl8gNRAg7WqYGSNBlot9TmN2VHATjxAWmjEfK+ddjz5H/xtERvR24UWIbXk4UsFLSnLy0chrOHjQx6GDPqqfkYzDkX2hzapVk6haNSnbfY4nzSsIpp2F5vwZQXpocrF1PkWxgqpho4qM+OQannnqT7ZvS0PXBV2vbcBzL+auPIWUkq1bUnG5DKqclljArVUUpTjIVbFNwwjt5nA4cDjU6pa8kRiJdyH0leEbtOb4CU3/i2D6BIp3rpfUUM0ibQPSOgs7eA2QgIg7AVeGggSZ/WvSXJ+gGQtC5QKO3Cu1tRiJN2Omf0+sqtjSrhSztyYeKSvmbkcAbRO6a3zMY0uZgG01Bm0TQqQirQYUl2tWrpybcuXcUdullPz4wxo+fH8x+/Zl0rz5aTw+sCV1651AQC1Px/YXj9Isl7Q6g2l/3kB6ehCXS8fpzF319IULdvDwg79z8IAPKSX1G5TnvQ+vpvoZqiSIopRmOQY4zZs354EHHqB58+YsWrSIZs1Keu2jwiX0fxD6mojeh1AF6INojinYwWJawFTbhCOxP4hAKMOw/AXd/RHB9K+xrXPRxKxQUr5jyZS4PTDH0p3jY9RCCp1Tdz+P5Xsp6jl2oD+aYx5w7LCYDtKDxI8QwWO2e0ITjXNJd30OMfp7pARpl0N3jUDomwkFXhLL+yR2sFuuj1/YPvpgMe+/83d4KfnU3zcxe9ZWJk3uTe06KeH9Vq3cx6gvlrFzRzptLq9Jj+sbkZBQvL/ACCFITs59uZhdu9K59aafyMw8OrS1/L999Ok5gT/m9EfXVb4lRSmtcvztfvLJJ7nuuuswTZPrrruOJ598sjDaVWoIfTWxJmsKkYnQlxd+g3LJ8DwPIi08FCWEF8QBdPeb2L4HAU+4JpKUodpYpncguRu+iFEZnVCQozkng9gf9Zi0mmN5Hw8tJZeJSOlCWmcRTB+NNM9HSidSJiGlG8t3N9Jsm+vXKrTVURmVQxyAjdDXIYQPITIQIhPdMxSh/5vr4xcmrzcYEdwc4fNZ3HHrz+Gff5m0ju5dx/Pd2JX8+ccWhg2dR+cOY0lLjX1tCtKBA14WLtjBjh1p+X7s78asiFoOb9uSw4f9zJ1TvIYeFUXJX9n24IwdO5bu3bvTpk0bkpKSWLt2bWG1q9SQdnViDrlIN7LYpr0PhJZuH9dDI4QV6nUKdCeYPiarZ2MZ0j4D23870spd755tXobm+Cn2cJN0IrQtSKtC9POCPbCDnRD6WqRMAfsMAMzMj0DsRmj7kFZtIHeTaMOntM5C6rGDHKEdjLHdj+YcjeVtkqfzFIYtW1LjPrZp4yGW/LOLs86uxFNPzshK6Bfi9Zrs3JHO55/+y/0P5bJw50mybcngF2Yx5usVOJ06gYBFq8vO4O33rsLjyZ+epK1b0wgEor9g2FKye1f0nKvSwDRtvvpyGd+MXk7Ab9Gxc13uurd5nnq+FKU0iNuD8+677zJnzhyCwVDX/2mnncacOXN47733Cq1xpYE0Lwa73HEVoAVIJ3ag8Jfb5kyiu94jVq9TiIWROADdMQ7L+zJm+o9Yme/nOrgBsopDuoi5fk8EsoLCeNxIq3E4uDna7CpI62zyGtwAWIGbOH5eTaha+/nE+g4ghERoxbPyduXKiRHLx48lJUz+dQOrV+3HjvHm+/0Wk39dX9BNDBv1xTK+HbMSv98iLS2A328xe+ZWnvu/mfl2jotaViMhIfoaSltybtO8pBEoOe6/9zdef3U+69YeZMuWVD4ZuZQeXcdF5EJSlFNB3ABn5syZvP3223g8oQRa1atX53//+x8zZswotMaVDjrBjC+Q5kVIaSCljrTOIZjxJcUlt8qxhPEbmuvruJN5hQhV/dZcY0HbdGInkZUIpn/C8eUYpHRhB9uBrBTn5LvR3cMwEvugewYitJUndv7j2TUw0z/FNpuGrpFdFst/E1bmECAYtXuona2jj1MMlCvnpnGT2O+frgtcTp2kZCdWjKR5EMpwXFg+GbkkaijN77f4aeLafLsZd+xUl9OqJkVMSPZ4DNpdUZt69UvmKsbsrFq5j5l/bIl4XwMBix070pj8y7oibJmiFL64Q1QJCQlRhTUdDgeJiWqJZZ7JSpiZHxCaIGtzIr0MhUHoyzASnoozH+V4Es2Ygx2odWInsxtjZowM5dPR1gJubH8PLP+DsffXtuJI7APCG5qkLVehOWZgZr6BNE8+s7a0G2FmfBG13fLdhe4eGZqDBEjpBLsidqDHSZ+zoHzwUQcuvfhLLCsyiDEcGp271qN27RRq1ynLmtUHIvbxeAxuuqVgh918PpMJ369mxtRN7Nkde4jIlhK/z8Tlyt0qqey43AbfT+zB8A8X8/OkdbjdBn37nUPffmef9LGLoyX/7I755SQz02TB/B10vbZB4TdKUYpI3ADH7XazdetWzjjj6FDA1q1bVTXxkxK9pLf4kBgJj+cyuCFUCVyeXKAmrWaY6d8RSiKnk90EZd31LoiMcJbh0H99GJ6XCKZNzva5J8MO3Ia0G6A7vwJxCNtsg+3vC+Qtp0xhqlI1iXc/aM8jD/4e/n21bMmTg44uFR/xSUf6953Inj2ZaEIQCFrceEsTrrq6ToG1KzMzSI9u49my+XBUz82xqp6WyH//7SX1sJ92V9bB4Ti5a5tcxsVjT7bksSdbntRxSoIqpyWixahV5nLpalm8csqJm8l47dq1PPLII7Rs2ZIzzjiDHTt2MHv2bF599VXOOuuswm5nTCqTcT7SNuJI6hPuqciJlG6Cqb8DhfNH05HcGqEditEOZyjAkdGTkuMp0dcpDw4d8jHt902Ypk3by2tSuUpk76uUkiX/7GbfvkyaNTst1xmOY/ln8S7efG0Ba9bsp3btFB569EIualktYp/PPlnK66/Ow+fLPoh2uXQcjtDoeTBoc8+A5tz/YOFMfC7pTNOm9SVfsmd3ZkRZi4REB9P/vIFKlQuuB/5U+b0qDUrbtYqXyTjbUg1paWlMmzaNPXv2cPrpp4dXUxUXKsDJR9omHEm9ojIUA1mTgY2jCfyExMx8HWleUmjNM5K6oOnRBSCldBBMnUlehv1K9HUqhhbM386tN06KWJXldhu892F72rarFd7W89rxLF60K8/H93gMhn98DZdcekbOOyts3ZLK/fdOZvXqA2hCULGihzffuZLm51ct0POq36uSo7RdqxMq1ZCcnEy3bt0Koj1KcWPXBLsSUtsaMYYvpY7lvxXbfwvCmA/oSPMiCnu4zfbfiPC8dlz9K2doUnIxndN0qnj5xdkRwQ2E5tq8+PysiADnRCcwe70mX436TwU4uXRGjTL8MOl6du/KIBCwqH5GsppaoJyScsxkrJwqBGbmGxiJtyGxCCXjcyLN87ADnRH68lBBzDwMBeUnO9gdoW1Gc40B6QQRRJrnY3lzX9yzuEo97GfC96tZu+YA5zSuRJdu9Yt9RuFjrVl9IOb2LZtTMU0bwwgNN/W/uTHz523Pdv5NPKmHCz8BYUmnam4ppzoV4Chh0m5AMG0KmmMaiH1IqyGaaxSO5J6ElnQHsAOdsXxPEyt5YcESWP5HsQK3hWpj2aeBPL2Q25D/Nm44RI9uoRwlXq9JQoLBO/9byISfepaYG1S5cm727Inu7ta00L8jWrepyR13N2P4B4txODRsOzTxOCcej0HHznXzs8kljpSSf5fuYeeOdM5pXElNGFaUXIgb4LzxxhtxuzUfeeSRAmuQchLENnT3e2jGAqQsg+2/ETt4HXlbYZQQro+le55BMxZm1dEKfYPWnD8j7VrYgRvzvfm5IlOQ1nlFc+4C8NSTMzh82B9OepiZaeL3Wwx5aTZvv9++aBuXSxdcdDqTfozOsaLrGgvm76DlxUcTNz748AXc0P8cFi/aRbkUN5s3H+bZ//sTy5RYlo1hCGwbJGBbkoREB3XqpNC9R8NCfEXFy759mdzYZyJbt6aiaYJA0KZL13oMHXY5mqaGnhQlnrgBTp06BbdcVMkHYju66zOEsRRp1cIOdMNIGAgiHSFsBAcQnmEIbROW/9G8H15fhOaYFF6WHd4ufOiu0UUX4JQiwaDFor93RmV0tizJ9GmbcnWMjIwAb7/5FxO+X4Nt2XToWJfHnryIlJS8z5EyTZvp0zaxYd1B6tYrT5vLa4aHl7JTpkzsuTW6Ltiw/lBEgANQsWICV7UP/X1pceHpnH9BVSZOWENGeoB2V9YmKcnJmK+Xs2+fl67dGtC2Xc18yYlTUj38wO+sX38Q85jkjD//tI7GTSrT78bGRdgyRSne4gY41157LQCmabJs2TJM00RKyZ49xTNF/SlF24QjqS/gQwgLqa0NDStBREASyjg8BitwW6jSd66YaM6P0d2fErdcgzi+KGIQzTkezfEDiMOAM1SfKtATaV5G/B6kTHTXx2jOSYDADnTC8t9GiZ80rG1CiF1Iu162c5aEEFm9pNELGXMTWEgp6dd7IqtW7SeQlfl33LcrmTtnG5On9onI3puTffsy6Xnt9+zfl4nPZ+J2G1SslMC4H7pTvrwn2+eedXZFPAkG3szIuTWarlG3Xs7V5WvVSuHBhy+I2PbSkDZA6VvtkVcHD/r4+6+dEcENhCZef/n5MhXgKEo2cpyDM2DAAILBIHv27MGyLCpXrkynTp0Ko21KHLr7LSAzXAwz9N94uUWcCG090mqew1EtdNd7aK6vCQVOsfeSUmCbx+YksTES70Ho/yLEMRNB9U1oxt/Y/u5Y/sdjns9IvC2rUncAAM31JcKYh5nxFaEqIgE0xyQ0xxSkTMYOXI+0inM+lDSMxAdDVeKlASKAHeiB5XuCWEGeYWi0u7IW06duIhg8Gkw6XTrduuc8JDNv7nbWrTsYDm4glDdm795Mfv9tAx0718t1y194ZiY7tqeGb6QZGUH8/jRefG4Wb717VbbP7dKtPm+9+Rd+v4WdlRnZ6dSoVassF1xY8udJFSVvZjBiHtOxMtIDhdsYRSlhcvyamJ6ezieffEKTJk34/vvv8ftPbDVDMBjk8ccfp2/fvvTo0YNp06ZFPP7ZZ5/RsWNH+vfvT//+/dmwYcMJnadEE3vQnN+iOceAiJ8vRNP/jqr0DcQuXkkwNCE3B7rrf1k1qLIPbiARy/fw0SbrfyH0/yKDmyOPCS+aayxCnxFVt0oYcxH6pnBwE9o/gNA3Zi1HD2Ik3oruGYbmmIfmmIKROADN+UmOr6Wo6AnPZAV6PoSWjhABNOf3aI4JcZ8zeGgbatQsS2KiA7dbJyHB4KyzKvLYExfmeL6VK/YRjFFYMzMjyH//7c11u6WU/D5lY1QvgWna/DY559/DxEQnE37syeXtauJwaHg8Bl2vbcDXY7ud8PLktLQAmzcdJhg8tQtEVj09KWYPmsOhcWV7NY1AUbKTYw+OYYR28Xq9uN3ucHXxvPrxxx9JSUnhtdde4+DBg1x77bW0a9cu/Pjy5ct59dVXOeecc07o+CWathXDMxCh/0fom76O7n4Dy/cIdqBP9P6yLHD8MFGM3WRomTeyWg57+tBc38VM8nf0WCCtupiZ74M8WoVZMxYB2WU/DmIkPgFoSLsGZsbbIE8P9XLEfJ4PoS9HiP1ZvTuhfUL3SR+6+yPs4LUgi0+hRKEvRXN+jGbMjAoOQ8OEo7Ime0cKBi1mz9pK4yaVaNiwAnXrl+PiS86g+fmnZQUGNiDw+yxmTN/MwYM+Lrq4GrVrpwChfCdOpx7R+wPgSTCoVSslT68hbrrPuGlAI51eLZnhH3fM0zlj8ftM/u+pP5j00zoMXWAYGo8+cdEpOxQjhODVN9px560/EwxamKbE7TEoW9bFgAfPL+rmnfKOZItWk72Lp2wzGQOMHj2agwcP4nQ6mTp1KgkJCXz++ed5PlFGRgZSSpKSkjh48GBUL06HDh2oV68ee/fupU2bNtx11105HtO27aiCgkVJ1zUsK868lThsuQMfHYH0GI+6cPMzmqgZsTUovybIK0QGCC40miPZgOQAING5EicvI0Ts7NO23EWQV7GYRvZBCoAbN2PQRGSRwqD8iiCvEiokmhMNQQ3cTMHiewK8CBw/vyIBJy9iMgWbKTGOkYiTVzDE1VGPSOnDYhbgQ+dihKiQtd2Lz5xCZsZ+yiRdhttVP8/XKR5T/kKAgYRef7zPYiUSxNyILT6fScf2X7N2zQEyMoI4HBqGoTH844506OQiwHPYzEPaGr/8WIeX/u8yDuxzIiX0veEchr15BaZp0/zckezckR7+QytEaNn24mV3kZzszPXr6N9nAr9NXh/x+2QYgms61eOzL7uGt9m2ZNQX/zJy+GLS0wNc07Eejz5xERUq5M+8qfvv/ZXvx62KSByYkGAw4tPOdLjm1F0qvnHDQT4Z+Q8bNhyi1aVn0K9/E8oUYuX33DiRv38l1a5d6Tz20O9MmbIeJFzerhZvvN2eatViZ9QtbkrbtXI4Ys83zDHAOdbq1aupWbMmbveJZ7FNT0/nnnvu4frrr6dz587h7e+99x59+/YlKSmJAQMG0KdPH9q2bZvtsUpDqQbdPRjNOT5qtRIcySJ8H7b/tuMfQXe9geYam5X0LoAdbIXlHQq4QOwDmUj2k3XTcCR3BXEoboHN0CdDAxxZvUm9o3cSh3EkX40QuXvdUiZgZgxHWnVxJF8NIjU83CalAJlCMO1XdM9zaI7fonpEpHRhZryLtCKHcIT+N0biA6EYQ0jAxPI9jBk4G5x3YpoWum4jBGze0I7a1Ydx8gU6LRzJl8eskXW0vTp2sCOW96WI7V989i/Dhs6LygBc9XTJ/OXfIURq+DPh92tsWJvCVZd0BwSeBIM33rqS9lfX4bln/mT0l/+Fe2A0XfD2u1dyTafcz78B2L0rg+5dx5Ga6icjI0hiooOyKS6+n9gjon7RoCdm8NPENeFkfQ6HRqVKCfw6tQ9JSbkPqGJJTw9wQbNP8fujP49Nzq3MhJ96ntTxlYJ1qkwIDwYt2rUeza6d6eEvBJouqFQxgRmz+uFyF//0cqXtWsUr1ZDjHJzFixfTtWtXWrVqxaBBg9i4ceMJN2Lnzp3ceOONdO3aNSK4kVJy0003Ub58eZxOJ61bt2bFihUnfJ6SRBiLYgY3IZLYK5kElv8xgqnTMDM/JJg2Ccv7P0LlEwTISuS0Eklz/phVnTu7OQ4alu9BgqlTYwc3ALIsZsaHSLtSaEhMhgIVKeN8tKQG4gCQQDDjC6TVECkdoX9WI4IZnwMeEKlx2hRAWs2O2+bFSHwAITIQWgZCZCJEAN09DM1zK26Pj6TkIJ4EC7fHonqt6Sxc9HV2b0/uaDsgxtyj8EuVTtasrMb333TKWg5+9LvETxPXRAU3AJ2uXY20vRGfCZfL5oyaaVzUamfo1WaajBm9nDmztjL+21URw0u2JXn26ZmYZt6+nVU5LZFpM/sx+JU2DHjwfJ4Y1JIuXevxzFN/8tEHizh40Mf2balMnLA6IhNxMGhz8KCPcd+uzNP5Yjl8yBe3q3/Xrlg9nIpS+KZP3cShg76I3k7bkqSnB3I1Z00pPDmGmoMHD+aNN96gbt26rFmzhmeffZYxY8bk+UT79u3j1ltv5dlnn6Vly5YRj6Wnp9OpUyd++eUXEhISWLBgAd27d8/zOUoiaVdHahviTOw1kMHserHKIK0mJ3ReoS/Nds4NgLTOwg7cfMwWOxScyCSOrUUlraYE06YgtHVAEEQGmmMmmjPGvB4RQFpZ8yns2pgZY7ICHiLm1Wj62jjviSOrh+ro6hxhzOFIIsKIUwmJwxEdRCQmmqCPB244ZmsmuusdNOcvgI1ttsXyPZR9aQpZhnir12zLw219+rFwAXTt8Q1J5bbw48/laXPZ45Qt0yBuKYb6DfehG9GvRdNt6tY/xPzZodft85uMHbMiZtmDQNBi4V87ovLP5MTl0unStT7L/9tLn54TCAYtAgGbWTO38PHwJTz86AU4HFpUD4vXazJ39jZuvvXcPJ3veFVOS8Lh0KNek6YJmjcv2EKRipJbGzccivnlJCMjyPr1B4ugRUo8OQY4ycnJ1K0bGvuuX7/+CQ9PffTRR6SmpvLBBx/wwQcfANCzZ0+8Xi+9evXi4Ycf5sYbb8TpdNKyZUtat259QucpaWz/rWjGXxw7h+XI0JDlvwVpF8y8A2nXRcoZEauYIh6XDqR5dBhIc/yC7h4GIoNQzpouWL4nCZVwCLVX2vXD+1vWOWjGn0htV/gcUnqw/DdEBw0xJgxLWRHBvjiNj0xTL/Q1QOz6RvEX8RwTRIj9OJK6gkgL7685fkYzFhJM+4G4hUVlWWzzYjRjDkIcnXwvpYdfJvRnySKLCVPGc3r1dBITTYKBDdjyBoT5Jjfc2JjFi3ZF3MyFgM0bqyHlxvDk6iNsW7BmZSinjMdj0O3aBkz9PXZvqhAi5jBPbj315AwyMo6+Hp/PIhCw+OXn9dgxOoYMQ1Cj5smXDjAMjYFPX8xLz88Kvy+aJnB7DB569IIcnn1ytmw+zKyZW0lMdHDFVbVPeLjt4EEfPp/JaaclFusCl6Zps2tnOinl3Cc9tHiqqVe/PG63EfE7ApCY6KBBg6Kp1afEluMcnEceeQSPx8NFF13E8uXLWbFiBR07hlZL9OrVq1AaGU9pmIMDIIzfMDxDQWQCJtKqheV9AWkX4MoRsR9HcmcgI8Y8l9By8GDaBJCVEfr8UH6XiEreLuxgByzvC9mcJB3N9Q2aMRVkMlagL9JsS27mvmiO39A9zx53TgM7eDGW992IfXX3s2jOidkEM5EyMgw+fq8T99z9AiAxkjohtG0x3gc3lvdp7GCXbI6WhpHwOMJYnJX7xsTy38pF57ro3X8WAx5dgtsTGWzYdgrB1GkMfmEe34xejm5oCAFut8E337anYbP+IA4enYPj01i9sgId23QjIcFJ4yaV+PyrLkz+dT1PPTkjKsGex2OwYPEtJCbm/cbl95mc03BEeNLy8cetVj2ZjRsORXTPezwGk37rleeVW/HMmL6JD95dxI4daVx4UXXuu785Z9bNOWHgiXrt1Xl89vFShBDoukBKGPHpNXnqAduzO4OHH5jCokW70ISgUqUEXvvfFcUyD9C3Y1YwdPBcgkELy5Z07lKPl15ufdJzR0rbvI54LMumwxXfsGVLangFo2FoVK2axJQZffOUYLOolLZrFW8OTo4BznvvvRf3sQEDBpxcq05SaQlwQiwQu7N6J2KvespvQluN7nkeoa8iNN8n9IspzeaYvoFgh/JsGIm3Zi0HjySlk2DqDCABtM0g3SDzbyhBc36K7h5OqHfGzGqfjjRbYGa+zpF5RqGJ2t9lk78HgkGB0ylJTzf4Z2FVnGI4zZpVQ+hLQwkHRez0B5a/N5ZvUM6NFTsR2l6kVQdIovm5n/Dtz1/R6OzoStu2nYCV8QXSrs+2raks/Gsn5cq7uaRV9dBqALEL3f0qmmM2YJB24ApGvHc5U6fsZceONFwuna7XNuCue87jgft+Y/Hfu8jMDGIYoWXVQ169nK7X1o86b24EgxaNG46IWnoOUKGCh59/68UD901hyT+70XVBmTIuXnuzHZdcesYJnS8nBf2HeP687dx+86SoYbGkJAd/Lb41Vzd9KSVXXf41mzcdjgz8Egx+m9qHatWLT2HMP2Zs5r67J+M77vXquqBHr0Y89X+XnHCPTmm7aWbn0CEfQwfP4ZdJ65AS2neow1PPtKJCheyzfhcXpe1axQtw4v727tq1i9NOOy3cW3Os2rVr51/LlCx6oVfHlnYDzIxvCC3VdnB0uCmS0LbHOYJAd7+VVX3cC9hIqw5m5hu5yL2TMztwKyDR3R9lBS9W6J+xEN3zMpb35dB+wQ5ozp+ItVRdSp3MjMp8P6Yemn6YLRub0K3rbeEeAaHtyJr4HH1+KTWkXSt3jZVVkdbR4K7dFbXIzIj96yWEhZSh4Kz6GWWiK0PL07C8/8PKGqVyapI/Z4xn3doD4aGnzz5Zyqw/tvD9Tz2Y9edWpk3dSEqKmx7XN6J2nZTctTkGh0Pn6mvOZPIv6yOCHLdbp3ffs6hUOZFvvruWffsyycwIckaNMsV6KCYn475dGXM+BcDcOdto265WjsdYtHAnu3ZlRKWsME2br0cv5/EnW8Z5ZuH74N2/o4IbCNU/+/67laxasY/xE3uU6GtaGFJS3Lz6ejtefb1dzjsrRSZugPPZZ58xaNAgnn322agP+5dfflngDVNyy4fm+B2hL8+q8t0RiJeLITOrZtS0ULXxQG+keTE5rbiyzXPQHHtirPbyR/ec6KtxJN5OMP1ncrFIL5rYh+b8HqFtRlpN0V1jo+YJCRFAc0zG8t2B0DeBXQU70A3N+QOhuTVHbjQ60myOwx5Kr54Vw88/9tuLbTVCj5sV2pn1fubdY09cxIcfnUejs6eSkHj0hmLbGti1QOZ++GPenG2sWXMgYl5NwG+xefNhZv25lcuvqMXlV9Q6oXbG8tLLrdm6JZXVq/aj6wLTtLn4kuoMePBomYyKFROgYjYHKSGCATtuksNgLlei7diZHnPQNRiw2bzp8Ik3rgDs2BF/NVowKFm75gD/LN7Nec1zzn6uKMVd3ABn0KBQt/zHH3/M+vXrOeuss5g6deopM/m3RBD7cST2A+0gQniR0o3u+oBgxpehm2gEL0ZSP4S2PTyvRTP+wvLdhh24I9vTWP570RxzkNJ3TM6a2BN4hbCR2iGEvvCYXDWpoO0D+3TiTtgFhL4cI/F2wESIAFJOI34CQRNHcjeQCSBspFULM+NdhPEPSBe2eT7YNYAchgfsWtjBy9Acs8LlJkI3PINg+qc5Pz+OylUSeeSRF9mwNkiDs/7Etg0cDg1BCsHMN/N0rGX/7sEfZ9XGv0t352twA5BcxsX4iT34b9leNm86RIOGFahbr/hkjs5PnbrWZfq0TWRmRg5Rmqad6zk4jZtUxoyRNM3jMbio5cn3ZOan8847jV93rY85xwpCXw3WrT2gAhylVMjxK/bjjz/O0qVLAdi4cSMDBw4s8EYpuaO73wZt9zHlDHwgUjE8z0Xtqzl/ighuQvt70d0jQBzK/kT2mZjpo7DNS5EyKW5wEyaDWSubguieZ3CUaYcj6QYcZdqgu59H9zyGnnAvmuMn4OiNRff8XziHzZH2QewyAkKEloGH8t54EfpadNen2P67Qkvb7XPIbXBieV/F8t2NtE9H2uWwg50Jpv0G9tk5Pzkbycku6td+E+n7Cd1+Hsv7TqhnKw+9NwCnV0/GHWMuSEJCaNJvQTmncSU6dq5XaoMbgHZX1KZ12xokJBgIEUpc6HYbvPxq21xngq5dO4X27evg9hy9RoZDo1w5N9d2b1BQTT8hDz16AR6PEff3VyDyfUJ3Wqqfcd+t5IvP/mWDWkatFKIcJxn36tWLsWPHhn/u378/o0aNKvCG5UbpmmScV4dwlGkbNwNyMHUOcHTCm55wL7pjTvS+diKmdyjSzF3PnDDmYHgeQmjxKxmHPlEOpNUQoa+JKMR55NMmRGg5tbTOxsx4B6GvwEi8GyGieyqOfU52pHQQTJuWVasrvpI2wc7vM7m05ZccOOCNeC/KlnUxc96NJ7RaqqQojGslpWTB/B1M/X0jSUlOrr2uATVrZf8ZOp5l2Xz15X989cUyvF6Tq66uw4AHz49ZKLOorV93kNdemcfU3zdGfHlwOjXqN6jAD5N6ntAcnFjXau6cbdx128+ACJUGEHBDv3N46plL1DyfIlTS/gbmJM+TjI+1ceNGateuzZYtW7BjJcJQCpXm+Bnd8xyxsxzDsSuijrJj97wIC2RKrs8tzWYQN/Ny1iEFQBD0ZTGKTx77/17Ql+Ao0zarvbEneyIrgdhP/Nd7hEZownTebk5HpSOMpSATsxIonsAcojzauTOdNav3U6NG2biTg11ug7HfX8cjD/zOyhX7QEC9euX53ztXRgQ3a9ccYMzXy9mzJ5PL29WiY+e6MZes+nwmo0ct46eJa3G7Dfr2P4fOXeqdsjccIQQXtax2UsNJuq5x0y1NuOmWE0u8WZjOrFuOjz6+hs2bDvPs038yd842DIdG5y71eOa5Vvn2OfD7TO654xcyj0tj8M3Xy2nTtmaBrbxTlCNyDHCeeuopHnroIfbv30/lypV54YXs8p4o8QhtBULfiLTqIO1GJ34cYw6658W4y5qP7rgnYihE0zfGyPMCoZVPefmjnIDluxnd/XGu885k20xxZAn40TYde1wp3Vj+XmiOOaD/k8PQWFmQobkDQl+E7n4XoW1E2jWxfPdF1a86luYYi+55A6QjFMDJMgQzPgwvlc9vlmUz8PHpTPpxHS6XTjBo0ax5VYZ/3CFmj0zt2ilM+KlnuBfn+OWok35ay5OPTg/lNrEkf0zfxOefLmXs+OsihreCQYte3b9n3dqD4dVD//23l/lztzPk1exrvymlS81aZflidBeklAUS3M6bt51YyxO9mSbjv1ulAhylwOX4FfXcc89l4sSJzJ49m++//57GjQsw+VyplIGReBNG0i3o7sEYSTdjJN5KdBXt3NFdw3MssQAGmmPWMT97QeyN2iv0Ny30B0gYv2Ek9sNIvCFrbkycTLhiJ7o7H+o4xREaugoFNlK6sc0LsQM3Y2YOBpwx5+OE9ndhel8EBMKYh5F4L5rxD0I7hGYsDdWqMv6MfU79X3TPmwjhR2jpocKhYjeOxLvIudfoxIwc/g+//ryeQMAiLS2Az2exaOEOnnkqdhuPKF/eExXc+H0mTz0xA5/PDC9Vzsw0WbfuIN+OiazpNmXyBjasj0w17800+eH71WzceCh/XlwMGRkBZs/cyj+Ld8Wd4KoUjYLqubNMm6MrGiMFzRPPtK0ouZVjgPPDDz9wzTXX0K5du/A/Jfd0z2sIfQVC+BBaZui/+jJ091sndLz4OWkizgrSdczPTuLluEGmYCTchZHwBJqxDM34D93zf1lBWPQfJ931BeDPZe+NQMqj58193Xo3lu8WzPSvsDLfCbVdVsfMeD/qdYQqkFcmmD4WaYbyjeju16OCQCF8GO7XY55Nc37H8bWshJChYqT6P7ltdJ6M+nxZVHK5QMAOBz158e/SPTFvUj6vyaQf10VsmzVza9SKIQhVQ/77rx15Om9ufTd2BRc0+4wB90zmpht+5LKWX7J2TXQCRKV0uejialG5gSA0Ob5LtxNLRKkoeZFjgDNy5Eg+/PBDfv311/A/JbckmuOX2HlcnD+d0BFt6+ysUgrZsbDNtllFLFMBHTtwHTIi6Mka/gm2QhgLjpsbA0JfgtBnRx1Z6MtyqEAecQYs/83YwQuR1unYZpNcBjkSaV4YlWRPWhdklbAog5QJSOnENi8hmDYe7KPJJ4UWp+K9toWYPTLiUHj5e+QJBYi03DQ4BxKhL0JzjkXoCwCb9PTYk7RtW+a5jpQnwRG3VyQ5OTIgrFIlEYcj+tde0wQVKmSfD+lELP9vL88/MwufzyQtLUBGRpCdO9O5se/E0KRTpdRKTHQy7I12uN1G+DOXkGDQ5vKatLtCJYtVCl6Oc3DOOOMMatasWRhtKaXizZWJvwopO7bvPrSkBVk5aULbooIG6cRIvBGh7QQk0mqGmfkCiFQ0xxRCPTpBbH8PNH1N3HNpznFY3ksj2yzScl4mnkWIUK4dMyOUGFIYf6AZD2b7nNBrsTCS7gOpYXoHI802R19/sCN2sD1oW0GWQTP+wki8H0QGdvAqbP8NoeKdMYbkQpOpo2/uMng50lgYVeASEURazXJ+odnKwEi8HaFvJBRc6Uj7NK66+gZ++H5PVGBSs1bZXC9PPuLscypSvoIbrzcY8VlwewwMh841V43h9GpJ3HX3efTs1YiPRy6JyFJ8pA7Wpa3zf07EN6OXx+yRysgMsmD+Di6+JG9L5k/G3j0ZpGcEqVmzLJp2ak6oLmzXdKrLuU0r8+MPa0lN9dO2XS1aXFD1lJ3QrhSuHAMct9vN7bffTqNGjcIfykceeaTAG1Y6CKR5PhgLI3oIpNQiKnXH5gWCCG0nmmMqEh07eFWovEL656EcOPpyQMuqgn30JiJJR9OOyViqL8aReCfB9J+wfI8itJ1I6wygTFYdpnhtiPx46J7BCG1H3iYXi4yj/6vtJDTnJ06SsfAS6KyJxwKMhCcJpo89LnGhAXZtdPfQrCKbWXmAtC1ojl+x/DeHJhhHFOr0YPlvjXleO9gRzfkt6BsQwpfVQ+bC8t2b45LznOju/yH0tZGTwrUtDH59NtN+PwevN0ggYKPrAqdTZ8grbfJ8DiEEn3zeiX59JoaGnyQEgzbSDk02Nk3J6lX7mT93Oy++3Jr3P7qaRx/8nWDQxrYllSsnMuLTa0J1sPKBlJIVy/exe1cGO7anxe1dSj3sj7k9v+3dkxFRPysx0cErr12eqzIMysmrVr0M9wxoXtTNUE5BOebBmTBhQtS2a6+9tsAalBclIg+OtimUbVgEEMIfGiaSboIZX2Vl2j2OOITueQ7NmE3oG/+Ry6MBBpbv3lAiuyyO5DYILefkWVK6sM2zQdbEDnRHWqHJ4sKYjJHwZMwVVsH0UWAfWWGVhqPM5VHDbdmf04nlvwPbf2fWuf7ESBgYmsQbtS+AiBoqklLH9vfF8j8W+QSxE0dyl6j2SOnB8j4O4jC6+2OOFOm0/DdntUPEyQHhR3NMQnP8jpRlsQO9kNZ5uX6t8TjKXIw4Jsg72k6DHev+4IvP/uOfxbupW68cN992LrVrp0TsZ9uSeXO3sWnjYerVL5/tt1/TtFkwbzsHDvr4Y/omJv24FtOMfD+TyzhZ+M+tCCFYuWIfLpdOvfrl8+0b9b59mdzc7yc2bTqErmv4vCYSiXVcO1wunZnzbgyVfMjGyebrkFLSsf1Y1q87EPFeuD0GP/zUk3r1S28Sw8JW2nKrlGal7VrlOQ/OsmXLaNy4MZUqVSqwRp0S7FoE039Cc3yP0FchrUbYwevi5J6RGAl3IfR1MRLehQpN6u73sINXHlPMMrfzGPzojsVIuSTUy+EbgB3oD7JKKO8LGeEVTEDoHPYxy8e1A0Tn1olPSgNpVw8NGR3ZZl4CMhmJL5yg8Mj5pF0Voe3j+CE9ISzQ9kQdXzOWEJpwfPz8Ji/CmIvlfQNpNUZzfgMikFUmwiTuZGtcodes7UZzTEd3v4vl74s0ryBmJc6wDHT3u2iOnwEb22yH5XsY5JFssPHm09hUqpzAY9kUYjx40EfvHt+zc0c6lmWjaRpn1k3hqzHdYlZ8NgwtvPT2zWHzo4IbCBVV3LzpMHXrladxk8rZvK5I6ekBfv15Hbt3Z3Je89NoeXG1mEHRQwOmsHbN/ohza1ooQ/CRYTGPx+Du+5rnGNzkh+X/7WPrlsNR70UwYDHq82W8OESVnlGU0ipugDNv3jwaN27Mzz//HPVYq1atCrRRxYsfzfUFumMiCAs70AHLfwc5FaiMIMthB27LcTeh/4fQN8fM5ntUEN31GZbv/4BQIBIapjkaGMSaI3Pk51Bg4UN3v4O0qmIkPg34jgluBJbv/sj2ahsRYjfZ3+iPnhsMLO/TSLtWqEfErom0mgImwfT3MBKGgv7vMc8y0PSdcZaAe0KB0fHb7YrEGuqSUge7KprzU3T3cEIrviTS+BvNOR4zY2SclmfiSOoD2p6jpSL0ldj+5Vj+h+I8xw4N8enrwu+/5vgZTf+bYOZb6M5xSJkYbsPRNmpIswU5BYzPPf0nmzcdPma+jMXqVfsZNnQeL76c/Y25YuUEtmxJjdpuBm1SysWvBxbLiuV76dvrB0zTxu8zcbsdND63Ep992QWX6+hr2Lcvk0V/74oKJmwbKlfxULtOCmXLurmh/zmFNvdm9650NC163pVlSbZsKV6FMJWSze+3OHjQS/nynpgJNpXCFzfAufPO0LBCcnIyTz31VKE1qHiRGIn3Zq0cCs0X0FxfIRyzMdO/IZeJoHNNaNtAatnGEUKA5pgaDnAs/wNoxl9IbR9CZCKlG/DlYiKwA90z7LjaVAASzflrKMAR+zAS7kfoGwi9Vj9SGuEALHYJBYGZ8Sa6+5OselRZpAHCCwikfTpmxttozh/RHNPCbYgeJnOAdCK0NSC2gDw6pCet5mCXRWpHe4OOvC7LvBJH4u0Rw1ehrMmr0BxTge5R74bmnADavqjnaK7RWIH+ICtEv4X6X5jWBpxG8JjnmEixD0dSX8BGCCsrT8+RHD9ukG5M3zPRxzuGbUt+m7wB87iK1oGAzdhvVvD4wJbZTka+465mPLL894il6E6nRsuLq+Wp50RKyX13TyYt9ej7kpkZZOk/uxn1xb/cfufRSdiZGUF0PfaHztA1vvqmW67Pmxeph/3MyJpr1LptjYjX17hJ5ZiTnN1uI9zbZVk2+/d5KZvijgjYFCU3bFvy1pt/8enHS5A26IbgvgHnc+c9zdRk6iKW4zLxDRs2kJoa/U3wVCD0fxD68ohaSkIEENo2hDEz389nW40gpwzFkFUc04sw/kB3D8W2zsXy34TluwXL+yTSrpO7VU7a7jjb1wMBjMR+R3P4iPSsicxmVGAjpYGULqTUkVYjdNdohL4yNFyU9S80EdpEiCCavhkj8TE0x8y4vVWhcwRBHEZzfYUjuTO65xGODslpBDM+RtpnZp07AWmXxcwchhB7iDUUFRq+mhb7NRtzYidQlE6EvgwI/SGb+ecWhrw0mw/fX8TChX8gY6ySE5ofIYLhid9CgGUJfN46WL5HCKb9DHb2K5aklHEn55qmTd+eE8hu+txV7evwwEMtcLsNkpKduFw6519wOm+9e1W25z3e5s2H2bM7eqze57MY9+2qiG3VzygTc+jM4dBod2XBLAue8tsGWrb4nGee+pPnn5nJZS2/5JvR/4Ufr1wlkd59z8JzTCFMh0MjpZyL3n3OYvSoZbRo+iltLx3FeY0/ZsjgOWr5upInwz9czKcjl+DNNPH5TDLSg7z79kK+Gb28qJt2ysuxC2L9+vVcdNFFlCtXLhyNzp4dnR+lNArd2KJvwEJkoun/YpmX5+8J7VrYZmM0Y1EOAYpE9zyC5vgHIbxHV/34+2MHr0Poa5DaluyHuqQBlAURo5telg0vM8+ultSRn6W0CHU72WjGimyHyI4Kkl1XVazXrzmmIa3PsQNZq6Fkdcz0caBtQZCJtOsCBsKYG/OYUmrxV0XZpyGlRlTxUmGBrIBp2tx28yQW/72LzMwgTqdGm2X7aXK+jssV+ZxYr98wJLt27sVpdyUlJechIl3XuLBlNebP3RZz6G7T5sPMm7s926GeO+85jxtuPId1aw9SqXICp5+ev5XHN6w/yO9TNnDlVaFyFpomeOX1y7nv7skEA6GSEW63TtkUN/fen/+raA4e9PHw/VPw+SJ7aAa/MJuLLq4enrD97AuX0rhJZT7/9F/SUv1ccVVt7r6vObNnbWXo4LkRvVxfj/oPTRMMfOrifG+vUvpIKRnx0T9RSTu9XpMP3ltE337nFFHLFMhFD86MGTNYsWIFc+bMYfbs2adMcAOhia+xegJCZQROL5hzWheRm7kummPu0eXRQiKED931BZrzK2yrLkh36IYebnNoyEfKxKyejg+x/DdnDWkdc37pxg5eitA35GE5uMzqsZBZ7cn5GaHgy85DduPQcUOZlI9j10DaDTkSr0vzApCxhm+c2IEeMY9tB3oTyg90lJQa2FWQ1jn8MGE1ixbuDGcBDgRspv1Wg0MHXJjBoy/YyiZHX3qqM6p0QnZeHtom7li+GQzNx8lJYqKTc5tW4fTTk0lPD/D9uFV89vFSVq3cl6s21KxZlspVYg9pWZbkoft/59+lRyeBV6qUwNUd6lCrdlnOPqci9z/Ugsm/98nzhGLblvz801p6XvsdN/adyA/fr47qWfn9tw0xhwBM0+anH44OjwohuK5HQ3785XpmzO7P08+2okIFD++8tTDmjemrL5YRDKpSAkrOLEuSlho73cG+vaVnlVJJFbcH56+//uKVV14hMTGRwYMHn5LJ/qTZBqQnetWPdGAHOhTMSe2KgJtQHpzY4gcQgVB+nKyCkdJsAfpapEzB9vdEyspAQtYEVwfSaoQQaWiurwnFuha22RDNOTlPy8FPRKjXycpbTh2IyKsTn4GZ8RFG4j0g/KG5yMLE8j4at9CptOtjZg7G8LwQKraJibTqYGa+BQgmjF8ddTO0LI1rr+rKsHdncmnbbQgBf8+vgtsjObvJHhyOo9FbRobBxx+cTUZqdGBhWTZffr6Mr75cRmZGkHZX1uahRy6gZq2yPPnUxQwZPBszGBkJOhx63Orjscyds43bbvoJy5ZIW+Jw6FzTqS6vvdku23kCQgje/+hq+vScQHp69HCc32cycvg/vPtBez4esYT/vb6AQMDEtsGTYLBi+T6Sy+QtcSHAYw9P5fffNoQrUS9evItfJq1j+CfXhNvr91sxh+ksS0bU2opn1870mNstS5KWFqB8eU/MxxXlCMPQqH5GGbbGmNBfv4FKQVDU4vbg/O9//+O1117joYce4o033ijMNhUjToIZnyOtBlm9H06kfSZmxidA/nb3H2EHryQXHWsxCXFkjlBG1nyT/wim/YqZPgE7GFryLM2LOdorpWH5HySY+gfB9LHYwcvQjFV5Dm7yGqSE7kky7vPi9eqEgksXuudxhD4fzTERzTkaYpRmkHZDgmlTMTPexvQOJZg6DTt4ffbtMq8kmDYDM/0TgmnfY2aMwTIrM2P6JnbuiH0z3L0rkVt6daBB1VtpePot9O9xHX/PGci6NeXJSDdIPezA59MZ9019Jo47m7PPqRh1jMcfmcYbw+azaeNh9uzJ5LuxK+lyzbekpfrpcX1DypZxR2TeNQyNSpUTuPSy3GUe3rsng5tumEggYGOZEtsOBQe//ryOX39en+Pzzzq7Eu992B63O7o3SUrYsukwe3Zn8MZr8/H5QsENhIp4zpi2iTmztuWqnUcs/28vUyYfDW6OHGve3G0smH+0Xlbby2vG/Ky43QZXXZ1zFfizz4mdAiMp2ZGrYURFAfi/51rhdkf2Fbg9BoP+L3r1p1K44vbgOBwOzjzzTADefffdQmtQsWPXwMwYAdq20EoaWeW4Hbxojj9AHMKWlwHVYhwkL5JDvQ8J94BIz3sPx7Gkje4egpSnI81Lwsn9onlAutAcf+Yxkd+JBTdSJsZMfnfk8eyyHQstHc0x5ZiSE6DzNnagO5bvCSKH93Sk1SJvDcQR7uVJTw/Qu8f3bNmcGrNAJUBKiouLW1Vn2dK91KxVlvseOJ8WF1Tl+usOgFhBhUppLFtSid27EilT1qBnr7Minr9502Em/7I+ov6UadocTvXz3bcrufX2poyf2IP/G/QH8+aGeonaXVGLF4e0RtejA+Fl/+5h7pxtlC3r4sr2dTh8yMfgF2aHg45j+XwW345dwTWd6ub4rjQ5t0rMYMLh0Lig5enMmrkVQxdRBUgyM00m/7qeVrkMxgDmzd0WtXrsyLHmztnGRS1Dv2PVzyjDgAfP5/13FxHwhya/u90G13ZvQNNmp+V4nscHtqRvrx/wHdMz5/YYDHzq4lJbyuHfpXuY8tsGnA6Nzl3r56kXUIntiitrM/Kzjrz1xgI2bjhM/QblefixCzm/RdWibtopL1frnO1Yfx1PBeIwuudpNGM+oIEsg+l9DmmG6jMJ/T+MxLsJJXMz8fEWuqc9lvcFcjOPJh5pNcHy34XuPsmeM+FFc04CbHB9hh28Csv7Ysy2acYfxE9KF+fwJ9RzA0JkxHxu6HEHyMSslWKxz3f0uUdvp5pzArbZKmbOnBP11hsLWL/uIIFA5OdfCEhIcKBpgk+/7My5TY8PeuHzr7rw8ksV+HHCGgIBi0taVef5wZdR7rgcNMv/24vh0KIKbPq8Jn/N38GttzfljBpl+GJ0F0zTDs1DihHY2Lbk0Yd+5/ffNhIMWmiaxtMD/8Dl0rMt3un15mLVHlA2xc0tt5/LF5/+Gx6q03SBJ8FB2TIu3nx9ftQQ3pF9EhLylk4hJcUdkRjwCJdLp9xxPSv3Djify1rXZOKE1ZimTcdOdWmeyxvLuU2r8M231/L6q/NYsWIf1aol88BDLU5oxVcgYPHTxLX88vM6ypZx0bff2Zx/QcHM0ztRLz0/i7HfrMDvN9E0wfAPFzPw6Yvpf1OTnJ+sZOviS6oXal01JXfi/uXZvXs3Y8eORUoZ/v8jevXqVSiNK2qhHDArjibRE3sxEh7DTP8KaZ+JkfAg4rhq05pjCnawFdLM23Lc44VWBOkcH3TEKmkgpUFoyCdy31AgcGSbD83xO3awPdKMTNQo9GXonjeizpWrdh7XixNqy5H5NTbHLinPTUBkB9ohrZbonqGxl23H5UVzTMTKxwDnx4lro4IbCM1L+b/nWtGpSz0SEmJnRk5MdDLklbYMeaVttuc4vVoSMsb3B4dDo1adyBVfhhF/6HLyL+uZOmXjMUFG6FrmVJm8a7cG2T5+rMeeuIgzzyzHyOH/cOigj4tbVWfPngw++mBxzOAGwOnQua5Hw1yfA6D91XV48blZUduFJujctV7U9nMaV+KcxieWcb3JuZX58uuuJ/TcI4JBixt6/8DKFfvwZpoIEVq+fv9DLbjrnpMv95Ef/lm8i7HfrAhfJ9uWmKbF0MFzaX/1mVSukljELVSU/Bf3L2bnzp3Zu3cv+/btC///kX+nBG0DQl8dkSE4JBBK9qcvhxg1lYTwojvHn/TppVWbWAFHKEgQSNuTNS8oCct3N7Z5aVZwEZ8QXjTHT8BBhPYPiG2Eyj+8iRD+Ex4OO5LITkqwg5dhep9CWnWQdgWQSbk+rhCgOf9EynLYgWuRUoSPm5vnxlrSfyLS0gKM+3YlmRmxh+uklLS7slbc4ObIPqtW7mP2zK0cPhQ/UDu3aRXOOCMZw4h8kwxD44Z+8YYUo3337cqIOSu5Ua6ci+t7n5XzjlmOrEb69fc+zPv7Fm65rSn/LNodM7hxuXRcLp3HnryIho2i5x1lJ7mMi0+/7Ez58m6SkpwkJTkoU8bJ8JHXULFSwZd3yKtfJq1jVVZwA6HPq9dr8vabf3HgQPzFAoVp8s/rY0681nTBjOmbi6BFilLw4t4RBwwYUJjtKHaEtisr++5x24WN0LYSupnGuXPnJllfTufXVxMqBxEriLKx7UqYaaOBRECHgB+tzGVkd5OXEjTHZDTH5JNu39G2RP6sOf5Ac8zKaocLyFvFaCG86K6PEfqa43qpsu8BktKDHeyYp3PFsnTJbm7sO5FAwIrZe3PEsKHzePX1djEf270rg1tu/IktWw5j6BqBgMU9A5pz/4PR84GEEHz5TVcefXAqC+ZvR9MElaskMuyNdpxRo0yu2x0vKeDxDEMghKBCBQ8TfuqZba9QTpb8syvueVtcUJU33r4yanm4bUvmzN7K7JlbKV/BQ7drG1DltOjeg/NbVGXe37ewYf1hDh/y0vS8KvlW7Ty/HT8h+giHQ+Ov+Tu4+pozi6BVkXRDQ2gCaUVeL4FAN0rnfCNFyd9aA6WItBqEijQev106sc3zkdY5McsqSOnBDnQ++QbYVciukKbQ9gPH3AC1neS0+qowsoaHltMfaXeo5yKvk5GFtjn83KPHPdKTk0Bo7o1FaAjPBDxZw4LZDwflxLYl99zxa8zl0MeSEn78YS3PvXhZzF6cu+/4hXVrD2AdczMZ/sFizjqrYsz5HRUrJvDF6C6kHvbj9ZpUrpIQXgq9Yf1B3nhtAQsX7KBiRQ9339eczl3rRS3t7t6zIYv/3pltL47DofH0s62oW68cF15U7aQn0laukohhRM8fcrt1Lm1dIyq4CQYtbr/l52OSJeq8+9ZCPhjRgcta1+B4hqFxwYXVin3V45RyoVVusYK97MppFKYu3erxxWf/4rOOS3Vg27S7omCyTCtKUTvxr2+lnayAHeiBlEdzYUipg0zMSgrnwPS+mpX078gfsQSk2QQ72OkEThhKfBc+l90QadWIU4ASwBeqlB3emEJ+DdEUpdD76YkdEMlELO8AzPSxBNMmY/nuxfbfgpnxPpb3NU7845yK5hqBpfdh8JsTqXJazrl2hAapMRJ8bdl8mDWr90cENxAasvjsk6XZHrNMWRdVTksMBy+bNx3m2s7fMWXyBvbv97J69QGeenIGH76/KOq5HTvVpdVlNfAkGDHfO4/H4L4Hzqf/TY1peXH1fFkl1KZtzZjn03WNbtdFz+2ZOGHNcckSLbxekwcHTCnRifV69z0bZ4waVi6XwYUtT3ZVZf5o2KgiDzzUApdLx+3W8SQYuFw6r795RdTEd0UpLeL24CxcuDDuk1q0yOvS25LJ8j2BtOqH5tyIVOxgKyz/3SBDCZykeQnBtB/RHJMQ2kESXG1IyzyPvNxohbYW3fNSVlkIHTt4NZZ3IJCAmfkRRsIdoK+PuIkcmTysu98KVSoPXg0yBTt4adZS7xMPdGIX0Dw5uT2WlCCtM5FmC6Q2Onr+kzCxg90IDcuRqwrtOTfuMI6kniAOkugOcNU1MHFcLSZNyH7pdGKig0ox5oMcPuzPGvaJvmEfPBh7Lk4waDFh/GrGj1uFoWtc37sRnbvW54P3/sbrNSN6Brxek/ffWcTNt54b0Xuk6xofDL+aRX/vYu7srQhNsGXzYf5asIMKFTzcfmezXC0HzwunU2fMuOsYcPdkNm44FBr6qujh7feuipm5OFayRADbslm6ZE+JXVbbuEllnn7mEl5+cTaGQ0dKSYLHwedfdT6pIcD8dte959GpS12mT9uMw6FxZfs6VKigkhkqpVfcAOebb0K9A1u2bCEYDNK4cWNWrFhBYmIio0aNKrQGFi2BHbwWO3ht/F1klfCNVnfHnjMT//D7MJJuAo4sm7bRHL+gOSYjRBBpV8Dy34n0l8dIeCJiTgqAED4014hQgANY3ifQHLGLSeZGvKGkWNvzsm/uG1AGM+NzEOlozklIDoeDnFAhzwYIfWWokviRsUFxAN05CuGYgpR7EVoAy4IN68qzfPGdXHNNr2x7KzTnVyAOhvP/aBpcdc0Wpv9Wk8zM2JOIXW6dZ5+/NOZy7foNKsTM4ON0alwRY3jKtiW33/IzixbuDN/8/126mz+mb2bZsj1RPUEQqla8edNhGp0VOXlXCMH5LarSoGEFLMsulGR1tWun8PNvvdmxI41gwKZGzTJxMyPHu9lLSdQk65Kmb79z6NylHgv/2klikoPzW1SN+fkoatWql6H/TbmfvK4oJVnc38A333yTN998k/LlyzN+/HgGDx7Md999h9NZPMaUSz4Lzfk5EDyud8YK39SFth/d/T+EtoV4sagQRyuC6563Yu6Tm5VIOT8uQhW7pSsX+7pDAYl05rrWlJRuLN9AwAmyPMH0b7H9fbHtlKyaWjZCX4aROADd/XLoSWI/jqSeaK4v0PRt6IYfTZM4HJIGjfbTsfurjP3202zPqxkzo5Ibduy2gdpnHkZosedANT+/Kp271o/5mMul88Lg1ng8R4duXC6dipUSueX2c6P2nzt7G4v/3hnRs5GZaTJlykbKlYv97ToYsGMu6921K51+vX/g/KafcFHzz+jYfgwrV+Su5tTJOv30ZGrWKptt2Ydefc7CEyMnjifBQZNzo3MJlTTJZVw0a34aP/6whgvO+4wLz/uUV4fMzXWuIUVR8leOXzGOXRZuWRYHDhwo0AadCjTHjziSL0d3jc4xc3CoiOaXIJNiPi6to8t8hbEgZu/JkaXl2Z8np8ckod4pf9z9pQQ70AUzYziW/z4s7yCwq0UU/Yz1HCk1LN+AyFVQsgJW4PpQyQlhZxUUlaGl7s6fQrl7nF+AOByV/+cIl9vmwlZfkZ6ezXssy0Vtcjptxv36KykpsVeArfgv+1QJ117XgNFju9G5az0uuPB0Hni4Bb/81itmj8q8udtiTgy2TJuzz6kYlQLe5dK54qpaUUMLlmXTu/sE/lqwAzNoEwzarFq5nz49J8QdGitsHTqeyTUd6+J2GzidOgmJDpKTnYz45JpSkTnY77e4rvN3jPtuFYcO+ti3z8snI5fQu8eEmDWzFEUpWDmuourRowcdO3akfv36rFu3jvvvv/+ETmTbNs8//zyrV6/G6XRGFfCcPn0677//PoZh0L17d66/Pvu6QSWVMOaie17OWxI7kYHlfRrd82r4eaG/l24s/4NHd4uzJDsURJQHDsUNBnJsQg73n1Bw0wnLfx+a43cgiLTrE8z4FCPhEdBXhguWHn9cKW1090ehydnHBByaY2acs/nQ3YNBBGLkKYpUt8FBVi5Ko0HDCjEftwL9EcY/EddDSh23qw7paYnEWsmWkBg//80R5zatwv/eyTnZY/kKnpjZhh0OnXObnsYFF1bjhWdnkpYeAAkdO9flpSFtoo4zZ/Y2DhzwRg1pBYM2479bye13NsuxLQVNCMGwN9px2x1NmTd3G+XKubmyfZ1s8wnlJykl8+dt56/5O6hQ0UPHzvXydYLt5F/WsW+/F/OYDMyWJflv2V6ef2YmLwxunW/nUhQlZzkGODfccANdu3Zlw4YNVK9enfLlT6xC6tSpUwkEAowdO5YlS5bwyiuv8OGHHwIQDAYZOnQo48aNw+Px0KdPH9q2bUulSieWnbQ4010j85ihF5BJ2MFrkbISuusjhLYDaTXE8t2PtEM9OEJbFLPSdigQcmKmj8GR1BkZp4K3lI6sScrTTzzhn52CI/nIEnkzqzxEe8yM0Wiu4eiuT2L2WIWCnCCa40fswE3HHNBFrE5GIQB9Dcicb07793moesYCjKQvEGI3ttUYWz4OhJYlS/MSLN896O4PCBUhNZFWbWzfO1zebiHTp22KKBng9hjcmI+p7bt0rcf/Xl8QtV3TBVddXZvERCdXX3Mme/ZkUKaMK24wsG1LKpYVHYz5fCabNh6Oe/60VD+vDp3HpB/XYtuSq66uw6D/u6RAJ582aFghbsCZW0v+2cWH7y1iw/pDnNusCvcOaE6dM6N74444dom61xvE5TJ4dcg8PvuyU76VVFi6dA+ZGbED7jFfr6DPDWfnOemhoignLscAZ+XKlYwdOxa//2jvwNChQ/N8okWLFnHppaEaTk2bNuW///4LP7Z+/Xpq1KhB2bKh1PTNmzfn77//pkOHDtkeU9cFKSnFJ7OprmtR7ZHyABYLECShcRE+dsYpIwngBoJErsBx49QeIjElEWif9Y9QCphjpkP55KiYWXOEAI0r0Mo8hCR+YCVEOdzOy7BpiMWHxCt2Gf/5YHi+Om6rD905GadTR6MeQVwQVY7xyPP9GJ73MDz7cfAQQiQhZSe8DIuzvw3CIhSUxL6p+P2Cv+aexpUdnkHTQ8NAujYTH39Rpuw4NHFkKfO9SNkfmxUIKqA56kJZ+GB4Za7vPo6VK/ah64JAwKJT53o8/FjLfFsdk5KSwOix13HbzT+GAikpSUx0Muqba6lWLSW8X/ny2afSv+jiM9C06NVbiYkOWl1aI+bviW1LulzzLWvXHCAQCD3vp4lrWfT3Lub/fSsuV8GmyfL5TD4esZhvx6zA4dDof1MT+t/UJGJybqzfqd9/28AtN07E5wsV2Ny8+TBTJm/g19/7cvY5lWOe6/NPl4Tz7xw5N8D9905h+Zp78mWI7OyzKuFwagRjJIi0bZtff97ARS2j8/2UFrGulVI8nSrXKse/YAMHDqRfv36cdlrO1Xmzk56eTlLS0Xkkuq5jmiaGYZCenk5ycnL4scTERNLT03M8pmXJYpUELCUlIaI9mnMUuvsdQjdhQBrYViM0Y3fUcI2UiQRT/0A4pmK4XwqXgZB2BTIzz0Ha2b9OI3EHWoyrKSVY8k+IU+DyyD5wkACvEOoxcQG+fFoq7seU32cd8+jNN/axAwTlNwSs+ZgZYwAXwhiKkfBYzKE1KW2QThCRE7WlhIBf8M7rzbjnwX9xe46f4+Ij03oQM/37Y7bpwJHVJVnvtYBvv7+OlSv2sW1bKg0bVuSMGmVIT8/fOS31G6Tw6OMXMX3aRqpXL8t9DzSnYsWEPH2265xZlqbNqrBo0U78vtB7ZTg0ypf30PbyGjGPNfPPLWzaeCgc3ECokvn+fZmMHfMfXeJMpM4PlmVzfffvWbVifzjYWL1qBr9NXs+HI45+sTn+d0pKyaMPT4mYlG1ZkoyMIE8PnM7nX3WJeb6vvvw3ZkX4jIwA8+Zu4exzTr63+Kqra/PcM38SjPFVQ0pITfUVq79X+e34a6UUX6XtWlWqlBxze45fQytWrEjPnj259NJLw/9ORFJSEhkZR4dQbNvGMIyYj2VkZEQEPCWR0Jehu99DiEBW9ewMhHYYTV8ORK5EklLH8vcBHOiuT8M3bCFAaNsxkm4GbT2a6yP0hAfQXO+DiJzoapvnx1+xlE2dqaN5b4II4UOITOIFNyc6TzL0WvwIYeZYdFOIAELfijDmhc5pXo7lfRQpYw3NaJB1zGMF/BofvNWU8WPqk5AYOyeQ0NYj9MW5an+jsypy5VV18lQ6Ibf27cvkqsu/ZshLs5k+dTNjv1lOu9aj465+2r4tlZl/bmHL5uhhp48/78Rdd5/HaVUTqVDRw3XXNeDVNy6PWYMIYM3q/RHBzREZGUFWFfDqqz+mb2bN6gMRbfN6TWb9uYVl/+6J+7z09CC7d8VOxLh40a64z4u3uuuk0hocJ7mMi0++6BTzeG6PQYeO+ZuHSFGU7OUY4FSrVo0RI0Ywa9YsZs+ezezZs0/oROeddx4zZ4YmjS5ZsoT69Y9+OzzzzDPZvHkzhw4dIhAI8Pfff9OsWdFPijwZmnM8MYdjhI3puwnQjwkYJLprNJpjHELbHjFxNvTH0o8jqQ+66xN0x5/ors9xJHdFaGvD+9mBG4kYsyIrIJGJZJfhOFbAEe8P/pFyCcf+Kxh+hLY6/JMdvBZkUsRqLCmN0MqyGCu0XG6bhmcdJLlMINubl+b8Ol9bfSLefG0Be3ZnhldS+f0W6WkBnng0Mp9RIGBx/z2TubLt1zxw729cfcU33Hnbz/iPCRBcLp0HH7mA2fNv4rruDZn4wxruvPVnWrb4nEFPTI/KFlyrVgpOZ3QG3oQER7bzWWKxLJsxXy+nW6dv6dh+DCM+Whw3sAJYMH9HzPkqlmWz6O+dcZ/nduvoeuyLWj6beUPX926ExxPdxZlcxpmv82IuvKga//dcK1yuUDuFAE+CQafO9WhxQclMZKgoJVWOQ1TBYJCNGzeycePG8LZWrVrl+URXXnklc+bMoXfv3kgpGTJkCD/99BOZmZn06tWLgQMHcttttyGlpHv37lSpUpR5MTJA2wP2acCJTrbcH3PVEIDumArHTPYN7edFc39CrLkvQgQjvmkKEUDKALrn5VBiPAB5GmbGx+iepxHadsKrf2JMPM6NeN9sjx8Kivfc4/fN28mdSLv6MRsSCKZ/heF5EYyFgMA2L8X234CReF/M819+1Ra+H1sX2xLoRqz3FDTjH7SkjkhZAct/E9KMXTyzIE2ZvAHTjP6crFl9gLRUP8llXAC8/b+/mD5tM36/FV5xNXvmVl4ZMpfnXrws4rmff/ovX325LGLfH39YS9myLgY+fUl4vzaX16RceTc+nxlefaVpAk+Ckeesxw8NmMKM6ZvDQ0ebNv7Frz+v57sJ3WPOV6pyWiIutx4eTjvC4dBjZog+9vHre5/Fd2NX4DvmuR6PwR13xf9SdF2Phkz9fRNzZm0lELBwuXQ0XePDER3yfYn6zbeey8WXVGfiD2vweU2u7nAm519QNdscQYqi5D8hc5GgYc2aNaxbt47atWvTqFGjwmhXrgSDVj6PI1ro7mFozgmE5mTYWP5+2P4B5JRHBo4d18zEkXx1Vo6WyH2kdAF2zOXNoSuhxZifEy/vjCCYuhi07eiuj9D0xUiZjNA35Lh8Ojsn220vJWSknU9i8jKE8Ec9FitIOrLtyM+W74E4pRiOVHEP9TzoCfejGbNjBpNrV6fg8VhUOyMtZiZmEOHs0JbpYseWG6hc/oFCvRG1PP8z9uyJ/gw7HBqLl90eXjV1XuOPOXw4Og2Ax2OwbNWdEW2+5ILP2RVjGCchwWDpijsjbuh792Tw9KA/+HPGZqSEC1tWY8grbfM0HLdyxT56Xjs+qgxDQoKD19+6gvZX14l6zr59mbRt9VXUvJiUcm7mLrgJV1b+n1hzBfx+i6eenM6vP6/H4dAImja33n4ujz5+UbbXTkrJkn92h5eJX33NmSQlqcSl+aW0zesozUrbtYo3ByfHHpxRo0YxadIkmjRpwqeffkqHDh247bZ8qAFUDGmuD9CcP0TclHXXV1mFN/vm8igS3TMoTnADlr83unMiiEMxnis4Pu9K9uGnE8R2HEm9gcxQQrwcn5OzvNzfYwVD6WkGP407lz63/HdMWzTAgbTLgXYYpA4igBXoge74Oassw9Fj6e6RSLs20rz8uDNGfmStzDfRkltBjKX3Z9ZLx3f4feABpIxOUHhs6Qvd8FOx6pdc0zqJD4Z3p3adlNy/CSfh+t5nMXLEPxE9GYYhaHlx9Ygl4bEmyALhlUTHvq54if18PpNg0MZ1TGHISpUTGfFJR0zTRkqJwxE9ZJWTvxfuxI7xocvMDDJ/7raYAU7Figl89mUnHrhvCmlpfqSEKlUS+WBkh3BwE4/LpfPGW1fy9LOt2LUznRo1y+YqUBFC0Oy802h23sktmCgJtm1N5buxK9m7N5PLWtfgiqtqF6u6WIpSGHIMcCZNmsTo0aMxDINgMEjv3r1LaYAj0V1fR+WoCWUS/izXAY7m/BLNMTNOkOACuzZWoBe664vjkss5CPXsxFgtZNUGfUdE4CWlEzvQCd09nFAtq6M3mBPOY5NNz012j5kmZM0Xx+8XjB9Tjz43fhnRiySlhh28BMv7JkJbBdoBpHU2INGd42LMA/Kiuz7DjApwjucAygHR8zaEAENrTDB1BpprJJoxEyk0NP0QQkRnIw4GBEllNnPTDT/yx5z+hZJd9977z2fx4l38kzVBVtMElSonMOyNyNfdvEVV5s/dHvX8c5tWiWpn4yaV+Xth9PtRo2bZiODmWCdz86tUKQHD0PAft0Td5dKpWjV2Bm6A8y84ndkLbmLd2gM4HDq1amdf6uF45ct7KF9eFYs83oxpmxhwz29YViij9U8T19KgYXlGj70Wl0tn375M1q87yBk1ynD66SV7MYeiZCfHAEdKGV7t5HA4cDgKJ+to4TOB/2/vvqOjqL4Ajn9nZmsKJPTee+/SERGRjhSpEVBBrIgNBTsWfihiVwQpoki10KVJEZQqvfcOoYSU7TPz+2OTwGZ3QwIpJL7POZ4jW2be7iTZu+/dd6898F3SJSTDOnRPy8D338RbzC5w7g0o6FpxdHdnJPkssnF54jZnF7paBkk57PcMSQLkS+ie+mDYhveSeQMd2bQIcJGyCWcgacmLCb5zykSw+jW6DnN/qkTn7sexhrjxuGWiHjuALKdsDOpBNv6F6riGrlW9MVEln0h8TYEKAKZtJ4/q7I5i+drnffBuFVeYNHEHBfLn58EOTxMS8jwAlrwvoOqr/F6v0aRx8UIIMTEO/t1+gfoNMj8p1GxWmDGzK7t3XWLf3suUKBlOk6Yl/IKWN99pQa+H5uNyqrjdGkajjNGo8PYY/5/J0W82p9/Dv+J0qmiajiSB2WLgnTGZU0m3dZsymE0KtsRcsSSKIvFQj8rBn4g3oKtU+c6K/gk3uN0qLwxf4ZPgbbO5ObD/CrNm7uHQwav8Mv8gZpOCy6XSolVJPvuynV9LEEHIDW75U12/fn2ee+456tevz7Zt23L87qbgjKCVBOWU3z2SBIaQl/EkfI6u3hP0CLqughS4aqyug64VRlcbABqapynood4Kwq5uSHIshtBHAx9YsuGxTUjMtfkK2bg+YEXg4OO68TrS/xgJXSvgrZ4cIH9m1/YqXLwQjqJoKAqEhqXWCsKIJF1MbBuRSCueuFyVcjwKmqfxLV5Z4mPVRpCiOKEkgUdVOX32Z76YUIv33/2LWfO6U7FSPow8jsoGuKnwodMps21zYc6eDicsHK7HBG57kVlq1ipEzVqBi9QBVK6cn2Ur+zLt+53s3hVN1WoFGPxY7YC5MrVqF+LXhb346vOt7N59iQoVI3n62YbUqh38+HfCbFaYOechnhy6lPPn45ElCAsz8dlX7ShYKPUChVlN13W2bD7P0iVHMBkVunWv7NeVPSfbszsaTfP/wmO3e5j83Q6uXnXgcqq4EpPP1689zZi31/P+2NZZPVRByHRpSjJes2YNR48epUKFCrRqdff0U8noJGPJsAFDyAggcN0YzVMbT8IPQZ8fERFCgqc1knLG776kAEd1DsNgfZ8bW7cVwIIn/nsMYVFBko8tuGP/BhwY89zrl7ibmlslDN+4+ka/cyfd98+GIuzeUYC+Aw8SFuZbVM/jMeFymgkJjUvDWMy4Y/8EfD/0ZONCFOt7JNXf8W4BD8EdPxf0VPIlpGtI8nEkZQeK5ZuAQd/P0yvzynOtkCSoVCkfS1b0JSIihNiEOajy+3g8bgwGjQ3rivHs4/cRe92M2azw95ZB5A3QHFMITtd1Thy/jtutUqFivoBLfHa7m99+PcTmv89SqnReeverluoySUYmQ+q6zqiRa1j4+yEcDg+SLGEyKjz/QiOGDMsdX9z27Y2md49fA+ZsGY2yT9uRJGazwq79Q+84Rye3Ja7mZrntWt12kvHq1avZvXs3w4cP57HHHkNRlNvaJp4T6J5meGwfYwh5joDbteUTtzyGx/EihpBX/YIQ71LTRQzWd1IEHCq6noAhZCSq4xkUy+c+eTi6bkZz9gJkJPkISbuH0vR60rQbSsJjfx6D9TtStjxIem6tOpcZ9PCDTBhbn/6D9jPq3c3IiX8LFcWFNST48tWN3VEWVOcjpAxuADR3Z3StKLJ5mnemyHMPqnMQ6MFKBWgolv8hm35JXOJzEKjWj8Muc/hgRPJYTpy4zsULCUREhKC5O+F0tGHE8O/Zu8fN2dPeJFWr1cDwEY1EcHMbJElKNTk7JsbBQ53mEn3Zht3mwWiU+X7yDqb90DnD+kGlZtuW8yz8/VDybi9d1XGoHiaM30TnbhUpUiR4vlBmWLn8OJ9/uplzZ+OpXqMgL41snOosXlpUrVaAyEiLX4BjDTGgegJ/l/V4NNxuVSQhC7nOLQOcL774gsmTJwPw6aefMmTIkFwb4ADoniZACBCgcaVWJg3Pvw/V1QfFND1gAb1A82WSBLp8Es3dHEm6jmz+Ce+lcaO5O6C6emIIHYyk7CRlr6HUpC1f0wJa6VQfoWoSAwbvp3a9aFq18Z2durn4n//5DOiaFV3Ph+YcjObuFvQcutoA1dYgLQPmzLmJFC0zH6vkhlSW6gxGnbkzfXNA9JsCV4vFyoTPhjFvzn6WLTlK3ggLnTpXoOW9ubdfUHb6+ottnD8fnzyL4HZ7k2Cff24Fq9f1x2TK3DyQZcuOBSw+KMsSa1afpE+/6pl6/pvNmbWPd99anxxs/bX+NNu2nmfmnIfuaClRkiQmTe1I/z6/4XapqKqOpul0e6gyZ8/Esn7dab+/QWXKRmC15tbcSuG/7JZ/UQwGA/nze5MAw8PDExv65WZGVMfjKJaJKXY5WVAdz6TtEGppvNui/aeDUws6FMskVPv/UJ2PI8ln0PUioJswhncAKSY5eTmjyst7/9DZMYSOSPVxRqPGK29swWjSCHT5b65h4zsuGc3VC9U5/M4Hm+jI4avkKfQTVmva6vzEXLMkj7F0mbx+39ItFgMDHqlJocKhvDl6LX+uOoGu67RrX54P/tc6aPfu3OT8+XgmT/yXrZvPU7psXoYOq0eNmsF7M9ntbs6djadwkdA015HZv+8yM3/cE3CJ5Py5eOrXmsLwFxrx+NA6t/sybslsVpBlKbmoYRJJlgJWdM4sqqox7sO//eoG2e0exo/7h+k/Be6nlVaVq+Rn4+ZBrF1zkmtXHTS8pxhly0Zw5PBVenSdhzMxUV1RvK97zAd3T9qBIGSkWwY4tWrV4sUXX6ROnTrs2rWLatWqZcW4spXmGgyEoJgngXQVXSuD6ngpMZk1Dc/3NEcJUhgwtQrBsvFPVLsOhKJr3pkH2bgQJIfPzqwbM0ES6JGAB6TYNAU9NycT33i87nffzSwWNU3H9p+xciGbZ6E6h6BYvkA2/Q640Dz3oDpe9SZ1p9Pk73bw9kdpy0GKjzN6S+VbjRiNMp9++UDAx+3ccZERz/ruPPlj2TES4l1Mmtop3WNMyePRmDt7H7N/3oem6XTvWYV+A2pk+ofq8eMxHD8aQ/kKkZQukzfgY06dvE7XjnOw2701cvbujWbVihN8+U07Wrcp4/Ma/lh2lC8+3cKxozEYjQq6rvNw76q88U4Lnw7gKSVtW06tdYPN5ubT8ZuIjDTTo9edFxONi3Wy4a8zyIpE8xYlCQkx0rVbJaZ9vwtV9R2Hrunc37bsHZ8zra5ddQSta7R7d/A+XOlhMim0fcC3/lCFivlYsrwPk7/bwY5/L1KxUj6GPFGXipXyBTmKIORst0wy1nWdVatWcfz4ccqXL899992qLknWyfhKxnfm5sQt2TQJxfIlcPMMh4x3Zse/QaT3/sTKxDe1CFPM3yCbvw1QNFBGdQ5Fc3fEGNYzzYnHt6p1483x8S6DqR4ZVQPVAyGhwba+3+p8ErraAEnZmZwErOsy6OG44xeAHpGu43XvMpcXX59GqzZnAs4mJbHZFKZ8W5s92/vQslUp2ncsT2iod7YhZYLdU08sZfmyY35T92azwqp1A1Kt5XIruq7z+ODF/LPxTHJrAYvVQO3ahfhxVrdMqbXjcHh4auhSNv1zFoNRwe1SadmqFJ991c6vDs7wp/9gyeKjfjtvChcJZcOmgUiShMejMXDAArZtPY/b5ftzYLUaGPxYbV58JfCON03TaXbPNC5dTNvvaclSeVjzVxSx152sWnkCg0Gh4T1F0pUfs2jBYUa+tBqDQULHG8B8/pU3YJs+dSf/++DvxD5R3tmcL75ux333l0nz8e+U06lSr+bkgAFf1Wr5WbSsT5aNJSPltsTV3Cy3Xavb7iaekJDA7t27OX78OB6Ph5MnT2b44HIlPRLvzqTEfybOuHgSPgI9v9+Hqa6DrtYh5SXR1Mp4c4JSsqCrNZGVrX7PuV3eWR01eXZHUeDfLUWRgzQ3TAtdK4Gk7PbZ4SRJGkgOZOMv6T7e0yP20LTluaD5TACaBksXlGX8+/XIk8dMz4erJgc3gZw8cT3gsUwmhQvn49M9xiRut8qIZ1ewZvVJn75JDruH3bui2fDX6ds+dmo+fG8D//x9FofD27jT6VRZv+4UE8Zv8nvsxo1nA24rvnLZTnRiC4kli4+wc8dFv+AGvMsq06fuItj3pAsX4okN0GIimEsXE1i98gRNGk7jrdfXMmrkau5r8SOTJ/6bpuefOxvHKy+twuHwEB/vJiHejc3m4Zmn/uDaNQcDB9dm7YYo3nq3JWM+aMU/WwdlaXAD3sC5X1QNv+afVquBZ59P2yyxIAi3dstPxlGjRlGyZElOnDhBgQIFGD16dFaMK4dTUSyf4d8VXEUxz8GdMBX0PIl9qbxViSEM1T4acHi3TVv+h2yag+6ph64VSax0TOLjjei6Fdn4C5LxD1LufsookqxxT7OzmM1a4IAslbk/7/1GNPeDgY8tOZGUvWkciY6k7EK2vMeDXRZhNmsplthuUFWJZQvL8PwT9yHLBooUuXUdloaNimIw+B/M5VKpUCF9XbVv9vYb61i65GjA+2w2N5s3nbvtYwej6zrz5hxIbrKZxOFQmT1zH+Bdvpn4zXZ69/gFhz3wz47HoxET481BW7LoCHZb8OUlu92NK0DwAxAeZgoYQAVTqVI+nn3Ku5yVkODGZnPjdKpMGL+ZA/tvXfhx0cLD6AHOJwHLEq9FwUKh9Hy4Kl0fqpzczDSrjXytCX37V8diMWA2K0REmBn9ZvOAbS0EQbg9t8zBiYmJoWfPnixYsIB69eoF/aYm3ES6CgGWjCQJUPaDVhp3/O/IpnlI8l50rTKa62FAwhjeLbGPlQ1dt6KYv8Sd8A2K6Xdk4x94821sSNIVZNOq2+g7ZULX1YAtIQK+FAlA9y416XpyUJGmfB9PHXRPXTBP8r9PB12rdOM88mEkZQ+6VjQx1ykp9lZRrC8jGzcSqNK0puGzVOVwKHz0XkMAFEWmx8O3zucYOqwev/96iPh4d/KHcdLSy+1+AMbFOpk/70DATuEAFotCoUwogqfr4HQGDkbsdjdxsU46t59DdHSCz6xSSpIE69aeolLl/ISFmVKdMStZKngLiPA8Zlq0KsW6Nad8EoxNJhlN0/HctHXZYjHQqnUpjh2L8TuOy63y2y8HeXV06kX5bAnugInMqqphDxLMZQeDQWb0m815aWQTYq87yJffmmoekyAI6Zem36ijR73ffC5cuPAf2EWVAfQ8BOs+ricVrtPzoTmHoto/Q3M+BXoBFMtYkM4jSd6lAUmyg3Qdg/UdVMco3HFLE7dFaz6BRlp3VHmXwSqj2t9IDFjS/pLS0g7C9/GApCEp+wn6XqjFATdKyHAMYQNQrP/DEDoCY1hnkC4CIBuXIhs3Ikl2v9c6bkx9/vdOQy5dsOJ0KGzaWIR+Xbpy/mwh8uWzMHFyhzTlzxQrHs5vix6mQ8fy5M9vpWKlfLzzXiteeDl41epbuXTJFrTuCHiDr85dK9728YORZYk6df3rB0kSNGxUjB+m7+bSpdSDG/D+rMTFeoP0pJmGQMxmhbfebZHqsT7+5H7q1C2MxaIQHm7CbFYYEFWTSVM7UqduYSLzWWjcpDgzfu5K8RLhAQMRTdX9ZqUCufe+0gHHKssSre4NXA4hLtbJ/n2X07WUllHMZoWChUJFcCMImeCWMzivv/46o0aN4ujRozz33HO89dZbWTGuHM6M5uyJbJ4XYKv5kwGfIRvnIhuXB6ydg3IQSfnb27sK1+1vEddD8SRMBELR3PUxhvdDJy7x27kE6KkeO31dxi1o7nZI8qEgwZEJSbYjmX5CNvzj+z7JNoxhfXHH/4xs/M0b6KWw/s/ifP91LWw2I19/6luFNm9emY8m3E+zFmnfpVW6TF4++6pdmh9/K9evO4IuzcgyTPuxCxGZVEzw3fdb0afnr7hcN/pWmc0G3nynBa+8uCpNgQLAxg1nGfES1G9QlGeHN+TTTzajKBIul7fHVe26hRn9RnPq1U+9O3eevGZmzevO8WMxnD8fT+Uq+cmf39sks2Ur36Dj1/kH0AJMekkStO9Y4ZZjrl2nMJ26VGTxwsPYbN5kfovFQL+oGpRPsdyoqpq3hcfMfRiNMi63Rq/eVXnrFrvCBEHIGW4Z4FSqVInZs2dz/fp1FEUhLCxrq33mVKpzBCAhm+cCOuhWVMdwdM+9/g+WT6JYPwoaQHh7Yb2YuDSV+nlvnpXx7Rslo7nbAgYU83hk82y8O6YUNLUguloFST4HyjGC7fJKy3m9wZIVXS2N5uqKbPwTXV+SPCt1g4zuqYMhZAT+HdxB5wrGsF7oapmA55s5vQo2W+Af3+vXnTwzbBkLl/ZOtbJuZoq+ZMNkkgPmpjRoWNQnKEha9k1PJ+0kbrfK+rWnuXrVToNGRSlTJoJq1QuybFVfpk/Zxb69l6lVuxCPDKpF4SKhyYFFWuzZE83laBsFCobwxFP16PFwFf7ZeJawcBPNmpfAaEzfNvey5SKSr0fsdSc/z9zLhr9OEx5mIk8eM+F5zMz+eV/A5+o6acqnkiSJD8e1pmPnCiz47RCKIvNQj8rc07i432Mnfr2dObP243SqyUHfL3MPkD+/leEjvMm+R49cY+aPe7hwIYFW95aiS7dKojGlIOQQQX9T9+7dy+jRo5k7dy5r1qzhrbfeIjw8nJEjR95VW8XvXgZU50uozudAikvcDh34A8GbW3OLb9VSwm3VorkReGjIpuVIxr+QpHjfbeXyZSR5Pd4ZHC15NictvMc3o7nvBT0UpMvoWhU0VwfAgua+H9n8Pcinks/pnd1pjq5VDFqJ2Bvk2IC86LrVbxbHbjMSbOkLvAnCP0zbxVvvpt4BXtd1du28xMWLCdSoWTDVvkjpUaVqgYABi8kk07KVt1Jy7HUn7769nsWLjqB6NJq3KMm777eiREn/BpqBHD50lf59fsPp8KBpOqrqrbEz5oNWFCsWzmuvN/N7zuDHavPP32f9iswFYjDIxMQ4KFDQu4uvQIEQOnW582W1K1fsdG4/m+sxjlsuld3s4oUESpUOXM/nZpIk0aJlKVq0TL0i9ZTvdwYstjft+10MH9GIFcuP8fwzK3C7vRWB1/55kimTdzL/9x6p7soTBOHuEHQedsKECYwdOxaj0ciECRP47rvvmD9/Pt99911Wji8XMIGen9R6SEk4uVWAc7vLUjfnrUiSDUm6HGDGxJO4PVxL/Hc6ghutCJ6Eyaj2ceh6QWTjXyjm7zCGd8MYfh9IF/HET0Nzt0XTItC1UEBGNuxGNk9E89QNmgskSW6QrqF5GntnhXQpOXeoS49ThIQGTxpVVZ3Tp2JTHX/0pQQ6PDCLAX1+5+URK2nT8ifeGLUmaCK93e5m9s/7eHH4Cj6bsJkLF4JvIS9dJi/3P1DW59u+rEiEhZvpO6AGuq7Tr/evLFpwGJfT+wG6ft1pHuoyj7i4W3eK13WdJx5bzNUrduITt0I7nSq//XqQJYuOBH1esxYlef6FRpgT82GsVgN5IwInUhsNMmXKRtxyLOn19RdbuXrFnq7gRpKgUuWMLUgXLOcmLs6Jy+XhlRdX43B4kisf2+0eTp26zozpuzN0HIIgZI6gAY6u61SpUoWLFy9it9upUaMGYWFhIsk4E2ie+4Db262T3l1UGdXiQdcN6GoN3Amz0dVaSIaVKOaJSNJNW7gl7zKTIWwAsnEVkhSTOBNlQ5IvopgnJ+YVBTuPzMXz+Vgw+ymunJuAruVPfg1deuynXsOLQYMci9Vwyxyc555eztEj17DZ3MTHu3G5VH6df5B5c/b7PTYmxkGHtrMY8856fvv1EN9+tY229/7Etq3ngx7/k8/a8uzzDShaLIy8ec107lKR3xf3IiLCwuZN5zh5MtZnx4+m6dhtbhb8djDVcQMcOnSV6Gib3/W32zz8NGNPqs99/Im6bNr2KN9Mas/833vSslXg96lkqXCmTdnJ5eiMLQi2auWJgDudUlOteoEMb4BarXrgdhSVq+Tn4IGrqKr/GJ0OlcULgweQgiDcPYJGK1pipt/69etp0qQJAC6Xi4QE/yaUQtpJ8iFk0ywkwwogcclGrY7m6o6uW25ZX+Zmum5GdQ5B1zM+WTVY3RtdB83dHE/8TDwJPyVXIvZ2Qfd9jjfIsSHJJwPugpJS7AhLyemUePqxwrw2cg2N6+7gm89LJc8uGQw6P/6ylC8mr+bBzudRbipGaDTK5Iu00DOVLeKXo23s+PeiX18iu93DtCm7/B7/xadbOH8+PrkejMulYbN5eGnEyqAzPgaDzLCn6vPXPwPZvvtxPvmsbfIS2NHD19ACfIDa7R7277sSdNxJnA5P0CrIaVl+Cg830aRpCSIjLSxaEPgDe8/uy3zy8SZat5jB1s0ZV7Mnz21svQ/Pk/FLQm+83Ryr1ZD8Pnrbehh4692WhIYa0dTA1zWt/bcEQcheQQOcJk2a0KdPH7788kuioqI4deoUw4YNo0OHDlk5vpxLPoFsmoZsmgHSebz1XEZiCItCsYzHEPIWxvC2SPJhAFTHSDwJ36G5+iUW9wsJGux4b5dQnX3RnE+jubqi62Z0zTdACv7cNL0AdF0O0LtKQlZOICkHkZTdJOXqSNLVoEe6uY+W/32Bb9d1OHooL1s2RZAQ78bp1PhsXB22/HNjC7Si6DzQ4SQTpx3m/bH3UrVaAUqWzEPUoJr8vuRhwsODfxDFJ7h8giKf++L9l4j+WHos4KzDxQsJXLgQOOjfuvkc/R7+lUb1ptC3168+hf0qVMqHHGCnjjXEQNVq+YOOO0m16gUD7vSxWAx06VYpwDMC+2zCplR/HpwOFZvNw3PPLM+wGliDH6vtV8X3Vk4cu56hQRZ4d4fN+60HD7YvR9myeXngwXLM+aU71WsUoGDBEEqUCPcLIq0hBh4ZVDNDxyEIQuZItRfV0aNHyZcvH5GRkZw6dYqDBw/Stm3brBxfqu7WXlSy6TsUy2S83cR1vPk1Rry7k258SHpzWErgjl9E4IRZDUNYDyT5dHJV5KReUt6rZkF1PormfAKks8iGneiaGUk5jmxaiCSfCJh0nNoy1Y3gqADoBmTlQpDHmAEZXa2I6uyNwfohSPHpPl9q3C6ZcgUfT/63JGn06HOYCd+uven4psT3IPD2+0AiIkK4ejWBpo2mJbcjSGI0ykQNqsnoN5r73N6m1Y+cOH7d71hGo8zGLYPIl893d9Jf607zxONLfPoNWSwGvpnUnpatSqHrOl06zOHwoavJgZOiSEREWli9bkCaZgn+XH2CZ4b9gapquN0aISFGyleIYNa87qnu9FFVjcOHruKwe3i4xy9+s1iBhIQY+GVBrwxpzKjrOu+/+xc/zdiLJHl7M0mStyZMank5VquBkaOaEjUwcwKMc+fiePmFVWzd7F12LFc+gmvXHNgSvL97bo9G/wHVGf1m89va8Zbb5bb+RrlZbrtWwXpR3bLZ5t3sbgxwrsfuxBDWPx3NLy24438GLUiJdikGxTI2MVfFv6u3rptwxy0GvZDP7YbQXsiGQwHOl57CgNItE441TUaSpOTKyDcfX9cB3QySO9VZnGBiY41ULznY57a27U/x4afrCA93YQ1RQMuPO342kLadR3Djl3vd2lM8OXQpHreKx6NjsShE5rOyYMnDfgHL95P+5ZOPN+O4aflHUSTq1S/CrHnd/c7Rosl0zp31T0IuXyGS5av7Ad4Cc++P2cDC3w/j8Wi0bFWSt8e0pHiJtL+W06dimTN7H5cuJtCiZSnatS+H0agQe92JqulERvouX25Yf5oRz63Abvfgcnl8KgmnxmI1sGhZb86diWPit9s5fy6eJk1LMOzpeuTPH4LJJKf7Q//SxQT27onGGmLg9KlYNM0bVIx4bgWXL9sC9r6yWAxs/ndwhu9icrtV7mvxIxcvJiQHfJLkXRobP6EtNpubeg2KZNguu9wot31o5ma57VqJACcLRESEEGv/GMU8Oc2tEHTdiOZqj+Z5EN3ThGCrhop1NIppUYDnh6DaX0dzd7xxoxSDMbz1bQUV/sdP/wxMUmNRzd0S1fEcxrDHQXIgSfY0H8/tkpg+uRrvvHZjq7PZLGOxSDgcbnQd2ncy8N4HvQkJSd+sws2/3MeOXmPG9N2cPhVL0+Yl6NW7WsClLY9HY/jTy1nz50lkWUKSIH9+Kz/Pe8iv0/X3k3bwwZgNQc//xdcP0L5jhUyZBTh3Lo4Xh6/k3+3embfyFSIZ/+n9VKlagHPn4nig9cw05eikpCgSJrOSvOMLvNWBk/58REZaePb5hkQNrHnHr0vTdDo9OJuDB/xzkcLDTXwzqT1Nmpa4o3OktGL5MV58fiUJ8b5J61argVGvN6NfVI0MPV9ulNs+NHOz3HatggU4omJVBpPSWD/mBjeyaQGyaSW6pwYe29d4l7NAUjYhm+YiSQmAhq7LAYIWCV33LYAmKVsTj+E/i5QUfKSn9YKuG5AkT5qDE0kC1XUfqv0T7yuMW4xsWoIkH0Y2LgUp9e3bAIoBqtWIQVFAVb3LF263itMJSUHg0sUScdc3MmlqpzS/lpTKlY+8Za0c8CYMfzXxQQ4fusqunZcoWiyMxk2K++Vo2GxuPvnIv2v3zUa+tJp1a08z9qOMrSfl8Wj07v6LzyzEgf1X6NPzV9ZsiGLe7P0BdwYFYjIpSJL3mKrqrbGTsuHmzZWar151MO7Dv5Ek6Y6XkGRZonSZvAEDHJdbzZAK0NGXEhj/0SZWrTiOxWKgcpX8QbulnzgRc8fnEwQh64kAJ4Np7rbI5mncqq5NUrBwI2CwgWEXsvE3NHcvZPO3KOZpgD0x58ZMwOJ7uoLuaZritjCCFcGTJNC1SHRd9hYgxJVqsOM9t46uRaBp4SCdRrmppE+goEfXlRRLZqForl7e+9TKKNZxfrV4UpJlnQaNr9Krj4FdOyJwOj0cOxrj8xiXU2XDX2c4fz4+TT2nMkLFSvlSzUP5Y+lRn7ybQGw2Dwt+O0TtOoW4HG2nUOFQOnSqkGpSdFqsW3OK69edfjk1brfGb78c5MKFhKBdv5MoikT1GgUZ88G9LFl0mCnf70xTjg54g4HPP90SMMCJi3Vy+bKdYsXDgzbmvNnAwbVYvfK43xKay6liDbmzP1txsU66dJjD1av25ONfvmwPGPyFhBqpWdu/t1cgdrubj8f9wy9zD+Bya9x7bylGv9VcLGsJQjYRRW0yjAeX/gmGsIGAK7kgnf92axOapwpJszQ3kyQHsmkhSNEo5u+Tt1Z773MCBm/9GS0UXQ9F1yLx2L5FUv7FENoHY55GGMM6I8nRoAdr9gmq62Hccatwx61A10qh68ZUm29KkgqSC5ftUa7HmLEleD+gbDYDmuadYfFlRE0MaFLS3N1RHc+ja2Gp7uiKvmTlvkZdWPi7hyOHr3HsaEzAxzqdKiuXHw98kGzw+adb0vQ4l0vl3bfW89mEzYx5Zz0tmkxn397oOzr32bNxAbuXOxweThyPoWmzEoSE+P/cgTe3xWCUiYy0UKRoGNHRCZQrH4nRkL4/Edeu2n3G4HSqvPLiKhrVn0qXjnNoWOd7pkzeccvj1KlbOOBSlyTB119sS9eYUpo39wCxcS6f4Cmpv5bRdOP1Go0yhQqF0O7BIPlxN9F1ncFRC5n5415iY1047B6WLz9Ot05z01S4URCEjCdmcDKIYvkID7/dNDOho+sGNLUqsnIE744qBdXxKLqnGYawx4BAReoUZMNWvJfG9w+jJLlRXW3Q3F1Bt6Cr9RODm2dvJDUrp1Cs75FaqwXZuBTN1RlJvoAkX0o8tne8uq4SuOmmHaPpCsMGD6dClbXUqH2FPTvzs3F9MT77bg2ly8ZjMplAl/HY3watfJCzS2iuvmiu3kjyv8iWKciG7Xhnqm6M+dXhLTh7OhyPB27MhukEmpka+8EGOnetmGnNK1Nz8MAVJk38l2NHr1GtegEunA9e3fhmuk7ybErS0s9zTy1nxZp+t53DUqNmQeQAW99DQo3Uq1+Udu3L8d032zl6NCZ5lskaYqDdg+XQdfhj2VEuX7azfNkx/lp3irr1iqQ5CTlJ4SKhGG4Kit55cx2LFx7B5VRxJfZ7+uSjTRQpEkaHTsGbZ548cR2TWfHbmq9psOUOt4tv3XLeJ1k8idVqoEmzEuzaeQnVo9GhUwVGvHQPJtOtZ5x277rEnt3Rya8RvB3QbQlufp1/gEcG1bqjMQuCkH4iwMkQ8cimX0gZkIAH9Ly4Y9eDdA30SLwzN5p3GSlFA0pdtyYW/MtDoA9yXZdBL4juaZV8m7fAnm+ujSQ5UpmNAeSTGMP6AEafHk/ePBsj3kAi5QeAFbSijHqjOT27OZjxvQe73UNIiJGohwazcFlDIvPr6GolAs1O+ZPRtfqotvqoxGHM8yDgDQ48HonVy0vh8aScPUjqkeV9b6xWNwajhtsdypKFR7I0EdTpVFm44BBvjV6X/O1/757LAWdQ0ur8+TjOnI6jZKm076K6WZ26haldpzD/br+AM3G7dfIsRPvyGI0Ks3/pzozpu1nw6yHMFgP9o2pQvUZBuneZ67NF22bzsOGvM0HPZTbLOJ2+r9ViNfDyq02S/223u/ntl4N+3cvtdg9ff7mV9h3LExPjxGo1+G1rL1goJGi141Klbt2PKjXlykVgNMoBji/x5NP1qVsv9e7ogRw8cCVgfprd7mHXzku3N9D/iI0bzvC/DzZy+NBVChUO5bkRDeneo0p2D0vIBUSAkxHkSwSecQFZPo6KMUVOiozH9imG0KF4Z3bcgILmbpW4G0oF3YROygabJjRXD99zKEfTPVxvI8s4Ai+TudF1xWeLeNJ2b819P2XLWljzVxS//3qQo0euUaNmITp2roDFYkBPe2uhFMLxxE/2dkyXr6BpwZfLFEUnb4SDfPkdDByyhwXzK7Bti4VrMann9GSk7yft4NPxm7DbPT7jDBbcGIwSsiRhMinoOj79jVK6kw1IkiQxZVonvv16O/Pm7Mft0ejQsQLDRzRMznuxWo0MHVaPocPqJT9v8nf/pjnPRpLgpVca06p1aU6dvM6E8Zs5eeI6xUuE8+LL99C+441ZmdjrrqCv5+yZOFq3+JEL5721kx54sDzvj703uf5PvnxW2j5QlpUrjicHa97xG3jq2frpfWt89IuqwbSpu3wCHINBpmSpPNSpm7Z8m5TKlo0g0JeSpARmIbDNm84xZPDi5BnF06dieXPUWuLjXGLWS7hjIsDJCFoRAi036TpoauClGl2tjjt2BbJxNUjX0D0N0LWk1gIynoSJGEKfTuyoDaCi2keha75VanWtOJLiX+8mbQInw+pqeST5fGIQBCChuh8gqV9WeLiJAY9kbLE1XauKO34xyCcBiYb3/Mvmf8757NRRDCpdexzls+/WAOBwKHz8fiPMJoWmzTJ223AwixYcZsLHm1Ldai1JEBJixOVSMRhl6tQpzLeTOnBg/2UMRplN/5zj8wlb/JKRixYLY82fJ/n910OYTAp9+lenY6cKQVsyBGK2GBj+QiOGv9Aozc8JDTWhKBLu4L1Lk3mX1lSqVitA1WoFaNc+2FIkFChoxWo1+hXvkySIj3cTG3vjC8HyZUe5etXOjJldk28bN74N7761nl/mHUBVdXRdx2xW+HfbBeo3KOqzFJYeRYuGMWNmF0a+tJqTJ7zFG5u3KMm4T9rc9vJg/YZFKV0mL0cO3yjcKEne7vGptQz5r/to7N9+vwd2u4dPx2+mf1SNgNW6BSGtRB2cDKFiDG+KJPvOIngTenuiOd64zeNqSMpOkGzonrpAiN8jJMMaDCEjfXYlpXU7d9L2b9/brOhqZSRlX2KvqKTbzWiubqCHo2sl0NztADOS4W8k+Qy6WhVdrUWw3VvpderkdXp0nYfD4cFm8xAa6iYsj5OmLc9xYE9+yla4ToFCNub+VIdW95bmy2/apfnD6U5qQHR44GcOHgjelgKgZKk8DBxUi1k/7+Xc2TgKFAxh6LB69OlXDUmScDpVBvT5lT27L+N2q1isBoyJMwjHjsYkB0/WEAMdOlZg3Pg2tzXWtLp2zcE99aakaRbHajUw+s3m9O1fPU3H/nX+Ad4YtTb5NcmyhCx7t5hrKSa8LBaFJcv7UrrMjSUoVYVGdScTG+tMfrzFYqBtu7J8+sUDaXuBqbh2zYHJJGdI4cDrMQ7eHL2WP5YdQ1V16jcowvtjW1O+QuQdHzsnuJ3fq7o1JvkEuklMJoUNmwf6FdwUMoaogyOknXwi4Oe6JIFs2M5tZWXIx5GkS+ha5eSGloHonnvR3fXAuNEnqLl1SwYZ1dkbxfwb3tknbzsJXa2EpOwI0DjTiWyak5iMbEWxfAK6BeT4xOfK6Go1PAlfA3ee7FuqdF7WbIhi0YIjHDl8lXz5PXzz5W4WzK+A6pE5sC8fkiQz9MlavPhy4ywrnX/pYup/FKxWAw/1qMyE8Zuw2bwFCU+djOX9d//izJlYXh7ZhLmz97F3z2UkGQxGGY9b45FBtZgxbbfPzJDd5mHxwsMMeaJuqlvTz52LY+niozidHlrfV4aq1Qqk6zVFRloIz2Mi5tqtq28rikSnzsGTg1N6qEcVChYM5csvtnL2dCx16xXh+PEY9u297PdYo1HhzJlYnwBnyuQd2O0en2DI4fCwfNkxzpyOpUTJ28tXSpKy0vOdyBth4bOv2qGqmndHlvHWycn/dSVL5WHvngA/Cyb5tpqyCsLNsizAiYuL4+WXXyY+Ph63282rr75K3bp1fR7z3nvvsX37dkJDvYXrvv76a8LDc0ANCd1C0F1Lejq/gUgxGEKe9S476QaQXGjO/qjO4QSOoq4iGbem6NKd1FQzqUBfUnJu0n0G0IoiIeENbjS8FQPUxJmbIEOTko5hR8cOUqzvY5U9KOZJqM5n0/eagwgNNdG7bzUABg1YQELCjelq77Z2naWLj/LyyCbBDpHh6tQtzJo/TwbMETKZFXr1rsq5s3E4HL75OXa7h6mTd9LuwfJ8+N4Gv8TbyROD58Fs2XQuaIDz+6+HeG3kanTN22Pqq8+30bd/NV5/q0W6XlehQqGpBjiSDEWLhvPVt+0IT+cHT/OWJWnesmTyv5MSSlMm+TpdKpUr++arbPrnrN97BWA0KRzYf+WOA5zMoCiyT60oIbgRL97DM0/+4bNMZbUaGPJE3dteghSEJFn2EzR16lQaN27Mjz/+yIcffsi7777r95i9e/cyefJkZsyYwYwZM3JGcAOgF0dXy5Dy7fTuiuqdrkMZrK8iKfuRJAeSHI8kuZDNs5CNSwI+XlL+JVBw5S3oVwTV3RLN3RWP/W00dzs0T31Ux3A89leRzfORJBeS5EGS3IkNPdOQiEHKIoVJtzmRTb+n6/Wm1bat5wPefuZ0LAkJWVdn5KWRjbFajT6v3WxWeOqZ+vz1z0DeerclO/69GDBYMRoVZv64J2AysiRJAbubK4pMZL7AswzXYxy8NnI1ToeKy+VtoeBweJg1cx9btwR+v4IJ1uFbUSSKFQ/jx5ldWbcxilppLHp3q3OFhBh9cousVgM9e1WhQEHfZdjKlfNjNPr/mVI92m3vNhPuHq3blGHc+PsoUjQUWZbIk8fE08814JnnGmT30IRcIMtmcAYNGuStkwKoqorZ7PstUNM0Tp48yZtvvsnly5fp2bMnPXv2zKrh3TGP7RPMeR5H02MT14dUb0Dh7pz2g0hXkQzbkjuHJ98s2ZFNM3z7TQEQi8H6PoETnGV0tS6q/b3k21T3Q3iXkxwo1g8B/51HgWZv0tePKv19jm5l0z9ngyb1GgwyZnPWrbRWqVqA+b/34LNPNrNz5yVKlAjnmeca+sxQlC0XwdEj1/xmeZKSjgMFP4pBQlPxu89olLmvTRl0XWfBb4f47tt/uXrFTpNmJahZsxAGRcaZomq2w+GtlNygYdE0v65evaty6OAVfpqxF7NZweVSKVYsjNfeaEbr+8qkK9H5VgoVDuW3Rb34aOzfbNhwhjzhZgY9Vivgrpmhw+rxw7SdPrM9RqNMteoFxO6kXKJj54p06FQBl0u7raatghBMpiQZz507l+nTp/vc9sEHH1CrVi2io6MZMmQIo0aNolGjGzs94uPj+eGHHxg8eDCqqvLII4/wwQcfUKVK8HoImqaleXtrVpBlHbe6EZ1oZOoiS6UB0PQ9uPkBnQsotMJAbyTJv7WApp/EQWfA7nefRHGs0hqf21z613j4mkA9p8CChbnIkvf903UVN5/g4cfExxsJFOAEZ+TWszsGFHpilsak47ipS0hwUa3iN8THB0hENCsMGFCTjya0TdcxFUVOtSeTx6Px7/YLKIpEnbpF0v3hvm3rebp2nOUTlFksBh7sUIHHh9bl4e7zsNl830uzReHTz9vx2sjVeNwauu7tBP7jrIeoWasw//vgL778fAs2241kXbNZQZIlbAkpA2J49PG6jBt/f7rGDRAd7e3wXax4OJUqZX8AoSgyf288zfBn/uDYsWvIkkSHThWY8Fk78uQVORp3k1v9Xgl3j9x2rYLlu2XpLqqDBw/ywgsv8Morr9CqVSuf+1RVxW63Exbm/eAfN24clSpVolu3bkGPd/fsovIKlJkuGxejWN/F2/NJQ9ctoOXHHT8LSDnFrmIMb4MkX/O5VdcNaK7uqI7RPrcbQgciG3b4jUPXJVTHM2iux5NvU8zjEpekUt9tlfTTkHS7rltQnb2RDVuRlOOAC3QzYPL+v6QmFhYMAS0Sd8LMVJOi02vJoiO8+spqvy7P4J0pWfxHnzT1NrpZajsINvx1muee+gO3RwMdrCFGJk5uT5266Sv+9ueqE7w5ei3R0TZkWeKhHpV58+0WmMwKI19azZLFR7DbPMgymEwGnhnegCefro/Ho7Fn9yVMJoWq1QogSRLXrjloXH+q39JW0pJWyiDfajXww8yu1Kuf/oJ1wRw+dJUP39vAls3nyZPXxODHavPo43UydGYniabp7Nl9CZdLo2WrMths3gA+LtaJyaRgtoi9EXej3LYzJzfLbdcq23dRHTlyhOHDh/Ppp58GnJU5ceIEI0aM4Ndff0XTNLZv385DDz2UVcPLJC4U6/s+QYUkOdDlaGTzTDTnsBSPV/DY38AQMoobAZEJ9DBU51C/o+taYZ+CfDeY0D333vRvG7J5XoCKx9yUgGzA20riCWTDNjDsBC0C1TkYzd0DzakjKZuRlAPoWvHE49uRTQuR5JPoak20m2rlZBS73YOuBY7BGzculu7gJjWXo2088dgSn5mXhAQ3A/svYOOWQenaSty6TRnW3Vea69edWK1Gn3H+7+P7eKhHZZYsOoLZrNCtexVq1CwIeJfcbg6mPB6NQQMWBMzbUVWdYsXCuHLVDro3MFAUiQEDa952cON0qhzYf5nwcBPlynu3N585HUuPbvOwJXh3hdlsbj4dv5lTp2J5971Wtzhi+uzdE82QRxcTH+dCkiRkWWL8p/dz3/1l0p3cLAjCf1uWBTjjx4/H5XLx/vvvAxAWFsY333zD1KlTKVWqFG3atKFz5848/PDDGI1GunbtSsWKFbNqeJlCUg4Hvl1yIRtXBQhwQPe0wRM/Ddn8I5J8Bs1zD5qrb2KbB1+aawCycS03LzXpuoKulkXXbtrKK10lWD55Ug8qdAPuhC9Ba4jmeizQI9HVxuhq45tuM6K5+gc8bkZp3qJkwGXIkBADD6RSZO52LPj9kE9hwSSaDn8sPUb3nukrHy9JUsD+WJIk0aRpCZo0vXVxwiWLjnDkyLWg91evWZB332vF0iU3tomntqU8Nb//eog3Rq9BAjyqTunSeZg0pSOTJv6LM8CusHmz9/P8C42Ij3MxeeIOdu++RNXqBRgytC5ly0Wk+/xOh4eovr9z/bpvIP7sU3+wfHVfipcQScWCIKRdlgU433zzTcDbBw8enPz/Q4YMYciQIVk1pEyn6+FAkP4FWkTw52lVUe3v3/r4ai1U++uJCcMAHnS1Mh7bhBQPLERqBfgkyYOOhsE6EU9Cw1ueNysVLhLKs8835KvPt+J0ens+hYQYaN6iFM1blLz1AdLhyhV7wC3JbpdKTBa2grjZ4oWHAzaGBG+tkCeerEehwqEMHHxnZe337olm1Mg/fbbrHj50jUf6LyAs1BSw6abJpLB65QneeWs9LqcHj0dn755oFv52iBk/d03Xsp6u60yZvCPg+6+qGvPnHuC5EWmvziwIgiAWszOTVhS0SHT5fIoifFbUDJr50Nyd0dztkOQj6Hpe0IsHeJQJ1TEExTLRZ7nsZpKkgbIdbz+tO6/qmpGefLo+TZuVYPkff1PvnvXUruuiYP786B4HkHGVTps2LcEPU3f7JQArBpl7Ggd6XzNfaJgpua7RzSQJnnyq3m01hgxkxvTduFy+wYWm6Vy6ZKN0w7zIsuQ3u+Vyqcz8aa9PkrOq6thsHt56fR2/L344Tec+fjyGQQMWculigt8YANxujStX/BPvczq73c3iRUc4cugalavkp0PH8iK/SBAykPhtyjRuDKGPg3w1xQeUgup8JEWOzJ0yoWvVkv8lyYeRjOtAt6C524JeCM01GPRIFPNkkM/cUVPHQM6djePbr7ezZfM5SpfJyxNPZtyHL0Cd+udo0HIs4ElsCLoFtKmJSc131l06SUSkhWIlwjh14joulzfnxRpioG3bslSvUTBDzpFefftVZ/myY37b5CMjLTz9XMbNtl28kBBweU6WJVq1LsXmTed8xmA2K9zbujSrVp4IeLy9e6LRNP2WSci6rjN4wELOnokN2mA1JMRAi5al0vxacoJz5+Lo3mUeCfFubDY3ISEGPh73N78u6EWhwqHZPTxByBVEqchMIhtXIikHkxN7bxTGU1HMkzCEPAXSmQw/r2L+CENYfxTzVyiWTzGGd0I2LgMkNPdDuOMXo7l6o+u+ncR1XUb33MPtzN6cOBFDhwdmMfvnvRw6eJWVy48zoO/v/LE0/Z3OA9MxhIxGkuzJNYIkyQ7yRRTTpDs+ututMmTwInr3+IWzp+OQEjt/129QhA/+15rxn6VvG3pGanhPMZ56pj5ms0JoqJGwMBMREWa+n945Qyu9tm5TGkuA2QO3S6VTl0pMmtqRcuUiUBTv9vTuPavwyedtCQ3z70gP3p1caQmid+28xNWr9lSDmxo1C9G6Ten0vJy73huvreXKZVvybKHN5iH6ko0xb6/P5pEJQu4hZnAyiWRY6/0QTnm7BKCB4W+MYf1xxy0CMqZis6RsT9wKnpSk6Z3uV6xvormbkrQtXXU8i2TYDvJZvDVxzKCH47G/fVvn/WTcJhIS3MkzALoODrt3maJtu3J3vpVYugCSf78aSXIjm1agOl+6o8NPmbyTjRvO+uSfKIp3B0+XrpVSeWbWeOrZBvTqXZW//z5LeJiJZi1KYjJlbC+Ang9XZfrUXZw/F5+cB2O1Gnh8aB3y57fSpGkJVqzpj83mxmRSkoOrqIE1mTxxh897Z7Eo9OlXPU0F265fdyIF+fnIk8fE22PupX3Hcrmqq7Su66xfd8qv2aiq6kFnxARBSD8R4GQWPRJdV5CkwEnGkqSh40A2LciwnUjedg6BcmwUZOMGNHf7xH+H44mfg2TYiCQfQtdKoXta4S3ml36b/jkbcHkjNs7JpUsJFCniX9QwLZJKNEmSmWC9vnT9zpsl/vzTXp8PaPB+2OzYcZFr1xwZ2pDxdhUsFJqpwVZIiJHfFj3MjOm7+GPpMfJGmBk4qBZt2pb1e9zNnh3ekDOnY1m6+Cgms4LLqXJfmzK8/Gra+oPVrVsYt9v/d8RqNfDiK40ZOLh2rqrXkcQb/AVeEhQEIWOIACcjSDHIhjW4dQmkxqAXRnN1Rzb9QtBdVHhr4kjKgawbpw8Z3dMcneZ3fKTIfFYuX/afrdI1CA9P/5JX7HUn77y1jiWLjqKqGk2bleD7n6tgCd3rEzDqugXNlbZE1tS4AyS2AkhIQe/LjZIqm0ZEWChbLiK5Dk5qDAaZ8Z+2ZeRrTTl2LIbSZfJStGjaA9rwPGZGvtaEj8b+k9yg1GI1UKp0Xnr0St+2/JxCkiQeaFeW5X8c96lvZDTKdOiU9k7tgiCkLksrGWe0u6GSsWRYhSHkNdBlJFlH17XEKsIDkY2LUKxj8G7RtgeoGmxBdTyXYTM4krIdQ+iTfjuldN2MO3Yl/pWTM8av8w/yxqg1PkmoJrNCuwfL8ekXD6TrWLqu07n9HI4cvtFtWpYlKlZy88dfy5ENV71ffCUVzd0K1T4WSPtyjdPh4dSpeBRFT/4Af++d9fz4wx6/7tblykew4s/MrfNzt7h4IYHOHWYTH+fC6VQxGCSMRoXvpnSkabNb1+u5U1u3nGfG9N1cvWKnXfty9OhVBavVmOsqria5csXOw93nE33JhsulYjIpFC0Wxpz53ckboHZSWrndKvv3XcZiMVCxUr4s7euUW69VbpTbrlWwSsYiwLkT0nWM4W39KgTruhlP/E/oWkXAhmTYgWL+H5JyBknyJD5GBj0P7riFZGTgoZg/RjbPwdv0UgEkVPu7aO4HM+wcKem6zqefbGbSxH8xGhXcLpUWLUsy4YsH/JY0bmXTP2d5fPBiv/5KBoNE3XqF6dHbQpduHszmmqCVS9ex583Zz7tvrUeWJdwejXLlIvhuSkdCrAa6dZ7H5egEbDYPZrM3x+THWV0zpHt2TvDqy6v5Zd4Bv6KKxUuEs3ZDVLY1QMxtf4hvpqoa69ae4tjRGCpWykfzFiXvaIlq9coTvDhiJaqqoak6hQuH8t2UjpSvcOuZuIyQm69VbpPbrpUIcDKBbFyAYv0QSfIdg67LaM5BqM7hN90aj2L9CNm4FPCgexrjsY8CPeO/HQfaJp4V4uJcHDt6jSJFwihc5Pa2uv78017ee+cvv5yYJCEhRsLzmPh1Qa90nWPHvxfo3+d3n6J5iiJRrnwES1f0xeVUWbDgEEsXHyNvHhPDnq6fLd2qFy04xKefbOH8uXjKlY/g1VFNaZbBBQ0DaVx/KtHR/r9LZrPCmr+ifLYux8U6OXs2juLFwzO9fUJ2/CF2u1W2bD6Pqmo0bFQs4O6yu83JE9fp0G6Wz8+3JEGBAiH8tWlghu64Cya3fWjmZrntWmV7L6rcyUPg5FcdpJTNIcNQ7e+g2t/J9FHpWkV0Z9a3uQgPN1G7zp3NeFSsGJnqt1ibzY3T6eGD9zbw2ZdpX/6aPnUXzgCJxGfPxLF/32VMJsW7G8zmRgL+WHaMJ56qx/AsrJ47d/Y+3nlzffJS3769lxn62BK+m9KBZs0zN8gJDTMSHe1/u6bpWK3ePxOqqjHm7b+YM2sfRqOM263Rt191Rr/VPNckx27edI5hjy/x5iNJ3uKGn3x2P20fSN9sYZK01ALKCHNm7cOTYolV18Fmd7Phr9O0ujd3bbMXhLTIPXsvs4HmaU7gJGKzd+ZESLf6DYtSvkJEqtugVVVn1Yrj6TruxQsJAWutKIrM5cs2Bj+ykOhoGwnxbuLj3TidKpO+/Ze/1p1O70u4Lbqu89HYf/wK+jkcHsaN/TvTzz/o0drJgUwSo1GmeYuSybM0X3+xjblz9uN0qsnv0exZ+/j2q22ZPr6sEBfn4vFBi7h+3Ul8vJv4OBe2BDfPP7Occ+fi0nwcVdX44tMt1K0xiYplvuaB+2ayYX3m/hxduBAfsCGrrulcCbAB4G6m6zqHDl5h186LAXfYCUJaiQDnTuiFUB0j0HUzuu7Nd/Hu7OmCrtbO7tHlSJIkMePnbjzUozIWS/AgJ71T7q3blAl4PJdLxWCQuR7j9AuA7HYPP/6wO13nuV0JCW6/JpNJjh4O3GwzLs7F0SPXsNtTzhamX/+oGnTuWhGTWSEszITVaqBa9QJ8/On9yY+Z+v1Ov75YdruH7yfvvOPz3w1W/HEs4Hyspuks+C1w49xAxr63kYnfbCc21gXA0SPXGPr4Enb8eyGDRuqv1b2lCQnxn5BXVZ2GjYpl2nkz2rGj17j/3p/o3mUeUX1/p1G9qen+MiMISUSAc4c0Vz/c8bPQnI9i4DE8CZNQHaOze1g5Wni4iQ/+15q9h4bxYPtyfsGMySTT9aH01YTp0686BQuFYjbfCHKsVgPPDG8ASEGLzcXGBg46MlpIiBFrgA8o8Cb63szj0Xj79bXcU28KD3WeS8M6U5gwfhN3kk4nyxIfjruPVWv788ln9zPvtx78sqBXcjd0XdeDvhexQQKznCY21um3zAPeXljX09hsNT7exU8/7vGfibN7+PzTLRkyzkAe7FCesmUjfIL4kBADvXpXpWSpnNGF3ePR6N/7N06euI7d7iE+3k3sdSfPPbOcEydisnt4Qg4kcnAyglYO1fkMJmsINjX3JG7dDd794F4OH/6VC+fj8agaiixTvkJEmgvJJQkPN7Fg8cP8MH0Xq1acIG9eMwMfrUXr+8pgt7tRA0zvW60GOnXJmlwmWZZ48un6fPHpFp8PR4vVwAsv3ePz2AnjNzF37gGcTjW56vD33+2gQAErUQPvrKt4sWLhFCsWTkyMg6+/3Mo/f5+lTJkIBg6uRZWqBdi/z7+idNVqWZ+MnRmaNS+JLPsvB1qtBlq1TlsOy8ULCRgMcsCu6EcOBZ6Jywgmk8LsX7oz88c9LPz9MCEhRvpH1chRdXU2bjhDgs3tN5OqejRm/7yPka81zZ6BCTmWCHCEu1r+/FaWrezLxr/OcPx4DJWr5Kdho6K3tW05T14zzzzXkNffbOWzg8BqNfLu+614Y9Ra3G4VVdWxhhgoXz6SHj2zrtjc0GF1kST45svtJCS4yJffyiuvNqFd+/LJj9E0nR+m7Q64VDTxm3+JGliLc2fjcDg8XL5sIyTERPUaBW75fmmazqZ/zhIdbaN0qTw88fhSYmOdOJ0q/2w8y/x5B3jhxXs4cTwmuSCfJIHZYuDNd1pmyvuR1SpWykePXlX4Zf5B7Dbv+xsSYqBZ85Lc0zhtyzxFi4X5bbUH73uV2YGg1WrksSF1eWxI3Uw9T2a5cjlwTzK3W+PihYSsH5CQ44lt4hkot229y62CXacD+y8z88e9XL5s4/62ZenYuaLPklZW0XUdh8ODxWLwC0ycTpUalScGbI1hNMqUKp2Xkydi8Hh0bwBiNpC/gJXvp3WiYqV8Ac935nQs/Xr/Rsw1B0hgT/wWnfIvQ9FiYUyc3IEvP9/KwQNXqFI1P88Ob0jVagUy7LWnlNW/U7qus2b1SebO3o/brdKte2Ue7FA+Xb2wPhr7N9On7vKZibNaDcya150aNbOnK31WuNNrdfpULO3azPSb/QoJMfDeh/fS9aHKdzpEIVFu+6wSdXCyQG77ocmtcvp1at1iBqdOxvrdrihS0NmD/PmtbNg8KGBydqcHZ3PwwJWAQdPNLBaFFWv6U6xY2pvD7t93mSWLjgDQvmN5qlVP3wd8TrxWuq4zZdIOvpv4LzHXHFStVoDX32xOgxyU7Hs7MuJavfX6WubPO5A8g2axKJQrF8n8BT0zvMHsf1lO/L1KjQhwskBu+6HJrXL6dVrz50mefmJZcjFESfLuKlMUOWiBxLAwI59/3c6vHsrpU7E8eP9MHI5bb8c1mWT+2To4za0EPpuwme+++RdX4lZfk1Fh6JN1b1lb6No1B0sXHSEuzsWDHSpSukzaAyoh+2TE75Wu6yxdfJQff9iNzeamU5eK9I+qgdV6e42AhcBy+t/AlEShP0HIJe5tXZofZnbhi8+2cOxoDNWqF6Bo0bBUt7TrOt4lqBRsdjeyLJNaU1jwLn81blIizcHN0SPX+O6b7T6Bk0P18N032+nUuWLQ9gEb1p/miceXAN4t/F98toUOnSrwv4/vC5hH5PFo/LH0KMuWHiNvXjMP96lGrdpZU7k7O7lcKtOm7GTu7P2oqk637pUY8kTdHB8ISJJEh04VclRytHD3EgGOIORA9RsUZdqMLsn/3rjhDPPmHMBmC1wTx+3RaHiP/xJJhQqRWCyK3/Nk2fthYw0xono0KlSM5JPP0168cuWK43g8/pPDqqqzcvnxgAGO06ny9LBlPrkrdruHpYuP0LZdWb9qwh6PxsABC9i14yI2mwdZlvh1/kFGjmrCI4PubDfZ3UzXdR4btIjtW88nB5DffrWdVStO8MuCnunKFxKE3Ez8JgjCXULTdK5dc+Bypb96a5OmxaleowBms/+vtNVq4JGBNQPmziiKzEcT7sdiNWAweGdIrCEGypaLZMnyPkz4rC1zf+3Bb4seJjIy7V2uDQYZOcBfF0mSMBgD/9nZuuVcwFo+NpuH+XMO+N2+bMnR5OAGvO+fw+Fh7PsbiUlj3ZqcaNvWC+zYfsFndszpVDl2LIY1q09m48gE4e4iZnAE4S6w4LdDvP/uX1y/7kQxyPQbUIORrzVJc8VmSZKY/mMXpiYuW8TGOrFajZQrF8GAR2rSpm2ZoM+9t3VpFv/Rm59/2svZM3G0bFWKLt0qYbEYqFAx8M6rW2nfoTzjx/0TYJze+wJKJRswUOCzdMnR5ODmZkajzKZ/ztHuwdvrH3W327njIu4ABQltCW62b7tAm7Zls2FUgnD3EQGOIGSzdWtP8drIP5Nr27jdGjN/3IPqUdNVY8ZsMTDsqfoMe6o+Fy7Ec/26k3LlIjAab737pEyZCF4b3ey2X0NKxYqH8/aYlrz9xrrkKtG6pvPWuy0pVjxwQmCDhkUD3h4SYqB7r6p+t4eHm5BlKeDur9CQnJ2LkprChUMxmRS/IMdqNQR9bwXhv0jsospAuS0zPbe6265Tr4fms32bf58is0Vh287H0pU4euWKnWeGLWPHjosYE3dWjfmwFZ06Z313eYDoSwmsXHECgPvblqFgodBUH792zUmeemIZuqbjdqtYLEbatC3DJ5+19evKvePfC/Tv87tf0cPISAt/bx2UpsAuJ3I6PDRvPJ1r1xw+tYrCwk2s2xCV5kTwjHa3/V4JweW2ayW2iWeB3PZDkzF0JPkASB50tSp3w6Th3XadGtefSnS0/3isVgN/rOpL8RJp7yX0UOe57Nsb7ZPga7Ea+HlON2rVLpwh481sly/bWLzwCLGxTtp3qEj5CnmDVmL+ftIOPh73D6bEvB6DUWHqD51z/U6qo0eu8dzTf3D8WAySBEWKhvHZl+2ytZDg3fZ7JQSX266VCHCyQG77oblTkrwfQ+jzIMUCEuhGPLb/oauNs3Vcd9t1enzQItb8edKvcnBYmIktOx5Nc4GzI4ev0q3TXL9Gj5IEnbpU5NMvHsioIWeZtFyrq1ftbPr7LKFhJpo0LZ5rZ24COX8+Hk3VKFY8/Lbal2Sku+33Sggut10rUQdHyGJ2DKFDkOS4GzdJYAgdjjtuIei5+xt2eox46R7++fusX2n/519slK7qrZcu2QImJes6nDsbF+AZmcvpVPlz1QkuX7bRsFExKlfJnF5M+fJZad/xv1k3pWjRsOwegiDctUSAI2QK2bgGpEDbnTVk42I01+CsHtJdq3qNgvw89yHGffg3e3ZfolDhUJ5+rgFdulZK33GqFwi4xdxsVmjesmRGDTdNDh68Qv/ev+FyqXg8GpIkcX/bskz43D+XRhAEITOIAEfIHNI1wH8LryS5kOQrWT+eu1zNWoWY8XPXOzpG3ggLQ5+sy+SJO5Jng4xGmbx5zVla+E7XdZ58fCnXrvrWolm14ji/zDtAz4f9d0QJgiBkNFHoT8gUuqch4P9NXddD0Dz3ZP2A/iOGj2jExxPup16DIpQtF8Ejg2qxcFlvIrJwZ83RI9e4dCnB73a73cPPP+3NsnEIgvDfJmZwhEyhaxXR3PcjG1cjSXbvbboFXa2G7sm4eiuCL0mSeLBDeR4MVkwvCyQtSQVyO1WaBUEQbocIcIRMo9rfQ/csRTbNB9xo7s5orocQE4e5W8VK+bCGGPz6W1ksBrp1T19ekSAIwu0SnzRCJpLR3B3xJEzBkzADzfUwkHsrzApeiiLz2ZcPYA0xYDZ7d4GFhBipUjU/A6JqZvPoBEH4r8iyGRxd12nZsiVlypQBoE6dOrz44os+j5kzZw6zZs3CYDDw5JNP0rp166waniAIGahJ0xKsXjuAX+Yd4OLFBJo0K8F9bcqkubeWIAjCncqyAOfUqVNUr16db7/9NuD90dHRzJgxg/nz5+N0OunXrx/NmjXDZDJl1RAFQcgAa9ec5KvPt3L2bBz16hXh+RfvoXyFyOweliAI/zFZ9nVq7969XLx4kaioKIYMGcKxY8d87t+1axd169bFZDIRHh5OqVKlOHDgQFYNTxCEDDB3zj6eemIZ27Ze4ML5BJYtPUa3znM5fOhqdg9NEIT/mEyZwZk7dy7Tp0/3ue3NN99k6NChtG/fnq1bt/Lyyy8zf/785Pvj4+MJD79Rbjk0NJT4+PhUz6MoEhERIRk7+DugKPJdNR4hMHGdMofHozH2vb99ml9qmo7d5uaLz7byw0/d0n3MnH6tVq88zpuvr+HI4asUKRLGK681pd+A3JmHlNOv1X/Jf+VaZUqA06tXL3r16uVzm91uR1G8CYcNGjTg4sWL6LqevJ00LCyMhIQbtTMSEhJ8Ap5AVFW/q/pp5Lb+HrmVuE6Z49y5OJxO/+KOug6b/zl7W+95Tr5WG/46zdBHl+BweN+T06djeeXFlVy5kkDUwKwrvJhVcvK1+q/JbdcqWC+qLFui+vLLL5NndQ4cOECxYsV8amXUqlWLbdu24XQ6iYuL4+jRo1SqJLaUCkJOERFhQdMC9+4tXCQ0i0eT/T7+3z/JwU0Su93DhPGbg75PgiBknCxLMh46dCgvv/wya9euRVEUPvzwQwCmTp1KqVKlaNOmDVFRUfTr1w9d1xkxYgRmszmrhicIwh0KCTHSpVslFv5+CIfjRkE/q9XAU882yMaRZY9jR68FvN2W4CYu1kneLKwuLQj/RZKu6zn2q4Tbrd5V02y5bdovtxLXKfM4nSpvjFrDwgWHMSgysizx0sh7bntJJqdeq8OHrtKr+3ziYl1+94WHm9i26zEUJXdtmc+p1+q/KLddq2BLVKKSsSAIGcZsVhg3vg1vvN2Cq1fsFC0WhsmkZPewstTePdH07vmLT7J1EqvVwJNP188xwY3T4cFglHPMeAXhZiLAEQQhw4WHmwgP/2/WsBr7/kbsNv/gRpbh+Rcb8diQOlk/qHT65++zvDlqLcePx2AyKTzcpyqvjm6WXJlaEHICEeAIgiBkoJ07Lga8XZZlevetHrQR6d3iwP7LPD5oEfbEGSiHw8OcWfu4csXO51+1y+bRCULaiXlHQRCEDJQvvzXg7QajjNV693+n/Pbr7Tidvl3fHQ6VlcuPE30pIcizBOHuIwIcQRCEDDTsqXp+gYzFotCnb7Uc0Yvr8KGrAbexm0wKp0/HZcOIBOH23P2/bYIgCDlI777VePyJOlgsBkJDjZjMCp27VuLV0U2ze2hpUrtOYRTFfxnN5VIpWy4i6wckCLfp7p8vFQRByEEkSeL5F+5h6LB6nD0TR+HCoeTJm3Nqej3xZD0WLjiMLcGdfJvVaqBHrypERoraPULOIQIcQRCEVBw5fJXVq05gNhto36E8hQqnrSpzSIiRipXyZfLoMl7pMnmZ+0t33h+zge1bL5Anr5lHH6/FY0PqZvfQBCFdRKG/DJTbiiflVuI65RzZfa3Gjf2b6VN2oqo6iiKh6zBufBs6damYbWO6W2X3tRLSLrddq2zvRSUIgpCT/Lv9Aj9M3YXDoeJ2azgcKk6nyisvreJ6jCO7hycIwi2IAEcQBCGABb8f8muWCWBQZNb8eTIbRiQIQnqIAEcQBCEAXUvlvhy7sC8I/x0iwBEEQQigU5eKWCz++zA8Ho1WrUtnw4gEQUgPEeAIgiAE0KBhUfoOqIHFYkBRJEwmGbNF4YNxrcV2aUHIAcQ2cUEQhCBGv9GMHj0rs2rlCSwWA+07lqdYscA7NgRBuLuIAEcQBCEVVaoWoErVAtk9DEEQ0kksUQmCIAiCkOuIAEcQBEEQhFxHBDiCIAiCIOQ6IgdHEIQcw+1WWbzwCEsXHyE8j5m+/atTv0HR7B6WIAh3IRHgCIKQI7jdKgP6/M7evdHYbR4kCZYuOcrzIxoxZJhoBCkIgi+xRCUIQo6wdPFR9iUGN+CtJuywe5gwfhNXr9qzeXSCINxtRIAjCEKO8MeyY9hs/r2hjEaZzf+cy4YRCYJwNxMBjiAIOUJEhBlZlgLeFxZmyuLRCIJwtxMBjiAIOULvvtUxmRS/201mA42bFs+GEQmCcDcTAY4gCDlCrdqFeHV0U8xmhbAwE6FhRvIXsDL9x84YDOJPmSAIvsQuKkEQcoyogTXp2q0SmzedIzTMSMNGxURwIwhCQCLAEQQhR8mT18z9D5TN7mEIgnCXE199BEEQBEHIdUSAIwiCIAhCriMCHEEQBEEQch0R4AiCIAiCkOuIJGNBEAThP03XdQ4fuookSVSoGIkkBS4oKeQsWRbgfPfdd6xfvx6A2NhYLl++zIYNG3we895777F9+3ZCQ0MB+PrrrwkPD8+qIQqCIAj/MTv+vcAzw/7g+nUHABGRVr6e+CA1axXK5pEJd0rSdV3P6pM+8cQTDBgwgBYtWvjc3rdvX7766ivy5cuXpuO43SoxMbbMGOJtiYgIuavGIwQmrlPOIa5VzpETr1XsdSctmkwnPt7tc3t4uIm/Ng3MtS1AcuK1Sk3BgoEnQrI8B2f58uXkyZPHL7jRNI2TJ0/y5ptv0qdPH+bNm5fVQxMEQRD+QxYtPIyq+n/HV1WNZUuOZsOIhIyUKUtUc+fOZfr06T63ffDBB9SqVYuJEyfyySef+D3HZrMxYMAABg8ejKqqPPLII9SoUYMqVaoEPY+iSEREhGT4+G+Xosh31XiEwMR1yjnEtco5cuK1io9z43D4d6h3OlXi4tw57vWkVVZdK6fTw6qVx4mJcdK8eUlKlc6b6ee8WaYEOL169aJXr15+tx85coQ8efJQunRpv/usViuPPPIIVqsVgMaNG3PgwIFUAxxV1e+qabbcNu2XW4nrlHOIa5Vz5MRrVaNWQaxWIzab7xKV2axQs2aBHPd60iorrtXePdE80u93PB4NTdNRVZ0BA2vy2uimGZ7EfVcsUW3cuJGWLVsGvO/EiRP069cPVVVxu91s376d6tWrZ+XwBEEQhP+QJk2LU6duIazWG9/1rVYDDRoVo37Dotk4spxNVTUeG7SImBgn8fFubDYPTqfKzB/3sGb1ySwbR5ZuEz9+/DjNmjXzuW3q1KmUKlWKNm3a0LlzZx5++GGMRiNdu3alYsWKWTk8QRAE4T9EkiS+n96ZWTP3Mm/OASQJHu5TlYf7VBNbxe/Azh2XsCW4/W632zz8PHMvrduUyZJxZMsuqowidlEJt0Ncp5xDXKucQ1yrnCOzr9XfG88wbMhS4uNcfvc1bV6CGTO7Zuj57oolKkEQBEEQcre69YqgBdidZrUa6NI161ZmRIAjCIIgCEKGsVgM/O/j+7BYDBgM3qW+kBAjNWoVpOtDlbNsHKJVgyAIgiAIGapDpwpUrV6AebP3c+WKndZtytDm/jIYDFk3ryICHEEQBEEQMlzZshG8/GqTbDu/WKISBEEQBCHXEQGOIAiCIAi5jghwBEEQBEHIdUSAIwiCIAhCriMCHEEQBEEQch0R4AiCIAiCkOuIAEcQBEEQhFxHBDiCIAiCIOQ6IsARBEEQBCHXEQGOIAiCIAi5jghwBEEQBEHIdUSAIwiCIAhCriPpuq5n9yAEQRAEQRAykpjBEQRBEAQh1xEBjiAIgiAIuY4IcARBEARByHVEgCMIgiAIQq4jAhxBEARBEHIdEeAIgiAIgpDriABHEARBEIRcx5DdA8gtdF2nZcuWlClTBoA6derw4osvZu+gBB+apvH2229z8OBBTCYT7733HqVLl87uYQlBdOvWjfDwcABKlCjBhx9+mM0jElLauXMnH3/8MTNmzODkyZO8+uqrSJJExYoVeeutt5Bl8R36bnHztdq7dy/Dhg1L/rzq27cvHTp0yN4BZgIR4GSQU6dOUb16db799tvsHooQxMqVK3G5XMyePZsdO3YwduxYvvnmm+welhCA0+kEYMaMGdk8EiGYSZMmsWDBAqxWKwAffvghzz//PPfccw9vvvkmq1atom3bttk8SgH8r9W+ffsYPHgwjz76aDaPLHOJ8DqD7N27l4sXLxIVFcWQIUM4duxYdg9JSGHbtm20aNEC8M6w7dmzJ5tHJARz4MAB7HY7jz76KI888gg7duzI7iEJKZQqVYovvvgi+d979+6lUaNGALRs2ZKNGzdm19CEFFJeqz179rBmzRr69+/PqFGjiI+Pz8bRZR4R4NyGuXPn0qlTJ5//ChQowNChQ5kxYwZPPPEEL7/8cnYPU0ghPj6esLCw5H8rioLH48nGEQnBWCwWHnvsMb7//nveeecdXnrpJXGt7jLt2rXDYLixCKDrOpIkARAaGkpcXFx2DU1IIeW1qlWrFq+88go//fQTJUuW5KuvvsrG0WUesUR1G3r16kWvXr18brPb7SiKAkCDBg24ePGizy+8kP3CwsJISEhI/remaT6/9MLdo2zZspQuXRpJkihbtiwRERFER0dTtGjR7B6aEMTN+TYJCQnkyZMnG0cjpKZt27bJ16dt27aMGTMmm0eUOcQMTgb58ssvmT59OuCdXi9WrJgIbu4y9erVY926dQDs2LGDSpUqZfOIhGDmzZvH2LFjAbh48SLx8fEULFgwm0clpKZatWps2rQJgHXr1tGgQYNsHpEQzGOPPcauXbsA+Pvvv6levXo2jyhziK+vGWTo0KG8/PLLrF27FkVRxI6Pu1Dbtm3ZsGEDffr0Qdd1Pvjgg+wekhBEz549ee211+jbty+SJPHBBx+I2ba73MiRI3njjTf45JNPKFeuHO3atcvuIQlBvP3224wZMwaj0UiBAgVy7QyOpOu6nt2DEARBEARByEhiiUoQBEEQhFxHBDiCIAiCIOQ6IsARBEEQBCHXEQGOIAiCIAi5jghwBEEQBEHIdUSAIwi52JkzZ6hXrx5RUVHJ/3355Zfs37+fL7/8Mk3HiImJYeHChbd1/meeeSZdj4+Ojubtt99O93mioqI4evRoup93t9qyZQsHDhzI7mEIQo4mCksIQi5XoUKFgE0rq1atmqbnHzx4kNWrV9O5c+d0nzutQVSSggUL3laAk9vMnz+fDh06UKVKleweiiDkWCLAEYT/oE2bNjFr1iwmTJhA69atKVeuHOXKlaNhw4ZMmjQJg8FA8eLFGTduHN9++y0HDhxg9uzZ9O7dO/kYHTp0oEGDBhw+fJi8efPyySefsGzZMubPn4+maTz33HO89NJLbNiwgaioKKpUqcLhw4eJj4/ns88+o3jx4nz99desXLkSVVXp27cvzZs354UXXmDOnDkBj69pGqNHjyYuLo5r167Rq1cv+vXrF/A17ty5k/fffx9d1ylcuDAff/wxx44dY8yYMSiKgtlsZsyYMWiaxogRIyhatChnzpyhY8eOHD58mH379nHvvffywgsvEBUVRdmyZTl+/Di6rjNhwgQKFizI2LFj2bZtGwCdOnVi4MCBvPrqq5hMJs6ePculS5cYO3Ys1atXZ+nSpUybNg1Zlqlfvz4vvfQSX3zxBWfOnOHKlSucO3eO1157jcjISNavX8/evXupUKECxYoVy5KfCUHIbUSAIwi53JEjR4iKikr+98cff+xz//nz5/nll1+IjIzkueeeY9CgQXTs2JHffvuN+Ph4hg0bxqxZs3yCGwCHw0Hnzp1p2LAh48aNY/bs2eTNm5c8efLwzTff+I2jVq1ajB49mgkTJrB48WKaN2/OunXrmDt3Li6Xi/Hjx9OsWbNUj9+oUSM6duzIAw88wMWLF4mKigoa4LzxxhtMmDCB8uXL89NPP3H06FHeeOMN3n//fapWrcrKlSsZO3Ysr7zyCqdPn2bKlCk4HA7atGnDunXrsFqttG7dmhdeeAHwtvp49913+emnn5g4cSLNmjXjzJkzzJkzB4/HQ79+/WjcuDEAxYoV491332XOnDnMnj2bF154gS+++IL58+djtVp5+eWX2bBhAwAmk4nJkyezYcMGpkyZwvfff0+LFi3o0KGDCG4E4Q6IAEcQcrlAS1QnTpxI/v/IyEgiIyMBeO2115g4cSI///wz5cqV4/777w96XIPBQMOGDYEbfb7q1KlD2bJlAz6+WrVqABQpUoTLly9z/PhxatWqhaIoWK1WXn/9dc6cOZPq8Tt06MD06dNZvnw5YWFhqXYYv3LlCuXLlwegf//+AFy6dCl5aa5hw4aMHz8egJIlSxIeHo7JZKJAgQJEREQA+PSTSwpe6tWrx+rVqylSpAgNGjRAkiSMRiO1a9dOzgNKOkeRIkXYvn07p06d4urVqwwdOhTwNqM8ffq032NdLlfQ1yMIQvqIJGNB+I+7uQv07NmzefbZZ/nxxx8BWLFiBbIso2ma3/M8Hk9yIuy2bduoUKGC3/FSU65cOfbt24emabjdbgYPHuzzAR/o+FOmTKFOnTp8/PHHPPjgg6TWaaZQoULJgdx3333HihUrKFSoUPIxt2zZQpkyZQDS1Bh3z549AGzfvp0KFSpQvnz55OUpt9vNv//+S+nSpQMer0SJEhQtWpQpU6YwY8YMBgwYQO3atYOeW5KkVF+bIAi3JmZwBEFIVqtWLQYPHkxERAShoaHce++9uFwuDh06xLRp0xg0aJDP4ydNmsS5c+coVqwYI0aMYNGiRWk+V9WqVWnRogV9+/ZF0zT69u2LyWRK9fjbt2/n7bffZuHChURERKAoStBZj3feeYdRo0YhyzIFCxZk0KBBFC9enDFjxqDrOoqipKvh6q+//sq0adOwWq2MGzeOyMhINm/eTO/evXG73Tz44INBuzLny5ePQYMGERUVhaqqFC9enPbt2wc9V+3atfn4448pUaJE8iyUIAjpI5ptCoJwW+677z6WLl2K2WzOkcdPj6ioKN5++20RbAhCDiKWqARBEARByHXEDI4gCIIgCLmOmMERBEEQBCHXEQGOIAiCIAi5jghwBEEQBEHIdUSAIwiCIAhCriMCHEEQBEEQcp3/A6f4dkKd3f4oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the data to from its components:\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca['Component_1'],x_pca['Component_2'],c = cancer['target'],cmap='plasma')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "plt.title('Benign vs malign brest cancer (PCA)')\n",
    "sns.set_style('darkgrid')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c1ae6c",
   "metadata": {},
   "source": [
    "### Part 2: Apply SVM Classifier\n",
    "\n",
    "* Next step will be split the data into train/test. It will be used 80% ofit to train;\n",
    "* Then, to find the best classification line, it will be used the scikit-learn `SVC()` method;\n",
    "    * OBS: To select the right 'C' and 'gamma' parameters, we will use a GridSearch method to test a small range of parameters. Because the test data is a bit small, it will be applied a Cross Validation on each combination of parameters;   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1f302f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#to ease understanding:\n",
    "X = x_pca\n",
    "y = cancer['target']\n",
    "\n",
    "#split data\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size = 0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a550830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV 1/5] END .................................C=0.1, gamma=1; total time=   0.1s\n",
      "[CV 2/5] END .................................C=0.1, gamma=1; total time=   0.0s\n",
      "[CV 3/5] END .................................C=0.1, gamma=1; total time=   0.0s\n",
      "[CV 4/5] END .................................C=0.1, gamma=1; total time=   0.0s\n",
      "[CV 5/5] END .................................C=0.1, gamma=1; total time=   0.0s\n",
      "[CV 1/5] END ...............................C=0.1, gamma=0.1; total time=   0.0s\n",
      "[CV 2/5] END ...............................C=0.1, gamma=0.1; total time=   0.0s\n",
      "[CV 3/5] END ...............................C=0.1, gamma=0.1; total time=   0.0s\n",
      "[CV 4/5] END ...............................C=0.1, gamma=0.1; total time=   0.0s\n",
      "[CV 5/5] END ...............................C=0.1, gamma=0.1; total time=   0.0s\n",
      "[CV 1/5] END ..............................C=0.1, gamma=0.01; total time=   0.0s\n",
      "[CV 2/5] END ..............................C=0.1, gamma=0.01; total time=   0.0s\n",
      "[CV 3/5] END ..............................C=0.1, gamma=0.01; total time=   0.0s\n",
      "[CV 4/5] END ..............................C=0.1, gamma=0.01; total time=   0.0s\n",
      "[CV 5/5] END ..............................C=0.1, gamma=0.01; total time=   0.0s\n",
      "[CV 1/5] END .............................C=0.1, gamma=0.001; total time=   0.0s\n",
      "[CV 2/5] END .............................C=0.1, gamma=0.001; total time=   0.0s\n",
      "[CV 3/5] END .............................C=0.1, gamma=0.001; total time=   0.0s\n",
      "[CV 4/5] END .............................C=0.1, gamma=0.001; total time=   0.0s\n",
      "[CV 5/5] END .............................C=0.1, gamma=0.001; total time=   0.0s\n",
      "[CV 1/5] END ............................C=0.1, gamma=0.0001; total time=   0.0s\n",
      "[CV 2/5] END ............................C=0.1, gamma=0.0001; total time=   0.0s\n",
      "[CV 3/5] END ............................C=0.1, gamma=0.0001; total time=   0.0s\n",
      "[CV 4/5] END ............................C=0.1, gamma=0.0001; total time=   0.0s\n",
      "[CV 5/5] END ............................C=0.1, gamma=0.0001; total time=   0.0s\n",
      "[CV 1/5] END ...................................C=1, gamma=1; total time=   0.0s\n",
      "[CV 2/5] END ...................................C=1, gamma=1; total time=   0.0s\n",
      "[CV 3/5] END ...................................C=1, gamma=1; total time=   0.0s\n",
      "[CV 4/5] END ...................................C=1, gamma=1; total time=   0.0s\n",
      "[CV 5/5] END ...................................C=1, gamma=1; total time=   0.0s\n",
      "[CV 1/5] END .................................C=1, gamma=0.1; total time=   0.0s\n",
      "[CV 2/5] END .................................C=1, gamma=0.1; total time=   0.0s\n",
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      "[CV 1/5] END ................................C=1000, gamma=1; total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END ................................C=1000, gamma=1; total time=   0.0s\n",
      "[CV 3/5] END ................................C=1000, gamma=1; total time=   0.0s\n",
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      "[CV 3/5] END .............................C=1000, gamma=0.01; total time=   0.0s\n",
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      "[CV 5/5] END ............................C=1000, gamma=0.001; total time=   0.0s\n",
      "[CV 1/5] END ...........................C=1000, gamma=0.0001; total time=   0.0s\n",
      "[CV 2/5] END ...........................C=1000, gamma=0.0001; total time=   0.0s\n",
      "[CV 3/5] END ...........................C=1000, gamma=0.0001; total time=   0.0s\n",
      "[CV 4/5] END ...........................C=1000, gamma=0.0001; total time=   0.0s\n",
      "[CV 5/5] END ...........................C=1000, gamma=0.0001; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model = SVC()\n",
    "\n",
    "#creating a dist of params to test\n",
    "param_grid = {'C':[0.1, 1,10,100,1000],\n",
    "              'gamma': [1,0.1,0.01, 0.001,0.0001]}\n",
    "\n",
    "S_grid = GridSearchCV(model, param_grid, verbose = 3, refit=True)\n",
    "\n",
    "S_grid.fit(X_train, y_train) #fit the grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "abc3deb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, gamma=0.01)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The best model was:\n",
    "S_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625f0966",
   "metadata": {},
   "source": [
    "Last, we have to evaluate the model. For that, two methods will be used:\n",
    " - Confusion Matrix;\n",
    " - Classification Report;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dfb575a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grab the best model and make predictions\n",
    "predictions = S_grid.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "82932583",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40  2]\n",
      " [ 3 69]]\n"
     ]
    }
   ],
   "source": [
    "#Evaluate:\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(confusion_matrix(y_valid,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f5ba2f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94        42\n",
      "           1       0.97      0.96      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.95      0.96      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_valid,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372c3035",
   "metadata": {},
   "source": [
    "### Diagnosis\n",
    "\n",
    "1. Confusion Matrix: \n",
    "    - Out of 114 samples of brest cancer in the test data, the model predicted 109 correctly and 5 incorrectly (0.96 accuracy). The most important is to reduce False Negative predictions (i.e, predicting the cancer is BENIGN, when it is MALIGNANT), as the risk is much greater of resulting in complications to the pacient. In test, out of 5 misevaluations, 3 were False Negatives, and two were False Positives;\n",
    "\n",
    "2. Classification Report:\n",
    "    - Looking at the Classification Report, if we assess the model by Rightfully classifying a Malignant Cancer, we can only aprove the model if we accept a minimum Recall of 96%. On the other hand, if we consider the misdiagnosis equaly important, we can only aprove the model if we accept a f1-score of above 97%. "
   ]
  }
 ],
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